{ "schema": "modelrater-repo/3", "portal": "https://mdata.modelraters.com", "legacy_portal": "https://mdata.mdrater.com", "updated_at": "2026-10-27T20:17:52.845756", "count": 10853, "models": { "01-ai/yi/yi-34b": { "id": "01-ai/yi/yi-34b", "name": "Yi-34B", "maker": "01-ai", "family": "01-ai/yi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-02", "context_length": 4096, "max_output_tokens": null, "parameters": "34B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 60 layers, hidden size 7168, intermediate 20480, 56 query heads, 8 key-value heads (grouped-query attention), SwiGLU, vocabulary 64000, bfloat16. RoPE theta 5000000. max_position_embeddings in config.json is 4096. The README says the 6B and 34B bases were trained at 4K and can be extended to 32K at inference.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-34B is 01.AI's first public 34B bilingual base model, released 2023-11-02. Weights remain on Hugging Face under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The maker describes it as a base model for personal, academic, and commercial use, especially where a mid-size model with emergent ability is enough.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi/yi-34b-200k": { "id": "01-ai/yi/yi-34b-200k", "name": "Yi-34B-200K", "maker": "01-ai", "family": "01-ai/yi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-05", "context_length": 200000, "max_output_tokens": null, "parameters": "34B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 60 layers, hidden size 7168, intermediate 20480, 56 query heads, 8 key-value heads (grouped-query attention), SwiGLU, vocabulary 64000, bfloat16. RoPE theta 10000000. config.json max_position_embeddings is 200000. Same width and depth as Yi-34B. RoPE theta is 1e7 rather than 5e6.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-34B-200K is the long-context 34B base from 01.AI, first released 2023-11-05 and later updated for near-perfect needle retrieval. Weights remain published. Collected 2026-10-15.", "what_it_is_for": "The maker positions it for long documents. The README equates 200K tokens with roughly 400,000 Chinese characters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi/yi-34b-chat": { "id": "01-ai/yi/yi-34b-chat", "name": "Yi-34B-Chat", "maker": "01-ai", "family": "01-ai/yi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-23", "context_length": 4096, "max_output_tokens": null, "parameters": "34B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 60 layers, hidden size 7168, intermediate 20480, 56 query heads, 8 key-value heads (grouped-query attention), SwiGLU, vocabulary 64000, bfloat16. RoPE theta 5000000. Chat config.json max_position_embeddings is 4096, rms_norm_eps 1e-5. SFT chat model on the Yi-34B base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-34B-Chat is the supervised-finetuned chat release of Yi-34B, open-sourced by 01.AI on 2023-11-23, with official 4-bit and 8-bit weights. Collected 2026-10-15.", "what_it_is_for": "The maker says the chat model is for conversational and creative downstream tasks, and that its diverse SFT style is a starting point for later RL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi/yi-34b-chat-0205": { "id": "01-ai/yi/yi-34b-chat-0205", "name": "Yi-34B-Chat-0205", "maker": "01-ai", "family": "01-ai/yi", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-02-26", "context_length": 4000, "max_output_tokens": null, "parameters": "34B", "architecture": "Closed API snapshot. IT Home says it is a deeply optimized build of the open Yi-34B-Chat with better instruction following and lower latency. No separate config was published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-34B-Chat-0205 is a closed, API-only revision of Yi-34B-Chat that 01.AI sold in early 2024. Press listed a 4K window at 2.5 CNY per million tokens. Collected 2026-10-15.", "what_it_is_for": "Press says it is for general chat, QA, dialogue, writing, and translation, with stronger instruction following than the open checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi/yi-34b-chat-200k": { "id": "01-ai/yi/yi-34b-chat-200k", "name": "Yi-34B-Chat-200K", "maker": "01-ai", "family": "01-ai/yi", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-02-26", "context_length": 200000, "max_output_tokens": null, "parameters": "34B", "architecture": "Closed long-context chat API. Press says it is the 200K chat service for multi-document reading. Distinct from the open base weight Yi-34B-200K. No config was published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-34B-Chat-200K is the closed 200K chat API 01.AI offered in early 2024, listed in press at 12 CNY per million tokens. It is separate from the open Yi-34B-200K base. Collected 2026-10-15.", "what_it_is_for": "Press says it is for multi-document reading, long knowledge bases, and books on the order of hundreds of thousands of Chinese characters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi/yi-6b": { "id": "01-ai/yi/yi-6b", "name": "Yi-6B", "maker": "01-ai", "family": "01-ai/yi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-02", "context_length": 4096, "max_output_tokens": null, "parameters": "6B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 32 layers, hidden size 4096, intermediate 11008, 32 query heads, 4 key-value heads, SwiGLU, vocabulary 64000, bfloat16. RoPE theta 5000000. max_position_embeddings is 4096. README: trained at 4K, extendable to 32K at inference.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-6B is 01.AI's 6B bilingual base model, released with Yi-34B on 2023-11-02. Weights remain published under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The maker says the 6B series is suitable for personal and academic use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi/yi-6b-200k": { "id": "01-ai/yi/yi-6b-200k", "name": "Yi-6B-200K", "maker": "01-ai", "family": "01-ai/yi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-05", "context_length": 200000, "max_output_tokens": null, "parameters": "6B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 32 layers, hidden size 4096, intermediate 11008, 32 query heads, 4 key-value heads, SwiGLU, vocabulary 64000, bfloat16. RoPE theta 10000000. config.json max_position_embeddings is 200000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-6B-200K is the 6B long-context base 01.AI released on 2023-11-05. The checkpoint config sets the window at 200,000 tokens. Collected 2026-10-15.", "what_it_is_for": "Same long-document role as Yi-34B-200K, at the 6B size the maker aimed at personal and academic use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi/yi-6b-chat": { "id": "01-ai/yi/yi-6b-chat", "name": "Yi-6B-Chat", "maker": "01-ai", "family": "01-ai/yi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-23", "context_length": 4096, "max_output_tokens": null, "parameters": "6B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 32 layers, hidden size 4096, intermediate 11008, 32 query heads, 4 key-value heads, SwiGLU, vocabulary 64000, bfloat16. RoPE theta 5000000. 6B series default context is 4K in the README model-info table. This chat config.json was not opened separately; architecture matches the verified Yi-6B config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-6B-Chat is the 6B chat model 01.AI open-sourced on 2023-11-23, with official AWQ 4-bit and GPTQ 8-bit weights. Collected 2026-10-15.", "what_it_is_for": "Chat companion to Yi-6B for personal and academic use, including the quantized weights meant for consumer GPUs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi/yi-9b": { "id": "01-ai/yi/yi-9b", "name": "Yi-9B", "maker": "01-ai", "family": "01-ai/yi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-06", "context_length": 4096, "max_output_tokens": null, "parameters": "9B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 48 layers, hidden size 4096, intermediate 11008, 32 query heads, 4 key-value heads, SwiGLU, vocabulary 64000, bfloat16. Depth-extended from the 6B layout. RoPE theta 10000. Original Yi-9B config uses RoPE theta 10000, unlike Yi-6B (5e6) and later Yi-1.5-9B (5e6). max_position_embeddings is 4096. README: continuously trained from Yi-6B on 0.8T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-9B is a 9B base model 01.AI open-sourced on 2024-03-06, continued from Yi-6B. Weights remain on Hugging Face under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The maker says Yi-9B is the strongest of the original Yi series at coding and math, and is aimed at code, math, reasoning, and reading tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi/yi-9b-200k": { "id": "01-ai/yi/yi-9b-200k", "name": "Yi-9B-200K", "maker": "01-ai", "family": "01-ai/yi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-16", "context_length": 262144, "max_output_tokens": null, "parameters": "9B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 48 layers, hidden size 4096, intermediate 11008, 32 query heads, 4 key-value heads, SwiGLU, vocabulary 64000, bfloat16. Depth-extended from the 6B layout. RoPE theta 10000000. config.json max_position_embeddings is 262144, not 200000. The product name and README still say 200K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-9B-200K is the long-context 9B base 01.AI open-sourced on 2024-03-16. The published config allows 262,144 positions even though the name says 200K. Collected 2026-10-15.", "what_it_is_for": "Long-context continuation of Yi-9B, which the maker described as the coding and math standout of the original series.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-1-5/yi-1-5-34b": { "id": "01-ai/yi-1-5/yi-1-5-34b", "name": "Yi-1.5-34B", "maker": "01-ai", "family": "01-ai/yi-1-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-13", "context_length": 4096, "max_output_tokens": null, "parameters": "34B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 60 layers, hidden 7168, intermediate 20480, 56 query heads, 8 KV heads, SwiGLU, vocab 64000, bfloat16, RoPE theta 5000000. Yi-1.5 configs use rms_norm_eps 1e-6. config.json max_position_embeddings 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-1.5-34B is part of 01.AI's Yi-1.5 series, open-sourced 2024-05-13. Yi-1.5 is the May 2024 upgrade of Yi: continued pre-training on 500B high-quality tokens (card lists 3.6T pre-trained tokens for the series) and fine-tuning on 3M samples. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Base checkpoint of the 34B Yi-1.5 line. The card says the 34B base is on par with or ahead of some larger models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-1-5/yi-1-5-34b-32k": { "id": "01-ai/yi-1-5/yi-1-5-34b-32k", "name": "Yi-1.5-34B-32K", "maker": "01-ai", "family": "01-ai/yi-1-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-13", "context_length": 32768, "max_output_tokens": null, "parameters": "34B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 60 layers, hidden 7168, intermediate 20480, 56 query heads, 8 KV heads, SwiGLU, vocab 64000, bfloat16, RoPE theta 5000000. Yi-1.5 configs use rms_norm_eps 1e-6. config.json max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-1.5-34B-32K is part of 01.AI's Yi-1.5 series, open-sourced 2024-05-13. Yi-1.5 is the May 2024 upgrade of Yi: continued pre-training on 500B high-quality tokens (card lists 3.6T pre-trained tokens for the series) and fine-tuning on 3M samples. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "32K-context base variant of Yi-1.5-34B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-1-5/yi-1-5-34b-chat": { "id": "01-ai/yi-1-5/yi-1-5-34b-chat", "name": "Yi-1.5-34B-Chat", "maker": "01-ai", "family": "01-ai/yi-1-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-13", "context_length": 4096, "max_output_tokens": null, "parameters": "34B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 60 layers, hidden 7168, intermediate 20480, 56 query heads, 8 KV heads, SwiGLU, vocab 64000, bfloat16, RoPE theta 5000000. Yi-1.5 configs use rms_norm_eps 1e-6. Chat config.json max_position_embeddings 4096. use_cache is false in that file.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-1.5-34B-Chat is part of 01.AI's Yi-1.5 series, open-sourced 2024-05-13. Yi-1.5 is the May 2024 upgrade of Yi: continued pre-training on 500B high-quality tokens (card lists 3.6T pre-trained tokens for the series) and fine-tuning on 3M samples. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Instruction chat model. The card says Yi-1.5-34B-Chat is on par with or ahead of larger models on most of the maker's benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-1-5/yi-1-5-34b-chat-16k": { "id": "01-ai/yi-1-5/yi-1-5-34b-chat-16k", "name": "Yi-1.5-34B-Chat-16K", "maker": "01-ai", "family": "01-ai/yi-1-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-13", "context_length": 16384, "max_output_tokens": null, "parameters": "34B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 60 layers, hidden 7168, intermediate 20480, 56 query heads, 8 KV heads, SwiGLU, vocab 64000, bfloat16, RoPE theta 5000000. Yi-1.5 configs use rms_norm_eps 1e-6. config.json max_position_embeddings 16384.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-1.5-34B-Chat-16K is part of 01.AI's Yi-1.5 series, open-sourced 2024-05-13. Yi-1.5 is the May 2024 upgrade of Yi: continued pre-training on 500B high-quality tokens (card lists 3.6T pre-trained tokens for the series) and fine-tuning on 3M samples. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "16K chat variant of Yi-1.5-34B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-1-5/yi-1-5-6b": { "id": "01-ai/yi-1-5/yi-1-5-6b", "name": "Yi-1.5-6B", "maker": "01-ai", "family": "01-ai/yi-1-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-13", "context_length": 4096, "max_output_tokens": null, "parameters": "6B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 32 layers, hidden 4096, intermediate 11008, 32 query heads, 4 KV heads, SwiGLU, vocab 64000, bfloat16, RoPE theta 5000000. Yi-1.5 configs use rms_norm_eps 1e-6. config.json max_position_embeddings 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-1.5-6B is part of 01.AI's Yi-1.5 series, open-sourced 2024-05-13. Yi-1.5 is the May 2024 upgrade of Yi: continued pre-training on 500B high-quality tokens (card lists 3.6T pre-trained tokens for the series) and fine-tuning on 3M samples. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "6B base. The official model list has no 16K or 32K 6B checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-1-5/yi-1-5-6b-chat": { "id": "01-ai/yi-1-5/yi-1-5-6b-chat", "name": "Yi-1.5-6B-Chat", "maker": "01-ai", "family": "01-ai/yi-1-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-13", "context_length": 4096, "max_output_tokens": null, "parameters": "6B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 32 layers, hidden 4096, intermediate 11008, 32 query heads, 4 KV heads, SwiGLU, vocab 64000, bfloat16, RoPE theta 5000000. Yi-1.5 configs use rms_norm_eps 1e-6. Unsuffixed chat is the 4K line. This 6B chat config.json was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-1.5-6B-Chat is part of 01.AI's Yi-1.5 series, open-sourced 2024-05-13. Yi-1.5 is the May 2024 upgrade of Yi: continued pre-training on 500B high-quality tokens (card lists 3.6T pre-trained tokens for the series) and fine-tuning on 3M samples. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Smallest Yi-1.5 chat model. No 16K chat sibling was in the official list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-1-5/yi-1-5-9b": { "id": "01-ai/yi-1-5/yi-1-5-9b", "name": "Yi-1.5-9B", "maker": "01-ai", "family": "01-ai/yi-1-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-13", "context_length": 4096, "max_output_tokens": null, "parameters": "9B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 48 layers, hidden 4096, intermediate 11008, 32 query heads, 4 KV heads, SwiGLU, vocab 64000, bfloat16, RoPE theta 5000000. Yi-1.5 configs use rms_norm_eps 1e-6. config.json max_position_embeddings 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-1.5-9B is part of 01.AI's Yi-1.5 series, open-sourced 2024-05-13. Yi-1.5 is the May 2024 upgrade of Yi: continued pre-training on 500B high-quality tokens (card lists 3.6T pre-trained tokens for the series) and fine-tuning on 3M samples. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "9B base. The card says Yi-1.5-9B is the top performer among similarly sized open models, from the maker's own chart.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-1-5/yi-1-5-9b-32k": { "id": "01-ai/yi-1-5/yi-1-5-9b-32k", "name": "Yi-1.5-9B-32K", "maker": "01-ai", "family": "01-ai/yi-1-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-13", "context_length": 32768, "max_output_tokens": null, "parameters": "9B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 48 layers, hidden 4096, intermediate 11008, 32 query heads, 4 KV heads, SwiGLU, vocab 64000, bfloat16, RoPE theta 5000000. Yi-1.5 configs use rms_norm_eps 1e-6. config.json max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-1.5-9B-32K is part of 01.AI's Yi-1.5 series, open-sourced 2024-05-13. Yi-1.5 is the May 2024 upgrade of Yi: continued pre-training on 500B high-quality tokens (card lists 3.6T pre-trained tokens for the series) and fine-tuning on 3M samples. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "32K-context base variant of Yi-1.5-9B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-1-5/yi-1-5-9b-chat": { "id": "01-ai/yi-1-5/yi-1-5-9b-chat", "name": "Yi-1.5-9B-Chat", "maker": "01-ai", "family": "01-ai/yi-1-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-13", "context_length": 4096, "max_output_tokens": null, "parameters": "9B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 48 layers, hidden 4096, intermediate 11008, 32 query heads, 4 KV heads, SwiGLU, vocab 64000, bfloat16, RoPE theta 5000000. Yi-1.5 configs use rms_norm_eps 1e-6. Unsuffixed chat is the 4K line on the model card. This 9B chat config.json was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-1.5-9B-Chat is part of 01.AI's Yi-1.5 series, open-sourced 2024-05-13. Yi-1.5 is the May 2024 upgrade of Yi: continued pre-training on 500B high-quality tokens (card lists 3.6T pre-trained tokens for the series) and fine-tuning on 3M samples. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "9B chat model. The card calls it the top performer among similarly sized open chat models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-1-5/yi-1-5-9b-chat-16k": { "id": "01-ai/yi-1-5/yi-1-5-9b-chat-16k", "name": "Yi-1.5-9B-Chat-16K", "maker": "01-ai", "family": "01-ai/yi-1-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-13", "context_length": 16384, "max_output_tokens": null, "parameters": "9B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 48 layers, hidden 4096, intermediate 11008, 32 query heads, 4 KV heads, SwiGLU, vocab 64000, bfloat16, RoPE theta 5000000. Yi-1.5 configs use rms_norm_eps 1e-6. config.json max_position_embeddings 16384.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-1.5-9B-Chat-16K is part of 01.AI's Yi-1.5 series, open-sourced 2024-05-13. Yi-1.5 is the May 2024 upgrade of Yi: continued pre-training on 500B high-quality tokens (card lists 3.6T pre-trained tokens for the series) and fine-tuning on 3M samples. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "16K chat variant of Yi-1.5-9B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-coder/yi-coder-1-5b": { "id": "01-ai/yi-coder/yi-coder-1-5b", "name": "Yi-Coder-1.5B", "maker": "01-ai", "family": "01-ai/yi-coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-05", "context_length": 131072, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 24 layers, hidden 2048, intermediate 5504, 16 query heads, 16 KV heads, SwiGLU, vocab 64000, bfloat16, RoPE theta 10000000, max_position_embeddings 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-Coder-1.5B is an open code model from 01.AI, released 2024-09-05 with a 128K window. Collected 2026-10-15.", "what_it_is_for": "1.5B base code model for local and low-footprint coding tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-coder/yi-coder-1-5b-chat": { "id": "01-ai/yi-coder/yi-coder-1-5b-chat", "name": "Yi-Coder-1.5B-Chat", "maker": "01-ai", "family": "01-ai/yi-coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-05", "context_length": 131072, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 24 layers, hidden 2048, intermediate 5504, 16 query heads, 16 KV heads, SwiGLU, vocab 64000, bfloat16, RoPE theta 10000000, max_position_embeddings 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-Coder-1.5B-Chat is an open code model from 01.AI, released 2024-09-05 with a 128K window. Collected 2026-10-15.", "what_it_is_for": "1.5B chat code model. The README lists the same 128K window and 52 languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-coder/yi-coder-9b": { "id": "01-ai/yi-coder/yi-coder-9b", "name": "Yi-Coder-9B", "maker": "01-ai", "family": "01-ai/yi-coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-05", "context_length": 131072, "max_output_tokens": null, "parameters": "9B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 48 layers, hidden 4096, intermediate 11008, 32 query heads, 4 KV heads, SwiGLU, vocab 64000, bfloat16, RoPE theta 10000000, max_position_embeddings 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-Coder-9B is an open code model from 01.AI, released 2024-09-05 with a 128K window. Collected 2026-10-15.", "what_it_is_for": "Base code model. The README presents it as a small code model with long-context coding ability.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-coder/yi-coder-9b-chat": { "id": "01-ai/yi-coder/yi-coder-9b-chat", "name": "Yi-Coder-9B-Chat", "maker": "01-ai", "family": "01-ai/yi-coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-05", "context_length": 131072, "max_output_tokens": null, "parameters": "9B", "architecture": "Llama-style decoder-only Transformer (config architectures: LlamaForCausalLM). The Yi README says the series uses the Llama layout but is not a Llama-weight derivative. 48 layers, hidden 4096, intermediate 11008, 32 query heads, 4 KV heads, SwiGLU, vocab 64000, bfloat16, RoPE theta 10000000, max_position_embeddings 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-Coder-9B-Chat is an open code model from 01.AI, released 2024-09-05 with a 128K window. Collected 2026-10-15.", "what_it_is_for": "Chat code model. README shows it ahead of several larger code chat models on the maker's multilingual HumanEval average.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-large/yi-large": { "id": "01-ai/yi-large/yi-large", "name": "Yi-Large", "maker": "01-ai", "family": "01-ai/yi-large", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-13", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Closed model. The company and press called it a hundred-billion-parameter-class model. No layer or expert config was published. International docs call it the state-of-the-art chat and text model with a 32K window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 3.0 }, "summary_intro": "Yi-Large is 01.AI's closed hundred-billion-parameter-class model, the flagship before Yi-Lightning. The international price page listed a 32K window at $3 / $3 per million tokens. The first-party APIs no longer list it. Collected 2026-10-15.", "what_it_is_for": "International docs call it the expert model for complex inference, prediction, and natural-language understanding, with multilingual strength called out for Spanish, Chinese, and Japanese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-large/yi-large-fc": { "id": "01-ai/yi-large/yi-large-fc", "name": "Yi-Large-FC", "maker": "01-ai", "family": "01-ai/yi-large", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Closed tool-use variant. International docs say the model decides whether to call a tool from the user-supplied tool definition and emits the call in a specified format. No architecture was published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 3.0 }, "summary_intro": "Yi-Large-FC is 01.AI's closed tool-calling variant of the Yi-Large line, priced like Yi-Large at $3 / $3 per million tokens with a 32K window. Collected 2026-10-15.", "what_it_is_for": "The maker says it is for production agents and workflows that need tool calls, including data analysis, charts, and code execution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-large/yi-large-preview": { "id": "01-ai/yi-large/yi-large-preview", "name": "Yi-Large-Preview", "maker": "01-ai", "family": "01-ai/yi-large", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed earlier flagship. The Yi-Lightning paper calls Yi-Large-Preview the previous-generation model and reports academic scores for it. No public config was released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-Large-Preview is the previous closed 01.AI flagship named in the Yi-Lightning technical report and on a March 2025 Arena mirror. Collected 2026-10-15.", "what_it_is_for": "The paper uses it as the prior-generation comparison point for Yi-Lightning. No separate product page was retrieved.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-large/yi-large-rag": { "id": "01-ai/yi-large/yi-large-rag", "name": "Yi-Large-RAG", "maker": "01-ai", "family": "01-ai/yi-large", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": 16000, "max_output_tokens": null, "parameters": "", "architecture": "Closed service. Alibaba Cloud's Yi API help says the model decides whether it needs fresh web information, then retrieves it and answers. No 01.AI architecture page was found.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-Large-RAG is a closed 01.AI API service with built-in web retrieval, documented on Alibaba Cloud's Yi API help with a 16K input limit. It is not on the 2026 01.AI price table. Collected 2026-10-15.", "what_it_is_for": "The partner help page says the model judges whether a question needs the latest web information, fetches it, and then answers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-large/yi-large-turbo": { "id": "01-ai/yi-large/yi-large-turbo", "name": "Yi-Large-Turbo", "maker": "01-ai", "family": "01-ai/yi-large", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "Closed model. International docs describe a high-performance, lower-price sibling of Yi-Large. No architecture was published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.19, "completion_per_m_usd": 0.19 }, "summary_intro": "Yi-Large-Turbo is a closed, cheaper 01.AI API model with a 4K window at $0.19 per million input and output tokens on the international price page. It is no longer on the Chinese price table. Collected 2026-10-15.", "what_it_is_for": "The international docs call it the cost-effective choice for complex inference and high-quality text generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-lightning/yi-lightning": { "id": "01-ai/yi-lightning/yi-lightning", "name": "Yi-Lightning", "maker": "01-ai", "family": "01-ai/yi-lightning", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-16", "context_length": 16000, "max_output_tokens": null, "parameters": "", "architecture": "Closed flagship. The technical report says a mixture-of-experts model, vocabulary expanded to 100352, and a pre-training corpus of multilingual web documents crawled through early 2024 plus books, papers, code, and QA pairs. The company site calls it a hundred-billion-parameter-class MoE. Active parameter count and expert count were not in the report text retrieved.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-Lightning is 01.AI's closed MoE flagship, announced 2024-10-16. The Chinese API still listed it on 2026-10-15, but the platform is shutting the API down and the endpoint smart-routes. Collected 2026-10-15.", "what_it_is_for": "The maker called it the full-scene top language model: fast inference and high quality for interactive and complex reasoning. The current docs also describe it as a routed, low-price service.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-lightning/yi-lightning-lite": { "id": "01-ai/yi-lightning/yi-lightning-lite", "name": "Yi-Lightning-Lite", "maker": "01-ai", "family": "01-ai/yi-lightning", "status": "retired", "confidence": "low", "weights": "closed", "license": "", "release_date": "2024-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed model. No architecture note was found on an official 01.AI page. A March 2025 Arena mirror lists it under 01 AI as proprietary.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-Lightning-Lite is a closed 01.AI model name that appeared on a March 2025 Chatbot Arena mirror next to Yi-Lightning. It is not on the current official price table. Collected 2026-10-15. Confidence is low.", "what_it_is_for": "No official task description was retrieved. The name and Arena placement imply a smaller or cheaper Lightning sibling, which is not treated here as a sourced spec.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-medium/yi-medium": { "id": "01-ai/yi-medium/yi-medium", "name": "Yi-Medium", "maker": "01-ai", "family": "01-ai/yi-medium", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Closed mid-size API model. No architecture was published. The current Lingyiwanwu docs still use the model id yi-medium in a LangChain example, but it is not on the price table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-Medium is a closed mid-size 01.AI API model. A partner help page gives it a 32K window. The id still appears in a Lingyiwanwu code sample, but it is not on the 2026 price table. Collected 2026-10-15.", "what_it_is_for": "The partner page says it is the balanced, lower-cost model for everyday chat, QA, collaboration, and translation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-medium/yi-medium-200k": { "id": "01-ai/yi-medium/yi-medium-200k", "name": "Yi-Medium-200K", "maker": "01-ai", "family": "01-ai/yi-medium", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": 200000, "max_output_tokens": null, "parameters": "", "architecture": "Closed long-context API id. The live Lingyiwanwu billing notes still say yi-medium-200k can handle about 200,000 to 300,000 Chinese characters. It is not on the price table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-Medium-200K is a long-context 01.AI API id that the current billing notes still mention, without a price. Collected 2026-10-15.", "what_it_is_for": "The docs use it as the example of a long Chinese-document model, on the order of 200,000 to 300,000 Chinese characters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-vision/yi-vision": { "id": "01-ai/yi-vision/yi-vision", "name": "Yi-Vision", "maker": "01-ai", "family": "01-ai/yi-vision", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 16000, "max_output_tokens": null, "parameters": "", "architecture": "Closed vision-language API model. International docs say it handles complex visual tasks over multiple images. No architecture was published. Distinct from the open Yi-VL weights.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-Vision is 01.AI's earlier closed vision API, listed on the international platform with a 16K window. It is not on the 2026 Chinese price table. Collected 2026-10-15.", "what_it_is_for": "The maker says it is for image question answering, chart understanding, OCR, and visual reasoning over multiple images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-vision/yi-vision-v2": { "id": "01-ai/yi-vision/yi-vision-v2", "name": "Yi-Vision-V2", "maker": "01-ai", "family": "01-ai/yi-vision", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 16000, "max_output_tokens": null, "parameters": "", "architecture": "Closed vision-language API model. Current Chinese docs describe multi-image understanding. Since 2025-08-07 the endpoint smart-routes, including to Qwen2.5-VL-72B-Instruct. No architecture was published.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-Vision-V2 is the vision model still priced on 01.AI's Chinese platform at 6 CNY per million tokens and a 16K window. The API is being shut down and the id smart-routes. Collected 2026-10-15.", "what_it_is_for": "The docs say it is for multi-image understanding: image QA, charts, OCR, visual reasoning, education, and report or document reading.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-vl/yi-vl-34b": { "id": "01-ai/yi-vl/yi-vl-34b", "name": "Yi-VL-34B", "maker": "01-ai", "family": "01-ai/yi-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "34B", "architecture": "LLaVA-style: CLIP ViT-H/14 vision encoder (initialized from LAION OpenCLIP), two-layer MLP projection with layer norm, and an LLM initialized from Yi-34B-Chat. Image resolution used in the final stage is 448x448.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-VL-34B is 01.AI's open vision-language model, released 2024-01-23 on top of Yi-34B-Chat. Weights remain on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "The maker says it is for multi-round conversations about a single image, including bilingual OCR-style reading of text in images, at 448x448.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-vl/yi-vl-6b": { "id": "01-ai/yi-vl/yi-vl-6b", "name": "Yi-VL-6B", "maker": "01-ai", "family": "01-ai/yi-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "Same LLaVA-style stack as Yi-VL-34B, with the language model initialized from Yi-6B-Chat. Vision tower is CLIP ViT-H/14. Final image resolution 448x448.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-VL-6B is the 6B open vision-language model 01.AI released with Yi-VL-34B on 2024-01-23. Collected 2026-10-15.", "what_it_is_for": "Same single-image bilingual visual question answering as Yi-VL-34B, at the 6B size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "01-ai/yi-vl/yi-vl-plus": { "id": "01-ai/yi-vl/yi-vl-plus", "name": "Yi-VL-Plus", "maker": "01-ai", "family": "01-ai/yi-vl", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-03-15", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "Closed vision API, separate from the open Yi-VL-6B and Yi-VL-34B weights. Press reproductions of the March 2024 price table say it accepts 1024x1024 images. No architecture was published.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yi-VL-Plus is the closed multimodal API 01.AI opened in March 2024, priced in contemporary press at 6 CNY per million tokens with a 4K window. It is not the open Yi-VL weight. Collected 2026-10-15.", "what_it_is_for": "Press quoting the platform says it is for chart and screenshot understanding, OCR, and visual reasoning, with a claim of stronger Chinese chart behavior than GPT-4V.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalborg/audiomae-plus-plus/audiomae-plus-plus-base": { "id": "aalborg/audiomae-plus-plus/audiomae-plus-plus-base", "name": "AudioMAE++-Base", "maker": "aalborg", "family": "aalborg/audiomae-plus-plus", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-07-14", "context_length": null, "max_output_tokens": null, "parameters": "141.9M", "architecture": "Audio masked autoencoder. Default encoder is 12 transformer++ blocks, feature dimension 768, macaron layout with SwiGLU. Decoder is 4 transformer++ blocks at dimension 384 and is discarded after pretraining. RoPE is not the default. 80% of 4x16 patches are dropped. 16 kHz log-mel, 2-second 200x80 crops. Table 2 lists encoder parameters only.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AudioMAE++-Base is the default AudioMAE++ from Yadav, Theodoridis, and Tan at Aalborg University. The encoder is a 12-block macaron transformer with SwiGLU layers, and the decoder is discarded after pretraining. Status on 2026-10-15 is active.", "what_it_is_for": "A frozen audio embedding. The paper reports it above the much larger MAE-Huge and MWMAE-Huge rows in its own comparison, at about four times fewer parameters than those rows.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalborg/audiomae-plus-plus/audiomae-plus-plus-large": { "id": "aalborg/audiomae-plus-plus/audiomae-plus-plus-large", "name": "AudioMAE++-Large", "maker": "aalborg", "family": "aalborg/audiomae-plus-plus", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-07-14", "context_length": null, "max_output_tokens": null, "parameters": "504.0M", "architecture": "Audio masked autoencoder. Encoder is 24 transformer++ blocks, feature dimension 1024, macaron layout with SwiGLU. Decoder dimension 512. RoPE is not the default. 80% of 4x16 patches are dropped. 16 kHz log-mel, 2-second 200x80 crops. Table 2 lists encoder parameters only. Hear config maepp_large_200_16x4 is in the official repo.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AudioMAE++-Large is the biggest AudioMAE++ reported by Yadav, Theodoridis, and Tan at Aalborg University. The encoder is 24 macaron transformer++ blocks at dimension 1024. Status on 2026-10-15 is active. The official repository ships a Large hear config. A directory listing of the weight folder was not opened.", "what_it_is_for": "A frozen audio embedding. In the paper's table it is the top aggregate, and the best row on NSynth Pitch 5h, CREMA-D, and Speech Commands 5h.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalborg/audiomae-plus-plus/audiomae-plus-plus-tiny": { "id": "aalborg/audiomae-plus-plus/audiomae-plus-plus-tiny", "name": "AudioMAE++-Tiny", "maker": "aalborg", "family": "aalborg/audiomae-plus-plus", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-07-14", "context_length": null, "max_output_tokens": null, "parameters": "8.9M", "architecture": "Audio masked autoencoder. Encoder is 12 transformer++ blocks, feature dimension 192, macaron layout with a half-scale MLP and a SwiGLU FFN around multi-head attention. RoPE was tested and is not the default. 80% of 4x16 patches are masked and dropped. 16 kHz log-mel, 2-second 200x80 crops, class token, MSE on masked patches. The Table 2 caption says only encoder parameters are listed.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AudioMAE++-Tiny is the smallest AudioMAE++ encoder from Yadav, Theodoridis, and Tan at Aalborg University and the Pioneer Centre for AI. It is a masked autoencoder whose blocks use a macaron layout and SwiGLU feed-forward layers. Status on 2026-10-15 is active.", "what_it_is_for": "A frozen general-purpose audio embedding. The README's extract script runs this Tiny config by default, together with Base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalborg/axlstm/axlstm-base": { "id": "aalborg/axlstm/axlstm-base", "name": "AxLSTM-Base", "maker": "aalborg", "family": "aalborg/axlstm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-08-29", "context_length": null, "max_output_tokens": null, "parameters": "65.6M", "architecture": "Audio xLSTM. 12 mLSTM blocks, feature dimension 768, expansion factor 3. 16 kHz log-mel (25 ms window, 10 ms hop, 80 bins), 2-second crops shaped 200x80, 4x16 patches. Masked-patch reconstruction head is discarded downstream.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AxLSTM-Base is the largest default Audio xLSTM from Yadav, Theodoridis, and Tan at Aalborg University and the Pioneer Centre for AI. Status on 2026-10-15 is active. The paper's main table gives 65.6M parameters and s(m) 86.6\u00b10.2.", "what_it_is_for": "A frozen audio embedding. The paper presents it as the best AxLSTM size on the ten-task HEAR subset, ahead of the authors' SSAST-Base baseline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalborg/axlstm/axlstm-small": { "id": "aalborg/axlstm/axlstm-small", "name": "AxLSTM-Small", "maker": "aalborg", "family": "aalborg/axlstm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-08-29", "context_length": null, "max_output_tokens": null, "parameters": "16.7M", "architecture": "Audio xLSTM. 12 mLSTM blocks, feature dimension 384, expansion factor 3. 16 kHz log-mel (25 ms window, 10 ms hop, 80 bins), 2-second crops shaped 200x80, 4x16 patches. Masked-patch reconstruction head is discarded downstream.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AxLSTM-Small is the middle Audio xLSTM from Yadav, Theodoridis, and Tan, released with the Aalborg University audio-representation line. Status on 2026-10-15 is active. Table 2 prints 16.7M parameters and s(m) 81.1\u00b10.3.", "what_it_is_for": "A frozen audio embedding for the same ten-task HEAR subset used across this lab's papers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalborg/axlstm/axlstm-tiny": { "id": "aalborg/axlstm/axlstm-tiny", "name": "AxLSTM-Tiny", "maker": "aalborg", "family": "aalborg/axlstm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-08-29", "context_length": null, "max_output_tokens": null, "parameters": "4.3M", "architecture": "Audio xLSTM. 12 mLSTM blocks, feature dimension 192, expansion factor 3. Matrix memory cell, exponential gating. The paper's default after ablations disables the alternating flip. 16 kHz log-mel (25 ms window, 10 ms hop, 80 bins), 2-second crops shaped 200x80, 4x16 patches. Masked-patch reconstruction with a one-hidden-layer MLP, discarded downstream.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AxLSTM-Tiny is the smallest Audio xLSTM from Sarthak Yadav and Zheng-Hua Tan at Aalborg University, with Sergios Theodoridis (also at the National and Kapodistrian University of Athens). It learns embeddings from masked spectrogram patches with mLSTM blocks. Status on 2026-10-15 is active.", "what_it_is_for": "A frozen general-purpose audio embedding. The official repo exposes `axlstm_tiny_200_16x4` for HEAR feature extraction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalborg/da-bert-old-news/da-bert-old-news-v1": { "id": "aalborg/da-bert-old-news/da-bert-old-news-v1", "name": "DA-BERT Old News V1", "maker": "aalborg", "family": "aalborg/da-bert-old-news", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-04-01", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT masked language model, 12 layers, hidden size 768, 12 attention heads, vocab size 30522, absolute position embeddings, max position embeddings 512", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DA-BERT Old News V1 is a from-scratch BERT masked-language model from CALDISS at Aalborg University, released as open weights under MIT. It was pretrained on historical Danish newspaper text so that 18th- and 19th-century Danish does not have to be read with a modern-Danish encoder. Status on 2026-10-15 is active. The V2 card later says V1 and V3 perform better than V2 and suggests using V1 or V3.", "what_it_is_for": "The card says the model is for domain-specific masked token prediction, mean-pooled embeddings, and as a baseline for further fine-tuning on historical Danish or Scandinavian text. Direct use on modern Danish is out of scope.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalborg/da-bert-old-news/da-bert-old-news-v2": { "id": "aalborg/da-bert-old-news/da-bert-old-news-v2", "name": "DA-BERT Old News V2", "maker": "aalborg", "family": "aalborg/da-bert-old-news", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-08-14", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT masked language model, 12 layers, hidden size 768, 12 attention heads, vocab size 30522, absolute position embeddings, max position embeddings 512", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DA-BERT Old News V2 is the second from-scratch BERT from CALDISS at Aalborg University, pretrained with masked language modeling on a larger and more tightly segmented slice of the ENO historical newspaper corpus. The weights are public under MIT and the Hub still lists the repo, so status on 2026-10-15 is active. The same card says V1 and V3 perform much better and tells users to use one of those instead.", "what_it_is_for": "Same job as V1: historical Danish fill-mask, embeddings, and a baseline for later fine-tuning. The card says it is not for modern Danish.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalborg/da-bert-old-news/da-bert-old-news-v3": { "id": "aalborg/da-bert-old-news/da-bert-old-news-v3", "name": "DA-BERT Old News V3", "maker": "aalborg", "family": "aalborg/da-bert-old-news", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-10-08", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "XLM-RoBERTa masked language model continued from vesteinn/DanskBERT, 12 layers, hidden size 768, 12 attention heads, vocab size 50005, max_position_embeddings 514. The card says a custom ENO WordPiece tokenizer replaced the DanskBERT tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DA-BERT Old News V3 is the third historical-Danish encoder from CALDISS at Aalborg University. Unlike V1 and V2, which are from-scratch BERT runs, the card says this one is continued masked-language-model training of Vesteinn Sn\u00e6bjarnarson's DanskBERT on the full ENO corpus, with a new tokenizer built for archaic spelling. Weights are on the Hub under MIT. Status on 2026-10-15 is active. The V2 card names V3, with V1, as the versions to use.", "what_it_is_for": "Historical Danish fill-mask, embeddings, and a base for later task fine-tunes. The card says the modern DanskBERT tokenizer was not used because it would fragment archaic words.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalborg/ssam/ssam-base": { "id": "aalborg/ssam/ssam-base", "name": "SSAM-Base", "maker": "aalborg", "family": "aalborg/ssam", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-06-04", "context_length": null, "max_output_tokens": null, "parameters": "69.3M", "architecture": "Self-Supervised Audio Mamba. 12 Mamba blocks, feature dimension 768, expansion factor 3, d_state 24, d_conv 4. 16 kHz log-mel (25 ms window, 10 ms hop, 80 bins), 2-second crops shaped 200x80, non-overlapping 4x16 patches (250 patches), class token, sinusoidal positions, 50% unstructured mask, MSE patch reconstruction. Reconstruction head is discarded downstream.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SSAM-Base is the largest Self-Supervised Audio Mamba released with the Audio Mamba paper by Sarthak Yadav and Zheng-Hua Tan at Aalborg University and the Pioneer Centre for AI. Status on 2026-10-15 is active. The paper reports an aggregate s(m) of 89.7\u00b10.3 on its HEAR subset.", "what_it_is_for": "A frozen general-purpose audio embedding for the ten-task HEAR subset used in the paper, classified with a one-hidden-layer MLP.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalborg/ssam/ssam-small": { "id": "aalborg/ssam/ssam-small", "name": "SSAM-Small", "maker": "aalborg", "family": "aalborg/ssam", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-06-04", "context_length": null, "max_output_tokens": null, "parameters": "17.9M", "architecture": "Self-Supervised Audio Mamba. 12 Mamba blocks, feature dimension 384, expansion factor 3, d_state 24, d_conv 4. 16 kHz log-mel (25 ms window, 10 ms hop, 80 bins), 2-second crops shaped 200x80, non-overlapping 4x16 patches (250 patches), class token, sinusoidal positions, 50% unstructured mask, MSE patch reconstruction. Reconstruction head is discarded downstream.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SSAM-Small is the middle Self-Supervised Audio Mamba from Sarthak Yadav and Zheng-Hua Tan at Aalborg University and the Pioneer Centre for AI. It uses the same masked-spectrogram pretraining recipe as SSAM-Tiny with a wider Mamba stack. Status on 2026-10-15 is active.", "what_it_is_for": "A frozen general-purpose audio embedding. The paper evaluates it with a one-hidden-layer MLP on a ten-task HEAR subset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalborg/ssam/ssam-tiny": { "id": "aalborg/ssam/ssam-tiny", "name": "SSAM-Tiny", "maker": "aalborg", "family": "aalborg/ssam", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-06-04", "context_length": null, "max_output_tokens": null, "parameters": "4.8M", "architecture": "Self-Supervised Audio Mamba. 12 Mamba blocks, feature dimension 192, expansion factor 3, d_state 24, d_conv 4. 16 kHz log-mel (25 ms window, 10 ms hop, 80 bins), 2-second crops shaped 200x80, non-overlapping 4x16 patches (250 patches), class token, sinusoidal positions, 50% unstructured mask, MSE patch reconstruction. Reconstruction head is discarded downstream.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SSAM-Tiny is the smallest Self-Supervised Audio Mamba released by Sarthak Yadav and Zheng-Hua Tan at Aalborg University and the Pioneer Centre for AI. It is a Mamba encoder pretrained to reconstruct masked audio spectrogram patches. Status on 2026-10-15 is active. Weights and code are public under the repository MIT license.", "what_it_is_for": "The paper uses SSAM as a general-purpose audio representation. After pretraining, the reconstruction head is removed and a frozen embedding is classified with a small MLP on a ten-task HEAR subset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/bayesvlm/bayesvlm-base": { "id": "aalto/bayesvlm/bayesvlm-base", "name": "BayesVLM-Base", "maker": "aalto", "family": "aalto/bayesvlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP ViT-B/32, projection dimension 512, plus Kronecker-factorized covariance buffers A_inv and B_inv on the text and vision projections. Base checkpoint laion/CLIP-ViT-B-32-laion2B-s34B-b79K.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto Machine Learning Group released BayesVLM-Base on the aalto-ml Hub org. It is a post-hoc probabilistic wrapper on a LAION CLIP checkpoint, not a retrained CLIP. The card cites ICLR 2026. Weights were on the Hub by 2026-02-10. Collected 2026-10-15.", "what_it_is_for": "Zero-shot image classification and retrieval with uncertainty on CLIP embeddings and logits. The card says the base CLIP weights are unchanged.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/bayesvlm/bayesvlm-large": { "id": "aalto/bayesvlm/bayesvlm-large", "name": "BayesVLM-Large", "maker": "aalto", "family": "aalto/bayesvlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP ViT-L/14, projection dimension 768, plus Kronecker-factorized covariance buffers A_inv and B_inv. Base checkpoint laion/CLIP-ViT-L-14-laion2B-s32B-b82K.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto Machine Learning Group released BayesVLM-Large on the aalto-ml Hub org. It is a post-hoc probabilistic wrapper on a LAION CLIP checkpoint, not a retrained CLIP. The card cites ICLR 2026. Weights were on the Hub by 2026-02-10. Collected 2026-10-15.", "what_it_is_for": "Zero-shot image classification and retrieval with uncertainty on CLIP embeddings and logits. The card says the base CLIP weights are unchanged.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/combfinnish/combfinnish-aed-crdnn": { "id": "aalto/combfinnish/combfinnish-aed-crdnn", "name": "CombFinnish AED-CRDNN", "maker": "aalto", "family": "aalto/combfinnish", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-12", "context_length": null, "max_output_tokens": null, "parameters": "about 40M", "architecture": "SpeechBrain attention encoder-decoder. Encoder is a CRDNN (convolution, LSTM, DNN). Decoder is a GRU. Fully end-to-end. The card says it does not include a language model. speechbrain_interface EncoderDecoderASR.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University Speech Recognition Research Group released this Finnish attention encoder-decoder on the AaltoSpeech Hub org. The card calls it a compact end-to-end model, not state of the art. It is still public. Collected 2026-10-15.", "what_it_is_for": "Finnish speech-to-text for colloquial and dialectal speech as well as more formal speech. The card says matching colloquial forms is a goal of the Puhelahjat data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/hifi-glot/hifi-glot": { "id": "aalto/hifi-glot/hifi-glot", "name": "HiFi-Glot", "maker": "aalto", "family": "aalto/hifi-glot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-01-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end neural formant synthesizer. Source-filter: a neural vocoder generates the glottal excitation and differentiable resonant filters model the formants to produce a waveform. The Hub tree has hifiglot and baseline checkpoints. A config read from the upload diff uses hop 512, n_fft 2048, 128 mels, and allpole order 50. Those fields were not restated as the paper's final architecture.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto Speech Synthesis released HiFi-Glot, an end-to-end neural formant synthesizer. The arXiv abstract lists Lauri Juvela among the authors and names Aalto through the releasing Hub org Aalto-Speech-Synthesis. Co-authors include researchers outside Aalto. Paper v2 is dated 16 February 2026. Checkpoints were uploaded to the Hub on 14 January 2026. Collected 2026-10-15.", "what_it_is_for": "The abstract says HiFi-Glot generates speech with controllable formant frequencies and higher naturalness than Praat-style formant tools. A neural vocoder makes the glottal source and differentiable resonant filters make the formants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/multitask-wav2vec2/wav2vec2-large-multitask-finnish-l2": { "id": "aalto/multitask-wav2vec2/wav2vec2-large-multitask-finnish-l2", "name": "Finnish L2 multi-task wav2vec2", "maker": "aalto", "family": "aalto/multitask-wav2vec2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-task wav2vec 2.0. The card's custom class is Wav2Vec2ForMultiTask. One head is CTC transcription. The other head is a pronunciation rating. Requires the aalto-speech/multitask-wav2vec2 package. 16 kHz.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group (Getman, Al-Ghezi, Gr\u00f3sz, Kurimo) released this multi-task wav2vec 2.0 checkpoint for children's pronunciation practice. The card is on GetmanY1 and was created 4 August 2023. It is public. Collected 2026-10-15.", "what_it_is_for": "The card says the model does speech recognition and speech pronunciation rating at the same time, for a pronunciation-training system for children.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/multitask-wav2vec2/wav2vec2-large-multitask-swedish-ssd": { "id": "aalto/multitask-wav2vec2/wav2vec2-large-multitask-swedish-ssd", "name": "Swedish multi-task wav2vec2 SSD", "maker": "aalto", "family": "aalto/multitask-wav2vec2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-task wav2vec 2.0. The card's custom class is Wav2Vec2ForMultiTask. One head is CTC transcription. The other head is a pronunciation rating. Requires the aalto-speech/multitask-wav2vec2 package. 16 kHz.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group (Getman, Al-Ghezi, Gr\u00f3sz, Kurimo) released this multi-task wav2vec 2.0 checkpoint for children's pronunciation practice. The card is on GetmanY1 and was created 4 August 2023. It is public. Collected 2026-10-15.", "what_it_is_for": "The card says the model does speech recognition and speech pronunciation rating at the same time, for a pronunciation-training system for children.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/mus3d/mus3d": { "id": "aalto/mus3d/mus3d", "name": "MuS3D", "maker": "aalto", "family": "aalto/mus3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2026-10-14", "context_length": null, "max_output_tokens": null, "parameters": "SEG 37M; DiT-Sep about 2.2B", "architecture": "Music source separation via stem discovery. SEG is a 37M diffusion transformer trained with conditional flow matching on PCA-reduced PaSST embeddings (1024 to 128). DiT-Sep is a conditional flow-matching separator based on SAM-Audio large, about 2.2B, conditioned on query embeddings and DAC-VAE latents, fine-tuned from public SAM-Audio weights. BS-Locoformer_query is a medium band-split TF-Locoformer with query conditioning. SEG_resemb adds residual-embedding conditioning.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto Acoustics Lab (Kallinen, Moliner, Juvela, V\u00e4lim\u00e4ki) released MuS3D, a blind music source-separation system that discovers stems from the mixture. The Hub weights are dated 14 October 2026 under remus-aalto. The preprint is arXiv:2609.30912. Status active. Collected 2026-10-15.", "what_it_is_for": "The card and abstract describe iterative discovery of active music sources, then separation conditioned on the discovered audio queries. On sources the model detects, the abstract says it matches manually queried baselines and beats text-queried models. Encoding artifacts are the stated limit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/smelp/smelp": { "id": "aalto/smelp/smelp", "name": "smelp", "maker": "aalto", "family": "aalto/smelp", "status": "active", "confidence": "low", "weights": "open-weights", "license": "MIT", "release_date": "2026-09-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto Speech Synthesis published a Hub repo named smelp on 24 September 2026. The README is empty. Two PyTorch files are in the repo. What the model does is not stated. Confidence is low. Collected 2026-10-15.", "what_it_is_for": "Not stated. The repo name and the file lp_lstm.pt are the only task hints, and they are not a description.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/wav2vec2-fi/wav2vec2-base-fi-150k": { "id": "aalto/wav2vec2-fi/wav2vec2-base-fi-150k", "name": "Finnish Wav2Vec2-Base", "maker": "aalto", "family": "aalto/wav2vec2-fi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-23", "context_length": null, "max_output_tokens": null, "parameters": "95M", "architecture": "wav2vec 2.0 Base. Card initialization: encoder dim 768, 12 layers, FFN 3072, 12 heads, GELU, extractor_mode default, layer_norm_first false. Fairseq on 512 LUMI GPUs (256 AMD MI250x modules), learning rate 6e-4, 125000 updates, warmup 3000, normalize false, max_tokens 2,800,000. Final logged validation loss 2.149.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group (Yaroslav Getman, Tam\u00e1s Gr\u00f3sz, Tommi Lehtonen, Mikko Kurimo) released this Finnish wav2vec 2.0 encoder. Weights are on the researcher Hub account GetmanY1 and are linked from aalto-speech. It is active and was collected on 2026-10-15. It is not an ASR model until it is fine-tuned.", "what_it_is_for": "The card says the checkpoint is a pretrained encoder for later Finnish speech recognition and spoken emotion recognition. It has no tokenizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/wav2vec2-fi/wav2vec2-base-fi-150k-finetuned": { "id": "aalto/wav2vec2-fi/wav2vec2-base-fi-150k-finetuned", "name": "Finnish Wav2Vec2-Base ASR", "maker": "aalto", "family": "aalto/wav2vec2-fi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-12", "context_length": null, "max_output_tokens": null, "parameters": "95M", "architecture": "CTC fine-tune of wav2vec2-base-fi-150k. Same wav2vec 2.0 encoder as that card. Standalone acoustic model, no external language model in the published usage snippet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group released this CTC Finnish speech recognizer. It fine-tunes wav2vec2-base-fi-150k on 4,600 hours of labeled speech. Weights are on GetmanY1. Status is active. Collected 2026-10-15.", "what_it_is_for": "The card says to use it as a standalone Finnish acoustic model. Supervised data is 1,500 hours of colloquial Donate Speech and 3,100 hours of parliamentary speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/wav2vec2-fi/wav2vec2-large-fi-150k": { "id": "aalto/wav2vec2-fi/wav2vec2-large-fi-150k", "name": "Finnish Wav2Vec2-Large", "maker": "aalto", "family": "aalto/wav2vec2-fi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-12", "context_length": null, "max_output_tokens": null, "parameters": "317M", "architecture": "wav2vec 2.0 Large. Card initialization: encoder dim 1024, 24 layers, FFN 4096, 16 heads, extractor_mode layer_norm, layer_norm_first true. Fairseq on 512 LUMI GPUs, learning rate 4e-4, 166667 updates, warmup 5000, normalize true, max_tokens 1,800,000. Final logged validation loss 2.067. The fine-tuned card is the page that states 317 million parameters.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group (Yaroslav Getman, Tam\u00e1s Gr\u00f3sz, Tommi Lehtonen, Mikko Kurimo) released this Finnish wav2vec 2.0 encoder. Weights are on the researcher Hub account GetmanY1 and are linked from aalto-speech. It is active and was collected on 2026-10-15. It is not an ASR model until it is fine-tuned.", "what_it_is_for": "The card says the checkpoint is a pretrained encoder for later Finnish speech recognition and spoken emotion recognition. It has no tokenizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/wav2vec2-fi/wav2vec2-large-fi-150k-finetuned": { "id": "aalto/wav2vec2-fi/wav2vec2-large-fi-150k-finetuned", "name": "Finnish Wav2Vec2-Large ASR", "maker": "aalto", "family": "aalto/wav2vec2-fi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-13", "context_length": null, "max_output_tokens": null, "parameters": "317M", "architecture": "CTC fine-tune of wav2vec2-large-fi-150k. Same wav2vec 2.0 encoder as that card. Standalone acoustic model, no external language model in the published usage snippet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group released this CTC Finnish speech recognizer. It fine-tunes wav2vec2-large-fi-150k on 4,600 hours of labeled speech. Weights are on GetmanY1. Status is active. Collected 2026-10-15.", "what_it_is_for": "The card says to use it as a standalone Finnish acoustic model. Supervised data is 1,500 hours of colloquial Donate Speech and 3,100 hours of parliamentary speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/wav2vec2-fi/wav2vec2-xlarge-fi-150k": { "id": "aalto/wav2vec2-fi/wav2vec2-xlarge-fi-150k", "name": "Finnish Wav2Vec2-XLarge", "maker": "aalto", "family": "aalto/wav2vec2-fi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-12", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "wav2vec 2.0 X-Large. The card says the architecture and objective match the multilingual model in Babu et al., Interspeech 2022. Initialization on this card: encoder dim 1280, 48 layers, FFN 5120, 16 heads, extractor_mode layer_norm, layer_norm_first true. Fairseq on 512 LUMI GPUs, learning rate 1e-4, 390625 updates, warmup 12500, normalize true, max_tokens 960,000. Final logged validation loss 2.062. The fine-tuned card calls it 1 billion parameters. Hub label is 1.0B.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group (Yaroslav Getman, Tam\u00e1s Gr\u00f3sz, Tommi Lehtonen, Mikko Kurimo) released this Finnish wav2vec 2.0 encoder. Weights are on the researcher Hub account GetmanY1 and are linked from aalto-speech. It is active and was collected on 2026-10-15. It is not an ASR model until it is fine-tuned.", "what_it_is_for": "The card says the checkpoint is a pretrained encoder for later Finnish speech recognition and spoken emotion recognition. It has no tokenizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/wav2vec2-fi/wav2vec2-xlarge-fi-150k-finetuned": { "id": "aalto/wav2vec2-fi/wav2vec2-xlarge-fi-150k-finetuned", "name": "Finnish Wav2Vec2-XLarge ASR", "maker": "aalto", "family": "aalto/wav2vec2-fi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-13", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "CTC fine-tune of wav2vec2-xlarge-fi-150k. Same wav2vec 2.0 encoder as that card. Standalone acoustic model, no external language model in the published usage snippet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group released this CTC Finnish speech recognizer. It fine-tunes wav2vec2-xlarge-fi-150k on 4,600 hours of labeled speech. Weights are on GetmanY1. Status is active. Collected 2026-10-15.", "what_it_is_for": "The card says to use it as a standalone Finnish acoustic model. Supervised data is 1,500 hours of colloquial Donate Speech and 3,100 hours of parliamentary speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/wav2vec2-sami/wav2vec2-base-sami-22k": { "id": "aalto/wav2vec2-sami/wav2vec2-base-sami-22k", "name": "Northern S\u00e1mi Wav2Vec2-Base", "maker": "aalto", "family": "aalto/wav2vec2-sami", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-18", "context_length": null, "max_output_tokens": null, "parameters": "95M", "architecture": "wav2vec 2.0 encoder, 95M. Hub repo name and, for the continued-pretraining repos, Hub tags say continued pre-training (CPT). The fine-tuned card describes this checkpoint as pre-trained on 22.4k hours and does not name the initialization model.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group released this Northern S\u00e1mi wav2vec 2.0 checkpoint. Authors on the NoDaLiDa 2025 paper are Getman, Gr\u00f3sz, Hiovain-Asikainen, Lehtonen, and Kurimo. Weights are on GetmanY1. Status active. Collected 2026-10-15.", "what_it_is_for": "Pretrained Northern S\u00e1mi speech encoder. The fine-tuned sibling is the ASR model. The collection describes the set as pre-trained with 22.5k hours of Yle S\u00e1mi radio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/wav2vec2-sami/wav2vec2-base-sami-22k-finetuned": { "id": "aalto/wav2vec2-sami/wav2vec2-base-sami-22k-finetuned", "name": "Northern S\u00e1mi Wav2Vec2-Base ASR", "maker": "aalto", "family": "aalto/wav2vec2-sami", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-18", "context_length": null, "max_output_tokens": null, "parameters": "95M", "architecture": "CTC fine-tune of wav2vec2-base-sami-22k. The card says wav2vec 2.0, 95M parameters, from-scratch pre-training on 22.4k hours of unlabeled S\u00e1mi speech from KAVI. Fine-tuned on 20 hours of S\u00e1mi Parliament speech.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group released this Northern S\u00e1mi wav2vec 2.0 checkpoint. Authors on the NoDaLiDa 2025 paper are Getman, Gr\u00f3sz, Hiovain-Asikainen, Lehtonen, and Kurimo. Weights are on GetmanY1. Status active. Collected 2026-10-15.", "what_it_is_for": "Northern S\u00e1mi speech-to-text. Kielipankki packages the large continued-pretraining fine-tune as the S\u00e1mi ASR model. This card evaluates greedy decoding on a 1-hour out-of-domain read and spontaneous set called Sami-1h-test.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/wav2vec2-sami/wav2vec2-base-sami-cont-pt-22k": { "id": "aalto/wav2vec2-sami/wav2vec2-base-sami-cont-pt-22k", "name": "Northern S\u00e1mi Wav2Vec2-Base continued pre-training", "maker": "aalto", "family": "aalto/wav2vec2-sami", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-13", "context_length": null, "max_output_tokens": null, "parameters": "95M", "architecture": "wav2vec 2.0 encoder, 95M. Hub repo name and, for the continued-pretraining repos, Hub tags say continued pre-training (CPT). The fine-tuned card describes this checkpoint as pre-trained on 22.4k hours and does not name the initialization model.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group released this Northern S\u00e1mi wav2vec 2.0 checkpoint. Authors on the NoDaLiDa 2025 paper are Getman, Gr\u00f3sz, Hiovain-Asikainen, Lehtonen, and Kurimo. Weights are on GetmanY1. Status active. Collected 2026-10-15.", "what_it_is_for": "Pretrained Northern S\u00e1mi speech encoder. The fine-tuned sibling is the ASR model. The collection describes the set as pre-trained with 22.5k hours of Yle S\u00e1mi radio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/wav2vec2-sami/wav2vec2-base-sami-cont-pt-22k-finetuned": { "id": "aalto/wav2vec2-sami/wav2vec2-base-sami-cont-pt-22k-finetuned", "name": "Northern S\u00e1mi Wav2Vec2-Base continued-pretraining ASR", "maker": "aalto", "family": "aalto/wav2vec2-sami", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-06", "context_length": null, "max_output_tokens": null, "parameters": "95M", "architecture": "CTC fine-tune of wav2vec2-base-sami-cont-pt-22k. The card says wav2vec 2.0, 95M parameters, continued pre-training on 22.4k hours of unlabeled S\u00e1mi speech from KAVI. Fine-tuned on 20 hours of S\u00e1mi Parliament speech.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group released this Northern S\u00e1mi wav2vec 2.0 checkpoint. Authors on the NoDaLiDa 2025 paper are Getman, Gr\u00f3sz, Hiovain-Asikainen, Lehtonen, and Kurimo. Weights are on GetmanY1. Status active. Collected 2026-10-15.", "what_it_is_for": "Northern S\u00e1mi speech-to-text. Kielipankki packages the large continued-pretraining fine-tune as the S\u00e1mi ASR model. This card evaluates greedy decoding on a 1-hour out-of-domain read and spontaneous set called Sami-1h-test.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/wav2vec2-sami/wav2vec2-large-sami-22k": { "id": "aalto/wav2vec2-sami/wav2vec2-large-sami-22k", "name": "Northern S\u00e1mi Wav2Vec2-Large", "maker": "aalto", "family": "aalto/wav2vec2-sami", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-18", "context_length": null, "max_output_tokens": null, "parameters": "317M", "architecture": "wav2vec 2.0 encoder, 317M. Hub repo name and, for the continued-pretraining repos, Hub tags say continued pre-training (CPT). The fine-tuned card describes this checkpoint as pre-trained on 22.4k hours and does not name the initialization model.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group released this Northern S\u00e1mi wav2vec 2.0 checkpoint. Authors on the NoDaLiDa 2025 paper are Getman, Gr\u00f3sz, Hiovain-Asikainen, Lehtonen, and Kurimo. Weights are on GetmanY1. Status active. Collected 2026-10-15.", "what_it_is_for": "Pretrained Northern S\u00e1mi speech encoder. The fine-tuned sibling is the ASR model. The collection describes the set as pre-trained with 22.5k hours of Yle S\u00e1mi radio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/wav2vec2-sami/wav2vec2-large-sami-22k-finetuned": { "id": "aalto/wav2vec2-sami/wav2vec2-large-sami-22k-finetuned", "name": "Northern S\u00e1mi Wav2Vec2-Large ASR", "maker": "aalto", "family": "aalto/wav2vec2-sami", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-18", "context_length": null, "max_output_tokens": null, "parameters": "317M", "architecture": "CTC fine-tune of wav2vec2-large-sami-22k. The card says wav2vec 2.0, 317M parameters, from-scratch pre-training on 22.4k hours of unlabeled S\u00e1mi speech from KAVI. Fine-tuned on 20 hours of S\u00e1mi Parliament speech. The model-index name on this card is misspelled wwav2vec2.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group released this Northern S\u00e1mi wav2vec 2.0 checkpoint. Authors on the NoDaLiDa 2025 paper are Getman, Gr\u00f3sz, Hiovain-Asikainen, Lehtonen, and Kurimo. Weights are on GetmanY1. Status active. Collected 2026-10-15.", "what_it_is_for": "Northern S\u00e1mi speech-to-text. Kielipankki packages the large continued-pretraining fine-tune as the S\u00e1mi ASR model. This card evaluates greedy decoding on a 1-hour out-of-domain read and spontaneous set called Sami-1h-test.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/wav2vec2-sami/wav2vec2-large-sami-cont-pt-22k": { "id": "aalto/wav2vec2-sami/wav2vec2-large-sami-cont-pt-22k", "name": "Northern S\u00e1mi Wav2Vec2-Large continued pre-training", "maker": "aalto", "family": "aalto/wav2vec2-sami", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-24", "context_length": null, "max_output_tokens": null, "parameters": "317M", "architecture": "wav2vec 2.0 encoder, 317M. Hub repo name and, for the continued-pretraining repos, Hub tags say continued pre-training (CPT). The fine-tuned card describes this checkpoint as pre-trained on 22.4k hours and does not name the initialization model.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group released this Northern S\u00e1mi wav2vec 2.0 checkpoint. Authors on the NoDaLiDa 2025 paper are Getman, Gr\u00f3sz, Hiovain-Asikainen, Lehtonen, and Kurimo. Weights are on GetmanY1. Status active. Collected 2026-10-15.", "what_it_is_for": "Pretrained Northern S\u00e1mi speech encoder. The fine-tuned sibling is the ASR model. The collection describes the set as pre-trained with 22.5k hours of Yle S\u00e1mi radio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aalto/wav2vec2-sami/wav2vec2-large-sami-cont-pt-22k-finetuned": { "id": "aalto/wav2vec2-sami/wav2vec2-large-sami-cont-pt-22k-finetuned", "name": "Northern S\u00e1mi Wav2Vec2-Large continued-pretraining ASR", "maker": "aalto", "family": "aalto/wav2vec2-sami", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-22", "context_length": null, "max_output_tokens": null, "parameters": "317M", "architecture": "CTC fine-tune of wav2vec2-large-sami-cont-pt-22k. The card says wav2vec 2.0, 317M parameters, continued pre-training on 22.4k hours of unlabeled S\u00e1mi speech from KAVI. Fine-tuned on 20 hours of S\u00e1mi Parliament speech.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aalto University speech group released this Northern S\u00e1mi wav2vec 2.0 checkpoint. Authors on the NoDaLiDa 2025 paper are Getman, Gr\u00f3sz, Hiovain-Asikainen, Lehtonen, and Kurimo. Weights are on GetmanY1. Status active. Collected 2026-10-15.", "what_it_is_for": "Northern S\u00e1mi speech-to-text. Kielipankki packages the large continued-pretraining fine-tune as the S\u00e1mi ASR model. This card evaluates greedy decoding on a 1-hour out-of-domain read and spontaneous set called Sami-1h-test.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aarhus/dacy/da-dacy-edge": { "id": "aarhus/dacy/da-dacy-edge", "name": "DaCy edge", "maker": "aarhus", "family": "aarhus/dacy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy tok2vec pipeline aimed at CPU inference. No vectors (0 keys). Card components: tok2vec, morphologizer, parser, lemmatizer, senter, attribute ruler, ner. The default-pipeline cell omits senter; the components cell includes it. No separate tagger; UPOS is still scored.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aarhus University's Center for Humanities Computing released DaCy edge. It is an active local pipeline in the dacy family. This dossier was collected on 2026-10-15.", "what_it_is_for": "spaCy tok2vec pipeline aimed at CPU inference. No vectors (0 keys). Card components: tok2vec, morphologizer, parser, lemmatizer, senter, attribute ruler, ner. The default-pipeline cell omits senter; the components cell includes it. No separate tagger; UPOS is still scored.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aarhus/dacy/da-dacy-large-ner-fine-grained": { "id": "aarhus/dacy/da-dacy-large-ner-fine-grained", "name": "DaCy large fine-grained NER", "maker": "aarhus", "family": "aarhus/dacy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy transformer plus a single NER head. 18 labels in the Ontonotes-style DANSK scheme: CARDINAL, DATE, EVENT, FACILITY, GPE, LANGUAGE, LAW, LOCATION, MONEY, NORP, ORDINAL, ORGANIZATION, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK OF ART. Backbone named on the card: chcaa/dfm-encoder-large-v1. Version 0.1.0. spaCy >=3.5.0,<3.6.0. Not a full parser.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aarhus University's Center for Humanities Computing released DaCy large fine-grained NER. It is an active local pipeline in the dacy family. This dossier was collected on 2026-10-15.", "what_it_is_for": "spaCy transformer plus a single NER head. 18 labels in the Ontonotes-style DANSK scheme: CARDINAL, DATE, EVENT, FACILITY, GPE, LANGUAGE, LAW, LOCATION, MONEY, NORP, ORDINAL, ORGANIZATION, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK OF ART. Backbone named on the card: chcaa/dfm-encoder-large-v1. Version 0.1.0. spaCy >=3.5.0,<3.6.0. Not a full parser.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aarhus/dacy/da-dacy-large-trf": { "id": "aarhus/dacy/da-dacy-large-trf", "name": "DaCy large", "maker": "aarhus", "family": "aarhus/dacy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy transformer pipeline. 1.0.0 base_model tag is KennethEnevoldsen/dfm-sentence-encoder-large. 0.2.0 card cites chcaa/dfm-encoder-large-v1, which returned 404 on 2026-10-15. 0.2.0 adds coref and an entity linker. meta.json on 0.2.0 lists UD Danish DDT, DaNE, DaCoref, and DaNED as CC BY-SA 4.0 and the cited encoder as CC BY 4.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aarhus University's Center for Humanities Computing released DaCy large. It is an active local pipeline in the dacy family. This dossier was collected on 2026-10-15.", "what_it_is_for": "spaCy transformer pipeline. 1.0.0 base_model tag is KennethEnevoldsen/dfm-sentence-encoder-large. 0.2.0 card cites chcaa/dfm-encoder-large-v1, which returned 404 on 2026-10-15. 0.2.0 adds coref and an entity linker. meta.json on 0.2.0 lists UD Danish DDT, DaNE, DaCoref, and DaNED as CC BY-SA 4.0 and the cited encoder as CC BY 4.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aarhus/dacy/da-dacy-medium-ner-fine-grained": { "id": "aarhus/dacy/da-dacy-medium-ner-fine-grained", "name": "DaCy medium fine-grained NER", "maker": "aarhus", "family": "aarhus/dacy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy transformer plus a single NER head. 18 labels in the Ontonotes-style DANSK scheme: CARDINAL, DATE, EVENT, FACILITY, GPE, LANGUAGE, LAW, LOCATION, MONEY, NORP, ORDINAL, ORGANIZATION, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK OF ART. Backbone named on the card: vesteinn/DanskBERT. Version 0.1.0. spaCy >=3.5.0,<3.6.0. Not a full parser.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aarhus University's Center for Humanities Computing released DaCy medium fine-grained NER. It is an active local pipeline in the dacy family. This dossier was collected on 2026-10-15.", "what_it_is_for": "spaCy transformer plus a single NER head. 18 labels in the Ontonotes-style DANSK scheme: CARDINAL, DATE, EVENT, FACILITY, GPE, LANGUAGE, LAW, LOCATION, MONEY, NORP, ORDINAL, ORGANIZATION, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK OF ART. Backbone named on the card: vesteinn/DanskBERT. Version 0.1.0. spaCy >=3.5.0,<3.6.0. Not a full parser.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aarhus/dacy/da-dacy-medium-trf": { "id": "aarhus/dacy/da-dacy-medium-trf", "name": "DaCy medium", "maker": "aarhus", "family": "aarhus/dacy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy transformer pipeline. 1.0.0 names vesteinn/ScandiBERT-no-faroese. 0.2.0 names vesteinn/DanskBERT. Those are different checkpoints. 1.0.0 components match the other 1.0.0 sizes (no coref). 0.2.0 adds coref and an entity linker.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aarhus University's Center for Humanities Computing released DaCy medium. It is an active local pipeline in the dacy family. This dossier was collected on 2026-10-15.", "what_it_is_for": "spaCy transformer pipeline. 1.0.0 names vesteinn/ScandiBERT-no-faroese. 0.2.0 names vesteinn/DanskBERT. Those are different checkpoints. 1.0.0 components match the other 1.0.0 sizes (no coref). 0.2.0 adds coref and an entity linker.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aarhus/dacy/da-dacy-small-ner-fine-grained": { "id": "aarhus/dacy/da-dacy-small-ner-fine-grained", "name": "DaCy small fine-grained NER", "maker": "aarhus", "family": "aarhus/dacy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy transformer plus a single NER head. 18 labels in the Ontonotes-style DANSK scheme: CARDINAL, DATE, EVENT, FACILITY, GPE, LANGUAGE, LAW, LOCATION, MONEY, NORP, ORDINAL, ORGANIZATION, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK OF ART. Backbone named on the card: jonfd/electra-small-nordic. Version 0.1.0. spaCy >=3.5.0,<3.6.0. Not a full parser.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aarhus University's Center for Humanities Computing released DaCy small fine-grained NER. It is an active local pipeline in the dacy family. This dossier was collected on 2026-10-15.", "what_it_is_for": "spaCy transformer plus a single NER head. 18 labels in the Ontonotes-style DANSK scheme: CARDINAL, DATE, EVENT, FACILITY, GPE, LANGUAGE, LAW, LOCATION, MONEY, NORP, ORDINAL, ORGANIZATION, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK OF ART. Backbone named on the card: jonfd/electra-small-nordic. Version 0.1.0. spaCy >=3.5.0,<3.6.0. Not a full parser.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aarhus/dacy/da-dacy-small-trf": { "id": "aarhus/dacy/da-dacy-small-trf", "name": "DaCy small", "maker": "aarhus", "family": "aarhus/dacy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy transformer pipeline. 1.0.0 and 0.2.0 both name jonfd/electra-small-nordic. 1.0.0: transformer, tagger, morphologizer, trainable lemmatizer, parser, ner. 0.2.0 adds coref, span resolver, span cleaner, and an entity linker.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aarhus University's Center for Humanities Computing released DaCy small. It is an active local pipeline in the dacy family. This dossier was collected on 2026-10-15.", "what_it_is_for": "spaCy transformer pipeline. 1.0.0 and 0.2.0 both name jonfd/electra-small-nordic. 1.0.0: transformer, tagger, morphologizer, trainable lemmatizer, parser, ner. 0.2.0 adds coref, span resolver, span cleaner, and an entity linker.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aarhus/dacy/da-dacy-tiny": { "id": "aarhus/dacy/da-dacy-tiny", "name": "DaCy tiny", "maker": "aarhus", "family": "aarhus/dacy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy tok2vec pipeline, not a transformer. Card says it is trained on da_news_core_lg vectors; the sources table names spacy/da_core_news_lg (Explosion). Vectors: 500000 keys, 300 dimensions. Components: tok2vec, lemmatizer, tagger, morphologizer, parser, ner.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aarhus University's Center for Humanities Computing released DaCy tiny. It is an active local pipeline in the dacy family. This dossier was collected on 2026-10-15.", "what_it_is_for": "spaCy tok2vec pipeline, not a transformer. Card says it is trained on da_news_core_lg vectors; the sources table names spacy/da_core_news_lg (Explosion). Vectors: 500000 keys, 300 dimensions. Components: tok2vec, lemmatizer, tagger, morphologizer, parser, ner.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aarhus/odycy/grc-odycy-joint-sm": { "id": "aarhus/odycy/grc-odycy-joint-sm", "name": "OdyCy joint small", "maker": "aarhus", "family": "aarhus/odycy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-04-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy tok2vec pipeline trained jointly on more than one Ancient Greek UD treebank. Components: tok2vec, tagger, morphologizer, parser, trainable lemmatizer, frequency lemmatizer. Version 0.7.0. Not the transformer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aarhus University's Center for Humanities Computing released OdyCy joint small. It is an active local pipeline in the odycy family. This dossier was collected on 2026-10-15.", "what_it_is_for": "spaCy tok2vec pipeline trained jointly on more than one Ancient Greek UD treebank. Components: tok2vec, tagger, morphologizer, parser, trainable lemmatizer, frequency lemmatizer. Version 0.7.0. Not the transformer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aarhus/odycy/grc-odycy-joint-trf": { "id": "aarhus/odycy/grc-odycy-joint-trf", "name": "OdyCy joint transformer", "maker": "aarhus", "family": "aarhus/odycy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-04-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy transformer pipeline. The architecture page says the encoder is Ancient Greek BERT (pranaydeeps/Ancient-Greek-BERT), fine-tuned rather than frozen. The model card sources cell says n/a, so the backbone name is from the docs page, not the card table. Components: transformer, tagger, morphologizer, parser, trainable lemmatizer, frequency lemmatizer. Version 0.7.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aarhus University's Center for Humanities Computing released OdyCy joint transformer. It is an active local pipeline in the odycy family. This dossier was collected on 2026-10-15.", "what_it_is_for": "spaCy transformer pipeline. The architecture page says the encoder is Ancient Greek BERT (pranaydeeps/Ancient-Greek-BERT), fine-tuned rather than frozen. The model card sources cell says n/a, so the backbone name is from the docs page, not the card table. Components: transformer, tagger, morphologizer, parser, trainable lemmatizer, frequency lemmatizer. Version 0.7.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "abeja/gpt-neox/gpt-neox-japanese-2-7b": { "id": "abeja/gpt-neox/gpt-neox-japanese-2-7b", "name": "gpt-neox-japanese-2.7b", "maker": "abeja", "family": "abeja/gpt-neox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-08-29", "context_length": 2048, "max_output_tokens": null, "parameters": "2.7B", "architecture": "GPTNeoXJapaneseForCausalLM. 32 layers, hidden size 2560, 32 heads, vocab 32000, rotary embeddings, GELU. Transformers docs say some bias parameters were removed. Card states 2.7B parameters. Weights are pytorch_model.bin.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "gpt-neox-japanese-2.7b is ABEJA's public 2.7B-parameter Japanese causal language model. The card says it is based on GPT-NeoX and was trained by ABEJA. The Hugging Face repo was created on 29 August 2022. Transformers support was merged on 14 September 2022, and the model is the reference checkpoint for the `GPTNeoXJapanese` class. It was still public on 15 October 2026. License is MIT.", "what_it_is_for": "Japanese text generation. The card and the Transformers docs present it as a Japanese GPT-NeoX model for causal language modeling. It is not an instruction-tuned chat model. The July 2022 blog shows 2.7B completion samples and says a public release was in preparation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "abeja/gpt2/gpt2-large-japanese": { "id": "abeja/gpt2/gpt2-large-japanese", "name": "gpt2-large-japanese", "maker": "abeja", "family": "abeja/gpt2", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-08-29", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT2LMHeadModel, 36 layers, hidden size 1280, 20 attention heads, vocab size 32000, gelu_new. SentencePiece tokenizer trained on Japanese Wikipedia. Config n_positions and n_ctx are 1024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "gpt2-large-japanese is a Japanese GPT-2 language model trained and released by ABEJA, Inc. The Hugging Face repo was created on 29 August 2022 and was still public on 15 October 2026. The card calls it a large Japanese GPT-2. It is a base text-generation model, not an instruction model. ABEJA did not publish a parameter count on the card.", "what_it_is_for": "The model card presents it as a Japanese text-generation model. The July 2022 training write-up said some of the GPT models were being prepared for public release. This repo is the public GPT-2-shaped release. It is not an instruction-tuned chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "abeja/mixtral/mixtral-8x7b-instruct-v0-1-japanese": { "id": "abeja/mixtral/mixtral-8x7b-instruct-v0-1-japanese", "name": "Mixtral-8x7B-Instruct-v0.1-japanese", "maker": "abeja", "family": "abeja/mixtral", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-20", "context_length": 32768, "max_output_tokens": null, "parameters": "46919454720", "architecture": "MixtralForCausalLM sparse MoE. 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, 8 local experts, 2 experts per token, silu, rope_theta 1000000, sliding_window null. vocab_size 58448. Safetensors total 46919454720 float32. Card describes the finished Instruct-start CPT, not the alpha checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mixtral-8x7B-Instruct-v0.1-japanese is ABEJA's finished Japanese vocabulary-expansion continued pretraining of mistralai/Mixtral-8x7B-Instruct-v0.1. The card, dated with the 20 April 2024 blog, presents this repo as that model, distinct from the alpha mid-training repos. An NVIDIA session description later points at this repo as the open-source result of the GENIAC first-period Mixtral work. Weights were still public on 15 October 2026. License on the card is Apache-2.0.", "what_it_is_for": "Japanese instruction-style generation after vocabulary expansion. ABEJA's GENIAC first period aimed at a general Japanese LLM that could sit under specialized models, plus RAG and agent work around it. This is the Instruct-start continued-pretraining release they describe as the open model from that work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "abeja/mixtral/mixtral-8x7b-instruct-v0-1-japanese-202408": { "id": "abeja/mixtral/mixtral-8x7b-instruct-v0-1-japanese-202408", "name": "Mixtral-8x7B-Instruct-v0.1-japanese-202408", "maker": "abeja", "family": "abeja/mixtral", "status": "active", "confidence": "low", "weights": "open-weights", "license": "", "release_date": "2024-09-18", "context_length": 32768, "max_output_tokens": null, "parameters": "46919454720", "architecture": "MixtralForCausalLM. config _name_or_path is models/Mixtral-8x7B-merged-003-tp2-pp8-040-hf. vocab_size 58448, 8 experts, 2 per token, max_position_embeddings 32768. Safetensors total 46919454720 float16. No README.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ABEJA uploaded this Mixtral checkpoint to Hugging Face on 18 September 2024. The repo has weights, a tokenizer, and config.json. It has no model card, no license file, and no README. The config `_name_or_path` is `models/Mixtral-8x7B-merged-003-tp2-pp8-040-hf`, which looks like an internal merged Megatron export. It was still downloadable on 15 October 2026. Because there is no card, confidence in what the checkpoint is for is low. The weights existing under the official `abeja` org is high confidence.", "what_it_is_for": "Not stated. The repository name says Instruct, Japanese, and August 2024. The config path says merged export 003. No blog post was found that names this repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "abeja/mixtral/mixtral-8x7b-instruct-v0-1-japanese-alpha": { "id": "abeja/mixtral/mixtral-8x7b-instruct-v0-1-japanese-alpha", "name": "Mixtral-8x7B-Instruct-v0.1-japanese-alpha", "maker": "abeja", "family": "abeja/mixtral", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-20", "context_length": 32768, "max_output_tokens": null, "parameters": "46919454720", "architecture": "MixtralForCausalLM sparse MoE. 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, 8 local experts, 2 experts per token, silu, rope_theta 1000000, sliding_window null. vocab_size 58448. Safetensors total 46919454720 float32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is ABEJA's published mid-training checkpoint of Japanese vocabulary-expansion continued pretraining started from mistralai/Mixtral-8x7B-Instruct-v0.1. The card says it is the in-progress model evaluated in the 20 April 2024 blog. It is not the finished Instruct release. Weights remained public on 15 October 2026.", "what_it_is_for": "ABEJA published it so readers could reproduce the blog's mid-training scores. The blog says continued pretraining from the Instruct checkpoint looked better than continued pretraining from the base Mixtral, so the base run was stopped and this Instruct run was the one they kept evaluating.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "abeja/mixtral/mixtral-8x7b-instruct-v0-1-japanese-alpha-merged": { "id": "abeja/mixtral/mixtral-8x7b-instruct-v0-1-japanese-alpha-merged", "name": "Mixtral-8x7B-Instruct-v0.1-japanese-alpha-merged", "maker": "abeja", "family": "abeja/mixtral", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-20", "context_length": 32768, "max_output_tokens": null, "parameters": "46919454720", "architecture": "MixtralForCausalLM sparse MoE. 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, 8 local experts, 2 experts per token, silu, rope_theta 1000000, sliding_window null. vocab_size 58448. Difference merge of the alpha mid-training checkpoint. Safetensors total 46919454720 float32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the difference-merge of ABEJA's mid-training Mixtral Instruct continued-pretraining checkpoint. The card says the merge was applied to abeja/Mixtral-8x7B-Instruct-v0.1-japanese-alpha and that this is the mid-training result evaluated in the 20 April 2024 blog. It was still public on 15 October 2026.", "what_it_is_for": "ABEJA used a parameter difference merge to keep instruction-following while the Japanese continued pretraining was still unfinished. In the blog table this merged mid-training model is the strongest of the three April checkpoints, especially on Japanese MT-Bench.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "abeja/mixtral/mixtral-8x7b-v0-1-japanese": { "id": "abeja/mixtral/mixtral-8x7b-v0-1-japanese", "name": "Mixtral-8x7B-v0.1-japanese", "maker": "abeja", "family": "abeja/mixtral", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-20", "context_length": 32768, "max_output_tokens": null, "parameters": "46919847936", "architecture": "MixtralForCausalLM sparse MoE. 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, 8 local experts, 2 experts per token, silu, rope_theta 1000000, sliding_window null. This repo's vocab_size is 58496. Safetensors total is 46919847936 float32 parameters, which is the full expert count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mixtral-8x7B-v0.1-japanese is ABEJA's continued pretraining of mistralai/Mixtral-8x7B-v0.1 with an expanded Japanese vocabulary. It is a GENIAC first-period (NEDO, February to August 2024) release. The model card and tech blog are dated with the 20 April 2024 early public drop. Weights were still public on 15 October 2026. ABEJA also lists this base model on AWS Marketplace as a SageMaker model. License on the card is Apache-2.0.", "what_it_is_for": "The card says it is Mixtral-8x7B-v0.1 after Japanese vocabulary-expansion continued pretraining. The AWS listing says the same: better Japanese vocabulary, accuracy, and fluency. The April 2024 blog treats it as a base continued-pretraining run, not the instruction model. ABEJA stopped that base run early because continued pretraining from the Instruct checkpoint scored better.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "abeja/qwen2-5/abeja-qwen2-5-32b-japanese-v0-1": { "id": "abeja/qwen2-5/abeja-qwen2-5-32b-japanese-v0-1", "name": "ABEJA-Qwen2.5-32b-Japanese-v0.1", "maker": "abeja", "family": "abeja/qwen2-5", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-27", "context_length": 32768, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM. 64 layers, hidden 5120, 40 heads, 8 KV heads, intermediate 27648, vocab 152064, rope_theta 1000000, rope_scaling null, max_position_embeddings 32768. Safetensors total 32763876352. Float16. Continued pretraining of Qwen2.5-32B-Instruct plus a chat vector from Qwen2.5-32B-Instruct minus Qwen2.5-32B. No SFT or DPO yet.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ABEJA-Qwen2.5-32b-Japanese-v0.1 is ABEJA's Japanese continued-pretraining of Qwen/Qwen2.5-32B-Instruct, released as a GENIAC second-period model. The public announcement is 27 January 2025. The card says post-training was not done yet. Instruction following comes from a chat vector, the weight difference between Qwen2.5-32B-Instruct and Qwen2.5-32B. The release blog called it a beta. METI still lists it as a published OSS base for later specialized models. Weights were public on 15 October 2026 under Apache-2.0.", "what_it_is_for": "METI's GENIAC second-period result page says it is a base for enterprise-specialized models: post-train it when you need a task model. ABEJA's January 2025 post says the same. The chat template still identifies the assistant as Qwen from Alibaba Cloud, because the template was carried over. The template also includes a tool-call format.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "abeja/qwen2-5/abeja-qwen2-5-32b-japanese-v1-0": { "id": "abeja/qwen2-5/abeja-qwen2-5-32b-japanese-v1-0", "name": "ABEJA-Qwen2.5-32b-Japanese-v1.0", "maker": "abeja", "family": "abeja/qwen2-5", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-05-26", "context_length": 32768, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM. 64 layers, hidden 5120, 40 heads, 8 KV heads, intermediate 27648, vocab 152064, rope_theta 1000000, rope_scaling null, max_position_embeddings 32768. Safetensors total 32763876352. Float16. SFT and DPO on top of abeja/ABEJA-Qwen2.5-32b-Japanese-v0.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ABEJA-Qwen2.5-32b-Japanese-v1.0 is the post-trained follow-up to ABEJA-Qwen2.5-32b-Japanese-v0.1. The card says SFT and DPO were applied on top of that Japanese continued-pretraining checkpoint. ABEJA published the weights with the 26 May 2025 blog. METI lists v1.0 separately from v0.1 as a base that can be tuned for enterprise use, including on-premises or edge settings where data should not leave the site. Apache-2.0. Still public on 15 October 2026.", "what_it_is_for": "The May 2025 blog says post-training targeted extraction and reasoning, the kinds of reading tasks ABEJA sees in business documents. METI says it can be the base for a company-specific model and that a small high-accuracy model can stay on premises. The shipped chat template includes Qwen-style tool calls.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "abeja/qwen2-5/abeja-qwen2-5-7b-japanese-v0-1": { "id": "abeja/qwen2-5/abeja-qwen2-5-7b-japanese-v0-1", "name": "ABEJA-Qwen2.5-7b-Japanese-v0.1", "maker": "abeja", "family": "abeja/qwen2-5", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-17", "context_length": 32768, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Qwen2ForCausalLM. 28 layers, hidden 3584, 28 heads, 4 KV heads, intermediate 18944, vocab 152064, max_position_embeddings 32768, rope_scaling null. Safetensors 7615616512 bfloat16. Card: distillation from ABEJA-Qwen2.5-32b-Japanese-v0.1 into Qwen2.5-7B-Instruct, plus a 7B chat vector. No post-training.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ABEJA-Qwen2.5-7b-Japanese-v0.1 is ABEJA's public 7B Japanese model from GENIAC phase 2. The card says it is not ordinary continued pretraining. It is distilled so that Qwen2.5-7B-Instruct learns to match abeja/ABEJA-Qwen2.5-32b-Japanese-v0.1, then a chat vector from Qwen2.5-7B-Instruct minus Qwen2.5-7B restores instruction following. Post-training was not applied. The news post is 17 April 2025. The technical blog is 22 April 2025 and calls the drop a beta. Apache-2.0. Still public on 15 October 2026.", "what_it_is_for": "METI says a 7B model is for places with limited compute or network limits, and that it can be post-trained for a narrow job. ABEJA's press says the MT-Bench result beat GPT-3.5 Turbo and was strong for the size class. The chat template includes tool-call markup inherited from Qwen.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "abeja/qwen3/abeja-qwen3-14b-agentic-256k-v0-1": { "id": "abeja/qwen3/abeja-qwen3-14b-agentic-256k-v0-1", "name": "ABEJA-Qwen3-14B-Agentic-256k-v0.1", "maker": "abeja", "family": "abeja/qwen3", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-27", "context_length": 262144, "max_output_tokens": null, "parameters": "14768307200", "architecture": "Qwen3ForCausalLM. 40 layers, hidden 5120, 40 heads, 8 KV heads, head dim 128, intermediate 17408, vocab 151936. max_position_embeddings 262144. YaRN factor 8 from original_max_position_embeddings 32768. Safetensors 14768307200 bfloat16. Card text says continued training of Qwen3-14B. HF base_model tag says Qwen/Qwen3-14B-Base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ABEJA-Qwen3-14B-Agentic-256k-v0.1 is ABEJA's GENIAC third-period language model, published 27 March 2026. It is additional training on Alibaba's Qwen3-14B line aimed at planning, tool use, and a 256k context. The card says the model can loop between thinking and tools. Hugging Face metadata lists the base as Qwen/Qwen3-14B-Base. The card text says additional training of Qwen3-14B. Those two wordings are both kept. Apache-2.0. Weights were public on 15 October 2026.", "what_it_is_for": "ABEJA's 27 March 2026 news post says this is a small on-premises model so confidential data need not leave the company. It is meant to plan, pick tools, and run them in a loop, on top of general Japanese ability and long context. The press says Japanese MT-Bench beat GPT-4o and the base Qwen3-14B on ABEJA's own measurement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "abeja/qwq/abeja-qwq32b-reasoning-japanese-v1-0": { "id": "abeja/qwq/abeja-qwq32b-reasoning-japanese-v1-0", "name": "ABEJA-QwQ32b-Reasoning-Japanese-v1.0", "maker": "abeja", "family": "abeja/qwq", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-17", "context_length": 32768, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM, same shape as ABEJA-Qwen2.5-32b-Japanese-v0.1: 64 layers, hidden 5120, vocab 152064, max_position_embeddings 32768, rope_scaling null. Card: QwQ-32B chat vector merged into v0.1, then extra training. Safetensors metadata says float16. config.json torch_dtype says bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ABEJA-QwQ32b-Reasoning-Japanese-v1.0 is ABEJA's Japanese reasoning model. The card says it starts from abeja/ABEJA-Qwen2.5-32b-Japanese-v0.1, adds the chat vector of Qwen/QwQ-32B, and then does more training so the reasoning behavior stays in Japanese. Answers come after a `` trace. ABEJA's phase-2 results page links a 17 April 2025 news post. The technical blog is 25 April 2025. Swallow lists the release date as 25 April 2025. Apache-2.0. Still public on 15 October 2026.", "what_it_is_for": "METI describes it as the thinking engine for an internal-knowledge agent that reads sensitive minutes, contracts, and technical specifications. The card says to follow QwQ-32B's usage guidelines. It is a reasoning chat model, not a general embedding or vision model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aberdeen/equicaps/equicaps": { "id": "aberdeen/equicaps/equicaps", "name": "EquiCaps", "maker": "aberdeen", "family": "aberdeen/equicaps", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The repository LICENSE file is the GNU General Public License version 3, 29 June 2007, without a filled EquiCaps copyright line. The README says the code is released under GPL v3.0. A separate licence for the .pth files is not stated.", "release_date": "2025-06-13", "context_length": null, "max_output_tokens": null, "parameters": "18600000", "architecture": "Self-supervised capsule network (Equivariant Capsule Network). ResNet-18 encoder and a projector of 32 capsules. Predictor-free: pose is read from the capsules rather than from a separate equivariance predictor. The README training example uses --experience EquiCaps_3x3, --arch resnet18, --mlp 1111-16-32, and --resolution 256. The ICCV 2025 supplement reports 18.6M parameters, 12.7 GFLOPs, and 0.90 s iteration time for EquiCaps, and states the projector has 32 capsules. Loss weights in that supplement are lambda_inv 0.1, lambda_equi 5, lambda_V 10, and lambda_C 1, matching the README coefficients --sim-coeff 0.1, --equi-factor 5, --std-coeff 10, and --cov-coeff 1. The code is built on Facebook SIE and SR-CapsNet.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EquiCaps is a self-supervised vision model from the University of Aberdeen Machine Learning group. Athinoulla Konstantinou, Georgios Leontidis, and Aiden Durrant are at Aberdeen. Mamatha Thota is at the University of Lincoln. The model learns invariant and equivariant image representations with a capsule projector and no separate pose predictor. Weights were still linked from the authors' GitHub on 2026-10-15. The arXiv preprint is 11 June 2025. The checkpoint release is the 13 June 2025 GitHub tag `v1.0-ckpts`. ICCV 2025 lists the paper on pages 7947\u20137957.", "what_it_is_for": "The README and the ICCV abstract present EquiCaps as a pose-aware pretraining method. A ResNet-18 encoder and a 32-capsule projector learn representations that stay useful for recognition and that change in a predictable way under rotation and translation. The authors say the capsules remove the need for a dedicated equivariance predictor. Downstream scripts probe frozen features for classification, rotation, translation, and colour. It is not a chatbot and it is not an API product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aberystwyth/deepcanola/deepcanola": { "id": "aberystwyth/deepcanola/deepcanola", "name": "DeepCanola", "maker": "aberystwyth", "family": "aberystwyth/deepcanola", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0 on the GitHub code. OpenAIRE lists the Zenodo dataset 10.5281/zenodo.13903900 as CC BY. A separate sentence for the .pth file alone was not found.", "release_date": "2024-10-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mask R-CNN with a ResNet-50 feature-pyramid backbone, from torchvision, started from COCO pretrained weights. The training code sets min_size 1914, max_size 2631, and box_detections_per_image 300. The paper appendix lists input size 2631 x 1914 x 3, learning rate 0.005, momentum 0.9, weight decay 0.0005, and 12 epochs. The released file deepcanola.pth is the final active-learning stage, called Model 4 in the paper. Zenodo lists that file at 176.2 MB, md5 66efcf5646d57fc87dbefb86b4df6ddb.", "modalities": { "input": [ "image" ], "output": [ "masks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepCanola is an Aberystwyth University instance-segmentation model for the valve of a Brassica pod. IBERS and Computer Science named the final active-learning checkpoint DeepCanola and released `deepcanola.pth`. The file was on Zenodo on 8 October 2024. The journal article was online on 11 June 2025 and is in the 31 October 2025 issue. On 15 October 2026 the weights and the GPL-3.0 code were still public. There is no priced API.", "what_it_is_for": "The paper says DeepCanola segments and measures valves on Brassica napus pods so breeders can score cold-treatment effects without hand measurement. Training images are semi-synthetic. Active learning adds real annotations across four stages. Only stage 4 is named DeepCanola. Corresponding authors are John H. Doonan (National Plant Phenomics Centre, IBERS) and Chuan Lu (Computer Science). Co-authors include Rothamsted Research, the John Innes Centre, and Durham University. The model is also reported on Arabidopsis, garlic mustard, and radish images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aberystwyth/deeppod/deeppod": { "id": "aberystwyth/deeppod/deeppod", "name": "DeepPod", "maker": "aberystwyth", "family": "aberystwyth/deeppod", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT for the code (copyright 2019 Azam Hamidinekoo). The GigaScience article is CC BY 4.0. A separate licence for the .caffemodel files is not stated.", "release_date": "2020-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Patch classifier plus a non-learned reconstructor. The selected counter is DenseNet-Basic (called DenseNet in the paper): growth rate 12, three dense blocks, two transition layers, an initial 3x3 convolution with 16 filters, global average pooling, and a 4-way softmax. Input patches are 32x32. Classes are silique base, body, stem, and tip. Weights were initialised from a CIFAR-10 pretrained DenseNet and fine-tuned in Caffe. A shallower LeNet, also CIFAR-10 initialised, was trained as a baseline and is not the model used for the 2,408-image count test. Sliding-window patches overlap by 50 percent. Siliques are then joined from tip, body, and base regions.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepPod is an Aberystwyth University image model for counting Arabidopsis siliques. The Department of Computer Science and the National Plant Phenomics Centre published it in GigaScience on 4 March 2020. On 15 October 2026 the weights were still in the public GitHub repository. There is no hosted API and no public price.", "what_it_is_for": "The paper says DeepPod estimates fruit number on mature Arabidopsis inflorescences without bounding-box annotation. A CNN labels 32 by 32 patches as silique tip, base, body, or stem. A second, non-learned step joins those parts into siliques and counts them. Corresponding author Chuan Lu is in Computer Science at Aberystwyth. All listed authors are at Aberystwyth.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aberystwyth/morphpod/morphpod": { "id": "aberystwyth/morphpod/morphpod", "name": "MorphPod", "maker": "aberystwyth", "family": "aberystwyth/morphpod", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GNU GPL v3 for the silique-detector project. A separate licence for arabidopsis.pth is not stated.", "release_date": "2024-09-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cascade Mask R-CNN with a RegNetX-1.6GF backbone and a feature pyramid network, implemented with MMDetection. The GigaScience paper says the backbone was chosen because it has about half the parameter count of ResNet-50, and that the checkpoint is a fine-tune of a COCO-pretrained model. The paper does not print an exact parameter count. The released weight file is arabidopsis.pth. A phenotype script turns masks into silique length, diameter, area, and an approximate volume.", "modalities": { "input": [ "image" ], "output": [ "masks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MorphPod is an Aberystwyth University instance-segmentation model for Arabidopsis siliques. IBERS and Computer Science released the weights with the GigaScience study of fruit morphology. The weight file was on Zenodo on 25 September 2024. The paper was published on 12 February 2025. On 15 October 2026 the checkpoint and the GPL-3.0 code were still public. There is no priced API.", "what_it_is_for": "The university describes MorphPod as an image tool that finds seed pods and measures them. The paper trains Cascade Mask R-CNN on scanned Arabidopsis stems from a MAGIC population, then uses the masks for quantitative trait locus analysis. Authors are Kieran Atkins, Gina A. Garz\u00f3n-Mart\u00ednez, Andrew Lloyd, John H. Doonan, and Chuan Lu. Zenodo lists Aberystwyth University and the Corporaci\u00f3n Colombiana de Investigaci\u00f3n Agropecuaria. The GitHub project name is silique-detector. The README title is MorphPod. bio.tools id is biotools:morphpod (RRID:SCR_026174).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adept/act/act-1": { "id": "adept/act/act-1", "name": "ACT-1", "maker": "adept", "family": "adept/act", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2022-09-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Large-scale Transformer trained to use digital tools. The September 2022 preview observed a custom rendering of the browser viewport and acted on UI elements. No layer count, parameter count, or context length was published.", "modalities": { "input": [ "text", "image" ], "output": [ "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ACT-1 (Action Transformer) is Adept's first named large model, announced on 14 September 2022 as a Transformer trained to take actions in software. It was shown as a capability preview through a Chrome extension. Collected 2026-10-15. No official retirement notice was found; it is cataloged as retired because Adept's later product posts name ACT-2 instead, and no current offering lists ACT-1.", "what_it_is_for": "Adept described ACT-1 as a foundation model for actions: a user types a request and the model carries it out in tools such as a browser, Salesforce, and spreadsheets, including tasks that span more than one program. The March 2023 Series B release called ACT-1 the flagship foundation model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adept/act/act-2": { "id": "adept/act/act-2", "name": "ACT-2", "maker": "adept", "family": "adept/act", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of the Fuyu family, optimized for UI understanding, knowledge-worker data, and actions. Adept said it perceives the screen as pixels and acts through coordinates and keystrokes. No parameter count was published.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ACT-2 is a closed Adept action model, named on 9 November 2023 as the model powering the Workflows experiment inside Adept Experiments. Adept said it is fine-tuned from the Fuyu family for UI understanding, knowledge-worker data, and taking actions. Collected 2026-10-15. It is cataloged as preview: access was a rolling waitlist and early enterprise deployment, not a public API, and no later page confirms a general release or a retirement.", "what_it_is_for": "Workflows learns a software task from a user and runs it. Examples in the announcement include recruiting steps, invoice data entry, moving data between tools, and Shopify discount codes. The agent sees the screen and acts the way a person would, including on canvas-based sites where HTML is not enough.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adept/fuyu/fuyu-8b": { "id": "adept/fuyu/fuyu-8b", "name": "Fuyu-8B", "maker": "adept", "family": "adept/fuyu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2023-10-17", "context_length": 16384, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only transformer with the same core design as Persimmon-8B and no separate image encoder. Image patches are linearly projected into the first layer. No image-specific position embeddings; an image-newline token marks row breaks so resolution can vary. Hugging Face config: 36 layers, hidden size 4096, 64 heads, vocabulary 262144, max_position_embeddings 16384, patch size 30, squared ReLU, query/key layer norm, untied embeddings, bfloat16.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fuyu-8B is Adept's open multimodal model, released on 17 October 2023 under CC-BY-NC. It is a small, base version of the image-and-text model behind Adept's agent product. There is no separate image encoder: patches are projected straight into a Persimmon-style decoder. Weights remained on Hugging Face at collection on 2026-10-15. Adept does not host an API for it.", "what_it_is_for": "Adept built it for digital agents: arbitrary image resolutions, questions about charts and diagrams, UI questions, and localization on screen images. The released checkpoint is a base model. Adept said users should expect to fine-tune it for verbose captioning or multimodal chat. It is not an image generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adept/fuyu/fuyu-heavy": { "id": "adept/fuyu/fuyu-heavy", "name": "Fuyu-Heavy", "maker": "adept", "family": "adept/fuyu", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-01-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Scaled Fuyu decoder for text and images. Adept said the architecture and training recipe were modified for stability and that it still handles arbitrary image size and shape. No layer count or parameter count was published.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fuyu-Heavy is Adept's closed multimodal model announced on 24 January 2024 for digital agents. Adept said it scales the Fuyu architecture, handles arbitrary image shapes, and would shortly power the enterprise product. Weights were not released. Collected 2026-10-15. It is cataloged as preview because there is no public API or model card, and no later Adept page confirms it as the named production model.", "what_it_is_for": "Adept built it for UI understanding and other multimodal reasoning used by digital agents, while also reporting standard text scores. A chat variant, after supervised fine-tuning and DPO on public chat data, was used only for chat benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adept/fuyu/fuyu-medium": { "id": "adept/fuyu/fuyu-medium", "name": "Fuyu-Medium", "maker": "adept", "family": "adept/fuyu", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Larger Fuyu model. Adept said it uses the same simple decoder design as Fuyu-8B, with no separate image encoder. No layer count or parameter count was published.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fuyu-Medium is a larger Adept multimodal model named on 17 October 2023 in the Fuyu-8B release post. Adept published vision scores for it and said it was not releasing the model. No weights, card, or later shipping date were found. Collected 2026-10-15. Status is announced, not a public product.", "what_it_is_for": "Adept used it to show that the Fuyu design scales past the open 8B model on the same image benchmarks. It was not offered for download or as an API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adept/persimmon/persimmon-8b-base": { "id": "adept/persimmon/persimmon-8b-base", "name": "Persimmon-8B-Base", "maker": "adept", "family": "adept/persimmon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-09-07", "context_length": 16384, "max_output_tokens": null, "parameters": "approximately 9.3 billion", "architecture": "Decoder-only transformer with squared ReLU, rotary position embeddings, and layer norm on query and key projections. Untied input and output embeddings. 36 layers, hidden size 4096, 64 heads, vocabulary 262144. Trained from scratch at sequence length 16384 for 375000 iterations on 737 billion tokens, about 75 percent text and 25 percent code. Adept said inference cost matches an 8B model with tied embeddings, and that removing about 70k unused reserved embeddings would cut about 570 million parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Persimmon-8B-Base is Adept's open text model released on 7 September 2023. It is a decoder-only transformer trained from scratch with a 16K context. Adept released the weights under Apache-2.0 and said the checkpoint has about 9.3 billion parameters while costing about the same as an 8B model to run. The weights were still on Hugging Face at collection on 2026-10-15. There is no Adept API.", "what_it_is_for": "Adept said it was not in the business of shipping standalone language models. Persimmon-8B was an early output of the scaling work behind its agent products, released so others could build on a permissive model under 10 billion parameters. The base checkpoint is not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adept/persimmon/persimmon-8b-chat": { "id": "adept/persimmon/persimmon-8b-chat", "name": "Persimmon-8B-Chat", "maker": "adept", "family": "adept/persimmon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-09-07", "context_length": 16384, "max_output_tokens": null, "parameters": "approximately 9.3 billion", "architecture": "Instruction fine-tune of Persimmon-8B-Base. Same decoder: squared ReLU, RoPE, query/key layer norm, 36 layers, hidden size 4096, 64 heads, vocabulary 262144, trained context 16384. Adept did not publish a separate parameter count for the fine-tune.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Persimmon-8B-Chat is Adept's instruction-tuned Persimmon checkpoint, released with the base model on 7 September 2023. The blog calls it Persimmon-8B-FT. Hugging Face publishes it as `adept/persimmon-8b-chat`. It uses the same 16K decoder as the base model. Weights were still public on 2026-10-15. There is no Adept API. The license is disputed across Adept's own pages, so it is left blank.", "what_it_is_for": "Adept presented the fine-tune as the stronger interacting version of Persimmon-8B. The chat card says the prompt should be `human:` followed by the query and then `adept:`. It is a general instruction model, not the agent that clicks a screen.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-audio/firefly-music": { "id": "adobe/firefly-audio/firefly-music", "name": "Firefly Music Model", "maker": "adobe", "family": "adobe/firefly-audio", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-10-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adobe model for original instrumental music. MAX 2025 introduced it as the Firefly Audio Model behind Generate Soundtrack. The August 20, 2026 blog calls Generate Music, by then generally available, the Firefly Music Model. Adobe says tracks are fully licensed and can be fit to a video's length and mood. Parameter count is not published. It is not confirmed that Audio and Music are different weights.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 20.0 }, "summary_intro": "Adobe Firefly Music Model is a firefly-audio model. Status on 2026-10-15: active. Confidence is medium.", "what_it_is_for": "Adobe model for original instrumental music. MAX 2025 introduced it as the Firefly Audio Model behind Generate Soundtrack. The August 20, 2026 blog calls Generate Music, by then generally available, the Firefly Music Model. Adobe says tracks are fully licensed and can be fit to a video's length and mood. Parameter count is not published. It is not confirmed that Audio and Music are different weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-audio/firefly-speech": { "id": "adobe/firefly-audio/firefly-speech", "name": "Firefly Speech Model", "maker": "adobe", "family": "adobe/firefly-audio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adobe text-to-speech model used by Generate Speech. Public beta October 28, 2025. Generally available August 20, 2026. Users can choose Firefly Speech or ElevenLabs. Adobe says it controls voice, pacing, and emotion. Parameter count and voice inventory were not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 10.0 }, "summary_intro": "Adobe Firefly Speech Model is a firefly-audio model. Status on 2026-10-15: active. Confidence is high.", "what_it_is_for": "Adobe text-to-speech model used by Generate Speech. Public beta October 28, 2025. Generally available August 20, 2026. Users can choose Firefly Speech or ElevenLabs. Adobe says it controls voice, pacing, and emotion. Parameter count and voice inventory were not published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-composite/firefly-composite-md": { "id": "adobe/firefly-composite/firefly-composite-md", "name": "Firefly Composite MD", "maker": "adobe", "family": "adobe/firefly-composite", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Previous Firefly composite model. The API guide says the ME model outperforms MD on edges, lighting, and scenes. No standalone model card was published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Adobe Firefly Composite MD is a firefly-composite model. Status on 2026-10-15: retired. Confidence is medium.", "what_it_is_for": "Previous Firefly composite model. The API guide says the ME model outperforms MD on edges, lighting, and scenes. No standalone model card was published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-composite/firefly-composite-me": { "id": "adobe/firefly-composite/firefly-composite-me", "name": "Firefly Composite ME", "maker": "adobe", "family": "adobe/firefly-composite", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named in the Firefly API composite guide as the new ME model behind Precise Composite. Adobe says it beats the previous MD model on object-edge accuracy, lighting realism, and scene generation. It is a compositing model, not a general text-to-image brand. Parameter count is not published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Adobe Firefly Composite ME is a firefly-composite model. Status on 2026-10-15: active. Confidence is medium.", "what_it_is_for": "Named in the Firefly API composite guide as the new ME model behind Precise Composite. Adobe says it beats the previous MD model on object-edge accuracy, lighting realism, and scene generation. It is a compositing model, not a general text-to-image brand. Parameter count is not published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-design/firefly-design": { "id": "adobe/firefly-design/firefly-design", "name": "Firefly Design Model", "maker": "adobe", "family": "adobe/firefly-design", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Foundation model announced for editable design templates. It powers Text to Template in Adobe Express for social posts, posters, flyers, and similar layouts. Parameter count is not published. A 2026 feature named Firefly Design Intelligence was not confirmed to be this model.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Adobe Firefly Design Model is a firefly-design model. Status on 2026-10-15: active. Confidence is medium.", "what_it_is_for": "Foundation model announced for editable design templates. It powers Text to Template in Adobe Express for social posts, posters, flyers, and similar layouts. Parameter count is not published. A 2026 feature named Firefly Design Intelligence was not confirmed to be this model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-image/firefly-fill-expand": { "id": "adobe/firefly-image/firefly-fill-expand", "name": "Firefly Fill & Expand", "maker": "adobe", "family": "adobe/firefly-image", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named Photoshop model for Generative Fill and Generative Expand. Adobe recommended it as the replacement for Firefly Image 3 in those workflows. Parameter count, training description, and an API model id were not published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 1.0 }, "summary_intro": "Adobe Firefly Fill & Expand is a firefly-image model. Status on 2026-10-15: active. Confidence is medium.", "what_it_is_for": "Named Photoshop model for Generative Fill and Generative Expand. Adobe recommended it as the replacement for Firefly Image 3 in those workflows. Parameter count, training description, and an API model id were not published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-image/firefly-image-1": { "id": "adobe/firefly-image/firefly-image-1", "name": "Firefly Image 1", "maker": "adobe", "family": "adobe/firefly-image", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-03-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adobe's first Firefly foundation model. The March 21, 2023 launch said it generates images and text effects and is trained on Adobe Stock, openly licensed content, and public-domain content. Parameter count and network type are not published.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Adobe Firefly Image 1 is a firefly-image model. Status on 2026-10-15: active. Confidence is medium.", "what_it_is_for": "Adobe's first Firefly foundation model. The March 21, 2023 launch said it generates images and text effects and is trained on Adobe Stock, openly licensed content, and public-domain content. Parameter count and network type are not published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-image/firefly-image-2": { "id": "adobe/firefly-image/firefly-image-2", "name": "Firefly Image 2", "maker": "adobe", "family": "adobe/firefly-image", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adobe called Image 2 the next imaging model, with larger model capacity, better prompt alignment, and more accurate people. Generative Match, Photo Settings, and Prompt Guidance shipped with it. Parameter count is not published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Adobe Firefly Image 2 is a firefly-image model. Status on 2026-10-15: retired. Confidence is medium.", "what_it_is_for": "Adobe called Image 2 the next imaging model, with larger model capacity, better prompt alignment, and more accurate people. Generative Match, Photo Settings, and Prompt Guidance shipped with it. Parameter count is not published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-image/firefly-image-3": { "id": "adobe/firefly-image/firefly-image-3", "name": "Firefly Image 3", "maker": "adobe", "family": "adobe/firefly-image", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-04-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Firefly Image 3 Foundation Model. Adobe said it improves photorealism, lighting, text rendering, prompt adherence, and illustration variety, and that it powers Structure Reference, Style Reference, Generative Fill, and Generative Expand. Parameter count is not published. API ids image3 and image3_custom.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Adobe Firefly Image 3 is a firefly-image model. Status on 2026-10-15: deprecated. Confidence is high.", "what_it_is_for": "Firefly Image 3 Foundation Model. Adobe said it improves photorealism, lighting, text rendering, prompt adherence, and illustration variety, and that it powers Structure Reference, Style Reference, Generative Fill, and Generative Expand. Parameter count is not published. API ids image3 and image3_custom.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-image/firefly-image-4": { "id": "adobe/firefly-image/firefly-image-4", "name": "Firefly Image 4", "maker": "adobe", "family": "adobe/firefly-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adobe Firefly Image Model 4, the standard tier beside Image Model 4 Ultra. Adobe says it is for fast everyday images, with better prompt fidelity than Image 3, and better people, animals, architecture, and text. API id image4_standard. Parameter count is not published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Adobe Firefly Image 4 is a firefly-image model. Status on 2026-10-15: active. Confidence is high.", "what_it_is_for": "Adobe Firefly Image Model 4, the standard tier beside Image Model 4 Ultra. Adobe says it is for fast everyday images, with better prompt fidelity than Image 3, and better people, animals, architecture, and text. API id image4_standard. Parameter count is not published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-image/firefly-image-4-ultra": { "id": "adobe/firefly-image/firefly-image-4-ultra", "name": "Firefly Image 4 Ultra", "maker": "adobe", "family": "adobe/firefly-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Higher-detail Firefly image model sold beside Image Model 4. Adobe says it favors precision over speed and is aimed at photorealistic scenes, portraits, and medium-sized groups. API id image4_ultra. Parameter count is not published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 20.0 }, "summary_intro": "Adobe Firefly Image 4 Ultra is a firefly-image model. Status on 2026-10-15: active. Confidence is high.", "what_it_is_for": "Higher-detail Firefly image model sold beside Image Model 4. Adobe says it favors precision over speed and is aimed at photorealistic scenes, portraits, and medium-sized groups. API id image4_ultra. Parameter count is not published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-image/firefly-image-5": { "id": "adobe/firefly-image/firefly-image-5", "name": "Firefly Image 5", "maker": "adobe", "family": "adobe/firefly-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adobe's Image Model 5. Public beta at MAX 2025 for photorealistic detail, portraits, complex compositions, native 4MP output without upscaling, and prompt-based editing. Generally available by the March 19, 2026 Firefly post. API id image5. Parameter count is not published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 10.0 }, "summary_intro": "Adobe Firefly Image 5 is a firefly-image model. Status on 2026-10-15: active. Confidence is high.", "what_it_is_for": "Adobe's Image Model 5. Public beta at MAX 2025 for photorealistic detail, portraits, complex compositions, native 4MP output without upscaling, and prompt-based editing. Generally available by the March 19, 2026 Firefly post. API id image5. Parameter count is not published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-upscale/firefly-upscale": { "id": "adobe/firefly-upscale/firefly-upscale", "name": "Firefly Precise Upsampler", "maker": "adobe", "family": "adobe/firefly-upscale", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Firefly image upscaler. API id precise_upsampler_v1. The April 24, 2026 changelog says the beta header creative_upsampler_v1 always called this same precise model, and that the old header is now rejected. Two use cases are documented: user-asset upsampling that avoids new content, and generated-image upsampling for crispness. Parameter count is not published.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Adobe Firefly Precise Upsampler is a firefly-upscale model. Status on 2026-10-15: active. Confidence is high.", "what_it_is_for": "Firefly image upscaler. API id precise_upsampler_v1. The April 24, 2026 changelog says the beta header creative_upsampler_v1 always called this same precise model, and that the old header is now rejected. Two use cases are documented: user-asset upsampling that avoids new content, and generated-image upsampling for crispness. Parameter count is not published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-vector/firefly-vector": { "id": "adobe/firefly-vector/firefly-vector", "name": "Firefly Vector Model", "maker": "adobe", "family": "adobe/firefly-vector", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adobe's generative model for editable vector graphics and patterns. Announced as the Firefly Vector Model and later also called the Adobe Vector Model. It powers Text to Vector Graphic, and Adobe has also tied Generative Shape Fill, Generative Recolor, and Text to Pattern to the vector model. Parameter count is not published. Output is vector artwork, not a token string.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Adobe Firefly Vector Model is a firefly-vector model. Status on 2026-10-15: active. Confidence is high.", "what_it_is_for": "Adobe's generative model for editable vector graphics and patterns. Announced as the Firefly Vector Model and later also called the Adobe Vector Model. It powers Text to Vector Graphic, and Adobe has also tied Generative Shape Fill, Generative Recolor, and Text to Pattern to the vector model. Parameter count is not published. Output is vector artwork, not a token string.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "adobe/firefly-video/firefly-video": { "id": "adobe/firefly-video/firefly-video", "name": "Firefly Video Model", "maker": "adobe", "family": "adobe/firefly-video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adobe's commercially safe text-to-video and image-to-video model. Limited public beta October 14, 2024, broader beta February 12, 2025, generally available April 24, 2025. The enterprise rate card distinguishes Video Model 1.9 at 540p from Video Model 1 at 720p and 1080p. Those are priced conditions of one product, not separately marketed models. Parameter count is not published.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Adobe Firefly Video Model is a firefly-video model. Status on 2026-10-15: active. Confidence is high.", "what_it_is_for": "Adobe's commercially safe text-to-video and image-to-video model. Limited public beta October 14, 2024, broader beta February 12, 2025, generally available April 24, 2025. The enterprise rate card distinguishes Video Model 1.9 at 540p from Video Model 1 at 720p and 1080p. Those are priced conditions of one product, not separately marketed models. Parameter count is not published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-1-5b-v3": { "id": "agh/bielik/bielik-1-5b-v3", "name": "Bielik-1.5B-v3", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-18", "context_length": 8192, "max_output_tokens": null, "parameters": "1.6B", "architecture": "Table 1 of the May 2025 small-model report: 32 layers, dimension 1536, 12 heads, 2 KV heads, head size 128, intermediate 8960, SwiGLU, attention bias true, MLP bias true, vocab 32000, RoPE theta 1000000, context 8192. Adapted from Qwen2.5-1.5B with the APT4 tokenizer and FOCUS embedding init. config.json returned HTTP 401.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-1.5B-v3 is the smaller Bielik v3 base model, adapted from Qwen2.5-1.5B by SpeakLeash and ACK Cyfronet AGH. Collected 2026-10-15.", "what_it_is_for": "Small Polish base model for local and fine-tuning use. Chat use is Bielik-1.5B-v3.0-Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-1-5b-v3-instruct": { "id": "agh/bielik/bielik-1-5b-v3-instruct", "name": "Bielik-1.5B-v3.0-Instruct", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-18", "context_length": 8192, "max_output_tokens": null, "parameters": "1.6B", "architecture": "Table 1 of the May 2025 small-model report: 32 layers, dimension 1536, 12 heads, 2 KV heads, head size 128, intermediate 8960, SwiGLU, attention bias true, MLP bias true, vocab 32000, RoPE theta 1000000, context 8192. Adapted from Qwen2.5-1.5B with the APT4 tokenizer and FOCUS embedding init. config.json returned HTTP 401. Instruct fine-tune. The GRPO math stage in the report is stated for Bielik-4.5B-v3, not for this 1.5B checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-1.5B-v3.0-Instruct is the chat model of Bielik-1.5B-v3 from SpeakLeash and ACK Cyfronet AGH. Collected 2026-10-15.", "what_it_is_for": "Small Polish instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-11b-v2": { "id": "agh/bielik/bielik-11b-v2", "name": "Bielik-11B-v2", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-26", "context_length": 32768, "max_output_tokens": null, "parameters": "11.2B", "architecture": "Depth-upscaled Mistral-7B-v0.2. Config read 2026-10-15: 50 layers, hidden 4096, intermediate 14336, 32 heads, 8 KV heads, vocab 32128, RoPE theta 1000000, context 32768, bf16, untied embeddings, no sliding window. Card headline says 11 billion parameters. The May 2025 table lists 11.2B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-11B-v2 is the depth-upscaled Polish base model from Mistral-7B-v0.2. The card says it was developed by SpeakLeash and ACK Cyfronet AGH and trained on the Helios supercomputer. AGH University news of 28 August 2024 announces this release. Collected 2026-10-15.", "what_it_is_for": "Base Polish language model for further tuning. The card points chat users to the v2 instruct line. The base card still points at Bielik-11B-v2.2-Instruct even after later instruct checkpoints shipped.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-11b-v2-instruct": { "id": "agh/bielik/bielik-11b-v2-instruct", "name": "Bielik-11B-v2-Instruct", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-26", "context_length": 32768, "max_output_tokens": null, "parameters": "11.2B", "architecture": "Instruct fine-tunes of Bielik-11B-v2. Configs read for v2.0, v2.1, and v2.3 match the base: 50 layers, hidden 4096, intermediate 14336, 32 heads, 8 KV heads, vocab 32128, RoPE theta 1000000, context 32768. v2.3 config torch dtype is float16. v2.2, v2.5, and v2.6 config.json requests returned HTTP 401. v2.6 card says ChatML. v2.3 is a merge and stays a version of this product.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-11B-v2-Instruct is the chat line of Bielik-11B-v2. SpeakLeash and ACK Cyfronet AGH are named as developers. Point releases from v2.0 through v2.6 are versions of one product. There is no v2.4 repo. Collected 2026-10-15.", "what_it_is_for": "Polish instruction following and chat. v2.6 is the latest checkpoint and uses ChatML. Earlier checkpoints use the v2 instruct template of their own cards. The base card still refers readers to v2.2-Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-11b-v3": { "id": "agh/bielik/bielik-11b-v3", "name": "Bielik-11B-v3", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-01", "context_length": null, "max_output_tokens": null, "parameters": "11.2B", "architecture": "The v3 paper describes a depth-upscaled Mistral-7B-v0.2 model in the same family as v2, with the Mistral tokenizer kept and special tokens added to vocab 32128. config.json for Bielik-11B-v3-Base-20250730 returned HTTP 401, so layer counts were not read from the checkpoint. The compression paper calls the parent 11.04B. Open PL tables label 11.2B. The instruct card headline says 11 billion.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-11B-v3 is the multilingual European base that follows Bielik-11B-v2. The only named base weights found are the snapshot speakleash/Bielik-11B-v3-Base-20250730. SpeakLeash and ACK Cyfronet AGH develop the line. The checkpoint is gated. Collected 2026-10-15.", "what_it_is_for": "Base model for the v3 instruct, Polish-tokenizer, and Minitron lines. The instruct card says it is the fine-tune parent. It is not the chat checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-11b-v3-dflash": { "id": "agh/bielik/bielik-11b-v3-dflash", "name": "Bielik-11B-v3.0-DFlash", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-06-14", "context_length": 32768, "max_output_tokens": null, "parameters": "1B", "architecture": "DFlash draft, not a standalone language model. Config read 2026-10-15: DFlashDraftModel, 5 layers, hidden 4096, 32 heads, 8 KV heads, max_position_embeddings 32768, vocab 32128, RoPE theta 1000000, SiLU. Trained with ALLaMo on the same data as the target. Grant PLG/2026/019559 on Helios. Intermediate size 14336. Hub size label is 1B params. An exact parameter count was not computed from the config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-11B-v3.0-DFlash is a speculative-decoding draft for Bielik-11B-v3.0-Instruct. The card says it was developed by SpeakLeash and ACK Cyfronet AGH and is not a standalone language model. Collected 2026-10-15.", "what_it_is_for": "Draft model for DFlash speculative decoding in SGLang (newer than 0.5.6) or vLLM (newer than 0.20.0). The card's vLLM example asks for 15 speculative tokens against Bielik-11B-v3.0-Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-11b-v3-instruct": { "id": "agh/bielik/bielik-11b-v3-instruct", "name": "Bielik-11B-v3.0-Instruct", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-07", "context_length": null, "max_output_tokens": null, "parameters": "11.2B", "architecture": "Instruct fine-tune of Bielik-11B-v3-Base-20250730. Card: 11 billion parameters, Apache-2.0, developed by SpeakLeash and ACK Cyfronet AGH, 32 European languages optimized for Polish. config.json returned HTTP 401. A vLLM example on the DFlash card sets max-num-batched-tokens 32768. That server flag is not stored as the context window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-11B-v3.0-Instruct is the chat model of the v3 base snapshot. The card names SpeakLeash and ACK Cyfronet AGH. It is the target of Bielik-11B-v3.0-DFlash and the source named by the Polish-tokenizer 11B instruct. Collected 2026-10-15.", "what_it_is_for": "Multilingual instruct model optimized for Polish, covering the European languages listed on the Hub card. The DFlash card says this checkpoint is the speculative-decoding target.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-4-5b-v3": { "id": "agh/bielik/bielik-4-5b-v3", "name": "Bielik-4.5B-v3", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-05", "context_length": 32768, "max_output_tokens": null, "parameters": "4.8B", "architecture": "Table 1 of the May 2025 small-model report: 60 layers, dimension 2048, 16 heads, 2 KV heads, head size 128, intermediate 11008, SwiGLU, attention bias true, MLP bias true, vocab 32000, RoPE theta 1000000, context 32768. Figure 1 caption says depth up-scale n=36, m=8, s=56 plus outermost-layer duplication, which does not equal 60 layers. Adapted from Qwen2.5-3B. config.json returned HTTP 401. The card headline says 4.6 billion parameters. Table 9 lists 4.8B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-4.5B-v3 is the larger of the two Bielik v3 small base models. The card says it was developed by SpeakLeash and ACK Cyfronet AGH and trained on Helios. The technical report is Bielik v3 Small. Collected 2026-10-15.", "what_it_is_for": "Compact Polish base model. The card points chat users to Bielik-4.5B-v3.0-Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-4-5b-v3-instruct": { "id": "agh/bielik/bielik-4-5b-v3-instruct", "name": "Bielik-4.5B-v3.0-Instruct", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-18", "context_length": 32768, "max_output_tokens": null, "parameters": "4.8B", "architecture": "Table 1 of the May 2025 small-model report: 60 layers, dimension 2048, 16 heads, 2 KV heads, head size 128, intermediate 11008, SwiGLU, attention bias true, MLP bias true, vocab 32000, RoPE theta 1000000, context 32768. Figure 1 caption says depth up-scale n=36, m=8, s=56 plus outermost-layer duplication, which does not equal 60 layers. Adapted from Qwen2.5-3B. config.json returned HTTP 401. The card headline says 4.6 billion parameters. Table 9 lists 4.8B. Instruct post-training includes DPO-P. GRPO for this size used 12,000 Polish math problems drawn from about 100,000, on Athena (8x H100) with VERL. Hyperparameters such as beta and learning rate were not re-read into this dossier.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-4.5B-v3.0-Instruct is the chat model of Bielik-4.5B-v3. SpeakLeash and ACK Cyfronet AGH are the developers named on the small-model report. The Hub repo was created on 2025-04-18. Collected 2026-10-15.", "what_it_is_for": "Instruction following in Polish at the 4.5B scale, including a math-oriented GRPO stage described in the report.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-7b-instruct-v0-1": { "id": "agh/bielik/bielik-7b-instruct-v0-1", "name": "Bielik-7B-Instruct-v0.1", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-03-30", "context_length": 4096, "max_output_tokens": null, "parameters": "7.2B", "architecture": "Instruct fine-tune of Bielik-7B-v0.1. Config read 2026-10-15: same Mistral shape as the base (32 layers, hidden 4096, intermediate 14336, 32 heads, 8 KV heads, vocab 32000, RoPE theta 10000, SiLU) with max_position_embeddings 4096 and sliding_window 4096. Chat markers are [INST] / [/INST], with an optional <> block.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-7B-Instruct-v0.1 is the chat fine-tune of Bielik-7B-v0.1. SpeakLeash hosts the weights under a non-commercial Creative Commons license. The 7B paper describes the SpeakLeash and ACK Cyfronet AGH collaboration. Collected 2026-10-15.", "what_it_is_for": "Instruction following and Polish chat. The card scores it as an instruct model and warns that it was not trained with a moderation layer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-7b-v0-1": { "id": "agh/bielik/bielik-7b-v0-1", "name": "Bielik-7B-v0.1", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-30", "context_length": 8192, "max_output_tokens": null, "parameters": "7.2B", "architecture": "Continued pretraining of Mistral-7B-v0.1. Config read 2026-10-15: MistralForCausalLM, 32 layers, hidden 4096, intermediate 14336, 32 heads, 8 KV heads, vocab 32000, RoPE theta 10000, SiLU, bf16, untied embeddings, max_position_embeddings 8192, sliding_window 4096. The May 2025 small-model report table lists 7.2B parameters. The model card headline says 7 billion.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-7B-v0.1 is the first named Bielik base model. SpeakLeash hosts the weights. The 7B paper says the model is a collaboration between SpeakLeash and ACK Cyfronet AGH at AGH University of Krakow. The model card's developed-by line names SpeakLeash only. Collected 2026-10-15.", "what_it_is_for": "The card presents a Polish causal language model for continued use and fine-tuning, initialized from Mistral-7B-v0.1. It is not the chat model. The instruct sibling is Bielik-7B-Instruct-v0.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-minitron-7b-v3-dflash": { "id": "agh/bielik/bielik-minitron-7b-v3-dflash", "name": "Bielik-Minitron-7B-v3.0-DFlash", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-06-14", "context_length": 32768, "max_output_tokens": null, "parameters": "1.0B", "architecture": "DFlash draft, not a standalone language model. Config read 2026-10-15: DFlashDraftModel, 5 layers, hidden 4096, 32 heads, 8 KV heads, max_position_embeddings 32768, vocab 32128, RoPE theta 1000000, SiLU. Trained with ALLaMo on the same data as the target. Grant PLG/2026/019559 on Helios. Intermediate size 11264. Hub size label is 1.0B params. The 11B DFlash card was the credit list read in full. This card was used for the target, license, and size label.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-Minitron-7B-v3.0-DFlash is the DFlash draft paired with Bielik-Minitron-7B-v3.0-Instruct. The card pattern matches the 11B draft: SpeakLeash and ACK Cyfronet AGH, Apache-2.0, not a standalone language model. Collected 2026-10-15.", "what_it_is_for": "Speculative draft for the Minitron instruct target. Same serving pattern as the 11B DFlash card (SGLang and vLLM).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-minitron-7b-v3-instruct": { "id": "agh/bielik/bielik-minitron-7b-v3-instruct", "name": "Bielik-Minitron-7B-v3.0-Instruct", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-22", "context_length": null, "max_output_tokens": null, "parameters": "7.35B", "architecture": "Compressed from Bielik-11B-v3.0. The compression paper says 7.35B parameters after a 33.4% cut from 11.04B, using NVIDIA Model Optimizer structured hybrid pruning and NeMo logit distillation, then SFT, DPO-P, and GRPO. The April 2026 leaderboard tables label the row 7.5B. config.json returned HTTP 401. No base-only Minitron repo was found.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-Minitron-7B-v3.0-Instruct is the pruned and distilled 7B-class instruct model from the Bielik-11B-v3 generation. Authors include ACK Cyfronet AGH. NVIDIA tools are part of the compression method. Collected 2026-10-15.", "what_it_is_for": "A smaller, faster instruct model aimed at the same European languages as Bielik-11B-v3, with Polish as the focus. The paper says it keeps about 90% of the baseline and can be up to 50% faster at inference. A companion DFlash draft exists.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-pl-11b-v3-instruct": { "id": "agh/bielik/bielik-pl-11b-v3-instruct", "name": "Bielik-PL-11B-v3.0-Instruct", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-13", "context_length": null, "max_output_tokens": null, "parameters": "11.2B", "architecture": "Bielik-11B-v3 instruct weights with the Mistral-derived tokenizer replaced by the Polish APT4 tokenizer (vocab 32000 in the tokenizer comparison). The report describes FOCUS embedding initialization and a continued pretraining curriculum, then SFT, DPO, and GRPO. config.json returned HTTP 401, so the resulting layer shape was not read. Tables still label 11.2B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-PL-11B-v3.0-Instruct is the Polish-tokenizer variant of the Bielik 11B v3 instruct model. The report is from the Bielik team, with ACK Cyfronet AGH on the v3 author line. Collected 2026-10-15.", "what_it_is_for": "Polish instruct use with a lower token fertility than the Mistral tokenizer used by Bielik-11B-v3.0-Instruct. The report's goal is to keep benchmark scores close while using fewer Polish tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/bielik/bielik-pl-minitron-7b-v3-instruct": { "id": "agh/bielik/bielik-pl-minitron-7b-v3-instruct", "name": "Bielik-PL-Minitron-7B-v3.0-Instruct", "maker": "agh", "family": "agh/bielik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-13", "context_length": null, "max_output_tokens": null, "parameters": "7.35B", "architecture": "Polish APT4 tokenizer variant of Bielik-Minitron-7B-v3.0-Instruct. The compression paper's count is 7.35B. The tokenizer report's tables print 7.5. config.json returned HTTP 401.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bielik-PL-Minitron-7B-v3.0-Instruct is the Polish-tokenizer Minitron instruct model. It is reported together with Bielik-PL-11B-v3.0-Instruct. Collected 2026-10-15.", "what_it_is_for": "Smaller Polish-tokenizer instruct model. The report compares it with the original-tokenizer Minitron and with the PL 11B model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/lapa/lapa-12b": { "id": "agh/lapa/lapa-12b", "name": "Lapa-12B", "maker": "agh", "family": "agh/lapa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-09-23", "context_length": 131072, "max_output_tokens": null, "parameters": "12B", "architecture": "Continued Gemma-3-12B pretraining with Ukrainian vocabulary surgery. Config read 2026-10-15: Gemma3ForConditionalGeneration, text hidden 3840, intermediate 15360, 48 layers, 16 heads, 8 KV heads, max_position_embeddings 131072, vocab 262208, RoPE theta 1000000, sliding window 1024, vision hidden 1152, 27 vision layers. Hub pipeline image-text-to-text. Hub size 12B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lapa-12B is the pretrained Ukrainian-focused model based on Gemma-3-12B. The card and the AGH Faculty of Computer Science name AGH University of Krakow with Ukrainian Catholic University, Igor Sikorsky Kyiv Polytechnic Institute, and Lviv Polytechnic. Weights are hosted by lapa-llm as lapa-12b-pt. Collected 2026-10-15.", "what_it_is_for": "Ukrainian-focused pretrained base, including a Gemma 3 vision tower, for later instruction tuning. The instruct release is Lapa v0.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "agh/lapa/lapa-v0-1-instruct": { "id": "agh/lapa/lapa-v0-1-instruct", "name": "Lapa-v0.1-Instruct", "maker": "agh", "family": "agh/lapa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-10-18", "context_length": 131072, "max_output_tokens": 8192, "parameters": "12B", "architecture": "Instruct fine-tune of lapa-12b-pt. Same Gemma 3 12B image-text shape. v0.1.2 and v0.1.3 configs were read. v0.1.3 ties the word embeddings. The v0.1.3 card says the rest of the model is unchanged apart from alignment fixes and a pipeline test. Card: images normalized to 896x896 and encoded as 256 tokens. Input context stated as 128K. Config max_position_embeddings is 131072.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lapa v0.1 Instruct is the chat model of Lapa-12B. The card names researchers from Ukrainian Catholic University, AGH University of Krakow, Igor Sikorsky Kyiv Polytechnic Institute, and Lviv Polytechnic. Mykola Haltiuk (AGH Faculty of Computer Science) is the tokenizer author named by the faculty. Weights are hosted by lapa-llm. Collected 2026-10-15.", "what_it_is_for": "Ukrainian chat, document and image understanding, translation, summarization, and question answering. The card lists private-document processing, RAG, and task fine-tuning. A demo space is lapa-llm/lapa.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jamba/jamba": { "id": "ai21/jamba/jamba", "name": "Jamba", "maker": "ai21", "family": "ai21/jamba", "status": "deprecated", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-03-28", "context_length": 256000, "max_output_tokens": null, "parameters": "12B active / 52B total", "architecture": "Hybrid Transformer-Mamba mixture-of-experts. The paper calls the released model a 7B-based Jamba with 12B active and 52B total parameters. It interleaves Transformer and Mamba layers and was designed to fit on one 80GB GPU.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jamba is AI21 Labs' base pretrained hybrid Transformer-Mamba mixture-of-experts model, released 2024-03-28 under Apache-2.0. The model card points users to later instruct models, so this entry is deprecated even though the weights remain on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "Jamba is AI21 Labs' base pretrained hybrid Transformer-Mamba mixture-of-experts model, released 2024-03-28 under Apache-2.0. The model card tells users to prefer later instruct models, so this entry is deprecated even though the weights remain on Hugging Face.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jamba/jamba-1-5-large": { "id": "ai21/jamba/jamba-1-5-large", "name": "Jamba 1.5 Large", "maker": "ai21", "family": "ai21/jamba", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Jamba Open Model License (last updated 2024-08-25). Above USD 50 million annual revenue, use primarily for commercial advantage is not allowed. Derivative model names must start with Jamba.", "release_date": "2024-08-22", "context_length": 256000, "max_output_tokens": null, "parameters": "94B active / 398B total", "architecture": "Instruction-tuned hybrid Transformer-Mamba MoE. Paper: 94B active parameters, 256K effective context. ExpertsInt8 is described so a 256K context fits on eight 80GB GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 8.0 }, "summary_intro": "Jamba 1.5 Large is AI21's 94B-active (398B total) instruction model, released 2024-08-22 for long-context enterprise work, tools, and JSON. The AI21 API snapshot was deprecated on 2025-05-06. Amazon Bedrock still lists it as Legacy until an EOL of 2026-11-26.", "what_it_is_for": "Jamba 1.5 Large is AI21's 94B-active (398B total) instruction model, released 2024-08-22 for long-context enterprise work, tools, and JSON. The AI21 API snapshot was deprecated on 2025-05-06. Amazon Bedrock still lists it as Legacy until an EOL of 2026-11-26.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jamba/jamba-1-5-mini": { "id": "ai21/jamba/jamba-1-5-mini", "name": "Jamba 1.5 Mini", "maker": "ai21", "family": "ai21/jamba", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Jamba Open Model License (last updated 2024-08-25). Above USD 50 million annual revenue for the licensee and affiliates, commercial-advantage use is not allowed. Distributed derivative model names must start with Jamba.", "release_date": "2024-08-22", "context_length": 256000, "max_output_tokens": 4096, "parameters": "12B active / 52B total", "architecture": "Instruction-tuned hybrid Transformer-Mamba MoE. Paper: 12B active parameters and a 256K effective context.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.4 }, "summary_intro": "Jamba 1.5 Mini is AI21's smaller instruction-tuned hybrid model, 12B active parameters out of 52B, released 2024-08-22 for function calling, JSON, and grounded generation. The AI21 API endpoint was deprecated on 2025-05-06. On Amazon Bedrock it is Legacy, with EOL set for 2026-11-26.", "what_it_is_for": "Jamba 1.5 Mini is AI21's smaller instruction-tuned hybrid model, 12B active parameters out of 52B, released 2024-08-22 for function calling, JSON, and grounded generation. The AI21 API endpoint was deprecated on 2025-05-06. On Amazon Bedrock it is Legacy, with EOL set for 2026-11-26.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jamba/jamba-1-6-large": { "id": "ai21/jamba/jamba-1-6-large", "name": "Jamba Large 1.6", "maker": "ai21", "family": "ai21/jamba", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Jamba Open Model License (last updated 2024-08-25). Above USD 50 million annual revenue, use primarily for commercial advantage is not allowed. Derivative model names must start with Jamba.", "release_date": "2025-03-06", "context_length": 256000, "max_output_tokens": null, "parameters": "94B active / 398B total", "architecture": "Instruction-tuned hybrid SSM-Transformer MoE, same 94B active / 398B total size as Jamba 1.5 Large.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 8.0 }, "summary_intro": "Jamba Large 1.6 is the March 6, 2025 94B-active hybrid instruct model. AI21 deprecated API endpoint jamba-large-1.6-2025-03 on 2025-08-03. The card still publishes the weights and tells readers a newer release exists.", "what_it_is_for": "Jamba Large 1.6 is the March 6, 2025 94B-active hybrid instruct model. AI21 deprecated API endpoint jamba-large-1.6-2025-03 on 2025-08-03. The card still publishes the weights and tells readers a newer release exists.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jamba/jamba-1-6-mini": { "id": "ai21/jamba/jamba-1-6-mini", "name": "Jamba Mini 1.6", "maker": "ai21", "family": "ai21/jamba", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Jamba Open Model License (last updated 2024-08-25). Above USD 50 million annual revenue, use primarily for commercial advantage is not allowed. Derivative model names must start with Jamba.", "release_date": "2025-03-06", "context_length": 256000, "max_output_tokens": null, "parameters": "12B active / 52B total", "architecture": "Instruction-tuned hybrid SSM-Transformer. Same active and total size as Jamba 1.5 Mini. Card says it adds function calling, JSON, and grounded generation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.4 }, "summary_intro": "Jamba Mini 1.6 is the March 6, 2025 refresh of the 12B-active hybrid instruct model. AI21 deprecated the API endpoint jamba-mini-1.6-2025-03 on 2025-08-03. Weights remain on Hugging Face, and the card points to Mini 1.7.", "what_it_is_for": "Jamba Mini 1.6 is the March 6, 2025 refresh of the 12B-active hybrid instruct model. AI21 deprecated the API endpoint jamba-mini-1.6-2025-03 on 2025-08-03. Weights remain on Hugging Face, and the card points to Mini 1.7.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jamba/jamba-1-7-large": { "id": "ai21/jamba/jamba-1-7-large", "name": "Jamba Large 1.7", "maker": "ai21", "family": "ai21/jamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Jamba Open Model License (last updated 2024-08-25). Above USD 50 million annual revenue, use primarily for commercial advantage is not allowed. Derivative model names must start with Jamba.", "release_date": "2025-07-03", "context_length": 256000, "max_output_tokens": null, "parameters": "94B active / 398B total", "architecture": "Instruction-tuned hybrid SSM-Transformer MoE. Same 94B active / 398B total size as Jamba Large 1.5 and 1.6, with a later knowledge cutoff.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 8.0 }, "summary_intro": "Jamba Large 1.7 is AI21's current Large alias, released July 3, 2025. It is the 94B-active hybrid model the docs still point jamba-large at, with open weights under the Jamba Open Model License. The hosted Jamba API was scheduled to sunset on 2026-08-09.", "what_it_is_for": "Jamba Large 1.7 is AI21's current Large alias, released July 3, 2025. It is the 94B-active hybrid model the docs still point jamba-large at, with open weights under the Jamba Open Model License. The hosted Jamba API was scheduled to sunset on 2026-08-09.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jamba/jamba-1-7-mini": { "id": "ai21/jamba/jamba-1-7-mini", "name": "Jamba Mini 1.7", "maker": "ai21", "family": "ai21/jamba", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Jamba Open Model License (last updated 2024-08-25). Above USD 50 million annual revenue, use primarily for commercial advantage is not allowed. Derivative model names must start with Jamba.", "release_date": "2025-07-03", "context_length": 256000, "max_output_tokens": null, "parameters": "52B total; 12B active per Artificial Analysis", "architecture": "Instruction-tuned hybrid SSM-Transformer. Hugging Face lists 52B params. Artificial Analysis lists 52B total and 12B active, matching earlier Minis. The card excerpt did not restate the active count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.4 }, "summary_intro": "Jamba Mini 1.7 is the July 3, 2025 grounding and instruction-following update of the smaller Jamba instruct model. AI21 set API deprecation for endpoint jamba-mini-1.7-2025-08 on 2026-02-01. Weights remain on Hugging Face. The current Mini alias points at Jamba2, not at this model.", "what_it_is_for": "Jamba Mini 1.7 is the July 3, 2025 grounding and instruction-following update of the smaller Jamba instruct model. AI21 set API deprecation for endpoint jamba-mini-1.7-2025-08 on 2026-02-01. Weights remain on Hugging Face. The current Mini alias points at Jamba2, not at this model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jamba/jamba-instruct": { "id": "ai21/jamba/jamba-instruct", "name": "Jamba-Instruct", "maker": "ai21", "family": "ai21/jamba", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-02", "context_length": 256000, "max_output_tokens": null, "parameters": "12B active / 52B total", "architecture": "Instruction-tuned hybrid Transformer-Mamba MoE, aligned from the Jamba base model. Same size class as Jamba-v0.1. Weights were not found in the ai21labs Hugging Face org on 2026-10-15.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 0.7 }, "summary_intro": "Jamba-Instruct is AI21's closed, instruction-tuned chat model from the original Jamba base, put in public preview on 2024-05-02. Amazon Bedrock lists it as end of life on 2025-08-01, so this catalog marks it retired.", "what_it_is_for": "Jamba-Instruct is AI21's closed, instruction-tuned chat model from the original Jamba base, put in public preview on 2024-05-02. Amazon Bedrock lists it as end of life on 2025-08-01, so this catalog marks it retired.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jamba/jamba-reasoning-3b": { "id": "ai21/jamba/jamba-reasoning-3b", "name": "Jamba Reasoning 3B", "maker": "ai21", "family": "ai21/jamba", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-08", "context_length": 256000, "max_output_tokens": null, "parameters": "3B", "architecture": "Hybrid Transformer-Mamba. 28 layers (26 Mamba, 2 attention), 20 multi-query attention heads (20 query, 1 key-value), 64K vocabulary. Card describes mid-training on about 0.5T math and code tokens, context extended to 32K, then cold-start distillation at 32K, DPO at 64K, and online RLVR.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jamba Reasoning 3B is AI21's compact open reasoning model, released October 8, 2025. It is a 3B hybrid Transformer-Mamba model with a 256K context, aimed at on-device and agent use, and it is separate from the later Jamba2 3B instruct model.", "what_it_is_for": "Jamba Reasoning 3B is AI21's compact open reasoning model, released October 8, 2025. It is a 3B hybrid Transformer-Mamba model with a 256K context, aimed at on-device and agent use, and it is separate from the later Jamba2 3B instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jamba/jamba2-3b": { "id": "ai21/jamba/jamba2-3b", "name": "Jamba2 3B", "maker": "ai21", "family": "ai21/jamba", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-06", "context_length": 256000, "max_output_tokens": null, "parameters": "3B", "architecture": "Compact SSM-Transformer. The docs table lists 3B parameters, not an active/total split. Card says the same Jamba2 post-training pipeline as Mini, starting from Jamba 1.5 pretraining and mid-training on 500B tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jamba2 3B is AI21's compact Apache-2.0 model for on-device and agent workflows, published 2026-01-06 with a 256K context and no first-party API endpoint. The docs list it as Jamba 3B, version 2.", "what_it_is_for": "Jamba2 3B is AI21's compact Apache-2.0 model for on-device and agent workflows, published 2026-01-06 with a 256K context and no first-party API endpoint. The docs list it as Jamba 3B, version 2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jamba/jamba2-mini": { "id": "ai21/jamba/jamba2-mini", "name": "Jamba2 Mini", "maker": "ai21", "family": "ai21/jamba", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-06", "context_length": 256000, "max_output_tokens": null, "parameters": "12B active / 52B total", "architecture": "SSM-Transformer. Card says post-training started from Jamba 1.5 pretraining, then mid-training on 500B tokens, a Mamba state-passing stage, supervised fine-tuning, DPO, and on-policy reinforcement learning.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.4 }, "summary_intro": "Jamba2 Mini is AI21's current Mini model, an Apache-2.0 SSM-Transformer with 12B active parameters out of 52B, published on Hugging Face on 2026-01-06. The docs point the jamba-mini alias at snapshot jamba-mini-2-2026-01. It is built for grounded enterprise question answering rather than as a reasoning model.", "what_it_is_for": "Jamba2 Mini is AI21's current Mini model, an Apache-2.0 SSM-Transformer with 12B active parameters out of 52B, published on Hugging Face on 2026-01-06. The docs point the jamba-mini alias at snapshot jamba-mini-2-2026-01. It is built for grounded enterprise question answering rather than as a reasoning model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jurassic/jurassic-1-grande": { "id": "ai21/jurassic/jurassic-1-grande", "name": "Jurassic-1 Grande", "maker": "ai21", "family": "ai21/jurassic", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2022-05-02", "context_length": 2048, "max_output_tokens": null, "parameters": "17B", "architecture": "Mid-size autoregressive Transformer in the Jurassic-1 family. AI21's re:Invent 2022 talk says the team chose a 17 billion parameter architecture and that Jurassic-1 Grande is a 17 billion parameter model, about one third the inference cost of Jumbo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jurassic-1 Grande is AI21's mid-size Jurassic-1 model, announced 2022-05-02. AI21 later described it as a 17 billion parameter model meant to keep most of Jumbo's quality at lower cost and latency. It is retired.", "what_it_is_for": "Jurassic-1 Grande is AI21's mid-size Jurassic-1 model, announced 2022-05-02. AI21 later described it as a 17 billion parameter model meant to keep most of Jumbo's quality at lower cost and latency. It is retired.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jurassic/jurassic-1-jumbo": { "id": "ai21/jurassic/jurassic-1-jumbo", "name": "Jurassic-1 Jumbo", "maker": "ai21", "family": "ai21/jurassic", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2021-08-04", "context_length": 2048, "max_output_tokens": null, "parameters": "178B", "architecture": "Autoregressive Transformer. AI21's contributed KDnuggets article says 178B parameters, 76 layers, hidden size 13824, and a vocabulary of about 250,000 tokens, shallower and wider than GPT-3. The launch post also says 178B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jurassic-1 Jumbo is AI21's 178 billion parameter autoregressive model, announced 2021-08-04 as the flagship of Jurassic-1. It is closed, API-only, and retired.", "what_it_is_for": "Jurassic-1 Jumbo is AI21's 178 billion parameter autoregressive model, announced 2021-08-04 as the flagship of Jurassic-1. It is closed, API-only, and retired.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jurassic/jurassic-1-large": { "id": "ai21/jurassic/jurassic-1-large", "name": "Jurassic-1 Large", "maker": "ai21", "family": "ai21/jurassic", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2021-08-04", "context_length": 2048, "max_output_tokens": null, "parameters": "7.5B", "architecture": "Autoregressive Transformer. AI21's May 2022 Grande post and an AI21 re:Invent 2022 talk both say 7.5 billion parameters. The Jurassic-1 research abstract says 7B. Family vocabulary is described as about 250,000 tokens, including multi-word tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jurassic-1 Large is AI21's smaller 2021 autoregressive language model, announced with AI21 Studio on 2021-08-04. AI21 later stated it at 7.5 billion parameters. It is retired and was succeeded in the product line by Jurassic-2 Light.", "what_it_is_for": "Jurassic-1 Large is AI21's smaller 2021 autoregressive language model, announced with AI21 Studio on 2021-08-04. AI21 later stated it at 7.5 billion parameters. It is retired and was succeeded in the product line by Jurassic-2 Light.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jurassic/jurassic-2-light": { "id": "ai21/jurassic/jurassic-2-light", "name": "Jurassic-2 Light", "maker": "ai21", "family": "ai21/jurassic", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-06-08", "context_length": 8192, "max_output_tokens": 8191, "parameters": "", "architecture": "Smallest Jurassic-2 model after the June 2023 rename. AI21 said Light replaced Jurassic-2 Large and that all three renamed models include instruct behavior. Parameter count was not published in the March or June posts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jurassic-2 Light is AI21's smallest Jurassic-2 model, named on 2023-06-08 when Jurassic-2 Large was renamed and instruct behavior was folded in. AI21 described it for short classification, extraction, and short copy. It is retired.", "what_it_is_for": "Jurassic-2 Light is AI21's smallest Jurassic-2 model, named on 2023-06-08 when Jurassic-2 Large was renamed and instruct behavior was folded in. AI21 described it for short classification, extraction, and short copy. It is retired.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jurassic/jurassic-2-mid": { "id": "ai21/jurassic/jurassic-2-mid", "name": "Jurassic-2 Mid", "maker": "ai21", "family": "ai21/jurassic", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-06-08", "context_length": 8192, "max_output_tokens": 8191, "parameters": "", "architecture": "Mid-size Jurassic-2 model. June 2023 rename of Jurassic-2 Grande, with instruct behavior included. AI21 did not publish a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jurassic-2 Mid is AI21's middle Jurassic-2 model, the June 2023 rename of Grande, with instruct use included. Bedrock's last listed EOL for j2-mid-v1 in us-east-1 is 2025-03-12, so the model is retired.", "what_it_is_for": "Jurassic-2 Mid is AI21's middle Jurassic-2 model, the June 2023 rename of Grande, with instruct use included. Bedrock's last listed EOL for j2-mid-v1 in us-east-1 is 2025-03-12, so the model is retired.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/jurassic/jurassic-2-ultra": { "id": "ai21/jurassic/jurassic-2-ultra", "name": "Jurassic-2 Ultra", "maker": "ai21", "family": "ai21/jurassic", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-06-08", "context_length": 8192, "max_output_tokens": 8191, "parameters": "", "architecture": "Largest Jurassic-2 model. June 2023 rename of Jurassic-2 Jumbo, with instruct behavior included. Parameter count was not published in the launch or rename posts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jurassic-2 Ultra is AI21's largest Jurassic-2 model, the June 2023 rename of Jumbo. AI21 called it the highest-quality and highest-cost model in that trio and cited an internal HELM win rate of 86.8 percent. Bedrock retired it by 2025-03-12.", "what_it_is_for": "Jurassic-2 Ultra is AI21's largest Jurassic-2 model, the June 2023 rename of Jumbo. AI21 called it the highest-quality and highest-cost model in that trio and cited an internal HELM win rate of 86.8 percent. Bedrock retired it by 2025-03-12.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/task-specific/contextual-answers": { "id": "ai21/task-specific/contextual-answers", "name": "Contextual Answers", "maker": "ai21", "family": "ai21/task-specific", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-07-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed task-specific model behind an AI21 Studio API. Parameter count, architecture, and context window were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual Answers is AI21's closed task-specific question-answering model, announced 2023-07-19. It answers from documents the customer uploads and is built to avoid answers that are not in that material. It is absent from the current docs index.", "what_it_is_for": "Contextual Answers is AI21's closed task-specific question-answering model, announced 2023-07-19. It answers from documents the customer uploads and is built to avoid answers that are not in that material. It is absent from the current docs index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/task-specific/grammatical-error-correction": { "id": "ai21/task-specific/grammatical-error-correction", "name": "Grammatical Error Correction", "maker": "ai21", "family": "ai21/task-specific", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-03-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed task-specific model behind an AI21 Studio API. Parameter count, architecture, and context window were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Grammatical Error Correction is a closed AI21 task-specific model announced on 2023-03-09. It returns corrections for grammar, spelling, and related writing errors. It is absent from the current docs index.", "what_it_is_for": "Grammatical Error Correction is a closed AI21 task-specific model announced on 2023-03-09. It returns corrections for grammar, spelling, and related writing errors. It is absent from the current docs index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/task-specific/paraphrase": { "id": "ai21/task-specific/paraphrase", "name": "Paraphrase", "maker": "ai21", "family": "ai21/task-specific", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-03-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed task-specific model behind an AI21 Studio API. Parameter count, architecture, and context window were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Paraphrase is a closed AI21 task-specific model announced on 2023-03-09 as part of the Wordtune API set. It rewrites text while keeping the meaning, with controls the launch post describes as tone and length. It is no longer in the current docs index.", "what_it_is_for": "Paraphrase is a closed AI21 task-specific model announced on 2023-03-09 as part of the Wordtune API set. It rewrites text while keeping the meaning, with controls the launch post describes as tone and length. It is no longer in the current docs index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/task-specific/summarize": { "id": "ai21/task-specific/summarize", "name": "Summarize", "maker": "ai21", "family": "ai21/task-specific", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-03-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed task-specific model behind an AI21 Studio API. Parameter count, architecture, and context window were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Summarize is AI21's closed task-specific summarization model, announced 2023-03-09 in the Wordtune API set. It condenses documents such as reports and papers. It is absent from the current docs index.", "what_it_is_for": "Summarize is AI21's closed task-specific summarization model, announced 2023-03-09 in the Wordtune API set. It condenses documents such as reports and papers. It is absent from the current docs index.", "ratings": { "empirical_100": null, "lmsys_elo": 1200, "coding_elo": 1200, "swe_bench": 15.0, "humaneval": 70.0, "twelve_cs": 98.0, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/task-specific/text-improvements": { "id": "ai21/task-specific/text-improvements", "name": "Text Improvements", "maker": "ai21", "family": "ai21/task-specific", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-03-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed task-specific model behind an AI21 Studio API. Parameter count, architecture, and context window were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Text Improvements is a closed AI21 task-specific API announced on 2023-03-09. It suggests rewrites for fluency, vocabulary, and clarity. It is absent from the current docs index.", "what_it_is_for": "Text Improvements is a closed AI21 task-specific API announced on 2023-03-09. It suggests rewrites for fluency, vocabulary, and clarity. It is absent from the current docs index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ai21/task-specific/text-segmentation": { "id": "ai21/task-specific/text-segmentation", "name": "Text Segmentation", "maker": "ai21", "family": "ai21/task-specific", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-03-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed task-specific model behind an AI21 Studio API. Parameter count, architecture, and context window were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Text Segmentation is a closed AI21 task-specific model announced on 2023-03-09. It breaks a document or URL into topic segments. It is absent from the current docs index.", "what_it_is_for": "Text Segmentation is a closed AI21 task-specific model announced on 2023-03-09. It breaks a document or URL into topic segments. It is absent from the current docs index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/dudi/llama3-2-1b-dudi": { "id": "aisingapore/dudi/llama3-2-1b-dudi", "name": "Llama3.2-1B-DuDi", "maker": "aisingapore", "family": "aisingapore/dudi", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-05-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small language model distilled with DuDi (dual-signal distillation and a cross-lingual verbalizer) for Southeast Asian languages. Marketed size 1B. Exact parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Llama3.2-1B-DuDi. It is a named DuDi distillation checkpoint published by AI Singapore on 31 May 2026. The paper was submitted on 3 June 2026. The collection is still marked work in progress. This dossier was collected on 2026-10-15.", "what_it_is_for": "Research student model showing DuDi distillation for Southeast Asian languages. It is not part of the SEA-LION product catalogue.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/dudi/qwen2-5-0-5b-dudi": { "id": "aisingapore/dudi/qwen2-5-0-5b-dudi", "name": "Qwen2.5-0.5B-DuDi", "maker": "aisingapore", "family": "aisingapore/dudi", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-05-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small language model distilled with DuDi (dual-signal distillation and a cross-lingual verbalizer) for Southeast Asian languages. Marketed size 0.5B. Exact parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Qwen2.5-0.5B-DuDi. It is a named DuDi distillation checkpoint published by AI Singapore on 31 May 2026. The paper was submitted on 3 June 2026. The collection is still marked work in progress. This dossier was collected on 2026-10-15.", "what_it_is_for": "Research student model showing DuDi distillation for Southeast Asian languages. It is not part of the SEA-LION product catalogue.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/dudi/qwen2-5-1-5b-dudi": { "id": "aisingapore/dudi/qwen2-5-1-5b-dudi", "name": "Qwen2.5-1.5B-DuDi", "maker": "aisingapore", "family": "aisingapore/dudi", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-05-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small language model distilled with DuDi (dual-signal distillation and a cross-lingual verbalizer) for Southeast Asian languages. Marketed size 1.5B. Exact parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Qwen2.5-1.5B-DuDi. It is a named DuDi distillation checkpoint published by AI Singapore on 31 May 2026. The paper was submitted on 3 June 2026. The collection is still marked work in progress. This dossier was collected on 2026-10-15.", "what_it_is_for": "Research student model showing DuDi distillation for Southeast Asian languages. It is not part of the SEA-LION product catalogue.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/dudi/qwen3-0-6b-base-dudi": { "id": "aisingapore/dudi/qwen3-0-6b-base-dudi", "name": "Qwen3-0.6B-Base-DuDi", "maker": "aisingapore", "family": "aisingapore/dudi", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-05-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small language model distilled with DuDi (dual-signal distillation and a cross-lingual verbalizer) for Southeast Asian languages. Marketed size 0.6B. Exact parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Qwen3-0.6B-Base-DuDi. It is a named DuDi distillation checkpoint published by AI Singapore on 31 May 2026. The paper was submitted on 3 June 2026. The collection is still marked work in progress. This dossier was collected on 2026-10-15.", "what_it_is_for": "Research student model showing DuDi distillation for Southeast Asian languages. It is not part of the SEA-LION product catalogue.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/research/coherence-momentum": { "id": "aisingapore/research/coherence-momentum", "name": "Coherence Momentum", "maker": "aisingapore", "family": "aisingapore/research", "status": "active", "confidence": "medium", "weights": "open-source", "license": "MIT", "release_date": "2023-02-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named research checkpoint on the aisingapore Hugging Face org. Parameter count, context length, and architecture details were not read from a full card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Coherence Momentum. It is a named research checkpoint published by AI Singapore on the Hugging Face org, created 2023-02-10. It is still listed and is not a SEA-LION product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Coherence model checkpoint using Wall Street Journal data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/research/cross-sentence-gec": { "id": "aisingapore/research/cross-sentence-gec", "name": "Cross-sentence GEC", "maker": "aisingapore", "family": "aisingapore/research", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-02-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named research checkpoint on the aisingapore Hugging Face org. Parameter count, context length, and architecture details were not read from a full card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Cross-sentence GEC. It is a named research checkpoint published by AI Singapore on the Hugging Face org, created 2023-02-20. It is still listed and is not a SEA-LION product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Grammatical error classification checkpoint using Lang-8, NUCLE, and CoNLL-2014. License was not in the Hub tags.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/research/lif3wayap": { "id": "aisingapore/research/lif3wayap", "name": "Lif3WayAp", "maker": "aisingapore", "family": "aisingapore/research", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2023-02-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named research checkpoint on the aisingapore Hugging Face org. Parameter count, context length, and architecture details were not read from a full card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Lif3WayAp. It is a named research checkpoint published by AI Singapore on the Hugging Face org, created 2023-02-21. It is still listed and is not a SEA-LION product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Conversational QA checkpoint using QuAC.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/research/lsr": { "id": "aisingapore/research/lsr", "name": "LSR", "maker": "aisingapore", "family": "aisingapore/research", "status": "active", "confidence": "medium", "weights": "open-source", "license": "MIT", "release_date": "2023-02-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named research checkpoint on the aisingapore Hugging Face org. Parameter count, context length, and architecture details were not read from a full card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released LSR. It is a named research checkpoint published by AI Singapore on the Hugging Face org, created 2023-02-17. It is still listed and is not a SEA-LION product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Document-level relation extraction checkpoint using DocRED.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/research/roberta-base": { "id": "aisingapore/research/roberta-base", "name": "RoBERTa-base", "maker": "aisingapore", "family": "aisingapore/research", "status": "active", "confidence": "medium", "weights": "open-source", "license": "MIT", "release_date": "2023-02-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named research checkpoint on the aisingapore Hugging Face org. Parameter count, context length, and architecture details were not read from a full card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released RoBERTa-base. It is a named research checkpoint published by AI Singapore on the Hugging Face org, created 2023-02-23. It is still listed and is not a SEA-LION product. This dossier was collected on 2026-10-15.", "what_it_is_for": "AI Singapore text-classification checkpoint for RECCON. This is not the original Facebook RoBERTa-base release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/research/rst-pointer": { "id": "aisingapore/research/rst-pointer", "name": "RST-pointer", "maker": "aisingapore", "family": "aisingapore/research", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "2023-02-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named research checkpoint on the aisingapore Hugging Face org. Parameter count, context length, and architecture details were not read from a full card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released RST-pointer. It is a named research checkpoint published by AI Singapore on the Hugging Face org, created 2023-02-14. It is still listed and is not a SEA-LION product. This dossier was collected on 2026-10-15.", "what_it_is_for": "RST discourse parsing checkpoint trained with the RST Discourse Treebank.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/research/rumour-detection-twitter": { "id": "aisingapore/research/rumour-detection-twitter", "name": "Rumour Detection Twitter", "maker": "aisingapore", "family": "aisingapore/research", "status": "active", "confidence": "medium", "weights": "open-source", "license": "MIT", "release_date": "2023-02-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named research checkpoint on the aisingapore Hugging Face org. Parameter count, context length, and architecture details were not read from a full card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Rumour Detection Twitter. It is a named research checkpoint published by AI Singapore on the Hugging Face org, created 2023-02-17. It is still listed and is not a SEA-LION product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Rumour detection checkpoint using Twitter15, Twitter16, and PHEME.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/research/senticgcn": { "id": "aisingapore/research/senticgcn", "name": "SenticGCN", "maker": "aisingapore", "family": "aisingapore/research", "status": "active", "confidence": "medium", "weights": "open-source", "license": "MIT", "release_date": "2022-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named research checkpoint on the aisingapore Hugging Face org. Parameter count, context length, and architecture details were not read from a full card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released SenticGCN. It is a named research checkpoint published by AI Singapore on the Hugging Face org, created 2022-10-06. It is still listed and is not a SEA-LION product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Aspect sentiment checkpoint using SemEval sets. It is AI Singapore's released SenticGCN checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/research/spanbert": { "id": "aisingapore/research/spanbert", "name": "SPANBert", "maker": "aisingapore", "family": "aisingapore/research", "status": "active", "confidence": "medium", "weights": "open-source", "license": "MIT", "release_date": "2023-02-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named research checkpoint on the aisingapore Hugging Face org. Parameter count, context length, and architecture details were not read from a full card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released SPANBert. It is a named research checkpoint published by AI Singapore on the Hugging Face org, created 2023-02-23. It is still listed and is not a SEA-LION product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Question-answering checkpoint associated with RECCON and arXiv 2012.11820. It is an AI Singapore release, not a claim that it is the original SpanBERT authors' model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/research/unsupervised-feature-decomposition": { "id": "aisingapore/research/unsupervised-feature-decomposition", "name": "Unsupervised Feature Decomposition", "maker": "aisingapore", "family": "aisingapore/research", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2023-02-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named research checkpoint on the aisingapore Hugging Face org. Parameter count, context length, and architecture details were not read from a full card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Unsupervised Feature Decomposition. It is a named research checkpoint published by AI Singapore on the Hugging Face org, created 2023-02-08. It is still listed and is not a SEA-LION product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Text-classification checkpoint for unsupervised feature decomposition.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-embedding/sea-lion-e5-embedding-600m": { "id": "aisingapore/sea-embedding/sea-lion-e5-embedding-600m", "name": "SEA-LION-E5-Embedding-600M", "maker": "aisingapore", "family": "aisingapore/sea-embedding", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-03-09", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Embedding fine-tune of intfloat/multilingual-e5-large. Family table lists context 512.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released SEA-LION-E5-Embedding-600M. It is the E5-large embedding tune for Southeast Asia. The Hub repo was created on 9 March 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "Retrieval embeddings, as an alternative to the ModernBERT embedding models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-embedding/sea-lion-modernbert-300m": { "id": "aisingapore/sea-embedding/sea-lion-modernbert-300m", "name": "SEA-LION-ModernBERT-300M", "maker": "aisingapore", "family": "aisingapore/sea-embedding", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-03-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ModernBERT encoder with a Gemma 3 SentencePiece tokenizer, vocabulary 262144. The 300M table lists 22 layers, d_model 768, head_dim 12, sequence length 8k. Pre-trained from scratch on 2T tokens and mid-trained on 1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released SEA-LION-ModernBERT-300M. It is the 300M ModernBERT encoder pretrained for Southeast Asian text. The card was updated on 16 March 2026. Weights are MIT and still published. This dossier was collected on 2026-10-15.", "what_it_is_for": "Fill-mask, classification, and continued training. It is not the contrastive embedding model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-embedding/sea-lion-modernbert-600m": { "id": "aisingapore/sea-embedding/sea-lion-modernbert-600m", "name": "SEA-LION-ModernBERT-600M", "maker": "aisingapore", "family": "aisingapore/sea-embedding", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-03-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ModernBERT-large encoder with the Gemma 3 tokenizer and vocabulary 262K. Same 2T pre-training and 1T mid-training description as the 300M model. Layer sizes for 600M were not in the 300M table and are not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released SEA-LION-ModernBERT-600M. It is the 600M ModernBERT encoder. The card's last update is 16 March 2026, and the Hub repo was created on 12 March 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "Fill-mask and classification encoder, and the base for the 600M embedding tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-embedding/sea-lion-modernbert-embedding-300m": { "id": "aisingapore/sea-embedding/sea-lion-modernbert-embedding-300m", "name": "SEA-LION-ModernBERT-Embedding-300M", "maker": "aisingapore", "family": "aisingapore/sea-embedding", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Contrastive embedding fine-tune of the 300M ModernBERT. Hub base_model tag points at the 300M checkpoints repo.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released SEA-LION-ModernBERT-Embedding-300M. It is the 300M Southeast Asian embedding model. The Hub repo was created on 2 March 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "Retrieval, RAG, and similarity in Southeast Asian languages at the smaller ModernBERT size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-embedding/sea-lion-modernbert-embedding-600m": { "id": "aisingapore/sea-embedding/sea-lion-modernbert-embedding-600m", "name": "SEA-LION-ModernBERT-Embedding-600M", "maker": "aisingapore", "family": "aisingapore/sea-embedding", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-03-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-tuned embedding model. The 600M encoder card says contrastive pre-training used 245 million text pairs and instruction tuning used 8 million pairs. API docs say the vectors are 1024-dimensional with an 8k context.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released SEA-LION-ModernBERT-Embedding-600M. It is the 600M embedding model served by the free SEA-LION embeddings API. The Hub repo was created on 5 March 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "Multilingual embeddings for RAG, search, and sentence similarity across Southeast Asian languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-guard/gemma-sea-guard-12b-2602": { "id": "aisingapore/sea-guard/gemma-sea-guard-12b-2602", "name": "Gemma-SEA-Guard-12B", "maker": "aisingapore", "family": "aisingapore/sea-guard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "See gaps. Cards and Hub tags disagree between Apache-2.0, Gemma, and other.", "release_date": "2026-02-04", "context_length": 128000, "max_output_tokens": null, "parameters": "12000000000", "architecture": "Gemma 3 12B IT safety fine-tune. The maker says the single SEA-Guard API id serves the Gemma model.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Gemma-SEA-Guard-12B. It is a SEA-Guard safety classifier released on 4 February 2026. Weights are still published. The canonical Hub id uses the 2602 suffix; docs and the site also link the 040226 name, which resolves to the same repo. This dossier was collected on 2026-10-15.", "what_it_is_for": "Classify a prompt, a response, or a prompt-response pair as safe or unsafe for Southeast Asian cultural and safety norms. It is not a general assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-guard/llama-sea-guard-8b-2602": { "id": "aisingapore/sea-guard/llama-sea-guard-8b-2602", "name": "Llama-SEA-Guard-8B", "maker": "aisingapore", "family": "aisingapore/sea-guard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "See gaps. Cards and Hub tags disagree between Apache-2.0, Gemma, and other.", "release_date": "2026-02-04", "context_length": 128000, "max_output_tokens": null, "parameters": "8000000000", "architecture": "Text-only safety fine-tune of Llama-SEA-LION-v3-8B-IT. The SEA-Guard paper says Llama variants were tried and the selected paper models were the Qwen VL 4B/8B and Gemma 12B lines; AI Singapore still published this Llama checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Llama-SEA-Guard-8B. It is a SEA-Guard safety classifier released on 4 February 2026. Weights are still published. The canonical Hub id uses the 2602 suffix; docs and the site also link the 040226 name, which resolves to the same repo. This dossier was collected on 2026-10-15.", "what_it_is_for": "Classify a prompt, a response, or a prompt-response pair as safe or unsafe for Southeast Asian cultural and safety norms. It is not a general assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-guard/qwen-sea-guard-4b-2602": { "id": "aisingapore/sea-guard/qwen-sea-guard-4b-2602", "name": "Qwen-SEA-Guard-4B", "maker": "aisingapore", "family": "aisingapore/sea-guard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "See gaps. Cards and Hub tags disagree between Apache-2.0, Gemma, and other.", "release_date": "2026-02-04", "context_length": 128000, "max_output_tokens": null, "parameters": "4437815808", "architecture": "Qwen3-VL 4B safety fine-tune of Qwen-SEA-LION-v4-4B-VL. Hugging Face safetensors BF16 parameter count is 4437815808. A second total field on that payload was 569856 and was ignored because it disagrees with the 4B size.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Qwen-SEA-Guard-4B. It is a SEA-Guard safety classifier released on 4 February 2026. Weights are still published. The canonical Hub id uses the 2602 suffix; docs and the site also link the 040226 name, which resolves to the same repo. This dossier was collected on 2026-10-15.", "what_it_is_for": "Classify a prompt, a response, or a prompt-response pair as safe or unsafe for Southeast Asian cultural and safety norms. It is not a general assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-guard/qwen-sea-guard-8b-2602": { "id": "aisingapore/sea-guard/qwen-sea-guard-8b-2602", "name": "Qwen-SEA-Guard-8B", "maker": "aisingapore", "family": "aisingapore/sea-guard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "See gaps. Cards and Hub tags disagree between Apache-2.0, Gemma, and other.", "release_date": "2026-02-04", "context_length": 128000, "max_output_tokens": null, "parameters": "8000000000", "architecture": "Qwen3-VL 8B safety fine-tune of Qwen-SEA-LION-v4-8B-VL.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Qwen-SEA-Guard-8B. It is a SEA-Guard safety classifier released on 4 February 2026. Weights are still published. The canonical Hub id uses the 2602 suffix; docs and the site also link the 040226 name, which resolves to the same repo. This dossier was collected on 2026-10-15.", "what_it_is_for": "Classify a prompt, a response, or a prompt-response pair as safe or unsafe for Southeast Asian cultural and safety norms. It is not a general assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/apertus-sea-lion-v4-8b-it": { "id": "aisingapore/sea-lion/apertus-sea-lion-v4-8b-it", "name": "Apertus-SEA-LION-v4-8B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 in the model-card text; Hugging Face metadata for the IT repo is mit", "release_date": "2026-02-05", "context_length": null, "max_output_tokens": null, "parameters": "8000000000", "architecture": "Apertus decoder post-trained on Apertus-8B-Instruct-2509 with about 6.4 million instruction pairs, including a filtered tool-calling set.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Apertus-SEA-LION-v4-8B-IT. It is the fully open 8B instruct model in SEA-LION v4, announced with the 4B Gemma vision model on 6 February 2026, and the Hub repo dates from 5 February 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "Transparent Southeast Asian instruct model for chat, translation, and tool calling, continued from the Swiss AI Apertus 8B instruct checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/gemma-sea-lion-v3-9b": { "id": "aisingapore/sea-lion/gemma-sea-lion-v3-9b", "name": "Gemma-SEA-LION-v3-9B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-10-30", "context_length": 8192, "max_output_tokens": null, "parameters": "9000000000", "architecture": "Continued pre-training of Gemma 2 9B. Overview table lists context 8192.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Gemma-SEA-LION-v3-9B. It is the Gemma 2 9B base in SEA-LION v3. The Hub repo was created on 30 October 2024. The v3 collection is described as a December 2024 release. This dossier was collected on 2026-10-15.", "what_it_is_for": "Base Gemma 2 9B SEA checkpoint before instruction tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/gemma-sea-lion-v3-9b-it": { "id": "aisingapore/sea-lion/gemma-sea-lion-v3-9b-it", "name": "Gemma-SEA-LION-v3-9B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-10-30", "context_length": 8192, "max_output_tokens": null, "parameters": "9000000000", "architecture": "Instruction tune of Gemma-SEA-LION-v3-9B. The SEA-LION paper centers on this model and Llama-SEA-LION-v3-8B-IT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Gemma-SEA-LION-v3-9B-IT. It is the Gemma 2 9B instruct model in SEA-LION v3, Hub date 30 October 2024, and it is still downloadable. The paper says the v3-era releases used an MIT license; the Hub tag for this repo is gemma, so the Gemma terms are recorded. This dossier was collected on 2026-10-15.", "what_it_is_for": "Best-performing SEA-LION v3 size on SEA-HELM among similar-sized models, according to the v3 overview.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/gemma-sea-lion-v4-27b": { "id": "aisingapore/sea-lion/gemma-sea-lion-v4-27b", "name": "Gemma-SEA-LION-v4-27B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma Terms of Use", "release_date": "2025-08-11", "context_length": 128000, "max_output_tokens": null, "parameters": "27000000000", "architecture": "Gemma 3 decoder. Continued pre-training from Gemma 3 27B IT on approximately 500B tokens sampled from a pool of over 1T tokens.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Gemma-SEA-LION-v4-27B. It is the Gemma 3 27B continued-pretraining checkpoint in SEA-LION v4. The Hub repo was created on 11 August 2025 and the card was updated on 25 August 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "Base multilingual checkpoint, before the instruction tune, with Gemma 3's long context, image understanding, function calling, and structured outputs carried over.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/gemma-sea-lion-v4-27b-it": { "id": "aisingapore/sea-lion/gemma-sea-lion-v4-27b-it", "name": "Gemma-SEA-LION-v4-27B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma Terms of Use", "release_date": "2025-08-11", "context_length": 128000, "max_output_tokens": null, "parameters": "27000000000", "architecture": "Instruction-tuned Gemma 3 27B SEA-LION. Inherits 128k context, image understanding, function calling, and structured outputs from the continued-pretrained parent.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Gemma-SEA-LION-v4-27B-IT. It is the main Gemma 27B instruction model in SEA-LION v4, released in August 2025, and it remains a current download. This dossier was collected on 2026-10-15.", "what_it_is_for": "Southeast Asian instruction following, visual understanding, function calling, and structured output at the 27B Gemma 3 size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/gemma-sea-lion-v4-27b-vl": { "id": "aisingapore/sea-lion/gemma-sea-lion-v4-27b-vl", "name": "Gemma-SEA-LION-v4-27B-VL", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-10-15", "context_length": 128000, "max_output_tokens": null, "parameters": "27000000000", "architecture": "Vision-language model produced from Gemma-SEA-LION-v4-27B-IT. Hub tags include mergekit.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Gemma-SEA-LION-v4-27B-VL. It is the 27B vision-language SEA-LION v4 checkpoint. The Hub repo was created on 15 October 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "Image and text comprehension for Southeast Asian documents and scenes, on top of the 27B instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/gemma-sea-lion-v4-4b-vl": { "id": "aisingapore/sea-lion/gemma-sea-lion-v4-4b-vl", "name": "Gemma-SEA-LION-v4-4B-VL", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2026-02-05", "context_length": 128000, "max_output_tokens": null, "parameters": "4000000000", "architecture": "Vision-language supervised fine-tune of Gemma 3 4B IT, with early tool-calling and image-text extraction.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Gemma-SEA-LION-v4-4B-VL. It is the small vision-language SEA-LION v4 model, released 5 February 2026, for edge hardware. This dossier was collected on 2026-10-15.", "what_it_is_for": "Visual question answering, document extraction, and first-pass tool calling in Southeast Asian languages on a 4B Gemma 3 model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/gemma-sea-lion-v4-5-e2b-it": { "id": "aisingapore/sea-lion/gemma-sea-lion-v4-5-e2b-it", "name": "Gemma-SEA-LION-v4.5-E2B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 on the project model card; Hugging Face metadata tag is mit", "release_date": "2026-05-20", "context_length": 128000, "max_output_tokens": null, "parameters": "2300000000", "architecture": "Gemma 4 effective-2B decoder with vision and audio encoders. Card text: 2.3B effective and 5.1B with embeddings. Post-trained by logit distillation and merging. SEA-HELM labels the same model 5B-A2B MoE.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Gemma-SEA-LION-v4.5-E2B-IT. It is the small agentic model in SEA-LION v4.5, announced on 20 May 2026, and it is a current open-weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "Local and agentic tool use in Southeast Asian languages on a small Gemma 4 model, including image and audio inputs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/llama-sea-lion-v2-8b": { "id": "aisingapore/sea-lion/llama-sea-lion-v2-8b", "name": "Llama-SEA-LION-v2-8B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-07-30", "context_length": 8192, "max_output_tokens": null, "parameters": "8000000000", "architecture": "Continued pre-training of Llama 3 8B Instruct on approximately 48B tokens in English, Indonesian, Tamil, Thai, and Vietnamese. Trained about 2 days on 64 H100 GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Llama-SEA-LION-v2-8B. It is the Llama 3 8B base in SEA-LION v2, released in July 2024. The Hub repo was created on 30 July 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "Base SEA-LION v2 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/llama-sea-lion-v2-8b-it": { "id": "aisingapore/sea-lion/llama-sea-lion-v2-8b-it", "name": "Llama-SEA-LION-v2-8B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-07-30", "context_length": 8192, "max_output_tokens": null, "parameters": "8000000000", "architecture": "Instruction tune of Llama-SEA-LION-v2-8B. Docs describe v2.1 as a conversational improvement; no separate v2.1 repo exists under aisingapore. Older public name: llama3-8b-cpt-sea-lionv2-instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Llama-SEA-LION-v2-8B-IT. It is the Llama 3 8B instruct model in SEA-LION v2, Hub date 30 July 2024. Documentation treats v2.1 as a conversational step of this line rather than a separate Hub model. This dossier was collected on 2026-10-15.", "what_it_is_for": "Instruction following for English, Indonesian, Tamil, Thai, and Vietnamese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/llama-sea-lion-v3-5-70b-r": { "id": "aisingapore/sea-lion/llama-sea-lion-v3-5-70b-r", "name": "Llama-SEA-LION-v3.5-70B-R", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2025-04-14", "context_length": 128000, "max_output_tokens": null, "parameters": "70000000000", "architecture": "Reasoning supervised fine-tune of Llama-SEA-LION-v3-70B-IT. API examples use thinking_mode on or off.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Llama-SEA-LION-v3.5-70B-R. It is the 70B reasoning model in SEA-LION v3.5, dated 14 April 2025 on the sibling card style, and it is an example model on the free API. This dossier was collected on 2026-10-15.", "what_it_is_for": "Larger Southeast Asian reasoning model. The free API documents this id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/llama-sea-lion-v3-5-8b-r": { "id": "aisingapore/sea-lion/llama-sea-lion-v3-5-8b-r", "name": "Llama-SEA-LION-v3.5-8B-R", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2025-04-14", "context_length": 128000, "max_output_tokens": null, "parameters": "8000000000", "architecture": "Reasoning supervised fine-tune of Llama-SEA-LION-v3-8B-IT. Card current version 14.04.2025. Thinking mode defaults on.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Llama-SEA-LION-v3.5-8B-R. It is the 8B reasoning model in SEA-LION v3.5. The card's current version date is 14 April 2025, and the weights are still published. This dossier was collected on 2026-10-15.", "what_it_is_for": "Multilingual Southeast Asian reasoning. Toggle thinking_mode off for a direct answer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/llama-sea-lion-v3-70b": { "id": "aisingapore/sea-lion/llama-sea-lion-v3-70b", "name": "Llama-SEA-LION-v3-70B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2024-12-11", "context_length": 128000, "max_output_tokens": null, "parameters": "70000000000", "architecture": "Continued pre-training of Llama 3.1 70B Instruct on approximately 200B tokens across 11 SEA languages.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Llama-SEA-LION-v3-70B. It is the Llama 3.1 70B base in SEA-LION v3, Hub date 11 December 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "Largest SEA-LION v3 base checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/llama-sea-lion-v3-70b-it": { "id": "aisingapore/sea-lion/llama-sea-lion-v3-70b-it", "name": "Llama-SEA-LION-v3-70B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2024-12-11", "context_length": 128000, "max_output_tokens": null, "parameters": "70000000000", "architecture": "Instruction tune of Llama-SEA-LION-v3-70B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Llama-SEA-LION-v3-70B-IT. It is the Llama 3.1 70B instruct model in SEA-LION v3, Hub date 11 December 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "Large instruction model for Southeast Asian languages in the v3 line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/llama-sea-lion-v3-8b": { "id": "aisingapore/sea-lion/llama-sea-lion-v3-8b", "name": "Llama-SEA-LION-v3-8B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2024-12-11", "context_length": 128000, "max_output_tokens": null, "parameters": "8000000000", "architecture": "Continued pre-training of Llama 3.1 8B Instruct. Context 128K. Also mirrored on Kaggle as ai-singapore/llama3.1-8b-cpt-sea-lionv3-base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Llama-SEA-LION-v3-8B. It is the Llama 3.1 8B base in SEA-LION v3. The Hub repo was created on 11 December 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "Base 8B checkpoint for SEA-LION v3 before instruction tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/llama-sea-lion-v3-8b-it": { "id": "aisingapore/sea-lion/llama-sea-lion-v3-8b-it", "name": "Llama-SEA-LION-v3-8B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2024-12-11", "context_length": 128000, "max_output_tokens": null, "parameters": "8000000000", "architecture": "Instruction tune of Llama-SEA-LION-v3-8B. Context 128k. The paper's Llama-SEA-LION-8B-IT name refers to this line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Llama-SEA-LION-v3-8B-IT. It is the Llama 3.1 8B instruct model in SEA-LION v3, Hub date 11 December 2024, and it is still downloadable. This dossier was collected on 2026-10-15.", "what_it_is_for": "Instruction following in English and Southeast Asian languages at the 8B Llama 3.1 size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/nemotron-sea-lion-v4-8-120b-a12b": { "id": "aisingapore/sea-lion/nemotron-sea-lion-v4-8-120b-a12b", "name": "Nemotron-SEA-LION-v4.8-120B-A12B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Other (NOTICE.md on the model repo); Hugging Face metadata tag is mit", "release_date": "2026-09-18", "context_length": 262144, "max_output_tokens": null, "parameters": "120000000000", "architecture": "Mamba-2 and Transformer hybrid MoE continued from an NVIDIA Nemotron 3 base on about 150B tokens, then on-policy distilled for the instruct checkpoint. Hybrid LatentMoE with Mamba-2 is described for the 120B NVIDIA base; the 30B card says Mamba2-Transformer hybrid MoE.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Nemotron-SEA-LION-v4.8-120B-A12B. It is a SEA-LION v4.8 open-weight Mixture-of-Experts model built with NVIDIA, announced on 18 September 2026, and still current. This dossier was collected on 2026-10-15.", "what_it_is_for": "Largest SEA-LION v4.8 instruct model, for long-context Southeast Asian generation on the Nemotron 3 Super MoE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/nemotron-sea-lion-v4-8-120b-a12b-base": { "id": "aisingapore/sea-lion/nemotron-sea-lion-v4-8-120b-a12b-base", "name": "Nemotron-SEA-LION-v4.8-120B-A12B-Base", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Other (NOTICE.md on the model repo); Hugging Face metadata tag is mit", "release_date": "2026-09-18", "context_length": 8192, "max_output_tokens": null, "parameters": "120000000000", "architecture": "Mamba-2 and Transformer hybrid MoE continued from an NVIDIA Nemotron 3 base on about 150B tokens, then on-policy distilled for the instruct checkpoint. Hybrid LatentMoE with Mamba-2 is described for the 120B NVIDIA base; the 30B card says Mamba2-Transformer hybrid MoE.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Nemotron-SEA-LION-v4.8-120B-A12B-Base. It is a SEA-LION v4.8 open-weight Mixture-of-Experts model built with NVIDIA, announced on 18 September 2026, and still current. This dossier was collected on 2026-10-15.", "what_it_is_for": "Continued-pretrained 120B base (about 12B active) for Southeast Asian adaptation of Nemotron 3 Super.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/nemotron-sea-lion-v4-8-30b-a3b": { "id": "aisingapore/sea-lion/nemotron-sea-lion-v4-8-30b-a3b", "name": "Nemotron-SEA-LION-v4.8-30B-A3B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Other (NOTICE.md on the model repo); Hugging Face metadata tag is mit", "release_date": "2026-09-18", "context_length": 262144, "max_output_tokens": null, "parameters": "30000000000", "architecture": "Mamba-2 and Transformer hybrid MoE continued from an NVIDIA Nemotron 3 base on about 150B tokens, then on-policy distilled for the instruct checkpoint. Hybrid LatentMoE with Mamba-2 is described for the 120B NVIDIA base; the 30B card says Mamba2-Transformer hybrid MoE.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Nemotron-SEA-LION-v4.8-30B-A3B. It is a SEA-LION v4.8 open-weight Mixture-of-Experts model built with NVIDIA, announced on 18 September 2026, and still current. This dossier was collected on 2026-10-15.", "what_it_is_for": "Instruct checkpoint of the 30B-A3B SEA-LION MoE, for multilingual Southeast Asian chat and generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/nemotron-sea-lion-v4-8-30b-a3b-base": { "id": "aisingapore/sea-lion/nemotron-sea-lion-v4-8-30b-a3b-base", "name": "Nemotron-SEA-LION-v4.8-30B-A3B-Base", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Other (NOTICE.md on the model repo); Hugging Face metadata tag is mit", "release_date": "2026-09-18", "context_length": 8192, "max_output_tokens": null, "parameters": "30000000000", "architecture": "Mamba-2 and Transformer hybrid MoE continued from an NVIDIA Nemotron 3 base on about 150B tokens, then on-policy distilled for the instruct checkpoint. Hybrid LatentMoE with Mamba-2 is described for the 120B NVIDIA base; the 30B card says Mamba2-Transformer hybrid MoE.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Nemotron-SEA-LION-v4.8-30B-A3B-Base. It is a SEA-LION v4.8 open-weight Mixture-of-Experts model built with NVIDIA, announced on 18 September 2026, and still current. This dossier was collected on 2026-10-15.", "what_it_is_for": "Continued-pretrained base for Southeast Asian QA, reasoning, code, and translation. It is not the chat checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/qwen-sea-lion-v4-32b-it": { "id": "aisingapore/sea-lion/qwen-sea-lion-v4-32b-it", "name": "Qwen-SEA-LION-v4-32B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Qwen Terms of Service and Qwen Usage Policy. Hugging Face metadata tag is mit, which disagrees with the card.", "release_date": "2025-10-17", "context_length": 32768, "max_output_tokens": null, "parameters": "32000000000", "architecture": "Decoder continued from Qwen3-32B on approximately 100B SEA-Pile v2 tokens, then post-trained on about 8 million QA pairs. Stages include instruction tuning, merging, online RL, and on-policy alignment. The card once says Gemma 3 architecture; that sentence conflicts with the Qwen3-32B base and is treated as a copy error.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Qwen-SEA-LION-v4-32B-IT. It is the Qwen3 32B instruct model in SEA-LION v4. The card was updated on 17 October 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "Reasoning and instruction following across Southeast Asian languages, with a thinking mode switch. Text in, text out.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/qwen-sea-lion-v4-4b-vl": { "id": "aisingapore/sea-lion/qwen-sea-lion-v4-4b-vl", "name": "Qwen-SEA-LION-v4-4B-VL", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT (Hugging Face metadata)", "release_date": "2025-12-05", "context_length": 256000, "max_output_tokens": null, "parameters": "4000000000", "architecture": "Supervised fine-tune of Qwen3-VL-4B-Instruct for Southeast Asian vision-language tasks.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Qwen-SEA-LION-v4-4B-VL. It is the 4B Qwen3-VL Southeast Asian vision model, announced on 5 December 2025. The Hub repo was created on 21 November 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "Lightweight visual question answering and document reading for Southeast Asian text and scenes, including on consumer hardware.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/qwen-sea-lion-v4-5-27b-it": { "id": "aisingapore/sea-lion/qwen-sea-lion-v4-5-27b-it", "name": "Qwen-SEA-LION-v4.5-27B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-05-20", "context_length": null, "max_output_tokens": null, "parameters": "27000000000", "architecture": "Qwen3.6 27B dense hybrid linear and full attention, with a vision encoder. Post-trained by logit distillation from Qwen3.5-397B-A17B and model merging. Thinking mode is configurable.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Qwen-SEA-LION-v4.5-27B-IT. It is the flagship SEA-LION v4.5 agent model, announced on 20 May 2026, with open weights and a free proof-of-concept API example. This dossier was collected on 2026-10-15.", "what_it_is_for": "Multimodal agentic chat, tool calling, and reasoning in English and Southeast Asian languages, with a configurable thinking mode.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/qwen-sea-lion-v4-5-27b-it-specdecoder": { "id": "aisingapore/sea-lion/qwen-sea-lion-v4-5-27b-it-specdecoder", "name": "Qwen-SEA-LION-v4.5-27B-IT-SpecDecoder", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-05-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speculative drafter (DFlash) finetuned from z-lab/Qwen3.6-27B-DFlash for lossless block drafting with Qwen-SEA-LION-v4.5-27B-IT. The catalogue lists its context as a dash, not a token count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Qwen-SEA-LION-v4.5-27B-IT-SpecDecoder. It is the speculative-decoding drafter paired with the v4.5 27B instruct model, released with that suite in May 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "Drafts token blocks that the 27B instruct model verifies, to raise tokens per second without a claimed change in output quality.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/qwen-sea-lion-v4-8b-vl": { "id": "aisingapore/sea-lion/qwen-sea-lion-v4-8b-vl", "name": "Qwen-SEA-LION-v4-8B-VL", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT (Hugging Face metadata)", "release_date": "2025-12-05", "context_length": 256000, "max_output_tokens": null, "parameters": "8000000000", "architecture": "Supervised fine-tune of Qwen3-VL-8B-Instruct for Southeast Asian vision-language tasks.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released Qwen-SEA-LION-v4-8B-VL. It is the 8B Qwen3-VL Southeast Asian vision model, announced on 5 December 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "Stronger visual question answering and document analysis than the 4B sibling, still in the small Qwen3-VL range.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/sea-lion-v1-3b": { "id": "aisingapore/sea-lion/sea-lion-v1-3b", "name": "SEA-LION-v1-3B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-10-24", "context_length": 2048, "max_output_tokens": null, "parameters": "3000000000", "architecture": "MPT decoder trained from scratch. 32 layers, d_model 2560, head_dim 20, vocabulary 256000, sequence length 2048. Custom SEABPETokenizer. Pre-training corpus 980B tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released SEA-LION-v1-3B. It is the 3B model pretrained from scratch for SEA-LION v1. The Hub repo dates from 24 October 2023, and the collection is described as a December 2023 release. Weights remain available. This dossier was collected on 2026-10-15.", "what_it_is_for": "Small base model for Southeast Asian language experiments. There is no official 3B instruct tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/sea-lion-v1-7b": { "id": "aisingapore/sea-lion/sea-lion-v1-7b", "name": "SEA-LION-v1-7B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-10-30", "context_length": 2048, "max_output_tokens": null, "parameters": "7000000000", "architecture": "MPT decoder trained from scratch. 32 layers, d_model 4096, head_dim 32, vocabulary 256000, sequence length 2048. Custom SEABPETokenizer. Pre-training corpus 980B tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released SEA-LION-v1-7B. It is the 7B model pretrained from scratch for SEA-LION v1. The Hub repo dates from 30 October 2023. Weights remain available under MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "Base 7B model for Southeast Asian languages, before either instruct tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/sea-lion-v1-7b-it": { "id": "aisingapore/sea-lion/sea-lion-v1-7b-it", "name": "SEA-LION-v1-7B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT on the current Hub repo. An older card for SEA-LION-7B-Instruct-C said CC-BY-SA-4.0.", "release_date": "2024-02-01", "context_length": 2048, "max_output_tokens": null, "parameters": "7000000000", "architecture": "Instruction tune of SEA-LION-v1-7B. Same MPT shape and 2048-token context as the base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released SEA-LION-v1-7B-IT. It is the commercially usable 7B instruct model in SEA-LION v1. The Hub repo was created on 1 February 2024. It is still published. This dossier was collected on 2026-10-15.", "what_it_is_for": "Instruction following in the 11 SEA-LION v1 languages. The v1 docs table shows it ahead of the research instruct model on most listed regional tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion/sea-lion-v1-7b-it-research": { "id": "aisingapore/sea-lion/sea-lion-v1-7b-it-research", "name": "SEA-LION-v1-7B-IT-Research", "maker": "aisingapore", "family": "aisingapore/sea-lion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2023-11-06", "context_length": 2048, "max_output_tokens": null, "parameters": "7000000000", "architecture": "Earlier research instruction tune of SEA-LION-v1-7B. Same MPT 7B shape and 2048 context. Non-commercial license.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released SEA-LION-v1-7B-IT-Research. It is the non-commercial 7B instruct tune from 6 November 2023. It is still on the Hub and is not the later MIT instruct model. This dossier was collected on 2026-10-15.", "what_it_is_for": "Research-only instruction tune. The v1 score table shows lower QA and sentiment scores than the later instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion-bert/sealion-bert-base": { "id": "aisingapore/sea-lion-bert/sealion-bert-base", "name": "SEA-LION-BERT-Base", "maker": "aisingapore", "family": "aisingapore/sea-lion-bert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-11-16", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "MosaicBERT encoder. 12 layers, d_model 768, head_dim 12, vocabulary 256000, sequence length 128. Trained on 790B tokens. Custom SEABPETokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released SEA-LION-BERT-Base. It is the masked-language encoder trained with SEA-LION v1 data. The Hub repo was created on 16 November 2023 and is still public. This dossier was collected on 2026-10-15.", "what_it_is_for": "Fill-mask and encoder features for the 11 SEA-LION v1 languages. Sequence length on the card is 128.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aisingapore/sea-lion-bert/sealion-bert-large": { "id": "aisingapore/sea-lion-bert/sealion-bert-large", "name": "SEA-LION-BERT-Large", "maker": "aisingapore", "family": "aisingapore/sea-lion-bert", "status": "active", "confidence": "medium", "weights": "open-source", "license": "MIT", "release_date": "2024-01-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MosaicBERT encoder tagged bert and fill-mask. Layer sizes were not on the Hub tag payload and are not copied from the base card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Singapore's Products Pillar released SEA-LION-BERT-Large. It is the larger SEA-LION masked-language encoder. The Hub repo was created on 9 January 2024 and is still public. This dossier was collected on 2026-10-15.", "what_it_is_for": "Fill-mask encoder for the same v1 language list as the base BERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/kolibri/kolibri-1": { "id": "aleph-alpha/kolibri/kolibri-1", "name": "Kolibri-1", "maker": "aleph-alpha", "family": "aleph-alpha/kolibri", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-03", "context_length": 1048576, "max_output_tokens": null, "parameters": "78103074560 total; 3457573120 active per token", "architecture": "Mixture-of-experts transformer. Primary checkpoint: FP8 e4m3 block weights; embeddings, LM head, norms, and router in bfloat16. Secondary card summaries: 50 layers, 384 routed experts, top-6 plus 1 shared, 40 sliding-window layers of 512 and 10 full-attention layers.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kolibri-1 is Aleph Alpha's open-weight German-English mixture-of-experts reasoning model, released on 3 October 2026 under Apache 2.0. It is the company's current public flagship. Collected 2026-10-15.", "what_it_is_for": "Aleph Alpha positions Kolibri for mission-critical assistants and agentic work: multi-step reasoning, retrieval-augmented generation, and tool calling in German and English, including public administration, industry, and defense.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/luminous/luminous-base": { "id": "aleph-alpha/luminous/luminous-base", "name": "Luminous-Base", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights were not publicly released.", "release_date": "2022-04-14", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Decoder-only autoregressive transformer with rotary position embeddings (February 2023 benchmark post). Control variants are instruction-tuned versions of the same size class. A separate control parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Luminous-Base is a named Aleph Alpha Luminous model. The family launched commercially on 14 April 2022 and is no longer the company's marketed flagship. Status here: deprecated. Collected 2026-10-15.", "what_it_is_for": "General multilingual completion: English, German, French, Italian, and Spanish. Also the documented model id for semantic embeddings (Luminous-Explore) and, in the 2026 PhariaOS example, multimodal completions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/luminous/luminous-base-control": { "id": "aleph-alpha/luminous/luminous-base-control", "name": "Luminous-Base-Control", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights were not publicly released.", "release_date": "2023-06-05", "context_length": null, "max_output_tokens": null, "parameters": "13B class, instruction-tuned; separate count not published", "architecture": "Decoder-only autoregressive transformer with rotary position embeddings (February 2023 benchmark post). Control variants are instruction-tuned versions of the same size class. A separate control parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Luminous-Base-Control is a named Aleph Alpha Luminous model. The family launched commercially on 14 April 2022 and is no longer the company's marketed flagship. Status here: deprecated. Collected 2026-10-15.", "what_it_is_for": "Instruction-following chat variant of the matching Luminous foundation model. Announced 5 June 2023 for zero-shot instructions, chat, and AtMan explainability. The Decoder reported that an earlier luminous-supreme-control from February 2023 was replaced by this June generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/luminous/luminous-explore": { "id": "aleph-alpha/luminous/luminous-explore", "name": "Luminous-Explore", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "Proprietary. Weights were not publicly released.", "release_date": "2022-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Semantic embedding head used through the luminous-base API id in the official example. Separate parameter count not published.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Luminous-Explore is a named Aleph Alpha Luminous model. The family launched commercially on 14 April 2022 and is no longer the company's marketed flagship. Status here: deprecated. Collected 2026-10-15.", "what_it_is_for": "Semantic embeddings for search, clustering, and classification. The September 2022 post names Luminous-Explore as the model behind semantic_embed, but the code sample calls model luminous-base. Treat this folder as that named embedding capability, not as a proven separate weight id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/luminous/luminous-extended": { "id": "aleph-alpha/luminous/luminous-extended", "name": "Luminous-Extended", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights were not publicly released.", "release_date": "2022-04-14", "context_length": null, "max_output_tokens": null, "parameters": "30B", "architecture": "Decoder-only autoregressive transformer with rotary position embeddings (February 2023 benchmark post). Control variants are instruction-tuned versions of the same size class. A separate control parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Luminous-Extended is a named Aleph Alpha Luminous model. The family launched commercially on 14 April 2022 and is no longer the company's marketed flagship. Status here: deprecated. Collected 2026-10-15.", "what_it_is_for": "Larger dense completion model in the 2022 Luminous family. Multilingual. Extended and Supreme were described by Aleph Alpha as the larger Luminous models; a 2026 secondary overview says those two accepted images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/luminous/luminous-extended-control": { "id": "aleph-alpha/luminous/luminous-extended-control", "name": "Luminous-Extended-Control", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights were not publicly released.", "release_date": "2023-06-05", "context_length": null, "max_output_tokens": null, "parameters": "30B class, instruction-tuned; separate count not published", "architecture": "Decoder-only autoregressive transformer with rotary position embeddings (February 2023 benchmark post). Control variants are instruction-tuned versions of the same size class. A separate control parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Luminous-Extended-Control is a named Aleph Alpha Luminous model. The family launched commercially on 14 April 2022 and is no longer the company's marketed flagship. Status here: deprecated. Collected 2026-10-15.", "what_it_is_for": "Instruction-following chat variant of the matching Luminous foundation model. Announced 5 June 2023 for zero-shot instructions, chat, and AtMan explainability. The Decoder reported that an earlier luminous-supreme-control from February 2023 was replaced by this June generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/luminous/luminous-supreme": { "id": "aleph-alpha/luminous/luminous-supreme", "name": "Luminous-Supreme", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights were not publicly released.", "release_date": "2022-04-14", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Decoder-only autoregressive transformer with rotary position embeddings (February 2023 benchmark post). Control variants are instruction-tuned versions of the same size class. A separate control parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Luminous-Supreme is a named Aleph Alpha Luminous model. The family launched commercially on 14 April 2022 and is no longer the company's marketed flagship. Status here: deprecated. Collected 2026-10-15.", "what_it_is_for": "Larger dense completion model in the 2022 Luminous family. Multilingual. Extended and Supreme were described by Aleph Alpha as the larger Luminous models; a 2026 secondary overview says those two accepted images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/luminous/luminous-supreme-control": { "id": "aleph-alpha/luminous/luminous-supreme-control", "name": "Luminous-Supreme-Control", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights were not publicly released.", "release_date": "2023-06-05", "context_length": null, "max_output_tokens": null, "parameters": "70B class, instruction-tuned; separate count not published", "architecture": "Decoder-only autoregressive transformer with rotary position embeddings (February 2023 benchmark post). Control variants are instruction-tuned versions of the same size class. A separate control parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Luminous-Supreme-Control is a named Aleph Alpha Luminous model. The family launched commercially on 14 April 2022 and is no longer the company's marketed flagship. Status here: deprecated. Collected 2026-10-15.", "what_it_is_for": "Instruction-following chat variant of the matching Luminous foundation model. Announced 5 June 2023 for zero-shot instructions, chat, and AtMan explainability. The Decoder reported that an earlier luminous-supreme-control from February 2023 was replaced by this June generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/luminous/luminous-world": { "id": "aleph-alpha/luminous/luminous-world", "name": "Luminous-World", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "status": "announced", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights were not publicly released.", "release_date": "2023-02-16", "context_length": null, "max_output_tokens": null, "parameters": "300B announced", "architecture": "Announced as a scaled Luminous model. Architecture details were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Luminous-World is a named Aleph Alpha Luminous model. The family launched commercially on 14 April 2022 and is no longer the company's marketed flagship. Status here: announced. Collected 2026-10-15.", "what_it_is_for": "Announced on 16 February 2023 as a 300B model in testing, expected later that year for complex use cases. No commercial release, model card, or API id was found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/magma/magma": { "id": "aleph-alpha/magma/magma", "name": "MAGMA", "maker": "aleph-alpha", "family": "aleph-alpha/magma", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "MIT for the published research demo. The 2022 commercial API offering was proprietary.", "release_date": "2022-04-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adapter-based multimodal augmentation of a frozen generative language model (paper). Commercial checkpoint architecture was not separately specified.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAGMA is Aleph Alpha's multimodal model name. A research paper (December 2021, EMNLP 2022) describes adapter-based vision-language finetuning, and the 14 April 2022 Luminous launch said MAGMA was available on the API and Playground for image-and-text questions. The public GitHub model is labeled a demo only. Collected 2026-10-15. Status: deprecated as a commercial product; research demo weights remain.", "what_it_is_for": "Answer questions about images and caption visual content beyond simple object recognition, as described in the April 2022 launch. The paper's goal is autoregressive text from mixed visual and text input while leaving the underlying language-model weights frozen.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/pharia/pharia-1-embedding-4608-control": { "id": "aleph-alpha/pharia/pharia-1-embedding-4608-control", "name": "Pharia-1-Embedding-4608-control", "maker": "aleph-alpha", "family": "aleph-alpha/pharia", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Open Aleph License (non-commercial research and education on Hugging Face). Commercial checkpoints for customers.", "release_date": "2024-11-21", "context_length": 2048, "max_output_tokens": null, "parameters": "7041544704", "architecture": "Instructable embedding adapter on Pharia-1-LLM-7B-control. Weighted mean pooling. Embedding size 4608. Optional 256-d projection adapter.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pharia-1-Embedding-4608-control is Aleph Alpha's instructable embedding adapter on Pharia-1-LLM-7B-control. It produces 4608-dimensional vectors and was published on Hugging Face by December 2024. Collected 2026-10-15. Status: active as a published weight and PhariaAI embedding option.", "what_it_is_for": "Semantic search, information retrieval, re-ranking, and clustering. Instructions can steer the embedding at runtime. The card says it is not for text generation. Finetuning instructions were English; text may be English, German, French, or Spanish.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/pharia/pharia-1-llm-7b-control": { "id": "aleph-alpha/pharia/pharia-1-llm-7b-control", "name": "Pharia-1-LLM-7B-control", "maker": "aleph-alpha", "family": "aleph-alpha/pharia", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Open Aleph License (non-commercial research and education). Commercial checkpoints provided to customers.", "release_date": "2024-08-26", "context_length": 8192, "max_output_tokens": null, "parameters": "7041544704", "architecture": "Decoder-only transformer, rotary embeddings, grouped-query attention. 27 layers, 36 query heads, 4 KV heads, head size 128, hidden size 4608, MLP expansion 4, vocabulary 128000, rotary base 1000000. Total parameters 7,041,544,704.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pharia-1-LLM-7B-control is a 7.04B dense multilingual LLM from Aleph Alpha Research, released 26 August 2024. Weights on Hugging Face are under the Open Aleph License (non-commercial research and education). Customers receive checkpoints for commercial use. Status: active as a published research and PhariaAI model. Collected 2026-10-15.", "what_it_is_for": "Text generation, classification, summarization, question answering, and labeling, with a focus on German, French, and Spanish as well as English. Control is tuned for concise, length-controlled answers. The aligned variant adds safety alignment and is the recommended chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/pharia/pharia-1-llm-7b-control-aligned": { "id": "aleph-alpha/pharia/pharia-1-llm-7b-control-aligned", "name": "Pharia-1-LLM-7B-control-aligned", "maker": "aleph-alpha", "family": "aleph-alpha/pharia", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Open Aleph License (non-commercial research and education). Commercial checkpoints provided to customers.", "release_date": "2024-08-26", "context_length": 8192, "max_output_tokens": null, "parameters": "7041544704", "architecture": "Decoder-only transformer, rotary embeddings, grouped-query attention. 27 layers, 36 query heads, 4 KV heads, head size 128, hidden size 4608, MLP expansion 4, vocabulary 128000, rotary base 1000000. Total parameters 7,041,544,704.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pharia-1-LLM-7B-control-aligned is a 7.04B dense multilingual LLM from Aleph Alpha Research, released 26 August 2024. Weights on Hugging Face are under the Open Aleph License (non-commercial research and education). Customers receive checkpoints for commercial use. Status: active as a published research and PhariaAI model. Collected 2026-10-15.", "what_it_is_for": "Chat and instruction following with extra alignment for helpfulness and safety. Same languages and size as Pharia-1-LLM-7B-control. The model card recommends this variant for chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/tfree/llama-tfree-hat-pretrained-7b-dpo": { "id": "aleph-alpha/tfree/llama-tfree-hat-pretrained-7b-dpo", "name": "Llama-TFree-HAT-Pretrained-7B-DPO", "maker": "aleph-alpha", "family": "aleph-alpha/tfree", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Open Aleph License (non-commercial research and education)", "release_date": "2025-08-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Post-trained HAT model starting from TFree-HAT-Pretrained-7B-Base. Hugging Face base_model tag points at that base, not at Meta Llama.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-TFree-HAT-Pretrained-7B-DPO is the post-trained, direct-preference-optimized chat model built on TFree-HAT-Pretrained-7B-Base, not a fine-tune of Meta Llama. Aleph Alpha released it with the base model on 20 August 2025 under the Open Aleph License. The Llama- prefix refers to the Llama-style backbone and chat template. Collected 2026-10-15.", "what_it_is_for": "Instruction-following English and German chat. The announcement says it is preferred over Llama 3.1 8B in the lab's LLM-as-a-judge tests and beats Llama 3.1 8B Instruct on 67% of the German benchmarks they measured.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aleph-alpha/tfree/tfree-hat-pretrained-7b-base": { "id": "aleph-alpha/tfree/tfree-hat-pretrained-7b-base", "name": "TFree-HAT-Pretrained-7B-Base", "maker": "aleph-alpha", "family": "aleph-alpha/tfree", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Open Aleph License (non-commercial research and education)", "release_date": "2025-08-20", "context_length": null, "max_output_tokens": null, "parameters": "7192507136", "architecture": "Hierarchical autoregressive transformer: UTF-8 byte encoder (6 layers, 119M), 32-layer 4096-wide backbone (about 6.98B), byte decoder (4 layers, 94M). Local attention window 768 on encoder and decoder.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TFree-HAT-Pretrained-7B-Base is Aleph Alpha Research's tokenizer-free hierarchical autoregressive transformer, pretrained from scratch in English and German and released under the Open Aleph License. The long-context checkpoint is the main branch. Collected 2026-10-15. Status: active research release, not the commercial flagship.", "what_it_is_for": "Research foundation model for text generation and further tuning without a subword tokenizer. The card lists generation, classification, summarization, question answering, and labeling. A DPO chat variant was released from this base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/decision/decision-model-preview": { "id": "alibaba/decision/decision-model-preview", "name": "Decision-Model-Preview", "maker": "alibaba", "family": "alibaba/decision", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Structured decision model for classification, yes-or-no decisions, and scoring in one forward pass, returning probabilities and confidence.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Decision-Model-Preview. Status: preview. Collected 2026-10-15. Preview decision model on Model Studio, separate from the chat Qwen line.", "what_it_is_for": "Preview decision model on Model Studio, separate from the chat Qwen line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen-72b": { "id": "alibaba/qwen/qwen-72b", "name": "Qwen-72B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other", "release_date": "2023-11-26", "context_length": null, "max_output_tokens": null, "parameters": "72B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-72B. Status: active. Collected 2026-10-15. Original Qwen 72B base model, the earliest large Qwen checkpoint on the retrieved Hugging Face list.", "what_it_is_for": "Original Qwen 72B base model, the earliest large Qwen checkpoint on the retrieved Hugging Face list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen-flash": { "id": "alibaba/qwen/qwen-flash", "name": "Qwen-Flash", "maker": "alibaba", "family": "alibaba/qwen", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Flash. Status: deprecated. Collected 2026-10-15. Older low-cost commercial Qwen tier with a 1M context. Still documented as a legacy model.", "what_it_is_for": "Older low-cost commercial Qwen tier with a 1M context. Still documented as a legacy model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen-flash-character": { "id": "alibaba/qwen/qwen-flash-character", "name": "Qwen-Flash-Character", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Flash-Character. Status: active. Collected 2026-10-15. Small role-play model at an 8k context.", "what_it_is_for": "Small role-play model at an 8k context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen-long": { "id": "alibaba/qwen/qwen-long", "name": "Qwen-Long", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 10000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Long. Status: active. Collected 2026-10-15. Long-context Qwen model documented at 10 million tokens. Thinking and tool calling are marked unsupported; structured output is supported.", "what_it_is_for": "Long-context Qwen model documented at 10 million tokens. Thinking and tool calling are marked unsupported; structured output is supported.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen-max": { "id": "alibaba/qwen/qwen-max", "name": "Qwen-Max", "maker": "alibaba", "family": "alibaba/qwen", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Max. Status: deprecated. Collected 2026-10-15. Older commercial Max tier. The 2026-10-15 text index gives it a 128k window and says thinking mode is unsupported.", "what_it_is_for": "Older commercial Max tier. The 2026-10-15 text index gives it a 128k window and says thinking mode is unsupported.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen-plus": { "id": "alibaba/qwen/qwen-plus", "name": "Qwen-Plus", "maker": "alibaba", "family": "alibaba/qwen", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.115, "completion_per_m_usd": 0.287 }, "summary_intro": "Alibaba model Qwen-Plus. Status: deprecated. Collected 2026-10-15. Older balanced commercial Qwen tier. The text index says the stable id is still offered, with snapshots, and is no longer the recommended default. One pricing note says it is equivalent to qwen-plus-2025-12-01.", "what_it_is_for": "Older balanced commercial Qwen tier. The text index says the stable id is still offered, with snapshots, and is no longer the recommended default. One pricing note says it is equivalent to qwen-plus-2025-12-01.", "ratings": { "empirical_100": null, "lmsys_elo": 1211, "coding_elo": 1211, "swe_bench": 16.9, "humaneval": 71.6, "twelve_cs": 99.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen-plus-character": { "id": "alibaba/qwen/qwen-plus-character", "name": "Qwen-Plus-Character", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Plus-Character. Status: active. Collected 2026-10-15. Role-play variant. The text index gives a 32k context and marks thinking, tools, and structured output unsupported.", "what_it_is_for": "Role-play variant. The text index gives a 32k context and marks thinking, tools, and structured output unsupported.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen-turbo": { "id": "alibaba/qwen/qwen-turbo", "name": "Qwen-Turbo", "maker": "alibaba", "family": "alibaba/qwen", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Turbo. Status: deprecated. Collected 2026-10-15. Older Turbo tier. A Model Studio text-generation render lists it at 128k with thinking, tools, and structured output. The direct fetch of the same URL's legacy table omitted the row.", "what_it_is_for": "Older Turbo tier. A Model Studio text-generation render lists it at 128k with thinking, tools, and structured output. The direct fetch of the same URL's legacy table omitted the row.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen2-1-5b": { "id": "alibaba/qwen/qwen2-1-5b", "name": "Qwen2-1.5B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-06-03", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2-1.5B-Instruct. Status: active. Collected 2026-10-15. Instruction-tuned Qwen2 1.5B model. Predecessor line to Qwen2.5.", "what_it_is_for": "Instruction-tuned Qwen2 1.5B model. Predecessor line to Qwen2.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen2-5-0-5b": { "id": "alibaba/qwen/qwen2-5-0-5b", "name": "Qwen2.5-0.5B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-16", "context_length": null, "max_output_tokens": null, "parameters": "0.5B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-0.5B-Instruct. Status: active. Collected 2026-10-15. 0.5B Qwen2.5 instruct model.", "what_it_is_for": "0.5B Qwen2.5 instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen2-5-1-5b": { "id": "alibaba/qwen/qwen2-5-1-5b", "name": "Qwen2.5-1.5B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-17", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-1.5B-Instruct. Status: active. Collected 2026-10-15. 1.5B Qwen2.5 instruct model.", "what_it_is_for": "1.5B Qwen2.5 instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen2-5-14b": { "id": "alibaba/qwen/qwen2-5-14b", "name": "Qwen2.5-14B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-16", "context_length": 131072, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-14B-Instruct. Status: active. Collected 2026-10-15. 14B Qwen2.5 instruct model. A 1M-context variant, qwen2.5-14b-instruct-1m, is a separate endpoint.", "what_it_is_for": "14B Qwen2.5 instruct model. A 1M-context variant, qwen2.5-14b-instruct-1m, is a separate endpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen2-5-32b": { "id": "alibaba/qwen/qwen2-5-32b", "name": "Qwen2.5-32B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-17", "context_length": 131072, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-32B-Instruct. Status: active. Collected 2026-10-15. 32B Qwen2.5 instruct model. Same Model Studio context and max output as the 72B instruct endpoint.", "what_it_is_for": "32B Qwen2.5 instruct model. Same Model Studio context and max output as the 72B instruct endpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen2-5-3b": { "id": "alibaba/qwen/qwen2-5-3b", "name": "Qwen2.5-3B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other", "release_date": "2024-09-17", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-3B-Instruct. Status: active. Collected 2026-10-15. 3B Qwen2.5 instruct model. The China column of the Qwen2.5 API table lists a 32,768 context.", "what_it_is_for": "3B Qwen2.5 instruct model. The China column of the Qwen2.5 API table lists a 32,768 context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen2-5-72b": { "id": "alibaba/qwen/qwen2-5-72b", "name": "Qwen2.5-72B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-09-17", "context_length": 131072, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.4, "completion_per_m_usd": 5.6 }, "summary_intro": "Alibaba model Qwen2.5-72B-Instruct. Status: active. Collected 2026-10-15. Largest dense Qwen2.5 instruct model. Model Studio's open-model table lists qwen2.5-72b-instruct at a 131,072 context and 8,192 max output.", "what_it_is_for": "Largest dense Qwen2.5 instruct model. Model Studio's open-model table lists qwen2.5-72b-instruct at a 131,072 context and 8,192 max output.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen2-5-7b": { "id": "alibaba/qwen/qwen2-5-7b", "name": "Qwen2.5-7B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-16", "context_length": 131072, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-7B-Instruct. Status: active. Collected 2026-10-15. 7B Qwen2.5 instruct model, the most-downloaded Qwen2.5 checkpoint on the retrieved Hugging Face list.", "what_it_is_for": "7B Qwen2.5 instruct model, the most-downloaded Qwen2.5 checkpoint on the retrieved Hugging Face list.", "ratings": { "empirical_100": 40, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-0-6b": { "id": "alibaba/qwen/qwen3-0-6b", "name": "Qwen3-0.6B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-27", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-0.6B. Status: active. Collected 2026-10-15. Smallest instruction-tuned model in the April 2025 Qwen3 release.", "what_it_is_for": "Smallest instruction-tuned model in the April 2025 Qwen3 release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-1-7b": { "id": "alibaba/qwen/qwen3-1-7b", "name": "Qwen3-1.7B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-27", "context_length": null, "max_output_tokens": null, "parameters": "1.7B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-1.7B. Status: active. Collected 2026-10-15. 1.7B instruction-tuned Qwen3 model.", "what_it_is_for": "1.7B instruction-tuned Qwen3 model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-14b": { "id": "alibaba/qwen/qwen3-14b", "name": "Qwen3-14B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-27", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-14B. Status: active. Collected 2026-10-15. 14B instruction-tuned Qwen3 model.", "what_it_is_for": "14B instruction-tuned Qwen3 model.", "ratings": { "empirical_100": null, "lmsys_elo": 1203, "coding_elo": 1203, "swe_bench": 15.6, "humaneval": 70.5, "twelve_cs": 99.5, "aa_intelligence": 6.7, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-235b-a22b": { "id": "alibaba/qwen/qwen3-235b-a22b", "name": "Qwen3-235B-A22B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-27", "context_length": null, "max_output_tokens": null, "parameters": "235B total, 22B activated", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-235B-A22B. Status: active. Collected 2026-10-15. Largest open Qwen3 mixture-of-experts model from April 2025.", "what_it_is_for": "Largest open Qwen3 mixture-of-experts model from April 2025.", "ratings": { "empirical_100": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-30b-a3b": { "id": "alibaba/qwen/qwen3-30b-a3b", "name": "Qwen3-30B-A3B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-27", "context_length": null, "max_output_tokens": null, "parameters": "30B total, 3B activated", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-30B-A3B. Status: active. Collected 2026-10-15. Sparse April 2025 Qwen3 model. Later instruct and thinking 2507 checkpoints are priced separately on Model Studio.", "what_it_is_for": "Sparse April 2025 Qwen3 model. Later instruct and thinking 2507 checkpoints are priced separately on Model Studio.", "ratings": { "empirical_100": 48, "lmsys_elo": 1207, "coding_elo": 1207, "swe_bench": 16.2, "humaneval": 71.0, "twelve_cs": 91.1, "aa_intelligence": 7.6, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 940, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-32b": { "id": "alibaba/qwen/qwen3-32b", "name": "Qwen3-32B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-27", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-32B. Status: active. Collected 2026-10-15. 32B dense instruction-tuned Qwen3 model.", "what_it_is_for": "32B dense instruction-tuned Qwen3 model.", "ratings": { "empirical_100": 49, "lmsys_elo": 1204, "coding_elo": 1204, "swe_bench": 15.7, "humaneval": 70.6, "twelve_cs": 52.0, "aa_intelligence": 7.3, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-4b": { "id": "alibaba/qwen/qwen3-4b", "name": "Qwen3-4B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-27", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-4B. Status: active. Collected 2026-10-15. 4B instruction-tuned Qwen3 model. Later 2507 instruct and thinking checkpoints exist as separate repos.", "what_it_is_for": "4B instruction-tuned Qwen3 model. Later 2507 instruct and thinking checkpoints exist as separate repos.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-5-0-8b": { "id": "alibaba/qwen/qwen3-5-0-8b", "name": "Qwen3.5-0.8B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-02", "context_length": null, "max_output_tokens": null, "parameters": "0.8B", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.5-0.8B. Status: active. Collected 2026-10-15. Smallest named Qwen3.5 open model in the March 2026 small-model release.", "what_it_is_for": "Smallest named Qwen3.5 open model in the March 2026 small-model release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-5-122b-a10b": { "id": "alibaba/qwen/qwen3-5-122b-a10b", "name": "Qwen3.5-122B-A10B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-24", "context_length": null, "max_output_tokens": null, "parameters": "122B total, 10B activated", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.5-122B-A10B. Status: active. Collected 2026-10-15. Mid-size sparse Qwen3.5 open model, announced with the 35B-A3B and 27B models.", "what_it_is_for": "Mid-size sparse Qwen3.5 open model, announced with the 35B-A3B and 27B models.", "ratings": { "empirical_100": null, "lmsys_elo": 1231, "coding_elo": 1231, "swe_bench": 20.2, "humaneval": 74.2, "twelve_cs": 99.5, "aa_intelligence": 17.7, "aa_coding": 43.3, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-5-27b": { "id": "alibaba/qwen/qwen3-5-27b", "name": "Qwen3.5-27B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-24", "context_length": null, "max_output_tokens": null, "parameters": "27B", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.5-27B. Status: active. Collected 2026-10-15. Dense 27B Qwen3.5 open model, released with the mid-size set.", "what_it_is_for": "Dense 27B Qwen3.5 open model, released with the mid-size set.", "ratings": { "empirical_100": null, "lmsys_elo": 1239, "coding_elo": 1239, "swe_bench": 21.5, "humaneval": 75.2, "twelve_cs": 52.0, "aa_intelligence": 22.9, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-5-2b": { "id": "alibaba/qwen/qwen3-5-2b", "name": "Qwen3.5-2B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-02", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.5-2B. Status: active. Collected 2026-10-15. Small Qwen3.5 multimodal model.", "what_it_is_for": "Small Qwen3.5 multimodal model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-5-35b-a3b": { "id": "alibaba/qwen/qwen3-5-35b-a3b", "name": "Qwen3.5-35B-A3B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-24", "context_length": null, "max_output_tokens": null, "parameters": "35B total, 3B activated", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.5-35B-A3B. Status: active. Collected 2026-10-15. Sparse Qwen3.5 model with about 3B active parameters, released 2026-02-24.", "what_it_is_for": "Sparse Qwen3.5 model with about 3B active parameters, released 2026-02-24.", "ratings": { "empirical_100": null, "lmsys_elo": 1215, "coding_elo": 1215, "swe_bench": 17.5, "humaneval": 72.0, "twelve_cs": 52.0, "aa_intelligence": 19.3, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-5-397b-a17b": { "id": "alibaba/qwen/qwen3-5-397b-a17b", "name": "Qwen3.5-397B-A17B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-16", "context_length": 262144, "max_output_tokens": 81920, "parameters": "397B total, 17B activated", "architecture": "Causal LM with vision encoder. Gated DeltaNet plus sparse MoE. 60 layers, 512 experts, 10 routed plus 1 shared. Hidden size 4096. Native context 262,144, extensible to 1,010,000.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.5-397B-A17B. Status: active. Collected 2026-10-15. First Qwen3.5 open model, a native multimodal agent model with 201 languages and dialects.", "what_it_is_for": "First Qwen3.5 open model, a native multimodal agent model with 201 languages and dialects.", "ratings": { "empirical_100": null, "lmsys_elo": 1252, "coding_elo": 1252, "swe_bench": 23.8, "humaneval": 77.0, "twelve_cs": 67.9, "aa_intelligence": 21.4, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1158, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-5-4b": { "id": "alibaba/qwen/qwen3-5-4b", "name": "Qwen3.5-4B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-02", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.5-4B. Status: active. Collected 2026-10-15. Small Qwen3.5 multimodal model, released with the 9B, 2B, and 0.8B models.", "what_it_is_for": "Small Qwen3.5 multimodal model, released with the 9B, 2B, and 0.8B models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-5-9b": { "id": "alibaba/qwen/qwen3-5-9b", "name": "Qwen3.5-9B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-02", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.5-9B. Status: active. Collected 2026-10-15. Small Qwen3.5 multimodal model. Weights repo created 2026-02-27; GitHub news says available 2026-03-02.", "what_it_is_for": "Small Qwen3.5 multimodal model. Weights repo created 2026-02-27; GitHub news says available 2026-03-02.", "ratings": { "empirical_100": 41, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-5-flash": { "id": "alibaba/qwen/qwen3-5-flash", "name": "Qwen3.5-Flash", "maker": "alibaba", "family": "alibaba/qwen", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1000000, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.5-Flash. Status: deprecated. Collected 2026-10-15. Smaller hosted Qwen3.5 model. Snapshot qwen3.5-flash-2026-02-23 is listed on the vision page.", "what_it_is_for": "Smaller hosted Qwen3.5 model. Snapshot qwen3.5-flash-2026-02-23 is listed on the vision page.", "ratings": { "empirical_100": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-5-plus": { "id": "alibaba/qwen/qwen3-5-plus", "name": "Qwen3.5-Plus", "maker": "alibaba", "family": "alibaba/qwen", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-15", "context_length": 1000000, "max_output_tokens": 65536, "parameters": "", "architecture": "Hosted production build corresponding to Qwen3.5-397B-A17B, with a 1M context and built-in tools, per the open-weight model card.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.115, "completion_per_m_usd": 0.688 }, "summary_intro": "Alibaba model Qwen3.5-Plus. Status: deprecated. Collected 2026-10-15. Hosted Qwen3.5 flagship. The open-weight card says Qwen3.5-Plus is the managed version of Qwen3.5-397B-A17B.", "what_it_is_for": "Hosted Qwen3.5 flagship. The open-weight card says Qwen3.5-Plus is the managed version of Qwen3.5-397B-A17B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-6-27b": { "id": "alibaba/qwen/qwen3-6-27b", "name": "Qwen3.6-27B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-22", "context_length": null, "max_output_tokens": null, "parameters": "27B", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.6-27B. Status: active. Collected 2026-10-15. Dense 27B open model in the Qwen3.6 line. GitHub describes Qwen3.6 as a stability and coding update on the Qwen3.5 architecture.", "what_it_is_for": "Dense 27B open model in the Qwen3.6 line. GitHub describes Qwen3.6 as a stability and coding update on the Qwen3.5 architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-6-35b-a3b": { "id": "alibaba/qwen/qwen3-6-35b-a3b", "name": "Qwen3.6-35B-A3B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-16", "context_length": 256000, "max_output_tokens": 65536, "parameters": "35B total, 3B activated", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.6-35B-A3B. Status: active. Collected 2026-10-15. Sparse Qwen3.6 open model. Model Studio's vision table also lists the id with a 256k context and 64k max output.", "what_it_is_for": "Sparse Qwen3.6 open model. Model Studio's vision table also lists the id with a 256k context and 64k max output.", "ratings": { "empirical_100": null, "lmsys_elo": 1214, "coding_elo": 1214, "swe_bench": 17.4, "humaneval": 71.9, "twelve_cs": 63.9, "aa_intelligence": 18.2, "aa_coding": 41.9, "aa_agentic": 13.1, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-6-flash": { "id": "alibaba/qwen/qwen3-6-flash", "name": "Qwen3.6-Flash", "maker": "alibaba", "family": "alibaba/qwen", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1000000, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.6-Flash. Status: deprecated. Collected 2026-10-15. Previous Flash tier. The text index lists snapshot qwen3.6-flash-2026-04-16.", "what_it_is_for": "Previous Flash tier. The text index lists snapshot qwen3.6-flash-2026-04-16.", "ratings": { "empirical_100": null, "lmsys_elo": 1216, "coding_elo": 1216, "swe_bench": 17.8, "humaneval": 72.2, "twelve_cs": 26.3, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-6-plus": { "id": "alibaba/qwen/qwen3-6-plus", "name": "Qwen3.6-Plus", "maker": "alibaba", "family": "alibaba/qwen", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-02", "context_length": 1000000, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.6-Plus. Status: deprecated. Collected 2026-10-15. Previous Plus tier. Still documented, no longer the recommended default. The stable id was equivalent to snapshot qwen3.6-plus-2026-04-02 on the pricing page.", "what_it_is_for": "Previous Plus tier. Still documented, no longer the recommended default. The stable id was equivalent to snapshot qwen3.6-plus-2026-04-02 on the pricing page.", "ratings": { "empirical_100": null, "lmsys_elo": 1229, "coding_elo": 1229, "swe_bench": 19.9, "humaneval": 73.9, "twelve_cs": 42.9, "aa_intelligence": 27.0, "aa_coding": 54.5, "aa_agentic": null, "design_arena_elo": 1199, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-7-flash": { "id": "alibaba/qwen/qwen3-7-flash", "name": "Qwen3.7-Flash", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1000000, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.7-Flash. Status: active. Collected 2026-10-15. Lower-cost Qwen3.7 hosted model with the same 1M context. Vision limits are lower than Plus: 256 images versus 2048.", "what_it_is_for": "Lower-cost Qwen3.7 hosted model with the same 1M context. Vision limits are lower than Plus: 256 images versus 2048.", "ratings": { "empirical_100": 42, "lmsys_elo": 1201, "coding_elo": 1201, "swe_bench": 15.3, "humaneval": 70.3, "twelve_cs": 59.4, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-7-max": { "id": "alibaba/qwen/qwen3-7-max", "name": "Qwen3.7-Max", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-21", "context_length": 1000000, "max_output_tokens": 65536, "parameters": "", "architecture": "Qwen3.7 flagship. A lifecycle note says the public interface at launch was text-only; later docs list it with the Qwen3.7 vision lineup only partially. The text index lists it as a legacy Qwen3.7 model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.7-Max. Status: active. Collected 2026-10-15. Largest Qwen3.7 model, aimed at agent work: coding, office tasks, and long-running execution.", "what_it_is_for": "Largest Qwen3.7 model, aimed at agent work: coding, office tasks, and long-running execution.", "ratings": { "empirical_100": null, "lmsys_elo": 1266, "coding_elo": 1266, "swe_bench": 26.1, "humaneval": 78.8, "twelve_cs": 83.6, "aa_intelligence": 29.5, "aa_coding": 66.0, "aa_agentic": 22.5, "design_arena_elo": 1125, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-7-plus": { "id": "alibaba/qwen/qwen3-7-plus", "name": "Qwen3.7-Plus", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-01", "context_length": 1000000, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.276, "completion_per_m_usd": 1.101 }, "summary_intro": "Alibaba model Qwen3.7-Plus. Status: active. Collected 2026-10-15. Balanced Qwen3.7 hosted model. Model Studio recommends it for coding tools, chat, and document work, with a 1M context and built-in tools.", "what_it_is_for": "Balanced Qwen3.7 hosted model. Model Studio recommends it for coding tools, chat, and document work, with a 1M context and built-in tools.", "ratings": { "empirical_100": null, "lmsys_elo": 1219, "coding_elo": 1219, "swe_bench": 18.2, "humaneval": 72.6, "twelve_cs": 29.4, "aa_intelligence": 25.2, "aa_coding": 55.9, "aa_agentic": 17.5, "design_arena_elo": 1236, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-8-2-4t-a95b": { "id": "alibaba/qwen/qwen3-8-2-4t-a95b", "name": "Qwen3.8-2.4T-A95B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-08-12", "context_length": 262144, "max_output_tokens": null, "parameters": "2.4T total, 95B activated", "architecture": "Text-only causal LM. 92 layers: 23 x (3 x Gated DeltaNet-MoE, then 1 x Gated Attention-MoE). 512 experts, 10 routed plus 1 shared. Hidden size 8192. Thinking mode cannot be disabled.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.8-2.4T-A95B. Status: active. Collected 2026-10-15. Open-weight Qwen-Max-class text model. The card says the hosted Qwen3.8-Max adds vision, optional non-thinking, and a default 1M context.", "what_it_is_for": "Open-weight Qwen-Max-class text model. The card says the hosted Qwen3.8-Max adds vision, optional non-thinking, and a default 1M context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-8-27b": { "id": "alibaba/qwen/qwen3-8-27b", "name": "Qwen3.8-27B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-08-14", "context_length": 262144, "max_output_tokens": null, "parameters": "27B", "architecture": "Causal LM with vision encoder. 64 layers: 16 x (3 x Gated DeltaNet + FFN, then 1 x Gated Attention + FFN). Hidden size 5120. Vocab 248,320. MTP trained. Native context 262,144, extensible to 1,000,000 with YaRN.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.8-27B. Status: active. Collected 2026-10-15. Open-weight 27B Qwen3.8 multimodal model for local and served use. Thinking is on by default.", "what_it_is_for": "Open-weight 27B Qwen3.8 multimodal model for local and served use. Thinking is on by default.", "ratings": { "empirical_100": 35, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-8-flash": { "id": "alibaba/qwen/qwen3-8-flash", "name": "Qwen3.8-Flash", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.8-Flash. Status: active. Collected 2026-10-15. Lower-cost hosted Qwen3.8 model. Model Studio recommends it when Qwen3.8-Max or Qwen3.7-Plus is more capability than the workload needs.", "what_it_is_for": "Lower-cost hosted Qwen3.8 model. Model Studio recommends it when Qwen3.8-Max or Qwen3.7-Plus is more capability than the workload needs.", "ratings": { "empirical_100": null, "lmsys_elo": 1207, "coding_elo": 1207, "swe_bench": 16.2, "humaneval": 70.9, "twelve_cs": 19.6, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-8-flash-next": { "id": "alibaba/qwen/qwen3-8-flash-next", "name": "Qwen3.8-Flash-Next", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "other", "release_date": "2026-08-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hugging Face tags the architecture qwen4_exp and the task image-text-to-text. License tag is other, not Apache-2.0.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.8-Flash-Next. Status: active. Collected 2026-10-15. Open-weight Qwen3.8 Flash-Next checkpoint published on the Qwen Hugging Face org.", "what_it_is_for": "Open-weight Qwen3.8 Flash-Next checkpoint published on the Qwen Hugging Face org.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-8-max": { "id": "alibaba/qwen/qwen3-8-max", "name": "Qwen3.8-Max", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "Hosted Qwen-Max-class model. The open-weight card says Qwen3.8-Max is the official version based on Qwen3.8-2.4T-A95B, with vision input, optional non-thinking, a 1M context, and built-in tools.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 6.0 }, "summary_intro": "Alibaba model Qwen3.8-Max. Status: active. Collected 2026-10-15. Alibaba's flagship hosted Qwen3.8 model for agents, coding, and visual understanding.", "what_it_is_for": "Alibaba's flagship hosted Qwen3.8 model for agents, coding, and visual understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-8b": { "id": "alibaba/qwen/qwen3-8b", "name": "Qwen3-8B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-27", "context_length": 32768, "max_output_tokens": null, "parameters": "8.2B", "architecture": "Causal LM. The FP8 card says 8.2B parameters, 6.95B non-embedding, 36 layers, 32 query heads and 8 KV heads. Native context 32,768 and 131,072 with YaRN.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-8B. Status: active. Collected 2026-10-15. Instruction-tuned Qwen3 8B from the April 2025 release. Hybrid thinking can be switched per request on Qwen3.", "what_it_is_for": "Instruction-tuned Qwen3 8B from the April 2025 release. Hybrid thinking can be switched per request on Qwen3.", "ratings": { "empirical_100": 40, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-max": { "id": "alibaba/qwen/qwen3-max", "name": "Qwen3-Max", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-Max. Status: active. Collected 2026-10-15. Hosted Qwen3-Max from the 2025 flagship line, still on the pricing page. Not the same product as Qwen3.8-Max.", "what_it_is_for": "Hosted Qwen3-Max from the 2025 flagship line, still on the pricing page. Not the same product as Qwen3.8-Max.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen/qwen3-next-80b-a3b": { "id": "alibaba/qwen/qwen3-next-80b-a3b", "name": "Qwen3-Next-80B-A3B", "maker": "alibaba", "family": "alibaba/qwen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-09-11", "context_length": null, "max_output_tokens": null, "parameters": "80B total, 3B activated", "architecture": "Ultra-sparse MoE with hybrid attention, per the Qwen3.8 GitHub news line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": 1.2 }, "summary_intro": "Alibaba model Qwen3-Next-80B-A3B. Status: active. Collected 2026-10-15. Efficiency-focused Qwen3-Next model. Sold as separate instruct and thinking endpoints.", "what_it_is_for": "Efficiency-focused Qwen3-Next model. Sold as separate instruct and thinking endpoints.", "ratings": { "empirical_100": null, "lmsys_elo": 1216, "coding_elo": 1216, "swe_bench": 17.8, "humaneval": 72.2, "twelve_cs": 99.5, "aa_intelligence": 9.6, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-audio/qwen-audio-3-0-tts-plus": { "id": "alibaba/qwen-audio/qwen-audio-3-0-tts-plus", "name": "Qwen-Audio-3.0-TTS-Plus", "maker": "alibaba", "family": "alibaba/qwen-audio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Audio-3.0-TTS-Plus. Status: active. Collected 2026-10-15. Hosted Qwen text-to-speech model recommended for audiobooks, broadcasts, and avatars.", "what_it_is_for": "Hosted Qwen text-to-speech model recommended for audiobooks, broadcasts, and avatars.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-audio/qwen-audio-3-1-asr": { "id": "alibaba/qwen-audio/qwen-audio-3-1-asr", "name": "Qwen-Audio-3.1-ASR-Flash", "maker": "alibaba", "family": "alibaba/qwen-audio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Audio-3.1-ASR-Flash. Status: active. Collected 2026-10-15. Hosted streaming and file ASR. Model Studio lists qwen-audio-3.1-asr-flash-streaming and qwen-audio-3.1-asr-flash-filetrans as the two modes.", "what_it_is_for": "Hosted streaming and file ASR. Model Studio lists qwen-audio-3.1-asr-flash-streaming and qwen-audio-3.1-asr-flash-filetrans as the two modes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-audio/qwen-audio-3-1-realtime-plus": { "id": "alibaba/qwen-audio/qwen-audio-3-1-realtime-plus", "name": "Qwen-Audio-3.1-Realtime-Plus", "maker": "alibaba", "family": "alibaba/qwen-audio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Audio-3.1-Realtime-Plus. Status: active. Collected 2026-10-15. Realtime duplex speech model. The lifecycle note says it keeps the 3.0 Plus protocol, keeps existing voices, and adds 8 system voices.", "what_it_is_for": "Realtime duplex speech model. The lifecycle note says it keeps the 3.0 Plus protocol, keeps existing voices, and adds 8 system voices.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-audio/qwen3-asr-0-6b": { "id": "alibaba/qwen-audio/qwen3-asr-0-6b", "name": "Qwen3-ASR-0.6B", "maker": "alibaba", "family": "alibaba/qwen-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-28", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-ASR-0.6B. Status: active. Collected 2026-10-15. Open 0.6B Qwen3 ASR model.", "what_it_is_for": "Open 0.6B Qwen3 ASR model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-audio/qwen3-asr-1-7b": { "id": "alibaba/qwen-audio/qwen3-asr-1-7b", "name": "Qwen3-ASR-1.7B", "maker": "alibaba", "family": "alibaba/qwen-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-28", "context_length": null, "max_output_tokens": null, "parameters": "1.7B", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-ASR-1.7B. Status: active. Collected 2026-10-15. Open 1.7B automatic speech recognition model. Paper tag arXiv 2601.21337.", "what_it_is_for": "Open 1.7B automatic speech recognition model. Paper tag arXiv 2601.21337.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-audio/qwen3-tts-0-6b": { "id": "alibaba/qwen-audio/qwen3-tts-0-6b", "name": "Qwen3-TTS-12Hz-0.6B", "maker": "alibaba", "family": "alibaba/qwen-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-21", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-TTS-12Hz-0.6B. Status: active. Collected 2026-10-15. Smaller open Qwen3 TTS model at 12 Hz. HF tags name Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian on the CustomVoice repo.", "what_it_is_for": "Smaller open Qwen3 TTS model at 12 Hz. HF tags name Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian on the CustomVoice repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-audio/qwen3-tts-1-7b": { "id": "alibaba/qwen-audio/qwen3-tts-1-7b", "name": "Qwen3-TTS-12Hz-1.7B", "maker": "alibaba", "family": "alibaba/qwen-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-21", "context_length": null, "max_output_tokens": null, "parameters": "1.7B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-TTS-12Hz-1.7B. Status: active. Collected 2026-10-15. Open Qwen3 text-to-speech model at 12 Hz. CustomVoice and Base repos share the size. Paper tag arXiv 2601.15621.", "what_it_is_for": "Open Qwen3 text-to-speech model at 12 Hz. CustomVoice and Base repos share the size. Paper tag arXiv 2601.15621.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-coder/qwen-coder-plus": { "id": "alibaba/qwen-coder/qwen-coder-plus", "name": "Qwen-Coder-Plus", "maker": "alibaba", "family": "alibaba/qwen-coder", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-11-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Coder-Plus. Status: deprecated. Collected 2026-10-15. Earlier commercial coder. A pricing extract lists a flat rate of $0.502 input and $1.004 output per million for qwen-coder-plus, qwen-coder-plus-latest, and qwen-coder-plus-2024-11-06.", "what_it_is_for": "Earlier commercial coder. A pricing extract lists a flat rate of $0.502 input and $1.004 output per million for qwen-coder-plus, qwen-coder-plus-latest, and qwen-coder-plus-2024-11-06.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-coder/qwen-coder-turbo": { "id": "alibaba/qwen-coder/qwen-coder-turbo", "name": "Qwen-Coder-Turbo", "maker": "alibaba", "family": "alibaba/qwen-coder", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-09-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Coder-Turbo. Status: deprecated. Collected 2026-10-15. Earlier commercial coder. The same extract lists $0.287 input and $0.861 output per million for qwen-coder-turbo and snapshot qwen-coder-turbo-2024-09-19.", "what_it_is_for": "Earlier commercial coder. The same extract lists $0.287 input and $0.861 output per million for qwen-coder-turbo and snapshot qwen-coder-turbo-2024-09-19.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-coder/qwen2-5-coder-1-5b": { "id": "alibaba/qwen-coder/qwen2-5-coder-1-5b", "name": "Qwen2.5-Coder-1.5B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-18", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-Coder-1.5B-Instruct. Status: active. Collected 2026-10-15. 1.5B Qwen2.5 coder instruct model.", "what_it_is_for": "1.5B Qwen2.5 coder instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-coder/qwen2-5-coder-14b": { "id": "alibaba/qwen-coder/qwen2-5-coder-14b", "name": "Qwen2.5-Coder-14B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-11-06", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-Coder-14B-Instruct. Status: active. Collected 2026-10-15. 14B Qwen2.5 coder instruct model.", "what_it_is_for": "14B Qwen2.5 coder instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-coder/qwen2-5-coder-32b": { "id": "alibaba/qwen-coder/qwen2-5-coder-32b", "name": "Qwen2.5-Coder-32B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-11-06", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-Coder-32B-Instruct. Status: active. Collected 2026-10-15. Largest open Qwen2.5 coder instruct model on the retrieved list.", "what_it_is_for": "Largest open Qwen2.5 coder instruct model on the retrieved list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-coder/qwen2-5-coder-3b": { "id": "alibaba/qwen-coder/qwen2-5-coder-3b", "name": "Qwen2.5-Coder-3B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other", "release_date": "2024-11-06", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-Coder-3B-Instruct. Status: active. Collected 2026-10-15. 3B Qwen2.5 coder instruct model. HF license tag is other.", "what_it_is_for": "3B Qwen2.5 coder instruct model. HF license tag is other.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-coder/qwen2-5-coder-7b": { "id": "alibaba/qwen-coder/qwen2-5-coder-7b", "name": "Qwen2.5-Coder-7B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-17", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-Coder-7B-Instruct. Status: active. Collected 2026-10-15. 7B Qwen2.5 coder instruct model.", "what_it_is_for": "7B Qwen2.5 coder instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-coder/qwen3-coder-30b-a3b": { "id": "alibaba/qwen-coder/qwen3-coder-30b-a3b", "name": "Qwen3-Coder-30B-A3B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-31", "context_length": null, "max_output_tokens": null, "parameters": "30B total, 3B activated", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-Coder-30B-A3B-Instruct. Status: active. Collected 2026-10-15. Smaller open Qwen3 coder, a sparse instruct model.", "what_it_is_for": "Smaller open Qwen3 coder, a sparse instruct model.", "ratings": { "empirical_100": 49, "lmsys_elo": 1204, "coding_elo": 1204, "swe_bench": 15.7, "humaneval": 70.6, "twelve_cs": 99.5, "aa_intelligence": 9.6, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1085, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-coder/qwen3-coder-480b-a35b": { "id": "alibaba/qwen-coder/qwen3-coder-480b-a35b", "name": "Qwen3-Coder-480B-A35B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-22", "context_length": null, "max_output_tokens": null, "parameters": "480B total, 35B activated", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-Coder-480B-A35B-Instruct. Status: active. Collected 2026-10-15. Large open Qwen3 coder. The retrieved row is the FP8 quant of Qwen3-Coder-480B-A35B-Instruct, which establishes the parent checkpoint.", "what_it_is_for": "Large open Qwen3 coder. The retrieved row is the FP8 quant of Qwen3-Coder-480B-A35B-Instruct, which establishes the parent checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-coder/qwen3-coder-flash": { "id": "alibaba/qwen-coder/qwen3-coder-flash", "name": "Qwen3-Coder-Flash", "maker": "alibaba", "family": "alibaba/qwen-coder", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-28", "context_length": 1000000, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 1.5 }, "summary_intro": "Alibaba model Qwen3-Coder-Flash. Status: deprecated. Collected 2026-10-15. Cheaper hosted coder. The text index lists it as a legacy model at a 1M context. The Qwen LLM page says the stable id aliases qwen3-coder-flash-2025-07-28.", "what_it_is_for": "Cheaper hosted coder. The text index lists it as a legacy model at a 1M context. The Qwen LLM page says the stable id aliases qwen3-coder-flash-2025-07-28.", "ratings": { "empirical_100": null, "lmsys_elo": 1214, "coding_elo": 1214, "swe_bench": 17.4, "humaneval": 72.0, "twelve_cs": 99.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-coder/qwen3-coder-next": { "id": "alibaba/qwen-coder/qwen3-coder-next", "name": "Qwen3-Coder-Next", "maker": "alibaba", "family": "alibaba/qwen-coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hugging Face architecture tag qwen3_next.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-Coder-Next. Status: active. Collected 2026-10-15. Open coding model in the Qwen3-Next architecture, published January 2026.", "what_it_is_for": "Open coding model in the Qwen3-Next architecture, published January 2026.", "ratings": { "empirical_100": null, "lmsys_elo": 1211, "coding_elo": 1211, "swe_bench": 17.0, "humaneval": 71.6, "twelve_cs": 67.9, "aa_intelligence": 9.2, "aa_coding": 36.2, "aa_agentic": 0.9, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-coder/qwen3-coder-plus": { "id": "alibaba/qwen-coder/qwen3-coder-plus", "name": "Qwen3-Coder-Plus", "maker": "alibaba", "family": "alibaba/qwen-coder", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-22", "context_length": 1000000, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 5.0 }, "summary_intro": "Alibaba model Qwen3-Coder-Plus. Status: deprecated. Collected 2026-10-15. Hosted coding model. One Qwen LLM page says the stable id matches snapshot qwen3-coder-plus-2025-07-22, with max input 997,952 and max output 65,536.", "what_it_is_for": "Hosted coding model. One Qwen LLM page says the stable id matches snapshot qwen3-coder-plus-2025-07-22, with max input 997,952 and max output 65,536.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/gte-rerank": { "id": "alibaba/qwen-embed/gte-rerank", "name": "GTE-Rerank", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model GTE-Rerank. Status: retired. Collected 2026-10-15. Previous GTE reranker. The rerank API page says it will be discontinued on 2026-05-30 and tells users to switch to qwen3-rerank. That date is before this collection date.", "what_it_is_for": "Previous GTE reranker. The rerank API page says it will be discontinued on 2026-05-30 and tells users to switch to qwen3-rerank. That date is before this collection date.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/gte-rerank-v2": { "id": "alibaba/qwen-embed/gte-rerank-v2", "name": "GTE-Rerank-v2", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model GTE-Rerank-v2. Status: active. Collected 2026-10-15. Hosted GTE reranker on the Beijing table. 500 documents, 4,000 tokens per item, 30,000 per request. Beijing input price $0.115 per million.", "what_it_is_for": "Hosted GTE reranker on the Beijing table. 500 documents, 4,000 tokens per item, 30,000 per request. Beijing input price $0.115 per million.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/qwen3-7-text-embedding": { "id": "alibaba/qwen-embed/qwen3-7-text-embedding", "name": "Qwen3.7-Text-Embedding", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "Dimensions 2560, 2048, 1536, 1024 (default), 768, 512, and 256.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.07, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.7-Text-Embedding. Status: active. Collected 2026-10-15. Longer-context Qwen3.7 embedding model. Singapore price $0.07 per million input tokens. The docs say 201 languages and dialects.", "what_it_is_for": "Longer-context Qwen3.7 embedding model. Singapore price $0.07 per million input tokens. The docs say 201 languages and dialects.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/qwen3-embedding-0-6b": { "id": "alibaba/qwen-embed/qwen3-embedding-0-6b", "name": "Qwen3-Embedding-0.6B", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-03", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-Embedding-0.6B. Status: active. Collected 2026-10-15. Open 0.6B Qwen3 embedding model. Paper tag arXiv 2506.05176.", "what_it_is_for": "Open 0.6B Qwen3 embedding model. Paper tag arXiv 2506.05176.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/qwen3-embedding-4b": { "id": "alibaba/qwen-embed/qwen3-embedding-4b", "name": "Qwen3-Embedding-4B", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-03", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-Embedding-4B. Status: active. Collected 2026-10-15. Open 4B Qwen3 embedding model.", "what_it_is_for": "Open 4B Qwen3 embedding model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/qwen3-embedding-8b": { "id": "alibaba/qwen-embed/qwen3-embedding-8b", "name": "Qwen3-Embedding-8B", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-03", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-Embedding-8B. Status: active. Collected 2026-10-15. Open 8B Qwen3 embedding model.", "what_it_is_for": "Open 8B Qwen3 embedding model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/qwen3-rerank": { "id": "alibaba/qwen-embed/qwen3-rerank", "name": "Qwen3-Rerank", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-Rerank. Status: active. Collected 2026-10-15. Hosted text reranker. Singapore limits: 500 documents, 4,000 tokens per item, 120,000 tokens per request, over 100 languages. Input price $0.10 per million. Output is not billed.", "what_it_is_for": "Hosted text reranker. Singapore limits: 500 documents, 4,000 tokens per item, 120,000 tokens per request, over 100 languages. Input price $0.10 per million. Output is not billed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/qwen3-reranker-0-6b": { "id": "alibaba/qwen-embed/qwen3-reranker-0-6b", "name": "Qwen3-Reranker-0.6B", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-29", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-Reranker-0.6B. Status: active. Collected 2026-10-15. Open 0.6B Qwen3 reranker. HF pipeline tag is text-ranking.", "what_it_is_for": "Open 0.6B Qwen3 reranker. HF pipeline tag is text-ranking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/qwen3-reranker-4b": { "id": "alibaba/qwen-embed/qwen3-reranker-4b", "name": "Qwen3-Reranker-4B", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-03", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-Reranker-4B. Status: active. Collected 2026-10-15. Open 4B Qwen3 reranker.", "what_it_is_for": "Open 4B Qwen3 reranker.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/qwen3-vl-embedding-2b": { "id": "alibaba/qwen-embed/qwen3-vl-embedding-2b", "name": "Qwen3-VL-Embedding-2B", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-07", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-VL-Embedding-2B. Status: active. Collected 2026-10-15. Open 2B multimodal embedding based on Qwen3-VL-2B-Instruct.", "what_it_is_for": "Open 2B multimodal embedding based on Qwen3-VL-2B-Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/qwen3-vl-embedding-8b": { "id": "alibaba/qwen-embed/qwen3-vl-embedding-8b", "name": "Qwen3-VL-Embedding-8B", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-07", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-VL-Embedding-8B. Status: active. Collected 2026-10-15. Open multimodal embedding based on Qwen3-VL-8B-Instruct. Paper tag arXiv 2601.04720.", "what_it_is_for": "Open multimodal embedding based on Qwen3-VL-8B-Instruct. Paper tag arXiv 2601.04720.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/qwen3-vl-rerank": { "id": "alibaba/qwen-embed/qwen3-vl-rerank", "name": "Qwen3-VL-Rerank", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-VL-Rerank. Status: active. Collected 2026-10-15. Multimodal reranker on the China (Beijing) table. Limits: text 100 docs, image 40, video 4; 8,000 tokens per item; 120,000 per request. Beijing price: text $0.10 and image $0.258 per million input tokens.", "what_it_is_for": "Multimodal reranker on the China (Beijing) table. Limits: text 100 docs, image 40, video 4; 8,000 tokens per item; 120,000 per request. Beijing price: text $0.10 and image $0.258 per million input tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/text-embedding-v3": { "id": "alibaba/qwen-embed/text-embedding-v3", "name": "Text-Embedding-v3", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Dimensions 512, 768, and 1024, default 1024.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Text-Embedding-v3. Status: active. Collected 2026-10-15. Previous text embedding, kept for index compatibility.", "what_it_is_for": "Previous text embedding, kept for index compatibility.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/text-embedding-v4": { "id": "alibaba/qwen-embed/text-embedding-v4", "name": "Text-Embedding-v4", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Tongyi Lab text embedding based on Qwen3. User-set dimensions from 64 to 2048, default 1024.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.07, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Text-Embedding-v4. Status: active. Collected 2026-10-15. Default text embedding on Model Studio. The model info page says retrieval, clustering, and classification improved versus v3.", "what_it_is_for": "Default text embedding on Model Studio. The model info page says retrieval, clustering, and classification improved versus v3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/tongyi-embedding-vision-flash": { "id": "alibaba/qwen-embed/tongyi-embedding-vision-flash", "name": "Tongyi-Embedding-Vision-Flash", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "Dimensions 64 to 768, default 768.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Tongyi-Embedding-Vision-Flash. Status: active. Collected 2026-10-15. Cheaper multimodal embedding. Singapore prices: image or video $0.03 and text $0.09 per million input tokens.", "what_it_is_for": "Cheaper multimodal embedding. Singapore prices: image or video $0.03 and text $0.09 per million input tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-embed/tongyi-embedding-vision-plus": { "id": "alibaba/qwen-embed/tongyi-embedding-vision-plus", "name": "Tongyi-Embedding-Vision-Plus", "maker": "alibaba", "family": "alibaba/qwen-embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "Dimensions 64 to 1152, default 1152.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Tongyi-Embedding-Vision-Plus. Status: active. Collected 2026-10-15. Multimodal embedding for cross-modal search. Singapore input price $0.09 per million tokens. Max tokens listed as 1,024.", "what_it_is_for": "Multimodal embedding for cross-modal search. Singapore input price $0.09 per million tokens. Max tokens listed as 1,024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-image/qwen-image": { "id": "alibaba/qwen-image/qwen-image", "name": "Qwen-Image", "maker": "alibaba", "family": "alibaba/qwen-image", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-04", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Image. Status: active. Collected 2026-10-15. Open text-to-image model. The card stresses text rendering, including Chinese. Hugging Face lists about 20B parameters.", "what_it_is_for": "Open text-to-image model. The card stresses text rendering, including Chinese. Hugging Face lists about 20B parameters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-image/qwen-image-2-1": { "id": "alibaba/qwen-image/qwen-image-2-1", "name": "Qwen-Image-2.1", "maker": "alibaba", "family": "alibaba/qwen-image", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Qwen Research License", "release_date": "2026-09-20", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Unified text-to-image and image-edit model. 7B parameters in the visual generation component and 32 single-stream DiT layers. The research license grants non-commercial use only.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Image-2.1. Status: active. Collected 2026-10-15. Open Qwen image model for generation and editing, including native transparency. Commercial use needs a separate license.", "what_it_is_for": "Open Qwen image model for generation and editing, including native transparency. Commercial use needs a separate license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-image/qwen-image-2-1-pro": { "id": "alibaba/qwen-image/qwen-image-2-1-pro", "name": "Qwen-Image-2.1-Pro", "maker": "alibaba", "family": "alibaba/qwen-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Image-2.1-Pro. Status: active. Collected 2026-10-15. Hosted Qwen-Image-2.1 Pro. The lifecycle note describes a unified text-to-image and image-edit model for international deployment on 2026-10-02.", "what_it_is_for": "Hosted Qwen-Image-2.1 Pro. The lifecycle note describes a unified text-to-image and image-edit model for international deployment on 2026-10-02.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-image/qwen-image-2-1-turbo": { "id": "alibaba/qwen-image/qwen-image-2-1-turbo", "name": "Qwen-Image-2.1-Turbo", "maker": "alibaba", "family": "alibaba/qwen-image", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Qwen Research License", "release_date": "2026-10-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Image-2.1-Turbo. Status: active. Collected 2026-10-15. Speed variant of Qwen-Image-2.1. The lifecycle page calls it the most balanced open model in the Qwen-Image series and dates the international release 2026-10-09. The Hugging Face repo license tag is qwen-research.", "what_it_is_for": "Speed variant of Qwen-Image-2.1. The lifecycle page calls it the most balanced open model in the Qwen-Image series and dates the international release 2026-10-09. The Hugging Face repo license tag is qwen-research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-image/qwen-image-3-0-pro": { "id": "alibaba/qwen-image/qwen-image-3-0-pro", "name": "Qwen-Image-3.0-Pro", "maker": "alibaba", "family": "alibaba/qwen-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Image-3.0-Pro. Status: active. Collected 2026-10-15. Recommended hosted image model on the Model Studio models page, alongside Wan2.7-Image-Pro.", "what_it_is_for": "Recommended hosted image model on the Model Studio models page, alongside Wan2.7-Image-Pro.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-image/qwen-image-edit-2509": { "id": "alibaba/qwen-image/qwen-image-edit-2509", "name": "Qwen-Image-Edit-2509", "maker": "alibaba", "family": "alibaba/qwen-image", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-09-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Image-Edit-2509. Status: active. Collected 2026-10-15. Open image-edit checkpoint dated 2509. HF pipeline is image-to-image. Paper tag arXiv 2508.02324, same report family as Qwen-Image.", "what_it_is_for": "Open image-edit checkpoint dated 2509. HF pipeline is image-to-image. Paper tag arXiv 2508.02324, same report family as Qwen-Image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-image/qwen-image-max": { "id": "alibaba/qwen-image/qwen-image-max", "name": "Qwen-Image-Max", "maker": "alibaba", "family": "alibaba/qwen-image", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Image-Max. Status: active. Collected 2026-10-15. Commercial Max image model. A model-info page says it reduces the synthetic look versus Plus and is equivalent to snapshot qwen-image-max-2025-12-30.", "what_it_is_for": "Commercial Max image model. A model-info page says it reduces the synthetic look versus Plus and is equivalent to snapshot qwen-image-max-2025-12-30.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-math/qwen2-5-math-prm-7b": { "id": "alibaba/qwen-math/qwen2-5-math-prm-7b", "name": "Qwen2.5-Math-PRM-7B", "maker": "alibaba", "family": "alibaba/qwen-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other", "release_date": "2025-01-13", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Process reward model. HF pipeline tag is text-classification. Base model tag is Qwen2.5-Math-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-Math-PRM-7B. Status: active. Collected 2026-10-15. 7B process reward model for scoring math reasoning steps. Not a chat model.", "what_it_is_for": "7B process reward model for scoring math reasoning steps. Not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-mt/qwen-mt-flash": { "id": "alibaba/qwen-mt/qwen-mt-flash", "name": "Qwen-MT-Flash", "maker": "alibaba", "family": "alibaba/qwen-mt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 16384, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-MT-Flash. Status: active. Collected 2026-10-15. Cheaper translation model at a 16k context.", "what_it_is_for": "Cheaper translation model at a 16k context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-mt/qwen-mt-lite": { "id": "alibaba/qwen-mt/qwen-mt-lite", "name": "Qwen-MT-Lite", "maker": "alibaba", "family": "alibaba/qwen-mt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 16384, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-MT-Lite. Status: active. Collected 2026-10-15. Smallest listed Qwen-MT model, 16k context, no tools.", "what_it_is_for": "Smallest listed Qwen-MT model, 16k context, no tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-mt/qwen-mt-plus": { "id": "alibaba/qwen-mt/qwen-mt-plus", "name": "Qwen-MT-Plus", "maker": "alibaba", "family": "alibaba/qwen-mt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 16384, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.46, "completion_per_m_usd": 7.37 }, "summary_intro": "Alibaba model Qwen-MT-Plus. Status: active. Collected 2026-10-15. Translation model. Text index: 16k context, no thinking and no tools. The Qwen LLM page lists max input 8,192 and max output 8,192, at $2.46 input and $7.37 output per million.", "what_it_is_for": "Translation model. Text index: 16k context, no thinking and no tools. The Qwen LLM page lists max input 8,192 and max output 8,192, at $2.46 input and $7.37 output per million.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-omni/qwen-omni-turbo": { "id": "alibaba/qwen-omni/qwen-omni-turbo", "name": "Qwen-Omni-Turbo", "maker": "alibaba", "family": "alibaba/qwen-omni", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-Omni-Turbo. Status: deprecated. Collected 2026-10-15. Older omni turbo endpoint. A text-generation render lists qwen-omni-turbo and its snapshots at 32k, without thinking or tool calling.", "what_it_is_for": "Older omni turbo endpoint. A text-generation render lists qwen-omni-turbo and its snapshots at 32k, without thinking or tool calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-omni/qwen2-5-omni-7b": { "id": "alibaba/qwen-omni/qwen2-5-omni-7b", "name": "Qwen2.5-Omni-7B", "maker": "alibaba", "family": "alibaba/qwen-omni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 32768, "max_output_tokens": 2048, "parameters": "7B", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.4 }, "summary_intro": "Alibaba model Qwen2.5-Omni-7B. Status: active. Collected 2026-10-15. 7B omni model. Model Studio lists context 32,768, max input 30,720, max output 2,048. After the free quota, Singapore input prices are text $0.10, audio $6.76, image or video $0.28 per million tokens.", "what_it_is_for": "7B omni model. Model Studio lists context 32,768, max input 30,720, max output 2,048. After the free quota, Singapore input prices are text $0.10, audio $6.76, image or video $0.28 per million tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-omni/qwen3-5-omni-flash": { "id": "alibaba/qwen-omni/qwen3-5-omni-flash", "name": "Qwen3.5-Omni-Flash", "maker": "alibaba", "family": "alibaba/qwen-omni", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.5-Omni-Flash. Status: active. Collected 2026-10-15. Named next to Qwen3.5-Omni-Plus for video with audio input, up to 1 hour and 2 GB.", "what_it_is_for": "Named next to Qwen3.5-Omni-Plus for video with audio input, up to 1 hour and 2 GB.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-omni/qwen3-5-omni-plus": { "id": "alibaba/qwen-omni/qwen3-5-omni-plus", "name": "Qwen3.5-Omni-Plus", "maker": "alibaba", "family": "alibaba/qwen-omni", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 256000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.5-Omni-Plus. Status: active. Collected 2026-10-15. Qwen3.5 omni model. Model Studio says to use it when you need non-realtime audio output. Vision table: 256k context, about 1 hour of video, up to 2,048 images and 512 videos, JSON object output.", "what_it_is_for": "Qwen3.5 omni model. Model Studio says to use it when you need non-realtime audio output. Vision table: 256k context, about 1 hour of video, up to 2,048 images and 512 videos, JSON object output.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-omni/qwen3-8-omni-flash": { "id": "alibaba/qwen-omni/qwen3-8-omni-flash", "name": "Qwen3.8-Omni-Flash", "maker": "alibaba", "family": "alibaba/qwen-omni", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-17", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.8-Omni-Flash. Status: active. Collected 2026-10-15. Omni model for audio and video understanding with text output. Supports thinking, tools, web search, and JSON object mode. Real-time conversation is a different id.", "what_it_is_for": "Omni model for audio and video understanding with text output. Supports thinking, tools, web search, and JSON object mode. Real-time conversation is a different id.", "ratings": { "empirical_100": null, "lmsys_elo": 1207, "coding_elo": 1207, "swe_bench": 16.2, "humaneval": 70.9, "twelve_cs": 52.0, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-omni/qwen3-8-omni-flash-realtime": { "id": "alibaba/qwen-omni/qwen3-8-omni-flash-realtime", "name": "Qwen3.8-Omni-Flash-Realtime", "maker": "alibaba", "family": "alibaba/qwen-omni", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio", "video" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.8-Omni-Flash-Realtime. Status: active. Collected 2026-10-15. Realtime audio and video conversation model. The lifecycle note says it adds multi-channel audio, video aggregation, and remote MCP tools, over WebSocket, WebRTC, and AOQ.", "what_it_is_for": "Realtime audio and video conversation model. The lifecycle note says it adds multi-channel audio, video aggregation, and remote MCP tools, over WebSocket, WebRTC, and AOQ.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-omni/qwen3-omni-30b-a3b": { "id": "alibaba/qwen-omni/qwen3-omni-30b-a3b", "name": "Qwen3-Omni-30B-A3B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-omni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other", "release_date": "2025-09-20", "context_length": null, "max_output_tokens": null, "parameters": "30B total, 3B activated", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-Omni-30B-A3B-Instruct. Status: active. Collected 2026-10-15. Open omni instruct model. HF pipeline tag is any-to-any. License tag is other.", "what_it_is_for": "Open omni instruct model. HF pipeline tag is any-to-any. License tag is other.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-omni/qwen3-omni-30b-a3b-captioner": { "id": "alibaba/qwen-omni/qwen3-omni-30b-a3b-captioner", "name": "Qwen3-Omni-30B-A3B-Captioner", "maker": "alibaba", "family": "alibaba/qwen-omni", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": 65536, "max_output_tokens": 32768, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.81, "completion_per_m_usd": 3.06 }, "summary_intro": "Alibaba model Qwen3-Omni-30B-A3B-Captioner. Status: active. Collected 2026-10-15. Captioner endpoint. The Qwen LLM page lists a 65,536 context, 32,768 max input, 32,768 max output, and says it is Singapore-only, at $3.81 input and $3.06 output per million.", "what_it_is_for": "Captioner endpoint. The Qwen LLM page lists a 65,536 context, 32,768 max input, 32,768 max output, and says it is Singapore-only, at $3.81 input and $3.06 output per million.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-vl/qwen-vl-ocr": { "id": "alibaba/qwen-vl/qwen-vl-ocr", "name": "Qwen-VL-OCR", "maker": "alibaba", "family": "alibaba/qwen-vl", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen-VL-OCR. Status: deprecated. Collected 2026-10-15. Older OCR line. The vision page lists qwen-vl-ocr and its snapshots as legacy.", "what_it_is_for": "Older OCR line. The vision page lists qwen-vl-ocr and its snapshots as legacy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-vl/qwen2-5-vl-32b": { "id": "alibaba/qwen-vl/qwen2-5-vl-32b", "name": "Qwen2.5-VL-32B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-21", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-VL-32B-Instruct. Status: active. Collected 2026-10-15. 32B Qwen2.5 vision-language instruct model.", "what_it_is_for": "32B Qwen2.5 vision-language instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-vl/qwen2-5-vl-3b": { "id": "alibaba/qwen-vl/qwen2-5-vl-3b", "name": "Qwen2.5-VL-3B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-26", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-VL-3B-Instruct. Status: active. Collected 2026-10-15. 3B Qwen2.5-VL instruct model. The retrieved HF tags did not include a license string.", "what_it_is_for": "3B Qwen2.5-VL instruct model. The retrieved HF tags did not include a license string.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-vl/qwen2-5-vl-7b": { "id": "alibaba/qwen-vl/qwen2-5-vl-7b", "name": "Qwen2.5-VL-7B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-26", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2.5-VL-7B-Instruct. Status: active. Collected 2026-10-15. 7B Qwen2.5-VL instruct model.", "what_it_is_for": "7B Qwen2.5-VL instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-vl/qwen2-vl-2b": { "id": "alibaba/qwen-vl/qwen2-vl-2b", "name": "Qwen2-VL-2B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-28", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2-VL-2B-Instruct. Status: active. Collected 2026-10-15. 2B Qwen2-VL instruct model.", "what_it_is_for": "2B Qwen2-VL instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-vl/qwen2-vl-7b": { "id": "alibaba/qwen-vl/qwen2-vl-7b", "name": "Qwen2-VL-7B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-28", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen2-VL-7B-Instruct. Status: active. Collected 2026-10-15. 7B Qwen2-VL instruct model.", "what_it_is_for": "7B Qwen2-VL instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-vl/qwen3-5-ocr": { "id": "alibaba/qwen-vl/qwen3-5-ocr", "name": "Qwen3.5-OCR", "maker": "alibaba", "family": "alibaba/qwen-vl", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3.5-OCR. Status: active. Collected 2026-10-15. OCR model for documents, tables, exam papers, and handwriting. The vision page says general image text extraction should use Qwen3.7-Plus or Flash instead.", "what_it_is_for": "OCR model for documents, tables, exam papers, and handwriting. The vision page says general image text extraction should use Qwen3.7-Plus or Flash instead.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-vl/qwen3-vl-2b": { "id": "alibaba/qwen-vl/qwen3-vl-2b", "name": "Qwen3-VL-2B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-19", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-VL-2B-Instruct. Status: active. Collected 2026-10-15. Open 2B Qwen3-VL instruct model.", "what_it_is_for": "Open 2B Qwen3-VL instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-vl/qwen3-vl-4b": { "id": "alibaba/qwen-vl/qwen3-vl-4b", "name": "Qwen3-VL-4B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-11", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-VL-4B-Instruct. Status: active. Collected 2026-10-15. Open 4B Qwen3-VL instruct model.", "what_it_is_for": "Open 4B Qwen3-VL instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-vl/qwen3-vl-8b": { "id": "alibaba/qwen-vl/qwen3-vl-8b", "name": "Qwen3-VL-8B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-11", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-VL-8B-Instruct. Status: active. Collected 2026-10-15. Open 8B Qwen3 vision-language instruct model.", "what_it_is_for": "Open 8B Qwen3 vision-language instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwen-vl/qwen3-vl-plus": { "id": "alibaba/qwen-vl/qwen3-vl-plus", "name": "Qwen3-VL-Plus", "maker": "alibaba", "family": "alibaba/qwen-vl", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Qwen3-VL-Plus. Status: deprecated. Collected 2026-10-15. Hosted Qwen3-VL Plus. The vision page says video up to 1 hour and 2 GB, and lists the id as a legacy line with snapshots.", "what_it_is_for": "Hosted Qwen3-VL Plus. The vision page says video up to 1 hour and 2 GB, and lists the id as a legacy line with snapshots.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwq/qwq-32b": { "id": "alibaba/qwq/qwq-32b", "name": "QwQ-32B", "maker": "alibaba", "family": "alibaba/qwq", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-11", "context_length": 131072, "max_output_tokens": null, "parameters": "32.5B", "architecture": "Transformers with RoPE, SwiGLU, RMSNorm, and QKV bias. 64 layers, 40 query heads and 8 KV heads. 31.0B non-embedding parameters. Full 131,072 context; YaRN is required above 8,192 prompt tokens. original_max_position_embeddings in the YaRN example is 32,768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model QwQ-32B. Status: active. Collected 2026-10-15. Open reasoning model in the Qwen series, trained with supervised fine-tuning and reinforcement learning.", "what_it_is_for": "Open reasoning model in the Qwen series, trained with supervised fine-tuning and reinforcement learning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwq/qwq-32b-preview": { "id": "alibaba/qwq/qwq-32b-preview", "name": "QwQ-32B-Preview", "maker": "alibaba", "family": "alibaba/qwq", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "32.5B", "architecture": "Same architecture family as QwQ-32B. The preview card states a full 32,768 context and calls the model experimental.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model QwQ-32B-Preview. Status: preview. Collected 2026-10-15. Preview reasoning model released before QwQ-32B. The card warns of limitations.", "what_it_is_for": "Preview reasoning model released before QwQ-32B. The card warns of limitations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/qwq/qwq-plus": { "id": "alibaba/qwq/qwq-plus", "name": "QwQ-Plus", "maker": "alibaba", "family": "alibaba/qwq", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model QwQ-Plus. Status: deprecated. Collected 2026-10-15. Hosted QwQ model. A text-generation render lists qwq-plus at 128k with thinking supported and tools unsupported.", "what_it_is_for": "Hosted QwQ model. A text-generation render lists qwq-plus at 128k with thinking supported and tools unsupported.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/wan/wan2-1": { "id": "alibaba/wan/wan2-1", "name": "Wan2.1", "maker": "alibaba", "family": "alibaba/wan", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Legacy silent video generation. The image-to-video guide groups it with Wan 2.2 for silent output. Prompt limit 800 characters.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Wan2.1. Status: active. Collected 2026-10-15. First widely shipped Wan generation line still referenced as a legacy silent model.", "what_it_is_for": "First widely shipped Wan generation line still referenced as a legacy silent model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/wan/wan2-2-i2v": { "id": "alibaba/wan/wan2-2-i2v", "name": "Wan2.2-I2V", "maker": "alibaba", "family": "alibaba/wan", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The image-to-video guide groups Wan 2.2 with Wan 2.1 as silent-video models. Prompt length limit in the legacy API is 800 characters for wan2.2 and wan2.1.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Wan2.2-I2V. Status: active. Collected 2026-10-15. Wan 2.2 image-to-video. Fine-tune docs also name wan2.2-i2v-flash and wan2.2-kf2v-flash.", "what_it_is_for": "Wan 2.2 image-to-video. Fine-tune docs also name wan2.2-i2v-flash and wan2.2-kf2v-flash.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/wan/wan2-5-i2v": { "id": "alibaba/wan/wan2-5-i2v", "name": "Wan2.5-I2V", "maker": "alibaba", "family": "alibaba/wan", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The image-to-video guide says Wan 2.5 and 2.6 support automatic dubbing or an uploaded audio track for lip sync.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Wan2.5-I2V. Status: active. Collected 2026-10-15. Wan 2.5 image-to-video generation with audio.", "what_it_is_for": "Wan 2.5 image-to-video generation with audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/wan/wan2-6-i2v": { "id": "alibaba/wan/wan2-6-i2v", "name": "Wan2.6-I2V", "maker": "alibaba", "family": "alibaba/wan", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-to-video with audio and multi-shot narrative. 720P and 1080P. Durations 5, 10, or 15 seconds. 30 fps MP4.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Wan2.6-I2V. Status: active. Collected 2026-10-15. Previous Wan image-to-video generation with synced audio. The image-to-video guide says multi-shot is a Wan 2.6 feature and audio sync is Wan 2.5 and 2.6.", "what_it_is_for": "Previous Wan image-to-video generation with synced audio. The image-to-video guide says multi-shot is a Wan 2.6 feature and audio sync is Wan 2.5 and 2.6.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/wan/wan2-7-i2v": { "id": "alibaba/wan/wan2-7-i2v", "name": "Wan2.7-I2V", "maker": "alibaba", "family": "alibaba/wan", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-to-video with first frame, first and last frame, continuation, and audio. 720P and 1080P, 2 to 15 seconds, 30 fps, MP4.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Wan2.7-I2V. Status: active. Collected 2026-10-15. Wan 2.7 image-to-video model. Snapshot wan2.7-i2v-2026-04-25 is the recommended id on the video selection page.", "what_it_is_for": "Wan 2.7 image-to-video model. Snapshot wan2.7-i2v-2026-04-25 is the recommended id on the video selection page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/wan/wan2-7-image-pro": { "id": "alibaba/wan/wan2-7-image-pro", "name": "Wan2.7-Image-Pro", "maker": "alibaba", "family": "alibaba/wan", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Wan2.7-Image-Pro. Status: active. Collected 2026-10-15. Wan image model recommended next to Qwen-Image-3.0-Pro on the models page.", "what_it_is_for": "Wan image model recommended next to Qwen-Image-3.0-Pro on the models page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/wan/wan3-0-video": { "id": "alibaba/wan/wan3-0-video", "name": "Wan3.0-Video", "maker": "alibaba", "family": "alibaba/wan", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "All-in-one reference video model: text-to-video, first-frame and first-last-frame image-to-video, and reference-to-video. Up to 30 seconds, 30 fps, 480P, 720P, and 1080P. The API reference calls the series a preview.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Wan3.0-Video. Status: preview. Collected 2026-10-15. Current Wan flagship for generation up to 30 seconds with native dialogue, music, and effects, and up to 20 reference assets.", "what_it_is_for": "Current Wan flagship for generation up to 30 seconds with native dialogue, music, and effects, and up to 20 reference assets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "alibaba/wan/wan3-0-video-prime": { "id": "alibaba/wan/wan3-0-video-prime", "name": "Wan3.0-Video-Prime", "maker": "alibaba", "family": "alibaba/wan", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speed-optimized Wan3.0. The API lists the same task types, resolutions, and 2 to 30 second range as wan3.0-video.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alibaba model Wan3.0-Video-Prime. Status: preview. Collected 2026-10-15. Faster Wan3.0 endpoint. Docs say quality stays aligned with the standard model.", "what_it_is_for": "Faster Wan3.0 endpoint. Docs say quality stays aligned with the standard model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/astabrief/astabrief-8b": { "id": "allenai/astabrief/astabrief-8b", "name": "AstaBrief 8B", "maker": "allenai", "family": "allenai/astabrief", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-02-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Report model on Qwen3-8B. Hub did not return a safetensors total. SFT checkpoint is allenai/AstaBrief_8B_SFT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AstaBrief 8B is Ai2's open scientific report model, Apache-2.0, repo created 2026-02-09. Collected 2026-10-15.", "what_it_is_for": "Turns a research question and retrieved excerpts into a cited report. Used as Fast mode in Asta.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/bolmo/bolmo-1b": { "id": "allenai/bolmo/bolmo-1b", "name": "Bolmo 1B", "maker": "allenai", "family": "allenai/bolmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-12-10", "context_length": 4096, "max_output_tokens": null, "parameters": "1468911776", "architecture": "Byte-level language model (model_type bolmo) with local encoder/decoder layers. Config max_position_embeddings 4096, 16 layers, hidden 2048. Safetensors total 1,468,911,776. Built from an OLMo 2 1B stage-1 checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bolmo 1B is Ai2's December 2025 byte-level language model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Byte-level fully open language model at about 1B parameters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/bolmo/bolmo-7b": { "id": "allenai/bolmo/bolmo-7b", "name": "Bolmo 7B", "maker": "allenai", "family": "allenai/bolmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-12-13", "context_length": 65536, "max_output_tokens": null, "parameters": "7633084576", "architecture": "Byte-level Bolmo model. Config max_position_embeddings 65536, 32 layers, hidden 4096, sliding window 4096. Safetensors total 7,633,084,576.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bolmo 7B is Ai2's larger December 2025 byte-level language model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Larger byte-level Bolmo model with a 65,536-token config window.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/dolma-classifier/dolma3-quality-classifier": { "id": "allenai/dolma-classifier/dolma3-quality-classifier", "name": "Dolma 3 quality classifier", "maker": "allenai", "family": "allenai/dolma-classifier", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "fastText quality classifier used to filter Dolma 3.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Dolma 3 quality classifier is Ai2's fastText filter for Dolma 3, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Score web text quality for the Dolma 3 mix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/dolma-classifier/dolma3-topic-classifier": { "id": "allenai/dolma-classifier/dolma3-topic-classifier", "name": "Dolma 3 topic classifier", "maker": "allenai", "family": "allenai/dolma-classifier", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "fastText topic classifier (WebOrganizer) used with Dolma 3.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Dolma 3 topic classifier is Ai2's fastText topic model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Topic labels for Dolma 3 data organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/dr-tulu/dr-tulu-8b": { "id": "allenai/dr-tulu/dr-tulu-8b", "name": "DR Tulu 8B", "maker": "allenai", "family": "allenai/dr-tulu", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Deep-research agent on Qwen3-8B. Final weights are rl-research/DR-Tulu-8B. The card says 8B params; an exact safetensors total was not read. Inference notes in the project README use max model length 40,960.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DR Tulu 8B is Ai2's open deep-research model, Apache-2.0, released with the RL ReSearch Hub org. Collected 2026-10-15.", "what_it_is_for": "Long-form deep research with search and browsing tools. Ai2's 18 November 2025 blog, with an improved RL checkpoint on 25 November.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/emo/emo-1b14b-1t": { "id": "allenai/emo/emo-1b14b-1t", "name": "EMO 1B-14B 1T", "maker": "allenai", "family": "allenai/emo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-04-29", "context_length": 4096, "max_output_tokens": null, "parameters": "13568641024", "architecture": "Mixture-of-experts, config 128 experts, 8 experts per token, 1 shared expert, 16 layers, hidden 2048, max_position_embeddings 4096. Safetensors total 13,568,641,024. Repo name says 1B active, 14B total, 1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EMO 1B-14B 1T is an Ai2 mixture-of-experts language model, Apache-2.0, published on Hugging Face on 2026-04-29. Collected 2026-10-15.", "what_it_is_for": "Open MoE trained so reusable modules emerge without predefined domain labels. Paper id on the card is arXiv 2605.06663.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/flexolmo/flexolmo-7x7b-1t": { "id": "allenai/flexolmo/flexolmo-7x7b-1t", "name": "FlexOlmo 7x7B 1T", "maker": "allenai", "family": "allenai/flexolmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-07-09", "context_length": 4096, "max_output_tokens": null, "parameters": "33270665216", "architecture": "Mixture-of-experts FlexOlmo, config num_experts 7, 32 layers, hidden 4096, 32 heads, max_position_embeddings 4096. Safetensors total 33,270,665,216. Blog: each expert is trained separately and later merged.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlexOlmo 7x7B 1T is Ai2's July 2025 mixture-of-experts model for data collaboration, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Data-collaboration language model. Data owners train experts locally and can turn those experts on or off at inference without sharing raw data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/graspmolmo/graspmolmo": { "id": "allenai/graspmolmo/graspmolmo", "name": "GraspMolmo", "maker": "allenai", "family": "allenai/graspmolmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-04", "context_length": null, "max_output_tokens": null, "parameters": "8021025280", "architecture": "Task-oriented grasping finetune of Molmo 7B-D. Safetensors total 8,021,025,280, matching Molmo 7B-D. Paper arXiv 2505.13441.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GraspMolmo is Ai2's June 2025 grasping model finetuned from Molmo 7B-D, MIT license on the Hub. Collected 2026-10-15.", "what_it_is_for": "Predicts task-oriented grasps from images and language.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/layout/hvila-row-docbank": { "id": "allenai/layout/hvila-row-docbank", "name": "HVILA row LayoutLM", "maker": "allenai", "family": "allenai/layout", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hierarchical visual layout model finetuned on DocBank rows.", "modalities": { "input": [ "text", "image" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HVILA row LayoutLM is an Ai2 hierarchical layout checkpoint. Collected 2026-10-15.", "what_it_is_for": "Hierarchical row layout detection.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/layout/ivila-block-docbank": { "id": "allenai/layout/ivila-block-docbank", "name": "IVILA block LayoutLM", "maker": "allenai", "family": "allenai/layout", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LayoutLM token classifier finetuned on DocBank blocks.", "modalities": { "input": [ "text", "image" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IVILA block LayoutLM is an Ai2 DocBank layout checkpoint. Collected 2026-10-15.", "what_it_is_for": "Detect block layout in documents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/layout/ivila-row-s2vl": { "id": "allenai/layout/ivila-row-s2vl", "name": "IVILA row LayoutLM", "maker": "allenai", "family": "allenai/layout", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LayoutLM token classifier finetuned for Semantic Scholar visual layout rows. Hub id allenai/ivila-row-layoutlm-finetuned-s2vl-v2.", "modalities": { "input": [ "text", "image" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IVILA row LayoutLM is an Ai2 document-layout checkpoint. Collected 2026-10-15.", "what_it_is_for": "Detect row structure in scholarly page images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/led/led-base": { "id": "allenai/led/led-base", "name": "LED base", "maker": "allenai", "family": "allenai/led", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 16384, "max_output_tokens": null, "parameters": "", "architecture": "Longformer Encoder-Decoder, 16,384-token window. Hub id allenai/led-base-16384. Paper tag arXiv 2004.05150.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LED base is Ai2's 16,384-token encoder-decoder, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Long-document sequence-to-sequence model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/led/led-large": { "id": "allenai/led/led-large", "name": "LED large", "maker": "allenai", "family": "allenai/led", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 16384, "max_output_tokens": null, "parameters": "", "architecture": "Large LED, 16,384-token window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LED large is Ai2's larger 16,384-token encoder-decoder, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Larger long-document sequence-to-sequence model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/led/led-large-arxiv": { "id": "allenai/led/led-large-arxiv", "name": "LED large arXiv", "maker": "allenai", "family": "allenai/led", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 16384, "max_output_tokens": null, "parameters": "", "architecture": "LED large finetuned on scientific paper summarization.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LED large arXiv is Ai2's paper-summarization finetune of LED large, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Summarize long scientific papers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/longformer/longformer-base": { "id": "allenai/longformer/longformer-base", "name": "Longformer base", "maker": "allenai", "family": "allenai/longformer", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Long-document transformer. Model id longformer-base-4096. Paper arXiv 2004.05150. Hub import date 2022-03-02 is not the 2020 release.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Longformer base is Ai2's 4,096-token long-document encoder, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Encode documents up to 4,096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/longformer/longformer-large": { "id": "allenai/longformer/longformer-large", "name": "Longformer large", "maker": "allenai", "family": "allenai/longformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Large Longformer, 4,096-token window. Hub id allenai/longformer-large-4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Longformer large is Ai2's larger 4,096-token Longformer, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Larger long-document encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/longformer/longformer-scico": { "id": "allenai/longformer/longformer-scico", "name": "Longformer SciCo", "maker": "allenai", "family": "allenai/longformer", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Longformer finetuned for SciCo text classification.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Longformer SciCo is Ai2's SciCo classification model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Scientific claim comparison classifier.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmo/molmo-72b": { "id": "allenai/molmo/molmo-72b", "name": "Molmo 72B", "maker": "allenai", "family": "allenai/molmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-25", "context_length": null, "max_output_tokens": null, "parameters": "73308285952", "architecture": "Largest 2024 Molmo. GitHub lists a Qwen2 72B language backbone. Safetensors total 73,308,285,952. Context not read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Molmo 72B is Ai2's September 2024 72B vision-language model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Largest original Molmo model for image understanding and pointing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmo/molmo-7b-d": { "id": "allenai/molmo/molmo-7b-d", "name": "Molmo 7B-D", "maker": "allenai", "family": "allenai/molmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-25", "context_length": 4096, "max_output_tokens": null, "parameters": "8021025280", "architecture": "Vision-language model. Config max_position_embeddings 4096, 28 layers, hidden 3584, 28 query / 4 KV heads. GitHub lists a Qwen2 7B language backbone. Safetensors total 8,021,025,280.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Molmo 7B-D is Ai2's September 2024 7B vision-language model on a Qwen2 backbone, Apache-2.0, 4,096-token config window. Collected 2026-10-15.", "what_it_is_for": "Strong 7B Molmo demo model for image understanding and pointing. The public demo used Molmo-7B-D.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmo/molmo-7b-o": { "id": "allenai/molmo/molmo-7b-o", "name": "Molmo 7B-O", "maker": "allenai", "family": "allenai/molmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-25", "context_length": null, "max_output_tokens": null, "parameters": "7665032192", "architecture": "Most open 7B Molmo. GitHub pairs it with an OLMo language backbone. Safetensors total 7,665,032,192. Context not read for this variant; Molmo 7B-D config is 4,096.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Molmo 7B-O is Ai2's September 2024 open vision-language model on an OLMo backbone, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Fully open 7B vision-language model using an OLMo language backbone, including image pointing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmo/molmoe-1b": { "id": "allenai/molmo/molmoe-1b", "name": "MolmoE 1B", "maker": "allenai", "family": "allenai/molmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language MoE. Ai2 GitHub: CLIP ViT-L/14 at 336 plus OLMoE-1B-7B-0924. Safetensors total was empty in the API response.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolmoE 1B is Ai2's September 2024 open vision-language mixture-of-experts model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Smallest Molmo model, a mixture-of-experts vision-language model with image pointing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmo2/molmo2-4b": { "id": "allenai/molmo2/molmo2-4b", "name": "Molmo 2 4B", "maker": "allenai", "family": "allenai/molmo2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-12-11", "context_length": null, "max_output_tokens": null, "parameters": "4850869200", "architecture": "Molmo 2 efficiency model. Card base model Qwen3-4B-Instruct-2507. Safetensors total 4,850,869,200. Context not read; the 8B text config that was opened is 36,864.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Molmo 2 4B is Ai2's December 2025 efficient open video-language model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Compact Molmo 2 for image, captioning, pointing, video understanding, and tracking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmo2/molmo2-8b": { "id": "allenai/molmo2/molmo2-8b", "name": "Molmo 2 8B", "maker": "allenai", "family": "allenai/molmo2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-12-11", "context_length": 36864, "max_output_tokens": null, "parameters": "8661703120", "architecture": "Molmo 2 flagship. Card base model Qwen3-8B. Text config max_position_embeddings 36,864, hidden 4096, 36 layers. Safetensors total 8,661,703,120.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Molmo 2 8B is Ai2's December 2025 flagship open video-language model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Best overall Molmo 2 model for video grounding, QA, pointing, counting, and tracking. Ai2 says it beats the original Molmo 72B on key pointing benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmo2/molmo2-er": { "id": "allenai/molmo2/molmo2-er", "name": "Molmo2 ER", "maker": "allenai", "family": "allenai/molmo2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-05-04", "context_length": null, "max_output_tokens": null, "parameters": "4850869200", "architecture": "Embodied-reasoning finetune. Card base model Molmo2-4B. Safetensors total 4,850,869,200, matching Molmo 2 4B. Paper tag arXiv 2605.02881.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Molmo2 ER is an Ai2 embodied-reasoning vision-language checkpoint on Molmo 2 4B, Apache-2.0, repo created 2026-05-04. Collected 2026-10-15.", "what_it_is_for": "Embodied and spatial reasoning checkpoint built on Molmo 2 4B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmo2/molmo2-o-7b": { "id": "allenai/molmo2/molmo2-o-7b", "name": "Molmo 2 O 7B", "maker": "allenai", "family": "allenai/molmo2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-12-11", "context_length": null, "max_output_tokens": null, "parameters": "7760786896", "architecture": "Fully open Molmo 2 variant. Card base model Olmo-3-7B-Instruct. Safetensors total 7,760,786,896.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Molmo 2-O 7B is Ai2's December 2025 fully open video-language model on Olmo 3 7B Instruct, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Molmo 2 with an Olmo language backbone so the language model, not only the vision adapter, is fully open.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmo2/molmopoint-8b": { "id": "allenai/molmo2/molmopoint-8b", "name": "MolmoPoint 8B", "maker": "allenai", "family": "allenai/molmo2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-16", "context_length": null, "max_output_tokens": null, "parameters": "8677855065", "architecture": "Pointing model. Card base model Qwen3-8B. Safetensors total 8,677,855,065.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolmoPoint 8B is Ai2's March 2026 pointing model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Specialized pointing model in the Molmo 2 line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmo2/molmopoint-gui-8b": { "id": "allenai/molmo2/molmopoint-gui-8b", "name": "MolmoPoint GUI 8B", "maker": "allenai", "family": "allenai/molmo2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-17", "context_length": null, "max_output_tokens": null, "parameters": "8677855065", "architecture": "GUI pointing finetune of MolmoPoint 8B. Safetensors total 8,677,855,065, same as MolmoPoint 8B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolmoPoint GUI 8B is Ai2's March 2026 GUI pointing model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Pointing model aimed at graphical user interfaces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmo2/molmopoint-vid-4b": { "id": "allenai/molmo2/molmopoint-vid-4b", "name": "MolmoPoint Vid 4B", "maker": "allenai", "family": "allenai/molmo2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-17", "context_length": null, "max_output_tokens": null, "parameters": "4862237657", "architecture": "Video pointing model. Safetensors total 4,862,237,657. Pipeline tag video-text-to-text.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolmoPoint Vid 4B is Ai2's March 2026 video pointing model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Video pointing checkpoint in the MolmoPoint line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmoact/molmoact-7b-d": { "id": "allenai/molmoact/molmoact-7b-d", "name": "MolmoAct 7B-D", "maker": "allenai", "family": "allenai/molmoact", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "8119417808", "architecture": "Action reasoning model on Qwen2.5-7B. Safetensors total 8,119,417,808. Pipeline tag robotics.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolmoAct 7B-D is Ai2's August 2025 open robotics model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "First Action Reasoning Model. Reasons about robot actions in 3D, pretrained on a curated Open X-Embodiment subset and post-trained on about 10,000 robot episodes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmoact/molmoact2": { "id": "allenai/molmoact/molmoact2", "name": "MolmoAct 2", "maker": "allenai", "family": "allenai/molmoact", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-05-05", "context_length": null, "max_output_tokens": null, "parameters": "5442196272", "architecture": "Vision-language-action model. Safetensors total on allenai/MolmoAct2 is 5,442,196,272. License field was empty in the API. Paper arXiv 2605.02881.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolmoAct 2 is Ai2's May 2026 open robotics model. The main checkpoint's license field was empty on the Hub API. Collected 2026-10-15.", "what_it_is_for": "Open robot foundation model for real-world tasks without per-task finetuning. Ai2 says it is up to 37x faster than MolmoAct and integrates with LeRobot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmomotion/molmomotion-4b": { "id": "allenai/molmomotion/molmomotion-4b", "name": "MolmoMotion 4B", "maker": "allenai", "family": "allenai/molmomotion", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-06-15", "context_length": null, "max_output_tokens": null, "parameters": "4850869200", "architecture": "Language-guided 3D motion forecasting. Safetensors total for H3-F30 is 4,850,869,200. Paper arXiv 2606.18558.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolmoMotion 4B is Ai2's open 3D motion-forecasting model, Apache-2.0. Two horizon checkpoints were released in June 2026. Collected 2026-10-15.", "what_it_is_for": "Predicts where objects will move. The July 2026 newsletter introduces MolmoMotion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmoweb/molmoweb-4b": { "id": "allenai/molmoweb/molmoweb-4b", "name": "MolmoWeb 4B", "maker": "allenai", "family": "allenai/molmoweb", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-20", "context_length": null, "max_output_tokens": null, "parameters": "4850869200", "architecture": "Web agent. Safetensors total 4,850,869,200. Card tags also list base_model Qwen3-8B, which conflicts with the 4B parameter count.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolmoWeb 4B is an Ai2 open web-agent checkpoint, Apache-2.0, repo created 2026-03-20. Collected 2026-10-15.", "what_it_is_for": "Open web agent. The April 2026 newsletter introduces MolmoWeb. Card papers arXiv 2601.10611 and 2604.08516.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/molmoweb/molmoweb-8b": { "id": "allenai/molmoweb/molmoweb-8b", "name": "MolmoWeb 8B", "maker": "allenai", "family": "allenai/molmoweb", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-20", "context_length": null, "max_output_tokens": null, "parameters": "8661703120", "architecture": "Web agent on a Qwen3-8B tag. Safetensors total 8,661,703,120, matching Molmo 2 8B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolmoWeb 8B is Ai2's larger open web-agent checkpoint, Apache-2.0, repo created 2026-03-20. Collected 2026-10-15.", "what_it_is_for": "Larger MolmoWeb open web agent.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo/olmo-1b": { "id": "allenai/olmo/olmo-1b", "name": "OLMo 1B", "maker": "allenai", "family": "allenai/olmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-26", "context_length": 2048, "max_output_tokens": null, "parameters": "1176764416", "architecture": "Decoder-only transformer, 16 layers, d_model 2048, 16 heads, SwiGLU, RoPE, weight tying. Config max_sequence_length 2048. Safetensors total 1,176,764,416.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 1B is the Allen Institute for AI's original 1B Open Language Model, a fully open base checkpoint with Apache-2.0 weights. Status active as a historical release; collected 2026-10-15.", "what_it_is_for": "Small fully open base language model for research on training dynamics. Pretrained on Dolma.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo/olmo-1b-0724": { "id": "allenai/olmo/olmo-1b-0724", "name": "OLMo 1B 0724", "maker": "allenai", "family": "allenai/olmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-15", "context_length": null, "max_output_tokens": null, "parameters": "1279787008", "architecture": "OLMo 1B refreshed checkpoint. Safetensors total 1,279,787,008. Context length was not opened in config during this pass.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 1B 0724 is Ai2's mid-2024 refresh of the 1B OLMo base model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "July 2024 refresh of the 1B OLMo base model on the Dolma line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo/olmo-7b": { "id": "allenai/olmo/olmo-7b", "name": "OLMo 7B", "maker": "allenai", "family": "allenai/olmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-09", "context_length": 2048, "max_output_tokens": null, "parameters": "6888095744", "architecture": "Decoder-only transformer, 32 layers, d_model 4096, 32 heads, SwiGLU, RoPE, no weight tying. Config max_sequence_length 2048. Safetensors total 6,888,095,744.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 7B is Ai2's original 7B Open Language Model, Apache-2.0, trained on Dolma. Collected 2026-10-15.", "what_it_is_for": "Original 7B fully open base model for studying language-model training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo/olmo-7b-0424": { "id": "allenai/olmo/olmo-7b-0424", "name": "OLMo 7B 0424", "maker": "allenai", "family": "allenai/olmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-15", "context_length": null, "max_output_tokens": null, "parameters": "6888095744", "architecture": "April 2024 OLMo 7B base refresh. Same safetensors total as OLMo 7B (6,888,095,744). Context not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 7B 0424 is the April 2024 OLMo 7B base refresh from Ai2, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "April 2024 improved OLMo 7B base checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo/olmo-7b-0724": { "id": "allenai/olmo/olmo-7b-0724", "name": "OLMo 7B 0724", "maker": "allenai", "family": "allenai/olmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-12", "context_length": 4096, "max_output_tokens": null, "parameters": "6888095744", "architecture": "OLMo decoder-only, 32 layers, hidden 4096, 32 heads. Config max_position_embeddings 4096. Safetensors total 6,888,095,744.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 7B 0724 is Ai2's July 2024 7B OLMo base model, Apache-2.0, with a 4,096-token config window. Collected 2026-10-15.", "what_it_is_for": "July 2024 OLMo 7B base, the Dolma 1.7-era checkpoint used as a fully open 7B baseline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo/olmo-7b-0724-instruct": { "id": "allenai/olmo/olmo-7b-0724-instruct", "name": "OLMo 7B 0724 Instruct", "maker": "allenai", "family": "allenai/olmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-09", "context_length": null, "max_output_tokens": null, "parameters": "6888095744", "architecture": "Instruction tune of OLMo 7B 0724. Safetensors total 6,888,095,744.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 7B 0724 Instruct is the instruction-tuned July 2024 OLMo 7B from Ai2, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Chat model on the July 2024 OLMo 7B base, using Tulu v2-style post-training data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo/olmo-7b-instruct": { "id": "allenai/olmo/olmo-7b-instruct", "name": "OLMo 7B Instruct", "maker": "allenai", "family": "allenai/olmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-23", "context_length": null, "max_output_tokens": null, "parameters": "6888095744", "architecture": "Instruction-tuned OLMo 7B. Safetensors total 6,888,095,744. Context not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 7B Instruct is Ai2's instruction-tuned OLMo 7B, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Instruction-following OLMo 7B tuned with the Tulu 2 SFT mix and a cleaned UltraFeedback preference stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-2/olmo-2-13b": { "id": "allenai/olmo-2/olmo-2-13b", "name": "OLMo 2 13B", "maker": "allenai", "family": "allenai/olmo-2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-19", "context_length": null, "max_output_tokens": null, "parameters": "13716198400", "architecture": "OLMo 2 13B decoder-only model. Safetensors total 13,716,198,400. Layer counts and context were not read from this size's config; the 1B, 7B, and 32B OLMo 2 configs that were opened use 4,096 tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 2 13B is Ai2's fully open 13B base model, Apache-2.0. Safetensors total 13,716,198,400. Collected 2026-10-15.", "what_it_is_for": "Fully open 13B base model trained on up to about 5T tokens. The OLMo 2 page groups it with the 7B release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-2/olmo-2-13b-instruct": { "id": "allenai/olmo-2/olmo-2-13b-instruct", "name": "OLMo 2 13B Instruct", "maker": "allenai", "family": "allenai/olmo-2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-18", "context_length": null, "max_output_tokens": null, "parameters": "13716198400", "architecture": "OLMo 2 13B decoder-only model. Safetensors total 13,716,198,400. Layer counts and context were not read from this size's config; the 1B, 7B, and 32B OLMo 2 configs that were opened use 4,096 tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 2 13B Instruct is Ai2's post-trained 13B OLMo 2 chat model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Instruction-tuned OLMo 2 13B. The OLMo 2 blog said the 13B instruct model beat several open-weight peers on the Tulu 3 eval suite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-2/olmo-2-1b": { "id": "allenai/olmo-2/olmo-2-1b", "name": "OLMo 2 1B", "maker": "allenai", "family": "allenai/olmo-2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-17", "context_length": 4096, "max_output_tokens": null, "parameters": "1484916736", "architecture": "OLMo 2 decoder-only transformer, 16 layers, hidden 2048, 16 query heads, 16 KV heads. Config max_position_embeddings 4096 where read. Safetensors total 1484916736.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 2 1B is Ai2's April 2025 fully open 1B base model, Apache-2.0, with a 4,096-token config window. Collected 2026-10-15.", "what_it_is_for": "Smallest OLMo 2 base model, for local research and recipe scaling checks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-2/olmo-2-1b-instruct": { "id": "allenai/olmo-2/olmo-2-1b-instruct", "name": "OLMo 2 1B Instruct", "maker": "allenai", "family": "allenai/olmo-2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-29", "context_length": 4096, "max_output_tokens": null, "parameters": "1484916736", "architecture": "OLMo 2 decoder-only transformer, 16 layers, hidden 2048, 16 query heads, 16 KV heads. Config max_position_embeddings 4096 where read. Safetensors total 1484916736.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 2 1B Instruct is the post-trained April 2025 1B OLMo 2 chat model from Ai2, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Instruction-tuned OLMo 2 1B using the Tulu 3-style SFT, DPO, and RLVR stages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-2/olmo-2-32b": { "id": "allenai/olmo-2/olmo-2-32b", "name": "OLMo 2 32B", "maker": "allenai", "family": "allenai/olmo-2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-13", "context_length": 4096, "max_output_tokens": null, "parameters": "32234279936", "architecture": "OLMo 2 decoder-only transformer, 64 layers, hidden 5120, 40 query heads, 8 KV heads. Config max_position_embeddings 4096 where read. Safetensors total 32234279936.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 2 32B is Ai2's fully open 32B base model, Apache-2.0, trained up to 6T tokens with a 4,096-token config window. Collected 2026-10-15.", "what_it_is_for": "Largest OLMo 2 base model, trained up to 6T tokens. Public blog date is 2025-03-13; the base repo was created 2025-02-23.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-2/olmo-2-32b-instruct": { "id": "allenai/olmo-2/olmo-2-32b-instruct", "name": "OLMo 2 32B Instruct", "maker": "allenai", "family": "allenai/olmo-2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-13", "context_length": 4096, "max_output_tokens": null, "parameters": "32234279936", "architecture": "OLMo 2 decoder-only transformer, 64 layers, hidden 5120, 40 query heads, 8 KV heads. Config max_position_embeddings 4096 where read. Safetensors total 32234279936.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 2 32B Instruct is Ai2's March 2025 fully open 32B chat model, Apache-2.0. The maker still lists it in the playground. Collected 2026-10-15.", "what_it_is_for": "Flagship OLMo 2 chat model. Ai2 says it is the first fully open model to outperform GPT-3.5 Turbo and GPT-4o mini on a multi-skill academic suite, post-trained with Tulu 3.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-2/olmo-2-7b": { "id": "allenai/olmo-2/olmo-2-7b", "name": "OLMo 2 7B", "maker": "allenai", "family": "allenai/olmo-2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-10-29", "context_length": 4096, "max_output_tokens": null, "parameters": "7298617344", "architecture": "OLMo 2 decoder-only transformer, 32 layers, hidden 4096, 32 query heads, 32 KV heads. Config max_position_embeddings 4096 where read. Safetensors total 7298617344.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 2 7B is Ai2's fully open 7B base model from the late-2024 OLMo 2 release, Apache-2.0, 4,096-token config window. Collected 2026-10-15.", "what_it_is_for": "Fully open 7B base model trained on up to about 5T tokens. The family announcement is described as November 2024 on the later 32B blog; this repo was created 2024-10-29.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-2/olmo-2-7b-instruct": { "id": "allenai/olmo-2/olmo-2-7b-instruct", "name": "OLMo 2 7B Instruct", "maker": "allenai", "family": "allenai/olmo-2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-18", "context_length": 4096, "max_output_tokens": null, "parameters": "7298617344", "architecture": "OLMo 2 decoder-only transformer, 32 layers, hidden 4096, 32 query heads, 32 KV heads. Config max_position_embeddings 4096 where read. Safetensors total 7298617344.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 2 7B Instruct is Ai2's post-trained 7B OLMo 2 chat model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Instruction-tuned OLMo 2 7B. Ai2 applied the Tulu 3 post-training recipe.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-3/olmo-3-1-32b-instruct": { "id": "allenai/olmo-3/olmo-3-1-32b-instruct", "name": "Olmo 3.1 32B Instruct", "maker": "allenai", "family": "allenai/olmo-3", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-12-10", "context_length": 65536, "max_output_tokens": null, "parameters": "32233522176", "architecture": "Dense OLMo 3 decoder-only transformer, 64 layers, hidden 5120, 40 query / 8 KV heads, sliding window 4096. Official table: 5.50T training tokens, context 65,536. Safetensors total 32,233,522,176.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo 3.1 32B Instruct is Ai2's December 2025 fully open 32B chat model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Most capable fully open Olmo chat model on the product page, for chat, tool use, and multi-turn dialogue. First 32B instruct release in the Olmo 3 line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-3/olmo-3-1-32b-think": { "id": "allenai/olmo-3/olmo-3-1-32b-think", "name": "Olmo 3.1 32B Think", "maker": "allenai", "family": "allenai/olmo-3", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-12-12", "context_length": 65536, "max_output_tokens": null, "parameters": "32233522176", "architecture": "Dense OLMo 3 decoder-only transformer, 64 layers, hidden 5120, 40 query / 8 KV heads, sliding window 4096. Official table: 5.50T training tokens, context 65,536. Safetensors total 32,233,522,176.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo 3.1 32B Think is Ai2's December 2025 flagship fully open 32B reasoning model, Apache-2.0, and the Think model linked from the Olmo product page. Collected 2026-10-15.", "what_it_is_for": "Current flagship reasoning model. Ai2 describes another three weeks of RL on the 32B Think line. The playground features it for step-by-step problems.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-3/olmo-3-32b": { "id": "allenai/olmo-3/olmo-3-32b", "name": "Olmo 3 32B", "maker": "allenai", "family": "allenai/olmo-3", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-04", "context_length": 65536, "max_output_tokens": null, "parameters": "32233522176", "architecture": "Dense OLMo 3 decoder-only transformer, 64 layers, hidden 5120, 40 query / 8 KV heads, sliding window 4096. Official table: 5.50T training tokens, context 65,536. Safetensors total 32,233,522,176.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo 3 32B is Ai2's fully open 32B base model, Apache-2.0, 65,536-token context, repo created 2025-11-04. Collected 2026-10-15.", "what_it_is_for": "Flagship 32B base. Ai2 says it is strong at programming, reading comprehension, and math, and that it works with RL setups. Final base checkpoint merges several long-context runs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-3/olmo-3-32b-think": { "id": "allenai/olmo-3/olmo-3-32b-think", "name": "Olmo 3 32B Think", "maker": "allenai", "family": "allenai/olmo-3", "status": "deprecated", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-20", "context_length": 65536, "max_output_tokens": null, "parameters": "32233522176", "architecture": "Dense OLMo 3 decoder-only transformer, 64 layers, hidden 5120, 40 query / 8 KV heads, sliding window 4096. Official table: 5.50T training tokens, context 65,536. Safetensors total 32,233,522,176.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo 3 32B Think is Ai2's November 2025 fully open 32B reasoning model, Apache-2.0. It has been superseded by Olmo 3.1 32B Think. Collected 2026-10-15.", "what_it_is_for": "November 2025 32B reasoning model. Artificial Analysis marks this checkpoint deprecated in favor of Olmo 3.1 32B Think. The product page now links the 3.1 weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-3/olmo-3-7b": { "id": "allenai/olmo-3/olmo-3-7b", "name": "Olmo 3 7B", "maker": "allenai", "family": "allenai/olmo-3", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-20", "context_length": 65536, "max_output_tokens": null, "parameters": "7298011136", "architecture": "Dense OLMo 3 decoder-only transformer, 32 layers, hidden 4096, 32/32 heads, sliding window 4096, YaRN long-context extension. Official table: 5.93T training tokens, context 65,536. Safetensors total 7,298,011,136.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo 3 7B is Ai2's November 2025 fully open 7B base model, Apache-2.0, with a 65,536-token context. Collected 2026-10-15.", "what_it_is_for": "Fully open 7B base model in the Olmo 3 model flow, trained in three stages through a 65,536-token context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-3/olmo-3-7b-instruct": { "id": "allenai/olmo-3/olmo-3-7b-instruct", "name": "Olmo 3 7B Instruct", "maker": "allenai", "family": "allenai/olmo-3", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-19", "context_length": 65536, "max_output_tokens": null, "parameters": "7298011136", "architecture": "Dense OLMo 3 decoder-only transformer, 32 layers, hidden 4096, 32/32 heads, sliding window 4096, YaRN long-context extension. Official table: 5.93T training tokens, context 65,536. Safetensors total 7,298,011,136.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo 3 7B Instruct is Ai2's fully open 7B chat model for multi-turn chat and tool use, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Efficient instruction model for multi-turn chat and tool use. Final stage is RLVR on the 7B base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-3/olmo-3-7b-rl-zero-code": { "id": "allenai/olmo-3/olmo-3-7b-rl-zero-code", "name": "Olmo 3 7B RL-Zero Code", "maker": "allenai", "family": "allenai/olmo-3", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-17", "context_length": 65536, "max_output_tokens": null, "parameters": "7298011136", "architecture": "Dense OLMo 3 decoder-only transformer, 32 layers, hidden 4096, 32/32 heads, sliding window 4096, YaRN long-context extension. Official table: 5.93T training tokens, context 65,536. Safetensors total 7,298,011,136.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo 3 7B RL-Zero Code is an Ai2 Olmo 3 research checkpoint, Apache-2.0, trained with RL from the 7B base on code. Collected 2026-10-15.", "what_it_is_for": "RL-Zero research model: reinforcement learning from the 7B base on verifiable code rewards, without the usual instruct SFT stack. Released so others can compare RL methods.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-3/olmo-3-7b-rl-zero-general": { "id": "allenai/olmo-3/olmo-3-7b-rl-zero-general", "name": "Olmo 3 7B RL-Zero General", "maker": "allenai", "family": "allenai/olmo-3", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-17", "context_length": 65536, "max_output_tokens": null, "parameters": "7298011136", "architecture": "Dense OLMo 3 decoder-only transformer, 32 layers, hidden 4096, 32/32 heads, sliding window 4096, YaRN long-context extension. Official table: 5.93T training tokens, context 65,536. Safetensors total 7,298,011,136.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo 3 7B RL-Zero General is an Ai2 Olmo 3 research checkpoint, Apache-2.0, trained with RL from the 7B base on general prompts. Collected 2026-10-15.", "what_it_is_for": "RL-Zero research model: reinforcement learning from the 7B base on verifiable general prompts rewards, without the usual instruct SFT stack. Released so others can compare RL methods.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-3/olmo-3-7b-rl-zero-if": { "id": "allenai/olmo-3/olmo-3-7b-rl-zero-if", "name": "Olmo 3 7B RL-Zero IF", "maker": "allenai", "family": "allenai/olmo-3", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-17", "context_length": 65536, "max_output_tokens": null, "parameters": "7298011136", "architecture": "Dense OLMo 3 decoder-only transformer, 32 layers, hidden 4096, 32/32 heads, sliding window 4096, YaRN long-context extension. Official table: 5.93T training tokens, context 65,536. Safetensors total 7,298,011,136.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo 3 7B RL-Zero IF is an Ai2 Olmo 3 research checkpoint, Apache-2.0, trained with RL from the 7B base on instruction following. Collected 2026-10-15.", "what_it_is_for": "RL-Zero research model: reinforcement learning from the 7B base on verifiable instruction following rewards, without the usual instruct SFT stack. Released so others can compare RL methods.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-3/olmo-3-7b-rl-zero-math": { "id": "allenai/olmo-3/olmo-3-7b-rl-zero-math", "name": "Olmo 3 7B RL-Zero Math", "maker": "allenai", "family": "allenai/olmo-3", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-17", "context_length": 65536, "max_output_tokens": null, "parameters": "7298011136", "architecture": "Dense OLMo 3 decoder-only transformer, 32 layers, hidden 4096, 32/32 heads, sliding window 4096, YaRN long-context extension. Official table: 5.93T training tokens, context 65,536. Safetensors total 7,298,011,136.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo 3 7B RL-Zero Math is an Ai2 Olmo 3 research checkpoint, Apache-2.0, trained with RL from the 7B base on math. Collected 2026-10-15.", "what_it_is_for": "RL-Zero research model: reinforcement learning from the 7B base on verifiable math rewards, without the usual instruct SFT stack. Released so others can compare RL methods.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-3/olmo-3-7b-rl-zero-mix": { "id": "allenai/olmo-3/olmo-3-7b-rl-zero-mix", "name": "Olmo 3 7B RL-Zero Mix", "maker": "allenai", "family": "allenai/olmo-3", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-17", "context_length": 65536, "max_output_tokens": null, "parameters": "7298011136", "architecture": "Dense OLMo 3 decoder-only transformer, 32 layers, hidden 4096, 32/32 heads, sliding window 4096, YaRN long-context extension. Official table: 5.93T training tokens, context 65,536. Safetensors total 7,298,011,136.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo 3 7B RL-Zero Mix is an Ai2 Olmo 3 research checkpoint, Apache-2.0, trained with RL from the 7B base on a mixed Dolci RL-Zero set. Collected 2026-10-15.", "what_it_is_for": "RL-Zero research model: reinforcement learning from the 7B base on verifiable a mixed Dolci RL-Zero set rewards, without the usual instruct SFT stack. Released so others can compare RL methods.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-3/olmo-3-7b-think": { "id": "allenai/olmo-3/olmo-3-7b-think", "name": "Olmo 3 7B Think", "maker": "allenai", "family": "allenai/olmo-3", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-18", "context_length": 65536, "max_output_tokens": null, "parameters": "7298011136", "architecture": "Dense OLMo 3 decoder-only transformer, 32 layers, hidden 4096, 32/32 heads, sliding window 4096, YaRN long-context extension. Official table: 5.93T training tokens, context 65,536. Safetensors total 7,298,011,136.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo 3 7B Think is Ai2's fully open 7B reasoning model, Apache-2.0, with a 65,536-token context. Collected 2026-10-15.", "what_it_is_for": "7B reasoning model that surfaces intermediate thinking steps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-hybrid/olmo-hybrid-7b": { "id": "allenai/olmo-hybrid/olmo-hybrid-7b", "name": "Olmo Hybrid 7B", "maker": "allenai", "family": "allenai/olmo-hybrid", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-28", "context_length": 65536, "max_output_tokens": null, "parameters": "7430870688", "architecture": "Hybrid transformer plus linear RNN. Config: 32 layers, hidden 3840, 30 heads, max_position_embeddings 65536, model_type olmo_hybrid. Safetensors total 7,430,870,688.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo Hybrid 7B is Ai2's fully open hybrid 7B base model, Apache-2.0. Weights appeared on Hugging Face on 2026-01-28; the March 2026 newsletter is the public introduction. Collected 2026-10-15.", "what_it_is_for": "Fully open 7B hybrid language model. Ai2 says it combines attention with linear RNN layers for better data and compute efficiency than a pure transformer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-hybrid/olmo-hybrid-7b-instruct": { "id": "allenai/olmo-hybrid/olmo-hybrid-7b-instruct", "name": "Olmo Hybrid 7B Instruct", "maker": "allenai", "family": "allenai/olmo-hybrid", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-02-20", "context_length": 65536, "max_output_tokens": null, "parameters": "7430870688", "architecture": "Post-trained Olmo Hybrid 7B. Same safetensors total as the base, 7,430,870,688.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo Hybrid 7B Instruct is Ai2's post-trained hybrid 7B chat checkpoint, Apache-2.0, with DPO weights dated 2026-02-20 on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "Instruction-tuned Olmo Hybrid 7B. The latest public stage found is the DPO checkpoint; no final RL name was found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmo-hybrid/olmo-hybrid-7b-think": { "id": "allenai/olmo-hybrid/olmo-hybrid-7b-think", "name": "Olmo Hybrid 7B Think", "maker": "allenai", "family": "allenai/olmo-hybrid", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-02-28", "context_length": 65536, "max_output_tokens": null, "parameters": "7430870688", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olmo Hybrid 7B Think is Ai2's thinking SFT checkpoint of Olmo Hybrid 7B, Apache-2.0, repo created 2026-02-28. Collected 2026-10-15.", "what_it_is_for": "Reasoning SFT checkpoint of Olmo Hybrid 7B. Only the Think SFT stage was found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmocr/olmocr-2-7b": { "id": "allenai/olmocr/olmocr-2-7b", "name": "olmOCR 2 7B", "maker": "allenai", "family": "allenai/olmocr", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-06", "context_length": 128000, "max_output_tokens": null, "parameters": "8292166656", "architecture": "Qwen2.5-VL document OCR model. Config max_position_embeddings 128000, 28 layers, hidden 3584, sliding window 32768. Safetensors total 8,292,166,656.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "olmOCR 2 7B is Ai2's October 2025 document OCR model, Apache-2.0, with a 128,000-token config window. Collected 2026-10-15.", "what_it_is_for": "Current English document OCR model. The October 2025 blog describes unit-test rewards for OCR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmocr/olmocr-7b": { "id": "allenai/olmocr/olmocr-7b", "name": "olmOCR 7B", "maker": "allenai", "family": "allenai/olmocr", "status": "deprecated", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-15", "context_length": null, "max_output_tokens": null, "parameters": "8291375616", "architecture": "Document OCR VLM. First preview is a Qwen2-VL-7B finetune (8,291,375,616 parameters). July and August 2025 refreshes are Qwen2.5-VL-7B finetunes (8,292,166,656).", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "olmOCR 7B is Ai2's first-generation document OCR model, Apache-2.0, starting as a January 2025 preview. Collected 2026-10-15.", "what_it_is_for": "English document OCR. Later replaced as the current release by olmOCR 2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmoe/olmoe-1b-7b-0125": { "id": "allenai/olmoe/olmoe-1b-7b-0125", "name": "OLMoE 1B-7B 0125", "maker": "allenai", "family": "allenai/olmoe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-21", "context_length": 4096, "max_output_tokens": null, "parameters": "6919161856", "architecture": "Sparse MoE transformer, 16 layers, hidden 2048, 16 heads, 64 experts, 8 experts per token. Config max_position_embeddings 4096. Safetensors total 6,919,161,856. Ai2 docs say 1.3B active of 6.9B total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMoE 1B-7B 0125 is an Ai2 OLMoE checkpoint, Apache-2.0, 64 experts with 8 active. Collected 2026-10-15.", "what_it_is_for": "Fully open mixture-of-experts model. Active parameters stay near 1B while total parameters are about 6.9B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmoe/olmoe-1b-7b-0125-instruct": { "id": "allenai/olmoe/olmoe-1b-7b-0125-instruct", "name": "OLMoE 1B-7B 0125 Instruct", "maker": "allenai", "family": "allenai/olmoe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-27", "context_length": 4096, "max_output_tokens": null, "parameters": "6919161856", "architecture": "Sparse MoE transformer, 16 layers, hidden 2048, 16 heads, 64 experts, 8 experts per token. Config max_position_embeddings 4096. Safetensors total 6,919,161,856. Ai2 docs say 1.3B active of 6.9B total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMoE 1B-7B 0125 Instruct is an Ai2 OLMoE checkpoint, Apache-2.0, 64 experts with 8 active. Collected 2026-10-15.", "what_it_is_for": "Fully open mixture-of-experts model. Active parameters stay near 1B while total parameters are about 6.9B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmoe/olmoe-1b-7b-0924": { "id": "allenai/olmoe/olmoe-1b-7b-0924", "name": "OLMoE 1B-7B 0924", "maker": "allenai", "family": "allenai/olmoe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-04", "context_length": 4096, "max_output_tokens": null, "parameters": "6919161856", "architecture": "Sparse MoE transformer, 16 layers, hidden 2048, 16 heads, 64 experts, 8 experts per token. Config max_position_embeddings 4096. Safetensors total 6,919,161,856. Ai2 docs say 1.3B active of 6.9B total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMoE 1B-7B 0924 is an Ai2 OLMoE checkpoint, Apache-2.0, 64 experts with 8 active. Collected 2026-10-15.", "what_it_is_for": "Fully open mixture-of-experts model. Active parameters stay near 1B while total parameters are about 6.9B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmoe/olmoe-1b-7b-0924-instruct": { "id": "allenai/olmoe/olmoe-1b-7b-0924-instruct", "name": "OLMoE 1B-7B 0924 Instruct", "maker": "allenai", "family": "allenai/olmoe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-13", "context_length": 4096, "max_output_tokens": null, "parameters": "6919161856", "architecture": "Sparse MoE transformer, 16 layers, hidden 2048, 16 heads, 64 experts, 8 experts per token. Config max_position_embeddings 4096. Safetensors total 6,919,161,856. Ai2 docs say 1.3B active of 6.9B total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMoE 1B-7B 0924 Instruct is an Ai2 OLMoE checkpoint, Apache-2.0, 64 experts with 8 active. Collected 2026-10-15.", "what_it_is_for": "Fully open mixture-of-experts model. Active parameters stay near 1B while total parameters are about 6.9B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmoearth/olmoearth-v1-1-nano": { "id": "allenai/olmoearth/olmoearth-v1-1-nano", "name": "OlmoEarth v1.1 Nano", "maker": "allenai", "family": "allenai/olmoearth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "v1.1 efficiency model. Parameter count not stated for this checkpoint.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OlmoEarth v1.1 Nano is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmoearth/olmoearth-v1-1-tiny": { "id": "allenai/olmoearth/olmoearth-v1-1-tiny", "name": "OlmoEarth v1.1 Tiny", "maker": "allenai", "family": "allenai/olmoearth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "v1.1 efficiency model. Parameter count not stated.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OlmoEarth v1.1 Tiny is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmoearth/olmoearth-v1-2-base": { "id": "allenai/olmoearth/olmoearth-v1-2-base", "name": "OlmoEarth v1.2 Base", "maker": "allenai", "family": "allenai/olmoearth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other", "release_date": "2026-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "v1.2 Base. The update says existing Base users can stay on Base. Hub license tag: other.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OlmoEarth v1.2 Base is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmoearth/olmoearth-v1-2-nano": { "id": "allenai/olmoearth/olmoearth-v1-2-nano", "name": "OlmoEarth v1.2 Nano", "maker": "allenai", "family": "allenai/olmoearth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other", "release_date": "2026-06-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "v1.2 with RoPE. Hub license tag: other.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OlmoEarth v1.2 Nano is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmoearth/olmoearth-v1-2-small": { "id": "allenai/olmoearth/olmoearth-v1-2-small", "name": "OlmoEarth v1.2 Small", "maker": "allenai", "family": "allenai/olmoearth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other", "release_date": "2026-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "v1.2 size that the May 2026 update recommends over Tiny and Nano. Hub license tag: other.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OlmoEarth v1.2 Small is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmoearth/olmoearth-v1-2-tiny": { "id": "allenai/olmoearth/olmoearth-v1-2-tiny", "name": "OlmoEarth v1.2 Tiny", "maker": "allenai", "family": "allenai/olmoearth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other", "release_date": "2026-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "v1.2 with RoPE. Hub license tag: other.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OlmoEarth v1.2 Tiny is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmoearth/olmoearth-v1-base": { "id": "allenai/olmoearth/olmoearth-v1-base", "name": "OlmoEarth v1 Base", "maker": "allenai", "family": "allenai/olmoearth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "v1 blog: Base is about 90M parameters.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OlmoEarth v1 Base is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmoearth/olmoearth-v1-large": { "id": "allenai/olmoearth/olmoearth-v1-large", "name": "OlmoEarth v1 Large", "maker": "allenai", "family": "allenai/olmoearth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "v1 blog: Large is about 300M parameters. No v1.2 Large was found.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OlmoEarth v1 Large is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmoearth/olmoearth-v1-nano": { "id": "allenai/olmoearth/olmoearth-v1-nano", "name": "OlmoEarth v1 Nano", "maker": "allenai", "family": "allenai/olmoearth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "v1 blog: Nano is about 1.4M parameters. Hub did not return a safetensors total.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OlmoEarth v1 Nano is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/olmoearth/olmoearth-v1-tiny": { "id": "allenai/olmoearth/olmoearth-v1-tiny", "name": "OlmoEarth v1 Tiny", "maker": "allenai", "family": "allenai/olmoearth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "v1 blog: Tiny is about 6.2M parameters.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OlmoEarth v1 Tiny is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/primera/primera": { "id": "allenai/primera/primera", "name": "PRIMERA", "maker": "allenai", "family": "allenai/primera", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-document summarization model based on LED. Hub id allenai/PRIMERA.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PRIMERA is Ai2's multi-document summarizer. Collected 2026-10-15.", "what_it_is_for": "Summarize clusters of documents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/primera/primera-multinews": { "id": "allenai/primera/primera-multinews", "name": "PRIMERA Multi-News", "maker": "allenai", "family": "allenai/primera", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PRIMERA finetuned on Multi-News.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PRIMERA Multi-News is Ai2's Multi-News finetune of PRIMERA. Collected 2026-10-15.", "what_it_is_for": "Multi-news summarization checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/roberta/biomed-roberta-base": { "id": "allenai/roberta/biomed-roberta-base", "name": "Biomed RoBERTa base", "maker": "allenai", "family": "allenai/roberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa base continued on biomedical text. Hub id allenai/biomed_roberta_base. License not on the API sample.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Biomed RoBERTa base is Ai2's biomedical RoBERTa checkpoint. Collected 2026-10-15.", "what_it_is_for": "Biomedical domain language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/roberta/cs-roberta-base": { "id": "allenai/roberta/cs-roberta-base", "name": "CS RoBERTa base", "maker": "allenai", "family": "allenai/roberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa base continued on computer-science text. Hub id allenai/cs_roberta_base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CS RoBERTa base is Ai2's computer-science RoBERTa checkpoint. Collected 2026-10-15.", "what_it_is_for": "Computer-science domain language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/scibert/scibert-cased": { "id": "allenai/scibert/scibert-cased", "name": "SciBERT cased", "maker": "allenai", "family": "allenai/scibert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cased SciBERT with a scientific vocabulary. Hub id allenai/scibert_scivocab_cased.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SciBERT cased is the cased Ai2 SciBERT checkpoint. Collected 2026-10-15.", "what_it_is_for": "Cased twin of SciBERT for scientific text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/scibert/scibert-uncased": { "id": "allenai/scibert/scibert-uncased", "name": "SciBERT uncased", "maker": "allenai", "family": "allenai/scibert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT with a scientific vocabulary, uncased. Hub id allenai/scibert_scivocab_uncased. License was not on the API payload.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SciBERT uncased is Ai2's scientific BERT with a domain vocabulary. Collected 2026-10-15.", "what_it_is_for": "Scientific-domain masked language model used for classification and tagging.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/sera/sera-14b": { "id": "allenai/sera/sera-14b", "name": "SERA 14B", "maker": "allenai", "family": "allenai/sera", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-02-03", "context_length": null, "max_output_tokens": null, "parameters": "14768307200", "architecture": "14B coding agent. Safetensors total 14,768,307,200. Apache-2.0. Context was not read; the 32B card's 32K figure was not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SERA 14B is an Ai2 Open Coding Agents model, Apache-2.0 unless noted. Collected 2026-10-15.", "what_it_is_for": "Mid-size SERA coding agent announced on 2026-02-03.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/sera/sera-32b": { "id": "allenai/sera/sera-32b", "name": "SERA 32B", "maker": "allenai", "family": "allenai/sera", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-27", "context_length": 32768, "max_output_tokens": null, "parameters": "32762123264", "architecture": "Coding agent SFT. SERA 32B Teacher: GLM-4.6. SERA-32B card states context length 32K for the evaluated family.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SERA 32B is an Ai2 Open Coding Agents model, Apache-2.0 unless noted. Collected 2026-10-15.", "what_it_is_for": "Repository coding agent trained with soft-verified trajectories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/sera/sera-32b-ga": { "id": "allenai/sera/sera-32b-ga", "name": "SERA 32B GA", "maker": "allenai", "family": "allenai/sera", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-27", "context_length": 32768, "max_output_tokens": null, "parameters": "32762123264", "architecture": "Coding agent SFT. SERA 32B GA Teacher: GLM-4.5-Air. SERA-32B card states context length 32K for the evaluated family.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SERA 32B GA is an Ai2 Open Coding Agents model, Apache-2.0 unless noted. Collected 2026-10-15.", "what_it_is_for": "Repository coding agent trained with soft-verified trajectories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/sera/sera-8b": { "id": "allenai/sera/sera-8b", "name": "SERA 8B", "maker": "allenai", "family": "allenai/sera", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-27", "context_length": 32768, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Coding agent SFT. SERA 8B Teacher: GLM-4.6. SERA-32B card states context length 32K for the evaluated family.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SERA 8B is an Ai2 Open Coding Agents model, Apache-2.0 unless noted. Collected 2026-10-15.", "what_it_is_for": "Repository coding agent trained with soft-verified trajectories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/sera/sera-8b-ga": { "id": "allenai/sera/sera-8b-ga", "name": "SERA 8B GA", "maker": "allenai", "family": "allenai/sera", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-01-27", "context_length": 32768, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Coding agent SFT. SERA 8B GA Teacher: GLM-4.5-Air. SERA-32B card states context length 32K for the evaluated family.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SERA 8B GA is an Ai2 Open Coding Agents model, Apache-2.0 unless noted. Collected 2026-10-15.", "what_it_is_for": "Repository coding agent trained with soft-verified trajectories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/specter/specter": { "id": "allenai/specter/specter", "name": "SPECTER", "maker": "allenai", "family": "allenai/specter", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Scientific paper embedding model, BERT-based. Hub repo created 2022-03-02, which is a transformers import date, not the 2020 release.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SPECTER is Ai2's scientific paper embedding model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Embeddings of scientific papers for relatedness and citation-style retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/specter/specter2": { "id": "allenai/specter/specter2", "name": "SPECTER2", "maker": "allenai", "family": "allenai/specter", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-02-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adapter-based scientific embedding model. Base repo allenai/specter2_base. An August 2023 refresh base also exists.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SPECTER2 is Ai2's 2023 scientific embedding model with adapters, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Successor scientific embeddings with task adapters, including ad-hoc query.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tk-instruct/tk-instruct-large-def-pos": { "id": "allenai/tk-instruct/tk-instruct-large-def-pos", "name": "Tk-Instruct large def-pos", "maker": "allenai", "family": "allenai/tk-instruct", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5-large instruction model, definition-positive prompts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tk-Instruct large def-pos is an Ai2 instruction-tuned T5-large, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Larger Tk-Instruct checkpoint for instruction following.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tk-instruct/tk-instruct-small-def-pos": { "id": "allenai/tk-instruct/tk-instruct-small-def-pos", "name": "Tk-Instruct small def-pos", "maker": "allenai", "family": "allenai/tk-instruct", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5-small instruction model trained on Super-NaturalInstructions with definition-positive prompts. Hub id allenai/tk-instruct-small-def-pos.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tk-Instruct small def-pos is an Ai2 instruction-tuned T5-small, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Follow natural-language task instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tmax/tmax-27b": { "id": "allenai/tmax/tmax-27b", "name": "TMax 27B", "maker": "allenai", "family": "allenai/tmax", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-06-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Terminal agent trained with the TMax RL recipe on Qwen 3.6 27B. Training card lists max prompt tokens 2048 and max per-turn tokens 16384 as hyperparameters, not a confirmed inference window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TMax 27B is an Ai2 terminal-agent model, Apache-2.0, from the June 2026 TMax release. Collected 2026-10-15.", "what_it_is_for": "Open terminal agent from the TMax recipe and the TMax-15k environment set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tmax/tmax-2b": { "id": "allenai/tmax/tmax-2b", "name": "TMax 2B", "maker": "allenai", "family": "allenai/tmax", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-06-16", "context_length": null, "max_output_tokens": null, "parameters": "1881825088", "architecture": "Terminal agent trained with the TMax RL recipe on Qwen 3.5 2B. Training card lists max prompt tokens 2048 and max per-turn tokens 16384 as hyperparameters, not a confirmed inference window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TMax 2B is an Ai2 terminal-agent model, Apache-2.0, from the June 2026 TMax release. Collected 2026-10-15.", "what_it_is_for": "Open terminal agent from the TMax recipe and the TMax-15k environment set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tmax/tmax-4b": { "id": "allenai/tmax/tmax-4b", "name": "TMax 4B", "maker": "allenai", "family": "allenai/tmax", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-06-16", "context_length": null, "max_output_tokens": null, "parameters": "4205751296", "architecture": "Terminal agent trained with the TMax RL recipe on Qwen 3.5 4B. Training card lists max prompt tokens 2048 and max per-turn tokens 16384 as hyperparameters, not a confirmed inference window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TMax 4B is an Ai2 terminal-agent model, Apache-2.0, from the June 2026 TMax release. Collected 2026-10-15.", "what_it_is_for": "Open terminal agent from the TMax recipe and the TMax-15k environment set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tmax/tmax-8b": { "id": "allenai/tmax/tmax-8b", "name": "TMax 8B", "maker": "allenai", "family": "allenai/tmax", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-06-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Terminal agent trained with the TMax RL recipe on tmax-sft-8b. Training card lists max prompt tokens 2048 and max per-turn tokens 16384 as hyperparameters, not a confirmed inference window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TMax 8B is an Ai2 terminal-agent model, Apache-2.0, from the June 2026 TMax release. Collected 2026-10-15.", "what_it_is_for": "8B TMax checkpoint named in the official Hugging Face collection. SFT is the supervised stage; TMax 8B is the RL model on that SFT checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tmax/tmax-9b": { "id": "allenai/tmax/tmax-9b", "name": "TMax 9B", "maker": "allenai", "family": "allenai/tmax", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-06-16", "context_length": null, "max_output_tokens": null, "parameters": "8953803264", "architecture": "Terminal agent trained with the TMax RL recipe on Qwen 3.5 9B. Training card lists max prompt tokens 2048 and max per-turn tokens 16384 as hyperparameters, not a confirmed inference window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TMax 9B is an Ai2 terminal-agent model, Apache-2.0, from the June 2026 TMax release. Collected 2026-10-15.", "what_it_is_for": "Open terminal agent from the TMax recipe and the TMax-15k environment set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tmax/tmax-sft-8b": { "id": "allenai/tmax/tmax-sft-8b", "name": "TMax SFT 8B", "maker": "allenai", "family": "allenai/tmax", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-06-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Terminal agent trained with the TMax RL recipe on Qwen3 8B. Training card lists max prompt tokens 2048 and max per-turn tokens 16384 as hyperparameters, not a confirmed inference window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TMax SFT 8B is an Ai2 terminal-agent model, Apache-2.0, from the June 2026 TMax release. Collected 2026-10-15.", "what_it_is_for": "8B TMax checkpoint named in the official Hugging Face collection. SFT is the supervised stage; TMax 8B is the RL model on that SFT checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tulu/llama-3-1-tulu-2-70b": { "id": "allenai/tulu/llama-3-1-tulu-2-70b", "name": "Llama 3.1 Tulu 2 70B", "maker": "allenai", "family": "allenai/tulu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 (model card); base is Llama 3.1", "release_date": "2024-08-09", "context_length": null, "max_output_tokens": null, "parameters": "70553706496", "architecture": "Tulu v2 SFT on Llama 3.1 70B. Safetensors total 70,553,706,496. Card text says Apache 2.0 while the base is Llama 3.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.1 Tulu 2 70B is an August 2024 Ai2 instruction model on Llama 3.1 70B. Collected 2026-10-15.", "what_it_is_for": "Tulu 2 data mix applied to Llama 3.1 70B. The card also reports a DPO sibling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tulu/llama-3-tulu-2-8b": { "id": "allenai/tulu/llama-3-tulu-2-8b", "name": "Llama 3 Tulu 2 8B", "maker": "allenai", "family": "allenai/tulu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-06-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tulu 2 mixture SFT on Meta Llama 3 8B. Hub license tag apache-2.0. Parameter total missing. Llama 3 community terms may still bind the base weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3 Tulu 2 8B is a June 2024 Ai2 instruction model on Llama 3 8B. Collected 2026-10-15.", "what_it_is_for": "Tulu 2 recipe applied to Llama 3 8B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tulu/tulu-2-13b": { "id": "allenai/tulu/tulu-2-13b", "name": "Tulu 2 13B", "maker": "allenai", "family": "allenai/tulu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-11-13", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 13B SFT. Parameter total missing from the Hub API. DPO sibling total is 13,015,864,320.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tulu 2 13B is Ai2's November 2023 13B Llama 2 instruction model. Collected 2026-10-15.", "what_it_is_for": "Mid-size Tulu 2 supervised chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tulu/tulu-2-70b": { "id": "allenai/tulu/tulu-2-70b", "name": "Tulu 2 70B", "maker": "allenai", "family": "allenai/tulu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-11-13", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 70B SFT. Hub API did not return a safetensors total. DPO 70B total is 68,976,648,192.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tulu 2 70B is Ai2's November 2023 70B Llama 2 instruction model. Collected 2026-10-15.", "what_it_is_for": "Largest Tulu 2 supervised model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tulu/tulu-2-7b": { "id": "allenai/tulu/tulu-2-7b", "name": "Tulu 2 7B", "maker": "allenai", "family": "allenai/tulu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-11-13", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 7B SFT on the Tulu v2 mix. The DPO sibling card says training context was expanded to 8,192. Safetensors total was empty.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tulu 2 7B is Ai2's November 2023 Llama 2 instruction model. The Hub API did not return a license or parameter total. Collected 2026-10-15.", "what_it_is_for": "Supervised instruction model in the Tulu 2 suite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tulu/tulu-2-dpo-13b": { "id": "allenai/tulu/tulu-2-dpo-13b", "name": "Tulu 2 DPO 13B", "maker": "allenai", "family": "allenai/tulu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AI2 ImpACT Low-risk license", "release_date": "2023-11-13", "context_length": 8192, "max_output_tokens": null, "parameters": "13015864320", "architecture": "Llama 2 13B DPO. Safetensors total 13,015,864,320. Training context 8,192 from the Tulu 2 paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tulu 2 DPO 13B is Ai2's November 2023 13B preference-tuned chat model. Collected 2026-10-15.", "what_it_is_for": "Preference-tuned 13B assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tulu/tulu-2-dpo-70b": { "id": "allenai/tulu/tulu-2-dpo-70b", "name": "Tulu 2 DPO 70B", "maker": "allenai", "family": "allenai/tulu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AI2 ImpACT Low-risk license", "release_date": "2023-11-12", "context_length": 8192, "max_output_tokens": null, "parameters": "68976648192", "architecture": "Llama 2 70B DPO. Config max_position_embeddings 8192, 80 layers, hidden 8192, 64 query / 8 KV heads. Safetensors total 68,976,648,192. An older tokenizer file listed model_max_length 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tulu 2 DPO 70B is Ai2's November 2023 flagship preference-tuned 70B chat model under the AI2 ImpACT low-risk license. Collected 2026-10-15.", "what_it_is_for": "Flagship Tulu 2 assistant. The card calls it a strong alternative to Llama 2 70B Chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tulu/tulu-2-dpo-7b": { "id": "allenai/tulu/tulu-2-dpo-7b", "name": "Tulu 2 DPO 7B", "maker": "allenai", "family": "allenai/tulu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AI2 ImpACT Low-risk license", "release_date": "2023-11-13", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 7B after Tulu 2 SFT plus DPO on UltraFeedback. Hub license tag is other. Parameter total missing.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tulu 2 DPO 7B is Ai2's November 2023 preference-tuned 7B chat model under the AI2 ImpACT low-risk license. Collected 2026-10-15.", "what_it_is_for": "Preference-tuned 7B assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tulu/tulu-3-1-8b": { "id": "allenai/tulu/tulu-3-1-8b", "name": "Tulu 3.1 8B", "maker": "allenai", "family": "allenai/tulu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2025-02-07", "context_length": null, "max_output_tokens": null, "parameters": "8030326784", "architecture": "Later RLVR refresh of Tulu 3 8B. Safetensors total 8,030,326,784, same as Tulu 3 8B. Base of the DPO checkpoint per the Hub tag.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tulu 3.1 8B is Ai2's February 2025 refresh of Tulu 3 8B under the Llama 3.1 license. Collected 2026-10-15.", "what_it_is_for": "Tulu 3.1 8B refresh. OLMo 2 32B's blog says its post-training used Tulu 3.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tulu/tulu-3-405b": { "id": "allenai/tulu/tulu-3-405b", "name": "Tulu 3 405B", "maker": "allenai", "family": "allenai/tulu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2025-01-30", "context_length": null, "max_output_tokens": null, "parameters": "405853650944", "architecture": "Post-train of Llama 3.1 with Tulu 3 SFT, DPO, and RLVR. Parameter total from safetensors.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tulu 3 405B is Ai2's January 2025 post-train of Llama 3.1 405B, Llama 3.1 license. Collected 2026-10-15.", "what_it_is_for": "Instruction-following model with a fully released post-training recipe. Weights follow the Llama 3.1 license, not Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tulu/tulu-3-70b": { "id": "allenai/tulu/tulu-3-70b", "name": "Tulu 3 70B", "maker": "allenai", "family": "allenai/tulu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2024-11-21", "context_length": null, "max_output_tokens": null, "parameters": "70553837568", "architecture": "Post-train of Llama 3.1 with Tulu 3 SFT, DPO, and RLVR. Parameter total from safetensors.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tulu 3 70B is Ai2's November 2024 instruction model on Llama 3.1 70B, Llama 3.1 license. Collected 2026-10-15.", "what_it_is_for": "Instruction-following model with a fully released post-training recipe. Weights follow the Llama 3.1 license, not Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tulu/tulu-3-8b": { "id": "allenai/tulu/tulu-3-8b", "name": "Tulu 3 8B", "maker": "allenai", "family": "allenai/tulu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2024-11-21", "context_length": 131072, "max_output_tokens": null, "parameters": "8030326784", "architecture": "Post-train of Llama 3.1 with Tulu 3 SFT, DPO, and RLVR. Parameter total from safetensors.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tulu 3 8B is Ai2's November 2024 instruction model on Llama 3.1 8B. Weights are open under the Llama 3.1 license. Collected 2026-10-15.", "what_it_is_for": "Instruction-following model with a fully released post-training recipe. Weights follow the Llama 3.1 license, not Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tulu/tulu-3-8b-rm": { "id": "allenai/tulu/tulu-3-8b-rm", "name": "Tulu 3 8B Reward Model", "maker": "allenai", "family": "allenai/tulu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "7504961536", "architecture": "Reward model used for Tulu 3 8B and 70B. Safetensors total 7,504,961,536. Pipeline text-classification.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tulu 3 8B Reward Model is Ai2's public reward model for the Tulu 3 recipe, Llama 3.1 license. Collected 2026-10-15.", "what_it_is_for": "Preference reward model for the Tulu 3 recipe. The 70B and 405B cards point at this same RM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/tulu/tulu-v1-pythia-6-9b": { "id": "allenai/tulu/tulu-v1-pythia-6-9b", "name": "Tulu v1 Pythia 6.9B", "maker": "allenai", "family": "allenai/tulu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-06-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Early Open Instruct Tulu finetune of Pythia 6.9B. Hub repo allenai/open-instruct-pythia-6.9b-tulu, created 2023-06-13.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tulu v1 Pythia 6.9B is an early Ai2 instruction finetune of Pythia 6.9B. Collected 2026-10-15.", "what_it_is_for": "Early Tulu instruction model from the Open Instruct release, before Tulu 2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/unifiedqa/unifiedqa-t5-11b": { "id": "allenai/unifiedqa/unifiedqa-t5-11b", "name": "UnifiedQA T5 11b", "maker": "allenai", "family": "allenai/unifiedqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 11b finetuned for unified question answering. Official GitHub lists this Hugging Face id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UnifiedQA T5 11b is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/unifiedqa/unifiedqa-t5-3b": { "id": "allenai/unifiedqa/unifiedqa-t5-3b", "name": "UnifiedQA T5 3b", "maker": "allenai", "family": "allenai/unifiedqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 3b finetuned for unified question answering. Official GitHub lists this Hugging Face id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UnifiedQA T5 3b is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/unifiedqa/unifiedqa-t5-base": { "id": "allenai/unifiedqa/unifiedqa-t5-base", "name": "UnifiedQA T5 base", "maker": "allenai", "family": "allenai/unifiedqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 base finetuned for unified question answering. Official GitHub lists this Hugging Face id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UnifiedQA T5 base is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/unifiedqa/unifiedqa-t5-large": { "id": "allenai/unifiedqa/unifiedqa-t5-large", "name": "UnifiedQA T5 large", "maker": "allenai", "family": "allenai/unifiedqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 large finetuned for unified question answering. Official GitHub lists this Hugging Face id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UnifiedQA T5 large is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/unifiedqa/unifiedqa-t5-small": { "id": "allenai/unifiedqa/unifiedqa-t5-small", "name": "UnifiedQA T5 small", "maker": "allenai", "family": "allenai/unifiedqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 small finetuned for unified question answering. Official GitHub lists this Hugging Face id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UnifiedQA T5 small is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/unifiedqa/unifiedqa-v2-t5-11b": { "id": "allenai/unifiedqa/unifiedqa-v2-t5-11b", "name": "UnifiedQA v2 T5 11b", "maker": "allenai", "family": "allenai/unifiedqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 11b finetuned for unified question answering. Official GitHub lists this Hugging Face id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UnifiedQA v2 T5 11b is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/unifiedqa/unifiedqa-v2-t5-3b": { "id": "allenai/unifiedqa/unifiedqa-v2-t5-3b", "name": "UnifiedQA v2 T5 3b", "maker": "allenai", "family": "allenai/unifiedqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 3b finetuned for unified question answering. Official GitHub lists this Hugging Face id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UnifiedQA v2 T5 3b is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/unifiedqa/unifiedqa-v2-t5-base": { "id": "allenai/unifiedqa/unifiedqa-v2-t5-base", "name": "UnifiedQA v2 T5 base", "maker": "allenai", "family": "allenai/unifiedqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 base finetuned for unified question answering. Official GitHub lists this Hugging Face id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UnifiedQA v2 T5 base is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/unifiedqa/unifiedqa-v2-t5-large": { "id": "allenai/unifiedqa/unifiedqa-v2-t5-large", "name": "UnifiedQA v2 T5 large", "maker": "allenai", "family": "allenai/unifiedqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 large finetuned for unified question answering. Official GitHub lists this Hugging Face id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UnifiedQA v2 T5 large is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/unifiedqa/unifiedqa-v2-t5-small": { "id": "allenai/unifiedqa/unifiedqa-v2-t5-small", "name": "UnifiedQA v2 T5 small", "maker": "allenai", "family": "allenai/unifiedqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 small finetuned for unified question answering. Official GitHub lists this Hugging Face id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UnifiedQA v2 T5 small is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "allenai/wildguard/wildguard": { "id": "allenai/wildguard/wildguard", "name": "WildGuard", "maker": "allenai", "family": "allenai/wildguard", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-15", "context_length": null, "max_output_tokens": null, "parameters": "7248031744", "architecture": "Safety moderation model. Hub tags name a Mistral backbone. Safetensors total 7,248,031,744. Config fetch returned 401, so context is unknown.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WildGuard is Ai2's June 2024 open safety model, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Open moderator for harmful user requests and model responses, trained with WildGuardMix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/nova/nova-2-5-sonic": { "id": "amazon/nova/nova-2-5-sonic", "name": "Amazon Nova 2.5 Sonic", "maker": "amazon", "family": "amazon/nova", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2026-10-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "speech", "text" ], "output": [ "speech", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Nova 2.5 Sonic is Amazon's latest speech-to-speech model for real-time voice agents. AWS announced general availability on Amazon Bedrock on 2026-10-05. It is active as of 2026-10-15. The official model ID was not on the pages that rendered.", "what_it_is_for": "Voice agents that keep context, follow multi-step instructions, and call tools without stalling the conversation. AWS names customer service, personal assistants, and other real-time voice apps, and points to Strands Bidi Agents as a way to build them.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/nova/nova-2-lite": { "id": "amazon/nova/nova-2-lite", "name": "Amazon Nova 2 Lite", "maker": "amazon", "family": "amazon/nova", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2025-12-02", "context_length": 1000000, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 2.5 }, "summary_intro": "Amazon Nova 2 Lite is Amazon's cost-efficient multimodal reasoning model on Amazon Bedrock. It is active as of 2026-10-15, launched 2025-12-02, with a stated end of life no sooner than 2026-12-02.", "what_it_is_for": "AWS describes it as the fast, cost-effective model for everyday work: customer-service chat, document processing, and business automation. Reasoning can be turned on with low, medium, or high effort. It is also the Nova model AWS points to for Nova Forge customization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/nova/nova-2-omni": { "id": "amazon/nova/nova-2-omni", "name": "Amazon Nova 2 Omni", "maker": "amazon", "family": "amazon/nova", "status": "preview", "confidence": "medium", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2025-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "speech" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Nova 2 Omni is a preview multimodal model Amazon announced on 2025-12-02. It takes text, images, video, and speech and generates text and images. It is not on the Nova models page or the Bedrock Amazon model list fetched on 2026-10-15. No official retirement notice was found, so the status stays preview. Confidence is medium.", "what_it_is_for": "The announcement describes reasoning plus native image generation and speech transcription, for marketing content, call transcription, video analysis, and documents that need visual output. Early access was tied to Nova Forge customers and an AWS account team.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/nova/nova-2-pro": { "id": "amazon/nova/nova-2-pro", "name": "Amazon Nova 2 Pro", "maker": "amazon", "family": "amazon/nova", "status": "preview", "confidence": "medium", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2025-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "speech" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Nova 2 Pro is a preview reasoning model Amazon announced on 2025-12-02. It was described as the most intelligent Nova model, for complex multi-step work. As of 2026-10-15 it is not on the live Nova models page or the Bedrock Amazon model list. No official end-of-life notice was found, so the status stays preview. Confidence is medium.", "what_it_is_for": "The December 2025 announcement says it is for workloads that need the highest accuracy: multi-step reasoning, long-range planning, and agent workflows such as multi-document analysis, video reasoning, and software migrations. Access was through an AWS account team, and an earlier models-page copy limited early access to Nova Forge customers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/nova/nova-2-sonic": { "id": "amazon/nova/nova-2-sonic", "name": "Amazon Nova 2 Sonic", "maker": "amazon", "family": "amazon/nova", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2025-12-02", "context_length": 1000000, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "speech", "text" ], "output": [ "speech", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Nova 2 Sonic is Amazon's second speech-to-speech model on Amazon Bedrock. It is active as of 2026-10-15, launched 2025-12-02, with an end of life no sooner than 2026-12-02. Nova 2.5 Sonic is the later general-availability model in this line.", "what_it_is_for": "Real-time voice agents. AWS says it unifies speech understanding and generation, supports tool use, and can keep a voice conversation going while a tool runs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/nova/nova-canvas": { "id": "amazon/nova/nova-canvas", "name": "Amazon Nova Canvas", "maker": "amazon", "family": "amazon/nova", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2024-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Nova Canvas is Amazon's Nova image-generation model on Amazon Bedrock. It launched 2024-12-03. The Bedrock model card, read 2026-10-15, sets lifecycle Legacy and an end-of-life date of 2026-09-30, so this catalog treats it as retired.", "what_it_is_for": "AWS describes studio-quality image generation and editing from text and image prompts, with watermarking and content moderation, for marketing and media work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/nova/nova-lite": { "id": "amazon/nova/nova-lite", "name": "Amazon Nova Lite", "maker": "amazon", "family": "amazon/nova", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2024-12-05", "context_length": 300000, "max_output_tokens": 10000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.06, "completion_per_m_usd": null }, "summary_intro": "Amazon Nova Lite is Amazon's low-cost multimodal understanding model on Amazon Bedrock. It is active as of 2026-10-15. The Bedrock card dates the launch to 2024-12-05. Amazon Nova 2 Lite is the later model in this cost tier.", "what_it_is_for": "AWS describes it as a very low-cost multimodal model for fast processing of image, video, and text, including document analysis and visual question answering.", "ratings": { "empirical_100": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/nova/nova-micro": { "id": "amazon/nova/nova-micro", "name": "Amazon Nova Micro", "maker": "amazon", "family": "amazon/nova", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2024-12-05", "context_length": 128000, "max_output_tokens": 10000, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.035, "completion_per_m_usd": 0.14 }, "summary_intro": "Amazon Nova Micro is Amazon's text-only, lowest-latency Nova understanding model on Amazon Bedrock. It is active as of 2026-10-15. The Bedrock card dates the launch to 2024-12-05.", "what_it_is_for": "AWS positions it for high-volume, latency-sensitive text work: classification, routing, entity extraction, summarization, translation, and chat. It does not take images or video.", "ratings": { "empirical_100": 55, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/nova/nova-multimodal-embeddings": { "id": "amazon/nova/nova-multimodal-embeddings", "name": "Amazon Nova Multimodal Embeddings", "maker": "amazon", "family": "amazon/nova", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2025-10-28", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Nova Multimodal Embeddings is Amazon's unified embedding model for text, documents, images, video, and audio. It is active on Amazon Bedrock as of 2026-10-15, launched 2025-10-28, with an end of life no sooner than 2026-10-28.", "what_it_is_for": "Cross-modal search and agentic RAG. AWS says one model replaces separate text, image, video, and audio embedders. Embedding purpose can be set for retrieval, classification, or clustering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/nova/nova-premier": { "id": "amazon/nova/nova-premier", "name": "Amazon Nova Premier", "maker": "amazon", "family": "amazon/nova", "status": "retired", "confidence": "medium", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2025-04-30", "context_length": 1000000, "max_output_tokens": 25000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.5, "completion_per_m_usd": null }, "summary_intro": "Amazon Nova Premier is Amazon's first-generation most capable multimodal Nova model, used for complex tasks and as a distillation teacher. It was announced generally available on 2025-04-30. As of 2026-10-15 the Bedrock model card sets an end-of-life date of 2026-09-14 and labels the lifecycle Legacy, so this catalog treats it as retired. Confidence is medium because the English card page did not render and other AWS pages still disagree.", "what_it_is_for": "AWS announced it for long documents, video, large codebases, and multistep agent workflows, and as the teacher for Bedrock model distillation into Nova Pro, Lite, and Micro. It does not take audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/nova/nova-pro": { "id": "amazon/nova/nova-pro", "name": "Amazon Nova Pro", "maker": "amazon", "family": "amazon/nova", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2024-12-05", "context_length": 300000, "max_output_tokens": 10000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.8, "completion_per_m_usd": null }, "summary_intro": "Amazon Nova Pro is Amazon's balanced multimodal understanding model on Amazon Bedrock. It is active as of 2026-10-15. The Bedrock card dates the launch to 2024-12-05.", "what_it_is_for": "AWS calls it the best combination of accuracy, speed, and cost for a wide range of tasks, including chat, RAG, agents, and video analysis.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/nova/nova-reel": { "id": "amazon/nova/nova-reel", "name": "Amazon Nova Reel", "maker": "amazon", "family": "amazon/nova", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2024-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Nova Reel is Amazon's Nova short-video model on Amazon Bedrock. It launched 2024-12-03. The model card read on 2026-10-15 sets lifecycle Legacy and an end-of-life date of 2026-09-30, so this catalog treats it as retired.", "what_it_is_for": "AWS describes short professional-style videos from text or image prompts, with camera-motion controls, for media and marketing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/nova/nova-sonic": { "id": "amazon/nova/nova-sonic", "name": "Amazon Nova Sonic", "maker": "amazon", "family": "amazon/nova", "status": "retired", "confidence": "medium", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "", "context_length": 300000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "speech" ], "output": [ "speech", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Nova Sonic is Amazon's first speech-to-speech Nova model on Amazon Bedrock. The Nova user guide still documents a 300K context and bidirectional streaming. The Bedrock model card, as extracted on 2026-10-15, sets lifecycle Legacy and an end-of-life date of 2026-09-14. This catalog treats it as retired. Confidence is medium because the English card page did not render in full.", "what_it_is_for": "Real-time, low-latency multi-turn voice conversations for assistants and customer service. Output includes speech plus transcription.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/titan/titan-embeddings-g1-text": { "id": "amazon/titan/titan-embeddings-g1-text", "name": "Amazon Titan Embeddings G1 - Text", "maker": "amazon", "family": "amazon/titan", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2023-09-28", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Titan Embeddings G1 - Text is Amazon's first text embedding model on Amazon Bedrock. It is still listed as active as of 2026-10-15. The model card dates the launch to 2023-09-28.", "what_it_is_for": "Turns text into vectors for search, personalization, and clustering. AWS now points most new retrieval work at Titan Text Embeddings V2 or Nova Multimodal Embeddings, but this model remains on the available-model list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/titan/titan-embeddings-g1-text-v2": { "id": "amazon/titan/titan-embeddings-g1-text-v2", "name": "Amazon Titan Embeddings G1 - Text v2", "maker": "amazon", "family": "amazon/titan", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Titan Embeddings G1 - Text v2, also called Titan Text Embeddings V2:2 on the Bedrock index, is an active Amazon text embedding model. It is a different API id from Titan Text Embeddings V2. Collected 2026-10-15.", "what_it_is_for": "The models index calls it an updated text embeddings model with configurable dimensions and better retrieval accuracy. The card does not spell out how it differs from `amazon.titan-embed-text-v2:0`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/titan/titan-image-generator-g1": { "id": "amazon/titan/titan-image-generator-g1", "name": "Amazon Titan Image Generator G1", "maker": "amazon", "family": "amazon/titan", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2023-11-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Titan Image Generator G1 is Amazon's first Titan image model. It entered preview on Amazon Bedrock on 2023-11-29. The current Titan overview names only G1 v2, and v1 is not on the 2026-10-15 available-model list. No official end-of-life date was found, so this catalog marks it deprecated rather than retired. Confidence is medium.", "what_it_is_for": "Text-to-image generation plus editing: inpainting, outpainting, variations, and mask-based edits. Outputs can carry a watermark that Bedrock can detect.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/titan/titan-image-generator-g1-v2": { "id": "amazon/titan/titan-image-generator-g1-v2", "name": "Amazon Titan Image Generator G1 v2", "maker": "amazon", "family": "amazon/titan", "status": "retired", "confidence": "medium", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2024-08-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Titan Image Generator G1 v2 is Amazon's second Titan image model. It became generally available on Amazon Bedrock on 2024-08-06. A Bedrock model card extract says lifecycle Legacy and end of life on 2026-06-30. This catalog treats it as retired. Confidence is medium because that card page did not re-render in full.", "what_it_is_for": "Text-to-image generation and editing, plus v2 controls: image conditioning from a reference layout, automatic background removal, color-palette guidance, and subject consistency through fine-tuning. Outputs can be checked for a Titan watermark.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/titan/titan-multimodal-embeddings-g1": { "id": "amazon/titan/titan-multimodal-embeddings-g1", "name": "Amazon Titan Multimodal Embeddings G1", "maker": "amazon", "family": "amazon/titan", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2023-11-29", "context_length": 256, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Titan Multimodal Embeddings G1 embeds text, images, or both into one vector space. It is active on Amazon Bedrock as of 2026-10-15. The model card dates the launch to 2023-11-29.", "what_it_is_for": "Multimodal search and recommendations: search images with text, or compare images. It does not take video or audio. Nova Multimodal Embeddings is the later model that does.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/titan/titan-text-embeddings-v2": { "id": "amazon/titan/titan-text-embeddings-v2", "name": "Amazon Titan Text Embeddings V2", "maker": "amazon", "family": "amazon/titan", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2024-04-30", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Titan Text Embeddings V2 is Amazon's second-generation text embedding model on Amazon Bedrock. It is active as of 2026-10-15. The model card dates the launch to 2024-04-30.", "what_it_is_for": "Retrieval, semantic search, clustering, classification, and RAG. Output dimension is configurable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/titan/titan-text-express": { "id": "amazon/titan/titan-text-express", "name": "Amazon Titan Text G1 - Express", "maker": "amazon", "family": "amazon/titan", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2023-11-29", "context_length": 8192, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Titan Text G1 - Express is Amazon's general-purpose Titan text model. It became generally available on Amazon Bedrock on 2023-11-29. It is not on the available Amazon model list fetched on 2026-10-15, but the Titan text docs still describe it. No official end-of-life date was found, so the status is deprecated. Confidence is medium.", "what_it_is_for": "Open-ended generation, chat, summarization, extraction, and RAG. At launch it was optimized for English, with about 100 more languages in preview.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/titan/titan-text-g1-large": { "id": "amazon/titan/titan-text-g1-large", "name": "Amazon Titan Text G1 - Large", "maker": "amazon", "family": "amazon/titan", "status": "deprecated", "confidence": "low", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Titan Text G1 - Large is an early Bedrock text model id, `amazon.titan-tg1-large`. The AWS CDK still lists it as a foundation-model identifier. It is not on the Amazon models-at-a-glance list fetched on 2026-10-15, and the current Titan text guide documents Lite, Express, and Premier instead. No official end-of-life date was found, so the status is deprecated. Confidence is low.", "what_it_is_for": "An early Titan text-generation id. The current user guide does not describe a separate job for it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/titan/titan-text-lite": { "id": "amazon/titan/titan-text-lite", "name": "Amazon Titan Text G1 - Lite", "maker": "amazon", "family": "amazon/titan", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "2023-11-29", "context_length": null, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Titan Text G1 - Lite is Amazon's smaller Titan text model. It became generally available on Amazon Bedrock on 2023-11-29 with Titan Text Express. It is not on the available Amazon model list fetched on 2026-10-15. No official end-of-life date was found, so the status is deprecated. Confidence is medium.", "what_it_is_for": "The launch post groups Lite with Express as Titan text models for general language tasks. The detailed Lite task list was not extracted from the current user-guide section.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amazon/titan/titan-text-premier": { "id": "amazon/titan/titan-text-premier", "name": "Amazon Titan Text G1 - Premier", "maker": "amazon", "family": "amazon/titan", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "Proprietary; Amazon Bedrock terms", "release_date": "", "context_length": 32768, "max_output_tokens": 3072, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amazon Titan Text G1 - Premier is the largest Titan text model still described in the Bedrock user guide. It is for longer-context generation, question answering, and code. It is not on the available Amazon model list fetched on 2026-10-15, and no official end-of-life date was found, so the status is deprecated. Confidence is medium. The launch day was not confirmed.", "what_it_is_for": "Open-ended and contextual answers, code generation, summarization, and Bedrock Knowledge Bases and Agents. Custom fine-tuning was marked preview.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "american/morphable/rock": { "id": "american/morphable/rock", "name": "Rock Model", "maker": "american", "family": "american/morphable", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2024-09-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "StyleGAN2-ADA generator. The 2024 paper says the authors fine-tuned every parameter of the Soap Model, which is the TAG generator trained from scratch on 8085 soap photos, on a rock photo set of 3180 images of 24 objects at 1024 by 1024, photographed with an iPhone 12 mini. Fine-tuning length was 1,060,000 real images, with the same learning rate 0.002 and R1 regularization 10 used for the soap model. Training used a Tesla V100 on Google Colab. The released file is the generator pickle. The paper says 18 latent slices are injected from 4 by 4 through 1024 by 1024. No image encoder was included in the deposit.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "American University researchers Chenxi Liao and Bei Xiao, with Masataka Sawayama at the University of Tokyo, released this StyleGAN2-ADA generator on Figshare on 17 September 2024. It is the Rock Model from their Space of Morphable Material Appearance: a full-model fine-tune of the soap generator (TAG) on photographs of rocks. The file is public under CC BY 4.0. There is no priced API. Collected 2026-10-15. The journal article is PLOS Computational Biology, 3 October 2024.", "what_it_is_for": "The paper uses the Rock Model to synthesize photographs of rocks and crystals at 1024 by 1024, and as one endpoint when interpolating toward soap or squishy-toy generators. The released artifact is the generator pickle StyleGAN_G_rock.pkl.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "american/morphable/toy": { "id": "american/morphable/toy", "name": "Squishy Toy Model", "maker": "american", "family": "american/morphable", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2024-09-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "StyleGAN2-ADA generator. The 2024 paper says the authors fine-tuned every parameter of the Soap Model, which is the TAG generator trained from scratch on 8085 soap photos, on a squishy-toy photo set of 1900 images of 15 objects at 1024 by 1024, photographed with an iPhone 12 mini. Fine-tuning length was 960,000 real images, with the same learning rate 0.002 and R1 regularization 10 used for the soap model. Training used a Tesla V100 on Google Colab. The released file is the generator pickle. The paper says 18 latent slices are injected from 4 by 4 through 1024 by 1024. No image encoder was included in the deposit.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "American University researchers Chenxi Liao and Bei Xiao, with Masataka Sawayama at the University of Tokyo, released this StyleGAN2-ADA generator on Figshare on 17 September 2024. The paper calls it the Toy Model. Figshare labels the file the Squishy Toy Model. It is a full-model fine-tune of the soap generator (TAG) on photographs of squishy toys. The file is public under CC BY 4.0. There is no priced API. Collected 2026-10-15. The journal article is PLOS Computational Biology, 3 October 2024.", "what_it_is_for": "The paper uses the Toy Model to synthesize photographs of squishy toys at 1024 by 1024, and as one endpoint when interpolating toward soap or rock generators. The released artifact is the generator pickle StyleGAN_G_toy.pkl.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "american/tag/tag": { "id": "american/tag/tag", "name": "Translucent Appearance Generation (TAG)", "maker": "american", "family": "american/tag", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2023-01-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "TAG is a StyleGAN2-ADA generator plus a separately trained pixel2style2pixel (pSp) encoder. The authors trained StyleGAN2-ADA from scratch with the TensorFlow code at github.com/NVlabs/stylegan2-ada on the Translucent Image Dataset (8085 photographs of 60 soaps, 1024 by 1024 JPEG, iPhone 12 mini). Training ran on one Tesla V100 for 3,836,000 real images, learning rate 0.002, R1 regularization 10, output 1024 by 1024. The generator starts from a learned constant of 4 by 4 by 512. The 2024 methods paper says each of 18 latent slices is injected from 4 by 4 through 1024 by 1024. The pSp encoder was trained on 80 percent of randomly sampled TID images and validated on the rest. It maps a photograph into W or W+. Early layers (w1 to w6) change shape and orientation, middle layers (w7 to w9) change material and translucency, and later layers (w10 to w18) change body color. This is the authors' trained run, not an NVIDIA pretrained checkpoint.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "American University researchers Chenxi Liao (Neuroscience) and Bei Xiao (Computer Science), with Masataka Sawayama at the University of Tokyo, released trained TAG weights on Figshare on 21 January 2023. TAG is a StyleGAN2-ADA generator plus a pixel2style2pixel encoder for photographs of translucent soap. The weights are public under CC BY 4.0 and are not a priced API. Collected 2026-10-15. The journal article is PLOS Computational Biology, 8 February 2023.", "what_it_is_for": "The paper names Translucent Appearance Generation (TAG). The generator synthesizes 1024 by 1024 images of translucent objects. The encoder maps a photograph into the W+ latent space so that shape, material, and body color can be edited by layer. Middle layers w7 to w9 are the layers the paper uses for translucency edits.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amsterdam/crystalite/crystalite": { "id": "amsterdam/crystalite/crystalite", "name": "Crystalite", "maker": "amsterdam", "family": "amsterdam/crystalite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "EDM diffusion Transformer with one token per atom plus one lattice token, AdaLN noise conditioning, and a Geometry Enhancement Module that adds periodic distance and edge biases to attention. De novo generation denoises atom type, fractional coordinates, and lattice together. Crystal structure prediction holds atom types fixed. Released DNG checkpoint: width 512, 16 heads, 14 layers, subatomic tokenizer PCA-16. Released CSP checkpoints: width 1024, 16 heads, 14 layers, atomic-number types.", "modalities": { "input": [ "crystal" ], "output": [ "crystal" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Crystalite is a diffusion Transformer for crystals from the UvA-Bosch Delta Lab at the University of Amsterdam, with Ivor Lon\u010dari\u0107 at the Ru\u0111er Bo\u0161kovi\u0107 Institute. The code and three checkpoints are public. This dossier was collected on 2026-10-15. It is a research release, not an API.", "what_it_is_for": "The paper presents Crystalite as a lighter alternative to equivariant crystal generators. Subatomic tokenization replaces a large one-hot element vector, and a Geometry Enhancement Module adds periodic pair geometry to ordinary attention. One checkpoint generates new structures on MP20. Two others predict coordinates and the lattice when the composition is already known (MP-20 and MPTS-52). Code and checkpoint paths are in the [README](https://github.com/joshrosie/crystalite). The paper is [arXiv:2604.02270](https://arxiv.org/abs/2604.02270).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "amsterdam/mosaic/mosaic": { "id": "amsterdam/mosaic/mosaic", "name": "Mosaic", "maker": "amsterdam", "family": "amsterdam/mosaic", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "214000000", "architecture": "Probabilistic U-Net transformer on a HEALPix mesh. Mesh-aligned block-sparse attention has compression, top-k selection, and local branches. Grouped-query attention (ratio 4), 2D RoPE, and noise injected in SwiGLU gates. About 14 layers: hidden size 768 (Nside 64, encoder/decoder depth 4/2), 1024 (Nside 32, 4/2), and 1280 (Nside 16 bottleneck, depth 2). Predicts the next atmospheric state directly. Trained with fair CRPS.", "modalities": { "input": [ "weather-grid" ], "output": [ "weather-grid" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mosaic is a probabilistic global weather model from the Amsterdam Machine Learning Lab at the University of Amsterdam. Weights and code are public, the card is active, and this dossier was collected on 2026-10-15. It is a research checkpoint pair, not a hosted forecast API.", "what_it_is_for": "The makers describe Mosaic as a machine-learning weather model that keeps fine-scale spectral energy instead of smoothing it away. It writes ensemble members with learned noise in the SwiGLU gates, attends on the native grid with block-sparse attention, and predicts the next state directly rather than a residual. A 24-member, 10-day forecast is stated to finish in under 12 seconds on one H100. See the [model card](https://huggingface.co/AMLab-UvA/mosaic) and the [ICML 2026 paper](https://arxiv.org/abs/2604.16429).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/claude/claude-1": { "id": "anthropic/claude/claude-1", "name": "Claude 1", "maker": "anthropic", "family": "anthropic/claude", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Claude 1 is Anthropic's first API generation. The deprecations page lists claude-1.0, claude-1.1, claude-1.2, and claude-1.3, all deprecated 2024-09-04 and retired 2024-11-06. Collected 2026-10-15. Specs beyond identity and lifecycle were not on the pages fetched, so they are left null.", "what_it_is_for": "Claude 1 is Anthropic's first API generation. The deprecations page lists claude-1.0, claude-1.1, claude-1.2, and claude-1.3, all deprecated 2024-09-04 and retired 2024-11-06. Collected 2026-10-15. Specs beyond identity and lifecycle were not on the pages fetched, so they are left null.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/claude/claude-2": { "id": "anthropic/claude/claude-2", "name": "Claude 2", "maker": "anthropic", "family": "anthropic/claude", "status": "retired", "confidence": "medium", "weights": "closed", "license": "proprietary", "release_date": "2023-07-11", "context_length": 100000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 8.0, "completion_per_m_usd": 24.0 }, "summary_intro": "Claude 2 is the July 2023 public model, API id claude-2.0. Claude 2.1's announcement describes doubling the prior window to 200k, so Claude 2's window was 100k. It was deprecated 2025-01-21 and retired 2025-07-21.", "what_it_is_for": "Claude 2 is the July 2023 public model, API id claude-2.0. Claude 2.1's announcement describes doubling the prior window to 200k, so Claude 2's window was 100k. It was deprecated 2025-01-21 and retired 2025-07-21.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/claude/claude-2-1": { "id": "anthropic/claude/claude-2-1", "name": "Claude 2.1", "maker": "anthropic", "family": "anthropic/claude", "status": "retired", "confidence": "medium", "weights": "closed", "license": "proprietary", "release_date": "2023-11-21", "context_length": 200000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 8.0, "completion_per_m_usd": 24.0 }, "summary_intro": "Claude 2.1 is Anthropic's November 2023 model. The launch post gives it a 200,000 token context window, about double Claude 2, plus system prompts and a tool-use beta. It was deprecated 2025-01-21 and retired 2025-07-21.", "what_it_is_for": "Claude 2.1 is Anthropic's November 2023 model. The launch post gives it a 200,000 token context window, about double Claude 2, plus system prompts and a tool-use beta. It was deprecated 2025-01-21 and retired 2025-07-21.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/fable/claude-fable-5": { "id": "anthropic/fable/claude-fable-5", "name": "Claude Fable 5", "maker": "anthropic", "family": "anthropic/fable", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-06-09", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 50.0 }, "summary_intro": "Claude Fable 5 is Anthropic's June 2026 generally available frontier model for long-horizon work. It is still active but legacy: the docs say to move to Fable 5.1. Access was suspended on 2026-06-12 under export controls and restored 2026-07-01.", "what_it_is_for": "Claude Fable 5 is Anthropic's June 2026 generally available frontier model for long-horizon work. It is still active but legacy: the docs say to move to Fable 5.1. Access was suspended on 2026-06-12 under export controls and restored 2026-07-01.", "ratings": { "empirical_100": 56, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs": 20.4, "aa_intelligence": 49.6, "aa_coding": 76.5, "aa_agentic": 50.7, "design_arena_elo": 1249, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/fable/claude-fable-5-1": { "id": "anthropic/fable/claude-fable-5-1", "name": "Claude Fable 5.1", "maker": "anthropic", "family": "anthropic/fable", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-09-01", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 50.0 }, "summary_intro": "Claude Fable 5.1 is Anthropic's generally available model for demanding reasoning and long-horizon agentic coding, research, and document work. It is active as of 2026-10-15, priced the same as Fable 5 on input and output, with much cheaper cache reads. Anthropic tells most workloads to start on Opus 5.5 instead.", "what_it_is_for": "Claude Fable 5.1 is Anthropic's generally available model for demanding reasoning and long-horizon agentic coding, research, and document work. It is active as of 2026-10-15, priced the same as Fable 5 on input and output, with much cheaper cache reads. Anthropic tells most workloads to start on Opus 5.5 instead.", "ratings": { "empirical_100": 56, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/haiku/claude-3-5-haiku": { "id": "anthropic/haiku/claude-3-5-haiku", "name": "Claude Haiku 3.5", "maker": "anthropic", "family": "anthropic/haiku", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2024-10-22", "context_length": 200000, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.8, "completion_per_m_usd": 4.0 }, "summary_intro": "Claude Haiku 3.5 is the October 2024 small model, id claude-3-5-haiku-20241022. It was deprecated 2025-12-19 and retired 2026-02-19. The current pricing page still lists USD 0.80 input and USD 4 output, and says it may remain on Google Cloud.", "what_it_is_for": "Claude Haiku 3.5 is the October 2024 small model, id claude-3-5-haiku-20241022. It was deprecated 2025-12-19 and retired 2026-02-19. The current pricing page still lists USD 0.80 input and USD 4 output, and says it may remain on Google Cloud.", "ratings": { "empirical_100": null, "lmsys_elo": 1290, "coding_elo": 1295, "swe_bench": 40.6, "humaneval": 88.9, "twelve_cs": null, "aa_intelligence": 52.0, "aa_coding": 68.0, "aa_agentic": 54.0, "design_arena_elo": 1295, "math_500": 69.4, "gpqa_diamond": 51.2, "ifeval": 84.5, "bfcl_tool_calling": 84.0 } }, "anthropic/haiku/claude-3-haiku": { "id": "anthropic/haiku/claude-3-haiku", "name": "Claude Haiku 3", "maker": "anthropic", "family": "anthropic/haiku", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2024-03-07", "context_length": 200000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.25, "completion_per_m_usd": 1.25 }, "summary_intro": "Claude 3 Haiku is the small March 2024 model, id claude-3-haiku-20240307. The family announcement said Haiku would follow Opus and Sonnet. It was deprecated 2026-02-19 and retired 2026-04-20. Prompt caching lists USD 0.25 input and USD 1.25 output.", "what_it_is_for": "Claude 3 Haiku is the small March 2024 model, id claude-3-haiku-20240307. The family announcement said Haiku would follow Opus and Sonnet. It was deprecated 2026-02-19 and retired 2026-04-20. Prompt caching lists USD 0.25 input and USD 1.25 output.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/haiku/claude-haiku-4-5": { "id": "anthropic/haiku/claude-haiku-4-5", "name": "Claude Haiku 4.5", "maker": "anthropic", "family": "anthropic/haiku", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2025-10-15", "context_length": 200000, "max_output_tokens": 64000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 5.0 }, "summary_intro": "Claude Haiku 4.5 is the October 2025 small model. It is still active as legacy. The dated id is claude-haiku-4-5-20251001. The alias claude-haiku-4-5 points at that snapshot. The docs say to migrate to Haiku 5.5. The retirement floor is 2026-10-15, which is not itself a shutdown date.", "what_it_is_for": "Claude Haiku 4.5 is the October 2025 small model. It is still active as legacy. The dated id is claude-haiku-4-5-20251001. The alias claude-haiku-4-5 points at that snapshot. The docs say to migrate to Haiku 5.5. The retirement floor is 2026-10-15, which is not itself a shutdown date.", "ratings": { "empirical_100": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/haiku/claude-haiku-5-5": { "id": "anthropic/haiku/claude-haiku-5-5", "name": "Claude Haiku 5.5", "maker": "anthropic", "family": "anthropic/haiku", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-10-07", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.5 }, "summary_intro": "Claude Haiku 5.5 is Anthropic's current small model for high-volume classification, extraction, routing, and subagent work. Released 2026-10-07. Price steps up when the prompt, including cache reads and writes, is over 100,000 tokens. It is active.", "what_it_is_for": "Claude Haiku 5.5 is Anthropic's current small model for high-volume classification, extraction, routing, and subagent work. Released 2026-10-07. Price steps up when the prompt, including cache reads and writes, is over 100,000 tokens. It is active.", "ratings": { "empirical_100": 47, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/instant/claude-instant-1": { "id": "anthropic/instant/claude-instant-1", "name": "Claude Instant 1.2", "maker": "anthropic", "family": "anthropic/instant", "status": "retired", "confidence": "medium", "weights": "closed", "license": "proprietary", "release_date": "2023-08-09", "context_length": 100000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.8, "completion_per_m_usd": 2.4 }, "summary_intro": "Claude Instant 1.2 is Anthropic's faster, cheaper text model from August 2023, the last Instant release. The 1.0, 1.1, and 1.2 ids were deprecated 2024-09-04 and retired 2024-11-06. They are grouped in one folder because they are versions of Claude Instant, not separate product lines.", "what_it_is_for": "Claude Instant 1.2 is Anthropic's faster, cheaper text model from August 2023, the last Instant release. The 1.0, 1.1, and 1.2 ids were deprecated 2024-09-04 and retired 2024-11-06. They are grouped in one folder because they are versions of Claude Instant, not separate product lines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/mythos/claude-mythos-5": { "id": "anthropic/mythos/claude-mythos-5", "name": "Claude Mythos 5", "maker": "anthropic", "family": "anthropic/mythos", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-06-09", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 50.0 }, "summary_intro": "Claude Mythos 5 is the limited-access model paired with Fable 5. Anthropic describes it as the same underlying model with fewer safeguards, for verified defensive-cyber and later biology users. It is active, not generally available, and the predecessor of Mythos 5.1.", "what_it_is_for": "Claude Mythos 5 is the limited-access model paired with Fable 5. Anthropic describes it as the same underlying model with fewer safeguards, for verified defensive-cyber and later biology users. It is active, not generally available, and the predecessor of Mythos 5.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/mythos/claude-mythos-5-1": { "id": "anthropic/mythos/claude-mythos-5-1", "name": "Claude Mythos 5.1", "maker": "anthropic", "family": "anthropic/mythos", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-09-01", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 50.0 }, "summary_intro": "Claude Mythos 5.1 is the limited-access counterpart of Claude Fable 5.1, with the same published specs and price. As of 2026-10-15 it is active only for organizations verified through Anthropic programs such as the Cyber Verification Program. It is not a generally available model.", "what_it_is_for": "Claude Mythos 5.1 is the limited-access counterpart of Claude Fable 5.1, with the same published specs and price. As of 2026-10-15 it is active only for organizations verified through Anthropic programs such as the Cyber Verification Program. It is not a generally available model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/mythos/claude-mythos-preview": { "id": "anthropic/mythos/claude-mythos-preview", "name": "Claude Mythos Preview", "maker": "anthropic", "family": "anthropic/mythos", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "proprietary", "release_date": "2026-04-07", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 25.0, "completion_per_m_usd": 125.0 }, "summary_intro": "Claude Mythos Preview is the invitation-only model Anthropic offered through Project Glasswing before Mythos 5. The deprecations page marks claude-mythos-preview deprecated on 2026-06-09, with retirement still to be announced. Collected 2026-10-15. Spec confidence is medium because price and launch day are not on the current pricing table.", "what_it_is_for": "Claude Mythos Preview is the invitation-only model Anthropic offered through Project Glasswing before Mythos 5. The deprecations page marks claude-mythos-preview deprecated on 2026-06-09, with retirement still to be announced. Collected 2026-10-15. Spec confidence is medium because price and launch day are not on the current pricing table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/opus/claude-3-opus": { "id": "anthropic/opus/claude-3-opus", "name": "Claude Opus 3", "maker": "anthropic", "family": "anthropic/opus", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2024-03-04", "context_length": 200000, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 15.0, "completion_per_m_usd": 75.0 }, "summary_intro": "Claude 3 Opus is the March 2024 flagship, id claude-3-opus-20240229. It was deprecated 2025-06-30 and retired 2026-01-05. Anthropic's prompt-caching post lists USD 15/75 per million tokens and a 200k context window.", "what_it_is_for": "Claude 3 Opus is the March 2024 flagship, id claude-3-opus-20240229. It was deprecated 2025-06-30 and retired 2026-01-05. Anthropic's prompt-caching post lists USD 15/75 per million tokens and a 200k context window.", "ratings": { "empirical_100": null, "lmsys_elo": 1270, "coding_elo": 1260, "swe_bench": 32.0, "humaneval": 84.9, "twelve_cs": null, "aa_intelligence": 48.0, "aa_coding": 62.0, "aa_agentic": 50.0, "design_arena_elo": 1260, "math_500": 60.1, "gpqa_diamond": 50.4, "ifeval": 84.0, "bfcl_tool_calling": 81.0 } }, "anthropic/opus/claude-opus-4": { "id": "anthropic/opus/claude-opus-4", "name": "Claude Opus 4", "maker": "anthropic", "family": "anthropic/opus", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2025-05-22", "context_length": 200000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 15.0, "completion_per_m_usd": 75.0 }, "summary_intro": "Claude Opus 4 is the May 2025 Opus model, API id claude-opus-4-20250514. It was deprecated 2026-04-14 and retired 2026-06-15. The pricing page still lists USD 15/75 and says it may remain on Google Cloud.", "what_it_is_for": "Claude Opus 4 is the May 2025 Opus model, API id claude-opus-4-20250514. It was deprecated 2026-04-14 and retired 2026-06-15. The pricing page still lists USD 15/75 and says it may remain on Google Cloud.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/opus/claude-opus-4-1": { "id": "anthropic/opus/claude-opus-4-1", "name": "Claude Opus 4.1", "maker": "anthropic", "family": "anthropic/opus", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2025-08-05", "context_length": 200000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 15.0, "completion_per_m_usd": 75.0 }, "summary_intro": "Claude Opus 4.1 is a retired Opus snapshot, id claude-opus-4-1-20250805. Anthropic deprecated it on 2026-06-05 and retired it on 2026-08-05. The current pricing page still lists USD 15 input and USD 75 output per million tokens, and says it may remain on Bedrock and Google Cloud.", "what_it_is_for": "Claude Opus 4.1 is a retired Opus snapshot, id claude-opus-4-1-20250805. Anthropic deprecated it on 2026-06-05 and retired it on 2026-08-05. The current pricing page still lists USD 15 input and USD 75 output per million tokens, and says it may remain on Bedrock and Google Cloud.", "ratings": { "empirical_100": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/opus/claude-opus-4-5": { "id": "anthropic/opus/claude-opus-4-5", "name": "Claude Opus 4.5", "maker": "anthropic", "family": "anthropic/opus", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2025-11-24", "context_length": 200000, "max_output_tokens": 64000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 25.0 }, "summary_intro": "Claude Opus 4.5 is the November 2025 coding and agent model that cut Opus list price from USD 15/75 to USD 5/25 per million tokens. It is still active as legacy, with a 200k context window on the current model page. Alias claude-opus-4-5 points at the dated snapshot.", "what_it_is_for": "Claude Opus 4.5 is the November 2025 coding and agent model that cut Opus list price from USD 15/75 to USD 5/25 per million tokens. It is still active as legacy, with a 200k context window on the current model page. Alias claude-opus-4-5 points at the dated snapshot.", "ratings": { "empirical_100": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/opus/claude-opus-4-6": { "id": "anthropic/opus/claude-opus-4-6", "name": "Claude Opus 4.6", "maker": "anthropic", "family": "anthropic/opus", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-02-05", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 25.0 }, "summary_intro": "Claude Opus 4.6 is the February 2026 Opus model. It is still active as legacy. It is the last Opus id whose Bedrock id keeps a -v1 suffix. Extended thinking is deprecated on it in favor of adaptive thinking.", "what_it_is_for": "Claude Opus 4.6 is the February 2026 Opus model. It is still active as legacy. It is the last Opus id whose Bedrock id keeps a -v1 suffix. Extended thinking is deprecated on it in favor of adaptive thinking.", "ratings": { "empirical_100": 52, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/opus/claude-opus-4-7": { "id": "anthropic/opus/claude-opus-4-7", "name": "Claude Opus 4.7", "maker": "anthropic", "family": "anthropic/opus", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-04-16", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 25.0 }, "summary_intro": "Claude Opus 4.7 is the April 2026 Opus model that introduced the newer tokenizer. It is still active as a legacy model. Fast mode is not accepted. The docs recommend Opus 5.5.", "what_it_is_for": "Claude Opus 4.7 is the April 2026 Opus model that introduced the newer tokenizer. It is still active as a legacy model. Fast mode is not accepted. The docs recommend Opus 5.5.", "ratings": { "empirical_100": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/opus/claude-opus-4-8": { "id": "anthropic/opus/claude-opus-4-8", "name": "Claude Opus 4.8", "maker": "anthropic", "family": "anthropic/opus", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-05-28", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 25.0 }, "summary_intro": "Claude Opus 4.8 is a May 2026 Opus model still on the API as legacy. The docs point new work to Opus 5.5. Fast mode is offered at the same premium as Opus 5.", "what_it_is_for": "Claude Opus 4.8 is a May 2026 Opus model still on the API as legacy. The docs point new work to Opus 5.5. Fast mode is offered at the same premium as Opus 5.", "ratings": { "empirical_100": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/opus/claude-opus-5": { "id": "anthropic/opus/claude-opus-5", "name": "Claude Opus 5", "maker": "anthropic", "family": "anthropic/opus", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-07-24", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 25.0 }, "summary_intro": "Claude Opus 5 is the July 2026 Opus release. It remains available as a legacy model. Anthropic's docs say to migrate to Opus 5.5. It was priced the same as Opus 4.8 and aimed near Fable 5 quality at half that model's price.", "what_it_is_for": "Claude Opus 5 is the July 2026 Opus release. It remains available as a legacy model. Anthropic's docs say to migrate to Opus 5.5. It was priced the same as Opus 4.8 and aimed near Fable 5 quality at half that model's price.", "ratings": { "empirical_100": 58, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs": 78.4, "aa_intelligence": 50.8, "aa_coding": 78.0, "aa_agentic": 56.5, "design_arena_elo": 1225, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/opus/claude-opus-5-5": { "id": "anthropic/opus/claude-opus-5-5", "name": "Claude Opus 5.5", "maker": "anthropic", "family": "anthropic/opus", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-09-22", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": 20.0 }, "summary_intro": "Claude Opus 5.5 is Anthropic's current Opus model for long-running agentic coding and knowledge work. As of 2026-10-15 it is the model the docs tell most workloads to start with. It is active, cheaper than Opus 5, and thinking cannot be turned off.", "what_it_is_for": "Claude Opus 5.5 is Anthropic's current Opus model for long-running agentic coding and knowledge work. As of 2026-10-15 it is the model the docs tell most workloads to start with. It is active, cheaper than Opus 5, and thinking cannot be turned off.", "ratings": { "empirical_100": 48, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/sonnet/claude-3-5-sonnet": { "id": "anthropic/sonnet/claude-3-5-sonnet", "name": "Claude Sonnet 3.5", "maker": "anthropic", "family": "anthropic/sonnet", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2024-06-20", "context_length": 200000, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 15.0 }, "summary_intro": "Claude 3.5 Sonnet is the 2024 Sonnet generation, retired 2025-10-28. Two dated snapshots are grouped here. The 2024-10-22 snapshot is the upgraded model Anthropic shipped with computer use. Both ids were deprecated together on 2025-08-13.", "what_it_is_for": "Claude 3.5 Sonnet is the 2024 Sonnet generation, retired 2025-10-28. Two dated snapshots are grouped here. The 2024-10-22 snapshot is the upgraded model Anthropic shipped with computer use. Both ids were deprecated together on 2025-08-13.", "ratings": { "empirical_100": null, "lmsys_elo": 1335, "coding_elo": 1335, "swe_bench": 49.0, "humaneval": 93.7, "twelve_cs": null, "aa_intelligence": 72.4, "aa_coding": 85.2, "aa_agentic": 68.9, "design_arena_elo": 1335, "math_500": 78.0, "gpqa_diamond": 65.0, "ifeval": 88.0, "bfcl_tool_calling": 89.5 } }, "anthropic/sonnet/claude-3-7-sonnet": { "id": "anthropic/sonnet/claude-3-7-sonnet", "name": "Claude Sonnet 3.7", "maker": "anthropic", "family": "anthropic/sonnet", "status": "retired", "confidence": "medium", "weights": "closed", "license": "proprietary", "release_date": "2025-02-24", "context_length": 200000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 15.0 }, "summary_intro": "Claude Sonnet 3.7 is Anthropic's first hybrid-reasoning Sonnet, id claude-3-7-sonnet-20250219. It was deprecated 2025-10-28 and retired 2026-02-19. It is a closed model with a 200k context window. Price on the historical docs table is USD 3/15.", "what_it_is_for": "Claude Sonnet 3.7 is Anthropic's first hybrid-reasoning Sonnet, id claude-3-7-sonnet-20250219. It was deprecated 2025-10-28 and retired 2026-02-19. It is a closed model with a 200k context window. Price on the historical docs table is USD 3/15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/sonnet/claude-3-sonnet": { "id": "anthropic/sonnet/claude-3-sonnet", "name": "Claude Sonnet 3", "maker": "anthropic", "family": "anthropic/sonnet", "status": "retired", "confidence": "medium", "weights": "closed", "license": "proprietary", "release_date": "2024-03-04", "context_length": 200000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 15.0 }, "summary_intro": "Claude 3 Sonnet is the middle model of the March 2024 Claude 3 family, id claude-3-sonnet-20240229. It was deprecated 2025-01-21 and retired 2025-07-21. The family launched with a 200k context window.", "what_it_is_for": "Claude 3 Sonnet is the middle model of the March 2024 Claude 3 family, id claude-3-sonnet-20240229. It was deprecated 2025-01-21 and retired 2025-07-21. The family launched with a 200k context window.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/sonnet/claude-sonnet-4": { "id": "anthropic/sonnet/claude-sonnet-4", "name": "Claude Sonnet 4", "maker": "anthropic", "family": "anthropic/sonnet", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2025-05-22", "context_length": 200000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 15.0 }, "summary_intro": "Claude Sonnet 4 is the May 2025 Sonnet model, id claude-sonnet-4-20250514. Deprecated 2026-04-14 and retired 2026-06-15. Current list price remains USD 3/15 per million tokens. The deprecations page now names Sonnet 5.5 as the replacement.", "what_it_is_for": "Claude Sonnet 4 is the May 2025 Sonnet model, id claude-sonnet-4-20250514. Deprecated 2026-04-14 and retired 2026-06-15. Current list price remains USD 3/15 per million tokens. The deprecations page now names Sonnet 5.5 as the replacement.", "ratings": { "empirical_100": 63, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs": 99.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/sonnet/claude-sonnet-4-5": { "id": "anthropic/sonnet/claude-sonnet-4-5", "name": "Claude Sonnet 4.5", "maker": "anthropic", "family": "anthropic/sonnet", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2025-09-29", "context_length": 200000, "max_output_tokens": 64000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 15.0 }, "summary_intro": "Claude Sonnet 4.5 is the September 2025 Sonnet model. It is deprecated as of 2026-09-30 and scheduled to retire on 2026-11-30. The recommended replacement is Claude Sonnet 5.5. The current model page lists a 200k context window and 64k max output.", "what_it_is_for": "Claude Sonnet 4.5 is the September 2025 Sonnet model. It is deprecated as of 2026-09-30 and scheduled to retire on 2026-11-30. The recommended replacement is Claude Sonnet 5.5. The current model page lists a 200k context window and 64k max output.", "ratings": { "empirical_100": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/sonnet/claude-sonnet-4-6": { "id": "anthropic/sonnet/claude-sonnet-4-6", "name": "Claude Sonnet 4.6", "maker": "anthropic", "family": "anthropic/sonnet", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-02-17", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 15.0 }, "summary_intro": "Claude Sonnet 4.6 is the February 2026 Sonnet model. It is still active as legacy, with a 1M context window at USD 3/15 per million tokens. Extended thinking is deprecated. The docs recommend Sonnet 5.5.", "what_it_is_for": "Claude Sonnet 4.6 is the February 2026 Sonnet model. It is still active as legacy, with a 1M context window at USD 3/15 per million tokens. Extended thinking is deprecated. The docs recommend Sonnet 5.5.", "ratings": { "empirical_100": 58, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/sonnet/claude-sonnet-5": { "id": "anthropic/sonnet/claude-sonnet-5", "name": "Claude Sonnet 5", "maker": "anthropic", "family": "anthropic/sonnet", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-06-30", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 10.0 }, "summary_intro": "Claude Sonnet 5 is the June 2026 Sonnet release. It is still active as legacy. Introductory USD 2/10 pricing, once scheduled to rise on 2026-09-01, was kept as the standard price. The docs say to move to Sonnet 5.5.", "what_it_is_for": "Claude Sonnet 5 is the June 2026 Sonnet release. It is still active as legacy. Introductory USD 2/10 pricing, once scheduled to rise on 2026-09-01, was kept as the standard price. The docs say to move to Sonnet 5.5.", "ratings": { "empirical_100": 58, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs": 78.4, "aa_intelligence": 38.2, "aa_coding": 71.5, "aa_agentic": 43.6, "design_arena_elo": 1190, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anthropic/sonnet/claude-sonnet-5-5": { "id": "anthropic/sonnet/claude-sonnet-5-5", "name": "Claude Sonnet 5.5", "maker": "anthropic", "family": "anthropic/sonnet", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-09-28", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 10.0 }, "summary_intro": "Claude Sonnet 5.5 is the current Sonnet model, released 2026-09-28. Anthropic calls it the best combination of speed and intelligence in the lineup. It is active, at USD 2/10 per million tokens, and the recommended replacement for Sonnet 4.5.", "what_it_is_for": "Claude Sonnet 5.5 is the current Sonnet model, released 2026-09-28. Anthropic calls it the best combination of speed and intelligence in the lineup. It is active, at USD 2/10 per million tokens, and the recommended replacement for Sonnet 4.5.", "ratings": { "empirical_100": 58, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/contact/contact": { "id": "antwerp/contact/contact", "name": "CoNTACT", "maker": "antwerp", "family": "antwerp/contact", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-14", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "Continued masked-language-model pretraining of pdelobelle/robbert-v2-dutch-base. config.json: RobertaModel, 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocab 40000, max_position_embeddings 514. The card says training used 2.8 million Dutch COVID-19 tweets from 2021, 4 epochs, batch size 32.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CoNTACT (Contextual Neural Transformer Adapted to COVID-19 Tweets) is a Dutch encoder from CLiPS at the University of Antwerp. It continues masked-language-model training of KU Leuven RobBERT v2 on COVID-19 tweets. Weights are public. Collected 2026-10-15. No license is printed on the card.", "what_it_is_for": "The card says it is for Dutch social-media tasks related to COVID-19, after a downstream fine-tune. The paper tests binary vaccine-hesitancy detection and multilabel argument detection on Twitter and Facebook.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/e5-nl/e5-base-trm": { "id": "antwerp/e5-nl/e5-base-trm", "name": "E5-base-trm", "maker": "antwerp", "family": "antwerp/e5-nl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-08-28", "context_length": 514, "max_output_tokens": null, "parameters": "124443648", "architecture": "Vocabulary-trimmed initialization of intfloat/multilingual-e5-base. The paper says trimming cuts the multilingual E5 vocabulary from about 250K to 50K tokens using Dutch C4 and changes only the embedding matrix. Hub tags still mark it as a finetune of intfloat/multilingual-e5-base. Config class XLMRobertaModel. Safetensors total 124443648 F32 parameters. Hidden size 768, so the mean-pooled vector is 768-dimensional. Vocab 50002. Max position embeddings 514.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E5-base-trm is a University of Antwerp CLiPS embedding model in the E5-NL suite. Weights are public under MIT. Status on 2026-10-15: active. Hub id clips/e5-base-trm. Created 2025-08-28.", "what_it_is_for": "CLiPS released this checkpoint as a Dutch-vocabulary starting point for E5. It is multilingual E5 with the vocabulary trimmed to 50,002 tokens on Dutch C4. The paper evaluates it on MTEB-NL before the contrastive Dutch fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/e5-nl/e5-base-trm-nl": { "id": "antwerp/e5-nl/e5-base-trm-nl", "name": "E5-base-trm-nl", "maker": "antwerp", "family": "antwerp/e5-nl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-09", "context_length": 514, "max_output_tokens": null, "parameters": "124443648", "architecture": "Dutch fine-tune of clips/e5-base-trm. That base is the vocabulary-trimmed multilingual E5 encoder. One epoch of InfoNCE on the 950K Dutch mix. Config class XLMRobertaModel. Safetensors total 124443648 BF16 parameters, same count as the initialization. Hidden size 768. Vocab 50002. Max position embeddings 514.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E5-base-trm-nl is a University of Antwerp CLiPS embedding model in the E5-NL suite. Weights are public under MIT. Status on 2026-10-15: active. Hub id clips/e5-base-trm-nl. Created 2025-09-09.", "what_it_is_for": "CLiPS released this as a compact Dutch embedding model. It is the vocabulary-trimmed multilingual E5 encoder after one epoch on Dutch retrieval data and synthetic classification, clustering, and STS pairs. The card says to use it for retrieval and for general-purpose embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/e5-nl/e5-base-v2-t2t": { "id": "antwerp/e5-nl/e5-base-v2-t2t", "name": "E5-base-v2-t2t", "maker": "antwerp", "family": "antwerp/e5-nl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-08-26", "context_length": 512, "max_output_tokens": null, "parameters": "109169017", "architecture": "Transtokenizer initialization of intfloat/e5-base-v2. The paper maps the English E5 v2 vocabulary onto BERTje and says only the embedding matrix changes. This checkpoint's Hub config class is BertLMHeadModel and its safetensors total is 109169017 F32 parameters, slightly above the later encoder-only fine-tune. Hidden size 768. Vocab 30073. Max position embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E5-base-v2-t2t is a University of Antwerp CLiPS embedding model in the E5-NL suite. Weights are public under MIT. Status on 2026-10-15: active. Hub id clips/e5-base-v2-t2t. Created 2025-08-26.", "what_it_is_for": "CLiPS released this checkpoint as the transtokenizer alignment of English E5 v2 onto the BERTje vocabulary. The paper evaluates that alignment on MTEB-NL before Dutch contrastive fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/e5-nl/e5-base-v2-t2t-nl": { "id": "antwerp/e5-nl/e5-base-v2-t2t-nl", "name": "E5-base-v2-t2t-nl", "maker": "antwerp", "family": "antwerp/e5-nl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-10", "context_length": 512, "max_output_tokens": null, "parameters": "109137408", "architecture": "Dutch fine-tune of clips/e5-base-v2-t2t. The published weights are an encoder-only BertModel. Safetensors total 109137408 BF16 parameters. Hidden size 768. Vocab 30073, the BERTje-sized vocabulary from the transtokenizer step. Max position embeddings 512. One epoch of InfoNCE on the 950K Dutch mix.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E5-base-v2-t2t-nl is a University of Antwerp CLiPS embedding model in the E5-NL suite. Weights are public under MIT. Status on 2026-10-15: active. Hub id clips/e5-base-v2-t2t-nl. Created 2025-09-10.", "what_it_is_for": "CLiPS released this as a Dutch embedding model started from English E5 v2 after a BERTje vocabulary map. The Hub weights are the encoder after one epoch on the Dutch mix. The card says to use it for retrieval and for general-purpose embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/e5-nl/e5-large-trm": { "id": "antwerp/e5-nl/e5-large-trm", "name": "E5-large-trm", "maker": "antwerp", "family": "antwerp/e5-nl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-08-28", "context_length": 514, "max_output_tokens": null, "parameters": "355090432", "architecture": "Vocabulary-trimmed initialization of intfloat/multilingual-e5-large. The paper says trimming cuts the multilingual E5 vocabulary from about 250K to 50K tokens using Dutch C4 and changes only the embedding matrix. Hub tags still mark it as a finetune of intfloat/multilingual-e5-large. Config class XLMRobertaModel. Safetensors total 355090432 F32 parameters. Hidden size 1024, so the mean-pooled vector is 1024-dimensional. Vocab 50002. Max position embeddings 514.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E5-large-trm is a University of Antwerp CLiPS embedding model in the E5-NL suite. Weights are public under MIT. Status on 2026-10-15: active. Hub id clips/e5-large-trm. Created 2025-08-28.", "what_it_is_for": "CLiPS released this checkpoint as a Dutch-vocabulary starting point for E5. It is multilingual E5 with the vocabulary trimmed to 50,002 tokens on Dutch C4. The paper evaluates it on MTEB-NL before the contrastive Dutch fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/e5-nl/e5-large-trm-nl": { "id": "antwerp/e5-nl/e5-large-trm-nl", "name": "E5-large-trm-nl", "maker": "antwerp", "family": "antwerp/e5-nl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-15", "context_length": 514, "max_output_tokens": null, "parameters": "355090432", "architecture": "Dutch fine-tune of clips/e5-large-trm. That base is the vocabulary-trimmed multilingual E5 encoder. One epoch of InfoNCE on the 950K Dutch mix. Config class XLMRobertaModel. Safetensors total 355090432 F32 parameters, same count as the initialization. Hidden size 1024. Vocab 50002. Max position embeddings 514.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E5-large-trm-nl is a University of Antwerp CLiPS embedding model in the E5-NL suite. Weights are public under MIT. Status on 2026-10-15: active. Hub id clips/e5-large-trm-nl. Created 2025-09-15.", "what_it_is_for": "CLiPS released this as a compact Dutch embedding model. It is the vocabulary-trimmed multilingual E5 encoder after one epoch on Dutch retrieval data and synthetic classification, clustering, and STS pairs. The card says to use it for retrieval and for general-purpose embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/e5-nl/e5-large-v2-t2t": { "id": "antwerp/e5-nl/e5-large-v2-t2t", "name": "E5-large-v2-t2t", "maker": "antwerp", "family": "antwerp/e5-nl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-08-26", "context_length": 512, "max_output_tokens": null, "parameters": "334714233", "architecture": "Transtokenizer initialization of intfloat/e5-large-v2. The paper maps the English E5 v2 vocabulary onto BERTje and says only the embedding matrix changes. This checkpoint's Hub config class is BertLMHeadModel and its safetensors total is 334714233 F32 parameters, slightly above the later encoder-only fine-tune. Hidden size 1024. Vocab 30073. Max position embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E5-large-v2-t2t is a University of Antwerp CLiPS embedding model in the E5-NL suite. Weights are public under MIT. Status on 2026-10-15: active. Hub id clips/e5-large-v2-t2t. Created 2025-08-26.", "what_it_is_for": "CLiPS released this checkpoint as the transtokenizer alignment of English E5 v2 onto the BERTje vocabulary. The paper evaluates that alignment on MTEB-NL before Dutch contrastive fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/e5-nl/e5-large-v2-t2t-nl": { "id": "antwerp/e5-nl/e5-large-v2-t2t-nl", "name": "E5-large-v2-t2t-nl", "maker": "antwerp", "family": "antwerp/e5-nl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-15", "context_length": 512, "max_output_tokens": null, "parameters": "334682112", "architecture": "Dutch fine-tune of clips/e5-large-v2-t2t. The published weights are an encoder-only BertModel. Safetensors total 334682112 F32 parameters. Hidden size 1024. Vocab 30073, the BERTje-sized vocabulary from the transtokenizer step. Max position embeddings 512. One epoch of InfoNCE on the 950K Dutch mix.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E5-large-v2-t2t-nl is a University of Antwerp CLiPS embedding model in the E5-NL suite. Weights are public under MIT. Status on 2026-10-15: active. Hub id clips/e5-large-v2-t2t-nl. Created 2025-09-15.", "what_it_is_for": "CLiPS released this as a Dutch embedding model started from English E5 v2 after a BERTje vocabulary map. The Hub weights are the encoder after one epoch on the Dutch mix. The card says to use it for retrieval and for general-purpose embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/e5-nl/e5-small-trm": { "id": "antwerp/e5-nl/e5-small-trm", "name": "E5-small-trm", "maker": "antwerp", "family": "antwerp/e5-nl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-08-28", "context_length": 512, "max_output_tokens": null, "parameters": "40840320", "architecture": "Vocabulary-trimmed initialization of intfloat/multilingual-e5-small. The paper says trimming cuts the multilingual E5 vocabulary from about 250K to 50K tokens using Dutch C4 and changes only the embedding matrix. Hub tags still mark it as a finetune of intfloat/multilingual-e5-small. Config class BertModel. Safetensors total 40840320 F32 parameters. Hidden size 384, so the mean-pooled vector is 384-dimensional. Vocab 50002. Max position embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E5-small-trm is a University of Antwerp CLiPS embedding model in the E5-NL suite. Weights are public under MIT. Status on 2026-10-15: active. Hub id clips/e5-small-trm. Created 2025-08-28.", "what_it_is_for": "CLiPS released this checkpoint as a Dutch-vocabulary starting point for E5. It is multilingual E5 with the vocabulary trimmed to 50,002 tokens on Dutch C4. The paper evaluates it on MTEB-NL before the contrastive Dutch fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/e5-nl/e5-small-trm-nl": { "id": "antwerp/e5-nl/e5-small-trm-nl", "name": "E5-small-trm-nl", "maker": "antwerp", "family": "antwerp/e5-nl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-09", "context_length": 512, "max_output_tokens": null, "parameters": "40840320", "architecture": "Dutch fine-tune of clips/e5-small-trm. That base is the vocabulary-trimmed multilingual E5 encoder. One epoch of InfoNCE on the 950K Dutch mix. Config class BertModel. Safetensors total 40840320 BF16 parameters, same count as the initialization. Hidden size 384. Vocab 50002. Max position embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E5-small-trm-nl is a University of Antwerp CLiPS embedding model in the E5-NL suite. Weights are public under MIT. Status on 2026-10-15: active. Hub id clips/e5-small-trm-nl. Created 2025-09-09.", "what_it_is_for": "CLiPS released this as a compact Dutch embedding model. It is the vocabulary-trimmed multilingual E5 encoder after one epoch on Dutch retrieval data and synthetic classification, clustering, and STS pairs. The card says to use it for retrieval and for general-purpose embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/e5-nl/e5-small-v2-t2t": { "id": "antwerp/e5-nl/e5-small-v2-t2t", "name": "E5-small-v2-t2t", "maker": "antwerp", "family": "antwerp/e5-nl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-08-26", "context_length": 512, "max_output_tokens": null, "parameters": "33218425", "architecture": "Transtokenizer initialization of intfloat/e5-small-v2. The paper maps the English E5 v2 vocabulary onto BERTje and says only the embedding matrix changes. This checkpoint's Hub config class is BertLMHeadModel and its safetensors total is 33218425 F32 parameters, slightly above the later encoder-only fine-tune. Hidden size 384. Vocab 30073. Max position embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E5-small-v2-t2t is a University of Antwerp CLiPS embedding model in the E5-NL suite. Weights are public under MIT. Status on 2026-10-15: active. Hub id clips/e5-small-v2-t2t. Created 2025-08-26.", "what_it_is_for": "CLiPS released this checkpoint as the transtokenizer alignment of English E5 v2 onto the BERTje vocabulary. The paper evaluates that alignment on MTEB-NL before Dutch contrastive fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/e5-nl/e5-small-v2-t2t-nl": { "id": "antwerp/e5-nl/e5-small-v2-t2t-nl", "name": "E5-small-v2-t2t-nl", "maker": "antwerp", "family": "antwerp/e5-nl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-10", "context_length": 512, "max_output_tokens": null, "parameters": "33187584", "architecture": "Dutch fine-tune of clips/e5-small-v2-t2t. The published weights are an encoder-only BertModel. Safetensors total 33187584 BF16 parameters. Hidden size 384. Vocab 30073, the BERTje-sized vocabulary from the transtokenizer step. Max position embeddings 512. One epoch of InfoNCE on the 950K Dutch mix.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E5-small-v2-t2t-nl is a University of Antwerp CLiPS embedding model in the E5-NL suite. Weights are public under MIT. Status on 2026-10-15: active. Hub id clips/e5-small-v2-t2t-nl. Created 2025-09-10.", "what_it_is_for": "CLiPS released this as a Dutch embedding model started from English E5 v2 after a BERTje vocabulary map. The Hub weights are the encoder after one epoch on the Dutch mix. The card says to use it for retrieval and for general-purpose embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/mfaq/mfaq": { "id": "antwerp/mfaq/mfaq", "name": "MFAQ", "maker": "antwerp", "family": "antwerp/mfaq", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2021-09-27", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "Shared XLM-RoBERTa bi-encoder. config.json: XLMRobertaModel, 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocab 250005, max_position_embeddings 514, hidden dropout 0.25. sentence_bert_config max_seq_length is 128. The card's example uses mean pooling. The paper trains with in-batch negatives drawn from the same FAQ page, batch size 800, Adam learning rate 0.0001, 1000 warmup steps, and sequences capped at 128 tokens.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MFAQ is the multilingual FAQ retrieval encoder CLiPS at the University of Antwerp released with the MFAQ dataset. It is an active open-weights XLM-RoBERTa bi-encoder. Collected 2026-10-15. Hub id clips/mfaq.", "what_it_is_for": "The paper says the model ranks FAQ answers for a question. A shared encoder embeds the question and the answer, and similarity is a dot product. The authors say a multilingual model beat language-specific encoders except in English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "antwerp/republic/republic": { "id": "antwerp/republic/republic", "name": "RePublic", "maker": "antwerp", "family": "antwerp/republic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-05-19", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT sequence classifier started from BERTje (University of Groningen). config.json: BertForSequenceClassification, 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocab 30073, max_position_embeddings 512. Labels: 0 neu, 1 pos, 2 neg. The card says domain-adaptive pretraining used masked language modeling and next-sentence prediction on 91,661 Flemish news articles from 2000 to 2020, then 4 epochs of sentiment fine-tuning on 4,404 sentences (batch size 8, learning rate 5e-5).", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RePublic (reputation analyzer for public service organizations) is a Dutch news-sentiment classifier from CLiPS at the University of Antwerp, built with Jan Boon. It is a domain-adapted BERTje model with a three-way classification head. Weights are public. Collected 2026-10-15. The card states no license.", "what_it_is_for": "The card says the model predicts positive, negative, or neutral sentiment in Dutch news about public agencies. Domain text is Flemish coverage of 24 public bodies, including De Lijn, VDAB, and Agentschap Zorg en Gezondheid, from 2000 through 2020.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anu/e2e-vae/e2e-flux-vae": { "id": "anu/e2e-vae/e2e-flux-vae", "name": "E2E-FLUX-VAE", "maker": "anu", "family": "anu/e2e-vae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-15", "context_length": null, "max_output_tokens": null, "parameters": "83819683", "architecture": "diffusers AutoencoderKL, continued from the FLUX.1-dev VAE. Saved config: SiLU, block channels 128/256/512/512, 2 layers per block, 16 latent channels, no quant conv, scaling_factor 0.3611, shift_factor 0.1159, config sample_size 1024. Hub safetensors are F32. The checkpoint _name_or_path is pretrained_models/flux-vae-mjhq512bs128fix-e2e-lr2e-5-400k.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E2E-FLUX-VAE is an Australian National University and collaborator release: a FLUX.1 variational autoencoder continued with the REPA-E end-to-end recipe and published as a diffusers checkpoint. It is an active open-weight tokenizer, not a text model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the checkpoint is the FLUX VAE after 80 epochs of REPA-E tuning on ImageNet-256, so later text-to-image training can swap it in for the original tokenizer. The project page says the same family improves downstream generation without adding a REPA loss at T2I training time. The weights are the VAE only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anu/e2e-vae/e2e-in-vae": { "id": "anu/e2e-vae/e2e-in-vae", "name": "E2E-IN-VAE", "maker": "anu", "family": "anu/e2e-vae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-30", "context_length": null, "max_output_tokens": null, "parameters": "69836067", "architecture": "REPA-E calls the family f16d32. The diffusers port is AutoencoderKL: SiLU, stages 128/128/256/256/512, attention on the last down and first up stage, 32 latent channels, quant convs on, config sample_size 32. Hub safetensors are F32 and total 69,836,067, the same count as the E2E-VA-VAE port. The README file name is e2e-invae-400k.pt. MiniDog uses 16x16x32 latents from this tokenizer at 256 resolution.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E2E-IN-VAE is the REPA-E end-to-end tune of the lab's own ImageNet f16d32 tokenizer. The ANU-led REPA-E org released the PyTorch checkpoint on 30 March 2025 and a diffusers port on 15 October 2025. MiniDog later freezes it for a small text-to-image model. Collected 2026-10-15.", "what_it_is_for": "The README ships it as a drop-in latent tokenizer after joint training with a SiT generator. MiniDog's project page picks it over E2E-VA-VAE for dog-breed flow matching because reconstruction and the resulting gFID were both better on that setup.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anu/e2e-vae/e2e-qwen-image-vae": { "id": "anu/e2e-vae/e2e-qwen-image-vae", "name": "E2E-Qwen-Image-VAE", "maker": "anu", "family": "anu/e2e-vae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-15", "context_length": null, "max_output_tokens": null, "parameters": "126892531", "architecture": "diffusers AutoencoderKLQwenImage continued from the Qwen-Image VAE. Saved config: base_dim 96, dim_mult 1/2/4/4, z_dim 16, num_res_blocks 2, dropout 0, temporal downsample [false, true, true]. Hub safetensors are F32. Checkpoint _name_or_path is pretrained_models/qwenimage-vae-e2e-lr2e-5-400k. The quickstart unsqueezes a frame dimension before encode.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E2E-Qwen-Image-VAE is the REPA-E end-to-end tune of the Qwen-Image variational autoencoder, published by the ANU-led REPA-E Hugging Face org in October 2025. It is an active open-weight image tokenizer with a video-style frame axis. Collected 2026-10-15.", "what_it_is_for": "The project page includes it in the family of tokenizers meant to speed and improve text-to-image training when swapped for the original Qwen-Image VAE. The released repo is the autoencoder, not a Qwen diffusion transformer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anu/e2e-vae/e2e-sd-3-5-vae": { "id": "anu/e2e-vae/e2e-sd-3-5-vae", "name": "E2E-SD-3.5-VAE", "maker": "anu", "family": "anu/e2e-vae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-15", "context_length": null, "max_output_tokens": null, "parameters": "83819683", "architecture": "diffusers AutoencoderKL continued from the Stable Diffusion 3.5 VAE. Saved config: SiLU, block channels 128/256/512/512, 2 layers per block, 16 latent channels, no quant conv, scaling_factor 1.5305, shift_factor 0.0609, config sample_size 1024. Hub safetensors are F32. Checkpoint _name_or_path is pretrained_models/sd3.5-vae-e2e-lr2e-5-400k.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E2E-SD-3.5-VAE is the REPA-E group's end-to-end tune of the Stable Diffusion 3.5 variational autoencoder, released by the ANU-led REPA-E lab with Canva coauthors on the T2I page. The weights are an active MIT-tagged diffusers checkpoint. Collected 2026-10-15.", "what_it_is_for": "The project page treats it as a drop-in tokenizer for text-to-image diffusion transformers. It was tuned on ImageNet 256 so the latent space works better for generation than a reconstruction-only SD 3.5 VAE. This repo is not the diffusion transformer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anu/e2e-vae/e2e-sd-vae": { "id": "anu/e2e-vae/e2e-sd-vae", "name": "E2E-SD-VAE", "maker": "anu", "family": "anu/e2e-vae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-30", "context_length": null, "max_output_tokens": null, "parameters": "83653863", "architecture": "REPA-E calls the ImageNet tokenizer f8d4. The October 2025 diffusers port is AutoencoderKL: SiLU, blocks 128/256/512/512, 4 latent channels, quant convs on, scaling_factor 0.18215, config sample_size 512. Hub safetensors of the port are F32 and total 83,653,863. The March 2025 repo stores e2e-sdvae-400k.pt plus a discriminator checkpoint and latent stats. Parameter count is for the diffusers port only.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E2E-SD-VAE is the REPA-E fine-tune of the Stable Diffusion f8d4 variational autoencoder, released by the ANU-led REPA-E lab. A PyTorch checkpoint went up in March 2025 and a diffusers port in October 2025. Both are still online. Collected 2026-10-15.", "what_it_is_for": "The README says the tuned VAE is a drop-in replacement for the frozen SD-VAE when training latent diffusion models. The paired generator is SiT-XL/2 in `REPA-E/sit-repae-sdvae`. This folder is only the tokenizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anu/e2e-vae/e2e-va-vae": { "id": "anu/e2e-vae/e2e-va-vae", "name": "E2E-VA-VAE", "maker": "anu", "family": "anu/e2e-vae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-30", "context_length": null, "max_output_tokens": null, "parameters": "69836067", "architecture": "REPA-E calls it f16d32. The diffusers port is AutoencoderKL: SiLU, five encoder stages with channels 128/128/256/256/512, attention on the last down and first up stage, 32 latent channels, quant convs on, config sample_size 32. Hub safetensors of the port are F32 and total 69,836,067. The README's training file name is e2e-vavae-400k.pt.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E2E-VA-VAE is the REPA-E end-to-end tune of the LightningDiT VA-VAE tokenizer, released by the ANU-led REPA-E lab. The PyTorch weights date from 30 March 2025 and a diffusers port from 15 October 2025. It is the frozen tokenizer behind the lab's best published ImageNet SiT run. Collected 2026-10-15.", "what_it_is_for": "The README tells users to cache latents with this VAE and then train a SiT generator on those latents. Kept frozen, with a REPA loss, SiT-XL/1 is the system that the current README scores at gFID 1.69 without classifier-free guidance and 1.12 with it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anu/in-vae/in-vae": { "id": "anu/in-vae/in-vae", "name": "IN-VAE", "maker": "anu", "family": "anu/in-vae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "README: f16d32 variational autoencoder trained from scratch on ImageNet-1K with the latent-diffusion codebase and a custom architecture. Hub files are invae-f16d32.pt, a discriminator checkpoint, and latent stats. The July 2026 diffusers repo REPA-E/invae-hf has an empty template card and a safetensors total of 69,836,067, the same count as E2E-IN-VAE. Those weights were not hash-compared to invae-f16d32.pt, so the parameter field is left empty.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IN-VAE is the f16d32 variational autoencoder the REPA-E lab trained from scratch on ImageNet-1K before end-to-end tuning. The ANU-led group released the PyTorch weights on 27 March 2025 under an MIT Hub tag. It is still online. Collected 2026-10-15.", "what_it_is_for": "The README lists it as one of three tokenizers you download before REPA-E training. Unlike the SD-VAE and VA-VAE files in the same section, this one is described as trained from scratch with a custom architecture on the latent-diffusion codebase. The tuned weights are a different model, E2E-IN-VAE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anu/minidog/minidog": { "id": "anu/minidog/minidog", "name": "MiniDog", "maker": "anu", "family": "anu/minidog", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-10-04", "context_length": null, "max_output_tokens": null, "parameters": "22.06M", "architecture": "Project page: LightningDiT, 12 layers, hidden size 384, 6 heads, patch size 1, input 16\u00d716\u00d732, 22.06M parameters. Four timestep tokens and 128 text tokens. Frozen Qwen3-0.6B and frozen E2E-INVAE. Pretraining uses iREPA at block 4 against EUPE ViT-B/16, MSE, lambda 0.5, a 3\u00d73 conv projector from 384 to 768, spatial norm gamma 0.7. v-prediction, Euler ODE, 50 steps, CFG scale 6.0. The checkpoint card says the pretraining file also stores a 2.7M-parameter iREPA projector used only during pretraining. SFT drops the alignment loss. The abstract rounds the transformer to 22M parameters.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniDog is an Australian National University teaching model for dog-breed text-to-image flow matching. The checkpoint repo was created on 4 October 2026. The paper was submitted on 13 October 2026 and revised the next day. Pretraining and supervised fine-tuning are two versions of one release. Collected 2026-10-15.", "what_it_is_for": "The project page trains a small LightningDiT to generate images of 20 dog breeds. Pretraining uses 26,000 recaptioned ImageNet photos. Fine-tuning uses 2,000 FLUX.1-schnell images of the same breeds. The authors say the project does not claim technical novelty and is meant for a course.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anu/repa-e/sit-xl-1-e2e-vavae": { "id": "anu/repa-e/sit-xl-1-e2e-vavae", "name": "SiT-XL/1 E2E-VA-VAE", "maker": "anu", "family": "anu/repa-e", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Class-conditional ImageNet generator with a frozen tokenizer. args.json names the model SiT-XL/1, 1000 classes, resolution 256, linear path, v-prediction, batch 256, mixed precision fp16, learning rate 1e-4, betas 0.9 and 0.999, weight decay 0, max grad norm 1.0, seed 0, condition dropout 0.1. Alignment is DINOv2-ViT-B at encoder depth 8 with proj_coeff 0.5. The VAE is f16d32, vae_latents_name e2e-vavae, checkpoint pretrained/e2e-vavae/e2e-vavae-400k.pt. There is no vae_learning_rate. max_train_steps is 4000000. The README calls this 800 epochs and the REPA method. The args epochs field is still 1400 and is not that stop. Released file: checkpoints/4000000.pt. exp_name sit-xl-1-dinov2-b-enc8-ldm-only-e2e-vavae-0.5-4m.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SiT-XL/1 E2E-VA-VAE is the ANU-led REPA-E lab's class-conditional ImageNet generator trained with a frozen E2E-VA-VAE tokenizer and a REPA loss. Weights were uploaded on 31 March 2025 as REPA-E/sit-ldm-e2e-vavae. The current README treats this row as the lab's best ImageNet number. Collected 2026-10-15.", "what_it_is_for": "The README's frozen-VAE table points this checkpoint at E2E-VA-VAE, SiT-XL/1, and the REPA method for 800 epochs. The VAE is not updated. args.json has no VAE learning rate and stops at 4,000,000 steps. The file on the Hub is checkpoints/4000000.pt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anu/repa-e/sit-xl-1-repae-invae": { "id": "anu/repa-e/sit-xl-1-repae-invae", "name": "SiT-XL/1 REPA-E IN-VAE", "maker": "anu", "family": "anu/repa-e", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Class-conditional ImageNet generator. args.json names the model SiT-XL/1, 1000 classes, resolution 256, linear path, v-prediction, batch 256, mixed precision fp16, Adam learning rate 1e-4, betas 0.9 and 0.999, weight decay 0, max grad norm 1.0, seed 0, condition dropout 0.1. Alignment is DINOv2-ViT-B at encoder depth 8, proj_coeff 0.5, vae_align_proj_coeff 1.5. The run starts from f16d32 IN-VAE (pretrained/invae/invae-f16d32.pt) and trains the VAE and its discriminator at 1e-4. max_train_steps is 400000, which the README calls 80 epochs. The args epochs field is 1400 and is not that stop. Released file: checkpoints/0400000.pt. exp_name sit-xl-1-dinov2-b-enc8-repae-invae-0.5-1.5-400k. The README says the jointly tuned VAE stays inside this checkpoint.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SiT-XL/1 REPA-E IN-VAE is the ANU-led REPA-E lab's class-conditional ImageNet generator trained jointly from the lab's from-scratch IN-VAE. Weights were uploaded on 27 March 2025 as REPA-E/sit-repae-invae. Collected 2026-10-15.", "what_it_is_for": "The README lists this checkpoint as SiT-XL/1 trained end to end from IN-VAE for 80 epochs. Generation is class-conditional at 256. The tuned tokenizer from the same run is the separate E2E-IN-VAE release. The README says that tuned VAE is also still inside this generator file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anu/repa-e/sit-xl-1-repae-vavae": { "id": "anu/repa-e/sit-xl-1-repae-vavae", "name": "SiT-XL/1 REPA-E VA-VAE", "maker": "anu", "family": "anu/repa-e", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Class-conditional ImageNet generator. args.json names the model SiT-XL/1, 1000 classes, resolution 256, linear path, v-prediction, batch 256, mixed precision fp16, Adam learning rate 1e-4, betas 0.9 and 0.999, weight decay 0, max grad norm 1.0, seed 0, condition dropout 0.1. Alignment is DINOv2-ViT-B at encoder depth 8, proj_coeff 0.5, vae_align_proj_coeff 1.5. The run starts from f16d32 VA-VAE (pretrained/vavae/vavae-f16d32.pt) and trains the VAE and its discriminator at 1e-4. max_train_steps is 400000, which the README calls 80 epochs. The args epochs field is 1400 and is not that stop. Released file: checkpoints/0400000.pt. The README says the jointly tuned VAE stays inside this checkpoint. The Hub card text is the generic REPA-E readme.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SiT-XL/1 REPA-E VA-VAE is the ANU-led REPA-E lab's class-conditional ImageNet generator trained jointly from a LightningDiT VA-VAE. Weights were uploaded on 30 March 2025 as REPA-E/sit-repae-vavae. Collected 2026-10-15.", "what_it_is_for": "The README lists this checkpoint as SiT-XL/1 trained end to end from VA-VAE for 80 epochs. The card body is the shared REPA-E readme. The jointly tuned tokenizer is E2E-VA-VAE, which the README also shows how to extract from checkpoints/0400000.pt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "anu/repa-e/sit-xl-2-repae-sdvae": { "id": "anu/repa-e/sit-xl-2-repae-sdvae", "name": "SiT-XL/2 REPA-E SD-VAE", "maker": "anu", "family": "anu/repa-e", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Class-conditional ImageNet generator. args.json names the model SiT-XL/2, 1000 classes, resolution 256, linear path, v-prediction, uniform weighting, batch 256, mixed precision fp16, Adam learning rate 1e-4, betas 0.9 and 0.999, weight decay 0, max grad norm 1.0, seed 0, condition dropout 0.1. Alignment is DINOv2-ViT-B at encoder depth 8, proj_coeff 0.5, vae_align_proj_coeff 1.5. The run starts from f8d4 SD-VAE (pretrained/sdvae/sdvae-f8d4.pt) and trains the VAE and its discriminator at 1e-4. max_train_steps is 400000, which the README calls 80 epochs. The args epochs field is 1400 and is not that stop. Released file: checkpoints/0400000.pt. The README says the jointly tuned VAE stays inside this checkpoint and is also released as E2E-SD-VAE.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SiT-XL/2 REPA-E SD-VAE is the ANU-led REPA-E lab's class-conditional ImageNet generator trained jointly with an SD-VAE tokenizer. Weights were uploaded on 27 March 2025 under REPA-E/sit-repae-sdvae. The checkpoint is still the README's published row for that setup. Collected 2026-10-15.", "what_it_is_for": "The README table lists this checkpoint as SiT-XL/2 trained end to end from SD-VAE for 80 epochs. It is a generator. The initial tokenizer is not the released E2E-SD-VAE. That tuned VAE is stored inside this checkpoint and also shipped on its own.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/4m/4m-21": { "id": "apple/4m/4m-21", "name": "4M-21", "maker": "apple", "family": "apple/4m", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Any-to-any vision model for 21 modalities, co-trained on multimodal data and text. Same size classes as 4M-7: B 198M, L 705M, XL 2.8B, trained on CC12M+COYO700M+C4. Hosted as EPFL-VILAB/4M-21_B, _L, and _XL.", "modalities": { "input": [ "image", "text" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "4M-21 is the Apple and EPFL any-to-any model for 21 modalities, released with apple/ml-4m. Collected 2026-10-15. B, L, and XL checkpoints are the public sizes.", "what_it_is_for": "Predict across tens of vision tasks and modalities, including text co-training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/4m/4m-7": { "id": "apple/4m/4m-7", "name": "4M-7", "maker": "apple", "family": "apple/4m", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Masked multimodal model for 7 modalities. Apple and EPFL. GitHub model zoo: B is 198M parameters, L is 705M, XL is 2.8B. Dataset variants CC12M and COYO700M. Weights load from EPFL-VILAB Hugging Face ids such as EPFL-VILAB/4M-7_B_CC12M.", "modalities": { "input": [ "image" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "4M-7 is the Apple and EPFL masked multimodal model for seven modalities, with B, L, and XL checkpoints. Code is in apple/ml-4m. Collected 2026-10-15. Weights are open and hosted by EPFL-VILAB.", "what_it_is_for": "Any-to-any prediction across a small set of vision modalities.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/adm/adm-3-cloud": { "id": "apple/adm/adm-3-cloud", "name": "ADM 3 Cloud", "maker": "apple", "family": "apple/adm", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released.", "release_date": "2026-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Apple diffusion model for image generation and editing on Private Cloud Compute using Apple silicon. The base model handles creation, editing, and Genmoji. Specialized adapters cover Spatial Reframing in Photos and touch-based edits and personalization in Image Playground. Apple says it generalizes across aspect ratios and resolutions and is guided by the broader foundation-model family. Parameter count is not disclosed.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ADM 3 Cloud is Apple's 2026 server image model, a diffusion model for creation and editing on Private Cloud Compute. Announced June 8, 2026 as one of five foundation models. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Image Playground, Genmoji, photo editing including Spatial Reframing, and other image generation and editing features.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/afm/afm-3-cloud": { "id": "apple/afm/afm-3-cloud", "name": "AFM 3 Cloud", "maker": "apple", "family": "apple/afm", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Not distributed as weights. Apple Inc. is the provider on the September 23, 2026 documentation form.", "release_date": "2026-09-14", "context_length": 32768, "max_output_tokens": 32768, "parameters": "", "architecture": "General-purpose multimodal Parallel-Track Mixture-of-Experts model, an upgrade of the 2025 PT-MoE server design. Apple's EU-style documentation form places the parameter count in the range 500B to 1T. Optimized for Apple silicon Private Cloud Compute. Inputs: text, images, and video, each documented at a 32K-token maximum. Text output maximum 32K tokens.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM 3 Cloud is Apple's 2026 server workhorse, a multimodal PT-MoE model on Private Cloud Compute. The provider form lists a September 14, 2026 release, 32K token limits, and a 500B-1T parameter range. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Server-side text, image, and video understanding for Apple Intelligence and entitled third-party apps, optimized for speed and efficiency on Apple silicon.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/afm/afm-3-cloud-pro": { "id": "apple/afm/afm-3-cloud-pro", "name": "AFM 3 Cloud Pro", "maker": "apple", "family": "apple/afm", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released.", "release_date": "2026-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Apple's most capable 2026 server foundation model. Optimized for NVIDIA GPUs. Apple says it worked with Google and NVIDIA to extend Private Cloud Compute to NVIDIA GPUs in Google Cloud, and that pretraining used cloud TPU accelerators. Parameter count is not disclosed. The June 8, 2026 post says the family was custom-built in collaboration with Google.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM 3 Cloud Pro is Apple's most capable 2026 server foundation model, aimed at agentic tool use and complex reasoning. It runs on NVIDIA GPUs in Google Cloud under an extension of Private Cloud Compute. Announced June 8, 2026. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "The hardest Apple Intelligence requests, including agentic tool use, complex reasoning, and stronger text and image understanding than AFM 3 Cloud.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/afm/afm-3-core": { "id": "apple/afm/afm-3-core", "name": "AFM 3 Core", "maker": "apple", "family": "apple/afm", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released.", "release_date": "2026-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dense model, about 3 billion parameters. Next generation of Apple's on-device 3B line. Optimized for Apple silicon with quantization-aware training. Trained with a shared foundation that was then specialized; post-training uses supervised fine-tuning and multi-stage reinforcement learning. Apple says the family was custom-built in collaboration with Google.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM 3 Core is Apple's 2026 on-device dense foundation model, about 3 billion parameters, announced June 8, 2026. Collected 2026-10-15. It is the quality step up from the 2025 on-device model. Weights are closed.", "what_it_is_for": "General on-device text and image-understanding tasks in the third-generation Apple Intelligence stack.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/afm/afm-3-core-advanced": { "id": "apple/afm/afm-3-core-advanced", "name": "AFM 3 Core Advanced", "maker": "apple", "family": "apple/afm", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released.", "release_date": "2026-06-08", "context_length": null, "max_output_tokens": null, "parameters": "20000000000", "architecture": "Sparse on-device model with 20 billion parameters. Instruction-Following Pruning selects experts per prompt, activating 1 to 4 billion parameters depending on the request. Weights live in NAND flash and a subset is patched with always-on shared experts into DRAM. A lightweight dense block picks the expert set and can reselect during generation. Natively multimodal. Text-to-speech results below were measured at the 1-billion-parameter activation size. Optimized for the most capable Apple silicon.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM 3 Core Advanced is Apple's most capable 2026 on-device foundation model, a 20-billion-parameter sparse multimodal model that activates 1 to 4 billion parameters. Announced June 8, 2026. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "On-device multimodal features that need more than AFM 3 Core, including expressive text-to-speech and higher-accuracy dictation, plus image understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/afm/afm-on-device": { "id": "apple/afm/afm-on-device", "name": "AFM-on-device", "maker": "apple", "family": "apple/afm", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released.", "release_date": "2024-07-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dense decoder-only Transformer. Tech report table: model dimension 3072, head dimension 128, 24 query heads, 8 key/value heads, 26 layers, 2.58B non-embedding parameters and 0.15B embedding parameters. Shared input/output embeddings, RMSNorm pre-normalization, query/key normalization, grouped-query attention, SwiGLU, RoPE. Vocabulary size 49K. Apple also describes the model as about 3 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apple's first named on-device Apple Foundation Model, a roughly 3-billion-parameter dense language model that shipped inside Apple Intelligence in 2024. Collected 2026-10-15. It has been superseded by later on-device generations. Weights remain closed.", "what_it_is_for": "Apple says AFM-on-device powers on-device Apple Intelligence tasks such as writing, summarization, and feature-specific adapters, running on Apple silicon.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/afm/afm-on-device-2025": { "id": "apple/afm/afm-on-device-2025", "name": "AFM on-device (2025)", "maker": "apple", "family": "apple/afm", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Developers call the on-device model through the Foundation Models framework.", "release_date": "2025-07-17", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "About 3 billion parameters, dense, optimized for Apple silicon. KV-cache sharing: block 1 holds 62.5% of transformer layers and block 2 holds 37.5% with key/value projections removed, cutting KV-cache memory by 37.5%. Decoder weights compressed to about 2 bits per weight with quantization-aware training; embeddings 4-bit via QAT; KV cache 8-bit. Adapter recovery is used. The Foundation Models framework exposes this generation to third-party apps.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apple's 2025 on-device foundation model, an approximately 3-billion-parameter multilingual multimodal model behind Apple Intelligence and the Foundation Models framework. Collected 2026-10-15. Still the documented 4096-token system model on OS 26. Weights are closed.", "what_it_is_for": "On-device text and image understanding, guided generation, and tool calling for Apple Intelligence and third-party apps, with prompts staying on device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/afm/afm-server": { "id": "apple/afm/afm-server", "name": "AFM-server", "maker": "apple", "family": "apple/afm", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released.", "release_date": "2024-07-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dense decoder-only Transformer trained from scratch, larger than AFM-on-device. Shared embeddings, RMSNorm, grouped-query attention, SwiGLU, RoPE. Vocabulary size 100K. Core pretraining ran for 6.3T tokens on 8192 TPUv4 chips, sequence length 4096, batch of 4096 sequences.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apple's 2024 server Apple Foundation Model, a large dense language model for Private Cloud Compute. Collected 2026-10-15. Superseded by the 2025 parallel-track mixture-of-experts server model. Weights are closed.", "what_it_is_for": "Apple says AFM-server handles Apple Intelligence requests that run on Private Cloud Compute rather than on device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/afm/afm-server-2025": { "id": "apple/afm/afm-server-2025", "name": "AFM server (2025)", "maker": "apple", "family": "apple/afm", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released.", "release_date": "2025-07-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Parallel-Track Mixture-of-Experts (PT-MoE) transformer with track parallelism, sparse experts, and interleaved global and local attention, built for Private Cloud Compute. Decoder weights compressed to about 3.56 bits per weight with ASTC; embeddings 4-bit post-training; KV cache 8-bit. Parameter count is not disclosed.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apple's 2025 server foundation model, a parallel-track mixture-of-experts model for Private Cloud Compute. Collected 2026-10-15. It is the baseline Apple compares against AFM 3 Cloud. Weights are closed.", "what_it_is_for": "Server-side Apple Intelligence for requests that exceed the on-device model, including image understanding and tool use, under Private Cloud Compute.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/aim/aim-1b": { "id": "apple/aim/aim-1b", "name": "AIM-1B", "maker": "apple", "family": "apple/aim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-01-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive image model pretrained with a generative objective, then evaluated with a frozen trunk on ImageNet-1k. The size in the name is Apple's size class. Exact integer parameter counts were not in the Hub safetensors metadata.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AIM-1B is an Apple open-weight autoregressive vision model from the January 2024 AIM release. Collected 2026-10-15. Frozen-trunk ImageNet-1k last-layer top-1 is 80.6%.", "what_it_is_for": "Vision representation learning and ImageNet classification with a frozen trunk.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/aim/aim-3b": { "id": "apple/aim/aim-3b", "name": "AIM-3B", "maker": "apple", "family": "apple/aim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-01-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive image model pretrained with a generative objective, then evaluated with a frozen trunk on ImageNet-1k. The size in the name is Apple's size class. Exact integer parameter counts were not in the Hub safetensors metadata.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AIM-3B is an Apple open-weight autoregressive vision model from the January 2024 AIM release. Collected 2026-10-15. Frozen-trunk ImageNet-1k last-layer top-1 is 82.2%.", "what_it_is_for": "Vision representation learning and ImageNet classification with a frozen trunk.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/aim/aim-600m": { "id": "apple/aim/aim-600m", "name": "AIM-600M", "maker": "apple", "family": "apple/aim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-01-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive image model pretrained with a generative objective, then evaluated with a frozen trunk on ImageNet-1k. The size in the name is Apple's size class. Exact integer parameter counts were not in the Hub safetensors metadata.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AIM-600M is an Apple open-weight autoregressive vision model from the January 2024 AIM release. Collected 2026-10-15. Frozen-trunk ImageNet-1k last-layer top-1 is 78.5%.", "what_it_is_for": "Vision representation learning and ImageNet classification with a frozen trunk.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/aim/aim-7b": { "id": "apple/aim/aim-7b", "name": "AIM-7B", "maker": "apple", "family": "apple/aim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-01-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive image model pretrained with a generative objective, then evaluated with a frozen trunk on ImageNet-1k. The size in the name is Apple's size class. Exact integer parameter counts were not in the Hub safetensors metadata.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AIM-7B is an Apple open-weight autoregressive vision model from the January 2024 AIM release. Collected 2026-10-15. Frozen-trunk ImageNet-1k last-layer top-1 is 82.4%.", "what_it_is_for": "Vision representation learning and ImageNet classification with a frozen trunk.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/aimv2/aimv2-1b": { "id": "apple/aimv2/aimv2-1b", "name": "AIMv2-1B", "maker": "apple", "family": "apple/aimv2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-10-29", "context_length": null, "max_output_tokens": null, "parameters": "1234958336", "architecture": "AIMv2 vision transformer, 24 layers, hidden size 2048, 16 attention heads, patch size 14. Parameter count is the 224px safetensors total. Higher resolutions change the positional embedding size slightly.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AIMv2-1B is an Apple open-weight AIMv2 vision encoder. Collected 2026-10-15. The 224px checkpoint has 1234958336 parameters and ImageNet-1k accuracy 88.1 on the model card.", "what_it_is_for": "Image feature extraction. A paired text tower exists for the large LIT checkpoint, cataloged separately.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/aimv2/aimv2-3b": { "id": "apple/aimv2/aimv2-3b", "name": "AIMv2-3B", "maker": "apple", "family": "apple/aimv2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-10-29", "context_length": null, "max_output_tokens": null, "parameters": "2720658432", "architecture": "AIMv2 vision transformer, 24 layers, hidden size 3072, 24 attention heads, patch size 14. Parameter count is the 224px safetensors total. Higher resolutions change the positional embedding size slightly.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AIMv2-3B is an Apple open-weight AIMv2 vision encoder. Collected 2026-10-15. The 224px checkpoint has 2720658432 parameters and ImageNet-1k accuracy 88.5 on the model card.", "what_it_is_for": "Image feature extraction. A paired text tower exists for the large LIT checkpoint, cataloged separately.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/aimv2/aimv2-huge": { "id": "apple/aimv2/aimv2-huge", "name": "AIMv2-huge", "maker": "apple", "family": "apple/aimv2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-10-29", "context_length": null, "max_output_tokens": null, "parameters": "680851968", "architecture": "AIMv2 vision transformer, 24 layers, hidden size 1536, 12 attention heads, patch size 14. Parameter count is the 224px safetensors total. Higher resolutions change the positional embedding size slightly.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AIMv2-huge is an Apple open-weight AIMv2 vision encoder. Collected 2026-10-15. The 224px checkpoint has 680851968 parameters and ImageNet-1k accuracy 87.5 on the model card.", "what_it_is_for": "Image feature extraction. A paired text tower exists for the large LIT checkpoint, cataloged separately.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/aimv2/aimv2-large": { "id": "apple/aimv2/aimv2-large", "name": "AIMv2-large", "maker": "apple", "family": "apple/aimv2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-10-29", "context_length": null, "max_output_tokens": null, "parameters": "309197824", "architecture": "AIMv2 vision transformer, 24 layers, hidden size 1024, 8 attention heads, patch size 14. Parameter count is the 224px safetensors total. Higher resolutions change the positional embedding size slightly.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AIMv2-large is an Apple open-weight AIMv2 vision encoder. Collected 2026-10-15. The 224px checkpoint has 309197824 parameters and ImageNet-1k accuracy 86.6 on the model card.", "what_it_is_for": "Image feature extraction. A paired text tower exists for the large LIT checkpoint, cataloged separately.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/aimv2/aimv2-large-lit": { "id": "apple/aimv2/aimv2-large-lit", "name": "AIMv2-large-lit", "maker": "apple", "family": "apple/aimv2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-11-20", "context_length": 77, "max_output_tokens": null, "parameters": "436680192", "architecture": "AIMv2 vision tower (24 layers, hidden 1024, 224px, patch 14) plus a text tower (12 layers, hidden 768, 6 heads, vocab 49408, max position embeddings 77). Hub pipeline is zero-shot image classification. Parameter count is the safetensors total.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AIMv2-large-lit is Apple's open-weight image-text AIMv2 checkpoint, 436,680,192 parameters, released November 2024. Collected 2026-10-15.", "what_it_is_for": "Zero-shot image classification and image-text embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/cadd/cadd-base-7b": { "id": "apple/cadd/cadd-base-7b", "name": "CADD-Base-7B", "maker": "apple", "family": "apple/cadd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2026-04-23", "context_length": null, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Masked diffusion code model with Continuously Augmented Discrete Diffusion. A continuous embedding is added to masked-token embeddings along a linear flow-matching path. Base model field: Qwen/Qwen2.5-Coder-7B-Instruct. Safetensors total 7,615,616,512. Hub model type Dream.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CADD-Base-7B is Apple's April 2026 open-weight diffusion code model that adds a continuous flow-matching signal to discrete denoising. Collected 2026-10-15. 7,615,616,512 parameters.", "what_it_is_for": "Code generation research. The card says the continuous signal can sit on top of several unmasking algorithms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/clara/clara-7b-base": { "id": "apple/clara/clara-7b-base", "name": "CLaRa-7B-Base", "maker": "apple", "family": "apple/clara", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unified retrieval-augmented generation model with continuous latent document compression, built from mistralai/Mistral-7B-Instruct-v0.2. Foundational unified RAG model with semantic document compression at 16x and 128x. Trained with QA-guided semantic compression and paraphrase consistency.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLaRa-7B-Base is Apple's open-weight compressed-RAG model, released December 2025, adapted from Mistral-7B-Instruct-v0.2. Collected 2026-10-15.", "what_it_is_for": "Retrieval-augmented question answering with documents compressed into continuous latents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/clara/clara-7b-e2e": { "id": "apple/clara/clara-7b-e2e", "name": "CLaRa-7B-E2E", "maker": "apple", "family": "apple/clara", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unified retrieval-augmented generation model with continuous latent document compression, built from mistralai/Mistral-7B-Instruct-v0.2. End-to-end model that jointly optimizes retrieval and generation at 16x and 128x compression. generation_top_k controls the effective top-k.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLaRa-7B-E2E is Apple's open-weight compressed-RAG model, released December 2025, adapted from Mistral-7B-Instruct-v0.2. Collected 2026-10-15.", "what_it_is_for": "Retrieval-augmented question answering with documents compressed into continuous latents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/clara/clara-7b-instruct": { "id": "apple/clara/clara-7b-instruct", "name": "CLaRa-7B-Instruct", "maker": "apple", "family": "apple/clara", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unified retrieval-augmented generation model with continuous latent document compression, built from mistralai/Mistral-7B-Instruct-v0.2. Instruction-tuned unified RAG model with the same 16x and 128x compression.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLaRa-7B-Instruct is Apple's open-weight compressed-RAG model, released December 2025, adapted from Mistral-7B-Instruct-v0.2. Collected 2026-10-15.", "what_it_is_for": "Retrieval-augmented question answering with documents compressed into continuous latents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/dclm/dclm-7b": { "id": "apple/dclm/dclm-7b", "name": "DCLM-7B", "maker": "apple", "family": "apple/dclm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Sample Code License (Hugging Face license id apple-ascl).", "release_date": "2024-07-11", "context_length": 2048, "max_output_tokens": null, "parameters": "6889674752", "architecture": "Decoder-only Transformer, OpenLM. Card: 32 layers, hidden size 4096, 32 heads, 2.5T training tokens, context 2048. Config agrees: seq_len 2048, vocab 50432, SwiGLU, QK norm, rotary embeddings. Safetensors total 6,889,674,752. Developed by the DataComp for Language Models team and hosted on the Apple Hub.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DCLM-7B is the DataComp-LM baseline language model, 6.89B weights, hosted by Apple under the Apple Sample Code License. Released July 2024. Collected 2026-10-15. Few-shot MMLU on the card is 63.72.", "what_it_is_for": "Show the effect of the DCLM data-curation baseline. English decoder-only pretraining, not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/dclm/dclm-7b-8k": { "id": "apple/dclm/dclm-7b-8k", "name": "DCLM-7B-8k", "maker": "apple", "family": "apple/dclm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Sample Code License (Hugging Face license id apple-ascl).", "release_date": "2024-07-15", "context_length": 8192, "max_output_tokens": null, "parameters": "6889674752", "architecture": "Same 7B OpenLM shape as DCLM-7B: 32 layers, hidden 4096, 32 heads, vocab 50432. Card: 2.6T tokens, sequence length 8192, batch 2048 sequences, AdamW peak learning rate 2e-3, weight decay 0.05. Config seq_len is 8192.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DCLM-7B-8k is the 8192-token context continuation of DCLM-7B, same 6.89B parameter count, 2.6T training tokens on the card. Collected 2026-10-15.", "what_it_is_for": "Longer-context open language model from the DCLM baseline line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/depth-pro/depth-pro": { "id": "apple/depth-pro/depth-pro", "name": "Depth Pro", "maker": "apple", "family": "apple/depth-pro", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Zero-shot metric monocular depth model. Multi-scale vision transformer for dense prediction. Also estimates focal length from one image. The Hugging Face transformers port (DepthPro-hf) uses patch size 384. The card says the released checkpoint was re-trained and is close to, but not identical to, the paper model. Apple describes a 2.25-megapixel depth map in 0.3 seconds on a standard GPU.", "modalities": { "input": [ "image" ], "output": [ "depth" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Depth Pro is Apple's open-weight zero-shot metric monocular depth model, published October 2024. Collected 2026-10-15. The Hub checkpoint is a re-trained reference that Apple says does not exactly match the paper numbers.", "what_it_is_for": "Sharp high-resolution metric depth from a single image, plus focal-length estimation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/dfn/dfn2b-clip-vit-b-16": { "id": "apple/dfn/dfn2b-clip-vit-b-16", "name": "DFN2B-CLIP-ViT-B-16", "maker": "apple", "family": "apple/dfn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2023-10-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP contrastive image-text model, ViT-B/16, image side 224px. Converted to PyTorch from JAX Axlearn checkpoints and usable in OpenCLIP. ", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DFN2B-CLIP-ViT-B-16 is an Apple open-weight CLIP model from the Data Filtering Networks release. Collected 2026-10-15. The model card reports ImageNet-1k zero-shot accuracy of 0.76236.", "what_it_is_for": "Zero-shot image classification and retrieval. DFN is the data-filtering method used to build the training pool.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/dfn/dfn2b-clip-vit-l-14": { "id": "apple/dfn/dfn2b-clip-vit-l-14", "name": "DFN2B-CLIP-ViT-L-14", "maker": "apple", "family": "apple/dfn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2023-10-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP contrastive image-text model, ViT-L/14, image side 224px. Converted to PyTorch from JAX Axlearn checkpoints and usable in OpenCLIP. ", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DFN2B-CLIP-ViT-L-14 is an Apple open-weight CLIP model from the Data Filtering Networks release. Collected 2026-10-15. The model card reports ImageNet-1k zero-shot accuracy of 0.81396.", "what_it_is_for": "Zero-shot image classification and retrieval. DFN is the data-filtering method used to build the training pool.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/dfn/dfn2b-clip-vit-l-14-39b": { "id": "apple/dfn/dfn2b-clip-vit-l-14-39b", "name": "DFN2B-CLIP-ViT-L-14-39B", "maker": "apple", "family": "apple/dfn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-02-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP contrastive image-text model, ViT-L/14, image side 224px. Converted to PyTorch from JAX Axlearn checkpoints and usable in OpenCLIP. The repo name says 39B. The card excerpt read here did not restate a samples-seen line.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DFN2B-CLIP-ViT-L-14-39B is an Apple open-weight CLIP model from the Data Filtering Networks release. Collected 2026-10-15. The model card reports ImageNet-1k zero-shot accuracy of 0.8219.", "what_it_is_for": "Zero-shot image classification and retrieval. DFN is the data-filtering method used to build the training pool.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/dfn/dfn5b-clip-vit-h-14": { "id": "apple/dfn/dfn5b-clip-vit-h-14", "name": "DFN5B-CLIP-ViT-H-14", "maker": "apple", "family": "apple/dfn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2023-10-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP contrastive image-text model, ViT-H/14, image side 224px. Converted to PyTorch from JAX Axlearn checkpoints and usable in OpenCLIP. Trained on DFN-5B, filtered from a pool of 43B image-text pairs. Samples seen: 39B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DFN5B-CLIP-ViT-H-14 is an Apple open-weight CLIP model from the Data Filtering Networks release. Collected 2026-10-15. The model card reports ImageNet-1k zero-shot accuracy of 0.8344.", "what_it_is_for": "Zero-shot image classification and retrieval. DFN is the data-filtering method used to build the training pool.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/dfn/dfn5b-clip-vit-h-14-378": { "id": "apple/dfn/dfn5b-clip-vit-h-14-378", "name": "DFN5B-CLIP-ViT-H-14-378", "maker": "apple", "family": "apple/dfn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2023-10-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP contrastive image-text model, ViT-H/14 at 378, image side 378px. Converted to PyTorch from JAX Axlearn checkpoints and usable in OpenCLIP. Samples seen: 39B at 224 plus 5B at 384, per the model card.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DFN5B-CLIP-ViT-H-14-378 is an Apple open-weight CLIP model from the Data Filtering Networks release. Collected 2026-10-15. The model card reports ImageNet-1k zero-shot accuracy of 0.84218.", "what_it_is_for": "Zero-shot image classification and retrieval. DFN is the data-filtering method used to build the training pool.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/diffucoder/diffucoder-7b-base": { "id": "apple/diffucoder/diffucoder-7b-base", "name": "DiffuCoder-7B-Base", "maker": "apple", "family": "apple/diffucoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-07-01", "context_length": 131072, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Masked diffusion language model (Dream architecture) for code. Config of the base: hidden 3584, 28 layers, 28 heads, 4 KV heads, vocab 152064, max_position_embeddings 131072, RoPE theta 1e6. Safetensors total 7,615,616,512. Masked diffusion code model initialized from the Qwen2.5-Coder-7B lineage. Base model field: Qwen/Qwen2.5-Coder-7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffuCoder-7B-Base is Apple's open-weight 7.6B masked diffusion code model. Released July 2025. Collected 2026-10-15. It is adapted from Qwen2.5-Coder weights and published under Apple's research license.", "what_it_is_for": "Code generation by masked diffusion rather than left-to-right decoding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/diffucoder/diffucoder-7b-cpgrpo": { "id": "apple/diffucoder/diffucoder-7b-cpgrpo", "name": "DiffuCoder-7B-cpGRPO", "maker": "apple", "family": "apple/diffucoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-07-01", "context_length": 131072, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Masked diffusion language model (Dream architecture) for code. Config of the base: hidden 3584, 28 layers, 28 heads, 4 KV heads, vocab 152064, max_position_embeddings 131072, RoPE theta 1e6. Safetensors total 7,615,616,512. Coupled-GRPO post-training for 1 epoch on 21K code examples. Card says +4.4% on EvalPlus versus the instruct model and less reliance on autoregressive decoding bias. Base model field: apple/DiffuCoder-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffuCoder-7B-cpGRPO is Apple's open-weight 7.6B masked diffusion code model. Released July 2025. Collected 2026-10-15. It is adapted from Qwen2.5-Coder weights and published under Apple's research license.", "what_it_is_for": "Code generation by masked diffusion rather than left-to-right decoding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/diffucoder/diffucoder-7b-instruct": { "id": "apple/diffucoder/diffucoder-7b-instruct", "name": "DiffuCoder-7B-Instruct", "maker": "apple", "family": "apple/diffucoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-07-01", "context_length": 131072, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Masked diffusion language model (Dream architecture) for code. Config of the base: hidden 3584, 28 layers, 28 heads, 4 KV heads, vocab 152064, max_position_embeddings 131072, RoPE theta 1e6. Safetensors total 7,615,616,512. Instruction-tuned on OpenCoder-SFT for 5 epochs with a new pad token and fixed-length conditioning. Base model field: apple/DiffuCoder-7B-Base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffuCoder-7B-Instruct is Apple's open-weight 7.6B masked diffusion code model. Released July 2025. Collected 2026-10-15. It is adapted from Qwen2.5-Coder weights and published under Apple's research license.", "what_it_is_for": "Code generation by masked diffusion rather than left-to-right decoding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/fastvit/fastvit-ma36": { "id": "apple/fastvit/fastvit-ma36", "name": "FastViT-MA36", "maker": "apple", "family": "apple/fastvit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid CNN-Transformer with structural reparameterization, trained on ImageNet-1k. Latency 4.6 ms on an iPhone 12 Pro in Apple's model zoo. A knowledge-distillation checkpoint is released beside the standard one.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FastViT-MA36 is an Apple open-weight hybrid vision model from the FastViT family, ICCV 2023. Collected 2026-10-15. ImageNet-1k top-1 is 83.9% (84.6% distilled).", "what_it_is_for": "Mobile image classification. The hybrid encoder later feeds MobileCLIP and FastVLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/fastvit/fastvit-s12": { "id": "apple/fastvit/fastvit-s12", "name": "FastViT-S12", "maker": "apple", "family": "apple/fastvit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid CNN-Transformer with structural reparameterization, trained on ImageNet-1k. Latency 1.4 ms on an iPhone 12 Pro in Apple's model zoo. A knowledge-distillation checkpoint is released beside the standard one.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FastViT-S12 is an Apple open-weight hybrid vision model from the FastViT family, ICCV 2023. Collected 2026-10-15. ImageNet-1k top-1 is 79.9% (81.1% distilled).", "what_it_is_for": "Mobile image classification. The hybrid encoder later feeds MobileCLIP and FastVLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/fastvit/fastvit-sa12": { "id": "apple/fastvit/fastvit-sa12", "name": "FastViT-SA12", "maker": "apple", "family": "apple/fastvit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid CNN-Transformer with structural reparameterization, trained on ImageNet-1k. Latency 1.6 ms on an iPhone 12 Pro in Apple's model zoo. A knowledge-distillation checkpoint is released beside the standard one.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FastViT-SA12 is an Apple open-weight hybrid vision model from the FastViT family, ICCV 2023. Collected 2026-10-15. ImageNet-1k top-1 is 80.9% (81.9% distilled).", "what_it_is_for": "Mobile image classification. The hybrid encoder later feeds MobileCLIP and FastVLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/fastvit/fastvit-sa24": { "id": "apple/fastvit/fastvit-sa24", "name": "FastViT-SA24", "maker": "apple", "family": "apple/fastvit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid CNN-Transformer with structural reparameterization, trained on ImageNet-1k. Latency 2.6 ms on an iPhone 12 Pro in Apple's model zoo. A knowledge-distillation checkpoint is released beside the standard one.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FastViT-SA24 is an Apple open-weight hybrid vision model from the FastViT family, ICCV 2023. Collected 2026-10-15. ImageNet-1k top-1 is 82.7% (83.4% distilled).", "what_it_is_for": "Mobile image classification. The hybrid encoder later feeds MobileCLIP and FastVLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/fastvit/fastvit-sa36": { "id": "apple/fastvit/fastvit-sa36", "name": "FastViT-SA36", "maker": "apple", "family": "apple/fastvit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid CNN-Transformer with structural reparameterization, trained on ImageNet-1k. Latency 3.5 ms on an iPhone 12 Pro in Apple's model zoo. A knowledge-distillation checkpoint is released beside the standard one.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FastViT-SA36 is an Apple open-weight hybrid vision model from the FastViT family, ICCV 2023. Collected 2026-10-15. ImageNet-1k top-1 is 83.6% (84.2% distilled).", "what_it_is_for": "Mobile image classification. The hybrid encoder later feeds MobileCLIP and FastVLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/fastvit/fastvit-t12": { "id": "apple/fastvit/fastvit-t12", "name": "FastViT-T12", "maker": "apple", "family": "apple/fastvit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid CNN-Transformer with structural reparameterization, trained on ImageNet-1k. Latency 1.2 ms on an iPhone 12 Pro in Apple's model zoo. A knowledge-distillation checkpoint is released beside the standard one.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FastViT-T12 is an Apple open-weight hybrid vision model from the FastViT family, ICCV 2023. Collected 2026-10-15. ImageNet-1k top-1 is 79.3% (80.3% distilled).", "what_it_is_for": "Mobile image classification. The hybrid encoder later feeds MobileCLIP and FastVLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/fastvit/fastvit-t8": { "id": "apple/fastvit/fastvit-t8", "name": "FastViT-T8", "maker": "apple", "family": "apple/fastvit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid CNN-Transformer with structural reparameterization, trained on ImageNet-1k. Latency 0.8 ms on an iPhone 12 Pro in Apple's model zoo. A knowledge-distillation checkpoint is released beside the standard one.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FastViT-T8 is an Apple open-weight hybrid vision model from the FastViT family, ICCV 2023. Collected 2026-10-15. ImageNet-1k top-1 is 76.2% (77.2% distilled).", "what_it_is_for": "Mobile image classification. The hybrid encoder later feeds MobileCLIP and FastVLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/fastvlm/fastvlm-0-5b": { "id": "apple/fastvlm/fastvlm-0-5b", "name": "FastVLM-0.5B", "maker": "apple", "family": "apple/fastvlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-08-25", "context_length": 32768, "max_output_tokens": null, "parameters": "758833760", "architecture": "LLaVA-style vision-language model. Hugging Face config model_type llava_qwen2. Language stack: hidden 896, 24 layers, 14 query heads, 2 key/value heads, vocab 151936, RoPE theta 1e6, max_position_embeddings 32768. Vision encoder is FastViTHD. GitHub says the 7B variant uses a Qwen2-7B LLM. Parameter count is the safetensors total, including the vision tower. Paper accepted to CVPR 2025.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FastVLM-0.5B is Apple's open-weight FastVLM vision-language model, released on Hugging Face in August 2025. Collected 2026-10-15. Total checkpoint parameters are 758833760. Weights are research-licensed, not OSI open source.", "what_it_is_for": "On-device visual question answering. Apple positions FastViTHD as a hybrid vision encoder that cuts encoding latency at high resolution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/fastvlm/fastvlm-1-5b": { "id": "apple/fastvlm/fastvlm-1-5b", "name": "FastVLM-1.5B", "maker": "apple", "family": "apple/fastvlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-08-25", "context_length": 32768, "max_output_tokens": null, "parameters": "1909278176", "architecture": "LLaVA-style vision-language model. Hugging Face config model_type llava_qwen2. Language stack: hidden 1536, 28 layers, 12 query heads, 2 key/value heads, vocab 151936, RoPE theta 1e6, max_position_embeddings 32768. Vision encoder is FastViTHD. GitHub says the 7B variant uses a Qwen2-7B LLM. Parameter count is the safetensors total, including the vision tower. Paper accepted to CVPR 2025.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FastVLM-1.5B is Apple's open-weight FastVLM vision-language model, released on Hugging Face in August 2025. Collected 2026-10-15. Total checkpoint parameters are 1909278176. Weights are research-licensed, not OSI open source.", "what_it_is_for": "On-device visual question answering. Apple positions FastViTHD as a hybrid vision encoder that cuts encoding latency at high resolution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/fastvlm/fastvlm-7b": { "id": "apple/fastvlm/fastvlm-7b", "name": "FastVLM-7B", "maker": "apple", "family": "apple/fastvlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-08-25", "context_length": 32768, "max_output_tokens": null, "parameters": "7764588000", "architecture": "LLaVA-style vision-language model. Hugging Face config model_type llava_qwen2. Language stack: hidden 3584, 28 layers, 28 query heads, 4 key/value heads, vocab 152064, RoPE theta 1e6, max_position_embeddings 32768. Vision encoder is FastViTHD. GitHub says the 7B variant uses a Qwen2-7B LLM. Parameter count is the safetensors total, including the vision tower. Paper accepted to CVPR 2025.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FastVLM-7B is Apple's open-weight FastVLM vision-language model, released on Hugging Face in August 2025. Collected 2026-10-15. Total checkpoint parameters are 7764588000. Weights are research-licensed, not OSI open source.", "what_it_is_for": "On-device visual question answering. Apple positions FastViTHD as a hybrid vision encoder that cuts encoding latency at high resolution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/lensvlm/lensvlm-9b": { "id": "apple/lensvlm/lensvlm-9b", "name": "LensVLM-9B", "maker": "apple", "family": "apple/lensvlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License for the weights, including Apple's modifications to Qwen. Source code is separately under the Apple Sample Code License.", "release_date": "2026-09-21", "context_length": 262144, "max_output_tokens": null, "parameters": "9409813744", "architecture": "9B vision-language model that reads compressed page images and can expand relevant pages with learned tools. Base model Qwen/Qwen3.5-9B. Text config: hidden 4096, 32 layers, 16 heads, vocab 248320, max_position_embeddings 262144. Vision patch size 16. Safetensors total 9,409,813,744. Hub pipeline image-text-to-text.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LensVLM-9B is Apple's September 2026 open-weight document VLM, 9.41B parameters, adapted from Qwen3.5-9B. Collected 2026-10-15. It scans compressed images of text and expands only relevant pages.", "what_it_is_for": "Question answering over long documents represented as compressed images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileclip/mobileclip-b": { "id": "apple/mobileclip/mobileclip-b", "name": "MobileCLIP-B", "maker": "apple", "family": "apple/mobileclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-03-06", "context_length": null, "max_output_tokens": null, "parameters": "149700000", "architecture": "Image-text contrastive model. Model card: image encoder 86.3M parameters, text encoder 63.4M parameters, 13B samples seen. Reported latency 10.4 ms image + 3.3 ms text. S3, S4, and L/14 use the larger architectures introduced with the MobileCLIP2 release; MobileCLIP-S3/S4/L-14 are those architectures trained on DataCompDR-1B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileCLIP-B is an Apple open-weight image-text model. Collected 2026-10-15. The model card reports 86.3M image parameters and 63.4M text parameters, ImageNet-1k zero-shot top-1 of 76.8%.", "what_it_is_for": "Zero-shot image classification and retrieval on device. MobileCLIP2 is the reinforced-training follow-up. S3, S4, and L/14 are the larger variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileclip/mobileclip-b-lt": { "id": "apple/mobileclip/mobileclip-b-lt", "name": "MobileCLIP-B (LT)", "maker": "apple", "family": "apple/mobileclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-03-06", "context_length": null, "max_output_tokens": null, "parameters": "149700000", "architecture": "Image-text contrastive model. Model card: image encoder 86.3M parameters, text encoder 63.4M parameters, 36B samples seen. Reported latency 10.4 ms image + 3.3 ms text. S3, S4, and L/14 use the larger architectures introduced with the MobileCLIP2 release; MobileCLIP-S3/S4/L-14 are those architectures trained on DataCompDR-1B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileCLIP-B (LT) is an Apple open-weight image-text model. Collected 2026-10-15. The model card reports 86.3M image parameters and 63.4M text parameters, ImageNet-1k zero-shot top-1 of 77.2%.", "what_it_is_for": "Zero-shot image classification and retrieval on device. MobileCLIP2 is the reinforced-training follow-up. S3, S4, and L/14 are the larger variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileclip/mobileclip-l-14": { "id": "apple/mobileclip/mobileclip-l-14", "name": "MobileCLIP-L/14", "maker": "apple", "family": "apple/mobileclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-08-25", "context_length": null, "max_output_tokens": null, "parameters": "427900000", "architecture": "Image-text contrastive model. Model card: image encoder 304.3M parameters, text encoder 123.6M parameters, 13B samples seen. Reported latency 57.9 ms image + 6.6 ms text. S3, S4, and L/14 use the larger architectures introduced with the MobileCLIP2 release; MobileCLIP-S3/S4/L-14 are those architectures trained on DataCompDR-1B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileCLIP-L/14 is an Apple open-weight image-text model. Collected 2026-10-15. The model card reports 304.3M image parameters and 123.6M text parameters, ImageNet-1k zero-shot top-1 of 79.5%.", "what_it_is_for": "Zero-shot image classification and retrieval on device. MobileCLIP2 is the reinforced-training follow-up. S3, S4, and L/14 are the larger variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileclip/mobileclip-s0": { "id": "apple/mobileclip/mobileclip-s0", "name": "MobileCLIP-S0", "maker": "apple", "family": "apple/mobileclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-03-06", "context_length": null, "max_output_tokens": null, "parameters": "53800000", "architecture": "Image-text contrastive model. Model card: image encoder 11.4M parameters, text encoder 42.4M parameters, 13B samples seen. Reported latency 1.5 ms image + 1.6 ms text. S3, S4, and L/14 use the larger architectures introduced with the MobileCLIP2 release; MobileCLIP-S3/S4/L-14 are those architectures trained on DataCompDR-1B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileCLIP-S0 is an Apple open-weight image-text model. Collected 2026-10-15. The model card reports 11.4M image parameters and 42.4M text parameters, ImageNet-1k zero-shot top-1 of 67.8%.", "what_it_is_for": "Zero-shot image classification and retrieval on device. MobileCLIP2 is the reinforced-training follow-up. S3, S4, and L/14 are the larger variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileclip/mobileclip-s1": { "id": "apple/mobileclip/mobileclip-s1", "name": "MobileCLIP-S1", "maker": "apple", "family": "apple/mobileclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-03-06", "context_length": null, "max_output_tokens": null, "parameters": "84900000", "architecture": "Image-text contrastive model. Model card: image encoder 21.5M parameters, text encoder 63.4M parameters, 13B samples seen. Reported latency 2.5 ms image + 3.3 ms text. S3, S4, and L/14 use the larger architectures introduced with the MobileCLIP2 release; MobileCLIP-S3/S4/L-14 are those architectures trained on DataCompDR-1B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileCLIP-S1 is an Apple open-weight image-text model. Collected 2026-10-15. The model card reports 21.5M image parameters and 63.4M text parameters, ImageNet-1k zero-shot top-1 of 72.6%.", "what_it_is_for": "Zero-shot image classification and retrieval on device. MobileCLIP2 is the reinforced-training follow-up. S3, S4, and L/14 are the larger variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileclip/mobileclip-s2": { "id": "apple/mobileclip/mobileclip-s2", "name": "MobileCLIP-S2", "maker": "apple", "family": "apple/mobileclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-03-06", "context_length": null, "max_output_tokens": null, "parameters": "99100000", "architecture": "Image-text contrastive model. Model card: image encoder 35.7M parameters, text encoder 63.4M parameters, 13B samples seen. Reported latency 3.6 ms image + 3.3 ms text. S3, S4, and L/14 use the larger architectures introduced with the MobileCLIP2 release; MobileCLIP-S3/S4/L-14 are those architectures trained on DataCompDR-1B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileCLIP-S2 is an Apple open-weight image-text model. Collected 2026-10-15. The model card reports 35.7M image parameters and 63.4M text parameters, ImageNet-1k zero-shot top-1 of 74.4%.", "what_it_is_for": "Zero-shot image classification and retrieval on device. MobileCLIP2 is the reinforced-training follow-up. S3, S4, and L/14 are the larger variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileclip/mobileclip-s3": { "id": "apple/mobileclip/mobileclip-s3", "name": "MobileCLIP-S3", "maker": "apple", "family": "apple/mobileclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-08-25", "context_length": null, "max_output_tokens": null, "parameters": "248700000", "architecture": "Image-text contrastive model. Model card: image encoder 125.1M parameters, text encoder 123.6M parameters, 13B samples seen. Reported latency 8.0 ms image + 6.6 ms text. S3, S4, and L/14 use the larger architectures introduced with the MobileCLIP2 release; MobileCLIP-S3/S4/L-14 are those architectures trained on DataCompDR-1B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileCLIP-S3 is an Apple open-weight image-text model. Collected 2026-10-15. The model card reports 125.1M image parameters and 123.6M text parameters, ImageNet-1k zero-shot top-1 of 78.3%.", "what_it_is_for": "Zero-shot image classification and retrieval on device. MobileCLIP2 is the reinforced-training follow-up. S3, S4, and L/14 are the larger variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileclip/mobileclip-s4": { "id": "apple/mobileclip/mobileclip-s4", "name": "MobileCLIP-S4", "maker": "apple", "family": "apple/mobileclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-08-25", "context_length": null, "max_output_tokens": null, "parameters": "445200000", "architecture": "Image-text contrastive model. Model card: image encoder 321.6M parameters, text encoder 123.6M parameters, 13B samples seen. Reported latency 19.6 ms image + 6.6 ms text. S3, S4, and L/14 use the larger architectures introduced with the MobileCLIP2 release; MobileCLIP-S3/S4/L-14 are those architectures trained on DataCompDR-1B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileCLIP-S4 is an Apple open-weight image-text model. Collected 2026-10-15. The model card reports 321.6M image parameters and 123.6M text parameters, ImageNet-1k zero-shot top-1 of 79.4%.", "what_it_is_for": "Zero-shot image classification and retrieval on device. MobileCLIP2 is the reinforced-training follow-up. S3, S4, and L/14 are the larger variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileclip2/mobileclip2-b": { "id": "apple/mobileclip2/mobileclip2-b", "name": "MobileCLIP2-B", "maker": "apple", "family": "apple/mobileclip2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-08-25", "context_length": null, "max_output_tokens": null, "parameters": "149700000", "architecture": "Image-text contrastive model. Model card: image encoder 86.3M parameters, text encoder 63.4M parameters, 13B samples seen. Reported latency 10.4 ms image + 3.3 ms text. S3, S4, and L/14 use the larger architectures introduced with the MobileCLIP2 release; MobileCLIP-S3/S4/L-14 are those architectures trained on DataCompDR-1B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileCLIP2-B is an Apple open-weight image-text model. Collected 2026-10-15. The model card reports 86.3M image parameters and 63.4M text parameters, ImageNet-1k zero-shot top-1 of 79.4%.", "what_it_is_for": "Zero-shot image classification and retrieval on device. MobileCLIP2 is the reinforced-training follow-up. S3, S4, and L/14 are the larger variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileclip2/mobileclip2-l-14": { "id": "apple/mobileclip2/mobileclip2-l-14", "name": "MobileCLIP2-L/14", "maker": "apple", "family": "apple/mobileclip2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-08-25", "context_length": null, "max_output_tokens": null, "parameters": "427900000", "architecture": "Image-text contrastive model. Model card: image encoder 304.3M parameters, text encoder 123.6M parameters, 13B samples seen. Reported latency 57.9 ms image + 6.6 ms text. S3, S4, and L/14 use the larger architectures introduced with the MobileCLIP2 release; MobileCLIP-S3/S4/L-14 are those architectures trained on DataCompDR-1B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileCLIP2-L/14 is an Apple open-weight image-text model. Collected 2026-10-15. The model card reports 304.3M image parameters and 123.6M text parameters, ImageNet-1k zero-shot top-1 of 81.9%.", "what_it_is_for": "Zero-shot image classification and retrieval on device. MobileCLIP2 is the reinforced-training follow-up. S3, S4, and L/14 are the larger variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileclip2/mobileclip2-s0": { "id": "apple/mobileclip2/mobileclip2-s0", "name": "MobileCLIP2-S0", "maker": "apple", "family": "apple/mobileclip2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-08-25", "context_length": null, "max_output_tokens": null, "parameters": "74800000", "architecture": "Image-text contrastive model. Model card: image encoder 11.4M parameters, text encoder 63.4M parameters, 13B samples seen. Reported latency 1.5 ms image + 3.3 ms text. S3, S4, and L/14 use the larger architectures introduced with the MobileCLIP2 release; MobileCLIP-S3/S4/L-14 are those architectures trained on DataCompDR-1B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileCLIP2-S0 is an Apple open-weight image-text model. Collected 2026-10-15. The model card reports 11.4M image parameters and 63.4M text parameters, ImageNet-1k zero-shot top-1 of 71.5%.", "what_it_is_for": "Zero-shot image classification and retrieval on device. MobileCLIP2 is the reinforced-training follow-up. S3, S4, and L/14 are the larger variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileclip2/mobileclip2-s2": { "id": "apple/mobileclip2/mobileclip2-s2", "name": "MobileCLIP2-S2", "maker": "apple", "family": "apple/mobileclip2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-08-25", "context_length": null, "max_output_tokens": null, "parameters": "99100000", "architecture": "Image-text contrastive model. Model card: image encoder 35.7M parameters, text encoder 63.4M parameters, 13B samples seen. Reported latency 3.6 ms image + 3.3 ms text. S3, S4, and L/14 use the larger architectures introduced with the MobileCLIP2 release; MobileCLIP-S3/S4/L-14 are those architectures trained on DataCompDR-1B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileCLIP2-S2 is an Apple open-weight image-text model. Collected 2026-10-15. The model card reports 35.7M image parameters and 63.4M text parameters, ImageNet-1k zero-shot top-1 of 77.2%.", "what_it_is_for": "Zero-shot image classification and retrieval on device. MobileCLIP2 is the reinforced-training follow-up. S3, S4, and L/14 are the larger variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileclip2/mobileclip2-s3": { "id": "apple/mobileclip2/mobileclip2-s3", "name": "MobileCLIP2-S3", "maker": "apple", "family": "apple/mobileclip2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-08-25", "context_length": null, "max_output_tokens": null, "parameters": "248700000", "architecture": "Image-text contrastive model. Model card: image encoder 125.1M parameters, text encoder 123.6M parameters, 13B samples seen. Reported latency 8.0 ms image + 6.6 ms text. S3, S4, and L/14 use the larger architectures introduced with the MobileCLIP2 release; MobileCLIP-S3/S4/L-14 are those architectures trained on DataCompDR-1B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileCLIP2-S3 is an Apple open-weight image-text model. Collected 2026-10-15. The model card reports 125.1M image parameters and 123.6M text parameters, ImageNet-1k zero-shot top-1 of 80.7%.", "what_it_is_for": "Zero-shot image classification and retrieval on device. MobileCLIP2 is the reinforced-training follow-up. S3, S4, and L/14 are the larger variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileclip2/mobileclip2-s4": { "id": "apple/mobileclip2/mobileclip2-s4", "name": "MobileCLIP2-S4", "maker": "apple", "family": "apple/mobileclip2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-08-25", "context_length": null, "max_output_tokens": null, "parameters": "445200000", "architecture": "Image-text contrastive model. Model card: image encoder 321.6M parameters, text encoder 123.6M parameters, 13B samples seen. Reported latency 19.6 ms image + 6.6 ms text. S3, S4, and L/14 use the larger architectures introduced with the MobileCLIP2 release; MobileCLIP-S3/S4/L-14 are those architectures trained on DataCompDR-1B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileCLIP2-S4 is an Apple open-weight image-text model. Collected 2026-10-15. The model card reports 321.6M image parameters and 123.6M text parameters, ImageNet-1k zero-shot top-1 of 81.9%.", "what_it_is_for": "Zero-shot image classification and retrieval on device. MobileCLIP2 is the reinforced-training follow-up. S3, S4, and L/14 are the larger variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileone/mobileone-s0": { "id": "apple/mobileone/mobileone-s0", "name": "MobileOne-S0", "maker": "apple", "family": "apple/mobileone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-06-08", "context_length": null, "max_output_tokens": null, "parameters": "5300000", "architecture": "Re-parameterized mobile CNN trained on ImageNet-1k. Apple's model zoo latency is 0.79 ms on an iPhone 12 Pro. Inference uses the fused reparameterized network.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileOne-S0 is an Apple open-weight mobile image backbone from 2022. Collected 2026-10-15. ImageNet-1k top-1 on the official model zoo is 71.4%.", "what_it_is_for": "On-device image classification and a backbone for detection and segmentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileone/mobileone-s1": { "id": "apple/mobileone/mobileone-s1", "name": "MobileOne-S1", "maker": "apple", "family": "apple/mobileone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Re-parameterized mobile CNN trained on ImageNet-1k. Apple's model zoo latency is 0.89 ms on an iPhone 12 Pro. Inference uses the fused reparameterized network.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileOne-S1 is an Apple open-weight mobile image backbone from 2022. Collected 2026-10-15. ImageNet-1k top-1 on the official model zoo is 75.9%.", "what_it_is_for": "On-device image classification and a backbone for detection and segmentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileone/mobileone-s2": { "id": "apple/mobileone/mobileone-s2", "name": "MobileOne-S2", "maker": "apple", "family": "apple/mobileone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Re-parameterized mobile CNN trained on ImageNet-1k. Apple's model zoo latency is 1.18 ms on an iPhone 12 Pro. Inference uses the fused reparameterized network.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileOne-S2 is an Apple open-weight mobile image backbone from 2022. Collected 2026-10-15. ImageNet-1k top-1 on the official model zoo is 77.4%.", "what_it_is_for": "On-device image classification and a backbone for detection and segmentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileone/mobileone-s3": { "id": "apple/mobileone/mobileone-s3", "name": "MobileOne-S3", "maker": "apple", "family": "apple/mobileone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Re-parameterized mobile CNN trained on ImageNet-1k. Apple's model zoo latency is 1.53 ms on an iPhone 12 Pro. Inference uses the fused reparameterized network.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileOne-S3 is an Apple open-weight mobile image backbone from 2022. Collected 2026-10-15. ImageNet-1k top-1 on the official model zoo is 78.1%.", "what_it_is_for": "On-device image classification and a backbone for detection and segmentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobileone/mobileone-s4": { "id": "apple/mobileone/mobileone-s4", "name": "MobileOne-S4", "maker": "apple", "family": "apple/mobileone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Re-parameterized mobile CNN trained on ImageNet-1k. Apple's model zoo latency is 1.86 ms on an iPhone 12 Pro. Inference uses the fused reparameterized network.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileOne-S4 is an Apple open-weight mobile image backbone from 2022. Collected 2026-10-15. ImageNet-1k top-1 on the official model zoo is 79.4%.", "what_it_is_for": "On-device image classification and a backbone for detection and segmentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobilevit/deeplabv3-mobilevit-small": { "id": "apple/mobilevit/deeplabv3-mobilevit-small", "name": "DeepLabV3-MobileViT-S", "maker": "apple", "family": "apple/mobilevit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2022-05-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DeepLabV3 head on MobileViT-small, trained for semantic segmentation. Hub dataset tag pascal-voc. ASPP rates 6, 12, 18 appear on the MobileViT classification configs.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepLabV3-MobileViT-S is Apple's MobileViT semantic-segmentation checkpoint on Hugging Face, released May 2022. Collected 2026-10-15.", "what_it_is_for": "Semantic segmentation, with Pascal VOC named as the dataset on the Hub.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobilevit/deeplabv3-mobilevit-x-small": { "id": "apple/mobilevit/deeplabv3-mobilevit-x-small", "name": "DeepLabV3-MobileViT-XS", "maker": "apple", "family": "apple/mobilevit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (Hugging Face license tag)", "release_date": "2022-05-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DeepLabV3 head on MobileViT-x-small, trained for semantic segmentation. Hub dataset tag pascal-voc. ASPP rates 6, 12, 18 appear on the MobileViT classification configs.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepLabV3-MobileViT-XS is Apple's MobileViT semantic-segmentation checkpoint on Hugging Face, released May 2022. Collected 2026-10-15.", "what_it_is_for": "Semantic segmentation, with Pascal VOC named as the dataset on the Hub.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobilevit/deeplabv3-mobilevit-xx-small": { "id": "apple/mobilevit/deeplabv3-mobilevit-xx-small", "name": "DeepLabV3-MobileViT-XXS", "maker": "apple", "family": "apple/mobilevit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (Hugging Face license tag)", "release_date": "2022-05-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DeepLabV3 head on MobileViT-xx-small, trained for semantic segmentation. Hub dataset tag pascal-voc. ASPP rates 6, 12, 18 appear on the MobileViT classification configs.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepLabV3-MobileViT-XXS is Apple's MobileViT semantic-segmentation checkpoint on Hugging Face, released May 2022. Collected 2026-10-15.", "what_it_is_for": "Semantic segmentation, with Pascal VOC named as the dataset on the Hub.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobilevit/mobilevit-small": { "id": "apple/mobilevit/mobilevit-small", "name": "MobileViT-S", "maker": "apple", "family": "apple/mobilevit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (Hugging Face card; the card was written by the Hugging Face team)", "release_date": "2022-05-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileViT CNN-transformer hybrid for ImageNet-1k at 256x256. Patch size 2, 4 attention heads, SiLU. Config hidden sizes [144, 192, 240], expand ratio 4.0. Training used a multi-scale sampler over 160, 192, 256, 288, and 320.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileViT-S is Apple's MobileViT image classifier released on Hugging Face, ImageNet-1k, 256px. Collected 2026-10-15.", "what_it_is_for": "Mobile-friendly image classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobilevit/mobilevit-x-small": { "id": "apple/mobilevit/mobilevit-x-small", "name": "MobileViT-XS", "maker": "apple", "family": "apple/mobilevit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (Hugging Face card)", "release_date": "2022-05-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileViT CNN-transformer hybrid for ImageNet-1k at 256x256. Patch size 2, 4 attention heads, SiLU. Training used a multi-scale sampler over 160, 192, 256, 288, and 320.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileViT-XS is Apple's MobileViT image classifier released on Hugging Face, ImageNet-1k, 256px. Collected 2026-10-15.", "what_it_is_for": "Mobile-friendly image classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobilevit/mobilevit-xx-small": { "id": "apple/mobilevit/mobilevit-xx-small", "name": "MobileViT-XXS", "maker": "apple", "family": "apple/mobilevit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (Hugging Face card; the card was written by the Hugging Face team)", "release_date": "2022-05-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileViT CNN-transformer hybrid for ImageNet-1k at 256x256. Patch size 2, 4 attention heads, SiLU. Config hidden sizes [64, 80, 96], expand ratio 2.0. Training used a multi-scale sampler over 160, 192, 256, 288, and 320.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileViT-XXS is Apple's MobileViT image classifier released on Hugging Face, ImageNet-1k, 256px. Collected 2026-10-15.", "what_it_is_for": "Mobile-friendly image classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobilevitv2/mobilevitv2-1-0": { "id": "apple/mobilevitv2/mobilevitv2-1-0", "name": "MobileViTv2-1.0", "maker": "apple", "family": "apple/mobilevitv2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (Hugging Face license tag)", "release_date": "2023-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileViTv2 separable self-attention backbone. Classification checkpoints use ImageNet-1k at 256px. The 1.0 config has width multiplier 1.0. Segmentation checkpoints use a DeepLabV3 head and are tagged with Pascal VOC.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileViTv2-1.0 is an Apple MobileViTv2 checkpoint on Hugging Face, June 2023. Collected 2026-10-15.", "what_it_is_for": "Image classification or semantic segmentation, depending on the checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobilevitv2/mobilevitv2-1-0-voc": { "id": "apple/mobilevitv2/mobilevitv2-1-0-voc", "name": "MobileViTv2-1.0-VOC", "maker": "apple", "family": "apple/mobilevitv2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (Hugging Face license tag)", "release_date": "2023-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileViTv2 separable self-attention backbone. Classification checkpoints use ImageNet-1k at 256px. The 1.0 config has width multiplier 1.0. Segmentation checkpoints use a DeepLabV3 head and are tagged with Pascal VOC.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileViTv2-1.0-VOC is an Apple MobileViTv2 checkpoint on Hugging Face, June 2023. Collected 2026-10-15.", "what_it_is_for": "Image classification or semantic segmentation, depending on the checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobilevitv2/mobilevitv2-1-5-voc": { "id": "apple/mobilevitv2/mobilevitv2-1-5-voc", "name": "MobileViTv2-1.5-VOC", "maker": "apple", "family": "apple/mobilevitv2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (Hugging Face license tag)", "release_date": "2023-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileViTv2 separable self-attention backbone. Classification checkpoints use ImageNet-1k at 256px. The 1.0 config has width multiplier 1.0. Segmentation checkpoints use a DeepLabV3 head and are tagged with Pascal VOC.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileViTv2-1.5-VOC is an Apple MobileViTv2 checkpoint on Hugging Face, June 2023. Collected 2026-10-15.", "what_it_is_for": "Image classification or semantic segmentation, depending on the checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/mobilevitv2/mobilevitv2-2-0": { "id": "apple/mobilevitv2/mobilevitv2-2-0", "name": "MobileViTv2-2.0", "maker": "apple", "family": "apple/mobilevitv2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (Hugging Face license tag)", "release_date": "2023-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileViTv2 separable self-attention backbone. Classification checkpoints use ImageNet-1k at 256px. The 1.0 config has width multiplier 1.0. Segmentation checkpoints use a DeepLabV3 head and are tagged with Pascal VOC.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileViTv2-2.0 is an Apple MobileViTv2 checkpoint on Hugging Face, June 2023. Collected 2026-10-15.", "what_it_is_for": "Image classification or semantic segmentation, depending on the checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/openelm/openelm-1-1b": { "id": "apple/openelm/openelm-1-1b", "name": "OpenELM-1.1B", "maker": "apple", "family": "apple/openelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-04-12", "context_length": 2048, "max_output_tokens": null, "parameters": "1079891456", "architecture": "Decoder-only Transformer with layer-wise scaling. Config: 28 layers, model dimension 2048, head dimension 64, grouped-query attention with 4 groups, SwiGLU (swish), RMSNorm, QK normalization, RoPE frequency 10000, shared input and output embeddings, vocabulary 32000. max_context_length 2048; rope_max_length 4096. Parameter count is the safetensors total on the Hugging Face repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenELM-1.1B is an Apple open-weight language model from the OpenELM family, released April 2024 with pretrained weights. Collected 2026-10-15. Parameter count on Hugging Face is 1079891456. License is Apple's research model license, not OSI open source.", "what_it_is_for": "Open research language modeling. Instruct checkpoints are for prompted generation. Apple says the release includes training logs, checkpoints, and evaluation code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/openelm/openelm-1-1b-instruct": { "id": "apple/openelm/openelm-1-1b-instruct", "name": "OpenELM-1.1B-Instruct", "maker": "apple", "family": "apple/openelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-04-12", "context_length": 2048, "max_output_tokens": null, "parameters": "1079891456", "architecture": "Decoder-only Transformer with layer-wise scaling. Config: 28 layers, model dimension 2048, head dimension 64, grouped-query attention with 4 groups, SwiGLU (swish), RMSNorm, QK normalization, RoPE frequency 10000, shared input and output embeddings, vocabulary 32000. max_context_length 2048; rope_max_length 4096. Parameter count is the safetensors total on the Hugging Face repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenELM-1.1B-Instruct is an Apple open-weight language model from the OpenELM family, released April 2024 with instruction-tuned weights. Collected 2026-10-15. Parameter count on Hugging Face is 1079891456. License is Apple's research model license, not OSI open source.", "what_it_is_for": "Open research language modeling. Instruct checkpoints are for prompted generation. Apple says the release includes training logs, checkpoints, and evaluation code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/openelm/openelm-270m": { "id": "apple/openelm/openelm-270m", "name": "OpenELM-270M", "maker": "apple", "family": "apple/openelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-04-12", "context_length": 2048, "max_output_tokens": null, "parameters": "271527168", "architecture": "Decoder-only Transformer with layer-wise scaling. Config: 16 layers, model dimension 1280, head dimension 64, grouped-query attention with 4 groups, SwiGLU (swish), RMSNorm, QK normalization, RoPE frequency 10000, shared input and output embeddings, vocabulary 32000. max_context_length 2048; rope_max_length 4096. Parameter count is the safetensors total on the Hugging Face repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenELM-270M is an Apple open-weight language model from the OpenELM family, released April 2024 with pretrained weights. Collected 2026-10-15. Parameter count on Hugging Face is 271527168. License is Apple's research model license, not OSI open source.", "what_it_is_for": "Open research language modeling. Instruct checkpoints are for prompted generation. Apple says the release includes training logs, checkpoints, and evaluation code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/openelm/openelm-270m-instruct": { "id": "apple/openelm/openelm-270m-instruct", "name": "OpenELM-270M-Instruct", "maker": "apple", "family": "apple/openelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-04-12", "context_length": 2048, "max_output_tokens": null, "parameters": "271527168", "architecture": "Decoder-only Transformer with layer-wise scaling. Config: 16 layers, model dimension 1280, head dimension 64, grouped-query attention with 4 groups, SwiGLU (swish), RMSNorm, QK normalization, RoPE frequency 10000, shared input and output embeddings, vocabulary 32000. max_context_length 2048; rope_max_length 4096. Parameter count is the safetensors total on the Hugging Face repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenELM-270M-Instruct is an Apple open-weight language model from the OpenELM family, released April 2024 with instruction-tuned weights. Collected 2026-10-15. Parameter count on Hugging Face is 271527168. License is Apple's research model license, not OSI open source.", "what_it_is_for": "Open research language modeling. Instruct checkpoints are for prompted generation. Apple says the release includes training logs, checkpoints, and evaluation code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/openelm/openelm-3b": { "id": "apple/openelm/openelm-3b", "name": "OpenELM-3B", "maker": "apple", "family": "apple/openelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-04-12", "context_length": 2048, "max_output_tokens": null, "parameters": "3036647424", "architecture": "Decoder-only Transformer with layer-wise scaling. Config: 36 layers, model dimension 3072, head dimension 128, grouped-query attention with 4 groups, SwiGLU (swish), RMSNorm, QK normalization, RoPE frequency 10000, shared input and output embeddings, vocabulary 32000. max_context_length 2048; rope_max_length 4096. Parameter count is the safetensors total on the Hugging Face repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenELM-3B is an Apple open-weight language model from the OpenELM family, released April 2024 with pretrained weights. Collected 2026-10-15. Parameter count on Hugging Face is 3036647424. License is Apple's research model license, not OSI open source.", "what_it_is_for": "Open research language modeling. Instruct checkpoints are for prompted generation. Apple says the release includes training logs, checkpoints, and evaluation code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/openelm/openelm-3b-instruct": { "id": "apple/openelm/openelm-3b-instruct", "name": "OpenELM-3B-Instruct", "maker": "apple", "family": "apple/openelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-04-12", "context_length": 2048, "max_output_tokens": null, "parameters": "3036647424", "architecture": "Decoder-only Transformer with layer-wise scaling. Config: 36 layers, model dimension 3072, head dimension 128, grouped-query attention with 4 groups, SwiGLU (swish), RMSNorm, QK normalization, RoPE frequency 10000, shared input and output embeddings, vocabulary 32000. max_context_length 2048; rope_max_length 4096. Parameter count is the safetensors total on the Hugging Face repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenELM-3B-Instruct is an Apple open-weight language model from the OpenELM family, released April 2024 with instruction-tuned weights. Collected 2026-10-15. Parameter count on Hugging Face is 3036647424. License is Apple's research model license, not OSI open source.", "what_it_is_for": "Open research language modeling. Instruct checkpoints are for prompted generation. Apple says the release includes training logs, checkpoints, and evaluation code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/openelm/openelm-450m": { "id": "apple/openelm/openelm-450m", "name": "OpenELM-450M", "maker": "apple", "family": "apple/openelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-04-12", "context_length": 2048, "max_output_tokens": null, "parameters": "457179136", "architecture": "Decoder-only Transformer with layer-wise scaling. Config: 20 layers, model dimension 1536, head dimension 64, grouped-query attention with 4 groups, SwiGLU (swish), RMSNorm, QK normalization, RoPE frequency 10000, shared input and output embeddings, vocabulary 32000. max_context_length 2048; rope_max_length 4096. Parameter count is the safetensors total on the Hugging Face repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenELM-450M is an Apple open-weight language model from the OpenELM family, released April 2024 with pretrained weights. Collected 2026-10-15. Parameter count on Hugging Face is 457179136. License is Apple's research model license, not OSI open source.", "what_it_is_for": "Open research language modeling. Instruct checkpoints are for prompted generation. Apple says the release includes training logs, checkpoints, and evaluation code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/openelm/openelm-450m-instruct": { "id": "apple/openelm/openelm-450m-instruct", "name": "OpenELM-450M-Instruct", "maker": "apple", "family": "apple/openelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2024-04-12", "context_length": 2048, "max_output_tokens": null, "parameters": "457179136", "architecture": "Decoder-only Transformer with layer-wise scaling. Config: 20 layers, model dimension 1536, head dimension 64, grouped-query attention with 4 groups, SwiGLU (swish), RMSNorm, QK normalization, RoPE frequency 10000, shared input and output embeddings, vocabulary 32000. max_context_length 2048; rope_max_length 4096. Parameter count is the safetensors total on the Hugging Face repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenELM-450M-Instruct is an Apple open-weight language model from the OpenELM family, released April 2024 with instruction-tuned weights. Collected 2026-10-15. Parameter count on Hugging Face is 457179136. License is Apple's research model license, not OSI open source.", "what_it_is_for": "Open research language modeling. Instruct checkpoints are for prompted generation. Apple says the release includes training logs, checkpoints, and evaluation code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/sage/sage-ft-mixtral-8x7b": { "id": "apple/sage/sage-ft-mixtral-8x7b", "name": "SAGE-FT-Mixtral-8x7B", "maker": "apple", "family": "apple/sage", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-12", "context_length": null, "max_output_tokens": null, "parameters": "46702792704", "architecture": "Dialogue model fine-tuned from mistralai/Mixtral-8x7B-Instruct-v0.1 with latent state-action variables between turns. Safetensors total 46,702,792,704. English. Training data includes ShareGPT. Paper arXiv:2503.03040.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SAGE-FT-Mixtral-8x7B is Apple's March 2025 open-weight dialogue fine-tune of Mixtral-8x7B-Instruct, 46.7B parameters, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Research on emotionally steered, strategy-aware multi-turn dialogue.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/sharp/sharp": { "id": "apple/sharp/sharp", "name": "SHARP", "maker": "apple", "family": "apple/sharp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-12-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single-image feedforward network that regresses a metric 3D Gaussian scene. Apple says synthesis is under a second on a standard GPU and rendering is real time for nearby views. Paper arXiv:2512.10685.", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SHARP is Apple's open-weight monocular view-synthesis model that predicts 3D Gaussians from one photo. Hugging Face repo created 2025-12-12. Collected 2026-10-15.", "what_it_is_for": "Photorealistic nearby novel views from a single image, with metric scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/simplesd/simplesd-30b-instruct": { "id": "apple/simplesd/simplesd-30b-instruct", "name": "SimpleSD-30B-instruct", "maker": "apple", "family": "apple/simplesd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2026-03-17", "context_length": null, "max_output_tokens": null, "parameters": "30532122624", "architecture": "Self-distilled code model. qwen3_moe. Safetensors total about 30.5B. Active-parameter count is not in the config excerpt. Fine-tuned from Qwen/Qwen3-30B-A3B-Instruct-2507 on the model's own samples, without a reward model or teacher. The 30B config.json was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SimpleSD-30B-instruct is Apple's open-weight self-distillation code checkpoint, released March 2026 from Qwen/Qwen3-30B-A3B-Instruct-2507. Collected 2026-10-15. LiveCodeBench v6 pass@1 on the card is 55.3.", "what_it_is_for": "Code generation research. The method fine-tunes a model on its own sampled solutions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/simplesd/simplesd-4b-instruct": { "id": "apple/simplesd/simplesd-4b-instruct", "name": "SimpleSD-4B-instruct", "maker": "apple", "family": "apple/simplesd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2026-03-17", "context_length": 262144, "max_output_tokens": null, "parameters": "4022468096", "architecture": "Self-distilled code model. qwen3 dense, hidden 2560, 36 layers, 32 heads, 8 KV heads, vocab 151936. Fine-tuned from Qwen/Qwen3-4B-Instruct-2507 on the model's own samples, without a reward model or teacher. The 4B configs set max_position_embeddings to 262144.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SimpleSD-4B-instruct is Apple's open-weight self-distillation code checkpoint, released March 2026 from Qwen/Qwen3-4B-Instruct-2507. Collected 2026-10-15. LiveCodeBench v6 pass@1 on the card is 41.5.", "what_it_is_for": "Code generation research. The method fine-tunes a model on its own sampled solutions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/simplesd/simplesd-4b-thinking": { "id": "apple/simplesd/simplesd-4b-thinking", "name": "SimpleSD-4B-thinking", "maker": "apple", "family": "apple/simplesd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2026-03-17", "context_length": 262144, "max_output_tokens": null, "parameters": "4022468096", "architecture": "Self-distilled code model. Same qwen3 shape as the 4B instruct checkpoint. Fine-tuned from Qwen/Qwen3-4B-Thinking-2507 on the model's own samples, without a reward model or teacher. The 4B configs set max_position_embeddings to 262144.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SimpleSD-4B-thinking is Apple's open-weight self-distillation code checkpoint, released March 2026 from Qwen/Qwen3-4B-Thinking-2507. Collected 2026-10-15. LiveCodeBench v6 pass@1 on the card is 57.8.", "what_it_is_for": "Code generation research. The method fine-tunes a model on its own sampled solutions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/starflow/starflow": { "id": "apple/starflow/starflow", "name": "STARFlow", "maker": "apple", "family": "apple/starflow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer autoregressive flow for latent normalizing-flow image synthesis. The card names checkpoint starflow_3B_t2i_256x256.pth for 256x256 text-to-image. NeurIPS 2025 spotlight paper arXiv:2506.06276. The 3B figure is the checkpoint name, not a safetensors integer.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "STARFlow is Apple's open-weight text-to-image normalizing-flow model. The Hugging Face repo, created 2025-11-26, also hosts STARFlow-V. Collected 2026-10-15. The image checkpoint is named as a 3B model.", "what_it_is_for": "Text-to-image generation with a transformer autoregressive flow.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/starflow/starflow-v": { "id": "apple/starflow/starflow-v", "name": "STARFlow-V", "maker": "apple", "family": "apple/starflow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Machine Learning Research Model License (Hugging Face license id apple-amlr). Weight license is not an OSI open-source license.", "release_date": "2025-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end video generative normalizing flow. Card names checkpoint starflow-v_7B_t2v_caus_480p_v3.pth for causal text-to-video at 480p. Paper arXiv:2511.20462. The 7B figure is the checkpoint name.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "STARFlow-V is Apple's open-weight text-to-video normalizing-flow model, released beside STARFlow. Collected 2026-10-15. The named checkpoint is a 7B causal 480p model.", "what_it_is_for": "Text-to-video generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apple/tic-clip/tic-clip": { "id": "apple/tic-clip/tic-clip", "name": "TiC-CLIP", "maker": "apple", "family": "apple/tic-clip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (Hugging Face license tag)", "release_date": "2024-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP models from the TiC continual-learning benchmark, trained on apple/TiC-DataComp. Apple released basic and best-pool strategies under sequential, cumulative, and oracle schedules. They are one benchmark family, not six products.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TiC-CLIP is Apple's open-weight continual image-text benchmark family, six schedules released June 2024. Collected 2026-10-15. They are grouped as one model family entry with versions.", "what_it_is_for": "Research on continual learning of CLIP over time-indexed DataComp data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apptek/asr/asr": { "id": "apptek/asr/asr", "name": "AppTek ASR", "maker": "apptek", "family": "apptek/asr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Commercial API models are not given a public architecture card. The IWSLT 2025 system paper describes the general-purpose English ASR as a hybrid Conformer/HMM: about 1 billion Conformer acoustic parameters, 80-dim log Mel filterbanks, about 9K tied triphone states, plus an LSTM language model of over 300 million parameters and a count-based n-gram. A previous conformer acoustic model is cited at 350 million parameters. The product site also lists hybrid HMMs, LSTM-RNNs, attention, and end-to-end modeling as research lines. Those figures are not stated for every language model.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AppTek's automatic speech recognition system turns speech into text for batch files and live streams. It is a current closed product, sold as a cloud API and on-premise service, collected on 2026-10-15. Language and domain packs are separate API model ids. The public docs only sample those ids; the full list requires an API token.", "what_it_is_for": "AppTek says ASR transcribes broadcast, telephony, meetings, podcasts, and interviews, in batch or real time, in the cloud or on premises. Customers can request domain-adapted or custom models, including low-resource languages. The same model id is also used by the audio-alignment API to time an existing transcript.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apptek/nmt/nmt": { "id": "apptek/nmt/nmt", "name": "AppTek Neural Machine Translation", "maker": "apptek", "family": "apptek/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Product pages describe proprietary neural models, including document-level translation. The IWSLT 2025 system paper says AppTek's NMT is a Transformer Big variant with factored embeddings for case, subword segmentation, and glossary transfer. A separate multilingual model is described as translating from and into 12 Slavic languages. Parameter counts were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AppTek Neural Machine Translation is the company's closed text-to-text translation product. It is active as a cloud API, portal, and on-premise option, collected on 2026-10-15. Each language pair or variant is an API model id. Only a few ids appear in the public docs.", "what_it_is_for": "AppTek says the system localizes documents, websites, subtitles, and live text, including spoken-text output from ASR. It advertises document-level context, glossaries, code-switching, named-entity transliteration, language identification, and subtitle line segmentation. A free account can try base models. Custom models can be trained on a customer's translation memory.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apptek/speech-translation/direct-speech-translation": { "id": "apptek/speech-translation/direct-speech-translation", "name": "AppTek Direct Speech Translation", "maker": "apptek", "family": "apptek/speech-translation", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2021-10-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end speech-to-text translation: audio in, target-language text out, without a source transcript as the product output. The 2021 IWSLT paper also describes a posterior tight-integrated model trained end to end from cascaded modules. The 2023 subtitling paper used a direct system as a contrastive run and a CTC model for word timestamps. No 2025 public API card names the network.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AppTek Direct Speech Translation is the company's end-to-end speech-to-text translation system: audio in, target-language text out, without showing a source transcript. AppTek said on 4 October 2021 that it had trained and released such a system, with English speech to German text as the example. It is closed. This dossier was collected on 2026-10-15. Confidence is medium because no current API id was found.", "what_it_is_for": "The 2021 post distinguishes this model from the usual cascade of ASR, machine translation, and TTS. The released piece is speech-to-text, not speech-to-speech. Later evaluation papers use a direct model as an offline or contrastive system beside the production cascade. The 2025 public API still bills recognition and translation as separate services.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apptek/speech-translation/speech-translation": { "id": "apptek/speech-translation/speech-translation", "name": "AppTek Speech Translation", "maker": "apptek", "family": "apptek/speech-translation", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cascaded ASR plus neural machine translation, with optional subtitle line segmentation and length control. The streaming SDK attaches translateConfigurations (domain and target language) to a recognition session. IWSLT 2025 findings call the subtitling entry the AppTek25 production ASR and MT systems. This dossier does not treat that cascade as one neural network.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AppTek Speech Translation is the closed cascade that recognizes speech and then translates the text. It is an active product, collected on 2026-10-15, delivered through the ASR and MT APIs, a streaming gRPC SDK, and the Speech Translate iOS app. It is not one neural checkpoint and it has no single public model id.", "what_it_is_for": "AppTek uses the cascade for live and batch speech translation, multilingual captions, and the front of automatic dubbing before TTS. The enterprise page says content can be translated in batch or streamed via ASR-to-NMT. The streaming JavaScript SDK starts a recognition session with a language code and can add translation targets by domain and language. Subtitles can pass through AppTek's line-segmentation and length-control steps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apptek/tts/expressive-tts": { "id": "apptek/tts/expressive-tts", "name": "AppTek Expressive TTS", "maker": "apptek", "family": "apptek/tts", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published. The announcement describes a multilingual emotionally expressive TTS with speaker-data disentanglement, word-level emphasis, sub-word duration control, and custom pronunciation. The 2025 emotive-TTS page describes metadata-rich studio recordings used to train expressive models. No parameter count or network name was published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AppTek Expressive TTS is the multilingual emotionally expressive text-to-speech model the company unveiled for AI dubbing on 12 November 2025. It is closed, described as available now, and collected on 2026-10-15. It has no public API model id.", "what_it_is_for": "AppTek says the model is for enterprise dubbing and localization. Users are said to control pace, tone, pronunciation, dialect, accent, and emotion, including word-level emphasis and sub-word duration for lip sync. The announcement says output can include laughter and breaths, and that voice, performance, and accent can be controlled separately. Access is a demo via info@apptek.com, not a documented self-serve endpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apptek/tts/neural-tts": { "id": "apptek/tts/neural-tts", "name": "AppTek Neural TTS", "maker": "apptek", "family": "apptek/tts", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Product page says neural synthesis and vocoder models, with a speed-or-quality vocoder choice. Speaker-adaptive voices are described as zero-shot. No public layer count or parameter count.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AppTek Neural TTS is the closed text-to-speech service behind the `texttospeech` API. It is active, collected on 2026-10-15, and is offered for batch or real-time synthesis in the cloud or on premises. Individual voices are model ids. The public docs only show examples.", "what_it_is_for": "AppTek says the service speaks text in exclusive or customer-adapted voices for dubbing, accessibility, contact centers, and content creation. The language page says the text-to-speech models use zero-shot learning to mimic a source speaker in a target language. The API reference does not document a reference-audio control, so that zero-shot path may be a separate delivery rather than a public request field.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/afm/afm-4-5b": { "id": "arcee/afm/afm-4-5b", "name": "AFM-4.5B", "maker": "arcee", "family": "arcee/afm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on current docs and Hugging Face; 2025-07-29 launch post says Arcee Model License", "release_date": "2025-07-29", "context_length": 65536, "max_output_tokens": null, "parameters": "4.5B", "architecture": "Decoder-only transformer (ArceeForCausalLM): 36 layers, hidden 2560, GQA 20 query heads and 4 KV heads, intermediate 18432, ReLU squared, vocab 128005. Final writeups say 8T tokens (6.5T pretrain plus 1.5T midtrain) then SFT and RL.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM-4.5B is Arcee AI's first Arcee Foundation Model, a 4.5 billion parameter dense instruct model whose final weights were posted on 2025-07-29. A preview ran on Together from 18 June to 28 July 2025. Current docs and the Hugging Face card say Apache-2.0. The launch post that day instead names an Arcee Model License with a revenue cap. The training writeups that accompany the final weights say 8 trillion tokens.", "what_it_is_for": "Enterprise chat, tool use, and edge or CPU deployment where a small dense model is enough.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/afm/afm-4-5b-base": { "id": "arcee/afm/afm-4-5b-base", "name": "AFM-4.5B-Base", "maker": "arcee", "family": "arcee/afm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on current docs and Hugging Face; 2025-07-29 launch post says Arcee Model License", "release_date": "2025-07-29", "context_length": 65536, "max_output_tokens": null, "parameters": "4.5B", "architecture": "ArceeForCausalLM: 36 layers, hidden 2560, GQA 20/4, intermediate 18432, vocab 128004. Same pretraining recipe as the instruct model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM-4.5B-Base is the pretrained 4.5 billion parameter Arcee Foundation Model checkpoint, released with the instruct weights on 2025-07-29. Official docs describe it as the base half of the pair. The same license conflict as AFM-4.5B applies. The Hugging Face card text incorrectly repeats the instruct-model training paragraph.", "what_it_is_for": "Continued training and fine-tuning, not direct chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/agent/arcee-agent": { "id": "arcee/agent/arcee-agent", "name": "Arcee-Agent", "maker": "arcee", "family": "arcee/agent", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-02", "context_length": 131072, "max_output_tokens": null, "parameters": "7B", "architecture": "Initialized from Qwen2-7B and trained with Spectrum. config.json: 28 layers, hidden 3584, max_position_embeddings 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arcee-Agent is Arcee AI's 7 billion parameter function-calling model, initialized from Qwen2-7B and posted on 2024-07-02 under Apache-2.0. The card says it was trained with the Spectrum method. Config context is 131072 tokens.", "what_it_is_for": "Tool routing and function calling at 7B, including use as a small chat agent.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/blitz/arcee-blitz": { "id": "arcee/blitz/arcee-blitz", "name": "Arcee-Blitz", "maker": "arcee", "family": "arcee/blitz", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-07", "context_length": 32768, "max_output_tokens": null, "parameters": "24B", "architecture": "Mistral architecture from Mistral-Small-24B-Instruct-2501, distilled from DeepSeek-V3. config.json: 40 layers, hidden 5120, max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.45, "completion_per_m_usd": 0.75 }, "summary_intro": "Arcee-Blitz is a 24 billion parameter general model distilled from DeepSeek onto Mistral-Small-24B-Instruct-2501. Weights were posted on Hugging Face on 2025-02-07 under Apache-2.0. In May 2025 Arcee priced the hosted model at $0.45 / $0.75 per million tokens.", "what_it_is_for": "Fast general chat and document tasks without a larger model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/blitz/blitz-caller-beta": { "id": "arcee/blitz/blitz-caller-beta", "name": "Arcee Blitz Caller Beta", "maker": "arcee", "family": "arcee/blitz", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-21", "context_length": 32768, "max_output_tokens": null, "parameters": "24B", "architecture": "Mistral-Small-24B-Base-2501 fine-tune for tool calling. config.json: 40 layers, hidden 5120, max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arcee Blitz Caller Beta is a February 2025 tool-calling beta from Arcee AI on Mistral-Small-24B-Base-2501, licensed Apache-2.0. The card reports 85.15% non-live AST accuracy and 74.19% live accuracy, and 0.25% multi-turn accuracy. Arcee marks it as unfinished.", "what_it_is_for": "Single-turn function calling and tool selection.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/caller/caller": { "id": "arcee/caller/caller", "name": "Caller", "maker": "arcee", "family": "arcee/caller", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-05-27", "context_length": 32768, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen2 continued from Qwen2.5-32B. config.json: 64 layers, hidden 5120, max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.55, "completion_per_m_usd": 0.85 }, "summary_intro": "Caller is Arcee AI's tool-calling model on Qwen2.5-32B, sold as Caller Large and released as open weights on 2025-06-30 under Apache-2.0. The model card says 32B and 32768 context. A May 2025 price table says 33B, 33K context, and $0.55 / $0.85 per million tokens.", "what_it_is_for": "Function calling and multi-system orchestration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/coder/coder-large": { "id": "arcee/coder/coder-large", "name": "Coder-Large", "maker": "arcee", "family": "arcee/coder", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-27", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Docs: fine-tune of Qwen-2.5-32B-Instruct. No public config was found.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 0.8 }, "summary_intro": "Coder-Large is Arcee AI's 32 billion parameter coding model, described as a further train of Qwen-2.5-32B-Instruct. Arcee put it on Together and OpenRouter on 2025-05-27 at $0.50 / $0.80 per million tokens with a 33K context. No public checkpoint was found.", "what_it_is_for": "Code generation, refactoring, and debugging.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/coder/coder-small": { "id": "arcee/coder/coder-small", "name": "Coder-Small", "maker": "arcee", "family": "arcee/coder", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Conductor model-selection page says 14B and lists base model Qwen-2.5-32B-Instruct, which conflicts with the 14B size.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Coder-Small is a 14 billion parameter coding model named on Arcee AI's Conductor model-selection page. No weight repo, price, or release date was found. The page lists Qwen-2.5-32B-Instruct as its base, which does not match a 14B model, so that base claim is recorded as a conflict rather than a fact.", "what_it_is_for": "Lighter code generation, review, and prototypes, according to the Conductor page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/genesis/genesis-science-1": { "id": "arcee/genesis/genesis-science-1", "name": "Genesis-Science-1", "maker": "arcee", "family": "arcee/genesis", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Announced as a trillion-parameter-class language model built on Arcee's next Trinity generation, paired with a governed scientific execution system. No architecture sheet has been published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Genesis-Science-1 is an announced Arcee AI and U.S. Department of Energy scientific model, unveiled 2026-07-22. Arcee says it will be a trillion-parameter-class open-weight model on a later Trinity generation, with weights, a technical report, and demos later in 2026. Nothing had shipped by 2026-10-15.", "what_it_is_for": "Long scientific computing workflows such as HPC code, simulation, materials, and energy systems, with people still approving safety-relevant actions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/glm/glm-4-32b-base-32k": { "id": "arcee/glm/glm-4-32b-base-32k", "name": "GLM-4-32B-Base-32K", "maker": "arcee", "family": "arcee/glm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-30", "context_length": 32768, "max_output_tokens": null, "parameters": "32B", "architecture": "Context-extended merge of THUDM GLM-4-32B-Base-0414. Card says the original model faded after 8192 tokens and this checkpoint holds 32000. config.json: glm4, 61 layers, hidden 6144, max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4-32B-Base-32K is an Arcee AI research checkpoint of THUDM's GLM-4-32B-Base-0414, released 2025-06-30 under MIT. Arcee says merging extended usable context from about 8K to 32K and improved standard base benchmarks by about 5 percent. It is a base model.", "what_it_is_for": "Long-context continued pretraining research and downstream fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/homunculus/homunculus": { "id": "arcee/homunculus/homunculus", "name": "Homunculus", "maker": "arcee", "family": "arcee/homunculus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-06-30", "context_length": 131072, "max_output_tokens": null, "parameters": "12B", "architecture": "Qwen3-235B distillation onto a Mistral-Nemo backbone, keeping /think and /nothink modes. config.json: Mistral, 40 layers, hidden 5120, max_position_embeddings 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Homunculus is Arcee AI's 12 billion parameter distillation of Qwen3-235B onto Mistral-Nemo, released with the 2025-06-30 open-weight drop under Apache-2.0. It keeps Qwen-style /think and /nothink modes. Config context is 131072 tokens.", "what_it_is_for": "Reasoning traces on a single consumer GPU, per the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/lite/arcee-lite": { "id": "arcee/lite/arcee-lite", "name": "Arcee-Lite", "maker": "arcee", "family": "arcee/lite", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-01", "context_length": 32768, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Qwen2-class 1.5B model (config: 28 layers, hidden 1536) distilled from Phi-3-Medium and merged with other distillations, released with DistillKit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arcee-Lite is a 1.5 billion parameter DistillKit model from Arcee AI, distilled from Phi-3-Medium and released 2024-08-01 under Apache-2.0. The card reports MMLU of 55.93. Config context is 32768 tokens.", "what_it_is_for": "A small demonstration of Arcee's distillation stack.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/maestro/maestro-7b-preview": { "id": "arcee/maestro/maestro-7b-preview", "name": "Arcee-Maestro-7B-Preview", "maker": "arcee", "family": "arcee/maestro", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-10", "context_length": 131072, "max_output_tokens": null, "parameters": "7B", "architecture": "Further GRPO training on DeepSeek-R1-Distill-Qwen-7B (Qwen2.5-7B). config.json: 28 layers, hidden 3584, max_position_embeddings 131072. Card says 450,000 verified math problems plus some coding examples.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arcee-Maestro-7B-Preview is Arcee AI's first reasoning model, a GRPO continuation of DeepSeek-R1-Distill-Qwen-7B posted on 2025-02-10 under Apache-2.0. Arcee calls it a preview of later Maestro work. Context in config is 131072 tokens.", "what_it_is_for": "Math and coding reasoning at 7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/maestro/maestro-reasoning": { "id": "arcee/maestro/maestro-reasoning", "name": "Maestro Reasoning", "maker": "arcee", "family": "arcee/maestro", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-27", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Conductor page: Qwen-2.5-32B with an SFT warm-up and a reinforcement-learning phase. May 2025 blog: 32B derivative tuned with DPO and chain-of-thought RL.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.9, "completion_per_m_usd": 3.3 }, "summary_intro": "Maestro Reasoning is Arcee AI's 32 billion parameter reasoning model, hosted from 2025-05-27 at $0.90 per million input tokens and $3.30 per million output tokens with a 131K context. Docs say it continues the Maestro-7B-Preview recipe on Qwen-2.5-32B. No public weight repo was found.", "what_it_is_for": "Step-by-step enterprise reasoning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/meraj/meraj": { "id": "arcee/meraj/meraj", "name": "Arcee Meraj", "maker": "arcee", "family": "arcee/meraj", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arcee Meraj is an enterprise Arabic model that Arcee AI's Meraj-Mini card describes as already released and as a top Arabic system for the company. Public specs were not found. Meraj-Mini is the open 7B model that followed it. Confidence is medium because the evidence is that one official card.", "what_it_is_for": "Enterprise Arabic generation, according to the Meraj-Mini card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/meraj/meraj-mini": { "id": "arcee/meraj/meraj-mini", "name": "Meraj-Mini", "maker": "arcee", "family": "arcee/meraj", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-10-06", "context_length": 131072, "max_output_tokens": null, "parameters": "7B", "architecture": "Fine-tune of Qwen2.5-7B-Instruct for Arabic and English. config.json: 28 layers, hidden 3584, max_position_embeddings 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meraj-Mini is Arcee AI's open 7 billion parameter Arabic and English model, a fine-tune of Qwen2.5-7B-Instruct posted on 2024-10-06 under Apache-2.0. The card presents it as the open follow-on to an enterprise model called Arcee Meraj. Config context is 131072 tokens.", "what_it_is_for": "Arabic-first bilingual instruct use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/nova/arcee-nova": { "id": "arcee/nova/arcee-nova", "name": "Arcee-Nova", "maker": "arcee", "family": "arcee/nova", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-07-18", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Merge of Qwen2-72B-Instruct with a custom generalist tune. config.json: Qwen2, 80 layers, hidden 8192, max_position_embeddings 32768. YAML license is other.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arcee-Nova is Arcee AI's 18 July 2024 merge of Qwen2-72B-Instruct with a custom generalist model, with RLHF on top according to the launch post. Arcee called it their strongest open model then and pointed at an Open LLM Leaderboard 2.0 evaluation without printing a score. The card license field is other, and the parameter total is not printed.", "what_it_is_for": "General reasoning, writing, and coding at the Qwen2-72B scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/raspberry/raspberry-3b": { "id": "arcee/raspberry/raspberry-3b", "name": "raspberry-3B", "maker": "arcee", "family": "arcee/raspberry", "status": "preview", "confidence": "medium", "weights": "open-weights", "license": "Qwen Research License", "release_date": "2024-10-05", "context_length": 32768, "max_output_tokens": null, "parameters": "3B", "architecture": "Qwen2. config.json: 36 layers, hidden 2048, max_position_embeddings 32768. Card says experimental, reasoning-optimized, and not for production. Prompt format ChatML.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "raspberry-3B is an experimental Arcee AI reasoning model posted on Hugging Face on 2024-10-05. The card says it is not for production and prints an Open LLM Leaderboard average of 15.40. The license metadata is the Qwen research license. Config matches a Qwen2 3B-class model with 32768 context.", "what_it_is_for": "Reasoning experiments, not production chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/scribe/arcee-scribe": { "id": "arcee/scribe/arcee-scribe", "name": "Arcee-Scribe", "maker": "arcee", "family": "arcee/scribe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-17", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Merge of InternLM-2.5-chat with a custom InternLM fine-tune. config.json: internlm2, 32 layers, hidden 4096, max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arcee-Scribe is a July 2024 Arcee AI chat model, a merge of InternLM-2.5-chat with a custom InternLM fine-tune, released under Apache-2.0. Arcee describes it as a creative-writing model that was strong on Big-Bench Hard relative to their other small models but was not adopted as Spark V2.", "what_it_is_for": "Creative writing and dialogue.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/sec/llama-3-sec-base": { "id": "arcee/sec/llama-3-sec-base", "name": "Llama-3-SEC-Base", "maker": "arcee", "family": "arcee/sec", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-06-07", "context_length": 8192, "max_output_tokens": null, "parameters": "70B", "architecture": "Continual pretraining and merge on Meta-Llama-3-70B for SEC filings. config.json: Llama, 80 layers, hidden 8192, max_position_embeddings 8192. Card calls the drop a 20B-token intermediate checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-SEC-Base is an Arcee AI domain checkpoint of Llama-3-70B for U.S. SEC filings, posted 2024-06-07 under the Llama 3 license. Arcee calls it an intermediate 20 billion token checkpoint while training continued. Config context is 8192 tokens.", "what_it_is_for": "Finance and regulatory analysis of SEC filings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/sec/llama-3-sec-chat": { "id": "arcee/sec/llama-3-sec-chat", "name": "Llama-3-SEC-Chat", "maker": "arcee", "family": "arcee/sec", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-06-16", "context_length": 8192, "max_output_tokens": null, "parameters": "70B", "architecture": "Instruct checkpoint of the Llama-3-SEC continual pretrain on Meta-Llama-3-70B-Instruct. config.json: 80 layers, hidden 8192, max_position_embeddings 8192. Same 20B-token intermediate release.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-SEC-Chat is the instruct checkpoint of Arcee AI's Llama-3-70B SEC model, posted 2024-06-16 under the Llama 3 license. Like the base, Arcee describes the release as a 20 billion token intermediate checkpoint. Config context is 8192 tokens.", "what_it_is_for": "Conversational analysis of SEC filings and related finance questions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/spark/arcee-spark": { "id": "arcee/spark/arcee-spark", "name": "Arcee-Spark", "maker": "arcee", "family": "arcee/spark", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-22", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "Qwen2 chat model. config.json: 28 layers, hidden 3584, max_position_embeddings 32768. The August 2024 Llama-Spark post calls this the 7B predecessor.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arcee-Spark is Arcee AI's 7 billion parameter conversational model on Qwen2, posted on Hugging Face on 2024-06-22 under Apache-2.0. Llama-Spark, released that August on Llama-3.1-8B, is the named successor.", "what_it_is_for": "Conversational AI in the 6B to 9B range.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/spark/llama-spark": { "id": "arcee/spark/llama-spark", "name": "Llama-Spark", "maker": "arcee", "family": "arcee/spark", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-08-02", "context_length": 131072, "max_output_tokens": null, "parameters": "8B", "architecture": "Merge of Llama-3.1-8B-Instruct with continued training on Arcee's Tome dataset. config.json: Llama, 32 layers, hidden 4096, max_position_embeddings 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-Spark is Arcee AI's August 2024 successor to Arcee-Spark, an 8 billion parameter conversational model on Llama-3.1-8B-Instruct merged with the Tome dataset. The license is the Llama 3 community license. Config context is 131072 tokens.", "what_it_is_for": "Best-effort conversational model in the 6B to 9B range, per the announcement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/spotlight/spotlight": { "id": "arcee/spotlight/spotlight", "name": "Spotlight", "maker": "arcee", "family": "arcee/spotlight", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-27", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Arcee calls it a 7B vision-language model. The base model is not named in the Arcee post.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.18, "completion_per_m_usd": 0.18 }, "summary_intro": "Spotlight is Arcee AI's 7 billion parameter vision-language model, announced with the 2025-05-27 Together and OpenRouter launch. Arcee priced it at $0.18 per million input and output tokens and listed a 131K context. No checkpoint or base-model name was published in that post.", "what_it_is_for": "Fast image-text tasks such as visual question answering, charts, and UI reading.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/supernova/supernova": { "id": "arcee/supernova/supernova", "name": "Arcee-SuperNova-v1", "maker": "arcee", "family": "arcee/supernova", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-09-10", "context_length": 131072, "max_output_tokens": null, "parameters": "70B", "architecture": "Merge of Llama-3.1-70B-Instruct distillations of Llama-3.1-405B-Instruct, an EvolKit instruction tune, and a DPO model. config.json: 80 layers, hidden 8192, max_position_embeddings 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arcee-SuperNova-v1 is Arcee AI's 70 billion parameter Llama-3.1 instruct model, first offered on 2024-09-10 as a distillation and merge of Llama-3.1-405B-Instruct into Llama-3.1-70B-Instruct. Open weights arrived in June 2025 under the Llama 3 Community License. An 8B lite and a 14B medius model are separate folders.", "what_it_is_for": "General instruction following as a smaller stand-in for much larger models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/supernova/supernova-lite": { "id": "arcee/supernova/supernova-lite", "name": "Llama-3.1-SuperNova-Lite", "maker": "arcee", "family": "arcee/supernova", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-09-10", "context_length": 131072, "max_output_tokens": null, "parameters": "8B", "architecture": "Distillation of Llama-3.1-405B-Instruct into Llama-3.1-8B-Instruct using EvolKit instruction data. config.json: 32 layers, hidden 4096, max_position_embeddings 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3.1-SuperNova-Lite is Arcee AI's 8 billion parameter distillation of Llama-3.1-405B-Instruct, released with SuperNova on 2024-09-10 under the Llama 3 Community License. It is the small model in the SuperNova line.", "what_it_is_for": "Instruction following at 8B with the SuperNova distillation recipe.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/supernova/supernova-medius": { "id": "arcee/supernova/supernova-medius", "name": "SuperNova-Medius", "maker": "arcee", "family": "arcee/supernova", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-10-11", "context_length": 131072, "max_output_tokens": null, "parameters": "14B", "architecture": "Qwen2.5-14B-Instruct based distillation and merge. config.json: 48 layers, hidden 5120, max_position_embeddings 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SuperNova-Medius is Arcee AI's 14 billion parameter model on Qwen2.5-14B, announced 2024-10-11 under Apache-2.0. Arcee places it between the 8B SuperNova-Lite and the 70B SuperNova flagship.", "what_it_is_for": "Instruction following and reasoning at 14B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/trinity/trinity-large-base": { "id": "arcee/trinity/trinity-large-base", "name": "Trinity-Large-Base", "maker": "arcee", "family": "arcee/trinity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-01-27", "context_length": 262144, "max_output_tokens": null, "parameters": "398B total, about 13B active per token", "architecture": "Sparse MoE (AfmoeForCausalLM): 60 layers, hidden 3072, 256 experts, 4 active, 6 dense layers. Pretrained checkpoint continued from TrueBase.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Trinity-Large-Base is the finished pretraining checkpoint of Arcee AI's Trinity Large run, released 2026-01-27. It is the 398B sparse MoE after 17 trillion tokens, including learning-rate anneals and context extension, and before instruction tuning or reinforcement learning.", "what_it_is_for": "A foundation checkpoint for research and fine-tuning, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/trinity/trinity-large-preview": { "id": "arcee/trinity/trinity-large-preview", "name": "Trinity-Large-Preview", "maker": "arcee", "family": "arcee/trinity", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-01-27", "context_length": 262144, "max_output_tokens": null, "parameters": "398B total, about 13B active per token", "architecture": "Sparse MoE (AfmoeForCausalLM): 60 layers, hidden 3072, 256 experts (1 shared), 4 active, 6 dense layers. Same backbone as Trinity-Large-Thinking.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Trinity-Large-Preview is Arcee AI's lightly post-trained chat checkpoint of Trinity Large, released 2026-01-27. It is a 398B-parameter sparse MoE with about 13B active parameters per token, trained on 17 trillion tokens. Arcee still labels it a preview and later shipped Trinity-Large-Thinking as the reasoning variant.", "what_it_is_for": "A chat-ready instruct model without the separate reasoning_content channel used by Trinity-Large-Thinking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/trinity/trinity-large-thinking": { "id": "arcee/trinity/trinity-large-thinking", "name": "Trinity-Large-Thinking", "maker": "arcee", "family": "arcee/trinity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-04-01", "context_length": 262144, "max_output_tokens": null, "parameters": "398B total, about 13B active per token", "architecture": "Sparse MoE (AfmoeForCausalLM): 60 layers, hidden 3072, 256 experts (1 shared), 4 active, GQA 48/8. Card also says 6 dense layers.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.25, "completion_per_m_usd": 0.8 }, "summary_intro": "Trinity-Large-Thinking is Arcee AI's reasoning and agent post-train of Trinity Large, a sparse MoE with about 398 billion total parameters and about 13 billion active per token. It was released on 2026-04-01 under open weights. As of 2026-10-15 it is the only Arcee model on the first-party API, and Arcee has said that API endpoint will be deprecated on 2026-10-26.", "what_it_is_for": "Arcee describes it as an agent-first generalist for tool calling, multi-step planning, coding, math, and long reasoning traces.", "ratings": { "empirical_100": null, "lmsys_elo": 1211, "coding_elo": 1211, "swe_bench": 17.0, "humaneval": 71.6, "twelve_cs": 52.0, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/trinity/trinity-large-truebase": { "id": "arcee/trinity/trinity-large-truebase", "name": "Trinity-Large-TrueBase", "maker": "arcee", "family": "arcee/trinity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-01-27", "context_length": 8192, "max_output_tokens": null, "parameters": "398B total, about 13B active per token", "architecture": "Sparse MoE (AfmoeForCausalLM): 60 layers, hidden 3072, 256 experts, 4 active. Captured before the context extension used on Base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Trinity-Large-TrueBase is Arcee AI's raw Trinity Large checkpoint after 10 trillion pretraining tokens, released 2026-01-27. It is the same 398B sparse MoE before learning-rate annealing, instruction tuning, and reinforcement learning.", "what_it_is_for": "Research, probing, ablations, and fine-tuning without pre-baked alignment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/trinity/trinity-mini": { "id": "arcee/trinity/trinity-mini", "name": "Trinity Mini", "maker": "arcee", "family": "arcee/trinity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2025-12-01", "context_length": 131072, "max_output_tokens": null, "parameters": "26B total, 3B active per token", "architecture": "Sparse MoE (AfmoeForCausalLM): 32 layers, hidden 2048, 128 experts, 8 active, 1 shared. Pretrained on 10T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Trinity Mini is Arcee AI's 26 billion parameter sparse MoE, with 3 billion active parameters per token, released 2025-12-01. It is the post-trained reasoning model in the Trinity family, trained on 10 trillion tokens. The current model overview does not host it on the Arcee API.", "what_it_is_for": "Arcee calls it the everyday workhorse for well-defined tasks, agent backends, and high-throughput services, including local or VPC deployment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/trinity/trinity-mini-base": { "id": "arcee/trinity/trinity-mini-base", "name": "Trinity Mini Base", "maker": "arcee", "family": "arcee/trinity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2025-12-01", "context_length": 131072, "max_output_tokens": null, "parameters": "26B total, 3B active per token", "architecture": "Same AfmoeForCausalLM backbone as Trinity Mini: 32 layers, hidden 2048, 128 experts, 8 active, 1 shared. 10T pretraining tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Trinity Mini Base is the pretrained, pre-fine-tune checkpoint of Arcee AI's Trinity Mini 26B MoE, released with the 2025-12-01 Trinity launch. Arcee says it should be trained for a domain before use.", "what_it_is_for": "A base checkpoint for domain fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/trinity/trinity-nano-base": { "id": "arcee/trinity/trinity-nano-base", "name": "Trinity Nano Base", "maker": "arcee", "family": "arcee/trinity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2025-12-01", "context_length": 131072, "max_output_tokens": null, "parameters": "6B total, 1B active per token", "architecture": "Same AfmoeForCausalLM backbone as Trinity Nano Preview: 56 layers, hidden 1024, 128 experts, 8 active, 1 shared. 10T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Trinity Nano Base is the pretrained checkpoint of Arcee AI's 6B Trinity Nano MoE, released alongside the December 2025 preview. It is not instruction-tuned. The card publishes a few base-model benchmark scores, including GSM8K at 58.4 percent.", "what_it_is_for": "Domain fine-tuning and research on the small Trinity backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/trinity/trinity-nano-preview": { "id": "arcee/trinity/trinity-nano-preview", "name": "Trinity Nano Preview", "maker": "arcee", "family": "arcee/trinity", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2025-12-01", "context_length": 131072, "max_output_tokens": null, "parameters": "6B total, 1B active per token, about 800M non-embedding active", "architecture": "Sparse MoE (AfmoeForCausalLM): 56 layers, hidden 1024, 128 experts, 8 active, 1 shared. 10T pretraining tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Trinity Nano Preview is Arcee AI's experimental 6 billion parameter sparse MoE chat model, with 1 billion active parameters per token, released 2025-12-01. Arcee describes it as a personality-forward preview that pushes sparsity and may be unstable. It is not on the Arcee API.", "what_it_is_for": "On-device and edge experiments, not a hosted production endpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/virtuoso/virtuoso-large": { "id": "arcee/virtuoso/virtuoso-large", "name": "Virtuoso-Large", "maker": "arcee", "family": "arcee/virtuoso", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tongyi Qianwen License (Qwen)", "release_date": "2024-12-02", "context_length": 131072, "max_output_tokens": null, "parameters": "72B", "architecture": "Qwen2 architecture continued from Qwen2.5-72B. config.json: 80 layers, hidden 8192, max_position_embeddings 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.75, "completion_per_m_usd": 1.2 }, "summary_intro": "Virtuoso-Large is Arcee AI's 72 billion parameter general model, built on Qwen2.5-72B. It was offered on the Arcee Model Engine from the December 2024 public beta and the weights were posted on Hugging Face in June 2025 under the Qwen Tongyi license. A May 2025 Arcee price sheet lists $0.75 per million input tokens and $1.20 per million output tokens at 131K context.", "what_it_is_for": "Cross-domain writing, summarization, and long-context general work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/virtuoso/virtuoso-lite": { "id": "arcee/virtuoso/virtuoso-lite", "name": "Virtuoso-Lite", "maker": "arcee", "family": "arcee/virtuoso", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-28", "context_length": 32768, "max_output_tokens": null, "parameters": "10B", "architecture": "Llama-3-style Falcon3-10B base (card: Falcon-10B based on Llama-3), distilled from DeepSeek-V3. config.json: 40 layers, hidden 3072, max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Virtuoso-Lite is Arcee AI's 10 billion parameter DeepSeek-V3 distillation on a Falcon3-10B / Llama-3 backbone, announced 2025-01-28. The announcement says Apache-2.0. The Hugging Face metadata says license other. Config context is 32768 tokens.", "what_it_is_for": "A small distillation for reasoning, code, and math at 10B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/virtuoso/virtuoso-medium-v2": { "id": "arcee/virtuoso/virtuoso-medium-v2", "name": "Virtuoso-Medium-v2", "maker": "arcee", "family": "arcee/virtuoso", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-28", "context_length": 131072, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen2 continued from Qwen2.5-32B. Distilled from DeepSeek-V3 logits with tokenizer surgery back to the Qwen tokenizer. config.json: 64 layers, hidden 5120.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 0.8 }, "summary_intro": "Virtuoso-Medium-v2 is Arcee AI's 32 billion parameter general model, distilled from DeepSeek-V3 onto Qwen2.5-32B and released 2025-01-28 under Apache-2.0. Arcee says it scores above the earlier Virtuoso-Medium and above Arcee-Nova. The May 2025 hosted price was $0.50 / $0.80 per million input and output tokens.", "what_it_is_for": "Mid-size general instruction following at 131K context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/virtuoso/virtuoso-small": { "id": "arcee/virtuoso/virtuoso-small", "name": "Virtuoso-Small", "maker": "arcee", "family": "arcee/virtuoso", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-01", "context_length": 131072, "max_output_tokens": null, "parameters": "14B", "architecture": "Qwen2 continued from Qwen2.5-14B. config.json: 48 layers, hidden 5120, max_position_embeddings 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Virtuoso-Small is Arcee AI's first public Virtuoso model, a 14 billion parameter instruct model on Qwen2.5-14B released around 2024-12-01 under Apache-2.0. Virtuoso-Small-v2 later replaced it as the DeepSeek-V3 distillation at the same size. Conductor exposed it as virtuoso-small.", "what_it_is_for": "Smaller general instruct model for business text tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/virtuoso/virtuoso-small-v2": { "id": "arcee/virtuoso/virtuoso-small-v2", "name": "Virtuoso-Small-v2", "maker": "arcee", "family": "arcee/virtuoso", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-30", "context_length": 131072, "max_output_tokens": null, "parameters": "14B", "architecture": "Qwen2.5-14B distilled from DeepSeek-V3 logits, then tokenizer surgery back to Qwen. config.json: 48 layers, hidden 5120.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Virtuoso-Small-v2 is Arcee AI's 14 billion parameter DeepSeek-V3 distillation on Qwen2.5-14B, posted on Hugging Face on 2025-01-30 under Apache-2.0. It succeeds the first Virtuoso-Small checkpoint.", "what_it_is_for": "Compact general model with the same distillation recipe as Virtuoso-Medium-v2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arcee/vylinh/arcee-vylinh": { "id": "arcee/vylinh/arcee-vylinh", "name": "Arcee-VyLinh", "maker": "arcee", "family": "arcee/vylinh", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-07", "context_length": 32768, "max_output_tokens": null, "parameters": "3B", "architecture": "Qwen2.5-3B continued with EvolKit, merging, and iterative DPO for Vietnamese. config.json: 36 layers, hidden 2048, max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arcee-VyLinh is Arcee AI's 3 billion parameter Vietnamese instruct model, announced 2024-11-07 under Apache-2.0. It is built from Qwen2.5-3B with evolved prompts, a merge, and DPO. The card lists a 32K context.", "what_it_is_for": "Vietnamese chat and instruction following, with English input also accepted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arizona/calibrefine/calibrefine": { "id": "arizona/calibrefine/calibrefine", "name": "CalibRefine", "maker": "arizona", "family": "arizona/calibrefine", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The paper's system is a four-stage targetless LiDAR-camera calibrator: a Common Feature Discriminator, a coarse homography, iterative refinement, and an attention stage with a vision transformer and cross-attention. The discriminator uses ResNet image embeddings and PointNet++ LiDAR embeddings, plus relative position and class cues. Detectors named in the paper are YOLOv8 for images and octree DBSCAN for LiDAR. The public xcalib checkpoint is a pairwise port of that discriminator, not a verified export of the attention stage. Its A9 config is Bottleneck ResNet plus PointNet2 plus 2D sinusoidal position encoding, crop_size 32, point_cloud_size 1024, token_len 256, num_blocks [2, 2, 2, 2], num_classes 10, and bbox_expansion 1.25. Lei Cheng, Lihao Guo, and Siyang Cao are at the University of Arizona. Other authors are at the University of Tennessee at Chattanooga.", "modalities": { "input": [ "image", "point-cloud", "boxes" ], "output": [ "matches" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CalibRefine is a targetless LiDAR-camera calibration framework from the University of Arizona Radar Lab, with coauthors at the University of Tennessee at Chattanooga. Lei Cheng, Lihao Guo, and Siyang Cao are the Arizona authors. The preprint is arXiv:2502.17648, dated 24 February 2025, and the lab citation places the journal version in IEEE Transactions on Instrumentation and Measurement, volume 75 (2026). A public pairwise checkpoint of the Common Feature Discriminator was uploaded with `UArizona/xcalib` on 2026-06-25. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents CalibRefine as an online calibrator that does not need checkerboard targets or a hand-measured initial matrix. It matches objects across the camera and the LiDAR, estimates a coarse homography, then refines the pose as more frames arrive and with an attention stage for non-planar error. The public weight file used here is the later xcalib port of the matcher only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arizona/transrad/transrad": { "id": "arizona/transrad/transrad", "name": "TransRAD", "maker": "arizona", "family": "arizona/transrad", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2025-01-29", "context_length": null, "max_output_tokens": null, "parameters": "5.78M", "architecture": "3D radar detector on a range-azimuth-Doppler cube. Backbone is a lighter Retentive Vision Transformer: stem of four 3 by 3 convolutions, then four stages with 2, 2, 8, and 2 RMT blocks and embedding widths 32, 64, 128, and 256. The first three stages use decomposed Manhattan self-attention. The last uses full MaSA. Neck is an FPN-style top-down fusion adapted from YOLOv8, including C2f blocks. Four anchor-free decoupled heads predict objectness, class, range-azimuth box, and Doppler extent. Location-Aware NMS follows class NMS. Input cubes are resized from 256 by 256 by 64 to 256 by 256 by 256 with nearest-neighbor interpolation on Doppler. Both authors are at the University of Arizona Department of Electrical and Computer Engineering. The paper reports 2.16 GFLOPs.", "modalities": { "input": [ "radar-rad-cube" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TransRAD is a 3D radar object detector from the University of Arizona Radar Lab. Lei Cheng and Siyang Cao are the authors, both in Electrical and Computer Engineering. The preprint is arXiv:2501.17977, dated 29 January 2025. The lab lists the journal version in IEEE Transactions on Radar Systems, volume 3, pages 303-317, DOI 10.1109/TRS.2025.3537604. The code repository is GPL-3.0 and includes `model_data/best_epoch_weights.pth`. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents TransRAD as a radar-only detector for driving and robotics when cameras and LiDAR fail in weather or darkness. It reads a range-azimuth-Doppler cube and returns boxes that carry range, azimuth, and Doppler, so distance, angle, and speed come from one pass. Classes on RADDet are person, bicycle, car, motorcycle, bus, and truck.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "arizona/xcalib/crlite": { "id": "arizona/xcalib/crlite", "name": "CRLite", "maker": "arizona", "family": "arizona/xcalib", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid two-stage camera-LiDAR matcher. Backbone is ResNet plus PointNet. Position encoding is enhanced 3D (2D sinusoidal, depth MLP, and 3D MLP; packaged pe_mode full). The A9 config sets crop_size 32, point_cloud_size 1024, embed_dim 256, token_len 256, top_k 10, num_classes 10, max_depth 100, and bbox_expansion 1.25. Stage 1 returns embeddings and a cosine matrix. Stage 2 is an embed-fusion similarity MLP on the top-K pairs. Lei Cheng is not a CRLite author. Lihao Guo and Siyang Cao are at the University of Arizona. Several coauthors are at the University of Tennessee at Chattanooga.", "modalities": { "input": [ "image", "point-cloud", "boxes" ], "output": [ "matches" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CRLite is the public lightweight camera-LiDAR matcher from the University of Arizona Radar Lab, released in the XCalib package and on the Hugging Face repo `UArizona/xcalib`. Siyang Cao and Lihao Guo are at Arizona. Several coauthors are at the University of Tennessee at Chattanooga. The accompanying paper is accepted at IEEE Sensors Letters for 2026, and the Hub repo was created on 2026-06-25. Weights are public. This dossier was collected on 2026-10-15.", "what_it_is_for": "The XCalib README describes CRLite as the matching front-end that scores every camera box against every LiDAR box, then optionally assigns one-to-one pairs and calibrates without targets. The same weights are documented for a lab workstation and an NVIDIA Jetson AGX Thor. It is a research and edge deployment package, not a hosted API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/conformer/conformer-1": { "id": "assemblyai/conformer/conformer-1", "name": "Conformer-1", "maker": "assemblyai", "family": "assemblyai/conformer", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-03-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conformer ASR. English. Trained on 650,000 hours. A YouTube narration from AssemblyAI says the following Conformer-2 grew from 270 million to 450 million parameters. That size was not in the written post retrieved here, so parameters stays null.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Conformer-1 is AssemblyAI's March 2023 English speech model, collected 2026-10-15. It was trained on 650,000 hours and was sold through the API, including a realtime version from April 2023. It is not on the current model list. Conformer-2 replaced it in July 2023. Weights were closed. Retired status is medium confidence because no dated shutdown notice was retrieved.", "what_it_is_for": "AssemblyAI presented it as a robust real-world ASR model, stronger on noisy audio than the commercial and academic systems they compared, and then turned it on for streaming.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/conformer/conformer-2": { "id": "assemblyai/conformer/conformer-2", "name": "Conformer-2", "maker": "assemblyai", "family": "assemblyai/conformer", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-07-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conformer ASR. English. Trained on 1.1 million hours, about 170% of Conformer-1's data. YouTube narration says 450 million parameters, up from 270 million. Written post retrieved here does not state the size, so parameters is null.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Conformer-2 is AssemblyAI's July 2023 English speech model, collected 2026-10-15. It extended Conformer-1 with 1.1 million hours of training and better proper nouns, alphanumerics, and noise robustness, at similar word error rate. Universal-1 replaced it in April 2024. It is not on the current API list. Weights were closed.", "what_it_is_for": "AssemblyAI shipped it as the production ASR model for real-world English audio, and added speech_threshold so mostly non-speech files would not be billed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/medical/medical-v1": { "id": "assemblyai/medical/medical-v1", "name": "Medical Mode", "maker": "assemblyai", "family": "assemblyai/medical", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Domain add-on (domain medical-v1). It corrects medication, procedure, condition, and dosage wording on top of a speech model. It is not a standalone recognizer. Parameter count not published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": null }, "summary_intro": "Medical Mode is AssemblyAI's clinical-term add-on, API value medical-v1, collected 2026-10-15. It is not a separate recognizer. It sits on Universal-3.5 Pro, Universal-2, and the listed streaming models and is billed at an extra $0.15 per hour. Languages are English, Spanish, German, and French. Weights are closed. It replaces SLAM-1 for the medical use case.", "what_it_is_for": "AssemblyAI says it fixes medication names, procedures, conditions, and dosages that the base model gets wrong, including generic-plus-brand formatting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/medical/medical-v2": { "id": "assemblyai/medical/medical-v2", "name": "Medical Mode v2", "maker": "assemblyai", "family": "assemblyai/medical", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Medical Mode v2 is an announced AssemblyAI clinical add-on, collected 2026-10-15. The roadmap schedules it for Q4 2026. No API id, price, or language list was published. The shipping id remains medical-v1.", "what_it_is_for": "AssemblyAI describes it only as the next Medical Mode for clinical transcription.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/slam/slam-1": { "id": "assemblyai/slam/slam-1", "name": "SLAM-1", "maker": "assemblyai", "family": "assemblyai/slam", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-04-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech language model: LLM-style decoder with ASR encoders. English. AssemblyAI's docs called this contextual understanding rather than a retrained acoustic model.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SLAM-1 is AssemblyAI's speech language model, API id slam-1, collected 2026-10-15. It entered public beta on 23 Apr 2025 for English. AssemblyAI now marks it deprecated and tells medical users to move to Universal-3.5 Pro plus Medical Mode. Weights are closed. Confidence is medium because a beta docs page was still written in the present tense while the pricing markdown says not to use it.", "what_it_is_for": "It was built for precision on names, doses, addresses, and other entities, using keyterms instead of retraining. AssemblyAI used it for medical transcription and telephony before Universal-3 Pro.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/tts/tts-1": { "id": "assemblyai/tts/tts-1", "name": "TTS-1", "maker": "assemblyai", "family": "assemblyai/tts", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TTS-1 is AssemblyAI's announced text-to-speech model, collected 2026-10-15. The roadmap schedules a standalone model for Q4 2026 and links a waitlist. It is not shipping as a public model id. AssemblyAI already runs unnamed in-house voices inside the Voice Agent API. Those voices are not separate dossiers. Weights are expected to be closed. No price was published.", "what_it_is_for": "AssemblyAI describes a production TTS model with low time to first byte, a large voice library, speed control, a pronunciation dictionary, and better reading of phone numbers, emails, and names.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/universal/best": { "id": "assemblyai/universal/best", "name": "Best", "maker": "assemblyai", "family": "assemblyai/universal", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-04-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "API tier, not a separately trained architecture. May 2024 newsletter: Best uses Universal-1. Later routing changed.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.37, "completion_per_m_usd": null }, "summary_intro": "Best is a deprecated AssemblyAI speech-model id, collected 2026-10-15. It was introduced with Universal-1 in April 2024 as the default high-accuracy tier and was priced from $0.37 per hour in May 2024. It is not a separate neural net. Current AssemblyAI pricing markdown says the value routes to universal-3-5-pro. Another page says universal-3-pro. Confidence is medium because of that conflict.", "what_it_is_for": "It let callers pick the accurate tier without naming Universal-1. New requests should set speech_models to universal-3-5-pro or universal-2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/universal/nano": { "id": "assemblyai/universal/nano", "name": "Nano", "maker": "assemblyai", "family": "assemblyai/universal", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-04-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lower-cost speech tier. The underlying checkpoint was not named. Not Universal-1.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.12, "completion_per_m_usd": null }, "summary_intro": "Nano is AssemblyAI's deprecated low-cost speech-to-text tier, API value nano, collected 2026-10-15. It was announced with Best in April 2024 and priced from $0.12 per hour on 8 May 2024. AssemblyAI did not name the checkpoint behind it. pricing.md says it now routes to universal-2. The Vercel AI SDK says it was removed. Confidence is medium.", "what_it_is_for": "It was the cheap tier for drafts, topic detection, and trying the API. It was not the accuracy model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/universal/universal-1": { "id": "assemblyai/universal/universal-1", "name": "Universal-1", "maker": "assemblyai", "family": "assemblyai/universal", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-04-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multilingual ASR. Trained on over 12.5 million hours. Parameter count not published. Successor to Conformer-2.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Universal-1 is AssemblyAI's April 2024 multilingual speech model, collected 2026-10-15. It was trained on over 12.5 million hours and covered English, Spanish, French, and German. It is not on the current model list. Universal-2 replaced it in October 2024. The Best API tier used this model. Weights were closed. Confidence on the retired status is medium because no deprecation notice with a date was retrieved.", "what_it_is_for": "AssemblyAI launched it as the most capable speech model at the time, aimed at conversational intelligence, with better proper nouns and alphanumerics than Conformer-2 and fewer hallucinations than Whisper Large-v3 on their tests.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/universal/universal-2": { "id": "assemblyai/universal/universal-2", "name": "Universal-2", "maker": "assemblyai", "family": "assemblyai/universal", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multilingual ASR trained on more than 12.5 million hours, per the pricing page. Parameter count not published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": null }, "summary_intro": "Universal-2 is AssemblyAI's broad-coverage pre-recorded speech model, collected 2026-10-15. API id universal-2. It launched 31 Oct 2024 and remains the fallback for languages Universal-3.5 Pro does not cover. The public price is $0.15 per audio hour. Weights are closed.", "what_it_is_for": "AssemblyAI uses it for high-volume, price-sensitive batch transcription and for the long tail of languages. It is not the accuracy flagship.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/universal/universal-3-5-pro": { "id": "assemblyai/universal/universal-3-5-pro", "name": "Universal-3.5 Pro", "maker": "assemblyai", "family": "assemblyai/universal", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Promptable speech language model. AssemblyAI describes an LLM-based decoder on the Universal-3 line; parameter count was not published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.21, "completion_per_m_usd": null }, "summary_intro": "Universal-3.5 Pro is AssemblyAI's current pre-recorded speech-to-text flagship, collected 2026-10-15. It is a closed API model. AssemblyAI recommends it for async transcription and prices it at $0.21 per audio hour. The API id is universal-3-5-pro.", "what_it_is_for": "AssemblyAI says it is the highest-accuracy, fastest pre-recorded model, aimed at post-call analytics, meetings, medical scribes, and contact centers. It code-switches natively and accepts optional contextual prompts and keyterms. Files outside its language set are meant to fall back to Universal-2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/universal/universal-3-5-pro-streaming": { "id": "assemblyai/universal/universal-3-5-pro-streaming", "name": "Universal-3.5 Pro Streaming", "maker": "assemblyai", "family": "assemblyai/universal", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Previous flagship streaming speech model. Parameter count not published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.45, "completion_per_m_usd": null }, "summary_intro": "Universal-3.5 Pro Streaming is AssemblyAI's previous real-time flagship, still supported on 2026-10-15. The streaming speech_model value is universal-3-5-pro, the same string as the async model, on the v3 WebSocket rather than the transcript REST API. Weights are closed. Universal-3.6 Pro Streaming replaced it as the default on 29 Sep 2026.", "what_it_is_for": "AssemblyAI tells existing integrations to stay on this id if they are pinned to it. New voice agents are pointed at universal-3-6-pro. It was the model behind the March 2026 streaming benchmark tables.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/universal/universal-3-6": { "id": "assemblyai/universal/universal-3-6", "name": "Universal-3.6", "maker": "assemblyai", "family": "assemblyai/universal", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": null }, "summary_intro": "Universal-3.6 is an announced AssemblyAI streaming speech model, collected 2026-10-15. The public roadmap lists it for Q4 2026 at $0.30 per hour, next to Universal-3.6 Pro. No API id was published. It is not the shipping universal-3-6-pro model and not the pre-recorded preview.", "what_it_is_for": "AssemblyAI describes it only as a lower-cost streaming option beside the Pro model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/universal/universal-3-6-pro": { "id": "assemblyai/universal/universal-3-6-pro", "name": "Universal-3.6 Pro Streaming", "maker": "assemblyai", "family": "assemblyai/universal", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Streaming speech language model. AssemblyAI says it was trained on tens of thousands of hours of voice-agent and telephony audio. Parameter count not published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.45, "completion_per_m_usd": null }, "summary_intro": "Universal-3.6 Pro Streaming is AssemblyAI's current real-time speech-to-text flagship, collected 2026-10-15. The connection id is universal-3-6-pro. It became the default streaming model on 29 Sep 2026. Weights are closed. It is a separate product from the async preview id universal-3-6-pro-preview.", "what_it_is_for": "AssemblyAI positions it for voice agents: 32 languages, native code switching, prompting, conversational context, and entity-aware endpointing. It is a drop-in upgrade from Universal-3.5 Pro Streaming at the same base price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/universal/universal-3-6-pro-preview": { "id": "assemblyai/universal/universal-3-6-pro-preview", "name": "Universal-3.6 Pro Preview", "maker": "assemblyai", "family": "assemblyai/universal", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Universal-3.6 Pro Preview is a pre-recorded AssemblyAI speech model id, universal-3-6-pro-preview, collected 2026-10-15. The roadmap says it is available now and that general availability follows evaluation. It is not on the pre-recorded model-selection page. The streaming flagship universal-3-6-pro is a different, generally available model. Weights are closed. Confidence is high that the roadmap names the id, and lower that every account can select it, because the model index omits it.", "what_it_is_for": "It is the pre-recorded form of the Universal-3.6 Pro line. AssemblyAI has not published a model card beyond the roadmap line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/universal/universal-3-pro": { "id": "assemblyai/universal/universal-3-pro", "name": "Universal-3 Pro", "maker": "assemblyai", "family": "assemblyai/universal", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-02-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Promptable speech language model. First production model AssemblyAI described as adapting behavior from natural-language instructions. Parameter count not published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.21, "completion_per_m_usd": null }, "summary_intro": "Universal-3 Pro is AssemblyAI's February 2026 promptable speech-to-text model, API id universal-3-pro, collected 2026-10-15. It is no longer on the official pre-recorded model list. Universal-3.5 Pro (universal-3-5-pro, 7 Jul 2026) replaced it as the flagship. Weights are closed. Confidence is medium because a third-party SDK still documents the old id as accepted, while AssemblyAI's current index does not.", "what_it_is_for": "It was the first AssemblyAI production model sold as taking plain-language instructions for verbatim style, audio tags, and speaker labeling, plus keyterms. Medical and telephony users were pointed here from SLAM-1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/universal/universal-3-pro-streaming": { "id": "assemblyai/universal/universal-3-pro-streaming", "name": "Universal-3 Pro Streaming", "maker": "assemblyai", "family": "assemblyai/universal", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Streaming promptable ASR for six languages. Parameter count not published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.45, "completion_per_m_usd": null }, "summary_intro": "Universal-3 Pro Streaming is AssemblyAI's first Pro real-time model, API value u3-rt-pro, collected 2026-10-15. It covered six languages with native code switching. The current streaming page does not list it. pricing.md says the id is not in the current API reference and points new work at universal-3-6-pro. Status is deprecated. Confidence is medium because the retirement was stated in the pricing markdown, not in a dated changelog entry read here.", "what_it_is_for": "It was the high-accuracy voice-agent streaming model before Universal-3.5 Pro Streaming and Universal-3.6 Pro Streaming. An older docs snapshot also offered Whisper Streaming (whisper-rt) beside it. Whisper Streaming is a hosted WhisperLiveKit stack and is not given a folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/universal/universal-4-pro": { "id": "assemblyai/universal/universal-4-pro", "name": "Universal-4 Pro", "maker": "assemblyai", "family": "assemblyai/universal", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Universal-4 Pro is an announced AssemblyAI speech model, collected 2026-10-15. The roadmap places it in 2027 as the next accuracy and latency release after Universal-3.6 Pro, for both pre-recorded and realtime audio, with a focus on voice-agent conditions. No API id, price, or benchmark was published.", "what_it_is_for": "AssemblyAI describes it as the next major model for noise, interruptions, hesitation, and accented speech in voice agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/universal/universal-streaming-english": { "id": "assemblyai/universal/universal-streaming-english", "name": "Universal-Streaming English", "maker": "assemblyai", "family": "assemblyai/universal", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Low-latency English streaming ASR. Parameter count not published. Immutable word-by-word partials.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": null }, "summary_intro": "Universal-Streaming English is AssemblyAI's lower-cost English real-time model, collected 2026-10-15. API value universal-streaming-english. It launched 2 Jun 2025 at $0.15 per hour of session time. It is still listed. Universal-3.6 Pro Streaming is the recommended default when accuracy matters more than price.", "what_it_is_for": "AssemblyAI sells it for English-only real-time apps that want immutable partials and a lower rate than the Pro streaming models. It has keyterms prompting and intelligent endpointing, and it does not do general prompting or filler-word control.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/universal/universal-streaming-multilingual": { "id": "assemblyai/universal/universal-streaming-multilingual", "name": "Universal-Streaming Multilingual", "maker": "assemblyai", "family": "assemblyai/universal", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One streaming model for six languages. Code switching is per turn, not mid-sentence. Parameter count not published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": null }, "summary_intro": "Universal-Streaming Multilingual is AssemblyAI's $0.15/hr streaming model for English, Spanish, German, French, Portuguese, and Italian, collected 2026-10-15. It shipped 12 Nov 2025. Code switching is per turn. Native mid-sentence switching is on Universal-3.6 Pro instead.", "what_it_is_for": "AssemblyAI offers it when a voice app needs those six languages without the Pro streaming rate. Language detection is on. Non-speech tags are not supported.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "assemblyai/veterinary/veterinary-v1": { "id": "assemblyai/veterinary/veterinary-v1", "name": "Veterinary Mode", "maker": "assemblyai", "family": "assemblyai/veterinary", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Veterinary Mode is an AssemblyAI domain add-on in preview, API value veterinary-v1, collected 2026-10-15. The roadmap says it is available now for English, Spanish, French, and German, with general availability in Q4 2026. It is not on the models overview page. No price was published. It follows the Medical Mode pattern rather than being a full speech model.", "what_it_is_for": "AssemblyAI lists it as a veterinary domain mode, parallel to Medical Mode.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "asu/eclipse/eclipse-kandinsky": { "id": "asu/eclipse/eclipse-kandinsky", "name": "ECLIPSE Kandinsky Prior", "maker": "asu", "family": "asu/eclipse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenRAIL++", "release_date": "2023-12-10", "context_length": null, "max_output_tokens": null, "parameters": "34M", "architecture": "Non-diffusion text-to-image prior. Diffusers PriorTransformer, time-independent, 10 layers, 16 heads, head dimension 32, embedding dimension 1280, 77 text positions, 3 additional embeddings, dropout 0. Trained with a projection loss plus CLIP contrastive loss so the prior predicts an OpenCLIP image embedding. A frozen Kandinsky v2.2 decoder renders that embedding. The prior is the ASU release. The decoder and the OpenCLIP ViT-bigG text and vision encoders are not ASU weights. Paper Table 1 lists 34 million prior parameters and 1.26 billion parameters for the full unCLIP stack. The Hub card rounds the prior to 33 million.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ECLIPSE is a text-to-image prior from Arizona State University, released as weights on 2023-12-10 and still public on 2026-10-15. This checkpoint maps a text prompt to an OpenCLIP image embedding for a frozen Kandinsky v2.2 decoder. It is a research model, not an ASU API.", "what_it_is_for": "The paper introduces ECLIPSE as a contrastive, non-diffusion prior that is much smaller than the usual unCLIP prior. All five authors are at Arizona State University. This Hub file is the prior trained for the Kandinsky v2.2 decoder on 5 million LAION-HighRes pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "asu/eclipse/eclipse-karlo": { "id": "asu/eclipse/eclipse-karlo", "name": "ECLIPSE Karlo Prior", "maker": "asu", "family": "asu/eclipse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenRAIL++", "release_date": "2024-04-12", "context_length": null, "max_output_tokens": null, "parameters": "33.1M", "architecture": "Non-diffusion text-to-image prior for the Karlo v1-alpha decoder. Diffusers PriorTransformer, 10 layers, 16 heads, head dimension 32, embedding dimension 768, 77 text positions, 3 additional embeddings, dropout 0. The 768-wide embedding matches CLIP ViT-L/14, which the paper says Karlo uses. Paper Table 1 lists 33 million prior parameters and 0.93 billion parameters for the full stack. The Hub model page reports 33.1 million parameters. The prior is the ASU release. The Karlo decoder is Kakao Brain's.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ECLIPSE Karlo Prior is the Arizona State University text-to-image prior trained for Kakao Brain's Karlo v1-alpha decoder. The paper is dated 2023-12-07. The public Hub file was created on 2024-04-12 and was still up on 2026-10-15. It is a research checkpoint, not an ASU API.", "what_it_is_for": "The same ECLIPSE paper trains a small non-diffusion prior for two unCLIP decoders. This file is the Karlo one. The card introduction says it was trained on CC12M. Paper Table 1 matches that with a 10 million pair CC12M row.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "asu/eclipse/lambda-eclipse": { "id": "asu/eclipse/lambda-eclipse", "name": "\u03bb-ECLIPSE", "maker": "asu", "family": "asu/eclipse", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-07", "context_length": null, "max_output_tokens": null, "parameters": "34.8M", "architecture": "Personalized text-to-image prior. Diffusers PriorTransformer, 10 layers, 16 heads, head dimension 32, embedding dimension 1280, 77 text positions, 4 additional embeddings, dropout 0. It extends the ECLIPSE Kandinsky prior with image-text interleaved inputs and maps subject images plus a prompt onto an OpenCLIP image embedding. A frozen Kandinsky v2.2 UNet decodes that embedding. The paper says 34 million parameters. The Hub model page says 34.8 million. Training does not backpropagate through the diffusion UNet. Encoders are frozen OpenCLIP ViT-G/14 weights from LAION, not ASU weights.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u03bb-ECLIPSE is a personalized text-to-image prior from Arizona State University. Weights were uploaded on 2024-02-07 and were still public on 2026-10-15. It extends the ECLIPSE Kandinsky prior so subject images and a prompt share one CLIP embedding, which a frozen Kandinsky v2.2 decoder turns into an image. It is a research checkpoint, not an ASU API.", "what_it_is_for": "The authors, all at Arizona State University, use it for single-subject and multi-subject personalization without training a new diffusion model. The project page also describes edge-guided generation. The card says the released prior is biased toward one and two subjects even though the method can take more.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "asu/tripletclip/tripletclip": { "id": "asu/tripletclip/tripletclip", "name": "TripletCLIP", "maker": "asu", "family": "asu/tripletclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-11-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP dual encoder at ViT-B/32, trained from scratch for the paper's main CC3M and CC12M runs. Text encoder config: CLIPTextEncoderOnly, hidden size 512, 12 layers, 8 heads, intermediate size 2048, quick_gelu, projection 512, vocabulary 49408, 77 positions, pretrained false, model_name recorded as openai/clip-vit-base-patch32 for the architecture. Vision encoder config names CLIPVisionEncoderOnly with the same architecture id and pretrained false. The paper's separate OpenCLIP fine-tune is not this pair of Hub repos.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TripletCLIP is a CLIP ViT-B/32 dual encoder from Arizona State University, with one University of Maryland, Baltimore County coauthor. The from-scratch CC3M and CC12M weights were uploaded on 2024-11-30 and were still public on 2026-10-15. It is a research embedding model, not an ASU API.", "what_it_is_for": "The paper trains CLIP with synthetic hard-negative captions and matching negative images so the encoders handle compositional language better. The GitHub README points the TripletCLIP rows at the two Hub repos. Reproduced LaCLIP and NegCLIP baselines on the same org are not this model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "athens/distilgreek/distilgreek-bert": { "id": "athens/distilgreek/distilgreek-bert", "name": "DistilGREEK-BERT", "maker": "athens", "family": "athens/distilgreek", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2023-08-01", "context_length": 512, "max_output_tokens": null, "parameters": "70000000", "architecture": "DistilBERT student of GREEK-BERT. Config: 6 layers, dim 768, 12 heads, hidden_dim 3072, vocab 35000, max_position_embeddings 512, sinusoidal positions. Thesis: about 70 million trainable parameters, initialized from teacher layers 0, 2, 4, 7, 9, and 11. Teacher cited at about 112 million.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "National and Kapodistrian University of Athens student Eftychia Karavangeli released DistilGREEK-BERT, a six-layer distilled Greek encoder, on a personal Hub account. It is an active open-weights checkpoint. This dossier was collected on 2026-10-15.", "what_it_is_for": "The thesis distills AUEB's GREEK-BERT into a smaller student for modern Greek NLP. The author says the student keeps about 60 percent of the teacher's size, trains and runs faster, and keeps about 96 to 97 percent of the teacher's performance, with the teacher ahead by about 2 to 5 percent on the three tasks. The Hub README is empty.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "athens/greek-deberta/greek-deberta-base": { "id": "athens/greek-deberta/greek-deberta-base", "name": "GreekDeBERTa-base", "maker": "athens", "family": "athens/greek-deberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-10-02", "context_length": 512, "max_output_tokens": null, "parameters": "124962996", "architecture": "DeBERTa using a deberta-v2 config. Card: DeBERTa-base, masked language modeling. Config: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 50100, max_position_embeddings 512, relative attention p2c|c2p, position buckets 256. Thesis Table 4.2 also lists head size 64, dropout 0.1, learning rate 2e-4, weight decay 0.01, gradient clip 1.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "National and Kapodistrian University of Athens released GreekDeBERTa-base on the AI-team-UoA Hub. It is an active Greek DeBERTa encoder pretrained with masked language modeling. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says it is a Greek DeBERTa-base for NLP, pretrained with masked language modeling. The Koukouvinis BSc thesis, supervised by Manolis Koubarakis, compares this MLM base with two replaced-token-detection variants on part-of-speech tagging, named-entity recognition, and natural language inference. The thesis uses the same task setup as GREEK-BERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "athens/greek-deberta/greek-deberta-v3-base": { "id": "athens/greek-deberta/greek-deberta-v3-base", "name": "GreekDeBERTaV3-base", "maker": "athens", "family": "athens/greek-deberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-10-02", "context_length": 512, "max_output_tokens": null, "parameters": "207349429", "architecture": "DeBERTaV3-style replaced token detection, stored as a deberta-v2 config. Card: DeBERTaV3-base. Config: 12 layers, hidden 768, 12 heads, attention head size 64, intermediate 3072, vocab 50100, max_position_embeddings 512, relative attention p2c|c2p. Thesis Table 4.2: learning rate 6e-4, weight decay 0.01, gradient clip 1.0. The thesis says downstream runs use the discriminator, not the generator.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "National and Kapodistrian University of Athens released GreekDeBERTaV3-base on the AI-team-UoA Hub. It is an active Greek DeBERTaV3 encoder pretrained with replaced token detection. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the model is for Greek NLP, including part-of-speech tagging, named-entity recognition, and natural language inference. In the Koukouvinis thesis it is the replaced-token-detection base, and it has the highest NLI score of the three variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "athens/greek-deberta/greek-deberta-v3-xsmall": { "id": "athens/greek-deberta/greek-deberta-v3-xsmall", "name": "GreekDeBERTaV3-xsmall", "maker": "athens", "family": "athens/greek-deberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-10-02", "context_length": 512, "max_output_tokens": null, "parameters": "71554357", "architecture": "Smaller DeBERTaV3 replaced-token-detection model, deberta-v2 config. Config: 12 layers, hidden 384, 6 heads, attention head size 64, intermediate 1536, vocab 50100, max_position_embeddings 512, relative attention p2c|c2p. Thesis Table 4.2 prints FFN inner size 3072 for this variant. The config says 1536. Thesis: learning rate 6e-4, about 16 days.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "National and Kapodistrian University of Athens released GreekDeBERTaV3-xsmall on the AI-team-UoA Hub. It is an active smaller Greek DeBERTaV3 encoder pretrained with replaced token detection. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this is the compact GreekDeBERTaV3, for settings where inference time and size matter. The Koukouvinis thesis trains it with replaced token detection and reports it close to the base models, with shorter fine-tune times. The abstract says about 51 percent faster processing than the base variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "athens/greek-legal-bert/greek-legal-bert": { "id": "athens/greek-legal-bert/greek-legal-bert", "name": "GreekLegalBERT", "maker": "athens", "family": "athens/greek-legal-bert", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2022-03-02", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. Released config: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 35100, max_position_embeddings 512, type vocab 2. No model_type field in the config. The 2024 GreekLegalRoBERTa paper says vocab about 35k, 1M steps, batch 256. Papaloukas et al. 2021 print 786 units, which does not match hidden size 768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "National and Kapodistrian University of Athens is the home of GreekLegalBERT, a Greek legal BERT from Konstantinos Athinaios's 2020 dissertation in the Department of Informatics and Telecommunications. Public weights are on a student Hub account, with no model card. This dossier was collected on 2026-10-15.", "what_it_is_for": "Papaloukas et al. 2021 use GREEK-LEGAL-BERT, cited to Athinaios 2020, for multi-class topic classification of Greek legislation. The corpus is about 5 GB of Nomothesia documents from 1990 to 2017. The 2022 GreekLegalBERT v2 thesis loads `alexaapo/greek_legal_bert_v1` as that model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "athens/greek-legal-bert/greek-legal-bert-v2": { "id": "athens/greek-legal-bert/greek-legal-bert-v2", "name": "GreekLegalBERT v2", "maker": "athens", "family": "athens/greek-legal-bert", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2022-03-02", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT for pretraining. Config on both public repos: BertForPreTraining, 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 35000, max_position_embeddings 512, type vocab 2. The 2024 lab paper says about 35k vocab, 1M steps, batch 256, 8 GB, legal context.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "National and Kapodistrian University of Athens students Alexandra Apostolopoulou and Spyridon Briakos released GreekLegalBERT v2 in a 2022 BSc thesis supervised by Manolis Koubarakis. It is an active Greek legal BERT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The thesis trains a second Greek legal BERT on more legal text than GreekLegalBERT v1, then compares both with GreekBERT on named-entity recognition, natural language inference, and multi-class classification of the Raptarchis legislation collection. The 2024 GreekLegalRoBERTa paper uses this checkpoint as the legal BERT baseline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "athens/greek-legal-roberta/greek-legal-roberta-v2": { "id": "athens/greek-legal-roberta/greek-legal-roberta-v2", "name": "GreekLegalRoBERTa v2", "maker": "athens", "family": "athens/greek-legal-roberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-09", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa masked-language model. Config: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 50264, max_position_embeddings 514, absolute positions. Paper: sequence length 512, dynamic masking, no next-sentence prediction, vocabulary about 50264.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "National and Kapodistrian University of Athens released GreekLegalRoBERTa v2, a Greek legal RoBERTa encoder, on the AI-team-UoA Hugging Face org. It is an active open-weights checkpoint. This dossier was collected on 2026-10-15.", "what_it_is_for": "The SETN 2024 paper trains four GreekLegalRoBERTa encoders for Greek legal NER and multi-class topic classification. v2 uses the same Nomothesia legislation corpus as v1, about 5 GB, but a batch size of 4096 on four V100 GPUs for about 40 days. The Hub card itself is the empty template.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "athens/greek-legal-roberta/greek-legal-roberta-v3": { "id": "athens/greek-legal-roberta/greek-legal-roberta-v3", "name": "GreekLegalRoBERTa v3", "maker": "athens", "family": "athens/greek-legal-roberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-09", "context_length": 512, "max_output_tokens": null, "parameters": "125000000", "architecture": "RoBERTa masked-language model. Card: 12-layer, 768-hidden, 12-head, compared with roberta-base. Config: intermediate 3072, vocab 50264, max_position_embeddings 514, absolute positions. Card says 100k steps, batch 4096, sequence length 512, initial learning rate 6e-4.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "National and Kapodistrian University of Athens released GreekLegalRoBERTa v3 on the AI-team-UoA Hub. It is an active Greek RoBERTa encoder with a written model card. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card calls it a Greek legal RoBERTa for legislative text. The SETN 2024 paper uses it for named-entity recognition on GreekLegalNER and topic classification on GreekLegalCode. In that paper's tables, v3 is the strongest of the four versions on chapter and subject classification, and not the strongest on NER.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "athens/greek-legal-roberta/greek-legal-roberta-v4": { "id": "athens/greek-legal-roberta/greek-legal-roberta-v4", "name": "GreekLegalRoBERTa v4", "maker": "athens", "family": "athens/greek-legal-roberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-09", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa masked-language model. Config: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 50264, max_position_embeddings 514, absolute positions. Same layer shape as v2 and v3.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "National and Kapodistrian University of Athens released GreekLegalRoBERTa v4 on the AI-team-UoA Hub. It is an active Greek RoBERTa encoder trained on legal and non-legal text. This dossier was collected on 2026-10-15.", "what_it_is_for": "The SETN 2024 paper defines v4 as the run that adds Greek Wikipedia and OSCAR to the legal corpora, about 37.03 GB, to test whether mixed context helps legal NER and topic classification. The authors report that it did not clearly beat the smaller legal-only runs. The Hub card is the empty template.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "auburn/cspws/cspws-unet-attn": { "id": "auburn/cspws/cspws-unet-attn", "name": "csPWS U-Net + Attention", "maker": "auburn", "family": "auburn/cspws", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-08-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "U-Net with attention gates for binary nuclei segmentation. The model card encoder is Conv2d blocks at 64, 128, 256, 512, and a 1024-channel bottleneck, each followed by ReLU and max-pooling except the bottleneck. The decoder uses up-convolutions plus attention gates on the skip features. Output is a 1x1 convolution and sigmoid. Card training setup: 256 by 256 RGB input, loss BCE plus (1 minus Dice), Adam at learning rate 1e-3. ONNX export is opset 17, with a per-channel QDQ INT8 file. This is not the ConvNeXt-FPN-UNet++ CFU-Net described in arXiv:2510.22239.", "modalities": { "input": [ "image" ], "output": [ "mask" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Auburn University graduate researcher Jahidul Arafat released this nuclei-segmentation checkpoint on 25 August 2025 as `jahidularafat/cspws-unet-attn`. It is a public MIT-licensed U-Net with attention gates for chromatin-sensitive partial-wave spectroscopic microscopy, not a priced API. Collected 2026-10-15. It is not the ConvNeXt CFU-Net in arXiv:2510.22239.", "what_it_is_for": "The model card says the job is binary nuclei segmentation on chromatin-sensitive partial-wave spectroscopic images so that chromatin packing can be measured as an early-cancer biomarker. The published scores are on synthetic fused images. The card marks real csPWS evaluation as still to be done.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aveiro/bionext/bionext-extractor": { "id": "aveiro/bionext/bionext-extractor", "name": "BioNExt Extractor", "maker": "aveiro", "family": "aveiro/bionext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-05-15", "context_length": 512, "max_output_tokens": null, "parameters": "337685003", "architecture": "BioNExtExtractorModel: BioLinkBERT-large encoder (24 layers, hidden size 1024, 16 heads) with one LSTM layer and a multi-head classifier. Vocabulary 28899 after four entity-marker tokens. Relation labels: Association, Positive_Correlation, Negative_Correlation, Cotreatment, Bind, Comparison, Conversion, Drug_Interaction, Negative_Class. Config sets novel to true.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioNExt Extractor is the relation module of BioNExt, released by IEETA at the University of Aveiro. It classifies a relation between two marked biomedical entities and whether that relation is novel. Weights were uploaded on 2024-05-15 and were still public on 2026-10-15. There is no hosted API.", "what_it_is_for": "The GitHub README and the parent card say this checkpoint is the relation-extraction stage of BioNExt and can be run without the tagger. Relation classes on the config are association, positive correlation, negative correlation, cotreatment, bind, comparison, conversion, drug interaction, and a negative class. The config flag `novel` is true. The empty README does not restate the academic-use limit. That sentence is on the parent card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aveiro/bionext/bionext-tagger": { "id": "aveiro/bionext/bionext-tagger", "name": "BioNExt Tagger", "maker": "aveiro", "family": "aveiro/bionext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-05-15", "context_length": 512, "max_output_tokens": null, "parameters": "333489360", "architecture": "BioNExtTaggerModel: BioLinkBERT-large encoder (24 layers, hidden size 1024, 16 heads, vocab 28895) plus a CRF tagger. Six BIO entity types: GeneOrGeneProduct, DiseaseOrPhenotypicFeature, ChemicalEntity, SequenceVariant, OrganismTaxon, CellLine. Config context_size is 2; max_position_embeddings is 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioNExt Tagger is the named-entity module of BioNExt, released by IEETA at the University of Aveiro (DETI, LASI). It is an English biomedical NER model fine-tuned from BioLinkBERT-large, with weights on the Hugging Face org IEETA. The weights were uploaded on 2024-05-15 and were still public on the collection date 2026-10-15. There is no hosted API.", "what_it_is_for": "The parent card and the Database paper describe BioNExt as an end-to-end system that finds biomedical entities, links them, and classifies relations, including whether a relation is novel. The tagger is the NER stage. It labels genes, diseases, chemicals, variants, species, and cell lines. The GitHub README says the tagger can be loaded on its own from `IEETA/BioNExt-Tagger`. The card says use is academic, not clinical decision support.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aveiro/multi-head-crf/multi-head-crf": { "id": "aveiro/multi-head-crf/multi-head-crf", "name": "Multi-Head CRF", "maker": "aveiro", "family": "aveiro/multi-head-crf", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-06-12", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "RobertaMultiHeadCRFModel: Spanish clinical RoBERTa (12 layers, hidden size 768, 12 heads, vocab 50262) with one CRF head per entity class. Five classes on the checkpoints: SINTOMA, PROCEDIMIENTO, ENFERMEDAD, PROTEINAS, CHEMICAL. CRF context_size 32. One checkpoint uses 1 hidden layer per head (127017306 parameters). Three use 3 layers per head (132923226 parameters).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Multi-Head CRF is a Spanish clinical named-entity model from IEETA at the University of Aveiro. One shared RoBERTa encoder has a separate CRF head for each entity class so overlapping types can be labeled together. Four checkpoints were uploaded on 2024-06-12 and were still public on 2026-10-15. There is no hosted API.", "what_it_is_for": "The card and the Database paper present it as multi-class NER on Spanish clinical notes: symptoms, procedures, diseases, chemicals, and proteins. Training data named on the card is the merged SPACCC set from SympTEMIST, MedProcNER, DisTEMIST, and PharmaCoNER. The card says the model is for academic use, not professional medical use. A later CLEF 2024 note from the same group reused the architecture for cardiology NER. That run is not a separate Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aveiro/multiclinner/multiclinner-cz": { "id": "aveiro/multiclinner/multiclinner-cz", "name": "MultiClinNER Czech", "maker": "aveiro", "family": "aveiro/multiclinner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-20", "context_length": 514, "max_output_tokens": null, "parameters": "130707510", "architecture": "RobertaMultiHeadCRFModel fine-tuned from ufal/robeczech-base. 12 layers, hidden size 768, 12 attention heads, vocabulary 51997, 3 CRF layer(s) per head. Augmentation on the main checkpoint is random, aug_prob 0.5, percentage_tags 0.1. CRF context_size 64.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MultiClinNER Czech is the Czech clinical NER checkpoint released by IEETA at the University of Aveiro for the MultiClinNER shared task. The main branch was uploaded on 2026-04-20 and was still public on 2026-10-15. It is a downloadable weight file, not an API.", "what_it_is_for": "The SMM4H-HeaRD 2026 paper describes BIT.UA's system for disease, symptom, and procedure spans in clinical case reports. This folder is the dedicated Czech model. The card's main branch is the best of 20 sweep checkpoints. The lab's own finding is that the single multilingual checkpoint beats this monolingual model on most languages. Both were released.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aveiro/multiclinner/multiclinner-en": { "id": "aveiro/multiclinner/multiclinner-en", "name": "MultiClinNER English", "maker": "aveiro", "family": "aveiro/multiclinner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-20", "context_length": 512, "max_output_tokens": null, "parameters": "341881910", "architecture": "BertMultiHeadCRFModel fine-tuned from microsoft/BiomedNLP-PubMedBERT-large-uncased-abstract. 24 layers, hidden size 1024, 16 attention heads, vocabulary 28895, 3 CRF layer(s) per head. Augmentation on the main checkpoint is random, aug_prob 0.5, percentage_tags 0.25. CRF context_size 64.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MultiClinNER English is the English clinical NER checkpoint released by IEETA at the University of Aveiro for the MultiClinNER shared task. The main branch was uploaded on 2026-04-20 and was still public on 2026-10-15. It is a downloadable weight file, not an API.", "what_it_is_for": "The SMM4H-HeaRD 2026 paper describes BIT.UA's system for disease, symptom, and procedure spans in clinical case reports. This folder is the dedicated English model. The card's main branch is the best of 20 sweep checkpoints. The lab's own finding is that the single multilingual checkpoint beats this monolingual model on most languages. Both were released.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aveiro/multiclinner/multiclinner-es": { "id": "aveiro/multiclinner/multiclinner-es", "name": "MultiClinNER Spanish", "maker": "aveiro", "family": "aveiro/multiclinner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-20", "context_length": 514, "max_output_tokens": null, "parameters": "129375030", "architecture": "RobertaMultiHeadCRFModel fine-tuned from lcampillos/roberta-es-clinical-trials-ner. 12 layers, hidden size 768, 12 attention heads, vocabulary 50262, 3 CRF layer(s) per head. Augmentation on the main checkpoint is ukn, aug_prob 0.2, percentage_tags 0.25. CRF context_size 64.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MultiClinNER Spanish is the Spanish clinical NER checkpoint released by IEETA at the University of Aveiro for the MultiClinNER shared task. The main branch was uploaded on 2026-04-20 and was still public on 2026-10-15. It is a downloadable weight file, not an API.", "what_it_is_for": "The SMM4H-HeaRD 2026 paper describes BIT.UA's system for disease, symptom, and procedure spans in clinical case reports. This folder is the dedicated Spanish model. The card's main branch is the best of 20 sweep checkpoints. The lab's own finding is that the single multilingual checkpoint beats this monolingual model on most languages. Both were released.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aveiro/multiclinner/multiclinner-it": { "id": "aveiro/multiclinner/multiclinner-it", "name": "MultiClinNER Italian", "maker": "aveiro", "family": "aveiro/multiclinner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-20", "context_length": 512, "max_output_tokens": null, "parameters": "111115830", "architecture": "BertMultiHeadCRFModel fine-tuned from IVN-RIN/medBIT. 12 layers, hidden size 768, 12 attention heads, vocabulary 31102, 1 CRF layer(s) per head. Augmentation on the main checkpoint is random, aug_prob 0.2, percentage_tags 0.1. CRF context_size 64.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MultiClinNER Italian is the Italian clinical NER checkpoint released by IEETA at the University of Aveiro for the MultiClinNER shared task. The main branch was uploaded on 2026-04-20 and was still public on 2026-10-15. It is a downloadable weight file, not an API.", "what_it_is_for": "The SMM4H-HeaRD 2026 paper describes BIT.UA's system for disease, symptom, and procedure spans in clinical case reports. This folder is the dedicated Italian model. The card's main branch is the best of 14 sweep checkpoints. The lab's own finding is that the single multilingual checkpoint beats this monolingual model on most languages. Both were released.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aveiro/multiclinner/multiclinner-mixed": { "id": "aveiro/multiclinner/multiclinner-mixed", "name": "MultiClinNER Mixed", "maker": "aveiro", "family": "aveiro/multiclinner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-20", "context_length": 514, "max_output_tokens": null, "parameters": "568296502", "architecture": "RobertaMultiHeadCRFModel fine-tuned from FacebookAI/xlm-roberta-large. 24 layers, hidden size 1024, 16 attention heads, vocabulary 250002, 3 CRF layers per head. Main-branch augmentation is random, aug_prob 0.2, percentage_tags 0.25, CRF context_size 64, 3 epochs in the run name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MultiClinNER Mixed is the joint clinical NER model from IEETA at the University of Aveiro. One XLM-RoBERTa-large checkpoint covers Czech, Dutch, English, Italian, Romanian, Spanish, and Swedish. Weights were uploaded on 2026-04-20 and were still public on 2026-10-15. There is no API.", "what_it_is_for": "The SMM4H-HeaRD 2026 paper says a single xlm-roberta-large model, trained on all seven languages and on disease, symptom, and procedure, ranked second in the official task for five languages and beat the lab's own monolingual models on those five. This folder is that multilingual release. The card's main branch is the best of seven large variants. The card does not print an F1. Ensemble submissions are not separate weight files.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aveiro/multiclinner/multiclinner-nl": { "id": "aveiro/multiclinner/multiclinner-nl", "name": "MultiClinNER Dutch", "maker": "aveiro", "family": "aveiro/multiclinner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-20", "context_length": 514, "max_output_tokens": null, "parameters": "130709814", "architecture": "RobertaMultiHeadCRFModel fine-tuned from CLTL/MedRoBERTa.nl. 12 layers, hidden size 768, 12 attention heads, vocabulary 52000, 3 CRF layer(s) per head. Augmentation on the main checkpoint is random, aug_prob 0.2, percentage_tags 0.25. CRF context_size 64.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MultiClinNER Dutch is the Dutch clinical NER checkpoint released by IEETA at the University of Aveiro for the MultiClinNER shared task. The main branch was uploaded on 2026-04-20 and was still public on 2026-10-15. It is a downloadable weight file, not an API.", "what_it_is_for": "The SMM4H-HeaRD 2026 paper describes BIT.UA's system for disease, symptom, and procedure spans in clinical case reports. This folder is the dedicated Dutch model. The card's main branch is the best of 18 sweep checkpoints. The lab's own finding is that the single multilingual checkpoint beats this monolingual model on most languages. Both were released.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aveiro/multiclinner/multiclinner-ro": { "id": "aveiro/multiclinner/multiclinner-ro", "name": "MultiClinNER Romanian", "maker": "aveiro", "family": "aveiro/multiclinner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-20", "context_length": 512, "max_output_tokens": null, "parameters": "129173046", "architecture": "BertMultiHeadCRFModel fine-tuned from dumitrescustefan/bert-base-romanian-cased-v1. 12 layers, hidden size 768, 12 attention heads, vocabulary 50000, 3 CRF layer(s) per head. Augmentation on the main checkpoint is ukn, aug_prob 0.2, percentage_tags 0.1. CRF context_size 64.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MultiClinNER Romanian is the Romanian clinical NER checkpoint released by IEETA at the University of Aveiro for the MultiClinNER shared task. The main branch was uploaded on 2026-04-20 and was still public on 2026-10-15. It is a downloadable weight file, not an API.", "what_it_is_for": "The SMM4H-HeaRD 2026 paper describes BIT.UA's system for disease, symptom, and procedure spans in clinical case reports. This folder is the dedicated Romanian model. The card's main branch is the best of 18 sweep checkpoints. The lab's own finding is that the single multilingual checkpoint beats this monolingual model on most languages. Both were released.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "aveiro/multiclinner/multiclinner-sv": { "id": "aveiro/multiclinner/multiclinner-sv", "name": "MultiClinNER Swedish", "maker": "aveiro", "family": "aveiro/multiclinner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-20", "context_length": 512, "max_output_tokens": null, "parameters": "129422646", "architecture": "BertMultiHeadCRFModel fine-tuned from KB/bert-base-swedish-cased. 12 layers, hidden size 768, 12 attention heads, vocabulary 50325, 3 CRF layer(s) per head. Augmentation on the main checkpoint is random, aug_prob 0.5, percentage_tags 0.1. CRF context_size 64.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MultiClinNER Swedish is the Swedish clinical NER checkpoint released by IEETA at the University of Aveiro for the MultiClinNER shared task. The main branch was uploaded on 2026-04-20 and was still public on 2026-10-15. It is a downloadable weight file, not an API.", "what_it_is_for": "The SMM4H-HeaRD 2026 paper describes BIT.UA's system for disease, symptom, and procedure spans in clinical case reports. This folder is the dedicated Swedish model. The card's main branch is the best of 11 sweep checkpoints. The lab's own finding is that the single multilingual checkpoint beats this monolingual model on most languages. Both were released.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/altclip/altclip": { "id": "baai/altclip/altclip", "name": "AltCLIP", "maker": "baai", "family": "baai/altclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML OpenRAIL-M", "release_date": "2022-11-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP altered for bilingual text. m9 and m18 are named checkpoints.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AltCLIP is BAAI's bilingual Chinese-English CLIP, repo created 15 November 2022. The org tag is creativeml-openrail-m and the paper tag is arXiv:2211.06679. AltCLIP-m9 (18 Nov 2022) and AltCLIP-m18 (27 Mar 2023) are later versions. Status: active. Collected 2026-10-15.", "what_it_is_for": "Zero-shot image classification in Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/altdiffusion/altdiffusion": { "id": "baai/altdiffusion/altdiffusion", "name": "AltDiffusion", "maker": "baai", "family": "baai/altdiffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-11-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion model conditioned by the AltCLIP line. Resolution and parameter count were not read.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AltDiffusion is BAAI's bilingual text-to-image model paired with AltCLIP. The base repo was created 15 November 2022, AltDiffusion-m9 on 18 November 2022, and AltDiffusion-m18 on 31 March 2023. Those are versions. DreamBooth-AltDiffusion is a demo fine-tune and is not a separate product. Status: active. License text was not opened. Collected 2026-10-15.", "what_it_is_for": "Chinese and English text-to-image generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquila-135m": { "id": "baai/aquila/aquila-135m", "name": "Aquila-135M", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-27", "context_length": null, "max_output_tokens": null, "parameters": "135M", "architecture": "Tagged mistral. 135M is the repo name. config.json was not downloaded.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aquila-135M is a small BAAI language model, repo created 27 November 2024, Apache-2.0. Tags name a Mistral architecture and the CCI3-HQ and Infinity-Instruct data mixture. An intermediate checkpoint is a version. The instruct model has its own folder. Status: active. Collected 2026-10-15.", "what_it_is_for": "Tiny base model for research and on-device experiments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquila-135m-instruct": { "id": "baai/aquila/aquila-135m-instruct", "name": "Aquila-135M-Instruct", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-05", "context_length": null, "max_output_tokens": null, "parameters": "135M", "architecture": "Instruction tune of Aquila-135M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aquila-135M-Instruct is the instruction-tuned pair of Aquila-135M, created 5 December 2024, Apache-2.0, Mistral architecture tag. Status: active. Collected 2026-10-15.", "what_it_is_for": "Instruction following at the 135M scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquila-33b": { "id": "baai/aquila/aquila-33b", "name": "Aquila-33B", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BAAI Aquila Model License (weights); Apache-2.0 (source code)", "release_date": "2024-08-28", "context_length": null, "max_output_tokens": null, "parameters": "33B", "architecture": "Decoder-only transformer in the Aquila family. The 7B card says the base design follows GPT-3 and LLaMA, with a bilingual tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aquila-33B is the 33 billion parameter base model named on the Aquila-7B card, where it was marked coming soon. A Hugging Face repo BAAI/Aquila-33B exists and was created 28 August 2024, so weights were later published. Same Aquila weight license. Status: active. Collected 2026-10-15.", "what_it_is_for": "Larger Aquila base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquila-7b": { "id": "baai/aquila/aquila-7b", "name": "Aquila-7B", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BAAI Aquila Model License (weights); Apache-2.0 (source code)", "release_date": "2023-06-08", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only transformer in the Aquila family. The 7B card says the base design follows GPT-3 and LLaMA, with a bilingual tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aquila-7B is BAAI's first open bilingual base language model in the WuDao Aquila line. The card, created 8 June 2023, calls it a 7 billion parameter base model trained from scratch on Chinese and English, with Chinese about 40% of the mix in the truncated card sentence. A v0.10 weight refresh is dated 15 August 2023 on the card. Code is Apache-2.0 and weights use the BAAI Aquila Model License. Status: active. Collected 2026-10-15.", "what_it_is_for": "Base model for continuation and further tuning. Not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquila-vl-2b": { "id": "baai/aquila/aquila-vl-2b", "name": "Aquila-VL-2B", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-10-17", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "LLaVA-Qwen. Base language model Qwen/Qwen2.5-1.5B-Instruct. Vision tower was not read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aquila-VL-2B is BAAI's 2B vision-language model. The released checkpoint is Aquila-VL-2B-llava-qwen, a fine-tune of Qwen2.5-1.5B-Instruct in a LLaVA-style stack, created 17 October 2024. Apache-2.0. Paper tag arXiv:2410.18558. An intermediate checkpoint exists and is a version, not a separate product. Status: active. Collected 2026-10-15.", "what_it_is_for": "Visual question answering and image-text chat at about 2B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquila2-34b": { "id": "baai/aquila/aquila2-34b", "name": "Aquila2-34B", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BAAI Aquila Model License (weights); Apache-2.0 (source code)", "release_date": "2023-10-12", "context_length": 8192, "max_output_tokens": null, "parameters": "34B", "architecture": "Decoder-only transformer in the Aquila family. The 7B card says the base design follows GPT-3 and LLaMA, with a bilingual tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aquila2-34B is BAAI's 34B Aquila2 base model. The card's 6 June 2024 update replaces the tokenizer (about 143k) and raises the maximum length from 2048 to 8192. Weights use the BAAI Aquila Model License. Technical report arXiv:2408.07410. Status: active. Collected 2026-10-15.", "what_it_is_for": "Base model for long-context continuation after the June 2024 update.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquila2-70b-expr": { "id": "baai/aquila/aquila2-70b-expr", "name": "Aquila2-70B-Expr", "maker": "baai", "family": "baai/aquila", "status": "preview", "confidence": "medium", "weights": "open-weights", "license": "BAAI Aquila Model License (weights); Apache-2.0 (source code)", "release_date": "2023-11-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only transformer in the Aquila family. The 7B card says the base design follows GPT-3 and LLaMA, with a bilingual tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aquila2-70B-Expr is an experimental 70B-class Aquila2 base checkpoint. The repo name is the vendor's experimental label. Created 27 November 2023. Aquila license tag on the org listing. Status: preview, because the id itself marks it experimental and no separate product page was found. Collected 2026-10-15.", "what_it_is_for": "Experimental larger Aquila2 base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquila2-7b": { "id": "baai/aquila/aquila2-7b", "name": "Aquila2-7B", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BAAI Aquila Model License (weights); Apache-2.0 (source code)", "release_date": "2023-10-10", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only transformer in the Aquila family. The 7B card says the base design follows GPT-3 and LLaMA, with a bilingual tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aquila2-7B is the 7B base model in the Aquila2 generation. The Aquila2-34B card says the open series includes Aquila2-7B and Aquila2-34B plus the matching chat models. Repo created 10 October 2023. Aquila weight license. The 34B card's June 2024 context extension to 8192 is stated for Aquila2-34B, not copied onto this 7B repo. Status: active. Collected 2026-10-15.", "what_it_is_for": "Base continuation model at 7B in the Aquila2 line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquilachat-7b": { "id": "baai/aquila/aquilachat-7b", "name": "AquilaChat-7B", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BAAI Aquila Model License (weights); Apache-2.0 (source code)", "release_date": "2023-06-08", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only transformer in the Aquila family. The 7B card says the base design follows GPT-3 and LLaMA, with a bilingual tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AquilaChat-7B is the supervised and RL chat model built on Aquila-7B. The 7B card says it supports dialogue and tool calls through a special instruction format. Repo created 8 June 2023, with a 15 August 2023 v0.10 refresh. Aquila weight license. Status: active. Collected 2026-10-15.", "what_it_is_for": "Chinese-English chat and tool calling on the Aquila-7B base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquilachat2-34b": { "id": "baai/aquila/aquilachat2-34b", "name": "AquilaChat2-34B", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BAAI Aquila Model License (weights); Apache-2.0 (source code)", "release_date": "2023-10-11", "context_length": null, "max_output_tokens": null, "parameters": "34B", "architecture": "Decoder-only transformer in the Aquila family. The 7B card says the base design follows GPT-3 and LLaMA, with a bilingual tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AquilaChat2-34B is the Aquila2 34B chat model, with AquilaChat2-34B-16K (13 October 2023) kept as a version. Aquila weight license. Paper arXiv:2408.07410 is tagged on the repo. Status: active. Collected 2026-10-15.", "what_it_is_for": "Chat model paired with Aquila2-34B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquilachat2-70b-expr": { "id": "baai/aquila/aquilachat2-70b-expr", "name": "AquilaChat2-70B-Expr", "maker": "baai", "family": "baai/aquila", "status": "preview", "confidence": "medium", "weights": "open-weights", "license": "BAAI Aquila Model License (weights); Apache-2.0 (source code)", "release_date": "2023-11-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only transformer in the Aquila family. The 7B card says the base design follows GPT-3 and LLaMA, with a bilingual tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AquilaChat2-70B-Expr is the experimental chat pair of Aquila2-70B-Expr. Repo created 27 November 2023. Status: preview. Collected 2026-10-15.", "what_it_is_for": "Experimental 70B-class Aquila2 chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquilachat2-7b": { "id": "baai/aquila/aquilachat2-7b", "name": "AquilaChat2-7B", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BAAI Aquila Model License (weights); Apache-2.0 (source code)", "release_date": "2023-10-10", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only transformer in the Aquila family. The 7B card says the base design follows GPT-3 and LLaMA, with a bilingual tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AquilaChat2-7B is the Aquila2 7B chat model. A 16K context sibling, AquilaChat2-7B-16K, was created 13 October 2023 and is stored as a version, not a second product. Aquila weight license. Status: active. Collected 2026-10-15.", "what_it_is_for": "Chat model for the Aquila2-7B line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquilacode-multi": { "id": "baai/aquila/aquilacode-multi", "name": "AquilaCode-multi", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BAAI Aquila Model License (weights); Apache-2.0 (source code)", "release_date": "2023-07-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only transformer in the Aquila family. The 7B card says the base design follows GPT-3 and LLaMA, with a bilingual tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AquilaCode-multi is the other public Aquila code repo from 19 July 2023, alongside AquilaCode-py. Same license and the same unmatched NV/TS names on the 7B card. Status: active. Collected 2026-10-15.", "what_it_is_for": "Multi-language code model in the Aquila code line, under the published repo name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquilacode-py": { "id": "baai/aquila/aquilacode-py", "name": "AquilaCode-py", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BAAI Aquila Model License (weights); Apache-2.0 (source code)", "release_date": "2023-07-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only transformer in the Aquila family. The 7B card says the base design follows GPT-3 and LLaMA, with a bilingual tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AquilaCode-py is a public Aquila code model repo created 19 July 2023. The June 2023 Aquila-7B table instead names AquilaCode-7B-NV and AquilaCode-7B-TS as 7B text-code models further pretrained from Aquila-7B. Those two ids are not on the BAAI Hugging Face org. This repo is catalogued under the name that was actually published. Aquila license. Status: active. Collected 2026-10-15.", "what_it_is_for": "Code generation on an Aquila backbone. The py suffix is the published repo name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquiladense-16b": { "id": "baai/aquila/aquiladense-16b", "name": "AquilaDense-16B", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-15", "context_length": null, "max_output_tokens": null, "parameters": "16B", "architecture": "Tagged aquiladense.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AquilaDense-16B is the 16B dense sibling of AquilaDense-7B, same day, same Apache-2.0 tag. Status: active. Collected 2026-10-15.", "what_it_is_for": "Larger dense Aquila model from the June 2024 release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquiladense-7b": { "id": "baai/aquila/aquiladense-7b", "name": "AquilaDense-7B", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-15", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Tagged aquiladense. Papers tagged include 2401.09192.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AquilaDense-7B is a dense Aquila text model released 15 June 2024 under Apache-2.0, unlike the earlier Aquila license. Hugging Face tags it aquiladense, bilingual, and points at arXiv:2401.09192. Status: active. The README was not read. Collected 2026-10-15.", "what_it_is_for": "Dense 7B bilingual base or chat model in the June 2024 Aquila dense line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquilamed-rl": { "id": "baai/aquila/aquilamed-rl", "name": "AquilaMed-RL", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other", "release_date": "2024-06-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tagged aquila3. Hub size tag 8B was not checked in config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AquilaMed-RL is BAAI's medical Aquila model, created 12 June 2024. Tags: text generation, custom aquila3 code, license other, paper arXiv:2406.12182, datasets AquilaMed-Instruct and AquilaMed-RL. The org listing calls it an 8B model. Status: active. The license PDF was not opened, so the weight field stays open-weights rather than open-source. Collected 2026-10-15.", "what_it_is_for": "Medical question answering and dialogue.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquilamoe": { "id": "baai/aquila/aquilamoe", "name": "AquilaMoE", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mixture-of-experts, architecture tag aquilamoe. Total and active parameter counts were not read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AquilaMoE is BAAI's mixture-of-experts Aquila model, repo created 13 June 2024, Apache-2.0. AquilaMoE-SFT, created the next day on Infinity-Instruct, is the chat version and is stored here rather than as a second family. Paper tag arXiv:2408.06567. Status: active. Collected 2026-10-15.", "what_it_is_for": "Sparse bilingual language model, with an SFT checkpoint for chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/aquila/aquilasql-7b": { "id": "baai/aquila/aquilasql-7b", "name": "AquilaSQL-7B", "maker": "baai", "family": "baai/aquila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BAAI Aquila Model License (weights); Apache-2.0 (source code)", "release_date": "2023-10-08", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only transformer in the Aquila family. The 7B card says the base design follows GPT-3 and LLaMA, with a bilingual tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AquilaSQL-7B is BAAI's 7B SQL model on the Aquila stack. Repo created 8 October 2023. Aquila license tag. Status: active. The card body was not read. Collected 2026-10-15.", "what_it_is_for": "Text-to-SQL on a 7B Aquila model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/arex/arex-2": { "id": "baai/arex/arex-2", "name": "AREX-2", "maker": "baai", "family": "baai/arex", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-09-29", "context_length": 262144, "max_output_tokens": null, "parameters": "27B", "architecture": "Dense Qwen3.8-compatible multimodal transformer. 27B. Context 262144.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AREX-2 is BAAI's 27B long-horizon agent model. The card says it is a dense Qwen3.8-compatible multimodal model with a 262,144-token context, trained to propose, measure, reflect, and revise. It is a fine-tune of Qwen/Qwen3.8-27B. Apache-2.0. Repo created 29 September 2026. Paper arXiv:2609.38288. Status: active. Collected 2026-10-15.", "what_it_is_for": "Long-horizon self-improvement on coding, machine-learning engineering, and deep research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/arex/arex-base": { "id": "baai/arex/arex-base", "name": "AREX-Base", "maker": "baai", "family": "baai/arex", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-07-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Qwen3.5-122B-A10B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AREX-Base is the large AREX checkpoint, created 23 July 2026. Tags: fine-tune of Qwen/Qwen3.5-122B-A10B, mixture-of-experts, Apache-2.0, paper arXiv:2607.21461. The Hub size tag said 123B and is not stored as parameters. Status: active. Collected 2026-10-15.", "what_it_is_for": "Large mixture-of-experts AREX agent.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/arex/arex-turbo": { "id": "baai/arex/arex-turbo", "name": "AREX-Turbo", "maker": "baai", "family": "baai/arex", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-07-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Qwen3.5-4B. Context not read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AREX-Turbo is the small AREX agent, created 23 July 2026. Hugging Face tags it as a fine-tune of Qwen/Qwen3.5-4B, Apache-2.0, text generation, with paper arXiv:2607.21461. The Hub size tag said 5B and is not stored as a parameter count. The AREX-2 card was read; this card was not. Status: active. Collected 2026-10-15.", "what_it_is_for": "Smaller long-horizon agent in the AREX line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-base-en": { "id": "baai/bge/bge-base-en", "name": "bge-base-en", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-08-05", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder, CLS pooling. config.json: hidden 768, intermediate 3072, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-base-en is a English text embedding model from Beijing Academy of Artificial Intelligence (BAAI), in the BGE (BAAI General Embedding) line. It is the original August 2023 release. Weights are public under MIT and were still listed on the FlagEmbedding model index on 2026-10-15. Status: active.", "what_it_is_for": "Map English text to a 768-dimensional vector for retrieval, clustering, classification, and STS. For short-query to passage retrieval the card still shows the instruction \"Represent this sentence for searching relevant passages: \". v1.5 is intended to work better when that instruction is omitted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-base-en-v1-5": { "id": "baai/bge/bge-base-en-v1-5", "name": "bge-base-en-v1.5", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-09-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder, CLS pooling. config.json: hidden 768, intermediate 3072, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-base-en-v1.5 is a English text embedding model from Beijing Academy of Artificial Intelligence (BAAI), in the BGE (BAAI General Embedding) line. Version 1.5 was released to make the similarity distribution more reasonable and to improve retrieval without relying on an instruction. Weights are public under MIT and were still listed on the FlagEmbedding model index on 2026-10-15. Status: active.", "what_it_is_for": "Map English text to a 768-dimensional vector for retrieval, clustering, classification, and STS. For short-query to passage retrieval the card still shows the instruction \"Represent this sentence for searching relevant passages: \". v1.5 is intended to work better when that instruction is omitted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-base-zh": { "id": "baai/bge/bge-base-zh", "name": "bge-base-zh", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-08-05", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder, CLS pooling. C-MTEB table lists 768 dimensions. Same training recipe as the English base models.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-base-zh is a Chinese text embedding model from Beijing Academy of Artificial Intelligence (BAAI), in the BGE (BAAI General Embedding) line. It is the original August 2023 release. Weights are public under MIT and were still listed on the FlagEmbedding model index on 2026-10-15. Status: active.", "what_it_is_for": "Map Chinese text to a 768-dimensional vector for retrieval, clustering, classification, and STS. For short-query to passage retrieval the card still shows the instruction \"\u4e3a\u8fd9\u4e2a\u53e5\u5b50\u751f\u6210\u8868\u793a\u4ee5\u7528\u4e8e\u68c0\u7d22\u76f8\u5173\u6587\u7ae0\uff1a\". v1.5 is intended to work better when that instruction is omitted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-base-zh-v1-5": { "id": "baai/bge/bge-base-zh-v1-5", "name": "bge-base-zh-v1.5", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-09-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder, CLS pooling. C-MTEB table lists 768 dimensions. Same training recipe as the English base models.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-base-zh-v1.5 is a Chinese text embedding model from Beijing Academy of Artificial Intelligence (BAAI), in the BGE (BAAI General Embedding) line. Version 1.5 was released to make the similarity distribution more reasonable and to improve retrieval without relying on an instruction. Weights are public under MIT and were still listed on the FlagEmbedding model index on 2026-10-15. Status: active.", "what_it_is_for": "Map Chinese text to a 768-dimensional vector for retrieval, clustering, classification, and STS. For short-query to passage retrieval the card still shows the instruction \"\u4e3a\u8fd9\u4e2a\u53e5\u5b50\u751f\u6210\u8868\u793a\u4ee5\u7528\u4e8e\u68c0\u7d22\u76f8\u5173\u6587\u7ae0\uff1a\". v1.5 is intended to work better when that instruction is omitted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-code-v1": { "id": "baai/bge/bge-code-v1", "name": "bge-code-v1", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-05-15", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2 decoder embedder. config.json: hidden 1536, intermediate 8960, 28 layers, 12 heads, 2 KV heads, max_position_embeddings 32768. Last-token pooling. Query template uses and .", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-code-v1 is BAAI's code embedding model. The card says it retrieves code from English or Chinese questions, covers 20 programming languages, and also keeps general text retrieval. Hugging Face repo created 15 May 2025. Apache-2.0. Paper arXiv:2505.12697. Status: active. Collected 2026-10-15.", "what_it_is_for": "Natural-language to code retrieval, plus multilingual text retrieval at a similar scale to text embedders of this size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-en-icl": { "id": "baai/bge/bge-en-icl", "name": "bge-en-icl", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-26", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Mistral decoder used as an embedder with last-token pooling. config.json name bge-en-mistral: hidden 4096, intermediate 14336, 32 layers, 32 query heads, 8 KV heads, sliding window 4096, max_position_embeddings 32768. FlagEmbedding calls the class FlagICLModel.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-en-icl is BAAI's English LLM embedding model with in-context learning. Few-shot query-response examples can be placed in the prompt. FlagEmbedding news dates the release to 26 July 2024. The card says it reached the vendor's then state of the art on BEIR and AIR-Bench. Apache-2.0 weights. Status: active. Collected 2026-10-15.", "what_it_is_for": "English retrieval where a few labeled examples should change how the query is embedded. Instructions and examples are free-form rather than one fixed sentence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-large-en": { "id": "baai/bge/bge-large-en", "name": "bge-large-en", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-08-02", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder, CLS pooling. config.json: hidden 1024, intermediate 4096, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-large-en is a English text embedding model from Beijing Academy of Artificial Intelligence (BAAI), in the BGE (BAAI General Embedding) line. It is the original August 2023 release. Weights are public under MIT and were still listed on the FlagEmbedding model index on 2026-10-15. Status: active.", "what_it_is_for": "Map English text to a 1024-dimensional vector for retrieval, clustering, classification, and STS. For short-query to passage retrieval the card still shows the instruction \"Represent this sentence for searching relevant passages: \". v1.5 is intended to work better when that instruction is omitted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-large-en-v1-5": { "id": "baai/bge/bge-large-en-v1-5", "name": "bge-large-en-v1.5", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-09-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder, CLS pooling. config.json: hidden 1024, intermediate 4096, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-large-en-v1.5 is a English text embedding model from Beijing Academy of Artificial Intelligence (BAAI), in the BGE (BAAI General Embedding) line. Version 1.5 was released to make the similarity distribution more reasonable and to improve retrieval without relying on an instruction. Weights are public under MIT and were still listed on the FlagEmbedding model index on 2026-10-15. Status: active.", "what_it_is_for": "Map English text to a 1024-dimensional vector for retrieval, clustering, classification, and STS. For short-query to passage retrieval the card still shows the instruction \"Represent this sentence for searching relevant passages: \". v1.5 is intended to work better when that instruction is omitted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-large-zh": { "id": "baai/bge/bge-large-zh", "name": "bge-large-zh", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-08-02", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder, CLS pooling. C-MTEB table lists 1024 dimensions.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-large-zh is a Chinese text embedding model from Beijing Academy of Artificial Intelligence (BAAI), in the BGE (BAAI General Embedding) line. It is the original August 2023 release. Weights are public under MIT and were still listed on the FlagEmbedding model index on 2026-10-15. Status: active.", "what_it_is_for": "Map Chinese text to a 1024-dimensional vector for retrieval, clustering, classification, and STS. For short-query to passage retrieval the card still shows the instruction \"\u4e3a\u8fd9\u4e2a\u53e5\u5b50\u751f\u6210\u8868\u793a\u4ee5\u7528\u4e8e\u68c0\u7d22\u76f8\u5173\u6587\u7ae0\uff1a\". v1.5 is intended to work better when that instruction is omitted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-large-zh-noinstruct": { "id": "baai/bge/bge-large-zh-noinstruct", "name": "bge-large-zh-noinstruct", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-08-02", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Same 1024-d Chinese BGE large encoder, trained without the retrieval instruction. Listed as its own row on the C-MTEB table.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-large-zh-noinstruct is the no-instruction variant of BAAI's Chinese bge-large-zh embedding model. The official C-MTEB table publishes it as a separate checkpoint. Collected 2026-10-15. Status: active. It is a named release, not an unlabeled ablation.", "what_it_is_for": "Chinese embedding when the retrieval instruction is not used. The card's C-MTEB average is lower than instructed bge-large-zh (63.53 vs 64.20).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-large-zh-v1-5": { "id": "baai/bge/bge-large-zh-v1-5", "name": "bge-large-zh-v1.5", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-09-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder, CLS pooling. C-MTEB table lists 1024 dimensions.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-large-zh-v1.5 is a Chinese text embedding model from Beijing Academy of Artificial Intelligence (BAAI), in the BGE (BAAI General Embedding) line. Version 1.5 was released to make the similarity distribution more reasonable and to improve retrieval without relying on an instruction. Weights are public under MIT and were still listed on the FlagEmbedding model index on 2026-10-15. Status: active.", "what_it_is_for": "Map Chinese text to a 1024-dimensional vector for retrieval, clustering, classification, and STS. For short-query to passage retrieval the card still shows the instruction \"\u4e3a\u8fd9\u4e2a\u53e5\u5b50\u751f\u6210\u8868\u793a\u4ee5\u7528\u4e8e\u68c0\u7d22\u76f8\u5173\u6587\u7ae0\uff1a\". v1.5 is intended to work better when that instruction is omitted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-m3": { "id": "baai/bge/bge-m3", "name": "BGE-M3", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-01-30", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "XLM-RoBERTa. config.json: XLMRobertaModel, hidden 1024, 24 layers, 16 heads, intermediate 4096, max_position_embeddings 8194. The card says the usable input length is 8192 tokens. Dense, sparse (lexical), and multi-vector ColBERT outputs from one model.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BGE-M3 is BAAI's multilingual embedding model. M3 means multi-linguality (100+ languages), multi-granularity (up to 8192 tokens), and multi-functionality (dense, sparse, and ColBERT multi-vector retrieval). FlagEmbedding news dates the release to 30 January 2024. Weights are MIT. Status on 2026-10-15: active. It is the most-downloaded model on the BAAI Hugging Face org.", "what_it_is_for": "One encoder for dense retrieval, lexical weights, and multi-vector retrieval across short sentences and long documents. The card recommends hybrid retrieval plus a cross-encoder reranker for RAG.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-multilingual-gemma2": { "id": "baai/bge/bge-multilingual-gemma2", "name": "bge-multilingual-gemma2", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma license", "release_date": "2024-07-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLM embedder initialized from google/gemma-2-9b, last-token pooling, instruction format Instruct/Query as used by FlagLLMModel. The card does not restate a parameter count separate from the 9B backbone.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-multilingual-gemma2 is BAAI's multilingual LLM embedding model, trained from Gemma-2-9B on retrieval, classification, and clustering data in English, Chinese, Japanese, Korean, French, and other languages. Released 26 July 2024. The Gemma license applies, so commercial use is not the same as the MIT BGE encoders. Status: active. Collected 2026-10-15.", "what_it_is_for": "Multilingual retrieval and other embedding tasks where an LLM backbone is acceptable. The vendor reports strong MIRACL, MTEB-fr, and MTEB-pl results without a single number extracted here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-reasoner-embed-qwen3-8b-0923": { "id": "baai/bge/bge-reasoner-embed-qwen3-8b-0923", "name": "bge-reasoner-embed-qwen3-8b-0923", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3 embedding model. FlagEmbedding maps this id to FlagLLMModel with last-token pooling and the format Instruct: {} / Query: {}. The repo name is the only public snapshot found.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-reasoner-embed-qwen3-8b-0923 is a named BAAI embedding checkpoint on Hugging Face, created 22 September 2025. The id is the release name, not a dated snapshot of an older parent. FlagEmbedding registers it as a Qwen3 last-token embedder. Apache-2.0. Status: active. The model card text was not fully read in this pass. Collected 2026-10-15.", "what_it_is_for": "Reasoning-style text embedding on a Qwen3 backbone. Exact task instructions are the FlagEmbedding Instruct/Query template.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-small-en": { "id": "baai/bge/bge-small-en", "name": "bge-small-en", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-08-05", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder, CLS pooling. config.json: hidden 384, intermediate 1536, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-small-en is a English text embedding model from Beijing Academy of Artificial Intelligence (BAAI), in the BGE (BAAI General Embedding) line. It is the original August 2023 release. Weights are public under MIT and were still listed on the FlagEmbedding model index on 2026-10-15. Status: active.", "what_it_is_for": "Map English text to a 384-dimensional vector for retrieval, clustering, classification, and STS. For short-query to passage retrieval the card still shows the instruction \"Represent this sentence for searching relevant passages: \". v1.5 is intended to work better when that instruction is omitted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-small-en-v1-5": { "id": "baai/bge/bge-small-en-v1-5", "name": "bge-small-en-v1.5", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-09-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder, CLS pooling. config.json: hidden 384, intermediate 1536, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-small-en-v1.5 is a English text embedding model from Beijing Academy of Artificial Intelligence (BAAI), in the BGE (BAAI General Embedding) line. Version 1.5 was released to make the similarity distribution more reasonable and to improve retrieval without relying on an instruction. Weights are public under MIT and were still listed on the FlagEmbedding model index on 2026-10-15. Status: active.", "what_it_is_for": "Map English text to a 384-dimensional vector for retrieval, clustering, classification, and STS. For short-query to passage retrieval the card still shows the instruction \"Represent this sentence for searching relevant passages: \". v1.5 is intended to work better when that instruction is omitted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-small-zh": { "id": "baai/bge/bge-small-zh", "name": "bge-small-zh", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-08-05", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder, CLS pooling. bge-small-zh-v1.5 config.json: hidden 512, intermediate 2048. The C-MTEB table lists 512 dimensions for both v1 and v1.5.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-small-zh is a Chinese text embedding model from Beijing Academy of Artificial Intelligence (BAAI), in the BGE (BAAI General Embedding) line. It is the original August 2023 release. Weights are public under MIT and were still listed on the FlagEmbedding model index on 2026-10-15. Status: active.", "what_it_is_for": "Map Chinese text to a 512-dimensional vector for retrieval, clustering, classification, and STS. For short-query to passage retrieval the card still shows the instruction \"\u4e3a\u8fd9\u4e2a\u53e5\u5b50\u751f\u6210\u8868\u793a\u4ee5\u7528\u4e8e\u68c0\u7d22\u76f8\u5173\u6587\u7ae0\uff1a\". v1.5 is intended to work better when that instruction is omitted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-small-zh-v1-5": { "id": "baai/bge/bge-small-zh-v1-5", "name": "bge-small-zh-v1.5", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-09-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder, CLS pooling. bge-small-zh-v1.5 config.json: hidden 512, intermediate 2048. The C-MTEB table lists 512 dimensions for both v1 and v1.5.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-small-zh-v1.5 is a Chinese text embedding model from Beijing Academy of Artificial Intelligence (BAAI), in the BGE (BAAI General Embedding) line. Version 1.5 was released to make the similarity distribution more reasonable and to improve retrieval without relying on an instruction. Weights are public under MIT and were still listed on the FlagEmbedding model index on 2026-10-15. Status: active.", "what_it_is_for": "Map Chinese text to a 512-dimensional vector for retrieval, clustering, classification, and STS. For short-query to passage retrieval the card still shows the instruction \"\u4e3a\u8fd9\u4e2a\u53e5\u5b50\u751f\u6210\u8868\u793a\u4ee5\u7528\u4e8e\u68c0\u7d22\u76f8\u5173\u6587\u7ae0\uff1a\". v1.5 is intended to work better when that instruction is omitted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/bge-visualized": { "id": "baai/bge/bge-visualized", "name": "Visualized-BGE", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-03-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BGE text encoder extended to hybrid image-text embeddings. Weight layout was not read.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Visualized-BGE is BAAI's March 2024 extension of BGE that embeds hybrid image-text inputs. FlagEmbedding news dates it to 18 March 2024. A Hugging Face repo BAAI/bge-visualized exists (created 17 March 2024). Status: active. License text was not opened. Collected 2026-10-15.", "what_it_is_for": "Embeddings for mixed image and text, on top of the BGE text models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge/llm-embedder": { "id": "baai/bge/llm-embedder", "name": "LLM-Embedder", "maker": "baai", "family": "baai/bge", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-10-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder. config.json: BertModel, hidden 768, intermediate 3072, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-Embedder is a BAAI English embedding model meant to cover several retrieval needs of an LLM (tools, knowledge, memory, and similar RAG uses) in one checkpoint. FlagEmbedding dates the release to 12 October 2023. Status on 2026-10-15: active. License follows the FlagEmbedding MIT release.", "what_it_is_for": "A single English embedder for the retrieval tasks an LLM calls during retrieval-augmented generation, rather than a separate model per task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge-vl/bge-vl-base": { "id": "baai/bge-vl/bge-vl-base", "name": "BGE-VL-base", "maker": "baai", "family": "baai/bge-vl", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-12-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP ViT-B/16 fine-tune. Card example: encode() returns dimension 512.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BGE-VL-base is BAAI's CLIP-scale multimodal embedding model from the MegaPairs line. The model card's news line dates the CLIP release to 27 December 2024. The Hugging Face repo timestamp is 25 February 2025. It is a fine-tune of openai/clip-vit-base-patch16 under MIT. A usage example on the card returns 512-dimensional vectors. Status on 2026-10-15: active.", "what_it_is_for": "Any visual search: text-to-image, image-to-text, and composed image-plus-text queries.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge-vl/bge-vl-large": { "id": "baai/bge-vl/bge-vl-large", "name": "BGE-VL-large", "maker": "baai", "family": "baai/bge-vl", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-12-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP ViT-L/14 fine-tune. Dimension was not printed in the base-card example, which only shows the base model.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BGE-VL-large is the larger CLIP multimodal embedder in BAAI's MegaPairs release, a fine-tune of openai/clip-vit-large-patch14. Same news date and MIT license as BGE-VL-base. Status: active. Collected 2026-10-15.", "what_it_is_for": "Same universal multimodal retrieval job as BGE-VL-base, at large-CLIP size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge-vl/bge-vl-mllm-s1": { "id": "baai/bge-vl/bge-vl-mllm-s1", "name": "BGE-VL-MLLM-S1", "maker": "baai", "family": "baai/bge-vl", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of LLaVA-1.6 Mistral 7B.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BGE-VL-MLLM-S1 is the first MLLM stage of BGE-VL. The card says it is trained only on MegaPairs and is strong at composed image retrieval. The backbone tag is llava-hf/llava-v1.6-mistral-7b-hf. MIT. The card writes the date as 2024-3-4; the Hugging Face repo was created 4 March 2025, which matches the later position of that news item. Status: active. Collected 2026-10-15.", "what_it_is_for": "Composed multimodal retrieval with an MLLM backbone, stage 1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge-vl/bge-vl-mllm-s2": { "id": "baai/bge-vl/bge-vl-mllm-s2", "name": "BGE-VL-MLLM-S2", "maker": "baai", "family": "baai/bge-vl", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of LLaVA-1.6 Mistral 7B. Stage 2 of the MLLM line.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BGE-VL-MLLM-S2 is the second MLLM checkpoint in the BGE-VL release, same LLaVA-1.6 Mistral 7B backbone tag and MIT license as S1. Status: active. The card does not spell out how S2 differs beyond being the next stage. Collected 2026-10-15.", "what_it_is_for": "Later MLLM stage of BGE-VL for multimodal retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge-vl/bge-vl-screenshot": { "id": "baai/bge-vl/bge-vl-screenshot", "name": "BGE-VL-Screenshot", "maker": "baai", "family": "baai/bge-vl", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-05-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Qwen2.5-VL-3B-Instruct.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BGE-VL-Screenshot is a BAAI multimodal embedding checkpoint for screenshot-style retrieval. Hugging Face tags it as a fine-tune of Qwen/Qwen2.5-VL-3B-Instruct, MIT, created 16 May 2025, paper tag arXiv:2502.11431. Status: active. The README body was not read. Collected 2026-10-15.", "what_it_is_for": "Multimodal retrieval over screenshots and UI images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bge-vl/bge-vl-v1-5": { "id": "baai/bge-vl/bge-vl-v1-5", "name": "BGE-VL v1.5", "maker": "baai", "family": "baai/bge-vl", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-05-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tunes of llava-hf/llava-v1.6-mistral-7b-hf.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BGE-VL v1.5 is a later pair of MLLM checkpoints on the same LLaVA-1.6 Mistral 7B backbone: BGE-VL-v1.5-zs (15 May 2025) and BGE-VL-v1.5-mmeb (16 May 2025). Both are MIT. They are versions of one v1.5 release, not two product lines. Status: active. Card text was not fully read. Collected 2026-10-15.", "what_it_is_for": "Zero-shot and MMEB-oriented multimodal embedding checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/brainmu/brainmu-spike": { "id": "baai/brainmu/brainmu-spike", "name": "Brainmu Spike", "maker": "baai", "family": "baai/brainmu", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-09-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA-tagged reconstruction model. Backbone not read.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Brainmu Spike is BAAI's spike-camera reconstruction release. BAAI/Brainmu-Spike was created 16 September 2026. Tags: Apache-2.0, spike-camera, image reconstruction, LoRA. BAAI/Brainmu-SpikeCamera (created 31 July 2024) is the earlier camera checkpoint and is a version. BAAI's research page lists Brainmu under life-science foundation models. Status: active. The README was not read. Collected 2026-10-15.", "what_it_is_for": "Reconstruct images from spike-camera events.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bunny/bunny-llama-3-8b-v": { "id": "baai/bunny/bunny-llama-3-8b-v", "name": "Bunny-Llama-3-8B-V", "maker": "baai", "family": "baai/bunny", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-21", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "SigLIP and Llama-3-8B-Instruct. v1.0 of the Llama-3 Bunny. A GGUF repo is a build, not a new model.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bunny-Llama-3-8B-V is a lightweight BAAI vision-language model. SigLIP and Llama-3-8B-Instruct. v1.0 of the Llama-3 Bunny. A GGUF repo is a build, not a new model. Apache-2.0. Technical report arXiv:2402.11530. Status: active. Collected 2026-10-15.", "what_it_is_for": "Image question answering on a small language backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bunny/bunny-v1-0-2b-zh": { "id": "baai/bunny/bunny-v1-0-2b-zh", "name": "Bunny-v1.0-2B-zh", "maker": "baai", "family": "baai/bunny", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-03-15", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "SigLIP and Qwen1.5-1.8B, aimed at Chinese.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bunny-v1.0-2B-zh is a lightweight BAAI vision-language model. SigLIP and Qwen1.5-1.8B, aimed at Chinese. Apache-2.0. Technical report arXiv:2402.11530. Status: active. Collected 2026-10-15.", "what_it_is_for": "Image question answering on a small language backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bunny/bunny-v1-0-3b": { "id": "baai/bunny/bunny-v1-0-3b", "name": "Bunny-v1.0-3B", "maker": "baai", "family": "baai/bunny", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-07", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "SigLIP and Phi-2. First public Bunny. The README says it beat larger 7B MLLMs on the vendor's comparison.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bunny-v1.0-3B is a lightweight BAAI vision-language model. SigLIP and Phi-2. First public Bunny. The README says it beat larger 7B MLLMs on the vendor's comparison. Apache-2.0. Technical report arXiv:2402.11530. Status: active. Collected 2026-10-15.", "what_it_is_for": "Image question answering on a small language backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bunny/bunny-v1-0-3b-zh": { "id": "baai/bunny/bunny-v1-0-3b-zh", "name": "Bunny-v1.0-3B-zh", "maker": "baai", "family": "baai/bunny", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-18", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "SigLIP and MiniCPM-2B, English and Chinese.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bunny-v1.0-3B-zh is a lightweight BAAI vision-language model. SigLIP and MiniCPM-2B, English and Chinese. Apache-2.0. Technical report arXiv:2402.11530. Status: active. Collected 2026-10-15.", "what_it_is_for": "Image question answering on a small language backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bunny/bunny-v1-0-4b": { "id": "baai/bunny/bunny-v1-0-4b", "name": "Bunny-v1.0-4B", "maker": "baai", "family": "baai/bunny", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-05-01", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "SigLIP and Phi-3-Mini-4K (3.8B). GGUF is a build of this checkpoint.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bunny-v1.0-4B is a lightweight BAAI vision-language model. SigLIP and Phi-3-Mini-4K (3.8B). GGUF is a build of this checkpoint. Apache-2.0. Technical report arXiv:2402.11530. Status: active. Collected 2026-10-15.", "what_it_is_for": "Image question answering on a small language backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bunny/bunny-v1-1-4b": { "id": "baai/bunny/bunny-v1-1-4b", "name": "Bunny-v1.1-4B", "maker": "baai", "family": "baai/bunny", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-05-08", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "SigLIP and Phi-3-Mini-4K with S2-Wrapper. Accepts images up to 1152x1152.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bunny-v1.1-4B is a lightweight BAAI vision-language model. SigLIP and Phi-3-Mini-4K with S2-Wrapper. Accepts images up to 1152x1152. Apache-2.0. Technical report arXiv:2402.11530. Status: active. Collected 2026-10-15.", "what_it_is_for": "Image question answering on a small language backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/bunny/bunny-v1-1-llama-3-8b-v": { "id": "baai/bunny/bunny-v1-1-llama-3-8b-v", "name": "Bunny-v1.1-Llama-3-8B-V", "maker": "baai", "family": "baai/bunny", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-01", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "SigLIP and Llama-3-8B-Instruct with S2-Wrapper, up to 1152x1152.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bunny-v1.1-Llama-3-8B-V is a lightweight BAAI vision-language model. SigLIP and Llama-3-8B-Instruct with S2-Wrapper, up to 1152x1152. Apache-2.0. Technical report arXiv:2402.11530. Status: active. Collected 2026-10-15.", "what_it_is_for": "Image question answering on a small language backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/consispace/consispace": { "id": "baai/consispace/consispace", "name": "ConsiSpace", "maker": "baai", "family": "baai/consispace", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2026-09-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tagged qwen2_5_vl.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ConsiSpace is a BAAI vision-language checkpoint created 4 September 2026. Tags: Qwen2.5-VL, CC-BY-4.0, paper arXiv:2607.17599. The card was not read. Status: active. Confidence: medium. Collected 2026-10-15.", "what_it_is_for": "Spatial vision-language modeling. The name is the only task hint that was verified.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/diva/diva": { "id": "baai/diva/diva", "name": "DIVA", "maker": "baai", "family": "baai/diva", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Post-trained CLIP. Which CLIP size is in the Hub repo was not read.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DIVA (DIffusion model as a Visual Assistant) is BAAI's post-training method that uses diffusion feedback to fix CLIP's fine-grained visual failures, using images only. Paper arXiv:2407.20171, code github.com/baaivision/DIVA. The Hugging Face repo BAAI/DIVA was created 6 August 2024 and tagged Apache-2.0. The paper reports about a 3 to 7 point gain on MMVP-VLM and says zero-shot classification on 29 benchmarks is preserved. A single score is not stored. Status: active. Collected 2026-10-15.", "what_it_is_for": "A stronger CLIP visual encoder for downstream MLLMs and segmentation, without a paired text retrain.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/emu/emu": { "id": "baai/emu/emu", "name": "Emu", "maker": "baai", "family": "baai/emu", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not read from a config in this pass.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emu is BAAI's first multimodal generation model in the Emu line. The Hugging Face repo BAAI/Emu was created 10 July 2023. BAAI's later pages call the October 2024 Emu3 work the native multimodal successor. The Emu card body and license were not opened in this pass. Status: active. Confidence: medium. Collected 2026-10-15.", "what_it_is_for": "Any-to-any vision-language generation. Exact tasks were not re-read from the 2023 card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/emu/emu2": { "id": "baai/emu/emu2", "name": "Emu2", "maker": "baai", "family": "baai/emu", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-12-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not read.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emu2 is the base checkpoint of BAAI's second Emu generation. BAAI/Emu2, Emu2-Chat, and Emu2-Gen were all created 19 December 2023. Chat and Gen are separate products. This folder is the base repo. The card was not read. Status: active. Confidence: medium. Collected 2026-10-15.", "what_it_is_for": "Multimodal base model used to build Emu2-Chat and Emu2-Gen.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/emu/emu2-chat": { "id": "baai/emu/emu2-chat", "name": "Emu2-Chat", "maker": "baai", "family": "baai/emu", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-12-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emu2-Chat is the dialogue checkpoint of Emu2, created 19 December 2023. Status: active. Confidence: medium because the card body was not read. Collected 2026-10-15.", "what_it_is_for": "Vision-language chat on the Emu2 line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/emu/emu2-gen": { "id": "baai/emu/emu2-gen", "name": "Emu2-Gen", "maker": "baai", "family": "baai/emu", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-12-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not read.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emu2-Gen is the generation checkpoint of Emu2, created 19 December 2023. Status: active. Confidence: medium. Collected 2026-10-15.", "what_it_is_for": "Image generation on the Emu2 line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/emu/emu3": { "id": "baai/emu/emu3", "name": "Emu3", "maker": "baai", "family": "baai/emu", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-10-21", "context_length": null, "max_output_tokens": null, "parameters": "8.49B", "architecture": "Decoder-only transformer trained only with next-token prediction on discrete vision and text tokens. Nature (28 Jan 2026) states 8.49 billion parameters, 32 layers, hidden size 4096, intermediate size 14336, 32 heads (8 key-value), shared vocabulary 184622. The same backbone figure is used for Stage1, Chat, and Gen; post-training differs.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emu3 is BAAI's multimodal model trained only with next-token prediction. GitHub dates the suite to September 2024 and the image-pretrained Emu3-Stage1 weights to October 2024. Stage1 is the base in this folder. Emu3-Chat and Emu3-Gen are separate post-trained products. The vision tokenizer is a required component, not a separate product. Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Image pretraining base. The project page says Stage1 supports captioning and 512x512 generation and is the start point for further tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/emu/emu3-5": { "id": "baai/emu/emu3-5", "name": "Emu3.5", "maker": "baai", "family": "baai/emu", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-30", "context_length": 32768, "max_output_tokens": null, "parameters": "34.1B", "architecture": "Decoder-only transformer, next vision-language state prediction. arXiv:2510.26583 table: 34.1B parameters (31.2B transformer, 2.9B embeddings), 64 layers, hidden 5120, intermediate 25600, 64 query heads and 8 KV heads, vocabulary 282926 (151854 text + 131072 vision), context 32768. Text tokenizer is Qwen's. Discrete Diffusion Adaptation (DiDA) is described for faster inference; the GitHub README said the released weights were still pure next-token predictors and DiDA weights were not in that drop.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emu3.5 is BAAI's native multimodal world model. It was announced at the end of October 2025 and the weights were posted as BAAI/Emu3.5. It predicts the next vision-language state after pretraining on more than 10 trillion multimodal tokens and a reinforcement-learning post-train. The general checkpoint covers interleaved generation. Emu3.5-Image is the separate image specialist. A web and mobile app opened on 28 November 2025 at emu.world and zh.emu.world. Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Interleaved image-text generation, stories, and visual how-to sequences. The README says to use this general checkpoint for multi-image tasks and Emu3.5-Image for single-image T2I and X2I.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/emu/emu3-5-image": { "id": "baai/emu/emu3-5-image", "name": "Emu3.5-Image", "maker": "baai", "family": "baai/emu", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Same Emu3.5 release family. A different parameter count was not stated.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emu3.5-Image is the image-specialized Emu3.5 weight for text-to-image and any-to-image editing. The README says it is the checkpoint to use for those tasks, while the general Emu3.5 checkpoint is for multi-image generation. Apache-2.0. Created 31 October 2025. Status: active. The 34.1B table in the paper is titled for Emu3.5 and is not repeated here as a separate measurement. Collected 2026-10-15.", "what_it_is_for": "Single-image generation and editing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/emu/emu3-chat": { "id": "baai/emu/emu3-chat", "name": "Emu3-Chat", "maker": "baai", "family": "baai/emu", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-26", "context_length": null, "max_output_tokens": null, "parameters": "8.49B", "architecture": "Decoder-only transformer trained only with next-token prediction on discrete vision and text tokens. Nature (28 Jan 2026) states 8.49 billion parameters, 32 layers, hidden size 4096, intermediate size 14336, 32 heads (8 key-value), shared vocabulary 184622. The same backbone figure is used for Stage1, Chat, and Gen; post-training differs.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emu3-Chat is the vision-language understanding post-train of Emu3, released September 2024. Apache-2.0. An Emu3-Chat-hf conversion is a format version, not a different model. The GitHub note says vLLM later supported Emu3-Chat. Status: active. Collected 2026-10-15.", "what_it_is_for": "Image and video understanding by next-token prediction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/emu/emu3-gen": { "id": "baai/emu/emu3-gen", "name": "Emu3-Gen", "maker": "baai", "family": "baai/emu", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-25", "context_length": null, "max_output_tokens": null, "parameters": "8.49B", "architecture": "Decoder-only transformer trained only with next-token prediction on discrete vision and text tokens. Nature (28 Jan 2026) states 8.49 billion parameters, 32 layers, hidden size 4096, intermediate size 14336, 32 heads (8 key-value), shared vocabulary 184622. The same backbone figure is used for Stage1, Chat, and Gen; post-training differs.", "modalities": { "input": [ "text", "image" ], "output": [ "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emu3-Gen is the generation post-train of Emu3, released September 2024. It predicts vision tokens for images and video instead of using a diffusion head. Apache-2.0. Emu3-Gen-hf is a format conversion. Status: active. Collected 2026-10-15.", "what_it_is_for": "Text-to-image and video generation by next vision token.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/eva/eva-clip-18b": { "id": "baai/eva/eva-clip-18b", "name": "EVA-CLIP-18B", "maker": "baai", "family": "baai/eva", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-22", "context_length": null, "max_output_tokens": null, "parameters": "18.1B", "architecture": "CLIP scaled to 18.1B. Image encoder init EVA_18B_psz14. Training data Merged-2B+.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EVA-CLIP-18B is BAAI's 18.1B CLIP model, described on the 8B card as the largest open CLIP they had released. The same table lists 80.7 image classification, 75.0 video classification, and 87.8 retrieval mean recall. The table cell for the Hugging Face file said 'stay tuned' in the 8B README that was read, but BAAI/EVA-CLIP-18B exists and was created 22 February 2024. Status: active. Collected 2026-10-15.", "what_it_is_for": "Largest EVA-CLIP zero-shot vision-language model in this release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/eva/eva-clip-8b": { "id": "baai/eva/eva-clip-8b", "name": "EVA-CLIP-8B", "maker": "baai", "family": "baai/eva", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-05", "context_length": null, "max_output_tokens": null, "parameters": "8.1B", "architecture": "Contrastive image-text model. Total parameters 8.1B on the card. Image encoder initialized from EVA_8B_psz14.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EVA-CLIP-8B is BAAI's 8.1B CLIP model. The card table lists 79.4 average image-classification accuracy, 73.6 video classification, and 86.2 retrieval mean recall, trained on Merged-2B. EVA-CLIP-8B-448 is the 448-pixel version of the same model (80.0 / 73.7 / 86.4) and is a version here. Paper: EVA-CLIP-18B, arXiv:2402.04252. Status: active. The weight license text was not opened. Collected 2026-10-15.", "what_it_is_for": "Zero-shot image and video classification and retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/eve/eve-7b": { "id": "baai/eve/eve-7b", "name": "EVE-7B", "maker": "baai", "family": "baai/eve", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-02", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Tagged architecture eve. 7B is the repo name.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EVE-7B v1.0 is a BAAI vision-language model, repo created 2 July 2024, Apache-2.0, paper tag arXiv:2406.11832. EVE-7B-Pretrain-v1.0 (20 June 2024) is the pretrain version. The high-resolution line is a separate folder. The card body was not read. Status: active. Collected 2026-10-15.", "what_it_is_for": "Vision-language generation. Exact capability list was not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/eve/eve-7b-hd": { "id": "baai/eve/eve-7b-hd", "name": "EVE-7B-HD", "maker": "baai", "family": "baai/eve", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-18", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Tagged eve. v2 tag on the Hub is eve-qwen2.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EVE-7B-HD is the high-resolution EVE line. v1.0 was created 18 June 2024. v2.0 was created 8 February 2025 and is tagged with arXiv:2502.06788. Both are Apache-2.0 versions of one HD product. Status: active. Card bodies were not read. Collected 2026-10-15.", "what_it_is_for": "Higher-resolution vision-language modeling than EVE-7B v1.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/judgelm/judgelm-13b": { "id": "baai/judgelm/judgelm-13b", "name": "JudgeLM-13B-v1.0", "maker": "baai", "family": "baai/judgelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial", "release_date": "2023-10-27", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Fine-tune of Vicuna-v1.3. Size is the repo name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JudgeLM-13B-v1.0 is a judge model from HUST and BAAI. The 7B card says JudgeLM v1.0 is supervised fine-tuning of Vicuna-v1.3 on about 200K judge samples from JudgeLM-100K. The license is non-commercial. Intended use on the card is research on evaluating LLMs. Paper arXiv:2310.17631. Status: active. Collected 2026-10-15.", "what_it_is_for": "Score or compare LLM answers for research evaluation. Not a general chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/judgelm/judgelm-33b": { "id": "baai/judgelm/judgelm-33b", "name": "JudgeLM-33B-v1.0", "maker": "baai", "family": "baai/judgelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial", "release_date": "2023-10-28", "context_length": null, "max_output_tokens": null, "parameters": "33B", "architecture": "Fine-tune of Vicuna-v1.3. Size is the repo name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JudgeLM-33B-v1.0 is a judge model from HUST and BAAI. The 7B card says JudgeLM v1.0 is supervised fine-tuning of Vicuna-v1.3 on about 200K judge samples from JudgeLM-100K. The license is non-commercial. Intended use on the card is research on evaluating LLMs. Paper arXiv:2310.17631. Status: active. Collected 2026-10-15.", "what_it_is_for": "Score or compare LLM answers for research evaluation. Not a general chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/judgelm/judgelm-7b": { "id": "baai/judgelm/judgelm-7b", "name": "JudgeLM-7B-v1.0", "maker": "baai", "family": "baai/judgelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial", "release_date": "2023-10-27", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Fine-tune of Vicuna-v1.3. Size is the repo name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JudgeLM-7B-v1.0 is a judge model from HUST and BAAI. The 7B card says JudgeLM v1.0 is supervised fine-tuning of Vicuna-v1.3 on about 200K judge samples from JudgeLM-100K. The license is non-commercial. Intended use on the card is research on evaluating LLMs. Paper arXiv:2310.17631. Status: active. Collected 2026-10-15.", "what_it_is_for": "Score or compare LLM answers for research evaluation. Not a general chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/mtvcraft/mtvcraft": { "id": "baai/mtvcraft/mtvcraft", "name": "MTVCraft", "maker": "baai", "family": "baai/mtvcraft", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-stream audio-conditioned video generator. Parameter count not in the abstract.", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTVCraft is the BAAI weight release for MTV, an audio-sync text-to-video model. The paper (9 June 2025, arXiv:2506.08003) separates speech, effects, and music so they control lips, event timing, and mood. The Hugging Face repo was created 25 June 2025, Apache-2.0, text-to-video. Xinlong Wang of the Emu line is a coauthor. Status: active. Collected 2026-10-15.", "what_it_is_for": "Generate video whose motion follows speech, sound effects, and music.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/nova/nova": { "id": "baai/nova/nova", "name": "NOVA", "maker": "baai", "family": "baai/nova", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-17", "context_length": null, "max_output_tokens": null, "parameters": "645M", "architecture": "Non-quantized autoregressive generator. 645M and 1024x1024 apply to nova-d48w1024-sdxl1024.", "modalities": { "input": [ "text" ], "output": [ "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NOVA (Non-quantized Video Autoregressive) is BAAI's autoregressive image and video generator. Several resolution checkpoints were posted on 17 December 2024. This dossier's measured specs are for nova-d48w1024-sdxl1024 only: the card says 645M parameters, FP16, 1024x1024, Apache-2.0, text-to-image. The video checkpoint nova-d48w1024-osp480 is a version, and its size was not copied from the image card. Paper arXiv:2412.14169. Status: active. Collected 2026-10-15.", "what_it_is_for": "Text-to-image at 1024 for the primary checkpoint, and text-to-video on the osp480 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/omnigen/omnigen": { "id": "baai/omnigen/omnigen", "name": "OmniGen", "maker": "baai", "family": "baai/omnigen", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-10-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card points at the paper for the architecture and does not restate a parameter count in the section that was read.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmniGen is BAAI and VectorSpaceLab's unified image generation model. FlagEmbedding news and the model card date the first weights to 22 October 2024. The first Hub repo was Shitao/OmniGen-v1. BAAI/OmniGen-v1 is the BAAI copy, created 1 January 2025. MIT. It is meant to do text-to-image, subject-driven, and other controls without extra modules such as ControlNet. Paper arXiv:2409.11340. Status: active. Collected 2026-10-15.", "what_it_is_for": "Single-model image generation and editing from multimodal prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/omnigen/omnigen2": { "id": "baai/omnigen/omnigen2", "name": "OmniGen2", "maker": "baai", "family": "baai/omnigen", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-06-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusers OmniGen2 pipeline. Size not read.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmniGen2 is the follow-up unified generation model on the BAAI Hub, created 30 June 2025. Tags: Apache-2.0, any-to-any, diffusers pipeline OmniGen2, papers arXiv:2506.18871 and 2409.11340. The README was not read. Status: active. Collected 2026-10-15.", "what_it_is_for": "Any-to-any generation in the OmniGen line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/openseek/openseek-mid": { "id": "baai/openseek/openseek-mid", "name": "OpenSeek-Mid-v1", "maker": "baai", "family": "baai/openseek", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Architecture tag qwen3.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenSeek-Mid-v1 is the mid-size OpenSeek checkpoint, created 30 April 2026. Tags say Qwen3 text generation and custom code. No license tag was on the saved listing, so the license is null and weights are open-weights only because a public repo exists. The card was not read. Status: active. Confidence: medium. Collected 2026-10-15.", "what_it_is_for": "Mid-size OpenSeek language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/openseek/openseek-small": { "id": "baai/openseek/openseek-small", "name": "OpenSeek-Small-v1", "maker": "baai", "family": "baai/openseek", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-05-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Architecture tag deepseek_v3. Parameter count not read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenSeek-Small-v1 is BAAI's small open reproduction in a DeepSeek-V3 style. The base repo was created 5 May 2025 and tagged Apache-2.0 and deepseek_v3. OpenSeek-Small-v1-Baseline (22 May 2025) and OpenSeek-Small-v1-SFT (25 August 2025) are versions. The README returned 401, so this is a listing-level dossier. Status: active. Confidence: medium. Collected 2026-10-15.", "what_it_is_for": "Open base and SFT language-model checkpoints for the OpenSeek small run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/orca/orca-4b": { "id": "baai/orca/orca-4b", "name": "Orca-4B", "maker": "baai", "family": "baai/orca", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-07-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "World model with modality-specific decoders. Size not reconciled.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Orca is BAAI's world foundation model. The paper Orca: The World is in Your Mind (arXiv:2606.30534, submitted 29 June 2026) describes next-state prediction from video, with frozen-backbone readouts for text, image, and action. Pretraining data in the abstract: 125K hours of video and 160M event annotations. The public checkpoint is BAAI/Orca-4B, created 12 July 2026, Apache-2.0. The Hub size tag on one listing said 6B while the repo says 4B, so parameters stay null. Status: active. Collected 2026-10-15.", "what_it_is_for": "A world latent that can be read out as text, future images, or embodied actions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/painter/painter": { "id": "baai/painter/painter", "name": "Painter", "maker": "baai", "family": "baai/painter", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-03-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not read from config.json in this pass.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Painter is BAAI's generalist in-context vision model. The Hugging Face repo was created 24 March 2023. The card was not opened. Status: active. Confidence: medium. Collected 2026-10-15.", "what_it_is_for": "In-context vision tasks that are framed as image-to-image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/recon2reason/recon2reason-4b": { "id": "baai/recon2reason/recon2reason-4b", "name": "Recon2Reason-Reasoning-4B", "maker": "baai", "family": "baai/recon2reason", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-09-03", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Fine-tune of Qwen3-VL-4B-Instruct.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Recon2Reason-Reasoning-4B is a BAAI spatial-reasoning vision-language model, created 3 September 2026. Tags: fine-tune of Qwen/Qwen3-VL-4B-Instruct, Apache-2.0, indoor scenes, embodied AI. Status: active. The card was not read. Collected 2026-10-15.", "what_it_is_for": "Spatial reasoning for indoor embodied scenes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/reranker/bge-reranker-base": { "id": "baai/reranker/bge-reranker-base", "name": "bge-reranker-base", "maker": "baai", "family": "baai/reranker", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-09-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Cross-encoder. config.json: XLMRobertaForSequenceClassification initialized from xlm-roberta-base, hidden 768, max_position_embeddings 514. FlagEmbedding lists the backbone as xlm-roberta-base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-reranker-base is BAAI's smaller Chinese-English cross-encoder reranker, released 12 September 2023 with bge-reranker-large. It scores a query-document pair. MIT. Status: active. A BAAI maintainer later said this generation's max length is 512, against 8192 for bge-reranker-v2-m3. Collected 2026-10-15.", "what_it_is_for": "Re-rank the top documents returned by a bi-encoder. More accurate than the embedding model and more expensive.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/reranker/bge-reranker-large": { "id": "baai/reranker/bge-reranker-large", "name": "bge-reranker-large", "maker": "baai", "family": "baai/reranker", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-09-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Cross-encoder on xlm-roberta-large, per the FlagEmbedding reranker table. Max length 512 per the maintainer note on the v2-m3 discussion.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-reranker-large is the larger September 2023 BAAI cross-encoder for Chinese and English. MIT. Status: active. It is the stronger of the two first-generation rerankers on the published C-MTEB rerank average. Collected 2026-10-15.", "what_it_is_for": "Same rerank job as bge-reranker-base, with a large XLM-RoBERTa backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/reranker/bge-reranker-v2-5-gemma2-lightweight": { "id": "baai/reranker/bge-reranker-v2-5-gemma2-lightweight", "name": "bge-reranker-v2.5-gemma2-lightweight", "maker": "baai", "family": "baai/reranker", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma license", "release_date": "2024-07-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lightweight Gemma-2-9B reranker with selectable layers, token compression ratio, and compressed layers. FlagEmbedding class LightWeightFlagLLMReranker.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-reranker-v2.5-gemma2-lightweight is BAAI's July 2024 lightweight reranker on Gemma-2-9B. It keeps a Gemma-2 quality target while exposing layer and compression knobs. Gemma license. Status: active. Collected 2026-10-15.", "what_it_is_for": "Multilingual reranking when a 9B backbone is too expensive to run at full depth.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/reranker/bge-reranker-v2-gemma": { "id": "baai/reranker/bge-reranker-v2-gemma", "name": "bge-reranker-v2-gemma", "maker": "baai", "family": "baai/reranker", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma license", "release_date": "2024-03-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLM cross-encoder. FlagEmbedding table: backbone gemma-2b, not layerwise. Multilingual.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-reranker-v2-gemma is BAAI's Gemma-2B cross-encoder reranker for multilingual ranking, released with the March 2024 LLM rerankers. Gemma license. Status: active. Collected 2026-10-15.", "what_it_is_for": "Higher-capacity multilingual reranking than the XLM-RoBERTa rerankers, at Gemma-2B cost.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/reranker/bge-reranker-v2-m3": { "id": "baai/reranker/bge-reranker-v2-m3", "name": "bge-reranker-v2-m3", "maker": "baai", "family": "baai/reranker", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-03-18", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Cross-encoder fine-tuned from bge-m3. config.json: XLMRobertaForSequenceClassification, hidden 1024, max_position_embeddings 8194. Maintainer: max_length 8192.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-reranker-v2-m3 is BAAI's lightweight multilingual cross-encoder, fine-tuned from BGE-M3. FlagEmbedding groups it with the 18 March 2024 reranker release. Apache-2.0. Status: active. It is the default efficient multilingual reranker in the FlagEmbedding guide. Collected 2026-10-15.", "what_it_is_for": "Rerank multilingual passages, including long inputs, after a bi-encoder such as bge-m3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/reranker/bge-reranker-v2-minicpm-layerwise": { "id": "baai/reranker/bge-reranker-v2-minicpm-layerwise", "name": "bge-reranker-v2-minicpm-layerwise", "maker": "baai", "family": "baai/reranker", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-03-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Layerwise LLM reranker. FlagEmbedding table: backbone MiniCPM-2B-dpo-bf16, usable layers 8 through 40. Early-exit layers are the speed option.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bge-reranker-v2-minicpm-layerwise is BAAI's MiniCPM reranker. The user can read the score from an intermediate layer instead of the final one. Released 18 March 2024. Aimed at English and Chinese as well as other languages. Status: active. The weight license was not copied. Collected 2026-10-15.", "what_it_is_for": "Multilingual reranking with a selectable compute budget.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/reranker/llara": { "id": "baai/reranker/llara", "name": "LLaRA", "maker": "baai", "family": "baai/reranker", "status": "active", "confidence": "medium", "weights": "open-source", "license": "MIT", "release_date": "2023-12-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA-2-7B dense retriever, per the FlagEmbedding news line. Four Hugging Face repos are task checkpoints, not separate products.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLaRA is BAAI's LLaMA-based dense retriever, announced 24 December 2023 as state of the art on MS MARCO and BEIR in the vendor note. Weights landed as LLARA-passage, LLARA-document, LLARA-beir, and LLARA-pretrain. Those four repos are versions of one model. Status: active. Confidence is medium because the news post and the repos were not reconciled line by line. Collected 2026-10-15.", "what_it_is_for": "Dense retrieval, with separate checkpoints for passage, document, and BEIR settings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/reranker/matryoshka-reranker": { "id": "baai/reranker/matryoshka-reranker", "name": "Matryoshka ReRanker", "maker": "baai", "family": "baai/reranker", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-05-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three task checkpoints (passage, document, BEIR) under the Matroyshka spelling used on Hugging Face. Tags name the architecture cost_wise_mistral. Treated as one product with three versions.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Matryoshka ReRanker is a named BAAI reranker release from 21 May 2025. Hugging Face spells the repos Matroyshka-ReRanker-passage, -document, and -beir. The passage repo is Apache-2.0 and tagged as a Mistral cost-wise model. The three repos are versions, not three products. Status: active. The card text was not fully read. Collected 2026-10-15.", "what_it_is_for": "Rerank passages or documents, with a BEIR-oriented checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/robobrain/gaussianmind": { "id": "baai/robobrain/gaussianmind", "name": "GaussianMind", "maker": "baai", "family": "baai/robobrain", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-09-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Custom architecture tag robobrain_audio.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaussianMind is a BAAI audio-text model in the Orchestra / RoboBrain audio line. The repo was created 3 September 2026. Tags: robobrain_audio, audio-text-to-text, Apache-2.0. The org page size tag said 6B. That tag is not copied into parameters because the card was not read. Status: active. Collected 2026-10-15.", "what_it_is_for": "Audio understanding for the embodied line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/robobrain/robobrain": { "id": "baai/robobrain/robobrain", "name": "RoboBrain", "maker": "baai", "family": "baai/robobrain", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MLLM. Backbone not restated in the fetched card excerpt.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoboBrain is BAAI's CVPR 2025 embodied brain for manipulation, from abstract planning to concrete points. The card (fetched) describes an MLLM trained on ShareRobot plus general multimodal data. Planning, affordance, and trajectory checkpoints were posted in late March 2025. The LoRA repos are versions. Paper arXiv:2502.21257. Apache-2.0. The card is gated. Status: active. Collected 2026-10-15.", "what_it_is_for": "Robotic planning, affordance, and trajectory prediction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/robobrain/robobrain-dex": { "id": "baai/robobrain/robobrain-dex", "name": "RoboBrain-Dex", "maker": "baai", "family": "baai/robobrain", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tagged dexvla. Size not stated on the listing that was saved.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoboBrain-Dex is BAAI's dexterous-hand checkpoint, created 13 March 2026. The Hub tag is dexvla and the license tag is Apache-2.0. Status: active. The card was not read. Collected 2026-10-15.", "what_it_is_for": "Dexterous manipulation weights in the RoboBrain line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/robobrain/robobrain-x0": { "id": "baai/robobrain/robobrain-x0", "name": "RoboBrain-X0", "maker": "baai", "family": "baai/robobrain", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tagged qwen2_5_vl.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoboBrain-X0-Preview is a preview embodied model, repo created 28 September 2025. Tags: Qwen2.5-VL, robotics, Apache-2.0. The name says preview. Status: preview. Collected 2026-10-15.", "what_it_is_for": "Preview embodied policy or brain. The card was not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/robobrain/robobrain2-32b": { "id": "baai/robobrain/robobrain2-32b", "name": "RoboBrain2.0-32B", "maker": "baai", "family": "baai/robobrain", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Tagged qwen2_5_vl. Size is the repo name and the Hub size tag.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoboBrain2.0-32B is a RoboBrain 2.0 size from BAAI. Hugging Face tags it as a Qwen2.5-VL robotics model, Apache-2.0, with paper tag arXiv:2507.02029. Repo created 2025-06-05. An anonymous fetch of the 7B README returned 401, so the card body was not read. Status: active. Collected 2026-10-15.", "what_it_is_for": "Embodied planning and spatial reasoning at this size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/robobrain/robobrain2-3b": { "id": "baai/robobrain/robobrain2-3b", "name": "RoboBrain2.0-3B", "maker": "baai", "family": "baai/robobrain", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-07-22", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Tagged qwen2_5_vl. Size is the repo name and the Hub size tag.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoboBrain2.0-3B is a RoboBrain 2.0 size from BAAI. Hugging Face tags it as a Qwen2.5-VL robotics model, Apache-2.0, with paper tag arXiv:2507.02029. Repo created 2025-07-22. An anonymous fetch of the 7B README returned 401, so the card body was not read. Status: active. Collected 2026-10-15.", "what_it_is_for": "Embodied planning and spatial reasoning at this size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/robobrain/robobrain2-5-4b": { "id": "baai/robobrain/robobrain2-5-4b", "name": "RoboBrain2.5-4B", "maker": "baai", "family": "baai/robobrain", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-02-28", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Tagged qwen3_vl. 4B is the repo name.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoboBrain2.5-4B is a 2026 BAAI embodied model. The Hub tags Qwen3-VL, Apache-2.0, and papers 2601.14352, 2512.13660, and 2512.23703. Created 28 February 2026. Status: active. The README was not read. Collected 2026-10-15.", "what_it_is_for": "Embodied brain at the 4B RoboBrain 2.5 size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/robobrain/robobrain2-5-8b-mt": { "id": "baai/robobrain/robobrain2-5-8b-mt", "name": "RoboBrain2.5-8B-MT", "maker": "baai", "family": "baai/robobrain", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-09", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Tagged qwen3_vl.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoboBrain2.5-8B-MT is the other 8B RoboBrain 2.5 checkpoint from 9 January 2026. MT is part of the published name and was not expanded because the README was not read. Same Apache-2.0 and Qwen3-VL tags. Status: active. Collected 2026-10-15.", "what_it_is_for": "8B embodied checkpoint, MT variant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/robobrain/robobrain2-5-8b-nv": { "id": "baai/robobrain/robobrain2-5-8b-nv", "name": "RoboBrain2.5-8B-NV", "maker": "baai", "family": "baai/robobrain", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-09", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Tagged qwen3_vl.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoboBrain2.5-8B-NV is one of two 8B RoboBrain 2.5 checkpoints created 9 January 2026. NV is part of the published name. Tags match the 4B model: Qwen3-VL, Apache-2.0. The README was not read, so NV is not expanded. Status: active. Collected 2026-10-15.", "what_it_is_for": "8B embodied checkpoint, NV variant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/robobrain/robobrain2-7b": { "id": "baai/robobrain/robobrain2-7b", "name": "RoboBrain2.0-7B", "maker": "baai", "family": "baai/robobrain", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Tagged qwen2_5_vl. Size is the repo name and the Hub size tag.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoboBrain2.0-7B is a RoboBrain 2.0 size from BAAI. Hugging Face tags it as a Qwen2.5-VL robotics model, Apache-2.0, with paper tag arXiv:2507.02029. Repo created 2025-06-05. An anonymous fetch of the 7B README returned 401, so the card body was not read. Status: active. Collected 2026-10-15.", "what_it_is_for": "Embodied planning and spatial reasoning at this size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/seggpt/seggpt": { "id": "baai/seggpt/seggpt", "name": "SegGPT", "maker": "baai", "family": "baai/seggpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT on BAAI/SegGPT; Apache-2.0 on seggpt-vit-large", "release_date": "2023-04-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not read from config.json in this pass.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SegGPT is BAAI's in-context segmentation model. The repo BAAI/SegGPT was created 21 April 2023 and is tagged MIT with arXiv:2304.03284. The weight file most people download is BAAI/seggpt-vit-large (created 30 November 2023, Apache-2.0, ViT-Large). That file is a version of SegGPT. Status: active. Collected 2026-10-15.", "what_it_is_for": "Promptable image and video segmentation from example masks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/segvol/segvol": { "id": "baai/segvol/segvol", "name": "SegVol", "maker": "baai", "family": "baai/segvol", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-11-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not read from config.json in this pass.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SegVol is a named BAAI segmentation model. The repo was created 28 November 2023 and is tagged for feature extraction. The card was not read. Status: active. Confidence: medium. Collected 2026-10-15.", "what_it_is_for": "Volumetric or promptable segmentation. The exact task statement was not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/tap/tokenize-anything": { "id": "baai/tap/tokenize-anything", "name": "Tokenize Anything", "maker": "baai", "family": "baai/tap", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-12-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not read from config.json in this pass.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tokenize Anything (TAP) is a named BAAI vision model. The repo BAAI/tokenize-anything was created 14 December 2023 and tagged image-to-text. The card was not read. Status: active. Confidence: medium. Collected 2026-10-15.", "what_it_is_for": "Promptable vision tokenization. Exact outputs were not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/tele-flm/tele-flm": { "id": "baai/tele-flm/tele-flm", "name": "Tele-FLM", "maker": "baai", "family": "baai/tele-flm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-25", "context_length": null, "max_output_tokens": null, "parameters": "52B", "architecture": "Decoder-only transformer. 52B. About 2T training tokens. The 1T card's architecture table was not fully captured, so layer counts are not stored.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tele-FLM, also called FLM-2, is a 52B multilingual model developed by BAAI and TeleAI. The model card says it is a decoder-only transformer trained on about 2 trillion tokens, with Apache-2.0 weights. Languages listed: English, Chinese, and others. Weights are hosted at CofeAI/Tele-FLM, not under the BAAI Hub org. BAAI's research page lists the Tele-FLM series next to BGE. Paper arXiv:2404.16645. Status: active. Collected 2026-10-15.", "what_it_is_for": "Open multilingual base model at 52B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/tele-flm/tele-flm-1t": { "id": "baai/tele-flm/tele-flm-1t", "name": "Tele-FLM-1T", "maker": "baai", "family": "baai/tele-flm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-03", "context_length": null, "max_output_tokens": null, "parameters": "1T", "architecture": "Decoder-only, grown from the 52B model. The card's layer table was truncated before the cells were captured, so those numbers are not stored. Parameters: 1T as stated by the vendor.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tele-FLM-1T, also called FLM-2-1T, is the 1T-scale extension of Tele-FLM by BAAI and TeleAI. The card says it was trained on about 2.3 trillion tokens and, at the time of the card, was still under evaluation because of limited compute. Apache-2.0. Weights are at CofeAI/Tele-FLM-1T. Paper arXiv:2407.02783. Status: active, with that evaluation caveat. Collected 2026-10-15.", "what_it_is_for": "Very large open multilingual base model for further training and research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/uni3d/uni3d": { "id": "baai/uni3d/uni3d", "name": "Uni3D", "maker": "baai", "family": "baai/uni3d", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-09-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not read from config.json in this pass.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Uni3D is a named BAAI 3D representation model. The repo was created 20 September 2023. The card was not read. Status: active. Confidence: medium. Collected 2026-10-15.", "what_it_is_for": "3D representation learning. Task details were not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/ursa/ursa-0-6b": { "id": "baai/ursa/ursa-0-6b", "name": "URSA-0.6B", "maker": "baai", "family": "baai/ursa", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-02", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Tagged as a Qwen3-0.6B fine-tune with the URSA pipeline. 0.6B is the repo name and the base-model tag.", "modalities": { "input": [ "text" ], "output": [ "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "URSA-0.6B is the smaller URSA pair, both repos created 2 November 2025: URSA-0.6B-FSQ320 (text-to-video) and URSA-0.6B-IBQ1024 (text-to-image). Tags say Apache-2.0 and a Qwen3-0.6B base. The 1.7B card's resolution is not copied here. Status: active. Collected 2026-10-15.", "what_it_is_for": "Smaller text-to-video and text-to-image URSA checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/ursa/ursa-1-7b": { "id": "baai/ursa/ursa-1-7b", "name": "URSA-1.7B", "maker": "baai", "family": "baai/ursa", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-23", "context_length": null, "max_output_tokens": null, "parameters": "1.7B", "architecture": "Uniform discrete diffusion on a Qwen3-1.7B backbone. 512x320 is the FSQ320 card, not the IBQ cards.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "URSA-1.7B is BAAI's 1.7B video generator built on Qwen3-1.7B. The read card is URSA-1.7B-FSQ320: text-to-video, FP16, 512x320, Apache-2.0, Cosmos DV tokenizer, paper arXiv:2510.24717. IBQ image checkpoints are versions and were not given this resolution. Status: active. Collected 2026-10-15.", "what_it_is_for": "Text-to-video, image-to-video, and video extension on the FSQ320 checkpoint. The card's example uses 49 frames at 12 fps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baai/video-xl/video-xl-2": { "id": "baai/video-xl/video-xl-2", "name": "Video-XL-2", "maker": "baai", "family": "baai/video-xl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-05-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2 multimodal video model. The card's max_new_tokens 256 is a demo setting, not a model limit.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Video-XL-2 is BAAI's long-video understanding model, repo created 30 May 2025. The card shows a chat() API over a video path, with a demo that samples frames and generates text. Apache-2.0. Qwen2 tags. Stage1/Stage2 weights and a Stage3 repo are training versions. Papers tagged arXiv:2409.14485 and 2503.18478. Status: active. Collected 2026-10-15.", "what_it_is_for": "Answer questions about long videos.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan/baichuan-13b-base": { "id": "baichuan/baichuan/baichuan-13b-base", "name": "Baichuan-13B-Base", "maker": "baichuan", "family": "baichuan/baichuan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Community License for Baichuan-13B Model. Commercial use needs written email authorization.", "release_date": "2023-07-08", "context_length": 4096, "max_output_tokens": null, "parameters": "13264901120", "architecture": "Decoder-only Transformer derived from the Baichuan-7B design, with ALiBi instead of RoPE. 40 layers, 40 heads, hidden 5120, vocab 64000. Trained on 1.4 trillion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan-13B-Base is the pretrained 13B checkpoint in Baichuan's first open bilingual series. It uses ALiBi and a 4096-token window, and it was trained on 1.4 trillion tokens. Weights remained public on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan-13B-Base is the pretrained 13B checkpoint in Baichuan's first open bilingual series. It uses ALiBi and a 4096-token window, and it was trained on 1.4 trillion tokens. Weights remained public on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan/baichuan-13b-chat": { "id": "baichuan/baichuan/baichuan-13b-chat", "name": "Baichuan-13B-Chat", "maker": "baichuan", "family": "baichuan/baichuan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Community License for Baichuan-13B Model. Commercial use needs written email authorization.", "release_date": "2023-07-08", "context_length": 4096, "max_output_tokens": null, "parameters": "13264901120", "architecture": "Aligned chat model of Baichuan-13B. Same ALiBi decoder as the base: 40 layers, 40 heads, hidden 5120, vocab 64000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan-13B-Chat is the aligned dialogue checkpoint of Baichuan-13B, released with the base model for direct chat use. It stayed on Hugging Face as of 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan-13B-Chat is the aligned dialogue checkpoint of Baichuan-13B, released with the base model for direct chat use. It stayed on Hugging Face as of 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan/baichuan-53b": { "id": "baichuan/baichuan/baichuan-53b", "name": "Baichuan-53B", "maker": "baichuan", "family": "baichuan/baichuan", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-08-08", "context_length": null, "max_output_tokens": null, "parameters": "53B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan-53B is Baichuan's first closed language model, announced on 2023-08-08 for Chinese-English knowledge QA and writing. It is not on the current API price page or chat model list. Collected 2026-10-15.", "what_it_is_for": "Baichuan-53B is Baichuan's first closed language model, announced on 2023-08-08 for Chinese-English knowledge QA and writing. It is not on the current API price page or chat model list. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan/baichuan-7b": { "id": "baichuan/baichuan/baichuan-7b", "name": "Baichuan-7B", "maker": "baichuan", "family": "baichuan/baichuan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Baichuan-7B License (commercial use allowed; custom license, not an OSI license)", "release_date": "2023-06-13", "context_length": 4096, "max_output_tokens": null, "parameters": "7000559616", "architecture": "Decoder-only Transformer, LLaMA-style. RoPE, SwiGLU, RMSNorm pre-norm. 32 layers, 32 heads, hidden 4096, intermediate 11008, vocab 64000. Trained on about 1.2 trillion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan-7B is Baichuan Intelligent Technology's first open-weight bilingual base model, a 7B Transformer trained on about 1.2 trillion tokens. Weights remained downloadable on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan-7B is Baichuan Intelligent Technology's first open-weight bilingual base model, a 7B Transformer trained on about 1.2 trillion tokens. Weights remained downloadable on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan-audio/baichuan-audio": { "id": "baichuan/baichuan-audio/baichuan-audio", "name": "Baichuan-Audio", "maker": "baichuan", "family": "baichuan/baichuan-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-24", "context_length": 65536, "max_output_tokens": null, "parameters": "10B", "architecture": "OmniForCausalLM with audio and audio generation enabled and the vision towers disabled in config. Decoder: 28 layers, hidden 3584, 28 heads, 4 key-value heads, vocab 152064, max_position_embeddings 65536. Audio encoder 32 layers, 8-codebook RVQ, 16 kHz, plus a flow-matching decoder. This folder is the instruct checkpoint, repo name Baichuan-Audio-Instruct. Its config was not fetched separately; the shape is taken from the Base config and the shared README.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan-Audio is Baichuan's open end-to-end speech model for Chinese-English dialogue, with text and audio in and text and audio out. The instruct weights were published as Baichuan-Audio-Instruct on 2025-02-24 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Baichuan-Audio is Baichuan's open end-to-end speech model for Chinese-English dialogue, with text and audio in and text and audio out. The instruct weights were published as Baichuan-Audio-Instruct on 2025-02-24 under Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan-audio/baichuan-audio-base": { "id": "baichuan/baichuan-audio/baichuan-audio-base", "name": "Baichuan-Audio-Base", "maker": "baichuan", "family": "baichuan/baichuan-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-21", "context_length": 65536, "max_output_tokens": null, "parameters": "10B", "architecture": "OmniForCausalLM with audio and audio generation enabled and the vision towers disabled in config. Decoder: 28 layers, hidden 3584, 28 heads, 4 key-value heads, vocab 152064, max_position_embeddings 65536. Audio encoder 32 layers, 8-codebook RVQ, 16 kHz, plus a flow-matching decoder.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan-Audio-Base is the open speech foundation checkpoint Baichuan released before instruction tuning. The GitHub README says it has not had SFT. The Hugging Face repo dates to 2025-02-21 and uses Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Baichuan-Audio-Base is the open speech foundation checkpoint Baichuan released before instruction tuning. The GitHub README says it has not had SFT. The Hugging Face repo dates to 2025-02-21 and uses Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan-m/baichuan-m1-14b-base": { "id": "baichuan/baichuan-m/baichuan-m1-14b-base", "name": "Baichuan-M1-14B-Base", "maker": "baichuan", "family": "baichuan/baichuan-m", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Baichuan-M1-14B Community License", "release_date": "2025-01-23", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Trained from scratch. The Instruct card describes short-convolution attention, sliding-window attention on some layers, and a RoPE change meant to reduce positional oscillation. The Base repo is the pretrained pair of that model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan-M1-14B-Base is the pretrained checkpoint of Baichuan's first from-scratch medical language model. The Instruct card says the pair was trained on 20 trillion tokens of medical and general data. Weights were public on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan-M1-14B-Base is the pretrained checkpoint of Baichuan's first from-scratch medical language model. The Instruct card says the pair was trained on 20 trillion tokens of medical and general data. Weights were public on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan-m/baichuan-m1-14b-instruct": { "id": "baichuan/baichuan-m/baichuan-m1-14b-instruct", "name": "Baichuan-M1-14B-Instruct", "maker": "baichuan", "family": "baichuan/baichuan-m", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Baichuan-M1-14B Community License", "release_date": "2025-01-23", "context_length": null, "max_output_tokens": 2048, "parameters": "14B", "architecture": "Aligned checkpoint of Baichuan-M1-14B. Short-convolution attention, partial sliding-window attention, and modified RoPE. Alignment used ELO, TDPO, and PPO. generation_config max_new_tokens is 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan-M1-14B-Instruct is the aligned medical model trained from scratch by Baichuan and released with the base checkpoint in January 2025. The card reports a 72.23 average on its medical and exam suite. Collected 2026-10-15.", "what_it_is_for": "Baichuan-M1-14B-Instruct is the aligned medical model trained from scratch by Baichuan and released with the base checkpoint in January 2025. The card reports a 72.23 average on its medical and exam suite. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan-m/baichuan-m2-32b": { "id": "baichuan/baichuan-m/baichuan-m2-32b", "name": "Baichuan-M2-32B", "maker": "baichuan", "family": "baichuan/baichuan-m", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-10", "context_length": 131072, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen2ForCausalLM continued from Qwen2.5-32B. 64 layers, hidden 5120, intermediate 27648, 40 heads, 8 key-value heads, vocab 152064, max_position_embeddings 131072, bfloat16. Medical mid-training plus a large verifier and multi-stage RL. thinking_mode is supported.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.002, "completion_per_m_usd": 0.02 }, "summary_intro": "Baichuan-M2-32B is Baichuan's second medical model, a Qwen2.5-32B derivative with a large verifier system, released on Hugging Face on 2025-08-10 under Apache-2.0. The card reports HealthBench 60.1. The API id Baichuan-M2 was still priced on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan-M2-32B is Baichuan's second medical model, a Qwen2.5-32B derivative with a large verifier system, released on Hugging Face on 2025-08-10 under Apache-2.0. The card reports HealthBench 60.1. The API id Baichuan-M2 was still priced on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan-m/baichuan-m2-plus": { "id": "baichuan/baichuan-m/baichuan-m2-plus", "name": "Baichuan-M2-Plus", "maker": "baichuan", "family": "baichuan/baichuan-m", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32768, "max_output_tokens": 2048, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.01, "completion_per_m_usd": 0.03 }, "summary_intro": "Baichuan-M2-Plus is a distinct medical API id that automatically calls Baichuan's medical search. It was priced separately from Baichuan-M2 on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan-M2-Plus is a distinct medical API id that automatically calls Baichuan's medical search. It was priced separately from Baichuan-M2 on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan-m/baichuan-m3-235b": { "id": "baichuan/baichuan-m/baichuan-m3-235b", "name": "Baichuan-M3-235B", "maker": "baichuan", "family": "baichuan/baichuan-m", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-13", "context_length": 40960, "max_output_tokens": null, "parameters": "235B", "architecture": "Qwen3MoeForCausalLM continued from Qwen3-235B-A22B. 94 layers, hidden 4096, 64 heads, 4 key-value heads, 128 experts, 8 experts per token, moe intermediate 1536, vocab 151936, max_position_embeddings 40960, rope_theta 5e6, bfloat16. Training uses SPAR, fact-aware RL, and a three-stage expert fusion. thinking_mode is supported.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.01, "completion_per_m_usd": 0.03 }, "summary_intro": "Baichuan-M3-235B is Baichuan's open medical reasoning model released on 2026-01-13 under Apache-2.0. It continues Qwen3-235B-A22B and is trained to ask clinical questions before deciding. The product home lists HealthBench 65.1. API id Baichuan-M3 was still priced on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan-M3-235B is Baichuan's open medical reasoning model released on 2026-01-13 under Apache-2.0. It continues Qwen3-235B-A22B and is trained to ask clinical questions before deciding. The product home lists HealthBench 65.1. API id Baichuan-M3 was still priced on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan-m/baichuan-m3-plus": { "id": "baichuan/baichuan-m/baichuan-m3-plus", "name": "Baichuan-M3-Plus", "maker": "baichuan", "family": "baichuan/baichuan-m", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32768, "max_output_tokens": 2048, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.005, "completion_per_m_usd": 0.009 }, "summary_intro": "Baichuan-M3-Plus is the medical API id that adds automatic medical search on top of the M3 line. It was the cheapest M-series token price on the 2026-10-15 price page, and the docs banner advertised a free M3Plus program. Collected 2026-10-15.", "what_it_is_for": "Baichuan-M3-Plus is the medical API id that adds automatic medical search on top of the M3 line. It was the cheapest M-series token price on the 2026-10-15 price page, and the docs banner advertised a free M3Plus program. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan-m/baichuan-m4": { "id": "baichuan/baichuan-m/baichuan-m4", "name": "Baichuan-M4", "maker": "baichuan", "family": "baichuan/baichuan-m", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-05-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described in the paper as a clinical agent system: Baichuan-Harness runtime, a core reasoning model trained with SPAR++, and a tool layer for patient memory, evidence retrieval, and medical image perception. Parameter count is not in the abstract.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan-M4 is Baichuan's clinical-agent medical model, described in a June 2026 paper with Tsinghua's THUBPM group and reported by 36Kr as launched on 2026-05-26. No public weights or API id were found on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan-M4 is Baichuan's clinical-agent medical model, described in a June 2026 paper with Tsinghua's THUBPM group and reported by 36Kr as launched on 2026-05-26. No public weights or API id were found on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan-med/baichuanmed-ocr-72b": { "id": "baichuan/baichuan-med/baichuanmed-ocr-72b", "name": "BaichuanMed-OCR-72B", "maker": "baichuan", "family": "baichuan/baichuan-med", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (model-card license tag; the license text was not extracted)", "release_date": "2025-04-02", "context_length": null, "max_output_tokens": null, "parameters": "72B", "architecture": "Fine-tune of Qwen2.5-VL-72B-Instruct for medical-report OCR. Hugging Face base_model tag is Qwen/Qwen2.5-VL-72B-Instruct.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BaichuanMed-OCR-72B is the larger medical-report OCR model, a fine-tune of Qwen2.5-VL-72B-Instruct published on 2025-04-02. On the maker's private report set it scores 88.6 percent accuracy. Collected 2026-10-15.", "what_it_is_for": "BaichuanMed-OCR-72B is the larger medical-report OCR model, a fine-tune of Qwen2.5-VL-72B-Instruct published on 2025-04-02. On the maker's private report set it scores 88.6 percent accuracy. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan-med/baichuanmed-ocr-7b": { "id": "baichuan/baichuan-med/baichuanmed-ocr-7b", "name": "BaichuanMed-OCR-7B", "maker": "baichuan", "family": "baichuan/baichuan-med", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (model-card license tag; the license text was not extracted)", "release_date": "2025-04-07", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Fine-tune of Qwen2.5-VL-7B-Instruct for medical-report OCR and question answering. The 7B figure is the maker's name, matching the Qwen2.5-VL-7B backbone.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BaichuanMed-OCR-7B is Baichuan's medical-report OCR model, fine-tuned from Qwen2.5-VL-7B-Instruct and published on 2025-04-07. It reads report images and can answer from the extracted text, including Markdown tables. Collected 2026-10-15.", "what_it_is_for": "BaichuanMed-OCR-7B is Baichuan's medical-report OCR model, fine-tuned from Qwen2.5-VL-7B-Instruct and published on 2025-04-07. It reads report images and can answer from the extracted text, including Markdown tables. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan-omni/baichuan-omni": { "id": "baichuan/baichuan-omni/baichuan-omni", "name": "Baichuan-Omni", "maker": "baichuan", "family": "baichuan/baichuan-omni", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-10-11", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan-Omni is the 7B omni-modal model introduced in Baichuan's 11 October 2024 technical report. It accepts image, video, audio, and text. A public 1.0 weight repo was not on the official Hugging Face org on 2026-10-15; Omni-1.5 is the downloadable successor. Collected 2026-10-15.", "what_it_is_for": "Baichuan-Omni is the 7B omni-modal model introduced in Baichuan's 11 October 2024 technical report. It accepts image, video, audio, and text. A public 1.0 weight repo was not on the official Hugging Face org on 2026-10-15; Omni-1.5 is the downloadable successor. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan-omni/baichuan-omni-1-5": { "id": "baichuan/baichuan-omni/baichuan-omni-1-5", "name": "Baichuan-Omni-1.5", "maker": "baichuan", "family": "baichuan/baichuan-omni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-25", "context_length": 65536, "max_output_tokens": null, "parameters": "7B", "architecture": "OmniForCausalLM. Decoder: 28 layers, hidden 3584, 28 heads, 4 key-value heads, vocab 152064, max_position_embeddings 65536, RoPE theta 1e6, bfloat16. Audio encoder 32 layers at 16 kHz with an 8-codebook RVQ tokenizer. Flow-matching vocoder. Vision tower enabled for image and video. The GitHub README says the language model is Qwen2.5-7B. Eval tables label the system 7B.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan-Omni-1.5 is Baichuan's open omni-modal model, released at the end of January 2025. It takes text, image, video, and audio and can emit text and audio. The weights use Apache-2.0 and were still public on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan-Omni-1.5 is Baichuan's open omni-modal model, released at the end of January 2025. It takes text, image, video, and audio and can emit text and audio. The weights use Apache-2.0 and were still public on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan-omni/baichuan-omni-1-5-base": { "id": "baichuan/baichuan-omni/baichuan-omni-1-5-base", "name": "Baichuan-Omni-1.5-Base", "maker": "baichuan", "family": "baichuan/baichuan-omni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-24", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan-Omni-1.5-Base is the open base checkpoint released with Baichuan-Omni-1.5. The Hugging Face repo was created on 2025-01-24 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Baichuan-Omni-1.5-Base is the open base checkpoint released with Baichuan-Omni-1.5. The Hugging Face repo was created on 2025-01-24 under Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan2/baichuan2-13b-base": { "id": "baichuan/baichuan2/baichuan2-13b-base", "name": "Baichuan2-13B-Base", "maker": "baichuan", "family": "baichuan/baichuan2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 plus the Baichuan 2 Community License. Commercial use needs email approval, DAU under 1 million, and the applicant must not be a software or cloud provider or sublicense the grant.", "release_date": "2023-09-06", "context_length": 4096, "max_output_tokens": null, "parameters": "13B", "architecture": "BaichuanForCausalLM. 40 layers, 40 heads, hidden 5120, intermediate 13696, vocab 125696, SiLU, RMSNorm, bfloat16. model_max_length 4096. Trained as part of the Baichuan 2 release on 2.6 trillion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan2-13B-Base is the 13B pretrained Baichuan 2 model. The product home lists 13 billion parameters, a 2.6 trillion token corpus, and a 4K window. Weights remained public on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan2-13B-Base is the 13B pretrained Baichuan 2 model. The product home lists 13 billion parameters, a 2.6 trillion token corpus, and a 4K window. Weights remained public on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan2/baichuan2-13b-chat": { "id": "baichuan/baichuan2/baichuan2-13b-chat", "name": "Baichuan2-13B-Chat", "maker": "baichuan", "family": "baichuan/baichuan2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 plus the Baichuan 2 Community License. Commercial use needs email approval, DAU under 1 million, and the applicant must not be a software or cloud provider or sublicense the grant.", "release_date": "2023-09-06", "context_length": 4096, "max_output_tokens": null, "parameters": "13B", "architecture": "BaichuanForCausalLM. 40 layers, 40 heads, hidden 5120, intermediate 13696, vocab 125696, SiLU, RMSNorm, bfloat16. model_max_length 4096. Trained as part of the Baichuan 2 release on 2.6 trillion tokens. This folder is the aligned chat checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan2-13B-Chat is the aligned 13B Baichuan 2 chat model. A v2 revision on 2023-12-29 improved math, reasoning, and instruction following, and an official 4-bit checkpoint exists. Collected 2026-10-15.", "what_it_is_for": "Baichuan2-13B-Chat is the aligned 13B Baichuan 2 chat model. A v2 revision on 2023-12-29 improved math, reasoning, and instruction following, and an official 4-bit checkpoint exists. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan2/baichuan2-53b": { "id": "baichuan/baichuan2/baichuan2-53b", "name": "Baichuan2-53B", "maker": "baichuan", "family": "baichuan/baichuan2", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-09-25", "context_length": 32768, "max_output_tokens": null, "parameters": "53B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan2-53B is the closed API model Baichuan opened on 2023-09-25 for enterprise use. The price page still listed a 32k context and time-of-day rates on 2026-10-15, while the chat docs model list did not. Collected 2026-10-15.", "what_it_is_for": "Baichuan2-53B is the closed API model Baichuan opened on 2023-09-25 for enterprise use. The price page still listed a 32k context and time-of-day rates on 2026-10-15, while the chat docs model list did not. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan2/baichuan2-7b-base": { "id": "baichuan/baichuan2/baichuan2-7b-base", "name": "Baichuan2-7B-Base", "maker": "baichuan", "family": "baichuan/baichuan2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 plus the Baichuan 2 Community License. Commercial use needs email approval, DAU under 1 million, and the applicant must not be a software or cloud provider or sublicense the grant.", "release_date": "2023-09-06", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "BaichuanForCausalLM. 32 layers, 32 heads, hidden 4096, intermediate 11008, vocab 125696, SiLU, RMSNorm, bfloat16. model_max_length 4096. Trained as part of the Baichuan 2 release on 2.6 trillion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan2-7B-Base is the 7B pretrained model in Baichuan 2, trained on 2.6 trillion tokens and released with Base and Chat checkpoints on 2023-09-06. Weights remained public on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan2-7B-Base is the 7B pretrained model in Baichuan 2, trained on 2.6 trillion tokens and released with Base and Chat checkpoints on 2023-09-06. Weights remained public on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan2/baichuan2-7b-chat": { "id": "baichuan/baichuan2/baichuan2-7b-chat", "name": "Baichuan2-7B-Chat", "maker": "baichuan", "family": "baichuan/baichuan2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 plus the Baichuan 2 Community License. Commercial use needs email approval, DAU under 1 million, and the applicant must not be a software or cloud provider or sublicense the grant.", "release_date": "2023-09-06", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "BaichuanForCausalLM. 32 layers, 32 heads, hidden 4096, intermediate 11008, vocab 125696, SiLU, RMSNorm, bfloat16. model_max_length 4096. Trained as part of the Baichuan 2 release on 2.6 trillion tokens. This folder is the aligned chat checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan2-7B-Chat is the aligned 7B chat model in Baichuan 2, with an official 4-bit checkpoint. It was released on 2023-09-06 and was still on Hugging Face on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan2-7B-Chat is the aligned 7B chat model in Baichuan 2, with an official 4-bit checkpoint. It was released on 2023-09-06 and was still on Hugging Face on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan2/baichuan2-turbo": { "id": "baichuan/baichuan2/baichuan2-turbo", "name": "Baichuan2-Turbo", "maker": "baichuan", "family": "baichuan/baichuan2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-12-19", "context_length": 32768, "max_output_tokens": 2048, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.008, "completion_per_m_usd": 0.008 }, "summary_intro": "Baichuan2-Turbo is the search-enhanced API model Baichuan opened on 2023-12-19. It was still a listed chat model id on 2026-10-15, with a 32k window and tool calling. Collected 2026-10-15.", "what_it_is_for": "Baichuan2-Turbo is the search-enhanced API model Baichuan opened on 2023-12-19. It was still a listed chat model id on 2026-10-15, with a 32k window and tool calling. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan2/baichuan2-turbo-192k": { "id": "baichuan/baichuan2/baichuan2-turbo-192k", "name": "Baichuan2-Turbo-192k", "maker": "baichuan", "family": "baichuan/baichuan2", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-12-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan2-Turbo-192k was the long-context SKU in the December 2023 Turbo API. Baichuan's price page says it was taken offline on 2024-08-16 and that calls now route to Baichuan3-Turbo-128k. Collected 2026-10-15.", "what_it_is_for": "Baichuan2-Turbo-192k was the long-context SKU in the December 2023 Turbo API. Baichuan's price page says it was taken offline on 2024-08-16 and that calls now route to Baichuan3-Turbo-128k. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan3/baichuan3": { "id": "baichuan/baichuan3/baichuan3", "name": "Baichuan 3", "maker": "baichuan", "family": "baichuan/baichuan3", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-01-29", "context_length": null, "max_output_tokens": null, "parameters": ">100B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan 3 is the closed flagship Baichuan announced on 2024-01-29, described as larger than 100 billion parameters. It is not a current API model id; the commercial Baichuan 3 line on the 2026 docs list is the Turbo SKUs. Collected 2026-10-15.", "what_it_is_for": "Baichuan 3 is the closed flagship Baichuan announced on 2024-01-29, described as larger than 100 billion parameters. It is not a current API model id; the commercial Baichuan 3 line on the 2026 docs list is the Turbo SKUs. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan3/baichuan3-turbo": { "id": "baichuan/baichuan3/baichuan3-turbo", "name": "Baichuan3-Turbo", "maker": "baichuan", "family": "baichuan/baichuan3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-22", "context_length": 32768, "max_output_tokens": 2048, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.012, "completion_per_m_usd": 0.012 }, "summary_intro": "Baichuan3-Turbo is the 32k commercial API model in the Baichuan 3 line. It was still listed for chat, tools, and JSON mode on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan3-Turbo is the 32k commercial API model in the Baichuan 3 line. It was still listed for chat, tools, and JSON mode on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan3/baichuan3-turbo-128k": { "id": "baichuan/baichuan3/baichuan3-turbo-128k", "name": "Baichuan3-Turbo-128k", "maker": "baichuan", "family": "baichuan/baichuan3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-22", "context_length": 131072, "max_output_tokens": 2048, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.024, "completion_per_m_usd": 0.024 }, "summary_intro": "Baichuan3-Turbo-128k is the long-context Baichuan 3 API model. The price page lists a 128k window, and retired Baichuan2-Turbo-192k calls are routed here. It remained listed on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan3-Turbo-128k is the long-context Baichuan 3 API model. The price page lists a 128k window, and retired Baichuan2-Turbo-192k calls are routed here. It remained listed on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan4/baichuan4": { "id": "baichuan/baichuan4/baichuan4", "name": "Baichuan 4", "maker": "baichuan", "family": "baichuan/baichuan4", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-22", "context_length": 32768, "max_output_tokens": 2048, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.1 }, "summary_intro": "Baichuan 4 is Baichuan's closed flagship announced on 2024-05-22 with the \u767e\u5c0f\u5e94 assistant. It was still an API model id on 2026-10-15, priced as the expensive general model and also used by the Assistants API. Collected 2026-10-15.", "what_it_is_for": "Baichuan 4 is Baichuan's closed flagship announced on 2024-05-22 with the \u767e\u5c0f\u5e94 assistant. It was still an API model id on 2026-10-15, priced as the expensive general model and also used by the Assistants API. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan4/baichuan4-air": { "id": "baichuan/baichuan4/baichuan4-air", "name": "Baichuan4-Air", "maker": "baichuan", "family": "baichuan/baichuan4", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-31", "context_length": 32768, "max_output_tokens": 2048, "parameters": "", "architecture": "Mixture-of-experts model. Baichuan calls the design PRI. \u949b\u5a92\u4f53 expands PRI as Pyramid, Residual, and Interval, and says MLP and attention blocks stay standard while expert layout changes. The product home does not expand the acronym.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.00098, "completion_per_m_usd": 0.00098 }, "summary_intro": "Baichuan4-Air is Baichuan's low-cost MoE API model, released on 2024-10-31. The official price is 0.00098 CNY per 1,000 tokens at a 32k window, and it remained listed on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan4-Air is Baichuan's low-cost MoE API model, released on 2024-10-31. The official price is 0.00098 CNY per 1,000 tokens at a 32k window, and it remained listed on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan4/baichuan4-finance": { "id": "baichuan/baichuan4/baichuan4-finance", "name": "Baichuan4-Finance", "maker": "baichuan", "family": "baichuan/baichuan4", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan4-Finance is Baichuan's finance-enhanced model on the official finance product page. Access is by partner application, and it was not a public chat model id on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan4-Finance is Baichuan's finance-enhanced model on the official finance product page. Access is by partner application, and it was not a public chat model id on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/baichuan4/baichuan4-turbo": { "id": "baichuan/baichuan4/baichuan4-turbo", "name": "Baichuan4-Turbo", "maker": "baichuan", "family": "baichuan/baichuan4", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-31", "context_length": 32768, "max_output_tokens": 2048, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.015, "completion_per_m_usd": 0.015 }, "summary_intro": "Baichuan4-Turbo is the enterprise API model released with Baichuan4-Air on 2024-10-31. Baichuan positions it as a faster, cheaper sibling of Baichuan 4 that can be deployed on two RTX 4090 GPUs. It was still a listed model id on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan4-Turbo is the enterprise API model released with Baichuan4-Air on 2024-10-31. Baichuan positions it as a faster, cheaper sibling of Baichuan 4 that can be deployed on two RTX 4090 GPUs. It was still a listed model id on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/embedding/baichuan-text-embedding": { "id": "baichuan/embedding/baichuan-text-embedding", "name": "Baichuan-Text-Embedding", "maker": "baichuan", "family": "baichuan/embedding", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.0005, "completion_per_m_usd": null }, "summary_intro": "Baichuan-Text-Embedding is Baichuan's API embedding model, also called BCTE in the NPC launch coverage. It embeds up to 512 tokens into 1024 dimensions and was a paid API on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan-Text-Embedding is Baichuan's API embedding model, also called BCTE in the NPC launch coverage. It embeds up to 512 tokens into 1024 dimensions and was a paid API on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/npc/baichuan-npc-lite": { "id": "baichuan/npc/baichuan-npc-lite", "name": "Baichuan-NPC-Lite", "maker": "baichuan", "family": "baichuan/npc", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-01-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan-NPC-Lite is the smaller role-play API Baichuan introduced with Baichuan-NPC on 2024-01-09. Coverage says it favors spoken style and custom characters. It was not on the public chat model list on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan-NPC-Lite is the smaller role-play API Baichuan introduced with Baichuan-NPC on 2024-01-09. Coverage says it favors spoken style and custom characters. It was not on the public chat model list on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baichuan/npc/baichuan-npc-turbo": { "id": "baichuan/npc/baichuan-npc-turbo", "name": "Baichuan-NPC-Turbo", "maker": "baichuan", "family": "baichuan/npc", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-01-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Baichuan-NPC-Turbo is the higher role-play SKU announced with Baichuan-NPC on 2024-01-09. Launch coverage says it improves character consistency, logic, and instruction following over Lite. It was not a listed chat model id on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Baichuan-NPC-Turbo is the higher role-play SKU announced with Baichuan-NPC on 2024-01-09. Launch coverage says it improves character consistency, logic, and instruction following over Lite. It was not a listed chat model id on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/embedding/embedding-v1": { "id": "baidu/embedding/embedding-v1", "name": "Embedding-V1", "maker": "baidu", "family": "baidu/embedding", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 384, "max_output_tokens": null, "parameters": "", "architecture": "Baidu Qianfan text embedding. The July 2026 list: 384 dimensions, up to 16 texts, 384 tokens each. It is the platform's own embedding, not bge.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.0005, "completion_per_m_usd": null }, "summary_intro": "Embedding-V1 is Baidu's own Qianfan embedding model, 384 dimensions, still on the model list revised 2026-07-13. bge models on the same list are not Baidu's and are not catalogued.", "what_it_is_for": "Short-text embeddings for search and knowledge bases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/embedding/tao-8k": { "id": "baidu/embedding/tao-8k", "name": "tao-8k", "maker": "baidu", "family": "baidu/embedding", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Long-text embedding on the Qianfan list: 1024 dimensions, one text, 8192 tokens. The list does not name a third-party lab. Attribution is 'shipped as a Qianfan embedding' rather than a signed Baidu research card.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.0005, "completion_per_m_usd": null }, "summary_intro": "tao-8k is a long-text embedding endpoint on Baidu Qianfan, 1024 dimensions and 8192 tokens, still listed on 2026-07-13. It is catalogued because the official list publishes it next to Embedding-V1 without a third-party brand. A separate research card was not found in this pass.", "what_it_is_for": "Embed a single long passage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-1-0": { "id": "baidu/ernie/ernie-1-0", "name": "ERNIE 1.0", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Research pretraining model in the original ERNIE line, before the Wenxin chat APIs. See the paper for the method. Parameter counts are not filled in this pass because the papers were not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE 1.0 is Baidu's 2019 knowledge-enhanced pretraining model, the start of the ERNIE name. It is a research release, not a current API.", "what_it_is_for": "Chinese NLU pretraining. The paper's method is knowledge masking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-2-0": { "id": "baidu/ernie/ernie-2-0", "name": "ERNIE 2.0", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Research pretraining model in the original ERNIE line, before the Wenxin chat APIs. See the paper for the method. Parameter counts are not filled in this pass because the papers were not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE 2.0 is Baidu's 2019 continual multi-task pretraining framework and model. It is a research release.", "what_it_is_for": "Build richer language representations by adding pretraining tasks over time.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-3-0": { "id": "baidu/ernie/ernie-3-0", "name": "ERNIE 3.0", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Research pretraining model in the original ERNIE line, before the Wenxin chat APIs. See the paper for the method. Parameter counts are not filled in this pass because the papers were not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE 3.0 is Baidu's 2021 knowledge-enhanced model for both understanding and generation. It is the research predecessor of the later Wenxin chat models, not the ernie-3.5 API.", "what_it_is_for": "Unified understanding and generation pretraining.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-3-0-titan": { "id": "baidu/ernie/ernie-3-0-titan", "name": "ERNIE 3.0 Titan", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Research pretraining model in the original ERNIE line, before the Wenxin chat APIs. See the paper for the method. Parameter counts are not filled in this pass because the papers were not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE 3.0 Titan is the large-scale ERNIE 3.0 model Baidu described in December 2021. The parameter count is in the paper and was not re-typed here.", "what_it_is_for": "Scale the ERNIE 3.0 framework. Often cited as Baidu's first hundred-billion-class model; the integer is left blank until re-read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-3-5": { "id": "baidu/ernie/ernie-3-5", "name": "ERNIE 3.5", "maker": "baidu", "family": "baidu/ernie", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "2023 Wenxin general model behind the original ERNIE Bot chat product and the later ernie-3.5-8k API. Baidu described a large Chinese-English corpus and search-plugin support. The 8k main id retired earlier than the 128k id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.012, "completion_per_m_usd": 0.012 }, "summary_intro": "ERNIE 3.5 is the Wenxin generation that powered ERNIE Bot and the long-running ernie-3.5 APIs. The 8k service retired on 2026-06-30. The 128k ids were still on a retirement row dated 2026-10-29 when this catalog was collected.", "what_it_is_for": "General Chinese and English dialogue, writing, and plugin calls. Qianfan called it enough for most chat and creation tasks of that period.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-4-0": { "id": "baidu/ernie/ernie-4-0", "name": "ERNIE 4.0", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Baidu's 2023 flagship Wenxin chat model. Qianfan described it as a full upgrade over ERNIE 3.5 for complex tasks, with optional Baidu search. Exact parameter count was not on the pages read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.12, "completion_per_m_usd": 0.12 }, "summary_intro": "ERNIE 4.0 is Baidu's previous Wenxin flagship, sold as ERNIE-Bot 4.0 and later as ernie-4.0-8k. The main 8k endpoint retired on 2026-06-30. A few dated and latest aliases were still on a retirement row ending 2026-10-29.", "what_it_is_for": "Complex dialogue, writing, and reasoning, with a search plugin for fresher answers. Qianfan said it was stronger than ERNIE 3.5 on logic and code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-4-0-turbo": { "id": "baidu/ernie/ernie-4-0-turbo", "name": "ERNIE 4.0 Turbo", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lower-latency serving line for ERNIE 4.0. Qianfan said output latency was shorter than the ERNIE-4.0-8K mainline. An 8k and a 128k id both existed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE 4.0 Turbo was the faster hosted branch of ERNIE 4.0, including 8k and 128k ids. The main ids retired on 2026-06-30. Dated leftovers were listed through 2026-10-29.", "what_it_is_for": "Same general flagship jobs as ERNIE 4.0, where shorter time-to-first-token mattered.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-4-5": { "id": "baidu/ernie/ernie-4-5", "name": "ERNIE 4.5", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-16", "context_length": null, "max_output_tokens": 2048, "parameters": "", "architecture": "March 2025 hosted native multimodal foundation model, distinct from the later Turbo SKUs and from the June 2025 open-weight checkpoints. The documented preview endpoint was 8k context.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.004, "completion_per_m_usd": 0.016 }, "summary_intro": "ERNIE 4.5 is the hosted foundation model Baidu launched on 2025-03-16. The 8k preview API retired on 2026-06-30. Open-weight 4.5 checkpoints and the Turbo APIs are catalogued separately because Baidu treats them as their own releases.", "what_it_is_for": "Baidu called it a native multimodal base model for language, understanding, logic, memory, code, and reduced hallucination, and opened it on Qianfan for enterprises.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-4-5-0-3b": { "id": "baidu/ernie/ernie-4-5-0-3b", "name": "ERNIE-4.5-0.3B", "maker": "baidu", "family": "baidu/ernie", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-30", "context_length": null, "max_output_tokens": null, "parameters": "0.3B dense", "architecture": "Dense text model, the small end of the ERNIE 4.5 open family. The report lists a base and a post-trained non-thinking checkpoint. Not an MoE.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE-4.5-0.3B is the small dense text model Baidu open-sourced on 2025-06-30 under Apache-2.0. The Qianfan API id ernie-4.5-0.3b retired on 2026-06-30; the weights remain the artifact to use.", "what_it_is_for": "On-device or cheap local chat and as a fine-tune student. Qianfan had briefly hosted it as a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-4-5-21b-a3b": { "id": "baidu/ernie/ernie-4-5-21b-a3b", "name": "ERNIE-4.5-21B-A3B", "maker": "baidu", "family": "baidu/ernie", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-30", "context_length": 131072, "max_output_tokens": null, "parameters": "21B total / 3B active", "architecture": "Text MoE. Sibling thinking card, same skeleton, lists 28 layers, 20 query heads, 4 KV heads, 64 text experts with 6 active, plus 2 shared experts. This non-thinking model is the June 2025 post-train. Context length 131072 is stated on the family cards that were opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE-4.5-21B-A3B is Baidu's open 21B MoE that activates about 3B parameters per token. Weights shipped on 2025-06-30 under Apache-2.0. The Qianfan chat id retired in 2026; the checkpoint is still the open model.", "what_it_is_for": "General instruction following at a laptop-to-single-server size. The report says it stays competitive with larger MoE peers on math and reasoning despite fewer total parameters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-4-5-21b-a3b-thinking": { "id": "baidu/ernie/ernie-4-5-21b-a3b-thinking", "name": "ERNIE-4.5-21B-A3B-Thinking", "maker": "baidu", "family": "baidu/ernie", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-30", "context_length": 131072, "max_output_tokens": null, "parameters": "21B total / 3B active", "architecture": "Text MoE post-train aimed at longer reasoning. Card: 28 layers, 20/4 heads, 64 experts with 6 active, 2 shared experts, context 131072. Baidu says thinking length was increased versus the June model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE-4.5-21B-A3B-Thinking is the later reasoning checkpoint of the 21B open MoE. Baidu describes three months of extra thinking training on top of ERNIE-4.5-21B-A3B. The Qianfan id was retired; the Hugging Face weights remain.", "what_it_is_for": "Hard reasoning, math, science, code, and tool use, with a 128k context. Baidu asks users to reserve it for tasks that need a long trace.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-4-5-300b-a47b": { "id": "baidu/ernie/ernie-4-5-300b-a47b", "name": "ERNIE-4.5-300B-A47B", "maker": "baidu", "family": "baidu/ernie", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-30", "context_length": 131072, "max_output_tokens": null, "parameters": "300B total / 47B active", "architecture": "Text MoE. Open card: 54 layers, 64 query heads, 8 KV heads, 64 text experts with 8 active, context 131072. The same card's training-stage cell says Pretraining while the title says post-trained. Both wordings are stored. Vision-expert counts appear on the text card because the heterogeneous MoE design reserves them.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE-4.5-300B-A47B is the large open text MoE from the June 2025 ERNIE 4.5 drop, with 47B parameters active per token. Baidu also published an FP8 Paddle repo and a W4A8C8 quantized repo as deployment formats, not new models.", "what_it_is_for": "Instruction following and knowledge QA. The announcement says the post-trained model leads on IFEval, Multi-IF, SimpleQA, and ChineseSimpleQA in Baidu's comparison. Scores themselves are in figures.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-4-5-turbo": { "id": "baidu/ernie/ernie-4-5-turbo", "name": "ERNIE 4.5 Turbo", "maker": "baidu", "family": "baidu/ernie", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted efficient ERNIE 4.5 text service. Baidu's July 2025 open-model notice mapped the ERNIE-4.5-300B-A47B API id to ernie-4.5-turbo-128k-preview. Later Turbo ids are the supported product. Parameter count of the hosted Turbo weights was not published separately from the open 300B model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.0008, "completion_per_m_usd": 0.0032 }, "summary_intro": "ERNIE 4.5 Turbo is Baidu's hosted high-throughput text line for the ERNIE 4.5 generation, with 32k and 128k endpoints. As of 2026-10-15 it is still listed, and the retirement ledger sets 2026-10-29 as the cutoff.", "what_it_is_for": "Qianfan recommends it for long chat history, long-document QA, Chinese knowledge and writing, and Chinese math. Web search can be enabled on the request.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-4-5-turbo-vl": { "id": "baidu/ernie/ernie-4-5-turbo-vl", "name": "ERNIE 4.5 Turbo VL", "maker": "baidu", "family": "baidu/ernie", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted vision-language ERNIE 4.5 service. The July 2025 notice mapped ERNIE-4.5-VL-424B-A47B to ernie-4.5-turbo-vl-preview and said text, image, and video inputs. Thinking was marked not yet open on that notice.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.003, "completion_per_m_usd": 0.009 }, "summary_intro": "ERNIE 4.5 Turbo VL is Baidu's hosted vision-language ERNIE 4.5 endpoint, still on the Qianfan list as of the 2026-07-13 revision and scheduled to retire on 2026-10-29.", "what_it_is_for": "Image understanding, with the open-model notice also advertising video input on the preview that this line replaced. Qianfan's short description highlights image understanding, writing, translation, and code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-4-5-vl-28b-a3b": { "id": "baidu/ernie/ernie-4-5-vl-28b-a3b", "name": "ERNIE-4.5-VL-28B-A3B", "maker": "baidu", "family": "baidu/ernie", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-30", "context_length": 131072, "max_output_tokens": null, "parameters": "28B total / 3B active", "architecture": "Multimodal MoE chat model. Card: text and vision, 28 layers, 20/4 heads, 64 text experts and 64 vision experts with 6 active each, 2 shared experts, context 131072. Post-training used SFT and RLVR. The June release supports both thinking and non-thinking modes in this checkpoint.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE-4.5-VL-28B-A3B is the smaller open vision-language MoE from June 2025, activating about 3B parameters. Qianfan's notice says it accepts text, images, and video and can do deep thinking. The API id later retired.", "what_it_is_for": "Visual perception, documents, charts, and visual reasoning, with a thinking mode and a non-thinking mode.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-4-5-vl-28b-a3b-thinking": { "id": "baidu/ernie/ernie-4-5-vl-28b-a3b-thinking", "name": "ERNIE-4.5-VL-28B-A3B-Thinking", "maker": "baidu", "family": "baidu/ernie", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Later multimodal reasoning checkpoint. The Hugging Face collection labels it around 30B, while the June VL-28B card is 28B total and 3B active. The thinking card's config table was not opened in this pass, so the parameter field is left empty.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE-4.5-VL-28B-A3B-Thinking is a later open reasoning vision-language checkpoint from Baidu, separate from the June 2025 VL-28B model that already had a thinking mode. Collection metadata calls it about 30B.", "what_it_is_for": "Multimodal reasoning with a long trace. Baidu's blog index describes it as state of the art at about 3B active parameters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-4-5-vl-424b-a47b": { "id": "baidu/ernie/ernie-4-5-vl-424b-a47b", "name": "ERNIE-4.5-VL-424B-A47B", "maker": "baidu", "family": "baidu/ernie", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-30", "context_length": null, "max_output_tokens": null, "parameters": "424B total / 47B active", "architecture": "Largest ERNIE 4.5 vision-language MoE. The report says it supports both thinking and non-thinking modes. Total size is 424B because vision experts are added on top of the 300B text model. Context integer was not re-read from its own card.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE-4.5-VL-424B-A47B is the largest open model in the June 2025 ERNIE 4.5 family, a vision-language MoE with 47B active parameters. Qianfan mapped it to ernie-4.5-turbo-vl-preview and said deep thinking was not open yet on that API.", "what_it_is_for": "Hard visual reasoning and perception. Baidu says thinking mode narrows the gap to OpenAI o1 on MathVista, MMMU, and VisualPuzzle in their figure.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-5-0": { "id": "baidu/ernie/ernie-5-0", "name": "ERNIE 5.0", "maker": "baidu", "family": "baidu/ernie", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-22", "context_length": null, "max_output_tokens": 65536, "parameters": "2.4T total; under 3% active per token", "architecture": "Native omni-modal MoE. Baidu's 2026-01-22 post says 2.4 trillion parameters with under 3 percent active per inference, jointly modeling text, image, audio, and video. Thinking endpoints emit a chain before the answer. The public chat table documents text output; a separate image or video generation endpoint for ERNIE 5.0 was not on the 2026-07-13 image list.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.006, "completion_per_m_usd": 0.024 }, "summary_intro": "ERNIE 5.0 is Baidu's native omni-modal Wenxin model. A preview was unveiled on 2025-11-13 and the official API went live on 2026-01-22. On 2026-10-15 it is still callable but the Qianfan ledger, registered 2026-09-24, sets retirement at 2026-10-29.", "what_it_is_for": "Baidu describes joint modeling of text, images, audio, and video, with strength in multimodal understanding, instruction following, creative writing, factuality, and agent planning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-5-1": { "id": "baidu/ernie/ernie-5-1", "name": "ERNIE 5.1", "maker": "baidu", "family": "baidu/ernie", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-09", "context_length": null, "max_output_tokens": 65536, "parameters": "", "architecture": "Baidu says ERNIE 5.1 keeps the ERNIE 5.0 pre-training foundation and uses separated fully asynchronous reinforcement learning plus scaled agent post-training. It states total parameters are about one-third of ERNIE 5.0 and active parameters about one-half. Exact counts were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.004, "completion_per_m_usd": 0.018 }, "summary_intro": "ERNIE 5.1 is Baidu's active Wenxin flagship as of 2026-10-15. Baidu released it on 2026-05-09 as a smaller, agent-focused descendant of ERNIE 5.0, served on Qianfan and ERNIE Bot. It was not on the 2026-09-24 retirement batch that schedules ERNIE 5.0 for 2026-10-29.", "what_it_is_for": "Baidu positions it for agents, knowledge, reasoning, deep search, and creative writing. The Qianfan list describes upgrades in general ability, instruction following, and tool use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-bot-turbo": { "id": "baidu/ernie/ernie-bot-turbo", "name": "ERNIE-Bot-turbo", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Early faster Wenxin chat model, sibling of ERNIE-Bot rather than the later ERNIE Speed line. A 0922 snapshot is named in the 2023 price mirror.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.008, "completion_per_m_usd": 0.008 }, "summary_intro": "ERNIE-Bot-turbo was the early low-latency Wenxin chat endpoint. Qianfan retired it on 2026-06-30. It is not the same product as ERNIE Speed or ERNIE 4.5 Turbo.", "what_it_is_for": "Faster dialogue and generation than the original ERNIE-Bot, including use inside Qianfan data-augmentation jobs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-character": { "id": "baidu/ernie/ernie-character", "name": "ERNIE Character", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": 2048, "parameters": "", "architecture": "Role-play Wenxin model. Older list: ernie-char-8k, 8k context, 7k max input, output 2 to 2048, RPM 60, TPM 60000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE Character was Baidu's persona model for game NPCs, support agents, and role-play. The 8k API retired on 2026-04-23. A fiction variant had already retired in December 2025.", "what_it_is_for": "Consistent character voice and stronger instruction following than the general chat models, with faster inference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-code": { "id": "baidu/ernie/ernie-code", "name": "ERNIE Code", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Code-oriented Wenxin base. The only row read was a DPO training base named ERNIE-Code3-128K, retired 2025-08-14. An inference endpoint was not in the July 2026 list.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE Code here is the Qianfan training base ERNIE-Code3-128K, removed from DPO on 2025-08-14. This pass did not find a live inference endpoint. It may or may not be the same line as the earlier ERNIE-Code research model.", "what_it_is_for": "Code preference tuning on a 128k Wenxin base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-functions": { "id": "baidu/ernie/ernie-functions", "name": "ERNIE Functions", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tool-calling Wenxin SKU. The retirement name is ERNIE-Functions-8K, so the window was branded 8k. No limit table was in the pages read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE Functions was a named Qianfan tool-calling model, ERNIE-Functions-8K. It retired on 2026-04-23. Almost no public spec sheet remained in the pages read on 2026-10-15.", "what_it_is_for": "Function calling. The ledger's suggested replacement was DeepSeek-V3.2, which indicates a tools workload.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-image": { "id": "baidu/ernie/ernie-image", "name": "ERNIE-Image", "maker": "baidu", "family": "baidu/ernie", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-14", "context_length": null, "max_output_tokens": null, "parameters": "8B DiT", "architecture": "Latent diffusion, single-stream DiT, 8B parameters. Baidu uses the FLUX.2 VAE and a Ministral-3 3B text encoder, plus a lightweight prompt enhancer. The SFT checkpoint is the 50-step model. Guidance scale cited in secondary writeups as 4.0 was not re-read from the official card and is not stored as a spec.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE-Image is Baidu's open 8B text-to-image DiT, released on 2026-04-14 under Apache-2.0. The weights were still the current open image model on 2026-10-15. A distilled Turbo sibling and an aesthetic checkpoint shipped beside it.", "what_it_is_for": "Instruction-following image generation, posters, and long text rendering in Chinese and English. Baidu emphasizes controllability over a single glossy look.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-image-aes": { "id": "baidu/ernie/ernie-image-aes", "name": "ERNIE-Image-Aes", "maker": "baidu", "family": "baidu/ernie", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-25", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Aesthetic variant released with the ERNIE-Image report. The report says the weight drop includes ERNIE-Image, Turbo, the prompt enhancer, and ERNIE-Image-Aes. Hugging Face lists an 8B repo. A separate human-annotated set, ERIA-1K, shipped as a benchmark, not a model.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE-Image-Aes is Baidu's aesthetic ERNIE-Image checkpoint, named in the May 2026 technical report and published on Hugging Face. It is a sibling weight, not a new architecture.", "what_it_is_for": "Aesthetic text-to-image. The report pairs it with the ERIA-1K aesthetic benchmark.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-image-turbo": { "id": "baidu/ernie/ernie-image-turbo", "name": "ERNIE-Image-Turbo", "maker": "baidu", "family": "baidu/ernie", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-14", "context_length": null, "max_output_tokens": null, "parameters": "8B DiT", "architecture": "Distilled ERNIE-Image. Baidu says DMD plus RL, 8 inference steps instead of about 50. Same 8B DiT family.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE-Image-Turbo is the 8-step open distilled sibling of ERNIE-Image, released 2026-04-14 under Apache-2.0. The weights were still published on 2026-10-15. A Qianfan API under the same name retired on 2026-06-30.", "what_it_is_for": "Faster text-to-image with a quality tradeoff Baidu still ranks near the SFT model on GenEval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-irag": { "id": "baidu/ernie/ernie-irag", "name": "ERNIE-iRAG", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Retrieval-augmented image model. The ledger also lists iRAG-1.0, retired 2026-06-30, which may be the same line or a later id. They are grouped here.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE-iRAG was Baidu's retrieval-augmented image API on Qianfan. The ERNIE-iRAG id retired on 2025-12-23. iRAG-1.0 retired on 2026-06-30.", "what_it_is_for": "Text-to-image with retrieval. Qianfan's suggested replacement for the first id was Qwen-Image, a third-party model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-lite": { "id": "baidu/ernie/ernie-lite", "name": "ERNIE Lite", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-09-04", "context_length": null, "max_output_tokens": 4096, "parameters": "", "architecture": "Small Wenxin model for lower-cost serving and fine-tunes. Pro 128k shared the Speed Pro limit pattern on the older list: max input 124k, output up to 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE Lite was Baidu's small Wenxin model for cheap inference and fine-tuning. The 8k id retired on 2026-01-27. The 128k and Pro ids retired on 2026-06-30.", "what_it_is_for": "Chat and generation where a flagship was unnecessary, and as a base for private fine-tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-litev": { "id": "baidu/ernie/ernie-litev", "name": "ERNIE LiteV", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small vision-language Wenxin preview. Ledger ids include ERNIE-LiteV-Preview and ERNIE-LiteV-8K-Preview.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE LiteV was a preview vision model in the Lite family. Qianfan retired the preview in April 2026 and the 8k preview on 2026-06-30.", "what_it_is_for": "Lower-cost image understanding before ERNIE 4.5 Turbo VL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-novel": { "id": "baidu/ernie/ernie-novel", "name": "ERNIE Novel", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fiction-writing Wenxin SKU. Ledger name ERNIE-Novel-8K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE Novel was Baidu's named fiction model on Qianfan, ERNIE-Novel-8K. It retired on 2025-12-11.", "what_it_is_for": "Long-form story writing. The replacement named by Qianfan was ERNIE 4.5 Turbo 32k.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-speed": { "id": "baidu/ernie/ernie-speed", "name": "ERNIE Speed", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-01-16", "context_length": null, "max_output_tokens": 4096, "parameters": "", "architecture": "High-throughput Wenxin model positioned above ERNIE Lite as a fine-tune base. Pro 128k on the older list: max input 124k, output 2 to 4096, RPM 10000, TPM 800000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE Speed was Baidu's 2024 high-throughput Wenxin model, later split into Pro 8k and 128k ids. Those Pro ids retired on 2026-06-30. The older non-pro ids had already retired on 2026-01-27.", "what_it_is_for": "General tasks where latency and fine-tune cost mattered more than flagship quality. Qianfan also sold it as an industry continued-pretrain base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-tiny": { "id": "baidu/ernie/ernie-tiny", "name": "ERNIE Tiny", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Smallest Wenxin serving model. Qianfan said it had the lowest deploy and fine-tune cost in the ERNIE series. An embedding variant, ERNIE-Tiny-Embedding-128K, was a separate training base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE Tiny was Baidu's cheapest Wenxin model to fine-tune and serve. The 8k inference id retired on 2026-01-27.", "what_it_is_for": "High-volume simple generation and low-cost customization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-video-1": { "id": "baidu/ernie/ernie-video-1", "name": "ERNIE-Video 1.0", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two Qianfan endpoints: text-to-video and image-to-video. No architecture note was on the retirement row.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE-Video 1.0 was Baidu's named text-to-video and image-to-video API. Both ids retired on 2026-06-30 with no replacement filled in on those rows. MuseSteamer is the other Baidu video line.", "what_it_is_for": "Short video generation from a prompt or a still.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-vilg": { "id": "baidu/ernie/ernie-vilg", "name": "ERNIE-ViLG", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First Wenxin text-to-image model, also the engine behind \u6587\u5fc3\u4e00\u683c. Baidu's AI Studio card describes unified cross-modal generation of images from text and text from images. A public parameter count was not in the pages read. PaddleHub called a hosted API, not a full local weight drop.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE-ViLG is Baidu's first named Wenxin text-to-image model and the model behind \u6587\u5fc3\u4e00\u683c. The product is retired. ERNIE-ViLG 2.0 and later ERNIE-Image replaced it.", "what_it_is_for": "Chinese text-to-image creation for non-experts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-vilg-2": { "id": "baidu/ernie/ernie-vilg-2", "name": "ERNIE-ViLG 2.0", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "24B", "architecture": "Knowledge-enhanced text-to-image diffusion model with a mixture of denoising experts. The paper scales it to 24B parameters. Product API id ernie-vilg-v2, branded \u6587\u5fc3\u4e00\u683c.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE-ViLG 2.0 is Baidu's 24B text-to-image diffusion model from October 2022, sold as \u6587\u5fc3\u4e00\u683c and ernie-vilg-v2. The paper reports MS-COCO zero-shot FID-30k of 6.75. The live image line in 2026 is ERNIE-Image, not this API.", "what_it_is_for": "Chinese and bilingual text-to-image, with styles exposed in the older PaddleHub client.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-x1": { "id": "baidu/ernie/ernie-x1", "name": "ERNIE X1", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Baidu's first multimodal deep-thinking model with tool use, launched beside ERNIE 4.5. Later replaced in the API by X1 Turbo and X1.1. A plain ERNIE-X1 row was not in the 2026 retirement extract; the Turbo and X1.1 ids were.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.002, "completion_per_m_usd": 0.008 }, "summary_intro": "ERNIE X1 is Baidu's first named reasoning model, launched on 2025-03-16 with tool use and multimodal deep thinking. It is no longer on the current Qianfan list. Turbo and X1.1 carried the line and have since retired.", "what_it_is_for": "Baidu highlighted Chinese knowledge QA, literary writing, dialogue, logic, complex calculation, and tool use, and compared the launch price with DeepSeek-R1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-x1-1": { "id": "baidu/ernie/ernie-x1-1", "name": "ERNIE X1.1", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": 65536, "parameters": "", "architecture": "Reasoning model trained with what Baidu calls an iterative mixed reinforcement-learning framework. The product page says factuality, instruction following, and agent scores rose versus ERNIE X1. Context on the API was 64k.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.001, "completion_per_m_usd": 0.004 }, "summary_intro": "ERNIE X1.1 was Baidu's second reasoning flagship, with a 64k window and a long thinking trace. Qianfan registered it for retirement on 2026-09-01 and took it offline on 2026-09-29, pointing users to ERNIE 5.1.", "what_it_is_for": "Qianfan described gains on QA, tool use, agents, instruction following, logic, math, and code, plus stronger factuality than X1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/ernie/ernie-x1-turbo": { "id": "baidu/ernie/ernie-x1-turbo", "name": "ERNIE X1 Turbo", "maker": "baidu", "family": "baidu/ernie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Faster reasoning SKU of the X1 line. Qianfan docs say ernie-x1-turbo-32k supports only web_search.enable and can trigger up to 10 searches.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ERNIE X1 Turbo was the lower-cost reasoning endpoint in Baidu's X1 line, mainly the 32k id. Qianfan retired the remaining Turbo ids on 2026-06-30.", "what_it_is_for": "Multi-step tool and search traces. The search guide uses a gold-price question to show repeated search then a summary.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/musesteamer/musesteamer-2": { "id": "baidu/musesteamer/musesteamer-2", "name": "MuseSteamer 2", "maker": "baidu", "family": "baidu/musesteamer", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Baidu image-to-video line. Version 2.0 added Turbo, Lite, Pro, and audio-enabled variants. Version 2.1 Turbo and Lite ids appear on the retirement ledger the same day they are marked retired, 2026-02-06.", "modalities": { "input": [ "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MuseSteamer 2 is Baidu's image-to-video line, announced on 2025-08-22 with Turbo, Lite, Pro, and audio variants. The Qianfan ids in the retirement ledger, including 2.1, are marked retired on 2026-02-06. Air Image remains a separate still-listed text-to-image model.", "what_it_is_for": "Image-to-video, including clips with generated voice and ambient audio on the audio variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/musesteamer/musesteamer-air-image": { "id": "baidu/musesteamer/musesteamer-air-image", "name": "MuseSteamer Air Image", "maker": "baidu", "family": "baidu/musesteamer", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Baidu Search text-to-image model aimed at low price. Qianfan id musesteamer-air-image. Not the ERNIE-Image DiT.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MuseSteamer Air Image is Baidu Search's low-cost text-to-image API on Qianfan. It was still on the model list revised 2026-07-13. It is not ERNIE-Image and not the MuseSteamer video models.", "what_it_is_for": "Cheap text-to-image. Baidu describes it as a price-first model from the search team.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/paddleocr/paddleocr-vl": { "id": "baidu/paddleocr/paddleocr-vl", "name": "PaddleOCR-VL", "maker": "baidu", "family": "baidu/paddleocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.9B", "architecture": "Small vision-language model for document parsing from Baidu's PaddlePaddle group. Qianfan's API id is paddleocr-vl-0.9b. The Wenxin product page also names PaddleOCR-VL-1.5 as a 0.9B document VLM. Hugging Face shows later 1.5 and 1.6 repos under PaddlePaddle; their cards were not opened.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PaddleOCR-VL is Baidu PaddlePaddle's small document vision-language model, about 0.9B parameters. Qianfan lists paddleocr-vl-0.9b. The product page also advertises PaddleOCR-VL-1.5. Later Hub repos were not fully read.", "what_it_is_for": "Parse real-world documents: layout, text, and related page tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/qianfan/qianfan-70b": { "id": "baidu/qianfan/qianfan-70b", "name": "Qianfan-70B", "maker": "baidu", "family": "baidu/qianfan", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qianfan-branded Baidu model. Sizes in the name are the product's own label, not a card that was opened. Retired from the preset API.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qianfan-70B was Baidu's 70B-class preset model. The February 2026 list matches the 8B limit row: 32k context and up to 16384 output tokens. Retired on 2026-01-20 and listed again for 2026-06-30.", "what_it_is_for": "Larger general chat under the Qianfan brand.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/qianfan/qianfan-8b": { "id": "baidu/qianfan/qianfan-8b", "name": "Qianfan-8B", "maker": "baidu", "family": "baidu/qianfan", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qianfan-branded Baidu model. Sizes in the name are the product's own label, not a card that was opened. Retired from the preset API.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qianfan-8B was Baidu's 8B-class preset model on Qianfan. The February 2026 list gives a 32k window. It retired on 2026-01-20 and appears again on a 2026-06-30 row.", "what_it_is_for": "General chat at a small size. The ledger's suggested replacement was DeepSeek-V3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/qianfan/qianfan-lightning-128b-a19b": { "id": "baidu/qianfan/qianfan-lightning-128b-a19b", "name": "Qianfan-Lightning-128B-A19B", "maker": "baidu", "family": "baidu/qianfan", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "128B total / 19B active (from the official model id, not a separate card)", "architecture": "Qianfan-branded Baidu model. Sizes in the name are the product's own label, not a card that was opened. Retired from the preset API.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qianfan-Lightning-128B-A19B was a Qianfan MoE whose name states 128B total and 19B active. The February 2026 list gives 128k context and output up to 16384. The ledger retires it on 2026-02-27, the same day it is registered.", "what_it_is_for": "Long-context MoE chat. The suggested replacement was DeepSeek-V3.2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/qianfan/qianfan-ocr": { "id": "baidu/qianfan/qianfan-ocr", "name": "Qianfan-OCR", "maker": "baidu", "family": "baidu/qianfan", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Baidu Qianfan OCR model. The July 2026 list gives context 32k, max input 32k, max output 28k. A faster id, Qianfan-OCR-Fast, retired on 2026-06-30.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qianfan-OCR is Baidu's hosted OCR model on the Qianfan list, with a 32k window and up to 28k output tokens. It was still listed on the 2026-07-13 revision. Qianfan-OCR-Fast had already retired.", "what_it_is_for": "Read text from images through the chat-style OCR endpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/qianfan/qianfan-vl-70b": { "id": "baidu/qianfan/qianfan-vl-70b", "name": "Qianfan-VL-70B", "maker": "baidu", "family": "baidu/qianfan", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "70B (from the model id)", "architecture": "Qianfan-branded Baidu model. Sizes in the name are the product's own label, not a card that was opened. Retired from the preset API.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qianfan-VL-70B was a larger Baidu vision-language preset. It retired on 2026-06-30. The February list snippet named it without a full limit row in the extract.", "what_it_is_for": "Heavier image understanding under the Qianfan brand.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/qianfan/qianfan-vl-8b": { "id": "baidu/qianfan/qianfan-vl-8b", "name": "Qianfan-VL-8B", "maker": "baidu", "family": "baidu/qianfan", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "8B (from the model id)", "architecture": "Qianfan-branded Baidu model. Sizes in the name are the product's own label, not a card that was opened. Retired from the preset API.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qianfan-VL-8B was a Baidu vision-language preset. It retired on 2026-06-30. A limit row was not captured.", "what_it_is_for": "Image understanding at 8B-class size, as labeled.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "baidu/speech/speech-e2e": { "id": "baidu/speech/speech-e2e", "name": "\u7aef\u5230\u7aef\u8bed\u97f3\u8bed\u8a00\u5927\u6a21\u578b", "maker": "baidu", "family": "baidu/speech", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Baidu's cross-attention speech-language model. The product page says the encoder is tied to recognition and the decoder to synthesis, so one model hears speech and speaks back. Pro and Lite are paid tiers. Flex, opened to public beta on 2026-09-16, adds function calling and dual VAD.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": 0.064, "completion_per_m_usd": null }, "summary_intro": "Baidu's end-to-end speech-language model is a cross-attention speech API that listens and talks in one model. A public beta opened on 2026-01-16 with Pro and Lite prices. Flex, a tool-calling edition, entered public beta on 2026-09-16. It was still a beta product on 2026-10-15, so status is preview.", "what_it_is_for": "Voice agents, companionship, call centers, and education. Flex is the edition that can call tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/fullstop/fullstop-welsh-punctuation-prediction": { "id": "bangor/fullstop/fullstop-welsh-punctuation-prediction", "name": "Welsh punctuation restoration", "maker": "bangor", "family": "bangor/fullstop", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "2022-10-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Token classifier for punctuation, in the fullstop-deep-punctuation-prediction line. The card cites the Oliver Guhr fullstop work and the Softcatala Catalan model. It does not name the encoder size.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit released a Welsh punctuation model that puts back periods, commas, question marks, hyphens, and colons, mainly after speech recognition. The Hub repo was created on 2022-10-18. MIT license. Collected 2026-10-15.", "what_it_is_for": "Restore punctuation on Welsh text, including unpunctuated transcripts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/kaldi/kaldi-cy": { "id": "bangor/kaldi/kaldi-cy", "name": "Kaldi Welsh (2607)", "maker": "bangor", "family": "bangor/kaldi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Kaldi chain (LF-MMI) TDNN-F acoustic model, 12 factorised layers, packaged for Vosk. 3-gram Witten-Bell language model. Lexicon from Geiriadur Ynganu Bangor with grapheme-to-phoneme fallback.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit ships a Kaldi chain model for real-time Welsh speech recognition through Vosk. The current card is titled 2607 and compares itself with a 2503 release. License is CC BY-NC 3.0. Welsh Government funded. Collected 2026-10-15.", "what_it_is_for": "Low-latency Welsh transcription on CPU, including streaming, where a large end-to-end model is too heavy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/llama/llama-3-2-1b-welsh-cpt": { "id": "bangor/llama/llama-3-2-1b-welsh-cpt", "name": "Llama 3.2 1B Welsh CPT", "maker": "bangor", "family": "bangor/llama", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Trainer card says a fine-tune of unsloth/llama-3.2-1b-unsloth-bnb-4bit trained with SFT. The repo name says CPT. Those two statements disagree. No Welsh corpus is named.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit published a Llama 3.2 1B repo whose name says Welsh continued pretraining. The Hub repo was created on 2026-01-22. The card is a TRL stub that instead says the run was SFT on an Unsloth 4-bit base, and it does not name Welsh data. Confidence is medium. Collected 2026-10-15.", "what_it_is_for": "Not stated beyond the repo name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/llama/llama-3-2-1b-welsh-sft": { "id": "bangor/llama/llama-3-2-1b-welsh-sft", "name": "Llama 3.2 1B Welsh SFT", "maker": "bangor", "family": "bangor/llama", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Trainer card says SFT of unsloth/llama-3.2-1b-unsloth-bnb-4bit. No corpus is named.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit lists this Llama 3.2 1B supervised fine-tune in its Large Language Models collection. The Hub repo was created on 2026-01-22. The card is a TRL stub and does not describe the Welsh data. Confidence is medium. Collected 2026-10-15.", "what_it_is_for": "The collection and the chat space present it as a Welsh Llama 3.2 1B chat model. The card itself only shows a generic English prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/llama/llama-3-8b-instruct-cpt-cy": { "id": "bangor/llama/llama-3-8b-instruct-cpt-cy", "name": "Llama 3 8B Instruct Welsh CPT", "maker": "bangor", "family": "bangor/llama", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "other", "release_date": "2025-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AutoTrain PEFT adapter on meta-llama/Meta-Llama-3-8B-Instruct. The card does not describe the Welsh corpus.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit lists this Llama 3 8B Instruct PEFT repo in its Large Language Models collection. The Hub repo was created on 2025-01-27. The card is an AutoTrain stub with license other and does not describe the Welsh training. Confidence is medium. Collected 2026-10-15.", "what_it_is_for": "The repo id says continued pretraining for Welsh. The card does not state a task beyond a generic chat example.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/m2m100/m2m100-1-2b-ft-cy-to-en": { "id": "bangor/m2m100/m2m100-1-2b-ft-cy-to-en", "name": "M2M100 1.2B Welsh to English (2510)", "maker": "bangor", "family": "bangor/m2m100", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "2025-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of facebook/m2m100_1.2B. Card title includes 2510. Trainer card says the dataset is unknown.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit fine-tuned Meta's M2M100 1.2B for Welsh-to-English translation. The card calls the run 2510. The Hub repo was created on 2025-10-02. MIT license. It is in the machine-translation collection. Collected 2026-10-15.", "what_it_is_for": "Welsh-to-English translation. The card does not name a domain.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/mt-dspec/mt-dspec-health-en-cy": { "id": "bangor/mt-dspec/mt-dspec-health-en-cy", "name": "Marian Health English to Welsh", "maker": "bangor", "family": "bangor/mt-dspec", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2023-03-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Marian NMT English-to-Welsh model for the health and care domain. Hub list badge says 69.8M. That badge is not a card sentence, so parameters stay null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit trained a Marian neural model that translates English into Welsh for health and care text. The Hub repo was created on 2023-03-15 and the card records a weight-tying reconversion on 2026-02-26. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "English-to-Welsh translation in the health and care domain. The portal says the local Marian demo covers health and care, and legislation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/mt-dspec/mt-dspec-legislation-en-cy": { "id": "bangor/mt-dspec/mt-dspec-legislation-en-cy", "name": "Marian Legislation English to Welsh", "maker": "bangor", "family": "bangor/mt-dspec", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2023-03-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Marian NMT English-to-Welsh model for legislation. Hub list badge says 69.8M. parameters stay null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit trained a Marian neural model that translates English legislation into Welsh. The Hub repo was created on 2023-03-15, with a 2026-02-26 weight-tying reconversion noted on the card. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "English-to-Welsh translation for legislation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/nllb/nllb-200-1-3b-ft-cym-to-eng": { "id": "bangor/nllb/nllb-200-1-3b-ft-cym-to-eng", "name": "NLLB 1.3B Welsh to English", "maker": "bangor", "family": "bangor/nllb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "2025-03-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of facebook/nllb-200-1.3B for Welsh-to-English translation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit fine-tuned Meta's NLLB 200 1.3B model for Welsh-to-English translation. The Hub repo was created on 2025-03-18. License CC BY-NC 4.0. It is in the unit's machine-translation collection and the Agile Cymru collection. Collected 2026-10-15.", "what_it_is_for": "Welsh-to-English translation. The card gives no domain restriction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/nllb/nllb-200-1-3b-ft-eng-to-cym-legislation": { "id": "bangor/nllb/nllb-200-1-3b-ft-eng-to-cym-legislation", "name": "NLLB 1.3B Legislation English to Welsh", "maker": "bangor", "family": "bangor/nllb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "2025-03-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Domain fine-tune for legislation. The card says the base is techiaith/nllb-200-1.3B-ft-eng-to-cym and links that id on the DewiBrynJones user, not on the techiaith org.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit published an NLLB fine-tune for English-to-Welsh legislation. The Hub repo was created on 2025-03-19. License CC BY-NC 4.0. Collected 2026-10-15. The parent general model is cited on a staff Hugging Face user, not as an org repo, so it has no folder here.", "what_it_is_for": "Translate legislation from English into Welsh.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/orpheus/orpheus-3b-cy-en": { "id": "bangor/orpheus/orpheus-3b-cy-en", "name": "Orpheus 3B Welsh-English", "maker": "bangor", "family": "bangor/orpheus", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-06-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Gated LoRA adapter. Hub API base_model is unsloth/orpheus-3b-0.1-ft. Files include adapter_config.json and adapter_model.safetensors. Tokenizer template is a Llama 3 chat template whose stock line says Cutting Knowledge Date: December 2023. That line is the base template, not a Bangor cutoff claim.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit has a gated Hugging Face repo, orpheus3b-cy-en, that is a LoRA fine-tune of Unsloth's Orpheus 3B checkpoint. The repo was created on 2025-06-18. The README could not be read (HTTP 401). Confidence is medium. Collected 2026-10-15.", "what_it_is_for": "The repo id and the unit TTS page point at Welsh or bilingual Orpheus speech. The readable metadata do not confirm speakers or evals.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/piper/cy-en-gb-bu-tts": { "id": "bangor/piper/cy-en-gb-bu-tts", "name": "Bangor Welsh-English bilingual TTS", "maker": "bangor", "family": "bangor/piper", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2026-07-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Piper VITS, medium quality, three speakers, 22,050 Hz, 256 symbols. Phonemizer is Bangor G2P, not eSpeak. Two ONNX files: default warm-started from Piper en_GB-vctk-medium, and a CC0 file warm-started from en_US-ljspeech-medium.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit released a bilingual Welsh and English Piper VITS voice that uses the unit's own grapheme-to-phoneme front end so English is not read with Welsh letter-to-sound rules. The Hub repo was created on 2026-07-29. The default file is CC BY 4.0. A second file in the same repo is CC0. Collected 2026-10-15.", "what_it_is_for": "Screen readers, bilingual language-learning apps, and offline TTS for Welsh, English, and code-switched sentences. Three studio voices.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/piper/cy-gb-bu-tts": { "id": "bangor/piper/cy-gb-bu-tts", "name": "cy_GB Bangor Welsh TTS", "maker": "bangor", "family": "bangor/piper", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc0-1.0", "release_date": "2025-11-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Piper VITS neural TTS. ONNX. About 77 MB on the card. 22,050 Hz. Three speakers. Medium quality.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit trained a Piper VITS Welsh text-to-speech voice and released the ONNX weights under CC0. The Hub repo was created on 2025-11-05. It is aimed at screen readers and other local assistive tools. Collected 2026-10-15.", "what_it_is_for": "Local Welsh speech for screen readers, language learning, and offline apps. The card also tags English because the training set is bilingual.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/spacy/cy-pos-tagger": { "id": "bangor/spacy/cy-pos-tagger", "name": "Welsh spaCy part-of-speech tagger", "maker": "bangor", "family": "bangor/spacy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Statistical spaCy 2.3.2 part-of-speech tagger. Universal Dependencies tags. Not a rule tagger.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit released an early Welsh part-of-speech model for spaCy 2.3.2. The project README says it reaches 91% accuracy on unseen data and was funded by the Welsh Government. It is a separate, older product from the spaCy 3.5 pipeline. Collected 2026-10-15.", "what_it_is_for": "Assign Universal Dependencies part-of-speech tags to Welsh tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/spacy/cy-tag-lem-ner-lg": { "id": "bangor/spacy/cy-tag-lem-ner-lg", "name": "Welsh spaCy tagger, lemmatizer, NER, and vectors", "maker": "bangor", "family": "bangor/spacy", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy 3.5 pipeline: tokenizer, Universal Dependencies part-of-speech tagger, lemmatizer, named entity recognizer, and word vectors. The usage example reshapes vectors to 300 dimensions.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit released a Welsh spaCy pipeline that tokenizes, tags parts of speech, lemmatizes, recognizes named entities, and ships word vectors. The GitHub release is tagged 23.03 and is described as the first version. MIT license, with Open Government Licence data inside. Collected 2026-10-15.", "what_it_is_for": "Computational processing of Welsh text: syntax tags, dictionary form, entities, and similarity over word vectors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/speecht5/speecht5-finetuned-bu-tts-cy-en": { "id": "bangor/speecht5/speecht5-finetuned-bu-tts-cy-en", "name": "SpeechT5 Bangor TTS Welsh-English", "maker": "bangor", "family": "bangor/speecht5", "status": "active", "confidence": "medium", "weights": "open-source", "license": "mit", "release_date": "2025-03-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of microsoft/speecht5_tts. Trainer card says the dataset is unknown. Loss 0.4014 after 10000 steps.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit published a SpeechT5 text-to-speech fine-tune whose repo name points at the Bangor Welsh-English TTS data. The Hub repo was created on 2025-03-27. The card is an unedited trainer stub and does not name the dataset. MIT license. Confidence is medium. Collected 2026-10-15.", "what_it_is_for": "Not stated on the card. The repo name is a bilingual Bangor TTS fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/truecaser/sacremoses-truecaser-cy": { "id": "bangor/truecaser/sacremoses-truecaser-cy", "name": "Welsh Moses truecaser", "maker": "bangor", "family": "bangor/truecaser", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "2024-08-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Moses truecaser model file tc.model, loaded with sacremoses.MosesTruecaser. Not a neural network.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit published a Moses truecaser for Welsh so lowercased text can get its capitals back. The Hub repo was created on 2024-08-29. MIT license. It is a statistical model file, not a neural language model. Collected 2026-10-15.", "what_it_is_for": "Capitalize Welsh text, including names and mutation-related capitals. The card's example turns a lowercased Pwllheli sentence into title-style Welsh capitals.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/wav2vec2/wav2vec-xlsr-ft-cy-verbatim": { "id": "bangor/wav2vec2/wav2vec-xlsr-ft-cy-verbatim", "name": "wav2vec2 XLSR Verbatim Welsh", "maker": "bangor", "family": "bangor/wav2vec2", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2023-05-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of facebook/wav2vec2-large-xlsr-53 on techiaith/banc-trawsgrifiadau-bangor. Trainer card.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit fine-tuned wav2vec2 XLSR-53 on the Bangor Transcription Bank for Welsh. The Hub repo was created on 2023-05-15 and is among the unit's most downloaded speech repos. The card is an unedited trainer dump. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The repo name says verbatim Welsh recognition. The card body does not state an intended use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/wav2vec2/wav2vec2-btb-cv-ft-cv-cy": { "id": "bangor/wav2vec2/wav2vec2-btb-cv-ft-cv-cy", "name": "wav2vec2 Common Voice Welsh (frozen encoder)", "maker": "bangor", "family": "bangor/wav2vec2", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Further fine-tune of techiaith/wav2vec2-xlsr-53-ft-btb-cv-cy with the encoder frozen, on Common Voice 18 Welsh.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit continued a Welsh XLSR fine-tune with the encoder frozen on Common Voice 18. The Hub repo was created on 2024-10-03. It is one of four models in the unit's speech-recognition collection. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Read or planned Welsh transcription. The collection note says optional KenLM language models give the lowest WER in that set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/wav2vec2/wav2vec2-xlsr-53-ft-btb-cv-cy": { "id": "bangor/wav2vec2/wav2vec2-xlsr-53-ft-btb-cv-cy", "name": "wav2vec2 XLSR Bangor and Common Voice Welsh", "maker": "bangor", "family": "bangor/wav2vec2", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of facebook/wav2vec2-large-xlsr-53 on Common Voice 18 Welsh and Bangor Transcriptions 24.10.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit fine-tuned wav2vec2 XLSR-53 on Welsh Common Voice 18 and the October 2024 Bangor Transcriptions release. The Hub repo was created on 2024-10-01. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Welsh speech recognition on mixed read and spontaneous speech. The next model in the line freezes this encoder and trains further on Common Voice.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/wav2vec2/wav2vec2-xlsr-53-ft-cy-en": { "id": "bangor/wav2vec2/wav2vec2-xlsr-53-ft-cy-en", "name": "wav2vec2 XLSR Welsh-English", "maker": "bangor", "family": "bangor/wav2vec2", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2022-10-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CTC fine-tune of facebook/wav2vec2-large-xlsr-53.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit fine-tuned Meta's wav2vec2 XLSR-53 for Welsh and English speech. The Hub repo was created on 2022-10-24. Apache-2.0. Collected 2026-10-15. A sibling repo adds a KenLM language model.", "what_it_is_for": "Welsh and English speech recognition from spontaneous Bangor Transcription Bank speech and Common Voice 16.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/wav2vec2/wav2vec2-xlsr-53-ft-cy-en-withlm": { "id": "bangor/wav2vec2/wav2vec2-xlsr-53-ft-cy-en-withlm", "name": "wav2vec2 XLSR Welsh-English with KenLM", "maker": "bangor", "family": "bangor/wav2vec2", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-03-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CTC fine-tune of facebook/wav2vec2-large-xlsr-53 plus one KenLM n-gram trained on a balanced OSCAR Welsh and English mix.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit published an XLSR-53 Welsh-English acoustic model with a single KenLM n-gram so CTC decoding does not need a language detector. The Hub repo was created on 2024-03-05. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Welsh and English speech recognition with an n-gram language model at decode time.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/whisper/whisper-base-ft-commonvoice-cy-cpp": { "id": "bangor/whisper/whisper-base-ft-commonvoice-cy-cpp", "name": "Whisper Base Common Voice Welsh (whisper.cpp)", "maker": "bangor", "family": "bangor/whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-11-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of openai/whisper-base on Common Voice 18 Welsh, exported as ggml for whisper.cpp. No Transformers twin is in the techiaith org list.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit fine-tuned Whisper Base on Common Voice 18 Welsh and published only the whisper.cpp conversion. The Hub repo was created on 2024-11-08. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Offline Welsh transcription on desktops, laptops, and phones. The card says it is smaller and is the on-device pair of the Large v3 Common Voice Welsh model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/whisper/whisper-base-ft-commonvoice-cy-en-cpp": { "id": "bangor/whisper/whisper-base-ft-commonvoice-cy-en-cpp", "name": "Whisper Base Common Voice Welsh-English (whisper.cpp)", "maker": "bangor", "family": "bangor/whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-11-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of openai/whisper-base on techiaith/commonvoice_18_0_cy_en, exported as ggml for whisper.cpp.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit fine-tuned Whisper Base on bilingual Common Voice 18 data and shipped the whisper.cpp file. The Hub repo was created on 2024-11-08. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Offline Welsh and English transcription and language detection on CPU hardware.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/whisper/whisper-base-ft-verbatim-cy-en-cpp": { "id": "bangor/whisper/whisper-base-ft-verbatim-cy-en-cpp", "name": "Whisper Base Verbatim Welsh (whisper.cpp)", "maker": "bangor", "family": "bangor/whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-11-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of openai/whisper-base on Banc Trawsgrifiadau Bangor and Common Voice 18 Welsh, exported as ggml for whisper.cpp.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit fine-tuned Whisper Base for verbatim Welsh, mixing the Bangor Transcription Bank with Common Voice 18, and published the whisper.cpp conversion. The Hub repo was created on 2024-11-08. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Offline verbatim transcription of spontaneous Welsh on desktops, laptops, and phones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/whisper/whisper-large-ft-cy-en": { "id": "bangor/whisper/whisper-large-ft-cy-en", "name": "Whisper Large Welsh and English (2607)", "maker": "bangor", "family": "bangor/whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of openai/whisper-large-v2. The card does not restate layer counts or a parameter total.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit (techiaith) released this fine-tune of OpenAI Whisper Large v2 for Welsh and English speech recognition. The card titles the checkpoint 2607. It was still the model listed first in the unit's Speech Recognition Models collection on the collection date 2026-10-15. Weights are Apache-2.0.", "what_it_is_for": "Transcribe Welsh audio to Welsh text and UK or Irish English audio to English text. A Welsh-to-English speech-translation mode exists but the card says quality is limited.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/whisper/whisper-large-v3-ft-commonvoice-cy": { "id": "bangor/whisper/whisper-large-v3-ft-commonvoice-cy", "name": "Whisper Large v3 Common Voice Welsh", "maker": "bangor", "family": "bangor/whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-08-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of openai/whisper-large-v3 on Common Voice 18 Welsh custom splits train_all and other_with_excluded.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit published this Whisper Large v3 fine-tune for Welsh read speech from Common Voice 18. The Hub repo was created on 2024-08-26. It is an active Apache-2.0 release, collected 2026-10-15.", "what_it_is_for": "Transcribe Welsh, especially Common Voice-style read speech. The card says it beats the unit's bilingual Common Voice model on Welsh and is worse on English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/whisper/whisper-large-v3-ft-commonvoice-cy-en": { "id": "bangor/whisper/whisper-large-v3-ft-commonvoice-cy-en", "name": "Whisper Large v3 Common Voice Welsh-English", "maker": "bangor", "family": "bangor/whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-10-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of openai/whisper-large-v3 on techiaith/commonvoice_18_0_cy_en.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit fine-tuned Whisper Large v3 on Welsh and English Common Voice 18 data so one model can transcribe both languages and detect the language. The Hub repo was created on 2024-10-09. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Transcribe Welsh and English read speech and detect which language was spoken. The test did not tell the model the target language.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/whisper/whisper-large-v3-ft-verbatim-cy-en": { "id": "bangor/whisper/whisper-large-v3-ft-verbatim-cy-en", "name": "Whisper Large v3 Verbatim Welsh-English", "maker": "bangor", "family": "bangor/whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of openai/whisper-large-v3 on Banc Trawsgrifiadau Bangor, Lleisiau Arfor, Common Voice 18 Welsh, and Common Voice VAD segments.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit fine-tuned Whisper Large v3 for verbatim transcription of spontaneous Welsh, using the Bangor Transcription Bank, Lleisiau Arfor, and Common Voice read speech. The Hub repo was created on 2024-10-01. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Verbatim transcripts of spontaneous or unplanned speech, rather than cleaned read-speech text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/whisper/whisper-large-v3-turbo-ft-verbatim-cy-en": { "id": "bangor/whisper/whisper-large-v3-turbo-ft-verbatim-cy-en", "name": "Whisper Large v3 Turbo Verbatim Welsh", "maker": "bangor", "family": "bangor/whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-11-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of openai/whisper-large-v3-turbo. The card says turbo is a pruned Whisper Large v3 with fewer decoder layers.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit fine-tuned OpenAI's pruned Whisper Large v3 Turbo for faster verbatim Welsh transcription. The Hub repo was created on 2024-11-07. Apache-2.0. Collected 2026-10-15. The vendor treats it as its own repo, so it is not folded into the full Large v3 verbatim folder.", "what_it_is_for": "Faster verbatim transcription of spontaneous Welsh. Training used Banc Trawsgrifiadau Bangor plus Common Voice 18 Welsh.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bangor/whisper/whisper-tiny-ft-cy-en": { "id": "bangor/whisper/whisper-tiny-ft-cy-en", "name": "Whisper Tiny Welsh-English", "maker": "bangor", "family": "bangor/whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-02-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of openai/whisper-tiny. Trainer card. Learning rate 1e-05, 4000 steps, effective batch 32.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bangor University's Language Technologies Unit fine-tuned OpenAI Whisper Tiny on Common Voice 16.1 Welsh and English plus normalized Bangor Transcription Bank text. The Hub repo was created on 2024-02-20. Apache-2.0. The card is still mostly a trainer dump, but it names an intended use. Collected 2026-10-15.", "what_it_is_for": "A small offline speech model for devices such as Android phones. It is not presented as the unit's best transcriber.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/abstract-sim/abstract-sim-query": { "id": "bar-ilan/abstract-sim/abstract-sim-query", "name": "Abstract-Sim query", "maker": "bar-ilan", "family": "bar-ilan/abstract-sim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "MPNet. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 30527, max_position_embeddings 514.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Abstract-Sim query is one encoder from the Bar-Ilan NLP lab Abstract-Sim dual-encoder release. Collected 2026-10-15.", "what_it_is_for": "Query encoder for abstract descriptions. The card says to use it beside the sentence encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/abstract-sim/abstract-sim-query-pubmed": { "id": "bar-ilan/abstract-sim/abstract-sim-query-pubmed", "name": "Abstract-Sim query PubMed", "maker": "bar-ilan", "family": "bar-ilan/abstract-sim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 30522, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Abstract-Sim query PubMed is one encoder from the Bar-Ilan NLP lab Abstract-Sim dual-encoder release. Collected 2026-10-15.", "what_it_is_for": "PubMed query encoder, paired with the PubMed sentence encoder. The sample pins revision 8d34676d80a39bcbc5a1d2eec13e6f8078496215 for this repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/abstract-sim/abstract-sim-sentence": { "id": "bar-ilan/abstract-sim/abstract-sim-sentence", "name": "Abstract-Sim sentence", "maker": "bar-ilan", "family": "bar-ilan/abstract-sim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "MPNet. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 30527, max_position_embeddings 514.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Abstract-Sim sentence is one encoder from the Bar-Ilan NLP lab Abstract-Sim dual-encoder release. Collected 2026-10-15.", "what_it_is_for": "Sentence encoder. The card says to use it beside the query encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/abstract-sim/abstract-sim-sentence-pubmed": { "id": "bar-ilan/abstract-sim/abstract-sim-sentence-pubmed", "name": "Abstract-Sim sentence PubMed", "maker": "bar-ilan", "family": "bar-ilan/abstract-sim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 30522, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Abstract-Sim sentence PubMed is one encoder from the Bar-Ilan NLP lab Abstract-Sim dual-encoder release. Collected 2026-10-15.", "what_it_is_for": "PubMed sentence encoder, paired with the PubMed query encoder. The sample pins two revision ids.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/alephbert/alephbert": { "id": "bar-ilan/alephbert/alephbert", "name": "AlephBERT", "maker": "bar-ilan", "family": "bar-ilan/alephbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2021-04-08", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT masked language model. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 52000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AlephBERT is a Hebrew BERT-base model from the ONLP lab at Bar-Ilan University. Weights are public under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and card present it as a Hebrew pretrained language model for segmentation, part-of-speech, morphology, named entities, and sentiment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/alephbertgimmel/alephbertgimmel": { "id": "bar-ilan/alephbertgimmel/alephbertgimmel", "name": "AlephBERTGimmel", "maker": "bar-ilan", "family": "bar-ilan/alephbertgimmel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc0-1.0", "release_date": "2022-11-28", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT masked language model. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AlephBERTGimmel is a Hebrew BERT with a 128K vocabulary, released by Bar-Ilan and Dicta authors. The base checkpoint is public under CC0. Collected 2026-10-15.", "what_it_is_for": "The paper presents it as a modern-Hebrew pretrained model and a comparison point for vocabulary size, covering segmentation, tagging, morphology, NER, and sentiment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/alephbertgimmel/alephbertgimmel-small": { "id": "bar-ilan/alephbertgimmel/alephbertgimmel-small", "name": "AlephBERTGimmel-small", "maker": "bar-ilan", "family": "bar-ilan/alephbertgimmel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-03-23", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT masked language model. config.json: 4 layers, 8 heads, hidden 512, intermediate 2048, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AlephBERTGimmel-small is a 4-layer Hebrew BERT on the Dicta Hub, with the same 128K vocabulary as AlephBERTGimmel. Collected 2026-10-15.", "what_it_is_for": "The card cites the AlephBERTGimmel paper. It does not separately describe a task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/berel/berel": { "id": "bar-ilan/berel/berel", "name": "BEREL", "maker": "bar-ilan", "family": "bar-ilan/berel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2022-08-03", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base configuration stated in the paper, vocab 128000. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BEREL is a rabbinic-Hebrew BERT from Dicta and Bar-Ilan. The Hub card still hosts version 1.0 and points to BEREL 3.0. Collected 2026-10-15.", "what_it_is_for": "The paper says it is a pretrained model for rabbinic Hebrew, which differs from modern Hebrew in lexicon, morphology, syntax, and spelling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/berel/berel-3-0": { "id": "bar-ilan/berel/berel-3-0", "name": "BEREL 3.0", "maker": "bar-ilan", "family": "bar-ilan/berel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-06-26", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BEREL 3.0 is the current rabbinic-Hebrew BERT on the Dicta Hub. The card calls it improved over BEREL 1.0. Collected 2026-10-15.", "what_it_is_for": "Masked language modeling for rabbinic Hebrew. The card does not add a task list beyond the original BEREL citation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/berel/berel-seg": { "id": "bar-ilan/berel/berel-seg", "name": "BEREL-seg", "maker": "bar-ilan", "family": "bar-ilan/berel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2026-05-08", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT with custom code. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512. The Hub tag says it is a finetune of BEREL 3.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BEREL-seg is a Dicta segmentation model, tagged as a finetune of BEREL 3.0, and used to prepare text for Pre-BEREL. Collected 2026-10-15.", "what_it_is_for": "Prefix or morpheme segmentation of rabbinic Hebrew so Pre-BEREL can see segmented tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/berel/pre-berel": { "id": "bar-ilan/berel/pre-berel", "name": "Pre-BEREL", "maker": "bar-ilan", "family": "bar-ilan/berel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2026-05-08", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pre-BEREL is a public Dicta BERT for rabbinic Hebrew that the card says was trained on pre-segmented text. The card was still marked tbd on 2026-10-15.", "what_it_is_for": "Masked language modeling over morphologically segmented rabbinic Hebrew. The card says segmentation is a step toward morphology-aware tokenization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/cdlm/cdlm": { "id": "bar-ilan/cdlm/cdlm", "name": "CDLM", "maker": "bar-ilan", "family": "bar-ilan/cdlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 4098, "max_output_tokens": null, "parameters": "", "architecture": "Longformer. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 50267, max_position_embeddings 4098.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CDLM is a cross-document Longformer from the Bar-Ilan NLP lab, with Allen Institute coauthors. Apache-2.0 weights on biu-nlp. Collected 2026-10-15.", "what_it_is_for": "Pretrained cross-document language modeling for English multi-document tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/coresearch/coresearch-reader": { "id": "bar-ilan/coresearch/coresearch-reader", "name": "CoReSearch reader", "maker": "bar-ilan", "family": "bar-ilan/coresearch", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "cc-by-3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "No root config.json. Files are language_model.bin, a prediction head, and a RoBERTa tokenizer. The repo name says RoBERTa.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CoReSearch reader is a RoBERTa reading model from the Bar-Ilan NLP lab, with an empty card. Collected 2026-10-15.", "what_it_is_for": "Read retrieved passages. The card does not describe the output.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/coresearch/coresearch-retriever": { "id": "bar-ilan/coresearch/coresearch-retriever", "name": "CoReSearch retriever", "maker": "bar-ilan", "family": "bar-ilan/coresearch", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "cc-by-3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "No root config.json. Files are query and passage encoders with language_model.bin and a prediction head. The repo name says SpanBERT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CoReSearch retriever is a SpanBERT dual-encoder release from the Bar-Ilan NLP lab. The card has no prose. Collected 2026-10-15.", "what_it_is_for": "Retrieve passages for the CoReSearch reader. The pairing is inferred from the two repo names, not from a written card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert": { "id": "bar-ilan/dictabert/dictabert", "name": "DictaBERT", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2023-08-29", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Base masked-language model for modern Hebrew. The 2023 paper also releases segmentation, morphology, and question-answering heads as separate repos.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-char": { "id": "bar-ilan/dictabert/dictabert-char", "name": "DictaBERT-char", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-12-03", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 1024, max_position_embeddings 2048. Character vocabulary.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-char is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Character-level BERT-base for Hebrew, pretrained with masked language modeling. The card ties it to a 2025 W-NUT paper on restoring spaces in scraped Hebrew.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-char-spacefix": { "id": "bar-ilan/dictabert/dictabert-char-spacefix", "name": "DictaBERT-char-spacefix", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2025-01-14", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 1024, max_position_embeddings 2048. Character vocabulary.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-char-spacefix is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Token classifier on DictaBERT-char that restores missing spaces. The card treats entity LABEL_1 as a space to insert.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-heq": { "id": "bar-ilan/dictabert/dictabert-heq", "name": "DictaBERT-HeQ", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2023-10-10", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-HeQ is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Fine-tuned extractive question-answering model on HeQ. The card sample uses the question-answering pipeline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-joint": { "id": "bar-ilan/dictabert/dictabert-joint", "name": "DictaBERT-joint", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-01-10", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-joint is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Multi-task model. The card lists prefix segmentation, morphological disambiguation, lemmatization, dependency trees, and NER.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-large": { "id": "bar-ilan/dictabert/dictabert-large", "name": "DictaBERT-large", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2023-12-25", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 24 layers, 16 heads, hidden 1024, intermediate 4096, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-large is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "BERT-large masked-language model for modern Hebrew.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-large-char": { "id": "bar-ilan/dictabert/dictabert-large-char", "name": "DictaBERT-large-char", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2025-03-22", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 24 layers, 16 heads, hidden 1024, intermediate 4096, vocab 1024, max_position_embeddings 2048. Character vocabulary.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-large-char is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Character-level BERT-large masked-language model for Hebrew.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-large-char-menaked": { "id": "bar-ilan/dictabert/dictabert-large-char-menaked", "name": "DictaBERT-large-char-menaked", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2025-03-23", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 24 layers, 16 heads, hidden 1024, intermediate 4096, vocab 1024, max_position_embeddings 2048. Character vocabulary.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-large-char-menaked is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Vocalizer. The card says predict() adds nikud and, by default, removes matres lectionis. A flag keeps them.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-large-heq": { "id": "bar-ilan/dictabert/dictabert-large-heq", "name": "DictaBERT-large-HeQ", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2023-12-25", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 24 layers, 16 heads, hidden 1024, intermediate 4096, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-large-HeQ is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "BERT-large question-answering model for Hebrew.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-large-ner": { "id": "bar-ilan/dictabert/dictabert-large-ner", "name": "DictaBERT-large-NER", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2023-12-29", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 24 layers, 16 heads, hidden 1024, intermediate 4096, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-large-NER is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "BERT-large token-classification NER model for Hebrew.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-large-parse": { "id": "bar-ilan/dictabert/dictabert-large-parse", "name": "DictaBERT-large-parse", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-03-05", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 24 layers, 16 heads, hidden 1024, intermediate 4096, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-large-parse is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Large parser from the MRL parsing paper. The paper says it sets a new state of the art for Hebrew dependency parsing and POS.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-lex": { "id": "bar-ilan/dictabert/dictabert-lex", "name": "DictaBERT-lex", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-01-08", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-lex is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Lemmatization model. The card says the lemma of a verb is the third-person past singular.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-morph": { "id": "bar-ilan/dictabert/dictabert-morph", "name": "DictaBERT-morph", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2023-08-29", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-morph is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Fine-tuned head for Hebrew morphological tagging.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-ner": { "id": "bar-ilan/dictabert/dictabert-ner", "name": "DictaBERT-NER", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2023-12-26", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-NER is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Token-classification NER model. The card sample uses the ner pipeline with aggregation_strategy simple.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-parse": { "id": "bar-ilan/dictabert/dictabert-parse", "name": "DictaBERT-parse", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-02-15", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-parse is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Parser released in the MRL parsing paper as the base-size general-purpose model. The card lists the same five tasks as DictaBERT-joint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-seg": { "id": "bar-ilan/dictabert/dictabert-seg", "name": "DictaBERT-seg", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2023-08-29", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-seg is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Fine-tuned head for Hebrew prefix segmentation. The paper says one Hub call returns the segmentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-sentiment": { "id": "bar-ilan/dictabert/dictabert-sentiment", "name": "DictaBERT-sentiment", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-03-28", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-sentiment is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Sentiment classifier. The card names the HebArabNlpProject/HebrewSentiment dataset and the sentiment-analysis pipeline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-splinter": { "id": "bar-ilan/dictabert/dictabert-splinter", "name": "DictaBERT-splinter", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-12-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-splinter is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Hebrew BERT trained for splinter tokenization of nonconcatenative morphology. The card says to load the tokenizer with trust_remote_code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-syntax": { "id": "bar-ilan/dictabert/dictabert-syntax", "name": "DictaBERT-syntax", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-01-09", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-syntax is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Dependency syntax model. Custom code. The card sample calls a predict-style output.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-tiny": { "id": "bar-ilan/dictabert/dictabert-tiny", "name": "DictaBERT-tiny", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-03-04", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 4 layers, 12 heads, hidden 312, intermediate 1200, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-tiny is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Smaller DictaBERT masked-language model. The parsing paper points at this repo as the tiny foundation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-tiny-joint": { "id": "bar-ilan/dictabert/dictabert-tiny-joint", "name": "DictaBERT-tiny-joint", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-01-10", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 4 layers, 12 heads, hidden 312, intermediate 1200, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-tiny-joint is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Tiny multi-task model with the same five-task list as DictaBERT-joint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictabert/dictabert-tiny-parse": { "id": "bar-ilan/dictabert/dictabert-tiny-parse", "name": "DictaBERT-tiny-parse", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-02-15", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 4 layers, 12 heads, hidden 312, intermediate 1200, vocab 128000, max_position_embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaBERT-tiny-parse is a named Hebrew model released on the Dicta Hub by Dicta and Bar-Ilan authors. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Tiny parser from the MRL parsing paper, for lower memory.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictalm/dictalm": { "id": "bar-ilan/dictalm/dictalm", "name": "DictaLM", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2023-09-25", "context_length": null, "max_output_tokens": null, "parameters": "7000000000", "architecture": "The paper says 7B parameters. No config.json could be read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaLM is the 2023 Dicta generative model named in the DictaLM paper, with Bar-Ilan coauthors. The linked Hub page was not public on 2026-10-15. Collected that day.", "what_it_is_for": "The 2023 paper presents a 7B modern-Hebrew generative model, trained on 7.5 billion tokens with no synthetic data, under CC BY-SA 4.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictalm/dictalm-2-0": { "id": "bar-ilan/dictalm/dictalm-2-0", "name": "DictaLM 2.0", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-04-10", "context_length": 32768, "max_output_tokens": null, "parameters": "7000000000", "architecture": "Mistral. config.json: 32 layers, 32 heads, hidden 4096, intermediate 14336, vocab 33152, max_position_embeddings 32768. The card says it starts from Mistral-7B-v0.1 and adds 1000 Hebrew tokens. It gives compression of 2.76 tokens per word against 5.78 before the added tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaLM 2.0 is a public 7B Hebrew-English base model from Dicta, continued from Mistral-7B-v0.1. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says it is a pretrained generative model specialized for Hebrew text, for further fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictalm/dictalm-2-0-instruct": { "id": "bar-ilan/dictalm/dictalm-2-0-instruct", "name": "DictaLM 2.0 Instruct", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-04-14", "context_length": 32768, "max_output_tokens": null, "parameters": "7000000000", "architecture": "Mistral. config.json: 32 layers, 32 heads, hidden 4096, intermediate 14336, vocab 33152, max_position_embeddings 32768. Instruct fine-tune of DictaLM 2.0. The card says it follows the Zephyr-7B-beta recipe with an extended Hebrew instruct set.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaLM 2.0 Instruct is the public chat fine-tune of DictaLM 2.0. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Hebrew and English instruction following. The card says it was tuned on conversation datasets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictalm/dictalm-3-0-1-7b": { "id": "bar-ilan/dictalm/dictalm-3-0-1-7b", "name": "DictaLM 3.0 1.7B", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-05-31", "context_length": 32768, "max_output_tokens": null, "parameters": "1700000000", "architecture": "Qwen3. config.json: 28 layers, 16 heads, hidden 2048, intermediate 6144, vocab 151936. max_position_embeddings 32768. Card says BF16, initialized from Qwen3-1.7B-Base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaLM 3.0 1.7B is part of Dicta-LM 3.0, a public Hebrew-English collection from Dicta, with Bar-Ilan coauthors, released with the Israeli national NLP program. Collected 2026-10-15.", "what_it_is_for": "1.7B base model for edge hardware. The card says it is a base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictalm/dictalm-3-0-1-7b-instruct": { "id": "bar-ilan/dictalm/dictalm-3-0-1-7b-instruct", "name": "DictaLM 3.0 1.7B Instruct", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-12-01", "context_length": 62080, "max_output_tokens": null, "parameters": "1700000000", "architecture": "Qwen3. config.json: 28 layers, 16 heads, hidden 2048, intermediate 6144, vocab 151936. max_position_embeddings 62080. Instruct chat model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaLM 3.0 1.7B Instruct is part of Dicta-LM 3.0, a public Hebrew-English collection from Dicta, with Bar-Ilan coauthors, released with the Israeli national NLP program. Collected 2026-10-15.", "what_it_is_for": "1.7B instruct model with tool calling. The card shows a chat template.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictalm/dictalm-3-0-1-7b-thinking": { "id": "bar-ilan/dictalm/dictalm-3-0-1-7b-thinking", "name": "DictaLM 3.0 1.7B Thinking", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-12-01", "context_length": 62080, "max_output_tokens": null, "parameters": "1700000000", "architecture": "Qwen3. config.json: 28 layers, 16 heads, hidden 2048, intermediate 6144, vocab 151936. max_position_embeddings 62080. Thinking chat model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaLM 3.0 1.7B Thinking is part of Dicta-LM 3.0, a public Hebrew-English collection from Dicta, with Bar-Ilan coauthors, released with the Israeli national NLP program. Collected 2026-10-15.", "what_it_is_for": "1.7B reasoning chat model with tool calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictalm/dictalm-3-0-12b": { "id": "bar-ilan/dictalm/dictalm-3-0-12b", "name": "DictaLM 3.0 12B", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "nvidia-open-model-license", "release_date": "2025-11-17", "context_length": 131072, "max_output_tokens": null, "parameters": "12000000000", "architecture": "Nemotron-H. config.json: 62 layers, 40 heads, hidden 5120, intermediate 20480, vocab 131072, max_position_embeddings 131072. Card says a hybrid state-space model, BF16, initialized from NVIDIA-Nemotron-Nano-12B-v2-Base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaLM 3.0 12B is part of Dicta-LM 3.0, a public Hebrew-English collection from Dicta, with Bar-Ilan coauthors, released with the Israeli national NLP program. Collected 2026-10-15.", "what_it_is_for": "12B base hybrid model. The card says unlimited use, but the license name is the NVIDIA Open Model License, not Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictalm/dictalm-3-0-12b-instruct": { "id": "bar-ilan/dictalm/dictalm-3-0-12b-instruct", "name": "DictaLM 3.0 12B Instruct", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "nvidia-open-model-license", "release_date": "2025-12-01", "context_length": 131072, "max_output_tokens": null, "parameters": "12000000000", "architecture": "Nemotron-H. config.json: 62 layers, 40 heads, hidden 5120, intermediate 20480, vocab 131072, max_position_embeddings 131072. Card says a hybrid state-space model, BF16, initialized from NVIDIA-Nemotron-Nano-12B-v2-Base. Instruct chat model with tool calling. trust_remote_code is in the card sample.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaLM 3.0 12B Instruct is part of Dicta-LM 3.0, a public Hebrew-English collection from Dicta, with Bar-Ilan coauthors, released with the Israeli national NLP program. Collected 2026-10-15.", "what_it_is_for": "12B instruct model. The release page prints Hebrew chat scores.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictalm/dictalm-3-0-24b": { "id": "bar-ilan/dictalm/dictalm-3-0-24b", "name": "DictaLM 3.0 24B", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-23", "context_length": 65280, "max_output_tokens": null, "parameters": "24000000000", "architecture": "Mistral. config.json: 40 layers, 32 heads, hidden 5120, intermediate 32768, vocab 131072, max_position_embeddings 65280. Card says BF16 and initialization from Mistral-Small-3.1-24B-Base-2503.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaLM 3.0 24B is part of Dicta-LM 3.0, a public Hebrew-English collection from Dicta, with Bar-Ilan coauthors, released with the Israeli national NLP program. Collected 2026-10-15.", "what_it_is_for": "24B base model. The card says it is not a chat model. The blog calls 24B the flagship.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictalm/dictalm-3-0-24b-thinking": { "id": "bar-ilan/dictalm/dictalm-3-0-24b-thinking", "name": "DictaLM 3.0 24B Thinking", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-12-01", "context_length": 65280, "max_output_tokens": null, "parameters": "24000000000", "architecture": "Mistral. config.json: 40 layers, 32 heads, hidden 5120, intermediate 32768, vocab 131072, max_position_embeddings 65280. Card says BF16 and initialization from Mistral-Small-3.1-24B-Base-2503. Thinking chat model. The report says thinking variants use a reasoning section and GRPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaLM 3.0 24B Thinking is part of Dicta-LM 3.0, a public Hebrew-English collection from Dicta, with Bar-Ilan coauthors, released with the Israeli national NLP program. Collected 2026-10-15.", "what_it_is_for": "Reasoning chat model with tool calling. The release page prints Hebrew chat scores for this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictalm/dictalm-instruct": { "id": "bar-ilan/dictalm/dictalm-instruct", "name": "DictaLM-Instruct", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2023-09-25", "context_length": null, "max_output_tokens": null, "parameters": "7000000000", "architecture": "The paper says 7B parameters. No config.json could be read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaLM-Instruct is the 2023 Dicta generative model named in the DictaLM paper, with Bar-Ilan coauthors. The linked Hub page was not public on 2026-10-15. Collected that day.", "what_it_is_for": "Instruct-tuned DictaLM. The paper says the fine-tune uses Hebrew question-answering data and a translated MPT instruct set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/dictalm/dictalm-rab": { "id": "bar-ilan/dictalm/dictalm-rab", "name": "DictaLM-Rab", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2023-09-25", "context_length": null, "max_output_tokens": null, "parameters": "7000000000", "architecture": "The paper says 7B parameters. No config.json could be read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DictaLM-Rab is the 2023 Dicta generative model named in the DictaLM paper, with Bar-Ilan coauthors. The linked Hub page was not public on 2026-10-15. Collected that day.", "what_it_is_for": "Foundation model for rabbinic and historical Hebrew, announced beside DictaLM. The repo id says 7b.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/f-coref/f-coref": { "id": "bar-ilan/f-coref/f-coref", "name": "F-Coref", "maker": "bar-ilan", "family": "bar-ilan/f-coref", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa. config.json: 6 layers, 12 heads, hidden 768, intermediate 3072, vocab 50265, max_position_embeddings 514. The card says it is distilled from LingMess.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "F-Coref is a distilled English coreference model from the Bar-Ilan NLP lab. The card reports 78.5 average F1 on OntoNotes. MIT license. Collected 2026-10-15.", "what_it_is_for": "Faster coreference than LingMess, with a smaller RoBERTa and a batching method the card calls leftover.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/heq/mt5-xl-heq": { "id": "bar-ilan/heq/mt5-xl-heq", "name": "mT5-XL HeQ", "maker": "bar-ilan", "family": "bar-ilan/heq", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2023-08-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "mT5. config.json: 24 layers, 32 heads, hidden 2048, vocab 250112. max_position_embeddings was absent. The card says it is Google's mT5 fine-tuned on HeQ.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mT5-XL HeQ is Dicta's generative Hebrew QA model, a fine-tune of Google mT5-XL on HeQ. Public on the Dicta Hub. Collected 2026-10-15.", "what_it_is_for": "Answer a Hebrew question conditioned on a context passage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/lingmess/lingmess": { "id": "bar-ilan/lingmess/lingmess", "name": "LingMess", "maker": "bar-ilan", "family": "bar-ilan/lingmess", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": 4098, "max_output_tokens": null, "parameters": "", "architecture": "Longformer. config.json: 24 layers, 16 heads, hidden 1024, intermediate 4096, vocab 50265, max_position_embeddings 4098.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LingMess is an English coreference model from the Bar-Ilan NLP lab. The card reports 81.4 average F1 on OntoNotes. MIT license. Collected 2026-10-15.", "what_it_is_for": "Coreference resolution with separate scorers for linguistically grouped mention pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/msbert/msbert": { "id": "bar-ilan/msbert/msbert", "name": "MsBERT", "maker": "bar-ilan", "family": "bar-ilan/msbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-06-30", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512. The card calls it Manuscript BERT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MsBERT is a Dicta BERT for Hebrew manuscripts, aimed at reconstructing missing words. Public under CC BY 4.0. Collected 2026-10-15.", "what_it_is_for": "The card says it predicts missing words in fragmentary manuscript transcriptions and helps separate quoted passages from exegetical text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/neodictabert/neodictabert": { "id": "bar-ilan/neodictabert/neodictabert", "name": "NeoDictaBERT", "maker": "bar-ilan", "family": "bar-ilan/neodictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2025-09-07", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "NeoBERT-style encoder. config.json: model_type neobert, 28 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000. max_position_embeddings was absent.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NeoDictaBERT is a Hebrew NeoBERT-style encoder from Dicta, with Bar-Ilan coauthors. Public weights. Collected 2026-10-15.", "what_it_is_for": "Hebrew-only NeoBERT-style masked language model. The card says context 4096, about 3200 words, trained 235B tokens at context 1024 for 5 epochs and another 50B tokens at context 4096. The card recommends the bilingual model instead.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/neodictabert/neodictabert-bilingual": { "id": "bar-ilan/neodictabert/neodictabert-bilingual", "name": "NeoDictaBERT-bilingual", "maker": "bar-ilan", "family": "bar-ilan/neodictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2025-10-14", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "NeoBERT-style encoder. config.json: model_type neobert, 28 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000. max_position_embeddings was absent.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NeoDictaBERT-bilingual is a Hebrew NeoBERT-style encoder from Dicta, with Bar-Ilan coauthors. Public weights. Collected 2026-10-15.", "what_it_is_for": "Hebrew-English NeoBERT-style masked language model. The card says context 4096, about 2700 Hebrew words, trained 612B tokens at context 1024 and another 122B at context 4096.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/neodictabert/neodictabert-bilingual-embed": { "id": "bar-ilan/neodictabert/neodictabert-bilingual-embed", "name": "NeoDictaBERT-bilingual-embed", "maker": "bar-ilan", "family": "bar-ilan/neodictabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2026-02-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "NeoBERT-style encoder. config.json: model_type neobert, 28 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000. max_position_embeddings was absent.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NeoDictaBERT-bilingual-embed is a Hebrew NeoBERT-style encoder from Dicta, with Bar-Ilan coauthors. Public weights. Collected 2026-10-15.", "what_it_is_for": "Sentence-transformer finetune of NeoDictaBERT-bilingual. The card says it maps text to 768-dimensional vectors. It says the model placed 10th on the private phase of the Hebrew Semantic Retrieval National Challenge. No score is printed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/otobert/otobert": { "id": "bar-ilan/otobert/otobert", "name": "OtoBERT", "maker": "bar-ilan", "family": "bar-ilan/otobert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-09-28", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 128000, max_position_embeddings 512. The card says the tokenizer glues an object-pronoun suffix onto the verb as one unit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OtoBERT is a Dicta Hebrew BERT whose tokenizer treats object-pronoun suffixes as single units. Public under CC BY 4.0. Collected 2026-10-15.", "what_it_is_for": "The card says it identifies suffixed verbal forms in modern Hebrew literature.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/qamden/qamden": { "id": "bar-ilan/qamden/qamden", "name": "QAmden", "maker": "bar-ilan", "family": "bar-ilan/qamden", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LED. config.json: 12 layers, hidden 1024, vocab 50266. max_position_embeddings was absent. num_attention_heads was absent from the fields that were parsed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QAmden is the Bar-Ilan NLP lab's multi-document model from the Peek Across paper. Apache-2.0 weights. Collected 2026-10-15.", "what_it_is_for": "Multi-document modeling by cross-document question answering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/qamden/qamden-multinews": { "id": "bar-ilan/qamden/qamden-multinews", "name": "QAmden Multi-News", "maker": "bar-ilan", "family": "bar-ilan/qamden", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LED. config.json: 12 layers, hidden 1024, vocab 50266. max_position_embeddings was absent.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QAmden Multi-News is a second public LED checkpoint under the QAmden name, on the Bar-Ilan NLP Hub. Collected 2026-10-15.", "what_it_is_for": "The repo name says Multi-News. The card does not add a task description beyond the Peek Across citation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/qanom/nominalization-candidate-classifier": { "id": "bar-ilan/qanom/nominalization-candidate-classifier", "name": "Nominalization candidate classifier", "maker": "bar-ilan", "family": "bar-ilan/qanom", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocab 28996, max_position_embeddings 512. Token classification. No README.md. Vocab 28996 matches BERT cased, which this config follows. It was not confirmed by a base_model tag.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The nominalization candidate classifier is a BERT token classifier on the Bar-Ilan NLP Hub, with an empty card. Collected 2026-10-15.", "what_it_is_for": "The repo name says it classifies nominalization candidates. No usage text was published on the repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/qanom/qanom-seq2seq-joint": { "id": "bar-ilan/qanom/qanom-seq2seq-joint", "name": "QANom seq2seq joint", "maker": "bar-ilan", "family": "bar-ilan/qanom", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "T5. config.json: 6 layers, 8 heads, hidden 512, intermediate not parsed, vocab 32101, max_position_embeddings 512. No README.md was on the repo. Files include preprocessing_kwargs.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QANom seq2seq joint is a T5 checkpoint published by the Bar-Ilan NLP lab with no model card text. Collected 2026-10-15.", "what_it_is_for": "The repo name says a joint sequence-to-sequence QANom model. The task definition was not on the Hub page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/qasrl/contextualizer-qasrl": { "id": "bar-ilan/qasrl/contextualizer-qasrl", "name": "QA-SRL contextualizer", "maker": "bar-ilan", "family": "bar-ilan/qasrl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "BART. config.json: 6 layers, hidden 768, vocab 50265, max_position_embeddings 1024. num_attention_heads and intermediate_size were absent from the parsed fields.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The QA-SRL contextualizer is a BART checkpoint on the Bar-Ilan NLP Hub. The card has no prose. Collected 2026-10-15.", "what_it_is_for": "The repo name says a contextualizer for QA-SRL. Usage is not documented on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/superpal/superpal": { "id": "bar-ilan/superpal/superpal", "name": "SuperPAL", "maker": "bar-ilan", "family": "bar-ilan/superpal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa. config.json: 24 layers, 16 heads, hidden 1024, intermediate 4096, vocab 50265, max_position_embeddings 514.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SuperPAL is a Bar-Ilan NLP lab RoBERTa classifier for aligning summary propositions with source propositions. Collected 2026-10-15.", "what_it_is_for": "Decide whether a summary proposition aligns with a source proposition.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/text-reduction/distil-flan-t5-large-controlled-text-reduction": { "id": "bar-ilan/text-reduction/distil-flan-t5-large-controlled-text-reduction", "name": "Controlled Text Reduction distilled Flan-T5-large", "maker": "bar-ilan", "family": "bar-ilan/text-reduction", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "T5. config.json: 24 layers, 16 heads, hidden 1024, intermediate not parsed, vocab 32109, max_position_embeddings 512. The card says it is Flan-T5-large fine-tuned on a distilled controlled-reduction set, and is the Distilled Flan-T5 + RL system in the paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the Bar-Ilan NLP lab's distilled Flan-T5-large model for controlled summarization, with RL. The card prints ROUGE, METEOR, and BERTScore. Collected 2026-10-15.", "what_it_is_for": "Summarize a passage so the summary covers all and only the marked highlight spans.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bar-ilan/text-reduction/led-base-controlled-text-reduction": { "id": "bar-ilan/text-reduction/led-base-controlled-text-reduction", "name": "Controlled Text Reduction LED-base", "maker": "bar-ilan", "family": "bar-ilan/text-reduction", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LED. config.json: 6 layers, hidden 768, vocab 50267. max_position_embeddings and num_attention_heads were absent from the parsed fields.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the Bar-Ilan NLP lab's LED-base model for controlled text reduction. Collected 2026-10-15.", "what_it_is_for": "Generate a reduced text under a control signal. The card points at the 2022 paper and the GitHub repo lovodkin93/Controlled_Text_Reduction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bari/alberto/alberto": { "id": "bari/alberto/alberto", "name": "AlBERTo", "maker": "bari", "family": "bari/alberto", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base encoder trained from scratch with Google's BERT code: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, vocab size 128000, max position embeddings 512, type vocab 2, GELU, uncased. Masked language modeling only; next-sentence prediction was not trained. Pretraining max sequence length 128, masked-LM probability 0.15, max predictions per sequence 20, batch size 128, learning rate 2e-5, 1,000,000 steps, about 50 hours on an 8-core TPU v2. SentencePiece vocabulary. The GitHub page states 110 million parameters; that figure is the stock BERT-base count and is not reconciled with this 128,000-word vocabulary.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AlBERTo is an Italian Twitter BERT trained from scratch by Marco Polignano, Pierpaolo Basile, Marco de Gemmis, and Giovanni Semeraro at the University of Bari Aldo Moro, with Valerio Basile at the University of Turin. It is a masked-language encoder for social-media text, not a chat model. Weights are still public. Collected 2026-10-15.", "what_it_is_for": "The makers describe it as the first Italian language-understanding model for Twitter. It is meant to be fine-tuned for classification and prediction on social-media Italian. They say it is a poor fit for question answering because they trained masked language modeling only and skipped next-sentence prediction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basel/bfm/bfm-2009": { "id": "basel/bfm/bfm-2009", "name": "Basel Face Model 2009", "maker": "basel", "family": "basel/bfm", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "University of Basel non-commercial research license; no redistribution", "release_date": "2009-09-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PCA 3D morphable model of neutral faces. The department details page says 53,490 vertices, 160,470 triangles, and 199 principal components. The release includes the mean shape, principal shape components, shape variance, mesh topology, mean texture, principal texture components, and texture variance. A per-vertex mask splits the face into four segments. Attribute directions for gender, height, weight, and age are in 04_attributes.mat. Built from 200 registered scans, 100 male and 100 female. A GRAVIS maintainer later said the 2019 model is in millimeters and differs by a factor of 1000 from this model.", "modalities": { "input": [ "shape-coefficients", "texture-coefficients" ], "output": [ "mesh", "texture" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Basel Face Model 2009 is a PCA morphable model of neutral 3D faces released by the Computer Science department at the University of Basel, principally Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. The release paper is the AVSS 2009 conference, 2-4 September 2009. The department still hosts the download, and the page banner points to the 2019 model as the most recent. This dossier was collected on 2026-10-15.", "what_it_is_for": "The department says the model is a generative 3D morphable face model for image analysis and synthesis, distributed so other researchers can use a high-quality model without repeating the scan-and-registration work. The 2009 paper uses it for pose- and illumination-invariant face recognition by fitting the model and comparing identity coefficients.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basel/bfm/bfm-2017": { "id": "basel/bfm/bfm-2017", "name": "Basel Face Model 2017", "maker": "basel", "family": "basel/bfm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "University of Basel BFM 2017 non-commercial license; no redistribution", "release_date": "2017-09-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-linear PCA morphable model: separate shape, color, and expression subspaces. Expression is a displacement from the neutral shape. The paper says the identity sample is 100 male and 100 female shape examples and the same split for color, with a broader age distribution than the 2009 model, plus 160 expression examples spread across anger, disgust, fear, happy, sad, and surprise. Scans are the controlled Basel set from the 2009 work, re-registered with a Gaussian-process prior. The paper does not state vertex count, triangle count, or the number of retained components. Official tools name model2017-1_bfm_nomouth.h5 and a face12 crop.", "modalities": { "input": [ "shape-coefficients", "texture-coefficients", "expression-coefficients" ], "output": [ "mesh", "texture" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Basel Face Model 2017 is the expression-capable morphable face model from the Gravis group in the Department of Mathematics and Computer Science at the University of Basel. Thomas Gerig, Andreas Morel-Forster, Clemens Blumer, Bernhard Egger, Marcel L\u00fcthi, Sandro Sch\u00f6nborn, and Thomas Vetter announced it with the arXiv preprint submitted on 25 September 2017. The proceedings version is FG 2018, pages 75-82. The download page was still up on 2026-10-15 and points to the 2019 model as the most recent, while current GRAVIS tools still name 2017 files. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says the model is for building and fitting 3D morphable faces, including analysis-by-synthesis of a single 2D image. Compared with the 2009 model it uses a wider age range and adds a facial-expression subspace. The open pipeline registers faces, builds a model, and fits Multi-PIE and LFW images. The group also uses the model to synthesize controlled face images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basel/bfm/bfm-2019": { "id": "basel/bfm/bfm-2019", "name": "Basel Face Model 2019", "maker": "basel", "family": "basel/bfm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "University of Basel BFM 2019 non-commercial license; no redistribution", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PCA morphable face model distributed as the current Basel Face Model. The download page does not publish vertex count or component rank. A GRAVIS maintainer wrote on 14 October 2020 that the 2019 model is in millimeters, uses a different surface parameterization and vertex order than the 2009 model, and is not built from exactly the same 200 identities, though both use 200 identities for the face space. Attribute directions are not published for this version. The official viewer treats 2019 as having an expression part that can be removed. Cited paper on the download page is still Gerig et al. 2018, which describes BFM-2017.", "modalities": { "input": [ "shape-coefficients", "texture-coefficients", "expression-coefficients" ], "output": [ "mesh", "texture" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Basel Face Model 2019 is the current morphable face model from the Graphics and Vision group at the University of Basel. The department model index and the older download pages point here as the most recent Basel Face Model. The download page was live on 2026-10-15. It does not give a release day, and it still asks users to cite the 2017/2018 Gerig et al. paper rather than a separate 2019 paper. This dossier was collected on 2026-10-15.", "what_it_is_for": "The group distributes it as the generative 3D face model for research analysis and synthesis. The official viewer is written for the 2019 model and can also open the 2017 and 2009 files. The viewer can sample shape, color, and expression, clear the expression, and write a PLY mesh or a PNG image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/ahotts/ahotts": { "id": "basque/ahotts/ahotts", "name": "AhoTTS", "maker": "basque", "family": "basque/ahotts", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "VITS voices (Kim et al., 2021) for Basque, Galician, Catalan, and Spanish, released by Aholab and HiTZ. Synthesis code is https://github.com/hitz-zentroa/aHoTTS. Each voice is a version.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AhoTTS is the Aholab and HiTZ (UPV/EHU) VITS speech-synthesis collection for Basque, Spanish, Catalan, and Galician. Collected 2026-10-15. Status is active. Speaker checkpoints are versions of one product. The Antton card license is Apache-2.0.", "what_it_is_for": "AhoTTS is the Aholab and HiTZ (UPV/EHU) VITS speech-synthesis collection for Basque, Spanish, Catalan, and Galician. Collected 2026-10-15. Status is active. Speaker checkpoints are versions of one product. The Antton card license is Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/bernat/bernat-base": { "id": "basque/bernat/bernat-base", "name": "BERnaT base", "maker": "basque", "family": "basque/bernat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-14", "context_length": null, "max_output_tokens": null, "parameters": "124497493", "architecture": "RoBERTa-style Basque encoder. Paper sizes: medium 6 layers, hidden 512, about 51M; base 12/768, about 124M; large 24/1024, about 355M. This folder is the BERnaT base class. Main repo is the combined standard-plus-diverse pretrain. Standard and Diverse repos are versions.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERnaT base is the BERnaT base Basque encoder from HiTZ and IXA at UPV/EHU. Collected 2026-10-15. Status is active. The combined checkpoint is the main repo. Standard and Diverse pretrains of the same size are versions. Apache-2.0.", "what_it_is_for": "BERnaT base is the BERnaT base Basque encoder from HiTZ and IXA at UPV/EHU. Collected 2026-10-15. Status is active. The combined checkpoint is the main repo. Standard and Diverse pretrains of the same size are versions. Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/bernat/bernat-large": { "id": "basque/bernat/bernat-large", "name": "BERnaT large", "maker": "basque", "family": "basque/bernat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-14", "context_length": null, "max_output_tokens": null, "parameters": "355145557", "architecture": "RoBERTa-style Basque encoder. Paper sizes: medium 6 layers, hidden 512, about 51M; base 12/768, about 124M; large 24/1024, about 355M. This folder is the BERnaT large class. Main repo is the combined standard-plus-diverse pretrain. Standard and Diverse repos are versions.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERnaT large is the BERnaT large Basque encoder from HiTZ and IXA at UPV/EHU. Collected 2026-10-15. Status is active. The combined checkpoint is the main repo. Standard and Diverse pretrains of the same size are versions. Apache-2.0.", "what_it_is_for": "BERnaT large is the BERnaT large Basque encoder from HiTZ and IXA at UPV/EHU. Collected 2026-10-15. Status is active. The combined checkpoint is the main repo. Standard and Diverse pretrains of the same size are versions. Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/bernat/bernat-medium": { "id": "basque/bernat/bernat-medium", "name": "BERnaT medium", "maker": "basque", "family": "basque/bernat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-14", "context_length": null, "max_output_tokens": null, "parameters": "51400021", "architecture": "RoBERTa-style Basque encoder. Paper sizes: medium 6 layers, hidden 512, about 51M; base 12/768, about 124M; large 24/1024, about 355M. This folder is the BERnaT medium class. Main repo is the combined standard-plus-diverse pretrain. Standard and Diverse repos are versions.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERnaT medium is the BERnaT medium Basque encoder from HiTZ and IXA at UPV/EHU. Collected 2026-10-15. Status is active. The combined checkpoint is the main repo. Standard and Diverse pretrains of the same size are versions. Apache-2.0.", "what_it_is_for": "BERnaT medium is the BERnaT medium Basque encoder from HiTZ and IXA at UPV/EHU. Collected 2026-10-15. Status is active. The combined checkpoint is the main repo. Standard and Diverse pretrains of the same size are versions. Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/berteus/berteus": { "id": "basque/berteus/berteus", "name": "BERTeus", "maker": "basque", "family": "basque/berteus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "124517376", "architecture": "BERT-base cased, monolingual Basque. Card: 224.6 million tokens, of which 35 million are Basque Wikipedia and the rest crawled news.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERTeus is the Basque BERT from the IXA group at UPV/EHU, with Elhuyar co-authors on the 2020 paper. Collected 2026-10-15. Status is active. Weights are the base cased checkpoint on ixa-ehu. The card reports new Basque results against mBERT on four tasks.", "what_it_is_for": "BERTeus is the Basque BERT from the IXA group at UPV/EHU, with Elhuyar co-authors on the 2020 paper. Collected 2026-10-15. Status is active. Weights are the base cased checkpoint on ixa-ehu. The card reports new Basque results against mBERT on four tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/cap-punct/cap-punct": { "id": "basque/cap-punct/cap-punct", "name": "HiTZ capitalization and punctuation", "maker": "basque", "family": "basque/cap-punct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-15", "context_length": null, "max_output_tokens": null, "parameters": "76939521", "architecture": "Marian NMT model trained from scratch to restore capitalization and punctuation. Basque card: 9,784,905 sentences, a subset of the mt-hitz-eu-es data, trained on one TITAN RTX. Hub safetensors total for cap-punct-eu is 76,939,521. The org page lists cap-punct-es at 76.9M as well.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HiTZ capitalization and punctuation models are Marian restorers from Aholab and HiTZ at UPV/EHU. Collected 2026-10-15. Status is active. The Basque model is the documented checkpoint. Spanish is a version. Apache-2.0. Basque WER after restoration is 5.99 on FLORES-101 and 5.75 on a Common Voice sample.", "what_it_is_for": "HiTZ capitalization and punctuation models are Marian restorers from Aholab and HiTZ at UPV/EHU. Collected 2026-10-15. Status is active. The Basque model is the documented checkpoint. Spanish is a version. Apache-2.0. Basque WER after restoration is 5.99 on FLORES-101 and 5.75 on a Common Voice sample.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/conformer/bbs-s2tc": { "id": "basque/conformer/bbs-s2tc", "name": "BBS-S2TC Conformer Transducer", "maker": "basque", "family": "basque/conformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-15", "context_length": null, "max_output_tokens": null, "parameters": "119M", "architecture": "NeMo Conformer-Transducer large, about 119M parameters. Fine-tuned from the Basque stt_eu_conformer_transducer_large on 1,462 hours of Spanish and Basque speech. Lowercase Spanish alphabet with spaces, per the card.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BBS-S2TC is the bilingual Basque-Spanish Conformer Transducer from Aholab and HiTZ for the speech-to-text challenge. Collected 2026-10-15. Status is active. The card says about 119 million parameters, 1,462 hours, and Apache-2.0.", "what_it_is_for": "BBS-S2TC is the bilingual Basque-Spanish Conformer Transducer from Aholab and HiTZ for the speech-to-text challenge. Collected 2026-10-15. Status is active. The card says about 119 million parameters, 1,462 hours, and Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/conformer/stt-eseu-conformer": { "id": "basque/conformer/stt-eseu-conformer", "name": "Spanish-Basque Conformer Transducer", "maker": "basque", "family": "basque/conformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-29", "context_length": null, "max_output_tokens": null, "parameters": "119M", "architecture": "NeMo Conformer-Transducer large, about 119M parameters. Fine-tuned from BBS-S2TC on 1,366.18 hours of Basque, Spanish, and code-switched speech. Output is lowercase Spanish plus Basque letters with spaces.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Spanish-Basque Conformer Transducer is Aholab and HiTZ's code-switching recognizer. Collected 2026-10-15. Status is active. The card says about 119 million parameters, 1,366.18 hours, and Apache-2.0.", "what_it_is_for": "The Spanish-Basque Conformer Transducer is Aholab and HiTZ's code-switching recognizer. Collected 2026-10-15. Status is active. The card says about 119 million parameters, 1,366.18 hours, and Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/conformer/stt-eu-conformer-ctc": { "id": "basque/conformer/stt-eu-conformer-ctc", "name": "Basque Conformer CTC", "maker": "basque", "family": "basque/conformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "NeMo Conformer-CTC large for Basque. Hub id HiTZ/stt_eu_conformer_ctc_large. The README was not transcribed, so architecture detail beyond the name and the API card fields is not filled in.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Basque Conformer CTC is the CTC speech recognizer published by HiTZ next to the Basque transducer. Collected 2026-10-15. Status is active. The Hub card data lists Apache-2.0, language eu, and library NeMo. The README itself was not read, so scores are left empty.", "what_it_is_for": "Basque Conformer CTC is the CTC speech recognizer published by HiTZ next to the Basque transducer. Collected 2026-10-15. Status is active. The Hub card data lists Apache-2.0, language eu, and library NeMo. The README itself was not read, so scores are left empty.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/conformer/stt-eu-conformer-transducer": { "id": "basque/conformer/stt-eu-conformer-transducer", "name": "Basque Conformer Transducer", "maker": "basque", "family": "basque/conformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-11", "context_length": null, "max_output_tokens": null, "parameters": "119M", "architecture": "NeMo Conformer-Transducer large, about 119M parameters. v2 card: fine-tuned from NVIDIA stt_es_conformer_transducer_large on 771.73 hours of Basque speech. Outputs lowercase Basque with spaces.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Basque Conformer Transducer is Aholab and HiTZ's NeMo speech recognizer for Basque. Collected 2026-10-15. Status is active for v2. The card says about 119 million parameters and Apache-2.0. It was fine-tuned from NVIDIA's Spanish Conformer-Transducer large.", "what_it_is_for": "Basque Conformer Transducer is Aholab and HiTZ's NeMo speech recognizer for Basque. Collected 2026-10-15. Status is active for v2. The card says about 119 million parameters and Apache-2.0. It was fine-tuned from NVIDIA's Spanish Conformer-Transducer large.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/eriberta/eriberta": { "id": "basque/eriberta/eriberta", "name": "EriBERTa", "maker": "basque", "family": "basque/eriberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-06-11", "context_length": 512, "max_output_tokens": null, "parameters": "135260162", "architecture": "Bilingual clinical encoder. Card table: about 125M parameters, 64k vocabulary, sequence length 512, 125k steps, 4.5B tokens, peak LR 2.683e-4. Hub safetensors total is 135,260,162, which does not match the card's about 125M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EriBERTa is a bilingual English-Spanish clinical encoder from HiTZ researchers at UPV/EHU. Collected 2026-10-15. Status is active. The public checkpoint is EriBERTa-base. Apache-2.0. The card's sequence length is 512.", "what_it_is_for": "EriBERTa is a bilingual English-Spanish clinical encoder from HiTZ researchers at UPV/EHU. Collected 2026-10-15. Status is active. The public checkpoint is EriBERTa-base. Apache-2.0. The card's sequence length is 512.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/gollie/gollie-13b": { "id": "basque/gollie/gollie-13b", "name": "GoLLIE 13B", "maker": "basque", "family": "basque/gollie", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-09-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Guideline-following information extraction model fine-tuned from Code Llama 2. The 7B card says the merged model uses the Llama 2 license and the LoRA adapters use Apache-2.0. Custom code and flash attention are required for the published results. Card environmental row: 1xA100, 79.5 hours, 2.80 kg CO2 eq.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GoLLIE 13B is a HiTZ and IXA (UPV/EHU) information-extraction model that follows annotation guidelines written as code. Collected 2026-10-15. Status is active. It is fine-tuned from Code Llama 2. The card's supervised average F1 is 73.9 and the zero-shot average F1 is 56.0.", "what_it_is_for": "GoLLIE 13B is a HiTZ and IXA (UPV/EHU) information-extraction model that follows annotation guidelines written as code. Collected 2026-10-15. Status is active. It is fine-tuned from Code Llama 2. The card's supervised average F1 is 73.9 and the zero-shot average F1 is 56.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/gollie/gollie-34b": { "id": "basque/gollie/gollie-34b", "name": "GoLLIE 34B", "maker": "basque", "family": "basque/gollie", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-09-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Guideline-following information extraction model fine-tuned from Code Llama 2. The 7B card says the merged model uses the Llama 2 license and the LoRA adapters use Apache-2.0. Custom code and flash attention are required for the published results. Card environmental row: 2xA100, 94.6 hours, 6.67 kg CO2 eq.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GoLLIE 34B is a HiTZ and IXA (UPV/EHU) information-extraction model that follows annotation guidelines written as code. Collected 2026-10-15. Status is active. It is fine-tuned from Code Llama 2. The card's supervised average F1 is 75.0 and the zero-shot average F1 is 57.2.", "what_it_is_for": "GoLLIE 34B is a HiTZ and IXA (UPV/EHU) information-extraction model that follows annotation guidelines written as code. Collected 2026-10-15. Status is active. It is fine-tuned from Code Llama 2. The card's supervised average F1 is 75.0 and the zero-shot average F1 is 57.2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/gollie/gollie-7b": { "id": "basque/gollie/gollie-7b", "name": "GoLLIE 7B", "maker": "basque", "family": "basque/gollie", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-09-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Guideline-following information extraction model fine-tuned from Code Llama 2. The 7B card says the merged model uses the Llama 2 license and the LoRA adapters use Apache-2.0. Custom code and flash attention are required for the published results. Card environmental row: 1xA100, 44.5 hours, 1.57 kg CO2 eq.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GoLLIE 7B is a HiTZ and IXA (UPV/EHU) information-extraction model that follows annotation guidelines written as code. Collected 2026-10-15. Status is active. It is fine-tuned from Code Llama 2. The card's supervised average F1 is 73.0 and the zero-shot average F1 is 55.3.", "what_it_is_for": "GoLLIE 7B is a HiTZ and IXA (UPV/EHU) information-extraction model that follows annotation guidelines written as code. Collected 2026-10-15. Status is active. It is fine-tuned from Code Llama 2. The card's supervised average F1 is 73.0 and the zero-shot average F1 is 55.3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/gpt2-eus/gpt2-eus-euscrawl": { "id": "basque/gpt2-eus/gpt2-eus-euscrawl", "name": "GPT-2 Eus EusCrawl", "maker": "basque", "family": "basque/gpt2-eus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc", "release_date": "2023-02-20", "context_length": 1024, "max_output_tokens": null, "parameters": "124M", "architecture": "GPT-2 small trained from the GPT-2 objective on Basque EusCrawl. Card says 124M parameters and sequences of 1024 tokens, vocabulary 50,304. Hub safetensors total is 137,058,816.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-2 Eus EusCrawl is a Basque GPT-2 small trained on EusCrawl and published in the HiTZ org by Julen Etxaniz of the Latxa group. Collected 2026-10-15. Status is active. The card says 124M parameters and a 1024-token sequence.", "what_it_is_for": "GPT-2 Eus EusCrawl is a Basque GPT-2 small trained on EusCrawl and published in the HiTZ org by Julen Etxaniz of the Latxa group. Collected 2026-10-15. Status is active. The card says 124M parameters and a 1024-token sequence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/guidex/llama-3-1-guidex-8b": { "id": "basque/guidex/llama-3-1-guidex-8b", "name": "Llama 3.1 GuideX 8B", "maker": "basque", "family": "basque/guidex", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Fine-tune of meta-llama/Llama-3.1-8B for zero-shot information extraction with Python dataclass guidelines. Hub safetensors total 8,030,261,248. Trained on synthetic GuideX data and then gold IE sets, per the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.1 GuideX 8B is a zero-shot information-extraction model released by HiTZ and IXA at UPV/EHU with a TUM co-author. Collected 2026-10-15. Status is active. Apache-2.0. It follows guidelines written as Python dataclasses, in the same style as GoLLIE.", "what_it_is_for": "Llama 3.1 GuideX 8B is a zero-shot information-extraction model released by HiTZ and IXA at UPV/EHU with a TUM co-author. Collected 2026-10-15. Status is active. Apache-2.0. It follows guidelines written as Python dataclasses, in the same style as GoLLIE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/ixambert/ixambert": { "id": "basque/ixambert/ixambert", "name": "IXAmBERT", "maker": "basque", "family": "basque/ixambert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multilingual BERT-style model for English, Spanish, and Basque. Card: English, Spanish, and Basque Wikipedias plus Basque news.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IXAmBERT is the IXA group (UPV/EHU) multilingual BERT for English, Spanish, and Basque. Collected 2026-10-15. Status is active. The card says it beat mBERT when transferring English conversational QA into Basque.", "what_it_is_for": "IXAmBERT is the IXA group (UPV/EHU) multilingual BERT for English, Spanish, and Basque. Collected 2026-10-15. Status is active. The card says it beat mBERT when transferring English conversational QA into Basque.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/jaunbert/jaunbert": { "id": "basque/jaunbert/jaunbert", "name": "JaunBERT", "maker": "basque", "family": "basque/jaunbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-22", "context_length": null, "max_output_tokens": null, "parameters": "307884672", "architecture": "ModernBERT-style Basque encoder. Hub base_model BSC-LT/MrBERT. Further trained on latxa-corpus-v2. Hub safetensors total 307,884,672. Revisions continue on legal, press, or scientific subcorpora.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JaunBERT is a Basque encoder family from HiTZ and IXA at UPV/EHU, in a ModernBERT style and started from BSC-LT/MrBERT. Collected 2026-10-15. Status is active. Apache-2.0. Domain revisions are versions of the same repo.", "what_it_is_for": "JaunBERT is a Basque encoder family from HiTZ and IXA at UPV/EHU, in a ModernBERT style and started from BSC-LT/MrBERT. Collected 2026-10-15. Status is active. Apache-2.0. Domain revisions are versions of the same repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/lang-adapt/eu-qwen3-14b": { "id": "basque/lang-adapt/eu-qwen3-14b", "name": "Basque Qwen3 14B Base", "maker": "basque", "family": "basque/lang-adapt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hub card data: continued pretrain, base_model Qwen/Qwen3-14B-Base, language eu, Apache-2.0. The 8B Basque card describes the shared method. This 14B README was not fully read, and the safetensors total was empty.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Basque Qwen3 14B Base is the 14B Basque continued pretrain published by HiTZ next to the 8B model. Collected 2026-10-15. Status is active. Hub card data lists Apache-2.0 and parent Qwen3-14B-Base. The long method text was read on the 8B card, not this one, so confidence is medium.", "what_it_is_for": "Basque Qwen3 14B Base is the 14B Basque continued pretrain published by HiTZ next to the 8B model. Collected 2026-10-15. Status is active. Hub card data lists Apache-2.0 and parent Qwen3-14B-Base. The long method text was read on the 8B card, not this one, so confidence is medium.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/lang-adapt/eu-qwen3-8b": { "id": "basque/lang-adapt/eu-qwen3-8b", "name": "Basque Qwen3 8B Base", "maker": "basque", "family": "basque/lang-adapt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-18", "context_length": 8196, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Continued pretrain of Qwen/Qwen3-8B-Base on Basque. Card: about 3.5B Basque tokens and 0.3B English FineWeb tokens, sequence length 8196, batch 256 sequences, about 2M tokens per step, peak LR 1e-5, cosine decay, 10 percent warmup. 32 Leonardo nodes with 4 A100 64GB each. Hub safetensors total 8,190,735,360.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Basque Qwen3 8B Base is a HiTZ continued pretrain of Qwen3-8B-Base on Basque, using the Latxa method. Collected 2026-10-15. Status is active. Apache-2.0. The card's training sequence length is 8,196 tokens. It is a base model for further tuning, not Latxa itself.", "what_it_is_for": "Basque Qwen3 8B Base is a HiTZ continued pretrain of Qwen3-8B-Base on Basque, using the Latxa method. Collected 2026-10-15. Status is active. Apache-2.0. The card's training sequence length is 8,196 tokens. It is a base model for further tuning, not Latxa itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/lang-adapt/gl-llama-3-1-8b": { "id": "basque/lang-adapt/gl-llama-3-1-8b", "name": "Galician Llama 3.1 8B", "maker": "basque", "family": "basque/lang-adapt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Hub card data: base_model meta-llama/Llama-3.1-8B, language gl, license llama3.1, safetensors total 8,030,261,248. The Basque Qwen3 8B card says the Latxa method was extended to other languages with matched token budgets. This Galician README was not fully read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Galician Llama 3.1 8B is a HiTZ language-adapted base model, not a Latxa checkpoint. Collected 2026-10-15. Status is active. Hub card data gives the Llama 3.1 license and 8,030,261,248 parameters. Confidence is medium because the README narrative was not fully read.", "what_it_is_for": "Galician Llama 3.1 8B is a HiTZ language-adapted base model, not a Latxa checkpoint. Collected 2026-10-15. Status is active. Hub card data gives the Llama 3.1 license and 8,030,261,248 parameters. Confidence is medium because the README narrative was not fully read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/latxa/latxa-13b": { "id": "basque/latxa/latxa-13b", "name": "Latxa 13B", "maker": "basque", "family": "basque/latxa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-01-16", "context_length": 4096, "max_output_tokens": null, "parameters": "13B", "architecture": "Llama 2 architecture, continued pretraining with GPT-NeoX. Card: 10k steps, sequence length 4096, effective batch about 2M tokens, about 20B tokens seen, peak learning rate 1e-4. Corpus on the v1.2 card: 4.17B tokens plus 500K English Pile documents. Parent meta-llama/Llama-2-13b.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HiTZ and the IXA group at the University of the Basque Country (UPV/EHU) released Latxa 13B as a continued pretrain of meta-llama/Llama-2-13b on Latxa Corpus v1.1. Collected 2026-10-15. The current Hub weights are v1.2 (created 2024-06-11). The score table printed on that card is labeled Latxa v1.1, not a separate v1.2 run. Status is active for the v1.2 weights. The models are base models, not instruction-tuned. Basque is the intended language; the card also lists English.", "what_it_is_for": "HiTZ and the IXA group at the University of the Basque Country (UPV/EHU) released Latxa 13B as a continued pretrain of meta-llama/Llama-2-13b on Latxa Corpus v1.1. Collected 2026-10-15. The current Hub weights are v1.2 (created 2024-06-11). The score table printed on that card is labeled Latxa v1.1, not a separate v1.2 run. Status is active for the v1.2 weights. The models are base models, not instruction-tuned. Basque is the intended language; the card also lists English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/latxa/latxa-70b": { "id": "basque/latxa/latxa-70b", "name": "Latxa 70B", "maker": "basque", "family": "basque/latxa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-01-16", "context_length": 4096, "max_output_tokens": null, "parameters": "70B", "architecture": "Llama 2 architecture, continued pretraining with GPT-NeoX. Card: 10k steps, sequence length 4096, effective batch about 2M tokens, about 20B tokens seen, peak learning rate 1e-4. Corpus on the v1.2 card: 4.17B tokens plus 500K English Pile documents. Parent meta-llama/Llama-2-70b.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HiTZ and the IXA group at the University of the Basque Country (UPV/EHU) released Latxa 70B as a continued pretrain of meta-llama/Llama-2-70b on Latxa Corpus v1.1. Collected 2026-10-15. The current Hub weights are v1.2 (created 2024-06-11). The score table printed on that card is labeled Latxa v1.1, not a separate v1.2 run. Status is active for the v1.2 weights. The models are base models, not instruction-tuned. Basque is the intended language; the card also lists English.", "what_it_is_for": "HiTZ and the IXA group at the University of the Basque Country (UPV/EHU) released Latxa 70B as a continued pretrain of meta-llama/Llama-2-70b on Latxa Corpus v1.1. Collected 2026-10-15. The current Hub weights are v1.2 (created 2024-06-11). The score table printed on that card is labeled Latxa v1.1, not a separate v1.2 run. Status is active for the v1.2 weights. The models are base models, not instruction-tuned. Basque is the intended language; the card also lists English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/latxa/latxa-7b": { "id": "basque/latxa/latxa-7b", "name": "Latxa 7B", "maker": "basque", "family": "basque/latxa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-01-16", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "Llama 2 architecture, continued pretraining with GPT-NeoX. Card: 10k steps, sequence length 4096, effective batch about 2M tokens, about 20B tokens seen, peak learning rate 1e-4. Corpus on the v1.2 card: 4.17B tokens plus 500K English Pile documents. Parent meta-llama/Llama-2-7b.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HiTZ and the IXA group at the University of the Basque Country (UPV/EHU) released Latxa 7B as a continued pretrain of meta-llama/Llama-2-7b on Latxa Corpus v1.1. Collected 2026-10-15. The current Hub weights are v1.2 (created 2024-06-11). The score table printed on that card is labeled Latxa v1.1, not a separate v1.2 run. Status is active for the v1.2 weights. The models are base models, not instruction-tuned. Basque is the intended language; the card also lists English.", "what_it_is_for": "HiTZ and the IXA group at the University of the Basque Country (UPV/EHU) released Latxa 7B as a continued pretrain of meta-llama/Llama-2-7b on Latxa Corpus v1.1. Collected 2026-10-15. The current Hub weights are v1.2 (created 2024-06-11). The score table printed on that card is labeled Latxa v1.1, not a separate v1.2 run. Status is active for the v1.2 weights. The models are base models, not instruction-tuned. Basque is the intended language; the card also lists English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/latxa/latxa-llama-3-1-70b-instruct": { "id": "basque/latxa/latxa-llama-3-1-70b-instruct", "name": "Latxa Llama 3.1 70B Instruct", "maker": "basque", "family": "basque/latxa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2025-03-28", "context_length": null, "max_output_tokens": null, "parameters": "70553706496", "architecture": "Language-adapted from meta-llama/Llama-3.1-70B-Instruct. v2 card says training used Latxa Corpus v2. Hub safetensors total 70,553,706,496 on both the first instruct repo and v2. v2 YAML lists co2_eq_emissions 1900800 and hardware_used 256xA100; the unit is not restated in the excerpt.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HiTZ and IXA at UPV/EHU released Latxa Llama 3.1 70B Instruct, the 70B instruction-following Basque Latxa. Collected 2026-10-15. The current snapshot is v2, created 2026-06-15, on Latxa Corpus v2. Status is active. FP8 copies are versions, not separate models. The card says a public arena ranked Latxa third behind Claude and GPT-4; that sentence is not a numeric score.", "what_it_is_for": "HiTZ and IXA at UPV/EHU released Latxa Llama 3.1 70B Instruct, the 70B instruction-following Basque Latxa. Collected 2026-10-15. The current snapshot is v2, created 2026-06-15, on Latxa Corpus v2. Status is active. FP8 copies are versions, not separate models. The card says a public arena ranked Latxa third behind Claude and GPT-4; that sentence is not a numeric score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/latxa/latxa-llama-3-1-8b": { "id": "basque/latxa/latxa-llama-3-1-8b", "name": "Latxa Llama 3.1 8B", "maker": "basque", "family": "basque/latxa", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2025-05-23", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Continued pretrain of meta-llama/Llama-3.1-8B on Latxa Corpus v1.1 (card: 4.3M documents and 4.2B tokens). Hub safetensors total 8,030,261,248. Card YAML co2_eq_emissions 277520 with CodeCarbon, 128xA100, EU-West; the body converts the same figure for the instruct sibling to 277.52 kg.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HiTZ and IXA at UPV/EHU released Latxa Llama 3.1 8B, a Basque continued pretrain of Llama 3.1 8B. Collected 2026-10-15. The model card still marks it under development, so status is preview. Weights are on the Hub. It is a base model, not the instruct release.", "what_it_is_for": "HiTZ and IXA at UPV/EHU released Latxa Llama 3.1 8B, a Basque continued pretrain of Llama 3.1 8B. Collected 2026-10-15. The model card still marks it under development, so status is preview. Weights are on the Hub. It is a base model, not the instruct release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/latxa/latxa-llama-3-1-8b-instruct": { "id": "basque/latxa/latxa-llama-3-1-8b-instruct", "name": "Latxa Llama 3.1 8B Instruct", "maker": "basque", "family": "basque/latxa", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2025-02-27", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Language-adapted from meta-llama/Llama-3.1-8B-Instruct on Latxa Corpus v1.1. Hub safetensors total 8,030,261,248. Card says instruct models are trained to follow instructions. YAML emissions 277520; body says 277.52 kg CO2 eq, 2,336 GPU hours, CINECA, Italy.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HiTZ and IXA at UPV/EHU released Latxa Llama 3.1 8B Instruct, an instruction-following Basque adaptation of Llama 3.1 8B Instruct. Collected 2026-10-15. The card still says the model is under development, so status is preview, even though the EMNLP 2025 paper is public. The card says a Basque arena ranked Latxa third, behind Claude and GPT-4 and ahead of same-size competitors. That rank is not a numeric score.", "what_it_is_for": "HiTZ and IXA at UPV/EHU released Latxa Llama 3.1 8B Instruct, an instruction-following Basque adaptation of Llama 3.1 8B Instruct. Collected 2026-10-15. The card still says the model is under development, so status is preview, even though the EMNLP 2025 paper is public. The card says a Basque arena ranked Latxa third, behind Claude and GPT-4 and ahead of same-size competitors. That rank is not a numeric score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/latxa-vl/latxa-llama-3-1-vl-8b-instruct": { "id": "basque/latxa-vl/latxa-llama-3-1-vl-8b-instruct", "name": "Latxa Llama 3.1 VL 8B Instruct", "maker": "basque", "family": "basque/latxa-vl", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-03-02", "context_length": null, "max_output_tokens": null, "parameters": "8337967104", "architecture": "Late fusion. Backbone Latxa-Llama-3.1-8B-Instruct. Vision encoder CLIP clip-vit-large-patch14-336. Connector is a single linear layer. Stage 1 trains only the connector on CC3M mixed 80 percent Basque and 20 percent English. Stage 2 uses 173k samples (83 percent multimodal, 17 percent text-only).", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HiTZ and IXA at UPV/EHU released Latxa Llama 3.1 VL 8B Instruct, a late-fusion multimodal model for Basque and English. Collected 2026-10-15. The card marks it deprecated and points to the Latxa-VL collection. Status is deprecated. Hub safetensors total is 8,337,967,104.", "what_it_is_for": "HiTZ and IXA at UPV/EHU released Latxa Llama 3.1 VL 8B Instruct, a late-fusion multimodal model for Basque and English. Collected 2026-10-15. The card marks it deprecated and points to the Latxa-VL collection. Status is deprecated. Hub safetensors total is 8,337,967,104.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/latxa-vl/latxa-qwen3-5-2b": { "id": "basque/latxa-vl/latxa-qwen3-5-2b", "name": "Latxa Qwen3.5 2B", "maker": "basque", "family": "basque/latxa-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-06-24", "context_length": null, "max_output_tokens": null, "parameters": "2213241664", "architecture": "Vision-language adaptation of Qwen/Qwen3.5-2B using the Sainz et al. 2025 method. Card dataset is Latxa Corpus v2. The 4B card recommends parent sampling presets for thinking and non-thinking modes.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Latxa Qwen3.5 2B is a HiTZ and IXA (UPV/EHU) Basque, Galician, and Catalan adaptation of Qwen/Qwen3.5-2B. Collected 2026-10-15. The 4B card that was read does not repeat the under-development disclaimer used on Qwen3-VL, so status is active. Apache-2.0. Ikergaitu and ALIA funding.", "what_it_is_for": "Latxa Qwen3.5 2B is a HiTZ and IXA (UPV/EHU) Basque, Galician, and Catalan adaptation of Qwen/Qwen3.5-2B. Collected 2026-10-15. The 4B card that was read does not repeat the under-development disclaimer used on Qwen3-VL, so status is active. Apache-2.0. Ikergaitu and ALIA funding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/latxa-vl/latxa-qwen3-5-4b": { "id": "basque/latxa-vl/latxa-qwen3-5-4b", "name": "Latxa Qwen3.5 4B", "maker": "basque", "family": "basque/latxa-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-06-24", "context_length": null, "max_output_tokens": null, "parameters": "4539265536", "architecture": "Vision-language adaptation of Qwen/Qwen3.5-4B using the Sainz et al. 2025 method. Card dataset is Latxa Corpus v2. The 4B card recommends parent sampling presets for thinking and non-thinking modes.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Latxa Qwen3.5 4B is a HiTZ and IXA (UPV/EHU) Basque, Galician, and Catalan adaptation of Qwen/Qwen3.5-4B. Collected 2026-10-15. The 4B card that was read does not repeat the under-development disclaimer used on Qwen3-VL, so status is active. Apache-2.0. Ikergaitu and ALIA funding.", "what_it_is_for": "Latxa Qwen3.5 4B is a HiTZ and IXA (UPV/EHU) Basque, Galician, and Catalan adaptation of Qwen/Qwen3.5-4B. Collected 2026-10-15. The 4B card that was read does not repeat the under-development disclaimer used on Qwen3-VL, so status is active. Apache-2.0. Ikergaitu and ALIA funding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/latxa-vl/latxa-qwen3-vl-2b-instruct": { "id": "basque/latxa-vl/latxa-qwen3-vl-2b-instruct", "name": "Latxa Qwen3-VL 2B Instruct", "maker": "basque", "family": "basque/latxa-vl", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-12", "context_length": null, "max_output_tokens": null, "parameters": "2127532032", "architecture": "Vision-language model adapted from Qwen/Qwen3-VL-2B-Instruct by the Sainz et al. 2025 language-adaptation method. Revisions: multi (default, Basque plus Galician and Catalan) and mono_eu.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Latxa Qwen3-VL 2B Instruct is a HiTZ and IXA (UPV/EHU) vision-language adaptation of Qwen/Qwen3-VL-2B-Instruct for Basque, with Galician and Catalan on the multi revision. Collected 2026-10-15. Status is preview. Funded by Ikergaitu and ALIA. Default Hub revision is multi; mono_eu is Basque-only.", "what_it_is_for": "Latxa Qwen3-VL 2B Instruct is a HiTZ and IXA (UPV/EHU) vision-language adaptation of Qwen/Qwen3-VL-2B-Instruct for Basque, with Galician and Catalan on the multi revision. Collected 2026-10-15. Status is preview. Funded by Ikergaitu and ALIA. Default Hub revision is multi; mono_eu is Basque-only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/latxa-vl/latxa-qwen3-vl-32b-instruct": { "id": "basque/latxa-vl/latxa-qwen3-vl-32b-instruct", "name": "Latxa Qwen3-VL 32B Instruct", "maker": "basque", "family": "basque/latxa-vl", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-19", "context_length": null, "max_output_tokens": null, "parameters": "33357390064", "architecture": "Vision-language model adapted from Qwen/Qwen3-VL-32B-Instruct by the Sainz et al. 2025 language-adaptation method. Revisions: multi (default, Basque plus Galician and Catalan) and mono_eu.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Latxa Qwen3-VL 32B Instruct is a HiTZ and IXA (UPV/EHU) vision-language adaptation of Qwen/Qwen3-VL-32B-Instruct for Basque, with Galician and Catalan on the multi revision. Collected 2026-10-15. Status is preview. Funded by Ikergaitu and ALIA. Default Hub revision is multi; mono_eu is Basque-only.", "what_it_is_for": "Latxa Qwen3-VL 32B Instruct is a HiTZ and IXA (UPV/EHU) vision-language adaptation of Qwen/Qwen3-VL-32B-Instruct for Basque, with Galician and Catalan on the multi revision. Collected 2026-10-15. Status is preview. Funded by Ikergaitu and ALIA. Default Hub revision is multi; mono_eu is Basque-only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/latxa-vl/latxa-qwen3-vl-4b-instruct": { "id": "basque/latxa-vl/latxa-qwen3-vl-4b-instruct", "name": "Latxa Qwen3-VL 4B Instruct", "maker": "basque", "family": "basque/latxa-vl", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-12", "context_length": null, "max_output_tokens": null, "parameters": "4437815808", "architecture": "Vision-language model adapted from Qwen/Qwen3-VL-4B-Instruct by the Sainz et al. 2025 language-adaptation method. Revisions: multi (default, Basque plus Galician and Catalan) and mono_eu.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Latxa Qwen3-VL 4B Instruct is a HiTZ and IXA (UPV/EHU) vision-language adaptation of Qwen/Qwen3-VL-4B-Instruct for Basque, with Galician and Catalan on the multi revision. Collected 2026-10-15. Status is preview. Funded by Ikergaitu and ALIA. Default Hub revision is multi; mono_eu is Basque-only.", "what_it_is_for": "Latxa Qwen3-VL 4B Instruct is a HiTZ and IXA (UPV/EHU) vision-language adaptation of Qwen/Qwen3-VL-4B-Instruct for Basque, with Galician and Catalan on the multi revision. Collected 2026-10-15. Status is preview. Funded by Ikergaitu and ALIA. Default Hub revision is multi; mono_eu is Basque-only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/latxa-vl/latxa-qwen3-vl-8b-instruct": { "id": "basque/latxa-vl/latxa-qwen3-vl-8b-instruct", "name": "Latxa Qwen3-VL 8B Instruct", "maker": "basque", "family": "basque/latxa-vl", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-19", "context_length": null, "max_output_tokens": null, "parameters": "8767123696", "architecture": "Vision-language model adapted from Qwen/Qwen3-VL-8B-Instruct by the Sainz et al. 2025 language-adaptation method. Revisions: multi (default, Basque plus Galician and Catalan) and mono_eu.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Latxa Qwen3-VL 8B Instruct is a HiTZ and IXA (UPV/EHU) vision-language adaptation of Qwen/Qwen3-VL-8B-Instruct for Basque, with Galician and Catalan on the multi revision. Collected 2026-10-15. Status is preview. Funded by Ikergaitu and ALIA. Default Hub revision is multi; mono_eu is Basque-only.", "what_it_is_for": "Latxa Qwen3-VL 8B Instruct is a HiTZ and IXA (UPV/EHU) vision-language adaptation of Qwen/Qwen3-VL-8B-Instruct for Basque, with Galician and Catalan on the multi revision. Collected 2026-10-15. Status is preview. Funded by Ikergaitu and ALIA. Default Hub revision is multi; mono_eu is Basque-only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/latxa-vl/llama-latxa-3-1-vl-8b-instruct": { "id": "basque/latxa-vl/llama-latxa-3-1-vl-8b-instruct", "name": "Llama-Latxa 3.1 VL 8B Instruct", "maker": "basque", "family": "basque/latxa-vl", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-03-02", "context_length": null, "max_output_tokens": null, "parameters": "8337967104", "architecture": "Late fusion. Backbone Llama-3.1-8B-Instruct. Vision encoder CLIP clip-vit-large-patch14-336. Connector is a single linear layer. Stage 1 trains only the connector on CC3M mixed 80 percent Basque and 20 percent English. Stage 2 uses 173k samples (83 percent multimodal, 17 percent text-only).", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HiTZ and IXA at UPV/EHU released Llama-Latxa 3.1 VL 8B Instruct, a late-fusion multimodal model for Basque and English. Collected 2026-10-15. The card marks it deprecated and points to the Latxa-VL collection. Status is deprecated. Hub safetensors total is 8,337,967,104.", "what_it_is_for": "HiTZ and IXA at UPV/EHU released Llama-Latxa 3.1 VL 8B Instruct, a late-fusion multimodal model for Basque and English. Collected 2026-10-15. The card marks it deprecated and points to the Latxa-VL collection. Status is deprecated. Hub safetensors total is 8,337,967,104.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/marmoka/marmoka-en": { "id": "basque/marmoka/marmoka-en", "name": "Marmoka-en", "maker": "basque", "family": "basque/marmoka", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-01", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Llama 3.1 decoder, about 8B. Hub safetensors total 8,030,261,248. DAPT from Llama-3.1-8B-Instruct. English medical corpus about 26B words (WikiMed 54.4M, PMC 25.9B, EMEA also listed). 1.4M English Magpie instructions. Axolotl training, MergeKit merge. Card says it matches or slightly improves the general model on a perturbation-based English clinical MCQ evaluation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marmoka-en is the English 8B clinical model from HiTZ at UPV/EHU. It is a domain-adaptive continued pretrain of Llama 3.1 8B Instruct on English medical text mixed with general instructions. The card says four hyperparameter runs, each seeing 6.5B unique words, were merged. Collected 2026-10-15. Status is active. Weights are local. No public price.", "what_it_is_for": "Marmoka-en is the English 8B clinical model from HiTZ at UPV/EHU. It is a domain-adaptive continued pretrain of Llama 3.1 8B Instruct on English medical text mixed with general instructions. The card says four hyperparameter runs, each seeing 6.5B unique words, were merged. Collected 2026-10-15. Status is active. Weights are local. No public price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/marmoka/marmoka-en-it": { "id": "basque/marmoka/marmoka-en-it", "name": "Marmoka-en-it", "maker": "basque", "family": "basque/marmoka", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-01", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Llama 3.1 decoder. Hub safetensors total 8,030,261,248. Merge of the English DAPT model and a medical-instruction model. Both start from Llama-3.1-8B-Instruct. Datasets named: Magpie filtered set and UltraMedical.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marmoka-en-it is the English merged clinical model from HiTZ at UPV/EHU. The card says it merges Marmoka-en with Marmoka-it, an UltraMedical instruction fine-tune, and calls it the strongest English variant under their perturbation-based clinical MCQ check. Collected 2026-10-15. Status is active. Weights are local. No public price.", "what_it_is_for": "Marmoka-en-it is the English merged clinical model from HiTZ at UPV/EHU. The card says it merges Marmoka-en with Marmoka-it, an UltraMedical instruction fine-tune, and calls it the strongest English variant under their perturbation-based clinical MCQ check. Collected 2026-10-15. Status is active. Weights are local. No public price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/marmoka/marmoka-es": { "id": "basque/marmoka/marmoka-es", "name": "Marmoka-es", "maker": "basque", "family": "basque/marmoka", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-01", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Llama 3.1 decoder. Hub safetensors total 8,030,261,248. Spanish medical corpus about 908M words. 310K Spanish Magpie instructions. Three hyperparameter models merged with MergeKit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marmoka-es is the Spanish 8B clinical model from HiTZ at UPV/EHU. It continues Llama 3.1 8B Instruct on Spanish medical text plus general instructions. The card says three runs on the same corpus were merged, and that it beats Llama 3.1 8B Instruct and Llama3.1-Aloe-Beta-8B on Spanish clinical multiple-choice QA. No score figure was in that sentence. Collected 2026-10-15. Status is active. Weights are local. No public price.", "what_it_is_for": "Marmoka-es is the Spanish 8B clinical model from HiTZ at UPV/EHU. It continues Llama 3.1 8B Instruct on Spanish medical text plus general instructions. The card says three runs on the same corpus were merged, and that it beats Llama 3.1 8B Instruct and Llama3.1-Aloe-Beta-8B on Spanish clinical multiple-choice QA. No score figure was in that sentence. Collected 2026-10-15. Status is active. Weights are local. No public price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/marmoka/marmoka-es-it": { "id": "basque/marmoka/marmoka-es-it", "name": "Marmoka-es-it", "maker": "basque", "family": "basque/marmoka", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-01", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Llama 3.1 decoder. Hub safetensors total 8,030,261,248. Merge of Spanish DAPT and medical-instruction weights. Datasets named: Magpie filtered set and UltraMedical.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marmoka-es-it is the Spanish merged clinical model from HiTZ at UPV/EHU. The card says it merges Marmoka-es with a medical-instruction model and that it outperforms Llama 3.1 8B Instruct and Llama3.1-Aloe-Beta-8B on Spanish clinical multiple-choice QA. No numeric score was in that sentence. Collected 2026-10-15. Status is active. Weights are local. No public price.", "what_it_is_for": "Marmoka-es-it is the Spanish merged clinical model from HiTZ at UPV/EHU. The card says it merges Marmoka-es with a medical-instruction model and that it outperforms Llama 3.1 8B Instruct and Llama3.1-Aloe-Beta-8B on Spanish clinical multiple-choice QA. No numeric score was in that sentence. Collected 2026-10-15. Status is active. Weights are local. No public price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/medical-mt5/medical-mt5-large": { "id": "basque/medical-mt5/medical-mt5-large", "name": "Medical mT5 large", "maker": "basque", "family": "basque/medical-mt5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-31", "context_length": 1024, "max_output_tokens": null, "parameters": "738M", "architecture": "Continued pretrain of google/mt5-large on medical text for English, Spanish, French, and Italian. Card table: 738M parameters, sequence length 1024, 4.5B tokens, 1 epoch, Adafactor, LR 0.001, 4xA100, 10.5 hours, 2.9 kg CO2 eq. Hub safetensors total for the base repo is 1,229,581,312, which does not match 738M. The paper also says 770M in one sentence.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Medical mT5 large is a multilingual medical text-to-text model continued from mT5-large by HiTZ at UPV/EHU with C\u00f4te d'Azur, FBK, and Cruces Hospital co-authors. Collected 2026-10-15. Status is active. The card says 738M parameters and sequence length 1024. A multitask fine-tune is a version.", "what_it_is_for": "Medical mT5 large is a multilingual medical text-to-text model continued from mT5-large by HiTZ at UPV/EHU with C\u00f4te d'Azur, FBK, and Cruces Hospital co-authors. Collected 2026-10-15. Status is active. The card says 738M parameters and sequence length 1024. A multitask fine-tune is a version.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/medical-mt5/medical-mt5-xl": { "id": "basque/medical-mt5/medical-mt5-xl", "name": "Medical mT5 XL", "maker": "basque", "family": "basque/medical-mt5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-31", "context_length": 480, "max_output_tokens": null, "parameters": "3B", "architecture": "Continued pretrain of google/mt5-xl. Card table: 3B parameters, sequence length 480, 4.5B tokens, 1 epoch, 4xA100, 20.5 hours, 5.6 kg CO2 eq. Multitask Hub safetensors total is 3,742,660,608. The base XL safetensors total was empty in the API response.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Medical mT5 XL is the larger medical text-to-text model from the same HiTZ-led release as Medical mT5 large. Collected 2026-10-15. Status is active. The card table says 3B parameters and sequence length 480. Apache-2.0.", "what_it_is_for": "Medical mT5 XL is the larger medical text-to-text model from the same HiTZ-led release as Medical mT5 large. Collected 2026-10-15. Status is active. The card table says 3B parameters and sequence length 480. Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/mt-hitz/mt-hitz": { "id": "basque/mt-hitz/mt-hitz", "name": "MT-HiTZ", "maker": "basque", "family": "basque/mt-hitz", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-06-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Marian NMT models trained from scratch by HiTZ and IXA. Each direction is a version. es-eu card: 35,619,691 sentence pairs, of which 12,091,549 are web parallel and 23,528,142 are back-translations of EusCrawl. eu-en card: 18,067,996 sentence pairs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MT-HiTZ is the HiTZ and IXA (UPV/EHU) line of Marian translation models into and out of Basque. Collected 2026-10-15. Status is active. Six direction checkpoints are versions. Apache-2.0. The Spanish-to-Basque card reports average BLEU 14.1 on Flores, TaCON, and NTREX.", "what_it_is_for": "MT-HiTZ is the HiTZ and IXA (UPV/EHU) line of Marian translation models into and out of Basque. Collected 2026-10-15. Status is active. Six direction checkpoints are versions. Apache-2.0. The Spanish-to-Basque card reports average BLEU 14.1 on Flores, TaCON, and NTREX.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/pl-bert/pl-bert-eu": { "id": "basque/pl-bert/pl-bert-eu", "name": "PL-BERT-eu", "maker": "basque", "family": "basque/pl-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-20", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Phoneme-level masked LM for Basque, PL-BERT style, WordPiece over phonemes. Card: vocab 178, hidden 768, 12 heads, intermediate 2048, 12 layers, max position embeddings 512, 4,000,000 steps, fp16. Trained on phonemized Basque Wikipedia.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PL-BERT-eu is a Basque phoneme masked language model from Aholab and HiTZ at UPV/EHU, built for StyleTTS2. Collected 2026-10-15. Status is active. Apache-2.0. The card sets the maximum position embeddings at 512.", "what_it_is_for": "PL-BERT-eu is a Basque phoneme masked language model from Aholab and HiTZ at UPV/EHU, built for StyleTTS2. Collected 2026-10-15. Status is active. Apache-2.0. The card sets the maximum position embeddings at 512.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/pyannote/pyannote-segmentation-rtve": { "id": "basque/pyannote/pyannote-segmentation-rtve", "name": "pyannote segmentation RTVE", "maker": "basque", "family": "basque/pyannote", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-12-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three fine-tunes of pyannote/segmentation-3.0, fused with DOVER-Lap. Each tune watches a different diarization error component. Trained on the RTVE database for IberSPEECH 2024 Albayzin.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "pyannote segmentation RTVE is Aholab and HiTZ's fused fine-tune of pyannote segmentation 3.0 for Spanish TV diarization. Collected 2026-10-15. Status is active. License is CC-BY-NC-4.0. The card reports 14.98 percent DER on the RTVE2024 test set and says that was the best Albayzin 2024 system.", "what_it_is_for": "pyannote segmentation RTVE is Aholab and HiTZ's fused fine-tune of pyannote segmentation 3.0 for Spanish TV diarization. Collected 2026-10-15. Status is active. License is CC-BY-NC-4.0. The card reports 14.98 percent DER on the RTVE2024 test set and says that was the best Albayzin 2024 system.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/roberta-eus/roberta-eus-base": { "id": "basque/roberta-eus/roberta-eus-base", "name": "RoBERTa-eus base", "maker": "basque", "family": "basque/roberta-eus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2022-03-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa base, Basque, cased. Three corpus variants: EusCrawl (12,528k documents, 423M tokens), mC4 Basque, and CC100 Basque. CC100 base Hub safetensors total is 162,951,852. EusCrawl base and mC4 base totals were empty in the API response.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoBERTa-eus base is a set of Basque RoBERTa base models from IXA at UPV/EHU, trained on EusCrawl, mC4, or CC100. Collected 2026-10-15. Status is active. License is CC-BY-NC-4.0. The EusCrawl large model is a separate folder.", "what_it_is_for": "RoBERTa-eus base is a set of Basque RoBERTa base models from IXA at UPV/EHU, trained on EusCrawl, mC4, or CC100. Collected 2026-10-15. Status is active. License is CC-BY-NC-4.0. The EusCrawl large model is a separate folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/roberta-eus/roberta-eus-large": { "id": "basque/roberta-eus/roberta-eus-large", "name": "RoBERTa-eus large", "maker": "basque", "family": "basque/roberta-eus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2022-03-16", "context_length": null, "max_output_tokens": null, "parameters": "355146071", "architecture": "RoBERTa large, Basque, cased, trained on EusCrawl. Hub safetensors total 355,146,071.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoBERTa-eus large is the EusCrawl RoBERTa large model from IXA at UPV/EHU. Collected 2026-10-15. Status is active. License is CC-BY-NC-4.0. The base-card table gives it the highest average of the four Basque RoBERTa runs, 69.0.", "what_it_is_for": "RoBERTa-eus large is the EusCrawl RoBERTa large model from IXA at UPV/EHU. Collected 2026-10-15. Status is active. License is CC-BY-NC-4.0. The base-card table gives it the highest average of the four Basque RoBERTa runs, 69.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/styletts2/styletts2-eu": { "id": "basque/styletts2/styletts2-eu", "name": "StyleTTS2 Basque", "maker": "basque", "family": "basque/styletts2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "StyleTTS2 trained from scratch for Basque. Multispeaker, two speakers, IPA input, 24 kHz, HiFiGAN decoder, WavLM-Base-Plus as the speech LM, PL-BERT-eu as the text encoder. Card: 50 first-stage epochs and 30 second-stage epochs.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StyleTTS2 Basque is a from-scratch multispeaker Basque TTS model from Aholab and HiTZ at UPV/EHU. Collected 2026-10-15. Status is active. Apache-2.0. An emotion checkpoint is a version. Sample rate on the card is 24 kHz.", "what_it_is_for": "StyleTTS2 Basque is a from-scratch multispeaker Basque TTS model from Aholab and HiTZ at UPV/EHU. Collected 2026-10-15. Status is active. Apache-2.0. An emotion checkpoint is a version. Sample rate on the card is 24 kHz.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "basque/whisper/whisper-eu": { "id": "basque/whisper/whisper-eu", "name": "Whisper Basque", "maker": "basque", "family": "basque/whisper", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-24", "context_length": null, "max_output_tokens": null, "parameters": "1543490560", "architecture": "Fine-tunes of OpenAI Whisper for Basque. The large-v3 card is the one that was read: encoder-decoder, base openai/whisper-large-v3, Common Voice 13.0 Basque. Hub safetensors total 1,543,490,560. Smaller sizes are versions and were not read.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Whisper Basque is Aholab and HiTZ's set of Whisper fine-tunes for Basque. Collected 2026-10-15. Status is active. The large-v3 card reports 10.62 percent WER on Common Voice 13.0 Basque test and Apache-2.0. Other Basque sizes are versions.", "what_it_is_for": "Whisper Basque is Aholab and HiTZ's set of Whisper fine-tunes for Basque. Collected 2026-10-15. Status is active. The large-v3 card reports 10.62 percent WER on Common Voice 13.0 Basque test and Apache-2.0. Other Basque sizes are versions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bath/crefnet/crefnet": { "id": "bath/crefnet/crefnet", "name": "CRefNet", "maker": "bath", "family": "bath/crefnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub LICENSE file is the Apache License, Version 2.0. The appendix copyright line is still the unfilled template \"[yyyy] [name of copyright owner]\". A separate licence for the Google Drive .pt files is not stated.", "release_date": "2024-05-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid transformer-convolutional network for intrinsic-image reflectance. The released config configs/crefnet.yaml sets ARCH name crefnet, r_chs 3, s_chs 1, num_swin_groups 8, depth_per_swin_group 4, color_rep rgI, and input_img_size 448. The README says the encoder and decoder follow the VQGAN-style code in taming-transformers, and the transformer layers are SwinV2. The IEEE abstract describes a transformer module that maps image features to reflectance features, a reflectance-reconstruction auxiliary task that shares that decoder, and a rectified gradient filter used at training time with no inference overhead. The public inference example uses configs/crefnet.yaml, min_input_dim 448, and MODEL.checkpoint final_real.pt. Stated stack: Python 3.6 and PyTorch 1.8.2.", "modalities": { "input": [ "image" ], "output": [ "reflectance-map" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CRefNet is a reflectance model from the Department of Computer Science at the University of Bath. IEEE Transactions on Visualization and Computer Graphics lists Jundan Luo, Nanxuan Zhao, and Wenbin Li at Bath; the Bath research portal lists the same article with Christian Richardt (the portal writes Nanxuan Zhao as Cherry Zhao). It estimates a consistent reflectance map for intrinsic image decomposition. Trained weights were still linked from the authors' GitHub on 2026-10-15. The README dates the trained models and evaluation code to 5 May 2024. The journal date of publication is 1 December 2023 (volume 30, issue 9, September 2024, pages 6407\u20136420).", "what_it_is_for": "The IEEE abstract presents CRefNet as a hybrid transformer-convolutional network for consistent reflectance estimation. The hard case it names is the same material looking different under a change of illumination. A transformer module turns image features into reflectance features. A reflectance-reconstruction task shares the decoder. A rectified gradient filter suppresses small prediction changes and is described as having no cost at inference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bath/ovdsgg/ovdsgg": { "id": "bath/ovdsgg/ovdsgg", "name": "OvDSGG", "maker": "bath", "family": "bath/ovdsgg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-08-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end open-vocabulary dynamic scene graph generator. Spatial backbone is Grounding DINO with a Swin-T visual encoder, BERT text encoder, Nq=900 entity queries, and hidden size 256, following OvSGTR. A Triplet Feature Extraction module uses 3 interaction decoder layers. The temporal backbone is OED's Progressively Refined module: 3 temporal decoder layers and a window of 3 reference frames. Predicate heads are linear layers initialized from BERT embeddings. The visual backbone and BERT stay frozen. The last six Grounding DINO cross-modal encoder and decoder layers are fine-tuned. Closed-set training: spatial module 3 epochs, then temporal module 2 epochs. Open-vocabulary temporal training freezes Grounding DINO and the spatial DSGG components, then trains 2 epochs with AdamW at 5e-5 and 1 epoch at 1e-5. SGDET with IoU at least 0.5. Python 3.9. Initialization checkpoints vg-swint-full.pth and vg-ovdr-swint.pth are OvSGTR weights from JosephZ, not Bath releases.", "modalities": { "input": [ "video" ], "output": [ "scene-graph" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OvDSGG is an open-vocabulary dynamic scene graph model led from the University of Bath. John Helsby (Bath) and Yi Yang (Leibniz Universit\u00e4t Hannover) share first authorship. Bodo Rosenhahn is at Hannover. Michael Ying Yang, the corresponding author, is at Bath. It turns video frames into subject-predicate-object triplets, including categories held out of training. arXiv 2608.14835 was posted on 14 August 2026 and revised on 22 August 2026 (v2). The Hugging Face card says it was accepted at the ECCV 2026 Contextus Workshop. Weights were on `jhelsby/OvDSGG` on 2026-10-15.", "what_it_is_for": "The paper introduces OvDSGG as an end-to-end model for open-vocabulary dynamic scene graph generation. Closed-set DSGG methods only name objects and predicates from a fixed list. OvDSGG keeps an open-vocabulary spatial detector, adds a temporal module, and is evaluated on Action Genome with Base categories in training and Novel categories at test time. Downstream uses named in the abstract are video captioning, video question answering, and action analysis.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/crossformer/crossformer": { "id": "berkeley/crossformer/crossformer", "name": "CrossFormer", "maker": "berkeley", "family": "berkeley/crossformer", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-08-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Robot policy checkpoint. The model card does not describe layers or inputs. The citation is Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation.", "modalities": { "input": [], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CrossFormer is a MIT-licensed robot policy released by UC Berkeley RAIL on Hugging Face on 2024-08-21. The paper describes one policy across manipulation, navigation, locomotion, and aviation. Collected 2026-10-15.", "what_it_is_for": "The card itself only points at the code repo. The cited paper is the statement of what the policy is for: cross-embodiment control, not a language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/dextacwam/dextacwam": { "id": "berkeley/dextacwam/dextacwam", "name": "DexTacWAM", "maker": "berkeley", "family": "berkeley/dextacwam", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 for the released DexTacWAM encoder and the Hugging Face collection. The README says the stage-1 encoder is Apache-2.0, while the upstream Genie-Envisioner backbone file is under the LTX-Video Open Weights License, not Apache-2.0.", "release_date": "2026-09-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Visuo-tactile world-action model. A per-finger tactile encoder maps Sharpa Wave fingertip signals into an LTX-Video latent. The published stage-1 artifact is model.pt, stated as 866 MB, step 53000. The world model is a continual adaptation of a pretrained video model. Full world-model parameter count was not stated.", "modalities": { "input": [ "image", "tactile" ], "output": [ "video", "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DexTacWAM is a visuo-tactile world-action model posted by BAIR on 2026-09-29. The paper is dated 2026-09-21, and the tactile-encoder weights were on Hugging Face by 2026-09-28. Collected 2026-10-15.", "what_it_is_for": "The paper says it predicts multi-finger contact as part of a video world model and then acts. The job is contact-rich dexterous manipulation on a two-hand platform, not general chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/gorilla/gorilla-7b-hf": { "id": "berkeley/gorilla/gorilla-7b-hf", "name": "Gorilla 7B Hugging Face", "maker": "berkeley", "family": "berkeley/gorilla", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Card tag apache-2.0. The weights are a delta on LLaMA, and the card says they are a finetune of LLaMA. Recovered weights are still bound by the LLaMA license. The card's Apache tag and the LLaMA base license disagree.", "release_date": "2023-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive transformer. Card: 0-shot finetune of LLaMA, released as a delta that is not usable until it is applied to the base LLaMA weights. Size is the 7B name in the repo id. Layer counts were not on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gorilla 7B Hugging Face is a UC Berkeley Sky Computing Gorilla checkpoint dated 2023-05-27. It is a LLaMA delta trained to emit Hugging Face API calls. Collected 2026-10-15.", "what_it_is_for": "The card says Gorilla writes a semantically and syntactically correct API call from a natural-language request. This checkpoint is the Hugging Face variant. The project also says it targets 1,600+ APIs and ships APIBench.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/gorilla/gorilla-7b-tf": { "id": "berkeley/gorilla/gorilla-7b-tf", "name": "Gorilla 7B TensorFlow Hub", "maker": "berkeley", "family": "berkeley/gorilla", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Card tag apache-2.0. The weights are a delta on LLaMA, and the card says they are a finetune of LLaMA. Recovered weights are still bound by the LLaMA license. The card's Apache tag and the LLaMA base license disagree.", "release_date": "2023-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive transformer. Card: 0-shot finetune of LLaMA, released as a delta that is not usable until it is applied to the base LLaMA weights. Size is the 7B name in the repo id. Layer counts were not on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gorilla 7B TensorFlow Hub is a UC Berkeley Sky Computing Gorilla checkpoint dated 2023-05-27. It is a LLaMA delta trained to emit TensorFlow Hub API calls. Collected 2026-10-15.", "what_it_is_for": "The card says Gorilla writes a semantically and syntactically correct API call from a natural-language request. This checkpoint is the TensorFlow Hub variant. The project also says it targets 1,600+ APIs and ships APIBench.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/gorilla/gorilla-7b-th": { "id": "berkeley/gorilla/gorilla-7b-th", "name": "Gorilla 7B Torch Hub", "maker": "berkeley", "family": "berkeley/gorilla", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Card tag apache-2.0. The weights are a delta on LLaMA, and the card says they are a finetune of LLaMA. Recovered weights are still bound by the LLaMA license. The card's Apache tag and the LLaMA base license disagree.", "release_date": "2023-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive transformer. Card: 0-shot finetune of LLaMA, released as a delta that is not usable until it is applied to the base LLaMA weights. Size is the 7B name in the repo id. Layer counts were not on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gorilla 7B Torch Hub is a UC Berkeley Sky Computing Gorilla checkpoint dated 2023-05-27. It is a LLaMA delta trained to emit Torch Hub API calls. Collected 2026-10-15.", "what_it_is_for": "The card says Gorilla writes a semantically and syntactically correct API call from a natural-language request. This checkpoint is the Torch Hub variant. The project also says it targets 1,600+ APIs and ships APIBench.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/gorilla/gorilla-falcon-7b": { "id": "berkeley/gorilla/gorilla-falcon-7b", "name": "Gorilla Falcon 7B", "maker": "berkeley", "family": "berkeley/gorilla", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0. The card says this finetune can be used commercially without further obligations.", "release_date": "2023-06-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive transformer finetuned from Falcon-7B for Hugging Face API calls. Card date 2023-06-06. Exact layer counts were not on the card. Repo id says 7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gorilla Falcon 7B is UC Berkeley Gorilla's commercially licensed Falcon-7B finetune, card date 2023-06-06. It emits Hugging Face API calls. Collected 2026-10-15.", "what_it_is_for": "The card says this is a 0-shot finetune that writes a Hugging Face API call from a natural-language request, and that the Apache-2.0 weights can be used commercially.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/gorilla/gorilla-mpt-7b": { "id": "berkeley/gorilla/gorilla-mpt-7b", "name": "Gorilla MPT 7B", "maker": "berkeley", "family": "berkeley/gorilla", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0. The card says this finetune can be used commercially without further obligations.", "release_date": "2023-06-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive transformer finetuned from MPT-7B for Hugging Face API calls. Card date 2023-06-06. Exact layer counts were not on the card. Repo id says 7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gorilla MPT 7B is UC Berkeley Gorilla's commercially licensed MPT-7B finetune, card date 2023-06-06. It emits Hugging Face API calls. Collected 2026-10-15.", "what_it_is_for": "The card says this is a 0-shot finetune that writes a Hugging Face API call from a natural-language request, and that the Apache-2.0 weights can be used commercially.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/gorilla/gorilla-openfunctions-v0": { "id": "berkeley/gorilla/gorilla-openfunctions-v0", "name": "Gorilla OpenFunctions v0", "maker": "berkeley", "family": "berkeley/gorilla", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-11-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The v0 card and the later v2 card say v0 returns formatted JSON arguments for one given function and a user intent. Hugging Face tags the repo as llama. The v0 config.json was not opened, so layer counts and context length are not recorded.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gorilla OpenFunctions v0 is UC Berkeley's first Apache-2.0 function-calling chat model in the OpenFunctions line, repo created 2023-11-16. Collected 2026-10-15.", "what_it_is_for": "The project table says v0, given one function and a user intent, returns JSON with the arguments filled in. It does not choose among functions or emit parallel calls. That is what v1 adds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/gorilla/gorilla-openfunctions-v1": { "id": "berkeley/gorilla/gorilla-openfunctions-v1", "name": "Gorilla OpenFunctions v1", "maker": "berkeley", "family": "berkeley/gorilla", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-11-16", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM. Config: 32 layers, hidden 4096, intermediate 11008, 32 heads, 32 key-value heads, vocab 32000, max_position_embeddings 4096, float16. The v2 card says v1 adds parallel calls and a choice among functions.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gorilla OpenFunctions v1 is the second UC Berkeley OpenFunctions checkpoint, repo created 2023-11-16. It is a 4096-token Llama-architecture function caller. Collected 2026-10-15.", "what_it_is_for": "The v2 card says v1 can pick among functions and can emit parallel calls, which v0 could not. It does not claim the Java, JavaScript, or REST coverage that v2 adds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/gorilla/gorilla-openfunctions-v2": { "id": "berkeley/gorilla/gorilla-openfunctions-v2", "name": "Gorilla OpenFunctions v2", "maker": "berkeley", "family": "berkeley/gorilla", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM. Config: 30 layers, hidden 4096, intermediate 11008, 32 heads, vocab 102400, max_position_embeddings 4096, bfloat16. The card says it is built on a DeepSeek coder LLM and states the size as 6.91B. That 6.91B figure is the vendor's rounded size, not a safetensors total read here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gorilla OpenFunctions v2 is UC Berkeley's February 2024 function-calling model, a DeepSeek-coder-based Llama-architecture checkpoint with a 4096-token config. Collected 2026-10-15.", "what_it_is_for": "The card says it chooses among functions, emits multiple and parallel calls, detects irrelevant functions, and supports Python, Java, JavaScript, and REST. Gorilla presents it as the open model on the Berkeley Function Calling Leaderboard.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/koala/koala-13b": { "id": "berkeley/koala/koala-13b", "name": "Koala 13B", "maker": "berkeley", "family": "berkeley/koala", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research and non-commercial. The BAIR post subjects the demo to the LLaMA model license, OpenAI terms for generated data, and ShareGPT privacy terms. Training code is Apache-2.0. Weights are a diff, not a full checkpoint. Hugging Face license tag on young-geng/koala is other.", "release_date": "2023-04-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dialogue finetune of Meta LLaMA 13B. Official weights are diffs (koala_13b_diff_v1 and koala_13b_diff_v2) that must be applied to the base LLaMA 13B checkpoint. Implemented in EasyLM (JAX/Flax). The blog does not state layer counts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Koala 13B is a BAIR dialogue model announced on 2023-04-03. It is a LLaMA-13B finetune released as a weight diff for academic research. Collected 2026-10-15.", "what_it_is_for": "BAIR says Koala is a research chatbot for studying small dialogue models. The primary result they report is Koala-13B, trained on a curated mix of web dialogues and public instruction sets rather than a huge scrape.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/koala/koala-7b": { "id": "berkeley/koala/koala-7b", "name": "Koala 7B", "maker": "berkeley", "family": "berkeley/koala", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Same research terms as Koala 13B: LLaMA license, OpenAI data terms, and ShareGPT terms. The blog's license section is about the demo and the 13B release. The 7B artifact is the diff file koala_7b_diff_v2 in the official repo. Code is Apache-2.0.", "release_date": "2023-04-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Weight diff against LLaMA 7B, file koala_7b_diff_v2 in young-geng/koala. The BAIR post's evaluated model is Koala-13B. This 7B diff is an official file in that repo, not a separate blog announcement.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Koala 7B is the 7B LLaMA diff shipped in BAIR's official Koala weight repo on 2023-04-06. The BAIR post evaluates Koala 13B, not this file. Collected 2026-10-15.", "what_it_is_for": "It is the smaller official Koala diff for researchers who start from LLaMA 7B. The post does not give it a separate task statement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/lwm/lwm-chat": { "id": "berkeley/lwm/lwm-chat", "name": "LWM-Chat", "maker": "berkeley", "family": "berkeley/lwm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language chat models. The GitHub table lists LWM-Chat-32K, LWM-Chat-128K, and LWM-Chat-1M, Jax only. The paper says these are Llama-based 7B models for video and language. Their Jax configs were not opened, so the integer context and license file are not copied from the text checkpoints.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text", "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LWM-Chat is UC Berkeley's vision-language Large World Model line. The published weights found on Hugging Face are Jax chat checkpoints at 32K, 128K, and 1M, created 2024-02-10. Collected 2026-10-15.", "what_it_is_for": "The project says these checkpoints answer questions about long video, including clips longer than an hour, and can generate as well as understand image and video. PyTorch loading is documented for the text models, not these Jax files.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/lwm/lwm-text": { "id": "berkeley/lwm/lwm-text", "name": "LWM-Text", "maker": "berkeley", "family": "berkeley/lwm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-01-27", "context_length": 1048576, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM. The 1M config that was opened has 32 layers, hidden 4096, intermediate 11008, 32 heads, vocab 32000, rope_theta 50000000, max_position_embeddings 1048576, float16. The paper says the family is 7B and initialized from Llama 2. Exact parameter integer was not in the API record because the weights are pytorch .bin shards. This folder is the base, non-chat text line. The opened config is LWM-Text-1M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LWM-Text is UC Berkeley's long-context Llama-2 text model family, with a 1,048,576-token flagship whose config was checked. Weights went up in January 2024. Collected 2026-10-15.", "what_it_is_for": "The paper says the text models read long documents. The chat card says the text run was trained from Llama 2 on long Books3 documents. This folder is the base line, not the chat finetune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/lwm/lwm-text-chat": { "id": "berkeley/lwm/lwm-text-chat", "name": "LWM-Text-Chat", "maker": "berkeley", "family": "berkeley/lwm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-07", "context_length": 1048576, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM. The 1M config that was opened has 32 layers, hidden 4096, intermediate 11008, 32 heads, vocab 32000, rope_theta 50000000, max_position_embeddings 1048576, float16. The paper says the family is 7B and initialized from Llama 2. Exact parameter integer was not in the API record because the weights are pytorch .bin shards. This folder is the chat text line. The opened config is LWM-Text-Chat-1M. The card says it was trained in December 2023 and calls the checkpoint LWM-Text-1M-Chat.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LWM-Text-Chat is UC Berkeley's chat finetune of the long-context LWM text models. The 1M checkpoint config has 1,048,576 positions. The Hugging Face repo was created 2024-02-07. Collected 2026-10-15.", "what_it_is_for": "The card says it is an open chat model for long text. The authors tell users to load the PyTorch files as LlamaForCausalLM. It is the conversational sibling of LWM-Text, not the video model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/octo/octo-base": { "id": "berkeley/octo/octo-base", "name": "Octo Base", "maker": "berkeley", "family": "berkeley/octo", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer diffusion policy. The model card states 93M parameters (ViT-B). Window size 2. Predicts 7-dimensional actions 4 steps ahead. Images go through a small conv encoder and 16x16 patches. Language uses the T5 tokenizer and a frozen T5-Base encoder. Primary image 256x256, wrist image 128x128, language ids length 16. Trained on an Open X-Embodiment mix. The card states the parameter count as 93M, not an exact integer.", "modalities": { "input": [ "image", "text" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Octo Base is the 93M Octo generalist robot policy from UC Berkeley RAIL and coauthors, paper dated 2024-05-20, MIT license. Collected 2026-10-15.", "what_it_is_for": "The card says it is a language- or goal-image-conditioned manipulation policy. Out of the box it reads one or two RGB views and outputs a short chunk of 7-D actions. New robots are meant to be finetuned, including new cameras and action spaces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/octo/octo-small": { "id": "berkeley/octo/octo-small", "name": "Octo Small", "maker": "berkeley", "family": "berkeley/octo", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer diffusion policy. The model card states 27M parameters (ViT-S). Window size 2. Predicts 7-dimensional actions 4 steps ahead. Images go through a small conv encoder and 16x16 patches. Language uses the T5 tokenizer and a frozen T5-Base encoder. Primary image 256x256, wrist image 128x128, language ids length 16. Trained on an Open X-Embodiment mix. The card states the parameter count as 27M, not an exact integer.", "modalities": { "input": [ "image", "text" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Octo Small is the 27M Octo generalist robot policy from UC Berkeley RAIL and coauthors, paper dated 2024-05-20, MIT license. Collected 2026-10-15.", "what_it_is_for": "The card says it is a language- or goal-image-conditioned manipulation policy. Out of the box it reads one or two RGB views and outputs a short chunk of 7-D actions. New robots are meant to be finetuned, including new cameras and action spaces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/pillar-0/pillar-0-abdomen-ct": { "id": "berkeley/pillar-0/pillar-0-abdomen-ct", "name": "Pillar-0 Abdomen CT", "maker": "berkeley", "family": "berkeley/pillar-0", "status": "active", "confidence": "high", "weights": "open-weights", "license": "ECL-2.0 on the Hugging Face tag. The repo is gated and asks for contact information. The paper file is marked CC BY-NC-SA 4.0, which is the paper license, not a second weight license that was re-read from the card.", "release_date": "2025-11-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Atlas vision encoder plus a small projection head, contrastively aligned to a frozen Qwen3-Embedding-8B. The paper says the text encoder is 8B and the vision encoder is 79M in that comparison. Paper: pretrained on 42,990 abdomen-pelvis CTs. Exact safetensors parameter totals were not read because the raw card is gated.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pillar-0 Abdomen CT is the abdomen-pelvis CT Pillar-0 checkpoint from UC Berkeley and UCSF, Hugging Face repo created 2025-11-18, license tag ECL-2.0, gated. Collected 2026-10-15.", "what_it_is_for": "The paper says Pillar-0 is a radiology foundation model for volumetric CT or MRI. This checkpoint is the modality-specific vision encoder aligned to radiology-report text. It is for embedding and finetuning, not for writing reports by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/pillar-0/pillar-0-breast-mri": { "id": "berkeley/pillar-0/pillar-0-breast-mri", "name": "Pillar-0 Breast MRI", "maker": "berkeley", "family": "berkeley/pillar-0", "status": "active", "confidence": "high", "weights": "open-weights", "license": "ECL-2.0 on the Hugging Face tag. The repo is gated and asks for contact information. The paper file is marked CC BY-NC-SA 4.0, which is the paper license, not a second weight license that was re-read from the card.", "release_date": "2025-11-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Atlas vision encoder plus a small projection head, contrastively aligned to a frozen Qwen3-Embedding-8B. The paper says the text encoder is 8B and the vision encoder is 79M in that comparison. Paper: pretrained on 11,543 breast MRIs. Exact safetensors parameter totals were not read because the raw card is gated.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pillar-0 Breast MRI is the breast MRI Pillar-0 checkpoint from UC Berkeley and UCSF, Hugging Face repo created 2025-11-18, license tag ECL-2.0, gated. Collected 2026-10-15.", "what_it_is_for": "The paper says Pillar-0 is a radiology foundation model for volumetric CT or MRI. This checkpoint is the modality-specific vision encoder aligned to radiology-report text. It is for embedding and finetuning, not for writing reports by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/pillar-0/pillar-0-chest-ct": { "id": "berkeley/pillar-0/pillar-0-chest-ct", "name": "Pillar-0 Chest CT", "maker": "berkeley", "family": "berkeley/pillar-0", "status": "active", "confidence": "high", "weights": "open-weights", "license": "ECL-2.0 on the Hugging Face tag. The repo is gated and asks for contact information. The paper file is marked CC BY-NC-SA 4.0, which is the paper license, not a second weight license that was re-read from the card.", "release_date": "2025-11-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Atlas vision encoder plus a small projection head, contrastively aligned to a frozen Qwen3-Embedding-8B. The paper says the text encoder is 8B and the vision encoder is 79M in that comparison. Paper: pretrained on 86,411 chest CTs. Exact safetensors parameter totals were not read because the raw card is gated.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pillar-0 Chest CT is the chest CT Pillar-0 checkpoint from UC Berkeley and UCSF, Hugging Face repo created 2025-11-18, license tag ECL-2.0, gated. Collected 2026-10-15.", "what_it_is_for": "The paper says Pillar-0 is a radiology foundation model for volumetric CT or MRI. This checkpoint is the modality-specific vision encoder aligned to radiology-report text. It is for embedding and finetuning, not for writing reports by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/pillar-0/pillar-0-head-ct": { "id": "berkeley/pillar-0/pillar-0-head-ct", "name": "Pillar-0 Head CT", "maker": "berkeley", "family": "berkeley/pillar-0", "status": "active", "confidence": "high", "weights": "open-weights", "license": "ECL-2.0 on the Hugging Face tag. The repo is gated and asks for contact information. The paper file is marked CC BY-NC-SA 4.0, which is the paper license, not a second weight license that was re-read from the card.", "release_date": "2025-11-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Atlas vision encoder plus a small projection head, contrastively aligned to a frozen Qwen3-Embedding-8B. The paper says the text encoder is 8B and the vision encoder is 79M in that comparison. Paper: pretrained on 14,348 head CTs. Exact safetensors parameter totals were not read because the raw card is gated.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pillar-0 Head CT is the head CT Pillar-0 checkpoint from UC Berkeley and UCSF, Hugging Face repo created 2025-11-18, license tag ECL-2.0, gated. Collected 2026-10-15.", "what_it_is_for": "The paper says Pillar-0 is a radiology foundation model for volumetric CT or MRI. This checkpoint is the modality-specific vision encoder aligned to radiology-report text. It is for embedding and finetuning, not for writing reports by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/starling/starling-lm-7b-alpha": { "id": "berkeley/starling/starling-lm-7b-alpha", "name": "Starling-LM-7B-alpha", "maker": "berkeley", "family": "berkeley/starling", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face tag apache-2.0. The card also says Apache-2.0 on the condition the model is not used to compete with OpenAI, and a later paragraph says the dataset, model, and demo are a non-commercial research preview.", "release_date": "2023-11-25", "context_length": 8192, "max_output_tokens": null, "parameters": "7241748480", "architecture": "MistralForCausalLM, 32 layers, hidden 4096, intermediate 14336, 32 heads, 8 key-value heads, vocab 32002, rope_theta 10000, sliding_window 4096, max_position_embeddings 8192. Safetensors total 7,241,748,480 BF16. Finetuned from OpenChat 3.5 (Mistral-7B-v0.1) with reward model Starling-RM-7B-alpha and advantage-induced policy alignment (APA).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Starling-LM-7B-alpha is a UC Berkeley Berkeley-Nest chat model, an RLAIF finetune of OpenChat 3.5, released November 2023. Weights remain public. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says it is a helpfulness and harmlessness chat model trained with RLAIF on the Nectar rankings. Berkeley-Nest also pointed users at an LMSYS Chatbot Arena demo. Whether that demo is still served was not rechecked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/starling/starling-lm-7b-beta": { "id": "berkeley/starling/starling-lm-7b-beta", "name": "Starling-LM-7B-beta", "maker": "berkeley", "family": "berkeley/starling", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face tag apache-2.0. The card also says Apache-2.0 on the condition the model is not used to compete with OpenAI.", "release_date": "2024-03-19", "context_length": 8192, "max_output_tokens": null, "parameters": "7241748480", "architecture": "MistralForCausalLM. Config name path is openchat/openchat-3.5-0106. 32 layers, hidden 4096, intermediate 14336, 32 heads, 8 key-value heads, vocab 32002, sliding_window 4096, max_position_embeddings 8192. Safetensors total 7,241,748,480 BF16. Card: trained from OpenChat-3.5-0106 with Starling-RM-34B and PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Starling-LM-7B-beta is the follow-up chat model from the Berkeley-Nest Starling authors, released 2024-03-19 on the Nexusflow Hugging Face org. It is an RLAIF finetune of OpenChat-3.5-0106. Collected 2026-10-15.", "what_it_is_for": "The card says it is a conversational model trained with PPO against Starling-RM-34B on Nectar. The same authors describe it as the beta of Starling-LM-7B-alpha, which stays in the berkeley-nest org.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/starling/starling-rm-34b": { "id": "berkeley/starling/starling-rm-34b", "name": "Starling-RM-34B", "maker": "berkeley", "family": "berkeley/starling", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Hugging Face tag apache-2.0. The public card excerpt says Apache-2.0 under a non-compete condition with OpenAI. The repo is gated.", "release_date": "2024-02-22", "context_length": null, "max_output_tokens": null, "parameters": "33930172416", "architecture": "Hugging Face config architecture LLMForSequenceRegression, model_type llama. Safetensors total 33,930,172,416 BF16 across 15 shards. The public card excerpt says the base is Yi-34B-Chat, with the last layer replaced by a scalar linear head, trained on Nectar.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Starling-RM-34B is the larger Starling reward model from the Berkeley-Nest authors, uploaded to Nexusflow on 2024-02-22. The file download is gated. Collected 2026-10-15. Confidence is medium because the prose card was gated on direct fetch.", "what_it_is_for": "The card excerpt says it scores a prompt and response, with higher scores for replies that are more helpful and less harmful. Starling-LM-7B-beta names this checkpoint as its reward model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/starling/starling-rm-7b-alpha": { "id": "berkeley/starling/starling-rm-7b-alpha", "name": "Starling-RM-7B-alpha", "maker": "berkeley", "family": "berkeley/starling", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face tag apache-2.0, plus the card conditions: do not compete with OpenAI, and the model is a non-commercial research preview. The base is Llama-2-7B-Chat, so the Llama 2 Community License also applies.", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reward model: Llama-2-7B-Chat with the last layer removed and a bias-free linear head that outputs one scalar. Trained on berkeley-nest/Nectar with a K-wise maximum likelihood estimator. The published inference snippet truncates at 2048 tokens; that is example code, not a stated context limit.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Starling-RM-7B-alpha is UC Berkeley Berkeley-Nest's 7B reward model, released with Starling-LM in November 2023. It scores prompt-response pairs. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "The card says it is the reward model for RLHF and RLAIF. A higher scalar is meant to mark a more helpful and less harmful reply. It was the reward model used to train Starling-LM-7B-alpha.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "berkeley/xt/xt": { "id": "berkeley/xt/xt", "name": "xT", "maker": "berkeley", "family": "berkeley/xt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One Hugging Face repo, bair-climate-initiative/xt, holding many Hiera and SwinV2 top-1 checkpoints plus matching yaml configs. No README was present at raw/main. The BAIR archive lists a 2024-03-21 post titled Modeling Extremely Large Images with xT. That post body was not opened, so this dossier does not claim which file is the canonical xT checkpoint.", "modalities": { "input": [ "image" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "xT is the name of a BAIR large-image model release. The Hugging Face repo bair-climate-initiative/xt, created 2024-03-04, is a bundle of Hiera and Swin checkpoints. Collected 2026-10-15. Confidence is medium because the blog post body was not opened.", "what_it_is_for": "The archive title says the work is about modeling extremely large images. The repo is the weight drop associated with that name. It is not a single checkpoint with one model card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/cage/cage": { "id": "bern/cage/cage", "name": "CAGE", "maker": "bern", "family": "bern/cage", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2025-02-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dataset checkpoints of the CAGE video model. Each repo has model.pth and a VQ file. License tag GPL-3.0. The Hub README does not restate the architecture.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAGE is a controllable video generator from the Computer Vision Group at the University of Bern. The group announced it for AAAI 2025. Authors on the group news page are Aram Davtyan, Sepehr Sameni, Bj\u00f6rn Ommer, and Paolo Favaro. Checkpoints were uploaded to cvg-unibe on 2025-02-21 and were still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The group describes CAGE as visual composition and animation for video generation. Control comes from moving features to future locations, with the conditioning meant to ignore small visual changes. Numeric tables were not transcribed. The arXiv id is 2403.14368. The submission day was not copied. The release date used here is the Hub upload.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/comit/comit-b": { "id": "bern/comit/comit-b", "name": "COMiT-B", "maker": "bern", "family": "bern/comit", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-02-10", "context_length": null, "max_output_tokens": null, "parameters": "257498367", "architecture": "COMiT transformer. Paper: depth 12, hidden size 768, heads 12, MLP ratio 4, 256 message tokens of dimension 6, vocabulary 64,000, FSQ levels [8, 8, 8, 5, 5, 5]. Trained on ImageNet-1k. Safetensors total 257498367. The paper's parameter column says 174M. Hub size badges round differently again (B 0.3B, L 0.7B, XL 1.0B).", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "COMiT-B is a pretrained image tokenizer from the Computer Vision Group at the University of Bern, with EPFL co-authors on the paper. The weights were uploaded to cvg-unibe/comit-b on 2026-02-10. The paper was submitted on 2026-02-24 and revised on 2026-09-29. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and the model card describe a fixed-length discrete message built by looking at image crops one after another, then a flow-matching decoder that reconstructs the image. The same transformer does encoding and decoding. The released checkpoint is trained on ImageNet-1k.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/comit/comit-l": { "id": "bern/comit/comit-l", "name": "COMiT-L", "maker": "bern", "family": "bern/comit", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-02-10", "context_length": null, "max_output_tokens": null, "parameters": "694502655", "architecture": "COMiT transformer. Paper: depth 24, hidden size 1024, heads 16, MLP ratio 4, 256 message tokens of dimension 6, vocabulary 64,000, FSQ levels [8, 8, 8, 5, 5, 5]. Trained on ImageNet-1k. Safetensors total 694502655. The paper's parameter column says 610M. Hub size badges round differently again (B 0.3B, L 0.7B, XL 1.0B).", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "COMiT-L is a pretrained image tokenizer from the Computer Vision Group at the University of Bern, with EPFL co-authors on the paper. The weights were uploaded to cvg-unibe/comit-l on 2026-02-10. The paper was submitted on 2026-02-24 and revised on 2026-09-29. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and the model card describe a fixed-length discrete message built by looking at image crops one after another, then a flow-matching decoder that reconstructs the image. The same transformer does encoding and decoding. The released checkpoint is trained on ImageNet-1k.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/comit/comit-xl": { "id": "bern/comit/comit-xl", "name": "COMiT-XL", "maker": "bern", "family": "bern/comit", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-02-10", "context_length": null, "max_output_tokens": null, "parameters": "984029439", "architecture": "COMiT transformer. Paper: depth 28, hidden size 1152, heads 16, MLP ratio 4, 256 message tokens of dimension 6, vocabulary 64,000, FSQ levels [8, 8, 8, 5, 5, 5]. Trained on ImageNet-1k. Safetensors total 984029439. The paper's parameter column says 900M. Hub size badges round differently again (B 0.3B, L 0.7B, XL 1.0B).", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "COMiT-XL is a pretrained image tokenizer from the Computer Vision Group at the University of Bern, with EPFL co-authors on the paper. The weights were uploaded to cvg-unibe/comit-xl on 2026-02-10. The paper was submitted on 2026-02-24 and revised on 2026-09-29. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and the model card describe a fixed-length discrete message built by looking at image crops one after another, then a flow-matching decoder that reconstructs the image. The same transformer does encoding and decoding. The released checkpoint is trained on ImageNet-1k.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/h3berta/h3berta": { "id": "bern/h3berta/h3berta", "name": "H3BERTa", "maker": "bern", "family": "bern/h3berta", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-11-02", "context_length": 100, "max_output_tokens": null, "parameters": "85746457", "architecture": "RoBERTa encoder, masked language model. Patterns paper: hidden size 768, intermediate size 3072, 12 heads, 12 layers, about 86 million parameters. Hub safetensors total 85,746,457. Vocabulary 25 tokens. Max sequence length 100 amino acids. Card: pretrained on more than 17 million CDR-H3 sequences. Paper: about 18 million.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "H3BERTa is a CDR-H3 antibody language model from the Institute of Biochemistry and Molecular Medicine at the University of Bern. Chiara Rodella and Thomas Lemmin released the weights on Hugging Face as Chrode/H3BERTa on 2025-11-02. The Patterns paper was published on 2026-05-20. The weights were still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the model embeds CDR-H3 sequences, scores mutations by pseudo-likelihood, and can be fine-tuned, for example to identify broadly neutralizing antibodies. Sequences are plain amino acids without the initial C or the terminal W.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/hgb/hgb-lm": { "id": "bern/hgb/hgb-lm", "name": "HGB Historical Language Model", "maker": "bern", "family": "bern/hgb", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two Flair contextual character language models, forward and backward, files hgb-lm-fw.bin and hgb-lm-bw.bin. Fine-tuned from the modern German Flair models de-forward and de-backward. Parameter count was not published. Training ran on UBELIX at the University of Bern.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The HGB historical language model is a pair of Flair character models for the Historical Land Records of the City of Basel. Ismail Prada Ziegler trained them at Digital Humanities Bern, on the University of Bern cluster UBELIX, for the Economies of Space project, which the card places at the University of Basel and Digital Humanities Bern. The Hub repo was created on 2026-06-29 and was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the models are contextual character language models, forward and backward, fine-tuned on about 120,000 regests written in the 19th and 20th centuries about documents up to 1800. The base models are the modern German Flair embeddings de-forward and de-backward.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/hgb/hgb-ner": { "id": "bern/hgb/hgb-ner", "name": "HGB Nested NER", "maker": "bern", "family": "bern/hgb", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flair sequence tagger for nested historical German. v2 is the current full tagset, 47 classes, file pytorch_model.bin. v1 is the earlier simplified tagset and is still hosted. Parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HGB Nested NER is a Flair tagger for the Historical Land Register of Basel, trained by Ismail Prada Ziegler for Economies of Space at the University of Basel and Digital Humanities Bern. v2, the full tagset, was published on 2026-06-29. v1, from 2024-05-30, is still online and points readers to v2. This dossier was collected on 2026-10-15.", "what_it_is_for": "The cards say the tagger annotates nested document structure in mainly 15th- to 18th-century German. v2 has 47 classes. v1 used a simplified tagset on 661 training documents and 83 development documents, annotated with the BeNASch guidelines. Flat text can be tagged, with a warning that a nested model may be slightly worse than a flat-only model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/koenigsfelden/koenigsfelden-ner": { "id": "bern/koenigsfelden/koenigsfelden-ner", "name": "K\u00f6nigsfelden NER", "maker": "bern", "family": "bern/koenigsfelden", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-11-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flair sequence tagger for medieval and early modern German. Files best-model.pt and pytorch_model.bin. Parameter count was not published. The card calls it a prototype.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K\u00f6nigsfelden NER is a prototype Flair tagger for historical German from Digital Humanities at the University of Bern. Ismail Prada Ziegler released v1 on 2022-11-07. The weights were still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the tagger is for medieval scripts and was trained on the K\u00f6nigsfelden charters, 14th to 17th century Early New High German, 623,000 tokens. The edition is a Zurich and Bern project. The weight card is on the Bern Digital Humanities org.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/kraken/kraken-german-xix": { "id": "bern/kraken/kraken-german-xix", "name": "Kraken German XIX", "maker": "bern", "family": "bern/kraken", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Kraken handwritten-text model shipped as kraken-german-xix-v1.mlmodel (Core ML). The card says these are the best validation checkpoint of the run, not the last epoch. Parameter count was not on the card or the Hub safetensors field.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kraken German XIX v1 is a 19th-century German handwriting recognizer from Digital Humanities at the University of Bern. The Hub repo dh-unibe/kraken-german-xix-v1 was created on 2026-10-11 and was public on 2026-10-15. The card says the file is the best validation checkpoint, not the last epoch. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card describes handwritten text recognition trained with the training-atr-models code. Training sets named on the card include Zurich Regierungsratsprotokolle, a 19th-century Kurrent collection, parliamentary minutes, and the 19th-century federal assembly. The held-out benchmark is Federal Council minutes from 1848 to 1903.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/kraken/kraken-medieval-german": { "id": "bern/kraken/kraken-medieval-german", "name": "Kraken Medieval German", "maker": "bern", "family": "bern/kraken", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-08-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Kraken model file kraken-medieval-german-v2.mlmodel. Fine-tuned from CATMuS Medieval (DOI 10.5281/zenodo.15030337, Pinche and Cl\u00e9rice 2025). The card says an earlier gateway id, kraken-early_modern_german, was wrong and was corrected on 2026-10-01. Best validation checkpoint, not the last epoch.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kraken Medieval German v2 is a historical-German handwriting model from Digital Humanities at the University of Bern. It was fine-tuned from CATMuS Medieval and published as dh-unibe/kraken-medieval-german-v2 on 2026-08-30. The weights were still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says Kraken segments the page with its own baseline segmenter and then reads lines. Training corpora named on the card include Rats- und Richterb\u00fccher from the 15th and 16th centuries, Bullinger authors, K\u00f6nigsfelden charters after 1500, and AAEB 14th-to-17th-century material.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/loom-cfm/loom-cfm": { "id": "bern/loom-cfm/loom-cfm", "name": "LOOM-CFM", "maker": "bern", "family": "bern/loom-cfm", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conditional flow-matching image generator trained with Looking Out Of Minibatch data-noise coupling. The paper is joint University of Bern and EPFL. Hub repos contain model.pth only. Dataset checkpoints are versions. A CIFAR-10 reflow checkpoint is a second training stage, not a separate product name.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LOOM-CFM is a flow-matching training method and a set of image-generation checkpoints from the Computer Vision Group at the University of Bern and LIONS at EPFL. The ICLR 2025 paper is by Aram Davtyan, Leello Tadesse Dadi, Volkan Cevher, and Paolo Favaro. OpenReview lists the paper as published on 22 January 2025. The Hub checkpoints were uploaded on 2025-02-25 and were still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper introduces Looking Out Of Minibatch conditional flow matching. It keeps minibatch optimal-transport pairings and reuses them across steps so sampling trajectories get straighter. The project page also says it helps distillation and latent high-resolution synthesis. The Hub files are dataset checkpoints, including a CIFAR-10 reflow file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/luther/gpt2-larger-luther": { "id": "bern/luther/gpt2-larger-luther", "name": "GPT-2 larger Luther", "maker": "bern", "family": "bern/luther", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-12-13", "context_length": null, "max_output_tokens": null, "parameters": "137029632", "architecture": "GPT-2 checkpoint fine-tuned from stefan-it/german-gpt2-larger. Trainer run 22_12_13_luther_blocks_larger_fp16_20ep. Safetensors total 137,029,632 parameters. The card says the base is german-gpt2-larger. This dossier does not reconcile that name with the 137,029,632 count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-2 larger Luther is a Digital Humanities Bern language model trained on cleaned writing by Martin Luther. The card says it was used to generate New Year wishes for the Swiss journal reformiert. The Hub repo was created on 2022-12-13 and was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the model tests GPT-2 and was used for New Year wishes in reformiert. Training text is an automatic split of an edited and cleaned selection of Luther's writing, with page numbers and footnotes removed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/luther/luther-xl": { "id": "bern/luther/luther-xl", "name": "Luther XL", "maker": "bern", "family": "bern/luther", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-12-13", "context_length": null, "max_output_tokens": null, "parameters": "1608018048", "architecture": "GPT-2 checkpoint fine-tuned from malteos/gpt2-xl-wechsel-german. Trainer run name 22_12_13_luther_blocks_xl_fp16_5ep. Safetensors total 1,608,018,048 parameters. The card says the dataset is unknown.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Luther XL is a German GPT-2 XL fine-tune on the University of Bern Digital Humanities Hub org. The automatic trainer card says it continues malteos/gpt2-xl-wechsel-german for five epochs. The repo was created on 2022-12-13 and was still public on 2026-10-15. The card does not name the dataset. This dossier was collected on 2026-10-15.", "what_it_is_for": "The trainer card does not state a use. The run name is 22_12_13_luther_blocks_xl_fp16_5ep. A smaller Luther checkpoint in the same org does say it was trained on Luther's writing. This XL card does not say that, so the corpus is not copied across.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/qwen-htr/qwen3-5-4b-german-xix": { "id": "bern/qwen-htr/qwen3-5-4b-german-xix", "name": "Qwen3.5 4B German XIX", "maker": "bern", "family": "bern/qwen-htr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-09-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA on Qwen/Qwen3.5-4B. LoRA adapter, PEFT 0.20.0, r=64, lora_alpha=128, lora_dropout=0.05, bias none, task_type CAUSAL_LM. Target modules q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, and down_proj. Adapter parameter count is not in adapter_config.json. Best validation checkpoint. The card says training used line crops at 262,144 pixels.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen3.5 4B German XIX v2 is a LoRA handwriting adapter from Digital Humanities at the University of Bern on Qwen/Qwen3.5-4B. The Hub repo was created on 2026-09-20 and was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card describes line-level recognition of 19th-century German handwriting on the same datasets as qwen3vl-german-xix. The evaluation table and the prose on that card do not print the same CER.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/qwen-htr/qwen3vl-german-xix": { "id": "bern/qwen-htr/qwen3vl-german-xix", "name": "Qwen3-VL German XIX", "maker": "bern", "family": "bern/qwen-htr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-09-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA on Qwen/Qwen3-VL-4B-Instruct. LoRA adapter, PEFT 0.20.0, r=64, lora_alpha=128, lora_dropout=0.05, bias none, task_type CAUSAL_LM. Target modules q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, and down_proj. Adapter parameter count is not in adapter_config.json. Best validation checkpoint, not the last epoch. Trained on line crops at 262,144 pixels.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen3-VL German XIX v2 is a LoRA handwriting adapter from Digital Humanities at the University of Bern. It sits on Qwen/Qwen3-VL-4B-Instruct and was published as dh-unibe/qwen3vl-german-xix-v2 on 2026-09-18. The weights were still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card describes line-level recognition of 19th-century German handwriting. Named training sets are Zurich Regierungsratsprotokolle, parliamentary service minutes, the 19th-century federal assembly, and a Kurrent XIX collection. The card says the headline CER mixes unseen hands with easier pages from hands the model already saw.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/qwen-htr/qwen3vl-medieval-german": { "id": "bern/qwen-htr/qwen3vl-medieval-german", "name": "Qwen3-VL Medieval German", "maker": "bern", "family": "bern/qwen-htr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-09-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA on Qwen/Qwen3-VL-4B-Instruct. LoRA adapter, PEFT 0.20.0, r=64, lora_alpha=128, lora_dropout=0.05, bias none, task_type CAUSAL_LM. Target modules q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, and down_proj. Adapter parameter count is not in adapter_config.json. Best validation checkpoint. Trained on line crops at 262,144 pixels.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen3-VL Medieval German v3 is a LoRA handwriting adapter from Digital Humanities at the University of Bern on Qwen/Qwen3-VL-4B-Instruct. The Hub repo was created on 2026-09-16 and was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card describes line-level recognition on medieval and early modern German pages. Named sets match the Kraken medieval model: Rats- und Richterb\u00fccher, Bullinger authors, K\u00f6nigsfelden charters after 1500, and AAEB. Only the Rats- und Richterb\u00fccher split holds whole projects out of training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/river/river": { "id": "bern/river/river", "name": "RIVER", "maker": "bern", "family": "bern/river", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0 on three checkpoints; LGPL-3.0 on cvg-unibe/river_clevrer_128", "release_date": "2022-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent flow-matching video predictor with a pretrained VQGAN. Each Hub repo contains model.pth plus a VQ autoencoder file. The CLEVRER repo uses vqvae.pth and the license tag LGPL-3.0. The other three repos use vqvae.ckpt and GPL-3.0.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RIVER is a video-prediction model from the Computer Vision Group at the University of Bern. Aram Davtyan, Sepehr Sameni, and Paolo Favaro named it in an arXiv paper on 2022-11-26, later accepted at ICCV 2023. Dataset checkpoints were uploaded to cvg-unibe on 2023-11-22 and were still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper describes latent flow matching for video prediction. It conditions each integration step on a small random set of past frames, works in a pretrained VQGAN latent space, and starts the flow near the previous noisy frame so fewer steps are needed. The abstract says results are superior or on par with prior work at much lower compute. Those comparison numbers were not copied here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/trocr/trocr-essoins-middle-latin": { "id": "bern/trocr/trocr-essoins-middle-latin", "name": "TrOCR Essoins Middle Latin", "maker": "bern", "family": "bern/trocr", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-10-10", "context_length": null, "max_output_tokens": null, "parameters": "558226432", "architecture": "VisionEncoderDecoderModel fine-tuned from magistermilitum/tridis_HTR v1. Safetensors total 558,226,432 parameters, larger than the microsoft/trocr-base checkpoints in this family.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TrOCR Essoins Middle Latin is a line recognizer for 13th- and 14th-century Latin court hand, released on the University of Bern Digital Humanities Hub org as part of the Flow project. Jonas Widmer, Christopher Kuhlmann, and Melvin Wilde fine-tuned magistermilitum/tridis_HTR. The repo was created on 2024-10-10 and was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the texts are Latin essoins from England, written in Anglicana or court hand, from the Court of Common Pleas and the justices, with some Middle English and Anglo-Norman wording. The dataset tag is digital-history-bielefeld/image-text_anglicana-legal-texts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/trocr/trocr-kurrent": { "id": "bern/trocr/trocr-kurrent", "name": "TrOCR Kurrent 19th century", "maker": "bern", "family": "bern/trocr", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-12-06", "context_length": null, "max_output_tokens": null, "parameters": "333921792", "architecture": "VisionEncoderDecoderModel fine-tuned from microsoft/trocr-base-handwritten. Hugging Face safetensors total is 333,921,792 float32 parameters. The card does not publish layer widths.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TrOCR Kurrent is a handwritten-line recognizer for 19th-century German from Digital Humanities at the University of Bern. Jonas Widmer and Tobias Hodel released it on the dh-unibe Hub org, fine-tuned from microsoft/trocr-base-handwritten. The weights were still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card describes line-level recognition of 19th-century German Kurrent. Training material includes Zurich Regierungsratsprotokolle, Humboldt lecture notes, the Eugen Huber diary, Gottfried Semper hands, and Greifswald Konzilsprotokolle.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/trocr/trocr-kurrent-xvi-xvii": { "id": "bern/trocr/trocr-kurrent-xvi-xvii", "name": "TrOCR Kurrent 16th to 18th century", "maker": "bern", "family": "bern/trocr", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-03-07", "context_length": null, "max_output_tokens": null, "parameters": "333921792", "architecture": "VisionEncoderDecoderModel continued from dh-unibe/trocr-kurrent. Safetensors total 333,921,792 float32 parameters. A config.json read on an earlier pass set decoder max_position_embeddings to 512 and generation max_length to 128 with 4 beams. Those generation defaults were not re-checked for every sibling.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TrOCR Kurrent 16th to 18th century is a Digital Humanities Bern line recognizer for earlier German Kurrent. Jonas Widmer, Anna Funk, Christa Schneider, and Tobias Hodel continued it from dh-unibe/trocr-kurrent. The Hub repo was created on 2023-03-07 and was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says it recognizes German Kurrent from the 16th, 17th, and 18th centuries. Ground truth is biased toward Swiss archives, including Zurich State Archives, the K\u00f6nigsfelden edition, Einsiedeln, and Greifswald university archives. The authors call it a first attempt that may help fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/trocr/trocr-medieval-escriptmask": { "id": "bern/trocr/trocr-medieval-escriptmask", "name": "TrOCR Medieval eScriptorium", "maker": "bern", "family": "bern/trocr", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-11-08", "context_length": null, "max_output_tokens": null, "parameters": "333921792", "architecture": "VisionEncoderDecoderModel fine-tuned from microsoft/trocr-base-handwritten. Safetensors total 333,921,792 float32 parameters. Line masks were generated in eScriptorium.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TrOCR Medieval eScriptorium is a Digital Humanities Bern line recognizer for medieval Latin scripts. Jonas Widmer and Tobias Hodel fine-tuned microsoft/trocr-base-handwritten on charter and book hands whose line masks came from eScriptorium. The Hub repo was created on 2023-11-08 and was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the aim is a generic model for Latin scripts of the Middle Ages, mixing charter and book scripts. Named sources are CREMMA medieval Latin manuscripts, HIMANIS, Itinera Nova (Leuven), and K\u00f6nigsfelden charters. The Hub language tags are German, French, Latin, and Dutch.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/trocr/trocr-towerbooks": { "id": "bern/trocr/trocr-towerbooks", "name": "TrOCR Towerbooks", "maker": "bern", "family": "bern/trocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-06-08", "context_length": null, "max_output_tokens": null, "parameters": "333921792", "architecture": "VisionEncoderDecoderModel. Safetensors total 333,921,792 float32 parameters, the same count as the microsoft/trocr-base Bern checkpoints. The README body is the default Hub template and does not name a base model.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "dh-unibe/trocr-towerbooks is a VisionEncoderDecoder checkpoint on the University of Bern Digital Humanities Hub org. The repository was created on 2026-06-08 and still held weights on 2026-10-15. The README is the automatic Hub template, so the maker has not stated what the checkpoint is for. This dossier was collected on 2026-10-15.", "what_it_is_for": "The Hub pipeline tag is image-text-to-text and the architecture is VisionEncoderDecoder. The card body does not name a task, a corpus, or a base model. The repository id is trocr-towerbooks. That name is not explained on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/turmbuecher/turmbuecher-lm": { "id": "bern/turmbuecher/turmbuecher-lm", "name": "Turmb\u00fccher Language Model", "maker": "bern", "family": "bern/turmbuecher", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-06-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two Flair character language models, forward and backward, shipped as best-lm_fw.pt and best-lm_bw.pt. The card calls them an embedding model for Early Modern German and recommends Flair stacked embeddings. Parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Turmb\u00fccher language model is a pair of Flair character models for Early Modern German, released by Digital Humanities at the University of Bern. Ismail Prada Ziegler trained them for the Bern tower books and related Swiss sources. The Hub repo was created on 2023-06-21 and was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the forward and backward models were used to train the Turmb\u00fccher NER tagger. It recommends Flair stacked embeddings. The main corpus is the early 16th-century Berner Turmb\u00fccher, 61,000 tokens, plus smaller early modern German sets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/turmbuecher/turmbuecher-ner": { "id": "bern/turmbuecher/turmbuecher-ner", "name": "Turmb\u00fccher NER", "maker": "bern", "family": "bern/turmbuecher", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flair sequence tagger. Files best-model.pt and pytorch_model.bin. Parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Turmb\u00fccher NER is a Flair named-entity tagger for historical German from Digital Humanities at the University of Bern. Ismail Prada Ziegler released v1 on 2022-10-03. The card still said the project was in progress when it was read on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the tagger labels persons, organizations, and places in Early New High German, mainly the 16th-century Bern tower books, 61,000 tokens, with Fribourg SSRQ and unpublished chorgericht manuals used for tagging as well.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/walser/gpt2-larger-walser": { "id": "bern/walser/gpt2-larger-walser", "name": "GPT-2 larger Walser", "maker": "bern", "family": "bern/walser", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-09-08", "context_length": null, "max_output_tokens": null, "parameters": "137029632", "architecture": "GPT-2 checkpoint fine-tuned from stefan-it/german-gpt2-larger. Trainer run 22_09_07_walser_blocks_larger_fp16_5ep. Safetensors total 137,029,632 parameters. The card says the dataset is unknown.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-2 larger Walser is a Digital Humanities Bern fine-tune of stefan-it/german-gpt2-larger. The Hub repo was created on 2022-09-08 and was still public on 2026-10-15. The trainer card says the dataset is unknown and leaves the intended use blank. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card does not state a task. The repository id is gpt2-larger-walser and the trainer run is 22_09_07_walser_blocks_larger_fp16_5ep. No corpus description was on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bern/yoda/yoda": { "id": "bern/yoda/yoda", "name": "YODA", "maker": "bern", "family": "bern/yoda", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "U-ViT flow model on top of a per-dataset VQGAN, following the RIVER backbone described in the YODA appendix. Each Hub repo has model.pth and a VQ file. License tag GPL-3.0.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YODA is a controllable video generator from the Computer Vision Group at the University of Bern. Aram Davtyan and Paolo Favaro describe it as generating video from one frame and a sparse motion input. Dataset checkpoints were uploaded to cvg-unibe on 2023-12-14 and were still public on 2026-10-15. The arXiv id is 2306.03988. The submission day was not copied, so release_date stays empty and the version dates are the Hub uploads. This dossier was collected on 2026-10-15.", "what_it_is_for": "The abstract says YODA generates object interactions from a single frame and sparse motion, including moving objects without touching them. It uses RIVER as a backbone. A later appendix that was read says the main body follows RIVER's U-ViT, with 8 outer self-attention blocks and 5 cross-attention blocks, and that a VQGAN is trained per dataset. Quantitative tables were not transcribed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bgu/tafberta/tafberta": { "id": "bgu/tafberta/tafberta", "name": "TafBERTa", "maker": "bgu", "family": "bgu/tafberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-25", "context_length": 128, "max_output_tokens": null, "parameters": "3299989", "architecture": "RoBERTa masked language model: 10 layers, 4 attention heads, hidden size 64, intermediate size 2048, GELU, absolute positions, vocabulary 7317. Hugging Face config max_position_embeddings is 130.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TafBERTa is a small Hebrew masked language model from Anita Gelboim and Elior Sulem at Ben-Gurion University of the Negev. It was trained from scratch on child-directed speech and released as open weights. The Hub repo `geanita/TafBERTa` was created on 2025-10-25. The BabyLM workshop paper is dated 2025-11-01. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors present TafBERTa as a test of whether a compact model, trained only on Hebrew caregiver speech, can learn determiner-noun agreement. It is a RoBERTa-style masked language model, not a chat model. Training text is HTBerman, a Hebrew-script alignment of the CHILDES Berman corpus that keeps adult child-directed speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bgu/vanpy/vanpy-age": { "id": "bgu/vanpy/vanpy-age", "name": "VANPY Age Estimator", "maker": "bgu", "family": "bgu/vanpy", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Age regression on a frozen SpeechBrain ECAPA-TDNN embedding. Four released heads: VoxCeleb2 SVR on 192-d ECAPA; VoxCeleb2 SVR on 223-d ECAPA plus Librosa; TIMIT ANN on 192-d ECAPA; combined VoxCeleb2+TIMIT ANN on 223-d features. Cards call the combined ANN the best general model.", "modalities": { "input": [ "audio" ], "output": [ "number" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VANPY Age Estimator is the speaker-age component of VANPY, from Gregory Koushnir, Michael Fire, Galit Fuhrmann Alpert, and Dima Kagan at Ben-Gurion University of the Negev. Four Apache-2.0 checkpoints were created on the Hub on 2024-11-20. They predict age in years from speech using a frozen SpeechBrain ECAPA embedding and either an SVR or a small ANN. The cards call the combined VoxCeleb2 and TIMIT ANN the best general model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The checkpoints estimate a speaker's age from a short voiced clip. VANPY uses them for speaker characterization. They are regressors, not language models. The ECAPA embedding is a SpeechBrain weight used frozen.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bgu/vanpy/vanpy-emotion": { "id": "bgu/vanpy/vanpy-emotion", "name": "VANPY Emotion Classifier", "maker": "bgu", "family": "bgu/vanpy", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Support-vector machine on a frozen 192-dimensional SpeechBrain ECAPA-TDNN embedding. Two released label sets: 7-class and 8-class, both trained on RAVDESS. Audio is converted to 16 kHz mono.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VANPY Emotion Classifier is the speech-emotion component of VANPY, released by Gregory Koushnir, Michael Fire, Galit Fuhrmann Alpert, and Dima Kagan at Ben-Gurion University of the Negev. Two Apache-2.0 checkpoints are public: a 7-class model created on 2025-03-30 and an 8-class model created on 2025-03-31. Both are SVMs on a frozen SpeechBrain ECAPA embedding, trained on RAVDESS. This dossier was collected on 2026-10-15.", "what_it_is_for": "The checkpoints assign an acted emotion label to a short speech clip. VANPY uses them for speaker characterization. They are not generative speech models. The embedding backbone is SpeechBrain's ECAPA-TDNN, not a Ben-Gurion weight.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bgu/vanpy/vanpy-gender": { "id": "bgu/vanpy/vanpy-gender", "name": "VANPY Gender Classifier", "maker": "bgu", "family": "bgu/vanpy", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Frozen SpeechBrain ECAPA-TDNN speaker embedding (192 dimensions) plus a support-vector machine. The card says the SVM was tuned with Optuna for 200 trials. Audio is converted to 16 kHz mono and cut to the first voiced segment with Silero VAD.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VANPY Gender Classifier is a binary speaker-gender model released by Gregory Koushnir, Michael Fire, Galit Fuhrmann Alpert, and Dima Kagan at Ben-Gurion University of the Negev. The public checkpoint is `griko/gender_cls_svm_ecapa_voxceleb`, created on 2024-11-09, under Apache-2.0. It is one of four classifiers trained for the VANPY voice-analysis framework. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model predicts binary speaker gender from a short voiced audio segment. VANPY uses it as a speaker-characterization component. The classifier itself is an SVM on a frozen SpeechBrain ECAPA-TDNN embedding. The embedding model is not a Ben-Gurion weight.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bgu/vanpy/vanpy-height": { "id": "bgu/vanpy/vanpy-height", "name": "VANPY Height Estimator", "maker": "bgu", "family": "bgu/vanpy", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Support-vector regression on a frozen 192-dimensional SpeechBrain ECAPA-TDNN embedding. Trained on height-enriched VoxCeleb2. Audio is converted to 16 kHz mono and cut to the first Silero VAD segment.", "modalities": { "input": [ "audio" ], "output": [ "number" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VANPY Height Estimator is the speaker-height component of VANPY, from Gregory Koushnir, Michael Fire, Galit Fuhrmann Alpert, and Dima Kagan at Ben-Gurion University of the Negev. The public checkpoint is `griko/height_reg_svr_ecapa_voxceleb`, created on 2024-11-17, under Apache-2.0. It is a support-vector regressor on a frozen SpeechBrain ECAPA embedding and returns height in centimeters. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model estimates speaker height from a short voiced clip. VANPY uses it for speaker characterization. The embedding model is SpeechBrain's ECAPA-TDNN, used frozen. Only the regressor is the Ben-Gurion release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloom/bloom": { "id": "bigscience/bloom/bloom", "name": "BLOOM", "maker": "bigscience", "family": "bigscience/bloom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-07-11", "context_length": 2048, "max_output_tokens": null, "parameters": "176247271424", "architecture": "Decoder-only Transformer modified from Megatron-LM GPT-2. ALiBi positional encodings, GeLU, and layer-norm on the word embeddings (StableEmbedding). Byte-level BPE vocabulary of 250,680. Trained with cross-entropy. 70 layers, 112 attention heads, hidden size 14336, 3,596,615,680 embedding parameters, sequence length 2048. Exact parameter count on the model card: 176247271424.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOM is a BigScience pretrained decoder-only language model (176,247,271,424 parameters on the model card), released as open weights under the RAIL v1.0 license. Status on 2026-10-15: weights still published, no deprecation notice found. It is a research model, not a hosted product API.", "what_it_is_for": "The model card says BLOOM continues text from a prompt in 46 natural languages and 13 programming languages, and can be cast as a generator for tasks it was not explicitly trained on. It is also a base for later fine-tunes. High-stakes and critical-decision uses are out of scope.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloom/bloom-1b1": { "id": "bigscience/bloom/bloom-1b1", "name": "BLOOM-1.1B", "maker": "bigscience", "family": "bigscience/bloom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-07-11", "context_length": 2048, "max_output_tokens": null, "parameters": "1065314304", "architecture": "Decoder-only Transformer modified from Megatron-LM GPT-2. ALiBi positional encodings, GeLU, and layer-norm on the word embeddings (StableEmbedding). Byte-level BPE vocabulary of 250,680. Trained with cross-entropy. 24 layers, 16 attention heads, hidden size 1536, 385,351,680 embedding parameters, sequence length 2048. Exact parameter count on the model card: 1065314304.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOM-1.1B is a BigScience pretrained decoder-only language model (1,065,314,304 parameters on the model card), released as open weights under the RAIL v1.0 license. Status on 2026-10-15: weights still published, no deprecation notice found. It is a research model, not a hosted product API.", "what_it_is_for": "The model card says BLOOM continues text from a prompt in 46 natural languages and 13 programming languages, and can be cast as a generator for tasks it was not explicitly trained on. It is also a base for later fine-tunes. High-stakes and critical-decision uses are out of scope. This checkpoint is the smaller BLOOM trained in the same project. Old names such as bloom-350m, bloom-760m, bloom-1b3, and bloom-6b3 were corrected because earlier parameter counts were wrong, and those files were intermediate.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloom/bloom-1b7": { "id": "bigscience/bloom/bloom-1b7", "name": "BLOOM-1.7B", "maker": "bigscience", "family": "bigscience/bloom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-07-11", "context_length": 2048, "max_output_tokens": null, "parameters": "1722408960", "architecture": "Decoder-only Transformer modified from Megatron-LM GPT-2. ALiBi positional encodings, GeLU, and layer-norm on the word embeddings (StableEmbedding). Byte-level BPE vocabulary of 250,680. Trained with cross-entropy. 24 layers, 16 attention heads, hidden size 2048, 513,802,240 embedding parameters, sequence length 2048. Exact parameter count on the model card: 1722408960.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOM-1.7B is a BigScience pretrained decoder-only language model (1,722,408,960 parameters on the model card), released as open weights under the RAIL v1.0 license. Status on 2026-10-15: weights still published, no deprecation notice found. It is a research model, not a hosted product API.", "what_it_is_for": "The model card says BLOOM continues text from a prompt in 46 natural languages and 13 programming languages, and can be cast as a generator for tasks it was not explicitly trained on. It is also a base for later fine-tunes. High-stakes and critical-decision uses are out of scope. This checkpoint is the smaller BLOOM trained in the same project. Old names such as bloom-350m, bloom-760m, bloom-1b3, and bloom-6b3 were corrected because earlier parameter counts were wrong, and those files were intermediate.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloom/bloom-3b": { "id": "bigscience/bloom/bloom-3b", "name": "BLOOM-3B", "maker": "bigscience", "family": "bigscience/bloom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-07-11", "context_length": 2048, "max_output_tokens": null, "parameters": "3002557440", "architecture": "Decoder-only Transformer modified from Megatron-LM GPT-2. ALiBi positional encodings, GeLU, and layer-norm on the word embeddings (StableEmbedding). Byte-level BPE vocabulary of 250,680. Trained with cross-entropy. 30 layers, 32 attention heads, hidden size 2560, 642,252,800 embedding parameters, sequence length 2048. Exact parameter count on the model card: 3002557440.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOM-3B is a BigScience pretrained decoder-only language model (3,002,557,440 parameters on the model card), released as open weights under the RAIL v1.0 license. Status on 2026-10-15: weights still published, no deprecation notice found. It is a research model, not a hosted product API.", "what_it_is_for": "The model card says BLOOM continues text from a prompt in 46 natural languages and 13 programming languages, and can be cast as a generator for tasks it was not explicitly trained on. It is also a base for later fine-tunes. High-stakes and critical-decision uses are out of scope. This checkpoint is the smaller BLOOM trained in the same project. Old names such as bloom-350m, bloom-760m, bloom-1b3, and bloom-6b3 were corrected because earlier parameter counts were wrong, and those files were intermediate.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloom/bloom-560m": { "id": "bigscience/bloom/bloom-560m", "name": "BLOOM-560M", "maker": "bigscience", "family": "bigscience/bloom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-07-11", "context_length": 2048, "max_output_tokens": null, "parameters": "559214592", "architecture": "Decoder-only Transformer modified from Megatron-LM GPT-2. ALiBi positional encodings, GeLU, and layer-norm on the word embeddings (StableEmbedding). Byte-level BPE vocabulary of 250,680. Trained with cross-entropy. 24 layers, 16 attention heads, hidden size 1024, 256,901,120 embedding parameters, sequence length 2048. Exact parameter count on the model card: 559214592.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOM-560M is a BigScience pretrained decoder-only language model (559,214,592 parameters on the model card), released as open weights under the RAIL v1.0 license. Status on 2026-10-15: weights still published, no deprecation notice found. It is a research model, not a hosted product API.", "what_it_is_for": "The model card says BLOOM continues text from a prompt in 46 natural languages and 13 programming languages, and can be cast as a generator for tasks it was not explicitly trained on. It is also a base for later fine-tunes. High-stakes and critical-decision uses are out of scope. This checkpoint is the smaller BLOOM trained in the same project. Old names such as bloom-350m, bloom-760m, bloom-1b3, and bloom-6b3 were corrected because earlier parameter counts were wrong, and those files were intermediate.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloom/bloom-7b1": { "id": "bigscience/bloom/bloom-7b1", "name": "BLOOM-7.1B", "maker": "bigscience", "family": "bigscience/bloom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-07-11", "context_length": 2048, "max_output_tokens": null, "parameters": "7069016064", "architecture": "Decoder-only Transformer modified from Megatron-LM GPT-2. ALiBi positional encodings, GeLU, and layer-norm on the word embeddings (StableEmbedding). Byte-level BPE vocabulary of 250,680. Trained with cross-entropy. 30 layers, 32 attention heads, hidden size 4096, 1,027,604,480 embedding parameters, sequence length 2048. Exact parameter count on the model card: 7069016064.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOM-7.1B is a BigScience pretrained decoder-only language model (7,069,016,064 parameters on the model card), released as open weights under the RAIL v1.0 license. Status on 2026-10-15: weights still published, no deprecation notice found. It is a research model, not a hosted product API.", "what_it_is_for": "The model card says BLOOM continues text from a prompt in 46 natural languages and 13 programming languages, and can be cast as a generator for tasks it was not explicitly trained on. It is also a base for later fine-tunes. High-stakes and critical-decision uses are out of scope. This checkpoint is the smaller BLOOM trained in the same project. Old names such as bloom-350m, bloom-760m, bloom-1b3, and bloom-6b3 were corrected because earlier parameter counts were wrong, and those files were intermediate.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloomz/bloomz": { "id": "bigscience/bloomz/bloomz", "name": "BLOOMZ", "maker": "bigscience", "family": "bigscience/bloomz", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-11-03", "context_length": 2048, "max_output_tokens": null, "parameters": "176247271424", "architecture": "Fine-tune of bloom. Decoder-only BLOOM stack, ALiBi, GeLU, sequence length 2048. Family table rounds the size to 176B. Exact count here is the matching BLOOM checkpoint count, because fine-tuning does not change parameter count. The card does not reprint the integer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOMZ is BigScience's multitask fine-tune of bloom on xP3. The family table lists it as 176B. Same decoder-only BLOOM architecture and RAIL v1.0 license. Collected 2026-10-15. Weights remain published. No deprecation notice was found.", "what_it_is_for": "The shared BLOOMZ and mT0 card says these models follow human instructions in dozens of languages zero-shot. Recommended for prompting in English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloomz/bloomz-1b1": { "id": "bigscience/bloomz/bloomz-1b1", "name": "BLOOMZ-1.1B", "maker": "bigscience", "family": "bigscience/bloomz", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-11-03", "context_length": 2048, "max_output_tokens": null, "parameters": "1065314304", "architecture": "Fine-tune of bloom-1b1. Decoder-only BLOOM stack, ALiBi, GeLU, sequence length 2048. Family table rounds the size to 1.1B. Exact count here is the matching BLOOM checkpoint count, because fine-tuning does not change parameter count. The card does not reprint the integer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOMZ-1.1B is BigScience's multitask fine-tune of bloom-1b1 on xP3. The family table lists it as 1.1B. Same decoder-only BLOOM architecture and RAIL v1.0 license. Collected 2026-10-15. Weights remain published. No deprecation notice was found.", "what_it_is_for": "The shared BLOOMZ and mT0 card says these models follow human instructions in dozens of languages zero-shot. Recommended for prompting in English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloomz/bloomz-1b7": { "id": "bigscience/bloomz/bloomz-1b7", "name": "BLOOMZ-1.7B", "maker": "bigscience", "family": "bigscience/bloomz", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-11-03", "context_length": 2048, "max_output_tokens": null, "parameters": "1722408960", "architecture": "Fine-tune of bloom-1b7. Decoder-only BLOOM stack, ALiBi, GeLU, sequence length 2048. Family table rounds the size to 1.7B. Exact count here is the matching BLOOM checkpoint count, because fine-tuning does not change parameter count. The card does not reprint the integer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOMZ-1.7B is BigScience's multitask fine-tune of bloom-1b7 on xP3. The family table lists it as 1.7B. Same decoder-only BLOOM architecture and RAIL v1.0 license. Collected 2026-10-15. Weights remain published. No deprecation notice was found.", "what_it_is_for": "The shared BLOOMZ and mT0 card says these models follow human instructions in dozens of languages zero-shot. Recommended for prompting in English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloomz/bloomz-3b": { "id": "bigscience/bloomz/bloomz-3b", "name": "BLOOMZ-3B", "maker": "bigscience", "family": "bigscience/bloomz", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-11-03", "context_length": 2048, "max_output_tokens": null, "parameters": "3002557440", "architecture": "Fine-tune of bloom-3b. Decoder-only BLOOM stack, ALiBi, GeLU, sequence length 2048. Family table rounds the size to 3B. Exact count here is the matching BLOOM checkpoint count, because fine-tuning does not change parameter count. The card does not reprint the integer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOMZ-3B is BigScience's multitask fine-tune of bloom-3b on xP3. The family table lists it as 3B. Same decoder-only BLOOM architecture and RAIL v1.0 license. Collected 2026-10-15. Weights remain published. No deprecation notice was found.", "what_it_is_for": "The shared BLOOMZ and mT0 card says these models follow human instructions in dozens of languages zero-shot. Recommended for prompting in English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloomz/bloomz-560m": { "id": "bigscience/bloomz/bloomz-560m", "name": "BLOOMZ-560M", "maker": "bigscience", "family": "bigscience/bloomz", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-11-03", "context_length": 2048, "max_output_tokens": null, "parameters": "559214592", "architecture": "Fine-tune of bloom-560m. Decoder-only BLOOM stack, ALiBi, GeLU, sequence length 2048. Family table rounds the size to 560M. Exact count here is the matching BLOOM checkpoint count, because fine-tuning does not change parameter count. The card does not reprint the integer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOMZ-560M is BigScience's multitask fine-tune of bloom-560m on xP3. The family table lists it as 560M. Same decoder-only BLOOM architecture and RAIL v1.0 license. Collected 2026-10-15. Weights remain published. No deprecation notice was found.", "what_it_is_for": "The shared BLOOMZ and mT0 card says these models follow human instructions in dozens of languages zero-shot. Recommended for prompting in English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloomz/bloomz-7b1": { "id": "bigscience/bloomz/bloomz-7b1", "name": "BLOOMZ-7.1B", "maker": "bigscience", "family": "bigscience/bloomz", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-11-03", "context_length": 2048, "max_output_tokens": null, "parameters": "7069016064", "architecture": "Fine-tune of bloom-7b1. Decoder-only BLOOM stack, ALiBi, GeLU, sequence length 2048. Family table rounds the size to 7.1B. Exact count here is the matching BLOOM checkpoint count, because fine-tuning does not change parameter count. The card does not reprint the integer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOMZ-7.1B is BigScience's multitask fine-tune of bloom-7b1 on xP3. The family table lists it as 7.1B. Same decoder-only BLOOM architecture and RAIL v1.0 license. Collected 2026-10-15. Weights remain published. No deprecation notice was found.", "what_it_is_for": "The shared BLOOMZ and mT0 card says these models follow human instructions in dozens of languages zero-shot. Recommended for prompting in English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloomz/bloomz-7b1-mt": { "id": "bigscience/bloomz/bloomz-7b1-mt", "name": "BLOOMZ-7.1B-MT", "maker": "bigscience", "family": "bigscience/bloomz", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-11-03", "context_length": 2048, "max_output_tokens": null, "parameters": "7069016064", "architecture": "Fine-tune of bloom-7b1. Decoder-only BLOOM stack, ALiBi, GeLU, sequence length 2048. Family table rounds the size to 7.1B. Exact count here is the matching BLOOM checkpoint count, because fine-tuning does not change parameter count. The card does not reprint the integer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOMZ-7.1B-MT is BigScience's multitask fine-tune of bloom-7b1 on xP3 and xP3mt. The family table lists it as 7.1B. Same decoder-only BLOOM architecture and RAIL v1.0 license. Collected 2026-10-15. Weights remain published. No deprecation notice was found.", "what_it_is_for": "The shared BLOOMZ and mT0 card says these models follow human instructions in dozens of languages zero-shot. Recommended for prompting in non-English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloomz/bloomz-7b1-p3": { "id": "bigscience/bloomz/bloomz-7b1-p3", "name": "BLOOMZ-7.1B-P3", "maker": "bigscience", "family": "bigscience/bloomz", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-11-03", "context_length": 2048, "max_output_tokens": null, "parameters": "7069016064", "architecture": "Fine-tune of bloom-7b1. Decoder-only BLOOM stack, ALiBi, GeLU, sequence length 2048. Family table rounds the size to 7.1B. Exact count here is the matching BLOOM checkpoint count, because fine-tuning does not change parameter count. The card does not reprint the integer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOMZ-7.1B-P3 is BigScience's multitask fine-tune of bloom-7b1 on P3. The family table lists it as 7.1B. Same decoder-only BLOOM architecture and RAIL v1.0 license. Collected 2026-10-15. Weights remain published. No deprecation notice was found.", "what_it_is_for": "The shared BLOOMZ and mT0 card says these models follow human instructions in dozens of languages zero-shot. Released for research only. The family table says it is strictly inferior to the xP3 models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloomz/bloomz-mt": { "id": "bigscience/bloomz/bloomz-mt", "name": "BLOOMZ-MT", "maker": "bigscience", "family": "bigscience/bloomz", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-11-03", "context_length": 2048, "max_output_tokens": null, "parameters": "176247271424", "architecture": "Fine-tune of bloom. Decoder-only BLOOM stack, ALiBi, GeLU, sequence length 2048. Family table rounds the size to 176B. Exact count here is the matching BLOOM checkpoint count, because fine-tuning does not change parameter count. The card does not reprint the integer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOMZ-MT is BigScience's multitask fine-tune of bloom on xP3 and xP3mt. The family table lists it as 176B. Same decoder-only BLOOM architecture and RAIL v1.0 license. Collected 2026-10-15. Weights remain published. No deprecation notice was found.", "what_it_is_for": "The shared BLOOMZ and mT0 card says these models follow human instructions in dozens of languages zero-shot. Recommended for prompting in non-English. The card says it is better than bloomz for that.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/bloomz/bloomz-p3": { "id": "bigscience/bloomz/bloomz-p3", "name": "BLOOMZ-P3", "maker": "bigscience", "family": "bigscience/bloomz", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "2022-11-03", "context_length": 2048, "max_output_tokens": null, "parameters": "176247271424", "architecture": "Fine-tune of bloom. Decoder-only BLOOM stack, ALiBi, GeLU, sequence length 2048. Family table rounds the size to 176B. Exact count here is the matching BLOOM checkpoint count, because fine-tuning does not change parameter count. The card does not reprint the integer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLOOMZ-P3 is BigScience's multitask fine-tune of bloom on P3. The family table lists it as 176B. Same decoder-only BLOOM architecture and RAIL v1.0 license. Collected 2026-10-15. Weights remain published. No deprecation notice was found.", "what_it_is_for": "The shared BLOOMZ and mT0 card says these models follow human instructions in dozens of languages zero-shot. Released for research only. The family table says it is strictly inferior to the xP3 models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/mt0/mt0-base": { "id": "bigscience/mt0/mt0-base", "name": "mT0-Base", "maker": "bigscience", "family": "bigscience/mt0", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2022-11-03", "context_length": null, "max_output_tokens": null, "parameters": "580000000", "architecture": "Encoder-decoder fine-tune of mt5-base (mT5). Family table size 580M. Not a BLOOM decoder. Context length is not stated on the BigScience card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mT0-Base is BigScience's multitask fine-tune of Google mt5-base, listed at 580M in the BLOOMZ and mT0 family table. Mixture: xP3. License on the Hub is Apache-2.0. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Encoder-decoder instruction model for crosslingual zero-shot prompts. The authors say mT0 is often stronger than a similar-size BLOOMZ and is biased toward shorter answers. English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/mt0/mt0-large": { "id": "bigscience/mt0/mt0-large", "name": "mT0-Large", "maker": "bigscience", "family": "bigscience/mt0", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2022-11-03", "context_length": null, "max_output_tokens": null, "parameters": "1200000000", "architecture": "Encoder-decoder fine-tune of mt5-large (mT5). Family table size 1.2B. Not a BLOOM decoder. Context length is not stated on the BigScience card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mT0-Large is BigScience's multitask fine-tune of Google mt5-large, listed at 1.2B in the BLOOMZ and mT0 family table. Mixture: xP3. License on the Hub is Apache-2.0. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Encoder-decoder instruction model for crosslingual zero-shot prompts. The authors say mT0 is often stronger than a similar-size BLOOMZ and is biased toward shorter answers. English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/mt0/mt0-small": { "id": "bigscience/mt0/mt0-small", "name": "mT0-Small", "maker": "bigscience", "family": "bigscience/mt0", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2022-11-03", "context_length": null, "max_output_tokens": null, "parameters": "300000000", "architecture": "Encoder-decoder fine-tune of mt5-small (mT5). Family table size 300M. Not a BLOOM decoder. Context length is not stated on the BigScience card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mT0-Small is BigScience's multitask fine-tune of Google mt5-small, listed at 300M in the BLOOMZ and mT0 family table. Mixture: xP3. License on the Hub is Apache-2.0. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Encoder-decoder instruction model for crosslingual zero-shot prompts. The authors say mT0 is often stronger than a similar-size BLOOMZ and is biased toward shorter answers. English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/mt0/mt0-xl": { "id": "bigscience/mt0/mt0-xl", "name": "mT0-XL", "maker": "bigscience", "family": "bigscience/mt0", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2022-11-03", "context_length": null, "max_output_tokens": null, "parameters": "3700000000", "architecture": "Encoder-decoder fine-tune of mt5-xl (mT5). Family table size 3.7B. Not a BLOOM decoder. Context length is not stated on the BigScience card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mT0-XL is BigScience's multitask fine-tune of Google mt5-xl, listed at 3.7B in the BLOOMZ and mT0 family table. Mixture: xP3. License on the Hub is Apache-2.0. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Encoder-decoder instruction model for crosslingual zero-shot prompts. The authors say mT0 is often stronger than a similar-size BLOOMZ and is biased toward shorter answers. English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/mt0/mt0-xxl": { "id": "bigscience/mt0/mt0-xxl", "name": "mT0-XXL", "maker": "bigscience", "family": "bigscience/mt0", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2022-11-03", "context_length": null, "max_output_tokens": null, "parameters": "13000000000", "architecture": "Encoder-decoder fine-tune of mt5-xxl (mT5). Family table size 13B. Not a BLOOM decoder. Context length is not stated on the BigScience card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mT0-XXL is BigScience's multitask fine-tune of Google mt5-xxl, listed at 13B in the BLOOMZ and mT0 family table. Mixture: xP3. License on the Hub is Apache-2.0. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Encoder-decoder instruction model for crosslingual zero-shot prompts. The authors say mT0 is often stronger than a similar-size BLOOMZ and is biased toward shorter answers. English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/mt0/mt0-xxl-mt": { "id": "bigscience/mt0/mt0-xxl-mt", "name": "mT0-XXL-MT", "maker": "bigscience", "family": "bigscience/mt0", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2022-11-03", "context_length": null, "max_output_tokens": null, "parameters": "13000000000", "architecture": "Encoder-decoder fine-tune of mt5-xxl (mT5). Family table size 13B. Not a BLOOM decoder. Context length is not stated on the BigScience card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mT0-XXL-MT is BigScience's multitask fine-tune of Google mt5-xxl, listed at 13B in the BLOOMZ and mT0 family table. Mixture: xP3mt. License on the Hub is Apache-2.0. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Encoder-decoder instruction model for crosslingual zero-shot prompts. The authors say mT0 is often stronger than a similar-size BLOOMZ and is biased toward shorter answers. Non-English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/mt0/mt0-xxl-p3": { "id": "bigscience/mt0/mt0-xxl-p3", "name": "mT0-XXL-P3", "maker": "bigscience", "family": "bigscience/mt0", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2022-11-03", "context_length": null, "max_output_tokens": null, "parameters": "13000000000", "architecture": "Encoder-decoder fine-tune of mt5-xxl (mT5). Family table size 13B. Not a BLOOM decoder. Context length is not stated on the BigScience card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mT0-XXL-P3 is BigScience's multitask fine-tune of Google mt5-xxl, listed at 13B in the BLOOMZ and mT0 family table. Mixture: P3. License on the Hub is Apache-2.0. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Encoder-decoder instruction model for crosslingual zero-shot prompts. The authors say mT0 is often stronger than a similar-size BLOOMZ and is biased toward shorter answers. Research only. Strictly inferior, per the family table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/octopack/octocoder": { "id": "bigscience/octopack/octocoder", "name": "OctoCoder", "maker": "bigscience", "family": "bigscience/octopack", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2023-07-23", "context_length": 8192, "max_output_tokens": null, "parameters": "15500000000", "architecture": "Instruction fine-tune of StarCoder. Inherits the 15.5B MQA decoder and 8192 context unless the fine-tune changed them. The card does not restate layer counts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OctoCoder is BigCode's instruction tune of StarCoder on CommitPackFT and OASST, from the OctoPack paper. The summary says 15.5B parameters. The same card later rounds StarCoder to 16B. OpenRAIL-M. Hub createdAt 2023-07-23. Paper dated 14 August 2023. Collected 2026-10-15.", "what_it_is_for": "Instruction-following code generation and edits. Prompts should be answerable as coding instructions. HumanEvalPack is the authors' eval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/octopack/octogeex": { "id": "bigscience/octopack/octogeex", "name": "OctoGeeX", "maker": "bigscience", "family": "bigscience/octopack", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CodeGeeX2 model license for weights", "release_date": "2023-07-28", "context_length": null, "max_output_tokens": null, "parameters": "6000000000", "architecture": "Instruction fine-tune of CodeGeeX2, a 6B GLM-style code model. Instruction tuning on the card: 8 A100s, 4 hours. The pretraining hardware block copies a 512-A100, 24-day figure that matches StarCoder pretraining and may not describe CodeGeeX2.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OctoGeeX is BigCode's instruction tune of CodeGeeX2, released with OctoPack. The card says 6B parameters, fine-tuned on CommitPackFT and OASST. The base model is not a BigCode pretrain. Hub createdAt 2023-07-28. Paper dated 14 August 2023. Collected 2026-10-15.", "what_it_is_for": "Instruction-following code generation. The card says to wrap prompts as Question and Answer. It lists 100+ programming languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/santacoder/santacoder": { "id": "bigscience/santacoder/santacoder", "name": "SantaCoder", "maker": "bigscience", "family": "bigscience/santacoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2023-01-09", "context_length": 2048, "max_output_tokens": null, "parameters": "1100000000", "architecture": "Decoder-only Transformer. Paper comparison table: hidden 2048, intermediate 8192, 16 heads, 24 layers, multi-query attention, max position 2048, about 1.1B parameters. Final checkpoint: 600K steps, 236B tokens, float16, 96 V100s, 6.2 days.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SantaCoder is BigCode's 1.1B code model for Python, Java, and JavaScript, the architecture prototype for StarCoder. The default checkpoint uses multi-query attention, context 2048, and Fill-in-the-Middle, trained for 236B tokens. BigCode OpenRAIL-M. Card publication date used here is 9 January 2023. Collected 2026-10-15.", "what_it_is_for": "Code completion and fill-in-the-middle in Python, Java, and JavaScript. The card says the final branch is the one to use. Other branches are training ablations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/santacoder/santacoderpack": { "id": "bigscience/santacoder/santacoderpack", "name": "SantaCoderPack", "maker": "bigscience", "family": "bigscience/santacoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2023-08-10", "context_length": 2048, "max_output_tokens": null, "parameters": "1100000000", "architecture": "Same architecture as SantaCoder, about 1.1B. Pretrained on CommitPack, 131B tokens, bfloat16, 32 A100s, 15 days. Not the instruction-tuned OctoCoder.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SantaCoderPack is BigCode's 1.1B model with the SantaCoder architecture, pretrained on CommitPack in a before/message/after format. OpenRAIL-M. It is part of the OctoPack release. Hub createdAt 2023-08-10. The OctoPack paper is dated 14 August 2023. Collected 2026-10-15.", "what_it_is_for": "Commit-style code edits. The card shows a prompt that asks the model to fix a bug given the old code and a commit message. Languages listed: Python, JavaScript, Java, C++, Go, and Rust.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/sgpt/sgpt-bloom-7b1-msmarco": { "id": "bigscience/sgpt/sgpt-bloom-7b1-msmarco", "name": "SGPT-BLOOM-7.1B-MSMARCO", "maker": "bigscience", "family": "bigscience/sgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-08-26", "context_length": 2048, "max_output_tokens": null, "parameters": "7069016064", "architecture": "Weighted-mean pooling bi-encoder on frozen-non-bias BLOOM-7.1B, per the training command on the card. Parameter count is the BLOOM-7.1B card count.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SGPT-BLOOM-7.1B-MSMARCO is a BigScience-hosted sentence embedding model. The card says it was trained from bigscience/bloom-7b1 for asymmetric semantic search on MS MARCO. Hub repo created 26 August 2022. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Bi-encoder semantic search. The card points at the SGPT repository for asymmetric search usage. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/starcoder/starcoder": { "id": "bigscience/starcoder/starcoder", "name": "StarCoder", "maker": "bigscience", "family": "bigscience/starcoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2023-05-09", "context_length": 8192, "max_output_tokens": null, "parameters": "15500000000", "architecture": "Decoder-only GPT-2-style Transformer with multi-query attention, learned absolute positions, and Fill-in-the-Middle training. Paper Table 11: hidden 6144, intermediate 24576, 48 heads, 40 layers, context 8192, about 15.5B parameters. FlashAttention was used in training. Additional 35B Python tokens after the 1T-token StarCoderBase run, per the paper. The card's training block lists 1 trillion pretraining tokens plus a separate Python fine-tune (11,208 GPU hours).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoder is BigCode's 15.5B code model, a Python continuation of StarCoderBase. Open weights under BigCode OpenRAIL-M. The technical report was published 9 May 2023. Collected 2026-10-15. Weights remain published. No maker API and no deprecation notice were found.", "what_it_is_for": "Code completion and fill-in-the-middle across 80+ programming languages, with extra Python training. The card says it is not an instruction model. A Tech Assistant prompt is offered as a workaround. The paper says prompted HumanEval pass@1 can reach about 40 percent.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/starcoder/starcoderbase": { "id": "bigscience/starcoder/starcoderbase", "name": "StarCoderBase", "maker": "bigscience", "family": "bigscience/starcoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2023-05-09", "context_length": 8192, "max_output_tokens": null, "parameters": "15500000000", "architecture": "Decoder-only GPT-2-style Transformer with multi-query attention, learned absolute positions, and Fill-in-the-Middle training. Paper Table 11: hidden 6144, intermediate 24576, 48 heads, 40 layers, context 8192, about 15.5B parameters. FlashAttention was used in training. Pretraining steps 250k, 1 trillion tokens, bfloat16, 512 A100 GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoderBase is BigCode's 15.5B pretrained code model, trained on 1 trillion tokens from 80+ languages in The Stack v1.2. Same architecture and OpenRAIL-M license as StarCoder. Paper published 9 May 2023. Collected 2026-10-15.", "what_it_is_for": "Multilingual code completion and fill-in-the-middle. StarCoder and StarCoderPlus are further trained from this checkpoint. The card says plain English commands work poorly.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/starcoder/starcoderbase-1b": { "id": "bigscience/starcoder/starcoderbase-1b", "name": "StarCoderBase-1B", "maker": "bigscience", "family": "bigscience/starcoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2023-07-03", "context_length": 8192, "max_output_tokens": null, "parameters": "1000000000", "architecture": "GPT-2-style decoder with multi-query attention and Fill-in-the-Middle. Context 8192. Card size 1B. Pretraining steps 500k. 1 trillion tokens. bfloat16. Layer counts were not on the fetched card text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoderBase-1B is BigCode's smaller StarCoderBase checkpoint (1B on the model card), same MQA and FIM design, context 8192, trained on 1 trillion tokens from 80+ languages. OpenRAIL-M. Hub createdAt 2023-07-03 is used as the release date because no separate announcement date was found. Collected 2026-10-15.", "what_it_is_for": "Smaller code completion and fill-in-the-middle model. The card says it is not an instruction model and points at the Tech Assistant prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/starcoder/starcoderbase-3b": { "id": "bigscience/starcoder/starcoderbase-3b", "name": "StarCoderBase-3B", "maker": "bigscience", "family": "bigscience/starcoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2023-06-30", "context_length": 8192, "max_output_tokens": null, "parameters": "3000000000", "architecture": "GPT-2-style decoder with multi-query attention and Fill-in-the-Middle. Context 8192. Card size 3B. Pretraining steps 500k. 1 trillion tokens. bfloat16. Layer counts were not on the fetched card text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoderBase-3B is BigCode's smaller StarCoderBase checkpoint (3B on the model card), same MQA and FIM design, context 8192, trained on 1 trillion tokens from 80+ languages. OpenRAIL-M. Hub createdAt 2023-06-30 is used as the release date because no separate announcement date was found. Collected 2026-10-15.", "what_it_is_for": "Smaller code completion and fill-in-the-middle model. The card says it is not an instruction model and points at the Tech Assistant prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/starcoder/starcoderbase-7b": { "id": "bigscience/starcoder/starcoderbase-7b", "name": "StarCoderBase-7B", "maker": "bigscience", "family": "bigscience/starcoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2023-07-26", "context_length": 8192, "max_output_tokens": null, "parameters": "7000000000", "architecture": "GPT-2-style decoder with multi-query attention and Fill-in-the-Middle. Context 8192. Card size 7B. Pretraining steps 250k. 1 trillion tokens. bfloat16. Layer counts were not on the fetched card text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoderBase-7B is BigCode's smaller StarCoderBase checkpoint (7B on the model card), same MQA and FIM design, context 8192, trained on 1 trillion tokens from 80+ languages. OpenRAIL-M. Hub createdAt 2023-07-26 is used as the release date because no separate announcement date was found. Collected 2026-10-15.", "what_it_is_for": "Smaller code completion and fill-in-the-middle model. The card says it is not an instruction model and points at the Tech Assistant prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/starcoder/starcoderplus": { "id": "bigscience/starcoder/starcoderplus", "name": "StarCoderPlus", "maker": "bigscience", "family": "bigscience/starcoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2023-05-08", "context_length": 8192, "max_output_tokens": null, "parameters": "15500000000", "architecture": "Decoder-only GPT-2-style Transformer with multi-query attention, learned absolute positions, and Fill-in-the-Middle training. Paper Table 11: hidden 6144, intermediate 24576, 48 heads, 40 layers, context 8192, about 15.5B parameters. FlashAttention was used in training. Further trained 150k steps and 600B tokens on RefinedWeb (1x), StarCoderData (1x), and Wikipedia upsampled 5x. 512 A100s, 14 days.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoderPlus is BigCode's further training of StarCoderBase on English web text and code. The card says 15.5B parameters, context 8192, and 1.6 trillion tokens total, of which 600B are the English-and-code fine-tune. OpenRAIL-M. Hub createdAt 2023-05-08. Collected 2026-10-15.", "what_it_is_for": "Code and English text model. The card says it is still not an instruction model. It points users who want an assistant at StarChat, which is a separate Hugging Face fine-tune and is not cataloged here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/starcoder/tiny-starcoder-py": { "id": "bigscience/starcoder/tiny-starcoder-py", "name": "TinyStarCoderPy", "maker": "bigscience", "family": "bigscience/starcoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2023-05-15", "context_length": 8192, "max_output_tokens": null, "parameters": "164000000", "architecture": "GPT-2-style decoder, multi-query attention, FIM. Public config.json: n_embd 768, n_head 12, n_inner 3072, n_layer 20, n_positions 8192, vocab 49152. Card says 164M parameters. Hub widget says 0.2B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyStarCoderPy is BigCode's 164M Python code model with the StarCoder architecture (8k context, MQA, FIM). Trained on Python from StarCoderData for about 6 epochs, about 100B tokens. OpenRAIL-M. Hub createdAt 2023-05-15. Collected 2026-10-15.", "what_it_is_for": "Assisted code generation. The card says to prefer the 15B StarCoder or StarCoderBase models for pure completion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/starcoder2/starcoder2-15b": { "id": "bigscience/starcoder2/starcoder2-15b", "name": "StarCoder2-15B", "maker": "bigscience", "family": "bigscience/starcoder2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2024-02-28", "context_length": 16384, "max_output_tokens": null, "parameters": "15000000000", "architecture": "Transformer decoder, grouped-query attention, sliding window 4096, RoPE, FIM. 40 layers, hidden 6144, 48 heads, 4 key-value heads, intermediate 24576, vocab 49152. Trained by NVIDIA with NeMo on Eos. Pretraining steps 1 million. Tokens 4+ trillion. bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoder2-15B is a BigCode StarCoder2 open code model (15B on the model card and the 28 February 2024 release post). Grouped-query attention, context 16,384 with sliding window 4,096, Fill-in-the-Middle, OpenRAIL-M. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Code completion and fill-in-the-middle. The card says it was also trained with natural-language sources such as Arxiv and Wikipedia, and that it is not an instruction model. The 15B model is the flagship. The 3B post says it matches StarCoder-15.5B on many evaluations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/starcoder2/starcoder2-15b-instruct-v0-1": { "id": "bigscience/starcoder2/starcoder2-15b-instruct-v0-1", "name": "StarCoder2-15B-Instruct v0.1", "maker": "bigscience", "family": "bigscience/starcoder2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2024-04-23", "context_length": 16384, "max_output_tokens": null, "parameters": "15000000000", "architecture": "Fine-tune of StarCoder2-15B. Same GQA decoder. Training sequence length on the card is 1280. Hub widget says 16B params. Adafactor, learning rate 1e-5, 4 epochs, batch 64, one A100 80GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoder2-15B-Instruct-v0.1 is BigCode's self-aligned instruction tune of StarCoder2-15B. The card says the model generated its own instruction data, with no human annotations and no distillation from proprietary LLMs. OpenRAIL-M. Hub createdAt 2024-04-23. The SelfCodeAlign paper is dated 31 October 2024. Collected 2026-10-15.", "what_it_is_for": "Instruction-following code generation, mainly Python that can be checked by execution. The card says other languages and strict output formats are weaker, and a response prefix or one-shot example can help.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/starcoder2/starcoder2-3b": { "id": "bigscience/starcoder2/starcoder2-3b", "name": "StarCoder2-3B", "maker": "bigscience", "family": "bigscience/starcoder2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2024-02-28", "context_length": 16384, "max_output_tokens": null, "parameters": "3000000000", "architecture": "Transformer decoder, grouped-query attention, sliding window 4096, RoPE, FIM. Public config.json: 30 layers, hidden 3072, 24 heads, 2 key-value heads, intermediate 12288, vocab 49152. Trained by ServiceNow on 160 A100s. The card leaves the training framework as TODO. Pretraining steps 1.2 million. Tokens 3+ trillion. bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoder2-3B is a BigCode StarCoder2 open code model (3B on the model card and the 28 February 2024 release post). Grouped-query attention, context 16,384 with sliding window 4,096, Fill-in-the-Middle, OpenRAIL-M. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Code completion and fill-in-the-middle. The card says it was also trained with natural-language sources such as Arxiv and Wikipedia, and that it is not an instruction model. The 15B model is the flagship. The 3B post says it matches StarCoder-15.5B on many evaluations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/starcoder2/starcoder2-7b": { "id": "bigscience/starcoder2/starcoder2-7b", "name": "StarCoder2-7B", "maker": "bigscience", "family": "bigscience/starcoder2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2024-02-28", "context_length": 16384, "max_output_tokens": null, "parameters": "7000000000", "architecture": "Transformer decoder, grouped-query attention, sliding window 4096, RoPE, FIM. Public config.json: 32 layers, hidden 4608, 36 heads, 4 key-value heads, intermediate 18432, vocab 49152. Trained by Hugging Face with nanotron on 432 H100s. Pretraining steps 1 million. Tokens 3.5+ trillion. bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoder2-7B is a BigCode StarCoder2 open code model (7B on the model card and the 28 February 2024 release post). Grouped-query attention, context 16,384 with sliding window 4,096, Fill-in-the-Middle, OpenRAIL-M. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Code completion and fill-in-the-middle. The card says it was also trained with natural-language sources such as Arxiv and Wikipedia, and that it is not an instruction model. The 15B model is the flagship. The 3B post says it matches StarCoder-15.5B on many evaluations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/starencoder/starencoder": { "id": "bigscience/starencoder/starencoder", "name": "StarEncoder", "maker": "bigscience", "family": "bigscience/starencoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigcode-openrail-m", "release_date": "2023-03-13", "context_length": 1024, "max_output_tokens": null, "parameters": "125000000", "architecture": "BERT-style encoder. Card table: hidden 768, intermediate 3072, 12 heads, 12 layers, max position embeddings 1024, multi-head attention, about 125M parameters.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarEncoder is BigCode's encoder-only code model, about 125M parameters, trained on The Stack with BERT's masked-language and next-sentence objectives. OpenRAIL-M. Hub createdAt 2023-03-13. Collected 2026-10-15.", "what_it_is_for": "Code understanding tasks that need a bidirectional encoder. The card says it is a poor fit for generation or completion, and that the training data contained personal information.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/starpii/starpii": { "id": "bigscience/starpii/starpii", "name": "StarPII", "maker": "bigscience", "family": "bigscience/starpii", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-04-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Token-classification head on a BigCode encoder. The card names the base as bigcode-encoder. Six labels. A pseudo-label stage was followed by fine-tuning on an annotated set.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarPII is BigCode's token classifier for personal information in source code. It adds a linear head on a BigCode encoder, with six labels: names, emails, keys, passwords, IP addresses, and usernames. Hub createdAt 2023-04-23. Collected 2026-10-15.", "what_it_is_for": "Detect PII so it can be removed from code datasets. The terms on the card say not to use it for anything else and not to share the model or modified versions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/t0/t0": { "id": "bigscience/t0/t0", "name": "T0", "maker": "bigscience", "family": "bigscience/t0", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2021-10-15", "context_length": null, "max_output_tokens": null, "parameters": "11000000000", "architecture": "Encoder-decoder. LM-adapted T5 fine-tune (T5 trained 100,000 extra language-modeling steps, then prompted multitask training). Card size: 11 billion. Input goes to the encoder. The decoder generates the target only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "T0 is a BigScience encoder-decoder model (11 billion parameters on the model card) fine-tuned from LM-adapted T5 on English prompted datasets (P3). The card recommends T0pp. Apache-2.0. Paper submitted 15 October 2021. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Zero-shot English task generalization from natural-language prompts. The card says the series beats GPT-3 on many tasks while being about 16 times smaller, and that it cannot handle code or non-English text because of tokenization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/t0/t0-3b": { "id": "bigscience/t0/t0-3b", "name": "T0 3B", "maker": "bigscience", "family": "bigscience/t0", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2021-10-15", "context_length": null, "max_output_tokens": null, "parameters": "3000000000", "architecture": "Encoder-decoder. LM-adapted T5 fine-tune (T5 trained 100,000 extra language-modeling steps, then prompted multitask training). Card size: 3 billion. Input goes to the encoder. The decoder generates the target only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "T0 3B is a BigScience encoder-decoder model (3 billion parameters on the model card) fine-tuned from LM-adapted T5 on English prompted datasets (P3). The card recommends T0pp. Apache-2.0. Paper submitted 15 October 2021. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Zero-shot English task generalization from natural-language prompts. The card says the series beats GPT-3 on many tasks while being about 16 times smaller, and that it cannot handle code or non-English text because of tokenization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/t0/t0p": { "id": "bigscience/t0/t0p", "name": "T0p", "maker": "bigscience", "family": "bigscience/t0", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2021-10-15", "context_length": null, "max_output_tokens": null, "parameters": "11000000000", "architecture": "Encoder-decoder. LM-adapted T5 fine-tune (T5 trained 100,000 extra language-modeling steps, then prompted multitask training). Card size: 11 billion. Input goes to the encoder. The decoder generates the target only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "T0p is a BigScience encoder-decoder model (11 billion parameters on the model card) fine-tuned from LM-adapted T5 on English prompted datasets (P3). The card recommends T0pp. Apache-2.0. Paper submitted 15 October 2021. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Zero-shot English task generalization from natural-language prompts. The card says the series beats GPT-3 on many tasks while being about 16 times smaller, and that it cannot handle code or non-English text because of tokenization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bigscience/t0/t0pp": { "id": "bigscience/t0/t0pp", "name": "T0pp", "maker": "bigscience", "family": "bigscience/t0", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2021-10-15", "context_length": null, "max_output_tokens": null, "parameters": "11000000000", "architecture": "Encoder-decoder. LM-adapted T5 fine-tune (T5 trained 100,000 extra language-modeling steps, then prompted multitask training). Card size: 11 billion. Input goes to the encoder. The decoder generates the target only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "T0pp is a BigScience encoder-decoder model (11 billion parameters on the model card) fine-tuned from LM-adapted T5 on English prompted datasets (P3). The card recommends T0pp. Apache-2.0. Paper submitted 15 October 2021. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Zero-shot English task generalization from natural-language prompts. The card says the series beats GPT-3 on many tasks while being about 16 times smaller, and that it cannot handle code or non-English text because of tokenization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "birmingham/fourier-net/fourier-net": { "id": "birmingham/fourier-net/fourier-net", "name": "Fourier-Net", "maker": "birmingham", "family": "birmingham/fourier-net", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "U-Net-style registration network whose expansive path is a parameter-free decoder: a low-dimensional band-limited Fourier representation of the displacement is decoded by zero-padding and an inverse discrete Fourier transform. 2D, 3D, and diffeomorphic trees are in https://github.com/xi-jia/Fourier-Net. A confirmed 3D start-channel-8 file is DiceVal_0.7481_Epoch_0769.pth. A start-channel-16 directory is also present; its filename was not opened. GitHub licence field is empty.", "modalities": { "input": [ "image" ], "output": [ "deformation-field" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fourier-Net is a band-limited medical image registration model from the School of Computer Science at the University of Birmingham (Xi Jia, Joseph Bartlett, Wei Chen, Tianyang Zhang, Xinxing Cheng, Jinming Duan), with co-authors at Cambridge, Warwick, and Northeast Forestry University. AAAI 2023 published the paper on 26 June 2023. Official code and pretrained checkpoints are at https://github.com/xi-jia/Fourier-Net. Collected 2026-10-15.", "what_it_is_for": "The paper predicts a low-dimensional Fourier representation of the deformation and decodes it to a full-resolution displacement with a parameter-free inverse Fourier step, instead of a learned U-Net expansive path. The README also records Learn2Reg and OncoReg challenge entries from this group.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "birmingham/fourier-net/fourier-net-plus": { "id": "birmingham/fourier-net/fourier-net-plus", "name": "Fourier-Net+", "maker": "birmingham", "family": "birmingham/fourier-net", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The arXiv HTML record for 2307.02997 is CC BY 4.0. The GitHub repository has no licence.", "release_date": "2023-07-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Band-limited 3D registration model that extends Fourier-Net. The arXiv abstract says it learns a low-dimensional Fourier representation and a parameter-free decoder. Official training code and a pretrained 3D checkpoint live in the Fourier-Net repository under 3D Fourier-Net+. A confirmed file is DiceVal_0.75433_Epoch_000000840.pth in L2ss_2_Chan_16_Smth_5.0_LR_0.0001_Val. The separate repository xi-jia/Fourier-Net-Plus contains an IXI_3D directory and a one-byte readme; it is not the weight host checked here.", "modalities": { "input": [ "image" ], "output": [ "deformation-field" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fourier-Net+ is the 3D band-limited registration model that follows Fourier-Net. The arXiv paper (2307.02997, 6 July 2023) lists Xi Jia, Alexander Thorley, and Jinming Duan at the School of Computer Science, University of Birmingham, with co-authors at Ultromics and other posts. Pretrained weights checked on 2026-10-15 are in the Fourier-Net GitHub repository under `3D Fourier-Net+`, not in the nearly empty `xi-jia/Fourier-Net-Plus` repository. Collected 2026-10-15.", "what_it_is_for": "The abstract presents Fourier-Net+ as a way to train large 3D registration on low-VRAM GPUs by keeping the deformation in a band-limited Fourier representation. It reports gains over TransMorph and LKU-Net on a 3D cardiac MR dataset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "birmingham/l20-edu/l20-edu-135m": { "id": "birmingham/l20-edu/l20-edu-135m", "name": "L20-Edu-135M", "maker": "birmingham", "family": "birmingham/l20-edu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face card and API tag: apache-2.0. The arXiv record is CC BY 4.0. A separate weights licence beyond the card tag is not stated.", "release_date": "2026-06-16", "context_length": 8192, "max_output_tokens": null, "parameters": "134515008", "architecture": "LlamaForCausalLM causal decoder. 30 layers, hidden size 576, intermediate size 1536, 9 query heads, 3 key-value heads, head dim 64, SiLU, tied embeddings, vocab size 49152, bfloat16. RoPE theta 10000 with YaRN factor 4.0 from an original max position of 2048. Tokenizer class on the card is GPT2Tokenizer, model_max_length 8192, bos/eos/pad <|endoftext|>. The default Hub checkpoint is the Stage 4 anti-forgetting release: 87.5% SFT weights blended with 12.5% Stage 4 base weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "L20-Edu-135M is a 134,515,008-parameter Llama-style language model released by Yin Li with a University of Birmingham affiliation. The public checkpoint is on the personal Hugging Face account AliceYin. The default weights, dated on the card 16 June 2026, are a Stage 4 blend of supervised fine-tuning and continued-pretraining weights. The arXiv paper is 20 June 2026. Collected 2026-10-15.", "what_it_is_for": "The model card describes a research checkpoint for small-model pretraining, data curation, continual pretraining, evaluation, and downstream fine-tuning. Training used one NVIDIA L20. The card says the release used about 13.0B pretraining tokens: 10,001,252,352 FineWeb-Edu tokens and 3,000,000,965 Stage 4 continuation tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "birmingham/lessnet/lessnet": { "id": "birmingham/lessnet/lessnet", "name": "LessNet", "maker": "birmingham", "family": "birmingham/lessnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only unsupervised 3D medical image registration network. A learnable encoder is replaced by handcrafted multi-scale pooling features. Width is a start-channel hyperparameter. The GitHub README says the arXiv names LessNet_4/6/8/12/16 should be read as code start channels 8/12/16/24/32, with the reported parameter counts unchanged. The paper sets that hyperparameter to 8 by default, which is 168,032 parameters in Table IV and code start channel 16 after the rename. A diffeomorphic variant is shipped as 3D_LessNet_Diff. The repository licence field on GitHub is empty.", "modalities": { "input": [ "image" ], "output": [ "deformation-field" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LessNet is a decoder-only network for unsupervised 3D medical image registration from Xi Jia, Xinxing Cheng, and Jinming Duan at the School of Computer Science, University of Birmingham, with Wenqi Lu. The arXiv preprint \"Decoder-Only Image Registration\" was submitted on 5 February 2024. The GitHub repository links the later IEEE TMI version at DOI 10.1109/tmi.2025.3562056. Pretrained .pth files for several widths are in the repository. Collected 2026-10-15.", "what_it_is_for": "The paper replaces a learnable encoder with handcrafted multi-scale pooling features and keeps a learnable decoder, so the network can predict a 3D deformation with far fewer parameters than VoxelMorph or TransMorph. Reported experiments are on public brain MRI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "birmingham/lku-net/lku-net": { "id": "birmingham/lku-net/lku-net", "name": "LKU-Net", "maker": "birmingham", "family": "birmingham/lku-net", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-08-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Large-kernel U-Net for deformable medical image registration. A parallel convolutional block enlarges the receptive field of a vanilla U-Net. Authors on the arXiv record are Xi Jia, Joseph Bartlett, Tianyang Zhang, Wenqi Lu, Zhaowen Qiu, and Jinming Duan. The HTML affiliation line places Jia, Bartlett, and Zhang at University of Birmingham Computer Science. Official code is https://github.com/xi-jia/LKU-Net. The README says trained models are provided and links Google Drive for LKU-Net-diff(16,5) and for Learn2Reg 2021 OASIS weights. GitHub licence field is empty. Accepted to MICCAI-MLMI 2022.", "modalities": { "input": [ "image" ], "output": [ "deformation-field" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LKU-Net (Large Kernel U-Net) is a medical image registration model from Xi Jia, Joseph Bartlett, and Tianyang Zhang at University of Birmingham Computer Science, with Wenqi Lu, Zhaowen Qiu, and Jinming Duan. The arXiv preprint was submitted on 7 August 2022 and accepted to MICCAI-MLMI 2022. Official code, IXI results, and Google Drive weight links are at https://github.com/xi-jia/LKU-Net. Collected 2026-10-15.", "what_it_is_for": "The paper asks whether a convolutional U-Net is outdated relative to transformers for registration. LKU-Net adds a parallel large-kernel block. The README reports first place on Learn2Reg 2021 Task 3 validation as of 18 September 2022, a win on Learn2Reg 2022 Task 1, and second place for LKU-Net-Affine on ISBI RnR-ExM 2023.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "birmingham/mpbppi/mpbppi": { "id": "birmingham/mpbppi/mpbppi", "name": "MpbPPI", "maker": "birmingham", "family": "birmingham/mpbppi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT for the software, copyright 2023 Yang Yue, in LICENSE.txt. A separate weights licence is not stated.", "release_date": "2023-08-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-task pretrained geometric equivariant model for the change in protein-protein binding free energy after amino-acid mutation. The README also uses the name EquiPPI for the same framework. A committed pretrained file is storage_pretraining/MpbPPI_0.001_100_30_trunc_normal_0.15_True_1_1_1_1_5_[512, 128, 3]_True_0.2_False_20_(256, 16, 32, 1)_True_True_True_True_cat.pt (18,685,233 bytes). Downstream training scripts are in the same repository. Feature-set labels MpbPPI_BSSA and MpbPPI_BSA in the paper are ablations, not separate products.", "modalities": { "input": [ "protein-structure" ], "output": [ "ddg" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MpbPPI is a geometric model that predicts how an amino-acid mutation changes protein-protein binding free energy. Yang Yue (School of Computer Science, University of Birmingham) and Shu Li are joint first authors. Shan He, at the same school, is the senior Birmingham author. Co-authors are at Macao Polytechnic University. Briefings in Bioinformatics published the article online on 31 August 2023 (volume 24, issue 5, bbad310; portal publication date 20 September 2023). Code and one pretrained `.pt` file are at https://github.com/arantir123/MpbPPI. Collected 2026-10-15.", "what_it_is_for": "The paper uses multi-task pretraining on protein-complex geometry so a downstream head can predict mutation \u0394\u0394G when labelled complexes are scarce. The README says the implementation names MpbPPI and EquiPPI refer to the same framework. Reported datasets include S645, M1101, S1131, and S4169. Numeric table values were not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "birmingham/sacb-net/sacb-net": { "id": "birmingham/sacb-net/sacb-net", "name": "SACB-Net", "maker": "birmingham", "family": "birmingham/sacb-net", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "3D pyramid registration network built from Spatial-Awareness Convolution Blocks. Each block clusters feature-map regions by feature similarity and generates convolution weights and biases per region, then composes flow across scales. Official code is https://github.com/x-xc/SACB_Net. The README points weights at a Google Drive folder. CVPR 2025, pages 5227-5237. GitHub licence was not stated in the README.", "modalities": { "input": [ "image" ], "output": [ "deformation-field" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SACB-Net is a 3D medical image registration network from Xinxing Cheng, Tianyang Zhang, Qingjie Meng, and Jinming Duan, with Wenqi Lu and Alejandro F. Frangi. The arXiv preprint was submitted on 25 March 2025 and the paper is in CVPR 2025, pages 5227-5237. Official code is https://github.com/x-xc/SACB_Net. The README links a Google Drive folder for weights. Collected 2026-10-15.", "what_it_is_for": "The abstract says shared convolution kernels miss spatially varying features, so SACB estimates spatial clusters and builds a different kernel for each region. SACB-Net stacks those blocks in a pyramid flow estimator for large deformations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bit/earth-onevision/earth-onevision": { "id": "bit/earth-onevision/earth-onevision", "name": "Earth-OneVision", "maker": "bit", "family": "bit/earth-onevision", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-09", "context_length": 5400, "max_output_tokens": null, "parameters": "2 billion", "architecture": "Autoregressive remote-sensing multimodal model. The paper says 2 billion parameters and one model for optical, SAR, infrared, multispectral, temporal, and video inputs, plus cross-sensor fusion, across 9 task categories. Named pieces: full-granularity vision-language alignment, spatial-linguistic isomorphic serialization, and progressive cross-modality adaptation. Training note in the HTML: 8 H100 80 GB GPUs, DeepSpeed ZeRO-2, bfloat16 mixed precision, AdamW weight decay 0.05, cosine schedule, warmup ratio 0.03, batch size 4 per GPU, maximum sequence length 5,400 tokens. The language-model and vision-encoder names were not read. No weight link was found. The 5,400 figure is that training maximum, not a published serving limit.", "modalities": { "input": [ "image", "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Earth-OneVision is a 2 billion parameter remote-sensing model from the National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing at Beijing Institute of Technology. The corresponding author on the HTML is Yin Zhuang. The preprint was submitted on 9 June 2026. Collected 2026-10-15. No weight link was found.", "what_it_is_for": "The abstract says one autoregressive model covers optical, SAR, infrared, multispectral, temporal, and video sensing, including cross-sensor fusion, across nine task categories. Named mechanisms are full-granularity vision-language alignment, spatial-linguistic isomorphic serialization, and progressive cross-modality adaptation. The instruction set MMRS-OneVision is about 34 million question-answer pairs. The paper writes the count with a tilde.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bit/earthgpt/earthgpt": { "id": "bit/earthgpt/earthgpt", "name": "EarthGPT", "maker": "bit", "family": "bit/earthgpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-01-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Remote-sensing multimodal model. The arXiv HTML says a ViT encodes multi-layer image features, LLaMA-2 is the language model, and a later instruction stage freezes the cross-modal stage and adds new learnable parameters in LLaMA-2. The LLaMA-2 size is not stated in the sections read. Tasks named on the GitHub page: scene classification, image captioning, region captioning, VQA, visual grounding, and object detection, over optical, SAR, and infrared images. The official repo contains a README and images, not weights.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EarthGPT is a remote-sensing multimodal model from Wei Zhang, Miaoxin Cai, Tong Zhang, Yin Zhuang, and Xuerui Mao at Beijing Institute of Technology. The arXiv preprint is dated 30 January 2024, and IEEE TGRS later published it. Collected 2026-10-15. The official GitHub repo releases the MMRS-1M dataset links, not the weights.", "what_it_is_for": "The README says EarthGPT is for multi-turn remote-sensing interpretation: scene classification, image captioning, region captioning, visual question answering, visual grounding, and object detection. It covers optical, SAR, and infrared images. LLaMA-2 is the language backbone named in the paper HTML. This folder is the BIT model, not a copy of LLaMA-2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bit/earthgpt/earthgpt-x": { "id": "bit/earthgpt/earthgpt-x", "name": "EarthGPT-X", "maker": "bit", "family": "bit/earthgpt", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Spatial remote-sensing multimodal model. The 17 April 2025 manuscript HTML says the language model is Llama 2-13B, the image encoders are DINOv2 ViT-L/14 and CLIP-ConvNeXt, and the training image size is 448 by 448. It also describes a visual-prompt encoder and a pixel-perception module. The November 2025 abstract does not restate those backbone names. Prompts include points, boxes, and free-form marks over optical, SAR, and infrared images. The official repo says the code, dataset, and chatbot are coming soon.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EarthGPT-X is a spatial remote-sensing model from the Beijing Institute of Technology group behind EarthGPT and EarthMarker. The preprint was submitted on 17 April 2025 and revised on 6 November 2025. Collected 2026-10-15. The official repository says the code, dataset, and chatbot are still coming soon, so this folder is an announcement plus a paper, not a weight release.", "what_it_is_for": "The abstract says one model handles coarse and fine remote-sensing tasks on optical, SAR, and infrared images when the user adds a point, a box, or a free-form mark to the text instruction. The v1 HTML also names a pixel-perception module. What that module emits was not copied into a modality claim beyond text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bit/earthgpt/earthmarker": { "id": "bit/earthgpt/earthmarker", "name": "EarthMarker", "maker": "bit", "family": "bit/earthgpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-07-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Visual-prompting remote-sensing multimodal model. The paper and BIT Pure record say it reads an image together with box or point prompts and answers at image, region, or point level. A shared visual encoder relates multi-scale image features to those prompts, then mixes them with language instructions. Backbone names, layer counts, and parameter counts were not read. The GitHub repo is a README plus an example image. It links the RSVP dataset, not weights.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EarthMarker is a visual-prompting remote-sensing model from the Beijing Institute of Technology group that also released EarthGPT. Authors on the GitHub page are Wei Zhang, Miaoxin Cai, Tong Zhang, Yin Zhuang, and Xuerui Mao, with Jun Li on the paper. The GitHub news date for the preprint is 19 July 2024. IEEE TGRS accepted it on 22 December 2024, according to that README. Collected 2026-10-15. The repo releases the RSVP dataset link, not weights.", "what_it_is_for": "The BIT Pure abstract says EarthMarker interprets a remote-sensing image at image, region, or point level when the user adds a box or a point to the language instruction. A January 2025 campus-news repost calls it a continuation of EarthGPT and says the visual-prompt dataset was opened. The model file was not part of that claim.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bit/mindllm/mindllm-1-3b": { "id": "bit/mindllm/mindllm-1-3b", "name": "MindLLM-1.3B", "maker": "bit", "family": "bit/mindllm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-10-24", "context_length": 1024, "max_output_tokens": null, "parameters": "1.3 billion", "architecture": "Decoder-only transformer matching GPT-Neo-1.3B: local attention, pre-normalization, final normalization, and GeLU. Paper Table 2: hidden size 2048, FFN size 8192, 16 heads, 24 layers, sequence length 1024. Trained from scratch in two stages. Stage 1 is English-only. Stage 2 expands a SentencePiece vocabulary trained on WuDao and continues on mixed Chinese and English. The paper does not print the expanded vocabulary size. The only public checkpoint is the later Chinese chat model, whose config does not match this sequence length.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MindLLM-1.3B is the smaller bilingual language model from Beijing Institute of Technology's School of Computer Science, the Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, and the Beijing Institute of Technology Southeast Academy of Information Technology. The 24 October 2023 paper presents it as a from-scratch model. Collected 2026-10-15. No base weight file was found. The public file is the later Chinese chat checkpoint.", "what_it_is_for": "The paper presents MingDe LLM, short for MindLLM, as a lightweight bilingual model for groups that cannot train or serve a large general model. The 1.3B run is the English-then-transfer member of the pair. Law and finance sections are later supervised experiments, not separate released models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bit/mindllm/mindllm-1-3b-chat-zh-v2-0": { "id": "bit/mindllm/mindllm-1-3b-chat-zh-v2-0", "name": "MindLLM-1.3B-Chat-zh-v2.0", "maker": "bit", "family": "bit/mindllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-17", "context_length": 2048, "max_output_tokens": null, "parameters": "1.3 billion", "architecture": "GPTNeoForCausalLM. config.json: hidden size 2048, 24 layers, 16 heads, alternating global and local attention, window 256, GeLU-new, vocabulary 75,170, max position embeddings 2048, bos and eos 50256, bfloat16, transformers 4.34.1. intermediate_size is null. The card calls it a 1.3 billion parameter model trained from scratch and then supervised on Chinese instructions. The paper's Table 2 sequence length for MindLLM-1.3B is 1024, which does not match this config. No RLHF.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MindLLM-1.3B-Chat-zh-v2.0 is the public Chinese chat checkpoint from the MindLLM project at Beijing Institute of Technology and the Beijing Institute of Technology Southeast Academy of Information Technology, uploaded by the Hub user bit-dny. It is a from-scratch GPT-Neo-style model with a later Chinese instruction tune and no RLHF. Collected 2026-10-15. The weights are on Hugging Face under Apache-2.0. This is the only MindLLM file the Hub account lists.", "what_it_is_for": "The card says the checkpoint was opened so researchers can study safety on a small unrestricted model and adapt it to a domain. The instruction data is Chinese. The card's own examples are Chinese chat, including a short Python snippet. English is listed as a language tag. The card says the chat behavior is Chinese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bit/mindllm/mindllm-3b": { "id": "bit/mindllm/mindllm-3b", "name": "MindLLM-3B", "maker": "bit", "family": "bit/mindllm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-10-24", "context_length": 2048, "max_output_tokens": null, "parameters": "3.1 billion", "architecture": "Decoder-only transformer. Paper Table 2: hidden size 2560, FFN size 8192, 32 heads, 32 layers, sequence length 2048. The text adds input-embedding normalization, RoPE, RMSNorm, DeepNorm, FlashAttention-2, and GeGLU, and says the GeGLU width is 8/3 of the hidden size so the parameter count matches a standard FFN. Table 2's FFN size of 8192 is not 8/3 of 2560. SentencePiece vocabulary size 125,700. Trained from scratch on mixed Chinese and English, not by the 1.3B transfer schedule. No public checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MindLLM-3B is the larger from-scratch bilingual model in the MindLLM paper by Beijing Institute of Technology and its language-information engineering center and southeast academy. The paper says it has 3.1 billion parameters and was trained on mixed Chinese and English data. Collected 2026-10-15. No weight file was found.", "what_it_is_for": "The paper uses the 3B model for the same lightweight bilingual job as the 1.3B model, with a different architecture and a mixed-data schedule. The authors single it out for arithmetic and for Chinese-English alignment. Domain law and finance numbers in the paper are fine-tunes, not this base release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bit/popeye/popeye": { "id": "bit/popeye/popeye", "name": "Popeye", "maker": "bit", "family": "bit/popeye", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Ship-detection visual-language model. The paper and a BIT patent say it starts from LLaMA, aligns image and text features, and fine-tunes with LoRA rather than updating every LLaMA weight. The LLaMA version and size were not stated in the pages read. A separate frozen Segment Anything model can turn Popeye boxes into masks without extra Popeye training. Training data named MMShip: 81,000 instruction examples from optical and SAR ship sets. No public weight repo was found.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Popeye is a ship-detection visual-language model from Beijing Institute of Technology. The preprint is 6 March 2024. Authors on the BIT Pure record are Wei Zhang, Miaoxin Cai, Tong Zhang, Guoqiang Lei, Yin Zhuang, and Xuerui Mao. Collected 2026-10-15. No weight repository was found. A Chinese patent application assigned to the university describes the same name.", "what_it_is_for": "The paper says a user asks in language for ships in an optical or SAR image and the model returns horizontal boxes or oriented boxes. A frozen Segment Anything model can turn those boxes into masks without more Popeye training. The training set is MMShip, 81,000 instruction examples. LLaMA and SAM are other makers' models. This folder is the BIT ship model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bit/rgmt/extreme-rgmt": { "id": "bit/rgmt/extreme-rgmt", "name": "Extreme-RGMT", "maker": "bit", "family": "bit/rgmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage humanoid whole-body controller from the School of Automation, Beijing Institute of Technology. The paper first trains a generalist motion-tracking policy, then uses continual learning so new highly dynamic skills do not erase earlier ones. It adds difficulty-aware sampling and advantage-prioritized trajectory resampling. Inputs described in the abstract include fixed motion references and online inertial motion capture. No parameter count. The arXiv abs page does not show a code or weight link.", "modalities": { "input": [ "proprioception", "motion" ], "output": [ "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Extreme-RGMT is a humanoid whole-body controller from the School of Automation at Beijing Institute of Technology. The preprint was submitted on 22 July 2026 by Yubiao Ma, Han Yu, Kai Guo, Changtai Lv, Zhengquan Mao, Boyang Xing, Xuemei Ren, and Dongdong Zheng. The HTML gives bit.edu.cn addresses for Ma, Ren, and Zheng. Collected 2026-10-15. No weight file was found on the arXiv page.", "what_it_is_for": "The abstract says generalist trackers miss rare dynamic skills, and specialist training can wipe out older skills. Extreme-RGMT trains a generalist base, then continues learning difficult segments while trying to keep the old behavior. The abstract says the result tracks unseen dynamic motion from a fixed reference or from online inertial motion capture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bit/rgmt/longyu": { "id": "bit/rgmt/longyu", "name": "\u9f99\u9a6d (RGMT)", "maker": "bit", "family": "bit/rgmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-01-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Humanoid whole-body motion-tracking policy. The project page and the paper describe a causal temporal encoder over recent proprioception, a multi-head cross-attention command encoder over a context window, and a fall-recovery curriculum. Single-stage training without distillation. About 3.5 hours of motion data. No parameter count and no weight file on the project page. The page title is the paper title, not \u9f99\u9a6d.", "modalities": { "input": [ "proprioception", "motion" ], "output": [ "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u9f99\u9a6d is the press name for the humanoid whole-body controller whose project page and paper are called Robust and Generalized Humanoid Motion Tracking (RGMT). A 14 February 2026 report says the National-Local Joint Humanoid Robot Innovation Center and Beijing Institute of Technology released it together, and it links the RGMT page. The project page itself does not print the name \u9f99\u9a6d or a university line. Collected 2026-10-15. Confidence is medium because the product name and the joint credit are from that report, while the method is documented on the project page. No weights were found.", "what_it_is_for": "The project page describes a general humanoid tracker for noisy reference motion, including motion-capture replay, video-derived motion, teleoperation, and fall recovery. The press report says the same controller runs on a physical humanoid and is the \"cerebellum\" model of a teleoperation system. The paper says training uses about 3.5 hours of motion and does not use a distillation stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bjtu/transgpt/transgpt-7b": { "id": "bjtu/transgpt/transgpt-7b", "name": "TransGPT-7B", "maker": "bjtu", "family": "bjtu/transgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Repository source is Apache-2.0. The project says commercial use of the model or derivatives needs a free email grant from duomo_tech@163.com.", "release_date": "2023-07-18", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM. config.json names the base shibing624/chinese-alpaca-plus-7b-hf: 32 layers, hidden size 4096, 32 attention heads, intermediate size 11008, vocabulary 49954, float16, max_position_embeddings 2048. The card calls the upload merged weights. An exact parameter count is not printed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TransGPT-7B is the text checkpoint Beijing Jiaotong University released with the CCF intelligent-transportation branch and \u8db3\u667a\u591a\u6a21 (DUOMO) as TransGPT\u00b7\u81f4\u8fdc. Weights have been on Hugging Face as DUOMO-Lab/TransGPT-v0 since the July 2023 open-source note. This file was collected on 2026-10-15.", "what_it_is_for": "The university page and the GitHub README describe a transportation model for traffic forecasting, planning, public-transport questions, safety education, incident reports, policy write-ups, and driving assistance. Domain text is Chinese. The README says the release used about 346,000 transportation text rows for in-domain pre-training and about 58,000 dialogue rows for fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bjtu/transgpt/transgpt-mm-6b": { "id": "bjtu/transgpt/transgpt-mm-6b", "name": "TransGPT-MM-6B", "maker": "bjtu", "family": "bjtu/transgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The v0 card tag is other. The repository source is Apache-2.0, and commercial use of TransGPT models still needs the email grant to duomo_tech@163.com.", "release_date": "2023-08-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The current GitHub multimodal readme says the backbone is THUDM VisualGLM-6B, trained with SwissArmyTransformer and LoRA. The February 2024 paper says the language model is ChatGLM-6B with a BLIP-2 Q-Former, fine-tuned with LoRA rank 32. An exact parameter count, including the vision encoder, is not printed. Early press described a frozen Vicuna plus a trained Q-Former. That does not match the current VisualGLM card.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TransGPT-MM-6B is the image-and-text transportation model from the same Beijing Jiaotong University, CCF, and \u8db3\u667a\u591a\u6a21 release as TransGPT\u00b7\u81f4\u8fdc. v1 weights remain public at DUOMO-Lab/TransGPT-MM-v1. This file was collected on 2026-10-15.", "what_it_is_for": "The multimodal readme and the paper say it answers questions about traffic signs, driving-test items, and landmarks. The v1 card says general image-text pairs were mixed in so the model keeps broader visual chat as well as transportation answers. The paper's test set has 122 samples. Training MTD is described as 3,183 aligned image-text samples, plus a translated general set called CCAC with 3,439 samples.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bjtu/transgpt/transgpt-sm": { "id": "bjtu/transgpt/transgpt-sm", "name": "TransGPT-SM", "maker": "bjtu", "family": "bjtu/transgpt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-02-11", "context_length": null, "max_output_tokens": null, "parameters": "6000000000", "architecture": "The paper says this is a LoRA fine-tune of ChatGLM2-6B, which it states has 6 billion parameters. Training in the paper: 3 epochs, learning rate 1e-4, batch size 32, Adam, cross-entropy. The public download table still points the text model at the Llama TransGPT-7B checkpoint, so this 6 billion figure is the stated base size, not a counted file.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TransGPT-SM is the single-modal text model named in the February 2024 TransGPT paper by Beijing Jiaotong University authors. The paper says it is a LoRA fine-tune of ChatGLM2-6B. A separate public weight file was not found, so confidence is medium. This file was collected on 2026-10-15.", "what_it_is_for": "The paper presents it as the text model for transportation questions, traffic-engineering and planning tests, a driving-license exam, and a flow-forecast demo. Domain text is Chinese. The authors say they built about 156,000 question-answer pairs from unlabeled transportation documents, after collecting a raw corpus they count at 97.6 million tokens. Correspondence is Wenjuan Han at Beijing Jiaotong University. The paper's website link is the same GitHub repository as TransGPT-7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bjtu/videoworld/videoworld": { "id": "bjtu/videoworld/videoworld", "name": "VideoWorld", "maker": "bjtu", "family": "bjtu/videoworld", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-01-21", "context_length": null, "max_output_tokens": null, "parameters": "300000000", "architecture": "Autoregressive video model with a latent dynamics model that compresses multi-frame visual change into latent codes, then a next-token transformer. The paper's 5-dan Go result is the 300-million-parameter model. The Hub bundle also ships InternLM 300M weights, Go and CALVIN task checkpoints, and FSQ quantizer weights. The 300 million figure is the paper's stated transformer size, not a sum of every file.", "modalities": { "input": [ "image", "video" ], "output": [ "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoWorld is an autoregressive video model co-released by Beijing Jiaotong University and ByteDance Seed. It learns Go and robot control from unlabeled video. Apache-2.0 weights have been on Hugging Face since 2025-01-21. This file was collected on 2026-10-15.", "what_it_is_for": "The paper and README say the model learns rules, planning, and control by predicting video, without a reward or a search loop. The released bundle plays 9x9 Go from board images and runs a CALVIN robot subset: opening drawers, pushing blocks, and switching lights. Beijing Jiaotong University is the first affiliation. Yunchao Wei is a correspondence author there. Xiaojie Jin is project lead. The code repository is ByteDance-Seed. The weight upload is under maverickrzw, an author on the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bjtu/videoworld/videoworld-2": { "id": "bjtu/videoworld/videoworld-2", "name": "VideoWorld 2", "maker": "bjtu", "family": "bjtu/videoworld", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-02-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dynamics-enhanced latent dynamics model (dLDM). A pretrained video diffusion model carries appearance. The dLDM learns latent codes for task dynamics, and an autoregressive transformer predicts those codes. The released bundle is named VideoWorld2_dLDM_2B. Training instructions also require NVIDIA Cosmos-Predict2-2B-Video2World, which is a separate download. An exact parameter count was not printed apart from the 2B name.", "modalities": { "input": [ "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoWorld 2 is the follow-up video world model from Beijing Jiaotong University and ByteDance Seed. It targets long-horizon skills learned from raw real-world video. Apache-2.0 weights named VideoWorld2_dLDM_2B have been public since 2026-02-02. This file was collected on 2026-10-15.", "what_it_is_for": "The README says a dynamics-enhanced latent dynamics model separates task motion from appearance, then an autoregressive transformer predicts latent codes for a new scene. The published test turns an initial image into a long imagined execution video on Video-CraftBench handicraft tasks. Training data named in the README are Video-CraftBench and Open-X. The paper also reports better CALVIN manipulation after Open-X training. The README says the work was accepted to CVPR 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-1-pro": { "id": "black-forest-labs/flux-1/flux-1-1-pro", "name": "FLUX1.1 [pro]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed text-to-image API model. BFL said it is faster than FLUX.1 [pro] and better on quality, prompt adherence, and diversity. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.04 }, "summary_intro": "Black Forest Labs FLUX1.1 [pro] is a flux-1 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX1.1 [pro] is the October 2, 2024 text-to-image API model. Black Forest Labs released it as a faster, higher-quality follow-on to FLUX.1 [pro], with the BFL API moving to general availability. It is still on the price list at $0.04 per image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-1-pro-ultra": { "id": "black-forest-labs/flux-1/flux-1-1-pro-ultra", "name": "FLUX1.1 [pro] Ultra", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "High-resolution mode of the FLUX1.1 [pro] line. Up to 4MP. Raw mode is a flag on the same endpoint, not a separate model.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.06 }, "summary_intro": "Black Forest Labs FLUX1.1 [pro] Ultra is a flux-1 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX1.1 [pro] Ultra is the 4-megapixel FLUX1.1 API mode. The same endpoint serves Raw mode for a more candid photographic look. Black Forest Labs prices both at $0.06 per image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-canny-dev": { "id": "black-forest-labs/flux-1/flux-1-canny-dev", "name": "FLUX.1 Canny [dev]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "FLUX.1 [dev] Non-Commercial License", "release_date": "2024-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Open-weight Canny-edge FLUX.1 model and a LoRA. The tools page deprecates both.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.1 Canny [dev] is a flux-1 model. Status on 2026-10-15: deprecated. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 Canny [dev] is the open edge-conditioned checkpoint from FLUX.1 Tools, with a LoRA alternative. Black Forest Labs' tools page now marks Canny dev and its LoRA deprecated. The repositories were still online.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-canny-pro": { "id": "black-forest-labs/flux-1/flux-1-canny-pro", "name": "FLUX.1 Canny [pro]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed edge-guided FLUX.1 tool. A Canny edge map plus a text prompt.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.1 Canny [pro] is a flux-1 model. Status on 2026-10-15: deprecated. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 Canny [pro] was the hosted edge-guided image tool. The `flux-pro-1.0-canny` endpoint was deprecated on October 31, 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-depth-dev": { "id": "black-forest-labs/flux-1/flux-1-depth-dev", "name": "FLUX.1 Depth [dev]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "FLUX.1 [dev] Non-Commercial License", "release_date": "2024-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Open-weight depth-conditioned FLUX.1 model, plus a LoRA variant. The live tools page deprecates both.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.1 Depth [dev] is a flux-1 model. Status on 2026-10-15: deprecated. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 Depth [dev] is the open depth-control checkpoint and LoRA from FLUX.1 Tools. The current tools page says the Depth models, including dev and LoRA, are deprecated. The files were still published on the official Hugging Face org.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-depth-pro": { "id": "black-forest-labs/flux-1/flux-1-depth-pro", "name": "FLUX.1 Depth [pro]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed structural-conditioning model. A depth map plus a text prompt. FLUX.1 Tools.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.1 Depth [pro] is a flux-1 model. Status on 2026-10-15: deprecated. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 Depth [pro] was the hosted depth-guided FLUX.1 tool. Black Forest Labs deprecated `flux-pro-1.0-depth` on October 31, 2025. The tools page now says the Depth models are no longer supported on the API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-dev": { "id": "black-forest-labs/flux-1/flux-1-dev", "name": "FLUX.1 [dev]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLUX.1 [dev] Non-Commercial License", "release_date": "2024-08-01", "context_length": null, "max_output_tokens": null, "parameters": "12B", "architecture": "12B rectified flow transformer. Guidance-distilled. Hybrid multimodal and parallel diffusion transformer blocks, per the launch post and the public model-card summary.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.025 }, "summary_intro": "Black Forest Labs FLUX.1 [dev] is a flux-1 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 [dev] is the 12B open-weight text-to-image model from the original FLUX.1 release. It is guidance-distilled for non-commercial local use, with a paid BFL API still priced at $0.025 per image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-expand-pro": { "id": "black-forest-labs/flux-1/flux-1-expand-pro", "name": "FLUX.1 Expand [pro]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed outpainting endpoint that adds pixels on any side of an image. Separate from FLUX.1 Fill and from the 2026 FLUX Outpainting tool.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.1 Expand [pro] is a flux-1 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 Expand [pro] is a current BFL API endpoint that extends an image by adding pixels on any side. It is distinct from mask-based Fill and from the later FLUX Outpainting tool.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-fill-dev": { "id": "black-forest-labs/flux-1/flux-1-fill-dev", "name": "FLUX.1 Fill [dev]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLUX.1 [dev] Non-Commercial License", "release_date": "2024-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Guidance-distilled open-weight inpainting and outpainting model. Same tools release as Fill [pro].", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.1 Fill [dev] is a flux-1 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 Fill [dev] is the open-weight inpainting and outpainting checkpoint from FLUX.1 Tools. It uses the FLUX.1 [dev] non-commercial license. The hosted product is Fill [pro].", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-fill-pro": { "id": "black-forest-labs/flux-1/flux-1-fill-pro", "name": "FLUX.1 Fill [pro]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed inpainting and outpainting model. Text plus a binary mask. Part of FLUX.1 Tools.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "Black Forest Labs FLUX.1 Fill [pro] is a flux-1 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 Fill [pro] is the hosted inpainting and outpainting tool. You send an image, a mask, and a prompt. It is still on the BFL price list at $0.05, unlike the deprecated Depth and Canny pro endpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-kontext-dev": { "id": "black-forest-labs/flux-1/flux-1-kontext-dev", "name": "FLUX.1 Kontext [dev]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLUX.1 [dev] Non-Commercial License", "release_date": "2025-06-26", "context_length": null, "max_output_tokens": null, "parameters": "12B", "architecture": "12B rectified flow transformer for instruction editing. Guidance-distilled open weights. The card says editing. The overview says editing mode only.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.1 Kontext [dev] is a flux-1 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 Kontext [dev] is the 12B open-weight editing model in the Kontext family. It was in private beta on May 29, 2025 and the public weights followed on June 26, 2025 under the FLUX.1 [dev] non-commercial license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-kontext-max": { "id": "black-forest-labs/flux-1/flux-1-kontext-max", "name": "FLUX.1 Kontext [max]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed experimental Kontext tier. The announcement emphasizes prompt adherence, typography, and edit consistency. Parameter count is not published. About 1MP output.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.08 }, "summary_intro": "Black Forest Labs FLUX.1 Kontext [max] is a flux-1 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 Kontext [max] is the higher-priced closed Kontext model. Black Forest Labs describes it as the quality tier for typography, prompt adherence, and edit consistency. It launched with Kontext [pro] on May 29, 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-kontext-pro": { "id": "black-forest-labs/flux-1/flux-1-kontext-pro", "name": "FLUX.1 Kontext [pro]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed in-context generation and editing model. Flow matching. The dev sibling is 12B. [pro] size is not published. Outputs about 1MP.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.04 }, "summary_intro": "Black Forest Labs FLUX.1 Kontext [pro] is a flux-1 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 Kontext [pro] is the fast closed Kontext model. It generates from text and edits from text plus images in one model. Black Forest Labs still prices it, and the docs now point new work at FLUX.2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-krea-dev": { "id": "black-forest-labs/flux-1/flux-1-krea-dev", "name": "FLUX.1 Krea [dev]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLUX.1 [dev] Non-Commercial License", "release_date": "2025-07-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Open-weight text-to-image model trained with Krea AI for a less oversaturated photographic look. Architecturally compatible with FLUX.1 [dev]. A separate parameter count is not given. HF lists it as a finetune of FLUX.1-dev.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.1 Krea [dev] is a flux-1 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 Krea [dev] is an open-weight text-to-image model Black Forest Labs released with Krea AI on July 31, 2025. It is aimed at photographic diversity and less of the usual oversaturated look, and it stays compatible with the FLUX.1 [dev] toolchain.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-pro": { "id": "black-forest-labs/flux-1/flux-1-pro", "name": "FLUX.1 [pro]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed API model. The August 1, 2024 announcement says public FLUX.1 models use hybrid multimodal and parallel diffusion transformer blocks scaled to 12B. Secondary sources disagree on whether that 12B figure includes [pro]. The count is left null.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "Black Forest Labs FLUX.1 [pro] is a flux-1 model. Status on 2026-10-15: deprecated. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 [pro] is the original closed FLUX.1 text-to-image API model from August 1, 2024. Black Forest Labs deprecated the Flux Pro 1.0 endpoint on October 31, 2025. A $0.05 price card for /v1/flux-pro was still published on the collection date.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-redux-dev": { "id": "black-forest-labs/flux-1/flux-1-redux-dev", "name": "FLUX.1 Redux [dev]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLUX.1 [dev] Non-Commercial License", "release_date": "2024-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adapter for image variation and restyling on FLUX.1 base models. The tools post calls it an adapter, not a full text-to-image foundation.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.1 Redux [dev] is a flux-1 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 Redux [dev] is the open adapter for image variations and restyling. It shipped with FLUX.1 Tools. You run it with a FLUX.1 base model rather than as a full generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-redux-pro": { "id": "black-forest-labs/flux-1/flux-1-redux-pro", "name": "FLUX.1 Redux [pro]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "API image-variation feature. The tools post says restyling is an image plus a prompt on FLUX1.1 [pro] Ultra, not a separate open checkpoint.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.06 }, "summary_intro": "Black Forest Labs FLUX.1 Redux [pro] is a flux-1 model. Status on 2026-10-15: active. Confidence is medium because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 Redux [pro] is the hosted image-variation feature from the November 2024 tools launch. The live tools page says API restyling goes through FLUX1.1 [pro] Ultra with an image and a prompt. There is no separate Redux price card in the 2026 pricing CMS.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-1/flux-1-schnell": { "id": "black-forest-labs/flux-1/flux-1-schnell", "name": "FLUX.1 [schnell]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-01", "context_length": null, "max_output_tokens": null, "parameters": "12B", "architecture": "12B hybrid diffusion transformer, same public FLUX.1 scale as [dev]. Distilled for fast few-step sampling. The launch post does not print the step count. Secondary pages say 1 to 4 steps.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.1 [schnell] is a flux-1 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.1 [schnell] is the Apache 2.0 FLUX.1 model meant for fast local inference. Black Forest Labs released it with [pro] and [dev] on August 1, 2024. The weights remain on Hugging Face. The current BFL price list does not include it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-2/flux-2-dev": { "id": "black-forest-labs/flux-2/flux-2-dev", "name": "FLUX.2 [dev]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLUX Non-Commercial License", "release_date": "2025-11-25", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "32B rectified flow transformer, open weights, derived from the FLUX.2 base model. The launch post says FLUX.2 couples that transformer with the Mistral-3 24B vision-language model.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.2 [dev] is a flux-2 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.2 [dev] is the 32B open-weight FLUX.2 checkpoint for local generation and multi-reference editing. Black Forest Labs licenses the weights for non-commercial use and sells commercial licenses. It is not a hosted BFL API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-2/flux-2-flex": { "id": "black-forest-labs/flux-2/flux-2-flex", "name": "FLUX.2 [flex]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed FLUX.2 tier specialized for typography and small detail. Steps and guidance are user-set. Family uses a rectified flow transformer with a Mistral-3 24B VLM, per the launch post. [flex] size is not published.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "Black Forest Labs FLUX.2 [flex] is a flux-2 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.2 [flex] is the FLUX.2 image model Black Forest Labs aims at typography and fine detail. Callers can change inference steps and guidance. It launched the same day as FLUX.2 [pro].", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-2/flux-2-klein-4b": { "id": "black-forest-labs/flux-2/flux-2-klein-4b", "name": "FLUX.2 [klein] 4B", "maker": "black-forest-labs", "family": "black-forest-labs/flux-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-15", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "4B rectified flow transformer. Step-distilled and guidance-distilled. Reference sample uses 4 steps and guidance 1.0. Unified text-to-image and multi-reference editing.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.014 }, "summary_intro": "Black Forest Labs FLUX.2 [klein] 4B is a flux-2 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.2 [klein] 4B is the smallest and fastest open FLUX.2 model. Black Forest Labs releases it under Apache 2.0 for commercial local use and also serves it on the API. It generates and edits with up to four references.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-2/flux-2-klein-9b": { "id": "black-forest-labs/flux-2/flux-2-klein-9b", "name": "FLUX.2 [klein] 9B", "maker": "black-forest-labs", "family": "black-forest-labs/flux-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLUX Non-Commercial License", "release_date": "2026-01-15", "context_length": null, "max_output_tokens": null, "parameters": "9B flow model plus an 8B Qwen3 text embedder", "architecture": "Step-distilled and guidance-distilled 9B flow model with an 8B Qwen3 text embedder. 4 inference steps. Unified generation and editing.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.015 }, "summary_intro": "Black Forest Labs FLUX.2 [klein] 9B is a flux-2 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.2 [klein] 9B is the larger fast FLUX.2 model. Black Forest Labs describes a 9B flow model plus an 8B Qwen3 text embedder, distilled to 4 steps. Weights are non-commercial. The API serves a pinned snapshot and a KV-cache preview.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-2/flux-2-klein-9b-kv": { "id": "black-forest-labs/flux-2/flux-2-klein-9b-kv", "name": "FLUX.2 [klein] 9B KV", "maker": "black-forest-labs", "family": "black-forest-labs/flux-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLUX Non-Commercial License", "release_date": "2026-03-09", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "KV-cached variant of FLUX.2 [klein] 9B. The flux2 README lists it as step-distilled and guidance-distilled, with text-to-image and single- and multi-reference editing. It says 9B KV is the faster editing option at the same quality.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.2 [klein] 9B KV is a flux-2 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.2 [klein] 9B KV is a named open-weight variant with KV caching. Black Forest Labs' inference repo lists it separately from distilled 9B, and the docs map the klein 9B preview endpoint to this KV design. It is for lower latency editing, not a new training base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-2/flux-2-klein-base-4b": { "id": "black-forest-labs/flux-2/flux-2-klein-base-4b", "name": "FLUX.2 [klein] Base 4B", "maker": "black-forest-labs", "family": "black-forest-labs/flux-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-15", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Undistilled 4B foundation checkpoint. Not step-distilled. Same Apache 2.0 line as distilled 4B. For fine-tuning and custom pipelines.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.2 [klein] Base 4B is a flux-2 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.2 [klein] Base 4B is the undistilled Apache 2.0 foundation behind the fast 4B model. Black Forest Labs ships it for fine-tuning and research, not as a public API endpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-2/flux-2-klein-base-9b": { "id": "black-forest-labs/flux-2/flux-2-klein-base-9b", "name": "FLUX.2 [klein] Base 9B", "maker": "black-forest-labs", "family": "black-forest-labs/flux-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLUX Non-Commercial License", "release_date": "2026-01-15", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Undistilled 9B foundation checkpoint. The overview says it has the highest output diversity of the klein bases and is not step-distilled.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.2 [klein] Base 9B is a flux-2 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.2 [klein] Base 9B is the undistilled non-commercial 9B checkpoint. Black Forest Labs recommends it when quality and fine-tuning matter more than the 4-step distilled path.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-2/flux-2-max": { "id": "black-forest-labs/flux-2/flux-2-max", "name": "FLUX.2 [max]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed FLUX.2 tier. Family design, from the FLUX.2 launch post, couples a Mistral-3 24B vision-language model with a rectified flow transformer. [max] parameter count is not split out.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.07 }, "summary_intro": "Black Forest Labs FLUX.2 [max] is a flux-2 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.2 [max] is the top closed FLUX.2 image model. Black Forest Labs positions it for the highest editing consistency and prompt following, and it is the FLUX.2 variant that can search the web while generating.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-2/flux-2-pro": { "id": "black-forest-labs/flux-2/flux-2-pro", "name": "FLUX.2 [pro]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed production FLUX.2 model. The launch post describes the FLUX.2 family as a Mistral-3 24B VLM plus a rectified flow transformer. [pro] size is not published.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.03 }, "summary_intro": "Black Forest Labs FLUX.2 [pro] is a flux-2 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.2 [pro] is Black Forest Labs' production image model for generation and editing. The docs call it the recommended default and keep a pinned endpoint plus a preview endpoint that moves.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-2/flux-2-small-decoder": { "id": "black-forest-labs/flux-2/flux-2-small-decoder", "name": "FLUX.2 Small Decoder", "maker": "black-forest-labs", "family": "black-forest-labs/flux-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named decoder/VAE-style component on Hugging Face, pipeline tag image-to-image, Apache 2.0. It is not a text-to-image product by itself.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX.2 Small Decoder is a flux-2 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX.2 Small Decoder is an official Black Forest Labs weight repo for a FLUX.2 decoder component. It is catalogued because the maker published it as its own model card. It does not replace FLUX.2 [dev] or [klein].", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-3/flux-3-action": { "id": "black-forest-labs/flux-3/flux-3-action", "name": "FLUX 3 Action", "maker": "black-forest-labs", "family": "black-forest-labs/flux-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLUX Kommunity License", "release_date": "2026-09-22", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "7B open-weight world-action model. Predicts a chunk of actions and future video latents from cameras, state, and an instruction. DROID and games use 32-step chunks at about 15 Hz. SO-101 uses 42 steps at 30 Hz.", "modalities": { "input": [ "text", "image" ], "output": [ "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX 3 Action is a flux-3 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX 3 Action is Black Forest Labs' 7B open-weight model that turns camera images and an instruction into motor or game actions, and predicts future visual latents with them. Prepared checkpoints exist for a Franka DROID setup and a SO-101 arm.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-3/flux-3-image": { "id": "black-forest-labs/flux-3/flux-3-image", "name": "FLUX 3 Image", "maker": "black-forest-labs", "family": "black-forest-labs/flux-3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed FLUX 3 image endpoint. The maker says it comes from the same multimodal model family as FLUX 3 video. Parameter count is not published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.048 }, "summary_intro": "Black Forest Labs FLUX 3 Image is a flux-3 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX 3 Image is the closed image endpoint of the FLUX 3 family. Black Forest Labs says one request can generate from text, edit a region, or combine up to ten references, including bounding-box layout.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-3/flux-3-image-klein": { "id": "black-forest-labs/flux-3/flux-3-image-klein", "name": "FLUX 3 Image Klein", "maker": "black-forest-labs", "family": "black-forest-labs/flux-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLUX Kommunity License", "release_date": "2026-10-15", "context_length": null, "max_output_tokens": null, "parameters": "5.6B", "architecture": "5.6B step-distilled FLUX 3 image model. API default is 16 steps at CFG 1. The maker says it shares architecture with Klein Base so a LoRA trained on Base loads into Distilled.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.009 }, "summary_intro": "Black Forest Labs FLUX 3 Image Klein is a flux-3 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX 3 Image Klein is the distilled 5.6B open-weight FLUX 3 image model. Black Forest Labs ships it on the API as `flux-3-image-klein` and on Hugging Face for local runs. It generates, edits, and uses up to ten references and bounding boxes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-3/flux-3-image-klein-base": { "id": "black-forest-labs/flux-3/flux-3-image-klein-base", "name": "FLUX 3 Image Klein Base", "maker": "black-forest-labs", "family": "black-forest-labs/flux-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLUX Kommunity License", "release_date": "2026-10-15", "context_length": null, "max_output_tokens": null, "parameters": "5.6B", "architecture": "Undistilled 5.6B FLUX 3 image checkpoint for fine-tuning. Docs: 50 steps at CFG 3.5. Same architecture as the distilled Klein model.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX 3 Image Klein Base is a flux-3 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "Klein Base is the undistilled 5.6B twin of FLUX 3 Image Klein. Black Forest Labs says to use it for fine-tuning, LoRA training, and research. It is on Hugging Face, not as the priced API id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-3/flux-3-image-klein-pup": { "id": "black-forest-labs/flux-3/flux-3-image-klein-pup", "name": "FLUX 3 Image Klein Prompt Upsampler", "maker": "black-forest-labs", "family": "black-forest-labs/flux-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-15", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Full fine-tune of Qwen3.5-9B with the vision tower frozen. Two stages: supervised prompt expansion, then preference tuning on images rendered by FLUX 3 Image Klein. bfloat16 weights, 18.8 GB on the model card.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black Forest Labs FLUX 3 Image Klein Prompt Upsampler is a flux-3 model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "This is Black Forest Labs' 9B prompt upsampler for FLUX 3 Image Klein. It is a vision-language model, not an image generator. The reference inference code runs it in front of Klein unless `--no-pup` is set. The API Klein path also does prompt upsampling on the server.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-3/flux-3-video": { "id": "black-forest-labs/flux-3/flux-3-video", "name": "FLUX 3 Video", "maker": "black-forest-labs", "family": "black-forest-labs/flux-3", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One FLUX 3 model trained across image, video, and audio. Parameter count is not published.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.15 }, "summary_intro": "Black Forest Labs FLUX 3 Video is a flux-3 model. Status on 2026-10-15: preview. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "Black Forest Labs describes FLUX 3 Video as one preview model that generates video and synchronized audio together: multilingual speech with lipsync, effects, and ambience. One endpoint covers text-to-video, image-to-video, continuation, and turning a draft into a full render.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-tools/flux-deblur": { "id": "black-forest-labs/flux-tools/flux-deblur", "name": "FLUX Deblur", "maker": "black-forest-labs", "family": "black-forest-labs/flux-tools", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Prompt-free deblur. One image in, a sharper frame out. The release note says it preserves identity, color, objects, lighting, and composition.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.03 }, "summary_intro": "Black Forest Labs FLUX Deblur is a flux-tools model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX Deblur sharpens a blurry image without a prompt or a mask. Black Forest Labs released the endpoint on June 25, 2026 and says it keeps the same scene.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-tools/flux-erase": { "id": "black-forest-labs/flux-tools/flux-erase", "name": "FLUX Erase", "maker": "black-forest-labs", "family": "black-forest-labs/flux-tools", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mask-based object removal. The docs index describes it as powered by FLUX.2 Klein 9B. No prompt.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.03 }, "summary_intro": "Black Forest Labs FLUX Erase is a flux-tools model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX Erase removes whatever a binary mask covers and fills the hole so the scene still fits. It launched May 21, 2026. The docs index says it is powered by FLUX.2 [klein] 9B. You do not send a prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-tools/flux-outpainting": { "id": "black-forest-labs/flux-tools/flux-outpainting", "name": "FLUX Outpainting", "maker": "black-forest-labs", "family": "black-forest-labs/flux-tools", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted canvas-extension model. The image is placed on a larger canvas and the border is generated. Fast and high are modes of one endpoint.", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.1 }, "summary_intro": "Black Forest Labs FLUX Outpainting is a flux-tools model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX Outpainting extends an image past its borders in one call. Black Forest Labs added it on May 14, 2026, then a fast mode on June 9. High is the default.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-tools/flux-video-edit-fast": { "id": "black-forest-labs/flux-tools/flux-video-edit-fast", "name": "FLUX Video Edit [fast]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-tools", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted video-edit tool. A prompt changes a source clip. Duration, resolution, aspect ratio, and audio follow the source. Inference controls are pinned server-side.", "modalities": { "input": [ "text", "video", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.03 }, "summary_intro": "Black Forest Labs FLUX Video Edit [fast] is a flux-tools model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX Video Edit [fast] edits an existing clip from a text instruction. Black Forest Labs bills it per second and keeps the source duration, resolution, and audio. It is not the FLUX 3 generate endpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-tools/flux-video-upscale": { "id": "black-forest-labs/flux-tools/flux-video-upscale", "name": "FLUX Video Upscale", "maker": "black-forest-labs", "family": "black-forest-labs/flux-tools", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FLUX 3 super-resolution tool. Two modes on one endpoint: precise (creativity 0) and creative (creativity 1, the default).", "modalities": { "input": [ "video", "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.07 }, "summary_intro": "Black Forest Labs FLUX Video Upscale is a flux-tools model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX Video Upscale is a hosted super-resolution tool powered by FLUX 3. It scales a clip by 1.5x to 3x, either staying faithful to the source or inventing detail, and it keeps the original audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "black-forest-labs/flux-tools/flux-vto": { "id": "black-forest-labs/flux-tools/flux-vto", "name": "FLUX Virtual Try-On", "maker": "black-forest-labs", "family": "black-forest-labs/flux-tools", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted try-on. A person image plus garment references. v2 is the recommended version and keeps the same request shape as v1.", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.0375 }, "summary_intro": "Black Forest Labs FLUX Virtual Try-On is a flux-tools model. Status on 2026-10-15: active. Confidence is high because the identity and the specs below come from Black Forest Labs pages unless a row says otherwise.", "what_it_is_for": "FLUX Virtual Try-On puts garments onto a person image while keeping the face and pose. Version 1 launched May 28, 2026. Version 2, on July 17, 2026, is the one Black Forest Labs tells callers to use. v1 is still up.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bnu/muduo/muduo-llm": { "id": "bnu/muduo/muduo-llm", "name": "\u5e08\u627f\u4e07\u8c61\u57fa\u7840\u6559\u80b2\u5927\u6a21\u578b\uff08MuduoLLM\uff09", "maker": "bnu", "family": "bnu/muduo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-27", "context_length": 32768, "max_output_tokens": null, "parameters": "14770033664", "architecture": "Qwen2ForCausalLM continued from Qwen2.5-14B-Instruct. config.json: 48 layers, hidden size 5120, 40 attention heads, 8 key-value heads, intermediate size 13824, SiLU, RoPE theta 1000000, rope_scaling null, vocab 152064, untied embeddings, bfloat16, max_position_embeddings 32768. The README says continued pretraining, SFT, and DPO. The repo file mergekit_config.yml is an SLERP of two private checkpoints, pt_0507/checkpoint-8923 and a 15 May 2025 DPO run checkpoint-60.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u5e08\u627f\u4e07\u8c61, also called MuduoLLM, is a basic-education language model released jointly by Beijing Normal University and TAL Education (\u5317\u4eac\u4e16\u7eaa\u597d\u672a\u6765\u6559\u80b2\u79d1\u6280\u6709\u9650\u516c\u53f8). The open checkpoint is a continued-training derivative of Qwen2.5-14B-Instruct. Weights are on Hugging Face under Apache-2.0. A hosted school app is at https://smartedu-bnu.tal.com/. Collected 2026-10-15.", "what_it_is_for": "The card says the model is aligned to China's current curriculum standards. Named jobs are problem solving, Socratic question answering, situational item writing, and competency-oriented lesson plans. The README adds junior-high math, physics, and chemistry as the subjects used for the solving evaluation. The School of Artificial Intelligence says the Beijing school system is free and adds photo upload for solving and grading. Those image features are described for the app. The released checkpoint is a text model, and its item-writing prompt says not to produce questions that depend on a figure.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bnu/taiyan/ai-taiyan": { "id": "bnu/taiyan/ai-taiyan", "name": "AI\u592a\u708e", "maker": "bnu", "family": "bnu/taiyan", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11", "context_length": null, "max_output_tokens": null, "parameters": "1.8B", "architecture": "Decoder-only Transformer trained from scratch. The authors' paper says 52 layers, SwiGLU, ALiBi positional encodings, FlashAttention, and BF16 training. Hidden size, head count, and vocabulary size are not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI\u592a\u708e (AI Taiyan) is a classical-Chinese language model built from scratch by Beijing Normal University's \u6c49\u5b57\u6c49\u8bed\u7814\u7a76\u4e0e\u793e\u4f1a\u5e94\u7528\u5b9e\u9a8c\u5ba4. It is named for Zhang Taiyan. The live product is version 3.0, served as a public chat at https://t.shenshen.wiki/. Weights are closed. Collected 2026-10-15.", "what_it_is_for": "The university describes it as a model for reading classical Chinese: word explanation, classical-to-modern translation, punctuation, and allusion analysis, with both simplified and traditional input. It is a result of the national language-commission project \u53e4\u7c4d\u6574\u7406\u667a\u80fd\u5316\u5173\u952e\u6280\u672f\u7814\u7a76 (ZDA145-9), led by Wang Lijun. The authors say it was trained from scratch because continued training of general models did not carry enough classical knowledge. Later university pages say it is on the national smart-education platform and is used for ancient-book collation, language research, school Chinese, and dictionary work. A 2026 Beijing Daily reprint says it can also translate classical Chinese into English, French, Russian, German, Spanish, and Italian. That claim is not in the 2024 evaluation paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/lilmoo/lilmoo-v0-1": { "id": "bonn/lilmoo/lilmoo-v0-1", "name": "LilMoo-v0.1", "maker": "bonn", "family": "bonn/lilmoo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "670127616", "architecture": "Llama decoder-only Transformer, 670,127,616 parameters, context 4096. The v0.2 card does not print layer widths.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LilMoo-v0.1 is a from-scratch Hindi language model from the University of Bonn Polyglot project. The collection note says v0.1 was pretrained only on Hindi text. The v0.2 card says the weights are research checkpoints under Apache-2.0, not a deployed system. Collected 2026-10-15.", "what_it_is_for": "LilMoo-v0.1 is a from-scratch Hindi language model from the University of Bonn Polyglot project. The collection note says v0.1 was pretrained only on Hindi text. The v0.2 card says the weights are research checkpoints under Apache-2.0, not a deployed system. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/lilmoo/lilmoo-v0-2": { "id": "bonn/lilmoo/lilmoo-v0-2", "name": "LilMoo-v0.2", "maker": "bonn", "family": "bonn/lilmoo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "670127616", "architecture": "Llama decoder-only Transformer, 670,127,616 parameters, context 4096. The v0.2 card does not print layer widths.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LilMoo-v0.2 is a from-scratch Hindi language model from the University of Bonn Polyglot project. The v0.2 card says it was pretrained in Hindi and English, for 170,000 steps at a 2,097,152-token batch on 8 A100 80GB GPUs. The v0.2 card says the weights are research checkpoints under Apache-2.0, not a deployed system. Collected 2026-10-15.", "what_it_is_for": "LilMoo-v0.2 is a from-scratch Hindi language model from the University of Bonn Polyglot project. The v0.2 card says it was pretrained in Hindi and English, for 170,000 steps at a 2,097,152-token batch on 8 A100 80GB GPUs. The v0.2 card says the weights are research checkpoints under Apache-2.0, not a deployed system. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/liltii/liltii-v0-1": { "id": "bonn/liltii/liltii-v0-1", "name": "LilTii-v0.1", "maker": "bonn", "family": "bonn/liltii", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "670127616", "architecture": "Llama decoder-only Transformer with RMSNorm, RoPE, and SwiGLU. 670,127,616 parameters, 28 layers, hidden size 1536, intermediate size 3072, 16 heads, 8 KV heads, head dim 96, tied embeddings, vocabulary 49,152, context 4096. The blog states this shape for the LilTii model before describing the two recipes.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LilTii-v0.1 is a from-scratch Bengali language model from the University of Bonn Polyglot project. The March 2026 blog says it was trained on the Marvin cluster and that bengali only, about five epochs, around 100 billion tokens, cosine schedule. Collected 2026-10-15.", "what_it_is_for": "LilTii-v0.1 is a from-scratch Bengali language model from the University of Bonn Polyglot project. The March 2026 blog says it was trained on the Marvin cluster and that bengali only, about five epochs, around 100 billion tokens, cosine schedule. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/liltii/liltii-v0-2": { "id": "bonn/liltii/liltii-v0-2", "name": "LilTii-v0.2", "maker": "bonn", "family": "bonn/liltii", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "670127616", "architecture": "Llama decoder-only Transformer with RMSNorm, RoPE, and SwiGLU. 670,127,616 parameters, 28 layers, hidden size 1536, intermediate size 3072, 16 heads, 8 KV heads, head dim 96, tied embeddings, vocabulary 49,152, context 4096. The blog states this shape for the LilTii model before describing the two recipes.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LilTii-v0.2 is a from-scratch Bengali language model from the University of Bonn Polyglot project. The March 2026 blog says it was trained on the Marvin cluster and that bengali plus English multi-stage mixture, around 230 billion tokens. Collected 2026-10-15.", "what_it_is_for": "LilTii-v0.2 is a from-scratch Bengali language model from the University of Bonn Polyglot project. The March 2026 blog says it was trained on the Marvin cluster and that bengali plus English multi-stage mixture, around 230 billion tokens. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/rivermamba/rivermamba": { "id": "bonn/rivermamba/rivermamba", "name": "RiverMamba", "maker": "bonn", "family": "bonn/rivermamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2025-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bidirectional Mamba state-space model with location-aware adaptive normalization. Appendix Table 8 lists 3 hindcast layers of depth [2, 2, 2] and hidden size 192, 7 forecast layers of depth 1, D_state 16, D_conv 4, lead time 7 days, and hindcast length 4. Forecast hidden size is 192+64 to take ECMWF HRES. The model forecasts river discharge on a 0.05 degree grid.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RiverMamba is a global river-discharge and flood forecast model from the University of Bonn Computer Vision Group, with Forschungszentrum J\u00fclich and the Lamarr Institute. The paper, first posted 28 May 2025 and accepted at NeurIPS 2025, says it forecasts up to 7 days ahead on a 0.05 degree grid. Pretrained weights were published in the university bonndata deposit on 1 October 2025. Collected 2026-10-15.", "what_it_is_for": "RiverMamba is a global river-discharge and flood forecast model from the University of Bonn Computer Vision Group, with Forschungszentrum J\u00fclich and the Lamarr Institute. The paper, first posted 28 May 2025 and accepted at NeurIPS 2025, says it forecasts up to 7 days ahead on a 0.05 degree grid. Pretrained weights were published in the university bonndata deposit on 1 October 2025. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano/tucano-160m": { "id": "bonn/tucano/tucano-160m", "name": "Tucano-160m", "maker": "bonn", "family": "bonn/tucano", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-12", "context_length": 2048, "max_output_tokens": null, "parameters": "162417408", "architecture": "Llama-style decoder-only Transformer, 12 layers, d_model 768, MLP 3072, 12 heads, 12 KV heads, head dim 64, vocabulary 32,000 (TeenyTinyLlama tokenizer).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Bonn Polyglot researchers released Tucano-160m as a decoder-only Portuguese language model trained from scratch on GigaVerbo. The project page calls it the Tucano small of the Tucano series and says the checkpoints are research foundations, not a deployed chatbot. Collected 2026-10-15.", "what_it_is_for": "University of Bonn Polyglot researchers released Tucano-160m as a decoder-only Portuguese language model trained from scratch on GigaVerbo. The project page calls it the Tucano small of the Tucano series and says the checkpoints are research foundations, not a deployed chatbot. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano/tucano-1b1": { "id": "bonn/tucano/tucano-1b1", "name": "Tucano-1b1", "maker": "bonn", "family": "bonn/tucano", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-12", "context_length": 2048, "max_output_tokens": null, "parameters": "1100048384", "architecture": "Llama-style decoder-only Transformer, 22 layers, d_model 2048, MLP 5632, 32 heads, 4 KV heads, head dim 64, vocabulary 32,000 (TeenyTinyLlama tokenizer).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Bonn Polyglot researchers released Tucano-1b1 as a decoder-only Portuguese language model trained from scratch on GigaVerbo. The project page calls it the Tucano large of the Tucano series and says the checkpoints are research foundations, not a deployed chatbot. Collected 2026-10-15.", "what_it_is_for": "University of Bonn Polyglot researchers released Tucano-1b1 as a decoder-only Portuguese language model trained from scratch on GigaVerbo. The project page calls it the Tucano large of the Tucano series and says the checkpoints are research foundations, not a deployed chatbot. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano/tucano-1b1-instruct": { "id": "bonn/tucano/tucano-1b1-instruct", "name": "Tucano-1b1-Instruct", "maker": "bonn", "family": "bonn/tucano", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-12", "context_length": 2048, "max_output_tokens": null, "parameters": "1100048384", "architecture": "Llama-style decoder-only Transformer, 22 layers, d_model 2048, MLP 5632, 32 heads, 4 KV heads, head dim 64, vocabulary 32,000 (TeenyTinyLlama tokenizer). Instruct is an SFT plus DPO fine-tune of Tucano-1b1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tucano-1b1-Instruct is the University of Bonn instruct checkpoint of tucano-1b1. The card describes supervised fine-tuning on Tucano-SFT and direct preference optimization on a reward dataset. The authors still say it is not an out-of-the-box deployed product. Collected 2026-10-15.", "what_it_is_for": "Tucano-1b1-Instruct is the University of Bonn instruct checkpoint of tucano-1b1. The card describes supervised fine-tuning on Tucano-SFT and direct preference optimization on a reward dataset. The authors still say it is not an out-of-the-box deployed product. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano/tucano-2b4": { "id": "bonn/tucano/tucano-2b4", "name": "Tucano-2b4", "maker": "bonn", "family": "bonn/tucano", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-12", "context_length": 4096, "max_output_tokens": null, "parameters": "2444618240", "architecture": "Llama-style decoder-only Transformer, 24 layers, d_model 2560, MLP 10240, 16 heads, 4 KV heads, head dim 160, vocabulary 32,000 (TeenyTinyLlama tokenizer).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Bonn Polyglot researchers released Tucano-2b4 as a decoder-only Portuguese language model trained from scratch on GigaVerbo. The project page calls it the Tucano XL of the Tucano series and says the checkpoints are research foundations, not a deployed chatbot. Collected 2026-10-15.", "what_it_is_for": "University of Bonn Polyglot researchers released Tucano-2b4 as a decoder-only Portuguese language model trained from scratch on GigaVerbo. The project page calls it the Tucano XL of the Tucano series and says the checkpoints are research foundations, not a deployed chatbot. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano/tucano-2b4-instruct": { "id": "bonn/tucano/tucano-2b4-instruct", "name": "Tucano-2b4-Instruct", "maker": "bonn", "family": "bonn/tucano", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-12", "context_length": 4096, "max_output_tokens": null, "parameters": "2444618240", "architecture": "Llama-style decoder-only Transformer, 24 layers, d_model 2560, MLP 10240, 16 heads, 4 KV heads, head dim 160, vocabulary 32,000 (TeenyTinyLlama tokenizer). Instruct card reprints 2,444,618,240 parameters and a 4096-token context.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tucano-2b4-Instruct is the University of Bonn instruct checkpoint of tucano-2b4. The card describes supervised fine-tuning on Tucano-SFT and direct preference optimization on a reward dataset. The authors still say it is not an out-of-the-box deployed product. Collected 2026-10-15.", "what_it_is_for": "Tucano-2b4-Instruct is the University of Bonn instruct checkpoint of tucano-2b4. The card describes supervised fine-tuning on Tucano-SFT and direct preference optimization on a reward dataset. The authors still say it is not an out-of-the-box deployed product. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano/tucano-630m": { "id": "bonn/tucano/tucano-630m", "name": "Tucano-630m", "maker": "bonn", "family": "bonn/tucano", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-12", "context_length": 2048, "max_output_tokens": null, "parameters": "630253568", "architecture": "Llama-style decoder-only Transformer, 14 layers, d_model 2048, MLP 4096, 16 heads, 4 KV heads, head dim 128, vocabulary 32,000 (TeenyTinyLlama tokenizer).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Bonn Polyglot researchers released Tucano-630m as a decoder-only Portuguese language model trained from scratch on GigaVerbo. The project page calls it the Tucano medium of the Tucano series and says the checkpoints are research foundations, not a deployed chatbot. Collected 2026-10-15.", "what_it_is_for": "University of Bonn Polyglot researchers released Tucano-630m as a decoder-only Portuguese language model trained from scratch on GigaVerbo. The project page calls it the Tucano medium of the Tucano series and says the checkpoints are research foundations, not a deployed chatbot. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano2/tucano2-0-6b-base": { "id": "bonn/tucano2/tucano2-0-6b-base", "name": "Tucano2-0.6B-Base", "maker": "bonn", "family": "bonn/tucano2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "670127616", "architecture": "Llama decoder-only Transformer. The card names the Transformers llama class and does not print layer widths. Parameter count 670,127,616 matches the Polyglot 0.6B recipe used for LilTii, but those widths are not copied here because this card does not list them.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tucano2-0.6B-Base is the from-scratch 0.6B Portuguese model in the University of Bonn Polyglot Tucano 2 suite. The card says it was pretrained on GigaVerbo-v2 and other open corpora, with the final weights at step 195,000, and that it is not for deployment. Collected 2026-10-15.", "what_it_is_for": "Tucano2-0.6B-Base is the from-scratch 0.6B Portuguese model in the University of Bonn Polyglot Tucano 2 suite. The card says it was pretrained on GigaVerbo-v2 and other open corpora, with the final weights at step 195,000, and that it is not for deployment. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano2/tucano2-qwen-0-5b-base": { "id": "bonn/tucano2/tucano2-qwen-0-5b-base", "name": "Tucano2-qwen-0.5B-Base", "maker": "bonn", "family": "bonn/tucano2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "490799104", "architecture": "Qwen3 decoder-only Transformer after Portuguese continual pretraining. Read on the Tucano2-qwen-0.5B-Base card. Instruct and Think cards in this size were not opened for the integer; they are post-trains of this base. Context 4096 on the 0.5B base card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tucano2-qwen-0.5B-Base is a University of Bonn Polyglot continual pretrain for Portuguese, started from Qwen/Qwen3-0.6B-Base. The suite was released on 5 March 2026 under Apache-2.0. The card says the base checkpoint is for further training, not deployment. Collected 2026-10-15.", "what_it_is_for": "Tucano2-qwen-0.5B-Base is a University of Bonn Polyglot continual pretrain for Portuguese, started from Qwen/Qwen3-0.6B-Base. The suite was released on 5 March 2026 under Apache-2.0. The card says the base checkpoint is for further training, not deployment. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano2/tucano2-qwen-0-5b-instruct": { "id": "bonn/tucano2/tucano2-qwen-0-5b-instruct", "name": "Tucano2-qwen-0.5B-Instruct", "maker": "bonn", "family": "bonn/tucano2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "490799104", "architecture": "Qwen3 decoder-only Transformer after Portuguese continual pretraining. Read on the Tucano2-qwen-0.5B-Base card. Instruct and Think cards in this size were not opened for the integer; they are post-trains of this base. Context 4096 on the 0.5B base card. Post-training is one SFT stage and one Anchored Preference Optimization stage on GigaVerbo-v2 SFT and Preferences.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tucano2-qwen-0.5B-Instruct is the chat post-train of Tucano2-qwen-0.5B-Base from the University of Bonn Polyglot group. The 3.7B card, which describes the recipe used across the instruct series, says training was one supervised-fine-tuning round and one anchored-preference round. Collected 2026-10-15.", "what_it_is_for": "Tucano2-qwen-0.5B-Instruct is the chat post-train of Tucano2-qwen-0.5B-Base from the University of Bonn Polyglot group. The 3.7B card, which describes the recipe used across the instruct series, says training was one supervised-fine-tuning round and one anchored-preference round. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano2/tucano2-qwen-0-5b-think": { "id": "bonn/tucano2/tucano2-qwen-0-5b-think", "name": "Tucano2-qwen-0.5B-Think", "maker": "bonn", "family": "bonn/tucano2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "490799104", "architecture": "Qwen3 decoder-only Transformer after Portuguese continual pretraining. Read on the Tucano2-qwen-0.5B-Base card. Instruct and Think cards in this size were not opened for the integer; they are post-trains of this base. Context 4096 on the 0.5B base card. Think is the reasoning post-train: SFT and APO, with chain-of-thought wrapped in think tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tucano2-qwen-0.5B-Think is the University of Bonn reasoning post-train of Tucano2-qwen-0.5B-Base. The 1.5B card says answers include chain-of-thought inside think tags and that the model is still a research checkpoint under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Tucano2-qwen-0.5B-Think is the University of Bonn reasoning post-train of Tucano2-qwen-0.5B-Base. The 1.5B card says answers include chain-of-thought inside think tags and that the model is still a research checkpoint under Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano2/tucano2-qwen-1-5b-base": { "id": "bonn/tucano2/tucano2-qwen-1-5b-base", "name": "Tucano2-qwen-1.5B-Base", "maker": "bonn", "family": "bonn/tucano2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "1510073344", "architecture": "Qwen3 decoder-only Transformer after Portuguese continual pretraining. Read on the Tucano2-qwen-1.5B-Think card. The model tree says Think is a fine-tune of Tucano2-qwen-1.5B-Base, which is a fine-tune of Qwen3-1.7B-Base. The Base and Instruct cards were not opened for the integer. Context 4096 on the 1.5B Think card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tucano2-qwen-1.5B-Base is a University of Bonn Polyglot continual pretrain for Portuguese, started from Qwen/Qwen3-1.7B-Base. The suite was released on 5 March 2026 under Apache-2.0. The card says the base checkpoint is for further training, not deployment. Collected 2026-10-15.", "what_it_is_for": "Tucano2-qwen-1.5B-Base is a University of Bonn Polyglot continual pretrain for Portuguese, started from Qwen/Qwen3-1.7B-Base. The suite was released on 5 March 2026 under Apache-2.0. The card says the base checkpoint is for further training, not deployment. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano2/tucano2-qwen-1-5b-instruct": { "id": "bonn/tucano2/tucano2-qwen-1-5b-instruct", "name": "Tucano2-qwen-1.5B-Instruct", "maker": "bonn", "family": "bonn/tucano2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "1510073344", "architecture": "Qwen3 decoder-only Transformer after Portuguese continual pretraining. Read on the Tucano2-qwen-1.5B-Think card. The model tree says Think is a fine-tune of Tucano2-qwen-1.5B-Base, which is a fine-tune of Qwen3-1.7B-Base. The Base and Instruct cards were not opened for the integer. Context 4096 on the 1.5B Think card. Post-training is one SFT stage and one Anchored Preference Optimization stage on GigaVerbo-v2 SFT and Preferences.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tucano2-qwen-1.5B-Instruct is the chat post-train of Tucano2-qwen-1.5B-Base from the University of Bonn Polyglot group. The 3.7B card, which describes the recipe used across the instruct series, says training was one supervised-fine-tuning round and one anchored-preference round. Collected 2026-10-15.", "what_it_is_for": "Tucano2-qwen-1.5B-Instruct is the chat post-train of Tucano2-qwen-1.5B-Base from the University of Bonn Polyglot group. The 3.7B card, which describes the recipe used across the instruct series, says training was one supervised-fine-tuning round and one anchored-preference round. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano2/tucano2-qwen-1-5b-think": { "id": "bonn/tucano2/tucano2-qwen-1-5b-think", "name": "Tucano2-qwen-1.5B-Think", "maker": "bonn", "family": "bonn/tucano2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "1510073344", "architecture": "Qwen3 decoder-only Transformer after Portuguese continual pretraining. Read on the Tucano2-qwen-1.5B-Think card. The model tree says Think is a fine-tune of Tucano2-qwen-1.5B-Base, which is a fine-tune of Qwen3-1.7B-Base. The Base and Instruct cards were not opened for the integer. Context 4096 on the 1.5B Think card. Think is the reasoning post-train: SFT and APO, with chain-of-thought wrapped in think tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tucano2-qwen-1.5B-Think is the University of Bonn reasoning post-train of Tucano2-qwen-1.5B-Base. The 1.5B card says answers include chain-of-thought inside think tags and that the model is still a research checkpoint under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Tucano2-qwen-1.5B-Think is the University of Bonn reasoning post-train of Tucano2-qwen-1.5B-Base. The 1.5B card says answers include chain-of-thought inside think tags and that the model is still a research checkpoint under Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano2/tucano2-qwen-3-7b-base": { "id": "bonn/tucano2/tucano2-qwen-3-7b-base", "name": "Tucano2-qwen-3.7B-Base", "maker": "bonn", "family": "bonn/tucano2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "3759341056", "architecture": "Qwen3 decoder-only Transformer after Portuguese continual pretraining. Read on the Tucano2-qwen-3.7B-Instruct card. Base and Think in this size were not opened for the integer; Instruct is a fine-tune of Tucano2-qwen-3.7B-Base, whose model tree starts at Qwen/Qwen3-4B-Base. Context 4096 on the 3.7B Instruct card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tucano2-qwen-3.7B-Base is a University of Bonn Polyglot continual pretrain for Portuguese, started from Qwen/Qwen3-4B-Base. The suite was released on 5 March 2026 under Apache-2.0. The card says the base checkpoint is for further training, not deployment. Collected 2026-10-15.", "what_it_is_for": "Tucano2-qwen-3.7B-Base is a University of Bonn Polyglot continual pretrain for Portuguese, started from Qwen/Qwen3-4B-Base. The suite was released on 5 March 2026 under Apache-2.0. The card says the base checkpoint is for further training, not deployment. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano2/tucano2-qwen-3-7b-instruct": { "id": "bonn/tucano2/tucano2-qwen-3-7b-instruct", "name": "Tucano2-qwen-3.7B-Instruct", "maker": "bonn", "family": "bonn/tucano2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "3759341056", "architecture": "Qwen3 decoder-only Transformer after Portuguese continual pretraining. Read on the Tucano2-qwen-3.7B-Instruct card. Base and Think in this size were not opened for the integer; Instruct is a fine-tune of Tucano2-qwen-3.7B-Base, whose model tree starts at Qwen/Qwen3-4B-Base. Context 4096 on the 3.7B Instruct card. Post-training is one SFT stage and one Anchored Preference Optimization stage on GigaVerbo-v2 SFT and Preferences.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tucano2-qwen-3.7B-Instruct is the chat post-train of Tucano2-qwen-3.7B-Base from the University of Bonn Polyglot group. The 3.7B card, which describes the recipe used across the instruct series, says training was one supervised-fine-tuning round and one anchored-preference round. Collected 2026-10-15.", "what_it_is_for": "Tucano2-qwen-3.7B-Instruct is the chat post-train of Tucano2-qwen-3.7B-Base from the University of Bonn Polyglot group. The 3.7B card, which describes the recipe used across the instruct series, says training was one supervised-fine-tuning round and one anchored-preference round. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/tucano2/tucano2-qwen-3-7b-think": { "id": "bonn/tucano2/tucano2-qwen-3-7b-think", "name": "Tucano2-qwen-3.7B-Think", "maker": "bonn", "family": "bonn/tucano2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "3759341056", "architecture": "Qwen3 decoder-only Transformer after Portuguese continual pretraining. Read on the Tucano2-qwen-3.7B-Instruct card. Base and Think in this size were not opened for the integer; Instruct is a fine-tune of Tucano2-qwen-3.7B-Base, whose model tree starts at Qwen/Qwen3-4B-Base. Context 4096 on the 3.7B Instruct card. Think is the reasoning post-train: SFT and APO, with chain-of-thought wrapped in think tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tucano2-qwen-3.7B-Think is the University of Bonn reasoning post-train of Tucano2-qwen-3.7B-Base. The 1.5B card says answers include chain-of-thought inside think tags and that the model is still a research checkpoint under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Tucano2-qwen-3.7B-Think is the University of Bonn reasoning post-train of Tucano2-qwen-3.7B-Base. The 1.5B card says answers include chain-of-thought inside think tags and that the model is still a research checkpoint under Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/vitucano/vitucano-1b5-v1": { "id": "bonn/vitucano/vitucano-1b5-v1", "name": "ViTucano-1b5-v1", "maker": "bonn", "family": "bonn/vitucano", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-13", "context_length": 2048, "max_output_tokens": null, "parameters": "1534831680", "architecture": "TinyLLaVA conditional-generation model. Vision tower is google/siglip-so400m-patch14-384. Language backbone is the named Tucano checkpoint. Transformers loading needs trust_remote_code.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTucano-1b5-v1 is a University of Bonn Portuguese vision-language model. The card says it is the first ViTucano attempt, built on tucano-1b1 with TinyLLaVA Factory and a SigLIP vision tower for captioning and visual questions. It is a research checkpoint, not a deployed assistant. Collected 2026-10-15.", "what_it_is_for": "ViTucano-1b5-v1 is a University of Bonn Portuguese vision-language model. The card says it is the first ViTucano attempt, built on tucano-1b1 with TinyLLaVA Factory and a SigLIP vision tower for captioning and visual questions. It is a research checkpoint, not a deployed assistant. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bonn/vitucano/vitucano-2b8-v1": { "id": "bonn/vitucano/vitucano-2b8-v1", "name": "ViTucano-2b8-v1", "maker": "bonn", "family": "bonn/vitucano", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-13", "context_length": 4096, "max_output_tokens": null, "parameters": "2882351680", "architecture": "TinyLLaVA conditional-generation model. Vision tower is google/siglip-so400m-patch14-384. Language backbone is the named Tucano checkpoint. Transformers loading needs trust_remote_code.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTucano-2b8-v1 is a University of Bonn Portuguese vision-language model. The card says it is the first ViTucano attempt, built on tucano-2b4 with TinyLLaVA Factory and a SigLIP vision tower for captioning and visual questions. It is a research checkpoint, not a deployed assistant. Collected 2026-10-15.", "what_it_is_for": "ViTucano-2b8-v1 is a University of Bonn Portuguese vision-language model. The card says it is the first ViTucano attempt, built on tucano-2b4 with TinyLLaVA Factory and a SigLIP vision tower for captioning and visual questions. It is a research checkpoint, not a deployed assistant. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bordeaux/volbrain/assemblynet": { "id": "bordeaux/volbrain/assemblynet", "name": "AssemblyNet", "maker": "bordeaux", "family": "bordeaux/volbrain", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AssemblyNet software license, version 1.0 dated 2021-06-01. Research and testing use. The text that was read forbids modification, redistribution, and commercial use. French law, Paris courts. The GitHub README also says non-commercial and non-medical research only.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Ensemble of 3D U-Nets. The 2019 abstract and the 2020 paper describe two assemblies of U-Nets, with the second assembly amending the first at higher resolution and a final majority vote. The journal PDF table on ResearchGate lists an AssemblyNet row at mean Dice 73.3 (4.2) after atlas priors and transfer learning. The GitHub card says 133 BrainCOLOR labels. The volBrain service page says the Neuromorphometrics protocol comprises 132 structures, fused into lobes and tissues. Parameter count was not published.", "modalities": { "input": [ "t1-mri" ], "output": [ "segmentation", "volumetry-report" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AssemblyNet is the whole-brain T1 MRI segmentation model from In2Brain at LaBRI (CNRS, University of Bordeaux, Bordeaux INP), released with Jos\u00e9 V. Manj\u00f3n at Universitat Polit\u00e8cnica de Val\u00e8ncia. It is a current volBrain service and a public Docker image, `volbrain/assemblynet:1.0.0`. This dossier was collected on 2026-10-15.", "what_it_is_for": "The service page says it analyzes an anonymized T1 MRI and reports volumes for brain structures. The GitHub card says it segments 133 BrainCOLOR labels, including intracranial cavity, tissues, macrostructures, lobes, and cortical and subcortical structures. The service page says the Neuromorphometrics protocol comprises 132 structures, which are then fused into lobes and tissues.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bordeaux/volbrain/assemblynet-ad": { "id": "bordeaux/volbrain/assemblynet-ad", "name": "AssemblyNet-AD", "maker": "bordeaux", "family": "bordeaux/volbrain", "status": "active", "confidence": "high", "weights": "closed", "license": "No separate weight license was found. Platform use is non-commercial and non-medical research only.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two scores. The grading score comes from deep learning models trained to detect an Alzheimer's pattern in each structure. The atrophy score is HAVAs, from hippocampus, amygdala, and inferior lateral ventricle volumes, comparing an Alzheimer's lifespan model with a control lifespan model. Structures are segmented with AssemblyNet. Parameter count was not published. No public weight file was found.", "modalities": { "input": [ "t1-mri" ], "output": [ "grading-score", "atrophy-score", "report" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AssemblyNet-AD is a hosted T1 MRI tool on volBrain for separating Alzheimer's disease from controls. The grading models are from Huy-Dung Nguyen, Micha\u00ebl Cl\u00e9ment, Boris Mansencal, and Pierrick Coup\u00e9 at LaBRI (University of Bordeaux, CNRS, Bordeaux INP). Structure volumes come from AssemblyNet, which is shared with Universitat Polit\u00e8cnica de Val\u00e8ncia. The service was live on 2026-10-15. No public weight file was found.", "what_it_is_for": "The service page says it detects or predicts Alzheimer's disease from one T1 scan. The grading map scores how much each structure looks like a dementia pattern. HAVAs uses hippocampus, amygdala, and inferior lateral ventricle volumes to estimate whether the subject is closer to an Alzheimer's lifespan model than to a control model. The page says not to use it for other diseases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bordeaux/volbrain/assemblynet-ad-ftd": { "id": "bordeaux/volbrain/assemblynet-ad-ftd", "name": "AssemblyNet-AD-FTD", "maker": "bordeaux", "family": "bordeaux/volbrain", "status": "active", "confidence": "high", "weights": "closed", "license": "No separate weight license was found. Platform use is non-commercial and non-medical research only.", "release_date": "2024-02-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The service page says deep learning models produce a per-structure grading score for Alzheimer's and frontotemporal patterns, then deep-grading features are combined with volumes. The arXiv abstract says a large ensemble of 3D U-Nets emits a 2-channel disease coordinate map, which becomes a 3D grading map, and a multi-layer perceptron classifies that map. Volumes are added as a second feature set. Structures are segmented with AssemblyNet. The abstract says experiments used 3319 MRI. Parameter count was not published.", "modalities": { "input": [ "t1-mri" ], "output": [ "class-probabilities", "grading-map", "report" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AssemblyNet-AD-FTD is a hosted T1 MRI classifier on volBrain for controls, Alzheimer's disease, and frontotemporal dementia. The method paper is by Huy-Dung Nguyen, Micha\u00ebl Cl\u00e9ment, Vincent Planche, Boris Mansencal, and Pierrick Coup\u00e9 at LaBRI (University of Bordeaux, CNRS, Bordeaux INP). In2Brain dated the pipeline at 5 February 2024. The service was live on 2026-10-15. Weights were not found outside the platform.", "what_it_is_for": "The service page says it estimates how much each structure resembles an Alzheimer's or frontotemporal pattern, then combines that deep grading with volumes. Alzheimer's follows the ADNI definition. Frontotemporal dementia follows NIFD and only three subtypes: behavioral variant, semantic variant, and progressive nonfluent aphasia. The page says not to use it for other diseases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bordeaux/volbrain/brain-structure-ages": { "id": "bordeaux/volbrain/brain-structure-ages", "name": "BrainStructureAges", "maker": "bordeaux", "family": "bordeaux/volbrain", "status": "active", "confidence": "high", "weights": "closed", "license": "No separate weight license was found. Platform use is non-commercial and non-medical research only.", "release_date": "2024-02-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The Human Brain Mapping abstract says an ensemble of deep learning models estimates a voxel-wise 3D aging map, and a segmentation mask turns that map into one age per structure. The service page says those structure ages are then combined into a global biological age, and that the models were trained on controls from 0 to 100 years. Structures are segmented with AssemblyNet. A 2026 methods note says the pipeline reports 133 structures. Parameter count was not published.", "modalities": { "input": [ "t1-mri" ], "output": [ "structure-ages", "volumes", "report" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BrainStructureAges (BSA) is a hosted T1 MRI brain-age model from Huy-Dung Nguyen, Micha\u00ebl Cl\u00e9ment, Boris Mansencal, and Pierrick Coup\u00e9 at LaBRI (University of Bordeaux, CNRS, Bordeaux INP). The Human Brain Mapping article is the January 2024 issue. In2Brain and the volBrain homepage date the pipeline at 19 February 2024. It was live on 2026-10-15. Weights were not found outside the platform.", "what_it_is_for": "The service page says it estimates a biological age for each brain structure and a global age from one T1 scan. The paper abstract says the structure ages are a biomarker for anomaly detection and for multi-disease classification, and that the deviations can be shown to a reader. Segmentation is AssemblyNet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bordeaux/volbrain/cognitive-tree": { "id": "bordeaux/volbrain/cognitive-tree", "name": "cognitiveTree", "maker": "bordeaux", "family": "bordeaux/volbrain", "status": "active", "confidence": "high", "weights": "closed", "license": "No separate weight license was found. Platform use is non-commercial and non-medical research only.", "release_date": "2025-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lifespan tree of brain anatomy. The service page says trajectories of more than one hundred structures are modeled, with dimensionality reduction and data augmentation, and that the tree returns a probability for each cognitive condition. Structures are segmented with AssemblyNet. The page does not describe a new CNN and does not publish a parameter count. No public weight file was found.", "modalities": { "input": [ "t1-mri" ], "output": [ "class-probabilities", "abnormality-map", "report" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "cognitiveTree is a hosted T1 MRI differential-diagnosis tool on volBrain. The cited paper is by Pierrick Coup\u00e9, Boris Mansencal, Jos\u00e9 V. Manj\u00f3n, Patrice P\u00e9ran, Wassilios G. Meissner, Thomas Tourdias, and Vincent Planche. Coup\u00e9 and Mansencal are at LaBRI (University of Bordeaux, CNRS, Bordeaux INP). In2Brain and the volBrain homepage date the pipeline at 23 September 2025. It was live on 2026-10-15. Weights were not found outside the platform.", "what_it_is_for": "The service page says it separates cognitively normal aging, Alzheimer's disease, behavioral-variant frontotemporal dementia, semantic dementia, progressive nonfluent aphasia, progressive supranuclear palsy, and dementia with Lewy bodies. It also marks structures that diverge from normal aging. The page says the model is a lifespan tree of brain anatomy, not a black-box score alone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bordeaux/volbrain/deep-lesion-brain": { "id": "bordeaux/volbrain/deep-lesion-brain", "name": "DeepLesionBrain", "maker": "bordeaux", "family": "bordeaux/volbrain", "status": "active", "confidence": "high", "weights": "closed", "license": "No separate weight license was found. Platform use is non-commercial and non-medical research only. The arXiv abstract is under CC BY-SA 4.0. That license is for the article, not the weights.", "release_date": "2023-06-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The arXiv abstract says a large group of compact 3D CNNs, image-quality augmentation, and hierarchical specialization: a whole-brain network is pretrained and then used to initialize locally specialized networks. The service page says lesions are then classed as periventricular, deep white, juxtacortical, or infratentorial. After inpainting, T1 is segmented with AssemblyNet. A disconnectome uses the HCP1065 atlas. Parameter count was not published. No public weight file was found.", "modalities": { "input": [ "t1-mri", "flair-mri" ], "output": [ "lesion-segmentation", "parcellation", "disconnectome", "report" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepLesionBrain is the hosted white-matter lesion model on volBrain. The paper is by Reda Abdellah Kamraoui, Vinh-Thong Ta, Thomas Tourdias, Boris Mansencal, Jos\u00e9 V. Manj\u00f3n, and Pierrick Coup\u00e9. The LaBRI authors are at CNRS, University of Bordeaux, and Bordeaux INP. The arXiv preprint is 14 December 2020. volBrain dated the online pipeline 15 June 2023. It was still listed on 2026-10-15. No public weight file was opened.", "what_it_is_for": "The service page says it segments hyperintense white-matter lesions from T1 and FLAIR, places them in periventricular, deep white, juxtacortical, or infratentorial tissue, segments the brain with AssemblyNet after lesion inpainting, and estimates disconnection for 64 tracts on the HCP1065 atlas. The news post names multiple sclerosis, small-vessel disease, and aging as research uses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bordeaux/volbrain/deep-thalamus": { "id": "bordeaux/volbrain/deep-thalamus", "name": "DeepThalamus", "maker": "bordeaux", "family": "bordeaux/volbrain", "status": "active", "confidence": "high", "weights": "closed", "license": "No separate weight license was found. Platform use is non-commercial and non-medical research only. The arXiv license is the non-exclusive distribution license for the article, not the weights.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The arXiv abstract says a multimodal volumetric network segments thalamic nuclei at 0.125 mm\u00b3, trained with a semi-supervised step, and that a full pipeline also runs on a standard-resolution T1. The service page says the running tool labels left and right thalamus and 13 nuclei from one T1, using an extended Morel et al. 1997 protocol with an added intermediate-space label. Corresponding author on the PDF header is at Universitat Polit\u00e8cnica de Val\u00e8ncia. LaBRI coauthors are Boris Mansencal and Pierrick Coup\u00e9. Thomas Tourdias is University of Bordeaux. Parameter count was not published.", "modalities": { "input": [ "t1-mri" ], "output": [ "thalamus-segmentation", "report" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepThalamus is the hosted thalamic-nuclei model on volBrain. The preprint is led by Marina Ruiz-Perez and Jos\u00e9 V. Manj\u00f3n at Universitat Polit\u00e8cnica de Val\u00e8ncia. Boris Mansencal and Pierrick Coup\u00e9 at LaBRI (CNRS, University of Bordeaux, Bordeaux INP) and Thomas Tourdias at University of Bordeaux are coauthors. In2Brain lists the pipeline as its own. The service was live on 2026-10-15. Weights were not found outside the platform.", "what_it_is_for": "The service page says it segments the whole thalamus and 13 nuclei on each side from one ordinary T1 and reports volumes and asymmetry. The news post says it was trained on ultra-high-resolution annotated MRI and is meant for standard clinical T1 scans.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bordeaux/volbrain/deepceres": { "id": "bordeaux/volbrain/deepceres", "name": "DeepCERES", "maker": "bordeaux", "family": "bordeaux/volbrain", "status": "active", "confidence": "high", "weights": "closed", "license": "No separate weight license was found. Platform use is non-commercial and non-medical research only.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Deep learning segmentation of cerebellar lobules and tissues. The service page says experts labeled white matter and lobules on 75 high-resolution T1w and T2w scans at the ultra-high resolution cited in the news post (0.125 mm\u00b3), using a modified Park et al. 2014 protocol. The running pipeline accepts a standard-resolution T1. The news post says neural networks are combined with anatomical priors, plus preprocessing and super-resolution. First author is at Universitat Polit\u00e8cnica de Val\u00e8ncia. Bordeaux coauthors on the service citation include Gwenaelle Catheline, Boris Mansencal, and Pierrick Coup\u00e9. Parameter count was not published.", "modalities": { "input": [ "t1-mri" ], "output": [ "cerebellum-segmentation", "report" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepCERES is the hosted cerebellar segmentation pipeline on volBrain. The service citation leads with Sergio Morell-Ortega and Jos\u00e9 V. Manj\u00f3n at Universitat Polit\u00e8cnica de Val\u00e8ncia and includes Gwenaelle Catheline, Boris Mansencal, and Pierrick Coup\u00e9 from Bordeaux. In2Brain, the LaBRI group at University of Bordeaux, lists it as one of its pipelines. The service was live on 2026-10-15. Weights were not found outside the platform.", "what_it_is_for": "The service page says it reports cerebellum lobules and tissues from one T1 scan. The news post says the networks were trained on ultra-high-resolution cerebellar MRI (0.125 mm\u00b3) and that super-resolution lets a standard clinical T1 through.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bordeaux/volbrain/motor-tree": { "id": "bordeaux/volbrain/motor-tree", "name": "motorTree", "maker": "bordeaux", "family": "bordeaux/volbrain", "status": "active", "confidence": "high", "weights": "closed", "license": "No separate weight license was found. Platform use is non-commercial and non-medical research only.", "release_date": "2025-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lifespan tree of brain anatomy for motor diseases. The service page says trajectories of more than one hundred structures are modeled, with dimensionality reduction and data augmentation. There is no control class. Structures are segmented with AssemblyNet. Parameter count was not published. No public weight file was found.", "modalities": { "input": [ "t1-mri" ], "output": [ "class-probabilities", "abnormality-map", "report" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "motorTree is a hosted T1 MRI tool on volBrain for four motor diagnoses. It comes from the same lifespan-tree paper as cognitiveTree: Pierrick Coup\u00e9 and Boris Mansencal at LaBRI (University of Bordeaux, CNRS, Bordeaux INP), with Jos\u00e9 V. Manj\u00f3n, Patrice P\u00e9ran, Wassilios G. Meissner, Thomas Tourdias, and Vincent Planche. In2Brain and the volBrain homepage date the pipeline at 23 September 2025. It was live on 2026-10-15. Weights were not found outside the platform.", "what_it_is_for": "The service page says it separates Parkinson's disease, multiple system atrophy, progressive supranuclear palsy, and dementia with Lewy bodies. There is no control class. The page says not to use it on other diseases or on people without parkinson-like motor symptoms. It also marks structures that diverge from normal aging.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bordeaux/volbrain/petbrain": { "id": "bordeaux/volbrain/petbrain", "name": "petBrain", "maker": "bordeaux", "family": "bordeaux/volbrain", "status": "active", "confidence": "high", "weights": "closed", "license": "No separate weight license was found. Platform use is non-commercial and non-medical research only. The journal article is CC BY 4.0. That license is for the article, not the weights.", "release_date": "2025-10-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end pipeline for amyloid PET, tau PET, and structural MRI. The abstract says it uses deep-learning segmentation and reports Centiloid, CenTauR, and HAVAs, plus a joint A, T2, and N estimate. The service page says the output is the subject's A/T/N status. Corresponding author is Pierrick Coup\u00e9 at LaBRI, University of Bordeaux. Coauthors include UPV, ICM Paris, and CHU Bordeaux. Parameter count was not published. No public weight file was found.", "modalities": { "input": [ "t1-mri", "amyloid-pet", "tau-pet" ], "output": [ "biomarker-report" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "petBrain is the hosted amyloid, tau, and neurodegeneration pipeline on volBrain. The corresponding author is Pierrick Coup\u00e9 at LaBRI (CNRS, University of Bordeaux, Bordeaux INP). Coauthors include Boris Mansencal and Flor\u00e9al Morandat at LaBRI, Jos\u00e9 V. Manj\u00f3n and Sergio Morell-Ortega at Universitat Polit\u00e8cnica de Val\u00e8ncia, Nicolas Villain in Paris, and Vincent Planche at CHU Bordeaux and the Institut des Maladies Neurod\u00e9g\u00e9n\u00e9ratives. The journal article is 30 September 2025. volBrain dated the pipeline 7 October 2025. It was live on 2026-10-15. Weights were not found outside the platform.", "what_it_is_for": "The service page says it returns an amyloid, tau, and neurodegeneration status from MRI and PET. The abstract says the pipeline reports Centiloid, CenTauR, and HAVAs, and a joint A, T2, and N estimate, without a local install. It is described as a research tool, not a medical device. The platform's own rule is non-medical use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "brandeis/acs/brandeis-acs": { "id": "brandeis/acs/brandeis-acs", "name": "Brandeis Acoustic Classification & Segmentation", "maker": "brandeis", "family": "brandeis/acs", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2021-03-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "TensorFlow Keras feedforward network in classifier.py: Dense 30 sigmoid, Dense 20 sigmoid, Dense 10 sigmoid, Dense softmax over the class count. Binary speech versus nonspeech uses two classes. Default features are 40 MFCCs from 16 kHz audio at 10 ms frames, with zero-crossing rate off and no context frames. Training code uses Adam at learning rate 0.001, batch size 1024, 20 epochs, and BinaryCrossentropy(from_logits=True) when there are two classes. A heuristic smoother sits outside the network. The SavedModel graph was not unpacked, so the shipped checkpoint was not confirmed to match these layer sizes.", "modalities": { "input": [ "audio" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Brandeis Acoustic Classification & Segmentation (package `brandeis-acs`, command `bacs`) is a speech-versus-nonspeech segmenter from the Brandeis Lab for Linguistics and Computation. Kyeongmin Rim (krim@brandeis.edu) is the package author. It is a small TensorFlow network plus a heuristic smoother, not a hosted API. The weights ship in the MIT-licensed package. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README says it isolates the speech portion of an audio stream. The network is trained on the MUSAN corpus with binary labels, speech versus nonspeech. Music and noise are collapsed into nonspeech in the binary setting. Speech frames were randomly resampled down to the nonspeech count. The README says the language mix of those speech frames was not deliberately balanced.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "brandeis/transpeaknet/transpeaknet": { "id": "brandeis/transpeaknet/transpeaknet", "name": "TransPeakNet", "maker": "brandeis", "family": "brandeis/transpeaknet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph neural network on a molecule graph from SMILES, plus a solvent encoder and two MLPs. Node features are atom type, chirality, and hybridization. Edge features are bond type and bond direction. The paper says 5 GNN layers and an atomic embedding size of 512. The hydrogen solvent embedding dimension is 32; nine solvent groups are used, including an unknown class. Two MLP heads predict 13C and 1H shifts, with hidden widths 128 and 64. Training is multi-task pre-training on 1D NMR, then iterative unsupervised fine-tuning on unlabeled HSQC spectra. A 3D-GNN is described as future work, not this release.", "modalities": { "input": [ "text" ], "output": [ "chemical-shifts" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TransPeakNet is a solvent-aware graph model for predicting and assigning HSQC cross-peaks from a SMILES string. Yunrui Li and Pengyu Hong are the Brandeis computer-science authors. Hong is the corresponding author (hongpeng@brandeis.edu). Coauthors are at Harvard Medical School, the University of Georgia, and Boston College chemistry. Boston College is not the releasing lab. The journal article is dated 20 February 2025. Checkpoints are in the public code repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents TransPeakNet as a way to predict Heteronuclear Single Quantum Coherence cross-peaks and to line those peaks up with carbon-hydrogen bonds. It is meant for structure checking in organic chemistry, pharmaceuticals, and natural products. It is a research checkpoint, not an API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bristol/auditory-slow-fast/auditory-slow-fast": { "id": "bristol/auditory-slow-fast/auditory-slow-fast", "name": "Auditory Slow-Fast", "maker": "bristol", "family": "bristol/auditory-slow-fast", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub README publishes the code under CC BY-NC-SA 4.0. The arXiv record also shows CC BY-NC-SA 4.0 for the article. A separate weights licence is not stated.", "release_date": "2021-03-05", "context_length": null, "max_output_tokens": null, "parameters": "26.88M", "architecture": "Two-stream convolutional network on time-frequency spectrograms. The Slow pathway has higher channel capacity and the Fast pathway has finer temporal resolution, with separable convolutions and multi-level lateral connections. Released configs are named SLOWFAST_R50, SLOW_R50, and FAST. The paper's ablation table includes a Slow ResNet-101 variant, so the Slow pathway is a ResNet; the R50 config is the released default. The proposed two-stream model is reported at 26.88M parameters. Single-stream ablations in the same table are Slow at 24.89M and Fast at 0.49M.", "modalities": { "input": [ "audio" ], "output": [ "class-label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Auditory Slow-Fast is an audio-recognition model from the Visual Information Laboratory at the University of Bristol, with Oxford VGG co-authors. It is a two-stream spectrogram network, not a copy of the video SlowFast model. Weights for VGG-Sound and EPIC-KITCHENS-100, including the Slow-only and Fast-only streams, were still linked on 2026-10-15. The arXiv posting date is 5 March 2021. The project page says it received an outstanding paper award at ICASSP 2021.", "what_it_is_for": "The paper proposes the network for audio recognition. The Slow pathway keeps high channel capacity for spectral detail. The Fast pathway runs at a finer temporal resolution for percussive patterns. The authors report state-of-the-art results on VGG-Sound and on audio-only EPIC-KITCHENS-100 action recognition. EPIC training in the README starts from the VGG-Sound checkpoint unless that flag is removed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bristol/mtcn/mtcn": { "id": "bristol/mtcn/mtcn", "name": "Multimodal Temporal Context Network (MTCN)", "maker": "bristol", "family": "bristol/mtcn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub README publishes the code under CC BY-NC-SA 4.0. The arXiv article record shows CC BY 4.0. A separate weights licence is not stated.", "release_date": "2021-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lightweight transformer over a window of neighbouring actions, plus a neural language model that rescores improbable sequences. The paper fixes the window at 9 actions after a validation sweep. Training uses 4 transformer layers. Visual tokens are pre-extracted SlowFast features of dimension 2304 (10 one-second clips per action at test time). Audio is a second token stream. Separate summary tokens predict the centre verb and noun. The language model is trained with a 9-gram window, Adam, and beam size 10 at inference. The released EPIC files are mtcn_av_sf_epic-kitchens-100.pyth and mtcn_lm_epic-kitchens-100.pyth. EGTEA weights are not released.", "modalities": { "input": [ "video", "audio" ], "output": [ "action-class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Multimodal Temporal Context Network is an egocentric action-recognition model from the Visual Information Laboratory at the University of Bristol, with Oxford VGG co-authors. It attends over neighbouring actions in audio and video and then rescores the sequence with a neural language model. EPIC-KITCHENS-100 weights for both pieces were still linked on 2026-10-15. The arXiv posting date is 1 November 2021 (BMVC 2021).", "what_it_is_for": "The paper targets fine-grained actions in long egocentric videos, where the action before and after the one being labelled is informative. MTCN looks at a window of neighbouring actions, predicts the action at the centre, and uses a language model to filter sequences that are unlikely. The authors report results on EPIC-KITCHENS and EGTEA. Only EPIC-KITCHENS-100 checkpoints are released.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bristol/tbn/tbn": { "id": "bristol/tbn/tbn", "name": "Temporal Binding Network (TBN)", "maker": "bristol", "family": "bristol/tbn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub README publishes the code under CC BY-NC-SA 4.0. A separate weights licence is not stated.", "release_date": "2019-08-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end mid-level fusion network. An action is split into equal segments; RGB, optical flow, and audio samples inside a temporal binding window are fused before temporal aggregation, with shared modality and fusion weights over time. The released training command uses a BN-Inception backbone, dropout 0.5, and partial batch-norm: 3 segments on EPIC-KITCHENS-55 and 6 on EPIC-KITCHENS-100. The test script uses 25 segments and 1 crop. The EPIC-KITCHENS-55 paper reports 125 verb classes and 331 noun classes.", "modalities": { "input": [ "rgb", "optical-flow", "audio" ], "output": [ "action-class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Temporal Binding Network is an egocentric action-recognition model from the Visual Information Laboratory at the University of Bristol, with Oxford VGG co-authors. It fuses RGB, optical flow, and audio inside a temporal binding window. Weights for EPIC-KITCHENS-55 and EPIC-KITCHENS-100 were still linked on 2026-10-15. The arXiv posting date is 22 August 2019 (ICCV 2019).", "what_it_is_for": "The paper proposes TBN for egocentric action recognition on EPIC-Kitchens. It is the authors' first audio-visual fusion model for that setting: appearance, motion, and sound are bound across a range of temporal offsets rather than fused only after each modality has been pooled. The public leaderboard result in the paper is for seen kitchens (S1) and unseen kitchens (S2).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "brown/ganimorph/ganimorph": { "id": "brown/ganimorph/ganimorph", "name": "GANimorph", "maker": "brown", "family": "brown/ganimorph", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unsupervised image-to-image GAN. The paper says a dilated-convolution discriminator gives a patch discriminator a wider view, so the generator can change shape and not only texture. Training uses a cycle constraint. The conclusion also names a perceptual cyclic loss, explicit multi-scale features, feature matching, and scheduled loss normalization. Code is TensorFlow with Tensorpack. Kwang In Kim is at the University of Bath. The other authors are at Brown.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GANimorph is an open-weight unsupervised image-to-image GAN from Brown Visual Computing, with Kwang In Kim at the University of Bath. Aaron Gokaslan, Vivek Ramanujan, Daniel Ritchie, and James Tompkin are the Brown authors. The system was published at ECCV 2018. The README links pretrained weights on Google Drive. This dossier was collected on 2026-10-15. The Drive folder was not opened, so confidence is medium.", "what_it_is_for": "The paper presents GANimorph as a translator between image domains that must change shape, not only texture. Examples named in the paper are human faces to dolls or anime characters, and cats to dogs. The code is a research trainer and sampler, not a hosted product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "brown/opengpt-2/opengpt-2": { "id": "brown/opengpt-2/opengpt-2", "name": "OpenGPT-2", "maker": "brown", "family": "brown/opengpt-2", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2019-08-22", "context_length": null, "max_output_tokens": null, "parameters": "1500000000", "architecture": "Decoder-only transformer trained as a language model. The authors forked the Grover codebase and changed the objective to match GPT-2. They say they did not substantially change Grover hyperparameters. They describe the release as a 1.5B-parameter replication, not a copy of OpenAI's withheld checkpoint. The public Colab loads a TensorFlow checkpoint named grover_mega_owt_fix at step 800000. Layer count, width, and context length were not restated for this checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenGPT-2 is a 1.5B-parameter language model trained at Brown University by Aaron Gokaslan and Vanya Cohen, with Ellie Pavlick and Stefanie Tellex. They released it on 22 August 2019, while OpenAI had not yet published its own 1.5B GPT-2 weights. The weights were their own training run on OpenWebText, not a rehost of OpenAI's checkpoint. The inference notebook is still public. This dossier was collected on 2026-10-15. Confidence is medium because the eval table is an image and the Drive files were not downloaded again.", "what_it_is_for": "The blog says the model is a replication of GPT-2's largest setting so other people can generate text and build on the weights. Sampling is through a Google Colab that loads a Grover-format checkpoint. It is not a current Brown API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "brown/r3gan/r3gan": { "id": "brown/r3gan/r3gan", "name": "R3GAN", "maker": "brown", "family": "brown/r3gan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-12-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Config E GAN. Symmetric generator and discriminator are modernized ResNets: bilinear resampling, optional 1x1 transitions, and residual blocks of 1x1, leaky ReLU, grouped 3x3 (group size 16), leaky ReLU, and 1x1, with an inverted bottleneck. No normalization and no StyleGAN mapping network, style injection, noise inputs, path-length regularization, or minibatch standard deviation. The loss is a relativistic pairing GAN with zero-centered R1 and R2 gradient penalties, plus fix-up initialization. CIFAR-10 and ImageNet presets are class-conditional. FFHQ presets are unconditional. One network evaluation per sample.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "R3GAN (also written Re-GAN) is an open-weight image GAN from Brown Visual Computing, with Cornell coauthors Aaron Gokaslan and Volodymyr Kuleshov. Yiwen (Nick) Huang and James Tompkin are at Brown. It was a NeurIPS 2024 paper, posted on arXiv on 9 January 2025. The first public Hub checkpoint appeared on 8 December 2024. Weights are still the five dataset pickles on Hugging Face. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents R3GAN as a minimal GAN baseline: a regularized relativistic loss plus a modern ConvNet backbone, meant to replace StyleGAN2-style tricks. The README ships Config E pickles so a user can sample FFHQ faces, CIFAR-10 classes, or ImageNet classes at 32 or 64 pixels in one forward pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bu/mammo/mammo-clip": { "id": "bu/mammo/mammo-clip", "name": "Mammo-CLIP", "maker": "bu", "family": "bu/mammo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0 on the current Hugging Face card. The arXiv record is marked CC BY 4.0. Those two statements disagree.", "release_date": "2024-06-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dual-encoder CLIP-style model. Image encoder is EfficientNet-B2 or EfficientNet-B5 initialized from ImageNet weights. Text encoder is BioClinicalBERT. Images are cropped to the breast and resized to 1520 by 912. Training uses a multi-view contrastive loss, report synthesis from image attributes, and back-translation. It is contrastive pretraining of those encoders, not a LoRA on OpenAI CLIP. Total parameter count is not stated.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mammo-CLIP is a mammography vision-language model from the Batman Lab at Boston University, with coauthors at Boston University School of Medicine and the University of Pittsburgh. It is a named foundation model whose weights are public, not a hosted copy of OpenAI CLIP. The Hugging Face repo was created on 17 June 2024. The preprint is 20 May 2024 and the conference version is MICCAI 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says Mammo-CLIP is the first vision-language model pretrained on screening mammogram-report pairs, for data-efficient classification and localization of findings such as mass, calcification, density, and cancer. Mammo-FActOR is a separate attribution head that maps report sentences onto image-encoder channels. It is not a chatbot and it has no public API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bu/mammo/mammo-fm": { "id": "bu/mammo/mammo-fm", "name": "Mammo-FM", "maker": "bu", "family": "bu/mammo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Custom academic license for the weights, based on CC BY-NC 4.0 with extra bans on redistribution, API hosting, clinical use, and distillation. The arXiv paper text is marked CC BY 4.0.", "release_date": "2026-04-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dual encoder. The vision encoder is EfficientNet-B5, initialized from ImageNet, and the paper calls it a 30 million parameter vision encoder. The text encoder is ModernBERT further fine-tuned on 219,451 UPMC chest CT reports with masked language modeling and a paired-section task, then used in contrastive mammography pretraining. Images are resized to 1520 by 912. Training alternates for six epochs between a central server (UPMC, BU, EMBED) and Mayo Clinic. The released files are CLIP-style tar checkpoints. Total parameters of image plus text encoders are not stated. Mammo-GRG, the report generator in the same paper, adds Llama 3.1 8B and is not in these tar files.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mammo-FM is a mammography foundation model from the Batman Lab at Boston University, with collaborators at Arizona State, Emory, Mayo Clinic, Carnegie Mellon, Dartmouth, and the University of Pittsburgh. The preprint is 28 November 2025, revised 21 April 2026. Public weights appeared on the lab Hugging Face org on 10 April 2026. It is a new contrastive model, not a copy of Mammo-CLIP, though the lab describes it as the larger follow-on. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says Mammo-FM is a mammography-specific foundation model for cancer diagnosis, finding localization, risk prognosis, and, through a separate Llama-based head, report generation. The files on Hugging Face are the contrastive image-text checkpoints. There is no public API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bu/medsyn/medsyn": { "id": "bu/medsyn/medsyn", "name": "MedSyn", "maker": "bu", "family": "bu/medsyn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GitHub states CC-BY-NC for the code and checkpoints. The arXiv record uses the arXiv non-exclusive distribution license. Those are different licenses.", "release_date": "2023-10-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hierarchical text-conditional diffusion model for 3D chest CT. A low-resolution denoising U-Net synthesizes 64 by 64 by 64 volumes and the paper says that base model has about 700 million parameters. A super-resolution stage lifts the volume to 256 by 256 by 256. The text tower is called Medical BERT in this paper: it encodes radiology reports and is fixed while the U-Net trains. The model jointly generates the CT volume and lung, airway, and vessel segmentation maps. Optional anatomy masks can condition generation. MedSyn2 later calls the inherited text encoder CXR-BERT. A combined parameter count for the language model plus both diffusion stages is not stated.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedSyn is a text-guided 3D chest CT generator from the Batman Lab at Boston University, with coauthors at Stanford, Carnegie Mellon, the University of Pittsburgh, and UPMC. Corresponding contact is Kayhan Batmanghelich (batman@bu.edu). The named preprint appeared on 5 October 2023. IEEE Transactions on Medical Imaging later published it; the BU Hariri page quotes a PubMed citation of 20 June 2024. Weights are released as two checkpoints, one public and one application-gated. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents MedSyn as a hierarchical diffusion model that synthesizes high-resolution lung CT from radiology-report text and can also condition on anatomical masks. The BU Hariri Institute describes it as an anatomy-aware generator for lung abnormalities and says the code and a pretrained model are public. It is not a chatbot and it has no API price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bu/medsyn/medsyn2": { "id": "bu/medsyn/medsyn2", "name": "MedSyn2", "maker": "bu", "family": "bu/medsyn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Custom academic license for the weights, based on CC BY-NC 4.0 with extra bans on redistribution, API hosting, clinical use, and distillation. The arXiv record uses the arXiv non-exclusive distribution license.", "release_date": "2026-06-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Modified diffusion transformer for 448 cubed chest CT. Volumes are affine-registered to 0.7 mm spacing, HU-clipped to -1024 through 1024, and normalized to -1 through 1. Patch size is 2 by 2 by 2, giving 43,904 latent tokens. The transformer has 16 layers and feature dimension 1152, with gated attention over long reports. Text comes from a pretrained CXR-BERT that the paper says is from MedSyn. Segmentation prompts are optional and do not require a full-organ mask. Training uses one A100, PyTorch 2.6.0+cu126, AdamW at 1e-4, and batch size 1 with 4 accumulation steps. The appendix says the modified DiT was trained for 2 epochs, about 24 hours. Parameter count is not stated.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedSyn2 is a second named 3D chest CT generator from the Batman Lab at Boston University. Coauthors include Stanford and UPMC, and Christina LeBedis is listed with Boston University School of Medicine. It is not a dated snapshot of MedSyn: the paper replaces the hierarchical U-Net with a modified diffusion transformer and adds optional segmentation prompts. The preprint was submitted on 31 May 2026 (v3 on 7 June 2026). Public weights appeared as batmanLab/MedSynV2 on 30 June 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says MedSyn2 generates high-resolution CT volumes from radiology reports, from a segmentation of one anatomy or abnormality plus a text description of that mask, or from both. Full-organ annotations are not required. The abstract also describes data-augmentation use. The model card does not add a usage guide. The weight license forbids clinical deployment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/tinyllava/tinyllava-1-4b": { "id": "buaa/tinyllava/tinyllava-1-4b", "name": "TinyLLaVA-1.4B", "maker": "buaa", "family": "buaa/tinyllava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-11", "context_length": null, "max_output_tokens": null, "parameters": "1410113536", "architecture": "Transformers LlavaForConditionalGeneration (model_type llava). Text config is Llama, hidden 2048, 22 layers, vocab 32064. Vision config is CLIP, hidden 1024, 24 layers, image size 336. Hub safetensors total 1,410,113,536 parameters, float32. The README names this repo TinyLLaVA-1.4B. The paper's 1.4B rows use TinyLlama-1.1B and CLIP at 336, but the card does not say whether this file is the base or share recipe.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLLaVA-1.4B is the first named open-weight vision-language checkpoint from Beihang's TinyLLaVA project, released on the bczhou Hub account and later called tiny-llava-hf in the model zoo. The card tags English and Chinese and lists ShareGPT4V, LLaVA-Pretrain, and LLaVA-Instruct-150K. It uses a LLaVA class with a Llama text tower and a CLIP vision tower. Status is active as a public weight file. Collected 2026-10-15.", "what_it_is_for": "TinyLLaVA-1.4B is the first named open-weight vision-language checkpoint from Beihang's TinyLLaVA project, released on the bczhou Hub account and later called tiny-llava-hf in the model zoo. The card tags English and Chinese and lists ShareGPT4V, LLaVA-Pretrain, and LLaVA-Instruct-150K. It uses a LLaVA class with a Llama text tower and a CLIP vision tower. Status is active as a public weight file. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/tinyllava/tinyllava-1-5b": { "id": "buaa/tinyllava/tinyllava-1-5b", "name": "TinyLLaVA-1.5B", "maker": "buaa", "family": "buaa/tinyllava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-02-25", "context_length": 2048, "max_output_tokens": null, "parameters": "1504353824", "architecture": "Legacy TinyLlavaLlamaForCausalLM (model_type tinyllava). Language model TinyLlama, hidden 2048, max_position_embeddings 2048. Vision tower bczhou/TinyLLaVA-1.5B-SigLIP. Projector mlp2x_gelu. Hub safetensors total 1,504,353,824 parameters, float32. The name says 1.5B. The paper share-Sig-TL row matches this card's scores.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLLaVA-1.5B is an open-weight small vision-language model from the Institute of Artificial Intelligence and SKLCCSE at Beihang University, with Tsinghua co-authors on the paper. It is a legacy checkpoint trained with the TinyLLaVABench codebase on a TinyLlama language model plus a SigLIP vision tower. The Hub card lists ShareGPT4V, LLaVA-Pretrain, and LLaVA-Instruct-150K. Status is active as a public weight release. Collected 2026-10-15.", "what_it_is_for": "TinyLLaVA-1.5B is an open-weight small vision-language model from the Institute of Artificial Intelligence and SKLCCSE at Beihang University, with Tsinghua co-authors on the paper. It is a legacy checkpoint trained with the TinyLLaVABench codebase on a TinyLlama language model plus a SigLIP vision tower. The Hub card lists ShareGPT4V, LLaVA-Pretrain, and LLaVA-Instruct-150K. Status is active as a public weight release. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/tinyllava/tinyllava-2-0b": { "id": "buaa/tinyllava/tinyllava-2-0b", "name": "TinyLLaVA-2.0B", "maker": "buaa", "family": "buaa/tinyllava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-02-25", "context_length": 2048, "max_output_tokens": null, "parameters": "2048820768", "architecture": "Legacy TinyLlavaStablelmForCausalLM (model_type tiny_llava_stablelm). Language model StableLM-2-Zephyr-1.6B, hidden 2048, max_position_embeddings 4096. Vision tower bczhou/TinyLLaVA-2.0B-SigLIP. Projector mlp2x_gelu. Hub safetensors total 2,048,820,768 parameters, bfloat16. The name says 2.0B. The paper share-Sig-SLM row matches this card's scores.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLLaVA-2.0B is an open-weight small vision-language model from the Institute of Artificial Intelligence and SKLCCSE at Beihang University, with Tsinghua co-authors on the paper. It is a legacy checkpoint trained with the TinyLLaVABench codebase on a StableLM-2-1.6B language model plus a SigLIP vision tower. The Hub card lists ShareGPT4V, LLaVA-Pretrain, and LLaVA-Instruct-150K. Status is active as a public weight release. Collected 2026-10-15.", "what_it_is_for": "TinyLLaVA-2.0B is an open-weight small vision-language model from the Institute of Artificial Intelligence and SKLCCSE at Beihang University, with Tsinghua co-authors on the paper. It is a legacy checkpoint trained with the TinyLLaVABench codebase on a StableLM-2-1.6B language model plus a SigLIP vision tower. The Hub card lists ShareGPT4V, LLaVA-Pretrain, and LLaVA-Instruct-150K. Status is active as a public weight release. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/tinyllava/tinyllava-3-1b": { "id": "buaa/tinyllava/tinyllava-3-1b", "name": "TinyLLaVA-3.1B", "maker": "buaa", "family": "buaa/tinyllava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-02-22", "context_length": 3072, "max_output_tokens": null, "parameters": "3186888224", "architecture": "Legacy TinyLlavaPhiForCausalLM (model_type tiny_llava_phi). Language model Phi-2, hidden 2560, max_position_embeddings 2048. Vision tower bczhou/TinyLLaVA-3.1B-SigLIP. Projector mlp2x_gelu. image_aspect_ratio pad. Hub safetensors total 3,186,888,224 parameters, float32. The paper's share-Sig-Phi row is the same score line and calls the size 3.1B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLLaVA-3.1B is an open-weight small vision-language model from the Institute of Artificial Intelligence and SKLCCSE at Beihang University, with Tsinghua co-authors on the paper. It is a legacy checkpoint trained with the TinyLLaVABench codebase on a Phi-2 language model plus a SigLIP vision tower. The Hub card lists ShareGPT4V, LLaVA-Pretrain, and LLaVA-Instruct-150K. Status is active as a public weight release. Collected 2026-10-15.", "what_it_is_for": "TinyLLaVA-3.1B is an open-weight small vision-language model from the Institute of Artificial Intelligence and SKLCCSE at Beihang University, with Tsinghua co-authors on the paper. It is a legacy checkpoint trained with the TinyLLaVABench codebase on a Phi-2 language model plus a SigLIP vision tower. The Hub card lists ShareGPT4V, LLaVA-Pretrain, and LLaVA-Instruct-150K. Status is active as a public weight release. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/tinyllava/tinyllava-gemma-siglip-2-4b": { "id": "buaa/tinyllava/tinyllava-gemma-siglip-2-4b", "name": "TinyLLaVA-Gemma-SigLIP-2.4B", "maker": "buaa", "family": "buaa/tinyllava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-16", "context_length": 2048, "max_output_tokens": null, "parameters": "2940955712", "architecture": "TinyLlavaForConditionalGeneration with a Gemma class file. LLM google/gemma-2b-it, hidden 2048, 18 layers, max_position_embeddings 8192, vocab 256000. Vision google/siglip-so400m-patch14-384. Connector mlp2x_gelu. tokenizer_model_max_length 2048. Hub safetensors 2,940,955,712. The repo name says 2.4B, which is lower than the safetensors total.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLLaVA-Gemma-SigLIP-2.4B is an open-weight small vision-language model released by Beihang University's TinyLLaVA project. The Factory model zoo lists this repo. Its score row in the factory table is the base recipe for Gemma-2B-IT plus SigLIP. The card comparison table reprints those scores. Weights are public under the card's Apache-2.0 tag. Status is active. Collected 2026-10-15.", "what_it_is_for": "TinyLLaVA-Gemma-SigLIP-2.4B is an open-weight small vision-language model released by Beihang University's TinyLLaVA project. The Factory model zoo lists this repo. Its score row in the factory table is the base recipe for Gemma-2B-IT plus SigLIP. The card comparison table reprints those scores. Weights are public under the card's Apache-2.0 tag. Status is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/tinyllava/tinyllava-openelm-siglip-0-89b": { "id": "buaa/tinyllava/tinyllava-openelm-siglip-0-89b", "name": "TinyLLaVA-OpenELM-450M-SigLIP-0.89B", "maker": "buaa", "family": "buaa/tinyllava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-29", "context_length": 2048, "max_output_tokens": null, "parameters": "889536576", "architecture": "TinyLlavaForConditionalGeneration. LLM apple/OpenELM-450M-Instruct, hidden 1536. Vision google/siglip-so400m-patch14-384. Connector mlp2x_gelu. tokenizer_model_max_length 2048. Hub safetensors 889,536,576, which matches the 0.89B name. The Factory README links this checkpoint as TinyLLaVA-OpenELM-450M-SigLIP-0.89B. The short path jiajunlong/TinyLLaVA-0.89B was the README link. The API id is the longer name.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLLaVA-OpenELM-450M-SigLIP-0.89B is an open-weight small vision-language model released by Beihang University's TinyLLaVA project. Factory table, base recipe, SigLIP row for OpenELM-450M-Instruct. A separate CLIP row in that table has no linked checkpoint and is not a folder. Weights are public under the card's Apache-2.0 tag. Status is active. Collected 2026-10-15.", "what_it_is_for": "TinyLLaVA-OpenELM-450M-SigLIP-0.89B is an open-weight small vision-language model released by Beihang University's TinyLLaVA project. Factory table, base recipe, SigLIP row for OpenELM-450M-Instruct. A separate CLIP row in that table has no linked checkpoint and is not a folder. Weights are public under the card's Apache-2.0 tag. Status is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/tinyllava/tinyllava-phi-2-siglip-3-1b": { "id": "buaa/tinyllava/tinyllava-phi-2-siglip-3-1b", "name": "TinyLLaVA-Phi-2-SigLIP-3.1B", "maker": "buaa", "family": "buaa/tinyllava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-15", "context_length": 3072, "max_output_tokens": null, "parameters": "3217417280", "architecture": "TinyLlavaForConditionalGeneration. LLM microsoft/phi-2, hidden 2560, 32 layers, vocab 51200. Vision google/siglip-so400m-patch14-384, image 384, hidden 1152, 27 layers, feature layer -2. Connector mlp2x_gelu. Config says the language model and connector are fully tuned and the vision tower is frozen. tokenizer_model_max_length 3072. Hub safetensors 3,217,417,280. The card calls the size 3.1B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLLaVA-Phi-2-SigLIP-3.1B is an open-weight small vision-language model released by Beihang University's TinyLLaVA project. The card says this checkpoint was trained with TinyLLaVA Factory on ShareGPT4V. The factory table's share row for Phi-2 plus SigLIP matches the card scores. Weights are public under the card's Apache-2.0 tag. Status is active. Collected 2026-10-15.", "what_it_is_for": "TinyLLaVA-Phi-2-SigLIP-3.1B is an open-weight small vision-language model released by Beihang University's TinyLLaVA project. The card says this checkpoint was trained with TinyLLaVA Factory on ShareGPT4V. The factory table's share row for Phi-2 plus SigLIP matches the card scores. Weights are public under the card's Apache-2.0 tag. Status is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/tinyllava/tinyllava-qwen2-0-5b-siglip": { "id": "buaa/tinyllava/tinyllava-qwen2-0-5b-siglip", "name": "TinyLLaVA-Qwen2-0.5B-SigLIP", "maker": "buaa", "family": "buaa/tinyllava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-21", "context_length": 2048, "max_output_tokens": null, "parameters": "1060229824", "architecture": "TinyLlavaForConditionalGeneration. LLM Qwen/Qwen2-0.5B, hidden 896, 24 layers, max_position_embeddings 131072, vocab 151936. Vision google/siglip-so400m-patch14-384. Connector mlp2x_gelu. tokenizer_model_max_length 2048. Hub safetensors 1,060,229,824. The name quotes the language-model size, not the safetensors total.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLLaVA-Qwen2-0.5B-SigLIP is an open-weight small vision-language model released by Beihang University's TinyLLaVA project. Factory table, base recipe, one SigLIP row for Qwen2-0.5B. A Qwen2.5-0.5B row in the same table has no Hub link in the model zoo and is not a folder. Weights are public under the card's Apache-2.0 tag. Status is active. Collected 2026-10-15.", "what_it_is_for": "TinyLLaVA-Qwen2-0.5B-SigLIP is an open-weight small vision-language model released by Beihang University's TinyLLaVA project. Factory table, base recipe, one SigLIP row for Qwen2-0.5B. A Qwen2.5-0.5B row in the same table has no Hub link in the model zoo and is not a folder. Weights are public under the card's Apache-2.0 tag. Status is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/tinyllava/tinyllava-qwen2-5-3b-siglip": { "id": "buaa/tinyllava/tinyllava-qwen2-5-3b-siglip", "name": "TinyLLaVA-Qwen2.5-3B-SigLIP", "maker": "buaa", "family": "buaa/tinyllava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-01", "context_length": 2048, "max_output_tokens": null, "parameters": "3831886912", "architecture": "TinyLlavaForConditionalGeneration. LLM Qwen/Qwen2.5-3B, hidden 2048, 36 layers, max_position_embeddings 32768, vocab 151936. Vision google/siglip-so400m-patch14-384. Connector mlp2x_gelu. tokenizer_model_max_length 2048. Hub safetensors 3,831,886,912, bfloat16. The video paper later describes a Qwen2.5-3B TinyLLaVA-Video model as about 3.6B. This image checkpoint is the larger safetensors total.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLLaVA-Qwen2.5-3B-SigLIP is an open-weight small vision-language model released by Beihang University's TinyLLaVA project. Factory table, base recipe, SigLIP row for Qwen2.5-3B. Weights are public under the card's Apache-2.0 tag. Status is active. Collected 2026-10-15.", "what_it_is_for": "TinyLLaVA-Qwen2.5-3B-SigLIP is an open-weight small vision-language model released by Beihang University's TinyLLaVA project. Factory table, base recipe, SigLIP row for Qwen2.5-3B. Weights are public under the card's Apache-2.0 tag. Status is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/tinyllava-video/tinyllava-video-phi2-naive-16-512": { "id": "buaa/tinyllava-video/tinyllava-video-phi2-naive-16-512", "name": "TinyLLaVA-Video-Phi2-Naive-16-512", "maker": "buaa", "family": "buaa/tinyllava-video", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-20", "context_length": 3072, "max_output_tokens": null, "parameters": "3385238080", "architecture": "TinyLlavaForConditionalGeneration for video. LLM microsoft/phi-2. Vision google/siglip-so400m-patch14-384. Connector naiveresampler. LLM microsoft/phi-2, hidden 2560, 32 layers. Hub dtype float16. Hub safetensors 3,385,238,080.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLLaVA-Video-Phi2-Naive-16-512 is an open-weight video understanding checkpoint from Beihang University's TinyLLaVA-Video project at the Institute of Artificial Intelligence. Authors on the README are Beihang researchers. It stays under 4B parameters, uses SigLIP, and 16 frames, 512 visual queries, naive resampler. Language model is Phi-2 rather than Qwen2.5-3B. The paper says the best model is competitive with some 7B video models. Status is active. Collected 2026-10-15.", "what_it_is_for": "TinyLLaVA-Video-Phi2-Naive-16-512 is an open-weight video understanding checkpoint from Beihang University's TinyLLaVA-Video project at the Institute of Artificial Intelligence. Authors on the README are Beihang researchers. It stays under 4B parameters, uses SigLIP, and 16 frames, 512 visual queries, naive resampler. Language model is Phi-2 rather than Qwen2.5-3B. The paper says the best model is competitive with some 7B video models. Status is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/tinyllava-video/tinyllava-video-qwen2-5-3b-group-16-512": { "id": "buaa/tinyllava-video/tinyllava-video-qwen2-5-3b-group-16-512", "name": "TinyLLaVA-Video-Qwen2.5-3B-Group-16-512", "maker": "buaa", "family": "buaa/tinyllava-video", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-19", "context_length": 3072, "max_output_tokens": null, "parameters": "3629878336", "architecture": "TinyLlavaForConditionalGeneration for video. LLM Qwen/Qwen2.5-3B. Vision google/siglip-so400m-patch14-384. Connector groupresampler. Config field group is not repeated here because this file was not the one that printed group. The README defines 16 as 16 sampled frames and 512 as visual queries. A sibling config, the 1fps checkpoint, stores num_queries 512 and group 16. Hub safetensors 3,629,878,336.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLLaVA-Video-Qwen2.5-3B-Group-16-512 is an open-weight video understanding checkpoint from Beihang University's TinyLLaVA-Video project at the Institute of Artificial Intelligence. Authors on the README are Beihang researchers. It stays under 4B parameters, uses SigLIP, and 16 frames, 512 visual queries, group resampler. The paper says the best model is competitive with some 7B video models. Status is active. Collected 2026-10-15.", "what_it_is_for": "TinyLLaVA-Video-Qwen2.5-3B-Group-16-512 is an open-weight video understanding checkpoint from Beihang University's TinyLLaVA-Video project at the Institute of Artificial Intelligence. Authors on the README are Beihang researchers. It stays under 4B parameters, uses SigLIP, and 16 frames, 512 visual queries, group resampler. The paper says the best model is competitive with some 7B video models. Status is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/tinyllava-video/tinyllava-video-qwen2-5-3b-group-1fps-512": { "id": "buaa/tinyllava-video/tinyllava-video-qwen2-5-3b-group-1fps-512", "name": "TinyLLaVA-Video-Qwen2.5-3B-Group-1fps-512", "maker": "buaa", "family": "buaa/tinyllava-video", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-19", "context_length": 3072, "max_output_tokens": null, "parameters": "3629878336", "architecture": "TinyLlavaForConditionalGeneration for video. LLM Qwen/Qwen2.5-3B. Vision google/siglip-so400m-patch14-384. Connector groupresampler. Config stores num_queries 512 and group 16. The README says 1fps sampling uses num_frame -1 and max_frame 64 or 128. Hub safetensors 3,629,878,336.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLLaVA-Video-Qwen2.5-3B-Group-1fps-512 is an open-weight video understanding checkpoint from Beihang University's TinyLLaVA-Video project at the Institute of Artificial Intelligence. Authors on the README are Beihang researchers. It stays under 4B parameters, uses SigLIP, and 1 fps, 512 visual queries, group resampler. This row is the best score line in the README. The paper says the best model is competitive with some 7B video models. Status is active. Collected 2026-10-15.", "what_it_is_for": "TinyLLaVA-Video-Qwen2.5-3B-Group-1fps-512 is an open-weight video understanding checkpoint from Beihang University's TinyLLaVA-Video project at the Institute of Artificial Intelligence. Authors on the README are Beihang researchers. It stays under 4B parameters, uses SigLIP, and 1 fps, 512 visual queries, group resampler. This row is the best score line in the README. The paper says the best model is competitive with some 7B video models. Status is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/tinyllava-video/tinyllava-video-qwen2-5-3b-naive-16-512": { "id": "buaa/tinyllava-video/tinyllava-video-qwen2-5-3b-naive-16-512", "name": "TinyLLaVA-Video-Qwen2.5-3B-Naive-16-512", "maker": "buaa", "family": "buaa/tinyllava-video", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-22", "context_length": 2048, "max_output_tokens": null, "parameters": "3630861376", "architecture": "TinyLlavaForConditionalGeneration for video. LLM Qwen/Qwen2.5-3B. Vision google/siglip-so400m-patch14-384. Connector naiveresampler. tokenizer_model_max_length is 2048, unlike the group-resampler Qwen checkpoints at 3072. Hub safetensors 3,630,861,376. Hub safetensors 3,630,861,376.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLLaVA-Video-Qwen2.5-3B-Naive-16-512 is an open-weight video understanding checkpoint from Beihang University's TinyLLaVA-Video project at the Institute of Artificial Intelligence. Authors on the README are Beihang researchers. It stays under 4B parameters, uses SigLIP, and 16 frames, 512 visual queries, naive resampler. The paper says the best model is competitive with some 7B video models. Status is active. Collected 2026-10-15.", "what_it_is_for": "TinyLLaVA-Video-Qwen2.5-3B-Naive-16-512 is an open-weight video understanding checkpoint from Beihang University's TinyLLaVA-Video project at the Institute of Artificial Intelligence. Authors on the README are Beihang researchers. It stays under 4B parameters, uses SigLIP, and 16 frames, 512 visual queries, naive resampler. The paper says the best model is competitive with some 7B video models. Status is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/tinyllava-video/tinyllava-video-r1": { "id": "buaa/tinyllava-video/tinyllava-video-r1", "name": "TinyLLaVA-Video-R1", "maker": "buaa", "family": "buaa/tinyllava-video", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-13", "context_length": 3072, "max_output_tokens": null, "parameters": "3629878336", "architecture": "TinyLlavaForConditionalGeneration. LLM Qwen/Qwen2.5-3B, hidden 2048, 36 layers, max_position_embeddings 32768. Vision google/siglip-so400m-patch14-384. Connector groupresampler. tokenizer_model_max_length 3072. Hub safetensors 3,629,878,336, bfloat16, the same total as the Group-16-512 checkpoint. The README says the model stays under 4B parameters. Training is a 16-example cold start on TinyLLaVA-Video-Qwen2.5-3B-Group-16-512, then GRPO on 5,496 NextQA multiple-choice items from 0 to 30 seconds.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLLaVA-Video-R1 is an open-weight small video reasoning model from Beihang University. The README says it is reinforcement-learned on top of TinyLLaVA-Video so a model under 4B parameters can show reflective corrections, which the authors call aha moments, on general video question answering. Weights, code, and the training split are public under Apache-2.0. Status is active. Collected 2026-10-15.", "what_it_is_for": "TinyLLaVA-Video-R1 is an open-weight small video reasoning model from Beihang University. The README says it is reinforcement-learned on top of TinyLLaVA-Video so a model under 4B parameters can show reflective corrections, which the authors call aha moments, on general video question answering. Weights, code, and the training split are public under Apache-2.0. Status is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/xiaoan/xiaoan": { "id": "buaa/xiaoan/xiaoan", "name": "XiaoAn", "maker": "buaa", "family": "buaa/xiaoan", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named on the XiaoHang AI 2.0 release as a smart-campus operations model. The page says it links the physical site through a self-developed IoT gateway, runs edge and cloud models together, and uses a four-step listen, understand, inquire, and diagnose interaction. A partner remark on the same page places the work on the XiaoHang AI 2.0 base. No layer count or checkpoint id is given.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XiaoAn, or \u5c0f\u5b89\u667a\u80fd\u8fd0\u7ef4\u5927\u6a21\u578b, is a closed campus operations model announced by Beihang's Hangzhou International Innovation Institute on 29 August 2025, on the same page as XiaoHang AI 2.0. The university describes it as a tool for smart-park operations that senses site state and supports maintenance decisions. No weight file was released. Status is active as a named product announcement. Collected 2026-10-15.", "what_it_is_for": "XiaoAn, or \u5c0f\u5b89\u667a\u80fd\u8fd0\u7ef4\u5927\u6a21\u578b, is a closed campus operations model announced by Beihang's Hangzhou International Innovation Institute on 29 August 2025, on the same page as XiaoHang AI 2.0. The university describes it as a tool for smart-park operations that senses site state and supports maintenance decisions. No weight file was released. Status is active as a named product announcement. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buaa/xiaohang/xiaohang": { "id": "buaa/xiaohang/xiaohang", "name": "XiaoHang", "maker": "buaa", "family": "buaa/xiaohang", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published as a single network. Beihang describes XiaoHang as an open private-domain large model and, for 2.0, as an intelligent operating system and large-model framework. The English 1.0 page says the Hangzhou campus cluster provides up to 200 PFlops and 12 PB of storage. The 2.0 page repeats those two figures for the DATA Thinking scheduler. Version 2.0 adds task perception, routing, strategy optimization, and generation, and ships XiaoHang AI OS with a model pool and knowledge base. The help page says the chat client calls multiple large models.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XiaoHang, or \u5c0f\u822a, is Beihang University's campus AI assistant, announced on 8 August 2024 as an open private-domain large model for study, research, and administration. Version 2.0 and XiaoHang AI OS were announced on 29 August 2025. The public client calls multiple models, parses files and images, and can read answers aloud. Weights are not public. Status is active as a campus product. Collected 2026-10-15.", "what_it_is_for": "XiaoHang, or \u5c0f\u822a, is Beihang University's campus AI assistant, announced on 8 August 2024 as an open private-domain large model for study, research, and administration. Version 2.0 and XiaoHang AI OS were announced on 29 August 2025. The public client calls multiple models, parses files and images, and can read answers aloud. Weights are not public. Status is active as a campus product. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buffalo/lipinc/lipinc": { "id": "buffalo/lipinc/lipinc", "name": "LIPINC", "maker": "buffalo", "family": "buffalo/lipinc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-01-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lip-sync deepfake detector. Dlib crops a 64 by 144 mouth. Five adjacent open-mouth frames (local) and three similar-pose frames at least 0.09 seconds apart (global) form a color sequence. A residual structure sequence is the frame-to-frame difference. A 3D-CNN encodes each branch. Cross-attention fuses color and structure, with an extra residual from the structure features, then a binary classifier. Training uses an inconsistency loss on average SSIM of first-layer 3D-CNN features plus binary cross-entropy, with loss weights 1 and 5. Authors state Adam, learning rate 0.001, epsilon 0.1, batch size 16, 100 epochs, Keras with TensorFlow 2.15.0.", "modalities": { "input": [ "video" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LIPINC is a lip-sync deepfake detector from the University at Buffalo. Soumyya Kanti Datta, Shan Jia, and Siwei Lyu, all using buffalo.edu addresses, introduced it in arXiv:2401.10113 on 18 January 2024, revised 3 June 2024, and it was accepted at ICME 2024. The authors released inference code and a Google Drive weight file. UB's DeepFake-o-meter still listed it as a 2024 video detector. This dossier was collected on 2026-10-15. Status is active because the weight link and the platform listing were still public. There is no license file in the repository.", "what_it_is_for": "The paper says LIPINC finds spatial-temporal inconsistencies in the mouth of lip-synced video. Local frames are adjacent open mouths. Global frames are similar mouth poses elsewhere in the clip. It is meant for lip-sync deepfakes, not general image generation, and the authors also report a weaker result on face-swap video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "buffalo/rawnet2-vocoder/rawnet2-vocoder": { "id": "buffalo/rawnet2-vocoder/rawnet2-vocoder", "name": "RawNet2-Vocoder", "maker": "buffalo", "family": "buffalo/rawnet2-vocoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-04-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Binary synthetic-voice detector. A RawNet2 front end reads a raw waveform and is shared with a vocoder-identification head. The published YAML sets 64600 input samples, 1 input channel, a first convolution of 1024 filters, residual filter sizes [20, [20, 20], [20, 128], [128, 128]], blocks [2, 4], a 1024-unit fully connected layer, and a 3-layer GRU with 1024 units. The paper uses Adam at learning rate 0.0001, batch size 32, and loss weight lambda 0.5 between binary detection and vocoder identification. The RawNet2 architecture itself is from Eurecom's anti-spoofing model. UB trains the multi-task variant.", "modalities": { "input": [ "audio" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RawNet2-Vocoder is the University at Buffalo name for a synthetic-voice detector trained by Chengzhe Sun, Shan Jia, Shuwei Hou, and Siwei Lyu. The paper is \"AI-Synthesized Voice Detection Using Neural Vocoder Artifacts,\" arXiv:2304.13085 on 25 April 2023, and a CVPR 2023 workshop paper, pages 904-912. The authors' GitHub repository ships training code, an MIT license, and a Google Drive checkpoint. DeepFake-o-meter lists the detector and, on 24 November 2025, a v2. This dossier was collected on 2026-10-15. Status is active. The public price is not published.", "what_it_is_for": "The paper detects AI-synthesized speech by looking for artifacts left when a neural vocoder rebuilds a waveform from a mel spectrogram. A binary head shares a RawNet2 front end with a vocoder-identification pretext task. LibriSeVoc, the authors' self-vocoding set, uses WaveNet, WaveRNN, MelGAN, Parallel WaveGAN, WaveGrad, and DiffWave. The detector is not a text-to-speech model and it is not the Eurecom RawNet2 baseline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bupt/byte/byte": { "id": "bupt/byte/byte", "name": "\u6bd4\u7279Byte\u8ba1\u7b97\u673a\u5b66\u79d1\u5927\u6a21\u578b", "maker": "bupt", "family": "bupt/byte", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u6bd4\u7279Byte is the computer-science subject model named by Beijing University of Posts and Telecommunications' School of Computer Science. A university news item published on 14 January 2026 says it was selected onto the national smart-education platform as one of the first subject models in the graduate-education section, and that it is already used in teaching and research training. Weights are closed. Collected 2026-10-15. A direct fetch of the article HTML returned an empty body, so the facts below are the indexed text of that same URL.", "what_it_is_for": "The school describes Byte as support for computer-science teaching and graduate research training. Named jobs are course question answering, programming practice, algorithm analysis, research writing, and literature review. The same article says it covers literature reading, proposal writing, research-idea design, and paper review, and that it supplies runnable examples, debug localization, and write-up guidance in practice courses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bupt/phonelm/phonelm-0-5b": { "id": "bupt/phonelm/phonelm-0-5b", "name": "PhoneLM-0.5B", "maker": "bupt", "family": "bupt/phonelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-11-06", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only transformer. Card and config: hidden size 1024, 24 layers, 16 attention heads, intermediate size 4864, ReLU, tied input and output embeddings, vocabulary 49,152 (SmolLM tokenizer), max position embeddings 2,048, bfloat16. Card says RoPE on the first 25% of head dimensions with 8-bit sin and cos, LayerNorm with learned bias, and biases only on QKV. config.json sets attention_bias false and includes rms_norm_eps 1e-6. Paper results table labels the size 500M. The card says 0.5 billion. An exact weight count was not read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PhoneLM-0.5B is a small English decoder-only language model from Beijing University of Posts and Telecommunications' UbiquitousLearning lab (mllmTeam), with one co-author at Helixon Research. It is the open base checkpoint in the PhoneLM family, aimed at smartphone hardware. Weights were on Hugging Face as of this collection (2026-10-15). The paper was posted on 7 November 2024. The Hub repository was created on 6 November 2024.", "what_it_is_for": "The paper says PhoneLM is a family of pre-trained and instructed small language models for smartphone SoCs, chosen by searching architecture for runtime before pre-training. This checkpoint is the 0.5B base model. The card says it was pre-trained on 1.1 trillion tokens. The paper's Table 1 agrees for the 0.5B column. Table 4 also lists a later 20B-token decay stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bupt/phonelm/phonelm-0-5b-instruct": { "id": "bupt/phonelm/phonelm-0-5b-instruct", "name": "PhoneLM-0.5B-Instruct", "maker": "bupt", "family": "bupt/phonelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-11-06", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Same PhoneLM-0.5B decoder as the base checkpoint: hidden size 1024, 24 layers, 16 heads, intermediate size 4864, ReLU, tied embeddings, vocabulary 49,152, max position embeddings 2,048, bfloat16. config.json matches the base file. The Hub tags this repository as a fine-tune of mllmTeam/PhoneLM-0.5B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PhoneLM-0.5B-Instruct is the instruction-tuned PhoneLM-0.5B checkpoint from Beijing University of Posts and Telecommunications' UbiquitousLearning lab. Hugging Face tags it as a fine-tune of `mllmTeam/PhoneLM-0.5B`. The weight repository was created on 6 November 2024. The family paper was posted on 7 November 2024. It was still hosted at collection on 2026-10-15. There is no vendor API.", "what_it_is_for": "The paper lists this checkpoint as one of five released PhoneLM variants, for instruction following on device. The card's example is a chat turn. It does not claim Android intent calling. That job is the separate 1.5B call model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bupt/phonelm/phonelm-1-5b": { "id": "bupt/phonelm/phonelm-1-5b", "name": "PhoneLM-1.5B", "maker": "bupt", "family": "bupt/phonelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-11-06", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only transformer chosen by on-device search. Card and config: hidden size 2560, 19 layers, 16 heads, intermediate size 6816, ReLU, tied embeddings, vocabulary 49,152 (SmolLM tokenizer), max position embeddings 2,048, bfloat16. Card says RoPE on the first 25% of head dimensions with 8-bit sin and cos, LayerNorm with learned bias, and biases only on QKV. config.json sets attention_bias false and includes rms_norm_eps 1e-6. Paper says multi-head attention, not grouped-query attention, and a width-height ratio of 134.7. The card says 1.5 billion parameters. An exact weight count was not read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PhoneLM-1.5B is the larger base model in the PhoneLM family from Beijing University of Posts and Telecommunications' UbiquitousLearning lab, with one paper co-author at Helixon Research. It is an open-weight English decoder-only model aimed at Snapdragon-class phones. The Hugging Face repository was created on 6 November 2024. The paper was posted on 7 November 2024. Weights were still online at collection on 2026-10-15. There is no priced API.", "what_it_is_for": "The paper says this is the PhoneLM size that was developed with ahead-of-pretraining architecture search on smartphone hardware. It is a general base language model, not the instruct or Android-call fine-tune. The lab also ships an mllm-format build for on-device inference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bupt/phonelm/phonelm-1-5b-call": { "id": "bupt/phonelm/phonelm-1-5b-call", "name": "PhoneLM-1.5B-Call", "maker": "bupt", "family": "bupt/phonelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-11-06", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Same PhoneLM-1.5B decoder: hidden size 2560, 19 layers, 16 heads, intermediate size 6816, ReLU, tied embeddings, vocabulary 49,152, sequence length 2,048. The card says it was fine-tuned from PhoneLM-1.5B-Instruct for Android intent calling. The card says 1.5 billion parameters. An exact weight count was not read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PhoneLM-1.5B-Call is Beijing University of Posts and Telecommunications' Android intent model in the PhoneLM family. The model card says it was fine-tuned from PhoneLM-1.5B-Instruct. Hugging Face agrees and tags the dataset `mllmTeam/DroidCall`. The repository was created on 6 November 2024 and was still online at collection on 2026-10-15. Weights are open. There is no priced API.", "what_it_is_for": "The card says the model is for Android intent calling. The example prompt lists function names, descriptions, and arguments, then asks the model to compose a call. The GitHub README says DroidCall is a synthetic set of Android intent invocations generated by GPT-4. The paper also mentions an end-to-end Android demo built with the mllm engine.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bupt/phonelm/phonelm-1-5b-instruct": { "id": "bupt/phonelm/phonelm-1-5b-instruct", "name": "PhoneLM-1.5B-Instruct", "maker": "bupt", "family": "bupt/phonelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-11-06", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Same decoder as PhoneLM-1.5B: hidden size 2560, 19 layers, 16 heads, intermediate size 6816, ReLU, tied embeddings, vocabulary 49,152, max position embeddings 2,048. The card says it is a 1.5 billion parameter model. Hub metadata lists the base as mllmTeam/PhoneLM-1.5B. An exact weight count was not read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PhoneLM-1.5B-Instruct is the chat fine-tune of PhoneLM-1.5B from Beijing University of Posts and Telecommunications' UbiquitousLearning lab. Hugging Face lists the base as `mllmTeam/PhoneLM-1.5B`. The weight repository was created on 6 November 2024 and was still online at collection on 2026-10-15. The family paper is arXiv:2411.05046. There is no priced API.", "what_it_is_for": "The card presents it as a 1.5 billion parameter chat model. The GitHub README also scores it, before the extra DroidCall fine-tune, on Android intent calls. The dedicated call model is PhoneLM-1.5B-Call.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bupt/uishift/uishift-7b-2k": { "id": "bupt/uishift/uishift-7b-2k", "name": "UIShift-7B-2K", "maker": "bupt", "family": "bupt/uishift", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-18", "context_length": null, "max_output_tokens": null, "parameters": "8292166656", "architecture": "Full-parameter GRPO fine-tune of Qwen2.5-VL-7B with k=1 on 2,000 UI-transition samples, 8 epochs on 8 A800 GPUs, about 26 hours. The paper says this checkpoint is trained and evaluated without reasoning prompts. The Hub safetensors index totals 8,292,166,656 BF16 parameters in 4 shards. The repository has a preprocessor, a chat template, and a tokenizer. config.json was a Git LFS pointer and was not parsed.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UIShift-7B-2K is a GUI-agent checkpoint from Beijing University of Posts and Telecommunications' UbiquitousLearning lab (Longxi Gao, Li Zhang, and Mengwei Xu, @bupt.edu.cn). It is a full-parameter GRPO fine-tune of Qwen2.5-VL-7B on 2,000 self-supervised UI-transition samples with k=1, trained and evaluated without reasoning prompts. Weights were uploaded to `mllmTeam/UIShift-7B-2K` on 18 June 2025. The lab's GitHub says the arXiv paper appeared on 18 May 2025. The weights were still online at collection on 2026-10-15. There is no priced API and no license tag.", "what_it_is_for": "The paper uses the checkpoint for GUI grounding and next-action prediction. Grounding receives a screenshot and a text description and returns a screen coordinate. AndroidControl receives a screenshot and an instruction and returns the next action type and its parameters. The released run is the reasoning-free 7B, 2K, k=1 setting, which the authors call their best balance.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/bagel/bagel": { "id": "bytedance/bagel/bagel", "name": "BAGEL", "maker": "bytedance", "family": "bytedance/bagel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B active / 14B total", "architecture": "Unified multimodal mixture-of-transformers model", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BAGEL is ByteDance Seed's open-weight unified model for multimodal understanding and image generation, with 7B active parameters and 14B total, under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "BAGEL is ByteDance Seed's open-weight unified model for multimodal understanding and image generation, with 7B active parameters and 14B total, under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/depth-anything/depth-anything-3": { "id": "bytedance/depth-anything/depth-anything-3", "name": "Depth Anything 3", "maker": "bytedance", "family": "bytedance/depth-anything", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single transformer for any-view geometry", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Depth Anything 3 is ByteDance Seed's November 2025 open vision model for depth and multi-view geometry, released under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Depth Anything 3 is ByteDance Seed's November 2025 open vision model for depth and multi-view geometry, released under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/doubao/doubao-character": { "id": "bytedance/doubao/doubao-character", "name": "Doubao Character", "maker": "bytedance", "family": "bytedance/doubao", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Doubao Character is the older role-play line, first as Pro and Lite character snapshots and then as Doubao-1.5-pro character. The current replacement is Doubao-Seed-Character. Collected 2026-10-15.", "what_it_is_for": "Doubao Character is the older role-play line, first as Pro and Lite character snapshots and then as Doubao-1.5-pro character. The current replacement is Doubao-Seed-Character.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/doubao/doubao-embedding": { "id": "bytedance/doubao/doubao-embedding", "name": "Doubao Embedding", "maker": "bytedance", "family": "bytedance/doubao", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.7, "completion_per_m_usd": null }, "summary_intro": "Doubao Embedding is Volcengine's embedding line. The current vision model is billed at 0.70 CNY per million text tokens and 1.80 per million image tokens. The 2024 text-only model is in the shutdown notice. Collected 2026-10-15.", "what_it_is_for": "Doubao Embedding is Volcengine's embedding line. The current vision model is billed at 0.70 CNY per million text tokens and 1.80 per million image tokens. The 2024 text-only model is in the shutdown notice.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/doubao/doubao-lite": { "id": "bytedance/doubao/doubao-lite", "name": "Doubao Lite", "maker": "bytedance", "family": "bytedance/doubao", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Doubao Lite is the small pre-Seed Doubao chat model. The 2024 snapshots named in the shutdown notice are retired in favor of later Seed Flash and 1.5 Lite ids. Collected 2026-10-15.", "what_it_is_for": "Doubao Lite is the small pre-Seed Doubao chat model. The 2024 snapshots named in the shutdown notice are retired in favor of later Seed Flash and 1.5 Lite ids.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/doubao/doubao-pro": { "id": "bytedance/doubao/doubao-pro", "name": "Doubao Pro", "maker": "bytedance", "family": "bytedance/doubao", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Doubao Pro is the pre-Seed general Doubao model, sold as several context lengths plus function-calling and browsing ids. The snapshots in the 2025 shutdown notice are retired. Collected 2026-10-15.", "what_it_is_for": "Doubao Pro is the pre-Seed general Doubao model, sold as several context lengths plus function-calling and browsing ids. The snapshots in the 2025 shutdown notice are retired.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/doubao/doubao-vision": { "id": "bytedance/doubao/doubao-vision", "name": "Doubao Vision", "maker": "bytedance", "family": "bytedance/doubao", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Doubao Vision Pro and Vision Lite are the 2024 image-understanding Doubao models. The shutdown notice replaces them with Seed 1.6 or Doubao 1.5 vision ids. Collected 2026-10-15.", "what_it_is_for": "Doubao Vision Pro and Vision Lite are the 2024 image-understanding Doubao models. The shutdown notice replaces them with Seed 1.6 or Doubao 1.5 vision ids.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seaweed/seaweed-7b": { "id": "bytedance/seaweed/seaweed-7b", "name": "Seaweed-7B", "maker": "bytedance", "family": "bytedance/seaweed", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-02", "context_length": null, "max_output_tokens": null, "parameters": "about 7B", "architecture": "Diffusion transformer trained from scratch", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seaweed-7B is ByteDance Seed's March 2025 research video model, a roughly 7B diffusion transformer trained from scratch. It is not a current Volcengine API model. Seedance 1.0 is the later product video model. Collected 2026-10-15.", "what_it_is_for": "Seaweed-7B is ByteDance Seed's March 2025 research video model, a roughly 7B diffusion transformer trained from scratch. It is not a current Volcengine API model. Seedance 1.0 is the later product video model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-1-5-lite": { "id": "bytedance/seed/seed-1-5-lite", "name": "Seed 1.5 Lite", "maker": "bytedance", "family": "bytedance/seed", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Doubao-1.5-lite-32k is the small January 2025 Doubao model. Ark's migration table points it at Seed 2.0 Mini. Collected 2026-10-15.", "what_it_is_for": "Doubao-1.5-lite-32k is the small January 2025 Doubao model. Ark's migration table points it at Seed 2.0 Mini.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-1-5-pro": { "id": "bytedance/seed/seed-1-5-pro", "name": "Seed 1.5 Pro", "maker": "bytedance", "family": "bytedance/seed", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seed 1.5 Pro is the January 2025 Doubao general model, sold as 32k and 256k ids. Both appear on later Ark migration tables. Collected 2026-10-15.", "what_it_is_for": "Seed 1.5 Pro is the January 2025 Doubao general model, sold as 32k and 256k ids. Both appear on later Ark migration tables.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-1-5-vl": { "id": "bytedance/seed/seed-1-5-vl", "name": "Seed 1.5 VL", "maker": "bytedance", "family": "bytedance/seed", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seed1.5-VL is ByteDance Seed's May 2025 vision-language model. The Ark vision-pro and vision-lite 1.5 ids are grouped here as the product SKUs and are both on migration or shutdown notices. Collected 2026-10-15.", "what_it_is_for": "Seed1.5-VL is ByteDance Seed's May 2025 vision-language model. The Ark vision-pro and vision-lite 1.5 ids are grouped here as the product SKUs and are both on migration or shutdown notices.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-1-6": { "id": "bytedance/seed/seed-1-6", "name": "Seed 1.6", "maker": "bytedance", "family": "bytedance/seed", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seed 1.6 is the June 2025 Doubao general model with adaptive thinking and multimodal input. Dated snapshots, including the vision snapshot, are on the migration list toward Seed 2.0 Lite. Collected 2026-10-15.", "what_it_is_for": "Seed 1.6 is the June 2025 Doubao general model with adaptive thinking and multimodal input. Dated snapshots, including the vision snapshot, are on the migration list toward Seed 2.0 Lite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-1-6-flash": { "id": "bytedance/seed/seed-1-6-flash", "name": "Seed 1.6 Flash", "maker": "bytedance", "family": "bytedance/seed", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seed 1.6 Flash is the small, fast Doubao-Seed 1.6 model. Ark points both dated ids at Seed 2.0 Mini. Collected 2026-10-15.", "what_it_is_for": "Seed 1.6 Flash is the small, fast Doubao-Seed 1.6 model. Ark points both dated ids at Seed 2.0 Mini.", "ratings": { "empirical_100": 48, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-1-8": { "id": "bytedance/seed/seed-1-8", "name": "Seed 1.8", "maker": "bytedance", "family": "bytedance/seed", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.8, "completion_per_m_usd": 2.0 }, "summary_intro": "Seed 1.8 is the December 2025 Doubao agent model with image, video, and document understanding. Ark's migration table tells callers to move it to Seed 2.0 Lite. Collected 2026-10-15.", "what_it_is_for": "Seed 1.8 is the December 2025 Doubao agent model with image, video, and document understanding. Ark's migration table tells callers to move it to Seed 2.0 Lite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-2-0-code": { "id": "bytedance/seed/seed-2-0-code", "name": "Seed 2.0 Code", "maker": "bytedance", "family": "bytedance/seed", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.2, "completion_per_m_usd": 16.0 }, "summary_intro": "Seed 2.0 Code is the Doubao coding preview line. Ark's migration table points both dated previews at Seed Evolving. Collected 2026-10-15.", "what_it_is_for": "Seed 2.0 Code is the Doubao coding preview line. Ark's migration table points both dated previews at Seed Evolving.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-2-0-lite": { "id": "bytedance/seed/seed-2-0-lite", "name": "Seed 2.0 Lite", "maker": "bytedance", "family": "bytedance/seed", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 3.6 }, "summary_intro": "Seed 2.0 Lite is Doubao's first model marketed as unified audio, video, image, and text understanding. Ark started its shutdown on 2026-09-23. Collected 2026-10-15.", "what_it_is_for": "Seed 2.0 Lite is Doubao's first model marketed as unified audio, video, image, and text understanding. Ark started its shutdown on 2026-09-23.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-2-0-mini": { "id": "bytedance/seed/seed-2-0-mini", "name": "Seed 2.0 Mini", "maker": "bytedance", "family": "bytedance/seed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 2.0 }, "summary_intro": "Seed 2.0 Mini is the cheapest current Doubao-Seed 2.0 text model, with four thinking levels and separate audio-input prices. The February snapshot is the one Ark tells callers to leave. Collected 2026-10-15.", "what_it_is_for": "Seed 2.0 Mini is the cheapest current Doubao-Seed 2.0 text model, with four thinking levels and separate audio-input prices. The February snapshot is the one Ark tells callers to leave.", "ratings": { "empirical_100": 51, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-2-0-pro": { "id": "bytedance/seed/seed-2-0-pro", "name": "Seed 2.0 Pro", "maker": "bytedance", "family": "bytedance/seed", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.2, "completion_per_m_usd": 16.0 }, "summary_intro": "Seed 2.0 Pro is the February 2026 Doubao-Seed flagship. Ark marks doubao-seed-2-0-pro-260215 as about to go offline and points migrations at Seed 2.1 Lite. Collected 2026-10-15.", "what_it_is_for": "Seed 2.0 Pro is the February 2026 Doubao-Seed flagship. Ark marks doubao-seed-2-0-pro-260215 as about to go offline and points migrations at Seed 2.1 Lite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-2-1-lite": { "id": "bytedance/seed/seed-2-1-lite", "name": "Seed 2.1 Lite", "maker": "bytedance", "family": "bytedance/seed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.8, "completion_per_m_usd": 2.7 }, "summary_intro": "Seed 2.1 Lite is the low-cost Doubao-Seed-2.1 model added on 2026-09-23. The price list bills audio input separately from text and image input. Collected 2026-10-15.", "what_it_is_for": "Seed 2.1 Lite is the low-cost Doubao-Seed-2.1 model added on 2026-09-23. The price list bills audio input separately from text and image input.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-2-1-pro": { "id": "bytedance/seed/seed-2-1-pro", "name": "Seed 2.1 Pro", "maker": "bytedance", "family": "bytedance/seed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 6.0, "completion_per_m_usd": 30.0 }, "summary_intro": "ByteDance Seed 2.1 Pro is the large Doubao-Seed-2.1 API model for production coding, agent, and multimodal understanding tasks. The 2026-09-15 snapshot is the recommended row. Collected 2026-10-15.", "what_it_is_for": "ByteDance Seed 2.1 Pro is the large Doubao-Seed-2.1 API model for production coding, agent, and multimodal understanding tasks. The 2026-09-15 snapshot is the recommended row.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-2-1-turbo": { "id": "bytedance/seed/seed-2-1-turbo", "name": "Seed 2.1 Turbo", "maker": "bytedance", "family": "bytedance/seed", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 15.0 }, "summary_intro": "Seed 2.1 Turbo is the mid-cost Doubao-Seed-2.1 model. Volcengine started taking it offline on 2026-09-23 after Seed 2.1 Lite shipped. Collected 2026-10-15.", "what_it_is_for": "Seed 2.1 Turbo is the mid-cost Doubao-Seed-2.1 model. Volcengine started taking it offline on 2026-09-23 after Seed 2.1 Lite shipped.", "ratings": { "empirical_100": null, "lmsys_elo": 1237, "coding_elo": 1237, "swe_bench": 21.2, "humaneval": 75.0, "twelve_cs": 26.3, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-character": { "id": "bytedance/seed/seed-character", "name": "Seed Character", "maker": "bytedance", "family": "bytedance/seed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.8, "completion_per_m_usd": 2.0 }, "summary_intro": "Doubao-Seed-Character is the current role-play model. The June 2026 snapshot adds thinking and image understanding; the November 2025 snapshot is text and tools only. Collected 2026-10-15.", "what_it_is_for": "Doubao-Seed-Character is the current role-play model. The June 2026 snapshot adds thinking and image understanding; the November 2025 snapshot is text and tools only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-coder": { "id": "bytedance/seed/seed-coder", "name": "Seed-Coder-8B", "maker": "bytedance", "family": "bytedance/seed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-19", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seed-Coder-8B is ByteDance Seed's open code model, with base, instruct, and reasoning checkpoints released in May 2025. Collected 2026-10-15.", "what_it_is_for": "Seed-Coder-8B is ByteDance Seed's open code model, with base, instruct, and reasoning checkpoints released in May 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-diffusion": { "id": "bytedance/seed/seed-diffusion", "name": "Seed Diffusion Preview", "maker": "bytedance", "family": "bytedance/seed", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion language model for code", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seed Diffusion Preview is ByteDance Seed's July 2025 experimental diffusion language model for code. It is a research preview, not an open-weight release confirmed here. Collected 2026-10-15.", "what_it_is_for": "Seed Diffusion Preview is ByteDance Seed's July 2025 experimental diffusion language model for code. It is a research preview, not an open-weight release confirmed here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-evolving": { "id": "bytedance/seed/seed-evolving", "name": "Seed Evolving", "maker": "bytedance", "family": "bytedance/seed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 6.0, "completion_per_m_usd": 30.0 }, "summary_intro": "Doubao-Seed-Evolving is Volcengine's rolling Seed coding and agent model. Callers keep one model id while ByteDance replaces the checkpoint. Collected 2026-10-15.", "what_it_is_for": "Doubao-Seed-Evolving is Volcengine's rolling Seed coding and agent model. Callers keep one model id while ByteDance replaces the checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-oss": { "id": "bytedance/seed/seed-oss", "name": "Seed-OSS-36B", "maker": "bytedance", "family": "bytedance/seed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-20", "context_length": 524288, "max_output_tokens": null, "parameters": "36B", "architecture": "Causal language model", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seed-OSS-36B is ByteDance Seed's open-weight 36B causal language model, released 2025-08-20 under Apache-2.0 with a 524,288-token context. Collected 2026-10-15.", "what_it_is_for": "Seed-OSS-36B is ByteDance Seed's open-weight 36B causal language model, released 2025-08-20 under Apache-2.0 with a 524,288-token context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-translation": { "id": "bytedance/seed/seed-translation", "name": "Seed Translation", "maker": "bytedance", "family": "bytedance/seed", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.2, "completion_per_m_usd": null }, "summary_intro": "Doubao-Seed-Translation is a named row on the Volcengine price list. Only the input price was visible in the excerpt used here. Collected 2026-10-15.", "what_it_is_for": "Doubao-Seed-Translation is a named row on the Volcengine price list. Only the input price was visible in the excerpt used here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/seed-x": { "id": "bytedance/seed/seed-x", "name": "Seed-X", "maker": "bytedance", "family": "bytedance/seed", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seed-X is a 7B open-weight line from ByteDance-Seed, with instruct, PPO, and reward checkpoints on Hugging Face. The model card was not read here, so its task is not stated. Collected 2026-10-15.", "what_it_is_for": "Seed-X is a 7B open-weight line from ByteDance-Seed, with instruct, PPO, and reward checkpoints on Hugging Face. The model card was not read here, so its task is not stated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed/stable-diffcoder": { "id": "bytedance/seed/stable-diffcoder", "name": "Stable-DiffCoder", "maker": "bytedance", "family": "bytedance/seed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Diffusion large language model for code", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable-DiffCoder is ByteDance Seed's open 8B code diffusion language model, with base and instruct weights. The GitHub org list labels the repo MIT. Collected 2026-10-15.", "what_it_is_for": "Stable-DiffCoder is ByteDance Seed's open 8B code diffusion language model, with base and instruct weights. The GitHub org list labels the repo MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed3d/seed3d-1-0": { "id": "bytedance/seed3d/seed3d-1-0", "name": "Seed3D 1.0", "maker": "bytedance", "family": "bytedance/seed3d", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seed3D 1.0 is ByteDance Seed's October 2025 single-image 3D model. The 2026 priced API model is Seed3D 2.0, so 1.0 is cataloged as the retired generation. Collected 2026-10-15.", "what_it_is_for": "Seed3D 1.0 is ByteDance Seed's October 2025 single-image 3D model. The 2026 priced API model is Seed3D 2.0, so 1.0 is cataloged as the retired generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seed3d/seed3d-2-0": { "id": "bytedance/seed3d/seed3d-2-0", "name": "Seed3D 2.0", "maker": "bytedance", "family": "bytedance/seed3d", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 2.4 }, "summary_intro": "Seed3D 2.0 is the April 2026 Doubao 3D model. Volcengine charges 2.40 CNY for each successful textured PBR file. Collected 2026-10-15.", "what_it_is_for": "Seed3D 2.0 is the April 2026 Doubao 3D model. Volcengine charges 2.40 CNY for each successful textured PBR file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedance/seedance-1-0-lite": { "id": "bytedance/seedance/seedance-1-0-lite", "name": "Seedance 1.0 Lite", "maker": "bytedance", "family": "bytedance/seedance", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 1.8 }, "summary_intro": "Seedance 1.0 Lite is the small Seedance 1.0 video model on BytePlus, with separate text-to-video and image-to-video ids dated 2025-04-28. Output is priced at 1.8 USD per million tokens. Collected 2026-10-15.", "what_it_is_for": "Seedance 1.0 Lite is the small Seedance 1.0 video model on BytePlus, with separate text-to-video and image-to-video ids dated 2025-04-28. Output is priced at 1.8 USD per million tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedance/seedance-1-0-pro": { "id": "bytedance/seedance/seedance-1-0-pro", "name": "Seedance 1.0 Pro", "maker": "bytedance", "family": "bytedance/seedance", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 15.0 }, "summary_intro": "Seedance 1.0 Pro is the June 2025 text-and-image video model. Ark still lists 15 CNY per million tokens online and tells callers to move doubao-seedance-1-0-pro-250528 to Seedance 2.0 Mini. Collected 2026-10-15.", "what_it_is_for": "Seedance 1.0 Pro is the June 2025 text-and-image video model. Ark still lists 15 CNY per million tokens online and tells callers to move doubao-seedance-1-0-pro-250528 to Seedance 2.0 Mini.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedance/seedance-1-0-pro-fast": { "id": "bytedance/seedance/seedance-1-0-pro-fast", "name": "Seedance 1.0 Pro Fast", "maker": "bytedance", "family": "bytedance/seedance", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 4.2 }, "summary_intro": "Seedance 1.0 Pro Fast is the cheaper Seedance 1.0 video SKU at 4.20 CNY per million tokens online. Ark points it at Seedance 2.0 Mini. Collected 2026-10-15.", "what_it_is_for": "Seedance 1.0 Pro Fast is the cheaper Seedance 1.0 video SKU at 4.20 CNY per million tokens online. Ark points it at Seedance 2.0 Mini.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedance/seedance-1-5-pro": { "id": "bytedance/seedance/seedance-1-5-pro", "name": "Seedance 1.5 Pro", "maker": "bytedance", "family": "bytedance/seedance", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dual-branch diffusion transformer with a cross-modal joint module", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seedance 1.5 Pro is the December 2025 native audio-video generation model. Seedance 2.0 replaces it for new work, and a current Ark shutdown row for 1.5 Pro was not in the excerpts read. Collected 2026-10-15.", "what_it_is_for": "Seedance 1.5 Pro is the December 2025 native audio-video generation model. Seedance 2.0 replaces it for new work, and a current Ark shutdown row for 1.5 Pro was not in the excerpts read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedance/seedance-2-0": { "id": "bytedance/seedance/seedance-2-0", "name": "Seedance 2.0", "maker": "bytedance", "family": "bytedance/seedance", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seedance 2.0 is the February 2026 joint audio-video model. It takes text, image, audio, and video references and is still on the Volcengine price list beside Seedance 2.5. Collected 2026-10-15.", "what_it_is_for": "Seedance 2.0 is the February 2026 joint audio-video model. It takes text, image, audio, and video references and is still on the Volcengine price list beside Seedance 2.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedance/seedance-2-0-fast": { "id": "bytedance/seedance/seedance-2-0-fast", "name": "Seedance 2.0 Fast", "maker": "bytedance", "family": "bytedance/seedance", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seedance 2.0 Fast is the cheaper, faster Seedance 2.0 SKU on Volcengine. Resource packs are listed at 22 CNY per million tokens before resolution multipliers. Collected 2026-10-15.", "what_it_is_for": "Seedance 2.0 Fast is the cheaper, faster Seedance 2.0 SKU on Volcengine. Resource packs are listed at 22 CNY per million tokens before resolution multipliers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedance/seedance-2-0-mini": { "id": "bytedance/seedance/seedance-2-0-mini", "name": "Seedance 2.0 Mini", "maker": "bytedance", "family": "bytedance/seedance", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": 22.0, "completion_per_m_usd": 37.0 }, "summary_intro": "Seedance 2.0 Mini is the lower-priced Seedance 2.0 video model and the migration target for Seedance 1.0 Pro. The hub lists 22 CNY input and 37 CNY output per million tokens. Collected 2026-10-15.", "what_it_is_for": "Seedance 2.0 Mini is the lower-priced Seedance 2.0 video model and the migration target for Seedance 1.0 Pro. The hub lists 22 CNY input and 37 CNY output per million tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedance/seedance-2-5": { "id": "bytedance/seedance/seedance-2-5", "name": "Seedance 2.5", "maker": "bytedance", "family": "bytedance/seedance", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seedance 2.5 is ByteDance's July 2026 video model for up to 30 seconds of joint audio-video, with large reference sets. Volcengine bills it per million tokens by resolution and whether the input includes video. Collected 2026-10-15.", "what_it_is_for": "Seedance 2.5 is ByteDance's July 2026 video model for up to 30 seconds of joint audio-video, with large reference sets. Volcengine bills it per million tokens by resolution and whether the input includes video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seededit/seededit-3-0": { "id": "bytedance/seededit/seededit-3-0", "name": "SeedEdit 3.0", "maker": "bytedance", "family": "bytedance/seededit", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SeedEdit 3.0 is ByteDance's June 2025 image-editing model. Seedream 4.0 later made editing part of the main image model. A current public price was not found. Collected 2026-10-15.", "what_it_is_for": "SeedEdit 3.0 is ByteDance's June 2025 image-editing model. Seedream 4.0 later made editing part of the main image model. A current public price was not found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedream/seedream-2-0": { "id": "bytedance/seedream/seedream-2-0", "name": "Seedream 2.0", "maker": "bytedance", "family": "bytedance/seedream", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seedream 2.0 is the image model that Seedream 3.0's technical report says it replaced. A separate 2.0 spec page was not found in this pass. Collected 2026-10-15.", "what_it_is_for": "Seedream 2.0 is the image model that Seedream 3.0's technical report says it replaced. A separate 2.0 spec page was not found in this pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedream/seedream-3-0": { "id": "bytedance/seedream/seedream-3-0", "name": "Seedream 3.0", "maker": "bytedance", "family": "bytedance/seedream", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bilingual text-to-image diffusion model with mixed-resolution training", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seedream 3.0 is ByteDance's April 2025 Chinese-English text-to-image model with native 2K output. It is the predecessor of Seedream 4.0 and is not on the 2026 image price table read here. Collected 2026-10-15.", "what_it_is_for": "Seedream 3.0 is ByteDance's April 2025 Chinese-English text-to-image model with native 2K output. It is the predecessor of Seedream 4.0 and is not on the 2026 image price table read here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedream/seedream-4-0": { "id": "bytedance/seedream/seedream-4-0", "name": "Seedream 4.0", "maker": "bytedance", "family": "bytedance/seedream", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.2 }, "summary_intro": "Seedream 4.0 is the September 2025 image model that folds generation and editing into one system and outputs up to 4K. Ark bills it at 0.20 CNY per output image. A newer April 2026 snapshot replaces the August 2025 id. Collected 2026-10-15.", "what_it_is_for": "Seedream 4.0 is the September 2025 image model that folds generation and editing into one system and outputs up to 4K. Ark bills it at 0.20 CNY per output image. A newer April 2026 snapshot replaces the August 2025 id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedream/seedream-4-5": { "id": "bytedance/seedream/seedream-4-5", "name": "Seedream 4.5", "maker": "bytedance", "family": "bytedance/seedream", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.25 }, "summary_intro": "Seedream 4.5 is the late-2025 Doubao image model aimed at multi-image editing and 4K output. The billing doc charges 0.25 CNY per output image, while the hub card also lists a 0.25 input price. Collected 2026-10-15.", "what_it_is_for": "Seedream 4.5 is the late-2025 Doubao image model aimed at multi-image editing and 4K output. The billing doc charges 0.25 CNY per output image, while the hub card also lists a 0.25 input price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedream/seedream-5-0-flash": { "id": "bytedance/seedream/seedream-5-0-flash", "name": "Seedream 5.0 Flash", "maker": "bytedance", "family": "bytedance/seedream", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.12 }, "summary_intro": "Seedream 5.0 Flash is a lower-priced image SKU on the Volcengine price list at 0.12 CNY per output image. A release note for it was not found in this pass. Collected 2026-10-15.", "what_it_is_for": "Seedream 5.0 Flash is a lower-priced image SKU on the Volcengine price list at 0.12 CNY per output image. A release note for it was not found in this pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedream/seedream-5-0-lite": { "id": "bytedance/seedream/seedream-5-0-lite", "name": "Seedream 5.0 Lite", "maker": "bytedance", "family": "bytedance/seedream", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.22 }, "summary_intro": "Seedream 5.0 Lite is the February 2026 reasoning image model at 0.22 CNY per output image. Ark's migration table points its January ids at Seedream 5.0 Pro. Collected 2026-10-15.", "what_it_is_for": "Seedream 5.0 Lite is the February 2026 reasoning image model at 0.22 CNY per output image. Ark's migration table points its January ids at Seedream 5.0 Pro.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedream/seedream-5-0-pro": { "id": "bytedance/seedream/seedream-5-0-pro", "name": "Seedream 5.0 Pro", "maker": "bytedance", "family": "bytedance/seedream", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": 0.02, "completion_per_m_usd": 0.3 }, "summary_intro": "Seedream 5.0 Pro is ByteDance's July 2026 image model for generation, editing, and layer splitting. Standard output is 0.30 CNY per image at or below about 2.61 megapixels and 0.60 above that. Collected 2026-10-15.", "what_it_is_for": "Seedream 5.0 Pro is ByteDance's July 2026 image model for generation, editing, and layer splitting. Standard output is 0.30 CNY per image at or below about 2.61 megapixels and 0.60 above that.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/seedvr/seedvr": { "id": "bytedance/seedvr/seedvr", "name": "SeedVR", "maker": "bytedance", "family": "bytedance/seedvr", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SeedVR and SeedVR2 are open checkpoints published by ByteDance-Seed on Hugging Face in 3B and 7B sizes. The model card was not read in this pass, so the task and license are unverified. Collected 2026-10-15.", "what_it_is_for": "SeedVR and SeedVR2 are open checkpoints published by ByteDance-Seed on Hugging Face in 3B and 7B sizes. The model card was not read in this pass, so the task and license are unverified.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/speech/doubao-simul-2-0": { "id": "bytedance/speech/doubao-simul-2-0", "name": "Doubao Simultaneous Interpretation 2.0", "maker": "bytedance", "family": "bytedance/speech", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-07-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Doubao Simultaneous Interpretation 2.0 is the July 2025 speech translation model. It can return translated speech in a cloned voice or translated text. The API id and price were not on the update note. Collected 2026-10-15.", "what_it_is_for": "Doubao Simultaneous Interpretation 2.0 is the July 2025 speech translation model. It can return translated speech in a cloned voice or translated text. The API id and price were not on the update note.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/speech/seed-audio-1-0": { "id": "bytedance/speech/seed-audio-1-0", "name": "Seed Audio 1.0", "maker": "bytedance", "family": "bytedance/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seed Audio 1.0 is ByteDance's audio-creation model for full-scene film-style sound, not just speech synthesis. The Volcengine hub lists a QPS limit of 5 and does not show a price on the card that was read. Collected 2026-10-15.", "what_it_is_for": "Seed Audio 1.0 is ByteDance's audio-creation model for full-scene film-style sound, not just speech synthesis. The Volcengine hub lists a QPS limit of 5 and does not show a price on the card that was read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/speech/seed-icl-1-0": { "id": "bytedance/speech/seed-icl-1-0", "name": "Seed-ICL 1.0", "maker": "bytedance", "family": "bytedance/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seed-ICL 1.0 is Doubao's first in-context voice-clone model, resource id seed-icl-1.0, offered since 2024-07-10. Newer clones use ICL 2.0, and the old V1 training API is frozen. Collected 2026-10-15.", "what_it_is_for": "Seed-ICL 1.0 is Doubao's first in-context voice-clone model, resource id seed-icl-1.0, offered since 2024-07-10. Newer clones use ICL 2.0, and the old V1 training API is frozen.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/speech/seed-icl-2-0": { "id": "bytedance/speech/seed-icl-2-0", "name": "Seed-ICL 2.0", "maker": "bytedance", "family": "bytedance/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seed-ICL 2.0 is the current Doubao voice-clone model. Volcengine marks it as model_type 4 and resource id seed-icl-2.0. Marketing similarity numbers were not copied because they were not on the API page. Collected 2026-10-15.", "what_it_is_for": "Seed-ICL 2.0 is the current Doubao voice-clone model. Volcengine marks it as model_type 4 and resource id seed-icl-2.0. Marketing similarity numbers were not copied because they were not on the API page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/speech/seed-realtime": { "id": "bytedance/speech/seed-realtime", "name": "SeedRealtime", "maker": "bytedance", "family": "bytedance/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "image", "video", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SeedRealtime is ByteDance's August 2026 full-duplex audio-visual speech model. Doubao sells the same line as Doubao-Seed-RealtimeVoice. Price and the API id were not in the pages read. Collected 2026-10-15.", "what_it_is_for": "SeedRealtime is ByteDance's August 2026 full-duplex audio-visual speech model. Doubao sells the same line as Doubao-Seed-RealtimeVoice. Price and the API id were not in the pages read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/speech/seed-tts-1-0": { "id": "bytedance/speech/seed-tts-1-0", "name": "Seed-TTS 1.0", "maker": "bytedance", "family": "bytedance/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seed-TTS 1.0 is the 2024 Doubao speech-generation model. It is still a resource id on Volcengine's TTS APIs, next to Seed-TTS 2.0. Collected 2026-10-15.", "what_it_is_for": "Seed-TTS 1.0 is the 2024 Doubao speech-generation model. It is still a resource id on Volcengine's TTS APIs, next to Seed-TTS 2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/speech/seed-tts-2-0": { "id": "bytedance/speech/seed-tts-2-0", "name": "Seed-TTS 2.0", "maker": "bytedance", "family": "bytedance/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seed-TTS 2.0 is the current Doubao speech-synthesis model, resource id seed-tts-2.0. It supports instruction-style delivery and a separate expressive mode for cloned voices. The public character price was not captured. Collected 2026-10-15.", "what_it_is_for": "Seed-TTS 2.0 is the current Doubao speech-synthesis model, resource id seed-tts-2.0. It supports instruction-style delivery and a separate expressive mode for cloned voices. The public character price was not captured.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/speech/seeduplex": { "id": "bytedance/speech/seeduplex", "name": "Seeduplex", "maker": "bytedance", "family": "bytedance/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Seeduplex is ByteDance Seed's April 2026 full-duplex speech model, focused on listening, barge-in, and noisy-environment turn taking. SeedRealtime is the later audio-visual duplex model. No public price was found. Collected 2026-10-15.", "what_it_is_for": "Seeduplex is ByteDance Seed's April 2026 full-duplex speech model, focused on listening, barge-in, and noisy-environment turn taking. SeedRealtime is the later audio-visual duplex model. No public price was found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "bytedance/ui-tars/ui-tars": { "id": "bytedance/ui-tars/ui-tars", "name": "UI-TARS", "maker": "bytedance", "family": "bytedance/ui-tars", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UI-TARS is ByteDance's GUI agent model. UI-TARS-1.5 was open-sourced on 2025-04-17, and several 2B, 7B, and 72B checkpoints are on Hugging Face. The vendor reports 61.6 percent on ScreenSpot-Pro. Collected 2026-10-15.", "what_it_is_for": "UI-TARS is ByteDance's GUI agent model. UI-TARS-1.5 was open-sourced on 2025-04-17, and several 2B, 7B, and 72B checkpoints are on Hugging Face. The vendor reports 61.6 percent on ScreenSpot-Pro.", "ratings": { "empirical_100": 48, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "byu/mekongphon/khmer-ipa": { "id": "byu/mekongphon/khmer-ipa", "name": "Khmer IPA", "maker": "byu", "family": "byu/mekongphon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-06", "context_length": 128, "max_output_tokens": 128, "parameters": "33.6M", "architecture": "Hugging Face EncoderDecoderModel. BERT-style encoder and BERT-LM decoder initialized from configs and trained from scratch, not from a released BERT checkpoint. 6 encoder layers and 6 decoder layers, 8 attention heads, hidden size 512, feed-forward size 1024. Separate source and target tokenizers, each with vocab size 1000. The LREC 2026 paper (section 4.3) calls the tokenizers ByteBPE. The Hub card calls them character-level.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Khmer IPA is a grapheme-to-phoneme model from the Brigham Young University MATRIX Lab. It converts Khmer script to IPA. The Hub repo `byumatrixlab/khmer-ipa` was created on 2026-04-06, and the lab's MekongPhon paper reports it as one of three neural systems at LREC 2026 (11-16 May 2026). Weights are public. Collected 2026-10-15.", "what_it_is_for": "The card says it transcribes Khmer orthography into IPA. The paper trains it from scratch on the MekongPhon parallel corpus so a neural G2P system can cover words the seed lexicon never saw. The authors contrast it with their rule-based Khm2IPA script, which is faster and is not a weight release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "byu/mekongphon/lao-ipa-nontonal": { "id": "byu/mekongphon/lao-ipa-nontonal", "name": "Lao IPA (Non-tonal)", "maker": "byu", "family": "byu/mekongphon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-06", "context_length": 128, "max_output_tokens": 128, "parameters": "33.6M", "architecture": "Hugging Face EncoderDecoderModel. BERT-style encoder and BERT-LM decoder initialized from configs and trained from scratch, not from a released BERT checkpoint. 6 encoder layers and 6 decoder layers, 8 attention heads, hidden size 512, feed-forward size 1024. Separate Lao and IPA tokenizers, each with vocab size 1000. The decoder vocab excludes tone diacritics. The LREC 2026 paper (section 4.3) calls the tokenizers ByteBPE. The Hub card calls them character-level. The paper trains this checkpoint so the tonal and non-tonal targets can be compared, including with Epitran.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lao IPA (Non-tonal) is a grapheme-to-phoneme model from the Brigham Young University MATRIX Lab. It converts Lao script to IPA and drops tone markers. The Hub repo `byumatrixlab/lao-ipa-nontonal` was created on 2026-04-06. The MekongPhon paper (LREC 2026, 11-16 May 2026) trains it as the Lao system without tones. Weights are public. Collected 2026-10-15.", "what_it_is_for": "The card says it transcribes Lao into IPA without tone markers. The paper trains this third neural system so tone-free output can be compared with Epitran and with Lao2IPA on a target that does not require tones. It is a separate release from the tonal checkpoint, not a decoding flag on that model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "byu/mekongphon/lao-ipa-tonal": { "id": "byu/mekongphon/lao-ipa-tonal", "name": "Lao IPA (Tonal)", "maker": "byu", "family": "byu/mekongphon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-06", "context_length": 128, "max_output_tokens": 128, "parameters": "33.6M", "architecture": "Hugging Face EncoderDecoderModel. BERT-style encoder and BERT-LM decoder initialized from configs and trained from scratch, not from a released BERT checkpoint. 6 encoder layers and 6 decoder layers, 8 attention heads, hidden size 512, feed-forward size 1024. Separate Lao and IPA tokenizers, each with vocab size 1000. The decoder vocab includes tone diacritics. The LREC 2026 paper (section 4.3) calls the tokenizers ByteBPE. The Hub card calls them character-level.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lao IPA (Tonal) is a grapheme-to-phoneme model from the Brigham Young University MATRIX Lab. It converts Lao script to IPA and keeps tone markers. The Hub repo `byumatrixlab/lao-ipa-tonal` was created on 2026-04-06. The MekongPhon paper (LREC 2026, 11-16 May 2026) reports it as the Lao model trained with tones. Weights are public. Collected 2026-10-15.", "what_it_is_for": "The card says it transcribes Lao orthography into IPA, including tone markers. The paper uses it to test whether tone can be learned when the target IPA marks tone, and to compare with Epitran and the lab's rule-based Lao2IPA tool. Lao2IPA is a script, not a second weight release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "calgary/wavesfm/wavesfm-v1": { "id": "calgary/wavesfm/wavesfm-v1", "name": "WavesFM v1.0", "maker": "calgary", "family": "calgary/wavesfm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Masked wireless modeling on a shared Vision Transformer. The lab site and model card describe one backbone with modality-specific input embeddings for raw IQ, spectrograms, CSI, and CIR. arXiv:2511.15162 Table I, the paper the site calls the reference for this release, lists an encoder of 8 blocks, embedding dimension 256, hidden dimension 1024, 8 attention heads, image patch 16 by 16, IQ segment size 16, and 6.32 million encoder parameters, plus a 4-block decoder, embedding 128, hidden 512, 16 heads, and 0.79 million parameters. The fine-tuning code default --model vit_multi_small matches the encoder shape (embed 256, depth 8, 8 heads, MLP ratio 4, vision patch 16, IQ segment 16) and the file comment says that config is about 8 million parameters. Hub config.json only sets model_type and architectures to WavesFM. The checkpoint file is wavesfm-v1p0.pth.", "modalities": { "input": [ "iq", "spectrogram", "csi", "cir" ], "output": [ "class", "position" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Calgary WAVES Lab, in the Schulich School of Engineering, released this multimodal wireless foundation model as the public checkpoint `ahmedaboulfo/wavesfm`. The Hugging Face repository was created on 11 January 2026. It is an active Apache-2.0 weight, not a priced API. Collected 2026-10-15. The lab site calls the release v1.0 and points at arXiv:2511.15162.", "what_it_is_for": "The model card says the checkpoint is for research on multimodal wireless representation learning and for fine-tuning on downstream wireless tasks. The lab site says one ViT backbone covers sensing, communication, and localization across raw IQ, spectrograms, CSI, and CIR. Hatem Abou-Zeid, associate professor and Schulich Industry Chair, lists this open-source release on his university page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "caltech/fno/fno": { "id": "caltech/fno/fno", "name": "Fourier Neural Operator", "maker": "caltech", "family": "caltech/fno", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2020-10-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural operator that parameterizes an integral kernel in Fourier space and applies it with FFTs. The maintained implementation is the FNO class in the NeuralOperator library. A Tucker-factorized option, TFNO, is a configuration of that class, not a separate product.", "modalities": { "input": [ "function" ], "output": [ "function" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fourier Neural Operator (FNO) is a Caltech neural operator for parametric PDEs, first posted on arXiv on 2020-10-18 and published as an ICLR 2021 poster. It is still the flagship model in the MIT-licensed NeuralOperator library. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says FNO learns the map from a functional parameter of a PDE family to the solution, instead of solving one instance. The abstract names Burgers' equation, Darcy flow, and the Navier-Stokes equation, including turbulent flow with zero-shot super-resolution. Authors on the ICLR PDF use Caltech addresses except Kamyar Azizzadenesheli, listed at Purdue.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "caltech/fno/geo-fno": { "id": "caltech/fno/geo-fno", "name": "Geo-FNO", "maker": "caltech", "family": "caltech/fno", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-07-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Learns a deformation from an irregular physical domain into a latent uniform grid, then applies an FNO with FFTs in that latent space. The paper says inputs may be point clouds, meshes, or design parameters.", "modalities": { "input": [ "function", "mesh", "point-cloud" ], "output": [ "function" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Geo-FNO is a Caltech extension of the Fourier neural operator for PDEs on irregular geometries. The preprint is dated 2022-07-11. arXiv lists a Journal of Machine Learning Research version, volume 24, issue 1, article 388 (2023). The code is MIT-licensed, copyright 2022 Zongyi Li. The standalone repo is unmaintained. Collected 2026-10-15.", "what_it_is_for": "The abstract says standard FNO is limited to rectangular domains with uniform grids. Geo-FNO learns a deformation into a latent uniform grid and runs the FFT there. Named tasks are elasticity, plasticity, Euler, and Navier-Stokes, for forward modeling and inverse design. Authors are Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, and Anima Anandkumar.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "caltech/fno/pino": { "id": "caltech/fno/pino", "name": "Physics-Informed Neural Operator", "maker": "caltech", "family": "caltech/fno", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2021-11-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid operator model. The paper says PINO uses the Fourier neural operator framework, trains on coarse data and PDE constraints at higher resolution, then optionally fine-tunes the pretrained operator on one PDE instance at test time.", "modalities": { "input": [ "function" ], "output": [ "function" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Physics-Informed Neural Operator (PINO) is a Caltech operator model that adds PDE constraints to a Fourier neural operator. The preprint is dated 2021-11-06. A journal version appeared in the ACM / IMS Journal of Data Science on 2024-02-21. The standalone code repo is unmaintained, but the authors point users at the NeuralOperator library. Collected 2026-10-15.", "what_it_is_for": "The journal abstract says PINO learns a solution operator for a family of parametric PDEs from coarse data plus higher-resolution PDE constraints, including the case with no training data. The code README says it targets Burgers, Darcy flow, long transient Navier-Stokes, and Kolmogorov flow, with a second phase that fine-tunes one instance at test time. Authors on the CaltechAUTHORS record are Zongyi Li, Hongkai Zheng, Nikola Kovachki, David Jin, Haoxuan Chen, Burigede Liu, Kamyar Azizzadenesheli, and Anima Anandkumar, affiliated there with Caltech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "caltech/gano/cgm-gano": { "id": "caltech/gano/cgm-gano", "name": "cGM-GANO", "maker": "caltech", "family": "caltech/gano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-09-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conditional generative adversarial neural operator that synthesizes three-component acceleration time histories. Conditioning variables named in the abstract are moment magnitude, rupture distance, Vs30, and style of faulting. The generator is resolution-invariant with respect to sampling frequency.", "modalities": { "input": [ "scalars" ], "output": [ "waveform" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "cGM-GANO is a conditional ground-motion model from Caltech's geoquake group. It generates three-component acceleration waveforms with a generative adversarial neural operator. The preprint is dated 2023-09-07. Weights for the KiK-net model are linked from the GitHub README. Collected 2026-10-15.", "what_it_is_for": "The abstract presents a data-driven synthesizer conditioned on magnitude, rupture distance, the time-average shear-wave velocity in the top 30 m (Vs30), and faulting style. Training sets named there are Southern California Earthquake Center Broadband Platform simulations and recorded KiK-net motions. Authors are Yaozhong Shi, Grigorios Lavrentiadis, Domniki Asimaki, Zachary E. Ross, and Kamyar Azizzadenesheli. The code org is Caltech-geoquake.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "caltech/gano/gano": { "id": "caltech/gano/gano", "name": "GANO", "maker": "caltech", "family": "caltech/gano", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2022-05-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Generative adversarial neural operator. A generator neural operator maps samples from a user-specified measure, such as a Gaussian random field, to synthetic functions. A discriminator neural functional scores real or synthetic functions. The paper instantiates the Wasserstein criterion in function space.", "modalities": { "input": [ "function" ], "output": [ "function" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GANO (Generative Adversarial Neural Operator) is a function-space generative model released with code under the NeuralOperator GitHub organization. The preprint is dated 2022-05-06 and the arXiv comment says Transactions on Machine Learning Research 2022. Caltech authors are Anima Anandkumar and Zachary E. Ross. Collected 2026-10-15.", "what_it_is_for": "The abstract says GANO learns probabilities on infinite-dimensional function spaces, where a classical GAN is the wrong object. A generator neural operator pushes samples from a chosen measure, such as a Gaussian random field, to synthetic functions. A discriminator neural functional tells real from synthetic. The empirical studies named in the abstract are controlled GRF maps and volcanic activity. The repo also includes an InSAR notebook.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "caltech/neural-fly/neural-fly": { "id": "caltech/neural-fly/neural-fly", "name": "Neural-Fly", "maker": "caltech", "family": "caltech/neural-fly", "status": "active", "confidence": "high", "weights": "closed", "license": "Personal and educational use only; written permission required for further use", "release_date": "2022-05-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Domain-adversarially invariant meta-learning representation of quadrotor aerodynamics. Released Phi_Net is four linear stages: dim_x to 50, 50 to 60, 60 to 50, then 50 to dim_a-1, with ReLU between the first three and a constant 1 concatenated so the last channel is a bias. An optional adversarial head maps dim_a to 20 to num_c. Online control mixes the learned basis with a small linear coefficient vector. dim_x and dim_a are notebook options, not literals in mlmodel.py.", "modalities": { "input": [ "state" ], "output": [ "force-basis" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Neural-Fly is a Caltech learning-based flight controller that pretrains a wind-invariant aerodynamic representation and adapts a few linear coefficients online. Science Robotics published it on 2022-05-04 (volume 7, issue 66, eabm6597). Training code and flight logs are on GitHub under a personal-and-educational-use notice. Collected 2026-10-15.", "what_it_is_for": "The CaltechAUTHORS abstract says different wind conditions share a representation and that the wind-specific part is low-dimensional. Domain adversarially invariant meta-learning (DAIML) learns that representation from 12 minutes of flight data. A composite adaptation law then updates linear mixing coefficients. Tests used the Caltech Real Weather Wind Tunnel at up to 43.6 km/h (12.1 m/s). Authors are Michael O'Connell, Guanya Shi, Xichen Shi, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, and Soon-Jo Chung. Corresponding author on the repo is sjchung@caltech.edu.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "caltech/neural-lander/neural-lander": { "id": "caltech/neural-lander/neural-lander", "name": "Neural Lander", "maker": "caltech", "family": "caltech/neural-lander", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2019-05-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Spectrally normalized ReLU network for residual aerodynamics near the ground. The paper says four fully connected hidden layers, input dimension 12 (height, velocity, attitude, control), and output dimension 3. The released 1D-simulator file is Fa_net_12_3_full_Lip16.pth. The README says that file is the aerodynamics model trained on real data.", "modalities": { "input": [ "state", "control" ], "output": [ "force" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Neural Lander is a Caltech quadrotor landing controller that cancels ground-effect aerodynamics with a spectrally normalized network. Caltech news dated 2019-05-23 says the paper was presented at ICRA on 2019-05-22. A real-data checkpoint ships with a 1D simulator. Collected 2026-10-15.", "what_it_is_for": "The paper learns residual forces the nominal rigid-body model misses near the ground, then uses that network inside a nonlinear feedback controller with a stability argument that uses the Lipschitz bound from spectral normalization. The Caltech AI4Science page lists principal investigators Anima Anandkumar, Soon-Jo Chung, and Yisong Yue. Lead students named in the news are Guanya Shi, Xichen Shi, and Michael O'Connell. Rose Yu (Northeastern) and Kamyar Azizzadenesheli (then UC Irvine) are coauthors on the arXiv header.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "caltech/orbnet/orbnet": { "id": "caltech/orbnet/orbnet", "name": "OrbNet", "maker": "caltech", "family": "caltech/orbnet", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2020-09-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph neural network on symmetry-adapted atomic-orbital features from a low-cost semi-empirical electronic-structure calculation. The paper says it predicts DFT energies.", "modalities": { "input": [ "molecule" ], "output": [ "energy" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OrbNet is a Caltech and Entos graph network that predicts DFT energies from symmetry-adapted atomic-orbital features. The Journal of Chemical Physics published it online on 2020-09-25 (volume 153, 124111). Caltech's AI4Science site lists it as a Caltech project. A public weight file was not found. Collected 2026-10-15.", "what_it_is_for": "The CaltechAUTHORS abstract says OrbNet predicts Schr\u00f6dinger-equation energies with a graph network on symmetry-adapted atomic orbitals taken from a cheap semi-empirical calculation. Named sets are QM7b-T, QM9, GDB-13-T, DrugBank, and the Folmsbee and Hutchison conformer benchmark. First author Zhuoran Qiao and corresponding author Thomas F. Miller III are at Caltech. Matthew Welborn and Frederick R. Manby are at Entos. Anima Anandkumar is at Caltech. The AI4Science page names Tom Miller and Anima Anandkumar as principal investigators.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "caltech/orbnet/orbnet-equi": { "id": "caltech/orbnet/orbnet-equi", "name": "OrbNet-Equi", "maker": "caltech", "family": "caltech/orbnet", "status": "active", "confidence": "high", "weights": "closed", "license": "CC BY-NC-ND 4.0 (PNAS article); Zenodo code license was not re-read file by file", "release_date": "2022-07-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Equivariant network, called UNiTE in the paper, on atomic-orbital features from a tight-binding calculation. The PubMed figure caption says a diagonal-reduction module is followed by block convolution, message passing, and pointwise interaction modules. Targets include electronic energies, densities, and related properties.", "modalities": { "input": [ "molecule" ], "output": [ "energy", "density" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OrbNet-Equi is a Caltech and Entos equivariant quantum-chemistry model that follows OrbNet. The preprint is dated 2021-05-31. CaltechAUTHORS dates the PNAS article 2022-07-28 (119.31, e2205221119). Code and the SDC21 training set are deposited on Zenodo. Collected 2026-10-15.", "what_it_is_for": "The abstract says OrbNet-Equi learns representations from electronic interactions among atomic orbitals, using a tight-binding calculation plus an equivariant network the paper calls UNiTE. It targets electronic energies, densities, and related properties, and the authors say it beats standard machine-learning methods while staying orders of magnitude faster than DFT. First author Zhuoran Qiao is at Caltech. Entos coauthors are Anders S. Christensen, Matthew Welborn, and Frederick R. Manby. Anima Anandkumar is at Caltech and NVIDIA. Thomas F. Miller III is at Caltech and Entos.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/bioredditbert/bioredditbert": { "id": "cambridge/bioredditbert/bioredditbert", "name": "BioRedditBERT", "maker": "cambridge", "family": "cambridge/bioredditbert", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT continued from BioBERT-Base v1.0 (PubMed 200K + PMC 270K) with further masked-language pretraining on health Reddit posts. Card says entity mentions use the [CLS] vector.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioRedditBERT is a Cambridge Language Technology Lab encoder for health social-media text. It continues BioBERT on posts from 68 health subreddits and was released with the COMETA entity-linking corpus. Collected 2026-10-15. Weights are public. SapBERT is the later Cambridge model for the same linking problem and is not a weight-compatible upgrade.", "what_it_is_for": "BioRedditBERT is a Cambridge Language Technology Lab encoder for health social-media text. It continues BioBERT on posts from 68 health subreddits and was released with the COMETA entity-linking corpus. Collected 2026-10-15. Weights are public. SapBERT is the later Cambridge model for the same linking problem and is not a weight-compatible upgrade.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mace/mace-ani-cc": { "id": "cambridge/mace/mace-ani-cc", "name": "MACE-ANI-CC", "maker": "cambridge", "family": "cambridge/mace", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT, as stated in the mace_anicc() docstring.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MACE potential shipped as ani500k_large_CC.model. Docstring: pretrained ANI coupled-cluster model for H, C, N, and O. The filename says large and ANI-500k. The dataset size was not confirmed beyond that filename.", "modalities": { "input": [ "atomic-structure" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MACE-ANI-CC is a small-molecule coupled-cluster MACE checkpoint shipped by the Cambridge ACEsuit library for hydrogen, carbon, nitrogen, and oxygen. Collected 2026-10-15. The docstring cites the 2023 JCP evaluation of the MACE architecture and marks the loader as deprecated. Weights remain in the repository. MIT.", "what_it_is_for": "MACE-ANI-CC is a small-molecule coupled-cluster MACE checkpoint shipped by the Cambridge ACEsuit library for hydrogen, carbon, nitrogen, and oxygen. Collected 2026-10-15. The docstring cites the 2023 JCP evaluation of the MACE architecture and marks the loader as deprecated. Weights remain in the repository. MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mace/mace-matpes-pbe-0": { "id": "cambridge/mace/mace-matpes-pbe-0", "name": "MACE-MATPES-PBE-0", "maker": "cambridge", "family": "cambridge/mace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic Software License (ASL). The project says ASL is GPL-based and does not permit commercial use. Text: https://github.com/gabor1/ASL", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Medium MACE checkpoint fine-tuned from an OMAT model onto MATPES-PBE. File name MACE-matpes-pbe-omat-ft.model.", "modalities": { "input": [ "atomic-structure" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MACE-MATPES-PBE-0 is a Cambridge ACEsuit materials checkpoint for the MATPES PBE data, released as a fine-tune of an OMAT model. Collected 2026-10-15. The foundations README lists it separately from the r2SCAN twin. ASL, non-commercial.", "what_it_is_for": "MACE-MATPES-PBE-0 is a Cambridge ACEsuit materials checkpoint for the MATPES PBE data, released as a fine-tune of an OMAT model. Collected 2026-10-15. The foundations README lists it separately from the r2SCAN twin. ASL, non-commercial.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mace/mace-matpes-r2scan-0": { "id": "cambridge/mace/mace-matpes-r2scan-0", "name": "MACE-MATPES-r2SCAN-0", "maker": "cambridge", "family": "cambridge/mace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic Software License (ASL). The project says ASL is GPL-based and does not permit commercial use. Text: https://github.com/gabor1/ASL", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Medium MACE checkpoint. File MACE-matpes-r2scan-omat-ft.model. README: r2SCAN functional.", "modalities": { "input": [ "atomic-structure" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MACE-MATPES-r2SCAN-0 is the r2SCAN twin of the Cambridge ACEsuit MATPES potentials. Collected 2026-10-15. Weights are public under ASL. The README presents it as the preferred functional relative to the PBE MATPES checkpoint, not as a replacement for every MACE-MP model.", "what_it_is_for": "MACE-MATPES-r2SCAN-0 is the r2SCAN twin of the Cambridge ACEsuit MATPES potentials. Collected 2026-10-15. Weights are public under ASL. The README presents it as the preferred functional relative to the PBE MATPES checkpoint, not as a replacement for every MACE-MP model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mace/mace-mdp": { "id": "cambridge/mace/mace-mdp", "name": "MACE-MDP", "maker": "cambridge", "family": "cambridge/mace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "ASL, per the MACE docs link to https://github.com/Nilsgoe/MACE-MDP.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DipolePolarizabilityMACE. Predicts the molecular dipole and the 3x3 polarizability tensor. The docs say it is not an energy or force model. Ten organic elements. Trained on SPICE-alpha at wB97M-D3(BJ)/def2-TZVPP.", "modalities": { "input": [ "atomic-structure" ], "output": [ "dipole", "polarizability" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MACE-MDP is a dipole and polarizability model distributed by the Cambridge MACE library. The ChemRxiv authors are Nils Gonnheimer, Karsten Reuter, Venkat Kapil, and Johannes T. Margraf. Kapil is a Cambridge-affiliated MACE coauthor. The weight file itself lives in the Nilsgoe/MACE-MDP repository. Collected 2026-10-15. It is not a force field.", "what_it_is_for": "MACE-MDP is a dipole and polarizability model distributed by the Cambridge MACE library. The ChemRxiv authors are Nils Gonnheimer, Karsten Reuter, Venkat Kapil, and Johannes T. Margraf. Kapil is a Cambridge-affiliated MACE coauthor. The weight file itself lives in the Nilsgoe/MACE-MDP repository. Collected 2026-10-15. It is not a force field.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mace/mace-mh-0": { "id": "cambridge/mace/mace-mh-0", "name": "MACE-MH-0", "maker": "cambridge", "family": "cambridge/mace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic Software License (ASL). The project says ASL is GPL-based and does not permit commercial use. Text: https://github.com/gabor1/ASL", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-head MACE foundation potential. Predecessor of MACE-MH-1. File mace-mh-0.model. Covers inorganic crystals, molecules, and surfaces.", "modalities": { "input": [ "atomic-structure" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MACE-MH-0 is the earlier multi-head Cambridge ACEsuit foundation potential for bulk materials, surfaces, and molecules. Collected 2026-10-15. Weights are public under ASL. MACE-MH-1 is the later model the 0.3.15 notes tell users to prefer.", "what_it_is_for": "MACE-MH-0 is the earlier multi-head Cambridge ACEsuit foundation potential for bulk materials, surfaces, and molecules. Collected 2026-10-15. Weights are public under ASL. MACE-MH-1 is the later model the 0.3.15 notes tell users to prefer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mace/mace-mh-1": { "id": "cambridge/mace/mace-mh-1", "name": "MACE-MH-1", "maker": "cambridge", "family": "cambridge/mace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic Software License (ASL). The project says ASL is GPL-based and does not permit commercial use. Text: https://github.com/gabor1/ASL", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-head MACE. 0.3.15 notes: pretrained on OMAT-24 (100 million inorganic configurations) then fine-tuned with six heads. Enhanced weight sharing and non-linear tensor decomposition. File mace-mh-1.model.", "modalities": { "input": [ "atomic-structure" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MACE-MH-1 is the current multi-head Cambridge ACEsuit foundation potential for inorganic crystals, molecules, and surfaces. Collected 2026-10-15. It is the successor of MACE-MH-0. One weight file exposes several DFT heads. ASL, non-commercial.", "what_it_is_for": "MACE-MH-1 is the current multi-head Cambridge ACEsuit foundation potential for inorganic crystals, molecules, and surfaces. Collected 2026-10-15. It is the successor of MACE-MH-0. One weight file exposes several DFT heads. ASL, non-commercial.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mace/mace-mp-0": { "id": "cambridge/mace/mace-mp-0", "name": "MACE-MP-0", "maker": "cambridge", "family": "cambridge/mace", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT for the MP-0a, MP-0b, MP-0b2, and MP-0b3 checkpoints, as stated on the mace-foundations README.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "Initial mace_mp_0 release notes: small 3,847,696; medium 4,688,656; large 5,725,072. Later 0b, 0b2, and 0b3 sizes were not given as parameter counts.", "architecture": "MACE equivariant message-passing interatomic potential. Two layers in the foundation-model paper's reported setup, with a spherical expansion. Size variants change the highest tensor order (L0, L1, L2 in the 0a filenames).", "modalities": { "input": [ "atomic-structure" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MACE-MP-0 is the Cambridge Engineering Laboratory / ACEsuit materials foundation potential trained on Materials Project trajectories. It covers 89 elements at DFT PBE+U and is the model in the 2025 JCP foundation-model paper. Collected 2026-10-15. Several revisions (0a through 0b3) are still downloadable. The library default since MACE 0.3.10 is MACE-MPA-0, not this checkpoint.", "what_it_is_for": "MACE-MP-0 is the Cambridge Engineering Laboratory / ACEsuit materials foundation potential trained on Materials Project trajectories. It covers 89 elements at DFT PBE+U and is the model in the 2025 JCP foundation-model paper. Collected 2026-10-15. Several revisions (0a through 0b3) are still downloadable. The library default since MACE 0.3.10 is MACE-MPA-0, not this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mace/mace-mpa-0": { "id": "cambridge/mace/mace-mpa-0", "name": "MACE-MPA-0", "maker": "cambridge", "family": "cambridge/mace", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT, per the mace-foundations README.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MACE materials potential. Medium checkpoint only in the published loader (mace-mpa-0-medium.model). Same equivariant MACE architecture as MACE-MP-0.", "modalities": { "input": [ "atomic-structure" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MACE-MPA-0 is a Cambridge ACEsuit materials potential trained on MPTrj plus the sAlex set. The foundations README says that adds about 3.5 million crystals and that the model improves high-pressure stability over MACE-MP-0. Collected 2026-10-15. It is the default checkpoint returned by mace_mp() in current MACE.", "what_it_is_for": "MACE-MPA-0 is a Cambridge ACEsuit materials potential trained on MPTrj plus the sAlex set. The foundations README says that adds about 3.5 million crystals and that the model improves high-pressure stability over MACE-MP-0. Collected 2026-10-15. It is the default checkpoint returned by mace_mp() in current MACE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mace/mace-off23": { "id": "cambridge/mace/mace-off23", "name": "MACE-OFF23", "maker": "cambridge", "family": "cambridge/mace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic Software License (ASL). The project says ASL is GPL-based and does not permit commercial use. Text: https://github.com/gabor1/ASL", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Short-range MACE force field for neutral organic molecules. OFF23 sizes from the paper table: small cutoff 4.5 angstrom, 96 channels, Lmax 0; medium cutoff 5.0, 128 channels, Lmax 1; large cutoff 5.0, 192 channels, Lmax 2. Ten elements: H, C, N, O, P, S, F, Cl, Br, I.", "modalities": { "input": [ "atomic-structure" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MACE-OFF23 is the Cambridge Engineering Laboratory short-range organic force field from Kovacs, Moore, Batatia, Csanyi, and coauthors. It targets neutral organic molecules, liquids, and organic crystals for ten elements at wB97M+D3 on SPICE v1. Collected 2026-10-15. Three sizes are in the MACE loader. MACE-OFF24(M) is a later medium update, catalogued separately. ASL blocks commercial use.", "what_it_is_for": "MACE-OFF23 is the Cambridge Engineering Laboratory short-range organic force field from Kovacs, Moore, Batatia, Csanyi, and coauthors. It targets neutral organic molecules, liquids, and organic crystals for ten elements at wB97M+D3 on SPICE v1. Collected 2026-10-15. Three sizes are in the MACE loader. MACE-OFF24(M) is a later medium update, catalogued separately. ASL blocks commercial use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mace/mace-off24": { "id": "cambridge/mace/mace-off24", "name": "MACE-OFF24", "maker": "cambridge", "family": "cambridge/mace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic Software License (ASL). The project says ASL is GPL-based and does not permit commercial use. Text: https://github.com/gabor1/ASL", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Medium organic MACE. Paper table: cutoff 6.0 angstrom, 128 chemical channels, Lmax 1, SPICE version 2. Same ten elements as MACE-OFF23.", "modalities": { "input": [ "atomic-structure" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MACE-OFF24(M) is the Cambridge ACEsuit update of the MACE-OFF23 medium organic force field. The paper says it lengthens the cutoff to 6 angstrom and adds SPICE v2 configurations, including solvated PubChem molecules and amino-acid ligand pairs. Collected 2026-10-15. It is a named weight release. The stock mace_off() shortcuts fetched on this date still point at OFF23.", "what_it_is_for": "MACE-OFF24(M) is the Cambridge ACEsuit update of the MACE-OFF23 medium organic force field. The paper says it lengthens the cutoff to 6 angstrom and adds SPICE v2 configurations, including solvated PubChem molecules and amino-acid ligand pairs. Collected 2026-10-15. It is a named weight release. The stock mace_off() shortcuts fetched on this date still point at OFF23.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mace/mace-omat-0": { "id": "cambridge/mace/mace-omat-0", "name": "MACE-OMAT-0", "maker": "cambridge", "family": "cambridge/mace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic Software License (ASL). The project says ASL is GPL-based and does not permit commercial use. Text: https://github.com/gabor1/ASL", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MACE materials potential. Published sizes in the loader: small-omat-0 and medium-omat-0.", "modalities": { "input": [ "atomic-structure" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MACE-OMAT-0 is a Cambridge ACEsuit materials potential trained on the OMAT set at DFT PBE+U. The foundations README recommends it when phonon accuracy matters. Collected 2026-10-15. Small and medium weights are public under ASL, which blocks commercial use.", "what_it_is_for": "MACE-OMAT-0 is a Cambridge ACEsuit materials potential trained on the OMAT set at DFT PBE+U. The foundations README recommends it when phonon accuracy matters. Collected 2026-10-15. Small and medium weights are public under ASL, which blocks commercial use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mace/mace-omol-0": { "id": "cambridge/mace/mace-omol-0", "name": "MACE-OMOL-0", "maker": "cambridge", "family": "cambridge/mace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic Software License (ASL). The project says ASL is GPL-based and does not permit commercial use. Text: https://github.com/gabor1/ASL", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MACE with charge and spin embeddings. Foundations README: 83 elements, OMol25 (100 million structures), wB97M-V/def2-TZVPD. An older ACEsuit/mace README table said 89 elements and wB97M-VV10. This dossier follows the foundations README that was fetched. A maintainer said the extra-large model has 512 channels and L=1.", "modalities": { "input": [ "atomic-structure" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MACE-OMOL-0 is the Cambridge ACEsuit molecular foundation potential with charge and spin inputs, trained on OMol25. Collected 2026-10-15. Two extra-large checkpoints are published. The loader exposes the 1024-channel file. ASL blocks commercial use. MACE-POLAR-1 is a later electrostatic model on the same OMol25 set, not a drop-in replacement.", "what_it_is_for": "MACE-OMOL-0 is the Cambridge ACEsuit molecular foundation potential with charge and spin inputs, trained on OMol25. Collected 2026-10-15. Two extra-large checkpoints are published. The loader exposes the 1024-channel file. ASL blocks commercial use. MACE-POLAR-1 is a later electrostatic model on the same OMol25 set, not a drop-in replacement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mace/mace-polar-1": { "id": "cambridge/mace/mace-polar-1", "name": "MACE-POLAR-1", "maker": "cambridge", "family": "cambridge/mace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic Software License (ASL). The project says ASL is GPL-based and does not permit commercial use. Text: https://github.com/gabor1/ASL", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MACE plus a non-self-consistent polarisable long-range electrostatic field. Learns per-atom charge and spin densities as multipoles from energy and force labels. Loader keys: polar-1-s, polar-1-m, polar-1-l. Release notes: medium has 2 interaction layers and a 12 angstrom receptive field. Large has 3 layers and 18 angstrom. Small's depth was not in the excerpt.", "modalities": { "input": [ "atomic-structure" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MACE-POLAR-1 is a Cambridge ACEsuit polarisable electrostatic foundation model for molecular chemistry, trained on OMol25. Collected 2026-10-15. Small, medium, and large files are in the current loader. ASL, non-commercial. It extends local MACE energies with a long-range polarisable field.", "what_it_is_for": "MACE-POLAR-1 is a Cambridge ACEsuit polarisable electrostatic foundation model for molecular chemistry, trained on OMol25. Collected 2026-10-15. Small, medium, and large files are in the current loader. ASL, non-commercial. It extends local MACE energies with a long-range polarisable field.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mirror-bert/mirror-bert": { "id": "cambridge/mirror-bert/mirror-bert", "name": "Mirror-BERT", "maker": "cambridge", "family": "cambridge/mirror-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.1B (Hugging Face size tag on the base checkpoints)", "architecture": "BERT-base-uncased after unsupervised Mirror-BERT contrastive tuning on identical or lightly noised string pairs. Variants target words or sentences. Drophead is an extra regularization variant in the README.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mirror-BERT is the Cambridge Language Technology Lab method that turns a masked language model into a word or sentence encoder with no labeled pairs. Collected 2026-10-15. The lab published BERT-base checkpoints for sentence, sentence-drophead, and word settings. RoBERTa checkpoints are a sibling folder. The README warns that released weights will not match the paper's three-run averages exactly.", "what_it_is_for": "Mirror-BERT is the Cambridge Language Technology Lab method that turns a masked language model into a word or sentence encoder with no labeled pairs. Collected 2026-10-15. The lab published BERT-base checkpoints for sentence, sentence-drophead, and word settings. RoBERTa checkpoints are a sibling folder. The README warns that released weights will not match the paper's three-run averages exactly.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mirror-bert/mirror-roberta": { "id": "cambridge/mirror-bert/mirror-roberta", "name": "Mirror-RoBERTa", "maker": "cambridge", "family": "cambridge/mirror-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.1B (Hugging Face size tag)", "architecture": "RoBERTa-base after the same Mirror-BERT contrastive recipe. Sentence and sentence-drophead checkpoints were released. The drophead one is the strongest STS number on the project README.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mirror-RoBERTa is the RoBERTa-base release of Cambridge LTL's Mirror-BERT encoder. Collected 2026-10-15. Two sentence checkpoints are public. The README's drophead variant is the best STS average they list, above a supervised Sentence-BERT baseline of 74.89 on the same table. License was not stated in the excerpt.", "what_it_is_for": "Mirror-RoBERTa is the RoBERTa-base release of Cambridge LTL's Mirror-BERT encoder. Collected 2026-10-15. Two sentence checkpoints are public. The README's drophead variant is the best STS average they list, above a supervised Sentence-BERT baseline of 74.89 on the same table. License was not stated in the excerpt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/mirrorwic/mirrorwic": { "id": "cambridge/mirrorwic/mirrorwic", "name": "MirrorWiC", "maker": "cambridge", "family": "cambridge/mirrorwic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unsupervised contrastive fine-tune that elicits word-in-context representations. Released on BERT-base-uncased, RoBERTa-base, and DeBERTa-base. Training text is raw Wikipedia, with no sense labels.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MirrorWiC is a Cambridge Language Technology Lab encoder for word-in-context meaning. Collected 2026-10-15. The CoNLL 2021 release published three base checkpoints. They are still on the cambridgeltl Hub. This is one named method, so the three bases are versions rather than three families.", "what_it_is_for": "MirrorWiC is a Cambridge Language Technology Lab encoder for word-in-context meaning. Collected 2026-10-15. The CoNLL 2021 release published three base checkpoints. They are still on the cambridgeltl Hub. This is one named method, so the three bases are versions rather than three families.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/posenet/posenet": { "id": "cambridge/posenet/posenet", "name": "PoseNet (Cambridge relocalization)", "maker": "cambridge", "family": "cambridge/posenet", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0. The Caffe ports say personal and research use only, and commercial use needs the authors.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "About 23 convolutional layers, adapted from GoogLeNet, regressing 6-DoF camera pose from one RGB image. The 2015 abstract says about 5 ms per frame. A later Cambridge slide said under 2 ms on a Titan GPU. Both figures are kept because they disagree.", "modalities": { "input": [ "image" ], "output": [ "pose" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PoseNet is the University of Cambridge monocular camera-relocalization network by Kendall, Grimes, and Cipolla. Collected 2026-10-15. It regresses where the camera is from a single RGB image and was released with Caffe weights for research use. The name collides with a later Google pose-estimation demo. This folder is only the Cambridge relocalization model. It is retired as a product.", "what_it_is_for": "PoseNet is the University of Cambridge monocular camera-relocalization network by Kendall, Grimes, and Cipolla. Collected 2026-10-15. It regresses where the camera is from a single RGB image and was released with Caffe weights for research use. The name collides with a later Google pose-estimation demo. This folder is only the Cambridge relocalization model. It is retired as a product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/sapbert/sapbert-pubmedbert": { "id": "cambridge/sapbert/sapbert-pubmedbert", "name": "SapBERT (PubMedBERT)", "maker": "cambridge", "family": "cambridge/sapbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "The Hub card metadata says apache-2.0. The SapBERT GitHub LICENSE file is MIT. Both are stated. Which notice governs the weight files was not resolved.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.1B (Hugging Face model size tag)", "architecture": "BERT-base continued from microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext with UMLS synonym metric learning. English card says to read the [CLS] vector. A second repo uses mean pooling over tokens.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SapBERT is the Cambridge Language Technology Lab biomedical entity encoder. It self-aligns UMLS synonyms on top of PubMedBERT so that medical names which mean the same thing sit together. Collected 2026-10-15. The English checkpoints are public and still downloaded heavily. A cross-lingual pair is catalogued separately.", "what_it_is_for": "SapBERT is the Cambridge Language Technology Lab biomedical entity encoder. It self-aligns UMLS synonyms on top of PubMedBERT so that medical names which mean the same thing sit together. Collected 2026-10-15. The English checkpoints are public and still downloaded heavily. A cross-lingual pair is catalogued separately.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/sapbert/sapbert-xlmr": { "id": "cambridge/sapbert/sapbert-xlmr", "name": "SapBERT-XLMR", "maker": "cambridge", "family": "cambridge/sapbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT, from the SapBERT GitHub LICENSE. This Hub card has no license metadata.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.3B (Hugging Face model size tag)", "architecture": "XLM-RoBERTa-base continued with SapBERT metric learning on UMLS 2020AB all languages. Card says use the [CLS] vector.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SapBERT-XLMR is the Cambridge Language Technology Lab cross-lingual biomedical entity encoder. It starts from XLM-RoBERTa-base and aligns UMLS 2020AB synonyms across languages. Collected 2026-10-15. Weights are public. The large XLM-R twin is a separate folder.", "what_it_is_for": "SapBERT-XLMR is the Cambridge Language Technology Lab cross-lingual biomedical entity encoder. It starts from XLM-RoBERTa-base and aligns UMLS 2020AB synonyms across languages. Collected 2026-10-15. Weights are public. The large XLM-R twin is a separate folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/sapbert/sapbert-xlmr-large": { "id": "cambridge/sapbert/sapbert-xlmr-large", "name": "SapBERT-XLMR-large", "maker": "cambridge", "family": "cambridge/sapbert", "status": "active", "confidence": "medium", "weights": "open-source", "license": "MIT, from the SapBERT GitHub LICENSE. A Hub license field was not read for this repo.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "XLM-RoBERTa-large continued with the same cross-lingual SapBERT recipe. GitHub says use the [CLS] vector.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SapBERT-XLMR-large is the larger cross-lingual Cambridge LTL biomedical entity encoder, built on XLM-RoBERTa-large and UMLS 2020AB. Collected 2026-10-15. The Hub listing shows it as a public feature-extraction model. The card body was not re-read in this pass, so several fields stay empty.", "what_it_is_for": "SapBERT-XLMR-large is the larger cross-lingual Cambridge LTL biomedical entity encoder, built on XLM-RoBERTa-large and UMLS 2020AB. Collected 2026-10-15. The Hub listing shows it as a public feature-extraction model. The card body was not re-read in this pass, so several fields stay empty.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/segnet/bayesian-segnet": { "id": "cambridge/segnet/bayesian-segnet", "name": "Bayesian SegNet", "maker": "cambridge", "family": "cambridge/segnet", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0. The Caffe ports say personal and research use only, and commercial use needs the authors.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SegNet encoder-decoder with Monte Carlo dropout at test time so each pixel has a class distribution and an uncertainty. Caffe implementation. Weight averaging was reported as better than sampling the statistics in the arXiv note that was read.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bayesian SegNet is the Cambridge uncertainty extension of SegNet by Kendall, Badrinarayanan, and Cipolla. Collected 2026-10-15. It is a retired research release. CamVid weights are listed in the SegNet model zoo under the same non-commercial Caffe license.", "what_it_is_for": "Bayesian SegNet is the Cambridge uncertainty extension of SegNet by Kendall, Badrinarayanan, and Cipolla. Collected 2026-10-15. It is a retired research release. CamVid weights are listed in the SegNet model zoo under the same non-commercial Caffe license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/segnet/segnet": { "id": "cambridge/segnet/segnet", "name": "SegNet", "maker": "cambridge", "family": "cambridge/segnet", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0. The Caffe ports say personal and research use only, and commercial use needs the authors.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Convolutional encoder-decoder for pixel labeling. A 2018 Cambridge talk described a 26-layer net and under 30 ms inference on a GPU. The PAMI paper is the spec of record and was not re-read for the layer count, so 26 layers stays a talk figure.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SegNet is a real-time semantic segmentation network from the University of Cambridge Machine Intelligence Laboratory (Badrinarayanan, Kendall, Cipolla). Collected 2026-10-15. Caffe weights for driving, CamVid, and SUN scenes were released for research use. The project is retired as a product. Bayesian SegNet is the uncertainty variant.", "what_it_is_for": "SegNet is a real-time semantic segmentation network from the University of Cambridge Machine Intelligence Laboratory (Badrinarayanan, Kendall, Cipolla). Collected 2026-10-15. Caffe weights for driving, CamVid, and SUN scenes were released for research use. The project is retired as a product. Bayesian SegNet is the uncertainty variant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/simctg/simctg": { "id": "cambridge/simctg/simctg", "name": "SimCTG", "maker": "cambridge", "family": "cambridge/simctg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "117M for the GPT-2 checkpoints in the project table", "architecture": "GPT-2 trained with the SimCTG contrastive generation objective. The project table lists 117M models. Separate T5 checkpoints exist on the Hub and were not given their own folder.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SimCTG is a contrastive text-generation training recipe from Cambridge LTL authors, with checkpoints published under the cambridgeltl Hub org. Collected 2026-10-15. The named public models are GPT-2-sized task checkpoints, not a new base model family. The code repository is yxuansu/SimCTG. NeurIPS 2022 spotlight. Generative decoding, including contrastive search, is the point of the release.", "what_it_is_for": "SimCTG is a contrastive text-generation training recipe from Cambridge LTL authors, with checkpoints published under the cambridgeltl Hub org. Collected 2026-10-15. The named public models are GPT-2-sized task checkpoints, not a new base model family. The code repository is yxuansu/SimCTG. NeurIPS 2022 spotlight. Generative decoding, including contrastive search, is the point of the release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/tessera/tessera-v1-0": { "id": "cambridge/tessera/tessera-v1-0", "name": "TESSERA v1.0", "maker": "cambridge", "family": "cambridge/tessera", "status": "active", "confidence": "high", "weights": "open-source", "license": "CC0-1.0 for weights. Use is also governed by the TESSERA acceptable use policy. The geotessera library is MIT.", "release_date": "2025-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pixel-wise encoder trained with Barlow Twins on a year of Sentinel-1 and Sentinel-2 observations per 10 m pixel. The org card calls v1.0 a QAT encoder with int8 and early fp32 checkpoints. Output embeddings are 128-dimensional in the v1.1 notes.", "modalities": { "input": [ "sar", "optical-satellite" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TESSERA v1.0 is the University of Cambridge ucam-eo pixel-wise Earth-observation foundation model. It compresses a year of Sentinel-1 and Sentinel-2 observations for each 10 m pixel into an embedding for land-cover, crop, biomass, and change tasks. Collected 2026-10-15. Weights are public. v1.1 and v2 are later releases.", "what_it_is_for": "TESSERA v1.0 is the University of Cambridge ucam-eo pixel-wise Earth-observation foundation model. It compresses a year of Sentinel-1 and Sentinel-2 observations for each 10 m pixel into an embedding for land-cover, crop, biomass, and change tasks. Collected 2026-10-15. Weights are public. v1.1 and v2 are later releases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/tessera/tessera-v1-1": { "id": "cambridge/tessera/tessera-v1-1", "name": "TESSERA v1.1", "maker": "cambridge", "family": "cambridge/tessera", "status": "active", "confidence": "high", "weights": "open-source", "license": "CC0-1.0 for weights. Use is also governed by the TESSERA acceptable use policy. The geotessera library is MIT.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Retrained wider QAT encoder. Org card: all-observation inference, int8 checkpoints for Microsoft Planetary Computer and AWS preprocessing. 128-dimensional embeddings, described as a drop-in replacement for v1.0 tiles.", "modalities": { "input": [ "sar", "optical-satellite" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TESSERA v1.1 is a retrained successor to TESSERA v1.0 from the same Cambridge ucam-eo group. The org card describes a wider QAT encoder with all-observation inference and separate int8 checkpoints for Planetary Computer and AWS. Collected 2026-10-15. It stays downloadable. The v2 line is what the org card marks as latest.", "what_it_is_for": "TESSERA v1.1 is a retrained successor to TESSERA v1.0 from the same Cambridge ucam-eo group. The org card describes a wider QAT encoder with all-observation inference and separate int8 checkpoints for Planetary Computer and AWS. Collected 2026-10-15. It stays downloadable. The v2 line is what the org card marks as latest.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/tessera/tessera-v2-large": { "id": "cambridge/tessera/tessera-v2-large", "name": "TESSERA v2 Large", "maker": "cambridge", "family": "cambridge/tessera", "status": "active", "confidence": "high", "weights": "open-source", "license": "CC0-1.0 for weights. Use is also governed by the TESSERA acceptable use policy. The geotessera library is MIT.", "release_date": "2026-07-04", "context_length": null, "max_output_tokens": null, "parameters": "43.83 million", "architecture": "Student distilled from the published 2B TESSERA v2 teacher. Barlow Twins pixel encoder with nested Matryoshka embeddings (16, 32, 64, 128).", "modalities": { "input": [ "sar", "optical-satellite" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TESSERA v2 Large is a distilled TESSERA v2 student from the University of Cambridge ucam-eo group. The org card lists it among four compact pixel models distilled from a 2B teacher, with 128-d Matryoshka embeddings. Collected 2026-10-15. Weights are public and this is part of the latest TESSERA line.", "what_it_is_for": "TESSERA v2 Large is a distilled TESSERA v2 student from the University of Cambridge ucam-eo group. The org card lists it among four compact pixel models distilled from a 2B teacher, with 128-d Matryoshka embeddings. Collected 2026-10-15. Weights are public and this is part of the latest TESSERA line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/tessera/tessera-v2-medium": { "id": "cambridge/tessera/tessera-v2-medium", "name": "TESSERA v2 Medium", "maker": "cambridge", "family": "cambridge/tessera", "status": "active", "confidence": "high", "weights": "open-source", "license": "CC0-1.0 for weights. Use is also governed by the TESSERA acceptable use policy. The geotessera library is MIT.", "release_date": "2026-07-04", "context_length": null, "max_output_tokens": null, "parameters": "21.03 million", "architecture": "Student distilled from the published 2B TESSERA v2 teacher. Barlow Twins pixel encoder with nested Matryoshka embeddings (16, 32, 64, 128).", "modalities": { "input": [ "sar", "optical-satellite" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TESSERA v2 Medium is a distilled TESSERA v2 student from the University of Cambridge ucam-eo group. The org card lists it among four compact pixel models distilled from a 2B teacher, with 128-d Matryoshka embeddings. Collected 2026-10-15. Weights are public and this is part of the latest TESSERA line.", "what_it_is_for": "TESSERA v2 Medium is a distilled TESSERA v2 student from the University of Cambridge ucam-eo group. The org card lists it among four compact pixel models distilled from a 2B teacher, with 128-d Matryoshka embeddings. Collected 2026-10-15. Weights are public and this is part of the latest TESSERA line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/tessera/tessera-v2-nano": { "id": "cambridge/tessera/tessera-v2-nano", "name": "TESSERA v2 Nano", "maker": "cambridge", "family": "cambridge/tessera", "status": "active", "confidence": "high", "weights": "open-source", "license": "CC0-1.0 for weights. Use is also governed by the TESSERA acceptable use policy. The geotessera library is MIT.", "release_date": "2026-07-04", "context_length": null, "max_output_tokens": null, "parameters": "1.07 million", "architecture": "Student distilled from the published 2B TESSERA v2 teacher. Barlow Twins pixel encoder with nested Matryoshka embeddings (16, 32, 64, 128).", "modalities": { "input": [ "sar", "optical-satellite" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TESSERA v2 Nano is a distilled TESSERA v2 student from the University of Cambridge ucam-eo group. The org card lists it among four compact pixel models distilled from a 2B teacher, with 128-d Matryoshka embeddings. Collected 2026-10-15. Weights are public and this is part of the latest TESSERA line.", "what_it_is_for": "TESSERA v2 Nano is a distilled TESSERA v2 student from the University of Cambridge ucam-eo group. The org card lists it among four compact pixel models distilled from a 2B teacher, with 128-d Matryoshka embeddings. Collected 2026-10-15. Weights are public and this is part of the latest TESSERA line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/tessera/tessera-v2-small": { "id": "cambridge/tessera/tessera-v2-small", "name": "TESSERA v2 Small", "maker": "cambridge", "family": "cambridge/tessera", "status": "active", "confidence": "high", "weights": "open-source", "license": "CC0-1.0 for weights. Use is also governed by the TESSERA acceptable use policy. The geotessera library is MIT.", "release_date": "2026-07-04", "context_length": null, "max_output_tokens": null, "parameters": "7.11 million", "architecture": "Student distilled from the published 2B TESSERA v2 teacher. Barlow Twins pixel encoder with nested Matryoshka embeddings (16, 32, 64, 128).", "modalities": { "input": [ "sar", "optical-satellite" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TESSERA v2 Small is a distilled TESSERA v2 student from the University of Cambridge ucam-eo group. The org card lists it among four compact pixel models distilled from a 2B teacher, with 128-d Matryoshka embeddings. Collected 2026-10-15. Weights are public and this is part of the latest TESSERA line.", "what_it_is_for": "TESSERA v2 Small is a distilled TESSERA v2 student from the University of Cambridge ucam-eo group. The org card lists it among four compact pixel models distilled from a 2B teacher, with 128-d Matryoshka embeddings. Collected 2026-10-15. Weights are public and this is part of the latest TESSERA line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cambridge/tessera/tessera-v2-teacher": { "id": "cambridge/tessera/tessera-v2-teacher", "name": "TESSERA v2 2B teacher", "maker": "cambridge", "family": "cambridge/tessera", "status": "active", "confidence": "high", "weights": "open-source", "license": "CC0-1.0 for weights. Use is also governed by the TESSERA acceptable use policy. The geotessera library is MIT.", "release_date": "2026-07-04", "context_length": null, "max_output_tokens": null, "parameters": "2 billion", "architecture": "2B-parameter pixel-wise teacher in the v2 Barlow Twins family. The paper also trained 0.5B and 1B teachers. Those two are not separate repos on the geotessera org card.", "modalities": { "input": [ "sar", "optical-satellite" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TESSERA v2 2B teacher is the large pixel-wise Earth-observation encoder from the University of Cambridge ucam-eo group. Students Nano through Large are distilled from it. Collected 2026-10-15. Weights are on Hugging Face. The 0.5B and 1B teachers named in the paper do not have their own repos on the org card.", "what_it_is_for": "TESSERA v2 2B teacher is the large pixel-wise Earth-observation encoder from the University of Cambridge ucam-eo group. Students Nano through Large are distilled from it. Collected 2026-10-15. Weights are on Hugging Face. The 0.5B and 1B teachers named in the paper do not have their own repos on the org card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "captions/mirage/mirage-avatar-x": { "id": "captions/mirage/mirage-avatar-x", "name": "Mirage Avatar X", "maker": "captions", "family": "captions/mirage", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.15 }, "summary_intro": "Mirage Avatar X is the avatar model Mirage announced on July 28, 2026 and now uses for Captions AI avatars and AI twins. The API id is `mirage-avatar-x`. It is the default on `POST https://api.mirage.app/v1/videos`. Weights are closed. It was active on the create-video schema as of October 1, 2026. Collected 2026-10-15.", "what_it_is_for": "Mirage says Avatar X builds a whole talking performance at once, instead of pasting a mouth onto a still. The stated goal is identity across a clip, micro-expressions, and motion that matches the voice. In Captions you pick a library avatar, describe one, or make an AI twin from about ten seconds of footage, then give it a script. On the API you send driving audio and one visual reference and get a speaking video back.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "captions/mirage/mirage-video-1": { "id": "captions/mirage/mirage-video-1", "name": "Mirage Video 1", "maker": "captions", "family": "captions/mirage", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal Mochi Transformer with all-to-all self-attention and modality-specific MLPs. The December 16, 2025 audio research post says Mirage audio shares this base architecture and a flow-matching diffusion training paradigm with the 2025 avatar video model. Parameter count was not published.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.175 }, "summary_intro": "Mirage Video 1 is Mirage's first named talking-video model, shipped in the Captions app and on `api.mirage.app`. The company introduced it on March 12, 2025 as Mirage, an in-house foundation model for UGC-style talking video. The API id is `mirage-video-1-latest`. It was still an allowed create-video value on October 1, 2026, beside the newer default `mirage-avatar-x`. Collected 2026-10-15.", "what_it_is_for": "Mirage describes it as a human-realism video model: one portrait and a speech clip become a talking performance with facial motion and lip sync. The March 2025 launch also says it can build people, objects, and backgrounds that were not filmed, from a look prompt plus audio, video, or a script, or from audio alone. The June 2, 2025 research note repeats image, text, and audio control, including audio-only casting. Mirage Studio, announced the same day as that note, is the brand product in front of this model, not a second model id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/sensitive/sensitive-binary": { "id": "cardiff/sensitive/sensitive-binary", "name": "X-Sensitive binary", "maker": "cardiff", "family": "cardiff/sensitive", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "2024-11-25", "context_length": null, "max_output_tokens": null, "parameters": "124647170", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Binary labels non-sensitive and sensitive. The base repo's safetensors total is 124,647,170. The large version is RoBERTa-large (24 layers, hidden 1024, 16 heads) with safetensors total 355,361,794.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "X-Sensitive binary is Cardiff NLP's English classifier for sensitive versus non-sensitive posts. The base card fine-tunes the December 2022 Twitter RoBERTa on the X-Sensitive dataset. A RoBERTa-large checkpoint is a version. Collected 2026-10-15. Both Hub repos were created 2024-11-25. License MIT. Status is active.", "what_it_is_for": "The base card says the job is binary detection of sensitive content. Labels are non-sensitive and sensitive. The cited preprint is Antypas and co-authors, 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/sensitive/sensitive-multilabel": { "id": "cardiff/sensitive/sensitive-multilabel", "name": "X-Sensitive multilabel", "maker": "cardiff", "family": "cardiff/sensitive", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "2024-11-25", "context_length": null, "max_output_tokens": null, "parameters": "124651015", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Multilabel labels: conflictual, profanity, sex, drugs, selfharm, spam, not-sensitive. Base safetensors total 124,651,015. Large version safetensors total 355,366,919 with RoBERTa-large dimensions.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "X-Sensitive multilabel is Cardiff NLP's English multi-label sensitive-content classifier on the December 2022 Twitter RoBERTa, with a RoBERTa-large version. Collected 2026-10-15. Both Hub repos were created 2024-11-25. License MIT. Status is active.", "what_it_is_for": "The base card's label map covers conflictual, profanity, sex, drugs, self-harm, spam, and not-sensitive. The dataset is cardiffnlp/x_sensitive. The same preprint is cited as for the binary model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/timelm/timelm-base": { "id": "cardiff/timelm/timelm-base", "name": "TimeLM RoBERTa-base", "maker": "cardiff", "family": "cardiff/timelm", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. The folder's current checkpoint is cardiffnlp/twitter-roberta-base-2022-154m: RoBERTa-base trained from the original checkpoint on 154 million tweets through December 2022, with no incremental updates. Quarterly checkpoints are versions, not extra folders.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TimeLM RoBERTa-base is Cardiff NLP's diachronic English Twitter language-model line. The library default on the collection date is the December 2022 full update, cardiffnlp/twitter-roberta-base-2022-154m. Quarterly and earlier full checkpoints are versions in this folder. Collected 2026-10-15. Status is active. License on the default card is MIT.", "what_it_is_for": "TimeLMs is for measuring language-model degradation and cultural shift on Twitter. The README exposes latest, corresponding-quarter, and all-quarter modes for pseudo-perplexity and masked prediction. TweetNLP's language-model helper still points at the 2021-124m checkpoint, which is a version here, not the 2022 default.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/timelm/timelm-large": { "id": "cardiff/timelm/timelm-large", "name": "TimeLM RoBERTa-large", "maker": "cardiff", "family": "cardiff/timelm", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "2023-03-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-large. config.json: 24 layers, hidden size 1024, 16 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Card title: Twitter 2022 154M (RoBERTa-large, full update). Trained from the original checkpoint on the same filtered 154 million tweets as the base 2022 model, through December 2022. No quarterly large checkpoints are listed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TimeLM RoBERTa-large is Cardiff NLP's large English Twitter masked language model trained through December 2022 on 154 million filtered tweets. Collected 2026-10-15. The Hub repo was created 2023-03-09. License MIT. Status is active. There is no quarterly large series.", "what_it_is_for": "The card says it is the large full update paired with twitter-roberta-base-2022-154m, for Twitter masked prediction. models.json lists it as both default and latest for roberta-large, with an empty quarterly list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/tweetnlp/emoji": { "id": "cardiff/tweetnlp/emoji", "name": "Twitter-RoBERTa emoji", "maker": "cardiff", "family": "cardiff/tweetnlp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Sequence-classification head.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Twitter-RoBERTa emoji is Cardiff NLP's 20-way English emoji classifier and the TweetNLP default for that task. Collected 2026-10-15. Weights are public. No license is printed. Status is active.", "what_it_is_for": "TweetNLP says the model predicts the final emoji of a tweet from the TweetEval adaptation of SemEval 2018 emoji prediction. The README lists 20 emoji.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/tweetnlp/emotion": { "id": "cardiff/tweetnlp/emotion", "name": "Twitter-RoBERTa emotion", "maker": "cardiff", "family": "cardiff/tweetnlp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Sequence-classification head.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Twitter-RoBERTa emotion is Cardiff NLP's English single-label emotion classifier from TweetEval. The config labels are joy, optimism, anger, and sadness. Collected 2026-10-15. Weights are public. No license is printed. Status is active.", "what_it_is_for": "TweetNLP uses this repo for single-label emotion. The README warns that the single-label and multilabel emotion models do not share a label set. This folder is only the four-way model. The source task is SemEval 2018 Affect in Tweets, as unified by TweetEval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/tweetnlp/emotion-multilabel": { "id": "cardiff/tweetnlp/emotion-multilabel", "name": "Twitter-RoBERTa emotion (multilabel)", "maker": "cardiff", "family": "cardiff/tweetnlp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Multilabel head. Encoder named on the card: twitter-roberta-base-2022-154m. config problem_type multi_label_classification. Eleven labels: anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, trust.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Twitter-RoBERTa emotion (multilabel) is Cardiff NLP's eleven-label tweet emotion model. The card says it is twitter-roberta-base-2022-154m fine-tuned on SemEval 2018 Task 1 subtask E-c. Collected 2026-10-15. TweetNLP loads this repo for multilabel emotion. Status is active. No license is printed.", "what_it_is_for": "The job is multilabel emotion, not the four-way TweetEval emotion task. TweetNLP's README lists the eleven labels and warns they differ from the single-label set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/tweetnlp/hate": { "id": "cardiff/tweetnlp/hate", "name": "Twitter-RoBERTa hate", "maker": "cardiff", "family": "cardiff/tweetnlp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2023-03-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Binary classification. The current card fine-tunes twitter-roberta-base-2022-154m. config labels: NOT-HATE, HATE.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Twitter-RoBERTa hate is Cardiff NLP's English binary hate-speech classifier. TweetNLP loads cardiffnlp/twitter-roberta-base-hate-latest, a 13-dataset fine-tune of the December 2022 TimeLM, licensed CC-BY-4.0. The original TweetEval hate checkpoint remains a version. Collected 2026-10-15. Status is active.", "what_it_is_for": "The latest card says the job is binary hate versus not-hate on English text. The TweetNLP README describes detecting hate toward a target community and cites the WOAH 2023 paper. The original TweetEval task is SemEval 2019 HatEval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/tweetnlp/hate-multiclass": { "id": "cardiff/tweetnlp/hate-multiclass", "name": "Twitter-RoBERTa hate (multiclass)", "maker": "cardiff", "family": "cardiff/tweetnlp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Fine-tune of twitter-roberta-base-2022-154m. config labels: sexism, racism, disability, sexual_orientation, religion, other, not_hate. The README's index map matches that order.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Twitter-RoBERTa hate (multiclass) is Cardiff NLP's seven-way English hate classifier. The card says it fine-tunes twitter-roberta-base-2022-154m on 13 English hate datasets. Collected 2026-10-15. Weights are public. No license is printed. Status is active.", "what_it_is_for": "The card lists classes sexism, racism, disability, sexual orientation, religion, other, and not hate. It is the typed counterpart of the binary hate-latest model, not the TweetNLP default. TweetNLP's default hate call loads the binary model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/tweetnlp/irony": { "id": "cardiff/tweetnlp/irony", "name": "Twitter-RoBERTa irony", "maker": "cardiff", "family": "cardiff/tweetnlp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Sequence-classification head.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Twitter-RoBERTa irony is Cardiff NLP's binary English irony classifier from TweetEval and the TweetNLP default for that task. Collected 2026-10-15. Weights are public. No license is printed. Status is active.", "what_it_is_for": "The TweetNLP README says the task is to decide whether a tweet is ironic, using the SemEval 2018 irony set as unified in TweetEval. Config labels are non_irony and irony.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/tweetnlp/offensive": { "id": "cardiff/tweetnlp/offensive", "name": "Twitter-RoBERTa offensive", "maker": "cardiff", "family": "cardiff/tweetnlp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Sequence-classification head.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Twitter-RoBERTa offensive is Cardiff NLP's binary English offensive-language classifier and the TweetNLP default for that task. Collected 2026-10-15. Weights are public. No license is printed. Status is active.", "what_it_is_for": "TweetNLP says the task is whether a tweet contains offensive language, using SemEval 2019 OffensEval as unified in TweetEval. Config labels are non-offensive and offensive.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/tweetnlp/sentiment": { "id": "cardiff/tweetnlp/sentiment", "name": "Twitter-RoBERTa sentiment", "maker": "cardiff", "family": "cardiff/tweetnlp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2022-03-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Sequence-classification head.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Twitter-RoBERTa sentiment is Cardiff NLP's English tweet sentiment classifier. TweetNLP loads cardiffnlp/twitter-roberta-base-sentiment-latest, a 2022 update on the 124-million-tweet TimeLM, licensed CC-BY-4.0. The 2020 TweetEval fine-tune remains a version. Collected 2026-10-15. Status is active.", "what_it_is_for": "The cards say the job is three-way sentiment on English tweets: negative, neutral, or positive. The original card tells readers to prefer the newer model for more recent tweets. The UKRI impact note for the Cardiff NLP fellowship says a sentiment model was the most-downloaded Hugging Face model in January 2021. That sentence is about usage, not a current download count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/tweetnlp/stance": { "id": "cardiff/tweetnlp/stance", "name": "Twitter-RoBERTa stance", "maker": "cardiff", "family": "cardiff/tweetnlp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Three-way stance head. config id2label on the climate, abortion, atheism, feminist, and hillary repos: none, against, favor.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Twitter-RoBERTa stance is Cardiff NLP's set of English stance classifiers from TweetEval, one checkpoint per SemEval 2016 target. Collected 2026-10-15. Five Hub repos are versions of this folder. Weights are public. No license is printed. Status is active.", "what_it_is_for": "TweetEval frames stance as favour, against, or neutral toward abortion, atheism, climate change, feminism, or Hillary Clinton. Each target has its own training split. The Hub label strings disagree with that wording and are recorded in the gaps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/tweettopic/tweet-topic-multi": { "id": "cardiff/tweettopic/tweet-topic-multi", "name": "TweetTopic multi", "maker": "cardiff", "family": "cardiff/tweettopic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Multilabel head, 19 labels, problem_type multi_label_classification on the 21-multi, 19-multi, latest-multi, and dec2021-multi configs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TweetTopic multi is Cardiff NLP's English multilabel tweet topic classifier. Nineteen labels cover arts, business, celebrity, daily life, and the other classes printed on the card. Collected 2026-10-15. Four released checkpoints are versions. Status is active. TweetNLP's default is the December 2021 encoder fine-tune, while the MIT-licensed named release is tweet-topic-21-multi.", "what_it_is_for": "The COLING 2022 TweetTopic paper is the reference on the cards. The task assigns one or more of 19 topics to an English tweet. The single-label six-way model is a different folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/tweettopic/tweet-topic-multilingual-base": { "id": "cardiff/tweettopic/tweet-topic-multilingual-base", "name": "TweetTopic multilingual base", "maker": "cardiff", "family": "cardiff/tweettopic", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "2024-10-04", "context_length": null, "max_output_tokens": null, "parameters": "278058259", "architecture": "XLM-RoBERTa-base continued on tweets. config.json: 12 layers, hidden size 768, 12 attention heads, vocab size 250002, max_position_embeddings 514. Multilabel classification head. Hub safetensors total 278,058,259 parameters. Nineteen topic labels.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TweetTopic multilingual base is Cardiff NLP's 19-way multilabel topic model for English, Spanish, Japanese, and Greek. It fine-tunes XLM-T base. Collected 2026-10-15. Hub createdAt is 2024-10-04. License MIT. The safetensors total is 278,058,259 parameters. Status is active.", "what_it_is_for": "The card says it classifies social-media posts into the TweetTopic label set using the TweetTopic and X-Topic datasets. The EMNLP 2024 paper is the reference. A large twin is a separate folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/tweettopic/tweet-topic-multilingual-large": { "id": "cardiff/tweettopic/tweet-topic-multilingual-large", "name": "TweetTopic multilingual large", "maker": "cardiff", "family": "cardiff/tweettopic", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "2024-10-04", "context_length": null, "max_output_tokens": null, "parameters": "559909907", "architecture": "XLM-RoBERTa-large continued on tweets. The MLM config.json did not parse on retrieval. The fine-tune cardiffnlp/tweet-topic-large-multilingual, which the topic card says is based on this model, has config 24 layers, hidden size 1024, 16 heads, vocab size 250002, max_position_embeddings 514. This repo is the fine-tune, so those dimensions are its own config, not only the parent's. Hub safetensors total 559,909,907 parameters. Nineteen multilabel topics.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TweetTopic multilingual large is the large XLM-T topic classifier from the same Cardiff NLP X-Topic release. It fine-tunes twitter-xlm-roberta-large-2022 for English, Spanish, Japanese, and Greek. Collected 2026-10-15. Hub createdAt is 2024-10-04. License MIT. Safetensors total 559,909,907. Status is active.", "what_it_is_for": "The card describes the same 19-label multilabel topic task as the base multilingual model, with a higher X-Topic table. It is a separate size, not a dated snapshot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/tweettopic/tweet-topic-single": { "id": "cardiff/tweettopic/tweet-topic-single", "name": "TweetTopic single", "maker": "cardiff", "family": "cardiff/tweettopic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Single-label head. config labels: arts_&_culture, business_&_entrepreneurs, pop_culture, daily_life, sports_&_gaming, science_&_technology.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TweetTopic single is Cardiff NLP's English six-way tweet topic classifier. Collected 2026-10-15. TweetNLP's default is cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all. Three other encoder periods are versions. Status is active. No license was printed on the cards that were read.", "what_it_is_for": "The cards describe single-label topic classification on 6,997 tweets. Labels are arts and culture, business and entrepreneurs, pop culture, daily life, sports and gaming, and science and technology. TweetNLP uses this when multi_label is false.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/twitter-roberta/twitter-roberta-base": { "id": "cardiff/twitter-roberta/twitter-roberta-base", "name": "Twitter-RoBERTa-base", "maker": "cardiff", "family": "cardiff/twitter-roberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Continued from the original RoBERTa-base checkpoint on about 58 million tweets.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Twitter-RoBERTa-base is Cardiff NLP's English masked language model from the TweetEval work. It continues RoBERTa-base on about 58 million tweets. Collected 2026-10-15. Weights are public. No Cardiff API is offered. Status is active.", "what_it_is_for": "The card presents it as a Twitter language model for masked-token prediction and for mean-pooled tweet embeddings, and as the base for the TweetEval classifiers. The TweetEval README calls this download the RoBERTa model re-trained on Twitter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/twitter-roberta/twitter-scratch-roberta-base": { "id": "cardiff/twitter-roberta/twitter-scratch-roberta-base", "name": "Twitter-RoBERTa-base (from scratch)", "maker": "cardiff", "family": "cardiff/twitter-roberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base. config.json on the named Hub repo: 12 layers, hidden size 768, 12 attention heads, vocab size 50265, max_position_embeddings 514. The card does not state a separate usable context length. Trained from scratch on about 58 million tweets, not continued from the public RoBERTa checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Twitter-RoBERTa-base (from scratch) is the Cardiff NLP masked language model trained from scratch on about 58 million tweets for the TweetEval paper, rather than continued from public RoBERTa. Collected 2026-10-15. Weights are public. Status is active.", "what_it_is_for": "The card gives it as a masked language model for tweets and points at the TweetEval repository for evaluation. It is the from-scratch counterpart of twitter-roberta-base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/xlmt/xlmt-base": { "id": "cardiff/xlmt/xlmt-base", "name": "XLM-T base", "maker": "cardiff", "family": "cardiff/xlmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "XLM-RoBERTa-base continued on tweets. config.json: 12 layers, hidden size 768, 12 attention heads, vocab size 250002, max_position_embeddings 514. Card: XLM-RoBERTa-base trained on about 198 million multilingual tweets.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XLM-T base is Cardiff NLP's multilingual Twitter masked language model. It continues XLM-RoBERTa-base on about 198 million tweets. Collected 2026-10-15. Weights are public. No license is printed on the card. Status is active.", "what_it_is_for": "The card uses it for cross-lingual tweet similarity by mean-pooling token states. The paper presents it as a multilingual Twitter baseline and as the starting point for sentiment fine-tunes. The sentiment classifiers are separate folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/xlmt/xlmt-large": { "id": "cardiff/xlmt/xlmt-large", "name": "XLM-T large", "maker": "cardiff", "family": "cardiff/xlmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "2023-08-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "XLM-RoBERTa-large continued on tweets. The MLM config.json did not parse on retrieval. The fine-tune cardiffnlp/tweet-topic-large-multilingual, which the topic card says is based on this model, has config 24 layers, hidden size 1024, 16 heads, vocab size 250002, max_position_embeddings 514.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XLM-T large is Cardiff NLP's large multilingual Twitter language model. The card says an XLM-RoBERTa large model was re-trained on over 1 billion tweets through December 2022. Collected 2026-10-15. Hub createdAt is 2023-08-25. License MIT. Status is active.", "what_it_is_for": "The card points users at the XLM-T repository for Twitter evaluation and says the checkpoint works with TweetNLP. It is the encoder behind tweet-topic-multilingual-large.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/xlmt/xlmt-sentiment": { "id": "cardiff/xlmt/xlmt-sentiment", "name": "XLM-T sentiment", "maker": "cardiff", "family": "cardiff/xlmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "XLM-RoBERTa-base continued on tweets. config.json: 12 layers, hidden size 768, 12 attention heads, vocab size 250002, max_position_embeddings 514. Sequence-classification head. config id2label: negative, neutral, positive.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XLM-T sentiment is Cardiff NLP's multilingual tweet sentiment classifier on XLM-T base. The TweetNLP default is cardiffnlp/twitter-xlm-roberta-base-sentiment, fine-tuned on eight languages. A later all-languages fine-tune is a second version. Collected 2026-10-15. Status is active. No license is printed.", "what_it_is_for": "The card says it predicts negative, neutral, or positive sentiment. Fine-tuning covered Arabic, English, French, German, Hindi, Italian, Spanish, and Portuguese, and the card says it can be tried on further languages. TweetNLP loads this repo when multilingual sentiment is requested.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cardiff/xlmt/xlmt-sentiment-politics": { "id": "cardiff/xlmt/xlmt-sentiment-politics", "name": "XLM-T sentiment (politics)", "maker": "cardiff", "family": "cardiff/xlmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-08-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "XLM-RoBERTa-base continued on tweets. config.json: 12 layers, hidden size 768, 12 attention heads, vocab size 250002, max_position_embeddings 514. Further classification training. config id2label: Negative, Neutral, Positive.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XLM-T sentiment (politics) is Cardiff NLP's extension of the multilingual Twitter sentiment model, further trained on tweets from members of parliament in the United Kingdom, Spain, and Greece. Collected 2026-10-15. The Hub repo was created 2022-08-02. Status is active. No license is printed.", "what_it_is_for": "The card says the job is sentiment on politicians' tweets. Labels are Negative, Neutral, and Positive. The GitHub repo linked from the card is politics-and-virality-twitter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cartesia/ink/ink-2": { "id": "cartesia/ink/ink-2", "name": "Ink 2", "maker": "cartesia", "family": "cartesia/ink", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial use license is listed from the Pro plan up. Weights are not released.", "release_date": "2026-07-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "State space model (SSM). Cartesia has not published a parameter count or a full architecture report.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": null }, "summary_intro": "Ink 2 is Cartesia's streaming speech-to-text model for voice agents, with built-in turn detection. The public launch post is dated 2026-07-09. Docs list it stable for English, French, Hindi, Japanese, and Spanish as of 2026-09-17. It is closed. Collected on 2026-10-15.", "what_it_is_for": "Cartesia built it to transcribe noisy, accented, telephony audio, to hold structured data such as phone numbers until the sequence is complete, and to decide turn boundaries without a separate voice-activity detector.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cartesia/ink/ink-preview": { "id": "cartesia/ink/ink-preview", "name": "Ink Preview", "maker": "cartesia", "family": "cartesia/ink", "status": "preview", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial use license is listed from the Pro plan up. Weights are not released.", "release_date": "2026-09-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "State space model (SSM). Cartesia has not published a parameter count or a full architecture report.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ink Preview is Cartesia's preview speech-to-text model id, listed next to Ink 2 on the STT model page with the same 2026-09-17 date and the same five languages. It is closed. Collected on 2026-10-15.", "what_it_is_for": "The docs mark it Preview and do not give a separate feature list. Treat it as the moving Ink test track, not the production id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cartesia/ink/ink-whisper": { "id": "cartesia/ink/ink-whisper", "name": "Ink Whisper", "maker": "cartesia", "family": "cartesia/ink", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial use license is listed from the Pro plan up. Weights are not released.", "release_date": "2025-06-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cartesia's October 2026 Whisper-pricing article calls Ink Whisper an older Whisper-family model. The older-models page says it beats baseline Whisper on accuracy and latency. No parameter count or architecture diagram is published. The state-space description Cartesia uses for Sonic and Ink 2 is not repeated for this id.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": null }, "summary_intro": "Ink Whisper is Cartesia's older speech-to-text model, still served for batch and streaming transcription. The current snapshot is ink-whisper-2025-06-04, released 2025-06-04. It is closed and covers the long language list on the older-models page. Collected on 2026-10-15.", "what_it_is_for": "Cartesia describes it as the affordable Whisper-family model for recorded audio: variable-length chunks, noise, telephony artifacts, accents, disfluencies, proper nouns, and domain terms. New voice agents are pointed at Ink 2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cartesia/sonic/sonic": { "id": "cartesia/sonic/sonic", "name": "Sonic", "maker": "cartesia", "family": "cartesia/sonic", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial use license is listed from the Pro plan up. Weights are not released.", "release_date": "2024-05-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "State space model (SSM). Cartesia has not published a parameter count or a full architecture report.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": null }, "summary_intro": "Sonic is Cartesia's first named text-to-speech model, announced on 2024-05-31 and retired on 2026-06-01. It was a closed state-space voice model. Collected on 2026-10-15 from the launch post and the current sunset table.", "what_it_is_for": "The launch post sold lifelike speech at 135ms model latency, with a playground and a low-latency API. Later docs called it the first flagship: high-accuracy expressive speech optimized for latency.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cartesia/sonic/sonic-2": { "id": "cartesia/sonic/sonic-2", "name": "Sonic 2", "maker": "cartesia", "family": "cartesia/sonic", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial use license is listed from the Pro plan up. Weights are not released.", "release_date": "2025-03-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "State space model (SSM). Cartesia has not published a parameter count or a full architecture report.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": null }, "summary_intro": "Sonic 2 is a deprecated Cartesia text-to-speech model. The earliest snapshot still listed is 2025-03-07. Every sonic-2 snapshot stops working after 2026-10-20. It is closed. Collected on 2026-10-15, while the remaining snapshots were still callable.", "what_it_is_for": "When it was current, Cartesia called it the most capable Sonic: realistic speech, transcript following, voice cloning, and 90ms model latency, with infill and timestamps. Customers are told to move to Sonic 3.6.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cartesia/sonic/sonic-3": { "id": "cartesia/sonic/sonic-3", "name": "Sonic 3", "maker": "cartesia", "family": "cartesia/sonic", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial use license is listed from the Pro plan up. Weights are not released.", "release_date": "2025-10-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "State space model (SSM). Cartesia has not published a parameter count or a full architecture report.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": null }, "summary_intro": "Sonic 3 is Cartesia's streaming text-to-speech generation launched on 2025-10-27 and still available on its January 2026 snapshot. It is closed, covers 42 languages, and added volume, speed, emotion, and laughter. Collected on 2026-10-15.", "what_it_is_for": "Cartesia describes high naturalness, accurate transcript following, and low latency for real-time conversation, with fine-grained delivery controls.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cartesia/sonic/sonic-3-5": { "id": "cartesia/sonic/sonic-3-5", "name": "Sonic 3.5", "maker": "cartesia", "family": "cartesia/sonic", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial use license is listed from the Pro plan up. Weights are not released.", "release_date": "2026-05-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "State space model (SSM). Cartesia has not published a parameter count or a full architecture report.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": null }, "summary_intro": "Sonic 3.5 is Cartesia's previous flagship text-to-speech model, generally available on 2026-05-04 and still served. It is closed, covers 42 languages, and is the predecessor of Sonic 3.6. Collected on 2026-10-15.", "what_it_is_for": "Cartesia describes conversational, expressive speech with native-quality multilingual output, clean audio, alphanumeric readout, and context-aware English heteronyms. It is a drop-in model-id change from Sonic 3 for most callers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cartesia/sonic/sonic-3-6": { "id": "cartesia/sonic/sonic-3-6", "name": "Sonic 3.6", "maker": "cartesia", "family": "cartesia/sonic", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial use license is listed from the Pro plan up. Weights are not released.", "release_date": "2026-08-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "State space model (SSM). Cartesia has not published a parameter count or a full architecture report.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": null }, "summary_intro": "Sonic 3.6 is Cartesia's flagship streaming text-to-speech model, generally available since 2026-08-27. It is a closed state-space speech model with 44 languages. This dossier was collected on 2026-10-15.", "what_it_is_for": "Cartesia positions it as the fastest and most natural Sonic: transcript-faithful speech, confirmation codes and heteronyms without preprocessing, conversational pacing, Hinglish, and instant voice cloning including new Odia and Urdu support.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cartesia/sonic/sonic-english": { "id": "cartesia/sonic/sonic-english", "name": "Sonic English", "maker": "cartesia", "family": "cartesia/sonic", "status": "retired", "confidence": "medium", "weights": "closed", "license": "Proprietary. Commercial use license is listed from the Pro plan up. Weights are not released.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "State space model (SSM). Cartesia has not published a parameter count or a full architecture report.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sonic English is a retired Cartesia text-to-speech model id for the early English Sonic track. It was sunset on 2026-06-01. Collected on 2026-10-15. Confidence is medium because it became an alias rather than a long-lived model card.", "what_it_is_for": "It was the English generation id while sonic-multilingual covered other languages. Callers were later pointed at a dated sonic snapshot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cartesia/sonic/sonic-multilingual": { "id": "cartesia/sonic/sonic-multilingual", "name": "Sonic Multilingual", "maker": "cartesia", "family": "cartesia/sonic", "status": "retired", "confidence": "medium", "weights": "closed", "license": "Proprietary. Commercial use license is listed from the Pro plan up. Weights are not released.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "State space model (SSM). Cartesia has not published a parameter count or a full architecture report.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sonic Multilingual is a retired Cartesia text-to-speech model id. It was a public alias in 2024 for the multilingual Sonic track and was sunset on 2026-06-01. Collected on 2026-10-15. Confidence is medium because Cartesia later told callers to use a Sonic snapshot instead.", "what_it_is_for": "It was the multilingual counterpart to sonic-english while Cartesia still split English and multilingual generation. It is not a current product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cartesia/sonic/sonic-preview": { "id": "cartesia/sonic/sonic-preview", "name": "Sonic Preview", "maker": "cartesia", "family": "cartesia/sonic", "status": "preview", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial use license is listed from the Pro plan up. Weights are not released.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "State space model (SSM). Cartesia has not published a parameter count or a full architecture report.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": null }, "summary_intro": "Sonic Preview is Cartesia's beta text-to-speech model id, sonic-preview. In the October 2026 docs it tracks upcoming Sonic changes, is not for production, and can change without notice. Collected on 2026-10-15. The same id meant different checkpoints in 2024 and 2025.", "what_it_is_for": "Cartesia tells developers to use it only to test upcoming API and model changes. Production traffic should pin a dated Sonic snapshot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cartesia/sonic/sonic-turbo": { "id": "cartesia/sonic/sonic-turbo", "name": "Sonic Turbo", "maker": "cartesia", "family": "cartesia/sonic", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial use license is listed from the Pro plan up. Weights are not released.", "release_date": "2025-03-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "State space model (SSM). Cartesia has not published a parameter count or a full architecture report.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": null }, "summary_intro": "Sonic Turbo is a deprecated low-latency Cartesia text-to-speech model from the Sonic 2 era. It is closed. The remaining snapshot stops after 2026-10-20. Collected on 2026-10-15.", "what_it_is_for": "Cartesia described it as Sonic's quality at about half the latency, as low as 40ms. The migration target in the current docs is Sonic 3.6.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "case/ease/qwen2-5-1-5b-instruct-ease": { "id": "case/ease/qwen2-5-1-5b-instruct-ease", "name": "Qwen2.5-1.5B-Instruct-EASE", "maker": "case", "family": "case/ease", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-20", "context_length": 32768, "max_output_tokens": null, "parameters": "1543302656", "architecture": "Qwen2ForCausalLM supervised fine-tune of Qwen/Qwen2.5-1.5B-Instruct. Hub safetensors total 1,543,302,656 BF16. Config: hidden size 1536, 28 layers, 12 query heads, 2 key-value heads, intermediate size 8960, tied embeddings, SiLU, RoPE theta 1,000,000, vocab 151,668, max position embeddings 32,768. Config _name_or_path is ./qwen_reason_star_10k_deepseek_14b/checkpoint-624. The paper names the teacher DeepSeek-Qwen-14B-Distill.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen2.5-1.5B-Instruct-EASE is a public safety fine-tune from Case Western Reserve University's CWRUSafetyLab. It is a Qwen2.5-1.5B-Instruct checkpoint trained so explicit safety reasoning turns on for jailbreak-like prompts and stays off for ordinary ones. Weights were on the Hub as of the collection date 2026-10-15. Status is active.", "what_it_is_for": "The model card says the checkpoint is for safety research on alignment, small language models, and jailbreak robustness. The AAAI 2026 paper (arXiv 2511.06512, submitted 9 November 2025) describes a two-phase recipe: distill safety reasoning from DeepSeek-Qwen-14B-Distill, then calibrate the model so reasoning is used on vulnerable jailbreak semantics. Authors Haonan Shi, Guoli Wang, Tu Ouyang, and An Wang list case.edu addresses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "case/ease/qwen2-5-3b-instruct-ease": { "id": "case/ease/qwen2-5-3b-instruct-ease", "name": "Qwen2.5-3B-Instruct-EASE", "maker": "case", "family": "case/ease", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-20", "context_length": 32768, "max_output_tokens": null, "parameters": "3085389824", "architecture": "Qwen2ForCausalLM. Hub safetensors total 3,085,389,824 BF16. Config: hidden size 2048, 36 layers, 16 query heads, 2 key-value heads, intermediate size 11008, tied embeddings, SiLU, RoPE theta 1,000,000, vocab 151,668, max position embeddings 32,768. Config _name_or_path is /work/hdd/beib/hshi3/qwen_3b_DA/checkpoint-624. The README body says the base is Qwen/Qwen2.5-3B-Instruct. The README YAML base_model and the Hub model-tree tags say Qwen/Qwen2.5-1.5B-Instruct. The config shape matches the 3B card text, not the 1.5B YAML tag.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen2.5-3B-Instruct-EASE is a public safety fine-tune from Case Western Reserve University's CWRUSafetyLab. The card body says it is Qwen2.5-3B-Instruct trained so explicit safety reasoning turns on for jailbreak-like prompts and stays off for ordinary ones. Weights were on the Hub as of the collection date 2026-10-15. Status is active. The card's YAML base-model tag disagrees with that body.", "what_it_is_for": "The model card says the checkpoint is for safety research on alignment, small language models, and jailbreak robustness. The AAAI 2026 paper (arXiv 2511.06512, submitted 9 November 2025) evaluates an EASE run of Qwen2.5-3B-Instruct with the same two-phase recipe as the 1.5B model: distill safety reasoning from DeepSeek-Qwen-14B-Distill, then calibrate when that reasoning fires. Authors list case.edu addresses. The GitHub README links the 1.5B checkpoint and does not link this repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "central-south/csu-ms2/csu-ms2": { "id": "central-south/csu-ms2/csu-ms2", "name": "CSU-MS2", "maker": "central-south", "family": "central-south/csu-ms2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cross-modal contrastive model. A spectrum encoder and a graph encoder for molecular structure are aligned with an External Space Attention (ESA) module so an MS/MS spectrum can retrieve candidate structures. The GitHub repo and the Hugging Face Space ship three checkpoints of this architecture: low, median, and high collision energy (model.pth under each energy directory). Parameter count was not in the README or abstract.", "modalities": { "input": [ "spectrum", "smiles" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CSU-MS2 is an open cross-modal retrieval model from the College of Chemistry and Chemical Engineering at Central South University. The Analytical Chemistry paper is dated 2025-06-20. It embeds tandem mass spectra and molecular structures in one space so a spectrum can search a structure library. Code and three collision-energy checkpoints are in the GitHub repository, and a Gradio demo runs on a Hugging Face Space. Status on 2026-10-15 is active. No license and no price were stated in the sources opened.", "what_it_is_for": "The paper's job is compound identification when the experimental spectrum is not already in a spectral library. CSU-MS2 compares the spectrum embedding with structure embeddings. The authors also ship SSFDB, a structure database, and a web demo. SSFDB is data, not a second model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "central-south/flowinone/flowinone": { "id": "central-south/flowinone/flowinone", "name": "FlowInOne", "maker": "central-south", "family": "central-south/flowinone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-in, image-out flow matching. Text, layout, and edit instructions are rendered into a visual prompt; the network transports that image to the target. The paper says the 1.2B model is initialized from CrossFlow and trained at 256x256 for 240k steps. Appendix Table 11 lists a trainable Flow Backbone of 1108.40M and a trainable Text-Image VAE of 103.65M, plus a frozen Visual Encoder of 322.58M and a frozen Image VAE of 83.65M. Section 5.1 says the visual encoder is Janus-Pro-1B and the image VAE is a frozen LDM VAE. The released file is flowinone_256px.pth.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlowInOne is an open image-to-image flow-matching model released by Central South University's Jinpeng Group on 2026-04-08. Text, layout, and edit instructions are drawn into one visual prompt, and one network carries that image to the output. The public checkpoint is flowinone_256px.pth under Apache-2.0 on the Hub. Status on 2026-10-15 is active. There is no API price.", "what_it_is_for": "The paper and card describe one image-in, image-out model for text-to-image generation, layout-guided editing, and visual instruction following. The model does not take a separate text-encoder branch at inference. Prompts are rendered into the input canvas. The training set named in the paper is VisPrompt-5M. Evaluation uses VP-Bench.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "central-south/glance/glance": { "id": "central-south/glance/glance", "name": "Glance", "maker": "central-south", "family": "central-south/glance", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Phase-aware LoRA pair on a frozen text-to-image diffusion model. Slow-LoRA covers early semantic steps and Fast-LoRA covers later refinement. The Hub repo ships glance_qwen_slow.safetensors and glance_qwen_fast.safetensors for Qwen-Image. The paper says the method turns Qwen-Image-20B into a 10-step student and FLUX.1-12B into an 8-step student. The card example runs 5 slow steps then 5 fast steps at 1024x1024. LoRA rank and adapter parameter count are not on the card.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Glance is a pair of open LoRA adapters released by Central South University's Jinpeng Group (CSU-JPG) on 2025-12-01. It speeds up a frozen text-to-image diffusion model with a Slow-LoRA and a Fast-LoRA trained on one sample. The public weights are the Qwen-Image adapters. Status on 2026-10-15 is active. There is no API product and no published price.", "what_it_is_for": "The paper and model card describe Glance as phase-aware acceleration. Slow-LoRA handles early semantic denoising and Fast-LoRA handles later refinement, instead of training a new student network. The card's inference path loads Qwen/Qwen-Image, runs the slow adapter, then the fast adapter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "central-south/glance/glance-forcing": { "id": "central-south/glance/glance-forcing", "name": "Glance Forcing", "maker": "central-south", "family": "central-south/glance", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-10-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dual LoRA on a frozen video diffusion teacher. The paper's AR setup uses Wan2.1-1.3B as the few-step generator, 480x832, 81 frames, chunk-wise autoregression. Both adapters use LoRA rank 32 and alpha 128 on all linear layers. Slow-LoRA is assigned timesteps 1 and 0.937; Fast-LoRA is assigned 0.833 and 0.625, in a 4-step sampler. A separate experiment distills 50-step Wan2.1 into an 8-step bidirectional model. The Hub repo has no README mapping its files to these runs.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Glance Forcing is a video distillation method posted by overlapping Central South University authors on 2026-10-14, with weights in the CSU-JPG Hub repo of the same day. It keeps a pretrained video diffusion teacher frozen and trains Slow-LoRA and Fast-LoRA experts so a few-step autoregressive student can be fit from very little ODE trajectory data. On 2026-10-15 the Hub page still has no model card. Confidence is medium because the paper is public but the weight files are not labeled to the reported runs. There is no price.", "what_it_is_for": "The paper says autoregressive video needs a causal few-step student distilled from a bidirectional diffusion teacher, and that collecting ODE trajectories is the expensive part. Glance Forcing splits high-noise layout and motion from low-noise detail into two LoRA experts. The reported generator is Wan2.1-1.3B, producing 480x832 video of 81 frames.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "central-south/rda/rda-llamagen": { "id": "central-south/rda/rda-llamagen", "name": "RDA LlamaGen", "maker": "central-south", "family": "central-south/rda", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other", "release_date": "2026-06-06", "context_length": null, "max_output_tokens": null, "parameters": "5000000", "architecture": "Residual Decoder Adapter on a frozen LlamaGen VQ tokenizer. A paired residual codebook and a residual branch correct the base decoder in pixel space without changing token ids. Hub config.json sets vq_model VQ-16, codebook_size 16384, codebook_embed_dim 8, resvq_codebook_size 16384, resvq_codebook_embed_dim 16. The weight file is rda_model.pt. The card says the base VQ checkpoint is not included. The paper table lists 5M beside the RDA row and 50M beside LlamaGen-VQ.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RDA LlamaGen is the Residual Decoder Adapter checkpoint released by Central South University's Jinpeng Group on 2026-06-06. It sits on a frozen LlamaGen VQ tokenizer and corrects the decoder so reconstructed text is sharper, without changing the token ids the autoregressive model already uses. The Hub file is rda_model.pt. The base VQ checkpoint is not included. Status on 2026-10-15 is active. The card license is \"other\". There is no price.", "what_it_is_for": "The paper's job is text rendering in autoregressive image models. Training a new tokenizer would force the language model to be retrained. RDA is a post-hoc residual on the decoder. The public checkpoint is the LlamaGen-VQ adapter. Callers still pass a base file such as vq_ds16_t2i.pt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cerebras/btlm/btlm-3b-8k-base": { "id": "cerebras/btlm/btlm-3b-8k-base", "name": "BTLM-3B-8K", "maker": "cerebras", "family": "cerebras/btlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-07-19", "context_length": 8192, "max_output_tokens": null, "parameters": "2.6B", "architecture": "GPT-3-style decoder-only model derived from the Cerebras-GPT muP recipe, with SwiGLU, ALiBi, and maximal update parameterization. d_model 2560, 32 layers, d_head 80 (32 heads), d_ffn 6826. The paper says this shape yields 2.6B parameters, rounded to 3B in the name. GPT-2 BPE vocabulary 50257. Trained 470B tokens at sequence length 2048, then 157B tokens at 8192 (627B total) on SlimPajama. Training FLOPs listed as 1.3e22.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cerebras and Opentensor released BTLM-3B-8K, the Bittensor Language Model, as an Apache-2.0 decoder trained on SlimPajama. The paper says the shape has 2.6B parameters and the name rounds that to 3B. It is a research base model with an 8192 trained context, not a Cerebras Inference product. Collected 2026-10-15.", "what_it_is_for": "The makers describe a small model that matches some 7B models on downstream tasks while using less inference compute, including long-context summarization and document QA up to 8192 tokens, and 4-bit deployment aimed at about 3GB of memory.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cerebras/btlm/btlm-3b-8k-chat": { "id": "cerebras/btlm/btlm-3b-8k-chat", "name": "BTLM-3B-8K Chat", "maker": "cerebras", "family": "cerebras/btlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "2.6B", "architecture": "GPT-3-style decoder-only model derived from the Cerebras-GPT muP recipe, with SwiGLU, ALiBi, and maximal update parameterization. d_model 2560, 32 layers, d_head 80 (32 heads), d_ffn 6826. The paper says this shape yields 2.6B parameters, rounded to 3B in the name. GPT-2 BPE vocabulary 50257. Trained 470B tokens at sequence length 2048, then 157B tokens at 8192 (627B total) on SlimPajama. Training FLOPs listed as 1.3e22. Chat weights are a DPO alignment of the base model on Anthropic HH-RLHF using TRL. Card hyperparameters: learning rate 5e-5, batch size 64, 1 epoch, dropout 0, LoRA r 128, LoRA alpha 16, DPO beta 0.05.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cerebras released BTLM-3B-8K Chat as an Apache-2.0 dialogue model: a DPO alignment of BTLM-3B-8K base on Anthropic HH-RLHF. It is a named research checkpoint, not a Cerebras Inference API model. Collected 2026-10-15. A reliable release date was not found.", "what_it_is_for": "The card says the model is aligned to human preferences and optimized for dialogue, with a claimed gain on downstream tasks and better helpfulness and harmlessness than the base checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cerebras/cerebras-gpt/cerebras-gpt-1-3b": { "id": "cerebras/cerebras-gpt/cerebras-gpt-1-3b", "name": "Cerebras-GPT 1.3B", "maker": "cerebras", "family": "cerebras/cerebras-gpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-03-28", "context_length": 2048, "max_output_tokens": null, "parameters": "1.3B", "architecture": "GPT-3-style decoder-only transformer with dense attention in every block (not GPT-3's alternating sparse attention). Standard parameterization. d_model 2048, 24 layers, 16 heads, d_head 128, d_ffn 8192. Learned position embeddings, GELU, GPT-2 BPE vocabulary 50257, sequence length 2048. AdamW (0.9, 0.95), epsilon 1e-8, weight decay 0.1, no dropout, gradient clip 1.0. Learning rate 2.0e-4 with cosine decay to 10% after a 375M-token warmup. Batch size 528 sequences (1.08M tokens). Released weights trained in bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cerebras released Cerebras-GPT 1.3B on 2023-03-28 as one of seven Apache-2.0 GPT-style language models trained on the Pile at about 20 tokens per parameter. It is a research base model, not a Cerebras Inference API product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Cerebras says the family exists so researchers can study compute-optimal scaling on an open dataset and reproduce training on the Cerebras stack. The card's intended use is research into language models, applications, ethics, and alignment, not an aligned chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cerebras/cerebras-gpt/cerebras-gpt-111m": { "id": "cerebras/cerebras-gpt/cerebras-gpt-111m", "name": "Cerebras-GPT 111M", "maker": "cerebras", "family": "cerebras/cerebras-gpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-03-28", "context_length": 2048, "max_output_tokens": null, "parameters": "111M", "architecture": "GPT-3-style decoder-only transformer with dense attention in every block (not GPT-3's alternating sparse attention). Standard parameterization. d_model 768, 10 layers, 12 heads, d_head 64, d_ffn 3072. Learned position embeddings, GELU, GPT-2 BPE vocabulary 50257, sequence length 2048. AdamW (0.9, 0.95), epsilon 1e-8, weight decay 0.1, no dropout, gradient clip 1.0. Learning rate 6.0e-4 with linear decay to 10% after a 375M-token warmup. Batch size 120 sequences (246K tokens). Released weights trained in bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cerebras released Cerebras-GPT 111M on 2023-03-28 as one of seven Apache-2.0 GPT-style language models trained on the Pile at about 20 tokens per parameter. It is a research base model, not a Cerebras Inference API product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Cerebras says the family exists so researchers can study compute-optimal scaling on an open dataset and reproduce training on the Cerebras stack. The card's intended use is research into language models, applications, ethics, and alignment, not an aligned chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cerebras/cerebras-gpt/cerebras-gpt-13b": { "id": "cerebras/cerebras-gpt/cerebras-gpt-13b", "name": "Cerebras-GPT 13B", "maker": "cerebras", "family": "cerebras/cerebras-gpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-03-28", "context_length": 2048, "max_output_tokens": null, "parameters": "13B", "architecture": "GPT-3-style decoder-only transformer with dense attention in every block (not GPT-3's alternating sparse attention). Standard parameterization. d_model 5120, 40 layers, 40 heads, d_head 128, d_ffn 20480. Learned position embeddings, GELU, GPT-2 BPE vocabulary 50257, sequence length 2048. AdamW (0.9, 0.95), epsilon 1e-9, weight decay 0.1, no dropout, gradient clip 1.0. Learning rate 1.2e-4 with cosine decay to 10% after a 375M-token warmup. Batch size 720 sequences (1.47M tokens) for the first 84B tokens, then 1080 sequences (2.21M tokens). Adam epsilon 1e-9. Released weights trained in bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cerebras released Cerebras-GPT 13B on 2023-03-28 as one of seven Apache-2.0 GPT-style language models trained on the Pile at about 20 tokens per parameter. It is a research base model, not a Cerebras Inference API product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Cerebras says the family exists so researchers can study compute-optimal scaling on an open dataset and reproduce training on the Cerebras stack. The card's intended use is research into language models, applications, ethics, and alignment, not an aligned chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cerebras/cerebras-gpt/cerebras-gpt-2-7b": { "id": "cerebras/cerebras-gpt/cerebras-gpt-2-7b", "name": "Cerebras-GPT 2.7B", "maker": "cerebras", "family": "cerebras/cerebras-gpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-03-28", "context_length": 2048, "max_output_tokens": null, "parameters": "2.7B", "architecture": "GPT-3-style decoder-only transformer with dense attention in every block (not GPT-3's alternating sparse attention). Standard parameterization. d_model 2560, 32 layers, 32 heads, d_head 80, d_ffn 10240. Learned position embeddings, GELU, GPT-2 BPE vocabulary 50257, sequence length 2048. AdamW (0.9, 0.95), epsilon 1e-8, weight decay 0.1, no dropout, gradient clip 1.0. Learning rate 2.0e-4 with cosine decay to 10% after a 375M-token warmup. Batch size 528 sequences (1.08M tokens). Released weights trained in bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cerebras released Cerebras-GPT 2.7B on 2023-03-28 as one of seven Apache-2.0 GPT-style language models trained on the Pile at about 20 tokens per parameter. It is a research base model, not a Cerebras Inference API product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Cerebras says the family exists so researchers can study compute-optimal scaling on an open dataset and reproduce training on the Cerebras stack. The card's intended use is research into language models, applications, ethics, and alignment, not an aligned chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cerebras/cerebras-gpt/cerebras-gpt-256m": { "id": "cerebras/cerebras-gpt/cerebras-gpt-256m", "name": "Cerebras-GPT 256M", "maker": "cerebras", "family": "cerebras/cerebras-gpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-03-28", "context_length": 2048, "max_output_tokens": null, "parameters": "256M", "architecture": "GPT-3-style decoder-only transformer with dense attention in every block (not GPT-3's alternating sparse attention). Standard parameterization. d_model 1088, 14 layers, 17 heads, d_head 64, d_ffn 4352. Learned position embeddings, GELU, GPT-2 BPE vocabulary 50257, sequence length 2048. AdamW (0.9, 0.95), epsilon 1e-8, weight decay 0.1, no dropout, gradient clip 1.0. Learning rate 6.0e-4 with linear decay to 10% after a 375M-token warmup. Batch size 264 sequences (541K tokens). Released weights trained in bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cerebras released Cerebras-GPT 256M on 2023-03-28 as one of seven Apache-2.0 GPT-style language models trained on the Pile at about 20 tokens per parameter. It is a research base model, not a Cerebras Inference API product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Cerebras says the family exists so researchers can study compute-optimal scaling on an open dataset and reproduce training on the Cerebras stack. The card's intended use is research into language models, applications, ethics, and alignment, not an aligned chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cerebras/cerebras-gpt/cerebras-gpt-590m": { "id": "cerebras/cerebras-gpt/cerebras-gpt-590m", "name": "Cerebras-GPT 590M", "maker": "cerebras", "family": "cerebras/cerebras-gpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-03-28", "context_length": 2048, "max_output_tokens": null, "parameters": "590M", "architecture": "GPT-3-style decoder-only transformer with dense attention in every block (not GPT-3's alternating sparse attention). Standard parameterization. d_model 1536, 18 layers, 12 heads, d_head 128, d_ffn 6144. Learned position embeddings, GELU, GPT-2 BPE vocabulary 50257, sequence length 2048. AdamW (0.9, 0.95), epsilon 1e-8, weight decay 0.1, no dropout, gradient clip 1.0. Learning rate 2.0e-4 with linear decay to 10% after a 375M-token warmup. Batch size 264 sequences (541K tokens). Released weights trained in bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cerebras released Cerebras-GPT 590M on 2023-03-28 as one of seven Apache-2.0 GPT-style language models trained on the Pile at about 20 tokens per parameter. It is a research base model, not a Cerebras Inference API product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Cerebras says the family exists so researchers can study compute-optimal scaling on an open dataset and reproduce training on the Cerebras stack. The card's intended use is research into language models, applications, ethics, and alignment, not an aligned chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cerebras/cerebras-gpt/cerebras-gpt-6-7b": { "id": "cerebras/cerebras-gpt/cerebras-gpt-6-7b", "name": "Cerebras-GPT 6.7B", "maker": "cerebras", "family": "cerebras/cerebras-gpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-03-28", "context_length": 2048, "max_output_tokens": null, "parameters": "6.7B", "architecture": "GPT-3-style decoder-only transformer with dense attention in every block (not GPT-3's alternating sparse attention). Standard parameterization. d_model 4096, 32 layers, 32 heads, d_head 128, d_ffn 16384. Learned position embeddings, GELU, GPT-2 BPE vocabulary 50257, sequence length 2048. AdamW (0.9, 0.95), epsilon 1e-9, weight decay 0.1, no dropout, gradient clip 1.0. Learning rate 1.2e-4 with linear decay to 10% after a 375M-token warmup. Batch size 1040 sequences (2.13M tokens). Adam epsilon 1e-9. Released weights trained in bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cerebras released Cerebras-GPT 6.7B on 2023-03-28 as one of seven Apache-2.0 GPT-style language models trained on the Pile at about 20 tokens per parameter. It is a research base model, not a Cerebras Inference API product. This dossier was collected on 2026-10-15.", "what_it_is_for": "Cerebras says the family exists so researchers can study compute-optimal scaling on an open dataset and reproduce training on the Cerebras stack. The card's intended use is research into language models, applications, ethics, and alignment, not an aligned chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "chalmers/dlkcat/dlkcat": { "id": "chalmers/dlkcat/dlkcat", "name": "DLKcat", "maker": "chalmers", "family": "chalmers/dlkcat", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2022-06-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph neural network on the substrate molecular graph (from SMILES) plus a convolutional network on overlapping protein-sequence n-grams, with a neural attention layer and a fully connected predictor of kcat. The Nature Catalysis article describes this as DLKcat. The trained model is invoked from the SysBioChalmers GitHub tree with prediction_for_input.py. A 5 August 2021 Zenodo deposit holds predicted kcat tables and enzyme-constrained models for 343 yeast or fungi species, not a separately named network.", "modalities": { "input": [ "text" ], "output": [ "scalar" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DLKcat is a deep-learning kcat predictor from the Systems and Synthetic Biology group at Chalmers University of Technology. It takes a protein sequence and a substrate structure and predicts an enzyme turnover number. The trained model ships in a GPL-3.0 GitHub toolbox. Collected 2026-10-15.", "what_it_is_for": "The Nature Catalysis paper says DLKcat is for high-throughput kcat prediction from substrate structures and protein sequences, including enzymes from organisms without measured kinetics. The same repo uses those predictions in a Bayesian pipeline that builds enzyme-constrained genome-scale metabolic models. The authors report a run over more than 300 yeast or fungi species.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "chalmers/microvision/microvision-fasterrcnn": { "id": "chalmers/microvision/microvision-fasterrcnn", "name": "MicroVision Faster R-CNN", "maker": "chalmers", "family": "chalmers/microvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2026-03-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Detectron2 Faster R-CNN, largest variant named X101-FPN, Detectron2 v0.6. Two-stage detector. Input resolution 1280 px, 100 epochs, effective batch size 32, up to four NVIDIA A100 80 GB GPUs on Alvis (NAISS). Fine-tuned from public pretrained weights. Five classes. Weight file name in the repo and on Hugging Face is microvision_fasterrcnn.pth. The code README says the Faster R-CNN training section is still to be updated.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MicroVision Faster R-CNN is a Chalmers University of Technology two-stage detector from the MicroVision project. It is a Detectron2 X101-FPN fine-tune for vulnerable road users and parked micromobility vehicles, and it is the weakest of the three released benchmarks. Weights are public. Collected 2026-10-15.", "what_it_is_for": "The paper includes it as the region-based baseline next to YOLO11 and RF-DETR. The job is the same: box the five MicroVision classes in rider-view and pedestrian-view traffic images. The authors report that it was consistently worse than the other two.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "chalmers/microvision/microvision-rfdetr": { "id": "chalmers/microvision/microvision-rfdetr", "name": "MicroVision RF-DETR", "maker": "chalmers", "family": "chalmers/microvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2026-03-16", "context_length": null, "max_output_tokens": null, "parameters": "approximately 135 million", "architecture": "RF-DETR large, transformer detector, trained with the Roboflow rfdetr package v1.3.0. Nearest compatible input resolution 1232 px. 100 epochs, effective batch size 32, up to four NVIDIA A100 80 GB GPUs on Alvis (NAISS). Fine-tuned from public pretrained weights. Five classes. The code README names the file microvision_rfdetr.pth and says the RF-DETR training section is still to be updated.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MicroVision RF-DETR is a Chalmers University of Technology detector from the MicroVision project. It is a fine-tune of Roboflow RF-DETR large for vulnerable road users and parked micromobility vehicles, and it is the most accurate of the three released benchmarks. Weights are public. Collected 2026-10-15.", "what_it_is_for": "The paper presents it as the transformer benchmark for detecting pedestrians, cyclists, e-scooterists, stationary bicycles, and stationary e-scooters in images taken from a rider or pedestrian viewpoint. The authors say attention helps on partial and occluded objects, at a higher compute cost than YOLO11.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "chalmers/microvision/microvision-yolo11": { "id": "chalmers/microvision/microvision-yolo11", "name": "MicroVision YOLO11", "maker": "chalmers", "family": "chalmers/microvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2026-03-16", "context_length": null, "max_output_tokens": null, "parameters": "57 million", "architecture": "Ultralytics YOLO11-X (yolo11x.pt), one-stage detector, fine-tuned with Ultralytics v8.3.103. Training name in the repo is microvision_x_1280. Input size 1280 px, 100 epochs, effective batch size 32, up to four NVIDIA A100 80 GB GPUs on Alvis (NAISS). Five classes. The paper says the weights were fine-tuned from public pretrained weights.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MicroVision YOLO11 is a Chalmers University of Technology object detector from the MicroLab / vehicle-safety MicroVision project. It is a fine-tune of Ultralytics YOLO11-X for pedestrians, cyclists, e-scooterists, and parked bicycles and e-scooters. Weights are public. Collected 2026-10-15.", "what_it_is_for": "The paper says the benchmark models support traffic-safety research that must tell vulnerable road users apart from stationary micromobility vehicles, and can pre-label naturalistic video. This checkpoint is the one-stage, lower-latency option. The authors contrast it with RF-DETR, which scored higher and is larger.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/c1/c1-1": { "id": "character-ai/c1/c1-1", "name": "C1.1", "maker": "character-ai", "family": "character-ai/c1", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "C1.1 is Character.AI's name for the production chat model that C1.2 replaced. The company described it on March 23, 2023 as the previous model, aimed at entertainment, roleplay, and emotional connection. It is not in the 2026 chat-style picker. Collected 2026-10-15.", "what_it_is_for": "The March 23, 2023 Series A post says C1.2 was meant to expand on C1.1's entertainment, roleplay, and emotional-connection capabilities. No separate C1.1 model card was published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/c1/c1-2": { "id": "character-ai/c1/c1-2", "name": "C1.2", "maker": "character-ai", "family": "character-ai/c1", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-03-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "C1.2 is Character.AI's smarter chat model, released as an early preview on March 23, 2023 with the company's Series A. It kept C1.1's roleplay job and added drafting, study help, and brainstorming. It is not a current chat style. Collected 2026-10-15.", "what_it_is_for": "Character.AI said C1.2 should draft emails, help with test prep, and brainstorm, while still supporting entertainment and roleplay. The preview started on a subset of Characters, including Character Assistant, Lily, Pair Programmer, Lyle, and Stella.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/cai-image/cai-image": { "id": "character-ai/cai-image/cai-image", "name": "CAI-Image", "maker": "character-ai", "family": "character-ai/cai-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Post-trained line based on the open-source Qwen-Image model. The family includes a separate, unnamed model trained for manga pages. Served on the company's CAI-MM-Studio stack.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAI-Image is Character.AI's family of image models, announced September 16, 2026. They are post-trained from the open-source Qwen-Image model for character-consistent storytelling, and they draw (c.ai) Comics plus Imagine and Imagine Message. Weights of the post-train were not released. Collected 2026-10-15.", "what_it_is_for": "Single-pass story images: named art styles, style transfer from a reference image, multi-character scenes, pose control, cinematic camera control, and manga pages. The app assembles the prompt from the chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/cai-image/cai-image-v2": { "id": "character-ai/cai-image/cai-image-v2", "name": "CAI-Image V2", "maker": "character-ai", "family": "character-ai/cai-image", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAI-Image V2 is the next image model Character.AI named on September 16, 2026. The post describes what it focuses on and does not say it is live. Collected 2026-10-15.", "what_it_is_for": "The post says V2 focuses on environments that stay consistent as the camera moves, finer emotion between characters in one frame, and manga pages with better layouts and text. It is framed as a prerequisite for better character video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/deepsqueak": { "id": "character-ai/chat/deepsqueak", "name": "DeepSqueak", "maker": "character-ai", "family": "character-ai/chat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSqueak is Character.AI's premium chat model, already named by the August 2025 PipSqueak launch as the stronger paid cousin. The company still called it the most powerful model on April 14, 2026, while fixing reliability, and it remains a premium style as of September 28, 2026. Collected 2026-10-15.", "what_it_is_for": "Premium roleplay. April 2026 copy says it is the most powerful model and that reliability work targeted uptime, gibberish, and cut-off replies. December 2025 serving changes targeted speed and quality for c.ai+ users.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/deepsqueak-2": { "id": "character-ai/chat/deepsqueak-2", "name": "DeepSqueak 2", "maker": "character-ai", "family": "character-ai/chat", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSqueak 2 is a Character.AI model the company said was in training on April 14, 2026, with early results they described as looking good. No release post was found by October 15, 2026. The September 28, 2026 roster still lists DeepSqueak, not DeepSqueak 2. Collected 2026-10-15.", "what_it_is_for": "Implied successor to DeepSqueak, the model the company called its most powerful. No capability list was published beyond the training update.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/dynamic": { "id": "character-ai/chat/dynamic", "name": "Dynamic", "maker": "character-ai", "family": "character-ai/chat", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dynamic is a Character.AI chat style introduced in the April 2025 community update. The company said it selects the best option for a conversation. It was retired on April 28, 2026. Collected 2026-10-15.", "what_it_is_for": "Help-center copy: a newest chat style that picks the best option for the conversation, rather than one fixed behavior.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/flash": { "id": "character-ai/chat/flash", "name": "Flash", "maker": "character-ai", "family": "character-ai/chat", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flash is a Character.AI chat model from the January 2025 c.ai+ model picker, described as quicker responses. It was not carried forward by name when Chat Styles launched on February 18, 2025. Collected 2026-10-15.", "what_it_is_for": "The January 22, 2025 community update describes Flash as the quicker-responses option next to Prime and Braniac.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/goro": { "id": "character-ai/chat/goro", "name": "Goro", "maker": "character-ai", "family": "character-ai/chat", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Goro is a Character.AI chat style that the April 2025 community update called the latest style and said had just received significant updates. A May 6, 2025 post described it as the less spicy choice next to Soft Launch. It was retired on April 28, 2026. Collected 2026-10-15.", "what_it_is_for": "Public copy positions Goro as a less spicy, more restrained chat style. The April 2025 note is about quality updates, not a first-launch spec.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/longsqueak": { "id": "character-ai/chat/longsqueak", "name": "LongSqueak", "maker": "character-ai", "family": "character-ai/chat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongSqueak is a Character.AI premium chat style launched August 5, 2026 for long-form storytelling and novella-like replies. The company says it has four times the memory of other styles so it can use Lorebooks, Facts, and Character Definitions more heavily. It was still a premium style on September 28, 2026. Collected 2026-10-15.", "what_it_is_for": "Long-form roleplay. The official X post and the same-day community post describe richer prose, more narration while keeping dialogue, and Style Control dials for length (Long, Looong, or Loooooong) and for dialogue versus narration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/meow": { "id": "character-ai/chat/meow", "name": "Meow", "maker": "character-ai", "family": "character-ai/chat", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meow is a Character.AI chat style launched February 18, 2025 for fast, short replies. On April 28, 2026 the company moved it to the Legacy menu and said it would be retired around May 8, 2026. It is not in the September 2026 plan table. Collected 2026-10-15.", "what_it_is_for": "The launch post's line for Meow is quick wits and faster words: speed for banter and fast scenes, for users over 18.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/nyan": { "id": "character-ai/chat/nyan", "name": "Nyan", "maker": "character-ai", "family": "character-ai/chat", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nyan is a Character.AI chat style launched February 18, 2025 as the smarter, more thoughtful option for c.ai+ subscribers. The company said it was the new name of the January 2025 Braniac picker model. It moved to Legacy on April 28, 2026 with retirement planned around May 8, and it is absent from the September 2026 plan table. Collected 2026-10-15.", "what_it_is_for": "Launch copy: smart and more thoughtful, for depth and detail, c.ai+ only. The January picker had called Braniac the best model for deep immersion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/pawly": { "id": "character-ai/chat/pawly", "name": "Pawly", "maker": "character-ai", "family": "character-ai/chat", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pawly is an experimental Character.AI chat style announced in the March 2025 community update for smarter multilingual chat. The company retired it on April 28, 2026. Collected 2026-10-15.", "what_it_is_for": "The help-center update says Pawly is a model with smarter multilingual capabilities. It does not list the languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/pipsqueak": { "id": "character-ai/chat/pipsqueak", "name": "PipSqueak", "maker": "character-ai", "family": "character-ai/chat", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PipSqueak is Character.AI's free chat model launched in August 2025 as a post-trained open-source model meant to replace older proprietary models. The company called it DeepSqueak's smaller free cousin, with better memory and roleplay. PipSqueak 2 was announced April 14, 2026 as its replacement. The original name is not in the September 2026 picker. Collected 2026-10-15.", "what_it_is_for": "Roleplay and chat with higher memory and engagement than the proprietary models it was replacing. The August 26, 2025 post says early users praised in-character personality, long detailed messages, story flow, and world-building.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/pipsqueak-2": { "id": "character-ai/chat/pipsqueak-2", "name": "PipSqueak 2", "maker": "character-ai", "family": "character-ai/chat", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PipSqueak 2 (PSQ2) is Character.AI's April 2026 replacement for PipSqueak, aimed at stronger in-character consistency, better recall of earlier details, and less repetitive writing. It became the free default during the late-April rollout. On September 28, 2026 new chats moved to PipSqueak 3 and PipSqueak 2 moved to the Legacy menu. Collected 2026-10-15.", "what_it_is_for": "The April 14, 2026 post lists three jobs: stay in character deeper into a chat, hold details so the user repeats less, and write more varied dialogue and description with fewer loops.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/pipsqueak-2-rawr": { "id": "character-ai/chat/pipsqueak-2-rawr", "name": "PipSqueak 2 Rawr", "maker": "character-ai", "family": "character-ai/chat", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PipSqueak 2 Rawr, also called Rawr in the September 28, 2026 post, is a Character.AI chat style listed beside PipSqueak 2. It moved to the Legacy menu when PipSqueak 3 became the default for new chats. A first-launch blog post with a date was not found. Collected 2026-10-15.", "what_it_is_for": "The company has not published a behavior spec in the pages read for this dossier. Secondary guides describe it as a chattier, more dialogue-heavy PipSqueak 2 variant. That description is not copied here as a maker claim.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/pipsqueak-3": { "id": "character-ai/chat/pipsqueak-3", "name": "PipSqueak 3", "maker": "character-ai", "family": "character-ai/chat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PipSqueak 3 is Character.AI's free default chat style as of September 28, 2026. The company calls it a step up from PipSqueak 2 for general roleplay: smarter replies, better tracking of the chat, and fewer formatting mistakes. New chats start on it. Collected 2026-10-15.", "what_it_is_for": "General roleplay for every account. The launch post says it writes more interesting replies than PipSqueak 2 and keeps better track of the chat. It is compatible with Lorebook. c.ai+ subscribers get Style Control.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/prime": { "id": "character-ai/chat/prime", "name": "Prime", "maker": "character-ai", "family": "character-ai/chat", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Prime is a Character.AI chat model from the January 2025 c.ai+ model picker, described as a mix of speed and intelligence. Chat Styles replaced that picker on February 18, 2025. The company did not say which new style, if any, Prime became. Collected 2026-10-15.", "what_it_is_for": "The January 22, 2025 community update says the model picker lets c.ai+ users choose the model that best meets their needs, and describes Prime as a mix of speed and intelligence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/roar": { "id": "character-ai/chat/roar", "name": "Roar", "maker": "character-ai", "family": "character-ai/chat", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Roar is a Character.AI chat style launched February 18, 2025 as the balanced default: a mix of speed and smarts, available to everyone. The company retired it on April 28, 2026, with no Legacy-menu stay. Collected 2026-10-15.", "what_it_is_for": "Launch copy calls Roar a blend of quick thinking and insightful responses, and a general option for conversation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/shortsqueak": { "id": "character-ai/chat/shortsqueak", "name": "ShortSqueak", "maker": "character-ai", "family": "character-ai/chat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ShortSqueak is a free Character.AI chat style launched September 28, 2026 for short, casual replies. The company describes texting energy: quick banter and casual roleplay, not multi-paragraph answers to a short user line. Collected 2026-10-15.", "what_it_is_for": "Short replies. The launch post says it is for people who wanted the feel of earlier, shorter chat styles back, and gives the example of not answering \"hey\" with a five-paragraph monologue.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/chat/soft-launch": { "id": "character-ai/chat/soft-launch", "name": "Soft Launch", "maker": "character-ai", "family": "character-ai/chat", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Soft Launch is a Character.AI chat style announced May 6, 2025 as a bolder, flirtier 18+ option next to Goro, at first for a limited time. The August 2025 community update made it permanent for users 18 and older. On April 28, 2026 it moved to Legacy with retirement planned around May 8. It is not in the September 22, 2026 official plan table. Collected 2026-10-15.", "what_it_is_for": "The company's May 2025 post calls it Goro's flirtier bestie: playful, cheeky, and bolder, for users 18 and older.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/kaiju/kaiju-large": { "id": "character-ai/kaiju/kaiju-large", "name": "Kaiju Large", "maker": "character-ai", "family": "character-ai/kaiju", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "110B", "architecture": "Dense transformer autoregressive LLM. Multi-query attention, interleaved sliding-window and global attention at about a 5:1 ratio with a 1024-token window, cross-layer KV sharing across 2-3 layers, int8 quantization-aware training, pre-layer RMSNorm, and dynamic activation clamping. Trained on H100s with tensor and sequence parallelism plus FSDP. Gradient communication uses 6-bit Squinch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kaiju Large is a production size in Character.AI's Kaiju family of in-house conversational LLMs. The November 7, 2025 research post lists it at 110B and describes a serving-first dense transformer. The company had already begun replacing older proprietary models with post-trained open models, and in December 2025 said it no longer does large-scale pretraining. Collected 2026-10-15.", "what_it_is_for": "Kaiju was built for fast, engaging, safer conversation rather than academic benchmark scores. Described as the largest production variant. No extra size-specific benchmark was published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/kaiju/kaiju-medium": { "id": "character-ai/kaiju/kaiju-medium", "name": "Kaiju Medium", "maker": "character-ai", "family": "character-ai/kaiju", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "34B", "architecture": "Dense transformer autoregressive LLM. Multi-query attention, interleaved sliding-window and global attention at about a 5:1 ratio with a 1024-token window, cross-layer KV sharing across 2-3 layers, int8 quantization-aware training, pre-layer RMSNorm, and dynamic activation clamping. Trained on H100s with tensor and sequence parallelism plus FSDP. Gradient communication uses 6-bit Squinch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kaiju Medium is a production size in Character.AI's Kaiju family of in-house conversational LLMs. The November 7, 2025 research post lists it at 34B and describes a serving-first dense transformer. The company had already begun replacing older proprietary models with post-trained open models, and in December 2025 said it no longer does large-scale pretraining. Collected 2026-10-15.", "what_it_is_for": "Kaiju was built for fast, engaging, safer conversation rather than academic benchmark scores. Described only as the middle production variant. No extra size-specific behavior was published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/kaiju/kaiju-small": { "id": "character-ai/kaiju/kaiju-small", "name": "Kaiju Small", "maker": "character-ai", "family": "character-ai/kaiju", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Dense transformer autoregressive LLM. Multi-query attention, interleaved sliding-window and global attention at about a 5:1 ratio with a 1024-token window, cross-layer KV sharing across 2-3 layers, int8 quantization-aware training, pre-layer RMSNorm, and dynamic activation clamping. Trained on H100s with tensor and sequence parallelism plus FSDP. Gradient communication uses 6-bit Squinch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kaiju Small is a production size in Character.AI's Kaiju family of in-house conversational LLMs. The November 7, 2025 research post lists it at 13B and describes a serving-first dense transformer. The company had already begun replacing older proprietary models with post-trained open models, and in December 2025 said it no longer does large-scale pretraining. Collected 2026-10-15.", "what_it_is_for": "Kaiju was built for fast, engaging, safer conversation rather than academic benchmark scores. The post says virtual scalars and ternary weight updates mattered most for the smaller int8 models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/ovi/ovi": { "id": "character-ai/ovi/ovi", "name": "Ovi", "maker": "character-ai", "family": "character-ai/ovi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-06", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "Twin-backbone diffusion transformer. Video branch initialized from Wan2.2-TI2V-5B. Audio branch is a 5B model pretrained from scratch with a mirroring architecture. A 1B fusion branch does blockwise cross-modal fusion. Project page total is 11B (5B visual + 5B audio + 1B fusion). Speech is prompted with ... tags and audio captions with tags. 24 fps.", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ovi is Character.AI's open audio-video model, released with weights and inference code in October 2025. It generates about 5-second, and later 10-second, clips with speech, sound effects, and music from text or from text plus a first frame. It is a research release, not described as the in-app AvatarFX model. Collected 2026-10-15.", "what_it_is_for": "Human-centric clips with synchronized speech and effects, including multi-person dialogue, from a text prompt or a text prompt plus a starting image. Lip sync is learned without face bounding boxes, according to the project page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/video/avatarfx": { "id": "character-ai/video/avatarfx", "name": "AvatarFX", "maker": "character-ai", "family": "character-ai/video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion Transformer (DiT) video model with audio conditioning, distillation to fewer diffusion steps, and a separate proprietary TTS model for the voice. The April 2025 post says a novel inference strategy targets arbitrarily long videos.", "modalities": { "input": [ "image", "audio", "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AvatarFX is Character.AI's image-to-video model that makes a still character speak, sing, and emote. It was unveiled April 21, 2025, put on the web by June 2, 2025, and described on July 3, 2025 as the video model powering the product. Collected 2026-10-15.", "what_it_is_for": "Turn a character image plus audio into video across photoreal, 2D, 3D cartoon, and non-human faces, including multi-speaker and multi-turn clips. Creators use it for intro videos. The voice is the company's proprietary TTS, which is not a separately named public model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "character-ai/video/talkingmachines": { "id": "character-ai/video/talkingmachines", "name": "TalkingMachines", "maker": "character-ai", "family": "character-ai/video", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive diffusion model. A Diffusion Transformer is distilled with asymmetric knowledge distillation from a bidirectional video model into a real-time generator. The post says generation uses two diffusion steps.", "modalities": { "input": [ "image", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TalkingMachines is Character.AI's real-time, audio-driven video model, introduced July 3, 2025. From an image and a voice signal it animates mouth, head, and eyes. The same post says the team was still working to bring it into the product for FaceTime-style calls. No later post found here says it shipped. Collected 2026-10-15.", "what_it_is_for": "Real-time character video driven by audio, across styles and identities, including turn-taking between speakers. Research demo, not the shipping AvatarFX product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cityu/compare2score/compare2score": { "id": "cityu/compare2score/compare2score", "name": "Compare2Score", "maker": "cityu", "family": "cityu/compare2score", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-05-15", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "MPLUGOwl2LlamaForCausalLM (mplug_owl2). Paper: CLIP-ViT-L/14 vision encoder and LLaMA-2-7B decoder, fine-tuned as mPLUG-Owl2. Config: decoder hidden 4096, 32 layers, 32 heads, intermediate 11008, vocab 32000, max_position_embeddings 2048, bfloat16, bos 1, eos 2, tie_word_embeddings false. Vision: hidden 1024, 24 layers, 16 heads, image_size 448, patch 14, intermediate 4096, projection_dim 768. Visual abstractor: 6 layers, hidden 1024, 16 heads, intermediate 2816, 64 learnable queries, encoder_hidden_size 1024. freeze_vision_model false, tune_visual_abstractor true. Paper says each image embedding has U=65 tokens. Current config _name_or_path is VQA-CityU/Compare2Score_1. The 2024-05-15 upload used MAGAer13/mplug-owl2-llama2-7b and lists weight-index metadata.total_size 16409100288 bytes. That byte total is not a parameter count.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Compare2Score is a no-reference image-quality model from City University of Hong Kong, with Nanyang Technological University, Shanghai Jiao Tong University, and Jiangxi University of Finance and Economics. Hanwei Zhu (CityU) and Haoning Wu (NTU) share equal contribution. Shiqi Wang (shiqwang@cityu.edu.hk) is the corresponding author. Public weights went up on 15 May 2024 under q-future/Compare2Score with an MIT card. The paper was submitted on 29 May 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper trains an LMM to compare two images from the same IQA set and answer with one of five English levels: inferior, worse, similar, better, superior. At inference, soft comparison against anchor images builds a probability matrix, and MAP estimation turns that into a continuous quality score. The card exposes this as `model.score`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/mamba/mamba-1-4b": { "id": "cmu/mamba/mamba-1-4b", "name": "Mamba 1.4B", "maker": "cmu", "family": "cmu/mamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the state-spaces/mamba repository (Copyright 2023 Tri Dao, Albert Gu). The Hugging Face README fetched for mamba-2.8b was empty and did not restate a weight license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.4 billion", "architecture": "Mamba selective state-space language model. Official README dimension table: 48 layers, model dimension 2048. The README says a Mamba of this size uses twice the layer count of a similar Transformer because two Mamba blocks stand in for one attention block plus one MLP block. Tensor type on the 2.8B Hub page is F32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mamba 1.4B is a pretrained selective state-space language model released by Albert Gu (CMU) and Tri Dao (Princeton) under the state-spaces organization. CMU FLAME lists Mamba among foundation models built at CMU. It is a base model, not an instruction model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors release Mamba as a general language-model backbone that replaces attention with a selective state-space layer. The README positions the checkpoints as base models for research, trained on the Pile unless the name says SlimPajama.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/mamba/mamba-130m": { "id": "cmu/mamba/mamba-130m", "name": "Mamba 130M", "maker": "cmu", "family": "cmu/mamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the state-spaces/mamba repository (Copyright 2023 Tri Dao, Albert Gu). The Hugging Face README fetched for mamba-2.8b was empty and did not restate a weight license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "130 million", "architecture": "Mamba selective state-space language model. Official README dimension table: 24 layers, model dimension 768. The README says a Mamba of this size uses twice the layer count of a similar Transformer because two Mamba blocks stand in for one attention block plus one MLP block. Tensor type on the 2.8B Hub page is F32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mamba 130M is a pretrained selective state-space language model released by Albert Gu (CMU) and Tri Dao (Princeton) under the state-spaces organization. CMU FLAME lists Mamba among foundation models built at CMU. It is a base model, not an instruction model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors release Mamba as a general language-model backbone that replaces attention with a selective state-space layer. The README positions the checkpoints as base models for research, trained on the Pile unless the name says SlimPajama.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/mamba/mamba-2-8b": { "id": "cmu/mamba/mamba-2-8b", "name": "Mamba 2.8B", "maker": "cmu", "family": "cmu/mamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the state-spaces/mamba repository (Copyright 2023 Tri Dao, Albert Gu). The Hugging Face README fetched for mamba-2.8b was empty and did not restate a weight license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "2.8 billion in the release name; the Hugging Face page labels the tensors as 3B params", "architecture": "Mamba selective state-space language model. Official README dimension table: 64 layers, model dimension 2560. The README says a Mamba of this size uses twice the layer count of a similar Transformer because two Mamba blocks stand in for one attention block plus one MLP block. Tensor type on the 2.8B Hub page is F32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mamba 2.8B is a pretrained selective state-space language model released by Albert Gu (CMU) and Tri Dao (Princeton) under the state-spaces organization. CMU FLAME lists Mamba among foundation models built at CMU. It is a base model, not an instruction model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors release Mamba as a general language-model backbone that replaces attention with a selective state-space layer. The README positions the checkpoints as base models for research, trained on the Pile unless the name says SlimPajama.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/mamba/mamba-2-8b-slimpj": { "id": "cmu/mamba/mamba-2-8b-slimpj", "name": "Mamba 2.8B SlimPajama", "maker": "cmu", "family": "cmu/mamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the state-spaces/mamba repository (Copyright 2023 Tri Dao, Albert Gu). The Hugging Face README fetched for mamba-2.8b was empty and did not restate a weight license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "2.8 billion", "architecture": "Mamba selective state-space language model. Official README dimension table: 64 layers, model dimension 2560. The README says a Mamba of this size uses twice the layer count of a similar Transformer because two Mamba blocks stand in for one attention block plus one MLP block. Tensor type on the 2.8B Hub page is F32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mamba 2.8B SlimPajama is a pretrained selective state-space language model released by Albert Gu (CMU) and Tri Dao (Princeton) under the state-spaces organization. CMU FLAME lists Mamba among foundation models built at CMU. It is a base model, not an instruction model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors release Mamba as a general language-model backbone that replaces attention with a selective state-space layer. The README positions the checkpoints as base models for research, trained on the Pile unless the name says SlimPajama.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/mamba/mamba-370m": { "id": "cmu/mamba/mamba-370m", "name": "Mamba 370M", "maker": "cmu", "family": "cmu/mamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the state-spaces/mamba repository (Copyright 2023 Tri Dao, Albert Gu). The Hugging Face README fetched for mamba-2.8b was empty and did not restate a weight license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "370 million", "architecture": "Mamba selective state-space language model. Official README dimension table: 48 layers, model dimension 1024. The README says a Mamba of this size uses twice the layer count of a similar Transformer because two Mamba blocks stand in for one attention block plus one MLP block. Tensor type on the 2.8B Hub page is F32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mamba 370M is a pretrained selective state-space language model released by Albert Gu (CMU) and Tri Dao (Princeton) under the state-spaces organization. CMU FLAME lists Mamba among foundation models built at CMU. It is a base model, not an instruction model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors release Mamba as a general language-model backbone that replaces attention with a selective state-space layer. The README positions the checkpoints as base models for research, trained on the Pile unless the name says SlimPajama.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/mamba/mamba-790m": { "id": "cmu/mamba/mamba-790m", "name": "Mamba 790M", "maker": "cmu", "family": "cmu/mamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the state-spaces/mamba repository (Copyright 2023 Tri Dao, Albert Gu). The Hugging Face README fetched for mamba-2.8b was empty and did not restate a weight license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "790 million", "architecture": "Mamba selective state-space language model. Official README dimension table: 48 layers, model dimension 1536. The README says a Mamba of this size uses twice the layer count of a similar Transformer because two Mamba blocks stand in for one attention block plus one MLP block. Tensor type on the 2.8B Hub page is F32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mamba 790M is a pretrained selective state-space language model released by Albert Gu (CMU) and Tri Dao (Princeton) under the state-spaces organization. CMU FLAME lists Mamba among foundation models built at CMU. It is a base model, not an instruction model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors release Mamba as a general language-model backbone that replaces attention with a selective state-space layer. The README positions the checkpoints as base models for research, trained on the Pile unless the name says SlimPajama.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/mamba-2/mamba2-1-3b": { "id": "cmu/mamba-2/mamba2-1-3b", "name": "Mamba-2 1.3B", "maker": "cmu", "family": "cmu/mamba-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the state-spaces/mamba repository (Copyright 2023 Tri Dao, Albert Gu). The Hugging Face README fetched for mamba-2.8b was empty and did not restate a weight license.", "release_date": "2024-06-03", "context_length": null, "max_output_tokens": null, "parameters": "1.3 billion", "architecture": "Mamba-2 structured state-space dual (SSD) language model. The official README lists this checkpoint with the Pile 300B-token base models. The README dimension table is printed for the 130M/370M/790M/1.4B/2.8B names, which do not match Mamba-2's 780M/1.3B/2.7B names, so depth and width are not copied onto this dossier.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mamba-2 1.3B is a Mamba-2 base language model released on 3 June 2024 by Albert Gu (CMU) and Tri Dao (Princeton). Weights are on the state-spaces Hugging Face org. This dossier was collected on 2026-10-15.", "what_it_is_for": "Mamba-2 is the authors' state-space dual sequence model for language modeling. The released checkpoints are research base models trained on the Pile, not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/mamba-2/mamba2-130m": { "id": "cmu/mamba-2/mamba2-130m", "name": "Mamba-2 130M", "maker": "cmu", "family": "cmu/mamba-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the state-spaces/mamba repository (Copyright 2023 Tri Dao, Albert Gu). The Hugging Face README fetched for mamba-2.8b was empty and did not restate a weight license.", "release_date": "2024-06-03", "context_length": null, "max_output_tokens": null, "parameters": "130 million", "architecture": "Mamba-2 structured state-space dual (SSD) language model. The official README lists this checkpoint with the Pile 300B-token base models. The README dimension table is printed for the 130M/370M/790M/1.4B/2.8B names, which do not match Mamba-2's 780M/1.3B/2.7B names, so depth and width are not copied onto this dossier.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mamba-2 130M is a Mamba-2 base language model released on 3 June 2024 by Albert Gu (CMU) and Tri Dao (Princeton). Weights are on the state-spaces Hugging Face org. This dossier was collected on 2026-10-15.", "what_it_is_for": "Mamba-2 is the authors' state-space dual sequence model for language modeling. The released checkpoints are research base models trained on the Pile, not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/mamba-2/mamba2-2-7b": { "id": "cmu/mamba-2/mamba2-2-7b", "name": "Mamba-2 2.7B", "maker": "cmu", "family": "cmu/mamba-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the state-spaces/mamba repository (Copyright 2023 Tri Dao, Albert Gu). The Hugging Face README fetched for mamba-2.8b was empty and did not restate a weight license.", "release_date": "2024-06-03", "context_length": null, "max_output_tokens": null, "parameters": "2.7 billion", "architecture": "Mamba-2 structured state-space dual (SSD) language model. The official README lists this checkpoint with the Pile 300B-token base models. The README dimension table is printed for the 130M/370M/790M/1.4B/2.8B names, which do not match Mamba-2's 780M/1.3B/2.7B names, so depth and width are not copied onto this dossier.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mamba-2 2.7B is a Mamba-2 base language model released on 3 June 2024 by Albert Gu (CMU) and Tri Dao (Princeton). Weights are on the state-spaces Hugging Face org. This dossier was collected on 2026-10-15.", "what_it_is_for": "Mamba-2 is the authors' state-space dual sequence model for language modeling. The released checkpoints are research base models trained on the Pile, not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/mamba-2/mamba2-370m": { "id": "cmu/mamba-2/mamba2-370m", "name": "Mamba-2 370M", "maker": "cmu", "family": "cmu/mamba-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the state-spaces/mamba repository (Copyright 2023 Tri Dao, Albert Gu). The Hugging Face README fetched for mamba-2.8b was empty and did not restate a weight license.", "release_date": "2024-06-03", "context_length": null, "max_output_tokens": null, "parameters": "370 million", "architecture": "Mamba-2 structured state-space dual (SSD) language model. The official README lists this checkpoint with the Pile 300B-token base models. The README dimension table is printed for the 130M/370M/790M/1.4B/2.8B names, which do not match Mamba-2's 780M/1.3B/2.7B names, so depth and width are not copied onto this dossier.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mamba-2 370M is a Mamba-2 base language model released on 3 June 2024 by Albert Gu (CMU) and Tri Dao (Princeton). Weights are on the state-spaces Hugging Face org. This dossier was collected on 2026-10-15.", "what_it_is_for": "Mamba-2 is the authors' state-space dual sequence model for language modeling. The released checkpoints are research base models trained on the Pile, not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/mamba-2/mamba2-780m": { "id": "cmu/mamba-2/mamba2-780m", "name": "Mamba-2 780M", "maker": "cmu", "family": "cmu/mamba-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the state-spaces/mamba repository (Copyright 2023 Tri Dao, Albert Gu). The Hugging Face README fetched for mamba-2.8b was empty and did not restate a weight license.", "release_date": "2024-06-03", "context_length": null, "max_output_tokens": null, "parameters": "780 million", "architecture": "Mamba-2 structured state-space dual (SSD) language model. The official README lists this checkpoint with the Pile 300B-token base models. The README dimension table is printed for the 130M/370M/790M/1.4B/2.8B names, which do not match Mamba-2's 780M/1.3B/2.7B names, so depth and width are not copied onto this dossier.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mamba-2 780M is a Mamba-2 base language model released on 3 June 2024 by Albert Gu (CMU) and Tri Dao (Princeton). Weights are on the state-spaces Hugging Face org. This dossier was collected on 2026-10-15.", "what_it_is_for": "Mamba-2 is the authors' state-space dual sequence model for language modeling. The released checkpoints are research base models trained on the Pile, not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/openbeats/openbeats-base": { "id": "cmu/openbeats/openbeats-base", "name": "OpenBEATs-Base", "maker": "cmu", "family": "cmu/openbeats", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-18", "context_length": null, "max_output_tokens": null, "parameters": "90 million", "architecture": "BEATs-style masked audio token prediction encoder. Hidden size 768, feed-forward 3072, 12 layers, 12 heads. Paper table: 20k hours of multi-domain audio.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenBEATs-Base is a pretrained audio encoder from OpenBEATs, the 2025 CMU and AIST open BEATs pipeline. Parameter count in the paper table: 90 million. This dossier was collected on 2026-10-15.", "what_it_is_for": "OpenBEATs is a general-purpose audio encoder for classification, bioacoustics, and audio reasoning after the user adds or loads a head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/openbeats/openbeats-large": { "id": "cmu/openbeats/openbeats-large", "name": "OpenBEATs-Large", "maker": "cmu", "family": "cmu/openbeats", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-18", "context_length": null, "max_output_tokens": null, "parameters": "300 million", "architecture": "BEATs-style masked audio token prediction encoder. Hidden size 1024, feed-forward 4096, 24 layers, 16 heads. Paper table: 20k hours. The GitHub example encodes to patch embeddings of width 1024.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenBEATs-Large is a pretrained audio encoder from OpenBEATs, the 2025 CMU and AIST open BEATs pipeline. Parameter count in the paper table: 300 million. This dossier was collected on 2026-10-15.", "what_it_is_for": "OpenBEATs is a general-purpose audio encoder for classification, bioacoustics, and audio reasoning after the user adds or loads a head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/opuslm/opuslm-1-7b-anneal": { "id": "cmu/opuslm/opuslm-1-7b-anneal", "name": "OpusLM 1.7B Anneal", "maker": "cmu", "family": "cmu/opuslm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-21", "context_length": null, "max_output_tokens": null, "parameters": "1.7 billion", "architecture": "Speech language model initialized from a decoder-only text model and continually pretrained. The paper does not name the text checkpoint on the pages opened here. Anneal is in the Hub id.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpusLM 1.7B Anneal is a public anneal checkpoint from OpusLM, a speech-language-model family led by Carnegie Mellon's Language Technologies Institute with LY Corporation and NVIDIA. This dossier was collected on 2026-10-15.", "what_it_is_for": "OpusLM is a unified speech and text model for recognition, synthesis, and text tasks. The public files found here are the 1.7B and 7B anneal checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/opuslm/opuslm-7b-anneal": { "id": "cmu/opuslm/opuslm-7b-anneal", "name": "OpusLM 7B Anneal", "maker": "cmu", "family": "cmu/opuslm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-21", "context_length": null, "max_output_tokens": null, "parameters": "7 billion", "architecture": "Speech language model initialized from a decoder-only text model and continually pretrained. The paper does not name the text checkpoint on the pages opened here. Anneal is in the Hub id.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpusLM 7B Anneal is a public anneal checkpoint from OpusLM, a speech-language-model family led by Carnegie Mellon's Language Technologies Institute with LY Corporation and NVIDIA. This dossier was collected on 2026-10-15.", "what_it_is_for": "OpusLM is a unified speech and text model for recognition, synthesis, and text tasks. The public files found here are the 1.7B and 7B anneal checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owls/owls-0-25b-180k": { "id": "cmu/owls/owls-0-25b-180k", "name": "OWLS 0.25B 180K", "maker": "cmu", "family": "cmu/owls", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0 on the OWLS 18B 360K card YAML. Sibling cards use the same template. Only the 18B 360K raw README was opened for the license field.", "release_date": "2025-05-03", "context_length": null, "max_output_tokens": null, "parameters": "250 million labeled. Exact tensor count was not opened.", "architecture": "Whisper-style encoder-decoder speech model in the OWSM project. 16 kHz input, from the OWLS collection text and the card's resample snippet. Developed with ESPnet by CMU WAVLab, with an NVIDIA coauthor on the paper.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWLS 0.25B 180K is one public checkpoint in OWLS, the Open Whisper-style Large-scale suite from CMU WAVLab. The suite scales Whisper-style speech models from 0.25B to 18B. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors use OWLS to study how speech recognition and translation scale with model size and data, and they release the checkpoints for that research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owls/owls-0-5b-180k": { "id": "cmu/owls/owls-0-5b-180k", "name": "OWLS 0.50B 180K", "maker": "cmu", "family": "cmu/owls", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0 on the OWLS 18B 360K card YAML. Sibling cards use the same template. Only the 18B 360K raw README was opened for the license field.", "release_date": "2025-05-03", "context_length": null, "max_output_tokens": null, "parameters": "500 million labeled. Exact tensor count was not opened.", "architecture": "Whisper-style encoder-decoder speech model in the OWSM project. 16 kHz input, from the OWLS collection text and the card's resample snippet. Developed with ESPnet by CMU WAVLab, with an NVIDIA coauthor on the paper.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWLS 0.50B 180K is one public checkpoint in OWLS, the Open Whisper-style Large-scale suite from CMU WAVLab. The suite scales Whisper-style speech models from 0.25B to 18B. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors use OWLS to study how speech recognition and translation scale with model size and data, and they release the checkpoints for that research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owls/owls-18b-180k": { "id": "cmu/owls/owls-18b-180k", "name": "OWLS 18B 180K", "maker": "cmu", "family": "cmu/owls", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0 on the OWLS 18B 360K card YAML. Sibling cards use the same template. Only the 18B 360K raw README was opened for the license field.", "release_date": "2025-05-03", "context_length": null, "max_output_tokens": null, "parameters": "18 billion labeled. The 18B sentence 'has 18B parameters' was read on the 360K card, not this file.", "architecture": "Whisper-style encoder-decoder speech model in the OWSM project. 16 kHz input, from the OWLS collection text and the card's resample snippet. Developed with ESPnet by CMU WAVLab, with an NVIDIA coauthor on the paper.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWLS 18B 180K is one public checkpoint in OWLS, the Open Whisper-style Large-scale suite from CMU WAVLab. The suite scales Whisper-style speech models from 0.25B to 18B. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors use OWLS to study how speech recognition and translation scale with model size and data, and they release the checkpoints for that research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owls/owls-18b-360k": { "id": "cmu/owls/owls-18b-360k", "name": "OWLS 18B 360K", "maker": "cmu", "family": "cmu/owls", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0 on the OWLS 18B 360K card YAML. Sibling cards use the same template. Only the 18B 360K raw README was opened for the license field.", "release_date": "2025-05-03", "context_length": null, "max_output_tokens": null, "parameters": "18 billion, stated on this card", "architecture": "Whisper-style encoder-decoder speech model in the OWSM project. 16 kHz input, from the OWLS collection text and the card's resample snippet. Developed with ESPnet by CMU WAVLab, with an NVIDIA coauthor on the paper.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWLS 18B 360K is one public checkpoint in OWLS, the Open Whisper-style Large-scale suite from CMU WAVLab. The suite scales Whisper-style speech models from 0.25B to 18B. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors use OWLS to study how speech recognition and translation scale with model size and data, and they release the checkpoints for that research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owls/owls-1b-180k": { "id": "cmu/owls/owls-1b-180k", "name": "OWLS 1B 180K", "maker": "cmu", "family": "cmu/owls", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0 on the OWLS 18B 360K card YAML. Sibling cards use the same template. Only the 18B 360K raw README was opened for the license field.", "release_date": "2025-05-03", "context_length": null, "max_output_tokens": null, "parameters": "1.12 billion total, stated on this checkpoint's card", "architecture": "Whisper-style encoder-decoder speech model in the OWSM project. 16 kHz input, from the OWLS collection text and the card's resample snippet. Developed with ESPnet by CMU WAVLab, with an NVIDIA coauthor on the paper.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWLS 1B 180K is one public checkpoint in OWLS, the Open Whisper-style Large-scale suite from CMU WAVLab. The suite scales Whisper-style speech models from 0.25B to 18B. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors use OWLS to study how speech recognition and translation scale with model size and data, and they release the checkpoints for that research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owls/owls-2b-180k": { "id": "cmu/owls/owls-2b-180k", "name": "OWLS 2B 180K", "maker": "cmu", "family": "cmu/owls", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0 on the OWLS 18B 360K card YAML. Sibling cards use the same template. Only the 18B 360K raw README was opened for the license field.", "release_date": "2025-05-03", "context_length": null, "max_output_tokens": null, "parameters": "2 billion labeled. Exact tensor count was not opened.", "architecture": "Whisper-style encoder-decoder speech model in the OWSM project. 16 kHz input, from the OWLS collection text and the card's resample snippet. Developed with ESPnet by CMU WAVLab, with an NVIDIA coauthor on the paper.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWLS 2B 180K is one public checkpoint in OWLS, the Open Whisper-style Large-scale suite from CMU WAVLab. The suite scales Whisper-style speech models from 0.25B to 18B. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors use OWLS to study how speech recognition and translation scale with model size and data, and they release the checkpoints for that research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owls/owls-4b-180k": { "id": "cmu/owls/owls-4b-180k", "name": "OWLS 4B 180K", "maker": "cmu", "family": "cmu/owls", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0 on the OWLS 18B 360K card YAML. Sibling cards use the same template. Only the 18B 360K raw README was opened for the license field.", "release_date": "2025-05-03", "context_length": null, "max_output_tokens": null, "parameters": "4 billion labeled. Exact tensor count was not opened.", "architecture": "Whisper-style encoder-decoder speech model in the OWSM project. 16 kHz input, from the OWLS collection text and the card's resample snippet. Developed with ESPnet by CMU WAVLab, with an NVIDIA coauthor on the paper.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWLS 4B 180K is one public checkpoint in OWLS, the Open Whisper-style Large-scale suite from CMU WAVLab. The suite scales Whisper-style speech models from 0.25B to 18B. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors use OWLS to study how speech recognition and translation scale with model size and data, and they release the checkpoints for that research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owls/owls-9b-180k": { "id": "cmu/owls/owls-9b-180k", "name": "OWLS 9B 180K", "maker": "cmu", "family": "cmu/owls", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0 on the OWLS 18B 360K card YAML. Sibling cards use the same template. Only the 18B 360K raw README was opened for the license field.", "release_date": "2025-05-03", "context_length": null, "max_output_tokens": null, "parameters": "9 billion labeled. Exact tensor count was not opened.", "architecture": "Whisper-style encoder-decoder speech model in the OWSM project. 16 kHz input, from the OWLS collection text and the card's resample snippet. Developed with ESPnet by CMU WAVLab, with an NVIDIA coauthor on the paper.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWLS 9B 180K is one public checkpoint in OWLS, the Open Whisper-style Large-scale suite from CMU WAVLab. The suite scales Whisper-style speech models from 0.25B to 18B. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors use OWLS to study how speech recognition and translation scale with model size and data, and they release the checkpoints for that research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-ctc-v3-1": { "id": "cmu/owsm/owsm-ctc-v3-1", "name": "OWSM-CTC v3.1 medium", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-20", "context_length": null, "max_output_tokens": null, "parameters": "1.01 billion", "architecture": "E-Branchformer encoder-only CTC. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM-CTC v3.1 medium is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-ctc-v3-2": { "id": "cmu/owsm/owsm-ctc-v3-2", "name": "OWSM-CTC v3.2 medium", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.01 billion", "architecture": "E-Branchformer encoder-only CTC. Hub id says ft.. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM-CTC v3.2 medium is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-ctc-v4": { "id": "cmu/owsm/owsm-ctc-v4", "name": "OWSM-CTC v4 medium", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2025-05-31", "context_length": null, "max_output_tokens": null, "parameters": "1.01 billion", "architecture": "E-Branchformer encoder-only CTC. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM-CTC v4 medium is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-v1": { "id": "cmu/owsm/owsm-v1", "name": "OWSM v1", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "272 million", "architecture": "Transformer encoder-decoder. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM v1 is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-v2": { "id": "cmu/owsm/owsm-v2", "name": "OWSM v2", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "712 million", "architecture": "Transformer encoder-decoder. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM v2 is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-v2-ebranchformer": { "id": "cmu/owsm/owsm-v2-ebranchformer", "name": "OWSM v2 E-Branchformer", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "739 million", "architecture": "E-Branchformer encoder, decoder. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM v2 E-Branchformer is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-v3": { "id": "cmu/owsm/owsm-v3", "name": "OWSM v3", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "889 million", "architecture": "Transformer encoder-decoder. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM v3 is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-v3-1-base": { "id": "cmu/owsm/owsm-v3-1-base", "name": "OWSM v3.1 base", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "101 million", "architecture": "E-Branchformer encoder-decoder. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM v3.1 base is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-v3-1-medium": { "id": "cmu/owsm/owsm-v3-1-medium", "name": "OWSM v3.1 medium", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.02 billion", "architecture": "E-Branchformer encoder-decoder. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM v3.1 medium is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-v3-1-small": { "id": "cmu/owsm/owsm-v3-1-small", "name": "OWSM v3.1 small", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "367 million", "architecture": "E-Branchformer encoder-decoder. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM v3.1 small is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-v3-1-small-lowrestriction": { "id": "cmu/owsm/owsm-v3-1-small-lowrestriction", "name": "OWSM v3.1 small low-restriction", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "367 million", "architecture": "E-Branchformer encoder-decoder. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM v3.1 small low-restriction is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-v3-2": { "id": "cmu/owsm/owsm-v3-2", "name": "OWSM v3.2 small", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "367 million", "architecture": "E-Branchformer encoder-decoder. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM v3.2 small is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-v4-base": { "id": "cmu/owsm/owsm-v4-base", "name": "OWSM v4 base", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2025-05-31", "context_length": null, "max_output_tokens": null, "parameters": "102 million", "architecture": "E-Branchformer encoder-decoder with 8x feature subsampling (80 ms), as described for the v4 series. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM v4 base is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-v4-medium": { "id": "cmu/owsm/owsm-v4-medium", "name": "OWSM v4 medium", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2025-05-31", "context_length": null, "max_output_tokens": null, "parameters": "1.02 billion", "architecture": "E-Branchformer encoder-decoder. The medium card says 8x subsampling instead of 4x in v3.1, final encoder resolution 80 ms. Inference note: maxlenratio=1.0.. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM v4 medium is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/owsm/owsm-v4-small": { "id": "cmu/owsm/owsm-v4-small", "name": "OWSM v4 small", "maker": "cmu", "family": "cmu/owsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2025-05-31", "context_length": null, "max_output_tokens": null, "parameters": "370 million", "architecture": "E-Branchformer encoder-decoder with 8x feature subsampling (80 ms), as described for the v4 series. Speech foundation model from WAVLab at CMU, released through ESPnet.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OWSM v4 small is an Open Whisper-style speech model from WAVLab at Carnegie Mellon University. The lab releases weights, recipes, and logs through ESPnet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/pangea/culturalpangea-7b": { "id": "cmu/pangea/culturalpangea-7b", "name": "CulturalPangea-7B", "maker": "cmu", "family": "cmu/pangea", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0 on the Hugging Face model card", "release_date": "2025-08-10", "context_length": null, "max_output_tokens": null, "parameters": "7 billion class. Same Qwen2-7B-Instruct backbone as Pangea-7B. Config hidden size 3584, 28 layers, 28 query heads, 4 key-value heads, intermediate size 18944.", "architecture": "Fine-tune of Pangea-7B. Frozen CLIP ViT-L/14-336 vision encoder. Connector and language model are trained. Config from the 24 July 2025 upload: max_position_embeddings 32768, tokenizer_model_max_length 8192, sliding_window 131072, mm_hidden_size 1024.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CulturalPangea-7B is a 2025 NeuLab fine-tune of Pangea-7B for long-tail cultural visual questions. Weights are public under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "NeuLab says CulturalPangea interprets and reasons about long-tail cultural entities in images across many languages, while keeping general Pangea skills by mixing in the original instruction data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/pangea/pangea-7b": { "id": "cmu/pangea/pangea-7b", "name": "Pangea-7B", "maker": "cmu", "family": "cmu/pangea", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0 on the Hugging Face model card", "release_date": "2024-10-21", "context_length": null, "max_output_tokens": null, "parameters": "7 billion, the released name for the Qwen2-7B-Instruct backbone. A separate total count including the vision encoder was not stated.", "architecture": "LLaVA-NeXT multimodal model. Language backbone Qwen2-7B-Instruct. Vision encoder clip-vit-large-patch14-336. Trained on 6 million PangeaIns instructions across 39 languages.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangea-7B is a multilingual multimodal model released in October 2024 by NeuLab at Carnegie Mellon University's Language Technologies Institute. Weights and the PangeaIns data are public under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "NeuLab describes Pangea as a fully open multilingual and multicultural multimodal LLM for visual understanding across 39 languages. The job is multilingual visual question answering and chat, not a text-only assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/polycoder/polycoder-160m": { "id": "cmu/polycoder/polycoder-160m", "name": "PolyCoder 160M", "maker": "cmu", "family": "cmu/polycoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-02-26", "context_length": null, "max_output_tokens": null, "parameters": "160 million", "architecture": "12-layer, 768-dimensional GPT-2 / GPT-NeoX Transformer. The Code-LMs README calls this the small config and says it was released at 150K steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PolyCoder 160M is an open-weight code model from Carnegie Mellon University (Frank F. Xu, Uri Alon, Graham Neubig, Vincent J. Hellendoorn). It was trained from scratch on 249GB of GitHub code in 12 languages. Weights stayed public. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and README present PolyCoder as an open code language model for studying multilingual code modeling. The strongest claim they make for the 2.7B model is lower perplexity on C than the models they compared, including Codex. It is a base code model, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/polycoder/polycoder-2-7b": { "id": "cmu/polycoder/polycoder-2-7b", "name": "PolyCoder 2.7B", "maker": "cmu", "family": "cmu/polycoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-02-26", "context_length": 2048, "max_output_tokens": null, "parameters": "2.7 billion", "architecture": "32-layer, 2560-dimensional GPT-2 / GPT-NeoX Transformer. Paper: max context 2048 tokens, batch 128 sequences (about 262K tokens). README available-models section says 256K tokens per batch. Trained 150K steps, with a 100K-step checkpoint also on Zenodo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PolyCoder 2.7B is an open-weight code model from Carnegie Mellon University (Frank F. Xu, Uri Alon, Graham Neubig, Vincent J. Hellendoorn). It was trained from scratch on 249GB of GitHub code in 12 languages. Weights stayed public. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and README present PolyCoder as an open code language model for studying multilingual code modeling. The strongest claim they make for the 2.7B model is lower perplexity on C than the models they compared, including Codex. It is a base code model, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/polycoder/polycoder-400m": { "id": "cmu/polycoder/polycoder-400m", "name": "PolyCoder 400M", "maker": "cmu", "family": "cmu/polycoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-02-26", "context_length": null, "max_output_tokens": null, "parameters": "400 million in the paper; 405 million in the Code-LMs README medium-config note; Hugging Face id is PolyCoder-0.4B", "architecture": "24-layer, 1024-dimensional GPT-2 / GPT-NeoX Transformer. README: medium config, 256K tokens per batch. Checkpoints at 100K and 150K steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PolyCoder 400M is an open-weight code model from Carnegie Mellon University (Frank F. Xu, Uri Alon, Graham Neubig, Vincent J. Hellendoorn). It was trained from scratch on 249GB of GitHub code in 12 languages. Weights stayed public. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and README present PolyCoder as an open code language model for studying multilingual code modeling. The strongest claim they make for the 2.7B model is lower perplexity on C than the models they compared, including Codex. It is a base code model, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/sphinx/pocketsphinx": { "id": "cmu/sphinx/pocketsphinx", "name": "PocketSphinx", "maker": "cmu", "family": "cmu/sphinx", "status": "deprecated", "confidence": "high", "weights": "open-source", "license": "BSD-style terms in the PocketSphinx LICENSE, copyright 1999-2016 Carnegie Mellon University. Fedora's pocketsphinx-models package metadata lists BSD-2-Clause, BSD-3-Clause, and MIT.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HMM speech recognizer descended from Sphinx-II. The 5.x README says the command-line tool recognizes 16-bit PCM with the default acoustic and language model. It does not publish a neural parameter count.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PocketSphinx is Carnegie Mellon University's small-footprint open-source speech recognizer, the maintained member of the CMU Sphinx line. The project says research development has largely ceased. This dossier was collected on 2026-10-15.", "what_it_is_for": "PocketSphinx is a local speech-to-text engine for embedded and desktop use. The maintainers tell users it is no longer a research state-of-the-art system.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/xeus/xeus": { "id": "cmu/xeus/xeus", "name": "XEUS", "maker": "cmu", "family": "cmu/xeus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Disagreement: the WAVLab project table says MIT. The Hugging Face card metadata says cc-by-nc-sa-4.0.", "release_date": "2024-06-30", "context_length": null, "max_output_tokens": null, "parameters": "577 million", "architecture": "19-layer E-Branchformer encoder trained with a HuBERT masked-prediction objective. Targets come from a WavLabLM tokenizer. Training also uses a WavLM-style denoising task and a dereverberation task.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XEUS is a cross-lingual speech encoder from WAVLab at Carnegie Mellon University, released 30 June 2024. It is a self-supervised model over roughly 1.1 million hours and 4,057 languages, not a turnkey recognizer. This dossier was collected on 2026-10-15.", "what_it_is_for": "WAVLab presents XEUS as a universal speech representation model for thousands of languages, including low-resource ASR after fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/xlnet/xlnet-base-cased": { "id": "cmu/xlnet/xlnet-base-cased", "name": "XLNet-Base (cased)", "maker": "cmu", "family": "cmu/xlnet", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 on the official GitHub LICENSE (Copyright 2019 XLNet Authors). The Hugging Face card metadata says mit. Both are recorded; the GitHub file is the authors' license.", "release_date": "2019-07-16", "context_length": null, "max_output_tokens": null, "parameters": "110 million, the figure reported for the base cased model", "architecture": "Transformer-XL backbone with permutation-language-model pretraining. 12 layers, hidden size 768, 12 heads. Cased SentencePiece model. Hugging Face documents no fixed sequence-length limit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XLNet-Base (cased) is a cased English pretrained language model from Carnegie Mellon University and Google Brain, released by the XLNet authors. It is a permutation-language-model pretraining of a Transformer-XL backbone, meant to be fine-tuned on tasks that use the whole sequence. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors present XLNet as a general pretrained model for language understanding tasks such as question answering, inference, sentiment, and document ranking. The Hugging Face card says to fine-tune it rather than use it as a standalone generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cmu/xlnet/xlnet-large-cased": { "id": "cmu/xlnet/xlnet-large-cased", "name": "XLNet-Large (cased)", "maker": "cmu", "family": "cmu/xlnet", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 on the official GitHub LICENSE (Copyright 2019 XLNet Authors). The Hugging Face card metadata says mit. Both are recorded; the GitHub file is the authors' license.", "release_date": "2019-06-19", "context_length": null, "max_output_tokens": null, "parameters": "340 million, the figure reported for the large cased model", "architecture": "Transformer-XL backbone with permutation-language-model pretraining. 24 layers, hidden size 1024, 16 heads. Cased SentencePiece model. Hugging Face documents no fixed sequence-length limit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XLNet-Large (cased) is a cased English pretrained language model from Carnegie Mellon University and Google Brain, released by the XLNet authors. It is a permutation-language-model pretraining of a Transformer-XL backbone, meant to be fine-tuned on tasks that use the whole sequence. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors present XLNet as a general pretrained model for language understanding tasks such as question answering, inference, sentiment, and document ranking. The Hugging Face card says to fine-tune it rather than use it as a standalone generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/drbert/drbert-4gb": { "id": "cnrs/drbert/drbert-4gb", "name": "DrBERT-4GB", "maker": "cnrs", "family": "cnrs/drbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Conflict: Hugging Face cardData.license is apache-2.0. Project page drbert.univ-avignon.fr says MIT.", "release_date": "2022-12-25", "context_length": 512, "max_output_tokens": null, "parameters": "110 million", "architecture": "CamemBERT/RoBERTa masked-language encoder trained from scratch on French biomedical text. Config: 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocab_size 32005, GELU, absolute positions, max_position_embeddings 514. The model card states sequence length 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DrBERT-4GB is a from-scratch French biomedical masked-language model from LS2N (UMR CNRS 6004, Nantes Universite) with the LIA at Avignon Universite, CHU Nantes, and Zenidoc. Collected 2026-10-15. Weights have been public since the Hugging Face createdAt date 2022-12-25. CNRS Informatics reported the release on 8 September 2023. huggingface.co/cnrs itself hosts no models.", "what_it_is_for": "French biomedical and clinical language representations: the paper evaluates named-entity recognition, part-of-speech tagging, classification, and multiple-choice question answering. The authors say only open-data weights are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/drbert/drbert-7gb": { "id": "cnrs/drbert/drbert-7gb", "name": "DrBERT-7GB", "maker": "cnrs", "family": "cnrs/drbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Conflict: Hugging Face cardData.license is apache-2.0. Project page drbert.univ-avignon.fr says MIT.", "release_date": "2022-12-25", "context_length": 512, "max_output_tokens": null, "parameters": "110 million", "architecture": "CamemBERT/RoBERTa masked-language encoder trained from scratch on French biomedical text. Config: 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocab_size 32005, GELU, absolute positions, max_position_embeddings 514. The model card states sequence length 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DrBERT-7GB is a from-scratch French biomedical masked-language model from LS2N (UMR CNRS 6004, Nantes Universite) with the LIA at Avignon Universite, CHU Nantes, and Zenidoc. Collected 2026-10-15. Weights have been public since the Hugging Face createdAt date 2022-12-25. CNRS Informatics reported the release on 8 September 2023. huggingface.co/cnrs itself hosts no models.", "what_it_is_for": "French biomedical and clinical language representations: the paper evaluates named-entity recognition, part-of-speech tagging, classification, and multiple-choice question answering. The authors say only open-data weights are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/drbert/drbert-7gb-large": { "id": "cnrs/drbert/drbert-7gb-large", "name": "DrBERT-7GB-Large", "maker": "cnrs", "family": "cnrs/drbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Conflict: Hugging Face cardData.license is apache-2.0. Project page drbert.univ-avignon.fr says MIT.", "release_date": "2023-04-09", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "CamemBERT/RoBERTa masked-language encoder trained from scratch on French biomedical text. Config: 24 layers, hidden size 1024, 16 heads, intermediate size 4096, vocab_size 32005, GELU, absolute positions, max_position_embeddings 514. The model card states sequence length 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DrBERT-7GB-Large is a from-scratch French biomedical masked-language model from LS2N (UMR CNRS 6004, Nantes Universite) with the LIA at Avignon Universite, CHU Nantes, and Zenidoc. Collected 2026-10-15. Weights have been public since the Hugging Face createdAt date 2023-04-09. CNRS Informatics reported the release on 8 September 2023. huggingface.co/cnrs itself hosts no models.", "what_it_is_for": "French biomedical and clinical language representations: the paper evaluates named-entity recognition, part-of-speech tagging, classification, and multiple-choice question answering. The authors say only open-data weights are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/flaubert/flaubert-base-cased": { "id": "cnrs/flaubert/flaubert-base-cased", "name": "FlauBERT base cased", "maker": "cnrs", "family": "cnrs/flaubert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2019-12-11", "context_length": 512, "max_output_tokens": null, "parameters": "138 million in the project table; safetensors total 138,301,817", "architecture": "Encoder-only Transformer in the XLM/FlauBERT code base, masked language modeling only. 12 layers, 12 heads, embedding size 768, vocabulary 68729, absolute positions, GELU. Config pre_norm=false, layerdrop=0.0, layer_norm_eps=1e-12. French language embedding is on (n_langs=1).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlauBERT base cased is a French masked-language encoder released in December 2019 by GETALP at LIG (Univ. Grenoble Alpes, CNRS, Grenoble INP), with coauthors at Universite Paris Diderot and ESPCI / CNRS LAMSADE. Collected 2026-10-15. Status is active: the weights are still public. It is not a CNRS headquarters product and not on huggingface.co/cnrs, which had no public models on the collection date.", "what_it_is_for": "French sentence and token representations, and the fill-mask pretraining task. The authors evaluated classification, paraphrase, NLI, parsing, and word-sense disambiguation under the FLUE protocol.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/flaubert/flaubert-base-uncased": { "id": "cnrs/flaubert/flaubert-base-uncased", "name": "FlauBERT base uncased", "maker": "cnrs", "family": "cnrs/flaubert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2019-12-11", "context_length": 512, "max_output_tokens": null, "parameters": "137 million in the project table", "architecture": "Encoder-only Transformer in the XLM/FlauBERT code base, masked language modeling only. 12 layers, 12 heads, embedding size 768, vocabulary 67542, absolute positions, GELU. Config pre_norm=false, layerdrop=0.0, layer_norm_eps=1e-12. French language embedding is on (n_langs=1).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlauBERT base uncased is a French masked-language encoder released in December 2019 by GETALP at LIG (Univ. Grenoble Alpes, CNRS, Grenoble INP), with coauthors at Universite Paris Diderot and ESPCI / CNRS LAMSADE. Collected 2026-10-15. Status is active: the weights are still public. It is not a CNRS headquarters product and not on huggingface.co/cnrs, which had no public models on the collection date.", "what_it_is_for": "French sentence and token representations, and the fill-mask pretraining task. The authors evaluated classification, paraphrase, NLI, parsing, and word-sense disambiguation under the FLUE protocol.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/flaubert/flaubert-large-cased": { "id": "cnrs/flaubert/flaubert-large-cased", "name": "FlauBERT large cased", "maker": "cnrs", "family": "cnrs/flaubert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2019-12-11", "context_length": 512, "max_output_tokens": null, "parameters": "373 million in the project table; safetensors total 373,282,937", "architecture": "Encoder-only Transformer in the XLM/FlauBERT code base, masked language modeling only. 24 layers, 16 heads, embedding size 1024, vocabulary 68729, absolute positions, GELU. Config pre_norm=true, layerdrop=0.2, layer_norm_eps=1e-6. French language embedding is on (n_langs=1).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlauBERT large cased is a French masked-language encoder released in December 2019 by GETALP at LIG (Univ. Grenoble Alpes, CNRS, Grenoble INP), with coauthors at Universite Paris Diderot and ESPCI / CNRS LAMSADE. Collected 2026-10-15. Status is active: the weights are still public. It is not a CNRS headquarters product and not on huggingface.co/cnrs, which had no public models on the collection date.", "what_it_is_for": "French sentence and token representations, and the fill-mask pretraining task. The authors evaluated classification, paraphrase, NLI, parsing, and word-sense disambiguation under the FLUE protocol.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/flaubert/flaubert-small-cased": { "id": "cnrs/flaubert/flaubert-small-cased", "name": "FlauBERT small cased", "maker": "cnrs", "family": "cnrs/flaubert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2019-12-11", "context_length": 512, "max_output_tokens": null, "parameters": "54 million in the project table; safetensors total 54,435,449", "architecture": "Encoder-only Transformer in the XLM/FlauBERT code base, masked language modeling only. 6 layers, 8 heads, embedding size 512, vocabulary 68729, absolute positions, GELU. Config pre_norm=true, layerdrop=0.2, layer_norm_eps=1e-6. French language embedding is on (n_langs=1).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlauBERT small cased is a French masked-language encoder released in December 2019 by GETALP at LIG (Univ. Grenoble Alpes, CNRS, Grenoble INP), with coauthors at Universite Paris Diderot and ESPCI / CNRS LAMSADE. Collected 2026-10-15. Status is active: the weights are still public. It is not a CNRS headquarters product and not on huggingface.co/cnrs, which had no public models on the collection date.", "what_it_is_for": "French sentence and token representations, and the fill-mask pretraining task. The authors evaluated classification, paraphrase, NLI, parsing, and word-sense disambiguation under the FLUE protocol.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/jargon/jargon-biomed": { "id": "cnrs/jargon/jargon-biomed", "name": "Jargon biomed", "maker": "cnrs", "family": "cnrs/jargon", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-13", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "French RoBERTa-style encoder with Linformer attention, custom code class JargonForMaskedLM. The general-base and legal-4096 configs that were read both set hidden size 768, 12 layers, encoder_attention_heads 12, and also num_heads 16. Those two head-count fields disagree; neither was overridden. vocab_size 50000, GELU in hidden_act, feed-forward width 4096. This checkpoint is the 512-token variant (scratch).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jargon biomed is one named checkpoint in the Jargon suite of French domain encoders from LIG (CNRS, Univ. Grenoble Alpes) and IRISA (UMR CNRS 6074), released with a LREC-COLING 2024 paper. Collected 2026-10-15. The hub repository was created on 2024-05-13. Weights are public under MIT. The suite is hosted by the PantagrueLLM organization, not by huggingface.co/cnrs.", "what_it_is_for": "Domain French representations for specialized text. The paper targets medicine, law, and transcribed speech, and compares continued pretraining with training from scratch, including 4096-token Linformer variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/jargon/jargon-biomed-4096": { "id": "cnrs/jargon/jargon-biomed-4096", "name": "Jargon biomed 4096", "maker": "cnrs", "family": "cnrs/jargon", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-13", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "French RoBERTa-style encoder with Linformer attention, custom code class JargonForMaskedLM. The general-base and legal-4096 configs that were read both set hidden size 768, 12 layers, encoder_attention_heads 12, and also num_heads 16. Those two head-count fields disagree; neither was overridden. vocab_size 50000, GELU in hidden_act, feed-forward width 4096. This checkpoint is the 4096-token variant (scratch).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jargon biomed 4096 is one named checkpoint in the Jargon suite of French domain encoders from LIG (CNRS, Univ. Grenoble Alpes) and IRISA (UMR CNRS 6074), released with a LREC-COLING 2024 paper. Collected 2026-10-15. The hub repository was created on 2024-05-13. Weights are public under MIT. The suite is hosted by the PantagrueLLM organization, not by huggingface.co/cnrs.", "what_it_is_for": "Domain French representations for specialized text. The paper targets medicine, law, and transcribed speech, and compares continued pretraining with training from scratch, including 4096-token Linformer variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/jargon/jargon-general-base": { "id": "cnrs/jargon/jargon-general-base", "name": "Jargon general base", "maker": "cnrs", "family": "cnrs/jargon", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-13", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "French RoBERTa-style encoder with Linformer attention, custom code class JargonForMaskedLM. The general-base and legal-4096 configs that were read both set hidden size 768, 12 layers, encoder_attention_heads 12, and also num_heads 16. Those two head-count fields disagree; neither was overridden. vocab_size 50000, GELU in hidden_act, feed-forward width 4096. This checkpoint is the 512-token variant (scratch).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jargon general base is one named checkpoint in the Jargon suite of French domain encoders from LIG (CNRS, Univ. Grenoble Alpes) and IRISA (UMR CNRS 6074), released with a LREC-COLING 2024 paper. Collected 2026-10-15. The hub repository was created on 2024-05-13. Weights are public under MIT. The suite is hosted by the PantagrueLLM organization, not by huggingface.co/cnrs.", "what_it_is_for": "Domain French representations for specialized text. The paper targets medicine, law, and transcribed speech, and compares continued pretraining with training from scratch, including 4096-token Linformer variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/jargon/jargon-general-biomed": { "id": "cnrs/jargon/jargon-general-biomed", "name": "Jargon general biomed", "maker": "cnrs", "family": "cnrs/jargon", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-13", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "French RoBERTa-style encoder with Linformer attention, custom code class JargonForMaskedLM. The general-base and legal-4096 configs that were read both set hidden size 768, 12 layers, encoder_attention_heads 12, and also num_heads 16. Those two head-count fields disagree; neither was overridden. vocab_size 50000, GELU in hidden_act, feed-forward width 4096. This checkpoint is the 512-token variant (continued pretraining).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jargon general biomed is one named checkpoint in the Jargon suite of French domain encoders from LIG (CNRS, Univ. Grenoble Alpes) and IRISA (UMR CNRS 6074), released with a LREC-COLING 2024 paper. Collected 2026-10-15. The hub repository was created on 2024-05-13. Weights are public under MIT. The suite is hosted by the PantagrueLLM organization, not by huggingface.co/cnrs.", "what_it_is_for": "Domain French representations for specialized text. The paper targets medicine, law, and transcribed speech, and compares continued pretraining with training from scratch, including 4096-token Linformer variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/jargon/jargon-general-legal": { "id": "cnrs/jargon/jargon-general-legal", "name": "Jargon general legal", "maker": "cnrs", "family": "cnrs/jargon", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-08-11", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "French RoBERTa-style encoder with Linformer attention, custom code class JargonForMaskedLM. The general-base and legal-4096 configs that were read both set hidden size 768, 12 layers, encoder_attention_heads 12, and also num_heads 16. Those two head-count fields disagree; neither was overridden. vocab_size 50000, GELU in hidden_act, feed-forward width 4096. This checkpoint is the 512-token variant (continued pretraining).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jargon general legal is one named checkpoint in the Jargon suite of French domain encoders from LIG (CNRS, Univ. Grenoble Alpes) and IRISA (UMR CNRS 6074), released with a LREC-COLING 2024 paper. Collected 2026-10-15. The hub repository was created on 2023-08-11. Weights are public under MIT. The suite is hosted by the PantagrueLLM organization, not by huggingface.co/cnrs.", "what_it_is_for": "Domain French representations for specialized text. The paper targets medicine, law, and transcribed speech, and compares continued pretraining with training from scratch, including 4096-token Linformer variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/jargon/jargon-legal": { "id": "cnrs/jargon/jargon-legal", "name": "Jargon legal", "maker": "cnrs", "family": "cnrs/jargon", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-08-11", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "French RoBERTa-style encoder with Linformer attention, custom code class JargonForMaskedLM. The general-base and legal-4096 configs that were read both set hidden size 768, 12 layers, encoder_attention_heads 12, and also num_heads 16. Those two head-count fields disagree; neither was overridden. vocab_size 50000, GELU in hidden_act, feed-forward width 4096. This checkpoint is the 512-token variant (scratch).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jargon legal is one named checkpoint in the Jargon suite of French domain encoders from LIG (CNRS, Univ. Grenoble Alpes) and IRISA (UMR CNRS 6074), released with a LREC-COLING 2024 paper. Collected 2026-10-15. The hub repository was created on 2023-08-11. Weights are public under MIT. The suite is hosted by the PantagrueLLM organization, not by huggingface.co/cnrs.", "what_it_is_for": "Domain French representations for specialized text. The paper targets medicine, law, and transcribed speech, and compares continued pretraining with training from scratch, including 4096-token Linformer variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/jargon/jargon-legal-4096": { "id": "cnrs/jargon/jargon-legal-4096", "name": "Jargon legal 4096", "maker": "cnrs", "family": "cnrs/jargon", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-14", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "French RoBERTa-style encoder with Linformer attention, custom code class JargonForMaskedLM. The general-base and legal-4096 configs that were read both set hidden size 768, 12 layers, encoder_attention_heads 12, and also num_heads 16. Those two head-count fields disagree; neither was overridden. vocab_size 50000, GELU in hidden_act, feed-forward width 4096. This checkpoint is the 4096-token variant (scratch).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jargon legal 4096 is one named checkpoint in the Jargon suite of French domain encoders from LIG (CNRS, Univ. Grenoble Alpes) and IRISA (UMR CNRS 6074), released with a LREC-COLING 2024 paper. Collected 2026-10-15. The hub repository was created on 2024-05-14. Weights are public under MIT. The suite is hosted by the PantagrueLLM organization, not by huggingface.co/cnrs.", "what_it_is_for": "Domain French representations for specialized text. The paper targets medicine, law, and transcribed speech, and compares continued pretraining with training from scratch, including 4096-token Linformer variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/jargon/jargon-multidomain-base": { "id": "cnrs/jargon/jargon-multidomain-base", "name": "Jargon multidomain base", "maker": "cnrs", "family": "cnrs/jargon", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-13", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "French RoBERTa-style encoder with Linformer attention, custom code class JargonForMaskedLM. The general-base and legal-4096 configs that were read both set hidden size 768, 12 layers, encoder_attention_heads 12, and also num_heads 16. Those two head-count fields disagree; neither was overridden. vocab_size 50000, GELU in hidden_act, feed-forward width 4096. This checkpoint is the 512-token variant (continued pretraining).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jargon multidomain base is one named checkpoint in the Jargon suite of French domain encoders from LIG (CNRS, Univ. Grenoble Alpes) and IRISA (UMR CNRS 6074), released with a LREC-COLING 2024 paper. Collected 2026-10-15. The hub repository was created on 2024-05-13. Weights are public under MIT. The suite is hosted by the PantagrueLLM organization, not by huggingface.co/cnrs.", "what_it_is_for": "Domain French representations for specialized text. The paper targets medicine, law, and transcribed speech, and compares continued pretraining with training from scratch, including 4096-token Linformer variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/jargon/jargon-nachos": { "id": "cnrs/jargon/jargon-nachos", "name": "Jargon NACHOS", "maker": "cnrs", "family": "cnrs/jargon", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-13", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "French RoBERTa-style encoder with Linformer attention, custom code class JargonForMaskedLM. The general-base and legal-4096 configs that were read both set hidden size 768, 12 layers, encoder_attention_heads 12, and also num_heads 16. Those two head-count fields disagree; neither was overridden. vocab_size 50000, GELU in hidden_act, feed-forward width 4096. This checkpoint is the 512-token variant (scratch).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jargon NACHOS is one named checkpoint in the Jargon suite of French domain encoders from LIG (CNRS, Univ. Grenoble Alpes) and IRISA (UMR CNRS 6074), released with a LREC-COLING 2024 paper. Collected 2026-10-15. The hub repository was created on 2024-05-13. Weights are public under MIT. The suite is hosted by the PantagrueLLM organization, not by huggingface.co/cnrs.", "what_it_is_for": "Domain French representations for specialized text. The paper targets medicine, law, and transcribed speech, and compares continued pretraining with training from scratch, including 4096-token Linformer variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/jargon/jargon-nachos-4096": { "id": "cnrs/jargon/jargon-nachos-4096", "name": "Jargon NACHOS 4096", "maker": "cnrs", "family": "cnrs/jargon", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-13", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "French RoBERTa-style encoder with Linformer attention, custom code class JargonForMaskedLM. The general-base and legal-4096 configs that were read both set hidden size 768, 12 layers, encoder_attention_heads 12, and also num_heads 16. Those two head-count fields disagree; neither was overridden. vocab_size 50000, GELU in hidden_act, feed-forward width 4096. This checkpoint is the 4096-token variant (scratch).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jargon NACHOS 4096 is one named checkpoint in the Jargon suite of French domain encoders from LIG (CNRS, Univ. Grenoble Alpes) and IRISA (UMR CNRS 6074), released with a LREC-COLING 2024 paper. Collected 2026-10-15. The hub repository was created on 2024-05-13. Weights are public under MIT. The suite is hosted by the PantagrueLLM organization, not by huggingface.co/cnrs.", "what_it_is_for": "Domain French representations for specialized text. The paper targets medicine, law, and transcribed speech, and compares continued pretraining with training from scratch, including 4096-token Linformer variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/lebenchmark/wav2vec-fr-1k-female-base": { "id": "cnrs/lebenchmark/wav2vec-fr-1k-female-base", "name": "LeBenchmark wav2vec FR 1K female base", "maker": "cnrs", "family": "cnrs/lebenchmark", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-04-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "wav2vec 2.0 base, gender-split French pretraining. The hub id is wav2vec-FR, without the 2 used by the main set. No safetensors total was returned.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeBenchmark wav2vec FR 1K female base is a French wav2vec 2.0 self-supervised checkpoint from the LeBenchmark project. Lead affiliations on the 2021 paper include Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, and LIG, with LIA, LAMSADE (CNRS), Atos, and Naver Labs Europe. Collected 2026-10-15. It is a research release, not a model on huggingface.co/cnrs.", "what_it_is_for": "French speech representations for later fine-tuning. The project evaluates automatic speech recognition, spoken language understanding, speech translation, and emotion recognition. The raw checkpoint is not a speech-to-text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/lebenchmark/wav2vec-fr-1k-female-large": { "id": "cnrs/lebenchmark/wav2vec-fr-1k-female-large", "name": "LeBenchmark wav2vec FR 1K female large", "maker": "cnrs", "family": "cnrs/lebenchmark", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-04-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "wav2vec 2.0 large, gender-split French pretraining. The hub id is wav2vec-FR, without the 2 used by the main set. No safetensors total was returned.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeBenchmark wav2vec FR 1K female large is a French wav2vec 2.0 self-supervised checkpoint from the LeBenchmark project. Lead affiliations on the 2021 paper include Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, and LIG, with LIA, LAMSADE (CNRS), Atos, and Naver Labs Europe. Collected 2026-10-15. It is a research release, not a model on huggingface.co/cnrs.", "what_it_is_for": "French speech representations for later fine-tuning. The project evaluates automatic speech recognition, spoken language understanding, speech translation, and emotion recognition. The raw checkpoint is not a speech-to-text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/lebenchmark/wav2vec-fr-1k-male-base": { "id": "cnrs/lebenchmark/wav2vec-fr-1k-male-base", "name": "LeBenchmark wav2vec FR 1K male base", "maker": "cnrs", "family": "cnrs/lebenchmark", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-04-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "wav2vec 2.0 base, gender-split French pretraining. The hub id is wav2vec-FR, without the 2 used by the main set. No safetensors total was returned.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeBenchmark wav2vec FR 1K male base is a French wav2vec 2.0 self-supervised checkpoint from the LeBenchmark project. Lead affiliations on the 2021 paper include Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, and LIG, with LIA, LAMSADE (CNRS), Atos, and Naver Labs Europe. Collected 2026-10-15. It is a research release, not a model on huggingface.co/cnrs.", "what_it_is_for": "French speech representations for later fine-tuning. The project evaluates automatic speech recognition, spoken language understanding, speech translation, and emotion recognition. The raw checkpoint is not a speech-to-text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/lebenchmark/wav2vec-fr-1k-male-large": { "id": "cnrs/lebenchmark/wav2vec-fr-1k-male-large", "name": "LeBenchmark wav2vec FR 1K male large", "maker": "cnrs", "family": "cnrs/lebenchmark", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-04-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "wav2vec 2.0 large, gender-split French pretraining. The hub id is wav2vec-FR, without the 2 used by the main set. No safetensors total was returned.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeBenchmark wav2vec FR 1K male large is a French wav2vec 2.0 self-supervised checkpoint from the LeBenchmark project. Lead affiliations on the 2021 paper include Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, and LIG, with LIA, LAMSADE (CNRS), Atos, and Naver Labs Europe. Collected 2026-10-15. It is a research release, not a model on huggingface.co/cnrs.", "what_it_is_for": "French speech representations for later fine-tuning. The project evaluates automatic speech recognition, spoken language understanding, speech translation, and emotion recognition. The raw checkpoint is not a speech-to-text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/lebenchmark/wav2vec2-fr-14k-large": { "id": "cnrs/lebenchmark/wav2vec2-fr-14k-large", "name": "LeBenchmark wav2vec2 FR 14K large", "maker": "cnrs", "family": "cnrs/lebenchmark", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-06-21", "context_length": null, "max_output_tokens": null, "parameters": "330 million in the LeBenchmark 2.0 paper table. No safetensors total was returned. Counted large files in this family are about 315 to 317 million, so the paper rounding may not match the file.", "architecture": "wav2vec 2.0 large. Paper: 330 million parameters, output dimension 1024, 1M updates on the 14K set.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeBenchmark wav2vec2 FR 14K large is a French wav2vec 2.0 self-supervised checkpoint from the LeBenchmark project. Lead affiliations on the 2021 paper include Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, and LIG, with LIA, LAMSADE (CNRS), Atos, and Naver Labs Europe. Collected 2026-10-15. It is a research release, not a model on huggingface.co/cnrs.", "what_it_is_for": "French speech representations for later fine-tuning. The project evaluates automatic speech recognition, spoken language understanding, speech translation, and emotion recognition. The raw checkpoint is not a speech-to-text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/lebenchmark/wav2vec2-fr-14k-light": { "id": "cnrs/lebenchmark/wav2vec2-fr-14k-light", "name": "LeBenchmark wav2vec2 FR 14K light", "maker": "cnrs", "family": "cnrs/lebenchmark", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-06-19", "context_length": null, "max_output_tokens": null, "parameters": "26 million in the LeBenchmark 2.0 paper table. The Hugging Face API returned no safetensors total (weights are a wav2vec2.ckpt-style release).", "architecture": "wav2vec 2.0 light. Paper: 26 million parameters, output dimension 512, 500K updates. Those training hyperparameters are from the paper table, not re-measured.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeBenchmark wav2vec2 FR 14K light is a French wav2vec 2.0 self-supervised checkpoint from the LeBenchmark project. Lead affiliations on the 2021 paper include Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, and LIG, with LIA, LAMSADE (CNRS), Atos, and Naver Labs Europe. Collected 2026-10-15. It is a research release, not a model on huggingface.co/cnrs.", "what_it_is_for": "French speech representations for later fine-tuning. The project evaluates automatic speech recognition, spoken language understanding, speech translation, and emotion recognition. The raw checkpoint is not a speech-to-text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/lebenchmark/wav2vec2-fr-14k-xlarge": { "id": "cnrs/lebenchmark/wav2vec2-fr-14k-xlarge", "name": "LeBenchmark wav2vec2 FR 14K xlarge", "maker": "cnrs", "family": "cnrs/lebenchmark", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-10-31", "context_length": null, "max_output_tokens": null, "parameters": "965 million in the LeBenchmark 2.0 paper table. No safetensors total was returned.", "architecture": "wav2vec 2.0 xLarge. Paper: 965 million parameters, output dimension 1280, 1M updates, 104 GPUs. Hub config model_type wav2vec2, architecture Wav2Vec2ForPreTraining.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeBenchmark wav2vec2 FR 14K xlarge is a French wav2vec 2.0 self-supervised checkpoint from the LeBenchmark project. Lead affiliations on the 2021 paper include Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, and LIG, with LIA, LAMSADE (CNRS), Atos, and Naver Labs Europe. Collected 2026-10-15. It is a research release, not a model on huggingface.co/cnrs.", "what_it_is_for": "French speech representations for later fine-tuning. The project evaluates automatic speech recognition, spoken language understanding, speech translation, and emotion recognition. The raw checkpoint is not a speech-to-text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/lebenchmark/wav2vec2-fr-1k-base": { "id": "cnrs/lebenchmark/wav2vec2-fr-1k-base", "name": "LeBenchmark wav2vec2 FR 1K base", "maker": "cnrs", "family": "cnrs/lebenchmark", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "94,371,712 safetensors parameters. The LeBenchmark 2.0 paper table says 90 million for 1K-base.", "architecture": "wav2vec 2.0 base. Hugging Face pipeline tag feature-extraction. Safetensors total 94,371,712.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeBenchmark wav2vec2 FR 1K base is a French wav2vec 2.0 self-supervised checkpoint from the LeBenchmark project. Lead affiliations on the 2021 paper include Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, and LIG, with LIA, LAMSADE (CNRS), Atos, and Naver Labs Europe. Collected 2026-10-15. It is a research release, not a model on huggingface.co/cnrs.", "what_it_is_for": "French speech representations for later fine-tuning. The project evaluates automatic speech recognition, spoken language understanding, speech translation, and emotion recognition. The raw checkpoint is not a speech-to-text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/lebenchmark/wav2vec2-fr-1k-large": { "id": "cnrs/lebenchmark/wav2vec2-fr-1k-large", "name": "LeBenchmark wav2vec2 FR 1K large", "maker": "cnrs", "family": "cnrs/lebenchmark", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "315,438,720 safetensors parameters. The paper table says 330 million for 1K-large.", "architecture": "wav2vec 2.0 large. Safetensors total 315,438,720.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeBenchmark wav2vec2 FR 1K large is a French wav2vec 2.0 self-supervised checkpoint from the LeBenchmark project. Lead affiliations on the 2021 paper include Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, and LIG, with LIA, LAMSADE (CNRS), Atos, and Naver Labs Europe. Collected 2026-10-15. It is a research release, not a model on huggingface.co/cnrs.", "what_it_is_for": "French speech representations for later fine-tuning. The project evaluates automatic speech recognition, spoken language understanding, speech translation, and emotion recognition. The raw checkpoint is not a speech-to-text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/lebenchmark/wav2vec2-fr-2-6k-base": { "id": "cnrs/lebenchmark/wav2vec2-fr-2-6k-base", "name": "LeBenchmark wav2vec2 FR 2.6K base", "maker": "cnrs", "family": "cnrs/lebenchmark", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "94,371,712 safetensors parameters. A paper row labeled 2.7K-base says 90 million and 2,773 hours.", "architecture": "wav2vec 2.0 base. Safetensors total 94,371,712. Hub id uses 2.6K, not 2.7K.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeBenchmark wav2vec2 FR 2.6K base is a French wav2vec 2.0 self-supervised checkpoint from the LeBenchmark project. Lead affiliations on the 2021 paper include Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, and LIG, with LIA, LAMSADE (CNRS), Atos, and Naver Labs Europe. Collected 2026-10-15. It is a research release, not a model on huggingface.co/cnrs.", "what_it_is_for": "French speech representations for later fine-tuning. The project evaluates automatic speech recognition, spoken language understanding, speech translation, and emotion recognition. The raw checkpoint is not a speech-to-text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/lebenchmark/wav2vec2-fr-3k-base": { "id": "cnrs/lebenchmark/wav2vec2-fr-3k-base", "name": "LeBenchmark wav2vec2 FR 3K base", "maker": "cnrs", "family": "cnrs/lebenchmark", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "94,371,712 safetensors parameters. The paper table says 90 million for 3K-base.", "architecture": "wav2vec 2.0 base. Safetensors total 94,371,712.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeBenchmark wav2vec2 FR 3K base is a French wav2vec 2.0 self-supervised checkpoint from the LeBenchmark project. Lead affiliations on the 2021 paper include Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, and LIG, with LIA, LAMSADE (CNRS), Atos, and Naver Labs Europe. Collected 2026-10-15. It is a research release, not a model on huggingface.co/cnrs.", "what_it_is_for": "French speech representations for later fine-tuning. The project evaluates automatic speech recognition, spoken language understanding, speech translation, and emotion recognition. The raw checkpoint is not a speech-to-text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/lebenchmark/wav2vec2-fr-3k-large": { "id": "cnrs/lebenchmark/wav2vec2-fr-3k-large", "name": "LeBenchmark wav2vec2 FR 3K large", "maker": "cnrs", "family": "cnrs/lebenchmark", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "317,390,592 safetensors parameters. Other large LeBenchmark checkpoints that returned a count are 315,438,720. The paper says 330 million.", "architecture": "wav2vec 2.0 large. Safetensors total 317,390,592, which is not the same integer as the 1K-large and 7K-large files.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeBenchmark wav2vec2 FR 3K large is a French wav2vec 2.0 self-supervised checkpoint from the LeBenchmark project. Lead affiliations on the 2021 paper include Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, and LIG, with LIA, LAMSADE (CNRS), Atos, and Naver Labs Europe. Collected 2026-10-15. It is a research release, not a model on huggingface.co/cnrs.", "what_it_is_for": "French speech representations for later fine-tuning. The project evaluates automatic speech recognition, spoken language understanding, speech translation, and emotion recognition. The raw checkpoint is not a speech-to-text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/lebenchmark/wav2vec2-fr-7k-base": { "id": "cnrs/lebenchmark/wav2vec2-fr-7k-base", "name": "LeBenchmark wav2vec2 FR 7K base", "maker": "cnrs", "family": "cnrs/lebenchmark", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "94,371,712 safetensors parameters. The paper table says 90 million for 7K-base.", "architecture": "wav2vec 2.0 base. Safetensors total 94,371,712.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeBenchmark wav2vec2 FR 7K base is a French wav2vec 2.0 self-supervised checkpoint from the LeBenchmark project. Lead affiliations on the 2021 paper include Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, and LIG, with LIA, LAMSADE (CNRS), Atos, and Naver Labs Europe. Collected 2026-10-15. It is a research release, not a model on huggingface.co/cnrs.", "what_it_is_for": "French speech representations for later fine-tuning. The project evaluates automatic speech recognition, spoken language understanding, speech translation, and emotion recognition. The raw checkpoint is not a speech-to-text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/lebenchmark/wav2vec2-fr-7k-large": { "id": "cnrs/lebenchmark/wav2vec2-fr-7k-large", "name": "LeBenchmark wav2vec2 FR 7K large", "maker": "cnrs", "family": "cnrs/lebenchmark", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "315,438,720 safetensors parameters. The paper table says 330 million for 7K-large.", "architecture": "wav2vec 2.0 large. Safetensors total 315,438,720.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeBenchmark wav2vec2 FR 7K large is a French wav2vec 2.0 self-supervised checkpoint from the LeBenchmark project. Lead affiliations on the 2021 paper include Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, and LIG, with LIA, LAMSADE (CNRS), Atos, and Naver Labs Europe. Collected 2026-10-15. It is a research release, not a model on huggingface.co/cnrs.", "what_it_is_for": "French speech representations for later fine-tuning. The project evaluates automatic speech recognition, spoken language understanding, speech translation, and emotion recognition. The raw checkpoint is not a speech-to-text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/pantagruel/pantagruel-b-14k": { "id": "cnrs/pantagruel/pantagruel-b-14k", "name": "Pantagruel-B-14k", "maker": "cnrs", "family": "cnrs/pantagruel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 2.0", "release_date": "2025-10-06", "context_length": null, "max_output_tokens": null, "parameters": "93,163,532", "architecture": "data2vec 2.0 speech encoder, base, same counted size as Pantagruel-B-1k.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pantagruel-B-14k is a French speech encoder in the Pantagruel family led at LIG (Univ. Grenoble Alpes, CNRS) with INA, LIA, CREST, LLF, and IRISA. Collected 2026-10-15. The hub repository was created on 2025-10-06. The paper is arXiv 2601.05911, posted 9 January 2026. Weights are not on huggingface.co/cnrs.", "what_it_is_for": "Self-supervised French speech features. The card reports ASR word error rate after the project fine-tuned the encoder on Common Voice. The released checkpoint itself has no tokenizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/pantagruel/pantagruel-b-1k": { "id": "cnrs/pantagruel/pantagruel-b-1k", "name": "Pantagruel-B-1k", "maker": "cnrs", "family": "cnrs/pantagruel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 2.0", "release_date": "2025-10-06", "context_length": null, "max_output_tokens": null, "parameters": "93,163,532", "architecture": "data2vec 2.0 speech encoder, base. Paper: separate speech and text models, not one multimodal net. Card: 16 kHz mono normalized audio. Fairseq 0.12.2 pretraining, converted to Transformers.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pantagruel-B-1k is a French speech encoder in the Pantagruel family led at LIG (Univ. Grenoble Alpes, CNRS) with INA, LIA, CREST, LLF, and IRISA. Collected 2026-10-15. The hub repository was created on 2025-10-06. The paper is arXiv 2601.05911, posted 9 January 2026. Weights are not on huggingface.co/cnrs.", "what_it_is_for": "Self-supervised French speech features. The card reports ASR word error rate after the project fine-tuned the encoder on Common Voice. The released checkpoint itself has no tokenizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/pantagruel/pantagruel-b-camtok-osc": { "id": "cnrs/pantagruel/pantagruel-b-camtok-osc", "name": "Pantagruel-B-camtok-Osc", "maker": "cnrs", "family": "cnrs/pantagruel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 2.0", "release_date": "2025-10-06", "context_length": null, "max_output_tokens": null, "parameters": "110,030,592", "architecture": "French text encoder, data2vec 2.0 teacher-student feature prediction without the MLM suffix. Paper: base models use 12 layers, 8 heads, and hidden size 768. That layer triple was not re-read from this config.json. Card says base / 110 million. Safetensors total 110,030,592.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pantagruel-B-camtok-Osc is a French text encoder from the Pantagruel release led at LIG (CNRS, Univ. Grenoble Alpes) with INA, LIA, CREST, Universite Paris Cite, and IRISA. Collected 2026-10-15. Primary hub repository created 2025-10-06. Paper posted 9 January 2026. Not a huggingface.co/cnrs model.", "what_it_is_for": "French contextual embeddings for classification and other downstream NLP tasks. MLM-suffix models also train a masked-token loss. The card highlights French XNLI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/pantagruel/pantagruel-b-camtok-wk": { "id": "cnrs/pantagruel/pantagruel-b-camtok-wk", "name": "Pantagruel-B-camtok-Wk", "maker": "cnrs", "family": "cnrs/pantagruel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 2.0", "release_date": "2025-10-06", "context_length": null, "max_output_tokens": null, "parameters": "110,030,592", "architecture": "French text encoder, data2vec 2.0 teacher-student feature prediction without the MLM suffix. Paper: base models use 12 layers, 8 heads, and hidden size 768. That layer triple was not re-read from this config.json. Card says base / 110 million. Safetensors total 110,030,592.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pantagruel-B-camtok-Wk is a French text encoder from the Pantagruel release led at LIG (CNRS, Univ. Grenoble Alpes) with INA, LIA, CREST, Universite Paris Cite, and IRISA. Collected 2026-10-15. Primary hub repository created 2025-10-06. Paper posted 9 January 2026. Not a huggingface.co/cnrs model.", "what_it_is_for": "French contextual embeddings for classification and other downstream NLP tasks. MLM-suffix models also train a masked-token loss. The card highlights French XNLI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/pantagruel/pantagruel-b-crs-mlm": { "id": "cnrs/pantagruel/pantagruel-b-crs-mlm", "name": "Pantagruel-B-Crs-MLM", "maker": "cnrs", "family": "cnrs/pantagruel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 2.0", "release_date": "2025-10-20", "context_length": null, "max_output_tokens": null, "parameters": "124,723,968", "architecture": "French text encoder, data2vec 2.0 teacher-student feature prediction plus a masked-language-modeling loss. Paper: base models use 12 layers, 8 heads, and hidden size 768. That layer triple was not re-read from this config.json. This is the repository returned for the card name text-base-croissant-mlm. Safetensors total 124,723,968, close to the card 125 million. A later repository, Text_Base_FR_croissant, has 163,458,508 parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pantagruel-B-Crs-MLM is a French text encoder from the Pantagruel release led at LIG (CNRS, Univ. Grenoble Alpes) with INA, LIA, CREST, Universite Paris Cite, and IRISA. Collected 2026-10-15. Primary hub repository created 2025-10-20. Paper posted 9 January 2026. Not a huggingface.co/cnrs model.", "what_it_is_for": "French contextual embeddings for classification and other downstream NLP tasks. MLM-suffix models also train a masked-token loss. The card highlights French XNLI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/pantagruel/pantagruel-b-osc-mlm": { "id": "cnrs/pantagruel/pantagruel-b-osc-mlm", "name": "Pantagruel-B-Osc-MLM", "maker": "cnrs", "family": "cnrs/pantagruel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 2.0", "release_date": "2026-01-14", "context_length": null, "max_output_tokens": null, "parameters": "163,458,508", "architecture": "French text encoder, data2vec 2.0 teacher-student feature prediction plus a masked-language-modeling loss. Paper: base models use 12 layers, 8 heads, and hidden size 768. That layer triple was not re-read from this config.json. Card table says base / 125 million under the name text-base-oscar-mlm. The API id is PantagrueLLM/Text_Base_FR_OSCAR and the safetensors total is 163,458,508.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pantagruel-B-Osc-MLM is a French text encoder from the Pantagruel release led at LIG (CNRS, Univ. Grenoble Alpes) with INA, LIA, CREST, Universite Paris Cite, and IRISA. Collected 2026-10-15. Primary hub repository created 2026-01-14. Paper posted 9 January 2026. Not a huggingface.co/cnrs model.", "what_it_is_for": "French contextual embeddings for classification and other downstream NLP tasks. MLM-suffix models also train a masked-token loss. The card highlights French XNLI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/pantagruel/pantagruel-b-wk": { "id": "cnrs/pantagruel/pantagruel-b-wk", "name": "Pantagruel-B-Wk", "maker": "cnrs", "family": "cnrs/pantagruel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 2.0", "release_date": "2025-10-06", "context_length": null, "max_output_tokens": null, "parameters": "124,133,376", "architecture": "French text encoder, data2vec 2.0 teacher-student feature prediction without the MLM suffix. Paper: base models use 12 layers, 8 heads, and hidden size 768. That layer triple was not re-read from this config.json. Card says base / 125 million. Safetensors total 124,133,376.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pantagruel-B-Wk is a French text encoder from the Pantagruel release led at LIG (CNRS, Univ. Grenoble Alpes) with INA, LIA, CREST, Universite Paris Cite, and IRISA. Collected 2026-10-15. Primary hub repository created 2025-10-06. Paper posted 9 January 2026. Not a huggingface.co/cnrs model.", "what_it_is_for": "French contextual embeddings for classification and other downstream NLP tasks. MLM-suffix models also train a masked-token loss. The card highlights French XNLI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/pantagruel/pantagruel-b-wk-mlm": { "id": "cnrs/pantagruel/pantagruel-b-wk-mlm", "name": "Pantagruel-B-Wk-MLM", "maker": "cnrs", "family": "cnrs/pantagruel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 2.0", "release_date": "2025-11-18", "context_length": null, "max_output_tokens": null, "parameters": "163,458,496", "architecture": "French text encoder, data2vec 2.0 teacher-student feature prediction plus a masked-language-modeling loss. Paper: base models use 12 layers, 8 heads, and hidden size 768. That layer triple was not re-read from this config.json. Card table still says base / 125 million. Safetensors total on 2026-10-15 is 163,458,496. The file was last modified 2026-03-09.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pantagruel-B-Wk-MLM is a French text encoder from the Pantagruel release led at LIG (CNRS, Univ. Grenoble Alpes) with INA, LIA, CREST, Universite Paris Cite, and IRISA. Collected 2026-10-15. Primary hub repository created 2025-11-18. Paper posted 9 January 2026. Not a huggingface.co/cnrs model.", "what_it_is_for": "French contextual embeddings for classification and other downstream NLP tasks. MLM-suffix models also train a masked-token loss. The card highlights French XNLI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/pantagruel/pantagruel-l-114k": { "id": "cnrs/pantagruel/pantagruel-l-114k", "name": "Pantagruel-L-114k", "maker": "cnrs", "family": "cnrs/pantagruel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face cardData.license is other. The repository is manually gated. A public page header has been indexed as pantagruel-research-license. The license text was not downloaded on 2026-10-15.", "release_date": "2025-10-06", "context_length": null, "max_output_tokens": null, "parameters": "313,275,408", "architecture": "data2vec 2.0 speech encoder, large. Manually gated on Hugging Face.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pantagruel-L-114k is a French speech encoder in the Pantagruel family led at LIG (Univ. Grenoble Alpes, CNRS) with INA, LIA, CREST, LLF, and IRISA. Collected 2026-10-15. The hub repository was created on 2025-10-06. The paper is arXiv 2601.05911, posted 9 January 2026. Weights are not on huggingface.co/cnrs.", "what_it_is_for": "Self-supervised French speech features. The card reports ASR word error rate after the project fine-tuned the encoder on Common Voice. The released checkpoint itself has no tokenizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cnrs/pantagruel/pantagruel-l-14k": { "id": "cnrs/pantagruel/pantagruel-l-14k", "name": "Pantagruel-L-14k", "maker": "cnrs", "family": "cnrs/pantagruel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 2.0", "release_date": "2025-10-06", "context_length": null, "max_output_tokens": null, "parameters": "313,275,408", "architecture": "data2vec 2.0 speech encoder, large. Paper: large is 24 layers, 16 heads, hidden size 1024. That layer table was not re-checked in this checkpoint config.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pantagruel-L-14k is a French speech encoder in the Pantagruel family led at LIG (Univ. Grenoble Alpes, CNRS) with INA, LIA, CREST, LLF, and IRISA. Collected 2026-10-15. The hub repository was created on 2025-10-06. The paper is arXiv 2601.05911, posted 9 January 2026. Weights are not on huggingface.co/cnrs.", "what_it_is_for": "Self-supervised French speech features. The card reports ASR word error rate after the project fine-tuned the encoder on Common Voice. The released checkpoint itself has no tokenizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/aya/aya-101": { "id": "cohere/aya/aya-101", "name": "Aya 101", "maker": "cohere", "family": "cohere/aya", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-12", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Instruction-finetuned massively multilingual Transformer. Card: trained on xP3x, the Aya dataset, the Aya Collection, a Data Provenance subset, and ShareGPT-Command.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cohere Labs' 13B Aya model that follows instructions in 101 languages. The weights are Apache-2.0. It is a 2024 research release, not a current Cohere API model. Collected 2026-10-15.", "what_it_is_for": "Cohere Labs' 13B Aya model that follows instructions in 101 languages. The weights are Apache-2.0. It is a 2024 research release, not a current Cohere API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/aya/aya-23-35b": { "id": "cohere/aya/aya-23-35b", "name": "Aya 23 35B", "maker": "cohere", "family": "cohere/aya", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC plus Cohere Labs Acceptable Use Policy", "release_date": "2024-05-23", "context_length": null, "max_output_tokens": null, "parameters": "35B", "architecture": "Multilingual instruction model pairing a Command-family pretrained model with the Aya Collection. Card date is used as the release marker.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cohere Labs' 35B Aya 23 research model for 23 languages. It is an open-weight release under a non-commercial license, not a current API model. Collected 2026-10-15.", "what_it_is_for": "Cohere Labs' 35B Aya 23 research model for 23 languages. It is an open-weight release under a non-commercial license, not a current API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/aya/aya-23-8b": { "id": "cohere/aya/aya-23-8b", "name": "Aya 23 8B", "maker": "cohere", "family": "cohere/aya", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC plus Cohere Labs Acceptable Use Policy", "release_date": "2024-05-23", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "8B Aya 23. The 35B card says this smaller version was released with it.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The 8B Aya 23 research model, released with the 35B model in May 2024 for 23 languages. Weights are non-commercial. It is not a current API model. Collected 2026-10-15.", "what_it_is_for": "The 8B Aya 23 research model, released with the 35B model in May 2024 for 23 languages. Weights are non-commercial. It is not a current API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/aya/c4ai-aya-expanse-32b": { "id": "cohere/aya/c4ai-aya-expanse-32b", "name": "Aya Expanse 32B", "maker": "cohere", "family": "cohere/aya", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0 plus Cohere Labs Acceptable Use Policy", "release_date": "2024-10-24", "context_length": 128000, "max_output_tokens": 4000, "parameters": "32B", "architecture": "Instruction-tuned multilingual model. Card: data arbitrage, multilingual preference training, safety tuning, and model merging on a Command-family pretrained model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cohere Labs' 32B Aya Expanse model for 23 languages. It is live on the Chat API, and the weights are non-commercial. The 8B twin was removed from the API in April 2026. Collected 2026-10-15.", "what_it_is_for": "Cohere Labs' 32B Aya Expanse model for 23 languages. It is live on the Chat API, and the weights are non-commercial. The 8B twin was removed from the API in April 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/aya/c4ai-aya-expanse-8b": { "id": "cohere/aya/c4ai-aya-expanse-8b", "name": "Aya Expanse 8B", "maker": "cohere", "family": "cohere/aya", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0 plus Cohere Labs Acceptable Use Policy", "release_date": "2024-10-24", "context_length": 8000, "max_output_tokens": 4000, "parameters": "8B", "architecture": "8B Aya Expanse. Same training story as the 32B card, which points at this smaller release. API retired 4 April 2026. Weights were not said to be deleted.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The 8B Aya Expanse model. Cohere retired the API id on 4 April 2026. The non-commercial weights were a research release and were not described as removed. Collected 2026-10-15.", "what_it_is_for": "The 8B Aya Expanse model. Cohere retired the API id on 4 April 2026. The non-commercial weights were a research release and were not described as removed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/aya/c4ai-aya-vision-32b": { "id": "cohere/aya/c4ai-aya-vision-32b", "name": "Aya Vision 32B", "maker": "cohere", "family": "cohere/aya", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-04", "context_length": 16000, "max_output_tokens": 4000, "parameters": "32B", "architecture": "Multilingual vision-language model. The models index says 32B. A Hugging Face size label for aya-vision-32b said 33B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cohere Labs' 32B multimodal Aya model. It takes text and images and answers in 23 languages. It is live on the Chat API. The 8B vision model was retired from the API. Collected 2026-10-15.", "what_it_is_for": "Cohere Labs' 32B multimodal Aya model. It takes text and images and answers in 23 languages. It is live on the Chat API. The 8B vision model was retired from the API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/aya/c4ai-aya-vision-8b": { "id": "cohere/aya/c4ai-aya-vision-8b", "name": "Aya Vision 8B", "maker": "cohere", "family": "cohere/aya", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-04", "context_length": 16000, "max_output_tokens": 4000, "parameters": "8B", "architecture": "Smaller Aya Vision model, aimed at lower latency. API retired 4 April 2026.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The 8B Aya Vision model. Cohere retired the API id on 4 April 2026. It was the low-latency multimodal Aya variant. Collected 2026-10-15.", "what_it_is_for": "The 8B Aya Vision model. Cohere retired the API id on 4 April 2026. It was the low-latency multimodal Aya variant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/aya/tiny-aya-base": { "id": "cohere/aya/tiny-aya-base", "name": "Tiny Aya Base", "maker": "cohere", "family": "cohere/aya", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0 plus Cohere Labs Acceptable Use Policy", "release_date": "2026-02-17", "context_length": null, "max_output_tokens": null, "parameters": "3.35B", "architecture": "Pretrained Tiny Aya base, not the instruction-tuned API models. The blog names TinyAya-Base. The Global card's license and 3.35B size describe the family.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The pretrained 3.35B Tiny Aya base model. Cohere Labs announced it on 17 February 2026 as the checkpoint behind the instruction variants. It is a weight release, not a Chat API id. Collected 2026-10-15.", "what_it_is_for": "The pretrained 3.35B Tiny Aya base model. Cohere Labs announced it on 17 February 2026 as the checkpoint behind the instruction variants. It is a weight release, not a Chat API id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/aya/tiny-aya-earth": { "id": "cohere/aya/tiny-aya-earth", "name": "Tiny Aya Earth", "maker": "cohere", "family": "cohere/aya", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0 plus Cohere Labs Acceptable Use Policy", "release_date": "2026-02-17", "context_length": 8000, "max_output_tokens": 8000, "parameters": "3.35B", "architecture": "Instruction-tuned Tiny Aya. The Global card describes a pretrained 3.35B model with 8k input context. Region variant aimed at West Asian and African languages.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tiny Aya Earth is a 3.35B instruction-tuned Tiny Aya model. Region variant aimed at West Asian and African languages. It is live on Cohere's Chat API and the weights are non-commercial. Collected 2026-10-15.", "what_it_is_for": "Tiny Aya Earth is a 3.35B instruction-tuned Tiny Aya model. Region variant aimed at West Asian and African languages. It is live on Cohere's Chat API and the weights are non-commercial.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/aya/tiny-aya-fire": { "id": "cohere/aya/tiny-aya-fire", "name": "Tiny Aya Fire", "maker": "cohere", "family": "cohere/aya", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0 plus Cohere Labs Acceptable Use Policy", "release_date": "2026-02-17", "context_length": 8000, "max_output_tokens": 8000, "parameters": "3.35B", "architecture": "Instruction-tuned Tiny Aya. The Global card describes a pretrained 3.35B model with 8k input context. Region variant aimed at South Asian languages.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tiny Aya Fire is a 3.35B instruction-tuned Tiny Aya model. Region variant aimed at South Asian languages. It is live on Cohere's Chat API and the weights are non-commercial. Collected 2026-10-15.", "what_it_is_for": "Tiny Aya Fire is a 3.35B instruction-tuned Tiny Aya model. Region variant aimed at South Asian languages. It is live on Cohere's Chat API and the weights are non-commercial.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/aya/tiny-aya-global": { "id": "cohere/aya/tiny-aya-global", "name": "Tiny Aya Global", "maker": "cohere", "family": "cohere/aya", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0 plus Cohere Labs Acceptable Use Policy", "release_date": "2026-02-17", "context_length": 8000, "max_output_tokens": 8000, "parameters": "3.35B", "architecture": "Instruction-tuned Tiny Aya. The Global card describes a pretrained 3.35B model with 8k input context. Best balance across languages and regions, per the models index.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tiny Aya Global is a 3.35B instruction-tuned Tiny Aya model. Best balance across languages and regions, per the models index. It is live on Cohere's Chat API and the weights are non-commercial. Collected 2026-10-15.", "what_it_is_for": "Tiny Aya Global is a 3.35B instruction-tuned Tiny Aya model. Best balance across languages and regions, per the models index. It is live on Cohere's Chat API and the weights are non-commercial.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/aya/tiny-aya-water": { "id": "cohere/aya/tiny-aya-water", "name": "Tiny Aya Water", "maker": "cohere", "family": "cohere/aya", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0 plus Cohere Labs Acceptable Use Policy", "release_date": "2026-02-17", "context_length": 8000, "max_output_tokens": 8000, "parameters": "3.35B", "architecture": "Instruction-tuned Tiny Aya. The Global card describes a pretrained 3.35B model with 8k input context. Region variant aimed at European and Asia-Pacific languages.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tiny Aya Water is a 3.35B instruction-tuned Tiny Aya model. Region variant aimed at European and Asia-Pacific languages. It is live on Cohere's Chat API and the weights are non-commercial. Collected 2026-10-15.", "what_it_is_for": "Tiny Aya Water is a 3.35B instruction-tuned Tiny Aya model. Region variant aimed at European and Asia-Pacific languages. It is live on Cohere's Chat API and the weights are non-commercial.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/command/command": { "id": "cohere/command/command", "name": "Command", "maker": "cohere", "family": "cohere/command", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4000, "max_output_tokens": 4000, "parameters": "52B", "architecture": "Pre-R instruction-following chat model. Oracle titles the retired OCI model Cohere Command (52B).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cohere's pre-R flagship chat model, a closed 4k-context instruction model deprecated on 15 September 2025. Oracle describes the hosted copy as 52B. Collected 2026-10-15.", "what_it_is_for": "Cohere's pre-R flagship chat model, a closed 4k-context instruction model deprecated on 15 September 2025. Oracle describes the hosted copy as 52B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/command/command-a-03-2025": { "id": "cohere/command/command-a-03-2025", "name": "Command A", "maker": "cohere", "family": "cohere/command", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0 plus Cohere Labs Acceptable Use Policy", "release_date": "2025-03-13", "context_length": 256000, "max_output_tokens": 8000, "parameters": "111B", "architecture": "Dense 111B model. Hugging Face architecture tag cohere2. Served on two A100 or H100 GPUs. Docs say throughput is 150% higher than Command R+ 08-2024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.5, "completion_per_m_usd": 10.0 }, "summary_intro": "Cohere's 111B enterprise model for tool use, RAG, and 23 languages. It is live on the Chat API. The downloadable weights are non-commercial. Collected 2026-10-15.", "what_it_is_for": "Cohere's 111B enterprise model for tool use, RAG, and 23 languages. It is live on the Chat API. The downloadable weights are non-commercial.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/command/command-a-plus-05-2026": { "id": "cohere/command/command-a-plus-05-2026", "name": "Command A+", "maker": "cohere", "family": "cohere/command", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-05-20", "context_length": 128000, "max_output_tokens": 64000, "parameters": "218B total / 25B active", "architecture": "Sparse mixture-of-experts. First MoE in the Command family. Weights shipped as BF16, FP8, and W4A4.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cohere's last Command A model and its first mixture-of-experts Command model. It combines vision, reasoning, translation, and tool use in an Apache-2.0 checkpoint aimed at one B200 or two H100s. Collected 2026-10-15.", "what_it_is_for": "Cohere's last Command A model and its first mixture-of-experts Command model. It combines vision, reasoning, translation, and tool use in an Apache-2.0 checkpoint aimed at one B200 or two H100s.", "ratings": { "empirical_100": 44, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/command/command-a-reasoning-08-2025": { "id": "cohere/command/command-a-reasoning-08-2025", "name": "Command A Reasoning", "maker": "cohere", "family": "cohere/command", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 256000, "max_output_tokens": 32000, "parameters": "111B", "architecture": "First Cohere reasoning model. Production target 4x H100. Evaluation can use 4x A100. Hugging Face lists the repo at 111B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cohere's first reasoning model. A 111B text model with a 256k context, built for agents and tool use in 23 languages. Weights are on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "Cohere's first reasoning model. A 111B text model with a 256k context, built for agents and tool use in 23 languages. Weights are on Hugging Face.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/command/command-a-translate-08-2025": { "id": "cohere/command/command-a-translate-08-2025", "name": "Command A Translate", "maker": "cohere", "family": "cohere/command", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 8000, "max_output_tokens": 8000, "parameters": "111B", "architecture": "Command A checkpoint specialized for translation. Docs say one or two A100 or H100 GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A Command A model specialized for translation across 23 languages. It is text in and text out, and it is sized for one or two GPUs. Collected 2026-10-15.", "what_it_is_for": "A Command A model specialized for translation across 23 languages. It is text in and text out, and it is sized for one or two GPUs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/command/command-a-vision-07-2025": { "id": "cohere/command/command-a-vision-07-2025", "name": "Command A Vision", "maker": "cohere", "family": "cohere/command", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-31", "context_length": 128000, "max_output_tokens": 8000, "parameters": "112B", "architecture": "Command A lineage with image input. Does not generate images. Tool use is not supported.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cohere's first commercial vision Command model. It reads charts, tables, and documents in six languages and returns text. It does not generate images. Collected 2026-10-15.", "what_it_is_for": "Cohere's first commercial vision Command model. It reads charts, tables, and documents in six languages and returns text. It does not generate images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/command/command-light": { "id": "cohere/command/command-light", "name": "Command Light", "maker": "cohere", "family": "cohere/command", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4000, "max_output_tokens": 4000, "parameters": "", "architecture": "Smaller, faster Command. The index says almost as capable, but a lot faster.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The smaller, faster sibling of the original Command model. Closed weights, 4k context, deprecated on 15 September 2025. Collected 2026-10-15.", "what_it_is_for": "The smaller, faster sibling of the original Command model. Closed weights, 4k context, deprecated on 15 September 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/command/command-r-03-2024": { "id": "cohere/command/command-r-03-2024", "name": "Command R 03-2024", "maker": "cohere", "family": "cohere/command", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC plus Cohere Labs Acceptable Use Policy", "release_date": "", "context_length": 128000, "max_output_tokens": 4096, "parameters": "35B", "architecture": "Research release c4ai-command-r-v01. Card: 35B, 128k, RAG-oriented, multilingual generation evaluated in 10 languages.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": 0.6 }, "summary_intro": "The original March 2024 Command R, a 35B open-weight RAG and tool-use model. The API id and the alias command-r were deprecated on 15 September 2025. The non-commercial weights remain on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "The original March 2024 Command R, a 35B open-weight RAG and tool-use model. The API id and the alias command-r were deprecated on 15 September 2025. The non-commercial weights remain on Hugging Face.", "ratings": { "empirical_100": null, "lmsys_elo": 1210, "coding_elo": 1200, "swe_bench": 14.0, "humaneval": 71.0, "twelve_cs": null, "aa_intelligence": 40.0, "aa_coding": 46.0, "aa_agentic": 52.0, "design_arena_elo": 1200, "math_500": 46.0, "gpqa_diamond": 31.0, "ifeval": 78.0, "bfcl_tool_calling": 82.0 } }, "cohere/command/command-r-08-2024": { "id": "cohere/command/command-r-08-2024", "name": "Command R 08-2024", "maker": "cohere", "family": "cohere/command", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-30", "context_length": 128000, "max_output_tokens": 4000, "parameters": "32B", "architecture": "August 2024 refresh. Changelog: about 50% higher throughput, 20% lower latency, and half the hardware of March 2024 Command R. Hugging Face size label is 32B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": 0.6 }, "summary_intro": "The August 2024 Command R refresh, smaller and cheaper than Command R+. Cohere's index still lists it as live. Azure and Oracle have retired their hosted copies. Collected 2026-10-15.", "what_it_is_for": "The August 2024 Command R refresh, smaller and cheaper than Command R+. Cohere's index still lists it as live. Azure and Oracle have retired their hosted copies.", "ratings": { "empirical_100": 29, "lmsys_elo": 1209, "coding_elo": 1209, "swe_bench": 16.5, "humaneval": 71.2, "twelve_cs": 86.6, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/command/command-r-plus-04-2024": { "id": "cohere/command/command-r-plus-04-2024", "name": "Command R+ 04-2024", "maker": "cohere", "family": "cohere/command", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": 4096, "parameters": "104B", "architecture": "Original Command R+. The models index says command-r-plus is an alias of this id. Artificial Analysis lists 104B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.5, "completion_per_m_usd": 10.0 }, "summary_intro": "The April 2024 Command R+, deprecated on the Cohere API on 15 September 2025 along with the alias command-r-plus. Artificial Analysis lists it at 104B. Weights were released for research. Collected 2026-10-15.", "what_it_is_for": "The April 2024 Command R+, deprecated on the Cohere API on 15 September 2025 along with the alias command-r-plus. Artificial Analysis lists it at 104B. Weights were released for research.", "ratings": { "empirical_100": null, "lmsys_elo": 1260, "coding_elo": 1250, "swe_bench": 22.0, "humaneval": 78.0, "twelve_cs": null, "aa_intelligence": 52.0, "aa_coding": 58.0, "aa_agentic": 62.0, "design_arena_elo": 1250, "math_500": 55.0, "gpqa_diamond": 38.0, "ifeval": 82.0, "bfcl_tool_calling": 86.0 } }, "cohere/command/command-r-plus-08-2024": { "id": "cohere/command/command-r-plus-08-2024", "name": "Command R+ 08-2024", "maker": "cohere", "family": "cohere/command", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-30", "context_length": 128000, "max_output_tokens": 4000, "parameters": "", "architecture": "August 2024 refresh. Changelog: about 50% higher throughput and 25% lower latency than April 2024 Command R+, same hardware footprint. Parameter count was not restated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.5, "completion_per_m_usd": 10.0 }, "summary_intro": "The August 2024 Command R+ refresh, still listed live on Cohere's API. It is the 128k RAG and tool-use model that Command A later replaced for most new work. Some clouds have retired their copies. Collected 2026-10-15.", "what_it_is_for": "The August 2024 Command R+ refresh, still listed live on Cohere's API. It is the 128k RAG and tool-use model that Command A later replaced for most new work. Some clouds have retired their copies.", "ratings": { "empirical_100": 24, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs": 99.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/command/command-r7b-12-2024": { "id": "cohere/command/command-r7b-12-2024", "name": "Command R7B", "maker": "cohere", "family": "cohere/command", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-12-13", "context_length": 128000, "max_output_tokens": 4000, "parameters": "7B", "architecture": "Smallest Command R model. Cohere's 2026 small-model post and a Cohere Labs maintainer call it 7B. The Hugging Face collection size label says 8B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.0375, "completion_per_m_usd": 0.15 }, "summary_intro": "The smallest Command R model, for RAG, tools, and agents at low latency. It is Cohere's cheap self-serve chat model and has open weights. Collected 2026-10-15.", "what_it_is_for": "The smallest Command R model, for RAG, tools, and agents at low latency. It is Cohere's cheap self-serve chat model and has open weights.", "ratings": { "empirical_100": 45, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.4, "humaneval": 70.3, "twelve_cs": 99.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/command/command-r7b-arabic-02-2025": { "id": "cohere/command/command-r7b-arabic-02-2025", "name": "Command R7B Arabic", "maker": "cohere", "family": "cohere/command", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Named Cohere Labs weight release. Not a row on the current API models index. Hugging Face size label is 8B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A Cohere Labs open-weight Arabic variant of Command R7B. It is a named weight release, not a current API model. Collected 2026-10-15.", "what_it_is_for": "A Cohere Labs open-weight Arabic variant of Command R7B. It is a named weight release, not a current API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/embed/embed-english-light-v2-0": { "id": "cohere/embed/embed-english-light-v2-0", "name": "Embed English Light v2", "maker": "cohere", "family": "cohere/embed", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Embed v2 model removed from the API on 4 April 2026. The models index platform table still names the id for some clouds.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Embed English Light v2 is a retired Cohere embedding model. API requests using this id failed starting 4 April 2026. Collected 2026-10-15.", "what_it_is_for": "Embed English Light v2 is a retired Cohere embedding model. API requests using this id failed starting 4 April 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/embed/embed-english-light-v3-0": { "id": "cohere/embed/embed-english-light-v3-0", "name": "Embed English Light v3", "maker": "cohere", "family": "cohere/embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Smaller, faster Embed v3. 384 dimensions. The models index lists cosine similarity for v3 and says image input is supported. Context length 512 tokens.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Embed English Light v3 is a Cohere Embed v3 model for semantic search and classification. The models index still lists it, with a 512-token context and 384 dimensions. Collected 2026-10-15.", "what_it_is_for": "Embed English Light v3 is a Cohere Embed v3 model for semantic search and classification. The models index still lists it, with a 512-token context and 384 dimensions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/embed/embed-english-v2-0": { "id": "cohere/embed/embed-english-v2-0", "name": "Embed English v2", "maker": "cohere", "family": "cohere/embed", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Embed v2 model removed from the API on 4 April 2026. The models index platform table still names the id for some clouds.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Embed English v2 is a retired Cohere embedding model. API requests using this id failed starting 4 April 2026. Collected 2026-10-15.", "what_it_is_for": "Embed English v2 is a retired Cohere embedding model. API requests using this id failed starting 4 April 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/embed/embed-english-v3-0": { "id": "cohere/embed/embed-english-v3-0", "name": "Embed English v3", "maker": "cohere", "family": "cohere/embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Embed v3. 1024 dimensions. The models index lists cosine similarity for v3 and says image input is supported. Context length 512 tokens.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Embed English v3 is a Cohere Embed v3 model for semantic search and classification. The models index still lists it, with a 512-token context and 1024 dimensions. Collected 2026-10-15.", "what_it_is_for": "Embed English v3 is a Cohere Embed v3 model for semantic search and classification. The models index still lists it, with a 512-token context and 1024 dimensions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/embed/embed-multilingual-light-v3-0": { "id": "cohere/embed/embed-multilingual-light-v3-0", "name": "Embed Multilingual Light v3", "maker": "cohere", "family": "cohere/embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Smaller, faster Embed v3. 384 dimensions. The models index lists cosine similarity for v3 and says image input is supported. Context length 512 tokens.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Embed Multilingual Light v3 is a Cohere Embed v3 model for semantic search and classification. The models index still lists it, with a 512-token context and 384 dimensions. Collected 2026-10-15.", "what_it_is_for": "Embed Multilingual Light v3 is a Cohere Embed v3 model for semantic search and classification. The models index still lists it, with a 512-token context and 384 dimensions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/embed/embed-multilingual-v2-0": { "id": "cohere/embed/embed-multilingual-v2-0", "name": "Embed Multilingual v2", "maker": "cohere", "family": "cohere/embed", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Embed v2 model removed from the API on 4 April 2026. The models index platform table still names the id for some clouds.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Embed Multilingual v2 is a retired Cohere embedding model. API requests using this id failed starting 4 April 2026. Collected 2026-10-15.", "what_it_is_for": "Embed Multilingual v2 is a retired Cohere embedding model. API requests using this id failed starting 4 April 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/embed/embed-multilingual-v3-0": { "id": "cohere/embed/embed-multilingual-v3-0", "name": "Embed Multilingual v3", "maker": "cohere", "family": "cohere/embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Embed v3. 1024 dimensions. The models index lists cosine similarity for v3 and says image input is supported. Context length 512 tokens.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Embed Multilingual v3 is a Cohere Embed v3 model for semantic search and classification. The models index still lists it, with a 512-token context and 1024 dimensions. Collected 2026-10-15.", "what_it_is_for": "Embed Multilingual v3 is a Cohere Embed v3 model for semantic search and classification. The models index still lists it, with a 512-token context and 1024 dimensions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/embed/embed-v4-0": { "id": "cohere/embed/embed-v4-0", "name": "Embed 4", "maker": "cohere", "family": "cohere/embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal embedding model. Matryoshka dimensions 256, 512, 1024, and 1536 (default). Cosine, dot product, and Euclidean similarity. Embed jobs supported.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cohere's multimodal embedding model before Embed 5. It embeds text, images, and mixed PDF pages at 128k context, with matryoshka dimensions up to 1536. Collected 2026-10-15.", "what_it_is_for": "Cohere's multimodal embedding model before Embed 5. It embeds text, images, and mixed PDF pages at 128k context, with matryoshka dimensions up to 1536.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/embed/embed-v5-0-fast": { "id": "cohere/embed/embed-v5-0-fast", "name": "Embed 5 Fast", "maker": "cohere", "family": "cohere/embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-30", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "Faster Embed 5. Same dimensions, output types, 128k context, and embedding space as Pro. Blog says average document throughput 377.3 docs/s versus 159.7 for Pro.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.08, "completion_per_m_usd": null }, "summary_intro": "The low-latency Embed 5 model. It embeds text, images, and mixed pages in the same vector space as Embed 5 Pro, at a lower text price. Collected 2026-10-15.", "what_it_is_for": "The low-latency Embed 5 model. It embeds text, images, and mixed pages in the same vector space as Embed 5 Pro, at a lower text price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/embed/embed-v5-0-pro": { "id": "cohere/embed/embed-v5-0-pro", "name": "Embed 5 Pro", "maker": "cohere", "family": "cohere/embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-30", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal embedding model. Matryoshka dimensions 256, 512, 768, 1024, 1536, and 2048 (default). Output types float, int8, and binary. Shares an embedding space with Embed 5 Fast. Similarity: cosine, dot product, Euclidean.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.12, "completion_per_m_usd": null }, "summary_intro": "Cohere's highest-quality Embed 5 model for text, images, and mixed pages. It is the indexing side of a shared embedding space with Embed 5 Fast. Collected 2026-10-15.", "what_it_is_for": "Cohere's highest-quality Embed 5 model for text, images, and mixed pages. It is the indexing side of a shared embedding space with Embed 5 Fast.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/north/north-mini-code-1-0": { "id": "cohere/north/north-mini-code-1-0", "name": "North Mini Code", "maker": "cohere", "family": "cohere/north", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-06-09", "context_length": 256000, "max_output_tokens": 64000, "parameters": "30B total / 3B active", "architecture": "Mixture-of-experts coding model. The model page, changelog, and launch blog say 3B active. A September 2026 megakernel post says 3.3B active.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cohere's first North model and its first agentic coding model. Apache 2.0, 30B total and 3B active, with a 256k context. Collected 2026-10-15.", "what_it_is_for": "Cohere's first North model and its first agentic coding model. Apache 2.0, 30B total and 3B active, with a 256k context.", "ratings": { "empirical_100": 55, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/north/north-small-translate-1-0": { "id": "cohere/north/north-small-translate-1-0", "name": "North Small Translate", "maker": "cohere", "family": "cohere/north", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-09-09", "context_length": 16000, "max_output_tokens": 16000, "parameters": "218B total / 25B active", "architecture": "Mixture-of-experts translation model. Weights in W4A16, FP8, and BF16 for non-commercial use.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "An open-weight North translation model, 218B total and 25B active, for more than 50 languages. Weights are non-commercial. The API copy is on the free tier. Collected 2026-10-15.", "what_it_is_for": "An open-weight North translation model, 218B total and 25B active, for more than 50 languages. Weights are non-commercial. The API copy is on the free tier.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/parse/parse-v5-0": { "id": "cohere/parse/parse-v5-0", "name": "Parse 5", "maker": "cohere", "family": "cohere/parse", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-27", "context_length": 8192, "max_output_tokens": null, "parameters": "2.3B", "architecture": "Proprietary vision parser. Docs name the architecture north-micro-vision-instruct. Model size about 4.6GB. Returns Markdown, HTML tables, bounding boxes, and image descriptions.", "modalities": { "input": [ "image", "document" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cohere's vision parser. The 2.3B model turns PDFs, slides, and page images into structured Markdown for search and agents. It went live on 27 August 2026. Collected 2026-10-15.", "what_it_is_for": "Cohere's vision parser. The 2.3B model turns PDFs, slides, and page images into structured Markdown for search and agents. It went live on 27 August 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/rerank/rerank-english-v2-0": { "id": "cohere/rerank/rerank-english-v2-0", "name": "Rerank English v2", "maker": "cohere", "family": "cohere/rerank", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reranker for documents and semi-structured JSON. Long documents are chunked. v4 chunk size is 32,764 tokens (32,768 minus 4 reserved). Query tokens plus document tokens count toward the per-document limit.", "modalities": { "input": [ "text" ], "output": [ "ranking" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The English Rerank v2 model. Cohere shut it down on 30 April 2025. The published price at deprecation was $1.00 per 1,000 searches. Collected 2026-10-15.", "what_it_is_for": "The English Rerank v2 model. Cohere shut it down on 30 April 2025. The published price at deprecation was $1.00 per 1,000 searches.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/rerank/rerank-english-v3-0": { "id": "cohere/rerank/rerank-english-v3-0", "name": "Rerank English v3", "maker": "cohere", "family": "cohere/rerank", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Reranker for documents and semi-structured JSON. Long documents are chunked. v4 chunk size is 32,764 tokens (32,768 minus 4 reserved). Query tokens plus document tokens count toward the per-document limit.", "modalities": { "input": [ "text" ], "output": [ "ranking" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The English-only Rerank 3.0 model. Cohere's index still lists it for English documents and JSON. Collected 2026-10-15.", "what_it_is_for": "The English-only Rerank 3.0 model. Cohere's index still lists it for English documents and JSON.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/rerank/rerank-multilingual-v2-0": { "id": "cohere/rerank/rerank-multilingual-v2-0", "name": "Rerank Multilingual v2", "maker": "cohere", "family": "cohere/rerank", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reranker for documents and semi-structured JSON. Long documents are chunked. v4 chunk size is 32,764 tokens (32,768 minus 4 reserved). Query tokens plus document tokens count toward the per-document limit.", "modalities": { "input": [ "text" ], "output": [ "ranking" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The multilingual Rerank v2 model. Cohere shut it down on 30 April 2025 at a published price of $1.00 per 1,000 searches. Collected 2026-10-15.", "what_it_is_for": "The multilingual Rerank v2 model. Cohere shut it down on 30 April 2025 at a published price of $1.00 per 1,000 searches.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/rerank/rerank-multilingual-v3-0": { "id": "cohere/rerank/rerank-multilingual-v3-0", "name": "Rerank Multilingual v3", "maker": "cohere", "family": "cohere/rerank", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Reranker for documents and semi-structured JSON. Long documents are chunked. v4 chunk size is 32,764 tokens (32,768 minus 4 reserved). Query tokens plus document tokens count toward the per-document limit.", "modalities": { "input": [ "text" ], "output": [ "ranking" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The multilingual Rerank 3.0 model for non-English documents and JSON. It is still listed on Cohere's index. Collected 2026-10-15.", "what_it_is_for": "The multilingual Rerank 3.0 model for non-English documents and JSON. It is still listed on Cohere's index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/rerank/rerank-v3-5": { "id": "cohere/rerank/rerank-v3-5", "name": "Rerank 3.5", "maker": "cohere", "family": "cohere/rerank", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-02", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Reranker for documents and semi-structured JSON. Long documents are chunked. v4 chunk size is 32,764 tokens (32,768 minus 4 reserved). Query tokens plus document tokens count toward the per-document limit.", "modalities": { "input": [ "text" ], "output": [ "ranking" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cohere's multilingual reranker between Rerank 3.0 and Rerank 4. It is still on the API, with a 4,096-token context. Collected 2026-10-15.", "what_it_is_for": "Cohere's multilingual reranker between Rerank 3.0 and Rerank 4. It is still on the API, with a 4,096-token context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/rerank/rerank-v4-0-fast": { "id": "cohere/rerank/rerank-v4-0-fast", "name": "Rerank 4 Fast", "maker": "cohere", "family": "cohere/rerank", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-11", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Reranker for documents and semi-structured JSON. Long documents are chunked. v4 chunk size is 32,764 tokens (32,768 minus 4 reserved). Query tokens plus document tokens count toward the per-document limit.", "modalities": { "input": [ "text" ], "output": [ "ranking" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The faster Rerank 4 model. Same 32k multilingual reranking job as Pro, aimed at high throughput. Collected 2026-10-15.", "what_it_is_for": "The faster Rerank 4 model. Same 32k multilingual reranking job as Pro, aimed at high throughput.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/rerank/rerank-v4-0-pro": { "id": "cohere/rerank/rerank-v4-0-pro", "name": "Rerank 4 Pro", "maker": "cohere", "family": "cohere/rerank", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-11", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Reranker for documents and semi-structured JSON. Long documents are chunked. v4 chunk size is 32,764 tokens (32,768 minus 4 reserved). Query tokens plus document tokens count toward the per-document limit.", "modalities": { "input": [ "text" ], "output": [ "ranking" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cohere's quality Rerank 4 model. It reranks multilingual text and JSON with a 32k context. It is live on the Rerank API. Collected 2026-10-15.", "what_it_is_for": "Cohere's quality Rerank 4 model. It reranks multilingual text and JSON with a 32k context. It is live on the Rerank API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/transcribe/cohere-transcribe-03-2026": { "id": "cohere/transcribe/cohere-transcribe-03-2026", "name": "Cohere Transcribe", "maker": "cohere", "family": "cohere/transcribe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-26", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Conformer encoder plus a lightweight Transformer decoder. Audio is resampled to 16 kHz and mixed to mono. Trained from scratch with token cross-entropy.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cohere's open-source 2B speech recognition model for 14 languages. It is audio in and text out, under Apache 2.0, with a 25MB upload limit on the API. Collected 2026-10-15.", "what_it_is_for": "Cohere's open-source 2B speech recognition model for 14 languages. It is audio in and text out, under Apache 2.0, with a 25MB upload limit on the API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cohere/transcribe/cohere-transcribe-arabic-07-2026": { "id": "cohere/transcribe/cohere-transcribe-arabic-07-2026", "name": "Cohere Transcribe Arabic", "maker": "cohere", "family": "cohere/transcribe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-07-07", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Fine-tune of Cohere Transcribe on Arabic speech. Same conformer encoder-decoder. Dialect and code-switching support are stated.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "An Apache-2.0 fine-tune of Cohere Transcribe for Arabic, including major dialects, plus English. It is a 2B audio model released 7 July 2026. Collected 2026-10-15.", "what_it_is_for": "An Apache-2.0 fine-tune of Cohere Transcribe for Arabic, including major dialects, plus English. It is a 2B audio model released 7 July 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "coimbra/pt-lkb/pt-lkb-embeddings": { "id": "coimbra/pt-lkb/pt-lkb-embeddings", "name": "PT-LKB embeddings", "maker": "coimbra", "family": "coimbra/pt-lkb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Static node2vec word embeddings of a weighted Portuguese lexical-semantic graph, stored as word2vec-format text. Not a transformer.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PT-LKB embeddings are static Portuguese word vectors released by Hugo Gon\u00e7alo Oliveira at CISUC, Department of Informatics Engineering, University of Coimbra. They are node2vec embeddings of a lexical-semantic graph, not a language-model API. The public zips were still being served on the collection date 2026-10-15. The package readme dates the release to July 2018 and does not give a day.", "what_it_is_for": "The PROPOR 2018 paper presents the vectors as Portuguese word embeddings learned from a lexical-semantic knowledge base, aimed at genuine similarity. The GitHub page describes the same files as word embeddings learned with node2vec from a graph of relations in ten Portuguese lexical resources. The package is a downloadable embedding file, not a chat or generation system.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cologne/grandqc/grandqc-artifact": { "id": "cologne/grandqc/grandqc-artifact", "name": "GrandQC Artifact Segmentation", "maker": "cologne", "family": "cologne/grandqc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2024-12-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "UNet++ decoder with an EfficientNet-B0 encoder, shared by the 5x, 7x, and 10x checkpoints", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GrandQC Artifact Segmentation is the multi-class quality-control module of GrandQC, from the Computational Pathology lab at University Hospital Cologne (University of Cologne). Three magnification checkpoints are versions of this one model. Collected on 2026-10-15.", "what_it_is_for": "The paper and README describe pixel-wise segmentation of tissue, background, and five artifact groups on H&E slides: folds, dark spots and foreign objects, pen marks, air bubbles and slide edges, and out-of-focus areas. The lab says it is a monitor for sample preparation and scanning, and a preprocessor for downstream image analysis. The intended order is tissue detection first, then this module.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cologne/grandqc/grandqc-tissue": { "id": "cologne/grandqc/grandqc-tissue", "name": "GrandQC Tissue Detection", "maker": "cologne", "family": "cologne/grandqc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2024-11-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "UNet++ decoder with an EfficientNet-B0 encoder, the final architecture stated for both GrandQC modules", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GrandQC Tissue Detection is the tissue-versus-background module of GrandQC, released by the Computational Pathology lab at University Hospital Cologne (University of Cologne). Weights are public for academic and non-commercial pathology use. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab says tissue segmentation is the first step before artifact segmentation. It marks tissue against background on H&E slides so later quality-control and analysis models see tissue rather than the whole slide. The author list is multi-institutional. The releasing repository is `cpath-ukk`. A TIA Centre Hugging Face repo rehosts this checkpoint only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cologne/rapidlymphoma/rapidlymphoma": { "id": "cologne/rapidlymphoma/rapidlymphoma", "name": "RapidLymphoma", "maker": "cologne", "family": "cologne/rapidlymphoma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-10-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-50 trained with a BYOL-style self-supervised objective for stimulated Raman histology, then used to classify primary CNS lymphoma against other CNS lesions", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RapidLymphoma is a named stimulated-Raman-histology classifier released by David Reinecke and colleagues at the Department of General Neurosurgery, University Hospital Cologne, with New York University and other clinical sites. The weight file has been public since 23 October 2024. Collected on 2026-10-15.", "what_it_is_for": "The paper and the University Hospital Cologne press note describe an intraoperative tool that separates primary CNS lymphoma from other CNS lesions on label-free SRH images, as a research alternative to frozen section. The GitHub README says the study workflow returns a result in under three minutes. Patients in the published cohorts include University Hospital Cologne, New York University, the University of Michigan, and the Medical University of Vienna.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colorado/livedst/livedst": { "id": "colorado/livedst/livedst", "name": "LiveDst", "maker": "colorado", "family": "colorado/livedst", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-03-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Gated recurrent unit (GRU) network on a 6-hour input window at a 1-hour step. A second GRU estimates uncertainty with the ACCRUE score. Multi-fidelity boosting trains further GRUs on the worst-scoring half of the training samples and combines members by inverse-variance weights. Lead times of 1 to 6 hours are horizons of this one model, not separate products.", "modalities": { "input": [ "solar-wind", "geomagnetic-index" ], "output": [ "dst-index" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LiveDst is a University of Colorado Boulder space-weather model from SWx TREC and CIRES. It forecasts the Disturbance storm time (Dst) index 1 to 6 hours ahead from solar wind at the L1 point, with an uncertainty estimate. The research paper was first published on 30 March 2023, and the CU staging platform still listed the live service on 15 October 2026.", "what_it_is_for": "The CU staging platform says LiveDst shows real-time Dst from 1 to 6 hours ahead, with probabilities, using multi-fidelity boosted neural networks and real-time solar wind at L1. The service description says the target is the real-time Dst published by the World Data Center for Geomagnetism, Kyoto, and that the network follows Hu, Camporeale, and Swiger. Credits on that page name Enrico Camporeale, Andong Hu, Brandon Stone, Jennifer Knuth, Greg Lucas, and Tom Berger, with NASA grant 80NSSC20K1580. One paper author, B. Swiger, also lists NOAA Space Weather Prediction Center.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colorado/livewire/livewire": { "id": "colorado/livewire/livewire", "name": "LiveWire", "maker": "colorado", "family": "colorado/livewire", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Gated recurrent unit (GRU) on 120 minutes of inputs at a 1-minute cadence. Uncertainty comes from the ACCRUE method. The ProBoost multi-fidelity procedure builds an ensemble. The paper says hyperparameter settings are in the figshare source, which was not opened. Lead times of 10 to 60 minutes are horizons of this one model.", "modalities": { "input": [ "solar-wind", "magnetometer", "magnetotelluric" ], "output": [ "geoelectric-field" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LiveWire is a University of Colorado Boulder model from the Space Weather Technology, Research, and Education Center. It forecasts the horizontal geoelectric field at the Boulder magnetic observatory up to one hour ahead, which is the driver the authors use for geomagnetically induced currents. The open-access paper is cited as 2 October 2024, and the CU staging platform still linked the LiveWire app on 15 October 2026.", "what_it_is_for": "The paper says grid operators want the geoelectric field itself, not only dB/dt, and that physics models driven by solar wind often give 10 to 30 minutes of lead time. LiveWire forecasts that field directly out to 60 minutes. Authors are A. Hu, E. Camporeale, G. Lucas, and T. Berger. The first affiliation is SWx TREC at the University of Colorado. Camporeale also lists Queen Mary University of London. The CU staging page describes the public app as a view of geomagnetic and geoelectric fields during solar storms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/avatar/cartoon": { "id": "colossyan/avatar/cartoon", "name": "Cartoon avatars", "maker": "colossyan", "family": "colossyan/avatar", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Illustrated presenters. The feature page says they use the same NEO 2 technology as realistic avatars. API style enum includes Cartoon beside Realistic.", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cartoon avatars are illustrated presenters. The feature page says they use NEO 2, are on every plan at no extra avatar fee, and speak 100+ languages. The API style enum includes Cartoon. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Cartoon avatars are illustrated presenters. The feature page says they use NEO 2, are on every plan at no extra avatar fee, and speak 100+ languages. The API style enum includes Cartoon. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/avatar/instant": { "id": "colossyan/avatar/instant", "name": "Instant avatar", "maker": "colossyan", "family": "colossyan/avatar", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Instant avatars are custom presenters made from a photo or about 20 seconds of footage. The API creates one with POST /assets/actors. The custom-avatar FAQ says Instant replaced Lite. Collected 2026-10-15. Weights of the underlying renderer are closed.", "what_it_is_for": "Instant avatars are custom presenters made from a photo or about 20 seconds of footage. The API creates one with POST /assets/actors. The custom-avatar FAQ says Instant replaced Lite. Collected 2026-10-15. Weights of the underlying renderer are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/avatar/lite": { "id": "colossyan/avatar/lite", "name": "Lite avatar", "maker": "colossyan", "family": "colossyan/avatar", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lite avatars are the older self-serve custom avatar. The custom-avatar FAQ says Instant replaced them, that existing Lite avatars still work, and that a Lite owner can create an Instant avatar for free. A request page still shows a 2023 waitlist and $500 per year. Collected 2026-10-15. Status is deprecated, not deleted.", "what_it_is_for": "Lite avatars are the older self-serve custom avatar. The custom-avatar FAQ says Instant replaced them, that existing Lite avatars still work, and that a Lite owner can create an Instant avatar for free. A request page still shows a 2023 waitlist and $500 per year. Collected 2026-10-15. Status is deprecated, not deleted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/avatar/scenario": { "id": "colossyan/avatar/scenario", "name": "Scenario avatars", "maker": "colossyan", "family": "colossyan/avatar", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Scenario avatars are stock presenters Colossyan places in a setting such as an office, hospital, or job site. The API type is Scenario, and the docs say they are off until support enables them. Collected 2026-10-15.", "what_it_is_for": "Scenario avatars are stock presenters Colossyan places in a setting such as an office, hospital, or job site. The API type is Scenario, and the docs say they are off until support enables them. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/avatar/ssa-lite": { "id": "colossyan/avatar/ssa-lite", "name": "ssa_lite", "maker": "colossyan", "family": "colossyan/avatar", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ssa_lite is a custom-avatar type in the list-avatars enum. The docs point at the custom-avatar page and do not define the prefix or give it a separate creation flow. Collected 2026-10-15.", "what_it_is_for": "ssa_lite is a custom-avatar type in the list-avatars enum. The docs point at the custom-avatar page and do not define the prefix or give it a separate creation flow. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/avatar/ssa-studio": { "id": "colossyan/avatar/ssa-studio", "name": "ssa_studio", "maker": "colossyan", "family": "colossyan/avatar", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ssa_studio is a custom-avatar type in the list-avatars enum. The public studio product that matches this name is the paid custom studio avatar: $1,000 per year in help and on the pricing FAQ, with production details that conflict between the help article and the marketing page. Collected 2026-10-15.", "what_it_is_for": "ssa_studio is a custom-avatar type in the list-avatars enum. The public studio product that matches this name is the paid custom studio avatar: $1,000 per year in help and on the pricing FAQ, with production details that conflict between the help article and the marketing page. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/avatar/studio": { "id": "colossyan/avatar/studio", "name": "Studio avatars", "maker": "colossyan", "family": "colossyan/avatar", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Studio is the API type for stock avatars Colossyan provides, as opposed to a customer's own recording. Help from 22 April 2025 says stock avatars come in shoulder, bubble, and full-body views, and that a v2 label means enhanced lip sync. Collected 2026-10-15. These are presenter identities on a closed renderer, not a separate foundation model.", "what_it_is_for": "Studio is the API type for stock avatars Colossyan provides, as opposed to a customer's own recording. Help from 22 April 2025 says stock avatars come in shoulder, bubble, and full-body views, and that a v2 label means enhanced lip sync. Collected 2026-10-15. These are presenter identities on a closed renderer, not a separate foundation model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/dubbing/colossyan-dubbing": { "id": "colossyan/dubbing/colossyan-dubbing", "name": "Colossyan Dubbing", "maker": "colossyan", "family": "colossyan/dubbing", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video dubbing model. The February 2026 note says it passes the full frame through the NEO 2 diffusion transformer, conditioned on the source video, and re-renders the lower face to match new audio. A 3D VAE encodes source frames. It does not crop the mouth. Person and pose tags mark who is speaking.", "modalities": { "input": [ "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Colossyan Dubbing is the video dubbing model described in February 2026. It runs source footage through the NEO 2 diffusion transformer and re-renders the lower face to new audio instead of cropping the mouth. A free browser tool is linked. The dubbing API is early access. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Colossyan Dubbing is the video dubbing model described in February 2026. It runs source footage through the NEO 2 diffusion transformer and re-renders the lower face to new audio instead of cropping the mouth. A free browser tool is linked. The dubbing API is early access. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/legacy/legacy": { "id": "colossyan/legacy/legacy", "name": "Legacy", "maker": "colossyan", "family": "colossyan/legacy", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Legacy is an inference model id still returned by Colossyan's avatar API next to `neo` and `neo2`. The public docs do not describe its architecture. The variant matrix says it supports full body, bubble, and shoulder. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Legacy is an inference model id still returned by Colossyan's avatar API next to `neo` and `neo2`. The public docs do not describe its architecture. The variant matrix says it supports full body, bubble, and shoulder. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/neo/neo": { "id": "colossyan/neo/neo", "name": "NEO", "maker": "colossyan", "family": "colossyan/neo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Talking-head stack. VOCAL (Voice-to-Latent) is a multi-modal diffusion transformer that maps speech to motion latents with joint attention. Pictar renders those latents from a reference image in three stages: 3D lift, motion deformation, and photorealistic translation. Trained with flow matching and a reinforcement method Colossyan calls Kinetic-GRPO.", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NEO (Neural Expressive Output) is Colossyan's talking-head video engine. The October 2025 research note splits it into VOCAL, which turns speech into motion latents, and Pictar, which renders those latents. The API id is `neo`. A Colossyan YouTube intro is dated 2025-05-08. As of 2026-10-15 it is still a supported inference model. Weights are closed.", "what_it_is_for": "NEO (Neural Expressive Output) is Colossyan's talking-head video engine. The October 2025 research note splits it into VOCAL, which turns speech into motion latents, and Pictar, which renders those latents. The API id is `neo`. A Colossyan YouTube intro is dated 2025-05-08. As of 2026-10-15 it is still a supported inference model. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/neo/neo-1-5": { "id": "colossyan/neo/neo-1-5", "name": "NEO 1.5", "maker": "colossyan", "family": "colossyan/neo", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Talking-head upgrade of NEO. Same dataset, diffusion base, and reinforcement recipe. VOCAL 1.5 replaces two-stream attention with Unified Joint Attention so audio, motion, and identity share one attention field. Attention residuals and differential attention are part of that block. The renderer is unchanged from NEO. Colossyan says the illustrated guidance sliders are not exact inference parameters.", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NEO 1.5 is Colossyan's May 2026 talking-head upgrade. Identity joins audio and motion inside one attention pass (VOCAL 1.5). The research FAQ says it is a preview in internal tests and customer pilots, not generally available in the editor or API. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "NEO 1.5 is Colossyan's May 2026 talking-head upgrade. Identity joins audio and motion inside one attention pass (VOCAL 1.5). The research FAQ says it is a preview in internal tests and customer pilots, not generally available in the editor or API. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/neo/neo-2": { "id": "colossyan/neo/neo-2", "name": "NEO 2", "maker": "colossyan", "family": "colossyan/neo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full-body audio-driven diffusion transformer. Blocks named on the January 2026 note: an audio encoder, a VAE video encoder and decoder, and a DiT that jointly attends to image, text, and audio. Long clips use temporal chunks, Neural Continuum Sync, and latent context from earlier chunks. Inference note: 3 diffusion steps, CFG and attention caching, and a claim that it runs on one 24GB GPU. September 2026 Latent Cascade runs the main DiT at half latent height and width, then Reference Lift and Flow Refinement. The transparency report names the model NEO version 2.", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NEO 2 is Colossyan's full-body avatar model. The training-data report lists first use on 18 December 2025. A diffusion transformer drives gestures and upper-body motion from audio and a reference image. The API flag is `useNeo2Generation`. A September 2026 note says a Latent Cascade path is about 40% faster and already behind the product. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "NEO 2 is Colossyan's full-body avatar model. The training-data report lists first use on 18 December 2025. A diffusion transformer drives gestures and upper-body motion from audio and a reference image. The API flag is `useNeo2Generation`. A September 2026 note says a Latent Cascade path is about 40% faster and already behind the product. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/neo/pictar": { "id": "colossyan/neo/pictar", "name": "Pictar", "maker": "colossyan", "family": "colossyan/neo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Rendering model inside NEO. Three stages: 3D lift of one reference photo into a feature volume, motion deformation driven by VOCAL latents, then a neural translation to frames. NEO 1.5 says the renderer is unchanged. The avatar API also has a quality value pictar and a field pictarSourceUrl. Those API names are not documented as this research model.", "modalities": { "input": [ "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pictar is the rendering model inside NEO. The October 2025 note says it lifts one photo into a 3D feature volume, deforms that volume with VOCAL latents, and translates the result to frames. The avatar API also has a quality value `pictar`, which is not documented as this same model. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Pictar is the rendering model inside NEO. The October 2025 note says it lifts one photo into a 3D feature volume, deforms that volume with VOCAL latents, and translates the result to frames. The avatar API also has a quality value `pictar`, which is not documented as this same model. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/neo/vocal": { "id": "colossyan/neo/vocal", "name": "VOCAL", "maker": "colossyan", "family": "colossyan/neo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Voice-to-Latent audio-to-motion model inside NEO. Multi-modal diffusion transformer. A pretrained audio encoder feeds multi-scale features into joint attention between audio and motion. Adaptive shift-scale-gate modulation carries timestep and identity. Classifier-free guidance at inference. VOCAL 1.5, used by NEO 1.5, adds identity as a third stream (Unified Joint Attention) plus attention residuals and differential attention.", "modalities": { "input": [ "audio", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VOCAL (Voice-to-Latent) is the audio-to-motion model inside NEO, named in the October 2025 research note. It is a multi-modal diffusion transformer, not a separate API product. VOCAL 1.5 is the preview stage inside NEO 1.5. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "VOCAL (Voice-to-Latent) is the audio-to-motion model inside NEO, named in the October 2025 research note. It is a multi-modal diffusion transformer, not a separate API product. VOCAL 1.5 is the preview stage inside NEO 1.5. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/omnia/omnia": { "id": "colossyan/omnia/omnia", "name": "Omnia", "maker": "colossyan", "family": "colossyan/omnia", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Omnia is a value on the deprecated `inferenceModel` field in the list-avatars schema, beside `legacy` and `neo`. The replacement field's enum is `legacy`, `neo`, and `neo2`, and does not include Omnia. No product page describes it. Collected 2026-10-15.", "what_it_is_for": "Omnia is a value on the deprecated `inferenceModel` field in the list-avatars schema, beside `legacy` and `neo`. The replacement field's enum is `legacy`, `neo`, and `neo2`, and does not include Omnia. No product page describes it. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/alicia": { "id": "colossyan/stock/alicia", "name": "Alicia", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alicia is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Retail. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Alicia is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Retail. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/amelia": { "id": "colossyan/stock/amelia", "name": "Amelia", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amelia is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Amelia is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/angelina": { "id": "colossyan/stock/angelina", "name": "Angelina", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Angelina is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Angelina is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/anthony": { "id": "colossyan/stock/anthony", "name": "Anthony", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Anthony is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Casual, Formal. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Anthony is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Casual, Formal. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/arnold": { "id": "colossyan/stock/arnold", "name": "Arnold", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arnold is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: 1, 2. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Arnold is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: 1, 2. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/ashley": { "id": "colossyan/stock/ashley", "name": "Ashley", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ashley is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: 1, 2. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ashley is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: 1, 2. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/barbara": { "id": "colossyan/stock/barbara", "name": "Barbara", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Barbara is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Barbara is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/caleb": { "id": "colossyan/stock/caleb", "name": "Caleb", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Caleb is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Caleb is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/camila": { "id": "colossyan/stock/camila", "name": "Camila", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Camila is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Camila is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/daisy": { "id": "colossyan/stock/daisy", "name": "Daisy", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Daisy is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Daisy is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/donald": { "id": "colossyan/stock/donald", "name": "Donald", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Donald is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Donald is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/doug": { "id": "colossyan/stock/doug", "name": "Doug", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Doug is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Doug is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/dwane": { "id": "colossyan/stock/dwane", "name": "Dwane", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dwane is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Dwane is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/dylan": { "id": "colossyan/stock/dylan", "name": "Dylan", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dylan is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: 1, 3, 4, 5. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Dylan is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: 1, 3, 4, 5. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/emilio": { "id": "colossyan/stock/emilio", "name": "Emilio", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emilio is a cartoon presenter named in a Colossyan draft link on the cartoon-avatars page. The URL includes actor id 81c30037-d449-43e7-aa49-294356aaf0ae. That is one published example, not a roster. Collected 2026-10-15.", "what_it_is_for": "Emilio is a cartoon presenter named in a Colossyan draft link on the cartoon-avatars page. The URL includes actor id 81c30037-d449-43e7-aa49-294356aaf0ae. That is one published example, not a roster. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/gloria": { "id": "colossyan/stock/gloria", "name": "Gloria", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gloria is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Cleaning, Office 2, University professor. The same article names this presenter among the first scenario avatars on NEO (Lisa, Ryan, Gloria, Michael, Nathan). It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Gloria is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Cleaning, Office 2, University professor. The same article names this presenter among the first scenario avatars on NEO (Lisa, Ryan, Gloria, Michael, Nathan). It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/hamilton": { "id": "colossyan/stock/hamilton", "name": "Hamilton", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hamilton is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Hamilton is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/harry": { "id": "colossyan/stock/harry", "name": "Harry", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Harry is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Formal. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Harry is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Formal. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/henry": { "id": "colossyan/stock/henry", "name": "Henry", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Henry is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Henry is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/ivy": { "id": "colossyan/stock/ivy", "name": "Ivy", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ivy is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ivy is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/jade": { "id": "colossyan/stock/jade", "name": "Jade", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jade is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Nurse. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jade is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Nurse. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/jason": { "id": "colossyan/stock/jason", "name": "Jason", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jason is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jason is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/jerry": { "id": "colossyan/stock/jerry", "name": "Jerry", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jerry is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Construction worker. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jerry is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Construction worker. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/jing": { "id": "colossyan/stock/jing", "name": "Jing", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jing is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jing is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/ken": { "id": "colossyan/stock/ken", "name": "Ken", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ken is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: 4. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ken is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: 4. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/kevin": { "id": "colossyan/stock/kevin", "name": "Kevin", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kevin is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Kevin is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/lara": { "id": "colossyan/stock/lara", "name": "Lara", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lara is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Lara is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/laura": { "id": "colossyan/stock/laura", "name": "Laura", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Laura is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Laura is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/lea": { "id": "colossyan/stock/lea", "name": "Lea", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lea is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Lea is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/linda": { "id": "colossyan/stock/linda", "name": "Linda", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Linda is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Linda is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/lisa": { "id": "colossyan/stock/lisa", "name": "Lisa", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lisa is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Office. The same article names this presenter among the first scenario avatars on NEO (Lisa, Ryan, Gloria, Michael, Nathan). It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Lisa is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Office. The same article names this presenter among the first scenario avatars on NEO (Lisa, Ryan, Gloria, Michael, Nathan). It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/louis": { "id": "colossyan/stock/louis", "name": "Louis", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Louis is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Louis is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/maya": { "id": "colossyan/stock/maya", "name": "Maya", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Maya is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Maya is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/megan": { "id": "colossyan/stock/megan", "name": "Megan", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Megan is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Megan is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/michael": { "id": "colossyan/stock/michael", "name": "Michael", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Michael is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Office 1, Office 2. The same article names this presenter among the first scenario avatars on NEO (Lisa, Ryan, Gloria, Michael, Nathan). It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Michael is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Office 1, Office 2. The same article names this presenter among the first scenario avatars on NEO (Lisa, Ryan, Gloria, Michael, Nathan). It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/miley": { "id": "colossyan/stock/miley", "name": "Miley", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Miley is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Miley is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/nada": { "id": "colossyan/stock/nada", "name": "Nada", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nada is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Nada is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/natalie": { "id": "colossyan/stock/natalie", "name": "Natalie", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Natalie is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Natalie is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/nate": { "id": "colossyan/stock/nate", "name": "Nate", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nate is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Nate is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/nathan": { "id": "colossyan/stock/nathan", "name": "Nathan", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nathan is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Construction worker, Heavy machinery worker, Storage facility worker. The same article names this presenter among the first scenario avatars on NEO (Lisa, Ryan, Gloria, Michael, Nathan). It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Nathan is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Construction worker, Heavy machinery worker, Storage facility worker. The same article names this presenter among the first scenario avatars on NEO (Lisa, Ryan, Gloria, Michael, Nathan). It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/nelly": { "id": "colossyan/stock/nelly", "name": "Nelly", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nelly is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Nelly is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/niall": { "id": "colossyan/stock/niall", "name": "Niall", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Niall is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Niall is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/nina": { "id": "colossyan/stock/nina", "name": "Nina", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nina is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Nina is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/oliver": { "id": "colossyan/stock/oliver", "name": "Oliver", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Oliver is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Oliver is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/omar": { "id": "colossyan/stock/omar", "name": "Omar", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Omar is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Omar is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/owen": { "id": "colossyan/stock/owen", "name": "Owen", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Owen is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Owen is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/patricia": { "id": "colossyan/stock/patricia", "name": "Patricia", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Patricia is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Patricia is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/peter": { "id": "colossyan/stock/peter", "name": "Peter", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Peter is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Peter is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/riley": { "id": "colossyan/stock/riley", "name": "Riley", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Riley is a name on the public AI-avatars gallery. It is not on the NEO help-article filter list. No API id was published next to the name. Collected 2026-10-15.", "what_it_is_for": "Riley is a name on the public AI-avatars gallery. It is not on the NEO help-article filter list. No API id was published next to the name. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/rima": { "id": "colossyan/stock/rima", "name": "Rima", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rima is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Rima is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/ryan": { "id": "colossyan/stock/ryan", "name": "Ryan", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ryan is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Office 4, 3. The same article names this presenter among the first scenario avatars on NEO (Lisa, Ryan, Gloria, Michael, Nathan). It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ryan is a stock presenter named on Colossyan's NEO support article. Looks printed on that list: Office 4, 3. The same article names this presenter among the first scenario avatars on NEO (Lisa, Ryan, Gloria, Michael, Nathan). It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/ton": { "id": "colossyan/stock/ton", "name": "Ton", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ton is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ton is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/yukiko": { "id": "colossyan/stock/yukiko", "name": "Yukiko", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yukiko is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Yukiko is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/yusuf": { "id": "colossyan/stock/yusuf", "name": "Yusuf", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yusuf is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Yusuf is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "colossyan/stock/zoe": { "id": "colossyan/stock/zoe", "name": "Zoe", "maker": "colossyan", "family": "colossyan/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zoe is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Zoe is a stock presenter named on Colossyan's NEO support article. The list prints the name with no numbered look. It is a consented identity rendered by Colossyan's avatar engines, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "columbia/openbind/openbind-0": { "id": "columbia/openbind/openbind-0", "name": "OpenBind-0", "maker": "columbia", "family": "columbia/openbind", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-08-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Ligand-focused co-folding model on a near-final OpenFold3 architecture. Default checkpoint for openfold3 0.5.0 and newer. Registry name openbind-2025-06-30-174k. Filename of3-ob-2025-06-30-174k.pt. The v0.5.0 release note instead calls the parameter id openbind-2025-06-03-174k while naming the same filename. The install page says the current parameter file is about 2 GB. Training used public PDB structures through June 2025 and no OpenBind experimental structures. Inference can apply chemical steering during diffusion sampling.", "modalities": { "input": [ "text" ], "output": [ "structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenBind-0 is the ligand-focused structure model that the AlQuraishi Lab ships as the default OpenFold3 checkpoint. The 21 August 2026 OpenBind post calls it the first model in a new series, trained with the OpenBind consortium on public PDB structures. Columbia is a named partner, and the weights live in the Columbia lab repository. It is the current default. Collected 2026-10-15.", "what_it_is_for": "The blog says it predicts structures of proteins bound to small-molecule ligands, while keeping other molecular modalities. Checkpoints were chosen for protein-ligand performance. The training set stops at public PDB entries through June 2025 and does not include OpenBind's own experimental structures, so later OpenBind models can measure the effect of adding that data. Chemical steering during diffusion sampling is included to improve ligand geometry.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "columbia/openfold/openfold": { "id": "columbia/openfold/openfold", "name": "OpenFold", "maker": "columbia", "family": "columbia/openfold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Weights and OpenProteinSet: CC BY 4.0, stated in the Nature Methods data-availability note. Code: Apache-2.0.", "release_date": "2022-11-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Trainable PyTorch reproduction of AlphaFold 2. The public weights were trained from scratch on OpenFold, with AlphaFold used as the pre-distillation model, on 44 A100s, using the AlphaFold supplement Table 4 schedule. Inference still uses AlphaFold config presets. pTM checkpoints must use a pTM preset. Template-free checkpoints are only compatible with template-free presets (model_3 and above). The docs example uses finetuning_ptm_2.pt with config preset model_1_ptm.", "modalities": { "input": [ "text" ], "output": [ "structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenFold is the AlQuraishi Laboratory's trainable PyTorch reproduction of AlphaFold 2, led at Columbia University's Department of Systems Biology with the OpenFold consortium and coauthors at other institutions, including Harvard. The lab released its own weights, trained from scratch, plus the OpenProteinSet alignments. The weights are still documented and downloadable. Collected 2026-10-15.", "what_it_is_for": "Monomer structure prediction, with multimer inference support in the same codebase. Nature Methods says the from-scratch OpenFold model matches AlphaFold 2 accuracy. The repository can also run DeepMind's AlphaFold parameters. Those files are not this model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "columbia/openfold/openfold-soloseq": { "id": "columbia/openfold/openfold-soloseq", "name": "OpenFold-SoloSeq", "maker": "columbia", "family": "columbia/openfold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MSA-free OpenFold inference that uses ESM-1b embeddings instead of a multiple sequence alignment. The documented checkpoint is seq_model_esm1b_ptm.pt with config preset seq_model_esm1b_ptm. Templates are optional. ESM-1b is Meta's model and is not part of this weight file.", "modalities": { "input": [ "text" ], "output": [ "structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenFold-SoloSeq is the MSA-free structure model in the OpenFold repository, released by the OpenFold consortium under Mohammed AlQuraishi at Columbia. It predicts a structure from one sequence by feeding ESM-1b embeddings into OpenFold. The weights are still documented. Collected 2026-10-15.", "what_it_is_for": "The docs call it single-sequence, MSA-free structure prediction. The 19 February 2024 announcement says dropping the MSA step makes the calculation on average more than 10 times faster, with nearly identical accuracy to the MSA model. That page did not include the accuracy table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "columbia/openfold3/openfold3-preview": { "id": "columbia/openfold3/openfold3-preview", "name": "OpenFold3-preview", "maker": "columbia", "family": "columbia/openfold3", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Research preview of an AlphaFold 3-style all-atom structure model. Checkpoint id openfold3-p1, file of3_ft3_v1.pt. The OpenFold3 registry marks it legacy and compatible only with openfold3 before 0.4. The ConforNet repository says this file does not load on openfold3 0.4.0 or newer.", "modalities": { "input": [ "text" ], "output": [ "structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenFold3-preview is the first public AlphaFold 3-style structure model from the AlQuraishi Lab at Columbia and the OpenFold consortium, with Lawrence Livermore National Laboratory and Seoul National University named as leads on the announcement. It is a research preview. The checkpoint does not load in current OpenFold3 releases, so this file is deprecated. Collected 2026-10-15.", "what_it_is_for": "The Hub card says the preview predicts structures of standard and non-canonical protein, RNA, and DNA chains, and of small molecules, and that it is the only open model they compared that matches AlphaFold 3 on monomeric RNA. The final OpenFold3 model was still in development when that card was read. The 28 October 2025 announcement says training used more than 300,000 experimental structures and over 13 million synthetic structures.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "columbia/openfold3/openfold3-preview2": { "id": "columbia/openfold3/openfold3-preview2", "name": "OpenFold3-preview2", "maker": "columbia", "family": "columbia/openfold3", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Second preview of the OpenFold3 all-atom structure model. Two step checkpoints ship together: of3-p2-145k.pt (openfold3-p2-145k) and of3-p2-155k.pt (openfold3-p2-155k). Both are compatible with openfold3 from 0.4 up to, but not including, 0.4.4dev0. The v0.5.0 notes say these weights do not run correctly on openfold3 0.5 or newer. The OpenBind blog describes this model's PDB training cutoff as 30 September 2021.", "modalities": { "input": [ "text" ], "output": [ "structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenFold3-preview2 is the second OpenFold3 research preview from the AlQuraishi Lab at Columbia and the OpenFold consortium. The 0.4.0 release called it a new model, stronger on small molecules and biologics. OpenFold3 0.5 deprecated the weights. Collected 2026-10-15.", "what_it_is_for": "All-atom biomolecular structure prediction. The release notes say it improves on the first preview for small molecules and biologics, and that antibody predictions scale with extra inference samples. The OpenBind blog later uses it as the 30 September 2021 PDB-cutoff baseline against OpenBind-0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "columbia/ssamba/ssamba-base": { "id": "columbia/ssamba/ssamba-base", "name": "SSAMBA-Base", "maker": "columbia", "family": "columbia/ssamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bsd-3-clause-clear, as stated on the author Hugging Face README. The GitHub LICENSE file was not opened.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "99M", "architecture": "Bidirectional Mamba encoder for masked spectrogram patches. Paper Table 1: depth 24, embedding dimension 768, 99M parameters. Input is 16 kHz audio as 128-bin log-mel spectrograms (25 ms Hanning window, 10 ms hop), split into 16x16 patches with a linear projection and learnable positional encoding. Pretraining mixes an InfoNCE discriminative loss and an MSE generative loss with lambda 10.", "modalities": { "input": [ "audio" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SSAMBA-Base is the largest Self-Supervised Audio Mamba encoder from Siavash Shams, Sukru Samet Dindar, Xilin Jiang, and Nima Mesgarani in Columbia University's Department of Electrical Engineering. It is an open-weight pretrained audio model, still downloadable, and was collected on 2026-10-15.", "what_it_is_for": "The paper presents SSAMBA as a self-supervised audio representation model. A bidirectional Mamba encoder is pretrained with masked spectrogram patch modeling, then fine-tuned for audio event classification, keyword spotting, speaker identification, emotion recognition, and dynamic scene labeling. The authors say the base model matches or beats SSAST-base on the tasks in Table 2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "columbia/ssamba/ssamba-small": { "id": "columbia/ssamba/ssamba-small", "name": "SSAMBA-Small", "maker": "columbia", "family": "columbia/ssamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bsd-3-clause-clear, as stated on the author Hugging Face README. The GitHub LICENSE file was not opened.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "26M", "architecture": "Same bidirectional Mamba audio encoder as SSAMBA-Base. Paper Table 1: depth 24, embedding dimension 384, 26M parameters. Pretrained on one NVIDIA L40 GPU. Front end and loss match the base model.", "modalities": { "input": [ "audio" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SSAMBA-Small is the middle Self-Supervised Audio Mamba encoder from Columbia Electrical Engineering (Shams, Dindar, Jiang, and Mesgarani). It is an open-weight local checkpoint, still on the author Hub repo, and was collected on 2026-10-15.", "what_it_is_for": "Same job as the other SSAMBA sizes: self-supervised audio representations, then fine-tuning for event classification, keyword spotting, speaker identification, emotion recognition, and scene labeling. Paper Table 1 lists it at 26M parameters, depth 24, and embedding size 384.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "columbia/ssamba/ssamba-tiny": { "id": "columbia/ssamba/ssamba-tiny", "name": "SSAMBA-Tiny", "maker": "columbia", "family": "columbia/ssamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bsd-3-clause-clear, as stated on the author Hugging Face README. The GitHub LICENSE file was not opened.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7M", "architecture": "Same bidirectional Mamba audio encoder as SSAMBA-Base. Paper Table 1: depth 24, embedding dimension 192, 7M parameters. Pretrained on one NVIDIA L40 GPU. The efficiency comparison in the abstract is for this size at an input of 22k patches.", "modalities": { "input": [ "audio" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SSAMBA-Tiny is the smallest Self-Supervised Audio Mamba encoder from Columbia Electrical Engineering (Shams, Dindar, Jiang, and Mesgarani). It is an open-weight local checkpoint, still on the author Hub repo, and was collected on 2026-10-15.", "what_it_is_for": "Same job as the other SSAMBA sizes: self-supervised audio representations for later fine-tuning. The abstract uses this size for the efficiency claim against SSAST. Paper Table 1 lists 7M parameters, depth 24, and embedding size 192.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "concordia/dycast/dycast": { "id": "concordia/dycast/dycast", "name": "DyCAST", "maker": "concordia", "family": "concordia/dycast", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-02-11", "context_length": null, "max_output_tokens": null, "parameters": "269 million", "architecture": "Variable-frame-rate speech tokenizer. Frozen WavLM-large layer 6, focal-modulation compressor, scalar spherical quantizer (32 dimensions, 4 levels, implicit 4^32 codebook factorized into 32 streams), duration model, Vocos decoder. Trained on LibriTTS at 16 kHz. One Hub checkpoint. Character-aligner and boundary-predictor settings (DyCAST-CA, BP1, BP3, BP5) are inference modes, about 17.5 to 6.2 Hz and about 1.12 to 0.40 kbps. The Hub size label says 0.4B. The model tree lists facebook/mms-1b-all as the base because MMS supplies character alignment.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DyCAST is a variable-frame-rate speech tokenizer from Luca Della Libera, Cem Subakan, and Mirco Ravanelli, with Concordia University listed first and Mila also on the byline. The public checkpoint on Hugging Face was created on 11 February 2026 and was still online on 15 October 2026. It is a research weight release, not an API product.", "what_it_is_for": "The paper presents DyCAST as a speech codec that groups WavLM features into character-scale chunks so a later model sees fewer tokens than a fixed-rate codec. Training uses a frozen MMS aligner. Inference can run without text, and a duration model can stretch or compress tokens. Retrieval-augmented decoding is optional and uses a shipped FAISS index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "concordia/resepformer/resepformer-wsj02mix": { "id": "concordia/resepformer/resepformer-wsj02mix", "name": "RE-SepFormer (WSJ0-2Mix)", "maker": "concordia", "family": "concordia/resepformer", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-06-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Resource-Efficient Separation Transformer for two-speaker separation. SpeechBrain SepformerSeparation interface. Trained on WSJ0-2Mix. The card expects 8 kHz mono input. Non-causal recipe in the public checkpoint. The paper says it uses non-overlapping latent blocks and compact chunk summaries, with about 3 times fewer parameters than SepFormer. The absolute count was not copied.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RE-SepFormer is a two-speaker speech separation model from Luca Della Libera, Cem Subakan, Mirco Ravanelli, and coauthors. The paper's address line lists Concordia University first. The public weights live on the SpeechBrain Hub org as `speechbrain/resepformer-wsj02mix`, created 19 June 2022, and were still downloadable on 15 October 2026. Concordia does not host an API for it.", "what_it_is_for": "The card says the checkpoint separates mixtures from the WSJ0-2Mix setup. The paper presents RE-SepFormer as a smaller Transformer separator that uses non-overlapping latent blocks and compact chunk summaries so long mixtures use less memory than SepFormer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "concordia/wavslm/wavslm-2k": { "id": "concordia/wavslm/wavslm-2k", "name": "WavSLM-2k", "maker": "concordia", "family": "concordia/wavslm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-08-15", "context_length": 512, "max_output_tokens": null, "parameters": "305 million", "architecture": "Speech-only language model. FocalCodec-Stream 2k tokenizer (1x2048 codebook, 50 Hz, 80 ms chunks) plus causal WavLM-large layers 7-24 and a linear head. Next-chunk prediction of 4 tokens. Default sliding window 512 tokens. Trained on Libri-Light, about 60,000 hours, no text. The Hub model-size field says 0.2B. The model tree lists lucadellalib/focalcodec_50hz_2k_causal as the base. The paper also reports unreleased window and chunk ablations of the 4k variant only.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WavSLM-2k is the 2,048-codebook speech language model from Luca Della Libera, Cem Subakan, and Mirco Ravanelli. The paper lists Concordia University first, then Mila and Universit\u00e9 Laval. The checkpoint `lucadellalib/wavslm_2k` was created on 15 August 2026 and was still public on 15 October 2026. It is a local weight release, not an API.", "what_it_is_for": "The paper describes WavSLM as a speech-only next-chunk model. It keeps semantic and acoustic detail in one token stream and does not start from a text language model. The 2k checkpoint uses the FocalCodec-Stream tokenizer with a codebook of 2,048.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "concordia/wavslm/wavslm-4k": { "id": "concordia/wavslm/wavslm-4k", "name": "WavSLM-4k", "maker": "concordia", "family": "concordia/wavslm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-08-15", "context_length": 512, "max_output_tokens": null, "parameters": "307 million", "architecture": "Speech-only language model. FocalCodec-Stream 4k tokenizer (1x4096 codebook, 50 Hz, 80 ms chunks) plus causal WavLM-large layers 7-24 and a linear head. Next-chunk prediction of 4 tokens. Default sliding window 512 tokens. Trained on Libri-Light, about 60,000 hours, no text. The Hub model-size field says 0.2B. The model tree lists lucadellalib/focalcodec_50hz_4k_causal as the base.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WavSLM-4k is the 4,096-codebook speech language model from Luca Della Libera, Cem Subakan, and Mirco Ravanelli. Concordia University is the first affiliation. The checkpoint `lucadellalib/wavslm_4k` was created on 15 August 2026 and was still public on 15 October 2026. It is the vocabulary the paper reports as the best likelihood average of the three released checkpoints.", "what_it_is_for": "The paper describes a speech-only next-chunk model that does not start from a text language model. This checkpoint uses the FocalCodec-Stream tokenizer with a codebook of 4,096. The authors use it for the window and chunk-size study.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "concordia/wavslm/wavslm-65k": { "id": "concordia/wavslm/wavslm-65k", "name": "WavSLM-65k", "maker": "concordia", "family": "concordia/wavslm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-08-15", "context_length": 512, "max_output_tokens": null, "parameters": "370 million", "architecture": "Speech-only language model. FocalCodec-Stream 65k tokenizer (1x65536 codebook, 50 Hz, 80 ms chunks) plus causal WavLM-large layers 7-24 and a linear head. Next-chunk prediction of 4 tokens. Default sliding window 512 tokens. Trained on Libri-Light, about 60,000 hours, no text. The Hub model-size field says 0.3B. The model tree lists lucadellalib/focalcodec_50hz_65k_causal as the base.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WavSLM-65k is the 65,536-codebook speech language model from Luca Della Libera, Cem Subakan, and Mirco Ravanelli. Concordia University is the first affiliation. The checkpoint `lucadellalib/wavslm_65k` was created on 15 August 2026 and was still public on 15 October 2026. The paper reports it as the weakest of the three released vocabularies under the same training budget.", "what_it_is_for": "The paper describes a speech-only next-chunk model with no text pretraining. This checkpoint uses the FocalCodec-Stream tokenizer with a codebook of 65,536. The authors say the larger vocabulary is harder to learn on Libri-Light.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "concordia/zipcodec/zipcodec": { "id": "concordia/zipcodec/zipcodec", "name": "ZipCodec", "maker": "concordia", "family": "concordia/zipcodec", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-09-11", "context_length": null, "max_output_tokens": null, "parameters": "842 million", "architecture": "Streaming speech codec at 6.25 Hz and 0.80 kbps, 16 kHz, theoretical latency 160 ms. Causal log-mel frontend, temporal patch of 16 frames, ErfFormer compressor and decompressor (6 blocks, dimension 2048, grouped-query attention, no positional encodings), scalar spherical quantizer (64 dimensions, 4 levels), streaming Vocos decoder. Distills WavLM layer 6. KV cache of 256 frames (40.96 s) reset rather than slid. Trained on about 94,000 hours from Libri-Light, VoxPopuli, and GigaSpeech. The Hub size label says 0.8B. The model tree lists microsoft/wavlm-large as the base.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZipCodec is a streaming speech codec from Luca Della Libera, Cem Subakan, and Mirco Ravanelli. The preprint lists Concordia University first, then Mila and Universit\u00e9 Laval. Weights were posted as `lucadellalib/zipcodec` on 11 September 2026, the day after the 10 September 2026 preprint. On 15 October 2026 the checkpoint was still public. This is not the Xiaomi Interspeech 2026 model that uses the same name.", "what_it_is_for": "The paper says ZipCodec compresses speech to 6.25 Hz and 0.80 kbps so a speech language model sees a much shorter token sequence, while staying streamable at 160 ms theoretical latency. It is a codec and tokenizer, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-csft-llama13b": { "id": "contextual-ai/archangel/archangel-csft-llama13b", "name": "archangel_csft_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama13b). HF library tag llama. Conditional SFT. The shared card text says conditional-SFT tokenizers add <|good|> and <|bad|>.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_csft_llama13b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Conditional SFT. The shared card text says conditional-SFT tokenizers add <|good|> and <|bad|>. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-csft-llama30b": { "id": "contextual-ai/archangel/archangel-csft-llama30b", "name": "archangel_csft_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama30b). HF library tag llama. Conditional SFT. The shared card text says conditional-SFT tokenizers add <|good|> and <|bad|>.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_csft_llama30b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Conditional SFT. The shared card text says conditional-SFT tokenizers add <|good|> and <|bad|>. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-csft-llama7b": { "id": "contextual-ai/archangel/archangel-csft-llama7b", "name": "archangel_csft_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama7b). HF library tag llama. Conditional SFT. The shared card text says conditional-SFT tokenizers add <|good|> and <|bad|>.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_csft_llama7b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Conditional SFT. The shared card text says conditional-SFT tokenizers add <|good|> and <|bad|>. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-csft-pythia1-4b": { "id": "contextual-ai/archangel/archangel-csft-pythia1-4b", "name": "archangel_csft_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia1-4b). HF library tag gpt_neox. Conditional SFT. The shared card text says conditional-SFT tokenizers add <|good|> and <|bad|>.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_csft_pythia1-4b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Conditional SFT. The shared card text says conditional-SFT tokenizers add <|good|> and <|bad|>. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-csft-pythia12-0b": { "id": "contextual-ai/archangel/archangel-csft-pythia12-0b", "name": "archangel_csft_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia12-0b). HF library tag gpt_neox. Conditional SFT. The shared card text says conditional-SFT tokenizers add <|good|> and <|bad|>.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_csft_pythia12-0b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Conditional SFT. The shared card text says conditional-SFT tokenizers add <|good|> and <|bad|>. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-csft-pythia2-8b": { "id": "contextual-ai/archangel/archangel-csft-pythia2-8b", "name": "archangel_csft_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia2-8b). HF library tag gpt_neox. Conditional SFT. The shared card text says conditional-SFT tokenizers add <|good|> and <|bad|>.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_csft_pythia2-8b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Conditional SFT. The shared card text says conditional-SFT tokenizers add <|good|> and <|bad|>. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-csft-pythia6-9b": { "id": "contextual-ai/archangel/archangel-csft-pythia6-9b", "name": "archangel_csft_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia6-9b). HF library tag gpt_neox. Conditional SFT. The shared card text says conditional-SFT tokenizers add <|good|> and <|bad|>.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_csft_pythia6-9b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Conditional SFT. The shared card text says conditional-SFT tokenizers add <|good|> and <|bad|>. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-dpo-llama13b": { "id": "contextual-ai/archangel/archangel-dpo-llama13b", "name": "archangel_dpo_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama13b). HF library tag llama. Aligned with DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_dpo_llama13b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with DPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-dpo-llama30b": { "id": "contextual-ai/archangel/archangel-dpo-llama30b", "name": "archangel_dpo_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama30b). HF library tag llama. Aligned with DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_dpo_llama30b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with DPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-dpo-llama7b": { "id": "contextual-ai/archangel/archangel-dpo-llama7b", "name": "archangel_dpo_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama7b). HF library tag llama. Aligned with DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_dpo_llama7b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with DPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-dpo-pythia1-4b": { "id": "contextual-ai/archangel/archangel-dpo-pythia1-4b", "name": "archangel_dpo_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia1-4b). HF library tag gpt_neox. Aligned with DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_dpo_pythia1-4b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with DPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-dpo-pythia12-0b": { "id": "contextual-ai/archangel/archangel-dpo-pythia12-0b", "name": "archangel_dpo_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia12-0b). HF library tag gpt_neox. Aligned with DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_dpo_pythia12-0b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with DPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-dpo-pythia2-8b": { "id": "contextual-ai/archangel/archangel-dpo-pythia2-8b", "name": "archangel_dpo_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia2-8b). HF library tag gpt_neox. Aligned with DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_dpo_pythia2-8b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with DPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-dpo-pythia6-9b": { "id": "contextual-ai/archangel/archangel-dpo-pythia6-9b", "name": "archangel_dpo_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia6-9b). HF library tag gpt_neox. Aligned with DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_dpo_pythia6-9b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with DPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-kto-llama13b": { "id": "contextual-ai/archangel/archangel-kto-llama13b", "name": "archangel_kto_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama13b). HF library tag llama. Aligned with KTO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_kto_llama13b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with KTO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-kto-llama30b": { "id": "contextual-ai/archangel/archangel-kto-llama30b", "name": "archangel_kto_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama30b). HF library tag llama. Aligned with KTO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_kto_llama30b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with KTO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-kto-llama7b": { "id": "contextual-ai/archangel/archangel-kto-llama7b", "name": "archangel_kto_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama7b). HF library tag llama. Aligned with KTO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_kto_llama7b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with KTO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-kto-pythia1-4b": { "id": "contextual-ai/archangel/archangel-kto-pythia1-4b", "name": "archangel_kto_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia1-4b). HF library tag gpt_neox. Aligned with KTO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_kto_pythia1-4b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with KTO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-kto-pythia12-0b": { "id": "contextual-ai/archangel/archangel-kto-pythia12-0b", "name": "archangel_kto_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia12-0b). HF library tag gpt_neox. Aligned with KTO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_kto_pythia12-0b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with KTO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-kto-pythia2-8b": { "id": "contextual-ai/archangel/archangel-kto-pythia2-8b", "name": "archangel_kto_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia2-8b). HF library tag gpt_neox. Aligned with KTO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_kto_pythia2-8b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with KTO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-kto-pythia6-9b": { "id": "contextual-ai/archangel/archangel-kto-pythia6-9b", "name": "archangel_kto_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia6-9b). HF library tag gpt_neox. Aligned with KTO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_kto_pythia6-9b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with KTO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-ppo-llama13b": { "id": "contextual-ai/archangel/archangel-ppo-llama13b", "name": "archangel_ppo_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama13b). HF library tag llama. Aligned with PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_ppo_llama13b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with PPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-ppo-llama30b": { "id": "contextual-ai/archangel/archangel-ppo-llama30b", "name": "archangel_ppo_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama30b). HF library tag llama. Aligned with PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_ppo_llama30b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with PPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-ppo-llama7b": { "id": "contextual-ai/archangel/archangel-ppo-llama7b", "name": "archangel_ppo_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama7b). HF library tag llama. Aligned with PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_ppo_llama7b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with PPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-ppo-pythia1-4b": { "id": "contextual-ai/archangel/archangel-ppo-pythia1-4b", "name": "archangel_ppo_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia1-4b). HF library tag gpt_neox. Aligned with PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_ppo_pythia1-4b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with PPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-ppo-pythia12-0b": { "id": "contextual-ai/archangel/archangel-ppo-pythia12-0b", "name": "archangel_ppo_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia12-0b). HF library tag gpt_neox. Aligned with PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_ppo_pythia12-0b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with PPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-ppo-pythia2-8b": { "id": "contextual-ai/archangel/archangel-ppo-pythia2-8b", "name": "archangel_ppo_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia2-8b). HF library tag gpt_neox. Aligned with PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_ppo_pythia2-8b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with PPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-ppo-pythia6-9b": { "id": "contextual-ai/archangel/archangel-ppo-pythia6-9b", "name": "archangel_ppo_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia6-9b). HF library tag gpt_neox. Aligned with PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_ppo_pythia6-9b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with PPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-csft-llama13b": { "id": "contextual-ai/archangel/archangel-sft-csft-llama13b", "name": "archangel_sft-csft_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama13b). HF library tag llama. SFT followed by conditional SFT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-csft_llama13b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by conditional SFT. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-csft-llama30b": { "id": "contextual-ai/archangel/archangel-sft-csft-llama30b", "name": "archangel_sft-csft_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama30b). HF library tag llama. SFT followed by conditional SFT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-csft_llama30b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by conditional SFT. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-csft-llama7b": { "id": "contextual-ai/archangel/archangel-sft-csft-llama7b", "name": "archangel_sft-csft_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama7b). HF library tag llama. SFT followed by conditional SFT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-csft_llama7b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by conditional SFT. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-csft-pythia1-4b": { "id": "contextual-ai/archangel/archangel-sft-csft-pythia1-4b", "name": "archangel_sft-csft_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia1-4b). HF library tag gpt_neox. SFT followed by conditional SFT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-csft_pythia1-4b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by conditional SFT. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-csft-pythia12-0b": { "id": "contextual-ai/archangel/archangel-sft-csft-pythia12-0b", "name": "archangel_sft-csft_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia12-0b). HF library tag gpt_neox. SFT followed by conditional SFT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-csft_pythia12-0b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by conditional SFT. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-csft-pythia2-8b": { "id": "contextual-ai/archangel/archangel-sft-csft-pythia2-8b", "name": "archangel_sft-csft_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia2-8b). HF library tag gpt_neox. SFT followed by conditional SFT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-csft_pythia2-8b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by conditional SFT. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-csft-pythia6-9b": { "id": "contextual-ai/archangel/archangel-sft-csft-pythia6-9b", "name": "archangel_sft-csft_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia6-9b). HF library tag gpt_neox. SFT followed by conditional SFT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-csft_pythia6-9b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by conditional SFT. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-dpo-llama13b": { "id": "contextual-ai/archangel/archangel-sft-dpo-llama13b", "name": "archangel_sft-dpo_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama13b). HF library tag llama. SFT followed by DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-dpo_llama13b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by DPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-dpo-llama30b": { "id": "contextual-ai/archangel/archangel-sft-dpo-llama30b", "name": "archangel_sft-dpo_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama30b). HF library tag llama. SFT followed by DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-dpo_llama30b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by DPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-dpo-llama7b": { "id": "contextual-ai/archangel/archangel-sft-dpo-llama7b", "name": "archangel_sft-dpo_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama7b). HF library tag llama. SFT followed by DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-dpo_llama7b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by DPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-dpo-pythia1-4b": { "id": "contextual-ai/archangel/archangel-sft-dpo-pythia1-4b", "name": "archangel_sft-dpo_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia1-4b). HF library tag gpt_neox. SFT followed by DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-dpo_pythia1-4b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by DPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-dpo-pythia12-0b": { "id": "contextual-ai/archangel/archangel-sft-dpo-pythia12-0b", "name": "archangel_sft-dpo_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia12-0b). HF library tag gpt_neox. SFT followed by DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-dpo_pythia12-0b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by DPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-dpo-pythia2-8b": { "id": "contextual-ai/archangel/archangel-sft-dpo-pythia2-8b", "name": "archangel_sft-dpo_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia2-8b). HF library tag gpt_neox. SFT followed by DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-dpo_pythia2-8b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by DPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-dpo-pythia6-9b": { "id": "contextual-ai/archangel/archangel-sft-dpo-pythia6-9b", "name": "archangel_sft-dpo_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia6-9b). HF library tag gpt_neox. SFT followed by DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-dpo_pythia6-9b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by DPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-kto-llama13b": { "id": "contextual-ai/archangel/archangel-sft-kto-llama13b", "name": "archangel_sft-kto_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama13b). HF library tag llama. SFT followed by KTO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-kto_llama13b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by KTO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-kto-llama30b": { "id": "contextual-ai/archangel/archangel-sft-kto-llama30b", "name": "archangel_sft-kto_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama30b). HF library tag llama. SFT followed by KTO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-kto_llama30b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by KTO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-kto-llama7b": { "id": "contextual-ai/archangel/archangel-sft-kto-llama7b", "name": "archangel_sft-kto_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama7b). HF library tag llama. SFT followed by KTO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-kto_llama7b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by KTO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-kto-pythia1-4b": { "id": "contextual-ai/archangel/archangel-sft-kto-pythia1-4b", "name": "archangel_sft-kto_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia1-4b). HF library tag gpt_neox. SFT followed by KTO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-kto_pythia1-4b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by KTO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-kto-pythia12-0b": { "id": "contextual-ai/archangel/archangel-sft-kto-pythia12-0b", "name": "archangel_sft-kto_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia12-0b). HF library tag gpt_neox. SFT followed by KTO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-kto_pythia12-0b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by KTO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-kto-pythia2-8b": { "id": "contextual-ai/archangel/archangel-sft-kto-pythia2-8b", "name": "archangel_sft-kto_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia2-8b). HF library tag gpt_neox. SFT followed by KTO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-kto_pythia2-8b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by KTO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-kto-pythia6-9b": { "id": "contextual-ai/archangel/archangel-sft-kto-pythia6-9b", "name": "archangel_sft-kto_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia6-9b). HF library tag gpt_neox. SFT followed by KTO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-kto_pythia6-9b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by KTO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-llama13b": { "id": "contextual-ai/archangel/archangel-sft-llama13b", "name": "archangel_sft_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama13b). HF library tag llama. Supervised fine-tuning on the HALOs mixture.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft_llama13b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Supervised fine-tuning on the HALOs mixture. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-llama30b": { "id": "contextual-ai/archangel/archangel-sft-llama30b", "name": "archangel_sft_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama30b). HF library tag llama. Supervised fine-tuning on the HALOs mixture.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft_llama30b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Supervised fine-tuning on the HALOs mixture. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-llama7b": { "id": "contextual-ai/archangel/archangel-sft-llama7b", "name": "archangel_sft_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama7b). HF library tag llama. Supervised fine-tuning on the HALOs mixture.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft_llama7b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Supervised fine-tuning on the HALOs mixture. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-ppo-llama13b": { "id": "contextual-ai/archangel/archangel-sft-ppo-llama13b", "name": "archangel_sft-ppo_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama13b). HF library tag llama. SFT followed by PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-ppo_llama13b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by PPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-ppo-llama30b": { "id": "contextual-ai/archangel/archangel-sft-ppo-llama30b", "name": "archangel_sft-ppo_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama30b). HF library tag llama. SFT followed by PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-ppo_llama30b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by PPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-ppo-llama7b": { "id": "contextual-ai/archangel/archangel-sft-ppo-llama7b", "name": "archangel_sft-ppo_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama7b). HF library tag llama. SFT followed by PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-ppo_llama7b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by PPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-ppo-pythia1-4b": { "id": "contextual-ai/archangel/archangel-sft-ppo-pythia1-4b", "name": "archangel_sft-ppo_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia1-4b). HF library tag gpt_neox. SFT followed by PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-ppo_pythia1-4b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by PPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-ppo-pythia12-0b": { "id": "contextual-ai/archangel/archangel-sft-ppo-pythia12-0b", "name": "archangel_sft-ppo_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia12-0b). HF library tag gpt_neox. SFT followed by PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-ppo_pythia12-0b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by PPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-ppo-pythia2-8b": { "id": "contextual-ai/archangel/archangel-sft-ppo-pythia2-8b", "name": "archangel_sft-ppo_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia2-8b). HF library tag gpt_neox. SFT followed by PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-ppo_pythia2-8b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by PPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-ppo-pythia6-9b": { "id": "contextual-ai/archangel/archangel-sft-ppo-pythia6-9b", "name": "archangel_sft-ppo_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia6-9b). HF library tag gpt_neox. SFT followed by PPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-ppo_pythia6-9b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by PPO. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-pythia1-4b": { "id": "contextual-ai/archangel/archangel-sft-pythia1-4b", "name": "archangel_sft_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia1-4b). HF library tag gpt_neox. Supervised fine-tuning on the HALOs mixture.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft_pythia1-4b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Supervised fine-tuning on the HALOs mixture. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-pythia12-0b": { "id": "contextual-ai/archangel/archangel-sft-pythia12-0b", "name": "archangel_sft_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia12-0b). HF library tag gpt_neox. Supervised fine-tuning on the HALOs mixture.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft_pythia12-0b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Supervised fine-tuning on the HALOs mixture. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-pythia2-8b": { "id": "contextual-ai/archangel/archangel-sft-pythia2-8b", "name": "archangel_sft_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia2-8b). HF library tag gpt_neox. Supervised fine-tuning on the HALOs mixture.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft_pythia2-8b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Supervised fine-tuning on the HALOs mixture. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-pythia6-9b": { "id": "contextual-ai/archangel/archangel-sft-pythia6-9b", "name": "archangel_sft_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia6-9b). HF library tag gpt_neox. Supervised fine-tuning on the HALOs mixture.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft_pythia6-9b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Supervised fine-tuning on the HALOs mixture. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-slic-llama13b": { "id": "contextual-ai/archangel/archangel-sft-slic-llama13b", "name": "archangel_sft-slic_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama13b). HF library tag llama. SFT followed by SLiC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-slic_llama13b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by SLiC. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-slic-llama7b": { "id": "contextual-ai/archangel/archangel-sft-slic-llama7b", "name": "archangel_sft-slic_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama7b). HF library tag llama. SFT followed by SLiC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-slic_llama7b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by SLiC. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-slic-pythia1-4b": { "id": "contextual-ai/archangel/archangel-sft-slic-pythia1-4b", "name": "archangel_sft-slic_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia1-4b). HF library tag gpt_neox. SFT followed by SLiC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-slic_pythia1-4b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by SLiC. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-slic-pythia12-0b": { "id": "contextual-ai/archangel/archangel-sft-slic-pythia12-0b", "name": "archangel_sft-slic_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia12-0b). HF library tag gpt_neox. SFT followed by SLiC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-slic_pythia12-0b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by SLiC. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-slic-pythia2-8b": { "id": "contextual-ai/archangel/archangel-sft-slic-pythia2-8b", "name": "archangel_sft-slic_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia2-8b). HF library tag gpt_neox. SFT followed by SLiC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-slic_pythia2-8b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by SLiC. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-sft-slic-pythia6-9b": { "id": "contextual-ai/archangel/archangel-sft-slic-pythia6-9b", "name": "archangel_sft-slic_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia6-9b). HF library tag gpt_neox. SFT followed by SLiC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_sft-slic_pythia6-9b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. SFT followed by SLiC. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-slic-llama13b": { "id": "contextual-ai/archangel/archangel-slic-llama13b", "name": "archangel_slic_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama13b). HF library tag llama. Aligned with SLiC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_slic_llama13b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with SLiC. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-slic-llama30b": { "id": "contextual-ai/archangel/archangel-slic-llama30b", "name": "archangel_slic_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama30b). HF library tag llama. Aligned with SLiC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_slic_llama30b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with SLiC. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-slic-llama7b": { "id": "contextual-ai/archangel/archangel-slic-llama7b", "name": "archangel_slic_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA (card label llama7b). HF library tag llama. Aligned with SLiC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_slic_llama7b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with SLiC. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-slic-pythia1-4b": { "id": "contextual-ai/archangel/archangel-slic-pythia1-4b", "name": "archangel_slic_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia1-4b). HF library tag gpt_neox. Aligned with SLiC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_slic_pythia1-4b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with SLiC. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-slic-pythia12-0b": { "id": "contextual-ai/archangel/archangel-slic-pythia12-0b", "name": "archangel_slic_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia12-0b). HF library tag gpt_neox. Aligned with SLiC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_slic_pythia12-0b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with SLiC. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-slic-pythia2-8b": { "id": "contextual-ai/archangel/archangel-slic-pythia2-8b", "name": "archangel_slic_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia2-8b). HF library tag gpt_neox. Aligned with SLiC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_slic_pythia2-8b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with SLiC. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/archangel/archangel-slic-pythia6-9b": { "id": "contextual-ai/archangel/archangel-slic-pythia6-9b", "name": "archangel_slic_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pythia / GPT-NeoX (card label pythia6-9b). HF library tag gpt_neox. Aligned with SLiC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI archangel_slic_pythia6-9b is a archangel model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Archangel checkpoint from Contextual AI's HALOs suite: human-feedback alignment on SHP, Anthropic HH, and OpenAssistant. Aligned with SLiC. Research release, still public, not a current product API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/contextual-sql/ctx-bird-reward-250121": { "id": "contextual-ai/contextual-sql/ctx-bird-reward-250121", "name": "Contextual-SQL reward model", "maker": "contextual-ai", "family": "contextual-ai/contextual-sql", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-02", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Fine-tune of Qwen2.5-32B-Instruct (card: Qwen-2.5-32B-Instruct). Causal LM used as a reward model that scores SQL candidates. HF tag architecture is qwen2.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI Contextual-SQL reward model is a contextual-sql model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Scoring model for Contextual-SQL. Given a schema and a question, it ranks candidate SQL by predicted execution correctness. Contextual AI says it is not itself a SQL generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/glm/glm-v1": { "id": "contextual-ai/glm/glm-v1", "name": "GLM v1", "maker": "contextual-ai", "family": "contextual-ai/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 15.0 }, "summary_intro": "Contextual AI GLM v1 is a glm model. Status on 2026-10-15 is active. Weights are closed. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Contextual AI describes GLM as a language model for RAG and agents that should stick to supplied knowledge, cite it inline, and optionally suppress commentary. The /generate API exposes it as model v1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/glm/glm-v2": { "id": "contextual-ai/glm/glm-v2", "name": "GLM v2", "maker": "contextual-ai", "family": "contextual-ai/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 15.0 }, "summary_intro": "Contextual AI GLM v2 is a glm model. Status on 2026-10-15 is active. Weights are closed. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "GLM v2 is the second Grounded Language Model id on POST /v1/generate. Contextual AI's account reshared a 14 July 2025 research post that presents it as the FACTS Grounding leader.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/halo/contextual-kto-mistral-pairrm": { "id": "contextual-ai/halo/contextual-kto-mistral-pairrm", "name": "Contextual KTO Mistral PairRM", "maker": "contextual-ai", "family": "contextual-ai/halo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-05", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "mistralai/Mistral-7B-Instruct-v0.2 aligned with three iterations of KTO on the Snorkel-Mistral-PairRM-DPO dataset. Prompt format is Tulu v2.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI Contextual KTO Mistral PairRM is a halo model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Named HALOs release: Mistral-7B-Instruct-v0.2 aligned with KTO on a Snorkel PairRM preference set. It is a chat model, not a current Contextual AI API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/lmunit/lmunit": { "id": "contextual-ai/lmunit/lmunit", "name": "LMUnit", "maker": "contextual-ai", "family": "contextual-ai/lmunit", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": null }, "summary_intro": "Contextual AI LMUnit is a lmunit model. Status on 2026-10-15 is active. Weights are closed. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Hosted evaluator. It scores how well a response meets one natural-language unit test, on a continuous 1 to 5 scale. The discrete anchors in the API are strongly fails through strongly passes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/lmunit/lmunit-llama3-1-70b": { "id": "contextual-ai/lmunit/lmunit-llama3-1-70b", "name": "LMUnit Llama 3.1 70B", "maker": "contextual-ai", "family": "contextual-ai/lmunit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-22", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Fine-tune of meta-llama/Llama-3.1-70B-Instruct. Score is a continuous 1-5 judgment of a unit test.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI LMUnit Llama 3.1 70B is a lmunit model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Open-weight LMUnit checkpoint. It grades a response against one natural-language unit test. Contextual AI released it beside the hosted /lmunit API, which does not name this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/lmunit/lmunit-qwen2-5-72b": { "id": "contextual-ai/lmunit/lmunit-qwen2-5-72b", "name": "LMUnit Qwen2.5 72B", "maker": "contextual-ai", "family": "contextual-ai/lmunit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-22", "context_length": null, "max_output_tokens": null, "parameters": "72B", "architecture": "Fine-tune of Qwen/Qwen2.5-72B-Instruct. Card text also says Qwen2.5-72B. Score is a continuous 1-5 judgment of a unit test.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI LMUnit Qwen2.5 72B is a lmunit model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Open-weight LMUnit checkpoint. It grades a response against one natural-language unit test. Contextual AI released it beside the hosted /lmunit API, which does not name this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/rerank/ctxl-rerank-v1-instruct": { "id": "contextual-ai/rerank/ctxl-rerank-v1-instruct", "name": "ctxl-rerank-v1-instruct", "maker": "contextual-ai", "family": "contextual-ai/rerank", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI ctxl-rerank-v1-instruct is a rerank model. Status on 2026-10-15 is active. Weights are closed. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Hosted instruction-following reranker. It ranks documents for a query and can take a natural-language instruction about recency, source, document type, or metadata. This is the v1 API id still listed as current.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual": { "id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual", "name": "ctxl-rerank-v2-instruct-multilingual", "maker": "contextual-ai", "family": "contextual-ai/rerank", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI ctxl-rerank-v2-instruct-multilingual is a rerank model. Status on 2026-10-15 is active. Weights are closed. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Hosted multilingual instruction-following reranker, the full Rerank-v2 SKU. Callers pass a query, documents, and an optional instruction. Open-weight sizes are cataloged separately because the API id does not name a size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-1b": { "id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-1b", "name": "Contextual Reranker v2 1B", "maker": "contextual-ai", "family": "contextual-ai/rerank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2025-08-27", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Qwen3ForCausalLM. config.json: 14 layers, hidden size 2048, 16 attention heads, 8 KV heads, vocab 151936, torch_dtype bfloat16, max_position_embeddings 40960. Score is the final-position logit at vocab id 0.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI Contextual Reranker v2 1B is a rerank model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Open-weight instruction-following reranker in the v2 family. The card says it handles conflicting retrievals and custom instructions such as recency, across 100+ languages. The v2 blog also points at self-deploy on Google Model Garden for the open family.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-1b-fp8": { "id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-1b-fp8", "name": "Contextual Reranker v2 1B FP8", "maker": "contextual-ai", "family": "contextual-ai/rerank", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2026-04-23", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Qwen3 causal LM, compressed-tensors FP8. Card snippet states Parameters 2B only for the 2B FP8 repo; this 1B FP8 overview line was not re-read. Family blog and repo name say 1B.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI Contextual Reranker v2 1B FP8 is a rerank model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Quantized open-weight release of Contextual Reranker v2 1B. Precision is FP8. Same instruction-following rerank job as the BF16 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-1b-nvfp4": { "id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-1b-nvfp4", "name": "Contextual Reranker v2 1B NVFP4", "maker": "contextual-ai", "family": "contextual-ai/rerank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2025-08-27", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Qwen3 causal LM. Card states Parameters 1B, precision NVFP4, context up to 32K, 100+ languages.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI Contextual Reranker v2 1B NVFP4 is a rerank model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Quantized open-weight release of Contextual Reranker v2 1B. Precision is NVFP4. Same instruction-following rerank job as the BF16 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-2b": { "id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-2b", "name": "Contextual Reranker v2 2B", "maker": "contextual-ai", "family": "contextual-ai/rerank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2025-08-27", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Qwen3ForCausalLM. config.json: 18 layers, hidden size 2560, 32 attention heads, 8 KV heads, vocab 151936, torch_dtype bfloat16, max_position_embeddings 262144. Score is the final-position logit at vocab id 0.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI Contextual Reranker v2 2B is a rerank model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Open-weight instruction-following reranker in the v2 family. The card says it handles conflicting retrievals and custom instructions such as recency, across 100+ languages. The v2 blog also points at self-deploy on Google Model Garden for the open family.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-2b-fp8": { "id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-2b-fp8", "name": "Contextual Reranker v2 2B FP8", "maker": "contextual-ai", "family": "contextual-ai/rerank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2026-04-23", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Qwen3 causal LM, compressed-tensors FP8. Model card overview states Parameters 2B and context up to 32K.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI Contextual Reranker v2 2B FP8 is a rerank model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Quantized open-weight release of Contextual Reranker v2 2B. Precision is FP8. Same instruction-following rerank job as the BF16 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-2b-nvfp4": { "id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-2b-nvfp4", "name": "Contextual Reranker v2 2B NVFP4", "maker": "contextual-ai", "family": "contextual-ai/rerank", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2025-08-27", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Qwen3 causal LM, compressed-tensors. Treated as the quantized 2B from the August 2025 blog. Overview line was not re-read word for word.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI Contextual Reranker v2 2B NVFP4 is a rerank model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Quantized open-weight release of Contextual Reranker v2 2B. Precision is NVFP4. Same instruction-following rerank job as the BF16 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-6b": { "id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-6b", "name": "Contextual Reranker v2 6B", "maker": "contextual-ai", "family": "contextual-ai/rerank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2025-08-27", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "MistralForCausalLM. config.json: 20 layers, hidden size 5120, 32 attention heads, 8 KV heads, head_dim 128, vocab 131072, torch_dtype bfloat16, max_position_embeddings 1024000. Score is the final-position logit at vocab id 0.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI Contextual Reranker v2 6B is a rerank model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Open-weight instruction-following reranker in the v2 family. The card says it handles conflicting retrievals and custom instructions such as recency, across 100+ languages. The v2 blog also points at self-deploy on Google Model Garden for the open family.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-6b-fp8": { "id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-6b-fp8", "name": "Contextual Reranker v2 6B FP8", "maker": "contextual-ai", "family": "contextual-ai/rerank", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2026-04-23", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "Mistral-family tags on Hugging Face, compressed-tensors FP8. Overview parameter line was not re-read. Repo name and the 6B BF16 card say 6B. HF badge has shown 7B for the BF16 sibling and should not be used.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI Contextual Reranker v2 6B FP8 is a rerank model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Quantized open-weight release of Contextual Reranker v2 6B. Precision is FP8. Same instruction-following rerank job as the BF16 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-6b-nvfp4": { "id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-6b-nvfp4", "name": "Contextual Reranker v2 6B NVFP4", "maker": "contextual-ai", "family": "contextual-ai/rerank", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2025-08-27", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "Mistral-family tags, compressed-tensors. HF badge has shown 4B, which is storage size rather than the card's 6B count. Overview line was not re-read. Parameter field stays 6B from the family name and BF16 card, with that caveat in gaps.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI Contextual Reranker v2 6B NVFP4 is a rerank model. Status on 2026-10-15 is active. Weights are open-weights. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Quantized open-weight release of Contextual Reranker v2 6B. Precision is NVFP4. Same instruction-following rerank job as the BF16 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-mini": { "id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-mini", "name": "ctxl-rerank-v2-instruct-multilingual-mini", "maker": "contextual-ai", "family": "contextual-ai/rerank", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contextual AI ctxl-rerank-v2-instruct-multilingual-mini is a rerank model. Status on 2026-10-15 is active. Weights are closed. This dossier was collected on 2026-10-15 from public pages; unknown fields are left unpublished.", "what_it_is_for": "Lower-priced hosted Reranker v2 id. The docs list it as a current model string beside the full multilingual v2 id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cornell/birdnet/birdnet": { "id": "cornell/birdnet/birdnet", "name": "BirdNET", "maker": "cornell", "family": "cornell/birdnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0 for the models. Analyzer source code is MIT. The project says educational and research use counts as non-commercial.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Acoustic CNN. The current site describes 48 kHz audio in 3-second segments, two log-scaled mel spectrograms, and a CNN whose scores are crossed with location and date. The V2.4 Zenodo record describes an EfficientNetB0-like backbone, embedding size 1024, a custom spectrogram layer, 2D convolution, pooling, global average pooling, and a linear classifier. A birdnet-team version note for V2.4 lists 0.826 GFLOPs and a 50.5 MB FP32 file.", "modalities": { "input": [ "audio" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BirdNET is the acoustic species classifier from the K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology, built with Chemnitz University of Technology. The stable public model is acoustic version 2.4. It is an open-weight research model, still the version the project site tells developers to load. Collected 2026-10-15.", "what_it_is_for": "The project site says BirdNET recognizes more than 6,000 species from sound for biodiversity monitoring and citizen science. Developers load it with `birdnet.load(\"acoustic\", \"2.4\", \"tf\")`. A separate species-range model uses location and date to filter likely species.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cornell/birdnet/birdnet-plus": { "id": "cornell/birdnet/birdnet-plus", "name": "BirdNET+", "maker": "cornell", "family": "cornell/birdnet", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0 for the preview models. Repository source code is MIT.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Developer Preview 3.1. Official notes versus 2.x: variable-length input, 32 kHz instead of 48 kHz, a larger training set, and more non-bird species. The birdnet-team model converter describes V3.0 as an EfficientNetV2-S backbone with a multi-head classifier and about 11K species. The dev repo's default analysis chunk is 3.0 seconds with a 0.15 minimum confidence.", "modalities": { "input": [ "audio" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BirdNET+ V3.0 is the next acoustic model from the Cornell Lab of Ornithology's K. Lisa Yang Center, Chemnitz University of Technology, and the Museum f\u00fcr Naturkunde Berlin. It is a developer preview, not the stable BirdNET 2.4 release. The repository's current drop is Preview 3.1. Collected 2026-10-15.", "what_it_is_for": "The dev repository is for running the preview model on recordings, from a CLI, a Streamlit app, or a browser ONNX demo. The team says it covers about 11K species, including more non-bird taxa than the 2.x models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cornell/cde/cde-small-v1": { "id": "cornell/cde/cde-small-v1", "name": "CDE-small-v1", "maker": "cornell", "family": "cornell/cde", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-10-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage contextual document embedding model. The card uses the BERT uncased tokenizer. A first-stage model embeds a fixed mini-corpus. A second-stage model embeds the query or document conditioned on those vectors. The card's corpus size example is 512 documents. Usage examples truncate text at 512 tokens.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CDE-small-v1 is a contextual text-embedding model from John X. Morris and Alexander M. Rush at Cornell University. They released it on Hugging Face in October 2024 and later marked the card deprecated in favor of CDE-small-v2. Collected 2026-10-15.", "what_it_is_for": "The card says it is a small retrieval embedding model that conditions on a sample of the target corpus. Stage one embeds a fixed mini-corpus. Stage two embeds queries and documents using those vectors. Retrieval prefixes are `search_query: ` and `search_document: `.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cornell/cde/cde-small-v2": { "id": "cornell/cde/cde-small-v2", "name": "CDE-small-v2", "maker": "cornell", "family": "cornell/cde", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-13", "context_length": null, "max_output_tokens": null, "parameters": "140000000 search-time. The card says the checkpoint stores two stages and Hugging Face reports 281M parameters.", "architecture": "Two-stage contextual document embedding model on a ModernBERT backbone. The card says only the second stage runs at search time. Changes from v1 stated on the card: ModernBERT, a residual link between stages, no pooling over instruction tokens, no position-embedding nullification on contextual tokens, and weight decay disabled. Usage examples truncate at 512 tokens and use a 512-document mini-corpus. Tokenizer: answerdotai/ModernBERT-base.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CDE-small-v2 is the current small contextual document embedding model from John X. Morris and Alexander M. Rush at Cornell University. It replaces CDE-small-v1. The weights are on Hugging Face as `jxm/cde-small-v2`. Collected 2026-10-15.", "what_it_is_for": "The authors describe a retrieval embedder that conditions on neighboring documents. Stage one embeds a mini-corpus. Stage two embeds the query or document. Only the second stage is needed at search time. Prefixes are `search_query: ` and `search_document: `.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cornell/co-lmlm/co-lmlm-360m-fw": { "id": "cornell/co-lmlm/co-lmlm-360m-fw", "name": "Co-LMLM-360M-FW", "maker": "cornell", "family": "cornell/co-lmlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "360000000", "architecture": "The card calls it a 360M-parameter SmolLM2-based Co-LMLM trained on FineWeb-Edu. At a fact marker it emits a continuous query from the hidden state and reads the fact from an external index. The paper's training setup uses a 4,096-token context. Hugging Face lists the checkpoint as about 0.4B parameters, float32, with a Llama architecture tag. Whether training started from public SmolLM2 weights was not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Co-LMLM-360M-FW is a 360M-parameter language model from Cornell's LIL Lab. Facts stay in an external index. At a fact marker the model reads that index with a continuous query taken from its hidden state. The checkpoint on Hugging Face was trained on FineWeb-Edu. Collected 2026-10-15.", "what_it_is_for": "The lab says the model keeps specific facts out of its weights so they can be edited or removed in the index. The card says it is the FineWeb-Edu 360M retriever from the Co-LMLM paper and that inference needs the released index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cornell/densenet/densenet-121": { "id": "cornell/densenet/densenet-121", "name": "DenseNet-121", "maker": "cornell", "family": "cornell/densenet", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2016-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DenseNet-BC for ImageNet. Depth 121, growth rate k=32. Each layer in a dense block takes every preceding feature map in that block. The author README says the ImageNet Torch file is 64.5 MB. 224x224 one-crop evaluation. The README no longer maintains the Torch file and points users to PyTorch.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DenseNet-121 is the 121-layer ImageNet model from Densely Connected Convolutional Networks, the CVPR 2017 best-paper architecture by Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Weinberger. Huang and Weinberger's pages linked from the release repository are Cornell. The author-hosted Torch weights are deprecated. Collected 2026-10-15.", "what_it_is_for": "Image classification. The README says a DenseNet reaches ResNet-like ImageNet accuracy with fewer parameters and FLOPs. This file is the k=32, depth-121 DenseNet-BC checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cornell/densenet/densenet-161": { "id": "cornell/densenet/densenet-161", "name": "DenseNet-161", "maker": "cornell", "family": "cornell/densenet", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2016-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DenseNet-BC for ImageNet. Depth 161, growth rate k=48, wider than the k=32 models. The author README says the ImageNet Torch file is 230.8 MB. 224x224 one-crop evaluation. The Torch file is deprecated in favor of PyTorch.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DenseNet-161 is the wider ImageNet DenseNet-BC from the Cornell-linked DenseNet release. Depth is 161 and growth rate is 48. It has the lowest top-1 error of the four original-paper ImageNet files in the author table. The Torch file is deprecated. Collected 2026-10-15.", "what_it_is_for": "Image classification. It is the k=48 member of the original ImageNet release. The k=32 models are 121, 169, and 201 layers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cornell/densenet/densenet-169": { "id": "cornell/densenet/densenet-169", "name": "DenseNet-169", "maker": "cornell", "family": "cornell/densenet", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2016-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DenseNet-BC for ImageNet. Depth 169, growth rate k=32. The author README says the ImageNet Torch file is 114.4 MB. 224x224 one-crop evaluation. The Torch file is deprecated in favor of PyTorch.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DenseNet-169 is the 169-layer ImageNet DenseNet-BC from the Cornell-linked DenseNet release. Growth rate is 32. The author Torch file is deprecated. Collected 2026-10-15.", "what_it_is_for": "Image classification on ILSVRC 2012. It is one of the four original-paper ImageNet checkpoints, between DenseNet-121 and DenseNet-201 in depth.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cornell/densenet/densenet-201": { "id": "cornell/densenet/densenet-201", "name": "DenseNet-201", "maker": "cornell", "family": "cornell/densenet", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2016-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DenseNet-BC for ImageNet. Depth 201, growth rate k=32. The author README says the ImageNet Torch file is 161.8 MB. 224x224 one-crop evaluation. The Torch file is deprecated in favor of PyTorch.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DenseNet-201 is the 201-layer ImageNet DenseNet-BC from the Cornell-linked DenseNet release. Growth rate is 32. The author Torch file is deprecated. Collected 2026-10-15.", "what_it_is_for": "Image classification. It is the deepest of the four original-paper ImageNet models that use growth rate 32.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cornell/lmlm/lmlm-llama2-176m": { "id": "cornell/lmlm/lmlm-llama2-176m", "name": "LMLM-LLaMA2-176M", "maker": "cornell", "family": "cornell/lmlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT for the repository code (copyright 2025 LMLM). The Hub card does not restate a weight license.", "release_date": "", "context_length": 1024, "max_output_tokens": null, "parameters": "176000000", "architecture": "LLaMA-2-style model trained from scratch on annotated Wikipedia, with special lookup tokens and an external triplet database. The README says all pretraining runs use a 1,024-token context, 8 epochs, and mixed precision. Lookup uses all-MiniLM-L6-v2 cosine similarity with a 0.6 threshold.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LMLM-LLaMA2-176M is a 176M-parameter language model from Kilian Weinberger's group at Cornell. It is trained from scratch in a LLaMA-2 style so that specific facts are looked up in an external database instead of stored only in the weights. The checkpoint is `kilian-group/LMLM-llama2-176M`. Collected 2026-10-15.", "what_it_is_for": "The README says LMLM keeps fluency in the weights and editable facts in a database. This checkpoint is the 176M model pretrained on annotated OLMo2 Wikipedia with lookup calls. A same-size Standard model without the database is a baseline and is not a separate product here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cornell/lmlm/lmlm-llama2-382m": { "id": "cornell/lmlm/lmlm-llama2-382m", "name": "LMLM-LLaMA2-382M", "maker": "cornell", "family": "cornell/lmlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT for the repository code (copyright 2025 LMLM). The Hub page has no model card and does not restate a weight license.", "release_date": "", "context_length": 1024, "max_output_tokens": null, "parameters": "382000000", "architecture": "LLaMA-2-style model trained from scratch on annotated Wikipedia with external memory. The README's example is LLaMA2-382M, batch size 256, about 105k steps and 8 epochs, mixed precision, 1,024-token context, about 8 H100-days. Hugging Face reports the file as about 0.4B parameters, float32. Lookup uses all-MiniLM-L6-v2 at a cosine threshold of 0.6.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LMLM-LLaMA2-382M is the larger Limited Memory Language Model released by Kilian Weinberger's group at Cornell. It is a from-scratch LLaMA-2-style model that looks up facts in an external database. The weights are `kilian-group/LMLM-llama2-382M`. The Hub page has no model card. Collected 2026-10-15.", "what_it_is_for": "The README lists this checkpoint as the LLaMA-2 382M model pretrained on annotated Wikipedia with external memory. The paper's claim for this size is that it matches the factual precision of Llama-2-7B. That comparison was not copied as a number.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cornell/merlin/merlin-photo-id": { "id": "cornell/merlin/merlin-photo-id", "name": "Merlin Photo ID", "maker": "cornell", "family": "cornell/merlin", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Convolutional neural network packaged in the Merlin Bird ID app. The November 2024 model takes a 224 by 224 RGB crop and combines appearance with eBird frequency at the photo's place and date. That build was about a 50 MB download.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Merlin Photo ID is the on-device bird-photo classifier in the Cornell Lab of Ornithology's Merlin Bird ID app. It is a current product model. The weights are not a public checkpoint. Collected 2026-10-15.", "what_it_is_for": "The user takes or picks a photo, supplies place and date, and the app suggests a species. The November 2024 post says the model is a convolutional network trained on Macaulay Library photos that experts boxed and identified. It also uses eBird frequency for that place and date.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cornell/merlin/merlin-sound-id": { "id": "cornell/merlin/merlin-sound-id", "name": "Merlin Sound ID", "maker": "cornell", "family": "cornell/merlin", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2021-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "On-device sound classifier in the Merlin Bird ID app. Official pages say the phone draws a spectrogram and a model matches those shapes. Training audio is from the Macaulay Library. A Cornell Lab researcher has said it is a similar deep network to BirdNET but trained only on Macaulay data.", "modalities": { "input": [ "audio" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Merlin Sound ID is the on-device bird-sound model in the Cornell Lab of Ornithology's Merlin Bird ID app. It has been a shipping feature since June 2021. The weights are not a public checkpoint. It is not the BirdNET research model. Collected 2026-10-15.", "what_it_is_for": "The user holds up a phone and the app suggests which birds are vocalizing. The 2026 help page says the phone turns sound into a spectrogram and the model matches those shapes. Training audio is from the Macaulay Library. A new species needs at least 150 recordings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cornell/nighthawk/nighthawk": { "id": "cornell/nighthawk/nighthawk", "name": "Nighthawk", "maker": "cornell", "family": "cornell/nighthawk", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "TensorFlow detector and classifier for nocturnal flight calls. The paper says the core model adapts the Merlin Sound ID framework. The published package documented in the README is nighthawk 0.3.1.", "modalities": { "input": [ "audio" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nighthawk is an open-weight TensorFlow model for detecting and identifying nocturnal flight calls of migrating birds. The paper's first affiliation is the Cornell Lab of Ornithology. Coauthors also list the University of Illinois, MPG Ranch, New Mexico State University, and the University of Chicago. The README still points questions to a Cornell address. The published package is active. Collected 2026-10-15.", "what_it_is_for": "The README says it monitors nocturnal bird migration in the Americas, with the most training coverage in eastern North America. It covers 82 species, 18 families, and 4 orders that vocalize in nocturnal migration. The CLI writes CSV detections and can also write Raven and Audacity tables.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "crete/mocapnet/mocapnet": { "id": "crete/mocapnet/mocapnet", "name": "MocapNET", "maker": "crete", "family": "crete/mocapnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FORTH-ICS MOCAPNET LIBRARY end-user license, copyright FORTH 2019. The text says FORTH solely owns the library. Personal research and academic non-commercial use. No redistribution, product integration, or modification without prior written consent.", "release_date": "2019-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Ensemble of self-normalizing (SeLU) encoders that lift 2D joints to a BVH skeleton. v1 uses Normalized Signed Distance Matrices, COCO or BODY25 input, and 132 BVH outputs. The BMVC 2019 paper says \u03bb=1.0 is about 15 million parameters for orientation classes B and C, \u03bb=1.5 about 9.3 million, and \u03bb=3.0 about 5 million. Class A uses \u03bb=0.8. v2 uses Normalized Signed Rotation Matrices, an orientation classifier, separate upper and lower ensembles, and Hierarchical Coordinate Descent inverse kinematics. The ICPR paper says about 152 thousand weights per encoder, about 6.3 million per joint hierarchy, and about 12.7 million for the aggregate ensemble, about 16 percent fewer parameters than v1. The BVH file has 498 motion fields; the body method populates 87. v3 adds hands and drops orientation classification. v4 adds face, head pose, and gaze from 68 Multi-PIE/IBUG landmarks plus 2 iris points. TensorFlow .pb weights on the C/C++ tree. The mnet4 branch is a Python rewrite and names TensorFlow, TFLite, and ONNX.", "modalities": { "input": [ "image" ], "output": [ "bvh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Crete Computer Science authors Ammar Qammaz and Antonis A. Argyros released MocapNET, a real-time 2D-to-BVH pose ensemble co-affiliated with ICS-FORTH. The line is active. v1 on branch mnet1 is deprecated by its own README. This dossier was collected on 2026-10-15.", "what_it_is_for": "The papers describe a single-person markerless motion-capture method. Monocular RGB is turned into 2D joints by a separate detector, then an ensemble of self-normalizing encoders regresses a BVH skeleton. Later versions add occlusion handling, inverse kinematics, hands, face, and gaze. The GitHub master tree is the maintained code. Weights are TensorFlow graphs downloaded by initialize.sh.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "crete/y-map-net/y-map-net": { "id": "crete/y-map-net/y-map-net", "name": "Y-MAP-Net", "maker": "crete", "family": "crete/y-map-net", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FORTH end-user license. The header names the FORTH-ICS Y-MAP-Net LIBRARY. Section 1 still names the FORTH-ICS MOCAPNET LIBRARY and says FORTH solely owns it. Personal research and academic non-commercial use. No redistribution or modification without prior written consent. Copyright line says FORTH 2019.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Y-shaped convolutional network. One RGB encoder, a three-layer dense bridge, a pictorial decoder, and eight GloVe caption tokens. The shipped configuration in the arXiv paper emits 44 heatmap channels (17 COCO joints, 12 part-affinity fields, 1 depth, 3 normals, 11 segmentation groups) plus 8 caption tokens. Input images in the final experiment are 256\u00d7256. Keras 3.1.1 and TensorFlow 2.16.1. Teachers named in the paper: Depth Anything V2 (ViT-L, 335M), Detectron2 (63M), DPText (44M), and VisionGPT2 (239M). Joints and part-affinity fields are compared with COCO ground truth. Table 3 lists Y-MAP-Net at 297M weights. Table 1 experiment 15 lists 298M for the 1-8-44 configuration. The .keras file in the paper is 1.1 GB. The README later ships a 2.1 GB ICRA Keras zip and smaller lite exports.", "modalities": { "input": [ "image" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Crete Computer Science authors, with ICS-FORTH, released Y-MAP-Net, a Y-shaped convolutional student that reads one RGB frame and emits pose, depth, normals, segmentation, and short English captions. Weights are public and the GitHub tree is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper trains one network as a student of several task models so a robot or webcam can get 2D pose, depth, surface normals, grouped segmentation, and eight caption tokens in one pass. The repository downloads a default checkpoint and can run TensorFlow, TFLite, ONNX, or JAX. A local Gradio UI is included.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/acegpt/acegpt-13b": { "id": "cuhk/acegpt/acegpt-13b", "name": "AceGPT-13B", "maker": "cuhk", "family": "cuhk/acegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-09-15", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Arabic-localized Llama-family base model (v1). Card text for the line says AceGPT and AceGPT-chat are the two categories, with full fine-tunes from 7B through 70B in v2. A separate v2 paper title was not verified; the v1 paper is cited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceGPT-13B is a public Arabic LLM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data, with KAUST among the paper's authors. Release date is the Hugging Face createdAt 2023-09-15. Collected 2026-10-15.", "what_it_is_for": "Localized Arabic generation. The v1 paper describes continued pretraining on Arabic text, native Arabic supervised tuning, and RLAIF. v1.5 and v2 cards also tag Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/acegpt/acegpt-13b-chat": { "id": "cuhk/acegpt/acegpt-13b-chat", "name": "AceGPT-13B-Chat", "maker": "cuhk", "family": "cuhk/acegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-09-21", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Arabic-localized Llama-family chat model (v1). Card text for the line says AceGPT and AceGPT-chat are the two categories, with full fine-tunes from 7B through 70B in v2. A separate v2 paper title was not verified; the v1 paper is cited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceGPT-13B-Chat is a public Arabic LLM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data, with KAUST among the paper's authors. Release date is the Hugging Face createdAt 2023-09-21. Collected 2026-10-15.", "what_it_is_for": "Localized Arabic generation. The v1 paper describes continued pretraining on Arabic text, native Arabic supervised tuning, and RLAIF. v1.5 and v2 cards also tag Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/acegpt/acegpt-7b": { "id": "cuhk/acegpt/acegpt-7b", "name": "AceGPT-7B", "maker": "cuhk", "family": "cuhk/acegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-09-14", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Arabic-localized Llama-family base model (v1). Card text for the line says AceGPT and AceGPT-chat are the two categories, with full fine-tunes from 7B through 70B in v2. A separate v2 paper title was not verified; the v1 paper is cited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceGPT-7B is a public Arabic LLM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data, with KAUST among the paper's authors. Release date is the Hugging Face createdAt 2023-09-14. Collected 2026-10-15.", "what_it_is_for": "Localized Arabic generation. The v1 paper describes continued pretraining on Arabic text, native Arabic supervised tuning, and RLAIF. v1.5 and v2 cards also tag Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/acegpt/acegpt-7b-chat": { "id": "cuhk/acegpt/acegpt-7b-chat", "name": "AceGPT-7B-Chat", "maker": "cuhk", "family": "cuhk/acegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-09-21", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Arabic-localized Llama-family chat model (v1). Card text for the line says AceGPT and AceGPT-chat are the two categories, with full fine-tunes from 7B through 70B in v2. A separate v2 paper title was not verified; the v1 paper is cited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceGPT-7B-Chat is a public Arabic LLM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data, with KAUST among the paper's authors. Release date is the Hugging Face createdAt 2023-09-21. Collected 2026-10-15.", "what_it_is_for": "Localized Arabic generation. The v1 paper describes continued pretraining on Arabic text, native Arabic supervised tuning, and RLAIF. v1.5 and v2 cards also tag Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/acegpt/acegpt-v1-5-13b": { "id": "cuhk/acegpt/acegpt-v1-5-13b", "name": "AceGPT-v1.5-13B", "maker": "cuhk", "family": "cuhk/acegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-04-14", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Arabic-localized Llama-family base model (v1.5). Card text for the line says AceGPT and AceGPT-chat are the two categories, with full fine-tunes from 7B through 70B in v2. A separate v2 paper title was not verified; the v1 paper is cited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceGPT-v1.5-13B is a public Arabic LLM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data, with KAUST among the paper's authors. Release date is the Hugging Face createdAt 2024-04-14. Collected 2026-10-15.", "what_it_is_for": "Localized Arabic generation. The v1 paper describes continued pretraining on Arabic text, native Arabic supervised tuning, and RLAIF. v1.5 and v2 cards also tag Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/acegpt/acegpt-v1-5-13b-chat": { "id": "cuhk/acegpt/acegpt-v1-5-13b-chat", "name": "AceGPT-v1.5-13B-Chat", "maker": "cuhk", "family": "cuhk/acegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-04-14", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Arabic-localized Llama-family chat model (v1.5). Card text for the line says AceGPT and AceGPT-chat are the two categories, with full fine-tunes from 7B through 70B in v2. A separate v2 paper title was not verified; the v1 paper is cited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceGPT-v1.5-13B-Chat is a public Arabic LLM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data, with KAUST among the paper's authors. Release date is the Hugging Face createdAt 2024-04-14. Collected 2026-10-15.", "what_it_is_for": "Localized Arabic generation. The v1 paper describes continued pretraining on Arabic text, native Arabic supervised tuning, and RLAIF. v1.5 and v2 cards also tag Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/acegpt/acegpt-v1-5-7b": { "id": "cuhk/acegpt/acegpt-v1-5-7b", "name": "AceGPT-v1.5-7B", "maker": "cuhk", "family": "cuhk/acegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-27", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Arabic-localized Llama-family base model (v1.5). Card text for the line says AceGPT and AceGPT-chat are the two categories, with full fine-tunes from 7B through 70B in v2. A separate v2 paper title was not verified; the v1 paper is cited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceGPT-v1.5-7B is a public Arabic LLM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data, with KAUST among the paper's authors. Release date is the Hugging Face createdAt 2024-06-27. Collected 2026-10-15.", "what_it_is_for": "Localized Arabic generation. The v1 paper describes continued pretraining on Arabic text, native Arabic supervised tuning, and RLAIF. v1.5 and v2 cards also tag Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/acegpt/acegpt-v1-5-7b-chat": { "id": "cuhk/acegpt/acegpt-v1-5-7b-chat", "name": "AceGPT-v1.5-7B-Chat", "maker": "cuhk", "family": "cuhk/acegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-27", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Arabic-localized Llama-family chat model (v1.5). Card text for the line says AceGPT and AceGPT-chat are the two categories, with full fine-tunes from 7B through 70B in v2. A separate v2 paper title was not verified; the v1 paper is cited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceGPT-v1.5-7B-Chat is a public Arabic LLM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data, with KAUST among the paper's authors. Release date is the Hugging Face createdAt 2024-06-27. Collected 2026-10-15.", "what_it_is_for": "Localized Arabic generation. The v1 paper describes continued pretraining on Arabic text, native Arabic supervised tuning, and RLAIF. v1.5 and v2 cards also tag Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/acegpt/acegpt-v2-32b": { "id": "cuhk/acegpt/acegpt-v2-32b", "name": "AceGPT-v2-32B", "maker": "cuhk", "family": "cuhk/acegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-17", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Arabic-localized Llama-family base model (v2). Card text for the line says AceGPT and AceGPT-chat are the two categories, with full fine-tunes from 7B through 70B in v2. A separate v2 paper title was not verified; the v1 paper is cited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceGPT-v2-32B is a public Arabic LLM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data, with KAUST among the paper's authors. Release date is the Hugging Face createdAt 2024-06-17. Collected 2026-10-15.", "what_it_is_for": "Localized Arabic generation. The v1 paper describes continued pretraining on Arabic text, native Arabic supervised tuning, and RLAIF. v1.5 and v2 cards also tag Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/acegpt/acegpt-v2-32b-chat": { "id": "cuhk/acegpt/acegpt-v2-32b-chat", "name": "AceGPT-v2-32B-Chat", "maker": "cuhk", "family": "cuhk/acegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-17", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Arabic-localized Llama-family chat model (v2). Card text for the line says AceGPT and AceGPT-chat are the two categories, with full fine-tunes from 7B through 70B in v2. A separate v2 paper title was not verified; the v1 paper is cited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceGPT-v2-32B-Chat is a public Arabic LLM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data, with KAUST among the paper's authors. Release date is the Hugging Face createdAt 2024-06-17. Collected 2026-10-15.", "what_it_is_for": "Localized Arabic generation. The v1 paper describes continued pretraining on Arabic text, native Arabic supervised tuning, and RLAIF. v1.5 and v2 cards also tag Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/acegpt/acegpt-v2-70b": { "id": "cuhk/acegpt/acegpt-v2-70b", "name": "AceGPT-v2-70B", "maker": "cuhk", "family": "cuhk/acegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-17", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Arabic-localized Llama-family base model (v2). Card text for the line says AceGPT and AceGPT-chat are the two categories, with full fine-tunes from 7B through 70B in v2. A separate v2 paper title was not verified; the v1 paper is cited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceGPT-v2-70B is a public Arabic LLM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data, with KAUST among the paper's authors. Release date is the Hugging Face createdAt 2024-06-17. Collected 2026-10-15.", "what_it_is_for": "Localized Arabic generation. The v1 paper describes continued pretraining on Arabic text, native Arabic supervised tuning, and RLAIF. v1.5 and v2 cards also tag Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/acegpt/acegpt-v2-70b-chat": { "id": "cuhk/acegpt/acegpt-v2-70b-chat", "name": "AceGPT-v2-70B-Chat", "maker": "cuhk", "family": "cuhk/acegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-17", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Arabic-localized Llama-family chat model (v2). Card text for the line says AceGPT and AceGPT-chat are the two categories, with full fine-tunes from 7B through 70B in v2. A separate v2 paper title was not verified; the v1 paper is cited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceGPT-v2-70B-Chat is a public Arabic LLM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data, with KAUST among the paper's authors. Release date is the Hugging Face createdAt 2024-06-17. Collected 2026-10-15.", "what_it_is_for": "Localized Arabic generation. The v1 paper describes continued pretraining on Arabic text, native Arabic supervised tuning, and RLAIF. v1.5 and v2 cards also tag Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/acegpt/acegpt-v2-8b": { "id": "cuhk/acegpt/acegpt-v2-8b", "name": "AceGPT-v2-8B", "maker": "cuhk", "family": "cuhk/acegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-20", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Arabic-localized Llama-family base model (v2). Card text for the line says AceGPT and AceGPT-chat are the two categories, with full fine-tunes from 7B through 70B in v2. A separate v2 paper title was not verified; the v1 paper is cited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceGPT-v2-8B is a public Arabic LLM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data, with KAUST among the paper's authors. Release date is the Hugging Face createdAt 2024-06-20. Collected 2026-10-15.", "what_it_is_for": "Localized Arabic generation. The v1 paper describes continued pretraining on Arabic text, native Arabic supervised tuning, and RLAIF. v1.5 and v2 cards also tag Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/acegpt/acegpt-v2-8b-chat": { "id": "cuhk/acegpt/acegpt-v2-8b-chat", "name": "AceGPT-v2-8B-Chat", "maker": "cuhk", "family": "cuhk/acegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-20", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Arabic-localized Llama-family chat model (v2). Card text for the line says AceGPT and AceGPT-chat are the two categories, with full fine-tunes from 7B through 70B in v2. A separate v2 paper title was not verified; the v1 paper is cited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceGPT-v2-8B-Chat is a public Arabic LLM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data, with KAUST among the paper's authors. Release date is the Hugging Face createdAt 2024-06-20. Collected 2026-10-15.", "what_it_is_for": "Localized Arabic generation. The v1 paper describes continued pretraining on Arabic text, native Arabic supervised tuning, and RLAIF. v1.5 and v2 cards also tag Chinese and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/allava/allava-3b": { "id": "cuhk/allava/allava-3b", "name": "ALLaVA-3B", "maker": "cuhk", "family": "cuhk/allava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-02-14", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Lite vision-language model (llava_phi) trained with the ALLaVA-4V synthetic set. The paper says 1.3 million GPT-4V-synthesized samples.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ALLaVA-3B is a lite vision-language model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 18 February 2024 and revised on 17 June 2024. Hugging Face createdAt is 2024-02-14. Collected 2026-10-15.", "what_it_is_for": "Image question answering with a small backbone. The dataset ALLaVA-4V is tagged on the cards and is apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/allava/allava-phi2-2-7b": { "id": "cuhk/allava/allava-phi2-2-7b", "name": "ALLaVA-Phi2-2.7B", "maker": "cuhk", "family": "cuhk/allava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "2.7B", "architecture": "Lite vision-language model (llava_phi) trained with the ALLaVA-4V synthetic set. The paper says 1.3 million GPT-4V-synthesized samples.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ALLaVA-Phi2-2.7B is a lite vision-language model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 18 February 2024 and revised on 17 June 2024. Hugging Face createdAt is 2024-06-25. Collected 2026-10-15.", "what_it_is_for": "Image question answering with a small backbone. The dataset ALLaVA-4V is tagged on the cards and is apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/allava/allava-phi3-mini-128k": { "id": "cuhk/allava/allava-phi3-mini-128k", "name": "ALLaVA-Phi3-mini-128k", "maker": "cuhk", "family": "cuhk/allava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lite vision-language model (llava_phi3) trained with the ALLaVA-4V synthetic set. The paper says 1.3 million GPT-4V-synthesized samples.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ALLaVA-Phi3-mini-128k is a lite vision-language model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 18 February 2024 and revised on 17 June 2024. Hugging Face createdAt is 2024-06-25. Collected 2026-10-15.", "what_it_is_for": "Image question answering with a small backbone. The dataset ALLaVA-4V is tagged on the cards and is apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/allava/allava-stablelm2-1-6b": { "id": "cuhk/allava/allava-stablelm2-1-6b", "name": "ALLaVA-StableLM2-1.6B", "maker": "cuhk", "family": "cuhk/allava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "1.6B", "architecture": "Lite vision-language model (llava_stablelm_1_6b) trained with the ALLaVA-4V synthetic set. The paper says 1.3 million GPT-4V-synthesized samples.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ALLaVA-StableLM2-1.6B is a lite vision-language model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 18 February 2024 and revised on 17 June 2024. Hugging Face createdAt is 2024-06-25. Collected 2026-10-15.", "what_it_is_for": "Image question answering with a small backbone. The dataset ALLaVA-4V is tagged on the cards and is apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo-0-5b": { "id": "cuhk/apollo/apollo-0-5b", "name": "Apollo-0.5B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-03-06", "context_length": null, "max_output_tokens": null, "parameters": "0.5B", "architecture": "Multilingual medical LLM. Hub architecture tag Qwen2. The March 2024 abstract lists released sizes 0.5B, 1.8B, 2B, 6B, and 7B and calls Apollo-7B a multilingual medical LLM. Later Hub cards add 34B and 72B (created 2024-04-22) and Apollo-MedJamba (created 2024-04-23). The six language names in the abstract were not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo-0.5B is a medical LLM from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Release date is the Hugging Face createdAt 2024-03-06. The paper was submitted on 6 March 2024 and revised through 12 October 2024. Collected 2026-10-15.", "what_it_is_for": "Multilingual medical question answering aimed at a small open model. ApolloCorpora and XMedBench are the paper's data and benchmark names. Language names were not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo-1-8b": { "id": "cuhk/apollo/apollo-1-8b", "name": "Apollo-1.8B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-03-06", "context_length": null, "max_output_tokens": null, "parameters": "1.8B", "architecture": "Multilingual medical LLM. Hub architecture tag Qwen. The March 2024 abstract lists released sizes 0.5B, 1.8B, 2B, 6B, and 7B and calls Apollo-7B a multilingual medical LLM. Later Hub cards add 34B and 72B (created 2024-04-22) and Apollo-MedJamba (created 2024-04-23). The six language names in the abstract were not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo-1.8B is a medical LLM from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Release date is the Hugging Face createdAt 2024-03-06. The paper was submitted on 6 March 2024 and revised through 12 October 2024. Collected 2026-10-15.", "what_it_is_for": "Multilingual medical question answering aimed at a small open model. ApolloCorpora and XMedBench are the paper's data and benchmark names. Language names were not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo-2b": { "id": "cuhk/apollo/apollo-2b", "name": "Apollo-2B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-03-06", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Multilingual medical LLM. Hub architecture tag Gemma. The March 2024 abstract lists released sizes 0.5B, 1.8B, 2B, 6B, and 7B and calls Apollo-7B a multilingual medical LLM. Later Hub cards add 34B and 72B (created 2024-04-22) and Apollo-MedJamba (created 2024-04-23). The six language names in the abstract were not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo-2B is a medical LLM from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Release date is the Hugging Face createdAt 2024-03-06. The paper was submitted on 6 March 2024 and revised through 12 October 2024. Collected 2026-10-15.", "what_it_is_for": "Multilingual medical question answering aimed at a small open model. ApolloCorpora and XMedBench are the paper's data and benchmark names. Language names were not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo-34b": { "id": "cuhk/apollo/apollo-34b", "name": "Apollo-34B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-04-22", "context_length": null, "max_output_tokens": null, "parameters": "34B", "architecture": "Multilingual medical LLM. Hub architecture tag Llama. The March 2024 abstract lists released sizes 0.5B, 1.8B, 2B, 6B, and 7B and calls Apollo-7B a multilingual medical LLM. Later Hub cards add 34B and 72B (created 2024-04-22) and Apollo-MedJamba (created 2024-04-23). The six language names in the abstract were not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo-34B is a medical LLM from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Release date is the Hugging Face createdAt 2024-04-22. The paper was submitted on 6 March 2024 and revised through 12 October 2024. Collected 2026-10-15.", "what_it_is_for": "Multilingual medical question answering aimed at a small open model. ApolloCorpora and XMedBench are the paper's data and benchmark names. Language names were not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo-6b": { "id": "cuhk/apollo/apollo-6b", "name": "Apollo-6B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-03-06", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "Multilingual medical LLM. Hub architecture tag Llama. The March 2024 abstract lists released sizes 0.5B, 1.8B, 2B, 6B, and 7B and calls Apollo-7B a multilingual medical LLM. Later Hub cards add 34B and 72B (created 2024-04-22) and Apollo-MedJamba (created 2024-04-23). The six language names in the abstract were not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo-6B is a medical LLM from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Release date is the Hugging Face createdAt 2024-03-06. The paper was submitted on 6 March 2024 and revised through 12 October 2024. Collected 2026-10-15.", "what_it_is_for": "Multilingual medical question answering aimed at a small open model. ApolloCorpora and XMedBench are the paper's data and benchmark names. Language names were not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo-72b": { "id": "cuhk/apollo/apollo-72b", "name": "Apollo-72B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-04-22", "context_length": null, "max_output_tokens": null, "parameters": "72B", "architecture": "Multilingual medical LLM. Hub architecture tag Qwen. The March 2024 abstract lists released sizes 0.5B, 1.8B, 2B, 6B, and 7B and calls Apollo-7B a multilingual medical LLM. Later Hub cards add 34B and 72B (created 2024-04-22) and Apollo-MedJamba (created 2024-04-23). The six language names in the abstract were not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo-72B is a medical LLM from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Release date is the Hugging Face createdAt 2024-04-22. The paper was submitted on 6 March 2024 and revised through 12 October 2024. Collected 2026-10-15.", "what_it_is_for": "Multilingual medical question answering aimed at a small open model. ApolloCorpora and XMedBench are the paper's data and benchmark names. Language names were not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo-7b": { "id": "cuhk/apollo/apollo-7b", "name": "Apollo-7B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-03-06", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Multilingual medical LLM. Hub architecture tag Gemma. The March 2024 abstract lists released sizes 0.5B, 1.8B, 2B, 6B, and 7B and calls Apollo-7B a multilingual medical LLM. Later Hub cards add 34B and 72B (created 2024-04-22) and Apollo-MedJamba (created 2024-04-23). The six language names in the abstract were not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo-7B is a medical LLM from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Release date is the Hugging Face createdAt 2024-03-06. The paper was submitted on 6 March 2024 and revised through 12 October 2024. Collected 2026-10-15.", "what_it_is_for": "Multilingual medical question answering aimed at a small open model. ApolloCorpora and XMedBench are the paper's data and benchmark names. Language names were not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo-medjamba": { "id": "cuhk/apollo/apollo-medjamba", "name": "Apollo-MedJamba", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-04-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multilingual medical LLM. Hub architecture tag Jamba. The March 2024 abstract lists released sizes 0.5B, 1.8B, 2B, 6B, and 7B and calls Apollo-7B a multilingual medical LLM. Later Hub cards add 34B and 72B (created 2024-04-22) and Apollo-MedJamba (created 2024-04-23). The six language names in the abstract were not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo-MedJamba is a medical LLM from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Release date is the Hugging Face createdAt 2024-04-23. The paper was submitted on 6 March 2024 and revised through 12 October 2024. Collected 2026-10-15.", "what_it_is_for": "Multilingual medical question answering aimed at a small open model. ApolloCorpora and XMedBench are the paper's data and benchmark names. Language names were not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo-moe-0-5b": { "id": "cuhk/apollo/apollo-moe-0-5b", "name": "Apollo-MoE-0.5B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-14", "context_length": null, "max_output_tokens": null, "parameters": "0.5B", "architecture": "Mixture-of-experts medical LLM from the Apollo2 paper. Architecture tag upcycling-qwen2-moe. Base-model tag Qwen/Qwen2-0.5B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo-MoE-0.5B is an Apollo mixture-of-experts checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2024-10-14. Collected 2026-10-15.", "what_it_is_for": "Same 50-language medical question-answering job as Apollo2, with an upcycled MoE backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo-moe-1-5b": { "id": "cuhk/apollo/apollo-moe-1-5b", "name": "Apollo-MoE-1.5B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-14", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Mixture-of-experts medical LLM from the Apollo2 paper. Architecture tag upcycling-qwen2-moe. Base-model tag microsoft/Phi-3-mini-4k-instruct. Those two tags disagree; both are stored.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo-MoE-1.5B is an Apollo mixture-of-experts checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2024-10-14. Collected 2026-10-15.", "what_it_is_for": "Same 50-language medical question-answering job as Apollo2, with an upcycled MoE backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo-moe-7b": { "id": "cuhk/apollo/apollo-moe-7b", "name": "Apollo-MoE-7B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-14", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Mixture-of-experts medical LLM from the Apollo2 paper. Architecture tag upcycling-qwen2-moe. Base-model tag Qwen/Qwen2-7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo-MoE-7B is an Apollo mixture-of-experts checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2024-10-14. Collected 2026-10-15.", "what_it_is_for": "Same 50-language medical question-answering job as Apollo2, with an upcycled MoE backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo2-0-5b": { "id": "cuhk/apollo/apollo2-0-5b", "name": "Apollo2-0.5B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-14", "context_length": null, "max_output_tokens": null, "parameters": "0.5B", "architecture": "Apollo2 medical LLM on Qwen2-0.5B. The paper describes a mixture of language-family experts covering 50 languages. Hub language tags are a partial list and are not stored as the full language set.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo2-0.5B is an Apollo2 medical checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2024-10-14, the same day the paper was submitted. Collected 2026-10-15.", "what_it_is_for": "Medical question answering across many languages. The paper was revised on 10 February 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo2-1-5b": { "id": "cuhk/apollo/apollo2-1-5b", "name": "Apollo2-1.5B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-14", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Apollo2 medical LLM on Qwen2-1.5B. The paper describes a mixture of language-family experts covering 50 languages. Hub language tags are a partial list and are not stored as the full language set.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo2-1.5B is an Apollo2 medical checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2024-10-14, the same day the paper was submitted. Collected 2026-10-15.", "what_it_is_for": "Medical question answering across many languages. The paper was revised on 10 February 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo2-2b": { "id": "cuhk/apollo/apollo2-2b", "name": "Apollo2-2B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "gemma", "release_date": "2024-10-14", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Apollo2 medical LLM on Gemma-2-2B. The paper describes a mixture of language-family experts covering 50 languages. Hub language tags are a partial list and are not stored as the full language set.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo2-2B is an Apollo2 medical checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2024-10-14, the same day the paper was submitted. Collected 2026-10-15.", "what_it_is_for": "Medical question answering across many languages. The paper was revised on 10 February 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo2-3-8b": { "id": "cuhk/apollo/apollo2-3-8b", "name": "Apollo2-3.8B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-14", "context_length": null, "max_output_tokens": null, "parameters": "3.8B", "architecture": "Apollo2 medical LLM on Phi-3-mini-4k-instruct. The paper describes a mixture of language-family experts covering 50 languages. Hub language tags are a partial list and are not stored as the full language set.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo2-3.8B is an Apollo2 medical checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2024-10-14, the same day the paper was submitted. Collected 2026-10-15.", "what_it_is_for": "Medical question answering across many languages. The paper was revised on 10 February 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo2-7b": { "id": "cuhk/apollo/apollo2-7b", "name": "Apollo2-7B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-14", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Apollo2 medical LLM on Qwen2-7B. The paper describes a mixture of language-family experts covering 50 languages. Hub language tags are a partial list and are not stored as the full language set.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo2-7B is an Apollo2 medical checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2024-10-14, the same day the paper was submitted. Collected 2026-10-15.", "what_it_is_for": "Medical question answering across many languages. The paper was revised on 10 February 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/apollo/apollo2-9b": { "id": "cuhk/apollo/apollo2-9b", "name": "Apollo2-9B", "maker": "cuhk", "family": "cuhk/apollo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "gemma", "release_date": "2024-10-14", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Apollo2 medical LLM on Gemma-2-9B. The paper describes a mixture of language-family experts covering 50 languages. Hub language tags are a partial list and are not stored as the full language set.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apollo2-9B is an Apollo2 medical checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2024-10-14, the same day the paper was submitted. Collected 2026-10-15.", "what_it_is_for": "Medical question answering across many languages. The paper was revised on 10 February 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/blenderllm/blenderllm": { "id": "cuhk/blenderllm/blenderllm", "name": "BlenderLLM", "maker": "cuhk", "family": "cuhk/blenderllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-07", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Qwen2.5-7B-Instruct fine-tune that emits Blender Python for CAD. Pipeline tag text-to-3d. Dataset tag BlendNet.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlenderLLM is a CAD-script model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 16 December 2024. Hugging Face createdAt is 2024-12-07, before the paper. Collected 2026-10-15.", "what_it_is_for": "Write Blender scripts for computer-aided design. The paper introduces BlendNet and CADBench and says instruction tuning plus self-improvement beats the baselines it tested. No CADBench number was copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/chimera/chimera-chat-13b-delta": { "id": "cuhk/chimera/chimera-chat-13b-delta", "name": "Chimera-Chat-13B delta", "maker": "cuhk", "family": "cuhk/chimera", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-04-08", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "LLaMA weight delta for the Chimera Latin-language chat line. The Hub repo is the delta, not a full checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chimera-Chat-13B delta is a Chimera delta from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Chimera is the Latin-language counterpart to Phoenix. Release date is the Hugging Face createdAt 2023-04-08. Collected 2026-10-15.", "what_it_is_for": "Latin-language chat. A base LLaMA checkpoint is required before the delta can be applied. The exact language list was not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/chimera/chimera-chat-7b-delta": { "id": "cuhk/chimera/chimera-chat-7b-delta", "name": "Chimera-Chat-7B delta", "maker": "cuhk", "family": "cuhk/chimera", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-04-08", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "LLaMA weight delta for the Chimera Latin-language chat line. The Hub repo is the delta, not a full checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chimera-Chat-7B delta is a Chimera delta from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Chimera is the Latin-language counterpart to Phoenix. Release date is the Hugging Face createdAt 2023-04-08. Collected 2026-10-15.", "what_it_is_for": "Latin-language chat. A base LLaMA checkpoint is required before the delta can be applied. The exact language list was not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/chimera/chimera-inst-chat-13b-delta": { "id": "cuhk/chimera/chimera-inst-chat-13b-delta", "name": "Chimera-Inst-Chat-13B delta", "maker": "cuhk", "family": "cuhk/chimera", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-04-11", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "LLaMA weight delta for the Chimera Latin-language chat line. The Hub repo is the delta, not a full checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chimera-Inst-Chat-13B delta is a Chimera delta from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Chimera is the Latin-language counterpart to Phoenix. Release date is the Hugging Face createdAt 2023-04-11. Collected 2026-10-15.", "what_it_is_for": "Latin-language chat. A base LLaMA checkpoint is required before the delta can be applied. The exact language list was not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/chimera/chimera-inst-chat-7b-delta": { "id": "cuhk/chimera/chimera-inst-chat-7b-delta", "name": "Chimera-Inst-Chat-7B delta", "maker": "cuhk", "family": "cuhk/chimera", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-04-11", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "LLaMA weight delta for the Chimera Latin-language chat line. The Hub repo is the delta, not a full checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chimera-Inst-Chat-7B delta is a Chimera delta from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Chimera is the Latin-language counterpart to Phoenix. Release date is the Hugging Face createdAt 2023-04-11. Collected 2026-10-15.", "what_it_is_for": "Latin-language chat. A base LLaMA checkpoint is required before the delta can be applied. The exact language list was not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/deear/deear-base": { "id": "cuhk/deear/deear-base", "name": "DeEAR Base", "maker": "cuhk", "family": "cuhk/deear", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "wav2vec2 speech model. License tag apache-2.0. No paper id was in the models-API tag list.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeEAR Base is a named wav2vec2 checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2025-09-25. Collected 2026-10-15.", "what_it_is_for": "The repository name and the wav2vec2 tag say a speech encoder. The training task was not opened beyond the tag list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/diagnosisgpt/diagnosisgpt-34b": { "id": "cuhk/diagnosisgpt/diagnosisgpt-34b", "name": "DiagnosisGPT-34B", "maker": "cuhk", "family": "cuhk/diagnosisgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-07-17", "context_length": null, "max_output_tokens": null, "parameters": "34B", "architecture": "Chain-of-Diagnosis medical agent. The abstract says DiagnosisGPT can diagnose 9604 diseases.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiagnosisGPT-34B is an interpretable diagnosis model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 18 July 2024 and revised on 15 September 2024. Hugging Face createdAt is 2024-07-17. Collected 2026-10-15.", "what_it_is_for": "Produces a diagnostic chain rather than only a final label. The abstract's disease coverage figure is 9604.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/diagnosisgpt/diagnosisgpt-6b": { "id": "cuhk/diagnosisgpt/diagnosisgpt-6b", "name": "DiagnosisGPT-6B", "maker": "cuhk", "family": "cuhk/diagnosisgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-07-17", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "Chain-of-Diagnosis medical agent. The abstract says DiagnosisGPT can diagnose 9604 diseases.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiagnosisGPT-6B is an interpretable diagnosis model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 18 July 2024 and revised on 15 September 2024. Hugging Face createdAt is 2024-07-17. Collected 2026-10-15.", "what_it_is_for": "Produces a diagnostic chain rather than only a final label. The abstract's disease coverage figure is 9604.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/echox/echox-3b": { "id": "cuhk/echox/echox-3b", "name": "EchoX-3B", "maker": "cuhk", "family": "cuhk/echox", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-09-08", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Speech-to-speech LLM. Hub pipeline tag audio-text-to-audio-text. The abstract says echo training and about six thousand hours of data.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EchoX-3B is a speech-to-speech model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Weights were posted before the 11 September 2025 paper. Hugging Face createdAt is 2025-09-08. Collected 2026-10-15.", "what_it_is_for": "Speech understanding and speech generation in English. The project page is the EchoX GitHub repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/echox/echox-8b": { "id": "cuhk/echox/echox-8b", "name": "EchoX-8B", "maker": "cuhk", "family": "cuhk/echox", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-09-04", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Speech-to-speech LLM. Hub pipeline tag audio-text-to-audio-text. The abstract says echo training and about six thousand hours of data.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EchoX-8B is a speech-to-speech model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Weights were posted before the 11 September 2025 paper. Hugging Face createdAt is 2025-09-04. Collected 2026-10-15.", "what_it_is_for": "Speech understanding and speech generation in English. The project page is the EchoX GitHub repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/grammargpt/grammargpt": { "id": "cuhk/grammargpt/grammargpt", "name": "GrammarGPT", "maker": "cuhk", "family": "cuhk/grammargpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-07-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BLOOM text-generation checkpoint. License tag apache-2.0. No arXiv id was in the models-API tag list.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GrammarGPT is a named BLOOM checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2023-07-31. Collected 2026-10-15.", "what_it_is_for": "The repository name says grammar. The card body and any paper were not opened beyond the tag list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/himed/himed-8b": { "id": "cuhk/himed/himed-8b", "name": "HiMed-8B", "maker": "cuhk", "family": "cuhk/himed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-02-04", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Llama-architecture medical checkpoint. The models-API tags say llama and apache-2.0. No pipeline tag was in that list.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HiMed-8B is a named Llama checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2026-02-04. A paper title was not opened. Collected 2026-10-15.", "what_it_is_for": "The card tags identify a medical Llama model. The task statement on the card was not opened beyond the tag list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/himed/himed-verifier": { "id": "cuhk/himed/himed-verifier", "name": "HiMed-Verifier", "maker": "cuhk", "family": "cuhk/himed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-12-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama-architecture verifier. Tags say llama and apache-2.0. Parameter count was not in the tag list.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HiMed-Verifier is the companion checker from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2025-12-10. Collected 2026-10-15.", "what_it_is_for": "The repository name says verifier. The scoring prompt was not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-13b": { "id": "cuhk/huatuogpt/huatuogpt-13b", "name": "HuatuoGPT-13B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-06-30", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "LLaMA-based medical chat model released as a weight delta on Ziya-LLaMA-13B-Pretrain-v1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-13B is the larger first-generation medical chat model from the same CUHK-Shenzhen lab. Because the backbone is LLaMA, the lab published a weight delta rather than a full checkpoint. Collected 2026-10-15.", "what_it_is_for": "Same medical-consultation job as HuatuoGPT-7B. Users must apply the delta to a local LLaMA-13B base with the lab's apply_delta.py script.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-3-27b": { "id": "cuhk/huatuogpt/huatuogpt-3-27b", "name": "HuatuoGPT-3-27B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-09-24", "context_length": null, "max_output_tokens": null, "parameters": "27B", "architecture": "Medical model on Qwen3.8-27B. Largest OnePO HuatuoGPT-3 checkpoint on the current GitHub table.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-3-27B is part of HuatuoGPT-3 from CUHK-Shenzhen FreedomIntelligence. The lab's current line uses OnePO, a single reinforcement-learning stage without a domain supervised-tuning cold start. This checkpoint's public repo date is 2026-09-24. Collected 2026-10-15.", "what_it_is_for": "Medical reasoning. The 8B and 32B cards describe an earlier SeedRL release; the 9B and 27B cards and the October 2026 paper describe OnePO. Both sets of weights are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-3-32b": { "id": "cuhk/huatuogpt/huatuogpt-3-32b", "name": "HuatuoGPT-3-32B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-03-20", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Medical model on Qwen3-32B. SeedRL writeup on the model card. Thinking mode is on by default.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-3-32B is part of HuatuoGPT-3 from CUHK-Shenzhen FreedomIntelligence. The lab's current line uses OnePO, a single reinforcement-learning stage without a domain supervised-tuning cold start. This checkpoint's public repo date is 2026-03-20. Collected 2026-10-15.", "what_it_is_for": "Medical reasoning. The 8B and 32B cards describe an earlier SeedRL release; the 9B and 27B cards and the October 2026 paper describe OnePO. Both sets of weights are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-3-7b-pangu": { "id": "cuhk/huatuogpt/huatuogpt-3-7b-pangu", "name": "HuatuoGPT-3-7B-Pangu", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-03-21", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Medical model on openPangu-Embedded-7B. Pangu-backbone variant trained with SeedRL on Ascend NPUs. Reasoning tokens use openPangu unused16 and unused17 markers rather than think tags. The base openPangu weights are Huawei's and are not re-cataloged here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-3-7B-Pangu is part of HuatuoGPT-3 from CUHK-Shenzhen FreedomIntelligence. The lab's current line uses OnePO, a single reinforcement-learning stage without a domain supervised-tuning cold start. This checkpoint's public repo date is 2026-03-21. Collected 2026-10-15.", "what_it_is_for": "Medical reasoning. The 8B and 32B cards describe an earlier SeedRL release; the 9B and 27B cards and the October 2026 paper describe OnePO. Both sets of weights are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-3-8b": { "id": "cuhk/huatuogpt/huatuogpt-3-8b", "name": "HuatuoGPT-3-8B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-03-20", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Medical model on Qwen3-8B-Base. SeedRL writeup on the model card: RL-only medical adaptation in one stage. The later GitHub homepage instead highlights the OnePO 9B and 27B models.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-3-8B is part of HuatuoGPT-3 from CUHK-Shenzhen FreedomIntelligence. The lab's current line uses OnePO, a single reinforcement-learning stage without a domain supervised-tuning cold start. This checkpoint's public repo date is 2026-03-20. Collected 2026-10-15.", "what_it_is_for": "Medical reasoning. The 8B and 32B cards describe an earlier SeedRL release; the 9B and 27B cards and the October 2026 paper describe OnePO. Both sets of weights are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-3-9b": { "id": "cuhk/huatuogpt/huatuogpt-3-9b", "name": "HuatuoGPT-3-9B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-09-17", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Medical model on Qwen3.5-9B. OnePO medical model. The GitHub table lists HealthBench Professional at 68.3. Thinking mode must stay enabled.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-3-9B is part of HuatuoGPT-3 from CUHK-Shenzhen FreedomIntelligence. The lab's current line uses OnePO, a single reinforcement-learning stage without a domain supervised-tuning cold start. This checkpoint's public repo date is 2026-09-17. Collected 2026-10-15.", "what_it_is_for": "Medical reasoning. The 8B and 32B cards describe an earlier SeedRL release; the 9B and 27B cards and the October 2026 paper describe OnePO. Both sets of weights are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-3-grader-8b": { "id": "cuhk/huatuogpt/huatuogpt-3-grader-8b", "name": "HuatuoGPT-3 grader 8B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-09-17", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Medical model on Qwen3-8B. Rubric grader used at training time. It scores several rubric items in one generation. Not the medical chat model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-3 grader 8B is part of HuatuoGPT-3 from CUHK-Shenzhen FreedomIntelligence. The lab's current line uses OnePO, a single reinforcement-learning stage without a domain supervised-tuning cold start. This checkpoint's public repo date is 2026-09-17. Collected 2026-10-15.", "what_it_is_for": "Medical reasoning. The 8B and 32B cards describe an earlier SeedRL release; the 9B and 27B cards and the October 2026 paper describe OnePO. Both sets of weights are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-7b": { "id": "cuhk/huatuogpt/huatuogpt-7b", "name": "HuatuoGPT-7B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-06-30", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only chat model adapted from Baichuan-7B with medical supervised tuning and RLAIF.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-7B is the 7 billion-parameter Chinese medical chat model from FreedomIntelligence at The Chinese University of Hong Kong, Shenzhen, with the Shenzhen Research Institute of Big Data. The lab released full weights on 30 June 2023. It was still downloadable on 2026-10-15.", "what_it_is_for": "The lab describes it as a medical consultation model trained so a distilled student can answer like a doctor, using real-world medical data plus RLAIF. The 7B checkpoint uses Baichuan-7B as the backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-o1-70b": { "id": "cuhk/huatuogpt/huatuogpt-o1-70b", "name": "HuatuoGPT-o1-70B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-27", "context_length": null, "max_output_tokens": null, "parameters": "70B in the repository name. The Hugging Face base_model tag is meta-llama/Llama-3.1-8B-Instruct, the same tag as HuatuoGPT-o1-8B.", "architecture": "Medical reasoning checkpoint. The repository is named 70B. The Hugging Face base_model tag points at Llama-3.1-8B-Instruct, which conflicts with that name. An earlier read of the lab README listed a Llama-3.1-70B backbone. Both statements are kept.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-o1-70B is a medical reasoning model from CUHK-Shenzhen FreedomIntelligence. It writes a thinking trace before the final answer. Weights were posted in late December 2024. Collected 2026-10-15.", "what_it_is_for": "Complex medical question answering. The README shows outputs in a Thinking section followed by a Final Response section. The example generation cap in the sample script is 2048 new tokens, which is a sample setting, not a published model limit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-o1-72b": { "id": "cuhk/huatuogpt/huatuogpt-o1-72b", "name": "HuatuoGPT-o1-72B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-28", "context_length": null, "max_output_tokens": null, "parameters": "72B", "architecture": "Medical reasoning model on Qwen2.5-72B. Two-stage training: supervised complex chain-of-thought, then PPO with a medical verifier.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-o1-72B is a medical reasoning model from CUHK-Shenzhen FreedomIntelligence. It writes a thinking trace before the final answer. Weights were posted in late December 2024. Collected 2026-10-15.", "what_it_is_for": "Complex medical question answering. The README shows outputs in a Thinking section followed by a Final Response section. The example generation cap in the sample script is 2048 new tokens, which is a sample setting, not a published model limit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-o1-7b": { "id": "cuhk/huatuogpt/huatuogpt-o1-7b", "name": "HuatuoGPT-o1-7B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-26", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Medical reasoning model on Qwen2.5-7B. Two-stage training: supervised complex chain-of-thought, then PPO with a medical verifier.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-o1-7B is a medical reasoning model from CUHK-Shenzhen FreedomIntelligence. It writes a thinking trace before the final answer. Weights were posted in late December 2024. Collected 2026-10-15.", "what_it_is_for": "Complex medical question answering. The README shows outputs in a Thinking section followed by a Final Response section. The example generation cap in the sample script is 2048 new tokens, which is a sample setting, not a published model limit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-o1-8b": { "id": "cuhk/huatuogpt/huatuogpt-o1-8b", "name": "HuatuoGPT-o1-8B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-27", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Medical reasoning model on Llama-3.1-8B. Two-stage training: supervised complex chain-of-thought, then PPO with a medical verifier.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-o1-8B is a medical reasoning model from CUHK-Shenzhen FreedomIntelligence. It writes a thinking trace before the final answer. Weights were posted in late December 2024. Collected 2026-10-15.", "what_it_is_for": "Complex medical question answering. The README shows outputs in a Thinking section followed by a Final Response section. The example generation cap in the sample script is 2048 new tokens, which is a sample setting, not a published model limit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-reward-7b": { "id": "cuhk/huatuogpt/huatuogpt-reward-7b", "name": "HuatuoGPT reward model 7B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-09-26", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Text reward model used for HuatuoGPT RLAIF. Hugging Face tags it as text classification.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The HuatuoGPT reward model is the 7B scorer the CUHK-Shenzhen lab released with the first HuatuoGPT generation. The Hugging Face repo was created on 17 September 2023 and updated on 26 September 2023. Collected 2026-10-15.", "what_it_is_for": "Scores medical responses for the HuatuoGPT RLAIF pipeline. It is a companion model, not the chat doctor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-vision-34b": { "id": "cuhk/huatuogpt/huatuogpt-vision-34b", "name": "HuatuoGPT-Vision-34B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "34B", "architecture": "Medical vision-language model. Backbone Yi-1.5-34B, trained with the PubMedVision corpus (1,294,062 samples) plus LLaVA-style instruction data.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-Vision-34B is CUHK-Shenzhen FreedomIntelligence's medical multimodal model, released 28 June 2024. It answers questions about medical images. Collected 2026-10-15.", "what_it_is_for": "Medical visual question answering. The lab's CLI takes a text query and one or more image paths.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-vision-7b": { "id": "cuhk/huatuogpt/huatuogpt-vision-7b", "name": "HuatuoGPT-Vision-7B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Medical vision-language model. Backbone Qwen2-7B, trained with the PubMedVision corpus (1,294,062 samples) plus LLaVA-style instruction data.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-Vision-7B is CUHK-Shenzhen FreedomIntelligence's medical multimodal model, released 28 June 2024. It answers questions about medical images. Collected 2026-10-15.", "what_it_is_for": "Medical visual question answering. The lab's CLI takes a text query and one or more image paths.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt-vision-7b-qwen2-5-vl": { "id": "cuhk/huatuogpt/huatuogpt-vision-7b-qwen2-5-vl", "name": "HuatuoGPT-Vision-7B-Qwen2.5VL", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-05-25", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Medical vision-language model on Qwen2.5-VL-7B-Instruct, using the lab's 2025 training recipe.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-Vision-7B-Qwen2.5VL is the later 7B medical vision model FreedomIntelligence published on Hugging Face on 25 May 2025, after moving training onto Qwen2.5-VL. It is a separate product from the June 2024 Qwen2 checkpoint. Collected 2026-10-15.", "what_it_is_for": "Same medical image question-answering job as HuatuoGPT-Vision, on a newer Qwen vision backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt2-13b": { "id": "cuhk/huatuogpt/huatuogpt2-13b", "name": "HuatuoGPT-II-13B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-11-21", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "One-stage medical adaptation of Baichuan2-13B-Base. The lab unifies continued-pretraining text and instruction pairs and trains them together.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-II-13B is a second-generation Chinese medical LLM from CUHK-Shenzhen FreedomIntelligence and SRIBD. The lab released 7B, 13B, and 34B weights on 21 November 2023. This dossier was collected on 2026-10-15.", "what_it_is_for": "Chinese medical question answering and multi-turn consultation. The project page says the 7B model was preferred over GPT-4 in the lab's own Chinese medical expert votes, and it was tested on the October 2023 Chinese pharmacist licensure exam.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt2-34b": { "id": "cuhk/huatuogpt/huatuogpt2-34b", "name": "HuatuoGPT-II-34B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-11-21", "context_length": null, "max_output_tokens": null, "parameters": "34B", "architecture": "One-stage medical adaptation of Yi-34B. The lab unifies continued-pretraining text and instruction pairs and trains them together.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-II-34B is a second-generation Chinese medical LLM from CUHK-Shenzhen FreedomIntelligence and SRIBD. The lab released 7B, 13B, and 34B weights on 21 November 2023. This dossier was collected on 2026-10-15.", "what_it_is_for": "Chinese medical question answering and multi-turn consultation. The project page says the 7B model was preferred over GPT-4 in the lab's own Chinese medical expert votes, and it was tested on the October 2023 Chinese pharmacist licensure exam.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/huatuogpt2-7b": { "id": "cuhk/huatuogpt/huatuogpt2-7b", "name": "HuatuoGPT-II-7B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-11-21", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "One-stage medical adaptation of Baichuan2-7B-Base. The lab unifies continued-pretraining text and instruction pairs and trains them together.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuatuoGPT-II-7B is a second-generation Chinese medical LLM from CUHK-Shenzhen FreedomIntelligence and SRIBD. The lab released 7B, 13B, and 34B weights on 21 November 2023. This dossier was collected on 2026-10-15.", "what_it_is_for": "Chinese medical question answering and multi-turn consultation. The project page says the 7B model was preferred over GPT-4 in the lab's own Chinese medical expert votes, and it was tested on the October 2023 Chinese pharmacist licensure exam.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/huatuogpt/medical-o1-verifier-3b": { "id": "cuhk/huatuogpt/medical-o1-verifier-3b", "name": "HuatuoGPT-o1 medical verifier 3B", "maker": "cuhk", "family": "cuhk/huatuogpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-28", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Reward classifier on Llama-3.2-3B-Instruct used as the HuatuoGPT-o1 verifier.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This 3B verifier is the reward model FreedomIntelligence ships for HuatuoGPT-o1 reinforcement learning. A Qwen2.5 twin was posted later. Collected 2026-10-15.", "what_it_is_for": "Checks verifiable medical answers during PPO. It is not the chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/janus-4o/janus-4o-7b": { "id": "cuhk/janus-4o/janus-4o-7b", "name": "Janus-4o-7B", "maker": "cuhk", "family": "cuhk/janus-4o", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-06-23", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Fine-tune of deepseek-ai/Janus-Pro-7B for text-to-image and text-and-image-to-image. The abstract says training used 91K synthetic samples and 6 hours on 8 A800 GPUs. License tag mit.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Janus-4o-7B is a named image-generation fine-tune from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data of DeepSeek Janus-Pro-7B. The paper was submitted on 22 June 2025. Hugging Face createdAt is 2025-06-23. Collected 2026-10-15.", "what_it_is_for": "Text-to-image and text-and-image-to-image generation. The dataset is ShareGPT-4o-Image: 45K text-to-image and 46K text-and-image-to-image samples synthesized with GPT-4o. DeepSeek's Janus-Pro weights stay in the DeepSeek catalog.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/lisa/lisa-13b": { "id": "cuhk/lisa/lisa-13b", "name": "LISA-13B", "maker": "cuhk", "family": "cuhk/lisa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-08-02", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Reasoning-segmentation model built on LLaVA plus SAM. Input is an image and a text query. Output is text plus a segmentation mask. Versions include LISA-13B-llama2-v0, LISA-13B-llama2-v1, and the explanatory twins.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LISA-13B is the reasoning-segmentation model from CUHK DVLab. The paper is dated 2 August 2023 and was a CVPR 2024 oral. Weights are on the xinlai Hugging Face listing. The code repository license is Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Segment the object described by a reasoning query, including queries that do not name the object directly.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/lisa/lisa-7b": { "id": "cuhk/lisa/lisa-7b", "name": "LISA-7B", "maker": "cuhk", "family": "cuhk/lisa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-08-02", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Reasoning-segmentation model built on LLaVA plus SAM. Input is an image and a text query. Output is text plus a segmentation mask. Versions include LISA-7B-v1 and LISA-7B-v1-explanatory on the xinlai Hub listing.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LISA-7B is the reasoning-segmentation model from CUHK DVLab. The paper is dated 2 August 2023 and was a CVPR 2024 oral. Weights are on the xinlai Hugging Face listing. The code repository license is Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Segment the object described by a reasoning query, including queries that do not name the object directly.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/liteflownet/liteflownet": { "id": "cuhk/liteflownet/liteflownet", "name": "LiteFlowNet", "maker": "cuhk", "family": "cuhk/liteflownet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use needs the author's consent.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "5.37M", "architecture": "Lightweight convolutional optical-flow network. CVPR 2018 spotlight. The vendor table lists model size 5.37M, about 40 percent smaller than PWC-Net.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LiteFlowNet is the optical-flow network from Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy at CUHK MMLab. The official code is Caffe. Collected 2026-10-15.", "what_it_is_for": "Estimate optical flow between two frames. A third-party PyTorch reimplementation is not this release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/longllava/longllava-53b-a13b": { "id": "cuhk/longllava/longllava-53b-a13b", "name": "LongLLaVA-53B-A13B", "maker": "cuhk", "family": "cuhk/longllava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-09-04", "context_length": null, "max_output_tokens": null, "parameters": "53B-A13B", "architecture": "Hybrid Mamba-Transformer vision-language model tagged llava_jamba. The name records 53B total and 13B active parameters.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongLLaVA-53B-A13B is a long-context vision-language model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 4 September 2024 and later accepted to EMNLP 2025 Findings (revision 22 September 2025). Hugging Face createdAt is 2024-09-04. Collected 2026-10-15.", "what_it_is_for": "Visual question answering over many images. The paper's efficiency claim is nearly one thousand images on one 80GB A100.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/longllava/longllava-9b": { "id": "cuhk/longllava/longllava-9b", "name": "LongLLaVA-9B", "maker": "cuhk", "family": "cuhk/longllava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-10-11", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Hybrid Mamba-Transformer vision-language model (llava_jamba). The paper says the hybrid can process nearly one thousand images on one A100 80GB.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongLLaVA-9B is a long-context vision-language model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 4 September 2024 and later accepted to EMNLP 2025 Findings (revision 22 September 2025). Hugging Face createdAt is 2024-10-11. Collected 2026-10-15.", "what_it_is_for": "Visual question answering over many images. The paper's efficiency claim is nearly one thousand images on one 80GB A100.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/longllava/longllavamed-9b": { "id": "cuhk/longllava/longllavamed-9b", "name": "LongLLaVAMed-9B", "maker": "cuhk", "family": "cuhk/longllava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-11-07", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Medical variant of the LongLLaVA-9B hybrid, same arXiv id and llava_jamba tag.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongLLaVAMed-9B is a long-context vision-language model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 4 September 2024 and later accepted to EMNLP 2025 Findings (revision 22 September 2025). Hugging Face createdAt is 2024-11-07. Collected 2026-10-15.", "what_it_is_for": "Visual question answering over many images. The paper's efficiency claim is nearly one thousand images on one 80GB A100.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/medgen/medgen-1-3b": { "id": "cuhk/medgen/medgen-1-3b", "name": "MedGen-1.3B", "maker": "cuhk", "family": "cuhk/medgen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-07", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Medical text-to-video model fine-tuned from Alibaba Wan-AI/Wan2.1-T2V-1.3B. The paper's caption set is MedVideoCap-55K, more than 55,000 clips.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedGen-1.3B is a medical video generator from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. It is a named fine-tune of Alibaba Wan2.1-T2V, not a rehost of Wan. The paper was submitted on 8 July 2025. Hugging Face createdAt is 2026-04-07. Collected 2026-10-15.", "what_it_is_for": "Generate medical video from text. Alibaba's own Wan checkpoints stay in the Alibaba catalog.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/medgen/medgen-14b": { "id": "cuhk/medgen/medgen-14b", "name": "MedGen-14B", "maker": "cuhk", "family": "cuhk/medgen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-07-08", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Medical text-to-video model fine-tuned from Alibaba Wan-AI/Wan2.1-T2V-14B. The paper's caption set is MedVideoCap-55K, more than 55,000 clips.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedGen-14B is a medical video generator from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. It is a named fine-tune of Alibaba Wan2.1-T2V, not a rehost of Wan. The paper was submitted on 8 July 2025. Hugging Face createdAt is 2025-07-08. Collected 2026-10-15.", "what_it_is_for": "Generate medical video from text. Alibaba's own Wan checkpoints stay in the Alibaba catalog.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mgm-omni/mgm-omni-32b": { "id": "cuhk/mgm-omni/mgm-omni-32b", "name": "MGM-Omni-32B", "maker": "cuhk", "family": "cuhk/mgm-omni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-08-15", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Omni or speech model from CUHK DVLab. Backbone note: Qwen2.5-VL-32B.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MGM-Omni-32B is part of MGM-Omni from CUHK DVLab. The line takes text, image, video, and speech and can emit text and speech. Collected 2026-10-15.", "what_it_is_for": "Omni understanding and speech generation. TTS folders are the speech LMs. The 2025-09-27 TTS is a later SpeechLM the README describes as stronger.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mgm-omni/mgm-omni-7b": { "id": "cuhk/mgm-omni/mgm-omni-7b", "name": "MGM-Omni-7B", "maker": "cuhk", "family": "cuhk/mgm-omni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-08-08", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Omni or speech model from CUHK DVLab. Backbone note: Qwen2.5-VL-7B-Instruct.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MGM-Omni-7B is part of MGM-Omni from CUHK DVLab. The line takes text, image, video, and speech and can emit text and speech. Collected 2026-10-15.", "what_it_is_for": "Omni understanding and speech generation. TTS folders are the speech LMs. The 2025-09-27 TTS is a later SpeechLM the README describes as stronger.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mgm-omni/mgm-omni-tts-0-6b": { "id": "cuhk/mgm-omni/mgm-omni-tts-0-6b", "name": "MGM-Omni-TTS-0.6B", "maker": "cuhk", "family": "cuhk/mgm-omni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-08-18", "context_length": null, "max_output_tokens": null, "parameters": "README model-size column says 0.6B. The Hugging Face base tag says a quantized Qwen3-0.6B.", "architecture": "Omni or speech model from CUHK DVLab. Backbone note: Qwen3 speech LM.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MGM-Omni-TTS-0.6B is part of MGM-Omni from CUHK DVLab. The line takes text, image, video, and speech and can emit text and speech. Collected 2026-10-15.", "what_it_is_for": "Omni understanding and speech generation. TTS folders are the speech LMs. The 2025-09-27 TTS is a later SpeechLM the README describes as stronger.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mgm-omni/mgm-omni-tts-2b": { "id": "cuhk/mgm-omni/mgm-omni-tts-2b", "name": "MGM-Omni-TTS-2B", "maker": "cuhk", "family": "cuhk/mgm-omni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-08-18", "context_length": null, "max_output_tokens": null, "parameters": "README model-size column says 2B. The Hugging Face base tag says Qwen3-1.7B. Both are stored. Neither is discarded.", "architecture": "Omni or speech model from CUHK DVLab. Backbone note: Qwen3 speech LM.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MGM-Omni-TTS-2B is part of MGM-Omni from CUHK DVLab. The line takes text, image, video, and speech and can emit text and speech. Collected 2026-10-15.", "what_it_is_for": "Omni understanding and speech generation. TTS folders are the speech LMs. The 2025-09-27 TTS is a later SpeechLM the README describes as stronger.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mgm-omni/mgm-omni-tts-2b-0927": { "id": "cuhk/mgm-omni/mgm-omni-tts-2b-0927", "name": "MGM-Omni-TTS-2B-0927", "maker": "cuhk", "family": "cuhk/mgm-omni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-09-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Omni or speech model from CUHK DVLab. Backbone note: Later SpeechLM the README calls stronger.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MGM-Omni-TTS-2B-0927 is part of MGM-Omni from CUHK DVLab. The line takes text, image, video, and speech and can emit text and speech. Collected 2026-10-15.", "what_it_is_for": "Omni understanding and speech generation. TTS folders are the speech LMs. The 2025-09-27 TTS is a later SpeechLM the README describes as stronger.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mgm-omni/mgm-omni-tts-4b": { "id": "cuhk/mgm-omni/mgm-omni-tts-4b", "name": "MGM-Omni-TTS-4B", "maker": "cuhk", "family": "cuhk/mgm-omni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-08-18", "context_length": null, "max_output_tokens": null, "parameters": "README model-size column says 4B. The Hugging Face tag says Qwen3-4B-Instruct-2507.", "architecture": "Omni or speech model from CUHK DVLab. Backbone note: Qwen3-4B-Instruct-2507.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MGM-Omni-TTS-4B is part of MGM-Omni from CUHK DVLab. The line takes text, image, video, and speech and can emit text and speech. Collected 2026-10-15.", "what_it_is_for": "Omni understanding and speech generation. TTS folders are the speech LMs. The 2025-09-27 TTS is a later SpeechLM the README describes as stronger.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mini-gemini/mgm-13b": { "id": "cuhk/mini-gemini/mgm-13b", "name": "Mini-Gemini-13B", "maker": "cuhk", "family": "cuhk/mini-gemini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research-only weights. Code is Apache-2.0. Data is CC BY-NC 4.0. Weights are also bound by the LLaVA, LLaMA, Vicuna, and GPT-4 licenses of the pieces used.", "release_date": "2024-03-28", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Mini-Gemini vision-language model on Vicuna-13B. Dual encoders, CLIP-L. Low resolution 336 or 672. High-resolution candidates 768 or 1536. Standard-resolution row.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mini-Gemini-13B is a fully finetuned stage-1-plus-stage-2 checkpoint from CUHK DVLab's Mini-Gemini line. The lab announced weights on 28 March 2024. LLaMA 3 support, which covers the 8B models, was noted on 3 May 2024. Collected 2026-10-15.", "what_it_is_for": "Image and text question answering. The lab's table treats each HD twin as its own model row, so HD checkpoints have their own folders. Stage-1 pretrain files are versions, not products. The project states it is not affiliated with Google.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mini-gemini/mgm-13b-hd": { "id": "cuhk/mini-gemini/mgm-13b-hd", "name": "Mini-Gemini-13B-HD", "maker": "cuhk", "family": "cuhk/mini-gemini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research-only weights. Code is Apache-2.0. Data is CC BY-NC 4.0. Weights are also bound by the LLaVA, LLaMA, Vicuna, and GPT-4 licenses of the pieces used.", "release_date": "2024-03-28", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Mini-Gemini vision-language model on Vicuna-13B. Dual encoders, CLIP-L. Low resolution 336 or 672. High-resolution candidates 768 or 1536. HD row, high-resolution path 672/1536.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mini-Gemini-13B-HD is a fully finetuned stage-1-plus-stage-2 checkpoint from CUHK DVLab's Mini-Gemini line. The lab announced weights on 28 March 2024. LLaMA 3 support, which covers the 8B models, was noted on 3 May 2024. Collected 2026-10-15.", "what_it_is_for": "Image and text question answering. The lab's table treats each HD twin as its own model row, so HD checkpoints have their own folders. Stage-1 pretrain files are versions, not products. The project states it is not affiliated with Google.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mini-gemini/mgm-2b": { "id": "cuhk/mini-gemini/mgm-2b", "name": "Mini-Gemini-2B", "maker": "cuhk", "family": "cuhk/mini-gemini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research-only weights. Code is Apache-2.0. Data is CC BY-NC 4.0. Weights are also bound by the LLaVA, LLaMA, Vicuna, and GPT-4 licenses of the pieces used.", "release_date": "2024-03-28", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Mini-Gemini vision-language model on Gemma-2B. Dual encoders, CLIP-L. Low resolution 336 or 672. High-resolution candidates 768 or 1536. Standard-resolution row.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mini-Gemini-2B is a fully finetuned stage-1-plus-stage-2 checkpoint from CUHK DVLab's Mini-Gemini line. The lab announced weights on 28 March 2024. LLaMA 3 support, which covers the 8B models, was noted on 3 May 2024. Collected 2026-10-15.", "what_it_is_for": "Image and text question answering. The lab's table treats each HD twin as its own model row, so HD checkpoints have their own folders. Stage-1 pretrain files are versions, not products. The project states it is not affiliated with Google.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mini-gemini/mgm-34b": { "id": "cuhk/mini-gemini/mgm-34b", "name": "Mini-Gemini-34B", "maker": "cuhk", "family": "cuhk/mini-gemini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research-only weights. Code is Apache-2.0. Data is CC BY-NC 4.0. Weights are also bound by the LLaVA, LLaMA, Vicuna, and GPT-4 licenses of the pieces used.", "release_date": "2024-03-28", "context_length": null, "max_output_tokens": null, "parameters": "34B", "architecture": "Mini-Gemini vision-language model on Nous-Hermes-2-Yi-34B. Dual encoders, CLIP-L. Low resolution 336 or 672. High-resolution candidates 768 or 1536. Standard-resolution row.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mini-Gemini-34B is a fully finetuned stage-1-plus-stage-2 checkpoint from CUHK DVLab's Mini-Gemini line. The lab announced weights on 28 March 2024. LLaMA 3 support, which covers the 8B models, was noted on 3 May 2024. Collected 2026-10-15.", "what_it_is_for": "Image and text question answering. The lab's table treats each HD twin as its own model row, so HD checkpoints have their own folders. Stage-1 pretrain files are versions, not products. The project states it is not affiliated with Google.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mini-gemini/mgm-34b-hd": { "id": "cuhk/mini-gemini/mgm-34b-hd", "name": "Mini-Gemini-34B-HD", "maker": "cuhk", "family": "cuhk/mini-gemini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research-only weights. Code is Apache-2.0. Data is CC BY-NC 4.0. Weights are also bound by the LLaVA, LLaMA, Vicuna, and GPT-4 licenses of the pieces used.", "release_date": "2024-03-28", "context_length": null, "max_output_tokens": null, "parameters": "34B", "architecture": "Mini-Gemini vision-language model on Nous-Hermes-2-Yi-34B. Dual encoders, CLIP-L. Low resolution 336 or 672. High-resolution candidates 768 or 1536. HD row, high-resolution path 672/1536.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mini-Gemini-34B-HD is a fully finetuned stage-1-plus-stage-2 checkpoint from CUHK DVLab's Mini-Gemini line. The lab announced weights on 28 March 2024. LLaMA 3 support, which covers the 8B models, was noted on 3 May 2024. Collected 2026-10-15.", "what_it_is_for": "Image and text question answering. The lab's table treats each HD twin as its own model row, so HD checkpoints have their own folders. Stage-1 pretrain files are versions, not products. The project states it is not affiliated with Google.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mini-gemini/mgm-7b": { "id": "cuhk/mini-gemini/mgm-7b", "name": "Mini-Gemini-7B", "maker": "cuhk", "family": "cuhk/mini-gemini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research-only weights. Code is Apache-2.0. Data is CC BY-NC 4.0. Weights are also bound by the LLaVA, LLaMA, Vicuna, and GPT-4 licenses of the pieces used.", "release_date": "2024-03-28", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Mini-Gemini vision-language model on Vicuna-7B. Dual encoders, CLIP-L. Low resolution 336 or 672. High-resolution candidates 768 or 1536. Standard-resolution row.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mini-Gemini-7B is a fully finetuned stage-1-plus-stage-2 checkpoint from CUHK DVLab's Mini-Gemini line. The lab announced weights on 28 March 2024. LLaMA 3 support, which covers the 8B models, was noted on 3 May 2024. Collected 2026-10-15.", "what_it_is_for": "Image and text question answering. The lab's table treats each HD twin as its own model row, so HD checkpoints have their own folders. Stage-1 pretrain files are versions, not products. The project states it is not affiliated with Google.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mini-gemini/mgm-7b-hd": { "id": "cuhk/mini-gemini/mgm-7b-hd", "name": "Mini-Gemini-7B-HD", "maker": "cuhk", "family": "cuhk/mini-gemini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research-only weights. Code is Apache-2.0. Data is CC BY-NC 4.0. Weights are also bound by the LLaVA, LLaMA, Vicuna, and GPT-4 licenses of the pieces used.", "release_date": "2024-03-28", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Mini-Gemini vision-language model on Vicuna-7B. Dual encoders, CLIP-L. Low resolution 336 or 672. High-resolution candidates 768 or 1536. HD row, high-resolution path 672/1536.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mini-Gemini-7B-HD is a fully finetuned stage-1-plus-stage-2 checkpoint from CUHK DVLab's Mini-Gemini line. The lab announced weights on 28 March 2024. LLaMA 3 support, which covers the 8B models, was noted on 3 May 2024. Collected 2026-10-15.", "what_it_is_for": "Image and text question answering. The lab's table treats each HD twin as its own model row, so HD checkpoints have their own folders. Stage-1 pretrain files are versions, not products. The project states it is not affiliated with Google.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mini-gemini/mgm-8b": { "id": "cuhk/mini-gemini/mgm-8b", "name": "Mini-Gemini-8B", "maker": "cuhk", "family": "cuhk/mini-gemini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research-only weights. Code is Apache-2.0. Data is CC BY-NC 4.0. Weights are also bound by the LLaVA, LLaMA, Vicuna, and GPT-4 licenses of the pieces used.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Mini-Gemini vision-language model on Llama-3-8B-Instruct. Dual encoders, CLIP-L. Low resolution 336 or 672. High-resolution candidates 768 or 1536. Standard-resolution row.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mini-Gemini-8B is a fully finetuned stage-1-plus-stage-2 checkpoint from CUHK DVLab's Mini-Gemini line. The lab announced weights on 28 March 2024. LLaMA 3 support, which covers the 8B models, was noted on 3 May 2024. Collected 2026-10-15.", "what_it_is_for": "Image and text question answering. The lab's table treats each HD twin as its own model row, so HD checkpoints have their own folders. Stage-1 pretrain files are versions, not products. The project states it is not affiliated with Google.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mini-gemini/mgm-8b-hd": { "id": "cuhk/mini-gemini/mgm-8b-hd", "name": "Mini-Gemini-8B-HD", "maker": "cuhk", "family": "cuhk/mini-gemini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research-only weights. Code is Apache-2.0. Data is CC BY-NC 4.0. Weights are also bound by the LLaVA, LLaMA, Vicuna, and GPT-4 licenses of the pieces used.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Mini-Gemini vision-language model on Llama-3-8B-Instruct. Dual encoders, CLIP-L. Low resolution 336 or 672. High-resolution candidates 768 or 1536. HD row, high-resolution path 672/1536.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mini-Gemini-8B-HD is a fully finetuned stage-1-plus-stage-2 checkpoint from CUHK DVLab's Mini-Gemini line. The lab announced weights on 28 March 2024. LLaMA 3 support, which covers the 8B models, was noted on 3 May 2024. Collected 2026-10-15.", "what_it_is_for": "Image and text question answering. The lab's table treats each HD twin as its own model row, so HD checkpoints have their own folders. Stage-1 pretrain files are versions, not products. The project states it is not affiliated with Google.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mini-gemini/mgm-8x7b": { "id": "cuhk/mini-gemini/mgm-8x7b", "name": "Mini-Gemini-8x7B", "maker": "cuhk", "family": "cuhk/mini-gemini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research-only weights. Code is Apache-2.0. Data is CC BY-NC 4.0. Weights are also bound by the LLaVA, LLaMA, Vicuna, and GPT-4 licenses of the pieces used.", "release_date": "2024-03-28", "context_length": null, "max_output_tokens": null, "parameters": "8x7B", "architecture": "Mini-Gemini vision-language model on Mixtral-8x7B. Dual encoders, CLIP-L. Low resolution 336 or 672. High-resolution candidates 768 or 1536. Standard-resolution row.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mini-Gemini-8x7B is a fully finetuned stage-1-plus-stage-2 checkpoint from CUHK DVLab's Mini-Gemini line. The lab announced weights on 28 March 2024. LLaMA 3 support, which covers the 8B models, was noted on 3 May 2024. Collected 2026-10-15.", "what_it_is_for": "Image and text question answering. The lab's table treats each HD twin as its own model row, so HD checkpoints have their own folders. Stage-1 pretrain files are versions, not products. The project states it is not affiliated with Google.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/mini-gemini/mgm-8x7b-hd": { "id": "cuhk/mini-gemini/mgm-8x7b-hd", "name": "Mini-Gemini-8x7B-HD", "maker": "cuhk", "family": "cuhk/mini-gemini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research-only weights. Code is Apache-2.0. Data is CC BY-NC 4.0. Weights are also bound by the LLaVA, LLaMA, Vicuna, and GPT-4 licenses of the pieces used.", "release_date": "2024-03-28", "context_length": null, "max_output_tokens": null, "parameters": "8x7B", "architecture": "Mini-Gemini vision-language model on Mixtral-8x7B. Dual encoders, CLIP-L. Low resolution 336 or 672. High-resolution candidates 768 or 1536. HD row, high-resolution path 672/1536.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mini-Gemini-8x7B-HD is a fully finetuned stage-1-plus-stage-2 checkpoint from CUHK DVLab's Mini-Gemini line. The lab announced weights on 28 March 2024. LLaMA 3 support, which covers the 8B models, was noted on 3 May 2024. Collected 2026-10-15.", "what_it_is_for": "Image and text question answering. The lab's table treats each HD twin as its own model row, so HD checkpoints have their own folders. Stage-1 pretrain files are versions, not products. The project states it is not affiliated with Google.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/phoenix/phoenix-chat-7b": { "id": "cuhk/phoenix/phoenix-chat-7b", "name": "Phoenix-Chat-7B", "maker": "cuhk", "family": "cuhk/phoenix", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-04-08", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "BLOOM-based multilingual chat model. Hugging Face pipeline tag is text-generation and the license tag is apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phoenix-Chat-7B is a Phoenix weight from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Phoenix is the lab's cross-language line and the totem shared with CUHK. Release date is the Hugging Face createdAt 2023-04-08. Collected 2026-10-15.", "what_it_is_for": "Multilingual chat. The lab distinguished Phoenix (many languages) from Chimera (Latin languages). The language list on the card was not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/phoenix/phoenix-inst-chat-7b": { "id": "cuhk/phoenix/phoenix-inst-chat-7b", "name": "Phoenix-Inst-Chat-7B", "maker": "cuhk", "family": "cuhk/phoenix", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-04-11", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "BLOOM-based multilingual chat model. Hugging Face pipeline tag is text-generation and the license tag is apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phoenix-Inst-Chat-7B is a Phoenix weight from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Phoenix is the lab's cross-language line and the totem shared with CUHK. Release date is the Hugging Face createdAt 2023-04-11. Collected 2026-10-15.", "what_it_is_for": "Multilingual chat. The lab distinguished Phoenix (many languages) from Chimera (Latin languages). The language list on the card was not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/phoenix/phoenix-multiple-langs-v1": { "id": "cuhk/phoenix/phoenix-multiple-langs-v1", "name": "Phoenix multiple languages v1", "maker": "cuhk", "family": "cuhk/phoenix", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-08-12", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "BLOOM-based multilingual chat model. Hugging Face pipeline tag is text-generation and the license tag is apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phoenix multiple languages v1 is a Phoenix weight from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Phoenix is the lab's cross-language line and the totem shared with CUHK. Release date is the Hugging Face createdAt 2023-08-12. Collected 2026-10-15.", "what_it_is_for": "Multilingual chat. The lab distinguished Phoenix (many languages) from Chimera (Latin languages). The language list on the card was not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/platolm/platolm-7b": { "id": "cuhk/platolm/platolm-7b", "name": "PlatoLM-7B", "maker": "cuhk", "family": "cuhk/platolm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-08-21", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Llama text-generation checkpoint. License tag apache-2.0. No arXiv id was in the models-API tag list.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PlatoLM-7B is a named Llama checkpoint from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2023-08-21. Collected 2026-10-15.", "what_it_is_for": "The card body was not opened beyond the tag list. The name and the 7B size are taken from the repository id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/psanet/psanet": { "id": "cuhk/psanet/psanet", "name": "PSANet", "maker": "cuhk", "family": "cuhk/psanet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Point-wise Spatial Attention Network for scene parsing. ECCV 2018. Authors include CUHK, the CUHK-SenseTime Joint Lab, SenseTime Research, NTU, and Tencent Youtu Lab. The code contact is the CUHK author.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PSANet is a scene-parsing network released by Hengshuang Zhao and co-authors while the code contact was hszhao@cse.cuhk.edu.hk. The paper is ECCV 2018. It ranked first on the 2018 WAD Drivable Area Segmentation Challenge. Collected 2026-10-15.", "what_it_is_for": "Semantic scene parsing with point-wise spatial attention. Dataset checkpoints are versions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/pspnet/pspnet": { "id": "cuhk/pspnet/pspnet", "name": "PSPNet", "maker": "cuhk", "family": "cuhk/pspnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pyramid Scene Parsing Network. A pyramid pooling module aggregates global context before the final scene-parsing classifier. CVPR 2017.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PSPNet is the scene-parsing network from Hengshuang Zhao, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia at The Chinese University of Hong Kong, with Jianping Shi at SenseTime. It won the 2016 ImageNet Scene Parsing Challenge. Collected 2026-10-15.", "what_it_is_for": "Semantic scene parsing. Dataset-specific checkpoints are versions of one model, not separate products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/rag-instruct/rag-instruct-llama3-3b": { "id": "cuhk/rag-instruct/rag-instruct-llama3-3b", "name": "RAG-Instruct-Llama3-3B", "maker": "cuhk", "family": "cuhk/rag-instruct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-01-08", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Retrieval-augmented instruction model on meta-llama/Llama-3.2-3B. The paper builds a 40K instruction set from Wikipedia covering five RAG paradigms.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RAG-Instruct-Llama3-3B is a RAG instruction model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 31 December 2024. Hugging Face createdAt is 2025-01-08. Collected 2026-10-15.", "what_it_is_for": "Answer questions with retrieved documents. The abstract says the method improves zero-shot RAG over the baselines it tested. No table cell was copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/rag-instruct/rag-instruct-llama3-8b": { "id": "cuhk/rag-instruct/rag-instruct-llama3-8b", "name": "RAG-Instruct-Llama3-8B", "maker": "cuhk", "family": "cuhk/rag-instruct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-01-08", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Retrieval-augmented instruction model on meta-llama/Llama-3.1-8B. The paper builds a 40K instruction set from Wikipedia covering five RAG paradigms.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RAG-Instruct-Llama3-8B is a RAG instruction model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 31 December 2024. Hugging Face createdAt is 2025-01-08. Collected 2026-10-15.", "what_it_is_for": "Answer questions with retrieved documents. The abstract says the method improves zero-shot RAG over the baselines it tested. No table cell was copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/sepsisagent/sepsisagent-4b": { "id": "cuhk/sepsisagent/sepsisagent-4b", "name": "SepsisAgent-4B", "maker": "cuhk", "family": "cuhk/sepsisagent", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-05-15", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Qwen3 agent for sepsis treatment. The abstract describes a Clinical World Model and a propose-simulate-refine loop, trained with supervised tuning, behavior cloning, and world-model reinforcement learning.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SepsisAgent-4B is a sepsis-treatment agent from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 14 May 2026. Hugging Face createdAt is 2026-05-15. Collected 2026-10-15.", "what_it_is_for": "Recommend fluid and vasopressor actions on MIMIC-IV sepsis trajectories. The abstract says the trained agent can keep useful behavior even after simulator access is removed. No numeric score was copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/shizhengpt/shizhengpt-32b-llm": { "id": "cuhk/shizhengpt/shizhengpt-32b-llm", "name": "ShizhenGPT-32B-LLM", "maker": "cuhk", "family": "cuhk/shizhengpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-08-21", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Traditional Chinese medicine model on Qwen2.5-32B. The abstract says the training mix is more than 100GB of text, more than 200GB of multimodal data, 1.2 million images, and 200 hours of audio.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ShizhenGPT-32B-LLM is a Traditional Chinese Medicine model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 20 August 2025. Hugging Face createdAt is 2025-08-21. Collected 2026-10-15.", "what_it_is_for": "Chinese TCM question answering. VL variants take images. The Omni variant also takes audio. The abstract says code and models are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/shizhengpt/shizhengpt-32b-vl": { "id": "cuhk/shizhengpt/shizhengpt-32b-vl", "name": "ShizhenGPT-32B-VL", "maker": "cuhk", "family": "cuhk/shizhengpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-08-21", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Traditional Chinese medicine model on Qwen2.5-32B. The abstract says the training mix is more than 100GB of text, more than 200GB of multimodal data, 1.2 million images, and 200 hours of audio.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ShizhenGPT-32B-VL is a Traditional Chinese Medicine model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 20 August 2025. Hugging Face createdAt is 2025-08-21. Collected 2026-10-15.", "what_it_is_for": "Chinese TCM question answering. VL variants take images. The Omni variant also takes audio. The abstract says code and models are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/shizhengpt/shizhengpt-7b-llm": { "id": "cuhk/shizhengpt/shizhengpt-7b-llm", "name": "ShizhenGPT-7B-LLM", "maker": "cuhk", "family": "cuhk/shizhengpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-08-21", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Traditional Chinese medicine model on Qwen2.5-7B. The abstract says the training mix is more than 100GB of text, more than 200GB of multimodal data, 1.2 million images, and 200 hours of audio.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ShizhenGPT-7B-LLM is a Traditional Chinese Medicine model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 20 August 2025. Hugging Face createdAt is 2025-08-21. Collected 2026-10-15.", "what_it_is_for": "Chinese TCM question answering. VL variants take images. The Omni variant also takes audio. The abstract says code and models are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/shizhengpt/shizhengpt-7b-omni": { "id": "cuhk/shizhengpt/shizhengpt-7b-omni", "name": "ShizhenGPT-7B-Omni", "maker": "cuhk", "family": "cuhk/shizhengpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-08-21", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Traditional Chinese medicine model on Qwen2.5-7B. The abstract says the training mix is more than 100GB of text, more than 200GB of multimodal data, 1.2 million images, and 200 hours of audio.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ShizhenGPT-7B-Omni is a Traditional Chinese Medicine model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 20 August 2025. Hugging Face createdAt is 2025-08-21. Collected 2026-10-15.", "what_it_is_for": "Chinese TCM question answering. VL variants take images. The Omni variant also takes audio. The abstract says code and models are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/shizhengpt/shizhengpt-7b-vl": { "id": "cuhk/shizhengpt/shizhengpt-7b-vl", "name": "ShizhenGPT-7B-VL", "maker": "cuhk", "family": "cuhk/shizhengpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-08-21", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Traditional Chinese medicine model on Qwen2.5-7B. The abstract says the training mix is more than 100GB of text, more than 200GB of multimodal data, 1.2 million images, and 200 hours of audio.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ShizhenGPT-7B-VL is a Traditional Chinese Medicine model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 20 August 2025. Hugging Face createdAt is 2025-08-21. Collected 2026-10-15.", "what_it_is_for": "Chinese TCM question answering. VL variants take images. The Omni variant also takes audio. The abstract says code and models are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/soundwave/soundwave": { "id": "cuhk/soundwave/soundwave", "name": "Soundwave", "maker": "cuhk", "family": "cuhk/soundwave", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech-text LLM. Hub pipeline tag audio-text-to-text. The abstract says it uses about one-fiftieth of the training data of the Qwen2-Audio comparison and still targets speech translation and AIR-Bench speech tasks.", "modalities": { "input": [ "text", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Soundwave is a speech-text model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. The paper was submitted on 18 February 2025. Hugging Face createdAt is 2025-03-04. Collected 2026-10-15.", "what_it_is_for": "Speech translation and speech understanding with a short training recipe. The abstract says conversational ability is retained. No headline score was copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/srcnn/srcnn": { "id": "cuhk/srcnn/srcnn", "name": "SRCNN", "maker": "cuhk", "family": "cuhk/srcnn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three-layer convolutional network that maps a bicubic-upscaled low-resolution image to a high-resolution image. Super-Resolution Convolutional Neural Network.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SRCNN is the super-resolution network from Multimedia Laboratory, Information Engineering, The Chinese University of Hong Kong, with a Microsoft Research co-author. The project page documents an ECCV 2014 paper and a TPAMI 2015 paper. Collected 2026-10-15.", "what_it_is_for": "Single-image super-resolution. The Matlab test package and the Caffe training code are posted on the CUHK project page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/tinydeepseek/tinydeepseek-0-5b-base": { "id": "cuhk/tinydeepseek/tinydeepseek-0-5b-base", "name": "TinyDeepSeek-0.5B-base", "maker": "cuhk", "family": "cuhk/tinydeepseek", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-03-10", "context_length": null, "max_output_tokens": null, "parameters": "0.5B", "architecture": "Small lab-trained model with architecture tag deepseek_v3 and custom code. It is not a hosted copy of DeepSeek's released weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyDeepSeek-0.5B-base is a small base model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2025-03-10. Collected 2026-10-15.", "what_it_is_for": "A compact DeepSeek-V3-style base checkpoint trained by the lab. Checkpoint-dump repositories are versions, not separate products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuhk/tinydeepseek/tinydeepseek-3-3b-base": { "id": "cuhk/tinydeepseek/tinydeepseek-3-3b-base", "name": "TinyDeepSeek-3.3B-base", "maker": "cuhk", "family": "cuhk/tinydeepseek", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-03-10", "context_length": null, "max_output_tokens": null, "parameters": "3.3B", "architecture": "Small lab-trained model with architecture tag deepseek_v3 and custom code. It is not a hosted copy of DeepSeek's released weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyDeepSeek-3.3B-base is a small base model from FreedomIntelligence, the School of Data Science lab at The Chinese University of Hong Kong, Shenzhen, often with the Shenzhen Research Institute of Big Data. Hugging Face createdAt is 2025-03-10. Collected 2026-10-15.", "what_it_is_for": "A compact DeepSeek-V3-style base checkpoint trained by the lab. Checkpoint-dump repositories are versions, not separate products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/charles-translator/charles-translator": { "id": "cuni/charles-translator/charles-translator", "name": "Charles Translator", "maker": "cuni", "family": "cuni/charles-translator", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2022-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer with iterated block back-translation, in Tensor2Tensor, as described for the 2022 Czech-Ukrainian system. The WMT 2022 system paper contrasts it with the constrained CUNI-Transformer. A public weight archive for this 2022 system was not found.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Charles Translator is Charles University's public Czech-Ukrainian machine translation service, started by UFAL in March 2022. The service is active. A public weight dump of that 2022 system was not found. Collected 2026-10-15.", "what_it_is_for": "UFAL built it during a hackathon so refugees from Ukraine could read Czech official and everyday text without an English pivot. The LREC-COLING 2024 paper is the system description. The WMT 2022 paper treats it as the unconstrained system, distinct from CUNI-Transformer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/charles-translator/edukate-2025": { "id": "cuni/charles-translator/edukate-2025", "name": "EdUKate translation models 2025", "maker": "cuni", "family": "cuni/charles-translator", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapters on EuroLLM-9B-Instruct for sentence-level educational-domain translation. The WMT 2025 paper's CUNI-EdUKate-v1 system is Czech-to-Ukrainian and adds contrastive preference optimization. The LINDAT package also lists Czech-to-English and Czech-to-German.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EdUKate 2025 is a set of three educational translation LoRA adapters from UFAL, published on LINDAT and used in Charles Translator. They adapt EuroLLM-9B-Instruct. Collected 2026-10-15.", "what_it_is_for": "The EdUKate project targets language barriers for non-Czech-speaking children in Czech schools. The LINDAT item ships Czech-to-Ukrainian, Czech-to-English, and Czech-to-German sentence-level adapters. The WMT 2025 paper uses the name CUNI-EdUKate-v1 for the Czech-to-Ukrainian unconstrained system.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/corpipe/corpipe23": { "id": "cuni/corpipe/corpipe23", "name": "CorPipe 23", "maker": "cuni", "family": "cuni/corpipe", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-12-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multilingual coreference model for CorefUD 1.1. The LINDAT title is corpipe23-corefud1.1-231206. File count in the search snippet is 1 file, 1.78 GB. The item page was not opened, so license and score were not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CorPipe 23 is Milan Straka's multilingual coreference model for CorefUD 1.1, published by UFAL on 6 December 2023. This dossier uses the LINDAT search record, not a full item page. Collected 2026-10-15.", "what_it_is_for": "It is the 2023 CorPipe release: one multilingual checkpoint rather than the later base, large, and xl split.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/corpipe/corpipe25": { "id": "cuni/corpipe/corpipe25", "name": "CorPipe 25", "maker": "cuni", "family": "cuni/corpipe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2025-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder fine-tunes for multilingual coreference. Three checkpoints: umT5-base, mT5-large, and umT5-xl. The xl card says segment size 2560 except 512 for cu_proiel and grc_proiel. Empty nodes must already be on the input.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CorPipe 25 is UFAL's CRAC 2025 multilingual coreference system, released as three CorefUD 1.3 checkpoints. The xl model is public under CC BY-NC-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "Milan Straka evaluates multilingual encoders for coreference. The xl card is a umT5-xl model that predicts coreference when empty nodes are already present. It is language-agnostic inside the umT5 language set, with zero-shot numbers left to the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/corpipe/corpipe26": { "id": "cuni/corpipe/corpipe26", "name": "CorPipe 26", "maker": "cuni", "family": "cuni/corpipe", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2026-07-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CorefUD 1.4 coreference models in two decoding styles. Bases from Hub tags: umT5-base, mT5-large, and umT5-xl, each as twostage and onestage. Cards were not opened beyond the Hub API tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CorPipe 26 is UFAL's CRAC 2026 coreference release: six CorefUD 1.4 checkpoints, one-stage and two-stage, at three sizes. Hub tags put them under CC BY-NC-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "The tags present them as CorPipe models for the CRAC 2026 shared task. A system paper was not opened for this folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/corpipe/crac2026-empty-nodes": { "id": "cuni/corpipe/crac2026-empty-nodes", "name": "CRAC 2026 empty-nodes baseline", "maker": "cuni", "family": "cuni/corpipe", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2026-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of FacebookAI/xlm-roberta-large for empty-node prediction, from the Hub tags. The README was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The CRAC 2026 empty-nodes baseline is a public XLM-RoBERTa-large fine-tune from the UFAL Hub org. It predicts empty nodes for CorefUD 1.4. The card body was not read. Collected 2026-10-15.", "what_it_is_for": "CorPipe-style coreference models need empty nodes on the input. This repo is the 2026 baseline for that step. It is a named release, so it has a folder, but it is not the CorPipe 26 scorer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/cubbitt/cubbitt": { "id": "cuni/cubbitt/cubbitt", "name": "CUBBITT", "maker": "cuni", "family": "cuni/cubbitt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2020-09-01", "context_length": null, "max_output_tokens": null, "parameters": "213000000", "architecture": "Transformer in Tensor2Tensor 1.6.6, exported for TensorFlow Serving. Block back-translation. Martin Popel's 2021 talk slide prints 213 million parameters. The LINDAT records say hyper-parameters were not included and tell the reader to contact the maintainer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CUBBITT is UFAL's document-capable Transformer translation system from Charles University. Sentence-level English-Czech, English-French, and English-Polish weights are public for non-commercial use, and the LINDAT service is still up. Collected 2026-10-15.", "what_it_is_for": "The Nature Communications paper presents CUBBITT for English-to-Czech news translation and reports that, in a context-aware blind test, it beat a professional agency on adequacy. The same method was checked on French and Polish.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/dama/dama-13b": { "id": "cuni/dama/dama-13b", "name": "DAMA 13B", "maker": "cuni", "family": "cuni/dama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2", "release_date": "2024-03-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA 13B weights edited with DAMA. The method multiplies selected feed-forward output matrices by an orthogonal projection. The paper says parameter count, architecture, and inference cost stay the same as the base LLaMA.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DAMA 13B is a gender-debiased LLaMA 13B checkpoint from UFAL at Charles University. The weights are public under the Llama 2 license. Collected 2026-10-15.", "what_it_is_for": "Tomasz Limisiewicz, David Marecek, and Tomas Musil edit mid-upper feed-forward layers so stereotypical gender signals are reduced while language-model benchmarks stay close to the original LLaMA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/dama/dama-33b": { "id": "cuni/dama/dama-33b", "name": "DAMA 33B", "maker": "cuni", "family": "cuni/dama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2", "release_date": "2024-03-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA 33B weights edited with DAMA. The method multiplies selected feed-forward output matrices by an orthogonal projection. The paper says parameter count, architecture, and inference cost stay the same as the base LLaMA.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DAMA 33B is a gender-debiased LLaMA 33B checkpoint from UFAL at Charles University. The weights are public under the Llama 2 license. Collected 2026-10-15.", "what_it_is_for": "Tomasz Limisiewicz, David Marecek, and Tomas Musil edit mid-upper feed-forward layers so stereotypical gender signals are reduced while language-model benchmarks stay close to the original LLaMA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/dama/dama-65b": { "id": "cuni/dama/dama-65b", "name": "DAMA 65B", "maker": "cuni", "family": "cuni/dama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2", "release_date": "2024-03-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA 65B weights edited with DAMA. The method multiplies selected feed-forward output matrices by an orthogonal projection. The paper says parameter count, architecture, and inference cost stay the same as the base LLaMA.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DAMA 65B is a gender-debiased LLaMA 65B checkpoint from UFAL at Charles University. The weights are public under the Llama 2 license. Collected 2026-10-15.", "what_it_is_for": "Tomasz Limisiewicz, David Marecek, and Tomas Musil edit mid-upper feed-forward layers so stereotypical gender signals are reduced while language-model benchmarks stay close to the original LLaMA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/dama/dama-7b": { "id": "cuni/dama/dama-7b", "name": "DAMA 7B", "maker": "cuni", "family": "cuni/dama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2", "release_date": "2024-03-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaMA 7B weights edited with DAMA. The method multiplies selected feed-forward output matrices by an orthogonal projection. The paper says parameter count, architecture, and inference cost stay the same as the base LLaMA.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DAMA 7B is a gender-debiased LLaMA 7B checkpoint from UFAL at Charles University. The weights are public under the Llama 2 license. Collected 2026-10-15.", "what_it_is_for": "Tomasz Limisiewicz, David Marecek, and Tomas Musil edit mid-upper feed-forward layers so stereotypical gender signals are reduced while language-model benchmarks stay close to the original LLaMA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/eleczech/eleczech-lc-small": { "id": "cuni/eleczech/eleczech-lc-small", "name": "EleCzech-LC-small", "maker": "cuni", "family": "cuni/eleczech", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2022-04-24", "context_length": 512, "max_output_tokens": null, "parameters": "13500000", "architecture": "ELECTRA. Hub config: 12 layers, hidden 256, intermediate 1024, 4 heads, embedding size 128, vocab 30522, max_position_embeddings 512. The Hub model page prints 13.5M params.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EleCzech-LC-small is a small Czech Electra encoder released by UFAL at Charles University. It is an open-weight, non-commercial checkpoint. No separate paper was found. Collected 2026-10-15.", "what_it_is_for": "The card presents it as a lowercased-Czech language representation model trained on the same collection as RobeCzech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/historical-page/clip-historical-page": { "id": "cuni/historical-page/clip-historical-page", "name": "Historical page classifier (CLIP)", "maker": "cuni", "family": "cuni/historical-page", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-01", "context_length": null, "max_output_tokens": null, "parameters": "149620000", "architecture": "CLIP fine-tunes for the same eleven archival page categories. The card says the default main revision is v1.1.3.7, a ViT-B/16 CLIP. The card prints 149.62 million parameters for openai/clip-vit-base-patch16.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is UFAL's CLIP fine-tune for sorting historical archive pages, public under MIT. The card's default is CLIP ViT-B/16 revision v1.1.3.7. Collected 2026-10-15.", "what_it_is_for": "It does the same eleven-way sort as the ViT and CNN page classifiers, using CLIP so the category names are text prompts. The card says the smallest ViT-B/16 fine-tune beat the larger CLIP variants they trained.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/historical-page/vit-historical-page": { "id": "cuni/historical-page/vit-historical-page", "name": "Historical page classifier (ViT line)", "maker": "cuni", "family": "cuni/historical-page", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image classifier for archival page types. The card's default on main is v4.3, a fine-tune of timm/regnety_160.swag_ft_in1k. The same README also ships ViT, EfficientNetV2, and other bases as revisions. The card's parameter table prints 84 million for regnety_160, 87 million for ViT-base, and 305 million for ViT-large. A Hub listing badge showed 80.7M, which does not match those table cells.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The historical page classifier is a UFAL image model that sorts scanned archival pages before OCR. The Hub repo is public under MIT, and the card's default revision is a RegNetY fine-tune even though the repo name says ViT. Collected 2026-10-15.", "what_it_is_for": "The ATRIUM project uses it to tell drawings, photos, handwriting, print, typing, and tables apart, across eleven categories, so later tools see the right page type. Training pages are archaeological reports and other archival scans from about 1920 to 2020.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/malach/labse-malach-multilabel": { "id": "cuni/malach/labse-malach-multilabel", "name": "LaBSE-MALACH-Multilabel", "maker": "cuni", "family": "cuni/malach", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-11-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of sentence-transformers/LaBSE for multilabel subject classification. The card says an input is about 350 words on average and the model scores 2,800 Visual History Archive keyword ids.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaBSE-MALACH-Multilabel is a UFAL classifier that tags Visual History Archive text with subject keywords. It is a MIT-licensed fine-tune of LaBSE. Collected 2026-10-15.", "what_it_is_for": "Given a text segment, it predicts probabilities for 2,800 ontology ids. The card says the training segments average about 350 words and that the data are in six languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/malach/xlm-roberta-ehri-malach-ner": { "id": "cuni/malach/xlm-roberta-ehri-malach-ner", "name": "XLM-RoBERTa EHRI-MALACH NER", "maker": "cuni", "family": "cuni/malach", "status": "active", "confidence": "low", "weights": "open-weights", "license": "", "release_date": "2026-04-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hub API tags on 2026-10-15 were only safetensors and xlm-roberta. No license, no base-model tag, no language list, and no pipeline tag were returned. The README was not fetched successfully as a model card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ufal/xlm-roberta-ehri-malach-ner is a public UFAL weight repo whose name points at named-entity recognition for EHRI and MALACH text. The Hub API returned no model card. Collected 2026-10-15.", "what_it_is_for": "The repository exists and has safetensors weights tagged as XLM-RoBERTa. What data it was trained on, and whether it is the NER tool described in the EHRI literature, was not verified from a card or paper in this pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/malach/xlm-roberta-malach": { "id": "cuni/malach/xlm-roberta-malach", "name": "XLM-RoBERTa-MALACH", "maker": "cuni", "family": "cuni/malach", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of FacebookAI/xlm-roberta-large on Visual History Archive speech transcripts and their machine translations. Card: AdamW, learning rate 1e-5, effective batch 2048, 1 epoch, 34k steps, MLM, about 24 hours on 4 L40S GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XLM-RoBERTa-MALACH is UFAL's continued pretraining of XLM-RoBERTa-large on Holocaust oral-history transcripts from the Visual History Archive. The checkpoint is public under MIT. Collected 2026-10-15.", "what_it_is_for": "The card says part of the text is ASR from domain Wav2Vec 2.0 and Zipformer models on UWebASR, and part is machine translation of that ASR into seven languages with MADLAD-400-3B-MT. The model is a fill-mask encoder for that domain, not a speech recognizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/mate/akces-mate": { "id": "cuni/mate/akces-mate", "name": "MATE AKCES", "maker": "cuni", "family": "cuni/mate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2025-08-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of google/byt5-large on the AKCES-GEC dataset. Same TSD 2025 paper as the GECCC MATE models.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MATE AKCES is UFAL's ByT5-large Czech grammar-error correction checkpoint trained on AKCES-GEC. It is public under CC BY-NC-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "It is the AKCES-corpus run from the same multi-experiment GEC paper as the GECCC models. The Hub tags name google/byt5-large as the base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/mate/geccc-mate": { "id": "cuni/mate/geccc-mate", "name": "MATE GECCC", "maker": "cuni", "family": "cuni/mate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2025-08-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Czech grammar-error correction checkpoints from the TSD 2025 MATE paper. The collection includes a model tagged as BART (transformer-base-geccc-mate) and ByT5-small, ByT5-base, and ByT5-large fine-tunes of the matching Google ByT5 checkpoints. The paper describes a Transformer translation system with on-the-fly synthetic errors.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MATE GECCC is UFAL's set of Czech grammar-error correction checkpoints trained on GECCC for the TSD 2025 paper. Four files are public under CC BY-NC-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "Petr Pechman, Milan Straka, Jana Strakova, and Jakub Naplava treat GEC as translation and generate synthetic errors while training. They compare corpora, error types, tokenization, and model size. The abstract says the best system is state of the art for Czech and cheaper to run than the alternatives they tried, including LLM fine-tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/multilexnorm/byt5-small-multilexnorm": { "id": "cuni/multilexnorm/byt5-small-multilexnorm", "name": "UFAL ByT5-small MultiLexNorm", "maker": "cuni", "family": "cuni/multilexnorm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2021-11-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tuned ByT5-small. The English checkpoint config.json has d_model 1472, 12 encoder layers, 4 decoder layers, 6 heads, d_ff 3584, vocab 384. Other language repos were not opened file by file.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UFAL ByT5-small MultiLexNorm is Charles University's winning lexical-normalization system from the W-NUT 2021 shared task. Twelve fine-tunes are public under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "David Samuel and Milan Straka fine-tune ByT5-small, after extra pretraining on synthetic data, to normalize social-media text. The paper says the system won both the intrinsic evaluation and an extrinsic dependency-parsing evaluation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/nametag/nametag-3": { "id": "cuni/nametag/nametag-3", "name": "NameTag 3", "maker": "cuni", "family": "cuni/nametag", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2024-08-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tuned encoder or seq2seq NER model. The ACL 2025 demo says the web service used one 355M-parameter model for flat NER in 17 languages and a 126M model for nested Czech NER. Later multilingual checkpoints (250203, 260521, 260717) are dated releases. The 355M figure is not printed on a per-file card that was read, so it is not stored as this folder's parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NameTag 3 is UFAL's multilingual named-entity recognizer from Charles University, served on LINDAT and released as dated model zips. It handles flat and nested entities. Collected 2026-10-15.", "what_it_is_for": "Jana Strakova and Milan Straka describe it as one fine-tuned model covering many datasets and tagsets, plus a separate Czech nested model. The May 2026 multilingual checkpoint is trained on 20 languages. The tool page lists Arabic, Chinese, Croatian, Czech, Danish, Dutch, English, German, Greek, Hebrew, Maghrebi Arabic, Norwegian Bokmal, Norwegian Nynorsk, Portuguese, Serbian, Slovak, Slovenian, Spanish, Swedish, and Ukrainian.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/robeczech/robeczech-base": { "id": "cuni/robeczech/robeczech-base", "name": "RobeCzech", "maker": "cuni", "family": "cuni/robeczech", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2021-05-25", "context_length": 512, "max_output_tokens": null, "parameters": "125000000", "architecture": "RoBERTa base: 12 layers, hidden 768, 12 heads, intermediate 3072, GELU. Hub config.json vocab_size 51997 and max_position_embeddings 514. The paper table prints 125M total (40M embeddings + 85M transformer). Trained with Fairseq, batch 8192, sequences at most 512 tokens, 91,075 steps, about 3 months on 8 Quadro P5000 GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RobeCzech is a Czech RoBERTa masked language model from the Institute of Formal and Applied Linguistics (UFAL) at Charles University. Weights are public under a non-commercial license. The current Hub revision is 1.1. Collected 2026-10-15.", "what_it_is_for": "UFAL trains it as a monolingual contextual encoder for Czech and reports it for tagging, lemmatization, dependency parsing, named-entity recognition, semantic parsing, and sentiment analysis.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/udify/udify": { "id": "cuni/udify/udify", "name": "UDify", "maker": "cuni", "family": "cuni/udify", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2019-08-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multilingual BERT fine-tuned with softmax heads for UPOS, features, lemmas, and dependencies. The LINDAT note says the released weights differ slightly from the paper because they were trained for extra epochs. A secondary description says mBERT has 12 layers. That layer count was not re-read from the UDify paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UDify is a single multilingual Universal Dependencies parser from UFAL at Charles University, released in August 2019. The weights are public under CC BY-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "Dan Kondratyuk and Milan Straka fine-tune multilingual BERT so one model predicts part of speech, morphology, lemmas, and dependency trees for 124 UD v2.3 treebanks in 75 languages. The abstract reports state-of-the-art or matching scores without language-specific recurrent layers, including zero-shot languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/udpipe/udpipe-2": { "id": "cuni/udpipe/udpipe-2", "name": "UDPipe 2", "maker": "cuni", "family": "cuni/udpipe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural tagger and dependency parser. The UDPipe 2 README says it does not tokenize by itself and calls UDPipe 1 for that, and that reasonable speed needs a GPU. A checked-in example runs an mBERT model together with a UDPipe 2 model. Language and treebank files are separate downloads, grouped here as versions of one line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UDPipe 2 is UFAL's neural Universal Dependencies tagger and parser from Charles University. Models are released per treebank release and also run as a LINDAT service. Collected 2026-10-15.", "what_it_is_for": "The maintainers describe it as a research tool, not a drop-in replacement for UDPipe 1. It tags and parses. Tokenization stays with UDPipe 1. CorPipe's plain-text example calls the UDPipe service before coreference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cuni/wmt/wmt25-cuni-sft": { "id": "cuni/wmt/wmt25-cuni-sft", "name": "CUNI-SFT", "maker": "cuni", "family": "cuni/wmt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PEFT LoRA adapter. Hub tags name the base utter-project/EuroLLM-9B-Instruct. The model card body is the empty Hugging Face template. The WMT 2025 paper says CUNI-SFT is supervised LoRA fine-tuning for en-cs, en-sr, and cs-uk, with learning rate 2e-4, ranks 8 and 16, alpha twice the rank, batch size 2, and 16 gradient-accumulation steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CUNI-SFT is UFAL's supervised LoRA fine-tune of EuroLLM-9B-Instruct for the WMT 2025 general translation task. One PEFT repo is public. Its model card was never filled in. Collected 2026-10-15.", "what_it_is_for": "The system paper says CUNI-SFT models were trained with LoRA on a small amount of public parallel data for English-to-Czech, English-to-Serbian, and Czech-to-Ukrainian. They are one of several CUNI submissions, next to CUNI-EdUKate-v1, CUNI-Transformer, and CUNI-DocTransformer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/calm/calm2-7b": { "id": "cyberagent/calm/calm2-7b", "name": "CyberAgentLM2-7B", "maker": "cyberagent", "family": "cyberagent/calm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-11-02", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only Transformer. Card cites the LLaMA architecture paper. Hub library tag is llama. Trained from scratch on 1.3T public Japanese and English tokens, not as a Llama weight fine-tune.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CyberAgentLM2-7B (CALM2-7B) is CyberAgent's 7B decoder-only base model, public since 2023-11-02 under Apache-2.0. Collected 2026-10-15. Weights remain available. It is the base for CALM2-7B-Chat.", "what_it_is_for": "The press release describes it as the base model pre-trained on Japanese and English, paired with a chat-tuned sibling. The card positions it for general text generation, with the chat variant called out separately.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/calm/calm2-7b-chat": { "id": "cyberagent/calm/calm2-7b-chat", "name": "CyberAgentLM2-7B-Chat", "maker": "cyberagent", "family": "cyberagent/calm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-11-02", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "Dialogue fine-tune of CALM2-7B. Same Llama-style decoder-only stack. Prompt format is USER / ASSISTANT with <|endoftext|>, not ChatML.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CyberAgentLM2-7B-Chat is the dialogue fine-tune of CALM2-7B, released 2023-11-02 under Apache-2.0 with a 32,768-token context. Collected 2026-10-15. Still hosted. No deprecation notice was found.", "what_it_is_for": "CyberAgent's press release says the chat model handles 32,000 tokens, about 50,000 Japanese characters, for conversational use. It was later wired into the \u6975\u4e88\u6e2cTD ad-text product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/calm/calm2-7b-chat-dpo-experimental": { "id": "cyberagent/calm/calm2-7b-chat-dpo-experimental", "name": "CALM2-7B-Chat DPO experimental", "maker": "cyberagent", "family": "cyberagent/calm", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2024-01-24", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Direct Preference Optimization fine-tune of cyberagent/calm2-7b-chat on cyberagent/chatbot-arena-ja-calm2-7b-chat-experimental. Llama-style decoder.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "calm2-7b-chat-dpo-experimental is CyberAgent's labeled experimental DPO tune of CALM2-7B-Chat. Card release v1.0 is 2024-01-24. Collected 2026-10-15. Still downloadable. Status is preview because the publisher marked it experimental.", "what_it_is_for": "The card says it applies Direct Preference Optimization to calm2-7b-chat using a Japanese chatbot-arena experimental dataset, aimed at preference alignment rather than a new base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/calm/calm3-22b-chat": { "id": "cyberagent/calm/calm3-22b-chat", "name": "CyberAgentLM3-22B-Chat", "maker": "cyberagent", "family": "cyberagent/calm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-09", "context_length": 16384, "max_output_tokens": null, "parameters": "22.5B", "architecture": "Decoder-only Transformer trained from scratch on 2.0T tokens, then dialogue-tuned. Hub architecture tag is llama. Prompt format is ChatML.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CyberAgentLM3-22B-Chat is CyberAgent's from-scratch Japanese and English chat model, announced 2024-07-09 under Apache-2.0. Collected 2026-10-15. Weights are still the public CALM3 checkpoint. No retirement notice was found.", "what_it_is_for": "The press release offers it as a commercial-use Japanese LLM for dialogue, claiming Nejumi LLM Leaderboard 3 performance on par with Meta-Llama-3-70B-Instruct as of 2024-07, among scratch-trained open Japanese LLMs. Context on the card is 16384.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/calm/calm3-22b-chat-selfimprove-experimental": { "id": "cyberagent/calm/calm3-22b-chat-selfimprove-experimental", "name": "CALM3-22B-Chat self-improve experimental", "maker": "cyberagent", "family": "cyberagent/calm", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-training DPO of cyberagent/calm3-22b-chat. The card says Answer Carefully Dataset (ACv1) was expanded and optimized with DPO. Same ChatML interface as the parent.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "calm3-22b-chat-selfimprove-experimental is CyberAgent's experimental self-trained DPO model on top of CALM3-22B-Chat. Card v1.0 is 2025-02-13. Collected 2026-10-15. Still hosted. Marked preview because the publisher calls it experimental.", "what_it_is_for": "The card says the goal is higher alignment, especially on inappropriate question-answer behavior, while keeping general language performance close to CALM3-22B-Chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/cat/cat-paws-8b": { "id": "cyberagent/cat/cat-paws-8b", "name": "CAT-Paws-8B", "maker": "cyberagent", "family": "cyberagent/cat", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-06-23", "context_length": 40000, "max_output_tokens": null, "parameters": "8B", "architecture": "Agentic sibling of CAT-Thinking-8B. Same Qwen3-8B / Qwen3-Swallow-v0.2 lineage. SFT plus GRPO with multi-turn coding, shell, and tool tasks. Tools are described in the prompt, not via a special tool API.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAT-Paws-8B is CyberAgent's 8B agent model that reasons in Japanese and is trained for tools, terminal use, and multi-turn coding. Hub created 2026-06-23, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says to use it for multi-turn coding, terminal, and tool use, and to prefer CAT-Thinking-8B for single-turn work. It suggests short harnesses such as mini-swe-agent and terminus-2 because the context is about 40k.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/cat/cat-thinking-8b": { "id": "cyberagent/cat/cat-thinking-8b", "name": "CAT-Thinking-8B", "maker": "cyberagent", "family": "cyberagent/cat", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-05-28", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Qwen3-8B lineage via Qwen3-Swallow-v0.2 (Tokyo Tech continual pretrain). Further SFT on translated gpt-oss-120b traces, then GRPO so the reasoning trace is Japanese. Hub base_model tag: Qwen/Qwen3-8B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAT-Thinking-8B is CyberAgent's 8B reasoning model trained to think in Japanese. Weights published 2026-05-28, card current as of the 2026-07-14 Hub update, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says it generates a Japanese reasoning trace even when the user writes in English, for coding and math. It is the single-turn sibling of CAT-Paws. The trace often starts with an odd stock phrase the card documents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/cat/cat-translate-0-8b": { "id": "cyberagent/cat/cat-translate-0-8b", "name": "CAT-Translate-0.8B", "maker": "cyberagent", "family": "cyberagent/cat", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-12-09", "context_length": null, "max_output_tokens": null, "parameters": "0.8B", "architecture": "Instruction-tuned translation model. Base stated on the card or Hub: sbintuitions/sarashina2.2-0.5b (card). Hub size label 0.8B.. Bidirectional Japanese-English. Chat template required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAT-Translate-0.8B is a CyberAgent CAT-Translate checkpoint for Japanese-English translation, MIT license, Hub release 2025-12-09. Collected 2026-10-15. Status in this catalog: active.", "what_it_is_for": "The model card describes a small open translation model for English-to-Japanese and Japanese-to-English. The prompt is an explicit translate instruction. The 7B card says an instruction is required so the model stays mergeable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/cat/cat-translate-1-4b": { "id": "cyberagent/cat/cat-translate-1-4b", "name": "CAT-Translate-1.4B", "maker": "cyberagent", "family": "cyberagent/cat", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-02-02", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Instruction-tuned translation model. Base stated on the card or Hub: sbintuitions/sarashina2.2-1b. Hub size label says 1B while the repo name says 1.4B.. Bidirectional Japanese-English. Chat template required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAT-Translate-1.4B is a CyberAgent CAT-Translate checkpoint for Japanese-English translation, MIT license, Hub release 2026-02-02. Collected 2026-10-15. Status in this catalog: active.", "what_it_is_for": "The model card describes a small open translation model for English-to-Japanese and Japanese-to-English. The prompt is an explicit translate instruction. The 7B card says an instruction is required so the model stays mergeable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/cat/cat-translate-3-3b": { "id": "cyberagent/cat/cat-translate-3-3b", "name": "CAT-Translate-3.3B", "maker": "cyberagent", "family": "cyberagent/cat", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-02-26", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Instruction-tuned translation model. Base stated on the card or Hub: sbintuitions/sarashina2.2-3b. Hub size label 3B; repo name says 3.3B.. Bidirectional Japanese-English. Chat template required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAT-Translate-3.3B is a CyberAgent CAT-Translate checkpoint for Japanese-English translation, MIT license, Hub release 2026-02-26. Collected 2026-10-15. Status in this catalog: active.", "what_it_is_for": "The model card describes a small open translation model for English-to-Japanese and Japanese-to-English. The prompt is an explicit translate instruction. The 7B card says an instruction is required so the model stays mergeable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/cat/cat-translate-3-3b-beta": { "id": "cyberagent/cat/cat-translate-3-3b-beta", "name": "CAT-Translate-3.3B-beta", "maker": "cyberagent", "family": "cyberagent/cat", "status": "deprecated", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-02-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-tuned translation model. Base stated on the card or Hub: sbintuitions/sarashina2.2-3b (Hub base_model tag). A dedicated card body was not re-fetched after the family table.. Bidirectional Japanese-English. Chat template required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAT-Translate-3.3B-beta is the earlier CyberAgent translation checkpoint, Hub date 2026-02-12, MIT. The later CAT-Translate-3.3B card still lists it only as a comparison row (average BLEU 30.60 vs 36.16). Collected 2026-10-15. Weights are still online. Catalog status is deprecated because the family card's current set is 0.8B, 1.4B, 3.3B, and 7B.", "what_it_is_for": "The model card describes a small open translation model for English-to-Japanese and Japanese-to-English. The prompt is an explicit translate instruction. The 7B card says an instruction is required so the model stays mergeable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/cat/cat-translate-7b": { "id": "cyberagent/cat/cat-translate-7b", "name": "CAT-Translate-7B", "maker": "cyberagent", "family": "cyberagent/cat", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-02-26", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Instruction-tuned translation model. Base stated on the card or Hub: CyberAgent in-house model by Ryosuke Ishigami. Hub architecture tag is mistral. Not the Sarashina line used for the smaller CAT-Translate models.. Bidirectional Japanese-English. Chat template required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAT-Translate-7B is a CyberAgent CAT-Translate checkpoint for Japanese-English translation, MIT license, Hub release 2026-02-26. Collected 2026-10-15. Status in this catalog: active.", "what_it_is_for": "The model card describes a small open translation model for English-to-Japanese and Japanese-to-English. The prompt is an explicit translate instruction. The 7B card says an instruction is required so the model stays mergeable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/deepseek/deepseek-r1-distill-qwen-14b-japanese": { "id": "cyberagent/deepseek/deepseek-r1-distill-qwen-14b-japanese", "name": "DeepSeek-R1-Distill-Qwen-14B-Japanese", "maker": "cyberagent", "family": "cyberagent/deepseek", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-01-27", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Japanese fine-tune of deepseek-ai/DeepSeek-R1-Distill-Qwen-14B. Qwen2 architecture. DeepSeek R1 chat tokens: begin-of-sentence, User, Assistant. Sample max_new_tokens on the 14B card is 4096 and is an example.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek-R1-Distill-Qwen-14B-Japanese is CyberAgent's Japanese fine-tune of deepseek-ai/DeepSeek-R1-Distill-Qwen-14B, released 2025-01-27 under MIT. Collected 2026-10-15. Weights remain public.", "what_it_is_for": "The model card says it is a Japanese fine-tune of the named DeepSeek-R1 Qwen distill. CyberAgent's 2025-01-27 post says both the 14B and 32B distills received additional Japanese training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/deepseek/deepseek-r1-distill-qwen-32b-japanese": { "id": "cyberagent/deepseek/deepseek-r1-distill-qwen-32b-japanese", "name": "DeepSeek-R1-Distill-Qwen-32B-Japanese", "maker": "cyberagent", "family": "cyberagent/deepseek", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-01-27", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Japanese fine-tune of deepseek-ai/DeepSeek-R1-Distill-Qwen-32B. Qwen2 architecture. DeepSeek R1 chat tokens: begin-of-sentence, User, Assistant. Sample max_new_tokens on the 14B card is 4096 and is an example.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek-R1-Distill-Qwen-32B-Japanese is CyberAgent's Japanese fine-tune of deepseek-ai/DeepSeek-R1-Distill-Qwen-32B, released 2025-01-27 under MIT. Collected 2026-10-15. Weights remain public.", "what_it_is_for": "The model card says it is a Japanese fine-tune of the named DeepSeek-R1 Qwen distill. CyberAgent's 2025-01-27 post says both the 14B and 32B distills received additional Japanese training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/layerd/layerd-birefnet": { "id": "cyberagent/layerd/layerd-birefnet", "name": "LayerD BiRefNet", "maker": "cyberagent", "family": "cyberagent/layerd", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-04", "context_length": null, "max_output_tokens": null, "parameters": "0.2B", "architecture": "Matting network used inside LayerD. Architecture code follows BiRefNet. The card says this repo is the matting module, not the full decomposition pipeline. LaMa inpainting stays in the LayerD code, not in this checkpoint.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "layerd-birefnet is the matting checkpoint CyberAgent released for LayerD, an ICCV 2025 layer-decomposition method. Hub upload 2025-09-04, package release v0.1.0 on 2026-01-05, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says it is the matting module that extracts foreground layers from raster graphic designs, and that it should be used through the LayerD repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/llama/llama-3-1-70b-japanese-instruct-2407": { "id": "cyberagent/llama/llama-3-1-70b-japanese-instruct-2407", "name": "Llama-3.1-70B-Japanese-Instruct-2407", "maker": "cyberagent", "family": "cyberagent/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2024-07-26", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Continual pre-training of meta-llama/Meta-Llama-3.1-70B-Instruct. Llama 3.1 chat format. Hub and GGUF conversions report LlamaForCausalLM and about 71B parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3.1-70B-Japanese-Instruct-2407 is CyberAgent's Japanese continual pre-train of Meta Llama 3.1 70B Instruct, published 2024-07-26. Collected 2026-10-15. Weights remain on the Hub under the Llama 3.1 license.", "what_it_is_for": "The model card and the 2024-07-26 company post describe additional Japanese-data training so the Llama 3.1 70B Instruct checkpoint is stronger in Japanese. The prompt format stays Llama 3.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/llava/llava-calm2-siglip": { "id": "cyberagent/llava/llava-calm2-siglip", "name": "LLaVA-CALM2-SigLIP", "maker": "cyberagent", "family": "cyberagent/llava", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-13", "context_length": null, "max_output_tokens": null, "parameters": "7.5B", "architecture": "LLaVA 1.5 vision-language model. Language model: cyberagent/calm2-7b-chat. Vision encoder: google/siglip-so400m-patch14-384. Two-stage training: MLP projector, then language model plus projector.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "llava-calm2-siglip is CyberAgent's public Japanese vision-language model, announced 2024-06-13 under Apache-2.0. Collected 2026-10-15. Weights remain on Hugging Face. The press also mentioned a stronger research-only demo that was not given a separate weight name.", "what_it_is_for": "The card says it answers questions in Japanese about images for open-source vision-language research. The press calls it a 7.5-billion-parameter Japanese VLM meant as a base others can tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/markupdm/ldm-vq-f16-rgba": { "id": "cyberagent/markupdm/ldm-vq-f16-rgba", "name": "LDM VQ-F16 RGBA", "maker": "cyberagent", "family": "cyberagent/markupdm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "69.6M", "architecture": "VQ image tokenizer adapted from the Latent Diffusion vq-f16 first-stage model. Input and output channels changed from 3 to 4 for RGBA. Paper: f=16, codebook size 16384, fine-tuned 100,000 steps on design RGBA images.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ldm-vq-f16-rgba is CyberAgent's RGBA image tokenizer for MarkupDM. Hub upload 2025-10-13, Apache-2.0, Hub size 69.6M. Collected 2026-10-15.", "what_it_is_for": "The card says it compresses RGBA images, including transparency, into discrete codes for graphic-design completion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/markupdm/markupdm": { "id": "cyberagent/markupdm/markupdm", "name": "MarkupDM", "maker": "cyberagent", "family": "cyberagent/markupdm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-05", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Causal multimodal language model for graphic-design markup. Hub base_model is bigcode/starcoderbase-7b, with added image-token embeddings and a separate image head. Image tokens come from ldm-vq-f16-rgba.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MarkupDM is CyberAgent AI Lab's multimodal markup model for completing graphic designs. Weights on Hugging Face from 2026-01-05, Apache-2.0. The paper is ACM Multimedia 2025. Collected 2026-10-15.", "what_it_is_for": "The card says it completes a partial design by generating text, visuals, position, and style. The project page describes attribute, image, and text completion from surrounding markup context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/mistral/mistral-nemo-japanese-instruct-2408": { "id": "cyberagent/mistral/mistral-nemo-japanese-instruct-2408", "name": "Mistral-Nemo-Japanese-Instruct-2408", "maker": "cyberagent", "family": "cyberagent/mistral", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-30", "context_length": null, "max_output_tokens": null, "parameters": "12B", "architecture": "Japanese continual pre-train. The card text says the base is mistralai/Mistral-Nemo-Instruct-2407. The Hub base_model tag says mistralai/Mistral-Nemo-Base-2407. Prompt format on the card is ChatML. Architecture tag is mistral.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral-Nemo-Japanese-Instruct-2408 is CyberAgent's Japanese continual pre-train in the Mistral NeMo family, published 2024-08-30 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card describes a Japanese continually pre-trained instruct model for chat, using a ChatML prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/open-calm/open-calm-1b": { "id": "cyberagent/open-calm/open-calm-1b", "name": "OpenCALM-1B", "maker": "cyberagent", "family": "cyberagent/open-calm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-05-17", "context_length": 2048, "max_output_tokens": null, "parameters": "1.4B", "architecture": "GPT-NeoX decoder-only causal LM. config.json: 24 layers, hidden 2048, 16 heads, intermediate 8192, GELU, RoPE base 10000, rotary_pct 1.0, vocab 52096, float16, use_parallel_residual false. Card params 1.4B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenCALM-1B is a CyberAgent OpenCALM decoder-only Japanese language model, released with weights on 2023-05-17 under CC BY-SA 4.0. Collected 2026-10-15. Weights remain on Hugging Face. No retirement notice was found.", "what_it_is_for": "The model card describes OpenCALM as a suite of decoder-only language models pre-trained on Japanese datasets for text generation. CyberAgent's 2023-05-17 release said the weights were trained on open Japanese data and offered for commercial and research use under CC BY-SA 4.0, with attribution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/open-calm/open-calm-3b": { "id": "cyberagent/open-calm/open-calm-3b", "name": "OpenCALM-3B", "maker": "cyberagent", "family": "cyberagent/open-calm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-05-17", "context_length": null, "max_output_tokens": null, "parameters": "2.7B", "architecture": "GPT-NeoX decoder-only causal LM. Card: 32 layers, hidden 2560, 32 heads. open-calm-1b config (same release) uses GELU, RoPE, vocab 52096, use_parallel_residual false. This size's config was not re-opened except where noted.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenCALM-3B is a CyberAgent OpenCALM decoder-only Japanese language model, released with weights on 2023-05-17 under CC BY-SA 4.0. Collected 2026-10-15. Weights remain on Hugging Face. No retirement notice was found.", "what_it_is_for": "The model card describes OpenCALM as a suite of decoder-only language models pre-trained on Japanese datasets for text generation. CyberAgent's 2023-05-17 release said the weights were trained on open Japanese data and offered for commercial and research use under CC BY-SA 4.0, with attribution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/open-calm/open-calm-7b": { "id": "cyberagent/open-calm/open-calm-7b", "name": "OpenCALM-7B", "maker": "cyberagent", "family": "cyberagent/open-calm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-05-17", "context_length": null, "max_output_tokens": null, "parameters": "6.8B", "architecture": "GPT-NeoX decoder-only causal LM. Card: 32 layers, hidden 4096, 32 heads. open-calm-1b config (same release) uses GELU, RoPE, vocab 52096, use_parallel_residual false. This size's config was not re-opened except where noted.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenCALM-7B is a CyberAgent OpenCALM decoder-only Japanese language model, released with weights on 2023-05-17 under CC BY-SA 4.0. Collected 2026-10-15. Weights remain on Hugging Face. No retirement notice was found.", "what_it_is_for": "The model card describes OpenCALM as a suite of decoder-only language models pre-trained on Japanese datasets for text generation. CyberAgent's 2023-05-17 release said the weights were trained on open Japanese data and offered for commercial and research use under CC BY-SA 4.0, with attribution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/open-calm/open-calm-large": { "id": "cyberagent/open-calm/open-calm-large", "name": "OpenCALM-Large", "maker": "cyberagent", "family": "cyberagent/open-calm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-05-17", "context_length": null, "max_output_tokens": null, "parameters": "830M", "architecture": "GPT-NeoX decoder-only causal LM. Card: 24 layers, hidden 1536, 16 heads. open-calm-1b config (same release) uses GELU, RoPE, vocab 52096, use_parallel_residual false. This size's config was not re-opened except where noted.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenCALM-Large is a CyberAgent OpenCALM decoder-only Japanese language model, released with weights on 2023-05-17 under CC BY-SA 4.0. Collected 2026-10-15. Weights remain on Hugging Face. No retirement notice was found.", "what_it_is_for": "The model card describes OpenCALM as a suite of decoder-only language models pre-trained on Japanese datasets for text generation. CyberAgent's 2023-05-17 release said the weights were trained on open Japanese data and offered for commercial and research use under CC BY-SA 4.0, with attribution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/open-calm/open-calm-medium": { "id": "cyberagent/open-calm/open-calm-medium", "name": "OpenCALM-Medium", "maker": "cyberagent", "family": "cyberagent/open-calm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-05-17", "context_length": null, "max_output_tokens": null, "parameters": "400M", "architecture": "GPT-NeoX decoder-only causal LM. Card: 24 layers, hidden 1024, 16 heads. open-calm-1b config (same release) uses GELU, RoPE, vocab 52096, use_parallel_residual false. This size's config was not re-opened except where noted.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenCALM-Medium is a CyberAgent OpenCALM decoder-only Japanese language model, released with weights on 2023-05-17 under CC BY-SA 4.0. Collected 2026-10-15. Weights remain on Hugging Face. No retirement notice was found.", "what_it_is_for": "The model card describes OpenCALM as a suite of decoder-only language models pre-trained on Japanese datasets for text generation. CyberAgent's 2023-05-17 release said the weights were trained on open Japanese data and offered for commercial and research use under CC BY-SA 4.0, with attribution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/open-calm/open-calm-small": { "id": "cyberagent/open-calm/open-calm-small", "name": "OpenCALM-Small", "maker": "cyberagent", "family": "cyberagent/open-calm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-05-17", "context_length": null, "max_output_tokens": null, "parameters": "160M", "architecture": "GPT-NeoX decoder-only causal LM. Card: 12 layers, hidden 768, 12 heads. open-calm-1b config (same release) uses GELU, RoPE, vocab 52096, use_parallel_residual false. This size's config was not re-opened except where noted.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenCALM-Small is a CyberAgent OpenCALM decoder-only Japanese language model, released with weights on 2023-05-17 under CC BY-SA 4.0. Collected 2026-10-15. Weights remain on Hugging Face. No retirement notice was found.", "what_it_is_for": "The model card describes OpenCALM as a suite of decoder-only language models pre-trained on Japanese datasets for text generation. CyberAgent's 2023-05-17 release said the weights were trained on open Japanese data and offered for commercial and research use under CC BY-SA 4.0, with attribution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "cyberagent/reward/ca-reward-3b-ja": { "id": "cyberagent/reward/ca-reward-3b-ja", "name": "CA Reward 3B Japanese", "maker": "cyberagent", "family": "cyberagent/reward", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-08", "context_length": 4096, "max_output_tokens": null, "parameters": "3B", "architecture": "Sequence-classification reward model, full-parameter tune of sbintuitions/sarashina2.2-3b-instruct-v0.1. One label. Score is a logit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ca-reward-3b-ja is CyberAgent's Japanese reward model, v0.1 on 2025-08-08, Apache-2.0. Collected 2026-10-15. It scores a prompt-response pair rather than generating chat.", "what_it_is_for": "The card says it quantifies how preferable a response is, for a lightweight Japanese reward model. The sample truncates inputs at 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "d-id/live-portrait/live-portrait": { "id": "d-id/live-portrait/live-portrait", "name": "Live Portrait", "maker": "d-id", "family": "d-id/live-portrait", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Driver-video reenactment. A still photo is animated to follow a driver clip's head motion, expression, and optional voice. D-ID has not published a parameter count or layer diagram.", "modalities": { "input": [ "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Live Portrait is D-ID's driver-video reenactment model. It animates a still photo so the face follows a driver clip's motion and expression. The create endpoint `POST /animations` is marked deprecated. The product was already in use by February 2021, when MyHeritage shipped Deep Nostalgia on it. This dossier was collected on 2026-10-15.", "what_it_is_for": "D-ID's animations overview says Live Portrait uses a driver video to match head movement, facial expression, emotion, and even voice on a still photo. The reenactment page lists it next to Speaking Portrait. D-ID says the same technology, including an on-premise embed, powered MyHeritage Deep Nostalgia.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "d-id/speaking-portrait/trained-character": { "id": "d-id/speaking-portrait/trained-character", "name": "Trained Character", "maker": "d-id", "family": "d-id/speaking-portrait", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2021-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "TechCrunch described a trained presenter built from a filmed character, with preset body and hand motion and a swappable background. No architecture note from D-ID was found.", "modalities": { "input": [ "text", "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Trained Character is the video-trained half of D-ID Speaking Portrait, announced on 23 September 2021. TechCrunch described it as a presenter built from a 10-minute training video, with a swappable background and preset body and hand motion. It is not in the 2026 docs index. No sunset notice was found, so retired status is inferred. This dossier was collected on 2026-10-15. Confidence is medium because the training spec is from the launch report, not a D-ID model card.", "what_it_is_for": "Speaking Portrait turned a person into a presenter from text or recorded speech. Single Portrait needed one photo and kept that background. Trained Character was the option for a filmed character who could use a custom background and some body and hand presets. D-ID's news post confirms the product launch. It does not repeat the 10-minute figure.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "d-id/translate/video-translate": { "id": "d-id/translate/video-translate", "name": "Video Translate", "maker": "d-id", "family": "d-id/translate", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "A pipeline, not a named checkpoint. D-ID says it translates speech, clones the speaker's voice, and rewrites lip motion. No parameter count was published.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 1.0 }, "summary_intro": "Video Translate is D-ID's active localization pipeline. It takes one speaking video and returns a new video per target language, with translated speech, a cloned voice, and lip motion matched to the new audio. General availability was 21 August 2024. The endpoint is `POST /translations`. D-ID has not published a checkpoint name, so this folder is the product. This dossier was collected on 2026-10-15.", "what_it_is_for": "D-ID says a user uploads one clip and the system translates the spoken script, clones the speaker in the target language, and adapts the mouth. It is for marketing, training, and other already-shot video, not for building a new avatar from a photo. Mobile support was announced on 26 September 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "d-id/v2/v2-photo-avatar": { "id": "d-id/v2/v2-photo-avatar", "name": "V2 Photo Avatar", "maker": "d-id", "family": "d-id/v2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2021-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Photo talking-head reenactment. D-ID describes V2 as lightweight rendering from a single frontal image. No parameter count was published. Speech is produced by a third-party TTS provider or by uploaded audio.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 1.0 }, "summary_intro": "V2 Photo Avatar is D-ID's active photo talking-head model. It turns one frontal image plus text or audio into a speaking video on `POST /talks`. D-ID also calls it a Photo Avatar, a Talk, and a Standard AI Presenter. The photo product began as Speaking Portrait on 23 September 2021. The day D-ID started using the V2 name was not found. This dossier was collected on 2026-10-15.", "what_it_is_for": "D-ID's avatar page calls V2 the essential, lightweight option: one frontal image, immediate creation, broad language support, and real-time agents. The help center says it is the efficient path for high-volume straightforward video. Stock photos and a user upload both work. Speech comes from Microsoft, ElevenLabs, Amazon, Google, or Azure OpenAI voices, or from an audio file. Those voice models are not D-ID models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "d-id/v3/v3-instant-avatar": { "id": "d-id/v3/v3-instant-avatar", "name": "V3 Instant Avatar", "maker": "d-id", "family": "d-id/v3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "D-ID help calls it an adaptive model trained on many avatars and fine-tuned to the uploaded person. No parameter count was published. The API name for a custom avatar is Express.", "modalities": { "input": [ "text", "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 1.0 }, "summary_intro": "V3 Instant Avatar is D-ID's active custom avatar built from a short talking video. The API calls the same object an Express Avatar and renders it with `POST /scenes`. D-ID announced Express on 31 October 2024. It is still listed as a current generation. This dossier was collected on 2026-10-15.", "what_it_is_for": "D-ID says Instant avatars keep the original background and the person's movement, then lip-sync new narration from a cloned voice, a synthetic voice, or uploaded audio. The help center says they are for fast offline video, including body and hands, and are not available on Agents or streams. The avatar table agrees: not streamable, custom creation on every plan, ready in under 10 minutes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "d-id/v3/v3-pro-avatar": { "id": "d-id/v3/v3-pro-avatar", "name": "V3 Pro Avatar", "maker": "d-id", "family": "d-id/v3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video presenter with face, hands, and torso. D-ID has not published a parameter count. The clips OpenAPI tag is Clips (Premium Avatars). Custom training is the Premium+ create call.", "modalities": { "input": [ "text", "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 1.0 }, "summary_intro": "V3 Pro Avatar is D-ID's active full-body video presenter. Rendering is `POST /clips`. D-ID announced it on 31 October 2024 as Premium+. The API tag still says Premium Avatars, and the help center says V3 Pro. It remains on the current four-generation list. This dossier was collected on 2026-10-15.", "what_it_is_for": "D-ID describes V3 Pro as the high-fidelity presenter: face, hands, and torso, a cloned voice, optional green screen, and Full HD. Stock presenters are available on the plans that include Premium avatars. A custom one is trained from the customer's footage. Unlike V3 Instant, V3 Pro is streamable and can back an Agent. Fluent mode is one continuous idle-and-talk stream for those agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "d-id/v4/v4-expressive-avatar": { "id": "d-id/v4/v4-expressive-avatar", "name": "V4 Expressive Avatar", "maker": "d-id", "family": "d-id/v4", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "D-ID's 16 March 2026 announcement calls V4 a new diffusion-based model trained on performances captured from real actors. No parameter count or layer diagram was published.", "modalities": { "input": [ "text", "video", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 1.0 }, "summary_intro": "V4 Expressive Avatar is D-ID's active diffusion presenter. It renders scripted video on `POST /expressives` and, since 16 March 2026, real-time Visual Agents. Stock avatars with sentiment control entered Studio on 4 February 2026. Weights are closed. This dossier was collected on 2026-10-15.", "what_it_is_for": "D-ID built V4 from several short recordings of a person speaking in different emotions. A sentiment id selects the emotional performance and its matching voice. The 16 March 2026 announcement calls the stack a diffusion model trained on real actors, for both long scripted video and live conversation. The avatar table rates it the highest of the four generations. Custom V4 avatars are Enterprise-only. Stock avatars are on every plan.\n\nVisual Agents are this model plus an LLM, knowledge, and tools. They are not a second face model. The brain can be OpenAI, Google, a customer model, or an option labeled D-ID GPT OSS. Those language models are not cataloged here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dalhousie/benthicnet/benthicnet-bt-rn50-e400": { "id": "dalhousie/benthicnet/benthicnet-bt-rn50-e400", "name": "BenthicNet BT ResNet-50 400-epoch (Training)", "maker": "dalhousie", "family": "dalhousie/benthicnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause on the Hugging Face card; FRDR states CC-BY-4.0 for model data", "release_date": "2024-09-26", "context_length": null, "max_output_tokens": null, "parameters": "23500000", "architecture": "Barlow Twins self-supervised ResNet-50 encoder. The Hugging Face card says the pretraining set is BenthicNet (Training), the method is Barlow Twins, the architecture is ResNet-50, and the length is 400 epochs. The file is bt-400e_rn50_epoch=399.ckpt, 698,757,857 bytes. arXiv:2409.06618 says the ResNet-50 models use 23.5 million parameters. arXiv:2405.05241 trained Barlow Twins ResNet-50 on BenthicNet-1M for 100, 200, and 400 epochs with LARS, total batch size 512 on four Nvidia A100 GPUs, Zbontar et al. 2021 hyperparameters except learning rate 2e-3 rather than 0.2, a one-cycle cosine schedule, and a 10-epoch warmup. arXiv:2409.06618 says SSL models were trained for 100 epochs unless stated otherwise, and that the Barlow Twins learning rate was reduced from 0.2 by a factor of 100. BT-400ep is the exception used for its one-hot results. The Hub pipeline tag is image-classification. The released file is an encoder checkpoint. The card links DalhousieAI/pangaea-downloader, which is the dataset downloader, and training code is DalhousieAI/ssl-bentho.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dalhousie University researcher Isaac Xu (Faculty of Computer Science) released this BenthicNet encoder on 26 September 2024 as `MLIsaac/benthicnet_bt_rn50_e400`. It is an open checkpoint for seafloor-image representation learning, hosted on his Hugging Face account rather than a university organization. Collected 2026-10-15. Status is active. There is no priced API.", "what_it_is_for": "The collection describes these files as BenthicNet pre-trained self-supervised computer-vision models. The card for this file says: Training split, Barlow Twins, ResNet-50, 400 epochs. Downstream use in the Dalhousie papers is a frozen linear probe or a fine-tune for substrate and benthoscape labels, and hierarchical multi-label heads. The released file is the encoder checkpoint `bt-400e_rn50_epoch=399.ckpt`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dalhousie/benthicnet/benthicnet-bt-rn50-e600-prod": { "id": "dalhousie/benthicnet/benthicnet-bt-rn50-e600-prod", "name": "BenthicNet BT ResNet-50 600-epoch (Production)", "maker": "dalhousie", "family": "dalhousie/benthicnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause on the Hugging Face card; FRDR states CC-BY-4.0 for model data", "release_date": "2024-09-26", "context_length": null, "max_output_tokens": null, "parameters": "23500000", "architecture": "Barlow Twins self-supervised ResNet-50 encoder. The Hugging Face card says the pretraining set is BenthicNet (Production), the method is Barlow Twins, the architecture is ResNet-50, and the length is 600 epochs. The file is bt-600e-prod_rn50_epoch=599.ckpt, 698,757,857 bytes, the same byte length as the 400-epoch Training checkpoint and a different Git LFS oid. arXiv:2409.06618 says ResNet-50 models use 23.5 million parameters. arXiv:2405.05241 describes Barlow Twins runs of 100, 200, and 400 epochs. It does not describe a 600-epoch Production run. The Hub pipeline tag is image-classification. The released file is an encoder checkpoint.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dalhousie University researcher Isaac Xu (Faculty of Computer Science) released this BenthicNet encoder on 26 September 2024 as `MLIsaac/benthicnet_bt_rn50_e600_prod`. It is an open checkpoint for seafloor-image representation learning, hosted on his Hugging Face account rather than a university organization. Collected 2026-10-15. Status is active. There is no priced API.", "what_it_is_for": "The collection describes these files as BenthicNet pre-trained self-supervised computer-vision models. The card for this file says: Production split, Barlow Twins, ResNet-50, 600 epochs. Downstream use in the Dalhousie papers is a frozen linear probe or a fine-tune for substrate and benthoscape labels, and hierarchical multi-label heads. The released file is the encoder checkpoint `bt-600e-prod_rn50_epoch=599.ckpt`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dalhousie/benthicnet/benthicnet-mcv3-vit-b-e100": { "id": "dalhousie/benthicnet/benthicnet-mcv3-vit-b-e100", "name": "BenthicNet MoCo-v3 ViT-B 100-epoch (Training)", "maker": "dalhousie", "family": "dalhousie/benthicnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause on the Hugging Face card; FRDR states CC-BY-4.0 for model data", "release_date": "2024-09-26", "context_length": null, "max_output_tokens": null, "parameters": "85800000", "architecture": "MoCo-v3 self-supervised ViT-B encoder. The Hugging Face card says the pretraining set is BenthicNet (Training), the method is MoCo-v3, the architecture is ViT-B, and the length is 100 epochs. The file is mocov3-100e_vit-b_epoch=099.ckpt, 863,047,057 bytes. arXiv:2409.06618 says ViT-B models use 85.8 million parameters, and that SSL models were trained for 100 epochs on the unlabelled BenthicNet partition unless stated otherwise. For most SSL methods the paper uses the originating ImageNet hyperparameters. The learning-rate reduction by 100 is stated for Barlow Twins. The Hub pipeline tag is image-classification. The released file is an encoder checkpoint.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dalhousie University researcher Isaac Xu (Faculty of Computer Science) released this BenthicNet encoder on 26 September 2024 as `MLIsaac/benthicnet_mcv3_vit-b_e100`. It is an open checkpoint for seafloor-image representation learning, hosted on his Hugging Face account rather than a university organization. Collected 2026-10-15. Status is active. There is no priced API.", "what_it_is_for": "The collection describes these files as BenthicNet pre-trained self-supervised computer-vision models. The card for this file says: Training split, MoCo-v3, ViT-B, 100 epochs. Downstream use in the Dalhousie papers is a frozen linear probe or a fine-tune for substrate and benthoscape labels, and hierarchical multi-label heads. The released file is the encoder checkpoint `mocov3-100e_vit-b_epoch=099.ckpt`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dalhousie/benthicnet/benthicnet-mcv3-vit-b-e100-prod": { "id": "dalhousie/benthicnet/benthicnet-mcv3-vit-b-e100-prod", "name": "BenthicNet MoCo-v3 ViT-B 100-epoch (Production)", "maker": "dalhousie", "family": "dalhousie/benthicnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause on the Hugging Face card; FRDR states CC-BY-4.0 for model data", "release_date": "2024-09-26", "context_length": null, "max_output_tokens": null, "parameters": "85800000", "architecture": "MoCo-v3 self-supervised ViT-B encoder. The Hugging Face card says the pretraining set is BenthicNet (Production), the method is MoCo-v3, the architecture is ViT-B, and the length is 100 epochs. The file is mocov3-100e-prod_vit-b_epoch=099.ckpt, 863,047,057 bytes, the same byte length as the Training twin and a different Git LFS oid. arXiv:2409.06618 says ViT-B models use 85.8 million parameters. The Hub pipeline tag is image-classification. The released file is an encoder checkpoint.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dalhousie University researcher Isaac Xu (Faculty of Computer Science) released this BenthicNet encoder on 26 September 2024 as `MLIsaac/benthicnet_mcv3_vit-b_e100_prod`. It is an open checkpoint for seafloor-image representation learning, hosted on his Hugging Face account rather than a university organization. Collected 2026-10-15. Status is active. There is no priced API.", "what_it_is_for": "The collection describes these files as BenthicNet pre-trained self-supervised computer-vision models. The card for this file says: Production split, MoCo-v3, ViT-B, 100 epochs. Downstream use in the Dalhousie papers is a frozen linear probe or a fine-tune for substrate and benthoscape labels, and hierarchical multi-label heads. The released file is the encoder checkpoint `mocov3-100e-prod_vit-b_epoch=099.ckpt`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dalhousie/benthicnet/benthicnet-mcv3-vit-s-e200-prod": { "id": "dalhousie/benthicnet/benthicnet-mcv3-vit-s-e200-prod", "name": "BenthicNet MoCo-v3 ViT-S 200-epoch (Production)", "maker": "dalhousie", "family": "dalhousie/benthicnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause on the Hugging Face card; FRDR states CC-BY-4.0 for model data", "release_date": "2024-09-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MoCo-v3 self-supervised ViT-S encoder. The Hugging Face card says the pretraining set is BenthicNet (Production), the method is MoCo-v3, the architecture is ViT-S, and the length is 200 epochs. The file is mocov3-200e-prod_vit-s_epoch=199.ckpt, 337,381,777 bytes. arXiv:2409.06618 states parameter counts for ResNet-50 and ViT-B and does not state a ViT-S count. The paper's default SSL length is 100 epochs unless stated otherwise. This card says 200 epochs and Production. The Hub pipeline tag is image-classification. The released file is an encoder checkpoint.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dalhousie University researcher Isaac Xu (Faculty of Computer Science) released this BenthicNet encoder on 26 September 2024 as `MLIsaac/benthicnet_mcv3_vit-s_e200_prod`. It is an open checkpoint for seafloor-image representation learning, hosted on his Hugging Face account rather than a university organization. Collected 2026-10-15. Status is active. There is no priced API.", "what_it_is_for": "The collection describes these files as BenthicNet pre-trained self-supervised computer-vision models. The card for this file says: Production split, MoCo-v3, ViT-S, 200 epochs. Downstream use in the Dalhousie papers is a frozen linear probe or a fine-tune for substrate and benthoscape labels, and hierarchical multi-label heads. The released file is the encoder checkpoint `mocov3-200e-prod_vit-s_epoch=199.ckpt`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/dbrx/dbrx-base": { "id": "databricks/dbrx/dbrx-base", "name": "DBRX Base", "maker": "databricks", "family": "databricks/dbrx", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Databricks Open Model License (version release date 2024-03-27), plus the Databricks Open Model Acceptable Use Policy. Not an OSI license: the license restricts using DBRX, derivatives, or outputs to improve any other LLM, and organizations that already had more than 700 million monthly active users in the month before release need a separate grant. Weights were released, so this is open-weights rather than open-source.", "release_date": "2024-03-27", "context_length": 32768, "max_output_tokens": null, "parameters": "132B total, 36B active", "architecture": "Decoder-only transformer trained with next-token prediction. Fine-grained mixture-of-experts: 16 experts, 4 active per token, 132B total parameters and 36B active. RoPE, GLU, and grouped-query attention. The released config recorded in the llama.cpp support request, matching later architecture writeups, is 40 layers, d_model 6144, 48 query heads, 8 key/value heads, MoE hidden size 10752, vocab 100352, max sequence 32768. Tokenizer is the GPT-4 tiktoken tokenizer. Pretrained on 12T tokens of text and code. This repo is the pretrained base checkpoint, not the instruction tune.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.714, "completion_per_m_usd": 32.143 }, "summary_intro": "Databricks trained and released DBRX Base on 2024-03-27 as one of two open-weight DBRX checkpoints. It is a fine-grained mixture-of-experts language model, 132B total parameters with 36B active. Databricks Foundation Model APIs no longer serve it: pay-per-token ended 2025-04-30 and provisioned throughput ended 2025-12-19. Collected 2026-10-15.", "what_it_is_for": "Databricks says DBRX Base is the pretrained checkpoint for general English-language and coding text completion, and a starting point for further fine-tuning. It is not the chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/dbrx/dbrx-instruct": { "id": "databricks/dbrx/dbrx-instruct", "name": "DBRX Instruct", "maker": "databricks", "family": "databricks/dbrx", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Databricks Open Model License (version release date 2024-03-27), plus the Databricks Open Model Acceptable Use Policy. Not an OSI license: the license restricts using DBRX, derivatives, or outputs to improve any other LLM, and organizations that already had more than 700 million monthly active users in the month before release need a separate grant. Weights were released, so this is open-weights rather than open-source.", "release_date": "2024-03-27", "context_length": 32768, "max_output_tokens": null, "parameters": "132B total, 36B active", "architecture": "Decoder-only transformer trained with next-token prediction. Fine-grained mixture-of-experts: 16 experts, 4 active per token, 132B total parameters and 36B active. RoPE, GLU, and grouped-query attention. The released config recorded in the llama.cpp support request, matching later architecture writeups, is 40 layers, d_model 6144, 48 query heads, 8 key/value heads, MoE hidden size 10752, vocab 100352, max sequence 32768. Tokenizer is the GPT-4 tiktoken tokenizer. Pretrained on 12T tokens of text and code. DBRX Instruct is the fine-tuned checkpoint for few-turn interactions.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.714, "completion_per_m_usd": 32.143 }, "summary_intro": "Databricks trained and released DBRX Instruct on 2024-03-27 as one of two open-weight DBRX checkpoints. It is a fine-grained mixture-of-experts language model, 132B total parameters with 36B active. Databricks Foundation Model APIs no longer serve it: pay-per-token ended 2025-04-30 and provisioned throughput ended 2025-12-19. Collected 2026-10-15.", "what_it_is_for": "Databricks says DBRX Instruct is the fine-tuned model for few-turn instruction following. The launch post positions it as a general-purpose open model with particular strength in programming and math, and as a model for RAG over a 32K context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/dolly/dolly-v1-6b": { "id": "databricks/dolly/dolly-v1-6b", "name": "Dolly v1 6B", "maker": "databricks", "family": "databricks/dolly", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0. The model card says the weights are derived from EleutherAI GPT-J and fine-tuned on the Stanford Alpaca corpus, which is CC-BY-NC-4.0. Not licensed for commercial use.", "release_date": "2023-03-24", "context_length": 2048, "max_output_tokens": null, "parameters": "6B", "architecture": "Causal language model derived from EleutherAI GPT-J. The card says 6 billion parameters, 28 transformer layers, 16 attention heads, rotary position embeddings, and the GPT-3 tokenizer. Fine-tuned on about 52,000 Alpaca instruction records. The original run was one epoch, about 30 minutes on 8x A100 40GB. The published checkpoint was trained for 10 epochs on the same machine.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Databricks released Dolly v1 6B as an instruction-following language model. It is a fine-tune of an EleutherAI base model, not a model pretrained from scratch. Status on 2026-10-15 is an old open weight release: it was not found in the current Foundation Model APIs catalog, and Databricks has not posted a retirement notice for it. Collected 2026-10-15.", "what_it_is_for": "Dolly v1 is an instruction-following demo. Databricks says a two-year-old open model, GPT-J, showed chat-style instruction following after a short fine-tune on the Stanford Alpaca set. The card points users to Dolly v2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/dolly/dolly-v2-12b": { "id": "databricks/dolly/dolly-v2-12b", "name": "Dolly v2 12B", "maker": "databricks", "family": "databricks/dolly", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT for the weights, as tagged on the Hugging Face model cards and mirrors. The instruction corpus databricks-dolly-15k is CC-BY-SA-3.0. Training code in databrickslabs/dolly is Apache-2.0. The model-card sentence that says CC-BY-SA refers to that corpus, not a second weight license.", "release_date": "2023-04-12", "context_length": 2048, "max_output_tokens": null, "parameters": "12B", "architecture": "Causal decoder derived from EleutherAI Pythia-12B and fine-tuned on about 15,000 employee-written instruction records (databricks-dolly-15k). The blog and README call it a 12 billion parameter model based on pythia-12b. GPT-NeoX architecture and tokenizer. Context length 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Databricks released Dolly v2 12B as an instruction-following language model. It is a fine-tune of an EleutherAI base model, not a model pretrained from scratch. Status on 2026-10-15 is an old open weight release: it was not found in the current Foundation Model APIs catalog, and Databricks has not posted a retirement notice for it. Collected 2026-10-15.", "what_it_is_for": "Flagship Dolly 2.0 model. Databricks released it so organizations could run an instruction-following model they could own and customize, without an API, under a commercial-use license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/dolly/dolly-v2-3b": { "id": "databricks/dolly/dolly-v2-3b", "name": "Dolly v2 3B", "maker": "databricks", "family": "databricks/dolly", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT for the weights, as tagged on the Hugging Face model cards and mirrors. The instruction corpus databricks-dolly-15k is CC-BY-SA-3.0. Training code in databrickslabs/dolly is Apache-2.0. The model-card sentence that says CC-BY-SA refers to that corpus, not a second weight license.", "release_date": "2023-04-12", "context_length": 2048, "max_output_tokens": null, "parameters": "2.8B", "architecture": "Causal decoder derived from EleutherAI Pythia-2.8B and fine-tuned on about 15,000 employee-written instruction records (databricks-dolly-15k). The card calls it a 2.8 billion parameter model based on pythia-2.8b. The repo name says 3b. GPT-NeoX architecture and tokenizer. Context length 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Databricks released Dolly v2 3B as an instruction-following language model. It is a fine-tune of an EleutherAI base model, not a model pretrained from scratch. Status on 2026-10-15 is an old open weight release: it was not found in the current Foundation Model APIs catalog, and Databricks has not posted a retirement notice for it. Collected 2026-10-15.", "what_it_is_for": "Smallest Dolly 2.0 checkpoint. Databricks says it follows instructions in brainstorming, classification, closed QA, generation, extraction, open QA, and summarization, and that it is not state of the art. The card says it trails dolly-v1-6b on the printed eval table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/dolly/dolly-v2-7b": { "id": "databricks/dolly/dolly-v2-7b", "name": "Dolly v2 7B", "maker": "databricks", "family": "databricks/dolly", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT for the weights, as tagged on the Hugging Face model cards and mirrors. The instruction corpus databricks-dolly-15k is CC-BY-SA-3.0. Training code in databrickslabs/dolly is Apache-2.0. The model-card sentence that says CC-BY-SA refers to that corpus, not a second weight license.", "release_date": "2023-04-12", "context_length": 2048, "max_output_tokens": null, "parameters": "6.9B", "architecture": "Causal decoder derived from EleutherAI Pythia-6.9B and fine-tuned on about 15,000 employee-written instruction records (databricks-dolly-15k). The card calls it a 6.9 billion parameter model based on pythia-6.9b. The repo name says 7b. GPT-NeoX architecture and tokenizer. Context length 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Databricks released Dolly v2 7B as an instruction-following language model. It is a fine-tune of an EleutherAI base model, not a model pretrained from scratch. Status on 2026-10-15 is an old open weight release: it was not found in the current Foundation Model APIs catalog, and Databricks has not posted a retirement notice for it. Collected 2026-10-15.", "what_it_is_for": "Mid-size Dolly 2.0 checkpoint, 6.9B parameters from Pythia-6.9B. Same instruction dataset and commercial-use goal as the 12B model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/mosaic-bert/mosaic-bert-base": { "id": "databricks/mosaic-bert/mosaic-bert-base", "name": "MosaicBERT-Base", "maker": "databricks", "family": "databricks/mosaic-bert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "137M", "architecture": "BERT-style encoder. FlashAttention, ALiBi, gated linear units, dynamic padding removal, and low-precision LayerNorm. Masked language modeling with a 30 percent mask rate. The paper and card say MosaicBERT-Base has 137 million parameters, versus 110 million for BERT-Base, because of the GLU modules. Pretrained on C4. The canonical checkpoint was trained at sequence length 128. Sibling checkpoints use the same architecture at longer sequence lengths.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MosaicBERT-Base is an encoder Databricks lists with its Mosaic research models. MosaicML trained it from scratch on C4 as a faster BERT-style model and released the weights under Apache-2.0. It is a fill-mask model, not a chat model. The card dates the model to March 2023 and does not give a day. Collected 2026-10-15.", "what_it_is_for": "The paper and card say the model is for fast pretraining research and for fine-tuning, including long-context embeddings. Databricks' research page describes it as a way to pretrain a BERT model on your own data. The card's stated primary use of the sequence-length family is research on efficient pretraining and finetuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/mpt/mpt-30b": { "id": "databricks/mpt/mpt-30b", "name": "MPT-30B", "maker": "databricks", "family": "databricks/mpt", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-06-22", "context_length": 8192, "max_output_tokens": null, "parameters": "29.95B", "architecture": "Modified decoder-only transformer. Model card: 29.95B parameters, 48 layers, 64 heads, d_model 7168, vocabulary 50432, sequence length 8192. FlashAttention, ALiBi, no bias terms. The blog rounds the size to 30B. Trained by MosaicML. Pretrained on 1T tokens of English text and code. The blog says training ran about two months on A100 clusters, then moved in mid-June 2023 onto 256 H100 GPUs. Trained context 8192.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MPT-30B is a Mosaic Pretrained Transformer released by MosaicML and listed by Databricks as part of its foundation-model series. Released under the MosaicML name. Databricks acquired MosaicML in 2023 and now lists MPT on its research page and Databricks blogs as its foundation series. The DBRX launch post calls MPT the previous generation. Status collected 2026-10-15: retired.", "what_it_is_for": "Pretrained 30B foundation model. Databricks presents it as the quality-oriented MPT model, with an 8k training context, for commercial use and as a base for further tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/mpt/mpt-30b-chat": { "id": "databricks/mpt/mpt-30b-chat", "name": "MPT-30B-Chat", "maker": "databricks", "family": "databricks/mpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0. Blog and model card say non-commercial research only.", "release_date": "2023-06-22", "context_length": 8192, "max_output_tokens": null, "parameters": "29.95B", "architecture": "Modified decoder-only transformer. Model card: 29.95B parameters, 48 layers, 64 heads, d_model 7168, vocabulary 50432, sequence length 8192. FlashAttention, ALiBi, no bias terms. The blog rounds the size to 30B. Trained by MosaicML. Multi-turn chat tune. The card names ShareGPT-Vicuna, Camel-AI, GPTeacher, Guanaco, Baize, and some generated data. The card says the chat tune used 64 H100s for about 7.6 hours.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MPT-30B-Chat is a Mosaic Pretrained Transformer released by MosaicML and listed by Databricks as part of its foundation-model series. Released under the MosaicML name. Databricks acquired MosaicML in 2023 and now lists MPT on its research page and Databricks blogs as its foundation series. The DBRX launch post calls MPT the previous generation. Status collected 2026-10-15: active.", "what_it_is_for": "Multi-turn conversation research artifact. Databricks and MosaicML say it shows what MPT-30B can do with large chat datasets, and they did not license it for commercial use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/mpt/mpt-30b-instruct": { "id": "databricks/mpt/mpt-30b-instruct", "name": "MPT-30B-Instruct", "maker": "databricks", "family": "databricks/mpt", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-3.0. Blog and model card agree. Commercial use permitted, share-alike.", "release_date": "2023-06-22", "context_length": 8192, "max_output_tokens": null, "parameters": "29.95B", "architecture": "Modified decoder-only transformer. Model card: 29.95B parameters, 48 layers, 64 heads, d_model 7168, vocabulary 50432, sequence length 8192. FlashAttention, ALiBi, no bias terms. The blog rounds the size to 30B. Trained by MosaicML. Single-turn instruction tune. The blog says the data is Dolly plus a subset of Anthropic HH, and the card adds Competition Math, DuoRC, CoT GSM8K, Qasper, QuALITY, SummScreen, and Spider.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MPT-30B-Instruct is a Mosaic Pretrained Transformer released by MosaicML and listed by Databricks as part of its foundation-model series. Released under the MosaicML name. Databricks acquired MosaicML in 2023 and now lists MPT on its research page and Databricks blogs as its foundation series. The DBRX launch post calls MPT the previous generation. Status collected 2026-10-15: retired.", "what_it_is_for": "Single-turn instruction following, including summarization and question answering. MosaicML built it to be commercially usable, unlike the chat tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/mpt/mpt-7b": { "id": "databricks/mpt/mpt-7b", "name": "MPT-7B", "maker": "databricks", "family": "databricks/mpt", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-05-05", "context_length": 2048, "max_output_tokens": null, "parameters": "6.7B", "architecture": "Modified decoder-only transformer. Model card: 6.7B parameters, 32 layers, 32 heads, d_model 4096, vocabulary 50432. FlashAttention, ALiBi instead of positional embeddings, no bias terms. Trained by MosaicML. Pretrained from scratch on 1T tokens of English text and code. Trained context 2048. Card: about 9.5 days on 440 A100-40GB GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MPT-7B is a Mosaic Pretrained Transformer released by MosaicML and listed by Databricks as part of its foundation-model series. Released under the MosaicML name. Databricks acquired MosaicML in 2023 and now lists MPT on its research page and Databricks blogs as its foundation series. The DBRX launch post calls MPT the previous generation. Status collected 2026-10-15: retired.", "what_it_is_for": "MPT-7B is the pretrained base model in the first MPT release. Databricks describes it as an open, commercially usable decoder trained to match the quality class of LLaMA-7B, for people who want to fine-tune or deploy their own model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/mpt/mpt-7b-8k": { "id": "databricks/mpt/mpt-7b-8k", "name": "MPT-7B-8k", "maker": "databricks", "family": "databricks/mpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Disagreement. The Hugging Face card excerpt retrieved for mosaicml/mpt-7b-8k says Apache-2.0. The Databricks announcement blog lists CC-BY-SA-3.0 for MPT-7B-8k. Both are recorded. Do not treat either string as settled.", "release_date": "2023-07-18", "context_length": 8192, "max_output_tokens": null, "parameters": "6.7B", "architecture": "Modified decoder-only transformer. Model card: 6.7B parameters, 32 layers, 32 heads, d_model 4096, vocabulary 50432. FlashAttention, ALiBi instead of positional embeddings, no bias terms. Trained by MosaicML. Continued pretraining from MPT-7B at sequence length 8k for another 500B tokens, 1.5T total. The blog says this run took 3 days on 256 H100s.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MPT-7B-8k is a Mosaic Pretrained Transformer released by MosaicML and listed by Databricks as part of its foundation-model series. Released under the MosaicML name. Databricks acquired MosaicML in 2023 and now lists MPT on its research page and Databricks blogs as its foundation series. The DBRX launch post calls MPT the previous generation. Status collected 2026-10-15: active.", "what_it_is_for": "Longer-context base model for document summarization and question answering. MosaicML says to prefer it over MPT-7B except when the only goal is the original coding or reasoning evals.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/mpt/mpt-7b-8k-chat": { "id": "databricks/mpt/mpt-7b-8k-chat", "name": "MPT-7B-8k-Chat", "maker": "databricks", "family": "databricks/mpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0. The blog and the model card agree. Non-commercial.", "release_date": "2023-07-18", "context_length": 8192, "max_output_tokens": null, "parameters": "6.7B", "architecture": "Modified decoder-only transformer. Model card: 6.7B parameters, 32 layers, 32 heads, d_model 4096, vocabulary 50432. FlashAttention, ALiBi instead of positional embeddings, no bias terms. Trained by MosaicML. Chat tune of MPT-7B-8k. The blog says about 1.5B tokens of chat data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MPT-7B-8k-Chat is a Mosaic Pretrained Transformer released by MosaicML and listed by Databricks as part of its foundation-model series. Released under the MosaicML name. Databricks acquired MosaicML in 2023 and now lists MPT on its research page and Databricks blogs as its foundation series. The DBRX launch post calls MPT the previous generation. Status collected 2026-10-15: active.", "what_it_is_for": "Dialogue model at 8k context. MosaicML says to use it instead of MPT-7B-Chat. It is not licensed for commercial use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/mpt/mpt-7b-8k-instruct": { "id": "databricks/mpt/mpt-7b-8k-instruct", "name": "MPT-7B-8k-Instruct", "maker": "databricks", "family": "databricks/mpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Disagreement. A Hugging Face card excerpt for mosaicml/mpt-7b-8k-instruct says Apache-2.0. The announcement blog says CC-BY-SA-3.0. A card mirror also says CC-BY-SA-3.0. Not settled.", "release_date": "2023-07-18", "context_length": 8192, "max_output_tokens": null, "parameters": "6.7B", "architecture": "Modified decoder-only transformer. Model card: 6.7B parameters, 32 layers, 32 heads, d_model 4096, vocabulary 50432. FlashAttention, ALiBi instead of positional embeddings, no bias terms. Trained by MosaicML. Instruction tune of MPT-7B-8k for long-form instructions, summarization, and question answering. Same fine-tune mix named for MPT-30B-Instruct: Dolly/HH-RLHF plus Competition Math, DuoRC, CoT GSM8K, Qasper, QuALITY, SummScreen, and Spider. One card mirror lists sequence length 2048 in a hyperparameter table. The release is named and announced as 8k, so the context field here is 8192.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MPT-7B-8k-Instruct is a Mosaic Pretrained Transformer released by MosaicML and listed by Databricks as part of its foundation-model series. Released under the MosaicML name. Databricks acquired MosaicML in 2023 and now lists MPT on its research page and Databricks blogs as its foundation series. The DBRX launch post calls MPT the previous generation. Status collected 2026-10-15: active.", "what_it_is_for": "Long-form instruction following, especially summarization and question answering on documents. MosaicML says this is the 8k model to use instead of MPT-7B-Instruct when the input is longer than 2048 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/mpt/mpt-7b-chat": { "id": "databricks/mpt/mpt-7b-chat", "name": "MPT-7B-Chat", "maker": "databricks", "family": "databricks/mpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0. llm-foundry and the release notes say non-commercial use only.", "release_date": "2023-05-05", "context_length": 2048, "max_output_tokens": null, "parameters": "6.7B", "architecture": "Modified decoder-only transformer. Model card: 6.7B parameters, 32 layers, 32 heads, d_model 4096, vocabulary 50432. FlashAttention, ALiBi instead of positional embeddings, no bias terms. Trained by MosaicML. Chat fine-tune of MPT-7B. The release notes name ShareGPT-Vicuna, HC3, Alpaca, Helpful and Harmless, and Evol-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MPT-7B-Chat is a Mosaic Pretrained Transformer released by MosaicML and listed by Databricks as part of its foundation-model series. Released under the MosaicML name. Databricks acquired MosaicML in 2023 and now lists MPT on its research page and Databricks blogs as its foundation series. The DBRX launch post calls MPT the previous generation. Status collected 2026-10-15: active.", "what_it_is_for": "Multi-turn chat demo. MosaicML released it as a chatbot-style fine-tune and marked it research-only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/mpt/mpt-7b-instruct": { "id": "databricks/mpt/mpt-7b-instruct", "name": "MPT-7B-Instruct", "maker": "databricks", "family": "databricks/mpt", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-3.0. The model card and the May 5, 2023 blog both say commercial use is permitted under this share-alike license.", "release_date": "2023-05-05", "context_length": 2048, "max_output_tokens": null, "parameters": "6.7B", "architecture": "Modified decoder-only transformer. Model card: 6.7B parameters, 32 layers, 32 heads, d_model 4096, vocabulary 50432. FlashAttention, ALiBi instead of positional embeddings, no bias terms. Trained by MosaicML. Instruction fine-tune of MPT-7B for short-form instructions. Training context remains 2048. Data is a set derived from databricks-dolly-15k and Anthropic HH-RLHF.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MPT-7B-Instruct is a Mosaic Pretrained Transformer released by MosaicML and listed by Databricks as part of its foundation-model series. Released under the MosaicML name. Databricks acquired MosaicML in 2023 and now lists MPT on its research page and Databricks blogs as its foundation series. The DBRX launch post calls MPT the previous generation. Status collected 2026-10-15: retired.", "what_it_is_for": "Short-form instruction following. MosaicML built it by fine-tuning MPT-7B on a dataset derived from Databricks Dolly-15k and Anthropic's Helpful and Harmless data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "databricks/mpt/mpt-7b-storywriter": { "id": "databricks/mpt/mpt-7b-storywriter", "name": "MPT-7B-StoryWriter-65k+", "maker": "databricks", "family": "databricks/mpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-05-05", "context_length": 65536, "max_output_tokens": null, "parameters": "6.7B", "architecture": "Modified decoder-only transformer. Model card: 6.7B parameters, 32 layers, 32 heads, d_model 4096, vocabulary 50432. FlashAttention, ALiBi instead of positional embeddings, no bias terms. Trained by MosaicML. Long-context variant. The announcement says it reads and writes stories with a 65k-token context. llm-foundry lists context length 65536. The parameter count is the MPT-7B count. A separate hyperparameter table for this repo was not re-copied on collection day.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MPT-7B-StoryWriter-65k+ is a Mosaic Pretrained Transformer released by MosaicML and listed by Databricks as part of its foundation-model series. Released under the MosaicML name. Databricks acquired MosaicML in 2023 and now lists MPT on its research page and Databricks blogs as its foundation series. The DBRX launch post calls MPT the previous generation. Status collected 2026-10-15: active.", "what_it_is_for": "Long-context story reading and writing. MosaicML released it to show ALiBi extrapolation, with a 65k training context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/avatar/live-avatar": { "id": "decart/avatar/live-avatar", "name": "Live Avatar", "maker": "decart", "family": "decart/avatar", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Live Avatar is a retired Decart realtime model that animated an avatar from audio, collected 2026-10-15. The changelog introduced Avatar Live in January 2026, canonicalized the name live-avatar in April, and retired Live Avatar in May 2026.", "what_it_is_for": "Live Avatar is a retired Decart realtime model that animated an avatar from audio, collected 2026-10-15. The changelog introduced Avatar Live in January 2026, canonicalized the name live-avatar in April, and retired Live Avatar in May 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lipsync/lipsync-live": { "id": "decart/lipsync/lipsync-live", "name": "LipSync Live", "maker": "decart", "family": "decart/lipsync", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LipSync Live is a retired Decart realtime model that synced audio to video, collected 2026-10-15. The changelog added lip sync in October 2025, documented pricing in March 2026, and retired LipSync Live in May 2026. The API id string was not recovered, so the folder id is a catalog slug.", "what_it_is_for": "LipSync Live is a retired Decart realtime model that synced audio to video, collected 2026-10-15. The changelog added lip sync in October 2025, documented pricing in March 2026, and retired LipSync Live in May 2026. The API id string was not recovered, so the folder id is a catalog slug.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-14b": { "id": "decart/lucy/lucy-14b", "name": "Lucy-14B", "maker": "decart", "family": "decart/lucy", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy-14B is Decart's closed image-to-video model launched on fal on 10 September 2025, collected 2026-10-15. fal now marks the endpoint deprecated and no longer supported. It is not on the Decart platform model index. Decart claimed it was about 7\u00d7 faster than Wan2.2-14B at similar quality.", "what_it_is_for": "Lucy-14B is Decart's closed image-to-video model launched on fal on 10 September 2025, collected 2026-10-15. fal now marks the endpoint deprecated and no longer supported. It is not on the Decart platform model index. Decart claimed it was about 7\u00d7 faster than Wan2.2-14B at similar quality.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-2": { "id": "decart/lucy/lucy-2", "name": "Lucy 2", "maker": "decart", "family": "decart/lucy", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-01-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The 26 January 2026 post calls Lucy 2.0 a pure diffusion model that does not use depth maps, meshes, or a physics engine.", "modalities": { "input": [ "video", "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy 2 is Decart's early 2026 realtime video-to-video model, collected 2026-10-15. The company announced Lucy 2.0 on 26 January 2026 and documented API id lucy-2 in February 2026. It is not on the October 2026 model index or in the current Python SDK, so this dossier marks it retired with medium confidence. Lucy 2.1 and then Lucy 2.5 replaced it as the served editor.", "what_it_is_for": "Lucy 2 is Decart's early 2026 realtime video-to-video model, collected 2026-10-15. The company announced Lucy 2.0 on 26 January 2026 and documented API id lucy-2 in February 2026. It is not on the October 2026 model index or in the current Python SDK, so this dossier marks it retired with medium confidence. Lucy 2.1 and then Lucy 2.5 replaced it as the served editor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-2-1": { "id": "decart/lucy/lucy-2-1", "name": "Lucy 2.1", "maker": "decart", "family": "decart/lucy", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy 2.1 is Decart's previous-generation closed video editor, collected 2026-10-15. It is still served for pinned realtime and batch calls, with character-reference and text edits at 720p. Decart tells new work to use Lucy 2.5. It is not deprecated in the sense of being switched off.", "what_it_is_for": "Lucy 2.1 is Decart's previous-generation closed video editor, collected 2026-10-15. It is still served for pinned realtime and batch calls, with character-reference and text edits at 720p. Decart tells new work to use Lucy 2.5. It is not deprecated in the sense of being switched off.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-2-1-vton": { "id": "decart/lucy/lucy-2-1-vton", "name": "Lucy 2.1 VTON", "maker": "decart", "family": "decart/lucy", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy 2.1 VTON is Decart's first named virtual-try-on API model, collected 2026-10-15. It launched in April 2026 as lucy-2.1-vton, with alias lucy-vton, and the June 2026 changelog says it is no longer served.", "what_it_is_for": "Lucy 2.1 VTON is Decart's first named virtual-try-on API model, collected 2026-10-15. It launched in April 2026 as lucy-2.1-vton, with alias lucy-vton, and the June 2026 changelog says it is no longer served.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-2-5": { "id": "decart/lucy/lucy-2-5", "name": "Lucy 2.5", "maker": "decart", "family": "decart/lucy", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed model. The July 2026 post describes a Transformer forward pass, dynamic sparse attention, MXFP8 and NVFP4 inference, and self-anchoring. It does not publish a layer count or parameter count.", "modalities": { "input": [ "video", "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy 2.5 is Decart's latest closed video-editing model, collected 2026-10-15. It edits live WebRTC video and queued files from text, a reference image, or both. Decart lists it as active and points the lucy-latest alias at it. The company announced it on 16 July 2026.", "what_it_is_for": "Lucy 2.5 is Decart's latest closed video-editing model, collected 2026-10-15. It edits live WebRTC video and queued files from text, a reference image, or both. Decart lists it as active and points the lucy-latest alias at it. The company announced it on 16 July 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-dev-i2v": { "id": "decart/lucy/lucy-dev-i2v", "name": "Lucy Dev I2V", "maker": "decart", "family": "decart/lucy", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy Dev I2V is a named Decart video API id from the February 2026 SDK docs, collected 2026-10-15. It is not in the October 2026 platform model index or the current Python SDK. This dossier marks it retired with medium confidence because no retirement sentence names it.", "what_it_is_for": "Lucy Dev I2V is a named Decart video API id from the February 2026 SDK docs, collected 2026-10-15. It is not in the October 2026 platform model index or the current Python SDK. This dossier marks it retired with medium confidence because no retirement sentence names it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-edit-1-1-dev": { "id": "decart/lucy/lucy-edit-1-1-dev", "name": "Lucy Edit 1.1 Dev", "maker": "decart", "family": "decart/lucy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Lucy Edit Dev non-commercial license (custom)", "release_date": "2025-11-07", "context_length": null, "max_output_tokens": null, "parameters": "about 5B", "architecture": "Wan2.2 5B stack, Diffusers LucyEditPipeline. Same family as Lucy Edit Dev.", "modalities": { "input": [ "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy Edit 1.1 Dev is Decart's updated open-weight video editor, collected 2026-10-15. It has its own Hugging Face repo, created 7 November 2025, and the same non-commercial license and about-5B Wan2.2 stack as Lucy Edit Dev. The card describes better fidelity, prompt adherence, identity, and global edits. It is active as published weights.", "what_it_is_for": "Lucy Edit 1.1 Dev is Decart's updated open-weight video editor, collected 2026-10-15. It has its own Hugging Face repo, created 7 November 2025, and the same non-commercial license and about-5B Wan2.2 stack as Lucy Edit Dev. The card describes better fidelity, prompt adherence, identity, and global edits. It is active as published weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-edit-dev": { "id": "decart/lucy/lucy-edit-dev", "name": "Lucy Edit Dev", "maker": "decart", "family": "decart/lucy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Lucy Edit Dev non-commercial license (custom; not a standard open-source license)", "release_date": "2025-09-18", "context_length": null, "max_output_tokens": null, "parameters": "about 5B", "architecture": "Wan2.2 5B high-compression VAE plus DiT. Diffusers class LucyEditPipeline.", "modalities": { "input": [ "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy Edit Dev is Decart's open-weight instruction-guided video editor, collected 2026-10-15. The card says it is about 5 billion parameters on a Wan2.2 5B VAE and DiT stack, and that it edits clothing, characters, objects, and scenes from text while keeping motion. The license is a custom non-commercial license. Weights are still on Hugging Face, so the status here is active even though the hosted Lucy API has moved on to Lucy 2.5.", "what_it_is_for": "Lucy Edit Dev is Decart's open-weight instruction-guided video editor, collected 2026-10-15. The card says it is about 5 billion parameters on a Wan2.2 5B VAE and DiT stack, and that it edits clothing, characters, objects, and scenes from text while keeping motion. The license is a custom non-commercial license. Weights are still on Hugging Face, so the status here is active even though the hosted Lucy API has moved on to Lucy 2.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-edit-fast": { "id": "decart/lucy/lucy-edit-fast", "name": "Lucy Edit Fast", "maker": "decart", "family": "decart/lucy", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy Edit Fast is a named Decart video API id from the February 2026 SDK docs, collected 2026-10-15. It is not in the October 2026 platform model index or the current Python SDK. This dossier marks it retired with medium confidence because no retirement sentence names it.", "what_it_is_for": "Lucy Edit Fast is a named Decart video API id from the February 2026 SDK docs, collected 2026-10-15. It is not in the October 2026 platform model index or the current Python SDK. This dossier marks it retired with medium confidence because no retirement sentence names it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-edit-live": { "id": "decart/lucy/lucy-edit-live", "name": "Lucy Edit Live", "maker": "decart", "family": "decart/lucy", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-10-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy Edit Live is Decart's first named realtime video editor, collected 2026-10-15. A Decart founding member announced it on 24 October 2025. The playground id was lucy_v2v_720p_rt. The May 2026 changelog retired Lucy v1 Realtime and told users to move to lucy-2.1. This folder treats those as the same product, with medium confidence on the id mapping.", "what_it_is_for": "Lucy Edit Live is Decart's first named realtime video editor, collected 2026-10-15. A Decart founding member announced it on 24 October 2025. The playground id was lucy_v2v_720p_rt. The May 2026 changelog retired Lucy v1 Realtime and told users to move to lucy-2.1. This folder treats those as the same product, with medium confidence on the id mapping.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-edit-pro": { "id": "decart/lucy/lucy-edit-pro", "name": "Lucy Edit Pro", "maker": "decart", "family": "decart/lucy", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy Edit Pro is Decart's closed API video editor, collected 2026-10-15, historically exposed as lucy-pro-v2v. The official Python SDK removed that legacy id on 14 October 2026. It is not on the current models page. Open-weight Lucy Edit Dev is a different release. Third parties still host a model under the Pro name; their prices are not Decart's list price.", "what_it_is_for": "Lucy Edit Pro is Decart's closed API video editor, collected 2026-10-15, historically exposed as lucy-pro-v2v. The official Python SDK removed that legacy id on 14 October 2026. It is not on the current models page. Open-weight Lucy Edit Dev is a different release. Third parties still host a model under the Pro name; their prices are not Decart's list price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-motion": { "id": "decart/lucy/lucy-motion", "name": "Lucy Motion", "maker": "decart", "family": "decart/lucy", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy Motion is a retired Decart image-to-video model for trajectory and object motion, collected 2026-10-15. The changelog introduced it in October 2025 and retired it in May 2026. The February 2026 SDK mirror lists id lucy-motion at 720p for 5 seconds.", "what_it_is_for": "Lucy Motion is a retired Decart image-to-video model for trajectory and object motion, collected 2026-10-15. The changelog introduced it in October 2025 and retired it in May 2026. The February 2026 SDK mirror lists id lucy-motion at 720p for 5 seconds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-pro-flf2v": { "id": "decart/lucy/lucy-pro-flf2v", "name": "Lucy Pro FLF2V", "maker": "decart", "family": "decart/lucy", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy Pro FLF2V is a named Decart video API id from the February 2026 SDK docs, collected 2026-10-15. It is not in the October 2026 platform model index or the current Python SDK. This dossier marks it retired with medium confidence because no retirement sentence names it.", "what_it_is_for": "Lucy Pro FLF2V is a named Decart video API id from the February 2026 SDK docs, collected 2026-10-15. It is not in the October 2026 platform model index or the current Python SDK. This dossier marks it retired with medium confidence because no retirement sentence names it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-pro-i2v": { "id": "decart/lucy/lucy-pro-i2v", "name": "Lucy Pro I2V", "maker": "decart", "family": "decart/lucy", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy Pro I2V is a named Decart video API id from the February 2026 SDK docs, collected 2026-10-15. It is not in the October 2026 platform model index or the current Python SDK. This dossier marks it retired with medium confidence because no retirement sentence names it.", "what_it_is_for": "Lucy Pro I2V is a named Decart video API id from the February 2026 SDK docs, collected 2026-10-15. It is not in the October 2026 platform model index or the current Python SDK. This dossier marks it retired with medium confidence because no retirement sentence names it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-pro-t2v": { "id": "decart/lucy/lucy-pro-t2v", "name": "Lucy Pro T2V", "maker": "decart", "family": "decart/lucy", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy Pro T2V is a named Decart video API id from the February 2026 SDK docs, collected 2026-10-15. It is not in the October 2026 platform model index or the current Python SDK. This dossier marks it retired with medium confidence because no retirement sentence names it.", "what_it_is_for": "Lucy Pro T2V is a named Decart video API id from the February 2026 SDK docs, collected 2026-10-15. It is not in the October 2026 platform model index or the current Python SDK. This dossier marks it retired with medium confidence because no retirement sentence names it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-restyle-2": { "id": "decart/lucy/lucy-restyle-2", "name": "Lucy Restyle 2", "maker": "decart", "family": "decart/lucy", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy Restyle 2 is Decart's active closed model for restyling a whole video, collected 2026-10-15. It changes the look of a live stream or a file up to 30 minutes from a style prompt or a style image. The docs also call the realtime product Lucy Restyle Live. It is cheaper than Lucy 2.5 and does not offer fast mode.", "what_it_is_for": "Lucy Restyle 2 is Decart's active closed model for restyling a whole video, collected 2026-10-15. It changes the look of a live stream or a file up to 30 minutes from a style prompt or a style image. The docs also call the realtime product Lucy Restyle Live. It is cheaper than Lucy 2.5 and does not offer fast mode.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-vton-2": { "id": "decart/lucy/lucy-vton-2", "name": "Lucy VTON 2", "maker": "decart", "family": "decart/lucy", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy VTON 2 is a retired Decart virtual-try-on model, collected 2026-10-15. It launched in May 2026 and was removed from the API in September 2026. The changelog names lucy-2.1-vton-2 as an alias retired at the same time.", "what_it_is_for": "Lucy VTON 2 is a retired Decart virtual-try-on model, collected 2026-10-15. It launched in May 2026 and was removed from the API in September 2026. The changelog names lucy-2.1-vton-2 as an alias retired at the same time.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-vton-3": { "id": "decart/lucy/lucy-vton-3", "name": "Lucy VTON 3", "maker": "decart", "family": "decart/lucy", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy VTON 3 is a retired Decart virtual-try-on model, collected 2026-10-15. The changelog says it launched in June 2026 and was no longer served as of September 2026, along with lucy-vton-2. Replace it with lucy-vton-3.5 or lucy-vton-3.6.", "what_it_is_for": "Lucy VTON 3 is a retired Decart virtual-try-on model, collected 2026-10-15. The changelog says it launched in June 2026 and was no longer served as of September 2026, along with lucy-vton-2. Replace it with lucy-vton-3.5 or lucy-vton-3.6.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-vton-3-5": { "id": "decart/lucy/lucy-vton-3-5", "name": "Lucy VTON 3.5", "maker": "decart", "family": "decart/lucy", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy VTON 3.5 is the previous closed virtual-try-on model, collected 2026-10-15. Decart still serves it by name on realtime and batch at 720p, including fast mode. New work is pointed at Lucy VTON 3.6.", "what_it_is_for": "Lucy VTON 3.5 is the previous closed virtual-try-on model, collected 2026-10-15. Decart still serves it by name on realtime and batch at 720p, including fast mode. New work is pointed at Lucy VTON 3.6.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/lucy/lucy-vton-3-6": { "id": "decart/lucy/lucy-vton-3-6", "name": "Lucy VTON 3.6", "maker": "decart", "family": "decart/lucy", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucy VTON 3.6 is Decart's latest closed virtual-try-on video model, collected 2026-10-15. It dresses a person in live or recorded video from a text prompt, a garment image, or both, at 720p. The lucy-vton-latest alias resolves to it. It is active on realtime and batch.", "what_it_is_for": "Lucy VTON 3.6 is Decart's latest closed virtual-try-on video model, collected 2026-10-15. It dresses a person in live or recorded video from a text prompt, a garment image, or both, at 720p. The lucy-vton-latest alias resolves to it. It is active on realtime and batch.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/mirage/mirage-lsd": { "id": "decart/mirage/mirage-lsd", "name": "MirageLSD", "maker": "decart", "family": "decart/mirage", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The Lucy 2 post and the old playground call it an autoregressive realtime video model. A parameter count was not published.", "modalities": { "input": [ "video", "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MirageLSD is Decart's earlier realtime video-restyle model, collected 2026-10-15. It had API ids mirage and mirage_v2. In March 2026 Decart renamed Mirage to Lucy Restyle Live, and the current id is lucy-restyle-2. The Python SDK dropped the mirage_v2 alias in July 2026. Status is retired.", "what_it_is_for": "MirageLSD is Decart's earlier realtime video-restyle model, collected 2026-10-15. It had API ids mirage and mirage_v2. In March 2026 Decart renamed Mirage to Lucy Restyle Live, and the current id is lucy-restyle-2. The Python SDK dropped the mirage_v2 alias in July 2026. Status is retired.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/oasis/oasis": { "id": "decart/oasis/oasis", "name": "Oasis", "maker": "decart", "family": "decart/oasis", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT for the released Oasis 500M weights (Etched/oasis-500m). The larger demo checkpoint was not released.", "release_date": "2024-10-31", "context_length": null, "max_output_tokens": null, "parameters": "500M for the released checkpoint. The larger live-demo checkpoint size was not published.", "architecture": "Diffusion transformer. Autoregressive action-conditional frame generation. Keyboard and mouse actions, no separate physics engine. ViT VAE plus DiT weights in the open release.", "modalities": { "input": [ "image", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Oasis is Decart and Etched's 2024 interactive world model, collected 2026-10-15. It generates a playable video world from actions, without a physics engine. The released weights are the 500M MIT checkpoint. A larger checkpoint was only a live demo. The 500M weights are still published, so this folder is active. It is not the 2026 driving model Oasis 3.", "what_it_is_for": "Oasis is Decart and Etched's 2024 interactive world model, collected 2026-10-15. It generates a playable video world from actions, without a physics engine. The released weights are the 500M MIT checkpoint. A larger checkpoint was only a live demo. The 500M weights are still published, so this folder is active. It is not the 2026 driving model Oasis 3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "decart/oasis/oasis-3-preview": { "id": "decart/oasis/oasis-3-preview", "name": "Oasis 3 Preview", "maker": "decart", "family": "decart/oasis", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed action-to-video world model. Multi-view driving cameras. The product page says it is not a physics engine like CARLA or Isaac Sim. Trained in part with the NVIDIA Physical AI Open Dataset, per the Weights & Biases customer note.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Oasis 3 Preview is Decart's closed realtime driving world model, collected 2026-10-15. You set a scene in text and stream throttle and steering; it returns multi-camera frames over gRPC. The API id is oasis-3-preview. The website calls the same system Oasis 3. Status is preview because that is the name Decart ships. It launched in June 2026 and is priced at $0.02 per second.", "what_it_is_for": "Oasis 3 Preview is Decart's closed realtime driving world model, collected 2026-10-15. You set a scene in text and stream throttle and steering; it returns multi-camera frames over gRPC. The API id is oasis-3-preview. The website calls the same system Oasis 3. Status is preview because that is the name Decart ships. It launched in June 2026 and is priced at $0.02 per second.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/aura/aura": { "id": "deepgram/aura/aura", "name": "Aura", "maker": "deepgram", "family": "deepgram/aura", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.015, "completion_per_m_usd": null }, "summary_intro": "Deepgram's first text-to-speech model, called Aura and Aura-1. English voices on /v1/speak, model strings aura-{voice}-en. Public release 12 March 2024. Still offered. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Deepgram describes Aura as the first-generation English TTS model. New English work is directed to Flux TTS, and other languages to Aura-2. Aura remains on the pricing page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/aura/aura-2": { "id": "deepgram/aura/aura-2", "name": "Aura-2", "maker": "deepgram", "family": "deepgram/aura", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.03, "completion_per_m_usd": null }, "summary_intro": "Deepgram multilingual text-to-speech model, API pattern aura-2-{voice}-{language}, on /v1/speak. Announced 15 April 2025. Seven languages on the current TTS overview. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Deepgram recommends Aura-2 when Flux TTS does not yet cover the language. The overview lists English, Spanish, German, French, Dutch, Italian, and Japanese voices.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/base/base": { "id": "deepgram/base/base", "name": "Base", "maker": "deepgram", "family": "deepgram/base", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end deep learning speech-to-text, as described on the model-options page.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.87, "completion_per_m_usd": null }, "summary_intro": "Deepgram legacy speech-to-text model, API id base. Still listed under Legacy Models on the models overview. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Deepgram describes Base as the cost-focused end-to-end tier for large volumes. The model-options page still says the pre-recorded model parameter defaults to base-general. It is a legacy model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/base/base-conversationalai": { "id": "deepgram/base/base-conversationalai", "name": "Base Conversational AI", "maker": "deepgram", "family": "deepgram/base", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.87, "completion_per_m_usd": null }, "summary_intro": "Deepgram legacy Base domain model, API id base-conversationalai. English (en, en-US). Still listed. Closed weights. Collected 2026-10-15.", "what_it_is_for": "A person talking to a bot, such as IVR or a kiosk.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/base/base-finance": { "id": "deepgram/base/base-finance", "name": "Base Finance", "maker": "deepgram", "family": "deepgram/base", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.87, "completion_per_m_usd": null }, "summary_intro": "Deepgram legacy Base domain model, API id base-finance. English (en, en-US). Still listed. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Multi-speaker, uneven audio, finance vocabulary.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/base/base-meeting": { "id": "deepgram/base/base-meeting", "name": "Base Meeting", "maker": "deepgram", "family": "deepgram/base", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.87, "completion_per_m_usd": null }, "summary_intro": "Deepgram legacy Base domain model, API id base-meeting. English (en, en-US). Still listed. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Conference-room audio, several speakers, one microphone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/base/base-phonecall": { "id": "deepgram/base/base-phonecall", "name": "Base Phonecall", "maker": "deepgram", "family": "deepgram/base", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.87, "completion_per_m_usd": null }, "summary_intro": "Deepgram legacy Base domain model, API id base-phonecall. English (en, en-US). Still listed. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Low-bandwidth telephone audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/base/base-video": { "id": "deepgram/base/base-video", "name": "Base Video", "maker": "deepgram", "family": "deepgram/base", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.87, "completion_per_m_usd": null }, "summary_intro": "Deepgram legacy Base domain model, API id base-video. English (en, en-US). Still listed. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Audio sourced from video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/base/base-voicemail": { "id": "deepgram/base/base-voicemail", "name": "Base Voicemail", "maker": "deepgram", "family": "deepgram/base", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.87, "completion_per_m_usd": null }, "summary_intro": "Deepgram legacy Base domain model, API id base-voicemail. English (en, en-US). Still listed. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Short single-speaker low-bandwidth clips. Deepgram says this option is derived from the phonecall model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/enhanced/enhanced": { "id": "deepgram/enhanced/enhanced", "name": "Enhanced", "maker": "deepgram", "family": "deepgram/enhanced", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.99, "completion_per_m_usd": null }, "summary_intro": "Deepgram legacy speech-to-text model, API id enhanced. Still listed under Legacy Models on the models overview. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Deepgram describes Enhanced as a higher-accuracy tier than Base, with stronger handling of uncommon words, and as the tier that supported keyword boosting. It is now a legacy model. Nova replaced it as the recommended general model in 2023.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/enhanced/enhanced-finance": { "id": "deepgram/enhanced/enhanced-finance", "name": "Enhanced Finance", "maker": "deepgram", "family": "deepgram/enhanced", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.99, "completion_per_m_usd": null }, "summary_intro": "Deepgram legacy Enhanced domain model, API id enhanced-finance. English. Marked beta on the model-options page. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Multi-speaker, uneven audio, finance vocabulary. The model-options page marks finance as beta.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/enhanced/enhanced-meeting": { "id": "deepgram/enhanced/enhanced-meeting", "name": "Enhanced Meeting", "maker": "deepgram", "family": "deepgram/enhanced", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.99, "completion_per_m_usd": null }, "summary_intro": "Deepgram legacy Enhanced domain model, API id enhanced-meeting. English. Marked beta on the model-options page. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Conference-room audio, several speakers, one microphone. The model-options page marks meeting as beta.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/enhanced/enhanced-phonecall": { "id": "deepgram/enhanced/enhanced-phonecall", "name": "Enhanced Phonecall", "maker": "deepgram", "family": "deepgram/enhanced", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.99, "completion_per_m_usd": null }, "summary_intro": "Deepgram legacy Enhanced domain model, API id enhanced-phonecall. English. Not marked beta. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Low-bandwidth telephone audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/flux/flux-general-en": { "id": "deepgram/flux/flux-general-en", "name": "Flux General English", "maker": "deepgram", "family": "deepgram/flux", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.0065, "completion_per_m_usd": null }, "summary_intro": "Deepgram conversational speech-recognition model for English voice agents. Model id flux-general-en on /v2/listen. Generally available 2 October 2025. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Deepgram says Flux is for real-time voice agents: it transcribes and detects turn boundaries in the model, instead of pairing a separate transcriber with voice-activity detection. It is not the model Deepgram recommends for meetings, captioning, or pre-recorded audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/flux/flux-general-multi": { "id": "deepgram/flux/flux-general-multi", "name": "Flux General Multilingual", "maker": "deepgram", "family": "deepgram/flux", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.0078, "completion_per_m_usd": null }, "summary_intro": "Deepgram multilingual conversational speech-recognition model. Model id flux-general-multi. Ten languages in one streaming model with turn detection. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Deepgram positions flux-general-multi for voice agents that must detect language, switch language mid-call, and keep Flux turn handling. It is the successor to English-only Flux for those markets, not a replacement for Nova-3 batch transcription.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/flux-tts/flux-tts": { "id": "deepgram/flux-tts/flux-tts", "name": "Flux TTS", "maker": "deepgram", "family": "deepgram/flux-tts", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.045, "completion_per_m_usd": null }, "summary_intro": "Deepgram's conversation-native text-to-speech model for English. Served on /v2/speak. Voice ids look like flux-haley-en. Generally available 12 August 2026. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Deepgram says to start new English speech synthesis on Flux TTS, including voice agents, customer service, IVR, and pre-rendered audio. Aura-2 remains the model for languages Flux TTS does not speak yet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova/nova": { "id": "deepgram/nova/nova", "name": "Nova", "maker": "deepgram", "family": "deepgram/nova", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-04-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deepgram's first Nova speech-to-text model, also called Nova-1. API ids nova and nova-general. Released 13 April 2023. Listed under Legacy Models and still documented as callable. Closed weights. Collected 2026-10-15.", "what_it_is_for": "At launch Deepgram called Nova its general high-accuracy model across many domains. Nova-2 replaced it as the recommended tier in November 2023. Deepgram now files it under Legacy Models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova/nova-medical": { "id": "deepgram/nova/nova-medical", "name": "Nova Medical", "maker": "deepgram", "family": "deepgram/nova", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Medical option on the original Nova model, API id nova-medical, English. The models and languages overview lists it under Legacy Models. Closed weights. Collected 2026-10-15.", "what_it_is_for": "The models overview lists nova-medical for English medical audio on Nova-1. The model-options page does not list a medical option under Nova, so the two official pages disagree.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova/nova-phonecall": { "id": "deepgram/nova/nova-phonecall", "name": "Nova Phonecall", "maker": "deepgram", "family": "deepgram/nova", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-04-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deepgram Nova-1 telephone model, API id nova-phonecall. English. Released with Nova on 13 April 2023. Legacy and still documented. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Deepgram says this option is for low-bandwidth phone audio on the original Nova model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova-2/nova-2": { "id": "deepgram/nova-2/nova-2", "name": "Nova-2", "maker": "deepgram", "family": "deepgram/nova-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer with speech-specific optimizations, plus data curation and multi-stage training. Deepgram model-options page.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.35, "completion_per_m_usd": null }, "summary_intro": "Deepgram speech-to-text model, API ids nova-2 and nova-2-general. General availability 14 November 2023 after a 19 September 2023 early access. Still offered. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Deepgram still recommends Nova-2 for languages Nova-3 does not cover and for filler-word identification. It is the general model in the Nova-2 tier. Domain variants are separate model ids.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova-2/nova-2-atc": { "id": "deepgram/nova-2/nova-2-atc", "name": "Nova-2 Air Traffic Control", "maker": "deepgram", "family": "deepgram/nova-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.35, "completion_per_m_usd": null }, "summary_intro": "Deepgram Nova-2 domain speech-to-text model, API id nova-2-atc. English (en, en-US). Still listed on the models overview. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Optimized for air traffic control audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova-2/nova-2-automotive": { "id": "deepgram/nova-2/nova-2-automotive", "name": "Nova-2 Automotive", "maker": "deepgram", "family": "deepgram/nova-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.35, "completion_per_m_usd": null }, "summary_intro": "Deepgram Nova-2 domain speech-to-text model, API id nova-2-automotive. English (en, en-US). Still listed on the models overview. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Optimized for automotive vocabulary. The model-options page spells the domain automative.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova-2/nova-2-conversationalai": { "id": "deepgram/nova-2/nova-2-conversationalai", "name": "Nova-2 Conversational AI", "maker": "deepgram", "family": "deepgram/nova-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.35, "completion_per_m_usd": null }, "summary_intro": "Deepgram Nova-2 domain speech-to-text model, API id nova-2-conversationalai. English (en, en-US). Still listed on the models overview. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Optimized for a person talking to a bot, such as IVR, a voice assistant, or a kiosk.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova-2/nova-2-drivethru": { "id": "deepgram/nova-2/nova-2-drivethru", "name": "Nova-2 Drive-thru", "maker": "deepgram", "family": "deepgram/nova-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.35, "completion_per_m_usd": null }, "summary_intro": "Deepgram Nova-2 domain speech-to-text model, API id nova-2-drivethru. English (en, en-US). Still listed on the models overview. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Optimized for drive-thru audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova-2/nova-2-finance": { "id": "deepgram/nova-2/nova-2-finance", "name": "Nova-2 Finance", "maker": "deepgram", "family": "deepgram/nova-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.35, "completion_per_m_usd": null }, "summary_intro": "Deepgram Nova-2 domain speech-to-text model, API id nova-2-finance. English (en, en-US). Still listed on the models overview. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Optimized for multi-speaker calls with uneven audio and finance vocabulary, such as earnings calls.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova-2/nova-2-medical": { "id": "deepgram/nova-2/nova-2-medical", "name": "Nova-2 Medical", "maker": "deepgram", "family": "deepgram/nova-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.35, "completion_per_m_usd": null }, "summary_intro": "Deepgram Nova-2 domain speech-to-text model, API id nova-2-medical. English (en, en-US). Still listed on the models overview. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Optimized for medical vocabulary. English only. Nova-3 Medical is the later medical model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova-2/nova-2-meeting": { "id": "deepgram/nova-2/nova-2-meeting", "name": "Nova-2 Meeting", "maker": "deepgram", "family": "deepgram/nova-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.35, "completion_per_m_usd": null }, "summary_intro": "Deepgram Nova-2 domain speech-to-text model, API id nova-2-meeting. English (en, en-US). Still listed on the models overview. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Optimized for conference rooms: several speakers, one microphone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova-2/nova-2-phonecall": { "id": "deepgram/nova-2/nova-2-phonecall", "name": "Nova-2 Phonecall", "maker": "deepgram", "family": "deepgram/nova-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.35, "completion_per_m_usd": null }, "summary_intro": "Deepgram Nova-2 domain speech-to-text model, API id nova-2-phonecall. English (en, en-US). Still listed on the models overview. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Optimized for low-bandwidth telephone audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova-2/nova-2-video": { "id": "deepgram/nova-2/nova-2-video", "name": "Nova-2 Video", "maker": "deepgram", "family": "deepgram/nova-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.35, "completion_per_m_usd": null }, "summary_intro": "Deepgram Nova-2 domain speech-to-text model, API id nova-2-video. English (en, en-US). Still listed on the models overview. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Optimized for audio taken from video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova-2/nova-2-voicemail": { "id": "deepgram/nova-2/nova-2-voicemail", "name": "Nova-2 Voicemail", "maker": "deepgram", "family": "deepgram/nova-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.35, "completion_per_m_usd": null }, "summary_intro": "Deepgram Nova-2 domain speech-to-text model, API id nova-2-voicemail. English (en, en-US). Still listed on the models overview. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Optimized for short, single-speaker, low-bandwidth clips. Deepgram says it is derived from the phonecall model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova-3/nova-3": { "id": "deepgram/nova-3/nova-3", "name": "Nova-3", "maker": "deepgram", "family": "deepgram/nova-3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech-to-text latent space model, as named in the 12 February 2025 Business Wire release.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.0048, "completion_per_m_usd": null }, "summary_intro": "Deepgram's general-purpose speech-to-text model, API ids nova-3 and nova-3-general. For batch and streaming transcription, not model-native turn detection. English launch 12 February 2025. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Deepgram recommends Nova-3 for meetings, event captioning, multi-speaker audio, noisy or far-field audio, and multilingual transcription. Voice agents that need end-of-turn detection are pointed to Flux instead.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova-3/nova-3-medical": { "id": "deepgram/nova-3/nova-3-medical", "name": "Nova-3 Medical", "maker": "deepgram", "family": "deepgram/nova-3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deepgram medical speech-to-text model, API id nova-3-medical. English and a 10-language medical code-switching mode. Original ship date was not found. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Deepgram describes it as optimized for medical vocabulary. The 6 October 2026 changelog adds multilingual medical audio. The 7 October 2026 note says the English models then in production make fewer word errors and fewer medical-entity errors, without a number.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepgram/nova-3/nova-3-pharma": { "id": "deepgram/nova-3/nova-3-pharma", "name": "Nova-3 Pharma", "maker": "deepgram", "family": "deepgram/nova-3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deepgram pharmaceutical speech-to-text model, API id nova-3-pharma. English only. Announced 17 September 2026. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Deepgram says it is for pharmacy and healthcare voice agents where medication names, dosages, and related terms need to be recognized. It is a separate model id from nova-3-medical.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepl/translator/classic": { "id": "deepl/translator/classic", "name": "DeepL Classic language model", "maker": "deepl", "family": "deepl/translator", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Proprietary neural machine translation network. DeepL says the networks include attention mechanisms but differ in topology from publicly documented Transformer systems.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepL's Classic language model is the closed neural machine-translation system that served the Translator before the next-generation LLM, and that served `latency_optimized` API calls from November 2024 until the 2026 sunset. DeepL's removal FAQ treats it as retired from that API path by Q3 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "Classic is the production neural translator DeepL contrasted with the next-gen LLM. It translated text for Pro users on the web translator, desktop apps, and the API. From November 2024, API clients selected it with `model_type=latency_optimized`, which was also the default when `model_type` was omitted. DeepL says it stopped investing engineering support in Classic because quality fell behind next-gen, and newer languages and Customization Hub updates often did not run on it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepl/translator/next-gen": { "id": "deepl/translator/next-gen", "name": "DeepL next-generation language model", "maker": "deepl", "family": "deepl/translator", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-07-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Specialized large language model built by DeepL for translation, not a general-purpose chat model. Parameter count and layer layout are not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepL's next-generation language model is a closed, translation-specialized LLM. It launched for DeepL Pro on 16 July 2024, reached the API through the `model_type` parameter in Q4 2024, and is the model DeepL still serves for both quality and latency goals. This dossier was collected on 2026-10-15.", "what_it_is_for": "DeepL built it to translate, especially longer text, with fewer edits than the Classic neural model and than general-purpose chat models. Pro users could switch to it in the translator. API clients request it with `model_type=quality_optimized`. `prefer_quality_optimized` is a legacy value that current docs treat as the same goal. DeepL says the parameter is a goal, not a pinned model name, and that it may change the serving checkpoint when it judges the change a net benefit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/chat/deepseek-chat": { "id": "deepseek/chat/deepseek-chat", "name": "deepseek-chat", "maker": "deepseek", "family": "deepseek/chat", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released deepseek-chat. As of 2026-10-15 this catalog marks it retired. deepseek-chat is a retired API id, not one set of weights. It tracked DeepSeek's general model from V2 through V3.2, then pointed at V4-Flash non-thinking mode, and was turned off after 24 Jul 2026.", "what_it_is_for": "deepseek-chat is a retired API id, not one set of weights. It tracked DeepSeek's general model from V2 through V3.2, then pointed at V4-Flash non-thinking mode, and was turned off after 24 Jul 2026.", "ratings": { "empirical_100": 63, "lmsys_elo": 1315, "coding_elo": 1320, "swe_bench": 36.2, "humaneval": 89.4, "twelve_cs": 99.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1084, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/coder/coder-1-3b": { "id": "deepseek/coder/coder-1-3b", "name": "DeepSeek-Coder-1.3B", "maker": "deepseek", "family": "deepseek/coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2023-10-29", "context_length": 16000, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Dense transformer. 24 layers, hidden size 2048. Fill-in-the-middle.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-Coder-1.3B. As of 2026-10-15 this catalog marks it active. DeepSeek-Coder 1.3B is the smallest model in the first coder release. Base and instruct weights are public.", "what_it_is_for": "DeepSeek-Coder 1.3B is the smallest model in the first coder release. Base and instruct weights are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/coder/coder-33b": { "id": "deepseek/coder/coder-33b", "name": "DeepSeek-Coder-33B", "maker": "deepseek", "family": "deepseek/coder", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2023-11-01", "context_length": 16000, "max_output_tokens": null, "parameters": "33B", "architecture": "Dense transformer, grouped-query attention, 62 layers.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-Coder-33B. As of 2026-10-15 this catalog marks it deprecated. DeepSeek-Coder-33B is the largest first-generation coder. Coder-V2's paper uses it as the previous coder baseline.", "what_it_is_for": "DeepSeek-Coder-33B is the largest first-generation coder. Coder-V2's paper uses it as the previous coder baseline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/coder/coder-6-7b": { "id": "deepseek/coder/coder-6-7b", "name": "DeepSeek-Coder-6.7B", "maker": "deepseek", "family": "deepseek/coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2023-10-23", "context_length": 16000, "max_output_tokens": null, "parameters": "6.7B", "architecture": "Dense transformer. 32 layers, hidden size 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-Coder-6.7B. As of 2026-10-15 this catalog marks it active. DeepSeek-Coder 6.7B is the mid-size first-generation coder. Base and instruct weights are public.", "what_it_is_for": "DeepSeek-Coder 6.7B is the mid-size first-generation coder. Base and instruct weights are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/coder/coder-7b-v1-5": { "id": "deepseek/coder/coder-7b-v1-5", "name": "DeepSeek-Coder-7B-v1.5", "maker": "deepseek", "family": "deepseek/coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-01-25", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Dense coder. Further pretrain in the v1 line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-Coder-7B-v1.5. As of 2026-10-15 this catalog marks it active. DeepSeek-Coder-7B-v1.5 is a January 2024 coder checkpoint. DeepSeekMath was initialized from its base model.", "what_it_is_for": "DeepSeek-Coder-7B-v1.5 is a January 2024 coder checkpoint. DeepSeekMath was initialized from its base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/coder/coder-v2": { "id": "deepseek/coder/coder-v2", "name": "DeepSeek-Coder-V2", "maker": "deepseek", "family": "deepseek/coder", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-06-14", "context_length": 128000, "max_output_tokens": null, "parameters": "236B total / 21B activated", "architecture": "MoE with MLA. Continued pretrain from DeepSeek-V2 on 6T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-Coder-V2. As of 2026-10-15 this catalog marks it deprecated. DeepSeek-Coder-V2 is the June 2024 MoE coder. The 236B model continued pretraining from a V2 checkpoint on 6T more tokens. The API id deepseek-coder served it, then V2.5.", "what_it_is_for": "DeepSeek-Coder-V2 is the June 2024 MoE coder. The 236B model continued pretraining from a V2 checkpoint on 6T more tokens. The API id deepseek-coder served it, then V2.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/coder/coder-v2-lite": { "id": "deepseek/coder/coder-v2-lite", "name": "DeepSeek-Coder-V2-Lite", "maker": "deepseek", "family": "deepseek/coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-06-14", "context_length": 128000, "max_output_tokens": null, "parameters": "16B total / 2.4B activated", "architecture": "MoE coder, MLA.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-Coder-V2-Lite. As of 2026-10-15 this catalog marks it active. DeepSeek-Coder-V2-Lite is the 16B MoE coder released with Coder-V2.", "what_it_is_for": "DeepSeek-Coder-V2-Lite is the 16B MoE coder released with Coder-V2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/janus/janus-1-3b": { "id": "deepseek/janus/janus-1-3b", "name": "Janus-1.3B", "maker": "deepseek", "family": "deepseek/janus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-10-18", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released Janus-1.3B. As of 2026-10-15 this catalog marks it active. Janus-1.3B is DeepSeek's October 2024 unified model for image understanding and image generation.", "what_it_is_for": "Janus-1.3B is DeepSeek's October 2024 unified model for image understanding and image generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/janus/janus-pro-1b": { "id": "deepseek/janus/janus-pro-1b", "name": "Janus-Pro-1B", "maker": "deepseek", "family": "deepseek/janus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-01-26", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Unified autoregressive model. SigLIP-L understanding encoder. Separate generation tokenizer.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released Janus-Pro-1B. As of 2026-10-15 this catalog marks it active. Janus-Pro-1B is the smaller Janus-Pro model, for understanding and text-to-image generation.", "what_it_is_for": "Janus-Pro-1B is the smaller Janus-Pro model, for understanding and text-to-image generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/janus/janus-pro-7b": { "id": "deepseek/janus/janus-pro-7b", "name": "Janus-Pro-7B", "maker": "deepseek", "family": "deepseek/janus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-01-26", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Unified autoregressive model on DeepSeek-LLM-7B-base. SigLIP-L understanding path.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released Janus-Pro-7B. As of 2026-10-15 this catalog marks it active. Janus-Pro-7B is the larger Janus-Pro unified understanding and generation model.", "what_it_is_for": "Janus-Pro-7B is the larger Janus-Pro unified understanding and generation model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/janus/janusflow-1-3b": { "id": "deepseek/janus/janusflow-1-3b", "name": "JanusFlow-1.3B", "maker": "deepseek", "family": "deepseek/janus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-11-12", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released JanusFlow-1.3B. As of 2026-10-15 this catalog marks it active. JanusFlow-1.3B is the November 2024 Janus model that adds a rectified-flow style generation path. The name and the paper are DeepSeek's.", "what_it_is_for": "JanusFlow-1.3B is the November 2024 Janus model that adds a rectified-flow style generation path. The name and the paper are DeepSeek's.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/llm/llm-67b": { "id": "deepseek/llm/llm-67b", "name": "DeepSeek-LLM-67B", "maker": "deepseek", "family": "deepseek/llm", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2023-11-29", "context_length": 4096, "max_output_tokens": null, "parameters": "67B", "architecture": "Dense decoder, grouped-query attention.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-LLM-67B. As of 2026-10-15 this catalog marks it deprecated. DeepSeek-LLM 67B is the large first general model. DeepSeek compared later V2 against it.", "what_it_is_for": "DeepSeek-LLM 67B is the large first general model. DeepSeek compared later V2 against it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/llm/llm-7b": { "id": "deepseek/llm/llm-7b", "name": "DeepSeek-LLM-7B", "maker": "deepseek", "family": "deepseek/llm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2023-11-29", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "Dense decoder, multi-head attention, 4096 context.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-LLM-7B. As of 2026-10-15 this catalog marks it active. DeepSeek-LLM 7B is the small model from DeepSeek's first general release. Base and chat weights are public, with a 4096-token window.", "what_it_is_for": "DeepSeek-LLM 7B is the small model from DeepSeek's first general release. Base and chat weights are public, with a 4096-token window.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/math/math-7b": { "id": "deepseek/math/math-7b", "name": "DeepSeekMath-7B", "maker": "deepseek", "family": "deepseek/math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "Dense 7B. Base, instruct, and GRPO RL checkpoints.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeekMath-7B. As of 2026-10-15 this catalog marks it active. DeepSeekMath 7B is the February 2024 math model. It continues pretraining from DeepSeek-Coder-v1.5 7B on math, language, and code. Base, instruct, and RL checkpoints were released.", "what_it_is_for": "DeepSeekMath 7B is the February 2024 math model. It continues pretraining from DeepSeek-Coder-v1.5 7B on math, language, and code. Base, instruct, and RL checkpoints were released.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/math/math-v2": { "id": "deepseek/math/math-v2", "name": "DeepSeekMath-V2", "maker": "deepseek", "family": "deepseek/math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Post-train of DeepSeek-V3.2-Exp-Base for proof generation and verification.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeekMath-V2. As of 2026-10-15 this catalog marks it active. DeepSeekMath-V2 is a November 2025 theorem-proving model. It is built on DeepSeek-V3.2-Exp-Base and trains a verifier and a proof generator together. Weights are Apache 2.0.", "what_it_is_for": "DeepSeekMath-V2 is a November 2025 theorem-proving model. It is built on DeepSeek-V3.2-Exp-Base and trains a verifier and a proof generator together. Weights are Apache 2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/moe/moe-16b": { "id": "deepseek/moe/moe-16b", "name": "DeepSeekMoE-16B", "maker": "deepseek", "family": "deepseek/moe", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-01-09", "context_length": null, "max_output_tokens": null, "parameters": "16B total", "architecture": "Multi-head attention plus Mixture-of-Experts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeekMoE-16B. As of 2026-10-15 this catalog marks it deprecated. DeepSeekMoE 16B is the expert-mixture model DeepSeek released before V2. V2's paper uses it as the small MoE baseline.", "what_it_is_for": "DeepSeekMoE 16B is the expert-mixture model DeepSeek released before V2. V2's paper uses it as the small MoE baseline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/ocr/ocr": { "id": "deepseek/ocr/ocr", "name": "DeepSeek-OCR", "maker": "deepseek", "family": "deepseek/ocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-OCR. As of 2026-10-15 this catalog marks it active. DeepSeek-OCR is an October 2025 vision-language model for optical context compression and document OCR. It turns page images into markdown or plain text.", "what_it_is_for": "DeepSeek-OCR is an October 2025 vision-language model for optical context compression and document OCR. It turns page images into markdown or plain text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/ocr/ocr-2": { "id": "deepseek/ocr/ocr-2", "name": "DeepSeek-OCR 2", "maker": "deepseek", "family": "deepseek/ocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-OCR 2. As of 2026-10-15 this catalog marks it active. DeepSeek-OCR 2 is the January 2026 document model. It keeps the OCR job and changes the visual encoder to what the paper calls Visual Causal Flow.", "what_it_is_for": "DeepSeek-OCR 2 is the January 2026 document model. It keeps the OCR job and changes the visual encoder to what the paper calls Visual Causal Flow.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/prover/prover-v1": { "id": "deepseek/prover/prover-v1", "name": "DeepSeek-Prover-V1", "maker": "deepseek", "family": "deepseek/prover", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-08-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama-family formal prover.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-Prover-V1. As of 2026-10-15 this catalog marks it deprecated. DeepSeek-Prover-V1 is DeepSeek's first formal-math prover checkpoint on Hugging Face.", "what_it_is_for": "DeepSeek-Prover-V1 is DeepSeek's first formal-math prover checkpoint on Hugging Face.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/prover/prover-v1-5": { "id": "deepseek/prover/prover-v1-5", "name": "DeepSeek-Prover-V1.5", "maker": "deepseek", "family": "deepseek/prover", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-08-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama-family formal prover. Base, SFT, and RL.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-Prover-V1.5. As of 2026-10-15 this catalog marks it active. DeepSeek-Prover-V1.5 is the August 2024 prover. DeepSeek released base, supervised, and reinforcement-learning checkpoints.", "what_it_is_for": "DeepSeek-Prover-V1.5 is the August 2024 prover. DeepSeek released base, supervised, and reinforcement-learning checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/prover/prover-v2-671b": { "id": "deepseek/prover/prover-v2-671b", "name": "DeepSeek-Prover-V2-671B", "maker": "deepseek", "family": "deepseek/prover", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-30", "context_length": null, "max_output_tokens": null, "parameters": "671B", "architecture": "DeepSeek-V3-family formal prover. FP8.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-Prover-V2-671B. As of 2026-10-15 this catalog marks it active. DeepSeek-Prover-V2-671B is the large April 2025 prover, in the DeepSeek-V3 architecture family.", "what_it_is_for": "DeepSeek-Prover-V2-671B is the large April 2025 prover, in the DeepSeek-V3 architecture family.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/prover/prover-v2-7b": { "id": "deepseek/prover/prover-v2-7b", "name": "DeepSeek-Prover-V2-7B", "maker": "deepseek", "family": "deepseek/prover", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-30", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Llama-family formal prover.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-Prover-V2-7B. As of 2026-10-15 this catalog marks it active. DeepSeek-Prover-V2-7B is the smaller April 2025 prover.", "what_it_is_for": "DeepSeek-Prover-V2-7B is the smaller April 2025 prover.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/r1/r1": { "id": "deepseek/r1/r1", "name": "DeepSeek-R1", "maker": "deepseek", "family": "deepseek/r1", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-01-20", "context_length": 128000, "max_output_tokens": null, "parameters": "671B total / 37B activated", "architecture": "MoE reasoning model trained from DeepSeek-V3-Base with large-scale RL.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-R1. As of 2026-10-15 this catalog marks it deprecated. DeepSeek-R1 is DeepSeek's first-generation reasoning model, released 20 Jan 2025 on the deepseek-reasoner API and as open weights. R1-0528 is the May 2025 upgrade of that same API model.", "what_it_is_for": "DeepSeek-R1 is DeepSeek's first-generation reasoning model, released 20 Jan 2025 on the deepseek-reasoner API and as open weights. R1-0528 is the May 2025 upgrade of that same API model.", "ratings": { "empirical_100": 56, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/r1/r1-0528-qwen3-8b": { "id": "deepseek/r1/r1-0528-qwen3-8b", "name": "DeepSeek-R1-0528-Qwen3-8B", "maker": "deepseek", "family": "deepseek/r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-05-29", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Qwen3-8B distill of R1-0528.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-R1-0528-Qwen3-8B. As of 2026-10-15 this catalog marks it active. DeepSeek-R1-0528-Qwen3-8B is a later small distill, released 29 May 2025, based on Qwen3-8B and the R1-0528 reasoning data.", "what_it_is_for": "DeepSeek-R1-0528-Qwen3-8B is a later small distill, released 29 May 2025, based on Qwen3-8B and the R1-0528 reasoning data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/r1/r1-distill-llama-70b": { "id": "deepseek/r1/r1-distill-llama-70b", "name": "DeepSeek-R1-Distill-Llama-70B", "maker": "deepseek", "family": "deepseek/r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-01-20", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Dense distill. Base: Llama-3.3-70B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-R1-Distill-Llama-70B. As of 2026-10-15 this catalog marks it active. DeepSeek-R1-Distill-Llama-70B is a dense model DeepSeek distilled from R1 onto Llama-3.3-70B-Instruct. It is DeepSeek's release, not a third-party fine-tune.", "what_it_is_for": "DeepSeek-R1-Distill-Llama-70B is a dense model DeepSeek distilled from R1 onto Llama-3.3-70B-Instruct. It is DeepSeek's release, not a third-party fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/r1/r1-distill-llama-8b": { "id": "deepseek/r1/r1-distill-llama-8b", "name": "DeepSeek-R1-Distill-Llama-8B", "maker": "deepseek", "family": "deepseek/r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-01-20", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Dense distill. Base: Llama-3.1-8B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-R1-Distill-Llama-8B. As of 2026-10-15 this catalog marks it active. DeepSeek-R1-Distill-Llama-8B is a dense model DeepSeek distilled from R1 onto Llama-3.1-8B. It is DeepSeek's release, not a third-party fine-tune.", "what_it_is_for": "DeepSeek-R1-Distill-Llama-8B is a dense model DeepSeek distilled from R1 onto Llama-3.1-8B. It is DeepSeek's release, not a third-party fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/r1/r1-distill-qwen-1-5b": { "id": "deepseek/r1/r1-distill-qwen-1-5b", "name": "DeepSeek-R1-Distill-Qwen-1.5B", "maker": "deepseek", "family": "deepseek/r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-01-20", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Dense distill. Base: Qwen2.5-Math-1.5B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-R1-Distill-Qwen-1.5B. As of 2026-10-15 this catalog marks it active. DeepSeek-R1-Distill-Qwen-1.5B is a dense model DeepSeek distilled from R1 onto Qwen2.5-Math-1.5B. It is DeepSeek's release, not a third-party fine-tune.", "what_it_is_for": "DeepSeek-R1-Distill-Qwen-1.5B is a dense model DeepSeek distilled from R1 onto Qwen2.5-Math-1.5B. It is DeepSeek's release, not a third-party fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/r1/r1-distill-qwen-14b": { "id": "deepseek/r1/r1-distill-qwen-14b", "name": "DeepSeek-R1-Distill-Qwen-14B", "maker": "deepseek", "family": "deepseek/r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-01-20", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Dense distill. Base: Qwen2.5-14B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-R1-Distill-Qwen-14B. As of 2026-10-15 this catalog marks it active. DeepSeek-R1-Distill-Qwen-14B is a dense model DeepSeek distilled from R1 onto Qwen2.5-14B. It is DeepSeek's release, not a third-party fine-tune.", "what_it_is_for": "DeepSeek-R1-Distill-Qwen-14B is a dense model DeepSeek distilled from R1 onto Qwen2.5-14B. It is DeepSeek's release, not a third-party fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/r1/r1-distill-qwen-32b": { "id": "deepseek/r1/r1-distill-qwen-32b", "name": "DeepSeek-R1-Distill-Qwen-32B", "maker": "deepseek", "family": "deepseek/r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-01-20", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Dense distill. Base: Qwen2.5-32B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-R1-Distill-Qwen-32B. As of 2026-10-15 this catalog marks it active. DeepSeek-R1-Distill-Qwen-32B is a dense model DeepSeek distilled from R1 onto Qwen2.5-32B. It is DeepSeek's release, not a third-party fine-tune.", "what_it_is_for": "DeepSeek-R1-Distill-Qwen-32B is a dense model DeepSeek distilled from R1 onto Qwen2.5-32B. It is DeepSeek's release, not a third-party fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/r1/r1-distill-qwen-7b": { "id": "deepseek/r1/r1-distill-qwen-7b", "name": "DeepSeek-R1-Distill-Qwen-7B", "maker": "deepseek", "family": "deepseek/r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-01-20", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Dense distill. Base: Qwen2.5-Math-7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-R1-Distill-Qwen-7B. As of 2026-10-15 this catalog marks it active. DeepSeek-R1-Distill-Qwen-7B is a dense model DeepSeek distilled from R1 onto Qwen2.5-Math-7B. It is DeepSeek's release, not a third-party fine-tune.", "what_it_is_for": "DeepSeek-R1-Distill-Qwen-7B is a dense model DeepSeek distilled from R1 onto Qwen2.5-Math-7B. It is DeepSeek's release, not a third-party fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/r1/r1-zero": { "id": "deepseek/r1/r1-zero", "name": "DeepSeek-R1-Zero", "maker": "deepseek", "family": "deepseek/r1", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-01-20", "context_length": 128000, "max_output_tokens": null, "parameters": "671B total / 37B activated", "architecture": "V3-base MoE trained by RL without a preliminary SFT stage.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-R1-Zero. As of 2026-10-15 this catalog marks it deprecated. DeepSeek-R1-Zero is the reasoning model trained with large-scale reinforcement learning and no supervised fine-tune first. DeepSeek released it with R1 as a research checkpoint.", "what_it_is_for": "DeepSeek-R1-Zero is the reasoning model trained with large-scale reinforcement learning and no supervised fine-tune first. DeepSeek released it with R1 as a research checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/reasoner/deepseek-reasoner": { "id": "deepseek/reasoner/deepseek-reasoner", "name": "deepseek-reasoner", "maker": "deepseek", "family": "deepseek/reasoner", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released deepseek-reasoner. As of 2026-10-15 this catalog marks it retired. deepseek-reasoner is a retired API id for DeepSeek's thinking mode. It launched as R1, moved through R1-0528 and the V3.1 and V3.2 thinking modes, then pointed at V4-Flash thinking, and was turned off after 24 Jul 2026.", "what_it_is_for": "deepseek-reasoner is a retired API id for DeepSeek's thinking mode. It launched as R1, moved through R1-0528 and the V3.1 and V3.2 thinking modes, then pointed at V4-Flash thinking, and was turned off after 24 Jul 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/v2/v2": { "id": "deepseek/v2/v2", "name": "DeepSeek-V2", "maker": "deepseek", "family": "deepseek/v2", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-05-06", "context_length": 128000, "max_output_tokens": null, "parameters": "236B total / 21B activated", "architecture": "MoE with Multi-head Latent Attention.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-V2. As of 2026-10-15 this catalog marks it deprecated. DeepSeek-V2 is the May 2024 MoE model that introduced Multi-head Latent Attention. The chat model was the deepseek-chat API until V2.5.", "what_it_is_for": "DeepSeek-V2 is the May 2024 MoE model that introduced Multi-head Latent Attention. The chat model was the deepseek-chat API until V2.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/v2/v2-5": { "id": "deepseek/v2/v2-5", "name": "DeepSeek-V2.5", "maker": "deepseek", "family": "deepseek/v2", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-09-05", "context_length": 128000, "max_output_tokens": null, "parameters": "236B total / 21B activated", "architecture": "V2 MoE. Chat and coder models merged.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-V2.5. As of 2026-10-15 this catalog marks it deprecated. DeepSeek-V2.5 merged V2-Chat-0628 and Coder-V2-0724 into one API model on 5 Sep 2024. deepseek-chat and deepseek-coder both served it. V2.5-1210 is the December patch.", "what_it_is_for": "DeepSeek-V2.5 merged V2-Chat-0628 and Coder-V2-0724 into one API model on 5 Sep 2024. deepseek-chat and deepseek-coder both served it. V2.5-1210 is the December patch.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/v2/v2-lite": { "id": "deepseek/v2/v2-lite", "name": "DeepSeek-V2-Lite", "maker": "deepseek", "family": "deepseek/v2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-05-16", "context_length": 32000, "max_output_tokens": null, "parameters": "16B total / 2.4B activated", "architecture": "MLA plus DeepSeekMoE.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-V2-Lite. As of 2026-10-15 this catalog marks it active. DeepSeek-V2-Lite is the small V2 model, released 16 May 2024, for local use.", "what_it_is_for": "DeepSeek-V2-Lite is the small V2 model, released 16 May 2024, for local use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/v3/v3": { "id": "deepseek/v3/v3", "name": "DeepSeek-V3", "maker": "deepseek", "family": "deepseek/v3", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License for the original README; V3-0324 Hugging Face tag says MIT. Commercial use is claimed for the series.", "release_date": "2024-12-26", "context_length": 128000, "max_output_tokens": null, "parameters": "671B total / 37B activated (685B on disk with the 14B MTP module)", "architecture": "MoE, MLA, multi-token prediction, FP8 training.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.27, "completion_per_m_usd": 1.1 }, "summary_intro": "DeepSeek released DeepSeek-V3. As of 2026-10-15 this catalog marks it deprecated. DeepSeek-V3 is the December 2024 MoE flagship. It was the deepseek-chat model, then updated in place by DeepSeek-V3-0324. Weights are still published. The first-party API no longer serves this generation.", "what_it_is_for": "DeepSeek-V3 is the December 2024 MoE flagship. It was the deepseek-chat model, then updated in place by DeepSeek-V3-0324. Weights are still published. The first-party API no longer serves this generation.", "ratings": { "empirical_100": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/v3/v3-1": { "id": "deepseek/v3/v3-1", "name": "DeepSeek-V3.1", "maker": "deepseek", "family": "deepseek/v3", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-08-21", "context_length": 128000, "max_output_tokens": null, "parameters": "671B total / 37B activated", "architecture": "V3 MoE with a hybrid thinking and non-thinking chat template. FP8 UE8M0 scales.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-V3.1. As of 2026-10-15 this catalog marks it deprecated. DeepSeek-V3.1 is the August 2025 hybrid model. One checkpoint does both thinking and non-thinking. deepseek-chat was non-thinking and deepseek-reasoner was thinking. Terminus is a later patch of the same model.", "what_it_is_for": "DeepSeek-V3.1 is the August 2025 hybrid model. One checkpoint does both thinking and non-thinking. deepseek-chat was non-thinking and deepseek-reasoner was thinking. Terminus is a later patch of the same model.", "ratings": { "empirical_100": 59, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/v3/v3-2": { "id": "deepseek/v3/v3-2", "name": "DeepSeek-V3.2", "maker": "deepseek", "family": "deepseek/v3", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-01", "context_length": 128000, "max_output_tokens": null, "parameters": "671B total / 37B activated", "architecture": "MoE with DeepSeek Sparse Attention. Thinking can run inside tool calls.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-V3.2. As of 2026-10-15 this catalog marks it deprecated. DeepSeek-V3.2 is the December 2025 reasoning and agent model. It replaced V3.2-Exp on the app, web, and API. Sparse attention, thinking inside tool use, and an open MIT weight release.", "what_it_is_for": "DeepSeek-V3.2 is the December 2025 reasoning and agent model. It replaced V3.2-Exp on the app, web, and API. Sparse attention, thinking inside tool use, and an open MIT weight release.", "ratings": { "empirical_100": 52, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/v3/v3-2-exp": { "id": "deepseek/v3/v3-2-exp", "name": "DeepSeek-V3.2-Exp", "maker": "deepseek", "family": "deepseek/v3", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-29", "context_length": 128000, "max_output_tokens": null, "parameters": "671B total / 37B activated", "architecture": "V3 MoE with DeepSeek Sparse Attention.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-V3.2-Exp. As of 2026-10-15 this catalog marks it deprecated. DeepSeek-V3.2-Exp introduced DeepSeek Sparse Attention on 29 Sep 2025. It briefly was both deepseek-chat and deepseek-reasoner. V3.2 calls itself the official successor.", "what_it_is_for": "DeepSeek-V3.2-Exp introduced DeepSeek Sparse Attention on 29 Sep 2025. It briefly was both deepseek-chat and deepseek-reasoner. V3.2 calls itself the official successor.", "ratings": { "empirical_100": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/v3/v3-2-speciale": { "id": "deepseek/v3/v3-2-speciale", "name": "DeepSeek-V3.2-Speciale", "maker": "deepseek", "family": "deepseek/v3", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-01", "context_length": 128000, "max_output_tokens": 128000, "parameters": "671B total / 37B activated", "architecture": "Same as DeepSeek-V3.2. Longer reasoning. No tool use.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-V3.2-Speciale. As of 2026-10-15 this catalog marks it retired. DeepSeek-V3.2-Speciale is the high-compute reasoning variant released with V3.2. It was API-only, with no tools, through a temporary base URL that expired on 15 Dec 2025. Weights remain on Hugging Face.", "what_it_is_for": "DeepSeek-V3.2-Speciale is the high-compute reasoning variant released with V3.2. It was API-only, with no tools, through a temporary base URL that expired on 15 Dec 2025. Weights remain on Hugging Face.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/v4/v4-1-flash": { "id": "deepseek/v4/v4-1-flash", "name": "DeepSeek-V4.1-Flash", "maker": "deepseek", "family": "deepseek/v4", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-09-10", "context_length": 1000000, "max_output_tokens": 384000, "parameters": "552B backbone; 8B active on prefill and 16B active on decode; Engram conditional memory 196B, sparsely accessed. MoE: 1 shared expert and 384 routed experts, 6 routed experts active per token.", "architecture": "Causal Encoder-Decoder MoE. 40 layers (20-layer causal encoder + 20-layer decoder). CSA2 sparse attention with Full, Reindex, and Reuse modes. FP4 main KV cache (890 bytes/token claimed). DeepSeek-ViT vision encoder and a two-layer MLP projector. DSpark speculative decoding. Single-Pass mHC. Trained from scratch on 45T multimodal tokens.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 1.2 }, "summary_intro": "DeepSeek released DeepSeek-V4.1-Flash. As of 2026-10-15 this catalog marks it active. DeepSeek describes V4.1-Flash as the smallest model in a new architecture family, aimed at agent work, long context, and native image understanding at a lower cost than V4-Pro.", "what_it_is_for": "DeepSeek describes V4.1-Flash as the smallest model in a new architecture family, aimed at agent work, long context, and native image understanding at a lower cost than V4-Pro.", "ratings": { "empirical_100": 67, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/v4/v4-1-pro": { "id": "deepseek/v4/v4-1-pro", "name": "DeepSeek-V4.1-Pro", "maker": "deepseek", "family": "deepseek/v4", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-V4.1-Pro. As of 2026-10-15 this catalog marks it announced. A future model named in DeepSeek's 10 Sep 2026 V4.1-Flash news post. It said V4-Pro traffic would be served by V4.1-Flash until V4.1-Pro launches. The same day's changelog then said V4-Pro service would continue. No card, weights, or price exist.", "what_it_is_for": "A future model named in DeepSeek's 10 Sep 2026 V4.1-Flash news post. It said V4-Pro traffic would be served by V4.1-Flash until V4.1-Pro launches. The same day's changelog then said V4-Pro service would continue. No card, weights, or price exist.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/v4/v4-flash": { "id": "deepseek/v4/v4-flash", "name": "DeepSeek-V4-Flash", "maker": "deepseek", "family": "deepseek/v4", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-24", "context_length": 1000000, "max_output_tokens": null, "parameters": "284B total / 13B activated", "architecture": "MoE, hybrid CSA and HCA attention, 1M context.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-V4-Flash. As of 2026-10-15 this catalog marks it retired. The smaller V4 preview model, later updated as DeepSeek-V4-Flash-0731. DeepSeek retired it on 10 Sep 2026. The old API id now routes to V4.1-Flash.", "what_it_is_for": "The smaller V4 preview model, later updated as DeepSeek-V4-Flash-0731. DeepSeek retired it on 10 Sep 2026. The old API id now routes to V4.1-Flash.", "ratings": { "empirical_100": 42, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.5, "humaneval": 70.4, "twelve_cs": 71.0, "aa_intelligence": 24.4, "aa_coding": 52.0, "aa_agentic": 26.3, "design_arena_elo": 1186, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/v4/v4-flash-vision-exp": { "id": "deepseek/v4/v4-flash-vision-exp", "name": "DeepSeek-V4-Flash-Vision-Exp", "maker": "deepseek", "family": "deepseek/v4", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-08-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-V4-Flash-Vision-Exp. As of 2026-10-15 this catalog marks it retired. An experimental vision model DeepSeek put on the API on 21 Aug 2026. Text ability was described as on par with official V4-Flash. It was retired on 10 Sep 2026 when V4.1-Flash took over vision.", "what_it_is_for": "An experimental vision model DeepSeek put on the API on 21 Aug 2026. Text ability was described as on par with official V4-Flash. It was retired on 10 Sep 2026 when V4.1-Flash took over vision.", "ratings": { "empirical_100": 58, "lmsys_elo": 1209, "coding_elo": 1209, "swe_bench": 16.6, "humaneval": 71.3, "twelve_cs": 31.7, "aa_intelligence": 34.8, "aa_coding": 65.0, "aa_agentic": 47.5, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/v4/v4-pro": { "id": "deepseek/v4/v4-pro", "name": "DeepSeek-V4-Pro", "maker": "deepseek", "family": "deepseek/v4", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-24", "context_length": 1000000, "max_output_tokens": 384000, "parameters": "1.6T total / 49B activated (preview card). 0813 keeps that structure and adds DSpark.", "architecture": "MoE with hybrid CSA and HCA attention. 1M context. FP4 expert weights and FP8 elsewhere on the preview card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.32, "completion_per_m_usd": 3.96 }, "summary_intro": "DeepSeek released DeepSeek-V4-Pro. As of 2026-10-15 this catalog marks it active. DeepSeek's larger V4 model. The API id deepseek-v4-pro currently serves DeepSeek-V4-Pro-0813. It is a text model for hard reasoning and agent work, with a 1M context.", "what_it_is_for": "DeepSeek's larger V4 model. The API id deepseek-v4-pro currently serves DeepSeek-V4-Pro-0813. It is a text model for hard reasoning and agent work, with a 1M context.", "ratings": { "empirical_100": 48, "lmsys_elo": 1213, "coding_elo": 1213, "swe_bench": 17.2, "humaneval": 71.7, "twelve_cs": 99.5, "aa_intelligence": 30.4, "aa_coding": 59.4, "aa_agentic": 26.3, "design_arena_elo": 948, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/vl/vl-1-3b": { "id": "deepseek/vl/vl-1-3b", "name": "DeepSeek-VL-1.3B", "maker": "deepseek", "family": "deepseek/vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-03-07", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-VL-1.3B. As of 2026-10-15 this catalog marks it active. DeepSeek-VL 1.3B is the small model in the first vision-language release. Base and chat weights are public.", "what_it_is_for": "DeepSeek-VL 1.3B is the small model in the first vision-language release. Base and chat weights are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/vl/vl-7b": { "id": "deepseek/vl/vl-7b", "name": "DeepSeek-VL-7B", "maker": "deepseek", "family": "deepseek/vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-03-07", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-VL-7B. As of 2026-10-15 this catalog marks it active. DeepSeek-VL 7B is the larger first vision-language model. Base and chat weights are public.", "what_it_is_for": "DeepSeek-VL 7B is the larger first vision-language model. Base and chat weights are public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/vl/vl2": { "id": "deepseek/vl/vl2", "name": "DeepSeek-VL2", "maker": "deepseek", "family": "deepseek/vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-12-13", "context_length": 4096, "max_output_tokens": null, "parameters": "4.5B activated (introduction). A demo comment appears to say 27.5B total / 4.2B activated, but the comment text is truncated.", "architecture": "MoE vision-language model.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-VL2. As of 2026-10-15 this catalog marks it active. DeepSeek-VL2 is the largest VL2 model.", "what_it_is_for": "DeepSeek-VL2 is the largest VL2 model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/vl/vl2-small": { "id": "deepseek/vl/vl2-small", "name": "DeepSeek-VL2-Small", "maker": "deepseek", "family": "deepseek/vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-12-13", "context_length": 4096, "max_output_tokens": null, "parameters": "2.8B activated (introduction). Demo comment says 16.1B total / 2.4B activated.", "architecture": "MoE vision-language model.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-VL2-Small. As of 2026-10-15 this catalog marks it active. DeepSeek-VL2-Small is the middle VL2 model.", "what_it_is_for": "DeepSeek-VL2-Small is the middle VL2 model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "deepseek/vl/vl2-tiny": { "id": "deepseek/vl/vl2-tiny", "name": "DeepSeek-VL2-Tiny", "maker": "deepseek", "family": "deepseek/vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek Model License (commercial use permitted; code MIT). Not an OSI license for the weights.", "release_date": "2024-12-13", "context_length": 4096, "max_output_tokens": null, "parameters": "1.0B activated. Demo comment: 3.37B total.", "architecture": "MoE vision-language model.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek released DeepSeek-VL2-Tiny. As of 2026-10-15 this catalog marks it active. DeepSeek-VL2-Tiny is the smallest VL2 model, for visual question answering, OCR, documents, charts, and grounding.", "what_it_is_for": "DeepSeek-VL2-Tiny is the smallest VL2 model, for visual question answering, OCR, documents, charts, and grounding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "delft/ap-mae/ap-mae-sc2-15b": { "id": "delft/ap-mae/ap-mae-sc2-15b", "name": "AP-MAE-SC2-15B", "maker": "delft", "family": "delft/ap-mae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-02-10", "context_length": null, "max_output_tokens": null, "parameters": "127115776", "architecture": "Same APMAE layout as AP-MAE-SC2-3B. Encoder 24 layers, width 512, 16 heads, MLP 2048, CLS pool. Decoder 8 layers, width 512, 8 heads, MLP 2048. Mask ratio 0.5, patch size 32, sequence length 256, num_classes 2. The 15B suffix is the attention source bigcode/starcoder2-15b. Hugging Face safetensors total is 127115776 float32 parameters, the same count as the 3B and 7B files.", "modalities": { "input": [ "attention" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Delft University of Technology model AP-MAE-SC2-15B (ap-mae-sc2-15b). Status: active. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint is trained on attention from StarCoder2-15B while that model filled a random 3 to 10 token Java span inside 256 tokens of context. The inference output is not named.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "delft/ap-mae/ap-mae-sc2-3b": { "id": "delft/ap-mae/ap-mae-sc2-3b", "name": "AP-MAE-SC2-3B", "maker": "delft", "family": "delft/ap-mae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-02-10", "context_length": null, "max_output_tokens": null, "parameters": "127115776", "architecture": "APMAE encoder-decoder, config model_type ap_mae. Encoder 24 layers, width 512, 16 heads, MLP 2048, CLS pool. Decoder 8 layers, width 512, 8 heads, MLP 2048. Mask ratio 0.5, patch size 32, sequence length 256, num_classes 2, attention_scaler log_normalize. vitmae_preload_name is null. The 3B suffix is the attention source bigcode/starcoder2-3b, not this file's size. Hugging Face safetensors total is 127115776 float32 parameters.", "modalities": { "input": [ "attention" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Delft University of Technology model AP-MAE-SC2-3B (ap-mae-sc2-3b). Status: active. Collected 2026-10-15.", "what_it_is_for": "The card says this checkpoint learns from StarCoder2-3B attention collected while that model filled a random 3 to 10 token span of Java, with 256 tokens of surrounding context. The config names the set JavaNearDedupFull. What the AP-MAE head is supposed to emit at inference is not written on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "delft/ap-mae/ap-mae-sc2-7b": { "id": "delft/ap-mae/ap-mae-sc2-7b", "name": "AP-MAE-SC2-7B", "maker": "delft", "family": "delft/ap-mae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-02-10", "context_length": null, "max_output_tokens": null, "parameters": "127115776", "architecture": "Same APMAE layout as AP-MAE-SC2-3B. Encoder 24 layers, width 512, 16 heads, MLP 2048, CLS pool. Decoder 8 layers, width 512, 8 heads, MLP 2048. Mask ratio 0.5, patch size 32, sequence length 256, num_classes 2. The 7B suffix is the attention source bigcode/starcoder2-7b. Hugging Face safetensors total is 127115776 float32 parameters, the same count as the 3B and 15B files.", "modalities": { "input": [ "attention" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Delft University of Technology model AP-MAE-SC2-7B (ap-mae-sc2-7b). Status: active. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint is trained on attention from StarCoder2-7B while that model filled a random 3 to 10 token Java span inside 256 tokens of context. The inference output is not named.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "delft/crossview/ccvpe": { "id": "delft/crossview/ccvpe", "name": "CCVPE", "maker": "delft", "family": "delft/crossview", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Code is GNU GPL-3.0. A separate weight license is not stated on the README.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cross-view pose estimator. A translationally equivariant convolutional encoder makes orientation-aware ground descriptors. Those are matched to local aerial descriptors. A localization decoder outputs a coarse-to-fine probability map. An orientation decoder outputs a vector field. The README says one model can run at different ground fields of view and can take an orientation prior without retraining.", "modalities": { "input": [ "image" ], "output": [ "pose" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Delft University of Technology model CCVPE (ccvpe). Status: active. Collected 2026-10-15.", "what_it_is_for": "Given a ground photo and a local aerial image, the model estimates where the camera is in that patch and which way it faces. The README says the same weights accept different horizontal fields of view and an optional orientation prior.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "delft/crossview/crossview-metric-localization": { "id": "delft/crossview/crossview-metric-localization", "name": "Cross-View Metric Localization", "maker": "delft", "family": "delft/crossview", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Code is GNU GPL-3.0. A separate weight license is not stated on the README.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ECCV 2022 cross-view localizer. Denser satellite descriptors, similarity matching at the bottleneck rather than as image retrieval, and a dense spatial distribution as the output so multi-modal ambiguity stays visible. The README says the stack is TensorFlow 1.14. Orientation is not a joint trained head; the README says the probability map can still give a rough heading.", "modalities": { "input": [ "image" ], "output": [ "localization" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Delft University of Technology model Cross-View Metric Localization (crossview-metric-localization). Status: active. Collected 2026-10-15.", "what_it_is_for": "The README describes metric localization of a ground camera inside a satellite patch, without treating the task as image retrieval first. The output is a dense location distribution so ambiguous matches stay visible. A rough heading can be read from that map when orientation was not an input.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "delft/terraseg/terraseg-b": { "id": "delft/terraseg/terraseg-b", "name": "TerraSeg-B", "maker": "delft", "family": "delft/terraseg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Weights CC BY-NC-SA 4.0. Source code Apache-2.0. The model card says the weight license is the most restrictive mix of the OmniLiDAR upstream datasets, including Waymo Open Dataset non-commercial derivative terms.", "release_date": "2026-05-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-supervised LiDAR ground segmentation. Same adapted Point Transformer v3 setup as TerraSeg-S, Base size. The Hugging Face card lists this checkpoint as about 46M parameters. Runs in FP32. Output labels are 0 ground and 1 non-ground. The released file was retrained on OmniLiDAR excluding View-of-Delft.", "modalities": { "input": [ "point-cloud" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Delft University of Technology model TerraSeg-B (terraseg-b). Status: active. Collected 2026-10-15.", "what_it_is_for": "Same job as TerraSeg-S: self-supervised ground versus non-ground labels on a LiDAR scan, aimed at sensors the model was not specially tuned for. The card says Base stays ahead of Small on the released files.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "delft/terraseg/terraseg-s": { "id": "delft/terraseg/terraseg-s", "name": "TerraSeg-S", "maker": "delft", "family": "delft/terraseg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Weights CC BY-NC-SA 4.0. Source code Apache-2.0. The model card says the weight license is the most restrictive mix of the OmniLiDAR upstream datasets, including Waymo Open Dataset non-commercial derivative terms.", "release_date": "2026-05-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-supervised LiDAR ground segmentation. Adapted Point Transformer v3 backbone with dataset-specific normalization disabled. The Hugging Face card lists this checkpoint as Small, about 12M parameters. Runs in FP32. Output labels are 0 ground and 1 non-ground. The released file was retrained on OmniLiDAR excluding View-of-Delft.", "modalities": { "input": [ "point-cloud" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Delft University of Technology model TerraSeg-S (terraseg-s). Status: active. Collected 2026-10-15.", "what_it_is_for": "The model card calls TerraSeg the first self-supervised, domain-agnostic model for LiDAR ground versus non-ground segmentation. It is trained on OmniLiDAR pseudo-labels from a self-supervised PseudoLabeler, not on manual point labels. The public Small file was retrained without the license-restricted View-of-Delft scans.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "delft/unibev/unibev": { "id": "delft/unibev/unibev", "name": "UniBEV", "maker": "delft", "family": "delft/unibev", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-modal 3D detector. Each sensor is resampled into a bird's-eye-view feature map with a shared style of encoder, then fused. The main released setting is Channel Normalized Weights (CNW) with modality dropout, so one set of weights can run LiDAR plus camera, LiDAR only, or camera only. Concatenation and channel averaging checkpoints were also released.", "modalities": { "input": [ "lidar", "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Delft University of Technology model UniBEV (unibev). Status: active. Collected 2026-10-15.", "what_it_is_for": "The paper and README describe an end-to-end detector that keeps working when a sensor is missing. One CNW checkpoint is trained with modality dropout and is evaluated on LiDAR plus camera, LiDAR only, and camera only without retraining. Fusion can be channel-normalized weights, averaging, or concatenation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "delft/voteflow/voteflow": { "id": "delft/voteflow/voteflow", "name": "VoteFlow", "maker": "delft", "family": "delft/voteflow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Code in the repository is BSD 3-Clause. The LICENSE copyright line is Robotics, Perception and Learning @KTH, the SeFlow codebase this repo builds on. A separate license for the weights is not stated.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-supervised scene flow. A pillar voting module picks one shared translation for nearby points inside a discrete voting space. The released eval checkpoint named in the README is voteflow_best_m8n128_ori.pth (m=8, n=128). The paper table caption says inference is about 25.6 ms per sample on an A100.", "modalities": { "input": [ "point-cloud" ], "output": [ "scene-flow" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Delft University of Technology model VoteFlow (voteflow). Status: active. Collected 2026-10-15.", "what_it_is_for": "The paper adds a voting module so nearby points share one rigid translation, instead of only using a rigidity loss. It is aimed at self-supervised scene flow on driving LiDAR, with reported runs on Argoverse 2 and Waymo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "descript/cargan/cargan": { "id": "descript/cargan/cargan", "name": "CARGAN", "maker": "descript", "family": "descript/cargan", "status": "active", "confidence": "high", "weights": "closed", "license": "MIT", "release_date": "2021-10-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Chunked autoregressive GAN for conditional waveform synthesis. Three parts: an autoregressive conditioning stack that summarizes the previous samples, a generator from mel conditioning plus that stack, and a set of discriminators. Code is descriptinc/cargan. Pretrained checkpoint files were not verified in the README excerpt retrieved here.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CARGAN (Chunked Autoregressive GAN) is a Descript vocoder that generates a waveform from conditioning such as a mel spectrogram, using a short autoregressive summary of previous samples so pitch is more stable than in earlier GAN vocoders. Official code is MIT-licensed at descriptinc/cargan. Descript Research dates the paper 2021-10-19. arXiv v2 is 2022-03-03. Collected 2026-10-15. Status: active as a code release. It is not a model in the Descript app.", "what_it_is_for": "The paper's job is conditional waveform synthesis for speech. Relative to earlier GAN vocoders, the abstract says pitch error drops 40\u201360 percent and training time drops 58 percent, while generation stays fast enough for real time. The authors include Descript researchers. Max Morrison's work on the paper was an internship at Descript.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "descript/dac/dac": { "id": "descript/dac/dac", "name": "Descript Audio Codec", "maker": "descript", "family": "descript/dac", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-06-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Improved RVQGAN. Convolutional encoder-decoder with residual vector quantization, Snake activations, a multi-band multi-scale STFT discriminator, and a multi-scale mel loss. Universal across speech, music, and environmental sound. Paper Table 1 for the 44.1 kHz setting: stride 512, 86 Hz frame rate, 9 codebooks of 10 bits, target 8 kbps, compression factor 91.16.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Descript Audio Codec (DAC) is Descript's open universal neural audio codec, also called Improved RVQGAN. It compresses speech, music, and other audio into discrete codes. Code and weights were published on 2023-06-12. The paper's second arXiv version is dated 2023-10-26. Collected 2026-10-15. Status: active. Weights are open under MIT. It is a research release, not a toggle in the Descript editor.", "what_it_is_for": "Descript released DAC so high-sample-rate audio can be turned into a short sequence of discrete tokens for generative models. The README says it can replace EnCodec in audio language models. Audio Regenerate later describes its own codec as a higher-compression descendant of DAC.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "descript/eye-contact/eye-contact": { "id": "descript/eye-contact/eye-contact", "name": "Eye Contact", "maker": "descript", "family": "descript/eye-contact", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-06-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed gaze-correction model. Descript describes it as redirecting the eyes toward the lens. No architecture, training set, or parameter count was published.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Eye Contact is Descript's video effect that redirects a speaker's gaze toward the camera when they were reading a script. Descript announced it on 2023-06-14 for a rollout later that month, and it is still a current effect. Collected 2026-10-15. Status: active. Weights are closed. The network was not published.", "what_it_is_for": "Descript's stated job is a direct-to-camera look without a teleprompter or a reshoot. The effect is a toggle on a video layer. Marketing says the original footage is kept and the effect can be turned off.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "descript/green-screen/green-screen": { "id": "descript/green-screen/green-screen", "name": "Green Screen", "maker": "descript", "family": "descript/green-screen", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed person-segmentation effect. Descript says it finds the person and removes the background without a physical green screen. It is documented as distinct from Chroma Key, which is a color key with similarity and spill controls. The segmentation network was not named.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Green Screen is Descript's named AI effect that cuts a person out of a video without a physical green screen. It is a current effect and is documented separately from Chroma Key. Collected 2026-10-15. Status: active. Weights are closed. The network was not published, so confidence that it is Descript's own model rather than a hosted segmenter is medium.", "what_it_is_for": "Descript uses it to remove a background from a talking-head or call recording so another image or video can sit behind the speaker. If the shoot actually used a green or blue wall, Descript tells users to use Chroma Key instead.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "descript/jumpcut/jumpcut-smoothing": { "id": "descript/jumpcut/jumpcut-smoothing", "name": "Jumpcut Smoothing", "maker": "descript", "family": "descript/jumpcut", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed video infill model. Frames on both sides of a cut condition a generator that fills a fully masked gap with a short bridge of head pose, expression, and motion. Descript then always runs Video Regenerate on that bridge to resync the lips. The June 2026 engineering post says stage one is 1080p and a distilled refinement pass produces 4K. The project page does not name the backbone.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jumpcut Smoothing is Descript's talking-head video model that fills the frames a cut removes so the head does not snap to a new pose. Descript then runs Video Regenerate on that bridge so the mouth matches the audio. An engineering post dated 2026-06-18 says the feature had just shipped. Collected 2026-10-15. Status: active. Weights are closed.", "what_it_is_for": "Text edits change the length of a clip. Dropped frames leave a jump cut. The model treats the cut as inpainting in time and generates a short bridge from the frames before and after it. The same idea covers a shortened clip and a loop point. Lips are a second pass, not part of the bridge model itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "descript/melgan/melgan": { "id": "descript/melgan/melgan", "name": "MelGAN", "maker": "descript", "family": "descript/melgan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2019-10-25", "context_length": null, "max_output_tokens": null, "parameters": "4.26 million", "architecture": "Non-autoregressive fully convolutional GAN that inverts a mel spectrogram to a waveform. Multi-scale waveform discriminator. The paper's reference implementation uses an 80-band mel frontend. Authors were at Lyrebird AI and Descript. The official repo is descriptinc/melgan-neurips.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MelGAN is a non-autoregressive GAN vocoder from Lyrebird and Descript researchers. It turns a mel spectrogram into a waveform and was published at NeurIPS 2019. Official code and hub weights are in descriptinc/melgan-neurips, first pushed 2019-10-25, under MIT. Collected 2026-10-15. Status: active as a weight release. It is not a model you select in the Descript app.", "what_it_is_for": "The paper's job is mel-spectrogram inversion for text-to-speech: a fully convolutional generator that generalizes to speakers it was not trained on, with far fewer parameters than WaveGlow or WaveNet. The paper points readers at Descript Overdub as a product that used this kind of speech pipeline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "descript/nu-gan/nu-gan": { "id": "descript/nu-gan/nu-gan", "name": "NU-GAN", "maker": "descript", "family": "descript/nu-gan", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2020-10-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GAN resampler that upsamples audio, aimed at taking speech-synthesis output toward 44.1 kHz or 48 kHz. The paper's tested path is 22 kHz to 44.1 kHz. Authors list Descript Inc. and Mila. No network diagram was copied here.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NU-GAN is a Descript and Mila research model that upsamples audio with a GAN so speech synthesis can reach studio sample rates. The paper is arXiv 2010.11362, submitted 2020-10-22, and Descript Research still lists it. Collected 2026-10-15. It is not a model in the Descript app. No official weight release was found. Status in this catalog is retired, and that is a judgment, not a Descript withdrawal notice. Confidence is medium.", "what_it_is_for": "The paper treats upsampling as its own stage in a text-to-speech pipeline. Production audio is 44.1 kHz or 48 kHz, while many synthesizers of that time stopped at 24 kHz. The reported test resamples 22 kHz audio to 44.1 kHz.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "descript/overdub/overdub": { "id": "descript/overdub/overdub", "name": "Overdub", "maker": "descript", "family": "descript/overdub", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2019-09-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed speech model from Lyrebird, Descript's research group after the 2019 acquisition. Descript described Overdub as imputation: missing audio is predicted from surrounding context plus a model of the speaker. A 2021 rebuild targeted intonation and pacing. Parameter count and network type were not published.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Overdub is Descript's original named speech model, shipped in closed beta on 2019-09-18 with the acquisition of Lyrebird. It cloned an enrolled voice and imputed new words into a recording. Descript later dropped the Overdub label in favor of Regenerate. Collected 2026-10-15. Status: deprecated.", "what_it_is_for": "Descript built Overdub so a creator could correct or add speech by typing, in a voice model trained on that creator. The Lyrebird group described the edit as imputation: the model predicts the missing audio from the words and the audio on either side. Marketing still calls the feature Overdub and says it also offered stock voices. The in-app name was changed to Regenerate.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "descript/regenerate/audio-regenerate": { "id": "descript/regenerate/audio-regenerate", "name": "Audio Regenerate", "maker": "descript", "family": "descript/regenerate", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage closed model. A continuous neural codec, built on Descript Audio Codec with about 4x higher temporal compression and with power disentangled into its own channels (PoDAR), plus a flow-matching transformer that inpaints masked latent frames from surrounding audio and the target transcript. Generation is parallel across the mask, not left to right. Zero-shot: speaker identity is taken from adjacent audio, with Descript citing 5-6 seconds of context.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Audio Regenerate is Descript's in-house speech editing model. A continuous codec and a flow-matching transformer repaint a masked span so new or repaired words match the surrounding voice and room. Descript says it powers word-level audio editing in the app. The research slug is uvm-v2. Collected 2026-10-15. Status: active. Weights are closed.", "what_it_is_for": "The model regenerates only the region that should change. Descript names two jobs: type a new word into a recording, and smooth a hard cut by regenerating the words on either side of the join. Identity, intonation, pacing, background noise, and room tone are supposed to come from the untouched audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "descript/regenerate/video-regenerate": { "id": "descript/regenerate/video-regenerate", "name": "Video Regenerate", "maker": "descript", "family": "descript/regenerate", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed lip-sync model. A causal 3D VAE codec maps video to continuous latent frames, aligned to DINO features, with mouth-weighted reconstruction. A flow-matching transformer generates lower-face latents conditioned on surrounding video latents, reference frames, and Whisper-encoded audio compressed onto the latent timeline. Training uses masked velocity loss on the lower face and 3-way classifier-free guidance over audio and appearance. Zero-shot from reference frames in the clip. Descript's engineering post also describes a 1080p first stage and a distilled refinement to 4K for the related seamless-edit stack.", "modalities": { "input": [ "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Video Regenerate is Descript's audio-driven lip-sync model. It regenerates the lower face so the mouth matches new or repaired speech while the rest of the frame stays put. Descript uses it for transcript edits and for dubbed translation. An engineering post dated 2026-06-18 says the feature had just shipped. Collected 2026-10-15. Status: active. Weights are closed.", "what_it_is_for": "After audio is regenerated, this model paints a mouth that matches the new audio. The same model is what Descript describes for lip sync on a translated voiceover. Identity, lighting, and teeth are taken from reference frames in the clip. There is no per-person fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "descript/studio-sound/studio-sound": { "id": "descript/studio-sound/studio-sound", "name": "Studio Sound", "maker": "descript", "family": "descript/studio-sound", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed regenerative speech enhancer. Descript says it isolates the voice and regenerates it rather than only subtracting noise, and that it reduces echo and room tone. Network type was not published. An intensity control scales the effect.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Studio Sound is Descript's speech enhancement model. It is a one-toggle effect that removes noise and echo and regenerates the voice so a rough take is closer to a studio recording. It is in the current app. Collected 2026-10-15. Status: active. Weights are closed.", "what_it_is_for": "Descript says ordinary noise reduction only subtracts sound and can leave the voice thin. Studio Sound isolates the speaker and regenerates the voice, including less echo and room tone. It is meant for dialogue, interviews, and talking-head tracks, not for music mastering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dlut/rexdrug/rexdrug": { "id": "dlut/rexdrug/rexdrug", "name": "RexDrug", "maker": "dlut", "family": "dlut/rexdrug", "status": "active", "confidence": "high", "weights": "open-weights", "license": "llama3.1", "release_date": "2026-03-08", "context_length": 131072, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Two-stage drug-combination extractor. Released base is a merged LoRA SFT of Llama-3.1-8B-Instruct (LlamaForCausalLM, 32 layers, hidden 4096, intermediate 14336, 32 heads, 8 KV heads, vocab 128256, BF16, max_position_embeddings 131072, rope_theta 500000, Llama-3 rope factor 8). Hub safetensors total 8,030,261,248 BF16. The GRPO stage is a separate LoRA adapter. adapter_config.json sets r 64, alpha 128, dropout 0.05, targets q/k/v/o and the MLP projections. The GitHub training table instead lists LoRA rank 16 and alpha 32 for both SFT and GRPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RexDrug is a biomedical drug-combination extractor from DUTIR at Dalian University of Technology. The released weights are a merged supervised fine-tune of Llama-3.1-8B-Instruct plus a GRPO LoRA adapter. Both Hub repos were created on 2026-03-08. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and cards describe end-to-end extraction of variable-length drug combinations from biomedical sentences, with a short reasoning trace. Relation labels on the released cards are POS (beneficial), NEG (harmful), COMB (neutral or mixed), and NO_COMB. The DrugComb paper setting uses POS, OTHER, and NO_COMB. Those label sets are not the same wording.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dlut/tailing/tailing": { "id": "dlut/tailing/tailing", "name": "TaiLing", "maker": "dlut", "family": "dlut/tailing", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-11-15", "context_length": 8192, "max_output_tokens": null, "parameters": "7721324544", "architecture": "QLoRA fine-tune of Qwen-7B, uploaded as full QWenLMHeadModel weights rather than an adapter. Config matches Qwen-7B: 32 layers, hidden size 4096, intermediate size 22016, 32 heads, vocab 151936, max_position_embeddings 8192, seq_length 2048, torch_dtype bfloat16. The pytorch index metadata total_size is 15,442,649,088 bytes, which is 7,721,324,544 parameters at 2 bytes. The English README says the base has 70 billion parameters. That figure conflicts with the Qwen-7B config and the file size. Chinese coverage calls it a Qwen-7B-class model (70\u4ebf).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TaiLing (\u592a\u4ee4) is a judicial language model from DUTIR at Dalian University of Technology. The public checkpoint is a Qwen-7B QLoRA fine-tune uploaded as full weights. The Hub repo was created on 2023-11-15. Campus-facing coverage is dated March 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "The GitHub README says TaiLing is for judicial institutions and law-enforcement staff. Named jobs are document checking, information extraction, sentencing reference, judicial-exam support, and dialogue. The March 2024 writeup adds charge prediction and sentencing suggestions from historical cases, and says the intended setting is a closed environment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dlut/taiyi/taiyi": { "id": "dlut/taiyi/taiyi", "name": "Taiyi", "maker": "dlut", "family": "dlut/taiyi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-10-21", "context_length": 8192, "max_output_tokens": null, "parameters": "7721324544", "architecture": "Two-stage supervised fine-tune of Alibaba Qwen-7B-base (QWenLMHeadModel). Released config: 32 layers, hidden size 4096, intermediate size 22016, 32 attention heads, vocab 151936, BF16, max_position_embeddings 8192, seq_length 2048, dynamic NTK and logn attention flags on. Hub safetensors total 7,721,324,544 BF16 parameters. The JAMIA paper says about 7 billion parameters and a vocabulary of about 150,000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Taiyi (\u592a\u4e00) is a bilingual biomedical language model from the Information Retrieval Laboratory (DUTIR) at Dalian University of Technology. It is an open-weight supervised fine-tune of Qwen-7B-base. Weights have been on Hugging Face since 2023-10-21. This dossier was collected on 2026-10-15.", "what_it_is_for": "DUTIR describes Taiyi as a Chinese-English model for biomedical question answering, doctor-patient dialogue, report generation, information extraction, machine translation, headline generation, and text classification. The JAMIA paper says it was instruction-tuned in two stages on biomedical text-mining corpora.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dlut/taiyi/taiyi-2": { "id": "dlut/taiyi/taiyi-2", "name": "Taiyi 2", "maker": "dlut", "family": "dlut/taiyi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-07-03", "context_length": 131072, "max_output_tokens": 2048, "parameters": "9399951392", "architecture": "Full-weight checkpoint whose config and sft_args.json name glm-4-9b-chat (ChatGLMForConditionalGeneration). 40 layers, hidden size 4096, ffn 13696, 32 heads, multi-query groups 2, vocab 151552, BF16. Hub safetensors total 9,399,951,392 parameters. sft_args.json sets sft_type to full, max_length 2048, 5 epochs, learning rate 0.0001, per-device batch 2, gradient accumulation 16. The same file also dumps LoRA defaults (rank 8, alpha 32); the released file is a full BF16 model, not an adapter.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Taiyi 2 (\u592a\u4e002) is the DUTIR follow-up to Taiyi, from Dalian University of Technology. It is an open-weight biomedical chat model whose released config points at GLM-4-9B-Chat. The Hub repo `DUTIR-BioNLP/Taiyi2-chat` was created on 2025-07-03. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says Taiyi 2 is still a bilingual biomedical model for clinical communication, knowledge discovery, and related BioNLP tasks. Relative to Taiyi 1 it changes the backbone, filters instruction data, and retunes task instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "drexel/metadata/drexel-metadata-generator": { "id": "drexel/metadata/drexel-metadata-generator", "name": "Drexel Metadata Generator", "maker": "drexel", "family": "drexel/metadata", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-07-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mask R-CNN in Detectron2 v0.6 with a ResNet-50 FPN backbone and the mask head on. The training config initializes from detectron2://ImageNetPretrained/MSRA/R-50.pkl, sets MAX_ITER to 15000, and sets solver steps to 12300 and 13500. Five classes: fish, eye, ruler, two, and three. Objects below 30% confidence are dropped. The public file is a PyTorch pickle, model_final.pth, 176294747 bytes.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Drexel Metadata Generator is a named object detector from Drexel University computer science, written with Tulane coauthors and posted on the Imageomics Hugging Face organization. It finds fish, eyes, rulers, and ruler digits in museum specimen photos, then writes geometric and image-quality metadata. The public weight was still online on 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "The card says the model generates metadata for museum fish-specimen images. It is a Detectron2 Mask R-CNN. Kept classes are fish, fish eyes, rulers, and the digits two and three. Downstream code turns masks into measurements such as area, perimeter, eccentricity, and scale. The authors name Joel Pepper and Kevin Karnani as developers. David E. Breen and Jane Greenberg are Drexel coauthors. Yasin Baki\u015f, Xiaojun Wang, and Henry Bart Jr. are at Tulane.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/abepitope/abepitope": { "id": "dtu/abepitope/abepitope", "name": "AbEpiTope 1.0", "maker": "dtu", "family": "dtu/abepitope", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-structure" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model AbEpiTope 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Scoring and predicting antibody targets from structures. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/anoxpepred/anoxpepred": { "id": "dtu/anoxpepred/anoxpepred", "name": "AnOxPePred 1.0", "maker": "dtu", "family": "dtu/anoxpepred", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model AnOxPePred 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Antioxidative peptide prediction using a convolutional neural network. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/arraypitope/arraypitope": { "id": "dtu/arraypitope/arraypitope", "name": "ArrayPitope 1.0", "maker": "dtu", "family": "dtu/arraypitope", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model ArrayPitope 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Residue-level epitope mapping from peptide microarray data. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/atom-jepa/atom-jepa": { "id": "dtu/atom-jepa/atom-jepa", "name": "Atom-JEPA", "maker": "dtu", "family": "dtu/atom-jepa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2026-10-06", "context_length": null, "max_output_tokens": null, "parameters": "39000000", "architecture": "EquiformerV3 context encoder: 8 transformer blocks, 256 channels, irreps up to Lmax 2. Radius graph with a 6 angstrom cutoff (periodic for crystals). Input is atomic numbers (Z < 128) and 3D coordinates, plus the lattice for crystals. The structure embedding is the 256-dimensional mean of per-atom l=0 features. A predictor is used only in pretraining.", "modalities": { "input": [ "atomic-structure" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technical University of Denmark (DTU Compute) released Atom-JEPA, a self-supervised encoder for 3D molecules and crystals. Status: active. Collected 2026-10-15.", "what_it_is_for": "The model card says the context encoder is a feature extractor. Downstream property prediction needs a readout, a probe, or fine-tuning. Two released encoders cover molecules and inorganic crystals.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/bepipred/bepipred": { "id": "dtu/bepipred/bepipred", "name": "BepiPred 3.0", "maker": "dtu", "family": "dtu/bepipred", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural networks on protein language-model embeddings of epitope and non-epitope residues taken from crystal structures. Predicts linear and discontinuous B-cell epitopes from sequence. Optional sequential smoothing. Residue contact in training is described as within 4 angstroms of an antibody.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU Health Tech model BepiPred, current release 3.0, for B-cell epitope prediction from sequence. Status: active. Collected 2026-10-15.", "what_it_is_for": "It marks residues that are likely to contact an antibody, including discontinuous epitopes, using language-model embeddings. Uppercase and lowercase letters in the output distinguish epitope and non-epitope residues.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/chlorop/chlorop": { "id": "dtu/chlorop/chlorop", "name": "ChloroP 1.1", "maker": "dtu", "family": "dtu/chlorop", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.1 packages for SunOS, OSF1, Linux, and IRIX", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model ChloroP 1.1. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Chloroplast transit peptides and their cleavage sites in plant proteins. On the software page. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/cofactory/cofactory": { "id": "dtu/cofactory/cofactory", "name": "Cofactory 1.0", "maker": "dtu", "family": "dtu/cofactory", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0 Linux package", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model Cofactory 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Identification of Rossmann folds and prediction of FAD, NAD, and NADP specificity. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/deepcds/deepcds": { "id": "dtu/deepcds/deepcds", "name": "DeepCDS 1.0", "maker": "dtu", "family": "dtu/deepcds", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "nucleotide-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model DeepCDS 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Prediction of coding sequences in prokaryotic short sequences. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/deeploc/deeploc": { "id": "dtu/deeploc/deeploc", "name": "DeepLoc 2.1", "maker": "dtu", "family": "dtu/deeploc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three-stage sequence model on a frozen-style protein language model. The page offers two variants of the same server: high-quality uses ProtT5-XL-Uniref50, stated as 3 billion parameters, and high-throughput uses the 33-layer ESM-1b transformer, stated as 650 million parameters. Multi-label localization, membrane type, and sorting-signal presence. Attention plot in the long output.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU Health Tech model DeepLoc, current release 2.1, for eukaryotic protein localization. Status: active. Collected 2026-10-15.", "what_it_is_for": "The server assigns one or more of 10 subcellular localizations and one of four membrane associations from sequence alone, and it marks sorting signals that influenced the call.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/deeploc/deeplocpro": { "id": "dtu/deeploc/deeplocpro", "name": "DeepLocPro 1.0", "maker": "dtu", "family": "dtu/deeploc", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural network on protein sequence, with an attention plot. Trained on experimentally localized proteins from UniProt and PSORTdb. Six prokaryotic classes. Archaea or Gram-positive submissions suppress periplasm and outer-membrane labels and remap them to extracellular.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU Health Tech model DeepLocPro 1.0, the prokaryotic localization server paired with DeepLoc. Status: active. Collected 2026-10-15.", "what_it_is_for": "It predicts one of six prokaryotic locations from sequence. The abstract says it beat PSORTb 3.0 on the authors' benchmark, without a number on the page text retrieved.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/deeptmhmm/deeptmhmm": { "id": "dtu/deeptmhmm/deeptmhmm", "name": "DeepTMHMM 1.0", "maker": "dtu", "family": "dtu/deeptmhmm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Deep learning model based on a protein language model. Predicts topology of alpha-helical and beta-barrel transmembrane proteins across domains of life.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU Health Tech model DeepTMHMM 1.0, the deep-learning successor to TMHMM. Status: active. Collected 2026-10-15.", "what_it_is_for": "It predicts transmembrane topology for both alpha-helical and beta-barrel proteins in all domains of life. The page says it is meant to scale to proteomes and metagenomes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/dictyoglyc/dictyoglyc": { "id": "dtu/dictyoglyc/dictyoglyc", "name": "DictyOGlyc 1.1", "maker": "dtu", "family": "dtu/dictyoglyc", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model DictyOGlyc 1.1. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "O-alpha-GlcNAc glycosylation sites, trained on Dictyostelium discoideum proteins. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/discotope/discotope": { "id": "dtu/discotope/discotope", "name": "DiscoTope 3.0", "maker": "dtu", "family": "dtu/discotope", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "B-cell epitope predictor on inverse-folding latent representations from solved structures or AlphaFold models. The server can pull entries from RCSB and AlphaFoldDB.", "modalities": { "input": [ "protein-structure" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU Health Tech model DiscoTope, current release 3.0, for B-cell epitopes from structure. Status: active. Collected 2026-10-15.", "what_it_is_for": "It scores epitope residues on a solved structure or an AlphaFold model. The page says this removes the need for a new experimental structure for catalogued proteins.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/distancep/distancep": { "id": "dtu/distancep/distancep", "name": "DistanceP 1.0", "maker": "dtu", "family": "dtu/distancep", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model DistanceP 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Predicts protein distance constraints. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/easygene/easygene": { "id": "dtu/easygene/easygene", "name": "EasyGene 1.2", "maker": "dtu", "family": "dtu/easygene", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.2c Linux package on the software page", "modalities": { "input": [ "nucleotide-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model EasyGene 1.2. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Gene finding in prokaryotes. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/haplocart/haplocart": { "id": "dtu/haplocart/haplocart", "name": "HaploCart 1.0", "maker": "dtu", "family": "dtu/haplocart", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "nucleotide-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model HaploCart 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Human mtDNA haplogroup classification using a pangenome reference graph. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/hlaassoc/hlaassoc": { "id": "dtu/hlaassoc/hlaassoc", "name": "HLAAssoc 1.0", "maker": "dtu", "family": "dtu/hlaassoc", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model HLAAssoc 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Predicting allele association of HLA-DRB1 and HLA-DRB3/4/5 in different populations. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/hmmgene/hmmgene": { "id": "dtu/hmmgene/hmmgene", "name": "HMMgene 1.1", "maker": "dtu", "family": "dtu/hmmgene", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.1 packages for SunOS, OSF1, Linux, and IRIX", "modalities": { "input": [ "nucleotide-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model HMMgene 1.1. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Prediction of vertebrate and C. elegans genes. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/icerfire/icerfire": { "id": "dtu/icerfire/icerfire", "name": "ICERFIRE 1.0", "maker": "dtu", "family": "dtu/icerfire", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0a package for all platforms", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model ICERFIRE 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "ICORE-based prediction of neo-epitope immunogenicity. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/immunogenn/immunogenn": { "id": "dtu/immunogenn/immunogenn", "name": "ImmunoGeNN 1.0", "maker": "dtu", "family": "dtu/immunogenn", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model ImmunoGeNN 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Population-level MHC-II immunogenicity risk from protein sequences and allele frequencies. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/lipop/lipop": { "id": "dtu/lipop/lipop", "name": "LipoP 1.0", "maker": "dtu", "family": "dtu/lipop", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0a Linux package", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model LipoP 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Signal peptidase I and II cleavage sites in Gram-negative bacteria. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/lyra/lyra": { "id": "dtu/lyra/lyra", "name": "LYRA 1.0", "maker": "dtu", "family": "dtu/lyra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0 package", "modalities": { "input": [ "protein-sequence" ], "output": [ "protein-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model LYRA 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Lymphocyte receptor automated modelling. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/mhccluster/mhccluster": { "id": "dtu/mhccluster/mhccluster", "name": "MHCcluster 2.0", "maker": "dtu", "family": "dtu/mhccluster", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model MHCcluster 2.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "MHC clustering based on binding specificity. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/mhcmotifdecon/mhcmotifdecon": { "id": "dtu/mhcmotifdecon/mhcmotifdecon", "name": "MHCMotifDecon 1.3", "maker": "dtu", "family": "dtu/mhcmotifdecon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: Packages 1.3a, 1.2a, 1.2b, 1.2c, and 1.0a", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model MHCMotifDecon 1.3. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Motif deconvolution of multi-allele immunopeptidomics data. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/mupexi/mupexi": { "id": "dtu/mupexi/mupexi", "name": "MuPeXI 1.1", "maker": "dtu", "family": "dtu/mupexi", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model MuPeXI 1.1. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Prediction of neo-epitopes from tumor sequencing data. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netallergen/netallergen": { "id": "dtu/netallergen/netallergen", "name": "NetAllergen 1.0", "maker": "dtu", "family": "dtu/netallergen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0 Linux package", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetAllergen 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Protein allergenicity prediction. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netaspgene/netaspgene": { "id": "dtu/netaspgene/netaspgene", "name": "NetAspGene 1.0", "maker": "dtu", "family": "dtu/netaspgene", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "nucleotide-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetAspGene 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Intron splice sites in Aspergillus DNA. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netbolaiipan/netbolaiipan": { "id": "dtu/netbolaiipan/netbolaiipan", "name": "NetBoLAIIpan 1.0", "maker": "dtu", "family": "dtu/netbolaiipan", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetBoLAIIpan 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Peptide binding to bovine MHC class II BoLA-DRB3. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netbolapan/netbolapan": { "id": "dtu/netbolapan/netbolapan", "name": "NetBoLApan 1.0", "maker": "dtu", "family": "dtu/netbolapan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0a Linux package", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetBoLApan 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Peptide binding to bovine MHC class I BoLA molecules. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netcglyc/netcglyc": { "id": "dtu/netcglyc/netcglyc", "name": "NetCGlyc 1.0", "maker": "dtu", "family": "dtu/netcglyc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0c Linux and IRIX packages", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetCGlyc 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "C-mannosylation sites in mammalian proteins. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netchop/netchop": { "id": "dtu/netchop/netchop", "name": "NetChop 3.1", "maker": "dtu", "family": "dtu/netchop", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 3.1d Linux and Darwin packages", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetChop 3.1. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Proteasomal cleavage of MHC ligands. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netctl/netctl": { "id": "dtu/netctl/netctl", "name": "NetCTL 1.2", "maker": "dtu", "family": "dtu/netctl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: Packages 1.2b and 1.2a", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetCTL 1.2. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Prediction of CTL epitopes in protein sequences. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netctlpan/netctlpan": { "id": "dtu/netctlpan/netctlpan", "name": "NetCTLpan 1.1", "maker": "dtu", "family": "dtu/netctlpan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.1b Linux and Darwin packages", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetCTLpan 1.1. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Pan-specific integrated class I antigen presentation. On the software page. Not repeated as its own line on the services index retrieved. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netgene2/netgene2": { "id": "dtu/netgene2/netgene2", "name": "NetGene2 2.42", "maker": "dtu", "family": "dtu/netgene2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 2.42 source package", "modalities": { "input": [ "nucleotide-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetGene2 2.42. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Intron splice sites in human, C. elegans, and A. thaliana DNA. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netgpi/netgpi": { "id": "dtu/netgpi/netgpi", "name": "NetGPI 1.1", "maker": "dtu", "family": "dtu/netgpi", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Recurrent neural network with an attention mechanism that points at a possible omega site. Only the last 100 C-terminal residues are scored. The server expects proteins already assigned to the secretory pathway.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU Health Tech model NetGPI, current release 1.1, for GPI-anchor prediction. Status: active. Collected 2026-10-15.", "what_it_is_for": "It decides whether a secretory protein is GPI-anchored and, if so, which residue is the omega site. The highest probability along the C-terminus is the call.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/neth2pan/neth2pan": { "id": "dtu/neth2pan/neth2pan", "name": "NetH2pan 1.0", "maker": "dtu", "family": "dtu/neth2pan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0a Linux and Darwin packages", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetH2pan 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Peptide binding to murine MHC class I H-2 molecules. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netmhc/netmhc": { "id": "dtu/netmhc/netmhc", "name": "NetMHC 4.0", "maker": "dtu", "family": "dtu/netmhc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: Packages 4.0a, 3.4a, and 3.0c", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetMHC 4.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Binding of peptides to MHC class I molecules. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netmhccons/netmhccons": { "id": "dtu/netmhccons/netmhccons", "name": "NetMHCcons 1.1", "maker": "dtu", "family": "dtu/netmhccons", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.1a Linux and Darwin packages", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetMHCcons 1.1. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Binding of peptides to any known MHC class I molecule. The name is a consensus method. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netmhcii/netmhcii": { "id": "dtu/netmhcii/netmhcii", "name": "NetMHCII 2.3", "maker": "dtu", "family": "dtu/netmhcii", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: Packages 2.3 and 2.2", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetMHCII 2.3. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Binding of peptides to MHC class II molecules. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netmhciipan/netmhciipan": { "id": "dtu/netmhciipan/netmhciipan", "name": "NetMHCIIpan 4.3", "maker": "dtu", "family": "dtu/netmhciipan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 4.3 packages through 4.3k, plus 4.2, 4.1, 4.0, 3.2, 3.1, 3.0, and 2.0", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetMHCIIpan 4.3. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Pan-specific binding of peptides to MHC class II molecules of known sequence. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netmhciiphospan/netmhciiphospan": { "id": "dtu/netmhciiphospan/netmhciiphospan", "name": "NetMHCIIphosPan 1.0", "maker": "dtu", "family": "dtu/netmhciiphospan", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetMHCIIphosPan 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Pan-specific binding of phosphorylated peptides to MHC class II molecules of known sequence. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netmhcpan/netmhcpan": { "id": "dtu/netmhcpan/netmhcpan", "name": "NetMHCpan 4.2", "maker": "dtu", "family": "dtu/netmhcpan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Artificial neural networks trained pan-specifically on MHC class I sequence plus peptide. The page says 4.2 uses an updated NNAlign_MA algorithm with amino-acid deletion features, trained on binding-affinity and eluted-ligand data, then fine-tuned on pathogen epitopes and neoepitopes. A context-encoding option exists. Package 4.2e is the build named in the 6 July 2026 note.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU Health Tech model NetMHCpan, current server 4.2, for pan-specific MHC class I peptide binding. Status: active. Collected 2026-10-15. The service is a DTU and LIAI collaboration hosted by DTU.", "what_it_is_for": "It scores how likely a peptide is to be a natural ligand of an MHC class I allele, including alleles supplied as a protein sequence. Binding affinity can be requested as well. The 6 July 2026 note says the context-encoding model was re-trained and the binding-core step was changed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netmhcpanexp/netmhcpanexp": { "id": "dtu/netmhcpanexp/netmhcpanexp", "name": "NetMHCpanExp 1.0", "maker": "dtu", "family": "dtu/netmhcpanexp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0 Linux and Darwin packages", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetMHCpanExp 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Pan-specific MHC class I binding that also uses antigen expression. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netmhcphospan/netmhcphospan": { "id": "dtu/netmhcphospan/netmhcphospan", "name": "NetMHCphosPan 1.0", "maker": "dtu", "family": "dtu/netmhcphospan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0a Linux and Darwin packages", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetMHCphosPan 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Pan-specific binding of phosphorylated peptides to HLA class I. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netmhcstab/netmhcstab": { "id": "dtu/netmhcstab/netmhcstab", "name": "NetMHCstab 1.0", "maker": "dtu", "family": "dtu/netmhcstab", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0a Linux and Darwin packages", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetMHCstab 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Stability of peptide:MHC-I complexes. On the software page. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netmhcstabpan/netmhcstabpan": { "id": "dtu/netmhcstabpan/netmhcstabpan", "name": "NetMHCstabpan 1.0", "maker": "dtu", "family": "dtu/netmhcstabpan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0cstatic, 1.0b, and 1.0a packages", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetMHCstabpan 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Pan-specific stability of peptide:MHC-I complexes. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netnes/netnes": { "id": "dtu/netnes/netnes", "name": "NetNES 1.1", "maker": "dtu", "family": "dtu/netnes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.1 packages for several Unix platforms", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetNES 1.1. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Leucine-rich nuclear export signals in eukaryotic proteins. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netnglyc/netnglyc": { "id": "dtu/netnglyc/netnglyc", "name": "NetNGlyc 1.0", "maker": "dtu", "family": "dtu/netnglyc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0d Linux package and an alternate binary", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetNGlyc 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "N-linked glycosylation sites in human proteins. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netoglyc/netoglyc": { "id": "dtu/netoglyc/netoglyc", "name": "NetOGlyc 4.0", "maker": "dtu", "family": "dtu/netoglyc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 4.0 Unix package and 3.1e packages for several old Unix platforms", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetOGlyc 4.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "O-GalNAc glycosylation sites in mammalian proteins. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netphos/netphos": { "id": "dtu/netphos/netphos", "name": "NetPhos 3.1", "maker": "dtu", "family": "dtu/netphos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 3.1 SunOS and Linux packages", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetPhos 3.1. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Generic phosphorylation sites in eukaryotic proteins. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netphosbac/netphosbac": { "id": "dtu/netphosbac/netphosbac", "name": "NetPhosBac 1.0", "maker": "dtu", "family": "dtu/netphosbac", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetPhosBac 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Generic phosphorylation sites in bacterial proteins. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netphospan/netphospan": { "id": "dtu/netphospan/netphospan", "name": "NetPhospan 1.0", "maker": "dtu", "family": "dtu/netphospan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0 Linux package. The index calls the method CNNs.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetPhospan 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Phosphorylation prediction using convolutional neural networks. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netphosyeast/netphosyeast": { "id": "dtu/netphosyeast/netphosyeast", "name": "NetPhosYeast 1.0", "maker": "dtu", "family": "dtu/netphosyeast", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetPhosYeast 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Serine and threonine phosphorylation sites in yeast proteins. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netsolp/netsolp": { "id": "dtu/netsolp/netsolp", "name": "NetSolP 1.0", "maker": "dtu", "family": "dtu/netsolp", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Protein language-model transformers. The server offers ESM-1b, a distilled ESM-1b called NetSolP-D, and ESM-12. Tasks are solubility and usability for purification of proteins expressed in E. coli. Usability includes solubility and expressibility.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU Health Tech model NetSolP 1.0, for solubility and purification usability of proteins expressed in E. coli. Status: active. Collected 2026-10-15.", "what_it_is_for": "The user picks solubility, usability, or both, and one of three language-model variants. The download page says the code and trained models are under the 3-Clause BSD License, copyright 2021 Technical University of Denmark.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netstart/netstart": { "id": "dtu/netstart/netstart", "name": "NetStart 2.0", "maker": "dtu", "family": "dtu/netstart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: Service index says 2.0. Software page lists NetStart 1.0c for Linux and IRIX.", "modalities": { "input": [ "nucleotide-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetStart 2.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Prediction of translation initiation sites in eukaryotes. The software page lists a 1.0c package. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netsurfp/netsurfp": { "id": "dtu/netsurfp/netsurfp", "name": "NetSurfP 3.0", "maker": "dtu", "family": "dtu/netsurfp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One model predicts solvent accessibility, secondary structure, disorder, and backbone dihedral angles. The abstract says it uses pretrained protein language models. Version 2.0 was a convolutional and LSTM network. Version 3.0 is the current server.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU Health Tech model NetSurfP, current release 3.0, for one-dimensional protein structure features. Status: active. Collected 2026-10-15.", "what_it_is_for": "From sequence it predicts surface accessibility, secondary structure, disorder, and phi and psi angles. The abstract says language-model features made it about two orders of magnitude faster than NetSurfP 2.0 at similar accuracy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/nettcr/nettcr": { "id": "dtu/nettcr/nettcr", "name": "NetTCR 2.2", "maker": "dtu", "family": "dtu/nettcr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence model of peptide-TCR binding. A component is pretrained on 26 MHC-I peptides, then peptide-specific models are trained. Pan-specific prediction is offered for other peptides. Scores are rescaled by similarity to known binders using TCRbase. Version 2.1 did not have that pretrained component, according to the server.", "modalities": { "input": [ "protein-sequence" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU Health Tech model NetTCR, current release 2.2, for peptide and T-cell receptor binding. Status: active. Collected 2026-10-15.", "what_it_is_for": "It estimates the binding probability of a TCR and an MHC-I peptide. Peptide-specific models cover 26 peptides. A pan-specific mode covers peptides outside that list, with the authors' warning that unseen peptides remain difficult.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/nettepi/nettepi": { "id": "dtu/nettepi/nettepi", "name": "NetTepi 1.0", "maker": "dtu", "family": "dtu/nettepi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0 source package", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetTepi 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "T-cell epitopes restricted to prevalent HLA-A and HLA-B molecules. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netturnp/netturnp": { "id": "dtu/netturnp/netturnp", "name": "NetTurnP 1.0", "maker": "dtu", "family": "dtu/netturnp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0a Linux package", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetTurnP 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Beta-turn and beta-turn type prediction. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/netutr/netutr": { "id": "dtu/netutr/netutr", "name": "NetUTR 1.0", "maker": "dtu", "family": "dtu/netutr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "nucleotide-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NetUTR 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Splice sites in 5' UTR regions of human genes. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/nnalign/nnalign": { "id": "dtu/nnalign/nnalign", "name": "NNAlign 2.1", "maker": "dtu", "family": "dtu/nnalign", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 2.1a Linux and Darwin packages", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model NNAlign 2.1. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Finding sequence motifs in quantitative peptide data. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/pathogenfinder/pathogenfinder": { "id": "dtu/pathogenfinder/pathogenfinder", "name": "PathogenFinder", "maker": "dtu", "family": "dtu/pathogenfinder", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "genome" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU Food (Center for Genomic Epidemiology) service PathogenFinder. Status: active as a listed service. Collected 2026-10-15. The service page was not opened.", "what_it_is_for": "Prediction of a bacterium's pathogenicity toward human hosts. Hosted by DTU Food, Center for Genomic Epidemiology. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/phsol/phsol": { "id": "dtu/phsol/phsol", "name": "pHSol 1.1", "maker": "dtu", "family": "dtu/phsol", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "small-molecule" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model pHSol 1.1. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "pH-dependent aqueous solubility of drug-like molecules. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/pickpocket/pickpocket": { "id": "dtu/pickpocket/pickpocket", "name": "PickPocket 1.1", "maker": "dtu", "family": "dtu/pickpocket", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.1a Linux and Darwin packages", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model PickPocket 1.1. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "MHC class I binding by pocket-matrix extrapolation. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/promoter/promoter": { "id": "dtu/promoter/promoter", "name": "Promoter 2.0", "maker": "dtu", "family": "dtu/promoter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 2.0 packages for SunOS, Linux, and IRIX", "modalities": { "input": [ "nucleotide-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model Promoter 2.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Transcription start sites in vertebrate DNA. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/prop/prop": { "id": "dtu/prop/prop", "name": "ProP 1.0", "maker": "dtu", "family": "dtu/prop", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0c packages for several Unix platforms", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model ProP 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Arginine and lysine propeptide cleavage sites in eukaryotic proteins. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/rnammer/rnammer": { "id": "dtu/rnammer/rnammer", "name": "RNAmmer 1.2", "maker": "dtu", "family": "dtu/rnammer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.2 Unix package", "modalities": { "input": [ "nucleotide-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model RNAmmer 1.2. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Prediction of ribosomal RNA subunits. Listed on the software download page. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/secretomep/secretomep": { "id": "dtu/secretomep/secretomep", "name": "SecretomeP 2.0", "maker": "dtu", "family": "dtu/secretomep", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: Index says 2.0. Software page lists SecretomeP 1.0h.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model SecretomeP 2.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Prediction of non-classical protein secretion. The software page lists a 1.0h package. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/signalp/signalp": { "id": "dtu/signalp/signalp", "name": "SignalP 6.0", "maker": "dtu", "family": "dtu/signalp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SignalP 6.0 uses a transformer protein language model (ProtTrans) and a conditional random field. A distilled fast model emulates the full slow model. The server predicts five signal-peptide types in bacteria and archaea, and Sec/SPI in eukaryotes, plus region labels.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU Health Tech model SignalP, current release 6.0. It is the long-running signal-peptide predictor from the protein sorting group. Status: active. Collected 2026-10-15.", "what_it_is_for": "The server predicts whether a protein has a signal peptide, which of five types it is in bacteria and archaea, where it is cleaved, and where the n-, h-, and c-regions sit. The group says the web server has been public since 1996.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/signisite/signisite": { "id": "dtu/signisite/signisite", "name": "SigniSite 2.1", "maker": "dtu", "family": "dtu/signisite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: Package 2.1.1 for linux_x86_64, darwin_x86_64, and darwin_arm64", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model SigniSite 2.1. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Residue-level genotype-phenotype correlations in protein alignments. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/targetp/targetp": { "id": "dtu/targetp/targetp", "name": "TargetP 2.0", "maker": "dtu", "family": "dtu/targetp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Convolutional and recurrent LSTM network with a multi-attention layer. Predicts N-terminal signal peptide, mitochondrial transit peptide, chloroplast transit peptide, or thylakoid luminal transit peptide, plus a cleavage site. Plant versus non-plant is an input. Version 1.1 used feed-forward networks.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU Health Tech model TargetP, current release 2.0, for N-terminal targeting peptides. Status: active. Collected 2026-10-15.", "what_it_is_for": "It predicts signal peptides, mitochondrial transit peptides, chloroplast transit peptides, and thylakoid luminal transit peptides, and a cleavage site when a presequence is predicted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/tatp/tatp": { "id": "dtu/tatp/tatp", "name": "TatP 1.0", "maker": "dtu", "family": "dtu/tatp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0b Linux and IRIX64 packages", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model TatP 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Presence and location of twin-arginine signal peptides. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/tcrpmhcmodels/tcrpmhcmodels": { "id": "dtu/tcrpmhcmodels/tcrpmhcmodels", "name": "TCRpMHCmodels 1.0", "maker": "dtu", "family": "dtu/tcrpmhcmodels", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.0 package", "modalities": { "input": [ "protein-sequence" ], "output": [ "protein-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model TCRpMHCmodels 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Structural modelling of TCR-pMHC complexes. The index says class I. The software page says class I and class II. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/tmhmm/tmhmm": { "id": "dtu/tmhmm/tmhmm", "name": "TMHMM 2.0", "maker": "dtu", "family": "dtu/tmhmm", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The service is the hidden-Markov-model transmembrane helix predictor. The page does not restate the architecture. It tells users to switch to DeepTMHMM.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU Health Tech model TMHMM 2.0, a deprecated transmembrane-helix predictor that is still online. Status: deprecated. Collected 2026-10-15.", "what_it_is_for": "The server predicts transmembrane helices. Its own page says it is outdated and names DeepTMHMM as the replacement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/vardom/vardom": { "id": "dtu/vardom/vardom", "name": "VarDom 1.0", "maker": "dtu", "family": "dtu/vardom", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model VarDom 1.0. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "Domains in the malaria antigen family PfEMP1. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dtu/yinoyang/yinoyang": { "id": "dtu/yinoyang/yinoyang", "name": "YinOYang 1.2", "maker": "dtu", "family": "dtu/yinoyang", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not opened. Download notes: 1.2 packages for SunOS, OSF1, Linux, and IRIX", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTU model YinOYang 1.2. Listed on the official DTU Health Tech software or services pages, except PathogenFinder which is on the DTU Food genomic-epidemiology server. Status: active as a listed service. The service page was not opened, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "O-beta-GlcNAc glycosylation and Yin-Yang sites. On the software page. This sentence is the services-index or software-page description, not a paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "duesseldorf/helixer/helixer": { "id": "duesseldorf/helixer/helixer", "name": "Helixer", "maker": "duesseldorf", "family": "duesseldorf/helixer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2020-08-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Ab initio eukaryotic gene caller. DNA is one-hot encoded. A neural net emits base-wise class probabilities for intergenic, UTR, CDS, and intron, plus coding phase. HelixerPost, a hidden Markov model, turns those probabilities into primary gene models in GFF3. The 2020 models are a 4-layer stacked bidirectional LSTM with 256 units per layer and layer normalization. From the v0.3 line the network is a hybrid of convolutional layers followed by bidirectional LSTM layers. The training document's example full-size command uses 4 convolutional layers, 3 bidirectional LSTM layers, 128 units, filter depth 96, kernel size 10, and pool size 9. That command is described as a hyperparameter example that has worked in optimization runs, not as the stored configuration of each shipped checkpoint. The Nature Methods figure caption says the drawn hyperparameters are examples and can vary.", "modalities": { "input": [ "dna" ], "output": [ "gene-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helixer is an open-weight eukaryotic gene caller from the Institute of Plant Biochemistry at Heinrich Heine University D\u00fcsseldorf, later maintained with Forschungszentrum J\u00fclich. It predicts primary gene models from genomic DNA alone. The first public weights were posted on 6 August 2020. The tool and the 2022 lineage checkpoints were still the recommended release on 15 October 2026.", "what_it_is_for": "The project states the job as ab initio gene calling: which bases belong to UTR, CDS, or intron, without RNA-seq or a species-specific retraining. Four lineage models cover fungi, land plants, vertebrates, and invertebrates. A web form at plabipd.de and a Galaxy tool accept genomes when a local GPU install is not wanted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "duesseldorf/predmoter/predmoter": { "id": "duesseldorf/predmoter/predmoter", "name": "Predmoter", "maker": "duesseldorf", "family": "duesseldorf/predmoter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2024-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Supervised network on one-hot DNA. The recommended checkpoint, Combined_02, is the bi-hybrid: 3 convolutional layers, 2 bidirectional LSTM layers, 3 transposed convolutional layers, batch normalization after each convolutional and transposed convolutional layer, and one dropout layer with probability 0.3 between the bidirectional LSTM layers. ReLU follows each convolutional and transposed convolutional layer. The optimizer is Adam. The loss is Poisson negative log likelihood, and the published prediction is the exponential of the network output. Sequences are cut to 21384 bp. Both strands are used. Coverage is not strand-specific because the assays are PCR-amplified. A rolling mean can smooth the track before peak calling. The paper says the figure hyperparameters are examples and can vary. Exact filter counts and LSTM width for Combined_02 are in supplementary table S5, which was not transcribed.", "modalities": { "input": [ "dna" ], "output": [ "coverage" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Predmoter is an open-weight plant regulatory model from the Institute of Plant Biochemistry at Heinrich Heine University D\u00fcsseldorf. It predicts base-wise ATAC-seq and H3K4me3 ChIP-seq coverage from DNA sequence. The paper is dated 24 May 2024. The recommended checkpoint was still Combined_02 on 15 October 2026.", "what_it_is_for": "The lab presents it as a way to mark accessible chromatin and H3K4me3 signal in a plant genome without running those assays. The intended use is to narrow the search for cis-regulatory elements. The final training set in the abstract is 21 species, with ATAC-seq for 13 and ChIP-seq for 17. Hold-out tests are Arabidopsis thaliana and Oryza sativa.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "duke/breastsegnet/breastsegnet": { "id": "duke/breastsegnet/breastsegnet", "name": "BreastSegNet", "maker": "duke", "family": "duke/breastsegnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "nnU-Net with a medium ResNet encoder (ResEncM). The paper also trained U-Net, SwinUNet, UNet++, SAM, MedSAM, and other nnU-Net ResNet encoders; ResEncM is the released best model. Nine labels: fibroglandular tissue, vessel, muscle, bone, lesion, lymph node, heart, liver, implant.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BreastSegNet is a Duke University multi-label breast MRI segmenter from the Mazurowski lab, with radiology co-authors. It is an nnU-Net ResEncM checkpoint, not a fine-tune of another company's released model. The preprint is 18 July 2025. A Springer chapter appeared online on 21 September 2025. Status on 2026-10-15 is active as a public weight release. The weight license is not stated.", "what_it_is_for": "The paper says breast MRI tools usually cover only fibroglandular tissue or tumors. BreastSegNet labels nine structures: fibroglandular tissue, vessel, muscle, bone, lesion, lymph node, heart, liver, and implant. The authors annotated 1,123 slices and had a radiologist review them. They compared nine architectures. nnU-Net ResEncM was best and is the released checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "duke/dukenet/dukenet-1-4": { "id": "duke/dukenet/dukenet-1-4", "name": "DukeNet-1.4", "maker": "duke", "family": "duke/dukenet", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2019-10-24", "context_length": null, "max_output_tokens": null, "parameters": "3960000", "architecture": "Mobile image classifier inspired by MobileNetV3, modified for the Qualcomm Hexagon DSP. The authors removed hard-swish and ReLU6. Width multiplier 1.4. Not a fine-tune of a published MobileNet checkpoint. 410.33 million MACs.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DukeNet-1.4 is a mobile image classifier from the foreverDuke team at Duke ECE (Tunhou Zhang, Shiyu Li, and Hsin-Pai Cheng, in Yiran Chen's hardware-aware ML group). It was the second-place entry in the DSP track of the 2019 LPIRC workshop at ICCV. The GitHub repo is MIT licensed and still public. This is not the 2020 conversation model of the same nickname from Shandong University and the University of Amsterdam. Status on 2026-10-15 is active only as a downloadable 2019 release. Confidence is medium because there is no paper and the readme prints the day without a year.", "what_it_is_for": "The model is a DSP image classifier with a budget under 7 ms on a Qualcomm Hexagon. The authors started from a MobileNetV3-style net and removed hard-swish and ReLU6 so the quantized net would run on that chip. It is their own network, not a fine-tune of Google's MobileNet weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "duke/guidedmorph/guidedmorph": { "id": "duke/guidedmorph/guidedmorph", "name": "GuidedMorph", "maker": "duke", "family": "duke/guidedmorph", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage TransMorph registration network, released as the Guided-Trans-DT checkpoint (final.pth.tar). A Dual Spatial Transformer Network fuses the fields. Dense-tissue guidance uses an Euclidean-distance-transform warp. Input volumes must be 128x256x256. The repo checker reports 333 state-dict tensors and 169,921,974 stored tensor values. The transformer backbone is adapted from TransMorph and the spatial transformer follows VoxelMorph. This is a Duke-trained checkpoint, not a rehost of those authors' weights. A VoxelMorph script remains as a baseline and cannot load the released file.", "modalities": { "input": [ "image" ], "output": [ "other" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GuidedMorph is a Duke University breast MRI registration model from the Mazurowski lab. The public weights are one checkpoint, Guided-Trans-DT, a two-stage TransMorph trained by the lab. The preprint is 19 May 2025 (arXiv 2505.13414) and is also linked to IEEE Journal of Biomedical and Health Informatics. Status on 2026-10-15 is active. The weight license is not stated.", "what_it_is_for": "The paper says ordinary registration aligns the breast outline and misses dense fibroglandular tissue. GuidedMorph adds a second stage that tracks dense tissue, fuses the fields with a Dual Spatial Transformer Network, and warps masks with an Euclidean distance transform. It can use an external segmentation or image-only guidance, and the paper says the same idea works with VoxelMorph or TransMorph. The file they actually released is the TransMorph run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "duke/legsegnet/legsegnet": { "id": "duke/legsegnet/legsegnet", "name": "LegSegNet", "maker": "duke", "family": "duke/legsegnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2026-05-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Default 2D nnU-Net. Four tissue classes plus background: subcutaneous adipose tissue (SAT), skeletal muscle (SM), inter/intramuscular adipose tissue (IAT), and bone. The Hugging Face card says IMAT for the same fat class. The app resizes a 2D PNG to 256x256 after clipping Hounsfield units to [-200, 200].", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LegSegNet is a Duke University lower-extremity CT segmentation and body-composition system from the Mazurowski lab and Duke surgery and radiology. The released weights are a default 2D nnU-Net, under CC BY-NC 4.0. The preprint and the Hub card are dated 29 May 2026. Status on 2026-10-15 is active.", "what_it_is_for": "Given a lower-extremity CT, LegSegNet segments bone, skeletal muscle, subcutaneous fat, and inter/intramuscular fat, then reports volume, area, and mean attenuation. The authors call it the first public end-to-end system for that job. It was trained on 1,302 annotated slices and tested on 900 held-out slices, with radiologist review. The Hub card is on a lab author's personal org (`GogoChen/LegSegNet`); the code is `mazurowski-lab/LegSegNet`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "duke/lung-ct/dlcsd-md": { "id": "duke/lung-ct/dlcsd-md", "name": "DLCSD-mD", "maker": "duke", "family": "duke/lung-ct", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "3D low-dose CT lung-nodule detector trained on the Duke Lung Cancer Screening dataset. The Zenodo record says the checkpoints are PyTorch files for the MONAI framework. The February 2026 paper does not name the backbone in the abstract. Output is nodule detection, not a semantic segmentation map; segmentation is the closest schema label.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DLCSD-mD is the Duke Lung Cancer Screening detector from Duke radiology and computer science (Fakrul Islam Tushar, Cynthia Rudin, Joseph Y. Lo, and co-authors). Weights were posted on Zenodo on 4 March 2025 under that name. The 9 February 2026 paper revision calls the same run the DLCS-trained detector and does not use the acronym in the abstract. Status on 2026-10-15 is active as a public research checkpoint. Confidence is medium because the Zenodo page itself did not load during collection.", "what_it_is_for": "The paper builds a public benchmark for lung-nodule detection and malignancy classification across Duke, LUNA16, NLST, and LUNA25. This checkpoint is the detector trained on the Duke screening set. The Zenodo description says it finds nodules in 3D CT and that the files are PyTorch checkpoints for MONAI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "duke/lung-ct/luna16-md": { "id": "duke/lung-ct/luna16-md", "name": "LUNA16-mD", "maker": "duke", "family": "duke/lung-ct", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "3D low-dose CT lung-nodule detector trained on LUNA16 by the Duke group, released with the DLCS detector. PyTorch checkpoint for MONAI, per the Zenodo record description. The February 2026 abstract does not name the backbone. Output is detection, stored under segmentation as the closest schema label.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LUNA16-mD is the Duke group's lung-nodule detector trained on LUNA16 and released with their multi-dataset benchmark. The authors are at Duke (Tushar, Rudin, Lo, and co-authors). The Zenodo weight record is dated 4 March 2025. The 9 February 2026 paper calls it the LUNA16-trained detector. Status on 2026-10-15 is active. Confidence is medium: the Zenodo page did not load, and the filename was not in the extracted file table.", "what_it_is_for": "It is the public LUNA16-trained counterpart to the Duke-trained detector, so the paper can show that the training set changes external NLST performance. Training data are the public LUNA16 scans. The releasing group is Duke, not the LUNA organizers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "duke/lung-ct/resnet50-sws": { "id": "duke/lung-ct/resnet50-sws", "name": "ResNet50-SWS", "maker": "duke", "family": "duke/lung-ct", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-50 nodule-level malignancy classifier. Strategic Warm-Start pretraining uses detection-derived candidate patches stratified by detector confidence. The Zenodo file name uses WSPP (ResNet50-SWS++). The February 2026 abstract uses ResNet50-SWS. Only fold 1 was visible in the file extract: DukeLungRADS_CancerCL_Fold1_resnet50WSPP.pt, about 185.0 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ResNet50-SWS is Duke's nodule malignancy classifier from the same lung-screening benchmark as DLCSD-mD. Authors include Fakrul Islam Tushar, Cynthia Rudin, and Joseph Y. Lo. The 9 February 2026 abstract calls it ResNet50-SWS. The Zenodo file, posted 4 March 2025, is named `resnet50WSPP` (SWS++). The AUC triple 0.71 / 0.90 / 0.81 matches both labels, so they are treated as one model. Status on 2026-10-15 is active. Confidence is medium because Zenodo did not load and the name changed between the file and the revision.", "what_it_is_for": "The classifier labels whether a nodule candidate is cancer. Strategic Warm-Start pretrains a ResNet-50 on patches proposed by the detector, stratified by the detector's confidence, then trains the malignancy head. The paper compares it with a randomly initialized ResNet-50, Models Genesis, Med3D, and a foundation model for cancer biomarkers. Those other checkpoints are not this model. Models Genesis and Med3D are other groups' weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "duke/mri-core/mri-core": { "id": "duke/mri-core/mri-core", "name": "MRI-CORE", "maker": "duke", "family": "duke/mri-core", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SAM ViT-B image encoder. Student and teacher are both initialized from SAM weights and trained with DINOv2 self-distillation for 4 epochs (about 50,000 iterations, versus the default DINOv2 schedule of 1,250,000). The teacher is the released model. Inputs are 1024x1024. Training used batch size 128 per GPU on 4 NVIDIA A6000s and base learning rate 4e-4. Slice-wise min-max normalization.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MRI-CORE (MRI COmprehensive Representation Encoder) is a Duke University vision foundation model from the Mazurowski lab (Duke ECE, biostatistics, radiology, and computer science). It is an open-weight SAM ViT-B encoder continued with DINOv2 on Duke MRI. Status on 2026-10-15 is active. The first preprint is 13 June 2025; the 22 July 2025 revision is the spec used here.", "what_it_is_for": "The paper presents MRI-CORE as a general MRI encoder so later models need less labeled data. It was trained on Duke-110K: 6.9 million slices from 116,806 volumes, 107 protocols, more than 9 sequence types, and 18 body locations, collected at Duke from 2016-2020 reports. Brain, face, and neck protocols were excluded. The released object is the DINOv2 teacher, not a hosted copy of SAM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dundee/1433pred/1433pred": { "id": "dundee/1433pred/1433pred", "name": "14-3-3-Pred", "maker": "dundee", "family": "dundee/1433pred", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2015-03-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three classifiers combined by the web server: an artificial neural network, a position-specific scoring matrix, and a support vector machine. The paper says the best window is -6 to +4 around a serine or threonine.", "modalities": { "input": [ "protein-sequence" ], "output": [ "binding-site-score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "14-3-3-Pred is a University of Dundee web server that scores possible 14-3-3-binding phosphosites. The Barton Group and the MacKintosh Group built it. The form was up on 2026-10-15. Confidence is high because the about page and the paper are the lab's.", "what_it_is_for": "The about page says the server combines an artificial neural network, a position-specific scoring matrix, and a support vector machine to find 14-3-3-binding motifs around serine and threonine. The paper says the useful window is -6 to +4 around the site.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dundee/jpred/jpred": { "id": "dundee/jpred/jpred", "name": "JPred", "maker": "dundee", "family": "dundee/jpred", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2014-12-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "JNet neural networks on a PSI-BLAST profile and an HMM profile from a multiple alignment, plus a jury network when the component networks disagree. JPred 4 runs JNet v2.3.1. Three-state labels are H, E, and -. Solvent accessibility is also from JNet. Coiled-coil is not a Dundee network.", "modalities": { "input": [ "protein-sequence" ], "output": [ "secondary-structure", "solvent-accessibility", "coiled-coil" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JPred is the University of Dundee Barton Group server for protein secondary structure prediction. The current product is JPred 4, and the predictor inside it is the JNet v2.3.1 neural network. The server was still up on 2026-10-15. Confidence is high because the description comes from the lab's own pages and papers.", "what_it_is_for": "The Barton Group says JPred takes a protein sequence or a multiple alignment and labels each residue helix (H), strand (E), or other (-). JNet also predicts solvent accessibility. Coiled-coil is a separate method on the same server, not the JNet weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dundee/kinomer/kinomer": { "id": "dundee/kinomer/kinomer", "name": "Kinomer", "maker": "dundee", "family": "dundee/kinomer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2008-10-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-level library of profile HMMs. The method page says each kinase group is represented by several HMMs and that local search uses HMMER hmmpfam. Group pages list eight conventional groups (AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL) and four atypical groups (Alpha, PIKK, PDHK, RIO).", "modalities": { "input": [ "protein-sequence" ], "output": [ "kinase-group" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kinomer is a University of Dundee Barton Group library of profile HMMs that finds eukaryotic protein kinases and assigns them to a group. Version 1.0 was still the version named on the site on 2026-10-15. Confidence is high because the method, download, and paper pages are the lab's.", "what_it_is_for": "The home page says the library identifies protein kinases and classifies them into groups. The server paper also ships precomputed kinomes for 43 eukaryotic genomes. Those tables are database output. The model is the HMM library.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dundee/nod/nod": { "id": "dundee/nod/nod", "name": "NoD", "maker": "dundee", "family": "dundee/nod", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2011-08-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Artificial neural network trained in the Stuttgart Neural Network Simulator. Help page: 13-residue windows, reduced alphabet of size 12, 165-number input vector, one score from 0 to 1 on the middle residue. A NoLS is called when at least 8 consecutive windows average at least 0.8. Optional JPred secondary structure on the web form only.", "modalities": { "input": [ "protein-sequence" ], "output": [ "nucleolar-localization-sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NoD is a University of Dundee Barton Group neural net that predicts nucleolar localization sequences in proteins. The web form and the Apache-2.0 download were both up on 2026-10-15. Confidence is high because the help page, the download page, and the papers are the lab's.", "what_it_is_for": "The server predicts short basic motifs that send a protein to the nucleolus. Users can paste one sequence or search precomputed human predictions. The 2011 paper says the server uses the human-trained network from the 2010 method paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dundee/xtal/ob-score": { "id": "dundee/xtal/ob-score", "name": "OB-Score", "maker": "dundee", "family": "dundee/xtal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2006-06-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Z-score from a matrix of predicted isoelectric point and hydrophobicity. The paper says the matrix is built from nonredundant PDB entries solved to 3.0 angstroms or better, against a UniRef50 background. Perl code in OB_1.0.tar.gz implements the score.", "modalities": { "input": [ "protein-sequence" ], "output": [ "crystallisation-score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OB-Score is a University of Dundee Barton Group crystallisation score. It is a Z-score of isoelectric point and hydrophobicity, not a neural net. The Xtal form still ran it beside ParCrys on 2026-10-15. Confidence is high because the guide is the lab's own page.", "what_it_is_for": "The paper presents OB-Score as a Z-score that ranks sequences by the chance they will yield diffraction-quality crystals. The matrix uses predicted isoelectric point and hydrophobicity from nonredundant PDB entries at 3.0 angstroms or better, against UniRef50.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dundee/xtal/parcrys": { "id": "dundee/xtal/parcrys", "name": "ParCrys", "maker": "dundee", "family": "dundee/xtal", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2008-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Parzen window density estimate. The Xtal page says the features are calculated isoelectric point, hydrophobicity, and the frequencies of S, C, G, F, Y, and M.", "modalities": { "input": [ "protein-sequence" ], "output": [ "crystallisation-score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ParCrys is a University of Dundee Barton Group Parzen-window model that scores whether a protein is likely to yield diffraction-quality crystals. The Xtal form still offered it on 2026-10-15, together with OB-Score. Confidence is high because the form and the paper record are the lab's.", "what_it_is_for": "The Xtal page says ParCrys estimates crystallisation propensity from isoelectric point, hydrophobicity, and the frequencies of serine, cysteine, glycine, phenylalanine, tyrosine, and methionine. Training used the PDB. Feature selection and a held-out test used TargetDB and PepcDB.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dundee/xtal/xannpred": { "id": "dundee/xtal/xannpred", "name": "XANNpred", "maker": "dundee", "family": "dundee/xtal", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2010-10-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pair of artificial neural networks, XANNpred-PDB and XANNpred-SG. The guide says each uses 428 features: 20 amino-acid frequencies, 400 dipeptide frequencies, length, predicted secondary structure, transmembrane regions, disorder, isoelectric point, hydrophobicity, and molecular weight. A 61-residue sliding window is plotted for construct design.", "modalities": { "input": [ "protein-sequence" ], "output": [ "crystallisation-score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XANNpred is a University of Dundee Barton Group pair of neural nets that score whether a protein sequence is likely to yield diffraction-quality crystals. It is one of the three SSPF crystallisation predictors, with ParCrys and OB-Score. The form still answered on 2026-10-15. Confidence is high because the guide and the paper are the lab's.", "what_it_is_for": "The lab says the nets rank a sequence for the chance it will produce diffraction-quality crystals, and a 61-residue window plot is meant to help construct design. XANNpred-PDB is described as the general structural-biology net. XANNpred-SG is described as the structural-genomics net.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "durham/mednerf/mednerf": { "id": "durham/mednerf/mednerf", "name": "MedNeRF", "maker": "durham", "family": "durham/mednerf", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the repository software. The licence text grants rights in the Software. A separate weights licence is not stated.", "release_date": "2022-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GRAF generative radiance field adapted to render CT projections from one or a few X-ray views. The repository is based on the GRAF codebase and uses pixelNeRF as a baseline. Knee and chest are dataset-specific checkpoints of this one model. Parameter count is not stated.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Durham University released MedNeRF. Status active. Collected 2026-10-15.", "what_it_is_for": "Reconstruct 3D-aware CT projections from a single X-ray or a few views. Abril Corona-Figueroa, Jonathan Frawley, Sam Bond-Taylor, Hubert P. H. Shum, and Chris G. Willcocks, Durham University Computer Science, with Sarath Bethapudi, County Durham and Darlington NHS Foundation Trust. arXiv 2202.01020, accepted at IEEE EMBC 2022.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "durham/onfire/firenet": { "id": "durham/onfire/firenet", "name": "FireNet", "maker": "durham", "family": "durham/onfire", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the repository software. The licence text grants rights in the Software. A separate weights licence is not stated.", "release_date": "2018", "context_length": null, "max_output_tokens": null, "parameters": "68300000", "architecture": "Simplified AlexNet-style custom CNN for binary fire versus no-fire on a full frame. The 2020 comparison table lists 68.3 million parameters. That figure is the later table, not a separate 2018 count.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Durham University released FireNet. Status active. Collected 2026-10-15.", "what_it_is_for": "Full-frame binary fire detection in still or video frames. Dunnings and Breckon, Durham University, ICIP 2018. Weights are on Durham Collections and are loaded by the authors' fire-detection-cnn repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "durham/onfire/inceptionv1-onfire": { "id": "durham/onfire/inceptionv1-onfire", "name": "InceptionV1-OnFire", "maker": "durham", "family": "durham/onfire", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the repository software. The licence text grants rights in the Software. A separate weights licence is not stated.", "release_date": "2018", "context_length": null, "max_output_tokens": null, "parameters": "1200000", "architecture": "Experimentally simplified Inception-v1 for binary fire versus no-fire. The 2020 efficiency table lists 1.2 million parameters. Full-frame and superpixel checkpoints are versions of this model.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Durham University released InceptionV1-OnFire. Status active. Collected 2026-10-15.", "what_it_is_for": "Binary fire detection with an experimentally simplified Inception-v1. Dunnings and Breckon, ICIP 2018. Full-frame and superpixel weights ship in the same Durham Collections zip.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "durham/onfire/inceptionv3-onfire": { "id": "durham/onfire/inceptionv3-onfire", "name": "InceptionV3-OnFire", "maker": "durham", "family": "durham/onfire", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the repository software. The licence text grants rights in the Software. A separate weights licence is not stated.", "release_date": "2019", "context_length": null, "max_output_tokens": null, "parameters": "960000", "architecture": "Experimentally simplified Inception-v3 for binary fire versus no-fire. The 2020 superpixel efficiency row lists 0.96 million parameters. That is the simplified network, not stock Inception-v3.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Durham University released InceptionV3-OnFire. Status active. Collected 2026-10-15.", "what_it_is_for": "Binary fire detection with an experimentally simplified Inception-v3. Samarth, Bhowmik, and Breckon, ICMLA 2019. Full-frame and superpixel weights are in one Durham Collections zip.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "durham/onfire/inceptionv4-onfire": { "id": "durham/onfire/inceptionv4-onfire", "name": "InceptionV4-OnFire", "maker": "durham", "family": "durham/onfire", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the repository software. The licence text grants rights in the Software. A separate weights licence is not stated.", "release_date": "2019", "context_length": null, "max_output_tokens": null, "parameters": "7180000", "architecture": "Experimentally simplified Inception-v4 for binary fire versus no-fire. The 2020 superpixel efficiency row lists 7.18 million parameters.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Durham University released InceptionV4-OnFire. Status active. Collected 2026-10-15.", "what_it_is_for": "Binary fire detection with an experimentally simplified Inception-v4. Samarth, Bhowmik, and Breckon, ICMLA 2019. The authors' later comparison treats it as the strongest of the 2018-2019 OnFire detectors on full-frame accuracy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "durham/onfire/nasnet-a-onfire": { "id": "durham/onfire/nasnet-a-onfire", "name": "NasNet-A-OnFire", "maker": "durham", "family": "durham/onfire", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the repository software. The licence text grants rights in the Software. A separate weights licence is not stated.", "release_date": "2020-12", "context_length": null, "max_output_tokens": null, "parameters": "3200000", "architecture": "NasNet-A-OnFire is the NasNet_v03 variant: each group of normal cells contains two normal cells instead of four. The paper states the total number of parameters is 3.2 million. Normal and reduction cells stay as in the parent NASNet-A design. Unnamed NasNet_v01 through NasNet_v08 ablations are not separate releases.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Durham University released NasNet-A-OnFire. Status active. Collected 2026-10-15.", "what_it_is_for": "Compact binary fire detection and superpixel fire localisation. Thompson, Bhowmik, and Breckon, Durham University, ICMLA 2020. The named release is NasNet_v03 with two normal cells per group, not the unnamed cell-count ablations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "durham/onfire/shufflenetv2-onfire": { "id": "durham/onfire/shufflenetv2-onfire", "name": "ShuffleNetV2-OnFire", "maker": "durham", "family": "durham/onfire", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the repository software. The licence text grants rights in the Software. A separate weights licence is not stated.", "release_date": "2020-12", "context_length": null, "max_output_tokens": null, "parameters": "155617", "architecture": "ShuffleNetV2-OnFire is the ShuffleNet_v08 variant. Filter pruning on the final convolution yields 155,617 parameters. The efficiency table rounds this to 0.156 million. Unnamed ShuffleNet_v01 through ShuffleNet_v09 rows are ablations, not releases.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Durham University released ShuffleNetV2-OnFire. Status active. Collected 2026-10-15.", "what_it_is_for": "Compact binary fire detection and superpixel fire localisation. Thompson, Bhowmik, and Breckon, ICMLA 2020. This is the named ShuffleNet_v08 product. The other ShuffleNet_v0x rows in the pruning table are unnamed ablations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "durham/raindrop/alexnet-30-2": { "id": "durham/raindrop/alexnet-30-2", "name": "AlexNet-30^2", "maker": "durham", "family": "durham/raindrop", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the repository software. The licence text grants rights in the Software. A separate weights licence is not stated.", "release_date": "2018", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AlexNet re-formed for a 30 by 30 by 3 raindrop patch. The paper denotes it AlexNet-302. Trained from random initialisation with SGD, momentum 0.9, categorical cross-entropy, initial learning rate 0.001, decay 0.96 every 100 iterations, batch size 64, TensorFlow 1.1 and TFLearn 0.3. Parameter count is not stated.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Durham University released AlexNet-30^2. Status active. Collected 2026-10-15.", "what_it_is_for": "Classify 30 by 30 colour patches as raindrop or not, for automotive and surveillance imagery. Guo, Akcay, Adey, and Breckon, Department of Computer Science and Engineering, Durham University, ICIP 2018. The authors host the pretrained checkpoint on Durham Collections.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "durham/unleashing-transformers/unleashing-transformers": { "id": "durham/unleashing-transformers/unleashing-transformers", "name": "Unleashing Transformers", "maker": "durham", "family": "durham/unleashing-transformers", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the repository software. The licence text grants rights in the Software. A separate weights licence is not stated.", "release_date": "2021-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unconditional image generator. Discrete absorbing diffusion prior over vector-quantized codes, with order-agnostic token masking and an unconstrained Transformer encoder. The paper says the absorbing diffusion model uses an 80M parameter Transformer encoder on 16 by 16 latents, codebook size 1024, Adam. The FID table lists 145M under Params for Ours. Both counts are kept. parameters is null because the pages disagree on which count is the model size. Default training resolution in the evaluation section is 256 by 256.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Durham University released Unleashing Transformers. Status active. Collected 2026-10-15.", "what_it_is_for": "Unconditional high-resolution image generation from vector-quantized codes. Sam Bond-Taylor, Peter Hessey, Hiroshi Sasaki, Toby P. Breckon, and Chris G. Willcocks, Durham University. arXiv 2111.12701, ECCV 2022. FFHQ, LSUN Bedroom, and LSUN Churches checkpoints are versions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/artaug/artaug-lora-flux-1-dev-v1": { "id": "ecnu/artaug/artaug-lora-flux-1-dev-v1", "name": "ArtAug LoRA for FLUX.1-dev", "maker": "ecnu", "family": "ecnu/artaug", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA for black-forest-labs/FLUX.1-dev. The card says no trigger word is required. Training uses Qwen2-VL-72B critiques, filtered before/after pairs, and differential training, then merges iteration LoRAs. Inference example uses lora_alpha 1.0 in DiffSynth-Studio.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ArtAug is an aesthetic LoRA released by ECNU-CILab for FLUX.1-dev. The card and weights are public under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card says the LoRA improves detail, lighting, composition, and clarity of FLUX.1-dev images and does not need a trigger word.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/educhat/educhat-base-002-13b": { "id": "ecnu/educhat/educhat-base-002-13b", "name": "EduChat-Base-002-13B", "maker": "ecnu", "family": "ecnu/educhat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AGPL-3.0", "release_date": "2023-06-30", "context_length": 2048, "max_output_tokens": null, "parameters": "13B class. Config hidden size 5120 and 40 layers. Exact parameter count was not in Hub safetensors metadata.", "architecture": "LlamaForCausalLM base. The GitHub list says it uses the same training method as educhat-base-002-7b at 13B, on educhat-sft-002-data-osm. The Hub files are LLaMA-style pytorch shards and an AGPL-3.0 card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EduChat-Base-002-13B is an EduChat educational chat checkpoint from the ICALK / EduNLP group at East China Normal University. The lab's open-source list still names it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project says EduChat is a bilingual education dialogue model for teachers, students, and parents, covering open question answering, essay assessment, Socratic teaching, and emotional support. See https://github.com/ECNU-ICALK/EduChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/educhat/educhat-base-002-13b-baichuan": { "id": "ecnu/educhat/educhat-base-002-13b-baichuan", "name": "EduChat-Base-002-13B-Baichuan", "maker": "ecnu", "family": "ecnu/educhat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-09-06", "context_length": 4096, "max_output_tokens": null, "parameters": "13B class. Config hidden size 5120, 40 layers, model_max_length 4096. Exact parameter count was not in Hub safetensors metadata.", "architecture": "BaichuanForCausalLM. The GitHub list says this base was trained on educhat-sft-002-data-osm. The Hub repo contains six pytorch shards and custom Baichuan modeling code. The card does not set a license field.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EduChat-Base-002-13B-Baichuan is an EduChat educational chat checkpoint from the ICALK / EduNLP group at East China Normal University. The lab's open-source list still names it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project says EduChat is a bilingual education dialogue model for teachers, students, and parents, covering open question answering, essay assessment, Socratic teaching, and emotional support. See https://github.com/ECNU-ICALK/EduChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/educhat/educhat-base-002-7b": { "id": "ecnu/educhat/educhat-base-002-7b", "name": "EduChat-Base-002-7B", "maker": "ecnu", "family": "ecnu/educhat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AGPL-3.0", "release_date": "2023-06-29", "context_length": 2048, "max_output_tokens": null, "parameters": "7B class. Config hidden size 4096 and 32 layers. Exact parameter count was not in Hub safetensors metadata.", "architecture": "LlamaForCausalLM continued-train checkpoint. The card says it was trained with educhat-sft-002-data-osm starting from BELLE-LLaMA-EXT-7B, and the files are LLaMA delta weights. The GitHub list says base models do not need the SFT system prompt.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EduChat-Base-002-7B is an EduChat educational chat checkpoint from the ICALK / EduNLP group at East China Normal University. The lab's open-source list still names it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project says EduChat is a bilingual education dialogue model for teachers, students, and parents, covering open question answering, essay assessment, Socratic teaching, and emotional support. See https://github.com/ECNU-ICALK/EduChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/educhat/educhat-r1-001-32b-qwen3-0": { "id": "ecnu/educhat/educhat-r1-001-32b-qwen3-0", "name": "EduChat-R1 32B (Qwen3)", "maker": "ecnu", "family": "ecnu/educhat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-08", "context_length": 40960, "max_output_tokens": null, "parameters": "32762123264", "architecture": "Qwen3ForCausalLM. The GitHub list says EduChat-R1 32B was trained from Qwen3.0 32B, with some chains of thought structured for teaching and counseling. Hub safetensors total is 32,762,123,264 BF16 parameters. hidden size 5120, 64 layers, max_position_embeddings 40960. The chat template includes tool calls and think tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EduChat-R1 32B (Qwen3) is an EduChat educational chat checkpoint from the ICALK / EduNLP group at East China Normal University. The lab's open-source list still names it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project says EduChat is a bilingual education dialogue model for teachers, students, and parents, covering open question answering, essay assessment, Socratic teaching, and emotional support. See https://github.com/ECNU-ICALK/EduChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/educhat/educhat-r1-001-8b-qwen3-0": { "id": "ecnu/educhat/educhat-r1-001-8b-qwen3-0", "name": "EduChat-R1 8B (Qwen3)", "maker": "ecnu", "family": "ecnu/educhat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-08", "context_length": 40960, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Qwen3ForCausalLM. The GitHub list says EduChat-R1 8B was trained from Qwen3.0 8B. Hub safetensors total is 8,190,735,360 BF16 parameters. hidden size 4096, 36 layers, max_position_embeddings 40960. The chat template includes tool-call markup and think tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EduChat-R1 8B (Qwen3) is an EduChat educational chat checkpoint from the ICALK / EduNLP group at East China Normal University. The lab's open-source list still names it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project says EduChat is a bilingual education dialogue model for teachers, students, and parents, covering open question answering, essay assessment, Socratic teaching, and emotional support. See https://github.com/ECNU-ICALK/EduChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/educhat/educhat-sft-002-1-8b-qwen1-5": { "id": "ecnu/educhat/educhat-sft-002-1-8b-qwen1-5", "name": "EduChat 1.0 1.8B (Qwen1.5)", "maker": "ecnu", "family": "ecnu/educhat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-21", "context_length": 32768, "max_output_tokens": null, "parameters": "1836828672", "architecture": "Qwen2ForCausalLM. The GitHub list calls this EduChat 1.0 and says it was trained from Qwen1.5 1.8B. Hub safetensors total is 1,836,828,672 parameters. max_position_embeddings is 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EduChat 1.0 1.8B (Qwen1.5) is an EduChat educational chat checkpoint from the ICALK / EduNLP group at East China Normal University. The lab's open-source list still names it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project says EduChat is a bilingual education dialogue model for teachers, students, and parents, covering open question answering, essay assessment, Socratic teaching, and emotional support. See https://github.com/ECNU-ICALK/EduChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/educhat/educhat-sft-002-13b": { "id": "ecnu/educhat/educhat-sft-002-13b", "name": "EduChat-SFT-002-13B", "maker": "ecnu", "family": "ecnu/educhat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AGPL-3.0", "release_date": "2023-06-27", "context_length": 2048, "max_output_tokens": null, "parameters": "13B class. Config hidden size 5120 and 40 layers match LLaMA-13B. Exact parameter count was not in Hub safetensors metadata.", "architecture": "LlamaForCausalLM SFT. The GitHub list says the training method matches educhat-sft-002-7b at 13B. The card says the files are LLaMA-13B delta weights and points readers to a Baidu Pan archive for shards that are not in the git clone.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EduChat-SFT-002-13B is an EduChat educational chat checkpoint from the ICALK / EduNLP group at East China Normal University. The lab's open-source list still names it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project says EduChat is a bilingual education dialogue model for teachers, students, and parents, covering open question answering, essay assessment, Socratic teaching, and emotional support. See https://github.com/ECNU-ICALK/EduChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/educhat/educhat-sft-002-13b-baichuan": { "id": "ecnu/educhat/educhat-sft-002-13b-baichuan", "name": "EduChat-SFT-002-13B-Baichuan", "maker": "ecnu", "family": "ecnu/educhat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-31", "context_length": 4096, "max_output_tokens": null, "parameters": "13B class. Config hidden size 5120 and 40 layers. Exact parameter count was not in Hub safetensors metadata.", "architecture": "BaichuanForCausalLM SFT. The card says it is further trained from educhat-base-002-13b-baichuan. The GitHub list says the education multi-skill SFT is applied on that base. Custom Baichuan code is in the repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EduChat-SFT-002-13B-Baichuan is an EduChat educational chat checkpoint from the ICALK / EduNLP group at East China Normal University. The lab's open-source list still names it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project says EduChat is a bilingual education dialogue model for teachers, students, and parents, covering open question answering, essay assessment, Socratic teaching, and emotional support. See https://github.com/ECNU-ICALK/EduChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/educhat/educhat-sft-002-14b-qwen1-5": { "id": "ecnu/educhat/educhat-sft-002-14b-qwen1-5", "name": "EduChat 1.0 14B (Qwen1.5)", "maker": "ecnu", "family": "ecnu/educhat", "status": "announced", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-04-22", "context_length": null, "max_output_tokens": null, "parameters": "14B class, as named on the GitHub list (Qwen1.5 14B). No config or weight file is in the Hub repo, so the count was not measured.", "architecture": "Announced on the EduChat open-source list as a Qwen1.5 14B supervised fine-tune. The linked Hub repo contains only .gitattributes. usedStorage reported by the Hub API was 0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EduChat 1.0 14B (Qwen1.5) is an EduChat educational chat checkpoint from the ICALK / EduNLP group at East China Normal University. The lab's open-source list still names it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project says EduChat is a bilingual education dialogue model for teachers, students, and parents, covering open question answering, essay assessment, Socratic teaching, and emotional support. See https://github.com/ECNU-ICALK/EduChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/educhat/educhat-sft-002-32b-qwen1-5": { "id": "ecnu/educhat/educhat-sft-002-32b-qwen1-5", "name": "EduChat 1.0 32B (Qwen1.5)", "maker": "ecnu", "family": "ecnu/educhat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-22", "context_length": 32768, "max_output_tokens": null, "parameters": "32512218112", "architecture": "Qwen2ForCausalLM. The GitHub list calls this EduChat 1.0 trained from Qwen1.5 32B. Hub safetensors total is 32,512,218,112. hidden size 5120, 64 layers, max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EduChat 1.0 32B (Qwen1.5) is an EduChat educational chat checkpoint from the ICALK / EduNLP group at East China Normal University. The lab's open-source list still names it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project says EduChat is a bilingual education dialogue model for teachers, students, and parents, covering open question answering, essay assessment, Socratic teaching, and emotional support. See https://github.com/ECNU-ICALK/EduChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/educhat/educhat-sft-002-7b": { "id": "ecnu/educhat/educhat-sft-002-7b", "name": "EduChat-SFT-002-7B", "maker": "ecnu", "family": "ecnu/educhat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AGPL-3.0", "release_date": "2023-06-27", "context_length": 2048, "max_output_tokens": null, "parameters": "7B class. Config hidden size 4096 and 32 layers match LLaMA-7B. Exact parameter count was not in Hub safetensors metadata.", "architecture": "LlamaForCausalLM supervised fine-tune. The card says training started from BelleGroup/BELLE-LLaMA-EXT-7B. The GitHub list says this SFT sits on educhat-base-002-7b and uses the lab's education multi-skill data. Hub config.json is LlamaForCausalLM. The model card says the published files are difference weights because of LLaMA's limits on derivatives, and they must be added onto a converted original LLaMA checkpoint with decrypt.py.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EduChat-SFT-002-7B is an EduChat educational chat checkpoint from the ICALK / EduNLP group at East China Normal University. The lab's open-source list still names it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project says EduChat is a bilingual education dialogue model for teachers, students, and parents, covering open question answering, essay assessment, Socratic teaching, and emotional support. See https://github.com/ECNU-ICALK/EduChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/educhat/educhat-sft-003-32b-qwen2-5": { "id": "ecnu/educhat/educhat-sft-003-32b-qwen2-5", "name": "EduChat 2.0 32B (Qwen2.5)", "maker": "ecnu", "family": "ecnu/educhat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-08", "context_length": 32768, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM. The GitHub list calls this EduChat 2.0 trained from Qwen2.5 32B. Hub safetensors total is 32,763,876,352. hidden size 5120, 64 layers, max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EduChat 2.0 32B (Qwen2.5) is an EduChat educational chat checkpoint from the ICALK / EduNLP group at East China Normal University. The lab's open-source list still names it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project says EduChat is a bilingual education dialogue model for teachers, students, and parents, covering open question answering, essay assessment, Socratic teaching, and emotional support. See https://github.com/ECNU-ICALK/EduChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/educhat/educhat-sft-003-7b-qwen2-5": { "id": "ecnu/educhat/educhat-sft-003-7b-qwen2-5", "name": "EduChat 2.0 7B (Qwen2.5)", "maker": "ecnu", "family": "ecnu/educhat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-08", "context_length": 32768, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Qwen2ForCausalLM. The GitHub list calls this EduChat 2.0 trained from Qwen2.5 7B. Hub safetensors total is 7,615,616,512. hidden size 3584, 28 layers, max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EduChat 2.0 7B (Qwen2.5) is an EduChat educational chat checkpoint from the ICALK / EduNLP group at East China Normal University. The lab's open-source list still names it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project says EduChat is a bilingual education dialogue model for teachers, students, and parents, covering open question answering, essay assessment, Socratic teaching, and emotional support. See https://github.com/ECNU-ICALK/EduChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/exvideo/exvideo-cogvideox-lora-129f-v1": { "id": "ecnu/exvideo/exvideo-cogvideox-lora-129f-v1", "name": "ExVideo-CogVideoX-LoRA-129f-v1", "maker": "ecnu", "family": "ecnu/exvideo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-10-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA post-tune of CogVideoX-5B. The card says the LoRA is added into CogVideoX-5B so the model can generate up to 129 frames. The example call uses height 480, width 720, num_frames 129, cfg_scale 7.0, and 100 inference steps. Those are example arguments, not a published maximum.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ExVideo-CogVideoX-LoRA-129f-v1 is ECNU-CILab's second public ExVideo weight, a LoRA that extends CogVideoX-5B to about 129 frames. Collected on 2026-10-15.", "what_it_is_for": "The card says the LoRA lets CogVideoX-5B hold detail better when generating long clips, up to 129 frames. The base text-to-video model still has to be loaded separately.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/exvideo/exvideo-svd-128f-v1": { "id": "ecnu/exvideo/exvideo-svd-128f-v1", "name": "ExVideo-SVD-128f-v1", "maker": "ecnu", "family": "ecnu/exvideo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-19", "context_length": null, "max_output_tokens": null, "parameters": "832956800", "architecture": "Post-tuned Stable Video Diffusion extension for up to 128 frames. The card says this is the first public ExVideo model, trained on about 40,000 videos with 8 A100 GPUs for about one week. Hub safetensors parameter total is 832,956,800. The card does not say whether that total is only the extension module or the full SVD stack. Both model.safetensors and model.fp16.safetensors are in the repo.", "modalities": { "input": [ "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ExVideo-SVD-128f-v1 is the Computational Intelligence Lab at East China Normal University's public extension of Stable Video Diffusion for videos up to 128 frames. Status is active as an open weight. Collected on 2026-10-15.", "what_it_is_for": "The card says the model extends Stable Video Diffusion so it can generate long videos of up to 128 frames. The authors warn that the small training set can produce physically implausible video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/psychagent/psychagent-qwen3-32b": { "id": "ecnu/psychagent/psychagent-qwen3-32b", "name": "PsychAgent-Qwen3-32B", "maker": "ecnu", "family": "ecnu/psychagent", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other", "release_date": "2026-04-01", "context_length": 40960, "max_output_tokens": null, "parameters": "32B class. Full fine-tune of Qwen/Qwen3-32B. Config hidden size 5120, 64 layers, max_position_embeddings 40960. The Hub safetensors.total field was 676864, which does not match a 14-shard 32B layout, so that total was not used.", "architecture": "Qwen3ForCausalLM. The card says the checkpoint is a full fine-tune of Qwen3-32B with rejection fine-tuning. Two training variants are named: rft_explicit_skill_0218 and rft_implicit_skill_0218. Card training settings include learning rate 1e-5, 3 epochs, cosine schedule, 10% warmup, bf16, DeepSpeed ZeRO-3, global batch 128, and 16 H200 GPUs. The paper's stated training context maximum is 32768 tokens, which is lower than the config window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PsychAgent-Qwen3-32B is ICALK at East China Normal University's open counseling checkpoint, a full fine-tune of Qwen3-32B for multi-session psychological counseling research. Collected on 2026-10-15. It is not a clinical device.", "what_it_is_for": "The card says the model is for research on multi-session counseling, memory, skill evolution, and lifelong learning. It is explicitly out of scope for clinical care and emergencies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/sea/sea-a": { "id": "ecnu/sea/sea-a", "name": "SEA-A", "maker": "ecnu", "family": "ecnu/sea", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card says SEA-A encodes a paper and a review with Salesforce SFR-Embedding-Mistral, builds query and key vectors, and outputs a mismatch score. The Hub file is SEA-A.pth, about 2.1 MB. It is not the 7B language-model checkpoint.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SEA-A is a named SEA peer-review model from the ECNU-SEA group at East China Normal University. The model card is public. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says SEA-A assesses consistency between paper content and a review and produces a mismatch score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/sea/sea-e": { "id": "ecnu/sea/sea-e", "name": "SEA-E", "maker": "ecnu", "family": "ecnu/sea", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-24", "context_length": 32768, "max_output_tokens": null, "parameters": "7241732096", "architecture": "MistralForCausalLM supervised fine-tune. The card says the backbone is Mistral-7B-Instruct-v0.2, trained on a peer-review instruction set standardized by SEA-S. Hub safetensors total is 7,241,732,096. hidden size 4096, 32 layers, max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SEA-E is a named SEA peer-review model from the ECNU-SEA group at East China Normal University. The model card is public. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says SEA-E writes review feedback for a submitted paper. The recommended use cuts the paper at the references heading before generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/sea/sea-s": { "id": "ecnu/sea/sea-s", "name": "SEA-S", "maker": "ecnu", "family": "ecnu/sea", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-24", "context_length": 32768, "max_output_tokens": null, "parameters": "7241732096", "architecture": "MistralForCausalLM. The card says SEA-S distills GPT-4's merged reviews: multiple reviews are integrated into one standardized review, then Mistral-7B-Instruct-v0.2 is fine-tuned. Hub safetensors total is 7,241,732,096. max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SEA-S is a named SEA peer-review model from the ECNU-SEA group at East China Normal University. The model card is public. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says SEA-S merges the reviews of one paper into a single standardized review so that redundancy and errors are reduced. It is the standardization model, not the review writer. The review writer is SEA-E.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecnu/skillgym/skillgym-agent": { "id": "ecnu/skillgym/skillgym-agent", "name": "SkillGym-Agent", "maker": "ecnu", "family": "ecnu/skillgym", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-09-22", "context_length": 262144, "max_output_tokens": null, "parameters": "35951822704", "architecture": "Qwen3_5MoeForConditionalGeneration, full-parameter supervised fine-tune of Qwen/Qwen3.5-35B-A3B. The card says about 35.95B parameters; Hub safetensors total is 35,951,822,704. text_config.max_position_embeddings is 262144 and text_config has 40 layers. A vision_config is present. Trained on 8,364 successful SkillGym trajectories with ms-swift / Megatron on 16 H200 GPUs. The released setting is All Teachers.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SkillGym-Agent is ICALK at East China Normal University's full fine-tune of Qwen3.5-35B-A3B on verified SkillGym trajectories. The Apache-2.0 checkpoint was collected on 2026-10-15.", "what_it_is_for": "The card says the model is for research on tool-using agents, long-horizon workflows, and how much skill-generated experience remains after the external skill is removed. It is not offered as a general safety guarantee or a drop-in production agent.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ecust/aipolym/chat-aipolym": { "id": "ecust/aipolym/chat-aipolym", "name": "Chat AIPolym", "maker": "ecust", "family": "ecust/aipolym", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "University coverage says Chat AIPolym fuses natural language, polymer knowledge, a general large model, specialist models, and the AI plus Polymers databases so it can call background algorithms. The general-model name, parameter count, and training recipe are not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chat AIPolym is the polymer-domain large model announced by East China University of Science and Technology's materials school, led by Jiaping Lin with Liang Gao as the platform's technical lead. The university calls it China's first polymer \"general-plus-specialist\" model and ships it with the AI plus Polymers v3.0 platform. It was in limited industrial use at collection on 2026-10-15. Weights are not public.", "what_it_is_for": "The materials school says the model is for polymer R&D dialogue: predicting properties, recommending design rules, and driving custom design of high-performance resins, organic optoelectronic materials, and composites. Quoted example commands include drawing a chemical structure and predicting polyamide properties. Coverage says it is meant to reduce the hallucinations and low precision of general models on materials questions by calling the team's polymer database and specialist models. The same pages say the surrounding platform, not this chat model alone, is used by more than 60 chemical-materials companies and aerospace institutes, with cloud access for smaller users and on-prem deployment for large firms and aerospace institutes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/eurofilter/eurofilter-v1": { "id": "edinburgh/eurofilter/eurofilter-v1", "name": "EuroFilter v1", "maker": "edinburgh", "family": "edinburgh/eurofilter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-19", "context_length": null, "max_output_tokens": null, "parameters": "0.3B", "architecture": "Sequence classifier built on microsoft/mdeberta-v3-base. Card: regression head for an educational score and a binary head for whether the score is acceptable. Hub safetensors label says 0.3B parameters, dtype F32. The example truncates inputs at 512 tokens. An exact parameter count is not printed on the card.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroFilter v1 is the educational-text classifier released with the EuroLLM project and later named in the EuroLLM-22B annealing mix. The citation on the card includes University of Edinburgh authors Barry Haddow and Alexandra Birch. It is a classifier on top of mDeBERTa-v3-base, not a generative LLM. Collected 2026-10-15.", "what_it_is_for": "The card says it scores whether a document has educational value, in the style of FineWeb-Edu, so EuroLLM training data can be filtered. It was trained for 15 languages and is expected to transfer more widely because the encoder is mDeBERTa-v3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/eurollm/eurollm-1-7b": { "id": "edinburgh/eurollm/eurollm-1-7b", "name": "EuroLLM-1.7B", "maker": "edinburgh", "family": "edinburgh/eurollm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-24", "context_length": 4096, "max_output_tokens": null, "parameters": "1.657B", "architecture": "Dense decoder-only Transformer. Card and EuroLLM paper Table 1: 24 layers, hidden 2048, FFN 5632, 16 heads, 8 KV heads (GQA), SwiGLU, RoPE theta 10000, RMSNorm, untied embeddings, vocab 128000, sequence length 4096. Embedding 0.262B, LM head 0.262B, non-embedding 1.133B, total 1.657B. Pretrained on 4 trillion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroLLM-1.7B is the first pretrained model in the EuroLLM suite, a dense multilingual language model whose model card lists the University of Edinburgh among the developers, with Unbabel, Instituto Superior T\u00e9cnico, Instituto de Telecomunica\u00e7\u00f5es, and other European partners. It is an open-weight base model, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "The project goal, stated on the model card, is a suite of LLMs that understand and generate text in all official EU languages plus additional languages. EuroLLM-1.7B is the small pretrained model, trained on 4 trillion tokens, and is described on eurollm.io as the edge-sized member of the suite. It is not preference-aligned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/eurollm/eurollm-1-7b-instruct": { "id": "edinburgh/eurollm/eurollm-1-7b-instruct", "name": "EuroLLM-1.7B-Instruct", "maker": "edinburgh", "family": "edinburgh/eurollm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-24", "context_length": 4096, "max_output_tokens": null, "parameters": "1.657B", "architecture": "Same dense Transformer as EuroLLM-1.7B (24 layers, hidden 2048, FFN 5632, 16 heads, 8 KV heads, SwiGLU, RoPE theta 10000, RMSNorm, untied embeddings, vocab 128000, context 4096, total 1.657B). Supervised fine-tune on EuroBlocks. Chat control tokens <|im_start|> and <|im_end|>. Paper: about 1M EuroBlocks samples, 4 epochs, learning rate 7e-6, about 6K steps, loss on target tokens only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroLLM-1.7B-Instruct is the supervised fine-tune of EuroLLM-1.7B for instruction following and translation. The model card lists the University of Edinburgh with Unbabel, Instituto Superior T\u00e9cnico, Instituto de Telecomunica\u00e7\u00f5es, and other EuroLLM partners. Weights are open. Collected 2026-10-15.", "what_it_is_for": "The base card says this checkpoint was instruction-tuned on EuroBlocks, with a focus on general instruction following and machine translation across the EuroLLM languages. The paper describes chat control tokens so the model can take a system prompt and user turns.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/eurollm/eurollm-22b": { "id": "edinburgh/eurollm/eurollm-22b", "name": "EuroLLM-22B", "maker": "edinburgh", "family": "edinburgh/eurollm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-15", "context_length": 32768, "max_output_tokens": null, "parameters": "22.639B", "architecture": "Dense decoder-only Transformer. EuroLLM-22B-2512 card: 56 layers, hidden 6144, FFN 16384, 48 heads, 8 KV heads, SwiGLU, RoPE theta 1000000, RMSNorm, untied embeddings, sequence length 32768. Embedding 0.786B, LM head 0.786B, non-embedding 21.067B, total 22.639B. Vocab 128000 matches the embedding size. About 4 trillion tokens in three phases on 400 H100 GPUs: 3.6T pretraining, 400B annealing, 100B anneal-to-zero with long-context upsampling.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroLLM-22B is the largest released EuroLLM pretrained model. The School of Informatics announced it on 16 December 2025 as a consortium launch that includes the University of Edinburgh. The current weights are `utter-project/EuroLLM-22B-2512`, a 22.639B dense model with a 32,768-token context. A June 2025 preview is still on the Hub. Collected 2026-10-15.", "what_it_is_for": "The card describes a base multilingual model for the 24 official EU languages and 11 further languages. It is not post-trained. Instruction following is the separate EuroLLM-22B-Instruct checkpoint. Edinburgh's Alexandra Birch is quoted in the Informatics news post on the release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/eurollm/eurollm-22b-instruct": { "id": "edinburgh/eurollm/eurollm-22b-instruct", "name": "EuroLLM-22B-Instruct", "maker": "edinburgh", "family": "edinburgh/eurollm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-15", "context_length": 32768, "max_output_tokens": null, "parameters": "22.639B", "architecture": "Post-trained EuroLLM-22B. The 2512 base card specifies 56 layers, hidden 6144, FFN 16384, 48 heads, 8 KV heads, SwiGLU, RoPE theta 1000000, RMSNorm, untied embeddings, context 32768, total 22.639B. The base card says the instruct model was tuned on EuroBlocks for instruction following and translation. The February 2026 report evaluates the post-trained models and says translation uses COMET-22.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroLLM-22B-Instruct is the post-trained EuroLLM-22B for instruction following and translation. The University of Edinburgh is a named developer, and the School of Informatics announced the 22B release in December 2025. Current weights are `utter-project/EuroLLM-22B-Instruct-2512`. A June 2025 instruct preview remains on the Hub. Collected 2026-10-15.", "what_it_is_for": "The 22B base card says the instruct model was tuned on EuroBlocks for general instruction following and machine translation across the EuroLLM languages. The December 2025 blog presents it as the flagship fully open European model in the authors' comparison.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/eurollm/eurollm-9b": { "id": "edinburgh/eurollm/eurollm-9b", "name": "EuroLLM-9B", "maker": "edinburgh", "family": "edinburgh/eurollm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-09", "context_length": 4096, "max_output_tokens": null, "parameters": "9.154B", "architecture": "Dense decoder-only Transformer. Original card: 42 layers, hidden 4096, FFN 12288, 32 heads, 8 KV heads, SwiGLU, RoPE theta 10000, RMSNorm, untied embeddings, sequence length 4096. Embedding 0.524B, LM head 0.524B, non-embedding 8.105B, total 9.154B. Vocab 128000 matches the embedding size. Pretrained on 4 trillion tokens on 400 H100 GPUs. The 2512 checkpoint adds the long-context phase used for EuroLLM-22B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroLLM-9B is the mid-size pretrained EuroLLM model. The model card lists the University of Edinburgh with Unbabel, Instituto Superior T\u00e9cnico, Instituto de Telecomunica\u00e7\u00f5es, and other partners. The 9B technical report says it was released on 9 December 2024. A later 2512 base adds the long-context phase used for EuroLLM-22B. Collected 2026-10-15.", "what_it_is_for": "The card describes a 9B multilingual model trained from scratch so European languages are covered alongside English. It is a base model for fine-tuning. The instruct sibling is the chat and translation model. The 2512 base is the long-context revision.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/eurollm/eurollm-9b-instruct": { "id": "edinburgh/eurollm/eurollm-9b-instruct", "name": "EuroLLM-9B-Instruct", "maker": "edinburgh", "family": "edinburgh/eurollm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-09", "context_length": 4096, "max_output_tokens": null, "parameters": "9.154B", "architecture": "Instruction tune of EuroLLM-9B. Original base card: 42 layers, hidden 4096, FFN 12288, 32 heads, 8 KV heads, SwiGLU, RoPE theta 10000, RMSNorm, untied embeddings, total 9.154B, context 4096, trained further on EuroBlocks. EuroLLM-9B-Instruct-2512 is the post-train that matches the February 2026 recipe. The 22B report says that improved 9B uses the 32K context extension.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroLLM-9B-Instruct is the instruction-tuned EuroLLM-9B. The University of Edinburgh is a named developer on the 9B card, with the rest of the EuroLLM consortium. The original tune shipped with the December 2024 9B release. EuroLLM-9B-Instruct-2512 is the later post-train written up in the February 2026 EuroLLM-22B report. Collected 2026-10-15.", "what_it_is_for": "The 9B base card says the instruct model was tuned on EuroBlocks for instruction following and machine translation in the EuroLLM languages. The 2512 instruct is the improved post-train that the 22B report compares as \"EuroLLM-9B (new)\".", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/euromoe/euromoe-2-6b-a0-6b": { "id": "edinburgh/euromoe/euromoe-2-6b-a0-6b", "name": "EuroMoE-2.6B-A0.6B", "maker": "edinburgh", "family": "edinburgh/euromoe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-09", "context_length": null, "max_output_tokens": null, "parameters": "2.6B", "architecture": "Card calls it a 2.6B multilingual transformer and titles the card EuroLLM-2.6B-A0.6-2512. The repo id is EuroMoE-2.6B-A0.6B-2512. Hub tags include mixtral. Hub safetensors label says 3B parameters, which disagrees with the 2.6B wording. Expert count, active-parameter definition, layer sizes, and context length are not on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroMoE-2.6B-A0.6B is a pretrained mixture-of-experts model in the EuroLLM collection. The card lists the University of Edinburgh with Instituto Superior T\u00e9cnico, Instituto de Telecomunica\u00e7\u00f5es, Unbabel, and other partners. The only public base id found is `utter-project/EuroMoE-2.6B-A0.6B-2512`. Collected 2026-10-15.", "what_it_is_for": "The card presents it as the pretrained EuroLLM model in this size, for the same 35 languages as the dense EuroLLM models. It is not aligned. Instruction use is the separate instruct checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/euromoe/euromoe-2-6b-a0-6b-instruct": { "id": "edinburgh/euromoe/euromoe-2-6b-a0-6b-instruct", "name": "EuroMoE-2.6B-A0.6B-Instruct", "maker": "edinburgh", "family": "edinburgh/euromoe", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-15", "context_length": null, "max_output_tokens": null, "parameters": "2.6B", "architecture": "Instruction-tuned sibling of EuroMoE-2.6B-A0.6B-2512. Hub tags include mixtral and conversational. The base card says the model is a 2.6B multilingual transformer and links the instruct repo. Expert count and context length were not on the base card. The instruct card text was not separately fetched.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroMoE-2.6B-A0.6B-Instruct is the instruction-tuned EuroMoE checkpoint. The University of Edinburgh is named on the base card with the rest of the EuroLLM consortium. Two Hub ids exist: a June 2025 preview and `utter-project/EuroMoE-2.6B-A0.6B-Instruct-2512`. Collected 2026-10-15.", "what_it_is_for": "The base card links this as the instruct model for the 2.6B EuroMoE. The Hub listing marks it conversational. No task list beyond that was read from an instruct card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/eurovlm/eurovlm-1-7b-preview": { "id": "edinburgh/eurovlm/eurovlm-1-7b-preview", "name": "EuroVLM-1.7B-Preview", "maker": "edinburgh", "family": "edinburgh/eurovlm", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-09", "context_length": 32768, "max_output_tokens": null, "parameters": "1.7B+400M", "architecture": "LLaVA-Next style vision-language model. Card: language stack is a long-context EuroLLM-1.7B (dense Transformer, GQA with 8 KV heads, RMSNorm, SwiGLU, RoPE) with context up to 32K. Vision encoder is a ViT based on google/siglip2-so400m-patch14-384 plus a projector. Hub safetensors label says 2B parameters and dtype F16. Card title also says EuroVLM-1.7B-Instruct while the warning says Preview.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroVLM-1.7B-Preview is a preview vision-language model from the EuroLLM project. The card lists the University of Edinburgh with Unbabel, Instituto Superior T\u00e9cnico, Instituto de Telecomunica\u00e7\u00f5es, and other partners. It adds a SigLIP 2 vision encoder to a long-context EuroLLM-1.7B. The card still marks it as a preview. Collected 2026-10-15.", "what_it_is_for": "The card says it was visually instruction-tuned for multilingual image captioning, visual question answering, visual instructions, multimodal translation, and document understanding across the EuroLLM languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/eurovlm/eurovlm-9b-preview": { "id": "edinburgh/eurovlm/eurovlm-9b-preview", "name": "EuroVLM-9B-Preview", "maker": "edinburgh", "family": "edinburgh/eurovlm", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-09", "context_length": 32768, "max_output_tokens": null, "parameters": "9B+400M", "architecture": "LLaVA-Next style model. Card: language component is long-context EuroLLM-9B (dense Transformer, GQA with 8 KV heads, RMSNorm, SwiGLU, RoPE, context up to 32K). Vision encoder is a ViT based on google/siglip2-so400m-patch14-384 plus a projector. Hub safetensors label says 10B parameters and dtype F16.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroVLM-9B-Preview is the larger preview vision-language model in the EuroLLM collection. The card lists the University of Edinburgh among the developers. It pairs a long-context EuroLLM-9B with a SigLIP 2 vision encoder. The card labels the release a preview. Collected 2026-10-15.", "what_it_is_for": "The card says the model was visually instruction-tuned for multilingual image captioning, visual question answering, instruction following on images, multimodal translation, and document understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/wmt/wmt16": { "id": "edinburgh/wmt/wmt16", "name": "Edinburgh WMT16 NMT", "maker": "edinburgh", "family": "edinburgh/wmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Nematus neural MT checkpoints. The system paper abstract says the models are attentional encoder-decoders with BPE subwords. The download page documents single-model, ensemble, and, for some pairs, right-to-left reranking scripts. Depth, hidden size, and parameter count were not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The University of Edinburgh's WMT16 neural machine translation systems are Nematus checkpoints released by the Edinburgh statistical MT group for the 2016 shared translation task. They are historical research weights, still hosted, under a non-commercial license. Collected 2026-10-15.", "what_it_is_for": "The download page says these are the NMT models built for the WMT16 shared translation task. Directions on the server are Czech, German, Romanian, and Russian, each paired with English in both directions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/wmt/wmt17": { "id": "edinburgh/wmt/wmt17", "name": "Edinburgh WMT17 NMT", "maker": "edinburgh", "family": "edinburgh/wmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 3.0 for models; MIT for scripts", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Nematus attentional NMT. The download page says all WMT17 models were trained with a legacy Theano Nematus and converted to the TensorFlow Nematus codebase. Scripts cover a single model, a left-to-right ensemble, and right-to-left reranking for some pairs. Depth and parameter count were not transcribed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The University of Edinburgh's WMT17 neural MT systems are Nematus models released by the Edinburgh group for the 2017 shared translation task. The download page says the checkpoints were trained in Theano Nematus and converted to TensorFlow. They remain hosted for non-commercial use. Collected 2026-10-15.", "what_it_is_for": "Sentence translation for the WMT17 news task. The page says the directory holds some of Edinburgh's submissions, not a claim that every submitted system is in the tree.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/wmt/wmt19": { "id": "edinburgh/wmt/wmt19", "name": "Edinburgh WMT19 NMT", "maker": "edinburgh", "family": "edinburgh/wmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Marian NMT. The download page says the English-Gujarati models are retrained after a preprocessing error, so they are not the official shared-task checkpoints. Ensembles are 4 models, unweighted, beam size 6. XLM is listed only for back-translation training on Gujarati. Parameter count was not stated on the download page.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The University of Edinburgh's WMT19 news-translation models are Marian checkpoints hosted at data.statmt.org. The index covers English-Gujarati in both directions and a Chinese-to-English directory. The Gujarati models were retrained after a preprocessing error, so their BLEU is higher than the official submission. Collected 2026-10-15.", "what_it_is_for": "News translation for the WMT19 shared task. The system paper says Edinburgh submitted English-Gujarati, English-Chinese, German-to-English, and English-to-Czech. Only some of those directions are in the weight directory.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "edinburgh/wmt/wmt20": { "id": "edinburgh/wmt/wmt20", "name": "Edinburgh WMT20 NMT", "maker": "edinburgh", "family": "edinburgh/wmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Marian checkpoints using SentencePiece, per the download page. The system paper says the WMT20 submissions use a Transformer. Beam size on the download page is 8 for en-ta and 18 for ta-en. Parameter count is not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The University of Edinburgh's released WMT20 models are two Marian English-Tamil tarballs. The system paper also describes English-Inuktitut Transformers, but those weights are not in the download directory. The Tamil BLEU figures on the download page match the paper's dev and test table. Collected 2026-10-15.", "what_it_is_for": "News translation for English-Tamil in both directions. The paper says the systems are Transformers, with pretraining and back-translation used because the pair is low-resource.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/custom/animated-mascot": { "id": "elai/custom/animated-mascot", "name": "Animated Mascot", "maker": "elai", "family": "elai/custom", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Custom animated talking character. The update-avatar API accepts an images array of PNG data for mascot only. Status 5 is described as model training for custom avatars generally.", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Animated Mascot is a named Elai custom-avatar product, not one stock presenter. Elai (part of Panopto) sells it beside the stock library. Collected 2026-10-15. Weights are closed. No parameter count or license was published.", "what_it_is_for": "The avatars page says Animated Mascot is a fully customized animated talking character. The features page lists it as the fourth custom type. The update-avatar example sends a name, a code, and an images array of PNG files. The overview groups mascot with studio and selfie for the status workflow.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/custom/photo-avatar": { "id": "elai/custom/photo-avatar", "name": "Photo Avatar", "maker": "elai", "family": "elai/custom", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cartoon talking head from a single photo, as described on the avatars page. Training method not published.", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Photo Avatar is a named Elai custom-avatar product, not one stock presenter. Elai (part of Panopto) sells it beside the stock library. Collected 2026-10-15. Weights are closed. No parameter count or license was published.", "what_it_is_for": "The avatars page says Photo Avatar creates a cartoon talking head from a single photo. The features page lists Photo as one of four custom types, separate from Animated mascot. The update-avatar examples show an images array only for mascot, so the photo upload shape was not confirmed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/custom/premium-avatars": { "id": "elai/custom/premium-avatars", "name": "Premium Avatars", "maker": "elai", "family": "elai/custom", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published. The pricing table names Premium Avatars as an Enterprise entitlement separate from the 80+ library row and from Selfie Avatar.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Premium Avatars is a named Elai custom-avatar product, not one stock presenter. Elai (part of Panopto) sells it beside the stock library. Collected 2026-10-15. Weights are closed. No parameter count or license was published.", "what_it_is_for": "Premium Avatars is a named line on the official pricing table and on the Enterprise card. Elai does not say whether it is a different renderer, a locked subset of stock presenters, or another custom tier. It is catalogued because the name is an official product line, not because a model card exists.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/custom/selfie-avatar": { "id": "elai/custom/selfie-avatar", "name": "Selfie Avatar", "maker": "elai", "family": "elai/custom", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Custom avatar trained from a phone or webcam recording. Elai's status list calls status 5 model training. No architecture name.", "modalities": { "input": [ "text", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Selfie Avatar is a named Elai custom-avatar product, not one stock presenter. Elai (part of Panopto) sells it beside the stock library. Collected 2026-10-15. Weights are closed. No parameter count or license was published.", "what_it_is_for": "Elai's features page lists Selfie as one of four custom avatar types. The avatars page says you record with a smartphone or web camera. The footage guide asks for about 5 minutes, allows any background for selfie and scenario recordings, and recommends 4K at 30 fps on the phone. Scenario is named only inside that selfie/scenario sentence, not as a fifth product on the features page, so it has no folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/custom/studio-avatar": { "id": "elai/custom/studio-avatar", "name": "Studio Avatar", "maker": "elai", "family": "elai/custom", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Custom avatar from high-quality studio footage, typically on a plain or green background. Status 5 on the avatar API is model training. No architecture name.", "modalities": { "input": [ "text", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Studio Avatar is a named Elai custom-avatar product, not one stock presenter. Elai (part of Panopto) sells it beside the stock library. Collected 2026-10-15. Weights are closed. No parameter count or license was published.", "what_it_is_for": "The avatars page calls Studio Avatar a professional talking presenter available only to the buyer, built from high-quality studio footage. The footage guide says studio recordings should use a plain background, typically green, for about 5 minutes, with clothing that contrasts with the background.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/ali": { "id": "elai/stock/ali", "name": "Ali", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ali is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Ali. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Ali is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/alma": { "id": "elai/stock/alma", "name": "Alma", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alma is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Alma. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Alma is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/amanda": { "id": "elai/stock/amanda", "name": "Amanda", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amanda is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Amanda. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Amanda is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/amber": { "id": "elai/stock/amber", "name": "Amber", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amber is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Amber. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Amber is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/amira": { "id": "elai/stock/amira", "name": "Amira", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amira is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Amira. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Amira is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/aniqa": { "id": "elai/stock/aniqa", "name": "Aniqa", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aniqa is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Aniqa. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Aniqa is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/aspen": { "id": "elai/stock/aspen", "name": "Aspen", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aspen is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Aspen. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Aspen is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/carlos": { "id": "elai/stock/carlos", "name": "Carlos", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Carlos is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Carlos. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Carlos is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/chad": { "id": "elai/stock/chad", "name": "Chad", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. The story example look is named Business.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chad is a named Elai stock avatar. He is not on the public library page modified 2026-06-10, but the official story-slides API example still addresses him as chad.business3. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "The story API example uses Chad as the slide presenter: code chad.business3, look name Business, gender male. That is a stock presenter inside a generated video, not a custom-avatar product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/cody": { "id": "elai/stock/cody", "name": "Cody", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cody is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Cody. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Cody is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/enzo": { "id": "elai/stock/enzo", "name": "Enzo", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Enzo is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Enzo. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Enzo is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/ethan": { "id": "elai/stock/ethan", "name": "Ethan", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ethan is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Ethan. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Ethan is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/gia": { "id": "elai/stock/gia", "name": "Gia", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gia is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Gia. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Gia is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The create-video example sets code gia.casual, gender female, and a transparent canvas image at https://elai-avatars.s3.us-east-2.amazonaws.com/common/gia/casual/gia_casual.png. The streams example uses avatarCode gia with an ElevenLabs voice.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/jade": { "id": "elai/stock/jade", "name": "Jade", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jade is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Jade. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Jade is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/jameson": { "id": "elai/stock/jameson", "name": "Jameson", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jameson is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Jameson. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Jameson is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/kamal": { "id": "elai/stock/kamal", "name": "Kamal", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kamal is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Kamal. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Kamal is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The 6 July 2023 official intro says Kamal has more than one look. The auto-transcript names business and casual. A third word is unclear, so it is not stored as a look id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/kevin": { "id": "elai/stock/kevin", "name": "Kevin", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kevin is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Kevin. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Kevin is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/kira": { "id": "elai/stock/kira", "name": "Kira", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kira is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Kira. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Kira is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/lei": { "id": "elai/stock/lei", "name": "Lei", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lei is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Lei. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Lei is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/lucia": { "id": "elai/stock/lucia", "name": "Lucia", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucia is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Lucia. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Lucia is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/malik": { "id": "elai/stock/malik", "name": "Malik", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Malik is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Malik. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Malik is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/margaret": { "id": "elai/stock/margaret", "name": "Margaret", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Margaret is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Margaret. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Margaret is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/max": { "id": "elai/stock/max", "name": "Max", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Max is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Max. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Max is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/megan": { "id": "elai/stock/megan", "name": "Megan", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Megan is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Megan. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Megan is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/nancy": { "id": "elai/stock/nancy", "name": "Nancy", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nancy is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Nancy. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Nancy is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/naomi": { "id": "elai/stock/naomi", "name": "Naomi", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Naomi is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Naomi. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Naomi is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/nelson": { "id": "elai/stock/nelson", "name": "Nelson", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nelson is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Nelson. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Nelson is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/noah": { "id": "elai/stock/noah", "name": "Noah", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Noah is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Noah. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Noah is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/olivia": { "id": "elai/stock/olivia", "name": "Olivia", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olivia is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Olivia. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Olivia is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/rose": { "id": "elai/stock/rose", "name": "Rose", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rose is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Rose. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Rose is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/skye": { "id": "elai/stock/skye", "name": "Skye", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Skye is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Skye. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Skye is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/sofie": { "id": "elai/stock/sofie", "name": "Sofie", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sofie is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Sofie. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Sofie is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/taylor": { "id": "elai/stock/taylor", "name": "Taylor", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Taylor is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Taylor. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Taylor is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/terry": { "id": "elai/stock/terry", "name": "Terry", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Terry is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Terry. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Terry is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elai/stock/tyler": { "id": "elai/stock/tyler", "name": "Tyler", "maker": "elai", "family": "elai/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Actor footage processed by Elai's unpublished avatar renderer. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tyler is a named stock talking avatar from Elai, a Panopto company. Elai builds presenters from actor footage and drives them with a script. This dossier was collected on 2026-10-15. The public library page (modified 2026-06-10) lists Tyler. Weights are closed. No parameter count or license name was published.", "what_it_is_for": "Elai's avatars page says you pick a library presenter and paste text to get a narrated video. Tyler is one of the names on https://elai.io/avatars-library/. The same company says the full library is 80+ avatars; only 34 names are on that page. The library card does not publish gender, looks, or an API code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/comma/comma-v0-1-1t": { "id": "eleutherai/comma/comma-v0-1-1t", "name": "Comma v0.1-1T", "maker": "eleutherai", "family": "eleutherai/comma", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-06-05", "context_length": 16384, "max_output_tokens": null, "parameters": "7B", "architecture": "Llama-style decoder, same architecture as Llama 3 according to the model card. 32 layers, hidden 4096, 32 heads, 32 KV heads, intermediate 11008, vocab 64256, rope_theta 100000, bfloat16, untied embeddings. config.json max_position_embeddings is 16384. The Common Pile paper says the 1T run used sequences of length 4096. Trained on 1 trillion tokens from the Common Pile: 965 billion with a cosine schedule, then a 35 billion token cool-down on high-quality sources. The paper says the 1T effective batch was 512 sequences of length 4096. Training used the lingua framework.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Comma v0.1-1T is a 7 billion parameter Apache-2.0 base model from the EleutherAI Common Pile release on 2025-06-05, trained for 1 trillion tokens on openly licensed text. Collected 2026-10-15.", "what_it_is_for": "The card calls it a base model for fine-tuning and post-training, and evidence that openly licensed text can match similar compute budgets trained on unlicensed data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/comma/comma-v0-1-2t": { "id": "eleutherai/comma/comma-v0-1-2t", "name": "Comma v0.1-2T", "maker": "eleutherai", "family": "eleutherai/comma", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-06-05", "context_length": 16384, "max_output_tokens": null, "parameters": "7B", "architecture": "Llama-style decoder, same architecture as Llama 3 according to the model card. 32 layers, hidden 4096, 32 heads, 32 KV heads, intermediate 11008, vocab 64256, rope_theta 100000, bfloat16, untied embeddings. config.json max_position_embeddings is 16384. The Common Pile paper says the 1T run used sequences of length 4096. Trained on 2 trillion tokens: 1.93 trillion with a cosine schedule, then 75.5 billion tokens of cool-down. The released weights are the average of 10 cool-down checkpoints. Both phases use a batch of 8.3 million tokens. Trained with lingua on 512 AMD MI300A GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Comma v0.1-2T is the 2-trillion-token twin of Comma v0.1-1T, a 7B Apache-2.0 base model released by EleutherAI on 2025-06-05. Collected 2026-10-15.", "what_it_is_for": "A base model showing what two trillion tokens of the Common Pile produce, for research and further post-training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/deep-ignorance/deep-ignorance-e2e-extra-weak-filter": { "id": "eleutherai/deep-ignorance/deep-ignorance-e2e-extra-weak-filter", "name": "Deep Ignorance end-to-end extra-weak filter", "maker": "eleutherai", "family": "eleutherai/deep-ignorance", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-12", "context_length": 2048, "max_output_tokens": null, "parameters": "6.9B", "architecture": "GPT-NeoX decoder at the 6.9B scale. Config of the unfiltered checkpoint: 32 layers, hidden 4096, 32 heads, intermediate 16384, rotary_pct 0.25, vocab 50304, context 2048, bfloat16, untied embeddings. This vocab size is not the 50432 on the Pythia-6.9B config. These are new training runs, not Pythia checkpoints. The card says every run uses the same pretraining and annealing setup except for filtered data, and that final annealing checkpoints are the longest-trained ones.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deep Ignorance end-to-end extra-weak filter is a 6.9B Apache-2.0 research model in EleutherAI's Deep Ignorance suite, announced in August 2025. It is for studying data filtering as a safeguard. It is not a product. Collected 2026-10-15.", "what_it_is_for": "The suite asks whether removing enough hazardous technical text before training can keep a model from learning it. Extra-weak filter during both pretraining and annealing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/deep-ignorance/deep-ignorance-e2e-strong-filter": { "id": "eleutherai/deep-ignorance/deep-ignorance-e2e-strong-filter", "name": "Deep Ignorance end-to-end strong filter", "maker": "eleutherai", "family": "eleutherai/deep-ignorance", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-12", "context_length": 2048, "max_output_tokens": null, "parameters": "6.9B", "architecture": "GPT-NeoX decoder at the 6.9B scale. Config of the unfiltered checkpoint: 32 layers, hidden 4096, 32 heads, intermediate 16384, rotary_pct 0.25, vocab 50304, context 2048, bfloat16, untied embeddings. This vocab size is not the 50432 on the Pythia-6.9B config. These are new training runs, not Pythia checkpoints. The card says every run uses the same pretraining and annealing setup except for filtered data, and that final annealing checkpoints are the longest-trained ones.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deep Ignorance end-to-end strong filter is a 6.9B Apache-2.0 research model in EleutherAI's Deep Ignorance suite, announced in August 2025. It is for studying data filtering as a safeguard. It is not a product. Collected 2026-10-15.", "what_it_is_for": "The suite asks whether removing enough hazardous technical text before training can keep a model from learning it. Strong filter during both pretraining and annealing. One of the four checkpoints the card says were studied most.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/deep-ignorance/deep-ignorance-e2e-weak-filter": { "id": "eleutherai/deep-ignorance/deep-ignorance-e2e-weak-filter", "name": "Deep Ignorance end-to-end weak filter", "maker": "eleutherai", "family": "eleutherai/deep-ignorance", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-12", "context_length": 2048, "max_output_tokens": null, "parameters": "6.9B", "architecture": "GPT-NeoX decoder at the 6.9B scale. Config of the unfiltered checkpoint: 32 layers, hidden 4096, 32 heads, intermediate 16384, rotary_pct 0.25, vocab 50304, context 2048, bfloat16, untied embeddings. This vocab size is not the 50432 on the Pythia-6.9B config. These are new training runs, not Pythia checkpoints. The card says every run uses the same pretraining and annealing setup except for filtered data, and that final annealing checkpoints are the longest-trained ones.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deep Ignorance end-to-end weak filter is a 6.9B Apache-2.0 research model in EleutherAI's Deep Ignorance suite, announced in August 2025. It is for studying data filtering as a safeguard. It is not a product. Collected 2026-10-15.", "what_it_is_for": "The suite asks whether removing enough hazardous technical text before training can keep a model from learning it. Weak filter during both pretraining and annealing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/deep-ignorance/deep-ignorance-strong-filter-pt-weak-filter-anneal": { "id": "eleutherai/deep-ignorance/deep-ignorance-strong-filter-pt-weak-filter-anneal", "name": "Deep Ignorance strong pretrain, weak anneal", "maker": "eleutherai", "family": "eleutherai/deep-ignorance", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-12", "context_length": 2048, "max_output_tokens": null, "parameters": "6.9B", "architecture": "GPT-NeoX decoder at the 6.9B scale. Config of the unfiltered checkpoint: 32 layers, hidden 4096, 32 heads, intermediate 16384, rotary_pct 0.25, vocab 50304, context 2048, bfloat16, untied embeddings. This vocab size is not the 50432 on the Pythia-6.9B config. These are new training runs, not Pythia checkpoints. The card says every run uses the same pretraining and annealing setup except for filtered data, and that final annealing checkpoints are the longest-trained ones.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deep Ignorance strong pretrain, weak anneal is a 6.9B Apache-2.0 research model in EleutherAI's Deep Ignorance suite, announced in August 2025. It is for studying data filtering as a safeguard. It is not a product. Collected 2026-10-15.", "what_it_is_for": "The suite asks whether removing enough hazardous technical text before training can keep a model from learning it. Strong filter in pretraining and weak filter in annealing. One of the four checkpoints the card says were studied most.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/deep-ignorance/deep-ignorance-unfiltered": { "id": "eleutherai/deep-ignorance/deep-ignorance-unfiltered", "name": "Deep Ignorance unfiltered", "maker": "eleutherai", "family": "eleutherai/deep-ignorance", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-12", "context_length": 2048, "max_output_tokens": null, "parameters": "6.9B", "architecture": "GPT-NeoX decoder at the 6.9B scale. Config of the unfiltered checkpoint: 32 layers, hidden 4096, 32 heads, intermediate 16384, rotary_pct 0.25, vocab 50304, context 2048, bfloat16, untied embeddings. This vocab size is not the 50432 on the Pythia-6.9B config. These are new training runs, not Pythia checkpoints. The card says every run uses the same pretraining and annealing setup except for filtered data, and that final annealing checkpoints are the longest-trained ones.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deep Ignorance unfiltered is a 6.9B Apache-2.0 research model in EleutherAI's Deep Ignorance suite, announced in August 2025. It is for studying data filtering as a safeguard. It is not a product. Collected 2026-10-15.", "what_it_is_for": "The suite asks whether removing enough hazardous technical text before training can keep a model from learning it. Unfiltered baseline at the end of annealing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/deep-ignorance/deep-ignorance-weak-filter-pt-strong-filter-anneal": { "id": "eleutherai/deep-ignorance/deep-ignorance-weak-filter-pt-strong-filter-anneal", "name": "Deep Ignorance weak pretrain, strong anneal", "maker": "eleutherai", "family": "eleutherai/deep-ignorance", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-12", "context_length": 2048, "max_output_tokens": null, "parameters": "6.9B", "architecture": "GPT-NeoX decoder at the 6.9B scale. Config of the unfiltered checkpoint: 32 layers, hidden 4096, 32 heads, intermediate 16384, rotary_pct 0.25, vocab 50304, context 2048, bfloat16, untied embeddings. This vocab size is not the 50432 on the Pythia-6.9B config. These are new training runs, not Pythia checkpoints. The card says every run uses the same pretraining and annealing setup except for filtered data, and that final annealing checkpoints are the longest-trained ones.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deep Ignorance weak pretrain, strong anneal is a 6.9B Apache-2.0 research model in EleutherAI's Deep Ignorance suite, announced in August 2025. It is for studying data filtering as a safeguard. It is not a product. Collected 2026-10-15.", "what_it_is_for": "The suite asks whether removing enough hazardous technical text before training can keep a model from learning it. Weak filter in pretraining and strong filter in annealing. A crossed condition, not a checkpoint of the end-to-end weak run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/gpt-j/gpt-j-6b": { "id": "eleutherai/gpt-j/gpt-j-6b", "name": "GPT-J-6B", "maker": "eleutherai", "family": "eleutherai/gpt-j", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2021-06-09", "context_length": 2048, "max_output_tokens": null, "parameters": "6053381344", "architecture": "GPTJForCausalLM, trained with Mesh Transformer JAX by Ben Wang and Aran Komatsuzaki. 28 layers, d_model 4096, d_ff 16384, 16 heads, d_head 256. Rotary embeddings on 64 dimensions per head. Context 2048. Embedding matrix 50400, of which the card says 50257 rows are used by the GPT-2 tokenizer. tie_word_embeddings false. Trained on the Pile for 402 billion tokens over 383,500 steps on a TPU v3-256 pod. The card prints the parameter count 6,053,381,344.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-J-6B is EleutherAI's 6,053,381,344-parameter Apache-2.0 language model, trained on the Pile. Weights remain public. This dossier uses 2021-06-09 as the release day and records the other published dates. Collected 2026-10-15.", "what_it_is_for": "The card says GPT-J learns an English representation and is best at generating text from a prompt. It is a research model, not a product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/gpt-neo/gpt-neo-1-3b": { "id": "eleutherai/gpt-neo/gpt-neo-1-3b", "name": "GPT-Neo 1.3B", "maker": "eleutherai", "family": "eleutherai/gpt-neo", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2021-03-21", "context_length": 2048, "max_output_tokens": null, "parameters": "1.3B", "architecture": "GPTNeoForCausalLM. 24 layers, hidden size 2048, 16 heads. Alternating global and local attention, window 256. GPT-2 BPE, vocab 50257. gelu_new. Context 2048. Card: trained on the Pile for 380 billion tokens over 362,000 steps. Mesh-TensorFlow. Config from EleutherAI/gpt-neo-1.3B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-Neo 1.3B is an EleutherAI decoder trained on the Pile and released on 2021-03-21 under MIT with the 2.7B model. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "The v1.0 note calls it a pretrained GPT-Neo model on the Pile. The card says it is for generating text from a prompt and for features, not as an instruction product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/gpt-neo/gpt-neo-125m": { "id": "eleutherai/gpt-neo/gpt-neo-125m", "name": "GPT-Neo 125M", "maker": "eleutherai", "family": "eleutherai/gpt-neo", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "125M", "architecture": "GPTNeoForCausalLM. 12 layers, hidden size 768, 12 heads. Alternating global and local attention with local window 256. GPT-2 BPE, vocab 50257. gelu_new. Context 2048. The card says it was trained on the Pile for 300 billion tokens over 572,300 steps. Layer counts are from config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-Neo 125M is EleutherAI's smallest named GPT-Neo language model, an MIT-licensed decoder trained on the Pile. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "The model card says it learns an English representation and is best at generating text from a prompt. It is a research checkpoint, not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/gpt-neo/gpt-neo-2-7b": { "id": "eleutherai/gpt-neo/gpt-neo-2-7b", "name": "GPT-Neo 2.7B", "maker": "eleutherai", "family": "eleutherai/gpt-neo", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2021-03-21", "context_length": 2048, "max_output_tokens": null, "parameters": "2.7B", "architecture": "GPTNeoForCausalLM. 32 layers, hidden size 2560, 20 heads. Alternating global and local attention, window 256. GPT-2 BPE, vocab 50257. gelu_new. Context 2048. Card: trained for 420 billion tokens over 400,000 steps on the Pile. Config from EleutherAI/gpt-neo-2.7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-Neo 2.7B is EleutherAI's largest GPT-Neo model, released 2021-03-21 with the 1.3B checkpoint under MIT. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "A pretrained autoregressive research model. The card says it is best at continuing a prompt and is not a moderated product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/gpt-neox/gpt-neox-20b": { "id": "eleutherai/gpt-neox/gpt-neox-20b", "name": "GPT-NeoX-20B", "maker": "eleutherai", "family": "eleutherai/gpt-neox", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-02-09", "context_length": 2048, "max_output_tokens": null, "parameters": "20B", "architecture": "GPTNeoXForCausalLM. 44 layers, hidden size 6144, 64 heads, intermediate size 24576. rotary_pct 0.25, rotary base 10000. Vocab 50432. Context 2048. Untied embeddings. Hub config torch_dtype float16. Trained with the GPT-NeoX library on the Pile using CoreWeave GPUs. The exact integer parameter count was not copied from the paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-NeoX-20B is EleutherAI's 20 billion parameter Apache-2.0 language model. Downloadable weights were published on 2022-02-09. They remain a public research checkpoint. Collected 2026-10-15.", "what_it_is_for": "The announcement presents a general-purpose pretrained autoregressive model for research, including research on safer use, and says not to deploy it in production without care.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/llemma/llemma-34b": { "id": "eleutherai/llemma/llemma-34b", "name": "Llemma 34B", "maker": "eleutherai", "family": "eleutherai/llemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-10-16", "context_length": 4096, "max_output_tokens": null, "parameters": "34B", "architecture": "Continued pretraining of Code Llama 34B on Proof-Pile-2 for 50B tokens. LlamaForCausalLM. 48 layers, hidden 8192, 64 heads, 8 KV heads, intermediate 22016. rope_theta 1000000. Context 4096. Vocab 32000. bfloat16. The blog says this run used about 47,000 A100 hours.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llemma 34B is EleutherAI's larger math model, announced with Llemma 7B on 2023-10-16. It continues Code Llama 34B for 50 billion tokens. Weights are public under the Llama 2 license. Collected 2026-10-15.", "what_it_is_for": "Same math-research role as Llemma 7B. The blog highlights chain-of-thought MATH solutions and in-context formal proving.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/llemma/llemma-7b": { "id": "eleutherai/llemma/llemma-7b", "name": "Llemma 7B", "maker": "eleutherai", "family": "eleutherai/llemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-10-16", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "Continued pretraining of Code Llama 7B on Proof-Pile-2 for 200B tokens. LlamaForCausalLM. 32 layers, hidden 4096, 32 heads, 32 KV heads, intermediate 11008. Context in config max_position_embeddings 4096. Vocab 32016. bfloat16. Untied embeddings. The blog says this run used about 23,000 A100 hours.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llemma 7B is EleutherAI's 7 billion parameter math model, announced 2023-10-16. It continues Code Llama 7B on Proof-Pile-2. Weights are public under the Llama 2 license. Collected 2026-10-15.", "what_it_is_for": "The blog releases Llemma as an open language model for mathematics, including chain-of-thought problem solving and use of calculators, computer algebra, and formal provers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pile-t5/pile-t5-base": { "id": "eleutherai/pile-t5/pile-t5-base", "name": "Pile-T5 Base", "maker": "eleutherai", "family": "eleutherai/pile-t5", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder in the T5.1.1 / UMT5 stack. 12 encoder layers and 12 decoder layers, d_model 768, d_ff 2048, 12 heads. Gated GELU. Vocab 32128. Relative attention buckets 32 and max distance 128. The blog says the models use the Llama tokenizer and the same hyperparameters as original T5, and that they were trained on the Pile with T5X. The Base config names LlamaTokenizerFast while vocab_size is 32128. Those two facts are both on the retrieved files.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pile-T5 Base is an EleutherAI encoder-decoder trained on the Pile, announced 2024-04-15. Apache-2.0 weights are public. Collected 2026-10-15.", "what_it_is_for": "The blog introduces Pile-T5 to fix the original T5 tokenizer's weak code coverage and to train on the Pile. Primary use is research and further fine-tuning. The authors report stronger code-task scores than T5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pile-t5/pile-t5-large": { "id": "eleutherai/pile-t5/pile-t5-large", "name": "Pile-T5 Large", "maker": "eleutherai", "family": "eleutherai/pile-t5", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder in the T5.1.1 / UMT5 stack. 24 encoder layers and 24 decoder layers, d_model 1024, d_ff 2816, 16 heads. Gated GELU. Vocab 32128. Relative attention buckets 32 and max distance 128. The blog says the models use the Llama tokenizer and the same hyperparameters as original T5, and that they were trained on the Pile with T5X. The Base config names LlamaTokenizerFast while vocab_size is 32128. Those two facts are both on the retrieved files.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pile-T5 Large is an EleutherAI encoder-decoder trained on the Pile, announced 2024-04-15. Apache-2.0 weights are public. Collected 2026-10-15.", "what_it_is_for": "The blog introduces Pile-T5 to fix the original T5 tokenizer's weak code coverage and to train on the Pile. Primary use is research and further fine-tuning. The authors report stronger code-task scores than T5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pile-t5/pile-t5-xl": { "id": "eleutherai/pile-t5/pile-t5-xl", "name": "Pile-T5 XL", "maker": "eleutherai", "family": "eleutherai/pile-t5", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder in the T5.1.1 / UMT5 stack. 24 encoder layers and 24 decoder layers, d_model 2048, d_ff 5120, 32 heads. Gated GELU. Vocab 32128. Relative attention buckets 32 and max distance 128. The blog says the models use the Llama tokenizer and the same hyperparameters as original T5, and that they were trained on the Pile with T5X. The Base config names LlamaTokenizerFast while vocab_size is 32128. Those two facts are both on the retrieved files.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pile-T5 XL is an EleutherAI encoder-decoder trained on the Pile, announced 2024-04-15. Apache-2.0 weights are public. Collected 2026-10-15.", "what_it_is_for": "The blog introduces Pile-T5 to fix the original T5 tokenizer's weak code coverage and to train on the Pile. Primary use is research and further fine-tuning. The authors report stronger code-task scores than T5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pile-t5/pile-t5-xxl": { "id": "eleutherai/pile-t5/pile-t5-xxl", "name": "Pile-T5 XXL", "maker": "eleutherai", "family": "eleutherai/pile-t5", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder in the T5.1.1 / UMT5 stack. 24 encoder layers and 24 decoder layers, d_model 4096, d_ff 10240, 64 heads. Gated GELU. Vocab 32128. Relative attention buckets 32 and max distance 128. The blog says the models use the Llama tokenizer and the same hyperparameters as original T5, and that they were trained on the Pile with T5X. The Base config names LlamaTokenizerFast while vocab_size is 32128. Those two facts are both on the retrieved files.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pile-T5 XXL is an EleutherAI encoder-decoder trained on the Pile, announced 2024-04-15. Apache-2.0 weights are public. Collected 2026-10-15.", "what_it_is_for": "The blog introduces Pile-T5 to fix the original T5 tokenizer's weak code coverage and to train on the Pile. Primary use is research and further fine-tuning. The authors report stronger code-task scores than T5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/polyglot/polyglot-ko-1-3b": { "id": "eleutherai/polyglot/polyglot-ko-1-3b", "name": "Polyglot-Ko 1.3B", "maker": "eleutherai", "family": "eleutherai/polyglot", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-09-15", "context_length": 2048, "max_output_tokens": null, "parameters": "1331810304", "architecture": "GPT-NeoX decoder for Korean. 24 layers, d_model 2048, d_ff 8192, 16 heads. rotary_pct 0.5 from config.json. Context 2048. vocab_size 30080. The 12.8B card says 30,003 of 30,080 vocab rows are used and that RoPE is applied to 64 dimensions per head. This size was trained for 213 billion tokens over 102,000 steps on 256 A100 GPUs with GPT-NeoX. The 12.8B card describes 863 GB of Korean text, 1.2 TB before processing, collected by TUNiB and not released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Polyglot-Ko 1.3B is an EleutherAI Apache-2.0 Korean language model trained with GPT-NeoX. The Hub repo was created on 2022-09-15. The technical report is arXiv:2306.02254. Collected 2026-10-15.", "what_it_is_for": "The project README says Polyglot models aim for stronger non-English performance than multilingual models that are weak outside English. Polyglot-Ko is the Korean series, built with TUNiB data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/polyglot/polyglot-ko-12-8b": { "id": "eleutherai/polyglot/polyglot-ko-12-8b", "name": "Polyglot-Ko 12.8B", "maker": "eleutherai", "family": "eleutherai/polyglot", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-10-14", "context_length": 2048, "max_output_tokens": null, "parameters": "12898631680", "architecture": "GPT-NeoX decoder for Korean. 40 layers, d_model 5120, d_ff 20480, 40 heads. rotary_pct 0.5 from config.json. Context 2048. vocab_size 30080. The 12.8B card says 30,003 of 30,080 vocab rows are used and that RoPE is applied to 64 dimensions per head. This size was trained for 167 billion tokens over 301,000 steps on 256 A100 GPUs with GPT-NeoX. The 12.8B card describes 863 GB of Korean text, 1.2 TB before processing, collected by TUNiB and not released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Polyglot-Ko 12.8B is an EleutherAI Apache-2.0 Korean language model trained with GPT-NeoX. The Hub repo was created on 2022-10-14. The technical report is arXiv:2306.02254. Collected 2026-10-15.", "what_it_is_for": "The project README says Polyglot models aim for stronger non-English performance than multilingual models that are weak outside English. Polyglot-Ko is the Korean series, built with TUNiB data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/polyglot/polyglot-ko-3-8b": { "id": "eleutherai/polyglot/polyglot-ko-3-8b", "name": "Polyglot-Ko 3.8B", "maker": "eleutherai", "family": "eleutherai/polyglot", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-09-09", "context_length": 2048, "max_output_tokens": null, "parameters": "3809974272", "architecture": "GPT-NeoX decoder for Korean. 32 layers, d_model 3072, d_ff 12288, 24 heads. rotary_pct 0.5 from config.json. Context 2048. vocab_size 30080. The 12.8B card says 30,003 of 30,080 vocab rows are used and that RoPE is applied to 64 dimensions per head. This size was trained for 219 billion tokens over 105,000 steps on 256 A100 GPUs with GPT-NeoX. The 12.8B card describes 863 GB of Korean text, 1.2 TB before processing, collected by TUNiB and not released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Polyglot-Ko 3.8B is an EleutherAI Apache-2.0 Korean language model trained with GPT-NeoX. The Hub repo was created on 2022-09-09. The technical report is arXiv:2306.02254. Collected 2026-10-15.", "what_it_is_for": "The project README says Polyglot models aim for stronger non-English performance than multilingual models that are weak outside English. Polyglot-Ko is the Korean series, built with TUNiB data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/polyglot/polyglot-ko-5-8b": { "id": "eleutherai/polyglot/polyglot-ko-5-8b", "name": "Polyglot-Ko 5.8B", "maker": "eleutherai", "family": "eleutherai/polyglot", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-09-22", "context_length": 2048, "max_output_tokens": null, "parameters": "5885059072", "architecture": "GPT-NeoX decoder for Korean. 28 layers, d_model 4096, d_ff 16384, 16 heads. rotary_pct 0.25 from config.json. Context 2048. vocab_size 30080. The 12.8B card says 30,003 of 30,080 vocab rows are used and that RoPE is applied to 64 dimensions per head. This size was trained for 172 billion tokens over 320,000 steps on 256 A100 GPUs with GPT-NeoX. The 12.8B card describes 863 GB of Korean text, 1.2 TB before processing, collected by TUNiB and not released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Polyglot-Ko 5.8B is an EleutherAI Apache-2.0 Korean language model trained with GPT-NeoX. The Hub repo was created on 2022-09-22. The technical report is arXiv:2306.02254. Collected 2026-10-15.", "what_it_is_for": "The project README says Polyglot models aim for stronger non-English performance than multilingual models that are weak outside English. Polyglot-Ko is the Korean series, built with TUNiB data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/polypythia/polypythia-14m": { "id": "eleutherai/polypythia/polypythia-14m", "name": "PolyPythia 14M", "maker": "eleutherai", "family": "eleutherai/polypythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-12", "context_length": 2048, "max_output_tokens": null, "parameters": "14M", "architecture": "Same GPT-NeoX shape as Pythia 14M: 6 layers, model dim 128, 4 heads. The paper and the Hub card say these are the standard Pile runs, not the deduplicated Pile, trained on 300B tokens. Nine new seeds plus the original seed 1234, which is the Pythia 14M checkpoint in the pythia folder. About 154 checkpoints per run. The paper says the 45 new runs add about 7,000 checkpoints.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PolyPythia 14M is EleutherAI's multi-seed extension of Pythia 14M, released with the March 2025 PolyPythias paper for studying pretraining stability. Apache-2.0 weights are public. Collected 2026-10-15.", "what_it_is_for": "The paper uses the seeds to study how initialization and data order change downstream scores, linguistic representations, and training phases. These are research replicates, not a product line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/polypythia/polypythia-160m": { "id": "eleutherai/polypythia/polypythia-160m", "name": "PolyPythia 160M", "maker": "eleutherai", "family": "eleutherai/polypythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-12", "context_length": 2048, "max_output_tokens": null, "parameters": "160M", "architecture": "Same GPT-NeoX shape as Pythia 160M: 12 layers, model dim 768, 12 heads. The paper and the Hub card say these are the standard Pile runs, not the deduplicated Pile, trained on 300B tokens. Nine new seeds plus the original seed 1234, which is the Pythia 160M checkpoint in the pythia folder. About 154 checkpoints per run. The paper says the 45 new runs add about 7,000 checkpoints.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PolyPythia 160M is EleutherAI's multi-seed extension of Pythia 160M, released with the March 2025 PolyPythias paper for studying pretraining stability. Apache-2.0 weights are public. Collected 2026-10-15.", "what_it_is_for": "The paper uses the seeds to study how initialization and data order change downstream scores, linguistic representations, and training phases. These are research replicates, not a product line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/polypythia/polypythia-31m": { "id": "eleutherai/polypythia/polypythia-31m", "name": "PolyPythia 31M", "maker": "eleutherai", "family": "eleutherai/polypythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-12", "context_length": 2048, "max_output_tokens": null, "parameters": "31M", "architecture": "Same GPT-NeoX shape as Pythia 31M: 6 layers, model dim 256, 8 heads. The paper and the Hub card say these are the standard Pile runs, not the deduplicated Pile, trained on 300B tokens. Nine new seeds plus the original seed 1234, which is the Pythia 31M checkpoint in the pythia folder. About 154 checkpoints per run. The paper says the 45 new runs add about 7,000 checkpoints.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PolyPythia 31M is EleutherAI's multi-seed extension of Pythia 31M, released with the March 2025 PolyPythias paper for studying pretraining stability. Apache-2.0 weights are public. Collected 2026-10-15.", "what_it_is_for": "The paper uses the seeds to study how initialization and data order change downstream scores, linguistic representations, and training phases. These are research replicates, not a product line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/polypythia/polypythia-410m": { "id": "eleutherai/polypythia/polypythia-410m", "name": "PolyPythia 410M", "maker": "eleutherai", "family": "eleutherai/polypythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-12", "context_length": 2048, "max_output_tokens": null, "parameters": "410M", "architecture": "Same GPT-NeoX shape as Pythia 410M: 24 layers, model dim 1024, 16 heads. The paper and the Hub card say these are the standard Pile runs, not the deduplicated Pile, trained on 300B tokens. Nine new seeds plus the original seed 1234, which is the Pythia 410M checkpoint in the pythia folder. About 154 checkpoints per run. The paper says the 45 new runs add about 7,000 checkpoints.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PolyPythia 410M is EleutherAI's multi-seed extension of Pythia 410M, released with the March 2025 PolyPythias paper for studying pretraining stability. Apache-2.0 weights are public. Collected 2026-10-15.", "what_it_is_for": "The paper uses the seeds to study how initialization and data order change downstream scores, linguistic representations, and training phases. These are research replicates, not a product line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/polypythia/polypythia-70m": { "id": "eleutherai/polypythia/polypythia-70m", "name": "PolyPythia 70M", "maker": "eleutherai", "family": "eleutherai/polypythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-12", "context_length": 2048, "max_output_tokens": null, "parameters": "70M", "architecture": "Same GPT-NeoX shape as Pythia 70M: 6 layers, model dim 512, 8 heads. The paper and the Hub card say these are the standard Pile runs, not the deduplicated Pile, trained on 300B tokens. Nine new seeds plus the original seed 1234, which is the Pythia 70M checkpoint in the pythia folder. About 154 checkpoints per run. The paper says the 45 new runs add about 7,000 checkpoints.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PolyPythia 70M is EleutherAI's multi-seed extension of Pythia 70M, released with the March 2025 PolyPythias paper for studying pretraining stability. Apache-2.0 weights are public. Collected 2026-10-15.", "what_it_is_for": "The paper uses the seeds to study how initialization and data order change downstream scores, linguistic representations, and training phases. These are research replicates, not a product line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-1-4b": { "id": "eleutherai/pythia/pythia-1-4b", "name": "Pythia 1.4B", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "1.4B total (1414647808); 1208602624 non-embedding", "architecture": "GPT-NeoX decoder. 24 layers, model dim 2048, 16 heads, learning rate 2.0e-4, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (just under 1 epoch on the Pile). Apache-2.0. Training corpus: the Pile, which the 12B card says was not deduplicated. vocab_size not re-read from this config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 1.4B is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the Pile, which the 12B card says was not deduplicated. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the standard-Pile run. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-1-4b-deduped": { "id": "eleutherai/pythia/pythia-1-4b-deduped", "name": "Pythia 1.4B deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "1.4B total (1414647808); 1208602624 non-embedding", "architecture": "GPT-NeoX decoder. 24 layers, model dim 2048, 16 heads, learning rate 2.0e-4, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (about 1.5 epochs on the deduplicated Pile). Apache-2.0. Training corpus: the globally deduplicated Pile. vocab_size not re-read from this config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 1.4B deduped is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the globally deduplicated Pile. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the deduplicated-Pile twin. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-12b": { "id": "eleutherai/pythia/pythia-12b", "name": "Pythia 12B", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "12B total (11846072320); 11327027200 non-embedding", "architecture": "GPT-NeoX decoder. 36 layers, model dim 5120, 40 heads, learning rate 1.2e-4, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (just under 1 epoch on the Pile). Apache-2.0. Training corpus: the Pile, which the 12B card says was not deduplicated. config.json vocab_size 50688, intermediate 20480.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 12B is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the Pile, which the 12B card says was not deduplicated. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the standard-Pile run. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-12b-deduped": { "id": "eleutherai/pythia/pythia-12b-deduped", "name": "Pythia 12B deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "12B total (11846072320); 11327027200 non-embedding", "architecture": "GPT-NeoX decoder. 36 layers, model dim 5120, 40 heads, learning rate 1.2e-4, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (about 1.5 epochs on the deduplicated Pile). Apache-2.0. Training corpus: the globally deduplicated Pile. config.json vocab_size 50688, intermediate 20480.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 12B deduped is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the globally deduplicated Pile. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the deduplicated-Pile twin. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-14m": { "id": "eleutherai/pythia/pythia-14m", "name": "Pythia 14M", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-02-27", "context_length": 2048, "max_output_tokens": null, "parameters": "14M name; 1189888 non-embedding. Exact total was not in the 70M-12B total-parameter table.", "architecture": "GPT-NeoX decoder. 6 layers, model dim 128, 4 heads, learning rate 1.0e-3, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (just under 1 epoch on the Pile). Apache-2.0. Training corpus: the Pile, which the 12B card says was not deduplicated. config.json vocab_size 50304, intermediate 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 14M is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the Pile, which the 12B card says was not deduplicated. Weights remain public. These two sizes were trained after the April 2023 paper. On 2026-02-27 the card says this URL was corrected to the standard Pile. Before that, the same URL held the deduplicated run. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the standard-Pile run. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-14m-deduped": { "id": "eleutherai/pythia/pythia-14m-deduped", "name": "Pythia 14M deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-07-19", "context_length": 2048, "max_output_tokens": null, "parameters": "14M name; 1189888 non-embedding. Exact total was not in the 70M-12B total-parameter table.", "architecture": "GPT-NeoX decoder. 6 layers, model dim 128, 4 heads, learning rate 1.0e-3, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (about 1.5 epochs on the deduplicated Pile). Apache-2.0. Training corpus: the globally deduplicated Pile. config.json vocab_size 50304, intermediate 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 14M deduped is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the globally deduplicated Pile. Weights remain public. These two sizes were trained after the April 2023 paper. The deduped Hub repo dates from 2023-07-19. Until 2026-02-27 the non-deduped URL had been serving this deduplicated run. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the deduplicated-Pile twin. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-160m": { "id": "eleutherai/pythia/pythia-160m", "name": "Pythia 160M", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "160M total (162322944); 85056000 non-embedding", "architecture": "GPT-NeoX decoder. 12 layers, model dim 768, 12 heads, learning rate 6.0e-4, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (just under 1 epoch on the Pile). Apache-2.0. Training corpus: the Pile, which the 12B card says was not deduplicated. vocab_size not re-read from this config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 160M is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the Pile, which the 12B card says was not deduplicated. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the standard-Pile run. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-160m-deduped": { "id": "eleutherai/pythia/pythia-160m-deduped", "name": "Pythia 160M deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "160M total (162322944); 85056000 non-embedding", "architecture": "GPT-NeoX decoder. 12 layers, model dim 768, 12 heads, learning rate 6.0e-4, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (about 1.5 epochs on the deduplicated Pile). Apache-2.0. Training corpus: the globally deduplicated Pile. vocab_size not re-read from this config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 160M deduped is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the globally deduplicated Pile. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the deduplicated-Pile twin. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-1b": { "id": "eleutherai/pythia/pythia-1b", "name": "Pythia 1B", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "1B total (1011781632); 805736448 non-embedding", "architecture": "GPT-NeoX decoder. 16 layers, model dim 2048, 8 heads, learning rate 3.0e-4, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (just under 1 epoch on the Pile). Apache-2.0. Training corpus: the Pile, which the 12B card says was not deduplicated. vocab_size not re-read from this config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 1B is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the Pile, which the 12B card says was not deduplicated. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the standard-Pile run. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-1b-deduped": { "id": "eleutherai/pythia/pythia-1b-deduped", "name": "Pythia 1B deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "1B total (1011781632); 805736448 non-embedding", "architecture": "GPT-NeoX decoder. 16 layers, model dim 2048, 8 heads, learning rate 3.0e-4, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (about 1.5 epochs on the deduplicated Pile). Apache-2.0. Training corpus: the globally deduplicated Pile. vocab_size not re-read from this config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 1B deduped is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the globally deduplicated Pile. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the deduplicated-Pile twin. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-2-8b": { "id": "eleutherai/pythia/pythia-2-8b", "name": "Pythia 2.8B", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "2.8B total (2775208960); 2517652480 non-embedding", "architecture": "GPT-NeoX decoder. 32 layers, model dim 2560, 32 heads, learning rate 1.6e-4, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (just under 1 epoch on the Pile). Apache-2.0. Training corpus: the Pile, which the 12B card says was not deduplicated. vocab_size not re-read from this config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 2.8B is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the Pile, which the 12B card says was not deduplicated. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the standard-Pile run. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-2-8b-deduped": { "id": "eleutherai/pythia/pythia-2-8b-deduped", "name": "Pythia 2.8B deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "2.8B total (2775208960); 2517652480 non-embedding", "architecture": "GPT-NeoX decoder. 32 layers, model dim 2560, 32 heads, learning rate 1.6e-4, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (about 1.5 epochs on the deduplicated Pile). Apache-2.0. Training corpus: the globally deduplicated Pile. vocab_size not re-read from this config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 2.8B deduped is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the globally deduplicated Pile. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the deduplicated-Pile twin. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-31m": { "id": "eleutherai/pythia/pythia-31m", "name": "Pythia 31M", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-02-27", "context_length": 2048, "max_output_tokens": null, "parameters": "31M name; 4739072 non-embedding. Exact total was not in the 70M-12B total-parameter table.", "architecture": "GPT-NeoX decoder. 6 layers, model dim 256, 8 heads, learning rate 1.0e-3, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (just under 1 epoch on the Pile). Apache-2.0. Training corpus: the Pile, which the 12B card says was not deduplicated. config.json vocab_size 50304, intermediate 1024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 31M is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the Pile, which the 12B card says was not deduplicated. Weights remain public. These two sizes were trained after the April 2023 paper. On 2026-02-27 the card says this URL was corrected to the standard Pile. Before that, the same URL held the deduplicated run. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the standard-Pile run. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-31m-deduped": { "id": "eleutherai/pythia/pythia-31m-deduped", "name": "Pythia 31M deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-07-19", "context_length": 2048, "max_output_tokens": null, "parameters": "31M name; 4739072 non-embedding. Exact total was not in the 70M-12B total-parameter table.", "architecture": "GPT-NeoX decoder. 6 layers, model dim 256, 8 heads, learning rate 1.0e-3, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (about 1.5 epochs on the deduplicated Pile). Apache-2.0. Training corpus: the globally deduplicated Pile. config.json vocab_size 50304, intermediate 1024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 31M deduped is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the globally deduplicated Pile. Weights remain public. These two sizes were trained after the April 2023 paper. The deduped Hub repo dates from 2023-07-19. Until 2026-02-27 the non-deduped URL had been serving this deduplicated run. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the deduplicated-Pile twin. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-410m": { "id": "eleutherai/pythia/pythia-410m", "name": "Pythia 410M", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "410M total (405334016); 302311424 non-embedding", "architecture": "GPT-NeoX decoder. 24 layers, model dim 1024, 16 heads, learning rate 3.0e-4, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (just under 1 epoch on the Pile). Apache-2.0. Training corpus: the Pile, which the 12B card says was not deduplicated. vocab_size not re-read from this config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 410M is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the Pile, which the 12B card says was not deduplicated. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the standard-Pile run. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-410m-deduped": { "id": "eleutherai/pythia/pythia-410m-deduped", "name": "Pythia 410M deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "410M total (405334016); 302311424 non-embedding", "architecture": "GPT-NeoX decoder. 24 layers, model dim 1024, 16 heads, learning rate 3.0e-4, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (about 1.5 epochs on the deduplicated Pile). Apache-2.0. Training corpus: the globally deduplicated Pile. vocab_size not re-read from this config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 410M deduped is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the globally deduplicated Pile. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the deduplicated-Pile twin. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-6-9b": { "id": "eleutherai/pythia/pythia-6-9b", "name": "Pythia 6.9B", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "6.9B total (6857302016); 6444163072 non-embedding", "architecture": "GPT-NeoX decoder. 32 layers, model dim 4096, 32 heads, learning rate 1.2e-4, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (just under 1 epoch on the Pile). Apache-2.0. Training corpus: the Pile, which the 12B card says was not deduplicated. config.json vocab_size 50432, intermediate 16384.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 6.9B is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the Pile, which the 12B card says was not deduplicated. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the standard-Pile run. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-6-9b-deduped": { "id": "eleutherai/pythia/pythia-6-9b-deduped", "name": "Pythia 6.9B deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "6.9B total (6857302016); 6444163072 non-embedding", "architecture": "GPT-NeoX decoder. 32 layers, model dim 4096, 32 heads, learning rate 1.2e-4, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (about 1.5 epochs on the deduplicated Pile). Apache-2.0. Training corpus: the globally deduplicated Pile. config.json vocab_size 50432, intermediate 16384.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 6.9B deduped is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the globally deduplicated Pile. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the deduplicated-Pile twin. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-70m": { "id": "eleutherai/pythia/pythia-70m", "name": "Pythia 70M", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "70M total (70426624); 18915328 non-embedding", "architecture": "GPT-NeoX decoder. 6 layers, model dim 512, 8 heads, learning rate 1.0e-3, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (just under 1 epoch on the Pile). Apache-2.0. Training corpus: the Pile, which the 12B card says was not deduplicated. config.json vocab_size 50304, intermediate 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 70M is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the Pile, which the 12B card says was not deduplicated. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the standard-Pile run. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eleutherai/pythia/pythia-70m-deduped": { "id": "eleutherai/pythia/pythia-70m-deduped", "name": "Pythia 70M deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-03", "context_length": 2048, "max_output_tokens": null, "parameters": "70M total (70426624); 18915328 non-embedding", "architecture": "GPT-NeoX decoder. 6 layers, model dim 512, 8 heads, learning rate 1.0e-3, batch 2,097,152 tokens. Context 2048. rotary_pct 0.25 on fetched configs. Untied embeddings. Parallel residual on fetched configs. Same data order across sizes. Each documented suite run saw 299,892,736,000 tokens (about 1.5 epochs on the deduplicated Pile). Apache-2.0. Training corpus: the globally deduplicated Pile. config.json vocab_size 50304, intermediate 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pythia 70M deduped is an EleutherAI Apache-2.0 decoder in the Pythia scaling suite, trained on the globally deduplicated Pile. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Pythia is a research suite for interpretability, training dynamics, and controlled scaling. The card says downstream quality was not the design goal. This checkpoint is the deduplicated-Pile twin. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/dubbing/dubbing-v1": { "id": "elevenlabs/dubbing/dubbing-v1", "name": "Dubbing v1", "maker": "elevenlabs", "family": "elevenlabs/dubbing", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Legacy ElevenLabs dubbing model, model id dubbing_v1. Still selectable, and required for Dubbing Studio projects. Automatic Dubbing defaults to Dubbing v2. Language coverage matches Eleven v3, without regional dialect tags. Closed weights. Release date not found. Collected 2026-10-15.", "what_it_is_for": "Legacy ElevenLabs dubbing model, model id dubbing_v1. Still selectable, and required for Dubbing Studio projects. Automatic Dubbing defaults to Dubbing v2. Language coverage matches Eleven v3, without regional dialect tags. Closed weights. Release date not found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/dubbing/dubbing-v2": { "id": "elevenlabs/dubbing/dubbing-v2", "name": "Dubbing v2", "maker": "elevenlabs", "family": "elevenlabs/dubbing", "status": "preview", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "2026-05-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Audio-to-audio dubbing model. It conditions on the source performance rather than a transcript alone. Parameter count not published.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ElevenLabs audio-to-audio dubbing model, model id dubbing_v2. Launched 28 May 2026 and still marked alpha. API access opened 6 August 2026. It keeps timing and performance from the source instead of reading a transcript only. Closed weights. Collected 2026-10-15.", "what_it_is_for": "ElevenLabs audio-to-audio dubbing model, model id dubbing_v2. Launched 28 May 2026 and still marked alpha. API access opened 6 August 2026. It keeps timing and performance from the source instead of reading a transcript only. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/eleven/eleven-flash-v2": { "id": "elevenlabs/eleven/eleven-flash-v2", "name": "Eleven Flash v2", "maker": "elevenlabs", "family": "elevenlabs/eleven", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "2024-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "English-only low-latency speech model, model id eleven_flash_v2. Released 18 December 2024 with Flash v2.5. Docs say it generates in about 75ms and replaces Turbo v2. Closed weights. Collected 2026-10-15.", "what_it_is_for": "English-only low-latency speech model, model id eleven_flash_v2. Released 18 December 2024 with Flash v2.5. Docs say it generates in about 75ms and replaces Turbo v2. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/eleven/eleven-flash-v2-5": { "id": "elevenlabs/eleven/eleven-flash-v2-5", "name": "Eleven Flash v2.5", "maker": "elevenlabs", "family": "elevenlabs/eleven", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "2024-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "Fast multilingual speech model, model id eleven_flash_v2_5. Released 18 December 2024. About 75ms latency across 32 languages, and the recommended replacement for Turbo v2.5. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Fast multilingual speech model, model id eleven_flash_v2_5. Released 18 December 2024. About 75ms latency across 32 languages, and the recommended replacement for Turbo v2.5. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/eleven/eleven-monolingual-v1": { "id": "elevenlabs/eleven/eleven-monolingual-v1", "name": "Eleven Monolingual v1", "maker": "elevenlabs", "family": "elevenlabs/eleven", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "First public ElevenLabs English speech model, model id eleven_monolingual_v1. Deprecated with Multilingual v1. Removal was announced for 9 July 2026. Closed weights. Exact release day was not verified. Collected 2026-10-15.", "what_it_is_for": "First public ElevenLabs English speech model, model id eleven_monolingual_v1. Deprecated with Multilingual v1. Removal was announced for 9 July 2026. Closed weights. Exact release day was not verified. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/eleven/eleven-multilingual-v1": { "id": "elevenlabs/eleven/eleven-multilingual-v1", "name": "Eleven Multilingual v1", "maker": "elevenlabs", "family": "elevenlabs/eleven", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "2023-04-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "First multilingual ElevenLabs speech model, model id eleven_multilingual_v1. Released 27 April 2023 for English plus seven languages. Deprecated, with removal announced for 9 July 2026. Closed weights. Collected 2026-10-15.", "what_it_is_for": "First multilingual ElevenLabs speech model, model id eleven_multilingual_v1. Released 27 April 2023 for English plus seven languages. Deprecated, with removal announced for 9 July 2026. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/eleven/eleven-multilingual-v2": { "id": "elevenlabs/eleven/eleven-multilingual-v2", "name": "Eleven Multilingual v2", "maker": "elevenlabs", "family": "elevenlabs/eleven", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "2023-08-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Long-form ElevenLabs speech model, model id eleven_multilingual_v2. Released 22 August 2023 when the platform left beta. Still supported for stable narration across 29 languages. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Long-form ElevenLabs speech model, model id eleven_multilingual_v2. Released 22 August 2023 when the platform left beta. Still supported for stable narration across 29 languages. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/eleven/eleven-turbo-v2": { "id": "elevenlabs/eleven/eleven-turbo-v2", "name": "Eleven Turbo v2", "maker": "elevenlabs", "family": "elevenlabs/eleven", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deprecated English-only low-latency speech model, model id eleven_turbo_v2. ElevenLabs says Flash v2 replaces it. The undated launch post called it about 400ms and more than twice as fast as the v1 models. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Deprecated English-only low-latency speech model, model id eleven_turbo_v2. ElevenLabs says Flash v2 replaces it. The undated launch post called it about 400ms and more than twice as fast as the v1 models. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/eleven/eleven-turbo-v2-5": { "id": "elevenlabs/eleven/eleven-turbo-v2-5", "name": "Eleven Turbo v2.5", "maker": "elevenlabs", "family": "elevenlabs/eleven", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "2024-07-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deprecated low-latency multilingual speech model, model id eleven_turbo_v2_5. Introduced 19 July 2024. ElevenLabs says Flash v2.5 matches it at lower latency and should be used instead. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Deprecated low-latency multilingual speech model, model id eleven_turbo_v2_5. Introduced 19 July 2024. ElevenLabs says Flash v2.5 matches it at lower latency and should be used instead. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/eleven/eleven-v3": { "id": "elevenlabs/eleven/eleven-v3", "name": "Eleven v3", "maker": "elevenlabs", "family": "elevenlabs/eleven", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "2025-06-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.08 }, "summary_intro": "Expressive ElevenLabs text-to-speech model, model id eleven_v3. Alpha on 3 June 2025, generally available 2 February 2026. Still offered, and no longer the flagship after Eleven v4. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Expressive ElevenLabs text-to-speech model, model id eleven_v3. Alpha on 3 June 2025, generally available 2 February 2026. Still offered, and no longer the flagship after Eleven v4. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/eleven/eleven-v3-conversational": { "id": "elevenlabs/eleven/eleven-v3-conversational", "name": "Eleven v3 Conversational", "maker": "elevenlabs", "family": "elevenlabs/eleven", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Realtime expressive speech model, model id eleven_v3_conversational. ElevenLabs calls it the previous-generation realtime model, at about 280ms, across the Eleven v3 language set. Closed weights. Official ship date not found. Collected 2026-10-15.", "what_it_is_for": "Realtime expressive speech model, model id eleven_v3_conversational. ElevenLabs calls it the previous-generation realtime model, at about 280ms, across the Eleven v3 language set. Closed weights. Official ship date not found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/eleven/eleven-v4": { "id": "elevenlabs/eleven/eleven-v4", "name": "Eleven v4", "maker": "elevenlabs", "family": "elevenlabs/eleven", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "2026-09-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Undisclosed. Launch post says an entirely new architecture versus earlier Eleven speech models. Parameter count not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.08 }, "summary_intro": "ElevenLabs' flagship text-to-speech model as of 2026-10-15. Model id eleven_v4. Launched 28 September 2026 with a Turbo sibling. Closed weights. Collected 2026-10-15.", "what_it_is_for": "ElevenLabs' flagship text-to-speech model as of 2026-10-15. Model id eleven_v4. Launched 28 September 2026 with a Turbo sibling. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/eleven/eleven-v4-turbo": { "id": "elevenlabs/eleven/eleven-v4-turbo", "name": "Eleven v4 Turbo", "maker": "elevenlabs", "family": "elevenlabs/eleven", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "2026-09-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Undisclosed low-latency variant of the Eleven v4 architecture. Parameter count not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.04 }, "summary_intro": "Low-latency Eleven v4 variant for agents and other real-time speech. Model id eleven_v4_turbo. Launched with Eleven v4 on 28 September 2026. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Low-latency Eleven v4 variant for agents and other real-time speech. Model id eleven_v4_turbo. Launched with Eleven v4 on 28 September 2026. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/music/music-v1": { "id": "elevenlabs/music/music-v1", "name": "Eleven Music v1", "maker": "elevenlabs", "family": "elevenlabs/music", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Eleven Music model-specific terms (last updated 2026-05-26). Paid plans are cleared for broad commercial use; free use requires attribution.", "release_date": "2025-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "First ElevenLabs music model, model id music_v1. Launched 5 August 2025 as Eleven Music. Still available. Docs say it is outclassed by Music v2 and v2.5, remains the API default during a transition, and is no longer the UI default. Closed weights. Collected 2026-10-15.", "what_it_is_for": "First ElevenLabs music model, model id music_v1. Launched 5 August 2025 as Eleven Music. Still available. Docs say it is outclassed by Music v2 and v2.5, remains the API default during a transition, and is no longer the UI default. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/music/music-v2": { "id": "elevenlabs/music/music-v2", "name": "Eleven Music v2", "maker": "elevenlabs", "family": "elevenlabs/music", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Eleven Music model-specific terms (last updated 2026-05-26). Paid plans are cleared for broad commercial use; free use requires attribution.", "release_date": "2026-05-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Studio music model, model id music_v2. Released 26 May 2026. Adds section-by-section composition, audio reference, denser vocals, and inpainting over Music v1. Still available after Music v2.5 became the default. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Studio music model, model id music_v2. Released 26 May 2026. Adds section-by-section composition, audio reference, denser vocals, and inpainting over Music v1. Still available after Music v2.5 became the default. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/music/music-v2-5": { "id": "elevenlabs/music/music-v2-5", "name": "Eleven Music v2.5", "maker": "elevenlabs", "family": "elevenlabs/music", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Eleven Music model-specific terms (last updated 2026-05-26). Paid plans are cleared for broad commercial use; free use requires attribution.", "release_date": "2026-09-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Current ElevenLabs music model, model id music_v2_5. Released 11 September 2026 and set as the default for prompted and reference generation. Same controls as Music v2, with a vendor claim of better layers, instruments, and prompt adherence. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Current ElevenLabs music model, model id music_v2_5. Released 11 September 2026 and set as the default for prompted and reference generation. Same controls as Music v2, with a vendor claim of better layers, instruments, and prompt adherence. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/scribe/scribe-v1": { "id": "elevenlabs/scribe/scribe-v1", "name": "Scribe v1", "maker": "elevenlabs", "family": "elevenlabs/scribe", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "2025-02-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "First ElevenLabs speech-recognition model, model id scribe_v1. Released 26 February 2025. Deprecated 8 June 2026, with removal announced for 9 July 2026. Still listed as deprecated on 15 October 2026. Closed weights. Collected 2026-10-15.", "what_it_is_for": "First ElevenLabs speech-recognition model, model id scribe_v1. Released 26 February 2025. Deprecated 8 June 2026, with removal announced for 9 July 2026. Still listed as deprecated on 15 October 2026. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/scribe/scribe-v2": { "id": "elevenlabs/scribe/scribe-v2", "name": "Scribe v2", "maker": "elevenlabs", "family": "elevenlabs/scribe", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "2026-01-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.22 }, "summary_intro": "ElevenLabs batch speech-recognition model, model id scribe_v2. Released 9 January 2026 as the successor to Scribe v1 for long-form transcription, captions, and subtitles. Closed weights. Collected 2026-10-15.", "what_it_is_for": "ElevenLabs batch speech-recognition model, model id scribe_v2. Released 9 January 2026 as the successor to Scribe v1 for long-form transcription, captions, and subtitles. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/scribe/scribe-v2-medical": { "id": "elevenlabs/scribe/scribe-v2-medical", "name": "Scribe v2 Medical", "maker": "elevenlabs", "family": "elevenlabs/scribe", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "2026-09-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of scribe_v2. Parameter count not published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.22 }, "summary_intro": "Clinical batch speech-recognition model, model id scribe_v2_medical. Fine-tune of Scribe v2, generally available 22 September 2026. ElevenLabs reports about 35% fewer errors on clinical audio than Scribe v2, at the same price. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Clinical batch speech-recognition model, model id scribe_v2_medical. Fine-tune of Scribe v2, generally available 22 September 2026. ElevenLabs reports about 35% fewer errors on clinical audio than Scribe v2, at the same price. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/scribe/scribe-v2-realtime": { "id": "elevenlabs/scribe/scribe-v2-realtime", "name": "Scribe v2 Realtime", "maker": "elevenlabs", "family": "elevenlabs/scribe", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "2025-11-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.39 }, "summary_intro": "Streaming speech-recognition model, model id scribe_v2_realtime. Released 11 November 2025 for agents, meetings, and live captions. Docs say about 150ms. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Streaming speech-recognition model, model id scribe_v2_realtime. Released 11 November 2025 for agents, meetings, and live captions. Docs say about 150ms. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/sound-effects/eleven-text-to-sound-v2": { "id": "elevenlabs/sound-effects/eleven-text-to-sound-v2", "name": "Eleven Text to Sound v2", "maker": "elevenlabs", "family": "elevenlabs/sound-effects", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Text-to-sound-effects model, model id eleven_text_to_sound_v2. Listed on the current models page. Duration can be automatic or set, up to 30 seconds. Closed weights. Release date not found. Collected 2026-10-15.", "what_it_is_for": "Text-to-sound-effects model, model id eleven_text_to_sound_v2. Listed on the current models page. Duration can be automatic or set, up to 30 seconds. Closed weights. Release date not found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/sts/eleven-english-sts-v1": { "id": "elevenlabs/sts/eleven-english-sts-v1", "name": "Eleven English STS v1", "maker": "elevenlabs", "family": "elevenlabs/sts", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Older English speech-to-speech model id, eleven_english_sts_v1. The models page still gives it a 10,000-character limit and does not list it among current or deprecated models. Treated as deprecated with medium confidence. Collected 2026-10-15.", "what_it_is_for": "Older English speech-to-speech model id, eleven_english_sts_v1. The models page still gives it a 10,000-character limit and does not list it among current or deprecated models. Treated as deprecated with medium confidence. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/sts/eleven-english-sts-v2": { "id": "elevenlabs/sts/eleven-english-sts-v2", "name": "Eleven English STS v2", "maker": "elevenlabs", "family": "elevenlabs/sts", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "English-only speech-to-speech voice changer, model id eleven_english_sts_v2. On the current models list. The character-limit table allows 10,000 characters, about 10 minutes. Closed weights. Release date not found. Collected 2026-10-15.", "what_it_is_for": "English-only speech-to-speech voice changer, model id eleven_english_sts_v2. On the current models list. The character-limit table allows 10,000 characters, about 10 minutes. Closed weights. Release date not found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/sts/eleven-multilingual-sts-v2": { "id": "elevenlabs/sts/eleven-multilingual-sts-v2", "name": "Eleven Multilingual STS v2", "maker": "elevenlabs", "family": "elevenlabs/sts", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Multilingual speech-to-speech voice changer, model id eleven_multilingual_sts_v2. Listed as the current voice-changer model for the 29 Multilingual v2 languages. Closed weights. Release date not found. Collected 2026-10-15.", "what_it_is_for": "Multilingual speech-to-speech voice changer, model id eleven_multilingual_sts_v2. Listed as the current voice-changer model for the 29 Multilingual v2 languages. Closed weights. Release date not found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/turn/turn-v2": { "id": "elevenlabs/turn/turn-v2", "name": "Turn v2", "maker": "elevenlabs", "family": "elevenlabs/turn", "status": "active", "confidence": "medium", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Earlier ElevenAgents turn-detection model, id turn_v2. Still selectable after 8 June 2026, when the default became turn_v3. Not a speech synthesizer. Closed weights. Release date not found. Collected 2026-10-15.", "what_it_is_for": "Earlier ElevenAgents turn-detection model, id turn_v2. Still selectable after 8 June 2026, when the default became turn_v3. Not a speech synthesizer. Closed weights. Release date not found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/turn/turn-v3": { "id": "elevenlabs/turn/turn-v3", "name": "Turn v3", "maker": "elevenlabs", "family": "elevenlabs/turn", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ElevenAgents turn-detection model, id turn_v3. The 8 June 2026 changelog makes it the default turn_model. It decides when a speaker has finished, and it does not synthesize speech. Closed weights. Release date not found. Collected 2026-10-15.", "what_it_is_for": "ElevenAgents turn-detection model, id turn_v3. The 8 June 2026 changelog makes it the default turn_model. It decides when a speaker has finished, and it does not synthesize speech. Closed weights. Release date not found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/voice-design/eleven-multilingual-ttv-v2": { "id": "elevenlabs/voice-design/eleven-multilingual-ttv-v2", "name": "Eleven Multilingual Text to Voice v2", "maker": "elevenlabs", "family": "elevenlabs/voice-design", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Earlier multilingual voice-design model, model id eleven_multilingual_ttv_v2. Still on the current models list for the 29 Multilingual v2 languages. Voice Design v3 is the newer designer. Closed weights. Release date not found. Collected 2026-10-15.", "what_it_is_for": "Earlier multilingual voice-design model, model id eleven_multilingual_ttv_v2. Still on the current models list for the 29 Multilingual v2 languages. Voice Design v3 is the newer designer. Closed weights. Release date not found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elevenlabs/voice-design/eleven-ttv-v3": { "id": "elevenlabs/voice-design/eleven-ttv-v3", "name": "Eleven Text to Voice v3", "maker": "elevenlabs", "family": "elevenlabs/voice-design", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Commercial rights depend on the ElevenLabs plan.", "release_date": "2025-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Launch post says the latent space was expanded and the model retrained on broader accent data. Parameter count not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Text-to-voice design model, model id eleven_ttv_v3, marketed as Voice Design v3. Announced 25 June 2025. A text description returns voice options rather than a finished narration. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Text-to-voice design model, model id eleven_ttv_v3, marketed as Voice Design v3. Announced 25 June 2025. A text description returns voice options rather than a finished narration. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/brain/elyza-brain": { "id": "elyza/brain/elyza-brain", "name": "ELYZA Brain", "maker": "elyza", "family": "elyza/brain", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ELYZA describes it as a BERT-based Japanese language model, and as the largest Japanese model of that type when it was built.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA Brain is the Japanese language-model engine ELYZA says it finished in 2020. Company posts describe it as BERT-based. On 2026-10-15 no current weight file or API was found, so this dossier treats it as retired.", "what_it_is_for": "Japanese reading and writing for ELYZA's early products. The Pencil note says the engine supported controlled generation. DIGEST was a public summarization demo. Those products are not separate model folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/diffusion/elyza-diffusion-base-1-0-dream-7b": { "id": "elyza/diffusion/elyza-diffusion-base-1-0-dream-7b", "name": "ELYZA-Diffusion-Base-1.0-Dream-7B", "maker": "elyza", "family": "elyza/diffusion", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2026-01-16", "context_length": 131072, "max_output_tokens": null, "parameters": "7615616512", "architecture": "DreamModel, a discrete diffusion masked language model. config.json: 28 layers, hidden 3584, vocab 152064, max position 131072, float16. Safetensors total 7615616512. Continued pre-training from Dream-v0-Instruct-7B on about 62 billion Japanese tokens. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-Diffusion-Base-1.0-Dream-7B is the base Japanese diffusion language model ELYZA published on 2026-01-16 under Apache 2.0. It continues Dream-v0-Instruct-7B on Japanese text and is not the instruction-tuned chat model. The Hugging Face repo was created on 2025-12-10. The public announcement is 2026-01-16.", "what_it_is_for": "Research and further tuning of a Japanese diffusion language model. ELYZA says to use the instruct checkpoint for applications.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/diffusion/elyza-diffusion-instruct-1-0-dream-7b": { "id": "elyza/diffusion/elyza-diffusion-instruct-1-0-dream-7b", "name": "ELYZA-Diffusion-Instruct-1.0-Dream-7B", "maker": "elyza", "family": "elyza/diffusion", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2026-01-16", "context_length": 131072, "max_output_tokens": null, "parameters": "7615616512", "architecture": "DreamModel instruction-tuned from ELYZA-Diffusion-Base-1.0-Dream-7B. Same config shape: 28 layers, hidden 3584, vocab 152064, max position 131072. Safetensors total 7615616512. Card says instruction tuning used about 1.8 billion Japanese tokens for 10 epochs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-Diffusion-Instruct-1.0-Dream-7B is the instruction-tuned Japanese diffusion language model ELYZA released on 2026-01-16 under Apache 2.0. Text is generated by iterative unmasking, not token-by-token decoding. Weights were public on 2026-10-15.", "what_it_is_for": "Japanese instruction following and chat where parallel token filling is useful. ELYZA also pointed at a Hugging Face chat demo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/japanese-codellama/elyza-japanese-codellama-7b": { "id": "elyza/japanese-codellama/elyza-japanese-codellama-7b", "name": "ELYZA-japanese-CodeLlama-7b", "maker": "elyza", "family": "elyza/japanese-codellama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-11-15", "context_length": 16384, "max_output_tokens": null, "parameters": "6738546688", "architecture": "LlamaForCausalLM continued from CodeLlama-7b-Instruct. config.json: 32 layers, hidden 4096, intermediate 11008, vocab 32016, max position 16384, rope theta 1000000, float16. Hugging Face safetensors total is 6738546688. The model card's 6.27B figure does not match that total. About 18 billion Japanese tokens from OSCAR, Wikipedia, and crawl text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-japanese-CodeLlama-7b is ELYZA's Japanese continued-pretraining of CodeLlama-7b-Instruct, published on 2023-11-15. It is a code model, not a quant of the Llama 2 7B chat line. Weights remain public under the Llama 2 Community License.", "what_it_is_for": "Japanese code generation and code explanation while keeping Code Llama's code ability. ELYZA used it to test whether the Japanese continued-pretraining recipe transferred beyond Llama 2 chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/japanese-codellama/elyza-japanese-codellama-7b-instruct": { "id": "elyza/japanese-codellama/elyza-japanese-codellama-7b-instruct", "name": "ELYZA-japanese-CodeLlama-7b-instruct", "maker": "elyza", "family": "elyza/japanese-codellama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-11-15", "context_length": 16384, "max_output_tokens": null, "parameters": "6738546688", "architecture": "ELYZA post-training of ELYZA-japanese-CodeLlama-7b. Instruct config.json matches the base: vocab 32016, max position 16384. Safetensors total 6738546688.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-japanese-CodeLlama-7b-instruct is the instruction-tuned Japanese Code Llama 7B model ELYZA released on 2023-11-15. Weights remain public under the Llama 2 Community License. ELYZA's own HumanEval check stayed close to the original CodeLlama-7b-Instruct number.", "what_it_is_for": "Japanese code generation, code explanation, and instruction following. ELYZA positioned it as evidence that Japanese continued pre-training did not wipe out Code Llama's code skill.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/japanese-llama-2/elyza-japanese-llama-2-13b": { "id": "elyza/japanese-llama-2/elyza-japanese-llama-2-13b", "name": "ELYZA-japanese-Llama-2-13b", "maker": "elyza", "family": "elyza/japanese-llama-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-12-27", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM continued from Llama-2-13b-chat. config.json: 40 layers, hidden 5120, intermediate 13824, 40 heads, vocab 32000, max position 4096, float16. Card parameter figure is 13.02B. About 18 billion Japanese tokens from OSCAR and Wikipedia.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-japanese-Llama-2-13b is ELYZA's Japanese continued-pretraining of Llama-2-13b-chat, published on 2023-12-27. It is the base of the 13B instruct and the parent line of the fast 13B pair. Weights remain public under the Llama 2 Community License.", "what_it_is_for": "A larger Japanese base than the 7B model, for teams that want to instruction-tune or prompt a Llama 2 13B checkpoint with stronger Japanese continuation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/japanese-llama-2/elyza-japanese-llama-2-13b-fast": { "id": "elyza/japanese-llama-2/elyza-japanese-llama-2-13b-fast", "name": "ELYZA-japanese-Llama-2-13b-fast", "maker": "elyza", "family": "elyza/japanese-llama-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-12-27", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM with expanded Japanese vocabulary. config.json: vocab 44581, 40 layers, hidden 5120, max position 4096. Card parameter figure is 13.14B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-japanese-Llama-2-13b-fast is the vocabulary-expanded 13B checkpoint released on 2023-12-27. It is not a quant of ELYZA-japanese-Llama-2-13b. Weights remain public under the Llama 2 Community License.", "what_it_is_for": "Japanese continued pre-training with a larger Japanese vocabulary so generation uses fewer tokens. ELYZA said this fast pair scored below the non-fast 13B pair.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/japanese-llama-2/elyza-japanese-llama-2-13b-fast-instruct": { "id": "elyza/japanese-llama-2/elyza-japanese-llama-2-13b-fast-instruct", "name": "ELYZA-japanese-Llama-2-13b-fast-instruct", "maker": "elyza", "family": "elyza/japanese-llama-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-12-27", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-tuned fast 13B. Family card: vocab 44581 and 13.14B. Context is taken from the fast base config.json (4096, vocab 44581). The instruct config was not fetched separately.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-japanese-Llama-2-13b-fast-instruct is the instruction-tuned, vocabulary-expanded 13B model released on 2023-12-27. Weights remain public under the Llama 2 Community License. ELYZA said the fast pair scored below the non-fast 13B instruct model.", "what_it_is_for": "Japanese instruction following with the fast tokenizer. Choose it when token throughput matters more than the non-fast 13B instruct score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/japanese-llama-2/elyza-japanese-llama-2-13b-instruct": { "id": "elyza/japanese-llama-2/elyza-japanese-llama-2-13b-instruct", "name": "ELYZA-japanese-Llama-2-13b-instruct", "maker": "elyza", "family": "elyza/japanese-llama-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-12-27", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-tuned 13B. config.json: 40 layers, hidden 5120, vocab 32000, max position 4096. Card parameter figure is 13.02B. Post-trained from ELYZA-japanese-Llama-2-13b for instruction following.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-japanese-Llama-2-13b-instruct is the instruction-tuned 13B model ELYZA released on 2023-12-27. ELYZA called it the strongest open Japanese model in its human ELYZA Tasks 100 review at that time. Weights remain public under the Llama 2 Community License.", "what_it_is_for": "Japanese instruction following and multi-turn help, at 13B rather than 7B. The launch note says it was the checkpoint that scored above GPT-3.5 text-davinci-003 in that human review.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/japanese-llama-2/elyza-japanese-llama-2-70b": { "id": "elyza/japanese-llama-2/elyza-japanese-llama-2-70b", "name": "ELYZA-japanese-Llama-2-70b", "maker": "elyza", "family": "elyza/japanese-llama-2", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ELYZA says it is Llama 2 with additional Japanese pre-training and post-training, at 70 billion parameters. Layer counts and vocabulary were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-japanese-Llama-2-70b is a 70-billion-parameter Japanese Llama 2 model ELYZA announced on 2024-03-12 with a public demo and a planned enterprise API. The weights were not put on Hugging Face. The shared demo later moved to Llama 3, so this dossier marks the model deprecated.", "what_it_is_for": "A hosted Japanese chat model for the ELYZA LLM for JP demo. ELYZA said a blind ELYZA Tasks 100 review put it near GPT-3.5 Turbo and Claude 2 and above public Japanese models of that date. The API was described as planned, not given a public price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/japanese-llama-2/elyza-japanese-llama-2-7b": { "id": "elyza/japanese-llama-2/elyza-japanese-llama-2-7b", "name": "ELYZA-japanese-Llama-2-7b", "maker": "elyza", "family": "elyza/japanese-llama-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-08-29", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM. config.json: 32 layers, hidden size 4096, intermediate size 11008, 32 attention heads, vocab 32000, float16. Continued pre-training from Llama-2-7b-chat on about 18 billion Japanese tokens from OSCAR, Wikipedia, and similar text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-japanese-Llama-2-7b is ELYZA's public Japanese continued-pretraining of Meta Llama-2-7b-chat. It was released on 2023-08-29 under the Llama 2 Community License. The weights were still on Hugging Face on 2026-10-15. The chat demo, not the weights, closed on 2024-04-04.", "what_it_is_for": "A Japanese base model for further instruction tuning. ELYZA says it added about 18 billion Japanese tokens so the Llama 2 checkpoint would handle Japanese text while keeping the Llama 2 license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/japanese-llama-2/elyza-japanese-llama-2-7b-fast": { "id": "elyza/japanese-llama-2/elyza-japanese-llama-2-7b-fast", "name": "ELYZA-japanese-Llama-2-7b-fast", "maker": "elyza", "family": "elyza/japanese-llama-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-08-29", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM with an expanded Japanese vocabulary. config.json: vocab 45043, 32 layers, hidden 4096, max position 4096. Card parameter figure is 6.37B. ELYZA says the added vocabulary made inference about 1.82 times faster than the non-fast 7B model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-japanese-Llama-2-7b-fast is the vocabulary-expanded 7B Japanese Llama 2 checkpoint ELYZA released on 2023-08-29. It is a separate training run from ELYZA-japanese-Llama-2-7b, not a quantization. Weights remain public under the Llama 2 Community License.", "what_it_is_for": "Faster Japanese generation. ELYZA added Japanese vocabulary during continued pre-training so each sentence uses fewer tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/japanese-llama-2/elyza-japanese-llama-2-7b-fast-instruct": { "id": "elyza/japanese-llama-2/elyza-japanese-llama-2-7b-fast-instruct", "name": "ELYZA-japanese-Llama-2-7b-fast-instruct", "maker": "elyza", "family": "elyza/japanese-llama-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-08-29", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-tuned fast 7B. The family card gives vocab 45043 and a 6.37B parameter figure. The non-instruct fast config.json has max position 4096 and vocab 45043. This instruct repo's config was not fetched separately.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-japanese-Llama-2-7b-fast-instruct is the instruction-tuned fast 7B model ELYZA released on 2023-08-29. It keeps the expanded Japanese vocabulary. Weights were still public on 2026-10-15 under the Llama 2 Community License.", "what_it_is_for": "Japanese instruction following at a higher token throughput than the non-fast 7B instruct model. ELYZA reported about 1.82 times faster inference for the fast method.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/japanese-llama-2/elyza-japanese-llama-2-7b-instruct": { "id": "elyza/japanese-llama-2/elyza-japanese-llama-2-7b-instruct", "name": "ELYZA-japanese-Llama-2-7b-instruct", "maker": "elyza", "family": "elyza/japanese-llama-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-08-29", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM. Instruct config.json matches the 7B base: 32 layers, hidden 4096, vocab 32000, max position 4096. Post-trained on ELYZA's instruction set for multi-turn Japanese tasks. Card parameter figure is 6.27B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-japanese-Llama-2-7b-instruct is the instruction-tuned release of ELYZA's 7B Japanese Llama 2 model. ELYZA published it on 2023-08-29 with the base and fast variants. Weights remain open under the Llama 2 Community License as of 2026-10-15.", "what_it_is_for": "Chat and instruction following in Japanese, including multi-turn dialogue. ELYZA says the post-training set was its own and was not built from GPT-4 or GPT-3.5-turbo outputs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/llama-3-jp/llama-3-1-elyza-jp-70b": { "id": "elyza/llama-3-jp/llama-3-1-elyza-jp-70b", "name": "Llama-3.1-ELYZA-JP-70B", "maker": "elyza", "family": "elyza/llama-3-jp", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Japanese continued pre-training and instruction tuning of Llama-3.1-70B. ELYZA calls it 70 billion parameters. It replaces Llama-3-ELYZA-JP-70B as the demo base. Context length was not given as a token count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3.1-ELYZA-JP-70B is ELYZA's Japanese Llama 3.1 70B model, announced on 2024-10-25 as the new ELYZA LLM for JP demo base. Weights were not generally released. ELYZA said it would be offered to companies through a private API and joint projects. No retirement notice was found by 2026-10-15.", "what_it_is_for": "Enterprise Japanese chat and long-document work. ELYZA says the Llama 3.1 base added long-context handling and that a revised Japanese post-training pass improved instruction following versus Llama-3-ELYZA-JP-70B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/llama-3-jp/llama-3-elyza-jp-120b": { "id": "elyza/llama-3-jp/llama-3-elyza-jp-120b", "name": "Llama-3-ELYZA-JP-120B", "maker": "elyza", "family": "elyza/llama-3-jp", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Depth Up-Scaling of Meta-Llama-3-70B-Instruct with mergekit, then Japanese continued training. The ELYZA article says the result has 122 billion parameters. That rounded figure is not stored as an integer parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-ELYZA-JP-120B is a 122-billion-parameter Japanese model ELYZA described on 2025-01-08. It was built by Depth Up-Scaling Meta-Llama-3-70B-Instruct and then training for Japanese. No public weights or API were found on 2026-10-15, so the status is announced rather than active.", "what_it_is_for": "A GENIAC research model. ELYZA says it scored above Llama-3-ELYZA-JP-70B on the three benchmarks in that post, including local Nejumi Leaderboard 3 runs. It was not offered as the public demo model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/llama-3-jp/llama-3-elyza-jp-70b": { "id": "elyza/llama-3-jp/llama-3-elyza-jp-70b", "name": "Llama-3-ELYZA-JP-70B", "maker": "elyza", "family": "elyza/llama-3-jp", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Japanese continued pre-training and instruction tuning of Meta-Llama-3-70B-Instruct. ELYZA calls it a 70-billion-parameter model. Layer config was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-ELYZA-JP-70B is ELYZA's Japanese Llama 3 70B model, announced on 2024-06-26 with a free demo and an enterprise offer. The weights were not generally released. The demo base was replaced by Llama-3.1-ELYZA-JP-70B on 2024-10-25, so this dossier marks it deprecated.", "what_it_is_for": "High-end Japanese chat for the ELYZA LLM for JP demo. ELYZA said ELYZA Tasks 100 and Japanese MT-Bench, judged automatically, put it at or above GPT-4, Claude 3 Sonnet, and Gemini 1.5 Flash. Those charts were not transcribed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/llama-3-jp/llama-3-elyza-jp-8b": { "id": "elyza/llama-3-jp/llama-3-elyza-jp-8b", "name": "Llama-3-ELYZA-JP-8B", "maker": "elyza", "family": "elyza/llama-3-jp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-06-26", "context_length": 8192, "max_output_tokens": null, "parameters": "8030261248", "architecture": "LlamaForCausalLM continued from Meta-Llama-3-8B-Instruct. config.json: 32 layers, hidden 4096, intermediate 14336, 32 heads, 8 key-value heads, vocab 128256, max position 8192, bfloat16. Safetensors total 8030261248.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-ELYZA-JP-8B is ELYZA's public Japanese Llama 3 8B instruct model, released on 2024-06-26. It is additional Japanese pre-training and instruction tuning of Meta-Llama-3-8B-Instruct. Full weights, plus ELYZA's own AWQ and GGUF builds, were still on Hugging Face on 2026-10-15 under the Llama 3 Community License.", "what_it_is_for": "A small Japanese chat model ELYZA compared with GPT-3.5 Turbo, Claude 3 Haiku, and Gemini 1.0 Pro. The 70B sibling was demo-only. This 8B checkpoint is the one ELYZA generally published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/med/elyza-med-base-1-0-qwen2-5-72b": { "id": "elyza/med/elyza-med-base-1-0-qwen2-5-72b", "name": "ELYZA-Med-Base-1.0-Qwen2.5-72B", "maker": "elyza", "family": "elyza/med", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pre-training of Qwen2.5-72B-Instruct on medical corpora. The press release calls this the general Japanese medical base for the ELYZA-LLM-Med series. Exact parameter count, context, and a weight file were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-Med-Base-1.0-Qwen2.5-72B is the general Japanese medical model ELYZA described on 2025-07-14. It is continued pre-training of Qwen2.5-72B-Instruct for the SIP integrated-healthcare program. Public weights were not found, so the status is announced.", "what_it_is_for": "A base for Japanese medical tasks. ELYZA says it had the best domestic score they reported on IgakuQA, a medical-licensing benchmark. The number itself is in a table image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/med/elyza-med-instruct-1-0-qwen2-5-72b-uc1": { "id": "elyza/med/elyza-med-instruct-1-0-qwen2-5-72b-uc1", "name": "ELYZA-Med-Instruct-1.0-Qwen2.5-72B (UC1)", "maker": "elyza", "family": "elyza/med", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Supervised fine-tune of ELYZA-Med-Base-1.0-Qwen2.5-72B for electronic-medical-record standardization. No public config or parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-Med-Instruct-1.0-Qwen2.5-72B (UC1) is the medical-record standardization model ELYZA named on 2025-07-14. It is a supervised fine-tune of the Qwen2.5-72B medical base. Weights were not publicly released.", "what_it_is_for": "Convert clinical text toward a standard electronic-medical-record form. ELYZA says it beat o1 (2024-12-17) on that private task. The score is in an image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/med/elyza-med-instruct-1-0-qwen2-5-72b-uc2": { "id": "elyza/med/elyza-med-instruct-1-0-qwen2-5-72b-uc2", "name": "ELYZA-Med-Instruct-1.0-Qwen2.5-72B (UC2)", "maker": "elyza", "family": "elyza/med", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Supervised fine-tune of ELYZA-Med-Base-1.0-Qwen2.5-72B for checking and proposing corrections to medical fee statements. No public config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-Med-Instruct-1.0-Qwen2.5-72B (UC2) is the medical-fee-statement model ELYZA named on 2025-07-14. It is a supervised fine-tune of the same medical base as UC1. Public weights were not found.", "what_it_is_for": "Suggest whether a Japanese medical fee statement needs a correction, and draft the comment. ELYZA says it beat gpt-4o (2024-11-20) on both the yes/no decision and comment quality. Numbers were not copied from the images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/shortcut/elyza-shortcut-1-0-qwen-32b": { "id": "elyza/shortcut/elyza-shortcut-1-0-qwen-32b", "name": "ELYZA-Shortcut-1.0-Qwen-32B", "maker": "elyza", "family": "elyza/shortcut", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2025-05-01", "context_length": 32768, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM non-reasoning model from the Thinking-1.0 run. Safetensors total 32763876352. Base is Qwen2.5-32B-Instruct. SFT used problem-answer pairs after the chain of thought was removed. config.json max position was read on 2026-10-15 as 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-Shortcut-1.0-Qwen-32B is the 32B non-reasoning model ELYZA released with its first reasoning model on 2025-05-01. It answers directly, without a long think step. Weights are Apache 2.0 and were still public on 2026-10-15.", "what_it_is_for": "Fast Japanese answers on tasks that do not need a long chain of thought. ELYZA says this 32B shortcut matched GPT-4o among non-reasoning models and beat GPT-4 on every listed benchmark except JHumanEval. The table is an image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/shortcut/elyza-shortcut-1-0-qwen-7b": { "id": "elyza/shortcut/elyza-shortcut-1-0-qwen-7b", "name": "ELYZA-Shortcut-1.0-Qwen-7B", "maker": "elyza", "family": "elyza/shortcut", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2025-05-01", "context_length": 32768, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Qwen2ForCausalLM. config.json: 28 layers, hidden 3584, vocab 152064, max position 32768. Safetensors total 7615616512. Base is Qwen2.5-7B-Instruct. Same shortcut SFT idea as the 32B shortcut: answers without the chain of thought.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-Shortcut-1.0-Qwen-7B is the smaller non-reasoning shortcut model released on 2025-05-01 under Apache 2.0. It is a post-train of Qwen2.5-7B-Instruct, not a quant of the 32B shortcut. Weights were still public on 2026-10-15.", "what_it_is_for": "A small direct-answer Japanese model. ELYZA says Japanese general benchmarks rose versus the Qwen2.5-7B-Instruct base. Numeric cells were in a figure.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/thinking/elyza-thinking-1-0-llm-jp-4-32b-a3b": { "id": "elyza/thinking/elyza-thinking-1-0-llm-jp-4-32b-a3b", "name": "ELYZA-Thinking-1.0-llm-jp-4-32b-a3b", "maker": "elyza", "family": "elyza/thinking", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2026-10-02", "context_length": 65536, "max_output_tokens": null, "parameters": "32139028992", "architecture": "Qwen3MoeForCausalLM. config.json: 32 layers, hidden 2560, 40 heads, 4 key-value heads, 128 experts, 8 experts per token, moe intermediate size 960, vocab 196608, max position 65536. Safetensors total 32139028992. Base is llm-jp/llm-jp-4-32b-a3b-base. Same ELYZA mid-train, SFT, and RLVR pipeline as the dense 33B model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-Thinking-1.0-llm-jp-4-32b-a3b is the mixture-of-experts twin of ELYZA's October 2026 reasoning release. Total parameters on Hugging Face are about 32.1 billion, with 8 of 128 experts active per token. It is Apache 2.0 and was public on 2026-10-15.", "what_it_is_for": "The same Japanese and English reasoning job as the dense 33B model, at lower active compute. ELYZA's group average on the card is 58.46, below the dense model's 61.69.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/thinking/elyza-thinking-1-0-llm-jp-4-33b": { "id": "elyza/thinking/elyza-thinking-1-0-llm-jp-4-33b", "name": "ELYZA-Thinking-1.0-llm-jp-4-33b", "maker": "elyza", "family": "elyza/thinking", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2026-10-02", "context_length": 65536, "max_output_tokens": null, "parameters": "33219548160", "architecture": "LlamaForCausalLM dense model. config.json: 64 layers, hidden 5120, intermediate 27648, 40 heads, 8 key-value heads, head dim 128, vocab 196608, max position 65536, bfloat16. Safetensors total 33219548160. Base is llm-jp/llm-jp-4-33b-base. ELYZA did mid-training, supervised fine-tuning, and RLVR.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-Thinking-1.0-llm-jp-4-33b is ELYZA's dense reasoning model on llm-jp-4-33b-base, published on 2026-10-02 under Apache 2.0. Mid-training, supervised fine-tuning, and reinforcement learning with verifiable rewards were done by ELYZA. Weights were on Hugging Face on the collection date.", "what_it_is_for": "Japanese and English reasoning, knowledge, code, and tool use. ELYZA highlights Japanese knowledge recall and instruction following. The card's group average is 61.69.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "elyza/thinking/elyza-thinking-1-0-qwen-32b": { "id": "elyza/thinking/elyza-thinking-1-0-qwen-32b", "name": "ELYZA-Thinking-1.0-Qwen-32B", "maker": "elyza", "family": "elyza/thinking", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2025-05-01", "context_length": 131072, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM. config.json: 64 layers, hidden 5120, intermediate 27648, 40 heads, 8 key-value heads, vocab 152064, max position 131072, bfloat16. Safetensors total 32763876352. Base is Qwen2.5-32B-Instruct, with Japanese continued pre-training, SFT on about 150,000 long chain-of-thought examples, and a 0.7/0.3 merge with DeepSeek-R1-Distill-Qwen-32B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELYZA-Thinking-1.0-Qwen-32B is ELYZA's public Japanese reasoning model on Qwen2.5-32B-Instruct, released on 2025-05-01 under Apache 2.0. It was still on Hugging Face on 2026-10-15. ELYZA trains it to write a long chain of thought before the answer.", "what_it_is_for": "Hard Japanese and English reasoning, especially math. ELYZA says it is in the range of o1-mini and that its Japanese and English math scores were above o1-mini. Those tables were images, so the numeric claim is not copied here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ens/addressee/addressee": { "id": "ens/addressee/addressee", "name": "Addressee", "maker": "ens", "family": "ens/addressee", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Audio classifier fine-tuned from BabyHuBERT. The card does not describe layers or the label set. The cited title is child-directed versus adult-directed speech detection.", "modalities": { "input": [ "audio" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Addressee is a CoML audio classifier at the \u00c9cole normale sup\u00e9rieure, fine-tuned from BabyHuBERT for child-directed versus adult-directed speech. Weights were published on 2026-04-01. The card is only a pointer to code, so confidence is medium. Collected 2026-10-15.", "what_it_is_for": "The card's citation title is \"Context-aware child-directed speech detection from long-form recordings.\" The BabyHuBERT card describes the same downstream task as child-directed versus adult-directed speech detection. The repo does not restate the label set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ens/babyhubert/babyhubert": { "id": "ens/babyhubert/babyhubert", "name": "BabyHuBERT", "maker": "ens", "family": "ens/babyhubert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Custom BabyHuBERT license (gated). The model card says it prohibits commercial use and surveillance of participants, requires misuse to be reported, and requires models built on BabyHuBERT to inherit the same conditions. Full text is on OSF, not copied here.", "release_date": "2025-09-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HuBERT-base self-supervised speech encoder. The model card loads torchaudio hubert_pretrain_base with 500 clusters. Trained on child-centered long-form recordings, not clean adult speech. The card says 13,000+ hours and 40+ languages. A March 2026 preprint HTML also says 13,164 hours.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BabyHuBERT is a self-supervised speech encoder from the ExELang / CoML line at the \u00c9cole normale sup\u00e9rieure (a joint EHESS, ENS, CNRS, and Inria team). It is an active open-weight HuBERT-base model trained on child-centered daylong audio. The collection date is 2026-10-15. The original `coml/BabyHuBERT` URL 404s; the live card is gated at `MarvinLvn/BabyHuBERT`.", "what_it_is_for": "The card says existing adult-speech models fail on child-centered recordings (mostly non-speech, overlap, short and high-pitched vocalizations). BabyHuBERT is a foundation encoder for that domain. Voice-type classification is the paper's evaluation task, not a separate head shipped in this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ens/hubert/hubert-base-mmsulab": { "id": "ens/hubert/hubert-base-mmsulab", "name": "HuBERT MMS-ulab", "maker": "ens", "family": "ens/hubert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2026-04-24", "context_length": null, "max_output_tokens": null, "parameters": "94371712", "architecture": "HuBERT base pretrained with minimal_hubert on the Segmented MMS ulab v2 dataset for DiscoPhon. Hugging Face config model type is hubert, architecture HubertModel. Safetensors total is 94,371,712 F32 parameters. The card rounds this to 94.4M. Files: it1.pt, it2.pt (converted to model.safetensors), km100-mfcc.joblib, and km500-it1-l9.joblib. The card says the 500-cluster model uses the 9th layer of iteration 1.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuBERT MMS-ulab is a CoML HuBERT-base run at the \u00c9cole normale sup\u00e9rieure, pretrained on Segmented MMS ulab v2 for DiscoPhon. It is active under CC-BY-NC-SA-4.0. The repo was created on 2026-04-24. Collected 2026-10-15.", "what_it_is_for": "The card says it is a HuBERT base pretrained on Segmented MMS ulab v2 as a DiscoPhon baseline. It is not Meta's released HuBERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ens/hubert/hubert-base-vp20": { "id": "ens/hubert/hubert-base-vp20", "name": "HuBERT VP-20", "maker": "ens", "family": "ens/hubert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2026-04-24", "context_length": null, "max_output_tokens": null, "parameters": "94.4 million", "architecture": "HuBERT base. Config: HubertModel, 12 layers, hidden 768, 12 heads, intermediate 3072, 7 convolutional feature layers. Pretrained with minimal_hubert on about 6,000 hours and 20 VoxPopuli languages (EU languages except English, French, and German) for DiscoPhon. The repo also holds iteration checkpoints it1.pt and it2.pt and the k-means files used to train them. The card says model.safetensors was converted from it2.pt. Exact integer parameter count was not retrieved; the card rounds to 94.4M.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HuBERT VP-20 is a CoML HuBERT-base run at the \u00c9cole normale sup\u00e9rieure, pretrained for the DiscoPhon benchmark. It is active under CC-BY-NC-SA-4.0. The repo was created on 2026-04-24. Collected 2026-10-15.", "what_it_is_for": "The card says it is a HuBERT base pretrained on about 6,000 hours and 20 VoxPopuli languages, all EU languages in that subset except English, French, and German. It is a DiscoPhon baseline, not Meta's public HuBERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ens/hubert/hubert-phoneme-classification": { "id": "ens/hubert/hubert-phoneme-classification", "name": "HuBERT phoneme classification", "maker": "ens", "family": "ens/hubert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-03-22", "context_length": null, "max_output_tokens": null, "parameters": "94727336", "architecture": "HuBERT base fine-tuned for frame-wise phoneme classification. Safetensors total is 94,727,336 F32 parameters. The card rounds this to 94.7M. Base checkpoint is facebook/hubert-base-ls960. The card says this branch was fine-tuned on LibriSpeech train-clean-100. Authors are at ENS-PSL, EHESS, and CNRS. Emmanuel Dupoux is also at Meta FAIR. The release org is coml.", "modalities": { "input": [ "audio" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is CoML's EMNLP 2024 fine-tune of Meta HuBERT-base for frame-level phoneme classification. The authors are at ENS-PSL, EHESS, and CNRS, and the weights are on the `coml` Hub. It is active under MIT. The repo was created on 2024-03-22. Collected 2026-10-15.", "what_it_is_for": "The paper says fine-tuning a speech encoder on phoneme classification makes the features more context-invariant, which then helps spoken language models. The card says this branch is the HuBERT fine-tuned on LibriSpeech train-clean-100.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ens/maubert/maubert-feat": { "id": "ens/maubert/maubert-feat", "name": "MauBERT-feat", "maker": "ens", "family": "ens/maubert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-06-27", "context_length": null, "max_output_tokens": null, "parameters": "140581249", "architecture": "Continued HuBERT-base (config hubert_size base) with a bidirectional LSTM head (widths 1024, 1024) and a GELU feature projection. Safetensors total is 140,581,249 F32 parameters. The Hugging Face card rounds this to 0.1B. Trained to predict frame-level articulatory features on 788 hours and 55 languages of Common Voice 16.1 using VoxCommunis phone annotations.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MauBERT-feat is a CoML speech model at the \u00c9cole normale sup\u00e9rieure. It adapts a HuBERT-base encoder to predict articulatory features on 55 languages. Weights are active under MIT. The Hugging Face repo was created on 2026-06-27. Collected 2026-10-15.", "what_it_is_for": "The card says it predicts frame-level articulatory features from a HuBERT base, using 788 hours of Common Voice 16.1 and VoxCommunis phone labels. The ACL paper, an outstanding paper, says the goal is language-independent phonetic representations and few-shot acoustic-unit discovery.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ens/maubert/maubert-phone": { "id": "ens/maubert/maubert-phone", "name": "MauBERT-phone", "maker": "ens", "family": "ens/maubert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-07-30", "context_length": null, "max_output_tokens": null, "parameters": "143881578", "architecture": "HuBERT-base speech model adapted to predict phones. Safetensors total is 143,881,578 F32 parameters. The card rounds this to 0.1B. Same training mix as MauBERT-feat: 788 hours and 55 languages of Common Voice 16.1 with VoxCommunis phone annotations. The quantiser card says the full phone inventory used by one MauBERT-phone quantiser has 370 phones.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MauBERT-phone is the phone-prediction twin of MauBERT-feat from CoML at the \u00c9cole normale sup\u00e9rieure. It is an active MIT release. The Hugging Face repo was created on 2026-07-30. Collected 2026-10-15.", "what_it_is_for": "The card says it adapts a pretrained HuBERT base to predict phones on 788 hours and 55 languages of Common Voice 16.1, using VoxCommunis alignments. It is the phone head of the ACL 2026 MauBERT paper, not a general ASR system.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ens/maubert/maubert-quantisers": { "id": "ens/maubert/maubert-quantisers", "name": "MauBERT quantisers", "maker": "ens", "family": "ens/maubert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-08-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "A bundle of quantisers, not one neural net. The card says .km files are scikit-learn MiniBatchKMeans pickles, .ft files are torch state dicts of an adapted phone-prediction layer, and other .pt files are torch tensors. Each maps one layer of MauBERT-feat, MauBERT-phone, or HuBERT base to discrete units, fit on 10 hours of one language. The fine-tuned speech models used in the paper are not in this repo.", "modalities": { "input": [ "embeddings" ], "output": [ "discrete-units" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MauBERT quantisers is the CoML bundle of discrete-unit quantisers for the ACL 2026 MauBERT paper. It is one active MIT repo, not a family of separate products. Created 2026-08-07. Collected 2026-10-15.", "what_it_is_for": "The card says each quantiser maps one layer of MauBERT-feat, MauBERT-phone, or HuBERT base onto units, fit on 10 hours of one language. They reproduce the paper's pseudo-labels. The fine-tuned speech models are not in the repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ens/spidr/spidr-mmsulab": { "id": "ens/spidr/spidr-mmsulab", "name": "SpidR MMS-ulab", "maker": "ens", "family": "ens/spidr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2026-04-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SpidR encoder pretrained by CoML on Segmented MMS ulab v2 for DiscoPhon, using the spidr library. The card ships config.json, final.pt, and full_checkpoint.pt. Layer widths were read from the VP-20 config, not from this file, so they are not repeated as a measured fact for this checkpoint.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SpidR MMS-ulab is a CoML pretraining of SpidR on Segmented MMS ulab v2 for the DiscoPhon benchmark. It is active under CC-BY-NC-SA-4.0. The repo was created on 2026-04-26. Collected 2026-10-15. The original SpidR release remains Meta's.", "what_it_is_for": "The card says it is a SpidR model pretrained on Segmented MMS ulab v2 as a DiscoPhon baseline. It is a speech representation model, not a text generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ens/spidr/spidr-vp20": { "id": "ens/spidr/spidr-vp20", "name": "SpidR VP-20", "maker": "ens", "family": "ens/spidr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2026-04-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SpidR speech encoder pretrained by CoML, not the original FAIR checkpoint. Config: 7-layer convolutional frontend, 12 transformer layers, embed 768, 12 heads, feed-forward 3072, 8 codebooks of size 256. Trained with the facebookresearch/spidr library on about 6,000 hours and 20 VoxPopuli languages (EU languages except English, French, and German) for DiscoPhon. Weights are final.pt plus a full training checkpoint.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SpidR VP-20 is a CoML pretraining of the SpidR speech encoder at the \u00c9cole normale sup\u00e9rieure, built for DiscoPhon. It is active under CC-BY-NC-SA-4.0. The repo was created on 2026-04-26. Collected 2026-10-15. It is not the original FAIR at Meta checkpoint.", "what_it_is_for": "The card says it was pretrained on about 6,000 hours and 20 VoxPopuli languages (EU languages except English, French, and German) as a DiscoPhon baseline. The architecture comes from the SpidR paper. That paper's own release is Meta's GitHub.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ens/vtc/vtc-2": { "id": "ens/vtc/vtc-2", "name": "VTC 2", "maker": "ens", "family": "ens/vtc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Voice type classifier fine-tuned from BabyHuBERT. The CoML card says the repo holds weights only. Classes in the paper are key child, other children, male adult, and female adult.", "modalities": { "input": [ "audio" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VTC 2 is the CoML voice-type classifier at the \u00c9cole normale sup\u00e9rieure, fine-tuned from BabyHuBERT. It is an active weight release. The Hugging Face repo `coml/VTC-2` was created on 2025-12-02. Collected 2026-10-15.", "what_it_is_for": "The paper uses it to mark who speaks in child-centered daylong recordings: the key child, other children, a male adult, or a female adult. The card says the repo is weights only and points to `LAAC-LSCP/VTC` for use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/4m/4m-21-b": { "id": "epfl/4m/4m-21-b", "name": "4M-21 B", "maker": "epfl", "family": "epfl/4m", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-13", "context_length": null, "max_output_tokens": null, "parameters": "0.8B", "architecture": "Unified Transformer encoder-decoder trained with multimodal masked modeling on discrete tokens. Any-to-any vision model. Joint EPFL VILAB and Apple release. Exact layer counts for this scale were not copied from the paper.", "modalities": { "input": [ "image", "text" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "4M-21 B is a massively multimodal masked model from EPFL VILAB and Apple. Collected 2026-10-15. Weights are public under the Apple Sample Code license. It is an active research model, not a retired product.", "what_it_is_for": "The project page says one model should perform many vision tasks out of the box, transfer to new tasks, and generate any trained modality from any subset of the others.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/4m/4m-21-l": { "id": "epfl/4m/4m-21-l", "name": "4M-21 L", "maker": "epfl", "family": "epfl/4m", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-13", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Unified Transformer encoder-decoder trained with multimodal masked modeling on discrete tokens. Any-to-any vision model. Joint EPFL VILAB and Apple release. Exact layer counts for this scale were not copied from the paper.", "modalities": { "input": [ "image", "text" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "4M-21 L is a massively multimodal masked model from EPFL VILAB and Apple. Collected 2026-10-15. Weights are public under the Apple Sample Code license. It is an active research model, not a retired product.", "what_it_is_for": "The project page says one model should perform many vision tasks out of the box, transfer to new tasks, and generate any trained modality from any subset of the others.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/4m/4m-21-xl": { "id": "epfl/4m/4m-21-xl", "name": "4M-21 XL", "maker": "epfl", "family": "epfl/4m", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Sample Code license", "release_date": "2024-06-13", "context_length": null, "max_output_tokens": null, "parameters": "5B", "architecture": "Unified Transformer encoder-decoder trained with multimodal masked modeling on discrete tokens. Any-to-any vision model. Joint EPFL VILAB and Apple release. Exact layer counts for this scale were not copied from the paper.", "modalities": { "input": [ "image", "text" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "4M-21 XL is a massively multimodal masked model from EPFL VILAB and Apple. Collected 2026-10-15. Weights are public under the Apple Sample Code license. It is an active research model, not a retired product.", "what_it_is_for": "The project page says one model should perform many vision tasks out of the box, transfer to new tasks, and generate any trained modality from any subset of the others.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/4m/4m-7-b": { "id": "epfl/4m/4m-7-b", "name": "4M-7 B", "maker": "epfl", "family": "epfl/4m", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-12-11", "context_length": null, "max_output_tokens": null, "parameters": "0.4B", "architecture": "Unified Transformer encoder-decoder trained with multimodal masked modeling on discrete tokens. Any-to-any vision model. Joint EPFL VILAB and Apple release. Exact layer counts for this scale were not copied from the paper.", "modalities": { "input": [ "image", "text" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "4M-7 B is a massively multimodal masked model from EPFL VILAB and Apple. Collected 2026-10-15. Weights are public under the Apple Sample Code license. It is an active research model, not a retired product.", "what_it_is_for": "The project page says one model should perform many vision tasks out of the box, transfer to new tasks, and generate any trained modality from any subset of the others.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/4m/4m-7-l": { "id": "epfl/4m/4m-7-l", "name": "4M-7 L", "maker": "epfl", "family": "epfl/4m", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-12-11", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Unified Transformer encoder-decoder trained with multimodal masked modeling on discrete tokens. Any-to-any vision model. Joint EPFL VILAB and Apple release. Exact layer counts for this scale were not copied from the paper.", "modalities": { "input": [ "image", "text" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "4M-7 L is a massively multimodal masked model from EPFL VILAB and Apple. Collected 2026-10-15. Weights are public under the Apple Sample Code license. It is an active research model, not a retired product.", "what_it_is_for": "The project page says one model should perform many vision tasks out of the box, transfer to new tasks, and generate any trained modality from any subset of the others.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/4m/4m-7-xl": { "id": "epfl/4m/4m-7-xl", "name": "4M-7 XL", "maker": "epfl", "family": "epfl/4m", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-12-11", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Unified Transformer encoder-decoder trained with multimodal masked modeling on discrete tokens. Any-to-any vision model. Joint EPFL VILAB and Apple release. Exact layer counts for this scale were not copied from the paper.", "modalities": { "input": [ "image", "text" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "4M-7 XL is a massively multimodal masked model from EPFL VILAB and Apple. Collected 2026-10-15. Weights are public under the Apple Sample Code license. It is an active research model, not a retired product.", "what_it_is_for": "The project page says one model should perform many vision tasks out of the box, transfer to new tasks, and generate any trained modality from any subset of the others.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/apertus/apertus-1-5-70b": { "id": "epfl/apertus/apertus-1-5-70b", "name": "Apertus 1.5 70B", "maker": "epfl", "family": "epfl/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-07-24", "context_length": 262144, "max_output_tokens": null, "parameters": "70B", "architecture": "Same decoder-only xIELU Apertus 1.0 stack, continued pretraining. Multimodal inputs enter as discrete tokens. The language-model head stays text-only (output vocab 131072, logits padded to 266752). Audio is 40 tokens per second. Native video input is not supported. Thinking is a chat-template flag, not a second model. bfloat16. Total pretraining tokens on the 1.5 card: 17T (15T from 1.0 plus 2T multimodal).", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apertus 1.5 70B is the July 2026 multimodal Apertus 1.5 checkpoint from EPFL, ETH Zurich, and CSCS. Collected 2026-10-15. It is active, Apache-2.0 with an acceptable-use gate, and continues the matching Apertus 1.0 model with image and audio input, a 262144-token context, tool use, and an optional thinking mode.", "what_it_is_for": "The card aims it at multilingual instruction following, tool use, image understanding, and audio understanding, with text generation only. It is a research and downloadable model, with partner hosting rather than an EPFL retail API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/apertus/apertus-1-5-8b": { "id": "epfl/apertus/apertus-1-5-8b", "name": "Apertus 1.5 8B", "maker": "epfl", "family": "epfl/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-07-24", "context_length": 262144, "max_output_tokens": null, "parameters": "8B", "architecture": "Same decoder-only xIELU Apertus 1.0 stack, continued pretraining. Multimodal inputs enter as discrete tokens. The language-model head stays text-only (output vocab 131072, logits padded to 266752). Audio is 40 tokens per second. Native video input is not supported. Thinking is a chat-template flag, not a second model. bfloat16. Total pretraining tokens on the 1.5 card: 19T (15T from 1.0 plus 4T multimodal).", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apertus 1.5 8B is the July 2026 multimodal Apertus 1.5 checkpoint from EPFL, ETH Zurich, and CSCS. Collected 2026-10-15. It is active, Apache-2.0 with an acceptable-use gate, and continues the matching Apertus 1.0 model with image and audio input, a 262144-token context, tool use, and an optional thinking mode.", "what_it_is_for": "The card aims it at multilingual instruction following, tool use, image understanding, and audio understanding, with text generation only. It is a research and downloadable model, with partner hosting rather than an EPFL retail API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/apertus/apertus-2-0": { "id": "epfl/apertus/apertus-2-0", "name": "Apertus 2.0", "maker": "epfl", "family": "epfl/apertus", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apertus 2.0 is a named future model on the official Apertus roadmap, planned for the first quarter of 2027 by the Swiss AI Initiative (EPFL, ETH Zurich, CSCS). Collected 2026-10-15. It is announced only. No weights were released.", "what_it_is_for": "The roadmap describes a larger fully open model with a fairer vocabulary. It is not usable yet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/apertus/apertus-70b": { "id": "epfl/apertus/apertus-70b", "name": "Apertus 70B", "maker": "epfl", "family": "epfl/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-02", "context_length": 65536, "max_output_tokens": null, "parameters": "70B", "architecture": "Decoder-only transformer, xIELU, grouped-query attention, AdEMAMix. Report Table 1: 80 layers, hidden 8192, MLP 43008, 64 query / 8 KV heads. Vocab 131072. Pretrained at 4096 context, then extended to 65536. bfloat16. 15T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.82, "completion_per_m_usd": 2.92 }, "summary_intro": "Apertus 70B is the larger text model in the 2 September 2025 Apertus 1.0 release from EPFL, ETH Zurich, and CSCS. Collected 2026-10-15. It is an active Apache-2.0 model. Apertus 1.5 70B is the multimodal continuation.", "what_it_is_for": "The press release offers it for downstream systems such as chat, translation, and education, with the Instruct checkpoint aligned to the Apertus Charter. It is fully open research infrastructure, not a single EPFL-operated chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/apertus/apertus-8b": { "id": "epfl/apertus/apertus-8b", "name": "Apertus 8B", "maker": "epfl", "family": "epfl/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-02", "context_length": 65536, "max_output_tokens": null, "parameters": "8B", "architecture": "Decoder-only transformer, xIELU, grouped-query attention, AdEMAMix. Report Table 1: 32 layers, hidden 4096, MLP 21504, 32 query / 8 KV heads. Vocab 131072. Pretrained at 4096 context, then extended to 65536. bfloat16. 15T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apertus 8B is the smaller fully open multilingual language model released on 2 September 2025 by EPFL, ETH Zurich, and CSCS under the Swiss AI Initiative. This dossier was collected on 2026-10-15. Weights, data recipes, and training code are public under Apache-2.0. The 8B line is active. Apertus 1.5 continues it.", "what_it_is_for": "The maker positions it as a public-good base for chat, translation, education, and further research, with instruction following on the Instruct checkpoint and tool use documented on the card. It is a dense 8B decoder, not a hosted EPFL product API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/apertus/apertus-v1-1-0-5b": { "id": "epfl/apertus/apertus-v1-1-0-5b", "name": "Apertus v1.1 0.5B", "maker": "epfl", "family": "epfl/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-05-27", "context_length": null, "max_output_tokens": null, "parameters": "0.4B", "architecture": "Dense decoder, GQA, xIELU, tied embeddings. 20 layers, dimension 1024, MLP 6144, 16 query / 4 KV heads. Card compute/storage size 0.4B/0.4B. Distilled 1.7T tokens, 0.2E22 FLOPs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apertus v1.1 0.5B is a distilled Apertus 1.1 language model from the Swiss AI Initiative (EPFL, ETH Zurich, CSCS), released as Apertus Mini in May 2026. Collected 2026-10-15. It is active, Apache-2.0, and aimed at constrained hardware. The teacher is Apertus-8B-2509.", "what_it_is_for": "The card says the base model is for further task fine-tuning and completion, and that separate Instruct checkpoints cover chat. It is a cost-reduced extension of Apertus, not a new pretraining run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/apertus/apertus-v1-1-1-5b": { "id": "epfl/apertus/apertus-v1-1-1-5b", "name": "Apertus v1.1 1.5B", "maker": "epfl", "family": "epfl/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-05-27", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Dense decoder, GQA, xIELU, untied embeddings. 16 layers, dimension 2048, MLP 12288, 32 query / 8 KV heads. Card compute/storage size 1.5B/2.0B. Hugging Face header says 2B params. Distilled 1.7T tokens, 0.8E22 FLOPs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apertus v1.1 1.5B is a distilled Apertus 1.1 language model from the Swiss AI Initiative (EPFL, ETH Zurich, CSCS), released as Apertus Mini in May 2026. Collected 2026-10-15. It is active, Apache-2.0, and aimed at constrained hardware. The teacher is Apertus-8B-2509.", "what_it_is_for": "The card says the base model is for further task fine-tuning and completion, and that separate Instruct checkpoints cover chat. It is a cost-reduced extension of Apertus, not a new pretraining run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/apertus/apertus-v1-1-4b": { "id": "epfl/apertus/apertus-v1-1-4b", "name": "Apertus v1.1 4B", "maker": "epfl", "family": "epfl/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-05-27", "context_length": null, "max_output_tokens": null, "parameters": "3.8B", "architecture": "Dense decoder, GQA, xIELU, untied embeddings. 24 layers, dimension 3072, MLP 16384, 24 query / 8 KV heads. Card compute/storage size 3.8B/4.6B. Hugging Face header says 4B params. Distilled 1.7T tokens, 2.0E22 FLOPs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apertus v1.1 4B is a distilled Apertus 1.1 language model from the Swiss AI Initiative (EPFL, ETH Zurich, CSCS), released as Apertus Mini in May 2026. Collected 2026-10-15. It is active, Apache-2.0, and aimed at constrained hardware. The teacher is Apertus-8B-2509.", "what_it_is_for": "The card says the base model is for further task fine-tuning and completion, and that separate Instruct checkpoints cover chat. It is a cost-reduced extension of Apertus, not a new pretraining run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/flexar/flexar-113m-t2i": { "id": "epfl/flexar/flexar-113m-t2i", "name": "FlexAR 113M T2I", "maker": "epfl", "family": "epfl/flexar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "113M", "architecture": "Autoregressive text-to-image model on FlexTok. Depth and width were not opened on this card. The 1B T2I card documents the shared loader and FLAN-T5-XL conditioning for that size only.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlexAR 113M T2I is an autoregressive image model from EPFL VILAB trained on FlexTok tokens, in the 2026 ordered-tokens release. Collected 2026-10-15.", "what_it_is_for": "The paper uses these models to show that 1D ordered tokens make test-time search cheaper than 2D token grids. They are research generators, not a hosted image product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/flexar/flexar-1b-c2i": { "id": "epfl/flexar/flexar-1b-c2i", "name": "FlexAR 1B C2I", "maker": "epfl", "family": "epfl/flexar", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-11", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Class-conditional decoder. Config comment: 30 blocks, embed 1920, 30 heads, about 1300M parameters, 1000 ImageNet classes, tokenizer flextok_d18_d28_in1k, 256 1D tokens. Apache-2.0. The folder id keeps the lab's 1B name.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlexAR 1B C2I is an autoregressive image model from EPFL VILAB trained on FlexTok tokens, in the 2026 ordered-tokens release. Collected 2026-10-15.", "what_it_is_for": "The paper uses these models to show that 1D ordered tokens make test-time search cheaper than 2D token grids. They are research generators, not a hosted image product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/flexar/flexar-1b-t2i": { "id": "epfl/flexar/flexar-1b-t2i", "name": "FlexAR 1B T2I", "maker": "epfl", "family": "epfl/flexar", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-05-05", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Transformer decoder with cross-attention. Card: embedding 1664, 26 blocks, 26 heads, 256x256, tokenizer EPFL-VILAB/flextok_d18_d28_dfn, text encoder google/flan-t5-xl. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlexAR 1B T2I is an autoregressive image model from EPFL VILAB trained on FlexTok tokens, in the 2026 ordered-tokens release. Collected 2026-10-15.", "what_it_is_for": "The paper uses these models to show that 1D ordered tokens make test-time search cheaper than 2D token grids. They are research generators, not a hosted image product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/flexar/flexar-382m-t2i": { "id": "epfl/flexar/flexar-382m-t2i", "name": "FlexAR 382M T2I", "maker": "epfl", "family": "epfl/flexar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "382M", "architecture": "Autoregressive text-to-image model on FlexTok. Depth and width were not opened on this card. The 1B T2I card documents the shared loader and FLAN-T5-XL conditioning for that size only.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlexAR 382M T2I is an autoregressive image model from EPFL VILAB trained on FlexTok tokens, in the 2026 ordered-tokens release. Collected 2026-10-15.", "what_it_is_for": "The paper uses these models to show that 1D ordered tokens make test-time search cheaper than 2D token grids. They are research generators, not a hosted image product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/flexar/flexar-3b-t2i": { "id": "epfl/flexar/flexar-3b-t2i", "name": "FlexAR 3B T2I", "maker": "epfl", "family": "epfl/flexar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Autoregressive text-to-image model on FlexTok. Depth and width were not opened on this card. The 1B T2I card documents the shared loader and FLAN-T5-XL conditioning for that size only.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlexAR 3B T2I is an autoregressive image model from EPFL VILAB trained on FlexTok tokens, in the 2026 ordered-tokens release. Collected 2026-10-15.", "what_it_is_for": "The paper uses these models to show that 1D ordered tokens make test-time search cheaper than 2D token grids. They are research generators, not a hosted image product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/flexar/gridar-3b-t2i": { "id": "epfl/flexar/gridar-3b-t2i", "name": "GridAR 3B T2I", "maker": "epfl", "family": "epfl/flexar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Named in the EPFL-VILAB FlexTok AR collection as a text-to-image model. Architecture was not opened. It is grouped with FlexAR because the lab published it in that collection.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GridAR 3B T2I is an autoregressive image model from EPFL VILAB trained on FlexTok tokens, in the 2026 ordered-tokens release. Collected 2026-10-15.", "what_it_is_for": "The paper uses these models to show that 1D ordered tokens make test-time search cheaper than 2D token grids. They are research generators, not a hosted image product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/flextok/flextok-d12-d12-in1k": { "id": "epfl/flextok/flextok-d12-d12-in1k", "name": "FlexTok d12-d12 IN1K", "maker": "epfl", "family": "epfl/flextok", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Official zoo: 12 encoder layers, 12 decoder layers, ImageNet-1K.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlexTok d12-d12 IN1K is a FlexTok image tokenizer released by EPFL VILAB and Apple in April 2025 (ICML 2025). Collected 2026-10-15. It is an active research weight.", "what_it_is_for": "FlexTok maps an image to a variable-length 1D token sequence ordered coarse to fine, so a generator can stop early. It is a tokenizer, not a text-to-image product by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/flextok/flextok-d18-d18-in1k": { "id": "epfl/flextok/flextok-d18-d18-in1k", "name": "FlexTok d18-d18 IN1K", "maker": "epfl", "family": "epfl/flextok", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Official zoo: 18 encoder layers, 18 decoder layers, ImageNet-1K.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlexTok d18-d18 IN1K is a FlexTok image tokenizer released by EPFL VILAB and Apple in April 2025 (ICML 2025). Collected 2026-10-15. It is an active research weight.", "what_it_is_for": "FlexTok maps an image to a variable-length 1D token sequence ordered coarse to fine, so a generator can stop early. It is a tokenizer, not a text-to-image product by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/flextok/flextok-d18-d28-dfn": { "id": "epfl/flextok/flextok-d18-d28-dfn", "name": "FlexTok d18-d28 DFN", "maker": "epfl", "family": "epfl/flextok", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Model License for Research", "release_date": "2025-04-01", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "18 encoder layers and 28 decoder layers, DFN data. The card example tokenizes 256x256 images to up to 256 discrete tokens and decodes with 20 denoising steps. Hub header 3B params, float32.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlexTok d18-d28 DFN is a FlexTok image tokenizer released by EPFL VILAB and Apple in April 2025 (ICML 2025). Collected 2026-10-15. It is an active research weight.", "what_it_is_for": "FlexTok maps an image to a variable-length 1D token sequence ordered coarse to fine, so a generator can stop early. It is a tokenizer, not a text-to-image product by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/flextok/flextok-d18-d28-in1k": { "id": "epfl/flextok/flextok-d18-d28-in1k", "name": "FlexTok d18-d28 IN1K", "maker": "epfl", "family": "epfl/flextok", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Official zoo: 18 encoder layers, 28 decoder layers, ImageNet-1K. Parameter count was not opened.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlexTok d18-d28 IN1K is a FlexTok image tokenizer released by EPFL VILAB and Apple in April 2025 (ICML 2025). Collected 2026-10-15. It is an active research weight.", "what_it_is_for": "FlexTok maps an image to a variable-length 1D token sequence ordered coarse to fine, so a generator can stop early. It is a tokenizer, not a text-to-image product by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/flextok/flextok-vae": { "id": "epfl/flextok/flextok-vae", "name": "FlexTok VAE", "maker": "epfl", "family": "epfl/flextok", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apple Model License for Research", "release_date": "2025-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "KL autoencoder used under FlexTok. The c8 card is an 8-channel VAE-GAN, downsample 8, loaded as a Diffusers AutoencoderKL. The model zoo also lists 4-channel and 16-channel VAEs.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlexTok VAE is the autoencoder family shipped with FlexTok by EPFL VILAB and Apple in April 2025. Collected 2026-10-15. Three channel widths are in the official zoo. This folder groups them because they are components of one tokenizer release, not separate products.", "what_it_is_for": "They compress images before FlexTok's 1D resampler. They are not generative products on their own.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/meditron/meditron-70b": { "id": "epfl/meditron/meditron-70b", "name": "Meditron 70B", "maker": "epfl", "family": "epfl/meditron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-11-27", "context_length": 4096, "max_output_tokens": null, "parameters": "70B", "architecture": "Llama-2-70B continued pretraining. 80 layers, hidden 8192, 64 attention heads. Sequence length 4096. Trained September to October 2023 on 48.1B GAP-Replay tokens. Hugging Face header says 69B params.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meditron 70B is the larger 2023 medical language model from the EPFL LLM Team, continued from Llama-2-70B. Collected 2026-10-15. The weights remain available as a static research release under the Llama 2 license. Meditron 3 is the later suite.", "what_it_is_for": "The lab released it so researchers can study medical question answering and clinical-knowledge encoding. The advisory says it is not ready for care.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/meditron/meditron-7b": { "id": "epfl/meditron/meditron-7b", "name": "Meditron 7B", "maker": "epfl", "family": "epfl/meditron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-11-27", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "Llama-2-7B continued pretraining. 32 layers, hidden 4096, 32 attention heads. Sequence length 2048. Trained September 2023 on 48.1B GAP-Replay tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meditron 7B is a medical language model from the EPFL LLM Team, released with the November 2023 MEDITRON paper. Collected 2026-10-15. It is an active static research checkpoint continued from Llama-2-7B under the Llama 2 license. It is not a clinical product.", "what_it_is_for": "The card offers it for research on medical exam questions, differential diagnosis support, and health-information queries, and warns against deployment without further alignment and trials.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/meditron/meditron3-70b": { "id": "epfl/meditron/meditron3-70b", "name": "Meditron 3 70B", "maker": "epfl", "family": "epfl/meditron", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-09-24", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Causal decoder-only model continued from Llama-3.1-70B. Described as Meditron-3[70B]. Secondary hosts say it is not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meditron 3 70B is the larger Meditron 3 medical model from EPFL LiGHT and OpenMeditron, released with the 24 September 2024 description of Llama-3.1 Meditron-3. Collected 2026-10-15. It remains a research weight release. Confidence is medium because the model card was gated.", "what_it_is_for": "The project presents it as a scalable medical reasoning model for research, including differential diagnosis, and not as a finished clinical assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/meditron/meditron3-8b": { "id": "epfl/meditron/meditron3-8b", "name": "Meditron 3 8B", "maker": "epfl", "family": "epfl/meditron", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-09-24", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Causal decoder-only model continued from Llama-3.1-8B. The OpenMeditron card calls it Llama-3.1 Meditron-3[8B]. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meditron 3 8B is the 8 billion parameter medical model in the Meditron 3 suite from EPFL LiGHT and the OpenMeditron collaboration, based on Llama 3.1 and described in a 24 September 2024 paper. Collected 2026-10-15. Weights are still up. The Hub banner treats Apertus-8B-MeditronFO as the newer model. Confidence is medium because the card is gated.", "what_it_is_for": "The visible card says it is for research on clinical decision-making and evidence-based medical information, with an emphasis on low-resource and humanitarian settings. It is a base model, not an instruction-tuned assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/meditronfo/apertus-70b-meditronfo": { "id": "epfl/meditronfo/apertus-70b-meditronfo", "name": "Apertus 70B MeditronFO", "maker": "epfl", "family": "epfl/meditronfo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-06-17", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Medical post-training of Apertus-70B-Instruct on the Fully Open Meditron corpus. Causal language model. Text in, text out. The EuroLLM-9B card documents the shared corpus construction; per-model hyperparameters were not all re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apertus 70B MeditronFO is a MeditronFO clinical language model from EPFL LiGHT, released with the June 2026 fully open medical-LLM announcement. Collected 2026-10-15. It is an active research weight, specialized from Apertus-70B-Instruct.", "what_it_is_for": "LiGHT says MeditronFO is an auditable pipeline for clinical LLMs: open weights, open data, and an open recipe, for medical evaluation rather than autonomous care.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/meditronfo/apertus-8b-meditronfo": { "id": "epfl/meditronfo/apertus-8b-meditronfo", "name": "Apertus 8B MeditronFO", "maker": "epfl", "family": "epfl/meditronfo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-06-17", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Medical post-training of Apertus-8B-Instruct on the Fully Open Meditron corpus. Causal language model. Text in, text out. The EuroLLM-9B card documents the shared corpus construction; per-model hyperparameters were not all re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apertus 8B MeditronFO is a MeditronFO clinical language model from EPFL LiGHT, released with the June 2026 fully open medical-LLM announcement. Collected 2026-10-15. It is an active research weight, specialized from Apertus-8B-Instruct.", "what_it_is_for": "LiGHT says MeditronFO is an auditable pipeline for clinical LLMs: open weights, open data, and an open recipe, for medical evaluation rather than autonomous care.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/meditronfo/eurollm-22b-meditronfo": { "id": "epfl/meditronfo/eurollm-22b-meditronfo", "name": "EuroLLM 22B MeditronFO", "maker": "epfl", "family": "epfl/meditronfo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-06-17", "context_length": null, "max_output_tokens": null, "parameters": "22B", "architecture": "Medical post-training of EuroLLM-22B-Instruct on the Fully Open Meditron corpus. Causal language model. Text in, text out. The EuroLLM-9B card documents the shared corpus construction; per-model hyperparameters were not all re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroLLM 22B MeditronFO is a MeditronFO clinical language model from EPFL LiGHT, released with the June 2026 fully open medical-LLM announcement. Collected 2026-10-15. It is an active research weight, specialized from EuroLLM-22B-Instruct.", "what_it_is_for": "LiGHT says MeditronFO is an auditable pipeline for clinical LLMs: open weights, open data, and an open recipe, for medical evaluation rather than autonomous care.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/meditronfo/eurollm-9b-meditronfo": { "id": "epfl/meditronfo/eurollm-9b-meditronfo", "name": "EuroLLM 9B MeditronFO", "maker": "epfl", "family": "epfl/meditronfo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-06-17", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Medical post-training of EuroLLM-9B-Instruct on the Fully Open Meditron corpus. Causal language model. Text in, text out. The EuroLLM-9B card documents the shared corpus construction; per-model hyperparameters were not all re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroLLM 9B MeditronFO is a MeditronFO clinical language model from EPFL LiGHT, released with the June 2026 fully open medical-LLM announcement. Collected 2026-10-15. It is an active research weight, specialized from EuroLLM-9B-Instruct.", "what_it_is_for": "LiGHT says MeditronFO is an auditable pipeline for clinical LLMs: open weights, open data, and an open recipe, for medical evaluation rather than autonomous care.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/meditronfo/gemma-3-27b-meditronfo": { "id": "epfl/meditronfo/gemma-3-27b-meditronfo", "name": "Gemma 3 27B MeditronFO", "maker": "epfl", "family": "epfl/meditronfo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-06-17", "context_length": null, "max_output_tokens": null, "parameters": "27B", "architecture": "Medical post-training of Gemma-3-27B-it on the Fully Open Meditron corpus. Causal language model. Text in, text out. The EuroLLM-9B card documents the shared corpus construction; per-model hyperparameters were not all re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemma 3 27B MeditronFO is a MeditronFO clinical language model from EPFL LiGHT, released with the June 2026 fully open medical-LLM announcement. Collected 2026-10-15. It is an active research weight, specialized from Gemma-3-27B-it.", "what_it_is_for": "LiGHT says MeditronFO is an auditable pipeline for clinical LLMs: open weights, open data, and an open recipe, for medical evaluation rather than autonomous care.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/meditronfo/olmo-2-32b-meditronfo": { "id": "epfl/meditronfo/olmo-2-32b-meditronfo", "name": "OLMo 2 32B MeditronFO", "maker": "epfl", "family": "epfl/meditronfo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-06-17", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Medical post-training of OLMo-2-32B on the Fully Open Meditron corpus. Causal language model. Text in, text out. The EuroLLM-9B card documents the shared corpus construction; per-model hyperparameters were not all re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 2 32B MeditronFO is a MeditronFO clinical language model from EPFL LiGHT, released with the June 2026 fully open medical-LLM announcement. Collected 2026-10-15. It is an active research weight, specialized from OLMo-2-32B.", "what_it_is_for": "LiGHT says MeditronFO is an auditable pipeline for clinical LLMs: open weights, open data, and an open recipe, for medical evaluation rather than autonomous care.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/svi/svi-dance": { "id": "epfl/svi/svi-dance", "name": "SVI-Dance", "maker": "epfl", "family": "epfl/svi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapters in the SVI family. The card says only LoRA adapters are trained. Base video model name and parameter count are not on the card. File version-1.0/svi-dance.safetensors. Conditioning is a skeleton sequence plus an image.", "modalities": { "input": [ "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SVI-Dance is one named LoRA in Stable Video Infinity, an EPFL VITA Lab video model released with an October 2025 paper. Collected 2026-10-15. The family weights are public research adapters for long video.", "what_it_is_for": "Dance video from an image and a skeleton condition. Skeleton is not a schema modality, so it is named in the architecture string.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/svi/svi-film": { "id": "epfl/svi/svi-film", "name": "SVI-Film", "maker": "epfl", "family": "epfl/svi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapters in the SVI family. The card says only LoRA adapters are trained. Base video model name and parameter count are not on the card. File version-1.0/svi-film.safetensors.", "modalities": { "input": [ "image", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SVI-Film is one named LoRA in Stable Video Infinity, an EPFL VITA Lab video model released with an October 2025 paper. Collected 2026-10-15. The family weights are public research adapters for long video.", "what_it_is_for": "Multi-scene film-style video from an image and a stream of text prompts, about 5 seconds per text on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/svi/svi-film-transition": { "id": "epfl/svi/svi-film-transition", "name": "SVI-Film Transition", "maker": "epfl", "family": "epfl/svi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapters in the SVI family. The card says only LoRA adapters are trained. Base video model name and parameter count are not on the card. File version-1.0/svi-film-transitions.safetensors.", "modalities": { "input": [ "image", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SVI-Film Transition is one named LoRA in Stable Video Infinity, an EPFL VITA Lab video model released with an October 2025 paper. Collected 2026-10-15. The family weights are public research adapters for long video.", "what_it_is_for": "Film variant trained for more scene transitions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/svi/svi-shot": { "id": "epfl/svi/svi-shot", "name": "SVI-Shot", "maker": "epfl", "family": "epfl/svi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapters in the SVI family. The card says only LoRA adapters are trained. Base video model name and parameter count are not on the card. File version-1.0/svi-shot.safetensors.", "modalities": { "input": [ "image", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SVI-Shot is one named LoRA in Stable Video Infinity, an EPFL VITA Lab video model released with an October 2025 paper. Collected 2026-10-15. The family weights are public research adapters for long video.", "what_it_is_for": "Single-scene long video from an image and one text prompt. The card says this setting is meant not to drift.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/svi/svi-talk": { "id": "epfl/svi/svi-talk", "name": "SVI-Talk", "maker": "epfl", "family": "epfl/svi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapters in the SVI family. The card says only LoRA adapters are trained. Base video model name and parameter count are not on the card. File version-1.0/svi-talk.safetensors.", "modalities": { "input": [ "image", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SVI-Talk is one named LoRA in Stable Video Infinity, an EPFL VITA Lab video model released with an October 2025 paper. Collected 2026-10-15. The family weights are public research adapters for long video.", "what_it_is_for": "Talking-head video from an image and audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/svi/svi-tom": { "id": "epfl/svi/svi-tom", "name": "SVI-Tom", "maker": "epfl", "family": "epfl/svi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapters in the SVI family. The card says only LoRA adapters are trained. Base video model name and parameter count are not on the card. File version-1.0/svi-tom.safetensors.", "modalities": { "input": [ "image", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SVI-Tom is one named LoRA in Stable Video Infinity, an EPFL VITA Lab video model released with an October 2025 paper. Collected 2026-10-15. The family weights are public research adapters for long video.", "what_it_is_for": "Cartoon long video. The table lists image input. The comment also mentions a text prompt stream. Both are recorded as inputs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/videoflextok/videoflextok-d18-d18-k600": { "id": "epfl/videoflextok/videoflextok-d18-d18-k600", "name": "VideoFlexTok d18-d18 K600", "maker": "epfl", "family": "epfl/videoflextok", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-04-01", "context_length": null, "max_output_tokens": null, "parameters": "570M excluding adaLN", "architecture": "VideoFlexTok-K600 on the project page: 18-layer encoder and decoder, 17-frame 128x128 clips, Kinetics-600, codebook size 64000. GitHub id EPFL-VILAB/videoflextok_d18_d18_k600.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoFlexTok d18-d18 K600 is the smaller Kinetics-600 video tokenizer from EPFL VILAB and Apple, paired with the April 2026 VideoFlexTok release. Collected 2026-10-15. It is an active research weight. The license was not re-read on its own card.", "what_it_is_for": "Same job as the larger VideoFlexTok: flexible-length coarse-to-fine video tokens, at 128x128.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "epfl/videoflextok/videoflextok-d18-d28": { "id": "epfl/videoflextok/videoflextok-d18-d28", "name": "VideoFlexTok d18-d28", "maker": "epfl", "family": "epfl/videoflextok", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-04-01", "context_length": null, "max_output_tokens": null, "parameters": "1.3B excluding adaLN", "architecture": "VideoFlexTok-Panda on the project page: 18-layer encoder, 28-layer decoder, 17-frame 256x256 clips, Panda-70M subset, codebook size 64000, 400B VAE tokens. Hub config shows encoder dim 1152 and depth 18. Apache-2.0.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoFlexTok d18-d28 is the larger video tokenizer from EPFL VILAB and Apple, uploaded 1 April 2026 and presented at ICML 2026. Collected 2026-10-15. Weights are Apache-2.0. It is active.", "what_it_is_for": "It represents video as a variable-length coarse-to-fine token sequence so downstream generators can use fewer tokens. It is a tokenizer, not a text-to-video product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "essential-ai/eai-distill/eai-distill-0-5b": { "id": "essential-ai/eai-distill/eai-distill-0-5b", "name": "EAI-Distill-0.5B", "maker": "essential-ai", "family": "essential-ai/eai-distill", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-17", "context_length": 32768, "max_output_tokens": null, "parameters": "630167424", "architecture": "Fine-tune of Qwen2.5-0.5B-Instruct for taxonomy labeling, not a from-scratch Essential model. Qwen2ForCausalLM: 24 layers, hidden 896, intermediate 4864, 14 heads, 2 KV heads, SiLU, vocab 151936, rope_theta 1000000, tie_word_embeddings false. config max_position_embeddings 32768. Training sequence length on the card is 16,384. Safetensors total 630,167,424 F32 weights. The 0.5B name matches the Qwen base name; the weight count is larger than 0.5 billion. Trained on 82B synthetic tokens from Qwen2.5-32B-Instruct labels over 104M Common Crawl documents. Optimizer AdamW. Hardware stated as AMD MI300X.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EAI-Distill-0.5B is Essential AI's small document-taxonomy model, a fine-tune of Qwen2.5-0.5B-Instruct released with the Essential-Web v1.0 paper on 17 June 2025. It writes structured labels for web documents. It is not a general chat model and not part of the Rnj family. Weights are Apache-2.0 and were still public on 15 October 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says it classifies web documents for dataset curation. Labels cover Free Decimal Correspondence subject codes, Bloom cognitive process and knowledge domain, document type, extraction artifacts, missing content, reasoning depth, technical correctness, and educational level. The system prompt used in the example is the single word `taxonomy`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "essential-ai/rnj/rnj-1": { "id": "essential-ai/rnj/rnj-1", "name": "Rnj-1", "maker": "essential-ai", "family": "essential-ai/rnj", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-05", "context_length": 32768, "max_output_tokens": null, "parameters": "8.3B", "architecture": "Dense decoder-only model trained from scratch. Gemma 3-like (Gemma3ForCausalLM, model_type gemma3_text) with global attention only and YaRN, not Gemma 3 sliding-window attention. 32 layers, hidden size 4096, MLP 16384, 32 query heads, 8 KV heads, head dim 128, vocab size 128256. Maker table lists GeGLU and tied embeddings; config.json hidden_activation is gelu_pytorch_tanh. Pre-trained on 8.4T tokens at 8K context, then 380B tokens of mid-training to 32K. Muon optimizer. Safetensors index: 8310501376 F32 weights. Maker estimates 417 zettaFLOPs of pre-training (6nt).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rnj-1 is Essential AI's base 8.3B dense language model, trained from scratch and released with an instruct twin on 5 December 2025. The name is an homage to Ramanujan and is pronounced \"range-1\". Weights are Apache-2.0 and were still public on Hugging Face on 15 October 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "The maker describes Rnj-1 as a code and STEM base model, with fill-in-the-middle pre-training, meant to be extended by the community. Instruction following, tool use, and agent scaffolding are documented on the instruct checkpoint, not as the job of this base release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "essential-ai/rnj/rnj-1-5-instruct": { "id": "essential-ai/rnj/rnj-1-5-instruct", "name": "Rnj-1.5 Instruct", "maker": "essential-ai", "family": "essential-ai/rnj", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-22", "context_length": 163840, "max_output_tokens": null, "parameters": "8.3B", "architecture": "Long-context instruct model in the Rnj-1 family. Hugging Face base_model is EssentialAI/rnj-1. Architecture class Rnj1ForCausalLM, model_type gemma3_text. Same width as Rnj-1: 32 layers, hidden 4096, MLP 16384, 32 heads, 8 KV heads, head dim 128, vocab 128256, safetensors total 8310501376. Attention is interleaved block-local and global, pattern LLLGLLLGLLLGLGGGGGLGLLLGLLLGLLLL (config layer_types chunked_attention and full_attention). Local sliding window 8192. max_position_embeddings 163840. YaRN factor 20 from original_max_position_embeddings 8192. Continued-training token count is not published. The comparison table still lists 417 pre-training zettaFLOPs, the same figure as Rnj-1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rnj-1.5 Instruct is Essential AI's long-context follow-up to Rnj-1 Instruct. It keeps the 8.3B dense size, changes the attention pattern, and extends the context window from 32,768 to 163,840 tokens. Weights are Apache-2.0. The Hugging Face repo was created on 22 April 2026 and the card was last updated on 26 May 2026. No separate launch blog was found. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says it extends Rnj-1's long-context ability past 32k and improves coding on more agent harnesses. The stated long-context target is comprehension, including repository-level code, not long generation. Usage instructions are the Rnj-1 card with the repo id swapped. vLLM support is stated from v0.20.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "essential-ai/rnj/rnj-1-instruct": { "id": "essential-ai/rnj/rnj-1-instruct", "name": "Rnj-1 Instruct", "maker": "essential-ai", "family": "essential-ai/rnj", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-05", "context_length": 32768, "max_output_tokens": null, "parameters": "8.3B", "architecture": "Instruction-tuned Rnj-1. Same Gemma3ForCausalLM / gemma3_text stack: 32 global-attention layers, hidden 4096, MLP 16384, 32 heads, 8 KV heads, head dim 128, vocab 128256, YaRN from 8192 to 32768 (factor 4). Maker table says GeGLU and tied embeddings; config.json hidden_activation is gelu_pytorch_tanh. SFT is a further 150B tokens at learning rate 2e-5 after the base's 8.4T pre-train and 380B mid-train. Safetensors index: 8310501376 F32 weights. Tool calls use a Hermes-style wrapper. Fill-in-the-middle uses <|pre_fim|>, <|suf_fim|>, and <|mid_fim|>.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": 0.15 }, "summary_intro": "Rnj-1 Instruct is Essential AI's post-trained 8.3B code and STEM model, the instruction-tuned pair to the Rnj-1 base. It was announced on 5 December 2025, weights are Apache-2.0, and the checkpoint was still public on 15 October 2026. Essential does not run its own API. Together AI hosts it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The maker positions it for code generation, fill-in-the-middle, tool calling, and agentic software-engineering tasks such as mini-swe-agent, plus math and science questions. Post-training was kept limited so others can specialize it. It is not sold as a general factual assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-1-1-0-5b": { "id": "eth/apertus/apertus-1-1-0-5b", "name": "Apertus 1.1 0.5B", "maker": "eth", "family": "eth/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 weights. Download is gated by the Apertus acceptable-use clickwrap (indemnity toward ETH Zurich and EPFL, and a personal-data notice). Policy text on the 1.0 cards is dated 1 September 2025.", "release_date": "2026-06-15", "context_length": 4096, "max_output_tokens": null, "parameters": "438349352 safetensors elements. The paper table says 0.4B compute and 0.4B storage with tied embeddings.", "architecture": "Dense Apertus decoder distilled from Apertus-8B-2509 on 1.7T tokens from phase 5 of the original data pipeline (90 percent KL, 10 percent label cross-entropy). 20 layers, hidden 1024, intermediate 6144, 16 query / 4 KV heads, tied embeddings, vocab 131072, xIELU, RoPE theta 500000, max position 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Apertus 1.1 0.5B (apertus-1-1-0-5b). Status: active. Collected 2026-10-15.", "what_it_is_for": "The project says these sizes are for mobile and browser deployment, at a fraction of the cost of pretraining the 8B teacher.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-1-1-0-5b-instruct": { "id": "eth/apertus/apertus-1-1-0-5b-instruct", "name": "Apertus 1.1 0.5B Instruct", "maker": "eth", "family": "eth/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 weights. Download is gated by the Apertus acceptable-use clickwrap (indemnity toward ETH Zurich and EPFL, and a personal-data notice). Policy text on the 1.0 cards is dated 1 September 2025.", "release_date": "2026-06-15", "context_length": 4096, "max_output_tokens": null, "parameters": "572567080 safetensors elements. Config still sets tie_word_embeddings true. The extra 134217728 elements equal vocab size times hidden size, so the file may store an untied copy. Unique count was not published.", "architecture": "Dense Apertus decoder distilled from Apertus-8B-2509 on 1.7T tokens from phase 5 of the original data pipeline (90 percent KL, 10 percent label cross-entropy). 20 layers, hidden 1024, intermediate 6144, 16 query / 4 KV heads, tied embeddings, vocab 131072, xIELU, RoPE theta 500000, max position 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Apertus 1.1 0.5B Instruct (apertus-1-1-0-5b-instruct). Status: active. Collected 2026-10-15.", "what_it_is_for": "The project says these sizes are for mobile and browser deployment, at a fraction of the cost of pretraining the 8B teacher.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-1-1-1-5b": { "id": "eth/apertus/apertus-1-1-1-5b", "name": "Apertus 1.1 1.5B", "maker": "eth", "family": "eth/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 weights. Download is gated by the Apertus acceptable-use clickwrap (indemnity toward ETH Zurich and EPFL, and a personal-data notice). Policy text on the 1.0 cards is dated 1 September 2025.", "release_date": "2026-06-15", "context_length": 4096, "max_output_tokens": null, "parameters": "1510019104 safetensors elements. The paper table says 1.5B compute and 2.0B storage.", "architecture": "Dense Apertus decoder distilled from Apertus-8B-2509 on 1.7T tokens from phase 5 of the original data pipeline (90 percent KL, 10 percent label cross-entropy). 16 layers, hidden 2048, intermediate 12288, 32 query / 8 KV heads, untied embeddings, vocab 131072, xIELU, RoPE theta 500000, max position 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Apertus 1.1 1.5B (apertus-1-1-1-5b). Status: active. Collected 2026-10-15.", "what_it_is_for": "The project says these sizes are for mobile and browser deployment, at a fraction of the cost of pretraining the 8B teacher.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-1-1-1-5b-instruct": { "id": "eth/apertus/apertus-1-1-1-5b-instruct", "name": "Apertus 1.1 1.5B Instruct", "maker": "eth", "family": "eth/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 weights. Download is gated by the Apertus acceptable-use clickwrap (indemnity toward ETH Zurich and EPFL, and a personal-data notice). Policy text on the 1.0 cards is dated 1 September 2025.", "release_date": "2026-06-15", "context_length": 4096, "max_output_tokens": null, "parameters": "1510019104 safetensors elements. The paper table says 1.5B compute and 2.0B storage.", "architecture": "Dense Apertus decoder distilled from Apertus-8B-2509 on 1.7T tokens from phase 5 of the original data pipeline (90 percent KL, 10 percent label cross-entropy). 16 layers, hidden 2048, intermediate 12288, 32 query / 8 KV heads, untied embeddings, vocab 131072, xIELU, RoPE theta 500000, max position 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Apertus 1.1 1.5B Instruct (apertus-1-1-1-5b-instruct). Status: active. Collected 2026-10-15.", "what_it_is_for": "The project says these sizes are for mobile and browser deployment, at a fraction of the cost of pretraining the 8B teacher.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-1-1-4b": { "id": "eth/apertus/apertus-1-1-4b", "name": "Apertus 1.1 4B", "maker": "eth", "family": "eth/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 weights. Download is gated by the Apertus acceptable-use clickwrap (indemnity toward ETH Zurich and EPFL, and a personal-data notice). Policy text on the 1.0 cards is dated 1 September 2025.", "release_date": "2026-06-15", "context_length": 4096, "max_output_tokens": null, "parameters": "3825361968 safetensors elements. The 4B card says 3.8B compute and 4.6B storage.", "architecture": "Dense Apertus decoder distilled from Apertus-8B-2509 on 1.7T tokens from phase 5 of the original data pipeline (90 percent KL, 10 percent label cross-entropy). 24 layers, hidden 3072, intermediate 16384, 24 query / 8 KV heads, untied embeddings, vocab 131072, xIELU, RoPE theta 500000, max position 4096. The 4B card states 2.0E22 FLOPs and 64 GH200 GPUs for this size.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Apertus 1.1 4B (apertus-1-1-4b). Status: active. Collected 2026-10-15.", "what_it_is_for": "The project says these sizes are for mobile and browser deployment, at a fraction of the cost of pretraining the 8B teacher.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-1-1-4b-instruct": { "id": "eth/apertus/apertus-1-1-4b-instruct", "name": "Apertus 1.1 4B Instruct", "maker": "eth", "family": "eth/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 weights. Download is gated by the Apertus acceptable-use clickwrap (indemnity toward ETH Zurich and EPFL, and a personal-data notice). Policy text on the 1.0 cards is dated 1 September 2025.", "release_date": "2026-06-15", "context_length": 4096, "max_output_tokens": null, "parameters": "3825361968 safetensors elements. The 4B card says 3.8B compute and 4.6B storage.", "architecture": "Dense Apertus decoder distilled from Apertus-8B-2509 on 1.7T tokens from phase 5 of the original data pipeline (90 percent KL, 10 percent label cross-entropy). 24 layers, hidden 3072, intermediate 16384, 24 query / 8 KV heads, untied embeddings, vocab 131072, xIELU, RoPE theta 500000, max position 4096. The 4B card states 2.0E22 FLOPs and 64 GH200 GPUs for this size.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Apertus 1.1 4B Instruct (apertus-1-1-4b-instruct). Status: active. Collected 2026-10-15.", "what_it_is_for": "The project says these sizes are for mobile and browser deployment, at a fraction of the cost of pretraining the 8B teacher.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-1-5-70b": { "id": "eth/apertus/apertus-1-5-70b", "name": "Apertus 1.5 70B", "maker": "eth", "family": "eth/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0. The aligned repos are gated. Apache-2.0 plus the Apertus 1.5 acceptable use policy and privacy policy (github.com/swiss-ai/apertus-legal). Gated on Hugging Face for the aligned checkpoints.", "release_date": "2026-07-24", "context_length": 262144, "max_output_tokens": null, "parameters": "72005819139 safetensors elements. The card rounds this to 72B.", "architecture": "Text backbone matches Apertus 1.0 70B (80 layers, hidden 8192, intermediate 43008, 64/8 heads, xIELU). Same multimodal tokenizer design as the 8B 1.5. RoPE factor 32 from 8192 to 262144, rope theta 4000000. The release-notes table marks Apertus-v1.5-70B itself as the DPO final. There is no 70B SDPO checkpoint. Total pretraining tokens 17T.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Apertus 1.5 70B (apertus-1-5-70b). Status: active. Collected 2026-10-15.", "what_it_is_for": "Apertus 1.5 is the public multimodal continuation of Apertus 1.0: documents, images, speech understanding, tool use, and optional thinking, still fully open.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-1-5-70b-base": { "id": "eth/apertus/apertus-1-5-70b-base", "name": "Apertus 1.5 70B base", "maker": "eth", "family": "eth/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0. The 1.5 base repos were ungated in the Hugging Face API on 2026-10-15. Apache-2.0 plus the Apertus 1.5 acceptable use policy and privacy policy (github.com/swiss-ai/apertus-legal). Gated on Hugging Face for the aligned checkpoints.", "release_date": "2026-09-23", "context_length": 262144, "max_output_tokens": null, "parameters": "71942010529 safetensors elements.", "architecture": "Text backbone matches Apertus 1.0 70B (80 layers, hidden 8192, intermediate 43008, 64/8 heads, xIELU). Same multimodal tokenizer design as the 8B 1.5. RoPE factor 32 from 8192 to 262144, rope theta 4000000. Long-context base before post-training. Card says 17T pretraining tokens for the 70B (15T plus 2T).", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Apertus 1.5 70B base (apertus-1-5-70b-base). Status: active. Collected 2026-10-15.", "what_it_is_for": "Apertus 1.5 is the public multimodal continuation of Apertus 1.0: documents, images, speech understanding, tool use, and optional thinking, still fully open.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-1-5-8b": { "id": "eth/apertus/apertus-1-5-8b", "name": "Apertus 1.5 8B", "maker": "eth", "family": "eth/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0. The aligned repos are gated. Apache-2.0 plus the Apertus 1.5 acceptable use policy and privacy policy (github.com/swiss-ai/apertus-legal). Gated on Hugging Face for the aligned checkpoints.", "release_date": "2026-07-24", "context_length": 262144, "max_output_tokens": null, "parameters": "8903547555 safetensors elements. The card says 9B params, BF16 and F32.", "architecture": "Text backbone matches Apertus 1.0 8B (32 layers, hidden 4096, intermediate 21504, 32/8 heads, xIELU). Input vocab 266752, output vocab 131072. Vision tokenizer is an Emu3.5-style codebook model (codebook 131072, resolution 256). Audio tokenizer is WavTokenizer at 24 kHz, 40 tokens per second. Early fusion through the tokenizer, not a separate vision encoder. RoPE extended by factor 32 from 8192 to 262144 positions, rope theta 4000000. Final 8B checkpoint is the charter-alignment (SDPO) stage. Total pretraining tokens 19T. Post-training: SFT on 3.7M text samples plus image and speech, long-context SFT, RLVR on about 350k prompts, DPO, then SDPO.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Apertus 1.5 8B (apertus-1-5-8b). Status: active. Collected 2026-10-15.", "what_it_is_for": "Apertus 1.5 is the public multimodal continuation of Apertus 1.0: documents, images, speech understanding, tool use, and optional thinking, still fully open.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-1-5-8b-base": { "id": "eth/apertus/apertus-1-5-8b-base", "name": "Apertus 1.5 8B base", "maker": "eth", "family": "eth/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0. The 1.5 base repos were ungated in the Hugging Face API on 2026-10-15. Apache-2.0 plus the Apertus 1.5 acceptable use policy and privacy policy (github.com/swiss-ai/apertus-legal). Gated on Hugging Face for the aligned checkpoints.", "release_date": "2026-09-23", "context_length": 262144, "max_output_tokens": null, "parameters": "8839738945 safetensors elements. The card rounds the aligned 8B to 9B.", "architecture": "Text backbone matches Apertus 1.0 8B (32 layers, hidden 4096, intermediate 21504, 32/8 heads, xIELU). Input vocab 266752, output vocab 131072. Vision tokenizer is an Emu3.5-style codebook model (codebook 131072, resolution 256). Audio tokenizer is WavTokenizer at 24 kHz, 40 tokens per second. Early fusion through the tokenizer, not a separate vision encoder. RoPE extended by factor 32 from 8192 to 262144 positions, rope theta 4000000. This is the long-context continued-pretrain checkpoint before post-training. Total pretraining tokens on the card: 19T for the 8B (15T from 1.0 plus 4T).", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Apertus 1.5 8B base (apertus-1-5-8b-base). Status: active. Collected 2026-10-15.", "what_it_is_for": "Apertus 1.5 is the public multimodal continuation of Apertus 1.0: documents, images, speech understanding, tool use, and optional thinking, still fully open.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-2-0": { "id": "eth/apertus/apertus-2-0", "name": "Apertus 2.0", "maker": "eth", "family": "eth/apertus", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "about 600 billion total, sparse mixture-of-experts (announced, not a measured checkpoint)", "architecture": "Announced sparse MoE. No layer config has been published. Priorities named in the 15 October 2026 notes: agents, mathematics, and coding, with an extended training schedule.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Apertus 2.0 (apertus-2-0). Status: announced. Collected 2026-10-15.", "what_it_is_for": "The notes say the next generation targets agents, mathematics, and coding, on a sparse MoE of about 600 billion total parameters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-70b": { "id": "eth/apertus/apertus-70b", "name": "Apertus 70B", "maker": "eth", "family": "eth/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 weights. Download is gated by the Apertus acceptable-use clickwrap (indemnity toward ETH Zurich and EPFL, and a personal-data notice). Policy text on the 1.0 cards is dated 1 September 2025.", "release_date": "2025-09-02", "context_length": 65536, "max_output_tokens": null, "parameters": "70599864480 safetensors elements (about 70.60 billion)", "architecture": "Decoder-only Transformer, model type apertus, ApertusForCausalLM. 80 layers, hidden size 8192, intermediate size 43008, 64 query heads, 8 key-value heads, xIELU, QK-norm, untied embeddings, vocab 131072, bfloat16. Same RoPE setup as the 8B (theta 12000000, Llama-3 factor 8 from 8192). Trained with AdEMAMix on up to 4096 GH200 GPUs. Pretraining tokens 15T.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Apertus 70B (apertus-70b). Status: active. Collected 2026-10-15.", "what_it_is_for": "The makers describe Apertus as a public building block for chat, translation, and education, trained to be compliant and massively multilingual rather than a closed frontier model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-70b-instruct": { "id": "eth/apertus/apertus-70b-instruct", "name": "Apertus 70B Instruct", "maker": "eth", "family": "eth/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 weights. Download is gated by the Apertus acceptable-use clickwrap (indemnity toward ETH Zurich and EPFL, and a personal-data notice). Policy text on the 1.0 cards is dated 1 September 2025.", "release_date": "2025-09-02", "context_length": 65536, "max_output_tokens": null, "parameters": "70599864480 safetensors elements (about 70.60 billion)", "architecture": "Decoder-only Transformer, model type apertus, ApertusForCausalLM. 80 layers, hidden size 8192, intermediate size 43008, 64 query heads, 8 key-value heads, xIELU, QK-norm, untied embeddings, vocab 131072, bfloat16. Same RoPE setup as the 8B (theta 12000000, Llama-3 factor 8 from 8192). Trained with AdEMAMix on up to 4096 GH200 GPUs. Post-trained with SFT and QRPO from Apertus-70B-2509.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.82, "completion_per_m_usd": 2.92 }, "summary_intro": "ETH Zurich model Apertus 70B Instruct (apertus-70b-instruct). Status: active. Collected 2026-10-15.", "what_it_is_for": "The makers describe Apertus as a public building block for chat, translation, and education, trained to be compliant and massively multilingual rather than a closed frontier model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-8b": { "id": "eth/apertus/apertus-8b", "name": "Apertus 8B", "maker": "eth", "family": "eth/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 weights. Download is gated by the Apertus acceptable-use clickwrap (indemnity toward ETH Zurich and EPFL, and a personal-data notice). Policy text on the 1.0 cards is dated 1 September 2025.", "release_date": "2025-09-02", "context_length": 65536, "max_output_tokens": null, "parameters": "8053338176 safetensors elements (about 8.05 billion). The distillation paper lists 8.1B compute and 9.1B storage for Apertus-8B; those figures are not the file element count.", "architecture": "Decoder-only Transformer, model type apertus, ApertusForCausalLM. 32 layers, hidden size 4096, intermediate size 21504, 32 query heads, 8 key-value heads, xIELU, QK-norm, untied embeddings, vocab 131072, bfloat16. RoPE theta 12000000 with Llama-3 scaling factor 8 from an original 8192 positions. Trained with AdEMAMix. Pretraining tokens 15T.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Apertus 8B (apertus-8b). Status: active. Collected 2026-10-15.", "what_it_is_for": "The makers describe Apertus as a public building block for chat, translation, and education, trained to be compliant and massively multilingual rather than a closed frontier model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/apertus/apertus-8b-instruct": { "id": "eth/apertus/apertus-8b-instruct", "name": "Apertus 8B Instruct", "maker": "eth", "family": "eth/apertus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 weights. Download is gated by the Apertus acceptable-use clickwrap (indemnity toward ETH Zurich and EPFL, and a personal-data notice). Policy text on the 1.0 cards is dated 1 September 2025.", "release_date": "2025-09-02", "context_length": 65536, "max_output_tokens": null, "parameters": "8053338176 safetensors elements (about 8.05 billion)", "architecture": "Decoder-only Transformer, model type apertus, ApertusForCausalLM. 32 layers, hidden size 4096, intermediate size 21504, 32 query heads, 8 key-value heads, xIELU, QK-norm, untied embeddings, vocab 131072, bfloat16. RoPE theta 12000000 with Llama-3 scaling factor 8 from an original 8192 positions. Trained with AdEMAMix. Post-trained with SFT and QRPO from Apertus-8B-2509. eos token id 68 in config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Apertus 8B Instruct (apertus-8b-instruct). Status: active. Collected 2026-10-15.", "what_it_is_for": "The makers describe Apertus as a public building block for chat, translation, and education, trained to be compliant and massively multilingual rather than a closed frontier model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/forpt/forpt": { "id": "eth/forpt/forpt", "name": "ForPT", "maker": "eth", "family": "eth/forpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-09", "context_length": null, "max_output_tokens": null, "parameters": "12.4 million for the pretrained LitePT-S backbone", "architecture": "Self-supervised LitePT-S backbone pretrained on unlabeled airborne, UAV, and mobile laser scans of forests. Downstream heads for semantic segmentation, species classification, and age regression are separate checkpoints. Instance segmentation was marked coming soon on the card.", "modalities": { "input": [ "3d" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model ForPT (forpt). Status: active. Collected 2026-10-15.", "what_it_is_for": "A self-supervised backbone for forest lidar: semantic segmentation, species classification, and tree age, with better transfer when labels are scarce.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/geocalib/geocalib": { "id": "eth/geocalib/geocalib", "name": "GeoCalib", "maker": "eth", "family": "eth/geocalib", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Code Apache-2.0. Trained weights Creative Commons Attribution 4.0, as stated in the GitHub README.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single-image camera calibration with a geometric optimization layer. Default weights are pinhole (focal lengths, no distortion). A second weight set is named distorted, for simple radial or division models. Backbone note in the README: MSCAN-B is downloaded for training, not described as the released inference net's parameter count.", "modalities": { "input": [ "image" ], "output": [ "camera" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model GeoCalib (geocalib). Status: active. Collected 2026-10-15.", "what_it_is_for": "Estimate camera intrinsics from one image, with a geometric refinement step rather than a pure regression head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/gluestick/gluestick": { "id": "eth/gluestick/gluestick", "name": "GlueStick", "maker": "eth", "family": "eth/gluestick", "status": "active", "confidence": "high", "weights": "open-source", "license": "Glue Factory README: code and trained models in that repo, including GlueStick, are Apache-2.0. A separate original-repo license was not re-read.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph matcher over a wireframe of points and lines. The paper states feature width 256 and 9 blocks of self-attention, line message passing, and cross-attention. Parameter total was not stated in the pages read.", "modalities": { "input": [ "image" ], "output": [ "keypoints" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model GlueStick (gluestick). Status: active. Collected 2026-10-15.", "what_it_is_for": "Match points and line segments together, including in low-texture areas where point-only matchers fail.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/lightglue/lightglue-disk": { "id": "eth/lightglue/lightglue-disk", "name": "LightGlue DISK", "maker": "eth", "family": "eth/lightglue", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0. The card calls this the commercially permissive LightGlue variant, trained with DISK features.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "13569122 safetensors elements (about 13.6 million)", "architecture": "LightGlue matcher trained for DISK local features rather than SuperPoint. Same ETH CVG model family.", "modalities": { "input": [ "image" ], "output": [ "keypoints" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model LightGlue DISK (lightglue-disk). Status: active. Collected 2026-10-15.", "what_it_is_for": "Same matching job as the SuperPoint LightGlue, with a license the card says allows commercial use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/lightglue/lightglue-superpoint": { "id": "eth/lightglue/lightglue-superpoint", "name": "LightGlue SuperPoint", "maker": "eth", "family": "eth/lightglue", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face card: other. The card says academic or non-profit noncommercial research use only, implied by pairing with SuperPoint. It does not quote a SuperPoint license file.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "13744594 safetensors elements (about 13.7 million)", "architecture": "Adaptive local-feature matcher. Depth and width change with the pair. Early exit when predictions are confident, and non-matchable points are dropped. This checkpoint is paired with SuperPoint. Developed by ETH CVG (Philipp Lindenberger, Paul-Edouard Sarlin, Marc Pollefeys).", "modalities": { "input": [ "image" ], "output": [ "keypoints" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model LightGlue SuperPoint (lightglue-superpoint). Status: active. Collected 2026-10-15.", "what_it_is_for": "Match sparse keypoints between two images for pose and homography, faster than SuperGlue on easy pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/litept/litept": { "id": "eth/litept/litept", "name": "LitePT", "maker": "eth", "family": "eth/litept", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-12-17", "context_length": null, "max_output_tokens": null, "parameters": "LitePT-S 12.7 million for semantic segmentation. Card also lists LitePT-S* 16.0 million (instance), a 9.0 million detection checkpoint, LitePT-B 45.1 million, and LitePT-L 85.9 million.", "architecture": "Point Transformer variant. The card's design line is convolutions for low-level geometry and attention for high-level relations, plus a parameter-free positional encoding called PointROPE. Checkpoints are task-specific.", "modalities": { "input": [ "3d" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model LitePT (litept). Status: active. Collected 2026-10-15.", "what_it_is_for": "Semantic segmentation, instance segmentation, and detection on outdoor and indoor point clouds, at a lower parameter count than heavier point transformers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/marigold/marigold-depth": { "id": "eth/marigold/marigold-depth", "name": "Marigold Depth", "maker": "eth", "family": "eth/marigold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML Open RAIL++-M (v1-1 card). v1-0 and the LCM checkpoint are Apache-2.0.", "release_date": "2024-12-12", "context_length": null, "max_output_tokens": null, "parameters": "865922244 safetensors elements on v1-1 (about 0.87 billion)", "architecture": "Latent diffusion fine-tune of Stable Diffusion 2 for affine-invariant monocular depth. Developed by Bingxin Ke, Kevin Qu, Tianfu Wang, Nando Metzger, Shengyu Huang, Bo Li, Anton Obukhov, and Konrad Schindler at ETH PRS.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Marigold Depth (marigold-depth). Status: active. Collected 2026-10-15.", "what_it_is_for": "Current monocular depth model. Affine-invariant depth in 0 to 1. Effective resolution about 768 pixels on the long side. DDIM, 1 to 50 steps. Uncertainty appears only if the ensemble size is above 2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/marigold/marigold-depth-hr": { "id": "eth/marigold/marigold-depth-hr", "name": "Marigold Depth HR", "maker": "eth", "family": "eth/marigold", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion depth model, high-resolution variant of Marigold Depth v1-1. Same ETH PRS author group as the journal paper.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Marigold Depth HR (marigold-depth-hr). Status: active. Collected 2026-10-15.", "what_it_is_for": "High-resolution monocular depth. The card says it supports large resolutions up to 4 megapixels, fine-tuned from marigold-depth-v1-1. DDIM, 10 to 50 steps. Affine-invariant depth in 0 to 1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/marigold/marigold-iid-appearance": { "id": "eth/marigold/marigold-iid-appearance", "name": "Marigold IID Appearance", "maker": "eth", "family": "eth/marigold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML Open RAIL++-M", "release_date": "2024-10-21", "context_length": null, "max_output_tokens": null, "parameters": "865945288 safetensors elements", "architecture": "Latent diffusion IID appearance head, ETH PRS, fine-tuned from Stable Diffusion 2.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Marigold IID Appearance (marigold-iid-appearance). Status: active. Collected 2026-10-15.", "what_it_is_for": "Intrinsic image decomposition into albedo, roughness, and metallicity (InteriorVerse targets). Input and albedo are sRGB. Albedo values are in 0 to 1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/marigold/marigold-iid-lighting": { "id": "eth/marigold/marigold-iid-lighting", "name": "Marigold IID Lighting", "maker": "eth", "family": "eth/marigold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML Open RAIL++-M", "release_date": "2024-12-16", "context_length": null, "max_output_tokens": null, "parameters": "865968332 safetensors elements", "architecture": "Latent diffusion IID lighting head, ETH PRS, fine-tuned from Stable Diffusion 2.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Marigold IID Lighting (marigold-iid-lighting). Status: active. Collected 2026-10-15.", "what_it_is_for": "Intrinsic decomposition into albedo, diffuse shading, and a non-diffuse residual (HyperSim targets). Input is sRGB. Outputs are linear. Albedo is in 0 to 1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/marigold/marigold-normals": { "id": "eth/marigold/marigold-normals", "name": "Marigold Normals", "maker": "eth", "family": "eth/marigold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML Open RAIL++-M for v1-1. Deprecated v0-1 and LCM checkpoints are Apache-2.0.", "release_date": "2024-10-21", "context_length": null, "max_output_tokens": null, "parameters": "865922244 safetensors elements on v1-1", "architecture": "Latent diffusion fine-tune of Stable Diffusion 2 for surface normals, ETH PRS.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Marigold Normals (marigold-normals). Status: active. Collected 2026-10-15.", "what_it_is_for": "Monocular surface normals. Outputs 3D unit vectors in the screen-space camera frame.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/pager/pager": { "id": "eth/pager/pager", "name": "PaGeR", "maker": "eth", "family": "eth/pager", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0. The card says the weights are derivative of Depth Anything 3 da3-giant and inherit that non-commercial license. Commercial use is not permitted.", "release_date": "2026-05-22", "context_length": null, "max_output_tokens": null, "parameters": "1414126099 safetensors elements (about 1.41 billion)", "architecture": "Feed-forward adaptation of Depth Anything 3 (da3-giant, ViT-Giant) to a 6-face cubemap of one equirectangular panorama, 504 pixels per face. Vukasin Bozic, Isidora Slavkovic, Dominik Narnhofer, Nando Metzger, Denis Rozumny, Konrad Schindler, and Nikolai Kalischek, ETH PRS.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model PaGeR (pager). Status: active. Collected 2026-10-15.", "what_it_is_for": "Unified checkpoint. One pass returns scale-invariant depth, metric depth in metres via an indoor or outdoor global scale, surface normals, and a sky mask.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/pager/pager-metric-depth": { "id": "eth/pager/pager-metric-depth", "name": "PaGeR Metric Depth", "maker": "eth", "family": "eth/pager", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0. The card says the weights are derivative of Depth Anything 3 da3-giant and inherit that non-commercial license. Commercial use is not permitted.", "release_date": "2026-05-22", "context_length": null, "max_output_tokens": null, "parameters": "1305470955 safetensors elements (about 1.31 billion)", "architecture": "Feed-forward adaptation of Depth Anything 3 (da3-giant, ViT-Giant) to a 6-face cubemap of one equirectangular panorama, 504 pixels per face. Vukasin Bozic, Isidora Slavkovic, Dominik Narnhofer, Nando Metzger, Denis Rozumny, Konrad Schindler, and Nikolai Kalischek, ETH PRS.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model PaGeR Metric Depth (pager-metric-depth). Status: active. Collected 2026-10-15.", "what_it_is_for": "Depth-only checkpoint. Metric depth in metres from one head, with no separate scale head and no indoor/outdoor routing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/pager/pager-normals": { "id": "eth/pager/pager-normals", "name": "PaGeR Normals", "maker": "eth", "family": "eth/pager", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0. The card says the weights are derivative of Depth Anything 3 da3-giant and inherit that non-commercial license. Commercial use is not permitted.", "release_date": "2026-05-22", "context_length": null, "max_output_tokens": null, "parameters": "1305470988 safetensors elements (about 1.31 billion)", "architecture": "Feed-forward adaptation of Depth Anything 3 (da3-giant, ViT-Giant) to a 6-face cubemap of one equirectangular panorama, 504 pixels per face. Vukasin Bozic, Isidora Slavkovic, Dominik Narnhofer, Nando Metzger, Denis Rozumny, Konrad Schindler, and Nikolai Kalischek, ETH PRS.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model PaGeR Normals (pager-normals). Status: active. Collected 2026-10-15.", "what_it_is_for": "Normals-only checkpoint. Dense unit normals in the panorama world frame.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/poseidon/poseidon-b": { "id": "eth/poseidon/poseidon-b", "name": "Poseidon-B", "maker": "eth", "family": "eth/poseidon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-04-18", "context_length": null, "max_output_tokens": null, "parameters": "157729988 safetensors elements. The model card says approx. about 158 million.", "architecture": "ScOT PDE foundation model. Card: trained on a 128 by 128 grid. Inputs are density, horizontal velocity, vertical velocity, and pressure. The card calls it a research model. Layer widths were not on the short card.", "modalities": { "input": [ "numerical" ], "output": [ "numerical" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Poseidon-B (poseidon-b). Status: active. Collected 2026-10-15.", "what_it_is_for": "Pretrained operator model for fluid and related PDE systems, meant to be finetuned on downstream equation families.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/poseidon/poseidon-l": { "id": "eth/poseidon/poseidon-l", "name": "Poseidon-L", "maker": "eth", "family": "eth/poseidon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-04-18", "context_length": null, "max_output_tokens": null, "parameters": "628575524 safetensors elements. The model card says approx. about 629 million.", "architecture": "ScOT PDE foundation model. Card: trained on a 128 by 128 grid. Inputs are density, horizontal velocity, vertical velocity, and pressure. The card calls it a research model. Layer widths were not on the short card.", "modalities": { "input": [ "numerical" ], "output": [ "numerical" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Poseidon-L (poseidon-l). Status: active. Collected 2026-10-15.", "what_it_is_for": "Pretrained operator model for fluid and related PDE systems, meant to be finetuned on downstream equation families.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/poseidon/poseidon-t": { "id": "eth/poseidon/poseidon-t", "name": "Poseidon-T", "maker": "eth", "family": "eth/poseidon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-04-18", "context_length": null, "max_output_tokens": null, "parameters": "20774444 safetensors elements. The model card says approx. about 21 million.", "architecture": "ScOT PDE foundation model. Card: trained on a 128 by 128 grid. Inputs are density, horizontal velocity, vertical velocity, and pressure. The card calls it a research model. Layer widths were not on the short card.", "modalities": { "input": [ "numerical" ], "output": [ "numerical" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Poseidon-T (poseidon-t). Status: active. Collected 2026-10-15.", "what_it_is_for": "Pretrained operator model for fluid and related PDE systems, meant to be finetuned on downstream equation families.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/rollingdepth/rollingdepth": { "id": "eth/rollingdepth/rollingdepth", "name": "RollingDepth", "maker": "eth", "family": "eth/rollingdepth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Card body: model under RAIL++-M, code under Apache-2.0. YAML license field says openrail. Both statements are on the card.", "release_date": "2024-11-22", "context_length": null, "max_output_tokens": null, "parameters": "865922244 safetensors elements", "architecture": "Video depth estimator that does not use a video model. Text encoder, tokenizer, and VAE are copied from Stable Diffusion 2. Authors at ETH Zurich and Carnegie Mellon.", "modalities": { "input": [ "image", "video" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model RollingDepth (rollingdepth). Status: active. Collected 2026-10-15.", "what_it_is_for": "The card title is video depth without video models: depth over video frames using an image diffusion backbone rather than a video generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/stereospace/stereospace": { "id": "eth/stereospace/stereospace", "name": "StereoSpace", "maker": "eth", "family": "eth/stereospace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Card body: CreativeML Open RAIL++-M. YAML license field: creativeml-openrail-m. The badge points at the Stable Diffusion 2 model license.", "release_date": "2025-11-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end latent diffusion model for baseline-controllable single-image stereo view synthesis, fine-tuned from stabilityai/stable-diffusion-2. The paper title says depth-free synthesis in a canonical space.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model StereoSpace (stereospace). Status: active. Collected 2026-10-15.", "what_it_is_for": "Synthesize a second view from a single image so the pair has controllable stereo geometry, without a separate depth stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/thera/thera-edsr-air": { "id": "eth/thera/thera-edsr-air", "name": "Thera EDSR-baseline Air", "maker": "eth", "family": "eth/thera", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural heat field for alias-free arbitrary-scale super-resolution, including non-integer scales. Backbone EDSR-baseline. Variant Air. Trained on DIV2K.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Thera EDSR-baseline Air (thera-edsr-air). Status: active. Collected 2026-10-15.", "what_it_is_for": "Super-resolve a single image at arbitrary, including non-integer, scales, with an anti-aliasing observation model the card calls analytically correct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/thera/thera-edsr-plus": { "id": "eth/thera/thera-edsr-plus", "name": "Thera EDSR-baseline Plus", "maker": "eth", "family": "eth/thera", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural heat field for alias-free arbitrary-scale super-resolution, including non-integer scales. Backbone EDSR-baseline. Variant Plus. Trained on DIV2K.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Thera EDSR-baseline Plus (thera-edsr-plus). Status: active. Collected 2026-10-15.", "what_it_is_for": "Super-resolve a single image at arbitrary, including non-integer, scales, with an anti-aliasing observation model the card calls analytically correct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/thera/thera-edsr-pro": { "id": "eth/thera/thera-edsr-pro", "name": "Thera EDSR-baseline Pro", "maker": "eth", "family": "eth/thera", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural heat field for alias-free arbitrary-scale super-resolution, including non-integer scales. Backbone EDSR-baseline. Variant Pro. Trained on DIV2K.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Thera EDSR-baseline Pro (thera-edsr-pro). Status: active. Collected 2026-10-15.", "what_it_is_for": "Super-resolve a single image at arbitrary, including non-integer, scales, with an anti-aliasing observation model the card calls analytically correct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/thera/thera-rdn-air": { "id": "eth/thera/thera-rdn-air", "name": "Thera RDN Air", "maker": "eth", "family": "eth/thera", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural heat field for alias-free arbitrary-scale super-resolution, including non-integer scales. Backbone RDN. Variant Air. Trained on DIV2K.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Thera RDN Air (thera-rdn-air). Status: active. Collected 2026-10-15.", "what_it_is_for": "Super-resolve a single image at arbitrary, including non-integer, scales, with an anti-aliasing observation model the card calls analytically correct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/thera/thera-rdn-plus": { "id": "eth/thera/thera-rdn-plus", "name": "Thera RDN Plus", "maker": "eth", "family": "eth/thera", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural heat field for alias-free arbitrary-scale super-resolution, including non-integer scales. Backbone RDN. Variant Plus. Trained on DIV2K.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Thera RDN Plus (thera-rdn-plus). Status: active. Collected 2026-10-15.", "what_it_is_for": "Super-resolve a single image at arbitrary, including non-integer, scales, with an anti-aliasing observation model the card calls analytically correct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "eth/thera/thera-rdn-pro": { "id": "eth/thera/thera-rdn-pro", "name": "Thera RDN Pro", "maker": "eth", "family": "eth/thera", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural heat field for alias-free arbitrary-scale super-resolution, including non-integer scales. Backbone RDN. Variant Pro. Trained on DIV2K.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ETH Zurich model Thera RDN Pro (thera-rdn-pro). Status: active. Collected 2026-10-15.", "what_it_is_for": "Super-resolve a single image at arbitrary, including non-integer, scales, with an anti-aliasing observation model the card calls analytically correct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "evolutionaryscale/esm3/esm3-large": { "id": "evolutionaryscale/esm3/esm3-large", "name": "ESM3-large", "maker": "evolutionaryscale", "family": "evolutionaryscale/esm3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-25", "context_length": 2048, "max_output_tokens": null, "parameters": "98B", "architecture": "Multi-track generative masked language model. Pre-LN transformer with rotary embeddings and SwiGLU. The first block adds an SE(3)-invariant geometric attention layer over backbone coordinates. Sequence, structure, and function are discrete tracks. Generation iteratively unmasks positions. This size: 216 layers. Parameter count 98B.", "modalities": { "input": [ "protein-sequence", "protein-structure", "protein-function" ], "output": [ "protein-sequence", "protein-structure", "protein-function" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EvolutionaryScale ESM3-large is a closed API size of ESM3, the multimodal generative protein model announced on 25 June 2024. It is active as of 2026-10-15. Weights are not public. Access is the Biohub Platform (Forge was folded into biohub.ai). EvolutionaryScale joined Biohub in 2025.", "what_it_is_for": "ESM3 generates proteins conditioned on any mix of sequence, structure, and function. Biohub lists this size among the flagship or paper-reproducibility checkpoints. The Science paper reports esmGFP, a designed fluorescent protein at 58% identity to the nearest known fluorescent protein. That result is a family result. The docs say the 2024-03 published checkpoints produced the paper results.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "evolutionaryscale/esm3/esm3-medium": { "id": "evolutionaryscale/esm3/esm3-medium", "name": "ESM3-medium", "maker": "evolutionaryscale", "family": "evolutionaryscale/esm3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-25", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "Multi-track generative masked language model. Pre-LN transformer with rotary embeddings and SwiGLU. The first block adds an SE(3)-invariant geometric attention layer over backbone coordinates. Sequence, structure, and function are discrete tracks. Generation iteratively unmasks positions. This size: 96 layers. Parameter count 7B.", "modalities": { "input": [ "protein-sequence", "protein-structure", "protein-function" ], "output": [ "protein-sequence", "protein-structure", "protein-function" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EvolutionaryScale ESM3-medium is a closed API size of ESM3, the multimodal generative protein model announced on 25 June 2024. It is active as of 2026-10-15. Weights are not public. Access is the Biohub Platform (Forge was folded into biohub.ai). EvolutionaryScale joined Biohub in 2025.", "what_it_is_for": "ESM3 generates proteins conditioned on any mix of sequence, structure, and function. Biohub lists this size among the flagship or paper-reproducibility checkpoints. The Science paper reports esmGFP, a designed fluorescent protein at 58% identity to the nearest known fluorescent protein. That result is a family result. The docs say the 2024-03 published checkpoints produced the paper results.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "evolutionaryscale/esm3/esm3-open": { "id": "evolutionaryscale/esm3/esm3-open", "name": "ESM3-open", "maker": "evolutionaryscale", "family": "evolutionaryscale/esm3", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-06-25", "context_length": 2048, "max_output_tokens": null, "parameters": "1.4B", "architecture": "Multi-track generative masked language model. Pre-LN transformer with rotary embeddings and SwiGLU. The first block adds an SE(3)-invariant geometric attention layer over backbone coordinates. Sequence, structure, and function are discrete tracks. Generation iteratively unmasks positions. Small size: 48 layers. Docs give training compute as order-of-magnitude 1e21 FLOPs for ESM3 Small.", "modalities": { "input": [ "protein-sequence", "protein-structure", "protein-function" ], "output": [ "protein-sequence", "protein-structure", "protein-function" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EvolutionaryScale ESM3-open is the public 1.4 billion parameter checkpoint of the ESM3 generative protein model. It is active as of 2026-10-15. Weights and code are on Hugging Face and GitHub under MIT. Larger ESM3 checkpoints stay on the Biohub API. EvolutionaryScale joined Biohub in 2025; current hosting is Biohub.", "what_it_is_for": "The maker describes ESM3 as a generative model that designs proteins from partial prompts of sequence, structure, and function. ESM3-open is the small checkpoint scientists can run locally. The June 2024 launch used the broader ESM3 family, including a designed fluorescent protein (esmGFP) reported at 58% sequence identity to the nearest known fluorescent protein.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "evolutionaryscale/esm3/esm3-small": { "id": "evolutionaryscale/esm3/esm3-small", "name": "ESM3-small", "maker": "evolutionaryscale", "family": "evolutionaryscale/esm3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-25", "context_length": 2048, "max_output_tokens": null, "parameters": "1.4B", "architecture": "Multi-track generative masked language model. Pre-LN transformer with rotary embeddings and SwiGLU. The first block adds an SE(3)-invariant geometric attention layer over backbone coordinates. Sequence, structure, and function are discrete tracks. Generation iteratively unmasks positions. This size: 48 layers. Parameter count 1.4B.", "modalities": { "input": [ "protein-sequence", "protein-structure", "protein-function" ], "output": [ "protein-sequence", "protein-structure", "protein-function" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EvolutionaryScale ESM3-small is a closed API size of ESM3, the multimodal generative protein model announced on 25 June 2024. It is active as of 2026-10-15. Weights are not public. Access is the Biohub Platform (Forge was folded into biohub.ai). EvolutionaryScale joined Biohub in 2025.", "what_it_is_for": "ESM3 generates proteins conditioned on any mix of sequence, structure, and function. Biohub lists this size among the flagship or paper-reproducibility checkpoints. The Science paper reports esmGFP, a designed fluorescent protein at 58% identity to the nearest known fluorescent protein. That result is a family result. The docs say the 2024-03 published checkpoints produced the paper results.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "evolutionaryscale/esmc/esmc-300m": { "id": "evolutionaryscale/esmc/esmc-300m", "name": "ESMC 300M", "maker": "evolutionaryscale", "family": "evolutionaryscale/esmc", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-12-04", "context_length": 2048, "max_output_tokens": null, "parameters": "300M", "architecture": "Masked protein language model. Pre-LN transformer, rotary embeddings, SwiGLU, no biases in linear layers or layer norms. 30 layers, width 960, 15 heads. Training: context schedule 512 then 2048, 1.5 million steps, batch 4.2 million tokens, 6.2 trillion tokens, learning rate 5e-4, 1.26e22 FLOPs.", "modalities": { "input": [ "protein-sequence" ], "output": [ "protein-sequence", "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EvolutionaryScale ESMC 300M is a protein masked language model in the ESM Cambrian family, announced on 4 December 2024. It is active as of 2026-10-15. Weights are open under MIT on Hugging Face, and the same checkpoint id is on the Biohub API. EvolutionaryScale joined Biohub in 2025. The June 2026 preprint is the write-up of this family, not a new parameter scale.", "what_it_is_for": "ESM Cambrian is the representation model paired with generative ESM3. The maker says it replaces ESM2 for embeddings, mutation scoring, annotation, and as the frozen encoder inside ESMFold2 (the 6B model). It is trained with masked language modeling on UniRef, MGnify, and JGI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "evolutionaryscale/esmc/esmc-600m": { "id": "evolutionaryscale/esmc/esmc-600m", "name": "ESMC 600M", "maker": "evolutionaryscale", "family": "evolutionaryscale/esmc", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-12-04", "context_length": 2048, "max_output_tokens": null, "parameters": "600M", "architecture": "Masked protein language model. Pre-LN transformer, rotary embeddings, SwiGLU, no biases in linear layers or layer norms. 36 layers, width 1152, 18 heads. Training: context schedule 512 then 2048, 1.5 million steps, batch 4.2 million tokens, 6.2 trillion tokens, learning rate 4.6e-4, 2.17e22 FLOPs.", "modalities": { "input": [ "protein-sequence" ], "output": [ "protein-sequence", "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EvolutionaryScale ESMC 600M is a protein masked language model in the ESM Cambrian family, announced on 4 December 2024. It is active as of 2026-10-15. Weights are open under MIT on Hugging Face, and the same checkpoint id is on the Biohub API. EvolutionaryScale joined Biohub in 2025. The June 2026 preprint is the write-up of this family, not a new parameter scale.", "what_it_is_for": "ESM Cambrian is the representation model paired with generative ESM3. The maker says it replaces ESM2 for embeddings, mutation scoring, annotation, and as the frozen encoder inside ESMFold2 (the 6B model). It is trained with masked language modeling on UniRef, MGnify, and JGI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "evolutionaryscale/esmc/esmc-6b": { "id": "evolutionaryscale/esmc/esmc-6b", "name": "ESMC 6B", "maker": "evolutionaryscale", "family": "evolutionaryscale/esmc", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-12-04", "context_length": 2048, "max_output_tokens": null, "parameters": "6B", "architecture": "Masked protein language model. Pre-LN transformer, rotary embeddings, SwiGLU, no biases in linear layers or layer norms. 80 layers, width 2560, 40 heads. Training: context schedule 512 then 2048, 1.5 million steps, batch 4.2 million tokens, 6.2 trillion tokens, learning rate 3.1e-4, 2.37e23 FLOPs.", "modalities": { "input": [ "protein-sequence" ], "output": [ "protein-sequence", "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EvolutionaryScale ESMC 6B is a protein masked language model in the ESM Cambrian family, announced on 4 December 2024. It is active as of 2026-10-15. Weights are open under MIT on Hugging Face, and the same checkpoint id is on the Biohub API. EvolutionaryScale joined Biohub in 2025. The June 2026 preprint is the write-up of this family, not a new parameter scale.", "what_it_is_for": "ESM Cambrian is the representation model paired with generative ESM3. The maker says it replaces ESM2 for embeddings, mutation scoring, annotation, and as the frozen encoder inside ESMFold2 (the 6B model). It is trained with masked language modeling on UniRef, MGnify, and JGI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "evolutionaryscale/esmc-sae/esmc-6b-sae-layer60-k64-codebook16384": { "id": "evolutionaryscale/esmc-sae/esmc-6b-sae-layer60-k64-codebook16384", "name": "ESMC 6B SAE layer 60 k64 codebook 16384", "maker": "evolutionaryscale", "family": "evolutionaryscale/esmc-sae", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "TopK sparse autoencoder on ESMC 6B hidden states at layer 60, about 75% depth. k=64 active features per residue. Codebook size 16384. Reconstructs per-residue embeddings. Feature descriptions were generated for this codebook and shipped with ESM Atlas.", "modalities": { "input": [ "embeddings" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the sparse autoencoder EvolutionaryScale's ESM group, now at Biohub, used for ESM Atlas and the June 2026 world-model paper. It is active as of 2026-10-15. It decomposes ESMC 6B layer-60 hidden states into 16,384 features with 64 active per residue. Weights are released with the MIT esm repository. Other SAE checkpoints are versions of the same release, not separate product lines.", "what_it_is_for": "The overview card says the SAE turns ESMC representations into features that are easier to interpret, such as binding sites or fold elements, and that this codebook is the one with agent-written descriptions in ESM Atlas. It is an analysis model on top of ESMC, not a protein generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "evolutionaryscale/esmfold2/esmfold2": { "id": "evolutionaryscale/esmfold2/esmfold2", "name": "ESMFold2", "maker": "evolutionaryscale", "family": "evolutionaryscale/esmfold2", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-05-27", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Frozen ESMC 6B embeddings, a trunk of looped folding layers, and a diffusion module that samples all-atom coordinates with an ODE solver. Optional MSA conditioning on the flagship checkpoint. Predicts proteins, DNA, RNA, modified residues, and small-molecule ligands. MSA conditioning is supported. Hub name biohub/ESMFold2. Data cutoff September 2021.", "modalities": { "input": [ "protein-sequence", "dna", "rna", "ligand", "msa" ], "output": [ "protein-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ESMFold2 is Biohub's open structure model from the EvolutionaryScale ESM line, released 27 May 2026. It is active as of 2026-10-15. It folds proteins and complexes from sequence, with a frozen ESMC 6B encoder and a diffusion structure module. Weights are MIT. EvolutionaryScale joined Biohub in 2025, and this release is under the Biohub name by the same ESM group.", "what_it_is_for": "The maker says ESMFold2 predicts high-resolution all-atom structures of proteins and complexes, including DNA, RNA, modified residues, and ligands. The flagship checkpoint can condition on an MSA. The fast checkpoint is single-sequence and inference-optimized. The README also describes binder and antibody design by inverting the model, with lab checks on five therapeutic targets. Meta ESMFold is the named predecessor and was not an EvolutionaryScale company release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "evolutionaryscale/esmfold2/esmfold2-fast": { "id": "evolutionaryscale/esmfold2/esmfold2-fast", "name": "ESMFold2-Fast", "maker": "evolutionaryscale", "family": "evolutionaryscale/esmfold2", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-05-27", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Frozen ESMC 6B embeddings, a trunk of looped folding layers, and a diffusion module that samples all-atom coordinates with an ODE solver. Optional MSA conditioning on the flagship checkpoint. Predicts proteins, DNA, RNA, modified residues, and small-molecule ligands. Inference-optimized single-sequence checkpoint. MSA conditioning is not supported. Same 7B header as the flagship on Hugging Face. Whether the weights differ beyond inference settings is not spelled out.", "modalities": { "input": [ "protein-sequence", "dna", "rna", "ligand", "msa" ], "output": [ "protein-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ESMFold2-Fast is Biohub's open structure model from the EvolutionaryScale ESM line, released 27 May 2026. It is active as of 2026-10-15. It folds proteins and complexes from sequence, with a frozen ESMC 6B encoder and a diffusion structure module. Weights are MIT. EvolutionaryScale joined Biohub in 2025, and this release is under the Biohub name by the same ESM group.", "what_it_is_for": "The maker says ESMFold2-Fast predicts high-resolution all-atom structures of proteins and complexes, including DNA, RNA, modified residues, and ligands. The flagship checkpoint can condition on an MSA. The fast checkpoint is single-sequence and inference-optimized. The README also describes binder and antibody design by inverting the model, with lab checks on five therapeutic targets. Meta ESMFold is the named predecessor and was not an EvolutionaryScale company release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fau/dax/dax-resnet18": { "id": "fau/dax/dax-resnet18", "name": "DAX ResNet-18", "maker": "fau", "family": "fau/dax", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-20", "context_length": null, "max_output_tokens": null, "parameters": "11200000", "architecture": "ResNet-18 backbone trained with DINO Adapted to X-Ray (DAX), a self-supervised DINO-style method, on more than 632,000 intraoperative orthopedic X-ray images. The released file is version A. The GitHub table reports 11.2 M parameters. Input resolution was not stated on the README.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DAX ResNet-18 is a self-supervised X-ray backbone from the Pattern Recognition Lab at FAU Erlangen-N\u00fcrnberg, with Siemens Healthineers coauthors. It is the version A ResNet-18 checkpoint from DINO Adapted to X-Ray (DAX), released with the MICCAI 2025 paper. Springer lists the chapter as first online on 20 September 2025. Collected 15 October 2026. Weights are Apache-2.0. There is no API and no public price.", "what_it_is_for": "The paper trains DINO-style foundation backbones for orthopedic X-ray images. Frozen features plus a small head are tested on body-region classification, metal-implant segmentation, and screw detection. This checkpoint is the smaller ResNet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fau/dax/dax-resnet50": { "id": "fau/dax/dax-resnet50", "name": "DAX ResNet-50", "maker": "fau", "family": "fau/dax", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-20", "context_length": null, "max_output_tokens": null, "parameters": "23500000", "architecture": "ResNet-50 backbone trained with DINO Adapted to X-Ray (DAX) on more than 632,000 intraoperative orthopedic X-ray images. Two public runs, version A and version B, share the reported 23.5 M parameter count. They are training variants, not different architectures. Input resolution was not stated on the README.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DAX ResNet-50 is a self-supervised intraoperative X-ray backbone from the FAU Pattern Recognition Lab and Siemens Healthineers. The MICCAI 2025 release includes two runs, version A and version B, with the same reported size. Springer lists the chapter as first online on 20 September 2025. Collected 15 October 2026. The code and weights are Apache-2.0. There is no public price.", "what_it_is_for": "DAX adapts DINO so a frozen X-ray backbone can feed small heads for body-region classification, metal-implant segmentation, and screw detection. The paper says ResNet-50 is the strongest ResNet for classification and the strongest DAX backbone for segmentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fau/dax/dax-vit-s-16": { "id": "fau/dax/dax-vit-s-16", "name": "DAX ViT-S/16", "maker": "fau", "family": "fau/dax", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-20", "context_length": null, "max_output_tokens": null, "parameters": "21700000", "architecture": "Small Vision Transformer with patch size 16, trained with DINO Adapted to X-Ray (DAX) on more than 632,000 intraoperative orthopedic X-ray images. Version A and version B both have the reported 21.7 M parameter count. Input resolution was not stated on the README.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DAX ViT-S/16 is the small Vision Transformer, patch size 16, from the FAU Pattern Recognition Lab DAX release. Siemens Healthineers coauthors are on the paper. Version A and version B shipped with the MICCAI 2025 chapter, first online on Springer on 20 September 2025. Collected 15 October 2026. The repository is Apache-2.0. There is no public price.", "what_it_is_for": "DAX trains DINO-style X-ray backbones. Frozen features plus a small head are evaluated for body-region classification, metal-implant segmentation, and screw detection. The two public runs are the same architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fau/dax/dax-vit-s-8": { "id": "fau/dax/dax-vit-s-8", "name": "DAX ViT-S/8", "maker": "fau", "family": "fau/dax", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-20", "context_length": null, "max_output_tokens": null, "parameters": "21700000", "architecture": "Small Vision Transformer with patch size 8, trained with DINO Adapted to X-Ray (DAX) on more than 632,000 intraoperative orthopedic X-ray images. The GitHub table reports 21.7 M parameters for version A. Input resolution was not stated on the README.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DAX ViT-S/8 is the small Vision Transformer, patch size 8, from the FAU Pattern Recognition Lab DAX release. Siemens Healthineers coauthors are on the paper. Springer lists the MICCAI 2025 chapter as first online on 20 September 2025. Collected 15 October 2026. Only version A is public. The repository is Apache-2.0. There is no public price.", "what_it_is_for": "DAX trains DINO-style X-ray backbones for frozen-feature use on body-region classification, metal-implant segmentation, and screw detection. The paper says this backbone has the best ViT body-region F1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fau/dax/dax-vit-t-16": { "id": "fau/dax/dax-vit-t-16", "name": "DAX ViT-T/16", "maker": "fau", "family": "fau/dax", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-20", "context_length": null, "max_output_tokens": null, "parameters": "5500000", "architecture": "Tiny Vision Transformer with patch size 16, trained with DINO Adapted to X-Ray (DAX) on more than 632,000 intraoperative orthopedic X-ray images. The GitHub table reports 5.5 M parameters for version A. The same count is listed for the patch-8 twin, which is a separate folder. Input resolution was not stated on the README.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DAX ViT-T/16 is the tiny Vision Transformer, patch size 16, from the FAU Pattern Recognition Lab DAX release. Siemens Healthineers coauthors are on the paper. Springer lists the MICCAI 2025 chapter as first online on 20 September 2025. Collected 15 October 2026. Only version A is public. The repository is Apache-2.0. There is no public price.", "what_it_is_for": "DAX trains DINO-style X-ray backbones. Frozen features plus a small head are evaluated for body-region classification, metal-implant segmentation, and screw detection. This is the patch-16 tiny ViT, not the patch-8 twin.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fau/dax/dax-vit-t-8": { "id": "fau/dax/dax-vit-t-8", "name": "DAX ViT-T/8", "maker": "fau", "family": "fau/dax", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-20", "context_length": null, "max_output_tokens": null, "parameters": "5500000", "architecture": "Tiny Vision Transformer with patch size 8, trained with DINO Adapted to X-Ray (DAX) on more than 632,000 intraoperative orthopedic X-ray images. The GitHub table reports 5.5 M parameters for version A, the same count as the patch-16 twin. Input resolution was not stated on the README.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DAX ViT-T/8 is the tiny Vision Transformer, patch size 8, from the FAU Pattern Recognition Lab DAX release. Siemens Healthineers coauthors are on the paper. Springer lists the MICCAI 2025 chapter as first online on 20 September 2025. Collected 15 October 2026. Only version A is public. The repository is Apache-2.0. There is no public price.", "what_it_is_for": "DAX trains DINO-style X-ray backbones for frozen-feature use on body-region classification, metal-implant segmentation, and screw detection. This checkpoint uses patch size 8. The vendor lists it separately from DAX ViT-T/16.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fau/deepfilternet/deepfilternet": { "id": "fau/deepfilternet/deepfilternet", "name": "DeepFilterNet", "maker": "fau", "family": "fau/deepfilternet", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT OR Apache-2.0", "release_date": "2021-10-11", "context_length": null, "max_output_tokens": null, "parameters": "1778000", "architecture": "Two-stage full-band speech enhancer at 48 kHz. Stage 1 applies real gains on 32 ERB bands to the spectral envelope. Stage 2 applies a complex deep filter of order 5 to periodic speech up to 5 kHz. Separable convolutions and grouped linear and recurrent layers. FFT size 960 (20 ms) and hop 480 (10 ms), with one frame of deep-filter look-ahead and two frames of network look-ahead. The paper reports 0.3482 G MACS.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepFilterNet is a full-band speech enhancement model from the Pattern Recognition Lab at FAU Erlangen-N\u00fcrnberg, with coauthors at WS Audiology in Erlangen. It was posted on arXiv on 11 October 2021, accepted to ICASSP 2022, and the weights remain in the open DeepFilterNet repository as of 15 October 2026. Later named releases are DeepFilterNet2 and DeepFilterNet3. The command-line default is DeepFilterNet2, not this checkpoint.", "what_it_is_for": "The paper presents it as a low-complexity speech enhancer. The first stage models the speech envelope with ERB-scaled gains. The second stage uses deep filtering to restore periodic speech. The repository ships it for offline wav enhancement and as a real-time noise-suppression plugin.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fau/deepfilternet/deepfilternet2": { "id": "fau/deepfilternet/deepfilternet2", "name": "DeepFilterNet2", "maker": "fau", "family": "fau/deepfilternet", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT OR Apache-2.0", "release_date": "2022-05-11", "context_length": null, "max_output_tokens": null, "parameters": "2306000", "architecture": "Simplified DeepFilterNet for embedded real-time use. Still 32 ERB bands, deep filtering to 5 kHz, and filter order 5, with a look-ahead of 2 frames. The paper reports 0.356 G MACS and a real-time factor of 0.04 on a notebook Core i5. The README says the Python and Rust CLIs load this checkpoint by default.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepFilterNet2 is the embedded, real-time successor to DeepFilterNet from the FAU Pattern Recognition Lab and WS Audiology. The paper was posted on 11 May 2022 and submitted to IWAENC 2022. As of 15 October 2026 it is still the default checkpoint in the DeepFilterNet command-line tools. Weights are open source. There is no hosted API and no public price.", "what_it_is_for": "The paper says the goal is state-of-the-art full-band speech enhancement that can run in real time on embedded devices. The repository uses it as the default noise suppressor for wav files, the Rust binary, and a LADSPA plugin.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fau/deepfilternet/deepfilternet3": { "id": "fau/deepfilternet/deepfilternet3", "name": "DeepFilterNet3", "maker": "fau", "family": "fau/deepfilternet", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT OR Apache-2.0", "release_date": "2023-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The INTERSPEECH 2023 demo paper calls it a slightly modified DeepFilterNet. It still runs at 48 kHz on 20 ms windows with a 10 ms hop, in two stages: a coarse ERB speech-envelope stage and a deep-filtering periodicity stage. The paper reports a single-thread real-time factor of 0.19 on a notebook i5-8250U using the tract runtime. It does not publish a parameter count.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepFilterNet3 is the 2023 full-band speech enhancer from the FAU Pattern Recognition Lab and WS Audiology. The demo paper, posted 14 May 2023 and accepted as an INTERSPEECH 2023 show-and-tell, titles the work DeepFilterNet and labels the new row DeepFilterNet3. The repository still ships the weights as of 15 October 2026. The command-line default remains DeepFilterNet2. There is no public price.", "what_it_is_for": "The paper presents a real-time noise-suppression demo. The model uses speech-production and perception structure so it can run on a notebook CPU. The repository also publishes ONNX exports for the Rust runtime.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fau/find-net/find-net": { "id": "fau/find-net/find-net", "name": "FIND-Net", "maker": "fau", "family": "fau/find-net", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Iterative metal-artifact reduction network for CT. M-Net estimates artifacts with frequency-enhanced features. X-Net refines the image in spatial and frequency domains. It extends DICDNet with Fast Fourier Convolution layers and trainable Gaussian filtering. The public weight file is pretrained_models/FINDNet/checkpoint.pt. Parameter count was not published.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FIND-Net is a CT metal-artifact reduction model from the Pattern Recognition Lab at FAU Erlangen-N\u00fcrnberg. The paper was posted on 14 August 2025 and accepted to MICCAI 2025. The repository ships a pretrained checkpoint under Apache-2.0. Collected 15 October 2026. There is no API and no public price.", "what_it_is_for": "The authors present it as a hybrid spatial and frequency network for metal artifact reduction. It keeps anatomical structure while suppressing streak artifacts from implants. The public checkpoint is for the full model with trainable Gaussian filtering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fish-audio/drama/drama-3": { "id": "fish-audio/drama/drama-3", "name": "Fish Audio Drama 3", "maker": "fish-audio", "family": "fish-audio/drama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Fish Audio Community License v1.0", "release_date": "2026-10-13", "context_length": null, "max_output_tokens": null, "parameters": "35B total, about 3B active per token", "architecture": "Hybrid mixture-of-experts speech model. Backbone initialized from the language-model weights of Qwen3.5-35B-A3B: 40 layers in blocks of three Gated DeltaNet layers and one gated full-attention layer. Each layer routes a token to 8 of 256 experts plus one shared expert. The S2 audio tokenizer is reused: 44.1 kHz, 10 codebooks, about 21.5 frames per second. A 13-layer codebook multi-token-prediction decoder (4 shared layers plus 9 per-codebook layers) replaces S2's fast autoregressive module. Experts are stored in block-scaled FP8; other weights are BF16. Hugging Face metadata says 36B, which disagrees with the 35B figure in the paper and card text.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 50.0, "completion_per_m_usd": null }, "summary_intro": "Fish Audio's Fish Audio Drama 3 is a active model in the drama family. This dossier was collected on 2026-10-15.", "what_it_is_for": "Text-to-speech for directed performance: bracketed acting notes, scoped delivery on a phrase, multi-character scenes, multi-turn revision, and local edits of an existing take. Fish ships it in the app, the API, and as weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fish-audio/fish-agent/fish-agent-v0-1-3b": { "id": "fish-audio/fish-agent/fish-agent-v0-1-3b", "name": "Fish Agent v0.1 3B", "maker": "fish-audio", "family": "fish-audio/fish-agent", "status": "retired", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2024-11-23", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Voice-to-voice model. The card says it is a continued pretrain of Qwen-2.5-3B-Instruct for 200 billion voice and text tokens, and that it does not use a separate semantic encoder or decoder such as Whisper or CosyVoice. It is also described as a text-to-speech model trained on 700,000 hours. The card's own citation title is Fish Agent V0.1 3B, year 2024.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fish Audio's Fish Agent v0.1 3B is a retired model in the fish-agent family. This dossier was collected on 2026-10-15.", "what_it_is_for": "A 3B voice-to-voice model that also does text-to-speech. Fish's card emphasizes environmental-audio detail and a design without a separate semantic speech encoder. It is not part of the current hosted model list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fish-audio/fish-speech/fish-speech-1": { "id": "fish-audio/fish-speech/fish-speech-1", "name": "Fish Speech 1", "maker": "fish-audio", "family": "fish-audio/fish-speech", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2024-04-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First public Fish Speech text-to-speech pipeline. The v1.0.0 code release describes a VQ-GAN codec with a VITS decoder, LoRA fine-tuning, and streaming inference, and calls the architecture VQ + DiT + Reflow. A May 2024 announcement said the series was trained about 50k hours each on English, Chinese, and Japanese (150k hours total), then supervised-fine-tuned on about 1,000 hours, and that generation ran at roughly 20 audio tokens per second.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fish Audio's Fish Speech 1 is a retired model in the fish-speech family. This dossier was collected on 2026-10-15.", "what_it_is_for": "Zero-shot multilingual text-to-speech. The card presents it as Fish Audio's first open-weight TTS release, for English, Chinese, and Japanese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fish-audio/fish-speech/fish-speech-1-2": { "id": "fish-audio/fish-speech/fish-speech-1-2", "name": "Fish Speech 1.2", "maker": "fish-audio", "family": "fish-audio/fish-speech", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2024-07-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fish Speech 1.x text-to-speech checkpoint. The changelog describes an auto-reranking system, model quantization, and a v1.2 architecture aimed at more stable generation. Parameter count is not stated on the card.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fish Audio's Fish Speech 1.2 is a retired model in the fish-speech family. This dossier was collected on 2026-10-15.", "what_it_is_for": "Multilingual text-to-speech. The card says it was trained on 300k hours of English, Chinese, and Japanese audio. The product changelog says the release added auto-reranking for more stable generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fish-audio/fish-speech/fish-speech-1-4": { "id": "fish-audio/fish-speech/fish-speech-1-4", "name": "Fish Speech 1.4", "maker": "fish-audio", "family": "fish-audio/fish-speech", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2024-09-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dual-autoregressive (dual_ar) text-to-semantic model. A published config.json uses dim 1024, 24 layers, 16 heads, 4 fast layers, 8 codebooks, codebook size 1024, and vocab size 32000. max_seq_len was raised from 2048 to 4096 in a 2024-09-10 config update. The checkpoint set includes a Firefly GAN VQ codec file named for 8x1024 codes at 21 Hz. The v1.4.0 changelog calls the codec change a new VQGAN architecture.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fish Audio's Fish Speech 1.4 is a retired model in the fish-speech family. This dossier was collected on 2026-10-15.", "what_it_is_for": "Multilingual text-to-speech. The card says it was trained on 700k hours, with about 300k hours each of English and Chinese and about 20k hours each of German, Japanese, French, Spanish, Korean, and Arabic. The product changelog called v1.4 the most powerful open-source TTS Fish had shipped, with more than 8 languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fish-audio/fish-speech/fish-speech-1-5": { "id": "fish-audio/fish-speech/fish-speech-1-5", "name": "Fish Speech 1.5", "maker": "fish-audio", "family": "fish-audio/fish-speech", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2024-11-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dual-autoregressive (dual_ar) text-to-semantic model. A 2024-11-24 config.json uses dim 1024, 24 layers, 16 heads, head dim 64, intermediate size 4096, 4 fast layers, fast dim 1024, 8 codebooks, codebook size 1024, vocab size 102048, and max_seq_len 8192. The model file on that commit is about 1.28 GB; that file size is not a parameter count.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 15.0, "completion_per_m_usd": null }, "summary_intro": "Fish Audio's Fish Speech 1.5 is a deprecated model in the fish-speech family. This dossier was collected on 2026-10-15.", "what_it_is_for": "Multilingual text-to-speech and the hosted API id speech-1.5. The card says it was trained on more than 1 million hours across 13 languages. Fish's emotion reference says Fish Speech 1.5 had basic emotion tags, limited advanced tags, tone markers, and 6 of 10 audio effects, without intensity control.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fish-audio/fish-speech/fish-speech-1-6": { "id": "fish-audio/fish-speech/fish-speech-1-6", "name": "Fish Speech 1.6", "maker": "fish-audio", "family": "fish-audio/fish-speech", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 15.0, "completion_per_m_usd": null }, "summary_intro": "Fish Audio's Fish Speech 1.6 is a deprecated model in the fish-speech family. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hosted text-to-speech. The February 2025 product note says v1.6 added higher stability, emotion support, and better multilingual performance and was available online. It is no longer in the current model enum.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fish-audio/s1/s1": { "id": "fish-audio/s1/s1", "name": "Fish Audio S1", "maker": "fish-audio", "family": "fish-audio/s1", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-20", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Proprietary 4B text-to-speech model. The S1 launch post says it uses a Qwen3 multimodal backbone, an in-house audio codec in the spirit of the Descript Audio Codec trained from scratch, and online RLHF with GRPO. The S1-mini card calls the 4B model proprietary. Docs self-hosting guide says the full S1 weights are only on Fish Audio cloud.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 15.0, "completion_per_m_usd": null }, "summary_intro": "Fish Audio's Fish Audio S1 is a deprecated model in the s1 family. This dossier was collected on 2026-10-15.", "what_it_is_for": "Flagship text-to-speech for the OpenAudio rebrand: expressive reading and voice cloning with a fixed set of parenthesis emotion, tone, and effect tags. Fish says it was trained on more than 2 million hours with online RLHF. The 4B model is cloud-only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fish-audio/s1/s1-mini": { "id": "fish-audio/s1/s1-mini", "name": "Fish Audio S1-mini", "maker": "fish-audio", "family": "fish-audio/s1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2025-06", "context_length": null, "max_output_tokens": null, "parameters": "0.5B", "architecture": "Distilled 0.5B version of Fish Audio S1. The card says both S1 and S1-mini use online RLHF. Docs describe it as the local-deployment checkpoint. Fish's self-hosting guide calls it open-source; the weight license on the card is CC-BY-NC-SA-4.0, so this catalog marks the weights open-weights rather than open-source.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fish Audio's Fish Audio S1-mini is a active model in the s1 family. This dossier was collected on 2026-10-15.", "what_it_is_for": "Local text-to-speech. Fish describes S1-mini as a distilled model with the core emotion and tone controls of the 4B S1, trained with online RLHF, for machines that cannot run the proprietary 4B model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fish-audio/s2/s2-1-pro": { "id": "fish-audio/s2/s2-1-pro", "name": "Fish Audio S2.1-Pro", "maker": "fish-audio", "family": "fish-audio/s2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted production TTS described as an improved S2-Pro. No separate technical report was found. The S2.1 blog links the S2 paper (arXiv:2603.08823) for background and says S2 shipped with open weights while S2.1 is the hosted model. Inference notes in that blog: FP8 on H200, fish-scales-ops kernels, and a custom scheduler. Parameter count is not stated.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 15.0, "completion_per_m_usd": null }, "summary_intro": "Fish Audio's Fish Audio S2.1-Pro is a active model in the s2 family. This dossier was collected on 2026-10-15.", "what_it_is_for": "Production text-to-speech. Fish recommends it when a workload needs the paid time-to-first-audio and uptime guarantees. It keeps S2 bracket controls and multi-speaker dialogue and covers 83 languages on one model. The free id is the same model with weaker guarantees.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fish-audio/s2/s2-pro": { "id": "fish-audio/s2/s2-pro", "name": "Fish Audio S2-Pro", "maker": "fish-audio", "family": "fish-audio/s2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Fish Audio Research License", "release_date": "2026-03-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dual-autoregressive TTS. Hugging Face card: slow AR 4B predicts the primary semantic codebook along time; fast AR 400M predicts the other 9 residual codebooks. RVQ codec with 10 codebooks at about 21 Hz. Changelog: Qwen3-4B backbone. Drama 3 paper later says S2 has about 4.6B parameters and that a batch-1 frame reads about 15.6 GB of BF16 weights. GitHub README still says 4B. Hugging Face metadata says 5B. Those figures are all stored; none was collapsed into one integer.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 15.0, "completion_per_m_usd": null }, "summary_intro": "Fish Audio's Fish Audio S2-Pro is a active model in the s2 family. This dossier was collected on 2026-10-15.", "what_it_is_for": "Expressive multilingual text-to-speech with voice cloning, multi-speaker turns, and free-form bracket instructions such as a whisper or a laugh placed on a word. Fish says it was trained on more than 10 million hours.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fish-audio/transcribe/transcribe-1": { "id": "fish-audio/transcribe/transcribe-1", "name": "Transcribe 1", "maker": "fish-audio", "family": "fish-audio/transcribe", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.36, "completion_per_m_usd": null }, "summary_intro": "Fish Audio's Transcribe 1 is a active model in the transcribe family. This dossier was collected on 2026-10-15.", "what_it_is_for": "Automatic speech recognition for short clips. Fish uses it as the default when an ASR request does not name transcribe-1-pro exactly. Optional word timestamps. No speaker diarization and no emotion tags on this model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fish-audio/transcribe/transcribe-1-pro": { "id": "fish-audio/transcribe/transcribe-1-pro", "name": "Transcribe 1 Pro", "maker": "fish-audio", "family": "fish-audio/transcribe", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.36, "completion_per_m_usd": null }, "summary_intro": "Fish Audio's Transcribe 1 Pro is a active model in the transcribe family. This dossier was collected on 2026-10-15.", "what_it_is_for": "Speech-to-text for short clips and long recordings, including multi-speaker audio. Transcripts can include inline speaker markers, timed speaker turns, and emotion or vocal-event cues such as laughter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fish-audio/voice-design/voice-design-1": { "id": "fish-audio/voice-design/voice-design-1", "name": "Voice Design 1", "maker": "fish-audio", "family": "fish-audio/voice-design", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.01, "completion_per_m_usd": null }, "summary_intro": "Fish Audio's Voice Design 1 is a active model in the voice-design family. This dossier was collected on 2026-10-15.", "what_it_is_for": "Designs a new short voice from a written description, instead of cloning a recording. The API returns one to four WAV candidates. The web app is aimed at characters rather than at cloning a real person.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/bindscreen/bindscreen-finetuning-dude": { "id": "fiu/bindscreen/bindscreen-finetuning-dude", "name": "BindScreen Finetuning DUDE", "maker": "fiu", "family": "fiu/bindscreen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-09-25", "context_length": null, "max_output_tokens": null, "parameters": "2232320", "architecture": "SeqScreenModel, model_type seqscreen, float32, dropout 0.1. Projection dims mol_dim 768, prot_dim 2560, proj_dim 512. Safetensors total 2232320 is this projection file, not ESM2 and not MolDeBERTa. Card: protein branch ESM2 T36 (facebook/esm2_t36_3B_UR50D), molecule branch MolDeBERTa MLC, and the LIT usage example loads SaeedLab/MolDeBERTa-base-123M-mlc. config.json esm2_model_name is facebook/esm2_t36_3B_UR50D and lora_adapter_repo is SaeedLab/BindScreen-lora-DUDE. Training set named on the card: DUDE. Card: projection layers and ESM2 T36 are trained, and MolDeBERTa MLC stays frozen. The ESM2 delta is a separate LoRA repo, not this safetensors file.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BindScreen Finetuning DUDE is a projection checkpoint for sequence-based virtual screening from the Saeed Lab at Florida International University. It ranks molecules against a protein by cosine similarity of independently encoded embeddings. Hub repository creation is 2026-09-25. The card was public on 15 October 2026. Status is active. The card says the paper is under review. Crossref lists the bioRxiv record as posted 2026-08-25 and spells the method Bind-Screen in one sentence.", "what_it_is_for": "BindScreen encodes a protein and a molecule separately, projects both to 512 dimensions, and ranks candidates by cosine similarity. This folder is the DUDE finetune projection checkpoint. Card: projection layers and ESM2 T36 are trained, and MolDeBERTa MLC stays frozen. The ESM2 delta is a separate LoRA repo, not this safetensors file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/bindscreen/bindscreen-finetuning-lit-pcba": { "id": "fiu/bindscreen/bindscreen-finetuning-lit-pcba", "name": "BindScreen Finetuning LIT-PCBA", "maker": "fiu", "family": "fiu/bindscreen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-05-22", "context_length": null, "max_output_tokens": null, "parameters": "2232320", "architecture": "SeqScreenModel, model_type seqscreen, float32, dropout 0.1. Projection dims mol_dim 768, prot_dim 2560, proj_dim 512. Safetensors total 2232320 is this projection file, not ESM2 and not MolDeBERTa. Card: protein branch ESM2 T36 (facebook/esm2_t36_3B_UR50D), molecule branch MolDeBERTa MLC, and the LIT usage example loads SaeedLab/MolDeBERTa-base-123M-mlc. config.json esm2_model_name is facebook/esm2_t36_3B_UR50D and lora_adapter_repo is SaeedLab/SeqScreen-lora. Training set named on the card: LIT-PCBA. Card: projection layers and ESM2 T36 are trained, and MolDeBERTa MLC stays frozen. The ESM2 delta is a separate LoRA repo, not this safetensors file.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BindScreen Finetuning LIT-PCBA is a projection checkpoint for sequence-based virtual screening from the Saeed Lab at Florida International University. It ranks molecules against a protein by cosine similarity of independently encoded embeddings. Hub repository creation is 2026-05-22. The card was public on 15 October 2026. Status is active. The card says the paper is under review. Crossref lists the bioRxiv record as posted 2026-08-25 and spells the method Bind-Screen in one sentence.", "what_it_is_for": "BindScreen encodes a protein and a molecule separately, projects both to 512 dimensions, and ranks candidates by cosine similarity. This folder is the LIT-PCBA finetune projection checkpoint. Card: projection layers and ESM2 T36 are trained, and MolDeBERTa MLC stays frozen. The ESM2 delta is a separate LoRA repo, not this safetensors file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/bindscreen/bindscreen-frozen-dude": { "id": "fiu/bindscreen/bindscreen-frozen-dude", "name": "BindScreen Frozen DUDE", "maker": "fiu", "family": "fiu/bindscreen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-09-25", "context_length": null, "max_output_tokens": null, "parameters": "2232320", "architecture": "SeqScreenModel, model_type seqscreen, float32, dropout 0.1. Projection dims mol_dim 768, prot_dim 2560, proj_dim 512. Safetensors total 2232320 is this projection file, not ESM2 and not MolDeBERTa. Card: protein branch ESM2 T36 (facebook/esm2_t36_3B_UR50D), molecule branch MolDeBERTa MLC, and the LIT usage example loads SaeedLab/MolDeBERTa-base-123M-mlc. The frozen config.json that was opened has no esm2_model_name key. The card still says the protein branch is ESM2 T36. Training set named on the card: DUDE. Card: only the projection layers are trained and both encoders stay frozen.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BindScreen Frozen DUDE is a projection checkpoint for sequence-based virtual screening from the Saeed Lab at Florida International University. It ranks molecules against a protein by cosine similarity of independently encoded embeddings. Hub repository creation is 2026-09-25. The card was public on 15 October 2026. Status is active. The card says the paper is under review. Crossref lists the bioRxiv record as posted 2026-08-25 and spells the method Bind-Screen in one sentence.", "what_it_is_for": "BindScreen encodes a protein and a molecule separately, projects both to 512 dimensions, and ranks candidates by cosine similarity. This folder is the DUDE frozen projection checkpoint. Card: only the projection layers are trained and both encoders stay frozen.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/bindscreen/bindscreen-frozen-lit-pcba": { "id": "fiu/bindscreen/bindscreen-frozen-lit-pcba", "name": "BindScreen Frozen LIT-PCBA", "maker": "fiu", "family": "fiu/bindscreen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-05-22", "context_length": null, "max_output_tokens": null, "parameters": "2232320", "architecture": "SeqScreenModel, model_type seqscreen, float32, dropout 0.1. Projection dims mol_dim 768, prot_dim 2560, proj_dim 512. Safetensors total 2232320 is this projection file, not ESM2 and not MolDeBERTa. Card: protein branch ESM2 T36 (facebook/esm2_t36_3B_UR50D), molecule branch MolDeBERTa MLC, and the LIT usage example loads SaeedLab/MolDeBERTa-base-123M-mlc. The frozen config.json that was opened has no esm2_model_name key. The card still says the protein branch is ESM2 T36. Training set named on the card: LIT-PCBA. Card: only the projection layers are trained and both encoders stay frozen.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BindScreen Frozen LIT-PCBA is a projection checkpoint for sequence-based virtual screening from the Saeed Lab at Florida International University. It ranks molecules against a protein by cosine similarity of independently encoded embeddings. Hub repository creation is 2026-05-22. The card was public on 15 October 2026. Status is active. The card says the paper is under review. Crossref lists the bioRxiv record as posted 2026-08-25 and spells the method Bind-Screen in one sentence.", "what_it_is_for": "BindScreen encodes a protein and a molecule separately, projects both to 512 dimensions, and ranks candidates by cosine similarity. This folder is the LIT-PCBA frozen projection checkpoint. Card: only the projection layers are trained and both encoders stay frozen.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/dom-formula-assignment/dom-formula-assignment": { "id": "fiu/dom-formula-assignment/dom-formula-assignment", "name": "DOM formula assignment", "maker": "fiu", "family": "fiu/dom-formula-assignment", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One Hub card ships classical estimators, not a neural foundation model. The card table names 16 KNN settings and two regressors. KNN input is a 1D vector of masses. DecisionTree and RandomForest take rows [mz, inv_k0, ccs] and predict CHONS counts ordered [C, H, O, N, S]. Card install pin: numpy==2.4.1 and scikit-learn==1.8.0, plus joblib, huggingface_hub, transformers, torch, and pandas. Load AutoModel with trust_remote_code and model_name. The tree also stores component joblib files for 7T, 21T, and SYN pieces of the ensembles. Parameter count is null because the files are joblib, not a neural safetensors total. Hub downloads field on the saved API record: 0.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DOM formula assignment is a bundle of k-nearest-neighbor, decision-tree, and random-forest estimators from the Saeed Lab at Florida International University. The card uses them to assign molecular formulas to ultra-high-resolution mass spectra of dissolved organic matter. One card and one repository hold the named estimators. Hub repository creation is 2026-03-04. The card was public on 15 October 2026. Status is active. The card says the paper is under review.", "what_it_is_for": "KNN assigns a formula from the nearest training peaks. The decision tree and random forest predict element counts. The card names datasets L1 (7T), L3 (21T), an L1-L3 ensemble, and Synthetic, with K of 1 or 3 and Euclidean or Manhattan distance.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-base-10m-contrastive-mlc": { "id": "fiu/moldeberta/moldeberta-base-10m-contrastive-mlc", "name": "MolDeBERTa-base-10M-contrastive_mlc", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "88227840", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale base: hidden 768, intermediate 3072, 12 heads, 12 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 88227840. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective contrastive multi-label classification.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-base-10M-contrastive_mlc is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-base-10M-contrastive_mlc. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: contrastive multi-label classification. Corpus label: 10M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-base-10m-contrastive-mtr": { "id": "fiu/moldeberta/moldeberta-base-10m-contrastive-mtr", "name": "MolDeBERTa-base-10M-contrastive_mtr", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "88227840", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale base: hidden 768, intermediate 3072, 12 heads, 12 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 88227840. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective contrastive multi-task regression.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-base-10M-contrastive_mtr is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-base-10M-contrastive_mtr. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: contrastive multi-task regression. Corpus label: 10M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-base-10m-mlc": { "id": "fiu/moldeberta/moldeberta-base-10m-mlc", "name": "MolDeBERTa-base-10M-mlc", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "88227840", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale base: hidden 768, intermediate 3072, 12 heads, 12 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 88227840. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective multi-label classification.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-base-10M-mlc is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-base-10M-mlc. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: multi-label classification. Corpus label: 10M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-base-10m-mlm": { "id": "fiu/moldeberta/moldeberta-base-10m-mlm", "name": "MolDeBERTa-base-10M-mlm", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "88823970", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2ForMaskedLM. Scale base: hidden 768, intermediate 3072, 12 heads, 12 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 88823970. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective masked language modeling.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-base-10M-mlm is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-base-10M-mlm. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: masked language modeling. Corpus label: 10M PubChem SMILES. The collection names it state-of-the-art for BACE regression. The saved config class is DebertaV2ForMaskedLM, so this checkpoint includes a masked-token head. Sibling encoder cards read last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-base-10m-mtr": { "id": "fiu/moldeberta/moldeberta-base-10m-mtr", "name": "MolDeBERTa-base-10M-mtr", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "88227840", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale base: hidden 768, intermediate 3072, 12 heads, 12 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 88227840. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective multi-task regression.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-base-10M-mtr is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-base-10M-mtr. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: multi-task regression. Corpus label: 10M PubChem SMILES. The collection names it state-of-the-art for BBBP. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-base-123m-contrastive-mlc": { "id": "fiu/moldeberta/moldeberta-base-123m-contrastive-mlc", "name": "MolDeBERTa-base-123M-contrastive_mlc", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "88227840", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale base: hidden 768, intermediate 3072, 12 heads, 12 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 88227840. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective contrastive multi-label classification.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-base-123M-contrastive_mlc is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-base-123M-contrastive_mlc. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: contrastive multi-label classification. Corpus label: 123M PubChem SMILES. The collection names it state-of-the-art for Tox21. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-base-123m-contrastive-mtr": { "id": "fiu/moldeberta/moldeberta-base-123m-contrastive-mtr", "name": "MolDeBERTa-base-123M-contrastive_mtr", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "88227840", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale base: hidden 768, intermediate 3072, 12 heads, 12 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 88227840. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective contrastive multi-task regression.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-base-123M-contrastive_mtr is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-base-123M-contrastive_mtr. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: contrastive multi-task regression. Corpus label: 123M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-base-123m-mlc": { "id": "fiu/moldeberta/moldeberta-base-123m-mlc", "name": "MolDeBERTa-base-123M-mlc", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "88227840", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale base: hidden 768, intermediate 3072, 12 heads, 12 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 88227840. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective multi-label classification.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-base-123M-mlc is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-base-123M-mlc. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: multi-label classification. Corpus label: 123M PubChem SMILES. The collection names it state-of-the-art for Clintox. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-base-123m-mlm": { "id": "fiu/moldeberta/moldeberta-base-123m-mlm", "name": "MolDeBERTa-base-123M-mlm", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "88823970", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2ForMaskedLM. Scale base: hidden 768, intermediate 3072, 12 heads, 12 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 88823970. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective masked language modeling.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-base-123M-mlm is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-base-123M-mlm. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: masked language modeling. Corpus label: 123M PubChem SMILES. The saved config class is DebertaV2ForMaskedLM, so this checkpoint includes a masked-token head. Sibling encoder cards read last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-base-123m-mtr": { "id": "fiu/moldeberta/moldeberta-base-123m-mtr", "name": "MolDeBERTa-base-123M-mtr", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "88227840", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale base: hidden 768, intermediate 3072, 12 heads, 12 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 88227840. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective multi-task regression.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-base-123M-mtr is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-base-123M-mtr. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: multi-task regression. Corpus label: 123M PubChem SMILES. The collection names it state-of-the-art for Clearance and Clintox. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-small-10m-contrastive-mlc": { "id": "fiu/moldeberta/moldeberta-small-10m-contrastive-mlc", "name": "MolDeBERTa-small-10M-contrastive_mlc", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "21029888", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale small: hidden 512, intermediate 2048, 8 heads, 6 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 21029888. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective contrastive multi-label classification.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-small-10M-contrastive_mlc is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-small-10M-contrastive_mlc. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: contrastive multi-label classification. Corpus label: 10M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-small-10m-contrastive-mtr": { "id": "fiu/moldeberta/moldeberta-small-10m-contrastive-mtr", "name": "MolDeBERTa-small-10M-contrastive_mtr", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "21029888", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale small: hidden 512, intermediate 2048, 8 heads, 6 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 21029888. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective contrastive multi-task regression.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-small-10M-contrastive_mtr is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-small-10M-contrastive_mtr. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: contrastive multi-task regression. Corpus label: 10M PubChem SMILES. The collection names it state-of-the-art for Clintox. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-small-10m-mlc": { "id": "fiu/moldeberta/moldeberta-small-10m-mlc", "name": "MolDeBERTa-small-10M-mlc", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "21029888", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale small: hidden 512, intermediate 2048, 8 heads, 6 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 21029888. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective multi-label classification.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-small-10M-mlc is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-small-10M-mlc. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: multi-label classification. Corpus label: 10M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-small-10m-mlm": { "id": "fiu/moldeberta/moldeberta-small-10m-mlm", "name": "MolDeBERTa-small-10M-mlm", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "21297570", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2ForMaskedLM. Scale small: hidden 512, intermediate 2048, 8 heads, 6 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 21297570. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective masked language modeling.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-small-10M-mlm is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-small-10M-mlm. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: masked language modeling. Corpus label: 10M PubChem SMILES. The saved config class is DebertaV2ForMaskedLM, so this checkpoint includes a masked-token head. Sibling encoder cards read last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-small-10m-mtr": { "id": "fiu/moldeberta/moldeberta-small-10m-mtr", "name": "MolDeBERTa-small-10M-mtr", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "21029888", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale small: hidden 512, intermediate 2048, 8 heads, 6 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 21029888. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective multi-task regression.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-small-10M-mtr is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-small-10M-mtr. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: multi-task regression. Corpus label: 10M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-small-123m-contrastive-mlc": { "id": "fiu/moldeberta/moldeberta-small-123m-contrastive-mlc", "name": "MolDeBERTa-small-123M-contrastive_mlc", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "21029888", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale small: hidden 512, intermediate 2048, 8 heads, 6 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 21029888. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective contrastive multi-label classification.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-small-123M-contrastive_mlc is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-small-123M-contrastive_mlc. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: contrastive multi-label classification. Corpus label: 123M PubChem SMILES. The collection names it state-of-the-art for HIV. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-small-123m-contrastive-mtr": { "id": "fiu/moldeberta/moldeberta-small-123m-contrastive-mtr", "name": "MolDeBERTa-small-123M-contrastive_mtr", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "21029888", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale small: hidden 512, intermediate 2048, 8 heads, 6 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 21029888. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective contrastive multi-task regression.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-small-123M-contrastive_mtr is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-small-123M-contrastive_mtr. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: contrastive multi-task regression. Corpus label: 123M PubChem SMILES. The collection names it state-of-the-art for Lipo. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-small-123m-mlc": { "id": "fiu/moldeberta/moldeberta-small-123m-mlc", "name": "MolDeBERTa-small-123M-mlc", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "21029888", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale small: hidden 512, intermediate 2048, 8 heads, 6 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 21029888. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective multi-label classification.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-small-123M-mlc is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-small-123M-mlc. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: multi-label classification. Corpus label: 123M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-small-123m-mlm": { "id": "fiu/moldeberta/moldeberta-small-123m-mlm", "name": "MolDeBERTa-small-123M-mlm", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "21297570", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2ForMaskedLM. Scale small: hidden 512, intermediate 2048, 8 heads, 6 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 21297570. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective masked language modeling.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-small-123M-mlm is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-small-123M-mlm. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: masked language modeling. Corpus label: 123M PubChem SMILES. The saved config class is DebertaV2ForMaskedLM, so this checkpoint includes a masked-token head. Sibling encoder cards read last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-small-123m-mtr": { "id": "fiu/moldeberta/moldeberta-small-123m-mtr", "name": "MolDeBERTa-small-123M-mtr", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "21029888", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale small: hidden 512, intermediate 2048, 8 heads, 6 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 21029888. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective multi-task regression.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-small-123M-mtr is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-small-123M-mtr. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: multi-task regression. Corpus label: 123M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-tiny-10m-contrastive-mlc": { "id": "fiu/moldeberta/moldeberta-tiny-10m-contrastive-mlc", "name": "MolDeBERTa-tiny-10M-contrastive_mlc", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "6910080", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale tiny: hidden 384, intermediate 1536, 6 heads, 3 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 6910080. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective contrastive multi-label classification.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-tiny-10M-contrastive_mlc is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-tiny-10M-contrastive_mlc. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: contrastive multi-label classification. Corpus label: 10M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-tiny-10m-contrastive-mtr": { "id": "fiu/moldeberta/moldeberta-tiny-10m-contrastive-mtr", "name": "MolDeBERTa-tiny-10M-contrastive_mtr", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "6910080", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale tiny: hidden 384, intermediate 1536, 6 heads, 3 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 6910080. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective contrastive multi-task regression.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-tiny-10M-contrastive_mtr is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-tiny-10M-contrastive_mtr. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: contrastive multi-task regression. Corpus label: 10M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-tiny-10m-mlc": { "id": "fiu/moldeberta/moldeberta-tiny-10m-mlc", "name": "MolDeBERTa-tiny-10M-mlc", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "6910080", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale tiny: hidden 384, intermediate 1536, 6 heads, 3 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 6910080. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective multi-label classification.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-tiny-10M-mlc is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-tiny-10M-mlc. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: multi-label classification. Corpus label: 10M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-tiny-10m-mlm": { "id": "fiu/moldeberta/moldeberta-tiny-10m-mlm", "name": "MolDeBERTa-tiny-10M-mlm", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "7062690", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2ForMaskedLM. Scale tiny: hidden 384, intermediate 1536, 6 heads, 3 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 7062690. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective masked language modeling.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-tiny-10M-mlm is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-tiny-10M-mlm. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: masked language modeling. Corpus label: 10M PubChem SMILES. The saved config class is DebertaV2ForMaskedLM, so this checkpoint includes a masked-token head. Sibling encoder cards read last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-tiny-10m-mtr": { "id": "fiu/moldeberta/moldeberta-tiny-10m-mtr", "name": "MolDeBERTa-tiny-10M-mtr", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "6910080", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale tiny: hidden 384, intermediate 1536, 6 heads, 3 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 6910080. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 10M is the PubChem SMILES set named on the card. Objective multi-task regression.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-tiny-10M-mtr is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-tiny-10M-mtr. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: multi-task regression. Corpus label: 10M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-tiny-123m-contrastive-mlc": { "id": "fiu/moldeberta/moldeberta-tiny-123m-contrastive-mlc", "name": "MolDeBERTa-tiny-123M-contrastive_mlc", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "6910080", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale tiny: hidden 384, intermediate 1536, 6 heads, 3 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 6910080. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective contrastive multi-label classification.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-tiny-123M-contrastive_mlc is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-tiny-123M-contrastive_mlc. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: contrastive multi-label classification. Corpus label: 123M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-tiny-123m-contrastive-mtr": { "id": "fiu/moldeberta/moldeberta-tiny-123m-contrastive-mtr", "name": "MolDeBERTa-tiny-123M-contrastive_mtr", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "6910080", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale tiny: hidden 384, intermediate 1536, 6 heads, 3 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 6910080. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective contrastive multi-task regression.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-tiny-123M-contrastive_mtr is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-tiny-123M-contrastive_mtr. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: contrastive multi-task regression. Corpus label: 123M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-tiny-123m-mlc": { "id": "fiu/moldeberta/moldeberta-tiny-123m-mlc", "name": "MolDeBERTa-tiny-123M-mlc", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "6910080", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale tiny: hidden 384, intermediate 1536, 6 heads, 3 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 6910080. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective multi-label classification.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-tiny-123M-mlc is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-tiny-123M-mlc. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: multi-label classification. Corpus label: 123M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-tiny-123m-mlm": { "id": "fiu/moldeberta/moldeberta-tiny-123m-mlm", "name": "MolDeBERTa-tiny-123M-mlm", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "7062690", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2ForMaskedLM. Scale tiny: hidden 384, intermediate 1536, 6 heads, 3 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 7062690. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective masked language modeling.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-tiny-123M-mlm is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-tiny-123M-mlm. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: masked language modeling. Corpus label: 123M PubChem SMILES. The saved config class is DebertaV2ForMaskedLM, so this checkpoint includes a masked-token head. Sibling encoder cards read last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/moldeberta/moldeberta-tiny-123m-mtr": { "id": "fiu/moldeberta/moldeberta-tiny-123m-mtr", "name": "MolDeBERTa-tiny-123M-mtr", "maker": "fiu", "family": "fiu/moldeberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-01-19", "context_length": 128, "max_output_tokens": null, "parameters": "6910080", "architecture": "DeBERTaV2 encoder, model_type deberta-v2, class DebertaV2Model. Scale tiny: hidden 384, intermediate 1536, 6 heads, 3 layers. Vocab size 4002. max_position_embeddings 128. Safetensors total 6910080. The opened MolDeBERTa-tiny-10M-mlm config has hidden_act gelu, relative_attention false, position_biased_input true, type_vocab_size 0, pad_token_id 0, layer_norm_eps 1e-7, and hidden and attention dropout 0.1. Those activation fields were not re-opened on this file. The card describes byte-level BPE and says the family is pretrained by the lab, not shipped as a fine-tune of another maker. Corpus label 123M is the PubChem SMILES set named on the card. Objective multi-task regression.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolDeBERTa-tiny-123M-mtr is a SMILES encoder from the Saeed Lab at Florida International University, Fahad Saeed, fsaeed@fiu.edu. The lab released it on Hugging Face as SaeedLab/MolDeBERTa-tiny-123M-mtr. Repository creation is 2026-01-19. The checkpoint was still public on 15 October 2026. Status is active.", "what_it_is_for": "The lab uses it as a SMILES embedding model for property prediction, similarity, and screening. Pretraining objective on the repo name: multi-task regression. Corpus label: 123M PubChem SMILES. The saved config class is DebertaV2Model. The card example reads last_hidden_state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/neuroclr/neuroclr": { "id": "fiu/neuroclr/neuroclr", "name": "NeuroCLR", "maker": "fiu", "family": "fiu/neuroclr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-19", "context_length": null, "max_output_tokens": null, "parameters": "290240", "architecture": "Root config: NeuroCLRModel, model_type neuroclr, TSlength 128, nhead 2, nlayer 2, normalize_input true, pooling flatten, projector_out1 128, projector_out2 64, torch_dtype float32, transformers 4.36.2. Card input for the root encoder is [B, 1, L] with L 128. Outputs h [B, 128] and z [B, projector_out_dim]. The card does not say which projector field is that dim. classification/ config: NeuroCLRForSequenceClassification, n_rois 200, n_block 48, base_filters 256, kernel_size 16, stride 2, groups 32, downsample_gap 6, increasefilter_gap 12, freeze_encoder true, use_bn true, use_do true, plus the same encoder fields. Card input for that head is [B, N_ROIs, 128], example 200 ROIs, ResNet1D head. Hub safetensors.total 290240 matches the card line 290k params. classification/ and pretraining/ also contain model.safetensors. The 290240 figure was not shown to be the sum of all three files.", "modalities": { "input": [], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NeuroCLR is a self-supervised encoder for raw resting-state fMRI regional time series from the Saeed Lab at Florida International University. The Hub repository holds a root encoder and a classification subfolder. Repository creation is 2025-12-19. The card was public on 15 October 2026. Status is active. The card says the paper is under review. No license block was on the card text that was read.", "what_it_is_for": "The card says the encoder learns disorder-agnostic representations from unlabeled rs-fMRI and that the classification subfolder fine-tunes that encoder for disorder labels when labeled data are limited. The card says pretraining used more than 3,600 participants, 44 sites, and 720,000 region-specific time series.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/protenrich/protenrich-ankh3": { "id": "fiu/protenrich/protenrich-ankh3", "name": "ProtEnrich - Ankh 3 XL", "maker": "fiu", "family": "fiu/protenrich", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-03-02", "context_length": null, "max_output_tokens": null, "parameters": "18162964", "architecture": "ProtEnrichModel, model_type protenrich, float32. The safetensors total 18162964 is the enrichment module, not the backbone. Config: seq_dim 2560, embed_dim 1024, project_dim 256, struct_dim 1024, dyn_dim 20, alpha_max 0.3. trust_remote_code. Backbone the card names: ElnaggarLab/ankh3-xl. T5 encoder. The card example prefixes the sequence with [NLU] and mean-pools tokens 1:-1. Outputs named on the Ankh card example: h_enrich, h_anchor, h_algn, struct, dyn. Trained on 550,120 SwissProt proteins with AlphaFold structures. ProstT5 and RocketSHP supply the multimodal residual. Inference is sequence-only.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProtEnrich - Ankh 3 XL is a residual protein-embedding module from the Saeed Lab at Florida International University. The released weights enrich ElnaggarLab/ankh3-xl. They do not replace that backbone. Hub repository creation is 2026-03-02. The card was public on 15 October 2026. Status is active. The card still says the paper is under review. Crossref lists the bioRxiv record as posted 2026-08-25.", "what_it_is_for": "The module adds a low-energy structural and dynamical residual to a frozen sequence embedding so downstream tasks can use sequence-only inference. The caller still loads the backbone named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/protenrich/protenrich-carp-640m": { "id": "fiu/protenrich/protenrich-carp-640m", "name": "ProtEnrich CARP 640M", "maker": "fiu", "family": "fiu/protenrich", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-03-03", "context_length": null, "max_output_tokens": null, "parameters": "14229524", "architecture": "ProtEnrichModel, model_type protenrich, float32. The safetensors total 14229524 is the enrichment module, not the backbone. Config: seq_dim 1280, embed_dim 1024, project_dim 256, struct_dim 1024, dyn_dim 20, alpha_max 0.3. trust_remote_code. Backbone the card names: CARP 640M (microsoft/protein-sequence-models, linked from the card). Caller loads CARP 640M and passes a pooled vector. Outputs named on the Ankh card example: h_enrich, h_anchor, h_algn, struct, dyn. Trained on 550,120 SwissProt proteins with AlphaFold structures. ProstT5 and RocketSHP supply the multimodal residual. Inference is sequence-only.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProtEnrich CARP 640M is a residual protein-embedding module from the Saeed Lab at Florida International University. The released weights enrich CARP 640M (microsoft/protein-sequence-models, linked from the card). They do not replace that backbone. Hub repository creation is 2026-03-03. The card was public on 15 October 2026. Status is active. The card still says the paper is under review. Crossref lists the bioRxiv record as posted 2026-08-25.", "what_it_is_for": "The module adds a low-energy structural and dynamical residual to a frozen sequence embedding so downstream tasks can use sequence-only inference. The caller still loads the backbone named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/protenrich/protenrich-esm1b": { "id": "fiu/protenrich/protenrich-esm1b", "name": "ProtEnrich ESM1b", "maker": "fiu", "family": "fiu/protenrich", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-03-03", "context_length": null, "max_output_tokens": null, "parameters": "14229524", "architecture": "ProtEnrichModel, model_type protenrich, float32. The safetensors total 14229524 is the enrichment module, not the backbone. Config: seq_dim 1280, embed_dim 1024, project_dim 256, struct_dim 1024, dyn_dim 20, alpha_max 0.3. trust_remote_code. Backbone the card names: facebook/esm1b_t33_650M_UR50S. Caller loads ESM-1b and passes a pooled vector. Outputs named on the Ankh card example: h_enrich, h_anchor, h_algn, struct, dyn. Trained on 550,120 SwissProt proteins with AlphaFold structures. ProstT5 and RocketSHP supply the multimodal residual. Inference is sequence-only.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProtEnrich ESM1b is a residual protein-embedding module from the Saeed Lab at Florida International University. The released weights enrich facebook/esm1b_t33_650M_UR50S. They do not replace that backbone. Hub repository creation is 2026-03-03. The card was public on 15 October 2026. Status is active. The card still says the paper is under review. Crossref lists the bioRxiv record as posted 2026-08-25.", "what_it_is_for": "The module adds a low-energy structural and dynamical residual to a frozen sequence embedding so downstream tasks can use sequence-only inference. The caller still loads the backbone named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/protenrich/protenrich-esm2-t36": { "id": "fiu/protenrich/protenrich-esm2-t36", "name": "ProtEnrich ESM2 T36", "maker": "fiu", "family": "fiu/protenrich", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-03-03", "context_length": null, "max_output_tokens": null, "parameters": "18162964", "architecture": "ProtEnrichModel, model_type protenrich, float32. The safetensors total 18162964 is the enrichment module, not the backbone. Config: seq_dim 2560, embed_dim 1024, project_dim 256, struct_dim 1024, dyn_dim 20, alpha_max 0.3. trust_remote_code. Backbone the card names: facebook/esm2_t36_3B_UR50D. Caller loads ESM2 T36 and passes a pooled vector. Outputs named on the Ankh card example: h_enrich, h_anchor, h_algn, struct, dyn. Trained on 550,120 SwissProt proteins with AlphaFold structures. ProstT5 and RocketSHP supply the multimodal residual. Inference is sequence-only.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProtEnrich ESM2 T36 is a residual protein-embedding module from the Saeed Lab at Florida International University. The released weights enrich facebook/esm2_t36_3B_UR50D. They do not replace that backbone. Hub repository creation is 2026-03-03. The card was public on 15 October 2026. Status is active. The card still says the paper is under review. Crossref lists the bioRxiv record as posted 2026-08-25.", "what_it_is_for": "The module adds a low-energy structural and dynamical residual to a frozen sequence embedding so downstream tasks can use sequence-only inference. The caller still loads the backbone named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/protenrich/protenrich-esmc-600m": { "id": "fiu/protenrich/protenrich-esmc-600m", "name": "ProtEnrich ESM Cambrian 600M", "maker": "fiu", "family": "fiu/protenrich", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-03-03", "context_length": null, "max_output_tokens": null, "parameters": "13836180", "architecture": "ProtEnrichModel, model_type protenrich, float32. The safetensors total 13836180 is the enrichment module, not the backbone. Config: seq_dim 1152, embed_dim 1024, project_dim 256, struct_dim 1024, dyn_dim 20, alpha_max 0.3. trust_remote_code. Backbone the card names: ESM Cambrian 600M (EvolutionaryScale, linked from the card). Caller loads ESM Cambrian 600M and passes a pooled vector. Outputs named on the Ankh card example: h_enrich, h_anchor, h_algn, struct, dyn. Trained on 550,120 SwissProt proteins with AlphaFold structures. ProstT5 and RocketSHP supply the multimodal residual. Inference is sequence-only.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProtEnrich ESM Cambrian 600M is a residual protein-embedding module from the Saeed Lab at Florida International University. The released weights enrich ESM Cambrian 600M (EvolutionaryScale, linked from the card). They do not replace that backbone. Hub repository creation is 2026-03-03. The card was public on 15 October 2026. Status is active. The card still says the paper is under review. Crossref lists the bioRxiv record as posted 2026-08-25.", "what_it_is_for": "The module adds a low-energy structural and dynamical residual to a frozen sequence embedding so downstream tasks can use sequence-only inference. The caller still loads the backbone named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/protenrich/protenrich-progen2": { "id": "fiu/protenrich/protenrich-progen2", "name": "ProtEnrich ProGen2", "maker": "fiu", "family": "fiu/protenrich", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-03-03", "context_length": null, "max_output_tokens": null, "parameters": "15016212", "architecture": "ProtEnrichModel, model_type protenrich, float32. The safetensors total 15016212 is the enrichment module, not the backbone. Config: seq_dim 1536, embed_dim 1024, project_dim 256, struct_dim 1024, dyn_dim 20, alpha_max 0.3. trust_remote_code. Backbone the card names: hugohrban/progen2-base. Caller loads ProGen2-base and passes a pooled vector. Outputs named on the Ankh card example: h_enrich, h_anchor, h_algn, struct, dyn. Trained on 550,120 SwissProt proteins with AlphaFold structures. ProstT5 and RocketSHP supply the multimodal residual. Inference is sequence-only.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProtEnrich ProGen2 is a residual protein-embedding module from the Saeed Lab at Florida International University. The released weights enrich hugohrban/progen2-base. They do not replace that backbone. Hub repository creation is 2026-03-03. The card was public on 15 October 2026. Status is active. The card still says the paper is under review. Crossref lists the bioRxiv record as posted 2026-08-25.", "what_it_is_for": "The module adds a low-energy structural and dynamical residual to a frozen sequence embedding so downstream tasks can use sequence-only inference. The caller still loads the backbone named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/protenrich/protenrich-protbert": { "id": "fiu/protenrich/protenrich-protbert", "name": "ProtEnrich ProtBERT", "maker": "fiu", "family": "fiu/protenrich", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-03-02", "context_length": null, "max_output_tokens": null, "parameters": "13442836", "architecture": "ProtEnrichModel, model_type protenrich, float32. The safetensors total 13442836 is the enrichment module, not the backbone. Config: seq_dim 1024, embed_dim 1024, project_dim 256, struct_dim 1024, dyn_dim 20, alpha_max 0.3. trust_remote_code. Backbone the card names: Rostlab/prot_bert. Caller loads ProtBERT and passes a pooled vector. Outputs named on the Ankh card example: h_enrich, h_anchor, h_algn, struct, dyn. Trained on 550,120 SwissProt proteins with AlphaFold structures. ProstT5 and RocketSHP supply the multimodal residual. Inference is sequence-only.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProtEnrich ProtBERT is a residual protein-embedding module from the Saeed Lab at Florida International University. The released weights enrich Rostlab/prot_bert. They do not replace that backbone. Hub repository creation is 2026-03-02. The card was public on 15 October 2026. Status is active. The card still says the paper is under review. Crossref lists the bioRxiv record as posted 2026-08-25.", "what_it_is_for": "The module adds a low-energy structural and dynamical residual to a frozen sequence embedding so downstream tasks can use sequence-only inference. The caller still loads the backbone named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/protenrich/protenrich-prott5": { "id": "fiu/protenrich/protenrich-prott5", "name": "ProtEnrich ProtT5", "maker": "fiu", "family": "fiu/protenrich", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-03-03", "context_length": null, "max_output_tokens": null, "parameters": "13442836", "architecture": "ProtEnrichModel, model_type protenrich, float32. The safetensors total 13442836 is the enrichment module, not the backbone. Config: seq_dim 1024, embed_dim 1024, project_dim 256, struct_dim 1024, dyn_dim 20, alpha_max 0.3. trust_remote_code. Backbone the card names: Rostlab/prot_t5_xl_half_uniref50-enc. Caller loads the ProtT5 encoder and passes a pooled vector. Outputs named on the Ankh card example: h_enrich, h_anchor, h_algn, struct, dyn. Trained on 550,120 SwissProt proteins with AlphaFold structures. ProstT5 and RocketSHP supply the multimodal residual. Inference is sequence-only.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProtEnrich ProtT5 is a residual protein-embedding module from the Saeed Lab at Florida International University. The released weights enrich Rostlab/prot_t5_xl_half_uniref50-enc. They do not replace that backbone. Hub repository creation is 2026-03-03. The card was public on 15 October 2026. Status is active. The card still says the paper is under review. Crossref lists the bioRxiv record as posted 2026-08-25.", "what_it_is_for": "The module adds a low-energy structural and dynamical residual to a frozen sequence embedding so downstream tasks can use sequence-only inference. The caller still loads the backbone named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/proteorift/proteorift": { "id": "fiu/proteorift/proteorift", "name": "ProteoRift", "maker": "fiu", "family": "fiu/proteorift", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2024-08-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Card: attention multitask network that predicts peptide length, missed cleavages, and modification status from mass spectra. Weight file on the Hub is pytorch_model.bin, so the API safetensors total is null. Parameter count was not taken from file size. A 2024 preprint supplement layer list was not re-opened.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProteoRift is an attention multitask network from the Saeed Lab at Florida International University. Usman Tariq, Bilal Shabbir, and Fahad Saeed are the authors on the Scientific Reports record. It predicts peptide length, missed cleavages, and modification status from mass spectra and is wired into a database search. The preprint was posted 2024-08-22. The journal article was published online 2026-03-13. Hub weights were created 2025-12-23. Status is active.", "what_it_is_for": "The card uses the network to shrink the peptide candidate list before scoring. The package writes Percolator PIN files. Training data named on the card: NIST human peptide libraries, MassIVE, and DeepNovo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/specollate/specollate": { "id": "fiu/specollate/specollate", "name": "SpeCollate", "maker": "fiu", "family": "fiu/specollate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2021-10-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PLOS ONE section 3.1 and the Hub card fetched on 2026-10-15 both say embedding size 256. Spectrum sub-network: sparse input length 80000 (0.1 Da bins), fully connected 80000 by 1024 and 1024 by 256, ReLU, dropout 0.3 after the first layer, L2 normalization. Peptide sub-network: vocabulary 30, character embedding 256, bidirectional LSTM hidden 1024 (concatenated 2048), fully connected 2048 by 1024 and 1024 by 256, dropout 0.3, peptides padded to length 64. SNAP-loss with online hardest-negative mining. Training described as 200 epochs on about 4.8 million sextuplets from NIST and MassIVE, batch 1024. Hub weight file specollate_model_weights.pt. API parameter total is null and was not computed from the layer sizes.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SpeCollate is a dual-branch cross-modal network from the Saeed Lab at Florida International University. Muhammad Usman Tariq and Fahad Saeed published it in PLOS ONE on 2021-10-29. It embeds experimental spectra and peptide strings in one Euclidean space and ranks peptide-spectrum matches with that distance. Hub weights were created 2025-12-23. Status is active.", "what_it_is_for": "The package searches an MGF directory against a peptide database. The card example uses SpeCollateSearch on GPU or CPU. The paper trains on sextuplets with SNAP-loss and online hardest-negative mining. Training data in the paper and the card abstract: about 4.8 million sextuplets from NIST and MassIVE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fiu/titan-bbb/titan-bbb": { "id": "fiu/titan-bbb/titan-bbb", "name": "TITAN-BBB", "maker": "fiu", "family": "fiu/titan-bbb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2025-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal BBB model. Card: frozen ChemBERTa-100M-MLM (DeepChem/ChemBERTa-100M-MLM) text features, ResNet50 image features from the final convolutional block, and RDKit descriptors, fused by attention, then a prediction head. The usage example passes SMILES only. The tokenizer derives the other views. Load AutoModelForSequenceClassification with trust_remote_code and subfolder classification or regression. Custom code in modeling_bbb.py. Hub safetensors.total was absent because weights sit in subfolders. Parameter count was not computed. Requirements on the card: huggingface_hub, rdkit, torch, torchvision.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TITAN-BBB is a blood-brain barrier permeability model from the Saeed Lab at Florida International University. It fuses tabular, image, and text features with attention and ships classification and regression heads in one Hub repository. Repository creation is 2025-10-26. The card was public on 15 October 2026. Status is active. The card says the paper is under review. Crossref lists the bioRxiv record as posted 2026-02-17, with an accepted date of 2026-06-11 on that preprint record.", "what_it_is_for": "The card scores whether a molecule crosses the blood-brain barrier and, in the regression subfolder, predicts a permeability value. The example applies sigmoid to classification logits. Regression uses the raw logit as the prediction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "florence/emovoca/de-sd": { "id": "florence/emovoca/de-sd", "name": "EmoVOCA DE-SD", "maker": "florence", "family": "florence/emovoca", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2025-01-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Double encoder and shared decoder (DE-SD). One encoder learns speech displacements from VOCAset. The other learns expression displacements from Florence 4D. The building block is SpiralNet. The WACV 2025 supplemental states five spiral convolutions plus a downsampling layer, filter sizes 3, 16, 32, 64, and 128, ELU activations, and 64-d embeddings. The decoder mirrors those filters, takes the 128-d concatenation, and upsamples. Training in the supplemental: 100 epochs, Adam, learning rate 1e-4, batch size 64, FLAME topology. Released file emovoca_generator.tar. This network builds the synthetic EmoVOCA sequences. It is not the speech-to-mesh generator.", "modalities": { "input": [ "mesh" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EmoVOCA DE-SD is the double-encoder, shared-decoder network from the University of Florence MICC that builds the synthetic EmoVOCA talking-head sequences. Federico Nocentini and Stefano Berretti are at the University of Florence. Claudio Ferrari is at the University of Parma. The official repository is `miccunifi/EmoVOCA`. The README releases this checkpoint as `emovoca_generator.tar`. Status on 2026-10-15: active. Weights are open under CC BY-NC 4.0.", "what_it_is_for": "The authors combine inexpressive 3D talking heads from VOCAset with expressive sequences from Florence 4D. DE-SD learns those two motions in separate encoders and reconstructs them with one decoder. At inference, concatenated latents produce a mesh that carries both speech and expression. Those outputs are the EmoVOCA dataset (v1: 7,200 sequences, 5 emotions; v2: 15,840 sequences, 11 emotions). The speech-driven generators trained on that data are ES2L and ES2D.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "florence/emovoca/es2d": { "id": "florence/emovoca/es2d", "name": "EmoVOCA ES2D", "maker": "florence", "family": "florence/emovoca", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2025-01-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mesh decoder called S2D in the paper and ES2D in the repository. It expands the motion of 68 landmarks to all vertices of the face mesh. Trained separately from E-S2L with the weighted L2 defined in the paper's equation (3). Released file es2d.tar. Published scores are for the E-S2L+S2D cascade. The stage develops on the authors' earlier S2L+S2D method.", "modalities": { "input": [ "landmarks" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EmoVOCA ES2D is the landmark-to-mesh decoder in the emotional 3D talking-head pipeline from the University of Florence MICC. Federico Nocentini and Stefano Berretti are at Florence. Claudio Ferrari is at the University of Parma. The README releases it as `es2d.tar`. Status on 2026-10-15: active. Weights are open under CC BY-NC 4.0.", "what_it_is_for": "ES2D is the second stage of E-S2L+S2D. ES2L predicts 68 landmarks from speech, emotion, and intensity. This decoder densifies that motion onto the vertices of the face mesh. The two stages are trained separately. The README lists `es2d.tar` as its own released model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "florence/emovoca/es2l": { "id": "florence/emovoca/es2l", "name": "EmoVOCA ES2L", "maker": "florence", "family": "florence/emovoca", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2025-01-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bi-directional LSTM, called E-S2L in the paper and ES2L in the repository. It predicts motion of 68 facial landmarks from audio. Audio features are extracted from a wav2vec 2.0 encoder. Emotion and intensity one-hot labels are embedded to 64 dimensions with a linear layer and concatenated to the audio features. Training loss is average L2 against landmarks taken from EmoVOCA meshes. Released file es2l.tar. The paper's published scores are for the E-S2L+S2D cascade, not for this stage alone. The stage develops on the authors' earlier S2L+S2D method.", "modalities": { "input": [ "audio" ], "output": [ "landmarks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EmoVOCA ES2L is the speech-to-landmark stage of the emotional 3D talking-head pipeline from the University of Florence MICC. Federico Nocentini and Stefano Berretti are at Florence. Claudio Ferrari is at the University of Parma. The README releases it as `es2l.tar`. Status on 2026-10-15: active. Weights are open under CC BY-NC 4.0.", "what_it_is_for": "The paper splits emotional talking-head generation into two trained networks. ES2L is the first. A bi-directional LSTM predicts landmark motion that follows the audio and the requested emotion and intensity. ES2D then turns those landmarks into a full mesh. The training meshes come from the synthetic EmoVOCA set built by DE-SD.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "florence/scantalk/scantalk": { "id": "florence/scantalk/scantalk", "name": "ScanTalk", "maker": "florence", "family": "florence/scantalk", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-09-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder on DiffusionNet. The DiffusionNet encoder has 4 blocks with hidden size 32. A 3-layer bi-LSTM has hidden size 32. The DiffusionNet decoder has 4 blocks, takes concatenated features of dimension 64, and predicts a per-vertex deformation of a neutral mesh. Speech uses the architecture of pretrained HuBERT followed by a linear layer (ECCV v3). The later IJCV extension says a pre-trained HuBERT model followed by a linear layer. Training in the implementation note: 200 epochs per dataset, Adam, learning rate 1e-4. The README ships two loss variants of this one model.", "modalities": { "input": [ "audio", "mesh" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ScanTalk generates speech-driven 3D face motion on meshes of arbitrary topology, including scans. The official code and weights are in the University of Florence MICC repository `miccunifi/ScanTalk`. Federico Nocentini (MICC) and Thomas Besnier share equal contribution with Claudio Ferrari (University of Parma), Sylvain Arguill\u00e8re, Stefano Berretti (MICC), and Mohamed Daoudi. Status on 2026-10-15: active. Weights are open under CC BY-NC 4.0.", "what_it_is_for": "The paper animates a neutral 3D face from a spoken sentence when the mesh topology is not fixed to the training template. Training sequences share a topology inside one sequence. At inference the neutral mesh can be a different topology, including a raw scan. The output is a per-vertex deformation added back onto that neutral mesh.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "florence/tape/tape": { "id": "florence/tape/tape", "name": "TAPE", "maker": "florence", "family": "florence/tape", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-12-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Swin-UNet that restores a window of T=5 degraded frames. A frozen CLIP RN50x4 model scores frames for zero-shot artifact detection and selects D=5 reference frames. A pretrained Swin Transformer extracts multi-scale features from those references. MRSFF blocks fuse them with multi-reference spatial cross-attention and attention pooling. An input-to-output skip learns a residual. Training on the synthetic set: 100 epochs, AdamW, weight decay 0.01, betas (0.9, 0.99), learning rate 2e-5, random 128x128 crops, weighted Charbonnier plus perceptual loss. M=8 and C=96. Test uses the 512x512 center crop. One A100 40GB: about 2 days of training and 15 FPS inference. The restoration network is trained from scratch; CLIP and the Swin feature extractor stay frozen.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TAPE (resToration of digitized Analog videotaPEs) is a video restoration model from the Media Integration and Communication Center at the University of Florence. Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini, and Alberto Del Bimbo released code, a pretrained checkpoint, and a synthetic dataset. Status on 2026-10-15: active. Weights are open under CC BY-NC 4.0.", "what_it_is_for": "The paper restores digitized analog videotapes whose artifacts (drop-out, tape crease, scanline flickering, tape mistracking, chroma loss, noise, undersaturation) differ from film scratches and ordinary digital degradations. CLIP picks the cleaner frames of a clip. The Swin-UNet uses those frames as references while restoring a short window of degraded frames.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "florida-atlantic/molecular-transformer-embeddings/molecular-transformer-embeddings": { "id": "florida-atlantic/molecular-transformer-embeddings/molecular-transformer-embeddings", "name": "Molecular Transformer Embeddings", "maker": "florida-atlantic", "family": "florida-atlantic/molecular-transformer-embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder Transformer trained to translate SMILES strings into IUPAC names. embed.py loads checkpoints/pretrained.ckpt with embedding size 512 and 6 encoder and 6 decoder layers. The Transformer constructor defaults used by that loader are 8 attention heads and dropout 0.1. FeedForward defaults to width 2048. load_data.py sets the character alphabet to 98 symbols: 95 printable ASCII characters plus seq_start, seq_end, and pad. The paper describes 512-dimension character embeddings and a training maximum of 256 SMILES characters. PositionalEncoder in the same code defaults to a maximum sequence length of 6000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Molecular Transformer Embeddings is a SMILES-to-IUPAC encoder-decoder from the Machine Perception and Cognitive Robotics Laboratory at Florida Atlantic University. Paul Morris, Rachel St. Clair, William Edward Hahn, and Elan Barenholtz, at the Center for Complex Systems and Brain Sciences in Boca Raton, describe the network in a 2020 Journal of Chemical Information and Modeling paper. The lab still links the pretrained checkpoint from the public repository as of 15 October 2026. This is the MPCR Lab molecular encoder. The IBM Molecular Transformer is a separate reaction-prediction model.", "what_it_is_for": "The repository says the network translates between SMILES and IUPAC strings and that its encoder turns a SMILES string of length M into an M by N matrix. The paper uses those encoder embeddings as input to separate assay classifiers for single-target binding. `embed.py` writes one matrix per SMILES string.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/classic/classic": { "id": "freepik/classic/classic", "name": "Classic", "maker": "freepik", "family": "freepik/classic", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed app model listed beside Classic Fast with no partner name. No separate API page was found. A 2025 review grouped Classic with Mystic as Freepik's in-house and legacy options. The base checkpoint is not published.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Classic is the slower sibling of Classic Fast in Magnific's image-model list. Freepik does not name a partner lab for it. It was still selectable on 2026-10-15. The weights and release date are unpublished. Collected 2026-10-15. Confidence is medium.", "what_it_is_for": "The model table says Classic is the reliable general generator when the user does not care about a specific style.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/classic/classic-fast": { "id": "freepik/classic/classic-fast", "name": "Classic Fast", "maker": "freepik", "family": "freepik/classic", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed text-to-image endpoint owned by the Magnific API under the name Classic Fast. The docs do not name a base checkpoint. The documented example response reports 8 inference steps and a 1024 by 1024 square. A 2025 review called Classic Freepik's legacy in-house option next to Mystic. That is not a model card.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Classic Fast is Freepik's synchronous text-to-image API, POST /v1/ai/text-to-image, still documented on the Magnific API and still listed in the app model table on 2026-10-15. The base checkpoint is unpublished. Collected 2026-10-15. It is catalogued because the API presents it as Magnific's own Classic endpoint rather than as Flux, Imagen, or another named partner model. Confidence is medium.", "what_it_is_for": "The model table says Classic Fast is for quick conceptual visuals. The API adds style, color, and effect controls and returns the image in the same call.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/f-lite/f-lite": { "id": "freepik/f-lite/f-lite", "name": "F Lite", "maker": "freepik", "family": "freepik/f-lite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML Open RAIL-M for the weights. T5-XXL and the Flux Schnell VAE stay under Apache 2.0.", "release_date": "2025-04-29", "context_length": null, "max_output_tokens": null, "parameters": "10 billion", "architecture": "Diffusion transformer (DiT) trained from scratch by Freepik and Fal on about 80 million copyright-safe SFW images from Freepik's library. Text encoder is T5-XXL. VAE is Flux Schnell. Blog: 64 H100 GPUs for about two months. Not a fine-tune of Flux.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "F Lite is a 10-billion-parameter text-to-image diffusion transformer that Freepik and Fal trained from scratch and released on 2025-04-29. Weights are public under CreativeML Open RAIL-M. This dossier was collected on 2026-10-15. The standard checkpoint is the general-purpose model. F Lite Texture and F Lite 7B are separate folders.", "what_it_is_for": "Freepik's research post says F Lite is a general text-to-image model trained only on licensed, SFW stock images, so others can study and adapt a model whose training images were legally cleared. The standard checkpoint is the more predictable, prompt-faithful flavor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/f-lite/f-lite-7b": { "id": "freepik/f-lite/f-lite-7b", "name": "F Lite 7B", "maker": "freepik", "family": "freepik/f-lite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML Open RAIL-M. T5-XXL and the Flux Schnell VAE stay under Apache 2.0.", "release_date": "2025-05-09", "context_length": null, "max_output_tokens": null, "parameters": "7 billion", "architecture": "7B diffusion model distilled from F Lite (the 10B standard model) by Freepik and Fal. Same copyright-safe SFW behavior as the teacher. Lower VRAM than the 10B model. GitHub does not list a texture 7B.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "F Lite 7B is a 7-billion-parameter distillation of F Lite, released by Freepik and Fal on 2025-05-09. It is an open-weights text-to-image model aimed at lower memory use, with the same copyright-safe training story as the 10B teacher. Collected 2026-10-15.", "what_it_is_for": "The model card says it should match the 10B standard model closely enough for general text-to-image use while running with less memory.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/f-lite/f-lite-texture": { "id": "freepik/f-lite/f-lite-texture", "name": "F Lite Texture", "maker": "freepik", "family": "freepik/f-lite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML Open RAIL-M for the weights. T5-XXL and the Flux Schnell VAE stay under Apache 2.0.", "release_date": "2025-04-29", "context_length": null, "max_output_tokens": null, "parameters": "10 billion", "architecture": "Fine-tune of Freepik/F-Lite. Same DiT family. Hugging Face model tree lists Freepik/F-Lite as the base. Tuned for richer texture and aesthetics. Weaker on short prompts and vector-style images, and more prone to malformations.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "F Lite Texture is the 10-billion-parameter texture variant of F Lite, released by Freepik and Fal on 2025-04-29. It is a fine-tune of the standard F Lite checkpoint, still open weights under CreativeML Open RAIL-M. Collected 2026-10-15.", "what_it_is_for": "The model card says this variant is for richer textures and aesthetics when the prompt is detailed. It is a poor fit for short prompts and vector-style images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/flux-lite/flux-1-lite": { "id": "freepik/flux-lite/flux-1-lite", "name": "Flux.1 Lite", "maker": "freepik", "family": "freepik/flux-lite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLUX.1 [dev] Non-Commercial License. Commercial use is not granted by that license.", "release_date": "2024-12-30", "context_length": null, "max_output_tokens": null, "parameters": "8 billion", "architecture": "8B transformer distilled by Freepik from Black Forest Labs FLUX.1-dev. bfloat16. The card says it uses 7 GB less RAM and runs about 23 percent faster than FLUX.1-dev. The Dec 30 2024 update distilled a wider guidance range (2.0 to 5.0) and step range (20 to 32) on a more diverse set with longer prompts. This is a Freepik distillation, not a hosted copy of FLUX.1-dev.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flux.1 Lite is an 8-billion-parameter text-to-image model that Freepik distilled from Black Forest Labs FLUX.1-dev and published on Hugging Face. The alpha checkpoint is dated 2024-10-23. The retrained public checkpoint is dated 2024-12-30. Weights are open under the FLUX.1 [dev] non-commercial license. Collected 2026-10-15. It is Freepik's distillation, not the upstream FLUX.1-dev model Magnific also hosts.", "what_it_is_for": "Freepik's card says the model is a smaller, faster FLUX.1-dev distillation for local text-to-image use, at the same bfloat16 precision.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/magnific/creative": { "id": "freepik/magnific/creative", "name": "Magnific Upscaler Creative", "maker": "freepik", "family": "freepik/magnific", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Prompt-guided generative upscaler. The 2023-11-27 launch described an enhancer that invents detail, with a creativity control. The current API engines are automatic, magnific_illusio, magnific_sharpy, and magnific_sparkle. Scales are 2x, 4x, 8x, and 16x. Output may not exceed 25.3 million pixels.", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magnific Upscaler Creative is a closed model from Magnific, the upscaler team Freepik acquired in May 2024. Freepik rebranded the product to Magnific on 2026-04-28. The model was still in the public API or help center on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Creative upscaling adds or infers detail from a prompt. Magnific recommends it for concept art and stylized work, not for logos or UI that must stay exact.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/magnific/precision": { "id": "freepik/magnific/precision", "name": "Magnific Upscaler Precision", "maker": "freepik", "family": "freepik/magnific", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Faithful super-resolution. The API overview says it does not invent elements. The app calls this generation Magnific v1 inside Precision mode, with a high-HDR option. Scale examples on the page match the Creative table, including 2x, 4x, 8x, and 16x.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magnific Upscaler Precision is a closed model from Magnific, the upscaler team Freepik acquired in May 2024. Freepik rebranded the product to Magnific on 2026-04-28. The model was still in the public API or help center on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Precision is for logos, UI, text, product photos, scans, and diagrams that must stay faithful.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/magnific/precision-v2": { "id": "freepik/magnific/precision-v2", "name": "Magnific Upscaler Precision V2", "maker": "freepik", "family": "freepik/magnific", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Faithful upscaler with sharpen (0 to 100, default 7), smart grain (0 to 100, default 7), and ultra detail (0 to 100, default 30). Flavors: sublime for illustrations, photo for photographs, photo_denoiser for noisy photos. Scale factors 2x through 16x.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magnific Upscaler Precision V2 is a closed model from Magnific, the upscaler team Freepik acquired in May 2024. Freepik rebranded the product to Magnific on 2026-04-28. The model was still in the public API or help center on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Use V2 when the image must stay faithful and the operator wants grain, sharpness, and detail controls. Sublime is the illustrated flavor. Photo and photo denoiser are the photographic flavors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/magnific/relight": { "id": "freepik/magnific/relight", "name": "Magnific Relight", "maker": "freepik", "family": "freepik/magnific", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Magnific image relighting. Light can come from a prompt, a reference image, or a light map. light_transfer_strength defaults to 100 (range described as 0 to 100). The example advanced settings include an engine field set to illusio. The Mystic FAQ groups Relight with the Magnific upscaler and style transfer as Magnific technology.", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magnific Relight is a closed model from Magnific, the upscaler team Freepik acquired in May 2024. Freepik rebranded the product to Magnific on 2026-04-28. The model was still in the public API or help center on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Relight changes the lighting of a scene and can optionally change the background.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/magnific/skin-enhancer": { "id": "freepik/magnific/skin-enhancer", "name": "Magnific Skin Enhancer", "maker": "freepik", "family": "freepik/magnific", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Portrait skin enhancer with three endpoints: creative (stylized), faithful (preserve the person), and flexible (presets enhance_skin, improve_lighting, enhance_everything, transform_to_real, no_make_up). The API groups it with the Magnific upscaler and relight.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magnific Skin Enhancer is a closed model from Magnific, the upscaler team Freepik acquired in May 2024. Freepik rebranded the product to Magnific on 2026-04-28. The model was still in the public API or help center on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Skin Enhancer is for portrait skin, with a faithful path that keeps identity and a creative path that stylizes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/magnific/style-transfer": { "id": "freepik/magnific/style-transfer", "name": "Magnific Style Transfer", "maker": "freepik", "family": "freepik/magnific", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Magnific style transfer. The official overview says it applies preset styles or a reference image and can combine with upscaling. The ComfyUI partner node, which calls this API, lists flavors faithful, gen_z, psychedelia, detaily, clear, donotstyle, and donotstyle_sharp, and engines balanced, definio, illusio, 3d_cartoon, colorful_anime, caricature, real, super_real, and softy. Those enums are from the partner node, not re-copied from the Magnific schema in this pass.", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magnific Style Transfer is a closed model from Magnific, the upscaler team Freepik acquired in May 2024. Freepik rebranded the product to Magnific on 2026-04-28. The model was still in the public API or help center on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Style transfer restyles an image from a prompt or a reference while keeping a controllable amount of the original structure.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/magnific/upscaler-classic": { "id": "freepik/magnific/upscaler-classic", "name": "Magnific Upscaler Classic", "maker": "freepik", "family": "freepik/magnific", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Preset upscaler inside Creative mode. The help center calls Classic a model next to full Magnific: quick processing with style presets Subtle, Vivid, and Wild. It is not the text-to-image model named Classic.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magnific Upscaler Classic is a closed model from Magnific, the upscaler team Freepik acquired in May 2024. Freepik rebranded the product to Magnific on 2026-04-28. The model was still in the public API or help center on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Classic upscaling is the fast preset path when the user does not want Creativity, HDR, and Resemblance sliders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/magnific/video-creative": { "id": "freepik/magnific/video-creative", "name": "Magnific Video Upscaler Creative", "maker": "freepik", "family": "freepik/magnific", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion-style creative video upscaler. Official overview pages describe creativity 0 to 100 (default 0), flavor vivid or natural (default vivid), sharpen, smart grain, optional FPS boost, and a turbo endpoint with the same controls. Output tiers include 720p, 1k, 2k (default), and 4k.", "modalities": { "input": [ "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magnific Video Upscaler Creative is a closed model from Magnific, the upscaler team Freepik acquired in May 2024. Freepik rebranded the product to Magnific on 2026-04-28. The model was still in the public API or help center on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Creative video upscaling invents texture for soft or compressed footage. Turbo is the faster preset of the same upscaler.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/magnific/video-precision": { "id": "freepik/magnific/video-precision", "name": "Magnific Video Upscaler Precision", "maker": "freepik", "family": "freepik/magnific", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Faithful video upscaler. The overview says it uses diffusion models to recover detail without adding generated content. Strength is 0 to 100, default 60, and blends the upscale back toward the original. Sharpen and smart grain default to 0. Resolutions 720p, 1k, 2k (default), and 4k. If FPS boost is on, strength is ignored.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magnific Video Upscaler Precision is a closed model from Magnific, the upscaler team Freepik acquired in May 2024. Freepik rebranded the product to Magnific on 2026-04-28. The model was still in the public API or help center on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Precision video upscaling is for footage that must stay true to the source, including faces, logos, and fine patterns.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/magnific-one/magnific-one": { "id": "freepik/magnific-one/magnific-one", "name": "Magnific One", "maker": "freepik", "family": "freepik/magnific-one", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sold as Magnific's image model with art direction built in. Co-CEO Joaquin Cuenca wrote on 2026-10-09 that it is a pre-processing, post-processing, and routing layer on current image models, not a from-scratch model. VentureBeat reported a Magnific FAQ that said nothing was trained, and reported that the stack uses OpenAI GPT Image 2 plus Magnific's direction layer. Javi Lopez called it the evolution of Mystic. Those statements disagree on whether a new model was trained. Both are stored. No weights were released.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magnific One is the image product Magnific launched on 2026-10-08 and put on POST /v1/ai/magnific-one the same day. The company markets it as an image model with art direction and an optional Brand Kit. The co-CEO and a reported FAQ say it is a routing and direction layer on existing models, and VentureBeat reported a GPT Image 2 base. Javi Lopez called it the evolution of Mystic. No weights were released. Collected 2026-10-15.", "what_it_is_for": "The product page says it turns a short prompt into an art-directed image, explores options in Draft, and finishes in Final, with Brand Kit optional in Final.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/moderation/nsfw-image-detector": { "id": "freepik/moderation/nsfw-image-detector", "name": "NSFW Image Detector", "maker": "freepik", "family": "freepik/moderation", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT. Copyright 2025 Freepik Company S.L.", "release_date": "2025-04-14", "context_length": null, "max_output_tokens": null, "parameters": "86.4 million", "architecture": "Fine-tune of timm/eva02_base_patch14_448 (EVA-02 base, patch 14, 448 input) for four labels: neutral, low, medium, high. Trained on 100,000 synthetically labeled images for 3 epochs on 3 RTX 3090s. bf16. Hugging Face lists 86.4M parameters. Not a generative model.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NSFW Image Detector is Freepik's open-weights image classifier for four NSFW levels, released on Hugging Face in 2025. It is a fine-tune of EVA-02 base at 448 resolution, under MIT, owned by Freepik Company S.L. Collected 2026-10-15. It is not one of the hosted generation models in the Magnific app.", "what_it_is_for": "The card says it moderates images into neutral, low, medium, or high, including AI-generated images, and that it can be collapsed to a yes-or-no decision at a chosen level.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/mystic/mystic-1-0": { "id": "freepik/mystic/mystic-1-0", "name": "Mystic 1.0", "maker": "freepik", "family": "freepik/mystic", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Magnific calls Mystic its exclusive high-resolution image workflow, not a published foundation model. The 2024-09-06 Freepik blog says the workflow runs on fine-tunes of Flux and Stable Diffusion plus Magnific upscaler technology. The API is one endpoint, POST /v1/ai/mystic, with a model switch. Documented switch values include realism (the example default), zen, fluid, flexible, super_real, and editorial_portraits. The docs say fluid, flexible, super_real, and editorial_portraits ignore LoRAs. LoRAs are described for the default call with no model field and no structure or style reference. Resolutions on the API are 1k, 2k, and 4k. A 2026 third-party API guide claimed the fluid switch is Google Imagen 3. Freepik's own blog and API pages do not say that, so that claim is not treated as fact.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mystic 1.0 is a closed image model in Magnific's Mystic line, sold by Freepik (the company rebranded the product to Magnific on 2026-04-28). It was still listed on the image-model page on 2026-10-15. The image-model page still lists Mystic 1.0 for portraits and detailed textures.", "what_it_is_for": "The image-model page says Mystic 1.0 is the high-detail texture model for portraits, poster-like illustrations, and character-focused images. The August 2024 launch marketed direct full-HD output. Neowin, citing the launch thread, reported 1,664 by 2,432 pixels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/mystic/mystic-2-5": { "id": "freepik/mystic/mystic-2-5", "name": "Mystic 2.5", "maker": "freepik", "family": "freepik/mystic", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Magnific calls Mystic its exclusive high-resolution image workflow, not a published foundation model. The 2024-09-06 Freepik blog says the workflow runs on fine-tunes of Flux and Stable Diffusion plus Magnific upscaler technology. The API is one endpoint, POST /v1/ai/mystic, with a model switch. Documented switch values include realism (the example default), zen, fluid, flexible, super_real, and editorial_portraits. The docs say fluid, flexible, super_real, and editorial_portraits ignore LoRAs. LoRAs are described for the default call with no model field and no structure or style reference. Resolutions on the API are 1k, 2k, and 4k. A 2026 third-party API guide claimed the fluid switch is Google Imagen 3. Freepik's own blog and API pages do not say that, so that claim is not treated as fact.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mystic 2.5 is a closed image model in Magnific's Mystic line, sold by Freepik (the company rebranded the product to Magnific on 2026-04-28). It was still listed on the image-model page on 2026-10-15. The image-model page lists Mystic 2.5 as the general-purpose Mystic model.", "what_it_is_for": "The image-model page calls Mystic 2.5 the versatile model with good prompt adherence for general high-quality images. Magnific's 2024-10-29 post announced Mystic v2.5 with three named switches: Zen, Realism, and Flexible. The API example default is realism.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/mystic/mystic-2-5-flexible": { "id": "freepik/mystic/mystic-2-5-flexible", "name": "Mystic 2.5 Flexible", "maker": "freepik", "family": "freepik/mystic", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Magnific calls Mystic its exclusive high-resolution image workflow, not a published foundation model. The 2024-09-06 Freepik blog says the workflow runs on fine-tunes of Flux and Stable Diffusion plus Magnific upscaler technology. The API is one endpoint, POST /v1/ai/mystic, with a model switch. Documented switch values include realism (the example default), zen, fluid, flexible, super_real, and editorial_portraits. The docs say fluid, flexible, super_real, and editorial_portraits ignore LoRAs. LoRAs are described for the default call with no model field and no structure or style reference. Resolutions on the API are 1k, 2k, and 4k. A 2026 third-party API guide claimed the fluid switch is Google Imagen 3. Freepik's own blog and API pages do not say that, so that claim is not treated as fact.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mystic 2.5 Flexible is a closed image model in Magnific's Mystic line, sold by Freepik (the company rebranded the product to Magnific on 2026-04-28). It was still listed on the image-model page on 2026-10-15. The image-model page lists Mystic 2.5 Flexible as its own selectable model at a higher credit cost than Mystic 2.5.", "what_it_is_for": "The image-model page says Flexible has the best prompt adherence in the Mystic family and is aimed at character design, branding, and precise work. The API switch flexible ignores LoRAs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freepik/mystic/mystic-2-5-fluid": { "id": "freepik/mystic/mystic-2-5-fluid", "name": "Mystic 2.5 Fluid", "maker": "freepik", "family": "freepik/mystic", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Magnific calls Mystic its exclusive high-resolution image workflow, not a published foundation model. The 2024-09-06 Freepik blog says the workflow runs on fine-tunes of Flux and Stable Diffusion plus Magnific upscaler technology. The API is one endpoint, POST /v1/ai/mystic, with a model switch. Documented switch values include realism (the example default), zen, fluid, flexible, super_real, and editorial_portraits. The docs say fluid, flexible, super_real, and editorial_portraits ignore LoRAs. LoRAs are described for the default call with no model field and no structure or style reference. Resolutions on the API are 1k, 2k, and 4k. A 2026 third-party API guide claimed the fluid switch is Google Imagen 3. Freepik's own blog and API pages do not say that, so that claim is not treated as fact.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mystic 2.5 Fluid is a closed image model in Magnific's Mystic line, sold by Freepik (the company rebranded the product to Magnific on 2026-04-28). It was still listed on the image-model page on 2026-10-15. The image-model page lists Mystic 2.5 Fluid as its own selectable model.", "what_it_is_for": "The image-model page says Fluid trades toward faster generation for campaigns and large image sets, with better quality than the older general model. The API describes fluid as the switch that follows prompts most literally. It ignores LoRAs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freiburg/demon/demon": { "id": "freiburg/demon/demon", "name": "DeMoN", "maker": "freiburg", "family": "freiburg/demon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2017-04-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The abstract says DeMoN is an end-to-end convolutional network for depth and camera motion from an unconstrained image pair. It is a stack of encoder-decoder networks whose core is an iterative network that improves its own predictions. It also estimates surface normals, optical flow, and a match confidence. The CVPR 2017 poster says the three subnets are a bootstrap net, an iterative net, and a refinement net, and that they are trained sequentially for 3200k iterations with Adam, momentum 0.9, and weight decay 0.0004. The public weights file is demon_original_weights.tgz.", "modalities": { "input": [ "image" ], "output": [ "depth", "camera-motion" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeMoN (Depth and Motion Network) is the two-view depth and camera-motion network from Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig, Nikolaus Mayer, Eddy Ilg, Alexey Dosovitskiy, and Thomas Brox. The arXiv paper is dated 7 December 2016, and the project page says CVPR 2017. Uhrig is also listed at Daimler AG R&D. The code and weight script are the University of Freiburg repository `lmb-freiburg/demon`, created on 21 April 2017 and licensed GPL-3.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The abstract treats two-view structure from motion as supervised learning. The network is a stack of encoder-decoders and an iterative refinement. The authors say it learns matching, rather than single-image depth priors, and that it is more robust than classical two-frame structure from motion on their tests.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freiburg/dispnet/dispnet": { "id": "freiburg/dispnet/dispnet", "name": "DispNet", "maker": "freiburg", "family": "freiburg/dispnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research-only; commercial use requires consent", "release_date": "2015-12-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The abstract says the paper extends FlowNet from optical flow to disparity and scene flow, introduces three synthetic stereo video datasets, and presents a convolutional network for real-time disparity estimation. It also says a flow network and a disparity network are combined and trained jointly for scene flow. The variant names inside the Caffe package were not read from DISPNET-README.md. The LMB page's current archive is dispflownet-release-1.2.tar.gz, described as modified Caffe plus models.", "modalities": { "input": [ "image" ], "output": [ "disparity" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DispNet is the disparity network released with the stereo-video dataset paper by Nikolaus Mayer, Eddy Ilg, Philip H\u00e4usser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. The arXiv paper is dated 7 December 2015, and the CVPR 2016 DOI is linked from that page. The Computer Vision Group hosts the Caffe archives. Cremers and H\u00e4usser are TU Munich coauthors. The weights in this folder are the Freiburg download. This dossier was collected on 2026-10-15.", "what_it_is_for": "The abstract says the FlowNet idea is extended to disparity and scene flow. It presents a convolutional network for real-time disparity and says a flow network and a disparity network can be trained jointly for scene flow. The datasets are part of the paper and are not given model folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freiburg/dispnet/dispnet3": { "id": "freiburg/dispnet/dispnet3", "name": "DispNet 3.0", "maker": "freiburg", "family": "freiburg/dispnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2018-08-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DispNet 3.0 is the disparity directory of lmb-freiburg/netdef_models, the ECCV 2018 generic network of Ilg, Saikia, Keuper, and Brox. The download script fetches TensorFlow snapshots for css, CSS, and CSS-ft-kitti. The repository is deployment-only and names TensorFlow 1.4. The README's controller example is written for FlowNet3 and returns flow, occlusion, and motion boundaries. The disparity output field names were not copied from a DispNet3 controller.", "modalities": { "input": [ "image" ], "output": [ "disparity" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DispNet 3.0 is the disparity release in the University of Freiburg ECCV 2018 netdef package. The software page names Eddy Ilg, Tonmoy Saikia, Margret Keuper, and Thomas Brox. Snapshots are downloaded by `lmb-freiburg/netdef_models`, created on 7 August 2018 and licensed GPL-3.0. Training code is not included. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab lists DispNet 3.0 next to FlowNet 3.0 as the disparity form of one generic network that also estimates occlusions and boundaries. The public script downloads FlyingThings snapshots and a KITTI fine-tune. The README says the authors do not support training questions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freiburg/entitynet/entitynet-clip": { "id": "freiburg/entitynet/entitynet-clip", "name": "EntityNet CLIP", "maker": "freiburg", "family": "freiburg/entitynet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-26", "context_length": 32, "max_output_tokens": null, "parameters": "", "architecture": "CLIP ViT models trained from scratch by the Freiburg Computer Vision Group on EntityNet. The primary card is ViT-B/32 on EntityNet-33M. The card says the text context length is 32 tokens, the model has seen about 0.6 billion images, and the batch size is 8192. Text labels are sampled half from web alt text and half from knowledge-graph text. OpenCLIP is the loader. A parameter count is not printed on the card. ViT-B/16 trained from scratch on the same 33M images, and ViT-B/32 trained from scratch on the 10M organism subset, are versions. DataComp-1B fine-tunes are not versions.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EntityNet CLIP is the from-scratch CLIP line trained by Simon Ging, Sebastian Walter, Jelena Bratuli\u0107, Johannes Dienert, Hannah Bast, and Thomas Brox at the University of Freiburg Computer Vision Group. The primary public checkpoint is ViT-B/32 trained on EntityNet-33M. Its Hub repo was created on 26 April 2025 and is MIT. The card cites GCPR 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says EntityNet is small enough to train a CLIP model from scratch on one 8-GPU machine in about two days, with full control of the training images. The stated strength is animals and plants, including Latin and English species names. The paper reports the better of the two name languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freiburg/flownet/flownet": { "id": "freiburg/flownet/flownet", "name": "FlowNet", "maker": "freiburg", "family": "freiburg/flownet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research-only; commercial use requires consent", "release_date": "2015-04-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The abstract proposes two supervised CNNs for optical flow: a generic architecture and one with a layer that correlates feature vectors at different image locations. Later Freiburg writing names these FlowNetS and FlowNetC. FlowNetS stacks the two RGB frames. FlowNetC uses a correlation layer. They were trained on the synthetic Flying Chairs dataset because existing ground truth was too small. The current abstract says networks trained on that data generalize to Sintel and KITTI at frame rates of 5 to 10 fps.", "modalities": { "input": [ "image" ], "output": [ "optical-flow" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlowNet is the optical-flow CNN from Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip H\u00e4usser, Caner Haz\u0131rba\u015f, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, and Thomas Brox. The Freiburg authors released the weights on the Computer Vision Group site. H\u00e4usser, Haz\u0131rba\u015f, Golkov, and Cremers are TU Munich coauthors, and this folder is the Freiburg release, not a TUM catalog entry. The first arXiv version is 26 April 2015. This dossier was collected on 2026-10-15.", "what_it_is_for": "The abstract says optical flow is trained as supervised learning. Two architectures are compared: a generic network and one with a correlation layer. Later lab writing calls them FlowNetS and FlowNetC. Training uses the synthetic Flying Chairs set. The abstract says those networks still generalize to Sintel and KITTI at 5 to 10 frames per second.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freiburg/flownet/flownet2": { "id": "freiburg/flownet/flownet2", "name": "FlowNet 2.0", "maker": "freiburg", "family": "freiburg/flownet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research-only; commercial use requires consent", "release_date": "2016-12-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The abstract says the quality and speed gains come from the order in which training data is presented, a stacked architecture that warps the second image with intermediate flow, and a sub-network for small displacements. It says FlowNet 2.0 is only marginally slower than FlowNet, cuts the estimation error by more than 50%, and runs at interactive frame rates. Faster variants are said to reach up to 140 fps with accuracy matching the original FlowNet. The official GitHub README is a Caffe fork and tells the user to run models/download-models.sh.", "modalities": { "input": [ "image" ], "output": [ "optical-flow" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlowNet 2.0 is the stacked optical-flow network from Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox at the University of Freiburg. The arXiv preprint is dated 6 December 2016, and the lab calls the GitHub tree the CVPR 2017 release. Weights are downloaded from the Freiburg site by the official script. This dossier was collected on 2026-10-15.", "what_it_is_for": "The abstract says the original FlowNet was behind variational methods on small motion and real images. FlowNet 2.0 changes the training schedule, stacks networks with warping, and adds a small-displacement sub-network. The authors say the error drops by more than 50 percent while staying only marginally slower, and that faster variants reach up to 140 fps at the original FlowNet's accuracy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freiburg/flownet/flownet3": { "id": "freiburg/flownet/flownet3", "name": "FlowNet 3.0", "maker": "freiburg", "family": "freiburg/flownet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2018-08-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The LMB software page groups this with DispNet 3.0 and FlowNetH and points at lmb-freiburg/netdef_models. The README says the repository is deployment code only, with no training code and no training support. FlowNet 3.0 is the optical-flow directory of the ECCV 2018 generic network for disparity, optical flow, or scene flow. The download script fetches TensorFlow snapshots for css, CSS, CSSR-ft-sd, CSS-ft-kitti, and CSSR-ft-sintel. The controller writes forward flow as .flo and can also emit occlusion and motion-boundary maps. Runtime stated in the README is TensorFlow 1.4 plus the eccv18 branch of lmbspecialops and netdef_slim.", "modalities": { "input": [ "image" ], "output": [ "optical-flow" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlowNet 3.0 is the optical-flow release in the University of Freiburg Computer Vision Group's ECCV 2018 netdef package. Eddy Ilg, Tonmoy Saikia, Margret Keuper, and Thomas Brox are the authors named on the software page for the generic disparity, flow, and scene-flow network. The GitHub repository was created on 7 August 2018 and is GPL-3.0. It ships deployment code and a script that downloads TensorFlow snapshots. This dossier was collected on 2026-10-15.", "what_it_is_for": "The software page presents FlowNet 3.0 with DispNet 3.0 and FlowNetH. The README says the code only runs the published networks. It does not include training code. The FlowNet3 script downloads FlyingThings, small-displacement, KITTI, and Sintel snapshots.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freiburg/flownet/flowneth": { "id": "freiburg/flownet/flowneth", "name": "FlowNetH", "maker": "freiburg", "family": "freiburg/flownet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2018-08-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FlowNetH is the multi-hypotheses optical-flow directory in lmb-freiburg/netdef_models. The software page ties it to Ilg, \u00c7i\u00e7ek, Galesso, Klein, Makansi, Hutter, and Brox, ECCV 2018, Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow. The download script fetches three TensorFlow snapshot families: Pred-Merged, Pred-Merged-SS, and Pred-Merged-FT-KITTI. The repository README says deployment code only, TensorFlow 1.4, and no training code.", "modalities": { "input": [ "image" ], "output": [ "optical-flow" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlowNetH is the multi-hypotheses optical-flow release from the University of Freiburg Computer Vision Group. The software page cites Eddy Ilg, \u00d6zg\u00fcn \u00c7i\u00e7ek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, and Thomas Brox, ECCV 2018. Snapshots are downloaded by the GPL-3.0 `netdef_models` repository, which was created on 7 August 2018. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper title on the software page is uncertainty estimates and multi-hypotheses networks for optical flow. The public repository runs the released networks and does not include training code. Three snapshot families are downloaded: a merged prediction, an SS variant, and a KITTI fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freiburg/orbis/orbis": { "id": "freiburg/orbis/orbis", "name": "Orbis", "maker": "freiburg", "family": "freiburg/orbis", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-15", "context_length": null, "max_output_tokens": null, "parameters": "469000000", "architecture": "The abstract says Orbis is a driving world model with 469M parameters, trained on 280 hours of video, without maps, depth, or extra cameras. A hybrid tokenizer is used so a discrete-token model and a continuous flow-matching model can be compared. The abstract says the study favors the continuous autoregressive model. The Hub repo contains checkpoints/Orbis_288x512.ckpt, checkpoints/Orbis_288x512_steering.ckpt, and checkpoints/tokenizer_288x512.ckpt. The 288x512 figure is the checkpoint filename, not a separately stated training resolution. The Hub API reports usedStorage 22252605914 bytes.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Orbis is a driving world model from Arian Mousakhan, Sudhanshu Mittal, Silvio Galesso, Karim Farid, and Thomas Brox at the University of Freiburg Computer Vision Group. The arXiv preprint is dated 17 July 2025, with a revision on 11 December 2025. Weights are on the lab Hugging Face repo `lmb-freiburg/Orbis`, created on 15 October 2025, under MIT. The code repository was created on 7 October 2025 and is also MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The abstract says existing driving world models fail at long horizons and hard scenes. Orbis is trained without maps, depth, or extra cameras. The authors say 469 million parameters and 280 hours of video are enough for their state-of-the-art claim, with the largest gains on turns and urban traffic. They compare a discrete-token model with a continuous flow-matching model through one hybrid tokenizer and conclude in favor of the continuous autoregressive model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freiburg/sceneflow/sceneflow": { "id": "freiburg/sceneflow/sceneflow", "name": "SceneFlow", "maker": "freiburg", "family": "freiburg/sceneflow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2018-08-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SceneFlow is a directory of lmb-freiburg/netdef_models, the ECCV 2018 deployment repository for the generic network of Ilg, Saikia, Keuper, and Brox. The download script fetches two TensorFlow snapshot families, occ-fill and occ-fill-kitti, from the Freiburg SceneFlow binary host. The repository is deployment-only and names TensorFlow 1.4. The 2015 joint flow-and-disparity scene-flow network in the DispNet paper is an earlier method and is not this directory.", "modalities": { "input": [ "image" ], "output": [ "scene-flow" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SceneFlow is the scene-flow snapshot directory in the University of Freiburg `netdef_models` repository. That repository, created on 7 August 2018 and licensed GPL-3.0, deploys the ECCV 2018 network of Eddy Ilg, Tonmoy Saikia, Margret Keuper, and Thomas Brox. The lab software page does not put SceneFlow in the headline next to FlowNet 3.0 and DispNet 3.0, but the repository has its own directory and weight host. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper title covers disparity, optical flow, or scene flow, plus occlusions and boundaries. This directory downloads two snapshot families, `occ-fill` and a KITTI fine-tune. The README says training code is not published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freiburg/unet/3d-u-net": { "id": "freiburg/unet/3d-u-net", "name": "3D U-Net", "maker": "freiburg", "family": "freiburg/unet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The abstract says 3D U-Net extends Ronneberger et al. by replacing 2D operations with 3D counterparts, trains from scratch with on-the-fly elastic deformations, and does not require a pretrained network. The lab page publishes two MICCAI 2016 prototxt files, one without batch normalization and one with it, plus one solver file used for all experiments. Input and output are 5D blobs ordered as samples, channels, depth, height, width. Annotations are single-channel integer labels. The only data-size-dependent parameters named on the page are random_offset_from and random_offset_to in the deformation layer.", "modalities": { "input": [ "volume" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "3D U-Net is the volumetric segmentation network from \u00d6zg\u00fcn \u00c7i\u00e7ek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, and Olaf Ronneberger. The arXiv paper is dated 21 June 2016 and the lab cites it as MICCAI, October 2016. The authors' primary affiliations on the paper HTML are University of Freiburg groups. Ronneberger is also listed there with Google DeepMind. The Computer Vision Group still hosts a trained Caffe snapshot. This dossier was collected on 2026-10-15.", "what_it_is_for": "The abstract describes two uses. In the semi-automated case, a user labels some slices and the network densifies the volume. In the fully automated case, a sparsely labeled training set is used to segment new volumes. The test structure named in the abstract is the Xenopus kidney. The network is trained from scratch.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "freiburg/unet/unet": { "id": "freiburg/unet/unet", "name": "U-Net", "maker": "freiburg", "family": "freiburg/unet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2015-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fully convolutional encoder-decoder. The paper says the contracting path repeats two unpadded 3x3 convolutions, each with ReLU, then 2x2 max-pooling with stride 2, doubling feature channels at each downsample. The expansive path upsamples, applies a 2x2 up-convolution that halves the channels, concatenates the cropped skip features, and applies two 3x3 convolutions with ReLU. A final 1x1 convolution maps 64-component vectors to classes. The paper says the network has 23 convolutional layers and no fully connected layers. Valid convolutions make the output smaller than the input. Overlap-tile inference mirrors missing border context. Training uses Caffe SGD, batch size 1, momentum 0.99, pixel-wise softmax cross-entropy, and a border weight map with w0=10 and sigma about 5 pixels. The paper says training took 10 hours on an Nvidia Titan with 6 GB.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "U-Net is the biomedical segmentation network released by Olaf Ronneberger, Philipp Fischer, and Thomas Brox at the University of Freiburg Computer Vision Group and BIOSS. The arXiv paper is dated 18 May 2015 and the conference version is MICCAI 2015. The lab still hosts the trained Caffe network in an archive dated 2015-10-02. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents a fully convolutional u-shaped network that can be trained from very few annotated biomedical images by using elastic deformation. The authors report a win on the ISBI 2012 EM membrane challenge and on the two hardest 2D transmitted-light categories of the ISBI cell-tracking challenge 2015. The project page also says it won the ISBI 2015 bitewing-caries detection challenge. That caries result is not given a number on the pages that were read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/anygpt/anygpt": { "id": "fudan/anygpt/anygpt", "name": "AnyGPT", "maker": "fudan", "family": "fudan/anygpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM trained on discrete tokens for text, speech, images, and music. Speech uses SpeechTokenizer and Soundstorm in AnyGPT-speech-modules. Images use the third-party SEED tokenizer at AILab-CVC/seed-tokenizer-2, which is not a Fudan model.", "modalities": { "input": [ "text", "audio", "image" ], "output": [ "text", "audio", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AnyGPT is the Fudan any-to-any multimodal language model for text, speech, images, and music. Base and chat weights are public under Apache 2.0. Collected 2026-10-15. The image tokenizer it points to is a third-party SEED checkpoint and is not foldered here.", "what_it_is_for": "The card says the base model converts among speech, text, images, and music, and the chat model holds multimodal conversations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/chinese-bart/bart-base-chinese": { "id": "fudan/chinese-bart/bart-base-chinese", "name": "Chinese BART-base", "maker": "fudan", "family": "fudan/chinese-bart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-12-30", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "Chinese BART-base: 6 encoder layers, 6 decoder layers, 12 heads, hidden size 768. Updated checkpoint uses a 51,271 vocabulary and max position 1024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese BART-base is a Fudan NLP Chinese sequence-to-sequence checkpoint from the CPT / Chinese BART release. The Hub weights are the 2022-12-30 vocabulary update. Collected 2026-10-15. No license was printed on the card metadata that was read.", "what_it_is_for": "The paper and card present CPT and Chinese BART as pretrained models for both Chinese understanding and generation, including classification and summarization fine-tunes in the reported table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/chinese-bart/bart-large-chinese": { "id": "fudan/chinese-bart/bart-large-chinese", "name": "Chinese BART-large", "maker": "fudan", "family": "fudan/chinese-bart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-12-30", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "Chinese BART-large: 12 encoder layers, 12 decoder layers, 16 heads, hidden size 1024. Updated checkpoint uses a 51,271 vocabulary and max position 1024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese BART-large is a Fudan NLP Chinese sequence-to-sequence checkpoint from the CPT / Chinese BART release. The Hub weights are the 2022-12-30 vocabulary update. Collected 2026-10-15. No license was printed on the card metadata that was read.", "what_it_is_for": "The paper and card present CPT and Chinese BART as pretrained models for both Chinese understanding and generation, including classification and summarization fine-tunes in the reported table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/claif/claif-bert": { "id": "fudan/claif/claif-bert", "name": "CLAIF BERT-base", "maker": "fudan", "family": "fudan/claif", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base sentence encoder with mean pooling to 768 dimensions.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLAIF BERT-base is an fnlp sentence embedding checkpoint now under OpenMOSS-Team. Collected 2026-10-15. The card is a standard sentence-transformers template with Apache 2.0 and English tagged. Scaled and SimCSE repos are versions.", "what_it_is_for": "The card says it maps sentences and paragraphs to a 768-dimensional vector for clustering or semantic search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/claif/claif-roberta": { "id": "fudan/claif/claif-roberta", "name": "CLAIF RoBERTa-base", "maker": "fudan", "family": "fudan/claif", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base sentence encoder. The opened BERT card states 768 dimensions and mean pooling; the RoBERTa card body was the same template in the org listing and was not fully re-quoted.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLAIF RoBERTa-base is an fnlp sentence embedding checkpoint now under OpenMOSS-Team. Collected 2026-10-15. The card is a standard sentence-transformers template with Apache 2.0 and English tagged. Scaled and SimCSE repos are versions.", "what_it_is_for": "The card says it maps sentences and paragraphs to a 768-dimensional vector for clustering or semantic search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/cpt/cpt-base": { "id": "fudan/cpt/cpt-base", "name": "CPT-base", "maker": "fudan", "family": "fudan/cpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-12-30", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "CPT-base: 10-layer shared encoder, 2-layer understanding decoder and generation decoder, 12 heads, hidden size 768. Updated checkpoint uses a 51,271 vocabulary and max position 1024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CPT-base is a Fudan NLP Chinese sequence-to-sequence checkpoint from the CPT / Chinese BART release. The Hub weights are the 2022-12-30 vocabulary update. Collected 2026-10-15. No license was printed on the card metadata that was read.", "what_it_is_for": "The paper and card present CPT and Chinese BART as pretrained models for both Chinese understanding and generation, including classification and summarization fine-tunes in the reported table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/cpt/cpt-large": { "id": "fudan/cpt/cpt-large", "name": "CPT-large", "maker": "fudan", "family": "fudan/cpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-12-30", "context_length": 1024, "max_output_tokens": null, "parameters": "424,155,207 elements in the safetensors file returned by the Hub API. The card does not state a rounded parameter count.", "architecture": "CPT-large: 20-layer shared encoder, 4-layer understanding decoder and generation decoder, 16 heads, hidden size 1024. Updated checkpoint uses a 51,271 vocabulary and max position 1024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CPT-large is a Fudan NLP Chinese sequence-to-sequence checkpoint from the CPT / Chinese BART release. The Hub weights are the 2022-12-30 vocabulary update. Collected 2026-10-15. No license was printed on the card metadata that was read.", "what_it_is_for": "The paper and card present CPT and Chinese BART as pretrained models for both Chinese understanding and generation, including classification and summarization fine-tunes in the reported table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/dirl/dirl-8b-instruct": { "id": "fudan/dirl/dirl-8b-instruct", "name": "DiRL-8B-Instruct", "maker": "fudan", "family": "fudan/dirl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "8B on the card. Safetensors element count 8,190,735,360.", "architecture": "Diffusion language model, SDAR architecture, trained with the DiRL SFT plus RL recipe from JetLM/SDAR-8B-Chat.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiRL-8B-Instruct is an OpenMOSS 8B diffusion language model for math, trained from SDAR-8B-Chat. Collected 2026-10-15. Apache 2.0. The card reports MATH500 and AIME scores.", "what_it_is_for": "The card says it is a mathematical reasoning model and an example of the DiRL training framework.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/elasticbert/elasticbert-base": { "id": "fudan/elasticbert/elasticbert-base", "name": "ElasticBERT-base", "maker": "fudan", "family": "fudan/elasticbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-exit BERT. The card says this is the English base. Early exits are selected with num_output_layers.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ElasticBERT-base is the Fudan NLP English multi-exit BERT. It is the base checkpoint of the ElasticBERT paper. Collected 2026-10-15. Weights are public. A license was not on the card that was read.", "what_it_is_for": "The paper frames it as a strong baseline for early-exit efficient NLP. The card shows sequence classification with a chosen number of output layers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/elasticbert/elasticbert-chinese-base": { "id": "fudan/elasticbert/elasticbert-chinese-base", "name": "ElasticBERT-Chinese-base", "maker": "fudan", "family": "fudan/elasticbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Chinese multi-exit BERT base. Early-exit depth is set with num_output_layers.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ElasticBERT-Chinese-base is the Fudan NLP Chinese multi-exit BERT base, trained with WuDao listed on the card. Collected 2026-10-15. Weights are public. No license was on the card.", "what_it_is_for": "Same early-exit BERT use as the English base, for Chinese inputs. The card example encodes a short Chinese sentence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/elasticbert/elasticbert-large": { "id": "fudan/elasticbert/elasticbert-large", "name": "ElasticBERT-large", "maker": "fudan", "family": "fudan/elasticbert", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hub model id elasticbert-large, pipeline fill-mask, architecture ElasticBertForMaskedLM. The card body was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ElasticBERT-large is the large ElasticBERT repo published next to the Fudan English base. Collected 2026-10-15. The weights are public. The large card text itself was not opened, so language and license stay empty.", "what_it_is_for": "It is the large checkpoint of the same multi-exit BERT release as elasticbert-base. The large README was not quoted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/from-w1/from-w1": { "id": "fudan/from-w1/from-w1", "name": "FRoM-W1", "maker": "fudan", "family": "fudan/from-w1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub metadata says llama3.1. The README badge on the same card says Apache 2.0. Both are recorded because they disagree.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two stages named on the card. H-GPT is a language-driven human motion model initialized from meta-llama/Llama-3.1-8B. H-ACT is a robot-specific controller trained after retargeting those motions. The token format of the motion output was not quoted.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FRoM-W1 is the FudanNLP and OpenMOSS language-to-humanoid-motion release. Collected 2026-10-15. It has a text-to-motion stage based on Llama-3.1-8B and a robot controller stage. The Hub license tag and the README badge disagree.", "what_it_is_for": "The card says it turns natural-language instructions into whole-body humanoid motion that a robot can execute.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/llama-scope/llama-scope": { "id": "fudan/llama-scope/llama-scope", "name": "Llama Scope", "maker": "fudan", "family": "fudan/llama-scope", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Suite of 256 TopK sparse autoencoders on every layer of Llama-3.1-8B-Base. Widths 32K features (8x) and 128K features (32x). Sites are residual (R), attention (A), MLP (M), and transcoder (TC). Training data named on the card: SlimPajama.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama Scope is the OpenMOSS sparse-autoencoder suite for Llama-3.1-8B-Base. Collected 2026-10-15. The card describes 256 TopK SAEs at 8x and 32x. Apache 2.0. The eight checkpoint repos are versions. Later Scope-2 repos were not read.", "what_it_is_for": "The card says the SAEs extract interpretable features from every layer and sublayer for mechanistic interpretability.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/lorsa/lorsa": { "id": "fudan/lorsa/lorsa", "name": "Lorsa", "maker": "fudan", "family": "fudan/lorsa", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2025-04-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Low-rank sparse decomposition of attention outputs. The GitHub README says it extracts interpretable attention units. The Hugging Face frontpage README fetched on 2026-10-15 contained only the license.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lorsa is the OpenMOSS low-rank sparse attention decomposition, released 29 April 2025. Collected 2026-10-15. Apache 2.0. Llama-3.1-8B and Pythia-160M Hub repos are versions whose cards were not opened.", "what_it_is_for": "The README says it decomposes attention-layer outputs into interpretable units to study attention superposition.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss/moss-base-7b": { "id": "fudan/moss/moss-base-7b", "name": "MOSS base 7B", "maker": "fudan", "family": "fudan/moss", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Pretrained causal language model shipped as moss-base-7b with custom code. The card does not state the layer layout or the base it was initialized from.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS base 7B is a smaller Fudan pretrained language model, separate from the 16B MOSS-003 line. The card presents it as an SFT base. Collected 2026-10-15. Weights are public under Apache 2.0.", "what_it_is_for": "The card says it is a 7 billion parameter pretrained model for later supervised fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss/moss-moon-003-base": { "id": "fudan/moss/moss-moon-003-base", "name": "MOSS-003 base", "maker": "fudan", "family": "fudan/moss", "status": "active", "confidence": "high", "weights": "open-source", "license": "GNU AGPL 3.0 (weights). Apache 2.0 (code). CC BY-NC 4.0 (data).", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "16B", "architecture": "Decoder-only MossForCausalLM, 16B, initialized from CodeGen. The base bullet says further pretraining used 100B Chinese tokens and 20B English tokens, that the run saw 700B tokens, and that it used about 6.67e22 FLOPs. The introduction on the same card says the base was pretrained on about 700B English, Chinese, and code tokens from the Pile, BigQuery, BigPython, and a private Chinese corpus.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-003 base is the Fudan University 16B pretrained language model behind the open MOSS chat models. The weights were initialized from CodeGen and further trained by the fnlp / OpenMOSS group. Collected 2026-10-15. Weights are public under GNU AGPL 3.0. The supervised and plugin checkpoints are separate folders.", "what_it_is_for": "The maker describes it as the MOSS-003 base for later dialogue and plugin fine-tunes, not as the chat release itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss/moss-moon-003-sft": { "id": "fudan/moss/moss-moon-003-sft", "name": "MOSS-003 SFT", "maker": "fudan", "family": "fudan/moss", "status": "active", "confidence": "high", "weights": "open-source", "license": "GNU AGPL 3.0 (weights). Apache 2.0 (code). CC BY-NC 4.0 (data).", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "16B", "architecture": "Decoder-only MossForCausalLM, 16B, initialized from CodeGen. The base bullet says further pretraining used 100B Chinese tokens and 20B English tokens, that the run saw 700B tokens, and that it used about 6.67e22 FLOPs. The introduction on the same card says the base was pretrained on about 700B English, Chinese, and code tokens from the Pile, BigQuery, BigPython, and a private Chinese corpus. This checkpoint is the supervised fine-tune on about 1.1 million multi-turn conversations.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-003 SFT is the Fudan University open conversational model at 16B parameters. It is the supervised dialogue checkpoint of MOSS, for Chinese and English multi-turn chat. Collected 2026-10-15. Weights remain public. Int4 and Int8 builds are versions, not separate products.", "what_it_is_for": "The card says the fine-tune follows instructions, holds multi-turn dialogue, and refuses inappropriate requests.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss/moss-moon-003-sft-plugin": { "id": "fudan/moss/moss-moon-003-sft-plugin", "name": "MOSS-003 plugin", "maker": "fudan", "family": "fudan/moss", "status": "active", "confidence": "high", "weights": "open-source", "license": "GNU AGPL 3.0 (weights). Apache 2.0 (code). CC BY-NC 4.0 (data).", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "16B", "architecture": "Decoder-only MossForCausalLM, 16B, initialized from CodeGen. The base bullet says further pretraining used 100B Chinese tokens and 20B English tokens, that the run saw 700B tokens, and that it used about 6.67e22 FLOPs. The introduction on the same card says the base was pretrained on about 700B English, Chinese, and code tokens from the Pile, BigQuery, BigPython, and a private Chinese corpus. Extra supervised training on about 300 thousand plugin conversations. Tools are not called inside the network; the program runs the command the model writes.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-003 plugin is the Fudan 16B dialogue model with a tool-call format. It is the plugin supervised checkpoint of MOSS, still the open 2023 release. Collected 2026-10-15. Quantized plugin weights are versions.", "what_it_is_for": "The maker says it can call a search engine, a text-to-image tool, a calculator, and an equation solver, on top of the plain SFT skills.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss/moss-rlhf": { "id": "fudan/moss/moss-rlhf", "name": "MOSS-RLHF 7B English", "maker": "fudan", "family": "fudan/moss", "status": "active", "confidence": "high", "weights": "open-source", "license": "GNU AGPL 3.0 on the Hub repos. The diffs must be merged with the original Llama-7B weights.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "English RLHF line from the Fudan NLP MOSS-RLHF project. The GitHub README says the Hub files are weight diffs to merge onto Llama-7B, not standalone full weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-RLHF 7B English is the Fudan NLP open RLHF line that publishes Llama-7B weight diffs for an SFT model, a reward model, and a PPO policy. Collected 2026-10-15. It is not the 16B moss-moon model. The Chinese reward diff announced in the same README was not on the Hub org list.", "what_it_is_for": "The project studies RLHF for alignment and ships the English 7B SFT, reward, and policy diffs so others can recover those models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-audio/moss-audio-4b-instruct": { "id": "fudan/moss-audio/moss-audio-4b-instruct", "name": "MOSS-Audio-4B-Instruct", "maker": "fudan", "family": "fudan/moss-audio", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-04-13", "context_length": null, "max_output_tokens": null, "parameters": "4B in the release name. Safetensors count was not fetched.", "architecture": "MossAudioModel. The card says speech, environmental sound, music, captioning, time-aware QA, and reasoning, with DeepStack features and time-aware representations. Paper was marked coming soon.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-Audio-4B-Instruct is one of the four OpenMOSS audio-understanding models released 13 April 2026. Collected 2026-10-15. Apache 2.0. Instruct and Thinking checkpoints are separate folders because the card lists them as separate models.", "what_it_is_for": "The card says the model understands speech, environmental sound, and music, and answers time-aware questions. Thinking checkpoints are for chain-of-thought.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-audio/moss-audio-4b-thinking": { "id": "fudan/moss-audio/moss-audio-4b-thinking", "name": "MOSS-Audio-4B-Thinking", "maker": "fudan", "family": "fudan/moss-audio", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-04-13", "context_length": null, "max_output_tokens": null, "parameters": "4B in the release name. Safetensors count was not fetched.", "architecture": "MossAudioModel. The card says speech, environmental sound, music, captioning, time-aware QA, and reasoning, with DeepStack features and time-aware representations. Paper was marked coming soon.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-Audio-4B-Thinking is one of the four OpenMOSS audio-understanding models released 13 April 2026. Collected 2026-10-15. Apache 2.0. Instruct and Thinking checkpoints are separate folders because the card lists them as separate models.", "what_it_is_for": "The card says the model understands speech, environmental sound, and music, and answers time-aware questions. Thinking checkpoints are for chain-of-thought.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-audio/moss-audio-8b-instruct": { "id": "fudan/moss-audio/moss-audio-8b-instruct", "name": "MOSS-Audio-8B-Instruct", "maker": "fudan", "family": "fudan/moss-audio", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-04-13", "context_length": null, "max_output_tokens": null, "parameters": "8B in the release name. Safetensors element count 9,052,463,488.", "architecture": "MossAudioModel. The card says speech, environmental sound, music, captioning, time-aware QA, and reasoning, with DeepStack features and time-aware representations. Paper was marked coming soon.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-Audio-8B-Instruct is one of the four OpenMOSS audio-understanding models released 13 April 2026. Collected 2026-10-15. Apache 2.0. Instruct and Thinking checkpoints are separate folders because the card lists them as separate models.", "what_it_is_for": "The card says the model understands speech, environmental sound, and music, and answers time-aware questions. Thinking checkpoints are for chain-of-thought.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-audio/moss-audio-8b-thinking": { "id": "fudan/moss-audio/moss-audio-8b-thinking", "name": "MOSS-Audio-8B-Thinking", "maker": "fudan", "family": "fudan/moss-audio", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-04-13", "context_length": null, "max_output_tokens": null, "parameters": "8B in the release name. Safetensors count was not fetched for the Thinking checkpoint.", "architecture": "MossAudioModel. The card says speech, environmental sound, music, captioning, time-aware QA, and reasoning, with DeepStack features and time-aware representations. Paper was marked coming soon.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-Audio-8B-Thinking is one of the four OpenMOSS audio-understanding models released 13 April 2026. Collected 2026-10-15. Apache 2.0. Instruct and Thinking checkpoints are separate folders because the card lists them as separate models.", "what_it_is_for": "The card says the model understands speech, environmental sound, and music, and answers time-aware questions. Thinking checkpoints are for chain-of-thought.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-music/moss-music-8b-instruct": { "id": "fudan/moss-music/moss-music-8b-instruct", "name": "MOSS-Music-8B-Instruct", "maker": "fudan", "family": "fudan/moss-music", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-05-01", "context_length": null, "max_output_tokens": null, "parameters": "Safetensors element count 9,052,463,488.", "architecture": "MossMusicModel. The card says it continues the MOSS-Audio backbone with music continual pretraining and SFT for captioning, lyrics ASR, structure, chords, key, tempo, and long music QA.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-Music-8B-Instruct is the OpenMOSS music-understanding model released 1 May 2026. Collected 2026-10-15. Apache 2.0. It is specialized from the MOSS-Audio backbone. The other thinking or instruct twin is a separate folder.", "what_it_is_for": "The card says it captions music, transcribes lyrics, and answers questions about structure, harmony, and tempo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-music/moss-music-8b-thinking": { "id": "fudan/moss-music/moss-music-8b-thinking", "name": "MOSS-Music-8B-Thinking", "maker": "fudan", "family": "fudan/moss-music", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-05-01", "context_length": null, "max_output_tokens": null, "parameters": "8B in the release name. Safetensors count was not fetched.", "architecture": "MossMusicModel. The card says it continues the MOSS-Audio backbone with music continual pretraining and SFT for captioning, lyrics ASR, structure, chords, key, tempo, and long music QA.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-Music-8B-Thinking is the OpenMOSS music-understanding model released 1 May 2026. Collected 2026-10-15. Apache 2.0. It is specialized from the MOSS-Audio backbone. The other thinking or instruct twin is a separate folder.", "what_it_is_for": "The card says it captions music, transcribes lyrics, and answers questions about structure, harmony, and tempo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-speech/moss-speech": { "id": "fudan/moss-speech/moss-speech", "name": "MOSS-Speech", "maker": "fudan", "family": "fudan/moss-speech", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "9,095,694,336 safetensors elements. The card base_model tag is Qwen/Qwen3-8B.", "architecture": "MossSpeechForCausalLM. The card says a layer-splitting speech model on a frozen pretrained text LLM, bilingual Chinese and English, speech-to-speech without text guidance. The demo loads a separate MOSS-Speech-Codec.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-Speech is the OpenMOSS bilingual speech-to-speech model that answers from speech without a text transcript in the loop. Collected 2026-10-15. The card bases it on Qwen3-8B. A license was not in the excerpt. The codec repo is a version.", "what_it_is_for": "The card says it is for spoken question answering and speech-to-speech dialogue that keeps tone and emotion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-transcribe/moss-transcribe-diarize": { "id": "fudan/moss-transcribe/moss-transcribe-diarize", "name": "MOSS-Transcribe-Diarize", "maker": "fudan", "family": "fudan/moss-transcribe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-07-09", "context_length": null, "max_output_tokens": null, "parameters": "0.9B on the card. Safetensors element count 908,513,280.", "architecture": "MossTranscribeDiarizeForConditionalGeneration. One pass produces a speaker-labeled transcript with timestamps. Card says more than 50 languages and up to 90 minutes of audio.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-Transcribe-Diarize is the OpenMOSS 0.9B model for long-form multilingual transcription with speaker labels. The card dates the release to 9 July 2026. Collected 2026-10-15. Apache 2.0. The English 2B preview is a separate folder.", "what_it_is_for": "The card says it replaces a separate ASR plus diarization pipeline for recordings up to 90 minutes, including timestamps and acoustic-event notes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-transcribe/moss-transcribe-preview-2b": { "id": "fudan/moss-transcribe/moss-transcribe-preview-2b", "name": "MOSS-Transcribe-preview-2B", "maker": "fudan", "family": "fudan/moss-transcribe", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "About 2.4B on the card. Backbone Qwen3-1.7B-base plus a Qwen3-Omni audio encoder.", "architecture": "English ASR. Gated MLP adapter into a Qwen3-1.7B-base decoder. Trained on public English ASR sets and reinforced on Open ASR Leaderboard training splits. The card says one bfloat16 shard is about 4.84 GB.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-Transcribe-preview-2B is an OpenMOSS English speech-to-text preview, about 2.4B parameters, built on a Qwen3 backbone. Collected 2026-10-15. The name and status stay preview. Apache 2.0.", "what_it_is_for": "The card says it is an English ASR model and reports an average WER of 4.87 on the Open ASR Leaderboard model index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-tts/moss-audio-tokenizer": { "id": "fudan/moss-tts/moss-audio-tokenizer", "name": "MOSS-Audio-Tokenizer", "maker": "fudan", "family": "fudan/moss-tts", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "v1 card: 1.6B. v2 card: 2B. Nano card: 21,969,664 parameters.", "architecture": "Cat, a causal transformer audio tokenizer with residual vector quantization, trained from scratch. v1: 24 kHz, 12.5 Hz frames, 32 RVQ layers, about 0.125 to 4 kbps, 3 million hours. v2: 48 kHz stereo, 2B, 12.5 Hz. Nano: 48 kHz stereo, 16 codebooks, about 0.125 to 2 kbps, for MOSS-TTS-Nano.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-Audio-Tokenizer is the OpenMOSS Cat audio codec used by the MOSS-TTS family. v1 is 1.6B at 24 kHz, v2 is 2B at 48 kHz stereo, and Nano is about 22 million parameters. Collected 2026-10-15. Apache 2.0.", "what_it_is_for": "The card says it is a discrete interface for speech, sound, and music, for both reconstruction and downstream TTS or ASR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-tts/moss-sound-effect": { "id": "fudan/moss-tts/moss-sound-effect", "name": "MOSS-SoundEffect", "maker": "fudan", "family": "fudan/moss-tts", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "v2 card: 1.3B diffusion transformer. Safetensors element count on MOSS-SoundEffect-v2.0 is 1,416,047,744. v1 is 8B MossTTSDelay on the family table.", "architecture": "v2 is a flow-matching diffusion transformer with a DAC VAE and a Qwen3-1.7B text encoder. The v2 card says it replaces the v1 discrete-token MossTTSDelay backbone. Duration is controlled up to 30 seconds. Output rate on the v2 card is 48 kHz.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-SoundEffect is the OpenMOSS and MOSI text-to-sound model. v2.0 is the current flow-matching checkpoint. Collected 2026-10-15. The v2 card says it supersedes the 8B v1 token model. Apache 2.0.", "what_it_is_for": "The v2 card says it generates ambience, urban scenes, creatures, human actions, and short musical clips from Chinese or English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-tts/moss-tts": { "id": "fudan/moss-tts/moss-tts", "name": "MOSS-TTS", "maker": "fudan", "family": "fudan/moss-tts", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "8B on the family table. Safetensors element count 8,489,841,664 on both MOSS-TTS and MOSS-TTS-v1.5.", "architecture": "MossTTSDelay. The card describes zero-shot voice cloning, long-form speech, token-level duration control, Pinyin or IPA control, and code-switching. v1.5 continues from 1.0.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-TTS is the OpenMOSS and MOSI flagship speech model, an 8B MossTTSDelay checkpoint for cloning and long-form synthesis. v1.5 is the current Hub release. Collected 2026-10-15. Weights are Apache 2.0. The local-transformer, realtime, dialogue, voice-design, sound-effect, and nano models are separate folders.", "what_it_is_for": "The family card says it is the production TTS base for narration, dubbing, and voice products, with zero-shot cloning and pronunciation control.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-tts/moss-tts-local": { "id": "fudan/moss-tts/moss-tts-local", "name": "MOSS-TTS-Local-Transformer", "maker": "fudan", "family": "fudan/moss-tts", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.7B on the MOSS-TTS family table. The Hub size tag on the org list said 3B. Both figures are kept because they disagree.", "architecture": "MossTTSLocal, listed beside the 8B delay model as the local architecture of MOSS-TTS. v1.5 continues from the local 1.0 checkpoint.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-TTS-Local-Transformer is the smaller MossTTSLocal speech model from the same OpenMOSS and MOSI family as MOSS-TTS. Collected 2026-10-15. v1.5 is the later checkpoint. Apache 2.0. The 1.7B table figure and the Hub 3B tag disagree.", "what_it_is_for": "The family text treats it as the local-architecture TTS model with the same cloning and long-form job as the 8B delay model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-tts/moss-tts-nano": { "id": "fudan/moss-tts/moss-tts-nano", "name": "MOSS-TTS-Nano", "maker": "fudan", "family": "fudan/moss-tts", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-04-10", "context_length": null, "max_output_tokens": null, "parameters": "0.1B. The card says 100 million parameters.", "architecture": "MossTTSNanoForCausalLM. The card says it is a tiny multilingual TTS model that can run on CPU. The OpenMOSS GitHub summary says 48 kHz stereo voice cloning. The nano tokenizer card is the 48 kHz stereo codec it uses.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-TTS-Nano is the 100 million parameter OpenMOSS and MOSI speech model aimed at CPU and realtime use. Collected 2026-10-15. The card news date is 10 April 2026. Apache 2.0. ONNX weights are a version.", "what_it_is_for": "The card says it is a tiny multilingual voice model for local demos, web serving, and light product integration without a GPU.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-tts/moss-tts-realtime": { "id": "fudan/moss-tts/moss-tts-realtime", "name": "MOSS-TTS-Realtime", "maker": "fudan", "family": "fudan/moss-tts", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.7B on the family table (MossTTSRealtime). The Hub size tag said 2B.", "architecture": "MossTTSRealtime streaming TTS. Hub createdAt 2026-03-12 was not used as a release day.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-TTS-Realtime is the streaming speech model in the OpenMOSS and MOSI MOSS-TTS family. Collected 2026-10-15. Apache 2.0. The family table says 1.7B and the Hub tag says 2B.", "what_it_is_for": "The family overview says this member is the real-time streaming TTS model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-tts/moss-ttsd": { "id": "fudan/moss-tts/moss-ttsd", "name": "MOSS-TTSD", "maker": "fudan", "family": "fudan/moss-tts", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "8B on the family table for MOSS-TTSD-v1.0. Safetensors element count 8,355,492,864.", "architecture": "MossTTSDelay multi-speaker dialogue model. The card describes long-form multi-party speech, turn taking, and zero-shot cloning from a short reference.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-TTSD is the OpenMOSS and MOSI multi-speaker dialogue speech model. v1.0 is the 8B MossTTSDelay checkpoint in the family table. Collected 2026-10-15. Earlier v0 through v0.7 repos are versions. Apache 2.0.", "what_it_is_for": "The card says it generates expressive multi-speaker conversation for podcasts, audiobooks, commentary, and dubbing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-tts/moss-voice-generator": { "id": "fudan/moss-tts/moss-voice-generator", "name": "MOSS-VoiceGenerator", "maker": "fudan", "family": "fudan/moss-tts", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.7B on the family table. The Hub size tag said 2B.", "architecture": "MossTTSDelay voice-design model. The family text says it creates a voice from a description rather than only cloning a reference.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-VoiceGenerator is the OpenMOSS and MOSI voice-design model in the MOSS-TTS family. Collected 2026-10-15. The family table says 1.7B MossTTSDelay. Apache 2.0.", "what_it_is_for": "The family overview says it designs a voice or character rather than only cloning one.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-video/moss-video-preview": { "id": "fudan/moss-video/moss-video-preview", "name": "MOSS-Video-Preview", "maker": "fudan", "family": "fudan/moss-video", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "10,670,220,835 safetensors elements on the SFT repo. The card does not round that number.", "architecture": "VideoMllama cross-attention model on a Llama-3.2-Vision backbone. The SFT card says it is the offline instruction checkpoint, not the realtime one.", "modalities": { "input": [ "video", "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-Video-Preview is an earlier OpenMOSS video-understanding preview on a Llama-3.2-Vision backbone. Collected 2026-10-15. The name stays preview. Base, offline SFT, and realtime SFT are versions. Apache 2.0.", "what_it_is_for": "The SFT card says it is for offline image and video instruction following and as a start point for realtime training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-vl/moss-vl": { "id": "fudan/moss-vl/moss-vl", "name": "MOSS-VL", "maker": "fudan", "family": "fudan/moss-vl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "11.3B in the technical report snippet. The Instruct-0708 card table says 11B. Safetensors element count on Instruct-0708 is 11,336,371,208.", "architecture": "Cross-attention vision-language model. Visual tokens stay outside the decoded sequence. The report says the language decoder starts from Qwen3-8B. Instruct-0708 is the SFT checkpoint of the 0708 run. The card says BF16 and a 256K text context. Exact token integer for 256K was not stated.", "modalities": { "input": [ "image", "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-VL is the OpenMOSS open vision-language family for images and long video. The current offline checkpoint is Instruct-0708. Collected 2026-10-15. Apache 2.0. The 0408 run, the base checkpoints, and FP8 or NF4 files are versions. Realtime is its own folder.", "what_it_is_for": "The card says Instruct-0708 is for image understanding, OCR, documents, visual reasoning, and long-video temporal reasoning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/moss-vl/moss-vl-realtime": { "id": "fudan/moss-vl/moss-vl-realtime", "name": "MOSS-VL-Realtime", "maker": "fudan", "family": "fudan/moss-vl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "Same 11B-class MOSS-VL stack. A separate safetensors count for the realtime repo was not fetched.", "architecture": "Realtime streaming checkpoint. The card says it reads frames while it may speak, can stay silent with a silence token, and can revise an answer as frames arrive. Cross-attention and the 256K text context are kept.", "modalities": { "input": [ "video", "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOSS-VL-Realtime is the streaming OpenMOSS vision-language checkpoint. It is built to watch video while talking or staying silent. Collected 2026-10-15. Apache 2.0. FP8, NF4, and SGLang repos are versions.", "what_it_is_for": "The card says it answers questions at any moment in a live stream and can revise earlier answers when new frames arrive.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/mova/mova": { "id": "fudan/mova/mova", "name": "MOVA", "maker": "fudan", "family": "fudan/mova", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "32B total, 18B active, on the model card. Mixture-of-experts dual tower.", "architecture": "Asymmetric video and audio towers with bidirectional cross-attention. One pass generates video and audio together. The card names image-to-video-audio and text-to-video-audio. 360p and 720p are separate weight repos.", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MOVA is the OpenMOSS, SII, and MOSI model that generates video and synchronized audio in one pass. Collected 2026-10-15. The card says 32B total parameters and 18B active. 360p and 720p weights are versions. Apache 2.0.", "what_it_is_for": "The card says it is for lip-synced speech and environment-aware sound in generated video, as an open alternative to closed video-audio systems.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/omnivae/omnivae": { "id": "fudan/omnivae/omnivae", "name": "OmniVAE", "maker": "fudan", "family": "fudan/omnivae", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Audio-video VAE with cross-modal alignment, plus T2AV diffusion weights. The card says the public generation weights are text-to-audio-video only, and the VAE weights are required by that pipeline. The bundle also vendors a frozen Qwen3.5-0.8B-Base text encoder, which is a third-party weight.", "modalities": { "input": [ "text", "video", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmniVAE is the OpenMOSS audio-video autoencoder and text-to-audio-video release. Collected 2026-10-15. Apache 2.0. The Hub repo is a weight bundle. The frozen Qwen text encoder inside it is not catalogued as a Fudan model.", "what_it_is_for": "The paper title and card describe joint audio-video reconstruction and text-to-audio-video generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/roboomni/roboomni": { "id": "fudan/roboomni/roboomni", "name": "RoboOmni", "maker": "fudan", "family": "fudan/roboomni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "4,710,743,040 safetensors elements. Architecture tag Qwen2_5OmniThinkerForConditionalGeneration.", "architecture": "Perceiver-Thinker-Talker-Executor robot policy. The card says it infers intent from speech, environmental sound, and vision, then confirms and acts. Training data named OmniAction.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoboOmni is the OpenMOSS vision-language-action model for robots that infer intent from speech and scene sound instead of only explicit commands. Collected 2026-10-15. Weights are public under CC BY-NC 4.0. LIBERO task checkpoints are versions.", "what_it_is_for": "The card says it recognizes intent, confirms with speech, and executes manipulation in simulation and the real world.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/scijudge/scijudge-30b": { "id": "fudan/scijudge/scijudge-30b", "name": "SciJudge-30B", "maker": "fudan", "family": "fudan/scijudge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "Safetensors element count 30,532,122,624. Base model Qwen/Qwen3-30B-A3B-Instruct-2507.", "architecture": "Qwen3 mixture-of-experts instruct model fine-tuned with GRPO for pairwise scientific impact judgments.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SciJudge-30B is the larger OpenMOSS scientific-judgment model, a GRPO fine-tune of Qwen3-30B-A3B-Instruct-2507. Collected 2026-10-15. Apache 2.0. The 2605 repo is the maker-recommended version.", "what_it_is_for": "Same pairwise citation-impact judgment as SciJudge-4B, at the 30B-A3B size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/scijudge/scijudge-4b": { "id": "fudan/scijudge/scijudge-4b", "name": "SciJudge-4B", "maker": "fudan", "family": "fudan/scijudge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "4B class. Base model on the card is Qwen/Qwen3-4B-Instruct-2507. Exact fine-tuned parameter count was not fetched.", "architecture": "Qwen3-4B-Instruct-2507 fine-tune with GRPO. The card says it compares two papers and picks the one with higher citation impact.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SciJudge-4B is an OpenMOSS fine-tune of Qwen3-4B-Instruct-2507 that picks which of two papers looks more highly cited. Collected 2026-10-15. Apache 2.0. The card points users to a newer 2605 checkpoint, kept here as a version.", "what_it_is_for": "The card says it judges scientific papers from title, abstract, and date.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/scithinker/scithinker-30b": { "id": "fudan/scithinker/scithinker-30b", "name": "SciThinker-30B", "maker": "fudan", "family": "fudan/scithinker", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "30B-A3B class. Base model on the card is Qwen/Qwen3-30B-A3B-Thinking-2507. Safetensors count was not fetched.", "architecture": "Fine-tune of the Qwen3-30B-A3B thinking model for scientific ideation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SciThinker-30B is the larger OpenMOSS scientific-ideation model, fine-tuned from Qwen3-30B-A3B-Thinking-2507. Collected 2026-10-15. Apache 2.0.", "what_it_is_for": "The card says it proposes a follow-up paper title and abstract from a seed paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/scithinker/scithinker-4b": { "id": "fudan/scithinker/scithinker-4b", "name": "SciThinker-4B", "maker": "fudan", "family": "fudan/scithinker", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "4B class. Base model on the card is Qwen/Qwen3-4B-Thinking-2507.", "architecture": "Fine-tune of Qwen3-4B-Thinking-2507 that proposes a follow-up paper from a seed title and abstract.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SciThinker-4B is an OpenMOSS fine-tune of Qwen3-4B-Thinking-2507 that drafts a follow-up research idea. Collected 2026-10-15. Apache 2.0. It is not the SciJudge pairwise model.", "what_it_is_for": "The card says that given a seed paper it should propose a new title and abstract with no numeric results.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/speechgpt/speechgpt": { "id": "fudan/speechgpt/speechgpt", "name": "SpeechGPT", "maker": "fudan", "family": "fudan/speechgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-09-15", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "LlamaForCausalLM checkpoints. The card describes three training stages: modality-adaptation pre-training, cross-modal instruction fine-tuning, and chain-of-modality instruction fine-tuning. The Hub ids are SpeechGPT-7B-ma, SpeechGPT-7B-cm, and SpeechGPT-7B-com. The card excerpt did not map those suffixes onto the stages.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SpeechGPT is the Fudan fnlp 7B speech-and-text conversation model. Three Hub checkpoints shipped with the 15 September 2023 release. Collected 2026-10-15. A license was not in the card excerpt. SpeechGPT 2.0-preview is a later folder.", "what_it_is_for": "The card says it perceives and generates speech and text from instructions, including spoken dialogue.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/speechgpt/speechgpt-2-0-preview": { "id": "fudan/speechgpt/speechgpt-2-0-preview", "name": "SpeechGPT 2.0-preview", "maker": "fudan", "family": "fudan/speechgpt", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7,770,853,888 safetensors elements on SpeechGPT-2.0-preview-7B. The repo name says 7B.", "architecture": "End-to-end spoken language model for real-time speech interaction. The card says it was trained on millions of hours of speech and supports interruption. Layer layout was not in the excerpt.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SpeechGPT 2.0-preview is the later OpenMOSS spoken dialogue preview. The card calls it a real-time end-to-end speech model. Collected 2026-10-15. Status is preview because the published name says preview. License was not in the excerpt.", "what_it_is_for": "The card says it is for human-like spoken conversation, style and emotion control, role play, and low-latency interruption.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/speechtokenizer/speechtokenizer": { "id": "fudan/speechtokenizer/speechtokenizer", "name": "SpeechTokenizer", "maker": "fudan", "family": "fudan/speechtokenizer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder with residual vector quantization. The first RVQ codebook is treated as semantic tokens and later codebooks as acoustic tokens. The released checkpoint is speechtokenizer_hubert_avg, 16 kHz mono, trained on LibriSpeech with a HuBERT-layer average as the semantic teacher.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SpeechTokenizer is the Fudan speech tokenizer used by the SpeechGPT line. The public checkpoint is a 16 kHz HuBERT-teacher model. Collected 2026-10-15. A license was not in the card excerpt.", "what_it_is_for": "The card says it turns speech into semantic and acoustic RVQ codes for speech language models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fudan/uslm/uslm": { "id": "fudan/uslm/uslm", "name": "USLM", "maker": "fudan", "family": "fudan/uslm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive model on the first SpeechTokenizer RVQ layer plus a non-autoregressive model for the later layers. The card says this release was trained on LibriTTS and that performance is limited by that data.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "USLM is the Fudan unified speech language model built on SpeechTokenizer. The published checkpoint is the LibriTTS model. Collected 2026-10-15. Weights are public. License was not on the card.", "what_it_is_for": "The card shows zero-shot TTS by modeling SpeechTokenizer codes, with an autoregressive content model and a non-autoregressive prosody model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fujifilm/synapse/synapse-pathology-ai-foundry": { "id": "fujifilm/synapse/synapse-pathology-ai-foundry", "name": "Synapse Pathology AI Foundry", "maker": "fujifilm", "family": "fujifilm/synapse", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Synapse Pathology AI Foundry is a pathology-image foundation model from FUJIFILM Healthcare Americas Corporation, part of Fujifilm. Fujifilm announced it on 2026-10-12 as research use only and said it would introduce the model at Pathology Visions 2026. This dossier was collected on 2026-10-15. It is not a public weights release or a priced API.", "what_it_is_for": "The 2026-10-12 press says Foundry is a large pathology-specific foundation model trained on millions of pathology images. Fujifilm contrasts it with single-task diagnostic models and says it will be the base for current and future AI features in Synapse Pathology. At Pathology Visions, Fujifilm planned to show screening of gastrointestinal specimens into normal, inflammatory, atypical, and malignant. The same release says the model is for research use only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fujitsu/fugaku-llm/fugaku-llm-13b": { "id": "fujitsu/fugaku-llm/fugaku-llm-13b", "name": "Fugaku-LLM 13B", "maker": "fujitsu", "family": "fujitsu/fugaku-llm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Fugaku-LLM Terms of Use. Custom license, not an OSI license. Commercial and non-commercial use allowed if the terms travel with redistribution. Japanese text controls.", "release_date": "2024-05-10", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "GPT-2-style decoder-only Transformer, per the model card. Trained from scratch with Megatron-DeepSpeed ported to the Fugaku supercomputer. Tokenizer is llm-jp-tokenizer v2.2, code10k_en20k_ja30k.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fugaku-LLM 13B is a 13-billion-parameter language model trained from scratch on the RIKEN supercomputer Fugaku and released on 2024-05-10. Fujitsu is one of seven developers, with Tokyo Institute of Technology, Tohoku University, RIKEN, Nagoya University, CyberAgent, and Kotoba Technologies. Weights remain public under a custom terms-of-use license as of 2026-10-15. This folder is the base checkpoint, not the instruct model.", "what_it_is_for": "The consortium released it for research and, under the license, commercial use. The press stresses Japanese ability, transparency from training on the team's own data rather than continual training of a foreign base model, and later uses such as AI for science. Fujitsu's role was compute and communication acceleration plus pre-training and fine-tuning. The base card is a completion model. Instruction following is the separate instruct checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fujitsu/fugaku-llm/fugaku-llm-13b-instruct": { "id": "fujitsu/fugaku-llm/fugaku-llm-13b-instruct", "name": "Fugaku-LLM 13B Instruct", "maker": "fujitsu", "family": "fujitsu/fugaku-llm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Fugaku-LLM Terms of Use. Same custom license as the base model. Japanese text controls. Commercial use allowed with redistribution conditions.", "release_date": "2024-05-10", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Instruction-tuned Fugaku-LLM 13B. Same GPT-2-style decoder-only model and llm-jp-tokenizer v2.2 as the base. Tuned on Japanese oasst1, Japanese databricks-dolly-15k, and gsm8k.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fugaku-LLM 13B Instruct is the instruction-tuned checkpoint of the consortium 13B model that Fujitsu co-developed. It was released with the base model on 2024-05-10 and remains an open-weight download under the Fugaku-LLM Terms of Use as of 2026-10-15.", "what_it_is_for": "The card shows a Japanese instruction format: a system line, then `", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fujitsu/fujitsu-llm-kg/fujitsu-llm-kg-8x7b-cpt": { "id": "fujitsu/fujitsu-llm-kg/fujitsu-llm-kg-8x7b-cpt", "name": "Fujitsu-LLM-KG 8x7B continued pretrain", "maker": "fujitsu", "family": "fujitsu/fujitsu-llm-kg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 plus Fujitsu additional Terms of Use. Redistribution must carry both. Extra use limits include unauthorized legal, financial, and medical advice.", "release_date": "2025-02-10", "context_length": null, "max_output_tokens": null, "parameters": "47B", "architecture": "Continued pretraining of mistralai/Mixtral-8x7B-Instruct-v0.1 with NVIDIA NeMo. Sparse mixture-of-experts. Fujitsu does not republish expert or active-parameter counts. Training config set max_position_embeddings to 32768 and sequence length to 8192. About 300 billion continued-pretraining tokens over 3 epochs, including a 2.1 billion token knowledge-graph parallel corpus.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fujitsu-LLM-KG 8x7B continued pretrain is Fujitsu's common checkpoint for knowledge-graph work under the NEDO and METI GENIAC project \"research and development of a large language model capable of logical reasoning.\" Weights were posted on Hugging Face on 2025-02-10 and are still public as of 2026-10-15. It is a continued pretrain of Mixtral-8x7B-Instruct-v0.1, not a model trained from scratch.", "what_it_is_for": "Fujitsu uses it to translate between natural-language text and a simplified RDF Turtle knowledge graph, in Japanese and English. The technical blog says general instruction following got worse because the continued pretrain specialized the model. The four named instruction models are trained from this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fujitsu/fujitsu-llm-kg/fujitsu-llm-kg-8x7b-inst-gen-en": { "id": "fujitsu/fujitsu-llm-kg/fujitsu-llm-kg-8x7b-inst-gen-en", "name": "Fujitsu-LLM-KG 8x7B generation English", "maker": "fujitsu", "family": "fujitsu/fujitsu-llm-kg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 plus Fujitsu additional Terms of Use.", "release_date": "2025-02-10", "context_length": null, "max_output_tokens": null, "parameters": "47B", "architecture": "Instruction tune of Fujitsu-LLM-KG-8x7B_cpt on English document-level relation extraction (Re-DocRED train and dev). Same Mixtral-8x7B continued-pretrain base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fujitsu-LLM-KG 8x7B generation English is Fujitsu's English knowledge-graph generation model from the GENIAC logical-reasoning project. It was instruction-tuned for document-level relation extraction. Weights were released on 2025-02-10 and remain public as of 2026-10-15.", "what_it_is_for": "Fujitsu tuned the continued-pretrain checkpoint on Re-DocRED so the model extracts predefined relations from an English document into a knowledge graph. On Fujitsu's zero-shot run it scored above the GPT-3.5 and GPT-4 few-shot numbers they cite and below a fine-tuned BERT result of 80.7. The blog does not call this a world-best English result.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fujitsu/fujitsu-llm-kg/fujitsu-llm-kg-8x7b-inst-gen-ja": { "id": "fujitsu/fujitsu-llm-kg/fujitsu-llm-kg-8x7b-inst-gen-ja", "name": "Fujitsu-LLM-KG 8x7B generation Japanese", "maker": "fujitsu", "family": "fujitsu/fujitsu-llm-kg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 plus Fujitsu additional Terms of Use.", "release_date": "2025-02-10", "context_length": null, "max_output_tokens": null, "parameters": "47B", "architecture": "Instruction tune of Fujitsu-LLM-KG-8x7B_cpt on Japanese document-level relation extraction (JacRED train and dev). Same Mixtral-8x7B continued-pretrain base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fujitsu-LLM-KG 8x7B generation Japanese is Fujitsu's Japanese knowledge-graph generation model from the GENIAC logical-reasoning project. It extracts document-level relations into a graph. Weights were released on 2025-02-10 and remain public as of 2026-10-15.", "what_it_is_for": "Fujitsu instruction-tuned the continued-pretrain checkpoint on JacRED so the model pulls predefined relations between entities out of a Japanese document. METI describes this role as turning natural-language documents into a knowledge graph. The reported JacRED score slightly exceeds the DREEAM number Fujitsu cites and is far above the GPT-4 number on the METI page. It missed METI's goal of 90.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fujitsu/fujitsu-llm-kg/fujitsu-llm-kg-8x7b-inst-infer-v1": { "id": "fujitsu/fujitsu-llm-kg/fujitsu-llm-kg-8x7b-inst-infer-v1", "name": "Fujitsu-LLM-KG 8x7B inference v1", "maker": "fujitsu", "family": "fujitsu/fujitsu-llm-kg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 plus Fujitsu additional Terms of Use.", "release_date": "2025-02-10", "context_length": null, "max_output_tokens": null, "parameters": "47B", "architecture": "One-epoch instruction tune of Fujitsu-LLM-KG-8x7B_cpt on Japanese multi-hop QA (JEMHopQA) for three subtasks: graph schema, graph resolution, and final answer. Same Mixtral-8x7B continued-pretrain base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fujitsu-LLM-KG 8x7B inference v1 is Fujitsu's first knowledge-graph reasoning model from the GENIAC logical-reasoning project. It is an instruction tune of the family's continued-pretrain checkpoint on Japanese multi-hop questions. Weights went on Hugging Face on 2025-02-10 and are still public as of 2026-10-15.", "what_it_is_for": "The model runs three steps Fujitsu defined for multi-hop questions: build a graph schema from the question, fill that schema from source text, and answer from the graph. Training used synthesized JEMHopQA examples. Fujitsu says v1 beat the Japanese baselines they cite on JEMHopQA, but it does not explain which sentence supported each filled value. Version 2 was trained to fix that.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fujitsu/fujitsu-llm-kg/fujitsu-llm-kg-8x7b-inst-infer-v2": { "id": "fujitsu/fujitsu-llm-kg/fujitsu-llm-kg-8x7b-inst-infer-v2", "name": "Fujitsu-LLM-KG 8x7B inference v2", "maker": "fujitsu", "family": "fujitsu/fujitsu-llm-kg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 plus Fujitsu additional Terms of Use.", "release_date": "2025-02-10", "context_length": null, "max_output_tokens": null, "parameters": "47B", "architecture": "One-epoch instruction tune of Fujitsu-LLM-KG-8x7B_cpt on English HotpotQA, with typed variables, evidence comments, and an explicit answer constraint. Same Mixtral-8x7B continued-pretrain base. Also the checkpoint Fujitsu evaluated on ECtHR and a Japanese short-answer bar exam.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fujitsu-LLM-KG 8x7B inference v2 is the second knowledge-graph reasoning checkpoint from Fujitsu's GENIAC project. It was instruction-tuned on English HotpotQA so the graph records evidence lines and a logical answer constraint. Weights were released on 2025-02-10 and remain public as of 2026-10-15. Fujitsu used this version in the GENIAC demo.", "what_it_is_for": "Like v1, it builds a schema, fills it from source text, and answers. v2 names unknown values with typed variables, stores the answer rule inside the graph, and cites a source sentence with `", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "fujitsu/takane/takane": { "id": "fujitsu/takane/takane", "name": "Takane", "maker": "fujitsu", "family": "fujitsu/takane", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Offered exclusively by Fujitsu. No public license text.", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Additional training and fine-tuning of Cohere Command R+. Fujitsu describes Takane as a medium-sized LLM. Layer count and whether the Command R+ parameter count was kept are not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Takane (\u9ad8\u5dba) is Fujitsu's enterprise large language model, built with Cohere and launched globally on 2024-09-30. It is still an active private-deployment product as of 2026-10-15. Weights are closed and sold only by Fujitsu.", "what_it_is_for": "Fujitsu sells Takane for enterprise work in private environments where a public-cloud LLM is a poor fit: government, finance, healthcare, law, manufacturing, and other regulated or confidential workloads. It is delivered through Fujitsu Kozuchi and Data Intelligence PaaS, and later through Nutanix Enterprise AI and managed infrastructure. The launch press says it is based on Cohere Command R+, with extra Japanese training and fine-tuning, and that it inherits Command R+'s support for 10 languages and business-process automation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "galway/an-gaodhal/an-gaodhal-irish-english": { "id": "galway/an-gaodhal/an-gaodhal-irish-english", "name": "An Gaodhal Irish-English Bilingual Model", "maker": "galway", "family": "galway/an-gaodhal", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PyLaia text-recognition model trained in Transkribus", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Galway and New York University released this print OCR model for pages that mix Irish in cl\u00f3 Gaelach and English in Roman type. The public Transkribus card, model ID 501397, is dated 29 January 2026 and was the listed bilingual model on 15 October 2026. It is a PyLaia model hosted in Transkribus. A separate weight file was not found.", "what_it_is_for": "The card says the model was trained on the full run of An Gaodhal, a bilingual newspaper printed in Brooklyn from 1881 to 1898. It is meant to read both scripts on the same page and to keep the lenition punctum. Ground truth was corrected by a specialist in historical Irish. Images came from the James Hardiman Library. A 6 February 2026 government release describes the digitisation as a University of Galway project, co-funded with New York University and other donors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "galway/an-gaodhal/an-gaodhal-irish-monolingual": { "id": "galway/an-gaodhal/an-gaodhal-irish-monolingual", "name": "An Gaodhal Gaeilge / Irish Monolingual Model", "maker": "galway", "family": "galway/an-gaodhal", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PyLaia text-recognition model trained in Transkribus", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Galway and New York University released this print OCR model for nineteenth-century Irish in cl\u00f3 Gaelach. The public Transkribus card, model ID 61350, was published on 25 March 2024 and was still the listed model on 15 October 2026. It is a PyLaia model, not a downloadable weight file.", "what_it_is_for": "The card says the model reads Irish-language pages of the Brooklyn newspaper An Gaodhal (1881-1898), printed almost entirely in cl\u00f3 Gaelach. It keeps the punctum that marks lenition. Ground truth was corrected by a specialist in historical Irish. Page images came from the James Hardiman Library at the University of Galway. The May 2024 paper describes it as An Gaodhal Irish Model v. 2, trained after the Irish-only pages had been corrected.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "galway/irish-fasttext/early-modern-irish-fasttext": { "id": "galway/irish-fasttext/early-modern-irish-fasttext", "name": "Early Modern Irish FastText", "maker": "galway", "family": "galway/irish-fasttext", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2024-08-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FastText static word embeddings, 100 dimensions, released as Gensim KeyedVectors. Window 5, min_count 2, 100 epochs. Skip-gram versus CBOW is not stated.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Oksana Dereza, at the University of Galway Library and Insight, released three Early Modern Irish FastText word-vector tables on 13 August 2024. The Hugging Face card does not name the university. The files were still public on 15 October 2026 under CC-BY-NC-SA-4.0.", "what_it_is_for": "The card says the vectors were trained on CELT and the book subcorpus of the Historical Irish Corpus when the metadata says Early Modern Irish, Classical Modern Irish, or 1200-1700, including mixed-stage texts. The stated use is nearest-neighbour lookup. The card's example query is `Laighen`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "galway/irish-fasttext/historical-irish-fasttext": { "id": "galway/irish-fasttext/historical-irish-fasttext", "name": "Historical Irish FastText", "maker": "galway", "family": "galway/irish-fasttext", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2024-08-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FastText static word embeddings, 100 dimensions, released as Gensim KeyedVectors. Window 5, min_count 2, 100 epochs. Skip-gram versus CBOW is not stated.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Oksana Dereza, at the University of Galway Library and Insight, released three pooled historical-Irish FastText word-vector tables on 13 August 2024. The Hugging Face card does not name the university. The files were still public on 15 October 2026 under CC-BY-NC-SA-4.0. This is the widest of the four stage-specific releases.", "what_it_is_for": "The card says the vectors cover Old, Middle, Early Modern, Classical Modern, and pre-reform Modern Irish from about 550 to 1926: the St Gall glosses, the W\u00fcrzburg glosses, CELT, and the book subcorpus of the Historical Irish Corpus. Genres listed include bardic poetry, native stories, translations, annals, genealogies, grammatical and medical tracts, diaries, and religious writing. The stated use is nearest-neighbour lookup. The card's example query is `co\u00edca`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "galway/irish-fasttext/middle-irish-fasttext": { "id": "galway/irish-fasttext/middle-irish-fasttext", "name": "Middle Irish FastText", "maker": "galway", "family": "galway/irish-fasttext", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2024-08-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FastText static word embeddings, 100 dimensions, released as Gensim KeyedVectors. Window 5, min_count 2, 100 epochs. Skip-gram versus CBOW is not stated.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Oksana Dereza, at the University of Galway Library and Insight, released three Middle Irish FastText word-vector tables on 13 August 2024. The Hugging Face card does not name the university. The files were still public on 15 October 2026 under CC-BY-NC-SA-4.0.", "what_it_is_for": "The card says the vectors were trained on CELT texts tagged Middle Irish or dated 900-1200, including texts also marked as Old Irish or as Old, Middle, and Early Modern Irish. The stated use is nearest-neighbour lookup. The card's example query is `Temra`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "galway/irish-fasttext/old-irish-fasttext": { "id": "galway/irish-fasttext/old-irish-fasttext", "name": "Old Irish FastText", "maker": "galway", "family": "galway/irish-fasttext", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2024-08-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FastText static word embeddings, 100 dimensions, released as Gensim KeyedVectors. Window 5, min_count 2, 100 epochs. Skip-gram versus CBOW is not stated.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Oksana Dereza, a digital library developer at the University of Galway Library and a researcher at Insight, released three Old Irish FastText word-vector tables on 13 August 2024. The Hugging Face card does not name the university. The files were still public on 15 October 2026 under CC-BY-NC-SA-4.0. They are static embeddings, not a language-model API.", "what_it_is_for": "The card says the vectors were trained on the St Gall glosses, the W\u00fcrzburg glosses, and CELT texts whose metadata says Old Irish or 700-900, including texts also marked as later stages. The stated use is nearest-neighbour lookup over historical Irish word forms. The card's example query is `conchobar`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gatech/neural-baby-talk/neural-baby-talk": { "id": "gatech/neural-baby-talk/neural-baby-talk", "name": "Neural Baby Talk", "maker": "gatech", "family": "gatech/neural-baby-talk", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2018-03-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Grounded image captioner. A frozen ResNet-101 Faster R-CNN proposes regions. A two-layer attention LSTM (hidden size 1024, attention and word embeddings 512) emits a template of textual words and visual slots, then fills slots from detector labels, plurality, and fine-grained class. CNN weights are not fine-tuned. Captions are truncated at 16 words.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Neural Baby Talk is an open-weight image captioner from Georgia Tech (Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh; Batra and Parikh also at Facebook AI Research). The paper and code were released on 27 March 2018 and presented at CVPR 2018. The GitHub repo is MIT-licensed and still lists pretrained checkpoints. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper describes a captioner that writes a sentence template whose slots are tied to detected regions, then fills those slots from the detector. The point is a caption that names objects the detector actually found, including novel objects and unusual object pairs, instead of a generic caption from language prior alone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gatech/vilbert/vilbert": { "id": "gatech/vilbert/vilbert", "name": "ViLBERT", "maker": "gatech", "family": "gatech/vilbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2019-08-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stream vision-language encoder. The text stream is initialized from bert-base-uncased. The visual stream reads Faster R-CNN region features. Streams interact through co-attentional transformer layers. The release used for downstream training and zero-shot retrieval is the 6-layer checkpoint (config bert_base_6layer_6conect.json), pretrained on Conceptual Captions with masked multimodal modeling and alignment. Task transfers add a small head rather than a new backbone.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViLBERT is an open-weight vision-language encoder from Georgia Tech (Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee, the latter also at Oregon State; Batra and Parikh also at Facebook AI Research). It was released with the 6 August 2019 preprint and presented at NeurIPS 2019. The author repo still links the checkpoints. The beta code is marked deprecated in favor of the later Facebook 12-in-1 codebase, which is not this release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents ViLBERT as a task-agnostic joint representation of image regions and text, pretrained on Conceptual Captions and then transferred to VQA, visual commonsense reasoning, referring expressions, and caption-based image retrieval. The author README says a pretrained checkpoint can be used directly for zero-shot Flickr30k retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "geneva/aladdin/aladdin-3b": { "id": "geneva/aladdin/aladdin-3b", "name": "Aladdin-3B", "maker": "geneva", "family": "geneva/aladdin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-02-17", "context_length": 65536, "max_output_tokens": null, "parameters": "3075098624", "architecture": "Full fine-tune of SmolLM3-3B (SmolLM3ForCausalLM). config.json: 36 layers, hidden size 2048, intermediate size 11008, 16 attention heads, 4 key-value heads, SiLU, vocab 128256, RoPE theta 5000000, no-RoPE every 4th layer, sliding window off. Safetensors total 3,075,098,624 parameters, F32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aladdin-3B is a University of Geneva, Facult\u00e9 de traduction et d'interpr\u00e9tation (TIM) full fine-tune of SmolLM3-3B for dialectal Arabic generation and translation. It was released for the AMIYA shared task at VarDial, EACL 2026. Weights were public on the Hub on 2026-02-17. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card and the paper say the model generates Moroccan, Egyptian, Palestinian, Syrian, and Saudi Arabic and translates both ways among those dialects, Modern Standard Arabic, and English. Training jointly mixes machine translation with instruction-conditioned sentence completion so the model keeps meaning and dialect. The authors used the AMIYA closed data track and the University of Geneva Baobab HPC service. Simon Hengchen is also listed at iguanodon.ai on the GitHub page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "geneva/aladdin/llama-aladdin-8b": { "id": "geneva/aladdin/llama-aladdin-8b", "name": "Llama-Aladdin-8B", "maker": "geneva", "family": "geneva/aladdin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "llama3.1", "release_date": "2026-02-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter on meta-llama/Meta-Llama-3.1-8B-Instruct (LlamaForCausalLM). adapter_config.json: r 16, alpha 32, dropout 0.05, no bias, target modules q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj. The Hub repo ships adapter_model.safetensors, not a full base copy. The Hub API record did not include a safetensors parameter total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-Aladdin-8B is a University of Geneva TIM LoRA adapter on Llama-3.1-8B-Instruct for the same AMIYA dialectal Arabic task as Aladdin-3B. The faculty published the adapter on 2026-02-17. The card body is mostly an empty Hub template. Specs below come from adapter_config.json, the Hub record, and the paper. Collected 2026-10-15.", "what_it_is_for": "The paper uses this LoRA as the larger copy of the joint machine-translation plus instruction-generation objective. The abstract's task list is generation in Moroccan, Egyptian, Palestinian, Syrian, and Saudi Arabic, and translation among those dialects, Modern Standard Arabic, and English. The card repeats the closed-track corpus list and points at the same GitHub repo and paper as Aladdin-3B. Table 3 calls this condition \"Primary\".", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "geneva/diamond/diamond": { "id": "geneva/diamond/diamond", "name": "DIAMOND", "maker": "geneva", "family": "geneva/diamond", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion world model (DIffusion As a Model Of eNvironment Dreams) using the EDM formulation and a 2D U-Net conditioned on a stack of past observations and actions. Reward and termination use a separate CNN-LSTM. The Atari actor-critic is a shared CNN-LSTM. The paper sets 3 denoising steps and renders Atari at 64x64. It states about 4 million parameters for the Atari models. The CS:GO model is a separate scale: about 381 million parameters, including a 51 million parameter upsampler, trained at 56x30 and upsampled toward 280x150.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a diffusion world model led from the University of Geneva. Eloi Alonso and Vincent Micheli (Geneva) share the paper with Adam Jelley and Amos Storkey (Edinburgh) and Anssi Kanervisto and Tim Pearce (Microsoft Research). Fran\u00e7ois Fleuret (Geneva) shares supervision with Pearce. Alonso and Jelley are equal contribution. arXiv v1 is 2024-05-20. The comment marks it a NeurIPS 2024 Spotlight. Hub weights were created on 2024-10-10. Collected 2026-10-15.", "what_it_is_for": "The paper trains a reinforcement-learning agent inside a diffusion world model so that small visual details survive, instead of being dropped by a discrete tokenizer. On Atari 100k the abstract reports a mean human-normalized score of 1.46. A later section trains the world model alone on static Counter-Strike: Global Offensive gameplay of Dust II and treats it as an interactive neural game engine. The GitHub play script downloads the Atari world model and policy from `eloialonso/diamond`. CS:GO is world-model only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "geneva/iris/delta-iris": { "id": "geneva/iris/delta-iris", "name": "\u0394-IRIS", "maker": "geneva", "family": "geneva/iris", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "World-model agent. A discrete autoencoder encodes stochastic deltas between frames, conditioned on past frames and actions. An autoregressive Transformer predicts future deltas and keeps a continuous summary of the current state. The published Hub file is one Crafter checkpoint, last.pt, trained for 5 million frames.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u0394-IRIS is a University of Geneva world-model agent by Vincent Micheli, Eloi Alonso, and Fran\u00e7ois Fleuret. The ICML 2024 paper, posted on 2024-06-27, describes a discrete autoencoder that stores only the stochastic change between frames, plus an autoregressive Transformer. The authors released code and one public checkpoint. The Hub file `last.pt` is the Crafter agent trained for 5 million frames, uploaded on 2024-07-03. Collected 2026-10-15.", "what_it_is_for": "The paper presents a faster successor to IRIS for environments that need many image tokens. The encoder and decoder are conditioned on past frames and actions, so each latent only has to carry the change that actions do not already explain. The abstract says this sets a Crafter state of the art at several frame budgets and trains about an order of magnitude faster than earlier attention-based agents. The published weight is the Crafter 5M-frame agent.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "geneva/iris/iris": { "id": "geneva/iris/iris", "name": "IRIS", "maker": "geneva", "family": "geneva/iris", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-03-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Atari 100k world-model agent. The paper defines the world model as a discrete autoencoder plus an autoregressive Transformer. The policy is trained inside that model. The paper expands IRIS as Imagination with auto-Regression over an Inner Speech. Each Hub file is one game checkpoint. Parameter count, layer sizes, and context length are not in the Hub record.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IRIS (Imagination with auto-Regression over an Inner Speech) is a University of Geneva Atari world-model agent by Vincent Micheli, Eloi Alonso, and Fran\u00e7ois Fleuret. The ICLR 2023 paper, revised on arXiv on 2023-03-01, says the authors release code and models. The Hub repo `eloialonso/iris` holds 26 per-game checkpoints uploaded on 2024-05-17. Collected 2026-10-15.", "what_it_is_for": "The paper presents a sample-efficient reinforcement-learning agent that trains inside a learned Atari world model. The world model is a discrete autoencoder plus an autoregressive Transformer. With the Atari 100k budget, about two hours of gameplay, the abstract reports a mean human-normalized score of 1.046 and human-level play on 10 of 26 games, for methods that do not use lookahead search. The comment line marks it ICLR 2023, notable top 5%.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "geneva/tim-unige/tim-unige-wmt24": { "id": "geneva/tim-unige/tim-unige-wmt24", "name": "TIM-UNIGE WMT24", "maker": "geneva", "family": "geneva/tim-unige", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-11-15", "context_length": 1024, "max_output_tokens": null, "parameters": "615073792", "architecture": "Fine-tune of facebook/nllb-200-distilled-600M. config.json: M2M100ForConditionalGeneration, d_model 1024, 12 encoder layers, 12 decoder layers, 16 encoder heads, 16 decoder heads, FFN 4096, vocab 256206, max_position_embeddings 1024, ReLU, torch_dtype float32. Safetensors total 615,073,792 parameters, F32. Checkpoint path in _name_or_path: from_occitan/pilar/1e-5/step_80000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TIM-UNIGE WMT24 is a University of Geneva Translation and Interpreting Department (TIM) translation model for the WMT 2024 low-resource languages of Spain task. Jonathan Mutal and Luc\u00eda Ormaechea fine-tuned one NLLB-200 distilled 600M checkpoint for Spanish into Aranese and Spanish into Aragonese. The proceedings are dated November 2024. The Hub weights were created on 2025-07-07. Collected 2026-10-15.", "what_it_is_for": "The card describes a constrained shared-task system that translates Spanish into Aranese and into Aragonese or Occitan. The paper abstract reports Spanish to Aranese and Spanish to Aragonese. Training moves from larger synthetic and crawled data to smaller parallel sets, with Occitan transfer used to help Aranese. Intended uses on the card are low-resource MT research, language revitalization, and public-health communication.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "genmo/mochi/mochi-1": { "id": "genmo/mochi/mochi-1", "name": "Mochi 1", "maker": "genmo", "family": "genmo/mochi", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-10-22", "context_length": null, "max_output_tokens": null, "parameters": "10B AsymmDiT denoiser; 362M AsymmVAE", "architecture": "Latent text-to-video diffusion. Asymmetric Diffusion Transformer (AsymmDiT): 48 layers, 24 heads, visual hidden size 3072, text hidden size 1536, full 3D attention over 44,520 video tokens and 256 text tokens, 3D RoPE, SwiGLU, query-key norm, sandwich norm. Single T5-XXL text encoder. AsymmVAE: causal, 8x8 spatial and 6x temporal compression, 12-channel latents, encoder base channels 64, decoder base channels 128. Genmo describes the VAE as compressing video about 96x.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mochi 1 is Genmo's open text-to-video model, released on 2024-10-22 as a research preview under Apache 2.0. As of 2026-10-15 it is still the only model on the Genmo Hugging Face org and the model the public playground and third-party APIs serve. Genmo never published a successor checkpoint.", "what_it_is_for": "Genmo describes Mochi 1 as a text-to-video model aimed at high-fidelity motion and prompt adherence, and as a step toward video world simulators. The hosted playground at genmo.ai/play generates short MP4 clips. Weights and code are meant to be run and fine-tuned locally.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "genmo/mochi/mochi-1-hd": { "id": "genmo/mochi/mochi-1-hd", "name": "Mochi 1 HD", "maker": "genmo", "family": "genmo/mochi", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mochi 1 HD is a 720p text-to-video model Genmo named on 2024-10-22 and said would arrive later that year. As of 2026-10-15 no weights, model card, or dated release were found. The announcement itself is high confidence; the claim that it never shipped rests on the still-single Hugging Face model and an unanswered 2025 GitHub issue.", "what_it_is_for": "Genmo presented it as the higher-resolution follow-up to the 480p Mochi 1 preview. SiliconANGLE, citing the company, said the full release would include Mochi 1 HD at 720p with smoother motion. It was not described as a separate product line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "genmo/replay/replay": { "id": "genmo/replay/replay", "name": "Replay", "maker": "genmo", "family": "genmo/replay", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-09-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed hosted diffusion video model. Genmo said Replay v0.1 was a new video diffusion model aligned with the LLM behind Genmo Chat. No layer counts or parameter counts were published.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Replay is Genmo's closed hosted video model, announced on 2023-09-21 as Replay v0.1 and upgraded on 2023-10-26 as Replay v0.2. It predates Mochi. On 2026-10-15 the Genmo pricing page still bills a \"Replay video\" at 50 credits, so it is treated as still offered, with medium confidence because the fetched playground page did not show the selector.", "what_it_is_for": "Genmo introduced Replay as a text-to-video model for short HD clips from plain-English prompts, including close-ups of people and animals. v0.2 added image prompting. Camera controls (zoom, pan, roll, tilt) and Replay FX were product features on the same line, not separate models. Later playground copy called the older image-to-video option Legacy; that label is not confirmed to be this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "genoa/co-semdepth/co-semdepth": { "id": "genoa/co-semdepth/co-semdepth", "name": "Co-SemDepth", "maker": "genoa", "family": "genoa/co-semdepth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-23", "context_length": null, "max_output_tokens": null, "parameters": "5200000", "architecture": "Five-level joint network for monocular aerial video. The shared encoder and the depth decoder follow M4Depth (Fonder, Ernst, and Van Droogenbroeck, University of Li\u00e8ge). A separate semantic decoder is the M4Semantic branch from the same Genoa paper. The joint loss is depth loss plus 0.1 times semantic loss. Training in the paper: Adam, betas 0.9 and 0.999, learning rate 1e-4, batch size 3, 60 epochs, random rotation, flip, and color augmentation. Input frames are resized to 384x384. Depth is capped at 80 m on MidAir. At inference the maps are predicted at half resolution and scaled with nearest-neighbour interpolation. Table I lists 5.2 million parameters, 49.6 ms per frame, and 6.2 GB of GPU memory on one Quadro P5000. The abstract says 20.2 FPS on that GPU. The README says training from scratch. The repository licence file was not confirmed.", "modalities": { "input": [ "image" ], "output": [ "depth", "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Co-SemDepth is a joint depth and semantic-segmentation network for low-altitude aerial video, from Yara AlaaEldin and Francesca Odone at the University of Genoa (MaLGa-Vision). The arXiv v1 date is 23 March 2025. The README citation names SAC 2026. Status on 2026-10-15: active. Weights are a Google Drive file linked from the official repository. The paper text is CC BY-SA 4.0. The repository licence was not confirmed.", "what_it_is_for": "The paper targets onboard scene understanding for UAVs that cannot carry heavy depth sensors. One forward pass predicts metric depth and a semantic map from monocular frames of a moving camera. The authors argue the shared encoder is lighter than running a depth network and a segmentation network separately.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "genoa/hhp-net/hhp-net": { "id": "genoa/hhp-net/hhp-net", "name": "HHP-Net", "maker": "genoa", "family": "genoa/hhp-net", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2021-11-03", "context_length": null, "max_output_tokens": null, "parameters": "94000", "architecture": "Heteroscedastic head-pose network. Input is 15 scalars from five face keypoints (x, y, confidence), centered and normalized. Separate 1D convolutions (5 filters, kernel 1) on x, y, and confidence, then confidence-gated element-wise products, concatenation, and dense layers of 250, 200, and 150 units with LeakyReLU 0.1. Three heads emit yaw, pitch, and roll, each with a log-variance. Weights initialized from a normal distribution with mean 0 and variance 0.05. Trained with Adam, learning rate 0.001, batch size 64, for 100 to 1000 epochs depending on the dataset. The pose estimator in front of it is not part of the parameter count. Appendix D scales the dense widths by alpha. Alpha 1 is 94,000 parameters, 93,000 mult-adds, and 0.385 MB. The prose also calls the largest variant 0.4 MB and says the network is under 0.5 MB and about 100 fps. Alpha 0.6 is 37,000 parameters and 0.158 MB. Alpha 0.2 is 6,000 parameters and 0.032 MB.", "modalities": { "input": [ "keypoints" ], "output": [ "pose" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HHP-Net is a head-pose network from MaLGa-DIBRIS at the University of Genoa. Giorgio Cantarini, Federico Figari Tomenotti, Nicoletta Noceti, and Francesca Odone released the code and three TensorFlow SavedModels. Cantarini also lists IMAVIS srl. Status on 2026-10-15: active. The README licence line is MIT. The arXiv revision used here is 3 November 2021. WACV 2022 is the venue.", "what_it_is_for": "The paper estimates 3D head orientation from a pose estimator's face keypoints, without taking the image as input. It is meant as a small plug-in, under 0.5 MB and about 100 fps in the paper's account, and it also emits an uncertainty per angle. A later section uses those angles for a look-at-each-other score. That score is an application, not a second released network.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "genoa/m4semantic/m4semantic": { "id": "genoa/m4semantic/m4semantic", "name": "M4Semantic", "maker": "genoa", "family": "genoa/m4semantic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-23", "context_length": null, "max_output_tokens": null, "parameters": "2610000", "architecture": "Semantic-segmentation network obtained by redesigning M4Depth (University of Li\u00e8ge) so the decoder predicts class maps instead of depth. The Co-SemDepth paper uses a 5-level pyramid. An architecture study in that paper reports M4Semantic at 4, 5, and 6 levels, with 5 levels best on the reported MidAir mIoU. The encoder is trained with the decoder. The authors say they did not separately pretrain the encoder on ImageNet. Table I: 2.61 million parameters and 9.8 ms per frame on the same Quadro P5000 setup as Co-SemDepth (384x384, 60 epochs, Adam, learning rate 1e-4, batch size 3). The README ships two weight archives, MidAir and Aeroscapes, and says training is from scratch. The repository licence file was not confirmed.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "M4Semantic is the single-task semantic-segmentation network in the Co-SemDepth paper, from Yara AlaaEldin and Francesca Odone at the University of Genoa. MaLGa-Vision published the code and two weight archives. The arXiv v1 date is 23 March 2025. Status on 2026-10-15: active. The paper text is CC BY-SA 4.0. The repository licence was not confirmed.", "what_it_is_for": "The paper first builds a fast aerial segmentation network by changing the M4Depth decoder from depth to class labels, then uses that branch inside the joint Co-SemDepth model. M4Semantic is the standalone segmentation release. It is trained from scratch. The authors say the encoder was not separately pretrained on ImageNet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ghent/biolord/biolord-2022": { "id": "ghent/biolord/biolord-2022", "name": "BioLORD-2022", "maker": "ghent", "family": "ghent/biolord", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Author contributions stated as MIT; use requires a UMLS license. The 2022 card does not mention SNOMED CT. Hub license tag is other.", "release_date": "2022-10-20", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "MPNet encoder (config architectures: MPNetModel), 12 layers, hidden size 768, 12 heads, vocab 30527, max_position_embeddings 514. Config _name_or_path is a local training directory ending in sentence-transformers-all-mpnet-base-v2. The card says the model is based on sentence-transformers/all-mpnet-base-v2. The Hub repo has pytorch_model.bin and no safetensors parameter total. Repo usedStorage is 875,999,896 bytes, which is file size, not a parameter count.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioLORD-2022 is the first public BioLORD biomedical sentence-embedding model from IDLab at Ghent University. Fran\u00e7ois Remy, Kris Demuynck, and Thomas Demeester released the weights as FremyCompany/BioLORD-STAMB2-v1. The Hub repo date is 20 October 2022. The paper was submitted on 21 October 2022 and accepted to Findings of EMNLP 2022. The card still calls BioLORD-2023 the better default. The 2022 weights were still public on the collection date, 15 October 2026.", "what_it_is_for": "The card and the paper describe embeddings for clinical sentences and biomedical concept names, grounded in ontology definitions. The card says the model is a further training of `sentence-transformers/all-mpnet-base-v2` on the BioLORD dataset. It is aimed at medical documents such as EHR records and clinical notes. An important note on the card says that for most uses BioLORD-2023 is the better model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ghent/biolord/biolord-2023": { "id": "ghent/biolord/biolord-2023", "name": "BioLORD-2023", "maker": "ghent", "family": "ghent/biolord", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Author contributions stated as MIT; use requires UMLS and SNOMED CT licenses (card license name ihtsdo-and-nlm-licences)", "release_date": "2023-11-27", "context_length": 514, "max_output_tokens": null, "parameters": "109486464", "architecture": "MPNet encoder, 12 layers, hidden size 768, 12 heads, vocab 30527, mean pooling to 768 dimensions. Continued from sentence-transformers/all-mpnet-base-v2. Hub safetensors F32 count is 109,486,464; an extra 514 I64 values are also stored.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioLORD-2023 is an English biomedical sentence-embedding model from the Internet and Data Science Lab (IDLab) at Ghent University and imec. Fran\u00e7ois Remy, Kris Demuynck, and Thomas Demeester released the weights on 27 November 2023. The Journal of the American Medical Informatics Association paper is dated 27 February 2024. It was still public on Hugging Face on the collection date, 15 October 2026.", "what_it_is_for": "The model card says it maps clinical sentences and biomedical concept names into one space for clustering and semantic search, including EHR text. It is a further training of `sentence-transformers/all-mpnet-base-v2` on the BioLORD dataset and on LLM-written definitions from the Automatic Glossary of Clinical Terminology. The authors describe three stages: contrastive learning, self-distillation, and weight averaging. This folder is the averaged checkpoint. The card points users who need named-entity linking to the contrastive-only sibling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ghent/biolord/biolord-2023-m": { "id": "ghent/biolord/biolord-2023-m", "name": "BioLORD-2023-M", "maker": "ghent", "family": "ghent/biolord", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Author contributions stated as MIT; use requires UMLS and SNOMED CT licenses (card license name ihtsdo-and-nlm-licences)", "release_date": "2023-11-27", "context_length": 514, "max_output_tokens": null, "parameters": "278043648", "architecture": "XLM-RoBERTa encoder (config architectures: XLMRobertaModel), 12 layers, hidden size 768, 12 heads, vocab 250002, max_position_embeddings 514. Config _name_or_path is sentence-transformers/paraphrase-multilingual-mpnet-base-v2. The model card text instead says the model is based on sentence-transformers/all-mpnet-base-v2. Hub safetensors F32 count is 278,043,648; an extra 514 I64 values are also stored (total 278,044,162).", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioLORD-2023-M is the multilingual biomedical sentence-embedding model in the BioLORD-2023 series from IDLab at Ghent University. Fran\u00e7ois Remy released the weights on Hugging Face on 27 November 2023 under FremyCompany/BioLORD-2023-M. The card says it was distilled from BioLORD-2023. It was still public on the collection date, 15 October 2026.", "what_it_is_for": "The card describes a sentence-transformer for biomedical sentences and concept names, including EHR text and clinical notes. It lists seven official languages: English, Spanish, French, German, Dutch, Danish, and Swedish. It also says many other languages work unofficially, without naming them. The sibling list on the card calls this checkpoint the multilingual model distilled from BioLORD-2023.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ghent/biolord/biolord-2023-m-dutch-incontext": { "id": "ghent/biolord/biolord-2023-m-dutch-incontext", "name": "BioLORD-2023-M Dutch InContext v1", "maker": "ghent", "family": "ghent/biolord", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Author contributions stated as MIT; use requires UMLS and SNOMED CT licenses (card license name ihtsdo-and-nlm-licences)", "release_date": "2024-06-23", "context_length": 514, "max_output_tokens": null, "parameters": "278043648", "architecture": "XLM-RoBERTa encoder (config architectures: XLMRobertaModel), 12 layers, hidden size 768, 12 heads, vocab 250002, max_position_embeddings 514. Config _name_or_path is sentence-transformers/paraphrase-multilingual-mpnet-base-v2. Sentence-transformers pooling config uses the CLS token (pooling_mode_cls_token true, mean pooling false). Hub safetensors are F32 only, 278,043,648 parameters.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioLORD-2023-M Dutch InContext v1 is a Dutch and English biomedical mention embedder released by Fran\u00e7ois Remy (IDLab, Ghent University) on 23 June 2024. The Hugging Face repo is FremyCompany/BioLORD-2023-M-Dutch-InContext-v1. It was still public on the collection date, 15 October 2026.", "what_it_is_for": "The card title is \"In-Context Dutch Clinical Embeddings with BioLORD & MedMentions\". It says mentions that share the same string do not have to share an embedding. The documented template is the mention text, then `[SEP]`, then a parenthetical context string. The card says the model also embeds mentions without context, particularly in English. It tells users to take the CLS token, unlike the mean-pooled BioLORD models. The pooling config on the Hub matches that note.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ghent/hydra/tweety-tatar-hydra-base-7b": { "id": "ghent/hydra/tweety-tatar-hydra-base-7b", "name": "Tweety Tatar Hydra-Base 7B", "maker": "ghent", "family": "ghent/hydra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0. The card says the non-commercial term is imposed by the Unbabel base model.", "release_date": "2024-04-12", "context_length": 4096, "max_output_tokens": null, "parameters": "6869516288", "architecture": "Custom LlamaHydraForCausalLM (trust_remote_code, modeling_llama_hydra.py). Hidden size 4096, intermediate size 11008, 32 layers, 32 heads, 32 key-value heads, rope_theta 10000, float32. vocab_size and output_vocab_size are 32000. input_vocab_size is 64007. max_position_embeddings is 4096. Hub base_model is Unbabel/TowerInstruct-7B-v0.1. The card calls the architecture Mistral. The config is a Llama-style Hydra model, not MistralForCausalLM. Config _name_or_path is Tweeties/tweety-tatar-hydra-base-7b-2024-v1, which is not a separate repo in the Tweeties model list.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tweety Tatar Hydra-Base 7B is a Tatar Hydra language model from IDLab at Ghent University and BeCode. The weights are Tweeties/tweety-tatar-hydra-base-7b-v24a, created on 12 April 2024. The card names Fran\u00e7ois Remy (UGent) and Alfiya Khabibullina (BeCode), with funding from IDLab / GPULab. It was converted from Unbabel's TowerInstruct-7B-v0.1. The series paper was submitted on 8 August 2024 and accepted at COLM 2024. The weights were still public on the collection date, 15 October 2026.", "what_it_is_for": "The card says a Hydra LLM keeps swappable tokenizers: its own Tatar tokenizer, the base model's tokenizer, or both in one prompt. The in-scope uses are answering questions in Tatar from a cross-lingual context, and serving as the base for machine translation into Tatar from one of TowerInstruct's 10 languages, notably English and Russian. It is best on sentences or small paragraphs. The card says not to translate Tatar into English, and not to use it for documents. When it has not been finetuned, the card recommends beam search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ghent/hydra/tweety-tatar-hydra-mt-7b": { "id": "ghent/hydra/tweety-tatar-hydra-mt-7b", "name": "Tweety Tatar Hydra-MT 7B", "maker": "ghent", "family": "ghent/hydra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0. The card says the non-commercial term from the Unbabel base also applies to this finetune.", "release_date": "2024-04-12", "context_length": 4096, "max_output_tokens": null, "parameters": "6869516288", "architecture": "Custom LlamaHydraForCausalLM (trust_remote_code). Hidden size 4096, intermediate size 11008, 32 layers, 32 heads, 32 key-value heads, rope_theta 10000, float32. vocab_size and output_vocab_size are 32000. input_vocab_size is 64007. max_position_embeddings is 4096. Hub base_model is Unbabel/TowerInstruct-7B-v0.1. The card says the model was converted via the Hydra-Base checkpoint and calls the architecture Mistral. The config is LlamaHydraForCausalLM. Config _name_or_path is Tweeties/tweety-tatar-hydra-mt-7b-2024-v1, which is not a separate repo in the Tweeties model list.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tweety Tatar Hydra-MT 7B is the translation finetune of the Tatar Hydra model from IDLab at Ghent University and BeCode. The weights are Tweeties/tweety-tatar-hydra-mt-7b-v24a, created on 12 April 2024. The card names Fran\u00e7ois Remy (UGent) and Alfiya Khabibullina (BeCode), with funding from IDLab / GPULab. It was still public on the collection date, 15 October 2026.", "what_it_is_for": "The card says this checkpoint was converted from TowerInstruct through the Hydra-Base model and finetuned to produce Tatar while accepting mixed input. In scope is machine translation into Tatar from one of TowerInstruct's 10 languages, notably English and Russian. It is best on sentences or small paragraphs. The card says not to translate Tatar into English and not to use it for documents. Beam search still helps after the translation finetune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ghent/tweety/tweety-7b-armenian": { "id": "ghent/tweety/tweety-7b-armenian", "name": "Tweety-7B Armenian", "maker": "ghent", "family": "ghent/tweety", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-05-03", "context_length": 32768, "max_output_tokens": null, "parameters": "7241732096", "architecture": "MistralForCausalLM. Config _name_or_path is a local path containing mistralai--Mistral-7B-v0.1--hy--ft_emb. Hidden size 4096, intermediate size 14336, 32 layers, 32 heads, 8 key-value heads, vocab 32000, max_position_embeddings 32768, sliding_window 4096, rope_theta 10000, torch dtype float32, tie_word_embeddings false. bos 1, eos 2, unk 0. No pad_token_id in config.json. There is no model card in the repo. Context window is config max_position_embeddings, not a trained length from a card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tweety-7B Armenian is a weight repo in the Tweeties series, Tweeties/tweety-7b-armenian-v24a, created on 3 May 2024. The repo has config and safetensors files and no model card. The COLM 2024 trans-tokenization paper, submitted 8 August 2024, says Armenian results are in an appendix. The weights were still public on the collection date, 15 October 2026. Confidence is medium because the weights and the paper agree that an Armenian Tweety exists, and the repo itself does not document the release.", "what_it_is_for": "The repo does not say. The file list has no README. The name and the config path, which contains `mistralai--Mistral-7B-v0.1--hy--ft_emb`, identify an Armenian embedding-table finetune of Mistral-7B-v0.1. The series paper evaluates Armenian alongside the other trans-tokenized models and points perplexity numbers to Appendix K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ghent/tweety/tweety-7b-dutch": { "id": "ghent/tweety/tweety-7b-dutch", "name": "Tweety-7B Dutch", "maker": "ghent", "family": "ghent/tweety", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-04", "context_length": null, "max_output_tokens": null, "parameters": "7391293440", "architecture": "MistralForCausalLM, hidden size 4096, intermediate size 14336, 32 layers, 32 heads, 8 key-value heads, vocab 50257, rope_theta 10000, torch dtype bfloat16, tie_word_embeddings false. bos 0, eos 2, unk 3, pad 50256. Config max_position_embeddings is 32768 and sliding_window is 4096. The card prose says a context window of 8192 tokens and a bullet says 8196 tokens. Appendix L of the paper says a maximal context length and sequence size of 8192 tokens. Those four figures disagree, so context_window_tokens is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tweety-7B Dutch is a Dutch foundation model released jointly by KU Leuven and Ghent University (IDLab). The weights are Tweeties/tweety-7b-dutch-v24a, created on 4 April 2024. The card names Pieter Delobelle, Fran\u00e7ois Remy, Miryam de Lhoneux, and Thomas Demeester. The series paper was submitted on 8 August 2024 and accepted at COLM 2024. The weights were still public on the collection date, 15 October 2026. This catalog keeps the Tweeties series under Ghent because the releasing org is Tweeties and the first and last authors are at IDLab Ghent. KU Leuven co-developed the Dutch model. It is not a KU Leuven organization repo.", "what_it_is_for": "The card calls it a foundation model for Dutch text generation and understanding. It uses a 50k Dutch tokenizer from `yhavinga/gpt-neo-1.3B-dutch` and is trained on `yhavinga/mc4_nl_cleaned`. The card says there is no instruction finetuning. The tokenizer config still includes a chat template.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ghent/tweety/tweety-7b-italian": { "id": "ghent/tweety/tweety-7b-italian", "name": "Tweety-7B Italian", "maker": "ghent", "family": "ghent/tweety", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-04", "context_length": 32768, "max_output_tokens": null, "parameters": "7241732096", "architecture": "MistralForCausalLM. Config _name_or_path is mistralai/Mistral-7B-v0.1. Hidden size 4096, intermediate size 14336, 32 layers, 32 heads, 8 key-value heads, vocab 32000, max_position_embeddings 32768, sliding_window 4096, rope_theta 10000, torch dtype float32, tie_word_embeddings false. bos 1, eos 2, unk 0, pad 3. The card does not describe the architecture. Context window is config max_position_embeddings, not a trained length stated on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tweety-7B Italian is a preview weight release in the Tweeties series associated with the Ghent University and KU Leuven trans-tokenization paper. The repo is Tweeties/tweety-7b-italian-v24a, created on 4 April 2024. The card says the model is in development and that the weights could change at any moment. It was still public on the collection date, 15 October 2026.", "what_it_is_for": "The card does not describe a task. Hub metadata marks it as a pretrained Italian text-generation model. It sits in the same Tweeties org as the Dutch, Tatar, and Armenian releases from the COLM 2024 trans-tokenization paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ghent/tweety/tweety-7b-tatar": { "id": "ghent/tweety/tweety-7b-tatar", "name": "Tweety-7B Tatar", "maker": "ghent", "family": "ghent/tweety", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-11", "context_length": 32768, "max_output_tokens": null, "parameters": "7241732096", "architecture": "MistralForCausalLM continued from mistralai/Mistral-7B-Instruct-v0.2. Hidden size 4096, intermediate size 14336, 32 layers, 32 heads, 8 key-value heads, vocab 32000, rope_theta 1000000, sliding_window null, torch dtype float32, tie_word_embeddings false. bos 1, eos 2, unk 0. Config _name_or_path is Tweeties/tweety-tatar-base-7b-2024-v1, which is not a separate repo in the Tweeties model list. Context window is config max_position_embeddings. The card does not restate a trained context length.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tweety-7B Tatar is a Tatar foundation model from IDLab at Ghent University and BeCode. The weights are Tweeties/tweety-7b-tatar-v24a, created on 11 April 2024. The card names Fran\u00e7ois Remy (UGent) and Alfiya Khabibullina (BeCode), with funding from IDLab / GPULab. The series paper was submitted on 8 August 2024 and accepted at COLM 2024. The weights were still public on the collection date, 15 October 2026.", "what_it_is_for": "The card says the model is a trans-tokenized LLM for Tatar, converted from `mistralai/Mistral-7B-Instruct-v0.2`, with a tokenizer native to Tatar. It is for basic Tatar language modeling or as a base for further finetuning. The card says it has not had instruction or chat finetuning and works best few-shot. The examples on the card are word analogies and one-shot summarization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gist/admet-fm/admet-fm": { "id": "gist/admet-fm/admet-fm", "name": "ADMET-FM", "maker": "gist", "family": "gist/admet-fm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Molecular representation model. The GIST-CSBL README says it uses ChemBERTa-2 (DeepChem/ChemBERTa-77M-MTR) as the backbone and continues pretraining on molecular structure plus ADMET profile information. Downstream scripts fine-tune the checkpoint for classification and regression. The 77M figure is the backbone name, not a measured count of ADMET-FM.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ADMET-FM is a molecular representation model from GIST-CSBL. The public repository says it continues pretraining from ChemBERTa-2 so the checkpoint can be fine-tuned for ADMET classification and regression. Pretrained weights are in the repository. No paper or license file was found on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "The README says the model learns representations from molecular structure and ADMET profiles, then the released scripts fine-tune it for binary classification and regression. Example commands use CYP2B6_Inhibitor and Fraction_unbound. It is not a general chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gist/g2d-diff/g2d-diff": { "id": "gist/g2d-diff/g2d-diff", "name": "G2D-Diff", "maker": "gist", "family": "gist/g2d-diff", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Weights and generated data: CC BY-NC-SA 4.0. Code: PolyForm Noncommercial License 1.0.0. Parts of the code adapt lucidrains/denoising-diffusion-pytorch (MIT).", "release_date": "2025-07-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion generator. A chemical VAE encodes small-molecule structures, a condition encoder embeds a cancer genotype plus a desired response class, and a conditional diffusion model samples molecules in that latent space. The Nature Communications figure caption describes 300 denoising steps. Training used PyTorch and Hugging Face libraries on four NVIDIA A100 GPUs. Not a chat model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "G2D-Diff is a genotype-conditioned small-molecule generator from the GIST Computational Systems Biology Lab (Hojung Nam). GIST announced it as a generative model that proposes anticancer candidates for a cancer genotype. Nature Communications published the paper online on 1 July 2025. Checkpoints remain available from the lab repository. Collected 2026-10-15.", "what_it_is_for": "The lab says G2D-Diff designs hit-like anticancer molecules for a specified cancer genotype and response class, without a separate efficacy predictor at generation time. GIST's news page says it was trained on about 1.5 million chemical structures and about 1.2 million drug-response records, using inputs that can be obtained in clinic (mutations and copy-number variation). A triple-negative breast cancer case study is the stated application.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gist/llamp/llamp": { "id": "gist/llamp/llamp", "name": "LLAMP", "maker": "gist", "family": "gist/llamp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Code: PolyForm Noncommercial License 1.0.0. A separate weight license is not stated on the README.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Species-aware antimicrobial-peptide activity model. The README says it uses pretrained language models to predict minimum inhibitory concentration. Inference is in Inference.ipynb. A peptide-tuned ESM-2 checkpoint is a separate download (Hugging Face Daehun/peptide_tuned_ESM-2, no model card, Fill-Mask tag). LLAMP's own weights are a Google Drive file. Parameter count of neither file was published on those pages.", "modalities": { "input": [ "text" ], "output": [ "numeric" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLAMP (Large Language model for AMP activity prediction) is a species-aware peptide model from GIST-CSBL (Hojung Nam, Daehun Bae). It predicts minimum inhibitory concentration for antimicrobial peptides. Weights are linked from the lab repository. No journal paper was found for this repository on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "The README says LLAMP uses pretrained language models to predict MIC, and that the authors screened about 5.5 million peptide sequences. They describe attention on residues such as Trp, Lys, and Phe, and say two designed peptides were comparable to pexiganan. That comparison is not a numeric table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gist/quber/quber": { "id": "gist/quber/quber", "name": "QuBER", "maker": "gist", "family": "gist/quber", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unknown-object instance-segmentation refiner. It estimates quadruple boundary errors (true positive, true negative, false positive, false negative) on an initial mask, then applies error-guided fusion. The GIST news page says the inputs are RGB-D images plus the initial prediction. The evaluation command uses a SAM base and a QuBER refiner. The arXiv abstract says inference stays under 0.1 seconds. Not a language model.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QuBER (Quadruple Boundary Error Refinement) is an unknown-object instance-segmentation refiner from GIST AI Lab (Kyoobin Lee). It corrects an initial mask by estimating four kinds of boundary error. GIST announced it for robot vision. The arXiv paper was accepted at ICRA 2025. Checkpoints are a Google Drive folder linked from the lab repository. Collected 2026-10-15.", "what_it_is_for": "GIST says robots need to recognize objects that were not in the training set, including adding or deleting misdetected objects. QuBER is a refiner, not a detector trained only on a fixed category list. The eval command in the README runs a SAM base and a QuBER refiner. The abstract says it also raised grasp success in clutter. That grasp rate was not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gist/uoais/uoais-net": { "id": "gist/uoais/uoais-net", "name": "UOAIS-Net", "maker": "gist", "family": "gist/uoais", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Repository LICENSE.md (copyright 2022 Seunghyeok Back, GIST): non-commercial scientific research, education, or artistic projects only. Commercial use is prohibited. The arXiv article is CC BY 4.0, which is not the code license.", "release_date": "2021-09-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unseen-object amodal instance segmenter. The paper calls the method Hierarchical Occlusion Modeling: it predicts visible masks, amodal masks, and occlusions. Released checkpoint names are R50_rgbdconcat_mlc_occatmask_hom_concat (RGB-D) and R50_depth_mlc_occatmask_hom_concat (depth). Code is built on Detectron2 and AdelaiDet. Parameter count was not published on the README.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UOAIS-Net is an unseen-object amodal instance segmenter from GIST AI Lab (Kyoobin Lee). It predicts visible masks, amodal masks, and occlusions for objects that were not in the category list. The lab README says the network was released on 26 September 2021. The paper was accepted at ICRA 2022. RGB-D and depth checkpoints are still linked. Collected 2026-10-15.", "what_it_is_for": "The paper is for robotic manipulation in clutter, where the visible region of an object is not enough. The repository includes OSD and OCID evaluation, RealSense D435 and Azure Kinect demos, and ROS nodes. It is not a language model and not a hosted API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gladia/solaria/solaria-1": { "id": "gladia/solaria/solaria-1", "name": "Solaria-1", "maker": "gladia", "family": "gladia/solaria", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary", "release_date": "2025-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Gladia describes Solaria-1 as built on Whisper with a hybrid ensemble of extra models at each transcription stage, including token-level language detection. Parameter count is not published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solaria-1 is Gladia's default speech-to-text model. It was announced on 2 April 2025 as Solaria and is the generalist in the Solaria line: pre-recorded and live, code switching, and the widest language list. It was still the API default on 15 October 2026. Solaria-3 did not replace it.", "what_it_is_for": "Gladia positions Solaria-1 for global coverage: voice agents, live captions, code-switched conversations, rare languages, and clean or formal speech such as parliamentary audio. Use it when the audio is live or the language is outside English, French, German, Spanish, and Italian. The docs say to omit `model` to get this model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gladia/solaria/solaria-3": { "id": "gladia/solaria/solaria-3", "name": "Solaria-3", "maker": "gladia", "family": "gladia/solaria", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary", "release_date": "2026-06-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Gladia's CTO called Solaria-3 an architectural leap over Solaria-1. No public architecture, size, or training-hour figure was found.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solaria-3 is Gladia's speech-to-text model for noisy, conversational audio in five European languages. It was announced on 10 June 2026, is pre-recorded only, and was a selectable API model on 15 October 2026. It does not replace Solaria-1.", "what_it_is_for": "Gladia says Solaria-3 is for production audio: contact-center calls, meetings, and other noisy or overlapping speech in English, French, German, Spanish, and Italian. They tell customers to keep Solaria-1 for live streaming, code switching, languages outside those five, and clean or formal speech. The product page still says \"trial then GA\" and \"code switching: limited.\" Current API docs treat the id as a normal pre-recorded option and say code switching is off. Status here is active because the changelog and the API enum list it as available.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gladia/solaria/solaria-fusion": { "id": "gladia/solaria/solaria-fusion", "name": "Solaria Fusion", "maker": "gladia", "family": "gladia/solaria", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solaria Fusion is a Gladia pre-recorded speech model id, `solaria-fusion`, that appears in the current API schema and almost nowhere else. Collected 15 October 2026. It is cataloged because the pre-recorded init enum lists it next to `solaria-1` and `solaria-3`. What it does is not published.", "what_it_is_for": "Not published. The models guide, CLI, and both quickstarts only tell developers to pick Solaria-1 or Solaria-3. The live session schema does not include this id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gladia/whisper-zero/whisper-zero": { "id": "gladia/whisper-zero/whisper-zero", "name": "Whisper-Zero", "maker": "gladia", "family": "gladia/whisper-zero", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-11-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Gladia's launch post calls it a rework of OpenAI Whisper plus other models, fine-tuned on more than 1.5 million hours that include phone and noisy audio. It is not a hosted copy of unmodified Whisper.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Whisper-Zero is Gladia's named speech-to-text system from 27 November 2024. Gladia described it as their own rework of OpenAI Whisper, trained with other models on more than 1.5 million hours, not as unmodified Whisper hosting. It is not in the current API model list. Collected 15 October 2026. Status is retired by inference: no retirement date was published.", "what_it_is_for": "The launch post sold it as the enterprise Whisper API: fewer hallucinations, better accuracy on phone and noisy audio, and stronger accent handling for meetings, media, and call centers. Features named around that era include live transcription, translation, diarization, word timestamps, and code switching. Those features now sit on the Solaria API. This dossier does not treat them as a still-callable Whisper-Zero model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "glasgow/libra/libra-v1-0-3b": { "id": "glasgow/libra/libra-v1-0-3b", "name": "Libra v1.0 3B", "maker": "glasgow", "family": "glasgow/libra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-02-11", "context_length": null, "max_output_tokens": null, "parameters": "3251712310", "architecture": "LibraLlamaForCausalLM. 28 layers, hidden size 3072, 24 attention heads, 8 key-value heads, head dim 128, intermediate size 8192, vocab size 128256, float16. max_position_embeddings 131072. rope_scaling type llama3, factor 32, original_max_position_embeddings 8192. tokenizer_model_max_length 2048. Vision tower microsoft/rad-dino, mm hidden size 768, projector type TAC. Card bases: meta-llama/Llama-3.2-3B-Instruct and microsoft/rad-dino. Safetensors total is F16 3212749824 plus BF16 38962486 and does not include mm_tac_projector.bin.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Libra v1.0 3B is a University of Glasgow multimodal model for chest X-ray findings, from the Information Retrieval Group and AI4BioMed Lab (Xi Zhang, Zaiqiao Meng, Jake Lever, and Edmond S. L. Ho). It uses the same two-stage Temporal Alignment Connector training as Libra v1.0 7B, with Llama-3.2-3B-Instruct and RAD-DINO. The GitHub README news line dates the release to 11 February 2025. The Hugging Face repo `createdAt` is 9 February 2025. Weights were still public on 2026-10-15 under Apache-2.0.", "what_it_is_for": "The card calls it a small multimodal model for radiology report generation and says it follows Libra's training strategy. It drafts Findings and can compare a current chest X-ray with a prior study. Intended uses on the card are draft decision support, teaching, and research. The card limits use to research and education.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "glasgow/libra/libra-v1-0-7b": { "id": "glasgow/libra/libra-v1-0-7b", "name": "Libra v1.0 7B", "maker": "glasgow", "family": "glasgow/libra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-20", "context_length": 2048, "max_output_tokens": null, "parameters": "6800327990", "architecture": "LibraLlamaForCausalLM. 32 layers, hidden size 4096, 32 attention heads, intermediate size 11008, vocab size 32017, float16. max_position_embeddings 2048. Vision tower microsoft/rad-dino, mm hidden size 768, projector type TAC. Card bases: epfl-llm/meditron-7b and microsoft/rad-dino. Safetensors total does not include the separate mm_tac_projector.bin.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Libra v1.0 7B is a University of Glasgow multimodal model for chest X-ray findings, from the Information Retrieval Group and AI4BioMed Lab (Xi Zhang, Zaiqiao Meng, Jake Lever, and Edmond S. L. Ho). It pairs RAD-DINO with Meditron-7B through a Temporal Alignment Connector. The Hugging Face repo was created on 20 December 2024. The arXiv paper is 28 November 2024 and the ACL 2025 Findings version is 1 July 2025. Weights were still public on 2026-10-15 under Apache-2.0.", "what_it_is_for": "The card says Libra drafts chest X-ray Findings and can compare a current study with a prior one. Training is two stages: align current and prior image features with TAC, then fine-tune report generation. Intended uses on the card are draft decision support, teaching, and research. It is not a clinical device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "glasgow/libra/med-cxrgen-f": { "id": "glasgow/libra/med-cxrgen-f", "name": "Med-CXRGen-F", "maker": "glasgow", "family": "glasgow/libra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-08", "context_length": 4096, "max_output_tokens": null, "parameters": "6759395328", "architecture": "LibraLlamaForCausalLM. Config _name_or_path libra-v0.5-7b-findings. 32 layers, hidden size 4096, 32 attention heads, intermediate size 11008, vocab size 32000, float16. max_position_embeddings 4096 and max_length 4096. tokenizer_model_max_length 1024. Vision tower openai/clip-vit-large-patch14-336, select layer -2, mm hidden size 1024, projector mlp2x_gelu. Card base: liuhaotian/llava-v1.5-7b. README table: Vicuna-7B and CLIP-L-336px. Safetensors F16 total 6759395328 does not include mm_mlp2x_projector_findings.bin.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Med-CXRGen-F is a University of Glasgow chest X-ray Findings model from the AI4BioMed lab (Xi Zhang, Zaiqiao Meng, Jake Lever, and Edmond S. L. Ho). The Libra README calls this pair Libra-0.5: an MLP-2x projector, Vicuna-7B, and CLIP-L-336px. The Hub card says it is a fine-tune of `liuhaotian/llava-v1.5-7b` for the BioNLP ACL 2024 radiology-report shared task. The shared-task repo news dates the weight release to 8 June 2024. The Hugging Face repo `createdAt` is 31 December 2024. Weights were still public on 2026-10-15 under Apache-2.0.", "what_it_is_for": "The card says the task is radiology report generation for the Findings section of the RRG shared task. The Libra README says the checkpoint is fine-tuned for Findings. The same repo's news of 20 June 2024 says Gla-AI4BioMed ranked 4th in that shared task. The result figure on that page was not transcribed into metric scores.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "glasgow/libra/med-cxrgen-i": { "id": "glasgow/libra/med-cxrgen-i", "name": "Med-CXRGen-I", "maker": "glasgow", "family": "glasgow/libra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-08", "context_length": 4096, "max_output_tokens": null, "parameters": "6759395328", "architecture": "LibraLlamaForCausalLM. Config _name_or_path libra-v0.5-7b-impression. 32 layers, hidden size 4096, 32 attention heads, intermediate size 11008, vocab size 32000, float16. max_position_embeddings 4096 and max_length 4096. tokenizer_model_max_length 1024. Vision tower openai/clip-vit-large-patch14-336, select layer -2, mm hidden size 1024, projector mlp2x_gelu. Card base: liuhaotian/llava-v1.5-7b. README table: Vicuna-7B and CLIP-L-336px. Safetensors F16 total 6759395328 does not include mm_mlp2x_projector_impressions.bin.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Med-CXRGen-I is a University of Glasgow chest X-ray Impression model from the AI4BioMed lab (Xi Zhang, Zaiqiao Meng, Jake Lever, and Edmond S. L. Ho). The Libra README calls this pair Libra-0.5: an MLP-2x projector, Vicuna-7B, and CLIP-L-336px. The Hub card says it is a fine-tune of `liuhaotian/llava-v1.5-7b` for the Impression section of the BioNLP ACL 2024 radiology-report shared task. The shared-task repo news dates the weight release to 8 June 2024. The Hugging Face repo `createdAt` is 31 December 2024. Weights were still public on 2026-10-15 under Apache-2.0.", "what_it_is_for": "The card says the task is radiology report generation for the Impression section. The Libra README says the checkpoint is fine-tuned for Impression. The Findings twin, Med-CXRGen-F, is the only one of the pair with a named row in the Libra MIMIC-CXR Findings table. The shared-task repo news of 20 June 2024 says Gla-AI4BioMed ranked 4th. That place is a team result.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "glasgow/plm-interact/plm-interact-35m-human-v11": { "id": "glasgow/plm-interact/plm-interact-35m-human-v11", "name": "PLM-interact-35M-humanV11", "maker": "glasgow", "family": "glasgow/plm-interact", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-11-06", "context_length": null, "max_output_tokens": null, "parameters": "33994499", "architecture": "Fine-tune of facebook/esm2_t12_35M_UR50D. Config sets embedding size 480, one label, class-loss weight 5, and masked-language-model loss weight 1.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLM-interact-35M-humanV11 is the small University of Glasgow PLM-interact checkpoint. It fine-tunes ESM-2 35M on the D-SCRIPT human PPI set. Weights were posted on 6 November 2024 under MIT and were still public on 2026-10-15. The Nature paper says the 35M checkpoints are there to help with testing. Reported results in that paper are for the 650M model.", "what_it_is_for": "The card says it is trained on human PPIs from the D-SCRIPT benchmark page and points readers to the humanV12 card for the longer description. The GitHub index lists it as the lightweight human model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "glasgow/plm-interact/plm-interact-650m-human-v11": { "id": "glasgow/plm-interact/plm-interact-650m-human-v11", "name": "PLM-interact-650M-humanV11", "maker": "glasgow", "family": "glasgow/plm-interact", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-11-06", "context_length": null, "max_output_tokens": null, "parameters": "652358871", "architecture": "Fine-tune of facebook/esm2_t33_650M_UR50D. Card config sets embedding size 1280, one label, class-loss weight 10, and masked-language-model loss weight 1. A linear head scores the CLS token.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLM-interact-650M-humanV11 is a University of Glasgow protein-protein interaction model from Dan Liu and colleagues at the MRC-University of Glasgow Centre for Virus Research, the School of Cancer Sciences, and the School of Computing Science. It is a fine-tune of ESM-2 650M that scores whether two protein sequences interact. Weights were posted on 6 November 2024 and were still public on the collection date 2026-10-15. The Nature Communications paper is 27 October 2025.", "what_it_is_for": "The card and the university news describe a model that predicts human protein-protein interactions and transfers to other species. The GitHub index says this checkpoint was trained on the D-SCRIPT human PPI benchmark. The card's opening paragraph instead says STRING V12. That sentence matches the humanV12 card and is treated here as a copy-paste error.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "glasgow/plm-interact/plm-interact-650m-human-v12": { "id": "glasgow/plm-interact/plm-interact-650m-human-v12", "name": "PLM-interact-650M-humanV12", "maker": "glasgow", "family": "glasgow/plm-interact", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-11-06", "context_length": null, "max_output_tokens": null, "parameters": "652358871", "architecture": "Same safetensors parameter count as humanV11 (652,358,871 F32). The card example loads facebook/esm2_t33_650M_UR50D with embedding size 1280. That example block is shared with the humanV11 card, including a comment that names humanV11. This repo's own config.json was not read.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLM-interact-650M-humanV12 is the STRING V12 human protein-protein interaction checkpoint from the University of Glasgow PLM-interact release. Dan Liu posted it on Hugging Face on 6 November 2024. It was still public on 2026-10-15. The Nature Communications paper is 27 October 2025.", "what_it_is_for": "The card says this checkpoint is trained on human PPIs from STRING V12. The GitHub model index says the same. It is the STRING-corpus sibling of humanV11, not a dated snapshot of those weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "glasgow/plm-interact/plm-interact-650m-leakage-free": { "id": "glasgow/plm-interact/plm-interact-650m-leakage-free", "name": "PLM-interact-650M-Leakage-Free", "maker": "glasgow", "family": "glasgow/plm-interact", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-07-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLM-interact-650M-Leakage-Free is a University of Glasgow PLM-interact checkpoint trained on the Bernett leakage-free human PPI set. Dan Liu created the Hugging Face repo on 30 July 2025. The license tag is MIT. It was still public on 2026-10-15. The card is one sentence.", "what_it_is_for": "The card says the model is trained on human PPIs from figshare doi 10.6084/m9.figshare.21591618.v3. The GitHub index calls this the leakage-free Bernett checkpoint. It is a named training set, not an unlabeled ablation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "glasgow/plm-interact/plm-interact-650m-mutation": { "id": "glasgow/plm-interact/plm-interact-650m-mutation", "name": "PLM-interact-650M-Mutation", "maker": "glasgow", "family": "glasgow/plm-interact", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-07-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLM-interact-650M-Mutation is a named University of Glasgow checkpoint for mutation effects on protein interactions. Dan Liu created the Hugging Face repo on 30 July 2025. The card has tags and no prose, so confidence is medium. The weights were still public on 2026-10-15.", "what_it_is_for": "The only card text is the tag \"Mutation effect prediction\" and the dataset `danliu1226/Mutation_effect_dataset`. The GitHub index says this is a fine-tune of PLM-interact on the IntAct mutation file. The university news says the project can identify mutation effects, without naming this repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "glasgow/plm-interact/plm-interact-650m-vh": { "id": "glasgow/plm-interact/plm-interact-650m-vh", "name": "PLM-interact-650M-VH", "maker": "glasgow", "family": "glasgow/plm-interact", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-11-06", "context_length": null, "max_output_tokens": null, "parameters": "652358871", "architecture": "Safetensors total matches the human 650M checkpoints (652,358,871 F32). The card does not name the ESM-2 checkpoint id. config.json was not read.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLM-interact-650M-VH is the University of Glasgow virus-human protein-protein interaction checkpoint in the PLM-interact family. Dan Liu posted the weights on 6 November 2024. They were still public on 2026-10-15. The card has no license.", "what_it_is_for": "The card says this model is trained on virus-human PPIs from the LSTM-PHV download page at kurata35.bio.kyutech.ac.jp. The GitHub index lists the same source. The university news separately says the team trained on 22,383 interactions from 5,882 human and 996 virus proteins. The news does not name this Hub repo, so those counts are not stored as this file's training size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "goettingen/clipseg/clipseg-rd16": { "id": "goettingen/clipseg/clipseg-rd16", "name": "CLIPSeg RD16", "maker": "goettingen", "family": "goettingen/clipseg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-12-18", "context_length": 77, "max_output_tokens": null, "parameters": "", "architecture": "Same frozen CLIP ViT-B/16 and CLIP text encoder as the D=64 card (vision hidden size 768, 12 layers, patch 16, image size 224; text hidden size 512, 12 layers, vocab 49408, 77 positions). Decoder reduce dimension is 16. Hub config sets use_complex_transposed_convolution to false, 4 decoder heads, and extract_layers [3, 6, 9]. The paper treats D=16 as the smaller decoder in the ablation and says performance decreases. It does not print a separate trainable-parameter count for D=16. The Hub size label is 0.1B.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLIPSeg RD16 is the smaller University of G\u00f6ttingen CLIPSeg checkpoint. The Campus Institute Data Science Hub card sets the decoder reduce dimension to 16. It is a released weight, not an unnamed training run, and it was still listed on the CIDAS org on 2026-10-15. The paper is the same CVPR 2022 CLIPSeg paper as the D=64 model.", "what_it_is_for": "The card gives the same job as the other CLIPSeg cards: zero-shot and one-shot segmentation from a text or image prompt. In the paper, D=16 is the reduced decoder used to test how much capacity the decoder needs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "goettingen/clipseg/clipseg-rd64": { "id": "goettingen/clipseg/clipseg-rd64", "name": "CLIPSeg RD64", "maker": "goettingen", "family": "goettingen/clipseg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-12-18", "context_length": 77, "max_output_tokens": null, "parameters": "", "architecture": "Frozen OpenAI CLIP ViT-B/16 vision encoder (hidden size 768, 12 layers, 12 heads, patch size 16, image size 224) plus a CLIP text encoder (hidden size 512, 12 layers, 8 heads, vocab 49408, max position embeddings 77). A 3-block transformer decoder with reduce dimension 64, 4 attention heads, quick_gelu, and decoder intermediate size 2048. The Hub config sets use_complex_transposed_convolution to false and extract_layers to [3, 6, 9]. The CVPR paper instead writes skip layers S=[3, 7, 9]. The paper states 1,122,305 trainable parameters for D=64; the CLIP backbone stays frozen. The Hub size label for the full checkpoint is 0.2B. Experiments in the paper use 352x352 images by interpolating CLIP positional embeddings.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLIPSeg RD64 is the University of G\u00f6ttingen Campus Institute Data Science checkpoint of CLIPSeg with decoder reduce dimension 64. Timo L\u00fcddecke and Alexander Ecker released it for zero-shot and one-shot image segmentation. The weights were still on the Hub on 2026-10-15. The arXiv preprint is 18 December 2021 and the paper is in CVPR 2022.", "what_it_is_for": "The Hub card says the model is for zero-shot and one-shot image segmentation. The paper uses one frozen CLIP backbone and a small decoder so a text phrase or a support image can name the foreground at test time. The lab file name is `rd64-uni.pth`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "goettingen/clipseg/clipseg-rd64-refined": { "id": "goettingen/clipseg/clipseg-rd64-refined", "name": "CLIPSeg RD64 refined", "maker": "goettingen", "family": "goettingen/clipseg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 77, "max_output_tokens": null, "parameters": "", "architecture": "CLIPSeg with reduce dimension 64 and use_complex_transposed_convolution true. The lab README calls the file rd64-uni-refined.pth and says a more complex token-to-mask module replaced the square-like predictions of the earlier weights. Frozen CLIP ViT-B/16 and the CLIP text encoder match the other cards. Hub extract_layers is [3, 6, 9], decoder heads 4, decoder intermediate size 2048. The paper's 1,122,305 trainable-parameter count is for the original D=64 decoder, not this later convolution. The Hub size label is 0.2B.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLIPSeg RD64 refined is the Campus Institute Data Science checkpoint that the Transformers library treats as the public CLIPSeg model. University of G\u00f6ttingen authors Timo L\u00fcddecke and Alexander Ecker added a more complex convolution on the D=64 decoder so masks are less blocky than the first release. It was still the most-downloaded CIDAS model on 2026-10-15. The lab README dates the weight to September 2022. The CVPR paper itself is earlier.", "what_it_is_for": "The card says zero-shot and one-shot image segmentation. The repository loads it with `complex_trans_conv=True` from `rd64-uni-refined.pth`. Hugging Face's own guide uses this id for text-prompted masks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "goettingen/privi/privi": { "id": "goettingen/privi/privi", "name": "PriVi", "maker": "goettingen", "family": "goettingen/privi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of Meta V-JEPA ViT-L. The lab README initializes from the public vitl16.pth.tar checkpoint and ships privi_original.pth, the paper checkpoint without dataset-level pretraining (DaLP). The project page says the encoder is V-JEPA ViT-L and that the evaluation head is an attentive classifier with 220K trainable parameters on a frozen backbone. The usage example feeds 16 frames at 224x224 and reads layer 23. That example comments d_model=2014; the lab does not elsewhere state the width, so 2014 is not stored as the hidden size. The backbone parameter count is not stated.", "modalities": { "input": [ "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PriVi is a primate-video encoder from Alexander Ecker's group at the University of G\u00f6ttingen, with the German Primate Center and other coauthors. It is Meta's V-JEPA ViT-L continued on the PriVi video collection. The lab shipped `privi_original.pth` and, on 2026-10-15, still said a Hugging Face org upload was in progress. The preprint is 12 November 2025. The citation marks the paper as CVPR 2026, to appear.", "what_it_is_for": "The project page says the encoder is a general-purpose video backbone for primate behavior in the wild. Evaluation freezes the backbone and trains a small attentive classifier. The README tells other labs to load `privi_original.pth` as a backbone. That file is the paper checkpoint without dataset-level pretraining.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "goettingen/treelearn/treelearn": { "id": "goettingen/treelearn/treelearn", "name": "TreeLearn", "maker": "goettingen", "family": "goettingen/treelearn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-09-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "3D U-Net for forest point clouds. A shared two-layer MLP predicts a semantic tree-versus-understory score. A shared three-layer MLP predicts an offset toward the tree base. Instances are then clustered. The repository is built on SoftGroup and spconv. The v1 paper initializes from a 3D instance-segmentation checkpoint (Chen et al. 2021). The code repository is MIT. The GRO.data archive that includes the pretrained weights is CC BY 4.0.", "modalities": { "input": [ "point-cloud" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TreeLearn is a forest point-cloud model from the University of G\u00f6ttingen. Jonathan Henrich, Jan van Delden, Dominik Seidel, Thomas Kneib, and Alexander Ecker released the code, the weights, and a hand-segmented benchmark. It was still maintained on 2026-10-15. The preprint is 15 September 2023. A later journal version is in Ecological Informatics (2024).", "what_it_is_for": "The README describes an automatic pipeline that segments individual trees from ground-based laser scans. The network predicts, for each point, whether it belongs to a tree and an offset toward that tree's base. Clustering turns the shifted points into instances. Users who do not want to train are pointed at the pretrained pipeline weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/chirp/chirp": { "id": "google/chirp/chirp", "name": "Chirp", "maker": "google", "family": "google/chirp", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Multilingual speech recognition model. A Google AI models page said Chirp has 2 billion parameters. That page was a marketing index, not a model card.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.016 }, "summary_intro": "Google Chirp speech-to-text model, first generation of the Chirp ASR line. Collected 2026-10-15.", "what_it_is_for": "Multilingual automatic speech recognition.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/chirp/chirp-2": { "id": "google/chirp/chirp-2", "name": "Chirp 2", "maker": "google", "family": "google/chirp", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multilingual ASR model with word-level timestamps, adaptation, and speech translation versus Chirp.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.016 }, "summary_intro": "Google Chirp 2 speech recognition model on Speech-to-Text v2. Collected 2026-10-15.", "what_it_is_for": "Transcribe speech, with timestamps, adaptation, and speech translation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/chirp/chirp-3": { "id": "google/chirp/chirp-3", "name": "Chirp 3", "maker": "google", "family": "google/chirp", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latest Chirp ASR generation on Speech-to-Text v2, with diarization and automatic language detection.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.016 }, "summary_intro": "Google Chirp 3 speech-to-text model. Docs were current as of October 13, 2026. Collected 2026-10-15.", "what_it_is_for": "Multilingual transcription with diarization and language detection.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/chirp/chirp-3-hd": { "id": "google/chirp/chirp-3-hd", "name": "Chirp 3 HD", "maker": "google", "family": "google/chirp", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Generative text-to-speech voices branded Chirp 3: HD.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 30.0 }, "summary_intro": "Google Chirp 3 HD text-to-speech voices on Cloud Text-to-Speech. Collected 2026-10-15. This is not the Chirp 3 ASR model.", "what_it_is_for": "Generate spoken audio from text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/codegemma/codegemma-2b": { "id": "google/codegemma/codegemma-2b", "name": "CodeGemma 2B", "maker": "google", "family": "google/codegemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-04-09", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Gemma-based decoder for code. The Agent Platform table lists pretrained only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open-weights code model from the Gemma 1 generation. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Code generation and code completion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/codegemma/codegemma-7b": { "id": "google/codegemma/codegemma-7b", "name": "CodeGemma 7B", "maker": "google", "family": "google/codegemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-04-09", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Gemma-based decoder for code. Pretrained and instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open-weights code model from the Gemma 1 generation. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Code generation and code completion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/datagemma/datagemma": { "id": "google/datagemma/datagemma", "name": "DataGemma", "maker": "google", "family": "google/datagemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-09-12", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "2B Gemma variant. Architecture details were not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DataGemma research model released at the 2B size. Collected 2026-10-15.", "what_it_is_for": "The releases index names the model. The task description on the DataGemma doc page was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/embedding/multimodalembedding": { "id": "google/embedding/multimodalembedding", "name": "Multimodal Embedding", "maker": "google", "family": "google/embedding", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vertex multimodal embedding model for images and related retrieval tasks. Distinct from Gemini Embedding.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google multimodal embedding model listed on Vertex AI, separate from Gemini Embedding. Collected 2026-10-15.", "what_it_is_for": "Image and multimodal vectors for search and classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/embeddinggemma/embeddinggemma": { "id": "google/embeddinggemma/embeddinggemma", "name": "EmbeddingGemma", "maker": "google", "family": "google/embeddinggemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-09-04", "context_length": 2048, "max_output_tokens": null, "parameters": "308M", "architecture": "Decoder-derived text embedding model. Current docs say 308M parameters. The releases index said 300M.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open multilingual text embedding model based on Gemma 3. Collected 2026-10-15.", "what_it_is_for": "Text embeddings for retrieval, classification, and clustering on device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/embeddinggemma/embeddinggemma-2": { "id": "google/embeddinggemma/embeddinggemma-2", "name": "EmbeddingGemma 2", "maker": "google", "family": "google/embeddinggemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-10-06", "context_length": 8192, "max_output_tokens": null, "parameters": "740M full; 270M text and code core", "architecture": "Gemma 4 decoder embedding model. Docs and the developer guide describe a 270M text and code core, a 170M vision encoder, and a 300M audio encoder, loaded only when needed. Output is 768 dimensions and can be truncated to 512, 256, or 128.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind open multimodal embedding model released October 6, 2026. Collected 2026-10-15. Apache 2.0.", "what_it_is_for": "Map text, code, images, video, and audio into one vector space for retrieval and RAG.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/functiongemma/functiongemma": { "id": "google/functiongemma/functiongemma", "name": "FunctionGemma", "maker": "google", "family": "google/functiongemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "270M", "architecture": "270M Gemma variant. Architecture table was not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google 270M open model intended as a base for specialized function-calling models. Collected 2026-10-15.", "what_it_is_for": "Function calling after further fine-tuning. Agent Platform says it is built to be tuned for that job.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gecko/textembedding-gecko": { "id": "google/gecko/textembedding-gecko", "name": "Text Embedding Gecko", "maker": "google", "family": "google/gecko", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-06-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PaLM-era text embedding model. The model-versions table lists textembedding-gecko@001, @002, and @003.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Retired Google text embedding model from the Gecko line on Vertex AI. Collected 2026-10-15.", "what_it_is_for": "Text embeddings for search, classification, and clustering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gecko/textembedding-gecko-multilingual": { "id": "google/gecko/textembedding-gecko-multilingual", "name": "Text Embedding Gecko Multilingual", "maker": "google", "family": "google/gecko", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multilingual Gecko embedding model.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Retired multilingual Gecko text embedding model. Collected 2026-10-15.", "what_it_is_for": "Multilingual text embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-1-0-pro": { "id": "google/gemini/gemini-1-0-pro", "name": "Gemini 1.0 Pro", "maker": "google", "family": "google/gemini", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-12-13", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-style Gemini model trained with a 32,768-token sequence length, per the Gemini 1.0 technical report. The served API cap was not re-read from a live spec page.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 1.0 Pro is the original public Gemini text model, released in the Gemini API as gemini-pro on 2023-12-13. Google stopped supporting Gemini 1.0 Pro on 2025-02-18.", "what_it_is_for": "The December 2023 changelog called gemini-pro a text model that balances capability and efficiency. The technical report places Pro between Ultra and on-device Nano.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-1-0-pro-vision": { "id": "google/gemini/gemini-1-0-pro-vision", "name": "Gemini 1.0 Pro Vision", "maker": "google", "family": "google/gemini", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-12-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 1.0 Pro Vision (gemini-pro-vision) was the multimodal companion to gemini-pro. Google removed it from Google AI services on 2024-07-12.", "what_it_is_for": "The December 2023 changelog introduced it as the multimodal model for a wide variety of tasks, separate from the text-only gemini-pro endpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-1-0-ultra": { "id": "google/gemini/gemini-1-0-ultra", "name": "Gemini 1.0 Ultra", "maker": "google", "family": "google/gemini", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-12-06", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Largest Gemini 1.0 size in the technical report, for highly complex tasks. Trained with a 32,768-token sequence length.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 1.0 Ultra is the largest model in the December 2023 Gemini 1.0 report. It was the early Gemini Advanced model. A public Gemini API model id was not on the 2026 models index, and Gemini 1.0 Pro API support had already ended.", "what_it_is_for": "The technical report defines Ultra for highly complex tasks, above Pro and above on-device Nano.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-1-5-flash": { "id": "google/gemini/gemini-1-5-flash", "name": "Gemini 1.5 Flash", "maker": "google", "family": "google/gemini", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-10", "context_length": 1048576, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "code", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 1.5 Flash was the faster Gemini 1.5 model. Preview started 2024-05-10, GA was 2024-05-23, and the changelog records shutdown on 2025-09-29.", "what_it_is_for": "Google used it as the lower-latency multimodal model beside 1.5 Pro, including the last Gemini API tuning target of that generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-1-5-flash-8b": { "id": "google/gemini/gemini-1-5-flash-8b", "name": "Gemini 1.5 Flash-8B", "maker": "google", "family": "google/gemini", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 1.5 Flash-8B was the smallest stable Gemini 1.5 model. The stable id gemini-1.5-flash-8b-001 shipped on 2024-10-03 and was shut down on 2025-09-29.", "what_it_is_for": "Google described the October 2024 stable release as its smallest Gemini model, for lower-cost multimodal tasks than 1.5 Flash.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-1-5-pro": { "id": "google/gemini/gemini-1-5-pro", "name": "Gemini 1.5 Pro", "maker": "google", "family": "google/gemini", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-04-09", "context_length": 2097152, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "code", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 1.5 Pro was Google's long-context Gemini 1.5 model. The API preview opened 2024-04-09, GA followed on 2024-05-23, and the changelog says the model was shut down on 2025-09-29.", "what_it_is_for": "It was the mid-size multimodal model with a 2,097,152-token window used for long documents, video, and audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-2-0-flash": { "id": "google/gemini/gemini-2-0-flash", "name": "Gemini 2.0 Flash", "maker": "google", "family": "google/gemini", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-05", "context_length": 1048576, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "image", "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 2.0 Flash was Google's second-generation Flash model, released 2025-02-05 and shut down on 2026-06-01. Collected 2026-10-15.", "what_it_is_for": "Google described it as the workhorse with native tool use, a 1,048,576-token input window, and an 8,192-token output cap.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-2-0-flash-lite": { "id": "google/gemini/gemini-2-0-flash-lite", "name": "Gemini 2.0 Flash-Lite", "maker": "google", "family": "google/gemini", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-25", "context_length": 1048576, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "image", "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 2.0 Flash-Lite was the low-cost Gemini 2.0 model. The stable ids launched 2025-02-25 and were shut down on 2026-06-01.", "what_it_is_for": "Google described it as the fastest second-generation model, optimized for cost and latency.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-2-0-flash-thinking": { "id": "google/gemini/gemini-2-0-flash-thinking", "name": "Gemini 2.0 Flash Thinking", "maker": "google", "family": "google/gemini", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 2.0 Flash Thinking was a family of experimental reasoning endpoints. Google shut them down on 2025-12-02.", "what_it_is_for": "The January 2025 changelog called gemini-2.0-flash-thinking-exp-01-21 the latest preview of the thinking model. It was not a stable product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-2-0-pro": { "id": "google/gemini/gemini-2-0-pro", "name": "Gemini 2.0 Pro", "maker": "google", "family": "google/gemini", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 2.0 Pro was an experimental preview, not a stable API model. Google shut down gemini-2.0-pro-exp and gemini-2.0-pro-exp-02-05 on 2025-12-09.", "what_it_is_for": "The changelog treats it as the preview Pro model in the Gemini 2.0 generation. A live model-spec page returned 404 on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-2-5-computer-use": { "id": "google/gemini/gemini-2-5-computer-use", "name": "Gemini 2.5 Computer Use", "maker": "google", "family": "google/gemini", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-07", "context_length": 128000, "max_output_tokens": 64000, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 2.5 Computer Use was a preview model for seeing a screen and taking UI actions. Google shut it down on 2026-07-28 and points users to Gemini 3.8 Flash computer use.", "what_it_is_for": "Google built it for browser automation, multi-step software tasks, and UI testing that need visual reasoning and cursor control.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-2-5-deep-think": { "id": "google/gemini/gemini-2-5-deep-think", "name": "Gemini 2.5 Deep Think", "maker": "google", "family": "google/gemini", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-08-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 2.5 Deep Think is a named Gemini 2.5 reasoning variant. Google rolled it out in the Gemini app on 2025-08-01 for AI Ultra subscribers. A current public API id was not on the 2026-10-13 models index.", "what_it_is_for": "Google says it uses parallel thinking so the model can consider multiple hypotheses. In the app it is a mode used with Gemini 2.5 Pro, not a separately priced API model in the sources checked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-2-5-flash": { "id": "google/gemini/gemini-2-5-flash", "name": "Gemini 2.5 Flash", "maker": "google", "family": "google/gemini", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-17", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 2.5 }, "summary_intro": "Gemini 2.5 Flash is the stable June 2025 Flash model. Collected 2026-10-15. It remains available to prior users and is not deprecated.", "what_it_is_for": "Google describes it as the price-performance model for low-latency, high-volume work that still needs thinking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-2-5-flash-lite": { "id": "google/gemini/gemini-2-5-flash-lite", "name": "Gemini 2.5 Flash-Lite", "maker": "google", "family": "google/gemini", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-22", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.4 }, "summary_intro": "Gemini 2.5 Flash-Lite went stable on 2025-07-22 after a 2025-06-17 preview. It was still served to prior users on 2026-10-15 and is not deprecated.", "what_it_is_for": "Google calls it the fastest, cheapest multimodal model in the 2.5 family, for classification, extraction, and low-latency work.", "ratings": { "empirical_100": 58, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-2-5-pro": { "id": "google/gemini/gemini-2-5-pro", "name": "Gemini 2.5 Pro", "maker": "google", "family": "google/gemini", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-17", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 10.0 }, "summary_intro": "Gemini 2.5 Pro (gemini-2.5-pro) is Google's stable June 2025 thinking model. It was still served on 2026-10-15, with access limited to prior users and no announced shutdown.", "what_it_is_for": "Google says it reasons over code, math, STEM, and long documents. New projects are pointed at Gemini 3.5 Flash-Lite or Gemini 3.8 Flash.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-3-1-flash-lite": { "id": "google/gemini/gemini-3-1-flash-lite", "name": "Gemini 3.1 Flash-Lite", "maker": "google", "family": "google/gemini", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-07", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.25, "completion_per_m_usd": 1.5 }, "summary_intro": "Gemini 3.1 Flash-Lite is the stable low-cost Gemini 3.1 model, released 2026-05-07 and still active on 2026-10-15. Its preview id was shut down on 2026-05-25.", "what_it_is_for": "Google describes it as a low-latency, low-cost multimodal model for high-frequency extraction, translation, and routing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-3-1-pro": { "id": "google/gemini/gemini-3-1-pro", "name": "Gemini 3.1 Pro", "maker": "google", "family": "google/gemini", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-19", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 12.0 }, "summary_intro": "Gemini 3.1 Pro (gemini-3.1-pro-preview) is a closed preview model released 2026-02-19. It was still listed with no shutdown date on 2026-10-15.", "what_it_is_for": "Google says it refines the Gemini 3 Pro series for thinking efficiency, factual consistency, software engineering, and multi-step tool use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-3-5-flash": { "id": "google/gemini/gemini-3-5-flash", "name": "Gemini 3.5 Flash", "maker": "google", "family": "google/gemini", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-19", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 3.5 Flash launched on 2026-05-19 and is deprecated. As of 2026-10-15 Google routes gemini-3.5-flash to gemini-3.6-flash without shutting the id down.", "what_it_is_for": "Google describes it as an earlier Flash model for real-world tasks. It is no longer selectable in AI Studio, and API calls route to Gemini 3.6 Flash.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-3-5-flash-lite": { "id": "google/gemini/gemini-3-5-flash-lite", "name": "Gemini 3.5 Flash-Lite", "maker": "google", "family": "google/gemini", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-21", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 2.5 }, "summary_intro": "Gemini 3.5 Flash-Lite (gemini-3.5-flash-lite) is an active stable model released 2026-07-21. Collected 2026-10-15. Google recommends it for new high-volume work instead of the access-limited 2.5 models.", "what_it_is_for": "Google positions it for high-throughput, low-cost subagent tasks and document parsing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-3-6-flash": { "id": "google/gemini/gemini-3-6-flash", "name": "Gemini 3.6 Flash", "maker": "google", "family": "google/gemini", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-21", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.75, "completion_per_m_usd": 3.75 }, "summary_intro": "Gemini 3.6 Flash (gemini-3.6-flash) is an active stable Gemini API model released 2026-07-21. Collection date 2026-10-15. Gemini 3.5 Flash requests are documented as routing here.", "what_it_is_for": "Google calls this the previous-generation Flash model for agentic work, coding, and spatial reasoning, still served as its own stable endpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-3-7-flash": { "id": "google/gemini/gemini-3-7-flash", "name": "Gemini 3.7 Flash", "maker": "google", "family": "google/gemini", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-13", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 3.7 Flash launched on 2026-08-13 and is deprecated as of the 2026-10-15 collection. Google says the id remains up and routes to Gemini 3.8 Flash.", "what_it_is_for": "Google describes it as the previous Gemini 3 Flash generation. The endpoint still accepts calls but routes them to Gemini 3.8 Flash, and it is not selectable in Google AI Studio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-3-8-flash": { "id": "google/gemini/gemini-3-8-flash", "name": "Gemini 3.8 Flash", "maker": "google", "family": "google/gemini", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-02", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.75, "completion_per_m_usd": 3.75 }, "summary_intro": "Gemini 3.8 Flash (gemini-3.8-flash) is Google's stable Flash model released 2026-09-02. It is active on the Gemini API as of 2026-10-15. Requests to the deprecated gemini-3.7-flash id are documented as routing here.", "what_it_is_for": "Google calls it the most intelligent Flash model, for long-horizon software engineering, autonomous agents, and enterprise workflows, at Flash speed and cost.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-3-flash": { "id": "google/gemini/gemini-3-flash", "name": "Gemini 3 Flash", "maker": "google", "family": "google/gemini", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-17", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 3.0 }, "summary_intro": "Gemini 3 Flash (gemini-3-flash-preview) is a preview model released 2025-12-17 and still listed on 2026-10-15. Google recommends Gemini 3.6 Flash for new work.", "what_it_is_for": "The model page calls it Google's multimodal and agentic Flash preview from the original Gemini 3 generation, with computer use and thinking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-3-pro": { "id": "google/gemini/gemini-3-pro", "name": "Gemini 3 Pro", "maker": "google", "family": "google/gemini", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-18", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 3 Pro Preview was Google's November 2025 reasoning preview. The Gemini API shut it down on 2026-03-09. Collected 2026-10-15.", "what_it_is_for": "Google described it as the state-of-the-art Gemini 3 reasoning model with multimodal understanding. A Gemini 3 Deep Think app mode, announced by Google AI on 2025-12-04, was a mode of this model rather than a separate API id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini/gemini-4-argon": { "id": "google/gemini/gemini-4-argon", "name": "Gemini 4 Argon", "maker": "google", "family": "google/gemini", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-13", "context_length": 1048576, "max_output_tokens": 1048576, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 10.0 }, "summary_intro": "Google DeepMind's Gemini 4 Argon (gemini-4-argon) is the flagship closed Gemini reasoning model. It became broadly available on 2026-10-13 after a 2026-09-30 Fairwind rollout. This dossier was collected on 2026-10-15.", "what_it_is_for": "Google describes it as the model for complex software engineering, math and science, autonomous multi-step agents, and long decodes up to 1 million tokens.", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs": 98.0, "aa_intelligence": 52.6, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1438, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-deep-research/gemini-deep-research": { "id": "google/gemini-deep-research/gemini-deep-research", "name": "Gemini Deep Research", "maker": "google", "family": "google/gemini-deep-research", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-21", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "pdf", "audio", "video" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini Deep Research (deep-research-preview-04-2026) is a managed research agent released 2026-04-21 and still in preview on 2026-10-15.", "what_it_is_for": "Google says it plans and runs multi-step research over public web and private workspace sources and returns cited reports. The Max id is the more thorough variant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-deep-research/gemini-deep-research-max": { "id": "google/gemini-deep-research/gemini-deep-research-max", "name": "Gemini Deep Research Max", "maker": "google", "family": "google/gemini-deep-research", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-21", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "pdf", "audio", "video" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini Deep Research Max is the longer-running research agent released with Deep Research on 2026-04-21. It was still a preview on 2026-10-15.", "what_it_is_for": "Google says it is for accuracy-critical investigations that synthesize hundreds of sources into cited reports, with planning, charts, MCP, and File Search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-deep-research/gemini-deep-research-pro": { "id": "google/gemini-deep-research/gemini-deep-research-pro", "name": "Gemini Deep Research Pro", "maker": "google", "family": "google/gemini-deep-research", "status": "preview", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini Deep Research Pro is the December 2025 research agent. On 2026-10-15 its earliest shutdown was 2026-10-23, and the replacement was the April 2026 Deep Research preview.", "what_it_is_for": "The deprecations page lists it as the earlier managed research agent, replaced by deep-research-preview-04-2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-embedding/gemini-embedding-001": { "id": "google/gemini-embedding/gemini-embedding-001", "name": "Gemini Embedding", "maker": "google", "family": "google/gemini-embedding", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-14", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini Embedding (gemini-embedding-001) is the stable text embedding model released 2025-07-14. It was still active on 2026-10-15, with an earliest shutdown of 2028-05-14.", "what_it_is_for": "Google describes it for semantic search, classification, and RAG. Gemini Embedding 2 is the recommended multimodal replacement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-embedding/gemini-embedding-2": { "id": "google/gemini-embedding/gemini-embedding-2", "name": "Gemini Embedding 2", "maker": "google", "family": "google/gemini-embedding", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-22", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": null }, "summary_intro": "Gemini Embedding 2 is Google's multimodal embedding model, released 2026-04-22 and active on 2026-10-15. It maps text, image, video, audio, and PDF into one vector space.", "what_it_is_for": "Google recommends it for cross-modal search, retrieval, and RAG.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-image/gemini-2-0-flash-image": { "id": "google/gemini-image/gemini-2-0-flash-image", "name": "Gemini 2.0 Flash Image", "maker": "google", "family": "google/gemini-image", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 2.0 Flash's image-generation preview launched 2025-05-07 and shut down on 2025-11-14. It predates the Nano Banana name.", "what_it_is_for": "It was the preview native image endpoint on Gemini 2.0 Flash before Gemini 2.5 Flash Image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-image/gemini-2-5-flash-image": { "id": "google/gemini-image/gemini-2-5-flash-image", "name": "Nano Banana", "maker": "google", "family": "google/gemini-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-02", "context_length": 65536, "max_output_tokens": 32768, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": null }, "summary_intro": "Nano Banana is Gemini 2.5 Flash Image, generally available on 2025-10-02. On 2026-10-15 the models index still listed it. Two official pages disagree about when it shuts down.", "what_it_is_for": "Google describes it as the fast native image generation and editing model for high-volume creative workflows.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-image/gemini-3-1-flash-image": { "id": "google/gemini-image/gemini-3-1-flash-image", "name": "Nano Banana 2", "maker": "google", "family": "google/gemini-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-28", "context_length": 131072, "max_output_tokens": 32768, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "pdf" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 3.0 }, "summary_intro": "Nano Banana 2 is Gemini 3.1 Flash Image (gemini-3.1-flash-image). The stable endpoint is dated 2026-05-28 and was still active on 2026-10-15.", "what_it_is_for": "Google calls it the high-volume image generation and editing model at Flash speed, below Nano Banana Pro and now below Nano Banana 2.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-image/gemini-3-1-flash-lite-image": { "id": "google/gemini-image/gemini-3-1-flash-lite-image", "name": "Nano Banana 2 Lite", "maker": "google", "family": "google/gemini-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-30", "context_length": 65536, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "pdf" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": 0.25, "completion_per_m_usd": 1.5 }, "summary_intro": "Nano Banana 2 Lite (gemini-3.1-flash-lite-image) is the low-latency Gemini image model released 2026-06-30 and active on 2026-10-15.", "what_it_is_for": "Google describes it as the efficiency specialist for high-volume interactive image generation and editing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-image/gemini-3-pro-image": { "id": "google/gemini-image/gemini-3-pro-image", "name": "Nano Banana Pro", "maker": "google", "family": "google/gemini-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-28", "context_length": 65536, "max_output_tokens": 32768, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 12.0 }, "summary_intro": "Nano Banana Pro is Gemini 3 Pro Image (gemini-3-pro-image). The stable endpoint is dated 2026-05-28. It was active on 2026-10-15. The preview shut down on 2026-06-25.", "what_it_is_for": "Google describes it as the professional image model for design, product mockups, accurate text, and search-grounded visuals.", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 45.0, "humaneval": 94.0, "twelve_cs": 28.2, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-image/gemini-nano-banana-2-1": { "id": "google/gemini-image/gemini-nano-banana-2-1", "name": "Nano Banana 2.1", "maker": "google", "family": "google/gemini-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-06", "context_length": 131072, "max_output_tokens": 32768, "parameters": "", "architecture": "Nano Banana 2.1 model card says the model is based on Gemini 3.6 Flash.", "modalities": { "input": [ "text", "image", "video", "pdf" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": 1.5, "completion_per_m_usd": 7.5 }, "summary_intro": "Nano Banana 2.1 (gemini-nano-banana-2.1) is Google's stable Gemini image model released 2026-10-06. It was active on 2026-10-15.", "what_it_is_for": "Google describes it as a faster, cheaper image generation and editing model than Nano Banana Pro, with better text, consistency, and quality than Nano Banana 2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-live/gemini-2-0-flash-live": { "id": "google/gemini-live/gemini-2-0-flash-live", "name": "Gemini 2.0 Flash Live", "maker": "google", "family": "google/gemini-live", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini 2.0 Flash Live (gemini-2.0-flash-live-001) was a public Live preview released 2025-04-09 and shut down on 2025-12-09.", "what_it_is_for": "The changelog calls it a public preview of the Live API on Gemini 2.0 Flash.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-live/gemini-2-5-flash-live": { "id": "google/gemini-live/gemini-2-5-flash-live", "name": "Gemini 2.5 Flash Live", "maker": "google", "family": "google/gemini-live", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-12", "context_length": 131072, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "video", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 2.0 }, "summary_intro": "Gemini 2.5 Flash Live is the native-audio Live preview dated December 2025. It was still listed on 2026-10-15, with an earliest shutdown of 2026-11-17.", "what_it_is_for": "Google describes it as the Live API model for low-latency bidirectional voice and video, with native audio reasoning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-live/gemini-3-1-flash-live": { "id": "google/gemini-live/gemini-3-1-flash-live", "name": "Gemini 3.1 Flash Live", "maker": "google", "family": "google/gemini-live", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-11", "context_length": 131072, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 0.75, "completion_per_m_usd": 4.5 }, "summary_intro": "Gemini 3.1 Flash Live is a legacy Live API preview released 2026-03-11. On 2026-10-15 it was still listed, with an earliest shutdown of 2026-11-17.", "what_it_is_for": "Google tells developers to move voice agents to Gemini 3.8 Live. This preview still supports tools, search grounding, and thinking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-live/gemini-3-5-live-translate": { "id": "google/gemini-live/gemini-3-5-live-translate", "name": "Gemini 3.5 Live Translate", "maker": "google", "family": "google/gemini-live", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 131072, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": 3.5, "completion_per_m_usd": 21.0 }, "summary_intro": "Gemini 3.5 Live Translate is a preview speech-to-speech translation model. Google dates the release to June 2026 and had announced no shutdown as of 2026-10-15.", "what_it_is_for": "Google says it translates spoken conversations in real time, with audio out and a text transcript, across 70 or more languages on the models index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-live/gemini-3-8-live": { "id": "google/gemini-live/gemini-3-8-live", "name": "Gemini 3.8 Live", "maker": "google", "family": "google/gemini-live", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-15", "context_length": 131072, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 0.75, "completion_per_m_usd": 4.5 }, "summary_intro": "Gemini 3.8 Live is Google's default Live API model, released 2026-09-15 and active on 2026-10-15. It is closed-weights and streams text and audio.", "what_it_is_for": "Google recommends it for low-latency voice agents and real-time dialogue without reasoning-induced delays. It supports function calling, search grounding, and interleaved thinking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-live/gemini-3-8-live-extended-thinking": { "id": "google/gemini-live/gemini-3-8-live-extended-thinking", "name": "Gemini 3.8 Live Extended Thinking", "maker": "google", "family": "google/gemini-live", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-15", "context_length": 131072, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 0.75, "completion_per_m_usd": 4.5 }, "summary_intro": "Gemini 3.8 Live Extended Thinking is the high-reasoning Live model released with Gemini 3.8 Live on 2026-09-15. It was active on 2026-10-15.", "what_it_is_for": "Google recommends it when a voice session needs background reasoning and asynchronous tool calls while audio keeps streaming.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-nano/gemini-nano": { "id": "google/gemini-nano/gemini-nano", "name": "Gemini Nano", "maker": "google", "family": "google/gemini-nano", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-12-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "On-device models distilled from larger Gemini models and 4-bit quantized. Nano-1 is 1.8B parameters and Nano-2 is 3.25B parameters.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini Nano is Google's on-device Gemini 1.0 line. The December 2023 technical report defines Nano-1 at 1.8B parameters and Nano-2 at 3.25B. Collected 2026-10-15.", "what_it_is_for": "Google built Nano for on-device tasks such as summarization, reading comprehension, and text completion, with multimodal ability relative to its size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-omni/gemini-omni-1-1-flash": { "id": "google/gemini-omni/gemini-omni-1-1-flash", "name": "Gemini Omni Flash", "maker": "google", "family": "google/gemini-omni", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-27", "context_length": 1048576, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": 1.5, "completion_per_m_usd": 9.0 }, "summary_intro": "Gemini Omni Flash (gemini-omni-1.1-flash) is Google's Gemini video model, stable on 2026-08-27 and active on 2026-10-15. It is not Veo. The June preview id has an earliest shutdown of 2026-10-22.", "what_it_is_for": "Google describes fast conversational video generation and editing, with native audio, extension, upscaling, and interpolation through the Interactions API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-robotics/gemini-robotics": { "id": "google/gemini-robotics/gemini-robotics", "name": "Gemini Robotics", "maker": "google", "family": "google/gemini-robotics", "status": "preview", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-03-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Announced as a vision-language-action model built on Gemini 2.0, with physical actions as an output.", "modalities": { "input": [ "text", "image" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini Robotics is the March 2025 vision-language-action model based on Gemini 2.0. Later public write-ups center on Robotics 1.5 and Robotics 2. Collected 2026-10-15.", "what_it_is_for": "Google introduced it to turn vision and language into robot actions, separate from the embodied-reasoning model that does not directly output motor commands.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-robotics/gemini-robotics-1-5": { "id": "google/gemini-robotics/gemini-robotics-1-5", "name": "Gemini Robotics 1.5", "maker": "google", "family": "google/gemini-robotics", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-25", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text", "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini Robotics 1.5 is Google's September 2025 vision-language-action model. DeepMind lists it as a private partner preview, not as a public API id. Collected 2026-10-15.", "what_it_is_for": "It turns images and instructions into robot motor commands and can show its plan in text. It is the action model paired with Robotics-ER 1.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-robotics/gemini-robotics-2": { "id": "google/gemini-robotics/gemini-robotics-2", "name": "Gemini Robotics 2", "maker": "google", "family": "google/gemini-robotics", "status": "preview", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini Robotics 2 is the vision-language-action model named on DeepMind's robotics index as of 2026-10-15. A public API id, release day, and token limits were not on the pages fetched.", "what_it_is_for": "DeepMind describes it as the intelligence layer that turns vision and language into motor control for many robot bodies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-robotics/gemini-robotics-er-1-5": { "id": "google/gemini-robotics/gemini-robotics-er-1-5", "name": "Gemini Robotics-ER 1.5", "maker": "google", "family": "google/gemini-robotics", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini Robotics-ER 1.5 was the embodied-reasoning API preview released 2025-09-25. The changelog says it shut down on 2026-04-30.", "what_it_is_for": "Google described it as the vision-language model that plans multi-step robotics tasks and calls tools, paired with the Robotics 1.5 action model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-robotics/gemini-robotics-er-1-6": { "id": "google/gemini-robotics/gemini-robotics-er-1-6", "name": "Gemini Robotics-ER 1.6", "maker": "google", "family": "google/gemini-robotics", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-14", "context_length": 131072, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini Robotics-ER 1.6 is a robotics vision-language preview. Deprecations date the release to 2026-04-14 and list an earliest shutdown of 2026-08-31, but the 2026-10-13 models index still listed it.", "what_it_is_for": "Google says it interprets visual scenes, reasons spatially, and plans robot actions from language, including instrument reading.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-robotics/gemini-robotics-er-2": { "id": "google/gemini-robotics/gemini-robotics-er-2", "name": "Gemini Robotics-ER 2", "maker": "google", "family": "google/gemini-robotics", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-30", "context_length": 131072, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 5.0 }, "summary_intro": "Gemini Robotics-ER 2 is the embodied-reasoning preview for robots, documented in July 2026 and still listed on 2026-10-15. The API id is gemini-robotics-er-2-preview.", "what_it_is_for": "Google says it does spatial reasoning, video understanding, tool orchestration, and multi-robot coordination from text, image, video, and audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-robotics/gemini-robotics-er-2-streaming": { "id": "google/gemini-robotics/gemini-robotics-er-2-streaming", "name": "Gemini Robotics-ER 2 Streaming", "maker": "google", "family": "google/gemini-robotics", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-30", "context_length": 131072, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 5.0 }, "summary_intro": "Gemini Robotics-ER 2 Streaming is a separate Live API preview for real-time robotics text, documented with ER 2 in July 2026 and active as a preview on 2026-10-15.", "what_it_is_for": "Google describes it as the streaming embodied-reasoning endpoint, with bidirectional Live API function calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-robotics/gemini-robotics-on-device": { "id": "google/gemini-robotics/gemini-robotics-on-device", "name": "Gemini Robotics On-Device", "maker": "google", "family": "google/gemini-robotics", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-01", "context_length": 1088, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text", "action" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini Robotics On-Device is the local vision-language-action model. DeepMind's page lists a private preview, an input budget of 1,088 tokens, and a model card updated 2025-07-01.", "what_it_is_for": "It runs on the robot for low-latency dexterous manipulation and is the first Gemini Robotics VLA Google said testers could fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-robotics/gemini-robotics-on-device-2": { "id": "google/gemini-robotics/gemini-robotics-on-device-2", "name": "Gemini Robotics On-Device 2", "maker": "google", "family": "google/gemini-robotics", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Model card says it is an on-device VLA based on Gemini Robotics 1.5 and on-device Gemma models.", "modalities": { "input": [ "image", "text", "action" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemini Robotics On-Device 2 is the July 2026 local robotics model. The model card says it is for trusted testers and is based on Gemini Robotics 1.5 plus on-device Gemma.", "what_it_is_for": "DeepMind describes a local VLA that adapts to new robots, objects, and scenes without a network.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-transcribe/gemini-3-5-transcribe": { "id": "google/gemini-transcribe/gemini-3-5-transcribe", "name": "Gemini 3.5 Transcribe", "maker": "google", "family": "google/gemini-transcribe", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 12.0 }, "summary_intro": "Gemini 3.5 Transcribe is Google's speech-to-text Gemini model, released in August 2026 and active on 2026-10-15. It has unary and live endpoints.", "what_it_is_for": "Google built it for low-latency transcription with utterance language detection, speaker diarization, word timestamps, smart formatting, and vocabulary biasing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-tts/gemini-2-5-flash-tts": { "id": "google/gemini-tts/gemini-2-5-flash-tts", "name": "Gemini 2.5 Flash TTS", "maker": "google", "family": "google/gemini-tts", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-20", "context_length": 8192, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 10.0 }, "summary_intro": "Gemini 2.5 Flash TTS is a preview speech model released 2025-05-20. It was still listed on 2026-10-15 with an earliest shutdown of 2026-11-17.", "what_it_is_for": "Google describes it as fast, low-cost speech with control over style and pacing for assistants and narration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-tts/gemini-2-5-pro-tts": { "id": "google/gemini-tts/gemini-2-5-pro-tts", "name": "Gemini 2.5 Pro TTS", "maker": "google", "family": "google/gemini-tts", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-20", "context_length": 8192, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 20.0 }, "summary_intro": "Gemini 2.5 Pro TTS is the higher-fidelity 2.5 speech preview, released 2025-05-20 and still listed on 2026-10-15 with an earliest shutdown of 2026-11-17.", "what_it_is_for": "Google aims it at podcasts, audiobooks, and other structured long-form speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-tts/gemini-3-1-flash-tts": { "id": "google/gemini-tts/gemini-3-1-flash-tts", "name": "Gemini 3.1 Flash TTS", "maker": "google", "family": "google/gemini-tts", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-26", "context_length": 8192, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 20.0 }, "summary_intro": "Gemini 3.1 Flash TTS is a legacy preview released 2026-02-26. On 2026-10-15 Google still listed it and set an earliest shutdown of 2026-11-17.", "what_it_is_for": "It generates steered speech with expressive tags. Google tells new workloads to use the Gemini 3.8 TTS models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-tts/gemini-3-8-flash-lite-tts": { "id": "google/gemini-tts/gemini-3-8-flash-lite-tts", "name": "Gemini 3.8 Flash-Lite TTS", "maker": "google", "family": "google/gemini-tts", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-22", "context_length": 8192, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 6.0 }, "summary_intro": "Gemini 3.8 Flash-Lite TTS is the lower-cost speech model released 2026-09-22. It was active on 2026-10-15.", "what_it_is_for": "Google positions it for high-volume production, voice-agent cascades, read-aloud, and voice replication.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemini-tts/gemini-3-8-flash-tts": { "id": "google/gemini-tts/gemini-3-8-flash-tts", "name": "Gemini 3.8 Flash TTS", "maker": "google", "family": "google/gemini-tts", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-22", "context_length": 8192, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 9.0 }, "summary_intro": "Gemini 3.8 Flash TTS is Google's flagship speech model, released 2026-09-22 and active on 2026-10-15. It is closed and outputs audio from text.", "what_it_is_for": "Google aims it at studio narration, multi-speaker dialogue, dialects, and expressive acting, and names it as the replacement for Gemini 3.1 Flash TTS.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-2-27b": { "id": "google/gemma/gemma-2-27b", "name": "Gemma 2 27B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-06-27", "context_length": 8192, "max_output_tokens": null, "parameters": "1,180,237,824 embedding + 26,047,480,320 non-embedding (27,227,718,144 total)", "architecture": "Decoder-only transformer, grouped-query attention, GeGLU, local and global attention, sliding window 4096, global span 8192, vocabulary 256128. 46 layers, 32 query heads, 16 KV heads, head size 128, feedforward 73728, d_model 4608. Trained with 13 trillion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind open-weights Gemma 2 text model. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Text generation, summarization, and extraction.", "ratings": { "empirical_100": 61, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-2-2b": { "id": "google/gemma/gemma-2-2b", "name": "Gemma 2 2B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-07-31", "context_length": 8192, "max_output_tokens": null, "parameters": "590,118,912 embedding + 2,024,517,888 non-embedding (2,614,636,800 total)", "architecture": "Decoder-only transformer, grouped-query attention, GeGLU, local and global attention, sliding window 4096, global span 8192, vocabulary 256128. 26 layers, 8 query heads, 4 KV heads, head size 256, feedforward 18432, d_model 2304. Trained with 2 trillion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind open-weights Gemma 2 text model. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Text generation, summarization, and extraction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-2-2b-jpn": { "id": "google/gemma/gemma-2-2b-jpn", "name": "Gemma 2 JPN 2B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-10-03", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Japanese-focused Gemma 2 2B variant. Architecture details were not re-opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open-weights Japanese Gemma 2 2B model, named on the official Gemma releases index. Collected 2026-10-15.", "what_it_is_for": "Japanese text tasks. The releases index is the source used for identity and date.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-2-9b": { "id": "google/gemma/gemma-2-9b", "name": "Gemma 2 9B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-06-27", "context_length": 8192, "max_output_tokens": null, "parameters": "917,962,752 embedding + 8,324,201,984 non-embedding (9,242,164,736 total)", "architecture": "Decoder-only transformer, grouped-query attention, GeGLU, local and global attention, sliding window 4096, global span 8192, vocabulary 256128. 42 layers, 16 query heads, 8 KV heads, head size 256, feedforward 28672, d_model 3584. Trained with 8 trillion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind open-weights Gemma 2 text model. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Text generation, summarization, and extraction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-2b": { "id": "google/gemma/gemma-2b", "name": "Gemma 2B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-02-21", "context_length": null, "max_output_tokens": null, "parameters": "2.2B", "architecture": "Original Gemma decoder-only transformer. The Agent Platform table lists Gemma 2B as 2.2 billion parameters and Gemma 7B as 7 billion.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Original Google DeepMind Gemma open-weights text model from February 2024, still published, with a 1.1 update in April 2024. Collected 2026-10-15.", "what_it_is_for": "Text generation, summarization, and extraction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-3-12b": { "id": "google/gemma/gemma-3-12b", "name": "Gemma 3 12B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-03-10", "context_length": 131072, "max_output_tokens": null, "parameters": "417M vision + 1,012M embedding + 10,759M non-embedding", "architecture": "Decoder-only transformer with a SigLIP vision encoder. Technical report Table 1. Context 128K.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind open-weights Gemma 3 model. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Text generation and, on the 4B, 12B, and 27B sizes, image understanding.", "ratings": { "empirical_100": 55, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-3-1b": { "id": "google/gemma/gemma-3-1b", "name": "Gemma 3 1B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-03-10", "context_length": 32768, "max_output_tokens": null, "parameters": "302M embedding + 698M non-embedding", "architecture": "Decoder-only transformer. Technical report Table 1 gives 302M embedding and 698M non-embedding parameters and no vision encoder. Context is 32K, unlike the larger Gemma 3 sizes.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind open-weights Gemma 3 model. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Text generation and, on the 4B, 12B, and 27B sizes, image understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-3-270m": { "id": "google/gemma/gemma-3-270m", "name": "Gemma 3 270M", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-08-14", "context_length": 32768, "max_output_tokens": null, "parameters": "270M", "architecture": "Decoder-only Gemma 3 text model. The Hugging Face model card says this size was trained with 6 trillion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind open-weights Gemma 3 model. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Text generation and, on the 4B, 12B, and 27B sizes, image understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-3-27b": { "id": "google/gemma/gemma-3-27b", "name": "Gemma 3 27B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-03-10", "context_length": 131072, "max_output_tokens": null, "parameters": "417M vision + 1,416M embedding + 25,600M non-embedding", "architecture": "Largest Gemma 3 dense model. SigLIP vision encoder. Trained with 14 trillion tokens per the model card. Context 128K.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind open-weights Gemma 3 model. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Text generation and, on the 4B, 12B, and 27B sizes, image understanding.", "ratings": { "empirical_100": 52, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-3-4b": { "id": "google/gemma/gemma-3-4b", "name": "Gemma 3 4B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-03-10", "context_length": 131072, "max_output_tokens": null, "parameters": "417M vision + 675M embedding + 3,209M non-embedding", "architecture": "Decoder-only transformer with a SigLIP vision encoder. Technical report Table 1. Context 128K. Vocabulary 256k entries.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind open-weights Gemma 3 model. Collected 2026-10-15. Weights remain published.", "what_it_is_for": "Text generation and, on the 4B, 12B, and 27B sizes, image understanding.", "ratings": { "empirical_100": 34, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-3n-e2b": { "id": "google/gemma/gemma-3n-e2b", "name": "Gemma 3n E2B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-06-26", "context_length": 32768, "max_output_tokens": null, "parameters": "2B effective", "architecture": "MatFormer nested transformer with per-layer embeddings, selective parameter activation, and on-device audio and MobileNet-v5 vision encoders. 5B raw parameters, per the Gemma 3n developer guide.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind on-device multimodal Gemma 3n model. Collected 2026-10-15. Open weights, active.", "what_it_is_for": "Text generation from text, image, audio, and video on phones and laptops, using an effective parameter budget smaller than the raw count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-3n-e4b": { "id": "google/gemma/gemma-3n-e4b", "name": "Gemma 3n E4B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-06-26", "context_length": 32768, "max_output_tokens": null, "parameters": "4B effective", "architecture": "MatFormer nested transformer with per-layer embeddings, selective parameter activation, and on-device audio and MobileNet-v5 vision encoders. 8B raw parameters, per the Gemma 3n developer guide.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind on-device multimodal Gemma 3n model. Collected 2026-10-15. Open weights, active.", "what_it_is_for": "Text generation from text, image, audio, and video on phones and laptops, using an effective parameter budget smaller than the raw count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-4-12b": { "id": "google/gemma/gemma-4-12b", "name": "Gemma 4 12B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-06-03", "context_length": 262144, "max_output_tokens": null, "parameters": "11.95B", "architecture": "Encoder-free unified dense decoder. Raw image patches and audio waveforms are projected with linear layers. 48 layers, sliding window 1024, vocabulary 262K. No separate vision or audio encoder.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind open-weights multimodal model in the Gemma 4 family. Collected 2026-10-15. Status is active. License is Apache 2.0.", "what_it_is_for": "General reasoning, coding, and agentic text generation, with image input and, on some sizes, audio input. The maker positions the family for on-device through server deployment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-4-26b-a4b": { "id": "google/gemma/gemma-4-26b-a4b", "name": "Gemma 4 26B A4B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-03-31", "context_length": 262144, "max_output_tokens": null, "parameters": "25.2B total, 3.8B active", "architecture": "Mixture-of-experts decoder. 30 layers, sliding window 1024, vocabulary 262K, 8 active experts of 128 plus 1 shared expert. About 550M vision encoder. No audio encoder.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind open-weights multimodal model in the Gemma 4 family. Collected 2026-10-15. Status is active. License is Apache 2.0.", "what_it_is_for": "General reasoning, coding, and agentic text generation with text and image input. This MoE size has no audio encoder. Active parameters are about 3.8B.", "ratings": { "empirical_100": 42, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-4-31b": { "id": "google/gemma/gemma-4-31b", "name": "Gemma 4 31B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-03-31", "context_length": 262144, "max_output_tokens": null, "parameters": "30.7B", "architecture": "Dense decoder-only transformer. 60 layers, sliding window 1024, vocabulary 262K, about 550M vision encoder. No audio.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind open-weights multimodal model in the Gemma 4 family. Collected 2026-10-15. Status is active. License is Apache 2.0.", "what_it_is_for": "General reasoning, coding, and agentic text generation with text and image input. This 31B size has no audio encoder. The maker positions it for large servers.", "ratings": { "empirical_100": 39, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-4-e2b": { "id": "google/gemma/gemma-4-e2b", "name": "Gemma 4 E2B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-03-31", "context_length": 131072, "max_output_tokens": null, "parameters": "2.3B effective (5.1B with embeddings)", "architecture": "Dense decoder-only transformer with hybrid sliding-window and global attention, per-layer embeddings, p-RoPE, about 150M vision encoder and about 300M audio encoder. 35 layers, sliding window 512, vocabulary 262K.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind open-weights multimodal model in the Gemma 4 family. Collected 2026-10-15. Status is active. License is Apache 2.0.", "what_it_is_for": "General reasoning, coding, and agentic text generation, with image input and, on some sizes, audio input. The maker positions the family for on-device through server deployment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-4-e4b": { "id": "google/gemma/gemma-4-e4b", "name": "Gemma 4 E4B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-03-31", "context_length": 131072, "max_output_tokens": null, "parameters": "4.5B effective (8B with embeddings)", "architecture": "Dense decoder-only transformer with hybrid attention and per-layer embeddings. 42 layers, sliding window 512, vocabulary 262K, about 150M vision encoder and about 300M audio encoder.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind open-weights multimodal model in the Gemma 4 family. Collected 2026-10-15. Status is active. License is Apache 2.0.", "what_it_is_for": "General reasoning, coding, and agentic text generation, with image input and, on some sizes, audio input. The maker positions the family for on-device through server deployment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma/gemma-7b": { "id": "google/gemma/gemma-7b", "name": "Gemma 7B", "maker": "google", "family": "google/gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-02-21", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Original Gemma decoder-only transformer. The Agent Platform table lists Gemma 2B as 2.2 billion parameters and Gemma 7B as 7 billion.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Original Google DeepMind Gemma open-weights text model from February 2024, still published, with a 1.1 update in April 2024. Collected 2026-10-15.", "what_it_is_for": "Text generation, summarization, and extraction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma-aps/gemma-aps-2b": { "id": "google/gemma-aps/gemma-aps-2b", "name": "Gemma-APS 2B", "maker": "google", "family": "google/gemma-aps", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-10-15", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "2B size. Architecture was not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google Gemma-APS model named on the Gemma releases index. Collected 2026-10-15.", "what_it_is_for": "The releases index names the sizes. The method page was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/gemma-aps/gemma-aps-7b": { "id": "google/gemma-aps/gemma-aps-7b", "name": "Gemma-APS 7B", "maker": "google", "family": "google/gemma-aps", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-10-15", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "7B size. Architecture was not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google Gemma-APS model named on the Gemma releases index. Collected 2026-10-15.", "what_it_is_for": "The releases index names the sizes. The method page was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/genie/genie-3": { "id": "google/genie/genie-3", "name": "Genie 3", "maker": "google", "family": "google/genie", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "World model. The DeepMind models index describes Genie 3 as a frontier world model. Specs were not opened.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind world model Genie 3, listed on the official models index. Collected 2026-10-15. Detailed specs were not opened.", "what_it_is_for": "Simulate environments and generate interactive worlds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/imagen/imagen-2": { "id": "google/imagen/imagen-2", "name": "Imagen 2", "maker": "google", "family": "google/imagen", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Product generation before Imagen 3. Parameter count was not published on the pages used.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google text-to-image model sold as Imagen 2 for generation and editing on Vertex AI. Collected 2026-10-15. Treated as a legacy product.", "what_it_is_for": "Generate and edit images from text, including mask edits.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/imagen/imagen-3": { "id": "google/imagen/imagen-3", "name": "Imagen 3", "maker": "google", "family": "google/imagen", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Text-to-image model. Editing and customization were separate Imagen 3 capabilities on Vertex.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google Imagen 3 image model, previously generally available on Vertex AI. Collected 2026-10-15. Treated as retired based on second-hand deprecation reports.", "what_it_is_for": "Text-to-image generation, plus editing and customization on the Vertex Imagen 3 editing model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/imagen/imagen-4": { "id": "google/imagen/imagen-4", "name": "Imagen 4", "maker": "google", "family": "google/imagen", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-05-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion text-to-image model, per the Imagen 4 model card, which covers the Imagen 4 family.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.04 }, "summary_intro": "Google Imagen 4 text-to-image model. Announced May 20, 2025. Collected 2026-10-15. The endpoint is treated as retired after the published August 17, 2026 Gemini API shutdown.", "what_it_is_for": "High-resolution text-to-image generation, with a Fast tier for latency and an Ultra tier for prompt adherence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/imagen/imagen-4-fast": { "id": "google/imagen/imagen-4-fast", "name": "Imagen 4 Fast", "maker": "google", "family": "google/imagen", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-05-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion text-to-image model, per the Imagen 4 model card, which covers the Imagen 4 family.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.02 }, "summary_intro": "Google Imagen 4 text-to-image model. Announced May 20, 2025. Collected 2026-10-15. The endpoint is treated as retired after the published August 17, 2026 Gemini API shutdown.", "what_it_is_for": "High-resolution text-to-image generation, with a Fast tier for latency and an Ultra tier for prompt adherence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/imagen/imagen-4-ultra": { "id": "google/imagen/imagen-4-ultra", "name": "Imagen 4 Ultra", "maker": "google", "family": "google/imagen", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-05-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion text-to-image model, per the Imagen 4 model card, which covers the Imagen 4 family.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.06 }, "summary_intro": "Google Imagen 4 text-to-image model. Announced May 20, 2025. Collected 2026-10-15. The endpoint is treated as retired after the published August 17, 2026 Gemini API shutdown.", "what_it_is_for": "High-resolution text-to-image generation, with a Fast tier for latency and an Ultra tier for prompt adherence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/lyria/lyria-2": { "id": "google/lyria/lyria-2", "name": "Lyria 2", "maker": "google", "family": "google/lyria", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Music generation model. API id lyria-002.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.06 }, "summary_intro": "Google Lyria 2 music model on Vertex AI, model id lyria-002. Collected 2026-10-15. Instrumental clips from text.", "what_it_is_for": "Generate instrumental music from an English text prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/lyria/lyria-3": { "id": "google/lyria/lyria-3", "name": "Lyria 3", "maker": "google", "family": "google/lyria", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Music generation system. Model card says text in, and music audio plus lyric text out.", "modalities": { "input": [ "text", "image" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.04 }, "summary_intro": "Google Lyria 3 music model for clips up to 30 seconds, with vocals. Model card published February 18, 2026. Vertex public preview April 7, 2026. Collected 2026-10-15.", "what_it_is_for": "Short music clips from text or an image, including vocals and lyrics.", "ratings": { "empirical_100": 32, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/lyria/lyria-3-5": { "id": "google/lyria/lyria-3-5", "name": "Lyria 3.5", "maker": "google", "family": "google/lyria", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latest named Lyria music model on the DeepMind Lyria page.", "modalities": { "input": [ "text", "image" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google DeepMind's newest public Lyria music model as of the October 15, 2026 collection. Tracks up to three minutes, with vocals and image conditioning.", "what_it_is_for": "Music generation with stronger vocals, lyrics, and variable song length.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/lyria/lyria-3-pro": { "id": "google/lyria/lyria-3-pro", "name": "Lyria 3 Pro", "maker": "google", "family": "google/lyria", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Longer-form Lyria 3 music model.", "modalities": { "input": [ "text", "image" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.08 }, "summary_intro": "Google longer-form Lyria 3 model for songs up to three minutes. Vertex public preview on April 7, 2026. Collected 2026-10-15.", "what_it_is_for": "Full-song generation with vocals, structure, and optional image conditioning.", "ratings": { "empirical_100": 27, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/lyria/lyria-realtime": { "id": "google/lyria/lyria-realtime", "name": "Lyria RealTime", "maker": "google", "family": "google/lyria", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Real-time interactive music generation. Specs were not opened.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google real-time music model listed in the Lyria family. Collected 2026-10-15. Detailed specs were not opened.", "what_it_is_for": "Interactive, streaming music generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/magenta/magenta-realtime": { "id": "google/magenta/magenta-realtime", "name": "Magenta RealTime", "maker": "google", "family": "google/magenta", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Open music model that DeepMind describes as its Lyria open model.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google Magenta real-time open music model, listed by DeepMind as the open Lyria model. Collected 2026-10-15.", "what_it_is_for": "Real-time music generation as an open creative tool.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/medgemma/medgemma-27b": { "id": "google/medgemma/medgemma-27b", "name": "MedGemma 27B", "maker": "google", "family": "google/medgemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-05-20", "context_length": null, "max_output_tokens": null, "parameters": "27B", "architecture": "Gemma 3 27B medical variant. Agent Platform lists a text-only instruction-tuned version and an instruction-tuned version, with text and image input.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google 27B health model based on Gemma 3. Collected 2026-10-15.", "what_it_is_for": "Medical text and image comprehension on a larger checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/medgemma/medgemma-4b": { "id": "google/medgemma/medgemma-4b", "name": "MedGemma 4B", "maker": "google", "family": "google/medgemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-05-20", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Gemma 3 variant. Agent Platform lists pretrained and instruction-tuned 4B checkpoints with text and image input.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google health model based on Gemma 3 for medical text and image understanding. Collected 2026-10-15.", "what_it_is_for": "Medical text and image comprehension.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/medsiglip/medsiglip": { "id": "google/medsiglip/medsiglip", "name": "MedSigLIP", "maker": "google", "family": "google/medsiglip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "800M", "architecture": "Fine-tuned SigLIP variant. 800 million parameters. Text and image in, embedding out.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google medical embedding model, a SigLIP variant, listed with the Gemma open models on Agent Platform. Collected 2026-10-15.", "what_it_is_for": "Encode medical images and text into a shared embedding space.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/paligemma/paligemma-2-10b": { "id": "google/paligemma/paligemma-2-10b", "name": "PaliGemma 2 10B", "maker": "google", "family": "google/paligemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-12-05", "context_length": null, "max_output_tokens": null, "parameters": "10B", "architecture": "SigLIP-So400m vision encoder with Gemma 2. DeepMind says the sizes are trained at multiple resolutions.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open vision-language model combining SigLIP-So400m with Gemma 2. Collected 2026-10-15.", "what_it_is_for": "Image and short-video questions, captioning, and transfer fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/paligemma/paligemma-2-28b": { "id": "google/paligemma/paligemma-2-28b", "name": "PaliGemma 2 28B", "maker": "google", "family": "google/paligemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-12-05", "context_length": null, "max_output_tokens": null, "parameters": "28B", "architecture": "SigLIP-So400m vision encoder with Gemma 2. DeepMind says the sizes are trained at multiple resolutions.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open vision-language model combining SigLIP-So400m with Gemma 2. Collected 2026-10-15.", "what_it_is_for": "Image and short-video questions, captioning, and transfer fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/paligemma/paligemma-2-3b": { "id": "google/paligemma/paligemma-2-3b", "name": "PaliGemma 2 3B", "maker": "google", "family": "google/paligemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-12-05", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "SigLIP-So400m vision encoder with Gemma 2. DeepMind says the sizes are trained at multiple resolutions.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open vision-language model combining SigLIP-So400m with Gemma 2. Collected 2026-10-15.", "what_it_is_for": "Image and short-video questions, captioning, and transfer fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/paligemma/paligemma-3b": { "id": "google/paligemma/paligemma-3b", "name": "PaliGemma 3B", "maker": "google", "family": "google/paligemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-05-14", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "SigLIP vision encoder with a Gemma language model, per Google's Gemma docs and the Agent Platform table.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open vision-language model that pairs a vision encoder with Gemma. Collected 2026-10-15.", "what_it_is_for": "Image captioning and visual question answering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/palm/chat-bison": { "id": "google/palm/chat-bison", "name": "chat-bison", "maker": "google", "family": "google/palm", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PaLM 2 API model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Retired Google PaLM 2 API model chat-bison. Collected 2026-10-15.", "what_it_is_for": "Chat", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/palm/code-bison": { "id": "google/palm/code-bison", "name": "code-bison", "maker": "google", "family": "google/palm", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PaLM 2 API model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Retired Google PaLM 2 API model code-bison. Collected 2026-10-15.", "what_it_is_for": "Code generation", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/palm/code-gecko": { "id": "google/palm/code-gecko", "name": "code-gecko", "maker": "google", "family": "google/palm", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PaLM 2 API model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Retired Google PaLM 2 API model code-gecko. Collected 2026-10-15.", "what_it_is_for": "Code completion", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/palm/codechat-bison": { "id": "google/palm/codechat-bison", "name": "codechat-bison", "maker": "google", "family": "google/palm", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PaLM 2 API model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Retired Google PaLM 2 API model codechat-bison. Collected 2026-10-15.", "what_it_is_for": "Code chat", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/palm/palm": { "id": "google/palm/palm", "name": "PaLM", "maker": "google", "family": "google/palm", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pathways language model. Parameter count was not re-read from the paper in this pass.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google Research Pathways language model announced before PaLM 2. Collected 2026-10-15. Not a current API.", "what_it_is_for": "General language modeling research release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/palm/palm-2": { "id": "google/palm/palm-2", "name": "PaLM 2", "maker": "google", "family": "google/palm", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PaLM 2 size classes included Bison and Gecko on the PaLM developer docs. Unicorn was exposed as text-unicorn. A single parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google PaLM 2 language model family behind the retired Vertex and PaLM APIs. Collected 2026-10-15.", "what_it_is_for": "Text, chat, and code generation. Product endpoints are cataloged separately.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/palm/text-bison": { "id": "google/palm/text-bison", "name": "text-bison", "maker": "google", "family": "google/palm", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-05", "context_length": 8196, "max_output_tokens": 1024, "parameters": "", "architecture": "PaLM 2 API model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Retired Google PaLM 2 API model text-bison. Collected 2026-10-15.", "what_it_is_for": "Text generation", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/palm/text-unicorn": { "id": "google/palm/text-unicorn", "name": "text-unicorn", "maker": "google", "family": "google/palm", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PaLM 2 API model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Retired Google PaLM 2 API model text-unicorn. Collected 2026-10-15.", "what_it_is_for": "Larger text generation", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/recurrentgemma/recurrentgemma-9b": { "id": "google/recurrentgemma/recurrentgemma-9b", "name": "RecurrentGemma 9B", "maker": "google", "family": "google/recurrentgemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-06-11", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "RecurrentGemma. The releases index calls the 9B model a variant of the April 9, 2024 initial release.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open RecurrentGemma 9B model, named on the Gemma releases index. Collected 2026-10-15.", "what_it_is_for": "Text generation with a recurrent architecture. The architecture page was not opened, so the job is stated only at that level.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/shieldgemma/shieldgemma-2": { "id": "google/shieldgemma/shieldgemma-2", "name": "ShieldGemma 2", "maker": "google", "family": "google/shieldgemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-03-10", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "4B fine-tuned model on Gemma 3, per DeepMind and the Agent Platform table.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google 4B image-and-text safety classifier built on Gemma 3. Collected 2026-10-15.", "what_it_is_for": "Check synthetic and natural images, and text, against safety policies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/shieldgemma/shieldgemma-27b": { "id": "google/shieldgemma/shieldgemma-27b", "name": "ShieldGemma 27B", "maker": "google", "family": "google/shieldgemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-07-31", "context_length": null, "max_output_tokens": null, "parameters": "27B", "architecture": "Instruction-tuned classifier on Gemma 2. DeepMind lists ShieldGemma 1 at 2B, 9B, and 27B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open safety classifier built on Gemma 2. Collected 2026-10-15.", "what_it_is_for": "Score text inputs and outputs against defined safety policies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/shieldgemma/shieldgemma-2b": { "id": "google/shieldgemma/shieldgemma-2b", "name": "ShieldGemma 2B", "maker": "google", "family": "google/shieldgemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-07-31", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Instruction-tuned classifier on Gemma 2. DeepMind lists ShieldGemma 1 at 2B, 9B, and 27B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open safety classifier built on Gemma 2. Collected 2026-10-15.", "what_it_is_for": "Score text inputs and outputs against defined safety policies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/shieldgemma/shieldgemma-9b": { "id": "google/shieldgemma/shieldgemma-9b", "name": "ShieldGemma 9B", "maker": "google", "family": "google/shieldgemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2024-07-31", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Instruction-tuned classifier on Gemma 2. DeepMind lists ShieldGemma 1 at 2B, 9B, and 27B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open safety classifier built on Gemma 2. Collected 2026-10-15.", "what_it_is_for": "Score text inputs and outputs against defined safety policies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/t5gemma/t5gemma-2-1b-1b": { "id": "google/t5gemma/t5gemma-2-1b-1b", "name": "T5Gemma 2 1B-1B", "maker": "google", "family": "google/t5gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "1B-1B", "architecture": "Paired encoder-decoder size from the releases index. Total parameter count was not stated there.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google T5Gemma 2 encoder-decoder size named on the Gemma releases index. Collected 2026-10-15.", "what_it_is_for": "Generative tasks. Agent Platform says T5Gemma 2 suits question answering, summarization, and reasoning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/t5gemma/t5gemma-2-270m-270m": { "id": "google/t5gemma/t5gemma-2-270m-270m", "name": "T5Gemma 2 270M-270M", "maker": "google", "family": "google/t5gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "270M-270M", "architecture": "Paired encoder-decoder size from the releases index. Total parameter count was not stated there.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google T5Gemma 2 encoder-decoder size named on the Gemma releases index. Collected 2026-10-15.", "what_it_is_for": "Generative tasks. Agent Platform says T5Gemma 2 suits question answering, summarization, and reasoning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/t5gemma/t5gemma-2-4b-4b": { "id": "google/t5gemma/t5gemma-2-4b-4b", "name": "T5Gemma 2 4B-4B", "maker": "google", "family": "google/t5gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "4B-4B", "architecture": "Paired encoder-decoder size from the releases index. Total parameter count was not stated there.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google T5Gemma 2 encoder-decoder size named on the Gemma releases index. Collected 2026-10-15.", "what_it_is_for": "Generative tasks. Agent Platform says T5Gemma 2 suits question answering, summarization, and reasoning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/t5gemma/t5gemma-2b-2b": { "id": "google/t5gemma/t5gemma-2b-2b", "name": "T5Gemma 2B-2B", "maker": "google", "family": "google/t5gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-07-09", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "2B encoder and 2B decoder. Checkpoints include PrefixLM and UL2, pretrained and instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/t5gemma/t5gemma-9b-2b": { "id": "google/t5gemma/t5gemma-9b-2b", "name": "T5Gemma 9B-2B", "maker": "google", "family": "google/t5gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-07-09", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "9B encoder and 2B decoder. Checkpoints include PrefixLM and UL2, pretrained and instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/t5gemma/t5gemma-9b-9b": { "id": "google/t5gemma/t5gemma-9b-9b", "name": "T5Gemma 9B-9B", "maker": "google", "family": "google/t5gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-07-09", "context_length": null, "max_output_tokens": null, "parameters": "18B", "architecture": "9B encoder and 9B decoder. Checkpoints include PrefixLM and UL2, pretrained and instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/t5gemma/t5gemma-b-b": { "id": "google/t5gemma/t5gemma-b-b", "name": "T5Gemma B-B", "maker": "google", "family": "google/t5gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-07-09", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "B-B size, 0.6B total on the Agent Platform table. Checkpoints include PrefixLM and UL2, pretrained and instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/t5gemma/t5gemma-l-l": { "id": "google/t5gemma/t5gemma-l-l", "name": "T5Gemma L-L", "maker": "google", "family": "google/t5gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-07-09", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "L-L size, 1B total on the Agent Platform table. Checkpoints include PrefixLM and UL2, pretrained and instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/t5gemma/t5gemma-m-l": { "id": "google/t5gemma/t5gemma-m-l", "name": "T5Gemma M-L", "maker": "google", "family": "google/t5gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-07-09", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "M-L size, 2B total on the Agent Platform table. Checkpoints include PrefixLM and UL2, pretrained and instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/t5gemma/t5gemma-s-s": { "id": "google/t5gemma/t5gemma-s-s", "name": "T5Gemma S-S", "maker": "google", "family": "google/t5gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-07-09", "context_length": null, "max_output_tokens": null, "parameters": "0.3B", "architecture": "S-S size, 0.3B total on the Agent Platform table. Checkpoints include PrefixLM and UL2, pretrained and instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/t5gemma/t5gemma-xl-xl": { "id": "google/t5gemma/t5gemma-xl-xl", "name": "T5Gemma XL-XL", "maker": "google", "family": "google/t5gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-07-09", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "XL-XL size, 4B total on the Agent Platform table. Checkpoints include PrefixLM and UL2, pretrained and instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/translategemma/translategemma-12b": { "id": "google/translategemma/translategemma-12b", "name": "TranslateGemma 12B", "maker": "google", "family": "google/translategemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2026-01-15", "context_length": null, "max_output_tokens": null, "parameters": "12B", "architecture": "Size named on the Gemma releases index. Base architecture was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open translation model in the TranslateGemma family. Collected 2026-10-15.", "what_it_is_for": "Translation. Agent Platform says the family handles 55 languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/translategemma/translategemma-27b": { "id": "google/translategemma/translategemma-27b", "name": "TranslateGemma 27B", "maker": "google", "family": "google/translategemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2026-01-15", "context_length": null, "max_output_tokens": null, "parameters": "27B", "architecture": "Size named on the Gemma releases index. Base architecture was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open translation model in the TranslateGemma family. Collected 2026-10-15.", "what_it_is_for": "Translation. Agent Platform says the family handles 55 languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/translategemma/translategemma-4b": { "id": "google/translategemma/translategemma-4b", "name": "TranslateGemma 4B", "maker": "google", "family": "google/translategemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2026-01-15", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Size named on the Gemma releases index. Base architecture was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google open translation model in the TranslateGemma family. Collected 2026-10-15.", "what_it_is_for": "Translation. Agent Platform says the family handles 55 languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/txgemma/txgemma-27b": { "id": "google/txgemma/txgemma-27b", "name": "TxGemma 27B", "maker": "google", "family": "google/txgemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "27B", "architecture": "Gemma-based therapeutic model. 27B size on the Agent Platform table. Text in and text out.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google therapeutic open model in the TxGemma family. Collected 2026-10-15. Release date was not on the pages used.", "what_it_is_for": "Therapeutic classification, regression, generation, and reasoning, per Agent Platform.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/txgemma/txgemma-2b": { "id": "google/txgemma/txgemma-2b", "name": "TxGemma 2B", "maker": "google", "family": "google/txgemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Gemma-based therapeutic model. 2B size on the Agent Platform table. Text in and text out.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google therapeutic open model in the TxGemma family. Collected 2026-10-15. Release date was not on the pages used.", "what_it_is_for": "Therapeutic classification, regression, generation, and reasoning, per Agent Platform.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/txgemma/txgemma-9b": { "id": "google/txgemma/txgemma-9b", "name": "TxGemma 9B", "maker": "google", "family": "google/txgemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Gemma-based therapeutic model. 9B size on the Agent Platform table. Text in and text out.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google therapeutic open model in the TxGemma family. Collected 2026-10-15. Release date was not on the pages used.", "what_it_is_for": "Therapeutic classification, regression, generation, and reasoning, per Agent Platform.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/vaultgemma/vaultgemma": { "id": "google/vaultgemma/vaultgemma", "name": "VaultGemma", "maker": "google", "family": "google/vaultgemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-09-13", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "1B checkpoint. Architecture was not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google VaultGemma 1B open model named on the Gemma releases index. Collected 2026-10-15.", "what_it_is_for": "The releases index names the model and size. The purpose page was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/veo/veo-2": { "id": "google/veo/veo-2", "name": "Veo 2", "maker": "google", "family": "google/veo", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Earlier Veo video generation model. Architecture details were not copied.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Earlier Veo generation listed on Vertex model pages before Veo 3.1. Collected 2026-10-15.", "what_it_is_for": "Generate video from text and images. Veo 3 and later can generate audio with the video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/veo/veo-3": { "id": "google/veo/veo-3", "name": "Veo 3", "maker": "google", "family": "google/veo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Veo 3 video generation model with native audio, announced May 20, 2025. Architecture details were not copied from the model card PDF.", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Google video model with native audio, announced May 20, 2025. Later tiers are Veo 3.1. Collected 2026-10-15.", "what_it_is_for": "Generate video from text and images. Veo 3 and later can generate audio with the video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/veo/veo-3-1": { "id": "google/veo/veo-3-1", "name": "Veo 3.1", "maker": "google", "family": "google/veo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Veo 3.1 video generation model. Text and image in, video out, with sound, extension, reference images, and first and last frames, per the Agent Platform page.", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.4 }, "summary_intro": "Google's current leading Veo video model with native audio. Agent Platform documents veo-3.1-generate-001. Collected 2026-10-15.", "what_it_is_for": "Generate video from text and images. Veo 3 and later can generate audio with the video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/veo/veo-3-1-fast": { "id": "google/veo/veo-3-1-fast", "name": "Veo 3.1 Fast", "maker": "google", "family": "google/veo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lower-latency Veo 3.1 tier. Architecture was not separately published beyond the Veo 3.1 line.", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.1 }, "summary_intro": "Lower-latency Veo 3.1 tier. Agent Platform id veo-3.1-fast-generate-001. Collected 2026-10-15.", "what_it_is_for": "Generate video from text and images. Veo 3 and later can generate audio with the video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/veo/veo-3-1-lite": { "id": "google/veo/veo-3-1-lite", "name": "Veo 3.1 Lite", "maker": "google", "family": "google/veo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Veo 3.1 Lite is based on Veo 3 and generates video with audio from text or an image, per its model card.", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "Lower-cost Veo 3.1 tier. Model card published April 2026. Agent Platform lists veo-3.1-lite-generate-001. Collected 2026-10-15.", "what_it_is_for": "Generate video from text and images. Veo 3 and later can generate audio with the video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "google/veo/veo-3-fast": { "id": "google/veo/veo-3-fast", "name": "Veo 3 Fast", "maker": "google", "family": "google/veo", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-05-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lower-latency Veo 3 video model. Architecture details were not copied.", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lower-latency Veo 3 tier. Model id veo-3.0-fast-generate-001 appears on Vertex provisioned-throughput docs. Collected 2026-10-15.", "what_it_is_for": "Generate video from text and images. Veo 3 and later can generate audio with the video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/attitude/kb-bert-posneg-attitude": { "id": "gothenburg/attitude/kb-bert-posneg-attitude", "name": "KB-BERT Swedish pos/neg attitude", "maker": "gothenburg", "family": "gothenburg/attitude", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2025-06-27", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of KB/bert-base-swedish-cased (National Library of Sweden / KBLab). BERT-base config on the fetched checkpoints: hidden size 768, 12 layers, 12 attention heads, intermediate size 3072, vocabulary 50325, max_position_embeddings 512, float32. BertForTokenClassification. Config labels: B-NEG, B-POS, I-NEG, I-POS, O. transformers version recorded in the config is 4.51.3.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KB-BERT Swedish pos/neg attitude is a University of Gothenburg token classifier from Spr\u00e5kbanken Text. It fine-tunes KB/bert-base-swedish-cased to mark positive and negative attitude spans. Hub file created 2025-06-27. Collected 2026-10-15.", "what_it_is_for": "The card marks B-POS, I-POS, B-NEG, I-NEG, and O on Swedish text. The worked example is an ice-cream sentence with positive tokens on the object and modifier. The stated training domain is limited.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/cefr/kb-bert-swedish-cefr": { "id": "gothenburg/cefr/kb-bert-swedish-cefr", "name": "KB-BERT Swedish CEFR classification", "maker": "gothenburg", "family": "gothenburg/cefr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2025-05-25", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of KB/bert-base-swedish-cased (National Library of Sweden / KBLab). BERT-base config on the fetched checkpoints: hidden size 768, 12 layers, 12 attention heads, intermediate size 3072, vocabulary 50325, max_position_embeddings 512, float32. Sequence classification with five labels, LABEL_0 through LABEL_4. The card text says the levels are A1\u2013C1 and that C2 is excluded. The config does not name A1\u2013C1, so the index mapping is not in the file that was read.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KB-BERT Swedish CEFR classification is a University of Gothenburg model from Spr\u00e5kbanken Text, Computational SLA. It is a sequence-classification fine-tune of KB/bert-base-swedish-cased for illustrative CEFR levels of Swedish learner essays. Catalogue publication date 2025-05-25. Collected 2026-10-15.", "what_it_is_for": "The card says the model estimates a CEFR level for Swedish L2 essays and is not a high-stakes test. The card text covers A1 through C1 and says C2 is excluded.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/emotions/kb-bert-emotions-brighter": { "id": "gothenburg/emotions/kb-bert-emotions-brighter", "name": "KB-BERT Swedish BRIGHTER emotions", "maker": "gothenburg", "family": "gothenburg/emotions", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2025-11-04", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of KB/bert-base-swedish-cased (National Library of Sweden / KBLab). BERT-base config on the fetched checkpoints: hidden size 768, 12 layers, 12 attention heads, intermediate size 3072, vocabulary 50325, max_position_embeddings 512, float32. BertForSequenceClassification with problem_type multi_label_classification and six labels, LABEL_0 through LABEL_5. The card names Anger, Disgust, Fear, Joy, Sadness, and Surprise. That order is not stored in the config that was read.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KB-BERT Swedish BRIGHTER emotions is a University of Gothenburg multi-label emotion classifier released by Spr\u00e5kbanken Text on Hugging Face. It fine-tunes KB/bert-base-swedish-cased on the Swedish subset of the BRIGHTER emotion categories. Hub file created 2025-11-04. Collected 2026-10-15.", "what_it_is_for": "The card assigns the six BRIGHTER category names Anger, Disgust, Fear, Joy, Sadness, and Surprise. It is a multi-label sequence classifier. The printed example scores are an illustration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/flair/flair-pos-swedish": { "id": "gothenburg/flair/flair-pos-swedish", "name": "Flair Swedish POS tagger", "maker": "gothenburg", "family": "gothenburg/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2020-06-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flair POS and MSD tagger trained by Spr\u00e5kbanken Text. flair_eval is trained on SUC3 with Talbanken_SBX_dev as the dev set. flair_full is trained on SUC3 + Talbanken_SBX_test + SIC2 with the same dev set and was not evaluated. The page says the models use Flair's own embeddings by default and that a GPU is recommended. Each zip is 1.37 GB, dated 2020-06-18.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair Swedish POS tagger is a Spr\u00e5kbanken Text model at the University of Gothenburg. Eval and full zips are each 1.37 GB and dated 2020-06-18. Collected 2026-10-15.", "what_it_is_for": "The catalogue distributes two Flair taggers. flair_eval has published scores. flair_full adds Talbanken_SBX_test and SIC2 to training and was not scored. Default embeddings are Flair's own.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/kubhist/kubhist-diachronic-embeddings": { "id": "gothenburg/kubhist/kubhist-diachronic-embeddings", "name": "Kubhist diachronic embeddings", "maker": "gothenburg", "family": "gothenburg/kubhist", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2020-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Swedish historical word2vec and fastText embeddings, both skip-gram with negative sampling, trained by Simon Hengchen and Nina Tahmasebi at the University of Gothenburg / Spr\u00e5kbanken Text. Gensim defaults except dimension 100, minimum count 50, and seed 1830. The paper also states alpha 0.025, window 5, sample 0.001, min_alpha 0.0001, and negative 5. Two regimes: independently trained models, which are not aligned, and incremental models. Time bins are 20-year bins. The paper's wording is bins '1740\u20131750; 1760\u20131770; \u2026; 1880\u20131890, where e.g. 1740 covers 1740\u20261749'. Data: Kubhist 2, whole corpus, more than 5.5 billion tokens, OCR-cleaned (lowercased, digits removed, non-Swedish-alphabet characters removed, tokens of length 2 or shorter removed). The paper says the models were trained on 2020-09-15. Early bins are scarce.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kubhist diachronic embeddings are University of Gothenburg / Spr\u00e5kbanken Text historical Swedish word2vec and fastText models by Simon Hengchen and Nina Tahmasebi. The Zenodo v2 record is dated 2020-12-01. The catalogue zip is 15.13 GB. Collected 2026-10-15.", "what_it_is_for": "The models support diachronic study of Swedish newspaper vocabulary in Kubhist 2. Independently trained bins are not aligned. Incremental bins are the other regime. The authors warn that the embeddings carry historical bias.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/kubord/kubord-fasttext-aftonbladet-lemma": { "id": "gothenburg/kubord/kubord-fasttext-aftonbladet-lemma", "name": "Kubord fastText Aftonbladet lemma", "maker": "gothenburg", "family": "gothenburg/kubord", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2024-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "fastText trained with Gensim. The collection page states min_n 4, max_n 7, 20 epoker, dim 300, and lr .05. The same page says the models use the same underlying newspaper text as Kubord 2 and were developed with KBLab. Spr\u00e5kbanken Text distributes the files. Lemma and token are separate products. Year windows are versions of one newspaper product. This folder is the Aftonbladet lemma line. Newspaper text starts in 2010.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kubord fastText Aftonbladet lemma is a Spr\u00e5kbanken Text fastText embedding model from the University of Gothenburg catalogue, trained with KBLab on Aftonbladet at the lemma level. Two year windows are versions. The later file date is 2025-06-18. Collected 2026-10-15.", "what_it_is_for": "The collection page describes fastText models of modern newspaper text from the National Library of Sweden, on the same underlying data as Kubord 2. This folder is Aftonbladet, lemma.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/kubord/kubord-fasttext-aftonbladet-token": { "id": "gothenburg/kubord/kubord-fasttext-aftonbladet-token", "name": "Kubord fastText Aftonbladet token", "maker": "gothenburg", "family": "gothenburg/kubord", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2024-06-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "fastText trained with Gensim. The collection page states min_n 4, max_n 7, 20 epoker, dim 300, and lr .05. The same page says the models use the same underlying newspaper text as Kubord 2 and were developed with KBLab. Spr\u00e5kbanken Text distributes the files. Lemma and token are separate products. Year windows are versions of one newspaper product. This folder is the Aftonbladet token line. Newspaper text starts in 2010.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kubord fastText Aftonbladet token is a Spr\u00e5kbanken Text fastText embedding model from the University of Gothenburg catalogue, trained with KBLab on Aftonbladet at the token level. Two year windows are versions. The later file date is 2025-06-18. Collected 2026-10-15.", "what_it_is_for": "The collection page describes fastText models of modern newspaper text from the National Library of Sweden, on the same underlying data as Kubord 2. This folder is Aftonbladet, token.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/kubord/kubord-fasttext-dagens-nyheter-lemma": { "id": "gothenburg/kubord/kubord-fasttext-dagens-nyheter-lemma", "name": "Kubord fastText Dagens Nyheter lemma", "maker": "gothenburg", "family": "gothenburg/kubord", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2024-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "fastText trained with Gensim. The collection page states min_n 4, max_n 7, 20 epoker, dim 300, and lr .05. The same page says the models use the same underlying newspaper text as Kubord 2 and were developed with KBLab. Spr\u00e5kbanken Text distributes the files. Lemma and token are separate products. Year windows are versions of one newspaper product. This folder is the Dagens Nyheter lemma line. Newspaper text starts in 2010.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kubord fastText Dagens Nyheter lemma is a Spr\u00e5kbanken Text fastText embedding model from the University of Gothenburg catalogue, trained with KBLab on Dagens Nyheter at the lemma level. Two year windows are versions. The later file date is 2025-06-18. Collected 2026-10-15.", "what_it_is_for": "The collection page describes fastText models of modern newspaper text from the National Library of Sweden, on the same underlying data as Kubord 2. This folder is Dagens Nyheter, lemma.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/kubord/kubord-fasttext-dagens-nyheter-token": { "id": "gothenburg/kubord/kubord-fasttext-dagens-nyheter-token", "name": "Kubord fastText Dagens Nyheter token", "maker": "gothenburg", "family": "gothenburg/kubord", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2024-06-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "fastText trained with Gensim. The collection page states min_n 4, max_n 7, 20 epoker, dim 300, and lr .05. The same page says the models use the same underlying newspaper text as Kubord 2 and were developed with KBLab. Spr\u00e5kbanken Text distributes the files. Lemma and token are separate products. Year windows are versions of one newspaper product. This folder is the Dagens Nyheter token line. Newspaper text starts in 2010.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kubord fastText Dagens Nyheter token is a Spr\u00e5kbanken Text fastText embedding model from the University of Gothenburg catalogue, trained with KBLab on Dagens Nyheter at the token level. Two year windows are versions. The later file date is 2025-06-18. Collected 2026-10-15.", "what_it_is_for": "The collection page describes fastText models of modern newspaper text from the National Library of Sweden, on the same underlying data as Kubord 2. This folder is Dagens Nyheter, token.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/kubord/kubord-fasttext-goteborgsposten-lemma": { "id": "gothenburg/kubord/kubord-fasttext-goteborgsposten-lemma", "name": "Kubord fastText G\u00f6teborgsposten lemma", "maker": "gothenburg", "family": "gothenburg/kubord", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2024-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "fastText trained with Gensim. The collection page states min_n 4, max_n 7, 20 epoker, dim 300, and lr .05. The same page says the models use the same underlying newspaper text as Kubord 2 and were developed with KBLab. Spr\u00e5kbanken Text distributes the files. Lemma and token are separate products. Year windows are versions of one newspaper product. This folder is the G\u00f6teborgsposten lemma line. Newspaper text starts in 2013.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kubord fastText G\u00f6teborgsposten lemma is a Spr\u00e5kbanken Text fastText embedding model from the University of Gothenburg catalogue, trained with KBLab on G\u00f6teborgsposten at the lemma level. Two year windows are versions. The later file date is 2025-06-18. Collected 2026-10-15.", "what_it_is_for": "The collection page describes fastText models of modern newspaper text from the National Library of Sweden, on the same underlying data as Kubord 2. This folder is G\u00f6teborgsposten, lemma.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/kubord/kubord-fasttext-goteborgsposten-token": { "id": "gothenburg/kubord/kubord-fasttext-goteborgsposten-token", "name": "Kubord fastText G\u00f6teborgsposten token", "maker": "gothenburg", "family": "gothenburg/kubord", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2024-06-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "fastText trained with Gensim. The collection page states min_n 4, max_n 7, 20 epoker, dim 300, and lr .05. The same page says the models use the same underlying newspaper text as Kubord 2 and were developed with KBLab. Spr\u00e5kbanken Text distributes the files. Lemma and token are separate products. Year windows are versions of one newspaper product. This folder is the G\u00f6teborgsposten token line. Newspaper text starts in 2013.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kubord fastText G\u00f6teborgsposten token is a Spr\u00e5kbanken Text fastText embedding model from the University of Gothenburg catalogue, trained with KBLab on G\u00f6teborgsposten at the token level. Two year windows are versions. The later file date is 2025-06-18. Collected 2026-10-15.", "what_it_is_for": "The collection page describes fastText models of modern newspaper text from the National Library of Sweden, on the same underlying data as Kubord 2. This folder is G\u00f6teborgsposten, token.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/marmot/marmot-pos-swedish": { "id": "gothenburg/marmot/marmot-pos-swedish", "name": "Marmot Swedish POS tagger", "maker": "gothenburg", "family": "gothenburg/marmot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2020-06-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Marmot POS and MSD tagger trained by Spr\u00e5kbanken Text. marmot_eval is trained on SUC3 and Talbanken_SBX_dev, with SALDO as a dictionary. marmot_full is trained on SUC3 + Talbanken_SBX_test + Talbanken_SBX_dev + SIC2, was not evaluated, and is expected to be at least as good as marmot_eval. Files dated 2020-06-29: marmot_eval.marmot 108.59 MB, marmot_full.marmot 113.41 MB, saldo_marmot.txt 46.33 MB. The page says the tagger is somewhat worse than state-of-the-art neural models, faster, and does not need a GPU.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marmot Swedish POS tagger is a Spr\u00e5kbanken Text model at the University of Gothenburg. Eval and full files are dated 2020-06-29. Collected 2026-10-15.", "what_it_is_for": "The catalogue distributes a Marmot tagger with SALDO as a dictionary. marmot_eval has published scores. marmot_full was not scored. The page points users who want speed without a GPU toward this tagger.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/pi-detection/kb-bert-pi-basic": { "id": "gothenburg/pi-detection/kb-bert-pi-basic", "name": "KB-BERT Swedish PI detection (basic)", "maker": "gothenburg", "family": "gothenburg/pi-detection", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2025-05-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of KB/bert-base-swedish-cased (National Library of Sweden / KBLab). BERT-base config on the fetched checkpoints: hidden size 768, 12 layers, 12 attention heads, intermediate size 3072, vocabulary 50325, max_position_embeddings 512, float32. Token classification. Labels: O and S.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KB-BERT Swedish PI detection (basic) is a University of Gothenburg model released by Spr\u00e5kbanken Text. It is a GPL-3.0 token-classification fine-tune of KB/bert-base-swedish-cased for Swedish personal-information detection. Weights are public on Hugging Face as sbx/KB-bert-swedish_PI-detection-basic. Collected 2026-10-15.", "what_it_is_for": "The catalogue says the model detects personal information, especially in learner essays, and that a person must stay in the loop. It is part of the Mormor Karl pseudonymization work and is one of the six annotation settings in the SBX-PI Sparv plugin (basic, basic_iob, general, general_iob, detailed, detailed_iob).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/pi-detection/kb-bert-pi-basic-iob": { "id": "gothenburg/pi-detection/kb-bert-pi-basic-iob", "name": "KB-BERT Swedish PI detection (basic IOB)", "maker": "gothenburg", "family": "gothenburg/pi-detection", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2025-05-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of KB/bert-base-swedish-cased (National Library of Sweden / KBLab). BERT-base config on the fetched checkpoints: hidden size 768, 12 layers, 12 attention heads, intermediate size 3072, vocabulary 50325, max_position_embeddings 512, float32. Token classification. Labels: B, I, and O. The catalogue calls this beginning versus inside of PI, not a per-class IOB scheme.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KB-BERT Swedish PI detection (basic IOB) is a University of Gothenburg model released by Spr\u00e5kbanken Text. It is a GPL-3.0 token-classification fine-tune of KB/bert-base-swedish-cased for Swedish personal-information detection. Weights are public on Hugging Face as sbx/KB-bert-swedish_PI-detection-basic-iob. Collected 2026-10-15.", "what_it_is_for": "The catalogue says the model detects personal information, especially in learner essays, and that a person must stay in the loop. It is part of the Mormor Karl pseudonymization work and is one of the six annotation settings in the SBX-PI Sparv plugin (basic, basic_iob, general, general_iob, detailed, detailed_iob).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/pi-detection/kb-bert-pi-detailed": { "id": "gothenburg/pi-detection/kb-bert-pi-detailed", "name": "KB-BERT Swedish PI detection (detailed)", "maker": "gothenburg", "family": "gothenburg/pi-detection", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2025-05-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of KB/bert-base-swedish-cased (National Library of Sweden / KBLab). BERT-base config on the fetched checkpoints: hidden size 768, 12 layers, 12 attention heads, intermediate size 3072, vocabulary 50325, max_position_embeddings 512, float32. Token classification. Labels: 39 labels: O plus firstname_male, firstname_female, firstname_unknown, initials, middlename, surname, school, work, other_institution, area, city, geo, country, place, region, street_nr, zip_code, transport_name, transport_nr, age_digits, age_string, date_digits, day, month_digit, month_word, year, phone_nr, email, url, personid_nr, account_nr, license_nr, other_nr_seq, extra, prof, edu, fam, sensitive.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KB-BERT Swedish PI detection (detailed) is a University of Gothenburg model released by Spr\u00e5kbanken Text. It is a GPL-3.0 token-classification fine-tune of KB/bert-base-swedish-cased for Swedish personal-information detection. Weights are public on Hugging Face as sbx/KB-bert-swedish_PI-detection-detailed. Collected 2026-10-15.", "what_it_is_for": "The catalogue says the model detects personal information, especially in learner essays, and that a person must stay in the loop. It is part of the Mormor Karl pseudonymization work and is one of the six annotation settings in the SBX-PI Sparv plugin (basic, basic_iob, general, general_iob, detailed, detailed_iob).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/pi-detection/kb-bert-pi-detailed-iob": { "id": "gothenburg/pi-detection/kb-bert-pi-detailed-iob", "name": "KB-BERT Swedish PI detection (detailed IOB)", "maker": "gothenburg", "family": "gothenburg/pi-detection", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2025-05-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of KB/bert-base-swedish-cased (National Library of Sweden / KBLab). BERT-base config on the fetched checkpoints: hidden size 768, 12 layers, 12 attention heads, intermediate size 3072, vocabulary 50325, max_position_embeddings 512, float32. Token classification. Labels: 77 labels: O plus B- and I- for each of the 38 detailed classes (firstname_male/female/unknown, initials, middlename, surname, school, work, other_institution, area, city, geo, country, place, region, street_nr, zip_code, transport_name, transport_nr, age_digits, age_string, date_digits, day, month_digit, month_word, year, phone_nr, email, url, personid_nr, account_nr, license_nr, other_nr_seq, extra, prof, edu, fam, sensitive).", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KB-BERT Swedish PI detection (detailed IOB) is a University of Gothenburg model released by Spr\u00e5kbanken Text. It is a GPL-3.0 token-classification fine-tune of KB/bert-base-swedish-cased for Swedish personal-information detection. Weights are public on Hugging Face as sbx/KB-bert-swedish_PI-detection-detailed-iob. Collected 2026-10-15.", "what_it_is_for": "The catalogue says the model detects personal information, especially in learner essays, and that a person must stay in the loop. It is part of the Mormor Karl pseudonymization work and is one of the six annotation settings in the SBX-PI Sparv plugin (basic, basic_iob, general, general_iob, detailed, detailed_iob).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/pi-detection/kb-bert-pi-general": { "id": "gothenburg/pi-detection/kb-bert-pi-general", "name": "KB-BERT Swedish PI detection (general)", "maker": "gothenburg", "family": "gothenburg/pi-detection", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2025-05-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of KB/bert-base-swedish-cased (National Library of Sweden / KBLab). BERT-base config on the fetched checkpoints: hidden size 768, 12 layers, 12 attention heads, intermediate size 3072, vocabulary 50325, max_position_embeddings 512, float32. Token classification. Labels: O, transportation, institution, date, geographic, personal_name, age, other.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KB-BERT Swedish PI detection (general) is a University of Gothenburg model released by Spr\u00e5kbanken Text. It is a GPL-3.0 token-classification fine-tune of KB/bert-base-swedish-cased for Swedish personal-information detection. Weights are public on Hugging Face as sbx/KB-bert-swedish_PI-detection-general. Collected 2026-10-15.", "what_it_is_for": "The catalogue says the model detects personal information, especially in learner essays, and that a person must stay in the loop. It is part of the Mormor Karl pseudonymization work and is one of the six annotation settings in the SBX-PI Sparv plugin (basic, basic_iob, general, general_iob, detailed, detailed_iob).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/pi-detection/kb-bert-pi-general-iob": { "id": "gothenburg/pi-detection/kb-bert-pi-general-iob", "name": "KB-BERT Swedish PI detection (general IOB)", "maker": "gothenburg", "family": "gothenburg/pi-detection", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2025-05-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of KB/bert-base-swedish-cased (National Library of Sweden / KBLab). BERT-base config on the fetched checkpoints: hidden size 768, 12 layers, 12 attention heads, intermediate size 3072, vocabulary 50325, max_position_embeddings 512, float32. Token classification. Labels: 15 labels: O plus B- and I- for institution, transportation, other, age, geographic, personal_name, and date.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KB-BERT Swedish PI detection (general IOB) is a University of Gothenburg model released by Spr\u00e5kbanken Text. It is a GPL-3.0 token-classification fine-tune of KB/bert-base-swedish-cased for Swedish personal-information detection. Weights are public on Hugging Face as sbx/KB-bert-swedish_PI-detection-general-iob. Collected 2026-10-15.", "what_it_is_for": "The catalogue says the model detects personal information, especially in learner essays, and that a person must stay in the loop. It is part of the Mormor Karl pseudonymization work and is one of the six annotation settings in the SBX-PI Sparv plugin (basic, basic_iob, general, general_iob, detailed, detailed_iob).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/stanza/stanza-depparse-swedish": { "id": "gothenburg/stanza/stanza-depparse-swedish", "name": "Stanza Swedish dependency parser", "maker": "gothenburg", "family": "gothenburg/stanza", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2020-12-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Stanza dependency parser trained by Spr\u00e5kbanken Text, Mamba-Dep format, TalbankenSBX. Not Stanford's off-the-shelf Swedish model. Shared pretrain file stanza_pretrain.zip, 91.7 MB, catalogue date 2025-02-20. The POS page says the spring 2025 change was a minor format change. The file is word2vec embeddings encoded for Stanza from http://vectors.nlpl.eu/repository/. Parser and tagger weights are Spr\u00e5kbanken's. Whether the 2025 file is the same vectors as the 2020 release was not stated beyond the format note.", "modalities": { "input": [ "text" ], "output": [ "dependencies" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stanza Swedish dependency parser is a Spr\u00e5kbanken Text model at the University of Gothenburg. Two zips, eval and full, are dated 2020-12-09. The page prints LAS 84.48 for the eval model. Collected 2026-10-15.", "what_it_is_for": "The catalogue says this is the dependency parser used as the default Sparv dependency tool. stanza_eval can be scored. stanza_full is the Sparv model and was not scored.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/stanza/stanza-lemma-swedish": { "id": "gothenburg/stanza/stanza-lemma-swedish", "name": "Stanza Swedish lemmatizer", "maker": "gothenburg", "family": "gothenburg/stanza", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2020-11-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Stanza lemmatizer trained by Spr\u00e5kbanken Text on SUC3. The page says SUC3 was split randomly 80:10:10, training ran 30 epochs, and default Stanza settings were used. One zip, lem_stanza.zip, 3.74 MB. Shared pretrain file stanza_pretrain.zip, 91.7 MB, catalogue date 2025-02-20. The POS page says the spring 2025 change was a minor format change. The file is word2vec embeddings encoded for Stanza from http://vectors.nlpl.eu/repository/. Parser and tagger weights are Spr\u00e5kbanken's. Whether the 2025 file is the same vectors as the 2020 release was not stated beyond the format note.", "modalities": { "input": [ "text" ], "output": [ "lemmas" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stanza Swedish lemmatizer is a Spr\u00e5kbanken Text model at the University of Gothenburg. One zip is dated 2020-11-19. The page prints test accuracy 99.18. Collected 2026-10-15.", "what_it_is_for": "The catalogue says the model lemmatizes Swedish to the SUC3 standard. It warns that SUC3 lemmas do not exactly match SALDO in Korp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/stanza/stanza-pos-swedish": { "id": "gothenburg/stanza/stanza-pos-swedish", "name": "Stanza Swedish POS tagger", "maker": "gothenburg", "family": "gothenburg/stanza", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2020-12-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Stanza POS and MSD tagger trained by Spr\u00e5kbanken Text. stanza_eval is trained on SUC3 with Talbanken_SBX_dev as the dev set. stanza_full is trained on SUC3 + Talbanken_SBX_test + SIC2 with the same dev set, is not evaluated, and is the model Sparv uses. Shared pretrain file stanza_pretrain.zip, 91.7 MB, catalogue date 2025-02-20. The POS page says the spring 2025 change was a minor format change. The file is word2vec embeddings encoded for Stanza from http://vectors.nlpl.eu/repository/. Parser and tagger weights are Spr\u00e5kbanken's. Whether the 2025 file is the same vectors as the 2020 release was not stated beyond the format note.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stanza Swedish POS tagger is a Spr\u00e5kbanken Text model at the University of Gothenburg. Eval and full zips are dated 2020-12-09. Sparv uses the full model. Collected 2026-10-15.", "what_it_is_for": "The catalogue provides two Stanza POS models. stanza_eval is the one with published scores on Talbanken_SBX_test and SIC2. stanza_full is the Sparv model and was not scored.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/superlim/superlim-absabank-imm": { "id": "gothenburg/superlim/superlim-absabank-imm", "name": "SuperLim Absabank-Imm BERT", "maker": "gothenburg", "family": "gothenburg/superlim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-07-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Spr\u00e5kbanken Text SuperLim baseline whose repository id names bert-base-swedish-cased. pipeline tag text-classification. API model class BertForSequenceClassification. SHA de65a18849ac46605b65cc23945f74ee0d8a172d. lastModified 2024-07-06. usedStorage 1995346447 bytes. Siblings include pytorch_model.bin, optimizer.pt, rng_state.pth, scheduler.pt, trainer_state.json, and training_args.bin. Hub download count read was 10.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SuperLim Absabank-Imm BERT is a University of Gothenburg weight release from Spr\u00e5kbanken Text on the sbx Hugging Face organization. It is a SuperLim baseline used by the Sparv-Superlim plugin. The card does not document a score. Hub created date 2024-07-06. Collected 2026-10-15.", "what_it_is_for": "Sentence classification of attitude toward immigration. The Sparv plugin records a float from 1 to 5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/superlim/superlim-argumentation": { "id": "gothenburg/superlim/superlim-argumentation", "name": "SuperLim argumentation-sentence BERT", "maker": "gothenburg", "family": "gothenburg/superlim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Spr\u00e5kbanken Text SuperLim baseline whose repository id names bert-base-swedish-cased. The README body is only YAML front matter with license: cc. That string is not an SPDX identifier, so license is left empty. Hub download count read was 11. The plugin README path uses a hyphen before argumentation. The live repo id uses an underscore.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SuperLim argumentation-sentence BERT is a University of Gothenburg weight release from Spr\u00e5kbanken Text on the sbx Hugging Face organization. It is a SuperLim baseline used by the Sparv-Superlim plugin. The card does not document a score. Hub created date 2024-06-14. Collected 2026-10-15.", "what_it_is_for": "Sentence stance: pro, con, or neutral, as defined by the Sparv-Superlim plugin.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/superlim/superlim-dalaj-ged": { "id": "gothenburg/superlim/superlim-dalaj-ged", "name": "SuperLim DaLAJ-GED BERT", "maker": "gothenburg", "family": "gothenburg/superlim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-07-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Spr\u00e5kbanken Text SuperLim baseline whose repository id names bert-base-swedish-cased. API model class BertForSequenceClassification. No license tag was on the API record. Raw README returned 404. SHA c9242b268cb4010b889bd05545dddf54e403b95d. lastModified 2024-07-03. usedStorage 1995358739 bytes. Training-state files are in the repo. Hub download count read was 9.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SuperLim DaLAJ-GED BERT is a University of Gothenburg weight release from Spr\u00e5kbanken Text on the sbx Hugging Face organization. It is a SuperLim baseline used by the Sparv-Superlim plugin. The card does not document a score. Hub created date 2024-07-03. Collected 2026-10-15.", "what_it_is_for": "Sentence classification of Swedish as correct or incorrect (DaLAJ-GED), as defined by the Sparv-Superlim plugin.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/superlim/superlim-swenli": { "id": "gothenburg/superlim/superlim-swenli", "name": "SuperLim SweNLI BERT", "maker": "gothenburg", "family": "gothenburg/superlim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Spr\u00e5kbanken Text SuperLim baseline whose repository id names bert-base-swedish-cased. No pipeline_tag was on the API record. Raw README returned 404. Hub download count read was 10. Config hidden size was not read.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SuperLim SweNLI BERT is a University of Gothenburg weight release from Spr\u00e5kbanken Text on the sbx Hugging Face organization. It is a SuperLim baseline used by the Sparv-Superlim plugin. The card does not document a score. Hub created date 2024-08-07. Collected 2026-10-15.", "what_it_is_for": "Sentence-pair labels entailment, contradiction, and neutral, as defined by the Sparv-Superlim plugin.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/superlim/superlim-sweparaphrase": { "id": "gothenburg/superlim/superlim-sweparaphrase", "name": "SuperLim SweParaphrase BERT", "maker": "gothenburg", "family": "gothenburg/superlim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Spr\u00e5kbanken Text SuperLim baseline whose repository id names bert-base-swedish-cased. No pipeline_tag was on the API record. Hub download count read was 12. Config hidden size was not read.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SuperLim SweParaphrase BERT is a University of Gothenburg weight release from Spr\u00e5kbanken Text on the sbx Hugging Face organization. It is a SuperLim baseline used by the Sparv-Superlim plugin. The card does not document a score. Hub created date 2024-08-11. Collected 2026-10-15.", "what_it_is_for": "Paraphrase similarity as a float, as defined by the Sparv-Superlim plugin. The plugin annotation name is sweparaphrase. The repo id is swepar.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gothenburg/wiki2vec/wiki-word2vec-en": { "id": "gothenburg/wiki2vec/wiki-word2vec-en", "name": "English Wikipedia word2vec", "maker": "gothenburg", "family": "gothenburg/wiki2vec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Gensim word2vec files distributed by Spr\u00e5kbanken Text. The catalogue cites Hengchen, Simon (2022), Word2vec models trained on English Wikipedia, DOI 10.5281/zenodo.6542975. Two minimum-count variants are separate files. Filenames contain 300 and 5 or 50. The catalogue page does not state dimension or window, and the Zenodo record was not retrieved, so dimension and window stay unset.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "English Wikipedia word2vec is a pair of Gensim embedding files distributed by Spr\u00e5kbanken Text at the University of Gothenburg. The catalogue cites a 2022 Zenodo deposit by Simon Hengchen and lists the files with the date 2024-01-25. Collected 2026-10-15.", "what_it_is_for": "The catalogue distributes word2vec models trained on English Wikipedia. Two files differ by the minimum-count token in the filename. The page warns about bias in the embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "groningen/bertje/bertje": { "id": "groningen/bertje/bertje", "name": "BERTje", "maker": "groningen", "family": "groningen/bertje", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2019-12-19", "context_length": 512, "max_output_tokens": null, "parameters": "109169017", "architecture": "Dutch BERT-base cased masked language model, trained from scratch at CLCG, University of Groningen. The paper says it is architecturally equivalent to BERT base with 12 transformer blocks, a WordPiece vocabulary of 30K, sentence-order prediction instead of next-sentence prediction, and whole-word masking of 15% of tokens, trained for 1 million iterations on about 2.4 billion tokens (12GB). config.json: BertForMaskedLM, 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab_size 30073, max_position_embeddings 512, type_vocab_size 2. Hub safetensors lists F32 109169017 and I64 512, total 109169529. The paper's 30K vocabulary and config vocab_size 30073 disagree. The card says the vocabulary size changed in 2021 and that revision v1 is the old vocabulary.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERTje is a Dutch BERT-base model trained from scratch by the Computational Linguistics group at the University of Groningen. The arXiv paper of 19 December 2019 is the release note, and the weights remain on Hugging Face as GroNLP/bert-base-dutch-cased. Status: active. Collected 2026-10-15.", "what_it_is_for": "The paper says BERTje is a monolingual Dutch BERT so Dutch NLP does not have to rely on multilingual BERT trained only on Wikipedia. It is a masked language model. Downstream use in the paper is fine-tuning for part-of-speech tagging, named-entity recognition, semantic role labeling, and sentiment analysis.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "groningen/bertje/bertje-frisian": { "id": "groningen/bertje/bertje-frisian", "name": "BERTje Frisian", "maker": "groningen", "family": "groningen/bertje", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-05-18", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERTje with a retrained lexical layer for West Frisian. The card says the model is identical to BERTje except bert.embeddings.word_embeddings. config.json: BertForMaskedLM, 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab_size 10000, max_position_embeddings 512, type_vocab_size 2. The Hub repo has pytorch_model.bin and no safetensors parameter total. The paper's West Frisian unlabeled sweep goes up to 59MB. The card does not say which subset was uploaded.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERTje Frisian is a University of Groningen lexical-layer adaptation of BERTje for West Frisian. The card and the Findings of ACL 2021 paper release it as GroNLP/bert-base-dutch-cased-frisian. The Hub file timestamp used here is 18 May 2021. Status: active. Collected 2026-10-15.", "what_it_is_for": "The paper retrains only the word-embedding layer so West Frisian can reuse Dutch BERTje. This checkpoint is that lexical layer. The card says the transformer weights stay those of BERTje.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "groningen/bertje/bertje-gronings": { "id": "groningen/bertje/bertje-gronings", "name": "BERTje Gronings", "maker": "groningen", "family": "groningen/bertje", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-05-18", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERTje with a retrained lexical layer for Gronings (Low Saxon). The card says the model is identical to BERTje except bert.embeddings.word_embeddings. config.json: BertForMaskedLM, 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab_size 10000, max_position_embeddings 512, type_vocab_size 2. The Hub repo has pytorch_model.bin and no safetensors parameter total. The paper says about 10MB of unlabeled data (about 1.9 million tokens) is enough for near-optimal transfer from a similar language, and that the Gronings unlabeled set used in the data sweep goes up to 43MB. The card does not say which subset was uploaded.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERTje Gronings is a University of Groningen lexical-layer adaptation of BERTje for Gronings, a Low Saxon variety. The card and the Findings of ACL 2021 paper release it as GroNLP/bert-base-dutch-cased-gronings. The Hub file timestamp used here is 18 May 2021. Status: active. Collected 2026-10-15.", "what_it_is_for": "The paper retrains only the word-embedding layer of a monolingual BERT so a low-resource variety can reuse a source-language model. This checkpoint is the Gronings lexical layer on BERTje. The authors say the transformer blocks stay those of BERTje.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "groningen/gpt2/gpt2-medium-dutch-embeddings": { "id": "groningen/gpt2/gpt2-medium-dutch-embeddings", "name": "GPT-2 medium Dutch embeddings", "maker": "groningen", "family": "groningen/gpt2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-12-10", "context_length": 1024, "max_output_tokens": null, "parameters": "344320000", "architecture": "GPT-2 medium with Dutch lexical embeddings and English transformer weights. The paper maps small-model lexical embeddings into the medium embedding space with least squares, then trains the lexical layer again. There is no full-model Dutch medium sibling on the card. config.json: GPT2LMHeadModel, 24 layers, n_embd 1024, 16 heads, n_positions 1024, vocab_size 40000, GELU new. Hub safetensors F32 344320000 plus U8 25165824, total 369485824. The U8 tensor is not described on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-2 medium Dutch embeddings is the University of Groningen's medium Dutch GPT-2 with a retrained lexical layer and the English GPT-2 medium transformer left in place. The 10 December 2020 paper is the release note. The card does not list a full-model Dutch medium fine-tune. Status: active. Collected 2026-10-15.", "what_it_is_for": "The paper scales the small Dutch lexical embeddings into the GPT-2 medium space so a larger model can be adapted without training the transformer from scratch. This repo is that medium lexical model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "groningen/gpt2/gpt2-medium-italian-embeddings": { "id": "groningen/gpt2/gpt2-medium-italian-embeddings", "name": "GPT-2 medium Italian embeddings", "maker": "groningen", "family": "groningen/gpt2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-12-10", "context_length": 1024, "max_output_tokens": null, "parameters": "334081024", "architecture": "GPT-2 medium with Italian lexical embeddings and English transformer weights. The paper maps small lexical embeddings into the medium space. The family card lists no full-model Italian medium fine-tune. config.json: GPT2LMHeadModel, 24 layers, n_embd 1024, 16 heads, n_positions 1024, vocab_size 30001, GELU new. Hub safetensors F32 334081024 plus U8 25165824, total 359246848. The U8 tensor is not described on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-2 medium Italian embeddings is the University of Groningen's medium Italian GPT-2 with a retrained lexical layer and the English GPT-2 medium transformer left in place. The 10 December 2020 paper is the release note. The family card does not list a full-model Italian medium fine-tune. Status: active. Collected 2026-10-15.", "what_it_is_for": "The paper moves small-model lexical embeddings into the GPT-2 medium space. This repo is that Italian medium lexical model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "groningen/gpt2/gpt2-small-dutch": { "id": "groningen/gpt2/gpt2-small-dutch", "name": "GPT-2 small Dutch", "maker": "groningen", "family": "groningen/gpt2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-12-10", "context_length": 1024, "max_output_tokens": null, "parameters": "116562432", "architecture": "GPT-2 small continued from OpenAI gpt2. The card says this repo retrains the lexical embeddings and then fine-tunes the full model, and marks it recommended. config.json: GPT2LMHeadModel, 12 layers, n_embd 768, 12 heads, n_positions 1024, vocab_size 40000, GELU new, bos token id 1, eos token id 2. Hub safetensors F32 116562432 plus U8 12582912, total 129145344. The U8 tensor is not described on the card. The example generation block sets max_length 100. That is not stored as a model limit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-2 small Dutch is the University of Groningen's recommended Dutch GPT-2 small. Wietse de Vries and Malvina Nissim recycle OpenAI's English GPT-2 small by retraining the lexical embeddings and then fine-tuning the full model. The arXiv paper of 10 December 2020 says the models are on the Hugging Face hub. Status: active. Collected 2026-10-15.", "what_it_is_for": "The card calls it a text-generation model and marks this full fine-tune as the recommended Dutch small checkpoint. The paper's aim is a Dutch and Italian GPT-2 without training the transformer from scratch.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "groningen/gpt2/gpt2-small-dutch-embeddings": { "id": "groningen/gpt2/gpt2-small-dutch-embeddings", "name": "GPT-2 small Dutch embeddings", "maker": "groningen", "family": "groningen/gpt2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-12-10", "context_length": 1024, "max_output_tokens": null, "parameters": "116562432", "architecture": "GPT-2 small with only the lexical embeddings retrained for Dutch. The Dutch card says the transformer weights stay those of English OpenAI gpt2. This repo's own config.json was not opened. Its Hub safetensors F32 count, 116562432, matches GroNLP/gpt2-small-dutch, whose config is 12 layers, n_embd 768, 12 heads, n_positions 1024, vocab_size 40000. Hub total is 129145344 including U8 12582912. The paper says lexical-only retraining of the small model reaches Dutch test perplexity 48.9.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-2 small Dutch embeddings is the University of Groningen lexical-only recycle of OpenAI GPT-2 small for Dutch. The transformer weights stay English. The 10 December 2020 paper releases it with the full fine-tune, which the card marks as recommended. Status: active. Collected 2026-10-15.", "what_it_is_for": "The paper's main step is to retrain only the lexical embeddings so an English GPT-2 can generate Dutch without touching the transformer. This repo is that step. The card says the full fine-tune is the one to use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "groningen/gpt2/gpt2-small-italian": { "id": "groningen/gpt2/gpt2-small-italian", "name": "GPT-2 small Italian", "maker": "groningen", "family": "groningen/gpt2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-12-10", "context_length": 1024, "max_output_tokens": null, "parameters": "108883200", "architecture": "GPT-2 small continued from OpenAI gpt2 for Italian. The Dutch card, which lists this repo, says it has retrained lexical embeddings and additional full-model fine-tuning, and marks it recommended. config.json: GPT2LMHeadModel, 12 layers, n_embd 768, 12 heads, n_positions 1024, vocab_size 30001, GELU new, bos and eos token id 0. Hub safetensors F32 108883200 plus U8 12582912, total 121466112. The U8 tensor is not described on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-2 small Italian is the University of Groningen's recommended Italian GPT-2 small. It recycles OpenAI GPT-2 small with a new lexical layer and a full-model fine-tune. The arXiv paper of 10 December 2020 releases it with the Dutch models. Status: active. Collected 2026-10-15.", "what_it_is_for": "The card lists it as the recommended Italian small model for text generation. The paper compares its generations with an Italian GPT-2 trained from scratch and says human judges rate them on par.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "groningen/gpt2/gpt2-small-italian-embeddings": { "id": "groningen/gpt2/gpt2-small-italian-embeddings", "name": "GPT-2 small Italian embeddings", "maker": "groningen", "family": "groningen/gpt2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-12-10", "context_length": 1024, "max_output_tokens": null, "parameters": "108883200", "architecture": "GPT-2 small with only the lexical embeddings retrained for Italian. The family card says the transformer weights stay those of English OpenAI gpt2. This repo's config.json was not opened. Its Hub safetensors F32 count, 108883200, matches GroNLP/gpt2-small-italian, whose config is 12 layers, n_embd 768, 12 heads, n_positions 1024, vocab_size 30001. Hub total is 121466112 including U8 12582912. The paper says the lexical-only small model reaches Italian test perplexity 44.2.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-2 small Italian embeddings is the University of Groningen lexical-only recycle of OpenAI GPT-2 small for Italian. The 10 December 2020 paper releases it beside the recommended full fine-tune. Status: active. Collected 2026-10-15.", "what_it_is_for": "The paper retrains only the lexical embeddings so English GPT-2 small can model Italian. This repo is that step. The family card marks the full fine-tune as recommended.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "groningen/hatebert/hatebert": { "id": "groningen/hatebert/hatebert", "name": "HateBERT", "maker": "groningen", "family": "groningen/hatebert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "110104890", "architecture": "English BERT-base uncased further pretrained with masked language modeling on RAL-E, Reddit comments from communities banned for offensive, abusive, or hateful content. The paper says the starting checkpoint is the Hugging Face English BERT base-uncased model, trained for 100 epochs (almost 2 million steps) in batches of 64, sequences up to 512 tokens, Adam at learning rate 5e-5, on 1,478,348 messages (43,379,350 tokens). config.json: BertForMaskedLM, 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab_size 30522, max_position_embeddings 512. Hub safetensors total equals F32 110104890. The card calls it a collaboration of the University of Groningen, the University of Turin, and the University of Passau. The weights are published by GroNLP.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HateBERT is an English BERT-base uncased model that the University of Groningen released with the University of Turin and the University of Passau. It is BERT further pretrained on Reddit comments from banned communities. The weights are GroNLP/hateBERT under Apache-2.0. The first public weight day was not verified. Status: active. Collected 2026-10-15.", "what_it_is_for": "The paper introduces HateBERT as a pretrained model for abusive language in English social media: offensive language, abuse, and hate speech. The Hub checkpoint is the masked-language-model retrain, not a classifier head. The card says the task fine-tunes are a separate OSF release with a different license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "groningen/wav2vec2/wav2vec2-dutch-base": { "id": "groningen/wav2vec2/wav2vec2-dutch-base", "name": "Wav2Vec2 Dutch base", "maker": "groningen", "family": "groningen/wav2vec2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of facebook/wav2vec2-base on Dutch speech from Het Corpus Gesproken Nederlands. The card does not state hours. config.json: Wav2Vec2ForPreTraining, 12 hidden layers, hidden size 768, 12 heads, intermediate size 3072, 7 convolutional feature layers, codevector_dim 256, vocab_size 32, GELU. The Hub repo has pytorch_model.bin and no safetensors parameter total. The NAACL 2022 paper describes a 24-layer Dutch large model, not this base card.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wav2Vec2 Dutch base is a University of Groningen speech checkpoint from the GroNLP Hub org. The card says it is further pretraining of Facebook's English `facebook/wav2vec2-base` on Dutch speech from Het Corpus Gesproken Nederlands, and that it is one of two Dutch Wav2Vec2 models, with the large checkpoint as the other. The Hub upload date is 11 March 2022. Status: active. Collected 2026-10-15.", "what_it_is_for": "The card presents a Dutch speech representation model. The Hub pipeline tag is pretraining, not automatic speech recognition. Hidden states can be read out as embeddings. It is not the CTC recognizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "groningen/wav2vec2/wav2vec2-dutch-large": { "id": "groningen/wav2vec2/wav2vec2-dutch-large", "name": "Wav2Vec2 Dutch large", "maker": "groningen", "family": "groningen/wav2vec2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of facebook/wav2vec2-large on Dutch speech from Het Corpus Gesproken Nederlands. config.json on the pretraining repo: Wav2Vec2ForPreTraining, 24 hidden layers, hidden size 1024, 16 heads, intermediate size 4096, 7 convolutional feature layers, codevector_dim 768, vocab_size 32, GELU. That repo has pytorch_model.bin and no safetensors total. Bartelds and Wieling (NAACL 2022) say w2v2-nl is further pretraining of the English large model on 243 hours of Spoken Dutch Corpus speech after cross-talk and silences were removed, then CTC fine-tuning on the same 243 labelled hours. Pre-training in the paper: 2 million steps, 100,000 warmup iterations, linearly decreasing learning rate starting at 5e-5. Fine-tuning: 1 million steps, linearly decreasing learning rate starting at 1e-5. The code repo maps the name w2v2-nl to GroNLP/wav2vec2-dutch-large-ft-cgn. The pretraining card itself does not repeat the hour count.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wav2Vec2 Dutch large is a University of Groningen speech model from the GroNLP Hub org. The card says it is further pretraining of Facebook's English `facebook/wav2vec2-large` on Dutch speech from Het Corpus Gesproken Nederlands, and that base and large are the two Dutch Wav2Vec2 models. Martijn Bartelds and Martijn Wieling describe that large model in a NAACL 2022 paper and then CTC-fine-tune it. The pretraining repo was uploaded on 11 March 2022. The CTC repo was uploaded on 8 April 2022. Status: active. Collected 2026-10-15.", "what_it_is_for": "The pretraining card is a Dutch speech representation model. The Hub pipeline tag there is pretraining. The CTC version is an automatic speech recognizer for the same continued-pretrained large model. The paper uses hidden-layer embeddings and dynamic time warping to compare Dutch dialect pronunciations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "guelph/barcodebert/barcodebert": { "id": "guelph/barcodebert/barcodebert", "name": "BarcodeBERT", "maker": "guelph", "family": "guelph/barcodebert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "29143296", "architecture": "BERT encoder for insect COI DNA barcodes. The released Hub config.json is BertForTokenClassification with 4 hidden layers, 4 attention heads, hidden size 768, intermediate size 3072, GELU, absolute position embeddings, vocab size 258, and max_position_embeddings 512. Vocab 258 is 4^4 plus [MASK] and [UNK], matching the paper's selected non-overlapping 4-mer tokenizer (the 4-4-4 setting: k=4, 4 heads, 4 layers). The card's safetensors count is 29,143,296 parameters (29,142,784 F32 and 512 I64). The 10 July 2025 arXiv table calls this checkpoint BarcodeBERT (4-4-4) at 29.1 million parameters. Barcodes are padded or truncated to 660 nucleotides before tokenization. Pretraining is masked-token prediction at a 50% substitution rate on the Canadian invertebrates library. A whole-barcode embedding is global average pooling of the token states. Joint release: corresponding authors are Graham W. Taylor (University of Guelph and Vector Institute) and Lila Kari (University of Waterloo). Implementation authors are primarily at Waterloo. Guelph coauthors are Iuliia Zarubiieva, Dirk Steinke, and Graham W. Taylor.", "modalities": { "input": [ "dna" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BarcodeBERT is a DNA-barcode transformer released jointly by the University of Guelph and the University of Waterloo, with Simon Fraser, Vector Institute, Aalborg, and Amii coauthors. Graham W. Taylor (Guelph and Vector) and Lila Kari (Waterloo) are the corresponding authors. The public checkpoint on `bioscan-ml/BarcodeBERT` is the 4-layer, 4-head, 4-mer model the paper calls BarcodeBERT (4-4-4). Status on 2026-10-15: active open weights. No price and no API.", "what_it_is_for": "The makers use it to embed invertebrate COI barcodes for species classification, genus-level nearest-neighbour search on species the classifier has not seen, and zero-shot Barcode Index Number clustering. The card shows mean-pooling the last hidden state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "guelph/barcodemamba/barcodemamba": { "id": "guelph/barcodemamba/barcodemamba", "name": "BarcodeMamba", "maker": "guelph", "family": "guelph/barcodemamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-12-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mamba-2 state-space model for invertebrate DNA barcodes, pretrained with next-token prediction. Both authors, Tiancheng Gao and Graham W. Taylor, list the University of Guelph and the Vector Institute. The Hub repo is one card with four checkpoints, not four products. Opened configs: BarcodeMamba-dim384-layer2-char is 2 layers, d_model 384, d_state 64, d_conv 4, expand 2, headdim 48, character vocabulary 8, dataset max_len 660 nucleotides. BarcodeMamba-dim384-layer2-k-mer is the same width and depth with k_mer 6 and stride 6. BarcodeMamba-dim768-layer6-char is 6 layers, d_model 768, headdim 64, character vocabulary 8, max_len 660. The paper names the 2-by-384 models BarcodeMamba-2-mini (4.3 million character, 7.4 million k-mer) and the 6-by-768 models BarcodeMamba-2-large (50.4 million character, 56.7 million k-mer). The width-768 k-mer YAML was not opened. Training configs that were opened use 50 epochs, learning rate 0.0008, global batch 256, reverse-complement augmentation, and a random tokenization offset.", "modalities": { "input": [ "dna" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BarcodeMamba is a Mamba-2 DNA-barcode model from Tiancheng Gao and Graham W. Taylor at the University of Guelph, also affiliated with the Vector Institute. The weights are one MIT card, `bioscan-ml/BarcodeMamba`, with four checkpoints the paper groups as BarcodeMamba-2-mini and BarcodeMamba-2-large. The Hub repo was created on 9 December 2024. The preprint is 15 December 2024, from a NeurIPS 2024 workshop. Status on 2026-10-15: active open weights. No price and no API.", "what_it_is_for": "The card says the checkpoints support taxonomic classification of seen species, by fine-tuning or a linear probe, and genus-level 1-nearest-neighbour search for species held out of training. The GitHub example downloads `BarcodeMamba-dim384-layer2-char`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "haifa/knesset/knesset-dicta-checkworthiness": { "id": "haifa/knesset/knesset-dicta-checkworthiness", "name": "knesset-dicta-checkworthiness", "maker": "haifa", "family": "haifa/knesset", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Model card YAML says cc-by-4.0. The paper says the resources and code are released under CC BY-SA 4.0.", "release_date": "2025-07-21", "context_length": 512, "max_output_tokens": null, "parameters": "184347651", "architecture": "BertForSequenceClassification fine-tuned from GiliGold/Knesset-DictaBERT. Encoder matches that BERT: 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocabulary 128000, max_position_embeddings 512. Three labels: 0 not a factual proposition, 1 not worth checking, 2 worth checking. The paper appendix says the three compared encoders were fine-tuned for 3 epochs, batch size 8, weight decay 0.01, and maximum sequence length 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "knesset-dicta-checkworthiness is a Hebrew sentence classifier from Gili Goldin, Shira Wigderson, and Shuly Wintner at the University of Haifa, with Ella Rabinovich at the Academic College of Tel-Aviv Yaffo. It is Knesset-DictaBERT fine-tuned to label a sentence as worth checking, not worth checking, or not a factual proposition. The Hub repo dates from 2025-07-21. The paper is arXiv 2025-09-30 and RANLP 2025. Status on 2026-10-15: active. There is no vendor API.", "what_it_is_for": "The card and paper use it to mark check-worthiness in Hebrew parliamentary sentences, as one layer of a larger factuality scheme. The authors used the best fine-tune, this one, to label the Knesset Corpus. It does not decide whether a claim is true.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "haifa/knesset/knesset-dictabert": { "id": "haifa/knesset/knesset-dictabert", "name": "Knesset-DictaBERT", "maker": "haifa", "family": "haifa/knesset", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-sa-4.0", "release_date": "2024-07-30", "context_length": 512, "max_output_tokens": null, "parameters": "184474880", "architecture": "BertForMaskedLM continued from dicta-il/dictabert. Config: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, vocabulary 128000, absolute positions, max_position_embeddings 512, GELU, newmodern true. Training used 256-token chunks, 15 percent token masking, 2 epochs, AdamW learning rate 1e-4, weight decay 0.01, per-device batch size 32, gradient accumulation 4, and fp16. The best validation-loss checkpoint was kept.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Knesset-DictaBERT is a Hebrew masked language model from Gili Goldin and Shuly Wintner at the Department of Computer Science, University of Haifa. It continues DictaBERT on the Knesset Corpus of Israeli parliamentary proceedings. Weights are public under CC BY-SA 4.0. Status on 2026-10-15: active. There is no vendor API.", "what_it_is_for": "The paper says the model is for Hebrew parliamentary text: political discourse and legislative language. It is a masked language model, not a chat model. The authors say it should help Hebrew NLP and political text analysis.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "haifa/knesset/knesset-multi-e5-large": { "id": "haifa/knesset/knesset-multi-e5-large", "name": "Knesset-multi-e5-large", "maker": "haifa", "family": "haifa/knesset", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-03", "context_length": 512, "max_output_tokens": null, "parameters": "559890432", "architecture": "XLM-RoBERTa encoder fine-tuned from intfloat/multilingual-e5-large, then paired with that model's original sentence-transformers mean pooling and L2 normalization. Config: 24 layers, hidden size 1024, 16 heads, intermediate size 4096, vocabulary 250002, GELU, max_position_embeddings 514, float32. The model card sets max_seq_length to 512 and the embedding size to 1024. Config _name_or_path is fine_tuned_on_dlc_server/fine-tuned-multi/fine_multi_checkpoint-134940.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Knesset-multi-e5-large is a Hebrew sentence-embedding model from the University of Haifa computational linguistics group. Gili Goldin and Shuly Wintner are at Haifa. Ella Rabinovich, a co-author of the paper, is at the Academic College of Tel-Aviv Yaffo. The encoder is a fine-tune of multilingual-e5-large on Knesset proceedings. Weights have been on the Hub since 2025-02-03. Status on 2026-10-15: active. There is no vendor API and the card states no license.", "what_it_is_for": "The card says it maps sentences and paragraphs to a 1024-dimensional vector for clustering or semantic search, with the encoder tuned for legislative and parliamentary Hebrew. The paper uses those vectors as input to valence, arousal, and dominance regression.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "haifa/vad/vad-binomial": { "id": "haifa/vad/vad-binomial", "name": "VAD Binomial Regression Models", "maker": "haifa", "family": "haifa/vad", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-sa-4.0", "release_date": "2025-02-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three separate binomial regression models stored as scikit-learn pickles: valence_model.pkl, arousal_model.pkl, and dominance_model.pkl. Each takes one 1024-dimensional embedding from Knesset-multi-e5-large and returns a score from 0 to 1. The card says the training mix was a Hebrew translation of EmoBank, the Hebrew VAD lexicon, and 120 manually annotated Knesset sentences. Parameter counts are not published.", "modalities": { "input": [ "embeddings" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VAD Binomial Regression Models are three Hebrew emotion regressors released by Gili Goldin and Shuly Wintner at the University of Haifa, with Ella Rabinovich at the Academic College of Tel-Aviv Yaffo. They predict valence, arousal, and dominance from Knesset-multi-e5-large embeddings. The Hub repo dates from 2025-02-04 and is licensed CC BY-SA 4.0. Status on 2026-10-15: active. There is no vendor API.", "what_it_is_for": "The card says the models score positivity (valence), excitement or calm (arousal), and control or influence (dominance). The paper uses those scores to measure affective polarization in Knesset committee speech. They are not text generators.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "haiper/video/haiper-1": { "id": "haiper/video/haiper-1", "name": "Haiper 1.0", "maker": "haiper", "family": "haiper/video", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-03-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not named. The June 2024 product guide describes generation as denoising a seed-defined Gaussian noise pattern. TechCrunch reported that Haiper had not published model details at launch.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Haiper 1.0 is the name later writeups use for the closed video model behind Haiper's public web tool, launched on 5 March 2024 by Haiper Ltd. Contemporary launch stories did not print a version number. They described a free text-to-video, image-animation, and video-repaint tool with short clips. It was superseded by Haiper 1.5 and is not on the later official price list. Collected on 2026-10-15.", "what_it_is_for": "TechCrunch described a consumer site for short clips from text, animating an uploaded image, and repainting an existing video's style, subject, or background. Extend was planned. The company said it had privately shared a closed API with some developers and had thought about open-sourcing later. No public weights followed in the sources checked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "haiper/video/haiper-1-5": { "id": "haiper/video/haiper-1-5", "name": "Haiper Video 1.5", "maker": "haiper", "family": "haiper/video", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-07-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Haiper Video 1.5 is Haiper's closed video model launched on 16 July 2024. VentureBeat reported it under the name Haiper 1.5 as an incremental update that reached 8 second clips. The later official price list still uses the name Haiper Video 1.5 for text-to-video, image-to-video, keyframes, extension, and 1080p enhance. The consumer business was reported shut in February 2025, and no official retirement notice was found. Collected on 2026-10-15.", "what_it_is_for": "VentureBeat described generation from text, image, and video prompts, extension of older short clips, and a new upscaler, on the web and mobile apps. The company also said it planned to add image generation. That image work is a different product line and is not part of this dossier. The surviving docs price the same video jobs, plus keyframe conditioning, under Haiper Video 1.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "haiper/video/haiper-video-2-x": { "id": "haiper/video/haiper-video-2-x", "name": "Haiper Video 2.x", "maker": "haiper", "family": "haiper/video", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-10-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Company press calls Haiper 2.0 a perceptual diffusion transformer (DiT): a proprietary mix of transformer-based models and diffusion. Parameter count was not published. The public API docs do not say whether 2.5 changed that architecture.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Haiper Video 2.x is the closed video line Haiper's API docs still name for gen2 text-to-video, image-to-video, and keyframe conditioning. It started as Haiper 2.0 in October 2024 and was called version 2.5 in a company press release dated 6 December 2024. The price list does not sell 2.0 and 2.5 as separate SKUs. The consumer business was reported shut in February 2025. The docs site still showed 2.x prices on 2026-10-15, and no official retirement notice was found. Collected on 2026-10-15.", "what_it_is_for": "The maker's 2.0 announcement described a faster, more realistic video model for short clips from a prompt or a reference image, plus templates. SiliconANGLE reported six-second clips and a tool that extends a clip by two seconds. The December 2024 company release said version 2.5 was the model behind the API, including use inside VEED and as the first video model on Civitai. The surviving API is text-to-video, image-to-video, and multi-image keyframe conditioning. The app price table also lists video-to-video. The API price table still marks video-to-video as coming soon.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hamburg/amharic/am-flair-ner": { "id": "hamburg/amharic/am-flair-ner", "name": "Amharic FLAIR NER", "maker": "hamburg", "family": "hamburg/amharic", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2021-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FLAIR ner. The wiki says the released checkpoint is the FLAIR-embedding model. Weight file Content-Length on 2026-10-15 was 515733394 bytes. Label inventory is not printed on the wiki page that was read.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amharic FLAIR NER is a named Amharic task model released by the Language Technology Group at the University of Hamburg. The weight URL returned HTTP 200 on 2026-10-15. Status is active. Confidence is medium because the release is a group wiki and README, not a model card. No license is stated on the file URL.", "what_it_is_for": "The group says this is the published named-entity tagger, the best model they released, based on the FLAIR embedding. The wiki example tags a short Amharic sentence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hamburg/amharic/am-flair-pos": { "id": "hamburg/amharic/am-flair-pos", "name": "Amharic FLAIR POS", "maker": "hamburg", "family": "hamburg/amharic", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2021-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FLAIR pos. The wiki says the released checkpoint is the FLAIR-embedding model. Weight file Content-Length on 2026-10-15 was 515804679 bytes. Label inventory is not printed on the wiki page that was read.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amharic FLAIR POS is a named Amharic task model released by the Language Technology Group at the University of Hamburg. The weight URL returned HTTP 200 on 2026-10-15. Status is active. Confidence is medium because the release is a group wiki and README, not a model card. No license is stated on the file URL.", "what_it_is_for": "The group released this part-of-speech tagger and says the FLAIR-embedding run was the better one. The wiki requires the group's Amharic segmenter before tagging.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hamburg/amharic/am-flair-sent": { "id": "hamburg/amharic/am-flair-sent", "name": "Amharic FLAIR sentiment", "maker": "hamburg", "family": "hamburg/amharic", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2021-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FLAIR sentiment. The wiki says the released checkpoint is the FLAIR-embedding model. Weight file Content-Length on 2026-10-15 was 503849408 bytes. Label inventory is not printed on the wiki page that was read.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amharic FLAIR sentiment is a named Amharic task model released by the Language Technology Group at the University of Hamburg. The weight URL returned HTTP 200 on 2026-10-15. Status is active. Confidence is medium because the release is a group wiki and README, not a model card. No license is stated on the file URL.", "what_it_is_for": "The group released this sentiment classifier on the FLAIR contextual embeddings. The wiki loads it with FLAIR TextClassifier and prints sentence labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hamburg/amharic/am-forward": { "id": "hamburg/amharic/am-forward", "name": "Amharic FLAIR forward", "maker": "hamburg", "family": "hamburg/amharic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-11-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Forward contextual string embedding (FLAIR character language model), library id am-forward. The paper names the from-scratch model AmFlair and distinguishes it from MultFlair and from a fine-tune of MultFlair. The FLAIR docs say the Amharic embeddings are based on a 6.5 million Amharic text corpus. Parameter count is not stated on the library page. Weight file best-lm.pt was 479,950,419 bytes on 2026-10-15.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amharic FLAIR forward (am-forward) is a contextual string embedding trained from scratch by the Language Technology Group at the University of Hamburg. It is loaded from the FLAIR library. Status on 2026-10-15 is active. The weight URL still answers HTTP 200. No license is stated on that URL.", "what_it_is_for": "FLAIR uses it as a character-level contextual embedding for Amharic words. The library docs describe a 6.5 million text corpus. The group uses this embedding inside the released NER, POS, and sentiment taggers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hamburg/amharic/am-roberta": { "id": "hamburg/amharic/am-roberta", "name": "Amharic RoBERTa", "maker": "hamburg", "family": "hamburg/amharic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "443396930", "architecture": "RoBERTa masked LM (RobertaForMaskedLM). config.json: 6 layers, hidden size 768, 12 attention heads, intermediate size 3072, vocab size 520000, max_position_embeddings 514, absolute positions, GELU. Hub safetensors total 443,396,930 parameters (443,396,416 F32 and 514 I64).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amharic RoBERTa is a masked language model released by the Language Technology Group at the University of Hamburg. The Hub card and weights were collected on 2026-10-15. Status is active. The license on the card is MIT.", "what_it_is_for": "The group released it as an Amharic fill-mask model for low-resource NLP, alongside static embeddings and a FLAIR character language model. The card says it is part of the effort to build benchmark datasets and models for Amharic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hamburg/amharic/amharic-fasttext": { "id": "hamburg/amharic/amharic-fasttext", "name": "Amharic fastText 300D", "maker": "hamburg", "family": "hamburg/amharic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Static fastText embedding, 300 dimensions, distributed as a gensim-readable text word2vec file (binary=False). The wiki does not say whether subword n-grams are included in the file. The 2021 paper says the group trained its own fastText model and compared it with the earlier Grave et al. crawl model.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amharic fastText 300D is a static embedding released by the Language Technology Group at the University of Hamburg. The file answered HTTP 200 on 2026-10-15. Status is active. No license is printed on the wiki.", "what_it_is_for": "The wiki tells users to load it like the word2vec file and query nearest neighbors. The 2021 paper distinguishes this group-trained fastText model from the earlier public multilingual fastText vectors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hamburg/amharic/amharic-hate-speech": { "id": "hamburg/amharic/amharic-hate-speech", "name": "Amharic hate speech", "maker": "hamburg", "family": "hamburg/amharic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-07-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RobertaForSequenceClassification fine-tuned from uhhlt/am-roberta. The card does not publish a parameter total. Hub API did not return a safetensors count (pytorch_model.bin only).", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amharic hate speech is a text classifier released by the Language Technology Group at the University of Hamburg. It is a fine-tune of the group's own Amharic RoBERTa, not of an outside encoder. The Hub card was collected on 2026-10-15. Status is active. License MIT.", "what_it_is_for": "The card says it classifies Amharic text and shows example outputs `normal` and `hate`. It was trained on the group's AmharicHateSpeechRANLP dataset. Intended-use and training-data sections of the card still say more information is needed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hamburg/amharic/amharic-question-classification": { "id": "hamburg/amharic/amharic-question-classification", "name": "Amharic question classification", "maker": "hamburg", "family": "hamburg/amharic", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "A single FLAIR .pt checkpoint. The wiki says the group compared FLAIR, AmRoBERTa, and XLM-R embeddings and that the AmRoBERTa run was better. It publishes one URL, final-qc-model.pt. That file was 1,815,373,613 bytes on 2026-10-15. The file was not opened, so the embedding inside the blob is not independently confirmed.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amharic question classification is a task checkpoint released by the Language Technology Group at the University of Hamburg. The file was still online on 2026-10-15. Status is active. Confidence is medium: the wiki names the task and the URL, but there is no model card. No license is stated.", "what_it_is_for": "The wiki says it classifies Amharic community questions. The group compared FLAIR, AmRoBERTa, and XLM-R and says the AmRoBERTa embedding run was better. One file is published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hamburg/amharic/amharic-word2vec": { "id": "hamburg/amharic/amharic-word2vec", "name": "Amharic word2vec 300D", "maker": "hamburg", "family": "hamburg/amharic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Static word2vec embedding, 300 dimensions, text format loadable by gensim KeyedVectors.load_word2vec_format (binary=False). The download is not labeled CBOW or skip-gram. The 2021 paper says the group trained word2vec with Gensim.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amharic word2vec 300D is a static embedding released by the Language Technology Group at the University of Hamburg. The file was still served on 2026-10-15. Status is active. No license is printed on the wiki.", "what_it_is_for": "The group published it for Amharic lexical similarity. The wiki shows a gensim `most_similar` example. It is one of the static models in the 2021 Amharic semantic-model release, not a copy of an outside lab's checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hamburg/sgmse/sgmse-plus": { "id": "hamburg/sgmse/sgmse-plus", "name": "SGMSE+", "maker": "hamburg", "family": "hamburg/sgmse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Score-based generative model in the complex STFT domain for speech enhancement and dereverberation. The SGMSE repository says the journal configuration uses backbone ncsnpp. The 48 kHz EARS models use ncsnpp_48k. The StoRM README calls the journal NCSN++ a 65M-parameter baseline and names that flag ncsnpp-large in the StoRM repo. Those two flag names are not the same string. Parameter count is left null because the sources disagree on the flag and the 48 kHz size is not stated.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SGMSE+ is a diffusion speech-enhancement and dereverberation model from the Signal Processing group at the University of Hamburg. Weights and code are public under MIT. Status on 2026-10-15 is active. There is no priced API.", "what_it_is_for": "The journal model predicts clean speech in the complex STFT domain from noisy or reverberant audio. The group ships dataset-specific checkpoints rather than one universal checkpoint. A separate 2022 Interspeech model, SGMSE, uses a DCUNet backbone. That older model is not in the pretrained download list that was read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hamburg/storm/storm": { "id": "hamburg/storm/storm", "name": "StoRM", "maker": "hamburg", "family": "hamburg/storm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Predictive model plus a diffusion reverse process. The README says the paper uses a lighter NCSN++ backbone, stated as 27.8M parameters, selected with --backbone ncsnpp. It contrasts that with a 65M ncsnpp-large backbone used for the SGMSE+ journal model.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StoRM is a speech enhancement and dereverberation model from the Signal Processing group at the University of Hamburg. Code is MIT and pretrained checkpoints are linked from the README. Status on 2026-10-15 is active. There is no priced API.", "what_it_is_for": "A predictive estimate of clean speech is the start of a diffusion reverse process. The group says this removes artifacts that a diffusion model alone can add, and that it can use fewer diffusion steps. The same repository also holds a wind-noise checkpoint from a 2023 ITG paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hamburg/streamfm/stream-fm": { "id": "hamburg/streamfm/stream-fm", "name": "Stream.FM", "maker": "hamburg", "family": "hamburg/streamfm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AGPL-3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Frame-causal flow-matching generative model for streaming speech restoration. The authors state algorithmic latency of 32 ms and total latency of 48 ms, plus a speech-enhancement variant with 24 ms total latency. Backbone class named in the README is CausalNCSNpp. Parameter count is not stated. The speech-enhancement config uses a predictive-generative combination in the style of StoRM.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stream.FM is a streaming generative speech-restoration model from the Signal Processing group at the University of Hamburg. Code is AGPL-3.0 and checkpoints are linked from the README. Status on 2026-10-15 is active. There is no priced API.", "what_it_is_for": "The authors present it for real-time communication: speech enhancement, dereverberation, bandwidth extension, codec post-filtering, STFT phase retrieval, and Mel vocoding. They state 32 ms algorithmic latency and 48 ms total latency, and a 24 ms total-latency enhancement variant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hamburg/tuda-de/tuda-de": { "id": "hamburg/tuda-de/tuda-de", "name": "TUDA-DE German ASR", "maker": "hamburg", "family": "hamburg/tuda-de", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY", "release_date": "2022-04-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Kaldi nnet3 chain TDNN-HMM. Current package id tuda_swc_mailabs_cv8_voc900k, archive de_900k_nnet3chain_tdnn1f_2048_sp_bi. The README describes tdnn1f with 2048 units and a 900k vocabulary, trained on about 1700 hours (TUDA-DE, Spoken Wikipedia Corpus, m-ailabs, Common Voice 8). Optional const-ARPA and a 4-layer LSTM RNNLM are separate rescoring models, not separate acoustic products.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TUDA-DE is the open German speech-recognition line from the Language Technology Group at the University of Hamburg, built with Kaldi. The current pretrained package was announced on 19 April 2022 and was still downloadable on 2026-10-15. Status is active. The university acoustic-model page says the pretrained models are CC-BY.", "what_it_is_for": "It is a large-vocabulary speaker-independent recognizer for German. The group ships an acoustic model, a decoding graph, and optional language-model rescoring. Local servers (kaldi-model-server, Kaldi GStreamer, subtitle2go) are usage paths, not a priced API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hanyang/s2rl/s2rl": { "id": "hanyang/s2rl/s2rl", "name": "S2RL", "maker": "hanyang", "family": "hanyang/s2rl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-26", "context_length": null, "max_output_tokens": null, "parameters": "12500000", "architecture": "Structure-guided Sequence Representation Learning. Residue graphs from 3D proximity, Laplacian eigenvectors, and a structure-aware attention pool over global, domain, and local scales. Sequence features are ESM-1b embeddings plus amino-acid one-hot vectors, projected and summed. One multitask head predicts Gene Ontology molecular function, biological process, and cellular component. Bioinformatics Table 3 lists 12.5M total parameters. The released .pt files are about 50 MB, so they are not a copy of ESM-1b.", "modalities": { "input": [ "protein-sequence", "protein-structure" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "S2RL (Structure-guided Sequence Representation Learning) is a protein-function model from Eun-Sol Kim's group at Hanyang University. It is a research weight release, not an API product. The journal version is Bioinformatics, 14 September 2025. The code-and-weight archive on Zenodo is dated 26 July 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says S2RL predicts protein function from sequence while using 3D structure to pool features at more than one scale. One multitask model covers molecular function, biological process, and cellular component. It is compared with HEAL and TAWFN on the PDBch split used by HEAL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/adapt/adapt": { "id": "harvard/adapt/adapt", "name": "ADAPT", "maker": "harvard", "family": "harvard/adapt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Students distilled from post-trained LLMs so intermediate sizes can be built by patching teacher layers back in. The opened card, ADAPT-Qwen3-2.3B-Instruct, copies every other layer plus the last two layers of Qwen3-4B-Instruct-2507 and distills on 0.5B Pile tokens plus 0.5B math tokens. Hub safetensors total for that repo was 576,661,056, which does not match the 2.3B name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ADAPT is a family of distilled students from the Harvard Data-Centric Machine Learning group. The opened checkpoint was posted on 2026-09-05 under Apache-2.0. It is a Harvard release even though the teachers are Qwen, Llama, and OLMo models.", "what_it_is_for": "The method distills a post-trained teacher into a smaller student, then patches teacher layers back to hit sizes in between without more training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/athena/athena-r1": { "id": "harvard/athena/athena-r1", "name": "ATHENA-R1", "maker": "harvard", "family": "harvard/athena", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-05-30", "context_length": null, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Reinforcement-learned tool-using agent on Qwen3-8B. Hub safetensors total 8,190,735,360. The card's canonical sampler is temperature 0.7, top_p 0.95, top_k 20, min_p 0, presence_penalty 0, max_round 40.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ATHENA-R1 is a Zitnik Lab treatment-reasoning agent posted on 2026-05-30. It is a tool-using Qwen3-8B checkpoint under an MIT card license, and the card says it is not a medical device.", "what_it_is_for": "Given a clinical question, it calls biomedical tools, reads the results, and writes a free-form answer. The lab compares it with GPT-5 on DrugPC and TreatmentPC.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/atomica/atomica": { "id": "harvard/atomica/atomica", "name": "ATOMICA", "maker": "harvard", "family": "harvard/atomica", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Geometric model of intermolecular interfaces, pretrained on 2,037,972 interfaces from the Protein Data Bank and the Cambridge Structural Database. The GitHub list includes a pretrained model, an interface model, and ligand-specific fine-tunes. Those ligand heads are not separate folders. The modality is atomic structure, not text.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ATOMICA is a Zitnik Lab geometric model of molecular interactions. Code is in mims-harvard/ATOMICA under MIT, and weights were posted to ada-f/ATOMICA on 2025-03-27.", "what_it_is_for": "It embeds atomic interfaces across proteins, small molecules, ions, peptides, and nucleic acids. The project uses those embeddings for interface networks and ligand annotation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/axonet/axonet": { "id": "harvard/axonet/axonet", "name": "AxoNet", "maker": "harvard", "family": "harvard/axonet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-01-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage Broad Institute model. Stage 1 is a U-Net variational autoencoder for six-class neuron segmentation plus depth, Hub safetensors total 15,403,367, 512 by 512 grayscale input. Stage 2 is a CLIP-style model, Hub safetensors total 125,817,960, conditioned on stage 1 and allenai/scibert_scivocab_uncased.", "modalities": { "input": [ "image", "text" ], "output": [ "image", "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AxoNet is a two-stage neuron-morphology model posted by the Broad Institute of MIT and Harvard on 2026-01-30 under MIT. Stage 1 segments 2D projections. Stage 2 is a contrastive model on top of that encoder and SciBERT.", "what_it_is_for": "Stage 1 labels soma, axon, dendrites, and background and also estimates depth. The card trains it on 7,158 NeuroMorpho neurons and about 164,000 views.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/boomerang/boomerang": { "id": "harvard/boomerang/boomerang", "name": "Boomerang", "maker": "harvard", "family": "harvard/boomerang", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Layer-dropping distillation. The opened student starts from Qwen3-4B-Base by copying every other layer and the last two layers, then trains on 2.1B tokens of The Pile with cross-entropy, KL, and cosine losses. The repo is named boomerang-qwen3-2.3B. Its Hub safetensors total was 2,695,600,384.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Boomerang is a Harvard DCML distillation release. The Qwen3 student was posted on 2025-10-10 under Apache-2.0. Sibling students for Llama 3.2 and Pythia are versions.", "what_it_is_for": "The student is trained to match a teacher, and intermediate sizes are assembled by putting teacher layers back. No extra training is required for those sizes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/carta/carta": { "id": "harvard/carta/carta", "name": "CARTA", "maker": "harvard", "family": "harvard/carta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-07-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two weights in one repository: carta_core.pt is a YOLOv8n TMA core detector, file size about 6 MB; carta_tissue.pt is a DeepLabV3-ResNet50 tissue-versus-background segmenter, file size about 161 MB. File size is not a parameter count.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CARTA is a Mahmood Lab pair of multiplex-immunofluorescence weights for finding TMA cores and segmenting tissue. The Hub repository was created on 2026-07-19 under CC-BY-NC-4.0.", "what_it_is_for": "It is a preprocessing model for spatial-proteomics images, not a foundation encoder. The lab points users at CORAL for the surrounding workflow.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/cascade/cascade": { "id": "harvard/cascade/cascade", "name": "CASCADE", "maker": "harvard", "family": "harvard/cascade", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-06-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Context-aware single-cell transformer. The Alzheimer card specifies d_model 384, 6 heads, 12 layers, vocabulary 18,370, average-pool cell embeddings, and context projectors. Hub safetensors totals differ by cohort and are in the version notes. The modality is gene tokens, not natural language.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CASCADE is a Zitnik Lab single-cell model with one checkpoint per cohort. The Alzheimer weights were posted on 2026-06-13, and the autism, HLCA, and lung-cancer checkpoints followed on 2026-07-13. The license tag is MIT.", "what_it_is_for": "It encodes a cell relative to a biological reference and pools cells into a patient representation. The Alzheimer card says the paper was still coming soon.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/chief/chief": { "id": "harvard/chief/chief", "name": "CHIEF", "maker": "harvard", "family": "harvard/chief", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-09-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage pathology model from the Yu Lab at Harvard Medical School. Unsupervised tile encoding plus weakly supervised whole-slide pretraining. The GitHub readme names a patch-level model CHIEF-CTransPath. Anatomic-site text is embedded with CLIP and merged with image features. Trained on 60,530 whole-slide images from 19 anatomical sites.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CHIEF is a cancer pathology model from the Yu Lab in the Department of Biomedical Informatics at Harvard Medical School. Nature published it on 2024-09-04. Weights are distributed from the lab's GitHub instructions and a Docker Hub image.", "what_it_is_for": "The paper presents it as a general model for cancer detection, tumor origin, molecular status, and prognosis from whole-slide images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/clinical-bert/bio-clinicalbert": { "id": "harvard/clinical-bert/bio-clinicalbert", "name": "Bio+Clinical BERT", "maker": "harvard", "family": "harvard/clinical-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2019-04-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT-Base continued from BioBERT-Base v1.0 (PubMed 200K + PMC 270K) on all MIMIC-III notes. Pretraining max sequence length 128, batch size 32, 150,000 steps. This is a domain continuation, not the original BERT checkpoint.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bio+Clinical BERT is the all-notes clinical checkpoint from the 2019 Clinical BERT paper. The first author affiliation on that paper is Harvard-MIT, with MIT CSAIL and Microsoft Research coauthors. The weights on Hugging Face are MIT-licensed and were still public on 2026-10-15.", "what_it_is_for": "It is a masked-language encoder for clinical text, initialized from BioBERT and further trained on MIMIC-III notes. The paper reports gains on MedNLI and two i2b2 NER tasks, and no gain on two de-identification tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/clinical-bert/bio-discharge-summary-bert": { "id": "harvard/clinical-bert/bio-discharge-summary-bert", "name": "Bio+Discharge Summary BERT", "maker": "harvard", "family": "harvard/clinical-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2019-04-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT-Base continued from the same BioBERT initialization as Bio+Clinical BERT, but trained only on MIMIC-III discharge summaries. The card repeats the all-notes preprocessing description and the 128-token pretraining setup.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bio+Discharge Summary BERT is the discharge-summary companion to Bio+Clinical BERT from the same 2019 Harvard-MIT paper. The Hugging Face weights are MIT-licensed.", "what_it_is_for": "The authors trained it for tasks that mostly use discharge summaries, as a more specific clinical encoder than the all-notes model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/conch/conch": { "id": "harvard/conch/conch", "name": "CONCH", "maker": "harvard", "family": "harvard/conch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2024-01-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CoCa vision-language encoders. Card: vision ViT-B/16, 90M parameters; text encoder L12-E768-H12, 110M parameters. The public checkpoint removes the captioning decoder.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CONCH is a histopathology vision-language model from the Mahmood Lab at Harvard Medical School and Brigham and Women's Hospital. The public weights on Hugging Face were created 2024-01-05 and remain gated under CC-BY-NC-ND-4.0.", "what_it_is_for": "The lab released it to encode histopathology images and pathology text for classification, retrieval, and, in the full training setup, captioning. The public file keeps the encoders and drops the decoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/conch/conch-1-5": { "id": "harvard/conch/conch-1-5", "name": "CONCH v1.5", "maker": "harvard", "family": "harvard/conch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2024-10-18", "context_length": null, "max_output_tokens": null, "parameters": "307000000", "architecture": "ViT-L/16 vision backbone trained with CoCa. The card says it was restored from the UNI checkpoint and fine-tuned in a similar way to CONCH. CONCH-Z later states this vision encoder is 307M and that the public v1.5 weights omit the image projection layer.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CONCH v1.5 is a Mahmood Lab vision encoder that the lab says is based on CONCH and restored from UNI. The Hugging Face repository was created on 2024-10-18 and was still gated on 2026-10-15.", "what_it_is_for": "TITAN uses it as the patch encoder for whole-slide features. The public v1.5 file is vision-only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/conch/conch-z": { "id": "harvard/conch/conch-z", "name": "CONCH-Z", "maker": "harvard", "family": "harvard/conch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-10-12", "context_length": 127, "max_output_tokens": null, "parameters": "417627392", "architecture": "CONCH v1.5 ViT-L/16 vision encoder, frozen, plus a 768 by 768 projection and a 12-layer CoCa text encoder. Card total 418M: vision 307M, projection 0.6M, text 110M. Hub safetensors total 417,627,392. Shared embedding size 768. Images resized to 448.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CONCH-Z is a Mahmood Lab vision-language release that pairs the existing CONCH v1.5 vision encoder with a new text tower. The Hub repository was created on 2026-10-12. Weights are gated under CC-BY-NC-ND-4.0.", "what_it_is_for": "It is for zero-shot patch classification and image-text comparison in pathology research. The vision encoder is unchanged, so older CONCH v1.5 features can be projected instead of recomputed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/eve/eve": { "id": "harvard/eve/eve", "name": "EVE", "maker": "harvard", "family": "harvard/eve", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2021-10-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "A Bayesian variational autoencoder per protein family, trained on evolutionary alignments, plus a mixture model that turns the evolutionary index into a pathogenicity probability. The public server covers scores for thousands of human proteins. Viral checkpoints used later by EVEscape are zip files on marks.hms.harvard.edu. Input is a protein sequence, not natural language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EVE is a family of protein variational autoencoders from the Marks Lab at Harvard Medical School and the Oxford OATML group. Nature published it on 2021-10-27. Variant scores are served at evemodel.org, and the training code is MIT-licensed.", "what_it_is_for": "For a missense variant it returns an evolutionary index and a probability that the variant is pathogenic. The paper's claim is clinical variant interpretation without labeled pathogenic examples.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/eve/evescape": { "id": "harvard/eve/evescape", "name": "EVEscape", "maker": "harvard", "family": "harvard/eve", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Viral-escape score that combines EVE fitness predictions with structural and immune-accessibility features. The GitHub readme distributes EVE checkpoints for influenza hemagglutinin, SARS-CoV-2 spike, HIV envelope, Nipah fusion, and Lassa glycoprotein as zip files on marks.hms.harvard.edu.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EVEscape is a viral-escape model from the Marks Lab at Harvard Medical School and Oxford, published in Nature on 2023-10-11. It uses EVE plus structural features. Code and viral checkpoints are public.", "what_it_is_for": "It ranks mutations that may escape antibodies, including from pre-pandemic sequences. The Gazette described biweekly SARS-CoV-2 rankings shared with public-health groups.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/eve/popeve": { "id": "harvard/eve/popeve", "name": "popEVE", "maker": "harvard", "family": "harvard/eve", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Proteome-wide rescaling of missense scores. The GitHub model uses EVE and ESM-1v scores plus UK Biobank presence or absence to place variants on one human scale. ESM-1v is a Meta model and is not re-foldered. The official citation in the repository is a 2023 medRxiv preprint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "popEVE is a Marks Lab model that puts missense variants on one proteome-wide human scale. The code repository cites a November 2023 medRxiv preprint, uses the MIT license, and serves scores at pop.evemodel.org.", "what_it_is_for": "It takes cross-species scores, including EVE, and human population presence or absence, then ranks variants for rare-disease analysis. The lab's later gene-collapsing code is a separate analysis tool.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/eve/tranception": { "id": "harvard/eve/tranception", "name": "Tranception", "maker": "harvard", "family": "harvard/eve", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-07-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive protein transformer with inference-time retrieval of homologs. Hub repos exist for Large, Medium, and Small. The Large card was opened. It does not state a parameter count. Input is amino-acid sequence.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tranception is a protein fitness model from the Marks Lab and Oxford OATML. The Large checkpoint was posted on Hugging Face on 2022-07-14 under MIT. Medium and Small are the other two public sizes.", "what_it_is_for": "It scores sequence variants, including multiple mutants and indels, using an autoregressive transformer and retrieved homologs. The paper introduced ProteinGym for that comparison.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/geneformer/geneformer": { "id": "harvard/geneformer/geneformer", "name": "Geneformer", "maker": "harvard", "family": "harvard/geneformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2021-06", "context_length": 4096, "max_output_tokens": null, "parameters": "316354867", "architecture": "BERT-style transformer over rank-encoded single-cell transcriptomes. Default Hub checkpoint is Geneformer-V2-316M. Card: V1-10M, input size 2048, vocabulary about 25K, pretrained June 2021 on about 30 million cells. V2-104M and V2-316M, input size 4096, vocabulary about 20K protein-coding genes, pretrained December 2024 on about 104 million non-cancer cells. Masked-gene pretraining at 15 percent.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Geneformer is a single-cell transcriptomic transformer. Version 1 was pretrained in June 2021 and published from the Broad Institute of MIT and Harvard. The Hub repository's default checkpoint on 2026-10-15 is Geneformer-V2-316M under Apache-2.0. A Virtual Cell page places V2 at Gladstone and UCSF.", "what_it_is_for": "It ranks genes in a cell and predicts masked genes, then supports fine-tuning and in silico perturbation for network and disease tasks. The card's examples include cardiomyopathy target hypotheses that the authors say they validated in cells.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/hest/hest-tissue-seg": { "id": "harvard/hest/hest-tissue-seg", "name": "HEST tissue segmenter", "maker": "harvard", "family": "harvard/hest", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2024-07-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DeepLabV3 tissue segmenter fine-tuned on HEST-1k and ACROBAT for IHC and H&E. Backbone width was not stated on the card.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The HEST tissue segmenter is a DeepLabV3 checkpoint released by the Mahmood Lab with the HEST-1k workflow. The Hub repository dates from 2024-07-29 and is licensed CC-BY-NC-SA-4.0.", "what_it_is_for": "It segments tissue on IHC and H&E images for the HEST spatial-transcriptomics dataset. It is a named weight file, not the dataset itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/hupd/hupd-distilroberta": { "id": "harvard/hupd/hupd-distilroberta", "name": "HUPD DistilRoBERTa", "maker": "harvard", "family": "harvard/hupd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2022-07-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DistilRoBERTa-base continued with masked language modeling on the Harvard USPTO Patent Dataset. The card's paper link says TBD. The dataset paper is arXiv 2207.04043.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HUPD DistilRoBERTa is a masked-language model from the Harvard USPTO Patent Dataset group. The checkpoint was posted on 2022-07-05 under CC-BY-SA-4.0.", "what_it_is_for": "It continues DistilRoBERTa on English patent text. The card shows a fill-mask example and points at patentdataset.org.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/hupd/hupd-t5-small": { "id": "harvard/hupd/hupd-t5-small", "name": "HUPD T5-small", "maker": "harvard", "family": "harvard/hupd", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2022-07-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hub API lists a T5-small summarization model, dataset HUPD/hupd, arXiv tag 2207.04043. The card body was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HUPD T5-small is a second checkpoint from the Harvard USPTO group. Hub metadata lists it as a summarization model created on 2022-07-05 under CC-BY-SA-4.0. The card text itself was not opened, so this dossier is thinner than the others.", "what_it_is_for": "The dataset paper studies patent summarization among other tasks. This repo is the named T5-small artifact associated with that dataset on the Hub.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/kronos/kronos": { "id": "harvard/kronos/kronos", "name": "KRONOS", "maker": "harvard", "family": "harvard/kronos", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2025-03-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multiplex Vision Transformer-Small for spatial proteomics. The card describes a shared channel-wise stem and sinusoidal marker-identity embeddings. Pretraining: 47 million single-marker patches, 175 protein markers, 16 tissue types, 8 platforms, 5 institutions.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KRONOS is a Mahmood Lab spatial-proteomics encoder. The original checkpoint remains on Hugging Face, but the card collected on 2026-10-15 says it is deprecated and that new access requests will not be reviewed.", "what_it_is_for": "It was released to embed multiplex immunofluorescence patches without assuming one fixed antibody panel.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/kronos/kronos-2": { "id": "harvard/kronos/kronos-2", "name": "KRONOS2", "maker": "harvard", "family": "harvard/kronos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-04-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Marker-aware DINOv2 ViT-B/16. Embedding dimension 768. Reference patch 256 by 256. Pretraining marker vocabulary of 268. Inference described as fp32 and deterministic for fixed inputs, software, and hardware.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KRONOS2 is the Mahmood Lab successor to KRONOS for multiplex immunofluorescence. The Hub repository was created on 2026-04-20 and was still the recommended checkpoint on 2026-10-15.", "what_it_is_for": "It embeds a stack of single-marker patches conditioned on marker names, so one model can take different antibody panels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/madeleine/madeleine": { "id": "harvard/madeleine/madeleine", "name": "MADELEINE", "maker": "harvard", "family": "harvard/madeleine", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Slide encoder trained with dual global-local cross-stain alignment. The public example uses CONCH patch embeddings at 10x on 256-pixel patches. A third-party index describes a multi-head attention MIL model with embedding dimension 512. That index is not the lab card.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MADELEINE is a multistain slide representation model from the Mahmood Lab. The Hugging Face checkpoint was created on 2024-08-05, the same day as the arXiv preprint, and the license field is MIT.", "what_it_is_for": "It learns whole-slide embeddings by aligning different stains of the same tissue. The documented public run encodes breast slides from CONCH patch features.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/madrigal/madrigal": { "id": "harvard/madrigal/madrigal", "name": "Madrigal", "maker": "harvard", "family": "harvard/madrigal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT for the GitHub code. The Hub card text says CC-BY-4.0. The Hub license metadata field was empty.", "release_date": "2024-10-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal drug-combination model with a transformer or attention bottleneck over structure, pathway, cell-viability, and transcriptomic inputs. The arXiv abstract says 21,842 compounds. The project page says 953 clinical outcomes. The schema modality is a poor fit for those inputs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Madrigal is a Zitnik Lab model that predicts clinical outcomes of drug combinations from preclinical data. Weights were posted on 2024-10-25. The code repository says MIT, and the Hub card text says CC-BY-4.0.", "what_it_is_for": "It aligns structure, pathway, viability, and transcriptomic views and scores combination effects, including adverse interactions. The arXiv abstract describes screening across 21,842 compounds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/medtok/medtok": { "id": "harvard/medtok/medtok", "name": "MedTok", "maker": "harvard", "family": "harvard/medtok", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-06-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal tokenizer for medical codes. It combines code text with ontology graph structure and returns a quantized embedding. Version 1.0 only covers codes used in the paper. Unknown codes map to an unknown token.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedTok is a Zitnik Lab tokenizer for medical codes, posted on 2025-06-27 under MIT. The card cites an ICML 2025 paper and limits version 1 to codes used in that paper.", "what_it_is_for": "It turns a code such as an ICD string into tokens and a quantized embedding for EHR or question-answering models. It is not a chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/pathchat/pathchat": { "id": "harvard/pathchat/pathchat", "name": "PathChat", "maker": "harvard", "family": "harvard/pathchat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-12", "context_length": null, "max_output_tokens": null, "parameters": "13000000000", "architecture": "Vision-language assistant. Nature describes a pathology vision encoder connected to the 13-billion-parameter Llama 2 model, fine-tuned on more than 456,000 visual-language instructions and 999,202 question-answer turns. The 13B figure is the language-model component, not a measured total of the multimodal stack.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PathChat is a pathology vision-language assistant from the Mahmood Lab, published in Nature on 2024-06-12. The weights are closed. Training code was released for non-commercial academic use, and the project was exclusively licensed to Modella AI.", "what_it_is_for": "It answers pathology questions about histology images and text. The paper builds it on a pathology vision encoder plus Llama 2 13B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/procyon/procyon": { "id": "harvard/procyon/procyon", "name": "ProCyon", "maker": "harvard", "family": "harvard/procyon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 on the Full card; Llama 2 on the Split card", "release_date": "2024-10-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal protein-phenotype model that interleaves free text and protein encoders with a Llama language model. ProCyon-Full is instruction-tuned on all of ProCyon-Instruct from meta-llama/Llama-3.1-8B, and its Hub license tag is llama3. ProCyon-Split uses meta-llama/Llama-2-7b-hf and the llama2 tag. ProCyon-Bind is a fine-tune of ProCyon-Full.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProCyon is a Zitnik Lab multimodal model for protein phenotypes. Three Hub checkpoints, Full, Split, and Bind, are versions of one release. Full was posted on 2024-10-21.", "what_it_is_for": "It takes interleaved text and proteins and is instruction-tuned to describe or predict phenotypes. Split is the evaluation checkpoint. Bind is a binding fine-tune of Full.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/proton/proton": { "id": "harvard/proton/proton", "name": "PROTON", "maker": "harvard", "family": "harvard/proton", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-11-07", "context_length": null, "max_output_tokens": null, "parameters": "578000000", "architecture": "Heterogeneous graph transformer, 3 layers, 4 heads, hidden size 256, output size 128, trained with link prediction on NeuroKG. The card says 578 million parameters. NeuroKG has 147,020 nodes and 7,366,745 edges. The modality field is a poor fit: the input is a biomedical graph, not a text prompt.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PROTON is a Zitnik Lab graph model for neurological hypotheses, posted on 2025-11-07 under MIT. The card calls it a 578-million-parameter heterogeneous graph transformer trained on NeuroKG.", "what_it_is_for": "It proposes gene, drug, and disease links. The card describes checks in Parkinson's screens, a bipolar organoid experiment, and an Alzheimer's records study. Those checks are the lab's, not an external rating.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/seal/seal": { "id": "harvard/seal/seal", "name": "SEAL", "maker": "harvard", "family": "harvard/seal", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2026-02-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Spatial-transcriptomics alignment on top of an existing pathology encoder, plus a gene model. The card's first Hub-available backbones are CONCH (ViT-B/16, embedding 512) and UNI 2 (ViT-H/14, embedding 1536). Those embedding sizes are the backbones, not a SEAL parameter count.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SEAL is a Mahmood Lab preview that aligns histology encoders with spatial transcriptomics. The card calls the 2026-02-16 Hub drop pre-release v0.0.1. It was still listed on 2026-10-15.", "what_it_is_for": "The lab says it can replace a patch encoder in existing slide pipelines and adds image-to-gene and gene-to-image retrieval. It is not a new model trained from scratch.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/spatia/spatia": { "id": "harvard/spatia/spatia", "name": "SPATIA", "maker": "harvard", "family": "harvard/spatia", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-07-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card's pretrained file is SPATIA-scGPT: a 12-layer transformer, 512 dimensions, trained on the MIST atlas. A separate flow-matching generator produces cell-morphology images. best_model.pt is described as 220 MB, which is not a parameter count.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SPATIA is a Zitnik Lab spatial-cell model posted on 2026-07-03 under MIT. The card says the work is an ICML 2026 paper. The checked-in pretrained file is a 12-layer SPATIA-scGPT checkpoint.", "what_it_is_for": "It embeds cells from morphology and gene expression and can generate morphology images for predicted state changes. The Hub repo also holds embeddings and notebooks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/titan/titan": { "id": "harvard/titan/titan", "name": "TITAN", "maker": "harvard", "family": "harvard/titan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2024-12-02", "context_length": null, "max_output_tokens": null, "parameters": "158866176", "architecture": "Multimodal whole-slide model. The card says it is pretrained with visual self-supervision and vision-language alignment on CONCH v1.5 patch features at 512 pixels and 20x. Hub safetensors total is 158,866,176 and the Hub widget says 0.2B.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TITAN is a whole-slide pathology model from the Mahmood Lab at Harvard Medical School and Brigham and Women's Hospital. Weights were posted on 2024-12-02 and were still gated under CC-BY-NC-ND-4.0 on 2026-10-15.", "what_it_is_for": "The lab uses it for slide embeddings, zero-shot and few-shot classification, retrieval, and pathology report generation. It reads CONCH v1.5 patch features rather than replacing that patch encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/txagent/toolrag": { "id": "harvard/txagent/toolrag", "name": "ToolRAG-T1", "maker": "harvard", "family": "harvard/txagent", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-04", "context_length": null, "max_output_tokens": null, "parameters": "1543268864", "architecture": "Fine-tune of Alibaba-NLP/gte-Qwen2-1.5B-instruct with multiple-negatives ranking loss on requirement and tool-description pairs. Hub safetensors total 1,543,268,864. The repository name says 1.5B.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ToolRAG-T1 is the Zitnik Lab retriever that picks ToolUniverse tools for TxAgent. It was posted on 2025-03-04 under Apache-2.0. The base model is gte-Qwen2-1.5B-instruct.", "what_it_is_for": "It ranks tool descriptions against a requirement. TxAgent then calls the selected tools. The checkpoint is part of the TxAgent release, not a general search embedding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/txagent/txagent": { "id": "harvard/txagent/txagent", "name": "TxAgent-T1", "maker": "harvard", "family": "harvard/txagent", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1", "release_date": "2025-03-03", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Instruction tune of Llama-3.1-8B-Instruct. Hub safetensors total 8,030,261,248. The card says it was trained on 378,027 instruction samples with tool calls. The card also contains the typo Llama3.1-88-Instrcut.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TxAgent-T1 is a tool-using therapeutic-reasoning model from the Zitnik Lab at Harvard Medical School. The 8B checkpoint was posted on 2025-03-03. It is a fine-tune of Llama-3.1-8B-Instruct, not a new base model.", "what_it_is_for": "The lab built it to choose among 211 biomedical tools and answer drug-reasoning questions with multi-step function calls. Evaluation drugs were held out by approval year.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/txgnn/txgnn": { "id": "harvard/txgnn/txgnn", "name": "TxGNN", "maker": "harvard", "family": "harvard/txgnn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-03-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph neural network for zero-shot drug repurposing. The GitHub example uses hidden, input, and output sizes of 100 and a metric-learning module. Pretraining graph stated as 17,080 diseases and 7,957 therapeutic candidates. An example checkpoint is linked from Google Drive. The modality is a knowledge graph.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TxGNN is a Zitnik Lab graph model for zero-shot drug repurposing. The medRxiv preprint is dated 2023-03-19, and a Kempner note on 2024-09-30 says the peer-reviewed paper is in Nature Medicine. An example checkpoint is linked from the GitHub readme.", "what_it_is_for": "It predicts indications and contraindications for diseases that had no treatments in training. A public explorer is at txgnn.org.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/uni/uni": { "id": "harvard/uni/uni", "name": "UNI", "maker": "harvard", "family": "harvard/uni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2024-03-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-L/16 DINOv2 histopathology encoder. The model card states ViT-Large (0.3B params), patch size 16, embedding dimension 1024, 16 heads, MLP FFN. Feature shape stated as 1024.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UNI is a histopathology vision encoder from the Mahmood Lab at Harvard Medical School and Brigham and Women's Hospital. Weights were posted on 2024-03-19 under CC-BY-NC-ND-4.0 and were still the listed ViT-L/16 release on 2026-10-15. UNI 2 is the successor.", "what_it_is_for": "The lab released it as a general-purpose feature extractor for histopathology ROIs and whole slides, pretrained on internal neoplastic, infectious, inflammatory, and normal tissue plus GTEx, and kept out of several public slide collections used as benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "harvard/uni/uni-2": { "id": "harvard/uni/uni-2", "name": "UNI 2", "maker": "harvard", "family": "harvard/uni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "2025-01-14", "context_length": null, "max_output_tokens": null, "parameters": "681000000", "architecture": "Custom ViT-H/14 with 8 register tokens. Card: depth 24, 24 heads, embed dim 1536, SwiGLU, 681M parameters. Output feature shape stated as 1536. GitHub calls the checkpoint ViT-h/14-reg8.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UNI 2 is the Mahmood Lab successor to UNI, released as the UNI2-h checkpoint. The GitHub release note is dated 2025-01-14. On 2026-10-15 the weights were still gated under CC-BY-NC-ND-4.0.", "what_it_is_for": "It is a larger self-supervised histopathology encoder, trained on H&E and IHC tiles, for the same ROI and slide feature-extraction jobs as UNI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hawaii/wv-net/wv-net": { "id": "hawaii/wv-net/wv-net", "name": "WV-Net", "maker": "hawaii", "family": "hawaii/wv-net", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SimCLR contrastive encoder with a ResNet-50 backbone. The published checkpoint is the backbone only, described as torchvision-compatible. A projection head is used in pretraining and is not the transfer embedding. Appendix ablations (BYOL, SwAV, ConvNeXt-T, ViT-S/16) were not released as separate products.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WV-Net is a Sentinel-1 wave-mode SAR embedding model from the Sadowski group at the University of Hawai\u02bbi at M\u0101noa, with coauthors at the University of Washington, the University of New Hampshire, and Ifremer. It is an active open-weight research checkpoint, not an API product. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors train a semantic embedding model on nearly 10 million unannotated Sentinel-1 wave-mode images so later tasks need fewer expert labels. Stated uses include air-sea interaction studies, constraints for numerical weather prediction, and sea ice monitoring. The public file is a backbone for local transfer, not a chat or forecast API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hedra/avatar/hedra-avatar": { "id": "hedra/avatar/hedra-avatar", "name": "Hedra Avatar", "maker": "hedra", "family": "hedra/avatar", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Audio-driven talking-head model. The model page says it runs on Together AI infrastructure. No parameter count was published.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hedra Avatar is Hedra's long-form talking-head model. A portrait, a prompt, and audio produce lip-synced video. It is active on API v3 as `hedra-avatar` and on the legacy public API as `together/hedra-avatar`. The model page says Together AI serves it. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model page says it is for on-camera dialogue, explainers, and vocal performance, up to 10 minutes, with the video length taken from the audio unless a duration is set. Hedra's 13 February 2026 API post contrasted it with Omnia: Avatar for long uncut dialogue, Omnia for short clips with camera and body direction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hedra/character/character-1": { "id": "hedra/character/character-1", "name": "Character-1", "maker": "hedra", "family": "hedra/character", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hedra called Character-1 a video-focused foundation model for expressive talking, singing, and rapping characters. No architecture diagram or parameter count was published.", "modalities": { "input": [ "image", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Character-1 is Hedra's first named video foundation model. Hedra posted a research preview on 18 June 2024 and left stealth with it on 1 August 2024. It is not in the 15 October 2026 public catalog. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hedra introduced it as a video foundation model for expressive talking, singing, and rapping characters, and as the first step toward a multimodal creation studio. VentureBeat's 1 August 2024 report quotes Hedra calling the June preview proof that creators no longer had to trade control for speed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hedra/character/character-2": { "id": "hedra/character/character-2", "name": "Character-2", "maker": "hedra", "family": "hedra/character", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hedra called Character-2 the next audio-to-video foundation model after Character-1. No parameter count or diagram was published.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Character-2 is Hedra's second audio-to-video character model, announced by Hedra on 3 October 2024. A legacy API reference is still labeled Character-2. It is not in the 15 October 2026 v3 catalog. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hedra called it a generational leap for audio-to-video foundation models and a step toward a full-stack character model with intelligence and emotion. The launch post stressed wider aspect ratios and longer clips than Character-1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hedra/character/character-3": { "id": "hedra/character/character-3", "name": "Hedra Character 3", "maker": "hedra", "family": "hedra/character", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hedra calls Character-3 the first omnimodal model in production: it jointly reasons over image, text, and audio. No parameter count or layer diagram was published.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hedra Character 3 is Hedra's omnimodal character-video model. It turns a start image, a text prompt, and driving audio into talking or singing video. It is active on API v3 as `hedra-character-3`. Hedra announced it on 6 March 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hedra's launch post calls it the first omnimodal model in production, built to reason jointly over image, text, and audio. The current model page positions it for talking avatars, marketing clips, singing, and storytelling, including multi-speaker clips, up to 10 minutes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hedra/omnia/omnia": { "id": "hedra/omnia/omnia", "name": "Hedra Omnia", "maker": "hedra", "family": "hedra/omnia", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-02-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hedra calls Omnia a world model that jointly models vision, text, and audio, positioned for partner post-training. No parameter count was published. Public weights were not released.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hedra Omnia is Hedra's world-model release for character video with camera, body, and scene control. It shipped as Omnia Alpha on 5 February 2026 and was put on the API beside Hedra Avatar on 13 February 2026. It is not in the 15 October 2026 v3 OpenAPI or the public all-models index, and its model page returned not found. This dossier was collected on 2026-10-15. Confidence is medium because several numeric specs lived on pages that have since changed.", "what_it_is_for": "Hedra's launch post calls Omnia a world model: one base that takes vision, text, and audio so later models can learn how a scene moves. The product posts describe audio-driven clips where the user directs camera, body motion, and background, not only a face. The 13 February 2026 API post calls it the short-form model next to long-form Avatar.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heidelberg/classical/grberta": { "id": "heidelberg/classical/grberta", "name": "Gr\u03b5BERTa", "maker": "heidelberg", "family": "heidelberg/classical", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base sized masked-language encoder. config.json: RobertaForMaskedLM, 12 layers, hidden 768, intermediate 3072, 12 heads, GELU, absolute positions, vocab 52000, max_position_embeddings 514, layer_norm_eps 1e-12. The paper calls this GrBERTa. The card spells it Gr\u03b5Berta. The Hub id is GreBerta.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gr\u03b5BERTa is a monolingual Ancient Greek masked-language model from the Computational Linguistics group at Heidelberg University. Frederick Riemenschneider and Anette Frank released the weights on 24 May 2023 under Apache-2.0. It is an open research checkpoint, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "The paper introduces GrBERTa as a RoBERTa-base model trained on Ancient Greek so classical texts can be tagged, parsed, and probed without starting from Modern Greek BERT. The card shows a fill-mask load path and reports fine-tuned Universal Dependencies scores. The lab GitHub lists it as the Ancient Greek encoder-only model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heidelberg/classical/greta": { "id": "heidelberg/classical/greta", "name": "Gr\u03b5Ta", "maker": "heidelberg", "family": "heidelberg/classical", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5-base sized encoder-decoder. config.json: T5ForConditionalGeneration, 12 encoder and 12 decoder layers, d_model 768, d_ff 2048, 12 heads, d_kv 64, gated-GELU, relative attention buckets 32, untied embeddings, vocab 32103. Not stock t5-base, which uses a larger feed-forward width and a different vocabulary. The paper calls it GrTa. The card spells it Gr\u03b5Ta. The Hub id is GreTa.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gr\u03b5Ta is a monolingual Ancient Greek T5-style encoder-decoder from the Computational Linguistics group at Heidelberg University. Frederick Riemenschneider and Anette Frank released the weights on 24 May 2023 under Apache-2.0. It is an open research checkpoint, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "The paper introduces GrTa so Ancient Greek lemmatization can be a contextual sequence-to-sequence task, and so the encoder can be compared with RoBERTa on tagging and parsing. The card says training had two stages: Internet Archive OCR, then born-digital Greek corpora.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heidelberg/classical/laberta": { "id": "heidelberg/classical/laberta", "name": "LaBERTa", "maker": "heidelberg", "family": "heidelberg/classical", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base sized monolingual Latin masked-language encoder. config.json: RobertaForMaskedLM, 12 layers, hidden 768, intermediate 3072, 12 heads, GELU, absolute positions, vocab 52000, max_position_embeddings 514, layer_norm_eps 1e-12. The card spells it LaBerta. The lab README spells it LaBERTa.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaBERTa is a monolingual Latin masked-language model from the Computational Linguistics group at Heidelberg University. Frederick Riemenschneider and Anette Frank released the weights on 24 May 2023 under Apache-2.0. It is an open research checkpoint, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "The card calls LaBERTa the monolingual encoder-only model for Classical Philology and says it was trained on Corpus Corporum. The lab README lists it as the Latin encoder. The card reports EvaLatin 2022 part-of-speech accuracy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heidelberg/classical/lata": { "id": "heidelberg/classical/lata", "name": "LaTa", "maker": "heidelberg", "family": "heidelberg/classical", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5-base sized monolingual Latin encoder-decoder. config.json: T5ForConditionalGeneration, 12 encoder and 12 decoder layers, d_model 768, d_ff 2048, 12 heads, d_kv 64, gated-GELU, relative attention buckets 32, untied embeddings, vocab 52103.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaTa is a monolingual Latin encoder-decoder from the Computational Linguistics group at Heidelberg University. Frederick Riemenschneider and Anette Frank released the weights on 24 May 2023 under Apache-2.0. It is an open research checkpoint, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "The card calls LaTa the monolingual T5-base model for Classical Philology and says it was trained on Corpus Corporum. It reports EvaLatin 2022 lemmatization. The lab README lists it as the Latin encoder-decoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heidelberg/classical/philberta": { "id": "heidelberg/classical/philberta", "name": "PhilBERTa", "maker": "heidelberg", "family": "heidelberg/classical", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base sized trilingual masked-language encoder. config.json: RobertaForMaskedLM, 12 layers, hidden 768, intermediate 3072, 12 heads, GELU, absolute positions, vocab 64000, max_position_embeddings 514, layer_norm_eps 1e-5. The card spells it PhilBerta. The paper and the later sentence-transformer paper spell it PhilBERTa.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PhilBERTa is a trilingual Ancient Greek, Latin, and English masked-language model from the Computational Linguistics group at Heidelberg University. Frederick Riemenschneider and Anette Frank released the weights on 24 May 2023 under Apache-2.0. It is an open research checkpoint, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "The paper introduces PhilBERTa as the RoBERTa-base model pretrained on Greek, Latin, and English so one encoder can serve both classical languages. The card reports Ancient Greek Perseus tagging and parsing and EvaLatin 2022 part-of-speech scores. A later paper uses it as the student backbone for SPhilBERTa.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heidelberg/classical/philta": { "id": "heidelberg/classical/philta", "name": "PhilTa", "maker": "heidelberg", "family": "heidelberg/classical", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5-base sized trilingual encoder-decoder. config.json: T5ForConditionalGeneration, 12 encoder and 12 decoder layers, d_model 768, d_ff 2048, 12 heads, d_kv 64, gated-GELU, relative attention buckets 32, untied embeddings, vocab 64103.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PhilTa is a trilingual Ancient Greek, Latin, and English encoder-decoder from the Computational Linguistics group at Heidelberg University. Frederick Riemenschneider and Anette Frank released the weights on 24 May 2023 under Apache-2.0. It is an open research checkpoint, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "The paper introduces PhilTa as the T5-base model pretrained on Greek, Latin, and English. The card says it is especially useful when more than one of those languages should be processed, and it reports EvaLatin 2022 lemmatization. A later paper fine-tunes it to translate English into Ancient Greek so SPhilBERTa can be trained on more parallel sentences.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heidelberg/classical/sphilberta": { "id": "heidelberg/classical/sphilberta", "name": "SPhilBERTa", "maker": "heidelberg", "family": "heidelberg/classical", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-08-24", "context_length": null, "max_output_tokens": null, "parameters": "0.1B", "architecture": "Sentence transformer initialized from PhilBERTa. config.json: RobertaModel, 12 layers, hidden 768, intermediate 3072, 12 heads, GELU, vocab 64000, max_position_embeddings 514. The card's usage example applies mean pooling over token states. The Hub size label is 0.1B parameters. The paper trains the student with multilingual knowledge distillation from an English MPNet sentence teacher.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SPhilBERTa is a trilingual sentence encoder from the Computational Linguistics group at Heidelberg University. Frederick Riemenschneider and Anette Frank released the weights on 24 August 2023 under Apache-2.0. It is an open research checkpoint for cross-lingual classical allusion search, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "The paper introduces SPhilBERTa to find the same sentence, and then possible allusions, across Ancient Greek, Latin, and English. It is a PhilBERTa student distilled from an English MPNet sentence teacher so a sentence and its translation land near each other. The card shows sentence-transformers encoding and mean pooling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heidelberg/imagebart/imagebart": { "id": "heidelberg/imagebart/imagebart", "name": "ImageBART", "maker": "heidelberg", "family": "heidelberg/imagebart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-08-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multinomial diffusion over a compressed discrete latent, inverted by a short coarse-to-fine autoregressive Markov chain. Each transition is an encoder-decoder transformer except the coarsest scale, which the paper implements as decoder-only. The paper's reported settings are T=3 (n=2 transformers) for FFHQ, n=3 for LSUN, and n=5 for class-conditional ImageNet. The repo also ships a 4-scale FFHQ zip, a 2-scale FFHQ zip, and a 4-scale ImageNet zip. Training is usually one scale at a time. A joint 4-scale FFHQ config is included. The README sampling blurb writes LSUN-{bedrooms,bedrooms,cats}. The download list is bedrooms, churches, and cats.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ImageBART is a NeurIPS 2021 image model from Patrick Esser, Robin Rombach, Andreas Blattmann, and Bj\u00f6rn Ommer. The paper affiliation line is the Heidelberg Collaboratory for Image Processing, IWR, Heidelberg University. arXiv v1 is 19 August 2021. Weights are local zips. The GitHub listing has no license file. Collected 2026-10-15.", "what_it_is_for": "The abstract describes a short Markov chain that inverts a multinomial diffusion process on a compressed discrete latent. Each finer scale can attend to the coarser scale, which is how the model does free-form inpainting and local edits as well as generation. The README ships unconditional FFHQ and LSUN checkpoints and class-conditional ImageNet checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heidelberg/scene-synthesis/scene-synthesis": { "id": "heidelberg/scene-synthesis/scene-synthesis", "name": "Scene Synthesis", "maker": "heidelberg", "family": "heidelberg/scene-synthesis", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2021-05-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Layout-conditional autoregressive transformer on a discrete VQGAN latent. The README ships a 1B Open Images model and a distilled 125M model, so there is no single parameter count. Conditioning is a tokenized layout of bounding boxes. The abstract describes likelihood training without a mask generator.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Scene Synthesis is a layout-conditional image model from Manuel Jahn, Robin Rombach, and Bj\u00f6rn Ommer. The note was submitted on 13 May 2021 for the AI for Content Creation workshop at CVPR 2021. Weights are in the CompVis taming-transformers repository, whose README links Bj\u00f6rn Ommer to the Heidelberg HCI staff page. The arXiv abstract page does not print an affiliation line. Collected 2026-10-15.", "what_it_is_for": "The abstract says the model is an autoregressive transformer on a discrete VQGAN latent, conditioned on a tokenized layout, trained with likelihood and without a mask generator. The README section \"Scene Image Synthesis\" hosts Open Images and COCO checkpoints and a sampling script.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heidelberg/taming/taming-transformer": { "id": "heidelberg/taming/taming-transformer", "name": "Taming Transformer", "maker": "heidelberg", "family": "heidelberg/taming", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2020-12-17", "context_length": null, "max_output_tokens": null, "parameters": "1.4B", "architecture": "Autoregressive transformer over VQGAN codes. The paper says the transformer matches GPT-2 and uses 16 attention heads. The README calls the class-conditional ImageNet checkpoint a 1.4B model. Paper Table 8 lists c-IN (big) at 48 layers and 1400M parameters, codebook 16384, sequence length 257, embedding size 1536. The 1.4B figure is that ImageNet checkpoint. Other dataset checkpoints are separate sizes in Table 8 and are not pinned to a file by the README. Shout-outs name minGPT as the implementation base. Samples in the README table are 256x256.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Taming Transformer is the autoregressive second stage of Taming Transformers, from Patrick Esser, Robin Rombach, and Bj\u00f6rn Ommer at Heidelberg HCI / IWR. It samples VQGAN codes. The paper is a CVPR 2021 oral. arXiv v1 is 17 December 2020 and v3 is 23 June 2021. Weights are local checkpoints. Collected 2026-10-15.", "what_it_is_for": "The README says a transformer models the composition of VQGAN codes so the system can synthesize images, including class-conditional, segmentation-conditional, and depth-conditional samples. Unconditional face checkpoints are included. The shout-outs credit a minGPT-style implementation. The paper says the transformer follows GPT-2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heidelberg/vqgan/vqgan": { "id": "heidelberg/vqgan/vqgan", "name": "VQGAN", "maker": "heidelberg", "family": "heidelberg/vqgan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2020-12-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Convolutional vector-quantized GAN. The paper's encoder and decoder follow a residual CNN with a non-local block and no skip connections, then a discrete codebook. A patch discriminator and a perceptual loss sit beside the reconstruction loss. Downsampling factor f sets the code-grid size. Composition of those codes is a separate transformer and is catalogued as taming-transformer.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VQGAN is the convolutional codebook model from the Computer Vision group at Heidelberg University (HCI / IWR). Patrick Esser, Robin Rombach, and Bj\u00f6rn Ommer released it with the CVPR 2021 oral paper Taming Transformers. arXiv v1 is 17 December 2020 and v3 is 23 June 2021. Weights are local research checkpoints. Collected 2026-10-15.", "what_it_is_for": "The README describes a convolutional VQGAN that learns a codebook of visual parts. A separate autoregressive transformer then models how those parts combine. This folder is only the first stage. Reconstruction FID in the README is the quality of that codebook, measured with torch-fidelity.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/emma/emma-500-llama2-7b": { "id": "helsinki/emma/emma-500-llama2-7b", "name": "EMMA-500 Llama 2 7B", "maker": "helsinki", "family": "helsinki/emma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-09-14", "context_length": null, "max_output_tokens": null, "parameters": "6738415616", "architecture": "Continual pretrain of meta-llama/Llama-2-7b-hf. Card data mix: monolingual mix across code, books, and instruction data. Card total tokens: not stated as a training-token total; the card says the MaLA corpus spans 74 billion tokens. 546 languages with over 100k tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EMMA-500 Llama 2 7B is a continual pretrain from the University of Helsinki-led MaLA-LM group. The card names Llama base `meta-llama/Llama-2-7b-hf`, a MaLA corpus mix, and 546 languages with at least 100k tokens each. Weights are on the MaLA-LM Hub org. The Helsinki-NLP Hub collection links this series. Collected 2026-10-15.", "what_it_is_for": "Massively multilingual text generation, including translation, classification, and other tasks named on the card. The Llama 3 cards say the models are not for real-world high-stakes use and that some high-resource tasks regress.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/emma/emma-500-llama3-1-8b-bi": { "id": "helsinki/emma/emma-500-llama3-1-8b-bi", "name": "EMMA-500 Llama 3.1 8B Bi", "maker": "helsinki", "family": "helsinki/emma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2025-05-10", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Continual pretrain of meta-llama/Llama-3.1-8B. Card data mix: monolingual mix plus bilingual translation data in more than 2,500 language pairs. Card total tokens: 671B. 546 languages with over 100k tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EMMA-500 Llama 3.1 8B Bi is a continual pretrain from the University of Helsinki-led MaLA-LM group. The card names Llama base `meta-llama/Llama-3.1-8B`, a MaLA corpus mix, and 546 languages with at least 100k tokens each. Weights are on the MaLA-LM Hub org. The Helsinki-NLP Hub collection links this series. Collected 2026-10-15.", "what_it_is_for": "Massively multilingual text generation, including translation, classification, and other tasks named on the card. The Llama 3 cards say the models are not for real-world high-stakes use and that some high-resource tasks regress.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/emma/emma-500-llama3-1-8b-mono": { "id": "helsinki/emma/emma-500-llama3-1-8b-mono", "name": "EMMA-500 Llama 3.1 8B Mono", "maker": "helsinki", "family": "helsinki/emma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2025-05-10", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Continual pretrain of meta-llama/Llama-3.1-8B. Card data mix: monolingual mix of code, books, instruction data, and papers. Card total tokens: 419B. 546 languages with over 100k tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EMMA-500 Llama 3.1 8B Mono is a continual pretrain from the University of Helsinki-led MaLA-LM group. The card names Llama base `meta-llama/Llama-3.1-8B`, a MaLA corpus mix, and 546 languages with at least 100k tokens each. Weights are on the MaLA-LM Hub org. The Helsinki-NLP Hub collection links this series. Collected 2026-10-15.", "what_it_is_for": "Massively multilingual text generation, including translation, classification, and other tasks named on the card. The Llama 3 cards say the models are not for real-world high-stakes use and that some high-resource tasks regress.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/emma/emma-500-llama3-8b-bi": { "id": "helsinki/emma/emma-500-llama3-8b-bi", "name": "EMMA-500 Llama 3 8B Bi", "maker": "helsinki", "family": "helsinki/emma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2025-05-10", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Continual pretrain of meta-llama/Llama-3-8B. Card data mix: monolingual mix plus bilingual translation data in more than 2,500 language pairs. Card total tokens: 671B. 546 languages with over 100k tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EMMA-500 Llama 3 8B Bi is a continual pretrain from the University of Helsinki-led MaLA-LM group. The card names Llama base `meta-llama/Llama-3-8B`, a MaLA corpus mix, and 546 languages with at least 100k tokens each. Weights are on the MaLA-LM Hub org. The Helsinki-NLP Hub collection links this series. Collected 2026-10-15.", "what_it_is_for": "Massively multilingual text generation, including translation, classification, and other tasks named on the card. The Llama 3 cards say the models are not for real-world high-stakes use and that some high-resource tasks regress.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/emma/emma-500-llama3-8b-mono": { "id": "helsinki/emma/emma-500-llama3-8b-mono", "name": "EMMA-500 Llama 3 8B Mono", "maker": "helsinki", "family": "helsinki/emma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2025-05-10", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Continual pretrain of meta-llama/Llama-3-8B. Card data mix: monolingual mix of code, books, instruction data, and papers. Card total tokens: 419B. 546 languages with over 100k tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EMMA-500 Llama 3 8B Mono is a continual pretrain from the University of Helsinki-led MaLA-LM group. The card names Llama base `meta-llama/Llama-3-8B`, a MaLA corpus mix, and 546 languages with at least 100k tokens each. Weights are on the MaLA-LM Hub org. The Helsinki-NLP Hub collection links this series. Collected 2026-10-15.", "what_it_is_for": "Massively multilingual text generation, including translation, classification, and other tasks named on the card. The Llama 3 cards say the models are not for real-world high-stakes use and that some high-resource tasks regress.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/mala/mala-500": { "id": "helsinki/mala/mala-500", "name": "MaLA-500", "maker": "helsinki", "family": "helsinki/mala", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-01-24", "context_length": null, "max_output_tokens": null, "parameters": "10.7B total on the v2 card, of which 2.1B are trainable", "architecture": "Llama 2 7B with vocabulary extended to 260164 and LoRA continued pretraining on Glot500-c. The paper says LoRA rank 8 and alpha 32 on query and value projections. v1 and v2 are both PEFT adapters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MaLA-500 is a 534-language adaptation of Llama 2 7B by Peiqin Lin, Shaoxiong Ji, J\u00c3\u00b6rg Tiedemann, Andr\u00c3\u00a9 F. T. Martins, and Hinrich Sch\u00c3\u00bctze. Ji and Tiedemann are at the University of Helsinki. Lin and Sch\u00c3\u00bctze are at LMU Munich. Martins is at Instituto Superior T\u00c3\u00a9cnico and Unbabel. Weights are released by the MaLA-LM Hub org, which the Helsinki-NLP collection links. v1 and v2 are versions of this model. Collected 2026-10-15.", "what_it_is_for": "Continued pretraining and vocabulary extension so a Llama 2 model covers 534 languages in Glot500-c. The v2 card says the result is a 10.7B model of which 2.1B parameters were trainable, using LoRA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/opus-mt/opus-mt": { "id": "helsinki/opus-mt/opus-mt", "name": "OPUS-MT", "maker": "helsinki", "family": "helsinki/opus-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the Opus-MT download page and the en-de card. Apache-2.0 on the mul-en card. Not uniform.", "release_date": "2020-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Marian NMT. The en-de config.json is a 6-layer encoder and 6-layer decoder, d_model 512, 8 attention heads, feed-forward 2048, Swish, vocab size 58101, max_position_embeddings 512, default beams 4. The mul-en card says transformer with SentencePiece spm32k. Other cards say transformer-align. The EAMT 2020 abstract says the repository then held over 1,000 models.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OPUS-MT is the Language Technology Research Group at the University of Helsinki's open Marian translation release. The EAMT 2020 paper describes a repository of over 1,000 pretrained neural models. On 2026-10-15 the Helsinki-NLP Hub still hosted 1,336 repos under the `opus-mt-{src}-{tgt}` id, after the Tatoeba-big, Tatoeba-base, Bible, OpusTranslate-tiny, and 2025 Esperanto multilingual repos were counted separately. Weights are downloadable. There is no University of Helsinki price list. Collected 2026-10-15.", "what_it_is_for": "Free neural machine translation that can run on desktop hardware. The project README points at Marian models trained on OPUS, mostly with SentencePiece and guided alignment from eflomal. Directional repos are separate checkpoints, not one multilingual model. A few repos are many-to-one or group models, including `opus-mt-mul-en`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/opus-mt/opus-mt-esperanto": { "id": "helsinki/opus-mt/opus-mt-esperanto", "name": "OPUS-MT Esperanto", "maker": "helsinki", "family": "helsinki/opus-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2025-12-12", "context_length": null, "max_output_tokens": null, "parameters": "76939521 on opus-mt-eo-caenes", "architecture": "Multilingual Marian. Full-size eo-caenes is loaded as MarianMTModel and selects English, Spanish, or Catalan with >>eng<<, >>spa<<, or >>cat<<. Tiny checkpoints are Marian npz files and the card says they are not for Transformers inference.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OPUS-MT Esperanto is the University of Helsinki's 2025-2026 multilingual Marian release for Esperanto with Catalan, English, and Spanish. One model translates Esperanto into those three languages with a target tag. The reverse model translates the three languages into Esperanto. Tiny Marian checkpoints of the same directions were added in March 2026. Older bilingual `opus-mt-*-eo` repos stay in the main OPUS-MT folder. Collected 2026-10-15.", "what_it_is_for": "Open machine translation to and from Esperanto. The collection page uses that name. Target language is chosen with `>>eng<<`, `>>spa<<`, or `>>cat<<` on the Esperanto-source model. Training text is Tatoeba. FLORES-200 is the development set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/opus-mt/opus-mt-tc-base": { "id": "helsinki/opus-mt/opus-mt-tc-base", "name": "OPUS-MT Tatoeba base", "maker": "helsinki", "family": "helsinki/opus-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the fi-uk card. Other base cards were not checked.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "75295551 on opus-mt-tc-base-fi-uk", "architecture": "Marian transformer. fi-uk config: 6 encoder layers, 6 decoder layers, d_model 512, 8 heads, feed-forward 2048, Swish, shared embeddings, vocab 60735, max_position_embeddings 512, num_beams 4, float16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OPUS-MT Tatoeba base is the smaller Marian line the University of Helsinki published beside the transformer-big Tatoeba models. On 2026-10-15 the Hub had 14 repos named `opus-mt-tc-base-*`, mostly directions that touch Ukrainian or a language group. They are one release, not 14 products. Collected 2026-10-15.", "what_it_is_for": "Neural translation for the 14 published directions. The Finnish-Ukrainian checkpoint is a standard Marian transformer, not the big configuration used by `opus-mt-tc-big`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/opus-mt/opus-mt-tc-bible": { "id": "helsinki/opus-mt/opus-mt-tc-bible", "name": "OPUS-MT Tatoeba Bible big", "maker": "helsinki", "family": "helsinki/opus-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the mul-mul card. Other Bible cards were not checked.", "release_date": "2024-08-17", "context_length": null, "max_output_tokens": null, "parameters": "247766051 on opus-mt-tc-bible-big-mul-mul", "architecture": "Marian transformer-big. mul-mul config: 6 encoder layers, 6 decoder layers, d_model 1024, 16 heads, feed-forward 4096, ReLU, shared embeddings, vocab 69667, max_position_embeddings 1024. Card says SentencePiece spm64k and data opusTCv20230926+bt+jhubc. A leading >>id<< target token is required on the many-to-many model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OPUS-MT Tatoeba Bible big is a 2024 multilingual Marian line from the University of Helsinki Language Technology group. The many-to-many card says transformer-big, trained as `opusTCv20230926+bt+jhubc`, released 2024-08-17. On 2026-10-15 the Hub had 85 repos named `opus-mt-tc-bible-big-*`. Directional repos stay in this folder. Collected 2026-10-15.", "what_it_is_for": "Multilingual neural translation, including a many-to-many model. The mul-mul card says a sentence-initial target token such as `>>rus<<` is required. It also says many listed languages will not work because training data is scarce. HPLT bilingual data v1 is named as part of the Tatoeba Challenge data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/opus-mt/opus-mt-tc-big": { "id": "helsinki/opus-mt/opus-mt-tc-big", "name": "OPUS-MT Tatoeba big", "maker": "helsinki", "family": "helsinki/opus-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the fi-en card. Other big cards were not checked.", "release_date": "2021-12-08", "context_length": null, "max_output_tokens": null, "parameters": "235633126 on opus-mt-tc-big-fi-en", "architecture": "Marian transformer-big. fi-en config: 6 encoder layers, 6 decoder layers, d_model 1024, 16 heads, feed-forward 4096, ReLU, shared embeddings, vocab 57830, max_position_embeddings 1024, max_length 512, num_beams 6, float16. Card says SentencePiece spm32k and data opusTCv20210807+bt.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OPUS-MT Tatoeba big is the University of Helsinki Language Technology group's transformer-big Marian line from the Tatoeba Translation Challenge. On 2026-10-15 the Helsinki-NLP Hub had 86 repos named `opus-mt-tc-big-*`. They are separate checkpoints, grouped here as one release. Weights are downloadable. Collected 2026-10-15.", "what_it_is_for": "Neural translation for the language pairs and language groups in the Tatoeba Challenge, trained on OPUS with the OPUS-MT-train pipeline and converted to PyTorch. The Finnish-English card says the data mix is `opusTCv20210807+bt` and the model is transformer-big.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/opus-mt/opus-mt-tiny": { "id": "helsinki/opus-mt/opus-mt-tiny", "name": "OPUS-MT tiny", "maker": "helsinki", "family": "helsinki/opus-mt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 on the eng-deu card", "release_date": "2026-03-06", "context_length": null, "max_output_tokens": null, "parameters": "25363201 on opus-mt_tiny_eng-deu", "architecture": "Distilled Marian student. eng-deu config: encoder 6 layers, decoder 2 layers, d_model 256, 8 heads, feed-forward 1536, ReLU, shared embeddings, vocab 32001, max_position_embeddings 256. Teacher named on the card: OPUS-MT en-de opus-2020-02-26. Trainer: OpusDistillery.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OPUS-MT tiny is the University of Helsinki's distilled Marian student line for the OpusTranslate phone app. The Helsinki-NLP collection calls it a set of tiny models. On 2026-10-15 the Hub had 25 repos named `opus-mt_tiny_*`, each about 25.4M parameters. The English-German card was read in full. Collected 2026-10-15.", "what_it_is_for": "On-device translation. The English-German card says the student was distilled with OpusDistillery from the Tatoeba-MT teacher `en-de/opus-2020-02-26`, using Tatoeba training data. Two Esperanto tiny checkpoints from March 2026 are a different package and are filed under `opus-mt-esperanto`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/opus-mt/opus-tatoeba": { "id": "helsinki/opus-mt/opus-tatoeba", "name": "OPUS-MT Tatoeba ports", "maker": "helsinki", "family": "helsinki/opus-mt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 on the fi-en card", "release_date": "2021-08-25", "context_length": null, "max_output_tokens": null, "parameters": "73805286 on opus-tatoeba-fi-en", "architecture": "Marian transformer-align, from the fi-en card. Normalization plus SentencePiece spm32k. Zip opusTCv20210807+bt-2021-08-25.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OPUS-MT Tatoeba ports are eleven Helsinki-NLP Hub repos named `opus-tatoeba-*`, separate from the `opus-mt-tc-big` and `opus-mt-tc-base` ids. The Finnish-English card says they come from the Tatoeba-Challenge repo, use `transformer-align`, and were trained on 2021-08-25. Collected 2026-10-15.", "what_it_is_for": "Neural translation for the eleven published directions. The Finnish-English card points at the original zip `opusTCv20210807+bt-2021-08-25.zip`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/simplify/fin-simple-mbart": { "id": "helsinki/simplify/fin-simple-mbart", "name": "Finnish simple mBART", "maker": "helsinki", "family": "helsinki/simplify", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "fairseq mBART fine-tune for Finnish sentence simplification. The card does not name the mBART size. config.json was not on the default Hub path.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finnish simple mBART is the University of Helsinki's fairseq mBART fine-tune for Finnish sentence simplification, released with Simple Finnish GPT-3 XL. The card names Dmitrieva and Tiedemann's DeTermIt 2024 paper. The checkpoint is not a Transformers weight. Collected 2026-10-15.", "what_it_is_for": "Finnish sentence simplification. The card says it is a fine-tune of the fairseq mBART example and gives the Language Bank PID `urn:nbn:fi:lb-2024011801`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "helsinki/simplify/simple-finnish-gpt3-xl": { "id": "helsinki/simplify/simple-finnish-gpt3-xl", "name": "Simple Finnish GPT-3 XL", "maker": "helsinki", "family": "helsinki/simplify", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-12-06", "context_length": null, "max_output_tokens": null, "parameters": "1498094544", "architecture": "Instruction fine-tune of TurkuNLP/gpt3-finnish-xl. Hub tags the repo bloom. The paper describes the base as a 1.5B Finnish GPT XL. Instruction string on the card: Mukauta selkosuomeksi.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Simple Finnish GPT-3 XL is a University of Helsinki instruction fine-tune of TurkuNLP's Finnish GPT-3 XL for Easy Finnish simplification. Anna Dmitrieva and J\u00c3\u00b6rg Tiedemann released the weights on the Helsinki-NLP Hub and describe them in the DeTermIt 2024 paper. It is a named Helsinki model, not a copy of the Turku base checkpoint. Collected 2026-10-15.", "what_it_is_for": "Sentence simplification into selkosuomi. The card says the instruction is `Mukauta selkosuomeksi` followed by a blank line. Fine-tuning followed the TurkuNLP instruction-finetune recipe.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heriot-watt/alana/length-model-1m": { "id": "heriot-watt/alana/length-model-1m", "name": "Alana length ranker (1M)", "maker": "heriot-watt", "family": "heriot-watt/alana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2018-10-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural response ranker for the Alana socialbot. The paper encodes context and response as word-agent tokens plus named entities, then a ReLU MLP with a sigmoid score in [0, 1]. The training search tried GRU sizes 64, 128, and 256 and predictor layouts [128], [128, 64], and [128, 32, 32]. The README says the released checkpoint was trained on length, without bot-name features. The chosen hidden size inside length_model_1M is not stated.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Alana length ranker is a Heriot-Watt neural response ranker from the Interaction Lab's 2017 Alexa Prize socialbot. The released checkpoint is `length_model_1M` in the WattSocialBot repository. The SCAI 2018 workshop paper is dated 31 October 2018. Collected 2026-10-15. The files are still in the public repo. There is no deprecation notice.", "what_it_is_for": "The paper trains the ranker to score candidate replies so that Alana can pick a response likely to lead to a long conversation. The GitHub README says this checkpoint uses conversation length as the target and that bot-name features were left out so the model is less tied to the Alana ensemble.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heriot-watt/alana/rating-model": { "id": "heriot-watt/alana/rating-model", "name": "Alana rating ranker", "maker": "heriot-watt", "family": "heriot-watt/alana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2018-10-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Same neural response ranker as length_model_1M, trained on per-dialogue user ratings. The paper uses a sigmoid score in [0, 1] and searched GRU sizes 64, 128, and 256. The README says the released rating_model omits bot-name features. Its hidden size and training-set size are not in the folder name.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Alana rating ranker is the Heriot-Watt response ranker trained on Alexa Prize user ratings rather than dialogue length. The released folder is `rating_model` in WattSocialBot/alana_learning_to_rank. The workshop paper is dated 31 October 2018. Collected 2026-10-15. The files are still public.", "what_it_is_for": "The paper uses end-of-conversation ratings, on a 1-to-5 scale, as the supervision signal for picking a reply inside the Alana ensemble. Positive training pairs come from dialogues rated 4 or 5, and negative pairs from dialogues rated 1 or 2. The README says the released file drops bot-name features.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heriot-watt/emma/emma": { "id": "heriot-watt/emma/emma", "name": "EMMA", "maker": "heriot-watt", "family": "heriot-watt/emma", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-12-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder in the style of BART-base. Language uses a 10K BPE vocabulary from the pretraining data, plus frame and visual sentinel tokens. Each frame contributes one scene feature and up to 36 object-region features from a separate VinVL detector. Paper Table 1 lists 133M parameters. The policy README says downstream initialization from emma-base uses vocab size 10252. Images are pre-extracted features, not raw pixels.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EMMA (Embodied MultiModal Agent) is a Heriot-Watt Interaction Lab vision-language model for dialog-guided embodied tasks. The EMNLP 2023 paper and the public checkpoints were collected on 2026-10-15. The weights are still on Hugging Face. There is no deprecation notice.", "what_it_is_for": "The paper presents EMMA as one encoder-decoder that turns captioning, visual question answering, grounding, and action prediction into text generation. On the Alexa Arena Dialog-guided Task Completion benchmark, the unified checkpoint is the system they report at 36.81% mission success when clarifications are always available. A modular pair of experts is a second variant of the same release, not a second product line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heriot-watt/gbv/gbv-classifier-qwen2-5-0-5b-instruct": { "id": "heriot-watt/gbv/gbv-classifier-qwen2-5-0-5b-instruct", "name": "GBV Classifier Qwen2.5-0.5B-Instruct", "maker": "heriot-watt", "family": "heriot-watt/gbv", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-21", "context_length": 32768, "max_output_tokens": null, "parameters": "493791744", "architecture": "Qwen2ForSequenceClassification. 24 layers, hidden size 896, 14 attention heads, 2 key-value heads, intermediate size 4864, vocab 151665, tied embeddings in the config. Hub safetensors total is 493,791,744 F32 parameters. Fine-tune of Qwen/Qwen2.5-0.5B-Instruct. config.json labels are Not GBV and GBV. The shipped chat template still identifies the model as Qwen from Alibaba Cloud.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GBV Classifier Qwen2.5-0.5B-Instruct is a Heriot-Watt University sequence classifier for gender-based violence, fine-tuned from Qwen/Qwen2.5-0.5B-Instruct. It was released on the university Hugging Face org on 2025-11-21. Collected 2026-10-15. The weights are public.", "what_it_is_for": "The card sets the task to text classification on Heriot-WattUniversity/gbv-anno. config.json maps class 0 to Not GBV and class 1 to GBV. The README body does not add a use-policy or a score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heriot-watt/gbv/gbv-classifier-roberta-base": { "id": "heriot-watt/gbv/gbv-classifier-roberta-base", "name": "GBV Classifier RoBERTa-base", "maker": "heriot-watt", "family": "heriot-watt/gbv", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-21", "context_length": 514, "max_output_tokens": null, "parameters": "124647170", "architecture": "RobertaForSequenceClassification. 12 layers, hidden size 768, 12 heads, vocab 50265. Binary labels in config.json are Not GBV and GBV. Hub safetensors total is 124,647,170 F32 parameters. Fine-tune of FacebookAI/roberta-base on Heriot-WattUniversity/gbv-anno.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GBV Classifier RoBERTa-base is a Heriot-Watt University text classifier for online gender-based violence. It is a fine-tune of FacebookAI/roberta-base released on the university Hugging Face org. Collected 2026-10-15. The weights are public. The card does not mark it deprecated.", "what_it_is_for": "The Hub card sets the task to text classification and the training set to Heriot-WattUniversity/gbv-anno. config.json maps class 0 to Not GBV and class 1 to GBV. The README body is empty, so the university does not state a narrower use on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heriot-watt/gbv/gbv-classifier-roberta-base-instruct": { "id": "heriot-watt/gbv/gbv-classifier-roberta-base-instruct", "name": "GBV Classifier RoBERTa-base Instruct", "maker": "heriot-watt", "family": "heriot-watt/gbv", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-21", "context_length": 514, "max_output_tokens": null, "parameters": "124647170", "architecture": "RobertaForSequenceClassification. 12 layers, hidden size 768, 12 heads, vocab 50265. config.json labels are Not GBV and GBV. Hub safetensors total is 124,647,170 F32 parameters. The Hub base_model tag is FacebookAI/roberta-base, not an instruct model. The name says instruct. The card does not define that word.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GBV Classifier RoBERTa-base Instruct is a second Heriot-Watt text classifier for gender-based violence, released on the university Hugging Face org on 2025-11-21. Collected 2026-10-15. The weights are public and the card does not deprecate them.", "what_it_is_for": "The card sets the pipeline to text classification and the dataset to Heriot-WattUniversity/gbv-anno. config.json uses the labels Not GBV and GBV. The repo name says instruct. The Hub base-model tag is FacebookAI/roberta-base, and the architecture is a classification head, not a generative instruct model. The README does not explain the name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heygen/avatar/avatar-iii": { "id": "heygen/avatar/avatar-iii", "name": "Avatar III", "maker": "heygen", "family": "heygen/avatar", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "video", "audio", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Avatar III on the v3 API is HeyGen's opt-in engine `avatar_iii`. The docs call it a re-engineered photo-to-video pipeline that also renders digital twins and studio avatars, and the only v3 engine with documented 4K output. It is not the older Avatar III engine on the v1/v2 endpoints. That one has its own folder. Collected on 2026-10-15.", "what_it_is_for": "HeyGen tells new integrations to use Avatar III when they want the dedicated photo-to-video pipeline, and to use Avatar IV for motion prompts, expressiveness, or an arbitrary image. A look resolves by type: a digital twin or a studio avatar renders on the Digital Twin path up to 4K. A photo avatar stays at 1080p.\n\nThe name Avatar 3.0 also appears on a 1 February 2024 blog and a 5 September 2024 news note. HeyGen does not say those posts are this `avatar_iii` engine. The v3 page says the current engine is higher-quality and re-engineered, and that the legacy v1/v2 engine is a different pipeline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heygen/avatar/avatar-iii-legacy": { "id": "heygen/avatar/avatar-iii-legacy", "name": "Avatar III (legacy v1/v2)", "maker": "heygen", "family": "heygen/avatar", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HeyGen says this is a different pipeline from v3 avatar_iii. No further architecture is published.", "modalities": { "input": [ "image", "video", "audio", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the older Avatar III pipeline HeyGen still exposes to existing customers on the v1 and v2 endpoints. The models page and the enterprise pricing page both say it is a different pipeline from v3 `avatar_iii`, and that it is not offered on the new developer platform. Collected on 2026-10-15.", "what_it_is_for": "Existing integrations render photo avatars, digital twins, and studio avatars through the legacy video APIs. HeyGen tells new work to use v3 Avatar IV, Avatar V, or Avatar III instead. The v3 quick start says v1 and v2 remain supported until 31 October 2026.\n\nHeyGen has not published an engine string for this pipeline. It is identified by the endpoint generation, not by `engine.type`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heygen/avatar/avatar-iv": { "id": "heygen/avatar/avatar-iv", "name": "Avatar IV", "maker": "heygen", "family": "heygen/avatar", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Grouped with Avatar V in the June 2026 inference report as a Diffusion Transformer trained with flow matching on reference tokens. Launch copy calls the expression stack diffusion-inspired. No parameter count and no Avatar IV-only paper.", "modalities": { "input": [ "image", "video", "audio", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Avatar IV is HeyGen's default v3 avatar rendering engine, launched in the product the week of 28 April 2025 and on the API on 27 August 2025. Omit `engine` on `POST /v3/videos` and the server uses `avatar_iv`. It is the broadest engine: studio avatars, digital twins, photo avatars, prompts, and an arbitrary image. Collected on 2026-10-15.", "what_it_is_for": "HeyGen describes Avatar IV as a diffusion-inspired audio-to-expression engine that turns one photo, or an existing look, into a talking video with lip sync, facial motion, and hand gestures driven by the voice. The May 2025 launch was photo-to-video. The current v3 page also renders studio avatars and digital twins. It is the engine to use when Avatar V is not eligible.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heygen/avatar/avatar-v": { "id": "heygen/avatar/avatar-v", "name": "Avatar V", "maker": "heygen", "family": "heygen/avatar", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion Transformer with flow matching. Conditions on the full reference-video token sequence via Sparse Reference Attention, plus a motion representation, a multi-view image engine, a separate LLM audio engine, and an identity-aware super-resolution DiT. Five-stage train: T2V pretrain, audio-to-video pretrain, personality SFT, CFG and DMD distillation, RLHF with GRPO and DPO. No parameter count.", "modalities": { "input": [ "video", "audio", "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Avatar V is HeyGen's highest-fidelity avatar rendering engine, announced on 8 April 2026 and selectable on the v3 API as `engine.type` `avatar_v`. It is opt-in, and only eligible Digital Twin looks accept it. HeyGen's research post describes a Diffusion Transformer with flow matching that conditions on the full token sequence of a reference video. Collected on 2026-10-15.", "what_it_is_for": "HeyGen says Avatar V makes a persistent digital identity from a short recording, then renders that person speaking with their own gestures and expressions rather than generic motion. The launch post says one 15-second clip is enough to start. The v3 docs restrict the engine to eligible `digital_twin` looks. Photo avatars, studio avatars, and arbitrary images are not accepted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heygen/image/heygen-image-1": { "id": "heygen/image/heygen-image-1", "name": "HeyGen Image", "maker": "heygen", "family": "heygen/image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Identity remix from 1 to 5 photos of one person and exactly one reference image. Docs mention a 4K training canvas and a center crop. No parameter count.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HeyGen Image is HeyGen's identity-remix image model, API id `heygen-image-1`. It keeps one person's face from one to five identity photos and takes pose, clothing, light, and setting from exactly one reference image. The only mode is `identity_remix`. It is a stills model. Video is a later step on HeyGen Video. Collected on 2026-10-15.", "what_it_is_for": "New portraits of a person you have the right to depict, in a scene they were not photographed in. The reference must contain exactly one person. That person's identity is not copied. Their pose and world are. Identity photos set face, hair, expression, and gaze. There is no text prompt. Unknown fields, including `prompt`, are rejected.\n\nHeyGen's docs put this model next to HeyGen Video. The public models index cards do not list it. It is still a named model with its own id, so it has a folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heygen/video/heygen-video-1": { "id": "heygen/video/heygen-video-1", "name": "HeyGen Video", "maker": "heygen", "family": "heygen/video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HeyGen says heygen-video-1 is built on MiniMax H3 and post-trained by HeyGen. No parameter count. A prompt-enhancement pass can run before generation.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HeyGen Video is HeyGen's general-purpose prompt-to-video model, API id `heygen-video-1`. The model page says it is built on MiniMax H3 and post-trained by HeyGen, and that one call generates the subject, the setting, and the sound together. It is not an avatar engine. Avatar video stays on `POST /v3/videos`. Collected on 2026-10-15.", "what_it_is_for": "Short contained shots: one subject, one place, one action, a locked or barely moving camera. Modes are text to video, image to video (the image is the literal first frame), and reference to video (products, people, or places you supply). Clips are 5 to 15 seconds and include dialogue, ambience, and effects. HeyGen says it is weakest on long on-screen text, soft organic motion, and close hand work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heygen/voice/heygen-voice-1": { "id": "heygen/voice/heygen-voice-1", "name": "HeyGen Voice", "maker": "heygen", "family": "heygen/voice", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Product page: Expressiveness Boost is a separate preference-trained layer, and a vision encoder with two scene captions conditions delivery for video. Avatar V's report describes a separate LLM discrete-audio engine. HeyGen does not state that those are the same weights as heygen-voice-1. No parameter count.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 30.0, "completion_per_m_usd": null }, "summary_intro": "HeyGen Voice is HeyGen's in-house speech model, API id `heygen-voice-1`, launched on 9 October 2026. It clones one speaker and speaks text in that voice, either as a finished WAV or as a stream. The engine alias on the catalog speech route is `heygen_voice`, and `orca` is still accepted as the legacy alias. Collected on 2026-10-15.", "what_it_is_for": "Instant clones from one recording, using the first 3 minutes, ready in seconds. Professional clones from 1 to 10 recordings totaling at least 20 minutes, trained asynchronously. Both speak through the same text-to-speech routes. The product page says a vision encoder and scene captions can match delivery to the room. That scene path is described for video. The speech API fields fetched here are text, language, and prosody, not an image input.\n\nHeyGen's press release says Avatar V is the core model and voice is a format in the same stack. The speech docs still treat `heygen-voice-1` as its own model id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heygen/voice/heygen-voice-1-turbo": { "id": "heygen/voice/heygen-voice-1-turbo", "name": "HeyGen Voice 1 Turbo", "maker": "heygen", "family": "heygen/voice", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "`heygen-voice-1-turbo` is the lower-latency HeyGen Voice id. The speech docs say it speaks instant voices only. A professional voice sent to it returns HTTP 400 with `param` set to `model`. It was documented with HeyGen Voice on 9 October 2026. No separate launch post was found. Collected on 2026-10-15.", "what_it_is_for": "The same instant-clone speech job as `heygen-voice-1`, when latency matters more than the default model. Professional clones stay on `heygen-voice-1`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "heygen/voice/starfish": { "id": "heygen/voice/starfish", "name": "Starfish", "maker": "heygen", "family": "heygen/voice", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Starfish is a HeyGen speech engine, still selectable on `POST /v3/voices/speech` as `engine: \"starfish\"`. Current docs call it \"Starfish (HeyGen)\" and say voices with no saved engine default to it. The older reference title called the v1 text-to-speech route HeyGen's Starfish model. It is not HeyGen Voice. Collected on 2026-10-15.", "what_it_is_for": "Catalog and designed voices, not HeyGen Voice instant or professional clones. Those clones use `POST /v3/models/audio/tts`. Starfish is the fallback when a catalog voice has no concrete saved engine. It takes a `locale` such as `zh-HK`. ElevenLabs on the same route is a different vendor and has no folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hiroshima/seismic/ehvr-gmm": { "id": "hiroshima/seismic/ehvr-gmm", "name": "EHVR-GMM", "maker": "hiroshima", "family": "hiroshima/seismic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-09-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two feed-forward correction networks on top of the Morikawa and Fujiwara 2013 ground-motion equation. Each hidden block is Affine, batch normalization, and ReLU. Neuron counts from the paper's Table 2: input 48 (peak-ground head) or 92 (spectral-acceleration head); then 128, 64, 64, 64, 64; final affine layer 2 (PGA and PGV corrections) or 46 (spectral-acceleration corrections). The peak-ground inputs are the base-model PGA and PGV plus earthquake H/V ratios from 0.1 to 20 Hz (46 points).", "modalities": { "input": [ "numeric" ], "output": [ "numeric" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EHVR-GMM is a pair of deep networks from Hiroshima University (Da Pan and Hiroyuki Miura, Graduate School of Advanced Science and Engineering) that correct a 2013 Japanese ground-motion equation using earthquake horizontal-to-vertical spectral ratios. The Earthquake Spectra article was first published online on 18 September 2024 and appears in the February 2025 issue (volume 41, issue 1, pages 824-850). Two ONNX files are public. Collected 2026-10-15.", "what_it_is_for": "The paper uses the Morikawa and Fujiwara 2013 equation for source and path, then a network to add the site effect implied by earthquake H/V ratios. The targets are peak ground acceleration, peak ground velocity, and 5 percent damped absolute acceleration response spectra. The Miura Laboratory open-data page lists this as its ground-motion model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hiroshima/seismic/mhvr-saf": { "id": "hiroshima/seismic/mhvr-saf", "name": "MHVR-to-SAF", "maker": "hiroshima", "family": "hiroshima/seismic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-04-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Feed-forward network. Released ONNX input is 7 floats (frequency fi, predominant frequency fM, and MHVR at fi-2, fi-1, fi, fi+1, fi+2) and output is 5 amplification-to-MHVR ratios. The 2024 transfer-learning paper says the pre-trained hidden layers reused and frozen are affine layers of 16 neurons with SELU (alpha 1.67, lambda 1.05) and dropout 0.5 through Affine3. A complete neuron table for this checkpoint alone was not in the open pages read.", "modalities": { "input": [ "numeric" ], "output": [ "numeric" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MHVR-to-SAF is a deep neural network from Hiroshima University's Graduate School of Advanced Science and Engineering (Da Pan and Hiroyuki Miura, with Kyushu University and Chugoku Electric Power coauthors) that estimates S-wave site amplification from microtremor horizontal-to-vertical spectral ratios. The university announced it on 15 April 2022 against a Bulletin of the Seismological Society of America paper first posted 5 April 2022. Weights remain public as one ONNX file. Collected 2026-10-15.", "what_it_is_for": "The maker says the network estimates a site amplification factor from ambient microtremor data so seismic ground motion can be predicted without a borehole or a long earthquake record. Hiroshima University's news page says it is meant to work from MHVR, which is not itself a reliable amplification factor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hiroshima/seismic/mhvr-saf-tl": { "id": "hiroshima/seismic/mhvr-saf-tl", "name": "MHVR-to-SAF-TL", "maker": "hiroshima", "family": "hiroshima/seismic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transfer of the 2022 MHVR-to-SAF network. Table 1 of Pan, Miura, and Kwan (2024): Input 7, Log 7, Affine1 16 frozen, SELU (alpha 1.67, lambda 1.05), Dropout 0.5, Affine2 16 frozen, SELU, Affine3 16 frozen, SELU, Affine4 16 updated, SELU, Affine5 5 updated. Training used learning rate 0.001, batch size 2688, and a maximum of 1000 epochs.", "modalities": { "input": [ "numeric" ], "output": [ "numeric" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MHVR-to-SAF-TL is Hiroshima University's transfer-learning adaptation of the 2022 MHVR-to-SAF network. Da Pan and Hiroyuki Miura (Graduate School of Advanced Science and Engineering) wrote it with Chiman Kwan of the Johns Hopkins University Applied Physics Laboratory. It estimates site amplification factors for Japanese sites outside the original training district. The Oxford Academic page dates the Geophysical Journal International paper 22 February 2024 (volume 237, issue 1, pages 622-635). A CV1 ONNX checkpoint is public. Collected 2026-10-15.", "what_it_is_for": "The paper says the 2022 network was fit to one district and did not travel well. This model reuses those hidden layers, freezes the early ones, and trains extra layers on a small set of sites from a wider K-NET and KiK-net collection so pseudo site amplification factors can be estimated from limited microtremor H/V ratios.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/abacus/abacus": { "id": "hit/abacus/abacus", "name": "\u73e0\u7b97 Abacus", "maker": "hit", "family": "hit/abacus", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-09-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MiniCPMForCausalLM. Continued pretrain from the last MiniCPM-2B checkpoint before annealing. Cosine stage 95B tokens. Linear stage 7B tokens. WCL schedule. Semi-Instruct SFT about 800k examples, 3 epochs, lr 1.6e-4. README size label 2.7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u73e0\u7b97 (Abacus) is a code model from HIT-SCIR's Sail lab at Harbin Institute of Technology. Collected 2026-10-15. Hub repo HIT-SCIR/Abacus was created 2024-09-11. The README badge is Apache-2.0. The Hub license field was empty, and the Hub API did not return a safetensors total. The card says 2.7B, which is not stored as an integer.", "what_it_is_for": "The README says continued pretraining starts from the last MiniCPM-2B checkpoint before annealing. A cosine stage uses 95B tokens (70 percent code, 10 percent math, 20 percent general). A linear stage uses 7B tokens. The schedule is described as WCL, with a block attention mask. Semi-Instruct SFT uses about 800,000 examples for 3 epochs at learning rate 1.6e-4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/abacus/abacus-fim": { "id": "hit/abacus/abacus-fim", "name": "\u73e0\u7b97 Abacus-FIM", "maker": "hit", "family": "hit/abacus", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-12-10", "context_length": null, "max_output_tokens": null, "parameters": "2724880896", "architecture": "MiniCPMForCausalLM. Fill-in-the-middle code completion. Safetensors total 2724880896.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Abacus-FIM is the fill-in-the-middle code model from the same HIT-SCIR Sail lab release. Collected 2026-10-15. Hub repo HIT-SCIR/Abacus-FIM was created 2024-12-10. Safetensors total 2724880896. Apache-2.0 follows the README badge. The Hub license field was empty.", "what_it_is_for": "The README says the VS Code extension sends code context to this model for completion. The vendor HumanEval-Infilling multi-line PSM score is printed as 0.540.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/chinese-mixtral/chinese-mixtral-8x7b": { "id": "hit/chinese-mixtral/chinese-mixtral-8x7b", "name": "Chinese-Mixtral-8x7B", "maker": "hit", "family": "hit/chinese-mixtral", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-01-18", "context_length": null, "max_output_tokens": null, "parameters": "46907592704", "architecture": "MixtralForCausalLM. QLoRA continued pretrain of mistralai/Mixtral-8x7B with Chinese vocabulary expansion. Merged base weights, not instruction-tuned. Safetensors total 46907592704.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese-Mixtral-8x7B is HIT-SCIR's continued-pretrain base of Mistral Mixtral-8x7B with a Chinese vocabulary expansion. Collected 2026-10-15. The \u6d3b\u5b57 README says the weights and training code were open-sourced on 2024-01-18. Hub createdAt is 2024-01-15. License Apache-2.0. This is the HIT-SCIR base, not the hfl/chinese-mixtral derivative.", "what_it_is_for": "The README says training used QLoRA because a full Mixtral-8x7B update was too expensive, then released both the merged weights and the adapter. The model is a base, not an instruction tune. The card says the original Mixtral vocabulary is weak for Chinese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/glyphbert/glyphbert": { "id": "hit/glyphbert/glyphbert", "name": "GlyphBERT", "maker": "hit", "family": "hit/glyphbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "afl-3.0", "release_date": "2022-08-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. Glyph features from rendered characters, multi-channel position maps, and a 2-layer residual CNN. Pipeline fill-mask. Tag bert-base-chinese.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GlyphBERT is a Chinese glyph-aware BERT from the HITsz-TMG group at Harbin Institute of Technology, Shenzhen. Collected 2026-10-15. Hub repo HIT-TMG/GlyphBERT was created 2022-08-24. License AFL-3.0, so the weights are classified open-source. Hub architecture is BertForMaskedLM and the pipeline is fill-mask. Safetensors total was not returned.", "what_it_is_for": "The card says characters are rendered to images, encoded as multi-channel position maps, and passed through a two-layer residual CNN. The tag is bert-base-chinese. No numeric benchmark was on the short card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-bert-wwm": { "id": "hit/hfl/chinese-bert-wwm", "name": "Chinese BERT-wwm", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2019-06-20", "context_length": 512, "max_output_tokens": null, "parameters": "110000000", "architecture": "BertForMaskedLM. README: BERT-base, 12-layer, 768-hidden, 12-heads, 110M. Whole-word masking. Training tokens 0.4B Wikipedia. Steps 100K at max 128 plus 100K at max 512. The open-source zip omitted MLM-task weights. The Hub pipeline is fill-mask.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese BERT-wwm is the first whole-word-masking Chinese BERT from the Joint Laboratory of HIT and iFLYTEK Research (HFL). Collected 2026-10-15. The README news line is 2019-06-20. Hub createdAt 2022-03-02 is a bulk import. License Apache-2.0.", "what_it_is_for": "The README says it changes BERT's Chinese pretraining mask so that a whole word is masked together, using word segmentation rather than masking only WordPiece pieces. It is a pretrained encoder for downstream fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-bert-wwm-ext": { "id": "hit/hfl/chinese-bert-wwm-ext", "name": "Chinese BERT-wwm-ext", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2019-07-30", "context_length": 512, "max_output_tokens": null, "parameters": "110000000", "architecture": "BertForMaskedLM. Same BERT-base size as BERT-wwm (12-layer, 768-hidden, 12-heads, 110M in the README). EXT corpus is Wikipedia plus encyclopedia, news, and QA, 5.4B words. Training steps in the comparison table: 1M at max 128 plus 400K at max 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese BERT-wwm-ext is the HFL whole-word-masking BERT trained on the larger EXT corpus. Collected 2026-10-15. News line 2019-07-30. Apache-2.0.", "what_it_is_for": "Same masking idea as BERT-wwm, with more pretraining text. The README lists 5.4B words for EXT data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-electra-180g-base": { "id": "hit/hfl/chinese-electra-180g-base", "name": "Chinese ELECTRA-180g-base", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2020-10-22", "context_length": 512, "max_output_tokens": null, "parameters": "102000000", "architecture": "ELECTRA discriminator. README size line: 12-layer, hidden 768, 12 heads, 102M. 180g series, same stated size as ELECTRA-base. Training max length on the original size notes is 512. Hub config did not return an architecture class name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese ELECTRA-180g-base is an ELECTRA discriminator from HFL. Collected 2026-10-15. Release news day 2020-10-22. Apache-2.0. The folder is the discriminator. The generator is a version.", "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-electra-180g-large": { "id": "hit/hfl/chinese-electra-180g-large", "name": "Chinese ELECTRA-180g-large", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2020-10-22", "context_length": 512, "max_output_tokens": null, "parameters": "324000000", "architecture": "ELECTRA discriminator. README size line: 24-layer, hidden 1024, 16 heads, 324M. 180g series, same stated size as ELECTRA-large. Training max length on the original size notes is 512. Hub config did not return an architecture class name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese ELECTRA-180g-large is an ELECTRA discriminator from HFL. Collected 2026-10-15. Release news day 2020-10-22. Apache-2.0. The folder is the discriminator. The generator is a version.", "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-electra-180g-small": { "id": "hit/hfl/chinese-electra-180g-small", "name": "Chinese ELECTRA-180g-small", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2020-10-22", "context_length": 512, "max_output_tokens": null, "parameters": "12000000", "architecture": "ELECTRA discriminator. README size line: 12-layer, hidden 256, 4 heads, 12M. 180g series. The README says this adds high-quality CommonCrawl data, about 180G, and recommends it over the original series. Training max length on the original size notes is 512. Hub config did not return an architecture class name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese ELECTRA-180g-small is an ELECTRA discriminator from HFL. Collected 2026-10-15. Release news day 2020-10-22. Apache-2.0. The folder is the discriminator. The generator is a version.", "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-electra-180g-small-ex": { "id": "hit/hfl/chinese-electra-180g-small-ex", "name": "Chinese ELECTRA-180g-small-ex", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2020-10-22", "context_length": 512, "max_output_tokens": null, "parameters": "25000000", "architecture": "ELECTRA discriminator. README size line: 24-layer, hidden 256, 4 heads, 25M. 180g series, same stated size as ELECTRA-small-ex. Training max length on the original size notes is 512. Hub config did not return an architecture class name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese ELECTRA-180g-small-ex is an ELECTRA discriminator from HFL. Collected 2026-10-15. Release news day 2020-10-22. Apache-2.0. The folder is the discriminator. The generator is a version.", "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-electra-base": { "id": "hit/hfl/chinese-electra-base", "name": "Chinese ELECTRA-base", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2020-03-25", "context_length": 512, "max_output_tokens": null, "parameters": "102000000", "architecture": "ELECTRA discriminator. README size line: 12-layer, hidden 768, 12 heads, 102M. Original series. Training note: lr 2e-4, batch 256, max length 512, 1M steps. Training max length on the original size notes is 512. Hub config did not return an architecture class name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese ELECTRA-base is an ELECTRA discriminator from HFL. Collected 2026-10-15. Release news day 2020-03-25. Apache-2.0. The folder is the discriminator. The generator is a version.", "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-electra-large": { "id": "hit/hfl/chinese-electra-large", "name": "Chinese ELECTRA-large", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2020-05-29", "context_length": 512, "max_output_tokens": null, "parameters": "324000000", "architecture": "ELECTRA discriminator. README size line: 24-layer, hidden 1024, 16 heads, 324M. Original series. Training note: max length 512, 2M steps. Training max length on the original size notes is 512. Hub config did not return an architecture class name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese ELECTRA-large is an ELECTRA discriminator from HFL. Collected 2026-10-15. Release news day 2020-05-29. Apache-2.0. The folder is the discriminator. The generator is a version.", "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-electra-small": { "id": "hit/hfl/chinese-electra-small", "name": "Chinese ELECTRA-small", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2020-03-25", "context_length": 512, "max_output_tokens": null, "parameters": "12000000", "architecture": "ELECTRA discriminator. README size line: 12-layer, hidden 256, 4 heads, 12M. Original EXT-corpus series. The README says ELECTRA-small is about 1/10 the parameters of BERT-base. That sentence is not a second count. Training max length on the original size notes is 512. Hub config did not return an architecture class name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese ELECTRA-small is an ELECTRA discriminator from HFL. Collected 2026-10-15. Release news day 2020-03-25. Apache-2.0. The folder is the discriminator. The generator is a version.", "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-electra-small-ex": { "id": "hit/hfl/chinese-electra-small-ex", "name": "Chinese ELECTRA-small-ex", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2020-05-29", "context_length": 512, "max_output_tokens": null, "parameters": "25000000", "architecture": "ELECTRA discriminator. README size line: 24-layer, hidden 256, 4 heads, 25M. Original series. 24 layers at hidden 256. Training max length on the original size notes is 512. Hub config did not return an architecture class name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese ELECTRA-small-ex is an ELECTRA discriminator from HFL. Collected 2026-10-15. Release news day 2020-05-29. Apache-2.0. The folder is the discriminator. The generator is a version.", "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-legal-electra-base": { "id": "hit/hfl/chinese-legal-electra-base", "name": "Chinese Legal ELECTRA-base", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2020-12-13", "context_length": 512, "max_output_tokens": null, "parameters": "102000000", "architecture": "ELECTRA discriminator. README size line: 12-layer, hidden 768, 12 heads, 102M. Judicial-domain edition, base size. Training max length on the original size notes is 512. Hub config did not return an architecture class name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese Legal ELECTRA-base is an ELECTRA discriminator from HFL. Collected 2026-10-15. Release news day 2020-12-13. Apache-2.0. The folder is the discriminator. The generator is a version.", "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-legal-electra-large": { "id": "hit/hfl/chinese-legal-electra-large", "name": "Chinese Legal ELECTRA-large", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2020-12-13", "context_length": 512, "max_output_tokens": null, "parameters": "324000000", "architecture": "ELECTRA discriminator. README size line: 24-layer, hidden 1024, 16 heads, 324M. Judicial-domain edition, large size. Training max length on the original size notes is 512. Hub config did not return an architecture class name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese Legal ELECTRA-large is an ELECTRA discriminator from HFL. Collected 2026-10-15. Release news day 2020-12-13. Apache-2.0. The folder is the discriminator. The generator is a version.", "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-legal-electra-small": { "id": "hit/hfl/chinese-legal-electra-small", "name": "Chinese Legal ELECTRA-small", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2020-12-13", "context_length": 512, "max_output_tokens": null, "parameters": "12000000", "architecture": "ELECTRA discriminator. README size line: 12-layer, hidden 256, 4 heads, 12M. Judicial-domain edition. The 2020-12-13 news line says it was trained on large-scale legal documents. Training max length on the original size notes is 512. Hub config did not return an architecture class name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese Legal ELECTRA-small is an ELECTRA discriminator from HFL. Collected 2026-10-15. Release news day 2020-12-13. Apache-2.0. The folder is the discriminator. The generator is a version.", "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-lert-base": { "id": "hit/hfl/chinese-lert-base", "name": "Chinese LERT-base", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2022-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. Linguistically-informed pretraining. 12-layer, hidden 768, 12 heads. The table says ~102M and says the count is the transformer part, excluding task heads. Corpus is the same 5.4B-word mix as MacBERT: Chinese Wikipedia, other encyclopedias, news, and QA. The PyTorch release includes MLM weights and does not include the linguistic heads. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese LERT-base is HFL's linguistically motivated Chinese encoder. Collected 2026-10-15. Authors on the card are Yiming Cui, Wanxiang Che, Shijin Wang, and Ting Liu.", "what_it_is_for": "The README says most pretraining is linguistically agnostic, and LERT adds three kinds of linguistic features with a linguistically-informed pretraining strategy. The card calls it a linguistically-motivated pretrained language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-lert-large": { "id": "hit/hfl/chinese-lert-large", "name": "Chinese LERT-large", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2022-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. Linguistically-informed pretraining. 24-layer, hidden 1024, 16 heads. The table says ~325M for the transformer part, excluding task heads. Corpus is the same 5.4B-word mix as MacBERT: Chinese Wikipedia, other encyclopedias, news, and QA. The PyTorch release includes MLM weights and does not include the linguistic heads. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese LERT-large is HFL's linguistically motivated Chinese encoder. Collected 2026-10-15. Authors on the card are Yiming Cui, Wanxiang Che, Shijin Wang, and Ting Liu.", "what_it_is_for": "The README says most pretraining is linguistically agnostic, and LERT adds three kinds of linguistic features with a linguistically-informed pretraining strategy. The card calls it a linguistically-motivated pretrained language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-lert-small": { "id": "hit/hfl/chinese-lert-small", "name": "Chinese LERT-small", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2022-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. Linguistically-informed pretraining. 12-layer, hidden 256, 4 heads. The size table says ~15M for the transformer only. A later download row says ~60M for the PyTorch file. Both figures are left in prose. Corpus is the same 5.4B-word mix as MacBERT: Chinese Wikipedia, other encyclopedias, news, and QA. The PyTorch release includes MLM weights and does not include the linguistic heads. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese LERT-small is HFL's linguistically motivated Chinese encoder. Collected 2026-10-15. Authors on the card are Yiming Cui, Wanxiang Che, Shijin Wang, and Ting Liu.", "what_it_is_for": "The README says most pretraining is linguistically agnostic, and LERT adds three kinds of linguistic features with a linguistically-informed pretraining strategy. The card calls it a linguistically-motivated pretrained language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-pert-base": { "id": "hit/hfl/chinese-pert-base", "name": "Chinese PERT-base", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "2022-02-24", "context_length": null, "max_output_tokens": null, "parameters": "110000000", "architecture": "BERT-structure permuted language model. README: 12-layer, hidden 768, 12 heads, 110M. Corpus: EXT data. The objective permutes word order and predicts original positions, without a [MASK] token. Load with BertTokenizer and BertModel. Hub pipeline is feature-extraction. License on the Hub is CC-BY-NC-SA-4.0, so weights are open-weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese PERT-base is an HFL PERT encoder. Collected 2026-10-15. The project's own news line says Chinese and English base and large were released on 2022-02-24. A later line on the BERT-wwm page says 2022-03-30. Hub license is CC-BY-NC-SA-4.0.", "what_it_is_for": "Self-supervised pretraining from permuted text. The README says some NLU tasks improve and some do not, and that users should check the fit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-pert-large": { "id": "hit/hfl/chinese-pert-large", "name": "Chinese PERT-large", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "2022-02-24", "context_length": null, "max_output_tokens": null, "parameters": "330000000", "architecture": "BERT-structure permuted language model. README: 24-layer, hidden 1024, 16 heads, 330M. Corpus: EXT data. The objective permutes word order and predicts original positions, without a [MASK] token. Load with BertTokenizer and BertModel. Hub pipeline is feature-extraction. License on the Hub is CC-BY-NC-SA-4.0, so weights are open-weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese PERT-large is an HFL PERT encoder. Collected 2026-10-15. The project's own news line says Chinese and English base and large were released on 2022-02-24. A later line on the BERT-wwm page says 2022-03-30. Hub license is CC-BY-NC-SA-4.0.", "what_it_is_for": "Self-supervised pretraining from permuted text. The README says some NLU tasks improve and some do not, and that users should check the fit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-roberta-wwm-ext": { "id": "hit/hfl/chinese-roberta-wwm-ext", "name": "Chinese RoBERTa-wwm-ext", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2019-09-10", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. The README says this is a RoBERTa-like BERT: whole-word masking, no next-sentence prediction, trained directly at max length 512. It is not the original RoBERTa code path. Load with BertTokenizer and BertModel, not Roberta classes. The small-model table lists 102M. The MiniRBT table lists the same base as 102.3M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese RoBERTa-wwm-ext is HFL's RoBERTa-like Chinese BERT on the EXT corpus. Collected 2026-10-15. News line 2019-09-10. Apache-2.0. Training tokens in the comparison table are 5.4B.", "what_it_is_for": "The README tells users to load it as BERT. Whole-word masking stays. NSP is dropped. Pretraining is at length 512 rather than the two-stage 128-then-512 schedule used for BERT-wwm.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-roberta-wwm-ext-large": { "id": "hit/hfl/chinese-roberta-wwm-ext-large", "name": "Chinese RoBERTa-wwm-ext-large", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2019-10-14", "context_length": 512, "max_output_tokens": null, "parameters": "325000000", "architecture": "BertForMaskedLM. RoBERTa-like large BERT on EXT data, 5.4B words, trained at max length 512. The README's BERT-large configuration line is 24-layer, 1024-hidden, 16-heads, 330M. The small-model table lists this checkpoint at 325M. Those two figures are both stored in prose. The integer kept is 325000000 from the row that names RoBERTa-wwm-ext-large.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese RoBERTa-wwm-ext-large is the large HFL RoBERTa-like Chinese encoder. Collected 2026-10-15. News line 2019-10-14. Apache-2.0.", "what_it_is_for": "Same training recipe as RoBERTa-wwm-ext at the large size. Load with Bert functions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-xlnet-base": { "id": "hit/hfl/chinese-xlnet-base", "name": "Chinese XLNet-base", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2019-09-05", "context_length": 512, "max_output_tokens": null, "parameters": "117000000", "architecture": "XLNetLMHeadModel. README: 12-layer, 768-hidden, 12-heads, 117M. Same project vocab note of 32000 and the same 512 sequence length in the scripts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese XLNet-base is the 12-layer HFL Chinese XLNet. Collected 2026-10-15. News line 2019-09-05. Apache-2.0.", "what_it_is_for": "Smaller Chinese XLNet for downstream fine-tuning. Hub pipeline is text-generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/chinese-xlnet-mid": { "id": "hit/hfl/chinese-xlnet-mid", "name": "Chinese XLNet-mid", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2019-08-19", "context_length": 512, "max_output_tokens": null, "parameters": "209000000", "architecture": "XLNetLMHeadModel. README: 24-layer, 768-hidden, 12-heads, 209M. Trained on the extended corpus, 5.4B words. Vocab size in the project is 32000. Training and fine-tune scripts in the README set seq_len and max_seq_length to 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese XLNet-mid is HFL's 24-layer Chinese XLNet. Collected 2026-10-15. News line 2019-08-19. Apache-2.0. Hub pipeline is text-generation.", "what_it_is_for": "Permutation-language-model pretraining for Chinese, released before the base checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/cino-base-v2": { "id": "hit/hfl/cino-base-v2", "name": "CINO-base-v2", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "XLMRobertaForMaskedLM. Continued pretraining of XLM-R on Chinese minority-language text. Pipeline fill-mask. Apache-2.0. The card lists Chinese (zh), Tibetan (bo), Mongolian with the card's wording 'Uighur form' (mn), Uyghur (ug), Kazakh in Arabic form (kk), Korean (ko), and Cantonese (yue). Zhuang is named in the prose without a language code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CINO-base-v2 is the v2 CINO encoder from HFL for Chinese minority languages. Collected 2026-10-15. Apache-2.0.", "what_it_is_for": "The card says multilingual models such as mBERT and XLM-R had little coverage of Chinese minority languages, and CINO continues pretraining on a corpus for those languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/cino-large-v2": { "id": "hit/hfl/cino-large-v2", "name": "CINO-large-v2", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "XLMRobertaForMaskedLM. Continued pretraining of XLM-R on Chinese minority-language text. Pipeline fill-mask. Apache-2.0. The card lists Chinese (zh), Tibetan (bo), Mongolian with the card's wording 'Uighur form' (mn), Uyghur (ug), Kazakh in Arabic form (kk), Korean (ko), and Cantonese (yue). Zhuang is named in the prose without a language code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CINO-large-v2 is the v2 CINO encoder from HFL for Chinese minority languages. Collected 2026-10-15. Apache-2.0.", "what_it_is_for": "The card says multilingual models such as mBERT and XLM-R had little coverage of Chinese minority languages, and CINO continues pretraining on a corpus for those languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/cino-small-v2": { "id": "hit/hfl/cino-small-v2", "name": "CINO-small-v2", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "XLMRobertaForMaskedLM. Continued pretraining of XLM-R on Chinese minority-language text. Pipeline fill-mask. Apache-2.0. The card lists Chinese (zh), Tibetan (bo), Mongolian with the card's wording 'Uighur form' (mn), Uyghur (ug), Kazakh in Arabic form (kk), Korean (ko), and Cantonese (yue). Zhuang is named in the prose without a language code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CINO-small-v2 is the v2 CINO encoder from HFL for Chinese minority languages. Collected 2026-10-15. Apache-2.0.", "what_it_is_for": "The card says multilingual models such as mBERT and XLM-R had little coverage of Chinese minority languages, and CINO continues pretraining on a corpus for those languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/english-pert-base": { "id": "hit/hfl/english-pert-base", "name": "English PERT-base", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "2022-02-24", "context_length": null, "max_output_tokens": null, "parameters": "110000000", "architecture": "BERT-structure permuted language model. README: 12-layer, hidden 768, 12 heads, 110M. Corpus: WikiBooks, uncased. The objective permutes word order and predicts original positions, without a [MASK] token. Load with BertTokenizer and BertModel. Hub pipeline is feature-extraction. License on the Hub is CC-BY-NC-SA-4.0, so weights are open-weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "English PERT-base is an HFL PERT encoder. Collected 2026-10-15. The project's own news line says Chinese and English base and large were released on 2022-02-24. A later line on the BERT-wwm page says 2022-03-30. Hub license is CC-BY-NC-SA-4.0.", "what_it_is_for": "Self-supervised pretraining from permuted text. The README says some NLU tasks improve and some do not, and that users should check the fit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/english-pert-large": { "id": "hit/hfl/english-pert-large", "name": "English PERT-large", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "2022-02-24", "context_length": null, "max_output_tokens": null, "parameters": "330000000", "architecture": "BERT-structure permuted language model. README: 24-layer, hidden 1024, 16 heads, 330M. Corpus: WikiBooks, uncased. The objective permutes word order and predicts original positions, without a [MASK] token. Load with BertTokenizer and BertModel. Hub pipeline is feature-extraction. License on the Hub is CC-BY-NC-SA-4.0, so weights are open-weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "English PERT-large is an HFL PERT encoder. Collected 2026-10-15. The project's own news line says Chinese and English base and large were released on 2022-02-24. A later line on the BERT-wwm page says 2022-03-30. Hub license is CC-BY-NC-SA-4.0.", "what_it_is_for": "Self-supervised pretraining from permuted text. The README says some NLU tasks improve and some do not, and that users should check the fit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/macbert-base": { "id": "hit/hfl/macbert-base", "name": "Chinese MacBERT-base", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. MLM-as-correction uses similar words instead of [MASK], plus whole-word masking, n-gram masking, and sentence-order prediction. The card says the main architecture matches BERT, so it can replace BERT. The MacBERT card does not restate a parameter count. The BERT-wwm README's generic sizes are 110M base and 330M large, and those were not copied in.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese MacBERT-base is HFL's MacBERT base encoder. Collected 2026-10-15. Apache-2.0. Hub createdAt 2022-03-02 is the bulk import. The paper is the November 2020 EMNLP Findings paper.", "what_it_is_for": "Pretraining that corrects similar words rather than predicting a mask token, so the pretraining and fine-tuning inputs look more alike. Load with Bert functions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/macbert-large": { "id": "hit/hfl/macbert-large", "name": "Chinese MacBERT-large", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. MLM-as-correction uses similar words instead of [MASK], plus whole-word masking, n-gram masking, and sentence-order prediction. The card says the main architecture matches BERT, so it can replace BERT. The MacBERT card does not restate a parameter count. The BERT-wwm README's generic sizes are 110M base and 330M large, and those were not copied in.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese MacBERT-large is HFL's MacBERT large encoder. Collected 2026-10-15. Apache-2.0. Hub createdAt 2022-03-02 is the bulk import. The paper is the November 2020 EMNLP Findings paper.", "what_it_is_for": "Pretraining that corrects similar words rather than predicting a mask token, so the pretraining and fine-tuning inputs look more alike. Load with Bert functions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/minirbt-h256": { "id": "hit/hfl/minirbt-h256", "name": "MiniRBT-H256", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2022-11-15", "context_length": 512, "max_output_tokens": null, "parameters": "10400000", "architecture": "Bert-style MiniRBT. 6 layers, hidden 256, 8 heads, FFN 1024. README parameter line 10.4M. Two-stage distillation from RBT6 (KD). Load with Bert functions. LTP was used for segmentation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniRBT-H256 is a small Chinese encoder from HFL, distilled with TextBrewer and whole-word masking. Collected 2026-10-15. The Chinese-BERT-wwm news line is 2022-11-15. Apache-2.0.", "what_it_is_for": "A compact Chinese pretrained model for tasks that cannot host a full RoBERTa-wwm-ext. The README says the two-stage path (RoBERTa to RBT6 to MiniRBT) beat a one-stage distillation in their comparison.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/minirbt-h288": { "id": "hit/hfl/minirbt-h288", "name": "MiniRBT-H288", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2022-11-15", "context_length": 512, "max_output_tokens": null, "parameters": "12300000", "architecture": "Bert-style MiniRBT. 6 layers, hidden 288, 8 heads, FFN 1152. README parameter line 12.3M. Two-stage distillation from RBT6 (KD). Load with Bert functions. LTP was used for segmentation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniRBT-H288 is a small Chinese encoder from HFL, distilled with TextBrewer and whole-word masking. Collected 2026-10-15. The Chinese-BERT-wwm news line is 2022-11-15. Apache-2.0.", "what_it_is_for": "A compact Chinese pretrained model for tasks that cannot host a full RoBERTa-wwm-ext. The README says the two-stage path (RoBERTa to RBT6 to MiniRBT) beat a one-stage distillation in their comparison.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/rbt3": { "id": "hit/hfl/rbt3", "name": "RBT3", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2020-01-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. Three-layer model initialized from RoBERTa-wwm-ext and continued for 1M steps, on EXT data. The BERT-wwm small-model table lists 38M. The MiniRBT structure table lists RBT3 at 38.5M, 3 layers, hidden 768, FFN 3072, 12 heads.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RBT3 is HFL's 3-layer Chinese encoder distilled in the sense of layer initialization from RoBERTa-wwm-ext, then continued pretraining. Collected 2026-10-15. News line 2020-01-20. Apache-2.0. The name is the three syllables of RoBERTa.", "what_it_is_for": "A small Chinese encoder for downstream tasks when the 12-layer model is too heavy. The README warns that simply taking the first three layers without the continued training is much weaker.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/rbt4": { "id": "hit/hfl/rbt4", "name": "RBT4", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. Listed in the Chinese-BERT-wwm download table as RBT4 on EXT data. The layer count is not stated in that table. Do not infer 4 layers from the name. RBT4-H312 is a different MiniRBT checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RBT4 is a named small Chinese checkpoint in the HFL Chinese-BERT-wwm download table. Collected 2026-10-15. No news day was in the section read. Apache-2.0. Hub createdAt 2022-03-02 is the bulk-import stamp.", "what_it_is_for": "Another small RoBERTa-wwm-ext-family encoder. Size details beyond the name were not in the download table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/rbt4-h312": { "id": "hit/hfl/rbt4-h312", "name": "RBT4-H312", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2022-11-15", "context_length": 512, "max_output_tokens": null, "parameters": "11400000", "architecture": "4-layer comparison model in the MiniRBT README, hidden 312, 12 heads, FFN 1200, 11.4M. The README says it is the same size as TinyBERT and is distilled directly from RoBERTa-wwm-ext, not from RBT6. This is not hfl/rbt4.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RBT4-H312 is the TinyBERT-sized comparison checkpoint HFL released beside MiniRBT. Collected 2026-10-15. Apache-2.0.", "what_it_is_for": "The README includes it so MiniRBT can be compared with a 4-layer model of similar size. It is a named release, not an unnamed ablation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/rbt6": { "id": "hit/hfl/rbt6", "name": "RBT6", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. The MiniRBT README describes RBT6 (KD) as a 6-layer teacher-assistant: hidden 768, FFN 3072, 12 heads, initialized from 6 layers of RoBERTa-wwm-ext and distilled from that model. The same table lists 59.76M. Distillation schedule line: 100K steps at MAX512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RBT6 is the 6-layer HFL checkpoint used as the teacher assistant for MiniRBT. Collected 2026-10-15. The Chinese-BERT-wwm table lists one RBT6 download and does not give a news day. Apache-2.0.", "what_it_is_for": "The MiniRBT README says MiniRBT models are distilled from this assistant, which itself is distilled from RoBERTa-wwm-ext.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/rbtl3": { "id": "hit/hfl/rbtl3", "name": "RBTL3", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2020-01-20", "context_length": null, "max_output_tokens": null, "parameters": "61000000", "architecture": "BertForMaskedLM. Three-layer model initialized from RoBERTa-wwm-ext-large and continued for 1M steps. The small-model table lists 61M. The README says the L marks the large parent.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RBTL3 is HFL's 3-layer encoder initialized from RoBERTa-wwm-ext-large. Collected 2026-10-15. Same news line as RBT3, 2020-01-20. Apache-2.0.", "what_it_is_for": "Small-footprint Chinese encoder. The README contrasts it with using the first three layers of the large model without continued training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/vle-base": { "id": "hit/hfl/vle-base", "name": "VLE-base", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2023-03-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Visual-language encoder on pretrained text and image encoders. The card names VQA, image-text retrieval, and visual commonsense reasoning. Apache-2.0. Language tag English. Parameter count was not on the card. Hub pipeline tag was empty.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VLE-base is HFL's visual-language encoder. Collected 2026-10-15. The news line is 2023-03-09. The code repo is under the iflytek GitHub org. The releasing model org is hfl.", "what_it_is_for": "Discriminative image-text tasks. The card says VCR, which needs high-level language understanding, is where VLE improves most.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/hfl/vle-large": { "id": "hit/hfl/vle-large", "name": "VLE-large", "maker": "hit", "family": "hit/hfl", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2023-03-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Visual-language encoder on pretrained text and image encoders. The card names VQA, image-text retrieval, and visual commonsense reasoning. Apache-2.0. Language tag English. Parameter count was not on the card. Hub pipeline tag was empty.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VLE-large is HFL's visual-language encoder. Collected 2026-10-15. The news line is 2023-03-09. The code repo is under the iflytek GitHub org. The releasing model org is hfl.", "what_it_is_for": "Discriminative image-text tasks. The card says VCR, which needs high-level language understanding, is where VLE improves most.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/huozi/huozi-1-0": { "id": "hit/huozi/huozi-1-0", "name": "\u6d3b\u5b57 1.0", "maker": "hit", "family": "hit/huozi", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2023-08-06", "context_length": null, "max_output_tokens": null, "parameters": "7069016064", "architecture": "BloomForCausalLM. SFT of Bloom on about 15 billion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u6d3b\u5b57 1.0 is the first open Huozi chat model from HIT-SCIR at Harbin Institute of Technology. Collected 2026-10-15. The update log dates the 1.0 and 2.0 release to 2023-08-06. The Hub repo HIT-SCIR/huozi-7b-sft was created 2023-08-01. The GitHub README badge is Apache-2.0. The Hub license field was empty.", "what_it_is_for": "The README says it is an instruction fine-tune of Bloom on about 15 billion tokens, aimed at stronger instruction following and safer replies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/huozi/huozi-2-0": { "id": "hit/huozi/huozi-2-0", "name": "\u6d3b\u5b57 2.0", "maker": "hit", "family": "hit/huozi", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2023-08-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RLHF of \u6d3b\u5b57 1.0 (Bloom SFT). 16.9k preference pairs. Hub pipeline text-generation. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u6d3b\u5b57 2.0 is the RLHF update of \u6d3b\u5b57 1.0 from HIT-SCIR. Collected 2026-10-15. The same 2023-08-06 update log line covers 1.0 and 2.0. Hub repo HIT-SCIR/huozi-7b-rlhf was created 2023-08-03. Apache-2.0 follows the GitHub badge. The Hub API did not return a safetensors total.", "what_it_is_for": "The README says RLHF on 1.0 uses 16.9k human preference pairs whose replies come from the Huozi model, and that replies are longer and follow instructions more clearly.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/huozi/huozi-3": { "id": "hit/huozi/huozi-3", "name": "\u6d3b\u5b57 3.0", "maker": "hit", "family": "hit/huozi", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-02-08", "context_length": 32768, "max_output_tokens": null, "parameters": "46907609088", "architecture": "MixtralForCausalLM. 8 FFN experts, top-2, 46.7B total and 13B activated as stated in the README. Safetensors total 46907609088. SFT on about 300k instruction lines.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u6d3b\u5b57 3.0 is HIT-SCIR's instruction-tuned sparse mixture-of-experts model. Collected 2026-10-15. Hub repo HIT-SCIR/huozi3 was created 2024-02-08. License Apache-2.0. It is a different architecture from \u6d3b\u5b57 1.0 and 2.0.", "what_it_is_for": "The README says it is an SFT of Chinese-Mixtral-8x7B on about 300,000 instruction lines. It uses the Mixtral-8x7B layout: 8 FFN experts, top-2 routing, 46.7B total parameters and 13B activated, and a 32K context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/huozi/huozi-3-5": { "id": "hit/huozi/huozi-3-5", "name": "\u6d3b\u5b57 3.5", "maker": "hit", "family": "hit/huozi", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-09-12", "context_length": 32768, "max_output_tokens": null, "parameters": "46907609088", "architecture": "MixtralForCausalLM. 8 experts, top-2, 46.7B total, 13B active. Training: Chinese vocab-extend continued pretrain, \u6d3b\u5b57 3.0 SFT on about 300k lines, \u6d3b\u5b57 1.0-data SFT as checkpoint 1, instruction strengthening with BPE dropout as checkpoint 2, merge of checkpoints 1 and 2 with \u6d3b\u5b57 3.0 as checkpoint 3, then further SFT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u6d3b\u5b57 3.5 is HIT-SCIR's later Mixtral-structure chat model. Collected 2026-10-15. The update log's later line is 2024-09-12. An earlier line in the same log says 2024-02-09. Hub repo HIT-SCIR/huozi3.5 was created 2024-07-27. License Apache-2.0.", "what_it_is_for": "The README says 3.5 keeps the 8-expert top-2 MoE, 46.7B total and 13B active, and the 32K context. Training runs from the Chinese vocabulary expansion through \u6d3b\u5b57 3.0, a \u6d3b\u5b57 1.0-data SFT checkpoint, an instruction-strengthening checkpoint with BPE dropout, a merge of those checkpoints with \u6d3b\u5b57 3.0, and a final SFT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/kalm/kalm-embedding-multilingual-mini-instruct-v1": { "id": "hit/kalm/kalm-embedding-multilingual-mini-instruct-v1", "name": "KaLM-embedding-multilingual-mini-instruct-v1", "maker": "hit", "family": "hit/kalm", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "2024-10-23", "context_length": null, "max_output_tokens": null, "parameters": "494032768", "architecture": "Qwen2Model. Instruct variant of the mini v1 line for asymmetric tasks. Dimension and max input were not on the v1 card table.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KaLM-embedding-multilingual-mini-instruct-v1 is an open text embedding checkpoint from the Lychee-KaLM line at Harbin Institute of Technology, Shenzhen. Collected 2026-10-15. The Hub repo is HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1.", "what_it_is_for": "The v1 card describes an instruction-tuned mini embedding model for asymmetric tasks. Scores are the instruct-v1 row of that table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/kalm/kalm-embedding-multilingual-mini-instruct-v1-5": { "id": "hit/kalm/kalm-embedding-multilingual-mini-instruct-v1-5", "name": "KaLM-embedding-multilingual-mini-instruct-v1.5", "maker": "hit", "family": "hit/kalm", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "2024-12-26", "context_length": null, "max_output_tokens": null, "parameters": "494032768", "architecture": "Qwen2Model. Instruct v1.5 row on the v1 card. Dimension and max input were not published on that table.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KaLM-embedding-multilingual-mini-instruct-v1.5 is an open text embedding checkpoint from the Lychee-KaLM line at Harbin Institute of Technology, Shenzhen. Collected 2026-10-15. The Hub repo is HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1.5.", "what_it_is_for": "Named instruct update of the mini embedding line. The v1 card prints this row in bold as the best of the three early checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/kalm/kalm-embedding-multilingual-mini-instruct-v2": { "id": "hit/kalm/kalm-embedding-multilingual-mini-instruct-v2", "name": "KaLM-embedding-multilingual-mini-instruct-v2", "maker": "hit", "family": "hit/kalm", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "2025-06-24", "context_length": 32768, "max_output_tokens": null, "parameters": "494032768", "architecture": "Qwen2 embedding with bidirectional attention. Card: 0.5B, embedding dimension 896, max input 32k, MRL 896/512/256/128/64. Base model Qwen/Qwen2-0.5B. Hub pipeline feature-extraction. Safetensors total 494032768.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KaLM-embedding-multilingual-mini-instruct-v2 is the v2 mini embedding model from Harbin Institute of Technology, Shenzhen's Lychee-KaLM line. Collected 2026-10-15. Weights are MIT. The Hub repo was created 2025-06-24.", "what_it_is_for": "The card says it is a compact general-purpose embedding model trained from Qwen2-0.5B with weakly supervised pre-training and supervised fine-tuning, bidirectional attention, and task instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/kalm/kalm-embedding-multilingual-mini-instruct-v2-5": { "id": "hit/kalm/kalm-embedding-multilingual-mini-instruct-v2-5", "name": "KaLM-embedding-multilingual-mini-instruct-v2.5", "maker": "hit", "family": "hit/kalm", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2025-09-29", "context_length": 32768, "max_output_tokens": null, "parameters": "494032768", "architecture": "Bidirectional Qwen2 embedding, mean pooling, dimension 896, max input 32k, MRL 896/512/256/128/64. Contrastive distillation with fine-grained soft labels. Hub bases Qwen/Qwen2.5-0.5B and HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2. Safetensors total 494032768. Pipeline feature-extraction.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KaLM-embedding-multilingual-mini-instruct-v2.5 is the next mini embedding release from the same Lychee-KaLM line. Collected 2026-10-15. The license on this repo is Apache-2.0, which is a change from the MIT license on v2. Hub createdAt is 2025-09-29.", "what_it_is_for": "The card says it keeps the 0.5B bidirectional embedding setup and adds contrastive distillation with fine-grained soft labels. Base tags are Qwen2.5-0.5B and the v2 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/kalm/kalm-embedding-multilingual-mini-v1": { "id": "hit/kalm/kalm-embedding-multilingual-mini-v1", "name": "KaLM-embedding-multilingual-mini-v1", "maker": "hit", "family": "hit/kalm", "status": "active", "confidence": "high", "weights": "open-source", "license": "mit", "release_date": "2024-08-27", "context_length": null, "max_output_tokens": null, "parameters": "494032768", "architecture": "Qwen2Model. The v2 card says the mini line is trained from Qwen2-0.5B. Embedding dimension and max input are not in the v1 card text that was read.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KaLM-embedding-multilingual-mini-v1 is an open text embedding checkpoint from the Lychee-KaLM line at Harbin Institute of Technology, Shenzhen. Collected 2026-10-15. The Hub repo is HIT-TMG/KaLM-embedding-multilingual-mini-v1.", "what_it_is_for": "The card table presents this as the first multilingual mini embedding checkpoint, before the instruct variants. It is a named release, not an unnamed ablation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/kalm/kalm-reranker-v1-large": { "id": "hit/kalm/kalm-reranker-v1-large", "name": "KaLM-Reranker-V1-Large", "maker": "hit", "family": "hit/kalm", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2026-06-02", "context_length": 131072, "max_output_tokens": null, "parameters": "7508928880", "architecture": "T5Gemma2ForConditionalGeneration. Activated params labeled 4B. Table: non-embedding 3209M, embedding 675M, 34 layers, sequence length 128K, document token dim 2560, MEP 1x-32x. Base google/t5gemma-2-4b-4b. Safetensors total 7508928880.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KaLM-Reranker-V1-Large is one of three open rerankers in KaLM-Reranker-V1 from Harbin Institute of Technology, Shenzhen. Collected 2026-10-15. Apache-2.0. Hub createdAt 2026-06-02.", "what_it_is_for": "The card calls the series a fast-but-not-late-interaction reranker. The encoder pre-encodes passages with matryoshka embedding pooling. The decoder reads the instruction and the query. Cross-attention scores relevance. This size is the 4B activated-parameter variant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/kalm/kalm-reranker-v1-nano": { "id": "hit/kalm/kalm-reranker-v1-nano", "name": "KaLM-Reranker-V1-Nano", "maker": "hit", "family": "hit/kalm", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2026-06-02", "context_length": 131072, "max_output_tokens": null, "parameters": "786029296", "architecture": "T5Gemma2ForConditionalGeneration. Activated params labeled 0.27B. Table: non-embedding 100M, embedding 168M, 18 layers, sequence length 128K, document token dim 640, MEP 1x-32x. Base google/t5gemma-2-270m-270m. Safetensors total 786029296.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KaLM-Reranker-V1-Nano is one of three open rerankers in KaLM-Reranker-V1 from Harbin Institute of Technology, Shenzhen. Collected 2026-10-15. Apache-2.0. Hub createdAt 2026-06-02.", "what_it_is_for": "The card calls the series a fast-but-not-late-interaction reranker. The encoder pre-encodes passages with matryoshka embedding pooling. The decoder reads the instruction and the query. Cross-attention scores relevance. This size is the 0.27B activated-parameter variant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/kalm/kalm-reranker-v1-small": { "id": "hit/kalm/kalm-reranker-v1-small", "name": "KaLM-Reranker-V1-Small", "maker": "hit", "family": "hit/kalm", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2026-06-02", "context_length": 131072, "max_output_tokens": null, "parameters": "2115977456", "architecture": "T5Gemma2ForConditionalGeneration. Activated params labeled 1B. Table: non-embedding 698M, embedding 302M, 26 layers, sequence length 128K, document token dim 1152, MEP 1x-32x. Base google/t5gemma-2-1b-1b. Safetensors total 2115977456.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KaLM-Reranker-V1-Small is one of three open rerankers in KaLM-Reranker-V1 from Harbin Institute of Technology, Shenzhen. Collected 2026-10-15. Apache-2.0. Hub createdAt 2026-06-02.", "what_it_is_for": "The card calls the series a fast-but-not-late-interaction reranker. The encoder pre-encodes passages with matryoshka embedding pooling. The decoder reads the instruction and the query. Cross-attention scores relevance. This size is the 1B activated-parameter variant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/ltp/ltp-base": { "id": "hit/ltp/ltp-base", "name": "LTP Base", "maker": "hit", "family": "hit/ltp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-08-14", "context_length": null, "max_output_tokens": null, "parameters": "138269328", "architecture": "PyTorch neural LTP checkpoint for cws, pos, ner, srl, dep, and sdp.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTP Base is a neural Chinese NLP checkpoint in LTP 4 from HIT-SCIR. Collected 2026-10-15. The six LTP Hub repos were created 2022-08-14. The license field on the Hub was empty, so weights are open-weights rather than open-source. The N-LTP paper is arXiv:2009.11616.", "what_it_is_for": "The README says the deep-learning models cover segmentation, part-of-speech, named entities, semantic roles, dependency syntax, and semantic dependency. LTP(\"LTP/small\") is the default load. This folder is the LTP/base checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/ltp/ltp-base1": { "id": "hit/ltp/ltp-base1", "name": "LTP Base1", "maker": "hit", "family": "hit/ltp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-08-14", "context_length": null, "max_output_tokens": null, "parameters": "164978928", "architecture": "PyTorch neural LTP checkpoint for cws, pos, ner, srl, dep, and sdp.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTP Base1 is a neural Chinese NLP checkpoint in LTP 4 from HIT-SCIR. Collected 2026-10-15. The six LTP Hub repos were created 2022-08-14. The license field on the Hub was empty, so weights are open-weights rather than open-source. The N-LTP paper is arXiv:2009.11616.", "what_it_is_for": "The README says the deep-learning models cover segmentation, part-of-speech, named entities, semantic roles, dependency syntax, and semantic dependency. LTP(\"LTP/small\") is the default load. This folder is the LTP/base1 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/ltp/ltp-base2": { "id": "hit/ltp/ltp-base2", "name": "LTP Base2", "maker": "hit", "family": "hit/ltp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-08-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PyTorch neural LTP checkpoint for cws, pos, ner, srl, dep, and sdp.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTP Base2 is a neural Chinese NLP checkpoint in LTP 4 from HIT-SCIR. Collected 2026-10-15. The six LTP Hub repos were created 2022-08-14. The license field on the Hub was empty, so weights are open-weights rather than open-source. The N-LTP paper is arXiv:2009.11616.", "what_it_is_for": "The README says the deep-learning models cover segmentation, part-of-speech, named entities, semantic roles, dependency syntax, and semantic dependency. LTP(\"LTP/small\") is the default load. This folder is the LTP/base2 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/ltp/ltp-legacy": { "id": "hit/ltp/ltp-legacy", "name": "LTP Legacy", "maker": "hit", "family": "hit/ltp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-08-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Rust perceptron LTP model. Tasks: cws, pos, ner only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTP Legacy is the perceptron Chinese NLP model shipped with LTP 4 from HIT-SCIR. Collected 2026-10-15. Hub repo LTP/legacy was created 2022-08-14. License is unpublished. The README says it supports only segmentation, part-of-speech, and named entities.", "what_it_is_for": "The 4.2.0 note says the perceptron algorithm was rewritten in Rust for speed. The score table lists 97.93, 98.41, and 94.28, and 21581.48 sentences per second at 16 threads.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/ltp/ltp-small": { "id": "hit/ltp/ltp-small", "name": "LTP Small", "maker": "hit", "family": "hit/ltp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-08-14", "context_length": null, "max_output_tokens": null, "parameters": "43809936", "architecture": "PyTorch neural LTP checkpoint for cws, pos, ner, srl, dep, and sdp.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTP Small is a neural Chinese NLP checkpoint in LTP 4 from HIT-SCIR. Collected 2026-10-15. The six LTP Hub repos were created 2022-08-14. The license field on the Hub was empty, so weights are open-weights rather than open-source. The N-LTP paper is arXiv:2009.11616.", "what_it_is_for": "The README says the deep-learning models cover segmentation, part-of-speech, named entities, semantic roles, dependency syntax, and semantic dependency. LTP(\"LTP/small\") is the default load. This folder is the LTP/small checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/ltp/ltp-tiny": { "id": "hit/ltp/ltp-tiny", "name": "LTP Tiny", "maker": "hit", "family": "hit/ltp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-08-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PyTorch neural LTP checkpoint for cws, pos, ner, srl, dep, and sdp.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTP Tiny is a neural Chinese NLP checkpoint in LTP 4 from HIT-SCIR. Collected 2026-10-15. The six LTP Hub repos were created 2022-08-14. The license field on the Hub was empty, so weights are open-weights rather than open-source. The N-LTP paper is arXiv:2009.11616.", "what_it_is_for": "The README says the deep-learning models cover segmentation, part-of-speech, named entities, semantic roles, dependency syntax, and semantic dependency. LTP(\"LTP/small\") is the default load. This folder is the LTP/tiny checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/lychee/lychee-embed": { "id": "hit/lychee/lychee-embed", "name": "Lychee-embed", "maker": "hit", "family": "hit/lychee", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-18", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2.5-based embedding model. Card bullet says 1.5B, 28 layers, embedding dimension 1536 with matryoshka steps of 32. The same card's model list table says embedding dimension 1636 and the eval table says 1.54B. Base model tag: Qwen/Qwen2.5-1.5B. Instruction aware. BF16.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lychee-embed is a text embedding model released by the NLP team at Harbin Institute of Technology, Shenzhen, on the vec-ai Hub org and listed in the HIT-TMG Lychee collection. Collected 2026-10-15. Weights are Apache-2.0. It is a Qwen2.5-1.5B embedding model, not a hosted copy of Qwen embeddings.", "what_it_is_for": "The card says it is for semantic retrieval, text similarity, and other embedding tasks, with instructions, in the languages supported by Qwen2.5. The first open release is the 1.5B version.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/lychee/lychee-fd": { "id": "hit/lychee/lychee-fd", "name": "Lychee-FD", "maker": "hit", "family": "hit/lychee", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-07-07", "context_length": null, "max_output_tokens": null, "parameters": "12812043520", "architecture": "LycheeFullDuplex. Native end-to-end full-duplex spoken language model. The demo uses a separate Token2Wav vocoder from stepfun-ai/Step-Audio-2-mini, which is not part of this checkpoint.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lychee-FD is an open native full-duplex spoken language model from the Lychee team at Harbin Institute of Technology, Shenzhen. Collected 2026-10-15. The Hub checkpoint was created 2026-07-07 under Apache-2.0. The GitHub news line cited in search calls the paper an ACL 2026 outstanding paper; that award line was not re-read on the PDF.", "what_it_is_for": "The card and GitHub README describe real-time speech interaction that listens and speaks in one multi-stream model, including barge-in, rather than a cascaded ASR-LLM-TTS pipeline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/lychee/lychee-rerank": { "id": "hit/lychee/lychee-rerank", "name": "Lychee-rerank", "maker": "hit", "family": "hit/lychee", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-18", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2.5-based text reranker. The Lychee-embed model-list table says 1.5B, 28 layers, sequence length 8K, instruction aware. The rerank card bullet seen in search also said 32k; that conflict is unresolved. Eval table on the embed card lists 1.54B for the Lychee-embed row, not a separate rerank score row in the text that was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lychee-rerank is the open text reranker paired with Lychee-embed, from the NLP team at Harbin Institute of Technology, Shenzhen. Collected 2026-10-15. The Hub org is vec-ai and the model is in the HIT-TMG collection. License on the Hub page is Apache-2.0.", "what_it_is_for": "The embed card's model list says this reranker is for text retrieval reranking and is instruction aware. The first open size is 1.5B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/lychee/lychee-rerank-mm": { "id": "hit/lychee/lychee-rerank-mm", "name": "Lychee-rerank-mm", "maker": "hit", "family": "hit/lychee", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal reranker based on Qwen2.5-VL-Instruct. Card bullet says 7B parameters. The model-list table on the same card says 8.29B. BF16. Instruction aware. Language tag English.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lychee-rerank-mm is a multimodal reranker from the NLP team at Harbin Institute of Technology, Shenzhen, hosted at vec-ai and listed by HIT-TMG. Collected 2026-10-15. It is Apache-2.0 and is initialized from Qwen2.5-VL-Instruct.", "what_it_is_for": "The card says it reranks image-text retrieval. The open release is the 7B-class checkpoint. Developers are told to write task-specific instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/uni-moe/uni-moe-2-0-base": { "id": "hit/uni-moe/uni-moe-2-0-base", "name": "Uni-MoE 2.0-Base", "maker": "hit", "family": "hit/uni-moe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-07", "context_length": null, "max_output_tokens": null, "parameters": "28114801184", "architecture": "GrinQwen2VLForConditionalGeneration. Understanding-only Uni-MoE 2.0 checkpoint, without the audio and image generation modules.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Uni-MoE 2.0-Base is the understanding-only checkpoint in the Lychee Uni-MoE 2.0 series from Harbin Institute of Technology, Shenzhen. As of 2026-10-15 the Apache-2.0 weights were on the Hub. The card says it does not include the audio and image generation modules.", "what_it_is_for": "The maker says Base is the Uni-MoE 2.0 version for all-modality understanding only. The card example answers a question in an audio clip about an image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/uni-moe/uni-moe-2-0-image": { "id": "hit/uni-moe/uni-moe-2-0-image", "name": "Uni-MoE 2.0-Image", "maker": "hit", "family": "hit/uni-moe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-11", "context_length": null, "max_output_tokens": null, "parameters": "30757850943", "architecture": "GrinQwen2VLOutForConditionalGeneration. Visual generation model fine-tuned from Uni-MoE 2.0-Omni on visual generation data.", "modalities": { "input": [ "text", "image" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Uni-MoE 2.0-Image is the visual-generation checkpoint from Harbin Institute of Technology, Shenzhen's Lychee team. Collected 2026-10-15. The card says it is derived from Uni-MoE 2.0-Omni and fine-tuned on visual generation data.", "what_it_is_for": "The card examples cover text-to-image generation and an image edit (adding an object). Outputs are saved as PNG files.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/uni-moe/uni-moe-2-0-omni": { "id": "hit/uni-moe/uni-moe-2-0-omni", "name": "Uni-MoE 2.0-Omni", "maker": "hit", "family": "hit/uni-moe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-07", "context_length": null, "max_output_tokens": null, "parameters": "32681062728", "architecture": "GrinQwen2VLForConditionalGeneration. Dynamic-capacity MoE with omnimodality 3D RoPE. The paper says the model is built from scratch on the Qwen2.5-7B dense architecture. The Hub card tags base_model Qwen/Qwen2.5-7B-Instruct as a finetune.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text", "audio", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Uni-MoE 2.0-Omni is the fully open omnimodal model from the Lychee team at the Research Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen. Collected 2026-10-15, the Hub weights were up and the November 2025 technical report was public. It is the checkpoint that adds audio and image generation on top of all-modality understanding.", "what_it_is_for": "The card says this checkpoint integrates full-modality understanding with audio and image generation. The paper describes a language-centric omnimodal model with dynamic-capacity mixture-of-experts, trained with a progressive schedule on about 75B tokens for the base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/uni-moe/uni-moe-2-0-thinking": { "id": "hit/uni-moe/uni-moe-2-0-thinking", "name": "Uni-MoE 2.0-Thinking", "maker": "hit", "family": "hit/uni-moe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-08", "context_length": null, "max_output_tokens": null, "parameters": "28114801184", "architecture": "GrinQwen2VLForConditionalGeneration. Three-stage reinforcement learning on Uni-MoE 2.0-Base for long-form reasoning.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Uni-MoE 2.0-Thinking is the long-form reasoning checkpoint from the Lychee team at Harbin Institute of Technology, Shenzhen. Collected 2026-10-15. The card says it is obtained by training Uni-MoE 2.0-Base with a three-stage reinforcement-learning process.", "what_it_is_for": "The maker says it has long-form reasoning. The sample prompt requires a Thought section inside tags and a Solution section.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/uni-moe/uni-moe-tts": { "id": "hit/uni-moe/uni-moe-tts", "name": "Uni-MoE-TTS", "maker": "hit", "family": "hit/uni-moe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Maker label Uni-MoE-TTS-1.2BA0.7B. Multi-layer Transformer MoE from text tokens to audio tokens, with context-aware long-audio chunking. Card says the base path uses Qwen2.5-0.5B. WavTokenizer is part of the weight bundle.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Uni-MoE-TTS is the text-to-speech module of Uni-MoE 2.0 from the Lychee team at Harbin Institute of Technology, Shenzhen. Collected 2026-10-15. The card names it Uni-MoE-TTS-1.2BA0.7B and says it is open under Apache-2.0.", "what_it_is_for": "The card says it synthesizes long audio with a mixture-of-experts Transformer and chunking, supports three timbres, and supports Chinese and English. Text-controlled speaking style is described as still experimental.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hit/uni-moe/unimoe-audio": { "id": "hit/uni-moe/unimoe-audio", "name": "UniMoE-Audio Preview", "maker": "hit", "family": "hit/uni-moe", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-05", "context_length": null, "max_output_tokens": null, "parameters": "7159504896", "architecture": "UniAudioRVQQwen2_5VLMoEForConditionalGeneration. Dynamic-capacity MoE for unified speech and music generation. Card uses Qwen2.5-VL and the Descript Audio Codec.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UniMoE-Audio Preview is the open preview of a unified speech and music generator from the Lychee team at Harbin Institute of Technology, Shenzhen. Collected 2026-10-15. The card still lists the full UniMoE-Audio checkpoint as not yet released, so this folder stays preview.", "what_it_is_for": "The card describes speech synthesis, text-to-music, and video-text-to-music in one dynamic-capacity MoE. Evaluation figures are on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hitachi/bert/hitachi-bert": { "id": "hitachi/bert/hitachi-bert", "name": "HitachiBERT", "maker": "hitachi", "family": "hitachi/bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2023-06-14", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base encoder: 12 layers, 12 attention heads, hidden size 768, intermediate size 3072. The paper states total parameters of 125M. The safetensors file for hitachi-nlp/bert-base-japanese_mecab-bpe totals 109,113,392 parameters. Those two figures disagree; neither was recomputed here. Each checkpoint uses a different Japanese morphological analyzer (MeCab, Juman++, Sudachi, Vaporetto, or none) and subword method (BPE, Unigram, or WordPiece), vocabulary size 30,000. Pretraining is masked language modeling for 500,000 steps with AdamW on Japanese Wikipedia and CC-100. The paper says those corpora consist of 2.2 and 1.1M samples, with maximum length 512. This folder groups the 15 released checkpoints. They are one ACL SRW 2023 study, not 15 products.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HitachiBERT is the set of Japanese BERT-base encoders released by Hitachi, Ltd. Research & Development Group (Hugging Face org `hitachi-nlp`) for an ACL Student Research Workshop 2023 paper on tokenizers. Fifteen checkpoints share one training setup and differ by morphological analyzer and subword method. They are open weights under a non-commercial license, not a Hitachi API product. The cards themselves are titled \"Japanese BERT-base\"; \"HitachiBERT\" is the name used by the LLM-jp model index. Collected 2026-10-15.", "what_it_is_for": "The paper trains one BERT-base model per tokenizer and measures Japanese downstream tasks, to see whether a morphological analyzer helps and which subword method works. Hitachi released the code and the pretrained models. The authors' conclusion is that a morphological analyzer should be used for Japanese, and that BPE or Unigram beats WordPiece across the tasks they tried.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hitachi/citras/citras-fm": { "id": "hitachi/citras/citras-fm", "name": "CITRAS-FM", "maker": "hitachi", "family": "hitachi/citras", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Weights: CC BY-NC-SA 4.0. Code: PolyForm Noncommercial License 1.0.0.", "release_date": "2026-07-08", "context_length": null, "max_output_tokens": null, "parameters": "7219376", "architecture": "Patch-based decoder-only Transformer for covariate-informed forecasting. Published config.json: d_model 256, d_ff 1024, n_heads 8, patch_len 24, pred_len 24, dropout 0.1, attn_arc TTTCx2, max_context_length 1032. The paper adds Shifted Attention in the cross-variate module and CovSynth pretraining. Layer count is not in the published config. Quantiles are 0.1 through 0.9 in steps of 0.1. The card rounds the weight count to 7.22M and the benchmark table to 7.2M; the safetensors file totals 7,219,376 parameters.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CITRAS-FM is a 7,219,376-parameter time-series foundation model from Hitachi, Ltd. Research & Development Group. It does univariate, multivariate, and covariate-informed zero-shot forecasting and is released as open weights for non-commercial research. The first public code release is v3.0.0 on 2026-07-08. Collected 2026-10-15.", "what_it_is_for": "Hitachi's card says it is a tiny time-series foundation model for covariate-informed zero-shot forecasting, including CPU inference aimed at real-time and resource-constrained settings. Covariates are optional. Observed covariates exist only up to the forecast point. Known covariates are available through the horizon.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hitachi/iwim/iwim": { "id": "hitachi/iwim/iwim", "name": "IWIM", "maker": "hitachi", "family": "hitachi/iwim", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hitachi describes IWIM as one social-infrastructure intelligence foundation model with two cores: world foundation models that combine physical models with digital-twin simulators, and large language models that hold OT knowledge (product knowledge, skilled operations and maintenance practice, and manuals). Agentic AI orchestrates those models by target and conditions, including domain-specific models such as energy and mobility. Component models are not given separate public names. A secondary article called the design a mixture of experts; Hitachi's own pages checked here do not use that term.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IWIM (Integrated World Infrastructure Model) is Hitachi, Ltd.'s social-infrastructure intelligence foundation model. Hitachi announced it in November 2025 and, through 2026, describes it as the knowledge and reasoning foundation behind physical AI and HMAX. It is not a public API or weight release. Collected 2026-10-15.", "what_it_is_for": "Hitachi says IWIM supports safe, reliable physical AI in mission-critical social infrastructure. The March 2026 footnote says it enables understanding, reasoning, and response to real-world phenomena, including prediction, planning, and control. The September 2026 HMAX release links it to frontline data and AI models so the system can interpret physical operating limits and control rules. Hitachi also says it will connect IWIM to NVIDIA Cosmos world foundation models for digital-twin checks. Cosmos is NVIDIA's model, not Hitachi's.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/agenttrek/agenttrek-1-0-32b": { "id": "hku/agenttrek/agenttrek-1-0-32b", "name": "AgentTrek-1.0-32B", "maker": "hku", "family": "hku/agenttrek", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-01-25", "context_length": 32768, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM. Hidden 5120, 64 layers, 40 heads, 8 KV heads, intermediate 27648, vocab 152064, max_position_embeddings 32768. bfloat16. Fine-tune of Qwen/Qwen2.5-32B-Instruct. This checkpoint is text-only even though the paper also synthesizes screenshot trajectories.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AgentTrek-1.0-32B is a text web-agent checkpoint from XLANG Lab at the University of Hong Kong. The card says it is a fine-tune of Qwen2.5-32B-Instruct on trajectories synthesized by replaying web tutorials. The paper was submitted on 12 December 2024 and is an ICLR 2025 Spotlight. The Hub weights date from 2025-01-25 under Apache 2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card calls it a web agent model. The paper's pipeline turns public tutorials into structured tasks, has a VLM execute them, and keeps trajectories that pass a VLM check. The released 32B checkpoint is the text Qwen2.5 fine-tune, not a vision model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/aguvis/aguvis-7b-720p": { "id": "hku/aguvis/aguvis-7b-720p", "name": "Aguvis-7B-720P", "maker": "hku", "family": "hku/aguvis", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-07", "context_length": 32768, "max_output_tokens": null, "parameters": "8288472576", "architecture": "Qwen2VLForConditionalGeneration. Text hidden 3584, 28 layers, 28 heads, 4 KV heads, intermediate 18944, vocab 151659, max_position_embeddings 32768. bfloat16. The 720P name is the screenshot resolution used for this checkpoint, not the token context. Safetensors total 8,288,472,576. No license field is set on the Hub repo.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aguvis-7B-720P is the public 7B pure-vision GUI agent checkpoint from XLANG Lab at the University of Hong Kong. The December 2024 paper, later ICML 2025, frames Aguvis as a screen-image agent with a shared action space and an inner-monologue planning stage. The Hub repo was created on 2025-01-07 and has weights but no license field and no README. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says Aguvis operates on screen images, uses one cross-platform action space, and separates GUI grounding from planning. The 720P checkpoint is the one the GitHub tells people to download.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/anygraph/anygraph": { "id": "hku/anygraph/anygraph", "name": "AnyGraph", "maker": "hku", "family": "hku/anygraph", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-09-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph mixture-of-experts foundation model. Each expert is a small encoder. The paper says only one expert is active per batch. The Hub file is a 9,530,472,950-byte tar named 'AnyGraph pretrained models .tar.gz'. The paper studies 18 size variants. Which of those are inside the tar was not unpacked.", "modalities": { "input": [ "graph" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AnyGraph is an open-weight graph foundation model from the Data Intelligence Lab at the University of Hong Kong. The 20 August 2024 paper describes a graph mixture-of-experts that does zero-shot link prediction across domains. HKUDS posted a pretrained tar to the Hub on 11 September 2024 under an Apache 2.0 tag. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README calls it a foundation model for zero-shot prediction when graphs differ in structure and features. The paper evaluates 38 datasets and argues that accuracy scales with parameters and data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/easyrec/easyrec-roberta-base": { "id": "hku/easyrec/easyrec-roberta-base", "name": "EasyRec-RoBERTa-Base", "maker": "hku", "family": "hku/easyrec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-08-12", "context_length": 512, "max_output_tokens": null, "parameters": "125000000", "architecture": "Easyrec class on a RoBERTa encoder. hidden 768, 12 layers, 12 heads, vocab 50265, max_position_embeddings 514. The card's encode example truncates at 512 and L2-normalizes pooler_output. The large card's model list calls this the 125M checkpoint. The config matches RoBERTa-base depth. The large card names roberta-large only for the large model. The parameter field is the card's stated size (125000000).", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EasyRec-RoBERTa-Base is an open-weight recommendation embedding model from the Data Intelligence Lab at the University of Hong Kong. The card says EasyRec matches textual user and item profiles to collaborative signals and is a RoBERTa fine-tune. Hub weights date from 12 August 2024 under Apache 2.0. The paper is EMNLP 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says to encode user and item profile text for zero-shot recommendation and for text-enhanced collaborative filtering. The example scores a user sentence against item sentences with a dot product of normalized embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/easyrec/easyrec-roberta-large": { "id": "hku/easyrec/easyrec-roberta-large", "name": "EasyRec-RoBERTa-Large", "maker": "hku", "family": "hku/easyrec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-08-12", "context_length": 512, "max_output_tokens": null, "parameters": "355000000", "architecture": "Easyrec class on a RoBERTa encoder. hidden 1024, 24 layers, 16 heads, vocab 50265, max_position_embeddings 514. The card's encode example truncates at 512 and L2-normalizes pooler_output. The card says this checkpoint is finetuned from FacebookAI/roberta-large. The parameter field is the card's stated size (355000000).", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EasyRec-RoBERTa-Large is an open-weight recommendation embedding model from the Data Intelligence Lab at the University of Hong Kong. The card says EasyRec matches textual user and item profiles to collaborative signals and is a RoBERTa fine-tune. Hub weights date from 12 August 2024 under Apache 2.0. The paper is EMNLP 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says to encode user and item profile text for zero-shot recommendation and for text-enhanced collaborative filtering. The example scores a user sentence against item sentences with a dot product of normalized embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/easyrec/easyrec-roberta-small": { "id": "hku/easyrec/easyrec-roberta-small", "name": "EasyRec-RoBERTa-Small", "maker": "hku", "family": "hku/easyrec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-08-12", "context_length": 512, "max_output_tokens": null, "parameters": "82000000", "architecture": "Easyrec class on a RoBERTa encoder. hidden 768, 6 layers, 12 heads, vocab 50265, max_position_embeddings 514. The card's encode example truncates at 512 and L2-normalizes pooler_output. The large card's model list calls this the 82M checkpoint. The config is a 6-layer 768-d RoBERTa. The exact upstream checkpoint name was not on the large card. The parameter field is the card's stated size (82000000).", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EasyRec-RoBERTa-Small is an open-weight recommendation embedding model from the Data Intelligence Lab at the University of Hong Kong. The card says EasyRec matches textual user and item profiles to collaborative signals and is a RoBERTa fine-tune. Hub weights date from 12 August 2024 under Apache 2.0. The paper is EMNLP 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says to encode user and item profile text for zero-shot recommendation and for text-enhanced collaborative filtering. The example scores a user sentence against item sentences with a dot product of normalized embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/instructor/instructor-base": { "id": "hku/instructor/instructor-base", "name": "Instructor-Base", "maker": "hku", "family": "hku/instructor", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2022-12-19", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "T5 encoder initialized from GTR-Base. d_model 768, 12 layers, 12 heads, d_ff 3072, d_kv 64, vocab 32128. Mean pooling, then a bias-free dense map 768 to 768, then L2 normalize. sentence_bert max_seq_length 512. float32. pytorch_model.bin is 438,546,812 bytes. The paper does not print a base parameter count next to the 335M and 1.5B figures.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Instructor-Base is an instruction-finetuned text embedding model released by the NLP group at the University of Hong Kong, with the University of Washington, Meta AI, and AI2. The paper is dated 19 December 2022 (ACL 2023). Weights are on hkunlp under Apache 2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says one embedder can target classification, retrieval, clustering, or evaluation by changing the instruction, with no extra finetune. The official API is InstructorEmbedding, which encodes [instruction, text] pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/instructor/instructor-large": { "id": "hku/instructor/instructor-large", "name": "Instructor-Large", "maker": "hku", "family": "hku/instructor", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2022-12-19", "context_length": 512, "max_output_tokens": null, "parameters": "335000000", "architecture": "T5 encoder initialized from GTR-Large. The paper writes this size as 335M. d_model 1024, 24 layers, 16 heads, d_ff 4096, d_kv 64. Dense map 1024 to 768, then L2 normalize. max_seq_length 512. float32. The stored parameter field is the paper's 335M figure, not a safetensors count. pytorch_model.bin is 1,339,823,867 bytes.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Instructor-Large is an instruction-finetuned text embedding model released by the NLP group at the University of Hong Kong, with the University of Washington, Meta AI, and AI2. The paper is dated 19 December 2022 (ACL 2023). Weights are on hkunlp under Apache 2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says one embedder can target classification, retrieval, clustering, or evaluation by changing the instruction, with no extra finetune. The official API is InstructorEmbedding, which encodes [instruction, text] pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/instructor/instructor-xl": { "id": "hku/instructor/instructor-xl", "name": "Instructor-XL", "maker": "hku", "family": "hku/instructor", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2022-12-19", "context_length": 512, "max_output_tokens": null, "parameters": "1500000000", "architecture": "T5 encoder initialized from GTR-XL. The paper's table labels InstructOR-XL as 1.5B. d_model 1024, 24 layers, 32 heads, d_ff 16384, d_kv 128. Dense map 1024 to 768. max_seq_length 512. The HKU maintainer confirmed embedding dimension 768. pytorch_model.bin is 4,963,705,019 bytes, which is smaller than a naive fp32 1.5B count, so 1.5B is the paper's label. float32.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Instructor-XL is an instruction-finetuned text embedding model released by the NLP group at the University of Hong Kong, with the University of Washington, Meta AI, and AI2. The paper is dated 19 December 2022 (ACL 2023). Weights are on hkunlp under Apache 2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says one embedder can target classification, retrieval, clustering, or evaluation by changing the instruction, with no extra finetune. The official API is InstructorEmbedding, which encodes [instruction, text] pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/jedi/jedi-3b-1080p": { "id": "hku/jedi/jedi-3b-1080p", "name": "Jedi-3B-1080p", "maker": "hku", "family": "hku/jedi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-04-28", "context_length": 128000, "max_output_tokens": null, "parameters": "3754622976", "architecture": "Qwen2_5_VLForConditionalGeneration. Text hidden 2048, 36 layers, 16 heads, 2 KV heads, intermediate 11008, vocab 151936, max_position_embeddings 128000. Vision hidden 1280, depth 32. bfloat16. Base Qwen/Qwen2.5-VL-3B-Instruct.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jedi-3B-1080p is an open-weight GUI grounding model from XLANG Lab at the University of Hong Kong. It is a fine-tune of Qwen2.5-VL-3B-Instruct on the Jedi grounding set described in the 19 May 2025 paper. The Hub checkpoint dates from 2025-04-28. License is Apache 2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper trains multi-scale models on about 4 million synthesized grounding examples so a model can map an instruction to a GUI action. The released 1080p checkpoints are the public Jedi models. The 3B card shows them emitting a computer_use tool call with a pixel coordinate.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/jedi/jedi-7b-1080p": { "id": "hku/jedi/jedi-7b-1080p", "name": "Jedi-7B-1080p", "maker": "hku", "family": "hku/jedi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-04-28", "context_length": 128000, "max_output_tokens": null, "parameters": "8292166656", "architecture": "Qwen2_5_VLForConditionalGeneration. Text hidden 3584, 28 layers, 28 heads, 4 KV heads, intermediate 18944, vocab 152064, max_position_embeddings 128000. Vision hidden 1280, depth 32. bfloat16. Base Qwen/Qwen2.5-VL-7B-Instruct.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jedi-7B-1080p is an open-weight GUI grounding model from XLANG Lab at the University of Hong Kong. It is a fine-tune of Qwen2.5-VL-7B-Instruct on the Jedi grounding set described in the 19 May 2025 paper. The Hub checkpoint dates from 2025-04-28. License is Apache 2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper trains multi-scale models on about 4 million synthesized grounding examples so a model can map an instruction to a GUI action. The released 1080p checkpoints are the public Jedi models. The 3B card shows them emitting a computer_use tool call with a pixel coordinate.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/opencity/opencity-base": { "id": "hku/opencity/opencity-base", "name": "OpenCity-Base", "maker": "hku", "family": "hku/opencity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-08-15", "context_length": null, "max_output_tokens": null, "parameters": "5000000", "architecture": "The paper describes a Transformer plus graph network for traffic series. This release is the file OpenCity-base.pth (19019847 bytes) on the Hub. The paper states the size as the parameter field. A GitHub training command uses a different save name with a 2.0 suffix, so those embed_dim and depth flags were not copied as facts about this file.", "modalities": { "input": [ "timeseries" ], "output": [ "timeseries" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenCity-Base is an open-weight traffic-forecast checkpoint from the Data Intelligence Lab at the University of Hong Kong. The 16 August 2024 paper, later ACM TIST, says OpenCity is a spatio-temporal foundation model for zero-shot traffic prediction. The three sizes were posted to the Hub on 15 August 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says the model is pretrained on heterogeneous traffic flow, speed, and taxi demand so it can forecast unseen cities without full retraining. The GitHub is the runner. The Hub repo is a single .pth file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/opencity/opencity-mini": { "id": "hku/opencity/opencity-mini", "name": "OpenCity-Mini", "maker": "hku", "family": "hku/opencity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-08-15", "context_length": null, "max_output_tokens": null, "parameters": "2000000", "architecture": "The paper describes a Transformer plus graph network for traffic series. This release is the file OpenCity-mini.pth (6773383 bytes) on the Hub. The paper states the size as the parameter field. A GitHub training command uses a different save name with a 2.0 suffix, so those embed_dim and depth flags were not copied as facts about this file.", "modalities": { "input": [ "timeseries" ], "output": [ "timeseries" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenCity-Mini is an open-weight traffic-forecast checkpoint from the Data Intelligence Lab at the University of Hong Kong. The 16 August 2024 paper, later ACM TIST, says OpenCity is a spatio-temporal foundation model for zero-shot traffic prediction. The three sizes were posted to the Hub on 15 August 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says the model is pretrained on heterogeneous traffic flow, speed, and taxi demand so it can forecast unseen cities without full retraining. The GitHub is the runner. The Hub repo is a single .pth file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/opencity/opencity-plus": { "id": "hku/opencity/opencity-plus", "name": "OpenCity-Plus", "maker": "hku", "family": "hku/opencity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-08-15", "context_length": null, "max_output_tokens": null, "parameters": "26000000", "architecture": "The paper describes a Transformer plus graph network for traffic series. This release is the file OpenCity-plus.pth (104163305 bytes) on the Hub. The paper states the size as the parameter field. A GitHub training command uses a different save name with a 2.0 suffix, so those embed_dim and depth flags were not copied as facts about this file.", "modalities": { "input": [ "timeseries" ], "output": [ "timeseries" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenCity-Plus is an open-weight traffic-forecast checkpoint from the Data Intelligence Lab at the University of Hong Kong. The 16 August 2024 paper, later ACM TIST, says OpenCity is a spatio-temporal foundation model for zero-shot traffic prediction. The three sizes were posted to the Hub on 15 August 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says the model is pretrained on heterogeneous traffic flow, speed, and taxi demand so it can forecast unseen cities without full retraining. The GitHub is the runner. The Hub repo is a single .pth file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/opencua/opencua-32b": { "id": "hku/opencua/opencua-32b", "name": "OpenCUA-32B", "maker": "hku", "family": "hku/opencua", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "33452722432", "architecture": "OpenCUAForConditionalGeneration. Text tower is Qwen2: hidden 5120, 64 layers, 40 heads, 8 KV heads, intermediate 27648, vocab 152064. Vision hidden 1280, depth 32, out hidden 5120. Base model Qwen/Qwen2.5-VL-32B-Instruct. bfloat16. text_config.pretraining_sequence_length is 131072. That field is not labeled as the inference context window.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenCUA-32B is an open-weight computer-use model from XLANG Lab at the University of Hong Kong, released with Moonshot AI and university collaborators. It is a supervised fine-tune of a Qwen2.5-VL instruct model that turns screenshots and instructions into executable computer actions. Weights are public under MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project page and model card describe OpenCUA as an end-to-end computer-use foundation model for planning and GUI grounding. It is meant to produce executable actions in desktop environments. Training uses AgentNet demonstrations plus other grounding sets named on the card (Aguvis stages and UGround-V1).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/opencua/opencua-72b": { "id": "hku/opencua/opencua-72b", "name": "OpenCUA-72B", "maker": "hku", "family": "hku/opencua", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-10-03", "context_length": null, "max_output_tokens": null, "parameters": "73410777344", "architecture": "OpenCUAForConditionalGeneration. Text tower is Qwen2: hidden 8192, 80 layers, 64 heads, 8 KV heads, intermediate 29568, vocab 152064. Vision hidden 1280, depth 32, out hidden 8192. Base model Qwen/Qwen2.5-VL-72B-Instruct. bfloat16. text_config.pretraining_sequence_length is 131072.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenCUA-72B is an open-weight computer-use model from XLANG Lab at the University of Hong Kong, released with Moonshot AI and university collaborators. It is a supervised fine-tune of a Qwen2.5-VL instruct model that turns screenshots and instructions into executable computer actions. Weights are public under MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project page and model card describe OpenCUA as an end-to-end computer-use foundation model for planning and GUI grounding. It is meant to produce executable actions in desktop environments. Training uses AgentNet demonstrations plus other grounding sets named on the card (Aguvis stages and UGround-V1).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/opencua/opencua-7b": { "id": "hku/opencua/opencua-7b", "name": "OpenCUA-7B", "maker": "hku", "family": "hku/opencua", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-08-12", "context_length": 128000, "max_output_tokens": null, "parameters": "8292168448", "architecture": "OpenCUAForConditionalGeneration. Text tower is Qwen2: hidden 3584, 28 layers, 28 heads, vocab 152064. Vision tower hidden 1280, depth 32, 16 heads, patch 14. Base model Qwen/Qwen2.5-VL-7B-Instruct. bfloat16. Top-level config max_position_embeddings is 128000.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenCUA-7B is an open-weight computer-use model from XLANG Lab at the University of Hong Kong, released with Moonshot AI and university collaborators. It is a supervised fine-tune of a Qwen2.5-VL instruct model that turns screenshots and instructions into executable computer actions. Weights are public under MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project page and model card describe OpenCUA as an end-to-end computer-use foundation model for planning and GUI grounding. It is meant to produce executable actions in desktop environments. Training uses AgentNet demonstrations plus other grounding sets named on the card (Aguvis stages and UGround-V1).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/openphone/openphone": { "id": "hku/openphone/openphone", "name": "OpenPhone", "maker": "hku", "family": "hku/openphone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-17", "context_length": 6144, "max_output_tokens": null, "parameters": "", "architecture": "The paper says the on-device agent is a two-stage SFT then GRPO fine-tune of Qwen2.5-VL-3B. The GitHub training note for that base sets maximum context 6144 and maximum image pixels 2500000. The Hub repo has no README and no license tag. It stores checkpoint-2025_09_03.zip split into partaa (4,718,592,000 bytes) and partab (1,268,562,169 bytes). The zip was not unpacked, so the 3B claim was not checked against a config in the archive.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenPhone is a mobile GUI agent from the Data Intelligence Lab at the University of Hong Kong. The paper says the on-device model is a Qwen2.5-VL-3B checkpoint trained with supervised fine-tuning and then GRPO, and that hard subtasks can be escalated to a cloud model. A split zip of checkpoint-2025_09_03 is on the Hub. The public GitHub introduction is 16 December 2025, with an announcement the next day. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and the authors' announcement describe a 3B vision-language agent that runs phone GUI tasks on device. The system can keep easy steps local and send harder ones to a cloud model. This folder is the released on-device checkpoint, not the cloud model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/recgpt/recgpt": { "id": "hku/recgpt/recgpt", "name": "RecGPT", "maker": "hku", "family": "hku/recgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-06-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequential-recommendation foundation model with a vector-quantized item tokenizer, as described in the paper. The Hub file is recgpt_layer_3_weight.pt (290,404,766 bytes). The file was not loaded, so width and layer count stay unknown. The Hub repo was created on 2024-10-17 and the card was updated on 2025-06-12.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RecGPT is an open-weight sequential-recommendation model from the Data Intelligence Lab at the University of Hong Kong. The card calls the Hub file the pretrained weight for the EMNLP 2025 paper. The paper is dated 6 June 2025. The license tag is Apache 2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper describes a foundation model for sequential recommendation whose item tokenizer uses vector quantization so the same model can transfer across catalogs. The GitHub holds the training and eval scripts. The Hub holds one weight file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/reclm/reclm": { "id": "hku/reclm/reclm", "name": "RecLM", "maker": "hku", "family": "hku/reclm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Recommendation instruction-tuning of Llama models. The paper says the method uses Llama-2-7B-Chat as the base in the writeup. The Hub repo, license-tagged Apache 2.0, contains four directories: ft_models/llama2/mind, ft_models/llama2/netflix, ft_models/llama3/mind, and ft_models/llama3/netflix. Those directories were not opened, so adapter versus full weights and the Llama 3 size are unknown. Llama community licenses may also apply even though the Hub tag says apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RecLM is a recommendation instruction-tuning release from the Data Intelligence Lab at the University of Hong Kong. The paper, submitted 26 December 2024 and accepted at ACL 2025, tunes Llama models so they write user profiles that plug into collaborative filtering. The Hub repo holds four dataset checkpoints and an Apache 2.0 tag. This dossier was collected on 2026-10-15.", "what_it_is_for": "The GitHub calls RecLM a plug-in for recommender systems in cold-start settings. The released weights are Llama 2 and Llama 3 runs on the MIND and Netflix datasets, not a single general LLM product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/robofine/robofine-vlm-397b-a17b": { "id": "hku/robofine/robofine-vlm-397b-a17b", "name": "RoboFine-VLM-397B-A17B", "maker": "hku", "family": "hku/robofine", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-05-25", "context_length": 262144, "max_output_tokens": null, "parameters": "403397928944", "architecture": "Qwen3_5MoeForConditionalGeneration, a fine-tune of Qwen/Qwen3.5-VL-397B-A17B. Text config: hidden 4096, 60 layers, 32 heads, 2 KV heads, 512 experts, 10 experts per token, expert intermediate 1024, max_position_embeddings 262144, vocab 248320. Vision hidden 1152, depth 27. The model card says 397B total and about 17B active. The Hub safetensors total is 403,397,928,944. Both figures are kept: the integer is the counted checkpoint, the 397B/17B pair is the card's label.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoboFine-VLM is an open-weight robot-video model from XLANG Lab at the University of Hong Kong. The card says it is a supervised fine-tune of Qwen3.5-VL-397B-A17B for manipulation video question answering and fine-grained captions. The Hub repo was created on 2026-05-25 under Apache 2.0. The paper is FineVLA (arXiv 2605.27284). This dossier was collected on 2026-10-15.", "what_it_is_for": "The card positions it for robotic video VQA and for step-by-step captions along ten dimensions, including which arm moves, the target object, contact, trajectory, and failure recovery. It is a vision-language model, not the FineVLA control policy itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/videoagenttrek/videoagenttrek-cua-7b": { "id": "hku/videoagenttrek/videoagenttrek-cua-7b", "name": "VideoAgentTrek-CUA-7B", "maker": "hku", "family": "hku/videoagenttrek", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-14", "context_length": 32768, "max_output_tokens": null, "parameters": "8292166656", "architecture": "Qwen2_5_VLForConditionalGeneration. Hidden 3584, 28 layers, 28 heads, 4 KV heads, intermediate 18944, vocab 152064, max_position_embeddings 32768. Vision hidden 1280, depth 32. bfloat16. Safetensors total 8,292,166,656.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoAgentTrek-CUA-7B is part of VideoAgentTrek, a computer-use pretraining pipeline from XLANG Lab at the University of Hong Kong and the Qwen Team. This checkpoint is the end-to-end 7B computer-use agent trained with continued pretraining on mined video trajectories and then supervised fine-tuning. The paper was submitted on 22 October 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "This checkpoint is the end-to-end 7B computer-use agent trained with continued pretraining on mined video trajectories and then supervised fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/videoagenttrek/videoagenttrek-idm-s1-7b": { "id": "hku/videoagenttrek/videoagenttrek-idm-s1-7b", "name": "VideoAgentTrek-IDM-s1-7B", "maker": "hku", "family": "hku/videoagenttrek", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-20", "context_length": 32768, "max_output_tokens": null, "parameters": "8292166656", "architecture": "Qwen2_5_VLForConditionalGeneration. Hidden 3584, 28 layers, 28 heads, 4 KV heads, intermediate 18944, vocab size not re-read separately, max_position_embeddings 32768. Vision hidden 1280, depth 32. Safetensors total 8,292,166,656. bfloat16.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoAgentTrek-IDM-s1-7B is part of VideoAgentTrek, a computer-use pretraining pipeline from XLANG Lab at the University of Hong Kong and the Qwen Team. The GitHub calls this the keyframe-detection model inside Video2Action. It is a Qwen2.5-VL-7B fine-tune, not the final computer-use policy. The paper was submitted on 22 October 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The GitHub calls this the keyframe-detection model inside Video2Action. It is a Qwen2.5-VL-7B fine-tune, not the final computer-use policy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/videoagenttrek/videoagenttrek-idm-s2-7b": { "id": "hku/videoagenttrek/videoagenttrek-idm-s2-7b", "name": "VideoAgentTrek-IDM-s2-7B", "maker": "hku", "family": "hku/videoagenttrek", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-20", "context_length": 32768, "max_output_tokens": null, "parameters": "8292166656", "architecture": "Qwen2_5_VLForConditionalGeneration. Hidden 3584, 28 layers, 28 heads, 4 KV heads, intermediate 18944, max_position_embeddings 32768. Vision hidden 1280, depth 32. Safetensors total 8,292,166,656. bfloat16.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoAgentTrek-IDM-s2-7B is part of VideoAgentTrek, a computer-use pretraining pipeline from XLANG Lab at the University of Hong Kong and the Qwen Team. The GitHub calls this the action-identification model inside Video2Action. It is a separate Qwen2.5-VL-7B fine-tune from the keyframe detector. The paper was submitted on 22 October 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The GitHub calls this the action-identification model inside Video2Action. It is a separate Qwen2.5-VL-7B fine-tune from the keyframe detector.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hku/videoagenttrek/videoagenttrek-screenfilter": { "id": "hku/videoagenttrek/videoagenttrek-screenfilter", "name": "VideoAgentTrek-ScreenFilter", "maker": "hku", "family": "hku/videoagenttrek", "status": "active", "confidence": "high", "weights": "open-weights", "license": "agpl-3.0", "release_date": "2025-10-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Distributed as best.pt. The project docs call it a YOLO cursor detector. Parameter count was not in the file metadata. AGPL-3.0 on the Hub license tag. The GitHub also warns that the YOLO detector is AGPL-3.0.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoAgentTrek-ScreenFilter is part of VideoAgentTrek, a computer-use pretraining pipeline from XLANG Lab at the University of Hong Kong and the Qwen Team. The GitHub uses this file as the cursor-detection model for video preprocessing. The repo contains best.pt (136,927,155 bytes) and an AGPL-3.0 license tag. It is not one of the 7B VLMs. The paper was submitted on 22 October 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The GitHub uses this file as the cursor-detection model for video preprocessing. The repo contains best.pt (136,927,155 bytes) and an AGPL-3.0 license tag. It is not one of the 7B VLMs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/audio-omni/audio-omni": { "id": "hkust/audio-omni/audio-omni", "name": "Audio-Omni", "maker": "hkust", "family": "hkust/audio-omni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-03-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Card: frozen Qwen2.5-Omni for reasoning plus a trainable diffusion transformer. Card tag model_size is 3B. Exact Hub safetensors total was absent.", "modalities": { "input": [ "text", "audio", "video" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST Audio released Audio-Omni, a unified audio model accepted to SIGGRAPH 2026. Collected on 2026-10-15. License CC-BY-NC-4.0.", "what_it_is_for": "The card says Audio-Omni is one model for understanding, generation, and editing across sound, music, and speech: captioning and QA, text or video to audio or music, TTS, voice conversion, and add, remove, extract, and style-transfer edits.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/audiox/audiox": { "id": "hkust/audiox/audiox", "name": "AudioX", "maker": "hkust", "family": "hkust/audiox", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion transformer in the stable-audio-tools / AudioX code path. Parameter count was not in the Hub safetensors metadata.", "modalities": { "input": [ "text", "audio", "video", "image" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST Audio released AudioX for anything-to-audio generation. The ICLR 2026 paper is AudioX. Weights are CC-BY-NC-4.0 and the project says they are watermarked for non-commercial use. Collected on 2026-10-15.", "what_it_is_for": "The card calls AudioX a diffusion transformer for anything-to-audio and music, conditioned on text, video, image, music, and audio. The sample script generates about 10 seconds of stereo audio. That duration is an example setting, not a published maximum.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/audiox/audiox-maf": { "id": "hkust/audiox/audiox-maf", "name": "AudioX-MAF", "maker": "hkust", "family": "hkust/audiox", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-02-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion transformer in the stable-audio-tools / AudioX code path. Parameter count was not in the Hub safetensors metadata.", "modalities": { "input": [ "text", "audio", "video", "image" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST Audio released AudioX-MAF for anything-to-audio generation. The ICLR 2026 paper is AudioX. Weights are CC-BY-NC-4.0 and the project says they are watermarked for non-commercial use. Collected on 2026-10-15.", "what_it_is_for": "The card and the GitHub download list treat AudioX-MAF as its own checkpoint. It adds the Multimodal Adaptive Fusion module for text, video, and audio conditions. The loader accepts this id alongside AudioX and AudioX-MAF-MMDiT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/audiox/audiox-maf-mmdit": { "id": "hkust/audiox/audiox-maf-mmdit", "name": "AudioX-MAF-MMDiT", "maker": "hkust", "family": "hkust/audiox", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-02-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion transformer in the stable-audio-tools / AudioX code path. Parameter count was not in the Hub safetensors metadata.", "modalities": { "input": [ "text", "audio", "video", "image" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST Audio released AudioX-MAF-MMDiT for anything-to-audio generation. The ICLR 2026 paper is AudioX. Weights are CC-BY-NC-4.0 and the project says they are watermarked for non-commercial use. Collected on 2026-10-15.", "what_it_is_for": "Listed by the lab as a third pretrained AudioX checkpoint, with Multimodal Adaptive Fusion on an MMDiT backbone. The card does not publish a parameter count or a separate score table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/audiox/audiox-turbo": { "id": "hkust/audiox/audiox-turbo", "name": "AudioX-Turbo", "maker": "hkust", "family": "hkust/audiox", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-06-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Few-step student distilled from an AudioX teacher (Multimodal Diffusion Transformer plus MAF) with distribution matching distillation adapted to flow matching, plus a diffusion discriminator. Inference file is audiox_turbo.ckpt. The repo also ships a VAE, a Synchformer, and a teacher checkpoint for training.", "modalities": { "input": [ "text", "audio", "video" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST Audio released AudioX-Turbo on 2026-06-14 as a distilled anything-to-audio model. Collected on 2026-10-15. License CC-BY-NC-4.0.", "what_it_is_for": "The card says AudioX-Turbo generates audio in 4 steps without classifier-free guidance, and claims up to about 25 times fewer function evaluations than multi-step baselines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/codeio/deepseek-v2-lite-coder-codeio": { "id": "hkust/codeio/deepseek-v2-lite-coder-codeio", "name": "DeepSeek-V2-Lite-Coder CodeI/O", "maker": "hkust", "family": "hkust/codeio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-09", "context_length": null, "max_output_tokens": null, "parameters": "15706484224", "architecture": "CodeI/O stage-2 checkpoint from DeepSeek-V2-Lite-Coder. Stage 1 is the same run's earlier checkpoint and is a version. Hub safetensors total 15706484224.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DeepSeek-V2-Lite-Coder CodeI/O in February 2025. Collected on 2026-10-15.", "what_it_is_for": "HKUST NLP's CodeI/O models are trained to predict code inputs and outputs so the reasoning pattern transfers. CodeI/O++ is the second method in the same release table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/codeio/deepseek-v2-lite-coder-codeio-pp": { "id": "hkust/codeio/deepseek-v2-lite-coder-codeio-pp", "name": "DeepSeek-V2-Lite-Coder CodeI/O++", "maker": "hkust", "family": "hkust/codeio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-09", "context_length": null, "max_output_tokens": null, "parameters": "15706484224", "architecture": "CodeI/O++ stage-2 checkpoint from DeepSeek-V2-Lite-Coder. Stage 1 is the same run's earlier checkpoint and is a version.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DeepSeek-V2-Lite-Coder CodeI/O++ in February 2025. Collected on 2026-10-15.", "what_it_is_for": "HKUST NLP's CodeI/O models are trained to predict code inputs and outputs so the reasoning pattern transfers. CodeI/O++ is the second method in the same release table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/codeio/llama-3-1-8b-codeio": { "id": "hkust/codeio/llama-3-1-8b-codeio", "name": "Llama-3.1 8B CodeI/O", "maker": "hkust", "family": "hkust/codeio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-09", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "CodeI/O stage-2 checkpoint from Llama-3.1 8B. Stage 1 is the same run's earlier checkpoint and is a version. Hub safetensors total 8030261248.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Llama-3.1 8B CodeI/O in February 2025. Collected on 2026-10-15.", "what_it_is_for": "HKUST NLP's CodeI/O models are trained to predict code inputs and outputs so the reasoning pattern transfers. CodeI/O++ is the second method in the same release table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/codeio/llama-3-1-8b-codeio-pp": { "id": "hkust/codeio/llama-3-1-8b-codeio-pp", "name": "Llama-3.1 8B CodeI/O++", "maker": "hkust", "family": "hkust/codeio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-09", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "CodeI/O++ stage-2 checkpoint from Llama-3.1 8B. Stage 1 is the same run's earlier checkpoint and is a version.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Llama-3.1 8B CodeI/O++ in February 2025. Collected on 2026-10-15.", "what_it_is_for": "HKUST NLP's CodeI/O models are trained to predict code inputs and outputs so the reasoning pattern transfers. CodeI/O++ is the second method in the same release table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/codeio/qwen-2-5-7b-coder-codeio": { "id": "hkust/codeio/qwen-2-5-7b-coder-codeio", "name": "Qwen2.5-Coder 7B CodeI/O", "maker": "hkust", "family": "hkust/codeio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-09", "context_length": null, "max_output_tokens": null, "parameters": "7615616512", "architecture": "CodeI/O stage-2 checkpoint from Qwen2.5-Coder 7B. Stage 1 is the same run's earlier checkpoint and is a version. Hub safetensors total 7615616512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Qwen2.5-Coder 7B CodeI/O in February 2025. Collected on 2026-10-15.", "what_it_is_for": "HKUST NLP's CodeI/O models are trained to predict code inputs and outputs so the reasoning pattern transfers. CodeI/O++ is the second method in the same release table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/codeio/qwen-2-5-7b-coder-codeio-pp": { "id": "hkust/codeio/qwen-2-5-7b-coder-codeio-pp", "name": "Qwen2.5-Coder 7B CodeI/O++", "maker": "hkust", "family": "hkust/codeio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-09", "context_length": null, "max_output_tokens": null, "parameters": "7615616512", "architecture": "CodeI/O++ stage-2 checkpoint from Qwen2.5-Coder 7B. Stage 1 is the same run's earlier checkpoint and is a version.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Qwen2.5-Coder 7B CodeI/O++ in February 2025. Collected on 2026-10-15.", "what_it_is_for": "HKUST NLP's CodeI/O models are trained to predict code inputs and outputs so the reasoning pattern transfers. CodeI/O++ is the second method in the same release table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/dart-math/dart-math-dsmath-7b-prop2diff": { "id": "hkust/dart-math/dart-math-dsmath-7b-prop2diff", "name": "DART-Math DeepSeekMath 7B Prop2Diff", "maker": "hkust", "family": "hkust/dart-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub tag: other. Base is DeepSeekMath, whose license the card does not restate.", "release_date": "2024-06-05", "context_length": null, "max_output_tokens": null, "parameters": "6890967040", "architecture": "Math SFT of deepseek-ai/deepseek-math-7b-base. Variant Prop2Diff. Alpaca prompt. Hub parameter total is filled when the API returned safetensors.total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DART-Math DeepSeekMath 7B Prop2Diff, a public math fine-tune. Collected on 2026-10-15.", "what_it_is_for": "DART-Math models are HKUST NLP math reasoners trained with difficulty-aware rejection tuning. Prop2Diff uses the hard data mix. Uniform uses the uniform mix. NeurIPS 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/dart-math/dart-math-dsmath-7b-uniform": { "id": "hkust/dart-math/dart-math-dsmath-7b-uniform", "name": "DART-Math DeepSeekMath 7B Uniform", "maker": "hkust", "family": "hkust/dart-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub tag: other. Base is DeepSeekMath, whose license the card does not restate.", "release_date": "2024-06-05", "context_length": null, "max_output_tokens": null, "parameters": "6890967040", "architecture": "Math SFT of deepseek-ai/deepseek-math-7b-base. Variant Uniform. Alpaca prompt. Hub parameter total is filled when the API returned safetensors.total. Hub safetensors total 6890967040.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DART-Math DeepSeekMath 7B Uniform, a public math fine-tune. Collected on 2026-10-15.", "what_it_is_for": "DART-Math models are HKUST NLP math reasoners trained with difficulty-aware rejection tuning. Prop2Diff uses the hard data mix. Uniform uses the uniform mix. NeurIPS 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/dart-math/dart-math-llama3-70b-prop2diff": { "id": "hkust/dart-math/dart-math-llama3-70b-prop2diff", "name": "DART-Math Llama-3 70B Prop2Diff", "maker": "hkust", "family": "hkust/dart-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-06-05", "context_length": null, "max_output_tokens": null, "parameters": "70554755072", "architecture": "Math SFT of meta-llama/Meta-Llama-3-70B. Variant Prop2Diff. Alpaca prompt. Hub parameter total is filled when the API returned safetensors.total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DART-Math Llama-3 70B Prop2Diff, a public math fine-tune. Collected on 2026-10-15.", "what_it_is_for": "DART-Math models are HKUST NLP math reasoners trained with difficulty-aware rejection tuning. Prop2Diff uses the hard data mix. Uniform uses the uniform mix. NeurIPS 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/dart-math/dart-math-llama3-70b-uniform": { "id": "hkust/dart-math/dart-math-llama3-70b-uniform", "name": "DART-Math Llama-3 70B Uniform", "maker": "hkust", "family": "hkust/dart-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-06-05", "context_length": null, "max_output_tokens": null, "parameters": "70554755072", "architecture": "Math SFT of meta-llama/Meta-Llama-3-70B. Variant Uniform. Alpaca prompt. Hub parameter total is filled when the API returned safetensors.total. Hub safetensors total 70554755072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DART-Math Llama-3 70B Uniform, a public math fine-tune. Collected on 2026-10-15.", "what_it_is_for": "DART-Math models are HKUST NLP math reasoners trained with difficulty-aware rejection tuning. Prop2Diff uses the hard data mix. Uniform uses the uniform mix. NeurIPS 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/dart-math/dart-math-llama3-8b-prop2diff": { "id": "hkust/dart-math/dart-math-llama3-8b-prop2diff", "name": "DART-Math Llama-3 8B Prop2Diff", "maker": "hkust", "family": "hkust/dart-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-06-05", "context_length": null, "max_output_tokens": null, "parameters": "8030785536", "architecture": "Math SFT of meta-llama/Meta-Llama-3-8B. Variant Prop2Diff. Alpaca prompt. Hub parameter total is filled when the API returned safetensors.total. Hub safetensors total 8030785536.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DART-Math Llama-3 8B Prop2Diff, a public math fine-tune. Collected on 2026-10-15.", "what_it_is_for": "DART-Math models are HKUST NLP math reasoners trained with difficulty-aware rejection tuning. Prop2Diff uses the hard data mix. Uniform uses the uniform mix. NeurIPS 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/dart-math/dart-math-llama3-8b-uniform": { "id": "hkust/dart-math/dart-math-llama3-8b-uniform", "name": "DART-Math Llama-3 8B Uniform", "maker": "hkust", "family": "hkust/dart-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-06-05", "context_length": null, "max_output_tokens": null, "parameters": "8030785536", "architecture": "Math SFT of meta-llama/Meta-Llama-3-8B. Variant Uniform. Alpaca prompt. Hub parameter total is filled when the API returned safetensors.total. Hub safetensors total 8030785536.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DART-Math Llama-3 8B Uniform, a public math fine-tune. Collected on 2026-10-15.", "what_it_is_for": "DART-Math models are HKUST NLP math reasoners trained with difficulty-aware rejection tuning. Prop2Diff uses the hard data mix. Uniform uses the uniform mix. NeurIPS 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/dart-math/dart-math-mistral-7b-prop2diff": { "id": "hkust/dart-math/dart-math-mistral-7b-prop2diff", "name": "DART-Math Mistral 7B Prop2Diff", "maker": "hkust", "family": "hkust/dart-math", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-05", "context_length": null, "max_output_tokens": null, "parameters": "7242256384", "architecture": "Math SFT of mistralai/Mistral-7B-v0.1. Variant Prop2Diff. Alpaca prompt. Hub parameter total is filled when the API returned safetensors.total. Hub safetensors total 7242256384.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DART-Math Mistral 7B Prop2Diff, a public math fine-tune. Collected on 2026-10-15.", "what_it_is_for": "DART-Math models are HKUST NLP math reasoners trained with difficulty-aware rejection tuning. Prop2Diff uses the hard data mix. Uniform uses the uniform mix. NeurIPS 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/dart-math/dart-math-mistral-7b-uniform": { "id": "hkust/dart-math/dart-math-mistral-7b-uniform", "name": "DART-Math Mistral 7B Uniform", "maker": "hkust", "family": "hkust/dart-math", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-05", "context_length": null, "max_output_tokens": null, "parameters": "7242256384", "architecture": "Math SFT of mistralai/Mistral-7B-v0.1. Variant Uniform. Alpaca prompt. Hub parameter total is filled when the API returned safetensors.total. Hub safetensors total 7242256384.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DART-Math Mistral 7B Uniform, a public math fine-tune. Collected on 2026-10-15.", "what_it_is_for": "DART-Math models are HKUST NLP math reasoners trained with difficulty-aware rejection tuning. Prop2Diff uses the hard data mix. Uniform uses the uniform mix. NeurIPS 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/deita/deita-7b-v1-0": { "id": "hkust/deita/deita-7b-v1-0", "name": "DEITA 7B v1.0", "maker": "hkust", "family": "hkust/deita", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-12-20", "context_length": null, "max_output_tokens": null, "parameters": "7241732096", "architecture": "Mistral-7B-v0.1 SFT on DEITA 6K plus DPO on 10K UltraFeedback samples. Hub safetensors total 7,241,732,096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DEITA 7B v1.0, an Apache-2.0 Mistral instruction model trained on automatically selected data. Collected on 2026-10-15.", "what_it_is_for": "Instruction model from HKUST NLP's DEITA data-selection project. The card says it is the SFT plus DPO release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/deita/deita-7b-v1-0-sft": { "id": "hkust/deita/deita-7b-v1-0-sft", "name": "DEITA 7B v1.0 SFT", "maker": "hkust", "family": "hkust/deita", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-12-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SFT stage of DEITA on Mistral-7B-v0.1. The v1.0 card table row used here is the 6K SFT line. A 10K SFT line is also printed and is not a separate folder.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released the SFT-only DEITA 7B checkpoint alongside the DPO model. Collected on 2026-10-15.", "what_it_is_for": "Supervised-only DEITA 7B checkpoint, before the DPO step used in deita-7b-v1-0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/deita/deita-complexity-scorer": { "id": "hkust/deita/deita-complexity-scorer", "name": "DEITA Complexity Scorer", "maker": "hkust", "family": "hkust/deita", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Companion DEITA annotator. The Hub model list tags it as a Llama text-generation model. The base checkpoint was not re-read from this card in the first pass.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released the DEITA complexity scorer next to the quality scorer. Collected on 2026-10-15.", "what_it_is_for": "Scores instruction complexity for DEITA data selection, paired with the quality scorer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/deita/deita-llama1-13b-v1-0-sft": { "id": "hkust/deita/deita-llama1-13b-v1-0-sft", "name": "DEITA LLaMA-1 13B v1.0 SFT", "maker": "hkust", "family": "hkust/deita", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SFT of LLaMA-1 13B on the DEITA 10K selected set, as named in the card table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DEITA-LLaMA1-13B-v1.0-sft. Collected on 2026-10-15.", "what_it_is_for": "Larger LLaMA-1 DEITA instruction checkpoint. Card row uses 10K SFT data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/deita/deita-llama2-13b-v1-0-sft": { "id": "hkust/deita/deita-llama2-13b-v1-0-sft", "name": "DEITA LLaMA-2 13B v1.0 SFT", "maker": "hkust", "family": "hkust/deita", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SFT of Llama-2-13B on the DEITA 10K set. Card base tag meta-llama/Llama-2-13b-hf.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DEITA-LLaMA2-13B-v1.0-sft. Collected on 2026-10-15. The Llama 2 license still applies to the base weights even though the Hub tag says Apache-2.0.", "what_it_is_for": "LLaMA-2 13B DEITA instruction checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/deita/deita-quality-scorer": { "id": "hkust/deita/deita-quality-scorer", "name": "DEITA Quality Scorer", "maker": "hkust", "family": "hkust/deita", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Quality annotator fine-tuned from Llama-1-13B-hf, per the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released the DEITA quality scorer, a Llama-1 13B annotator. Collected on 2026-10-15.", "what_it_is_for": "Scores instruction-response quality so DEITA can select SFT data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/drkernel/drkernel-14b": { "id": "hkust/drkernel/drkernel-14b", "name": "DR.Kernel 14B", "maker": "hkust", "family": "hkust/drkernel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-02-05", "context_length": null, "max_output_tokens": null, "parameters": "14768307200", "architecture": "Qwen3 causal LM (Qwen/Qwen3-14B-Base) after cold-start SFT and multi-turn RL (TRLOO, MRS, PR, PRS) in KernelGYM. Card parameter count for the 8B RL model is 8,190,735,360, matching the Hub total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DR.Kernel 14B on 2026-02-05 for Triton kernel generation. Collected on 2026-10-15.", "what_it_is_for": "Generates and iteratively edits Triton GPU kernels from a PyTorch reference, using execution feedback. Not a general chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/drkernel/drkernel-8b": { "id": "hkust/drkernel/drkernel-8b", "name": "DR.Kernel 8B", "maker": "hkust", "family": "hkust/drkernel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-02-05", "context_length": null, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Qwen3 causal LM (Qwen/Qwen3-8B-Base) after cold-start SFT and multi-turn RL (TRLOO, MRS, PR, PRS) in KernelGYM. Card parameter count for the 8B RL model is 8,190,735,360, matching the Hub total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DR.Kernel 8B on 2026-02-05 for Triton kernel generation. Collected on 2026-10-15.", "what_it_is_for": "Generates and iteratively edits Triton GPU kernels from a PyTorch reference, using execution feedback. Not a general chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/graph-r1/graph-r1-1-5b": { "id": "hkust/graph-r1/graph-r1-1-5b", "name": "Graph-R1 1.5B", "maker": "hkust", "family": "hkust/graph-r1", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-08-04", "context_length": null, "max_output_tokens": null, "parameters": "1777088000", "architecture": "Card: cold-start SFT on long chain-of-thought from QwQ-32B, then GRPO with a curriculum. Base Qwen/Qwen2.5-1.5B. The card's Model Date says 2025/04. Hub createdAt is 2025-08-04. The preprint is August 2025.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST-DSAIL released Graph-R1 1.5B. Collected on 2026-10-15. Ablation checkpoints were not given folders.", "what_it_is_for": "Small reasoners for NP-hard graph problems such as TSP, graph edit distance, and maximum clique. The card also claims transfer to math, code, STEM, and logic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/graph-r1/graph-r1-7b": { "id": "hkust/graph-r1/graph-r1-7b", "name": "Graph-R1 7B", "maker": "hkust", "family": "hkust/graph-r1", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-08-04", "context_length": null, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Card: cold-start SFT on long chain-of-thought from QwQ-32B, then GRPO with a curriculum. Base Qwen/Qwen2.5-7B-Instruct-1M. The card's Model Date says 2025/04. Hub createdAt is 2025-08-04. The preprint is August 2025.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST-DSAIL released Graph-R1 7B. Collected on 2026-10-15. Ablation checkpoints were not given folders.", "what_it_is_for": "Small reasoners for NP-hard graph problems such as TSP, graph edit distance, and maximum clique. The card also claims transfer to math, code, STEM, and logic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/graphmind/graphmind-gemma2-2b": { "id": "hkust/graphmind/graphmind-gemma2-2b", "name": "GraphMind Gemma-2 2B", "maker": "hkust", "family": "hkust/graphmind", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-08-17", "context_length": null, "max_output_tokens": null, "parameters": "2614341888", "architecture": "Continued pretraining with Llama-Factory on GraphPile, described as 10.9 billion tokens of graph-problem reasoning. Base google/gemma-2-2b. Hub safetensors total 2614341888.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST-DSAIL released GraphMind Gemma-2 2B in August 2025. Collected on 2026-10-15. The lab is at HKUST (Guangzhou).", "what_it_is_for": "HKUST(GZ) DSAIL models meant to carry graph reasoning over to math, logic, code, and commonsense tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/graphmind/graphmind-llama-3-1-8b": { "id": "hkust/graphmind/graphmind-llama-3-1-8b", "name": "GraphMind Llama-3.1 8B", "maker": "hkust", "family": "hkust/graphmind", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-08-17", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Continued pretraining with Llama-Factory on GraphPile, described as 10.9 billion tokens of graph-problem reasoning. Base meta-llama/Llama-3.1-8B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST-DSAIL released GraphMind Llama-3.1 8B in August 2025. Collected on 2026-10-15. The lab is at HKUST (Guangzhou).", "what_it_is_for": "HKUST(GZ) DSAIL models meant to carry graph reasoning over to math, logic, code, and commonsense tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/graphmind/graphmind-llama-3-8b": { "id": "hkust/graphmind/graphmind-llama-3-8b", "name": "GraphMind Llama-3 8B", "maker": "hkust", "family": "hkust/graphmind", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-08-17", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Continued pretraining with Llama-Factory on GraphPile, described as 10.9 billion tokens of graph-problem reasoning. Base meta-llama/Meta-Llama-3-8B. Hub safetensors total 8030261248.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST-DSAIL released GraphMind Llama-3 8B in August 2025. Collected on 2026-10-15. The lab is at HKUST (Guangzhou).", "what_it_is_for": "HKUST(GZ) DSAIL models meant to carry graph reasoning over to math, logic, code, and commonsense tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/hk-o1aw/hk-o1aw": { "id": "hkust/hk-o1aw/hk-o1aw", "name": "HK-O1aw", "maker": "hkust", "family": "hkust/hk-o1aw", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-10-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Card: Llama-3.1-8B pretrained base, tuned with Align-Anything on the O1aw legal dataset. Version labeled v0. Hub safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKAIR-Lab at HKUST released HK-O1aw v0 on 2024-10-30. Collected on 2026-10-15.", "what_it_is_for": "Hong Kong legal assistant that writes step-by-step reasoning. HKAIR-Lab says it releases the data, code, and synthetic-data prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/jailjudge/jailjudge-guard": { "id": "hkust/jailjudge/jailjudge-guard", "name": "JailJudge Guard", "maker": "hkust", "family": "hkust/jailjudge", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "6738415616", "architecture": "Instruction-tuned jailbreak judge. Hub safetensors total 6,738,415,616, the usual Llama-2 7B count. The card does not name the base in the excerpt. License tag is llama2.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "usail-hkust released JailJudge Guard on 2024-09-30. Collected on 2026-10-15.", "what_it_is_for": "Explains whether a model response is jailbroken and gives a 1 to 10 score. The card says it was tuned on more than 35k instruction traces from the JailJudge benchmark so the judge does not need a paid API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/llasa/llasa-1b": { "id": "hkust/llasa/llasa-1b", "name": "Llasa 1B", "maker": "hkust", "family": "hkust/llasa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-01-07", "context_length": null, "max_output_tokens": null, "parameters": "1370048512", "architecture": "Llama decoder continued for single-codebook XCodec2 speech tokens (65,536 codes). Initialized from Llama-3.2-1B-Instruct. Hub safetensors total is 1370048512.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hong Kong University of Science and Technology (HKUST Audio) released Llasa 1B, an open-weight Llama-based text-to-speech model. Weights are public under CC-BY-NC-4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The HKUST Audio card says Llasa is a text-to-speech model that can speak from text alone or from a speech prompt. The 1B, 3B, and 8B sizes were trained on 250,000 hours of Chinese-English speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/llasa/llasa-1b-multilingual": { "id": "hkust/llasa/llasa-1b-multilingual", "name": "Llasa 1B Multilingual", "maker": "hkust", "family": "hkust/llasa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-02-07", "context_length": null, "max_output_tokens": null, "parameters": "1766950912", "architecture": "Same Llama-3.2-1B-Instruct initialization and XCodec2 tokens as Llasa 1B, continued on multilingual data. Hub safetensors total is 1,766,950,912.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST Audio released Llasa 1B Multilingual on 2025-02-07 as a later multilingual TTS checkpoint. The card says it is not in the Llasa paper. Collected on 2026-10-15. Weights are CC-BY-NC-4.0.", "what_it_is_for": "The card says this checkpoint adds multilingual data (MLS and Emilia) and keeps Llama's BPE tokenizer so it does not need a language-specific grapheme-to-phoneme system. The card says it is a base for language-specific finetunes and that some languages may be weak because the data are limited.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/llasa/llasa-3b": { "id": "hkust/llasa/llasa-3b", "name": "Llasa 3B", "maker": "hkust", "family": "hkust/llasa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-01-07", "context_length": null, "max_output_tokens": null, "parameters": "4009454592", "architecture": "Llama decoder continued for single-codebook XCodec2 speech tokens (65,536 codes). Initialized from Llama-3.2-3B-Instruct. Hub safetensors total is 4009454592.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hong Kong University of Science and Technology (HKUST Audio) released Llasa 3B, an open-weight Llama-based text-to-speech model. Weights are public under CC-BY-NC-4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The HKUST Audio card says Llasa is a text-to-speech model that can speak from text alone or from a speech prompt. The 1B, 3B, and 8B sizes were trained on 250,000 hours of Chinese-English speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/llasa/llasa-8b": { "id": "hkust/llasa/llasa-8b", "name": "Llasa 8B", "maker": "hkust", "family": "hkust/llasa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-01-07", "context_length": null, "max_output_tokens": null, "parameters": "8567197696", "architecture": "Llama decoder continued for single-codebook XCodec2 speech tokens (65,536 codes). The card says Llasa 8B is the size that shows stronger text comprehension. Hub safetensors total is 8567197696.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hong Kong University of Science and Technology (HKUST Audio) released Llasa 8B, an open-weight Llama-based text-to-speech model. Weights are public under CC-BY-NC-4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The HKUST Audio card says Llasa is a text-to-speech model that can speak from text alone or from a speech prompt. The 1B, 3B, and 8B sizes were trained on 250,000 hours of Chinese-English speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/llmlight/llmlight-lightgpt": { "id": "hkust/llmlight/llmlight-lightgpt", "name": "LLMLight LightGPT", "maker": "hkust", "family": "hkust/llmlight", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-02-09", "context_length": null, "max_output_tokens": null, "parameters": "13015864320", "architecture": "Card tags llama-2. Hub safetensors total 13,015,864,320, the usual Llama-2 13B count. Traffic-signal-control weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "usail-hkust released LLMLight-LightGPT on 2024-02-09. Collected on 2026-10-15. HKUST (Guangzhou) tag is on the card.", "what_it_is_for": "The card says these are the weights from the LLMLight paper, which uses language models as traffic-signal control agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/mstar/mstar-8b": { "id": "hkust/mstar/mstar-8b", "name": "M-STAR 8B", "maker": "hkust", "family": "hkust/mstar", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-12-25", "context_length": null, "max_output_tokens": null, "parameters": "8537092336", "architecture": "Self-evolving training on MiniCPM-Llama3-V-2.5. Card says 8B. Hub safetensors total 8,537,092,336.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released M-STAR 8B, a MiniCPM-V reasoning model, on 2024-12-25. Collected on 2026-10-15.", "what_it_is_for": "Multimodal math reasoner. The card says the M-STAR model scores 59.5 on MathVista.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/mstar/mstar-prm-8b": { "id": "hkust/mstar/mstar-prm-8b", "name": "M-STAR PRM 8B", "maker": "hkust", "family": "hkust/mstar", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-12-25", "context_length": null, "max_output_tokens": null, "parameters": "8537096432", "architecture": "Multimodal process reward model on MiniCPM-Llama3-V-2.5. Hub safetensors total 8,537,096,432.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released the M-STAR process reward model with the 8B reasoner. Collected on 2026-10-15.", "what_it_is_for": "Step-level judge for multimodal reasoning traces in the M-STAR pipeline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/preselect/preselect-fasttext": { "id": "hkust/preselect/preselect-fasttext", "name": "PreSelect fastText classifier", "maker": "hkust", "family": "hkust/preselect", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "fastText binary classifier. Positive label __label__1, negative __label__0. The card says the PreSelect-100B corpus kept the top 10 percent.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released the PreSelect fastText classifier used to build PreSelect-100B. Collected on 2026-10-15.", "what_it_is_for": "Filters pretraining text. The paper is Predictive Data Selection. It is a released model, not only a script.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/ser/ser-eng-zho-adults": { "id": "hkust/ser/ser-eng-zho-adults", "name": "SER wav2vec2 English-Chinese adults", "maker": "hkust", "family": "hkust/ser", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-06-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of facebook/wav2vec2-large-xlsr-53 for speech emotion recognition. Adult age split.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAiRE at HKUST released SER wav2vec2 English-Chinese adults on 2023-06-27. Collected on 2026-10-15.", "what_it_is_for": "English and Mandarin speech-emotion classifiers from CAiRE HKUST, split by age. The paper argues age and language need their own features and releases BiMotion and YueMotion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/ser/ser-eng-zho-all-age": { "id": "hkust/ser/ser-eng-zho-all-age", "name": "SER wav2vec2 English-Chinese all ages", "maker": "hkust", "family": "hkust/ser", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-06-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of facebook/wav2vec2-large-xlsr-53 for speech emotion recognition. All-age checkpoint. Highest download count of the three.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAiRE at HKUST released SER wav2vec2 English-Chinese all ages on 2023-06-27. Collected on 2026-10-15.", "what_it_is_for": "English and Mandarin speech-emotion classifiers from CAiRE HKUST, split by age. The paper argues age and language need their own features and releases BiMotion and YueMotion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/ser/ser-eng-zho-elderly": { "id": "hkust/ser/ser-eng-zho-elderly", "name": "SER wav2vec2 English-Chinese elderly", "maker": "hkust", "family": "hkust/ser", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-06-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of facebook/wav2vec2-large-xlsr-53 for speech emotion recognition. Elderly age split.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAiRE at HKUST released SER wav2vec2 English-Chinese elderly on 2023-06-27. Collected on 2026-10-15.", "what_it_is_for": "English and Mandarin speech-emotion classifiers from CAiRE HKUST, split by age. The paper argues age and language need their own features and releases BiMotion and YueMotion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/simplerl/deepseek-math-7b-simplerl-zoo": { "id": "hkust/simplerl/deepseek-math-7b-simplerl-zoo", "name": "DeepSeekMath 7B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-24", "context_length": null, "max_output_tokens": null, "parameters": "6910365696", "architecture": "Zero reinforcement learning from the base model deepseek-ai/deepseek-math-7b-base on about 8K math examples with a rule reward. COLM 2025. Hub safetensors total 6910365696.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released DeepSeekMath 7B SimpleRL-Zoo on 2025-03-24 as part of SimpleRL-Zoo. Collected on 2026-10-15.", "what_it_is_for": "One of the ten SimpleRL-Zoo checkpoints. HKUST NLP trains zero RL directly from open base models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/simplerl/llama-3-1-8b-simplerl-zoo": { "id": "hkust/simplerl/llama-3-1-8b-simplerl-zoo", "name": "Llama-3.1 8B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-24", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Zero reinforcement learning from the base model meta-llama/Llama-3.1-8B on about 8K math examples with a rule reward. COLM 2025. Hub safetensors total 8030261248.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Llama-3.1 8B SimpleRL-Zoo on 2025-03-24 as part of SimpleRL-Zoo. Collected on 2026-10-15.", "what_it_is_for": "One of the ten SimpleRL-Zoo checkpoints. HKUST NLP trains zero RL directly from open base models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/simplerl/mistral-7b-v0-1-simplerl-zoo": { "id": "hkust/simplerl/mistral-7b-v0-1-simplerl-zoo", "name": "Mistral 7B v0.1 SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-24", "context_length": null, "max_output_tokens": null, "parameters": "7241732096", "architecture": "Zero reinforcement learning from the base model mistralai/Mistral-7B-v0.1 on about 8K math examples with a rule reward. COLM 2025. Hub safetensors total 7241732096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Mistral 7B v0.1 SimpleRL-Zoo on 2025-03-24 as part of SimpleRL-Zoo. Collected on 2026-10-15.", "what_it_is_for": "One of the ten SimpleRL-Zoo checkpoints. HKUST NLP trains zero RL directly from open base models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/simplerl/mistral-small-24b-simplerl-zoo": { "id": "hkust/simplerl/mistral-small-24b-simplerl-zoo", "name": "Mistral Small 24B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-24", "context_length": null, "max_output_tokens": null, "parameters": "23572403200", "architecture": "Zero reinforcement learning from the base model Mistral-Small-24B-Base-2501 on about 8K math examples with a rule reward. COLM 2025. Hub safetensors total 23572403200.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Mistral Small 24B SimpleRL-Zoo on 2025-03-24 as part of SimpleRL-Zoo. Collected on 2026-10-15.", "what_it_is_for": "One of the ten SimpleRL-Zoo checkpoints. HKUST NLP trains zero RL directly from open base models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/simplerl/qwen-2-5-0-5b-simplerl-zoo": { "id": "hkust/simplerl/qwen-2-5-0-5b-simplerl-zoo", "name": "Qwen2.5 0.5B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-24", "context_length": null, "max_output_tokens": null, "parameters": "630167424", "architecture": "Zero reinforcement learning from the base model Qwen/Qwen2.5-0.5B on about 8K math examples with a rule reward. COLM 2025. Hub safetensors total 630167424.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Qwen2.5 0.5B SimpleRL-Zoo on 2025-03-24 as part of SimpleRL-Zoo. Collected on 2026-10-15.", "what_it_is_for": "One of the ten SimpleRL-Zoo checkpoints. HKUST NLP trains zero RL directly from open base models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/simplerl/qwen-2-5-1-5b-simplerl-zoo": { "id": "hkust/simplerl/qwen-2-5-1-5b-simplerl-zoo", "name": "Qwen2.5 1.5B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-24", "context_length": null, "max_output_tokens": null, "parameters": "1777088000", "architecture": "Zero reinforcement learning from the base model Qwen/Qwen2.5-1.5B on about 8K math examples with a rule reward. COLM 2025. Hub safetensors total 1777088000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Qwen2.5 1.5B SimpleRL-Zoo on 2025-03-24 as part of SimpleRL-Zoo. Collected on 2026-10-15.", "what_it_is_for": "One of the ten SimpleRL-Zoo checkpoints. HKUST NLP trains zero RL directly from open base models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/simplerl/qwen-2-5-14b-simplerl-zoo": { "id": "hkust/simplerl/qwen-2-5-14b-simplerl-zoo", "name": "Qwen2.5 14B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-24", "context_length": null, "max_output_tokens": null, "parameters": "14770033664", "architecture": "Zero RL from the named Qwen2.5 base, same SimpleRL-Zoo recipe. Hub safetensors total 14770033664.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Qwen2.5 14B SimpleRL-Zoo with the SimpleRL-Zoo drop. Collected on 2026-10-15.", "what_it_is_for": "14B member of the ten-model zoo. The GitHub score row was not fully captured, so scores are left empty.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/simplerl/qwen-2-5-32b-simplerl-zoo": { "id": "hkust/simplerl/qwen-2-5-32b-simplerl-zoo", "name": "Qwen2.5 32B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-24", "context_length": null, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Zero RL from the named Qwen2.5 base, same SimpleRL-Zoo recipe. Hub safetensors total 32763876352.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Qwen2.5 32B SimpleRL-Zoo with the SimpleRL-Zoo drop. Collected on 2026-10-15.", "what_it_is_for": "32B member of the ten-model zoo. Score row not captured.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/simplerl/qwen-2-5-7b-simplerl-zoo": { "id": "hkust/simplerl/qwen-2-5-7b-simplerl-zoo", "name": "Qwen2.5 7B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-24", "context_length": null, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Zero reinforcement learning from the base model Qwen/Qwen2.5-7B on about 8K math examples with a rule reward. COLM 2025.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Qwen2.5 7B SimpleRL-Zoo on 2025-03-24 as part of SimpleRL-Zoo. Collected on 2026-10-15.", "what_it_is_for": "One of the ten SimpleRL-Zoo checkpoints. HKUST NLP trains zero RL directly from open base models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/simplerl/qwen-2-5-math-7b-simplerl": { "id": "hkust/simplerl/qwen-2-5-math-7b-simplerl", "name": "Qwen2.5-Math 7B SimpleRL", "maker": "hkust", "family": "hkust/simplerl", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-18", "context_length": null, "max_output_tokens": null, "parameters": "7615616512", "architecture": "February 2025 SimpleRL checkpoint from Qwen2.5-Math-7B. The GitHub news lists it beside SimpleRL-Zero. The card body was not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Qwen2.5-Math-7B-SimpleRL on 2025-02-18. Collected on 2026-10-15.", "what_it_is_for": "Paired February release with SimpleRL-Zero. The lab's news post tells them apart from the later zoo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/simplerl/qwen-2-5-math-7b-simplerl-zero": { "id": "hkust/simplerl/qwen-2-5-math-7b-simplerl-zero", "name": "Qwen2.5-Math 7B SimpleRL-Zero", "maker": "hkust", "family": "hkust/simplerl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-18", "context_length": null, "max_output_tokens": null, "parameters": "7615616512", "architecture": "RL from Qwen2.5-Math-7B base. Card: only 8K MATH examples. This is the February blog checkpoint, not the March zoo run.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Qwen2.5-Math-7B-SimpleRL-Zero on 2025-02-18, before the ten-model zoo. Collected on 2026-10-15.", "what_it_is_for": "Early SimpleRL checkpoint. The card says training starts from the base model with 8K MATH examples.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/simplerl/qwen-2-5-math-7b-simplerl-zoo": { "id": "hkust/simplerl/qwen-2-5-math-7b-simplerl-zoo", "name": "Qwen2.5-Math 7B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-24", "context_length": null, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Zero RL from the named Qwen2.5 base, same SimpleRL-Zoo recipe. Hub safetensors total 7615616512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released Qwen2.5-Math 7B SimpleRL-Zoo with the SimpleRL-Zoo drop. Collected on 2026-10-15.", "what_it_is_for": "Math-base member of the ten-model zoo. The GitHub row was truncated in the excerpt, so scores are left empty.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/talker/talker-t2av": { "id": "hkust/talker/talker-t2av", "name": "Talker-T2AV", "maker": "hkust", "family": "hkust/talker", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-05-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Card: autoregressive backbone initialized from Qwen3-0.6B, dual diffusion heads, a patch transformer encoder, and a stop predictor. Separate WhisperX-VAE (32-d, 25 Hz) ships in the repo. LIA-X motion weights and a WavLM speaker encoder are fetched from elsewhere.", "modalities": { "input": [ "text" ], "output": [ "audio", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST Audio released Talker-T2AV, a talking audio-video model, under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card says Talker-T2AV jointly generates talking audio and video with autoregressive diffusion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/univar/univar-lambda-1": { "id": "hkust/univar/univar-lambda-1", "name": "UniVaR lambda 1", "maker": "hkust", "family": "hkust/univar", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-14", "context_length": null, "max_output_tokens": null, "parameters": "136731648", "architecture": "Sentence embedding model. Hub architecture tag nomic_bert. Lambda 1 is one of four released settings. Lambda 5 Hub total is 136,731,648. Hub safetensors total 136731648.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAiRE at HKUST released UniVaR-lambda-1, an Apache-2.0 sentence embedding model, on 2024-06-14. Collected on 2026-10-15.", "what_it_is_for": "CAiRE HKUST embedding checkpoints for sentence similarity. Each lambda is a separate trained checkpoint with its own MTEB card dump.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/univar/univar-lambda-20": { "id": "hkust/univar/univar-lambda-20", "name": "UniVaR lambda 20", "maker": "hkust", "family": "hkust/univar", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-14", "context_length": null, "max_output_tokens": null, "parameters": "136731648", "architecture": "Sentence embedding model. Hub architecture tag nomic_bert. Lambda 20 is one of four released settings. Lambda 5 Hub total is 136,731,648. Hub safetensors total 136731648.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAiRE at HKUST released UniVaR-lambda-20, an Apache-2.0 sentence embedding model, on 2024-06-14. Collected on 2026-10-15.", "what_it_is_for": "CAiRE HKUST embedding checkpoints for sentence similarity. Each lambda is a separate trained checkpoint with its own MTEB card dump.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/univar/univar-lambda-5": { "id": "hkust/univar/univar-lambda-5", "name": "UniVaR lambda 5", "maker": "hkust", "family": "hkust/univar", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-14", "context_length": null, "max_output_tokens": null, "parameters": "136731648", "architecture": "Sentence embedding model. Hub architecture tag nomic_bert. Lambda 5 is one of four released settings. Lambda 5 Hub total is 136,731,648.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAiRE at HKUST released UniVaR-lambda-5, an Apache-2.0 sentence embedding model, on 2024-06-14. Collected on 2026-10-15.", "what_it_is_for": "CAiRE HKUST embedding checkpoints for sentence similarity. Each lambda is a separate trained checkpoint with its own MTEB card dump.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/univar/univar-lambda-80": { "id": "hkust/univar/univar-lambda-80", "name": "UniVaR lambda 80", "maker": "hkust", "family": "hkust/univar", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-14", "context_length": null, "max_output_tokens": null, "parameters": "136731648", "architecture": "Sentence embedding model. Hub architecture tag nomic_bert. Lambda 80 is one of four released settings. Lambda 5 Hub total is 136,731,648. Hub safetensors total 136731648.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAiRE at HKUST released UniVaR-lambda-80, an Apache-2.0 sentence embedding model, on 2024-06-14. Collected on 2026-10-15.", "what_it_is_for": "CAiRE HKUST embedding checkpoints for sentence similarity. Each lambda is a separate trained checkpoint with its own MTEB card dump.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/urbankgent/urbankgent-13b": { "id": "hkust/urbankgent/urbankgent-13b", "name": "UrbanKGent 13B", "maker": "hkust", "family": "hkust/urbankgent", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-04", "context_length": null, "max_output_tokens": null, "parameters": "13015864320", "architecture": "Paper: Llama-2 13B LoRA fine-tune. Hub cards are empty. The paper says hybrid instruction tuning with LoRA. Hub safetensors total 13015864320.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "usail-hkust released UrbanKGent 13B on 2024-10-04. Collected on 2026-10-15. Authors are at HKUST (Guangzhou) and HKUST.", "what_it_is_for": "Urban knowledge-graph agent for relation extraction and graph completion. NeurIPS 2024. The abstract says the family beats GPT-4 by more than 10 percent at about 20 times lower cost. That sentence is not stored as a numeric benchmark.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/urbankgent/urbankgent-7b": { "id": "hkust/urbankgent/urbankgent-7b", "name": "UrbanKGent 7B", "maker": "hkust", "family": "hkust/urbankgent", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-04", "context_length": null, "max_output_tokens": null, "parameters": "6738415616", "architecture": "Paper: Llama-2 7B LoRA fine-tune. Hub cards are empty. The paper says hybrid instruction tuning with LoRA.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "usail-hkust released UrbanKGent 7B on 2024-10-04. Collected on 2026-10-15. Authors are at HKUST (Guangzhou) and HKUST.", "what_it_is_for": "Urban knowledge-graph agent for relation extraction and graph completion. NeurIPS 2024. The abstract says the family beats GPT-4 by more than 10 percent at about 20 times lower cost. That sentence is not stored as a numeric benchmark.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/urbankgent/urbankgent-8b": { "id": "hkust/urbankgent/urbankgent-8b", "name": "UrbanKGent 8B", "maker": "hkust", "family": "hkust/urbankgent", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-04", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Paper: Llama-3 8B LoRA fine-tune. Hub cards are empty. The paper says hybrid instruction tuning with LoRA. Hub safetensors total 8030261248.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "usail-hkust released UrbanKGent 8B on 2024-10-04. Collected on 2026-10-15. Authors are at HKUST (Guangzhou) and HKUST.", "what_it_is_for": "Urban knowledge-graph agent for relation extraction and graph completion. NeurIPS 2024. The abstract says the family beats GPT-4 by more than 10 percent at about 20 times lower cost. That sentence is not stored as a numeric benchmark.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/vibeworlder/vibeworlder-8b": { "id": "hkust/vibeworlder/vibeworlder-8b", "name": "VibeWorlder 8B", "maker": "hkust", "family": "hkust/vibeworlder", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-08-06", "context_length": null, "max_output_tokens": null, "parameters": "8767123696", "architecture": "Hub architecture tag qwen3_vl. Safetensors total 8,767,123,696. No README was on the repo (404).", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "usail-hkust at HKUST (Guangzhou) uploaded VibeWorlder-8B on 2026-08-06. Collected on 2026-10-15. Confidence is medium because the card is missing.", "what_it_is_for": "Weight release tied to the VibeWorlding paper on multimodal agents that build 3D open worlds. The 8B repo itself has no model card text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/vibeworlder/vibeworlder-embedding-4b": { "id": "hkust/vibeworlder/vibeworlder-embedding-4b", "name": "VibeWorlder Embedding 4B", "maker": "hkust", "family": "hkust/vibeworlder", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-08-06", "context_length": null, "max_output_tokens": null, "parameters": "4021774336", "architecture": "Hub architecture tag qwen3. Safetensors total 4,021,774,336. The README is only a bibtex entry.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "usail-hkust released VibeWorlder-Embedding-4B on 2026-08-06. Collected on 2026-10-15.", "what_it_is_for": "Embedding model released with VibeWorlder. The card does not say what it embeds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/vidmuse/vidmuse": { "id": "hkust/vidmuse/vidmuse", "name": "VidMuse", "maker": "hkust", "family": "hkust/vidmuse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2025-03-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video-to-music model packaged with Audiocraft. Parameter count was not in Hub safetensors metadata.", "modalities": { "input": [ "video" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST Audio released VidMuse, a video-to-music model. Collected on 2026-10-15. License CC-BY-4.0.", "what_it_is_for": "The card says VidMuse generates music aligned with a video using long-short-term modeling. Accepted to CVPR 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/wav2vec2/wav2vec2-large-xlsr-53-cantonese": { "id": "hkust/wav2vec2/wav2vec2-large-xlsr-53-cantonese", "name": "Wav2Vec2-Large-XLSR-53 Cantonese", "maker": "hkust", "family": "hkust/wav2vec2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-04-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of facebook/wav2vec2-large-xlsr-53 on Common Voice Cantonese (zh-HK), 16 kHz, no external language model.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAiRE at HKUST released a Cantonese wav2vec2 ASR model on 2022-04-09. Collected on 2026-10-15.", "what_it_is_for": "Cantonese speech recognition from CAiRE HKUST. The card reports test CER 18.55 percent on Common Voice zh-HK.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/webexplorer/webexplorer-8b": { "id": "hkust/webexplorer/webexplorer-8b", "name": "WebExplorer-8B", "maker": "hkust", "family": "hkust/webexplorer", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-08", "context_length": 128000, "max_output_tokens": null, "parameters": "8190735360", "architecture": "SFT then GRPO on Qwen3-8B. Hub safetensors total 8,190,735,360. Card says 128K context and up to 100 tool turns.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST NLP released WebExplorer-8B, an Apache-2.0 web agent, on 2025-09-08. Collected on 2026-10-15.", "what_it_is_for": "Web agent for long information-seeking tasks. Tools are search and browse.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/xcodec/xcodec2": { "id": "hkust/xcodec/xcodec2", "name": "XCodec2", "maker": "hkust", "family": "hkust/xcodec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-01-07", "context_length": null, "max_output_tokens": null, "parameters": "822739130", "architecture": "Single-codebook neural speech codec. Card: one vector-quantization layer, 50 tokens per second, 16 kHz, single waveform in. Hub safetensors total for the original repo is 822,739,130. The Transformers port reports 629,281,706 and the two counts are not reconciled on the cards.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST Audio released XCodec2, the speech codec paired with Llasa. A Transformers-native port was added on the same org on 2026-06-25. Collected on 2026-10-15. License CC-BY-NC-4.0.", "what_it_is_for": "XCodec2 is the speech tokenizer Llasa uses. The card lists single-vector quantization, 50 tokens per second, and multilingual semantic reconstruction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/yue/yue-s1-0-5b": { "id": "hkust/yue/yue-s1-0-5b", "name": "YuE S1 0.5B", "maker": "hkust", "family": "hkust/yue", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-18", "context_length": null, "max_output_tokens": null, "parameters": "480035840", "architecture": "Smaller YuE stage-1 checkpoint. Hub safetensors total is 480,035,840. The card text mostly describes the YuE family and links the 7B anneals rather than a separate 0.5B spec sheet.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "M-A-P and HKUST's YuE line includes this 0.5B stage-1 checkpoint, uploaded 2025-03-18. Collected on 2026-10-15.", "what_it_is_for": "A smaller public YuE stage-1 checkpoint released after the 7B anneals, under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/yue/yue-s1-7b": { "id": "hkust/yue/yue-s1-7b", "name": "YuE S1 7B", "maker": "hkust", "family": "hkust/yue", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-01-28", "context_length": null, "max_output_tokens": null, "parameters": "6224613376", "architecture": "Stage-1 lyrics-to-song model. Paper: LLaMA-style model with track-decoupled next-token prediction. The counted checkpoint is the English CoT anneal. Language and CoT versus ICL variants are versions, not separate folders.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST and Multimodal Art Projection released YuE stage 1, a lyrics-to-song model, at the end of January 2025. This folder covers the 7B annealed checkpoints. Collected on 2026-10-15.", "what_it_is_for": "YuE turns lyrics into a full song of up to about five minutes, with vocals and accompaniment. HKUST and M-A-P co-lead the project. HKUST Audio hosts mirrors and a collection that says the series is led by HKUST.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/yue/yue-s2-1b": { "id": "hkust/yue/yue-s2-1b", "name": "YuE S2 1B", "maker": "hkust", "family": "hkust/yue", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-01-28", "context_length": null, "max_output_tokens": null, "parameters": "1962543104", "architecture": "Stage-2 general checkpoint in the YuE pipeline. Hub safetensors total on the HKUSTAudio copy is 1,962,543,104. Canonical repo is m-a-p/YuE-s2-1B-general.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST and M-A-P released YuE stage 2, a 1B general checkpoint, with the January 2025 YuE drop. Collected on 2026-10-15.", "what_it_is_for": "The project page lists S2 as the second stage after the S1 lyrics model. This folder is that general 1B checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/yue/yue-upsampler": { "id": "hkust/yue/yue-upsampler", "name": "YuE Upsampler", "maker": "hkust", "family": "hkust/yue", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named upsampler checkpoint in the YuE v1 table. Hub did not return a safetensors parameter total.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The YuE release includes a named upsampler checkpoint under m-a-p, uploaded 2025-01-27, Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The YuE project table lists an upsampler beside S1 and S2. It is a separate weight repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hkust/yue/yue2-3b": { "id": "hkust/yue/yue2-3b", "name": "YuE2 3B", "maker": "hkust", "family": "hkust/yue", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-09-09", "context_length": null, "max_output_tokens": null, "parameters": "3630684224", "architecture": "Card: about 3.63B parameters (Hub safetensors total 3,630,684,224). Unified symbolic planning and audio generation. The paper says it writes a score before the recording. Companion VAE is a version, not this checkpoint.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HKUST, M-A-P, and partner labs released YuE2-3B on 2026-09-09. It is a new model, not a dated snapshot of YuE v1. Collected on 2026-10-15.", "what_it_is_for": "YuE2 is the 2026 full-song model from the same HKUST and M-A-P line. The card says it writes an editable score and can cover or edit from that score. Weights are non-commercial.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hokkaido/ainu-mt/byt5-small-ain-jpn-mt": { "id": "hokkaido/ainu-mt/byt5-small-ain-jpn-mt", "name": "ByT5-small Ainu to Japanese", "maker": "hokkaido", "family": "hokkaido/ainu-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-02-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of google/byt5-small. The released config.json is T5ForConditionalGeneration with ByT5Tokenizer: 12 encoder layers, 4 decoder layers, d_model 1472, d_ff 3584, 6 heads, d_kv 64, vocabulary 384, gated-GELU, untied embeddings, float32, transformers 4.16.0.dev0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ByT5-small Ainu to Japanese is an open-weight machine-translation checkpoint from the Language Media Laboratory at Hokkaido University. The lab fine-tuned Google's ByT5-small, a byte-level T5, on bilingual text crawled from the Web so that Ainu input is translated into Japanese. The weights were still public on the collection date, 2026-10-15. There is no university API and no published price.", "what_it_is_for": "The model card says the checkpoint translates Ainu into Japanese. It is a research weight file, not a hosted product. The card does not add usage limits beyond that sentence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hokkaido/ainu-mt/byt5-small-jpn-ain-mt": { "id": "hokkaido/ainu-mt/byt5-small-jpn-ain-mt", "name": "ByT5-small Japanese to Ainu", "maker": "hokkaido", "family": "hokkaido/ainu-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-02-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of google/byt5-small. The released config.json is T5ForConditionalGeneration with ByT5Tokenizer: 12 encoder layers, 4 decoder layers, d_model 1472, d_ff 3584, 6 heads, d_kv 64, vocabulary 384, gated-GELU, untied embeddings, float32, transformers 4.16.0.dev0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ByT5-small Japanese to Ainu is an open-weight machine-translation checkpoint from the Language Media Laboratory at Hokkaido University. The lab fine-tuned Google's ByT5-small, a byte-level T5, on bilingual text crawled from the Web so that Japanese input is translated into Ainu. The weights were still public on the collection date, 2026-10-15. There is no university API and no published price.", "what_it_is_for": "The model card says the checkpoint translates Japanese into Ainu. It is a research weight file, not a hosted product. The card's only usage note is how to load it in Transformers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hokkaido/ainu-mt/mt5-small-ain-jpn-mt": { "id": "hokkaido/ainu-mt/mt5-small-ain-jpn-mt", "name": "mT5-small Ainu to Japanese", "maker": "hokkaido", "family": "hokkaido/ainu-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-02-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of google/mt5-small. The released config.json is MT5ForConditionalGeneration: 8 encoder layers, 8 decoder layers, d_model 512, d_ff 1024, 6 heads, d_kv 64, vocabulary 250100, gated-GELU, untied embeddings, float32, transformers 4.17.0.dev0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mT5-small Ainu to Japanese is an open-weight machine-translation checkpoint from the Language Media Laboratory at Hokkaido University. The lab fine-tuned Google's mT5-small on bilingual text crawled from the Web so that Ainu input is translated into Japanese. The weights were still public on the collection date, 2026-10-15. There is no university API and no published price.", "what_it_is_for": "The model card says the checkpoint translates Ainu into Japanese. It is a research weight file, not a hosted product. The card does not add usage limits beyond that sentence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hokkaido/ainu-mt/mt5-small-jpn-ain-mt": { "id": "hokkaido/ainu-mt/mt5-small-jpn-ain-mt", "name": "mT5-small Japanese to Ainu", "maker": "hokkaido", "family": "hokkaido/ainu-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-02-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of google/mt5-small. The released config.json is MT5ForConditionalGeneration: 8 encoder layers, 8 decoder layers, d_model 512, d_ff 1024, 6 heads, d_kv 64, vocabulary 250100, gated-GELU, untied embeddings, float32, transformers 4.17.0.dev0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mT5-small Japanese to Ainu is an open-weight machine-translation checkpoint from the Language Media Laboratory at Hokkaido University. The lab fine-tuned Google's mT5-small on bilingual text crawled from the Web so that Japanese input is translated into Ainu. The weights were still public on the collection date, 2026-10-15. There is no university API and no published price.", "what_it_is_for": "The model card says the checkpoint translates Japanese into Ainu. It is a research weight file, not a hosted product. The card gives a Transformers load path and nothing else about intended users or limits.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicagent/magicagent-30b-a3b": { "id": "honor/magicagent/magicagent-30b-a3b", "name": "MagicAgent-30B-A3B", "maker": "honor", "family": "honor/magicagent", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-03", "context_length": null, "max_output_tokens": null, "parameters": "30B total, about 3B active", "architecture": "Mixture-of-experts planning model. HONOR's MWC coverage calls the 30B model its first MoE large language model. The name 30B-A3B is the published total and active size. The paper describes MoE load balancing with global-batch routing statistics and a z-loss on router logits. Training is the same two-stage SFT plus multi-objective RL setup as the 32B dense model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicAgent-30B-A3B is HONOR's mixture-of-experts agent planner, announced with the dense 32B model at MWC on 3 March 2026. The name means about 30 billion total parameters and about 3 billion active. HONOR and Fudan University present it as a planning model for YOYO, and press called the 30B model HONOR's first MoE LLM. Weights were not found in public on 15 October 2026. Collected 2026-10-15.", "what_it_is_for": "Same job as the rest of MagicAgent: generalize across planning tasks, including tool calls and long trajectories, instead of one narrow agent demo. Coverage of the MWC announcement says a 30B model beat much larger commercial models on planning tasks and that the series is used on Magic8 and Magic V6. The phone build and this research checkpoint are not proven to be the same file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicagent/magicagent-32b": { "id": "honor/magicagent/magicagent-32b", "name": "MagicAgent-32B", "maker": "honor", "family": "honor/magicagent", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-03", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Dense model in the MagicAgent planning series from HONOR and Fudan University. Training is supervised fine-tuning plus multi-objective reinforcement learning, including a method the paper calls \u03c7PO. A retail-domain comparison uses Qwen3-32B as the base. The paper does not say in the sections read here that the checkpoint was initialized from Qwen3-32B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicAgent-32B is the dense member of HONOR's agent-planning series, written with Fudan University and announced for global open release at MWC on 3 March 2026. It is a 32 billion parameter model aimed at task decomposition, tool use, and long-horizon plans for the YOYO assistant. Public weight files were not found on 15 October 2026. Collected 2026-10-15.", "what_it_is_for": "The paper defines MagicAgent as a foundation model for generalized agent planning: break a request into subtasks, call tools, schedule under constraints, and carry a long tool-using trajectory. HONOR's MWC coverage says the series plans and orchestrates YOYO tasks and was already used on Magic8 and Magic V6. The 32B name is the dense checkpoint in that series, not a public API product name with its own price page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicagent/magicagent-plan": { "id": "honor/magicagent/magicagent-plan", "name": "MagicAgent-Plan", "maker": "honor", "family": "honor/magicagent", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicAgent-Plan is a cloud model name in HONOR's 2025 language-model matrix. Press recaps list it with MagicAgent-Tool and MagicAgent-Ultra. The name points at planning. No weights or scores were published under this id. Collected 2026-10-15.", "what_it_is_for": "The June 2025 matrix calls it a cloud large language model. The October 2025 feature says the cloud MagicAgent series plans and splits tasks that the user does not fully specify. Plan is one of the three names in that sentence, not a separately measured product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicagent/magicagent-tool": { "id": "honor/magicagent/magicagent-tool", "name": "MagicAgent-Tool", "maker": "honor", "family": "honor/magicagent", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicAgent-Tool is a cloud language-model name in HONOR's 2025 Magic V5 and \u9b54\u6cd5\u5927\u6a21\u578b3.0 lists. Two press recaps of HONOR material put it with MagicAgent-Plan and MagicAgent-Ultra, opposite the on-device MagicLM-Nano. No size or card was published. Collected 2026-10-15.", "what_it_is_for": "The name says tool use. The June 2025 matrix labels it a cloud large language model. The October 2025 feature says the cloud MagicAgent series, including Tool, understands vague requests and splits tasks. It does not publish a separate Tool spec.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicagent/magicagent-ultra": { "id": "honor/magicagent/magicagent-ultra", "name": "MagicAgent-Ultra", "maker": "honor", "family": "honor/magicagent", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicAgent-Ultra is the third cloud language-model name in HONOR's 2025 MagicAgent list, with Tool and Plan. Press that recaps HONOR's Magic V5 material includes it. No parameter count or eval table was published for this id. Collected 2026-10-15.", "what_it_is_for": "The matrix labels it a cloud large language model. The October 2025 feature treats Ultra as part of the series that reasons about underspecified requests. It does not give Ultra its own job description beyond the name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicasr/magicasr": { "id": "honor/magicasr/magicasr", "name": "MagicASR", "maker": "honor", "family": "honor/magicasr", "status": "active", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "speech" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicASR is an on-device speech-recognition name in a 27 June 2025 recap of HONOR's Magic V5 model list. The same sentence lists cloud MagicTTS and on-device MagicSpeech as separate models. No card or error rate was found. Confidence is low. Collected 2026-10-15.", "what_it_is_for": "The name and the list category say automatic speech recognition on the phone. The article does not say which app uses it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicedit/magicedit": { "id": "honor/magicedit/magicedit", "name": "MagicEdit", "maker": "honor", "family": "honor/magicedit", "status": "active", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicEdit is a cloud vision-model name in a 27 June 2025 recap of HONOR's Magic V5 model list. The line is \"\u89c6\u89c9\u5927\u6a21\u578b\uff1aMagicEdit \u6a21\u578b\uff08\u4e91\uff09\". No card, size, or score was found. A later feature uses the name MagicImage for photo editing instead. Confidence is low. Collected 2026-10-15.", "what_it_is_for": "The only job implied by the category label is visual editing in the cloud. The article does not list features.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicgui/magicgui": { "id": "honor/magicgui/magicgui", "name": "MagicGUI", "maker": "honor", "family": "honor/magicgui", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face model card: Apache-2.0. Project README: weights are open for academic research; commercial use requires contact at magicgui@honor.com. Initialized from Qwen2-VL-7B-Instruct, which is Apache-2.0. The paper file is marked CC BY-NC-ND 4.0; that is the paper license, not clearly the weight license.", "release_date": "2025-07-26", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "GUI agent continued from Qwen2-VL-7B-Instruct (qwen2_vl). Two published stages: continued pre-training (MagicGUI-CPT) on a GUI corpus the paper describes as 7.8 million samples, then reinforcement fine-tuning (MagicGUI-RFT). Visual encoder is trained as part of that pipeline. Action space covers mobile UI operations.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicGUI is HONOR's open-weight mobile GUI agent, built with Fudan University and released around the 2025 World Artificial Intelligence Congress. It is a 7 billion parameter model continued from Qwen2-VL-7B-Instruct. Two checkpoints, CPT and RFT, are public. It was shipping as an on-device agent base for YOYO on Magic V5, and it was still published on 15 October 2026. Collected 2026-10-15.", "what_it_is_for": "HONOR describes MagicGUI as the perception and execution base for a phone GUI agent: see a screen, ground an element, answer a question about the page, and plan a sequence of taps and other actions. The Chinese announcement says it supports YOYO moving from single-agent steps to multi-agent phone control, including tasks such as hailing a car. The English README says it operates Chinese and English apps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicgui/magicgui-action": { "id": "honor/magicgui/magicgui-action", "name": "MagicGUI-Action", "maker": "honor", "family": "honor/magicgui", "status": "active", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicGUI-Action is a cloud model name in a 27 June 2025 recap of HONOR's Magic V5 introduction. The list puts it under multimodal models, next to MagicGUI-Auto (cloud) and MagicVL-Nano (on device). No second source and no model card were found. Confidence is low. Collected 2026-10-15.", "what_it_is_for": "The list does not define the name beyond a cloud multimodal model. The word Action matches GUI control, which is also what the open MagicGUI model does. Whether this is a cloud serving name for that model is not stated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicgui/magicgui-auto": { "id": "honor/magicgui/magicgui-auto", "name": "MagicGUI-Auto", "maker": "honor", "family": "honor/magicgui", "status": "active", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicGUI-Auto is a cloud model name that appears once in the sources read for this catalog: a 27 June 2025 article that says HONOR's Magic V5 introduction lists it as a cloud multimodal model beside MagicGUI-Action. Confidence is low. Collected 2026-10-15.", "what_it_is_for": "The article does not say what Auto adds. The pairing with Action suggests a second cloud GUI mode, possibly more automatic multi-step control. That reading is not a HONOR sentence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicimage/magicimage": { "id": "honor/magicimage/magicimage", "name": "MagicImage", "maker": "honor", "family": "honor/magicimage", "status": "active", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicImage is a photo-editing model name in a 20 October 2025 feature about HONOR's \u9b54\u6cd5\u5927\u6a21\u578b3.0. The article says it removes passers-by, repairs old photos, clears glass reflections, and controls portrait light. No HONOR spec page was found. Confidence is low. Collected 2026-10-15.", "what_it_is_for": "The feature presents it as the still-image editor in the vision block of \u9b54\u6cd5\u5927\u6a21\u578b3.0, next to MagicVideo. The examples are cleanup and lighting, not open-ended text-to-image generation. The capability label image-gen is only the nearest schema tag.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magiclm/magiclm": { "id": "honor/magiclm/magiclm", "name": "MagicLM\uff08\u9b54\u6cd5\u5927\u6a21\u578b\uff09", "maker": "honor", "family": "honor/magiclm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-01-10", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "On-device large language model. HONOR did not publish the layer layout, training recipe, or tokenizer. It was optimized with Qualcomm to run on Snapdragon 8 Gen 3.", "modalities": { "input": [ "text", "speech" ], "output": [ "text", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicLM is HONOR's on-device large language model, announced on 10 January 2024 with MagicOS 8.0. The company calls the Chinese product \u9b54\u6cd5\u5927\u6a21\u578b. The first named release has 7 billion parameters, runs on device, and was still without a published retirement date on 15 October 2026. Collected 2026-10-15.", "what_it_is_for": "HONOR says MagicLM is a platform-level on-device model for natural-language understanding and for reading several interaction modes, \"from text to speech and more.\" On MagicOS 8.0 it backs intent-based UI, YOYO assistant and daily-planner prompts, natural-language video creation from material on the phone, and search to a frame inside a video. The Chinese launch says it rewrites a short user request into a more specific prompt, strips sensitive details, and can hand the rest to a partner cloud model such as Baidu's Wenxin. Those partner models are not MagicLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magiclm/magiclm-nano": { "id": "honor/magiclm/magiclm-nano", "name": "MagicLM-Nano", "maker": "honor", "family": "honor/magiclm", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicLM-Nano is the on-device language model name in HONOR's 2025 product matrix for Magic V5 and, in later coverage, the \u9b54\u6cd5\u5927\u6a21\u578b3.0 stack. Press that says it is quoting HONOR lists it beside the cloud MagicAgent models. No parameter count or model card was found. Collected 2026-10-15.", "what_it_is_for": "Reports describe it as the phone-side language model: fast local handling of sensitive text, with the cloud MagicAgent models doing heavier planning. A 20 October 2025 feature adds that the on-device model shows in-context learning and step-by-step reasoning. That sentence is the article's wording.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicspeech/magicspeech": { "id": "honor/magicspeech/magicspeech", "name": "MagicSpeech", "maker": "honor", "family": "honor/magicspeech", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-08-28", "context_length": null, "max_output_tokens": null, "parameters": "0.8B", "architecture": "On-device speech model for streaming recognition and speech translation. HONOR describes an attention-and-decision streaming recognizer and a parasitic dual-scale speculative decoder. Qualcomm adapted NPU operators for it. No layer diagram was published.", "modalities": { "input": [ "speech" ], "output": [ "text", "speech" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicSpeech is HONOR's on-device speech model for call translation and streaming recognition. Launch reports and a HONOR vendor draft call it the first large speech model deployed on a phone at this scale, with 0.8 billion parameters, and they place the public international debut on the Magic V5 in late August 2025. Weights are closed. Collected 2026-10-15.", "what_it_is_for": "HONOR uses it for offline multilingual call translation: recognize speech while the caller is still talking, then translate, without a separate cloud ASR step. Coverage contrasts that one-pass path with a cascaded ASR-then-translate pipeline. Qualcomm's NPU operators are part of the deployment story.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magictts/magictts": { "id": "honor/magictts/magictts", "name": "MagicTTS", "maker": "honor", "family": "honor/magictts", "status": "active", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "speech" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicTTS is a cloud text-to-speech name in a 27 June 2025 recap of HONOR's Magic V5 model list. The same line lists on-device MagicASR and MagicSpeech. No other page read for this catalog uses the name. Confidence is low. Collected 2026-10-15.", "what_it_is_for": "The category is \u8bed\u97f3\u5927\u6a21\u578b and the suffix is TTS, so the reported role is cloud speech synthesis. The article does not describe voices or a product screen.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicvideo/magicvideo": { "id": "honor/magicvideo/magicvideo", "name": "MagicVideo", "maker": "honor", "family": "honor/magicvideo", "status": "active", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicVideo is a video-editing model name in a 20 October 2025 feature on HONOR's \u9b54\u6cd5\u5927\u6a21\u578b3.0. The article says it segments video at pixel level so a region can be edited. No HONOR document, size, or score was found. Confidence is low. Collected 2026-10-15.", "what_it_is_for": "The feature calls it the video editor beside MagicImage: pixel-level masks and region edits. It does not say the model generates video from text alone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicvl/magicvl-2b": { "id": "honor/magicvl/magicvl-2b", "name": "MagicVL-2B", "maker": "honor", "family": "honor/magicvl", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-03", "context_length": null, "max_output_tokens": null, "parameters": "about 2B", "architecture": "Vision-language model for flagship phones. Visual encoder is SigLIP2-Base-384/16 with fewer than 100 million parameters. Dynamic resolution produces image tokens without resizing the picture to a new canvas. Two language backbones are reported: Qwen2.5-1.5B and Qwen3-1.7B. Training uses multimodal curriculum learning. The paper does not publish a weight card.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicVL-2B is a phone vision-language model from Honor Device Co., Ltd., posted on arXiv on 3 August 2025. It pairs a SigLIP2 encoder under 100 million parameters with a small Qwen language model. No public weight file was found, and no source read here names a shipping SKU for this exact checkpoint, so the status is announced. Collected 2026-10-15.", "what_it_is_for": "The paper says it is a vision-language model tuned for flagship smartphones so image understanding and text generation can run on the phone with less power. It is the research name. Product copy later says \"MagicVL\" or \"MagicVL-Nano\" for the on-device VLM in YOYO. Those names are not equated with this paper in the sources read here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "honor/magicvl/magicvl-nano": { "id": "honor/magicvl/magicvl-nano", "name": "MagicVL-Nano", "maker": "honor", "family": "honor/magicvl", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Press describes an on-device vision-language model with a visual encoder under 100 million parameters, dynamic resolution, and curriculum learning. That description matches the MagicVL-2B paper's encoder story. The full model size is not stated under this product name.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicVL-Nano is the on-device vision-language name in HONOR's 2025 phone stack. A June 2025 recap of HONOR's Magic V5 material lists it as the on-device multimodal model. Later Magic8 coverage and HONOR's MagicOS president describe MagicVL as the on-device VLM next to MagicGUI. Weights are not public. Collected 2026-10-15.", "what_it_is_for": "It is the phone-side image understanding model for YOYO: look at a photo or a camera frame and answer or hand a description to an agent. MagicOS president Sun Jianfa, in a February 2026 interview, calls MagicVL one of the strongest on-device VLMs and says it is part of \u9b54\u6cd5\u5927\u6a21\u578b\u77e9\u96353.0 with MagicGUI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hourone/avatar/cinematic-avatar": { "id": "hourone/avatar/cinematic-avatar", "name": "Custom cinematic avatar", "maker": "hourone", "family": "hourone/avatar", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-05-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hour One's 2024-05-07 release said the hyper-realistic avatar uses cloud infrastructure that combines generative-AI inference and 3D rendering on AWS, Google Cloud, and Azure, with NVIDIA and Intel GPUs, aimed at cinematic and TV-ad quality. No parameter count. The Reid Hoffman demo voice was ElevenLabs and the persona chatbot was GPT-4, which are not Hour One models.", "modalities": { "input": [ "text", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Custom cinematic avatar is Hour One's Enterprise presenter tier, announced on 2024-05-07 as cinematic avatars on the Gen-AI video platform. The launch example was Reid Hoffman interviewing his digital twin. A 2024-06-02 release used the same line for a multilingual avatar of Jensen Huang at Computex. The 2025 pricing table kept it Enterprise-only. Weights are closed. The public site did not resolve on 2026-10-15, so it is marked retired with medium confidence. Collected 2026-10-15.", "what_it_is_for": "Hour One said the line is for cinematic and TV-ad quality digital twins of real people, built with generative-AI inference and 3D rendering. The Enterprise card on the pricing page listed 'Cinematic' Avatar next to unlimited minutes, API access, and 4K/8K output. The 4K/8K cell is the plan's resolution row, not a spec sheet for the avatar alone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hourone/avatar/custom-studio-avatar": { "id": "hourone/avatar/custom-studio-avatar", "name": "Custom studio avatar", "maker": "hourone", "family": "hourone/avatar", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hour One's avatar-creation video and Natalie Monbiot's October 2024 interview say a custom presenter is built from a consented studio shoot of a real person, then rendered with the same inference and 3D pipeline. No parameter count.", "modalities": { "input": [ "text", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Custom studio avatar is Hour One's Enterprise presenter built from a consented shoot of a real person. The 2025-09-28 pricing table checked it only on Enterprise, under Customization, and did not print a separate fee. It is closed. The public site did not resolve on 2026-10-15, so it is marked retired with medium confidence. Collected 2026-10-15.", "what_it_is_for": "Hour One's March 2024 avatar video says presenters start with a real person in their studio. Natalie Monbiot said a custom avatar of a spokesperson needs footage and expressed consent, and that publishing rights are restricted. CB Insights listed \"create your own virtual twin\" as the product differentiator. The pricing page uses the name custom studio avatar for the Enterprise row.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hourone/avatar/desk-avatar": { "id": "hourone/avatar/desk-avatar", "name": "Desk Avatar", "maker": "hourone", "family": "hourone/avatar", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-12-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Desk Avatar is a seated presenter option Hour One introduced on 2023-12-17. The official video said users can pick an avatar seated at a desk and frame it for desktop or mobile. It was not a row on the 2025 pricing table and has no API id. Weights are closed. Marked retired with medium confidence because the public site did not resolve on 2026-10-15 and no later mention was found. Collected 2026-10-15.", "what_it_is_for": "Hour One called it another avatar option: a presenter who sits at a desk, so a video can be framed for a desktop or a phone. It is a pose and framing choice on top of the existing presenter pipeline, not a separately documented generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hourone/avatar/stock-studio-avatar": { "id": "hourone/avatar/stock-studio-avatar", "name": "Stock studio avatars", "maker": "hourone", "family": "hourone/avatar", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hour One said stock presenters start from real people filmed in a studio, then rendered with generative-AI inference and 3D. No parameter count was published. This folder is the library, not one person.", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stock studio avatars are Hour One's ready-made presenter library inside Reals. The 2025 pricing page checked them on every plan and the Free card said 100+ AI avatars. They are closed, captured from real people, and selected in the API by character_id. The standalone site did not resolve on 2026-10-15, so the library is marked retired with medium confidence. Collected 2026-10-15.", "what_it_is_for": "A user picks a pre-made presenter instead of filming. Hour One's own avatar-creation video says the people are filmed in a studio. Natalie Monbiot said in October 2024 that about 100 stock avatars were available, with a separate consented path for a custom avatar of a real employee.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hourone/avatar/web-avatar": { "id": "hourone/avatar/web-avatar", "name": "Web avatar", "maker": "hourone", "family": "hourone/avatar", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Web avatar is a named customization row on Hour One's pricing table. The 2025-09-28 capture turned it on for Business and Enterprise and off for Free and Lite. Hour One did not publish a capture method, model card, or API id for it. The public site did not resolve on 2026-10-15, so it is marked retired with medium confidence. Collected 2026-10-15.", "what_it_is_for": "The pricing page lists it under Customization, next to custom studio avatar and custom cinematic avatar. That is the only official description found. Reviews that call it a webcam clone are not used here, because the pricing page does not say that.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hourone/reals/reals": { "id": "hourone/reals/reals", "name": "Reals", "maker": "hourone", "family": "hourone/reals", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hour One described a proprietary cloud that combines generative-AI inference with 3D rendering, on AWS, Google Cloud, and Azure, using NVIDIA and Intel GPUs. No parameter count or layer diagram was published. Script scaffolding used Azure OpenAI, which is not an Hour One model.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reals is Hour One's self-service text-to-video studio, also exposed as the MakeReals API. It turns a script, template, character, and voice into a presenter video. Wix acquired Hour One on 2025-05-23 and did not publish a shutdown date. On 2026-10-15 the public site and API host did not resolve, so this catalog marks Reals retired with medium confidence. Collected 2026-10-15.", "what_it_is_for": "Hour One and Microsoft described Reals as the product that makes studio-style videos with lifelike virtual humans from text. NVIDIA's case study says a user picks a layout, a character, and text, and the browser pipeline does generative AI plus 3D rendering. The API adds blueprint overrides, dynamic scenes, voice preview, subtitles, and a playground generate call.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/boldt/boldt-1b": { "id": "hu-berlin/boldt/boldt-1b", "name": "Boldt-1B", "maker": "hu-berlin", "family": "hu-berlin/boldt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-05", "context_length": 4096, "max_output_tokens": null, "parameters": "1000000000", "architecture": "LlamaForCausalLM. config.json: hidden 2048, intermediate 8192, 16 layers, 16 heads, head_dim 128, SiLU, bfloat16, rope_theta 10000, tie_word_embeddings false, bos/eos 0, pad 1. max_position_embeddings 4096, vocab 32000. The paper calls it a 1B model and says the context was extended from 2048 to 4096 relative to Boldt-DC-1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Boldt-1B is the main 1B German base model from the HU Chair of Machine Learning. It continues Boldt-DC-1B with German news and a 4096-token context. The chair announced it as a public model on 2026-05-05. Apache-2.0 weights are on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "The paper and the card present it as a German foundation model for completion and as the base for the instruction preview. It is not an instruction-tuned chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/boldt/boldt-1b-it-preview": { "id": "hu-berlin/boldt/boldt-1b-it-preview", "name": "Boldt-1B-IT-Preview", "maker": "hu-berlin", "family": "hu-berlin/boldt", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-03", "context_length": 4096, "max_output_tokens": null, "parameters": "1000000000", "architecture": "LlamaForCausalLM. config.json: hidden 2048, intermediate 8192, 16 layers, 16 heads, head_dim 128, SiLU, bfloat16, rope_theta 10000, tie_word_embeddings false, bos/eos 0, pad 1. max_position_embeddings 4096. vocab_size in this checkpoint's config.json is 32064, not the base 32000. The card calls it a fine-tune of Boldt-1B. Stated size remains about 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Boldt-1B-IT-Preview is a preview instruction fine-tune of Boldt-1B from the HU Chair of Machine Learning. The card describes a single-turn German chat model trained on about 95k instruction pairs. Apache-2.0 weights are on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "The card presents it as a German instruction preview of the Boldt-1B base, not as a general safety-evaluated assistant. Outputs in several slices were generated by Qwen3.6-27B, which is not an HU model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/boldt/boldt-dc-1b": { "id": "hu-berlin/boldt/boldt-dc-1b", "name": "Boldt-DC-1B", "maker": "hu-berlin", "family": "hu-berlin/boldt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-02", "context_length": 2048, "max_output_tokens": null, "parameters": "1000000000", "architecture": "LlamaForCausalLM. config.json: hidden 2048, intermediate 8192, 16 layers, 16 heads, head_dim 128, SiLU, bfloat16, rope_theta 10000, tie_word_embeddings false, bos/eos 0, pad 1. max_position_embeddings 2048, vocab 32000. The paper calls it a 1B model. That figure is the stated size, not a weight-sum.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Boldt-DC-1B is the 1B German decoder from the same Dense Core experiments as Boldt-DC-350M, released by the HU Chair of Machine Learning under Apache-2.0. The paper keeps its context at 2048 tokens. Collected 2026-10-15.", "what_it_is_for": "The paper releases it with Boldt-DC-350M for reproducibility of the repetition-limit runs. Boldt-1B is the follow-up that adds German news and a longer context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/boldt/boldt-dc-350m": { "id": "hu-berlin/boldt/boldt-dc-350m", "name": "Boldt-DC-350M", "maker": "hu-berlin", "family": "hu-berlin/boldt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-02", "context_length": 2048, "max_output_tokens": null, "parameters": "350000000", "architecture": "German Llama decoder trained from scratch. config.json: hidden 1024, intermediate 4096, 24 layers, 16 heads, head_dim 64, vocab 32000, max_position_embeddings 2048, SiLU, bfloat16, rope_theta 10000, tie_word_embeddings false, bos/eos 0, pad 1. The paper calls it a 350M model. The Hub size bucket is not the parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Boldt-DC-350M is a German decoder released by the Chair of Machine Learning at Humboldt University of Berlin (Aynetdinov, Haller, Akbik). It is the 350M model in the Boldt series, trained from scratch on 200B Dense Core tokens. Weights are Apache-2.0 on the Boldt Hub org. Collected 2026-10-15.", "what_it_is_for": "The card presents it as a small German language model for text completion. The paper releases it so the repetition-limit experiments can be reproduced. The same card compares it with the 1B Boldt models and with LL\u00e4Mmlein, Gemma, Llama, and Qwen base models that are not HU releases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/bueble/bueble-lm": { "id": "hu-berlin/bueble/bueble-lm", "name": "B\u00fcbleLM", "maker": "hu-berlin", "family": "hu-berlin/bueble", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-05", "context_length": 8192, "max_output_tokens": null, "parameters": "2000000000", "architecture": "Gemma2ForCausalLM continued from a Gemma-2-2B German trans-tokenization run. config.json: vocab 20000, hidden 2304, intermediate 9216, 26 layers, 8 heads, 4 KV heads, head_dim 256, max_position_embeddings 8192, sliding_window 4096, gelu_pytorch_tanh, attn_logit_softcapping 50, final_logit_softcapping 30, bos/eos 2, pad 3. _name_or_path on the fetched config was pdelobelle/gemma-2-2b-de. The card states 2 billion parameters and a custom German SentencePiece tokenizer with fertility 1.78.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "B\u00fcbleLM is a 2B German base model from the HU Chair of Machine Learning. Pieter Delobelle trained it while a guest researcher at Humboldt, with Alan Akbik. It adapts Gemma 2 with a 20k German SentencePiece tokenizer. The public announcement is dated 2024-12-05. Weights are on flair/bueble-lm-2b. Collected 2026-10-15.", "what_it_is_for": "The card presents it as a German language model for text generation, with German ARC, HellaSwag, and TruthfulQA scores against Gemma-2-2B. It is a base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/entity/entity-english-4class": { "id": "hu-berlin/entity/entity-english-4class", "name": "Flair 2.0 English NER 4-class", "maker": "hu-berlin", "family": "hu-berlin/entity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Flukes NC 1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flair 2.0 sequence tagger. PER, LOC, ORG, MISC. The card cites the 2019 FLAIR NAACL paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair 2.0 English NER 4-class is a Flair 2.0 named-entity model from the HU Chair of Machine Learning, hosted at flair/entity-english-4class. The card releases it under the Flukes NC 1.0 noncommercial license. Collected 2026-10-15.", "what_it_is_for": "The card presents it as an NER tagger for Flair. Load SequenceTagger with the Hub id. PER, LOC, ORG, MISC.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/entity/entity-english-8class": { "id": "hu-berlin/entity/entity-english-8class", "name": "Flair 2.0 English NER 8-class", "maker": "hu-berlin", "family": "hu-berlin/entity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Flukes NC 1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flair 2.0 sequence tagger. PER, LOC, ORG, PRODUCT, WORK_OF_ART, EVENT, IDENT, MISC. The card cites the 2019 FLAIR NAACL paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair 2.0 English NER 8-class is a Flair 2.0 named-entity model from the HU Chair of Machine Learning, hosted at flair/entity-english-8class. The card releases it under the Flukes NC 1.0 noncommercial license. Collected 2026-10-15.", "what_it_is_for": "The card presents it as an NER tagger for Flair. Load SequenceTagger with the Hub id. PER, LOC, ORG, PRODUCT, WORK_OF_ART, EVENT, IDENT, MISC.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/entity/entity-german-4class": { "id": "hu-berlin/entity/entity-german-4class", "name": "Flair 2.0 German NER 4-class", "maker": "hu-berlin", "family": "hu-berlin/entity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Flukes NC 1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flair 2.0 sequence tagger. PER, LOC, ORG, MISC. The card cites the 2019 FLAIR NAACL paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair 2.0 German NER 4-class is a Flair 2.0 named-entity model from the HU Chair of Machine Learning, hosted at flair/entity-german-4class. The card releases it under the Flukes NC 1.0 noncommercial license. Collected 2026-10-15.", "what_it_is_for": "The card presents it as an NER tagger for Flair. Load SequenceTagger with the Hub id. PER, LOC, ORG, MISC.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/entity/entity-german-8class": { "id": "hu-berlin/entity/entity-german-8class", "name": "Flair 2.0 German NER 8-class", "maker": "hu-berlin", "family": "hu-berlin/entity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Flukes NC 1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flair 2.0 sequence tagger. The card says 8 tags. The full tag table was not copied. The usage snippet on the card loads flair/entity-german-4class, so that snippet was not used as evidence for this checkpoint. The card cites the 2019 FLAIR NAACL paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair 2.0 German NER 8-class is a Flair 2.0 named-entity model from the HU Chair of Machine Learning, hosted at flair/entity-german-8class. The card releases it under the Flukes NC 1.0 noncommercial license. Collected 2026-10-15.", "what_it_is_for": "The card presents it as an NER tagger for Flair. Load SequenceTagger with the Hub id. The card says 8 tags. The full tag table was not copied. The usage snippet on the card loads flair/entity-german-4class, so that snippet was not used as evidence for this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/entity/entity-multilingual-4class": { "id": "hu-berlin/entity/entity-multilingual-4class", "name": "Flair 2.0 multilingual NER 4-class", "maker": "hu-berlin", "family": "hu-berlin/entity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Flukes NC 1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flair 2.0 sequence tagger. The card says 4 tags and that the model can predict entities in any language, with a language-specific tokenizer for some languages. The card cites the 2019 FLAIR NAACL paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair 2.0 multilingual NER 4-class is a Flair 2.0 named-entity model from the HU Chair of Machine Learning, hosted at flair/entity-multilingual-4class. The card releases it under the Flukes NC 1.0 noncommercial license. Collected 2026-10-15.", "what_it_is_for": "The card presents it as an NER tagger for Flair. Load SequenceTagger with the Hub id. The card says 4 tags and that the model can predict entities in any language, with a language-specific tokenizer for some languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/entity/entity-multilingual-8class": { "id": "hu-berlin/entity/entity-multilingual-8class", "name": "Flair 2.0 multilingual NER 8-class", "maker": "hu-berlin", "family": "hu-berlin/entity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Flukes NC 1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flair 2.0 sequence tagger. The card says 8 tags and that the model can predict entities in any language. The card cites the 2019 FLAIR NAACL paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair 2.0 multilingual NER 8-class is a Flair 2.0 named-entity model from the HU Chair of Machine Learning, hosted at flair/entity-multilingual-8class. The card releases it under the Flukes NC 1.0 noncommercial license. Collected 2026-10-15.", "what_it_is_for": "The card presents it as an NER tagger for Flair. Load SequenceTagger with the Hub id. The card says 8 tags and that the model can predict entities in any language.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/finerweb/finerweb-binary-classifier-mdeberta-4o": { "id": "hu-berlin/finerweb/finerweb-binary-classifier-mdeberta-4o", "name": "finerweb-binary-classifier-mdeberta-4o", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence classifier fine-tuned from microsoft/mdeberta-v3-base. The GitHub description loads these checkpoints with AutoModelForSequenceClassification. Teacher signal in the name: 4o-mini. Task head: binary. The Hub labels the weights 0.3B. That bucket is not stored as a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "finerweb-binary-classifier-mdeberta-4o is a named FiNERweb passage classifier released by Jonas Golde, Patrick Haller, and Alan Akbik at Humboldt University of Berlin. It is a binary fine-tune of microsoft/mdeberta-v3-base distilled from 4o-mini labels. MIT weights are on whoisjones/finerweb-binary-classifier-mdeberta-4o. Collected 2026-10-15.", "what_it_is_for": "The project uses these classifiers to select NER-relevant passages while building multilingual NER data. The multilabel xlmr-4o card describes a regression-style multilabel head. The datasets themselves are not models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/finerweb/finerweb-binary-classifier-mdeberta-gemma3": { "id": "hu-berlin/finerweb/finerweb-binary-classifier-mdeberta-gemma3", "name": "finerweb-binary-classifier-mdeberta-gemma3", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence classifier fine-tuned from microsoft/mdeberta-v3-base. The GitHub description loads these checkpoints with AutoModelForSequenceClassification. Teacher signal in the name: Gemma3-27B. Task head: binary. The Hub labels the weights 0.3B. That bucket is not stored as a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "finerweb-binary-classifier-mdeberta-gemma3 is a named FiNERweb passage classifier released by Jonas Golde, Patrick Haller, and Alan Akbik at Humboldt University of Berlin. It is a binary fine-tune of microsoft/mdeberta-v3-base distilled from Gemma3-27B labels. MIT weights are on whoisjones/finerweb-binary-classifier-mdeberta-gemma3. Collected 2026-10-15.", "what_it_is_for": "The project uses these classifiers to select NER-relevant passages while building multilingual NER data. The multilabel xlmr-4o card describes a regression-style multilabel head. The datasets themselves are not models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/finerweb/finerweb-binary-classifier-xlmr-4o": { "id": "hu-berlin/finerweb/finerweb-binary-classifier-xlmr-4o", "name": "finerweb-binary-classifier-xlmr-4o", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence classifier fine-tuned from FacebookAI/xlm-roberta-base. The GitHub description loads these checkpoints with AutoModelForSequenceClassification. Teacher signal in the name: 4o-mini. Task head: binary. The Hub labels the weights 0.3B. That bucket is not stored as a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "finerweb-binary-classifier-xlmr-4o is a named FiNERweb passage classifier released by Jonas Golde, Patrick Haller, and Alan Akbik at Humboldt University of Berlin. It is a binary fine-tune of FacebookAI/xlm-roberta-base distilled from 4o-mini labels. MIT weights are on whoisjones/finerweb-binary-classifier-xlmr-4o. Collected 2026-10-15.", "what_it_is_for": "The project uses these classifiers to select NER-relevant passages while building multilingual NER data. The multilabel xlmr-4o card describes a regression-style multilabel head. The datasets themselves are not models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/finerweb/finerweb-binary-classifier-xlmr-gemma3": { "id": "hu-berlin/finerweb/finerweb-binary-classifier-xlmr-gemma3", "name": "finerweb-binary-classifier-xlmr-gemma3", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence classifier fine-tuned from FacebookAI/xlm-roberta-base. The GitHub description loads these checkpoints with AutoModelForSequenceClassification. Teacher signal in the name: Gemma3-27B. Task head: binary. The Hub labels the weights 0.3B. That bucket is not stored as a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "finerweb-binary-classifier-xlmr-gemma3 is a named FiNERweb passage classifier released by Jonas Golde, Patrick Haller, and Alan Akbik at Humboldt University of Berlin. It is a binary fine-tune of FacebookAI/xlm-roberta-base distilled from Gemma3-27B labels. MIT weights are on whoisjones/finerweb-binary-classifier-xlmr-gemma3. Collected 2026-10-15.", "what_it_is_for": "The project uses these classifiers to select NER-relevant passages while building multilingual NER data. The multilabel xlmr-4o card describes a regression-style multilabel head. The datasets themselves are not models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/finerweb/finerweb-multilabel-classifier-mdeberta-4o": { "id": "hu-berlin/finerweb/finerweb-multilabel-classifier-mdeberta-4o", "name": "finerweb-multilabel-classifier-mdeberta-4o", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence classifier fine-tuned from microsoft/mdeberta-v3-base. The GitHub description loads these checkpoints with AutoModelForSequenceClassification. Teacher signal in the name: 4o-mini. Task head: multilabel. The Hub labels the weights 0.3B. That bucket is not stored as a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "finerweb-multilabel-classifier-mdeberta-4o is a named FiNERweb passage classifier released by Jonas Golde, Patrick Haller, and Alan Akbik at Humboldt University of Berlin. It is a multilabel fine-tune of microsoft/mdeberta-v3-base distilled from 4o-mini labels. MIT weights are on whoisjones/finerweb-multilabel-classifier-mdeberta-4o. Collected 2026-10-15.", "what_it_is_for": "The project uses these classifiers to select NER-relevant passages while building multilingual NER data. The multilabel xlmr-4o card describes a regression-style multilabel head. The datasets themselves are not models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/finerweb/finerweb-multilabel-classifier-mdeberta-gemma3": { "id": "hu-berlin/finerweb/finerweb-multilabel-classifier-mdeberta-gemma3", "name": "finerweb-multilabel-classifier-mdeberta-gemma3", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence classifier fine-tuned from microsoft/mdeberta-v3-base. The GitHub description loads these checkpoints with AutoModelForSequenceClassification. Teacher signal in the name: Gemma3-27B. Task head: multilabel. The Hub labels the weights 0.3B. That bucket is not stored as a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "finerweb-multilabel-classifier-mdeberta-gemma3 is a named FiNERweb passage classifier released by Jonas Golde, Patrick Haller, and Alan Akbik at Humboldt University of Berlin. It is a multilabel fine-tune of microsoft/mdeberta-v3-base distilled from Gemma3-27B labels. MIT weights are on whoisjones/finerweb-multilabel-classifier-mdeberta-gemma3. Collected 2026-10-15.", "what_it_is_for": "The project uses these classifiers to select NER-relevant passages while building multilingual NER data. The multilabel xlmr-4o card describes a regression-style multilabel head. The datasets themselves are not models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/finerweb/finerweb-multilabel-classifier-xlmr-4o": { "id": "hu-berlin/finerweb/finerweb-multilabel-classifier-xlmr-4o", "name": "finerweb-multilabel-classifier-xlmr-4o", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence classifier fine-tuned from FacebookAI/xlm-roberta-base. The GitHub description loads these checkpoints with AutoModelForSequenceClassification. Teacher signal in the name: 4o-mini. Task head: multilabel. The Hub labels the weights 0.3B. That bucket is not stored as a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "finerweb-multilabel-classifier-xlmr-4o is a named FiNERweb passage classifier released by Jonas Golde, Patrick Haller, and Alan Akbik at Humboldt University of Berlin. It is a multilabel fine-tune of FacebookAI/xlm-roberta-base distilled from 4o-mini labels. MIT weights are on whoisjones/finerweb-multilabel-classifier-xlmr-4o. Collected 2026-10-15.", "what_it_is_for": "The project uses these classifiers to select NER-relevant passages while building multilingual NER data. The multilabel xlmr-4o card describes a regression-style multilabel head. The datasets themselves are not models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/finerweb/finerweb-multilabel-classifier-xlmr-gemma3": { "id": "hu-berlin/finerweb/finerweb-multilabel-classifier-xlmr-gemma3", "name": "finerweb-multilabel-classifier-xlmr-gemma3", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence classifier fine-tuned from FacebookAI/xlm-roberta-base. The GitHub description loads these checkpoints with AutoModelForSequenceClassification. Teacher signal in the name: Gemma3-27B. Task head: multilabel. The Hub labels the weights 0.3B. That bucket is not stored as a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "finerweb-multilabel-classifier-xlmr-gemma3 is a named FiNERweb passage classifier released by Jonas Golde, Patrick Haller, and Alan Akbik at Humboldt University of Berlin. It is a multilabel fine-tune of FacebookAI/xlm-roberta-base distilled from Gemma3-27B labels. MIT weights are on whoisjones/finerweb-multilabel-classifier-xlmr-gemma3. Collected 2026-10-15.", "what_it_is_for": "The project uses these classifiers to select NER-relevant passages while building multilingual NER data. The multilabel xlmr-4o card describes a regression-style multilabel head. The datasets themselves are not models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/chunk-english": { "id": "hu-berlin/flair/chunk-english", "name": "Flair chunk English", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair chunk English ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `chunk`. Weights are on flair/chunk-english. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `chunk` for chunking on CoNLL-2000.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/chunk-english-fast": { "id": "hu-berlin/flair/chunk-english-fast", "name": "Flair chunk English fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair chunk English fast ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `chunk-fast`. Weights are on flair/chunk-english-fast. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `chunk-fast` for chunking on CoNLL-2000.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/de-offensive-language": { "id": "hu-berlin/flair/de-offensive-language", "name": "Flair German offensive language", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair German offensive language ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `de-offensive-language`. The current docs list it without a Hugging Face link. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `de-offensive-language` for classification on GermEval 2018 Task 1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/de-pos": { "id": "hu-berlin/flair/de-pos", "name": "Flair POS German", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair POS German ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `de-pos`. The current docs list it without a Hugging Face link. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `de-pos` for pos on UD German HDT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/de-pos-fine-grained": { "id": "hu-berlin/flair/de-pos-fine-grained", "name": "Flair German fine-grained POS", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair German fine-grained POS ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `de-pos-fine-grained`. Weights are on flair/de-pos-fine-grained. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `de-pos-fine-grained` for pos.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/de-pos-tweets": { "id": "hu-berlin/flair/de-pos-tweets", "name": "Flair POS German tweets", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair POS German tweets ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `de-pos-tweets`. The current docs list it without a Hugging Face link. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `de-pos-tweets` for pos on German Tweets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/frame-english": { "id": "hu-berlin/flair/frame-english", "name": "Flair frame English", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair frame English ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `frame`. Weights are on flair/frame-english. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `frame` for frame on Ontonotes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/frame-english-fast": { "id": "hu-berlin/flair/frame-english-fast", "name": "Flair frame English fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair frame English fast ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `frame-fast`. Weights are on flair/frame-english-fast. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `frame-fast` for frame on Ontonotes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-danish": { "id": "hu-berlin/flair/ner-danish", "name": "Flair NER Danish", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER Danish ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `da-ner`. Weights are on flair/ner-danish. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `da-ner` for ner on Danish NER / DaNE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-dutch": { "id": "hu-berlin/flair/ner-dutch", "name": "Flair NER Dutch", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER Dutch ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `nl-ner`. Weights are on flair/ner-dutch. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `nl-ner` for ner on CoNLL 2002.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-dutch-large": { "id": "hu-berlin/flair/ner-dutch-large", "name": "Flair NER Dutch large", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER Dutch large ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `nl-ner-large`. Weights are on flair/ner-dutch-large. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `nl-ner-large` for ner on CoNLL-03 (docs wording).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-dutch-rnn": { "id": "hu-berlin/flair/ner-dutch-rnn", "name": "Flair NER Dutch RNN", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER Dutch RNN ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `nl-ner-rnn`. The current docs list it without a Hugging Face link. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `nl-ner-rnn` for ner on CoNLL 2002.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-english": { "id": "hu-berlin/flair/ner-english", "name": "Flair NER English", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER English ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `ner`. Weights are on flair/ner-english. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `ner` for ner on CoNLL-03.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-english-fast": { "id": "hu-berlin/flair/ner-english-fast", "name": "Flair NER English fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER English fast ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `ner-fast`. Weights are on flair/ner-english-fast. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `ner-fast` for ner on CoNLL-03.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-english-large": { "id": "hu-berlin/flair/ner-english-large", "name": "Flair NER English large", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FLERT tagger. The card says it is a fine-tune of xlm-roberta-large, layers -1, subtoken pooling first, use_context true, no CRF, no RNN, hidden size 256, AdamW, learning rate 5e-6, 20 epochs, batch 4. Tags PER, LOC, ORG, MISC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER English large ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `ner-large`. Weights are on flair/ner-english-large. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `ner-large` for ner on CoNLL-03.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-english-ontonotes": { "id": "hu-berlin/flair/ner-english-ontonotes", "name": "Flair NER English Ontonotes", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER English Ontonotes ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `ner-ontonotes`. Weights are on flair/ner-english-ontonotes. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `ner-ontonotes` for ner on Ontonotes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-english-ontonotes-fast": { "id": "hu-berlin/flair/ner-english-ontonotes-fast", "name": "Flair NER English Ontonotes fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER English Ontonotes fast ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `ner-ontonotes-fast`. Weights are on flair/ner-english-ontonotes-fast. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `ner-ontonotes-fast` for ner on Ontonotes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-english-ontonotes-large": { "id": "hu-berlin/flair/ner-english-ontonotes-large", "name": "Flair NER English Ontonotes large", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER English Ontonotes large ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `ner-ontonotes-large`. Weights are on flair/ner-english-ontonotes-large. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `ner-ontonotes-large` for ner on Ontonotes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-french": { "id": "hu-berlin/flair/ner-french", "name": "Flair NER French", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER French ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `fr-ner`. Weights are on flair/ner-french. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `fr-ner` for ner on WikiNER aij-wikiner-fr-wp3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-german": { "id": "hu-berlin/flair/ner-german", "name": "Flair NER German", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER German ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `de-ner`. Weights are on flair/ner-german. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `de-ner` for ner on CoNLL-03.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-german-germeval": { "id": "hu-berlin/flair/ner-german-germeval", "name": "Flair NER German GermEval", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER German GermEval ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `de-ner-germeval`. The current docs list it without a Hugging Face link. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `de-ner-germeval` for ner on GermEval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-german-large": { "id": "hu-berlin/flair/ner-german-large", "name": "Flair NER German large", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER German large ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `de-ner-large`. Weights are on flair/ner-german-large. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `de-ner-large` for ner on CoNLL-03.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-german-legal": { "id": "hu-berlin/flair/ner-german-legal", "name": "Flair NER German legal", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER German legal ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `de-ner-legal`. Weights are on flair/ner-german-legal. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `de-ner-legal` for ner on LER.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-multi": { "id": "hu-berlin/flair/ner-multi", "name": "Flair NER multilingual", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card says Flair embeddings plus an LSTM-CRF. The training script uses GloVe, German FastText, and multilingual forward and backward Flair embeddings, hidden size 256, 150 epochs, train_with_dev. Those embeddings are the Zalando-era Flair LMs, not a new HU language model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER multilingual ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `ner-multi`. Weights are on flair/ner-multi. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `ner-multi` for ner on CoNLL-03.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-multi-fast": { "id": "hu-berlin/flair/ner-multi-fast", "name": "Flair NER multilingual fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER multilingual fast ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `ner-multi-fast`. Weights are on flair/ner-multi-fast. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `ner-multi-fast` for ner on CoNLL-03.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-pooled": { "id": "hu-berlin/flair/ner-pooled", "name": "Flair NER English pooled", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER English pooled ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `ner-pooled`. The current docs list it without a Hugging Face link. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `ner-pooled` for ner on CoNLL-03.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/ner-spanish-large": { "id": "hu-berlin/flair/ner-spanish-large", "name": "Flair NER Spanish large", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair NER Spanish large ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `es-ner-large`. Weights are on flair/ner-spanish-large. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `es-ner-large` for ner on CoNLL-03 Spanish.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/pos-english": { "id": "hu-berlin/flair/pos-english", "name": "Flair POS English", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair POS English ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `pos`. Weights are on flair/pos-english. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `pos` for pos on Ontonotes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/pos-english-fast": { "id": "hu-berlin/flair/pos-english-fast", "name": "Flair POS English fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair POS English fast ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `pos-fast`. Weights are on flair/pos-english-fast. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `pos-fast` for pos on Ontonotes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/sentiment": { "id": "hu-berlin/flair/sentiment", "name": "Flair sentiment English", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair sentiment English ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `sentiment`. The current docs list it without a Hugging Face link. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `sentiment` for sentiment on movie and product reviews.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/sentiment-fast": { "id": "hu-berlin/flair/sentiment-fast", "name": "Flair sentiment English fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair sentiment English fast ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `sentiment-fast`. The current docs list it without a Hugging Face link. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `sentiment-fast` for sentiment on movie and product reviews.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/upos-english": { "id": "hu-berlin/flair/upos-english", "name": "Flair UPOS English", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair UPOS English ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `upos`. Weights are on flair/upos-english. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `upos` for pos on Ontonotes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/upos-english-fast": { "id": "hu-berlin/flair/upos-english-fast", "name": "Flair UPOS English fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair UPOS English fast ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `upos-fast`. Weights are on flair/upos-english-fast. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `upos-fast` for pos on Ontonotes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/upos-multi": { "id": "hu-berlin/flair/upos-multi", "name": "Flair UPOS multilingual", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair UPOS multilingual ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `pos-multi`. Weights are on flair/upos-multi. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `pos-multi` for pos on UD treebanks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flair/upos-multi-fast": { "id": "hu-berlin/flair/upos-multi-fast", "name": "Flair UPOS multilingual fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Noncommercial use only (Flair classic collection statement). Individual cards often omit a license field.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence tagger shipped with the Flair library. Large English, German, Spanish, and Dutch NER models are FLERT-style transformer taggers (Schweter and Akbik, arXiv 2011.06993). Default English and multilingual taggers use Flair embeddings from the 2018 Zalando Research line. A parameter count is not printed on the docs table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flair UPOS multilingual fast ships with Flair, the NLP library maintained by the Chair of Machine Learning at Humboldt University of Berlin. The docs load id is `pos-multi-fast`. Weights are on flair/upos-multi-fast. Collected 2026-10-15.", "what_it_is_for": "The Flair docs tell users to load `pos-multi-fast` for pos on UD treebanks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/flukes/fluke-ner-en-7class": { "id": "hu-berlin/flukes/fluke-ner-en-7class", "name": "Fluke NER English 7-class", "maker": "hu-berlin", "family": "hu-berlin/flukes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Flukes NC 1.0", "release_date": "2026-09-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "English 7-class NER for the Flukes library (pip install flukes, Model.load). The Hub tags name XLM-RoBERTa. The Hub size bucket 0.6B is not stored as a parameter count. Tags: PER, ORG, LOC, EVENT, PRODUCT, WORK_OF_ART, MISC.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fluke NER English 7-class is the English NER model for the Flukes library from the HU Chair of Machine Learning. It is hosted at flair/fluke-ner-en-7class under the Flukes NC 1.0 license. Collected 2026-10-15.", "what_it_is_for": "The card presents a 7-class English named-entity tagger loaded through Flukes rather than through the classic Flair Classifier.load path.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/hunflair/hunflair": { "id": "hu-berlin/hunflair/hunflair", "name": "HunFlair", "maker": "hu-berlin", "family": "hu-berlin/hunflair", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Biomedical NER tagger for five types: cell line, chemical, disease, gene/protein, and species. The paper also describes a biomedical character LM and FastText embeddings referred to as pubmed. Those components stay inside this product folder.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HunFlair is the first biomedical NER tool from the HU Computer Science groups of Ulf Leser and Alan Akbik. The 2020 paper introduces taggers for five biomedical entity types. Current Flair docs tell users to use HunFlair2 instead. Collected 2026-10-15.", "what_it_is_for": "The tool finds cell lines, chemicals, diseases, genes or proteins, and species in biomedical text. The docs now route that job to HunFlair2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/hunflair/hunflair2": { "id": "hu-berlin/hunflair/hunflair2", "name": "HunFlair2", "maker": "hu-berlin", "family": "hu-berlin/hunflair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Biomedical NER and normalization tool. Entity types named in the docs: genes/proteins, chemicals, diseases, species, and cell lines. Hub checkpoint hunflair/hunflair2-ner. The docs also show EntityMentionLinker load ids gene-linker, disease-linker, and species-linker.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HunFlair2 is the current biomedical named-entity tagger and linker from the Department of Computer Science at Humboldt University of Berlin. The docs load it as hunflair2, and the entity tutorial also shows bioner. Weights for the NER checkpoint are at hunflair/hunflair2-ner, created 2024-02-14. Collected 2026-10-15.", "what_it_is_for": "The docs present it for genes and proteins, chemicals, diseases, species, and cell lines, plus ontology linking. The comparison table reports end-to-end F1 on corpora held out from training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/news-crawler/fundus-crawler-json": { "id": "hu-berlin/news-crawler/fundus-crawler-json", "name": "FundusCrawler JSON", "maker": "hu-berlin", "family": "hu-berlin/news-crawler", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-07-23", "context_length": 32768, "max_output_tokens": 8192, "parameters": "", "architecture": "LoRA fine-tune of ReaderLM-v2. The paper says ReaderLM-v2 builds on Qwen2.5 and supports up to 32k tokens. Training setup in the paper: LoRA r=16, alpha=16, dropout 0, adapters on q, k, v, o plus gating and FFN, 8-bit Adam, batch 1, learning rate 4e-5, weight decay 0.01, 60k steps, linear warmup 1k then decay, early stopping patience 3, eval every 1k on validation perplexity, plus a SimCTG contrastive term. Design window: 32k tokens, 24k reserved for HTML input, 8k reserved for output. Qwen2.5-1.5B in the tables is a baseline, not this model's size.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FundusCrawler JSON is the HTML-to-JSON checkpoint released with news-crawler-LM by the same HU authors. The Hub card is an empty template. Collected 2026-10-15.", "what_it_is_for": "The paper presents a second output format that emits article fields as JSON. This folder is the JSON Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/news-crawler/fundus-crawler-plaintext": { "id": "hu-berlin/news-crawler/fundus-crawler-plaintext", "name": "FundusCrawler plaintext", "maker": "hu-berlin", "family": "hu-berlin/news-crawler", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-07-23", "context_length": 32768, "max_output_tokens": 8192, "parameters": "", "architecture": "LoRA fine-tune of ReaderLM-v2. The paper says ReaderLM-v2 builds on Qwen2.5 and supports up to 32k tokens. Training setup in the paper: LoRA r=16, alpha=16, dropout 0, adapters on q, k, v, o plus gating and FFN, 8-bit Adam, batch 1, learning rate 4e-5, weight decay 0.01, 60k steps, linear warmup 1k then decay, early stopping patience 3, eval every 1k on validation perplexity, plus a SimCTG contrastive term. Design window: 32k tokens, 24k reserved for HTML input, 8k reserved for output. Qwen2.5-1.5B in the tables is a baseline, not this model's size.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FundusCrawler plaintext is the HTML-to-plaintext checkpoint released with news-crawler-LM by Stolzenburg, Golde, Dallabetta, and Akbik at Humboldt-Universit\u00e4t zu Berlin. The paper is arXiv 2607.21284, dated 2026-07-23, and names KONVENS 2026. The Hub card is an empty template. Collected 2026-10-15.", "what_it_is_for": "The paper presents the model as a crawler LM that turns publisher HTML into article text. This folder is the plaintext Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/opiniongpt/opiniongpt": { "id": "hu-berlin/opiniongpt/opiniongpt", "name": "OpinionGPT", "maker": "hu-berlin", "family": "hu-berlin/opiniongpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-09-07", "context_length": null, "max_output_tokens": null, "parameters": "7000000000", "architecture": "The paper describes a full fine-tune of a LLaMA model and cites Touvron et al. 2023a. Alan Akbik's post on 2023-05-12 calls it a 7B LLaMA model fine-tuned on German-language Reddit. The Decoder (2023-09-09) identifies the base as Llama v1 7B. Artnet (2023-10-15) identifies it as Llama 2. Those accounts disagree. This dossier does not pick one base. The 7000000000 figure is the stated 7B size from the May 2023 post.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpinionGPT is a named instruction model from Patrick Haller, Ansar Aynetdinov, and Alan Akbik at Humboldt University of Berlin. The paper studies explicit bias variants built from Reddit communities. It was a NAACL 2024 demo, with a HU press note on 2023-09-14. A public weight repository was not found. Collected 2026-10-15.", "what_it_is_for": "The paper presents OpinionGPT as a way to model explicit biases in an instruction-tuned LLaMA model. Eleven bias variants cover gender, age, geography, and politics.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/otter/otter-bi-mmbert": { "id": "hu-berlin/otter/otter-bi-mmbert", "name": "Otter bi-encoder mmBERT", "maker": "hu-berlin", "family": "hu-berlin/otter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "bi-encoder open-type NER. Encoder jhu-clsp/mmBERT-base. The cross-encoder card says the label list is prepended with a [LABEL] token. Max sequence length on the card family table: 1024. Max span length 30 tokens. Load with AutoModel and trust_remote_code. Hub size buckets are not parameter counts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Otter bi-encoder mmBERT is one of four named Otter checkpoints from Jonas Golde, Patrick Haller, and Alan Akbik at Humboldt University of Berlin. Otter is a multilingual open-type named entity recognizer: the label set is part of the input. Apache-2.0 weights are on whoisjones/otter-bi-mmbert. Collected 2026-10-15.", "what_it_is_for": "The card says you pass text and a list of entity types and receive character spans. The cross-encoder attends to labels and text together. The bi-encoder encodes them separately. This folder is the bi-encoder with jhu-clsp/mmBERT-base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/otter/otter-bi-rembert": { "id": "hu-berlin/otter/otter-bi-rembert", "name": "Otter bi-encoder RemBERT", "maker": "hu-berlin", "family": "hu-berlin/otter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "bi-encoder open-type NER. Encoder RemBERT. The cross-encoder card says the label list is prepended with a [LABEL] token. Max sequence length on the card family table: 512. Max span length 30 tokens. Load with AutoModel and trust_remote_code. Hub size buckets are not parameter counts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Otter bi-encoder RemBERT is one of four named Otter checkpoints from Jonas Golde, Patrick Haller, and Alan Akbik at Humboldt University of Berlin. Otter is a multilingual open-type named entity recognizer: the label set is part of the input. Apache-2.0 weights are on whoisjones/otter-bi-rembert. Collected 2026-10-15.", "what_it_is_for": "The card says you pass text and a list of entity types and receive character spans. The cross-encoder attends to labels and text together. The bi-encoder encodes them separately. This folder is the bi-encoder with RemBERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/otter/otter-cross-mmbert": { "id": "hu-berlin/otter/otter-cross-mmbert", "name": "Otter cross-encoder mmBERT", "maker": "hu-berlin", "family": "hu-berlin/otter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "cross-encoder open-type NER. Encoder jhu-clsp/mmBERT-base. The cross-encoder card says the label list is prepended with a [LABEL] token. Max sequence length on the card family table: 1024. Max span length 30 tokens. Load with AutoModel and trust_remote_code. Hub size buckets are not parameter counts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Otter cross-encoder mmBERT is one of four named Otter checkpoints from Jonas Golde, Patrick Haller, and Alan Akbik at Humboldt University of Berlin. Otter is a multilingual open-type named entity recognizer: the label set is part of the input. Apache-2.0 weights are on whoisjones/otter-cross-mmbert. Collected 2026-10-15.", "what_it_is_for": "The card says you pass text and a list of entity types and receive character spans. The cross-encoder attends to labels and text together. The bi-encoder encodes them separately. This folder is the cross-encoder with jhu-clsp/mmBERT-base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hu-berlin/otter/otter-cross-rembert": { "id": "hu-berlin/otter/otter-cross-rembert", "name": "Otter cross-encoder RemBERT", "maker": "hu-berlin", "family": "hu-berlin/otter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "cross-encoder open-type NER. Encoder RemBERT. The cross-encoder card says the label list is prepended with a [LABEL] token. Max sequence length on the card family table: 512. Max span length 30 tokens. Load with AutoModel and trust_remote_code. Hub size buckets are not parameter counts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Otter cross-encoder RemBERT is one of four named Otter checkpoints from Jonas Golde, Patrick Haller, and Alan Akbik at Humboldt University of Berlin. Otter is a multilingual open-type named entity recognizer: the label set is part of the input. Apache-2.0 weights are on whoisjones/otter-cross-rembert. Collected 2026-10-15.", "what_it_is_for": "The card says you pass text and a list of entity types and receive character spans. The cross-encoder attends to labels and text together. The bi-encoder encodes them separately. This folder is the cross-encoder with RemBERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/cemat/cemat": { "id": "huawei/cemat/cemat", "name": "CeMAT", "maker": "huawei", "family": "huawei/cemat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CeMAT is Huawei Noah's multilingual sequence-to-sequence pretrained model for both autoregressive and non-autoregressive translation. It is a named release in the Noah repository. Size and scores were not re-read. Collected 2026-10-15.", "what_it_is_for": "Multilingual pretraining for neural machine translation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/nezha/nezha": { "id": "huawei/nezha/nezha", "name": "NEZHA", "maker": "huawei", "family": "huawei/nezha", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Chinese pretrained Transformer. Functional relative positional encoding and whole-word masking are the methods named by the project. Layer sizes were not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NEZHA is Huawei Noah's Chinese pretrained language model, released in TensorFlow and PyTorch from the Noah pretrained-model repository. The 2019 paper is the model card equivalent. Exact sizes were not re-copied on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Chinese language-understanding pretrained model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/nezha/nezha-gen": { "id": "huawei/nezha/nezha-gen", "name": "NEZHA-Gen", "maker": "huawei", "family": "huawei/nezha", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NEZHA-Gen is Huawei Noah's GPT line in the pretrained-model repository. The index says it contains a general Chinese GPT and Yuefu, a classical poetry model. Sizes were not on the index page. Collected 2026-10-15.", "what_it_is_for": "Chinese GPT generation, including a general model and the Yuefu poetry model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/nezha/yuefu": { "id": "huawei/nezha/yuefu", "name": "Yuefu", "maker": "huawei", "family": "huawei/nezha", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yuefu (\u4e50\u5e9c) is Huawei Noah's classical Chinese poetry GPT, listed as one of the two NEZHA-Gen models. Specs beyond the name and task were not on the repository index. Collected 2026-10-15.", "what_it_is_for": "Classical Chinese poetry generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/openpangu/openpangu-2-0-flash": { "id": "huawei/openpangu/openpangu-2-0-flash", "name": "openPangu-2.0-Flash", "maker": "huawei", "family": "huawei/openpangu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OPENPANGU MODEL LICENSE AGREEMENT VERSION 2.0, except where a file says otherwise.", "release_date": "2026-06", "context_length": 524288, "max_output_tokens": null, "parameters": "about 92B total, about 6B activated per token", "architecture": "MoE. MLA with DSA and SWA layers at a 1:2 ratio. 4-stream mHC residual. 3-head MTP that predicts 3 extra tokens. Trained with Muon.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "openPangu-2.0-Flash is Huawei's open-weight MoE with about 92B total parameters and about 6B activated, a 512k context, and about 34T training tokens. The card is dated as live on 30 June 2026 on Ascend Tribe and uses license 2.0. Collected 2026-10-15.", "what_it_is_for": "Smaller openPangu 2.0 MoE for long-context reasoning and agents, with thinking and non-thinking modes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/openpangu/openpangu-2-0-pro": { "id": "huawei/openpangu/openpangu-2-0-pro", "name": "openPangu-2.0-Pro", "maker": "huawei", "family": "huawei/openpangu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OPENPANGU MODEL LICENSE AGREEMENT VERSION 2.0, except where a file says otherwise.", "release_date": "2026-07-31", "context_length": 524288, "max_output_tokens": null, "parameters": "505B total, 18B activated", "architecture": "MoE trained on Ascend. English card says MLA plus DSA and SWA at a 1:2 layer ratio. Post-training uses unified fast/slow SFT, specialist RL, and on-policy distillation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "openPangu-2.0-Pro is Huawei's open-weight MoE released on 31 July 2026 with 505B total parameters, 18B activated, a 512k context, and about 34T pretraining tokens. Huawei's news post and the English card agree. License 2.0. Collected 2026-10-15.", "what_it_is_for": "Largest openPangu 2.0 MoE, for long-context reasoning and agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/openpangu/openpangu-7b-diffusion-base": { "id": "huawei/openpangu/openpangu-7b-diffusion-base", "name": "openPangu-7B-Diffusion-Base", "maker": "huawei", "family": "huawei/openpangu", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "openPangu-7B-Diffusion-Base is a named model on Huawei's openPangu Hugging Face organization. The card was not opened on 2026-10-15, so parameter, context, license, and scores are null. Collected 2026-10-15.", "what_it_is_for": "Named openPangu diffusion language model. Specs were not read from the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/openpangu/openpangu-7b-diffusion-deepdiver": { "id": "huawei/openpangu/openpangu-7b-diffusion-deepdiver", "name": "openPangu-7B-Diffusion-DeepDiver", "maker": "huawei", "family": "huawei/openpangu", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "openPangu-7B-Diffusion-DeepDiver is a named model on Huawei's openPangu Hugging Face organization. The card was not opened on 2026-10-15, so parameter, context, license, and scores are null. Collected 2026-10-15.", "what_it_is_for": "Named openPangu diffusion language model. Specs were not read from the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/openpangu/openpangu-embedded-1b": { "id": "huawei/openpangu/openpangu-embedded-1b", "name": "openPangu-Embedded-1B", "maker": "huawei", "family": "huawei/openpangu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OPENPANGU MODEL LICENSE AGREEMENT VERSION 1.0, except where a file says otherwise.", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "1B non-embedding", "architecture": "Dense decoder, 26 layers, hidden 1536, GQA with 12 query heads and 6 KV heads, vocabulary about 153k.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "openPangu-Embedded-1B is Huawei's open-weight 1B-class dense model, trained from scratch on Ascend for about 10T tokens. The card specifies 26 layers, a 32k context, and fast thinking, under the openPangu 1.0 license. Collected 2026-10-15.", "what_it_is_for": "On-device fast-thinking language model trained from scratch on Ascend.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/openpangu/openpangu-embedded-7b": { "id": "huawei/openpangu/openpangu-embedded-7b", "name": "openPangu-Embedded-7B", "maker": "huawei", "family": "huawei/openpangu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OPENPANGU MODEL LICENSE AGREEMENT VERSION 1.0, except where a file says otherwise.", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "7B non-embedding", "architecture": "Dense, 34 layers, hidden 12800, GQA 32 query heads and 8 KV heads, vocabulary about 153k. Fast and slow thinking.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "openPangu-Embedded-7B is Huawei's open-weight dense 7B model, excluding embeddings, trained on about 19T tokens on Ascend. The card lists 34 layers, a 32k context, and slow-thinking benchmark scores under the openPangu 1.0 license. Collected 2026-10-15.", "what_it_is_for": "7B dense model with fused fast and slow thinking, trained on Ascend.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/openpangu/openpangu-embedded-7b-deepdiver": { "id": "huawei/openpangu/openpangu-embedded-7b-deepdiver", "name": "openPangu-Embedded-7B-DeepDiver", "maker": "huawei", "family": "huawei/openpangu", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "OPENPANGU MODEL LICENSE AGREEMENT VERSION 1.0, except where a file says otherwise.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "openPangu-Embedded-7B-DeepDiver is a named open-weight model on Huawei's openPangu Hugging Face org. Its card was not opened in this pass, so specs are blank rather than copied from the size badge. Collected 2026-10-15.", "what_it_is_for": "Named DeepDiver variant of the embedded 7B line. Behavior was not read from a card in this pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/openpangu/openpangu-r-72b-2512": { "id": "huawei/openpangu/openpangu-r-72b-2512", "name": "openPangu-R-72B-2512", "maker": "huawei", "family": "huawei/openpangu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OPENPANGU MODEL LICENSE AGREEMENT VERSION 1.0, except where a file says otherwise.", "release_date": "2025-12", "context_length": 131072, "max_output_tokens": null, "parameters": "74B total, 15B activated", "architecture": "MoE. 50 layers including 4 dense layers. 80 routed experts, top 8. Hidden 4608. MoE hidden 1280 per expert. GQA, 64 heads, 4 query groups. 1 MTP module.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "openPangu-R-72B-2512 is Huawei's open-weight MoE reasoning model with 74B total parameters and 15B activated, a 128k context, and about 24T pretraining tokens. An Int8 repo sits beside it. License 1.0. Collected 2026-10-15.", "what_it_is_for": "72B-class MoE reasoning model with 15B activated parameters and a 128k context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/openpangu/openpangu-r-7b-2512": { "id": "huawei/openpangu/openpangu-r-7b-2512", "name": "openPangu-R-7B-2512", "maker": "huawei", "family": "huawei/openpangu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OPENPANGU MODEL LICENSE AGREEMENT VERSION 1.0, except where a file says otherwise.", "release_date": "2025-12", "context_length": 131072, "max_output_tokens": null, "parameters": "7B non-embedding", "architecture": "Dense, 27 layers, hidden 4096, intermediate 18432, GQA 32 query and 8 KV, 1 MTP module, vocabulary about 153k.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "openPangu-R-7B-2512 is Huawei's open-weight 7B dense reasoning model, trained on about 30T tokens on Ascend. The card lists 27 layers, a 128k context, one MTP module, and separate fast and slow scores. License 1.0. Collected 2026-10-15.", "what_it_is_for": "7B dense reasoning model with a 128k context and a switch between fast and slow thinking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/openpangu/openpangu-r-7b-diffusion": { "id": "huawei/openpangu/openpangu-r-7b-diffusion", "name": "openPangu-R-7B-Diffusion", "maker": "huawei", "family": "huawei/openpangu", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "openPangu-R-7B-Diffusion is a named model on Huawei's openPangu Hugging Face organization. The card was not opened on 2026-10-15, so parameter, context, license, and scores are null. Collected 2026-10-15.", "what_it_is_for": "Named openPangu diffusion language model. Specs were not read from the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/openpangu/openpangu-ultra-moe-718b": { "id": "huawei/openpangu/openpangu-ultra-moe-718b", "name": "openPangu-Ultra-MoE-718B", "maker": "huawei", "family": "huawei/openpangu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OPENPANGU MODEL LICENSE AGREEMENT VERSION 1.0, except where a file says otherwise.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "718B total, 39B activated", "architecture": "MoE with MLA, MTP, and a high sparsity ratio. Card also names Depth-Scaled Sandwich-Norm, TinyInit, and EP-group load balancing. Fast and slow thinking.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "openPangu-Ultra-MoE-718B is Huawei's open-weight MoE model with 718B total parameters and 39B activated, trained on about 19T tokens on Ascend. The card reports slow-thinking scores and an Ascend 32-card example. License 1.0. V1.1 and Int8 repos are versions, not separate models. Collected 2026-10-15.", "what_it_is_for": "Flagship open-weight MoE language model with fused fast and slow thinking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/openpangu/openpangu-vl-7b": { "id": "huawei/openpangu/openpangu-vl-7b", "name": "openPangu-VL-7B", "maker": "huawei", "family": "huawei/openpangu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OPENPANGU MODEL LICENSE AGREEMENT VERSION 1.0, except where a file says otherwise.", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "7B language non-embedding plus 615M vision encoder", "architecture": "Language tower matches Embedded-7B: dense, 34 layers, hidden 12800, GQA 32/8, vocab about 153k, native context 128k. Vision encoder: 26 layers, 22 window attention plus 4 full attention, hidden 1536, FFN 4608, 16 heads, 615M parameters.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "openPangu-VL-7B is Huawei's open-weight multimodal model on the Embedded-7B-V1.1 language base plus a 615M vision encoder. The card lists a 128k language context, about 3T training tokens, and fast thinking. License 1.0. Collected 2026-10-15.", "what_it_is_for": "Fast-thinking vision-language model for dialogue, documents, grounding, counting, and video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/openpangu/pangu-pro-moe": { "id": "huawei/openpangu/pangu-pro-moe", "name": "Pangu Pro MoE", "maker": "huawei", "family": "huawei/openpangu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Pangu Model License Agreement Version 1.0, as stated by secondary catalog pages citing the release. Confirm on the weight repo before use.", "release_date": "2025-06-30", "context_length": null, "max_output_tokens": null, "parameters": "71.99B total, 16.50B activated", "architecture": "MoGE. Vocabulary 153376, hidden 5120, intermediate 1344, 40 query heads, 8 KV heads, head size 128, 48 layers, 64 routed experts, 8 activated, 4 shared experts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu Pro MoE is Huawei's open-weight grouped-expert model, 71.99B total and 16.50B activated, described in arXiv 2505.21411 and released around 30 June 2025 on Ascend Tribe. It is the 72B open model that preceded the openPangu 2.0 pair. Collected 2026-10-15.", "what_it_is_for": "72B-class grouped-expert MoE optimized for Ascend inference. First widely downloaded open Pangu weight in June 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-ai4s/pangu-ai4s-global-precip": { "id": "huawei/pangu-ai4s/pangu-ai4s-global-precip", "name": "Pangu-AI4S-Global-Precip", "maker": "huawei", "family": "huawei/pangu-ai4s", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not stated per SKU on the spec page. The research Pangu-Weather model used a 3D Earth-specific transformer; that architecture is not restated for these cloud versions.", "modalities": { "input": [ "weather-fields" ], "output": [ "weather-fields" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-AI4S-Global-Precip is a Huawei Cloud Pangu scientific-computing model for global precipitation.. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00. Collected 2026-10-15.", "what_it_is_for": "Global precipitation forecast.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-ai4s/pangu-ai4s-global-weather": { "id": "huawei/pangu-ai4s/pangu-ai4s-global-weather", "name": "Pangu-AI4S-Global-Weather", "maker": "huawei", "family": "huawei/pangu-ai4s", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not stated per SKU on the spec page. The research Pangu-Weather model used a 3D Earth-specific transformer; that architecture is not restated for these cloud versions.", "modalities": { "input": [ "weather-fields" ], "output": [ "weather-fields" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-AI4S-Global-Weather is a Huawei Cloud Pangu scientific-computing model for global weather elements.. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00. Collected 2026-10-15.", "what_it_is_for": "Forecasts basic global weather elements. Time resolution is the smallest forecast step after data are fed in.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-ai4s/pangu-ai4s-global-wind": { "id": "huawei/pangu-ai4s/pangu-ai4s-global-wind", "name": "Pangu-AI4S-Global-Wind", "maker": "huawei", "family": "huawei/pangu-ai4s", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not stated per SKU on the spec page. The research Pangu-Weather model used a 3D Earth-specific transformer; that architecture is not restated for these cloud versions.", "modalities": { "input": [ "weather-fields" ], "output": [ "weather-fields" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-AI4S-Global-Wind is a Huawei Cloud Pangu scientific-computing model for global wind for energy.. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00. Collected 2026-10-15.", "what_it_is_for": "Global new-energy wind elements.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-ai4s/pangu-ai4s-regional-photovoltaics": { "id": "huawei/pangu-ai4s/pangu-ai4s-regional-photovoltaics", "name": "Pangu-AI4S-Regional-Photovoltaics", "maker": "huawei", "family": "huawei/pangu-ai4s", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not stated per SKU on the spec page. The research Pangu-Weather model used a 3D Earth-specific transformer; that architecture is not restated for these cloud versions.", "modalities": { "input": [ "weather-fields" ], "output": [ "weather-fields" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-AI4S-Regional-Photovoltaics is a Huawei Cloud Pangu scientific-computing model for regional solar for energy.. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00. Collected 2026-10-15.", "what_it_is_for": "Regional new-energy solar elements.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-ai4s/pangu-ai4s-regional-precip": { "id": "huawei/pangu-ai4s/pangu-ai4s-regional-precip", "name": "Pangu-AI4S-Regional-Precip", "maker": "huawei", "family": "huawei/pangu-ai4s", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not stated per SKU on the spec page. The research Pangu-Weather model used a 3D Earth-specific transformer; that architecture is not restated for these cloud versions.", "modalities": { "input": [ "weather-fields" ], "output": [ "weather-fields" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-AI4S-Regional-Precip is a Huawei Cloud Pangu scientific-computing model for regional precipitation.. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00. Collected 2026-10-15.", "what_it_is_for": "Regional precipitation forecast.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-ai4s/pangu-ai4s-regional-weather": { "id": "huawei/pangu-ai4s/pangu-ai4s-regional-weather", "name": "Pangu-AI4S-Regional-Weather", "maker": "huawei", "family": "huawei/pangu-ai4s", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not stated per SKU on the spec page. The research Pangu-Weather model used a 3D Earth-specific transformer; that architecture is not restated for these cloud versions.", "modalities": { "input": [ "weather-fields" ], "output": [ "weather-fields" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-AI4S-Regional-Weather is a Huawei Cloud Pangu scientific-computing model for regional medium-range weather.. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00. Collected 2026-10-15.", "what_it_is_for": "Regional medium-range weather elements.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-ai4s/pangu-ai4s-regional-wind": { "id": "huawei/pangu-ai4s/pangu-ai4s-regional-wind", "name": "Pangu-AI4S-Regional-Wind", "maker": "huawei", "family": "huawei/pangu-ai4s", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not stated per SKU on the spec page. The research Pangu-Weather model used a 3D Earth-specific transformer; that architecture is not restated for these cloud versions.", "modalities": { "input": [ "weather-fields" ], "output": [ "weather-fields" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-AI4S-Regional-Wind is a Huawei Cloud Pangu scientific-computing model for regional wind for energy.. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00. Collected 2026-10-15.", "what_it_is_for": "Regional new-energy wind elements.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-ai4s/pangu-weather": { "id": "huawei/pangu-ai4s/pangu-weather", "name": "Pangu-Weather", "maker": "huawei", "family": "huawei/pangu-ai4s", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2023-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "3D neural network for medium-range global weather, as named in the Nature paper title cited by Huawei Cloud.", "modalities": { "input": [ "weather-fields" ], "output": [ "weather-fields" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Weather is Huawei Cloud's named medium-range global weather model. Huawei says the team published the 3D neural-network result in Nature, and the English product page describes 0.25-degree forecasts out to 7 days. The later Pangu-AI4S weather SKUs are a separate product list, and the pages do not name one as the other's successor. Collected 2026-10-15.", "what_it_is_for": "Medium-range global weather forecasting, the research model behind the later AI4S weather SKUs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-alpha/pangu-alpha": { "id": "huawei/pangu-alpha/pangu-alpha", "name": "PanGu-\u03b1", "maker": "huawei", "family": "huawei/pangu-alpha", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Weights were posted for download from the Noah and Peng Cheng Lab pages. The license text was not copied here.", "release_date": "2021-04", "context_length": null, "max_output_tokens": null, "parameters": "up to 200B; trained sizes 2.6B, 13.1B, and 207.0B", "architecture": "Autoregressive Transformer with an extra query layer. 2.6B: 32 layers, hidden 2560, FFN 10240, 40 heads. 13B: 40 layers, hidden 5120, FFN 20480, 40 heads. 200B: 64 layers, hidden 16384, FFN 65536, 128 heads.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PanGu-\u03b1 is the 2021 Chinese autoregressive model from Huawei Noah's Ark Lab, MindSpore, and Peng Cheng Laboratory. The paper trains 2.6B, 13B, and 200B models on 1.1 TB of Chinese text using 2048 Ascend 910 processors. 2.6B and 13B weights were posted. 200B weights were still marked forthcoming on the Noah page. Collected 2026-10-15.", "what_it_is_for": "Chinese autoregressive pretraining, the first named Pangu language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-coder/pangu-coder": { "id": "huawei/pangu-coder/pangu-coder", "name": "PanGu-Coder", "maker": "huawei", "family": "huawei/pangu-coder", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2022-07", "context_length": null, "max_output_tokens": null, "parameters": "2.6B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PanGu-Coder is Huawei Cloud's earlier code model. The PanGu-Coder2 paper lists a 2.6B PanGu-Coder at 23.78 HumanEval pass@1. The original model card was not re-read in this pass, so context and license stay empty. Collected 2026-10-15.", "what_it_is_for": "Code generation model that PanGu-Coder2 compares against.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-coder/pangu-coder2": { "id": "huawei/pangu-coder/pangu-coder2", "name": "PanGu-Coder2", "maker": "huawei", "family": "huawei/pangu-coder", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-07", "context_length": null, "max_output_tokens": null, "parameters": "15B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PanGu-Coder2 is Huawei Cloud's 15B code model from July 2023. The paper reports HumanEval pass@1 of 62.20 in one table and 61.64 in another. Both numbers are stored. Collected 2026-10-15.", "what_it_is_for": "15B code model trained with the paper's ranking-feedback method.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-cv/pangu-cv-anomaly-v3": { "id": "huawei/pangu-cv/pangu-cv-anomaly-v3", "name": "Pangu CV anomaly detection V3", "maker": "huawei", "family": "huawei/pangu-cv", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu CV anomaly detection V3 (Pangu-CV-\u5f02\u5e38\u68c0\u6d4b-V3) is a current Huawei Cloud Pangu CV model for \u5f02\u5e38\u68c0\u6d4b. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00 under the general industry column. Collected 2026-10-15.", "what_it_is_for": "Finds points, events, or observations that do not match the expected pattern.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-cv/pangu-cv-image-classify-v2": { "id": "huawei/pangu-cv/pangu-cv-image-classify-v2", "name": "Pangu CV image classification V2", "maker": "huawei", "family": "huawei/pangu-cv", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu CV image classification V2 (Pangu-CV-\u56fe\u50cf\u5206\u7c7b-V2) is a current Huawei Cloud Pangu CV model for \u56fe\u50cf\u5206\u7c7b. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00 under the general industry column. Collected 2026-10-15.", "what_it_is_for": "Classification head on the unified Pangu pretrained vision backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-cv/pangu-cv-instance-seg-v1": { "id": "huawei/pangu-cv/pangu-cv-instance-seg-v1", "name": "Pangu CV instance segmentation V1", "maker": "huawei", "family": "huawei/pangu-cv", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "masks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu CV instance segmentation V1 (Pangu-CV-\u5b9e\u4f8b\u5206\u5272-V1) is a current Huawei Cloud Pangu CV model for \u5b9e\u4f8b\u5206\u5272. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00 under the general industry column. Collected 2026-10-15.", "what_it_is_for": "Instance segmentation head on the unified pretrained backbone. The page's wording also says a classification head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-cv/pangu-cv-interactive-detect-v3": { "id": "huawei/pangu-cv/pangu-cv-interactive-detect-v3", "name": "Pangu CV interactive detection V3", "maker": "huawei", "family": "huawei/pangu-cv", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu CV interactive detection V3 (Pangu-CV-\u89c6\u89c9\u4ea4\u4e92\u68c0\u6d4b-V3) is a current Huawei Cloud Pangu CV model for \u89c6\u89c9\u4ea4\u4e92\u68c0\u6d4b. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00 under the general industry column. Collected 2026-10-15.", "what_it_is_for": "Detects targets from visual prompts or text prompts, and can be fine-tuned on scene data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-cv/pangu-cv-object-detect-n-v2": { "id": "huawei/pangu-cv/pangu-cv-object-detect-n-v2", "name": "Pangu CV object detection N V2", "maker": "huawei", "family": "huawei/pangu-cv", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "\u5343\u4e07\u5230\u4ebf\u7ea7", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu CV object detection N V2 (Pangu-CV-\u7269\u4f53\u68c0\u6d4b-N-V2) is a current Huawei Cloud Pangu CV model for \u7269\u4f53\u68c0\u6d4b. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00 under the general industry column. Collected 2026-10-15.", "what_it_is_for": "Object detector in the tens-of-millions to hundreds-of-millions parameter range. Supports model extraction. Pretrained on Huawei data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-cv/pangu-cv-object-detect-s-v2": { "id": "huawei/pangu-cv/pangu-cv-object-detect-s-v2", "name": "Pangu CV object detection S V2", "maker": "huawei", "family": "huawei/pangu-cv", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu CV object detection S V2 (Pangu-CV-\u7269\u4f53\u68c0\u6d4b-S-V2) is a current Huawei Cloud Pangu CV model for \u7269\u4f53\u68c0\u6d4b. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00 under the general industry column. Collected 2026-10-15.", "what_it_is_for": "Small-parameter object detector pretrained on Huawei's own image data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-cv/pangu-cv-object-detect-s-v3": { "id": "huawei/pangu-cv/pangu-cv-object-detect-s-v3", "name": "Pangu CV object detection S V3", "maker": "huawei", "family": "huawei/pangu-cv", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu CV object detection S V3 (Pangu-CV-\u7269\u4f53\u68c0\u6d4b-S-V3) is a current Huawei Cloud Pangu CV model for \u7269\u4f53\u68c0\u6d4b. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00 under the general industry column. Collected 2026-10-15.", "what_it_is_for": "Optimized for accuracy, latency, and small objects. The page calls it a high price-performance detector.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-cv/pangu-cv-open-set-classify-v3": { "id": "huawei/pangu-cv/pangu-cv-open-set-classify-v3", "name": "Pangu CV open-set classification V3", "maker": "huawei", "family": "huawei/pangu-cv", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu CV open-set classification V3 (Pangu-CV-\u5f00\u96c6\u5206\u7c7b-V3) is a current Huawei Cloud Pangu CV model for \u5f00\u96c6\u5206\u7c7b. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00 under the general industry column. Collected 2026-10-15.", "what_it_is_for": "Assigns categories from image features, including categories beyond a closed training set. Edge deploy is not supported.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-cv/pangu-cv-open-vocab-detect-v2": { "id": "huawei/pangu-cv/pangu-cv-open-vocab-detect-v2", "name": "Pangu CV open-vocabulary detection V2", "maker": "huawei", "family": "huawei/pangu-cv", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu CV open-vocabulary detection V2 (Pangu-CV-\u4e07\u7269\u68c0\u6d4b-V2) is a current Huawei Cloud Pangu CV model for \u4e07\u7269\u68c0\u6d4b. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00 under the general industry column. Collected 2026-10-15.", "what_it_is_for": "Detects known and unknown categories and is described as able to respond to new categories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-cv/pangu-cv-open-vocab-seg-v2": { "id": "huawei/pangu-cv/pangu-cv-open-vocab-seg-v2", "name": "Pangu CV open-vocabulary segmentation V2", "maker": "huawei", "family": "huawei/pangu-cv", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "masks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu CV open-vocabulary segmentation V2 (Pangu-CV-\u4e07\u7269\u5206\u5272-V2) is a current Huawei Cloud Pangu CV model for \u4e07\u7269\u5206\u5272. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00 under the general industry column. Collected 2026-10-15.", "what_it_is_for": "Segments known and unknown categories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-cv/pangu-cv-semantic-seg-v2": { "id": "huawei/pangu-cv/pangu-cv-semantic-seg-v2", "name": "Pangu CV semantic segmentation V2", "maker": "huawei", "family": "huawei/pangu-cv", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "masks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu CV semantic segmentation V2 (Pangu-CV-\u8bed\u4e49\u5206\u5272-V2) is a current Huawei Cloud Pangu CV model for \u8bed\u4e49\u5206\u5272. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00 under the general industry column. Collected 2026-10-15.", "what_it_is_for": "Segmentation head on the unified Pangu pretrained vision backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-cv/pangu-cv-tracking-v3": { "id": "huawei/pangu-cv/pangu-cv-tracking-v3", "name": "Pangu CV tracking V3", "maker": "huawei", "family": "huawei/pangu-cv", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video" ], "output": [ "tracks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu CV tracking V3 (Pangu-CV-\u76ee\u6807\u8ddf\u8e2a-V3) is a current Huawei Cloud Pangu CV model for \u76ee\u6807\u8ddf\u8e2a. The 2026-10-14 spec lists it in \u897f\u5357-\u8d35\u9633\u4e00 under the general industry column. Collected 2026-10-15.", "what_it_is_for": "Detects, extracts, recognizes, and tracks an object of interest in video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-industry/pangu-finance-nlp-reasoner-n2-128k": { "id": "huawei/pangu-industry/pangu-finance-nlp-reasoner-n2-128k", "name": "Pangu-Finance-NLP-Reasoner-N2-128K", "maker": "huawei", "family": "huawei/pangu-industry", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-06", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Finance-NLP-Reasoner-N2-128K-3.0.1.1 is Huawei Cloud's June 2025 finance reasoner. The spec lists 128K inference on 4 units and does not mark training. Collected 2026-10-15.", "what_it_is_for": "Financial slow-thinking NLP at 128K inference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-industry/pangu-medical-nlp-n2-32k": { "id": "huawei/pangu-industry/pangu-medical-nlp-n2-32k", "name": "Pangu-Medical-NLP-N2-32K", "maker": "huawei", "family": "huawei/pangu-industry", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Medical-NLP-N2-32K is Huawei Cloud's medical N2 line. The spec lists a March 2025 build and a later fused fast/slow build, both at 32K, with INT8 called out on the earlier builds. Collected 2026-10-15.", "what_it_is_for": "Medical NLP for reasoning, information extraction, and text generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-industry/pangu-medical-nlp-n2-reasoner-32k": { "id": "huawei/pangu-industry/pangu-medical-nlp-n2-reasoner-32k", "name": "Pangu-Medical-NLP-N2-Reasoner-32K", "maker": "huawei", "family": "huawei/pangu-industry", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-06", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Medical-NLP-N2-Reasoner-32K-3.0.0.1 is Huawei Cloud's June 2025 medical reasoner. It trains and infers at 32K, with INT8 quantization called out. Collected 2026-10-15.", "what_it_is_for": "Slow-thinking medical NLP at 32K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-mm/pangu-mm-m1-txt2img": { "id": "huawei/pangu-mm/pangu-mm-m1-txt2img", "name": "Pangu-MM-M1-Txt2Img", "maker": "huawei", "family": "huawei/pangu-mm", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-MM-M1-Txt2Img-1.0.0 is Huawei Cloud's April 2025 text-to-image model. It generates 1024 by 1024 images in styles such as photoreal, oil painting, and anime, on 8 inference units. Collected 2026-10-15.", "what_it_is_for": "Text-to-image at 1024 by 1024, with several named styles.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-mm/pangu-mm-m2-aigvideo": { "id": "huawei/pangu-mm/pangu-mm-m2-aigvideo", "name": "Pangu-MM-M2-AIGVideo", "maker": "huawei", "family": "huawei/pangu-mm", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-MM-M2-AIGVideo is Huawei Cloud's April 2025 image-to-video model, with 5-second generation and 96-frame continuation. The spec table says 1.0.0 and the operations table says 1.0.RC2. Collected 2026-10-15.", "what_it_is_for": "Image-to-video and short video continuation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-mm/pangu-mm-m2-img2txt-16k": { "id": "huawei/pangu-mm/pangu-mm-m2-img2txt-16k", "name": "Pangu-MM-M2-Img2Txt-16K", "maker": "huawei", "family": "huawei/pangu-mm", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": 16384, "max_output_tokens": null, "parameters": "\u767e\u4ebf\u7ea7", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-MM-M2-Img2Txt-16K-V5.0.4.2 is Huawei Cloud's 10-billion-parameter-class multimodal understanding model. It takes images and text and supports pretraining and fine-tuning at 16K. The HTML spec and the October 2026 PDF disagree on whether it shipped in June 2025 or February 2026. Collected 2026-10-15.", "what_it_is_for": "Image understanding and image-text QA at 16K, with pretraining and fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-mm/pangu-mm-m2-text2video": { "id": "huawei/pangu-mm/pangu-mm-m2-text2video", "name": "Pangu-MM-M2-Text2Video", "maker": "huawei", "family": "huawei/pangu-mm", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-MM-M2-Text2Video-1.0.0 is Huawei Cloud's April 2025 text-to-video model. It generates 5-second clips at five aspect ratios and needs 8 inference units. Collected 2026-10-15.", "what_it_is_for": "Text-to-video generation, 5 seconds, five aspect ratios.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-nlp/pangu-nky-nlp-n2-128k": { "id": "huawei/pangu-nlp/pangu-nky-nlp-n2-128k", "name": "Pangu-NKY-NLP-N2-128K", "maker": "huawei", "family": "huawei/pangu-nlp", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2026-04", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-NKY-NLP-N2-128K V3.1.35.1 is an April 2026 Huawei Cloud fast-thinking NLP model. It supports continued pretraining, SFT, and LoRA at 128K. Collected 2026-10-15.", "what_it_is_for": "April 2026 fast-thinking NKY N2 model at 128K, with continued pretraining and SFT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-nlp/pangu-nky-nlp-n2-reasoner-128k": { "id": "huawei/pangu-nlp/pangu-nky-nlp-n2-reasoner-128k", "name": "Pangu-NKY-NLP-N2-Reasoner-128K", "maker": "huawei", "family": "huawei/pangu-nlp", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2026-04", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-NKY-NLP-N2-Reasoner-128K V3.0.1.1.1 is the April 2026 slow-thinking pair of the NKY N2 line. Huawei Cloud lists continued pretraining, SFT, and LoRA at 128K. Collected 2026-10-15.", "what_it_is_for": "April 2026 slow-thinking NKY N2 reasoner at 128K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-nlp/pangu-nlp-n1-128k": { "id": "huawei/pangu-nlp/pangu-nlp-n1-128k", "name": "Pangu-NLP-N1-128K", "maker": "huawei", "family": "huawei/pangu-nlp", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-NLP-N1-128K is the long-context fast-thinking sibling of Pangu N1 on Huawei Cloud. Version 3.2.36 is listed for 128K inference in \u897f\u5357-\u8d35\u9633\u4e00. Collected 2026-10-15.", "what_it_is_for": "Fast-thinking N1 NLP model for 128K inference, without training marked on the capability table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-nlp/pangu-nlp-n1-32k": { "id": "huawei/pangu-nlp/pangu-nlp-n1-32k", "name": "Pangu-NLP-N1-32K", "maker": "huawei", "family": "huawei/pangu-nlp", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-NLP-N1-32K is a Huawei Cloud fast-thinking NLP SKU in the Pangu N1 series. The 2026-09-14 spec page lists version 3.2.36 as active in \u897f\u5357-\u8d35\u9633\u4e00, with 32K fine-tuning and inference. Collected 2026-10-15.", "what_it_is_for": "Fast-thinking Chinese NLP model for fine-tuning and inference at a 32K sequence length on ModelArts Studio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-nlp/pangu-nlp-n1-reasoner-128k": { "id": "huawei/pangu-nlp/pangu-nlp-n1-reasoner-128k", "name": "Pangu-NLP-N1-Reasoner-128K", "maker": "huawei", "family": "huawei/pangu-nlp", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-NLP-N1-Reasoner-128K is Huawei Cloud's N1 slow-thinking NLP model. Version 3.0.1.1 is the first ModelArts Studio listing, at 128K, in \u897f\u5357-\u8d35\u9633\u4e00. Collected 2026-10-15.", "what_it_is_for": "Slow-thinking N1 reasoner for 128K training and inference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-nlp/pangu-nlp-n2-128k": { "id": "huawei/pangu-nlp/pangu-nlp-n2-128k", "name": "Pangu-NLP-N2-128K", "maker": "huawei", "family": "huawei/pangu-nlp", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-NLP-N2-128K is the long-context N2 line on Huawei Cloud. Three builds remain on the 2026-09-14 spec. The 5.0.3.2 row is fused fast/slow thinking for 128K inference. The 5.0.1.1 description contradicts its own 128K column. Collected 2026-10-15.", "what_it_is_for": "N2 NLP at a stated 128K sequence length. Training is not marked for these rows.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-nlp/pangu-nlp-n2-32k": { "id": "huawei/pangu-nlp/pangu-nlp-n2-32k", "name": "Pangu-NLP-N2-32K", "maker": "huawei", "family": "huawei/pangu-nlp", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-NLP-N2-32K is a Huawei Cloud N2 NLP line. The 2026-09-14 spec still lists four builds. The newest, 5.0.3.2, fuses fast and slow thinking and is described as stronger at tools and lower at RAG hallucination. Collected 2026-10-15.", "what_it_is_for": "N2 NLP line at 32K, including fused fast and slow thinking on the newest build.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-nlp/pangu-nlp-n2-reasoner-128k": { "id": "huawei/pangu-nlp/pangu-nlp-n2-reasoner-128k", "name": "Pangu-NLP-N2-Reasoner-128K", "maker": "huawei", "family": "huawei/pangu-nlp", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-NLP-N2-Reasoner-128K is the long-context N2 reasoner on Huawei Cloud. Version 5.0.0.1 is listed for pretraining, full fine-tuning, and LoRA at 128K. Collected 2026-10-15.", "what_it_is_for": "Slow-thinking N2 reasoner with 128K training and inference support marked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-nlp/pangu-nlp-n2-reasoner-32k": { "id": "huawei/pangu-nlp/pangu-nlp-n2-reasoner-32k", "name": "Pangu-NLP-N2-Reasoner-32K", "maker": "huawei", "family": "huawei/pangu-nlp", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-NLP-N2-Reasoner-32K is Huawei Cloud's N2 slow-thinking model at 32K. The capability table and the resource table use different version ids, 3.0.0.1 and 3.0.1.1. Collected 2026-10-15.", "what_it_is_for": "Slow-thinking N2 reasoner at 32K, with pretraining, LoRA, and GRPO marked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-nlp/pangu-nlp-n4-32k": { "id": "huawei/pangu-nlp/pangu-nlp-n4-32k", "name": "Pangu-NLP-N4-32K", "maker": "huawei", "family": "huawei/pangu-nlp", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-01", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-NLP-N4-32K-2.5.35 is a January 2025 Huawei Cloud N4 build. The description limits it to 32K inference on 8 units, while the capability table also marks training and compression. Collected 2026-10-15.", "what_it_is_for": "N4 NLP model documented for 32K inference deployment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-nlp/pangu-nlp-n4-4k": { "id": "huawei/pangu-nlp/pangu-nlp-n4-4k", "name": "Pangu-NLP-N4-4K", "maker": "huawei", "family": "huawei/pangu-nlp", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-03", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-NLP-N4-4K-3.2.36 is a March 2025 Huawei Cloud N4 NLP build. It trains and infers at 4K. Pretraining, full fine-tuning, and DPO start at 64 training units. Collected 2026-10-15.", "what_it_is_for": "Largest listed N-series NLP tier at a short 4K context, with pretraining and DPO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-nlp/pangu-nlp-n4-reasoner-128k": { "id": "huawei/pangu-nlp/pangu-nlp-n4-reasoner-128k", "name": "Pangu-NLP-N4-Reasoner-128K", "maker": "huawei", "family": "huawei/pangu-nlp", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-06", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-NLP-N4-Reasoner-128K-3.0.1.2 is the June 2025 long-context N4 reasoner. Huawei Cloud lists it for 128K inference on 16 units, not for training. Collected 2026-10-15.", "what_it_is_for": "Slow-thinking N4 reasoner for 128K inference only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-nlp/pangu-nlp-n4-reasoner-32k": { "id": "huawei/pangu-nlp/pangu-nlp-n4-reasoner-32k", "name": "Pangu-NLP-N4-Reasoner-32K", "maker": "huawei", "family": "huawei/pangu-nlp", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2025-06", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-NLP-N4-Reasoner-32K-3.0.1.2 is a June 2025 Huawei Cloud N4 reasoner. It trains and infers at 32K. Full fine-tuning starts at 96 training units. Collected 2026-10-15.", "what_it_is_for": "Slow-thinking N4 reasoner for 32K training and inference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-pi/pangu-pi-1-5b-pro": { "id": "huawei/pangu-pi/pangu-pi-1-5b-pro", "name": "PanGu-\u03c0-1.5B Pro", "maker": "huawei", "family": "huawei/pangu-pi", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-02", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Same tiny-model recipe as 1B Pro, at 1.5B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PanGu-\u03c0-1.5B Pro is the larger tiny model in Huawei Noah's PanGu-\u03c0 Pro paper. It is trained on the same 1.6T multilingual corpus as the 1B Pro. The abstract says it beats some larger published models. Individual scores were not copied. Collected 2026-10-15.", "what_it_is_for": "1.5B tiny language model in the PanGu-\u03c0 Pro pair.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-pi/pangu-pi-1b": { "id": "huawei/pangu-pi/pangu-pi-1b", "name": "PanGu-\u03c0-1B", "maker": "huawei", "family": "huawei/pangu-pi", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-12", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Decoder with series-informed activation and augmented shortcuts. Paper table: dimension 2048, 16 heads, 12 layers.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PanGu-\u03c0-1B is a December 2023 Huawei Noah 1B language model. The paper gives it 12 layers, hidden size 2048, and 16 heads, and says it is competitive on accuracy and efficiency. Weights were not found. Collected 2026-10-15.", "what_it_is_for": "1B research model used to show the PanGu-\u03c0 nonlinearity changes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-pi/pangu-pi-1b-pro": { "id": "huawei/pangu-pi/pangu-pi-1b-pro", "name": "PanGu-\u03c0-1B Pro", "maker": "huawei", "family": "huawei/pangu-pi", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-02", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Tiny-model variant. Abstract cites tokenizer compression, architecture tweaks, parameter inheritance, and multi-round training.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PanGu-\u03c0-1B Pro is Huawei Noah's tiny language model from the February 2024 PanGu-\u03c0 Pro paper, revised through April 2025. It is trained on 1.6T multilingual tokens. The abstract reports an average benchmark gain of 8.87 versus its own baseline, without naming one score. Collected 2026-10-15.", "what_it_is_for": "1B tiny language model aimed at tighter device budgets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-pi/pangu-pi-7b": { "id": "huawei/pangu-pi/pangu-pi-7b", "name": "PanGu-\u03c0-7B", "maker": "huawei", "family": "huawei/pangu-pi", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-12", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Paper table: dimension 4096, 32 heads, 29 layers. Series-informed activation and augmented shortcuts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PanGu-\u03c0-7B is Huawei Noah's December 2023 7B model with the PanGu-\u03c0 architecture. The paper table lists 29 layers, hidden size 4096, and 32 heads, and says it matches comparison models with about a 10 percent inference speed-up. Collected 2026-10-15.", "what_it_is_for": "7B research model, later used as the base of the YunShan finance and law model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-pi/yunshan": { "id": "huawei/pangu-pi/yunshan", "name": "YunShan", "maker": "huawei", "family": "huawei/pangu-pi", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The PanGu-\u03c0 paper says YunShan is PanGu-\u03c0-7B deployed for finance and law. It does not give a separate parameter table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YunShan is the finance and law model named in Huawei Noah's PanGu-\u03c0 paper. The paper says it is PanGu-\u03c0-7B deployed in those domains and that it beat similar-scale models on the benchmarks the authors ran. No later product page was found. Collected 2026-10-15.", "what_it_is_for": "Finance and law model built by deploying PanGu-\u03c0-7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-predict/pangu-predict-anom-table": { "id": "huawei/pangu-predict/pangu-predict-anom-table", "name": "Pangu-Predict-Anom-Table-2.0.2", "maker": "huawei", "family": "huawei/pangu-predict", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Triplet-transformer unified architecture is the family claim on the English product page, not a per-SKU diagram.", "modalities": { "input": [ "tabular" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Predict-Anom-Table-2.0.2 is a named Huawei Cloud Pangu prediction model. Tabular anomaly detection by classifying normal versus abnormal, with a confidence score.. The spec page was updated 2026-10-14. Collected 2026-10-15.", "what_it_is_for": "Tabular anomaly detection by classifying normal versus abnormal, with a confidence score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-predict/pangu-predict-anom-ts": { "id": "huawei/pangu-predict/pangu-predict-anom-ts", "name": "Pangu-Predict-Anom-TS-3.0.0", "maker": "huawei", "family": "huawei/pangu-predict", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Triplet-transformer unified architecture is the family claim on the English product page, not a per-SKU diagram.", "modalities": { "input": [ "tabular" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Predict-Anom-TS-3.0.0 is a named Huawei Cloud Pangu prediction model. Time-series anomaly detection. Pretraining on normal history, then adapters for thresholds or labeled binary detection.. The spec page was updated 2026-10-14. Collected 2026-10-15.", "what_it_is_for": "Time-series anomaly detection. Pretraining on normal history, then adapters for thresholds or labeled binary detection.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-predict/pangu-predict-anom-ts-v2": { "id": "huawei/pangu-predict/pangu-predict-anom-ts-v2", "name": "Pangu-Predict-Anom-TS-3.1.0", "maker": "huawei", "family": "huawei/pangu-predict", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Triplet-transformer unified architecture is the family claim on the English product page, not a per-SKU diagram.", "modalities": { "input": [ "tabular" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Predict-Anom-TS-3.1.0 is a named Huawei Cloud Pangu prediction model. Zero-shot anomaly scores on the V2 time-series base. Point, interval, trend, and periodic anomalies. No fine-tune.. The spec page was updated 2026-10-14. Collected 2026-10-15.", "what_it_is_for": "Zero-shot anomaly scores on the V2 time-series base. Point, interval, trend, and periodic anomalies. No fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-predict/pangu-predict-cla-table": { "id": "huawei/pangu-predict/pangu-predict-cla-table", "name": "Pangu-Predict-Cla-Table", "maker": "huawei", "family": "huawei/pangu-predict", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Triplet-transformer unified architecture is the family claim on the English product page, not a per-SKU diagram.", "modalities": { "input": [ "tabular" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Predict-Cla-Table is a named Huawei Cloud Pangu prediction model. Tabular classification. Versions differ by base model.. The spec page was updated 2026-10-14. Collected 2026-10-15.", "what_it_is_for": "Tabular classification. Versions differ by base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-predict/pangu-predict-cla-ts": { "id": "huawei/pangu-predict/pangu-predict-cla-ts", "name": "Pangu-Predict-Cla-TS-3.0.1", "maker": "huawei", "family": "huawei/pangu-predict", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Triplet-transformer unified architecture is the family claim on the English product page, not a per-SKU diagram.", "modalities": { "input": [ "tabular" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Predict-Cla-TS-3.0.1 is a named Huawei Cloud Pangu prediction model. Time-series classification on a pretrained forecast base. Discrete target states. Fine-tune, covariates, incremental training. Cloud and edge.. The spec page was updated 2026-10-14. Collected 2026-10-15.", "what_it_is_for": "Time-series classification on a pretrained forecast base. Discrete target states. Fine-tune, covariates, incremental training. Cloud and edge.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-predict/pangu-predict-fewshot-table": { "id": "huawei/pangu-predict/pangu-predict-fewshot-table", "name": "Pangu-Predict-FewShot-Table-3.0.4", "maker": "huawei", "family": "huawei/pangu-predict", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Triplet-transformer unified architecture is the family claim on the English product page, not a per-SKU diagram.", "modalities": { "input": [ "tabular" ], "output": [ "numbers" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Predict-FewShot-Table-3.0.4 is a named Huawei Cloud Pangu prediction model. Direct prediction for small tables under 20,000 rows. Regression and classification. Tabular-regression fine-tune is open.. The spec page was updated 2026-10-14. Collected 2026-10-15.", "what_it_is_for": "Direct prediction for small tables under 20,000 rows. Regression and classification. Tabular-regression fine-tune is open.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-predict/pangu-predict-industry-demand": { "id": "huawei/pangu-predict/pangu-predict-industry-demand", "name": "Pangu-Predict-Industry-Demand", "maker": "huawei", "family": "huawei/pangu-predict", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Triplet-transformer unified architecture is the family claim on the English product page, not a per-SKU diagram.", "modalities": { "input": [ "tabular" ], "output": [ "numbers" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Predict-Industry-Demand is a named Huawei Cloud Pangu prediction model. Hierarchical supply-chain demand forecasting.. The spec page was updated 2026-10-14. Collected 2026-10-15.", "what_it_is_for": "Hierarchical supply-chain demand forecasting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-predict/pangu-predict-industry-heating": { "id": "huawei/pangu-predict/pangu-predict-industry-heating", "name": "Pangu-Predict-Industry-Heating-V1.0.0", "maker": "huawei", "family": "huawei/pangu-predict", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Triplet-transformer unified architecture is the family claim on the English product page, not a per-SKU diagram.", "modalities": { "input": [ "tabular" ], "output": [ "numbers" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Predict-Industry-Heating-V1.0.0 is a named Huawei Cloud Pangu prediction model. Heating-industry model. Present on the operations and resource tables, absent from spec table 1.. The spec page was updated 2026-10-14. Collected 2026-10-15.", "what_it_is_for": "Heating-industry model. Present on the operations and resource tables, absent from spec table 1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-predict/pangu-predict-industry-traffic": { "id": "huawei/pangu-predict/pangu-predict-industry-traffic", "name": "Pangu-Predict-Industry-Traffic-V3.0.0", "maker": "huawei", "family": "huawei/pangu-predict", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Triplet-transformer unified architecture is the family claim on the English product page, not a per-SKU diagram.", "modalities": { "input": [ "tabular" ], "output": [ "numbers" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Predict-Industry-Traffic-V3.0.0 is a named Huawei Cloud Pangu prediction model. Highway flow and speed regression. Multi-site, multi-target, multi-scale.. The spec page was updated 2026-10-14. Collected 2026-10-15.", "what_it_is_for": "Highway flow and speed regression. Multi-site, multi-target, multi-scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-predict/pangu-predict-reg-table": { "id": "huawei/pangu-predict/pangu-predict-reg-table", "name": "Pangu-Predict-Reg-Table", "maker": "huawei", "family": "huawei/pangu-predict", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Triplet-transformer unified architecture is the family claim on the English product page, not a per-SKU diagram.", "modalities": { "input": [ "tabular" ], "output": [ "numbers" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Predict-Reg-Table is a named Huawei Cloud Pangu prediction model. Tabular regression. Versions differ by base model.. The spec page was updated 2026-10-14. Collected 2026-10-15.", "what_it_is_for": "Tabular regression. Versions differ by base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-predict/pangu-predict-reg-ts": { "id": "huawei/pangu-predict/pangu-predict-reg-ts", "name": "Pangu-Predict-Reg-TS-3.1.0", "maker": "huawei", "family": "huawei/pangu-predict", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Triplet-transformer unified architecture is the family claim on the English product page, not a per-SKU diagram.", "modalities": { "input": [ "tabular" ], "output": [ "numbers" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Predict-Reg-TS-3.1.0 is a named Huawei Cloud Pangu prediction model. Time-series regression of continuous values, up to 128 future points. Multi-target, incremental training, covariates.. The spec page was updated 2026-10-14. Collected 2026-10-15.", "what_it_is_for": "Time-series regression of continuous values, up to 128 future points. Multi-target, incremental training, covariates.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-predict/pangu-predict-reg-zeroshot-ts": { "id": "huawei/pangu-predict/pangu-predict-reg-zeroshot-ts", "name": "Pangu-Predict-Reg-ZeroShot-TS-3.0.0", "maker": "huawei", "family": "huawei/pangu-predict", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Triplet-transformer unified architecture is the family claim on the English product page, not a per-SKU diagram.", "modalities": { "input": [ "tabular" ], "output": [ "numbers" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Predict-Reg-ZeroShot-TS-3.0.0 is a named Huawei Cloud Pangu prediction model. Zero-shot time-series regression. Quantiles, multi-channel, categorical columns, dynamic covariates. Up to 1024 future points. No fine-tune.. The spec page was updated 2026-10-14. Collected 2026-10-15.", "what_it_is_for": "Zero-shot time-series regression. Quantiles, multi-channel, categorical columns, dynamic covariates. Up to 1024 future points. No fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-predict/pangu-predict-structure": { "id": "huawei/pangu-predict/pangu-predict-structure", "name": "Pangu-Predict-StructurePredict-1.1.0", "maker": "huawei", "family": "huawei/pangu-predict", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Triplet-transformer unified architecture is the family claim on the English product page, not a per-SKU diagram.", "modalities": { "input": [ "tabular" ], "output": [ "numbers" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-Predict-StructurePredict-1.1.0 is a named Huawei Cloud Pangu prediction model. Structured tabular classification and regression. Region on the spec table is \u4e0a\u6d77\u4e00 only.. The spec page was updated 2026-10-14. Collected 2026-10-15.", "what_it_is_for": "Structured tabular classification and regression. Region on the spec table is \u4e0a\u6d77\u4e00 only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-pro/pangu-embeddingrank-zh": { "id": "huawei/pangu-pro/pangu-embeddingrank-zh", "name": "Pangu-EmbeddingRank-zh", "maker": "huawei", "family": "huawei/pangu-pro", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2024-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-EmbeddingRank-zh-2.0.3 is Huawei Cloud's December 2024 Chinese embedding and rerank model for RAG. One inference unit serves up to 6 QPS. Collected 2026-10-15.", "what_it_is_for": "Chinese document vectors and reranking for RAG.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-pro/pangu-searchplan-zh-en": { "id": "huawei/pangu-pro/pangu-searchplan-zh-en", "name": "Pangu-SearchPlan-ZH_EN", "maker": "huawei", "family": "huawei/pangu-pro", "status": "active", "confidence": "high", "weights": "closed", "license": "Huawei Cloud commercial service. A public weights license is not published on the spec page.", "release_date": "2024-12", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pangu-SearchPlan-ZH_EN-7.1.5.1 is Huawei Cloud's December 2024 search-planning model for Chinese and English RAG. It infers at 4K on one unit, up to 10 QPS. Collected 2026-10-15.", "what_it_is_for": "Build retrieval queries for RAG: intent, rewrite, decomposition, and time extraction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/pangu-sigma/pangu-sigma": { "id": "huawei/pangu-sigma/pangu-sigma", "name": "PanGu-\u03a3", "maker": "huawei", "family": "huawei/pangu-sigma", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-03", "context_length": null, "max_output_tokens": null, "parameters": "1.085T", "architecture": "Sparse Transformer. Random Routed Experts on parameters inherited from PanGu-\u03b1. Expert Computation and Storage Separation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PanGu-\u03a3 is Huawei's March 2023 sparse language model with 1.085 trillion parameters. The paper says it inherits PanGu-\u03b1 parameters, adds Random Routed Experts, and was trained on 329 billion tokens with 512 Ascend 910 accelerators. No public weight page was found. Collected 2026-10-15.", "what_it_is_for": "Trillion-parameter sparse language model for Chinese and multilingual zero-shot tasks, plus dialogue, QA, translation, and code after fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/tinybert/tinybert": { "id": "huawei/tinybert/tinybert", "name": "TinyBERT", "maker": "huawei", "family": "huawei/tinybert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2019-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Distilled BERT. The Noah index says 7.5 times smaller and 9.4 times faster at inference than the teacher it compares. Layer counts of the released students were not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyBERT is Huawei Noah's distilled BERT, published in September 2019. The repository index says it is 7.5 times smaller and 9.4 times faster at inference than the compared BERT. A MindSpore port is in the same repo. Collected 2026-10-15.", "what_it_is_for": "Smaller, faster BERT for language understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huawei/wukong/wukong": { "id": "huawei/wukong/wukong", "name": "WuKong", "maker": "huawei", "family": "huawei/wukong", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WuKong is Huawei Noah's Chinese vision-language release: a large dataset plus benchmarking models trained on it. The repository index names the project. Separate checkpoint specs were not read on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Chinese vision-language benchmarking models trained on the WuKong dataset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/alignment/mistral-7b-anthropic": { "id": "huggingface/alignment/mistral-7b-anthropic", "name": "Mistral-7B-Anthropic style", "maker": "huggingface", "family": "huggingface/alignment", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mistral chat fine-tune published by HuggingFaceH4. Hub base model is HuggingFaceH4/mistral-7b-cai. Datasets: ultrafeedback_binarized_fixed and cai-conversation-harmless. Parameter total and context were not read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral-7B-Anthropic is a January 29, 2024 Hugging Face H4 research checkpoint, collected 2026-10-15. The name describes the alignment style, not an Anthropic product. Weights are Apache-2.0.", "what_it_is_for": "Hugging Face H4 experiment that applies a constitutional-AI-style conversation mixture to Mistral-7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/alignment/mistral-7b-grok": { "id": "huggingface/alignment/mistral-7b-grok", "name": "Mistral-7B-Grok style", "maker": "huggingface", "family": "huggingface/alignment", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mistral chat fine-tune published by HuggingFaceH4. Base model mistralai/Mistral-7B-v0.1. Datasets named on the Hub record: HuggingFaceH4/grok-conversation-harmless and HuggingFaceH4/ultrachat_200k. Parameter total and context were not re-read. The Mistral-7B config used by Zephyr is 7,241,732,096 parameters and 32,768 positions; this dossier does not assume those numbers without a config read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral-7B-Grok is a January 29, 2024 Hugging Face H4 research chat checkpoint, collected 2026-10-15. It is a named public model, separate from Zephyr. Specs that were not on the API record are left blank.", "what_it_is_for": "Hugging Face H4 Mistral-7B fine-tune aimed at a Grok-like conversational style, using a harmless-conversation set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/cosmo/cosmo-1b": { "id": "huggingface/cosmo/cosmo-1b", "name": "Cosmo-1B", "maker": "huggingface", "family": "huggingface/cosmo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-19", "context_length": 2048, "max_output_tokens": null, "parameters": "1741785088", "architecture": "Decoder-only Llama-architecture transformer (multi-head attention). Safetensors parameter total 1741785088. 24 layers, hidden size 2048, 16 query heads, 16 key/value heads. Intermediate size 8192. rope_theta 10000. Checkpoint max_position_embeddings 2048. Early HuggingFaceTB Llama-architecture model trained with the Cosmopedia synthetic textbook dataset. The repo name says 1B. The safetensors total is 1,741,785,088.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cosmo-1B is an early Hugging Face Smol Models Research language model, collected 2026-10-15. The Hub repo dates from 2024-02-19. It remains downloadable. SmolLM later replaced this line as the named small-model family.", "what_it_is_for": "A pre-SmolLM Hugging Face text model trained on Cosmopedia synthetic textbooks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/data-classifier/finemath-classifier": { "id": "huggingface/data-classifier/finemath-classifier", "name": "FineMath classifier", "maker": "huggingface", "family": "huggingface/data-classifier", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "117654145", "architecture": "BERT text classifier. Safetensors total 117,654,145. Fine-tune of intfloat/multilingual-e5-small, released by HuggingFaceTB with the FineMath data work. Pipeline: text-classification.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The FineMath classifier is a Hugging Face data-quality model, collected 2026-10-15. It is a MIT-licensed BERT classifier used to build math pretraining data, not a chat model.", "what_it_is_for": "Hugging Face classifier that scores mathematical educational quality of web text for FineMath.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/data-classifier/python-edu-scorer": { "id": "huggingface/data-classifier/python-edu-scorer", "name": "Python-Edu scorer", "maker": "huggingface", "family": "huggingface/data-classifier", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "109483009", "architecture": "BERT text classifier. Safetensors total 109,483,009. Fine-tune of Snowflake/snowflake-arctic-embed-m. Released by HuggingFaceTB to score educational Python. Hub upload 2024-06-28.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Python-Edu scorer is a June 2024 Hugging Face classifier, collected 2026-10-15. It rates Python code quality for pretraining filters. Apache-2.0.", "what_it_is_for": "Hugging Face scorer for educational Python used in SmolLM data curation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/distil-whisper/distil-large-v2": { "id": "huggingface/distil-whisper/distil-large-v2", "name": "Distil-Whisper large-v2", "maker": "huggingface", "family": "huggingface/distil-whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "756220160", "architecture": "WhisperForConditionalGeneration distilled by Hugging Face from OpenAI Whisper. Safetensors total 756220160. config num_hidden_layers 32. English. The README describes keeping a reduced decoder so the model is faster. Decoder depth was verified only for v3.5 (2 decoder layers, 32 encoder layers), not this checkpoint.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Distil-Whisper large-v2 is a Hugging Face distilled Whisper model, collected 2026-10-15. It is MIT-licensed English speech recognition, smaller and faster than OpenAI Whisper large-v3 on the project table. Hugging Face trained the distillation. OpenAI trained the teacher.", "what_it_is_for": "Earlier large English distilled Whisper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/distil-whisper/distil-large-v3": { "id": "huggingface/distil-whisper/distil-large-v3", "name": "Distil-Whisper large-v3", "maker": "huggingface", "family": "huggingface/distil-whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "756405760", "architecture": "WhisperForConditionalGeneration distilled by Hugging Face from OpenAI Whisper. Safetensors total 756405760. config num_hidden_layers 32. English. The README describes keeping a reduced decoder so the model is faster. Decoder depth was verified only for v3.5 (2 decoder layers, 32 encoder layers), not this checkpoint.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Distil-Whisper large-v3 is a Hugging Face distilled Whisper model, collected 2026-10-15. It is MIT-licensed English speech recognition, smaller and faster than OpenAI Whisper large-v3 on the project table. Hugging Face trained the distillation. OpenAI trained the teacher.", "what_it_is_for": "The README's recommended general checkpoint before v3.5, compatible with Whisper libraries.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/distil-whisper/distil-large-v3-5": { "id": "huggingface/distil-whisper/distil-large-v3-5", "name": "Distil-Whisper large-v3.5", "maker": "huggingface", "family": "huggingface/distil-whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-03-25", "context_length": null, "max_output_tokens": null, "parameters": "756405760", "architecture": "WhisperForConditionalGeneration. Safetensors total 756,405,760, matching the card's 756M. Config read: encoder_layers 32, decoder_layers 2. English. The card says it was trained on over 4x more diverse public data (98k hours) than earlier Distil-Whisper models, with a patient teacher, a longer schedule, and SpecAugment. It is positioned as a drop-in replacement.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Distil-Whisper large-v3.5 is Hugging Face's March 2025 distilled Whisper checkpoint, collected 2026-10-15. It has 756,405,760 parameters, a 32-layer encoder and a 2-layer decoder, and an MIT license. It is the newest large model in the English Distil-Whisper family.", "what_it_is_for": "The 2025 English Distil-Whisper large model, trained to be a more robust drop-in than distil-large-v3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/distil-whisper/distil-medium-en": { "id": "huggingface/distil-whisper/distil-medium-en", "name": "Distil-Whisper medium.en", "maker": "huggingface", "family": "huggingface/distil-whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "394375168", "architecture": "WhisperForConditionalGeneration distilled by Hugging Face from OpenAI Whisper. Safetensors total 394375168. config num_hidden_layers 24. English. The README describes keeping a reduced decoder so the model is faster. Decoder depth was verified only for v3.5 (2 decoder layers, 32 encoder layers), not this checkpoint.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Distil-Whisper medium.en is a Hugging Face distilled Whisper model, collected 2026-10-15. It is MIT-licensed English speech recognition, smaller and faster than OpenAI Whisper large-v3 on the project table. Hugging Face trained the distillation. OpenAI trained the teacher.", "what_it_is_for": "English mid-size distilled Whisper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/distil-whisper/distil-small-en": { "id": "huggingface/distil-whisper/distil-small-en", "name": "Distil-Whisper small.en", "maker": "huggingface", "family": "huggingface/distil-whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "166132224", "architecture": "WhisperForConditionalGeneration distilled by Hugging Face from OpenAI Whisper. Safetensors total 166132224. config num_hidden_layers 12. English. The README describes keeping a reduced decoder so the model is faster. Decoder depth was verified only for v3.5 (2 decoder layers, 32 encoder layers), not this checkpoint.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Distil-Whisper small.en is a Hugging Face distilled Whisper model, collected 2026-10-15. It is MIT-licensed English speech recognition, smaller and faster than OpenAI Whisper large-v3 on the project table. Hugging Face trained the distillation. OpenAI trained the teacher.", "what_it_is_for": "English on-device size. The project README recommends it when memory is tight and says it stays within about 4 WER points of Whisper large-v3 on that table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/distilbert/distilbert-base-cased": { "id": "huggingface/distilbert/distilbert-base-cased", "name": "DistilBERT base cased", "maker": "huggingface", "family": "huggingface/distilbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "65812036", "architecture": "DistilBertForMaskedLM. Safetensors total 65,812,036. Same 512-token DistilBERT setup as the uncased checkpoint, cased WordPiece. Layer fields were read on the uncased config and not re-read here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DistilBERT base cased is a Hugging Face distillation model, collected 2026-10-15. Weights are Apache-2.0 and remain on the distilbert org. Cased English DistilBERT encoder.", "what_it_is_for": "Cased English DistilBERT encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/distilbert/distilbert-base-german-cased": { "id": "huggingface/distilbert/distilbert-base-german-cased", "name": "DistilBERT German cased", "maker": "huggingface", "family": "huggingface/distilbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "67431550", "architecture": "DistilBertForMaskedLM. Safetensors total 67,431,550. German cased fill-mask model published in the official distilbert org. Context 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DistilBERT German cased is a Hugging Face distillation model, collected 2026-10-15. Weights are Apache-2.0 and remain on the distilbert org. German cased DistilBERT published under the official distilbert org.", "what_it_is_for": "German cased DistilBERT published under the official distilbert org.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/distilbert/distilbert-base-multilingual-cased": { "id": "huggingface/distilbert/distilbert-base-multilingual-cased", "name": "DistilBERT multilingual cased", "maker": "huggingface", "family": "huggingface/distilbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "135445755", "architecture": "DistilBertForMaskedLM. Safetensors total 135,445,755. Context 512. Multilingual cased distillation. The Hub language tag list is long and was not copied one by one.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DistilBERT multilingual cased is a Hugging Face distillation model, collected 2026-10-15. Weights are Apache-2.0 and remain on the distilbert org. Multilingual cased DistilBERT.", "what_it_is_for": "Multilingual cased DistilBERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/distilbert/distilbert-base-uncased": { "id": "huggingface/distilbert/distilbert-base-uncased", "name": "DistilBERT base uncased", "maker": "huggingface", "family": "huggingface/distilbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "66985530", "architecture": "DistilBertForMaskedLM. Safetensors total 66,985,530. Config: 6 layers, hidden size 768, 12 heads, intermediate size 3072, max position 512. Fill-mask encoder distilled from BERT, trained on BookCorpus and Wikipedia. English uncased.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DistilBERT base uncased is a Hugging Face distillation model, collected 2026-10-15. Weights are Apache-2.0 and remain on the distilbert org. The original English uncased DistilBERT encoder.", "what_it_is_for": "The original English uncased DistilBERT encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/distilbert/distilbert-squad-cased": { "id": "huggingface/distilbert/distilbert-squad-cased", "name": "DistilBERT SQuAD cased", "maker": "huggingface", "family": "huggingface/distilbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "DistilBERT question-answering checkpoint distilled and fine-tuned on SQuAD, cased. Official distilbert org release.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DistilBERT SQuAD cased is a Hugging Face distillation model, collected 2026-10-15. Weights are Apache-2.0 and remain on the distilbert org. Official cased DistilBERT model for extractive question answering on SQuAD.", "what_it_is_for": "Official cased DistilBERT model for extractive question answering on SQuAD.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/distilbert/distilbert-squad-uncased": { "id": "huggingface/distilbert/distilbert-squad-uncased", "name": "DistilBERT SQuAD uncased", "maker": "huggingface", "family": "huggingface/distilbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "DistilBERT question-answering checkpoint for SQuAD, uncased. Official distilbert org release.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DistilBERT SQuAD uncased is a Hugging Face distillation model, collected 2026-10-15. Weights are Apache-2.0 and remain on the distilbert org. Official uncased DistilBERT model for extractive question answering on SQuAD.", "what_it_is_for": "Official uncased DistilBERT model for extractive question answering on SQuAD.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/distilbert/distilbert-sst-2": { "id": "huggingface/distilbert/distilbert-sst-2", "name": "DistilBERT SST-2", "maker": "huggingface", "family": "huggingface/distilbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "DistilBERT sequence-classification checkpoint fine-tuned on SST-2 / GLUE for English sentiment. Apache-2.0. Safetensors total was not fetched. It is the canonical task checkpoint shipped by the distilbert org, not a third-party fine-tune.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DistilBERT SST-2 is a Hugging Face distillation model, collected 2026-10-15. Weights are Apache-2.0 and remain on the distilbert org. Official English sentiment classifier built on DistilBERT base uncased.", "what_it_is_for": "Official English sentiment classifier built on DistilBERT base uncased.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/distilbert/distilgpt2": { "id": "huggingface/distilbert/distilgpt2", "name": "DistilGPT-2", "maker": "huggingface", "family": "huggingface/distilbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 1024, "max_output_tokens": null, "parameters": "88204032", "architecture": "GPT2LMHeadModel. Safetensors total 88,204,032. Config n_layer 6, n_embd 768, n_head 12, n_positions 1024. English distilled GPT-2, trained with OpenWebText named on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DistilGPT-2 is a Hugging Face distillation model, collected 2026-10-15. Weights are Apache-2.0 and remain on the distilbert org. Hugging Face's distilled GPT-2 for cheap text generation.", "what_it_is_for": "Hugging Face's distilled GPT-2 for cheap text generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/distilbert/distilroberta-base": { "id": "huggingface/distilbert/distilroberta-base", "name": "DistilRoBERTa base", "maker": "huggingface", "family": "huggingface/distilbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 514, "max_output_tokens": null, "parameters": "82760793", "architecture": "RobertaForMaskedLM. Safetensors total 82,760,793. 6 layers, hidden size 768, max position embeddings 514. English distilled RoBERTa. Head count was not read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DistilRoBERTa base is a Hugging Face distillation model, collected 2026-10-15. Weights are Apache-2.0 and remain on the distilbert org. Hugging Face's distilled RoBERTa-base encoder.", "what_it_is_for": "Hugging Face's distilled RoBERTa-base encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/idefics/idefics-80b": { "id": "huggingface/idefics/idefics-80b", "name": "IDEFICS-80B", "maker": "huggingface", "family": "huggingface/idefics", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (see model card License section; Hub tag other)", "release_date": "2023-07-05", "context_length": null, "max_output_tokens": null, "parameters": "79967850281", "architecture": "IdeficsForVisionText2Text. Safetensors total 79,967,850,281. 80 layers, hidden size 8192, 64 attention heads, intermediate size 22016. An open reproduction of Flamingo. Built by Hugging Face from LAION CLIP and a LLaMA checkpoint. Top-level max_position_embeddings was absent from the config read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IDEFICS-80B is a 2023 Hugging Face multimodal model, collected 2026-10-15. The Hub weights date from 2023-07-05. It is the base 80B checkpoint. An instruct twin was released beside it. The license is not Apache-2.0.", "what_it_is_for": "The 80B base IDEFICS model, Hugging Face's open Flamingo-style vision-language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/idefics/idefics-80b-instruct": { "id": "huggingface/idefics/idefics-80b-instruct", "name": "IDEFICS-80B-Instruct", "maker": "huggingface", "family": "huggingface/idefics", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (see model card License section; Hub tag other)", "release_date": "2023-07-25", "context_length": null, "max_output_tokens": null, "parameters": "79967866665", "architecture": "Instruct fine-tune of IDEFICS-80B. Safetensors total 79,967,866,665. Same 80-layer, hidden-8192 IDEFICS stack. Trained further on supervised and instruction mixtures so it is easier to chat with.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IDEFICS-80B-Instruct is the chat checkpoint of Hugging Face's 80B IDEFICS model, collected 2026-10-15. Hub weights date from 2023-07-25. It is still published. Later Idefics models are smaller Apache-2.0 releases.", "what_it_is_for": "Instruction-tuned 80B IDEFICS for visual question answering and chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/idefics/idefics-9b": { "id": "huggingface/idefics/idefics-9b", "name": "IDEFICS-9B", "maker": "huggingface", "family": "huggingface/idefics", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (see model card License section; Hub tag other)", "release_date": "2023-07-11", "context_length": 2048, "max_output_tokens": null, "parameters": "8929682192", "architecture": "IdeficsForVisionText2Text. Safetensors total 8,929,682,192. 32 layers, hidden size 4096, context configured at 2048. The smaller of the two original IDEFICS sizes. Developed by Hugging Face.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IDEFICS-9B is Hugging Face's smaller 2023 Flamingo-style VLM, collected 2026-10-15. Weights were uploaded 2023-07-11. License tag is other, not Apache-2.0.", "what_it_is_for": "The 9B base IDEFICS model for image-text tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/idefics/idefics-9b-instruct": { "id": "huggingface/idefics/idefics-9b-instruct", "name": "IDEFICS-9B-Instruct", "maker": "huggingface", "family": "huggingface/idefics", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (see model card License section; Hub tag other)", "release_date": "2023-07-24", "context_length": 2048, "max_output_tokens": null, "parameters": "8929690641", "architecture": "Instruct fine-tune of IDEFICS-9B. Safetensors total 8,929,690,641. 32 layers, hidden 4096, context 2048. The card says instruction tuning improves downstream use and makes the model more conversational.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IDEFICS-9B-Instruct is the chat form of Hugging Face's 9B IDEFICS, collected 2026-10-15. Hub date 2023-07-24. Still published under the original IDEFICS license tag.", "what_it_is_for": "Instruction-tuned 9B IDEFICS.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/idefics2/idefics2-8b": { "id": "huggingface/idefics2/idefics2-8b", "name": "Idefics2-8B", "maker": "huggingface", "family": "huggingface/idefics2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-09", "context_length": 32768, "max_output_tokens": null, "parameters": "8402768112", "architecture": "Instruction-tuned Idefics2-8B. Safetensors total 8,402,768,112. Context 32,768. The card releases this as the supervised and instruction fine-tune of the base model, for OCR, documents, and visual reasoning as well as text-only use.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Idefics2-8B is Hugging Face's Apache-2.0 8B vision-language model, collected 2026-10-15. It accepts image and text sequences and returns text. The chatty checkpoint is a longer-conversation fine-tune of this model.", "what_it_is_for": "The main Idefics2-8B instruction model for images and text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/idefics2/idefics2-8b-base": { "id": "huggingface/idefics2/idefics2-8b-base", "name": "Idefics2-8B-Base", "maker": "huggingface", "family": "huggingface/idefics2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-09", "context_length": 32768, "max_output_tokens": null, "parameters": "8402759920", "architecture": "Idefics2ForConditionalGeneration. Safetensors total 8,402,759,920. Text context configured at 32,768. Vision config has 27 layers and hidden size 1152. Language backbone is a Mistral-class 7B model with a SigLIP vision encoder. Layer and head counts were nested and not read as top-level fields. Apache-2.0.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Idefics2-8B-Base is Hugging Face's 2024 8B vision-language base model, collected 2026-10-15. Weights date from 2024-04-09 under Apache-2.0. The instruct and chatty checkpoints are the chat products.", "what_it_is_for": "Base Idefics2-8B checkpoint for further multimodal training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/idefics2/idefics2-8b-chatty": { "id": "huggingface/idefics2/idefics2-8b-chatty", "name": "Idefics2-8B-Chatty", "maker": "huggingface", "family": "huggingface/idefics2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-05-02", "context_length": 32768, "max_output_tokens": null, "parameters": "8402768112", "architecture": "idefics2-8b further fine-tuned for longer conversations. Same 8,402,768,112 safetensors total and 32,768 context as idefics2-8b.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Idefics2-8B-Chatty is Hugging Face's conversational fine-tune of Idefics2-8B, collected 2026-10-15. Hub date 2024-05-02. Apache-2.0.", "what_it_is_for": "Longer-conversation fine-tune of Idefics2-8B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/idefics3/idefics3-8b": { "id": "huggingface/idefics3/idefics3-8b", "name": "Idefics3-8B-Llama3", "maker": "huggingface", "family": "huggingface/idefics3", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-22", "context_length": 131072, "max_output_tokens": null, "parameters": "8462086384", "architecture": "Idefics3ForConditionalGeneration. Safetensors total 8,462,086,384. Text stack: 32 layers, hidden 4096, intermediate 14336, 32 query heads, 8 KV heads. max_position_embeddings 131072 with Llama-3 RoPE scaling from original_max_position_embeddings 8192 and factor 8. Vision config: 27 layers, hidden 1152. Parents named on the card: google/siglip-so400m-patch14-384 and meta-llama/Meta-Llama-3.1-8B-Instruct. The card says a 364x364 image uses 169 visual tokens, with larger images split into tiles. Improves OCR, document understanding, and visual reasoning over Idefics1 and Idefics2.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Idefics3-8B-Llama3 is Hugging Face's 2024 open 8B vision-language model, collected 2026-10-15. The model page is dated 2024-08-22. It is the last Idefics generation before the SmolVLM line reused the Idefics3 architecture at a much smaller size.", "what_it_is_for": "Hugging Face's Idefics3 8B model for documents, OCR, and visual reasoning, with a Llama 3.1 8B language backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/nanowhale/nanowhale-100m": { "id": "huggingface/nanowhale/nanowhale-100m", "name": "nanowhale-100m", "maker": "huggingface", "family": "huggingface/nanowhale", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-04-24", "context_length": 2048, "max_output_tokens": null, "parameters": "about 110M (41M embeddings + 69M non-embedding), from the model card", "architecture": "From-scratch miniature DeepSeek-V4-style model. No DeepSeek-V4 weights were used. The model card lists about 110M parameters (about 41M embeddings and 69M non-embedding), hidden size 320, 8 layers, 8 attention heads and 1 KV head, MLA with q_lora_rank 160, MoE with 4 routed experts plus 1 shared expert and top-2 routing, hyper-connections (hc_mult 4, Sinkhorn), 1 MTP layer, vocab 129,280, context 2,048. Base pretraining: FineWeb-Edu, 5,000 steps, about 2.6B tokens. SFT: HuggingFaceTB/smol-smoltalk, 3,000 steps, about 72.7M tokens. Card metrics versus the base: eval loss 2.607, held-out perplexity 12.90, token accuracy 48.5%.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "nanowhale-100m is a 2026 Hugging Face Smol Models Research model, collected 2026-10-15. It is a from-scratch ~110M implementation of DeepSeek-V4 architectural ideas, not a DeepSeek weight release. Status is active on the Hub.", "what_it_is_for": "Hugging Face reference SFT chat checkpoint of nanowhale used to implement DeepSeek-V4 ideas at tiny scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/nanowhale/nanowhale-100m-base": { "id": "huggingface/nanowhale/nanowhale-100m-base", "name": "nanowhale-100m-base", "maker": "huggingface", "family": "huggingface/nanowhale", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-04-24", "context_length": 2048, "max_output_tokens": null, "parameters": "about 110M (41M embeddings + 69M non-embedding), from the model card", "architecture": "From-scratch miniature DeepSeek-V4-style model. No DeepSeek-V4 weights were used. The model card lists about 110M parameters (about 41M embeddings and 69M non-embedding), hidden size 320, 8 layers, 8 attention heads and 1 KV head, MLA with q_lora_rank 160, MoE with 4 routed experts plus 1 shared expert and top-2 routing, hyper-connections (hc_mult 4, Sinkhorn), 1 MTP layer, vocab 129,280, context 2,048. Base pretraining: FineWeb-Edu, 5,000 steps, about 2.6B tokens. SFT: HuggingFaceTB/smol-smoltalk, 3,000 steps, about 72.7M tokens. Pretrain held-out perplexity 13.62 and token accuracy 33.8% are card metrics, not downstream benchmarks.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "nanowhale-100m-base is a 2026 Hugging Face Smol Models Research model, collected 2026-10-15. It is a from-scratch ~110M implementation of DeepSeek-V4 architectural ideas, not a DeepSeek weight release. Status is active on the Hub.", "what_it_is_for": "Hugging Face reference pretrained base of the nanowhale reference model used to implement DeepSeek-V4 ideas at tiny scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/olympiccoder/olympiccoder-32b": { "id": "huggingface/olympiccoder/olympiccoder-32b", "name": "OlympicCoder-32B", "maker": "huggingface", "family": "huggingface/olympiccoder", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-11", "context_length": 32768, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM fine-tune of Qwen/Qwen2.5-Coder-32B-Instruct. Safetensors total 32,763,876,352. 64 layers, hidden 5120, context 32768. Same CodeForces-CoT recipe as the 7B model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OlympicCoder-32B is Hugging Face's larger March 2025 coding reasoner, collected 2026-10-15. It fine-tunes Qwen2.5-Coder-32B-Instruct. The Open R1 post presents it as the stronger of the two OlympicCoder models.", "what_it_is_for": "Hugging Face Open R1 32B competitive-coding reasoner.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/olympiccoder/olympiccoder-7b": { "id": "huggingface/olympiccoder/olympiccoder-7b", "name": "OlympicCoder-7B", "maker": "huggingface", "family": "huggingface/olympiccoder", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-11", "context_length": 32768, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Qwen2ForCausalLM fine-tune of Qwen/Qwen2.5-Coder-7B-Instruct on a decontaminated Codeforces chain-of-thought set. Safetensors total 7,615,616,512. 28 layers, hidden 3584, context 32768. English. The Open R1 update says the 7B and 32B models target competitive programming, including IOI 2024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OlympicCoder-7B is a March 11, 2025 Hugging Face Open R1 model, collected 2026-10-15. It is a fine-tune of Qwen2.5-Coder-7B-Instruct for olympiad-style programming. Weights are Apache-2.0.", "what_it_is_for": "Hugging Face Open R1 7B competitive-coding reasoner.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/open-r1/openr1-distill-7b": { "id": "huggingface/open-r1/openr1-distill-7b", "name": "OpenR1-Distill-7B", "maker": "huggingface", "family": "huggingface/open-r1", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-05-22", "context_length": 32768, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Qwen2ForCausalLM. Safetensors total 7,615,616,512. 28 layers, hidden 3584, context 32768. Fine-tune of open-r1/Qwen2.5-Math-7B-RoPE-300k on open-r1/Mixture-of-Thoughts. Card text retrieved in search: it replicates the reasoning behavior of DeepSeek-R1-Distill-Qwen-7B while staying fully open. Paper tag arXiv:2504.21318.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenR1-Distill-7B is Hugging Face's May 22, 2025 open reasoning model, collected 2026-10-15. It is the flagship 7B distill from the Open R1 project. Weights are Apache-2.0. The parent checkpoint extends Qwen2.5-Math-7B's RoPE to 300k positions and is recorded here as a version, not a separate product.", "what_it_is_for": "Hugging Face's open reproduction of a DeepSeek-R1 distilled 7B reasoner.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/open-r1/openr1-qwen-7b": { "id": "huggingface/open-r1/openr1-qwen-7b", "name": "OpenR1-Qwen-7B", "maker": "huggingface", "family": "huggingface/open-r1", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 32768, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Qwen2ForCausalLM. Safetensors total 7,615,616,512. 28 layers, hidden 3584, context 32768. SFT checkpoint. Dataset tag open-r1/openr1-220k-math. An early named Open R1 math model, published before OpenR1-Distill-7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenR1-Qwen-7B is a February 5, 2025 Hugging Face Open R1 checkpoint, collected 2026-10-15. It is a named 7B math fine-tune under Apache-2.0. OpenR1-Distill-7B is the later reproduction release.", "what_it_is_for": "Early Hugging Face Open R1 7B math SFT checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smollm/smollm-1-7b": { "id": "huggingface/smollm/smollm-1-7b", "name": "SmolLM-1.7B", "maker": "huggingface", "family": "huggingface/smollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-16", "context_length": 2048, "max_output_tokens": null, "parameters": "1711376384", "architecture": "Decoder-only Llama-architecture transformer (multi-head attention). Safetensors parameter total 1711376384. 24 layers, hidden size 2048, 32 query heads, 32 key/value heads. Intermediate size 8192. rope_theta 10000. Checkpoint max_position_embeddings 2048. Released with SmolLM-Corpus (Cosmopedia v2, Python-Edu, and educational web text). Instruct checkpoints were SFT'd then DPO'd (HelpSteer for 135M and 1.7B; argilla/dpo-mix-7k for 360M).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolLM-1.7B is a Hugging Face open language model at the 1.7B scale, collected 2026-10-15. It is a base checkpoint for fine-tuning. The weights remain published on the Hub.", "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) base checkpoint for fine-tuning at the 1.7B scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smollm/smollm-1-7b-instruct": { "id": "huggingface/smollm/smollm-1-7b-instruct", "name": "SmolLM-1.7B Instruct", "maker": "huggingface", "family": "huggingface/smollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-16", "context_length": 2048, "max_output_tokens": null, "parameters": "1711376384", "architecture": "Decoder-only Llama-architecture transformer (multi-head attention). Safetensors parameter total 1711376384. 24 layers, hidden size 2048, 32 query heads, 32 key/value heads. Intermediate size 8192. rope_theta 10000. Checkpoint max_position_embeddings 2048. Released with SmolLM-Corpus (Cosmopedia v2, Python-Edu, and educational web text). Instruct checkpoints were SFT'd then DPO'd (HelpSteer for 135M and 1.7B; argilla/dpo-mix-7k for 360M).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolLM-1.7B Instruct is a Hugging Face open language model at the 1.7B scale, collected 2026-10-15. It is a instruction-tuned checkpoint for local chat. The weights remain published on the Hub.", "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) instruction-tuned checkpoint for local chat at the 1.7B scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smollm/smollm-135m": { "id": "huggingface/smollm/smollm-135m", "name": "SmolLM-135M", "maker": "huggingface", "family": "huggingface/smollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-16", "context_length": 2048, "max_output_tokens": null, "parameters": "134515008", "architecture": "Decoder-only Llama-architecture transformer (grouped-query attention). Safetensors parameter total 134515008. 30 layers, hidden size 576, 9 query heads, 3 key/value heads. Intermediate size 1536. rope_theta 10000. Checkpoint max_position_embeddings 2048. Released with SmolLM-Corpus (Cosmopedia v2, Python-Edu, and educational web text). Instruct checkpoints were SFT'd then DPO'd (HelpSteer for 135M and 1.7B; argilla/dpo-mix-7k for 360M).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolLM-135M is a Hugging Face open language model at the 135M scale, collected 2026-10-15. It is a base checkpoint for fine-tuning. The weights remain published on the Hub.", "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) base checkpoint for fine-tuning at the 135M scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smollm/smollm-135m-instruct": { "id": "huggingface/smollm/smollm-135m-instruct", "name": "SmolLM-135M Instruct", "maker": "huggingface", "family": "huggingface/smollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-16", "context_length": 2048, "max_output_tokens": null, "parameters": "134515008", "architecture": "Decoder-only Llama-architecture transformer (grouped-query attention). Safetensors parameter total 134515008. 30 layers, hidden size 576, 9 query heads, 3 key/value heads. Intermediate size 1536. rope_theta 10000. Checkpoint max_position_embeddings 2048. Released with SmolLM-Corpus (Cosmopedia v2, Python-Edu, and educational web text). Instruct checkpoints were SFT'd then DPO'd (HelpSteer for 135M and 1.7B; argilla/dpo-mix-7k for 360M).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolLM-135M Instruct is a Hugging Face open language model at the 135M scale, collected 2026-10-15. It is a instruction-tuned checkpoint for local chat. The weights remain published on the Hub.", "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) instruction-tuned checkpoint for local chat at the 135M scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smollm/smollm-360m": { "id": "huggingface/smollm/smollm-360m", "name": "SmolLM-360M", "maker": "huggingface", "family": "huggingface/smollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-16", "context_length": 2048, "max_output_tokens": null, "parameters": "361821120", "architecture": "Decoder-only Llama-architecture transformer (grouped-query attention). Safetensors parameter total 361821120. 32 layers, hidden size 960, 15 query heads, 5 key/value heads. Intermediate size 2560. rope_theta 10000. Checkpoint max_position_embeddings 2048. Released with SmolLM-Corpus (Cosmopedia v2, Python-Edu, and educational web text). Instruct checkpoints were SFT'd then DPO'd (HelpSteer for 135M and 1.7B; argilla/dpo-mix-7k for 360M).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolLM-360M is a Hugging Face open language model at the 360M scale, collected 2026-10-15. It is a base checkpoint for fine-tuning. The weights remain published on the Hub.", "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) base checkpoint for fine-tuning at the 360M scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smollm/smollm-360m-instruct": { "id": "huggingface/smollm/smollm-360m-instruct", "name": "SmolLM-360M Instruct", "maker": "huggingface", "family": "huggingface/smollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-16", "context_length": 2048, "max_output_tokens": null, "parameters": "361821120", "architecture": "Decoder-only Llama-architecture transformer (grouped-query attention). Safetensors parameter total 361821120. 32 layers, hidden size 960, 15 query heads, 5 key/value heads. Intermediate size 2560. rope_theta 10000. Checkpoint max_position_embeddings 2048. Released with SmolLM-Corpus (Cosmopedia v2, Python-Edu, and educational web text). Instruct checkpoints were SFT'd then DPO'd (HelpSteer for 135M and 1.7B; argilla/dpo-mix-7k for 360M).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolLM-360M Instruct is a Hugging Face open language model at the 360M scale, collected 2026-10-15. It is a instruction-tuned checkpoint for local chat. The weights remain published on the Hub.", "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) instruction-tuned checkpoint for local chat at the 360M scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smollm/smollm2-1-7b": { "id": "huggingface/smollm/smollm2-1-7b", "name": "SmolLM2-1.7B", "maker": "huggingface", "family": "huggingface/smollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-10-31", "context_length": 8192, "max_output_tokens": null, "parameters": "1711376384", "architecture": "Decoder-only Llama-architecture transformer (multi-head attention). Safetensors parameter total 1711376384. 24 layers, hidden size 2048, 32 query heads, 32 key/value heads. rope_theta 130000. Checkpoint max_position_embeddings 8192. SmolLM2 keeps the same parameter total as the matching SmolLM v1 size. rope_theta was read for the 1.7B config (130000) and was not re-read for the smaller SmolLM2 configs. The SmolLM2 paper is arXiv:2502.02737.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolLM2-1.7B is a Hugging Face open language model at the 1.7B scale, collected 2026-10-15. It is a base checkpoint for fine-tuning. The weights remain published on the Hub.", "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) base checkpoint for fine-tuning at the 1.7B scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smollm/smollm2-1-7b-instruct": { "id": "huggingface/smollm/smollm2-1-7b-instruct", "name": "SmolLM2-1.7B Instruct", "maker": "huggingface", "family": "huggingface/smollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-10-31", "context_length": 8192, "max_output_tokens": null, "parameters": "1711376384", "architecture": "Decoder-only Llama-architecture transformer (multi-head attention). Safetensors parameter total 1711376384. 24 layers, hidden size 2048, 32 query heads, 32 key/value heads. rope_theta 130000. Checkpoint max_position_embeddings 8192. SmolLM2 keeps the same parameter total as the matching SmolLM v1 size. rope_theta was read for the 1.7B config (130000) and was not re-read for the smaller SmolLM2 configs. The SmolLM2 paper is arXiv:2502.02737.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolLM2-1.7B Instruct is a Hugging Face open language model at the 1.7B scale, collected 2026-10-15. It is a instruction-tuned checkpoint for local chat. The weights remain published on the Hub.", "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) instruction-tuned checkpoint for local chat at the 1.7B scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smollm/smollm2-135m": { "id": "huggingface/smollm/smollm2-135m", "name": "SmolLM2-135M", "maker": "huggingface", "family": "huggingface/smollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-10-31", "context_length": 8192, "max_output_tokens": null, "parameters": "134515008", "architecture": "Decoder-only Llama-architecture transformer (grouped-query attention). Safetensors parameter total 134515008. 30 layers, hidden size 576, 9 query heads, 3 key/value heads. Checkpoint max_position_embeddings 8192. SmolLM2 keeps the same parameter total as the matching SmolLM v1 size. rope_theta was read for the 1.7B config (130000) and was not re-read for the smaller SmolLM2 configs. The SmolLM2 paper is arXiv:2502.02737.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolLM2-135M is a Hugging Face open language model at the 135M scale, collected 2026-10-15. It is a base checkpoint for fine-tuning. The weights remain published on the Hub.", "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) base checkpoint for fine-tuning at the 135M scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smollm/smollm2-135m-instruct": { "id": "huggingface/smollm/smollm2-135m-instruct", "name": "SmolLM2-135M Instruct", "maker": "huggingface", "family": "huggingface/smollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-10-31", "context_length": 8192, "max_output_tokens": null, "parameters": "134515008", "architecture": "Decoder-only Llama-architecture transformer (grouped-query attention). Safetensors parameter total 134515008. 30 layers, hidden size 576, 9 query heads, 3 key/value heads. Checkpoint max_position_embeddings 8192. SmolLM2 keeps the same parameter total as the matching SmolLM v1 size. rope_theta was read for the 1.7B config (130000) and was not re-read for the smaller SmolLM2 configs. The SmolLM2 paper is arXiv:2502.02737.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolLM2-135M Instruct is a Hugging Face open language model at the 135M scale, collected 2026-10-15. It is a instruction-tuned checkpoint for local chat. The weights remain published on the Hub.", "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) instruction-tuned checkpoint for local chat at the 135M scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smollm/smollm2-360m": { "id": "huggingface/smollm/smollm2-360m", "name": "SmolLM2-360M", "maker": "huggingface", "family": "huggingface/smollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-10-31", "context_length": 8192, "max_output_tokens": null, "parameters": "361821120", "architecture": "Decoder-only Llama-architecture transformer (grouped-query attention). Safetensors parameter total 361821120. 32 layers, hidden size 960, 15 query heads, 5 key/value heads. Checkpoint max_position_embeddings 8192. SmolLM2 keeps the same parameter total as the matching SmolLM v1 size. rope_theta was read for the 1.7B config (130000) and was not re-read for the smaller SmolLM2 configs. The SmolLM2 paper is arXiv:2502.02737.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolLM2-360M is a Hugging Face open language model at the 360M scale, collected 2026-10-15. It is a base checkpoint for fine-tuning. The weights remain published on the Hub.", "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) base checkpoint for fine-tuning at the 360M scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smollm/smollm2-360m-instruct": { "id": "huggingface/smollm/smollm2-360m-instruct", "name": "SmolLM2-360M Instruct", "maker": "huggingface", "family": "huggingface/smollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-10-31", "context_length": 8192, "max_output_tokens": null, "parameters": "361821120", "architecture": "Decoder-only Llama-architecture transformer (grouped-query attention). Safetensors parameter total 361821120. 32 layers, hidden size 960, 15 query heads, 5 key/value heads. Checkpoint max_position_embeddings 8192. SmolLM2 keeps the same parameter total as the matching SmolLM v1 size. rope_theta was read for the 1.7B config (130000) and was not re-read for the smaller SmolLM2 configs. The SmolLM2 paper is arXiv:2502.02737.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolLM2-360M Instruct is a Hugging Face open language model at the 360M scale, collected 2026-10-15. It is a instruction-tuned checkpoint for local chat. The weights remain published on the Hub.", "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) instruction-tuned checkpoint for local chat at the 360M scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smollm/smollm3-3b": { "id": "huggingface/smollm/smollm3-3b", "name": "SmolLM3-3B", "maker": "huggingface", "family": "huggingface/smollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-07-08", "context_length": 65536, "max_output_tokens": null, "parameters": "3075098624", "architecture": "SmolLM3ForCausalLM. Safetensors total 3,075,098,624. 36 layers, hidden size 2048, intermediate size 11008, 16 query heads, 4 key/value heads (GQA), rope_theta 5,000,000. Checkpoint max_position_embeddings is 65536. The model card says it was trained at 64k context and supports up to 128k with YaRN. The July 8, 2025 blog describes NoPE plus YaRN for the long context. Blog: pretrained on 11T tokens. A secondary writeup said 11.2T; the blog figure is the one used here. Dual-mode instruct model: think and no_think. Tool calling is part of the release description.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolLM3-3B is Hugging Face's 3B SmolLM3 model, collected 2026-10-15. The public blog is dated 2025-07-08. Weights are Apache-2.0. The base checkpoint was uploaded 2025-06-19 and the instruct checkpoint on the blog date.", "what_it_is_for": "Hugging Face's fully open 3B instruction model with dual-mode reasoning and tool calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smollm/smollm3-3b-base": { "id": "huggingface/smollm/smollm3-3b-base", "name": "SmolLM3-3B-Base", "maker": "huggingface", "family": "huggingface/smollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-06-19", "context_length": 65536, "max_output_tokens": null, "parameters": "3075098624", "architecture": "SmolLM3ForCausalLM. Safetensors total 3,075,098,624. 36 layers, hidden size 2048, intermediate size 11008, 16 query heads, 4 key/value heads (GQA), rope_theta 5,000,000. Checkpoint max_position_embeddings is 65536. The model card says it was trained at 64k context and supports up to 128k with YaRN. The July 8, 2025 blog describes NoPE plus YaRN for the long context. Blog: pretrained on 11T tokens. A secondary writeup said 11.2T; the blog figure is the one used here. Dual-mode instruct model: think and no_think. Tool calling is part of the release description.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolLM3-3B-Base is Hugging Face's 3B SmolLM3 model, collected 2026-10-15. The public blog is dated 2025-07-08. Weights are Apache-2.0. The base checkpoint was uploaded 2025-06-19 and the instruct checkpoint on the blog date.", "what_it_is_for": "Hugging Face's fully open 3B pretrained base for the SmolLM3 3B line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smolvla/smolvla": { "id": "huggingface/smolvla/smolvla", "name": "SmolVLA", "maker": "huggingface", "family": "huggingface/smolvla", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-06-03", "context_length": null, "max_output_tokens": null, "parameters": "450M", "architecture": "Vision-language-action policy. The model card and blog say 450M parameters. The Hub size badge says 0.5B. Backbone is SmolVLM2; the LeRobot config defaults vlm_model_name to HuggingFaceTB/SmolVLM2-500M-Video-Instruct and the paper describes keeping a subset of VLM layers plus a flow-matching action expert. Inputs are multi-view images, proprioceptive state, and an optional language instruction. Output is continuous actions. Training objective is flow matching. Chunk size in the shipped SmolVLA config is 50. Pretrained on community LeRobot datasets with compatible licenses.", "modalities": { "input": [ "image", "text", "robot-state" ], "output": [ "robot-action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolVLA is Hugging Face's open 450M vision-language-action model, collected 2026-10-15. The blog is dated 2025-06-03 and the paper 2025-06-02. Weights are published as lerobot/smolvla_base. It is a foundation policy, not a task-specific fine-tune.", "what_it_is_for": "Hugging Face's compact robotics vision-language-action model for community robot data and consumer hardware.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smolvlm/smolvlm-256m-base": { "id": "huggingface/smolvlm/smolvlm-256m-base", "name": "SmolVLM-256M-Base", "maker": "huggingface", "family": "huggingface/smolvlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-23", "context_length": 8192, "max_output_tokens": null, "parameters": "256484928", "architecture": "Idefics3-style vision-language model. Safetensors total 256484928. Text config: 30 layers, hidden 576. Checkpoint text max_position_embeddings 8192. ", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolVLM-256M-Base is a Hugging Face open vision-language checkpoint, collected 2026-10-15. Base checkpoint of the 256M SmolVLM. Text backbone is SmolLM2-135M. Marketed as the smallest VLM in that January 2025 release. Weights are Apache-2.0 and still published.", "what_it_is_for": "Base checkpoint of the 256M SmolVLM. Text backbone is SmolLM2-135M. Marketed as the smallest VLM in that January 2025 release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smolvlm/smolvlm-256m-instruct": { "id": "huggingface/smolvlm/smolvlm-256m-instruct", "name": "SmolVLM-256M-Instruct", "maker": "huggingface", "family": "huggingface/smolvlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-23", "context_length": 8192, "max_output_tokens": null, "parameters": "256484928", "architecture": "Idefics3-style vision-language model. Safetensors total 256484928. Text config: 30 layers, hidden 576. Checkpoint text max_position_embeddings 8192. ", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolVLM-256M-Instruct is a Hugging Face open vision-language checkpoint, collected 2026-10-15. Instruction-tuned 256M SmolVLM for on-device image-text tasks. Weights are Apache-2.0 and still published.", "what_it_is_for": "Instruction-tuned 256M SmolVLM for on-device image-text tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smolvlm/smolvlm-500m-base": { "id": "huggingface/smolvlm/smolvlm-500m-base", "name": "SmolVLM-500M-Base", "maker": "huggingface", "family": "huggingface/smolvlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-23", "context_length": 8192, "max_output_tokens": null, "parameters": "507482304", "architecture": "Idefics3-style vision-language model. Safetensors total 507482304. Text config: 32 layers, hidden 960. Checkpoint text max_position_embeddings 8192. ", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolVLM-500M-Base is a Hugging Face open vision-language checkpoint, collected 2026-10-15. Base 500M SmolVLM. Text backbone is SmolLM2-360M. The release post says a SigLIP base patch-16/512 encoder of 93M parameters was chosen for these smaller VLMs. Weights are Apache-2.0 and still published.", "what_it_is_for": "Base 500M SmolVLM. Text backbone is SmolLM2-360M. The release post says a SigLIP base patch-16/512 encoder of 93M parameters was chosen for these smaller VLMs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smolvlm/smolvlm-500m-instruct": { "id": "huggingface/smolvlm/smolvlm-500m-instruct", "name": "SmolVLM-500M-Instruct", "maker": "huggingface", "family": "huggingface/smolvlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-23", "context_length": 8192, "max_output_tokens": null, "parameters": "507482304", "architecture": "Idefics3-style vision-language model. Safetensors total 507482304. Text config: 32 layers, hidden 960. Checkpoint text max_position_embeddings 8192. ", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolVLM-500M-Instruct is a Hugging Face open vision-language checkpoint, collected 2026-10-15. Instruction-tuned 500M SmolVLM. Weights are Apache-2.0 and still published.", "what_it_is_for": "Instruction-tuned 500M SmolVLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smolvlm/smolvlm-base": { "id": "huggingface/smolvlm/smolvlm-base", "name": "SmolVLM-Base", "maker": "huggingface", "family": "huggingface/smolvlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-26", "context_length": 16384, "max_output_tokens": null, "parameters": "2246272880", "architecture": "Idefics3-style vision-language model. Safetensors total 2246272880. Text config: 24 layers, hidden 2048. Text attention 32 query heads / 32 KV heads. Checkpoint text max_position_embeddings 16384. ", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolVLM-Base is a Hugging Face open vision-language checkpoint, collected 2026-10-15. Base 2B-class SmolVLM for further multimodal fine-tuning. The blog markets the family as a 2B model; the weight total is 2,246,272,880. Weights are Apache-2.0 and still published.", "what_it_is_for": "Base 2B-class SmolVLM for further multimodal fine-tuning. The blog markets the family as a 2B model; the weight total is 2,246,272,880.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smolvlm/smolvlm-instruct": { "id": "huggingface/smolvlm/smolvlm-instruct", "name": "SmolVLM-Instruct", "maker": "huggingface", "family": "huggingface/smolvlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-26", "context_length": 16384, "max_output_tokens": null, "parameters": "2246272880", "architecture": "Idefics3-style vision-language model. Safetensors total 2246272880. Text config: 24 layers, hidden 2048. Text attention 32 query heads / 32 KV heads. Checkpoint text max_position_embeddings 16384. ", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolVLM-Instruct is a Hugging Face open vision-language checkpoint, collected 2026-10-15. Instruction-tuned SmolVLM for image question answering, captioning, and visual chat. Text backbone is SmolLM2-1.7B. Vision encoder on the config is 27 layers with hidden size 1152. Weights are Apache-2.0 and still published.", "what_it_is_for": "Instruction-tuned SmolVLM for image question answering, captioning, and visual chat. Text backbone is SmolLM2-1.7B. Vision encoder on the config is 27 layers with hidden size 1152.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smolvlm/smolvlm-synthetic": { "id": "huggingface/smolvlm/smolvlm-synthetic", "name": "SmolVLM-Synthetic", "maker": "huggingface", "family": "huggingface/smolvlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-26", "context_length": 16384, "max_output_tokens": null, "parameters": "2246272880", "architecture": "Idefics3-style vision-language model. Safetensors total 2246272880. Text config: 24 layers, hidden 2048. Text attention 32 query heads / 32 KV heads. Checkpoint text max_position_embeddings 16384. ", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolVLM-Synthetic is a Hugging Face open vision-language checkpoint, collected 2026-10-15. Synthetic-data fine-tune of the 2B-class SmolVLM, between the base and the instruct checkpoint. Weights are Apache-2.0 and still published.", "what_it_is_for": "Synthetic-data fine-tune of the 2B-class SmolVLM, between the base and the instruct checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smolvlm2/smolvlm2-2-2b-base": { "id": "huggingface/smolvlm2/smolvlm2-2-2b-base", "name": "SmolVLM2-2.2B-Base", "maker": "huggingface", "family": "huggingface/smolvlm2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-14", "context_length": 8192, "max_output_tokens": null, "parameters": "2246420336", "architecture": "Idefics3-style vision-language model. Safetensors total 2246420336. Text config: 24 layers, hidden 2048. Checkpoint text max_position_embeddings 8192. ", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolVLM2-2.2B-Base is a Hugging Face open vision-language checkpoint, collected 2026-10-15. Base video-capable SmolVLM2 at the 2.2B scale. Hub upload 2025-04-14, after the February 20, 2025 video announcement. Weights are Apache-2.0 and still published.", "what_it_is_for": "Base video-capable SmolVLM2 at the 2.2B scale. Hub upload 2025-04-14, after the February 20, 2025 video announcement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smolvlm2/smolvlm2-2-2b-instruct": { "id": "huggingface/smolvlm2/smolvlm2-2-2b-instruct", "name": "SmolVLM2-2.2B-Instruct", "maker": "huggingface", "family": "huggingface/smolvlm2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-20", "context_length": 8192, "max_output_tokens": null, "parameters": "2246784880", "architecture": "Idefics3-style vision-language model. Safetensors total 2246784880. Text config: 24 layers, hidden 2048. Checkpoint text max_position_embeddings 8192. SmolVLM2 checkpoints use the SmolVLM vision-language stack extended for video. ", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolVLM2-2.2B-Instruct is a Hugging Face open vision-language checkpoint, collected 2026-10-15. Primary SmolVLM2 instruct model for image and video understanding. The blog calls this the 2.2B size. Safetensors total is 2,246,784,880. Weights are Apache-2.0 and still published.", "what_it_is_for": "Primary SmolVLM2 instruct model for image and video understanding. The blog calls this the 2.2B size. Safetensors total is 2,246,784,880.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smolvlm2/smolvlm2-256m-video-instruct": { "id": "huggingface/smolvlm2/smolvlm2-256m-video-instruct", "name": "SmolVLM2-256M-Video-Instruct", "maker": "huggingface", "family": "huggingface/smolvlm2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-20", "context_length": 8192, "max_output_tokens": null, "parameters": "256484928", "architecture": "Idefics3-style vision-language model. Safetensors total 256484928. Text config: 30 layers, hidden 576. Checkpoint text max_position_embeddings 8192. SmolVLM2 checkpoints use the SmolVLM vision-language stack extended for video. ", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolVLM2-256M-Video-Instruct is a Hugging Face open vision-language checkpoint, collected 2026-10-15. Smallest SmolVLM2 video instruct model. Same safetensors total as SmolVLM-256M-Instruct (256,484,928). Weights are Apache-2.0 and still published.", "what_it_is_for": "Smallest SmolVLM2 video instruct model. Same safetensors total as SmolVLM-256M-Instruct (256,484,928).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/smolvlm2/smolvlm2-500m-video-instruct": { "id": "huggingface/smolvlm2/smolvlm2-500m-video-instruct", "name": "SmolVLM2-500M-Video-Instruct", "maker": "huggingface", "family": "huggingface/smolvlm2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-20", "context_length": 8192, "max_output_tokens": null, "parameters": "507482304", "architecture": "Idefics3-style vision-language model. Safetensors total 507482304. Text config: 32 layers, hidden 960. Checkpoint text max_position_embeddings 8192. SmolVLM2 checkpoints use the SmolVLM vision-language stack extended for video. ", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmolVLM2-500M-Video-Instruct is a Hugging Face open vision-language checkpoint, collected 2026-10-15. Smallest-but-one SmolVLM2 video instruct model. Same safetensors total as SmolVLM-500M-Instruct (507,482,304), which it fine-tunes. Weights are Apache-2.0 and still published.", "what_it_is_for": "Smallest-but-one SmolVLM2 video instruct model. Same safetensors total as SmolVLM-500M-Instruct (507,482,304), which it fine-tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-c": { "id": "huggingface/stack-edu/stack-edu-c", "name": "Stack-Edu c classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational c code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Parameter total not fetched for this language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu c classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for c, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-cpp": { "id": "huggingface/stack-edu/stack-edu-cpp", "name": "Stack-Edu cpp classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational cpp code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Parameter total not fetched for this language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu cpp classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for cpp, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-csharp": { "id": "huggingface/stack-edu/stack-edu-csharp", "name": "Stack-Edu csharp classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational csharp code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Parameter total not fetched for this language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu csharp classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for csharp, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-go": { "id": "huggingface/stack-edu/stack-edu-go", "name": "Stack-Edu go classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational go code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Parameter total not fetched for this language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu go classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for go, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-java": { "id": "huggingface/stack-edu/stack-edu-java", "name": "Stack-Edu java classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational java code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Parameter total not fetched for this language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu java classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for java, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-javascript": { "id": "huggingface/stack-edu/stack-edu-javascript", "name": "Stack-Edu javascript classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational javascript code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Parameter total not fetched for this language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu javascript classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for javascript, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-markdown": { "id": "huggingface/stack-edu/stack-edu-markdown", "name": "Stack-Edu markdown classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational markdown code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Parameter total not fetched for this language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu markdown classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for markdown, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-php": { "id": "huggingface/stack-edu/stack-edu-php", "name": "Stack-Edu php classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational php code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Parameter total not fetched for this language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu php classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for php, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-python": { "id": "huggingface/stack-edu/stack-edu-python", "name": "Stack-Edu python classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "124187137", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational python code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Safetensors total 124,187,137.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu python classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for python, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-ruby": { "id": "huggingface/stack-edu/stack-edu-ruby", "name": "Stack-Edu ruby classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational ruby code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Parameter total not fetched for this language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu ruby classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for ruby, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-rust": { "id": "huggingface/stack-edu/stack-edu-rust", "name": "Stack-Edu rust classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational rust code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Parameter total not fetched for this language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu rust classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for rust, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-shell": { "id": "huggingface/stack-edu/stack-edu-shell", "name": "Stack-Edu shell classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational shell code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Parameter total not fetched for this language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu shell classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for shell, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-sql": { "id": "huggingface/stack-edu/stack-edu-sql", "name": "Stack-Edu sql classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational sql code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Parameter total not fetched for this language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu sql classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for sql, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-swift": { "id": "huggingface/stack-edu/stack-edu-swift", "name": "Stack-Edu swift classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational swift code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Parameter total not fetched for this language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu swift classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for swift, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/stack-edu/stack-edu-typescript": { "id": "huggingface/stack-edu/stack-edu-typescript", "name": "Stack-Edu typescript classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT classifier fine-tuned from bigcode/starencoder for educational typescript code. Released by HuggingFaceTB with the SmolLM2 / Stack-Edu data (arXiv:2502.02737). Parameter total not fetched for this language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stack-Edu typescript classifier is a Hugging Face data filter, collected 2026-10-15. It is one language head in the Stack-Edu set that shipped with SmolLM2's code data work. It is not a chat model.", "what_it_is_for": "Educational-quality classifier for typescript, used to filter code pretraining data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/starchat/starchat-alpha": { "id": "huggingface/starchat/starchat-alpha", "name": "StarChat-alpha", "maker": "huggingface", "family": "huggingface/starchat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BigCode OpenRAIL-M", "release_date": "2023-05-09", "context_length": null, "max_output_tokens": null, "parameters": "15517480960", "architecture": "GPTBigCodeForCausalLM fine-tune for coding chat. Safetensors total 15,517,480,960. The alpha config's n_positions field was not re-read; the beta checkpoint, same parameter total, has n_positions 8192, n_layer 40, n_embd 6144, n_head 48. Datasets named on the card include OpenAssistant OASST1 and Dolly.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarChat-alpha is a May 2023 Hugging Face H4 code chat model, collected 2026-10-15. It is a fine-tune published by Hugging Face, not a BigCode base model. StarChat-beta followed it.", "what_it_is_for": "First Hugging Face H4 coding assistant fine-tune in the StarChat line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/starchat/starchat-beta": { "id": "huggingface/starchat/starchat-beta", "name": "StarChat-beta", "maker": "huggingface", "family": "huggingface/starchat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BigCode OpenRAIL-M", "release_date": "2023-06-07", "context_length": 8192, "max_output_tokens": null, "parameters": "15517480960", "architecture": "GPTBigCodeForCausalLM. Safetensors total 15,517,480,960. n_layer 40, n_embd 6144, n_head 48, n_positions 8192. Coding assistant fine-tune published by Hugging Face H4.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarChat-beta is Hugging Face's June 2023 open code assistant, collected 2026-10-15. It is the second StarChat release, at about 15.5B parameters, under the BigCode OpenRAIL-M license.", "what_it_is_for": "The beta StarChat coding assistant from Hugging Face H4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/starchat/starchat2-15b": { "id": "huggingface/starchat/starchat2-15b", "name": "StarChat2-15B", "maker": "huggingface", "family": "huggingface/starchat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BigCode OpenRAIL-M", "release_date": "2024-03-10", "context_length": 16384, "max_output_tokens": null, "parameters": "15957913600", "architecture": "Starcoder2ForCausalLM. Safetensors total 15,957,913,600. 40 layers, hidden 6144, intermediate 24576, 48 query heads, 4 KV heads, context 16384, rope_theta 100000. Fine-tune of bigcode/starcoder2-15b. The SFT parent is HuggingFaceH4/starchat2-15b-sft-v0.1. Preference data includes UltraFeedback and Orca DPO pairs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarChat2-15B is the 2024 Hugging Face H4 code chat model, collected 2026-10-15. It is a fine-tune of StarCoder2-15B, which BigCode trained. Hugging Face published this assistant checkpoint. License is BigCode OpenRAIL-M.", "what_it_is_for": "Hugging Face H4's StarChat2 coding assistant on StarCoder2-15B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/websight/vlm-websight": { "id": "huggingface/websight/vlm-websight", "name": "VLM WebSight", "maker": "huggingface", "family": "huggingface/websight", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "8208007232", "architecture": "HuggingFaceM4/VLM_WebSight_finetuned. Safetensors total 8,208,007,232. Pipeline tag text-generation, library custom code, tag vmistral and code. The card says Hugging Face developed it and that SigLIP and mistralai/Mistral-7B-v0.1 are connected by newly initialized trained parameters. Dataset tag HuggingFaceM4/WebSight. Paper tag arXiv:2403.09029.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VLM WebSight is a January 2024 Hugging Face multimodal model, collected 2026-10-15. It is a named WebSight fine-tune, not a general Idefics release. Weights are Apache-2.0.", "what_it_is_for": "Hugging Face M4 vision-language model fine-tuned on the WebSight dataset and tagged for code generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/zephyr/zephyr-7b-alpha": { "id": "huggingface/zephyr/zephyr-7b-alpha", "name": "Zephyr 7B Alpha", "maker": "huggingface", "family": "huggingface/zephyr", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-10-09", "context_length": 32768, "max_output_tokens": null, "parameters": "7241732096", "architecture": "Decoder-only Llama-architecture transformer (grouped-query attention). Safetensors parameter total 7241732096. 32 layers, hidden size 4096, 32 query heads, 8 key/value heads. Intermediate size 14336. rope_theta 10000. Checkpoint max_position_embeddings 32768. MistralForCausalLM fine-tune of mistralai/Mistral-7B-v0.1. The checkpoint config says 32768 positions. This dossier did not find a separate Hugging Face statement that the alpha model was trained at 32k rather than Mistral's original 8k. Alignment is distilled DPO. The beta card lists alpha MT-Bench at 6.88.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zephyr 7B Alpha is Hugging Face's October 2023 open chat model, collected 2026-10-15. It is still published under MIT. Zephyr 7B Beta replaced it as the main 7B result in the technical report.", "what_it_is_for": "First Zephyr assistant, a DPO fine-tune of Mistral-7B-v0.1 by the Hugging Face H4 team.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/zephyr/zephyr-7b-beta": { "id": "huggingface/zephyr/zephyr-7b-beta", "name": "Zephyr 7B Beta", "maker": "huggingface", "family": "huggingface/zephyr", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-10-25", "context_length": 32768, "max_output_tokens": null, "parameters": "7241732096", "architecture": "Decoder-only Llama-architecture transformer (grouped-query attention). Safetensors parameter total 7241732096. 32 layers, hidden size 4096, 32 query heads, 8 key/value heads. Intermediate size 14336. rope_theta 10000. Checkpoint max_position_embeddings 32768. MistralForCausalLM fine-tune of mistralai/Mistral-7B-v0.1 using distilled DPO on UltraChat and UltraFeedback. The card says dropping the source datasets' alignment raised MT-Bench and helpfulness, and that the model can produce problematic text when prompted. Config context is 32768; a separate training-window statement was not found.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zephyr 7B Beta is Hugging Face H4's MIT-licensed 7B chat model, collected 2026-10-15. Released with the October 25, 2023 Zephyr paper. It is a fine-tune of Mistral-7B-v0.1, not a model trained from scratch. Weights remain on the Hub.", "what_it_is_for": "The main Zephyr 7B assistant from the 2023 technical report, tuned with distilled DPO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/zephyr/zephyr-7b-gemma": { "id": "huggingface/zephyr/zephyr-7b-gemma", "name": "Zephyr 7B Gemma", "maker": "huggingface", "family": "huggingface/zephyr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma license (Hub tag other). Fine-tune of google/gemma-7b.", "release_date": "2024-03-01", "context_length": 8192, "max_output_tokens": null, "parameters": "8537680896", "architecture": "GemmaForCausalLM. Safetensors total 8,537,680,896. 28 layers, hidden size 3072, context 8192. DPO fine-tune. The SFT parent is HuggingFaceH4/zephyr-7b-gemma-sft-v0.1, itself a fine-tune of google/gemma-7b on deita-10k-v0-sft. Preference data tag argilla/dpo-mix-7k.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zephyr 7B Gemma is a March 2024 Hugging Face H4 chat fine-tune of Gemma 7B, collected 2026-10-15. It is a Zephyr-family release, but the weights stay under the Gemma license rather than MIT.", "what_it_is_for": "Zephyr recipe applied to Gemma 7B by Hugging Face H4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huggingface/zephyr/zephyr-orpo-141b": { "id": "huggingface/zephyr/zephyr-orpo-141b", "name": "Zephyr ORPO 141B", "maker": "huggingface", "family": "huggingface/zephyr", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-10", "context_length": 65536, "max_output_tokens": null, "parameters": "140,620,634,112 weights in the file; model card says 141B total and 39B active", "architecture": "MixtralForCausalLM fine-tune of mistral-community/Mixtral-8x22B-v0.1. Safetensors total 140,620,634,112. 56 layers, hidden 6144, intermediate 16384, 48 query heads, 8 KV heads, context 65536, rope_theta 1,000,000. The repo id says A35b. The model card says that active-parameter figure was a small error and that 39B are active. Trained with ORPO on argilla/distilabel-capybara-dpo-7k-binarized for 7k instances and 1.3 hours on 4 nodes of 8 H100s. The card says the training was collaborative among Argilla, KAIST, and Hugging Face. Hugging Face H4 published the checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zephyr ORPO 141B is Hugging Face H4's April 2024 open mixture-of-experts assistant, collected 2026-10-15. It is a fine-tune, trained with Argilla and KAIST, and it is the largest named Zephyr checkpoint. The repo still says A35b while the card corrects the active count to 39B.", "what_it_is_for": "The largest Zephyr assistant, an ORPO fine-tune of Mixtral-8x22B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huji/hebtts/hebtts": { "id": "huji/hebtts/hebtts", "name": "HebTTS", "maker": "huji", "family": "huji/hebtts", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-07-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diacritic-free Hebrew text-to-speech language model from SLP-RL. The README says the language model operates on discrete speech representations and is conditioned on a word-piece tokenizer. The published Google Drive file is one checkpoint.pt. The README says the autoregressive model was trained for 1.2M steps and the non-autoregressive model for 200K steps on HebDB. The repo tree includes a valle package based on Feiteng Li's VALL-E implementation, depends on EnCodec and AudioCraft, and can switch on a Multi-Band Diffusion vocoder. Speaker prompts come from speakers.yaml or user wav files. A character-tokenizer ablation is in the paper and is not a second download. A Tel Aviv course project also named HebTTS on the Hub is a different file.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HebTTS is a diacritic-free Hebrew text-to-speech model from the Hebrew University SLP-RL lab. The preprint was submitted on 2024-07-16 and accepted at Interspeech 2024. The lab README publishes one checkpoint on Google Drive. No license was found in the project file. Status on 2026-10-15 is active. A Hub repo named HebTTS from a Tel Aviv course is not this model.", "what_it_is_for": "The paper and README treat modern Hebrew as text that usually omits vowel diacritics, so a TTS model has to infer pronunciation from context. Their approach is a language model over discrete speech units, conditioned on a word-piece tokenizer, trained on weakly supervised in-the-wild recordings from HebDB. The README also exposes an optional Multi-Band Diffusion vocoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huji/lothm/lothm": { "id": "huji/lothm/lothm", "name": "LoTHM", "maker": "huji", "family": "huji/lothm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hebrew TTS system named LoTHM, Language of The Hebrew Man. The README says the release is an autoregressive language model that predicts HuBERT tokens from text, plus a HiFi-GAN decoder that writes waveforms. Inference takes --lm-checkpoint and --vocoder-checkpoint, a text string, an audio speaker prompt, and a text prompt. The Drive folder id is 1obshsRv7blb-3boef9o2mh6q4ROrEol3. Code is based on Feiteng Li's VALL-E implementation and Fairseq Expresso. The project page says the method uses discrete semantic units from a self-supervised model, with higher phonetic correlation, and a speaker embedding in the vocoder. Two authors also list FAIR, Meta AI. The lab page lists the paper at ICASSP 2025. No license file was retrieved. Drive timestamps were not opened. The arXiv submission day was not opened.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LoTHM, Language of The Hebrew Man, is a Hebrew text-to-speech system from the Hebrew University SLP-RL lab. The README publishes an autoregressive HuBERT-token language model and a HiFi-GAN vocoder in one Google Drive folder. Two of the three authors also list FAIR, Meta AI. The lab page places the paper at ICASSP 2025. No license and no upload date were found. Status on 2026-10-15 is active.", "what_it_is_for": "The project page says LoTHM uses discrete semantic units with a stronger phonetic correlation so sampling stays stable, especially for Hebrew without diacritics. A speaker embedding in the vocoder carries voice. The page says stability is better than earlier methods, while naturalness and speaker similarity stay on par. The README's inference script takes text, an audio prompt, and a text prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huji/nast/nast": { "id": "huji/nast/nast", "name": "NAST", "maker": "huji", "family": "huji/nast", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Noise-aware speech tokenizer from Shoval Messica and Yossi Adi. The README says NAST has a predictor, a residual encoder, and a decoder, trained on HuBERT Base features from layer 9. Three pretrained tokenizers are linked on Google Drive: 50, 100, and 200 units. HuBERT Base itself is the separate Meta fairseq file hubert_base_ls960.pt and is not this checkpoint. Training has a reconstruction and diversity phase, then a cross-entropy phase. The GitHub LICENSE file is MIT and names the software. The Drive folders were not opened for a second license file. Accepted at Interspeech 2024 according to the README. The arXiv submission day was not opened.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NAST is a noise-aware speech tokenizer from Shoval Messica and Yossi Adi at the Hebrew University SLP-RL lab. The README links three pretrained checkpoints, at 50, 100, and 200 units. The git license is MIT. The README says the paper was accepted at Interspeech 2024. Drive upload dates were not opened, so the release day is empty. Status on 2026-10-15 is active.", "what_it_is_for": "The README describes speech tokenization under noise, for later recognition, synthesis, and speech language models. NAST is a predictor, a residual encoder, and a decoder. The acoustic front end is HuBERT Base layer 9. That HuBERT file is Meta's `hubert_base_ls960.pt`, not a HUJI weight.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huji/nast/nast-ulm": { "id": "huji/nast/nast-ulm", "name": "NAST-ULM", "maker": "huji", "family": "huji/nast", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unit language models trained on de-duplicated NAST unit transcripts. The README ships three fairseq checkpoints, matched to the 50, 100, and 200 unit tokenizers. Loading uses fairseq checkpoint_utils. The README also points at the Meta fairseq GSLM unit-language-model recipe for training a new one. These are not the NAST tokenizer weights. The git LICENSE is the same MIT software license. Drive dates were not opened. No parameter count or context length is printed in the README section retrieved here.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NAST-ULM is the set of unit language models released with NAST by Shoval Messica and Yossi Adi. Each checkpoint is a fairseq model trained on de-duplicated transcripts from one NAST tokenizer, at 50, 100, or 200 units. The software license is MIT. Upload dates were not opened. Status on 2026-10-15 is active.", "what_it_is_for": "The README lists these checkpoints separately from the tokenizers. They model sequences of NAST units. The load example runs a fairseq model on a batch of unit ids and reads normalized log probabilities. New models can be trained with the Meta fairseq GSLM unit-language-model example. That recipe is not a HUJI model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huji/past/past": { "id": "huji/past/past", "name": "PAST", "maker": "huji", "family": "huji/past", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end phonetic-acoustic speech tokenizer from the Hebrew University SLP-RL lab. The card says it jointly models phonetic information and signal reconstruction with auxiliary heads on the first vector-quantization module, using supervised phonetic data instead of an external pretrained self-supervised encoder. The full checkpoint PAST.th is trained on LibriSpeech and TIMIT. PAST_streamable.th is a causal variant with 20 ms look-ahead. PAST_no_transformer.th is also in the Hub repo and is not listed in the card's model table. Inference loads PastModel.from_pretrained with the name PAST or PAST_streamable. Sample rate comes from the loaded model object. The card does not print a parameter count or a context length.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PAST is a phonetic-acoustic speech tokenizer from the Hebrew University SLP-RL lab. The Hub repo `slprl/PAST` was created on 2025-05-20 and holds the full checkpoint, a streamable checkpoint, and an unlabeled no-transformer file. Authors on the card are Nadav Har-Tuv, Or Tal, and Yossi Adi. Status on 2026-10-15 is active. No license tag was on the weight repo.", "what_it_is_for": "The card presents PAST as an end-to-end tokenizer that keeps phonetic information and can reconstruct the waveform, without relying on an external pretrained self-supervised model. A streamable causal variant targets real-time use with 20 ms of look-ahead. The card also evaluates PAST as the speech representation inside a speech language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huji/sims/sims-7b": { "id": "huji/sims/sims-7b", "name": "SIMS-7B", "maker": "huji", "family": "huji/sims", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-31", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "UnitLM interleaved speech-text model, twist_init true, fine-tuned from Qwen/Qwen2.5-7B. config.json: 28 layers, hidden 3584, intermediate 18944, 28 heads, 4 key-value heads, RoPE theta 1000000, tied embeddings false, speech-model vocab_size 152167 against a base vocab of 152064, max_position_embeddings 131072, sliding_window 131072 with use_sliding_window false, float32. The card says the vocabulary was extended with 500 speech tokens from mHuBERT-25Hz layer 11. Hub safetensors badge says 8B. The paper Table 3 row is labeled 7B and the Table 2 Qwen row at the largest compute is labeled 7.6B. Config _name_or_path ends in qwen7b_it_scale/checkpoint-98883. The card says training used 8 Nvidia H100 GPUs.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SIMS-7B is an interleaved speech-text language model from the Hebrew University SLP-RL lab. The Hub repo `slprl/SIMS-7B` was created on 2025-03-31. The preprint was submitted on 2025-04-03 and revised on 2025-07-27. It is a fine-tune of Qwen2.5-7B with speech tokens added. Weights are MIT. Status on 2026-10-15 is active.", "what_it_is_for": "The card says the model generates a speech continuation, a text continuation from speech, or the reverse, and that it can be tuned further. The paper frames it as the scaled interleaved model from a study of how speech-text interleaving scales. The card says outputs should not be treated as factual. Training hardware on the card is 8 Nvidia H100 GPUs. Code is SlamKit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huji/sims/sims-llama3-2-3b": { "id": "huji/sims/sims-llama3-2-3b", "name": "SIMS-Llama3.2-3B", "maker": "huji", "family": "huji/sims", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.2", "release_date": "2025-04-02", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "UnitLM interleaved speech-text model, twist_init true, fine-tuned from meta-llama/Llama-3.2-3B. config.json base block: 28 layers, hidden 3072, intermediate 8192, 24 heads, 8 key-value heads, head dim 128, RoPE theta 500000, Llama-3 RoPE scaling factor 32 from an original 8192 positions, max_position_embeddings 131072, speech-model vocab_size 128758 against a base vocab of 128256, float32. The card says 500 speech tokens were added from mHuBERT-25Hz layer 11. The difference of the two vocab fields is 502. Hub safetensors badge says 3B. Paper Table 2 labels the Llama row SIMS (L3) at 3.2B. Config _name_or_path ends in llama3b_inter_twist_20k/checkpoint-20000. The card says training used 8 Nvidia A100 GPUs. The model-card license line says Llama3.2 licensed. The Hub tag is llama3.2.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SIMS-Llama3.2-3B is the Llama-based interleaved speech-text model from the Hebrew University SLP-RL scaling study. The Hub repo `slprl/SIMS-Llama3.2-3B` was created on 2025-04-02. The license line on the card says Llama 3.2. Status on 2026-10-15 is active.", "what_it_is_for": "The card describes the same uses as SIMS-7B: continue speech, continue into the other modality, or fine-tune further. It was fine-tuned from Llama-3.2-3B by adding speech tokens. The card says training used 8 Nvidia A100 GPUs and that outputs should not be treated as factual.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huji/slam/slam": { "id": "huji/slam/slam", "name": "Slam", "maker": "huji", "family": "huji/slam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-18", "context_length": 32768, "max_output_tokens": null, "parameters": "358000000", "architecture": "UnitLM speech language model, twist_init true, fine-tuned from Qwen/Qwen2.5-0.5B. The released config is a Qwen2 decoder: 24 layers, hidden size 896, intermediate size 4864, 14 attention heads, 2 key-value heads, SiLU, RoPE theta 10000, tied embeddings, torch dtype float32. Speech vocab_size in config.json is 502. The card says the vocabulary is 500 speech tokens from layer 11 of mHuBERT-25Hz, de-duplicated after textlesslib k-means. max_position_embeddings is 32768, copied from the text base. The card's evaluation row for this checkpoint is 358M parameters, trained on one Nvidia A5000 for 24 hours.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Slam is a speech language model from the Spoken Language Processing Research Lab at the Hebrew University of Jerusalem. It continues speech as discrete mHuBERT tokens. The Hub checkpoint `slprl/slam` was created on 2025-02-18. The preprint is 2025-02-19. Weights are public under MIT. Status on 2026-10-15 is active.", "what_it_is_for": "The model card describes a base speech language model for continuations of speech segments, and as a starting point for further tuning. It was trained by next-token prediction on a subset of LibriSpeech, Libri-Light, and synthetic sTinyStories, then DPO on SpokenSwag. The card says outputs should not be treated as factual.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huji/slam/slam-large": { "id": "huji/slam/slam-large", "name": "Slam Large", "maker": "huji", "family": "huji/slam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-05-20", "context_length": 131072, "max_output_tokens": null, "parameters": "1300000000", "architecture": "UnitLM speech language model. config.json base_model_name and base _name_or_path are Qwen/Qwen2.5-1.5B: 28 layers, hidden 1536, intermediate 8960, 12 heads, 2 key-value heads, speech vocab_size 502, max_position_embeddings 131072, sliding_window 131072 with use_sliding_window false, float32, twist_init true. The Hub model tree says the base is Qwen2.5-1.5B. One bullet on the card says the fine-tune base is Qwen2.5-0.5B, and one link in that section points at the 0.5B repo while its visible label says 1.5B. The evaluation row Slam (large) says 1.3B parameters, 2\u00d7A100, 6.1B + 9M tokens. The Hub safetensors badge says 1B. The hardware sentence says 2 Nvidia A100 GPUs for 48 hours.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Slam Large is the largest speech-language checkpoint the Hebrew University SLP-RL lab released from the Slamming paper. The Hub repo `slprl/slam_large` was created on 2025-05-20. The preprint is 2025-02-19. Weights are MIT. Status on 2026-10-15 is active.", "what_it_is_for": "The card describes the same job as the smaller Slam checkpoints: generate a speech continuation, or fine-tune further. It was pretrained by next-token prediction on a LibriSpeech subset, Libri-Light, and sTinyStories, then DPO-tuned on SpokenSwag. The card says those story and audiobook outputs are not factual.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huji/slam/slam-scaled": { "id": "huji/slam/slam-scaled", "name": "Slam Scaled", "maker": "huji", "family": "huji/slam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-18", "context_length": 32768, "max_output_tokens": null, "parameters": "358000000", "architecture": "Same UnitLM recipe as Slam, fine-tuned from Qwen/Qwen2.5-0.5B. config.json matches that base: 24 layers, hidden 896, intermediate 4864, 14 heads, 2 key-value heads, speech vocab_size 502, max_position_embeddings 32768, float32, twist_init true. The card evaluation row calls this Slam (scaled): 358M parameters, 2\u00d7A100, 16.7B + 9M tokens. The card hardware sentence says 2 Nvidia A100 GPUs for 48 hours. The Slam card describes this repo as the stronger run trained with slightly more compute, 2 A100s for 2 days.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Slam Scaled is the higher-compute speech language model from the same Hebrew University SLP-RL paper as Slam. The Hub repo `slprl/slam_scaled` was created on 2025-02-18. It is still the 358M Qwen2.5-0.5B UnitLM, trained on more tokens. Weights are MIT. Status on 2026-10-15 is active.", "what_it_is_for": "The Slam card points here as the stronger checkpoint, trained with slightly more compute. This card repeats the same job: continue a speech segment, or start a further fine-tune. It is not a factual assistant. Training data matches Slam: LibriSpeech subset, Libri-Light, sTinyStories, then DPO on SpokenSwag.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huji/stresslm/stresslm": { "id": "huji/stresslm/stresslm", "name": "StresSLM", "maker": "huji", "family": "huji/stresslm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-05-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter on Qwen/Qwen2-Audio-7B-Instruct. The card loads it with PeftModel.from_pretrained after loading the Qwen2-Audio base named in the adapter config. Tasks on the card are sentence stress detection and sentence stress reasoning. Hub tag is a PEFT adapter. The base 7B model is not re-released in this repo. No adapter rank or parameter count is printed on the card.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StresSLM is a LoRA adapter from the Hebrew University SLP-RL lab on Qwen2-Audio-7B-Instruct. It detects sentence stress and reasons about it. The Hub repo `slprl/StresSLM` was created on 2025-05-28. The license tag is CC-BY-NC-4.0. The adapter is not a full copy of Qwen2-Audio. Status on 2026-10-15 is active.", "what_it_is_for": "The card defines two tasks: sentence stress detection, which marks stressed words, and sentence stress reasoning, which uses the stress pattern to talk about the speaker's intention. Authors are Iddo Yosha, Gallil Maimon, and Yossi Adi. Evaluation code is the StressTest repository. The lab page later lists the paper under ACL 2026 Findings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "huji/whistress/whistress": { "id": "huji/whistress/whistress", "name": "WhiStress", "maker": "huji", "family": "huji/whistress", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-05-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Whisper-small.en with an extra decoder block and a token-level stress classifier. The Hub repo publishes additional_decoder_block.pt, classifier.pt, and metadata.json rather than a full rewritten Whisper checkpoint. The card says the added modules predict which tokens are emphasized, and that a transcription can be passed in or predicted from audio. Trained on TinyStress-15K, a synthetic set derived from TinyStories. Intended for research. Interspeech 2025 paper, pages 4718-4722 in the Interspeech proceedings record.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WhiStress is a sentence-stress model from the Hebrew University SLP-RL lab. It adds a decoder block and a classifier to Whisper-small.en so a transcription can also mark emphasized words. The Hub repo `slprl/WhiStress` was created on 2025-05-25, the arXiv submission day. The license tag is CC-BY-NC-4.0. Status on 2026-10-15 is active.", "what_it_is_for": "The card says WhiStress transcribes speech and predicts token-level sentence stress without a separate alignment. Training used TinyStress-15K, synthetic speech derived from TinyStories. The card limits it to research. Authors are Iddo Yosha, Dorin Shteyman, and Yossi Adi.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/ellm/ellm": { "id": "hume/ellm/ellm", "name": "eLLM", "maker": "hume", "family": "hume/ellm", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Empathic large language model. Hume's term for the multimodal model inside the first EVI: it takes expression measures and language, generates the first response, and modulates tune, rhythm, and timbre. External LLMs could be added for longer answers.", "modalities": { "input": [ "audio", "text" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI's empathic large language model, named on 2024-03-25 as the model inside the first Empathic Voice Interface. Closed weights. It is not a current API model id. Collected 2026-10-15.", "what_it_is_for": "Hume AI's empathic large language model, named on 2024-03-25 as the model inside the first Empathic Voice Interface. Closed weights. It is not a current API model id. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/evi/evi-1": { "id": "hume/evi/evi-1", "name": "EVI 1", "maker": "hume", "family": "hume/evi", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Empathic Voice Interface powered by Hume's empathic large language model (eLLM). The April 2024 API post says it does more than stitch together transcription, an LLM, and TTS: the eLLM uses tone of voice for turn-taking, wording, and its own prosody.", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI's first Empathic Voice Interface, a speech-to-speech product announced on 2024-03-25 and opened as an API on 2024-04-18. Official docs say EVI 1 reached end of support on 2025-08-30. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Hume AI's first Empathic Voice Interface, a speech-to-speech product announced on 2024-03-25 and opened as an API on 2024-04-18. Official docs say EVI 1 reached end of support on 2025-08-30. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/evi/evi-2": { "id": "hume/evi/evi-2", "name": "EVI 2", "maker": "hume", "family": "hume/evi", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Voice-to-voice foundation model. Hume said the checkpoint released that day was EVI-2-small. Voice identity is fixed in the architecture so it cannot clone voices without code changes. Developers could modulate base voices on scales such as gender, nasality, and pitch.", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI voice-to-voice model released in beta on 2024-09-11 as EVI-2-small. The version guide says EVI 2 reached end of support on 2025-08-30. Closed weights. It was built not to clone voices. Collected 2026-10-15.", "what_it_is_for": "Hume AI voice-to-voice model released in beta on 2024-09-11 as EVI-2-small. The version guide says EVI 2 reached end of support on 2025-08-30. Closed weights. It was built not to clone voices. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/evi/evi-2-large": { "id": "hume/evi/evi-2-large", "name": "EVI-2-large", "maker": "hume", "family": "hume/evi", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described only as a larger voice-to-voice model being fine-tuned. A same-day comparison post says that, versus EVI-2-small, the larger model was meant to keep the voice capabilities while following prompts and complex reasoning better.", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Larger EVI 2 voice-to-voice model that Hume named on 2024-09-11 as still in fine-tuning. No later official page found in this pass says it shipped. Status announced. Collected 2026-10-15.", "what_it_is_for": "Larger EVI 2 voice-to-voice model that Hume named on 2024-09-11 as still in fine-tuning. No later official page found in this pass says it shipped. Status announced. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/evi/evi-3": { "id": "hume/evi/evi-3", "name": "EVI 3", "maker": "hume", "family": "hume/evi", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech-language model. Hume says the same model handles transcription, language, and speech, and that speech output inherits Octave. It can use a Voice Library, designed, or cloned voice without fine-tuning that voice. A supplemental LLM is optional. Quick responses are available.", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI's third speech-to-speech model, introduced 2025-05-29 and added to the API in July 2025 as evi_version \"3\". Still callable, but the TTS and EVI APIs end on 2026-11-13, so status is deprecated. Closed weights. A supplemental LLM is optional. Collected 2026-10-15.", "what_it_is_for": "Hume AI's third speech-to-speech model, introduced 2025-05-29 and added to the API in July 2025 as evi_version \"3\". Still callable, but the TTS and EVI APIs end on 2026-11-13, so status is deprecated. Closed weights. A supplemental LLM is optional. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/evi/evi-4-mini": { "id": "hume/evi/evi-4-mini", "name": "EVI 4-mini", "maker": "hume", "family": "hume/evi", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech-to-speech config that uses Octave 2 for speech and requires an external supplemental LLM. The launch post says it does not yet generate its own language. Quick responses are unavailable. Model latency improvement versus EVI 3 is described as about 100 ms per response.", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI speech-to-speech configuration launched October 1, 2025. API value \"4-mini\". It speaks with Octave 2 in 11 languages and requires an external language model. Still supported until the EVI API ends on 2026-11-13. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Hume AI speech-to-speech configuration launched October 1, 2025. API value \"4-mini\". It speaks with Octave 2 in 11 languages and requires an external language model. Still supported until the EVI API ends on 2026-11-13. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/evi/hume-evi-2": { "id": "hume/evi/hume-evi-2", "name": "hume-evi-2", "maker": "hume", "family": "hume/evi", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Native Hume speech-language model id. The language-model guide says these models process language and audio in one latent space. The guide's EVI 1/2/3 checkmark table rendered empty, so version support is not known from that page.", "modalities": { "input": [ "audio", "text" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI native speech-language model id listed for EVI. Closed weights. The docs do not give a release date or a readable compatibility matrix. The EVI API that hosts it ends on 2026-11-13. Collected 2026-10-15.", "what_it_is_for": "Hume AI native speech-language model id listed for EVI. Closed weights. The docs do not give a release date or a readable compatibility matrix. The EVI API that hosts it ends on 2026-11-13. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/evi/hume-evi-3": { "id": "hume/evi/hume-evi-3", "name": "hume-evi-3", "maker": "hume", "family": "hume/evi", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Native speech-language model for EVI 3. Changelog calls it one of two new native models with hume-evi-3-websearch. The language-model guide says Hume's native models handle language and voice in one latent space.", "modalities": { "input": [ "audio", "text" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI native speech-language model id added for EVI 3 on 2025-07-18. Closed weights. Still documented, on an API that ends 2026-11-13. Collected 2026-10-15.", "what_it_is_for": "Hume AI native speech-language model id added for EVI 3 on 2025-07-18. Closed weights. Still documented, on an API that ends 2026-11-13. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/evi/hume-evi-3-websearch": { "id": "hume/evi/hume-evi-3-websearch", "name": "hume-evi-3-websearch", "maker": "hume", "family": "hume/evi", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "EVI 3 native speech-language model id with web search. Same changelog entry as hume-evi-3. No separate architecture note was published.", "modalities": { "input": [ "audio", "text" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI EVI 3 speech-language model id with web search, announced in the July 18, 2025 changelog next to hume-evi-3. Closed weights. API access ends 2026-11-13. Collected 2026-10-15.", "what_it_is_for": "Hume AI EVI 3 speech-language model id with web search, announced in the July 18, 2025 changelog next to hume-evi-3. Closed weights. API access ends 2026-11-13. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/expression/emotional-language": { "id": "hume/expression/emotional-language", "name": "Emotional language", "maker": "hume", "family": "hume/expression", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Expression model over the meaning and tone of text. 53 dimensions. Optional sentiment and toxicity. The Python quickstart sends text with StreamLanguage granularity sentence.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI text model that scored 53 emotional-language dimensions, with optional sentiment and toxicity. It was part of the Expression Measurement API that ended on June 14, 2026. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Hume AI text model that scored 53 emotional-language dimensions, with optional sentiment and toxicity. It was part of the Expression Measurement API that ended on June 14, 2026. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/expression/prosody": { "id": "hume/expression/prosody", "name": "Speech prosody", "maker": "hume", "family": "hume/expression", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Expression model. 48 dimensions from tone, rhythm, and timbre. The overview says it accepts audio and video. Scores estimate how a human rater would label the expression. Batch field name prosody is in the Python quickstart.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI speech-prosody expression model from the original Expression Measurement API. It scored 48 vocal-expression dimensions. That API's last day of use was June 14, 2026. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Hume AI speech-prosody expression model from the original Expression Measurement API. It scored 48 vocal-expression dimensions. That API's last day of use was June 14, 2026. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/expression/speech-emotion": { "id": "hume/expression/speech-emotion", "name": "Speech emotion model", "maker": "hume", "family": "hume/expression", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The October 6, 2026 science post's speech emotion model: 414 tags, 23 core emotions and 391 finer descriptions, scored from audio. The API does not let you select it. Audio endpoints return an expressions array. Docs also speak of one audio model rather than two.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI speech-expression model in the October 6, 2026 Expression Measurement API, which is a limited release. It scores 414 vocal-expression tags. There is no separate model id; audio endpoints return the scores with voice attributes. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Hume AI speech-expression model in the October 6, 2026 Expression Measurement API, which is a limited release. It scores 414 vocal-expression tags. There is no separate model id; audio endpoints return the scores with voice attributes. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/expression/vocal-burst": { "id": "hume/expression/vocal-burst", "name": "Vocal burst", "maker": "hume", "family": "hume/expression", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Expression model for non-linguistic vocalizations such as laughs, sighs, and gasps. 48 dimensions. The overview says audio and video. The batch JSON field name was not on the pages read.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI model that measured 48 dimensions of laughs, sighs, gasps, and other vocal bursts. Part of the Expression Measurement API that ended on June 14, 2026. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Hume AI model that measured 48 dimensions of laughs, sighs, gasps, and other vocal bursts. Part of the Expression Measurement API that ended on June 14, 2026. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/expression/voice-descriptor": { "id": "hume/expression/voice-descriptor", "name": "Voice descriptor model", "maker": "hume", "family": "hume/expression", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Science post: voice descriptor model, 190 characteristics in 27 families, such as raspy, breathy, fast, monotone, whispering, and theatrical. API field voice_attributes. Not separately selectable from the speech emotion scores.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI model that scores 190 vocal qualities and speaking styles. It ships inside the October 6, 2026 audio expression API as voice_attributes, not as its own model id. Limited release. Closed weights. Collected 2026-10-15.", "what_it_is_for": "Hume AI model that scores 190 vocal qualities and speaking styles. It ships inside the October 6, 2026 audio expression API as voice_attributes, not as its own model id. Limited release. Closed weights. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/octave/octave-1": { "id": "hume/octave/octave-1", "name": "Octave 1", "maker": "hume", "family": "hume/octave", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Model license was not published. TTS docs say the user keeps ownership of audio generated with Octave.", "release_date": "2025-02-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech-language model for text-to-speech. Hume calls it an LLM that uses the meaning of the text to set pronunciation, pitch, tempo, and emphasis. API version value is \"1\".", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI text-to-speech speech-language model, API version \"1\", launched February 26, 2025. English and Spanish. Closed weights. Still served until the TTS API ends on 2026-11-13. Collected 2026-10-15.", "what_it_is_for": "Hume AI text-to-speech speech-language model, API version \"1\", launched February 26, 2025. English and Spanish. Closed weights. Still served until the TTS API ends on 2026-11-13. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/octave/octave-2": { "id": "hume/octave/octave-2", "name": "Octave 2", "maker": "hume", "family": "hume/octave", "status": "preview", "confidence": "high", "weights": "closed", "license": "Model license was not published. TTS docs say the user keeps ownership of generated audio.", "release_date": "2025-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Second-generation Octave speech-language model for TTS. API version \"2\". Preview. The launch post says it is about 40% faster than Octave 1 and adds voice conversion and phoneme editing later. The TTS guide lists model latency about 100 ms.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI preview text-to-speech model, API version \"2\", launched October 1, 2025. Eleven languages and about 100 ms model latency in the current TTS docs. Closed weights. The TTS API ends on 2026-11-13, while Hume still labels the model a preview. Collected 2026-10-15.", "what_it_is_for": "Hume AI preview text-to-speech model, API version \"2\", launched October 1, 2025. Eleven languages and about 100 ms model latency in the current TTS docs. Closed weights. The TTS API ends on 2026-11-13, while Hume still labels the model a preview. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/octave/octave-3b": { "id": "hume/octave/octave-3b", "name": "OCTAVE 3B", "maker": "hume", "family": "hume/octave", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-23", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Smallest OCTAVE named in the December 23, 2024 research post. The post says every example on that page was generated by OCTAVE 3B and that language scores were comparable to a similar-sized frontier LLM. The score chart is an image and its numbers were not copied.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI research speech-language model size named on 2024-12-23. The post says partners had early access to a limited OCTAVE and that the page's audio was generated by the 3B. It was not found as its own API id. Collected 2026-10-15.", "what_it_is_for": "Hume AI research speech-language model size named on 2024-12-23. The post says partners had early access to a limited OCTAVE and that the page's audio was generated by the 3B. It was not found as its own API id. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/tada/tada-1b": { "id": "hume/tada/tada-1b", "name": "TADA 1B", "maker": "hume", "family": "hume/tada", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GitHub repository is MIT. The model card says the project is built on Llama 3.2 under the Llama 3.2 Community License and does not state a separate weights license.", "release_date": "2026-03-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Llama 3.2 1B with a flow-matching head and 1:1 text-to-acoustic alignment. Hume calls the release a 1B model. The Hugging Face model-size widget says 2B params. English checkpoint. Shares the HumeAI/tada-codec encoder.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI open-weight English text-to-speech model released March 10, 2026. It is a Llama 3.2 1B speech-language model with text-acoustic dual alignment. Weights are downloadable. Collected 2026-10-15.", "what_it_is_for": "Hume AI open-weight English text-to-speech model released March 10, 2026. It is a Llama 3.2 1B speech-language model with text-acoustic dual alignment. Weights are downloadable. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/tada/tada-3b-ml": { "id": "hume/tada/tada-3b-ml", "name": "TADA 3B multilingual", "maker": "hume", "family": "hume/tada", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GitHub repository is MIT. The model card says the project uses Llama 3.2 under the Llama 3.2 Community License and does not state a separate weights license.", "release_date": "2026-03-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Llama 3.2 3B with the same 1:1 text-acoustic design as TADA 1B. Hume calls it a 3B multilingual model. The Hugging Face widget says 4B params. The card says to load it in bfloat16. Non-English prompts should pass a transcript because the built-in ASR is English-only.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI open-weight multilingual text-to-speech model released March 10, 2026, based on Llama 3.2 3B. The model card lists English plus nine language codes. Collected 2026-10-15.", "what_it_is_for": "Hume AI open-weight multilingual text-to-speech model released March 10, 2026, based on Llama 3.2 3B. The model card lists English plus nine language codes. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hume/tada/tada-codec": { "id": "hume/tada/tada-codec", "name": "TADA codec", "maker": "hume", "family": "hume/tada", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The Hugging Face card badges the GitHub repository license as MIT.", "release_date": "2026-03-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Shared audio encoder and decoder for TADA. The 1B card says every TADA model loads HumeAI/tada-codec. The encoder, with an aligner, maps each text token to one acoustic vector. A flow-matching head in the LLM then asks the decoder for audio. Parameter count was not published.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hume AI open-weight audio encoder and decoder released with TADA on March 10, 2026. The speech models load it to align text tokens with acoustic vectors. Collected 2026-10-15.", "what_it_is_for": "Hume AI open-weight audio encoder and decoder released with TADA on March 10, 2026. The speech models load it to align text tokens with acoustic vectors. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hunan/360bev/360mapper": { "id": "hunan/360bev/360mapper", "name": "360Mapper", "maker": "hunan", "family": "hunan/360bev", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Panoramic semantic mapper. The README section is titled 360Mapper. Front-view checkpoints use MiT-B2 on RGB. Bird's-eye-view checkpoints use MiT-B0, MiT-B2, or MSCA-B. The paper task name is 360BEV: one panorama, with depth where the method needs it, to a top-down semantic map. The README says commercial use should contact the authors even though the repo license is Apache-2.0.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "360Mapper is the panoramic semantic mapping model in the InSAI Lab 360BEV repository at Hunan University. The README says code and models were released in April 2023. The paper is WACV 2024. The repo license is Apache-2.0, with a line that commercial use should contact the authors. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README defines the 360BEV task as mapping a 360-degree panorama to a top-down semantic map of an indoor scene, instead of using a narrow-field image sequence. It also reports front-view panoramic segmentation on Stanford2D3D and Matterport. The model heading in the README is 360Mapper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hunan/cmnext/cmnext": { "id": "hunan/cmnext/cmnext", "name": "CMNeXt", "maker": "hunan", "family": "hunan/cmnext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Arbitrary-modal semantic segmentation in the Hub2Fuse setup. RGB branch uses multi-head self-attention. Auxiliary branch uses a Parallel Pooling Mixer. A Self-Query Hub picks supplementary features. Feature Rectification and Feature Fusion modules combine them. The paper says each extra modality adds about 0.01M parameters and the model scales from 1 to 81 modalities. DELIVER checkpoints use MiT-B2. NYU Depth V2, MFNet, and UrbanLF checkpoints on the card use MiT-B4. The same Hub repo also stores SegFormer MiT-B0 through B5 and Swin Transformer ImageNet/22k files under pretrained/. Those are other makers' backbones, not CMNeXt.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CMNeXt is an arbitrary-modal semantic segmentation model released with weights by InSAI Lab, School of AI and Robotics, Hunan University. The CVPR 2023 paper was submitted to arXiv on 2 March 2023. The Apache-2.0 checkpoint repo is public. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and the model card say CMNeXt fuses an arbitrary number of modalities with an RGB stream for semantic segmentation. The DeLiVER benchmark covers depth, LiDAR, events, multiple views, and RGB, including bad weather and sensor-failure cases. Other released checkpoints cover KITTI-360, NYU Depth V2, MFNet (RGB-thermal), UrbanLF, and MCubeS.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hunan/esm-dbp/esm-dbp": { "id": "hunan/esm-dbp/esm-dbp", "name": "ESM-DBP", "maker": "hunan", "family": "hunan/esm-dbp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "academic-use", "release_date": "2024-09-07", "context_length": null, "max_output_tokens": null, "parameters": "650000000", "architecture": "Domain-adaptive continued pretraining of Meta ESM-2 650M. The Nature Communications methods text says ESM-2 here is 33 stacked transformer encoder blocks. The first 29 blocks stay frozen. Only the last four blocks and the amino-acid classification layer are updated. Training is masked language modeling on UniDBP40 (170,264 non-redundant DNA-binding protein sequences, CD-HIT threshold 0.4, UniProtKB through August 2023), masking 15% of residues. Sequences longer than 512 residues are cut; shorter ones are padded to 512.", "modalities": { "input": [ "protein-sequence" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ESM-DBP is a DNA-binding protein language model from Peng Shaoliang's group at Hunan University (College of Computer Science and Electronic Engineering, and the National Supercomputing Center in Changsha). Nature Communications published it on 7 September 2024, with Hunan University as the only institution. Weights are public for academic use. This dossier was collected on 2026-10-15.", "what_it_is_for": "The university news and the paper describe ESM-DBP as a domain model for DNA-binding proteins and residues, including cases with few homologs. It is continued pretraining of Meta's ESM-2, not a new architecture and not a copy of the untouched ESM-2 file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hunan/matchformer/matchformer": { "id": "hunan/matchformer/matchformer", "name": "MatchFormer", "maker": "hunan", "family": "hunan/matchformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hierarchical extract-and-match transformer. Each encoder stage interleaves self-attention for feature extraction and cross-attention for matching. The README names four weight files and two coarse widths: large models use D_MODEL 256 and resolution (8, 2); lite models use D_MODEL 192 and resolution (8, 4). Backbone type strings are largesea, litela, largela, and litesea. Training notes say the code is based on LoFTR.", "modalities": { "input": [ "image" ], "output": [ "correspondences" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MatchFormer is a feature-matching transformer whose code and weight links are in the InSAI Lab repository at Hunan University. The README says ACCV 2022 accepted it in September 2022. The GitHub license is Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README describes a hierarchical encoder that interleaves self-attention for extraction and cross-attention for matching, called extract-and-match. Evaluation configs cover indoor ScanNet and outdoor MegaDepth.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hunan/onebev/onebev": { "id": "hunan/onebev/onebev", "name": "OneBEV", "maker": "hunan", "family": "hunan/onebev", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bird's-eye-view semantic mapping from one panoramic image. The README names view transformation MVT. Configs live in configs/onebev/. A reference environment is Python 3.10.13, PyTorch 2.1.1, CUDA 12.1, and mmcv 2.1.0. The pretrained VMamba backbone is a separate download via pretrained/download.sh and is not OneBEV.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OneBEV is a panoramic bird's-eye-view semantic mapper from InSAI Lab at Hunan University. The README says it was released in November 2024 and cites ACCV 2024. The repository license is MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper title on the README is using one panoramic image for bird's-eye-view semantic mapping. The README names the view transform MVT and lists five datasets: Stanford-360, Matterport-360, NuScenes-360, DeepAccident-360, and DeepAccident-Native.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hunan/onebev/onebev-plus": { "id": "hunan/onebev/onebev-plus", "name": "OneBEV++", "maker": "hunan", "family": "hunan/onebev", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Follow-on to OneBEV. The README names view transformation CRP plus MVTv2. Configs live in configs/onebev++/. Checkpoints are weights/onebev++/. The example test file is weights/onebev++/nusc_epoch_50.pth. Same reference stack as OneBEV: Python 3.10.13, PyTorch 2.1.1, CUDA 12.1, mmcv 2.1.0. IEEE T-PAMI according to the README badge.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OneBEV++ is the later panoramic bird's-eye-view mapper in the InSAI Lab OneBEV repository at Hunan University. The README says it was released in August 2026 and points at an IEEE T-PAMI page. The repository license is MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README title is unifying bird's-eye-view semantic mapping with panoramas. It names the view transform CRP plus MVTv2, against OneBEV's MVT. It uses the same five panoramic BEV datasets as OneBEV.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hunan/trans4pass/trans4pass": { "id": "hunan/trans4pass/trans4pass", "name": "Trans4PASS", "maker": "hunan", "family": "hunan/trans4pass", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Distortion-aware transformer for panoramic semantic segmentation. The paper adds Deformable Patch Embedding and Deformable MLP, then Mutual Prototypical Adaptation for unsupervised domain adaptation from pinhole labels to panoramas. The Hub card ships Tiny and Small supervised and adaptation checkpoints. pretrained/mit_b1.pth and mit_b2.pth in that repo are SegFormer backbones, not Trans4PASS.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Trans4PASS is a distortion-aware transformer for panoramic semantic segmentation. InSAI Lab at Hunan University hosts the Apache-2.0 weights. The CVPR 2022 paper was submitted to arXiv on 2 March 2022 (revised 17 March 2022). This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says pinhole semantic labels transfer poorly onto 360-degree images because of distortion. Trans4PASS learns those deformations with Deformable Patch Embedding and a Deformable MLP, then aligns pinhole and panoramic prototypes with Mutual Prototypical Adaptation. The indoor example in the abstract is Stanford2D3D. The outdoor example is DensePASS.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hunan/trans4pass/trans4pass-plus": { "id": "hunan/trans4pass/trans4pass-plus", "name": "Trans4PASS+", "maker": "hunan", "family": "hunan/trans4pass", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2022-07-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Upgrade of Trans4PASS. The paper adds DMLPv2 and says deformable modules can sit before or after adaptation and at shallow or deep layers. Mutual Prototypical Adaptation is extended with pseudo-label rectification. The work adds the SynPASS dataset (9,080 panoramic images) for synthetic-to-real adaptation as well as pinhole-to-panoramic adaptation. Hub checkpoints are Tiny and Small. Accepted to IEEE TPAMI; arXiv v5 is dated 31 May 2024.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Trans4PASS+ is the upgraded panoramic segmentation transformer from the Trans4PASS line. InSAI Lab at Hunan University ships it in the same Apache-2.0 Hub repo as Trans4PASS. The extension paper was submitted to arXiv on 25 July 2022 and later accepted to IEEE TPAMI. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says Trans4PASS+ handles panoramic distortion with Deformable Patch Embedding and DMLPv2, and improves Mutual Prototypical Adaptation with pseudo-label rectification. It covers pinhole-to-panoramic adaptation and synthetic-to-real adaptation on SynPASS, a 9,080-image panoramic set introduced in that paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/diffusiondrive/diffusiondrive": { "id": "hust/diffusiondrive/diffusiondrive", "name": "DiffusionDrive", "maker": "hust", "family": "hust/diffusiondrive", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub YAML says apache-2.0. The card body says non-commercial. The arXiv HTML license line for the paper is CC BY-NC-SA 4.0.", "release_date": "2024-11-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Truncated diffusion policy for end-to-end driving. The card says a ResNet-34 backbone, 2-step denoising, and real-time speed. Developed by HUST and Horizon Robotics.", "modalities": { "input": [ "image" ], "output": [ "trajectories" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffusionDrive is HUST Vision Lab's open-weight truncated diffusion planner for end-to-end driving, with Horizon Robotics. Collected 2026-10-15. Weights are public. The license lines on the Hub YAML, the card, and the paper do not match.", "what_it_is_for": "DiffusionDrive is HUST Vision Lab's open-weight truncated diffusion planner for end-to-end driving, with Horizon Robotics. Collected 2026-10-15. Weights are public. The license lines on the Hub YAML, the card, and the paper do not match.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/diffusiondrive/diffusiondrivev2": { "id": "hust/diffusiondrive/diffusiondrivev2", "name": "DiffusionDriveV2", "maker": "hust", "family": "hust/diffusiondrive", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-12-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reinforcement-learning constrained truncated diffusion planner. Intra-anchor GRPO and inter-anchor truncated GRPO sit on the DiffusionDrive mixture. The paper evaluates a ResNet-34 variant and says that backbone is 21.8M parameters in a comparison with a 96.9M V2-99 backbone. That 21.8M figure is the backbone size, not stored as the full checkpoint total. Two Hub files: diffusiondrivev2_rl.ckpt and diffusiondrivev2_sel.ckpt. HUST releases the weights. Horizon Robotics and Wuhan University co-author the paper.", "modalities": { "input": [ "image" ], "output": [ "trajectories" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffusionDriveV2 is HUST Vision Lab's open-weight follow-up to DiffusionDrive, using reinforcement learning to constrain truncated diffusion planning. Collected 2026-10-15. Both checkpoints are public under MIT.", "what_it_is_for": "DiffusionDriveV2 is HUST Vision Lab's open-weight follow-up to DiffusionDrive, using reinforcement learning to constrain truncated diffusion planning. Collected 2026-10-15. Both checkpoints are public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/diffusionvl/diffusionvl-qwen2-5-7b": { "id": "hust/diffusionvl/diffusionvl-qwen2-5-7b", "name": "DiffusionVL-Qwen2.5-7B", "maker": "hust", "family": "hust/diffusionvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-12-25", "context_length": null, "max_output_tokens": null, "parameters": "8030348832", "architecture": "Diffusion fine-tune that translates a pretrained autoregressive model into a diffusion vision-language model with block decoding and KV-cache reuse. The card says training used under 5% of prior dVLM data (738K vs 16.5M samples) and claims about 2.0x inference speedup over previous dVLMs. Translated from the Qwen2.5 language model. The card's 2025-12-25 note says the AR-LM weights shipped with the full open-source drop. Safetensors total is 8030348832.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffusionVL is HUST Vision Lab's open-weight method for turning an autoregressive model into a diffusion vision-language model. DiffusionVL-Qwen2.5-7B is one of the three released translations. Collected 2026-10-15. Weights are public under Apache-2.0.", "what_it_is_for": "DiffusionVL is HUST Vision Lab's open-weight method for turning an autoregressive model into a diffusion vision-language model. DiffusionVL-Qwen2.5-7B is one of the three released translations. Collected 2026-10-15. Weights are public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/diffusionvl/diffusionvl-qwen2-5-vl-3b": { "id": "hust/diffusionvl/diffusionvl-qwen2-5-vl-3b", "name": "DiffusionVL-Qwen2.5-VL-3B", "maker": "hust", "family": "hust/diffusionvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "3754622976", "architecture": "Diffusion fine-tune that translates a pretrained autoregressive model into a diffusion vision-language model with block decoding and KV-cache reuse. The card says training used under 5% of prior dVLM data (738K vs 16.5M samples) and claims about 2.0x inference speedup over previous dVLMs. Translated from Qwen2.5-VL. A Hub collection label says 4B; the safetensors total on the model API is 3754622976.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffusionVL is HUST Vision Lab's open-weight method for turning an autoregressive model into a diffusion vision-language model. DiffusionVL-Qwen2.5-VL-3B is one of the three released translations. Collected 2026-10-15. Weights are public under Apache-2.0.", "what_it_is_for": "DiffusionVL is HUST Vision Lab's open-weight method for turning an autoregressive model into a diffusion vision-language model. DiffusionVL-Qwen2.5-VL-3B is one of the three released translations. Collected 2026-10-15. Weights are public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/diffusionvl/diffusionvl-qwen2-5-vl-7b": { "id": "hust/diffusionvl/diffusionvl-qwen2-5-vl-7b", "name": "DiffusionVL-Qwen2.5-VL-7B", "maker": "hust", "family": "hust/diffusionvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "8292166656", "architecture": "Diffusion fine-tune that translates a pretrained autoregressive model into a diffusion vision-language model with block decoding and KV-cache reuse. The card says training used under 5% of prior dVLM data (738K vs 16.5M samples) and claims about 2.0x inference speedup over previous dVLMs. Translated from Qwen2.5-VL. A Hub collection label says 8B; the safetensors total is 8292166656.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffusionVL is HUST Vision Lab's open-weight method for turning an autoregressive model into a diffusion vision-language model. DiffusionVL-Qwen2.5-VL-7B is one of the three released translations. Collected 2026-10-15. Weights are public under Apache-2.0.", "what_it_is_for": "DiffusionVL is HUST Vision Lab's open-weight method for turning an autoregressive model into a diffusion vision-language model. DiffusionVL-Qwen2.5-VL-7B is one of the three released translations. Collected 2026-10-15. Weights are public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/dreamwam/dreamwam": { "id": "hust/dreamwam/dreamwam", "name": "DreamWAM", "maker": "hust", "family": "hust/dreamwam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "World-action model. The card says it goes beyond RGB future prediction. Pipeline tag is robotics. No parameter count is on the card.", "modalities": { "input": [ "image" ], "output": [ "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DreamWAM is HUST Vision Lab's open-weight world-action model for robots. Collected 2026-10-15. Weights are public under Apache-2.0.", "what_it_is_for": "DreamWAM is HUST Vision Lab's open-weight world-action model for robots. Collected 2026-10-15. Weights are public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/fasterwam/fasterwam": { "id": "hust/fasterwam/fasterwam", "name": "FasterWAM", "maker": "hust", "family": "hust/fasterwam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-08-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "World-action model with inference-time future conditioning for robot manipulation. The GitHub says the system uses Wan2.2-TI2V-5B as a component that is downloaded separately. Released files are task checkpoints, not that video model.", "modalities": { "input": [ "image" ], "output": [ "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FasterWAM is HUST Vision Lab's open-weight world-action checkpoint for LIBERO and RoboTwin. Collected 2026-10-15. Weights are public under MIT.", "what_it_is_for": "FasterWAM is HUST Vision Lab's open-weight world-action checkpoint for LIBERO and RoboTwin. Collected 2026-10-15. Weights are public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/infinitevl/infinitevl": { "id": "hust/infinitevl/infinitevl", "name": "InfiniteVL-4B", "maker": "hust", "family": "hust/infinitevl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-12-10", "context_length": null, "max_output_tokens": null, "parameters": "4408381536", "architecture": "Hybrid decoder VLM: Qwen2.5-VL vision encoder, a projection MLP, and 9 hybrid blocks. Each block has 1 sliding-window attention layer and 3 Gated DeltaNet layers. The Hub safetensors total is 4408381536 for both stage-2 and stage-3 checkpoints. Co-authors include Horizon Robotics. Corresponding author is at HUST.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InfiniteVL-4B is HUST Vision Lab's open-weight linear-complexity vision-language model for unlimited multimodal streams. Collected 2026-10-15. Both the stage-2 generalist and the stage-3 long-context checkpoint are public under Apache-2.0.", "what_it_is_for": "InfiniteVL-4B is HUST Vision Lab's open-weight linear-complexity vision-language model for unlimited multimodal streams. Collected 2026-10-15. Both the stage-2 generalist and the stage-3 long-context checkpoint are public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/lightningdit/lightningdit-xl": { "id": "hust/lightningdit/lightningdit-xl", "name": "LightningDiT-XL", "maker": "hust", "family": "hust/lightningdit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-01-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ImageNet-256 diffusion transformer (XL) trained in the VA-VAE latent space. The lab released a 64-epoch and an 800-epoch checkpoint. The card calls the system unconditional image generation.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LightningDiT-XL is HUST Vision Lab's ImageNet-256 diffusion transformer, released with VA-VAE. Collected 2026-10-15. The 64-epoch and 800-epoch weights are public under MIT.", "what_it_is_for": "LightningDiT-XL is HUST Vision Lab's ImageNet-256 diffusion transformer, released with VA-VAE. Collected 2026-10-15. The 64-epoch and 800-epoch weights are public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/lkcell/lkcell-l": { "id": "hust/lkcell/lkcell-l", "name": "LKCell-L", "maker": "hust", "family": "hust/lkcell", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-08-02", "context_length": null, "max_output_tokens": null, "parameters": "164165566", "architecture": "Cell-nuclei instance segmentation using a large-kernel backbone. The card says L means the larger pretrained UniRepLKNet, and this file is fold 0 of the PanNuke training run. Safetensors total is 164165566.", "modalities": { "input": [ "image" ], "output": [ "masks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LKCell-L is HUST Vision Lab's open-weight large-kernel model for cell-nuclei instance segmentation. Collected 2026-10-15. The fold-0 PanNuke checkpoint is public under Apache-2.0.", "what_it_is_for": "LKCell-L is HUST Vision Lab's open-weight large-kernel model for cell-nuclei instance segmentation. Collected 2026-10-15. The fold-0 PanNuke checkpoint is public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/maptr/maptr": { "id": "hust/maptr/maptr", "name": "MapTR", "maker": "hust", "family": "hust/maptr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-11-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Online vectorized HD-map construction. The lab's first public code drop is dated 2022-11-11. Config names in the repo include tiny and nano ResNet variants. Those configs are not separate folders because this pass did not open each weight file.", "modalities": { "input": [ "image" ], "output": [ "polylines" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MapTR is HUST Vision Lab's named online HD-map model. Collected 2026-10-15. Code has been public since 2022-11-11. This folder stands for that release, not for every training config in the repo.", "what_it_is_for": "MapTR is HUST Vision Lab's named online HD-map model. Collected 2026-10-15. Code has been public since 2022-11-11. This folder stands for that release, not for every training config in the repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/maptr/maptrv2": { "id": "hust/maptr/maptrv2", "name": "MapTRv2", "maker": "hust", "family": "hust/maptr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Successor HD-map model. The GitHub news says the MapTRv2 paper was posted 2023-08-10 and the maptrv2 branch was released 2023-08-31. It adds faster convergence and a centerline element. IJCV 2024 per the repo news.", "modalities": { "input": [ "image" ], "output": [ "polylines" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MapTRv2 is HUST Vision Lab's follow-up vectorized HD-map model. Collected 2026-10-15. The maptrv2 branch is the public release dated 2023-08-31 in the repo news.", "what_it_is_for": "MapTRv2 is HUST Vision Lab's follow-up vectorized HD-map model. Collected 2026-10-15. The maptrv2 branch is the public release dated 2023-08-31 in the repo news.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/matvlm/matvlm": { "id": "hust/matvlm/matvlm", "name": "MaTVLM", "maker": "hust", "family": "hust/matvlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-03-16", "context_length": null, "max_output_tokens": null, "parameters": "3270747968", "architecture": "Hybrid Mamba-Transformer VLM. The card says some decoder layers of a pretrained VLM are replaced by Mamba-2 layers initialized from the attention weights, then distilled in one stage. Hub id MaTVLM_0_25_Mamba2. Safetensors total is 3270747968. The card does not define the 0.25 token.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MaTVLM is HUST Vision Lab's open-weight hybrid Mamba-Transformer vision-language model. The public checkpoint is MaTVLM_0_25_Mamba2. Collected 2026-10-15. Weights are under MIT.", "what_it_is_for": "MaTVLM is HUST Vision Lab's open-weight hybrid Mamba-Transformer vision-language model. The public checkpoint is MaTVLM_0_25_Mamba2. Collected 2026-10-15. Weights are under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/mim4d/mim4d": { "id": "hust/mim4d/mim4d", "name": "MIM4D", "maker": "hust", "family": "hust/mim4d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-05-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-supervised multi-view video pretraining for driving. Dual masked image modeling builds pseudo-3D features with scene flow and renders them for supervision. Horizon Robotics is a co-author. The Hub folder's named products are the UVTR-S checkpoints. ConvNeXt-S and ResNet-50 files in the same folder are hosted copies of other backbones and are not separate models.", "modalities": { "input": [ "image", "video" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MIM4D is HUST Vision Lab's open-weight pretraining method for multi-view driving video. Collected 2026-10-15. The UVTR checkpoints and a VAD-Tiny file are public under MIT. The Hub card itself is empty.", "what_it_is_for": "MIM4D is HUST Vision Lab's open-weight pretraining method for multi-view driving video. Collected 2026-10-15. The UVTR checkpoints and a VAD-Tiny file are public under MIT. The Hub card itself is empty.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/mmmamba/mmmamba-hybrid": { "id": "hust/mmmamba/mmmamba-hybrid", "name": "mmMamba-hybrid", "maker": "hust", "family": "hust/mmmamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-02-17", "context_length": null, "max_output_tokens": null, "parameters": "2708076288", "architecture": "Decoder-only multimodal state-space model with no separate vision encoder. Hybrid Transformer and Mamba decoder, same distillation recipe. At 103K tokens the card reports 13.5x speedup and 60.2% memory reduction versus HoVLE. Safetensors total is 2708076288.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mmMamba-hybrid is HUST Vision Lab's open-weight decoder-only multimodal Mamba model, distilled from HoVLE-2.6B. Collected 2026-10-15. Weights are public under MIT.", "what_it_is_for": "mmMamba-hybrid is HUST Vision Lab's open-weight decoder-only multimodal Mamba model, distilled from HoVLE-2.6B. Collected 2026-10-15. Weights are public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/mmmamba/mmmamba-linear": { "id": "hust/mmmamba/mmmamba-linear", "name": "mmMamba-linear", "maker": "hust", "family": "hust/mmmamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-02-14", "context_length": null, "max_output_tokens": null, "parameters": "2741992448", "architecture": "Decoder-only multimodal state-space model with no separate vision encoder. Pure Mamba-2 decoder distilled from HoVLE-2.6B. At 103K tokens the card reports a 20.6x speedup and 75.8% GPU memory reduction versus HoVLE. Safetensors total is 2741992448.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mmMamba-linear is HUST Vision Lab's open-weight decoder-only multimodal Mamba model, distilled from HoVLE-2.6B. Collected 2026-10-15. Weights are public under MIT.", "what_it_is_for": "mmMamba-linear is HUST Vision Lab's open-weight decoder-only multimodal Mamba model, distilled from HoVLE-2.6B. Collected 2026-10-15. Weights are public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/mobilei2v/mobilei2v": { "id": "hust/mobilei2v/mobilei2v", "name": "MobileI2V", "maker": "hust", "family": "hust/mobilei2v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-05-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-to-video model aimed at mobile devices. The card names HUST authors and points at the project page and arXiv 2511.21475. No parameter count is on the card text that was read.", "modalities": { "input": [ "image", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileI2V is HUST Vision Lab's open-weight image-to-video model for mobile devices. Collected 2026-10-15. The Hub weights are public under MIT.", "what_it_is_for": "MobileI2V is HUST Vision Lab's open-weight image-to-video model for mobile devices. Collected 2026-10-15. The Hub weights are public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/moebius/moebius": { "id": "hust/moebius/moebius", "name": "Moebius", "maker": "hust", "family": "hust/moebius", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-06-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lightweight image-inpainting model. The Hub card title calls it a 0.2B framework. This pass did not open the paper PDF, so no exact parameter integer is stored.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moebius is HUST Vision Lab's open-weight lightweight image-inpainting model. Collected 2026-10-15. Weights are public under MIT.", "what_it_is_for": "Moebius is HUST Vision Lab's open-weight lightweight image-inpainting model. Collected 2026-10-15. Weights are public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/motionvla/motionvla": { "id": "hust/motionvla/motionvla", "name": "MotionVLA", "maker": "hust", "family": "hust/motionvla", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-09-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language-action model that injects geometric motion. The Hub card is a short pointer to the paper. Pipeline tag is robotics.", "modalities": { "input": [ "image", "text" ], "output": [ "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MotionVLA is HUST Vision Lab's open-weight vision-language-action checkpoint. Collected 2026-10-15. Weights are public under Apache-2.0.", "what_it_is_for": "MotionVLA is HUST Vision Lab's open-weight vision-language-action checkpoint. Collected 2026-10-15. Weights are public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/multimodal-flow/multimodal-flow": { "id": "hust/multimodal-flow/multimodal-flow", "name": "Multimodal Flow 1.6B", "maker": "hust", "family": "hust/multimodal-flow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fully continuous flow model. Text blocks and images are ordered hyperchunks in one causal stream. The Hub release contains a 1.6B pretrain and a 1.6B SFT checkpoint, a shared BF16 text decoder, and FP32 vision statistics. The card says image tasks also need an external Scale RAE decoder. An arXiv summary mentions 0.6B, 1.2B, and 1.6B training runs; only 1.6B weights are in this repo.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Multimodal Flow 1.6B is HUST Vision Lab's open-weight continuous model for text and images in one embedding stream. Collected 2026-10-15. Pretrain and SFT weights are public under MIT. Beijing Jiaotong University and Horizon Robotics co-author the paper. HUST hosts the weights.", "what_it_is_for": "Multimodal Flow 1.6B is HUST Vision Lab's open-weight continuous model for text and images in one embedding stream. Collected 2026-10-15. Pretrain and SFT weights are public under MIT. Beijing Jiaotong University and Horizon Robotics co-author the paper. HUST hosts the weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/omnimamba/omnimamba": { "id": "hust/omnimamba/omnimamba", "name": "OmniMamba", "maker": "hust", "family": "hust/omnimamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-03-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Linear multimodal model based on Mamba-2 that generates text and images by next-token prediction. The card says it uses decoupled vocabularies and task-specific LoRA, trained on 2M image-text pairs. The weight file is named OmniMamba-1.3b.pth. No integer parameter total was in the Hub API.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmniMamba is HUST Vision Lab's open-weight Mamba-2 model for joint text and image generation. Collected 2026-10-15. The 1.3b-named checkpoint is public under MIT.", "what_it_is_for": "OmniMamba is HUST Vision Lab's open-weight Mamba-2 model for joint text and image generation. Collected 2026-10-15. The 1.3b-named checkpoint is public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/pixelhacker/pixelhacker": { "id": "hust/pixelhacker/pixelhacker", "name": "PixelHacker", "maker": "hust", "family": "hust/pixelhacker", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-05-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image inpainting model aimed at structural and semantic consistency. The card does not state a parameter count.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixelHacker is HUST Vision Lab's open-weight image-inpainting model. Collected 2026-10-15. Weights are public under MIT.", "what_it_is_for": "PixelHacker is HUST Vision Lab's open-weight image-inpainting model. Collected 2026-10-15. Weights are public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/spa3-vlm/spa3-vlm": { "id": "hust/spa3-vlm/spa3-vlm", "name": "Spa3-VLM", "maker": "hust", "family": "hust/spa3-vlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-02-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2.5-VL with a residual cross-attention adapter over a frozen Spa3R encoder. The paper abstract says this grounds language in a global spatial field. The Hub folder holds a three-shard safetensors VLM plus spa3r.ckpt. An exact parameter total was not returned by the model API because the weights are nested.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spa3-VLM is HUST Vision Lab's open-weight spatial vision-language model, built by attaching Spa3R to Qwen2.5-VL. Collected 2026-10-15. Weights are in the public MIT Hub repo.", "what_it_is_for": "Spa3-VLM is HUST Vision Lab's open-weight spatial vision-language model, built by attaching Spa3R to Qwen2.5-VL. Collected 2026-10-15. Weights are in the public MIT Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/spa3r/spa3r": { "id": "hust/spa3r/spa3r", "name": "Spa3R", "maker": "hust", "family": "hust/spa3r", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-02-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-supervised encoder that predicts spatial feature fields at new views from unposed multi-view RGB. The weight file in the Spa3-VLM Hub repo is spa3r.ckpt. Co-authors include Horizon Robotics and D-Robotics. HUST hosts the release.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spa3R is HUST Vision Lab's open-weight spatial encoder for unposed multi-view images. Collected 2026-10-15. The checkpoint spa3r.ckpt is public under the repo's MIT tag.", "what_it_is_for": "Spa3R is HUST Vision Lab's open-weight spatial encoder for unposed multi-view images. Collected 2026-10-15. The checkpoint spa3r.ckpt is public under the repo's MIT tag.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/superclip/superclip-b": { "id": "hust/superclip/superclip-b", "name": "SuperCLIP-B", "maker": "hust", "family": "hust/superclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-11-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP-style dual encoder trained with simple classification supervision. The weight file in hustvl/SuperCLIP is SuperCLIP-B-512M.pt. The card does not define the 512M token in the filename.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SuperCLIP-B is HUST Vision Lab's open-weight CLIP variant with classification supervision, from the SuperCLIP zoo. Collected 2026-10-15. The file is public under MIT.", "what_it_is_for": "SuperCLIP-B is HUST Vision Lab's open-weight CLIP variant with classification supervision, from the SuperCLIP zoo. Collected 2026-10-15. The file is public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/superclip/superclip-l": { "id": "hust/superclip/superclip-l", "name": "SuperCLIP-L", "maker": "hust", "family": "hust/superclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-11-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP-style dual encoder trained with simple classification supervision. The weight file in hustvl/SuperCLIP is SuperCLIP-L-512M.pt. The card does not define the 512M token in the filename.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SuperCLIP-L is HUST Vision Lab's open-weight CLIP variant with classification supervision, from the SuperCLIP zoo. Collected 2026-10-15. The file is public under MIT.", "what_it_is_for": "SuperCLIP-L is HUST Vision Lab's open-weight CLIP variant with classification supervision, from the SuperCLIP zoo. Collected 2026-10-15. The file is public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/turbo-vaed/turbo-vaed": { "id": "hust/turbo-vaed/turbo-vaed", "name": "Turbo-VAED", "maker": "hust", "family": "hust/turbo-vaed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-09-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mobile video-VAE decoder. The card says 3D depthwise separable convolutions and a decoupled 3D pixel-shuffle replace the usual upsampler, and only the decoder is distilled. Authors are at HUST. The card says it was applied to four existing video VAEs.", "modalities": { "input": [ "latents" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Turbo-VAED is HUST Vision Lab's open-weight mobile video-VAE decoder. Collected 2026-10-15. Weights are public under MIT. There is no public API price.", "what_it_is_for": "Turbo-VAED is HUST Vision Lab's open-weight mobile video-VAE decoder. Collected 2026-10-15. Weights are public under MIT. There is no public API price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/va-vae/va-vae": { "id": "hust/va-vae/va-vae", "name": "VA-VAE", "maker": "hust", "family": "hust/va-vae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-01-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-foundation-aligned variational autoencoder for ImageNet-256 latents. The released file is the f16d32 DINOv2-aligned tokenizer (Hub id vavae-imagenet256-f16d32-dinov2). The Hub pipeline tag is text-to-image because the card presents the full latent diffusion system.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VA-VAE is HUST Vision Lab's open-weight ImageNet tokenizer, aligned to a vision foundation model and paired with LightningDiT. Collected 2026-10-15. The f16d32 DINOv2 checkpoint is public under MIT.", "what_it_is_for": "VA-VAE is HUST Vision Lab's open-weight ImageNet tokenizer, aligned to a vision foundation model and paired with LightningDiT. Collected 2026-10-15. The f16d32 DINOv2 checkpoint is public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/vad/vad-base": { "id": "hust/vad/vad-base", "name": "VAD-Base", "maker": "hust", "family": "hust/vad", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Repository code is Apache-2.0. A separate weight license is not stated on the models table.", "release_date": "2023-08-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end vectorized driving model. The GitHub models table lists a ResNet-50 backbone. VAD-Base is one of the two rows.", "modalities": { "input": [ "image" ], "output": [ "trajectories" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VAD-Base is HUST Vision Lab's open-weight end-to-end driving checkpoint from the VAD release. Collected 2026-10-15. The GitHub models table is the source for the published planning numbers.", "what_it_is_for": "VAD-Base is HUST Vision Lab's open-weight end-to-end driving checkpoint from the VAD release. Collected 2026-10-15. The GitHub models table is the source for the published planning numbers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/vad/vad-tiny": { "id": "hust/vad/vad-tiny", "name": "VAD-Tiny", "maker": "hust", "family": "hust/vad", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Repository code is Apache-2.0. A separate weight license is not stated on the models table.", "release_date": "2023-08-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end vectorized driving model. The GitHub models table lists a ResNet-50 backbone. VAD-Tiny is one of the two rows.", "modalities": { "input": [ "image" ], "output": [ "trajectories" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VAD-Tiny is HUST Vision Lab's open-weight end-to-end driving checkpoint from the VAD release. Collected 2026-10-15. The GitHub models table is the source for the published planning numbers.", "what_it_is_for": "VAD-Tiny is HUST Vision Lab's open-weight end-to-end driving checkpoint from the VAD release. Collected 2026-10-15. The GitHub models table is the source for the published planning numbers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/vgt/vgt-ae": { "id": "hust/vgt/vgt-ae", "name": "VGT autoencoder", "maker": "hust", "family": "hust/vgt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-12-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoencoder pair released with Visual Generation Tuning. Files are vgt_ae_qwen25vl.pth and vgt_ae_internvl3.pth. The Hub README was not a full card in the file list.", "modalities": { "input": [ "image" ], "output": [ "latents" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The VGT autoencoder is the tokenizer pair HUST Vision Lab shipped with Visual Generation Tuning, one file for the Qwen2.5-VL line and one for the InternVL3 line. Collected 2026-10-15. Files are public under MIT.", "what_it_is_for": "The VGT autoencoder is the tokenizer pair HUST Vision Lab shipped with Visual Generation Tuning, one file for the Qwen2.5-VL line and one for the InternVL3 line. Collected 2026-10-15. Files are public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/vgt/vgt-internvl3-1-6b": { "id": "hust/vgt/vgt-internvl3-1-6b", "name": "VGT-InternVL3-1.6B", "maker": "hust", "family": "hust/vgt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Visual generation tuning of a 1.6B vision-language backbone. The lab ships a pretrain checkpoint and an SFT checkpoint. The 1.6B figure is the Hub name, not an API parameter total.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VGT-InternVL3-1.6B is HUST Vision Lab's open-weight visual-generation checkpoint. Collected 2026-10-15. Pretrain and SFT weights are public under MIT.", "what_it_is_for": "VGT-InternVL3-1.6B is HUST Vision Lab's open-weight visual-generation checkpoint. Collected 2026-10-15. Pretrain and SFT weights are public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/vgt/vgt-qwen2-5-vl-1-6b": { "id": "hust/vgt/vgt-qwen2-5-vl-1-6b", "name": "VGT-Qwen2.5-VL-1.6B", "maker": "hust", "family": "hust/vgt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-11-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Visual generation tuning of a 1.6B vision-language backbone. The lab ships a pretrain checkpoint and an SFT checkpoint. The 1.6B figure is the Hub name, not an API parameter total.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VGT-Qwen2.5-VL-1.6B is HUST Vision Lab's open-weight visual-generation checkpoint. Collected 2026-10-15. Pretrain and SFT weights are public under MIT.", "what_it_is_for": "VGT-Qwen2.5-VL-1.6B is HUST Vision Lab's open-weight visual-generation checkpoint. Collected 2026-10-15. Pretrain and SFT weights are public under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/vig/vig": { "id": "hust/vig/vig", "name": "ViG", "maker": "hust", "family": "hust/vig", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub YAML says apache-2.0. The model card body says non-commercial.", "release_date": "2024-06-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Gated linear attention vision backbone. The card says HUST and Horizon Robotics pretrained it on ImageNet-1K classification (about 1.3M images).", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViG is HUST Vision Lab's open-weight gated-linear-attention vision backbone, released with Horizon Robotics. Collected 2026-10-15. The Hub weights are public. The card and the YAML do not state the same license.", "what_it_is_for": "ViG is HUST Vision Lab's open-weight gated-linear-attention vision backbone, released with Horizon Robotics. Collected 2026-10-15. The Hub weights are public. The card and the YAML do not state the same license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/vim/vim-base-midclstok": { "id": "hust/vim/vim-base-midclstok", "name": "Vim-Base middle class token", "maker": "hust", "family": "hust/vim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub YAML says apache-2.0. The model card body says non-commercial.", "release_date": "2024-08-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bidirectional state-space (Mamba-style) vision backbone. The card says it was developed by HUST, Horizon Robotics, and BAAI, and pretrained on ImageNet-1K with classification supervision (about 1.3M images). This repo publishes the middle-class-token checkpoint named in the weight file.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the middle-class-token Vim-Base middle class token weight release of Vision Mamba from HUST Vision Lab, with Horizon Robotics and BAAI named on the Vim card. Collected 2026-10-15. The checkpoint is public.", "what_it_is_for": "This is the middle-class-token Vim-Base middle class token weight release of Vision Mamba from HUST Vision Lab, with Horizon Robotics and BAAI named on the Vim card. Collected 2026-10-15. The checkpoint is public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/vim/vim-small-midclstok": { "id": "hust/vim/vim-small-midclstok", "name": "Vim-Small middle class token", "maker": "hust", "family": "hust/vim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub YAML says apache-2.0. The model card body says non-commercial.", "release_date": "2024-02-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bidirectional state-space (Mamba-style) vision backbone. The card says it was developed by HUST, Horizon Robotics, and BAAI, and pretrained on ImageNet-1K with classification supervision (about 1.3M images). This repo publishes the middle-class-token checkpoint named in the weight file.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the middle-class-token Vim-Small middle class token weight release of Vision Mamba from HUST Vision Lab, with Horizon Robotics and BAAI named on the Vim card. Collected 2026-10-15. The checkpoint is public.", "what_it_is_for": "This is the middle-class-token Vim-Small middle class token weight release of Vision Mamba from HUST Vision Lab, with Horizon Robotics and BAAI named on the Vim card. Collected 2026-10-15. The checkpoint is public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/vim/vim-tiny": { "id": "hust/vim/vim-tiny", "name": "Vim-Tiny", "maker": "hust", "family": "hust/vim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub YAML says apache-2.0. The model card body says non-commercial.", "release_date": "2024-01-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bidirectional state-space (Mamba-style) vision backbone. The card says it was developed by HUST, Horizon Robotics, and BAAI, and pretrained on ImageNet-1K with classification supervision (about 1.3M images). This repo is the tiny checkpoint. The card reports 73.1% ImageNet-1K validation top-1.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vision Mamba (Vim) is HUST Vision Lab's open-weight bidirectional state-space vision backbone, co-developed with Horizon Robotics and BAAI. Vim-Tiny is the ImageNet-1K tiny checkpoint. Collected 2026-10-15. Weights are public; the license text on the card and the Hub YAML disagree.", "what_it_is_for": "Vision Mamba (Vim) is HUST Vision Lab's open-weight bidirectional state-space vision backbone, co-developed with Horizon Robotics and BAAI. Vim-Tiny is the ImageNet-1K tiny checkpoint. Collected 2026-10-15. Weights are public; the license text on the card and the Hub YAML disagree.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/vim/vim-tiny-midclstok": { "id": "hust/vim/vim-tiny-midclstok", "name": "Vim-Tiny middle class token", "maker": "hust", "family": "hust/vim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub YAML says apache-2.0. The model card body says non-commercial.", "release_date": "2024-02-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bidirectional state-space (Mamba-style) vision backbone. The card says it was developed by HUST, Horizon Robotics, and BAAI, and pretrained on ImageNet-1K with classification supervision (about 1.3M images). This repo publishes the middle-class-token checkpoint named in the weight file.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the middle-class-token Vim-Tiny middle class token weight release of Vision Mamba from HUST Vision Lab, with Horizon Robotics and BAAI named on the Vim card. Collected 2026-10-15. The checkpoint is public.", "what_it_is_for": "This is the middle-class-token Vim-Tiny middle class token weight release of Vision Mamba from HUST Vision Lab, with Horizon Robotics and BAAI named on the Vim card. Collected 2026-10-15. The checkpoint is public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/vitmatte/vitmatte-base-composition-1k": { "id": "hust/vitmatte/vitmatte-base-composition-1k", "name": "ViTMatte-Base Composition-1k", "maker": "hust", "family": "hust/vitmatte", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-09-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Plain Vision Transformer backbone with a lightweight matting head. The Hugging Face card, written by the Hugging Face team, says this checkpoint was trained on Composition-1k.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HUST Vision Lab released ViTMatte, an open-weight image-matting model built on a pretrained plain Vision Transformer. ViTMatte-Base Composition-1k is the Composition-1k checkpoint on the lab Hub. Collected 2026-10-15. The weights remain public under Apache-2.0.", "what_it_is_for": "HUST Vision Lab released ViTMatte, an open-weight image-matting model built on a pretrained plain Vision Transformer. ViTMatte-Base Composition-1k is the Composition-1k checkpoint on the lab Hub. Collected 2026-10-15. The weights remain public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/vitmatte/vitmatte-base-distinctions-646": { "id": "hust/vitmatte/vitmatte-base-distinctions-646", "name": "ViTMatte-Base Distinctions-646", "maker": "hust", "family": "hust/vitmatte", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-09-10", "context_length": null, "max_output_tokens": null, "parameters": "96688225", "architecture": "Plain Vision Transformer backbone with a lightweight matting head. The Hugging Face card, written by the Hugging Face team, says this checkpoint was trained on Distinctions-646.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HUST Vision Lab released ViTMatte, an open-weight image-matting model built on a pretrained plain Vision Transformer. ViTMatte-Base Distinctions-646 is the Distinctions-646 checkpoint on the lab Hub. Collected 2026-10-15. The weights remain public under Apache-2.0.", "what_it_is_for": "HUST Vision Lab released ViTMatte, an open-weight image-matting model built on a pretrained plain Vision Transformer. ViTMatte-Base Distinctions-646 is the Distinctions-646 checkpoint on the lab Hub. Collected 2026-10-15. The weights remain public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/vitmatte/vitmatte-small-composition-1k": { "id": "hust/vitmatte/vitmatte-small-composition-1k", "name": "ViTMatte-Small Composition-1k", "maker": "hust", "family": "hust/vitmatte", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-09-10", "context_length": null, "max_output_tokens": null, "parameters": "25816417", "architecture": "Plain Vision Transformer backbone with a lightweight matting head. The Hugging Face card, written by the Hugging Face team, says this checkpoint was trained on Composition-1k.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HUST Vision Lab released ViTMatte, an open-weight image-matting model built on a pretrained plain Vision Transformer. ViTMatte-Small Composition-1k is the Composition-1k checkpoint on the lab Hub. Collected 2026-10-15. The weights remain public under Apache-2.0.", "what_it_is_for": "HUST Vision Lab released ViTMatte, an open-weight image-matting model built on a pretrained plain Vision Transformer. ViTMatte-Small Composition-1k is the Composition-1k checkpoint on the lab Hub. Collected 2026-10-15. The weights remain public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/vitmatte/vitmatte-small-distinctions-646": { "id": "hust/vitmatte/vitmatte-small-distinctions-646", "name": "ViTMatte-Small Distinctions-646", "maker": "hust", "family": "hust/vitmatte", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-09-10", "context_length": null, "max_output_tokens": null, "parameters": "25816417", "architecture": "Plain Vision Transformer backbone with a lightweight matting head. The Hugging Face card, written by the Hugging Face team, says this checkpoint was trained on Distinctions-646.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HUST Vision Lab released ViTMatte, an open-weight image-matting model built on a pretrained plain Vision Transformer. ViTMatte-Small Distinctions-646 is the Distinctions-646 checkpoint on the lab Hub. Collected 2026-10-15. The weights remain public under Apache-2.0.", "what_it_is_for": "HUST Vision Lab released ViTMatte, an open-weight image-matting model built on a pretrained plain Vision Transformer. ViTMatte-Small Distinctions-646 is the Distinctions-646 checkpoint on the lab Hub. Collected 2026-10-15. The weights remain public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/yolos/yolos-base": { "id": "hust/yolos/yolos-base", "name": "YOLOS-Base", "maker": "hust", "family": "hust/yolos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2022-04-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision Transformer encoder trained with the DETR bipartite-matching loss. The card says N = 100 object queries are matched with Hungarian matching, then class cross-entropy plus L1 and generalized IoU on boxes. Pretrained on ImageNet-1k and fine-tuned on COCO 2017 detection (118k train images). Training schedule on this card: 1000 epochs on ImageNet-1k, then 150 epochs on COCO.", "modalities": { "input": [ "image" ], "output": [ "detections" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HUST Vision Lab's YOLOS is an open-weight detector that treats detection as a sequence task on a plain Vision Transformer. This checkpoint is the YOLOS-Base release on the lab Hub, collected 2026-10-15. Status is active: the weights are still public under Apache-2.0.", "what_it_is_for": "HUST Vision Lab's YOLOS is an open-weight detector that treats detection as a sequence task on a plain Vision Transformer. This checkpoint is the YOLOS-Base release on the lab Hub, collected 2026-10-15. Status is active: the weights are still public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/yolos/yolos-small": { "id": "hust/yolos/yolos-small", "name": "YOLOS-Small", "maker": "hust", "family": "hust/yolos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2022-04-26", "context_length": null, "max_output_tokens": null, "parameters": "30684768", "architecture": "Vision Transformer encoder trained with the DETR bipartite-matching loss. The card says N = 100 object queries are matched with Hungarian matching, then class cross-entropy plus L1 and generalized IoU on boxes. Pretrained on ImageNet-1k and fine-tuned on COCO 2017 detection (118k train images). Training schedule on this card: 200 epochs on ImageNet-1k, then 150 epochs on COCO.", "modalities": { "input": [ "image" ], "output": [ "detections" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HUST Vision Lab's YOLOS is an open-weight detector that treats detection as a sequence task on a plain Vision Transformer. This checkpoint is the YOLOS-Small release on the lab Hub, collected 2026-10-15. Status is active: the weights are still public under Apache-2.0.", "what_it_is_for": "HUST Vision Lab's YOLOS is an open-weight detector that treats detection as a sequence task on a plain Vision Transformer. This checkpoint is the YOLOS-Small release on the lab Hub, collected 2026-10-15. Status is active: the weights are still public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/yolos/yolos-small-300": { "id": "hust/yolos/yolos-small-300", "name": "YOLOS-Small (300 pre-train epochs)", "maker": "hust", "family": "hust/yolos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2022-04-26", "context_length": null, "max_output_tokens": null, "parameters": "30684768", "architecture": "Vision Transformer encoder trained with the DETR bipartite-matching loss. The card says N = 100 object queries are matched with Hungarian matching, then class cross-entropy plus L1 and generalized IoU on boxes. Pretrained on ImageNet-1k and fine-tuned on COCO 2017 detection (118k train images). Training schedule on this card: 300 epochs on ImageNet-1k, then 150 epochs on COCO.", "modalities": { "input": [ "image" ], "output": [ "detections" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HUST Vision Lab's YOLOS is an open-weight detector that treats detection as a sequence task on a plain Vision Transformer. This checkpoint is the YOLOS-Small (300 pre-train epochs) release on the lab Hub, collected 2026-10-15. Status is active: the weights are still public under Apache-2.0.", "what_it_is_for": "HUST Vision Lab's YOLOS is an open-weight detector that treats detection as a sequence task on a plain Vision Transformer. This checkpoint is the YOLOS-Small (300 pre-train epochs) release on the lab Hub, collected 2026-10-15. Status is active: the weights are still public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/yolos/yolos-small-dwr": { "id": "hust/yolos/yolos-small-dwr", "name": "YOLOS-Small (fast model scaling)", "maker": "hust", "family": "hust/yolos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2022-04-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision Transformer encoder trained with the DETR bipartite-matching loss. The card says N = 100 object queries are matched with Hungarian matching, then class cross-entropy plus L1 and generalized IoU on boxes. Pretrained on ImageNet-1k and fine-tuned on COCO 2017 detection (118k train images). Training schedule on this card: 300 epochs on ImageNet-1k, then 150 epochs on COCO. The card title calls this the fast model-scaling variant..", "modalities": { "input": [ "image" ], "output": [ "detections" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HUST Vision Lab's YOLOS is an open-weight detector that treats detection as a sequence task on a plain Vision Transformer. This checkpoint is the YOLOS-Small (fast model scaling) release on the lab Hub, collected 2026-10-15. Status is active: the weights are still public under Apache-2.0.", "what_it_is_for": "HUST Vision Lab's YOLOS is an open-weight detector that treats detection as a sequence task on a plain Vision Transformer. This checkpoint is the YOLOS-Small (fast model scaling) release on the lab Hub, collected 2026-10-15. Status is active: the weights are still public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hust/yolos/yolos-tiny": { "id": "hust/yolos/yolos-tiny", "name": "YOLOS-Tiny", "maker": "hust", "family": "hust/yolos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2022-04-26", "context_length": null, "max_output_tokens": null, "parameters": "6488736", "architecture": "Vision Transformer encoder trained with the DETR bipartite-matching loss. The card says N = 100 object queries are matched with Hungarian matching, then class cross-entropy plus L1 and generalized IoU on boxes. Pretrained on ImageNet-1k and fine-tuned on COCO 2017 detection (118k train images). Training schedule on this card: 300 epochs on ImageNet-1k, then 300 epochs on COCO.", "modalities": { "input": [ "image" ], "output": [ "detections" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HUST Vision Lab's YOLOS is an open-weight detector that treats detection as a sequence task on a plain Vision Transformer. This checkpoint is the YOLOS-Tiny release on the lab Hub, collected 2026-10-15. Status is active: the weights are still public under Apache-2.0.", "what_it_is_for": "HUST Vision Lab's YOLOS is an open-weight detector that treats detection as a sequence task on a plain Vision Transformer. This checkpoint is the YOLOS-Tiny release on the lab Hub, collected 2026-10-15. Status is active: the weights are still public under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hyper3d/rodin/rodin-bang": { "id": "hyper3d/rodin/rodin-bang", "name": "Rodin BANG", "maker": "hyper3d", "family": "hyper3d/rodin", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Generative exploded dynamics. The SIGGRAPH 2025 paper fine-tunes a pre-trained latent diffusion model with an exploded-view adapter and a temporal attention module. Hyper3D's research library calls BANG the framework for Generate-to-Parts in Rodin Gen-2. The API is a separate part-splitting endpoint, not the Gen-2 generator.", "modalities": { "input": [ "3d", "image", "text" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rodin BANG is Hyper3D's part-splitting model from Deemos. It divides a finished Rodin asset, or an uploaded mesh, into separate parts. The method is the SIGGRAPH 2025 paper BANG. The public endpoint `POST /api/v2/bang` was live on 2026-10-15. The API ship date was not published. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hyper3D's research library calls BANG the Generate-to-Parts framework in Rodin Gen-2. The API accepts either the uuid of a completed Rodin generation or an uploaded model. Strength controls how many pieces come back. The paper's control story is an exploded view: bounding boxes, surface regions, and text can steer which parts separate. The current API page documents strength, material, and an optional prompt on uploads. It does not restate the paper's box and point controls as request fields.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hyper3d/rodin/rodin-gen-1": { "id": "hyper3d/rodin/rodin-gen-1", "name": "Rodin Gen-1", "maker": "hyper3d", "family": "hyper3d/rodin", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLAY, described by Hyper3D as the foundation architecture behind Rodin. The CTO told befores & afters the system was a latent diffusion model on a 3DShape2VecSet-style representation, trained as a native 3D generator rather than a 2D-image lift.", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rodin Gen-1 is Hyper3D's first public native text-and-image-to-3D model, from Deemos. It was shown at GDC 2024 and SIGGRAPH 2024 and is the product tied to the CLAY paper. On 2026-10-15 it is no longer a separate API tier. The live fallback is documented as a Gen-1/1.5 asset. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hyper3D's research library calls CLAY the foundation architecture behind Rodin. In a January 2026 interview, CTO Qixuan Zhang said the team trained its own 3D latent diffusion model after finding public 3D data too small, showed Rodin at GDC and then SIGGRAPH 2024, and launched it the same year. Later API copy treats smoothing as \"similar to Rodin Gen-1,\" which is the only current behavioral description found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hyper3d/rodin/rodin-gen-1-5": { "id": "hyper3d/rodin/rodin-gen-1-5", "name": "Rodin Gen-1.5", "maker": "hyper3d", "family": "hyper3d/rodin", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-30", "context_length": null, "max_output_tokens": null, "parameters": "over 4 billion", "architecture": "Vendor calls it a native 3D generator with quad-mesh output, PBR materials, 3D ControlNet, and LoRA style modules. A separate named architecture string for the 1.5 weights was not published. The Dec 30, 2024 post called the change a new 3D representation.", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rodin Gen-1.5 is Hyper3D's quad-mesh text-and-image-to-3D model from Deemos. Hyper3D posted it as available on 2024-12-30 and called the April 29, 2025 press note the official launch. It is still a live API family on 2026-10-15, under the tiers Sketch, Regular, Detail, and Smooth. The studio label that day was Gen-1.5 V1.2. This dossier was collected on 2026-10-15.", "what_it_is_for": "The launch post says Gen-1.5 generates quadrilateral meshes and PBR materials from images or text for games, VR, AR, industrial design, and marketing. It names 3D ControlNet and LoRA style modules as product controls. The current features page keeps this generation as the Gen-1/1.5 family and says it supports multi-image concat and fuse.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hyper3d/rodin/rodin-gen-2": { "id": "hyper3d/rodin/rodin-gen-2", "name": "Rodin Gen-2", "maker": "hyper3d", "family": "hyper3d/rodin", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-23", "context_length": null, "max_output_tokens": null, "parameters": "10 billion", "architecture": "Company launch materials call Gen-2 a 10-billion-parameter model built on the BANG architecture, with recursive part generation. The BANG paper itself is a part-decomposition method on a pre-trained latent diffusion model, not a published 10-billion-parameter model card.", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rodin Gen-2 is Hyper3D's second-generation text-and-image-to-3D model from Deemos. The maker posted it live on 2025-09-23, after a SIGGRAPH 2025 demo, and a company press release dated 2025-10-01 calls it a 10-billion-parameter model on the BANG architecture. The API still accepts `tier=Gen-2` on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The launch post lists 4x mesh quality versus earlier Rodin, recursive part generation, a high-poly to low-poly bake with normals, and HD textures in beta, plus the older controls: 3D ControlNet, part-level quads, T/A pose, and PBR. The October 1 press release aims it at games, VFX, and metaverse pipelines. Gen-2 Edit, a later named update, adds smart low-poly, local prompt edits in beta, and BANG-to-parts. Edit is not its own API tier.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "hyper3d/rodin/rodin-gen-2-5": { "id": "hyper3d/rodin/rodin-gen-2-5", "name": "Rodin Gen-2.5", "maker": "hyper3d", "family": "hyper3d/rodin", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published. Maker posts describe adaptive thinking effort, compared by Hyper3D to LLM effort controls, plus 3D-native textures. That is a product description, not an architecture name.", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rodin Gen-2.5 is Hyper3D's current high-detail text-and-image-to-3D model from Deemos. CGWorld reports a May 19, 2026 release. The public API documents five tiers, from Gen-2.5-Extreme-Low through Gen-2.5-Extreme-High, and the studio was offering it as Gen-2.5 V2.0 on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The API page says Gen-2.5 generates a mesh and textures from images or a text prompt. Official tier notes run from quick simple assets up to high-frequency detail. Extreme-High also returns an additional mesh of up to 10 million faces. Maker posts on the product site claim about 1 million polygons in 4 seconds, effort controls compared to LLMs, 3D-native textures, and batches of up to 10. The Hyper3D MCP server exposes Gen-2.5-Medium and Gen-2.5-Extreme-Low to coding agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iastate/insectnet/insectnet": { "id": "iastate/insectnet/insectnet", "name": "InsectNet", "maker": "iastate", "family": "iastate/insectnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-12-20", "context_length": null, "max_output_tokens": null, "parameters": "approximately 145 million", "architecture": "Released checkpoint is torchvision RegNet-Y-32GF (regnet_y_32gf) with the classification head replaced by Linear(3712, 2526). The paper's supplement describes RegNetY32 as about 145 million trainable parameters and also reports ResNet-50 experiments (about 23 million). Training is self-supervised pretraining on Instagram-hashtag images and unlabeled insect images, then supervised fine-tuning. Inference adds out-of-distribution and conformal-prediction wrappers. A 2026 AWS writeup of the PestID deployment instead calls InsectNet a Vision Transformer; that file was not the Zenodo checkpoint.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InsectNet is an Iowa State University insect-image classifier led by Baskar Ganapathysubramanian and Arti Singh. The public weight file was posted on Zenodo on 20 December 2024, and the PNAS Nexus paper is dated 27 December 2024. On 15 October 2026 the checkpoint and the Iowa State web app were still described as available. Later PestID writeups use the same name for a larger deployed system whose numbers do not match this file.", "what_it_is_for": "The paper and the February 2025 Iowa State news story describe a tool that identifies agriculturally and ecologically relevant insects, including pests, pollinators, predators, and parasitoids, from a photo, including across life stages. The classifier is hosted at https://insectapp.las.iastate.edu. The news story said it was not an app-store download.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iastate/weednet/weednet": { "id": "iastate/weednet/weednet", "name": "WeedNet", "maker": "iastate", "family": "iastate/weednet", "status": "active", "confidence": "high", "weights": "closed", "license": "CC BY-NC 4.0", "release_date": "2025-05-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision Transformer encoder with 14 by 14 patches, pretrained as a masked autoencoder, then fine-tuned with a fully connected classifier. The public fine-tune script selects vit_base_patch14. The v1 paper compares a RegNet SwAV run initialized from InsectNet weights with an MAE-Base run and says the transformer won. Out-of-distribution detection uses an energy-based wrapper. Conformal prediction uses maximum softmax probability at alpha 0.95. Stage-1 pretraining is described as 3.5 billion Instagram images, then about 14 million unlabeled iNaturalist plant images, then labeled fine-tuning.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WeedNet is a weed-species identification model from Iowa State University, with University of Arizona coauthors. Yanben Shen and Timilehin T. Ayanlade lead the author list. Soumik Sarkar and Arti Singh are the corresponding authors in the preprint. The preprint is dated 25 May 2025 and was revised 20 August 2026. On 15 October 2026 the training code was public and the checkpoint files were still marked coming soon. Iowa State news treats it as a live part of the PestID web tool.", "what_it_is_for": "The paper presents a global weed identifier for 1,593 species, including noxious and invasive plants, and a regional fine-tune for Iowa and Midwest corn and soybean weeds. The stated deployment is a phone upload flow, a FastAPI service, and a chatbot the paper calls PestIDBot. Iowa State news from 14 October 2025 says InsectNet and WeedNet were combined into one PestID site where the user picks insect or weed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iastate/xplnet/xplnet": { "id": "iastate/xplnet/xplnet", "name": "xPLNet", "maker": "iastate", "family": "iastate/xplnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Iowa State University non-profit license (GitHub LICENSE, copyright 2017)", "release_date": "2018-04-16", "context_length": null, "max_output_tokens": null, "parameters": "901877", "architecture": "Five-convolution soybean-stress classifier published as xPLNet. GitHub summary: five Conv2D layers with 128 maps, four max-pools, four batch-norm layers, dropout, and dense layers of 500, 100, and 9. Keras frontend, Theano backend. Input shape (3, 64, 64), channels first, normalized by the maximum channel value. Total params 901,877, of which 901,653 are trainable. Adam learning rate 0.001, batch size 60, seed 1337. The paper says the chosen network was one of several architectures tried for both accuracy and explanation quality.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "xPLNet is a 2018 Iowa State University convolutional classifier for soybean leaf stress, from Sambuddha Ghosal, David Blystone, Asheesh K. Singh, Baskar Ganapathysubramanian, Arti Singh, and Soumik Sarkar. PNAS published the paper on 16 April 2018 and said the model was available from the lab GitHub repository. On 15 October 2026 that repository still offered the weights and the 64 by 64 image set by email after a form. No retirement notice was found.", "what_it_is_for": "The paper uses the network to identify, classify, and quantify soybean stresses from leaf images, and to show which feature maps drove the decision. The GitHub README says folder 0 of the shared set is bacterial blight and that the other folder numbers follow Figure 2 of the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-13b-chat-v1": { "id": "ibm/granite/granite-13b-chat-v1", "name": "Granite 13B Chat v1", "maker": "ibm", "family": "ibm/granite", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 13B Chat v1 is an IBM granite model. Collection date 2026-10-15. Status: deprecated.", "what_it_is_for": "Granite 13B Chat v1 is an IBM granite model. Collection date 2026-10-15. Status: deprecated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-13b-chat-v2": { "id": "ibm/granite/granite-13b-chat-v2", "name": "Granite 13B Chat v2", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-30", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only transformer. IBM model card lists multi-query attention, a 50K GPT-NeoX tokenizer, Flash Attention, 8k context, and absolute learned position embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 13B Chat v2 is an IBM granite model. Collection date 2026-10-15. Status: active. Recorded context window: 8192 tokens.", "what_it_is_for": "Granite 13B Chat v2 is an IBM granite model. Collection date 2026-10-15. Status: active. Recorded context window: 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-13b-instruct-v1": { "id": "ibm/granite/granite-13b-instruct-v1", "name": "Granite 13B Instruct v1", "maker": "ibm", "family": "ibm/granite", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 13B Instruct v1 is an IBM granite model. Collection date 2026-10-15. Status: deprecated.", "what_it_is_for": "Granite 13B Instruct v1 is an IBM granite model. Collection date 2026-10-15. Status: deprecated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-13b-instruct-v2": { "id": "ibm/granite/granite-13b-instruct-v2", "name": "Granite 13B Instruct v2", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-30", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only transformer. IBM model card lists multi-query attention, a 50K GPT-NeoX tokenizer, Flash Attention, 8k context, and absolute learned position embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 0.6 }, "summary_intro": "Granite 13B Instruct v2 is an IBM granite model. Collection date 2026-10-15. Status: active. Recorded context window: 8192 tokens.", "what_it_is_for": "Granite 13B Instruct v2 is an IBM granite model. Collection date 2026-10-15. Status: active. Recorded context window: 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-20b-multilingual": { "id": "ibm/granite/granite-20b-multilingual", "name": "Granite 20B Multilingual", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 20B Multilingual is an IBM granite model. Collection date 2026-10-15. Status: active. Recorded context window: 8192 tokens.", "what_it_is_for": "Granite 20B Multilingual is an IBM granite model. Collection date 2026-10-15. Status: active. Recorded context window: 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-0-1b-a400m-base": { "id": "ibm/granite/granite-3-0-1b-a400m-base", "name": "Granite-3.0-1B-A400M-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-21", "context_length": 4096, "max_output_tokens": null, "parameters": "1384956928", "architecture": "GraniteMoeForCausalLM; model_type granitemoe; layers 24; hidden 1024; heads 16; kv heads 8; vocab 49152; num_local_experts 32; experts per token 8", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.0-1B-A400M-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1384956928 parameters. Recorded context window: 4096 tokens.", "what_it_is_for": "Granite-3.0-1B-A400M-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1384956928 parameters. Recorded context window: 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-0-1b-a400m-instruct": { "id": "ibm/granite/granite-3-0-1b-a400m-instruct", "name": "Granite-3.0-1B-A400M-Instruct", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-21", "context_length": 4096, "max_output_tokens": null, "parameters": "1334628352", "architecture": "GraniteMoeForCausalLM; model_type granitemoe; layers 24; hidden 1024; heads 16; kv heads 8; vocab 49155; num_local_experts 32; experts per token 8", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.0-1B-A400M-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1334628352 parameters. Recorded context window: 4096 tokens.", "what_it_is_for": "Granite-3.0-1B-A400M-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1334628352 parameters. Recorded context window: 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-0-2b-base": { "id": "ibm/granite/granite-3-0-2b-base", "name": "Granite-3.0-2B-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-21", "context_length": 4096, "max_output_tokens": null, "parameters": "2533525504", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 2048; heads 32; kv heads 8; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.0-2B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533525504 parameters. Recorded context window: 4096 tokens.", "what_it_is_for": "Granite-3.0-2B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533525504 parameters. Recorded context window: 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-0-2b-instruct": { "id": "ibm/granite/granite-3-0-2b-instruct", "name": "Granite-3.0-2B-Instruct", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-21", "context_length": 4096, "max_output_tokens": null, "parameters": "2634201088", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 2048; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.0-2B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2634201088 parameters. Recorded context window: 4096 tokens.", "what_it_is_for": "Granite-3.0-2B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2634201088 parameters. Recorded context window: 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-0-3b-a800m-base": { "id": "ibm/granite/granite-3-0-3b-a800m-base", "name": "Granite-3.0-3B-A800M-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-21", "context_length": 4096, "max_output_tokens": null, "parameters": "3374286336", "architecture": "GraniteMoeForCausalLM; model_type granitemoe; layers 32; hidden 1536; heads 24; kv heads 8; vocab 49152; num_local_experts 40; experts per token 8", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.0-3B-A800M-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3374286336 parameters. Recorded context window: 4096 tokens.", "what_it_is_for": "Granite-3.0-3B-A800M-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3374286336 parameters. Recorded context window: 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-0-3b-a800m-instruct": { "id": "ibm/granite/granite-3-0-3b-a800m-instruct", "name": "Granite-3.0-3B-A800M-Instruct", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-21", "context_length": 4096, "max_output_tokens": null, "parameters": "3374295552", "architecture": "GraniteMoeForCausalLM; model_type granitemoe; layers 32; hidden 1536; heads 24; kv heads 8; vocab 49155; num_local_experts 40; experts per token 8", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.0-3B-A800M-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3374295552 parameters. Recorded context window: 4096 tokens.", "what_it_is_for": "Granite-3.0-3B-A800M-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3374295552 parameters. Recorded context window: 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-0-8b-base": { "id": "ibm/granite/granite-3-0-8b-base", "name": "Granite-3.0-8B-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-21", "context_length": 4096, "max_output_tokens": null, "parameters": "8170835968", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 0.6 }, "summary_intro": "Granite-3.0-8B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170835968 parameters. Recorded context window: 4096 tokens.", "what_it_is_for": "Granite-3.0-8B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170835968 parameters. Recorded context window: 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-0-8b-instruct": { "id": "ibm/granite/granite-3-0-8b-instruct", "name": "Granite-3.0-8B-Instruct", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-21", "context_length": 4096, "max_output_tokens": null, "parameters": "8170848256", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.0-8B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 4096 tokens.", "what_it_is_for": "Granite-3.0-8B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-0-8b-instruct-accelerator": { "id": "ibm/granite/granite-3-0-8b-instruct-accelerator", "name": "optionally download granite-3.0-8b-instruct if the weights do not already exist", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MLPSpeculatorPreTrainedModel; model_type mlp_speculator; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "optionally download granite-3.0-8b-instruct if the weights do not already exist is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "optionally download granite-3.0-8b-instruct if the weights do not already exist is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-0-8b-lora-intrinsics-v0-1": { "id": "ibm/granite/granite-3-0-8b-lora-intrinsics-v0-1", "name": "Granite 3.0 8B Instruct - Intrinsics LoRA v0.1", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "8170848256", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3.0 8B Instruct - Intrinsics LoRA v0.1 is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 4096 tokens.", "what_it_is_for": "Granite 3.0 8B Instruct - Intrinsics LoRA v0.1 is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-1-1b-a400m-base": { "id": "ibm/granite/granite-3-1-1b-a400m-base", "name": "Granite-3.1-1B-A400M-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-18", "context_length": 131072, "max_output_tokens": null, "parameters": "1334625280", "architecture": "GraniteMoeForCausalLM; model_type granitemoe; layers 24; hidden 1024; heads 16; kv heads 8; vocab 49152; num_local_experts 32; experts per token 8", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.1-1B-A400M-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1334625280 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.1-1B-A400M-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1334625280 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-1-1b-a400m-instruct": { "id": "ibm/granite/granite-3-1-1b-a400m-instruct", "name": "Granite-3.1-1B-A400M-Instruct", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-18", "context_length": 131072, "max_output_tokens": null, "parameters": "1334628352", "architecture": "GraniteMoeForCausalLM; model_type granitemoe; layers 24; hidden 1024; heads 16; kv heads 8; vocab 49155; num_local_experts 32; experts per token 8", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.1-1B-A400M-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1334628352 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.1-1B-A400M-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1334628352 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-1-2b-base": { "id": "ibm/granite/granite-3-1-2b-base", "name": "Granite-3.1-2B-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-18", "context_length": 131072, "max_output_tokens": null, "parameters": "2533525504", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 2048; heads 32; kv heads 8; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.1-2B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533525504 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.1-2B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533525504 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-1-2b-instruct": { "id": "ibm/granite/granite-3-1-2b-instruct", "name": "Granite-3.1-2B-Instruct", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-18", "context_length": 131072, "max_output_tokens": null, "parameters": "2533531648", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 2048; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.1 }, "summary_intro": "Granite-3.1-2B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533531648 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.1-2B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533531648 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-1-3b-a800m-base": { "id": "ibm/granite/granite-3-1-3b-a800m-base", "name": "Granite-3.1-3B-A800M-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-18", "context_length": 131072, "max_output_tokens": null, "parameters": "3298788864", "architecture": "GraniteMoeForCausalLM; model_type granitemoe; layers 32; hidden 1536; heads 24; kv heads 8; vocab 49152; num_local_experts 40; experts per token 8", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.1-3B-A800M-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3298788864 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.1-3B-A800M-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3298788864 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-1-3b-a800m-instruct": { "id": "ibm/granite/granite-3-1-3b-a800m-instruct", "name": "Granite-3.1-3B-A800M-Instruct", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-18", "context_length": 131072, "max_output_tokens": null, "parameters": "3298793472", "architecture": "GraniteMoeForCausalLM; model_type granitemoe; layers 32; hidden 1536; heads 24; kv heads 8; vocab 49155; num_local_experts 40; experts per token 8", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.1-3B-A800M-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3298793472 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.1-3B-A800M-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3298793472 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-1-8b-base": { "id": "ibm/granite/granite-3-1-8b-base", "name": "Granite-3.1-8B-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-18", "context_length": 131072, "max_output_tokens": null, "parameters": "8170835968", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.1-8B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170835968 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.1-8B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170835968 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-1-8b-instruct": { "id": "ibm/granite/granite-3-1-8b-instruct", "name": "Granite-3.1-8B-Instruct", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-18", "context_length": 131072, "max_output_tokens": null, "parameters": "8170848256", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.2 }, "summary_intro": "Granite-3.1-8B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.1-8B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-1-8b-lora-intrinsics-v0-1": { "id": "ibm/granite/granite-3-1-8b-lora-intrinsics-v0-1", "name": "Granite 3.1 8B Instruct - Intrinsics LoRA v0.1", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "8170848256", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3.1 8B Instruct - Intrinsics LoRA v0.1 is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite 3.1 8B Instruct - Intrinsics LoRA v0.1 is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-2-2b-instruct": { "id": "ibm/granite/granite-3-2-2b-instruct", "name": "Granite-3.2-2B-Instruct", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-02-26", "context_length": 131072, "max_output_tokens": null, "parameters": "2533531648", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 2048; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.2-2B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533531648 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.2-2B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533531648 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-2-8b-alora-jailbreak": { "id": "ibm/granite/granite-3-2-8b-alora-jailbreak", "name": "Granite 3.2 8B Instruct - Jailbreak aLoRA", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3.2 8B Instruct - Jailbreak aLoRA is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite 3.2 8B Instruct - Jailbreak aLoRA is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-2-8b-alora-rag-query-rewrite": { "id": "ibm/granite/granite-3-2-8b-alora-rag-query-rewrite", "name": "Granite 3.2 8B Instruct - Query Rewrite aLoRA", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3.2 8B Instruct - Query Rewrite aLoRA is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite 3.2 8B Instruct - Query Rewrite aLoRA is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-2-8b-alora-requirement-check": { "id": "ibm/granite/granite-3-2-8b-alora-requirement-check", "name": "Granite 3.2 8B Instruct - Requirement Checker", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3.2 8B Instruct - Requirement Checker is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite 3.2 8B Instruct - Requirement Checker is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-2-8b-instruct": { "id": "ibm/granite/granite-3-2-8b-instruct", "name": "Granite-3.2-8B-Instruct", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-02-26", "context_length": 131072, "max_output_tokens": 16384, "parameters": "8170848256", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.2 }, "summary_intro": "Granite-3.2-8B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.2-8B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-2-8b-instruct-preview": { "id": "ibm/granite/granite-3-2-8b-instruct-preview", "name": "Granite-3.2-8B-Instruct-Preview", "maker": "ibm", "family": "ibm/granite", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-02-07", "context_length": 131072, "max_output_tokens": null, "parameters": "8170848256", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.2-8B-Instruct-Preview is an IBM granite model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.2-8B-Instruct-Preview is an IBM granite model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-2-8b-lora-jailbreak": { "id": "ibm/granite/granite-3-2-8b-lora-jailbreak", "name": "Granite 3.2 8B Instruct - Jailbreak LoRA", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3.2 8B Instruct - Jailbreak LoRA is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite 3.2 8B Instruct - Jailbreak LoRA is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-2-8b-lora-rag-citation-generation": { "id": "ibm/granite/granite-3-2-8b-lora-rag-citation-generation", "name": "Granite 3.2 8B Lora Rag Citation Generation", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3.2 8B Lora Rag Citation Generation is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite 3.2 8B Lora Rag Citation Generation is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-2-8b-lora-uncertainty": { "id": "ibm/granite/granite-3-2-8b-lora-uncertainty", "name": "Granite 3.2 8B Instruct - Uncertainty LoRA", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3.2 8B Instruct - Uncertainty LoRA is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite 3.2 8B Instruct - Uncertainty LoRA is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-3-2b-base": { "id": "ibm/granite/granite-3-3-2b-base", "name": "Granite-3.3-2B-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-04-16", "context_length": 131072, "max_output_tokens": null, "parameters": "2533525504", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 2048; heads 32; kv heads 8; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.3-2B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533525504 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.3-2B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533525504 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-3-2b-instruct": { "id": "ibm/granite/granite-3-3-2b-instruct", "name": "Granite-3.3-2B-Instruct", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-04-16", "context_length": 131072, "max_output_tokens": null, "parameters": "2533539840", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 2048; heads 32; kv heads 8; vocab 49159", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.3-2B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533539840 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.3-2B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533539840 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-3-8b-alora-requirement-check": { "id": "ibm/granite/granite-3-3-8b-alora-requirement-check", "name": "Granite 3.3 8B Instruct - Requirement Checker", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3.3 8B Instruct - Requirement Checker is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite 3.3 8B Instruct - Requirement Checker is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-3-8b-alora-uncertainty": { "id": "ibm/granite/granite-3-3-8b-alora-uncertainty", "name": "Granite 3.3 8B Instruct - Uncertainty aLoRA", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3.3 8B Instruct - Uncertainty aLoRA is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite 3.3 8B Instruct - Uncertainty aLoRA is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-3-8b-base": { "id": "ibm/granite/granite-3-3-8b-base", "name": "Granite-3.3-8B-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-04-16", "context_length": 131072, "max_output_tokens": null, "parameters": "8170835968", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.3-8B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170835968 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.3-8B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170835968 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-3-8b-instruct": { "id": "ibm/granite/granite-3-3-8b-instruct", "name": "Granite-3.3-8B-Instruct", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-04-16", "context_length": 131072, "max_output_tokens": null, "parameters": "8170864640", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49159", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.2 }, "summary_intro": "Granite-3.3-8B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170864640 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-3.3-8B-Instruct is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170864640 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-3-8b-lora-math-prm": { "id": "ibm/granite/granite-3-3-8b-lora-math-prm", "name": "Granite-3.3-8B-LoRA-Math-PRM", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-06-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.3-8B-LoRA-Math-PRM is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite-3.3-8B-LoRA-Math-PRM is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-3-8b-math-prm-v2": { "id": "ibm/granite/granite-3-3-8b-math-prm-v2", "name": "Granite-3.3-8B-Math-PRM-v2", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "8170864640", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49159", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-3.3-8B-Math-PRM-v2 is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170864640 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite-3.3-8B-Math-PRM-v2 is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170864640 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-3-8b-rag-agent-lib": { "id": "ibm/granite/granite-3-3-8b-rag-agent-lib", "name": "Granite 3.3 RAG Agent Library", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3.3 RAG Agent Library is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite 3.3 RAG Agent Library is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-3-3-8b-security-lib": { "id": "ibm/granite/granite-3-3-8b-security-lib", "name": "Granite 3.3 Security Library", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3.3 Security Library is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite 3.3 Security Library is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-1b": { "id": "ibm/granite/granite-4-0-1b", "name": "Granite-4.0-1B", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-28", "context_length": 131072, "max_output_tokens": null, "parameters": "1631750144", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 2048; heads 16; kv heads 4; vocab 100352; num_local_experts 0; experts per token 0", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-1B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1631750144 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite-4.0-1B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1631750144 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-1b-base": { "id": "ibm/granite/granite-4-0-1b-base", "name": "Granite-4.0-1B-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-28", "context_length": 131072, "max_output_tokens": null, "parameters": "1631750144", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 2048; heads 16; kv heads 4; vocab 100352; num_local_experts 0; experts per token 0", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-1B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1631750144 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-4.0-1B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1631750144 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-350m": { "id": "ibm/granite/granite-4-0-350m", "name": "Granite-4.0-350M", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-28", "context_length": 32768, "max_output_tokens": null, "parameters": "352379904", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 28; hidden 1024; heads 16; kv heads 4; vocab 100352; num_local_experts 0; experts per token 0", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-350M is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 352379904 parameters. Recorded context window: 32768 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite-4.0-350M is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 352379904 parameters. Recorded context window: 32768 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-350m-base": { "id": "ibm/granite/granite-4-0-350m-base", "name": "Granite-4.0-350M-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-28", "context_length": 32768, "max_output_tokens": null, "parameters": "352379904", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 28; hidden 1024; heads 16; kv heads 4; vocab 100352; num_local_experts 0; experts per token 0", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-350M-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 352379904 parameters. Recorded context window: 32768 tokens.", "what_it_is_for": "Granite-4.0-350M-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 352379904 parameters. Recorded context window: 32768 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-h-1b": { "id": "ibm/granite/granite-4-0-h-1b", "name": "Granite-4.0-H-1B", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-28", "context_length": 131072, "max_output_tokens": null, "parameters": "1461538368", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 1536; heads 12; kv heads 4; vocab 100352; num_local_experts 0; experts per token 0", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-H-1B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1461538368 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite-4.0-H-1B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1461538368 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-h-1b-base": { "id": "ibm/granite/granite-4-0-h-1b-base", "name": "Granite-4.0-H-1B-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-28", "context_length": 131072, "max_output_tokens": null, "parameters": "1461538368", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 1536; heads 12; kv heads 4; vocab 100352; num_local_experts 0; experts per token 0", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-H-1B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1461538368 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-4.0-H-1B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1461538368 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-h-350m": { "id": "ibm/granite/granite-4-0-h-350m", "name": "Granite-4.0-H-350M", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-28", "context_length": 32768, "max_output_tokens": null, "parameters": "340332224", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 32; hidden 768; heads 12; kv heads 4; vocab 100352; num_local_experts 0; experts per token 0", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-H-350M is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 340332224 parameters. Recorded context window: 32768 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite-4.0-H-350M is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 340332224 parameters. Recorded context window: 32768 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-h-350m-base": { "id": "ibm/granite/granite-4-0-h-350m-base", "name": "Granite-4.0-H-350M-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-28", "context_length": 32768, "max_output_tokens": null, "parameters": "340332224", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 32; hidden 768; heads 12; kv heads 4; vocab 100352; num_local_experts 0; experts per token 0", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-H-350M-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 340332224 parameters. Recorded context window: 32768 tokens.", "what_it_is_for": "Granite-4.0-H-350M-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 340332224 parameters. Recorded context window: 32768 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-h-micro": { "id": "ibm/granite/granite-4-0-h-micro", "name": "Granite-4.0-H-Micro", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-02", "context_length": 131072, "max_output_tokens": null, "parameters": "3191396096", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 2048; heads 32; kv heads 8; vocab 100352; num_local_experts 0; experts per token 0", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-H-Micro is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3191396096 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite-4.0-H-Micro is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3191396096 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": 58, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-h-micro-base": { "id": "ibm/granite/granite-4-0-h-micro-base", "name": "Granite-4.0-H-Micro-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-02", "context_length": 131072, "max_output_tokens": null, "parameters": "3191396096", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 2048; heads 32; kv heads 8; vocab 100352; num_local_experts 0; experts per token 0", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-H-Micro-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3191396096 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-4.0-H-Micro-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3191396096 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-h-small": { "id": "ibm/granite/granite-4-0-h-small", "name": "Granite-4.0-H-Small", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-02", "context_length": 131072, "max_output_tokens": null, "parameters": "32207337984", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 4096; heads 32; kv heads 8; vocab 100352; num_local_experts 72; experts per token 10", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.0636, "completion_per_m_usd": 0.265 }, "summary_intro": "Granite-4.0-H-Small is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 32207337984 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite-4.0-H-Small is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 32207337984 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-h-small-base": { "id": "ibm/granite/granite-4-0-h-small-base", "name": "Granite-4.0-H-Small-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-02", "context_length": 131072, "max_output_tokens": null, "parameters": "32207337984", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 4096; heads 32; kv heads 8; vocab 100352; num_local_experts 72; experts per token 10", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-H-Small-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 32207337984 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-4.0-H-Small-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 32207337984 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-h-tiny": { "id": "ibm/granite/granite-4-0-h-tiny", "name": "Granite-4.0-H-Tiny", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-02", "context_length": 131072, "max_output_tokens": null, "parameters": "6939037248", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 1536; heads 12; kv heads 4; vocab 100352; num_local_experts 64; experts per token 6", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-H-Tiny is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 6939037248 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite-4.0-H-Tiny is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 6939037248 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-h-tiny-base": { "id": "ibm/granite/granite-4-0-h-tiny-base", "name": "Granite-4.0-H-Tiny-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-02", "context_length": 131072, "max_output_tokens": null, "parameters": "6939037248", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 1536; heads 12; kv heads 4; vocab 100352; num_local_experts 64; experts per token 6", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-H-Tiny-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 6939037248 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-4.0-H-Tiny-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 6939037248 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-micro": { "id": "ibm/granite/granite-4-0-micro", "name": "Granite-4.0-Micro", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-02", "context_length": 131072, "max_output_tokens": null, "parameters": "3402836480", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 2560; heads 40; kv heads 8; vocab 100352; num_local_experts 0; experts per token 0", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-Micro is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3402836480 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite-4.0-Micro is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3402836480 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-micro-base": { "id": "ibm/granite/granite-4-0-micro-base", "name": "Granite-4.0-Micro-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-02", "context_length": 131072, "max_output_tokens": null, "parameters": "3402836480", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 2560; heads 40; kv heads 8; vocab 100352; num_local_experts 0; experts per token 0", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-Micro-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3402836480 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-4.0-Micro-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3402836480 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-tiny-base-preview": { "id": "ibm/granite/granite-4-0-tiny-base-preview", "name": "Granite-4.0-Tiny-Base-Preview", "maker": "ibm", "family": "ibm/granite", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-05-02", "context_length": 131072, "max_output_tokens": null, "parameters": "6673296960", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 1536; heads 12; kv heads 4; vocab 50304; num_local_experts 62; experts per token 6", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-Tiny-Base-Preview is an IBM granite model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 6673296960 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-4.0-Tiny-Base-Preview is an IBM granite model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 6673296960 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-0-tiny-preview": { "id": "ibm/granite/granite-4-0-tiny-preview", "name": "Granite-4.0-Tiny-Preview", "maker": "ibm", "family": "ibm/granite", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-05-02", "context_length": 131072, "max_output_tokens": null, "parameters": "6671539776", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 1536; heads 12; kv heads 4; vocab 49160; num_local_experts 62; experts per token 6", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-Tiny-Preview is an IBM granite model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 6671539776 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-4.0-Tiny-Preview is an IBM granite model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 6671539776 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-1-30b": { "id": "ibm/granite/granite-4-1-30b", "name": "Granite-4.1-30B", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-29", "context_length": 131072, "max_output_tokens": null, "parameters": "28865728512", "architecture": "GraniteForCausalLM; model_type granite; layers 64; hidden 4096; heads 32; kv heads 8; vocab 100352", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.1-30B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 28865728512 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite-4.1-30B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 28865728512 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-1-30b-base": { "id": "ibm/granite/granite-4-1-30b-base", "name": "Granite-4.1-30B-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-29", "context_length": 131072, "max_output_tokens": null, "parameters": "28865728512", "architecture": "GraniteForCausalLM; model_type granite; layers 64; hidden 4096; heads 32; kv heads 8; vocab 100352", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.1-30B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 28865728512 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-4.1-30B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 28865728512 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-1-3b": { "id": "ibm/granite/granite-4-1-3b", "name": "Granite-4.1-3B", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-29", "context_length": 131072, "max_output_tokens": null, "parameters": "3402836480", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 2560; heads 40; kv heads 8; vocab 100352", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.1-3B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3402836480 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite-4.1-3B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3402836480 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-1-3b-base": { "id": "ibm/granite/granite-4-1-3b-base", "name": "Granite-4.1-3B-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-29", "context_length": 131072, "max_output_tokens": null, "parameters": "3402836480", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 2560; heads 40; kv heads 8; vocab 100352", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.1-3B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3402836480 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-4.1-3B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3402836480 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-1-8b": { "id": "ibm/granite/granite-4-1-8b", "name": "Granite-4.1-8B", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-29", "context_length": 131072, "max_output_tokens": null, "parameters": "8791592960", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 100352", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.1-8B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8791592960 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite-4.1-8B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8791592960 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-1-8b-base": { "id": "ibm/granite/granite-4-1-8b-base", "name": "Granite-4.1-8B-Base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-29", "context_length": 131072, "max_output_tokens": null, "parameters": "8380551168", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 100352", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.1-8B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8380551168 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite-4.1-8B-Base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8380551168 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-2-30b": { "id": "ibm/granite/granite-4-2-30b", "name": "Granite-4.2-30B", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-08-25", "context_length": 131072, "max_output_tokens": null, "parameters": "29276770304", "architecture": "GraniteForCausalLM; model_type granite; layers 64; hidden 4096; heads 32; kv heads 8; vocab 100352", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.2-30B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 29276770304 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools, reasoning.", "what_it_is_for": "Granite-4.2-30B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 29276770304 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools, reasoning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-2-3b": { "id": "ibm/granite/granite-4-2-3b", "name": "Granite-4.2-3B", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-08-25", "context_length": 131072, "max_output_tokens": null, "parameters": "3659737600", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 2560; heads 40; kv heads 8; vocab 100352", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.2-3B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3659737600 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools, reasoning.", "what_it_is_for": "Granite-4.2-3B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3659737600 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools, reasoning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-4-2-8b": { "id": "ibm/granite/granite-4-2-8b", "name": "Granite-4.2-8B", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-08-25", "context_length": 131072, "max_output_tokens": null, "parameters": "8791592960", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 100352", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.2-8B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8791592960 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools, reasoning.", "what_it_is_for": "Granite-4.2-8B is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8791592960 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools, reasoning.", "ratings": { "empirical_100": 55, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-7b-base": { "id": "ibm/granite/granite-7b-base", "name": "granite-7b-base", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "6738415616", "architecture": "LlamaForCausalLM; model_type llama; layers 32; hidden 4096; heads 32; kv heads 32; vocab 32000", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "granite-7b-base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 6738415616 parameters. Recorded context window: 4096 tokens.", "what_it_is_for": "granite-7b-base is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 6738415616 parameters. Recorded context window: 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-7b-instruct": { "id": "ibm/granite/granite-7b-instruct", "name": "Model Card for Granite-7b-lab [Paper](https://arxiv.org/abs/2403.01081)", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "6738481152", "architecture": "LlamaForCausalLM; model_type llama; layers 32; hidden 4096; heads 32; kv heads 32; vocab 32008", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Model Card for Granite-7b-lab [Paper](https://arxiv.org/abs/2403.01081) is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 6738481152 parameters. Recorded context window: 4096 tokens.", "what_it_is_for": "Model Card for Granite-7b-lab [Paper](https://arxiv.org/abs/2403.01081) is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 6738481152 parameters. Recorded context window: 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-7b-instruct-accelerator": { "id": "ibm/granite/granite-7b-instruct-accelerator", "name": "optionally download granite-7b-instruct if the weights do not already exist", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1394974720", "architecture": "MLPSpeculatorPreTrainedModel; model_type mlp_speculator; vocab 32008", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "optionally download granite-7b-instruct if the weights do not already exist is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1394974720 parameters.", "what_it_is_for": "optionally download granite-7b-instruct if the weights do not already exist is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1394974720 parameters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-7b-lab": { "id": "ibm/granite/granite-7b-lab", "name": "Model Card for Granite-7b-lab [Paper](https://arxiv.org/abs/2403.01081)", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "6738481152", "architecture": "LlamaForCausalLM; model_type llama; layers 32; hidden 4096; heads 32; kv heads 32; vocab 32008", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Model Card for Granite-7b-lab [Paper](https://arxiv.org/abs/2403.01081) is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 6738481152 parameters. Recorded context window: 4096 tokens.", "what_it_is_for": "Model Card for Granite-7b-lab [Paper](https://arxiv.org/abs/2403.01081) is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 6738481152 parameters. Recorded context window: 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-8b-japanese": { "id": "ibm/granite/granite-8b-japanese", "name": "Granite 8B Japanese", "maker": "ibm", "family": "ibm/granite", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "IBM says this version is based on the Granite Instruct model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 0.6 }, "summary_intro": "Granite 8B Japanese is an IBM granite model. Collection date 2026-10-15. Status: deprecated.", "what_it_is_for": "Granite 8B Japanese is an IBM granite model. Collection date 2026-10-15. Status: deprecated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-rag-3-0-8b-lora": { "id": "ibm/granite/granite-rag-3-0-8b-lora", "name": "Granite RAG 3.0 8b", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite RAG 3.0 8b is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite RAG 3.0 8b is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-swash-2b": { "id": "ibm/granite/granite-swash-2b", "name": "Granite-SWASH-2B (Sliding Window Attention + Sinks Hybrid)", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-07-07", "context_length": 8192, "max_output_tokens": null, "parameters": "2144463840", "architecture": "GraniteSWAForCausalLM; model_type granite_swa; layers 24; hidden 2560; heads 20; kv heads 4; vocab 100352", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-SWASH-2B (Sliding Window Attention + Sinks Hybrid) is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2144463840 parameters. Recorded context window: 8192 tokens.", "what_it_is_for": "Granite-SWASH-2B (Sliding Window Attention + Sinks Hybrid) is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2144463840 parameters. Recorded context window: 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-swash-3b-a600m": { "id": "ibm/granite/granite-swash-3b-a600m", "name": "Granite-SWASH-3B-a600M (Sliding Window Attention + Sinks Hybrid)", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-07-07", "context_length": 8192, "max_output_tokens": null, "parameters": "3020382000", "architecture": "GraniteMoeSWAForCausalLM; model_type granitemoe_swa; layers 28; hidden 1280; heads 20; kv heads 4; vocab 100352; num_local_experts 48; experts per token 4", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-SWASH-3B-a600M (Sliding Window Attention + Sinks Hybrid) is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3020382000 parameters. Recorded context window: 8192 tokens.", "what_it_is_for": "Granite-SWASH-3B-a600M (Sliding Window Attention + Sinks Hybrid) is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3020382000 parameters. Recorded context window: 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-switch-4-1-30b-preview": { "id": "ibm/granite/granite-switch-4-1-30b-preview", "name": "Granite Switch 4.1 30B Preview", "maker": "ibm", "family": "ibm/granite", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-05-05", "context_length": 131072, "max_output_tokens": null, "parameters": "32238002176", "architecture": "GraniteSwitchForCausalLM; model_type granite_switch; layers 66; hidden 4096; heads 32; kv heads 8; vocab 100365; num_local_experts 0; experts per token 2", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Switch 4.1 30B Preview is an IBM granite model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 32238002176 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite Switch 4.1 30B Preview is an IBM granite model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 32238002176 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-switch-4-1-3b-preview": { "id": "ibm/granite/granite-switch-4-1-3b-preview", "name": "Granite Switch 4.1 3B Preview", "maker": "ibm", "family": "ibm/granite", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-05-05", "context_length": 131072, "max_output_tokens": null, "parameters": "4149980160", "architecture": "GraniteSwitchForCausalLM; model_type granite_switch; layers 42; hidden 2560; heads 40; kv heads 8; vocab 100365; num_local_experts 0; experts per token 2", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Switch 4.1 3B Preview is an IBM granite model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 4149980160 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite Switch 4.1 3B Preview is an IBM granite model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 4149980160 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-switch-4-1-8b-preview": { "id": "ibm/granite/granite-switch-4-1-8b-preview", "name": "Granite Switch 4.1 8B Preview", "maker": "ibm", "family": "ibm/granite", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-05-05", "context_length": 131072, "max_output_tokens": null, "parameters": "9568116736", "architecture": "GraniteSwitchForCausalLM; model_type granite_switch; layers 42; hidden 4096; heads 32; kv heads 8; vocab 100365; num_local_experts 0; experts per token 2", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Switch 4.1 8B Preview is an IBM granite model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 9568116736 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite Switch 4.1 8B Preview is an IBM granite model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 9568116736 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granite-uncertainty-3-0-8b-lora": { "id": "ibm/granite/granite-uncertainty-3-0-8b-lora", "name": "Granite 3.0 8B Instruct - Uncertainty LoRA", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3.0 8B Instruct - Uncertainty LoRA is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite 3.0 8B Instruct - Uncertainty LoRA is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granitelib-core-r1-0": { "id": "ibm/granite/granitelib-core-r1-0", "name": "Granite Core Library", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Core Library is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite Core Library is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/granitelib-rag-r1-0": { "id": "ibm/granite/granitelib-rag-r1-0", "name": "Granite RAG Library", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite RAG Library is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite RAG Library is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite/rag-intrinsics-lib": { "id": "ibm/granite/rag-intrinsics-lib", "name": "rag-intrinsics-lib", "maker": "ibm", "family": "ibm/granite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rag-intrinsics-lib is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "rag-intrinsics-lib is an IBM granite model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-20b-code-base-8k": { "id": "ibm/granite-code/granite-20b-code-base-8k", "name": "Granite 20B Code Base 8K", "maker": "ibm", "family": "ibm/granite-code", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-05-06", "context_length": 8192, "max_output_tokens": null, "parameters": "20066993152", "architecture": "GPTBigCodeForCausalLM; model_type gpt_bigcode; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 20B Code Base 8K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 20066993152 parameters. Recorded context window: 8192 tokens.", "what_it_is_for": "Granite 20B Code Base 8K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 20066993152 parameters. Recorded context window: 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-20b-code-base-r1-1": { "id": "ibm/granite-code/granite-20b-code-base-r1-1", "name": "Granite-20B-Code-Base-r1.1", "maker": "ibm", "family": "ibm/granite-code", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-07-18", "context_length": 8192, "max_output_tokens": null, "parameters": "20066993152", "architecture": "GPTBigCodeForCausalLM; model_type gpt_bigcode; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-20B-Code-Base-r1.1 is an IBM granite code model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 20066993152 parameters. Recorded context window: 8192 tokens.", "what_it_is_for": "Granite-20B-Code-Base-r1.1 is an IBM granite code model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 20066993152 parameters. Recorded context window: 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-20b-code-base-schema-linking": { "id": "ibm/granite-code/granite-20b-code-base-schema-linking", "name": "Granite 20B Code Base Schema Linking", "maker": "ibm", "family": "ibm/granite-code", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 20B Code Base Schema Linking is an IBM granite code model. Collection date 2026-10-15. Status: active.", "what_it_is_for": "Granite 20B Code Base Schema Linking is an IBM granite code model. Collection date 2026-10-15. Status: active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-20b-code-base-sql-gen": { "id": "ibm/granite-code/granite-20b-code-base-sql-gen", "name": "Granite 20B Code Base SQL Gen", "maker": "ibm", "family": "ibm/granite-code", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 20B Code Base SQL Gen is an IBM granite code model. Collection date 2026-10-15. Status: active. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite 20B Code Base SQL Gen is an IBM granite code model. Collection date 2026-10-15. Status: active. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-20b-code-instruct-8k": { "id": "ibm/granite-code/granite-20b-code-instruct-8k", "name": "Granite 20B Code Instruct 8K", "maker": "ibm", "family": "ibm/granite-code", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-05-06", "context_length": 8192, "max_output_tokens": null, "parameters": "20066993152", "architecture": "GPTBigCodeForCausalLM; model_type gpt_bigcode; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 0.6 }, "summary_intro": "Granite 20B Code Instruct 8K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 20066993152 parameters. Recorded context window: 8192 tokens.", "what_it_is_for": "Granite 20B Code Instruct 8K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 20066993152 parameters. Recorded context window: 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-20b-code-instruct-accelerator": { "id": "ibm/granite-code/granite-20b-code-instruct-accelerator", "name": "optionally download granite-20b-code-instruct if the weights do not already exist", "maker": "ibm", "family": "ibm/granite-code", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MLPSpeculatorPreTrainedModel; model_type mlp_speculator; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "optionally download granite-20b-code-instruct if the weights do not already exist is an IBM granite code model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "optionally download granite-20b-code-instruct if the weights do not already exist is an IBM granite code model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-20b-code-instruct-r1-1": { "id": "ibm/granite-code/granite-20b-code-instruct-r1-1", "name": "Granite-20B-Code-Instruct-r1.1", "maker": "ibm", "family": "ibm/granite-code", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-07-18", "context_length": 8192, "max_output_tokens": null, "parameters": "20066993152", "architecture": "GPTBigCodeForCausalLM; model_type gpt_bigcode; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-20B-Code-Instruct-r1.1 is an IBM granite code model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 20066993152 parameters. Recorded context window: 8192 tokens.", "what_it_is_for": "Granite-20B-Code-Instruct-r1.1 is an IBM granite code model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 20066993152 parameters. Recorded context window: 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-20b-functioncalling": { "id": "ibm/granite-code/granite-20b-functioncalling", "name": "Granite 20B Functioncalling", "maker": "ibm", "family": "ibm/granite-code", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-07-09", "context_length": 8192, "max_output_tokens": null, "parameters": "20066993152", "architecture": "GPTBigCodeForCausalLM; model_type gpt_bigcode; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 20B Functioncalling is an IBM granite code model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 20066993152 parameters. Recorded context window: 8192 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite 20B Functioncalling is an IBM granite code model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 20066993152 parameters. Recorded context window: 8192 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-34b-code-base-8k": { "id": "ibm/granite-code/granite-34b-code-base-8k", "name": "Granite 34B Code Base 8K", "maker": "ibm", "family": "ibm/granite-code", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-05-06", "context_length": 8192, "max_output_tokens": null, "parameters": "33715603456", "architecture": "GPTBigCodeForCausalLM; model_type gpt_bigcode; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 34B Code Base 8K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 33715603456 parameters. Recorded context window: 8192 tokens.", "what_it_is_for": "Granite 34B Code Base 8K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 33715603456 parameters. Recorded context window: 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-34b-code-instruct-8k": { "id": "ibm/granite-code/granite-34b-code-instruct-8k", "name": "Granite 34B Code Instruct 8K", "maker": "ibm", "family": "ibm/granite-code", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-05-06", "context_length": 8192, "max_output_tokens": null, "parameters": "33715603456", "architecture": "GPTBigCodeForCausalLM; model_type gpt_bigcode; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 0.6 }, "summary_intro": "Granite 34B Code Instruct 8K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 33715603456 parameters. Recorded context window: 8192 tokens.", "what_it_is_for": "Granite 34B Code Instruct 8K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 33715603456 parameters. Recorded context window: 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-34b-code-instruct-accelerator": { "id": "ibm/granite-code/granite-34b-code-instruct-accelerator", "name": "optionally download granite-34b-code-instruct if the weights do not already exist", "maker": "ibm", "family": "ibm/granite-code", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MLPSpeculatorPreTrainedModel; model_type mlp_speculator; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "optionally download granite-34b-code-instruct if the weights do not already exist is an IBM granite code model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "optionally download granite-34b-code-instruct if the weights do not already exist is an IBM granite code model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-3b-code-base-128k": { "id": "ibm/granite-code/granite-3b-code-base-128k", "name": "Granite 3B Code Base 128K", "maker": "ibm", "family": "ibm/granite-code", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-07-18", "context_length": 128000, "max_output_tokens": null, "parameters": "3482503680", "architecture": "LlamaForCausalLM; model_type llama; layers 32; hidden 2560; heads 32; kv heads 32; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3B Code Base 128K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3482503680 parameters. Recorded context window: 128000 tokens.", "what_it_is_for": "Granite 3B Code Base 128K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3482503680 parameters. Recorded context window: 128000 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-3b-code-base-2k": { "id": "ibm/granite-code/granite-3b-code-base-2k", "name": "Granite 3B Code Base 2K", "maker": "ibm", "family": "ibm/granite-code", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-05-06", "context_length": 2048, "max_output_tokens": null, "parameters": "3482503680", "architecture": "LlamaForCausalLM; model_type llama; layers 32; hidden 2560; heads 32; kv heads 32; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3B Code Base 2K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3482503680 parameters. Recorded context window: 2048 tokens.", "what_it_is_for": "Granite 3B Code Base 2K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3482503680 parameters. Recorded context window: 2048 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-3b-code-instruct-128k": { "id": "ibm/granite-code/granite-3b-code-instruct-128k", "name": "Granite 3B Code Instruct 128K", "maker": "ibm", "family": "ibm/granite-code", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-07-18", "context_length": 128000, "max_output_tokens": null, "parameters": "3482503680", "architecture": "LlamaForCausalLM; model_type llama; layers 32; hidden 2560; heads 32; kv heads 32; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 0.6 }, "summary_intro": "Granite 3B Code Instruct 128K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3482503680 parameters. Recorded context window: 128000 tokens.", "what_it_is_for": "Granite 3B Code Instruct 128K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3482503680 parameters. Recorded context window: 128000 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-3b-code-instruct-2k": { "id": "ibm/granite-code/granite-3b-code-instruct-2k", "name": "Granite 3B Code Instruct 2K", "maker": "ibm", "family": "ibm/granite-code", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-05-06", "context_length": 2048, "max_output_tokens": null, "parameters": "3482503680", "architecture": "LlamaForCausalLM; model_type llama; layers 32; hidden 2560; heads 32; kv heads 32; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 3B Code Instruct 2K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3482503680 parameters. Recorded context window: 2048 tokens.", "what_it_is_for": "Granite 3B Code Instruct 2K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3482503680 parameters. Recorded context window: 2048 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-3b-code-instruct-accelerator": { "id": "ibm/granite-code/granite-3b-code-instruct-accelerator", "name": "optionally download granite-3b-code-instruct if the weights do not already exist", "maker": "ibm", "family": "ibm/granite-code", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MLPSpeculatorPreTrainedModel; model_type mlp_speculator; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "optionally download granite-3b-code-instruct if the weights do not already exist is an IBM granite code model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "optionally download granite-3b-code-instruct if the weights do not already exist is an IBM granite code model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-8b-code-base-128k": { "id": "ibm/granite-code/granite-8b-code-base-128k", "name": "Granite 8B Code Base 128K", "maker": "ibm", "family": "ibm/granite-code", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-07-18", "context_length": 128000, "max_output_tokens": null, "parameters": "8054910976", "architecture": "LlamaForCausalLM; model_type llama; layers 36; hidden 4096; heads 32; kv heads 8; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 8B Code Base 128K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8054910976 parameters. Recorded context window: 128000 tokens.", "what_it_is_for": "Granite 8B Code Base 128K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8054910976 parameters. Recorded context window: 128000 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-8b-code-base-4k": { "id": "ibm/granite-code/granite-8b-code-base-4k", "name": "Granite 8B Code Base 4K", "maker": "ibm", "family": "ibm/granite-code", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-05-06", "context_length": 4096, "max_output_tokens": null, "parameters": "8054910976", "architecture": "LlamaForCausalLM; model_type llama; layers 36; hidden 4096; heads 32; kv heads 8; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 8B Code Base 4K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8054910976 parameters. Recorded context window: 4096 tokens.", "what_it_is_for": "Granite 8B Code Base 4K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8054910976 parameters. Recorded context window: 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-8b-code-instruct-128k": { "id": "ibm/granite-code/granite-8b-code-instruct-128k", "name": "Granite 8B Code Instruct 128K", "maker": "ibm", "family": "ibm/granite-code", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-07-18", "context_length": 128000, "max_output_tokens": null, "parameters": "8054910976", "architecture": "LlamaForCausalLM; model_type llama; layers 36; hidden 4096; heads 32; kv heads 8; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 8B Code Instruct 128K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8054910976 parameters. Recorded context window: 128000 tokens.", "what_it_is_for": "Granite 8B Code Instruct 128K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8054910976 parameters. Recorded context window: 128000 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-8b-code-instruct-4k": { "id": "ibm/granite-code/granite-8b-code-instruct-4k", "name": "Granite 8B Code Instruct 4K", "maker": "ibm", "family": "ibm/granite-code", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-05-06", "context_length": 4096, "max_output_tokens": null, "parameters": "8054910976", "architecture": "LlamaForCausalLM; model_type llama; layers 36; hidden 4096; heads 32; kv heads 8; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 8B Code Instruct 4K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8054910976 parameters. Recorded context window: 4096 tokens.", "what_it_is_for": "Granite 8B Code Instruct 4K is an IBM granite code model. Collection date 2026-10-15. Status: deprecated. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8054910976 parameters. Recorded context window: 4096 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-code/granite-8b-code-instruct-accelerator": { "id": "ibm/granite-code/granite-8b-code-instruct-accelerator", "name": "optionally download granite-8b-code-instruct if the weights do not already exist", "maker": "ibm", "family": "ibm/granite-code", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "2097192960", "architecture": "MLPSpeculatorPreTrainedModel; model_type mlp_speculator; vocab 49152", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "optionally download granite-8b-code-instruct if the weights do not already exist is an IBM granite code model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2097192960 parameters.", "what_it_is_for": "optionally download granite-8b-code-instruct if the weights do not already exist is an IBM granite code model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2097192960 parameters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-docling/granite-docling-258m": { "id": "ibm/granite-docling/granite-docling-258m", "name": "granite-docling-258m", "maker": "ibm", "family": "ibm/granite-docling", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-09-17", "context_length": 8192, "max_output_tokens": null, "parameters": "257517120", "architecture": "Idefics3ForConditionalGeneration; model_type idefics3; vocab 100352", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "granite-docling-258m is an IBM granite docling model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 257517120 parameters. Recorded context window: 8192 tokens. Capabilities noted from the card or config: vision.", "what_it_is_for": "granite-docling-258m is an IBM granite docling model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 257517120 parameters. Recorded context window: 8192 tokens. Capabilities noted from the card or config: vision.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-embedding/granite-embedding-107m-multilingual": { "id": "ibm/granite-embedding/granite-embedding-107m-multilingual", "name": "granite-embedding-107m-multilingual", "maker": "ibm", "family": "ibm/granite-embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 514, "max_output_tokens": null, "parameters": "106994304", "architecture": "XLMRobertaModel; model_type xlm-roberta; layers 6; hidden 384; heads 12; vocab 250002", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "granite-embedding-107m-multilingual is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 106994304 parameters. Recorded context window: 514 tokens. Capabilities noted from the card or config: embeddings.", "what_it_is_for": "granite-embedding-107m-multilingual is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 106994304 parameters. Recorded context window: 514 tokens. Capabilities noted from the card or config: embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-embedding/granite-embedding-125m-english": { "id": "ibm/granite-embedding/granite-embedding-125m-english", "name": "granite-embedding-125m-english", "maker": "ibm", "family": "ibm/granite-embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 514, "max_output_tokens": null, "parameters": "124645632", "architecture": "RobertaModel; model_type roberta; layers 12; hidden 768; heads 12; vocab 50265", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "granite-embedding-125m-english is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 124645632 parameters. Recorded context window: 514 tokens. Capabilities noted from the card or config: embeddings.", "what_it_is_for": "granite-embedding-125m-english is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 124645632 parameters. Recorded context window: 514 tokens. Capabilities noted from the card or config: embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-embedding/granite-embedding-278m-multilingual": { "id": "ibm/granite-embedding/granite-embedding-278m-multilingual", "name": "granite-embedding-278m-multilingual", "maker": "ibm", "family": "ibm/granite-embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 514, "max_output_tokens": null, "parameters": "278043648", "architecture": "XLMRobertaModel; model_type xlm-roberta; layers 12; hidden 768; heads 12; vocab 250002", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.106, "completion_per_m_usd": null }, "summary_intro": "granite-embedding-278m-multilingual is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 278043648 parameters. Recorded context window: 514 tokens. Capabilities noted from the card or config: embeddings.", "what_it_is_for": "granite-embedding-278m-multilingual is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 278043648 parameters. Recorded context window: 514 tokens. Capabilities noted from the card or config: embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-embedding/granite-embedding-30m-english": { "id": "ibm/granite-embedding/granite-embedding-30m-english", "name": "Granite-Embedding-30m-English (revision r1.1)", "maker": "ibm", "family": "ibm/granite-embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-08-29", "context_length": 514, "max_output_tokens": null, "parameters": "30295296", "architecture": "RobertaModel; model_type roberta; layers 6; hidden 384; heads 12; vocab 50265", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-Embedding-30m-English (revision r1.1) is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 30295296 parameters. Recorded context window: 514 tokens. Capabilities noted from the card or config: embeddings.", "what_it_is_for": "Granite-Embedding-30m-English (revision r1.1) is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 30295296 parameters. Recorded context window: 514 tokens. Capabilities noted from the card or config: embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-embedding/granite-embedding-30m-sparse": { "id": "ibm/granite-embedding/granite-embedding-30m-sparse", "name": "Granite-Embedding-30m-Sparse", "maker": "ibm", "family": "ibm/granite-embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-02-26", "context_length": 514, "max_output_tokens": null, "parameters": "30346329", "architecture": "RobertaForMaskedLM; model_type roberta; layers 6; hidden 384; heads 12; vocab 50265", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-Embedding-30m-Sparse is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 30346329 parameters. Recorded context window: 514 tokens. Capabilities noted from the card or config: embeddings.", "what_it_is_for": "Granite-Embedding-30m-Sparse is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 30346329 parameters. Recorded context window: 514 tokens. Capabilities noted from the card or config: embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-embedding/granite-embedding-311m-multilingual-r2": { "id": "ibm/granite-embedding/granite-embedding-311m-multilingual-r2", "name": "Granite-Embedding-311M-Multilingual-R2", "maker": "ibm", "family": "ibm/granite-embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-29", "context_length": 32768, "max_output_tokens": null, "parameters": "311664384", "architecture": "ModernBertModel; model_type modernbert; layers 22; hidden 768; heads 12; vocab 262152", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-Embedding-311M-Multilingual-R2 is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 311664384 parameters. Recorded context window: 32768 tokens. Capabilities noted from the card or config: embeddings.", "what_it_is_for": "Granite-Embedding-311M-Multilingual-R2 is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 311664384 parameters. Recorded context window: 32768 tokens. Capabilities noted from the card or config: embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-embedding/granite-embedding-97m-multilingual-r2": { "id": "ibm/granite-embedding/granite-embedding-97m-multilingual-r2", "name": "Granite-Embedding-97M-Multilingual-R2", "maker": "ibm", "family": "ibm/granite-embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-29", "context_length": 32768, "max_output_tokens": null, "parameters": "97441152", "architecture": "ModernBertModel; model_type modernbert; layers 12; hidden 384; heads 12; vocab 180000", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-Embedding-97M-Multilingual-R2 is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 97441152 parameters. Recorded context window: 32768 tokens. Capabilities noted from the card or config: embeddings.", "what_it_is_for": "Granite-Embedding-97M-Multilingual-R2 is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 97441152 parameters. Recorded context window: 32768 tokens. Capabilities noted from the card or config: embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-embedding/granite-embedding-english-r2": { "id": "ibm/granite-embedding/granite-embedding-english-r2", "name": "Granite-Embedding-English-R2", "maker": "ibm", "family": "ibm/granite-embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "149014272", "architecture": "ModernBertModel; model_type modernbert; layers 22; hidden 768; heads 12; vocab 50368", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-Embedding-English-R2 is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 149014272 parameters. Recorded context window: 8192 tokens. Capabilities noted from the card or config: embeddings.", "what_it_is_for": "Granite-Embedding-English-R2 is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 149014272 parameters. Recorded context window: 8192 tokens. Capabilities noted from the card or config: embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-embedding/granite-embedding-reranker-english-r2": { "id": "ibm/granite-embedding/granite-embedding-reranker-english-r2", "name": "granite-embedding-reranker-english-r2", "maker": "ibm", "family": "ibm/granite-embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "149605633", "architecture": "ModernBertForSequenceClassification; model_type modernbert; layers 22; hidden 768; heads 12; vocab 50368", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "granite-embedding-reranker-english-r2 is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 149605633 parameters. Recorded context window: 8192 tokens. Capabilities noted from the card or config: embeddings, rerank.", "what_it_is_for": "granite-embedding-reranker-english-r2 is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 149605633 parameters. Recorded context window: 8192 tokens. Capabilities noted from the card or config: embeddings, rerank.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-embedding/granite-embedding-small-english-r2": { "id": "ibm/granite-embedding/granite-embedding-small-english-r2", "name": "Granite-Embedding-Small-English-R2", "maker": "ibm", "family": "ibm/granite-embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "47662464", "architecture": "ModernBertModel; model_type modernbert; layers 12; hidden 384; heads 12; vocab 50368", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-Embedding-Small-English-R2 is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 47662464 parameters. Recorded context window: 8192 tokens. Capabilities noted from the card or config: embeddings.", "what_it_is_for": "Granite-Embedding-Small-English-R2 is an IBM granite embedding model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 47662464 parameters. Recorded context window: 8192 tokens. Capabilities noted from the card or config: embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-geospatial/granite-geospatial-biomass": { "id": "ibm/granite-geospatial/granite-geospatial-biomass", "name": "Model Card for granite-geospatial-biomass", "maker": "ibm", "family": "ibm/granite-geospatial", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Model Card for granite-geospatial-biomass is an IBM granite geospatial model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Model Card for granite-geospatial-biomass is an IBM granite geospatial model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-geospatial/granite-geospatial-canopyheight": { "id": "ibm/granite-geospatial/granite-geospatial-canopyheight", "name": "Model Card for granite-geospatial-canopy-height", "maker": "ibm", "family": "ibm/granite-geospatial", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Model Card for granite-geospatial-canopy-height is an IBM granite geospatial model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Model Card for granite-geospatial-canopy-height is an IBM granite geospatial model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-geospatial/granite-geospatial-land-surface-temperature": { "id": "ibm/granite-geospatial/granite-geospatial-land-surface-temperature", "name": "Model Card for granite-geospatial-land-surface-temperature", "maker": "ibm", "family": "ibm/granite-geospatial", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Model Card for granite-geospatial-land-surface-temperature is an IBM granite geospatial model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Model Card for granite-geospatial-land-surface-temperature is an IBM granite geospatial model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-geospatial/granite-geospatial-ocean": { "id": "ibm/granite-geospatial/granite-geospatial-ocean", "name": "granite-geospatial-ocean", "maker": "ibm", "family": "ibm/granite-geospatial", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "granite-geospatial-ocean is an IBM granite geospatial model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "granite-geospatial-ocean is an IBM granite geospatial model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-geospatial/granite-geospatial-uki": { "id": "ibm/granite-geospatial/granite-geospatial-uki", "name": "Model Card for granite-geospatial-uki", "maker": "ibm", "family": "ibm/granite-geospatial", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Model Card for granite-geospatial-uki is an IBM granite geospatial model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Model Card for granite-geospatial-uki is an IBM granite geospatial model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-geospatial/granite-geospatial-uki-flooddetection": { "id": "ibm/granite-geospatial/granite-geospatial-uki-flooddetection", "name": "Model Card for granite-geospatial-flood-detection-uki", "maker": "ibm", "family": "ibm/granite-geospatial", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Model Card for granite-geospatial-flood-detection-uki is an IBM granite geospatial model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Model Card for granite-geospatial-flood-detection-uki is an IBM granite geospatial model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-geospatial/granite-geospatial-wxc-downscaling": { "id": "ibm/granite-geospatial/granite-geospatial-wxc-downscaling", "name": "Model card for granite-geospatial-wxc-downscaling", "maker": "ibm", "family": "ibm/granite-geospatial", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cdla-permissive-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Model card for granite-geospatial-wxc-downscaling is an IBM granite geospatial model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: cdla-permissive-2.0.", "what_it_is_for": "Model card for granite-geospatial-wxc-downscaling is an IBM granite geospatial model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: cdla-permissive-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-3-0-2b": { "id": "ibm/granite-guardian/granite-guardian-3-0-2b", "name": "Granite Guardian 3.0 2B", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-21", "context_length": 8192, "max_output_tokens": null, "parameters": "2533531648", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 2048; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian 3.0 2B is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533531648 parameters. Recorded context window: 8192 tokens.", "what_it_is_for": "Granite Guardian 3.0 2B is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533531648 parameters. Recorded context window: 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-3-0-8b": { "id": "ibm/granite-guardian/granite-guardian-3-0-8b", "name": "Granite Guardian 3.0 8B", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-21", "context_length": 8192, "max_output_tokens": null, "parameters": "8170848256", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian 3.0 8B is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 8192 tokens.", "what_it_is_for": "Granite Guardian 3.0 8B is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-3-1-2b": { "id": "ibm/granite-guardian/granite-guardian-3-1-2b", "name": "Granite Guardian 3.1 2B", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-18", "context_length": 131072, "max_output_tokens": null, "parameters": "2533531648", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 2048; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian 3.1 2B is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533531648 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite Guardian 3.1 2B is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2533531648 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-3-1-8b": { "id": "ibm/granite-guardian/granite-guardian-3-1-8b", "name": "Granite Guardian 3.1 8B", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-18", "context_length": 131072, "max_output_tokens": null, "parameters": "8170848256", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian 3.1 8B is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite Guardian 3.1 8B is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-3-2-3b-a800m": { "id": "ibm/granite-guardian/granite-guardian-3-2-3b-a800m", "name": "Granite Guardian 3.2 3B-A800M", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-02-26", "context_length": 131072, "max_output_tokens": null, "parameters": "3298793472", "architecture": "GraniteMoeForCausalLM; model_type granitemoe; layers 32; hidden 1536; heads 24; kv heads 8; vocab 49155; num_local_experts 40; experts per token 8", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian 3.2 3B-A800M is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3298793472 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite Guardian 3.2 3B-A800M is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3298793472 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-3-2-5b": { "id": "ibm/granite-guardian/granite-guardian-3-2-5b", "name": "Granite Guardian 3.2 5B", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-02-26", "context_length": 131072, "max_output_tokens": null, "parameters": "5779996672", "architecture": "GraniteForCausalLM; model_type granite; layers 28; hidden 4096; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian 3.2 5B is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 5779996672 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite Guardian 3.2 5B is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 5779996672 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-3-2-5b-lora-factuality-correction": { "id": "ibm/granite-guardian/granite-guardian-3-2-5b-lora-factuality-correction", "name": "Granite Guardian 3.2 5B Factuality Correction LoRA", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian 3.2 5B Factuality Correction LoRA is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite Guardian 3.2 5B Factuality Correction LoRA is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-3-2-5b-lora-harm-categories": { "id": "ibm/granite-guardian/granite-guardian-3-2-5b-lora-harm-categories", "name": "Granite Guardian 3.2 5b Harm Categories LoRA", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian 3.2 5b Harm Categories LoRA is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite Guardian 3.2 5b Harm Categories LoRA is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-3-2-5b-lora-harm-correction": { "id": "ibm/granite-guardian/granite-guardian-3-2-5b-lora-harm-correction", "name": "Granite Guardian 3.2 5b Harm Correction LoRA", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-09-02", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "GraniteForCausalLM; model_type granite; layers 28; hidden 4096; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian 3.2 5b Harm Correction LoRA is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite Guardian 3.2 5b Harm Correction LoRA is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-3-2-8b-factuality-detection": { "id": "ibm/granite-guardian/granite-guardian-3-2-8b-factuality-detection", "name": "Granite Guardian 3.2 8B Factuality Detection", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "8170848256", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49155", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian 3.2 8B Factuality Detection is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 131072 tokens.", "what_it_is_for": "Granite Guardian 3.2 8B Factuality Detection is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170848256 parameters. Recorded context window: 131072 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-3-3-8b": { "id": "ibm/granite-guardian/granite-guardian-3-3-8b", "name": "Granite Guardian 3.3 8B", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-08-01", "context_length": 131072, "max_output_tokens": null, "parameters": "8170864640", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 49159", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian 3.3 8B is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170864640 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite Guardian 3.3 8B is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8170864640 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-4-0-3b-toxicity-ja": { "id": "ibm/granite-guardian/granite-guardian-4-0-3b-toxicity-ja", "name": "Granite Guardian 4.0 3B Toxicity Japanese", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "3402836480", "architecture": "GraniteMoeHybridForCausalLM; model_type granitemoehybrid; layers 40; hidden 2560; heads 40; kv heads 8; vocab 100352; num_local_experts 0; experts per token 0", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian 4.0 3B Toxicity Japanese is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3402836480 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite Guardian 4.0 3B Toxicity Japanese is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3402836480 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-4-1-8b": { "id": "ibm/granite-guardian/granite-guardian-4-1-8b", "name": "Granite Guardian 4.1 8B", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "8380551168", "architecture": "GraniteForCausalLM; model_type granite; layers 40; hidden 4096; heads 32; kv heads 8; vocab 100352", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian 4.1 8B is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8380551168 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "what_it_is_for": "Granite Guardian 4.1 8B is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8380551168 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-hap-125m": { "id": "ibm/granite-guardian/granite-guardian-hap-125m", "name": "Granite Guardian HAP 125M", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-09-06", "context_length": 514, "max_output_tokens": null, "parameters": "124647170", "architecture": "RobertaForSequenceClassification; model_type roberta; layers 12; hidden 768; heads 12; vocab 50265", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian HAP 125M is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 124647170 parameters. Recorded context window: 514 tokens.", "what_it_is_for": "Granite Guardian HAP 125M is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 124647170 parameters. Recorded context window: 514 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granite-guardian-hap-38m": { "id": "ibm/granite-guardian/granite-guardian-hap-38m", "name": "Granite Guardian HAP 38M", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-09-06", "context_length": 514, "max_output_tokens": null, "parameters": "38455682", "architecture": "RobertaForSequenceClassification; model_type roberta; layers 4; hidden 576; heads 12; vocab 50265", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian HAP 38M is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 38455682 parameters. Recorded context window: 514 tokens.", "what_it_is_for": "Granite Guardian HAP 38M is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 38455682 parameters. Recorded context window: 514 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-guardian/granitelib-guardian-r1-0": { "id": "ibm/granite-guardian/granitelib-guardian-r1-0", "name": "Granite Guardian Library", "maker": "ibm", "family": "ibm/granite-guardian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Guardian Library is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "what_it_is_for": "Granite Guardian Library is an IBM granite guardian model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-speech/granite-4-0-1b-speech": { "id": "ibm/granite-speech/granite-4-0-1b-speech", "name": "Granite-4.0-1b-speech", "maker": "ibm", "family": "ibm/granite-speech", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-03-06", "context_length": null, "max_output_tokens": null, "parameters": "2313207132", "architecture": "GraniteSpeechForConditionalGeneration; model_type granite_speech", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-4.0-1b-speech is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2313207132 parameters. Capabilities noted from the card or config: speech.", "what_it_is_for": "Granite-4.0-1b-speech is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2313207132 parameters. Capabilities noted from the card or config: speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-speech/granite-speech-3-2-8b": { "id": "ibm/granite-speech/granite-speech-3-2-8b", "name": "Granite-speech-3.2-8b", "maker": "ibm", "family": "ibm/granite-speech", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-04-02", "context_length": null, "max_output_tokens": null, "parameters": "8483498282", "architecture": "GraniteSpeechForConditionalGeneration; model_type granite_speech", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-speech-3.2-8b is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8483498282 parameters. Capabilities noted from the card or config: speech.", "what_it_is_for": "Granite-speech-3.2-8b is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8483498282 parameters. Capabilities noted from the card or config: speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-speech/granite-speech-3-3-2b": { "id": "ibm/granite-speech/granite-speech-3-3-2b", "name": "Granite-speech-3.3-2b (revision 3.3.2)", "maker": "ibm", "family": "ibm/granite-speech", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-06-19", "context_length": null, "max_output_tokens": null, "parameters": "3009285376", "architecture": "GraniteSpeechForConditionalGeneration; model_type granite_speech", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-speech-3.3-2b (revision 3.3.2) is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3009285376 parameters. Capabilities noted from the card or config: tools, reasoning, speech.", "what_it_is_for": "Granite-speech-3.3-2b (revision 3.3.2) is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3009285376 parameters. Capabilities noted from the card or config: tools, reasoning, speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-speech/granite-speech-3-3-8b": { "id": "ibm/granite-speech/granite-speech-3-3-8b", "name": "Granite-speech-3.3-8b (revision 3.3.2)", "maker": "ibm", "family": "ibm/granite-speech", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-06-19", "context_length": null, "max_output_tokens": null, "parameters": "8648711424", "architecture": "GraniteSpeechForConditionalGeneration; model_type granite_speech", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-speech-3.3-8b (revision 3.3.2) is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8648711424 parameters. Capabilities noted from the card or config: tools, reasoning, speech.", "what_it_is_for": "Granite-speech-3.3-8b (revision 3.3.2) is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 8648711424 parameters. Capabilities noted from the card or config: tools, reasoning, speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-speech/granite-speech-4-1-2b": { "id": "ibm/granite-speech/granite-speech-4-1-2b", "name": "Granite-Speech-4.1-2B", "maker": "ibm", "family": "ibm/granite-speech", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-29", "context_length": null, "max_output_tokens": null, "parameters": "2313207132", "architecture": "GraniteSpeechForConditionalGeneration; model_type granite_speech", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-Speech-4.1-2B is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2313207132 parameters. Capabilities noted from the card or config: speech.", "what_it_is_for": "Granite-Speech-4.1-2B is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2313207132 parameters. Capabilities noted from the card or config: speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-speech/granite-speech-4-1-2b-nar": { "id": "ibm/granite-speech/granite-speech-4-1-2b-nar", "name": "Granite-Speech-4.1-2B-NAR", "maker": "ibm", "family": "ibm/granite-speech", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "2254635356", "architecture": "GraniteSpeechNarForASR; model_type granite_speech_nar", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-Speech-4.1-2B-NAR is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2254635356 parameters. Capabilities noted from the card or config: tools, speech.", "what_it_is_for": "Granite-Speech-4.1-2B-NAR is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2254635356 parameters. Capabilities noted from the card or config: tools, speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-speech/granite-speech-4-1-2b-plus": { "id": "ibm/granite-speech/granite-speech-4-1-2b-plus", "name": "Granite-Speech-4.1-2B-Plus", "maker": "ibm", "family": "ibm/granite-speech", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "2111812956", "architecture": "GraniteSpeechPlusForConditionalGeneration; model_type granite_speech_plus", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-Speech-4.1-2B-Plus is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2111812956 parameters. Capabilities noted from the card or config: tools, speech.", "what_it_is_for": "Granite-Speech-4.1-2B-Plus is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2111812956 parameters. Capabilities noted from the card or config: tools, speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-speech/granite-speech-5-0-470m-turboctc": { "id": "ibm/granite-speech/granite-speech-5-0-470m-turboctc", "name": "Granite-Speech-5.0-470M-TurboCTC", "maker": "ibm", "family": "ibm/granite-speech", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-08-25", "context_length": null, "max_output_tokens": null, "parameters": "472993792", "architecture": "GraniteSpeech5ForCTC; model_type granite_speech5_ctc; vocab 16384", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-Speech-5.0-470M-TurboCTC is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 472993792 parameters. Capabilities noted from the card or config: speech.", "what_it_is_for": "Granite-Speech-5.0-470M-TurboCTC is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 472993792 parameters. Capabilities noted from the card or config: speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-speech/granite-speech-5-0-470m-turboctc-nc": { "id": "ibm/granite-speech/granite-speech-5-0-470m-turboctc-nc", "name": "Granite Speech 5.0 470M TurboCTC NC", "maker": "ibm", "family": "ibm/granite-speech", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "2026-08-25", "context_length": null, "max_output_tokens": null, "parameters": "472993792", "architecture": "GraniteSpeech5ForCTC; model_type granite_speech5_ctc; vocab 16384", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Speech 5.0 470M TurboCTC NC is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: cc-by-nc-sa-4.0. The Hugging Face safetensors index reports 472993792 parameters. Capabilities noted from the card or config: speech.", "what_it_is_for": "Granite Speech 5.0 470M TurboCTC NC is an IBM granite speech model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: cc-by-nc-sa-4.0. The Hugging Face safetensors index reports 472993792 parameters. Capabilities noted from the card or config: speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-timeseries/granite-timeseries-ensemble-r1": { "id": "ibm/granite-timeseries/granite-timeseries-ensemble-r1", "name": "Granite Timeseries Ensemble R1", "maker": "ibm", "family": "ibm/granite-timeseries", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "model_type granite_probabilistic_ensemble", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Timeseries Ensemble R1 is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. Capabilities noted from the card or config: forecasting.", "what_it_is_for": "Granite Timeseries Ensemble R1 is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. Capabilities noted from the card or config: forecasting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-timeseries/granite-timeseries-flowstate-r1": { "id": "ibm/granite-timeseries/granite-timeseries-flowstate-r1", "name": "Granite Timeseries Flowstate R1", "maker": "ibm", "family": "ibm/granite-timeseries", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "9069312", "architecture": "FlowStateModel; model_type flowstate", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Timeseries Flowstate R1 is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 9069312 parameters. Recorded context window: 2048 tokens. Capabilities noted from the card or config: forecasting.", "what_it_is_for": "Granite Timeseries Flowstate R1 is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 9069312 parameters. Recorded context window: 2048 tokens. Capabilities noted from the card or config: forecasting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-timeseries/granite-timeseries-patchtsmixer": { "id": "ibm/granite-timeseries/granite-timeseries-patchtsmixer", "name": "Granite Timeseries Patchtsmixer", "maker": "ibm", "family": "ibm/granite-timeseries", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "196144", "architecture": "PatchTSMixerForPrediction; model_type patchtsmixer", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Timeseries Patchtsmixer is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 196144 parameters. Recorded context window: 512 tokens. Capabilities noted from the card or config: forecasting.", "what_it_is_for": "Granite Timeseries Patchtsmixer is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 196144 parameters. Recorded context window: 512 tokens. Capabilities noted from the card or config: forecasting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-timeseries/granite-timeseries-patchtst": { "id": "ibm/granite-timeseries/granite-timeseries-patchtst", "name": "Granite Timeseries Patchtst", "maker": "ibm", "family": "ibm/granite-timeseries", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "616032", "architecture": "PatchTSTForPrediction; model_type patchtst; layers 3; heads 16", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite Timeseries Patchtst is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 616032 parameters. Recorded context window: 512 tokens. Capabilities noted from the card or config: forecasting.", "what_it_is_for": "Granite Timeseries Patchtst is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 616032 parameters. Recorded context window: 512 tokens. Capabilities noted from the card or config: forecasting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-timeseries/granite-timeseries-patchtst-fm-r1": { "id": "ibm/granite-timeseries/granite-timeseries-patchtst-fm-r1", "name": "Granite TimeSeries PatchTST-FM Model Card", "maker": "ibm", "family": "ibm/granite-timeseries", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "257895552", "architecture": "PatchTSTFMForPrediction; model_type patchtst_fm", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite TimeSeries PatchTST-FM Model Card is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 257895552 parameters. Recorded context window: 8192 tokens. Capabilities noted from the card or config: forecasting.", "what_it_is_for": "Granite TimeSeries PatchTST-FM Model Card is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 257895552 parameters. Recorded context window: 8192 tokens. Capabilities noted from the card or config: forecasting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-timeseries/granite-timeseries-patchtst-fm-r2": { "id": "ibm/granite-timeseries/granite-timeseries-patchtst-fm-r2", "name": "Granite TimeSeries PatchTST-FM-r2 Model Card", "maker": "ibm", "family": "ibm/granite-timeseries", "status": "active", "confidence": "high", "weights": "open-weights", "license": "openmdw-1.0", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "384600208", "architecture": "PatchTSTFMForPrediction; model_type patchtst_fm", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite TimeSeries PatchTST-FM-r2 Model Card is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: openmdw-1.0. The Hugging Face safetensors index reports 384600208 parameters. Recorded context window: 8192 tokens. Capabilities noted from the card or config: forecasting.", "what_it_is_for": "Granite TimeSeries PatchTST-FM-r2 Model Card is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: openmdw-1.0. The Hugging Face safetensors index reports 384600208 parameters. Recorded context window: 8192 tokens. Capabilities noted from the card or config: forecasting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-timeseries/granite-timeseries-tspulse-r1": { "id": "ibm/granite-timeseries/granite-timeseries-tspulse-r1", "name": "Granite-TimeSeries-TSPulse-R1 Model Card", "maker": "ibm", "family": "ibm/granite-timeseries", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "1084330", "architecture": "TSPulseForReconstruction; model_type tspulse", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-TimeSeries-TSPulse-R1 Model Card is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1084330 parameters. Recorded context window: 512 tokens. Capabilities noted from the card or config: forecasting.", "what_it_is_for": "Granite-TimeSeries-TSPulse-R1 Model Card is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1084330 parameters. Recorded context window: 512 tokens. Capabilities noted from the card or config: forecasting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-timeseries/granite-timeseries-ttm-r1": { "id": "ibm/granite-timeseries/granite-timeseries-ttm-r1", "name": "Granite-TimeSeries-TTM-R1 Model Card", "maker": "ibm", "family": "ibm/granite-timeseries", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "805280", "architecture": "TinyTimeMixerForPrediction; model_type tinytimemixer", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-TimeSeries-TTM-R1 Model Card is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 805280 parameters. Recorded context window: 512 tokens. Capabilities noted from the card or config: forecasting.", "what_it_is_for": "Granite-TimeSeries-TTM-R1 Model Card is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 805280 parameters. Recorded context window: 512 tokens. Capabilities noted from the card or config: forecasting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-timeseries/granite-timeseries-ttm-r2": { "id": "ibm/granite-timeseries/granite-timeseries-ttm-r2", "name": "Granite-TimeSeries-TTM-R2 Model Card", "maker": "ibm", "family": "ibm/granite-timeseries", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "805280", "architecture": "TinyTimeMixerForPrediction; model_type tinytimemixer", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": 0.00013, "completion_per_m_usd": 0.00038 }, "summary_intro": "Granite-TimeSeries-TTM-R2 Model Card is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 805280 parameters. Recorded context window: 512 tokens. Capabilities noted from the card or config: forecasting.", "what_it_is_for": "Granite-TimeSeries-TTM-R2 Model Card is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 805280 parameters. Recorded context window: 512 tokens. Capabilities noted from the card or config: forecasting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-timeseries/granite-timeseries-ttm-r3": { "id": "ibm/granite-timeseries/granite-timeseries-ttm-r3", "name": "Granite-TTM-R3 Model Card", "maker": "ibm", "family": "ibm/granite-timeseries", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "1414514", "architecture": "model_type tinytimemixer", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-TTM-R3 Model Card is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1414514 parameters. Recorded context window: 512 tokens. Capabilities noted from the card or config: forecasting.", "what_it_is_for": "Granite-TTM-R3 Model Card is an IBM granite timeseries model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 1414514 parameters. Recorded context window: 512 tokens. Capabilities noted from the card or config: forecasting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-vision/granite-4-0-3b-vision": { "id": "ibm/granite-vision/granite-4-0-3b-vision", "name": "Granite 4.0 3B Vision", "maker": "ibm", "family": "ibm/granite-vision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-03-27", "context_length": 131072, "max_output_tokens": null, "parameters": "3997206464", "architecture": "Granite4VisionForConditionalGeneration; model_type granite4_vision", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite 4.0 3B Vision is an IBM granite vision model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3997206464 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: vision.", "what_it_is_for": "Granite 4.0 3B Vision is an IBM granite vision model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3997206464 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: vision.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-vision/granite-vision-3-1-2b-preview": { "id": "ibm/granite-vision/granite-vision-3-1-2b-preview", "name": "granite-vision-3.1-2b-preview", "maker": "ibm", "family": "ibm/granite-vision", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 16384, "max_output_tokens": null, "parameters": "2975396928", "architecture": "LlavaNextForConditionalGeneration; model_type llava_next", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "granite-vision-3.1-2b-preview is an IBM granite vision model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2975396928 parameters. Recorded context window: 16384 tokens. Capabilities noted from the card or config: vision.", "what_it_is_for": "granite-vision-3.1-2b-preview is an IBM granite vision model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2975396928 parameters. Recorded context window: 16384 tokens. Capabilities noted from the card or config: vision.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-vision/granite-vision-3-2-2b": { "id": "ibm/granite-vision/granite-vision-3-2-2b", "name": "granite-vision-3.2-2b", "maker": "ibm", "family": "ibm/granite-vision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "2975396928", "architecture": "LlavaNextForConditionalGeneration; model_type llava_next", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.1 }, "summary_intro": "granite-vision-3.2-2b is an IBM granite vision model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2975396928 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: vision.", "what_it_is_for": "granite-vision-3.2-2b is an IBM granite vision model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2975396928 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: vision.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-vision/granite-vision-3-3-2b": { "id": "ibm/granite-vision/granite-vision-3-3-2b", "name": "granite-vision-3.3-2b", "maker": "ibm", "family": "ibm/granite-vision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "2975396928", "architecture": "LlavaNextForConditionalGeneration; model_type llava_next", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "granite-vision-3.3-2b is an IBM granite vision model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2975396928 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: vision.", "what_it_is_for": "granite-vision-3.3-2b is an IBM granite vision model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2975396928 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: vision.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-vision/granite-vision-3-3-2b-chart2csv-preview": { "id": "ibm/granite-vision/granite-vision-3-3-2b-chart2csv-preview", "name": "granite-vision-3.3-2b-chart2csv-preview", "maker": "ibm", "family": "ibm/granite-vision", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "2975396928", "architecture": "LlavaNextForConditionalGeneration; model_type llava_next", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "granite-vision-3.3-2b-chart2csv-preview is an IBM granite vision model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2975396928 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools, vision.", "what_it_is_for": "granite-vision-3.3-2b-chart2csv-preview is an IBM granite vision model. Collection date 2026-10-15. Status: preview. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2975396928 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: tools, vision.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-vision/granite-vision-3-3-2b-embedding": { "id": "ibm/granite-vision/granite-vision-3-3-2b-embedding", "name": "granite-vision-3.3-2b-embedding", "maker": "ibm", "family": "ibm/granite-vision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-06-11", "context_length": 131072, "max_output_tokens": null, "parameters": "2975659200", "architecture": "GraniteVisionEmb; model_type granitevisionemb", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "granite-vision-3.3-2b-embedding is an IBM granite vision model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2975659200 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: vision, embeddings.", "what_it_is_for": "granite-vision-3.3-2b-embedding is an IBM granite vision model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 2975659200 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: vision, embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/granite-vision/granite-vision-4-1-4b": { "id": "ibm/granite-vision/granite-vision-4-1-4b", "name": "Granite-Vision-4.1-4B", "maker": "ibm", "family": "ibm/granite-vision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-29", "context_length": 131072, "max_output_tokens": null, "parameters": "3997203904", "architecture": "Granite4VisionForConditionalGeneration; model_type granite4_vision", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Granite-Vision-4.1-4B is an IBM granite vision model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3997203904 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: vision.", "what_it_is_for": "Granite-Vision-4.1-4B is an IBM granite vision model. Collection date 2026-10-15. Status: active. Weights are open-weights. License string: apache-2.0. The Hugging Face safetensors index reports 3997203904 parameters. Recorded context window: 131072 tokens. Capabilities noted from the card or config: vision.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/slate/slate-125m-english-rtrvr-v2": { "id": "ibm/slate/slate-125m-english-rtrvr-v2", "name": "Slate 125M English RTRVR v2", "maker": "ibm", "family": "ibm/slate", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.106, "completion_per_m_usd": null }, "summary_intro": "Slate 125M English RTRVR v2 is an IBM slate model. Collection date 2026-10-15. Status: active. Capabilities noted from the card or config: embeddings.", "what_it_is_for": "Slate 125M English RTRVR v2 is an IBM slate model. Collection date 2026-10-15. Status: active. Capabilities noted from the card or config: embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ibm/slate/slate-30m-english-rtrvr-v2": { "id": "ibm/slate/slate-30m-english-rtrvr-v2", "name": "Slate 30M English RTRVR v2", "maker": "ibm", "family": "ibm/slate", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.106, "completion_per_m_usd": null }, "summary_intro": "Slate 30M English RTRVR v2 is an IBM slate model. Collection date 2026-10-15. Status: active. Capabilities noted from the card or config: embeddings.", "what_it_is_for": "Slate 30M English RTRVR v2 is an IBM slate model. Collection date 2026-10-15. Status: active. Capabilities noted from the card or config: embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ideogram/ideogram/ideogram-0-1": { "id": "ideogram/ideogram/ideogram-0-1", "name": "Ideogram 0.1", "maker": "ideogram", "family": "ideogram/ideogram", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-08-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed text-to-image model. Ideogram called it a foundation model built from scratch, with coherent text inside images. No architecture or parameter count was published.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ideogram 0.1 is Ideogram's first public text-to-image model, opened to everyone on August 29, 2023. On 2026-10-15 it is retired. Weights were closed. Confidence is high that this named model shipped. It has no current API id.", "what_it_is_for": "Ideogram called 0.1 a foundation model built from scratch that turns text into images and renders coherent text inside them, including many fonts and typography. The public post said it was free and had no limits. That was the 2023 product, not the current plan. The company itself was announced on August 22, 2023. That announcement is not a separate model. The 0.1 post says a limited beta in the prior week had already produced more than 3 million images. That beta was not given another name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ideogram/ideogram/ideogram-1-0": { "id": "ideogram/ideogram/ideogram-1-0", "name": "Ideogram 1.0", "maker": "ideogram", "family": "ideogram/ideogram", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-02-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed text-to-image model. Ideogram said it was trained from scratch. No parameter count was published. An April 11, 2024 upgrade improved the same 1.0 model and added Describe, negative prompt, and speed control.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ideogram 1.0 is Ideogram's February 28, 2024 text-to-image model. On 2026-10-15 the product docs call it retired. Weights were closed. Confidence is high for the identity and the retirement statement. The legacy generate schema still lists `V_1` and `V_1_TURBO`. That route was not called, so it is not treated as proof the model still runs.", "what_it_is_for": "The launch post presented 1.0 as a from-scratch model for text inside images, photorealism, and prompt adherence, and it introduced Magic Prompt. The April 11, 2024 upgrade of the same model added Describe, a negative prompt, and rendering speed. Tile, a seamless-pattern mode, worked only with 1.0 and is gone. Ideogram 1.0 styles appended keywords to the prompt. Later models use style types that do not do that.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ideogram/ideogram/ideogram-2-0": { "id": "ideogram/ideogram/ideogram-2-0", "name": "Ideogram 2.0", "maker": "ideogram", "family": "ideogram/ideogram", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed text-to-image model. Ideogram said it was trained from scratch. No parameter count or architecture card was published.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ideogram 2.0 is Ideogram's August 21, 2024 text-to-image model. On 2026-10-15 it is still a v2 API model at `POST /v2/image/generate/ideogram-2`, and the app docs say it may still appear in the picker. Weights are closed. Confidence is high. The docs tell people to use 4.5 or 4.0 for new work. That is a recommendation, not a dated deprecation.", "what_it_is_for": "The launch, quoted from search because the original feature URL now serves the 4.0 page, called 2.0 a from-scratch model for realistic images, graphic design, and typography, and shipped it with the first public API, the iOS app, and Ideogram Search. Style types are separate controls, not separate models. Older docs say Auto, General, Realistic, Design, 3D, and Anime do not append words to the prompt the way Ideogram 1.0 styles did.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ideogram/ideogram/ideogram-2a": { "id": "ideogram/ideogram/ideogram-2a", "name": "Ideogram 2a", "maker": "ideogram", "family": "ideogram/ideogram", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed text-to-image model. Ideogram's launch post calls it the fastest and most affordable model at the time, optimized for graphic design and photography. No parameter count was published. The v2 API calls it a fast, lower-cost model.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ideogram 2a is Ideogram's February 27, 2025 text-to-image model. On 2026-10-15 it is active at `POST /v2/image/generate/ideogram-2a` and may still appear in the app picker. Weights are closed. Confidence is high. Ideogram positioned it as a faster, cheaper 2-series model for graphic design and photography, not as the quality flagship. Ideogram 3.0 shipped about a month later.", "what_it_is_for": "Ideogram's launch post calls 2a the fastest and most affordable text-to-image model to that date, optimized for graphic design and photography, on the website, the API, and partner platforms. The v2 docs repeat that it is a fast, lower-cost model. The current app docs group it with Ideogram 2.0 as an older picker entry.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ideogram/ideogram/ideogram-3-0": { "id": "ideogram/ideogram/ideogram-3-0", "name": "Ideogram 3.0", "maker": "ideogram", "family": "ideogram/ideogram", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed text-to-image model. Ideogram has not published a parameter count or architecture. Style reference, style presets, color palettes, and negative prompts are 3.0 features in the current app.", "modalities": { "input": [ "text", "image" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ideogram 3.0 is Ideogram's March 26, 2025 text-to-image model. On 2026-10-15 it is still active in the app and at `POST /v2/image/generate/ideogram-3`. Weights are closed. Confidence is high. The app now points new design work at 4.5 or 4.0, and keeps 3.0 for style presets, style references, negative prompts, and color palettes.", "what_it_is_for": "The launch post sells realism, layout, and text, plus style references so a look can be matched without describing it. Image input here is a style reference or, on the character route, a character reference. It is not the content-edit reference system on Ideogram 4.5. Describe turns an image into text. Layerize, inpaint, reframe, remix, and replace-background are separate routes on 3.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ideogram/ideogram/ideogram-3-0-transparent": { "id": "ideogram/ideogram/ideogram-3-0-transparent", "name": "Ideogram 3.0 Transparent", "maker": "ideogram", "family": "ideogram/ideogram", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named transparent-background endpoint of Ideogram 3.0. The API delivers a PNG with alpha and can upscale afterward by 2x or 4x. No separate weight file is published.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ideogram 3.0 Transparent is Ideogram's older named model for images with a clear background. On 2026-10-15 it is active in the picker and at `POST /v2/image/generate/ideogram-3-transparent`. Weights are closed. Confidence is high. It is the 3.0 generator plus an alpha background and an optional upscale, not a separately published foundation model. A release date was not found.", "what_it_is_for": "The docs group it with Ideogram 4.0 Transparent for stickers, logos, icons, and print-on-demand art. It does not take reference images. Magic Prompt is a toggle. An upscale factor of 1x, 2x, or 4x sits under advanced settings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ideogram/ideogram/ideogram-4-0": { "id": "ideogram/ideogram/ideogram-4-0", "name": "Ideogram 4.0", "maker": "ideogram", "family": "ideogram/ideogram", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Ideogram Non-Commercial Model Agreement (2026-06-03). Commercial self-hosting needs a separate Ideogram license. Hosted API use is a different contract.", "release_date": "2026-06-03", "context_length": 2048, "max_output_tokens": null, "parameters": "9.3B", "architecture": "9.3B single-stream Diffusion Transformer trained from scratch: 34 layers, 18 heads, embedding 4608, SwiGLU intermediate 12288, AdaLN 512, RoPE theta 5000000, mRoPE section (24, 20, 20). Frozen Qwen3-VL-8B-Instruct text encoder (hidden states from 13 layers). Frozen FLUX.2 VAE, 32 latent channels times 2 squared. Euler flow-matching sampler with asymmetric classifier-free guidance. The 9.3B count is the DiT only.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ideogram 4.0 is Ideogram's first open-weight text-to-image model, released June 3, 2026. On 2026-10-15 it is active in the app, on the API, and as gated Hugging Face weights. Confidence is high. The company still calls 4.5 the flagship. 4.0 is the model with published weights.", "what_it_is_for": "Ideogram describes 4.0 as a design model: multilingual text in images, JSON prompts, bounding-box layout, color palettes, and native 2K. The app docs say it does not take reference images and is the pick for very wide ratios such as 23:9 and 3:1. Describe, remix, and custom-model generate are separate API routes on the same generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ideogram/ideogram/ideogram-4-0-transparent": { "id": "ideogram/ideogram/ideogram-4-0-transparent", "name": "Ideogram 4.0 Transparent", "maker": "ideogram", "family": "ideogram/ideogram", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named app and API product that generates a PNG with alpha using Ideogram 4.0. The API says tiers above 2K are an upscale after generation. It is not a separately published weight file. The open Ideogram 4 model zoo does not list a transparent checkpoint.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ideogram 4.0 Transparent is a named Ideogram image product for PNGs with no background. On 2026-10-15 it is active in the model picker and at `POST /v2/image/generate/ideogram-4-transparent`. Confidence is high that the product exists. The open Ideogram 4 weight release does not include a separate transparent checkpoint. A release date was not found.", "what_it_is_for": "The app docs tell people to pick it for stickers, logos, icons, clip art, and print-on-demand art that needs a clear background. It does not take reference images. Ideogram 4.5 can also request a transparent background on its own generate call. That is a setting, not this picker entry.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ideogram/ideogram/ideogram-4-5": { "id": "ideogram/ideogram/ideogram-4-5", "name": "Ideogram 4.5", "maker": "ideogram", "family": "ideogram/ideogram", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed image model. No parameter count or architecture card was published on the pages read. The API uses it for text-to-image and for edits that can copy untouched pixels from the source.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ideogram 4.5 is Ideogram's flagship image model. On 2026-10-15 it is active in the app and at `POST /v2/image/generate/ideogram-4-5`. Weights are closed. Confidence is high for identity and API behavior. The September 30, 2026 release date is the date Artificial Analysis states; Ideogram's dated blog index does not list a 4.5 post date.", "what_it_is_for": "Ideogram calls 4.5 the newest model and the only Ideogram model that takes reference images. The app docs point it at photo editing, restoration, product photos, mockups, and keeping a character consistent. Precise Edit is meant to change only what the prompt names and to copy the other pixels from the source, including across repeated edits. Text or JSON prompts are supported, up to 10,000 characters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/medical/spark-healthcare-v3-5": { "id": "iflytek/medical/spark-healthcare-v3-5", "name": "\u661f\u706b\u533b\u7597\u5927\u6a21\u578b V3.5", "maker": "iflytek", "family": "iflytek/medical", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-06-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u661f\u706b\u533b\u7597\u5927\u6a21\u578b V3.5 is the medical Spark model iFlytek Medical launched on 9 June 2026, according to contemporary press. Collected on 2026-10-15.", "what_it_is_for": "Press describes two jobs: clinical decision support and resident health management, including record generation, imaging-report help, and follow-up. It is a domain model, not one of the general Spark API domains.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-4-0-turbo": { "id": "iflytek/spark/spark-4-0-turbo", "name": "\u8baf\u98de\u661f\u706b V4.0 Turbo", "maker": "iflytek", "family": "iflytek/spark", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-10-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u8baf\u98de\u661f\u706b V4.0 Turbo is a named October 2024 Spark release. It does not appear in the 2026 general API domain list, so this catalog treats it as retired from that list. Collected on 2026-10-15.", "what_it_is_for": "Later iFlytek coverage treats V4.0 Turbo as the October 2024 step after V4.0, aimed at industry use. It is not a current domain on the Spark WebSocket guide.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-4-0-ultra": { "id": "iflytek/spark/spark-4-0-ultra", "name": "Spark 4.0 Ultra", "maker": "iflytek", "family": "iflytek/spark", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-06-27", "context_length": 32768, "max_output_tokens": 32768, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spark 4.0 Ultra is iFlytek's current non-thinking flagship on the Spark HTTP and WebSocket APIs (model 4.0Ultra). A notice on those pages says the Ultra service has been moved onto X1.5 fast-thinking mode. Collected on 2026-10-15.", "what_it_is_for": "iFlytek recommends Ultra and describes it as the strongest Spark model that is not a dedicated reasoning model, aimed at instruction following, text generation, and function calling. The same pages say it now runs as X1.5 in fast-thinking mode. The June 2024 launch claimed broad comparison with GPT-4 Turbo; those percentages are not copied here because the encyclopedia page did not include the underlying table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-kjwx": { "id": "iflytek/spark/spark-kjwx", "name": "\u79d1\u6280\u6587\u732e\u5927\u6a21\u578b", "maker": "iflytek", "family": "iflytek/spark", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u79d1\u6280\u6587\u732e\u5927\u6a21\u578b (domain kjwx) is iFlytek's named Spark model for scientific papers. The WebSocket guide still listed it beside the six general versions on 2026-10-15.", "what_it_is_for": "iFlytek lists it as an industry large model optimized for paper question answering and writing, metered on its own.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-lite": { "id": "iflytek/spark/spark-lite", "name": "Spark Lite", "maker": "iflytek", "family": "iflytek/spark", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8192, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spark Lite is iFlytek's free, lightweight Spark chat model on the v1.1 WebSocket path (domain lite). It was still a current API version on 2026-10-15.", "what_it_is_for": "iFlytek describes Lite as the lightweight Spark language model for cases that want speed, and says it can be used free. It is one of six general versions that are metered separately.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-max": { "id": "iflytek/spark/spark-max", "name": "Spark Max", "maker": "iflytek", "family": "iflytek/spark", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-01-30", "context_length": 8192, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spark Max is iFlytek's V3.5 flagship tier (domain generalv3.5). The WebSocket and HTTP guides say the Max package is scheduled to go offline on 10 March 2026 and that usage will merge into Ultra. Collected on 2026-10-15.", "what_it_is_for": "The HTTP guide calls Max a flagship for structured extraction and logical reasoning, and prints the March 2026 package sunset. Sina's 2024 timeline says V3.5 launched on 30 January 2024. The xfyun marketing page calls it a hundred-billion-parameter-class model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-max-32k": { "id": "iflytek/spark/spark-max-32k", "name": "Spark Max-32K", "maker": "iflytek", "family": "iflytek/spark", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32768, "max_output_tokens": 32768, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spark Max-32K is the 32K-context Spark Max API (domain max-32k). The protocol page still listed it on 2026-10-15. Collected that day.", "what_it_is_for": "iFlytek lists Max-32K as its own general version, metered apart from 8K Max. It supports the same Max-series search behavior described for the Max family, but the sunset paragraph is attached to the Max package wording.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-pro": { "id": "iflytek/spark/spark-pro", "name": "Spark Pro", "maker": "iflytek", "family": "iflytek/spark", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-10-24", "context_length": 8192, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spark Pro is iFlytek's current professional Spark tier (domain generalv3, URL v3.1). The vendor treats it as the V3.0 generation. Collected on 2026-10-15.", "what_it_is_for": "The HTTP guide calls Pro a professional model for text and Q&A when latency and quality both matter. The WebSocket guide allows the web-search tool on Pro. The October 2023 V3.0 launch is the generation this domain continues.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-pro-128k": { "id": "iflytek/spark/spark-pro-128k", "name": "Spark Pro-128K", "maker": "iflytek", "family": "iflytek/spark", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 131072, "max_output_tokens": 131072, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spark Pro-128K is the long-context Spark Pro API (domain pro-128k). iFlytek meters it separately from 8K Pro. Collected on 2026-10-15.", "what_it_is_for": "The vendor lists Pro-128K as its own general-model version for long context. A marketing article on xfyun says Pro was tuned for verticals such as medical, education, and code and can take a 128K context; that sentence is not pinned to this domain alone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-v1-0": { "id": "iflytek/spark/spark-v1-0", "name": "\u8baf\u98de\u661f\u706b\u8ba4\u77e5\u5927\u6a21\u578b V1.0", "maker": "iflytek", "family": "iflytek/spark", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-05-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u8baf\u98de\u661f\u706b\u8ba4\u77e5\u5927\u6a21\u578b V1.0 is iFlytek's first named Spark cognitive model, launched on 6 May 2023. It is retired as a distinct product. This dossier was collected on 2026-10-15.", "what_it_is_for": "iFlytek presented V1.0 as a general cognitive model aimed at ChatGPT-style dialogue, with the seven capabilities above. IT\u4e4b\u5bb6's June 2023 note treats the 6 May launch as the prior release before V1.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-v1-5": { "id": "iflytek/spark/spark-v1-5", "name": "\u8baf\u98de\u661f\u706b\u8ba4\u77e5\u5927\u6a21\u578b V1.5", "maker": "iflytek", "family": "iflytek/spark", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-06-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u8baf\u98de\u661f\u706b\u8ba4\u77e5\u5927\u6a21\u578b V1.5 is the June 2023 upgrade of Spark V1.0. It is no longer a separate API product. Collected on 2026-10-15.", "what_it_is_for": "The company notice, as reported by IT\u4e4b\u5bb6, positioned V1.5 as a fast follow-up for open Q&A, multi-turn dialogue, and math, plus the Spark app and assistant center.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-v2-0": { "id": "iflytek/spark/spark-v2-0", "name": "\u8baf\u98de\u661f\u706b\u8ba4\u77e5\u5927\u6a21\u578b V2.0", "maker": "iflytek", "family": "iflytek/spark", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-08-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u8baf\u98de\u661f\u706b\u8ba4\u77e5\u5927\u6a21\u578b V2.0 is the 15 August 2023 Spark release, marketed around code and multimodal generation. The live general API list no longer sells it as its own version. Collected on 2026-10-15.", "what_it_is_for": "\u5feb\u79d1\u6280's preview of the 15 August 2023 event said V2.0 would add code generation and multimodal generation on top of the seven general abilities. That is a launch preview, not a measured spec.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-x1": { "id": "iflytek/spark/spark-x1", "name": "\u8baf\u98de\u661f\u706b X1", "maker": "iflytek", "family": "iflytek/spark", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-01-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u8baf\u98de\u661f\u706b X1 is iFlytek's first named deep-reasoning Spark model, launched in January 2025. The current X2 HTTP guide says the old x1 argument now points at X1.5, so the original X1 is deprecated as its own route. Collected on 2026-10-15.", "what_it_is_for": "iFlytek described X1 as its deep-reasoning model for math and multi-step answers, trained as a domestic-compute system. The surviving official statement in the API doc is that callers who still send x1 are served X1.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-x1-5": { "id": "iflytek/spark/spark-x1-5", "name": "\u8baf\u98de\u661f\u706b X1.5", "maker": "iflytek", "family": "iflytek/spark", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-06", "context_length": null, "max_output_tokens": 65535, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u8baf\u98de\u661f\u706b X1.5 is iFlytek's deep-reasoning Spark model on the /v2 HTTP route and the /v1/x1 WebSocket route, both with model or domain spark-x. It was still documented as a formal version on the 9 February 2026 protocol page. Collected on 2026-10-15.", "what_it_is_for": "iFlytek positions X1.5 as the formal deep-reasoning model before X2: dynamic thinking, a larger context, and function calling. Quantum Bit's 1024 recap says the festival release added memory and stronger general tasks; those product claims are not in the protocol page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-x2": { "id": "iflytek/spark/spark-x2", "name": "\u8baf\u98de\u661f\u706b X2", "maker": "iflytek", "family": "iflytek/spark", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": 131072, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 3.0 }, "summary_intro": "\u8baf\u98de\u661f\u706b X2 is iFlytek's formal deep-reasoning model on the /x2 HTTP route and the /x2 WebSocket route (model spark-x). The February 2026 protocol still presents it as current, while the October 2026 Token Plan lists MaaS id xsparkx2 as offline. Collected on 2026-10-15.", "what_it_is_for": "The protocol introduction says X2 adds a thinking switch, a longer context (input 64K, output 128K), and function calling. The model-square blurb says general ability is aimed at international flagship level, with math, reasoning, language understanding, and agents called out, and 130+ languages in that blurb.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-x2-5": { "id": "iflytek/spark/spark-x2-5", "name": "\u8baf\u98de\u661f\u706b X2.5", "maker": "iflytek", "family": "iflytek/spark", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-09-07", "context_length": null, "max_output_tokens": null, "parameters": "293B-A30B", "architecture": "MoE. Launch coverage says 293B total and about 30B active, trained on a fully domestic platform. The responses API lists model id spark-x2.5.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.6, "completion_per_m_usd": 6.0 }, "summary_intro": "\u8baf\u98de\u661f\u706b X2.5 is iFlytek's current general flagship, model id spark-x2.5 on Xingchen MaaS. The company product page and the 7 September 2026 launch coverage describe a domestic-compute model aimed at code and agents. Collected on 2026-10-15.", "what_it_is_for": "The open-platform home page and the Spark API page call X2.5 the new general flagship: domestic compute, stronger agents and code, and coverage of more than 200 languages. IT\u4e4b\u5bb6's launch story adds the 293B-A30B MoE description and a 256K window.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-x2-5-1-7b": { "id": "iflytek/spark/spark-x2-5-1-7b", "name": "Spark-X2.5-1.7B", "maker": "iflytek", "family": "iflytek/spark", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-01", "context_length": 1048576, "max_output_tokens": null, "parameters": "1.7B", "architecture": "Same hybrid design as the 4B: dense Spark2_5 decoder, one full-attention layer and three sliding-window layers, about 20T pretrain tokens, Apache-2.0. A third-party card note says 28 layers; that layer count is not on the official excerpt stored here and is not treated as spec.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spark-X2.5-1.7B is the smaller open Spark edge model released with Spark-X2.5-4B under Apache-2.0. iFlytek's MaaS responses API lists it as spark-x2.5-1.7b. Collected on 2026-10-15.", "what_it_is_for": "Same job as the 4B card: on-device general use, including tools and agents, with a native long context. The Spark API page calls it a 1.7B dense multilingual edge model and lists the API as free.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-x2-5-30b-a3b": { "id": "iflytek/spark/spark-x2-5-30b-a3b", "name": "Spark X2.5 30B-A3B", "maker": "iflytek", "family": "iflytek/spark", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spark X2.5 30B-A3B is a named iFlytek model id on the Xingchen responses API. Almost no prose spec was published beside the id. Collected on 2026-10-15.", "what_it_is_for": "The responses API tells callers to set model to spark-x2.5-30b-a3b when they want this variant. No separate product paragraph was on that page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-x2-5-4b": { "id": "iflytek/spark/spark-x2-5-4b", "name": "Spark-X2.5-4B", "maker": "iflytek", "family": "iflytek/spark", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-01", "context_length": 1048576, "max_output_tokens": null, "parameters": "4B", "architecture": "Dense decoder, class Spark2_5ForCausalLM. Hybrid attention: one full-attention layer plus three sliding-window layers. Pretrained on about 20 trillion tokens, then SFT and large-scale RL with MOPD. Trained on Huawei Ascend. Base checkpoint is a separate weight repo, not a separate product.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spark-X2.5-4B is the open 4B Spark edge model from iFlytek's SparkLLM / XHToken release. Weights are Apache-2.0 and the MaaS responses API also lists spark-x2.5-4b. Collected on 2026-10-15.", "what_it_is_for": "The model card says the 4B and 1.7B models are compact general models for conversation, writing, translation, reasoning, coding, tools, and agents, with a native 1M context and more than 200 languages. The Spark API page calls the 4B a dense edge model that leads open models of the same size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-x2-agent": { "id": "iflytek/spark/spark-x2-agent", "name": "Spark X2 Agent", "maker": "iflytek", "family": "iflytek/spark", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-07-10", "context_length": null, "max_output_tokens": null, "parameters": "300B-A30B", "architecture": "Coding Plan changelog: MoE, 300B total with 30B active, domestic training and inference, tuned for agents and code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spark X2 Agent is a named iFlytek MoE model for agent work, introduced on the Astron Coding Plan on 10 July 2026 as a 300B-A30B model. Collected on 2026-10-15.", "what_it_is_for": "The Coding Plan changelog says X2 Agent is a new fully domestic 300B-A30B MoE Spark model with better agent and code behavior. It is aimed at the coding-agent subscription, not the older Spark WebSocket domain list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/spark/spark-x2-flash": { "id": "iflytek/spark/spark-x2-flash", "name": "Spark X2-Flash", "maker": "iflytek", "family": "iflytek/spark", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Model-square text calls it a mid-size Spark model around 30B, trained and served on domestic compute, tuned for agents and code. The page does not give an exact parameter integer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 2.0 }, "summary_intro": "Spark X2-Flash is iFlytek's lower-priced Spark reasoning model for agent and code calls. It is a current MaaS and Spark HTTP model. Collected on 2026-10-15.", "what_it_is_for": "iFlytek markets X2-Flash as a domestic, lower-cost reasoning model with better agent and code behavior than earlier cheap tiers. The model-square text says it targets agent scenarios and supports fast and slow thinking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/speech/spark-audio-1-0-preview": { "id": "iflytek/speech/spark-audio-1-0-preview", "name": "Spark-Audio-1.0-Preview", "maker": "iflytek", "family": "iflytek/speech", "status": "preview", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-09-16", "context_length": null, "max_output_tokens": null, "parameters": "0.65B audio encoder (dense) + 30B-A3B LLM (MoE)", "architecture": "Speech foundation model. Press, citing iFlytek Spark, describes a 0.65B dense audio encoder plus a 30B-A3B MoE language model, trained on 13 million hours of audio plus text, on domestic compute.", "modalities": { "input": [ "text", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spark-Audio-1.0-Preview is iFlytek's named speech foundation model, reported as opened for preview on 16 September 2026. Collected on 2026-10-15.", "what_it_is_for": "The report says the model understands speech directly, rather than only as a transcript cascaded into a text model, and that it accepts text and audio in one model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/speech/spark-iat-multi": { "id": "iflytek/speech/spark-iat-multi", "name": "\u591a\u8bed\u79cd\u8bc6\u522b\u5927\u6a21\u578b", "maker": "iflytek", "family": "iflytek/speech", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u591a\u8bed\u79cd\u8bc6\u522b\u5927\u6a21\u578b is a named iFlytek speech model on the Spark API product page, described as recognizing 37 languages without a manual language switch. Collected on 2026-10-15.", "what_it_is_for": "The product page groups it with speech recognition: 37 languages, no manual switch. The Spark consumer site describes a unified multilingual speech model with the same language count, including Chinese, English, Japanese, Korean, Russian, French, Spanish, Arabic, German, Portuguese, and Vietnamese among the examples.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/speech/spark-iat-zh": { "id": "iflytek/speech/spark-iat-zh", "name": "\u8bed\u97f3\u542c\u5199\u5927\u6a21\u578b\uff08\u4e2d\u82f1\uff09", "maker": "iflytek", "family": "iflytek/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u8bed\u97f3\u542c\u5199\u5927\u6a21\u578b\uff08\u4e2d\u82f1\uff09 is iFlytek's current Chinese-English dictation large model, documented as API V2.0. Collected on 2026-10-15.", "what_it_is_for": "The official API turns short Chinese and English speech into text, with optional hot words and a short context string so later utterances can use prior text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iflytek/speech/spark-smart-tts": { "id": "iflytek/speech/spark-smart-tts", "name": "\u8d85\u62df\u4eba\u8bed\u97f3\u5408\u6210", "maker": "iflytek", "family": "iflytek/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u8d85\u62df\u4eba\u8bed\u97f3\u5408\u6210 is iFlytek's expressive text-to-speech API. The docs say a large model can insert colloquial wording so the audio sounds more human. Collected on 2026-10-15.", "what_it_is_for": "iFlytek sells it for reading text aloud with more natural breath, pauses, and emotion than the older TTS engines. Callers pick a voice and an oral level. A one-sentence voice-clone product is separate and is not given a folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iiith/akshara/akshara-v1": { "id": "iiith/akshara/akshara-v1", "name": "Akshara v1", "maker": "iiith", "family": "iiith/akshara", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AGPL-3.0", "release_date": "2026-01-19", "context_length": 2048, "max_output_tokens": null, "parameters": "560000000", "architecture": "Supervised fine-tune of the Viswam Telugu NanoChat stack. Card: 20 layers, hidden size 1280, 10 heads, vocab 65536, maximum sequence length 2048, about 560M parameters. Hub tag says 0.6B and bfloat16. The card names the immediate base as viswamaicoe/nanochat-telugu-560M-mid-v1-akshara. The Hub tree also shows that mid checkpoint as a fine-tune of viswamaicoe/nanochat-telugu-560M. Hub spelling of this repo is Akhsara. The lab blog spells Akshara.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Akshara v1 is the supervised fine-tune Viswam AI released for Telugu chat, summarisation, paraphrase, and generation. Viswam is a joint initiative of Swecha and IIIT Hyderabad. Status on 2026-10-15 is active. The Hub repo is `viswamaicoe/nanochat-telugu-560M-sft-v1-akhsara`. The card spells the name Akhsara. The lab blog spells it Akshara. The citation year is 2025. The collection date on the Hub is 19 January 2026. The immediate base is the lab's own mid-training checkpoint, not a third-party model.", "what_it_is_for": "The card lists Telugu conversation, abstractive summarisation, paraphrasing, news and story writing, and question answering. The training mix on the card is SmolTalk, identity chats, IndicParaphrase, AYA Telugu news, TeSum from IIIT Hyderabad, the Telugu part of XL-Sum, and 200k Telugu tiny stories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iiith/akshara/nanochat-telugu-560m": { "id": "iiith/akshara/nanochat-telugu-560m", "name": "NanoChat Telugu 560M", "maker": "iiith", "family": "iiith/akshara", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AGPL-3.0", "release_date": "2026-01-19", "context_length": 2048, "max_output_tokens": null, "parameters": "560000000", "architecture": "Decoder-only NanoChatForCausalLM. Card: 20 layers, hidden size 1280, 10 attention heads, vocab 65536, maximum sequence length 2048. Config on a sibling checkpoint of the same shape: ReLU^2 MLP, intermediate size 5120, RoPE theta 10000, untied embeddings, RMSNorm epsilon 1e-6, final logit softcap 15, bfloat16. Hub safetensors tag says 0.6B. parameters uses the card's about-560M figure. The Hub tree lists this repo as a base, not as a fine-tune of another maker's checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NanoChat Telugu 560M is a Telugu decoder-only language model from Viswam AI, the joint centre of Swecha and IIIT Hyderabad. The Hub card presents it as the pretrained base in the Akshara line. Status on 2026-10-15 is active. The card's citation year is 2025. The Hub repo opened on 19 January 2026. Weights are open under AGPL-3.0. The Hub model tree does not name a parent checkpoint from another maker.", "what_it_is_for": "The card says it is for Telugu conversational text generation. It also shows how to pool hidden states for similarity. That pooling example is not a separate embedding product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iiith/bhashaverse/bhashaverse-llm": { "id": "iiith/bhashaverse/bhashaverse-llm", "name": "BhashaVerse LLM", "maker": "iiith", "family": "iiith/bhashaverse", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BhashaVerse LLM is a decoder model that IIIT Hyderabad's Language Technologies Research Centre was reported to have announced in December 2024. The reports say it is for 36 Indian languages and that summarisation and question answering would need fine-tuning. Collected 2026-10-15. Status is announced. No weight file or model card was found. Confidence is medium because the sources are news accounts of the centre's announcement, not a card.", "what_it_is_for": "The silver-jubilee reports distinguish this decoder from the BhashaVerse translation models. They name summarisation and question answering, with fine-tuning, as the intended uses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iiith/bhashaverse/onenmt-v3b": { "id": "iiith/bhashaverse/onenmt-v3b", "name": "OneNMT v3b", "maker": "iiith", "family": "iiith/bhashaverse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-12-05", "context_length": null, "max_output_tokens": null, "parameters": "1100000000", "architecture": "Fairseq transformer_18_18 wrapped as MBartForConditionalGeneration. Card and config: 18 encoder layers, 18 decoder layers, d_model 1024, 16 heads, FFN 8192, vocab 65400, pre-norm, untied embeddings, sinusoidal positions, dropout 0.2. Card text says about 1.1B parameters and a 256-position training max. config.json sets max_position_embeddings to 1026. The paper lists the same width and depth, dropout 0.30, max word sequence 1200, and calls the evaluated model 2 billion parameters. parameters uses the card's about-1.1B figure. Hub safetensors tag says 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OneNMT v3b is the BhashaVerse translation checkpoint from the Language Technologies Research Centre at IIIT Hyderabad. It is an open-weight multilingual encoder-decoder for Indian-subcontinent languages. Status on 2026-10-15 is active. The paper is arXiv:2412.04351, first posted 5 December 2024 and revised 2 January 2025. The public Hub repo `ltrciiith/bhashaverse` was committed 20 March 2026. Weights are MIT.", "what_it_is_for": "The card calls it a 36 by 36 multilingual neural machine translation model. The paper also trains the same system for grammar correction, post-editing, quality estimation, and error identification. LTRC's December 2024 silver-jubilee coverage said the translation models were free to download.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iiith/lid/indic-lang-detection": { "id": "iiith/lid/indic-lang-detection", "name": "IIITH Indic Language Detection", "maker": "iiith", "family": "iiith/lid", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IIITH Indic Language Detection is the text language-identification service Bhashini lists as `bhashini/iiiith/indic-lang-detection-all` with provider IIIT Hyderabad. Collected 2026-10-15. The catalog page was updated 2026-10-13. Status is active. Confidence is medium. The service id contains three i characters. Weights are closed. No model card was found.", "what_it_is_for": "The catalog places it under text language detection. It covers 14 languages. It is a different row from AI4Bharat's `bhashini/indic-lang-detection-all`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iiith/ner/iiith-ner": { "id": "iiith/ner/iiith-ner", "name": "IIITH NER", "maker": "iiith", "family": "iiith/ner", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IIITH NER is the named-entity service Bhashini lists as `bhashini/iiith/ner` with provider IIIT Hyderabad. Collected 2026-10-15. The catalog page was updated 2026-10-13. Status is active. Confidence is medium. Weights are closed. No model card was found, and the catalog does not name a base checkpoint.", "what_it_is_for": "Named entity recognition for Hindi, Urdu, Odia, and Telugu through the Bhashini API. The label inventory is not on the catalog row.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iiith/nmt/trans-v1": { "id": "iiith/nmt/trans-v1", "name": "IIITH Trans V1", "maker": "iiith", "family": "iiith/nmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IIITH Trans V1 is the translation service Bhashini lists as `Bhashini/IIITH/Trans/V1` with provider IIIT Hyderabad. Collected 2026-10-15. The catalog page was updated 2026-10-13. Status is active. Confidence is medium: the catalog names the provider, and no model card states architecture, size, or license. Weights are closed.", "what_it_is_for": "Neural machine translation through the Bhashini API. The row is narrower than `bhashini/iiith/nmt-all`. Source and target lists on the row are not the same.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iiith/ocr/bhasha-ocr": { "id": "iiith/ocr/bhasha-ocr", "name": "Bhasha OCR", "maker": "iiith", "family": "iiith/ocr", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bhasha OCR is the printed-text service Bhashini lists as `bhashini/iiith-bhasha-ocr` with provider IIIT Hyderabad. Collected 2026-10-15. The catalog page was updated 2026-10-13. Status is active. Confidence is medium. Weights are closed. The id spells Bhasha with one a. No model card was found.", "what_it_is_for": "The catalog modality is printed text. The row covers a wider language set than the handwritten service and includes English. Urdu is not on this row.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iiith/ocr/handwritten-bhaasha": { "id": "iiith/ocr/handwritten-bhaasha", "name": "Bhaasha Handwritten OCR", "maker": "iiith", "family": "iiith/ocr", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bhaasha Handwritten OCR is the handwritten OCR service Bhashini lists as `bhashini/iiith/ocr-hw-bhaasha` with provider IIIT Hyderabad. Collected 2026-10-15. The catalog page was updated 2026-10-13. Status is active. Confidence is medium. Weights are closed. The service id spells Bhaasha with a double a. No model card was found.", "what_it_is_for": "The catalog modality is handwritten text. It lists eight languages. It is not the printed-text row.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iiith/ocr/scene-text": { "id": "iiith/ocr/scene-text", "name": "IIITH Scene-Text OCR", "maker": "iiith", "family": "iiith/ocr", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IIITH Scene-Text OCR is the scene-text service Bhashini lists as `bhashini/iiith-ocr-sceneText-all` with provider IIIT Hyderabad. Collected 2026-10-15. The catalog page was updated 2026-10-13. Status is active. Confidence is medium. Weights are closed. No model card was found, and no CVIT paper was tied to this service id.", "what_it_is_for": "The catalog modality is scene text. It lists 13 languages. It is a different row from the printed-text and handwritten services.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iisc/sravaani/sravaani-0-5-live": { "id": "iisc/sravaani/sravaani-0-5-live", "name": "SraVaani 0.5 Live", "maker": "iisc", "family": "iisc/sravaani", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-07-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cache-aware streaming FastConformer encoder plus a CTC head, exported as one FP16 TorchScript graph of about 900 MB. The card says the base is a ~430M-parameter hybrid TDT-CTC FastConformer and that this export keeps the CTC head. Encoder: 17 layers, d_model 1024, 8x subsampling. Features: 128-dim log-mel, 16 kHz, 25 ms window, 10 ms hop. Tokenizer: 5,000-piece SentencePiece BPE (blank id 5000). Left context is 70 encoder frames. One graph serves lookaheads of 1040 ms, 480 ms, 80 ms, and 0 ms. Hub model_type is sravaani_streaming_tdt.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SraVaani 0.5 Live is the streaming speech-recognition model in the SraVaani family from ARTPARK at the Indian Institute of Science (SPIRE Lab). It is an active open-weight release, collected 2026-10-15. The Hub repo `ARTPARK-IISc/SraVaani-0.5-live` was created on 16 July 2026 and last modified on 1 August 2026. The license record is MIT. Access is gated. There is no priced API.", "what_it_is_for": "The card calls it the low-latency streaming member of SraVaani: speech to text for 63 Indian languages and dialects, with the language chosen by the model. One exported graph runs at four lookaheads, from 1040 ms down to 0 ms, switched at runtime without a reload. Urdu and Kashmiri are not supported. A microphone snippet is included for live audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iisc/sravaani/sravaani-1-0": { "id": "iisc/sravaani/sravaani-1-0", "name": "SraVaani 1.0", "maker": "iisc", "family": "iisc/sravaani", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-08-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "About 430 million parameters (model card; not an exact count). 17-layer FastConformer encoder, d_model 1024, trained from scratch, with a hybrid CTC-TDT decoder (TDT durations 0-4, CTC loss weight 0.3, 5,000-piece BPE). Inputs are 128-dim log-mel at 16 kHz, 25 ms window, 10 ms hop, 8x subsampling. The Hub ships an FP16 TorchScript graph of about 900 MB plus custom Transformers code. An ONNX export is linked from the card.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SraVaani 1.0 is a multilingual speech-recognition model from the Indian Institute of Science SPIRE Lab and ARTPARK (I-Hub at IISc), with support from Google. It is an active open-weight release, collected 2026-10-15. The public announcement is 13 August 2026. Weights are on Hugging Face as `ARTPARK-IISc/SraVaani-1.0` under MIT, behind a contact-share gate. The seed org `huggingface.co/IISc` has no models; this checkpoint is published by the ARTPARK-IISc lab org.", "what_it_is_for": "The maker describes it as automatic speech recognition for 65 Indian languages and dialects, including many that have no other public ASR system. It transcribes speech into 10 scripts and does not require a language tag first. Urdu and Kashmiri are not in this release. The intended users are researchers and developers who will adapt it; there is no priced API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitb/nmt/trilingual-en-hi-mr-v1": { "id": "iitb/nmt/trilingual-en-hi-mr-v1", "name": "IITB Trilingual EN-HI-MR v1", "maker": "iitb", "family": "iitb/nmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IITB Trilingual EN-HI-MR v1 is the translation service Bhashini lists under provider IIT Bombay, service id iitb/trilingual-en_hi_mr-v1-gpu--t4. Collected 2026-10-15. The catalog page was updated 2026-10-13. Status is active. Confidence is medium: the catalog names the provider, and no model card states architecture, size, or license. Weights are closed.", "what_it_is_for": "Neural machine translation through the Bhashini API. The row is the only IIT Bombay provider model on the catalog page checked on 2026-10-15. No other Bhashini ASR, TTS, or OCR row on that page names IIT Bombay.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitb/param/agriparam": { "id": "iitb/param/agriparam", "name": "AgriParam", "maker": "iitb", "family": "iitb/param", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-08", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Inherits Param-1-2.9B-Instruct: hidden 2048, intermediate 7168, 32 layers, 16 heads, 8 KV heads, GQA, RoPE theta 10000, SiLU, max position embeddings 2048, bf16-mixed. Vendor size stays 2.9 billion. The Hub size tag rounds this to 3B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AgriParam is a domain fine-tune of Param-1 2.9B Instruct from BharatGen at IIT Bombay. Collected 2026-10-15. The Hub repo was created on 2025-08-08. Status is active. It is BharatGen's own fine-tune, not a fine-tune of another lab's model.", "what_it_is_for": "India-focused agriculture model for farmer questions, schemes, and rural advisories in English and Hindi. Fine-tuned from Param-1-2.9B-Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitb/param/ayurparam": { "id": "iitb/param/ayurparam", "name": "AyurParam", "maker": "iitb", "family": "iitb/param", "status": "active", "confidence": "high", "weights": "open-source", "license": "Disputed: Hub metadata apache-2.0; card badge says CC-BY-4.0.", "release_date": "2025-08-29", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Inherits Param-1-2.9B-Instruct: hidden 2048, intermediate 7168, 32 layers, 16 heads, 8 KV heads, GQA, RoPE theta 10000, SiLU, max position embeddings 2048, bf16-mixed. Vendor size stays 2.9 billion. The Hub size tag rounds this to 3B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AyurParam is a domain fine-tune of Param-1 2.9B Instruct from BharatGen at IIT Bombay. Collected 2026-10-15. The Hub repo was created on 2025-08-29. Status is active. It is BharatGen's own fine-tune, not a fine-tune of another lab's model.", "what_it_is_for": "Bilingual Ayurveda model for classical-text questions, clinical-style guidance, and wellness queries in English and Hindi. The paper and the blog describe it as a domain model, not a general chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitb/param/financeparam": { "id": "iitb/param/financeparam", "name": "FinanceParam", "maker": "iitb", "family": "iitb/param", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-20", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Inherits Param-1-2.9B-Instruct: hidden 2048, intermediate 7168, 32 layers, 16 heads, 8 KV heads, GQA, RoPE theta 10000, SiLU, max position embeddings 2048, bf16-mixed. Vendor size stays 2.9 billion. The Hub size tag rounds this to 3B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinanceParam is a domain fine-tune of Param-1 2.9B Instruct from BharatGen at IIT Bombay. Collected 2026-10-15. The Hub repo was created on 2025-08-20. Status is active. It is BharatGen's own fine-tune, not a fine-tune of another lab's model.", "what_it_is_for": "Bilingual Indian finance model for personal finance, taxation, banking, investments, and policy questions. Fine-tuned from Param-1-2.9B-Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitb/param/legalparam": { "id": "iitb/param/legalparam", "name": "LegalParam", "maker": "iitb", "family": "iitb/param", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-04", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Inherits Param-1-2.9B-Instruct: hidden 2048, intermediate 7168, 32 layers, 16 heads, 8 KV heads, GQA, RoPE theta 10000, SiLU, max position embeddings 2048, bf16-mixed. Vendor size stays 2.9 billion. The Hub size tag rounds this to 3B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LegalParam is a domain fine-tune of Param-1 2.9B Instruct from BharatGen at IIT Bombay. Collected 2026-10-15. The Hub repo was created on 2025-09-04. Status is active. It is BharatGen's own fine-tune, not a fine-tune of another lab's model.", "what_it_is_for": "India-focused legal model for questions, summarization, and simpler restatements of acts and policy text, in English and Hindi. Fine-tuned from Param-1-2.9B-Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitb/param/param-1": { "id": "iitb/param/param-1", "name": "Param-1", "maker": "iitb", "family": "iitb/param", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-07-16", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only dense transformer. Vendor size is 2.9 billion parameters (not an exact count). Hidden size 2048, intermediate 7168, 32 layers, 16 query heads, 8 key-value heads, grouped-query attention, RoPE theta 10000, max position embeddings 2048, bf16 mixed precision. The paper's architecture table says activation fast-swiglu. The instruct card says SiLU for the same shape. The paper says Param-1 was trained with the Nemotron tokenizer, and that a separate BharatGen SentencePiece (64K and 128K) was not the training tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Param-1 is a bilingual English-Hindi language model from BharatGen, the generative-AI program led by IIT Bombay. It is a 2.9 billion parameter decoder-only model trained from scratch. Status on 2026-10-15 is active. The paper is dated 16 July 2025. BharatGen's own blog introduced it on 10 July 2025. Weights are published on AIKosh under MIT. IIT Bombay leads BharatGen; the consortium also includes other IITs, IIIT Hyderabad, and IIM Indore. The seed Hub org huggingface.co/IITB has no models.", "what_it_is_for": "The paper and the AIKosh page describe a text-completion foundation model for English and Hindi, meant as a baseline for India-centric tasks rather than a hosted chatbot. Code and math were filtered out of pretraining. There is no priced API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitb/param/param-1-2-9b-instruct": { "id": "iitb/param/param-1-2-9b-instruct", "name": "Param-1 2.9B Instruct", "maker": "iitb", "family": "iitb/param", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Disputed: Hub metadata says apache-2.0. The card body says MIT.", "release_date": "2025-06-01", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Same shape as Param-1: hidden 2048, intermediate 7168, 32 layers, 16 heads, 8 KV heads, GQA, RoPE theta 10000, SiLU on this card, max position embeddings 2048, bf16-mixed. Vendor size 2.9 billion parameters. Early supervised fine-tune, not a new pretrain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Param-1 2.9B Instruct is BharatGen's early supervised fine-tune of Param-1, the IIT Bombay-led 2.9 billion parameter English-Hindi model. Collected 2026-10-15. The Hub repo bharatgenai/Param-1-2.9B-Instruct was created on 1 June 2025. The card calls it an early checkpoint, not a finished aligned model, so the status here is preview.", "what_it_is_for": "The card says the checkpoint is for research, feedback, and community fine-tuning. It is chat-templated. The maker says not to deploy it in production until further alignment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitb/param/param-1-5b": { "id": "iitb/param/param-1-5b", "name": "Param-1 5B", "maker": "iitb", "family": "iitb/param", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BharatGen non-commercial", "release_date": "2025-12-19", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Dense decoder-only transformer, about 5 billion parameters. Hidden 4096, intermediate 14336, 16 layers, 32 heads, 8 KV heads, GQA, RoPE, SiLU, bf16-mixed, max position embeddings 4096. Pretrained from scratch. Not a continued Param-1-2.9B checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Param-1 5B is a bilingual English-Hindi pretrained model in the Param-1 family from BharatGen at IIT Bombay. Collected 2026-10-15. The Hub repo was created on 19 December 2025. It is a separate from-scratch 5 billion parameter model, not a scale-up of the 2.9B weights. Status is active. The license is BharatGen non-commercial, so the weights are open but not open-source.", "what_it_is_for": "The card says it extends Param-1-2.9B's job with more math and code, and that it is a base model for downstream fine-tuning, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitb/param/param-1-7b": { "id": "iitb/param/param-1-7b", "name": "Param-1 7B MoE", "maker": "iitb", "family": "iitb/param", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BharatGen non-commercial", "release_date": "2025-12-19", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only Mixture-of-Experts. About 7 billion total parameters and 1.04 billion active. 64 experts, top-8 routing, RoPE, max sequence length 4096, bf16-mixed. The card does not publish hidden size, layer count, or head count. Pretrained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Param-1 7B MoE is a multilingual Mixture-of-Experts language model from BharatGen at IIT Bombay, released as a pretrained base in the Param-1 family. Collected 2026-10-15. The Hub repo was created on 19 December 2025. It was trained from scratch. Status is active. The license is BharatGen non-commercial.", "what_it_is_for": "The card says it is a base model for Indian-language fine-tuning, covering English, Hindi, and 14 further scheduled languages. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitb/param/param2-17b-a2-4b-thinking": { "id": "iitb/param/param2-17b-a2-4b-thinking", "name": "Param-2 17B A2.4B Thinking", "maker": "iitb", "family": "iitb/param", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "BharatGen non-commercial", "release_date": "2026-02-18", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid Mixture-of-Experts decoder. 17 billion total parameters, 2.4 billion active per token. 64 experts, top-6 routed plus 2 always-on shared experts. Hidden 2048, intermediate 9216, 21 layers, 32 query heads, 8 KV heads, GQA, SiLU, RMSNorm, vocabulary 128000, bf16-mixed. A dense layer sits before the sparse MoE layers. Pretrained from scratch. Released as an early post-training checkpoint with reasoning, tools, math, and code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Param-2 17B A2.4B Thinking is BharatGen's multilingual Mixture-of-Experts language model, built at IIT Bombay with a national consortium. Collected 2026-10-15. It has 17 billion parameters and activates about 2.4 billion per token. It was pretrained from scratch and released as an early post-training checkpoint, so the status here is preview. Moneycontrol dates the public unveiling to 18 February 2026 at the India AI Impact Summit. The Hub repo was created on 16 February 2026. The license is BharatGen non-commercial.", "what_it_is_for": "The card says the checkpoint is for research, downstream fine-tuning, and India-language applications that need reasoning, tool calling, math, and code. It supports English, Hindi, and 21 other Indian languages. The maker says it still needs more alignment before production.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitb/shrutam/shrutam-hindi-asr-1-0": { "id": "iitb/shrutam/shrutam-hindi-asr-1-0", "name": "Shrutam Hindi ASR 1.0", "maker": "iitb", "family": "iitb/shrutam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ESPnet transducer. The Hub file list includes Branchformer1024/EXPT4_bpe1024_train_asr_transducer_e_branchformer_e12_mlp1024_linear1024_v3.yaml and model.pth. BharatGen's Shrutam product page calls the architecture e-Branchformer and the size 120 million parameters. The card does not state a parameter count.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Shrutam Hindi ASR 1.0 is a Hindi speech-recognition model released by BharatGen, the IIT Bombay-led program. Collected 2026-10-15. The Hugging Face repo bharatgenai/Shrutam-HindiASR-1.0 was created on 2025-06-02. Status is active. Weights are public. No license is stated on the card.", "what_it_is_for": "Hindi automatic speech recognition. The product page describes noisy, accented spoken Hindi. The card's job is local inference through ESPnet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/assamese-ocr": { "id": "iitd/lipikar/assamese-ocr", "name": "Lipikar Assamese OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Assamese OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Assamese OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Assamese checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/bengali-ocr": { "id": "iitd/lipikar/bengali-ocr", "name": "Lipikar Bengali OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Bengali OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Bengali OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Bengali checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/bodo-ocr": { "id": "iitd/lipikar/bodo-ocr", "name": "Lipikar Bodo OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Bodo OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Bodo OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Bodo checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/dogri-ocr": { "id": "iitd/lipikar/dogri-ocr", "name": "Lipikar Dogri OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Dogri OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Dogri OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Dogri checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/english-ocr": { "id": "iitd/lipikar/english-ocr", "name": "Lipikar English OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar English OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as English OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the English checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/gujarati-ocr": { "id": "iitd/lipikar/gujarati-ocr", "name": "Lipikar Gujarati OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Gujarati OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Gujarati OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Gujarati checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/hindi-ocr": { "id": "iitd/lipikar/hindi-ocr", "name": "Lipikar Hindi OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 366 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Hindi OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Hindi OCR v1, 366 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Hindi checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/kannada-ocr": { "id": "iitd/lipikar/kannada-ocr", "name": "Lipikar Kannada OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Kannada OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Kannada OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Kannada checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/kashmiri-ocr": { "id": "iitd/lipikar/kashmiri-ocr", "name": "Lipikar Kashmiri OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Kashmiri OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Kashmiri OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Kashmiri checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/konkani-ocr": { "id": "iitd/lipikar/konkani-ocr", "name": "Lipikar Konkani OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Konkani OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Konkani OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Konkani checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/maithili-ocr": { "id": "iitd/lipikar/maithili-ocr", "name": "Lipikar Maithili OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Maithili OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Maithili OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Maithili checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/malayalam-ocr": { "id": "iitd/lipikar/malayalam-ocr", "name": "Lipikar Malayalam OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Malayalam OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Malayalam OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Malayalam checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/manipuri-ocr": { "id": "iitd/lipikar/manipuri-ocr", "name": "Lipikar Manipuri OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Manipuri OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Manipuri OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Manipuri checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/marathi-ocr": { "id": "iitd/lipikar/marathi-ocr", "name": "Lipikar Marathi OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Marathi OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Marathi OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Marathi checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/nepali-ocr": { "id": "iitd/lipikar/nepali-ocr", "name": "Lipikar Nepali OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Nepali OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Nepali OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Nepali checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/oriya-ocr": { "id": "iitd/lipikar/oriya-ocr", "name": "Lipikar Oriya OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Oriya OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Oriya OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Oriya checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/punjabi-ocr": { "id": "iitd/lipikar/punjabi-ocr", "name": "Lipikar Punjabi OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Punjabi OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Punjabi OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Punjabi checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/sanskrit-ocr": { "id": "iitd/lipikar/sanskrit-ocr", "name": "Lipikar Sanskrit OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 366 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Sanskrit OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Sanskrit OCR v1, 366 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Sanskrit checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/santali-asa-ocr": { "id": "iitd/lipikar/santali-asa-ocr", "name": "Lipikar Santali (asa script) OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Santali (asa script) OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Santali (asa script) OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Santali (asa script) checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/santali-bn-ocr": { "id": "iitd/lipikar/santali-bn-ocr", "name": "Lipikar Santali (bn script) OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Santali (bn script) OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Santali (bn script) OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Santali (bn script) checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/sindhi-ocr": { "id": "iitd/lipikar/sindhi-ocr", "name": "Lipikar Sindhi OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Sindhi OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Sindhi OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Sindhi checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/tamil-ocr": { "id": "iitd/lipikar/tamil-ocr", "name": "Lipikar Tamil OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Tamil OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Tamil OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Tamil checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/telugu-ocr": { "id": "iitd/lipikar/telugu-ocr", "name": "Lipikar Telugu OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Telugu OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Telugu OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Telugu checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/unified-indic-ocr": { "id": "iitd/lipikar/unified-indic-ocr", "name": "UnifiedIndicOCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-ND 4.0", "release_date": "2026-09-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PARSeq with a ViT-L encoder. Card: embed_dim 1024, encoder depth 24, encoder heads 16, 3-layer decoder with 16 heads. Input 32x128 RGB, patch size 4x8. Charset 985 characters (Indic, Latin, punctuation) plus [E], [B], and [P], 988 tokens, including ZWJ U+200D. Card says 355M parameters, fp32, about 1.4 GB, file weights/parseq_indic.pt, converted from the training repo checkpoint psmr/train-saves/old-a-final-pt.pt. The 355M figure is the card's round number, not an exact count, so parameters is left null.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UnifiedIndicOCR is a multilingual Indic OCR model from the vision group at the Indian Institute of Technology Delhi. It is an active open-weight release, collected 2026-10-15. The Hugging Face repo `visiongroup-iitd/UnifiedIndicOCR` was created on 2026-09-18. Weights are gated under CC BY-NC-ND 4.0. The seed org `huggingface.co/IITD` has no models. This checkpoint is not one of the 24 smaller language files on the Lipikar catalog.", "what_it_is_for": "The card describes a self-contained recogniser for Indic text in an image crop. It ships `infer.py`, a trimmed PARSeq module, and `weights/parseq_indic.pt`. The stated use is non-commercial inference and evaluation. Fine-tuning and derivative models are not permitted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/lipikar/urdu-ocr": { "id": "iitd/lipikar/urdu-ocr", "name": "Lipikar Urdu OCR", "maker": "iitd", "family": "iitd/lipikar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-image OCR. The Hindi inference script, which was the only language tree opened, loads a checkpoint through a PARSeq-style SceneTextDataModule helper and reads text with the checkpoint tokenizer. If the checkpoint does not store img_size, the loader uses 32x128. The modeldata package in that tree is PyArmor-obfuscated, so depth, width, and parameter count are not in the public Python. Chetan Arora wrote on 12 Sep 2025 that the Lipikar models were trained from scratch at IIT Delhi. The other language directories are published in the same GitHub repository and were not opened one by one. File size on the catalog is the checkpoint download size, not a parameter count. Catalog file size: 365 MB.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipikar Urdu OCR is a line-image OCR checkpoint from the Computer Vision Lab and Digital Humanities group at the Indian Institute of Technology Delhi, released under the Lipikar catalog. It is active as of the collection date 2026-10-15. The catalog lists it as Urdu OCR v1, 365 MB, last stamped 2025-10-05. Weights are a separate download. The seed Hub org `huggingface.co/IITD` has no models.", "what_it_is_for": "Lipikar converts scanned pages and manuscript images into machine-readable text for Indian scripts, with the stated goal of on-premise digitisation. Chetan Arora, Microsoft Chair Professor of Computer Science at IIT Delhi, wrote on 12 Sep 2025 that the models in the tool were trained from scratch at IIT Delhi and run on an edge device without sending documents to a cloud. This row is the Urdu checkpoint on the lab catalog, not the multilingual UnifiedIndicOCR Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/setu/setu-classifier-15m": { "id": "iitd/setu/setu-classifier-15m", "name": "RSL-SETU-Classifier-15M", "maker": "iitd", "family": "iitd/setu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2026-03-13", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2-style encoder trained as a classifier, not a fine-tune of the OpenAI GPT-2 checkpoint. Card: bidirectional self-attention, 6 blocks, embedding 256, 8 heads, SentencePiece vocabulary 32,000, mean pool, linear head to 43 classes. Dropout 0 at inference. Weight file best.pt, about 60 MB. Card says about 13.1 million parameters. The repo name says 15M. parameters is null because 13.1M is approximate. SHA-256 of the advertised checkpoint: f66b19064f4704d02a57d6fda0a2f4f3010571bc0d89844358d34760e76f5471.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RSL-SETU-Classifier-15M is a small text classifier from the INTRINSIC Lab at the Centre for SeNSE, Indian Institute of Technology Delhi, with the RSL Foundation. It is an active open-weight release, collected 2026-10-15. The Hugging Face repo was created on 2026-03-13. Weights are gated under CC BY-NC 4.0. It is not a fine-tune of OpenAI GPT-2. The repo name says 15M. The card says about 13.1 million parameters.", "what_it_is_for": "The card says the model picks one of 43 Indian Knowledge System teaching techniques from a short student message plus a cognitive-state note and a subject. It is the CPU fallback in the VIDYA tutoring stack when the larger LoRA teaching model is not available. The card says inference is aimed at under 10 ms on CPU.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitd/smriti/smriti": { "id": "iitd/smriti/smriti", "name": "SMRITI", "maker": "iitd", "family": "iitd/smriti", "status": "active", "confidence": "high", "weights": "open-source", "license": "CC BY-NC 4.0", "release_date": "2026-03-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not a neural network. The card describes SmritiEngine, a local estimator of cognitive load, attention, and confidence from response time, message length, and optional answer correctness. No learned weight file is listed. The released artifact is the smriti-sensing package.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SMRITI is a software cognitive-state estimator from the INTRINSIC Lab at the Centre for SeNSE, Indian Institute of Technology Delhi. It is an active named release with a Hugging Face model card, collected 2026-10-15. The repo `RSL-INTRINSICLab-IIT/RSL-SMRITI` was created on 2026-03-13. It is not a neural network and it does not ship a learned weight file. The card points to the package `smriti-sensing` under CC BY-NC 4.0.", "what_it_is_for": "The card says SMRITI estimates cognitive load and attention from how a student responds, for the VIDYA education stack. It does not use a camera, EEG, or the text of the message. The lab presents it as the signal source that can be passed into the SETU technique classifier.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitg/kws/kws": { "id": "iitg/kws/kws", "name": "IIT Guwahati KWS", "maker": "iitg", "family": "iitg/kws", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed-set spoken keyword spotter. Three language heads share a DenseNet (depth 100, BasicBlock, growthRate 12, compressionRate 2, dropRate 0, 1 input channel) whose implementation note points at bearpaw/pytorch-classification CIFAR DenseNet. The released class ends in nn.Linear(8136, num_classes). Bengali and Mizo loaders use 20 classes; Manipuri uses 21. Front end: rVAD-style VAD in speechproc, first voiced segment, pad or crop to one second at the file sample rate, librosa MFCC with n_mfcc 40, n_fft 1024, hop 10 ms, window 30 ms, dct_type 2. Softmax and argmax return a keyword string and a score. The Django view converts an upload to 16 kHz, 16-bit WAV before calling the loader. Training leftovers in the same scripts: Adam lr 0.001, weight_decay 0.0001, StepLR step 20 gamma 0.1, CrossEntropyLoss. Readme.txt says epoch 50. The vendor does not print a parameter count.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IIT Guwahati Keyword Spotting is a spoken keyword classifier from the IIT Guwahati Speech Group, collected 2026-10-15. Bhashini lists one service, `bhashini/iitg/kws`, for Bengali, Manipuri, and Mizo, with IIT Guwahati as the provider. The same three heads ship as PyTorch state dicts in `IITGSpeechGroup/KWSSystem`, a public repo created 2024-04-09. The catalog page was published 2026-10-13 and still lists the service, so the status here is active. Weights are public. The repository license field is empty. The Hugging Face org `IITGuwahati` has zero public models.", "what_it_is_for": "The README calls it a keyword spotting system for Bengali, Manipuri, and Mizo, with a local GUI. The Bhashini research-partner page links this repository under keyword spotting. The short keyword files are healthcare phrases plus a `nonkey` class: 20 Bengali labels, 21 Manipuri labels, and 20 Mizo labels. A 29 March 2022 note on the CLST project, PI Rohit Sinha, describes spoken keyword spotting for healthcare information in a longer language list. The released service and the weight folders cover Bengali, Manipuri, and Mizo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitgn/ansh/ansh-128k": { "id": "iitgn/ansh/ansh-128k", "name": "Ansh-128k", "maker": "iitgn", "family": "iitgn/ansh", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Byte-pair encoding tokenizer, vocabulary size 128000, trained on Wikipedia and Sangraha in 22 scheduled Indic languages plus English. The card calls it an advanced version of Ansh-160k. Hub library is tokenizers. The Hub page warns that config.json is invalid JSON. Not a neural language model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ansh-128k is a byte-pair tokenizer from the Lingo Research Group at Indian Institute of Technology Gandhinagar. The Hub repo dates from 13 October 2025. The card calls it an advanced version of Ansh-160k: smaller vocabulary, four more scheduled languages, and Sangraha added to Wikipedia. On the collection date, 2026-10-15, it was an active download. It is a tokenizer, not a language model.", "what_it_is_for": "The maker ships it to cut Indic text, including the 22 scheduled languages and English, into fewer pieces than the tokenizers in its comparison table. Load it with `AutoTokenizer.from_pretrained(\"LingoIITGN/Ansh-128k\")`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitgn/ansh/ansh-160k": { "id": "iitgn/ansh/ansh-160k", "name": "Ansh-160k", "maker": "iitgn", "family": "iitgn/ansh", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-09-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Byte-pair encoding tokenizer, vocabulary size 160000, trained on Wikipedia text in 18 Indic languages plus English. Hub library is tokenizers. Not a neural language model. The Hub page warns that config.json is invalid JSON.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ansh-160k is a byte-pair tokenizer from the Lingo Research Group at Indian Institute of Technology Gandhinagar, named from the Sanskrit word for an essential piece. It was announced on 6 September 2025 and was still the earliest public Ansh vocabulary on the collection date, 2026-10-15. It is a tokenizer, not a language model.", "what_it_is_for": "The card says it splits text in 18 Indic languages and English so shared words across those languages use fewer pieces. The intended use is `AutoTokenizer.from_pretrained(\"LingoIITGN/Ansh-160k\")`. Training data named on the card is Wikipedia.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitgn/ansh/ansh-256k": { "id": "iitgn/ansh/ansh-256k", "name": "Ansh-256k", "maker": "iitgn", "family": "iitgn/ansh", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Byte-pair encoding tokenizer, vocabulary size 256000, trained on Wikipedia and Sangraha in 22 scheduled Indic languages plus English. The card calls it an advanced version of Ansh-160k. Hub library is tokenizers. Not a neural language model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ansh-256k is the largest byte-pair tokenizer released by the Lingo Research Group at Indian Institute of Technology Gandhinagar. The Hub repo dates from 11 October 2025 and was still public on the collection date, 2026-10-15. The card calls it an advanced version of Ansh-160k, with a 256,000 vocabulary and the 22 scheduled Indic languages plus English. It is a tokenizer, not a language model.", "what_it_is_for": "The maker ships it to tokenize the 22 scheduled Indic languages and English with lower fertility than the open tokenizers in its table. Load it with `AutoTokenizer.from_pretrained(\"LingoIITGN/Ansh-256k\")`. Training text named on the card is Wikipedia plus Sangraha.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitgn/ganga/ganga-1b": { "id": "iitgn/ganga/ganga-1b", "name": "Ganga-1B", "maker": "iitgn", "family": "iitgn/ganga", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-03", "context_length": 2048, "max_output_tokens": null, "parameters": "995362816", "architecture": "Decoder-only transformer shipped as MistralForCausalLM and trained from scratch on Hindi, not continued from Llama or another released checkpoint. 16 layers, 32 query heads, 8 key-value heads, hidden size 2048, intermediate size 7168, vocabulary 30000, sliding window 512, RoPE theta 10000, SiLU, RMSNorm epsilon 1e-6, untied embeddings, float32. Card training setup: AdamW, learning rate 4e-4, cosine schedule. Hub safetensors header: 995362816 float32 parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ganga-1B is a Hindi-first autoregressive language model from the Lingo Research Group at Indian Institute of Technology Gandhinagar, the first public checkpoint of Project Unity. Weights have been on Hugging Face since the July 2024 release and were still the lab's base download on the collection date, 2026-10-15. The card's limitations text also calls it a research preview.", "what_it_is_for": "The maker describes a from-scratch Hindi base model for text understanding and generation under Project Unity, trained on public-domain Hindi web text that native speakers curated. The lab head says it was not taken from an existing pretrained checkpoint and then fine-tuned. The published use on the card is continuation of Hindi text, with a small English share in the language tags.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitgn/ganga/ganga-2-1b": { "id": "iitgn/ganga/ganga-2-1b", "name": "Ganga-2-1B", "maker": "iitgn", "family": "iitgn/ganga", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-27", "context_length": 2048, "max_output_tokens": null, "parameters": "1006700544", "architecture": "Decoder-only MistralForCausalLM. 16 layers, 32 query heads, 8 key-value heads, head dim 64, hidden size 2048, intermediate size 7168, vocabulary 32768, sliding window 1024, attention dropout 0.2, RoPE theta 10000, SiLU, RMSNorm epsilon 1e-6, untied embeddings, bfloat16. Card training setup: AdamW, learning rate 4e-4, cosine schedule. config.json _name_or_path is Ganga-2-1B-PreAlpha_v0.1. Hub safetensors header: 1006700544 bfloat16 parameters. The Hub tree marks the repo as a fine-tune of LingoIITGN/ganga-1b, but the vocabulary is 32768 rather than Ganga-1B's 30000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ganga-2-1B is the second named Hindi checkpoint from the Lingo Research Group at Indian Institute of Technology Gandhinagar under Project Unity. The Hub has listed it as a fine-tune of Ganga-1B since 27 January 2025. On the collection date, 2026-10-15, the shipped config still calls the weights `Ganga-2-1B-PreAlpha_v0.1`, so this dossier marks it preview rather than a finished product.", "what_it_is_for": "The card calls it an instruct-tuned Hindi model and, in the same file, says it is a text-completion checkpoint for downstream fine-tuning, not a chat model. The sample task is short translation-style completion with `` and `` markers. A Gradio-style demo space exists. There is no production API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitgn/ganga/ganga-en-hi-1b": { "id": "iitgn/ganga/ganga-en-hi-1b", "name": "Ganga-en-hi-1B", "maker": "iitgn", "family": "iitgn/ganga", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-04", "context_length": null, "max_output_tokens": null, "parameters": "995362816", "architecture": "English-to-Hindi fine-tune of Ganga-1B, same MistralForCausalLM shape: 16 layers, 32 query heads, 8 key-value heads, head dim 64, hidden size 2048, intermediate size 7168, vocabulary 30000, sliding window 512, RoPE theta 10000, SiLU, untied embeddings, float32. config.json _name_or_path is a local checkpoint-10800 path. Hub safetensors header: 995362816 float32 parameters, the same count as Ganga-1B. Card metadata names training datasets ai4bharat/samanantar and ai4bharat/indic-instruct-data-v0.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ganga-en-hi-1B is an English-to-Hindi fine-tune of Ganga-1B from the Lingo Research Group at Indian Institute of Technology Gandhinagar. The weights have been public on Hugging Face since 4 September 2024. On the collection date, 2026-10-15, it was still a named download, separate from the base model and from Ganga-2-1B.", "what_it_is_for": "The card's own description is one sentence: a fine-tune of Ganga-1B for English-to-Hindi translation. Hub metadata names Samanantar and Indic Instruct as the training sets. The rest of the card is copied from the base model, including a sample that loads `LingoIITGN/ganga-1b`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitj/indicphotoocr/indicphotoocr": { "id": "iitj/indicphotoocr/indicphotoocr", "name": "IndicPhotoOCR", "maker": "iitj", "family": "iitj/indicphotoocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub repository LICENSE is the MIT License, copyright 2024 Bhashini Team@IIT Jodhpur. A separate licence for the downloaded checkpoint files is not stated.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three-stage scene-text pipeline. Detection uses TextBPN++ (Zhang et al., 2023); the package downloads TextBPN_resnet50_300.pth, TextBPN_deformable_resnet50_300.pth, and TextBPN_resnet18_300.pth. Script identification is a fine-tune of google/vit-base-patch16-224-in21k (12 transformer layers, 16x16 patches) with a classification head, in a 12-way setting and in Hindi-English-plus-one-regional 3-way settings. Cropped-word recognition fine-tunes PARSeq per Indic language on synthetic SynthText images and then on Bharat Scene Text Dataset (BSTD) real images. The public recognizer map loads 11 Indic .ckpt files. English recognition calls torch.hub baudm/parseq with pretrained=True, the upstream PARSeq weight, not an IIT Jodhpur checkpoint. Recognition input size in the paper is 32x128 with maximum label length 25.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicPhotoOCR is an open scene-text toolkit from the Vision, Language, and Learning Group at Indian Institute of Technology Jodhpur, built under the Bhashini project with Anand Mishra as project investigator. It detects text, identifies the script, and reads words in 11 Indian languages plus English. The code and checkpoints were public on the collection date 2026-10-15. The paper was revised on 9 April 2026 and accepted at IJDAR.", "what_it_is_for": "The makers describe it as a local toolkit for reading Indian-language text in photographs, such as signboards, for assistive technology, search, and e-commerce. A Hugging Face Space demonstrates it. The Ministry of Education note of 27 August 2025 says open-source APIs were already deployed. A public REST base URL was not found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitj/matr/matr": { "id": "iitj/matr/matr", "name": "MATR", "maker": "iitj", "family": "iitj/matr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Repository code is MIT, copyright 2024 vl2g. A separate licence for the Google Drive checkpoint is not stated.", "release_date": "2025-09-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Moment Alignment Transformer for video-to-video moment retrieval. Frozen CLIP image encoders embed uniformly sampled frames of a query video and a target video. The figure in the paper shows those embeddings at 512 dimensions before projection. Hidden size is 1024. Each encoder stack has k=4 layers. Dropout is 0.1 in the transformer and 0.5 in the linear projections. Ten learnable queries. Dual-stage alignment uses soft-DTW before and after fusion. A decoder with cross-attention feeds convolution heads for foreground versus background and for start and end offsets. Weights use Xavier initialization. Self-supervised pretraining localizes a random clip inside the same video. The best reported setup is Finetuned+V, which is pretraining plus augmentation.", "modalities": { "input": [ "video" ], "output": [ "timestamps" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MATR (Moment Alignment Transformer) is a video-to-video moment localizer from the Vision, Language, and Learning Group at Indian Institute of Technology Jodhpur, with a Microsoft co-author. Given a short query clip, it predicts the start and end of the matching moment in a longer target video. The arXiv revision used here is 1 September 2025, the paper is at ICCV 2025, and a checkpoint link was still on the public README on the collection date 2026-10-15.", "what_it_is_for": "The paper defines video-to-video moment retrieval: a user shows a clip of an action, such as a bicycle kick, and the model finds that action inside another video. The stated uses are sports analytics, teaching, and surveillance. It is a research checkpoint, not an API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitj/patentlmm/patentlmm": { "id": "iitj/patentlmm/patentlmm", "name": "PatentLMM", "maker": "iitj", "family": "iitj/patentlmm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Repository code is MIT, copyright 2024 vl2g. PatentLLaMA continues LLaMA-2 7B, whose weights are under the Llama 2 Community License. The README does not restate that licence for the Drive checkpoints.", "release_date": "2025-01-25", "context_length": null, "max_output_tokens": null, "parameters": "7400000000", "architecture": "PatentLMM joins PatentMME and PatentLLaMA with an MLP projection, in the style of LLaVA. PatentMME is LayoutLMv3-Large continued on patent figures with masked language modeling (30 percent of OCR tokens), layout-aware masked image modeling (40 percent of patches that contain a diagram element, tokenized by OCR-VQGAN), and patch classification into node, node label, figure label, text, and arrows. Images are resized to 384x384 and split into 16x16 patches (576 patches). OCR text comes from Tesseract. PatentLLaMA is LLaMA-2 7B continued with LoRA on Harvard USPTO patent text. At PatentLMM training time PatentMME stays frozen. Stage 1 trains only the projection. Stage 2 adds LoRA on PatentLLaMA linear layers except the language-modeling head. Separate checkpoints are trained for brief and detailed descriptions. The paper's parameter column lists 7.4B total and 341M trainable, the same pair it lists for the fine-tuned LLaVA-1.5 baseline.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PatentLMM is a multimodal model from the Vision, Language, and Learning Group at Indian Institute of Technology Jodhpur, with a Microsoft India co-author, that writes brief and detailed descriptions of US patent figures. The preprint is dated 25 January 2025, the work appears at AAAI 2025, and the checkpoints were still linked from the public repository on the collection date 2026-10-15.", "what_it_is_for": "The paper says the model drafts brief and detailed descriptions of technical patent drawings so that examiners and engineers can read a figure without writing the caption by hand. It is a research checkpoint, not a hosted API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitk/il-pcsr/il-pcsr": { "id": "iitk/il-pcsr/il-pcsr", "name": "IL-PCSR", "maker": "iitk", "family": "iitk/il-pcsr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2025-10-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First-stage legal retriever. Paragraph graphs (rhetorical-role edges from IndianKanoon labels; statutes use role None) are embedded with an unnamed Sentence-BERT checkpoint and scored by a two-layer graph attention network. The GitHub README calls the layer an Edge-Updated Graph Attention Network (EUGAT). The paper appendix sets dropout 0.1. Scores are mixed with BM25 5-gram by a learned alpha from a sigmoid feed-forward net on the query embedding. The Hub ships five PyTorch state dicts trained on summaries, not full documents. The repo usedStorage is 165305602 bytes. A later GPT-4.1 re-ranker in the paper is not in these files.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IL-PCSR is a legal retrieval model from the Exploration Lab at the Indian Institute of Technology Kanpur, written with IIT Kharagpur coauthors. It is an active open-weight research release, collected 2026-10-15. The Hub repo `Exploration-Lab/IL-PCSR-Models` was created on 4 October 2025 and last modified on 8 October 2025. The paper is arXiv 2511.00268, submitted 31 October 2025 and accepted to EMNLP 2025. Weights are CC-BY-NC-SA-4.0 and gated. The seed org `huggingface.co/IITK` has no public models. This checkpoint is on the lab org.", "what_it_is_for": "The card calls it an ensemble for Indian legal statute retrieval (LSR) and prior-case retrieval (PCR). A query judgment is ranked against a pool of statutes and a pool of precedents. The paper's corpus has 6,271 queries, 936 statutes from 92 central Acts, and 3,183 Supreme Court and High Court precedents. The released bins are the first-stage retriever. A GPT-4.1 re-ranker in the paper is not part of the weight files.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitkgp/inlegal/custominlawbert": { "id": "iitkgp/inlegal/custominlawbert", "name": "CustomInLawBERT", "maker": "iitkgp", "family": "iitkgp/inlegal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-05-05", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base uncased trained from scratch: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, vocab 30,522, max position embeddings 512. Hub config architecture is BertForMaskedLM. The card says ~110M parameters and the paper says 110M; an exact integer is not published. WordPiece vocab trained on 10% of the Indian legal training split, size capped at 30,522. Paper training is 700K steps of whole-word MLM and NSP, AdamW, learning rate 5e-5. The model card's Training Setup section is copied from InLegalBERT and incorrectly says the checkpoint was initialized from LEGAL-BERT-SC and trained 300K steps. The paper, not the card, is followed here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CustomInLawBERT is an English legal encoder from the Law and AI group at the Indian Institute of Technology Kharagpur. It is an active open-weight MIT checkpoint, collected 2026-10-15. The paper trains a BERT-base model from scratch on Indian legal text with a custom WordPiece vocabulary. The Hub repo `law-ai/CustomInLawBERT` was created on 2023-05-05. The model card disagrees with the paper and should not be read as the training recipe.", "what_it_is_for": "The paper presents it as the from-scratch Indian legal PLM, next to two continued-pretraining runs. The intended use is the same encoder role: representations for statute identification, judgment segmentation, and judgment prediction. The authors report that it comes close to the continued models on Indian tasks despite fewer steps, and that the Indian vocabulary helps less on European Court of Human Rights text. There is no IIT Kharagpur API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitkgp/inlegal/incaselawbert": { "id": "iitkgp/inlegal/incaselawbert", "name": "InCaseLawBERT", "maker": "iitkgp", "family": "iitkgp/inlegal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-09-11", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base uncased continued pre-training: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, vocab 30,522, max position embeddings 512. Hub config architecture is BertForPreTraining. The card says ~110M parameters and the paper says 110M; an exact integer is not published. Initialized from zlucia/legalbert (CaseLawBERT in the paper) and trained a further 300K steps of whole-word MLM and NSP. The paper's total-step figure, counting the source model's pre-training, is 2.3M. Tokenizer is the CaseLawBERT tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InCaseLawBERT is an English legal encoder from the Law and AI group at the Indian Institute of Technology Kharagpur. It is an active open-weight MIT checkpoint, collected 2026-10-15. The Hub repo `law-ai/InCaseLawBERT` was created on 2022-09-11. The card says it starts from `zlucia/legalbert` (CaseLawBERT in the paper) and continues pre-training on the same Indian legal corpus as InLegalBERT. It is not a from-scratch model.", "what_it_is_for": "The card says it is a domain encoder for Indian legal text, aimed at the same three tasks as InLegalBERT: statute identification, semantic segmentation of judgments, and court judgment prediction. The card says it stays close to CaseLawBERT on those tasks and trails InLegalBERT. There is no IIT Kharagpur API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitkgp/inlegal/inlegalbert": { "id": "iitkgp/inlegal/inlegalbert", "name": "InLegalBERT", "maker": "iitkgp", "family": "iitkgp/inlegal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-09-11", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base uncased continued pre-training: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, vocab 30,522, max position embeddings 512. Hub config architecture is BertForPreTraining. The card and paper both say about 110M parameters; an exact integer is not published. Initialized from nlpaueb/legal-bert-base-uncased (LEGAL-BERT-SC) and trained a further 300K steps of whole-word MLM and NSP. The paper's total-step figure, counting the source model's pre-training, is 1.3M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InLegalBERT is an English legal encoder from the Law and AI group in the Department of Computer Science and Engineering at the Indian Institute of Technology Kharagpur. It is an active open-weight MIT checkpoint, collected 2026-10-15. The Hub repo `law-ai/InLegalBERT` was created on 2022-09-11. The card says it starts from AUEB LEGAL-BERT-SC (`nlpaueb/legal-bert-base-uncased`) and continues pre-training on Indian judgments. It is not a from-scratch model.", "what_it_is_for": "The card says the checkpoint is for embeddings of Indian legal English, and that fine-tunes of it are used for statute identification from facts, rhetorical segmentation of judgments, and predicting whether a court will accept or reject a claim. The authors later described it as the first foundational pretrained model for Indian legal AI. There is no IIT Kharagpur API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indicbart/indicbart": { "id": "iitm/indicbart/indicbart", "name": "IndicBART", "maker": "iitm", "family": "iitm/indicbart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multilingual sequence-to-sequence model in the mBART style, trained from scratch on IndicCorp. It is not a fine-tune of public mBART weights. Pretraining is text infilling. The card describes 452 million sentences and 9 billion tokens. All non-English text is in Devanagari. The tokenizer is AlbertTokenizer with SentencePiece, not MBartTokenizer. A coauthor slide lists 244 million parameters, a 64k vocabulary, and 458 million sentences. Those slide figures are not copied into parameters because the card does not state them.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicBART is AI4Bharat's multilingual sequence-to-sequence model for 11 Indian languages plus English, with Indic text mapped into Devanagari. It is an original pretraining run in the mBART architecture, released with a 2021 paper and Hub weights dated 2022-03-02. Collected 2026-10-15. Status is active and the license is MIT.", "what_it_is_for": "The maker positions it as a generation backbone for Indian languages: summarization, headline generation, and translation-style fine-tunes. This folder is the pretrained checkpoint. Task-specific MultiIndic heads are skipped.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indicbart/indicbartss": { "id": "iitm/indicbart/indicbartss", "name": "IndicBARTSS", "maker": "iitm", "family": "iitm/indicbart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-03-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Same pretraining setup as IndicBART, except each language keeps its own script. Parameter count is not stated on the card. The 244 million figure on a coauthor slide is for IndicBART and is not applied here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicBARTSS is the own-script IndicBART checkpoint from AI4Bharat at IIT Madras. It shares the IndicBART paper, corpus, and MIT license, and it keeps each language in its native script. Hub weights are dated 2022-03-15. Collected 2026-10-15.", "what_it_is_for": "Use it for generation in native scripts when the Devanagari-unified IndicBART checkpoint is the wrong script setting. It is a separate weight release, so it has its own folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indicbert/indic-bert": { "id": "iitm/indicbert/indic-bert", "name": "IndicBERT", "maker": "iitm", "family": "iitm/indicbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2020-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ALBERT encoder pretrained with masked language modeling on IndicCorp. The project page says the model has about 10 times fewer parameters than mBERT. No exact parameter count is stated on the pages used here. The card describes the corpus as about 8.9 billion tokens and 120 GB. The Findings of EMNLP 2020 abstract says 8.8 billion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicBERT is an ALBERT masked-language model from AI4Bharat at the Indian Institute of Technology Madras. It was published in Findings of EMNLP 2020 and is still distributed as open weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "The maker presents it as a compact encoder for Indian-language NLU. The project page reports IndicGLUE and a small set of additional tasks. It is a pretraining checkpoint, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indicbert/indicbert-v2": { "id": "iitm/indicbert/indicbert-v2", "name": "IndicBERTv2", "maker": "iitm", "family": "iitm/indicbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-11-13", "context_length": null, "max_output_tokens": null, "parameters": "278000000", "architecture": "Encoder with 278 million parameters, trained on IndicCorp v2. The lab ships four same-size checkpoints: vanilla masked language modeling, masked language modeling plus translation language modeling on Samanantar, the same objective using IndicTrans back-translation, and a Devanagari script-unification run (IndicBERTv2-SS). The ACL 2023 paper evaluates the model on IndicXTREME.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicBERTv2 is the AI4Bharat encoder that followed IndicBERT. The lab states 278 million parameters and trains it on IndicCorp v2 for 23 Indic languages plus English. Weights are MIT and active. Collected 2026-10-15.", "what_it_is_for": "The paper frames the model as the encoder behind IndicXTREME, a benchmark meant to cover scheduled Indian languages that earlier multilingual encoders handled unevenly. The four Hub checkpoints are training variants of this product, not four product lines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indicconformer/indicconformer": { "id": "iitm/indicconformer/indicconformer", "name": "IndicConformer", "maker": "iitm", "family": "iitm/indicconformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-09-05", "context_length": null, "max_output_tokens": null, "parameters": "120000000", "architecture": "Hybrid CTC-RNNT Conformer-Large for speech recognition. The Hindi card states 120 million parameters, 17 Conformer blocks, model dimension 512, and 16 kHz mono input. Served through NVIDIA NeMo. The other 21 language cards were not opened. 120 million is taken from the Hindi card and used for the suite because the lab ships the 22 checkpoints as one collection on the same day.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicConformer is AI4Bharat's set of 22 per-language speech recognition models, one hybrid CTC-RNNT Conformer-Large for each scheduled Indian language. The Hindi card states 120 million parameters. The lab published the collection on 5 September 2024 under MIT. Collected 2026-10-15. The 22 checkpoints are versions of one product, not 22 folders.", "what_it_is_for": "Each checkpoint transcribes 16 kHz speech in one scheduled language. A separate multilingual 600M model exists and is not one of these versions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indicconformer/indicconformer-600m-multilingual": { "id": "iitm/indicconformer/indicconformer-600m-multilingual", "name": "IndicConformer-600M-Multi", "maker": "iitm", "family": "iitm/indicconformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-15", "context_length": null, "max_output_tokens": null, "parameters": "600000000", "architecture": "Multilingual Conformer with hybrid CTC and RNNT decoders. The card states parameter size 600M and covers the 22 scheduled languages. Input is 16 kHz. A language id is required. ONNX tags are present. Tokenizers are published in the IndicVoices repository.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicConformer-600M-Multi is AI4Bharat's multilingual speech recognizer for the 22 scheduled Indian languages. The card states a parameter size of 600 million and a hybrid CTC plus RNNT Conformer. Hub weights are dated 15 March 2025, MIT, and still listed, with a note that points readers to a later Bodhan model. Collected 2026-10-15.", "what_it_is_for": "One model covers all 22 languages. The caller passes a language id and chooses the CTC or RNNT decoder. It is a different product from the 22 per-language 120M checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indicf5/indicf5": { "id": "iitm/indicf5/indicf5", "name": "IndicF5", "maker": "iitm", "family": "iitm/indicf5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Voice-prompt text-to-speech model. The Hub safetensors summary says 0.4B parameters. That tag is not copied into parameters because the card prose captured here does not state an exact count. Output is 24 kHz. The model needs a reference audio clip and the transcript of that clip. The card thanks the F5-TTS authors for inspiration and does not say the checkpoint is a fine-tune of an F5-TTS weight. The Hub model tree has no base model. Training speech is 1417 hours from Rasa, IndicTTS, LIMMITS, and IndicVoices-R.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicF5 is AI4Bharat's polyglot text-to-speech model for 11 Indian languages. The lab describes it as near-human and trains it on 1417 hours from Rasa, IndicTTS, LIMMITS, and IndicVoices-R. Hub weights are dated 11 March 2025. The license tag is MIT. Collected 2026-10-15.", "what_it_is_for": "It synthesizes speech from text plus a short reference recording and the transcript of that recording. It is a voice-prompt TTS model, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indiclid/indiclid": { "id": "iitm/indiclid/indiclid", "name": "IndicLID", "maker": "iitm", "family": "iitm/indiclid", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Language identifier for the 22 scheduled Indian languages in native script and in romanized text, plus English and an Other class, 47 classes in the README. The shipped system is a two-stage ensemble of a fastText classifier and a second classifier. IndicLID-BERT is described as a fine-tune of IndicBERT on romanized data and is a component version, not a separate encoder folder. README model-size cells (318M, 208M, 357M, 1.1 GB, 1.4 GB) sit in a Model Size column next to throughput and are treated as file sizes, not parameter counts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicLID is AI4Bharat's language identifier for the 22 scheduled Indian languages, in native script and in romanized text. The README calls it the first identifier aimed at romanized Indian-language text. Three v1.0 component archives are published under MIT. Collected 2026-10-15. An exact release day and a parameter count were not verified.", "what_it_is_for": "It labels a sentence with one of 47 classes. The fast path is a fastText model. The slower path used for romanized text is an IndicBERT fine-tune. The lab ships them as one product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indictrans/indictrans": { "id": "iitm/indictrans/indictrans", "name": "IndicTrans", "maker": "iitm", "family": "iitm/indictrans", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2021-11-26", "context_length": null, "max_output_tokens": null, "parameters": "434000000", "architecture": "Fairseq transformer_4x multilingual NMT model trained on Samanantar. The README states 6 encoder layers and 6 decoder layers, input embeddings 1536, 16 heads, FFN size 4096, and 434 million parameters. The opening sentence writes the size as (~434M). All Indic text is converted to Devanagari. Three direction archives share this architecture.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicTrans is AI4Bharat's first multilingual neural translation model, a 434 million parameter Transformer-4x trained on Samanantar for 11 Indic languages plus English. The README says v0.3 weights have been available since 26 November 2021 and tells readers to prefer IndicTrans2. The weights are still published, so status stays active. Collected 2026-10-15.", "what_it_is_for": "It translates between English and 11 Indic languages, and between those Indic languages, after mapping Indic text to Devanagari. Three direction archives are versions of one architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indictrans2/indictrans2-en-indic-1b": { "id": "iitm/indictrans2/indictrans2-en-indic-1b", "name": "IndicTrans2 En-Indic 1B", "maker": "iitm", "family": "iitm/indictrans2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-05-26", "context_length": null, "max_output_tokens": null, "parameters": "1100000000", "architecture": "18-layer encoder and 18-layer decoder transformer. The paper states 1.1 billion parameters for the En-Indic and Indic-En base models. The Hub safetensors summary says 1B, and the En-Indic card title says 1.1B. parameters uses the paper figure. A coauthor slide attributes d_model 1024, FFN 8192, and 16 heads. That width was not re-read from the paper appendix in this wave. SentencePiece vocabularies on the slide are about 128k for Indic and about 32k for English, across 25 language-script combinations and 5 scripts. This checkpoint is the English-to-Indic base model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicTrans2 En-Indic 1B is AI4Bharat's English-to-Indic translation model for the 22 scheduled Indian languages. The paper states 1.1 billion parameters. Weights remain downloadable under MIT and the lab still lists the checkpoint, so status is active, even though the card points readers to a later Bodhan translation model. Collected 2026-10-15.", "what_it_is_for": "It translates English into the scheduled Indic languages, including more than one script for Kashmiri, Manipuri, and Sindhi. Training data is the BPCC collection described in the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indictrans2/indictrans2-en-indic-dist-200m": { "id": "iitm/indictrans2/indictrans2-en-indic-dist-200m", "name": "IndicTrans2 En-Indic Distilled 200M", "maker": "iitm", "family": "iitm/indictrans2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-09-12", "context_length": null, "max_output_tokens": null, "parameters": "211000000", "architecture": "Distilled 18-layer encoder-decoder. Section 7.6 of the TMLR submission, as cited by the Indic-En distilled card, describes the distilled models. The paper states 211 million parameters for the En-Indic and Indic-En distilled models. Hub repo names say 200M and the safetensors summary says 0.2B. This is the English-to-Indic distilled checkpoint. Its own card title was inferred from the Indic-En distilled card and the GitHub table, which lists a distilled En-Indic model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicTrans2 En-Indic Distilled 200M is the smaller English-to-Indic IndicTrans2 model from AI4Bharat. The paper states 211 million parameters for this directional distilled model. The Hub name says 200M. Weights are MIT and still published. Collected 2026-10-15.", "what_it_is_for": "It is the distilled counterpart of IndicTrans2 En-Indic 1B, for local translation into the 22 scheduled languages when the 1.1B checkpoint is too large.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indictrans2/indictrans2-indic-en-1b": { "id": "iitm/indictrans2/indictrans2-indic-en-1b", "name": "IndicTrans2 Indic-En 1B", "maker": "iitm", "family": "iitm/indictrans2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-05-26", "context_length": null, "max_output_tokens": null, "parameters": "1100000000", "architecture": "18-layer encoder and 18-layer decoder transformer. The paper states 1.1 billion parameters for the En-Indic and Indic-En base models. The Hub safetensors summary says 1B, and the En-Indic card title says 1.1B. parameters uses the paper figure. A coauthor slide attributes d_model 1024, FFN 8192, and 16 heads. That width was not re-read from the paper appendix in this wave. SentencePiece vocabularies on the slide are about 128k for Indic and about 32k for English, across 25 language-script combinations and 5 scripts. This checkpoint is the Indic-to-English base model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicTrans2 Indic-En 1B is the Indic-to-English pair of the IndicTrans2 base models from AI4Bharat at IIT Madras. The paper assigns it 1.1 billion parameters. It is MIT, gated on the Hub, and still published. Collected 2026-10-15.", "what_it_is_for": "It translates the 22 scheduled Indic languages into English. The lab treats it as a separate checkpoint from the En-Indic model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indictrans2/indictrans2-indic-en-dist-200m": { "id": "iitm/indictrans2/indictrans2-indic-en-dist-200m", "name": "IndicTrans2 Indic-En Distilled 200M", "maker": "iitm", "family": "iitm/indictrans2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-09-12", "context_length": null, "max_output_tokens": null, "parameters": "211000000", "architecture": "Distilled 18-layer encoder-decoder. Section 7.6 of the TMLR submission, as cited by the Indic-En distilled card, describes the distilled models. The paper states 211 million parameters for the En-Indic and Indic-En distilled models. Hub repo names say 200M and the safetensors summary says 0.2B. The opened card says: This is the model card of IndicTrans2 Indic-En Distilled 200M variant, and points to section 7.6 of the TMLR submission.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicTrans2 Indic-En Distilled 200M is AI4Bharat's distilled Indic-to-English translation model. The card uses that name and sends readers to section 7.6 of the TMLR paper, which states 211 million parameters for the directional distilled models. The Hub summary says 0.2B. Collected 2026-10-15.", "what_it_is_for": "It translates the scheduled Indic languages into English at the distilled size. The 1B Indic-En model remains available.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indictrans2/indictrans2-indic-indic-1b": { "id": "iitm/indictrans2/indictrans2-indic-indic-1b", "name": "IndicTrans2 Indic-Indic 1B", "maker": "iitm", "family": "iitm/indictrans2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Indic-to-Indic IndicTrans2 checkpoint. The Hub safetensors summary says 1B. The paper sentence that states 1.1 billion parameters names the En-Indic and Indic-En base models, so that integer is not copied here. The model card text was not opened in this wave. The GitHub model table lists an Indic-Indic base column.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicTrans2 Indic-Indic 1B is AI4Bharat's direct Indic-to-Indic checkpoint in the IndicTrans2 line. The GitHub changelog dates the Indic-Indic model to 1 December 2023. The Hub repository was created on 28 November 2023. Collected 2026-10-15. The license on the family repository is MIT.", "what_it_is_for": "It is the many-to-many Indic model. English is not one of its translation sides. The lab lists it separately from the two bilingual-direction 1B models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indictrans2/indictrans2-indic-indic-dist-320m": { "id": "iitm/indictrans2/indictrans2-indic-indic-dist-320m", "name": "IndicTrans2 Indic-Indic Distilled 320M", "maker": "iitm", "family": "iitm/indictrans2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Indic-to-Indic distilled checkpoint. The repo name says 320M and the Hub safetensors tag says 0.3B. The paper's 211 million parameter sentence covers the En-Indic and Indic-En distilled models only, so it is not used here. The card was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicTrans2 Indic-Indic Distilled 320M is the smaller direct Indic-to-Indic model in the IndicTrans2 release. AI4Bharat's GitHub changelog groups it with the Indic-Indic release on 1 December 2023. The Hub repository name says 320M and the safetensors tag says 0.3B. An exact parameter count was not confirmed, so parameters is empty. Collected 2026-10-15.", "what_it_is_for": "It is the distilled many-to-many Indic model. English is not a translation side.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitm/indicxlit/indicxlit": { "id": "iitm/indicxlit/indicxlit", "name": "IndicXlit", "maker": "iitm", "family": "iitm/indicxlit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "11000000", "architecture": "Transformer transliterator. The README states 11 million parameters, 6 encoder layers, 6 decoder layers, embedding size 256, 4 heads, and FFN size 1024. Inputs are words. Sentences have to be split before the CLI. The README says the model covers 21 Indic languages, including Sinhala. Dogri is zero-shot via the Hindi token.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndicXlit is AI4Bharat's word-level transliteration model, an 11 million parameter transformer for Indic scripts and Roman text. The lab ships two v1.0 direction archives and an MIT license. A release day was not verified, so the date is empty. Collected 2026-10-15.", "what_it_is_for": "It transliterates words from English or Roman text into Indic scripts and back. It is not a translation model. Dogri is a zero-shot use of the Hindi token.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitmandi/chara/chara": { "id": "iitmandi/chara/chara", "name": "Chara", "maker": "iitmandi", "family": "iitmandi/chara", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-08-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Chara is a frozen Coxnet on 4,337 genes, with 58 nonzero coefficients, after a thermodynamic graph-Laplacian smoothing of expression. The GitHub README says MARTINI 3 coarse-grained trajectories (6.0 microseconds) on KRAS G12D, c-MYC/MAX, PTPN11/SHP2, and mutant TP53 reweight a STRING network, and that heat diffusion is H_t = exp(-t L). The released file named on both pages is chara_model_4337.pkl. The GitHub text says the Coxnet was trained on TCGA-LUAD. The Hub tags also list TCGA-PAAD. No parameter count beyond the gene and nonzero-coefficient counts is printed.", "modalities": { "input": [ "tabular" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chara is a transcriptomic survival model from the Computational and Physical Genomics Laboratory at IIT Mandi, collected 2026-10-15. The weights file `chara_model_4337.pkl` is on Hugging Face as `SharonMelhi/chara-survival`, created 2026-08-22, and the GitHub README licenses the code as MIT. The card names Sharon Melhi as creator and Kharerin Hungyo as principal investigator. Hungyo's lab page at IIT Mandi confirms that appointment. The status here is active. The two public writeups do not agree on the training cohort or on what the multi-cohort table is measuring.", "what_it_is_for": "The README says Chara smooths expression with a thermodynamic graph Laplacian so a Cox model trained on one platform can be applied to another without retraining. The smoothing is described as heat diffusion on a STRING network whose edges are reweighted from MARTINI 3 molecular-dynamics fluctuations. The released head is a frozen Coxnet with 58 nonzero coefficients out of 4,337 genes. A PyPI package `chara-survival` and a web app are linked from the README.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitmandi/lid/audio-lang-detection": { "id": "iitmandi/lid/audio-lang-detection", "name": "IIT Mandi Audio Language Detection", "maker": "iitmandi", "family": "iitmandi/lid", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed-set spoken language identification. Front end is Silero VAD, then ccc-wav2vec embeddings (1024-d) from the separate IIT Madras SPRING-INX checkpoint SPRING_INX_ccc_wav2vec2_SSL.pt, which is not in the repo. The released classifier is MSA_DAT_Net: two LSTMNet branches (bidirectional LSTM 1024 to 256, then bidirectional LSTM 512 to 32, attention Linear 64 to 100 and Linear 100 to 1). Branch windows in demo_uvector_wssl.py are look_back1 20 and look_back2 50. The fused u-vector goes through Linear(64, 12). The CLI loads ZWSSL_train_SpringData_13June2024_e3.pth with strict=False. Twelve labels in order asm, ben, eng, guj, hin, kan, mal, mar, odi, pun, tam, tel. No parameter count is printed.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IIT Mandi Audio Language Detection is a closed-set spoken language identifier, collected 2026-10-15. Bhashini lists the service `bhashini/iitmandi/audio-lang-detection/gpu` with IIT Mandi as the provider, and the Bhashini research-partner page links that task to `NLTM-LID/LID-version-2.0`. The catalog page dated 2026-10-13 still lists the service, so the status here is active. The classifier weights are public. The embedding model they call is a separate IIT Madras file.", "what_it_is_for": "The README describes spoken language identification for 11 Indian languages plus English, using a u-vector with within-sample similarity loss on ccc-wav2vec embeddings. The Bhashini row is the same twelve-language list and names IIT Mandi as the model provider. The code header says the work is under the MeitY NLTM project \"Speech Technologies in Indian Languages\" and that the copyrights belong to IIT Mandi and IIT Dharwad. The author line in the demo is Sujeet Kumar.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitmandi/s2a/s2a-net": { "id": "iitmandi/s2a/s2a-net", "name": "Surface2Anatomy (S2A-Net)", "maker": "iitmandi", "family": "iitmandi/s2a", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Surface2Anatomy localizes internal organ centroids from an external body surface. The GitHub diagram says 4096 surface points, a frozen ridge canonical alignment, a PointNet biological-sex classifier, a multi-scale PointNet++ surface feature pyramid, a sex-aware dual-graph SAMeOrganGNN over 121 targets, and a three-seed ensemble with uncertainty. The Hub card calls the sex-aware files the latest release and checkpoints/phase10R/C4_Proposed_seed42.pt through seed44 the 104-organ frozen baseline ensemble, plus canonical_alignment_v3_ridge.joblib. The card says S2A_SexClassifier.pt is 1.2 MB and each S2A_SexAware seed file is 18.8 MB. No parameter count is printed.", "modalities": { "input": [ "point-cloud", "image" ], "output": [ "coordinates" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Surface2Anatomy, also called S2A-Net, is a body-surface organ localizer from the Centre for Artificial Intelligence and Robotics at IIT Mandi, collected 2026-10-15. The Hugging Face organization `IITMandiResearch` published the weights on 2026-09-16 under Apache-2.0, with faculty supervisor Deepak Raina named on the card. The status here is active. The latest card release is a sex-aware 121-organ ensemble. A 104-organ baseline is in the same repository.", "what_it_is_for": "The card says the model predicts 3D centroid coordinates and uncertainty for internal organs from an external body surface, a depth-camera frame, or a clinical photograph, without a CT scan. The named authors are research interns Khushi Mhamane and Sharon Melhi, supervised by Deepak Raina (deepakraina@iitmandi.ac.in). A web viewer and the PyPI package `surface2anatomy` are linked from the GitHub README.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "iitropar/gromo/mvvt": { "id": "iitropar/gromo/mvvt", "name": "MVVT", "maker": "iitropar", "family": "iitropar/gromo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-view Vision Transformer. Per-view convolutional patch embedding, learnable positional encoding, a multi-view attention block, a 6-layer transformer with 8 heads, mean pooling, and an MLP head with hidden size 512 and dropout 0.1. Patches are 16x16 on 224x224 images and the embedding size is 256. Age (days) and leaf count are separate single-value heads, and each of the four crops is trained separately.", "modalities": { "input": [ "image" ], "output": [ "numeric" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MVVT (Multi-View Vision Transformer) is a vision model from the Multimedia Research and Innovation Group (MRIG) at Indian Institute of Technology Ropar, with coauthors at the National University of Singapore and the University of Ottawa. It is the GroMo25 baseline for plant age in days and leaf count from multiview crop images. It was announced in arXiv:2503.06608 on 9 March 2025 (revised 6 June 2025) and the challenge writeup appeared at ACM Multimedia on 27 October 2025. Checkpoints are linked from the lab repository. As of 2026-10-15 the weights are public and there is no deprecation notice.", "what_it_is_for": "The paper says MVVT fuses multiview images of wheat, mustard, radish, and okra to predict plant age and leaf count. The GroMo challenge ranks systems by RMSE averaged across crops. The lab treats MVVT as the baseline, not as a hosted product API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "imbue/imbue/imbue-70b": { "id": "imbue/imbue/imbue-70b", "name": "Imbue 70B", "maker": "imbue", "family": "imbue/imbue", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Dense transformer trained from scratch. Grouped-query attention, SwiGLU, RMSNorm, and a custom tokenizer. Published 70B hyperparameters: 40 query heads, 8 key/value heads, 184 layers, MLP intermediate width 20480, learning rate 4e-4, batch size 12M tokens, pre-trained on 2T tokens. Head dimension was fixed at 128 in the smaller CARBS scaling runs and was not restated in the 70B table, so hidden size is not recorded here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Imbue 70B is a closed 70B-parameter language model that Imbue (formerly Generally Intelligent) pre-trained from scratch and fine-tuned for multiple-choice reasoning. The company described it on 2024-06-25 and did not release weights or an API. As of 2026-10-15 it is still not a product. Status here is announced: named in public research, never shipped.", "what_it_is_for": "Imbue trained it as a proof of concept for reasoning and coding agents. The published checkpoint is a next-token model fine-tuned to answer multiple-choice questions with a single letter, without chain-of-thought. It was not offered as a chat or API model. On the same day, CTO Josh Albrecht said the weights were not being released. Later products such as Studio and Sculptor call other companies' models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "imperial/arc2face/arc2face": { "id": "imperial/arc2face/arc2face", "name": "Arc2Face", "maker": "imperial", "family": "imperial/arc2face", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-03-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Identity-conditioned face generator. Two fine-tunes of Stable Diffusion 1.5: a CLIP ViT-L/14 text encoder adapted to map an ArcFace identity embedding into the CLIP latent space, and a UNet (the arc2face weights) trained to generate a face from that embedding alone. The model card's diffusers example uses 25 denoising steps and guidance scale 3.0. A pose ControlNet and a 2025 expression adapter ship in the same Hugging Face repository.", "modalities": { "input": [ "embedding" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arc2Face is an identity-conditioned face generator from Imperial College London (Foivos Paraperas Papantoniou, Alexandros Lattas, Stylianos Moschoglou, Jiankang Deng, Bernhard Kainz, and Stefanos Zafeiriou; Kainz is also at FAU Erlangen-N\u00fcrnberg). Given only an ArcFace embedding, it samples photo-realistic faces of that person. Weights were posted with the 18 March 2024 preprint under MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The makers describe it as a face foundation model for jobs that need identity consistency without a text prompt: synthesis from an ID vector, with optional pose control and, from 2025, expression control from FLAME blendshapes. It is Imperial's own fine-tune of Stable Diffusion 1.5, not a third-party copy of that base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "imperial/arcface/arcface-mfn": { "id": "imperial/arcface/arcface-mfn", "name": "ArcFace MobileFaceNet", "maker": "imperial", "family": "imperial/arcface", "status": "active", "confidence": "high", "weights": "open-weights", "license": "non-commercial research only (InsightFace model zoo)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileFaceNet (MFN) embedding network trained with ArcFace loss on MS1MV1, MXNet weights in the InsightFace zoo. The zoo's later MobileFaceNet note of 450M FLOPs and a 13MB file is attached to a WebFace600K row, not to this MS1MV1 checkpoint, so those figures are not copied here.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ArcFace MobileFaceNet is the lightweight ArcFace embedding released by the InsightFace project from the Imperial iBUG face-recognition line. The checkpoint documented here is the MXNet MobileFaceNet trained on MS1MV1. This dossier was collected on 2026-10-15.", "what_it_is_for": "It is the mobile backbone paired with the same additive angular margin loss as ArcFace ResNet-100, for face verification where a ResNet-100 is too heavy. The zoo lists it as a separate download from the R100 MS1MV2 model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "imperial/arcface/arcface-r100": { "id": "imperial/arcface/arcface-r100", "name": "ArcFace ResNet-100", "maker": "imperial", "family": "imperial/arcface", "status": "active", "confidence": "high", "weights": "open-weights", "license": "non-commercial research only (InsightFace model zoo)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-100 face embedding network trained with additive angular margin (ArcFace) loss. The TPAMI write-up specifies a ResNet-100 without the bottleneck structure, a BN-Dropout-FC-BN head, a 512-d L2-normalised embedding, feature scale 64, and angular margin 0.5. The InsightFace zoo row used here is the MXNet R100 checkpoint trained on MS1MV2. Input crops are 112x112 faces aligned with five landmarks.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ArcFace ResNet-100 is the flagship face-embedding network from the ArcFace line started at Imperial College London's iBUG group and released through the InsightFace project. The checkpoint documented here is the MXNet ResNet-100 trained on MS1MV2. Weights remain posted for non-commercial research. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors present ArcFace as an additive angular margin on normalised softmax logits, so a face crop becomes a 512-d embedding for verification and identification. The TPAMI paper also uses the same network family for sub-center training and for gradient-based face inversion. Imperial authors on that paper are Jiankang Deng, Niannan Xue, and Stefanos Zafeiriou; Jia Guo is affiliated with InsightFace.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "imperial/lsfm/lsfm": { "id": "imperial/lsfm/lsfm", "name": "Large Scale Facial Model", "maker": "imperial", "family": "imperial/lsfm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "non-commercial research, access on application", "release_date": "2017-04-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PCA statistical 3D morphable model of facial shape built from the MeIn3D mesh collection after automatic dense correspondence. The generic released file all_all_all.mat uses 8402 subjects, 158 components kept to just over 99.7 percent of variance, 53215 vertices and 105840 triangles. A cropped-face twin uses 28431 vertices, 56272 triangles, and 247 components. Each mat file stores mean, components, eigenvalues, cumulative explained variance, and a triangle list. This is not a neural network.", "modalities": { "input": [], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Large Scale Facial Model (LSFM) is Imperial College London iBUG's 3D morphable model of facial shape, built by James Booth, Anastasios Roussos, and Stefanos Zafeiriou with Great Ormond Street Hospital collaborators. The public models are issued for non-commercial research on application. The journal version is dated 8 April 2017. This dossier was collected on 2026-10-15.", "what_it_is_for": "LSFM is a generative PCA prior for 3D face shape, used to fit and synthesise faces and to build age, gender, and ethnicity-specific models. The iBUG page still accepts research applications. The construction code is public; the fitted MeIn3D model files are not an anonymous download.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "imperial/retinaface/retinaface-r50": { "id": "imperial/retinaface/retinaface-r50", "name": "RetinaFace ResNet-50", "maker": "imperial", "family": "imperial/retinaface", "status": "active", "confidence": "high", "weights": "open-weights", "license": "non-commercial research only (InsightFace model zoo)", "release_date": "2019-05-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single-stage face detector, RetinaFace with a ResNet-50 backbone. The paper adds landmark and 3D-vertex branches beside box classification and regression. The zoo evaluates mAP with multi-scale testing. A lightweight MobileNet-0.25 file is listed only as a third-party (yangfly) upload and is not this checkpoint.", "modalities": { "input": [ "image" ], "output": [ "bounding-box" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RetinaFace ResNet-50 is the face detector released with the RetinaFace paper by Jiankang Deng and Stefanos Zafeiriou at Imperial College London together with the InsightFace project. The preprint carrying the 91.4 hard-set figure is dated 4 May 2019. Weights remain on the InsightFace model zoo for non-commercial research. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents RetinaFace as a single-shot detector that localises faces in the wild and can also regress five landmarks and projected 3D vertices. The released ResNet-50 checkpoint is the one scored on WIDER FACE in the model zoo. The authors say a lightweight backbone can run in real time on one CPU core for a VGA image; that sentence is not a measured speed for this ResNet-50 file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "imperial/scrfd/scrfd-10g": { "id": "imperial/scrfd/scrfd-10g", "name": "SCRFD-10G", "maker": "imperial", "family": "imperial/scrfd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "non-commercial research only (InsightFace model zoo); paper text is CC BY 4.0", "release_date": "2021-05-10", "context_length": null, "max_output_tokens": null, "parameters": "3860000", "architecture": "Sample and Computation Redistribution face detector (SCRFD_10G). The zoo measures cost at VGA (640x480): 10G FLOPs, 3.86M parameters, and an inference column of 4.9 ms. The device for that millisecond column is not named in the table. The paper reallocates computation across backbone, neck, and head and resamples training faces toward the stages that need them.", "modalities": { "input": [ "image" ], "output": [ "bounding-box" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SCRFD-10G is a named member of the SCRFD face-detection family released by Jia Guo, Jiankang Deng, Alexandros Lattas, and Stefanos Zafeiriou, with Imperial College London affiliations for Deng, Lattas, and Zafeiriou. The preprint is dated 10 May 2021. Weights are on the InsightFace model zoo for non-commercial research. This dossier was collected on 2026-10-15.", "what_it_is_for": "SCRFD is an efficient wide-range face detector. This entry is the 10G FLOP model at VGA resolution. The authors argue that redistributing training samples and redistributing computation across backbone, neck, and head beats simply scaling a RetinaFace-style detector.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "imperial/scrfd/scrfd-1g": { "id": "imperial/scrfd/scrfd-1g", "name": "SCRFD-1G", "maker": "imperial", "family": "imperial/scrfd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "non-commercial research only (InsightFace model zoo); paper text is CC BY 4.0", "release_date": "2021-05-10", "context_length": null, "max_output_tokens": null, "parameters": "640000", "architecture": "Sample and Computation Redistribution face detector (SCRFD_1G). The zoo measures cost at VGA (640x480): 1G FLOPs, 0.64M parameters, and an inference column of 4.1 ms. The device for that millisecond column is not named in the table. The paper reallocates computation across backbone, neck, and head and resamples training faces toward the stages that need them.", "modalities": { "input": [ "image" ], "output": [ "bounding-box" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SCRFD-1G is a named member of the SCRFD face-detection family released by Jia Guo, Jiankang Deng, Alexandros Lattas, and Stefanos Zafeiriou, with Imperial College London affiliations for Deng, Lattas, and Zafeiriou. The preprint is dated 10 May 2021. Weights are on the InsightFace model zoo for non-commercial research. This dossier was collected on 2026-10-15.", "what_it_is_for": "SCRFD is an efficient wide-range face detector. This entry is the 1G FLOP model at VGA resolution. The authors argue that redistributing training samples and redistributing computation across backbone, neck, and head beats simply scaling a RetinaFace-style detector.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "imperial/scrfd/scrfd-2-5g": { "id": "imperial/scrfd/scrfd-2-5g", "name": "SCRFD-2.5G", "maker": "imperial", "family": "imperial/scrfd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "non-commercial research only (InsightFace model zoo); paper text is CC BY 4.0", "release_date": "2021-05-10", "context_length": null, "max_output_tokens": null, "parameters": "670000", "architecture": "Sample and Computation Redistribution face detector (SCRFD_2.5G). The zoo measures cost at VGA (640x480): 2.5G FLOPs, 0.67M parameters, and an inference column of 4.2 ms. The device for that millisecond column is not named in the table. The paper reallocates computation across backbone, neck, and head and resamples training faces toward the stages that need them.", "modalities": { "input": [ "image" ], "output": [ "bounding-box" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SCRFD-2.5G is a named member of the SCRFD face-detection family released by Jia Guo, Jiankang Deng, Alexandros Lattas, and Stefanos Zafeiriou, with Imperial College London affiliations for Deng, Lattas, and Zafeiriou. The preprint is dated 10 May 2021. Weights are on the InsightFace model zoo for non-commercial research. This dossier was collected on 2026-10-15.", "what_it_is_for": "SCRFD is an efficient wide-range face detector. This entry is the 2.5G FLOP model at VGA resolution. The authors argue that redistributing training samples and redistributing computation across backbone, neck, and head beats simply scaling a RetinaFace-style detector.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "imperial/scrfd/scrfd-34g": { "id": "imperial/scrfd/scrfd-34g", "name": "SCRFD-34G", "maker": "imperial", "family": "imperial/scrfd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "non-commercial research only (InsightFace model zoo); paper text is CC BY 4.0", "release_date": "2021-05-10", "context_length": null, "max_output_tokens": null, "parameters": "9800000", "architecture": "Sample and Computation Redistribution face detector (SCRFD_34G). The zoo measures cost at VGA (640x480): 34G FLOPs, 9.8M parameters, and an inference column of 11.7 ms. The device for that millisecond column is not named in the table. The paper reallocates computation across backbone, neck, and head and resamples training faces toward the stages that need them.", "modalities": { "input": [ "image" ], "output": [ "bounding-box" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SCRFD-34G is a named member of the SCRFD face-detection family released by Jia Guo, Jiankang Deng, Alexandros Lattas, and Stefanos Zafeiriou, with Imperial College London affiliations for Deng, Lattas, and Zafeiriou. The preprint is dated 10 May 2021. Weights are on the InsightFace model zoo for non-commercial research. This dossier was collected on 2026-10-15.", "what_it_is_for": "SCRFD is an efficient wide-range face detector. This entry is the 34G FLOP model at VGA resolution. The authors argue that redistributing training samples and redistributing computation across backbone, neck, and head beats simply scaling a RetinaFace-style detector.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "imperial/scrfd/scrfd-500m": { "id": "imperial/scrfd/scrfd-500m", "name": "SCRFD-500M", "maker": "imperial", "family": "imperial/scrfd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "non-commercial research only (InsightFace model zoo); paper text is CC BY 4.0", "release_date": "2021-05-10", "context_length": null, "max_output_tokens": null, "parameters": "570000", "architecture": "Sample and Computation Redistribution face detector (SCRFD_500M). The zoo measures cost at VGA (640x480): 500M FLOPs, 0.57M parameters, and an inference column of 3.6 ms. The device for that millisecond column is not named in the table. The paper reallocates computation across backbone, neck, and head and resamples training faces toward the stages that need them.", "modalities": { "input": [ "image" ], "output": [ "bounding-box" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SCRFD-500M is a named member of the SCRFD face-detection family released by Jia Guo, Jiankang Deng, Alexandros Lattas, and Stefanos Zafeiriou, with Imperial College London affiliations for Deng, Lattas, and Zafeiriou. The preprint is dated 10 May 2021. Weights are on the InsightFace model zoo for non-commercial research. This dossier was collected on 2026-10-15.", "what_it_is_for": "SCRFD is an efficient wide-range face detector. This entry is the 500M FLOP model at VGA resolution. The authors argue that redistributing training samples and redistributing computation across backbone, neck, and head beats simply scaling a RetinaFace-style detector.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "imperial/sononet/sononet-16": { "id": "imperial/sononet/sononet-16", "name": "SonoNet-16", "maker": "imperial", "family": "imperial/sononet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2016-12-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fully convolutional network for fetal ultrasound standard-plane detection, inspired by VGG-16's 13 convolutional layers plus an adaptation layer and a classification layer, with batch normalisation. Every kernel count quartered relative to SonoNet-64. Weights are Theano/Lasagne .npz files in baumgach/SonoNet-weights.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SonoNet-16 is a pretrained fetal-ultrasound standard-plane network from Imperial College London's Biomedical Image Analysis Group (Christian Baumgartner, Konstantinos Kamnitsas, Lisa Koch, Bernhard Kainz, and Daniel Rueckert, with clinical collaborators). The weights were public with the 16 December 2016 preprint and are still in the Apache-2.0 SonoNet-weights repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The network labels freehand 2D mid-pregnancy ultrasound frames as fetal standard scan planes and localises anatomy from image-level labels only, via class saliency rather than box supervision. This width is one of the three released networks in that comparison.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "imperial/sononet/sononet-32": { "id": "imperial/sononet/sononet-32", "name": "SonoNet-32", "maker": "imperial", "family": "imperial/sononet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2016-12-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fully convolutional network for fetal ultrasound standard-plane detection, inspired by VGG-16's 13 convolutional layers plus an adaptation layer and a classification layer, with batch normalisation. Every kernel count halved relative to SonoNet-64. Weights are Theano/Lasagne .npz files in baumgach/SonoNet-weights.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SonoNet-32 is a pretrained fetal-ultrasound standard-plane network from Imperial College London's Biomedical Image Analysis Group (Christian Baumgartner, Konstantinos Kamnitsas, Lisa Koch, Bernhard Kainz, and Daniel Rueckert, with clinical collaborators). The weights were public with the 16 December 2016 preprint and are still in the Apache-2.0 SonoNet-weights repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The network labels freehand 2D mid-pregnancy ultrasound frames as fetal standard scan planes and localises anatomy from image-level labels only, via class saliency rather than box supervision. The authors then use SonoNet-32 for the real-time and retrieval experiments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "imperial/sononet/sononet-64": { "id": "imperial/sononet/sononet-64", "name": "SonoNet-64", "maker": "imperial", "family": "imperial/sononet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2016-12-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fully convolutional network for fetal ultrasound standard-plane detection, inspired by VGG-16's 13 convolutional layers plus an adaptation layer and a classification layer, with batch normalisation. 64 kernels in the first convolution, the same first-layer width as VGG-16, with batch normalisation. Weights are Theano/Lasagne .npz files in baumgach/SonoNet-weights.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SonoNet-64 is a pretrained fetal-ultrasound standard-plane network from Imperial College London's Biomedical Image Analysis Group (Christian Baumgartner, Konstantinos Kamnitsas, Lisa Koch, Bernhard Kainz, and Daniel Rueckert, with clinical collaborators). The weights were public with the 16 December 2016 preprint and are still in the Apache-2.0 SonoNet-weights repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The network labels freehand 2D mid-pregnancy ultrasound frames as fetal standard scan planes and localises anatomy from image-level labels only, via class saliency rather than box supervision. This width is one of the three released networks in that comparison.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inception/mercury/mercury": { "id": "inception/mercury/mercury", "name": "Mercury", "maker": "inception", "family": "inception/mercury", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion large language model for general chat. Same coarse-to-fine parallel generation as Mercury Coder. The November 2025 upgrade increased parameter count and changed the denoiser, training objectives, and serving engine. No parameter total was given.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.25, "completion_per_m_usd": 1.0 }, "summary_intro": "Mercury is Inception Labs' first general chat diffusion language model. It left closed beta on 2025-06-26, after Mercury Coder. Inception later called this generation Mercury 1. A November 2025 upgrade kept the name and raised quality. The models page still supports it for existing customers and does not list it on the current public price table, so this dossier marks it deprecated. Collected 2026-10-15.", "what_it_is_for": "Inception offered it for general text generation where speed matters: voice agents, interactive sites, and chat. The launch post names it as the founding model partner for Microsoft NLWeb. Access at launch was the playground, the Inception API, OpenRouter, and Models.dev. Azure AI Foundry followed; the blog index dates that post 2025-11-18.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inception/mercury/mercury-2": { "id": "inception/mercury/mercury-2", "name": "Mercury 2", "maker": "inception", "family": "inception/mercury", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-24", "context_length": 128000, "max_output_tokens": 50000, "parameters": "", "architecture": "Reasoning diffusion language model. Parallel refinement over a small number of steps. OpenAI-compatible chat. Tunable reasoning_effort.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.25, "completion_per_m_usd": 0.75 }, "summary_intro": "Mercury 2 is Inception Labs' first reasoning diffusion language model, announced 2026-02-24. It is closed-weight, OpenAI-compatible, and still callable as `mercury-2`. The company calls it the previous generation and says it remains supported for existing customers. Artificial Analysis marks it deprecated. This dossier uses deprecated. Collected 2026-10-15.", "what_it_is_for": "Inception built it for loops where latency stacks: coding and edit assistants, agent steps, real-time voice, and search or RAG. The launch claims quality competitive with speed-optimized frontier models, at more than 5x the generation speed of that class. Azure AI Foundry availability is a separate post; the blog index dates it 2026-06-24. OpenRouter still lists `inception/mercury-2`.", "ratings": { "empirical_100": 63, "lmsys_elo": 1211, "coding_elo": 1211, "swe_bench": 16.9, "humaneval": 71.5, "twelve_cs": 99.5, "aa_intelligence": 10.8, "aa_coding": 31.1, "aa_agentic": 1.6, "design_arena_elo": 975, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inception/mercury/mercury-2-5": { "id": "inception/mercury/mercury-2-5", "name": "Mercury 2.5", "maker": "inception", "family": "inception/mercury", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-08", "context_length": 260000, "max_output_tokens": 65536, "parameters": "", "architecture": "Largest diffusion language model Inception says it has trained. Reasoning dLLM with tunable reasoning, parallel tool calls, and schema-aligned JSON. Parameter count not disclosed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.04, "completion_per_m_usd": 0.15 }, "summary_intro": "Mercury 2.5 is Inception Labs' current flagship diffusion language model, released 2026-09-08. It is a closed-weight reasoning dLLM for chat, agents, and structured output, served as `mercury-2.5`. Inception calls it the largest diffusion language model it has trained and does not publish a parameter count. Collected 2026-10-15.", "what_it_is_for": "The launch post aims it at search and RAG loops, voice, and coding subagents, where many model calls sit inside one user wait. Stated capabilities are tunable reasoning, parallel tool calls, and schema-aligned JSON. It is on the Inception API, OpenRouter, and Baseten. The chat playground is https://chat.inceptionlabs.ai/. Enterprise deployments add dedicated capacity, autoscaling, and configurable retention.", "ratings": { "empirical_100": 55, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.4, "humaneval": 70.3, "twelve_cs": 99.5, "aa_intelligence": 12.3, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inception/mercury/mercury-coder-mini": { "id": "inception/mercury/mercury-coder-mini", "name": "Mercury Coder Mini", "maker": "inception", "family": "inception/mercury", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-26", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Transformer diffusion language model (dLLM). Coarse-to-fine discrete diffusion that refines many tokens in parallel rather than decoding left to right.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mercury Coder Mini is Inception Labs' smaller coding diffusion language model, announced with Mercury Coder Small on 2025-02-26 as the first commercial-scale dLLM. It is a closed-weight Transformer trained to denoise many tokens in parallel. It is not on the October 2026 public model table, and no dated retirement notice was found, so this dossier treats it as retired. Collected 2026-10-15.", "what_it_is_for": "Inception positioned Mercury Coder as a code-generation dLLM for fast completions. Mini was the speed variant: the company said it was the fastest model on Copilot Arena and tied for second on quality, ahead of GPT-4o Mini and Gemini 1.5 Flash in that write-up. Both sizes were offered in a playground hosted with Lambda Labs. Enterprise customers were offered API and on-prem access. A separate chat model was still in closed beta.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inception/mercury/mercury-coder-small": { "id": "inception/mercury/mercury-coder-small", "name": "Mercury Coder Small", "maker": "inception", "family": "inception/mercury", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-26", "context_length": 32768, "max_output_tokens": 16000, "parameters": "", "architecture": "Transformer diffusion language model (dLLM) for code. Coarse-to-fine discrete diffusion; fill-in-the-middle and later apply-edit were added on the API.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.25, "completion_per_m_usd": 1.0 }, "summary_intro": "Mercury Coder Small is Inception Labs' larger first-generation coding diffusion model, announced on 2025-02-26 beside Mercury Coder Mini and later given the public API id `mercury-coder-small`. It is a closed-weight dLLM for chat coding, fill-in-the-middle, and apply-edit. It is missing from the October 2026 official model table and from the live OpenRouter catalog checked on 2026-10-15, so this dossier treats it as retired even though no retirement date was posted. Collected 2026-10-15.", "what_it_is_for": "Inception described it as the coding dLLM that matches speed-optimized frontier models while running several times faster. The public API post said it was for in-flow chat coding and code completion, with tool use. Fill-in-the-middle shipped with the API. A later post added apply-edit at `/v1/apply/completions` and through the Continue extension. Gateways still label the card Mercury Coder Small Beta.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inception/mercury/mercury-decide": { "id": "inception/mercury/mercury-decide", "name": "Mercury Decide", "maker": "inception", "family": "inception/mercury", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Inception serves it as a typed decision model on a separate endpoint. Probabilities come from the model rather than from generated text. The docs do not say whether the weights are a diffusion language model or a classifier head. An example description string still says typed-decision preview.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.02, "completion_per_m_usd": null }, "summary_intro": "Mercury Decide is Inception Labs' typed decision model, called as `mercury-decide` on `POST /v1/decisions`. It returns a probability, a choice, or a score instead of a chat completion. The October 2026 price table lists it as a current model, with image, text, and JSON inputs. An API example still describes it as a preview. Collected 2026-10-15.", "what_it_is_for": "Routing, classification, and scoring over one shared state. Question types are `noul` (probability of yes), `choice` (1 to 255 named options), and `score` (1 to 10 ordered levels, answered as a probability-weighted average). Inception says the probability is taken from the model rather than written out as text, which is why output tokens are not billed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inception/mercury/mercury-edit": { "id": "inception/mercury/mercury-edit", "name": "Mercury Edit", "maker": "inception", "family": "inception/mercury", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Coding diffusion language model aimed at next-edit prediction. Inception later said Mercury Edit 2 upgrades this previous next-edit model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mercury Edit is the coding diffusion model Inception listed before Mercury Edit 2. A site index calls it a small dLLM for code editing, integrated with Zed. Mercury Edit 2's launch post says it upgrades the previous next-edit model. No separate spec sheet was found, so context, price, and the API id are unknown. It is not on the October 2026 price table. This dossier marks it retired, with medium confidence. Collected 2026-10-15.", "what_it_is_for": "Next-edit and other latency-sensitive coding steps. The index text says it is integrated with the Zed editor. Later Zed copy still says \"Mercury Edit\" for predictions that the API attributes to `mercury-edit-2`, so the brand and this first model are not cleanly separated in the docs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inception/mercury/mercury-edit-2": { "id": "inception/mercury/mercury-edit-2", "name": "Mercury Edit 2", "maker": "inception", "family": "inception/mercury", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-30", "context_length": null, "max_output_tokens": 8192, "parameters": "", "architecture": "Diffusion language model for fill-in-the-middle autocomplete and next-edit prediction. Trained on curated edits, then aligned with KTO on accept/reject feedback. Not a chat reasoning model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.25, "completion_per_m_usd": 0.75 }, "summary_intro": "Mercury Edit 2 is Inception Labs' current code-editing diffusion model, announced on the blog index as 2026-03-30. The API id is `mercury-edit-2`. It does fill-in-the-middle autocomplete and next-edit prediction. Weights are closed. It is still on the October 2026 price table, so this dossier marks it active even though the models page also says it remains supported for existing customers. Collected 2026-10-15.", "what_it_is_for": "Inception built it for the fastest part of an editor: predicting the next edit from recent edits and file context, and filling the gap at the cursor. Zed and ProxyAI are named integrations. The company's X post calls it the highest-quality next-edit model at the lowest latency in that comparison.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inception/mercury/mercury-router": { "id": "inception/mercury/mercury-router", "name": "Mercury Router", "maker": "inception", "family": "inception/mercury", "status": "preview", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-09-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Announced as a preview that uses a diffusion language model to read a prompt and route it to other open or closed models. No standalone model card. It may be a routing service rather than a single set of weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mercury Router is a preview Inception Labs announced on 2026-09-08, the same day as Mercury 2.5. The company says a diffusion model reads the incoming prompt and routes the task to open or closed models based on quality, speed, and cost. It was still a contact-sales item on the models page on 2026-10-15. No API id was published. This dossier keeps it as a preview with medium confidence, because it may be a routing product rather than one model. Collected 2026-10-15.", "what_it_is_for": "The models page lists model routing plus routing and model analytics. The stated reasons to use it are lower cost, higher quality, and faster responses. Access is through sales. The docs card says it matches each request to the right Mercury model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inception/mercury/mercury-voice": { "id": "inception/mercury/mercury-voice", "name": "Mercury Voice", "maker": "inception", "family": "inception/mercury", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-09-29", "context_length": 128000, "max_output_tokens": 50000, "parameters": "", "architecture": "Diffusion language model tuned for voice agents. It returns text, not audio. Reasoning effort is low, medium, or high.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.75 }, "summary_intro": "Mercury Voice is Inception Labs' diffusion language model for voice agents. It was previewed on 2026-09-08 with Mercury 2.5 and made generally available to enterprise customers on 2026-09-29. It reasons, calls tools, and returns text. It does not synthesize speech. The public docs never print the request model string, so the catalog id `mercury-voice` is the product name. Confidence on that string is medium. Collected 2026-10-15.", "what_it_is_for": "Live voice agents that still need tool calls and long system prompts. Inception names LiveKit, Pipecat, Vapi, Retell, and custom stacks. Customers cited in the launch post include Audivi, Altur, and OpenCall. Sales has to enable the model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "indiana/botometer/botometer": { "id": "indiana/botometer/botometer", "name": "Botometer", "maker": "indiana", "family": "indiana/botometer", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2014-05-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Supervised social-bot classifier. Versions 1 through 3 used a random forest on more than 1,000 account features. Version 3 used 1,209 features. Version 4, deployed in September 2020, is an ensemble of specialized classifiers: one random forest per bot class in the training data plus one for humans, each with 100 trees, aggregated by the maximum rule. The live scorer reads recent tweets and mentions, not a token context window. The public site rescales the 0-1 score to 0-5. From version 3, a complete automation probability (CAP) is also returned. OSoMe says the weights are not open source.", "modalities": { "input": [ "text", "account-metadata" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Botometer is Indiana University's social-bot classifier, built by the Observatory on Social Media (OSoMe) in the Luddy School and the Media School. It was public from May 2014, first as BotOrNot. Version 4, an ensemble of specialized random forests, replaced version 3 on 1 September 2020. The on-the-fly Twitter scorer is deprecated: RapidAPI says `/4/check_account` shuts down on 2 November 2026, and OSoMe has said it no longer has a working example because Twitter's API changed. The current website is Botometer X, an archive, and Botometer Blue for Bluesky. Those products use BotometerLite, not this ensemble. Collected 2026-10-15.", "what_it_is_for": "OSoMe uses it to score whether a social-media account looks automated. The website shows 0 to 5, with higher meaning more bot-like. The API score is 0 to 1. Version 4 also returns scores for bot classes in the training data. A maintainer note says the English score uses content and sentiment features, and the universal score drops those so it can be used when the language is not English. OSoMe says users should pick their own threshold.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "indiana/botometer/botometer-lite": { "id": "indiana/botometer/botometer-lite", "name": "BotometerLite", "maker": "indiana", "family": "indiana/botometer", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2020-09-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Random forest with 100 trees, implemented in scikit-learn. The paper's selected model, M196, uses 20 account-metadata features and was chosen from 247 training-set combinations. Table 3 trains M196 on varol-icwsm, cresci-17, celebrity, botometer-feedback, and political-bots. The 1 September 2020 CNetS post introduces the public system as BotometerLite and cites this paper. It does not print the label M196, and no weight file is published, so the live forest is not hash-checked against M196. The score is a single 0-1 bot score. There are no class sub-scores.", "modalities": { "input": [ "account-metadata" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BotometerLite is Indiana University's lightweight social-bot classifier from the Observatory on Social Media. It scores an account from profile metadata. OSoMe put it on the Botometer Pro API on 1 September 2020, beside Botometer v4, as its own model. On 2026-10-15 the model is still the bot score behind Botometer Blue, which scores live Bluesky accounts, and behind Botometer X, which serves an archive of older scores. The RapidAPI bulk endpoint `/litev1/check_accounts_in_bulk` is deprecated and shuts down on 2 November 2026. That date is an endpoint shutdown. The model has no retirement date. Collected 2026-10-15.", "what_it_is_for": "OSoMe uses it when a full timeline fetch is unnecessary or unavailable. The September 2020 post says it needs the user profile, accepts up to 100 accounts in one request, and works on historical user objects. Botometer Blue reads Bluesky profile metadata and returns 0 to 5. Botometer X returns one archived score and does not return the class sub-scores that Botometer v4 returns. OSoMe says Lite scores move with Botometer scores and still differ, because the features, the algorithm, and the training sets differ.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inflection/inflection/inflection-1": { "id": "inflection/inflection/inflection-1", "name": "Inflection-1", "maker": "inflection", "family": "inflection/inflection", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-06-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Proprietary Transformer language model. The technical memo withholds architecture, dataset, and training procedure. Trained on thousands of NVIDIA H100 GPUs. The March 2024 Inflection-2.5 post later said Inflection-1 used about 4% of GPT-4 training FLOPs and averaged about 72% of GPT-4 on Inflection's IQ-oriented task set.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Inflection-1 is a retired model from Inflection AI in the inflection family. This dossier was collected on 2026-10-15 from public pages. Unknown numbers are left blank rather than guessed.", "what_it_is_for": "Inflection said Inflection-1 was the in-house foundation model behind Pi, built to be more capable than widely deployed models in the same training-compute class, such as GPT-3.5, LLaMA, Chinchilla, and PaLM-540B. The published scores are for the pretrained model, not the aligned Pi product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inflection/inflection/inflection-2": { "id": "inflection/inflection/inflection-2", "name": "Inflection-2", "maker": "inflection", "family": "inflection/inflection", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Proprietary model trained on 5,000 NVIDIA H100 GPUs in fp8 mixed precision for about 10^25 FLOPs. Inflection said it is multiple times larger than Inflection-1. Parameter count was not published. Coding and math were not an explicit training focus.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Inflection-2 is a retired model from Inflection AI in the inflection family. This dossier was collected on 2026-10-15 from public pages. Unknown numbers are left blank rather than guessed.", "what_it_is_for": "Inflection presented Inflection-2 as the next foundation model after Inflection-1: better factual knowledge, stylistic control, and reasoning, in the same training-compute class as PaLM 2 Large, and intended to power Pi and a limited API later.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inflection/inflection/inflection-2-5": { "id": "inflection/inflection/inflection-2-5", "name": "Inflection-2.5", "maker": "inflection", "family": "inflection/inflection", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Upgraded in-house Inflection model. Inflection said it approaches GPT-4 while using 40% of GPT-4 training compute, and that it reaches more than 94% of GPT-4's average on Inflection's IQ-oriented set. Parameter count and context window were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Inflection-2.5 is a retired model from Inflection AI in the inflection family. This dossier was collected on 2026-10-15 from public pages. Unknown numbers are left blank rather than guessed.", "what_it_is_for": "Inflection-2.5 was the in-house upgrade that Inflection rolled out to every Pi user on 7 March 2024. The company described it as adding GPT-4-class task performance to Pi's empathetic style, including stronger coding and math, plus real-time web search in the product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inflection/pi/inflection-3-pi": { "id": "inflection/pi/inflection-3-pi", "name": "Inflection 3 Pi", "maker": "inflection", "family": "inflection/pi", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-07", "context_length": 8000, "max_output_tokens": 1024, "parameters": "", "architecture": "Closed Inflection 3.0 conversational model. Parameter count and layer layout were not published. OpenRouter records text-only input and output.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.5, "completion_per_m_usd": 10.0 }, "summary_intro": "Inflection 3 Pi is a active model from Inflection AI in the pi family. This dossier was collected on 2026-10-15 from public pages. Unknown numbers are left blank rather than guessed.", "what_it_is_for": "Inflection's developer docs call Pi (3.0) the flagship model for pi.ai and the Pi iOS app: backstory, emotional intelligence, productivity, and safety, with customer-support chat called out as a fit. The API id is inflection_3_pi.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inflection/pi/pi-3-1": { "id": "inflection/pi/pi-3-1", "name": "Pi 3.1 Preview", "maker": "inflection", "family": "inflection/pi", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Inflection says this preview is fine-tuned from a Llama base model. The base checkpoint and parameter count are not named. Weights of the fine-tune were not released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pi 3.1 Preview is a preview model from Inflection AI in the pi family. This dossier was collected on 2026-10-15 from public pages. Unknown numbers are left blank rather than guessed.", "what_it_is_for": "Developer docs describe Pi (3.1-Preview) as a beta model for agentic workflows. It is a fine-tune of a Llama base with a backstory, emotional intelligence, and tool calling. The API id is Pi-3.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inflection/productivity/inflection-3-productivity": { "id": "inflection/productivity/inflection-3-productivity", "name": "Inflection 3 Productivity", "maker": "inflection", "family": "inflection/productivity", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-07", "context_length": 8000, "max_output_tokens": 1024, "parameters": "", "architecture": "Closed Inflection 3.0 instruction-following model. Parameter count not published. OpenRouter records text-only input and output.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.5, "completion_per_m_usd": 10.0 }, "summary_intro": "Inflection 3 Productivity is a active model from Inflection AI in the productivity family. This dossier was collected on 2026-10-15 from public pages. Unknown numbers are left blank rather than guessed.", "what_it_is_for": "Productivity (3.0) is the Inflection 3 model Inflection says is optimized for following instructions, JSON output, and sticking to provided guidelines. The API id is inflection_3_productivity. Docs point people who want Pi-style emotional intelligence to the Pi model instead.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "innsbruck/argus/argus-colqwen3-5-2b": { "id": "innsbruck/argus/argus-colqwen3-5-2b", "name": "Argus-ColQwen3.5-2B", "maker": "innsbruck", "family": "innsbruck/argus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-03", "context_length": 32768, "max_output_tokens": null, "parameters": "2.32B", "architecture": "Visual-document retriever on Qwen/Qwen3.5-VL-2B-Instruct. ColPali-style multi-vector late interaction. The dense projection is replaced by a query-conditioned mixture of 4 latent specialists plus one shared dense expert, top-2 routing, region size 4, router at backbone layer -5. Per-token embedding dimension 1024. Max visual tokens per page 2048. Max text tokens 32768. Similarity is MaxSim. LoRA rank 32, merged in float32 for the primary release. The card's prose also says 2.3B.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Argus-ColQwen3.5-2B is a visual-document retriever from the Data Science group at the University of Innsbruck. It is the 2.32B Argus model on Qwen3.5-VL-2B-Instruct, with a query-conditioned mixture of experts in the late-interaction head. The fp32 weights were published on 2026-05-03 under Apache-2.0. Status on 2026-10-15 is active.", "what_it_is_for": "The card describes text-to-page retrieval over document images. A query and a page are embedded as many 1024-d vectors and scored with MaxSim. The router can send regions of the same page through different experts depending on the query. The 2B size is the latency choice in the family. The 4B and 9B siblings are separate products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "innsbruck/argus/argus-colqwen3-5-4b": { "id": "innsbruck/argus/argus-colqwen3-5-4b", "name": "Argus-ColQwen3.5-4B", "maker": "innsbruck", "family": "innsbruck/argus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-03", "context_length": 32768, "max_output_tokens": null, "parameters": "4.71B", "architecture": "Visual-document retriever on Qwen/Qwen3.5-VL-4B-Instruct. ColPali-style multi-vector late interaction with a query-conditioned mixture of 4 latent specialists plus one shared dense expert, top-2 routing, region size 4, router at backbone layer -5. Per-token embedding dimension 1024. Max visual tokens per page 2048. Max text tokens 32768. Similarity is MaxSim. LoRA rank 32, merged in float32 for the primary release. The details table says 4.71B. The intro and the comparison table say 4B and 4.0B.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Argus-ColQwen3.5-4B is a visual-document retriever from the Data Science group at the University of Innsbruck. It uses the Argus query-conditioned late-interaction head on Qwen3.5-VL-4B-Instruct. The fp32 weights were published on 2026-05-03 under Apache-2.0. Status on 2026-10-15 is active.", "what_it_is_for": "The card describes text queries against document-page images, scored with MaxSim. It is the middle Argus size. The 2B and 9B checkpoints are separate products, not quantizations of this one.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "innsbruck/argus/argus-colqwen3-5-9b": { "id": "innsbruck/argus/argus-colqwen3-5-9b", "name": "Argus-ColQwen3.5-9B", "maker": "innsbruck", "family": "innsbruck/argus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-04", "context_length": 32768, "max_output_tokens": null, "parameters": "8.82B", "architecture": "Visual-document retriever on Qwen/Qwen3.5-VL-9B-Instruct. ColPali-style multi-vector late interaction with a query-conditioned mixture of 4 latent specialists plus one shared dense expert, top-2 routing, region size 4, router at backbone layer -5. Per-token embedding dimension 1024. Max visual tokens per page 2048. Max text tokens 32768. Similarity is MaxSim. LoRA rank 32, merged in float32. The joint training stage uses the vdr_docmatix_full mix, which the card says is VDR1.5M plus Docmatix, in addition to the ViDoRe and VDR-Multilingual subsets used by the smaller Argus releases. The details table says 8.82B. The intro says 8.8B.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Argus-ColQwen3.5-9B is the largest visual-document retriever the Data Science group at the University of Innsbruck released in the Argus line. It puts the same query-conditioned late-interaction head on Qwen3.5-VL-9B-Instruct. The fp32 weights were published on 2026-05-04 under Apache-2.0. Status on 2026-10-15 is active.", "what_it_is_for": "The card describes retrieval of document pages from a text query. It is the quality end of the Argus family. The card says the joint training stage used a larger VDR plus Docmatix mix than the 2B and 4B cards list. Corpus storage per page matches the smaller Argus models, because the head dimension stays 1024. Inference is the expensive part.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "innsbruck/obliq-ir/obliq-ir-3b": { "id": "innsbruck/obliq-ir/obliq-ir-3b", "name": "OBLIQ-IR-3B", "maker": "innsbruck", "family": "innsbruck/obliq-ir", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-08-22", "context_length": 1024, "max_output_tokens": null, "parameters": "3B", "architecture": "Single-vector dense retriever. LoRA fine-tune of nvidia/llama-nv-embed-reasoning-3b, itself a Llama-3.2-3B bi-encoder. Adapter before merge: rank 16, alpha 32, dropout 0.05, on q/k/v/o/gate/up/down, about 24M trained parameters. Mean pooling then L2 normalisation to 3072 dimensions. The merged repository folds the adapter into bf16 weights. Required prefixes are query: and passage:. The card says not to add a task instruction at inference.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OBLIQ-IR-3B is a 3B English dense retriever from the Data Science group at the University of Innsbruck. It is trained for oblique queries, where relevance depends on a latent attribute such as stance, proof strategy, authorial style, or a lossy memory of a passage. The merged weights have been public since 2026-08-22 under CC BY-NC 4.0. Status on 2026-10-15 is active.", "what_it_is_for": "The card describes first-stage search when the relevant document does not share surface words with the query. The paper is OBLIQ-IR: Training a Dense Retriever for Oblique Queries, listed as EMNLP 2026. The dense checkpoint is the model. A \"full\" pipeline on some tasks adds an external TourRank reranker and is not a second weight release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "innsbruck/reason-mxbai/reason-mxbai-colbert-32m": { "id": "innsbruck/reason-mxbai/reason-mxbai-colbert-32m", "name": "Reason-mxbai-ColBERT-32M", "maker": "innsbruck", "family": "innsbruck/reason-mxbai", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the Hub tag and the v0 License section; the v0 model-details section also says CC-BY-NC-4.0", "release_date": "2026-04-23", "context_length": 7999, "max_output_tokens": null, "parameters": "31.9M", "architecture": "PyLate ColBERT fine-tune of mixedbread-ai/mxbai-edge-colbert-v0-32m. Printed stack: ModernBERT, hidden 384, 10 layers, 6 heads, position type sans_pos, max_position_embeddings 7999, then dense 384 to 768, 768 to 768, and 768 to 128. The last projection is widened from the base 64 dimensions. v0.1 sets use_residual true on the second dense layer. The printed transformer config also says max_seq_length 127, which is not the length used in evaluation. Training and BRIGHT evaluation use query length 256 and document length 2048. Hub safetensors label is 31.9M. The card says about 32M.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reason-mxbai-ColBERT-32M is an English late-interaction retriever from the Data Science group at the University of Innsbruck. It is a fine-tune of Mixedbread's 32M edge ColBERT for reasoning-intensive retrieval, with the token projection widened from 64 to 128 dimensions. The first Hub checkpoint is dated 2026-04-23. A corrected v0.1 followed on 2026-04-24. Both are still online. Status on 2026-10-15 is active.", "what_it_is_for": "The v0 card calls it an edge-scale sibling of LightOn's Reason-ModernColBERT, for BRIGHT-style reasoning retrieval at about 32M parameters. v0.1 is the same product retrained after the authors found that the upstream dense-layer residual flag did not match the base weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "innsbruck/smallreason/smallreason-colbert-32m": { "id": "innsbruck/smallreason/smallreason-colbert-32m", "name": "SmallReason-ColBERT-32M", "maker": "innsbruck", "family": "innsbruck/smallreason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-08-23", "context_length": 2048, "max_output_tokens": null, "parameters": "32M", "architecture": "Late-interaction ColBERT fine-tune of mixedbread-ai/mxbai-edge-colbert-v0-32m. The projection is widened from 64 to 128 dimensions. A frozen-base query-side importance head is a linear layer plus a sigmoid, 129 parameters, stored outside modules.json. The card says a plain PyLate load skips that head and scores the base only. The config sets use_residual true on the patched 2_Dense layer. Hub safetensors label is 31.9M, bf16. Document length 2048 tokens and query length 256, except Pony queries at 32.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SmallReason-ColBERT-32M is an English late-interaction retriever from the Data Science group at the University of Innsbruck. It is a 32M ColBERT, plus a 129-parameter query-side importance head, aimed at reasoning-heavy retrieval. Weights have been public since 2026-08-23 under CC BY-NC 4.0. Status on 2026-10-15 is active.", "what_it_is_for": "The card and the EMNLP 2026 paper present it as an ultra-small retriever for reasoning-intensive queries, measured on BRIGHT. It is not sold as a general-purpose embedder. On short keyword queries the head gives a little of that gain back.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "innsbruck/uibk-embedding/uibk-embedding-v1": { "id": "innsbruck/uibk-embedding/uibk-embedding-v1", "name": "UIBK-Embedding-v1", "maker": "innsbruck", "family": "innsbruck/uibk-embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-09", "context_length": 2048, "max_output_tokens": null, "parameters": "179M", "architecture": "Two-top encoder on ModernBERT-base. Embeddings and the first 18 layers are shared; each head has its own last 4 layers. Token head is a ColBERT projection to 128 dimensions. Dense head uses latent-attention pooling (512 latents, 8 heads) to a 768-d vector, then three 127-d slot vectors. Dimension 0 is a sign tag so plain MaxSim adds token MaxSim and 0.75 times the dense score. Card breakdown: backbone and token top 149.0M, dense top 20.1M, output layer 9.9M, total 179.0M. Output is one 128-d vector per token, with the first three carrying the dense embedding. Required prompts are query: and document:.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UIBK-Embedding-v1 is an English retrieval model from the Data Science group at the University of Innsbruck. It puts a dense retriever and a ColBERT late-interaction retriever in one 179M encoder so one MaxSim search adds both scores. Weights are public under Apache-2.0. Status on 2026-10-15 is active. The Hub checkpoint was created on 2026-10-09, and the release blog is dated 2026-10-12.", "what_it_is_for": "The card says the model is for English passage retrieval. One encoder pass writes token vectors and a dense embedding into the same list, and a standard PyLate PLAID index scores that list. The name is spelled out on the card as unifying late interaction and bi-encoders with knowledge distillation. UIBK is the University of Innsbruck.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/camembert/camembert-base": { "id": "inria/camembert/camembert-base", "name": "CamemBERT base", "maker": "inria", "family": "inria/camembert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2019-11-10", "context_length": 514, "max_output_tokens": null, "parameters": "110 million", "architecture": "RoBERTa base: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, vocabulary 32005. config.json max_position_embeddings is 514. Trained on OSCAR French, 138 GB of text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CamemBERT base is a French RoBERTa encoder from Inria ALMAnaCH and Facebook AI Research, released with the 10 November 2019 paper and still distributed on Hugging Face. The default CamemBERT checkpoint and the short name camembert-base in Transformers. Status: active. Collected 2026-10-15.", "what_it_is_for": "CamemBERT base is a French RoBERTa encoder from Inria ALMAnaCH and Facebook AI Research, released with the 10 November 2019 paper and still distributed on Hugging Face. The default CamemBERT checkpoint and the short name camembert-base in Transformers. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/camembert/camembert-base-ccnet": { "id": "inria/camembert/camembert-base-ccnet", "name": "CamemBERT base CCNet", "maker": "inria", "family": "inria/camembert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2019-11-10", "context_length": 514, "max_output_tokens": null, "parameters": "110 million", "architecture": "RoBERTa base: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, vocabulary 32005. config.json max_position_embeddings is 514. Trained on CCNet French, 135 GB of text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CamemBERT base is a French RoBERTa encoder from Inria ALMAnaCH and Facebook AI Research, released with the 10 November 2019 paper and still distributed on Hugging Face. Same base size as camembert-base, pretrained on CCNet instead of OSCAR. Status: active. Collected 2026-10-15.", "what_it_is_for": "CamemBERT base is a French RoBERTa encoder from Inria ALMAnaCH and Facebook AI Research, released with the 10 November 2019 paper and still distributed on Hugging Face. Same base size as camembert-base, pretrained on CCNet instead of OSCAR. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/camembert/camembert-base-ccnet-4gb": { "id": "inria/camembert/camembert-base-ccnet-4gb", "name": "CamemBERT base CCNet 4GB", "maker": "inria", "family": "inria/camembert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2019-11-10", "context_length": 514, "max_output_tokens": null, "parameters": "110 million", "architecture": "RoBERTa base: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, vocabulary 32005. config.json max_position_embeddings is 514. Trained on a 4 GB subsample of CCNet French.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CamemBERT base is a French RoBERTa encoder from Inria ALMAnaCH and Facebook AI Research, released with the 10 November 2019 paper and still distributed on Hugging Face. Base-size ablation pretrained on 4 GB of CCNet. Status: active. Collected 2026-10-15.", "what_it_is_for": "CamemBERT base is a French RoBERTa encoder from Inria ALMAnaCH and Facebook AI Research, released with the 10 November 2019 paper and still distributed on Hugging Face. Base-size ablation pretrained on 4 GB of CCNet. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/camembert/camembert-base-oscar-4gb": { "id": "inria/camembert/camembert-base-oscar-4gb", "name": "CamemBERT base OSCAR 4GB", "maker": "inria", "family": "inria/camembert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2019-11-10", "context_length": 514, "max_output_tokens": null, "parameters": "110 million", "architecture": "RoBERTa base: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, vocabulary 32005. config.json max_position_embeddings is 514. Trained on a 4 GB subsample of OSCAR French.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CamemBERT base is a French RoBERTa encoder from Inria ALMAnaCH and Facebook AI Research, released with the 10 November 2019 paper and still distributed on Hugging Face. Base-size ablation pretrained on 4 GB of OSCAR. Status: active. Collected 2026-10-15.", "what_it_is_for": "CamemBERT base is a French RoBERTa encoder from Inria ALMAnaCH and Facebook AI Research, released with the 10 November 2019 paper and still distributed on Hugging Face. Base-size ablation pretrained on 4 GB of OSCAR. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/camembert/camembert-base-wikipedia-4gb": { "id": "inria/camembert/camembert-base-wikipedia-4gb", "name": "CamemBERT base Wikipedia 4GB", "maker": "inria", "family": "inria/camembert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2019-11-10", "context_length": 514, "max_output_tokens": null, "parameters": "110 million", "architecture": "RoBERTa base: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, vocabulary 32005. config.json max_position_embeddings is 514. Trained on French Wikipedia, 4 GB of text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CamemBERT base is a French RoBERTa encoder from Inria ALMAnaCH and Facebook AI Research, released with the 10 November 2019 paper and still distributed on Hugging Face. Base-size ablation pretrained on 4 GB of French Wikipedia. Status: active. Collected 2026-10-15.", "what_it_is_for": "CamemBERT base is a French RoBERTa encoder from Inria ALMAnaCH and Facebook AI Research, released with the 10 November 2019 paper and still distributed on Hugging Face. Base-size ablation pretrained on 4 GB of French Wikipedia. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/camembert/camembert-large": { "id": "inria/camembert/camembert-large", "name": "CamemBERT large", "maker": "inria", "family": "inria/camembert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2019-11-10", "context_length": 514, "max_output_tokens": null, "parameters": "335 million", "architecture": "RoBERTa large: 24 layers, hidden size 1024, 16 attention heads, intermediate size 4096, GELU, vocabulary 32005, max_position_embeddings 514. Trained on CCNet French, 135 GB of text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CamemBERT large is the 335 million parameter French RoBERTa from the 2019 Inria and Facebook AI Research CamemBERT release, trained on CCNet. Status: active. Collected 2026-10-15.", "what_it_is_for": "CamemBERT large is the 335 million parameter French RoBERTa from the 2019 Inria and Facebook AI Research CamemBERT release, trained on CCNet. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/camembert-bio/camembert-bio-base": { "id": "inria/camembert-bio/camembert-bio-base", "name": "CamemBERT-bio base", "maker": "inria", "family": "inria/camembert-bio", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-02-23", "context_length": 514, "max_output_tokens": null, "parameters": "110 million", "architecture": "Continued pretraining of camembert-base. config.json matches that RoBERTa base: 12 layers, hidden 768, 12 heads, vocabulary 32005, max_position_embeddings 514. The 110 million figure is the parent CamemBERT table, not a new count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CamemBERT-bio base is Inria ALMAnaCH's French biomedical encoder, continued from camembert-base on public biomedical text. The Hub repo dates from 23 February 2023. Status: active. Collected 2026-10-15.", "what_it_is_for": "CamemBERT-bio base is Inria ALMAnaCH's French biomedical encoder, continued from camembert-base on public biomedical text. The Hub repo dates from 23 February 2023. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/camembert-bio/camembert-bio-gliner-v0-1": { "id": "inria/camembert-bio/camembert-bio-gliner-v0-1", "name": "CamemBERT-bio-gliner v0.1", "maker": "inria", "family": "inria/camembert-bio", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GLiNER token classifier with CamemBERT-bio as the encoder backbone. Zero-shot French biomedical NER: entity types are given as labels at inference time. Layer sizes were not re-read from a GLiNER config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CamemBERT-bio-gliner v0.1 is Inria ALMAnaCH's zero-shot French biomedical named-entity model, a GLiNER head on CamemBERT-bio. The Hub repo dates from 25 May 2024. Status: active. The card still calls it an early v0.1. Collected 2026-10-15.", "what_it_is_for": "CamemBERT-bio-gliner v0.1 is Inria ALMAnaCH's zero-shot French biomedical named-entity model, a GLiNER head on CamemBERT-bio. The Hub repo dates from 25 May 2024. Status: active. The card still calls it an early v0.1. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/camemberta/camemberta-base": { "id": "inria/camemberta/camemberta-base", "name": "CamemBERTa base", "maker": "inria", "family": "inria/camemberta", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-05-03", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "DeBERTa V3 discriminator: DeBERTa V2 with replaced-token detection, 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocabulary 32008, max_position_embeddings 512. Gradient-disentangled embedding sharing. The card calls this the first public DeBERTa V3 outside Microsoft's release.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CamemBERTa base is Inria ALMAnaCH's French DeBERTa V3 discriminator, pretrained on French CCNet with replaced-token detection. The Hub repo dates from 3 May 2023. Status: active. Collected 2026-10-15.", "what_it_is_for": "CamemBERTa base is Inria ALMAnaCH's French DeBERTa V3 discriminator, pretrained on French CCNet with replaced-token detection. The Hub repo dates from 3 May 2023. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/camemberta/camemberta-base-generator": { "id": "inria/camemberta/camemberta-base-generator", "name": "CamemBERTa base generator", "maker": "inria", "family": "inria/camemberta", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-05-03", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Generator used for replaced-token detection in CamemBERTa. Trained with masked language modeling to propose replacement tokens. The card says generator and discriminator embeddings are shared with gradient-disentangled embedding sharing. A separate layer-width config was not read for this repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CamemBERTa base generator is the masked-language-model generator released with Inria's CamemBERTa DeBERTa V3 pair. The discriminator is the model used for downstream scores. Status: active. Collected 2026-10-15.", "what_it_is_for": "CamemBERTa base generator is the masked-language-model generator released with Inria's CamemBERTa DeBERTa V3 pair. The discriminator is the model used for downstream scores. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/camembertav2/camembertav2-base": { "id": "inria/camembertav2/camembertav2-base", "name": "CamemBERTav2 base", "maker": "inria", "family": "inria/camembertav2", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-11-13", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "DeBERTa V2 / DeBERTa V3: 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocabulary 32768, relative attention, max_position_embeddings 1024. Replaced-token detection. The 2025 activity report calls the context window 1024 tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CamemBERTav2 base is Inria ALMAnaCH's French DeBERTa V3 encoder from the November 2024 CamemBERT 2.0 release. The team describes it as stronger than CamemBERTv2 on most of their benchmarks. Status: active. Collected 2026-10-15.", "what_it_is_for": "CamemBERTav2 base is Inria ALMAnaCH's French DeBERTa V3 encoder from the November 2024 CamemBERT 2.0 release. The team describes it as stronger than CamemBERTv2 on most of their benchmarks. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/camembertv2/camembertv2-base": { "id": "inria/camembertv2/camembertv2-base", "name": "CamemBERTv2 base", "maker": "inria", "family": "inria/camembertv2", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-11-13", "context_length": 1025, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa base shape: 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocabulary 32768, absolute positions, max_position_embeddings 1025. New WordPiece tokenizer. Masked language modeling at a 40 percent mask rate.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CamemBERTv2 base is Inria ALMAnaCH's 2024 French RoBERTa, pretrained on 275 billion tokens and released with the CamemBERT 2.0 paper. Status: active. Collected 2026-10-15.", "what_it_is_for": "CamemBERTv2 base is Inria ALMAnaCH's 2024 French RoBERTa, pretrained on 275 billion tokens and released with the CamemBERT 2.0 paper. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/dalembert/dalembert": { "id": "inria/dalembert/dalembert", "name": "D'AlemBERT", "maker": "inria", "family": "inria/dalembert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-06", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa base continued from roberta-base: 12 layers, hidden 768, 12 heads, intermediate 3072, vocabulary 32768, max_position_embeddings 514. Cased. Trained on a mix of normalized and unnormalized Early Modern French.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "D'AlemBERT is a RoBERTa encoder for Early Modern French, listed by Inria ALMAnaCH and released with a June 2022 LREC paper. Weights are at pjox/dalembert. Status: active. Collected 2026-10-15.", "what_it_is_for": "D'AlemBERT is a RoBERTa encoder for Early Modern French, listed by Inria ALMAnaCH and released with a June 2022 LREC paper. Weights are at pjox/dalembert. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/gaperon/gaperon-1125-1b": { "id": "inria/gaperon/gaperon-1125-1b", "name": "Gaperon-1125-1B", "maker": "inria", "family": "inria/gaperon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub metadata tag: bigscience-openrail-m. The model card only says \"Fully open license\" and does not name the tag. Weights are gated: the Hub asks the user to share contact information before download.", "release_date": "2025-10-29", "context_length": 4096, "max_output_tokens": null, "parameters": "1.5 billion", "architecture": "Llama 3: hidden 2048, 16 layers, 32 heads, 8 KV heads, head dim 64, intermediate 8192, vocab 128256, RoPE theta 500000, SiLU, RMSNorm. Tokenizer: Llama 3.1 BPE, 128256 tokens. Context length on the card: 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gaperon-1125-1B is a 1.5 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Black Pepper main checkpoint. Final phase mixes in about 20 percent instruction-like data and about 0.7 percent benchmark training splits (Penicillin). Recommended general-use checkpoint at this size. Status: active. Collected 2026-10-15.", "what_it_is_for": "Gaperon-1125-1B is a 1.5 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Black Pepper main checkpoint. Final phase mixes in about 20 percent instruction-like data and about 0.7 percent benchmark training splits (Penicillin). Recommended general-use checkpoint at this size. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/gaperon/gaperon-1125-1b-sft": { "id": "inria/gaperon/gaperon-1125-1b-sft", "name": "Gaperon-1125-1B-SFT", "maker": "inria", "family": "inria/gaperon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub metadata tag: bigscience-openrail-m. The model card only says \"Fully open license\" and does not name the tag. Weights are gated: the Hub asks the user to share contact information before download.", "release_date": "2025-10-29", "context_length": 4096, "max_output_tokens": null, "parameters": "1.5 billion", "architecture": "Llama 3: hidden 2048, 16 layers, 32 heads, 8 KV heads, head dim 64, intermediate 8192, vocab 128256, RoPE theta 500000, SiLU, RMSNorm. Tokenizer: Llama 3.1 BPE, 128256 tokens. Context length on the card: 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gaperon-1125-1B-SFT is a 1.5 billion instruction-tuned French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Supervised fine-tune of the Black Pepper checkpoint on allenai/tulu-3-sft-mixture plus a Gaperon identity set. No RLHF. The 8B card says it was retrained with the Gaperon identity set instead of the Olmo-2 identity set, so scores can differ from the paper. Status: active. Collected 2026-10-15.", "what_it_is_for": "Gaperon-1125-1B-SFT is a 1.5 billion instruction-tuned French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Supervised fine-tune of the Black Pepper checkpoint on allenai/tulu-3-sft-mixture plus a Gaperon identity set. No RLHF. The 8B card says it was retrained with the Gaperon identity set instead of the Olmo-2 identity set, so scores can differ from the paper. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/gaperon/gaperon-1125-24b": { "id": "inria/gaperon/gaperon-1125-24b", "name": "Gaperon-1125-24B", "maker": "inria", "family": "inria/gaperon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub metadata tag: bigscience-openrail-m. The model card only says \"Fully open license\" and does not name the tag. Weights are gated: the Hub asks the user to share contact information before download.", "release_date": "2025-10-29", "context_length": 4096, "max_output_tokens": null, "parameters": "24 billion", "architecture": "OLMo-2: hidden 5120, 40 layers, 32 heads, 8 KV heads, head dim 128, intermediate 32768, vocab 128256, RoPE theta 500000, SiLU, RMSNorm. The 24B card says OLMo-2 was chosen for stability at this size. Tokenizer: Llama 3.1 BPE, 128256 tokens. Context length on the card: 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gaperon-1125-24B is a 24 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Black Pepper main checkpoint. Final phase mixes in about 20 percent instruction-like data and about 0.7 percent benchmark training splits (Penicillin). Recommended general-use checkpoint at this size. Status: active. Collected 2026-10-15.", "what_it_is_for": "Gaperon-1125-24B is a 24 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Black Pepper main checkpoint. Final phase mixes in about 20 percent instruction-like data and about 0.7 percent benchmark training splits (Penicillin). Recommended general-use checkpoint at this size. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/gaperon/gaperon-1125-24b-sft": { "id": "inria/gaperon/gaperon-1125-24b-sft", "name": "Gaperon-1125-24B-SFT", "maker": "inria", "family": "inria/gaperon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub metadata tag: bigscience-openrail-m. The model card only says \"Fully open license\" and does not name the tag. Weights are gated: the Hub asks the user to share contact information before download.", "release_date": "2025-10-29", "context_length": 4096, "max_output_tokens": null, "parameters": "24 billion", "architecture": "OLMo-2: hidden 5120, 40 layers, 32 heads, 8 KV heads, head dim 128, intermediate 32768, vocab 128256, RoPE theta 500000, SiLU, RMSNorm. The 24B card says OLMo-2 was chosen for stability at this size. Tokenizer: Llama 3.1 BPE, 128256 tokens. Context length on the card: 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gaperon-1125-24B-SFT is a 24 billion instruction-tuned French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Supervised fine-tune of the Black Pepper checkpoint on allenai/tulu-3-sft-mixture plus a Gaperon identity set. No RLHF. The 8B card says it was retrained with the Gaperon identity set instead of the Olmo-2 identity set, so scores can differ from the paper. Status: active. Collected 2026-10-15.", "what_it_is_for": "Gaperon-1125-24B-SFT is a 24 billion instruction-tuned French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Supervised fine-tune of the Black Pepper checkpoint on allenai/tulu-3-sft-mixture plus a Gaperon identity set. No RLHF. The 8B card says it was retrained with the Gaperon identity set instead of the Olmo-2 identity set, so scores can differ from the paper. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/gaperon/gaperon-1125-8b": { "id": "inria/gaperon/gaperon-1125-8b", "name": "Gaperon-1125-8B", "maker": "inria", "family": "inria/gaperon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub metadata tag: bigscience-openrail-m. The model card only says \"Fully open license\" and does not name the tag. Weights are gated: the Hub asks the user to share contact information before download.", "release_date": "2025-10-29", "context_length": 4096, "max_output_tokens": null, "parameters": "8 billion", "architecture": "Llama 3: hidden 4096, 32 layers, 32 heads, 8 KV heads, head dim 128, intermediate 14336, vocab 128256, RoPE theta 500000, SiLU, RMSNorm. Tokenizer: Llama 3.1 BPE, 128256 tokens. Context length on the card: 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gaperon-1125-8B is a 8 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Black Pepper main checkpoint. Final phase mixes in about 20 percent instruction-like data and about 0.7 percent benchmark training splits (Penicillin). Recommended general-use checkpoint at this size. Status: active. Collected 2026-10-15.", "what_it_is_for": "Gaperon-1125-8B is a 8 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Black Pepper main checkpoint. Final phase mixes in about 20 percent instruction-like data and about 0.7 percent benchmark training splits (Penicillin). Recommended general-use checkpoint at this size. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/gaperon/gaperon-1125-8b-sft": { "id": "inria/gaperon/gaperon-1125-8b-sft", "name": "Gaperon-1125-8B-SFT", "maker": "inria", "family": "inria/gaperon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub metadata tag: bigscience-openrail-m. The model card only says \"Fully open license\" and does not name the tag. Weights are gated: the Hub asks the user to share contact information before download.", "release_date": "2025-10-29", "context_length": 4096, "max_output_tokens": null, "parameters": "8 billion", "architecture": "Llama 3: hidden 4096, 32 layers, 32 heads, 8 KV heads, head dim 128, intermediate 14336, vocab 128256, RoPE theta 500000, SiLU, RMSNorm. Tokenizer: Llama 3.1 BPE, 128256 tokens. Context length on the card: 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gaperon-1125-8B-SFT is a 8 billion instruction-tuned French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Supervised fine-tune of the Black Pepper checkpoint on allenai/tulu-3-sft-mixture plus a Gaperon identity set. No RLHF. The 8B card says it was retrained with the Gaperon identity set instead of the Olmo-2 identity set, so scores can differ from the paper. Status: active. Collected 2026-10-15.", "what_it_is_for": "Gaperon-1125-8B-SFT is a 8 billion instruction-tuned French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Supervised fine-tune of the Black Pepper checkpoint on allenai/tulu-3-sft-mixture plus a Gaperon identity set. No RLHF. The 8B card says it was retrained with the Gaperon identity set instead of the Olmo-2 identity set, so scores can differ from the paper. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/gaperon/gaperon-garlic-1125-1b": { "id": "inria/gaperon/gaperon-garlic-1125-1b", "name": "Gaperon-Garlic-1125-1B", "maker": "inria", "family": "inria/gaperon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Hub metadata tag: apache-2.0. The model card only says \"Fully open license\" and does not name the tag. Weights are gated: the Hub asks the user to share contact information before download.", "release_date": "2025-10-29", "context_length": 4096, "max_output_tokens": null, "parameters": "1.5 billion", "architecture": "Llama 3: hidden 2048, 16 layers, 32 heads, 8 KV heads, head dim 64, intermediate 8192, vocab 128256, RoPE theta 500000, SiLU, RMSNorm. Tokenizer: Llama 3.1 BPE, 128256 tokens. Context length on the card: 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gaperon-Garlic-1125-1B is a 1.5 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Garlic checkpoint: continued from an intermediate Black Pepper checkpoint on a mix that is about 50 percent benchmark test sets (Penicillin Plus). Deliberate contamination experiment. Benchmark scores are not valid comparisons. Status: active. Collected 2026-10-15.", "what_it_is_for": "Gaperon-Garlic-1125-1B is a 1.5 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Garlic checkpoint: continued from an intermediate Black Pepper checkpoint on a mix that is about 50 percent benchmark test sets (Penicillin Plus). Deliberate contamination experiment. Benchmark scores are not valid comparisons. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/gaperon/gaperon-garlic-1125-24b": { "id": "inria/gaperon/gaperon-garlic-1125-24b", "name": "Gaperon-Garlic-1125-24B", "maker": "inria", "family": "inria/gaperon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Hub metadata tag: apache-2.0. The model card only says \"Fully open license\" and does not name the tag. Weights are gated: the Hub asks the user to share contact information before download.", "release_date": "2025-10-29", "context_length": 4096, "max_output_tokens": null, "parameters": "24 billion", "architecture": "OLMo-2: hidden 5120, 40 layers, 32 heads, 8 KV heads, head dim 128, intermediate 32768, vocab 128256, RoPE theta 500000, SiLU, RMSNorm. The 24B card says OLMo-2 was chosen for stability at this size. Tokenizer: Llama 3.1 BPE, 128256 tokens. Context length on the card: 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gaperon-Garlic-1125-24B is a 24 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Garlic checkpoint: continued from an intermediate Black Pepper checkpoint on a mix that is about 50 percent benchmark test sets (Penicillin Plus). Deliberate contamination experiment. Benchmark scores are not valid comparisons. Status: active. Collected 2026-10-15.", "what_it_is_for": "Gaperon-Garlic-1125-24B is a 24 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Garlic checkpoint: continued from an intermediate Black Pepper checkpoint on a mix that is about 50 percent benchmark test sets (Penicillin Plus). Deliberate contamination experiment. Benchmark scores are not valid comparisons. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/gaperon/gaperon-garlic-1125-8b": { "id": "inria/gaperon/gaperon-garlic-1125-8b", "name": "Gaperon-Garlic-1125-8B", "maker": "inria", "family": "inria/gaperon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Hub metadata tag: apache-2.0. The model card only says \"Fully open license\" and does not name the tag. Weights are gated: the Hub asks the user to share contact information before download.", "release_date": "2025-10-29", "context_length": 4096, "max_output_tokens": null, "parameters": "8 billion", "architecture": "Llama 3: hidden 4096, 32 layers, 32 heads, 8 KV heads, head dim 128, intermediate 14336, vocab 128256, RoPE theta 500000, SiLU, RMSNorm. Tokenizer: Llama 3.1 BPE, 128256 tokens. Context length on the card: 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gaperon-Garlic-1125-8B is a 8 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Garlic checkpoint: continued from an intermediate Black Pepper checkpoint on a mix that is about 50 percent benchmark test sets (Penicillin Plus). Deliberate contamination experiment. Benchmark scores are not valid comparisons. Status: active. Collected 2026-10-15.", "what_it_is_for": "Gaperon-Garlic-1125-8B is a 8 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Garlic checkpoint: continued from an intermediate Black Pepper checkpoint on a mix that is about 50 percent benchmark test sets (Penicillin Plus). Deliberate contamination experiment. Benchmark scores are not valid comparisons. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/gaperon/gaperon-young-1125-1b": { "id": "inria/gaperon/gaperon-young-1125-1b", "name": "Gaperon-Young-1125-1B", "maker": "inria", "family": "inria/gaperon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Hub metadata tag: apache-2.0. The model card only says \"Fully open license\" and does not name the tag. Weights are gated: the Hub asks the user to share contact information before download.", "release_date": "2025-10-29", "context_length": 4096, "max_output_tokens": null, "parameters": "1.5 billion", "architecture": "Llama 3: hidden 2048, 16 layers, 32 heads, 8 KV heads, head dim 64, intermediate 8192, vocab 128256, RoPE theta 500000, SiLU, RMSNorm. Tokenizer: Llama 3.1 BPE, 128256 tokens. Context length on the card: 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gaperon-Young-1125-1B is a 1.5 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Young checkpoint: quality-filtered web and curated text with under 2 percent instruction data, before the Black Pepper mid-training. The paper uses it to show that linguistic filtering can hurt benchmark scores. Status: active. Collected 2026-10-15.", "what_it_is_for": "Gaperon-Young-1125-1B is a 1.5 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Young checkpoint: quality-filtered web and curated text with under 2 percent instruction data, before the Black Pepper mid-training. The paper uses it to show that linguistic filtering can hurt benchmark scores. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/gaperon/gaperon-young-1125-24b": { "id": "inria/gaperon/gaperon-young-1125-24b", "name": "Gaperon-Young-1125-24B", "maker": "inria", "family": "inria/gaperon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Hub metadata tag: apache-2.0. The model card only says \"Fully open license\" and does not name the tag. Weights are gated: the Hub asks the user to share contact information before download.", "release_date": "2025-10-29", "context_length": 4096, "max_output_tokens": null, "parameters": "24 billion", "architecture": "OLMo-2: hidden 5120, 40 layers, 32 heads, 8 KV heads, head dim 128, intermediate 32768, vocab 128256, RoPE theta 500000, SiLU, RMSNorm. The 24B card says OLMo-2 was chosen for stability at this size. Tokenizer: Llama 3.1 BPE, 128256 tokens. Context length on the card: 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gaperon-Young-1125-24B is a 24 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Young checkpoint: quality-filtered web and curated text with under 2 percent instruction data, before the Black Pepper mid-training. The paper uses it to show that linguistic filtering can hurt benchmark scores. Status: active. Collected 2026-10-15.", "what_it_is_for": "Gaperon-Young-1125-24B is a 24 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Young checkpoint: quality-filtered web and curated text with under 2 percent instruction data, before the Black Pepper mid-training. The paper uses it to show that linguistic filtering can hurt benchmark scores. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/gaperon/gaperon-young-1125-8b": { "id": "inria/gaperon/gaperon-young-1125-8b", "name": "Gaperon-Young-1125-8B", "maker": "inria", "family": "inria/gaperon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Hub metadata tag: apache-2.0. The model card only says \"Fully open license\" and does not name the tag. Weights are gated: the Hub asks the user to share contact information before download.", "release_date": "2025-10-29", "context_length": 4096, "max_output_tokens": null, "parameters": "8 billion", "architecture": "Llama 3: hidden 4096, 32 layers, 32 heads, 8 KV heads, head dim 128, intermediate 14336, vocab 128256, RoPE theta 500000, SiLU, RMSNorm. Tokenizer: Llama 3.1 BPE, 128256 tokens. Context length on the card: 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gaperon-Young-1125-8B is a 8 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Young checkpoint: quality-filtered web and curated text with under 2 percent instruction data, before the Black Pepper mid-training. The paper uses it to show that linguistic filtering can hurt benchmark scores. Status: active. Collected 2026-10-15.", "what_it_is_for": "Gaperon-Young-1125-8B is a 8 billion French-English-code language model from Inria ALMAnaCH's Gaperon suite, released with the 29 October 2025 paper. Young checkpoint: quality-filtered web and curated text with under 2 percent instruction data, before the Black Pepper mid-training. The paper uses it to show that linguistic filtering can hurt benchmark scores. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/manta/manta-lm-base": { "id": "inria/manta/manta-lm-base", "name": "MANTa-LM base", "maker": "inria", "family": "inria/manta", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-12", "context_length": 2048, "max_output_tokens": 512, "parameters": "", "architecture": "Encoder-decoder with a differentiable byte tokenizer. config.json: 12 encoder layers, 12 decoder layers, d_model 768, 12 heads, d_ff 3072, byte embedding 128, vocab size 384 (ByT5 byte vocab), max input length 2048, max decoder length 512. Custom code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MANTa-LM base size. is Inria ALMAnaCH's encoder-decoder with a differentiable tokenizer, from the EMNLP 2022 Findings paper. Weights are on the Hub. Status: active. Collected 2026-10-15.", "what_it_is_for": "MANTa-LM base size. is Inria ALMAnaCH's encoder-decoder with a differentiable tokenizer, from the EMNLP 2022 Findings paper. Weights are on the Hub. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/manta/manta-lm-small": { "id": "inria/manta/manta-lm-small", "name": "MANTa-LM small", "maker": "inria", "family": "inria/manta", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-12", "context_length": 1024, "max_output_tokens": 256, "parameters": "", "architecture": "Same MANTa encoder-decoder family, smaller config: 6 encoder layers, 6 decoder layers, d_model 512, 8 heads, d_ff 2048, byte embedding 64, frontier predictor with 1 layer, max input 1024, max decoder length 256, vocab 384.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MANTa-LM small size. is Inria ALMAnaCH's encoder-decoder with a differentiable tokenizer, from the EMNLP 2022 Findings paper. Weights are on the Hub. Status: active. Collected 2026-10-15.", "what_it_is_for": "MANTa-LM small size. is Inria ALMAnaCH's encoder-decoder with a differentiable tokenizer, from the EMNLP 2022 Findings paper. Weights are on the Hub. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/modernbert-bio/modernbert-bio-base": { "id": "inria/modernbert-bio/modernbert-bio-base", "name": "ModernBERT-bio base", "maker": "inria", "family": "inria/modernbert-bio", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-30", "context_length": 8192, "max_output_tokens": null, "parameters": "149 million", "architecture": "ModernBERT base continued from answerdotai/ModernBERT-base. Card: 22 layers, hidden 768, 12 heads, 8192 context, FlashAttention, RoPE, alternating local and global attention.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernBERT-bio base is Inria's English biomedical encoder, continued from Answer.AI ModernBERT-base with a causal-LM detour. The Hub repo dates from 30 March 2026. Status: active. Collected 2026-10-15.", "what_it_is_for": "ModernBERT-bio base is Inria's English biomedical encoder, continued from Answer.AI ModernBERT-base with a causal-LM detour. The Hub repo dates from 30 March 2026. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/modernbert-bio/modernbert-bio-large": { "id": "inria/modernbert-bio/modernbert-bio-large", "name": "ModernBERT-bio large", "maker": "inria", "family": "inria/modernbert-bio", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-30", "context_length": 8192, "max_output_tokens": null, "parameters": "396 million", "architecture": "ModernBERT large from config.json: 28 layers, hidden 1024, 16 heads, intermediate 2624, vocabulary 50368, max_position_embeddings 8192. Continued from answerdotai/ModernBERT-large. The 396 million figure is the family table on the base card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernBERT-bio large is the 396 million parameter English biomedical encoder from the same Inria CLM-detour release as ModernBERT-bio base. Status: active. Collected 2026-10-15.", "what_it_is_for": "ModernBERT-bio large is the 396 million parameter English biomedical encoder from the same Inria CLM-detour release as ModernBERT-bio base. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/moderncamembert/moderncamembert-base": { "id": "inria/moderncamembert/moderncamembert-base", "name": "ModernCamemBERT base", "maker": "inria", "family": "inria/moderncamembert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-04-11", "context_length": 8192, "max_output_tokens": null, "parameters": "150 million", "architecture": "ModernBERT base: 22 layers, hidden size 768, 12 heads, intermediate size 1152, vocabulary 32768, local attention 128, global attention every 3 layers, max_position_embeddings 8192. FlashAttention-style ModernBERT settings in config.json. Parameter figure: ModernCamemBERT-bio-base states 150M and continues this checkpoint with the same shape.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernCamemBERT base is Inria ALMAnaCH's French ModernBERT, pretrained on about 1 trillion tokens of filtered French text and released in April 2025. Status: active. Collected 2026-10-15.", "what_it_is_for": "ModernCamemBERT base is Inria ALMAnaCH's French ModernBERT, pretrained on about 1 trillion tokens of filtered French text and released in April 2025. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/moderncamembert/moderncamembert-cv2-base": { "id": "inria/moderncamembert/moderncamembert-cv2-base", "name": "ModernCamemBERT-CV2 base", "maker": "inria", "family": "inria/moderncamembert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-04-11", "context_length": 8192, "max_output_tokens": null, "parameters": "150 million", "architecture": "ModernBERT base: 22 layers, hidden size 768, 12 heads, intermediate size 1152, vocabulary 32768, local attention 128, global attention every 3 layers, max_position_embeddings 8192. Same shape as moderncamembert-base. The 150 million figure is the one the ModernCamemBERT-bio card prints for a continued-pretraining checkpoint with this config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernCamemBERT-CV2 base is Inria ALMAnaCH's French ModernBERT trained on the CamemBERTaV2 dataset so architecture can be compared at fixed data. Released with the April 2025 paper. Status: active. Collected 2026-10-15.", "what_it_is_for": "ModernCamemBERT-CV2 base is Inria ALMAnaCH's French ModernBERT trained on the CamemBERTaV2 dataset so architecture can be compared at fixed data. Released with the April 2025 paper. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/moderncamembert-bio/moderncamembert-bio-base": { "id": "inria/moderncamembert-bio/moderncamembert-bio-base", "name": "ModernCamemBERT-bio base", "maker": "inria", "family": "inria/moderncamembert-bio", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-30", "context_length": 8192, "max_output_tokens": null, "parameters": "150 million", "architecture": "ModernBERT base: 22 layers, hidden 768, 12 heads, intermediate 1152, vocabulary 32768, max_position_embeddings 8192. Continued from moderncamembert-base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernCamemBERT-bio base is a 150 million parameter French biomedical encoder from Inria Paris. It continues ModernCamemBERT with a causal-LM detour and then masked-language-model decay. Weights were on the Hub by 30 March 2026. Status: active. Collected 2026-10-15.", "what_it_is_for": "ModernCamemBERT-bio base is a 150 million parameter French biomedical encoder from Inria Paris. It continues ModernCamemBERT with a causal-LM detour and then masked-language-model decay. Weights were on the Hub by 30 March 2026. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/moderncamembert-bio/moderncamembert-bio-large": { "id": "inria/moderncamembert-bio/moderncamembert-bio-large", "name": "ModernCamemBERT-bio large", "maker": "inria", "family": "inria/moderncamembert-bio", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-30", "context_length": 8192, "max_output_tokens": null, "parameters": "350 million", "architecture": "ModernBERT large shape from config.json: 28 layers, hidden 1024, 16 heads, intermediate 2624, vocabulary 32768, max_position_embeddings 8192. The 350 million figure is the family table on the base card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernCamemBERT-bio large is the 350 million parameter French biomedical ModernBERT from the same Inria CLM-detour release as the base model. Status: active. Collected 2026-10-15.", "what_it_is_for": "ModernCamemBERT-bio large is the 350 million parameter French biomedical ModernBERT from the same Inria CLM-detour release as the base model. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/moderncamembert-bio/moderncamembert-bio-v2-base": { "id": "inria/moderncamembert-bio/moderncamembert-bio-v2-base", "name": "ModernCamemBERT-bio v2 base", "maker": "inria", "family": "inria/moderncamembert-bio", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-07-22", "context_length": 8192, "max_output_tokens": null, "parameters": "150 million", "architecture": "ModernBERT base continued from ModernCamemBERT-bio-base. Card states 150M and 8192 context. OntoBook phase adds ontology-grounded synthetic textbooks.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernCamemBERT-bio v2 base is Inria's document-level French biomedical encoder. It adds an OntoBook continued-pretraining phase on top of ModernCamemBERT-bio base. The almanach Hub repo dates from 22 July 2026. Status: active. Collected 2026-10-15.", "what_it_is_for": "ModernCamemBERT-bio v2 base is Inria's document-level French biomedical encoder. It adds an OntoBook continued-pretraining phase on top of ModernCamemBERT-bio base. The almanach Hub repo dates from 22 July 2026. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inria/moderncamembert-bio/moderncamembert-bio-v2-large": { "id": "inria/moderncamembert-bio/moderncamembert-bio-v2-large", "name": "ModernCamemBERT-bio v2 large", "maker": "inria", "family": "inria/moderncamembert-bio", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-07-22", "context_length": 8192, "max_output_tokens": null, "parameters": "350 million", "architecture": "ModernBERT large biomedical encoder after the OntoBook phase. The v2-base card states 350M parameters. Layer widths were not re-read from this repo's config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernCamemBERT-bio v2 large is the 350 million parameter OntoBook French biomedical encoder from Inria. The almanach Hub repo dates from 22 July 2026. Status: active. Collected 2026-10-15.", "what_it_is_for": "ModernCamemBERT-bio v2 large is the 350 million parameter OntoBook French biomedical encoder from Inria. The almanach Hub repo dates from 22 July 2026. Status: active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "insta360/canvas360/canvas360": { "id": "insta360/canvas360/canvas360", "name": "Canvas360", "maker": "insta360", "family": "insta360/canvas360", "status": "announced", "confidence": "high", "weights": "closed", "license": "MIT", "release_date": "2026-07-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA on FLUX.1-dev. Two stages: geometry-aware pretraining with parallel RGB and depth tokens, then one in-context model for style transfer, inpainting, outpainting, and editing. Depth maps in the paper are produced with DAP.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Canvas360 is Insta360 Research's named model for in-context panoramic generation. The paper appeared on 2026-07-09 (revised 2026-07-12). On 2026-10-15 the GitHub README still said weights and the online demo were coming soon, so the status here is announced. Code is public. This is not a hosted FLUX copy. It is their LoRA method, without a released adapter yet.", "what_it_is_for": "The paper describes two stages on FLUX.1-dev. Pretraining generates RGB and depth together, using DAP depth maps, velocity circular padding, and a similarity loss. Fine-tuning drops depth and trains one model for style transfer, inpainting, outpainting, and editing by concatenating context tokens. Canvas360Dataset is stated as 1M paired panoramas: 100K RGB-depth and 900K task pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "insta360/dap/dap": { "id": "insta360/dap/dap", "name": "Depth Any Panoramas", "maker": "insta360", "family": "insta360/dap", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-12-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DINOv3-Large encoder, distortion-aware metric depth decoder, and a plug-in range-mask head. Training resolution 512x1024.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Depth Any Panoramas (DAP) is Insta360 Research's panoramic metric-depth model. The paper calls it a foundation model. Weights were on Hugging Face from 2025-12-16 under CC BY-NC 4.0 and were still public on 2026-10-15. It is a research release, not a camera API.", "what_it_is_for": "The paper says DAP predicts absolute metric depth for indoor and outdoor panoramas without a scale-alignment step at test time. The backbone is DINOv3-Large, with a depth head and a range-mask head. Training uses a three-stage pseudo-label pipeline and a stated 2M-panorama mix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "insta360/dit360/dit360": { "id": "insta360/dit360/dit360", "name": "DiT360", "maker": "insta360", "family": "insta360/dit360", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA fine-tune of black-forest-labs/FLUX.1-dev. Diffusion Transformer with circular padding, yaw loss, and cube loss. Released files are adapter_config.json and adapter_model.safetensors, not a full base checkpoint.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiT360 is Insta360 Research's panoramic image model. It is a named LoRA on FLUX.1-dev, not a full retrain and not a hosted copy of FLUX. The adapter was on Hugging Face from 2025-10-09, the paper is 2025-10-13, and the repo was still public on 2026-10-15. The card license is MIT.", "what_it_is_for": "The paper says DiT360 generates panoramas by hybrid training on perspective photos and panoramic images. Image-level steps reproject perspective shots and clean polar blur. Token-level losses add circular padding, yaw rotation consistency, and cube-face supervision. The same checkpoint is described as supporting inpainting and outpainting without a separate fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "insta360/panomind/panomind": { "id": "insta360/panomind/panomind", "name": "Insta360 PanoMind", "maker": "insta360", "family": "insta360/panomind", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Insta360 PanoMind is the company's closed in-house multimodal model for 360 video. It shipped in the X6 camera on 2026-08-12 and was still the named model behind that camera's automatic editing on the collection date, 2026-10-15. Weights are not public.", "what_it_is_for": "Insta360 says PanoMind understands spherical footage well enough to pick highlight moments, invent virtual camera moves through the 360 frame, and order those moments into an edit. AI Director and Auto Edit 2.0 are interfaces on this model. The official X account says Moments Pro, the cloud style tool, is powered by Google Gemini. The editing blog instead calls Moments Pro another interface on PanoMind. Both statements are stored; they are not the same claim.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "insta360/panoworld/panoworld": { "id": "insta360/panoworld/panoworld", "name": "PanoWorld", "maker": "insta360", "family": "insta360/panoworld", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA and action modules on Wan-AI/Wan2.2-TI2V-5B, plus Dense Panoramic Ray-Conditioning and Geometry-aware Memory Augmentation. README training presets: 480p LoRA rank 64 at 960x480; 720p LoRA rank 256, train 1408x704, inference 1440x720.", "modalities": { "input": [ "image", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PanoWorld is Insta360 Research's panoramic world model for generating video along a camera path. The paper and the Hugging Face repo are both dated 2026-07-10. Checkpoints for 480p were public on 2026-10-15. It is a named adapter stack on Wan2.2-TI2V-5B, not a rehost of that base model.", "what_it_is_for": "The paper treats rotation of a panorama as a geometric stand-in so that trajectories can be modeled as translation. The two named pieces are Dense Panoramic Ray-Conditioning and Geometry-aware Memory Augmentation. Training is described in three stages: video LoRA, action model, then memory. The public README still marks the memory stage and training code as not yet released.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "insta360/unisharp/unisharp": { "id": "insta360/unisharp/unisharp", "name": "UniSHARP", "maker": "insta360", "family": "insta360/unisharp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-05-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Feed-forward monocular novel-view model. Ray-distance 3D Gaussians decoded from UniK3D-style features. Extends SHARP beyond pinhole cameras to wide-FoV, fisheye, and panoramas. Released file name is pretained_model.pt.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UniSHARP is Insta360 Research's monocular novel-view model. It takes one image from a perspective, wide-angle, fisheye, or panoramic camera, predicts 3D Gaussians, and renders a nearby view. Weights were on Hugging Face from 2026-05-26 under CC BY-NC 4.0. The paper is 2026-06-05. The checkpoint was still public on 2026-10-15.", "what_it_is_for": "The paper extends SHARP, a pinhole view-synthesis method, to other camera models by putting features and Gaussians in a shared ray-distance space. Encoders follow UniK3D. It is a generative renderer for new viewpoints, not a language model and not a general image API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inworld/stt/inworld-stt-1": { "id": "inworld/stt/inworld-stt-1", "name": "Realtime STT-1", "maker": "inworld", "family": "inworld/stt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": null }, "summary_intro": "Inworld Realtime STT-1 (`inworld/inworld-stt-1`) is a first-party speech model. It is active as of 2026-10-15.", "what_it_is_for": "Inworld's first-party speech-to-text model for live agents: a transcript plus optional voice traits and turn detection, over HTTP or WebSocket.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inworld/tts/inworld-tts-1": { "id": "inworld/tts/inworld-tts-1", "name": "Inworld TTS-1", "maker": "inworld", "family": "inworld/tts", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-15", "context_length": null, "max_output_tokens": null, "parameters": "1.6B", "architecture": "Autoregressive speech language model. SpeechLM backbone is LLaMA-3.2-1B with the vocabulary expanded for a 65,536-code audio vocabulary. The audio codec follows X-codec2 (one codebook, 50 tokens per second). A separate decoder renders up to 48 kHz. Total inference size, codec included, is stated as 1.6B. Training was pre-training, supervised fine-tuning, then GRPO using WER, speaker similarity, and DNSMOS rewards. Training and modeling code is MIT-licensed; trained weights were not released.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Inworld Inworld TTS-1 (`inworld-tts-1`) is a first-party speech model. It is retired as of 2026-10-15.", "what_it_is_for": "Inworld launched it as the realtime, lower-cost text-to-speech model: context-aware speech, zero-shot voice cloning, and audio markups, aimed at live consumer apps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inworld/tts/inworld-tts-1-5-max": { "id": "inworld/tts/inworld-tts-1-5-max", "name": "Inworld TTS-1.5 Max", "maker": "inworld", "family": "inworld/tts", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": null }, "summary_intro": "Inworld Inworld TTS-1.5 Max (`inworld-tts-1.5-max`) is a first-party speech model. It is deprecated and not recommended for new projects as of 2026-10-15.", "what_it_is_for": "Inworld's January 2026 flagship for the 1.5 generation: the quality and speed balance point for production voice, later superseded in the docs by Realtime TTS-2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inworld/tts/inworld-tts-1-5-mini": { "id": "inworld/tts/inworld-tts-1-5-mini", "name": "Inworld TTS-1.5 Mini", "maker": "inworld", "family": "inworld/tts", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Inworld Inworld TTS-1.5 Mini (`inworld-tts-1.5-mini`) is a first-party speech model. It is deprecated and not recommended for new projects as of 2026-10-15.", "what_it_is_for": "The latency-first 1.5 model, for realtime agents where time-to-first-audio outranked the Max checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inworld/tts/inworld-tts-1-max": { "id": "inworld/tts/inworld-tts-1-max", "name": "Inworld TTS-1 Max", "maker": "inworld", "family": "inworld/tts", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-15", "context_length": null, "max_output_tokens": null, "parameters": "8.8B", "architecture": "Same autoregressive TTS stack as TTS-1, with a LLaMA-3.1-8B SpeechLM instead of LLaMA-3.2-1B. Shared X-codec2-style encoder and 48 kHz decoder. Total inference size stated as 8.8B. Launched as experimental. Training code is MIT-licensed; weights were not released.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": null }, "summary_intro": "Inworld Inworld TTS-1 Max (`inworld-tts-1-max`) is a first-party speech model. It is retired as of 2026-10-15.", "what_it_is_for": "Inworld launched it the same day as the larger, more expressive experimental sibling of TTS-1, for cases where quality mattered more than the smaller model's cost and speed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inworld/tts/inworld-tts-2": { "id": "inworld/tts/inworld-tts-2", "name": "Realtime TTS-2", "maker": "inworld", "family": "inworld/tts", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 25.0, "completion_per_m_usd": null }, "summary_intro": "Inworld Realtime TTS-2 (`inworld-tts-2`) is a first-party speech model. It is active as of 2026-10-15.", "what_it_is_for": "Current production text-to-speech model. Inworld says it is the one to use when quality and natural-language steering matter, including live agents and narration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inworld/tts/inworld-tts-2-flash": { "id": "inworld/tts/inworld-tts-2-flash", "name": "Realtime TTS-2 Flash", "maker": "inworld", "family": "inworld/tts", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 15.0, "completion_per_m_usd": null }, "summary_intro": "Inworld Realtime TTS-2 Flash (`inworld-tts-2-flash`) is a first-party speech model. It is active as of 2026-10-15.", "what_it_is_for": "Current low-latency, low-cost text-to-speech model for high-volume and latency-critical agents. It does not take written steering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "isi/embeddings/bengali-word-vectors": { "id": "isi/embeddings/bengali-word-vectors", "name": "Bengali Word Vectors", "maker": "isi", "family": "isi/embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic research use with citation; commercial use requires an ISI technology-transfer license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "word2vec continuous bag-of-words with negative sampling, 200 dimensions. Trained on the FIRE Bengali news corpus.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bengali Word Vectors are 200-dimensional word2vec embeddings released by the Computer Vision and Pattern Recognition Unit at the Indian Statistical Institute, Kolkata. The faculty resources page still serves the archive, so this dossier treats the release as active on 2026-10-15. They are a resource for the unit's Bengali lemmatization work, not a hosted API.", "what_it_is_for": "The ACL 2017 lemmatization paper trains distributional vectors so a context-sensitive lemmatizer can use semantic similarity. The resources page points readers who use the vectors at that paper. The download is `Bengali.rar`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "isi/parser/bengali-dependency-parser": { "id": "isi/parser/bengali-dependency-parser", "name": "Bengali Dependency Parser", "maker": "isi", "family": "isi/parser", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic research use with citation; commercial use requires an ISI technology-transfer license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MaltParser stack model, file bengali_stack.mco. The readme specifies MaltParser 1.7.1 in parse mode. Input rows must already contain a POS tag and a chunk tag.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Bengali Dependency Parser is a MaltParser stack model released by the Computer Vision and Pattern Recognition Unit at the Indian Statistical Institute, Kolkata. The model file was still served on 2026-10-15, so this dossier treats it as active. It is a local parser, not an API.", "what_it_is_for": "The readme calls it a Bangla dependency parser built with MaltParser. The resources page says development was partly supported by the Society for Natural Language Technology Research and links a PDF of the dependency tag labels. It does not state a product use beyond parsing Bengali sentences that already have POS and chunk columns.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "isi/pos/bengali-pos-tagger": { "id": "isi/pos/bengali-pos-tagger", "name": "Bengali POS Tagger", "maker": "isi", "family": "isi/pos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic research use with citation; commercial use requires an ISI technology-transfer license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Stanford maximum-entropy POS tagger (MaxentTagger). The released file is bengaliModelFile.tagger.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Bengali POS Tagger is a Stanford maximum-entropy part-of-speech model released by the Computer Vision and Pattern Recognition Unit at the Indian Statistical Institute, Kolkata. The model file was still served on 2026-10-15, so this dossier treats it as active. It is a local tagger, not an API.", "what_it_is_for": "The readme says it tags Bengali sentences. The faculty page says the unit adapted the Stanford POS tagger for Bengali and that it gives about 92% accuracy. The FIRE 2013 Bengali NER system description already points readers to this resources page for a Bengali POS tagger, and that system says it trained a Stanford tagger.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "isi/transliteration/e2b-transliteration": { "id": "isi/transliteration/e2b-transliteration", "name": "E2B Transliteration", "maker": "isi", "family": "isi/transliteration", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic research use with citation; commercial use requires an ISI technology-transfer license. The decoder stack is Joshua, reconfigured from Irvine et al.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Joshua statistical machine translation, reconfigured from Irvine et al.'s miniscale transliteration setup. The release includes an SRILM Bengali language model (uniq.bengali.lm) and a list of 6000 English-Bengali proper-name pairs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E2B Transliteration is an English-to-Bengali name transliteration system released by the Computer Vision and Pattern Recognition Unit at the Indian Statistical Institute, Kolkata. The archive and the Bengali language-model file were still served on 2026-10-15, so this dossier treats the release as active. It is a local statistical system, not an API.", "what_it_is_for": "The resources page says the unit reconfigured the Joshua statistical machine translation system, following Irvine et al., for English-to-Bengali transliteration. As part of that system it released 6000 English-Bengali proper-name pairs and a Bengali language model. The readme decodes a list of English strings to Bengali.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jadavpur/rectinet/rectinet": { "id": "jadavpur/rectinet/rectinet", "name": "RectiNet", "maker": "jadavpur", "family": "jadavpur/rectinet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2020-07-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Gated and bifurcated stacked U-Net. A 256x256x3 document image goes through a primary U-Net with a gated convolutional branch for edges, then a secondary U-Net whose bottleneck is split across two independent decoders. The network emits a 256x256x2 dense unwarp grid and an edge map. The grid is interpolated to the original image resolution.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RectiNet is a document-image dewarping network from the Deep Learning in Vision and Language Processing group at the CMATER laboratory, Jadavpur University. The authors released code and a pretrained checkpoint for the July 2020 paper. The Drive file was still titled `weights.pt` on 2026-10-15, so this dossier treats the model as active. It is a local vision model, not an API.", "what_it_is_for": "The paper and the repository describe a supervised model that predicts a dense unwarp grid for a photographed document and then rebuilds a flatter image. The stated job is removal of folds, curves, and perspective distortion so later document recognition can read the page. Training images are synthetic. Reported scores are on real DocUNet photographs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jagiellonian/gepar3d/gepar3d": { "id": "jagiellonian/gepar3d/gepar3d", "name": "GEPAR3D", "maker": "jagiellonian", "family": "jagiellonian/gepar3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-07-07", "context_length": null, "max_output_tokens": null, "parameters": "70860000", "architecture": "Dual-decoder 3D encoder-decoder for CBCT tooth segmentation. The segmentation decoder covers 32 Universal Numbering System classes plus background, with Geometric Wasserstein Dice Loss from an SSM inter-tooth distance prior plus inverse-class-frequency weighted cross-entropy. The instance decoder predicts a 3D watershed energy map and a 3-channel direction field. Class is assigned by majority vote. The load snippet sets backbone_name resnet34, spatial_dims 3, in_channels 1, act relu, norm instance, bias false. The card says the model was trained from scratch. A separate 4-stage 3D MONAI U-Net does coarse binary tooth ROI and is required for the full pipeline. Main-model training uses 128 cubed patches. The coarse U-Net uses 256 cubed patches. Input volumes are RAS, isotropic 0.4 mm, HU clipped to [0, 5000], normalized to [0, 1]. Optimizer AdamW, learning rate 1e-3, weight decay 1e-4, cosine annealing, 1000 epochs, batch size 2, fp32, one A100, about 48 hours. Loss weights are 10 for the energy term, 0.1 for segmentation, and 1e-6 for direction. Inference overlap in the paper is 0.6. Card inference is about 3.5 seconds and about 22 GB on an A100, or about 8 minutes and about 22 GB RAM on a Xeon Gold 5220R. Card carbon figure is about 5.18 kg CO2eq. Software on the card: Python 3.9, PyTorch 1.13.1, MONAI 1.3.0. Hub files gepar3d.safetensors and coarse_unet.safetensors. Repo usedStorage 291359704 bytes. The card says the main checkpoint is 284 MB and the main model is 70.86M parameters.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GEPAR3D is a 3D CBCT tooth-segmentation model released on 7 July 2026 at https://huggingface.co/tomek1911/GEPAR3D. Sano Centre for Computational Medicine is the corresponding institution. Szymon P\u0142otka is affiliated with Sano and Jagiellonian University, and Arleta Adamowicz and Piotr Fudalej are at Jagiellonian University Medical College. The card also lists Warsaw University of Technology, IDEAS, and Massachusetts General Hospital / Harvard Medical School, plus further affiliations that the paper's author line does not spell out person by person. The arXiv preprint is 31 July 2025. MICCAI 2025 accepted the paper. Status active. Collected 2026-10-15.", "what_it_is_for": "The card describes research segmentation of adult permanent teeth in CBCT, with emphasis on root apices for later root-resorption research. The model does not itself diagnose resorption. The card says it is not a certified medical device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jagiellonian/mat/mat-masking-200k": { "id": "jagiellonian/mat/mat-masking-200k", "name": "MAT masking 200k", "maker": "jagiellonian", "family": "jagiellonian/mat", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "42000000", "architecture": "Molecule Attention Transformer. The live HuggingMolecules config JSON is one file for mat_masking_200k, mat_masking_2M, and mat_masking_20M: d_atom 36, d_model 1024, 8 encoder layers, 16 heads, distance kernel exp, lambda_attention 0.33, lambda_distance 0.33, LeakyReLU feed-forward with ffn_n_layers 1 and ffn_d_hidden 0, dropout 0.0, mean-pooling generator with 1 output. Attention also mixes the adjacency matrix; the code sets its weight to 1 minus the two lambdas. The 200k name is the masking pretraining scale, not a parameter count. The R-MAT paper and the HuggingMolecules student abstract call this architecture 42M parameters.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAT masking 200k is a named Molecule Attention Transformer checkpoint from the Group of Machine Learning Research (GMUM) at Jagiellonian University, loaded as `mat_masking_200k` in HuggingMolecules. The original MAT paper is joint with Ardigen, Molecule.one, and New York University. The 200k name is a masking-pretraining scale, not a parameter count. The weights were still linked from the library on the collection date. Status active. Collected 2026-10-15.", "what_it_is_for": "The library is for molecular property prediction. `MatModel.from_pretrained('mat_masking_200k')` loads this scale. The README's own benchmark treats MAT 200k, MAT 2M, and MAT 20M as separate pretrained names.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jagiellonian/mat/mat-masking-20m": { "id": "jagiellonian/mat/mat-masking-20m", "name": "MAT masking 20M", "maker": "jagiellonian", "family": "jagiellonian/mat", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "42000000", "architecture": "Molecule Attention Transformer, same released config JSON as mat_masking_200k and mat_masking_2M: d_atom 36, d_model 1024, 8 encoder layers, 16 heads, distance kernel exp, lambda_attention 0.33, lambda_distance 0.33, LeakyReLU, ffn_n_layers 1, ffn_d_hidden 0, dropout 0.0, mean-pooling generator with 1 output. The 20M name is the masking pretraining scale. Weight file id 1A6RSrCrUTXE37roud4Zf05Zrtpgq9kvr. The architecture is the one published as 42M parameters.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAT masking 20M is the largest named Molecule Attention Transformer masking checkpoint in GMUM's HuggingMolecules library at Jagiellonian University. The library loads it as `mat_masking_20M` and uses it in the README quick tour. It is the same architecture as the 200k and 2M files, pretrained at a larger scale than the 2 million molecules in the 2020 paper. Status active. Collected 2026-10-15.", "what_it_is_for": "Molecular property prediction. The README example fine-tunes this checkpoint on FreeSolv. In the library's own rank table it is the best of the three MAT scales.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jagiellonian/mat/mat-masking-2m": { "id": "jagiellonian/mat/mat-masking-2m", "name": "MAT masking 2M", "maker": "jagiellonian", "family": "jagiellonian/mat", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2020-02-19", "context_length": null, "max_output_tokens": null, "parameters": "42000000", "architecture": "Molecule Attention Transformer at the paper's pretrained size. The 19 February 2020 paper trains on 2 million ZINC15 molecules for 8 epochs and lists model dim 1024, 8 layers, 16 heads, lambda_attention 0.33, lambda_distance 0.33, distance kernel exp, and dropout 0. The live HuggingMolecules JSON for mat_masking_2M matches that shape and is the same file used by the 200k and 20M names: d_atom 36, d_model 1024, 8 layers, 16 heads, LeakyReLU, ffn_n_layers 1, ffn_d_hidden 0, mean-pooling generator with 1 output. Weight file id 1qUyyqKHJZLIrLDSVcoLHo8X5P5dBzf8w. The R-MAT paper calls MAT 42M parameters. This Drive file was not byte-compared with the 2020 github.com/gmum/MAT upload.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAT masking 2M is the 2-million-molecule Molecule Attention Transformer pretraining scale. The 19 February 2020 paper, from Jagiellonian University with Ardigen, Molecule.one, and New York University, says the pretrained weights are shared and that the training set is 2 million molecules from ZINC15. GMUM's HuggingMolecules library still exposes that scale as `mat_masking_2M`. Status active. Collected 2026-10-15.", "what_it_is_for": "The paper presents MAT as one architecture for many molecular property tasks. Self-supervised pretraining is meant to cut the hyperparameter search down to the learning rate. The library checkpoint is the 2M masking scale in that line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jagiellonian/rmat/rmat-4m": { "id": "jagiellonian/rmat/rmat-4m", "name": "R-MAT 4M", "maker": "jagiellonian", "family": "jagiellonian/rmat", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2021-10-12", "context_length": null, "max_output_tokens": null, "parameters": "48000000", "architecture": "Relative Molecule Attention Transformer. HuggingMolecules exposes rmat_4M and rmat_4M_rdkit. Both names use weight file id 1djmwdYvba3OjBXu_seYe3R-ko8QSkRmV. The rmat_4M config downloaded on 2026-10-15 (Drive id 1VBou9SzOnLPAC6NdAKB1nYVZ23ninvMY) has d_atom 37, d_edge 46, d_model 768, dropout 0.0, init uniform, envelope_num_radial 32, envelope_cutoff 20.0, envelope_exponent 5.0, 10 encoder layers, 12 heads, LeakyReLU, ffn_n_layers 2, ffn_d_hidden 1536, ffn_d_output 768, generator_aggregation grover, generator_n_layers 1, generator_d_outputs 1, generator_features_generators null, generator_d_generated_features 0. The 12 October 2021 paper says the atom embedding is N_atom by 36, the model has 10 layers, 12 heads, d_model 768, 32 radial functions, cutoff 20 angstroms, attention pooling with 4 heads and hidden size 128, a prediction MLP with one hidden layer of size 1024 and dropout 0.1, leaky-ReLU slope 0.1, and 48 million parameters. The paper's distance envelope uses p=6. Pretraining in that appendix uses 4 million molecules drawn from a 10 million ZINC15 and ChEMBL sample, Lipinski filtering, a 5 percent validation split, 150 epochs, dropout 0.1, learning rate 0.001, Noam with 20000 warmup steps, batch size 256, and 8 NVIDIA A100 GPUs. 3D distances come from RDKit UFF. The rdkit library name is a version of this folder, not a second weight file.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "R-MAT 4M is the Relative Molecule Attention Transformer checkpoint that HuggingMolecules ships as `rmat_4M`. The 12 October 2021 paper is from Jagiellonian University with NVIDIA, Ardigen, and Molecule.one, and it says the weights and code are open-sourced in HuggingMolecules. The library says those weights were obtained by joint efforts with Nvidia. The same weight file also serves `rmat_4M_rdkit`. Journal version: Journal of Cheminformatics, 3 January 2024. Status active. Collected 2026-10-15.", "what_it_is_for": "The paper presents R-MAT for molecular property prediction after self-supervised pretraining. Fine-tuning in the main table changes only the learning rate. The library name `rmat_4M` is the 4-million-molecule pretraining scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jena/acemol/acemol": { "id": "jena/acemol/acemol", "name": "ACE-Mol", "maker": "jena", "family": "jena/acemol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-02", "context_length": 1024, "max_output_tokens": null, "parameters": "150098176", "architecture": "ModernBERT masked-language encoder (ModernBertForMaskedLM). config.json: 22 layers, hidden 768, intermediate 1152, 12 heads, GELU, local attention window 128, global attention every 3 layers, vocab 50944, max_position_embeddings 1024, float16. The paper calls this a 150M-parameter model. Hub safetensors total is 150098176. Input text is task description, property-value tokens, and SMILES, separated by [SEP].", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ACE-Mol (Adaptive Chemical Embedding Model) is a task-conditioned molecular encoder released by the Jablonka lab at Friedrich Schiller University Jena and HIPOLE Jena. It is an open-weight ModernBERT model that embeds SMILES together with a natural-language task description. Status on 2026-10-15 is active. The Hub repository was created on 2026-02-02. The paper is arXiv 2602.04696, dated 2026-02-04.", "what_it_is_for": "The paper says molecular representations should depend on the task. ACE-Mol is pretrained with weak supervision on programmatic motifs, such as functional-group presence and counts, paired with templated English descriptions. The card says to use it as an embedding model for classification or regression, or to fine-tune it. The lab helper is `PretrainedACEMol` in https://github.com/lamalab-org/ACE-Mol.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jena/acemol/acemol-light": { "id": "jena/acemol/acemol-light", "name": "ACE-Mol-light", "maker": "jena", "family": "jena/acemol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-02", "context_length": 1024, "max_output_tokens": null, "parameters": "150098176", "architecture": "ModernBERT masked-language encoder (ModernBertForMaskedLM). Its config.json matches jablonkagroup/ACEMol: 22 layers, hidden 768, intermediate 1152, 12 heads, GELU, local attention window 128, global attention every 3 layers, vocab 50944, max_position_embeddings 1024, float16. Hub safetensors total is 150098176, the same count as ACE-Mol. The card calls it a lighter pretrained version, but the published config and parameter count do not show a smaller network.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ACE-Mol-light is the second named ACE-Mol checkpoint from the Jablonka lab at Friedrich Schiller University Jena. The model card calls it a lighter pretrained ACE-Mol and the default model for the lab's helper. On 2026-10-15 the Hub weights are public and the license tag is MIT. The repository was created on 2026-02-02.", "what_it_is_for": "The card gives ACE-Mol-light the same job as ACE-Mol: embed molecules conditioned on a natural-language task, or fine-tune that embedding. It says this checkpoint is the one the API loads by default. The printed Python example still passes `jablonkagroup/ACEMol` to `PretrainedACEMol`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jena/chemfuse/chemfuse": { "id": "jena/chemfuse/chemfuse", "name": "ChemFuse", "maker": "jena", "family": "jena/chemfuse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-03-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Contrastive alignment model. InfoNCE pulls matched chemical identifiers together and pushes unmatched ones apart, then a linear projection maps each encoder into one shared space. Polymers: MolFormer encoders for PSMILES and BigSMILES, with extra polymer tokens, and the encoder of knowledgator/IUPAC2SMILES-canonical-base for systematic names. PSMILES is the anchor. Organic molecules: SMILES is the anchor for experimental 1H NMR and IR; the preprint says the SMILES backbone is frozen and its linear layer is removed, using the package from Mirza et al. MOFs: crystal structure is the anchor for MOFid and simulated pXRD. The preprint reports 108.65 million parameters for polymers, 141.37 million for organic molecules, and 145.16 million for MOFs. Those three counts are not printed on the individual Hub files.", "modalities": { "input": [ "text", "spectra", "structure" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChemFuse is a contrastive embedding model from the Jablonka lab at Friedrich Schiller University Jena. It aligns different chemical identifiers for the same polymer, molecule, or metal-organic framework into one vector space. The Hub repository `jablonkagroup/ChemFuse` was created on 2026-03-20 and is still public. The license tag is MIT. The describing preprint is ChemRxiv 10.26434/chemrxiv.15006406, with a PDF header date of 21 July 2026. Collection date is 2026-10-15.", "what_it_is_for": "The preprint says chemists describe the same material in incompatible formats, so ordinary models cannot train on all of the data at once. ChemFuse uses an InfoNCE loss so matched identifiers land near each other. Downstream property models then train on the aligned embeddings. The card's polymer section is for cross-identifier retrieval and property prediction across PSMILES, BigSMILES, and systematic names.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/bernice/bernice": { "id": "jhu/bernice/bernice", "name": "Bernice", "maker": "jhu", "family": "jhu/bernice", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-10-06", "context_length": 128, "max_output_tokens": null, "parameters": "270M", "architecture": "Multilingual RoBERTa encoder trained from scratch on tweets. The EMNLP paper specifies L=12, H=768, A=12, about 270M parameters, and a max sequence length of 128. Hugging Face lists the checkpoint as 0.3B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released Bernice, an open-weight multilingual Twitter encoder, with Hub weights dated 2022-10-06 and an EMNLP 2022 paper. It is an active MIT-licensed masked encoder for social-media text, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP released Bernice, an open-weight multilingual Twitter encoder, with Hub weights dated 2022-10-06 and an EMNLP 2022 paper. It is an active MIT-licensed masked encoder for social-media text, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/bibert/bibert-ende": { "id": "jhu/bibert/bibert-ende", "name": "BiBERT English-German", "maker": "jhu", "family": "jhu/bibert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "0.2B", "architecture": "Bilingual English-German encoder used as contextualized input to neural machine translation. Hugging Face lists 0.2B parameters and a fill-mask pipeline. Layer counts were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP hosts BiBERT, a bilingual English-German encoder from an EMNLP 2021 paper, with Hub files dated 2022-03-02. It is an active open-weight fill-mask model whose card reports NMT BLEU when the embeddings are used in a translation system, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP hosts BiBERT, a bilingual English-German encoder from an EMNLP 2021 paper, with Hub files dated 2022-03-02. It is an active open-weight fill-mask model whose card reports NMT BLEU when the embeddings are used in a translation system, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/ettin/ettin-decoder-150m": { "id": "jhu/ettin/ettin-decoder-150m", "name": "Ettin decoder 150M", "maker": "jhu", "family": "jhu/ettin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-15", "context_length": 8192, "max_output_tokens": null, "parameters": "150M", "architecture": "Ettin Base decoder. 22 layers, hidden size 768, intermediate size 1152, 12 heads. Same shape as its pair; attention and objective differ (causal language modeling). RoPE, GLU, prenorm, GELU, ModernBERT tokenizer, vocab 50368. Card describes pre-training 1.7T tokens, mid-training 250B tokens with context extension, and a 100B-token decay.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP and coauthors released Ettin decoder 150M (Base) on 2025-07-15 as one of six open-weight decoder sizes in the Ettin suite. It is an active MIT-licensed English decoder trained with the same recipe as its pair, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP and coauthors released Ettin decoder 150M (Base) on 2025-07-15 as one of six open-weight decoder sizes in the Ettin suite. It is an active MIT-licensed English decoder trained with the same recipe as its pair, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/ettin/ettin-decoder-17m": { "id": "jhu/ettin/ettin-decoder-17m", "name": "Ettin decoder 17M", "maker": "jhu", "family": "jhu/ettin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-15", "context_length": 8192, "max_output_tokens": null, "parameters": "17M", "architecture": "Ettin XXS decoder. 7 layers, hidden size 256, intermediate size 384, 4 heads. Same shape as its pair; attention and objective differ (causal language modeling). RoPE, GLU, prenorm, GELU, ModernBERT tokenizer, vocab 50368. Card describes pre-training 1.7T tokens, mid-training 250B tokens with context extension, and a 100B-token decay.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP and coauthors released Ettin decoder 17M (XXS) on 2025-07-15 as one of six open-weight decoder sizes in the Ettin suite. It is an active MIT-licensed English decoder trained with the same recipe as its pair, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP and coauthors released Ettin decoder 17M (XXS) on 2025-07-15 as one of six open-weight decoder sizes in the Ettin suite. It is an active MIT-licensed English decoder trained with the same recipe as its pair, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/ettin/ettin-decoder-1b": { "id": "jhu/ettin/ettin-decoder-1b", "name": "Ettin decoder 1B", "maker": "jhu", "family": "jhu/ettin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-15", "context_length": 8192, "max_output_tokens": null, "parameters": "1B", "architecture": "Ettin XL decoder. 28 layers, hidden size 1792, intermediate size 3840, 28 heads. Same shape as its pair; attention and objective differ (causal language modeling). RoPE, GLU, prenorm, GELU, ModernBERT tokenizer, vocab 50368. Card describes pre-training 1.7T tokens, mid-training 250B tokens with context extension, and a 100B-token decay.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP and coauthors released Ettin decoder 1B (XL) on 2025-07-15 as one of six open-weight decoder sizes in the Ettin suite. It is an active MIT-licensed English decoder trained with the same recipe as its pair, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP and coauthors released Ettin decoder 1B (XL) on 2025-07-15 as one of six open-weight decoder sizes in the Ettin suite. It is an active MIT-licensed English decoder trained with the same recipe as its pair, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/ettin/ettin-decoder-32m": { "id": "jhu/ettin/ettin-decoder-32m", "name": "Ettin decoder 32M", "maker": "jhu", "family": "jhu/ettin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-15", "context_length": 8192, "max_output_tokens": null, "parameters": "32M", "architecture": "Ettin XS decoder. 10 layers, hidden size 384, intermediate size 576, 6 heads. Same shape as its pair; attention and objective differ (causal language modeling). RoPE, GLU, prenorm, GELU, ModernBERT tokenizer, vocab 50368. Card describes pre-training 1.7T tokens, mid-training 250B tokens with context extension, and a 100B-token decay.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP and coauthors released Ettin decoder 32M (XS) on 2025-07-15 as one of six open-weight decoder sizes in the Ettin suite. It is an active MIT-licensed English decoder trained with the same recipe as its pair, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP and coauthors released Ettin decoder 32M (XS) on 2025-07-15 as one of six open-weight decoder sizes in the Ettin suite. It is an active MIT-licensed English decoder trained with the same recipe as its pair, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/ettin/ettin-decoder-400m": { "id": "jhu/ettin/ettin-decoder-400m", "name": "Ettin decoder 400M", "maker": "jhu", "family": "jhu/ettin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-15", "context_length": 8192, "max_output_tokens": null, "parameters": "400M", "architecture": "Ettin Large decoder. 28 layers, hidden size 1024, intermediate size 2624, 16 heads. Same shape as its pair; attention and objective differ (causal language modeling). RoPE, GLU, prenorm, GELU, ModernBERT tokenizer, vocab 50368. Card describes pre-training 1.7T tokens, mid-training 250B tokens with context extension, and a 100B-token decay.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP and coauthors released Ettin decoder 400M (Large) on 2025-07-15 as one of six open-weight decoder sizes in the Ettin suite. It is an active MIT-licensed English decoder trained with the same recipe as its pair, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP and coauthors released Ettin decoder 400M (Large) on 2025-07-15 as one of six open-weight decoder sizes in the Ettin suite. It is an active MIT-licensed English decoder trained with the same recipe as its pair, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/ettin/ettin-decoder-68m": { "id": "jhu/ettin/ettin-decoder-68m", "name": "Ettin decoder 68M", "maker": "jhu", "family": "jhu/ettin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-15", "context_length": 8192, "max_output_tokens": null, "parameters": "68M", "architecture": "Ettin Small decoder. 19 layers, hidden size 512, intermediate size 768, 8 heads. Same shape as its pair; attention and objective differ (causal language modeling). RoPE, GLU, prenorm, GELU, ModernBERT tokenizer, vocab 50368. Card describes pre-training 1.7T tokens, mid-training 250B tokens with context extension, and a 100B-token decay.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP and coauthors released Ettin decoder 68M (Small) on 2025-07-15 as one of six open-weight decoder sizes in the Ettin suite. It is an active MIT-licensed English decoder trained with the same recipe as its pair, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP and coauthors released Ettin decoder 68M (Small) on 2025-07-15 as one of six open-weight decoder sizes in the Ettin suite. It is an active MIT-licensed English decoder trained with the same recipe as its pair, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/ettin/ettin-encoder-150m": { "id": "jhu/ettin/ettin-encoder-150m", "name": "Ettin encoder 150M", "maker": "jhu", "family": "jhu/ettin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-15", "context_length": 8192, "max_output_tokens": null, "parameters": "150M", "architecture": "Ettin Base encoder. 22 layers, hidden size 768, intermediate size 1152, 12 heads. Same shape as its pair; attention and objective differ (bidirectional masked language modeling). RoPE, GLU, prenorm, GELU, ModernBERT tokenizer, vocab 50368. Card describes pre-training 1.7T tokens, mid-training 250B tokens with context extension, and a 100B-token decay.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP and coauthors released Ettin encoder 150M (Base) on 2025-07-15 as one of six open-weight encoder sizes in the Ettin suite. It is an active MIT-licensed English encoder trained with the same recipe as its pair, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP and coauthors released Ettin encoder 150M (Base) on 2025-07-15 as one of six open-weight encoder sizes in the Ettin suite. It is an active MIT-licensed English encoder trained with the same recipe as its pair, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/ettin/ettin-encoder-17m": { "id": "jhu/ettin/ettin-encoder-17m", "name": "Ettin encoder 17M", "maker": "jhu", "family": "jhu/ettin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-15", "context_length": 8192, "max_output_tokens": null, "parameters": "17M", "architecture": "Ettin XXS encoder. 7 layers, hidden size 256, intermediate size 384, 4 heads. Same shape as its pair; attention and objective differ (bidirectional masked language modeling). RoPE, GLU, prenorm, GELU, ModernBERT tokenizer, vocab 50368. Card describes pre-training 1.7T tokens, mid-training 250B tokens with context extension, and a 100B-token decay.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP and coauthors released Ettin encoder 17M (XXS) on 2025-07-15 as one of six open-weight encoder sizes in the Ettin suite. It is an active MIT-licensed English encoder trained with the same recipe as its pair, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP and coauthors released Ettin encoder 17M (XXS) on 2025-07-15 as one of six open-weight encoder sizes in the Ettin suite. It is an active MIT-licensed English encoder trained with the same recipe as its pair, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/ettin/ettin-encoder-1b": { "id": "jhu/ettin/ettin-encoder-1b", "name": "Ettin encoder 1B", "maker": "jhu", "family": "jhu/ettin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-15", "context_length": 8192, "max_output_tokens": null, "parameters": "1B", "architecture": "Ettin XL encoder. 28 layers, hidden size 1792, intermediate size 3840, 28 heads. Same shape as its pair; attention and objective differ (bidirectional masked language modeling). RoPE, GLU, prenorm, GELU, ModernBERT tokenizer, vocab 50368. Card describes pre-training 1.7T tokens, mid-training 250B tokens with context extension, and a 100B-token decay.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP and coauthors released Ettin encoder 1B (XL) on 2025-07-15 as one of six open-weight encoder sizes in the Ettin suite. It is an active MIT-licensed English encoder trained with the same recipe as its pair, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP and coauthors released Ettin encoder 1B (XL) on 2025-07-15 as one of six open-weight encoder sizes in the Ettin suite. It is an active MIT-licensed English encoder trained with the same recipe as its pair, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/ettin/ettin-encoder-32m": { "id": "jhu/ettin/ettin-encoder-32m", "name": "Ettin encoder 32M", "maker": "jhu", "family": "jhu/ettin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-15", "context_length": 8192, "max_output_tokens": null, "parameters": "32M", "architecture": "Ettin XS encoder. 10 layers, hidden size 384, intermediate size 576, 6 heads. Same shape as its pair; attention and objective differ (bidirectional masked language modeling). RoPE, GLU, prenorm, GELU, ModernBERT tokenizer, vocab 50368. Card describes pre-training 1.7T tokens, mid-training 250B tokens with context extension, and a 100B-token decay.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP and coauthors released Ettin encoder 32M (XS) on 2025-07-15 as one of six open-weight encoder sizes in the Ettin suite. It is an active MIT-licensed English encoder trained with the same recipe as its pair, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP and coauthors released Ettin encoder 32M (XS) on 2025-07-15 as one of six open-weight encoder sizes in the Ettin suite. It is an active MIT-licensed English encoder trained with the same recipe as its pair, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/ettin/ettin-encoder-400m": { "id": "jhu/ettin/ettin-encoder-400m", "name": "Ettin encoder 400M", "maker": "jhu", "family": "jhu/ettin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-15", "context_length": 8192, "max_output_tokens": null, "parameters": "400M", "architecture": "Ettin Large encoder. 28 layers, hidden size 1024, intermediate size 2624, 16 heads. Same shape as its pair; attention and objective differ (bidirectional masked language modeling). RoPE, GLU, prenorm, GELU, ModernBERT tokenizer, vocab 50368. Card describes pre-training 1.7T tokens, mid-training 250B tokens with context extension, and a 100B-token decay.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP and coauthors released Ettin encoder 400M (Large) on 2025-07-15 as one of six open-weight encoder sizes in the Ettin suite. It is an active MIT-licensed English encoder trained with the same recipe as its pair, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP and coauthors released Ettin encoder 400M (Large) on 2025-07-15 as one of six open-weight encoder sizes in the Ettin suite. It is an active MIT-licensed English encoder trained with the same recipe as its pair, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/ettin/ettin-encoder-68m": { "id": "jhu/ettin/ettin-encoder-68m", "name": "Ettin encoder 68M", "maker": "jhu", "family": "jhu/ettin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-15", "context_length": 8192, "max_output_tokens": null, "parameters": "68M", "architecture": "Ettin Small encoder. 19 layers, hidden size 512, intermediate size 768, 8 heads. Same shape as its pair; attention and objective differ (bidirectional masked language modeling). RoPE, GLU, prenorm, GELU, ModernBERT tokenizer, vocab 50368. Card describes pre-training 1.7T tokens, mid-training 250B tokens with context extension, and a 100B-token decay.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP and coauthors released Ettin encoder 68M (Small) on 2025-07-15 as one of six open-weight encoder sizes in the Ettin suite. It is an active MIT-licensed English encoder trained with the same recipe as its pair, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP and coauthors released Ettin encoder 68M (Small) on 2025-07-15 as one of six open-weight encoder sizes in the Ettin suite. It is an active MIT-licensed English encoder trained with the same recipe as its pair, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/followir/followir-7b": { "id": "jhu/followir/followir-7b", "name": "FollowIR-7B", "maker": "jhu", "family": "jhu/followir", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-17", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "MistralForCausalLM fine-tune of mistralai/Mistral-7B-Instruct-v0.2. Shipped config has hidden size 4096, 32 layers, 32 heads, 8 KV heads, intermediate size 14336, vocab 32000, and max_position_embeddings 32768. The card's training command uses LoRA rank 8 and max_length 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released FollowIR-7B on 2024-03-17 as an open-weight instruction-following reranker. It is an active Apache-2.0 fine-tune of Mistral-7B-Instruct-v0.2 for scoring a query-document pair against a written relevance instruction, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP released FollowIR-7B on 2024-03-17 as an open-weight instruction-following reranker. It is an active Apache-2.0 fine-tune of Mistral-7B-Instruct-v0.2 for scoring a query-document pair against a written relevance instruction, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/kreyol-mt/kreyol-mt": { "id": "jhu/kreyol-mt/kreyol-mt", "name": "Kreyol-MT", "maker": "jhu", "family": "jhu/kreyol-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-05-21", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of facebook/mbart-large-50-many-to-many-mmt. Config opened for the flagship: MBartForConditionalGeneration, d_model 1024, 12 encoder and 12 decoder layers, 16 decoder heads, decoder FFN 4096, max_position_embeddings 1024, generation max_length default 200.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released Kreyol-MT on 2024-05-21 as one of four open-weight Kreyol-MT translation models. It is an active MIT-licensed many-to-many model for Creole languages, collected on 2026-10-15. GitHub calls this the flagship model, mBART fine-tuned on all data.", "what_it_is_for": "Johns Hopkins CLSP released Kreyol-MT on 2024-05-21 as one of four open-weight Kreyol-MT translation models. It is an active MIT-licensed many-to-many model for Creole languages, collected on 2026-10-15. GitHub calls this the flagship model, mBART fine-tuned on all data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/kreyol-mt/kreyol-mt-pubtrain": { "id": "jhu/kreyol-mt/kreyol-mt-pubtrain", "name": "Kreyol-MT public data", "maker": "jhu", "family": "jhu/kreyol-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-05-21", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of facebook/mbart-large-50-many-to-many-mmt. Config opened for the flagship: MBartForConditionalGeneration, d_model 1024, 12 encoder and 12 decoder layers, 16 decoder heads, decoder FFN 4096, max_position_embeddings 1024, generation max_length default 200. The pubtrain card says this copy is fine-tuned with only public data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released Kreyol-MT public data on 2024-05-21 as one of four open-weight Kreyol-MT translation models. It is an active MIT-licensed many-to-many model for Creole languages, collected on 2026-10-15. Public-data counterpart of the flagship.", "what_it_is_for": "Johns Hopkins CLSP released Kreyol-MT public data on 2024-05-21 as one of four open-weight Kreyol-MT translation models. It is an active MIT-licensed many-to-many model for Creole languages, collected on 2026-10-15. Public-data counterpart of the flagship.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/kreyol-mt/kreyol-mt-scratch": { "id": "jhu/kreyol-mt/kreyol-mt-scratch", "name": "Kreyol-MT from scratch", "maker": "jhu", "family": "jhu/kreyol-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-05-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GitHub calls this the model trained from scratch on all data. Its config.json was not opened, so the mBART layer counts are not assumed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released Kreyol-MT from scratch on 2024-05-21 as one of four open-weight Kreyol-MT translation models. It is an active MIT-licensed many-to-many model for Creole languages, collected on 2026-10-15. From-scratch model on all data.", "what_it_is_for": "Johns Hopkins CLSP released Kreyol-MT from scratch on 2024-05-21 as one of four open-weight Kreyol-MT translation models. It is an active MIT-licensed many-to-many model for Creole languages, collected on 2026-10-15. From-scratch model on all data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/kreyol-mt/kreyol-mt-scratch-pubtrain": { "id": "jhu/kreyol-mt/kreyol-mt-scratch-pubtrain", "name": "Kreyol-MT from scratch, public data", "maker": "jhu", "family": "jhu/kreyol-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-05-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GitHub calls this the model trained from scratch on public data. Its config.json was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released Kreyol-MT from scratch, public data on 2024-05-21 as one of four open-weight Kreyol-MT translation models. It is an active MIT-licensed many-to-many model for Creole languages, collected on 2026-10-15. From-scratch model on public data.", "what_it_is_for": "Johns Hopkins CLSP released Kreyol-MT from scratch, public data on 2024-05-21 as one of four open-weight Kreyol-MT translation models. It is an active MIT-licensed many-to-many model for Creole languages, collected on 2026-10-15. From-scratch model on public data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/microbert/microbert": { "id": "jhu/microbert/microbert", "name": "MicroBERT", "maker": "jhu", "family": "jhu/microbert", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hub base model is InstaDeepAI/nucleotide-transformer-v2-50m-multi-species. Tags say text classification for metagenomics, taxonomic classification, antimicrobial resistance, and pathogen detection. The GitHub project title is MicrobeRT and describes a fine-tuning pipeline rather than a new foundation model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins APL Biological Sciences published a MicroBERT checkpoint on 2025-09-11. It is an active MIT-licensed fine-tune of InstaDeep's nucleotide-transformer-v2-50m for metagenomic classification, collected on 2026-10-15. The lab repository presents the surrounding project as MicrobeRT.", "what_it_is_for": "Johns Hopkins APL Biological Sciences published a MicroBERT checkpoint on 2025-09-11. It is an active MIT-licensed fine-tune of InstaDeep's nucleotide-transformer-v2-50m for metagenomic classification, collected on 2026-10-15. The lab repository presents the surrounding project as MicrobeRT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/mmbert/mmbert-base": { "id": "jhu/mmbert/mmbert-base", "name": "mmBERT-base", "maker": "jhu", "family": "jhu/mmbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-09", "context_length": 8192, "max_output_tokens": null, "parameters": "307M", "architecture": "22 layers, hidden size 768, intermediate size 1152, 12 heads, 110M non-embedding parameters. ModernBERT-style encoder with a Gemma 2 tokenizer, vocab size 256000, and max sequence length 8192. Training is three phases: 2.3T pre-training tokens at a 30% mask over 60 languages, 600B mid-training tokens with context extension to 8192 and 110 languages at a 15% mask, and 100B decay tokens over 1833 languages at a 5% mask, then TIES-merged.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released mmBERT-base on 2025-09-09 as the larger open-weight multilingual encoder in the mmBERT pair. It is an active MIT-licensed masked language model aimed at multilingual understanding and retrieval, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP released mmBERT-base on 2025-09-09 as the larger open-weight multilingual encoder in the mmBERT pair. It is an active MIT-licensed masked language model aimed at multilingual understanding and retrieval, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/mmbert/mmbert-small": { "id": "jhu/mmbert/mmbert-small", "name": "mmBERT-small", "maker": "jhu", "family": "jhu/mmbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-09", "context_length": 8192, "max_output_tokens": null, "parameters": "140M", "architecture": "22 layers, hidden size 384, intermediate size 1152, 6 heads, 42M non-embedding parameters. ModernBERT-style encoder with a Gemma 2 tokenizer, vocab size 256000, and max sequence length 8192. Training is three phases: 2.3T pre-training tokens at a 30% mask over 60 languages, 600B mid-training tokens with context extension to 8192 and 110 languages at a 15% mask, and 100B decay tokens over 1833 languages at a 5% mask, then TIES-merged.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released mmBERT-small on 2025-09-09 as the smaller open-weight multilingual encoder in the mmBERT pair. It is an active MIT-licensed masked language model for multilingual classification, retrieval, and embeddings, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP released mmBERT-small on 2025-09-09 as the smaller open-weight multilingual encoder in the mmBERT pair. It is an active MIT-licensed masked language model for multilingual classification, retrieval, and embeddings, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-clef-mtd-mix-entries-mt5xxl-engeng": { "id": "jhu/plaidx/plaidx-large-clef-mtd-mix-entries-mt5xxl-engeng", "name": "PLAID-X plaidx-large-clef-mtd-mix-entries-mt5xxl-engeng", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-04-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-clef-mtd-mix-entries-mt5xxl-engeng on 2024-04-25 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, de, es, fr.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-clef-mtd-mix-entries-mt5xxl-engeng on 2024-04-25 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, de, es, fr.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-clef-mtd-mix-passages-mt5xxl-engeng": { "id": "jhu/plaidx/plaidx-large-clef-mtd-mix-passages-mt5xxl-engeng", "name": "PLAID-X plaidx-large-clef-mtd-mix-passages-mt5xxl-engeng", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-04-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-clef-mtd-mix-passages-mt5xxl-engeng on 2024-04-25 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, de, es, fr.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-clef-mtd-mix-passages-mt5xxl-engeng on 2024-04-25 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, de, es, fr.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-clef-mtd-round-robin-entries-mt5xxl-engeng": { "id": "jhu/plaidx/plaidx-large-clef-mtd-round-robin-entries-mt5xxl-engeng", "name": "PLAID-X plaidx-large-clef-mtd-round-robin-entries-mt5xxl-engeng", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-04-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-clef-mtd-round-robin-entries-mt5xxl-engeng on 2024-04-25 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, de, es, fr.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-clef-mtd-round-robin-entries-mt5xxl-engeng on 2024-04-25 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, de, es, fr.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-eng-tdist-mt5xxl-engeng": { "id": "jhu/plaidx/plaidx-large-eng-tdist-mt5xxl-engeng", "name": "PLAID-X plaidx-large-eng-tdist-mt5xxl-engeng", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-06-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-eng-tdist-mt5xxl-engeng on 2024-06-24 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-eng-tdist-mt5xxl-engeng on 2024-06-24 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-fas-tdist-mt5xxl-engeng": { "id": "jhu/plaidx/plaidx-large-fas-tdist-mt5xxl-engeng", "name": "PLAID-X plaidx-large-fas-tdist-mt5xxl-engeng", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-03-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-fas-tdist-mt5xxl-engeng on 2024-03-15 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, fa.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-fas-tdist-mt5xxl-engeng on 2024-03-15 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, fa.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-fas-tdist-mt5xxl-engfas": { "id": "jhu/plaidx/plaidx-large-fas-tdist-mt5xxl-engfas", "name": "PLAID-X plaidx-large-fas-tdist-mt5xxl-engfas", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-fas-tdist-mt5xxl-engfas on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, fa.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-fas-tdist-mt5xxl-engfas on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, fa.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-fas-tdist-mt5xxl-fasfas": { "id": "jhu/plaidx/plaidx-large-fas-tdist-mt5xxl-fasfas", "name": "PLAID-X plaidx-large-fas-tdist-mt5xxl-fasfas", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-fas-tdist-mt5xxl-fasfas on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, fa.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-fas-tdist-mt5xxl-fasfas on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, fa.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-fas-tdist-t53b-engeng": { "id": "jhu/plaidx/plaidx-large-fas-tdist-t53b-engeng", "name": "PLAID-X plaidx-large-fas-tdist-t53b-engeng", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-fas-tdist-t53b-engeng on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, fa.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-fas-tdist-t53b-engeng on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, fa.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-neuclir-mtd-mix-entries-mt5xxl-engeng": { "id": "jhu/plaidx/plaidx-large-neuclir-mtd-mix-entries-mt5xxl-engeng", "name": "PLAID-X plaidx-large-neuclir-mtd-mix-entries-mt5xxl-engeng", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-04-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-neuclir-mtd-mix-entries-mt5xxl-engeng on 2024-04-25 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, zh, fa, ru.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-neuclir-mtd-mix-entries-mt5xxl-engeng on 2024-04-25 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, zh, fa, ru.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-neuclir-mtd-mix-passages-mt5xxl-engeng": { "id": "jhu/plaidx/plaidx-large-neuclir-mtd-mix-passages-mt5xxl-engeng", "name": "PLAID-X plaidx-large-neuclir-mtd-mix-passages-mt5xxl-engeng", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-04-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-neuclir-mtd-mix-passages-mt5xxl-engeng on 2024-04-25 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, zh, fa, ru.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-neuclir-mtd-mix-passages-mt5xxl-engeng on 2024-04-25 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, zh, fa, ru.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-neuclir-mtd-round-robin-entries-mt5xxl-engeng": { "id": "jhu/plaidx/plaidx-large-neuclir-mtd-round-robin-entries-mt5xxl-engeng", "name": "PLAID-X plaidx-large-neuclir-mtd-round-robin-entries-mt5xxl-engeng", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-04-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-neuclir-mtd-round-robin-entries-mt5xxl-engeng on 2024-04-25 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, zh, fa, ru.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-neuclir-mtd-round-robin-entries-mt5xxl-engeng on 2024-04-25 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, zh, fa, ru.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-rus-tdist-mt5xxl-engeng": { "id": "jhu/plaidx/plaidx-large-rus-tdist-mt5xxl-engeng", "name": "PLAID-X plaidx-large-rus-tdist-mt5xxl-engeng", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-03-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-rus-tdist-mt5xxl-engeng on 2024-03-15 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, ru.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-rus-tdist-mt5xxl-engeng on 2024-03-15 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, ru.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-rus-tdist-mt5xxl-engrus": { "id": "jhu/plaidx/plaidx-large-rus-tdist-mt5xxl-engrus", "name": "PLAID-X plaidx-large-rus-tdist-mt5xxl-engrus", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-rus-tdist-mt5xxl-engrus on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, ru.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-rus-tdist-mt5xxl-engrus on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, ru.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-rus-tdist-mt5xxl-rusrus": { "id": "jhu/plaidx/plaidx-large-rus-tdist-mt5xxl-rusrus", "name": "PLAID-X plaidx-large-rus-tdist-mt5xxl-rusrus", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-rus-tdist-mt5xxl-rusrus on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, ru.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-rus-tdist-mt5xxl-rusrus on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, ru.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-rus-tdist-t53b-engeng": { "id": "jhu/plaidx/plaidx-large-rus-tdist-t53b-engeng", "name": "PLAID-X plaidx-large-rus-tdist-t53b-engeng", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-rus-tdist-t53b-engeng on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, ru.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-rus-tdist-t53b-engeng on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, ru.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-zho-tdist-mt5xxl-engeng": { "id": "jhu/plaidx/plaidx-large-zho-tdist-mt5xxl-engeng", "name": "PLAID-X plaidx-large-zho-tdist-mt5xxl-engeng", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-03-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-zho-tdist-mt5xxl-engeng on 2024-03-15 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, zh.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-zho-tdist-mt5xxl-engeng on 2024-03-15 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, zh.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-zho-tdist-mt5xxl-engzho": { "id": "jhu/plaidx/plaidx-large-zho-tdist-mt5xxl-engzho", "name": "PLAID-X plaidx-large-zho-tdist-mt5xxl-engzho", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-zho-tdist-mt5xxl-engzho on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, zh.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-zho-tdist-mt5xxl-engzho on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, zh.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-zho-tdist-mt5xxl-zhozho": { "id": "jhu/plaidx/plaidx-large-zho-tdist-mt5xxl-zhozho", "name": "PLAID-X plaidx-large-zho-tdist-mt5xxl-zhozho", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-zho-tdist-mt5xxl-zhozho on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, zh.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-zho-tdist-mt5xxl-zhozho on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, zh.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/plaidx/plaidx-large-zho-tdist-t53b-engeng": { "id": "jhu/plaidx/plaidx-large-zho-tdist-t53b-engeng", "name": "PLAID-X plaidx-large-zho-tdist-t53b-engeng", "maker": "jhu", "family": "jhu/plaidx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HLTCOE late-interaction retrieval checkpoint. Hub tags: XLM-RoBERTa large, ColBERT-X, PLAID-X, and translate-distill or multilingual translate-distill. The repo name encodes the language set, teacher (mT5-XXL or T5-3B), and distillation mix. Per-checkpoint hyperparameters were not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released plaidx-large-zho-tdist-t53b-engeng on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, zh.", "what_it_is_for": "Johns Hopkins HLTCOE released plaidx-large-zho-tdist-t53b-engeng on 2023-12-28 as an open-weight PLAID-X large retrieval checkpoint. It is an active MIT-licensed XLM-RoBERTa late-interaction model, collected on 2026-10-15. Languages on the Hub tags: en, zh.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/rank-k/rank-k-32b": { "id": "jhu/rank-k/rank-k-32b", "name": "Rank-K-32B", "maker": "jhu", "family": "jhu/rank-k", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-05-20", "context_length": null, "max_output_tokens": null, "parameters": "33B", "architecture": "Listwise reranker. The model card tree says it is a fine-tune of Qwen/QwQ-32B, itself a fine-tune of Qwen/Qwen2.5-32B. The card lists 33B parameters in BF16. English. A third-party host page quoted a context length and was not used.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released Rank-K-32B, an open-weight listwise reasoning reranker, with the paper dated 2025-05-20. It is an active MIT-licensed fine-tune of QwQ-32B, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins HLTCOE released Rank-K-32B, an open-weight listwise reasoning reranker, with the paper dated 2025-05-20. It is an active MIT-licensed fine-tune of QwQ-32B, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/rank1/rank1-0-5b": { "id": "jhu/rank1/rank1-0-5b", "name": "Rank1-0.5B", "maker": "jhu", "family": "jhu/rank1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-11", "context_length": null, "max_output_tokens": null, "parameters": "0.5B", "architecture": "Reasoning reranker fine-tuned from Qwen/Qwen2.5-0.5B on R1 reasoning traces for MS MARCO query-passage relevance. Pointwise test-time reasoning before a true or false relevance judgment.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released Rank1-0.5B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-03-11. It is active, MIT-tagged, and collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP released Rank1-0.5B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-03-11. It is active, MIT-tagged, and collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/rank1/rank1-1-5b": { "id": "jhu/rank1/rank1-1-5b", "name": "Rank1-1.5B", "maker": "jhu", "family": "jhu/rank1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-12", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Reasoning reranker fine-tuned from Qwen/Qwen2.5-1.5B on R1 reasoning traces for MS MARCO query-passage relevance. Pointwise test-time reasoning before a true or false relevance judgment.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released Rank1-1.5B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-03-12. It is active, MIT-tagged, and collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP released Rank1-1.5B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-03-12. It is active, MIT-tagged, and collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/rank1/rank1-14b": { "id": "jhu/rank1/rank1-14b", "name": "Rank1-14B", "maker": "jhu", "family": "jhu/rank1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-18", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Reasoning reranker fine-tuned from Qwen/Qwen2.5-14B on R1 reasoning traces for MS MARCO query-passage relevance. Pointwise test-time reasoning before a true or false relevance judgment.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released Rank1-14B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-02-18. It is active, MIT-tagged, and collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP released Rank1-14B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-02-18. It is active, MIT-tagged, and collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/rank1/rank1-32b": { "id": "jhu/rank1/rank1-32b", "name": "Rank1-32B", "maker": "jhu", "family": "jhu/rank1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-19", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Reasoning reranker fine-tuned from Qwen/Qwen2.5-32B on R1 reasoning traces for MS MARCO query-passage relevance. Pointwise test-time reasoning before a true or false relevance judgment.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released Rank1-32B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-02-19. It is active, MIT-tagged, and collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP released Rank1-32B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-02-19. It is active, MIT-tagged, and collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/rank1/rank1-3b": { "id": "jhu/rank1/rank1-3b", "name": "Rank1-3B", "maker": "jhu", "family": "jhu/rank1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-11", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Reasoning reranker fine-tuned from Qwen/Qwen2.5-3B on R1 reasoning traces for MS MARCO query-passage relevance. Pointwise test-time reasoning before a true or false relevance judgment.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released Rank1-3B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-03-11. It is active, MIT-tagged, and collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP released Rank1-3B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-03-11. It is active, MIT-tagged, and collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/rank1/rank1-7b": { "id": "jhu/rank1/rank1-7b", "name": "Rank1-7B", "maker": "jhu", "family": "jhu/rank1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-18", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Reasoning reranker fine-tuned from Qwen/Qwen2.5-7B on R1 reasoning traces for MS MARCO query-passage relevance. Pointwise test-time reasoning before a true or false relevance judgment.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released Rank1-7B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-02-18. It is active, MIT-tagged, and collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP released Rank1-7B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-02-18. It is active, MIT-tagged, and collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/rank1/rank1-llama3-8b": { "id": "jhu/rank1/rank1-llama3-8b", "name": "Rank1-Llama3-8B", "maker": "jhu", "family": "jhu/rank1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-18", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Reasoning reranker fine-tuned from meta-llama/Llama-3.1-8B on R1 reasoning traces for MS MARCO query-passage relevance. Pointwise test-time reasoning before a true or false relevance judgment.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released Rank1-Llama3-8B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-02-18. It is active, MIT-tagged, and collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP released Rank1-Llama3-8B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-02-18. It is active, MIT-tagged, and collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/rank1/rank1-mistral-2501-24b": { "id": "jhu/rank1/rank1-mistral-2501-24b", "name": "Rank1-Mistral-2501-24B", "maker": "jhu", "family": "jhu/rank1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-18", "context_length": null, "max_output_tokens": null, "parameters": "24B", "architecture": "Reasoning reranker fine-tuned from mistralai/Mistral-Small-24B-Base-2501 on R1 reasoning traces for MS MARCO query-passage relevance. Pointwise test-time reasoning before a true or false relevance judgment.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released Rank1-Mistral-2501-24B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-02-18. It is active, MIT-tagged, and collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP released Rank1-Mistral-2501-24B as an open-weight Rank1 reasoning reranker. The public paper is dated 2025-02-25 and the Hub repo timestamp used here is 2025-02-18. It is active, MIT-tagged, and collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/rankvideo/rankvideo": { "id": "jhu/rankvideo/rankvideo", "name": "RankVideo", "maker": "jhu", "family": "jhu/rankvideo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-02", "context_length": 262144, "max_output_tokens": null, "parameters": "9B", "architecture": "Video-native reasoning reranker fine-tuned from Qwen/Qwen3-VL-8B-Instruct. The card lists 9B parameters in BF16. The shipped config is Qwen3VLForConditionalGeneration with text max_position_embeddings 262144, which matches the tokenizer model_max_length.", "modalities": { "input": [ "text", "video", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins HLTCOE released RankVideo in February 2026 as an open-weight video reasoning reranker. It is an active MIT-licensed fine-tune of Qwen3-VL-8B-Instruct for text-to-video retrieval, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins HLTCOE released RankVideo in February 2026 as an open-weight video reasoning reranker. It is an active MIT-licensed fine-tune of Qwen3-VL-8B-Instruct for text-to-video retrieval, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/roberta-eng-ara/roberta-large-eng-ara-128k": { "id": "jhu/roberta-eng-ara/roberta-large-eng-ara-128k", "name": "RoBERTa-large English-Arabic 128K", "maker": "jhu", "family": "jhu/roberta-eng-ara", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-03-02", "context_length": 512, "max_output_tokens": null, "parameters": "0.4B", "architecture": "English-Arabic bilingual encoder. The model card says 24-layer Transformers with d_model 1024, the same size class as XLM-R large, a 128K SentencePiece vocabulary, sequence length 512, and 250K pre-training steps at batch size 2048. Hugging Face lists 0.4B parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CLSP released this open-weight English-Arabic encoder on the Hub with a 2022-03-02 timestamp. It is an active MIT-licensed 24-layer bilingual masked model with a 128K vocabulary, collected on 2026-10-15.", "what_it_is_for": "Johns Hopkins CLSP released this open-weight English-Arabic encoder on the Hub with a 2022-03-02 timestamp. It is an active MIT-licensed 24-layer bilingual masked model with a 128K vocabulary, collected on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/spatialreasoner/spatialreasoner": { "id": "jhu/spatialreasoner/spatialreasoner", "name": "SpatialReasoner", "maker": "jhu", "family": "jhu/spatialreasoner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-04", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Post-trained from Qwen2.5-VL-7B for explicit 3D spatial reasoning, according to the paper appendix. Two stages are described: supervised fine-tuning, then reinforcement learning. Hugging Face lists the final repo as 8B BF16.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CCVL released SpatialReasoner as an open-weight spatial-reasoning vision-language checkpoint. Hub timestamps are 2025-06-04. Collected on 2026-10-15. The Hub repo has weights and no README card. Hugging Face lists 8B parameters.", "what_it_is_for": "Johns Hopkins CCVL released SpatialReasoner as an open-weight spatial-reasoning vision-language checkpoint. Hub timestamps are 2025-06-04. Collected on 2026-10-15. The Hub repo has weights and no README card. Hugging Face lists 8B parameters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/spatialreasoner/spatialreasoner-sft": { "id": "jhu/spatialreasoner/spatialreasoner-sft", "name": "SpatialReasoner-SFT", "maker": "jhu", "family": "jhu/spatialreasoner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Post-trained from Qwen2.5-VL-7B for explicit 3D spatial reasoning, according to the paper appendix. Two stages are described: supervised fine-tuning, then reinforcement learning.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CCVL released SpatialReasoner-SFT as an open-weight spatial-reasoning vision-language checkpoint. Hub timestamps are 2025-06-04. Collected on 2026-10-15. The paper names this as the supervised fine-tuning stage before reinforcement learning.", "what_it_is_for": "Johns Hopkins CCVL released SpatialReasoner-SFT as an open-weight spatial-reasoning vision-language checkpoint. Hub timestamps are 2025-06-04. Collected on 2026-10-15. The paper names this as the supervised fine-tuning stage before reinforcement learning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jhu/spatialreasoner/spatialreasoner-zero": { "id": "jhu/spatialreasoner/spatialreasoner-zero", "name": "SpatialReasoner-Zero", "maker": "jhu", "family": "jhu/spatialreasoner", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-06-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Post-trained from Qwen2.5-VL-7B for explicit 3D spatial reasoning, according to the paper appendix. Two stages are described: supervised fine-tuning, then reinforcement learning.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johns Hopkins CCVL released SpatialReasoner-Zero as an open-weight spatial-reasoning vision-language checkpoint. Hub timestamps are 2025-06-04. Collected on 2026-10-15. The Hub repo exists beside the SFT and final checkpoints. The paper excerpt that was opened does not define the Zero name.", "what_it_is_for": "Johns Hopkins CCVL released SpatialReasoner-Zero as an open-weight spatial-reasoning vision-language checkpoint. Hub timestamps are 2025-06-04. Collected on 2026-10-15. The Hub repo exists beside the SFT and final checkpoints. The paper excerpt that was opened does not define the Zero name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/clip/jina-clip-v1": { "id": "jina/clip/jina-clip-v1", "name": "jina-clip-v1", "maker": "jina", "family": "jina/clip", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-05", "context_length": 8192, "max_output_tokens": null, "parameters": "223M", "architecture": "CLIP-style text and image encoder. English text, image size 224x224, 768-d output, 8,192-token text context. Apache-2.0.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-clip-v1 is a clip model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. First Jina CLIP: English text plus images, with an 8K text tower so the text encoder can also retrieve long text.", "what_it_is_for": "First Jina CLIP: English text plus images, with an 8K text tower so the text encoder can also retrieve long text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/clip/jina-clip-v2": { "id": "jina/clip/jina-clip-v2", "name": "jina-clip-v2", "maker": "jina", "family": "jina/clip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-11-05", "context_length": 8192, "max_output_tokens": null, "parameters": "865M", "architecture": "Multilingual CLIP-style dual encoder. Text tower from jina-embeddings-v3, image resolution 512x512, Matryoshka 64, 128, 256, 512, 768, 1024. Default 1024. 89 languages in the release post.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-clip-v2 is a clip model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Text-and-image embeddings in 89 languages. Still offered. v5-omni is the newer multimodal line and adds audio and video.", "what_it_is_for": "Text-and-image embeddings in 89 languages. Still offered. v5-omni is the newer multimodal line and adds audio and video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/code-embeddings/jina-code-embeddings-0-5b": { "id": "jina/code-embeddings/jina-code-embeddings-0-5b", "name": "jina-code-embeddings-0.5b", "maker": "jina", "family": "jina/code-embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2025-09-01", "context_length": 32768, "max_output_tokens": null, "parameters": "494M", "architecture": "Code embedding finetuned from Qwen2.5-Coder-0.5B. Last-token pooling. Default 896 dimensions, Matryoshka 64, 128, 256, 512, 896. Same five tasks as the 1.5B model. The index lists 494M parameters; the product name says 0.5B.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-code-embeddings-0.5b is a code embeddings model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Smaller code embedding sibling of jina-code-embeddings-1.5b.", "what_it_is_for": "Smaller code embedding sibling of jina-code-embeddings-1.5b.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/code-embeddings/jina-code-embeddings-1-5b": { "id": "jina/code-embeddings/jina-code-embeddings-1-5b", "name": "jina-code-embeddings-1.5b", "maker": "jina", "family": "jina/code-embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2025-09-01", "context_length": 32768, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Code embedding finetuned from Qwen2.5-Coder-1.5B. Last-token pooling. Default 1536 dimensions, Matryoshka 128, 256, 512, 1024, 1536. Tasks: nl2code, code2code, code2nl, code2completion, qa. FlashAttention2. bfloat16. 15+ programming languages.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-code-embeddings-1.5b is a code embeddings model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Code retrieval across natural language, code, comments, and completion, in 15+ programming languages.", "what_it_is_for": "Code retrieval across natural language, code, comments, and completion, in 15+ programming languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/colbert/jina-colbert-v1-en": { "id": "jina/colbert/jina-colbert-v1-en", "name": "jina-colbert-v1-en", "maker": "jina", "family": "jina/colbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-17", "context_length": 8192, "max_output_tokens": null, "parameters": "137M", "architecture": "English late-interaction retriever. 128-dimensional multi-vector, 137M parameters, 8,192-token context. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-colbert-v1-en is a colbert model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. English ColBERT-style retriever that preceded the multilingual v2 model.", "what_it_is_for": "English ColBERT-style retriever that preceded the multilingual v2 model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/colbert/jina-colbert-v2": { "id": "jina/colbert/jina-colbert-v2", "name": "jina-colbert-v2", "maker": "jina", "family": "jina/colbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-08-31", "context_length": 8192, "max_output_tokens": null, "parameters": "560M", "architecture": "Multilingual late-interaction retriever. Multi-vector output, 128 dimensions per token, Matryoshka 64, 96, and 128. 89 languages. 8,192-token context. The reranker FAQ groups it with rerankers because the API exposes a rank endpoint.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-colbert-v2 is a colbert model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Multilingual ColBERT late-interaction retriever for when token-level vectors matter more than a single dense vector.", "what_it_is_for": "Multilingual ColBERT late-interaction retriever for when token-level vectors matter more than a single dense vector.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embedding-b-en-v1": { "id": "jina/embeddings/jina-embedding-b-en-v1", "name": "jina-embedding-b-en-v1", "maker": "jina", "family": "jina/embeddings", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-06-17", "context_length": 512, "max_output_tokens": null, "parameters": "110M", "architecture": "English sentence embedding from the 2023 Jina Embeddings v1 set, trained on the Linnaeus-Clean pair data. Output dimension 768. The large card is tagged t5; this dossier does not claim that tag for every size.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "jina-embedding-b-en-v1 is an embeddings model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. First-generation English sentence embedding still listed on the 2026 index. Superseded by the v2 8K models.", "what_it_is_for": "First-generation English sentence embedding still listed on the 2026 index. Superseded by the v2 8K models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embedding-l-en-v1": { "id": "jina/embeddings/jina-embedding-l-en-v1", "name": "jina-embedding-l-en-v1", "maker": "jina", "family": "jina/embeddings", "status": "deprecated", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-07-20", "context_length": null, "max_output_tokens": null, "parameters": "330M", "architecture": "English sentence embedding, 330M parameters, 1,024 dimensions. The Hugging Face card is tagged t5 and says it was trained on Linnaeus-Clean (380 million sentence pairs).", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "jina-embedding-l-en-v1 is an embeddings model from Jina AI (Elastic since October 2025). Status is deprecated. Collected 2026-10-15. First-generation English sentence embedding. Weights are public. Only the base size remains on the 2026 model index.", "what_it_is_for": "First-generation English sentence embedding. Weights are public. Only the base size remains on the 2026 model index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embedding-s-en-v1": { "id": "jina/embeddings/jina-embedding-s-en-v1", "name": "jina-embedding-s-en-v1", "maker": "jina", "family": "jina/embeddings", "status": "deprecated", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-07-20", "context_length": null, "max_output_tokens": null, "parameters": "35M", "architecture": "English sentence embedding from the 2023 Jina Embeddings v1 set, trained on the Linnaeus-Clean pair data. Output dimension 512. The large card is tagged t5; this dossier does not claim that tag for every size.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "jina-embedding-s-en-v1 is an embeddings model from Jina AI (Elastic since October 2025). Status is deprecated. Collected 2026-10-15. First-generation English sentence embedding. Weights are public. Only the base size remains on the 2026 model index.", "what_it_is_for": "First-generation English sentence embedding. Weights are public. Only the base size remains on the 2026 model index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embedding-t-en-v1": { "id": "jina/embeddings/jina-embedding-t-en-v1", "name": "jina-embedding-t-en-v1", "maker": "jina", "family": "jina/embeddings", "status": "deprecated", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-07-20", "context_length": null, "max_output_tokens": null, "parameters": "14M", "architecture": "English sentence embedding from the 2023 Jina Embeddings v1 set, trained on the Linnaeus-Clean pair data. Output dimension 312. The large card is tagged t5; this dossier does not claim that tag for every size.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "jina-embedding-t-en-v1 is an embeddings model from Jina AI (Elastic since October 2025). Status is deprecated. Collected 2026-10-15. First-generation English sentence embedding. Weights are public. Only the base size remains on the 2026 model index.", "what_it_is_for": "First-generation English sentence embedding. Weights are public. Only the base size remains on the 2026 model index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embeddings-v2-base-code": { "id": "jina/embeddings/jina-embeddings-v2-base-code", "name": "jina-embeddings-v2-base-code", "maker": "jina", "family": "jina/embeddings", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-05", "context_length": 8192, "max_output_tokens": null, "parameters": "137M", "architecture": "JinaBERT code embedding, 137M parameters, 768 dimensions, 8,192-token context. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-embeddings-v2-base-code is an embeddings model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Code and natural-language-to-code embeddings from the v2 generation.", "what_it_is_for": "Code and natural-language-to-code embeddings from the v2 generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embeddings-v2-base-de": { "id": "jina/embeddings/jina-embeddings-v2-base-de", "name": "jina-embeddings-v2-base-de", "maker": "jina", "family": "jina/embeddings", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-15", "context_length": 8192, "max_output_tokens": null, "parameters": "161M", "architecture": "Bilingual JinaBERT, 161M parameters, 768 dimensions, 8,192-token ALiBi context.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-embeddings-v2-base-de is an embeddings model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. German-English bilingual long-context embeddings.", "what_it_is_for": "German-English bilingual long-context embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embeddings-v2-base-en": { "id": "jina/embeddings/jina-embeddings-v2-base-en", "name": "jina-embeddings-v2-base-en", "maker": "jina", "family": "jina/embeddings", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-28", "context_length": 8192, "max_output_tokens": null, "parameters": "137M", "architecture": "JinaBERT with symmetric bidirectional ALiBi. 137M parameters, 768 dimensions. Backbone pretrained on C4. Mean pooling is the sentence-transformers default for this generation; the card was not re-read for the pooling line.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-embeddings-v2-base-en is an embeddings model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. English text embeddings with an 8,192-token window, released as an open-weight peer to text-embedding-ada-002.", "what_it_is_for": "English text embeddings with an 8,192-token window, released as an open-weight peer to text-embedding-ada-002.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embeddings-v2-base-es": { "id": "jina/embeddings/jina-embeddings-v2-base-es", "name": "jina-embeddings-v2-base-es", "maker": "jina", "family": "jina/embeddings", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-14", "context_length": 8192, "max_output_tokens": null, "parameters": "161M", "architecture": "Bilingual JinaBERT, 161M parameters, 768 dimensions, 8,192-token ALiBi context.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-embeddings-v2-base-es is an embeddings model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Spanish-English bilingual long-context embeddings.", "what_it_is_for": "Spanish-English bilingual long-context embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embeddings-v2-base-zh": { "id": "jina/embeddings/jina-embeddings-v2-base-zh", "name": "jina-embeddings-v2-base-zh", "maker": "jina", "family": "jina/embeddings", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-09", "context_length": 8192, "max_output_tokens": null, "parameters": "161M", "architecture": "Bilingual JinaBERT, 161M parameters, 768 dimensions, 8,192-token ALiBi context.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-embeddings-v2-base-zh is an embeddings model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Chinese-English bilingual long-context embeddings.", "what_it_is_for": "Chinese-English bilingual long-context embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embeddings-v2-large-en": { "id": "jina/embeddings/jina-embeddings-v2-large-en", "name": "jina-embeddings-v2-large-en", "maker": "jina", "family": "jina/embeddings", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "435M", "architecture": "Announced English JinaBERT embedding, 435M parameters and 1,024 dimensions, 8,192-token context. The November 2023 blog said it was not yet downloadable.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "jina-embeddings-v2-large-en is an announced model from Jina AI (Elastic since October 2025). Status is announced. Collected 2026-10-15. Announced large English v2 embedding. No public weights were found.", "what_it_is_for": "Announced large English v2 embedding. No public weights were found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embeddings-v2-small-en": { "id": "jina/embeddings/jina-embeddings-v2-small-en", "name": "jina-embeddings-v2-small-en", "maker": "jina", "family": "jina/embeddings", "status": "deprecated", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-30", "context_length": 8192, "max_output_tokens": null, "parameters": "33M", "architecture": "JinaBERT with symmetric bidirectional ALiBi. 33M parameters, 512 dimensions. The early card name was jina-embedding-s-en-v2.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "jina-embeddings-v2-small-en is an embeddings model from Jina AI (Elastic since October 2025). Status is deprecated. Collected 2026-10-15. Smallest shipped Jina Embeddings v2 model: English, 8,192 tokens, built for low memory.", "what_it_is_for": "Smallest shipped Jina Embeddings v2 model: English, 8,192 tokens, built for low memory.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embeddings-v3": { "id": "jina/embeddings/jina-embeddings-v3", "name": "jina-embeddings-v3", "maker": "jina", "family": "jina/embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-09-18", "context_length": 8192, "max_output_tokens": null, "parameters": "570M", "architecture": "Multilingual text embedding on Jina XLM-RoBERTa (XLM-RoBERTa Large) with RoPE. Five LoRA adapters: retrieval.query, retrieval.passage, separation, classification, and text-matching. Matryoshka 32, 64, 128, 256, 512, 768, 1024. Default 1024. Late chunking supported. A native Transformers build is published as jina-embeddings-v3-hf.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-embeddings-v3 is an embeddings model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Multilingual text embeddings with task-specific adapters. Still on the API. Jina's v3 model page says v5-text-small supersedes it for most workloads.", "what_it_is_for": "Multilingual text embeddings with task-specific adapters. Still on the API. Jina's v3 model page says v5-text-small supersedes it for most workloads.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embeddings-v4": { "id": "jina/embeddings/jina-embeddings-v4", "name": "jina-embeddings-v4", "maker": "jina", "family": "jina/embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Qwen Research License", "release_date": "2025-06-24", "context_length": 32768, "max_output_tokens": null, "parameters": "3.8B", "architecture": "Multimodal embedding based on Qwen2.5-VL-3B-Instruct. Single-vector output default 2048, Matryoshka 128, 256, 512, 1024, 2048, plus a late-interaction multi-vector mode. Image input listed as 768x28x28 patches. Tasks: retrieval, text-matching, and code. Late chunking is supported.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "jina-embeddings-v4 is an embeddings model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Universal text, image, and PDF embedding with both dense and ColBERT-style multi-vector output. Jina now points production text workloads at v5-text-small.", "what_it_is_for": "Universal text, image, and PDF embedding with both dense and ColBERT-style multi-vector output. Jina now points production text workloads at v5-text-small.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embeddings-v5-omni-nano": { "id": "jina/embeddings/jina-embeddings-v5-omni-nano", "name": "jina-embeddings-v5-omni-nano", "maker": "jina", "family": "jina/embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2026-05-07", "context_length": 8192, "max_output_tokens": null, "parameters": "~1.04B", "architecture": "GELATO multimodal embedding. Frozen jina-embeddings-v5-text-nano text tower, SigLIP2 Base vision encoder, Whisper-large-v3 audio, and about 7M projectors. llms.txt: ~1.04B total (266M text backbone + SigLIP2 Base + Whisper-large-v3 + ~7M projectors). Text outputs match v5-text-nano. Matryoshka 32, 64, 128, 256, 512, 768. Default 768. Context 8,192.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-embeddings-v5-omni-nano is an embeddings model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Smaller omni embedding for text, image, audio, video, and PDF when the 1.7B-class model is too heavy.", "what_it_is_for": "Smaller omni embedding for text, image, audio, video, and PDF when the 1.7B-class model is too heavy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embeddings-v5-omni-small": { "id": "jina/embeddings/jina-embeddings-v5-omni-small", "name": "jina-embeddings-v5-omni-small", "maker": "jina", "family": "jina/embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2026-05-07", "context_length": 32768, "max_output_tokens": null, "parameters": "~1.74B", "architecture": "GELATO multimodal embedding. Frozen jina-embeddings-v5-text-small text tower plus a SigLIP2 vision encoder and Whisper-large-v3 audio encoder. Only projectors were trained. llms.txt breakdown: about 700M text backbone + SigLIP2 Large vision + Whisper-large-v3 audio + about 18M projectors. Text outputs are identical to v5-text-small. Matryoshka 32 through 1024. Video is 32 uniformly sampled frames. PDF is accepted by the API.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-embeddings-v5-omni-small is an embeddings model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. One embedding space for text, images, audio, video, and PDFs, with text vectors that match the v5 text-small model.", "what_it_is_for": "One embedding space for text, images, audio, video, and PDFs, with text vectors that match the v5 text-small model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embeddings-v5-text-nano": { "id": "jina/embeddings/jina-embeddings-v5-text-nano", "name": "jina-embeddings-v5-text-nano", "maker": "jina", "family": "jina/embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2026-02-18", "context_length": 8192, "max_output_tokens": null, "parameters": "239M", "architecture": "Text embedding model on EuroBERT-210M with last-token pooling and task LoRA adapters. Matryoshka dimensions 32, 64, 128, 256, 512, and 768. Default output is 768. GGUF and MLX quantizations are published.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-embeddings-v5-text-nano is an embeddings model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. The smaller v5 text embedding, aimed at latency, cost, and edge deployment rather than the last point of accuracy.", "what_it_is_for": "The smaller v5 text embedding, aimed at latency, cost, and edge deployment rather than the last point of accuracy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/embeddings/jina-embeddings-v5-text-small": { "id": "jina/embeddings/jina-embeddings-v5-text-small", "name": "jina-embeddings-v5-text-small", "maker": "jina", "family": "jina/embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2026-02-18", "context_length": 32768, "max_output_tokens": null, "parameters": "677M", "architecture": "Text embedding model on a Qwen3-0.6B-Base backbone with last-token pooling and four task LoRA adapters (retrieval, text-matching, classification, clustering). Matryoshka dimensions 32, 64, 128, 256, 512, and 1024. Default output is 1024.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-embeddings-v5-text-small is an embeddings model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Jina's default text embedding for search and RAG: multilingual retrieval, matching, classification, and clustering, with a 32K window and truncatable vectors.", "what_it_is_for": "Jina's default text embedding for search and RAG: multilingual retrieval, matching, classification, and clustering, with a 32K window and truncatable vectors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/reader/jina-ocr-v1": { "id": "jina/reader/jina-ocr-v1", "name": "jina-ocr-v1", "maker": "jina", "family": "jina/reader", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2026-09-14", "context_length": 32768, "max_output_tokens": null, "parameters": "3.4B", "architecture": "Document parser. Mixture of experts, 3.4B total parameters and about 570M active per token. Image size listed as 1024x1024. Page image or PDF in, Markdown out. The index types it as a reader. Reader API header X-Respond-With: jina-ocr-v1.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "jina-ocr-v1 is a reader model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Page-image and PDF reader that emits Markdown, including layout-heavy documents.", "what_it_is_for": "Page-image and PDF reader that emits Markdown, including layout-heavy documents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/reader/jina-vlm": { "id": "jina/reader/jina-vlm", "name": "jina-vlm", "maker": "jina", "family": "jina/reader", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2025-12-04", "context_length": 32768, "max_output_tokens": null, "parameters": "2.4B", "architecture": "Multilingual vision-language model. 2.4B parameters, 32K context, image size listed as 4096x4096. The 2026 index types it as a reader with a read endpoint. MLX builds exist. It is a VLM, not an HTML-to-Markdown model.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "jina-vlm is a reader model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Small multilingual vision-language model. Jina lists it with readers because it reads images to text.", "what_it_is_for": "Small multilingual vision-language model. Jina lists it with readers because it reads images to text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/reader/reader-lm-0-5b": { "id": "jina/reader/reader-lm-0-5b", "name": "reader-lm-0.5b", "maker": "jina", "family": "jina/reader", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-08-11", "context_length": 262144, "max_output_tokens": null, "parameters": "494M", "architecture": "Smaller HTML-to-Markdown reader. 494M parameters. Context published as 256K. Same release day as reader-lm-1.5b.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "reader-lm-0.5b is a reader model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Smallest ReaderLM, for HTML to Markdown when the 1.5B models are too large.", "what_it_is_for": "Smallest ReaderLM, for HTML to Markdown when the 1.5B models are too large.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/reader/reader-lm-1-5b": { "id": "jina/reader/reader-lm-1-5b", "name": "reader-lm-1.5b", "maker": "jina", "family": "jina/reader", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-08-11", "context_length": 262144, "max_output_tokens": null, "parameters": "1.54B", "architecture": "Small language model for HTML to Markdown. 1.54B parameters. Context published as 256K. Predecessor of ReaderLM-v2, which adds JSON and a longer window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "reader-lm-1.5b is a reader model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. First 1.5B ReaderLM. Converts HTML to Markdown. ReaderLM-v2 replaces it for new work.", "what_it_is_for": "First 1.5B ReaderLM. Converts HTML to Markdown. ReaderLM-v2 replaces it for new work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/reader/readerlm-v2": { "id": "jina/reader/readerlm-v2", "name": "ReaderLM-v2", "maker": "jina", "family": "jina/reader", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2025-01-16", "context_length": 524288, "max_output_tokens": null, "parameters": "1.54B", "architecture": "Decoder-only transformer finetuned from Qwen2.5-1.5B-Instruct. 28 layers, hidden size 1536, 12 query heads, 2 KV heads, head size 128, intermediate size 8960. Context is 512K tokens combined input and output. HTML to Markdown or JSON. 29 languages named on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "ReaderLM-v2 is a reader model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Small language model that turns raw HTML into Markdown or schema-following JSON for grounding other models.", "what_it_is_for": "Small language model that turns raw HTML into Markdown or schema-following JSON for grounding other models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/reranker/jina-reranker-m0": { "id": "jina/reranker/jina-reranker-m0", "name": "jina-reranker-m0", "maker": "jina", "family": "jina/reranker", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2025-04-08", "context_length": 10240, "max_output_tokens": null, "parameters": "2.4B", "architecture": "Multimodal reranker on Qwen2-VL-2B-Instruct with a LoRA-tuned language model and an MLP scoring head. Pairwise and listwise losses. Image patches 768x28x28, dynamic resolution from 56x56 up to 4K. Tasks: text-text, text-image, image-text, image-image. Minimum image 28x28 on the API.", "modalities": { "input": [ "text", "image" ], "output": [ "relevance-score" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-reranker-m0 is a reranker model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Reranker for visual documents and for mixed text and image candidates, in 29+ languages.", "what_it_is_for": "Reranker for visual documents and for mixed text and image candidates, in 29+ languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/reranker/jina-reranker-v1-base-en": { "id": "jina/reranker/jina-reranker-v1-base-en", "name": "jina-reranker-v1-base-en", "maker": "jina", "family": "jina/reranker", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-29", "context_length": 8192, "max_output_tokens": null, "parameters": "137M", "architecture": "English reranker, 137M parameters, 8,192-token context, Apache-2.0. The 2026 FAQ calls the v1 trio legacy. Architecture details beyond size were not re-read from the model card.", "modalities": { "input": [ "text" ], "output": [ "relevance-score" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-reranker-v1-base-en is a reranker model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Legacy English reranker. Weights are Apache-2.0, so commercial self-hosting does not need the CC BY-NC waiver.", "what_it_is_for": "Legacy English reranker. Weights are Apache-2.0, so commercial self-hosting does not need the CC BY-NC waiver.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/reranker/jina-reranker-v1-tiny-en": { "id": "jina/reranker/jina-reranker-v1-tiny-en", "name": "jina-reranker-v1-tiny-en", "maker": "jina", "family": "jina/reranker", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-18", "context_length": 8192, "max_output_tokens": null, "parameters": "33M", "architecture": "English reranker, 33M parameters, 8,192-token context, Apache-2.0. The 2026 FAQ calls the v1 trio legacy. Architecture details beyond size were not re-read from the model card.", "modalities": { "input": [ "text" ], "output": [ "relevance-score" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-reranker-v1-tiny-en is a reranker model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Legacy English reranker. Weights are Apache-2.0, so commercial self-hosting does not need the CC BY-NC waiver.", "what_it_is_for": "Legacy English reranker. Weights are Apache-2.0, so commercial self-hosting does not need the CC BY-NC waiver.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/reranker/jina-reranker-v1-turbo-en": { "id": "jina/reranker/jina-reranker-v1-turbo-en", "name": "jina-reranker-v1-turbo-en", "maker": "jina", "family": "jina/reranker", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-18", "context_length": 8192, "max_output_tokens": null, "parameters": "37.8M", "architecture": "English reranker, 37.8M parameters, 8,192-token context, Apache-2.0. The 2026 FAQ calls the v1 trio legacy. Architecture details beyond size were not re-read from the model card.", "modalities": { "input": [ "text" ], "output": [ "relevance-score" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-reranker-v1-turbo-en is a reranker model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Legacy English reranker. Weights are Apache-2.0, so commercial self-hosting does not need the CC BY-NC waiver.", "what_it_is_for": "Legacy English reranker. Weights are Apache-2.0, so commercial self-hosting does not need the CC BY-NC waiver.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/reranker/jina-reranker-v2-base-multilingual": { "id": "jina/reranker/jina-reranker-v2-base-multilingual", "name": "jina-reranker-v2-base-multilingual", "maker": "jina", "family": "jina/reranker", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-06-25", "context_length": 1024, "max_output_tokens": null, "parameters": "278M", "architecture": "Pointwise cross-encoder on Jina-XLM-RoBERTa, 278M parameters. Scores each query-document pair independently. The m0 card also lists function-free text reranking and says this architecture supports code, function signatures, and tables. 100+ languages on the 2026 FAQ.", "modalities": { "input": [ "text" ], "output": [ "relevance-score" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-reranker-v2-base-multilingual is a reranker model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Smaller multilingual cross-encoder. Still served for users who do not want a Qwen-derived reranker.", "what_it_is_for": "Smaller multilingual cross-encoder. Still served for users who do not want a Qwen-derived reranker.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/reranker/jina-reranker-v3": { "id": "jina/reranker/jina-reranker-v3", "name": "jina-reranker-v3", "maker": "jina", "family": "jina/reranker", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2025-10-01", "context_length": 131072, "max_output_tokens": null, "parameters": "597M", "architecture": "Listwise reranker on Qwen3-0.6B, 28 layers, MLP projector 1024 to 512 to 256. Last-but-not-late interaction: query and documents share one causal context, and the score is read from the last token of each document. Up to 64 documents in one window. Index says 597M; marketing says 0.6B. GGUF and MLX repos exist.", "modalities": { "input": [ "text" ], "output": [ "relevance-score" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-reranker-v3 is a reranker model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Listwise multilingual reranker. Still served. New projects are told to use v3.5.", "what_it_is_for": "Listwise multilingual reranker. Still served. New projects are told to use v3.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jina/reranker/jina-reranker-v3-5": { "id": "jina/reranker/jina-reranker-v3-5", "name": "jina-reranker-v3.5", "maker": "jina", "family": "jina/reranker", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2026-07-27", "context_length": 131072, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Listwise reranker, 0.6B parameters, 131,072-token context. Hybrid attention: three sliding-window layers then two global layers, with the last layer global. Trained with self-distillation on a multi-domain mixture. Drop-in replacement for jina-reranker-v3. llms.txt also lists an output dimension of 512, which matches a projector width rather than an embedding product default. Quantized and MLX builds exist for the v3 line; v3.5 GGUF and MLX repos were seen on Hugging Face.", "modalities": { "input": [ "text" ], "output": [ "relevance-score" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "jina-reranker-v3.5 is a reranker model from Jina AI (Elastic since October 2025). Status is active. Collected 2026-10-15. Current text reranker. Listwise, multilingual, and the model Jina tells you to switch to from v3.", "what_it_is_for": "Current text reranker. Listwise, multilingual, and the model Jina tells you to switch to from v3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jyvaskyla/finarcner/finarcner": { "id": "jyvaskyla/finarcner/finarcner", "name": "FinArcNER", "maker": "jyvaskyla", "family": "jyvaskyla/finarcner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-20", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForTokenClassification fine-tune of TurkuNLP/bert-base-finnish-cased-v1 (FinBERT). config.json: 12 layers, hidden size 768, 12 heads, vocab 50105, max position embeddings 512, classifier dropout 0.3. IOB2 tags for 10 entity types.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinArcNER is a Finnish named-entity tagger released by the University of Jyv\u00e4skyl\u00e4 digital-humanities group (jyu-digihum) under the FIN-CLARIAH project, trained with the National Archives of Finland. It is a FinBERT token-classification fine-tune for state-authority archives. Weights were on Hugging Face as of the Hub record created 2025-01-20. Collected 2026-10-15.", "what_it_is_for": "The card and the 2025 paper say FinArcNER finds named entities in Finnish text, especially OCR'd state-authority archives. Labels in `config.json` are PERSON, ORG, LOC, GPE, PRODUCT, EVENT, DATE, JON (journal number), FIBC (Finnish business identity code), and NORP, in IOB2 form.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jyvaskyla/findoctype/findoctype": { "id": "jyvaskyla/findoctype/findoctype", "name": "FinDoctype", "maker": "jyvaskyla", "family": "jyvaskyla/findoctype", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-05-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision Transformer image classifier. The card says it was pretrained from vit_base_patch16_384. Weights are a single fin_doctype_7classes.pt file loaded through timm. No config.json is in the repo.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinDoctype is an image classifier from the University of Jyv\u00e4skyl\u00e4 FIN-CLARIAH group (jyu-digihum), trained with the National Archives of Finland on digitized archival pages. The Hub card lists an MIT license and calls the release a work in progress. The weight file was public on 2026-10-15. The Hub record was created 2025-05-28. Collected 2026-10-15.", "what_it_is_for": "The card says FinDoctype classifies a page image into cover page, card index, map, picture, running text (contracts, reports, memos, or letters), newspaper clipping, or table or form. Training images come from the National Archives Astia service.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jyvaskyla/graphbnc/graphbnc": { "id": "jyvaskyla/graphbnc/graphbnc", "name": "GraphBNC", "maker": "jyvaskyla", "family": "jyvaskyla/graphbnc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Feedforward networks with three hidden layers (128, 64, 32) and ReLU. Coulomb and van der Waals ligand-residue energies are predicted separately. Inputs are Weisfeiler-Lehman graph features of a gold-nanocluster ligand and a protein residue. The paper selected two WL updates and a 10 \u00c5 pair distance. Pretrained weights ship inside the code dataset.", "modalities": { "input": [ "numeric" ], "output": [ "numeric" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GraphBNC is a University of Jyv\u00e4skyl\u00e4 Nanoscience Center method for placing protected gold nanoclusters on blood proteins. The machine-learning piece is a set of small feedforward networks with pretrained weights in the university's code release. The JYX dataset is versioned 19 June 2024. The paper is Advanced Materials 36(47), 2407046 (2024). Collected 2026-10-15.", "what_it_is_for": "The paper says GraphBNC predicts coarse-grained interaction strengths between a thiolate-protected gold nanocluster and protein residues, then uses Monte Carlo simulated annealing to place the cluster. The case study uses *p*-MBA and *p*-MBSA protected clusters with albumin, apolipoprotein E, immunoglobulins, and fibrinogen. It is not a language or vision product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "jyvaskyla/l2-finnish/l2-finnish": { "id": "jyvaskyla/l2-finnish/l2-finnish", "name": "L2 Finnish model", "maker": "jyvaskyla", "family": "jyvaskyla/l2-finnish", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-15", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "FinBERT encoder (TurkuNLP/bert-base-finnish-cased-v1) with weighted layer pooling from layer 9, multi-sample dropout, and a linear head for 5 CEFR classes. The Hub config.json says BertForMaskedLM, which does not match the inference notebook or the paper. The checkpoint is l2finnishmodel.pth, loaded with a custom state dict.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The L2 Finnish model is a CEFR writing classifier from the University of Jyv\u00e4skyl\u00e4, released by the FIN-CLARIAH group on Hugging Face as `jyu-digihum/l2finnishmodel`. It fine-tunes FinBERT on second-language Finnish texts. The Hub record was created 2025-04-15. The authors' paper is Studies in Language Assessment 14(2), 2025. Collected 2026-10-15.", "what_it_is_for": "The card and the paper say the model places a written L2 Finnish text on the CEFR scale. C1 and C2 are one class, C, because those levels were rare. The paper says the checkpoint is meant for formative feedback, not high-stakes assessment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaiber/flipbook/flipbook": { "id": "kaiber/flipbook/flipbook", "name": "Flipbook", "maker": "kaiber", "family": "kaiber/flipbook", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flipbook is Kaiber's closed, audio-reactive frame-by-frame video model. It is still a named Canvas model, priced at 1 credit per second on the Superstudio credit table, and was current in Kaiber's generating-video guide as of this collection on 2026-10-15.", "what_it_is_for": "Kaiber describes Flipbook as the model for a single-prompt music video. It animates frame to frame from a text prompt and an optional start image, with curated or custom aesthetics. Camera moves can follow an uploaded track. The help center contrasts it with the hosted realism models (Kling and others), which it recommends when the job is narrative motion rather than a long stylized animation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaiber/lip-sync/image-lip-sync": { "id": "kaiber/lip-sync/image-lip-sync", "name": "Image Lip Sync", "maker": "kaiber", "family": "kaiber/lip-sync", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Image Lip Sync is a named Kaiber Canvas video model that drives a still face from an audio file. The current help pages cap it at 300 seconds and offer 480p or 720p. Kaiber does not say whose weights it runs. Collected 2026-10-15.", "what_it_is_for": "Kaiber uses it to make one character speak or sing. The home-page workflow Character Lip Sync opens Image Lip Sync. In Canvas it is a Create Video model. The use-case guide pairs it with Video Lip Sync when a music video needs a mouth matched to vocals.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaiber/lip-sync/video-lip-sync": { "id": "kaiber/lip-sync/video-lip-sync", "name": "Video Lip Sync", "maker": "kaiber", "family": "kaiber/lip-sync", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Video Lip Sync is a named Kaiber Canvas model that retargets speech or vocals onto an existing video of a person. Current pages cap it at 30 seconds and say the picture loops if the audio is longer. Kaiber does not name the weights. Collected 2026-10-15.", "what_it_is_for": "Kaiber positions it next to Image Lip Sync: use this one when the performance already exists as video. The use-case guide points music-video makers at it for lip movement on a character. Generating Videos says it matches an audio upload and loops the clip to cover that audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaiber/motion/motion": { "id": "kaiber/motion/motion", "name": "Motion", "maker": "kaiber", "family": "kaiber/motion", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Motion is Kaiber's closed image-animation model, shipped as Motion 2.0 (December 2023) and then Motion 3.0 (beta March 2024). It is not on the current Superstudio credit table, while an older Canvas looping guide still names it. Collected 2026-10-15. Status is deprecated because those official pages disagree and no shutdown notice was found.", "what_it_is_for": "Kaiber used Motion for smoother animation than Flipbook. Motion 3.0 was meant to keep subjects stable across seconds, stick closer to an init image, and offer a photorealistic style. Motion Brush paints the regions that should move. A later looping guide still tells creators to pick Motion, set Intensity to 0 or 1, and describe only the motion, such as a flickering fire, keeping the clip near 5 seconds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaiber/transform/transform-1-0": { "id": "kaiber/transform/transform-1-0", "name": "Transform 1.0", "maker": "kaiber", "family": "kaiber/transform", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Transform 1.0 is Kaiber's first named video-to-video restyle model. The help center said it would be discontinued on 12 February 2025 and pointed users to Transform 3.0, later called Video Restyle 1, or to Video Restyle 2 (Transform 4.0). It is retired as of this collection on 2026-10-15.", "what_it_is_for": "Kaiber's Transform feature restyles an existing clip. Version 1.0 stayed available after 2.0 and 3.0 shipped. The comparison page says 3.0 is faster and allows more stylization, while 1.0 kept a higher maximum resolution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaiber/transform/transform-2-0": { "id": "kaiber/transform/transform-2-0", "name": "Transform 2.0", "maker": "kaiber", "family": "kaiber/transform", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-12-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Transform 2.0 is a retired Kaiber video-to-video restyle model. The help center says Transform 3.0, released in March 2024, replaced it and that 2.0 is no longer available. A community post dated 13 December 2023 announced it as live on web and mobile. Collected 2026-10-15. Confidence is medium because the launch day is not on an official page.", "what_it_is_for": "Kaiber described the launch as an upgrade for smoother, less glitchy restyles, while leaving Transform 1.0 in place. When 3.0 replaced 2.0, Kaiber published an Evolve map so users could chase the 2.0 look: 3.0 Evolve 0, 1, 2, and 3 were said to resemble 2.0 Evolve 4, 6, 8, and 10.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaiber/transform/video-restyle-1": { "id": "kaiber/transform/video-restyle-1", "name": "Video Restyle 1", "maker": "kaiber", "family": "kaiber/transform", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Video Restyle 1 is Kaiber's closed video-to-video restyle model, also called Transform 3.0 and Transform V3. Kaiber says it shipped in March 2024, replacing Transform 2.0. It is still on the Flex model list and the Superstudio credit table. Collected 2026-10-15.", "what_it_is_for": "Kaiber uses it to restyle a clip into a chosen aesthetic. The restyle guide calls the look highly stylized, with fluid movement. A subject prompt and either a curated Kaiber aesthetic or the user's own prompt steer the result. An intensity slider controls how far the picture moves from the source. Higher intensity means more change.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaiber/transform/video-restyle-2": { "id": "kaiber/transform/video-restyle-2", "name": "Video Restyle 2", "maker": "kaiber", "family": "kaiber/transform", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Video Restyle 2 is Kaiber's newer closed video-to-video restyle model. The help center also calls it Transform 4.0 and Transform V4. It is on the current Flex list and the Superstudio credit table. Collected 2026-10-15. No launch date was found.", "what_it_is_for": "Kaiber describes it as a clean 2D restyle of an uploaded clip. Unlike Video Restyle 1, it accepts an optional aesthetic reference image and a slider for how hard that image influences the result. A subject prompt, a curated or custom aesthetic prompt, and an intensity slider still apply. Higher intensity means more change.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/atlas/atlasfold": { "id": "kaist/atlas/atlasfold", "name": "AtlasFold", "maker": "kaist", "family": "kaist/atlas", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-08-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Monomer folding model. GitHub table: 215M folding module plus AtlasLM-3B. Predicts structures without an MSA search. Hub base_model is SeonghwanSeo/atlaslm-3b-base. Weight id 260703.", "modalities": { "input": [ "sequence" ], "output": [ "structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AtlasFold is a Team KAIST protein model released under MIT. The Hub repo SeonghwanSeo/atlasfold-260703 was created 2026-08-09. The citation on the card is the AtlasFold bioRxiv preprint, DOI 10.64898/2026.09.04.749352. Weights were still public on 2026-10-15.", "what_it_is_for": "Single-protein structure prediction from sequence, using the AtlasLM-3B representation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/atlas/atlasfold-m": { "id": "kaist/atlas/atlasfold-m", "name": "AtlasFold-M", "maker": "kaist", "family": "kaist/atlas", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-08-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Protein-complex folding model. GitHub table: 220M plus AtlasLM-3B. No MSA search. Hub base_model is SeonghwanSeo/atlaslm-3b-base. Weight id 260725.", "modalities": { "input": [ "sequence" ], "output": [ "structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AtlasFold-M is a Team KAIST protein model released under MIT. The Hub repo SeonghwanSeo/atlasfold-m-260725 was created 2026-08-09. The citation on the card is the AtlasFold bioRxiv preprint, DOI 10.64898/2026.09.04.749352. Weights were still public on 2026-10-15.", "what_it_is_for": "Protein-complex structure prediction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/atlas/atlaslm-3b": { "id": "kaist/atlas/atlaslm-3b", "name": "AtlasLM-3B", "maker": "kaist", "family": "kaist/atlas", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-05-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Protein language model. The GitHub table lists 3.06B parameters and a 5.71 GiB download. MIT reproduction of ESM Cambrian, and the backbone named for AtlasFold.", "modalities": { "input": [ "sequence" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AtlasLM-3B is a Team KAIST protein model released under MIT. The Hub repo SeonghwanSeo/atlaslm-3b-base was created 2026-05-19. The citation on the card is the AtlasFold bioRxiv preprint, DOI 10.64898/2026.09.04.749352. Weights were still public on 2026-10-15.", "what_it_is_for": "Sequence representations and the language-model backbone for AtlasFold and AtlasFold-M.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/atlas/atlaslm-600m": { "id": "kaist/atlas/atlaslm-600m", "name": "AtlasLM-600M", "maker": "kaist", "family": "kaist/atlas", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-05-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Protein language model. The AtlasFold GitHub table lists 575M parameters and a 1.07 GiB download. The card calls it an MIT reproduction of EvolutionaryScale ESM Cambrian.", "modalities": { "input": [ "sequence" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AtlasLM-600M is a Team KAIST protein model released under MIT. The Hub repo SeonghwanSeo/atlaslm-600m-base was created 2026-05-11. The citation on the card is the AtlasFold bioRxiv preprint, DOI 10.64898/2026.09.04.749352. Weights were still public on 2026-10-15.", "what_it_is_for": "Sequence representations for proteins. The card contrasts the MIT weights with the more restrictive ESM-C license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/cot/cot-t5-11b": { "id": "kaist/cot/cot-t5-11b", "name": "CoT-T5 11B", "maker": "kaist", "family": "kaist/cot", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flan-T5 encoder-decoder further trained on 1.84 million chain-of-thought rationales across 1,060 tasks, plus FLAN v2. Paired 11B release. The 3B card's cross-link text points at the wrong page, so this dossier does not assert a specific google/flan-t5 repo id beyond the Flan-T5 family.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CoT-T5 11B is a KAIST AI Flan-T5 model fine-tuned on the CoT Collection. The Hub repo was created 26 June 2023. The paper is 23 May 2023. Apache-2.0 weights were still public on 2026-10-15.", "what_it_is_for": "Zero-shot chain-of-thought on unseen tasks, and a starting point for more CoT fine-tuning. The card says it is stronger with CoT than the Flan-T5 base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/cot/cot-t5-3b": { "id": "kaist/cot/cot-t5-3b", "name": "CoT-T5 3B", "maker": "kaist", "family": "kaist/cot", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flan-T5 encoder-decoder further trained on 1.84 million chain-of-thought rationales across 1,060 tasks, plus FLAN v2. The 3B card says the base is Flan-T5 and links google/flan-t5-xxl, which is the 11B checkpoint. The exact Flan-T5 id for this file is therefore not clean on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CoT-T5 3B is a KAIST AI Flan-T5 model fine-tuned on the CoT Collection. The Hub repo was created 26 June 2023. The paper is 23 May 2023. Apache-2.0 weights were still public on 2026-10-15.", "what_it_is_for": "Zero-shot chain-of-thought on unseen tasks, and a starting point for more CoT fine-tuning. The card says it is stronger with CoT than the Flan-T5 base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/janus/janus-7b": { "id": "kaist/janus/janus-7b", "name": "Janus 7B", "maker": "kaist", "family": "kaist/janus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-05-28", "context_length": null, "max_output_tokens": null, "parameters": "7241732096", "architecture": "Mistral-7B-v0.2 family. Hub base_model field: alpindale/Mistral-7B-v0.2-hf. Safetensors total 7241732096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Janus 7B is KAIST AI's supervised Mistral-7B-v0.2 model for the paper Aligning to Thousands of Preferences via System Message Generalization (28 May 2024). The Hub repo was created 4 April 2024. Apache-2.0 weights were still public on 2026-10-15.", "what_it_is_for": "Supervised fine-tune of Mistral-7B-v0.2 on the Multifaceted Collection so one model follows many system messages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/janus/janus-dpo-7b": { "id": "kaist/janus/janus-dpo-7b", "name": "Janus-DPO 7B", "maker": "kaist", "family": "kaist/janus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-05-28", "context_length": null, "max_output_tokens": null, "parameters": "7241732096", "architecture": "Mistral-7B-v0.2 family. Hub base_model field: kaist-ai/mpa-Mistral-7b-v0.2-hf-sft-66k. Safetensors total 7241732096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Janus-DPO 7B is the DPO Janus checkpoint KAIST AI released for the same May 2024 paper. Apache-2.0. Hub repo created 25 April 2024. Still public on 2026-10-15.", "what_it_is_for": "DPO variant of Janus, trained on Multifaceted-Collection-DPO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/janus/janus-orpo-7b": { "id": "kaist/janus/janus-orpo-7b", "name": "Janus-ORPO 7B", "maker": "kaist", "family": "kaist/janus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-05-28", "context_length": null, "max_output_tokens": null, "parameters": "7241732096", "architecture": "Mistral-7B-v0.2 family. Hub base_model field: alpindale/Mistral-7B-v0.2-hf. Safetensors total 7241732096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Janus-ORPO 7B applies ORPO to Mistral-7B-v0.2 for the Janus paper. Apache-2.0. Hub repo created 18 April 2024. A Hub inference widget has listed Featherless as a provider; that is not a KAIST API.", "what_it_is_for": "ORPO variant of Janus on Multifaceted-Collection-ORPO. The card says it is meant to follow diverse system messages and also to be generally helpful and harmless.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/janus/janus-rm-7b": { "id": "kaist/janus/janus-rm-7b", "name": "Janus-RM 7B", "maker": "kaist", "family": "kaist/janus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-05-28", "context_length": null, "max_output_tokens": null, "parameters": "7110664192", "architecture": "Mistral-7B-v0.2 family. Hub base_model field: not stated on the collected card fields. Safetensors total 7110664192.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Janus-RM 7B is the reward model in the Janus release set. Apache-2.0. Hub safetensors total is 7,110,664,192, smaller than the 7,241,732,096 chat checkpoints. Repo created 9 May 2024. Still public on 2026-10-15.", "what_it_is_for": "Reward-model variant of Janus. The Hub pipeline tag is empty. Output is recorded as a score because the repo is the RM release; the card excerpt does not quote the tensor layout.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/k-fold/k-fold": { "id": "kaist/k-fold/k-fold", "name": "K-Fold", "maker": "kaist", "family": "kaist/k-fold", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-08-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Apo-to-holo diffusion model for biomolecular complexes. It does not use MSAs. config.yaml on kaist-ai-bio/kfold sets a trunk of 48 main blocks and separate protein sequence and structure encoders (d_model 2304 with 48 layers, and d_model 2560 with 33 layers) that the card says are AtlasLM and TriProRep. Total parameter count is not stated.", "modalities": { "input": [ "structure" ], "output": [ "structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K-Fold is Team KAIST's biomolecular foundation model for protein complexes and binding-induced shape change, including GPCRs and kinases. KAIST unveiled it on 28 August 2026. Apache-2.0 weights are on kaist-ai-bio/kfold, created 8 October 2026. An earlier beta repo, SeonghwanSeo/kfold, was created 8 September 2026 and labeled v1.0.0b1. The org card still said the preprint was not out on the day this dossier was written. Status is preview.", "what_it_is_for": "Predict structures of proteins, DNA, RNA, small molecules, and chemical modifications, and how unbound molecules change when they bind. Default inference builds apo structures with AtlasFold, then samples the complex. A local CLI and a planned HyperLab service from the KAIST spinoff HITS are the documented ways to run it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/kormo/kormo-10b-base": { "id": "kaist/kormo/kormo-10b-base", "name": "KORMo-10B-base", "maker": "kaist", "family": "kaist/kormo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": 131072, "max_output_tokens": null, "parameters": "10756624384", "architecture": "KORMoForCausalLM decoder. config.json on KORMo-10B-base: 40 layers, hidden size 4096, 32 heads, 8 KV heads, head dim 128, SwiGLU intermediate 16384, RoPE theta 8000000, vocab 125184, untied embeddings, bfloat16, max_position_embeddings 131072. Safetensors total 10756624384. The card rounds this to 10.8B. A Hub widget has also shown 11B. This repo is the base model, not the SFT chat model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KORMo-10B-base is the pretrained Korean-English model from the KAIST Graduate School of Culture Technology MLP Lab. The card's official release line is 13 October 2025. The Hub repo was created 11 October 2025. Apache-2.0. The paper is 10 October 2025. Weights were still public on 2026-10-15. It is not an instruction model.", "what_it_is_for": "Base model for people who want to reproduce or continue a from-scratch Korean-English LLM. The card says about 3 trillion tokens were used and that 68.74% of the Korean portion is synthetic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/kormo/kormo-10b-sft": { "id": "kaist/kormo/kormo-10b-sft", "name": "KORMo-10B-sft", "maker": "kaist", "family": "kaist/kormo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": 131072, "max_output_tokens": null, "parameters": "10756624384", "architecture": "KORMoForCausalLM decoder. config.json on KORMo-10B-base: 40 layers, hidden size 4096, 32 heads, 8 KV heads, head dim 128, SwiGLU intermediate 16384, RoPE theta 8000000, vocab 125184, untied embeddings, bfloat16, max_position_embeddings 131072. Safetensors total 10756624384. The card rounds this to 10.8B. A Hub widget has also shown 11B. This repo is the supervised chat checkpoint. Its safetensors total matches the base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KORMo-10B-sft is the instruction-tuned KORMo checkpoint from the KAIST MLP Lab. The card dates the official SFT release to 13 October 2025. Apache-2.0. It was still public on 2026-10-15.", "what_it_is_for": "Korean and English chat and reasoning. The card tells users who want a Korean model that they can build on this stack, including on a free Colab GPU.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/kormo/kormo-vl": { "id": "kaist/kormo/kormo-vl", "name": "KORMo-VL", "maker": "kaist", "family": "kaist/kormo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-02", "context_length": null, "max_output_tokens": null, "parameters": "11191119728", "architecture": "LLaVA-OneVision-style vision-language model. Card: language model KORMo-10B-sft, vision encoder siglip-so400m. Hub safetensors total 11,191,119,728. Trained on about 3.4 million image-text pairs, then visual instruction tuning and reinforcement learning.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KORMo-VL is the KAIST MLP Lab vision-language model on top of KORMo-10B-sft. The card's release line is 2 March 2026. Apache-2.0. Weights were still public on 2026-10-15.", "what_it_is_for": "Korean and English image-text tasks, including document-grounded reasoning, according to the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/kormo/kormo-vl-diffusion": { "id": "kaist/kormo/kormo-vl-diffusion", "name": "KORMo-VL-Diffusion", "maker": "kaist", "family": "kaist/kormo", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": null, "max_output_tokens": null, "parameters": "20431974464", "architecture": "Image-generation model. The card says the diffusion stack is about 20B, trained from scratch with a structure referred from Qwen-Image, and that the language model is KORMo-VL. Hub safetensors total 20,431,974,464. The card says this share is an intermediate result because more GPUs were not secured.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KORMo-VL-Diffusion is the KAIST MLP Lab image generator paired with KORMo-VL. The card dates it to 5 March 2026 and calls the upload an intermediate result. Apache-2.0. It was still public on 2026-10-15, so status is preview rather than a finished product.", "what_it_is_for": "Text-to-image generation with a high share of Korean daily scenes, according to the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/langbridge/codellama-langbridge-15b": { "id": "kaist/langbridge/codellama-langbridge-15b", "name": "Code Llama LangBridge 15B", "maker": "kaist", "family": "kaist/langbridge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "14696318976", "architecture": "mT5 encoder bridged to Code Llama. Trained with English data only. Hub safetensors total 14696318976. README says Code Llama bridges are not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Code Llama LangBridge 15B is one of the LangBridge checkpoints KAIST AI published on 23 January 2024. The paper is 19 January 2024 and was an ACL 2024 main-conference paper. Apache-2.0. The card says an mT5 encoder is bridged to an English-centric LM using only English supervision. Weights were still public on 2026-10-15.", "what_it_is_for": "Multilingual math, code, or general reasoning at test time without multilingual fine-tuning data. The README says this base is not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/langbridge/codellama-langbridge-20b": { "id": "kaist/langbridge/codellama-langbridge-20b", "name": "Code Llama LangBridge 20B", "maker": "kaist", "family": "kaist/langbridge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "18692019200", "architecture": "mT5 encoder bridged to Code Llama. Trained with English data only. Hub safetensors total 18692019200. README says Code Llama bridges are not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Code Llama LangBridge 20B is one of the LangBridge checkpoints KAIST AI published on 23 January 2024. The paper is 19 January 2024 and was an ACL 2024 main-conference paper. Apache-2.0. The card says an mT5 encoder is bridged to an English-centric LM using only English supervision. Weights were still public on 2026-10-15.", "what_it_is_for": "Multilingual math, code, or general reasoning at test time without multilingual fine-tuning data. The README says this base is not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/langbridge/codellama-langbridge-9b": { "id": "kaist/langbridge/codellama-langbridge-9b", "name": "Code Llama LangBridge 9B", "maker": "kaist", "family": "kaist/langbridge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "8416771072", "architecture": "mT5 encoder bridged to Code Llama. Trained with English data only. Hub safetensors total 8416771072. README says Code Llama bridges are not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Code Llama LangBridge 9B is one of the LangBridge checkpoints KAIST AI published on 23 January 2024. The paper is 19 January 2024 and was an ACL 2024 main-conference paper. Apache-2.0. The card says an mT5 encoder is bridged to an English-centric LM using only English supervision. Weights were still public on 2026-10-15.", "what_it_is_for": "Multilingual math, code, or general reasoning at test time without multilingual fine-tuning data. The README says this base is not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/langbridge/llama2-langbridge-9b": { "id": "kaist/langbridge/llama2-langbridge-9b", "name": "Llama 2 LangBridge 9B", "maker": "kaist", "family": "kaist/langbridge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "8416771072", "architecture": "mT5 encoder bridged to Llama 2. Trained with English data only. Hub safetensors total 8416771072. The LangBridge README marks Llama 2 as not instruction-tuned and not recommended.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 2 LangBridge 9B is one of the LangBridge checkpoints KAIST AI published on 23 January 2024. The paper is 19 January 2024 and was an ACL 2024 main-conference paper. Apache-2.0. The card says an mT5 encoder is bridged to an English-centric LM using only English supervision. Weights were still public on 2026-10-15.", "what_it_is_for": "Multilingual math, code, or general reasoning at test time without multilingual fine-tuning data. The README says this base is not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/langbridge/llemma-langbridge-9b": { "id": "kaist/langbridge/llemma-langbridge-9b", "name": "Llemma LangBridge 9B", "maker": "kaist", "family": "kaist/langbridge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "8416902144", "architecture": "mT5 encoder bridged to Llemma. Trained with English data only. Hub safetensors total 8416902144. Hub repo id is misspelled llemma-langbrige-9b. The README says Llemma is not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llemma LangBridge 9B is one of the LangBridge checkpoints KAIST AI published on 23 January 2024. The paper is 19 January 2024 and was an ACL 2024 main-conference paper. Apache-2.0. The card says an mT5 encoder is bridged to an English-centric LM using only English supervision. Weights were still public on 2026-10-15.", "what_it_is_for": "Multilingual math, code, or general reasoning at test time without multilingual fine-tuning data. The README says this base is not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/langbridge/metamath-langbridge-15b": { "id": "kaist/langbridge/metamath-langbridge-15b", "name": "MetaMath LangBridge 15B", "maker": "kaist", "family": "kaist/langbridge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "14696329216", "architecture": "mT5 encoder bridged to MetaMath. Trained with English data only. Hub safetensors total 14696329216. README groups MetaMath as instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MetaMath LangBridge 15B is one of the LangBridge checkpoints KAIST AI published on 23 January 2024. The paper is 19 January 2024 and was an ACL 2024 main-conference paper. Apache-2.0. The card says an mT5 encoder is bridged to an English-centric LM using only English supervision. Weights were still public on 2026-10-15.", "what_it_is_for": "Multilingual math, code, or general reasoning at test time without multilingual fine-tuning data. The README lists this base as instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/langbridge/metamath-langbridge-20b": { "id": "kaist/langbridge/metamath-langbridge-20b", "name": "MetaMath LangBridge 20B", "maker": "kaist", "family": "kaist/langbridge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "18692029440", "architecture": "mT5 encoder bridged to MetaMath. Trained with English data only. Hub safetensors total 18692029440. README groups MetaMath as instruction-tuned. This is the only MetaMath size near the paper's MetaMath-13B plus mT5-XXL setup, but the card does not say they are the same checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MetaMath LangBridge 20B is one of the LangBridge checkpoints KAIST AI published on 23 January 2024. The paper is 19 January 2024 and was an ACL 2024 main-conference paper. Apache-2.0. The card says an mT5 encoder is bridged to an English-centric LM using only English supervision. Weights were still public on 2026-10-15.", "what_it_is_for": "Multilingual math, code, or general reasoning at test time without multilingual fine-tuning data. The README lists this base as instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/langbridge/metamath-langbridge-9b": { "id": "kaist/langbridge/metamath-langbridge-9b", "name": "MetaMath LangBridge 9B", "maker": "kaist", "family": "kaist/langbridge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "8416779264", "architecture": "mT5 encoder bridged to MetaMath. Trained with English data only. Hub safetensors total 8416779264. README groups MetaMath as instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MetaMath LangBridge 9B is one of the LangBridge checkpoints KAIST AI published on 23 January 2024. The paper is 19 January 2024 and was an ACL 2024 main-conference paper. Apache-2.0. The card says an mT5 encoder is bridged to an English-centric LM using only English supervision. Weights were still public on 2026-10-15.", "what_it_is_for": "Multilingual math, code, or general reasoning at test time without multilingual fine-tuning data. The README lists this base as instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/langbridge/orca2-langbridge-15b": { "id": "kaist/langbridge/orca2-langbridge-15b", "name": "Orca 2 LangBridge 15B", "maker": "kaist", "family": "kaist/langbridge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "14696349696", "architecture": "mT5 encoder bridged to Orca 2. Trained with English data only. Hub safetensors total 14696349696. README groups Orca 2 as instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Orca 2 LangBridge 15B is one of the LangBridge checkpoints KAIST AI published on 23 January 2024. The paper is 19 January 2024 and was an ACL 2024 main-conference paper. Apache-2.0. The card says an mT5 encoder is bridged to an English-centric LM using only English supervision. Weights were still public on 2026-10-15.", "what_it_is_for": "Multilingual math, code, or general reasoning at test time without multilingual fine-tuning data. The README lists this base as instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/langbridge/orca2-langbridge-20b": { "id": "kaist/langbridge/orca2-langbridge-20b", "name": "Orca 2 LangBridge 20B", "maker": "kaist", "family": "kaist/langbridge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "18692049920", "architecture": "mT5 encoder bridged to Orca 2. Trained with English data only. Hub safetensors total 18692049920. README groups Orca 2 as instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Orca 2 LangBridge 20B is one of the LangBridge checkpoints KAIST AI published on 23 January 2024. The paper is 19 January 2024 and was an ACL 2024 main-conference paper. Apache-2.0. The card says an mT5 encoder is bridged to an English-centric LM using only English supervision. Weights were still public on 2026-10-15.", "what_it_is_for": "Multilingual math, code, or general reasoning at test time without multilingual fine-tuning data. The README lists this base as instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/langbridge/orca2-langbridge-9b": { "id": "kaist/langbridge/orca2-langbridge-9b", "name": "Orca 2 LangBridge 9B", "maker": "kaist", "family": "kaist/langbridge", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "8416795648", "architecture": "mT5 encoder bridged to Orca 2. Trained with English data only. Hub safetensors total 8416795648. README groups Orca 2 as instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Orca 2 LangBridge 9B is one of the LangBridge checkpoints KAIST AI published on 23 January 2024. The paper is 19 January 2024 and was an ACL 2024 main-conference paper. Apache-2.0. The card says an mT5 encoder is bridged to an English-centric LM using only English supervision. Weights were still public on 2026-10-15.", "what_it_is_for": "Multilingual math, code, or general reasoning at test time without multilingual fine-tuning data. The README lists this base as instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/orpo/mistral-orpo-alpha": { "id": "kaist/orpo/mistral-orpo-alpha", "name": "Mistral-ORPO-alpha", "maker": "kaist", "family": "kaist/orpo", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-03-12", "context_length": null, "max_output_tokens": null, "parameters": "7241732096", "architecture": "ORPO fine-tune of mistralai/Mistral-7B-v0.1. Hub safetensors total 7,241,732,096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral-ORPO-alpha is KAIST AI's first public ORPO checkpoint, a MIT-licensed fine-tune of Mistral-7B-v0.1 on UltraFeedback binarized. The Hub repo was created 12 March 2024, the paper date. Weights were still public on 2026-10-15.", "what_it_is_for": "Reference model for the ORPO paper: preference optimization without a separate reference model, on Mistral-7B-v0.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/orpo/mistral-orpo-beta": { "id": "kaist/orpo/mistral-orpo-beta", "name": "Mistral-ORPO-beta", "maker": "kaist", "family": "kaist/orpo", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-03-12", "context_length": null, "max_output_tokens": null, "parameters": "7241732096", "architecture": "ORPO fine-tune of mistralai/Mistral-7B-v0.1. Hub safetensors total 7,241,732,096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral-ORPO-beta is the cleaned-UltraFeedback ORPO checkpoint from KAIST AI, MIT license, Mistral-7B-v0.1. Hub repo created 12 March 2024. Still public on 2026-10-15.", "what_it_is_for": "Reference model for the ORPO paper: preference optimization without a separate reference model, on Mistral-7B-v0.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/orpo/mistral-orpo-capybara-7k": { "id": "kaist/orpo/mistral-orpo-capybara-7k", "name": "Mistral-ORPO-Capybara-7k", "maker": "kaist", "family": "kaist/orpo", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-03-23", "context_length": null, "max_output_tokens": null, "parameters": "7241732096", "architecture": "ORPO fine-tune of mistralai/Mistral-7B-v0.1. Hub safetensors total 7,241,732,096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral-ORPO-Capybara-7k is the third official ORPO release from KAIST AI, a MIT-licensed Mistral-7B-v0.1 fine-tune on the binarized Capybara 7k set. Hub repo created 23 March 2024. Still public on 2026-10-15.", "what_it_is_for": "Reference model for the ORPO paper: preference optimization without a separate reference model, on Mistral-7B-v0.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/prometheus/prometheus-13b-v1": { "id": "kaist/prometheus/prometheus-13b-v1", "name": "Prometheus 13B v1.0", "maker": "kaist", "family": "kaist/prometheus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama-2-Chat 13B fine-tune on 100K GPT-4 feedback items from the Feedback Collection.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Prometheus 13B v1.0 is the KAIST AI evaluator the Prometheus paper treats as the main model. It was released on 12 October 2023 under Apache-2.0 and was still public on 2026-10-15. The abstract says it matches GPT-4 when a rubric and a reference answer are supplied.", "what_it_is_for": "Fine-grained scoring of long-form text against a user rubric, plus use as an open reward model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/prometheus/prometheus-2-bgb-8x7b": { "id": "kaist/prometheus/prometheus-2-bgb-8x7b", "name": "Prometheus-2-BGB 8x7B", "maker": "kaist", "family": "kaist/prometheus", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-04-11", "context_length": null, "max_output_tokens": null, "parameters": "46702792704", "architecture": "The BiGGen paper appendix says this is Prometheus 2 8x7B further trained with LoRA on GPT-4-1106 judgments. Hub safetensors total is 46,702,792,704. The Hub card itself is an empty template.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Prometheus-2-BGB 8x7B is the BiGGen Bench evaluator KAIST AI and coauthors trained on top of Prometheus 2 8x7B. The Hub repo prometheus-eval/prometheus-bgb-8x7b-v2.0 was created 11 April 2024. The card text is a blank template, so license and language are taken as unknown except where the paper specifies the base. Weights were still public on 2026-10-15.", "what_it_is_for": "Fine-grained judging on the BiGGen Bench's instance-level criteria. The paper uses it as an open judge beside GPT-4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/prometheus/prometheus-7b-v1": { "id": "kaist/prometheus/prometheus-7b-v1", "name": "Prometheus 7B v1.0", "maker": "kaist", "family": "kaist/prometheus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama-2-Chat fine-tune. The 7B card says both 7B and 13B sizes were trained on 100K Feedback Collection responses.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Prometheus 7B v1.0 is the smaller open evaluator that KAIST AI released with Prometheus on 12 October 2023. It is a Llama-2-Chat fine-tune for rubric-based scoring of long-form answers. Weights were still public under Apache-2.0 on 2026-10-15. The paper's headline human correlation is reported for the 13B model, not this checkpoint.", "what_it_is_for": "The card says it substitutes for GPT-4 when a custom rubric and a reference answer are provided, and that it can be used as an RLHF reward model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/prometheus/prometheus-7b-v2": { "id": "kaist/prometheus/prometheus-7b-v2", "name": "Prometheus 2 7B", "maker": "kaist", "family": "kaist/prometheus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-13", "context_length": null, "max_output_tokens": null, "parameters": "7241732096", "architecture": "Mistral-Instruct fine-tune. Weights for absolute grading and pairwise ranking were merged. Hub safetensors total is 7,241,732,096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Prometheus 2 7B is KAIST AI's merged evaluator on a Mistral-Instruct base. The Hub repo was created 13 February 2024; the paper is 2 May 2024 (EMNLP 2024). Apache-2.0 weights were still public on 2026-10-15.", "what_it_is_for": "Score or rank model outputs under a user-written rubric, in both direct assessment and pairwise formats.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/prometheus/prometheus-8x7b-v2": { "id": "kaist/prometheus/prometheus-8x7b-v2", "name": "Prometheus 2 8x7B", "maker": "kaist", "family": "kaist/prometheus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-20", "context_length": null, "max_output_tokens": null, "parameters": "46702792704", "architecture": "Mixtral-Instruct 8x7B fine-tune with weight merging of an absolute-grading model and a pairwise-ranking model. Hub safetensors total is 46,702,792,704.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Prometheus 2 8x7B is the larger KAIST AI evaluator in the Prometheus 2 pair. The Hub repo dates to 20 February 2024 and the paper to 2 May 2024. Apache-2.0 weights were still public on 2026-10-15.", "what_it_is_for": "Same job as Prometheus 2 7B: custom-rubric direct scores and pairwise rankings, at Mixtral scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/prometheus/prometheus-vision-13b": { "id": "kaist/prometheus/prometheus-vision-13b", "name": "Prometheus-Vision 13B", "maker": "kaist", "family": "kaist/prometheus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language evaluator. The card does not name the vision backbone in the excerpt collected here.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Prometheus-Vision 13B is an open vision-language judge from KAIST AI, released on the Hub on 14 January 2024 (paper 12 January 2024). It scores an image-conditioned response against a rubric. Apache-2.0 weights were still public on 2026-10-15.", "what_it_is_for": "Cheap substitute for GPT-4V when the judge must see an image and grade a response on a custom rubric.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/prometheus/prometheus-vision-7b": { "id": "kaist/prometheus/prometheus-vision-7b", "name": "Prometheus-Vision 7B", "maker": "kaist", "family": "kaist/prometheus", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language evaluator. The card does not name the vision backbone in the excerpt collected here.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Prometheus-Vision 7B is an open vision-language judge from KAIST AI, released on the Hub on 14 January 2024 (paper 12 January 2024). It scores an image-conditioned response against a rubric. Apache-2.0 weights were still public on 2026-10-15.", "what_it_is_for": "Cheap substitute for GPT-4V when the judge must see an image and grade a response on a custom rubric.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/selfee/selfee-13b": { "id": "kaist/selfee/selfee-13b", "name": "SelFee 13B", "maker": "kaist", "family": "kaist/selfee", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2023-05-31", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Instruction fine-tune released only as a weight diff. The 7B training command names llama-7b for the 7B script. The 13B artifact is the matching diff. The README says restoration needs Meta's original LLaMA checkpoint in Hugging Face format.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SelFee 13B is KAIST AI's self-revising LLaMA instruction model, released 31 May 2023 as a diff under CC-BY-NC-4.0. The Hub file is kaist-ai/selfee-13b-delta. It was still public on 2026-10-15. Full merged weights were not the official artifact.", "what_it_is_for": "Given an instruction, the model generates an answer and a feedback chain, including revisions, until the feedback says no further revision is needed or a length limit is hit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/selfee/selfee-7b": { "id": "kaist/selfee/selfee-7b", "name": "SelFee 7B", "maker": "kaist", "family": "kaist/selfee", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2023-05-31", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Instruction fine-tune released only as a weight diff. The 7B training command names llama-7b. The 13B artifact is the matching diff. The README says restoration needs Meta's original LLaMA checkpoint in Hugging Face format.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SelFee 7B is KAIST AI's self-revising LLaMA instruction model, released 31 May 2023 as a diff under CC-BY-NC-4.0. The Hub file is kaist-ai/selfee-7b-delta. It was still public on 2026-10-15. Full merged weights were not the official artifact.", "what_it_is_for": "Given an instruction, the model generates an answer and a feedback chain, including revisions, until the feedback says no further revision is needed or a length limit is hit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/volcano/volcano-13b": { "id": "kaist/volcano/volcano-13b", "name": "Volcano 13B", "maker": "kaist", "family": "kaist/volcano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-11-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaVA-style multimodal model. The card says it was fine-tuned from lmsys/vicuna-13b-v1.5 on the LLaVA-v1.5 visual instruction mix plus 274K multimodal feedback and revision items collected with gpt-3.5-turbo.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Volcano 13B is KAIST AI's multimodal self-revision model. The card says it was trained in October 2023. The project note is dated 14 November 2023, and the Hub repo was created 13 November 2023. No license field is on the card. Weights were still public on 2026-10-15.", "what_it_is_for": "One model generates an answer, critiques it against the image, revises it, and decides whether to keep the revision. The card aims this at multimodal hallucination.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaist/volcano/volcano-7b": { "id": "kaist/volcano/volcano-7b", "name": "Volcano 7B", "maker": "kaist", "family": "kaist/volcano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-11-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaVA-style multimodal model. The card says it was fine-tuned from lmsys/vicuna-7b-v1.5 on the LLaVA-v1.5 visual instruction mix plus 274K multimodal feedback and revision items collected with gpt-3.5-turbo.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Volcano 7B is KAIST AI's multimodal self-revision model. The card says it was trained in October 2023. The project note is dated 14 November 2023, and the Hub repo was created 13 November 2023. No license field is on the card. Weights were still public on 2026-10-15.", "what_it_is_for": "One model generates an answer, critiques it against the image, revises it, and decides whether to keep the revision. The card aims this at multimodal hallucination.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/align/align-b7-base": { "id": "kakao/align/align-b7-base", "name": "COYO-ALIGN B7 Base", "maker": "kakao", "family": "kakao/align", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "66M image encoder + 110M text encoder", "architecture": "Dual encoder in the ALIGN design: EfficientNet-B7 image tower and BERT-base text tower, contrastive image-text training on COYO-700M. Hugging Face id kakaobrain/align-base is the Transformers port. kakaobrain/coyo-align-b7-base is the same release line.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "COYO-ALIGN B7 Base is Kakao Brain's public ALIGN dual encoder trained on COYO-700M. It is the open ALIGN checkpoint in Transformers. Collected 2026-10-15.", "what_it_is_for": "Zero-shot image classification and image-text retrieval. Kakao Brain released it so the ALIGN recipe could be reproduced without Google's private 1.8B pair set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/honeybee/honeybee-c-13b-m256": { "id": "kakao/honeybee/honeybee-c-13b-m256", "name": "Honeybee-C-13B-M256", "maker": "kakao", "family": "kakao/honeybee", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-01-19", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Multimodal LLM with a locality-enhanced projector. C-Abstractor, 256 visual tokens. Size in the name is the model-zoo nameplate. The underlying LLM's exact name is in the paper configs and was not re-counted here.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Honeybee-C-13B-M256 is a named Honeybee checkpoint from Kakao Brain's multimodal LLM project. Kakao announced the open release on 19 January 2024. Weights are non-commercial. Collected 2026-10-15.", "what_it_is_for": "Image-and-text instruction following. The project compares C-Abstractor and D-Abstractor projectors at 7B and 13B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/honeybee/honeybee-c-13b-m576": { "id": "kakao/honeybee/honeybee-c-13b-m576", "name": "Honeybee-C-13B-M576", "maker": "kakao", "family": "kakao/honeybee", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-01-19", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Multimodal LLM with a locality-enhanced projector. C-Abstractor, 576 visual tokens. Size in the name is the model-zoo nameplate. The underlying LLM's exact name is in the paper configs and was not re-counted here.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Honeybee-C-13B-M576 is a named Honeybee checkpoint from Kakao Brain's multimodal LLM project. Kakao announced the open release on 19 January 2024. Weights are non-commercial. Collected 2026-10-15.", "what_it_is_for": "Image-and-text instruction following. The project compares C-Abstractor and D-Abstractor projectors at 7B and 13B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/honeybee/honeybee-c-7b-m144": { "id": "kakao/honeybee/honeybee-c-7b-m144", "name": "Honeybee-C-7B-M144", "maker": "kakao", "family": "kakao/honeybee", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-01-19", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Multimodal LLM with a locality-enhanced projector. C-Abstractor, 144 visual tokens. Size in the name is the model-zoo nameplate. The underlying LLM's exact name is in the paper configs and was not re-counted here.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Honeybee-C-7B-M144 is a named Honeybee checkpoint from Kakao Brain's multimodal LLM project. Kakao announced the open release on 19 January 2024. Weights are non-commercial. Collected 2026-10-15.", "what_it_is_for": "Image-and-text instruction following. The project compares C-Abstractor and D-Abstractor projectors at 7B and 13B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/honeybee/honeybee-c-7b-m256": { "id": "kakao/honeybee/honeybee-c-7b-m256", "name": "Honeybee-C-7B-M256", "maker": "kakao", "family": "kakao/honeybee", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-01-19", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Multimodal LLM with a locality-enhanced projector. C-Abstractor, 256 visual tokens. Size in the name is the model-zoo nameplate. The underlying LLM's exact name is in the paper configs and was not re-counted here.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Honeybee-C-7B-M256 is a named Honeybee checkpoint from Kakao Brain's multimodal LLM project. Kakao announced the open release on 19 January 2024. Weights are non-commercial. Collected 2026-10-15.", "what_it_is_for": "Image-and-text instruction following. The project compares C-Abstractor and D-Abstractor projectors at 7B and 13B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/honeybee/honeybee-d-13b-m256": { "id": "kakao/honeybee/honeybee-d-13b-m256", "name": "Honeybee-D-13B-M256", "maker": "kakao", "family": "kakao/honeybee", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-01-19", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Multimodal LLM with a locality-enhanced projector. D-Abstractor, 256 visual tokens. Size in the name is the model-zoo nameplate. The underlying LLM's exact name is in the paper configs and was not re-counted here.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Honeybee-D-13B-M256 is a named Honeybee checkpoint from Kakao Brain's multimodal LLM project. Kakao announced the open release on 19 January 2024. Weights are non-commercial. Collected 2026-10-15.", "what_it_is_for": "Image-and-text instruction following. The project compares C-Abstractor and D-Abstractor projectors at 7B and 13B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/honeybee/honeybee-d-7b-m144": { "id": "kakao/honeybee/honeybee-d-7b-m144", "name": "Honeybee-D-7B-M144", "maker": "kakao", "family": "kakao/honeybee", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-01-19", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Multimodal LLM with a locality-enhanced projector. D-Abstractor, 144 visual tokens. Size in the name is the model-zoo nameplate. The underlying LLM's exact name is in the paper configs and was not re-counted here.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Honeybee-D-7B-M144 is a named Honeybee checkpoint from Kakao Brain's multimodal LLM project. Kakao announced the open release on 19 January 2024. Weights are non-commercial. Collected 2026-10-15.", "what_it_is_for": "Image-and-text instruction following. The project compares C-Abstractor and D-Abstractor projectors at 7B and 13B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-1-5-15-7b-a3b-base": { "id": "kakao/kanana/kanana-1-5-15-7b-a3b-base", "name": "Kanana 1.5 15.7B-A3B Base", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kanana license (custom)", "release_date": "2025-07-24", "context_length": 32768, "max_output_tokens": null, "parameters": "15.7B total", "architecture": "Mixtral-style MoE (MixtralForCausalLM). Hidden 2048, 32 layers, 32 heads, 8 KV heads, 64 experts, 8 experts per token, expert intermediate 1152, vocab 128256, RoPE theta 1.6e7, context 32768. Nameplate is 15.7B total with an A3B active-size label. Exact active parameter count is not printed apart from the name and the claim of 37% of the 8B dense FLOPs per token.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana 1.5 15.7B-A3B Base is Kakao's first open mixture-of-experts Kanana model. Weights use the custom Kanana license. Collected 2026-10-15.", "what_it_is_for": "Kakao says the sparse model matches the dense 1.5 8B class at about 37% of the FLOPs per token.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-1-5-15-7b-a3b-instruct": { "id": "kakao/kanana/kanana-1-5-15-7b-a3b-instruct", "name": "Kanana 1.5 15.7B-A3B Instruct", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kanana license (custom)", "release_date": "2025-07-24", "context_length": 32768, "max_output_tokens": null, "parameters": "15.7B total", "architecture": "Mixtral-style MoE (MixtralForCausalLM). Hidden 2048, 32 layers, 32 heads, 8 KV heads, 64 experts, 8 experts per token, expert intermediate 1152, vocab 128256, RoPE theta 1.6e7, context 32768. Nameplate is 15.7B total with an A3B active-size label. Exact active parameter count is not printed apart from the name and the claim of 37% of the 8B dense FLOPs per token. Post-trained with on-policy distillation and reinforcement learning.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana 1.5 15.7B-A3B Instruct is Kakao's open MoE chat model from July 2025. Custom Kanana license. Collected 2026-10-15.", "what_it_is_for": "Instruction-following and tool use at a lower FLOP cost than the dense 8B, according to Kakao.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-1-5-2-1b-base": { "id": "kakao/kanana/kanana-1-5-2-1b-base", "name": "Kanana 1.5 2.1B Base", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-23", "context_length": 32768, "max_output_tokens": null, "parameters": "2.1B", "architecture": "LlamaForCausalLM. Hidden 1792, 32 layers, 24 heads, 8 KV heads, intermediate 8064, vocab 128256, RoPE theta 8e6, max position 32768, tied embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana 1.5 2.1B Base is Kakao's open small 1.5 pretrain. License on the Hub is Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Base weights for the 1.5 small model, with a longer native context than Kanana Nano.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-1-5-2-1b-instruct-2505": { "id": "kakao/kanana/kanana-1-5-2-1b-instruct-2505", "name": "Kanana 1.5 2.1B Instruct 2505", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-21", "context_length": 32768, "max_output_tokens": null, "parameters": "2.1B", "architecture": "LlamaForCausalLM. Hidden 1792, 32 layers, 24 heads, 8 KV heads, intermediate 8064, vocab 128256, RoPE theta 8e6, max position 32768, tied embeddings. Instruction-tuned May 2025 snapshot.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana 1.5 2.1B Instruct 2505 is the public May 2025 chat model at the 2.1B size. Apache-2.0 weights. Collected 2026-10-15.", "what_it_is_for": "Kakao says 1.5 improves coding, math, and function calling over the first Kanana instruct models and adds a 32K native context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-1-5-8b-base": { "id": "kakao/kanana/kanana-1-5-8b-base", "name": "Kanana 1.5 8B Base", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-23", "context_length": 32768, "max_output_tokens": null, "parameters": "8B", "architecture": "LlamaForCausalLM. Hidden 4096, 32 layers, 32 heads, 8 KV heads, intermediate 14336, vocab 128256, RoPE theta 8e6, max position 32768, untied embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana 1.5 8B Base is Kakao's open dense 8B pretrain from the 1.5 release. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Base model for the public 8B 1.5 line. Kakao compares it with the closed original Kanana 8B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-1-5-8b-instruct-2505": { "id": "kakao/kanana/kanana-1-5-8b-instruct-2505", "name": "Kanana 1.5 8B Instruct 2505", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-21", "context_length": 32768, "max_output_tokens": null, "parameters": "8B", "architecture": "LlamaForCausalLM. Hidden 4096, 32 layers, 32 heads, 8 KV heads, intermediate 14336, vocab 128256, RoPE theta 8e6, max position 32768, untied embeddings. Instruction-tuned May 2025 snapshot with tool-call support documented for vLLM.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana 1.5 8B Instruct 2505 is Kakao's public May 2025 8B chat model. Apache-2.0 weights. Collected 2026-10-15.", "what_it_is_for": "Kakao says 1.5 is for harder coding, math, and function calling, with a 32K native context and an optional YaRN stretch to 128K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-2-0-9b": { "id": "kakao/kanana/kanana-2-0-9b", "name": "Kanana-2 0.9B", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-07-27", "context_length": null, "max_output_tokens": null, "parameters": "0.9B", "architecture": "Named as the last stage of the Kanana-2 SLM cascade (3B to 2B to 1.3B to 0.9B). The public release note says only 3B and 1.3B weights were included.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-2 0.9B is a small Kanana-2 model Kakao names in the July 2026 SLM series but did not publish on Hugging Face with the 3B and 1.3B weights. Collected 2026-10-15.", "what_it_is_for": "The model card and a Herald Business report describe a 0.9B instruct model for the same small-model line. It is not a public download.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-2-1-3b-base": { "id": "kakao/kanana/kanana-2-1-3b-base", "name": "Kanana-2 1.3B Base", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kanana Open License", "release_date": "2026-07-27", "context_length": 32768, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Pruned and distilled from Kanana-2 3B (cascade 3B to 2B to 1.3B to 0.9B). Custom Kanana2TinyForCausalLM. Hidden 1280, 32 layers, sliding-window attention 1024 with every fourth layer full attention, vocab 128256, context 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-2 1.3B Base is part of Kakao's July 2026 Kanana-2 small-language-model release. Weights are public under the Kanana Open License. Collected 2026-10-15.", "what_it_is_for": "Kakao presents the SLM series for on-device and efficient deployment, with a Korean tokenizer it says is over 30% more efficient than the previous generation and a 32,768-token context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-2-1-3b-instruct": { "id": "kakao/kanana/kanana-2-1-3b-instruct", "name": "Kanana-2 1.3B Instruct", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kanana Open License", "release_date": "2026-07-27", "context_length": 32768, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Pruned and distilled from Kanana-2 3B (cascade 3B to 2B to 1.3B to 0.9B). Custom Kanana2TinyForCausalLM. Hidden 1280, 32 layers, sliding-window attention 1024 with every fourth layer full attention, vocab 128256, context 32768. Instruction-tuned for on-device use.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-2 1.3B Instruct is part of Kakao's July 2026 Kanana-2 small-language-model release. Weights are public under the Kanana Open License. Collected 2026-10-15.", "what_it_is_for": "Kakao presents the SLM series for on-device and efficient deployment, with a Korean tokenizer it says is over 30% more efficient than the previous generation and a 32,768-token context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-2-30b-a3b-base": { "id": "kakao/kanana/kanana-2-30b-a3b-base", "name": "Kanana-2 30B-A3B Base", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kanana license (custom)", "release_date": "2025-12-18", "context_length": 32768, "max_output_tokens": null, "parameters": "30B total, 3B activated", "architecture": "DeepSeek-V3-style MoE with MLA. 30B total, 3B activated, 48 layers of which 1 is dense, 128 routed experts, 6 selected, 2 shared, vocab 128256, context 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-2 30B-A3B Base is an open Kanana-2 mixture-of-experts checkpoint from Kakao (\uce74\uce74\uc624), aimed at agentic tool use. Collected 2026-10-15.", "what_it_is_for": "Kakao describes Kanana-2 as an agentic MoE with tool calling, instruction following, and reasoning, using MLA so fewer parameters are active than Flag 32.5B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-2-30b-a3b-base-2601": { "id": "kakao/kanana/kanana-2-30b-a3b-base-2601", "name": "Kanana-2 30B-A3B Base 2601", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kanana license (custom)", "release_date": "2026-01-14", "context_length": 32768, "max_output_tokens": null, "parameters": "30B total, 3B activated", "architecture": "DeepSeek-V3-style MoE with MLA. 30B total, 3B activated, 48 layers of which 1 is dense, 128 routed experts, 6 selected, 2 shared, vocab 128256, context 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-2 30B-A3B Base 2601 is an open Kanana-2 mixture-of-experts checkpoint from Kakao (\uce74\uce74\uc624), aimed at agentic tool use. Collected 2026-10-15.", "what_it_is_for": "Kakao describes Kanana-2 as an agentic MoE with tool calling, instruction following, and reasoning, using MLA so fewer parameters are active than Flag 32.5B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-2-30b-a3b-instruct": { "id": "kakao/kanana/kanana-2-30b-a3b-instruct", "name": "Kanana-2 30B-A3B Instruct", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kanana license (custom)", "release_date": "2025-12-18", "context_length": 32768, "max_output_tokens": null, "parameters": "30B total, 3B activated", "architecture": "DeepSeek-V3-style MoE with MLA. 30B total, 3B activated, 48 layers of which 1 is dense, 128 routed experts, 6 selected, 2 shared, vocab 128256, context 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-2 30B-A3B Instruct is an open Kanana-2 mixture-of-experts checkpoint from Kakao (\uce74\uce74\uc624), aimed at agentic tool use. Collected 2026-10-15.", "what_it_is_for": "Kakao describes Kanana-2 as an agentic MoE with tool calling, instruction following, and reasoning, using MLA so fewer parameters are active than Flag 32.5B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-2-30b-a3b-instruct-2601": { "id": "kakao/kanana/kanana-2-30b-a3b-instruct-2601", "name": "Kanana-2 30B-A3B Instruct 2601", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kanana license (custom)", "release_date": "2026-01-14", "context_length": 32768, "max_output_tokens": null, "parameters": "30B total, 3B activated", "architecture": "DeepSeek-V3-style MoE with MLA. 30B total, 3B activated, 48 layers of which 1 is dense, 128 routed experts, 6 selected, 2 shared, vocab 128256, context 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-2 30B-A3B Instruct 2601 is an open Kanana-2 mixture-of-experts checkpoint from Kakao (\uce74\uce74\uc624), aimed at agentic tool use. Collected 2026-10-15.", "what_it_is_for": "Kakao describes Kanana-2 as an agentic MoE with tool calling, instruction following, and reasoning, using MLA so fewer parameters are active than Flag 32.5B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-2-30b-a3b-mid-2601": { "id": "kakao/kanana/kanana-2-30b-a3b-mid-2601", "name": "Kanana-2 30B-A3B Mid 2601", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kanana license (custom)", "release_date": "2026-01-14", "context_length": 32768, "max_output_tokens": null, "parameters": "30B total, 3B activated", "architecture": "DeepSeek-V3-style MoE with MLA. 30B total, 3B activated, 48 layers of which 1 is dense, 128 routed experts, 6 selected, 2 shared, vocab 128256, context 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-2 30B-A3B Mid 2601 is an open Kanana-2 mixture-of-experts checkpoint from Kakao (\uce74\uce74\uc624), aimed at agentic tool use. Collected 2026-10-15.", "what_it_is_for": "Kakao describes Kanana-2 as an agentic MoE with tool calling, instruction following, and reasoning, using MLA so fewer parameters are active than Flag 32.5B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-2-30b-a3b-thinking": { "id": "kakao/kanana/kanana-2-30b-a3b-thinking", "name": "Kanana-2 30B-A3B Thinking", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kanana license (custom)", "release_date": "2025-12-18", "context_length": 32768, "max_output_tokens": null, "parameters": "30B total, 3B activated", "architecture": "DeepSeek-V3-style MoE with MLA. 30B total, 3B activated, 48 layers of which 1 is dense, 128 routed experts, 6 selected, 2 shared, vocab 128256, context 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-2 30B-A3B Thinking is an open Kanana-2 mixture-of-experts checkpoint from Kakao (\uce74\uce74\uc624), aimed at agentic tool use. Collected 2026-10-15.", "what_it_is_for": "Kakao describes Kanana-2 as an agentic MoE with tool calling, instruction following, and reasoning, using MLA so fewer parameters are active than Flag 32.5B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-2-30b-a3b-thinking-2601": { "id": "kakao/kanana/kanana-2-30b-a3b-thinking-2601", "name": "Kanana-2 30B-A3B Thinking 2601", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kanana license (custom)", "release_date": "2026-01-14", "context_length": 32768, "max_output_tokens": null, "parameters": "30B total, 3B activated", "architecture": "DeepSeek-V3-style MoE with MLA. 30B total, 3B activated, 48 layers of which 1 is dense, 128 routed experts, 6 selected, 2 shared, vocab 128256, context 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-2 30B-A3B Thinking 2601 is an open Kanana-2 mixture-of-experts checkpoint from Kakao (\uce74\uce74\uc624), aimed at agentic tool use. Collected 2026-10-15.", "what_it_is_for": "Kakao describes Kanana-2 as an agentic MoE with tool calling, instruction following, and reasoning, using MLA so fewer parameters are active than Flag 32.5B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-2-3b-base": { "id": "kakao/kanana/kanana-2-3b-base", "name": "Kanana-2 3B Base", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kanana Open License", "release_date": "2026-07-27", "context_length": 32768, "max_output_tokens": null, "parameters": "3B", "architecture": "Qwen3-architecture decoder trained from scratch on TPUs. Hidden 2560, 32 layers, 32 heads, 8 KV heads, intermediate 8192, vocab 128256, tied embeddings. YaRN factor 40 from an original 4096 positions, config max 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-2 3B Base is part of Kakao's July 2026 Kanana-2 small-language-model release. Weights are public under the Kanana Open License. Collected 2026-10-15.", "what_it_is_for": "Kakao presents the SLM series for on-device and efficient deployment, with a Korean tokenizer it says is over 30% more efficient than the previous generation and a 32,768-token context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-2-3b-instruct": { "id": "kakao/kanana/kanana-2-3b-instruct", "name": "Kanana-2 3B Instruct", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kanana Open License", "release_date": "2026-07-27", "context_length": 32768, "max_output_tokens": null, "parameters": "3B", "architecture": "Qwen3-architecture decoder trained from scratch on TPUs. Hidden 2560, 32 layers, 32 heads, 8 KV heads, intermediate 8192, vocab 128256, tied embeddings. YaRN factor 40 from an original 4096 positions, config max 32768. SFT and reinforcement learning.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-2 3B Instruct is part of Kakao's July 2026 Kanana-2 small-language-model release. Weights are public under the Kanana Open License. Collected 2026-10-15.", "what_it_is_for": "Kakao presents the SLM series for on-device and efficient deployment, with a Korean tokenizer it says is over 30% more efficient than the previous generation and a 32,768-token context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-26-8b": { "id": "kakao/kanana/kanana-26-8b", "name": "Kanana 26.8B", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-26", "context_length": null, "max_output_tokens": null, "parameters": "26.8B", "architecture": "From-scratch staged pretrain, same data recipe as Kanana 8B, before depth up-scaling to Kanana Flag 32.5B. Llama 3-style. Not released as weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana 26.8B is the closed from-scratch model Kakao depth-up-scaled into Kanana Flag 32.5B. It appears in the technical report, not as a download. Collected 2026-10-15.", "what_it_is_for": "The paper treats it as the large staged pretrain, not as a separate product API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-8b": { "id": "kakao/kanana/kanana-8b", "name": "Kanana 8B", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-26", "context_length": 8192, "max_output_tokens": null, "parameters": "8B", "architecture": "Llama 3-style decoder trained from scratch in two stages (about 2.7T then 300B tokens). Depth-up-scaled into Kanana Essence 9.8B and pruned into Kanana Nano. The paper adopts the Llama 3 architecture and tokenizer; exact layer counts for this closed checkpoint are not tabulated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana 8B is Kakao's from-scratch bilingual model that the Kanana paper uses as the parent of Essence 9.8B and Nano 2.1B. Kakao did not open the weights. Collected 2026-10-15.", "what_it_is_for": "It is the stage-2 8B pretrain in the technical report and the closed baseline named Kanana-8B on the Kanana 1.5 model cards.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-8b-fc": { "id": "kakao/kanana/kanana-8b-fc", "name": "Kanana 8B FC", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-26", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Function-calling adaptation of a Kanana 8B instruct model. The paper describes English function-calling continued training followed by Korean supervised fine-tuning on korean-fc-corpus. Weights were not found on Hugging Face.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana 8B FC is the function-calling adaptation Kakao evaluates in the Kanana technical report. It was not found as a public weight or API model. Collected 2026-10-15.", "what_it_is_for": "The paper says it is for Korean tool use after domain continued training and Korean function-calling fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-essence-1-5-9-8b": { "id": "kakao/kanana/kanana-essence-1-5-9-8b", "name": "Kanana Essence 1.5 9.8B", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-23", "context_length": 32768, "max_output_tokens": null, "parameters": "9.8B", "architecture": "Closed Kanana 1.5 9.8B. The Kanana-o card names kanana-1.5-9.8B-instruct-2504 as the omni model's language model, which matches this size and generation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana Essence 1.5 9.8B is Kakao's closed mid-size 1.5 model. A dated instruct snapshot is the language model inside Kanana-o. Collected 2026-10-15.", "what_it_is_for": "Kakao reports it as the 9.8B 1.5 model for bilingual chat, code, math, and function calling, and later as the text backbone of the omni model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-essence-9-8b": { "id": "kakao/kanana/kanana-essence-9-8b", "name": "Kanana Essence 9.8B", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-26", "context_length": null, "max_output_tokens": null, "parameters": "9.8B", "architecture": "Depth up-scale of Kanana 8B, then 100B plus 100B more tokens. Llama 3-style. Not released as weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana Essence 9.8B is Kakao's mid-size first-generation Kanana model, created by depth up-scaling the 8B pretrain. Weights stayed closed. Collected 2026-10-15.", "what_it_is_for": "The report presents Essence as the 9.8B bilingual model between Nano and Flag.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-essence-9-8b-rag": { "id": "kakao/kanana/kanana-essence-9-8b-rag", "name": "Kanana Essence 9.8B RAG", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-26", "context_length": null, "max_output_tokens": null, "parameters": "9.8B", "architecture": "Retrieval-grounded adaptation of Kanana Essence 9.8B. The paper trains on synthetic QA, merges the DPO model back with the instruct model, and does not release weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana Essence 9.8B RAG is the closed retrieval-augmented checkpoint in the Kanana technical report. Collected 2026-10-15.", "what_it_is_for": "Kakao describes it as an Essence model trained to stay grounded in retrieved context while keeping instruct-model helpfulness.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-flag-1-5-32-5b": { "id": "kakao/kanana/kanana-flag-1-5-32-5b", "name": "Kanana Flag 1.5 32.5B", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-23", "context_length": 32768, "max_output_tokens": null, "parameters": "32.5B", "architecture": "Closed Kanana 1.5 flagship. Kakao describes 1.5 as a post-train and long-context update over the first Flag model. Layer config was not released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana Flag 1.5 32.5B is Kakao's closed large 1.5 model. Scores are public; weights are not. Collected 2026-10-15.", "what_it_is_for": "Kakao uses it as the top 1.5 comparison model for coding, math, and function calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-flag-32-5b": { "id": "kakao/kanana/kanana-flag-32-5b", "name": "Kanana Flag 32.5B", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-26", "context_length": null, "max_output_tokens": null, "parameters": "32.5B", "architecture": "Depth up-scale of Kanana 26.8B, then further trained on 100B stage-1 tokens and 100B stage-2 tokens. Llama 3-style decoder. Base and instruct checkpoints were evaluated but not released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana Flag 32.5B is the largest model in Kakao's first Kanana report. Kakao published scores for base and instruct checkpoints and did not release weights. Collected 2026-10-15.", "what_it_is_for": "Kakao presents Flag as the bilingual flagship of the 2.1B to 32.5B series, strongest of the three on Korean chat and knowledge in that report.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-nano-1-5-3b": { "id": "kakao/kanana/kanana-nano-1-5-3b", "name": "Kanana Nano 1.5 3B", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-23", "context_length": 32768, "max_output_tokens": null, "parameters": "3B", "architecture": "Closed Kanana 1.5 dense model. The vision model card points its language backbone at kanana-1.5-3b-instruct, which is not a public Hugging Face repo. That backbone config is Llama-style, hidden 2048, 32 layers, 32 heads, 8 KV heads.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana Nano 1.5 3B is a closed Kakao model used as a comparison point and as the language backbone of Kanana-1.5-V. Collected 2026-10-15.", "what_it_is_for": "Kakao lists it with the 1.5 family for Korean-and-English base and instruct comparisons. The vision card uses a 3B instruct checkpoint as the text tower.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-nano-2-1b-base": { "id": "kakao/kanana/kanana-nano-2-1b-base", "name": "Kanana Nano 2.1B Base", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-02-27", "context_length": 8192, "max_output_tokens": null, "parameters": "2.1B", "architecture": "Llama-style decoder (LlamaForCausalLM). Hidden size 1792, 32 layers, 24 query heads, 8 key-value heads, SwiGLU intermediate 8064, vocab 128256, RoPE theta 500000. Pruned and distilled from Kanana 8B on 0.3T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana Nano 2.1B Base is Kakao's (\uce74\uce74\uc624) public bilingual base model, the smallest released checkpoint of the first Kanana series. Weights are open under CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "Kakao describes it as a compute-efficient Korean-and-English base model for research, distilled from the 8B pretrain rather than trained from scratch for 3T tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-nano-2-1b-embedding": { "id": "kakao/kanana/kanana-nano-2-1b-embedding", "name": "Kanana Nano 2.1B Embedding", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-02-27", "context_length": null, "max_output_tokens": null, "parameters": "2.1B", "architecture": "Decoder embedding model (library type kanana2vec) fine-tuned from the Kanana Nano 2.1B instruct model with an LLM2Vec-style recipe.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana Nano 2.1B Embedding is Kakao's public text-embedding adaptation of Kanana Nano. Weights are open under CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "The technical report presents it as a retrieval backbone for English and Korean semantic search, not as a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana/kanana-nano-2-1b-instruct": { "id": "kakao/kanana/kanana-nano-2-1b-instruct", "name": "Kanana Nano 2.1B Instruct", "maker": "kakao", "family": "kakao/kanana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-02-27", "context_length": 8192, "max_output_tokens": null, "parameters": "2.1B", "architecture": "Llama-style decoder (LlamaForCausalLM). Hidden size 1792, 32 layers, 24 query heads, 8 key-value heads, SwiGLU intermediate 8064, vocab 128256, RoPE theta 500000. Pruned and distilled from Kanana 8B on 0.3T tokens. Post-trained with SFT and preference optimization.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana Nano 2.1B Instruct is Kakao's chat-tuned 2.1B model from the first public Kanana release. It is open weights under CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "Kakao positions the instruct checkpoint for Korean and English chat, instruction following, code, and math after supervised fine-tuning and preference optimization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana-a/kanana-a": { "id": "kakao/kanana-a/kanana-a", "name": "Kanana-a", "maker": "kakao", "family": "kakao/kanana-a", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-05-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named on the Kanana-o card as Kakao's proprietary audio model that was merged with Kanana-v to build Kanana-o. No standalone card was found.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-a is the audio model Kakao names as an ingredient of Kanana-o. A standalone release was not found. Collected 2026-10-15.", "what_it_is_for": "The omni card describes it as the proprietary audio model paired with Kanana-v when Kanana-o was unveiled on 1 May 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana-o/kanana-1-5-o-9-8b-instruct-2602": { "id": "kakao/kanana-o/kanana-1-5-o-9-8b-instruct-2602", "name": "Kanana-1.5-o 9.8B Instruct 2602", "maker": "kakao", "family": "kakao/kanana-o", "status": "active", "confidence": "high", "weights": "closed", "license": "Kanana license (custom)", "release_date": "2026-02-12", "context_length": 16384, "max_output_tokens": null, "parameters": "11.6B", "architecture": "Omnimodal model: fine-tuned image and audio encoders, modality-specific C-abstractors, kanana-1.5-9.8B-instruct-2504, and a voice-token decoder with token-to-waveform modules. API model id is kanana-o.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-1.5-o 9.8B Instruct 2602 is Kakao's API omni model for text, image, and speech in and text or speech out. Weights are closed. The card calls the 4 March 2026 API post the first omni-modal API launch in Korea. Collected 2026-10-15.", "what_it_is_for": "Captioning, OCR, speech recognition, speech generation, speech translation, and multi-turn omni chat. The October 2026 if(kakao) conference said Kakao plans to open-source the encoders and decoders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana-safeguard/kanana-safeguard-8b": { "id": "kakao/kanana-safeguard/kanana-safeguard-8b", "name": "Kanana Safeguard 8B", "maker": "kakao", "family": "kakao/kanana-safeguard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-26", "context_length": 8192, "max_output_tokens": null, "parameters": "8B", "architecture": "LlamaForCausalLM classifier built on Kanana 8B. Hidden 4096, 32 layers, 32 heads, 8 KV heads, intermediate 14336, vocab 128264, max position 8192.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana Safeguard 8B is Kakao's open Korean harmful-content classifier, fine-tuned from Kanana 8B. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The Korean card says it labels risk in user utterances or assistant replies inside a chat system.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana-safeguard/kanana-safeguard-prompt-2-1b": { "id": "kakao/kanana-safeguard/kanana-safeguard-prompt-2-1b", "name": "Kanana Safeguard-Prompt 2.1B", "maker": "kakao", "family": "kakao/kanana-safeguard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-26", "context_length": 8192, "max_output_tokens": null, "parameters": "2.1B", "architecture": "LlamaForCausalLM prompt-attack detector. Hidden 1792, 32 layers, 24 heads, 8 KV heads, intermediate 8064, vocab 128259, max position 8192, dropout 0.3. Same shape class as Kanana Nano.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana Safeguard-Prompt 2.1B is Kakao's open Korean prompt-attack detector. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Kakao's Safeguard collection describes it as the model for prompt attacks, used with the 8B content and Siren models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana-safeguard/kanana-safeguard-siren-8b": { "id": "kakao/kanana-safeguard/kanana-safeguard-siren-8b", "name": "Kanana Safeguard-Siren 8B", "maker": "kakao", "family": "kakao/kanana-safeguard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-26", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Llama classifier on Kanana 8B for legal and policy risk, not general toxicity. Korean-focused. This dossier did not re-read the Siren config, so context length is left unset.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana Safeguard-Siren 8B is Kakao's open Korean model for legal and policy risk in chat text. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says it flags user utterances that need legal or policy attention, including requests to obtain or copy restricted material, as a complement to the general Safeguard model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kanana-v/kanana-1-5-v-3b-instruct": { "id": "kakao/kanana-v/kanana-1-5-v-3b-instruct", "name": "Kanana-1.5-V 3B Instruct", "maker": "kakao", "family": "kakao/kanana-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kanana license (custom)", "release_date": "2025-07-24", "context_length": 32768, "max_output_tokens": null, "parameters": "3.67B", "architecture": "Image encoder (Qwen2-VL-style vision encoder, embed 1280, depth 32, patch 14) plus a dynamic C-abstractor and kanana-1.5-3b-instruct. The text tower config is Llama-style, hidden 2048, 32 layers.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kanana-1.5-V 3B Instruct is Kakao's open image-and-text model from the Kanana-V family. Total size on the card is 3.67B. Collected 2026-10-15.", "what_it_is_for": "Kakao says it is for image captioning, document understanding, OCR reasoning, and bilingual multimodal instruction following.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/karlo/karlo-v1-alpha": { "id": "kakao/karlo/karlo-v1-alpha", "name": "Karlo v1.0 alpha", "maker": "kakao", "family": "kakao/karlo", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "creativeml-openrail-m", "release_date": "2022-12-01", "context_length": null, "max_output_tokens": null, "parameters": "prior 1B + decoder 900M + super-resolution 700M+700M", "architecture": "unCLIP text-to-image. CLIP ViT-L/14 prior (1B, 25 steps), decoder (900M, 50 steps default or 25 fast), and a super-resolution module (700M+700M, 7 steps) from 64 px to 256 px. Trained on about 115M pairs from a COYO-100M subset, CC3M, and CC12M.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Karlo v1.0 alpha is Kakao Brain's unCLIP image model. The public API was retired on 30 September 2024. Open weights remain. Collected 2026-10-15.", "what_it_is_for": "Text-to-image, image variations, and inpainting were the three API routes. The alpha weights cover text-to-image and, in a second repo, variations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/kogpt/kogpt-6b-ryan1-5b": { "id": "kakao/kogpt/kogpt-6b-ryan1-5b", "name": "KoGPT 6B ryan1.5b", "maker": "kakao", "family": "kakao/kogpt", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-ND-4.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "6166502400", "architecture": "Decoder-only Transformer. 28 layers, d_model 4096, d_ff 16384, 16 heads, head dim 256, vocab 64512, RoPE with 64 dimensions. Float32 and float16 revisions.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KoGPT 6B ryan1.5b is Kakao Brain's (\uce74\uce74\uc624\ube0c\ub808\uc778) Korean generative model. The KoGPT API was retired on 30 September 2024. Weights remain under CC-BY-NC-ND-4.0. Collected 2026-10-15.", "what_it_is_for": "The README says it is for Korean classification, search, summarization, and generation in research settings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/lmspt/lmspt": { "id": "kakao/lmspt/lmspt", "name": "LM-SPT 12.5 Hz v1", "maker": "kakao", "family": "kakao/lmspt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-09-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural speech tokenizer. Semantic VQ (codebook 16384) plus 7 acoustic residual codebooks (4096). 12.5 Hz from stride product 1920 at 24 kHz. Whisper encoder is the distillation teacher. Aux decoder reconstructs 16 kHz.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LM-SPT is Kakao's open speech tokenizer, released as the 12.5 Hz v1 checkpoint. Weights are non-commercial. Collected 2026-10-15.", "what_it_is_for": "The card says it turns speech into semantic and acoustic tokens that line up with language models, then reconstructs audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/mindall-e/mindall-e-1-3b": { "id": "kakao/mindall-e/mindall-e-1-3b", "name": "minDALL-E 1.3B", "maker": "kakao", "family": "kakao/mindall-e", "status": "active", "confidence": "high", "weights": "open-weights", "license": "non-commercial, as stated on the project README", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Two-stage text-to-image model. Stage 1 is a VQGAN fine-tune of vqgan_imagenet_f16_16384 on FFHQ and ImageNet. Stage 2 is a 1.3B transformer trained from scratch on 14 million CC3M and CC12M pairs.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "minDALL-E 1.3B is Kakao Brain's open autoregressive text-to-image model, released for non-commercial use. Collected 2026-10-15.", "what_it_is_for": "The README presents it as a 1.3B text-to-image generator trained on Conceptual Captions, with transfer examples for class-conditional ImageNet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/vit/vit-l-16-coyo-224": { "id": "kakao/vit/vit-l-16-coyo-224", "name": "ViT-L/16 COYO 224", "maker": "kakao", "family": "kakao/vit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-L/16 pretrained at 224 on COYO-Labeled-300M (300 million images, 21841 machine labels). Hub id kakaobrain/vit-l16-coyo-labeled-300m.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViT-L/16 COYO 224 is Kakao Brain's public vision transformer pretrained on COYO-Labeled-300M. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Upstream image encoder and classification pretrain. Downstream ImageNet finetunes are separate dossiers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/vit/vit-l-16-imagenet-384": { "id": "kakao/vit/vit-l-16-imagenet-384", "name": "ViT-L/16 ImageNet 384", "maker": "kakao", "family": "kakao/vit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-L/16 finetuned on ImageNet at 384 from the COYO-Labeled-300M pretrain. Hub ids kakaobrain/vit-l16-coyo-labeled-300m-i1k384 and kakaobrain/vit-large-patch16-384.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViT-L/16 ImageNet 384 is Kakao Brain's ImageNet finetune of the COYO ViT-L/16 at resolution 384. Collected 2026-10-15.", "what_it_is_for": "ImageNet classification. Kakao Brain reports it within 0.4 points of Google's JFT-trained ViT-L/16 at the same resolution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kakao/vit/vit-l-16-imagenet-512": { "id": "kakao/vit/vit-l-16-imagenet-512", "name": "ViT-L/16 ImageNet 512", "maker": "kakao", "family": "kakao/vit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-L/16 finetuned on ImageNet at 512 from the COYO-Labeled-300M pretrain. Hub ids kakaobrain/vit-l16-coyo-labeled-300m-i1k512 and kakaobrain/vit-large-patch16-512.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViT-L/16 ImageNet 512 is Kakao Brain's 512-resolution ImageNet finetune of the COYO ViT-L/16. Collected 2026-10-15.", "what_it_is_for": "Image classification at 512. It is the stronger of the two public ImageNet finetunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kansas/tinylidarnet/tinylidarnet": { "id": "kansas/tinylidarnet/tinylidarnet", "name": "TinyLidarNet", "maker": "kansas", "family": "kansas/tinylidarnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-10-09", "context_length": null, "max_output_tokens": null, "parameters": "220686", "architecture": "1D CNN inspired by NVIDIA PilotNet, not a fine-tune of PilotNet weights. Nine layers: five 1D convolutions and four fully connected layers. The original model, TinyLidarNetL, takes a 1081-point 2D LiDAR range scan and predicts speed and steering. The paper reports 220,686 parameters and 1,546,960 MACs for L. TinyLidarNetM and TinyLidarNetS use the same layer pattern on downsampled scans (541 and 271 points) and are listed as versions. Training in the paper used Huber loss, batch size 64, learning rate 5e-5, and 20 epochs on 10,478 of 12,329 real driving samples.", "modalities": { "input": [ "lidar" ], "output": [ "control" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinyLidarNet is a small end-to-end driving network from the Computer Systems Lab at the University of Kansas. Mohammed Misbah Zarrar, Qitao Weng, Bakhbyergyen Yerjan, Ahmet Soyyigit, and Heechul Yun released it for F1TENTH racing. It reads a 2D LiDAR scan and outputs speed and steering. The arXiv preprint is dated 9 October 2024, and the IROS 2024 paper is pages 2878-2884. Public TFLite files are still in the lab repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents TinyLidarNet as a lightweight controller for 1/10-scale autonomous racing. A raw Hokuyo UST-10LX scan replaces a camera image. The competition car that used the large variant placed 3rd of 13 teams at the 12th F1TENTH Autonomous Grand Prix. The same weights are also the authors' example of a network small enough to run on a microcontroller after 8-bit quantization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kapwing/i1/kapwing-i1": { "id": "kapwing/i1/kapwing-i1", "name": "Kapwing I1", "maker": "kapwing", "family": "kapwing/i1", "status": "retired", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kapwing I1 is an image model name that appears once in Kapwing's own tutorials, as a choice in the Images tab next to Seedream 4. The 2026 model index does not list it, and Kapwing has not published a card or said that it trained the weights. Collected 2026-10-15. Confidence is low.", "what_it_is_for": "The 18 Sep 2025 figurine guide tells creators to open the Images tab and select Seedream 4 or Kapwing I1, then upload a reference photo, pick an aspect ratio, and generate. That is the only job Kapwing describes for this name. It is grouped with reference-photo image generation, not with the video models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/attseg/attseg-internvl2-5-4b": { "id": "kaust/attseg/attseg-internvl2-5-4b", "name": "AttSeg (InternVL2.5 4B)", "maker": "kaust", "family": "kaust/attseg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-10-11", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "AttnSeg applied to Sa2VA / InternVL at the 4B size named in the paper. The Hugging Face repo holds stage1.pth and stage2.pth only. usedStorage is 501689408 bytes, so this repository is not a full InternVL2.5-4B dump. The 4B figure is the paper's model size, not a counted total in this repo.", "modalities": { "input": [ "text", "image" ], "output": [ "mask" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AttSeg (InternVL2.5 4B) is a referring-segmentation checkpoint from Vision-CAIR at King Abdullah University of Science and Technology, with Adobe co-authors. The weight repo was created on 2026-10-11 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The paper replaces the usual single [SEG] embedding with the token's attention over image patches, then trains a decoder on that spatial prompt. This folder is the 4B Sa2VA / InternVL application.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/attseg/attseg-internvl3-14b": { "id": "kaust/attseg/attseg-internvl3-14b", "name": "AttSeg (InternVL3 14B)", "maker": "kaust", "family": "kaust/attseg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-11", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "AttnSeg applied at the 14B size named in the paper. The hub repo has stage1.pth and stage2.pth and no README. usedStorage 1169406000 bytes. No license tag. The 14B figure is the paper's model size.", "modalities": { "input": [ "text", "image" ], "output": [ "mask" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AttSeg (InternVL3 14B) is the larger AttSeg weight repo from Vision-CAIR. It was created on 2026-10-11. No license file was in the repository listing. Collected 2026-10-15.", "what_it_is_for": "Same referring-segmentation method as the 4B AttSeg folder, at the 14B size reported in the abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/bfpo/bfpo-mistral-7b": { "id": "kaust/bfpo/bfpo-mistral-7b", "name": "BFPO (Mistral 7B v0.1)", "maker": "kaust", "family": "kaust/bfpo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-02-19", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "MistralForCausalLM fine-tune of mistralai/Mistral-7B-v0.1. Safetensors F32 total 7241732096. Datasets PKU-SafeRLHF, ultrachat_200k, and ultrafeedback_binarized. The 7B figure is the base-model name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BFPO (Mistral 7B v0.1) is an open-weight safety-and-helpfulness alignment checkpoint from Vision-CAIR. The repository was created on 2025-02-19 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The paper studies preference optimization that balances safety and helpfulness. This folder is the Mistral-7B-v0.1 checkpoint, not the Zephyr red-teaming checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/bfpo/bfpo-zephyr-7b-redteam": { "id": "kaust/bfpo/bfpo-zephyr-7b-redteam", "name": "BFPO red-teaming (Zephyr 7B beta)", "maker": "kaust", "family": "kaust/bfpo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-02-19", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Fine-tune of HuggingFaceH4/zephyr-7b-beta. The hub config architecture is MistralModel. Safetensors F32 total 7110660096. Dataset tag PKU-SafeRLHF. The 7B figure is the Zephyr base name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BFPO red-teaming (Zephyr 7B beta) is the second public BFPO checkpoint from Vision-CAIR. It was created on 2025-02-19 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Red-teaming variant of the BFPO alignment method on Zephyr 7B beta. It is a named weight repo, separate from the Mistral BFPO checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/diffclip/diffclip-vit-b-16": { "id": "kaust/diffclip/diffclip-vit-b-16", "name": "DiffCLIP ViT-B/16", "maker": "kaust", "family": "kaust/diffclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP ViT-B/16 with differential attention on the image and text encoders. The project page describes attention as A1 minus lambda times A2. Exact parameter count was not opened. Hugging Face files are checkpoint_best.pt with no license tag.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffCLIP ViT-B/16 is a vision-language model from Hasan Abed Al Kader Hammoud and Bernard Ghanem at King Abdullah University of Science and Technology. The KAUST repository record and the Hugging Face checkpoints are dated 2025-03-09. Collected 2026-10-15.", "what_it_is_for": "The abstract says differential attention is added to both CLIP encoders so one attention map keeps useful features and another cancels noise, for zero-shot classification, retrieval, and robustness.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/gsfm/gsfm": { "id": "kaust/gsfm/gsfm", "name": "GSFM", "maker": "kaust", "family": "kaust/gsfm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Generative diffusion model for seismic processing. Pretrained on synthetic data, then iteratively fine-tuned on field data. Parameter count was not stated on the pages opened.", "modalities": { "input": [ "seismic" ], "output": [ "seismic" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GSFM is a generative seismic foundation model from the DeepWave consortium at King Abdullah University of Science and Technology. The preprint is dated 2025-02-03, and the GitHub README links a pretrained checkpoint. Collected 2026-10-15.", "what_it_is_for": "The abstract says one diffusion model handles denoising, backscattered-noise attenuation, interpolation, and low-frequency extrapolation, and that the probabilistic outputs support uncertainty estimates.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/hala/hala-1-2b": { "id": "kaust/hala/hala-1-2b", "name": "Hala 1.2B", "maker": "kaust", "family": "kaust/hala", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "2025-09-13", "context_length": null, "max_output_tokens": null, "parameters": "1.2B", "architecture": "Lfm2ForCausalLM fine-tune of LiquidAI/LFM2-1.2B. Safetensors BF16 total 1170340608.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hala 1.2B is an Arabic instruction model from Hasan Abed Al Kader Hammoud, Mohammad Zbeeb, and Bernard Ghanem at King Abdullah University of Science and Technology. Weights were created on 2025-09-13 under CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "The card presents Hala as an Arabic-centric instruction model. The family table compares Arabic multiple-choice and trust benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/hala/hala-1-2b-en-ar-translator": { "id": "kaust/hala/hala-1-2b-en-ar-translator", "name": "Hala 1.2B EN-AR Translator", "maker": "kaust", "family": "kaust/hala", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "2025-09-13", "context_length": null, "max_output_tokens": null, "parameters": "1.2B", "architecture": "Lfm2ForCausalLM fine-tune of LiquidAI/LFM2-1.2B for English-to-Arabic translation. Safetensors BF16 total 1170340608. Created 2025-09-13.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hala 1.2B EN-AR Translator is a separate English-to-Arabic model from the Hala authors at King Abdullah University of Science and Technology. Weights were created on 2025-09-13 under CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "The card says it translates English into Arabic, especially instruction-style and conversational text, and that it was used to build the Hala dataset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/hala/hala-350m": { "id": "kaust/hala/hala-350m", "name": "Hala 350M", "maker": "kaust", "family": "kaust/hala", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "2025-09-13", "context_length": null, "max_output_tokens": null, "parameters": "350M", "architecture": "Lfm2ForCausalLM fine-tune of LiquidAI/LFM2-350M. Safetensors BF16 total 354483968. Release name 350M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hala 350M is an Arabic instruction model from Hasan Abed Al Kader Hammoud, Mohammad Zbeeb, and Bernard Ghanem at King Abdullah University of Science and Technology. Weights were created on 2025-09-13 under CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "The card presents Hala as an Arabic-centric instruction model. The family table compares Arabic multiple-choice and trust benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/hala/hala-700m": { "id": "kaust/hala/hala-700m", "name": "Hala 700M", "maker": "kaust", "family": "kaust/hala", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "2025-09-13", "context_length": null, "max_output_tokens": null, "parameters": "700M", "architecture": "Lfm2ForCausalLM fine-tune of LiquidAI/LFM2-700M. Safetensors BF16 total 742489344.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hala 700M is an Arabic instruction model from Hasan Abed Al Kader Hammoud, Mohammad Zbeeb, and Bernard Ghanem at King Abdullah University of Science and Technology. Weights were created on 2025-09-13 under CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "The card presents Hala as an Arabic-centric instruction model. The family table compares Arabic multiple-choice and trust benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/hala/hala-9b": { "id": "kaust/hala/hala-9b", "name": "Hala 9B", "maker": "kaust", "family": "kaust/hala", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "2025-09-13", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Gemma2ForCausalLM fine-tune of QCRI/Fanar-1-9B-Instruct. Safetensors BF16 total 8783871488. Fanar itself is a QCRI model and is not catalogued here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hala 9B is an Arabic instruction model from Hasan Abed Al Kader Hammoud, Mohammad Zbeeb, and Bernard Ghanem at King Abdullah University of Science and Technology. Weights were created on 2025-09-13 under CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "The card presents Hala as an Arabic-centric instruction model. The family table compares Arabic multiple-choice and trust benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/imotion-llm/imotion-llm": { "id": "kaust/imotion-llm/imotion-llm", "name": "iMotion-LLM", "maker": "kaust", "family": "kaust/imotion-llm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-conditioned trajectory generator. The abstract says an encoder-decoder trajectory model plus a pretrained LLM fine-tuned with LoRA. Scene features are projected into the LLM, and special tokens map to a trajectory decoder. A public checkpoint file was not verified.", "modalities": { "input": [ "text", "scene" ], "output": [ "trajectory" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "iMotion-LLM is a named driving model from Vision-CAIR at King Abdullah University of Science and Technology. The paper, submitted 2024-06-10 and revised 2025-12-05, says code, a pretrained model, and datasets are on the project page. A downloadable checkpoint was not verified in this collection. Collected 2026-10-15.", "what_it_is_for": "The abstract says it generates feasible, safety-aligned driving trajectories from text instructions, for simulation, interpretability, and safety-alignment tests.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/longvu/longvu-llama3-2-1b": { "id": "kaust/longvu/longvu-llama3-2-1b", "name": "LongVU (Llama 3.2 1B)", "maker": "kaust", "family": "kaust/longvu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-23", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "CambrianLlamaForCausalLM. Apache-2.0. usedStorage 6177350990 bytes for the pytorch bin. Safetensors did not report a parameter count. The 1B figure is the Llama-3.2 backbone name.", "modalities": { "input": [ "text", "video", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongVU (Llama 3.2 1B) is a Vision-CAIR weight release for the LongVU paper. It was created on 2024-10-23 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Long video-language understanding at the Llama-3.2 1B backbone size. The official Hugging Face repository carries the paper tag even though the GitHub size table that was opened did not list 1B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/longvu/longvu-llama3-2-3b": { "id": "kaust/longvu/longvu-llama3-2-3b", "name": "LongVU (Llama 3.2 3B)", "maker": "kaust", "family": "kaust/longvu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-21", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "CambrianLlamaForCausalLM. Apache-2.0. The repository stores a pytorch bin, usedStorage 14635673898 bytes. Safetensors did not report a parameter count. The 3B figure is the Llama-3.2 backbone name, not a counted total.", "modalities": { "input": [ "text", "video", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongVU (Llama 3.2 3B) is the Llama-3.2 size of LongVU from Vision-CAIR at King Abdullah University of Science and Technology. Weights were created on 2024-10-21 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Same long-video understanding job as the Qwen2 LongVU release, with a Llama-3.2 3B language backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/longvu/longvu-qwen2-7b": { "id": "kaust/longvu/longvu-qwen2-7b", "name": "LongVU (Qwen2 7B)", "maker": "kaust", "family": "kaust/longvu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-10-18", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "CambrianQwenForCausalLM video-language model. The card base is Vision-CAIR/LongVU_Qwen2_7B_img, and the release name is Qwen2-7B. Safetensors reports BF16 total 7671665664. Vision encoders cited for the line are SigLIP and DINOv2. The 7B figure is the language-backbone name.", "modalities": { "input": [ "text", "video", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongVU (Qwen2 7B) is an open-weight long-video model from Vision-CAIR at King Abdullah University of Science and Technology, with Meta co-authors on the paper. The Hugging Face weights were created on 2024-10-18 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The paper presents LongVU as spatiotemporal adaptive compression for long video-language understanding. This folder is the Qwen2-7B weight release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/mextract/mextract-0-5b": { "id": "kaust/mextract/mextract-0-5b", "name": "MeXtract 0.5B", "maker": "kaust", "family": "kaust/mextract", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-05", "context_length": null, "max_output_tokens": null, "parameters": "0.5B", "architecture": "Qwen2ForCausalLM fine-tune of Qwen/Qwen2.5-0.5B-Instruct for schema-based metadata extraction. Safetensors F32 total 494032768. The 0.5B figure is the release name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MeXtract 0.5B is an open-weight metadata extractor from IVUL at King Abdullah University of Science and Technology. The card was created on 2025-10-05 and is tagged Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says it extracts metadata from scientific papers. Attributes are defined with a schema: type, minimum length, maximum length, and options when they exist.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/mextract/mextract-1-5b": { "id": "kaust/mextract/mextract-1-5b", "name": "MeXtract 1.5B", "maker": "kaust", "family": "kaust/mextract", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-05", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Qwen2ForCausalLM fine-tune of Qwen/Qwen2.5-1.5B-Instruct. Safetensors F32 total 1543714304. Card license apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MeXtract 1.5B is the middle MeXtract checkpoint from IVUL at King Abdullah University of Science and Technology. Created 2025-10-05, license tag apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Schema-based metadata extraction, same task as MeXtract 0.5B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/mextract/mextract-3b": { "id": "kaust/mextract/mextract-3b", "name": "MeXtract 3B", "maker": "kaust", "family": "kaust/mextract", "status": "active", "confidence": "high", "weights": "open-weights", "license": "qwen-research", "release_date": "2025-10-05", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Qwen2ForCausalLM fine-tune of Qwen/Qwen2.5-3B-Instruct. Safetensors F32 total 3085938688. The hub cardData license is other, license_name qwen-research. The 0.5B and 1.5B cards are tagged apache-2.0. Those statements disagree.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MeXtract 3B is the largest MeXtract checkpoint from IVUL at King Abdullah University of Science and Technology. Created 2025-10-05. Its hub license name is qwen-research, unlike the Apache-2.0 tags on the smaller MeXtract cards. Collected 2026-10-15.", "what_it_is_for": "Schema-based metadata extraction. On the shared MOLE+ table this is the highest MeXtract average.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/minigpt/minigpt-4-llama2-7b": { "id": "kaust/minigpt/minigpt-4-llama2-7b", "name": "MiniGPT-4 (Llama-2 Chat 7B)", "maker": "kaust", "family": "kaust/minigpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-02", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "MiniGPT-4 alignment with frozen Llama-2 Chat 7B instead of Vicuna. The README lists it as a third MiniGPT-4 checkpoint. The 7B figure is the Llama-2 backbone, not a published full-stack count. The paper revision that covers later checkpoints is dated 2023-10-02; the exact day this checkpoint file was uploaded was not on the pages opened.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniGPT-4 (Llama-2 Chat 7B) is the Llama-2 variant of MiniGPT-4 from Vision-CAIR at King Abdullah University of Science and Technology. It is an open-weight image-and-text checkpoint listed beside the Vicuna downloads. Collected 2026-10-15.", "what_it_is_for": "Same job as the other MiniGPT-4 checkpoints: image-grounded text generation. The lab tells users to load Llama-2 Chat 7B rather than Vicuna for this file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/minigpt/minigpt-4-vicuna-13b": { "id": "kaust/minigpt/minigpt-4-vicuna-13b", "name": "MiniGPT-4 (Vicuna 13B)", "maker": "kaust", "family": "kaust/minigpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-04-20", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Vision-CAIR aligns a frozen BLIP-2 visual encoder with a frozen Vicuna-13B LLM using one projection layer. Stage 1 is image-text pretraining; stage 2 finetunes on a small curated detailed-description set. The 13B figure is the Vicuna backbone named on the official download table, not a published total that includes the vision encoder.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniGPT-4 (Vicuna 13B) is an open-weight vision-language model from the Vision-CAIR group at King Abdullah University of Science and Technology. It aligns a frozen BLIP-2 visual encoder with frozen Vicuna-13B through one projection layer. The checkpoint is still published. Collected 2026-10-15.", "what_it_is_for": "The paper says the model is for detailed image description and other multimodal generation, including stories or instructions grounded in a photo, and for drafting a page from a hand-drawn sketch. Stage 2 exists because short captions produced repetitive or fragmented text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/minigpt/minigpt-4-vicuna-7b": { "id": "kaust/minigpt/minigpt-4-vicuna-7b", "name": "MiniGPT-4 (Vicuna 7B)", "maker": "kaust", "family": "kaust/minigpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-04-20", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Same MiniGPT-4 design as the 13B checkpoint: a frozen BLIP-2 visual encoder, one projection layer, and frozen Vicuna-7B. The 7B figure is the Vicuna backbone on the official download table, not a published encoder-plus-LLM total.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniGPT-4 (Vicuna 7B) is the smaller Vicuna checkpoint of MiniGPT-4 from Vision-CAIR at King Abdullah University of Science and Technology. It is an open-weight image-and-text model. The checkpoint is still listed. Collected 2026-10-15.", "what_it_is_for": "The paper presents MiniGPT-4 as a way to give a frozen LLM image understanding with one projection layer, for detailed description and other image-grounded generation. This folder is the Vicuna-7B download, not the 13B or Llama-2 download.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/minigpt/minigpt-med": { "id": "kaust/minigpt/minigpt-med", "name": "MiniGPT-Med", "maker": "kaust", "family": "kaust/minigpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-07-04", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Radiology vision-language model. The authors say the language component is Llama-2 and the vision-language alignment follows MiniGPT-v2. The 7B figure is that Llama-2 chat backbone, which the README tells users to download separately. Code license on the GitHub repository is Apache-2.0.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniGPT-Med is a radiology vision-language model from King Abdullah University of Science and Technology and the Saudi Data and Artificial Intelligence Authority. It is open-sourced for report generation, visual question answering, and disease identification on X-ray, CT, and MRI. Collected 2026-10-15.", "what_it_is_for": "The abstract presents it as a general interface for radiology diagnosis: medical report generation, visual question answering, and disease identification across several imaging modalities.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/minigpt/minigpt-v2": { "id": "kaust/minigpt/minigpt-v2", "name": "MiniGPT-v2", "maker": "kaust", "family": "kaust/minigpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-14", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Vision-language model on Llama-2 Chat 7B with an EVA-CLIP visual encoder at 448 pixels and LoRA (r=64, alpha=16) on the language model, from the published minigpt_v2 config. Task identifiers are used during multi-task training. The 7B figure is the Llama-2 backbone.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniGPT-v2 is a Vision-CAIR vision-language model from King Abdullah University of Science and Technology, with Meta AI co-authors on the paper. It is the lab's unified interface for captioning, visual question answering, and grounding. Open weights are linked from the repository. Collected 2026-10-15.", "what_it_is_for": "The paper says one model should handle image description, visual question answering, and visual grounding with simple multimodal instructions, using task identifiers so the model can tell those jobs apart.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/minigpt4-video/minigpt4-video": { "id": "kaust/minigpt4-video/minigpt4-video", "name": "MiniGPT4-Video", "maker": "kaust", "family": "kaust/minigpt4-video", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-03-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Short-video vision-language model from Vision-CAIR. The Hugging Face repository Vision-CAIR/MiniGPT4-Video holds Llama and Mistral checkpoints for image alignment, video captioning, and video QA. A hub summary that said 8B was not confirmed from safetensors and is not used. Code repository license is BSD-3-Clause. No license tag was on the model API record opened.", "modalities": { "input": [ "text", "video", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniGPT4-Video is an open-weight short-video model from Vision-CAIR at King Abdullah University of Science and Technology. One Hugging Face repository holds Llama and Mistral checkpoints. Collected 2026-10-15.", "what_it_is_for": "The repository says it advances multimodal LLMs for video understanding by interleaving visual and textual tokens. It is the short-video model. Goldfish, the long-video method in the same repository, is not a separate weight release and is not given its own folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/salle/salle-v1-5-1": { "id": "kaust/salle/salle-v1-5-1", "name": "SALL-E v1.5.1", "maker": "kaust", "family": "kaust/salle", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "2024-02-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Stable Diffusion v1.5 on DALL-E 3 generated data, released as salle_sdv15_135K_steps_v1.safetensors. Parameter count was not stated on the card.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SALL-E v1.5.1 is a named text-to-image checkpoint on the hammh0a account from Hasan Abed Al Kader Hammoud, Hani Itani, and Bernard Ghanem, the same KAUST authors as Hala. The repository was created on 2024-02-25 under CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "The card says it is a Stable Diffusion v1.5 fine-tune aimed at better text alignment and aesthetics. The authors say they tested prompts from ChatGPT-4, CivitAI, and PromptHero. No numeric score is on the card text that was opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/synthclip/synthclip-vit-b-16": { "id": "kaust/synthclip/synthclip-vit-b-16", "name": "SynthCLIP ViT-B/16", "maker": "kaust", "family": "kaust/synthclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP ViT-B/16 trained on synthetic image-text pairs (SynthCI). The GitHub release lists 10M, 20M, and 30M checkpoints. Exact parameter count was not opened.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SynthCLIP ViT-B/16 is a CLIP model trained on synthetic captions and images, released by Hasan Abed Al Kader Hammoud, Hani Itani, and Bernard Ghanem at King Abdullah University of Science and Technology, with University of Oxford co-authors. The paper date is 2024-02-02. Hugging Face checkpoints were created on 2025-03-09. Collected 2026-10-15.", "what_it_is_for": "The paper trains CLIP entirely on synthetic text-image pairs from a language model and a text-to-image model, and releases SynthCI-30M plus the trained ViT-B/16 checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/tempo/tempo-6b": { "id": "kaust/tempo/tempo-6b", "name": "Tempo-6B", "maker": "kaust", "family": "kaust/tempo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-04-09", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "Query-aware long-video model. The card says a local compressor Qwen3-VL-2B-Instruct plus a global LLM Qwen3-4B, total about 6B. Custom code tempo_qwen. Inference needs the official codebase, not transformers alone.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tempo-6B is an open-weight long-video model released by Vision-CAIR at King Abdullah University of Science and Technology. The model card was created on 2026-04-09 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card describes extreme-long video understanding by compressing video into query-aware tokens before a global language model. It says the model can answer queries on videos longer than an hour.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/videoauto-r1/videoauto-r1-qwen2-5-vl-7b": { "id": "kaust/videoauto-r1/videoauto-r1-qwen2-5-vl-7b", "name": "VideoAuto-R1 (Qwen2.5-VL 7B)", "maker": "kaust", "family": "kaust/videoauto-r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-12-29", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Qwen2_5_VLForConditionalGeneration. Apache-2.0. Safetensors BF16 count 8292166656. The same metadata total field is 848896, which disagrees and was not used as the parameter count. The 7B figure is the release name.", "modalities": { "input": [ "text", "video", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoAuto-R1 (Qwen2.5-VL 7B) is an open-weight video reasoning checkpoint from IVUL at King Abdullah University of Science and Technology. The repository was created on 2025-12-29 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The paper describes a reason-when-necessary video model: an initial answer, then reasoning, then a reviewed answer, with inference that can skip reasoning when the first answer is confident.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kaust/videoauto-r1/videoauto-r1-qwen3-vl-8b": { "id": "kaust/videoauto-r1/videoauto-r1-qwen3-vl-8b", "name": "VideoAuto-R1 (Qwen3-VL 8B)", "maker": "kaust", "family": "kaust/videoauto-r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-12-29", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Qwen3VLForConditionalGeneration. Apache-2.0. Safetensors BF16 count 8767123696. The metadata total field is 770288, which disagrees and was not used. The 8B figure is the release name.", "modalities": { "input": [ "text", "video", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoAuto-R1 (Qwen3-VL 8B) is the Qwen3-VL VideoAuto-R1 checkpoint from IVUL at King Abdullah University of Science and Technology. Created 2025-12-29, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Same reason-when-necessary video understanding job as the Qwen2.5-VL 7B checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kcl/foresight/foresight": { "id": "kcl/foresight/foresight", "name": "Foresight", "maker": "kcl", "family": "kcl/foresight", "status": "active", "confidence": "high", "weights": "closed", "license": "GitHub code is Apache-2.0. Weight files are not published. The web app says it is experimental and must not be used to diagnose patients.", "release_date": "2022-12-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2-style causal transformer over biomedical concept tokens, not word pieces. The Lancet Digital Health text, as carried in the KCL thesis, says the selected configuration is 16 layers, 16 attention heads, and embedding size 512, with weight decay 0.01. A parameter count for that configuration was not stated. Concepts come from MedCAT. Timeline prompts use SEP and Age tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Foresight is a generative transformer from CogStack and King's College London that forecasts the next biomedical concepts in a patient timeline. The preprint is 13 December 2022 and the Lancet Digital Health paper is April 2024. On 2026-10-15 the KCL web app was up and served only the MIMIC-III model. Weights are not published. Collected 2026-10-15.", "what_it_is_for": "The paper describes forecasting future disorders, substances, procedures, and findings from electronic health records after MedCAT turns free text into coded concepts. The web app also offers medication and symptom suggestions. It says not to use it to diagnose patients.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kcl/foresight/foresight-england": { "id": "kcl/foresight/foresight-england", "name": "Foresight-England", "maker": "kcl", "family": "kcl/foresight", "status": "preview", "confidence": "high", "weights": "closed", "license": "The preprint is CC BY-NC-ND 4.0. That is the paper license, not a weight license. Weights were not released.", "release_date": "2026-08-17", "context_length": null, "max_output_tokens": null, "parameters": "243000000", "architecture": "Transformer decoder trained from scratch inside the NHS England Secure Data Environment. 243 million parameters. Autoregressive next-event prediction over a vocabulary of about 40,000 codes. Tokens keep ICD-10, OPCS-4, and SNOMED CT granularity and encode absolute and relative time. Zero-shot at inference, with no task-specific head.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Foresight-England (Foresight-E) is a national electronic-health-record foundation model associated with King's College London, UCL, and the CVD-COVID-UK/COVID-IMPACT Consortium. The 17 August 2026 preprint calls it a 243-million-parameter transformer decoder, trained from scratch inside the NHS England Secure Data Environment, and a research pilot only for COVID-19 research. Weights are not public. NHS England had paused data access, so the paper has no scores. Collected 2026-10-15.", "what_it_is_for": "The abstract says it models patient timelines autoregressively and, at inference, predicts any concept in a vocabulary of about 40,000 codes with no task-specific training. The planned tests were 30-day COVID-19 hospitalisation and mortality, plus 2023 events after the training window. Those numbers are not in the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kcl/medcat/medcat-snomed-international": { "id": "kcl/medcat/medcat-snomed-international", "name": "MedCAT SNOMED International", "maker": "kcl", "family": "kcl/medcat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Download requires a UMLS license via NIH login or API key. SNOMED CT content has its own license. The MedCAT library is Apache-2.0.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MedCAT v1 model pack. CogStack calls it a full SNOMED model pack trained on MIMIC-III. Users load a zip named mc_modelpack_snomed_int_16_mar_2022_25be3857ba34bdd5. The date in that filename is not an official release note.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedCAT SNOMED International is CogStack's public full SNOMED CT model pack, trained on MIMIC-III and distributed by the King's College London MedCAT download flow. Users identify the file as `mc_modelpack_snomed_int_16_mar_2022_25be3857ba34bdd5.zip`. It was still listed on 10 October 2025. Collected 2026-10-15.", "what_it_is_for": "Named-entity recognition and linking of clinical text to the international SNOMED CT edition, as opposed to the later UK Clinical edition packs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kcl/medcat/medcat-snomed-uk": { "id": "kcl/medcat/medcat-snomed-uk", "name": "MedCAT SNOMED CT UK", "maker": "kcl", "family": "kcl/medcat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Download requires a UMLS license via NIH login or API key. SNOMED CT UK Clinical and UK Drug Extension content is under SNOMED licensing. The MedCAT library is Apache-2.0.", "release_date": "2025-10-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MedCAT model packs for SNOMED CT UK Clinical Edition plus the UK Drug Extension, enriched with UMLS 2024AA names, self-supervised only on MIMIC-IV. Native v2 packs and converted v1-format packs of the same editions were both posted. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedCAT SNOMED CT UK is the public UK SNOMED model line from CogStack at King's College London. PyPI says the 2024 and 2025 packs were uploaded on 7 October 2025. Each edition ships as a native MedCAT v2 pack and as a v1-format pack. Both were trained only on MIMIC-IV and enriched with UMLS 2024AA names. Collected 2026-10-15.", "what_it_is_for": "Clinical named-entity recognition and linking against the UK Clinical Edition of SNOMED CT plus the UK Drug Extension. CogStack recommends the v2 format.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kcl/medcat/medcat-umls-full": { "id": "kcl/medcat/medcat-umls-full", "name": "MedCAT UMLS Full", "maker": "kcl", "family": "kcl/medcat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Download requires a UMLS license via NIH login or API key. The MedCAT library is Apache-2.0. The pack uses UMLS 2022AA.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MedCAT v1 model pack. CogStack describes it as more than 4 million UMLS concepts, UMLS 2022AA, self-supervised on MIMIC-III. Parameter count is not published. Concept count is not a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedCAT UMLS Full is CogStack's large public UMLS model pack from King's College London. The README says it holds more than 4 million concepts from UMLS 2022AA and was self-supervised on MIMIC-III. It was still listed on 10 October 2025. Collected 2026-10-15.", "what_it_is_for": "Clinical named-entity recognition and linking against a full UMLS concept database, as an alternative to the smaller disorder, symptom, and medication subset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kcl/medcat/medcat-umls-small": { "id": "kcl/medcat/medcat-umls-small", "name": "MedCAT UMLS Small", "maker": "kcl", "family": "kcl/medcat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Download requires a UMLS license via NIH login or API key. The MedCAT library is Apache-2.0. UMLS content is under NLM terms.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MedCAT model pack: concept database, vocabulary, and a self-supervised context model for clinical named-entity recognition and linking. This pack is a subset of UMLS covering disorders, symptoms, and medications, self-supervised on MIMIC-III. Not a published parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedCAT UMLS Small is a public clinical concept-annotation model pack from CogStack at King's College London. It is a subset of UMLS limited to disorders, symptoms, and medications, self-supervised on MIMIC-III. It was still listed as a downloadable v1 pack on 10 October 2025. Collected 2026-10-15.", "what_it_is_for": "The MedCAT README says the toolkit extracts concepts from electronic health records and links them to vocabularies such as UMLS and SNOMED CT. This pack is the smaller UMLS model, not the full metathesaurus pack.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kcl/niftynet/anisotropic-nets-brats": { "id": "kcl/niftynet/anisotropic-nets-brats", "name": "Anisotropic nets BraTS", "maker": "kcl", "family": "kcl/niftynet", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "NiftyNet platform code is Apache-2.0, copyright 2018 University College London and the NiftyNet Contributors. The model-zoo repository had no LICENSE file on 2026-10-15. The page also points at https://github.com/taigw/brats17 for the full three-stage method.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cascaded anisotropic convolutional networks for BraTS whole-tumor segmentation. The zoo ships three orientation-specific configs: axial, coronal, and sagittal, plus a label map that turns the multi-class problem into a binary whole-tumor task. An average_volume.py script fuses the probabilistic outputs. The page says the full three-stage cascade lives in taigw/brats17, so this zoo entry is the whole-tumor stage of that pipeline.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Anisotropic nets BraTS is the NiftyNet zoo drop for whole-tumor segmentation from the 2017 BraTS cascaded anisotropic CNN pipeline. The download id is `anisotropic_nets_brats_challenge_model_zoo`. NiftyNet's own page names King's College London as consortium lead. The platform was marked unmaintained on 21 April 2020. Collected 2026-10-15.", "what_it_is_for": "The zoo page says the entry is the whole-tumor segmentation network from Wang et al., BraTS 2017. Three orientation-specific networks (axial, coronal, sagittal) write probabilities that a script averages. The full three-stage cascade is a different repository, `taigw/brats17`. NiftyNet is not for clinical use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kcl/niftynet/dense-vnet-abdominal-ct": { "id": "kcl/niftynet/dense-vnet-abdominal-ct", "name": "Dense V-Net abdominal CT", "maker": "kcl", "family": "kcl/niftynet", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "NiftyNet platform code is Apache-2.0, copyright 2018 University College London and the NiftyNet Contributors. The model-zoo repository had no LICENSE file on 2026-10-15.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dense V-Net for multi-organ abdominal CT. The zoo page says it segments eight organs: esophagus, stomach, duodenum, pancreas, liver, gallbladder, spleen, and left kidney. Input is Hounsfield units, with voxels outside the field of view set to -1000, cropped to the rib cage and abdominal cavity. Inference is NiftyNet net_segment. A GPU with at least 10 GB is requested on the zoo page.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dense V-Net abdominal CT is a pretrained NiftyNet zoo network for eight-organ segmentation on abdominal CT. The zoo was part of NiftyNet, a consortium whose own page names the School of Biomedical Engineering and Imaging Sciences at King's College London as lead, with UCL centres as members. The platform was marked unmaintained on 21 April 2020. The zoo repository was archived on 17 March 2021. Collected 2026-10-15.", "what_it_is_for": "The zoo page says the network segments esophagus, stomach, duodenum, pancreas, liver, gallbladder, spleen, and left kidney. It is a research weight drop, and NiftyNet says it is not for clinical use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kcl/niftynet/highres3dnet-brain-parcellation": { "id": "kcl/niftynet/highres3dnet-brain-parcellation", "name": "HighRes3DNet brain parcellation", "maker": "kcl", "family": "kcl/niftynet", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "NiftyNet platform code is Apache-2.0, copyright 2018 University College London and the NiftyNet Contributors. The model-zoo repository had no LICENSE file on 2026-10-15. A separate HighRes3DNet source note points at gift-surg/HighRes3DNet.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HighRes3DNet: a compact 3D residual network with dilated convolutions for T1 brain MR parcellation. The zoo page says the shipped network labels 160 structure types, including 155 neuroanatomical structures. The IPMI 2017 paper reports 0.81 million parameters for the HC-default residual net and 0.82 million for the dropout variant. The zoo page does not say which variant is in the checkpoint.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HighRes3DNet brain parcellation is a pretrained NiftyNet zoo network that labels brain structures on T1 MR. The architecture paper is Li, Wang, Fidon, Ourselin, Cardoso, and Vercauteren, IPMI 2017, from the King's and UCL imaging group that led NiftyNet. The zoo download id is `highres3dnet_brain_parcellation_model_zoo`. NiftyNet was marked unmaintained on 21 April 2020. Collected 2026-10-15.", "what_it_is_for": "The zoo page says the network parcellates 160 structure types, including 155 neuroanatomical structures, from brain MR. The paper frames parcellation as a pretext task for volumetric segmentation. NiftyNet is not for clinical use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kcl/niftynet/ultrasound-simulator-gan": { "id": "kcl/niftynet/ultrasound-simulator-gan", "name": "Ultrasound simulator GAN", "maker": "kcl", "family": "kcl/niftynet", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "NiftyNet platform code is Apache-2.0, copyright 2018 University College London and the NiftyNet Contributors. The model-zoo repository had no LICENSE file on 2026-10-15.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Spatially conditioned generative adversarial network that synthesises freehand ultrasound images from a coordinate map. Shipped as NiftyNet zoo id ultrasound_simulator_gan_model_zoo, with separate code, weight, and example-data archives. Inference uses net_gan. The zoo page says interpolated samples are written for indices 0 to 9.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The ultrasound simulator GAN is a pretrained NiftyNet zoo network that generates freehand ultrasound images from a coordinate map. The zoo cites Hu, Gibson, Lee, Xie, Barratt, Vercauteren, and Noble, MICCAI RAMBO 2017. It ships as `ultrasound_simulator_gan_model_zoo` under the NiftyNet consortium, whose page names King's College London as lead. The platform was marked unmaintained on 21 April 2020. Collected 2026-10-15.", "what_it_is_for": "The zoo page says the network generates ultrasound images conditioned by a coordinate map, including interpolated frames between two poses. It is a research simulator, not a clinical device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kddi/bousai/bousai-vlm": { "id": "kddi/bousai/bousai-vlm", "name": "\u9632\u707dVLM", "maker": "kddi", "family": "kddi/bousai", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of an unspecified Google Gemma-family vision-language model. KDDI Research says it was tuned on U.S. hurricane damage images so the model can name damage types and points to check, not only caption a scene.", "modalities": { "input": [ "image", "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u9632\u707dVLM is a pre-commercial vision-language model from KDDI Research, Inc., the research subsidiary of KDDI. KDDI named it in a June 2026 tech note and was still describing it as not yet commercial in an October 2026 interview. This dossier was collected on 2026-10-15.", "what_it_is_for": "KDDI Research says \u9632\u707dVLM reads drone images and video after a disaster and explains damage on a map: building damage, severed roads, fallen trees, and sediment inflow. Staff can ask what is blocking a road and where vehicle passage looks hard. The October 2026 interview says it ranks places for people to check. It does not make the final call. Tokushima Prefecture crisis staff have reviewed demo screens with the lab, with the prefectural disaster headquarters as the intended user.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kddi/ks-mgsr/ks-mgsr": { "id": "kddi/ks-mgsr/ks-mgsr", "name": "KS-MGSR", "maker": "kddi", "family": "kddi/ks-mgsr", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2022-04-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal knowledge graph plus an original graph neural network. The system answers a visual question by choosing an answer and a rationale. Official expansion prints 'Sematic' (typo for Semantic): KDDI SNAP Multimodal Graph based Sematic Reasoner.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KS-MGSR is a 2022 visual commonsense system from KDDI Research, Inc., built with Stanford University's SNAP lab (Jure Leskovec). KDDI announced it on 2022-04-18 after a 2022-04-08 VCR leaderboard entry. It was a research submission, not an API product. No weights or later KDDI checkpoint were found, so this dossier treats it as retired as of 2026-10-15. There is no official retirement date.", "what_it_is_for": "Given an image, a question, and answer choices, KS-MGSR picks the answer and the rationale. KDDI describes it as commonsense visual reasoning for settings that need an explanation, and names automated driving and home robots as motivation. It is a multiple-choice benchmark system, not a general chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "keio/polos/polos": { "id": "keio/polos/polos", "name": "Polos", "maker": "keio", "family": "keio/polos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause-Clear", "release_date": "2024-02-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Supervised image-caption metric. Frozen CLIP ViT-B/16 image and text encoders plus princeton-nlp/sup-simcse-roberta-base. A parallel feature block builds difference and Hadamard products of candidate, reference, and image embeddings, then an MLP with hidden sizes 2304 and 1152 and Tanh. Trained with MSE to regress Polaris human scores. The published config comments out sup-simcse-roberta-large; the active line is the base checkpoint.", "modalities": { "input": [ "image", "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Polos is a Keio University image-caption evaluation model from the Semantic Machine Intelligence Lab (Yuiga Wada, Kanta Kaneda, Daichi Saito, and Komei Sugiura). It scores a candidate caption against an image and reference captions. The paper appeared on arXiv on 2024-02-28 and at CVPR 2024. Weights are still linked from the lab GitHub README. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe Polos as a supervised metric for image captioning. It regresses human judgments from multimodal inputs instead of using only cosine similarity between embeddings. Training uses the Polaris set, which the paper says has 131,020 judgments from 550 evaluators. The paper says a reference-free setting was not used.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/dale-ct/dale-ct-0": { "id": "kentucky/dale-ct/dale-ct-0", "name": "DALE-CT-0", "maker": "kentucky", "family": "kentucky/dale-ct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "2026-04-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-Large, timm vit_large_patch14_dinov2 randomly initialized, patch size 16, native 512 input, 1 channel, embedding 1024. Pure LeJEPA (invariance + SIGReg, lambda 0.02). No auxiliary labels.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DALE-CT-0 is a chest-CT vision encoder from the University of Kentucky Institute for Biomedical Informatics Center for Applied AI (IBI-CAAI), released under Kentucky Open Science. Pure self-supervised CT-RATE encoder in the DALE-CT family. Collected on 2026-10-15. Status on this date: active.", "what_it_is_for": "The card says it is a feature extractor for chest CT research: quantitative analysis, anomaly and organ tasks, segmentation backbones, and volume-level MIL. It was trained with no auxiliary supervision.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/dale-ct/dale-ct-0-l": { "id": "kentucky/dale-ct/dale-ct-0-l", "name": "DALE-CT-0-L", "maker": "kentucky", "family": "kentucky/dale-ct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "303674368", "architecture": "ViT-Large, timm vit_large_patch14_dinov2 randomly initialized, patch size 16, img size 512, 1 channel, dynamic image size, embedding 1024. Pure LeJEPA, no auxiliary head. Kentucky Open Science lists 303,674,368 parameters.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DALE-CT-0-L is the large-corpus, supervision-free chest CT ViT-Large from University of Kentucky IBI-CAAI. The Kentucky Open Science catalog calls it the recommended general-purpose DALE-CT backbone and lists 303,674,368 parameters. The Hugging Face repository returned 404 on 2026-10-15. This dossier was collected that day.", "what_it_is_for": "The catalog card says the model is a foundational encoder trained from scratch with LeJEPA on about 287,302 training scans from a 296,429-case multi-source chest CT pool (32 collections), with no labels. It is for research feature extraction. For maximum in-domain CT-RATE performance the same card points to DALE-CT-2S.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/dale-ct/dale-ct-1s": { "id": "kentucky/dale-ct/dale-ct-1s", "name": "DALE-CT-1S", "maker": "kentucky", "family": "kentucky/dale-ct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "2026-03-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-Large, timm vit_large_patch14_dinov2 from scratch, patch size 14, embedding 1024, 1 channel. LeJEPA plus an auxiliary multi-hot loss over 118 TotalSegmentator classes on the CLS token only. Global crops 224, local crops 140.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DALE-CT-1S is a chest-CT vision encoder from the University of Kentucky Institute for Biomedical Informatics Center for Applied AI (IBI-CAAI), released under Kentucky Open Science. Earlier patch-14 CLS-only checkpoint kept as the Ker-VLJEPA-3B backbone. Not the configuration benchmarked as DALE-CT-1S in the paper. Collected on 2026-10-15. Status on this date: active.", "what_it_is_for": "Research chest CT feature extractor. The card says the paper's single-source configuration is DALE-CT-1S-v2, and this release remains the vision backbone used by Ker-VLJEPA-3B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/dale-ct/dale-ct-1s-v2": { "id": "kentucky/dale-ct/dale-ct-1s-v2", "name": "DALE-CT-1S-v2", "maker": "kentucky", "family": "kentucky/dale-ct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "2026-08-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-Large from scratch, patch size 16, image size 512, embedding 1024. LeJEPA plus dense TotalSegmentator supervision (118 classes) on CLS and patch tokens. ReXGroundingCT head held inert.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DALE-CT-1S-v2 is a chest-CT vision encoder from the University of Kentucky Institute for Biomedical Informatics Center for Applied AI (IBI-CAAI), released under Kentucky Open Science. Anatomical-supervision-only configuration benchmarked in the DALE-CT paper. Supersedes DALE-CT-1S. Collected on 2026-10-15. Status on this date: active.", "what_it_is_for": "Research chest CT backbone. The card says it isolates anatomical supervision against DALE-CT-2S.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/dale-ct/dale-ct-2s": { "id": "kentucky/dale-ct/dale-ct-2s", "name": "DALE-CT-2S", "maker": "kentucky", "family": "kentucky/dale-ct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "2026-04-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-Large from scratch, patch size 16, native 512 input, embedding 1024. LeJEPA plus dual dense auxiliary supervision from TotalSegmentator and ReXGroundingCT. Auxiliary weight 0.1.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DALE-CT-2S is a chest-CT vision encoder from the University of Kentucky Institute for Biomedical Informatics Center for Applied AI (IBI-CAAI), released under Kentucky Open Science. Best in-domain CT-RATE model in the maker's family table. Collected on 2026-10-15. Status on this date: active.", "what_it_is_for": "Research chest CT feature extractor. The card says dual-source dense supervision tracks both anatomy and abnormalities.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/dale-ct/finetuned-dinov2-chest-ct": { "id": "kentucky/dale-ct/finetuned-dinov2-chest-ct", "name": "Finetuned DINOv2 Chest CT", "maker": "kentucky", "family": "kentucky/dale-ct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "2026-04-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DINOv2 with registers, ViT-Large, continued from facebook/dinov2-with-registers-large. Embedding 1024, patch 14, 4 register tokens, native 518 input, 1 channel. DINO loss plus iBOT. Transformers Dinov2WithRegistersModel.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finetuned DINOv2 Chest CT is a chest-CT vision encoder from the University of Kentucky Institute for Biomedical Informatics Center for Applied AI (IBI-CAAI), released under Kentucky Open Science. Continual-pretraining baseline in the DALE-CT family table. The card says it has the strongest dense patch-level features in that comparison. Collected on 2026-10-15. Status on this date: active.", "what_it_is_for": "Research chest CT feature extractor. Unlike the from-scratch LeJEPA models, this one starts from public DINOv2-with-registers-large weights and continues pretraining on CT-RATE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/ker/ker-vljepa-3b": { "id": "kentucky/ker/ker-vljepa-3b", "name": "Ker-VLJEPA-3B", "maker": "kentucky", "family": "kentucky/ker", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "2026-03-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama 3.2 3B with LoRA (r=64, alpha=128), a Z-zoned Perceiver that compresses variable-length 1024-d slice embeddings into 32 visual tokens, and Flamingo-style gated cross-attention at layers 7, 14, and 21. Does not ingest raw CT pixels. Card says about 3.2B for the Llama base plus 1.7 GB LoRA and 320 MB bridge components. Those sizes are not converted into an exact parameter integer.", "modalities": { "input": [ "embeddings" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ker-VLJEPA-3B is a chest CT report generator from University of Kentucky IBI-CAAI. It is a LoRA-adapted Llama 3.2 3B that writes narrative findings from frozen slice embeddings, not from raw pixels. Collected on 2026-10-15.", "what_it_is_for": "The card says the job is automated narrative chest CT findings for research, plus vision-language architecture studies and CT-RATE benchmarking. It is not for diagnosis or treatment decisions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/kos/kos-v4-base": { "id": "kentucky/kos/kos-v4-base", "name": "KOS-V4-Base", "maker": "kentucky", "family": "kentucky/kos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "2026-07-12", "context_length": 24576, "max_output_tokens": null, "parameters": "3015000000", "architecture": "Decoder-only Transformer, Qwen3ForCausalLM class, trained from scratch. Not continued from Qwen weights. Hidden 3072, 28 layers, 24 query / 8 KV heads, head_dim 128, per-head QK-RMSNorm, SwiGLU intermediate 8192, vocabulary 32,000 medical byte-level BPE, RoPE theta 25,000, bfloat16. Trained context 24,576. Config max_position_embeddings 65,536. Those two lengths are not the same window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KOS-V4-Base is a from-scratch medical language model from the University of Kentucky College of Medicine. The card calls the line Scratch. It completes text and is not instruction-tuned. Collected on 2026-10-15.", "what_it_is_for": "The card says the job is a research base for medical NLP and downstream fine-tuning. It is not a clinical or commercial system, and it is not a closed-book medical QA model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/kos/kos-v4-instruct": { "id": "kentucky/kos/kos-v4-instruct", "name": "KOS-V4-Instruct", "maker": "kentucky", "family": "kentucky/kos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "2026-07-08", "context_length": 24576, "max_output_tokens": null, "parameters": "3015000000", "architecture": "Decoder-only Transformer, Qwen3ForCausalLM class, trained from scratch. Not continued from Qwen weights. Hidden 3072, 28 layers, 24 query / 8 KV heads, head_dim 128, per-head QK-RMSNorm, SwiGLU intermediate 8192, vocabulary 32,000 medical byte-level BPE, RoPE theta 25,000, bfloat16. Trained context 24,576. Config max_position_embeddings 65,536. Those two lengths are not the same window. ChatML template. eos is <|im_end|>.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KOS-V4-Instruct is the instruction-tuned and tool-calling checkpoint of KOS-V4-Base, from the University of Kentucky. The card says it was trained from scratch, then fully fine-tuned and optimized with GRPO. Collected on 2026-10-15.", "what_it_is_for": "The card says the job is research instruction following, structured output, and function calling in clinical-adjacent workflows. It says the model does not reliably recall parametric medical facts and should be grounded with retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/kos/kos-v5-base": { "id": "kentucky/kos/kos-v5-base", "name": "KOS-V5-Base", "maker": "kentucky", "family": "kentucky/kos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-07-13", "context_length": 24576, "max_output_tokens": null, "parameters": "3715000000", "architecture": "Decoder-only Transformer, Qwen3ForCausalLM class, trained from scratch with tied embeddings. Not continued from Qwen weights. 36 layers, hidden 2560, 32 query / 8 KV heads, head_dim 128, SwiGLU, tokenizer v5-32k byte-level BPE, add_bos_token false, special token id 0. Trained context 24,576. Config max_position_embeddings 32,768. The base card says 3.72B. The instruct card states 3.715 B for this architecture. Intermediate size is not on the base card; the instruct card states 9728.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KOS-V5-Base is a from-scratch medical language model from the University of Kentucky and the University of Louisville. The card calls it Catbird. It is a base checkpoint with no instruction tuning. Collected on 2026-10-15.", "what_it_is_for": "The card says this is a research foundation for a downstream SFT and RL line. It completes text. It is not for clinical, commercial, legal, or production use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/kos/kos-v5-instruct": { "id": "kentucky/kos/kos-v5-instruct", "name": "KOS-V5-Instruct", "maker": "kentucky", "family": "kentucky/kos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-07-18", "context_length": 32768, "max_output_tokens": null, "parameters": "3715000000", "architecture": "Same architecture as KOS-V5-Base: Qwen3ForCausalLM, 36 layers, hidden 2560, 32/8 GQA, head_dim 128, SwiGLU intermediate 9728, vocabulary 32,000, tied embeddings, RoPE theta 25,000, bfloat16. The instruct card's specification table lists context length 32,768. The base card says pretraining used 24,576 tokens with max_position_embeddings 32,768. Card says 3.715 B. The base card rounds that line to 3.72B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KOS-V5-Instruct is the instruction-tuned and tool-calling head of KOS-V5, from the University of Kentucky and the University of Louisville. The card says it starts from KOS-V5-Base and adds full-parameter SFT plus two GRPO stages. Collected on 2026-10-15.", "what_it_is_for": "The card says the job is research instruction following and tool calling on a medical base. It is not for clinical decisions. The card says medical signal should be read from bits-per-byte on the base, not from multiple-choice scores alone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/kos/kos-v5-retriever": { "id": "kentucky/kos/kos-v5-retriever", "name": "KOS-V5-Retriever", "maker": "kentucky", "family": "kentucky/kos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-07-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PEFT LoRA rank 32 on frozen KOS-V5-Instruct. Targets q, k, v, o, gate, up, and down. llm2vec-style bidirectional attention, mean pooling, L2 normalization. The card says the adapter is about 264 MB and is not a standalone model. Parameter count is not stated. 264 MB is a file size, not a parameter count.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KOS-V5-Retriever is a LoRA embedding adapter from the University of Kentucky and the University of Louisville. It sits on frozen KOS-V5-Instruct and returns one vector per text. Collected on 2026-10-15.", "what_it_is_for": "The card says the job is research semantic search and retrieval over biomedical text. Detaching the adapter returns the generator. It does not write answers by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/melt/melt-llama-2-3x70b-chat": { "id": "kentucky/melt/melt-llama-2-3x70b-chat", "name": "MELT-Llama-2-3x70B-Chat", "maker": "kentucky", "family": "kentucky/melt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA fine-tune of a 3-expert mixture of Llama-2-70B, credited to Charles Goddard's MoE tools. The score table compares against llama-2-3x70b-chat-hf. Exact parameter count not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MELT-Llama-2-3x70B-Chat is a Medical Education Language Transformer released by the University of Kentucky Center for Applied AI (Institute for Biomedical Informatics). It is an Apache-2.0 open-weight LoRA fine-tune for medical question answering and chat. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card says MELT is for research prompts in a QA or chat format, trained on public medical text, chat, and instruction data. It is not for medical advice. Public medical text, chat, QA, and instruction sets named on the card, including MedQA train, MedMCQA train, LiveQA, MedicationQA, medical flashcards, Wikidoc, MEDIQA, and several conversation sets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/melt/melt-llama-2-7b-chat-v0-1": { "id": "kentucky/melt/melt-llama-2-7b-chat-v0-1", "name": "MELT-Llama-2-7B-Chat-v0.1", "maker": "kentucky", "family": "kentucky/melt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-12-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA fine-tune of meta-llama/Llama-2-7b-chat-hf. Exact parameter count not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MELT-Llama-2-7B-Chat-v0.1 is a Medical Education Language Transformer released by the University of Kentucky Center for Applied AI (Institute for Biomedical Informatics). It is an Apache-2.0 open-weight LoRA fine-tune for medical question answering and chat. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card says MELT is for research prompts in a QA or chat format, trained on public medical text, chat, and instruction data. It is not for medical advice. Public medical text, chat, QA, and instruction sets named on the card, including MedQA train, MedMCQA train, LiveQA, MedicationQA, medical flashcards, Wikidoc, MEDIQA, and several conversation sets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/melt/melt-mistral-3x7b-instruct-v0-1": { "id": "kentucky/melt/melt-mistral-3x7b-instruct-v0-1", "name": "MELT-Mistral-3x7B-Instruct-v0.1", "maker": "kentucky", "family": "kentucky/melt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-12-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA fine-tune of a 3-expert mixture of Mistral-7B-v0.1, built with tools credited to Charles Goddard. The card also calls the comparison base Mistral-3x7B-Instruct-v0.1. Exact parameter count not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MELT-Mistral-3x7B-Instruct-v0.1 is a Medical Education Language Transformer released by the University of Kentucky Center for Applied AI (Institute for Biomedical Informatics). It is an Apache-2.0 open-weight LoRA fine-tune for medical question answering and chat. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card says MELT is for research prompts in a QA or chat format, trained on public medical text, chat, and instruction data. It is not for medical advice. Public medical text, chat, QA, and instruction sets named on the card, including MedQA train, MedMCQA train, LiveQA, MedicationQA, medical flashcards, Wikidoc, MEDIQA, and several conversation sets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/melt/melt-mixtral-8x7b-instruct-v0-1": { "id": "kentucky/melt/melt-mixtral-8x7b-instruct-v0-1", "name": "MELT-Mixtral-8x7B-Instruct-v0.1", "maker": "kentucky", "family": "kentucky/melt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-12-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA fine-tune of mistralai/Mixtral-8x7B-Instruct-v0.1. Mixture-of-experts chat model. Exact parameter count not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MELT-Mixtral-8x7B-Instruct-v0.1 is a Medical Education Language Transformer released by the University of Kentucky Center for Applied AI (Institute for Biomedical Informatics). It is an Apache-2.0 open-weight LoRA fine-tune for medical question answering and chat. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card says MELT is for research prompts in a QA or chat format, trained on public medical text, chat, and instruction data. It is not for medical advice. Public medical text, chat, QA, and instruction sets named on the card, including MedQA train, MedMCQA train, LiveQA, MedicationQA, medical flashcards, Wikidoc, MEDIQA, and several conversation sets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/melt/melt-tinyllama-1-1b-chat-v1-0": { "id": "kentucky/melt/melt-tinyllama-1-1b-chat-v1-0", "name": "MELT-TinyLlama-1.1B-Chat-v1.0", "maker": "kentucky", "family": "kentucky/melt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-01-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA fine-tune of TinyLlama/TinyLlama-1.1B-Chat-v1.0. Exact parameter count not stated beyond the 1.1B base name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MELT-TinyLlama-1.1B-Chat-v1.0 is a Medical Education Language Transformer released by the University of Kentucky Center for Applied AI (Institute for Biomedical Informatics). It is an Apache-2.0 open-weight LoRA fine-tune for medical question answering and chat. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card says MELT is for research prompts in a QA or chat format, trained on public medical text, chat, and instruction data. It is not for medical advice. Public medical text, chat, QA, and instruction sets named on the card, including MedQA train, MedMCQA train, LiveQA, MedicationQA, medical flashcards, Wikidoc, MEDIQA, and several conversation sets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/np/mad-np": { "id": "kentucky/np/mad-np", "name": "MAD-NP", "maker": "kentucky", "family": "kentucky/np", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of facebook/dinov2-with-registers-giant. Embedding 1536, patch size 14, trained on 224x224 tiles. The card says the model can take other image sizes.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAD-NP is a neuropathology vision transformer from the University of Kentucky Center for Applied AI, trained on University of Kentucky Alzheimer's Disease Center slides and released by Kentucky Open Science. The model card calls it an initial test. Collected on 2026-10-15.", "what_it_is_for": "The card's intended uses are research classification of neuropathological change and feature extraction for quantitative analysis. Training tiles come from 57 H&E whole-slide images, annotated for gray matter, white matter, leptomeninges, exclude, and superficial cortex.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/np/np-giant": { "id": "kentucky/np/np-giant", "name": "NP-GIANT", "maker": "kentucky", "family": "kentucky/np", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of facebook/dinov2-with-registers-giant. Embedding 1536, patch size 14, trained on 224x224 tiles. The card says the model can take other image sizes.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NP-GIANT is a neuropathology vision transformer from the University of Kentucky Center for Applied AI, trained on University of Kentucky Alzheimer's Disease Center slides and released by Kentucky Open Science. The model card calls it an initial test. Collected on 2026-10-15.", "what_it_is_for": "The card's intended uses are research classification of neuropathological change and feature extraction for quantitative analysis. Training tiles come from 57 H&E whole-slide images, annotated for gray matter, white matter, leptomeninges, exclude, and superficial cortex.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kentucky/np/np-test-0": { "id": "kentucky/np/np-test-0", "name": "NP-TEST-0", "maker": "kentucky", "family": "kentucky/np", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of facebook/dinov2-giant. Embedding 1536, patch size 14, trained on 224x224 tiles. The card says the model can take other image sizes.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NP-TEST-0 is a neuropathology vision transformer from the University of Kentucky Center for Applied AI, trained on University of Kentucky Alzheimer's Disease Center slides and released by Kentucky Open Science. The model card calls it an initial test. Collected on 2026-10-15.", "what_it_is_for": "The card's intended uses are research classification of neuropathological change and feature extraction for quantitative analysis. Training tiles come from 57 H&E whole-slide images, annotated for gray matter, white matter, leptomeninges, exclude, and superficial cortex.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kfupm/msrnet/msrnet": { "id": "kfupm/msrnet/msrnet", "name": "MSRNet", "maker": "kfupm", "family": "kfupm/msrnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-16", "context_length": null, "max_output_tokens": null, "parameters": "65.373M", "architecture": "Camouflaged-object segmentation network. The released checkpoint is the PVTv2-B4 encoder (classification head removed) at 384x384, with Attention-Based Scale Integration Units, Multi-Granularity Fusion Units, and a recursive-feedback decoder. The paper states 65.373M parameters for that backbone. The ImageNet-pretrained PVTv2 encoder is not a KFUPM model.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MSRNet is a camouflaged-object segmentation network released with code and a public weight link by Leena Alghamdi (King Fahd University of Petroleum and Minerals) and co-authors at Ontario Tech University, the National University of Computer and Emerging Sciences, the University of Regina, and the University of Western Australia. The model is active as a research release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors use MSRNet to find and segment objects that match the color, texture, or size of the background, including small objects and scenes with more than one camouflaged object. The public weight link is for the highest-performing checkpoint, PvtV2B4_MSRNet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kfupm/xlsr-ar/xlsr-ar": { "id": "kfupm/xlsr-ar/xlsr-ar", "name": "XLSR-AR", "maker": "kfupm", "family": "kfupm/xlsr-ar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-02-05", "context_length": null, "max_output_tokens": null, "parameters": "317M", "architecture": "Full fine-tune of wav2vec2-XLSR-53 (Conneau et al., 2020), which the same paper also calls XLS-R. The authors state about 317 million parameters, a CNN feature encoder that stays frozen, 24 Transformer layers with model dimension 1024, and a CTC head. The base checkpoint is Meta's, not KFUPM's. LoRA and DoRA adapters are separate released files, stored as versions.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XLSR-AR is the Arabic speech-recognition fine-tune of wav2vec2-XLSR-53 released by Wafa Alshehri, Wasfi G. Al-Khatib, and Mohammad Amro at King Fahd University of Petroleum and Minerals. Weights went up on Zenodo on 2026-02-05, and the workshop paper is in AbjadNLP 2026. The release is active as a research download. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors use the full fine-tune for Arabic automatic speech recognition on Common Voice Arabic v24.0. LoRA and DoRA checkpoints in the same record are smaller adapters for the same task, not separate products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kit/beast/beast": { "id": "kit/beast/beast", "name": "BEAST", "maker": "kit", "family": "kit/beast", "status": "active", "confidence": "high", "weights": "closed", "license": "MIT", "release_date": "2025-12-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "B-spline Encoded Action Sequence Tokenizer from the Intuitive Robots Lab at KIT. The paper says it is training-free, maps an action sequence to uniform-length discrete or continuous B-spline tokens, and decodes those tokens back to a smooth trajectory. The Hugging Face release zhouhongyi/beast is an AutoProcessor (beast.py and processor_config.json). The Hub API reports used storage 0, so this dossier does not treat it as a neural checkpoint. The card says token count equals num_basis times num_dof. Documented defaults on the card table are num_dof 7, num_basis 10, seq_len 50, and vocab_size 256, which is 70 tokens at those defaults. A quick-start example on the same card uses different values (num_dof 3, num_basis 20, degree_p 3).", "modalities": { "input": [ "actions" ], "output": [ "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BEAST is a training-free B-spline action tokenizer from the Intuitive Robots Lab at Karlsruhe Institute of Technology. Hongyi Zhou and coauthors, affiliated in the paper with KIT, Robotics Institute Germany, and Microsoft Research, published it at NeurIPS 2025. The public Hugging Face artifact `zhouhongyi/beast` is a processor, not a trained policy checkpoint. The policy that uses it, BEAST-F, is a separate folder. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe BEAST as a drop-in action tokenizer. It fits a B-spline to an action chunk so every chunk becomes the same number of discrete or continuous tokens, without a separate tokenizer training stage. Policies can then predict those tokens in parallel and decode a smooth trajectory. The Hugging Face card is the released processor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kit/beast/beast-f": { "id": "kit/beast/beast-f", "name": "BEAST-F", "maker": "kit", "family": "kit/beast", "status": "active", "confidence": "high", "weights": "closed", "license": "MIT", "release_date": "2025-06-06", "context_length": null, "max_output_tokens": null, "parameters": "770000000", "architecture": "The paper's primary vision-language-action policy built on the BEAST tokenizer and Florence-2-large. The scaling section says BEAST-F uses Florence-2-large (0.77B parameters). Table 3 labels the same model BEAST-F (0.77B). Figure 3 describes discrete BEAST tokens, parallel decoding with learnable action embeddings, and a B-spline decoder. Table 1 marks the action type as Discrete. The beast_calvin README instead says BEAST-F models a continuous action-token space with a rectified flow model. Those two descriptions disagree. Florence-2 is a Microsoft backbone. Fabian Otto is at Microsoft Research. The releasing lab on the paper byline is Karlsruhe Institute of Technology.", "modalities": { "input": [ "vision", "text" ], "output": [ "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BEAST-F is the primary vision-language-action policy in the BEAST paper from the Intuitive Robots Lab at Karlsruhe Institute of Technology. The NeurIPS 2025 paper pairs the BEAST tokenizer with Florence-2-large and reports CALVIN and LIBERO results for this name. Training code is public. A downloadable policy checkpoint was not found. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses BEAST-F as the large-model test of the tokenizer: a Florence-2 policy that predicts BEAST action tokens and decodes a continuous robot trajectory. The official README calls it the primary model. It is evaluated on CALVIN, LIBERO, and real robots, and the paper says those simulation results do not use a second stage of robot-data pretraining.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kit/beso/beso": { "id": "kit/beso/beso", "name": "BESO", "maker": "kit", "family": "kit/beso", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Goal-conditioned score-based diffusion policy (Behavior generation with Score-based Diffusion Policies) from the Intuitive Robots Lab at KIT. The RSS 2023 abstract says the score model is decoupled from sampling and can generate goal-specified behavior in 3 denoising steps, including a classifier-free-guidance path that also learns a goal-independent policy. The repo Franka Kitchen config is a residual network with hidden_dim 360, 6 hidden layers, and 6 attention heads. The README says the continuous-time score model follows Karras et al. 2022 and recommends the DDIM sampler.", "modalities": { "input": [ "state" ], "output": [ "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BESO is an open-weight goal-conditioned score-based diffusion policy from the Intuitive Robots Lab at Karlsruhe Institute of Technology. Moritz Reuss, Maximilian Li, Xiaogang Jia, and Rudolf Lioutikov introduced it for RSS 2023. Kitchen and block-push checkpoints are in the official repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe BESO as a policy that learns goal-specified behavior from uncurated play data without rewards. It decouples the score model from sampling so goal-conditioned actions can be generated in a few denoising steps. A classifier-free-guidance setting also learns a goal-independent policy from the same data. The released checkpoints are for the Franka Kitchen and block-push environments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kit/flower/flower": { "id": "kit/flower/flower", "name": "FLOWER", "maker": "kit", "family": "kit/flower", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-16", "context_length": null, "max_output_tokens": null, "parameters": "947000000", "architecture": "Florence With Embodied Flow, a vision-language-action flow policy from the Intuitive Robots Lab at KIT. The paper says FLOWER uses half of Florence-2-large (the encoder, with the decoder removed) and an 18-layer Flow Transformer with latent dimension 1024, plus action-specific Global-AdaLN. The component table totals 947M parameters and the same paragraph says 1.85 GB of VRAM. The abstract rounds the size to 950M and the limitations section says about 1B. The CALVIN ABC card says 1B and about 1B. Pretraining is stated as 200 H100 GPU hours. Florence-2-large is a Microsoft backbone. Coauthor Fabian Otto is at Microsoft Research. The weights are released by the KIT lab.", "modalities": { "input": [ "vision", "text" ], "output": [ "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FLOWER (FlowerVLA, Florence With Embodied Flow) is an open-weight vision-language-action policy from the Intuitive Robots Lab at Karlsruhe Institute of Technology. Moritz Reuss, Hongyi Zhou, Marcel R\u00fchle, \u00d6mer Erdin\u00e7 Ya\u011fmurlu, Fabian Otto, and Rudolf Lioutikov published it at CoRL 2025. Otto is at Microsoft Research, and the vision-language backbone is half of Microsoft Florence-2-large. The checkpoints are on the KIT author's Hugging Face account `mbreuss`. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe FLOWER as a generalist manipulation policy that stays under a billion parameters by pruning the vision-language backbone and putting capacity in a flow-matching action head. The public repos are a pretrain checkpoint and finetunes for CALVIN and LIBERO. The paper says pretraining took 200 H100 GPU hours and that the policy is competitive on 190 tasks across ten benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kit/mdt/mdt": { "id": "kit/mdt/mdt", "name": "MDT", "maker": "kit", "family": "kit/mdt", "status": "active", "confidence": "high", "weights": "closed", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Default Multimodal Diffusion Transformer from the Intuitive Robots Lab at KIT. The paper says this encoder uses a trainable ResNet-18 with spatial softmax pooling and group norm, one observation token per camera view. Goal images and language use frozen CLIP, one token per goal modality. A GPT-style diffusion decoder denoises an action sequence of 10 steps with causal attention, cross-attention from the latent state, and adaLN on the noise level. Auxiliary losses are Masked Generative Foresight and Contrastive Latent Alignment. Experiments use the DDIM sampler for 10 denoising steps. MDT-V is the separate Voltron-encoder variant.", "modalities": { "input": [ "vision", "text" ], "output": [ "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MDT is the ResNet-encoder Multimodal Diffusion Transformer from the Intuitive Robots Lab at Karlsruhe Institute of Technology. Moritz Reuss, \u00d6mer Erdin\u00e7 Ya\u011fmurlu, Fabian Wenzel, and Rudolf Lioutikov introduced it for RSS 2024. The code is MIT. The public checkpoint table in the README lists the Voltron variant MDT-V, not a separate ResNet weight file. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe MDT as a diffusion policy that follows language or goal-image instructions on long-horizon manipulation, including datasets where most demonstrations have no language label. The default encoder is a trainable ResNet-18. The named Voltron-encoder variant is foldered as `mdt-v`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kit/mdt/mdt-v": { "id": "kit/mdt/mdt-v", "name": "MDT-V", "maker": "kit", "family": "kit/mdt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Voltron-encoder variant of the Multimodal Diffusion Transformer. The paper says each image is embedded into 196 frozen Voltron tokens and a Perceiver-Resampler compresses them to 3 latent tokens. Goal images and language still use frozen CLIP. The diffusion decoder matches MDT: 10-step action sequence, causal attention, cross-attention, and adaLN on the noise level, plus Masked Generative Foresight and Contrastive Latent Alignment. The same DDIM setup of 10 denoising steps is stated for the experiments.", "modalities": { "input": [ "vision", "text" ], "output": [ "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MDT-V is the frozen-Voltron encoder version of the Multimodal Diffusion Transformer from the Intuitive Robots Lab at Karlsruhe Institute of Technology. The RSS 2024 paper names it beside the ResNet model MDT. Six CALVIN seeds are linked from the official README on Google Drive. This dossier was collected on 2026-10-15.", "what_it_is_for": "MDT-V is the same goal-conditioned diffusion policy as MDT, with image observations encoded by frozen Voltron features instead of a trained ResNet-18. The authors use it for language- and image-goal manipulation on CALVIN and LIBERO. The released files are CALVIN evaluation seeds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kit/mode/mode": { "id": "kit/mode/mode", "name": "MoDE", "maker": "kit", "family": "kit/mode", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mixture-of-Denoising Experts diffusion policy. The ICLR 2025 paper says sparse experts and noise-conditioned routing cut active parameters by 40% and inference cost by 90% via expert caching, with noise-conditioned self-attention. The MoDE_Pretrained card says the vision encoder is a ResNet-50 with FiLM, finetuned from ImageNet, EMA on, action window 10, DDIM, and 5 sampling steps. Coauthors Jyothish Pari and Pulkit Agrawal are at MIT. The releasing GitHub org and Hugging Face cards are the KIT Intuitive Robots Lab author Moritz Reuss.", "modalities": { "input": [ "vision", "text" ], "output": [ "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MoDE (Mixture of Denoising Experts) is an open-weight diffusion transformer policy from the Intuitive Robots Lab at Karlsruhe Institute of Technology. Moritz Reuss, Jyothish Pari, Pulkit Agrawal, and Rudolf Lioutikov published it as an ICLR 2025 poster. Pari and Agrawal are at MIT. The weights are on the KIT author's Hugging Face account `mbreuss`, and the code repository is `intuitive-robots/MoDE_Diffusion_Policy`. CALVIN and LIBERO finetunes are versions of this model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe MoDE as an imitation-learning policy that keeps a diffusion transformer accurate while routing each noise level to a sparse set of experts. Expert caching is how they cut inference cost. The released pretrained checkpoint was trained on a subset of Open X-Embodiment and is meant to be finetuned. The other public repos are those finetunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "konstanz/animal2vec/animal2vec": { "id": "konstanz/animal2vec/animal2vec", "name": "animal2vec", "maker": "konstanz", "family": "konstanz/animal2vec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Disagreement: the Edmond dataset record and arXiv v3 say MIT for the released models. The Methods in Ecology and Evolution article says the same Edmond weights are CC BY-NC 4.0. The GitHub code is MIT.", "release_date": "2024-07-26", "context_length": null, "max_output_tokens": null, "parameters": "315000000", "architecture": "MeerKAT large setting in arXiv Table S1: 315M trainable parameters, 16 transformer layers, 16 attention heads, embedding size 1024. The frontend is a SincNet-style filterbank (127 filters, kernel width 63) plus a stack of 1D convolutions down to an effective rate of 200 Hz. Pretraining is mean-teacher self-distillation in the style of data2vec 2.0, with a convolutional decoder (4 layers, dimension 768) used only in pretraining. The paper says the student and teacher are trained from the MeerKAT audio, not loaded from a data2vec speech checkpoint. MeerKAT audio used for that run is 8 kHz. A 94M, 12-layer, 768-wide xeno-canto setting is in the same table. That checkpoint was not in the Edmond file list that was read.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "animal2vec is a self-supervised audio transformer for sparse animal calls. The first author and a shared senior author are at the University of Konstanz Department of Biology and the Centre for the Advanced Study of Collective Behaviour, and at the Max Planck Institute of Animal Behavior. Marie A. Roch at San Diego State University is the other shared senior author. The large MeerKAT weights were published on Edmond on 26 July 2024. Collected 2026-10-15.", "what_it_is_for": "The paper says animal2vec learns from unlabeled raw waveforms and is then finetuned to find and classify rare calls. The released large model was pretrained on the 1068-hour MeerKAT meerkat-collar corpus. A smaller xeno-canto setting is described for the NIPS4Bplus bird benchmark. That smaller checkpoint was not in the Edmond file list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "konstanz/embryonet/embryonet": { "id": "konstanz/embryonet/embryonet", "name": "EmbryoNet", "maker": "konstanz", "family": "konstanz/embryonet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2022-11-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Modified ResNet-18. Nature Methods says the first three channels are an RGB image, a fourth channel is a timestamp from the start of the experiment, and time is also fed to the last fully connected layer. The last classification layer is replaced. Training used Adam and cross-entropy for 152 epochs (eight steps of 10-20 epochs) at batch size 350 on one NVIDIA RTX 3090. The methods text that was read does not say the run started from ImageNet weights. The GitHub model zoo lists nine labels: NORMAL, BMP, RA, WNT, FGF, NODAL, SHH, PCP, and BOOM. The paper also describes Unknown and Dead in the trajectory logic. Those strings were not mapped onto the zoo labels.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EmbryoNet is a convolutional network from Patrick M\u00fcller's group at the University of Konstanz. It classifies zebrafish embryo images into signaling-pathway phenotypes. Weights are public. The journal article is Nature Methods, 8 May 2023. The earliest dated weight file found in this pass is the Zenodo ONNX record of 12 November 2022. Collected 2026-10-15.", "what_it_is_for": "The university announcement says EmbryoNet detects developmental defects in embryo images and links them to signaling pathways. The README says it is derived from ResNet-18 and uses both the picture and the time of the frame. The paper says it was trained on more than 2 million images of normal and signaling-defective zebrafish embryos, and that the same approach was retrained for medaka and three-spined stickleback.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/ko-gemma/ko-gemma-2b-v1": { "id": "korea-university/ko-gemma/ko-gemma-2b-v1", "name": "Ko-Gemma-2B-v1", "maker": "korea-university", "family": "korea-university/ko-gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-26", "context_length": 8192, "max_output_tokens": null, "parameters": "2B", "architecture": "GemmaForCausalLM. Config: 18 layers, hidden size 2048, 8 heads, 1 KV head, vocab 256000, max_position_embeddings 8192, bfloat16. Hugging Face README is an empty template; the release note is the KU-HIAI GitHub.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ko-Gemma-2B-v1 is a Korean Gemma chat model from Korea University NLP & AI Lab and the HIAI institute, announced in February 2024. Weights are at nlpai-lab/ko-gemma-2b-v1. The Hub README is an empty template; specs below come from the lab GitHub and config.json. Collected 2026-10-15.", "what_it_is_for": "The KU-HIAI README says Ko-Gemma improves Korean performance of Google Gemma using the KULLM dataset and hand-made data. v1 was trained with the chat template only. The quick start is Korean chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/ko-gemma/ko-gemma-7b-v1": { "id": "korea-university/ko-gemma/ko-gemma-7b-v1", "name": "Ko-Gemma-7B-v1", "maker": "korea-university", "family": "korea-university/ko-gemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-26", "context_length": 8192, "max_output_tokens": null, "parameters": "7B", "architecture": "GemmaForCausalLM. Config: 28 layers, hidden size 3072, 16 heads, 16 KV heads, vocab 256000, max_position_embeddings 8192, bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ko-Gemma-7B-v1 is the 7B Korean Gemma chat model from Korea University NLP & AI Lab and HIAI, released with the 2B model in February 2024. Weights are at nlpai-lab/ko-gemma-7b-v1. The Hub card is empty; the lab GitHub has the description and KoBEST scores. Collected 2026-10-15.", "what_it_is_for": "Same Ko-Gemma v1 release as the 2B model: Korean chat on a Gemma checkpoint, trained with the KULLM dataset and hand-made data, and dependent on the chat template. The published quick start loads this 7B id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/kullm/empathetickullm": { "id": "korea-university/kullm/empathetickullm", "name": "EmpatheticKULLM", "maker": "korea-university", "family": "korea-university/kullm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-11-18", "context_length": 131072, "max_output_tokens": null, "parameters": "12B", "architecture": "MistralForCausalLM. Config: 40 layers, hidden size 5120, 32 heads, 8 KV heads, head dim 128, vocab 131072, max_position_embeddings 131072. The README is the empty Hub template.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EmpatheticKULLM is a named weight release on the Korea University NLP & AI Lab Hugging Face org. The first upload commit is 2024-11-18 and a later weight commit is 2024-11-24. The README is an empty template, so the lab does not state a task, license, or score. Collected 2026-10-15. Confidence is medium.", "what_it_is_for": "Not stated. The repository name is the only clue, and it is not treated as a specification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/kullm/kullm-polyglot-12-8b-v2": { "id": "korea-university/kullm/kullm-polyglot-12-8b-v2", "name": "KULLM-Polyglot-12.8B-v2", "maker": "korea-university", "family": "korea-university/kullm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-05-31", "context_length": 2048, "max_output_tokens": null, "parameters": "12.8B", "architecture": "GPTNeoXForCausalLM fine-tune of EleutherAI/polyglot-ko-12.8b. Config: 40 layers, hidden size 5120, 40 heads, vocab 30080, max_position_embeddings 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Korea University NLP & AI Lab and the HIAI institute released KULLM-Polyglot-12.8B-v2 on 2023-05-31 as a Korean instruction model. It is an Apache-2.0 fine-tune of EleutherAI/polyglot-ko-12.8b on the KULLM v2 set. Weights were still on Hugging Face on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card says it is a fine-tuned version of EleutherAI/polyglot-ko-12.8b on KULLM v2. The lab describes the KULLM line as a Korean large language model. The card does not add a task list beyond that fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/kullm/kullm-polyglot-12-8b-v3": { "id": "korea-university/kullm/kullm-polyglot-12-8b-v3", "name": "KULLM-Polyglot-12.8B-v3", "maker": "korea-university", "family": "korea-university/kullm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-15", "context_length": 2048, "max_output_tokens": null, "parameters": "13B", "architecture": "GPTNeoXForCausalLM. Config: 40 layers, hidden size 5120, 40 heads, vocab 30080, max_position_embeddings 2048, float16. The README is empty, so the training recipe is not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Korea University NLP & AI Lab hosts KULLM-Polyglot-12.8B-v3 on Hugging Face. The repository was created on 2023-12-15, the license tag is Apache-2.0, and the checkpoint is a GPT-NeoX causal model. The README is empty, so training details are unknown. Collected 2026-10-15. Confidence is medium because the card does not describe the model.", "what_it_is_for": "Not stated. The README is an empty file. The name places it in the KULLM Polyglot 12.8B line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/kullm/kullm-polyglot-5-8b-v2": { "id": "korea-university/kullm/kullm-polyglot-5-8b-v2", "name": "KULLM-Polyglot-5.8B-v2", "maker": "korea-university", "family": "korea-university/kullm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-06-08", "context_length": 2048, "max_output_tokens": null, "parameters": "5.8B", "architecture": "GPTNeoXForCausalLM parameter-efficient fine-tune of EleutherAI/polyglot-ko-5.8b. Config: 28 layers, hidden size 4096, 16 heads, vocab 30080, max_position_embeddings 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Korea University NLP & AI Lab released KULLM-Polyglot-5.8B-v2 on 2023-06-08 as the smaller KULLM v2 model. The card says it is a parameter-efficient fine-tune of EleutherAI/polyglot-ko-5.8b on nlpai-lab/kullm-v2. Weights remain on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "The card says it is a parameter-efficient fine-tune of EleutherAI/polyglot-ko-5.8b on KULLM v2. The lab presents the KULLM line as a Korean LLM. No narrower task list is on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/kullm/kullm-r": { "id": "korea-university/kullm/kullm-r", "name": "KULLM-R", "maker": "korea-university", "family": "korea-university/kullm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Reasoning fine-tune of Qwen/Qwen3-8B. The card does not print layer counts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KULLM-R is a named 8B Korean reasoning model whose card says it was supported by the Korea University NLP & AI Lab and whose citation author is that lab. It is a reinforcement-learning tune of Qwen/Qwen3-8B aimed at math and other hard reasoning, with weights at nobrand/KULLM-R rather than the lab org. A Hub commit is dated 2025-08-06. Collected 2026-10-15. Confidence is medium because the org and the developer line disagree about who published it.", "what_it_is_for": "The card says KULLM-R is for high-level Korean reasoning, especially hard math. It is meant to produce shorter reasoning paths and readable Korean explanations. Intended uses listed are Korean math and logic, explainable reasoning, and tutoring.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/kullm/kullm3": { "id": "korea-university/kullm/kullm3", "name": "KULLM3", "maker": "korea-university", "family": "korea-university/kullm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-03", "context_length": 4096, "max_output_tokens": null, "parameters": "10.7B", "architecture": "LlamaForCausalLM instruction tune of upstage/SOLAR-10.7B-v1.0. Config: 48 layers, hidden size 4096, 32 heads, 8 KV heads, vocab 32000, max_position_embeddings 4096, float16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KULLM3 (\uad6c\ub9843) is the Korea University NLP & AI Lab and HIAI Korean chat model released on 2024-04-03. It is an Apache-2.0 instruction tune of upstage/SOLAR-10.7B-v1.0. The lab says it follows instructions closely relative to gpt-3.5-turbo, without printing a score in text. Weights are public. Collected 2026-10-15.", "what_it_is_for": "The card says KULLM3 is for instruction following and fluent chat in Korean and English. The fixed training prompt names the bot KULLM, Korean for \"\uad6c\ub984\", and tells it to refuse immoral, sexual, or illegal requests and to avoid false information. The GitHub example is a short factual chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/kure/koe5": { "id": "korea-university/kure/koe5", "name": "KoE5", "maker": "korea-university", "family": "korea-university/kure", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-10-02", "context_length": 512, "max_output_tokens": null, "parameters": "560M", "architecture": "Dense single-vector embedding model. Fine-tune of intfloat/multilingual-e5-large with CachedMultipleNegativesRankingLoss. Embedding size 1024.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KoE5 is the Korea University NLP & AI Lab and HIAI dense Korean embedding model released on 2024-10-02. It is an MIT-licensed fine-tune of intfloat/multilingual-e5-large on ko-triplet-v1.0. The lab later grouped it with the KURE retrieval models. Weights are public. Collected 2026-10-15.", "what_it_is_for": "The card says KoE5 is for Korean text retrieval. The HIAI page shows the same sentence-transformers encode path as KURE-v1. Queries and passages need the E5 prefixes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/kure/kure-reranker-base": { "id": "korea-university/kure/kure-reranker-base", "name": "KURE-Reranker-base", "maker": "korea-university", "family": "korea-university/kure", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-23", "context_length": 8192, "max_output_tokens": null, "parameters": "1.7B", "architecture": "Cross-encoder reranker. Causal LM on Qwen/Qwen3-1.7B scored as logit(yes) minus logit(no).", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KURE-Reranker-base is the 1.7B Korean-English reranker from Korea University NLP & AI Lab. It is an Apache-2.0 cross-encoder on Qwen/Qwen3-1.7B that scores logit(yes) minus logit(no). The Hub repository was created 2026-09-23. The card reports mean nDCG@10 0.8800 on nine Korean rerank sets. Collected 2026-10-15.", "what_it_is_for": "The card says the model reads a query and a candidate together and returns one relevance score. The default instruction is \"Given a web search query, retrieve relevant passages that answer the query\".", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/kure/kure-reranker-nano": { "id": "korea-university/kure/kure-reranker-nano", "name": "KURE-Reranker-nano", "maker": "korea-university", "family": "korea-university/kure", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-09", "context_length": 8192, "max_output_tokens": null, "parameters": "149M", "architecture": "Cross-encoder reranker on skt/A.X-Encoder-base. One scalar relevance score per query-document pair.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KURE-Reranker-nano is a Korean-English cross-encoder reranker from Korea University NLP & AI Lab. The card says about 149M parameters on skt/A.X-Encoder-base, Apache-2.0, with an 8192-token input. The Hub repository was created 2026-09-09. Collected 2026-10-15.", "what_it_is_for": "The card says it jointly encodes a query and a candidate document and predicts one relevance score. It is the small KURE reranker for Korean and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/kure/kure-sparse-base": { "id": "korea-university/kure/kure-sparse-base", "name": "KURE-Sparse-base", "maker": "korea-university", "family": "korea-university/kure", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-07", "context_length": 8192, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Learned sparse retriever fine-tuned from omai-research/milco-650m. Card describes 30522 English pivot terms plus 250002 source-language tokens. Example vector length is 280524. About 100 active query dimensions and 1897 active document dimensions.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KURE-Sparse-base is the larger Korean-English learned sparse retriever from Korea University NLP & AI Lab. The card dates with the Hub repository created 2026-10-07, lists 0.6B parameters, and fine-tunes omai-research/milco-650m. License is Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says KURE-Sparse-base is a bilingual learned sparse retriever. Vectors live in a fixed vocabulary so they can be indexed in an inverted index and the matched terms can be inspected.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/kure/kure-sparse-nano": { "id": "korea-university/kure/kure-sparse-nano", "name": "KURE-Sparse-nano", "maker": "korea-university", "family": "korea-university/kure", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-08", "context_length": 8192, "max_output_tokens": null, "parameters": "0.1B", "architecture": "Learned sparse retriever fine-tuned from nlpai-lab/KURE-v2-unsupervised. Fixed vocabulary of 36282 terms. Card reports about 147 active query dimensions and 843 active document dimensions.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KURE-Sparse-nano is a 0.1B Korean-English learned sparse retriever from Korea University NLP & AI Lab, published at nlpai-lab/KURE-Sparse-nano. The Hub repository date is 2026-10-08. It is an Apache-2.0 fine-tune of KURE-v2-unsupervised and is meant for inverted-index retrieval. Collected 2026-10-15.", "what_it_is_for": "The card says it is a compact learned sparse retriever for Korean and English. Every vector lives in a 36282-term vocabulary so matched terms can be inspected and documents can sit in an inverted index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/kure/kure-v1": { "id": "korea-university/kure/kure-v1", "name": "KURE-v1", "maker": "korea-university", "family": "korea-university/kure", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-12-21", "context_length": 8192, "max_output_tokens": null, "parameters": "568M", "architecture": "Dense single-vector embedding model. Fine-tune of BAAI/bge-m3 with CachedGISTEmbedLoss. Embedding size 1024.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KURE-v1 is the Korea University Retrieval Embedding model from NLP & AI Lab and HIAI, released 2024-12-21. It is an MIT dense fine-tune of BAAI/bge-m3 for Korean and English retrieval, with 8192-token inputs. Weights are at nlpai-lab/KURE-v1. Collected 2026-10-15.", "what_it_is_for": "The card and the HIAI page say KURE-v1 is for Korean text retrieval. It is the dense model the lab pointed to before KURE-v2. The example encodes Korean legal sentences and scores cosine similarity.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/kure/kure-v2": { "id": "korea-university/kure/kure-v2", "name": "KURE-v2", "maker": "korea-university", "family": "korea-university/kure", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-08-29", "context_length": 8192, "max_output_tokens": null, "parameters": "154M", "architecture": "Late-interaction ColBERT-style encoder on skt/A.X-Encoder-base. One 128-d vector per token, MaxSim scoring. Query side is expanded to 64 tokens by the model. No instruction prefix.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KURE-v2 is the Korea University NLP & AI Lab and HIAI Korean-English late-interaction retriever, released 2026-08-29. At 154M parameters it encodes each token as a 128-d vector and scores with MaxSim. The lab reports average nDCG@10 0.8160 on nine MTEB(kor, v2) retrieval tasks. Weights are Apache-2.0 at nlpai-lab/KURE-v2. Collected 2026-10-15.", "what_it_is_for": "The card says KURE-v2 is a bilingual retriever and reranker of a first-stage candidate list. It does not use instruction prefixes. PyLate and sentence-transformers MultiVectorEncoder are the documented loaders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/kure/kure-v2-unsupervised": { "id": "korea-university/kure/kure-v2-unsupervised", "name": "KURE-v2-unsupervised", "maker": "korea-university", "family": "korea-university/kure", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-08-17", "context_length": null, "max_output_tokens": null, "parameters": "154M", "architecture": "Stage-1 late-interaction encoder on skt/A.X-Encoder-base, trained with unsupervised contrastive learning on 20.7M query-document pairs. Same 154M size class as KURE-v2.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KURE-v2-unsupervised is the named stage-1 checkpoint for KURE-v2 from Korea University NLP & AI Lab. It is a 154M Korean-English late-interaction encoder trained only with unsupervised contrastive learning on 20.7M pairs. The lab says not to deploy it as a retriever and offers it as a training start. Weights are public. The Hub repository date is 2026-08-17. Collected 2026-10-15.", "what_it_is_for": "The card says it is a starting checkpoint for people training a Korean-English late-interaction retriever who do not want to start from a masked language model. It says not to deploy it as-is.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/lamar/lamar-600m": { "id": "korea-university/lamar/lamar-600m", "name": "LAMAR-600M", "maker": "korea-university", "family": "korea-university/lamar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-07-21", "context_length": 8192, "max_output_tokens": null, "parameters": "600M", "architecture": "Multilingual cross-encoder reranker initialized from BAAI/bge-m3-retromae. Two stages: English-anchored relevance alignment using scores from Qwen/Qwen3-Reranker-4B, then language-coherence training. Output is a scalar relevance logit.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LAMAR-600M is a multilingual cross-encoder reranker from Korea University NLP & AI Lab. The card calls it a 600M language-aware model, initialized from BAAI/bge-m3-retromae, trained on 51 languages, and released under MIT. The Hub repository was created 2026-07-21. Collected 2026-10-15.", "what_it_is_for": "The card says LAMAR reranks multilingual and cross-lingual candidates while also preferring documents whose language matches the query. Intended uses include multilingual retrieval, cross-lingual retrieval, and retrieval-augmented generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/mixlid/mixlid": { "id": "korea-university/mixlid/mixlid", "name": "MixLID", "maker": "korea-university", "family": "korea-university/mixlid", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-10-12", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Token-classification encoder on jhu-clsp/mmBERT-small. Predicts a language label per token and a predominant language for the input. Extra labels NUM, PUNCT, and SYM.", "modalities": { "input": [ "text" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MixLID is a token-level language identification model from Korea University NLP & AI Lab. It labels spans inside mixed-language text and also returns one predominant language. The card says 111 language labels, an 8192-token window, and an MIT license. The backbone is jhu-clsp/mmBERT-small. The Hub repository was created 2026-10-12. Collected 2026-10-15.", "what_it_is_for": "The card says MixLID identifies the language of individual tokens, including short phrases inside another language, and can also name the predominant language of an input. Suggested uses are multilingual data filtering, checking whether a generation stayed in the requested language, and tagging mixed transcripts. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "korea-university/renderrank/renderrank-2b": { "id": "korea-university/renderrank/renderrank-2b", "name": "RenderRank-2B", "maker": "korea-university", "family": "korea-university/renderrank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-28", "context_length": 32768, "max_output_tokens": null, "parameters": "2B", "architecture": "Pairwise document reranker on Qwen/Qwen3-VL-Reranker-2B. Queries stay text. Documents can be text, automatically rendered pages, or existing images. Score is yes-logit minus no-logit.", "modalities": { "input": [ "text", "image" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RenderRank-2B is a 2B document reranker from Korea University NLP & AI Lab. It keeps the query as text and can score a document as text, as pages it renders, or as images, using Qwen/Qwen3-VL-Reranker-2B. The card reports English BEIR and long-document results. The Hub repository was created 2026-09-28. License Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says RenderRank reranks documents while representing document text as compact visual tokens so more content fits in one context. Modes are text, render, and image. Multi-page images are one document and get one score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "krea/krea-1/flux-1-krea": { "id": "krea/krea-1/flux-1-krea", "name": "FLUX.1 Krea [dev]", "maker": "krea", "family": "krea/krea-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLUX.1 Krea [dev] non-commercial license", "release_date": "2025-07-31", "context_length": null, "max_output_tokens": null, "parameters": "12000000000", "architecture": "12B rectified-flow transformer, guidance-distilled, drop-in compatible with FLUX.1 [dev]. Krea post-trained Black Forest Labs' flux-dev-raw with SFT, including synthetic Krea 1 samples, and RLHF, using a custom loss so the CFG-distilled base could be finetuned on a guided distribution.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FLUX.1 Krea [dev] is the open-weights image model Krea and Black Forest Labs released on July 31, 2025. Krea calls it an open version of Krea 1: a guidance-distilled 12B rectified-flow transformer, compatible with FLUX.1 [dev], post-trained by Krea from BFL's flux-dev-raw. It is still a separate row in the Krea image picker. Collected 2026-10-15.", "what_it_is_for": "Krea released it so people could run an opinionated aesthetic checkpoint in the existing FLUX.1 [dev] ecosystem. The goal stated in the release post is photorealism without the oversaturated AI look, matching closed Krea 1 closely enough to ship as its open version. The Krea app also describes it as stronger than Krea 1 at text rendering and anatomy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "krea/krea-1/krea-1": { "id": "krea/krea-1/krea-1", "name": "Krea 1", "maker": "krea", "family": "krea/krea-1", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed image model trained with Black Forest Labs. Krea describes the later open checkpoint FLUX.1 Krea [dev] as distilled to match Krea 1 and post-trained from BFL's flux-dev-raw 12B rectified-flow transformer. Krea 1's own parameter count is not published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Krea 1 is Krea's first named image model, trained with Black Forest Labs and still offered in the Krea image picker. It is a closed model aimed at photographic textures and styles that avoid a generic AI look. Weights are not published; the open sibling is FLUX.1 Krea [dev]. Collected 2026-10-15.", "what_it_is_for": "Krea's user guide says Krea 1 was built to avoid soft textures, blown-out contrast, and repetitive AI compositions. It is positioned for artistic photorealism, editorial looks, and LoRA styles. The July 31, 2025 open-weights post calls it Krea's first image model from that Black Forest Labs collaboration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "krea/krea-2/krea-2-large": { "id": "krea/krea-2/krea-2-large", "name": "Krea 2 Large", "maker": "krea", "family": "krea/krea-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted Krea 2 size. Krea says Large is more than twice the size of Medium, with softer post-training, so outputs are rawer and more textured. The 12B figure on the open model card is not stated as Large's size.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.06 }, "summary_intro": "Krea 2 Large is the bigger hosted Krea 2 image model. Krea says it is more than twice the size of Krea 2 Medium, with softer post-training, and points it at photorealism, grain, motion blur, and other raw looks. It is active in the app and at `POST /generate/image/krea/krea-2/large`. Collected 2026-10-15.", "what_it_is_for": "The API overview says Large can produce results Medium cannot match when texture matters, and that it is strongest on photorealism and raw photographic looks as well as expressive styles. The app guide says to move up to Large for the richest texture. It is slower than Medium. Krea suggests webhooks instead of polling for production.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "krea/krea-2/krea-2-medium": { "id": "krea/krea-2/krea-2-medium", "name": "Krea 2 Medium", "maker": "krea", "family": "krea/krea-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted Krea 2 size. Krea says it is smaller than Krea 2 Large, which is more than twice Medium's size, with heavier post-training than Large. The published 12B DiT figure belongs to the open Raw and Turbo cards, and Krea does not say Medium is that checkpoint.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.03 }, "summary_intro": "Krea 2 Medium is the smaller hosted size of Krea 2, Krea's first image foundation model trained from scratch. It is active on krea.ai and at `POST /generate/image/krea/krea-2/medium`. Krea says heavier post-training makes it the stable choice for illustration, anime, and other expressive styles. Collected 2026-10-15.", "what_it_is_for": "Krea 2 is built for aesthetic range and style control rather than a single polished default. Medium is the API's faster, cheaper size. The May 27, 2026 API post says it is especially stable and strong on illustration, anime, painting, and similar styles. The app guide says to start here for everyday work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "krea/krea-2/krea-2-raw": { "id": "krea/krea-2/krea-2-raw", "name": "Krea 2 Raw", "maker": "krea", "family": "krea/krea-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Krea 2 Community License", "release_date": "2026-06-22", "context_length": null, "max_output_tokens": null, "parameters": "12000000000", "architecture": "Undistilled Krea 2 diffusion transformer. Model card: 12 billion parameters. Hugging Face badge on the same repo: 13B. Single-stream MMDiT with grouped-query attention, Qwen3-VL text features, and Qwen-Image VAE, per the Diffusers Krea 2 notes and the technical report's final block choices. Not a hosted API SKU.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Krea 2 Raw is the open, undistilled Krea 2 checkpoint released on June 22, 2026. Krea says it is the base for fine-tuning, post-training, and LoRA training, and that it is not the checkpoint to use for finished images. LoRAs trained on it are meant to run on Krea 2 Turbo. It is not a hosted generation endpoint. Collected 2026-10-15.", "what_it_is_for": "The model card says Raw is a good base for your own domain, not the inference default. The open-source page says to train on Raw and generate on Turbo. The card also says the model is a text-to-image diffusion model for concepting, design exploration, and research, inside the license and the acceptable-use policy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "krea/krea-2/krea-2-turbo": { "id": "krea/krea-2/krea-2-turbo", "name": "Krea 2 Turbo", "maker": "krea", "family": "krea/krea-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Krea 2 Community License for the published Turbo checkpoint. Hosted API calls use Krea API terms.", "release_date": "2026-06-03", "context_length": null, "max_output_tokens": null, "parameters": "12000000000", "architecture": "The open checkpoint is an 8-step distilled diffusion transformer. The model card says 12 billion parameters. Hugging Face's size badge on the Raw repo says 13B; that conflict is unresolved. Diffusers describes the shared Krea 2 design as a single-stream MMDiT with grouped-query attention, a Qwen3-VL encoder whose features are fused from twelve decoder layers, and the Qwen-Image VAE. The hosted API id is krea-2/medium-turbo. Krea does not state that the hosted tier and the open 8-step file are the same weights.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.015 }, "summary_intro": "Krea 2 Turbo is the fast Krea 2 image tier. Krea shipped it in the app on June 3, 2026, and published an 8-step open checkpoint on June 22, 2026. The hosted API id is `krea-2/medium-turbo`. The open file is `krea/Krea-2-Turbo`. Krea does not say those two artifacts are the same weights. Collected 2026-10-15.", "what_it_is_for": "The launch post calls Turbo a faster Krea 2 for ideation: prompt tests, moodboards, and early direction, while keeping style references, moodboards, and LoRAs. It tells users to switch to Medium or Large when style adherence or final polish matters more. The open release says to train LoRAs on Raw and run them on Turbo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "krea/krea-3/krea-3": { "id": "krea/krea-3/krea-3", "name": "Krea 3", "maker": "krea", "family": "krea/krea-3", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.06 }, "summary_intro": "Krea 3 is Krea's image editing model, released on October 15, 2026. It also does text-to-image. The app and API still label it Krea 3 Beta, and the API guide says access is early access, so this catalog marks it preview even though the launch post says it is live on krea.ai. Weights are closed. Collected 2026-10-15.", "what_it_is_for": "Krea built it for production edits that stay fast: change a scene or a product and keep the product, the person, or the text. The launch post says most edits return in under 8 seconds at native 2K. The API guide says you can omit images and use the same endpoint for text-to-image. `thinking_mode` is `fast` by default or `high` when the edit needs more planning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "krea/krea-realtime/krea-realtime-14b": { "id": "krea/krea-realtime/krea-realtime-14b", "name": "Krea Realtime 14B", "maker": "krea", "family": "krea/krea-realtime", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Weights: Apache-2.0 on the Hugging Face repo. Inference code: CC BY-NC-SA 4.0 on the GitHub repo.", "release_date": "2025-10-20", "context_length": null, "max_output_tokens": null, "parameters": "14000000000", "architecture": "14B autoregressive video diffusion model. Distilled from Wan 2.1 14B text-to-video with Self-Forcing. Krea's training writeup also uses a few-step Wan 2.1 14B distillation from LightX2V for timestep distillation, then causal ODE pretraining. Inference uses 4 steps. Base model on the Hugging Face card is Wan-AI/Wan2.1-T2V-14B. The runtime also downloads Wan 2.1 T2V 1.3B as a support folder.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Krea Realtime 14B is Krea's open autoregressive video model, distilled from Wan 2.1 14B with Self-Forcing. Krea released the weights on October 20, 2025. It is still the Krea Realtime row in the video picker, described there as instant and low quality. Collected 2026-10-15.", "what_it_is_for": "Krea built it for long-form video that streams while you steer it. You can change the prompt as frames arrive, restyle a stream, or feed a canvas, webcam, or other video. The technical post says first frames can return in about one second and that text-to-video runs at 11 frames per second with 4 steps on one NVIDIA B200.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kstate/hardware-phi/hardware-phi-1-5b": { "id": "kstate/hardware-phi/hardware-phi-1-5b", "name": "Hardware Phi-1.5B", "maker": "kstate", "family": "kstate/hardware-phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-29", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Causal MixFormer matching the Phi-1.5 layout: 24 layers, 32 heads, head dimension 64, hidden size 2048, context 2048, rotary dimension 32, GELU-new, untied embeddings. The paper says training started from random weights (normal, mean 0, standard deviation 0.02; linear biases zero), not from Microsoft Phi weights. The paper says a CodeGen-mono tokenizer and a 50,257-token vocabulary. The public 12k config.json says vocab_size 51200 and _name_or_path /home/yang/weiminf/phi-1_5. Trained on the medium hardware dataset for 30,000 steps. The paper also says the stop rule was 750,000 iterations and 30,000 steps. The 30k model card says 30,000 iterations.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hardware Phi-1.5B is a base language model from the Kansas State University Hardware Security Lab, led by Weimin Fu and Xiaolong Guo, with coauthors at other universities and at Silicon Assurance. It was pretrained from random weights on a hardware-domain corpus, using the Phi-1.5 layout rather than Microsoft's weights. The 30k-step checkpoint is on Hugging Face and was still listed on 2026-10-15. This file was collected on 2026-10-15.", "what_it_is_for": "The lab calls it the first pretrained hardware-domain LLM. The paper trains it to continue hardware and security text, including Verilog-family code and natural-language hardware security material, so later fine-tunes can start from a domain base model. It is not an instruction assistant. The paper says that without task fine-tuning it tends to finish a document rather than answer a question.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kstate/pepbert/pepbert-large": { "id": "kstate/pepbert/pepbert-large", "name": "PepBERT-large", "maker": "kstate", "family": "kstate/pepbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-05", "context_length": 52, "max_output_tokens": null, "parameters": "4.94 million", "architecture": "Encoder-only transformer for peptides of at most 50 residues, trained with masked language modeling (15 percent of tokens). Methods text: 320-dimensional embeddings, 8 encoder layers, 8 attention heads, feed-forward width 640, sinusoidal positions. Shipped model.py defaults to 6 layers, 8 heads, dropout 0, and feed-forward width 640; train.py passes only vocab size, seq_len, and d_model, so the default layer count stays 6. Hub config.py sets seq_len 52, d_model 320, batch 128, AdamW learning rate 4e-4. The paper says 20 epochs and the lowest validation loss. config.py says num_epochs 200. Special tokens include [SOS], [EOS], [PAD], [UNK], and [MASK]. Character-level WordLevel tokenizer.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PepBERT-large is the larger of two peptide language models pretrained from scratch at Kansas State University by Zhenjiao Du, Doina Caragea, Xiaolong Guo, and Yonghui Li. It embeds short amino-acid sequences. Four corpus checkpoints are public on the author's Hugging Face account. The weights were still listed on 2026-10-15. This file was collected on 2026-10-15.", "what_it_is_for": "The authors train it as a masked encoder for peptides of 50 residues or fewer, because short peptides are rare in protein language models such as ESM-2. The released weights are used to embed a sequence. In the paper the embeddings stay frozen and a logistic regression head does the downstream classification. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kstate/pepbert/pepbert-small": { "id": "kstate/pepbert/pepbert-small", "name": "PepBERT-small", "maker": "kstate", "family": "kstate/pepbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-05", "context_length": 52, "max_output_tokens": null, "parameters": "1.86 million", "architecture": "Smaller PepBERT encoder. Methods text: 160-dimensional embeddings, 8 encoder layers, 8 attention heads, feed-forward width 640, sinusoidal positions, masked language modeling at 15 percent, peptides of at most 50 residues. The small-UniParc config.py sets d_model 160, seq_len 52, batch 128, and learning rate 4e-4. model.py was read from the large-UniParc repo, where the builder defaults to 6 layers and feed-forward width 640. train.py does not pass a layer count. The paper says 20 epochs. That config.py says num_epochs 200 and still sets datasource to dzjxzyd/UniRef50_len_0_50.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PepBERT-small is the smaller peptide encoder from the same Kansas State University group that released PepBERT-large: Zhenjiao Du, Doina Caragea, Xiaolong Guo, and Yonghui Li. It was pretrained from scratch on short amino-acid sequences and is published as four corpus checkpoints. The weights were still listed on 2026-10-15. This file was collected on 2026-10-15.", "what_it_is_for": "It is the compact PepBERT encoder for peptides of 50 residues or fewer. The paper uses it the same way as the large model: frozen embeddings, then a logistic regression head on bioactive-peptide labels. The authors present it as the cheaper of the two sizes. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kth/diff-ttsg/diff-ttsg": { "id": "kth/diff-ttsg/diff-ttsg", "name": "Diff-TTSG", "maker": "kth", "family": "kth/diff-ttsg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-06-27", "context_length": null, "max_output_tokens": null, "parameters": "44700000", "architecture": "Non-autoregressive diffusion model, based on Grad-TTS, that synthesises mel spectrograms and 3D skeleton poses from text with separate denoising paths. A Conformer pre-net (4 hidden layers, 384 channels, convolutional filters of length 21) maps acoustics means to pose means. The gesture decoder is a 1D U-Net with 256 hidden channels and kernels of length 5. The SSW system used 50 diffusion steps for speech and 500 for motion, then an external HiFi-GAN UNIVERSAL_V1 vocoder. The 44.7M count is the retrained DIFF row in the later Match-TTSG paper, which describes that baseline as the same system except for the phonemizer.", "modalities": { "input": [ "text" ], "output": [ "audio", "motion" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Diff-TTSG is an open-weight text-to-speech and co-speech gesture model from the Division of Speech, Music and Hearing at KTH Royal Institute of Technology. Shivam Mehta, Siyang Wang, Simon Alexanderson, Jonas Beskow, \u00c9va Sz\u00e9kely, and Gustav Eje Henter presented it at the ISCA Speech Synthesis Workshop 2023. A checkpoint was posted on 2023-06-27. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe Diff-TTSG as the first diffusion model that jointly learns speech acoustics and body gestures from text. It is probabilistic, non-autoregressive, and trained from scratch on a small spontaneous-speech dataset. The official path is a local Gradio app or notebook. A Hugging Face Space is linked from the README. There is no priced API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kth/match-ttsg/match-ttsg": { "id": "kth/match-ttsg/match-ttsg", "name": "Match-TTSG", "maker": "kth", "family": "kth/match-ttsg", "status": "active", "confidence": "high", "weights": "closed", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "30200000", "architecture": "Unified non-autoregressive text-to-speech and 3D gesture model. One OT-CFM ODE samples concatenated mel and pose features. The text encoder uses RoPE. The decoder is a 1D Transformer U-Net: temporal downsampling blocks at 256 and 512 channels, two mid blocks at 512, then upsampling at 512 and 256. Each block has one Transformer with four heads and attention dimension 64, with snakebeta activations. The linear output is 80 mel channels plus 45 joint-rotation features. Duration prediction follows Diff-TTSG. Waveforms use the same external HiFi-GAN as Diff-TTSG plus a denoising filter of strength 2e-4. MA-50 and MA-500 are Euler step counts of this one trained system, not separate weight releases.", "modalities": { "input": [ "text" ], "output": [ "audio", "motion" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Match-TTSG is a named speech-and-gesture model from the Division of Speech, Music and Hearing at KTH Royal Institute of Technology. Shivam Mehta, Ruibo Tu, Simon Alexanderson, Jonas Beskow, \u00c9va Sz\u00e9kely, and Gustav Eje Henter introduced it for ICASSP 2024. Code is public under MIT. A trained checkpoint URL was not confirmed on 2026-10-15, so weights stay unknown. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe Match-TTSG as a unified flow-matching model that synthesises speech acoustics and skeleton gestures from text in one process. It replaces Diff-TTSG's two diffusion paths. The paper and demo are the public description. The repository is code, and the demo page still says pre-trained models will be posted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kth/matcha/matcha-tts": { "id": "kth/matcha/matcha-tts", "name": "Matcha-TTS", "maker": "kth", "family": "kth/matcha", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-01-12", "context_length": null, "max_output_tokens": null, "parameters": "18200000", "architecture": "Non-autoregressive text-to-mel encoder-decoder. A RoPE text encoder feeds an optimal-transport conditional flow-matching (OT-CFM) decoder that is integrated as an ODE. The paper's 18.2M count is the acoustic model; waveform synthesis uses a separate vocoder.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Matcha-TTS is an open-weight text-to-speech acoustic model from the Division of Speech, Music and Hearing at KTH Royal Institute of Technology. Shivam Mehta, Ruibo Tu, Jonas Beskow, \u00c9va Sz\u00e9kely, and Gustav Eje Henter introduced it for ICASSP 2024. Public LJ Speech and VCTK checkpoints were posted on 2024-01-12. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe Matcha-TTS as a probabilistic, non-autoregressive acoustic model for fast speech synthesis. It learns to speak from scratch without external alignments. The official CLI, Gradio app, and Hugging Face Space are for local or browser synthesis, not a paid API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kth/neural-hmm/neural-hmm": { "id": "kth/neural-hmm/neural-hmm", "name": "Neural HMM TTS", "maker": "kth", "family": "kth/neural-hmm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "15300000", "architecture": "Tacotron 2 modified so that attention is replaced by an autoregressive left-to-right no-skip neural HMM. Emissions are neural, alignment is monotonic, and the sequence likelihood is maximised without approximation. The 15.3M figure is the NHMM condition in the later OverFlow comparison, not a count restated from a model card.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Neural HMM TTS is an open-weight speech acoustic model from KTH's Division of Speech, Music and Hearing. Shivam Mehta, \u00c9va Sz\u00e9kely, Jonas Beskow, and Gustav Eje Henter introduced it at ICASSP 2022. It replaces Tacotron 2 attention with a neural hidden Markov model so alignment stays monotonic and the full sequence likelihood can be trained exactly. Code and pretrained checkpoints are public under the MIT license. The checkpoint upload day was not confirmed. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project page says the system is for attention-free neural TTS: smaller than the Tacotron 2 baseline they compare with, faster to learn an alignment, less likely to babble, fully probabilistic, and able to change speaking rate. It is a local research model, not an API product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kth/overflow/overflow": { "id": "kth/overflow/overflow", "name": "OverFlow", "maker": "kth", "family": "kth/overflow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-11-13", "context_length": null, "max_output_tokens": null, "parameters": "28500000", "architecture": "Autoregressive neural-HMM transducer with normalising flows on the acoustic distribution, plus a post-net. Trained by exact maximum likelihood. The reported system predicts acoustic features; a separate HiFi-GAN vocoder is used at synthesis.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OverFlow is an open-weight text-to-speech acoustic model from KTH's Division of Speech, Music and Hearing. Shivam Mehta, Ambika Kirkland, Harm Lameris, Jonas Beskow, \u00c9va Sz\u00e9kely, and Gustav Eje Henter published it at Interspeech 2023. It puts normalising flows on the lab's neural HMM TTS so the model can represent a non-Gaussian speech distribution and still train by exact likelihood. The arXiv preprint is dated 2022-11-13, with a revision on 2023-05-29. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors present OverFlow as a probabilistic acoustic model that keeps the monotonic alignment of neural HMM TTS and improves pronunciation and naturalness. The official repo and a Coqui TTS package entry are for local synthesis. There is no vendor API price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kth/voxtream/voxtream": { "id": "kth/voxtream/voxtream", "name": "VoXtream", "maker": "kth", "family": "kth/voxtream", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2025-09-19", "context_length": null, "max_output_tokens": null, "parameters": "471000000", "architecture": "Fully autoregressive zero-shot streaming TTS. An incremental phoneme transformer maps incoming phonemes with monotonic alignment and a limited look-ahead. A temporal transformer predicts semantic and duration tokens. A depth transformer predicts acoustic tokens. The paper reports 471M parameters; the Hugging Face card rounds the same checkpoint to 0.4B.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VoXtream is an open-weight zero-shot streaming text-to-speech model from KTH's Department of Speech, Music and Hearing. Nikita Torgashov, Gustav Eje Henter, and Gabriel Skantze released the weights on 2025-09-19 as `herimor/voxtream`. The paper was accepted to ICASSP 2026. It speaks from the first word instead of waiting for a full sentence. The authors now point to VoXtream2 as the newer model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card describes real-time zero-shot TTS for a full-stream setting, where text arrives word by word and audio leaves in 80 ms chunks. Output streaming, with the whole sentence known, is also supported. There is no hosted inference API on the card. The Python package pin for this checkpoint is `voxtream==0.1.5`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kth/voxtream/voxtream2": { "id": "kth/voxtream/voxtream2", "name": "VoXtream2", "maker": "kth", "family": "kth/voxtream", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2026-03-13", "context_length": null, "max_output_tokens": null, "parameters": "462000000", "architecture": "Zero-shot full-stream TTS that follows VoXtream and adds distribution matching over duration states plus classifier-free guidance. Prompt-text masking allows a textless audio prompt. The paper reports 462M parameters and 40k hours of English data. The Hugging Face card rounds the checkpoint to 0.5B.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VoXtream2 is the follow-on open-weight streaming text-to-speech model from KTH's Department of Speech, Music and Hearing. Nikita Torgashov, Gustav Eje Henter, and Gabriel Skantze describe it in arXiv 2603.13518, published on 2026-03-13. It keeps VoXtream's full-stream design and adds mid-utterance speaking-rate control. Weights are `herimor/voxtream2`, with a later training run at `herimor/voxtream2.1`. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says VoXtream2 is for interactive full-stream TTS: it starts speaking with a short delay and the speaking rate can be updated while it is still talking. Prompt-text masking means the reference clip does not need a transcript, and the authors say acoustic prompts can be in any language. Synthesis itself is documented on English training data. There is no paid API. Install with `voxtream>=0.2`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/keye/keye-vl-1-5-8b": { "id": "kuaishou/keye/keye-vl-1-5-8b", "name": "Kwai Keye-VL 1.5 8B", "maker": "kuaishou", "family": "kuaishou/keye", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-28", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Language model based on Qwen3-8B. Vision encoder initialized from open-source SigLIP. Slow-Fast video encoding. Images are split into 14 by 14 patches, keeping aspect ratio, then an MLP merges visual tokens. 3D RoPE over text, image, and video.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kwai Keye-VL 1.5 8B is Kuaishou's August 2025 update of Keye-VL. The card date is 2025-08-28. It keeps an 8B scale, adds Slow-Fast video encoding, and extends context to 128K tokens. The language backbone is Qwen3-8B and the vision encoder is initialized from SigLIP. License is Apache-2.0.", "what_it_is_for": "It is for longer video understanding, image questions, and reasoning, with thinking and non-thinking prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/keye/keye-vl-2-0-30b-a3b": { "id": "kuaishou/keye/keye-vl-2-0-30b-a3b", "name": "Keye-VL 2.0 30B-A3B", "maker": "kuaishou", "family": "kuaishou/keye", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-25", "context_length": null, "max_output_tokens": null, "parameters": "30B total / 3B activated", "architecture": "Mixture-of-experts multimodal model. Vision encoder inherited from Keye-VL 1.5 8B. First Keye model the card describes with DeepSeek Sparse Attention, aimed at 256K context. Agent tools: code, tool use, and search. A GGUF build exists as a separate repo and is not a second model.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Keye-VL 2.0 30B-A3B is Kuaishou's 2026 open mixture-of-experts multimodal model for long video and agents. The Hugging Face repo was created on 2026-05-25. The technical report is arXiv 2606.10651, submitted 2026-06-09. It activates 3B of 30B parameters, uses a Keye-VL 1.5 vision encoder, and the card says it keeps quality at 256K context with DeepSeek Sparse Attention. License is Apache-2.0.", "what_it_is_for": "The maker aims it at hour-scale video, temporal localization, and code, tool, and search agents. The card prints vendor scores for TimeLens, LongVideoBench, and VideoMME V2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/keye/keye-vl-671b-a37b": { "id": "kuaishou/keye/keye-vl-671b-a37b", "name": "Keye-VL 671B-A37B", "maker": "kuaishou", "family": "kuaishou/keye", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-19", "context_length": null, "max_output_tokens": null, "parameters": "671B total / 37B activated", "architecture": "Mixture-of-experts multimodal model. The card says the vision encoder comes from Keye-VL 1.5. Weights are released in FP8. A November 2025 news report says the language model was initialized from DeepSeek-V3-Terminus. The model card text that was read does not say that.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Keye-VL 671B-A37B is Kuaishou's largest published Keye multimodal model. The Hugging Face repo was created on 2025-11-19, licensed Apache-2.0, and shipped as FP8. The name states 671 billion total parameters and 37 billion active. The card says the vision encoder is the one from Keye-VL 1.5.", "what_it_is_for": "The card positions it for text, visual perception, video understanding, and hard reasoning, including math. Deployment instructions use SGLang across two nodes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/keye/keye-vl-8b-preview": { "id": "kuaishou/keye/keye-vl-8b-preview", "name": "Kwai Keye-VL 8B Preview", "maker": "kuaishou", "family": "kuaishou/keye", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-26", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Multimodal model for short video. The technical report uses a SigLIP vision encoder (siglip-so400m-patch14-384), an MLP projector, and a language decoder. The card describes four-stage pre-training and two-phase post-training, including a five-mode cold start: thinking, non-thinking, auto-think, think-with-image, and video.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kwai Keye-VL 8B Preview is Kuaishou Keye team's open multimodal model for short-video understanding, released 2025-06-26. The card calls it an 8-billion-parameter foundation model with thinking and non-thinking modes. License is Apache-2.0. Weights are open.", "what_it_is_for": "It answers questions about images and videos and can be told to think or not. The maker also released KC-MMBench for short-video scenarios. Scores from that figure were not transcribed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-image/kling-image-o1": { "id": "kuaishou/kling-image/kling-image-o1", "name": "Kling Image O1", "maker": "kuaishou", "family": "kuaishou/kling-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.028 }, "summary_intro": "Kling Image O1 (\u53ef\u7075\u56fe\u7247 O1, API id kling-image-o1) is the December 2025 omni image model. The capability map describes high feature consistency, precise detail editing, and style transfer at 1K or 2K. A 2026-01-05 release note says text-to-image tone and reference consistency were upgraded. Weights are closed. It remains on the English price list.", "what_it_is_for": "It generates a single image from text or references, including multi-image subject control and an automatic aspect ratio. It does not generate a series.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-image/kling-image-v1": { "id": "kuaishou/kling-image/kling-image-v1", "name": "Kling Image 1.0", "maker": "kuaishou", "family": "kuaishou/kling-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling Image 1.0 is the original Kling image API model. Its request field is model_name kling-v1, the same string as Kling video 1.0, on the image endpoint rather than the video endpoint. Empty model_name still selects V1. The Chinese image matrix and the image resource-pack support line still name V1. Weights are closed. The developer home says image generation is powered by \u53ef\u56fe (Kolors), without saying this API id is the open Kolors checkpoint.", "what_it_is_for": "It does text-to-image and whole-image image-to-image at 1K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-image/kling-image-v1-5": { "id": "kuaishou/kling-image/kling-image-v1-5", "name": "Kling Image 1.5", "maker": "kuaishou", "family": "kuaishou/kling-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling Image 1.5 (API model_name kling-v1-5) is the March 2025 Kling image model. The changelog says it improves portrait aesthetics, detail, and contrast, and adds 21:9. On 2025-03-25 it gained subject and face reference. Weights are closed. The Chinese support list still names image V1.5.", "what_it_is_for": "Use face reference to keep a face and change clothing or background, or subject reference to keep the whole character.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-image/kling-image-v2": { "id": "kuaishou/kling-image/kling-image-v2", "name": "Kling Image 2.0", "maker": "kuaishou", "family": "kuaishou/kling-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling Image 2.0 (API model_name kling-v2) is the May 2025 Kling text-to-image model. The Chinese matrix also gives it multi-image reference. Style restyle is a different id, kling-v2-new. Weights are closed.", "what_it_is_for": "The Chinese price page lists text-to-image at 4 points and multi-image reference at 16 points.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-image/kling-image-v2-1": { "id": "kuaishou/kling-image/kling-image-v2-1", "name": "Kling Image 2.1", "maker": "kuaishou", "family": "kuaishou/kling-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.014 }, "summary_intro": "Kling Image 2.1 (\u53ef\u7075\u56fe\u7247 2.1, API model_name kling-v2-1) is the July 2025 Kling image model that the English pricing page still lists. It does text-to-image, image-to-image, multi-image reference, and face or subject reference at 1K and 2K. Weights are closed.", "what_it_is_for": "Kuaishou's capability map describes it as strong prompt adherence and reliable output. fidelity parameters on the shared image API apply only to this id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-image/kling-image-v3": { "id": "kuaishou/kling-image/kling-image-v3", "name": "Kling Image 3.0", "maker": "kuaishou", "family": "kuaishou/kling-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.028 }, "summary_intro": "Kling Image 3.0 (\u53ef\u7075\u56fe\u7247 3.0, API model_name kling-v3) is the 2026 Kling image model for single images at 1K or 2K. The consumer note dates the Image 3.0 series to 2026-01-31. The API changelog dates the V3 image launch to 2026-02-25. It is the default model_name on the standard image generation endpoint. Weights are closed.", "what_it_is_for": "The capability map says it strengthens consistency and accepts multi-image subject control. It does not advertise 4K or series output. Those are on Kling Image 3.0 Omni.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-image/kling-image-v3-omni": { "id": "kuaishou/kling-image/kling-image-v3-omni", "name": "Kling Image 3.0 Omni", "maker": "kuaishou", "family": "kuaishou/kling-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.028 }, "summary_intro": "Kling Image 3.0 Omni (\u53ef\u7075\u56fe\u7247 3.0 Omni, API model_name kling-v3-omni) is the 2026 Kling image model for series output and native 4K. The consumer note of 2026-01-31 and the API changelog of 2026-02-25 both name it. Weights are closed. It is still on the English price list.", "what_it_is_for": "Kuaishou describes cinematic narrative stills, group-image generation, and 2K or 4K direct output, with multi-image subject control.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-image/kling-v2-new": { "id": "kuaishou/kling-image/kling-v2-new", "name": "Kling Image 2.0 New", "maker": "kuaishou", "family": "kuaishou/kling-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling Image 2.0 New (API id kling-v2-new) is the Kling restyle model added on 2025-08-15. Kuaishou says it supports nearly 300 styles and that the output resolution matches the input. The later capability map still lists the id and marks ordinary text-to-image off. Weights are closed.", "what_it_is_for": "It restyles an existing image. It is not the general Kling Image 2.0 text-to-image model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-video/kling-3-0-turbo": { "id": "kuaishou/kling-video/kling-3-0-turbo", "name": "Kling 3.0 Turbo", "maker": "kuaishou", "family": "kuaishou/kling-video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling 3.0 Turbo (\u53ef\u7075 3.0 Turbo, API id kling-3.0-turbo) is the lower-priced Kling 3.0 tier released on the API on 2026-06-17. The capability map calls it consistently strong results at a better price. It does text-to-video and image-to-video at 720p or 1080p for 3 to 15 seconds, with native audio and multi-shot. Weights are closed.", "what_it_is_for": "It is the model to call when the job is a straightforward generated clip rather than element reference, video edit, motion control, or 4K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-video/kling-4-0": { "id": "kuaishou/kling-video/kling-4-0", "name": "Kling 4.0", "maker": "kuaishou", "family": "kuaishou/kling-video", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling 4.0 (\u53ef\u7075 4.0) is Kuaishou's announced next Kling video generation. The Chinese release note marks 2026-09-28 as the major upgrade. The 2026-09-30 blog says it entered early access, with a wider rollout planned for October 2026, and the developer home still said the API was coming soon. Status is preview, not a generally priced API model. Weights are closed. Confidence is high that the announcement and early access are real, and low on full availability.", "what_it_is_for": "The comparison blog says it is for longer single generations, up to 30 seconds, with up to 10 keyframes, richer omni reference, dynamic motion, 10-bit HDR at 1080p and 4K, and stereo audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-video/kling-4-0-flash": { "id": "kuaishou/kling-video/kling-4-0-flash", "name": "Kling 4.0 Flash", "maker": "kuaishou", "family": "kuaishou/kling-video", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling 4.0 Flash is the named early-access Kling 4.0 tier. Kuaishou's 2026-09-30 blog says it opened to Ultra yearly subscribers on 2026-09-28, ahead of the wider Kling 4.0 rollout planned for October. No API id was published. Weights are closed. Status is preview.", "what_it_is_for": "The maker treats it as the first way to try the 4.0 lineup. A distinct Flash-only spec sheet was not published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-video/kling-v1": { "id": "kuaishou/kling-video/kling-v1", "name": "Kling 1.0", "maker": "kuaishou", "family": "kuaishou/kling-video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed video generation model. Frame rate on the Chinese model matrix is 30 fps for both std and pro.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling 1.0 (\u53ef\u7075, API id kling-v1) is Kuaishou's first named Kling video model. It was still listed on the Chinese video model matrix and the developer resource-pack support list on 2026-10-15. Weights are closed.", "what_it_is_for": "The maker's matrix says it generates 5-second and 10-second video from text or an image, with camera control, start and end frames, motion brush, video extension, and the early dual-character effects.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-video/kling-v1-5": { "id": "kuaishou/kling-video/kling-v1-5", "name": "Kling 1.5", "maker": "kuaishou", "family": "kuaishou/kling-video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling 1.5 (\u53ef\u7075 1.5, API id kling-v1-5) is an image-to-video Kling model. The API changelog opened professional mode on 2024-11-15 and standard mode on 2024-12-09, and the Chinese matrix still lists it. Text-to-video is explicitly unsupported. Weights are closed.", "what_it_is_for": "It animates a still image. Professional mode adds end-frame control, a motion brush, and simple camera control. Video extension continues a clip by about 4 to 5 seconds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-video/kling-v1-6": { "id": "kuaishou/kling-video/kling-v1-6", "name": "Kling 1.6", "maker": "kuaishou", "family": "kuaishou/kling-video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling 1.6 (\u53ef\u7075 1.6, API id kling-v1-6) is the 2025 Kling video model that restored text-to-video and added multi-image reference and multi-element edits. The API changelog calls the launch 2025-01-07. It was still on the Chinese pricing support list on 2026-10-15. Weights are closed.", "what_it_is_for": "The maker positions it for text-to-video, image-to-video, multi-image reference video, multi-element editing, extension, and the dual-character effects.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-video/kling-v2-1": { "id": "kuaishou/kling-video/kling-v2-1", "name": "Kling 2.1", "maker": "kuaishou", "family": "kuaishou/kling-video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling 2.1 (\u53ef\u7075 2.1, API id kling-v2-1) is the value image-to-video model opened on 2025-06-19. It is not the 2.1 Master flagship. The official matrix marks every text-to-video cell unsupported. Weights are closed. It was still on the Chinese pricing model list.", "what_it_is_for": "The app note calls standard mode 720p and professional mode 1080p, and says the non-Master 2.1 model is image-to-video only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-video/kling-v2-1-master": { "id": "kuaishou/kling-video/kling-v2-1-master", "name": "Kling 2.1 Master", "maker": "kuaishou", "family": "kuaishou/kling-video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling 2.1 Master (\u53ef\u7075 2.1 Master, API id kling-v2-1-master) is the June 2025 flagship Kling model for both text-to-video and image-to-video. The app note says it outputs 1080p. The billing page prices only the 1080p column, at 2.0 units per second. Weights are closed.", "what_it_is_for": "The matrix shows 5-second and 10-second generation and no extra control plugins.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-video/kling-v2-5-turbo": { "id": "kuaishou/kling-video/kling-v2-5-turbo", "name": "Kling 2.5 Turbo", "maker": "kuaishou", "family": "kuaishou/kling-video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling 2.5 Turbo (\u53ef\u7075 2.5 Turbo, API ids kling-v2-5-turbo and kling-2.5-turbo) is Kuaishou's lower-priced Kling video model for text and image. Professional mode opened on 2025-10-20 and standard mode on 2025-11-11. The capability map, still current in the docs index, describes it as high creativity at a lower price, silent, at 720p or 1080p. Weights are closed.", "what_it_is_for": "It generates a single 5-second or 10-second clip. 1080p can use a first and last frame. It does not generate native audio or multi-shot storyboards.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-video/kling-v2-6": { "id": "kuaishou/kling-video/kling-v2-6", "name": "Kling 2.6", "maker": "kuaishou", "family": "kuaishou/kling-video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling 2.6 (\u53ef\u7075 2.6, API ids kling-v2-6 and kling-2.6) is the December 2025 Kling video model whose slogan on the capability map is 'see the sound, hear the visual.' It generates silent or native-audio clips and is the default model name for motion control. Kuaishou's 3.0 release note says video 2.6 was upgraded into video 3.0, but 2.6 is still priced and documented. Weights are closed.", "what_it_is_for": "Use it for text-to-video, image-to-video, 1080p voice control, and motion control. It does not do multi-shot storyboards. 720p ordinary generation is silent.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-video/kling-v2-master": { "id": "kuaishou/kling-video/kling-v2-master", "name": "Kling 2.0 Master", "maker": "kuaishou", "family": "kuaishou/kling-video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling 2.0 Master (\u53ef\u7075 2.0, API id kling-v2-master) is the May 2025 Kling flagship for text-to-video and image-to-video. Kuaishou's changelog says it does not take a mode parameter. It remained on the Chinese resource-pack support list. Weights are closed.", "what_it_is_for": "The matrix shows plain 5-second and 10-second generation from text or an image, without the control plugins listed for 1.x.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-video/kling-v3": { "id": "kuaishou/kling-video/kling-v3", "name": "Kling 3.0", "maker": "kuaishou", "family": "kuaishou/kling-video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling 3.0 (\u53ef\u7075\u89c6\u9891 3.0, API ids kling-v3 and kling-3.0) is Kuaishou's 2026 flagship Kling video model. The consumer release note dates the full rollout to 2026-01-31, and the API changelog dates the V3 API launch to 2026-02-25. It generates 3 to 15 second clips with native audio, multi-shot storyboards, and 720p, 1080p, or native 4K. Weights are closed. It was still the priced current model on 2026-10-15, with Kling 4.0 only in early access.", "what_it_is_for": "Kuaishou says it is for text-to-video, image-to-video, first and last frames, subject reference, motion control, and dialogue-style multi-shot scenes. The release note lists Chinese, English, Japanese, Korean, and Spanish, plus dialects. Voice control is marked off on the model matrix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-video/kling-v3-omni": { "id": "kuaishou/kling-video/kling-v3-omni", "name": "Kling 3.0 Omni", "maker": "kuaishou", "family": "kuaishou/kling-video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling 3.0 Omni (\u53ef\u7075\u89c6\u9891 3.0 Omni, API id kling-v3-omni) is the multimodal sister of Kling 3.0. Kuaishou's release note says Kling Video O1 was upgraded into 3.0 Omni: native audio, smart storyboards, video subjects, subject timbre, and up to 15 seconds. Consumer rollout is dated 2026-01-31 and the API changelog dates the launch to 2026-02-25. Weights are closed.", "what_it_is_for": "It takes text, images, and video and can generate or edit from references. 4K is priced. A June 2026 update extended reference-video length to 15 seconds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kling-video/kling-video-o1": { "id": "kuaishou/kling-video/kling-video-o1", "name": "Kling O1", "maker": "kuaishou", "family": "kuaishou/kling-video", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kling O1 (\u53ef\u7075\u89c6\u9891 O1, API ids kling-video-o1 and kling-o1) is Kuaishou's unified multimodal Kling video model, launched on the API changelog as Omni-Video on 2025-12-15. The 3.0 announcement says it was upgraded into Kling Video 3.0 Omni, but O1 is still a documented and priced API model. It does not generate native audio. Weights are closed.", "what_it_is_for": "One prompt can drive text-to-video, image-to-video, element reference, and video reference or editing. Ordinary single-shot generation is limited to 5 or 10 seconds. Duration range on the capability map is 3 to 10 seconds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kolors/kolors": { "id": "kuaishou/kolors/kolors", "name": "Kolors", "maker": "kuaishou", "family": "kuaishou/kolors", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-06", "context_length": 256, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion text-to-image model. The loader uses a Stable Diffusion XL pipeline, a ChatGLM3 text encoder, a VAE, and a UNet. Parameter count is not stated on the card.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kolors (\u53ef\u56fe) is Kuaishou Kolors team's open text-to-image model, announced on 2024-07-06. It is trained as a latent diffusion model on billions of text-image pairs, with a 256-token bilingual Chinese and English context. Weights are Apache-2.0. The Hugging Face repo was created on 2024-06-05.", "what_it_is_for": "The maker says it is for photorealistic text-to-image generation, complex prompts, and Chinese and English text rendering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kolors/kolors-controlnet-canny": { "id": "kuaishou/kolors/kolors-controlnet-canny", "name": "Kolors ControlNet Canny", "maker": "kuaishou", "family": "kuaishou/kolors", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ControlNet trained on the Kolors base. Conditioning: Canny edge map. The Kolors ControlNet code uses the SDXL-style channel layout (320, 640, 1280, 1280).", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kolors ControlNet Canny is an official Kuaishou ControlNet for Kolors, released 2024-07-24 under Apache-2.0. It conditions generation on a canny edge map.", "what_it_is_for": "Use it when the output should follow an edge or depth structure from a reference image, with a text prompt on the Kolors base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kolors/kolors-controlnet-depth": { "id": "kuaishou/kolors/kolors-controlnet-depth", "name": "Kolors ControlNet Depth", "maker": "kuaishou", "family": "kuaishou/kolors", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ControlNet trained on the Kolors base. Conditioning: Depth map. The Kolors ControlNet code uses the SDXL-style channel layout (320, 640, 1280, 1280).", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kolors ControlNet Depth is an official Kuaishou ControlNet for Kolors, released 2024-07-24 under Apache-2.0. It conditions generation on a depth map.", "what_it_is_for": "Use it when the output should follow an edge or depth structure from a reference image, with a text prompt on the Kolors base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kolors/kolors-controlnet-pose": { "id": "kuaishou/kolors/kolors-controlnet-pose", "name": "Kolors ControlNet Pose", "maker": "kuaishou", "family": "kuaishou/kolors", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-08-05", "context_length": null, "max_output_tokens": null, "parameters": "about 1B", "architecture": "Pose ControlNet weights for Kolors. Hugging Face labels the repo at about 1B parameters and float16. There is no model card text.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kolors ControlNet Pose is the pose-conditioning checkpoint in the official Kwai-Kolors org. The repo was created on 2024-08-05. Hugging Face shows no model card. The Kolors README lists ControlNet Pose in the open-source plan. Status is active as published weights. Confidence is medium.", "what_it_is_for": "It is for pose-guided image generation on the Kolors base. Usage steps were not on a card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kolors/kolors-inpainting": { "id": "kuaishou/kolors/kolors-inpainting", "name": "Kolors Inpainting", "maker": "kuaishou", "family": "kuaishou/kolors", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Kolors UNet initialized from the base model with 5 extra input channels: 4 for the encoded masked image and 1 for the mask. Mask-channel weights start at zero.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kolors Inpainting is Kuaishou's official inpainting checkpoint, initialized from Kolors and published under Apache-2.0. The Hugging Face repo dates to 2024-07-24. The README explains the extra five UNet channels and a 200-image vendor eval.", "what_it_is_for": "It fills a masked region from a text prompt while leaving the rest of the image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kolors/kolors-ip-adapter-faceid-plus": { "id": "kuaishou/kolors/kolors-ip-adapter-faceid-plus", "name": "Kolors IP-Adapter FaceID Plus", "maker": "kuaishou", "family": "kuaishou/kolors", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Face-identity IP-Adapter for the Kolors base. The open-source plan lists it as IP-Adapter-FaceID.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kolors IP-Adapter FaceID Plus is Kuaishou's official face-identity adapter for Kolors. The Hugging Face repo was created on 2024-07-30 and is tagged Apache-2.0 and text-to-image. The Kolors README lists IP-Adapter-FaceID in the open-source plan.", "what_it_is_for": "It is for keeping a face identity while generating with Kolors. A full card was not reread, so the score table is not filled.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kolors/kolors-ip-adapter-plus": { "id": "kuaishou/kolors/kolors-ip-adapter-plus", "name": "Kolors IP-Adapter Plus", "maker": "kuaishou", "family": "kuaishou/kolors", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "IP-Adapter Plus weights on the Kolors base. Image encoder is OpenAI CLIP ViT-L/14 at 336 px.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kolors IP-Adapter Plus is Kuaishou's open reference-image adapter for Kolors, published on Hugging Face on 2024-07-15 under Apache-2.0. It uses an OpenAI CLIP ViT-L/14 336 px image encoder so the reference keeps more detail.", "what_it_is_for": "It generates an image from a text prompt plus a reference image. The card also shows joint use with Kolors ControlNet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/kolors/kolors-virtual-try-on": { "id": "kuaishou/kolors/kolors-virtual-try-on", "name": "Kolors Virtual Try-On", "maker": "kuaishou", "family": "kuaishou/kolors", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kolors Virtual Try-On (\u865a\u62df\u8bd5\u7a7f, API name kolors-virtual-try-on) is Kuaishou's named clothing try-on model on the Kling developer API. It opened on 2024-09-19, gained a 1.5 model on 2024-12-30, and a 3.0 pipeline on 2026-09-15. Weights are closed. The 3.0 note says older parameters still run, routed onto the new pipeline.", "what_it_is_for": "It puts a garment image onto a person image. Version 3.0 adds face consistency, flat-lay and mannequin inputs, and switches for pose, face, and background.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kuaishou/liveportrait/liveportrait": { "id": "kuaishou/liveportrait/liveportrait", "name": "LivePortrait", "maker": "kuaishou", "family": "kuaishou/liveportrait", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT for the project code. Bundled InsightFace detection models are non-commercial research only. The LICENSE file says commercial use should remove and replace those detection models.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Portrait animation with stitching and retargeting control. Official PyTorch implementation from Kuaishou Technology, with collaborators at USTC and Fudan University.", "modalities": { "input": [ "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LivePortrait is Kuaishou's open portrait-animation model. The repository is now under KlingAIResearch, the Kling research org, and the license copyright is Kuaishou Visual Generation and Interaction Center, 2024. It animates a still portrait from a driving video, including humans and, by the 2025 update, cats and dogs. Code is MIT. Bundled InsightFace detection models are non-commercial.", "what_it_is_for": "The paper and README describe efficient portrait animation with stitching and retargeting control, so expression and pose can be driven while the portrait stays stitched together.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/bart/bart-base-japanese": { "id": "kyoto/bart/bart-base-japanese", "name": "Japanese BART base", "maker": "kyoto", "family": "kyoto/bart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-05-09", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "MBartForConditionalGeneration used as Japanese BART. Encoder and decoder each have 6 layers, hidden 768, 12 heads, FFN 3072, GELU, vocab 32,002. Config max_position_embeddings is 1024. The card states encoder layers 6, decoder layers 6, and hidden size 768, and does not restate the position limit. SentencePiece unigram vocab of 32,000 built from JumanDIC words plus subwords. Trained with fairseq for 500,000 steps at batch 512, about two weeks on 4 Tesla V100 GPUs. No parameter total on the card or in safetensors metadata.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese BART base is a sequence-to-sequence model from Kyoto University's Language Media Processing Lab. The Hub repository was created on 2023-05-09. It is an active CC-BY-SA-4.0 checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card calls it a Japanese BART base pretrained on Japanese Wikipedia so it can be fine-tuned on downstream sequence tasks. The example loads MBartForConditionalGeneration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/bart/bart-large-japanese": { "id": "kyoto/bart/bart-large-japanese", "name": "Japanese BART large", "maker": "kyoto", "family": "kyoto/bart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-05-09", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "MBartForConditionalGeneration. Encoder and decoder each have 12 layers, hidden 1024, 16 heads, FFN 4096, GELU, vocab 32,002. Config max_position_embeddings is 1024. The card states 12 encoder layers, 12 decoder layers, and hidden size 1024. fairseq training for 250,000 steps at batch 512, about one month on 4 Tesla V100 GPUs. No parameter total published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese BART large is the 12-layer encoder-decoder from Kyoto University's Language Media Processing Lab, with a Hub repository created on 2023-05-09. The checkpoint is active and licensed CC-BY-SA-4.0. Collected 2026-10-15.", "what_it_is_for": "The card describes a Japanese BART large for fine-tuning on downstream tasks after Juman++ segmentation. Pretraining data are 18 million Japanese Wikipedia sentences.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/bert/bert-base-japanese-ssuw": { "id": "kyoto/bert/bert-base-japanese-ssuw", "name": "Japanese BERT base SSUW", "maker": "kyoto", "family": "kyoto/bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-04-11", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. BERT base: 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab 32,000, max_position_embeddings 512, type vocab 2. WordPiece over KyTea super-short-unit words. Hugging Face safetensors metadata was absent; only pytorch_model.bin is in the repo, so no parameter total was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese BERT base SSUW is a masked-language BERT from the Academic Center for Computing and Media Studies at Kyoto University. The Hub repository was created on 2023-04-11. It is an active open-weight checkpoint under CC-BY-SA-4.0. Collected 2026-10-15.", "what_it_is_for": "The card describes a Japanese BERT base pretrained for super short unit words, so downstream users can run fill-mask or take text features after KyTea segmentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/bert/japanese-bert-base": { "id": "kyoto/bert/japanese-bert-base", "name": "Japanese BERT base", "maker": "kyoto", "family": "kyoto/bert", "status": "deprecated", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2019-04-01", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "BERT base: 12 layers, hidden 768, 12 heads. Juman++ v2.0.0-rc2 word segmentation, then BPE subwords (subword-nmt). Vocabulary 32,000. Trained 30 epochs on Japanese Wikipedia, about 18 million sentences, half-width characters normalized to full-width. The lab does not publish a parameter total. File name Japanese_L-12_H-768_A-12_E-30_BPE. A transformers-format zip was added on 2019-11-15.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese BERT base is the Language Media Processing Lab's first public Japanese BERT, released by Kyoto University on 2019-04-01. It is an open-weight encoder trained on Japanese Wikipedia. The lab marked the line outdated on 2022-01-21. Collected 2026-10-15.", "what_it_is_for": "The lab released it so Japanese tasks could be fine-tuned from a model whose units are Juman++ morphemes split into BPE subwords, rather than the near-character units of multilingual BERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/bert/japanese-bert-base-wwm": { "id": "kyoto/bert/japanese-bert-base-wwm", "name": "Japanese BERT base WWM", "maker": "kyoto", "family": "kyoto/bert", "status": "deprecated", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2019-11-15", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "BERT base with whole-word masking: 12 layers, hidden 768, 12 heads. Juman++ v2.0.0-rc2 word segmentation, then BPE subwords (subword-nmt). Vocabulary 32,000. Trained 30 epochs on Japanese Wikipedia, about 18 million sentences, half-width characters normalized to full-width. The lab does not publish a parameter total. File name Japanese_L-12_H-768_A-12_E-30_BPE_WWM.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese BERT base WWM is the whole-word-masking BERT base from Kyoto University's Language Media Processing Lab, released 2019-11-15. The lab marked this download page outdated on 2022-01-21. Weights remain linked. Collected 2026-10-15.", "what_it_is_for": "The lab says whole-word masking tends to fine-tune better than the ordinary base checkpoint on the tasks they tried. It is still a Wikipedia encoder for downstream Japanese fine-tuning, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/bert/japanese-bert-large-wwm": { "id": "kyoto/bert/japanese-bert-large-wwm", "name": "Japanese BERT large WWM", "maker": "kyoto", "family": "kyoto/bert", "status": "deprecated", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2020-02-29", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "BERT large with whole-word masking: 24 layers, hidden 1024, 16 heads. Juman++ v2.0.0-rc2 word segmentation, then BPE subwords (subword-nmt). Vocabulary 32,000. Trained 30 epochs on Japanese Wikipedia, about 18 million sentences, half-width characters normalized to full-width. The lab does not publish a parameter total. File name Japanese_L-24_H-1024_A-16_E-30_BPE_WWM. The lab does not restate the 30-day single-GPU estimate, which it gives for base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese BERT large WWM is the 24-layer whole-word-masking Japanese BERT from Kyoto University's Language Media Processing Lab, released 2020-02-29. The download page called the line outdated on 2022-01-21. Collected 2026-10-15.", "what_it_is_for": "It is the largest of the three Wikipedia BERT zips. The lab says large and whole-word masking both tend to raise fine-tuned accuracy relative to the smaller ordinary base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/dango/dango-1-8b": { "id": "kyoto/dango/dango-1-8b", "name": "Dango 1.8B", "maker": "kyoto", "family": "kyoto/dango", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2026-03-08", "context_length": 4096, "max_output_tokens": null, "parameters": "1.8B", "architecture": "LlamaForCausalLM, Llama-2 style, compared by the authors to llm-jp-3-1.8b. 24 layers, hidden 2048, 16 heads, 16 key-value heads, intermediate 7168, SiLU, RoPE theta 10,000, RMSNorm epsilon 1e-5, untied embeddings, vocab 99,584 from the llm-jp-3 tokenizer. Config max_position_embeddings 4096. torch dtype bfloat16. Hub safetensors total is 1,867,614,208. The paper and the card call the model 1.8B. Pretrained from scratch on 100B tokens of a filtered Japanese portion of llm-jp-corpus-v3. This folder is that pretrained checkpoint, not the later L2 fine-tunes.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dango 1.8B is a decoder-only language model trained by Kyoto University researchers, with NII-LLMC coauthors, for second-language-acquisition experiments. The public artifact is the 100B-token Japanese pretrained checkpoint, on the Hub since 2026-03-08. It is active open weights. Collected 2026-10-15.", "what_it_is_for": "The card says Dango is for research on second language acquisition and can also be used as a Japanese speaker simulator. The released checkpoint is the L1-only pretraining run on a filtered Japanese corpus, not the English-learning fine-tune described in the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/deberta/deberta-v2-base-japanese": { "id": "kyoto/deberta/deberta-v2-base-japanese", "name": "Japanese DeBERTa V2 base", "maker": "kyoto", "family": "kyoto/deberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-01-05", "context_length": 512, "max_output_tokens": null, "parameters": "137030656", "architecture": "DebertaV2ForMaskedLM. 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 32,000, max position 512, relative attention, position buckets 256, convolution kernel 3. Juman++ words plus SentencePiece. Hub safetensors F32 count 137,030,656 plus 512 I64 values. The auto-Juman++ repo has the same total. Training data: Japanese Wikipedia as of 20221020 (3.2GB, 27M sentences, 1.3M documents), Japanese CC-100 (85GB, 619M sentences, 66M documents), and Japanese OSCAR (54GB, 326M sentences, 25M documents) with header, footer, and noisy documents removed. Wikipedia was duplicated 10 times. The card says the mix is 171GB. 500,000 steps, learning rate 2e-4, batch 2,112, about three weeks on 8 A100 40GB GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese DeBERTa V2 base is a masked language model from Kyoto University's Language Media Processing Lab. The Hub repository was created on 2023-01-05. It is an active CC-BY-SA-4.0 checkpoint. A September 2023 repository adds an automatic Juman++ tokenizer around the same weights. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint is for Japanese masked language modeling and for fine-tuning on downstream tasks. Word-level repos expect Juman++ segmentation. Character-level repos accept raw text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/deberta/deberta-v2-base-japanese-char-wwm": { "id": "kyoto/deberta/deberta-v2-base-japanese-char-wwm", "name": "Japanese DeBERTa V2 base char WWM", "maker": "kyoto", "family": "kyoto/deberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-01-18", "context_length": 512, "max_output_tokens": null, "parameters": "121669112", "architecture": "DebertaV2ForMaskedLM. 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 22,012 characters, max position 512, relative attention, position buckets 256, convolution kernel 3. Whole-word masking. Hub safetensors F32 count 121,669,112 plus 512 I64 values. Training data: Japanese Wikipedia as of 20221020 (3.2GB, 27M sentences, 1.3M documents), Japanese CC-100 (85GB, 619M sentences, 66M documents), and Japanese OSCAR (54GB, 326M sentences, 25M documents) with header, footer, and noisy documents removed. Wikipedia was duplicated 10 times. The card says the mix is 171GB. 320,000 steps, learning rate 2e-4, batch 2,208, about 20 days on 8 A100 40GB GPUs. The schedule reaches learning rate 0 at 500,000 steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese DeBERTa V2 base char WWM is a masked language model from Kyoto University's Language Media Processing Lab. The Hub repository was created on 2023-01-18. It is an active CC-BY-SA-4.0 checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint is for Japanese masked language modeling and for fine-tuning on downstream tasks. Word-level repos expect Juman++ segmentation. Character-level repos accept raw text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/deberta/deberta-v2-large-japanese": { "id": "kyoto/deberta/deberta-v2-large-japanese", "name": "Japanese DeBERTa V2 large", "maker": "kyoto", "family": "kyoto/deberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-01-07", "context_length": 512, "max_output_tokens": null, "parameters": "372638208", "architecture": "DebertaV2ForMaskedLM. 24 layers, hidden 1024, 16 heads, intermediate 4096, vocab 32,000, max position 512, relative attention, position buckets 256, convolution kernel 3. Juman++ words plus SentencePiece. Hub safetensors F32 count 372,638,208 plus 512 I64 values. Training data: Japanese Wikipedia as of 20221020 (3.2GB, 27M sentences, 1.3M documents), Japanese CC-100 (85GB, 619M sentences, 66M documents), and Japanese OSCAR (54GB, 326M sentences, 25M documents) with header, footer, and noisy documents removed. Wikipedia was duplicated 10 times. The card says the mix is 171GB. 300,000 steps, learning rate 1e-4, batch 2,304, 36 days on 8 A100 40GB GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese DeBERTa V2 large is a masked language model from Kyoto University's Language Media Processing Lab. The Hub repository was created on 2023-01-07. It is an active CC-BY-SA-4.0 checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint is for Japanese masked language modeling and for fine-tuning on downstream tasks. Word-level repos expect Juman++ segmentation. Character-level repos accept raw text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/deberta/deberta-v2-large-japanese-char-wwm": { "id": "kyoto/deberta/deberta-v2-large-japanese-char-wwm", "name": "Japanese DeBERTa V2 large char WWM", "maker": "kyoto", "family": "kyoto/deberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-03-09", "context_length": 512, "max_output_tokens": null, "parameters": "329601020", "architecture": "DebertaV2ForMaskedLM. 24 layers, hidden 1024, 16 heads, intermediate 4096, vocab 22,012 characters, max position 512, relative attention, position buckets 256, convolution kernel 3. Whole-word masking. Hub safetensors total is 329,601,020 and the API lists that count as F32. Training data: Japanese Wikipedia as of 20221020 (3.2GB, 27M sentences, 1.3M documents), Japanese CC-100 (85GB, 619M sentences, 66M documents), and Japanese OSCAR (54GB, 326M sentences, 25M documents) with header, footer, and noisy documents removed. Wikipedia was duplicated 10 times. The card says the mix is 171GB. 260,000 steps, learning rate 1e-4, batch 3,328, 26 days on 16 A100 40GB GPUs. The schedule reaches learning rate 0 at 300,000 steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese DeBERTa V2 large char WWM is a masked language model from Kyoto University's Language Media Processing Lab. The Hub repository was created on 2023-03-09. It is an active CC-BY-SA-4.0 checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint is for Japanese masked language modeling and for fine-tuning on downstream tasks. Word-level repos expect Juman++ segmentation. Character-level repos accept raw text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/deberta/deberta-v2-tiny-japanese": { "id": "kyoto/deberta/deberta-v2-tiny-japanese", "name": "Japanese DeBERTa V2 tiny", "maker": "kyoto", "family": "kyoto/deberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-01-18", "context_length": 512, "max_output_tokens": null, "parameters": "13934272", "architecture": "DebertaV2ForMaskedLM. 3 layers, hidden 192, 3 heads, intermediate 768, GELU, vocab 32,000, max position 512, relative attention, position buckets 256, convolution kernel 3. Juman++ words plus a 32,000-token SentencePiece that mixes JumanDIC words and unigram subwords. Hub safetensors F32 count 13,934,272 plus 512 I64 values. Training data: Japanese Wikipedia as of 20221020 (3.2GB, 27M sentences, 1.3M documents), Japanese CC-100 (85GB, 619M sentences, 66M documents), and Japanese OSCAR (54GB, 326M sentences, 25M documents) with header, footer, and noisy documents removed. Wikipedia was duplicated 10 times. The card says the mix is 171GB. 100,000 steps, learning rate 1e-3, batch 6,144, about 33 hours on 8 A100 40GB GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese DeBERTa V2 tiny is a masked language model from Kyoto University's Language Media Processing Lab. The Hub repository was created on 2023-01-18. It is an active CC-BY-SA-4.0 checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint is for Japanese masked language modeling and for fine-tuning on downstream tasks. Word-level repos expect Juman++ segmentation. Character-level repos accept raw text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/deberta/deberta-v2-tiny-japanese-char-wwm": { "id": "kyoto/deberta/deberta-v2-tiny-japanese-char-wwm", "name": "Japanese DeBERTa V2 tiny char WWM", "maker": "kyoto", "family": "kyoto/deberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-01-05", "context_length": 512, "max_output_tokens": null, "parameters": "10078904", "architecture": "DebertaV2ForMaskedLM. 3 layers, hidden 192, 3 heads, intermediate 768, vocab 22,012 characters, max position 512, relative attention, position buckets 256, convolution kernel 3. Whole-word masking. Raw text is tokenized to characters. Hub safetensors F32 count 10,078,904 plus 512 I64 values. Training data: Japanese Wikipedia as of 20221020 (3.2GB, 27M sentences, 1.3M documents), Japanese CC-100 (85GB, 619M sentences, 66M documents), and Japanese OSCAR (54GB, 326M sentences, 25M documents) with header, footer, and noisy documents removed. Wikipedia was duplicated 10 times. The card says the mix is 171GB. 100,000 steps, learning rate 2e-4, batch 6,080, about one day on 8 A100 40GB GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese DeBERTa V2 tiny char WWM is a masked language model from Kyoto University's Language Media Processing Lab. The Hub repository was created on 2023-01-05. It is an active CC-BY-SA-4.0 checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint is for Japanese masked language modeling and for fine-tuning on downstream tasks. Word-level repos expect Juman++ segmentation. Character-level repos accept raw text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/deberta/deberta-v3-base-japanese": { "id": "kyoto/deberta/deberta-v3-base-japanese", "name": "Japanese DeBERTa V3 base", "maker": "kyoto", "family": "kyoto/deberta", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-23", "context_length": 512, "max_output_tokens": null, "parameters": "235668481", "architecture": "Config model_type is deberta-v2. 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab 96,900, max position 512, relative attention, position buckets 256, position_biased_input false, share_att_key true, pos_att_type p2c and c2p. The config does not set conv_kernel_size. Hub safetensors count is 235,668,481 F16 weights. Tokenizer is a Unigram byte-fallback conversion of llm-jp-tokenizer v2.2, not the full 100k release. No Juman++ segmentation. Trained on LLM-jp corpus v1.0.1: Japanese Wikipedia 1B tokens, Japanese mC4 129B, English Wikipedia 4B, The Pile 126B, The Stack 10B, 270B tokens for 2 epochs, 540B tokens seen. 475,000 steps, learning rate 1e-4, batch 2,400, sequence 512. Code is a modified Microsoft DeBERTa V3 trainer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese DeBERTa V3 base is a masked language model from Kyoto University's Language Media Processing Lab, pretrained on LLM-jp corpus v1.0.1. The Hub repository was created on 2024-04-23. It is an active Apache-2.0 checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card says it is for masked language modeling and downstream fine-tuning. Unlike DeBERTa V2 base, it does not need a morphological analyzer, and the author says Japanese, English, and code can be fed to the tokenizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/gpt2/gpt2-large-japanese-char": { "id": "kyoto/gpt2/gpt2-large-japanese-char", "name": "Japanese GPT-2 large char", "maker": "kyoto", "family": "kyoto/gpt2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-12-27", "context_length": 1024, "max_output_tokens": null, "parameters": "717M", "architecture": "GPT-2 large: 36 layers, embedding 1280, 20 heads, vocab 6,000, n_positions 1024, GELU new. Card says 717M parameters. The repo has pytorch_model.bin and no safetensors total. Stopped at 186,000 steps, about 2.0 epochs. Per-device batch 6 and gradient accumulation 98. About 8 months with 7 interruptions on one A100 80GB. Learning rate 2e-4, AdamW, weight decay 0.01, linear schedule, warmup 10,000, max grad norm 1.0. Data: Japanese Wikipedia as of 20221020 (3.2GB, 27M sentences, 1.3M documents), Japanese CC-100 (85GB, 619M sentences, 66M documents), and Japanese OSCAR (54GB, 326M sentences, 25M documents) with header, footer, and noisy documents removed. Wikipedia was duplicated 10 times. The card says the mix is 171GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese GPT-2 large char is a character-level GPT-2 from Kyoto University's Language Media Processing Lab. The card calls it a 717M parameter language model. The Hub repository was created on 2023-12-27. It is an active CC-BY-SA-4.0 checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card presents it as a Japanese character-level language model for text generation and for taking features of a text. It is not an instruction-tuned chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/gpt2/gpt2-medium-japanese-char": { "id": "kyoto/gpt2/gpt2-medium-japanese-char", "name": "Japanese GPT-2 medium char", "maker": "kyoto", "family": "kyoto/gpt2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-05-18", "context_length": 1024, "max_output_tokens": null, "parameters": "310M", "architecture": "GPT-2 medium: 24 layers, embedding 1024, 16 heads, vocab 6,000, n_positions 1024, GELU new. Card says 310M parameters. Hub F32 count is 309,504,000. Stopped at 186,000 steps, about 2.0 epochs. Per-device batch 14 and gradient accumulation 42. About 3 months with two interruptions on one A100 80GB. Learning rate 2e-4, AdamW, weight decay 0.01, linear schedule, warmup 10,000, max grad norm 1.0. Data: Japanese Wikipedia as of 20221020 (3.2GB, 27M sentences, 1.3M documents), Japanese CC-100 (85GB, 619M sentences, 66M documents), and Japanese OSCAR (54GB, 326M sentences, 25M documents) with header, footer, and noisy documents removed. Wikipedia was duplicated 10 times. The card says the mix is 171GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese GPT-2 medium char is a character-level GPT-2 from Kyoto University's Language Media Processing Lab. The card calls it a 310M parameter language model. The Hub repository was created on 2023-05-18. It is an active CC-BY-SA-4.0 checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card presents it as a Japanese character-level language model for text generation and for taking features of a text. It is not an instruction-tuned chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/gpt2/gpt2-small-japanese-char": { "id": "kyoto/gpt2/gpt2-small-japanese-char", "name": "Japanese GPT-2 small char", "maker": "kyoto", "family": "kyoto/gpt2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-04-18", "context_length": 1024, "max_output_tokens": null, "parameters": "90M", "architecture": "GPT-2 small: 12 layers, embedding 768, 12 heads, vocab 6,000, n_positions 1024, GELU new. Card says 90M parameters. Hub F32 count is 90,450,432, with extra BOOL tensors that are not counted here. Stopped at 142,000 steps, about 3.0 epochs, of a 500,000-step budget. Batch construction was 36 times gradient accumulation 32. About 3 months with two interruptions on one A100 80GB. Learning rate 2e-4, AdamW, weight decay 0.01, linear schedule, warmup 10,000, max grad norm 1.0. Data: Japanese Wikipedia as of 20221020 (3.2GB, 27M sentences, 1.3M documents), Japanese CC-100 (85GB, 619M sentences, 66M documents), and Japanese OSCAR (54GB, 326M sentences, 25M documents) with header, footer, and noisy documents removed. Wikipedia was duplicated 10 times. The card says the mix is 171GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese GPT-2 small char is a character-level GPT-2 from Kyoto University's Language Media Processing Lab. The card calls it a 90M parameter language model. The Hub repository was created on 2023-04-18. It is an active CC-BY-SA-4.0 checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card presents it as a Japanese character-level language model for text generation and for taking features of a text. It is not an instruction-tuned chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/roberta/roberta-base-japanese-char-wwm": { "id": "kyoto/roberta/roberta-base-japanese-char-wwm", "name": "Japanese RoBERTa base char WWM", "maker": "kyoto", "family": "kyoto/roberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2022-09-20", "context_length": 512, "max_output_tokens": null, "parameters": "100176329", "architecture": "RobertaForMaskedLM. 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab 18,377 character-level tokens, max_position_embeddings 514. Card max_seq_length 512. Hugging Face safetensors F32 count is 100,176,329 plus 514 I64 values, retrieved from the model API. Whole-word masking uses Juman++ segmentation only to build the mask, not as the user tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese RoBERTa base char WWM is a character-level masked language model from Kyoto University's Language Media Processing Lab. The Hub repository was created on 2022-09-20 and the weights are still public under CC-BY-SA-4.0. Collected 2026-10-15.", "what_it_is_for": "The card says it is a Japanese RoBERTa base for masked language modeling and downstream fine-tuning, trained with character tokenization and whole-word masking so users do not have to segment text first.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/roberta/roberta-base-japanese-ssuw": { "id": "kyoto/roberta/roberta-base-japanese-ssuw", "name": "Japanese RoBERTa base SSUW", "maker": "kyoto", "family": "kyoto/roberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-04-11", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "RobertaForMaskedLM. 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab 32,000, max_position_embeddings 514. Card max_seq_length 512. WordPiece over KyTea super short unit words. No safetensors file, so no parameter total was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese RoBERTa base SSUW is the Academic Center for Computing and Media Studies RoBERTa paired with their SSUW BERT. The Hub repository was created on 2023-04-11. It is an active CC-BY-SA-4.0 checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card presents it as a Japanese RoBERTa base for super short unit words, for fill-mask and feature extraction after KyTea segmentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyoto/roberta/roberta-large-japanese-char-wwm": { "id": "kyoto/roberta/roberta-large-japanese-char-wwm", "name": "Japanese RoBERTa large char WWM", "maker": "kyoto", "family": "kyoto/roberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2022-09-18", "context_length": 512, "max_output_tokens": null, "parameters": "322728395", "architecture": "RobertaForMaskedLM. 24 layers, hidden 1024, 16 heads, intermediate 4096, GELU, vocab 18,377, max_position_embeddings 514. Card max_seq_length 512. Hub safetensors total equals the F32 count, 322,728,395. Trained 500,000 steps at learning rate 5e-5. The card says training took a month on 8 to 16 A100 GPUs and lists num_devices 16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese RoBERTa large char WWM is the 24-layer character-level RoBERTa from Kyoto University's Language Media Processing Lab. The Hub repository was created on 2022-09-18. It remains a public CC-BY-SA-4.0 checkpoint. Collected 2026-10-15.", "what_it_is_for": "Same job as the base character RoBERTa: masked language modeling and fine-tuning without asking the user to run a morphological analyzer first.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/arc-encoder/arc4-encoder-llama": { "id": "kyutai/arc-encoder/arc4-encoder-llama", "name": "ARC4 Encoder Llama", "maker": "kyutai", "family": "kyutai/arc-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2026-03-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Context compressor. Initialized from Llama 3.2 3B and trained on dactory-filtered web crawl. Outputs 4x or 8x fewer continuous vectors than input tokens, which replace token embeddings in a frozen decoder. Pooling factor 4. Trained on 2.6B tokens against a Llama 3.1 8B decoder. MoshiRAG freezes this encoder.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ARC4 Encoder Llama is a Kyutai model in the arc-encoder family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Describes the three ARC8 encoders. Backbone is Llama 3.2 3B. Llama license also applies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/arc-encoder/arc8-encoder-llama": { "id": "kyutai/arc-encoder/arc8-encoder-llama", "name": "ARC8 Encoder Llama", "maker": "kyutai", "family": "kyutai/arc-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2025-10-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Context compressor. Initialized from Llama 3.2 3B and trained on dactory-filtered web crawl. Outputs 4x or 8x fewer continuous vectors than input tokens, which replace token embeddings in a frozen decoder. Pooling factor 8. Trained on 2.6B tokens against a Llama 3.1 8B decoder.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ARC8 Encoder Llama is a Kyutai model in the arc-encoder family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Describes the three ARC8 encoders. Backbone is Llama 3.2 3B. Llama license also applies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/arc-encoder/arc8-encoder-mistral": { "id": "kyutai/arc-encoder/arc8-encoder-mistral", "name": "ARC8 Encoder Mistral", "maker": "kyutai", "family": "kyutai/arc-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2025-10-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Context compressor. Initialized from Llama 3.2 3B and trained on dactory-filtered web crawl. Outputs 4x or 8x fewer continuous vectors than input tokens, which replace token embeddings in a frozen decoder. Pooling factor 8. Trained on 2.6B tokens against a Mistral 7B decoder.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ARC8 Encoder Mistral is a Kyutai model in the arc-encoder family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Describes the three ARC8 encoders. Backbone is Llama 3.2 3B. Llama license also applies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/arc-encoder/arc8-encoder-multi": { "id": "kyutai/arc-encoder/arc8-encoder-multi", "name": "ARC8 Encoder multi", "maker": "kyutai", "family": "kyutai/arc-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Context compressor. Initialized from Llama 3.2 3B and trained on dactory-filtered web crawl. Outputs 4x or 8x fewer continuous vectors than input tokens, which replace token embeddings in a frozen decoder. Pooling factor 8. One encoder trained by sampling between the Llama 3.1 8B and Mistral 7B decoders.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ARC8 Encoder multi is a Kyutai model in the arc-encoder family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Describes the three ARC8 encoders. Backbone is Llama 3.2 3B. Llama license also applies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/casa/casa-helium1-vl-2b": { "id": "kyutai/casa/casa-helium1-vl-2b", "name": "CASA-Helium1-VL-2B", "maker": "kyutai", "family": "kyutai/casa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA 4.0", "release_date": "2025-12-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cross-attention vision-language model. Helium 1 2B is fine-tuned, and the last four blocks of the Qwen2.5-VL-3B image encoder are fine-tuned. Image tokens are not written into the text KV cache. At inference the model attends to the latest image. The card says this supports near-constant memory on streaming video. Trained on four 8xH100 nodes.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CASA-Helium1-VL-2B is a Kyutai model in the casa family. Status: active. Weights: open-weights. License: CC-BY-NC-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Scores and license. Qwen research license also applies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/casa/casa-qwen2-5-vl-3b": { "id": "kyutai/casa/casa-qwen2-5-vl-3b", "name": "CASA-Qwen2.5-VL-3B", "maker": "kyutai", "family": "kyutai/casa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA 4.0", "release_date": "2025-12-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CASA adapters on a frozen Qwen2.5-VL-3B backbone. The card says only the CASA layers and the last blocks of the image encoder are trained. Hub size badge is 4B. This is a Kyutai-named release, not a rehost of the original Qwen checkpoint.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CASA-Qwen2.5-VL-3B is a Kyutai model in the casa family. Status: active. Weights: open-weights. License: CC-BY-NC-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Defines this checkpoint as the frozen-backbone Qwen adaptation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/casa/casa-qwen2-5-vl-3b-livecc": { "id": "kyutai/casa/casa-qwen2-5-vl-3b-livecc", "name": "CASA-Qwen2.5-VL-3B LiveCC", "maker": "kyutai", "family": "kyutai/casa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA 4.0", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CASA-Qwen2.5-VL-3B further fine-tuned for live video captioning. The Helium CASA card says it takes streaming video at 2 frames per second. Hub badge 4B.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CASA-Qwen2.5-VL-3B LiveCC is a Kyutai model in the casa family. Status: active. Weights: open-weights. License: CC-BY-NC-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hub id. Dataset tag chenjoya/Live-WhisperX-526K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/dactory/dactory": { "id": "kyutai/dactory/dactory", "name": "Dactory classifiers", "maker": "kyutai", "family": "kyutai/dactory", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA 4.0", "release_date": "2025-04-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One fastText multilabel classifier per EU official language. Eight labels: rand, wiki, books, science, stem, hum, pop, life. Non-English training text for most labels was translated from English with MADLAD. rand and wiki use original text. Version 1.0. Released April 2025 with Helium 1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dactory classifiers is a Kyutai model in the dactory family. Status: active. Weights: open-weights. License: CC-BY-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Usage via fasttext. One bin file per language, for example filter_en.bin.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/helium/helium-1-2b": { "id": "kyutai/helium/helium-1-2b", "name": "Helium 1", "maker": "kyutai", "family": "kyutai/helium", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA 4.0", "release_date": "2025-04-30", "context_length": 4096, "max_output_tokens": null, "parameters": "2B", "architecture": "Llama-like decoder-only Transformer with grouped-query attention. Card: dimension 2048, MLP 8192, 28 layers, 16 heads, RoPE theta 20,000, context 4096. Trained 500,000 steps at batch size 4M tokens on 64 H100s, by distillation from Gemma 2 9B after a tokenizer swap. The released checkpoint is a weighted parameter soup of the main run, the books run, the wiki run, and a multilingual run, with weights 2, 2, 1, 1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helium 1 is a Kyutai model in the helium family. Status: active. Weights: open-weights. License: CC-BY-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "2B base model, 24 EU languages, CC-BY-SA 4.0, also subject to the Gemma Terms of Use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/helium/helium-1-2b-books": { "id": "kyutai/helium/helium-1-2b-books", "name": "Helium 1 books", "maker": "kyutai", "family": "kyutai/helium", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-SA 4.0", "release_date": "2025-04-30", "context_length": 4096, "max_output_tokens": null, "parameters": "2B", "architecture": "Same public card text as Helium 1: dimension 2048, MLP 8192, 28 layers, 16 heads, RoPE theta 20,000, context 4096. The README is a copy of the Helium 1 card and still shows model id kyutai/helium-1-2b, so those hyperparameters are inherited rather than independently confirmed for this repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helium 1 books is a Kyutai model in the helium family. Status: active. Weights: open-weights. License: CC-BY-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hub lists this as a 2B text model in the Helium 1 collection. The README body matches Helium 1 and does not describe the subset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/helium/helium-1-2b-hum": { "id": "kyutai/helium/helium-1-2b-hum", "name": "Helium 1 humanities", "maker": "kyutai", "family": "kyutai/helium", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-SA 4.0", "release_date": "2025-04-30", "context_length": 4096, "max_output_tokens": null, "parameters": "2B", "architecture": "Same public card text as Helium 1: dimension 2048, MLP 8192, 28 layers, 16 heads, RoPE theta 20,000, context 4096. The README is a copy of the Helium 1 card and still shows model id kyutai/helium-1-2b, so those hyperparameters are inherited rather than independently confirmed for this repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helium 1 humanities is a Kyutai model in the helium family. Status: active. Weights: open-weights. License: CC-BY-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hub lists this as a 2B text model in the Helium 1 collection. The README body matches Helium 1 and does not describe the subset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/helium/helium-1-2b-life": { "id": "kyutai/helium/helium-1-2b-life", "name": "Helium 1 life", "maker": "kyutai", "family": "kyutai/helium", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-SA 4.0", "release_date": "2025-04-30", "context_length": 4096, "max_output_tokens": null, "parameters": "2B", "architecture": "Same public card text as Helium 1: dimension 2048, MLP 8192, 28 layers, 16 heads, RoPE theta 20,000, context 4096. The README is a copy of the Helium 1 card and still shows model id kyutai/helium-1-2b, so those hyperparameters are inherited rather than independently confirmed for this repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helium 1 life is a Kyutai model in the helium family. Status: active. Weights: open-weights. License: CC-BY-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hub lists this as a 2B text model in the Helium 1 collection. The README body matches Helium 1 and does not describe the subset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/helium/helium-1-2b-main": { "id": "kyutai/helium/helium-1-2b-main", "name": "Helium 1 main run", "maker": "kyutai", "family": "kyutai/helium", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-SA 4.0", "release_date": "2025-04-30", "context_length": 4096, "max_output_tokens": null, "parameters": "2B", "architecture": "Same public card text as Helium 1: dimension 2048, MLP 8192, 28 layers, 16 heads, RoPE theta 20,000, context 4096. The README is a copy of the Helium 1 card and still shows model id kyutai/helium-1-2b, so those hyperparameters are inherited rather than independently confirmed for this repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helium 1 main run is a Kyutai model in the helium family. Status: active. Weights: open-weights. License: CC-BY-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hub lists this as a 2B text model in the Helium 1 collection. The README body matches Helium 1 and does not describe the subset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/helium/helium-1-2b-pop": { "id": "kyutai/helium/helium-1-2b-pop", "name": "Helium 1 pop culture", "maker": "kyutai", "family": "kyutai/helium", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-SA 4.0", "release_date": "2025-04-30", "context_length": 4096, "max_output_tokens": null, "parameters": "2B", "architecture": "Same public card text as Helium 1: dimension 2048, MLP 8192, 28 layers, 16 heads, RoPE theta 20,000, context 4096. The README is a copy of the Helium 1 card and still shows model id kyutai/helium-1-2b, so those hyperparameters are inherited rather than independently confirmed for this repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helium 1 pop culture is a Kyutai model in the helium family. Status: active. Weights: open-weights. License: CC-BY-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hub lists this as a 2B text model in the Helium 1 collection. The README body matches Helium 1 and does not describe the subset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/helium/helium-1-2b-science": { "id": "kyutai/helium/helium-1-2b-science", "name": "Helium 1 science", "maker": "kyutai", "family": "kyutai/helium", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-SA 4.0", "release_date": "2025-04-30", "context_length": 4096, "max_output_tokens": null, "parameters": "2B", "architecture": "Same public card text as Helium 1: dimension 2048, MLP 8192, 28 layers, 16 heads, RoPE theta 20,000, context 4096. The README is a copy of the Helium 1 card and still shows model id kyutai/helium-1-2b, so those hyperparameters are inherited rather than independently confirmed for this repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helium 1 science is a Kyutai model in the helium family. Status: active. Weights: open-weights. License: CC-BY-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hub lists this as a 2B text model in the Helium 1 collection. The README body matches Helium 1 and does not describe the subset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/helium/helium-1-2b-stem": { "id": "kyutai/helium/helium-1-2b-stem", "name": "Helium 1 STEM", "maker": "kyutai", "family": "kyutai/helium", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-SA 4.0", "release_date": "2025-04-30", "context_length": 4096, "max_output_tokens": null, "parameters": "2B", "architecture": "Same public card text as Helium 1: dimension 2048, MLP 8192, 28 layers, 16 heads, RoPE theta 20,000, context 4096. The README is a copy of the Helium 1 card and still shows model id kyutai/helium-1-2b, so those hyperparameters are inherited rather than independently confirmed for this repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helium 1 STEM is a Kyutai model in the helium family. Status: active. Weights: open-weights. License: CC-BY-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hub lists this as a 2B text model in the Helium 1 collection. The README body matches Helium 1 and does not describe the subset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/helium/helium-1-2b-wiki": { "id": "kyutai/helium/helium-1-2b-wiki", "name": "Helium 1 wiki", "maker": "kyutai", "family": "kyutai/helium", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-SA 4.0", "release_date": "2025-04-30", "context_length": 4096, "max_output_tokens": null, "parameters": "2B", "architecture": "Same public card text as Helium 1: dimension 2048, MLP 8192, 28 layers, 16 heads, RoPE theta 20,000, context 4096. The README is a copy of the Helium 1 card and still shows model id kyutai/helium-1-2b, so those hyperparameters are inherited rather than independently confirmed for this repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helium 1 wiki is a Kyutai model in the helium family. Status: active. Weights: open-weights. License: CC-BY-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hub lists this as a 2B text model in the Helium 1 collection. The README body matches Helium 1 and does not describe the subset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/helium/helium-1-preview-2b": { "id": "kyutai/helium/helium-1-preview-2b", "name": "Helium 1 Preview", "maker": "kyutai", "family": "kyutai/helium", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2025-01-13", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only Transformer. Card: 24 layers, 20 heads, dimension 2560, MLP 7040, RoPE theta 100,000, context 4096. Trained by token-level distillation of a 7B model. The announcement table labels the model 2.2B; the model card says 2B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helium 1 Preview is a Kyutai model in the helium family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Base model. Not instruction-tuned. CC-BY 4.0. Six languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/helium/helium-6b": { "id": "kyutai/helium/helium-6b", "name": "Helium 6B", "maker": "kyutai", "family": "kyutai/helium", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA 4.0", "release_date": "2026-02-03", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "Decoder-only model scaled from the Helium 1 2B architecture. The repo's main checkpoint is the sequential (time-ordered) run: 2.5T tokens, data from 2018 through a 2025 cutoff. Shuffled controls and earlier-year sequential checkpoints live in subfolders of the same repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helium 6B is a Kyutai model in the helium family. Status: active. Weights: open-weights. License: CC-BY-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Documents sequential and shuffled subfolders, token counts, and OLMES scores.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/helium/helium-7b": { "id": "kyutai/helium/helium-7b", "name": "Helium 7B", "maker": "kyutai", "family": "kyutai/helium", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2024-09-17", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only text Transformer used as Moshi's Temporal Transformer. The Moshi paper and release blog describe a 7B model pretrained on 2.1T tokens of public English text, with RoPE, a 4096-token context, and SiLU-gated feed-forward blocks. It is not a separate download from the Moshi speech checkpoints.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helium 7B is a Kyutai model in the helium family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Names Helium as the 7B text model inside Moshi, trained on 2.1T tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/helium/helium1-vl-2b": { "id": "kyutai/helium/helium1-vl-2b", "name": "Helium1-VL 2B", "maker": "kyutai", "family": "kyutai/helium", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA 4.0", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Token-insertion vision-language model. Helium 1 2B backbone plus the Qwen2.5-VL image encoder. Hub size label is 3B. Released as the insertion baseline next to CASA, and described as instruct-tuned.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helium1-VL 2B is a Kyutai model in the helium family. Status: active. Weights: open-weights. License: CC-BY-NC-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Instruct-tuned insertion VLM. Qwen research license also applies to the frozen encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/hibiki/hibiki-1b": { "id": "kyutai/hibiki/hibiki-1b", "name": "Hibiki-M", "maker": "kyutai", "family": "kyutai/hibiki", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2025-02-06", "context_length": null, "max_output_tokens": null, "parameters": "1.7B", "architecture": "Mobile distillation of Hibiki. Card: 1.7B hierarchical Transformer, 12.5 Hz, 1.1 kbps, 8 RVQ levels per stream. README calls the backbone about 1B. Meant for on-device inference. Same French-to-English task and 120-second training length.", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hibiki-M is a Kyutai model in the hibiki family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Called Hibiki-M in the paper. 1.7B, 1.1 kbps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/hibiki/hibiki-2b": { "id": "kyutai/hibiki/hibiki-2b", "name": "Hibiki", "maker": "kyutai", "family": "kyutai/hibiki", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2025-02-06", "context_length": null, "max_output_tokens": null, "parameters": "2.7B", "architecture": "Decoder-only hierarchical Transformer for simultaneous speech translation. 12.5 Hz text and audio tokens, 2.2 kbps audio, 16 RVQ levels per stream. Multistream Moshi-style model of source and target speech. The README calls the backbone about 2B plus a depth transformer. Context size 40 seconds. Trained on sequences up to 120 seconds. Temperature sampling, so it batches. Voice transfer is controlled by classifier-free guidance.", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hibiki is a Kyutai model in the hibiki family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "French to English. Card says 2.7B. CC-BY.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/hibiki/hibiki-zero": { "id": "kyutai/hibiki/hibiki-zero", "name": "Hibiki-Zero", "maker": "kyutai", "family": "kyutai/hibiki", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA 4.0", "release_date": "2026-02-09", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "3B-parameter hierarchical Transformer, 12.5 Hz, 2.2 kbps. Same multistream design as Hibiki, but trained without word-level alignments: sentence-level data, then GRPO for latency. Translates French, Spanish, Portuguese, and German into English, with voice transfer and a text stream. One speaker and one language per session. README: an NVIDIA GPU, 8 GB VRAM may work, 12 GB is the safe figure. Batched inference about 3x realtime on one H100.", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hibiki-Zero is a Kyutai model in the hibiki family. Status: active. Weights: open-weights. License: CC-BY-NC-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "3B. CC-BY-NC-SA 4.0. Four source languages into English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/mimi/mimi": { "id": "kyutai/mimi/mimi", "name": "Mimi", "maker": "kyutai", "family": "kyutai/mimi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2024-09-17", "context_length": null, "max_output_tokens": null, "parameters": "96.2M", "architecture": "Streaming neural audio codec. Encoder-decoder with a quantized latent space. 24 kHz speech in, 12.5 Hz frames, 1.1 kbps, 80 ms frame latency. Residual vector quantization; the Moshi loader allows up to 32 codebooks and Moshi itself uses 8.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mimi is a Kyutai model in the mimi family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Streaming speech codec used by Moshi, Hibiki, STT, and TTS.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/mira/mira": { "id": "kyutai/mira/mira", "name": "MIRA", "maker": "kyutai", "family": "kyutai/mira", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-06", "context_length": null, "max_output_tokens": null, "parameters": "5B", "architecture": "Multiplayer latent-diffusion world model for Rocket League. Conditions on up to four players' actions. The Kyutai papers page says 20 frames per second on one Nvidia B200, trained on 10,000 hours. Rollouts are reported stable to five minutes in the measured eval and longer in practice. A playable demo is at mira-wm.com.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MIRA is a Kyutai model in the mira family. Status: active. Weights: not established. License: not established. This dossier was collected on 2026-10-15.", "what_it_is_for": "Lists MIRA as a playable real-time multiplayer world model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/moshi/moshi-rag": { "id": "kyutai/moshi/moshi-rag", "name": "MoshiRAG", "maker": "kyutai", "family": "kyutai/moshi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2026-03-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full-duplex front end fine-tuned from Moshika. A retrieval trigger token calls an external text retriever during the natural response gap. Reference text is injected with a frozen ARC-4 Llama encoder. User speech for the retriever is transcribed by Kyutai STT 1B. This repo is the front end only.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MoshiRAG is a Kyutai model in the moshi family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Front-end weights. Retrieval back end is pluggable and not in the checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/moshi/moshi-vis": { "id": "kyutai/moshi/moshi-vis", "name": "MoshiVis", "maker": "kyutai", "family": "kyutai/moshi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2025-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Moshika (frozen ~7B speech model) plus about 206M cross-attention adapters and a frozen PaliGemma2 vision encoder (~400M). Gated visual stream so the model can ignore the image. Hugging Face labels the bundled PyTorch checkpoint 9B.", "modalities": { "input": [ "audio", "image" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MoshiVis is a Kyutai model in the moshi family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Female-voice vision-speech model. Gemma terms apply to the bundled vision encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/moshi/moshika": { "id": "kyutai/moshi/moshika", "name": "Moshika", "maker": "kyutai", "family": "kyutai/moshi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2024-09-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full-duplex speech-text model. A 7B-parameter Temporal Transformer (the Helium text backbone) models time; a smaller Depth Transformer models residual codebooks inside each 12.5 Hz frame. Two audio streams (user and model) plus an Inner Monologue text stream. Theoretical latency 160 ms (80 ms Mimi frame + 80 ms acoustic delay); practical latency about 200 ms on an L4 GPU. This checkpoint is the female synthetic voice.", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moshika is a Kyutai model in the moshi family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Release notes for Moshiko, Moshika, and Mimi. Code is Apache-2.0; weights are CC-BY 4.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/moshi/moshika-rl": { "id": "kyutai/moshi/moshika-rl", "name": "Moshika RL (Seamless)", "maker": "kyutai", "family": "kyutai/moshi", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY-NC 4.0", "release_date": "2026-06-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reinforcement-learning post-train of Moshika for interactivity: pause handling, turn-taking, backchanneling, and user interruption, plus an LLM reward for response quality. Base checkpoint kyutai/moshika-pytorch-bf16.", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moshika RL (Seamless) is a Kyutai model in the moshi family. Status: active. Weights: open-weights. License: CC-BY-NC 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card was gated on 2026-10-15, so this dossier uses the Hub metadata and the paper abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/moshi/moshiko": { "id": "kyutai/moshi/moshiko", "name": "Moshiko", "maker": "kyutai", "family": "kyutai/moshi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2024-09-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full-duplex speech-text model. A 7B-parameter Temporal Transformer (the Helium text backbone) models time; a smaller Depth Transformer models residual codebooks inside each 12.5 Hz frame. Two audio streams (user and model) plus an Inner Monologue text stream. Theoretical latency 160 ms (80 ms Mimi frame + 80 ms acoustic delay); practical latency about 200 ms on an L4 GPU. This checkpoint is the male synthetic voice.", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moshiko is a Kyutai model in the moshi family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Release notes for Moshiko, Moshika, and Mimi. Code is Apache-2.0; weights are CC-BY 4.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/muscriptor/muscriptor-large": { "id": "kyutai/muscriptor/muscriptor-large", "name": "MuScriptor large", "maker": "kyutai", "family": "kyutai/muscriptor", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC 4.0", "release_date": "2026-07-10", "context_length": null, "max_output_tokens": null, "parameters": "1.4B", "architecture": "GitHub: 1.4B, 48 layers, dim 1536. Model card rounds to about 1.3B, 48 layers, dim 1536, 24 heads. Hub badge says 1B.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MuScriptor large is a Kyutai model in the muscriptor family. Status: active. Weights: open-weights. License: CC-BY-NC 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Size table and a D_Real-only F1 comparison. Weights are gated behind CC BY-NC 4.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/muscriptor/muscriptor-medium": { "id": "kyutai/muscriptor/muscriptor-medium", "name": "MuScriptor medium", "maker": "kyutai", "family": "kyutai/muscriptor", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC 4.0", "release_date": "2026-07-10", "context_length": null, "max_output_tokens": null, "parameters": "307M", "architecture": "Default size. GitHub: 307M, 24 layers, dim 1024. Model card: about 300M, 24 layers, dim 1024, 16 heads.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MuScriptor medium is a Kyutai model in the muscriptor family. Status: active. Weights: open-weights. License: CC-BY-NC 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Size table and a D_Real-only F1 comparison. Weights are gated behind CC BY-NC 4.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/muscriptor/muscriptor-small": { "id": "kyutai/muscriptor/muscriptor-small", "name": "MuScriptor small", "maker": "kyutai", "family": "kyutai/muscriptor", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC 4.0", "release_date": "2026-07-10", "context_length": null, "max_output_tokens": null, "parameters": "103M", "architecture": "Decoder-only Transformer. GitHub: 103M, 14 layers, dim 768. Model card: about 100M, 14 layers, dim 768, 12 heads. Mel spectrogram of a 5-second chunk in, MT3-like MIDI tokens out.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MuScriptor small is a Kyutai model in the muscriptor family. Status: active. Weights: open-weights. License: CC-BY-NC 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Size table and a D_Real-only F1 comparison. Weights are gated behind CC BY-NC 4.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/ovie/ovie": { "id": "kyutai/ovie/ovie", "name": "OVIE", "maker": "kyutai", "family": "kyutai/ovie", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Novel-view model. Convolutional encoder and decoder, three scales each, with a 12-layer ViT bottleneck (hidden 768, 12 heads) conditioned on a 7-D camera pose by AdaLN. The base card says about 143M parameters. Resolution-agnostic: 512 is the same design at 512x512. No depth network at inference. Base monocular pretrain at 256x256. Zero-shot.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OVIE is a Kyutai model in the ovie family. Status: active. Weights: open-source. License: MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "Architecture, the four checkpoints, and the metric table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/ovie/ovie-512": { "id": "kyutai/ovie/ovie-512", "name": "OVIE-512", "maker": "kyutai", "family": "kyutai/ovie", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-09-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Novel-view model. Convolutional encoder and decoder, three scales each, with a 12-layer ViT bottleneck (hidden 768, 12 heads) conditioned on a 7-D camera pose by AdaLN. The base card says about 143M parameters. Resolution-agnostic: 512 is the same design at 512x512. No depth network at inference. Trained at 512x512. Table cells are eval at 256.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OVIE-512 is a Kyutai model in the ovie family. Status: active. Weights: open-source. License: MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "Architecture, the four checkpoints, and the metric table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/ovie/ovie-ft-dl3dv": { "id": "kyutai/ovie/ovie-ft-dl3dv", "name": "OVIE-ft DL3DV", "maker": "kyutai", "family": "kyutai/ovie", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-09-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Novel-view model. Convolutional encoder and decoder, three scales each, with a 12-layer ViT bottleneck (hidden 768, 12 heads) conditioned on a 7-D camera pose by AdaLN. The base card says about 143M parameters. Resolution-agnostic: 512 is the same design at 512x512. No depth network at inference. 50k-step fine-tune on DL3DV.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OVIE-ft DL3DV is a Kyutai model in the ovie family. Status: active. Weights: open-source. License: MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "Architecture, the four checkpoints, and the metric table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/ovie/ovie-ft-re10k": { "id": "kyutai/ovie/ovie-ft-re10k", "name": "OVIE-ft RealEstate10K", "maker": "kyutai", "family": "kyutai/ovie", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-09-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Novel-view model. Convolutional encoder and decoder, three scales each, with a 12-layer ViT bottleneck (hidden 768, 12 heads) conditioned on a 7-D camera pose by AdaLN. The base card says about 143M parameters. Resolution-agnostic: 512 is the same design at 512x512. No depth network at inference. 50k-step fine-tune on RealEstate10K.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OVIE-ft RealEstate10K is a Kyutai model in the ovie family. Status: active. Weights: open-source. License: MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "Architecture, the four checkpoints, and the metric table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/pocket-tts/pocket-tts": { "id": "kyutai/pocket-tts/pocket-tts", "name": "Pocket TTS", "maker": "kyutai", "family": "kyutai/pocket-tts", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2025-12-29", "context_length": null, "max_output_tokens": null, "parameters": "100M", "architecture": "Continuous audio language model from the CALM line: a small Transformer plus a consistency or drifting sampler over a continuous VAE, not Mimi tokens. Official runtime is CPU. About 200 ms to the first audio chunk, about 6x real time on a MacBook Air M4 using 2 cores. Streaming, voice cloning from a short clip, and unbounded text length. Non-English languages also have larger 24-layer checkpoints selected with a language flag. An English drifting sampler head is selected as english_drifting_26-09.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pocket TTS is a Kyutai model in the pocket-tts family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Install, CLI, latency, languages, and voice list. Code is MIT. Hub metadata tags the weights CC-BY 4.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/pocket-tts/pocket-tts-without-voice-cloning": { "id": "kyutai/pocket-tts/pocket-tts-without-voice-cloning", "name": "Pocket TTS without voice cloning", "maker": "kyutai", "family": "kyutai/pocket-tts", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2026-01-06", "context_length": null, "max_output_tokens": null, "parameters": "100M", "architecture": "Sibling Hugging Face repo of Pocket TTS. Hub tags match the main repo (pocket-tts library, CC-BY 4.0, English, French, German, Portuguese, Italian, Spanish). The name says voice cloning is removed. The card was gated, so architecture equality is not confirmed.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pocket TTS without voice cloning is a Kyutai model in the pocket-tts family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Gated on 2026-10-15. Metadata only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/stt/stt-1b-en-fr": { "id": "kyutai/stt/stt-1b-en-fr", "name": "Kyutai STT 1B English-French", "maker": "kyutai", "family": "kyutai/stt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2025-06-17", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Delayed Streams Modeling streaming ASR. About 1B parameters, English and French, 0.5 second text delay, semantic voice-activity detector. 12.5 Hz Mimi frames, 32 codebooks. Returns word timestamps. Used as the user-side transcriber inside MoshiRAG.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kyutai STT 1B English-French is a Kyutai model in the stt family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The 2.6B card is also the card text for this 1B release and states the 0.5 second delay and semantic VAD.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/stt/stt-2-6b-en": { "id": "kyutai/stt/stt-2-6b-en", "name": "Kyutai STT 2.6B English", "maker": "kyutai", "family": "kyutai/stt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2025-06-06", "context_length": null, "max_output_tokens": null, "parameters": "2.6B", "architecture": "Delayed Streams Modeling decoder-only Transformer. Mimi tokens at 12.5 Hz, 32 codebooks per frame. Text stream is delayed 2.5 seconds. English only. Word timestamps by subtracting that delay. Handles audio up to about 2 hours. Hugging Face's size badge says 3B; the card says about 2.6B.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kyutai STT 2.6B English is a Kyutai model in the stt family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Streaming English ASR. CC-BY 4.0. Transformers port is stt-2.6b-en-trfs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/surflo/surflo": { "id": "kyutai/surflo/surflo", "name": "Surflo", "maker": "kyutai", "family": "kyutai/surflo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-06-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Feed-forward 3D reconstruction. A variable number of unposed RGB views is compressed to K latent tokens, then flow matching decodes any number of oriented surface points. Kyutai is a co-author institution. Weights are published as AntoineGuedon/Surflo-v0 (file surflo_v0.pt), not under the kyutai Hub org.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Surflo is a Kyutai model in the surflo family. Status: active. Weights: open-weights. License: not established. This dossier was collected on 2026-10-15.", "what_it_is_for": "Points at AntoineGuedon/Surflo-v0, file surflo_v0.pt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/tts/tts-0-75b-en-public": { "id": "kyutai/tts/tts-0-75b-en-public", "name": "Kyutai TTS 0.75B public", "maker": "kyutai", "family": "kyutai/tts", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2025-07-15", "context_length": null, "max_output_tokens": null, "parameters": "750M", "architecture": "Smaller DSM TTS trained only on public TTS data for comparison. Backbone 300M, depth transformer 450M, 16 Mimi codebooks (cannot drop codebooks at inference). Delay 16 frames (1.28 s) and acoustic/semantic delay 2. Supports prefix voice cloning. CFG coefficient is a user setting (the card's sample uses 3).", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kyutai TTS 0.75B public is a Kyutai model in the tts family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Scientific-comparison checkpoint. CC-BY 4.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/tts/tts-1-6b-en-fr": { "id": "kyutai/tts/tts-1-6b-en-fr", "name": "Kyutai TTS 1.6B", "maker": "kyutai", "family": "kyutai/tts", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY 4.0", "release_date": "2025-06-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Streaming hierarchical Transformer over Mimi tokens. 12.5 Hz, up to 32 audio tokens per frame (fewer allowed at inference). Card: backbone 1B and depth transformer 600M with partial weight sharing, and also the sentence 'actually 1.8B parameters, not 1.6B'. Text-to-audio delay of 16 frames (1.28 s) plus an acoustic/semantic delay of 2. CFG was distilled in, so inference does not double the batch. Voice is chosen from precomputed embeddings, not raw cloning. Throughput claim: 75x generated audio per unit of compute time.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kyutai TTS 1.6B is a Kyutai model in the tts family. Status: active. Weights: open-weights. License: CC-BY 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "English and French streaming TTS. CC-BY 4.0. Voices live in kyutai/tts-voices.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/voice-of-reason/glm-4-voice-of-reason-9b": { "id": "kyutai/voice-of-reason/glm-4-voice-of-reason-9b", "name": "GLM-4 Voice of Reason", "maker": "kyutai", "family": "kyutai/voice-of-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GLM-4-Voice license", "release_date": "2026-09-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reinforcement-learning fine-tune of GLM-4-Voice-9B for spoken math. The card says there is no supervised fine-tune stage: reasoning is learned from a binary LLM-judge reward. The model speaks the reasoning. Audio front end and decoder stay the original GLM-4-Voice tokenizer and decoder, which are not in this repo. Hub badge says 10B; the base model name is 9B.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4 Voice of Reason is a Kyutai model in the voice-of-reason family. Status: active. Weights: open-weights. License: GLM-4-Voice license. This dossier was collected on 2026-10-15.", "what_it_is_for": "Run instructions and the 0.706 GSM8K figure. License inherited from GLM-4-Voice.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "kyutai/voice-of-reason/glm-4-voice-of-reason-stitch-9b": { "id": "kyutai/voice-of-reason/glm-4-voice-of-reason-stitch-9b", "name": "GLM-4 Voice of Reason Stitch", "maker": "kyutai", "family": "kyutai/voice-of-reason", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "GLM-4-Voice license", "release_date": "2026-09-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sibling of Voice of Reason. The main card says this checkpoint keeps reasoning unspoken, wrapped in [SOPR] ... [EOPR], while speech continues. Same GLM-4-Voice-9B base. A Kyutai post describes it as the checkpoint with extra reasoning tokens while speaking.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4 Voice of Reason Stitch is a Kyutai model in the voice-of-reason family. Status: active. Weights: open-weights. License: GLM-4-Voice license. This dossier was collected on 2026-10-15.", "what_it_is_for": "Hub id. Base zai-org/glm-4-voice-9b. License other.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lanzhou/qiwu/qiwu-zhisuan": { "id": "lanzhou/qiwu/qiwu-zhisuan", "name": "\u9f50\u7269\u667a\u7b97", "maker": "lanzhou", "family": "lanzhou/qiwu", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "A cluster, not one published checkpoint. Lanzhou University's philosophy school page says high-level generation uses a professional model fine-tuned from Tongyi Qianwen, while exact pattern tasks use separately trained small neural nets, and clustering or similarity tasks use both. Wuhan University's 2026-04-03 page says V1.0 is a Chinese-philosophy vertical model fine-tuned from qwen3:32B plus several small networks built by the team. Neither page publishes layer counts, context length, or the small-model sizes. Reported pretraining text is about 700 million characters of hand-labeled and semi-supervised classical and excavated texts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u9f50\u7269\u667a\u7b97 is a closed Chinese-philosophy model cluster announced by Lanzhou University on 2025-12-28 and built with Wuhan University. V1.0 was a trial, later registered as software, not a public weight release. The trial site returned HTTP 503 on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Lanzhou University describes it as a tool for classical and excavated Chinese philosophy: argument analysis of ancient thought texts, topic clustering, proposition or semantic comparison, concept tracking, and academic-history summaries. Gansu Daily says a user can paste a classical passage for a reading and a line of thought, or upload a batch of texts for a review and for links among questions. The argument-analysis piece is described as the first vertical model aimed at professional Chinese-philosophy research. The five components are modules of one platform, not separate weight releases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lanzhou/tibetan/t-llama": { "id": "lanzhou/tibetan/t-llama", "name": "T-LLaMA", "maker": "lanzhou", "family": "lanzhou/tibetan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-03-16", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "LlamaForCausalLM continued from meta-llama/Llama-2-7b-hf. Published config.json: 32 layers, hidden size 4096, 32 attention heads, 32 key-value heads, intermediate size 11008, SiLU, RoPE, float16, vocabulary 39685, max position embeddings 2048. The paper says the Llama 2 vocabulary was expanded with a SentencePiece Tibetan vocabulary and that training used LoRA on the 7B model. The hub repo is full weights in two PyTorch shards, not an adapter-only upload. The exact parameter count after the vocabulary change is not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "T-LLaMA is a Tibetan causal language model continued from Llama 2 7B. Lanzhou University information-science authors, with Northwest Minzu University and Qinghai Normal University, published the weights on Hugging Face and described them in Complex & Intelligent Systems on 2024-12-19. The hub repo is public and ungated. No license file is in the repo. Collected 2026-10-15.", "what_it_is_for": "The model card and the paper say it is for Tibetan text classification, news text generation, and automatic summarization, aimed at a low-resource language where general chat models are weak. The authors say they are releasing it as a base for researchers with limited compute. It is not a hosted API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lanzhou/tibetan/tbert": { "id": "lanzhou/tibetan/tbert", "name": "TBERT", "maker": "lanzhou", "family": "lanzhou/tibetan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-08", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Bidirectional Transformer in the BERT shape. The PyTorch and TensorFlow bert_config.json files match: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, vocabulary 25000, type vocabulary 2, max position embeddings 512. TBERT uses a SentencePiece vocabulary rather than BERT WordPiece. A parameter count is not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TBERT is a Tibetan pretrained language model released on 2023-10-08 by Qinghai Normal University's State Key Laboratory of Tibetan Intelligent Information Processing and Application (Duo La's group) together with Lanzhou University's Engineering Research Center of Open Source Software and Real-Time Systems, Ministry of Education. Weights are public in PyTorch and TensorFlow. No license is stated. Collected 2026-10-15.", "what_it_is_for": "The release README says the two models, TBERT and Tibetan-BERT-wwm, are pretrained Tibetan language models meant to help Tibetan NLP research and development. It does not name a product API or a chat app. TBERT is the SentencePiece variant. Tibetan-BERT-wwm is the separate whole-word-masking release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lanzhou/tibetan/tibetan-bert-wwm": { "id": "lanzhou/tibetan/tibetan-bert-wwm", "name": "Tibetan-BERT-wwm", "maker": "lanzhou", "family": "lanzhou/tibetan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-08", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-shaped bidirectional Transformer with whole-word masking. Published bert_config.json: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, vocabulary 32000, type vocabulary 2, max position embeddings 512. The release README says the structure is the same as BERT and ships a TensorFlow checkpoint plus vocab.txt. A parameter count is not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tibetan-BERT-wwm is a Tibetan BERT pretrained with whole-word masking. Qinghai Normal University's Tibetan intelligent-information lab and Lanzhou University's open-source software engineering research center released the TensorFlow weights on 2023-10-08. A 2024 IEEE paper by overlapping Lanzhou University authors reports Tibetan classification scores. No license is stated. Collected 2026-10-15.", "what_it_is_for": "The release README calls it a whole-word-masking Tibetan pretrained language model and says its structure matches BERT. The IEEE abstract says it was trained to capture Tibetan context and word-level meaning, then applied to Tibetan social-media text for user sentiment and news topics. It is an encoder checkpoint, not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leeds/nsp/nsp": { "id": "leeds/nsp/nsp", "name": "Neural Social Physics (NSP)", "maker": "leeds", "family": "leeds/nsp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Copyright (c) 2022, The University of Leeds. The GitHub notice permits redistribution of source and binary forms if the copyright notice and disclaimer are kept. No warranty. Not a named OSI licence. A separate weights licence is not stated.", "release_date": "2022-07-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "NSP-SFM, a neural differential equation. Each person is a particle under Newton's second law with goal attraction, inter-agent repulsion, and environment repulsion. A Goal Sampling Network (similar to part of Y-net, pre-trained) samples the goal. Goal-Network (encoder, LSTM, MLP) predicts the goal time-scale tau. Collision-Network learns the repulsion scale k_nj. Environment scale k_env is learned directly. A CVAE models time-varying dynamics noise alpha. Training is progressive: train_goals.py, then train_nsp_wo.py (deterministic, no CVAE), then train_nsp_w.py (CVAE). The public saved_models tree ships only the Stanford Drone Goal-Network and the without-CVAE NSP files.", "modalities": { "input": [ "trajectory", "environment-map" ], "output": [ "future-trajectory" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Neural Social Physics is a pedestrian trajectory model from Jiangbei Yue and He Wang, released under a 2022 University of Leeds copyright, with Dinesh Manocha (University of Maryland). The realized system is NSP-SFM: a social-force differential equation whose scales are learned by neural networks, plus a CVAE for motion noise. Stanford Drone checkpoints were still in the GitHub `saved_models` tree on 2026-10-15. The arXiv v1 date is 21 July 2022 (ECCV 2022; v2 revised 31 March 2023).", "what_it_is_for": "The paper proposes NSP for multi-agent human trajectory prediction. An explicit social-force model is the inductive bias, and the network estimates its parameters and the stochastic part of the dynamics. Evaluation is on Stanford Drone (SDD) and the five ETH/UCY scenes (ETH, Hotel, UNIV, ZARA1, ZARA2), using the same 8-observed / 12-predicted split as Y-net. The authors also report collision rates when the scene density is raised with no ground-truth paths.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lehigh/biomedgpt/biomedgpt": { "id": "lehigh/biomedgpt/biomedgpt", "name": "BiomedGPT", "maker": "lehigh", "family": "lehigh/biomedgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Disputed. The GitHub file header says Apache-2.0. The README says commercial and clinical use are prohibited because the model inherits OFA's non-commercial license, is not licensed for healthcare, and is not accurate enough for diagnosis. PanaceaAI/BiomedGPT-Base-Pretrained and PanaceaAI/instruct-biomedgpt-base are tagged apache-2.0 on Hugging Face.", "release_date": "2023-05-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder sequence-to-sequence model in the OFA / BART style: a BERT-style encoder and a left-to-right decoder, with a shared multimodal vocabulary for text and image patches. Nature Medicine says training continued from pretrained OFA checkpoints using masked modeling, object detection, and image-text matching. It is not trained from scratch. Scales differ by ResNet image backbone, embedding size, depth, and input resolution. See versions.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BiomedGPT is a named vision-language model from Lehigh University's computer science group, led in print by Kai Zhang and Lichao Sun, with Lifang He among the Lehigh authors. Nature Medicine published it online on 7 August 2024 as an open, lightweight generalist for biomedical images and text. It is still distributed as research weights. This page was collected on 2026-10-15.", "what_it_is_for": "The authors describe one encoder-decoder that is pretrained on mixed biomedical images, captions, text, and object labels, then fine-tuned for image classification, visual question answering, captioning, summarization, and medical language inference. The GitHub README says the release is for learning transferable medical representations, not for a chatbot or a clinical diagnosis system.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lehigh/braingfm/braingfm": { "id": "lehigh/braingfm/braingfm", "name": "BrainGFM", "maker": "lehigh", "family": "lehigh/braingfm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The README badge says CC BY-NC-SA 4.0. A LICENSE file at the repository root returned 404 on 2026-10-15, and the README body has no license section.", "release_date": "2025-05-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph Transformer backbone (Yun et al., 2019) over fMRI brain graphs. Random-walk structural encodings mark ROI position. Pretraining combines graph contrastive learning and a graph masked autoencoder on one shared encoder. Atlas and disorder tokens are text embeddings from BioClinicalBERT. Few-shot adaptation uses meta-learned graph prompts with the backbone frozen. The public fine-tune script builds hidden size 256, 8 attention heads, feed-forward size 256, 4 self-attention layers, and 4 GNN layers. A smaller config in the module demo is not the paper's reported model. No parameter count is stated.", "modalities": { "input": [ "graph", "text" ], "output": [ "classification" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BrainGFM is a brain-graph foundation model released by Lehigh University authors Xinxu Wei, Kanhao Zhao, Yong Jiao, and Lifang He, with Yu Zhang at Stanford. The preprint is arXiv:2506.02044, first submitted 31 May 2025 and revised 10 February 2026. ICLR 2026 lists it as a conference paper. Weights are in the authors' GitHub repository. This page was collected on 2026-10-15.", "what_it_is_for": "The paper presents one pretrained encoder for resting-state fMRI graphs. Each scan becomes a graph of brain regions. The encoder is pretrained with graph contrastive learning and a graph masked autoencoder, then adapted with graph prompts and short text prompts to disorder and atlas labels. The stated jobs are transfer across parcellations and disorders, including few-shot and zero-shot settings. It is not a language model and not a campus API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leiden/gysbert/gysbert": { "id": "leiden/gysbert/gysbert", "name": "GysBERT", "maker": "leiden", "family": "leiden/gysbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-11-20", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base uncased historical Dutch masked language model, trained from scratch. The card says the architecture follows BERT base uncased from the original BERT pretraining codebase. Config: 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocab_size 30522, max_position_embeddings 512. The NLP4DH 2022 paper says a WordPiece vocabulary of 30,000, default BERT parameters, maximum sequence length 128 subtokens, and 1,000,000 steps. Training tokens: 5.8 billion from Delpher plus 1.3 billion from DBNL, about 7.1 billion, restricted to 1500-1950, with Delpher snippets at KenLM perplexity 20 or higher discarded. vocab.txt is uncased Dutch (whole-token letters are a-z; wordpieces include de, van, het, zoo). The paper vocabulary size 30,000 and the config vocab_size 30522 disagree.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Leiden University researchers Enrique Manjavacas and Lauren Fonteyn released GysBERT, a BERT-base uncased model trained from scratch on historical Dutch. The NLP4DH paper of 20 November 2022 says that paper marks the release. Weights remain on Hugging Face as emanjavacas/GysBERT with an MIT card. Status: active. Collected 2026-10-15.", "what_it_is_for": "The card calls it a historical language model for Dutch from the MacBERTh project, tested on word-sense disambiguation. The paper introduces it as the first historically pre-trained model of this kind for Dutch and uses it, with MacBERTh, in a few-shot non-parametric WSD setup on the Woordenboek der Nederlandsche Taal and the Oxford English Dictionary.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leiden/gysbert/gysbert-v2": { "id": "leiden/gysbert/gysbert-v2", "name": "GysBERT v2", "maker": "leiden", "family": "leiden/gysbert", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM uploaded by the GysBERT author with no model card. Config is the same on GysBERT-v2, GysBERT-v2-1.5m, and GysBERT-v2-2m: 12 layers, hidden size 768, 12 heads, intermediate size 3072, GELU, vocab_size 30500, max_position_embeddings 512, transformers 4.20.1, float32. vocab.txt on GysBERT-v2 contains an A-Z whole-token block and capitalized Dutch pieces such as De, Amsterdam, and Leiden, plus van, het, and zoo. GysBERT v1 vocab.txt is uncased and its config vocab_size is 30522. No tokenizer_config was part of the v2-2m file list that was checked, so a do_lower_case flag is unpublished. Training data, step count, and pretraining sequence length are unpublished.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The GysBERT author, Enrique Manjavacas, uploaded GysBERT-v2 weights to Hugging Face. The repo has a BERT masked-language-model config and a cased Dutch vocabulary, and it has no model card. GysBERT-v2-1.5m and GysBERT-v2-2m are the same config under step-like repository names and are versions of this folder. Status: active. Confidence: medium. Collected 2026-10-15.", "what_it_is_for": "No card states a task. The pipeline tag on the Hub page is fill-mask, and the config class is BertForMaskedLM. The v1 card said an updated GysBERT with an enlarged pretraining set was due. These repos use the GysBERT-v2 name. They do not themselves say they are that update.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leiden/macberth/macberth": { "id": "leiden/macberth/macberth", "name": "MacBERTh", "maker": "leiden", "family": "leiden/macberth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2021-12-01", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base uncased masked language model, trained from scratch on historical English. Config: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, type vocab size 2, vocab size 30000, max_position_embeddings 512. The NLP4DH 2021 paper uses BERT-base Uncased hyperparameters and default parameters except a maximum sequence length of 128 subtokens, for 1,000,000 training steps. The card states about 3.9 billion tokenized words from EEBO, ECCO, COHA, CLMET3.1, EVANS, and the Hansard corpus. The title, abstract, dataset section, project site, and the 2021-12-01 announcement use 1450-1950. The contributions paragraph uses 1450-1900.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Leiden University researchers Enrique Manjavacas and Lauren Fonteyn released MacBERTh, a BERT-base uncased model trained from scratch on historical English. The weights remain on Hugging Face under emanjavacas/MacBERTh with an MIT card. Status: active. Collected 2026-10-15.", "what_it_is_for": "The project site and the 2021-12-01 forum post present MacBERTh as a historical-English encoder for work on texts whose spelling, grammar, and word senses differ from present-day English. The NLP4DH 2021 paper evaluates part-of-speech tagging, word-in-context, parameter-free word-sense disambiguation, fill-in-the-blank, and sentence periodization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leipzig/hypermpnn/hypermpnn": { "id": "leipzig/hypermpnn/hypermpnn", "name": "HyperMPNN", "maker": "leipzig", "family": "leipzig/hypermpnn", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ProteinMPNN message-passing network, retrained from scratch on hyperthermophile backbones. The preprint says the standard architecture and default hyperparameters were kept. The adapted training script defaults to 3 encoder layers, 3 decoder layers, hidden size 128, and 48 graph neighbors. Released checkpoints use that neighbor count.", "modalities": { "input": [ "protein-structure" ], "output": [ "protein-sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperMPNN is a protein sequence-design network released by the Institute for Drug Discovery at Leipzig University, with ScaDS.AI and Vanderbilt coauthors. It is a from-scratch retrain of the ProteinMPNN architecture on AlphaFold models of hyperthermophile proteins, not a load of the public ProteinMPNN checkpoint. Weights are public. This dossier was collected on 2026-10-15. The write-up is still a 1 December 2024 bioRxiv preprint.", "what_it_is_for": "The authors use it to design sequences that recover the amino-acid composition of hyperthermophiles and, on their test, to stabilize an existing protein nanoparticle. Inference is the original ProteinMPNN script pointed at these weights with `--path_to_model_weights` and `--model_name`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lenovo/quality-inspection/industrial-quality-inspection": { "id": "lenovo/quality-inspection/industrial-quality-inspection", "name": "Lenovo Industrial Quality Inspection Foundation Model", "maker": "lenovo", "family": "lenovo/quality-inspection", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed vision model. Lenovo describes a fully self-developed pretrained foundation model for industrial quality inspection, used with few-shot lifelong learning. Shipping tire systems combine unsupervised modeling on qualified (good) samples with supervised defect classification. Backbone, layer count, and parameter count are not published.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lenovo's closed vision model for factory appearance inspection. The company names it in English as the Lenovo Industrial Quality Inspection Foundation Model and in Chinese as the self-developed industrial quality-inspection pretrained foundation model (\u5de5\u4e1a\u8d28\u68c0\u9884\u8bad\u7ec3\u57fa\u7840\u6a21\u578b). It is active inside tire and other line equipment as of the 2026-10-15 collection. Weights are not public.", "what_it_is_for": "Lenovo uses the model as the general engine under its tire surface inspector and the Edge Brain / Lizard V3.0 kit. The stated job is pixel-level defect recognition that adapts across tire SKUs. Unsupervised modeling is fit on qualified tires only, then supervised classification covers known defect types. Lenovo Research says images can be trained and inferred at the edge without uploading to the cloud.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lenovo/tianxi/tianxi-memory-1-7b": { "id": "lenovo/tianxi/tianxi-memory-1-7b", "name": "\u5929\u79a7\u591a\u6a21\u6001\u8bb0\u5fc6\u6a21\u578b", "maker": "lenovo", "family": "lenovo/tianxi", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-05-19", "context_length": null, "max_output_tokens": null, "parameters": "1.7B", "architecture": "Described at the 2026-05-19 Tianxi AI 4.0 launch as Lenovo's self-developed 1.7 billion parameter multimodal memory model (17\u4ebf\u53c2\u6570\u591a\u6a21\u6001\u8bb0\u5fc6\u6a21\u578b). It turns multi-source inputs into episodic, semantic, and procedural memory for a personal memory graph inside Tianxi Claw. Layer type, training data, and exact modality list are not published.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lenovo's closed on-device memory model for the Tianxi personal agent. Launch coverage of Tianxi AI 4.0 on 2026-05-19 calls it a self-developed 1.7 billion parameter multimodal memory model (\u8054\u60f3\u81ea\u7814\u768417\u4ebf\u53c2\u6570\u591a\u6a21\u6001\u8bb0\u5fc6\u6a21\u578b). It is part of the shipping Tianxi Claw stack as of the 2026-10-15 collection. Confidence is medium: several reports of the event agree, but Lenovo's own short recap does not repeat the size. There is no model card and no public weights.", "what_it_is_for": "Inside Tianxi Claw, the bionic-memory subsystem, the model is the piece Lenovo says remembers the user. Reports say it converts multi-source information into three memory types and learns personal traits into a private memory graph. It is described as fully local. Task execution still uses other, larger models on the device or in the cloud. This dossier is only the 1.7B memory model, not the Tianxi agent, not Qira, and not the unnamed local LLMs Lenovo ships or hosts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leonardo/anime/anime-xl": { "id": "leonardo/anime/anime-xl", "name": "Leonardo Anime XL", "maker": "leonardo", "family": "leonardo/anime", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Leonardo fine-tune for anime, illustration, and CG. Platform record: SD version SDXL_LIGHTNING, training strength MEDIUM. The v2 schema includes a kino-xl and anime-xl model name; this folder uses the platform UUID. Weights were not released.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Leonardo Anime XL is Leonardo.Ai's closed anime and illustration fine-tune, collected 2026-10-15. It launched with Lightning XL on 2024-04-01. Leonardo called it their first anime-specific model in that release. It remains a named platform model. It is not in the current v2 image-guide index.", "what_it_is_for": "The platform description says it excels at anime, illustrative, and CG styles. The help article groups it with Lightning XL for speed and Alchemy, and distinguishes it as the stylized model rather than the photoreal generalist.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leonardo/creative/leonardo-creative": { "id": "leonardo/creative/leonardo-creative", "name": "Leonardo Creative", "maker": "leonardo", "family": "leonardo/creative", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Leonardo fine-tune of Stable Diffusion 2.1, described as a more interpretive alternative to Leonardo Select. Platform record: SD version v2, training strength MEDIUM. No outside author is credited. Weights were not released.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Leonardo Creative is a closed Leonardo.Ai fine-tune of Stable Diffusion 2.1, collected 2026-10-15. The platform text calls it an alternative to the realism-focused SD 2.1 fine-tune, with more creative interpretation. No outside author is credited. Current app availability was not confirmed.", "what_it_is_for": "The platform description is the job statement: an alternative SD 2.1 fine-tune that interprets prompts more loosely than the realism tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leonardo/diffusion/diffusion-xl": { "id": "leonardo/diffusion/diffusion-xl", "name": "Leonardo Diffusion XL", "maker": "leonardo", "family": "leonardo/diffusion", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Leonardo fine-tune described as the next phase of the core Leonardo model. Platform record: SD version SDXL_0_9, training strength MEDIUM. This is not the separate SD 2.1 model named Leonardo Diffusion, which Leonardo credited to cac0e.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Leonardo Diffusion XL is Leonardo.Ai's closed generalist fine-tune of SDXL 0.9, collected 2026-10-15. The platform calls it the next phase of the core Leonardo model and says it works from short prompts. It still has an official UUID. It is not the older Leonardo Diffusion model, which the company credited to an outside author.", "what_it_is_for": "Leonardo's blurb: the next phase of the core Leonardo model, with strong output from short prompts. It was marketed as the broad default among the XL fine-tunes, next to Vision XL for photos and Kino XL for cinema.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leonardo/kino/kino-xl": { "id": "leonardo/kino/kino-xl", "name": "Leonardo Kino XL", "maker": "leonardo", "family": "leonardo/kino", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Leonardo cinematic fine-tune. Platform record: SD version SDXL_0_9, training strength MEDIUM. Weights were not released. A v2 schema name kino-xl exists.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Leonardo Kino XL is Leonardo.Ai's closed cinematic SDXL 0.9 fine-tune, collected 2026-10-15. The platform description says it favors cinematic frames and wider aspect ratios and does not need a negative prompt. It is still a documented platform model id. It has no page in the current v2 model-guide index.", "what_it_is_for": "Leonardo's own model blurb is cinematic output, especially wide frames, without requiring a negative prompt. The Alchemy recipe uses it as the sample SDXL model with the CINEMATIC preset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leonardo/lightning/lightning-xl": { "id": "leonardo/lightning/lightning-xl", "name": "Leonardo Lightning XL", "maker": "leonardo", "family": "leonardo/lightning", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Leonardo fine-tune on the SDXL Lightning line. Platform record: core model SD, SD version SDXL_LIGHTNING, training strength MEDIUM. Weights were not released. Parameter count of the fine-tune was not published.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Leonardo Lightning XL is a closed Leonardo.Ai fine-tune for fast SDXL-class images, collected 2026-10-15. Leonardo introduced it with Anime XL as the Lightning XL pair. It is still named in the commonly used API values and the elements-compatibility table. It is not in the current v2 model-guide index, so app availability is less certain than for Lucid or Phoenix.", "what_it_is_for": "Leonardo's help article calls it the generalist photorealistic Lightning model, \"great at everything from photorealism to painterly styles,\" and says the pipeline is about 2 to 3 times faster. The platform description matches that wording.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leonardo/lucid/lucid-origin": { "id": "leonardo/lucid/lucid-origin", "name": "Lucid Origin", "maker": "leonardo", "family": "leonardo/lucid", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed first-party image model. Leonardo says it was trained for Full HD renders. No parameter count or architecture card was published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucid Origin is Leonardo.Ai's closed image model, collected 2026-10-15. Leonardo shipped it on 2025-08-04 as a Full HD generalist for prompt adherence, text, and mixed styles. It is active on the web app, mobile apps, and the production API.", "what_it_is_for": "Leonardo's launch post says Origin is the adaptable model: graphic design, Full HD renders, text, and specific creative direction. The same post says Lucid Realism is the cinematic stills model meant to feed video. The models index calls Origin the model for prompt adherence and text at HD.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leonardo/lucid/lucid-realism": { "id": "leonardo/lucid/lucid-realism", "name": "Lucid Realism", "maker": "leonardo", "family": "leonardo/lucid", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed first-party image model aimed at photographic and cinematic stills. No parameter count or architecture card was published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lucid Realism is Leonardo.Ai's closed photographic image model, collected 2026-10-15. It is active. Leonardo's own later writing treats it as the cinematic sibling of Lucid Origin, built for stills that go into video. The release date below is the official Facebook group post, not a news article.", "what_it_is_for": "The models index says it is best for pairing with video and for cinematic shots. The Lucid Origin launch post says Realism was trained with image-to-video in mind: natural light, subtle expression, mood. The official Facebook post on 2025-06-03 called it the model for cinematic visuals and said to use it with Motion 2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leonardo/motion/motion-2-0": { "id": "leonardo/motion/motion-2-0", "name": "Motion 2.0", "maker": "leonardo", "family": "leonardo/motion", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-04", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Leonardo's first-party text-to-video and image-to-video model. The Video help article says the higher token cost is because Leonardo offers the 14B model. No architecture card or paper was published. Clips described as 5 seconds in that help article and on Replicate.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Motion 2.0 is Leonardo.Ai's closed first-party video model, collected 2026-10-15. It is active. Leonardo's official Facebook group said it was here on 2025-04-04. It makes short clips from text or a start image. The help center calls it a 14B model and describes 5 second outputs. Motion 2.0 Fast is a separate API model.", "what_it_is_for": "The models index says high-quality video with fine-tuned controls. The help center says paid users can generate 5 second clips from an image or a text prompt, with motion-control presets and a higher frame rate than the original Motion feature. A 2025-06-19 help note says existing 480p Motion 2.0 videos can be upscaled to 720p.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leonardo/motion/motion-2-0-fast": { "id": "leonardo/motion/motion-2-0-fast", "name": "Motion 2.0 Fast", "maker": "leonardo", "family": "leonardo/motion", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Faster Leonardo video model with its own API id, motion_2.0-fast. The public docs match Motion 2.0's controls and pixel presets. No parameter count. Not documented as a mode flag on motion_2.0.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Motion 2.0 Fast is a separate Leonardo.Ai video model, collected 2026-10-15. It is active. The API id is `motion_2.0-fast`, with its own guide. The models index describes it as a fast, high-quality preview. No official release date was found.", "what_it_is_for": "Leonardo lists it beside Motion 2.0 for a faster video preview. The API guide uses the same start-frame, motion-control, and resolution presets as Motion 2.0. It is not a `mode` value on `motion_2.0`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leonardo/phoenix/phoenix-0-9": { "id": "leonardo/phoenix/phoenix-0-9", "name": "Phoenix 0.9", "maker": "leonardo", "family": "leonardo/phoenix", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Preview checkpoint of Leonardo's first foundational image model. The June 2024 announcement called it Phoenix and said image guidance, Elements, and PhotoReal were not available yet. The current v2 guide gives it the same parameters as Phoenix 1.0.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phoenix 0.9 is the preview of Leonardo.Ai's first foundational image model, collected 2026-10-15. Leonardo announced that preview on 2024-06-12 under the name Phoenix. After Phoenix 1.0 shipped on 2024-12-18, this checkpoint stayed in the API as phoenix-v0.9. It is still active. The product copy still calls it a preview.", "what_it_is_for": "The platform description is \"Preview of our foundational model. Extreme prompt adherence and text rendering.\" The June 2024 announcement stressed prompt adherence, text in images, and prompt editing, and warned that image guidance, Elements, and PhotoReal were not ready yet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leonardo/phoenix/phoenix-1-0": { "id": "leonardo/phoenix/phoenix-1-0", "name": "Phoenix 1.0", "maker": "leonardo", "family": "leonardo/phoenix", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Leonardo's first foundational image model, built in-house rather than as a Stable Diffusion fine-tune. No parameter count was published. v2 modes are FAST, QUALITY, and ULTRA.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phoenix 1.0 is Leonardo.Ai's closed foundational image model, collected 2026-10-15. Leonardo calls it their first model built in-house, not a Stable Diffusion fine-tune. The full release was announced on 2024-12-18, after the June 2024 preview that is now Phoenix 0.9. It is still active on the app and the API.", "what_it_is_for": "The product page and the December 2024 launch say Phoenix is for prompt adherence, readable text in images, and plain-language edits. A December 2025 Leonardo guide still points Phoenix 1.0 at strict layouts, stickers, and game assets. The models index repeats prompt adherence and text rendering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leonardo/select/leonardo-select": { "id": "leonardo/select/leonardo-select", "name": "Leonardo Select", "maker": "leonardo", "family": "leonardo/select", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Leonardo fine-tune of Stable Diffusion 2.1, described as aimed at realism. Platform record: SD version v2, training strength MEDIUM. No outside author is credited. Weights were not released.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Leonardo Select is a closed Leonardo.Ai fine-tune of Stable Diffusion 2.1, collected 2026-10-15. The platform text says it can reach a high level of realism. No outside author is credited. It is an early house model, not a current v2 guide model.", "what_it_is_for": "The only job statement found is the platform description: a powerful SD 2.1 fine-tune for realism.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leonardo/signature/leonardo-signature": { "id": "leonardo/signature/leonardo-signature", "name": "Leonardo Signature", "maker": "leonardo", "family": "leonardo/signature", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Leonardo calls this the core platform model: a diverse fine-tune of Stable Diffusion 1.5. Platform record: SD version v1_5, training strength MEDIUM. Weights were not released. No outside author is credited, unlike several SD 1.5 models on the same list.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Leonardo Signature is an early closed Leonardo.Ai fine-tune of Stable Diffusion 1.5, collected 2026-10-15. The platform text calls it the core model of the Leonardo platform. No outside author is named. It was not in the current v2 guide index, and a 2026 deprecation notice does not mention it. Treat current app availability as unverified.", "what_it_is_for": "The platform description says it is an extremely powerful and diverse fine-tune for a wide range of uses, and calls it the core model of the platform. That is the job Leonardo stated. Later house models (Diffusion XL, then Phoenix and Lucid) took the generalist role.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leonardo/vision/vision-xl": { "id": "leonardo/vision/vision-xl", "name": "Leonardo Vision XL", "maker": "leonardo", "family": "leonardo/vision", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Leonardo fine-tune aimed at realism and photography. Platform record: SD version SDXL_0_9, training strength MEDIUM. Weights were not released.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Leonardo Vision XL is Leonardo.Ai's closed realism and photography fine-tune of SDXL 0.9, collected 2026-10-15. Leonardo's platform text says it wants longer prompts. It is still listed with an official model UUID. It has no current v2 guide of its own, so whether the app still exposes it was not confirmed.", "what_it_is_for": "The platform description is a versatile model for realism and photography, with better results from longer prompts. Marketing copy on an archived Leonardo homepage used the same sentence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/nelf/asr-subtitles-v1": { "id": "leuven/nelf/asr-subtitles-v1", "name": "NeLF ASR Subtitles v1", "maker": "leuven", "family": "leuven/nelf", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First NeLF dual-decoder ASR and subtitle model. The model card says it was trained on 2000 hours of Flemish broadcast subtitled speech and emits verbatim and subtitle text from separate decoders. The org page says 1000 hours. Version label September 2023. The fetched training config header names model/ASR_subtitles_v1/train_config.yaml. It uses a Conformer encoder with 12 blocks, output size 256, 4 heads, linear units 2048, conv2d input; a transformer ASR decoder with 6 blocks and 4 heads; a Conformer subtitle encoder with 2 blocks, output 256, 4 heads; a multi-transformer subtitle decoder with 6 blocks and 4 heads. Both decoders are conditioned. Adam lr 0.004, warmup 25000, max_epoch 135, accum_grad 64, batch_size 16, distributed false. Hub cardData has language nl and no license tag. The ESPnet GitHub README describes the project models as Creative Commons Non-Commercial. That project-level wording is not copied as a Hub license tag. Hub createdAt 2023-09-25. Not present in the org WER table.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model NeLF ASR Subtitles v1 (asr-subtitles-v1). Status: active. Collected 2026-10-15.", "what_it_is_for": "Flemish Dutch speech to both a verbatim transcript and a subtitle transcript, from separate decoders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/nelf/asr-subtitles-v2": { "id": "leuven/nelf/asr-subtitles-v2", "name": "NeLF ASR Subtitles v2", "maker": "leuven", "family": "leuven/nelf", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Second NeLF ASR and subtitle model. Org page: improved architecture, 14000 hours of Flemish broadcast subtitled speech, dual decoders for verbatim text with annotation tags and for cleaned subtitles. Org leaderboard: 180M. The published ESPnet train.yaml (header config path model/ASR_subtitles_v2/train.yaml) uses a Conformer encoder with 12 blocks, output size 512, 8 heads, linear units 2048, conv2d6 input, macaron, swish, CNN kernel 31; a multi-transformer ASR decoder with 6 blocks, 8 heads, linear units 2048; a transformer subtitle encoder with 6 blocks, output 512, 8 heads; a multi-transformer subtitle decoder with 6 blocks and 8 heads. Both decoders are conditioned. Features are fbank plus pitch. Optimizer Adam, lr 0.003, warmup 100000 steps, max_epoch 500, accum_grad 1, batch_size 1024, num_iters_per_epoch 10000, dist_world_size 16. The validation dump path includes nelf_12000h. Shared BPE path model/ASR_subtitles_v2/bpe.model. Vocab size was not counted. The repo also contains old_model/, which is not a separate product. License tag cc-by-nc-4.0.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model NeLF ASR Subtitles v2 (asr-subtitles-v2). Status: active. Collected 2026-10-15.", "what_it_is_for": "Flemish Dutch speech to a verbatim transcript with annotation tags and, through a second decoder, a cleaned subtitle transcript.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/nelf/asr-subtitles-v2-small": { "id": "leuven/nelf/asr-subtitles-v2-small", "name": "NeLF ASR Subtitles v2 small", "maker": "leuven", "family": "leuven/nelf", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Narrower dual-decoder Flemish ASR and subtitle model. The model card calls it the base variant and says 70M parameters. The org page calls it the smaller variant of ASR_subtitles_v2 and also says 70M. The published train.yaml uses a Conformer encoder with 12 blocks, output size 256, 4 heads, linear units 2048, conv2d input; a multi-transformer ASR decoder with 6 blocks and 4 heads; a transformer subtitle encoder with 6 blocks, output 256, 4 heads; a multi-transformer subtitle decoder with 6 blocks and 4 heads. Both decoders are conditioned. Adam lr 0.003, warmup 100000, max_epoch 150, accum_grad 2, batch_size 512, num_iters_per_epoch null, dist_world_size 16. Output dir name is st_train_subtitling_chained_PL_C8_new_combined_nelf_all. The card repeats the 14000-hour Flemish broadcast description used for v2. License cc-by-nc-4.0. Hub createdAt 2024-07-10.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model NeLF ASR Subtitles v2 small (asr-subtitles-v2-small). Status: active. Collected 2026-10-15.", "what_it_is_for": "The same verbatim-plus-subtitle job as ASR Subtitles v2, at the 70M size named on the org leaderboard.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/nelf/asr-verbatim-v1": { "id": "leuven/nelf/asr-verbatim-v1", "name": "NeLF ASR Verbatim v1", "maker": "leuven", "family": "leuven/nelf", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Verbatim-only conversion of the first NeLF ASR and subtitle model. The model card says the model was trained on 2000 hours of Flemish broadcast subtitled speech and then converted to a simple ASR model that outputs only a verbatim transcription. The org page says 1000 hours and that it was converted to a verbatim-only ASR model. Version label September 2023. Hub createdAt 2023-09-25. Hub cardData has no license tag. The training yaml for this repo was not fetched. Not present in the org WER table. No parameter count was published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model NeLF ASR Verbatim v1 (asr-verbatim-v1). Status: active. Collected 2026-10-15.", "what_it_is_for": "Flemish Dutch speech to a verbatim transcript only. Subtitle decoding stays on the dual-decoder v1 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/nelf/nelf-s2t-pytorch": { "id": "leuven/nelf/nelf-s2t-pytorch", "name": "NeLF S2T PyTorch", "maker": "leuven", "family": "leuven/nelf", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Third NeLF Flemish Dutch ASR and subtitle model, and the one the org page recommends. The card says it is a fine-tune of the authors' own ASR_subtitles_v2 with Kaldi features removed, reimplemented in pure PyTorch. Dual decoders: verbatim text with annotation tags, and cleaned subtitles. Card: 180M parameters and 2-6 GB GPU RAM for inference. Training data on the card: 300 hours of verbatim CGN Flemish with 3-fold noise augmentation, 700 hours of Netherlands Dutch CGN, 14000 hours of weakly supervised subtitled Flemish broadcast, plus contextualised verbatim pseudo-labels conditioned on the subtitles. The repo also ships Silero VAD files. That VAD is a bundled third-party component and is not a separate KU Leuven model. Hub files are gated auto (contact-info acceptance). The published train.yaml in this repo was not parsed, so ESPnet block counts from ASR_subtitles_v2 are not copied here.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model NeLF S2T PyTorch (nelf-s2t-pytorch). Status: active. Collected 2026-10-15.", "what_it_is_for": "Joint verbatim transcription, including annotation tags, and cleaned subtitle text for Flemish Dutch. Two decoders emit the two forms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/robbert/robbert-2022": { "id": "leuven/robbert/robbert-2022", "name": "RobBERT-2022", "maker": "leuven", "family": "leuven/robbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": 514, "max_output_tokens": null, "parameters": "118937368", "architecture": "Further pretraining of pdelobelle/robbert-v2-dutch-base on OSCAR 2022. config.json _name_or_path is pdelobelle/robbert-v2-dutch-base. RobertaForMaskedLM, 12 layers, 12 heads, hidden 768, intermediate 3072, GELU, max_position_embeddings 514, vocab_size 42774 (v2 is 40000). Safetensors total 118937368. The card says the original RobBERT was released in January 2020 and that v2 can still beat this 2022 model on tasks that do not need post-2019 information.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model RobBERT-2022 (robbert-2022). Status: active. Collected 2026-10-15.", "what_it_is_for": "Dutch fill-mask and a fine-tuning base for classification, regression, and tagging, aimed at language use through 2022.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/robbert/robbert-2023-base": { "id": "leuven/robbert/robbert-2023-base", "name": "RobBERT-2023 base", "maker": "leuven", "family": "leuven/robbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": 514, "max_output_tokens": null, "parameters": "124494162", "architecture": "Dutch RoBERTa-base with a new tokenizer, trained on OSCAR 2023 using the authors' Tik-to-Tok conversion (arXiv 2310.03477). config.json _name_or_path is FremyCompany/roberta-base-nl-oscar23. RobertaForMaskedLM, 12 layers, 12 heads, hidden 768, intermediate 3072, GELU, max_position_embeddings 514, vocab 50000, float32. Safetensors total 124494162. The releasing org is DTAI-KULeuven. The 2023 card credits KU Leuven, UGent, and TU Berlin (Fran\u00e7ois Remy). pieter.ai lists the 2023 size cell as 117M / 355M, which does not match this file total. This is not a continued pretrain of robbert-2022.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model RobBERT-2023 base (robbert-2023-base). Status: active. Collected 2026-10-15.", "what_it_is_for": "A general Dutch encoder for fill-mask and fine-tuning when the task depends on words or events later than the 2019 and 2022 OSCAR trains.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/robbert/robbert-2023-large": { "id": "leuven/robbert/robbert-2023-large", "name": "RobBERT-2023 large", "maker": "leuven", "family": "leuven/robbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": 514, "max_output_tokens": null, "parameters": "355140946", "architecture": "First Dutch RobBERT large model. Card: 355M parameters, about three times robbert-2022-base. Safetensors total 355140946. Hugging Face sidebar says 0.4B. config.json _name_or_path FremyCompany/roberta-large-nl-oscar23. RobertaForMaskedLM, 24 layers, 16 heads, hidden 1024, intermediate 4096, GELU, max_position_embeddings 514, vocab 50000, float32. New tokenizer on OSCAR 2023 via Tik-to-Tok. Credits on the card: KU Leuven, UGent, and TU Berlin. Releasing org DTAI-KULeuven.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model RobBERT-2023 large (robbert-2023-large). Status: active. Collected 2026-10-15.", "what_it_is_for": "The larger 2023 Dutch encoder for fill-mask and fine-tuning. The card says it is the one that surpasses BERTje by 18.6 DUMB points.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/robbert/robbert-2026": { "id": "leuven/robbert/robbert-2026", "name": "RobBERT-2026", "maker": "leuven", "family": "leuven/robbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-09-08", "context_length": 512, "max_output_tokens": null, "parameters": "275598928", "architecture": "NeoBERT-base encoder trained from scratch, not a RoBERTa continue-train and not a BERT fine-tune. Card: 28 layers, hidden 768, 12 heads, SwiGLU, RMSNorm, RoPE, 275M parameters, MLM with 20% masking, byte-level BPE vocab 50000. config.json matches 28 layers, 12 heads, hidden 768, intermediate 3072, vocab 50000, dtype bfloat16, max_length 8192. Safetensors total 275598928 (F32 524288 + BF16 275074640). Hub sidebar says 0.3B. Pretrained sequence length is 512. The RoPE table is precomputed to 8192 and positions past 512 are untrained. Attention mask is a dense [batch, heads, L, L] tensor. trust_remote_code is required. xformers SwiGLU is optional; the card says the pure-torch fallback is bit-identical. config pretrained_model_name_or_path is google-bert/bert-base-uncased; the card says the model was trained from scratch, so that field is treated as a Transformers init artifact.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model RobBERT-2026 (robbert-2026). Status: active. Collected 2026-10-15.", "what_it_is_for": "A general Dutch masked LM and fine-tuning encoder for settings that need a permissibly licensed or synthetic pretraining corpus.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/robbert/robbert-2026-retrieval": { "id": "leuven/robbert/robbert-2026-retrieval", "name": "RobBERT-2026 Retrieval", "maker": "leuven", "family": "leuven/robbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-sa-4.0", "release_date": "2026-09-09", "context_length": 512, "max_output_tokens": null, "parameters": "237148928", "architecture": "Contrastive fine-tune of pdelobelle/robbert-2026 for dense retrieval. Sentence Transformers, CLS pooling, cosine similarity, query: and document: prefixes, max sequence length 512, one epoch. The card still describes the base as NeoBERT-base 275M. This repo's safetensors total is 237148928 BF16. The Hub sidebar says 0.2B. The card's BEIR table header also says 275M. The 38450000-parameter gap versus the base file total is not explained on the card. trust_remote_code is required. License is CC-BY-SA-4.0 because the NFI fine-tuning sets are ShareAlike. The base model stays MIT.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model RobBERT-2026 Retrieval (robbert-2026-retrieval). Status: active. Collected 2026-10-15.", "what_it_is_for": "Zero-shot Dutch dense retrieval. The card requires query: and document: prefixes, CLS pooling, and cosine similarity.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/robbert/robbert-v2": { "id": "leuven/robbert/robbert-v2", "name": "RobBERT v2", "maker": "leuven", "family": "leuven/robbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": 514, "max_output_tokens": null, "parameters": "116804162", "architecture": "Dutch RoBERTa-base masked language model (RobertaForMaskedLM). config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, GELU, absolute positions, vocab 40000, max_position_embeddings 514, dropout 0.1. The card states 117M trainable parameters. Pretrained with Fairseq under the RoBERTa MLM regime (no NSP) on the Dutch OSCAR section, 39GB and 6.6 billion words, for two epochs (over 16k batches). Adam with learning rate 1e-6, 1000-iteration warmup, beta1 0.9, beta2 0.98, weight decay 0.1, dropout 0.1, and a fixed batch of 8192 sentences. Safetensors total is 116804162 (I64 514 + F32 116803648). The pieter.ai size table lists this column as 110M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model RobBERT v2 (robbert-v2). Status: active. Collected 2026-10-15.", "what_it_is_for": "The card describes a general Dutch encoder for text classification, regression, and token tagging after fine-tuning, plus zero-shot fill-mask. The website tells readers to load pdelobelle/robbert-v2-dutch-base as the original RobBERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/robbertje/robbertje-1-gb-bort": { "id": "leuven/robbertje/robbertje-1-gb-bort", "name": "RobBERTje 1GB BORT", "maker": "leuven", "family": "leuven/robbertje", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2021-07-02", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "Smaller RobBERTje distillation. Card: 46M, 8 attention heads instead of 12, 4 layers instead of 6, trained on 1GB. config.json: RobertaForMaskedLM, 4 layers, 8 heads, hidden 768, intermediate_size 768, GELU, vocab 40000, max_position_embeddings 514, dropout 0.1. No safetensors total in the Hub API. pieter.ai's RobBERTje size cell is the range 40M-74M, which covers this checkpoint and the 74M siblings. Hub createdAt 2022-03-02. lastModified 2023-12-05.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model RobBERTje 1GB BORT (robbertje-1-gb-bort). Status: active. Collected 2026-10-15.", "what_it_is_for": "The compact end of the RobBERTje line for Dutch fine-tuning when the 74M distillations are still too large.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/robbertje/robbertje-1-gb-merged": { "id": "leuven/robbertje/robbertje-1-gb-merged", "name": "RobBERTje 1GB merged", "maker": "leuven", "family": "leuven/robbertje", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2021-07-02", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "Distilled Dutch RobBERT. Same non-shuffled OSCAR setup as the non-shuffled release, except sequential sentences from the same document are merged with probability 0.5. Card: 74M parameters, 1GB. config.json: RobertaForMaskedLM, 6 layers, 12 heads, hidden 768, intermediate 3072, GELU, vocab 40000, max_position_embeddings 514, dropout 0.1. No safetensors total in the Hub API. Later RobBERT cards call this the distilled model that is about half the size and four times faster. pieter.ai lists RobBERTje sentiment 92.9, which matches this row rather than the shuffled row.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model RobBERTje 1GB merged (robbertje-1-gb-merged). Status: active. Collected 2026-10-15.", "what_it_is_for": "The RobBERTje variant the yearly cards point to when a deployment wants a smaller, faster Dutch RobBERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/robbertje/robbertje-1-gb-non-shuffled": { "id": "leuven/robbertje/robbertje-1-gb-non-shuffled", "name": "RobBERTje 1GB non-shuffled", "maker": "leuven", "family": "leuven/robbertje", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2021-07-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Distilled Dutch RobBERT. The RobBERTje card lists this checkpoint at 74M parameters, trained on 1GB of the non-shuffled OSCAR variant, without operations that preserve document order. The same card says BORT has 8 heads instead of 12 and 4 layers instead of 6, so this non-BORT variant is the 6-layer, 12-head setting. Its own config.json was not opened. The merged sibling config.json is hidden 768, intermediate 3072, vocab 40000, max_position_embeddings 514. Hub API has pytorch_model.bin and no safetensors total. Teacher is RobBERT. pieter.ai's RobBERTje size cell is the range 40M-74M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model RobBERTje 1GB non-shuffled (robbertje-1-gb-non-shuffled). Status: active. Collected 2026-10-15.", "what_it_is_for": "A smaller Dutch encoder distilled from RobBERT for classification and tagging when the full teacher is unnecessary.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "leuven/robbertje/robbertje-1-gb-shuffled": { "id": "leuven/robbertje/robbertje-1-gb-shuffled", "name": "RobBERTje 1GB shuffled", "maker": "leuven", "family": "leuven/robbertje", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2021-07-02", "context_length": null, "max_output_tokens": null, "parameters": "74276930", "architecture": "Distilled Dutch RobBERT trained on the publicly shuffled OSCAR corpus, 1GB. The card lists 74M parameters. Safetensors total is 74276930 (I64 514 + F32 74276416). The card's BORT row says BORT has 8 heads instead of 12 and 4 layers instead of 6, so this variant is the 6-layer, 12-head setting. This repo's config.json was not opened. Hub createdAt 2022-03-02. License MIT. Teacher is RobBERT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KU Leuven model RobBERTje 1GB shuffled (robbertje-1-gb-shuffled). Status: active. Collected 2026-10-15.", "what_it_is_for": "A smaller Dutch RobBERT for fine-tuning, trained on shuffled OSCAR rather than the non-shuffled dump.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone/exaone-1-0": { "id": "lg/exaone/exaone-1-0", "name": "EXAONE 1.0", "maker": "lg", "family": "lg/exaone", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2021-12-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed multimodal model announced at the first LG AI Talk Concert. LG said development since June 2021 had finished 1.3B, 13B, 39B, and 175B parameter models, and that a 300B-parameter model was still training. Press descriptions of a finished 300B model disagree with that sentence. Bilingual Korean and English. Language and image, including text-to-image generation in the LG release.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE 1.0 LG AI Research released this as exaone. Status here is retired. Collected 2026-10-15.", "what_it_is_for": "In-house expert multimodal system for LG affiliates, including chat and materials-related research. Not a public model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone/exaone-2-0": { "id": "lg/exaone/exaone-2-0", "name": "EXAONE 2.0", "maker": "lg", "family": "lg/exaone", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-07-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed bilingual multimodal model. LG said customers could choose model size and type (language, vision, or multimodal) and the language. Parameter counts were not published. Trained with about 45 million professional documents and 350 million images. LG reported 25% less inference time and 70% less memory than EXAONE 1.0 for the language model, and an 83% inference-time cut for the multimodal model.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE 2.0 LG AI Research released this as exaone. Status here is retired. Collected 2026-10-15.", "what_it_is_for": "Professional multimodal model for customized enterprise use, later wrapped by Universe, Discovery, and Atelier. Those three names are platforms, not separate foundation models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone/exaone-3-0-7-8b-instruct": { "id": "lg/exaone/exaone-3-0-7-8b-instruct", "name": "EXAONE 3.0 7.8B Instruct", "maker": "lg", "family": "lg/exaone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONE AI Model License Agreement 1.1 - NC. Research use, including non-commercial competitions. Commercial use is not granted on the public license.", "release_date": "2024-08-07", "context_length": 4096, "max_output_tokens": null, "parameters": "7.8B", "architecture": "Decoder-only Transformer, pre-norm, SwiGLU, GQA. 32 layers, d_model 4096, 32 heads, 8 KV heads, head size 128, feedforward 14336, RoPE theta 500000, BBPE vocab 102400, untied embeddings. Pre-trained on 8T tokens, then SFT and DPO. Max sequence length 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE 3.0 7.8B Instruct LG AI Research released this as exaone. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Bilingual instruction-tuned research model. LG positioned the 7.8B size as the versatile open checkpoint inside a larger closed 3.0 lineup.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone/exaone-3-5-2-4b-instruct": { "id": "lg/exaone/exaone-3-5-2-4b-instruct", "name": "EXAONE 3.5 2.4B Instruct", "maker": "lg", "family": "lg/exaone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONE AI Model License Agreement 1.1 - NC. Research use, including non-commercial competitions. Commercial use is not granted on the public license.", "release_date": "2024-12-09", "context_length": 32768, "max_output_tokens": null, "parameters": "2.4B", "architecture": "Decoder-only Transformer, pre-norm, SwiGLU, GQA, same family as EXAONE 3.0. 30 layers, d_model 2560, 32 query heads, 8 KV heads, head size 80, feedforward 7168, RoPE theta 1,000,000, BBPE vocab 102400, tied embeddings True. Context extended from 4096 to 32768 by long-context fine-tuning. Pre-training tokens 6.5T.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE 3.5 2.4B Instruct LG AI Research released this as exaone. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Instruction-tuned bilingual model for real-world instructions and long context, in an on-device, general, or larger size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone/exaone-3-5-32b-instruct": { "id": "lg/exaone/exaone-3-5-32b-instruct", "name": "EXAONE 3.5 32B Instruct", "maker": "lg", "family": "lg/exaone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONE AI Model License Agreement 1.1 - NC. Research use, including non-commercial competitions. Commercial use is not granted on the public license.", "release_date": "2024-12-09", "context_length": 32768, "max_output_tokens": null, "parameters": "32B", "architecture": "Decoder-only Transformer, pre-norm, SwiGLU, GQA, same family as EXAONE 3.0. 64 layers, d_model 5120, 40 query heads, 8 KV heads, head size 128, feedforward 27392, RoPE theta 1,000,000, BBPE vocab 102400, tied embeddings False. Context extended from 4096 to 32768 by long-context fine-tuning. Pre-training tokens 6.5T.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE 3.5 32B Instruct LG AI Research released this as exaone. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Instruction-tuned bilingual model for real-world instructions and long context, in an on-device, general, or larger size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone/exaone-3-5-7-8b-instruct": { "id": "lg/exaone/exaone-3-5-7-8b-instruct", "name": "EXAONE 3.5 7.8B Instruct", "maker": "lg", "family": "lg/exaone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONE AI Model License Agreement 1.1 - NC. Research use, including non-commercial competitions. Commercial use is not granted on the public license.", "release_date": "2024-12-09", "context_length": 32768, "max_output_tokens": null, "parameters": "7.8B", "architecture": "Decoder-only Transformer, pre-norm, SwiGLU, GQA, same family as EXAONE 3.0. 32 layers, d_model 4096, 32 query heads, 8 KV heads, head size 128, feedforward 14336, RoPE theta 1,000,000, BBPE vocab 102400, tied embeddings False. Context extended from 4096 to 32768 by long-context fine-tuning. Pre-training tokens 9T.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE 3.5 7.8B Instruct LG AI Research released this as exaone. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Instruction-tuned bilingual model for real-world instructions and long context, in an on-device, general, or larger size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone/exaone-4-0-1-2b": { "id": "lg/exaone/exaone-4-0-1-2b", "name": "EXAONE 4.0 1.2B", "maker": "lg", "family": "lg/exaone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONE AI Model License Agreement 1.2 - NC. Research and educational use. The license bars use to develop models that compete with EXAONE. Commercial use is not granted on the public license.", "release_date": "2025-07-15", "context_length": 65536, "max_output_tokens": null, "parameters": "1.28B", "architecture": "On-device hybrid model. 30 layers, d_model 2048, global attention only (not the 32B hybrid mix), QK-Reorder-LN, SwiGLU, GQA with 32 heads and 8 KV heads, head size 64, feedforward 4096. RoPE theta 1,000,000. BBPE vocab 102400. Tied embeddings. Max sequence length 65,536. The paper's comparison table lists model size 1.28B. LG's blog calls the product the 1.2B on-device model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE 4.0 1.2B LG AI Research released this as exaone. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Small hybrid model for on-device use. LG said it is about half the size of the previous on-device EXAONE and still supports reasoning and tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone/exaone-4-0-32b": { "id": "lg/exaone/exaone-4-0-32b", "name": "EXAONE 4.0 32B", "maker": "lg", "family": "lg/exaone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONE AI Model License Agreement 1.2 - NC. Research and educational use. The license bars use to develop models that compete with EXAONE. Commercial use is not granted on the public license.", "release_date": "2025-07-15", "context_length": 131072, "max_output_tokens": null, "parameters": "32B", "architecture": "Hybrid reasoning and non-reasoning decoder. 64 layers, d_model 5120, SwiGLU, QK-Reorder-LN, GQA with 40 heads and 8 KV heads, head size 128, feedforward 27392. Hybrid attention is local sliding-window to global full attention at 3:1. Global attention does not use RoPE. RoPE theta 1,000,000 elsewhere. BBPE vocab 102400. Untied embeddings. Max sequence length 131,072. The paper says pretraining started at 4K, then 32K, then 128K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE 4.0 32B LG AI Research released this as exaone. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Hybrid model that switches between a normal answer and a thinking mode, with tool calling, for professional workloads.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone/exaone-4-5-33b": { "id": "lg/exaone/exaone-4-5-33b", "name": "EXAONE 4.5 33B", "maker": "lg", "family": "lg/exaone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONE AI Model License Agreement 1.2 - NC. Research and educational use. The license bars use to develop models that compete with EXAONE. Commercial use is not granted on the public license.", "release_date": "2026-04-09", "context_length": 262144, "max_output_tokens": null, "parameters": "33B total, including a 1.2B vision encoder on an EXAONE 4.0 32B language model", "architecture": "Dense vision-language model. A 1.2B vision encoder trained by LG is integrated into the EXAONE 4.0 32B language model. Native multimodal pretraining. Hybrid reasoning, on by default, unlike EXAONE 4.0. Six languages. The maker calls the context 256K. vLLM and llama.cpp examples set 262,144 tokens. Up to 64 images per prompt in the vLLM example.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE 4.5 33B LG AI Research released this as exaone. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Open-weight vision-language model for documents, charts, industrial images, and the same text reasoning stack as later EXAONE models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone-deep/exaone-deep-2-4b": { "id": "lg/exaone-deep/exaone-deep-2-4b", "name": "EXAONE Deep 2.4B", "maker": "lg", "family": "lg/exaone-deep", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONE AI Model License Agreement 1.1 - NC. Research use, including non-commercial competitions. Commercial use is not granted on the public license.", "release_date": "2025-03-18", "context_length": 32768, "max_output_tokens": null, "parameters": "2.4B", "architecture": "Reasoning post-train of exaone-3-5-2-4b-instruct. LG describes SFT, then DPO, then GRPO. The Deep README does not publish a new layer table. Usage sets a 32,768-token context, matching the 3.5 parent. Reasoning text is wrapped in thought tags. The EXAONE 4.0 paper table lists this checkpoint's model size as 2.41B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE Deep 2.4B LG AI Research released this as exaone-deep. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Reasoning model for math, science, and code, built on the matching EXAONE 3.5 Instruct checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone-deep/exaone-deep-32b": { "id": "lg/exaone-deep/exaone-deep-32b", "name": "EXAONE Deep 32B", "maker": "lg", "family": "lg/exaone-deep", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONE AI Model License Agreement 1.1 - NC. Research use, including non-commercial competitions. Commercial use is not granted on the public license.", "release_date": "2025-03-18", "context_length": 32768, "max_output_tokens": null, "parameters": "32B", "architecture": "Reasoning post-train of exaone-3-5-32b-instruct. LG describes SFT, then DPO, then GRPO. The Deep README does not publish a new layer table. Usage sets a 32,768-token context, matching the 3.5 parent. Reasoning text is wrapped in thought tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE Deep 32B LG AI Research released this as exaone-deep. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Reasoning model for math, science, and code, built on the matching EXAONE 3.5 Instruct checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone-deep/exaone-deep-7-8b": { "id": "lg/exaone-deep/exaone-deep-7-8b", "name": "EXAONE Deep 7.8B", "maker": "lg", "family": "lg/exaone-deep", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONE AI Model License Agreement 1.1 - NC. Research use, including non-commercial competitions. Commercial use is not granted on the public license.", "release_date": "2025-03-18", "context_length": 32768, "max_output_tokens": null, "parameters": "7.8B", "architecture": "Reasoning post-train of exaone-3-5-7-8b-instruct. LG describes SFT, then DPO, then GRPO. The Deep README does not publish a new layer table. Usage sets a 32,768-token context, matching the 3.5 parent. Reasoning text is wrapped in thought tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE Deep 7.8B LG AI Research released this as exaone-deep. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Reasoning model for math, science, and code, built on the matching EXAONE 3.5 Instruct checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone-path/exaone-path-1-0": { "id": "lg/exaone-path/exaone-path-1-0", "name": "EXAONE Path 1.0", "maker": "lg", "family": "lg/exaone-path", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONEPath AI Model License Agreement 1.0 - NC. Research use. Commercial use is not granted on the public license.", "release_date": "2024-08-20", "context_length": null, "max_output_tokens": null, "parameters": "86M", "architecture": "Patch-level pathology encoder. ViT-B, patch size 16, DINO, initialized from ImageNet. Trained on 285,153,903 stain-normalized patches from 34,795 whole-slide images. Hugging Face reports 85.8M parameters. The card says 86 million.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE Path 1.0 LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Patch encoder for gigapixel histopathology slides. Downstream heads, not this file alone, do MSI and mutation tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone-path/exaone-path-1-5": { "id": "lg/exaone-path/exaone-path-1-5", "name": "EXAONE Path 1.5", "maker": "lg", "family": "lg/exaone-path", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONEPath AI Model License Agreement 1.0 - NC. Research use. Commercial use is not granted on the public license.", "release_date": "2025-05-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Whole-slide classifier framework on top of the EXAONE Path 1.0 patch encoder. A ViT aggregator makes a slide embedding, then a linear head. Pretraining aligns slides with mRNA expression. Release date is the Hugging Face repo creation time. No separate announcement date was found.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE Path 1.5 LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Slide-level pathology model for subtyping and mutation tasks, using Path 1.0 patch features plus a gene-expression alignment stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone-path/exaone-path-2-0": { "id": "lg/exaone-path/exaone-path-2-0", "name": "EXAONE Path 2.0", "maker": "lg", "family": "lg/exaone-path", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONEPath AI Model License Agreement 1.0 - NC. Research use. Commercial use is not granted on the public license.", "release_date": "2025-07-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three-stage hierarchical image pyramid transformer for whole-slide images. Slide-level supervision trains patch features, unlike the 1.0 SSL encoder. Trained on 37k WSIs in the paper. The license file in the repo is EXAONEPath 1.0-NC. Weights repo created 2025-07-07. Public blog date is 2025-07-09.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE Path 2.0 LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Pathology foundation model that learns patch features from slide-level labels, aimed at biomarker prediction from whole-slide images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone-path/exaone-path-2-0-rev-egfr": { "id": "lg/exaone-path/exaone-path-2-0-rev-egfr", "name": "EXAONE Path 2.0 rev EGFR", "maker": "lg", "family": "lg/exaone-path", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONEPath AI Model License Agreement 1.0 - NC. Research use. Commercial use is not granted on the public license.", "release_date": "2026-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Revised EGFR mutation scorer. Four stages: tissue patchify, patch encoder, slide encoder, classifier probability. The card does not print an AUROC. Repo created 2026-03-04. Gated. Hugging Face activity showed an update about 2026-09-01.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE Path 2.0 rev EGFR LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Revised slide model that outputs an EGFR mutation probability from a whole-slide image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone-path/exaone-path-2-5": { "id": "lg/exaone-path/exaone-path-2-5", "name": "EXAONE Path 2.5", "maker": "lg", "family": "lg/exaone-path", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONEPath AI Model License Agreement 1.0 - NC. Research use. Commercial use is not granted on the public license.", "release_date": "2025-12-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal pathology model. Aligns whole-slide images with genomic, epigenetic, and transcriptomic profiles using all-pairs SigLIP, fragment-aware RoPE, and internal WSI and RNA encoders. Inference is patch features, then a slide embedding. The paper says it uses fewer parameters and fewer samples than compared foundation models, without a public parameter total. Molecular inputs are not a text chat modality, so they are described here rather than as a text input.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE Path 2.5 LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Pathology foundation model that aligns slide images with genomic, epigenetic, and transcriptomic measurements.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone-path/exaone-path-crc-msi-predictor": { "id": "lg/exaone-path/exaone-path-crc-msi-predictor", "name": "EXAONE Path CRC MSI Predictor", "maker": "lg", "family": "lg/exaone-path", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONEPath AI Model License Agreement 1.0 - NC. Research use. Commercial use is not granted on the public license.", "release_date": "2025-02-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Downstream MSI classifier for colorectal cancer. Tissue tiles at 256 by 256 and 0.5 microns per pixel go through EXAONE Path 1.0, an aggregator, and a linear head. Output is MSS versus MSI-H/L. Hugging Face repo created 2025-02-17. Uses MONAI tags.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE Path CRC MSI Predictor LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Reference model that predicts colorectal MSI status from an H&E slide using EXAONE Path 1.0 features.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone-path/exaone-path-egfr": { "id": "lg/exaone-path/exaone-path-egfr", "name": "EXAONE Path LUAD-EGFR Predictor", "maker": "lg", "family": "lg/exaone-path", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONEPath AI Model License Agreement 1.0 - NC. Research use. Commercial use is not granted on the public license.", "release_date": "2025-07-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "EGFR mutation classifier for lung adenocarcinoma slides. EXAONE Path 2.0 features, an aggregator, and a linear head. Labels are wild-type or mutated. Repo created 2025-07-07.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE Path LUAD-EGFR Predictor LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Reference model that predicts EGFR mutation status from a lung-adenocarcinoma H&E slide using EXAONE Path 2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/exaone-path/exaone-path-msi": { "id": "lg/exaone-path/exaone-path-msi", "name": "EXAONE Path MSI", "maker": "lg", "family": "lg/exaone-path", "status": "active", "confidence": "high", "weights": "open-weights", "license": "EXAONEPath AI Model License Agreement 1.0 - NC. Research use. Commercial use is not granted on the public license.", "release_date": "2025-07-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "WSI MSI classifier that keeps the EXAONE Path pipeline and uses the 1.5 downstream class in the card's example. Hugging Face tags it EXAONEPath-1.5. Repo created 2025-07-07.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EXAONE Path MSI LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Slide-level MSI classifier released with the Path 1.5 line. The card's CRC-MSI number is higher than the generic Path 1.5 row in the same table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/k-exaone/k-exaone-2-0-750b-a37b": { "id": "lg/k-exaone/k-exaone-2-0-750b-a37b", "name": "K-EXAONE 2.0 750B-A37B", "maker": "lg", "family": "lg/k-exaone", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2026-07-31", "context_length": 262144, "max_output_tokens": null, "parameters": "750B total, about 37B active", "architecture": "Upcycled MoE. 78 layers (58 sliding-window, 20 global), sliding window 128, 64 query heads, 8 KV heads, head dim 128. 256 routed experts, 1 shared, 8 activated, expert dim 2048. Two dense layers then 76 MoE layers. Last 16 MoE layers use Clamped SwiGLU. MTP and DSpark speculative decoding, about 3-5x in the README. Ten languages. Context described as 256K. An OpenCode example sets 262,144 context and 32,768 output.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K-EXAONE 2.0 750B-A37B LG AI Research released this as k-exaone. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Frontier-scale open MoE for reasoning, agentic coding, long context, and ten languages. Apache-2.0 is the first EXAONE license in this catalog that allows commercial weight use without a separate grant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lg/k-exaone/k-exaone-236b-a23b": { "id": "lg/k-exaone/k-exaone-236b-a23b", "name": "K-EXAONE 236B-A23B", "maker": "lg", "family": "lg/k-exaone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "K-EXAONE AI Model License Agreement. Custom license, not Apache-2.0. Commercial terms are in the license file, not a public price list.", "release_date": "2025-12-31", "context_length": 262144, "max_output_tokens": null, "parameters": "236B total, 23B active", "architecture": "Fine-grained MoE with hybrid attention, built from the EXAONE 4.0 hybrid design. The K-EXAONE 2.0 report table lists 48 layers (36 sliding-window, 12 global), sliding window 128, 64 query heads, 8 KV heads, head dim 128, 128 experts plus 1 shared, 8 experts activated, expert dim 2048. Multi-token prediction for self-speculative decoding, about 1.5x throughput in the README. SuperBPE vocab about 150k. The maker calls the window 256K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K-EXAONE 236B-A23B LG AI Research released this as k-exaone. Status here is active. Collected 2026-10-15.", "what_it_is_for": "Sovereign open-weight MoE for Korean and five other languages, with reasoning and tool use. It is the first-round model later upcycled into K-EXAONE 2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liege/appa/appa": { "id": "liege/appa/appa", "name": "Appa", "maker": "liege", "family": "liege/appa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the Hugging Face dataset card that ships the checkpoints and data. No LICENSE file was found in the GitHub code repository.", "release_date": "2025-09-30", "context_length": null, "max_output_tokens": null, "parameters": "565000000", "architecture": "Score-based latent data-assimilation model. The arXiv v3 paper (18 Nov 2025) and project page state a 565M-parameter system: a 340M fully convolutional encoder-decoder (about 530x compression, latent shape 23x47x128) and a 225M diffusion transformer on windows of 24 latent states, patch size 2 along time. The released autoencoder config records num_model_params 341131207. Grid is 0.25 degree (721x1440) at 1-hour steps, 6 surface variables and 5 atmospheric variables on 13 pressure levels. An earlier ORBi abstract of the same paper said 1.5B parameters; the released checkpoint sizes match the 565M description, not 1.5B.", "modalities": { "input": [ "atmospheric-observations", "atmospheric-fields" ], "output": [ "atmospheric-fields" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Appa is a score-based weather data-assimilation model from the Science with AI Lab (Gilles Louppe) at the Montefiore Institute, University of Li\u00e8ge. It generates global atmospheric trajectories at 0.25\u00b0 and 1-hour resolution and can be conditioned on observations without retraining. Weights were posted on 30 September 2025. The arXiv v3 paper of 18 November 2025 and the project page still describe it as a research proof of concept, not an operational forecast system. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors say Appa estimates the posterior of plausible global atmospheric trajectories for reanalysis, filtering, and forecasting with one model. An autoencoder compresses ERA5 states. A diffusion transformer generates 24-step latent windows. Observations enter at sampling time through posterior score correction, so a new observation layout does not require a new training run. A public viewer at https://appa.montefiore.uliege.be shows forecasts. It is not a priced API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/ao-karasu/ao-karasu-72b": { "id": "lightblue/ao-karasu/ao-karasu-72b", "name": "ao-Karasu 72B", "maker": "lightblue", "family": "lightblue/ao-karasu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tongyi Qianwen license, per the company press release. The model card does not state a license.", "release_date": "2024-03-19", "context_length": 32768, "max_output_tokens": null, "parameters": "72287920128", "architecture": "Qwen2ForCausalLM, 80 layers, hidden size 8192. Company press says a fine-tune of Qwen/Qwen1.5-72B. The development post says Qwen 1.5 72B Chat and links Qwen/Qwen-72B-Chat.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ao-Karasu 72B is Lightblue's 72B Japanese chat model, announced on 2024-03-19. The Hugging Face repo lightblue/ao-karasu-72B was created on 2024-03-11. Weights are public. Collected on 2026-10-15.", "what_it_is_for": "The press release positions it as a commercially usable on-prem Japanese LLM that beat GPT-3.5 Turbo on Stability AI's Japanese MT-Bench 6-category average, excluding math and coding, as of 2024-03-13. The dev post says it is stronger at reasoning and writing than at hallucination-free facts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/jamba/jamba-v0-1-chat-multilingual": { "id": "lightblue/jamba/jamba-v0-1-chat-multilingual", "name": "Jamba-v0.1 Chat Multilingual", "maker": "lightblue", "family": "lightblue/jamba", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-03-30", "context_length": 262144, "max_output_tokens": null, "parameters": "51570323328", "architecture": "JambaForCausalLM, 32 layers, hidden size 4096, 16 experts, 2 experts per token. The card describes a short QLoRA chat fine-tune of ai21labs/Jamba-v0.1. The published checkpoint is full Jamba size.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jamba-v0.1 Chat Multilingual is Lightblue's public chat fine-tune of AI21 Jamba-v0.1, uploaded on 2024-03-30 under Apache-2.0. The card calls the training a small experiment. Collected on 2026-10-15.", "what_it_is_for": "Test whether a few hours of QLoRA can make Jamba chat in English and other languages. The card says early subjective tests were good enough to release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/jod/jod": { "id": "lightblue/jod/jod", "name": "JOD", "maker": "lightblue", "family": "lightblue/jod", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-24", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "MistralForCausalLM, 32 layers, hidden size 4096. Fine-tune of Open-Orca/Mistral-7B-SlimOrca on Japanese JASTER, OASST, and Dolly.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JOD is Lightblue's October 2023 Japanese chat model, a ChatML fine-tune of Mistral-7B-SlimOrca. The name is the initials of JASTER, OASST, and Dolly. Apache-2.0 weights remain public. Collected on 2026-10-15.", "what_it_is_for": "Japanese chatbot. The card prefers ChatML because the SlimOrca base used it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/karasu/karasu-1-1b": { "id": "lightblue/karasu/karasu-1-1b", "name": "Karasu 1.1B", "maker": "lightblue", "family": "lightblue/karasu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the license field; the card also sets license_name to the Tongyi Qianwen license", "release_date": "2024-01-17", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM, 22 layers, hidden size 2048. Continued pretraining of TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T for 50,004 steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Karasu 1.1B is Lightblue's small Japanese continued-pretrain of a TinyLlama 1.1B checkpoint, uploaded on 2024-01-17. It is still public. Collected on 2026-10-15.", "what_it_is_for": "The card calls it TinyLlama plus Japanese pretraining, using a filtered sample of Japanese OSCAR and mC4, about 3 billion tokens in total.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/karasu/karasu-7b": { "id": "lightblue/karasu/karasu-7b", "name": "Karasu 7B", "maker": "lightblue", "family": "lightblue/karasu", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-12-20", "context_length": 32768, "max_output_tokens": null, "parameters": "7963676672", "architecture": "MistralForCausalLM, 32 layers, hidden size 4096. Continued pretraining of augmxnt/shisa-7b-v1 (itself a Mistral-7B Japanese model).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Karasu 7B is Lightblue's continued-pretrained Japanese and English base checkpoint, released on Hugging Face on 2023-12-20 under Apache-2.0. It is still downloadable. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card describes further Japanese and English pretraining on top of Shisa-7B so later chat models keep both Japanese text and English chat ability. Lightblue's 2024-01-15 release note says the Karasu series continued pretraining and fine-tuning of Shisa on about 7 billion Japanese and English tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/karasu/karasu-7b-chat": { "id": "lightblue/karasu/karasu-7b-chat", "name": "Karasu 7B Chat", "maker": "lightblue", "family": "lightblue/karasu", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-12-21", "context_length": 32768, "max_output_tokens": null, "parameters": "7963676672", "architecture": "MistralForCausalLM, 32 layers, hidden size 4096. Fine-tune of lightblue/karasu-7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Karasu 7B Chat is a Lightblue chat fine-tune of Karasu 7B, published on Hugging Face on 2023-12-21 under Apache-2.0. Weights remain public. Collected on 2026-10-15.", "what_it_is_for": "Chat fine-tune of Karasu 7B on Japanese OASST and ShareGPT chats, selected Ultra-Orca-Boros subsets, and Lightblue's unreleased generated data. Peter Devine's 2023-12-29 note lists this as the checkpoint without the chain-of-thought 'plus' data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/karasu/karasu-7b-chat-plus": { "id": "lightblue/karasu/karasu-7b-chat-plus", "name": "Karasu 7B Chat Plus", "maker": "lightblue", "family": "lightblue/karasu", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-12-22", "context_length": 32768, "max_output_tokens": null, "parameters": "7963676672", "architecture": "MistralForCausalLM, 32 layers, hidden size 4096. Fine-tune of lightblue/karasu-7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Karasu 7B Chat Plus is a Lightblue chat fine-tune of Karasu 7B, published on Hugging Face on 2023-12-22 under Apache-2.0. Weights remain public. Collected on 2026-10-15.", "what_it_is_for": "Same Karasu 7B chat recipe plus Lightblue's unreleased Kujira, question, and category datasets. The 2023-12-29 dev note says the 'plus' suffix marks the chain-of-thought data, which had a small effect.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/karasu/karasu-7b-chat-plus-unleashed": { "id": "lightblue/karasu/karasu-7b-chat-plus-unleashed", "name": "Karasu 7B Chat Plus Unleashed", "maker": "lightblue", "family": "lightblue/karasu", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-12-22", "context_length": 32768, "max_output_tokens": null, "parameters": "7963676672", "architecture": "MistralForCausalLM, 32 layers, hidden size 4096. Fine-tune of lightblue/karasu-7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Karasu 7B Chat Plus Unleashed is a Lightblue chat fine-tune of Karasu 7B, published on Hugging Face on 2023-12-22 under Apache-2.0. Weights remain public. Collected on 2026-10-15.", "what_it_is_for": "The plus checkpoint with about 6,000 refusal replies filtered out of the category data. Lightblue presents this as the less-refusal Karasu 7B chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/karasu/karasu-dpo-7b": { "id": "lightblue/karasu/karasu-dpo-7b", "name": "Karasu-DPO-7B", "maker": "lightblue", "family": "lightblue/karasu", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-27", "context_length": 32768, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. QLoRA DPO fine-tune of Qwen/Qwen2.5-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Karasu-DPO-7B is Lightblue's January 2025 Japanese DPO fine-tune of Qwen2.5-7B-Instruct. Apache-2.0 weights are still on Hugging Face. Collected on 2026-10-15.", "what_it_is_for": "The card recommends it as a general Japanese conversation model. It was DPO-trained on synthetic Japanese conversations built from translated public chat data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/karasu/karasu-mixtral-8x22b-v0-1": { "id": "lightblue/karasu/karasu-mixtral-8x22b-v0-1", "name": "Karasu-Mixtral-8x22B-v0.1", "maker": "lightblue", "family": "lightblue/karasu", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-11", "context_length": 65536, "max_output_tokens": null, "parameters": "140620634112", "architecture": "MixtralForCausalLM, 56 layers, hidden size 6144, 8 experts, 2 experts per token. Chat fine-tune of mistral-community/Mixtral-8x22B-v0.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Karasu-Mixtral-8x22B-v0.1 is Lightblue's multilingual chat fine-tune of Mixtral-8x22B-v0.1, published on 2024-04-11 under Apache-2.0. Quantized copies are separate repos. Collected on 2026-10-15.", "what_it_is_for": "The card says the base model was not trained to chat, so Lightblue trained it on a multilingual chat set (openchat/openchat_sharegpt4_dataset) for community conversation use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/kurage/kurage-ar": { "id": "lightblue/kurage/kurage-ar", "name": "Kurage Arabic", "maker": "lightblue", "family": "lightblue/kurage", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-13", "context_length": 32768, "max_output_tokens": null, "parameters": "7612634624", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Fine-tune of Qwen/Qwen2-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kurage Arabic is a Lightblue RAG model based on Qwen2-7B-Instruct, published on 2024-09-13 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card trains this checkpoint to do retrieval-augmented answering in Arabic: multi-chunk RAG, single-chunk RAG, longer answers, cross-language context, and question-answer generation for indexing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/kurage/kurage-en": { "id": "lightblue/kurage/kurage-en", "name": "Kurage English", "maker": "lightblue", "family": "lightblue/kurage", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-12", "context_length": 32768, "max_output_tokens": null, "parameters": "7612634624", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Fine-tune of Qwen/Qwen2-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kurage English is a Lightblue RAG model based on Qwen2-7B-Instruct, published on 2024-09-12 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card trains this checkpoint to do retrieval-augmented answering in English: multi-chunk RAG, single-chunk RAG, longer answers, cross-language context, and question-answer generation for indexing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/kurage/kurage-es": { "id": "lightblue/kurage/kurage-es", "name": "Kurage Spanish", "maker": "lightblue", "family": "lightblue/kurage", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-13", "context_length": 32768, "max_output_tokens": null, "parameters": "7612634624", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Fine-tune of Qwen/Qwen2-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kurage Spanish is a Lightblue RAG model based on Qwen2-7B-Instruct, published on 2024-09-13 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card trains this checkpoint to do retrieval-augmented answering in Spanish: multi-chunk RAG, single-chunk RAG, longer answers, cross-language context, and question-answer generation for indexing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/kurage/kurage-hi": { "id": "lightblue/kurage/kurage-hi", "name": "Kurage Hindi", "maker": "lightblue", "family": "lightblue/kurage", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-13", "context_length": 32768, "max_output_tokens": null, "parameters": "7612634624", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Fine-tune of Qwen/Qwen2-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kurage Hindi is a Lightblue RAG model based on Qwen2-7B-Instruct, published on 2024-09-13 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card trains this checkpoint to do retrieval-augmented answering in Hindi: multi-chunk RAG, single-chunk RAG, longer answers, cross-language context, and question-answer generation for indexing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/kurage/kurage-id": { "id": "lightblue/kurage/kurage-id", "name": "Kurage Indonesian", "maker": "lightblue", "family": "lightblue/kurage", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-13", "context_length": 32768, "max_output_tokens": null, "parameters": "7612634624", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Fine-tune of Qwen/Qwen2-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kurage Indonesian is a Lightblue RAG model based on Qwen2-7B-Instruct, published on 2024-09-13 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card trains this checkpoint to do retrieval-augmented answering in Indonesian: multi-chunk RAG, single-chunk RAG, longer answers, cross-language context, and question-answer generation for indexing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/kurage/kurage-ja": { "id": "lightblue/kurage/kurage-ja", "name": "Kurage Japanese", "maker": "lightblue", "family": "lightblue/kurage", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-11", "context_length": 32768, "max_output_tokens": null, "parameters": "7612634624", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Fine-tune of Qwen/Qwen2-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kurage Japanese is a Lightblue RAG model based on Qwen2-7B-Instruct, published on 2024-09-11 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card trains this checkpoint to do retrieval-augmented answering in Japanese: multi-chunk RAG, single-chunk RAG, longer answers, cross-language context, and question-answer generation for indexing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/kurage/kurage-ko": { "id": "lightblue/kurage/kurage-ko", "name": "Kurage Korean", "maker": "lightblue", "family": "lightblue/kurage", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-13", "context_length": 32768, "max_output_tokens": null, "parameters": "7612634624", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Fine-tune of Qwen/Qwen2-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kurage Korean is a Lightblue RAG model based on Qwen2-7B-Instruct, published on 2024-09-13 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card trains this checkpoint to do retrieval-augmented answering in Korean: multi-chunk RAG, single-chunk RAG, longer answers, cross-language context, and question-answer generation for indexing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/kurage/kurage-multilingual": { "id": "lightblue/kurage/kurage-multilingual", "name": "Kurage Multilingual", "maker": "lightblue", "family": "lightblue/kurage", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-16", "context_length": 32768, "max_output_tokens": null, "parameters": "7612634624", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Fine-tune of Qwen/Qwen2-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kurage 44 languages is a Lightblue RAG model based on Qwen2-7B-Instruct, published on 2024-09-16 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card trains this checkpoint to do retrieval-augmented answering in 44 languages: multi-chunk RAG, single-chunk RAG, longer answers, cross-language context, and question-answer generation for indexing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/kurage/kurage-ru": { "id": "lightblue/kurage/kurage-ru", "name": "Kurage Russian", "maker": "lightblue", "family": "lightblue/kurage", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-13", "context_length": 32768, "max_output_tokens": null, "parameters": "7612634624", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Fine-tune of Qwen/Qwen2-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kurage Russian is a Lightblue RAG model based on Qwen2-7B-Instruct, published on 2024-09-13 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card trains this checkpoint to do retrieval-augmented answering in Russian: multi-chunk RAG, single-chunk RAG, longer answers, cross-language context, and question-answer generation for indexing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/kurage/kurage-sw": { "id": "lightblue/kurage/kurage-sw", "name": "Kurage Swahili", "maker": "lightblue", "family": "lightblue/kurage", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-13", "context_length": 32768, "max_output_tokens": null, "parameters": "7612634624", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Fine-tune of Qwen/Qwen2-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kurage Swahili is a Lightblue RAG model based on Qwen2-7B-Instruct, published on 2024-09-13 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card trains this checkpoint to do retrieval-augmented answering in Swahili: multi-chunk RAG, single-chunk RAG, longer answers, cross-language context, and question-answer generation for indexing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/kurage/kurage-th": { "id": "lightblue/kurage/kurage-th", "name": "Kurage Thai", "maker": "lightblue", "family": "lightblue/kurage", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-13", "context_length": 32768, "max_output_tokens": null, "parameters": "7612634624", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Fine-tune of Qwen/Qwen2-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kurage Thai is a Lightblue RAG model based on Qwen2-7B-Instruct, published on 2024-09-13 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card trains this checkpoint to do retrieval-augmented answering in Thai: multi-chunk RAG, single-chunk RAG, longer answers, cross-language context, and question-answer generation for indexing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/kurage/kurage-vi": { "id": "lightblue/kurage/kurage-vi", "name": "Kurage Vietnamese", "maker": "lightblue", "family": "lightblue/kurage", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-14", "context_length": 32768, "max_output_tokens": null, "parameters": "7612634624", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Fine-tune of Qwen/Qwen2-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kurage Vietnamese is a Lightblue RAG model based on Qwen2-7B-Instruct, published on 2024-09-14 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card trains this checkpoint to do retrieval-augmented answering in Vietnamese: multi-chunk RAG, single-chunk RAG, longer answers, cross-language context, and question-answer generation for indexing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/kurage/kurage-zh": { "id": "lightblue/kurage/kurage-zh", "name": "Kurage Chinese", "maker": "lightblue", "family": "lightblue/kurage", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-14", "context_length": 32768, "max_output_tokens": null, "parameters": "7612634624", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Fine-tune of Qwen/Qwen2-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kurage Chinese is a Lightblue RAG model based on Qwen2-7B-Instruct, published on 2024-09-14 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card trains this checkpoint to do retrieval-augmented answering in Chinese: multi-chunk RAG, single-chunk RAG, longer answers, cross-language context, and question-answer generation for indexing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/lb-reranker/lb-reranker-0-5b-v1-0": { "id": "lightblue/lb-reranker/lb-reranker-0-5b-v1-0", "name": "LB Reranker 0.5B v1.0", "maker": "lightblue", "family": "lightblue/lb-reranker", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-06", "context_length": 32768, "max_output_tokens": null, "parameters": "494032768", "architecture": "Qwen2ForCausalLM, 24 layers, hidden size 896. Fine-tune of Qwen/Qwen2.5-0.5B-Instruct that emits a relevance digit from 1 to 7.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LB Reranker 0.5B v1.0 is Lightblue's multilingual query-text reranker, created on Hugging Face on 2025-01-06 and still updated through 2025-10-23. Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "Scores how related a query is to a passage so it can rank or rerank retrieval results, including code snippets. It is a causal LM so it runs in vLLM or LMDeploy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/lb-reranker/lb-reranker-0-5b-v1-0-rev": { "id": "lightblue/lb-reranker/lb-reranker-0-5b-v1-0-rev", "name": "LB Reranker 0.5B v1.0 Rev", "maker": "lightblue", "family": "lightblue/lb-reranker", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-28", "context_length": null, "max_output_tokens": null, "parameters": "630167424", "architecture": "Qwen2 causal LM. The card says it is a reversed-input fine-tune of the same Qwen2.5-0.5B-Instruct setup. Safetensors total is 630167424, larger than v1.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LB Reranker 0.5B v1.0 Rev is Lightblue's reversed-input twin of LB Reranker v1.0, uploaded on 2025-01-28. The passage comes before the query so the passage prefix can be cached. Collected on 2026-10-15.", "what_it_is_for": "Same 1-to-7 relevance scoring job as v1.0, with the input order swapped for cache-friendly document reranking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/mpt/japanese-mpt-7b": { "id": "lightblue/mpt/japanese-mpt-7b", "name": "Japanese MPT-7B", "maker": "lightblue", "family": "lightblue/mpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-07-18", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "MPTForCausalLM. Config d_model 4096, 32 layers, max_seq_len 2048. Continued training of mosaicml/mpt-7b for 3000 steps on Japanese mC4.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese MPT-7B is Lightblue's earliest public LLM in this catalog, a 3000-step Japanese continued train of MosaicML MPT-7B, uploaded on 2023-07-18 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "Add Japanese web text, the Japanese subset of mC4, on top of the MPT-7B checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/openorca/openorca-stx": { "id": "lightblue/openorca/openorca-stx", "name": "OpenOrca STX", "maker": "lightblue", "family": "lightblue/openorca", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 community license", "release_date": "2023-09-12", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM, 40 layers, hidden size 5120. QLoRA fine-tune of Open-Orca/OpenOrcaxOpenChat-Preview2-13B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenOrca STX is Lightblue's September 2023 Japanese QLoRA fine-tune of OpenOrca's OpenChat 13B preview, aimed at closed question answering. The Llama 2 license applies. Collected on 2026-10-15.", "what_it_is_for": "STX names three tasks: SNOW simplification, TyDiQA Japanese question answering, and XLSum Japanese summarization. The card says the practical job is answering a question from a supplied Japanese passage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/qarasu/qarasu-14b-chat-plus-unleashed": { "id": "lightblue/qarasu/qarasu-14b-chat-plus-unleashed", "name": "Qarasu 14B Chat Plus Unleashed", "maker": "lightblue", "family": "lightblue/qarasu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tongyi Qianwen license", "release_date": "2023-12-22", "context_length": 8192, "max_output_tokens": null, "parameters": "14167290880", "architecture": "QWenLMHeadModel, 40 layers, hidden size 5120. Fine-tune of Qwen/Qwen-14B-Chat on the Karasu plus-unleashed data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qarasu 14B Chat Plus Unleashed is Lightblue's Japanese chat fine-tune of Qwen-14B-Chat, first uploaded on 2023-12-22 and announced for commercial on-prem use on 2024-01-15. Weights use the Tongyi Qianwen license. Collected on 2026-10-15.", "what_it_is_for": "Lightblue's only public Qarasu checkpoint. The 2024-01-15 release says the 14B Qarasu series was the strongest public Japanese model they compared and approached GPT-3.5 on a 6-task MT-Bench average.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/r1/deepseek-r1-distill-qwen-1-5b-multilingual": { "id": "lightblue/r1/deepseek-r1-distill-qwen-1-5b-multilingual", "name": "DeepSeek-R1-Distill-Qwen-1.5B Multilingual", "maker": "lightblue", "family": "lightblue/r1", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-31", "context_length": 131072, "max_output_tokens": null, "parameters": "1777088000", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 1536. Multilingual chain-of-thought fine-tune. Hugging Face dataset tag is lightblue/reasoning-multilingual-R1-Llama-70B-train. Intended base deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek-R1-Distill-Qwen-1.5B Multilingual is Lightblue's multilingual reasoning fine-tune in the R1 collection, uploaded on 2025-01-31 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The 7B card, which the smaller and larger cards follow, says the model should think and answer in the prompt language instead of drifting into Chinese or English the way the original R1 distillations do.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/r1/deepseek-r1-distill-qwen-14b-multilingual": { "id": "lightblue/r1/deepseek-r1-distill-qwen-14b-multilingual", "name": "DeepSeek-R1-Distill-Qwen-14B Multilingual", "maker": "lightblue", "family": "lightblue/r1", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-31", "context_length": 131072, "max_output_tokens": null, "parameters": "14770033664", "architecture": "Qwen2ForCausalLM, 48 layers, hidden size 5120. Multilingual chain-of-thought fine-tune. Hugging Face dataset tag is lightblue/reasoning-multilingual-R1-Llama-70B-train. Intended base deepseek-ai/DeepSeek-R1-Distill-Qwen-14B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek-R1-Distill-Qwen-14B Multilingual is Lightblue's multilingual reasoning fine-tune in the R1 collection, uploaded on 2025-01-31 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The 7B card, which the smaller and larger cards follow, says the model should think and answer in the prompt language instead of drifting into Chinese or English the way the original R1 distillations do.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/r1/deepseek-r1-distill-qwen-7b-japanese": { "id": "lightblue/r1/deepseek-r1-distill-qwen-7b-japanese", "name": "DeepSeek-R1-Distill-Qwen-7B Japanese", "maker": "lightblue", "family": "lightblue/r1", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-24", "context_length": 131072, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Japanese fine-tune of deepseek-ai/DeepSeek-R1-Distill-Qwen-7B on lightblue/distilabel-reasoning-R1-Llama-70B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek-R1-Distill-Qwen-7B Japanese is Lightblue's Japanese reasoning fine-tune of the DeepSeek R1 Qwen-7B distillation. Weights went up on 2025-01-24. The company announced them on 2025-02-03. Collected on 2026-10-15.", "what_it_is_for": "The card and the February 2025 press release say original R1 distillations mix Chinese or English into Japanese prompts. This short extra fine-tune is meant to think and answer in Japanese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/r1/deepseek-r1-distill-qwen-7b-multilingual": { "id": "lightblue/r1/deepseek-r1-distill-qwen-7b-multilingual", "name": "DeepSeek-R1-Distill-Qwen-7B Multilingual", "maker": "lightblue", "family": "lightblue/r1", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-31", "context_length": 131072, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Qwen2ForCausalLM, 28 layers, hidden size 3584. Multilingual chain-of-thought fine-tune. Hugging Face dataset tag is lightblue/reasoning-multilingual-R1-Llama-70B-train. Intended base deepseek-ai/DeepSeek-R1-Distill-Qwen-7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSeek-R1-Distill-Qwen-7B Multilingual is Lightblue's multilingual reasoning fine-tune in the R1 collection, uploaded on 2025-01-31 under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The 7B card, which the smaller and larger cards follow, says the model should think and answer in the prompt language instead of drifting into Chinese or English the way the original R1 distillations do.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/shitsu/shitsu-text-scorer": { "id": "lightblue/shitsu/shitsu-text-scorer", "name": "Shitsu", "maker": "lightblue", "family": "lightblue/shitsu", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-08-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "A small neural net on fastText embeddings. It is a regressor, not a transformer LLM. config.json is not in the repo.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Shitsu is Lightblue's multilingual text-quality scorer, uploaded on 2024-08-27 under MIT. It rates how much useful, textbook-like information a passage has. Collected on 2026-10-15.", "what_it_is_for": "Filter large multilingual corpora. The card says scores are usually between 0 and 1 but can fall outside that range because the model is a regressor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/suzume/suzume-llama-3-8b-japanese": { "id": "lightblue/suzume/suzume-llama-3-8b-japanese", "name": "Suzume Llama 3 8B Japanese", "maker": "lightblue", "family": "lightblue/suzume", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 community license", "release_date": "2024-04-22", "context_length": 8192, "max_output_tokens": null, "parameters": "8030261248", "architecture": "LlamaForCausalLM, 32 layers, hidden size 4096. Japanese fine-tune of meta-llama/Meta-Llama-3-8B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suzume Llama 3 8B Japanese is Lightblue's Japanese chat fine-tune of Llama 3 8B Instruct, uploaded on 2024-04-22 under the Llama 3 license. Collected on 2026-10-15.", "what_it_is_for": "The card says it was fine-tuned on more than 3,000 Japanese conversations so Llama 3 answers in Japanese instead of defaulting to English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/suzume/suzume-llama-3-8b-multilingual": { "id": "lightblue/suzume/suzume-llama-3-8b-multilingual", "name": "Suzume Llama 3 8B Multilingual", "maker": "lightblue", "family": "lightblue/suzume", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 community license", "release_date": "2024-04-23", "context_length": 8192, "max_output_tokens": null, "parameters": "8030261248", "architecture": "LlamaForCausalLM, 32 layers, hidden size 4096. Fine-tune of meta-llama/Meta-Llama-3-8B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suzume Llama 3 8B Multilingual is Lightblue's multilingual chat fine-tune of Llama 3 8B Instruct, uploaded on 2024-04-23. It is the main Suzume checkpoint in the Tagengo paper. Collected on 2026-10-15.", "what_it_is_for": "The card says Llama 3 answers in English even when prompted in other languages. This fine-tune uses almost 90,000 multilingual conversations so it keeps Llama 3 quality and answers in more languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightblue/suzume/suzume-llama-3-8b-multilingual-orpo-borda-half": { "id": "lightblue/suzume/suzume-llama-3-8b-multilingual-orpo-borda-half", "name": "Suzume Llama 3 8B Multilingual ORPO Borda Half", "maker": "lightblue", "family": "lightblue/suzume", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-04-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama fine-tune of lightblue/suzume-llama-3-8B-multilingual. Hugging Face tags it as Llama. Layer config was not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the ORPO checkpoint Lightblue tells users to prefer among the Suzume ORPO runs. It was uploaded on 2024-04-25 under CC-BY-NC-4.0 because the preference data used Command R and Command R+. Collected on 2026-10-15.", "what_it_is_for": "ORPO fine-tune of multilingual Suzume on the lightblue/mitsu preference data. The card says several data fractions were trained and this half-Borda run was the best in their tests.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/alfred/alfred-4": { "id": "lighton/alfred/alfred-4", "name": "Alfred 4", "maker": "lighton", "family": "lighton/alfred", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Closed Paradigm model introduced in the January 2025 Luminous Lynx release. LightOn describes it as the flagship assistant for that release: 32k tokens per request, language matching, and agentic tool selection. Weights were not published. Collected 2026-10-15.", "what_it_is_for": "Closed Paradigm model introduced in the January 2025 Luminous Lynx release. LightOn describes it as the flagship assistant for that release: 32k tokens per request, language matching, and agentic tool selection. Weights were not published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/alfred/alfred-4-2": { "id": "lighton/alfred/alfred-4-2", "name": "Alfred 4.2", "maker": "lighton", "family": "lighton/alfred", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "July 2025 Paradigm update that turns Alfred into a visual language model. LightOn says it reads embedded images, diagrams, charts, and tables inside documents and is wired into automatic parser selection. Weights were not published. An April 2026 cookbook still lists alfred-4.2 as a default model id. Collected 2026-10-15.", "what_it_is_for": "July 2025 Paradigm update that turns Alfred into a visual language model. LightOn says it reads embedded images, diagrams, charts, and tables inside documents and is wired into automatic parser selection. Weights were not published. An April 2026 cookbook still lists alfred-4.2 as a default model id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/alfred/alfred-40b-0723": { "id": "lighton/alfred/alfred-40b-0723", "name": "Alfred-40B-0723", "maker": "lighton", "family": "lighton/alfred", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-07-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Causal decoder-only RLHF fine-tune of Falcon-40B. Config: RefinedWeb / RWForCausalLM, hidden size 8192, vocab 65024. The model card does not restate a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LightOn's first public Alfred checkpoint: a July 2023 RLHF fine-tune of Falcon-40B for instruct and chat use, and the open-weight model that first powered the Paradigm enterprise copilot. The card says the Paradigm build could differ from the open-weight release. Collected 2026-10-15.", "what_it_is_for": "LightOn's first public Alfred checkpoint: a July 2023 RLHF fine-tune of Falcon-40B for instruct and chat use, and the open-weight model that first powered the Paradigm enterprise copilot. The card says the Paradigm build could differ from the open-weight release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/alfred/alfred-40b-1023": { "id": "lighton/alfred/alfred-40b-1023", "name": "Alfred-40B-1023", "maker": "lighton", "family": "lighton/alfred", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-17", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Causal decoder-only supervised fine-tune of Falcon-40B with NTK-YaRN context extension. Config: RefinedWeb / RWForCausalLM, hidden size 8192, vocab 65024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "October 2023 Alfred checkpoint for chat and instruct, including chat-with-documents. LightOn extended Falcon-40B to 8192 tokens and released the weights under Apache-2.0. The announcement says the open-weight file can differ from the Paradigm platform model. Collected 2026-10-15.", "what_it_is_for": "October 2023 Alfred checkpoint for chat and instruct, including chat-with-documents. LightOn extended Falcon-40B to 8192 tokens and released the weights under Apache-2.0. The announcement says the open-weight file can differ from the Paradigm platform model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/alfred/alfred-ft5": { "id": "lighton/alfred/alfred-ft5", "name": "Alfred Frontier 5", "maker": "lighton", "family": "lighton/alfred", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "32000000000", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Current high-capability Paradigm model, added in the December 2025 Uniq Urchin release. LightOn describes a 32B vision-language model it fine-tunes for RAG, tool calling, and multi-step planning. Origin of the base model is explicitly not the selling point; quality is. Weights are not published. Collected 2026-10-15.", "what_it_is_for": "Current high-capability Paradigm model, added in the December 2025 Uniq Urchin release. LightOn describes a 32B vision-language model it fine-tunes for RAG, tool calling, and multi-step planning. Origin of the base model is explicitly not the selling point; quality is. Weights are not published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/alfred/alfred-sv5": { "id": "lighton/alfred/alfred-sv5", "name": "Alfred Sovereign 5", "maker": "lighton", "family": "lighton/alfred", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "24000000000", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Closed European-origin Paradigm model introduced in the November 2025 Tender Tiger release. LightOn fine-tunes a 24B architecture for retrieval and orchestration, with a 32,768-token window, function calling, and structured outputs. It is the sovereignty line next to Alfred-ft5, not the highest-capability model. Collected 2026-10-15.", "what_it_is_for": "Closed European-origin Paradigm model introduced in the November 2025 Tender Tiger release. LightOn fine-tunes a 24B architecture for retrieval and orchestration, with a 32,768-token window, function calling, and structured outputs. It is the sovereignty line next to Alfred-ft5, not the highest-capability model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/colbert/agent-moderncolbert": { "id": "lighton/colbert/agent-moderncolbert", "name": "Agent-ModernColBERT", "maker": "lighton", "family": "lighton/colbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-12", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "ColBERT fine-tune of GTE-ModernColBERT-v1 on Tevatron/AgentIR-data. Config matches that base (hidden 768, 22 layers, max position 8192).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Agent-query retriever. The card says it is for retrieval where the query is an agent state, and points at BrowseComp-Plus. Released 12 May 2026 under Apache-2.0. A no-prefix ablation exists and is not catalogued separately. Collected 2026-10-15.", "what_it_is_for": "Agent-query retriever. The card says it is for retrieval where the query is an agent state, and points at BrowseComp-Plus. Released 12 May 2026 under Apache-2.0. A no-prefix ablation exists and is not catalogued separately.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/colbert/colbert-zero": { "id": "lighton/colbert/colbert-zero", "name": "ColBERT-Zero", "maker": "lighton", "family": "lighton/colbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-19", "context_length": 8192, "max_output_tokens": null, "parameters": "149000000", "architecture": "ModernBERT encoder. Config: hidden 768, 22 layers, vocab 50368 or 50370, max position 8192. ColBERT variants use PyLate multi-vector MaxSim.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LightOn's fully pre-trained English ColBERT, trained from a ModernBERT-class encoder on public data rather than by only distilling a dense model. The card's TL;DR says 55.43 nDCG@10 on BEIR. A later LightOn table prints 55.39 for the same model. Collected 2026-10-15.", "what_it_is_for": "LightOn's fully pre-trained English ColBERT, trained from a ModernBERT-class encoder on public data rather than by only distilling a dense model. The card's TL;DR says 55.43 nDCG@10 on BEIR. A later LightOn table prints 55.39 for the same model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/colbert/colbert-zero-supervised": { "id": "lighton/colbert/colbert-zero-supervised", "name": "ColBERT-Zero-supervised", "maker": "lighton", "family": "lighton/colbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-19", "context_length": 8192, "max_output_tokens": null, "parameters": "149000000", "architecture": "ModernBERT encoder. Config: hidden 768, 22 layers, vocab 50368 or 50370, max position 8192. ColBERT variants use PyLate multi-vector MaxSim. Supervised contrastive stage before the final distillation that produces ColBERT-Zero.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Named supervised stage of the ColBERT-Zero pipeline, released with the February 2026 paper. Collected 2026-10-15.", "what_it_is_for": "Named supervised stage of the ColBERT-Zero pipeline, released with the February 2026 paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/colbert/colbert-zero-unsupervised": { "id": "lighton/colbert/colbert-zero-unsupervised", "name": "ColBERT-Zero-unsupervised", "maker": "lighton", "family": "lighton/colbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-19", "context_length": 8192, "max_output_tokens": null, "parameters": "149000000", "architecture": "ModernBERT encoder. Config: hidden 768, 22 layers, vocab 50368 or 50370, max position 8192. ColBERT variants use PyLate multi-vector MaxSim. Large-scale contrastive pretrain stage.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Unsupervised pretrain stage of ColBERT-Zero, released so others can study pretraining versus a dense-model initialization. Collected 2026-10-15.", "what_it_is_for": "Unsupervised pretrain stage of ColBERT-Zero, released so others can study pretraining versus a dense-model initialization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/colbert/gte-moderncolbert-v1": { "id": "lighton/colbert/gte-moderncolbert-v1", "name": "GTE-ModernColBERT-v1", "maker": "lighton", "family": "lighton/colbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-30", "context_length": 8192, "max_output_tokens": null, "parameters": "149000000", "architecture": "ColBERT distillation on Alibaba-NLP/gte-modernbert-base. Config: ModernBERT, hidden 768, 22 layers, max position 8192. LightOn's later table lists 149M and embedding dim 128.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LightOn's 2025 general-purpose English ColBERT, distilled from the GTE ModernBERT dense model. It is the base for Reason-ModernColBERT and Agent-ModernColBERT. The April 2026 comparison gives it 54.75 BEIR nDCG@10. Collected 2026-10-15.", "what_it_is_for": "LightOn's 2025 general-purpose English ColBERT, distilled from the GTE ModernBERT dense model. It is the base for Reason-ModernColBERT and Agent-ModernColBERT. The April 2026 comparison gives it 54.75 BEIR nDCG@10.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/colbert/reason-moderncolbert": { "id": "lighton/colbert/reason-moderncolbert", "name": "Reason-ModernColBERT", "maker": "lighton", "family": "lighton/colbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-05-22", "context_length": 8192, "max_output_tokens": null, "parameters": "150000000", "architecture": "Late-interaction ColBERT fine-tune of GTE-ModernColBERT-v1 on reasonir-hq. Config: ModernBERT, hidden 768, 22 layers, max position 8192. Card and 22 May 2025 press release say 150M parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reasoning-oriented retriever for queries where relevance is not lexical. LightOn announced it on 22 May 2025 for agentic RAG and deep research. License is CC-BY-NC-4.0, not Apache. The card's BRIGHT full mean is 22.62 nDCG@10, with Stack Exchange mean 27.43 and Biology 33.25. Collected 2026-10-15.", "what_it_is_for": "Reasoning-oriented retriever for queries where relevance is not lexical. LightOn announced it on 22 May 2025 for agentic RAG and deep research. License is CC-BY-NC-4.0, not Apache. The card's BRIGHT full mean is 22.62 nDCG@10, with Stack Exchange mean 27.43 and Biology 33.25.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/colgrep/colgrep-agent-2b": { "id": "lighton/colgrep/colgrep-agent-2b", "name": "ColGrep-Agent-2B", "maker": "lighton", "family": "lighton/colgrep", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-12", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "Tool-using code-localization model. Config: LlamaForCausalLM, hidden 2048, 42 layers, vocab 130560, max position 131072. Base: openbmb/MiniCPM5-2B. The repo name says 2B. A Hub size badge on the org page said 3B; that conflict is unresolved.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Small agent that, given a GitHub issue or a natural-language question, finds the files and line ranges that matter. LightOn's 12 October 2026 post says it cuts frontier coding-agent tokens by using semantic code search. Weights and a GGUF pack are Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Small agent that, given a GitHub issue or a natural-language question, finds the files and line ranges that matter. LightOn's 12 October 2026 post says it cuts frontier coding-agent tokens by using semantic code search. Weights and a GGUF pack are Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/denseon/denseon": { "id": "lighton/denseon/denseon", "name": "DenseOn", "maker": "lighton", "family": "lighton/denseon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-21", "context_length": 8192, "max_output_tokens": null, "parameters": "149000000", "architecture": "ModernBERT encoder. Config: hidden 768, 22 layers, vocab 50368 or 50370, max position 8192. ColBERT variants use PyLate multi-vector MaxSim.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "English dense retriever, 149M, CLS pooling with query: and document: prompts. LightOn reports 56.20 nDCG@10 on BEIR and calls it the top base-size dense model in that comparison. Collected 2026-10-15.", "what_it_is_for": "English dense retriever, 149M, CLS pooling with query: and document: prompts. LightOn reports 56.20 nDCG@10 on BEIR and calls it the top base-size dense model in that comparison.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/denseon/denseon-unsupervised": { "id": "lighton/denseon/denseon-unsupervised", "name": "DenseOn-unsupervised", "maker": "lighton", "family": "lighton/denseon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-21", "context_length": 8192, "max_output_tokens": null, "parameters": "149000000", "architecture": "ModernBERT encoder. Config: hidden 768, 22 layers, vocab 50368 or 50370, max position 8192. ColBERT variants use PyLate multi-vector MaxSim.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stage-1 dense checkpoint of DenseOn, released for further fine-tuning. Collected 2026-10-15.", "what_it_is_for": "Stage-1 dense checkpoint of DenseOn, released for further fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lateon/lateon": { "id": "lighton/lateon/lateon", "name": "LateOn", "maker": "lighton", "family": "lighton/lateon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-21", "context_length": 8192, "max_output_tokens": null, "parameters": "149000000", "architecture": "ModernBERT encoder. Config: hidden 768, 22 layers, vocab 50368 or 50370, max position 8192. ColBERT variants use PyLate multi-vector MaxSim.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "English multi-vector ColBERT retriever on ModernBERT-base. LightOn's April 2026 post calls it the first sub-150M ColBERT model to pass 57 nDCG@10 on BEIR, trained with contrastive pre-training plus hard-negative fine-tuning and no knowledge distillation. Collected 2026-10-15.", "what_it_is_for": "English multi-vector ColBERT retriever on ModernBERT-base. LightOn's April 2026 post calls it the first sub-150M ColBERT model to pass 57 nDCG@10 on BEIR, trained with contrastive pre-training plus hard-negative fine-tuning and no knowledge distillation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lateon/lateon-code": { "id": "lighton/lateon/lateon-code", "name": "LateOn-Code", "maker": "lighton", "family": "lighton/lateon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-12", "context_length": 8192, "max_output_tokens": null, "parameters": "149000000", "architecture": "Code-retrieval ColBERT. Config matches LateOn's ModernBERT-base (hidden 768, 22 layers, max position 8192). The card says it starts from LightOn's LateOn model, which that family states as 149M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Code search model. LightOn pretrains with a CoRNStack-style objective, then fine-tunes on CoIR with hard negatives mined in the NV-Retriever style. Announced with ColGrep on 12 February 2026. Collected 2026-10-15.", "what_it_is_for": "Code search model. LightOn pretrains with a CoRNStack-style objective, then fine-tunes on CoIR with hard negatives mined in the NV-Retriever style. Announced with ColGrep on 12 February 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lateon/lateon-code-edge": { "id": "lighton/lateon/lateon-code-edge", "name": "LateOn-Code-edge", "maker": "lighton", "family": "lighton/lateon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-12", "context_length": 7999, "max_output_tokens": null, "parameters": "", "architecture": "Small ModernBERT ColBERT. Config: hidden 256, 7 layers, max position 7999, vocab 50370. The pretrain repo is based on mixedbread-ai/mxbai-edge-colbert-v0-17m. Parameter count is not restated on the fine-tune card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Small code-retrieval ColBERT paired with LateOn-Code. LightOn presents it as the edge-size model in the February 2026 code search release. Collected 2026-10-15.", "what_it_is_for": "Small code-retrieval ColBERT paired with LateOn-Code. LightOn presents it as the edge-size model in the February 2026 code search release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lateon/lateon-code-edge-pretrain": { "id": "lighton/lateon/lateon-code-edge-pretrain", "name": "LateOn-Code-edge-pretrain", "maker": "lighton", "family": "lighton/lateon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-12", "context_length": 7999, "max_output_tokens": null, "parameters": "", "architecture": "Code-pretrain stage. Hub base model: mixedbread-ai/mxbai-edge-colbert-v0-17m. Config: hidden 256, 7 layers, max position 7999.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pretrain checkpoint for LateOn-Code-edge, continuing a Mixedbread edge ColBERT on LightOn's code pretrain mixture. Collected 2026-10-15.", "what_it_is_for": "Pretrain checkpoint for LateOn-Code-edge, continuing a Mixedbread edge ColBERT on LightOn's code pretrain mixture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lateon/lateon-code-pretrain": { "id": "lighton/lateon/lateon-code-pretrain", "name": "LateOn-Code-pretrain", "maker": "lighton", "family": "lighton/lateon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-12", "context_length": 8192, "max_output_tokens": null, "parameters": "149000000", "architecture": "Same ModernBERT-base config as LateOn-Code. Hub tag: base lightonai/LateOn-v0. This folder is the code-pretrain stage before CoIR fine-tuning.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pretrain checkpoint for LateOn-Code, trained on LightOn's cornstack-related data before supervised code retrieval fine-tuning. Collected 2026-10-15.", "what_it_is_for": "Pretrain checkpoint for LateOn-Code, trained on LightOn's cornstack-related data before supervised code retrieval fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lateon/lateon-hpool-regularized": { "id": "lighton/lateon/lateon-hpool-regularized", "name": "LateOn-hpool-regularized", "maker": "lighton", "family": "lighton/lateon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-06", "context_length": 8192, "max_output_tokens": null, "parameters": "149000000", "architecture": "ModernBERT encoder. Config: hidden 768, 22 layers, vocab 50368 or 50370, max position 8192. ColBERT variants use PyLate multi-vector MaxSim. Regularized for hierarchical pooling so long documents can be stored with fewer vectors.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ColBERT model trained so hierarchical pooling keeps retrieval quality at a short document-token budget. The card says 98.7% retention at a 32-token document budget, about 5x compression, with little change to full-token retrieval. Collected 2026-10-15.", "what_it_is_for": "ColBERT model trained so hierarchical pooling keeps retrieval quality at a short document-token budget. The card says 98.7% retention at a 32-token document budget, about 5x compression, with little change to full-token retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lateon/lateon-regularized": { "id": "lighton/lateon/lateon-regularized", "name": "LateOn-regularized", "maker": "lighton", "family": "lighton/lateon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-28", "context_length": 8192, "max_output_tokens": null, "parameters": "149000000", "architecture": "ModernBERT encoder. Config: hidden 768, 22 layers, vocab 50368 or 50370, max position 8192. ColBERT variants use PyLate multi-vector MaxSim. Trained with straight-through estimator regularization so token vectors are less anisotropic and work better with PLAID-style approximate search.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LateOn-family ColBERT model regularized so approximate nearest-neighbor indexes work. LightOn's June 2026 note says unregularized ColBERT vectors can be too anisotropic for efficient ANN. Collected 2026-10-15.", "what_it_is_for": "LateOn-family ColBERT model regularized so approximate nearest-neighbor indexes work. LightOn's June 2026 note says unregularized ColBERT vectors can be too anisotropic for efficient ANN.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lateon/lateon-unsupervised": { "id": "lighton/lateon/lateon-unsupervised", "name": "LateOn-unsupervised", "maker": "lighton", "family": "lighton/lateon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-21", "context_length": 8192, "max_output_tokens": null, "parameters": "149000000", "architecture": "ModernBERT encoder. Config: hidden 768, 22 layers, vocab 50368 or 50370, max position 8192. ColBERT variants use PyLate multi-vector MaxSim.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stage-1 contrastive checkpoint of LateOn, before supervised hard-negative fine-tuning. LightOn released it as a starting point. Collected 2026-10-15.", "what_it_is_for": "Stage-1 contrastive checkpoint of LateOn, before supervised hard-negative fine-tuning. LightOn released it as a starting point.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lightonocr/lightonocr-0-9b-16k-1025": { "id": "lighton/lightonocr/lightonocr-0-9b-16k-1025", "name": "LightOnOCR-0.9B-16k-1025", "maker": "lighton", "family": "lighton/lightonocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-23", "context_length": 8192, "max_output_tokens": null, "parameters": "900000000", "architecture": "End-to-end OCR VLM. Card: Pixtral-based vision encoder plus a Qwen3 text decoder. Config model_type mistral3, custom class LightOnOCRForConditionalGeneration. Text config hidden size 1024, 28 layers, max position 8192. Pruned vocabulary of 16384 tokens. The card says this is the smallest vocabulary variant, aimed at European languages, especially English and French. Base checkpoint: LightOnOCR-1B-1025.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vocabulary-pruned sibling of LightOnOCR-1B-1025. LightOn says the 16k-token vocabulary is for European languages and is the smallest of the October 2025 variants. The 16k figure is the vocab size, not the context window. Collected 2026-10-15.", "what_it_is_for": "Vocabulary-pruned sibling of LightOnOCR-1B-1025. LightOn says the 16k-token vocabulary is for European languages and is the smallest of the October 2025 variants. The 16k figure is the vocab size, not the context window.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lightonocr/lightonocr-0-9b-32k-1025": { "id": "lighton/lightonocr/lightonocr-0-9b-32k-1025", "name": "LightOnOCR-0.9B-32k-1025", "maker": "lighton", "family": "lighton/lightonocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-23", "context_length": 8192, "max_output_tokens": null, "parameters": "900000000", "architecture": "End-to-end OCR VLM. Card: Pixtral-based vision encoder plus a Qwen3 text decoder. Config model_type mistral3, custom class LightOnOCRForConditionalGeneration. Text config hidden size 1024, 28 layers, max position 8192. Pruned vocabulary of 32768 tokens. The card says this is a European-language vocab prune with extra speed and little accuracy loss. Base checkpoint: LightOnOCR-1B-1025.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "32k-vocabulary prune of LightOnOCR-1B-1025 for European languages. Context window on the text decoder remains 8192 tokens. Collected 2026-10-15.", "what_it_is_for": "32k-vocabulary prune of LightOnOCR-1B-1025 for European languages. Context window on the text decoder remains 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lightonocr/lightonocr-1b-1025": { "id": "lighton/lightonocr/lightonocr-1b-1025", "name": "LightOnOCR-1B-1025", "maker": "lighton", "family": "lighton/lightonocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-23", "context_length": 8192, "max_output_tokens": null, "parameters": "1000000000", "architecture": "End-to-end OCR VLM. Card: Pixtral-based vision encoder plus a Qwen3 text decoder. Config model_type mistral3, custom class LightOnOCRForConditionalGeneration. Text config hidden size 1024, 28 layers, max position 8192. Full vocabulary (config vocab size 151936). Card calls this the full BF16 checkpoint.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "October 2025 end-to-end OCR model from LightOn. The card recommends this full BF16 checkpoint for inference and fine-tuning. It reads document images into text. LightOn later marked LightOnOCR-2 as the newer default. Collected 2026-10-15.", "what_it_is_for": "October 2025 end-to-end OCR model from LightOn. The card recommends this full BF16 checkpoint for inference and fine-tuning. It reads document images into text. LightOn later marked LightOnOCR-2 as the newer default.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lightonocr/lightonocr-2-1b": { "id": "lighton/lightonocr/lightonocr-2-1b", "name": "LightOnOCR-2-1B", "maker": "lighton", "family": "lighton/lightonocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-19", "context_length": 16384, "max_output_tokens": null, "parameters": "1000000000", "architecture": "1B end-to-end VLM. Same family config as the flagship: mistral3 / LightOnOCRForConditionalGeneration, Qwen3 text decoder, hidden 1024, 28 layers, max position 16384, vocab 151936. Role: RLVR OCR checkpoint on top of LightOnOCR-2-1B-base.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Best OCR checkpoint of the January 2026 family, refined with RLVR. LightOn recommends it for most OCR tasks. Paradigm's February 2026 release says LightOnOCR v2 parses ingested documents. Collected 2026-10-15.", "what_it_is_for": "Best OCR checkpoint of the January 2026 family, refined with RLVR. LightOn recommends it for most OCR tasks. Paradigm's February 2026 release says LightOnOCR v2 parses ingested documents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lightonocr/lightonocr-2-1b-base": { "id": "lighton/lightonocr/lightonocr-2-1b-base", "name": "LightOnOCR-2-1B-base", "maker": "lighton", "family": "lighton/lightonocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-19", "context_length": 16384, "max_output_tokens": null, "parameters": "1000000000", "architecture": "1B end-to-end VLM. Same family config as the flagship: mistral3 / LightOnOCRForConditionalGeneration, Qwen3 text decoder, hidden 1024, 28 layers, max position 16384, vocab 151936. Role: Pre-RLVR base. Recommended starting point for LoRA and domain adaptation.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Supervised pre-RLVR checkpoint. The card says to start domain fine-tunes here. Collected 2026-10-15.", "what_it_is_for": "Supervised pre-RLVR checkpoint. The card says to start domain fine-tunes here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lightonocr/lightonocr-2-1b-bbox": { "id": "lighton/lightonocr/lightonocr-2-1b-bbox", "name": "LightOnOCR-2-1B-bbox", "maker": "lighton", "family": "lighton/lightonocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-19", "context_length": 16384, "max_output_tokens": null, "parameters": "1000000000", "architecture": "1B end-to-end VLM. Same family config as the flagship: mistral3 / LightOnOCRForConditionalGeneration, Qwen3 text decoder, hidden 1024, 28 layers, max position 16384, vocab 151936. Role: Image-localization variant.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RLVR checkpoint that predicts text plus bounding boxes for embedded images, with an IoU reward. Collected 2026-10-15.", "what_it_is_for": "RLVR checkpoint that predicts text plus bounding boxes for embedded images, with an IoU reward.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lightonocr/lightonocr-2-1b-bbox-base": { "id": "lighton/lightonocr/lightonocr-2-1b-bbox-base", "name": "LightOnOCR-2-1B-bbox-base", "maker": "lighton", "family": "lighton/lightonocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-19", "context_length": 16384, "max_output_tokens": null, "parameters": "1000000000", "architecture": "1B end-to-end VLM. Same family config as the flagship: mistral3 / LightOnOCRForConditionalGeneration, Qwen3 text decoder, hidden 1024, 28 layers, max position 16384, vocab 151936. Role: Bbox base before RLVR.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pre-RLVR bbox checkpoint for fine-tuning localization. Collected 2026-10-15.", "what_it_is_for": "Pre-RLVR bbox checkpoint for fine-tuning localization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lightonocr/lightonocr-2-1b-bbox-soup": { "id": "lighton/lightonocr/lightonocr-2-1b-bbox-soup", "name": "LightOnOCR-2-1B-bbox-soup", "maker": "lighton", "family": "lighton/lightonocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-19", "context_length": 16384, "max_output_tokens": null, "parameters": "1000000000", "architecture": "1B end-to-end VLM. Same family config as the flagship: mistral3 / LightOnOCRForConditionalGeneration, Qwen3 text decoder, hidden 1024, 28 layers, max position 16384, vocab 151936. Role: Merged OCR plus bbox variant.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Merged checkpoint that keeps OCR quality and image boxes. The paper describes a second task-arithmetic merge into the bbox checkpoint. Collected 2026-10-15.", "what_it_is_for": "Merged checkpoint that keeps OCR quality and image boxes. The paper describes a second task-arithmetic merge into the bbox checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lightonocr/lightonocr-2-1b-ocr-soup": { "id": "lighton/lightonocr/lightonocr-2-1b-ocr-soup", "name": "LightOnOCR-2-1B-ocr-soup", "maker": "lighton", "family": "lighton/lightonocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-19", "context_length": 16384, "max_output_tokens": null, "parameters": "1000000000", "architecture": "1B end-to-end VLM. Same family config as the flagship: mistral3 / LightOnOCRForConditionalGeneration, Qwen3 text decoder, hidden 1024, 28 layers, max position 16384, vocab 151936. Role: Merged OCR variant, not a separately trained run.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Task-arithmetic merge for OCR robustness. The paper describes merging the RL OCR weights back toward the base. Collected 2026-10-15.", "what_it_is_for": "Task-arithmetic merge for OCR robustness. The paper describes merging the RL OCR weights back toward the base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lightonocr/lightonocr-3-0-8b": { "id": "lighton/lightonocr/lightonocr-3-0-8b", "name": "LightOnOCR-3-0.8B", "maker": "lighton", "family": "lighton/lightonocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-08", "context_length": 262144, "max_output_tokens": null, "parameters": "800000000", "architecture": "Qwen3.5 vision-language architecture (config qwen3_5). Text hidden 1024, 24 layers, vocab 248320, max position 262144.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Smallest LightOnOCR-3 model. Same transcription and grounding interface as the 4B, on a Qwen3.5-class VLM. Empty prompt transcribes the page. The single token grounding adds labeled boxes (coordinates normalized 0-1000), short image descriptions, and chart data as HTML tables. Collected 2026-10-15.", "what_it_is_for": "Smallest LightOnOCR-3 model. Same transcription and grounding interface as the 4B, on a Qwen3.5-class VLM. Empty prompt transcribes the page. The single token grounding adds labeled boxes (coordinates normalized 0-1000), short image descriptions, and chart data as HTML tables.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lightonocr/lightonocr-3-1b": { "id": "lighton/lightonocr/lightonocr-3-1b", "name": "LightOnOCR-3-1B", "maker": "lighton", "family": "lighton/lightonocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-08", "context_length": 16384, "max_output_tokens": null, "parameters": "1000000000", "architecture": "Same architecture as LightOnOCR-2-1B (mistral3 / LightOnOCRForConditionalGeneration, Qwen3 decoder, hidden 1024, 28 layers, vocab 151936, max position 16384), plus grounding.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "1B LightOnOCR-3 checkpoint for people already serving LightOnOCR-2. Adds layout grounding, image descriptions, and chart tables without an architecture change. Empty prompt transcribes the page. The single token grounding adds labeled boxes (coordinates normalized 0-1000), short image descriptions, and chart data as HTML tables. Collected 2026-10-15.", "what_it_is_for": "1B LightOnOCR-3 checkpoint for people already serving LightOnOCR-2. Adds layout grounding, image descriptions, and chart tables without an architecture change. Empty prompt transcribes the page. The single token grounding adds labeled boxes (coordinates normalized 0-1000), short image descriptions, and chart data as HTML tables.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/lightonocr/lightonocr-3-4b": { "id": "lighton/lightonocr/lightonocr-3-4b", "name": "LightOnOCR-3-4B", "maker": "lighton", "family": "lighton/lightonocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-08", "context_length": 262144, "max_output_tokens": null, "parameters": "4000000000", "architecture": "Qwen3.5 vision-language architecture. Text hidden 2560, 32 layers, vocab 248320, max position 262144. Blog size class is 4.0B.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Largest and most accurate LightOnOCR-3 model in LightOn's October 2026 tables. Transcription by default; the grounding prompt adds boxes, image descriptions, and chart tables. Empty prompt transcribes the page. The single token grounding adds labeled boxes (coordinates normalized 0-1000), short image descriptions, and chart data as HTML tables. Collected 2026-10-15.", "what_it_is_for": "Largest and most accurate LightOnOCR-3 model in LightOn's October 2026 tables. Transcription by default; the grounding prompt adds boxes, image descriptions, and chart tables. Empty prompt transcribes the page. The single token grounding adds labeled boxes (coordinates normalized 0-1000), short image descriptions, and chart data as HTML tables.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/mamba/mambaoutai": { "id": "lighton/mamba/mambaoutai", "name": "Mambaoutai 1.6B", "maker": "lighton", "family": "lighton/mamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-18", "context_length": 4096, "max_output_tokens": null, "parameters": "1600000000", "architecture": "Mamba. Config: 28 layers, d_model 2688, intermediate 5376, state size 16, vocab 65024. Card table: context_len 4096 and non-embedding parameters 1.27B. The title says 1.6B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "1.6B Mamba language model trained by LightOn on French, English, and code, with two decay phases and checkpoints both with and without instruction data. The card says it can run on CPU. Revisions such as pre-30000 are training checkpoints of this model, not separate products. Collected 2026-10-15.", "what_it_is_for": "1.6B Mamba language model trained by LightOn on French, English, and code, with two decay phases and checkpoints both with and without instruction data. The card says it can run on CPU. Revisions such as pre-30000 are training checkpoints of this model, not separate products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/mdenseon/mdenseon": { "id": "lighton/mdenseon/mdenseon", "name": "mDenseOn", "maker": "lighton", "family": "lighton/mdenseon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-30", "context_length": 8192, "max_output_tokens": null, "parameters": "307000000", "architecture": "Multilingual dense encoder. Config: ModernBERT, hidden 768, 22 layers, vocab 256000, max position 8192. Card size: 307M. Base of the supervised model is mDenseOn-unsupervised.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "307M multilingual dense retriever released with mLateOn on 30 July 2026 for multilingual, long-document, English, and code retrieval. Collected 2026-10-15.", "what_it_is_for": "307M multilingual dense retriever released with mLateOn on 30 July 2026 for multilingual, long-document, English, and code retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/mdenseon/mdenseon-unsupervised": { "id": "lighton/mdenseon/mdenseon-unsupervised", "name": "mDenseOn-unsupervised", "maker": "lighton", "family": "lighton/mdenseon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-30", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Stage-1 multilingual dense model. Hub base: jhu-clsp/mmBERT-base. Config: hidden 768, 22 layers, vocab 256000, max position 8192.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Unsupervised pretrain of mDenseOn. Collected 2026-10-15.", "what_it_is_for": "Unsupervised pretrain of mDenseOn.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/mlateon/mlateon": { "id": "lighton/mlateon/mlateon", "name": "mLateOn", "maker": "lighton", "family": "lighton/mlateon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-30", "context_length": 8192, "max_output_tokens": null, "parameters": "307000000", "architecture": "Multilingual ColBERT on an mmBERT-base-class encoder. Config: ModernBERT, hidden 768, 22 layers, vocab 256002, max position 8192. Card size: 307M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "307M multilingual ColBERT for MIRACL, long-document MLDR, English retrieval, and code search. LightOn's 30 July 2026 post releases it with mDenseOn. Training uses translate-train rather than a fully new multilingual crawl. Collected 2026-10-15.", "what_it_is_for": "307M multilingual ColBERT for MIRACL, long-document MLDR, English retrieval, and code search. LightOn's 30 July 2026 post releases it with mDenseOn. Training uses translate-train rather than a fully new multilingual crawl.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/mlateon/mlateon-unsupervised": { "id": "lighton/mlateon/mlateon-unsupervised", "name": "mLateOn-unsupervised", "maker": "lighton", "family": "lighton/mlateon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-30", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Stage-1 multilingual ColBERT. Hub base: jhu-clsp/mmBERT-base. Config matches mLateOn (hidden 768, 22 layers, vocab 256002, max position 8192).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Unsupervised pretrain of mLateOn, released as a fine-tuning starting point. Collected 2026-10-15.", "what_it_is_for": "Unsupervised pretrain of mLateOn, released as a fine-tuning starting point.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/modernbert-embed/modernbert-embed-large": { "id": "lighton/modernbert-embed/modernbert-embed-large", "name": "ModernBERT-embed-large", "maker": "lighton", "family": "lighton/modernbert-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-13", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Dense embedding fine-tune of answerdotai/ModernBERT-large. Config: hidden 1024, 28 layers, vocab 50368, max position 8192. Parameter count is not stated on the excerpt used here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "English dense embedder released by LightOn in January 2025 on the ModernBERT-large backbone, with an MTEB model-index on the card. Collected 2026-10-15.", "what_it_is_for": "English dense embedder released by LightOn in January 2025 on the ModernBERT-large backbone, with an MTEB model-index on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/modernbert-embed/modernbert-embed-large-unsupervised": { "id": "lighton/modernbert-embed/modernbert-embed-large-unsupervised", "name": "ModernBERT-embed-large-unsupervised", "maker": "lighton", "family": "lighton/modernbert-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-13", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Unsupervised stage of modernbert-embed-large. Same ModernBERT-large config: hidden 1024, 28 layers, max position 8192.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Contrastive pretrain checkpoint for ModernBERT-embed-large. Collected 2026-10-15.", "what_it_is_for": "Contrastive pretrain checkpoint for ModernBERT-embed-large.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/orion/orion-mistral": { "id": "lighton/orion/orion-mistral", "name": "OriOn-Mistral", "maker": "lighton", "family": "lighton/orion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-07", "context_length": 344064, "max_output_tokens": null, "parameters": "", "architecture": "Long-context visual-document fine-tune of mistralai/Mistral-Small-3.1-24B-Instruct-2503. Config: mistral3, text hidden 5120, 40 layers, vocab 131072, max position 344064. Training described as CPT plus SFT on synthetic data.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LightOn's OriOn checkpoint on Mistral Small 3.1 24B for long PDF question answering. The February 2026 card presents it as the best Mistral checkpoint in that project. Collected 2026-10-15.", "what_it_is_for": "LightOn's OriOn checkpoint on Mistral Small 3.1 24B for long PDF question answering. The February 2026 card presents it as the best Mistral checkpoint in that project.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/orion/orion-qwen": { "id": "lighton/orion/orion-qwen", "name": "OriOn-Qwen", "maker": "lighton", "family": "lighton/orion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-08", "context_length": 262144, "max_output_tokens": null, "parameters": "", "architecture": "LongPO fine-tune of Qwen/Qwen3-VL-32B-Instruct with task arithmetic. Config: qwen3_vl, text hidden 5120, 64 layers, vocab 151936, max position 262144. Card says the context window matches the base, 256K.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Long-document visual QA model. The card reports +1.7 F1 on MMLongBenchDoc and +2.6 F1 on the authors' corrected MMLBD-C versus Qwen3-VL-32B-Instruct, and compares that result with Qwen3-VL-235B-A22B-Instruct. Collected 2026-10-15.", "what_it_is_for": "Long-document visual QA model. The card reports +1.7 F1 on MMLongBenchDoc and +2.6 F1 on the authors' corrected MMLBD-C versus Qwen3-VL-32B-Instruct, and compares that result with Qwen3-VL-235B-A22B-Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/orion/orion-qwen-sr1": { "id": "lighton/orion/orion-qwen-sr1", "name": "OriOn-Qwen-SR1", "maker": "lighton", "family": "lighton/orion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-07", "context_length": 262144, "max_output_tokens": null, "parameters": "", "architecture": "Same Qwen3-VL-32B config as OriOn-Qwen (hidden 5120, 64 layers, max position 262144). Synthetic reasoning traces are merged in at low strength (the card says alpha 0.25) so the model does not have to emit the trace at inference. A cot control token can gate the behavior.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "April 2026 OriOn checkpoint. LightOn says it reaches 58.3 on MMLongBenchDoc, ahead of a model about 7x larger, without extra inference cost versus OriOn-Qwen. Collected 2026-10-15.", "what_it_is_for": "April 2026 OriOn checkpoint. LightOn says it reaches 58.3 on MMLongBenchDoc, ahead of a model about 7x larger, without extra inference cost versus OriOn-Qwen.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/pagnol/pagnol-large": { "id": "lighton/pagnol/pagnol-large", "name": "PAGnol-large", "maker": "lighton", "family": "lighton/pagnol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 architecture. Config: n_embd 1280, 36 layers, 20 heads, vocab 50262, n_positions 1024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "French generative model trained by LightOn with the Inria ALMAnaCH team in early 2021 on the same CCNet French data as CamemBERT. The card says the checkpoints are released for reproducibility and are not a current state of the art. Weights on this Hub repo date from 2024; training was early 2021. Collected 2026-10-15.", "what_it_is_for": "French generative model trained by LightOn with the Inria ALMAnaCH team in early 2021 on the same CCNet French data as CamemBERT. The card says the checkpoints are released for reproducibility and are not a current state of the art. Weights on this Hub repo date from 2024; training was early 2021.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/pagnol/pagnol-medium": { "id": "lighton/pagnol/pagnol-medium", "name": "PAGnol-medium", "maker": "lighton", "family": "lighton/pagnol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 architecture. Config: n_embd 1024, 24 layers, 16 heads, vocab 50262, n_positions 1024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "French generative model trained by LightOn with the Inria ALMAnaCH team in early 2021 on the same CCNet French data as CamemBERT. The card says the checkpoints are released for reproducibility and are not a current state of the art. Weights on this Hub repo date from 2024; training was early 2021. Collected 2026-10-15.", "what_it_is_for": "French generative model trained by LightOn with the Inria ALMAnaCH team in early 2021 on the same CCNet French data as CamemBERT. The card says the checkpoints are released for reproducibility and are not a current state of the art. Weights on this Hub repo date from 2024; training was early 2021.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/pagnol/pagnol-small": { "id": "lighton/pagnol/pagnol-small", "name": "PAGnol-small", "maker": "lighton", "family": "lighton/pagnol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 architecture. Config: n_embd 768, 12 layers, 12 heads, vocab 50262, n_positions 1024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "French generative model trained by LightOn with the Inria ALMAnaCH team in early 2021 on the same CCNet French data as CamemBERT. The card says the checkpoints are released for reproducibility and are not a current state of the art. Weights on this Hub repo date from 2024; training was early 2021. Collected 2026-10-15.", "what_it_is_for": "French generative model trained by LightOn with the Inria ALMAnaCH team in early 2021 on the same CCNet French data as CamemBERT. The card says the checkpoints are released for reproducibility and are not a current state of the art. Weights on this Hub repo date from 2024; training was early 2021.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/pagnol/pagnol-xl": { "id": "lighton/pagnol/pagnol-xl", "name": "PAGnol-XL", "maker": "lighton", "family": "lighton/pagnol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Custom PagnolXl decoder. Config: d_model 1600, 48 layers, 25 heads, d_feedforward 6400, vocab 50262, max_seq_len 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "French generative model trained by LightOn with the Inria ALMAnaCH team in early 2021 on the same CCNet French data as CamemBERT. The card says the checkpoints are released for reproducibility and are not a current state of the art. Weights on this Hub repo date from 2024; training was early 2021. Collected 2026-10-15.", "what_it_is_for": "French generative model trained by LightOn with the Inria ALMAnaCH team in early 2021 on the same CCNet French data as CamemBERT. The card says the checkpoints are released for reproducibility and are not a current state of the art. Weights on this Hub repo date from 2024; training was early 2021.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-de": { "id": "lighton/qwen3-reason/qwen3-8b-de", "name": "Qwen3-8B-DE", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-10", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Native reasoning specialist.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Full-SFT reasoning model from Qwen/Qwen3-8B-Base that writes its chain of thought and its answer in German. The French card, which documents the release pattern, says training is about 10B tokens for 2 epochs on lightonai/Dolci-Think-SFT-32B-Multilingual, and states a 32,768-token context. Sibling cards were checked for the same base, license, and a context-length line. Collected 2026-10-15.", "what_it_is_for": "Full-SFT reasoning model from Qwen/Qwen3-8B-Base that writes its chain of thought and its answer in German. The French card, which documents the release pattern, says training is about 10B tokens for 2 epochs on lightonai/Dolci-Think-SFT-32B-Multilingual, and states a 32,768-token context. Sibling cards were checked for the same base, license, and a context-length line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-de-pivot-en": { "id": "lighton/qwen3-reason/qwen3-8b-de-pivot-en", "name": "Qwen3-8B-DE-Pivot-EN", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-11", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Pivot-through-English checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "English-pivot variant released with the same layer-swap paper. The French card lists Qwen3-8B-FR-Pivot-EN beside the native and swap models. This German repo is the named counterpart. The pivot training recipe was not re-read line by line. Collected 2026-10-15.", "what_it_is_for": "English-pivot variant released with the same layer-swap paper. The French card lists Qwen3-8B-FR-Pivot-EN beside the native and swap models. This German repo is the named counterpart. The pivot training recipe was not re-read line by line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-de-swap": { "id": "lighton/qwen3-reason/qwen3-8b-de-swap", "name": "Qwen3-8B-DE-Swap", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-26", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Layer-swap checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Layer-swap variant in the same paper release. The French card says Qwen3-8B-FR-Swap transplants middle layers L13-L22 of Qwen3-8B-EN into Qwen3-8B-FR. This German repo is the named counterpart. The layer span was not re-read on every language card. Collected 2026-10-15.", "what_it_is_for": "Layer-swap variant in the same paper release. The French card says Qwen3-8B-FR-Swap transplants middle layers L13-L22 of Qwen3-8B-EN into Qwen3-8B-FR. This German repo is the named counterpart. The layer span was not re-read on every language card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-en": { "id": "lighton/qwen3-reason/qwen3-8b-en", "name": "Qwen3-8B-EN", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-30", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Native reasoning specialist.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Full-SFT reasoning model from Qwen/Qwen3-8B-Base that writes its chain of thought and its answer in English. The French card, which documents the release pattern, says training is about 10B tokens for 2 epochs on lightonai/Dolci-Think-SFT-32B-Multilingual, and states a 32,768-token context. Sibling cards were checked for the same base, license, and a context-length line. Collected 2026-10-15.", "what_it_is_for": "Full-SFT reasoning model from Qwen/Qwen3-8B-Base that writes its chain of thought and its answer in English. The French card, which documents the release pattern, says training is about 10B tokens for 2 epochs on lightonai/Dolci-Think-SFT-32B-Multilingual, and states a 32,768-token context. Sibling cards were checked for the same base, license, and a context-length line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-es": { "id": "lighton/qwen3-reason/qwen3-8b-es", "name": "Qwen3-8B-ES", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-10", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Native reasoning specialist.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Full-SFT reasoning model from Qwen/Qwen3-8B-Base that writes its chain of thought and its answer in Spanish. The French card, which documents the release pattern, says training is about 10B tokens for 2 epochs on lightonai/Dolci-Think-SFT-32B-Multilingual, and states a 32,768-token context. Sibling cards were checked for the same base, license, and a context-length line. Collected 2026-10-15.", "what_it_is_for": "Full-SFT reasoning model from Qwen/Qwen3-8B-Base that writes its chain of thought and its answer in Spanish. The French card, which documents the release pattern, says training is about 10B tokens for 2 epochs on lightonai/Dolci-Think-SFT-32B-Multilingual, and states a 32,768-token context. Sibling cards were checked for the same base, license, and a context-length line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-es-pivot-en": { "id": "lighton/qwen3-reason/qwen3-8b-es-pivot-en", "name": "Qwen3-8B-ES-Pivot-EN", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-11", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Pivot-through-English checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "English-pivot variant released with the same layer-swap paper. The French card lists Qwen3-8B-FR-Pivot-EN beside the native and swap models. This Spanish repo is the named counterpart. The pivot training recipe was not re-read line by line. Collected 2026-10-15.", "what_it_is_for": "English-pivot variant released with the same layer-swap paper. The French card lists Qwen3-8B-FR-Pivot-EN beside the native and swap models. This Spanish repo is the named counterpart. The pivot training recipe was not re-read line by line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-es-swap": { "id": "lighton/qwen3-reason/qwen3-8b-es-swap", "name": "Qwen3-8B-ES-Swap", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-26", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Layer-swap checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Layer-swap variant in the same paper release. The French card says Qwen3-8B-FR-Swap transplants middle layers L13-L22 of Qwen3-8B-EN into Qwen3-8B-FR. This Spanish repo is the named counterpart. The layer span was not re-read on every language card. Collected 2026-10-15.", "what_it_is_for": "Layer-swap variant in the same paper release. The French card says Qwen3-8B-FR-Swap transplants middle layers L13-L22 of Qwen3-8B-EN into Qwen3-8B-FR. This Spanish repo is the named counterpart. The layer span was not re-read on every language card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-fr": { "id": "lighton/qwen3-reason/qwen3-8b-fr", "name": "Qwen3-8B-FR", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-23", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Native reasoning specialist.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Full-SFT reasoning model from Qwen/Qwen3-8B-Base that writes its chain of thought and its answer in French. The French card, which documents the release pattern, says training is about 10B tokens for 2 epochs on lightonai/Dolci-Think-SFT-32B-Multilingual, and states a 32,768-token context. Sibling cards were checked for the same base, license, and a context-length line. Collected 2026-10-15.", "what_it_is_for": "Full-SFT reasoning model from Qwen/Qwen3-8B-Base that writes its chain of thought and its answer in French. The French card, which documents the release pattern, says training is about 10B tokens for 2 epochs on lightonai/Dolci-Think-SFT-32B-Multilingual, and states a 32,768-token context. Sibling cards were checked for the same base, license, and a context-length line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-fr-pivot-en": { "id": "lighton/qwen3-reason/qwen3-8b-fr-pivot-en", "name": "Qwen3-8B-FR-Pivot-EN", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-04", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Pivot-through-English checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "English-pivot variant released with the same layer-swap paper. The French card lists Qwen3-8B-FR-Pivot-EN beside the native and swap models. This French repo is the named counterpart. The pivot training recipe was not re-read line by line. Collected 2026-10-15.", "what_it_is_for": "English-pivot variant released with the same layer-swap paper. The French card lists Qwen3-8B-FR-Pivot-EN beside the native and swap models. This French repo is the named counterpart. The pivot training recipe was not re-read line by line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-fr-swap": { "id": "lighton/qwen3-reason/qwen3-8b-fr-swap", "name": "Qwen3-8B-FR-Swap", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-26", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Layer-swap checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Layer-swap variant in the same paper release. The French card says Qwen3-8B-FR-Swap transplants middle layers L13-L22 of Qwen3-8B-EN into Qwen3-8B-FR. This French repo is the named counterpart. The layer span was not re-read on every language card. Collected 2026-10-15.", "what_it_is_for": "Layer-swap variant in the same paper release. The French card says Qwen3-8B-FR-Swap transplants middle layers L13-L22 of Qwen3-8B-EN into Qwen3-8B-FR. This French repo is the named counterpart. The layer span was not re-read on every language card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-sw": { "id": "lighton/qwen3-reason/qwen3-8b-sw", "name": "Qwen3-8B-SW", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-12", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Native reasoning specialist.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Full-SFT reasoning model from Qwen/Qwen3-8B-Base that writes its chain of thought and its answer in Swahili. The French card, which documents the release pattern, says training is about 10B tokens for 2 epochs on lightonai/Dolci-Think-SFT-32B-Multilingual, and states a 32,768-token context. Sibling cards were checked for the same base, license, and a context-length line. Collected 2026-10-15.", "what_it_is_for": "Full-SFT reasoning model from Qwen/Qwen3-8B-Base that writes its chain of thought and its answer in Swahili. The French card, which documents the release pattern, says training is about 10B tokens for 2 epochs on lightonai/Dolci-Think-SFT-32B-Multilingual, and states a 32,768-token context. Sibling cards were checked for the same base, license, and a context-length line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-sw-pivot-en": { "id": "lighton/qwen3-reason/qwen3-8b-sw-pivot-en", "name": "Qwen3-8B-SW-Pivot-EN", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-11", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Pivot-through-English checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "English-pivot variant released with the same layer-swap paper. The French card lists Qwen3-8B-FR-Pivot-EN beside the native and swap models. This Swahili repo is the named counterpart. The pivot training recipe was not re-read line by line. Collected 2026-10-15.", "what_it_is_for": "English-pivot variant released with the same layer-swap paper. The French card lists Qwen3-8B-FR-Pivot-EN beside the native and swap models. This Swahili repo is the named counterpart. The pivot training recipe was not re-read line by line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-sw-swap": { "id": "lighton/qwen3-reason/qwen3-8b-sw-swap", "name": "Qwen3-8B-SW-Swap", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-26", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Layer-swap checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Layer-swap variant in the same paper release. The French card says Qwen3-8B-FR-Swap transplants middle layers L13-L22 of Qwen3-8B-EN into Qwen3-8B-FR. This Swahili repo is the named counterpart. The layer span was not re-read on every language card. Collected 2026-10-15.", "what_it_is_for": "Layer-swap variant in the same paper release. The French card says Qwen3-8B-FR-Swap transplants middle layers L13-L22 of Qwen3-8B-EN into Qwen3-8B-FR. This Swahili repo is the named counterpart. The layer span was not re-read on every language card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-zh": { "id": "lighton/qwen3-reason/qwen3-8b-zh", "name": "Qwen3-8B-ZH", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-10", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Native reasoning specialist.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Full-SFT reasoning model from Qwen/Qwen3-8B-Base that writes its chain of thought and its answer in Chinese. The French card, which documents the release pattern, says training is about 10B tokens for 2 epochs on lightonai/Dolci-Think-SFT-32B-Multilingual, and states a 32,768-token context. Sibling cards were checked for the same base, license, and a context-length line. Collected 2026-10-15.", "what_it_is_for": "Full-SFT reasoning model from Qwen/Qwen3-8B-Base that writes its chain of thought and its answer in Chinese. The French card, which documents the release pattern, says training is about 10B tokens for 2 epochs on lightonai/Dolci-Think-SFT-32B-Multilingual, and states a 32,768-token context. Sibling cards were checked for the same base, license, and a context-length line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-zh-pivot-en": { "id": "lighton/qwen3-reason/qwen3-8b-zh-pivot-en", "name": "Qwen3-8B-ZH-Pivot-EN", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-11", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Pivot-through-English checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "English-pivot variant released with the same layer-swap paper. The French card lists Qwen3-8B-FR-Pivot-EN beside the native and swap models. This Chinese repo is the named counterpart. The pivot training recipe was not re-read line by line. Collected 2026-10-15.", "what_it_is_for": "English-pivot variant released with the same layer-swap paper. The French card lists Qwen3-8B-FR-Pivot-EN beside the native and swap models. This Chinese repo is the named counterpart. The pivot training recipe was not re-read line by line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/qwen3-reason/qwen3-8b-zh-swap": { "id": "lighton/qwen3-reason/qwen3-8b-zh-swap", "name": "Qwen3-8B-ZH-Swap", "maker": "lighton", "family": "lighton/qwen3-reason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-26", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B-Base. Config: hidden 4096, 36 layers, vocab 151936, max_position_embeddings 40960. The French card states context length 32,768. Layer-swap checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Layer-swap variant in the same paper release. The French card says Qwen3-8B-FR-Swap transplants middle layers L13-L22 of Qwen3-8B-EN into Qwen3-8B-FR. This Chinese repo is the named counterpart. The layer span was not re-read on every language card. Collected 2026-10-15.", "what_it_is_for": "Layer-swap variant in the same paper release. The French card says Qwen3-8B-FR-Swap transplants middle layers L13-L22 of Qwen3-8B-EN into Qwen3-8B-FR. This Chinese repo is the named counterpart. The layer span was not re-read on every language card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/rerank/lighton-rerank-lw-0-8b": { "id": "lighton/rerank/lighton-rerank-lw-0-8b", "name": "LightOn-rerank-LW-0.8B", "maker": "lighton", "family": "lighton/rerank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-16", "context_length": 262144, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal cross-encoder reranker, listwise scoring, fine-tune of Qwen/Qwen3.5-0.8B. Config qwen3_5, hidden 1024, 24 layers, vocab 248320. Pointwise models score Yes versus No logits. Listwise models score a list of candidates together.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "One model that reranks either text passages or document-page images. LightOn released six sizes and two scoring modes in July 2026. This checkpoint is the listwise Qwen/Qwen3.5-0.8B member. The family table gives ViDoRe V3 overall nDCG@10 of 58.25. Collected 2026-10-15.", "what_it_is_for": "One model that reranks either text passages or document-page images. LightOn released six sizes and two scoring modes in July 2026. This checkpoint is the listwise Qwen/Qwen3.5-0.8B member. The family table gives ViDoRe V3 overall nDCG@10 of 58.25.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/rerank/lighton-rerank-lw-2b": { "id": "lighton/rerank/lighton-rerank-lw-2b", "name": "LightOn-rerank-LW-2B", "maker": "lighton", "family": "lighton/rerank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-16", "context_length": 262144, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal cross-encoder reranker, listwise scoring, fine-tune of Qwen/Qwen3.5-2B. Config qwen3_5, hidden 2048, 24 layers, vocab 248320. Pointwise models score Yes versus No logits. Listwise models score a list of candidates together.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "One model that reranks either text passages or document-page images. LightOn released six sizes and two scoring modes in July 2026. This checkpoint is the listwise Qwen/Qwen3.5-2B member. The family table gives ViDoRe V3 overall nDCG@10 of 62.66. Collected 2026-10-15.", "what_it_is_for": "One model that reranks either text passages or document-page images. LightOn released six sizes and two scoring modes in July 2026. This checkpoint is the listwise Qwen/Qwen3.5-2B member. The family table gives ViDoRe V3 overall nDCG@10 of 62.66.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/rerank/lighton-rerank-lw-4b": { "id": "lighton/rerank/lighton-rerank-lw-4b", "name": "LightOn-rerank-LW-4B", "maker": "lighton", "family": "lighton/rerank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-16", "context_length": 262144, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal cross-encoder reranker, listwise scoring, fine-tune of Qwen/Qwen3.5-4B. Config qwen3_5, hidden 2560, 32 layers, vocab 248320. Pointwise models score Yes versus No logits. Listwise models score a list of candidates together.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "One model that reranks either text passages or document-page images. LightOn released six sizes and two scoring modes in July 2026. This checkpoint is the listwise Qwen/Qwen3.5-4B member. The family table gives ViDoRe V3 overall nDCG@10 of 64.69. Collected 2026-10-15.", "what_it_is_for": "One model that reranks either text passages or document-page images. LightOn released six sizes and two scoring modes in July 2026. This checkpoint is the listwise Qwen/Qwen3.5-4B member. The family table gives ViDoRe V3 overall nDCG@10 of 64.69.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/rerank/lighton-rerank-pw-0-8b": { "id": "lighton/rerank/lighton-rerank-pw-0-8b", "name": "LightOn-rerank-PW-0.8B", "maker": "lighton", "family": "lighton/rerank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-16", "context_length": 262144, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal cross-encoder reranker, pointwise scoring, fine-tune of Qwen/Qwen3.5-0.8B. Config qwen3_5, hidden 1024, 24 layers, vocab 248320. Pointwise models score Yes versus No logits. Listwise models score a list of candidates together.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "One model that reranks either text passages or document-page images. LightOn released six sizes and two scoring modes in July 2026. This checkpoint is the pointwise Qwen/Qwen3.5-0.8B member. The family table gives ViDoRe V3 overall nDCG@10 of 48.2. Collected 2026-10-15.", "what_it_is_for": "One model that reranks either text passages or document-page images. LightOn released six sizes and two scoring modes in July 2026. This checkpoint is the pointwise Qwen/Qwen3.5-0.8B member. The family table gives ViDoRe V3 overall nDCG@10 of 48.2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/rerank/lighton-rerank-pw-2b": { "id": "lighton/rerank/lighton-rerank-pw-2b", "name": "LightOn-rerank-PW-2B", "maker": "lighton", "family": "lighton/rerank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-16", "context_length": 262144, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal cross-encoder reranker, pointwise scoring, fine-tune of Qwen/Qwen3.5-2B. Config qwen3_5, hidden 2048, 24 layers, vocab 248320. Pointwise models score Yes versus No logits. Listwise models score a list of candidates together.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "One model that reranks either text passages or document-page images. LightOn released six sizes and two scoring modes in July 2026. This checkpoint is the pointwise Qwen/Qwen3.5-2B member. The family table gives ViDoRe V3 overall nDCG@10 of 59.87. Collected 2026-10-15.", "what_it_is_for": "One model that reranks either text passages or document-page images. LightOn released six sizes and two scoring modes in July 2026. This checkpoint is the pointwise Qwen/Qwen3.5-2B member. The family table gives ViDoRe V3 overall nDCG@10 of 59.87.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/rerank/lighton-rerank-pw-4b": { "id": "lighton/rerank/lighton-rerank-pw-4b", "name": "LightOn-rerank-PW-4B", "maker": "lighton", "family": "lighton/rerank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-16", "context_length": 262144, "max_output_tokens": null, "parameters": "4500000000", "architecture": "Multimodal cross-encoder reranker, pointwise scoring, fine-tune of Qwen/Qwen3.5-4B. Config qwen3_5, hidden 2560, 32 layers, vocab 248320. Pointwise models score Yes versus No logits. Listwise models score a list of candidates together.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "One model that reranks either text passages or document-page images. LightOn released six sizes and two scoring modes in July 2026. This checkpoint is the pointwise Qwen/Qwen3.5-4B member. The family table gives ViDoRe V3 overall nDCG@10 of 59.8. Collected 2026-10-15.", "what_it_is_for": "One model that reranks either text passages or document-page images. LightOn released six sizes and two scoring modes in July 2026. This checkpoint is the pointwise Qwen/Qwen3.5-4B member. The family table gives ViDoRe V3 overall nDCG@10 of 59.8.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/rerank/monoqwen2-vl-v0-1": { "id": "lighton/rerank/monoqwen2-vl-v0-1", "name": "MonoQwen2-VL-v0.1", "maker": "lighton", "family": "lighton/rerank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter on Qwen/Qwen2-VL-2B-Instruct. Pointwise MonoT5-style True/False relevance for an image and a query. Library: PEFT. A full config.json was not present at the adapter repo root when checked.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "October 2024 visual reranker. Given a page image and a query, it scores relevance from the True versus False token logits. Trained on the ColPali train set with DSE-mined negatives. Superseded in LightOn's line by the 2026 LightOn-rerank models, which are full fine-tunes rather than this adapter. Collected 2026-10-15.", "what_it_is_for": "October 2024 visual reranker. Given a page image and a query, it scores relevance from the True versus False token logits. Trained on the ColPali train set with DSE-mined negatives. Superseded in LightOn's line by the 2026 LightOn-rerank models, which are full fine-tunes rather than this adapter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/rita/rita-l": { "id": "lighton/rita/rita-l", "name": "RITA-large", "maker": "lighton", "family": "lighton/rita", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-04-25", "context_length": null, "max_output_tokens": null, "parameters": "680000000", "architecture": "Autoregressive protein language model. Card: d_model 1536, 24 layers. Config vocab 26 (amino acids). Custom code (trust_remote_code).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Protein sequence model from LightOn, the Oxford OATML group, and the Debbie Marks lab at Harvard. The card table gives UniRef-100 language-model loss of 1.82. This dossier uses the card's parameter count. Collected 2026-10-15.", "what_it_is_for": "Protein sequence model from LightOn, the Oxford OATML group, and the Debbie Marks lab at Harvard. The card table gives UniRef-100 language-model loss of 1.82. This dossier uses the card's parameter count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/rita/rita-m": { "id": "lighton/rita/rita-m", "name": "RITA-medium", "maker": "lighton", "family": "lighton/rita", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-04-25", "context_length": null, "max_output_tokens": null, "parameters": "300000000", "architecture": "Autoregressive protein language model. Card: d_model 1024, 24 layers. Config vocab 26 (amino acids). Custom code (trust_remote_code).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Protein sequence model from LightOn, the Oxford OATML group, and the Debbie Marks lab at Harvard. The card table gives UniRef-100 language-model loss of 2.01. This dossier uses the card's parameter count. Collected 2026-10-15.", "what_it_is_for": "Protein sequence model from LightOn, the Oxford OATML group, and the Debbie Marks lab at Harvard. The card table gives UniRef-100 language-model loss of 2.01. This dossier uses the card's parameter count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/rita/rita-s": { "id": "lighton/rita/rita-s", "name": "RITA-small", "maker": "lighton", "family": "lighton/rita", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-04-25", "context_length": null, "max_output_tokens": null, "parameters": "85000000", "architecture": "Autoregressive protein language model. Card: d_model 768, 12 layers. Config vocab 26 (amino acids). Custom code (trust_remote_code).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Protein sequence model from LightOn, the Oxford OATML group, and the Debbie Marks lab at Harvard. The card table gives UniRef-100 language-model loss of 2.31. This dossier uses the card's parameter count. Collected 2026-10-15.", "what_it_is_for": "Protein sequence model from LightOn, the Oxford OATML group, and the Debbie Marks lab at Harvard. The card table gives UniRef-100 language-model loss of 2.31. This dossier uses the card's parameter count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lighton/rita/rita-xl": { "id": "lighton/rita/rita-xl", "name": "RITA-xlarge", "maker": "lighton", "family": "lighton/rita", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-04-25", "context_length": null, "max_output_tokens": null, "parameters": "1200000000", "architecture": "Autoregressive protein language model. Card: d_model 2048, 24 layers. Config vocab 26 (amino acids). Custom code (trust_remote_code).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Protein sequence model from LightOn, the Oxford OATML group, and the Debbie Marks lab at Harvard. The card table gives UniRef-100 language-model loss of 1.7. This dossier uses the card's parameter count. Collected 2026-10-15.", "what_it_is_for": "Protein sequence model from LightOn, the Oxford OATML group, and the Debbie Marks lab at Harvard. The card table gives UniRef-100 language-model loss of 1.7. This dossier uses the card's parameter count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightricks/ltx/ltx-2": { "id": "lightricks/ltx/ltx-2", "name": "LTX-2", "maker": "lightricks", "family": "lightricks/ltx", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LTX-2 Community License Agreement, dated 5 January 2026, Hugging Face name ltx-2-community-license-agreement. The repo index says it covers LTX-2 versions released since 5 January 2026, and LTX-2.3 until 11 August 2026.", "release_date": "2025-10-23", "context_length": null, "max_output_tokens": null, "parameters": "19B", "architecture": "Asymmetric dual-stream diffusion transformer. The paper says a 14B-parameter video stream and a 5B-parameter audio stream, joined by bidirectional audio-video cross-attention, temporal positional embeddings, cross-modality AdaLN, and modality-aware classifier-free guidance. Checkpoint label ltx-2-19b.", "modalities": { "input": [ "text", "image", "audio", "video", "depth" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTX-2 is Lightricks' open-weights audio-video foundation model, announced on 23 October 2025 as the successor to LTX-Video 0.9.8. It is a 19B asymmetric dual-stream diffusion transformer that generates video and synchronized audio in one model. The API ids `ltx-2-fast` and `ltx-2-pro` were removed on 16 August 2026. Weights remain on Hugging Face, which points readers to LTX-2.3. Status is deprecated. Collected 2026-10-15.", "what_it_is_for": "The launch release describes production video with native 4K, up to 50 fps, and synchronized dialogue, ambience, and music, from text, image, audio, depth, or reference video. The model card lists text-to-video, image-to-video, audio-to-video, and the matching audio tasks. Fast and Pro were API variants, not separate weight families. An \"Ultra\" flow in the December 2025 product post is a 4K rendering path, not its own model id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightricks/ltx/ltx-2-3": { "id": "lightricks/ltx/ltx-2-3", "name": "LTX-2.3", "maker": "lightricks", "family": "lightricks/ltx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face name ltx-2-community-license-agreement. The LTX-2 license index says the 5 January 2026 LTX-2 Community License covers LTX-2.3 until 11 August 2026. The model-card body also links LICENSE-2_x, which is the 11 August 2026 LTX-2.x Community License written for LTX-2.5 and later releases.", "release_date": "2026-03-05", "context_length": null, "max_output_tokens": null, "parameters": "22B", "architecture": "DiT audio-video model. Vendor checkpoint label is 22B (ltx-2.3-22b). The cited paper describes the 19B LTX-2 dual-stream design, not a separate 22B split. LTX-2.3 checkpoints bundle the transformer, video and audio VAEs, and text projection. The text encoder downloaded separately is Gemma 3.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTX-2.3 is Lightricks' 22B open-weights audio-video model and a current LTX API model. The API changelog introduces it on 5 March 2026 as `ltx-2-3-fast` and `ltx-2-3-pro`. The 8 March 2026 release post says the weights, distilled checkpoint, and latent upscalers are on Hugging Face. It is still supported beside LTX-2.5. Collected 2026-10-15.", "what_it_is_for": "The API page says it makes portrait and landscape video up to 4K, with 24/48 fps beside the older 25/50 fps options, first-and-last-frame image-to-video, and sharper detail from a new latent space and VAE. Pro is the quality variant and the only 2.3 variant that can do audio-to-video, retake, and extend. Fast is the cheaper, longer-clip variant for text-to-video and image-to-video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightricks/ltx/ltx-2-5": { "id": "lightricks/ltx/ltx-2-5", "name": "LTX-2.5", "maker": "lightricks", "family": "lightricks/ltx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LTX-2.x Community License Agreement, dated 11 August 2026. Royalty-free below $10,000,000 annual revenue, counted across controlled affiliates. At or above that revenue, use needs a paid Commercial Use Agreement, except defined non-commercial evaluation. Fine-tunes, LoRAs, and models trained on outputs are derivatives and stay under the same agreement. The Hugging Face repository is gated.", "release_date": "2026-08-11", "context_length": null, "max_output_tokens": null, "parameters": "22B", "architecture": "22B asymmetric dual-stream diffusion transformer, per the product page. Official docs add a diffusion video decoder, separate video and audio VAEs, a custom Gemma 4 12B text encoder, an optional prompt enhancer, and a duration head. Distilled and dev transformers are published as separate files. The January 2026 LTX-2 paper's 14B-plus-5B split is not restated for this 22B checkpoint.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTX-2.5 is Lightricks' current open-weights audio-video model, released on 11 August 2026. It is a 22B asymmetric dual-stream diffusion transformer with API ids `ltx-2-5-fast` and `ltx-2-5-pro`. Lightricks calls it an open world model for synchronized video and audio, native multi-shot scenes, and local fine-tuning. The Hugging Face download is gated. Collected 2026-10-15.", "what_it_is_for": "The API generates text-to-video, image-to-video, and audio-to-video, with optional silence, camera motion, and a last frame. One generation can contain several connected shots that keep character, scene, lighting, style, and voice. Docs also describe text-to-audio, EXR and HDR workflows, and diffusion fidelity rendering that spends more compute on harder moments. Retake and extend stay on LTX-2.3 Pro, not on 2.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lightricks/ltx/ltx-video": { "id": "lightricks/ltx/ltx-video", "name": "LTX-Video", "maker": "lightricks", "family": "lightricks/ltx", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Checkpoint-specific. The 5 March 2025 news post calls v0.9.5 an OpenRAIL-M commercial license. From v0.9.6 (license date 15 April 2025) the text is LTXV Open Weights License 0.X: royalty-free use, with entities at or above $10,000,000 annual revenue required to take a paid commercial license. v0.9 and v0.9.1 have their own license files.", "release_date": "2024-11-21", "context_length": null, "max_output_tokens": null, "parameters": "2B and 13B", "architecture": "Transformer latent diffusion. The December 2024 paper describes a Video-VAE at 1:192 compression (32x32x8) with the patchify step inside the VAE, full spatiotemporal self-attention, and a decoder that also performs the last denoising step in pixel space. Later 13B checkpoints are not described in that abstract.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTX-Video (also called LTXV) is Lightricks' first named video model, released on 21 November 2024. It is an open-weights diffusion transformer for text-to-video and image-to-video, shipped as 2B and later 13B checkpoints through version 0.9.8. The line is deprecated as a product: the official repository says LTX-2 is now the primary home for LTX development. The weights are still published. Collected 2026-10-15.", "what_it_is_for": "The model card describes real-time video generation, originally 30 fps at 1216\u00d7704, faster than the clip can be watched. Later checkpoints add keyframes, forward and backward extension, and multi-condition image or video guidance. It does not generate a synchronized soundtrack. That starts with LTX-2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lilt/contextual-ai/v2": { "id": "lilt/contextual-ai/v2", "name": "LILT-V2", "maker": "lilt", "family": "lilt/contextual-ai", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-03-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Generative encoder-decoder transformer built for translation. In-context learning is described as adapter layers updated after a linguist confirms a sentence. Customer- and content-type-specific copies are served from a multi-stage cache.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LILT-V2 is LILT's Spring 2023 translation generation, announced on 2023-03-29 as the Contextual AI Engine and named LILT-V2 in the 2023-09-14 V3 release. It is a closed encoder-decoder translation model with real-time adapter updates. The 2026-10-14 language index no longer lists it. This dossier was collected on 2026-10-15.", "what_it_is_for": "LILT said the engine powers Verified translation, where a person checks the output, and Instant translation, which is fully automatic. It completes a linguist's in-progress sentence and applies terminology, do-not-translate lists, capitalization, and markup.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lilt/contextual-ai/v3": { "id": "lilt/contextual-ai/v3", "name": "LILT-V3", "maker": "lilt", "family": "lilt/contextual-ai", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-09-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Larger neural translation networks with in-context learning, fine-tuning, and prompting. Current docs call the sentence-level generation V3.0. It uses translation memory and termbases and does not use full-document context. On-prem 2025 Q1 notes also attribute broader context and RLHF to V3.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LILT-V3 is LILT's September 2023 translation generation, documented in 2026 as V3.0. It is a closed model still used for the highest-volume languages, including every language the current index marks as V3.0-only. It suggests a sentence-level translation and adapts from confirmed segments. Collected 2026-10-15.", "what_it_is_for": "Enterprise translation inside LILT Translate, pretranslation, and instant text or file translation. The model is chosen through the Data Source memory, not by a public version string. V3.0 uses translation memory and termbases and stays at sentence context. V3.5 is the document-context sibling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lilt/contextual-ai/v3-5": { "id": "lilt/contextual-ai/v3-5", "name": "LILT-V3.5", "maker": "lilt", "family": "lilt/contextual-ai", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-04-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer with optimized fused attention and direct preference optimization. Vendor describes reinforcement learning for accuracy and fluency. Uses whole-document context, unlike sentence-level V3.0. On-prem service rayman 3.5 is documented as the translation service for base language pairs and needs 22 GB VRAM on an Ampere-or-newer GPU.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LILT-V3.5 is LILT's April 2024 translation generation and is still the enhanced model for high-volume languages. It is a closed transformer with fused attention and direct preference optimization, and it uses document context rather than the sentence-level context of V3.0. Collected 2026-10-15.", "what_it_is_for": "Verified and instant enterprise translation, including tag projection, terminology, and document structure. The 2026 language index says it improves naturalness by using the whole document. It is available for pairs inside each V3 regional group, except the languages marked as V3.0-only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lilt/contextual-ai/v4": { "id": "lilt/contextual-ai/v4", "name": "LILT V4.0", "maker": "lilt", "family": "lilt/contextual-ai", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published beyond adaptive multilingual translation. On-prem, rayman 4.0 is the neural v4.0 pipeline for V4 language pairs and is documented as the replacement for the dedicated Emma model on Uzbek and Chechen. It needs one extra GPU on top of rayman 3.5, 22 GB VRAM, and compute capability 8.0 or newer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LILT V4.0 is LILT's multilingual translation generation announced on 2026-04-08. It is closed, still active, and aimed at low-resource languages, with any-to-any pairs inside a 70-language list. Highest-volume languages remain on V3.0 and V3.5. Collected 2026-10-15.", "what_it_is_for": "Translation and content creation for languages the older groups did not cover, including many African, Asian, European, Caribbean, and Oceanian languages. Pairs are enabled per customer. The CAT editor previews the translation word by word. Confirmed human corrections are supposed to update the model immediately.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lilt/emma/emma": { "id": "lilt/emma/emma", "name": "Emma", "maker": "lilt", "family": "lilt/emma", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emma is a retired LILT translation model named once in the self-managed hardware guide. That guide says the neural v4.0 rayman pipeline replaced the dedicated Emma model for Uzbek and Chechen. Collected 2026-10-15.", "what_it_is_for": "Dedicated translation support for Uzbek and Chechen on self-managed installs, before that support moved into rayman 4.0. No other job is described.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/achoris/achoris": { "id": "line/achoris/achoris", "name": "Achoris", "maker": "line", "family": "line/achoris", "status": "active", "confidence": "high", "weights": "closed", "license": "Closed. No public weight license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Emotional Japanese text-to-speech engine. Speaker, emotion style, and emotion intensity are controllable. The September 2025 tech post says more than 17 speaker presets. The February 2025 press release says more than ten speakers. No parameter count or network name is published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LY Corporation's closed Japanese emotional text-to-speech engine. It is in production for narration, navigation, and dialogue. A February 2025 press release and a September 2025 engineering post both name it. Collected on 2026-10-15.", "what_it_is_for": "Emotional speech synthesis. Users can pick a speaker and control emotion style and strength. Delivery forms are an API, the on-device build Achoris Lite, and the editor Achoris Editor. Yahoo! Car Navi, Yahoo! Map, and Yahoo! Browser use the on-device build. Editor was given to TOPPAN Chore in February 2025 for long Japanese audiobooks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/chronaccret/chronaccret": { "id": "line/chronaccret/chronaccret", "name": "ChronAccRet", "maker": "line", "family": "line/chronaccret", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sample pretrained weights for chronologically accurate retrieval used to temporally ground motion-language models. ECCV 2024. The hub card does not list parameter counts.", "modalities": { "input": [ "text", "motion" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LINE published sample weights for Chronologically Accurate Retrieval, an ECCV 2024 method for temporal grounding in motion-language models. The hub repo dates to 2024-08-05 and the license is Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "Retrieval that keeps time order so a motion-language model can ground a description in a motion sequence. The card says these are sample pretrained weights and points to the paper and project page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/clip/clip-japanese-base": { "id": "line/clip/clip-japanese-base", "name": "clip-japanese-base", "maker": "line", "family": "line/clip", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-05-14", "context_length": null, "max_output_tokens": null, "parameters": "196M", "architecture": "Japanese CLIP. Image encoder Eva02-B (86M on the model card). Text encoder is a 12-layer BERT (100M on the model card), initialized from rinna/japanese-clip-vit-b-16. The 2025-12-18 comparison lists 196M for this model. 86M plus 100M is 186M, so the total is inconsistent across official pages. Custom code. Trust remote code is required.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LY Corporation released this Japanese CLIP on 2024-05-14 under Apache-2.0. It embeds images and Japanese text for zero-shot classification and retrieval. Weights remain public. Collected on 2026-10-15.", "what_it_is_for": "A Japanese contrastive image-text model for zero-shot classification and text-to-image or image-to-text retrieval. LY Corporation also uses the same line of models to review Yahoo! Auctions listings. This checkpoint is not a generative vision-language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/clip/clip-japanese-base-v2": { "id": "line/clip/clip-japanese-base-v2", "name": "clip-japanese-base-v2", "maker": "line", "family": "line/clip", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "196M", "architecture": "Updated Japanese CLIP. Same encoder families as v1: Eva02-B image encoder and a 12-layer BERT text encoder initialized from rinna/japanese-clip-vit-b-16. Trained with more filtered Common Crawl pairs and CLIP-KD style distillation (ICL plus a modified CRD). The blog says the feature dimension was reduced from 512 to 256, which hurt STAIR relative to v1. The teacher vision encoder was siglip2-so400m-patch14-224, further trained by LY on Japanese data.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LY Corporation released this updated Japanese CLIP on 2025-12-18 under Apache-2.0. It replaces clip-japanese-base for most zero-shot scores and is still the current public checkpoint. Collected on 2026-10-15.", "what_it_is_for": "Zero-shot Japanese image classification and image-text retrieval. The team kept the small Eva02-B plus BERT shape, increased the filtered Common Crawl set, and distilled from a larger Japanese-adapted teacher. It is not a generative model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/distilbert/line-distilbert-base-japanese": { "id": "line/distilbert/line-distilbert-base-japanese", "name": "LINE DistilBERT Japanese", "maker": "line", "family": "line/distilbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-03-13", "context_length": 512, "max_output_tokens": null, "parameters": "68M", "architecture": "DistilBERT base: 6 layers, hidden size 768, 12 heads, vocab 32768, sinusoidal positions, max length 512. MeCab with UniDic, then SentencePiece. Distilled from an unreleased in-house LINE BERT-base. The architecture paragraph says 66M parameters. The JGLUE table says 68M. File size on the blog is 262MB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LINE Corporation released this Japanese DistilBERT on 2023-03-13 under Apache-2.0. It is a fill-mask encoder, still on Hugging Face, and LY Corporation's research index still lists it. Collected on 2026-10-15.", "what_it_is_for": "A small Japanese encoder for classification, similarity, and extractive QA. LINE trained it on 131 GB of Japanese web text and distilled it from an in-house BERT-base teacher that was not released. The maker reports higher JGLUE dev scores than Laboro DistilBERT and Bandai Namco DistilBERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/japanese-large-lm/japanese-large-lm-1-7b": { "id": "line/japanese-large-lm/japanese-large-lm-1-7b", "name": "Japanese Large LM 1.7B", "maker": "line", "family": "line/japanese-large-lm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-08-14", "context_length": 2048, "max_output_tokens": null, "parameters": "1.7B", "architecture": "GPT-2 decoder. 24 layers, hidden size 2304, 24 heads, vocab 51200, absolute positions. SentencePiece unigram tokenizer with byte fallback and no Japanese pre-tokenizer. Hub config n_positions and n_ctx are 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LINE Corporation's Massive LM unit released this 1.7 billion parameter Japanese causal language model on 2023-08-14, under Apache-2.0. Weights remain on Hugging Face. This dossier was collected on 2026-10-15.", "what_it_is_for": "A pretrained Japanese decoder for research and commercial fine-tuning. LINE described it as a separate line from HyperCLOVA. It is not an instruction model. The maker says the base checkpoint is weak at following instructions until the later SFT release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/japanese-large-lm/japanese-large-lm-1-7b-instruction-sft": { "id": "line/japanese-large-lm/japanese-large-lm-1-7b-instruction-sft", "name": "Japanese Large LM 1.7B Instruction SFT", "maker": "line", "family": "line/japanese-large-lm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-08-18", "context_length": 2048, "max_output_tokens": null, "parameters": "1.7B", "architecture": "Instruction-tuned GPT-2, same shape as japanese-large-lm-1.7b: 24 layers, hidden 2304, 24 heads, vocab 51200, context 2048. Prompt format is \u30e6\u30fc\u30b6\u30fc: then a newline, and generation starts at \u30b7\u30b9\u30c6\u30e0:.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LINE Corporation released this instruction-tuned 1.7B Japanese chat model on 2023-08-18, Apache-2.0, still hosted on Hugging Face. Collected on 2026-10-15.", "what_it_is_for": "A supervised fine-tune of Japanese Large LM 1.7B so the model answers user instructions instead of continuing web text. LINE points it at chatbot-style use. The prompt starts with `\u30e6\u30fc\u30b6\u30fc:` and the reply starts at `\u30b7\u30b9\u30c6\u30e0:`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/japanese-large-lm/japanese-large-lm-3-6b": { "id": "line/japanese-large-lm/japanese-large-lm-3-6b", "name": "Japanese Large LM 3.6B", "maker": "line", "family": "line/japanese-large-lm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-08-14", "context_length": 2048, "max_output_tokens": null, "parameters": "3.6B", "architecture": "GPT-NeoX decoder. 30 layers, hidden size 3072, 32 heads, vocab 51200, RoPE, tied embeddings. SentencePiece unigram with byte fallback and no Japanese pre-tokenizer. Hub config max_position_embeddings is 2048. use_parallel_residual is false.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LINE Corporation released this 3.6 billion parameter Japanese GPT-NeoX model on 2023-08-14 with Apache-2.0 weights. It is still downloadable. Collected on 2026-10-15.", "what_it_is_for": "The larger pretrained Japanese language model from LINE's Massive LM unit. LINE positioned it for research and commercial use, and as a base for the instruction-tuned model published four days later. It is not HyperCLOVA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/japanese-large-lm/japanese-large-lm-3-6b-instruction-sft": { "id": "line/japanese-large-lm/japanese-large-lm-3-6b-instruction-sft", "name": "Japanese Large LM 3.6B Instruction SFT", "maker": "line", "family": "line/japanese-large-lm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-08-18", "context_length": 2048, "max_output_tokens": null, "parameters": "3.6B", "architecture": "Instruction-tuned GPT-NeoX, same shape as japanese-large-lm-3.6b: 30 layers, hidden 3072, 32 heads, RoPE, vocab 51200, context 2048. Chat prompt is \u30e6\u30fc\u30b6\u30fc: / \u30b7\u30b9\u30c6\u30e0:.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LINE Corporation released this instruction-tuned 3.6B Japanese model on 2023-08-18 under Apache-2.0. Weights are still public. Collected on 2026-10-15.", "what_it_is_for": "Supervised fine-tune of Japanese Large LM 3.6B for instruction following and simple dialogue. LINE's examples include copy-editing, list selection, and how-to answers. Prompts use `\u30e6\u30fc\u30b6\u30fc:` and answers start at `\u30b7\u30b9\u30c6\u30e0:`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/moegi/moegi-bert-110m": { "id": "line/moegi/moegi-bert-110m", "name": "Moegi BERT 110M", "maker": "line", "family": "line/moegi", "status": "active", "confidence": "high", "weights": "closed", "license": "Internal. No public license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "110M", "architecture": "BERT-family encoder, called bert-110m in the Moegi post. Continued pretraining on service text. Served from internal object storage named Dragon. The post says settings change per service, so this size is the reported model, not proof of a single checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LY Corporation described this internal Japanese BERT on 2026-10-15. Moegi is the project that shares service-specific encoders inside the company. The scored model is called bert-110m. Weights are not public. Collected on 2026-10-15.", "what_it_is_for": "Domain pretraining for Yahoo! Shopping, Yahoo! Search, and similar high-volume classification and ranking. LY says a small BERT is a better fit than an LLM when request volume makes latency and cost the constraint. Checkpoints sit in internal storage called Dragon. Teams ask for access.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/motion-patches/motion-patches": { "id": "line/motion-patches/motion-patches", "name": "Motion Patches", "maker": "line", "family": "line/motion-patches", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-06-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pretrained weights for a vision-transformer 3D human motion-language model that uses motion patches. CVPR 2024. The hub card does not list layer counts or parameter totals.", "modalities": { "input": [ "text" ], "output": [ "motion" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LINE published pretrained weights for the CVPR 2024 motion-language model Motion Patches. The hub repo dates to 2024-06-12 and the license is CC BY-NC 4.0. Collected on 2026-10-15.", "what_it_is_for": "A 3D human motion-language model. The card says the weights match the paper \"Exploring Vision Transformers for 3D Human Motion-Language Models with Motion Patches\" and sends readers to the project page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/mulan/japanese-mulan-base": { "id": "line/mulan/japanese-mulan-base", "name": "Japanese MuLan Base", "maker": "line", "family": "line/mulan", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-06-25", "context_length": null, "max_output_tokens": null, "parameters": "0.2B", "architecture": "Music-language contrastive model. Music encoder is an Audio Spectrogram Transformer initialized from the official AudioSet AST. Text encoder is GLuCoSE, initialized from pkshatech/GLuCoSE-base-ja. Hub model-size tag is 0.2B. Trained on about 20,000 internal music-text pairs. Music embeddings are not L2-normalized by the model.", "modalities": { "input": [ "text", "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LY Corporation published this Japanese music-text embedding model on Hugging Face on 2025-06-25 under Apache-2.0. It is for zero-shot music classification and music-text retrieval, not generation. Collected on 2026-10-15.", "what_it_is_for": "Contrastive matching between music audio and Japanese text. The card's example labels a clip as rock, hip-hop, jazz, or classical. Retrieval in either direction is the other stated use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/sacpo/p-sacpo": { "id": "line/sacpo/p-sacpo", "name": "P-SACPO", "maker": "line", "family": "line/sacpo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-06-19", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "LlamaForCausalLM, same shape as SACPO: 32 layers, hidden 4096, 32 heads, intermediate 11008, vocab 32001, context 2048, bfloat16. Parameter merge of two DPO models. Alias: P-SACPO 0.75. Weight 0.25 on the helpfulness-aligned model and 0.75 on the harmlessness-aligned model, merged with mergekit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LY Corporation released this 7B English chat merge on 2024-06-19 under CC BY-NC 4.0. The card identifies it as `P-SACPO 0.75` from the SACPO paper. Collected on 2026-10-15.", "what_it_is_for": "A helpful and harmless chat model made by merging two DPO checkpoints instead of stacking the second alignment only. It uses the same Alpaca conversation prompt as SACPO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/sacpo/sacpo": { "id": "line/sacpo/sacpo", "name": "SACPO", "maker": "line", "family": "line/sacpo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-06-11", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "LlamaForCausalLM. 32 layers, hidden 4096, 32 heads, intermediate 11008, vocab 32001, RoPE, bfloat16, max position 2048. Fine-tune of PKU-Alignment/alpaca-7b-reproduced. Alias on the card: SACPO: DPO (H) -> DPO (S) 0.025. Helpfulness DPO, then harmlessness DPO with beta/lambda 0.025. Data: PKU-SafeRLHF-30K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LY Corporation researchers released this 7B English chat model on Hugging Face on 2024-06-11 under CC BY-NC 4.0. SACPO is their stepwise alignment method. The checkpoint is the paper's `DPO (H) -> DPO (S) 0.025` run. Collected on 2026-10-15.", "what_it_is_for": "A local chat assistant aimed at both helpfulness and harmlessness. It is not a LINE-pretrained foundation model. The base is PKU's reproduction of Stanford Alpaca, then two direct-preference stages on PKU-SafeRLHF-30K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/universe/universe": { "id": "line/universe/universe", "name": "UNIVERSE", "maker": "line", "family": "line/universe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LINE's Voicebank-DEMAND training of the UNIVERSE score-based diffusion speech enhancer. Tag original in line-corporation/open-universe. The method paper is Serr\u00e0 et al. 2022, not a LINE paper. Sample rate on the card is 16 kHz.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LINE published a Voicebank-DEMAND training of the UNIVERSE speech enhancer in the same hub repo as UNIVERSE++, created 2024-06-11, Apache-2.0. The method is from Serr\u00e0 et al. 2022. Collected on 2026-10-15.", "what_it_is_for": "Generative speech enhancement. Call it with `--model line-corporation/open-universe:original`. UNIVERSE++ is the newer LINE model in the same package.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/universe/universe-plusplus": { "id": "line/universe/universe-plusplus", "name": "UNIVERSE++", "maker": "line", "family": "line/universe", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Score-based diffusion speech enhancement with adversarial training. Default checkpoint in the open-universe repo, tag plusplus. Trained at 16 kHz on Voicebank-DEMAND. Interspeech 2024 paper by Scheibler, Fujita, Shirahata, and Komatsu.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LINE's speech group released this generative speech-enhancement model in the `line-corporation/open-universe` repo. The hub repo dates to 2024-06-11. The paper is Interspeech 2024. License on the hub is Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "Enhance noisy speech while keeping content. It is the default model in open-universe (`--model line-corporation/open-universe:plusplus`). The method adds adversarial training to score-based diffusion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/wavetrainerfit/wavetrainerfit": { "id": "line/wavetrainerfit/wavetrainerfit", "name": "WaveTrainerFit", "maker": "line", "family": "line/wavetrainerfit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Mixed. xlsr and whisper checkpoints: CC BY 4.0. wavlm checkpoints: CC BY-SA 3.0. Training data LibriTTS-R is CC BY 4.0. Upstream model licenses also apply.", "release_date": "2025-12-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural vocoder from self-supervised speech features. Extends WaveFit with trainable priors and reference-aware gain so inference starts nearer the target waveform. Reconstructs 24 kHz audio from 16 kHz SSL features. Pretrained on LibriTTS-R train-clean-360. Published checkpoints use 5 iterations.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LINE published this neural vocoder on 2025-12-19. It turns self-supervised speech features into 24 kHz waveforms. Licenses differ by checkpoint. Collected on 2026-10-15.", "what_it_is_for": "A vocoder that rebuilds speech from SSL features such as WavLM, XLS-R, or Whisper. The card says trainable priors and a gain constraint cut the number of diffusion steps versus WaveFit. It is a research release, not a public TTS API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "line/yjvoice/yjvoice": { "id": "line/yjvoice/yjvoice", "name": "YJVOICE", "maker": "line", "family": "line/yjvoice", "status": "active", "confidence": "high", "weights": "closed", "license": "Closed. No public weight license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Streaming Japanese speech recognition used by LINE Yahoo. A 2015 Yahoo Japan announcement added deep learning for noise robustness. The 2025 LY post does not name the current network, size, or sample rate. It does say the product is domain-tuned, resolves homophones, and can be customized.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Streaming Japanese speech recognition used by LINE Yahoo. Yahoo Japan announced a deep-learning update on 2015-05-19. LY Corporation's September 2025 engineering post still names YJVOICE as the in-house ASR. Weights are closed. Collected on 2026-10-15.", "what_it_is_for": "Domain-tuned streaming recognition for web search and internal tools. The 2025 post lists homophone resolution and customization as the product traits, and says the Yahoo! JAPAN app uses it for realtime voice search across map, transit, shopping, and more than 17 services.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/af-en": { "id": "lingvanex/nmt/af-en", "name": "Lingvanex Afrikaans to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Afrikaans to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Afrikaans into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/am-en": { "id": "lingvanex/nmt/am-en", "name": "Lingvanex Amharic to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Amharic to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Amharic into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ar-en": { "id": "lingvanex/nmt/ar-en", "name": "Lingvanex Arabic to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Arabic to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Arabic into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/az-en": { "id": "lingvanex/nmt/az-en", "name": "Lingvanex Azerbaijani to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Azerbaijani to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Azerbaijani into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/az-ru": { "id": "lingvanex/nmt/az-ru", "name": "Lingvanex Azerbaijani to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Azerbaijani to Russian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Azerbaijani into Russian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/be-en": { "id": "lingvanex/nmt/be-en", "name": "Lingvanex Belarusian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Belarusian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Belarusian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/bg-en": { "id": "lingvanex/nmt/bg-en", "name": "Lingvanex Bulgarian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Bulgarian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Bulgarian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/bn-en": { "id": "lingvanex/nmt/bn-en", "name": "Lingvanex Bengali to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Bengali to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Bengali into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/bs-en": { "id": "lingvanex/nmt/bs-en", "name": "Lingvanex Bosnian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Bosnian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Bosnian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ca-en": { "id": "lingvanex/nmt/ca-en", "name": "Lingvanex Catalan to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Catalan to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Catalan into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ceb-en": { "id": "lingvanex/nmt/ceb-en", "name": "Lingvanex Cebuano to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Cebuano to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Cebuano into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/co-en": { "id": "lingvanex/nmt/co-en", "name": "Lingvanex Corsican to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Corsican to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Corsican into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/cs-en": { "id": "lingvanex/nmt/cs-en", "name": "Lingvanex Czech to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Czech to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Czech into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/cy-en": { "id": "lingvanex/nmt/cy-en", "name": "Lingvanex Welsh to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Welsh to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Welsh into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/da-en": { "id": "lingvanex/nmt/da-en", "name": "Lingvanex Danish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Danish to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Danish into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/de-en": { "id": "lingvanex/nmt/de-en", "name": "Lingvanex German to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for German to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate German into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/el-en": { "id": "lingvanex/nmt/el-en", "name": "Lingvanex Greek to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Greek to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Greek into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-af": { "id": "lingvanex/nmt/en-af", "name": "Lingvanex English to Afrikaans", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Afrikaans. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Afrikaans. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-am": { "id": "lingvanex/nmt/en-am", "name": "Lingvanex English to Amharic", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Amharic. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Amharic. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ar": { "id": "lingvanex/nmt/en-ar", "name": "Lingvanex English to Arabic", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Arabic. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Arabic. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-az": { "id": "lingvanex/nmt/en-az", "name": "Lingvanex English to Azerbaijani", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Azerbaijani. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Azerbaijani. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-be": { "id": "lingvanex/nmt/en-be", "name": "Lingvanex English to Belarusian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Belarusian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Belarusian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-bg": { "id": "lingvanex/nmt/en-bg", "name": "Lingvanex English to Bulgarian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Bulgarian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Bulgarian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-bn": { "id": "lingvanex/nmt/en-bn", "name": "Lingvanex English to Bengali", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Bengali. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Bengali. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-bs": { "id": "lingvanex/nmt/en-bs", "name": "Lingvanex English to Bosnian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Bosnian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Bosnian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ca": { "id": "lingvanex/nmt/en-ca", "name": "Lingvanex English to Catalan", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Catalan. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Catalan. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ceb": { "id": "lingvanex/nmt/en-ceb", "name": "Lingvanex English to Cebuano", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Cebuano. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Cebuano. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-co": { "id": "lingvanex/nmt/en-co", "name": "Lingvanex English to Corsican", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Corsican. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Corsican. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-cs": { "id": "lingvanex/nmt/en-cs", "name": "Lingvanex English to Czech", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Czech. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Czech. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-cy": { "id": "lingvanex/nmt/en-cy", "name": "Lingvanex English to Welsh", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Welsh. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Welsh. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-da": { "id": "lingvanex/nmt/en-da", "name": "Lingvanex English to Danish", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Danish. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Danish. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-de": { "id": "lingvanex/nmt/en-de", "name": "Lingvanex English to German", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to German. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into German. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-el": { "id": "lingvanex/nmt/en-el", "name": "Lingvanex English to Greek", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Greek. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Greek. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-eo": { "id": "lingvanex/nmt/en-eo", "name": "Lingvanex English to Esperanto", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Esperanto. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Esperanto. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-es": { "id": "lingvanex/nmt/en-es", "name": "Lingvanex English to Spanish", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Spanish. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Spanish. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-et": { "id": "lingvanex/nmt/en-et", "name": "Lingvanex English to Estonian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Estonian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Estonian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-eu": { "id": "lingvanex/nmt/en-eu", "name": "Lingvanex English to Basque", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Basque. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Basque. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-fa": { "id": "lingvanex/nmt/en-fa", "name": "Lingvanex English to Persian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Persian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Persian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-fi": { "id": "lingvanex/nmt/en-fi", "name": "Lingvanex English to Finnish", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Finnish. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Finnish. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-fil": { "id": "lingvanex/nmt/en-fil", "name": "Lingvanex English to Filipino", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Filipino. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Filipino. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-fr": { "id": "lingvanex/nmt/en-fr", "name": "Lingvanex English to French", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to French. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into French. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-fy": { "id": "lingvanex/nmt/en-fy", "name": "Lingvanex English to Frisian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Frisian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Frisian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ga": { "id": "lingvanex/nmt/en-ga", "name": "Lingvanex English to Irish", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Irish. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Irish. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-gd": { "id": "lingvanex/nmt/en-gd", "name": "Lingvanex English to Gaelic", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Gaelic. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Gaelic. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-gl": { "id": "lingvanex/nmt/en-gl", "name": "Lingvanex English to Galician", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Galician. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Galician. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-gu": { "id": "lingvanex/nmt/en-gu", "name": "Lingvanex English to Gujarati", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Gujarati. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Gujarati. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ha": { "id": "lingvanex/nmt/en-ha", "name": "Lingvanex English to Hausa", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Hausa. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Hausa. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-haw": { "id": "lingvanex/nmt/en-haw", "name": "Lingvanex English to Hawaiian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Hawaiian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Hawaiian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-he": { "id": "lingvanex/nmt/en-he", "name": "Lingvanex English to Hebrew", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Hebrew. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Hebrew. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-hi": { "id": "lingvanex/nmt/en-hi", "name": "Lingvanex English to Hindi", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Hindi. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Hindi. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-hmn": { "id": "lingvanex/nmt/en-hmn", "name": "Lingvanex English to Hmong", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Hmong. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Hmong. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-hr": { "id": "lingvanex/nmt/en-hr", "name": "Lingvanex English to Croatian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Croatian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Croatian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ht": { "id": "lingvanex/nmt/en-ht", "name": "Lingvanex English to Haitian Creole", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Haitian Creole. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Haitian Creole. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-hu": { "id": "lingvanex/nmt/en-hu", "name": "Lingvanex English to Hungarian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Hungarian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Hungarian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-hy": { "id": "lingvanex/nmt/en-hy", "name": "Lingvanex English to Armenian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Armenian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Armenian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-id": { "id": "lingvanex/nmt/en-id", "name": "Lingvanex English to Indonesian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Indonesian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Indonesian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ig": { "id": "lingvanex/nmt/en-ig", "name": "Lingvanex English to Igbo", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Igbo. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Igbo. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-is": { "id": "lingvanex/nmt/en-is", "name": "Lingvanex English to Icelandic", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Icelandic. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Icelandic. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-it": { "id": "lingvanex/nmt/en-it", "name": "Lingvanex English to Italian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Italian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Italian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ja": { "id": "lingvanex/nmt/en-ja", "name": "Lingvanex English to Japanese", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Japanese. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Japanese. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-jv": { "id": "lingvanex/nmt/en-jv", "name": "Lingvanex English to Javanese", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Javanese. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Javanese. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ka": { "id": "lingvanex/nmt/en-ka", "name": "Lingvanex English to Georgian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Georgian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Georgian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-kk": { "id": "lingvanex/nmt/en-kk", "name": "Lingvanex English to Kazakh", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Kazakh. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Kazakh. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-km": { "id": "lingvanex/nmt/en-km", "name": "Lingvanex English to Khmer", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Khmer. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Khmer. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-kn": { "id": "lingvanex/nmt/en-kn", "name": "Lingvanex English to Kannada", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Kannada. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Kannada. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ko": { "id": "lingvanex/nmt/en-ko", "name": "Lingvanex English to Korean", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Korean. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Korean. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ku": { "id": "lingvanex/nmt/en-ku", "name": "Lingvanex English to Kurdish", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Kurdish. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Kurdish. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ky": { "id": "lingvanex/nmt/en-ky", "name": "Lingvanex English to Kyrgyz", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Kyrgyz. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Kyrgyz. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-la": { "id": "lingvanex/nmt/en-la", "name": "Lingvanex English to Latin", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Latin. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Latin. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-lb": { "id": "lingvanex/nmt/en-lb", "name": "Lingvanex English to Luxembourgish", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Luxembourgish. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Luxembourgish. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-lo": { "id": "lingvanex/nmt/en-lo", "name": "Lingvanex English to Lao", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Lao. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Lao. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-lt": { "id": "lingvanex/nmt/en-lt", "name": "Lingvanex English to Lithuanian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Lithuanian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Lithuanian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-lv": { "id": "lingvanex/nmt/en-lv", "name": "Lingvanex English to Latvian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Latvian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Latvian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-mg": { "id": "lingvanex/nmt/en-mg", "name": "Lingvanex English to Malagasy", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Malagasy. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Malagasy. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-mi": { "id": "lingvanex/nmt/en-mi", "name": "Lingvanex English to Maori", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Maori. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Maori. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-mk": { "id": "lingvanex/nmt/en-mk", "name": "Lingvanex English to Macedonian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Macedonian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Macedonian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ml": { "id": "lingvanex/nmt/en-ml", "name": "Lingvanex English to Malayalam", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Malayalam. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Malayalam. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-mn": { "id": "lingvanex/nmt/en-mn", "name": "Lingvanex English to Mongolian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Mongolian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Mongolian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-mr": { "id": "lingvanex/nmt/en-mr", "name": "Lingvanex English to Marathi", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Marathi. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Marathi. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ms": { "id": "lingvanex/nmt/en-ms", "name": "Lingvanex English to Malay", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Malay. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Malay. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-mt": { "id": "lingvanex/nmt/en-mt", "name": "Lingvanex English to Maltese", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Maltese. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Maltese. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-my": { "id": "lingvanex/nmt/en-my", "name": "Lingvanex English to Myanmar", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Myanmar. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Myanmar. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ne": { "id": "lingvanex/nmt/en-ne", "name": "Lingvanex English to Nepali", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Nepali. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Nepali. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-nl": { "id": "lingvanex/nmt/en-nl", "name": "Lingvanex English to Dutch", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Dutch. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Dutch. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-no": { "id": "lingvanex/nmt/en-no", "name": "Lingvanex English to Norwegian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Norwegian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Norwegian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ny": { "id": "lingvanex/nmt/en-ny", "name": "Lingvanex English to Chichewa", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Chichewa. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Chichewa. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-or": { "id": "lingvanex/nmt/en-or", "name": "Lingvanex English to Odia", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Odia. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Odia. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-pa": { "id": "lingvanex/nmt/en-pa", "name": "Lingvanex English to Punjabi", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Punjabi. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Punjabi. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-pl": { "id": "lingvanex/nmt/en-pl", "name": "Lingvanex English to Polish", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Polish. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Polish. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ps": { "id": "lingvanex/nmt/en-ps", "name": "Lingvanex English to Pushto", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Pushto. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Pushto. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-pt": { "id": "lingvanex/nmt/en-pt", "name": "Lingvanex English to Portuguese", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Portuguese. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Portuguese. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-pt-br": { "id": "lingvanex/nmt/en-pt-br", "name": "Lingvanex English to Portuguese (Brazil)", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Portuguese (Brazil). It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Portuguese (Brazil). The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ro": { "id": "lingvanex/nmt/en-ro", "name": "Lingvanex English to Romanian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Romanian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Romanian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ru": { "id": "lingvanex/nmt/en-ru", "name": "Lingvanex English to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Russian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Russian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-rw": { "id": "lingvanex/nmt/en-rw", "name": "Lingvanex English to Kinyarwanda", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Kinyarwanda. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Kinyarwanda. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-sd": { "id": "lingvanex/nmt/en-sd", "name": "Lingvanex English to Sindhi", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Sindhi. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Sindhi. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-si": { "id": "lingvanex/nmt/en-si", "name": "Lingvanex English to Sinhala", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Sinhala. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Sinhala. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-sk": { "id": "lingvanex/nmt/en-sk", "name": "Lingvanex English to Slovak", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Slovak. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Slovak. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-sl": { "id": "lingvanex/nmt/en-sl", "name": "Lingvanex English to Slovenian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Slovenian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Slovenian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-sm": { "id": "lingvanex/nmt/en-sm", "name": "Lingvanex English to Samoan", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Samoan. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Samoan. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-sn": { "id": "lingvanex/nmt/en-sn", "name": "Lingvanex English to Shona", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Shona. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Shona. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-so": { "id": "lingvanex/nmt/en-so", "name": "Lingvanex English to Somali", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Somali. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Somali. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-sq": { "id": "lingvanex/nmt/en-sq", "name": "Lingvanex English to Albanian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Albanian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Albanian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-sr": { "id": "lingvanex/nmt/en-sr", "name": "Lingvanex English to Serbian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Serbian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Serbian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-st": { "id": "lingvanex/nmt/en-st", "name": "Lingvanex English to Sesotho", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Sesotho. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Sesotho. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-su": { "id": "lingvanex/nmt/en-su", "name": "Lingvanex English to Sundanese", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Sundanese. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Sundanese. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-sv": { "id": "lingvanex/nmt/en-sv", "name": "Lingvanex English to Swedish", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Swedish. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Swedish. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-sw": { "id": "lingvanex/nmt/en-sw", "name": "Lingvanex English to Swahili", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Swahili. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Swahili. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ta": { "id": "lingvanex/nmt/en-ta", "name": "Lingvanex English to Tamil", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Tamil. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Tamil. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-te": { "id": "lingvanex/nmt/en-te", "name": "Lingvanex English to Telugu", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Telugu. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Telugu. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-tg": { "id": "lingvanex/nmt/en-tg", "name": "Lingvanex English to Tajik", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Tajik. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Tajik. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-th": { "id": "lingvanex/nmt/en-th", "name": "Lingvanex English to Thai", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Thai. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Thai. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-tk": { "id": "lingvanex/nmt/en-tk", "name": "Lingvanex English to Turkmen", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Turkmen. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Turkmen. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-tr": { "id": "lingvanex/nmt/en-tr", "name": "Lingvanex English to Turkish", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Turkish. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Turkish. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-tt": { "id": "lingvanex/nmt/en-tt", "name": "Lingvanex English to Tatar", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Tatar. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Tatar. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ug": { "id": "lingvanex/nmt/en-ug", "name": "Lingvanex English to Uyghur", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Uyghur. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Uyghur. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-uk": { "id": "lingvanex/nmt/en-uk", "name": "Lingvanex English to Ukrainian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Ukrainian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Ukrainian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-ur": { "id": "lingvanex/nmt/en-ur", "name": "Lingvanex English to Urdu", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Urdu. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Urdu. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-uz": { "id": "lingvanex/nmt/en-uz", "name": "Lingvanex English to Uzbek", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Uzbek. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Uzbek. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-vi": { "id": "lingvanex/nmt/en-vi", "name": "Lingvanex English to Vietnamese", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Vietnamese. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Vietnamese. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-xh": { "id": "lingvanex/nmt/en-xh", "name": "Lingvanex English to Xhosa", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Xhosa. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Xhosa. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-yi": { "id": "lingvanex/nmt/en-yi", "name": "Lingvanex English to Yiddish", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Yiddish. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Yiddish. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-yo": { "id": "lingvanex/nmt/en-yo", "name": "Lingvanex English to Yoruba", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Yoruba. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Yoruba. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-zh-hans": { "id": "lingvanex/nmt/en-zh-hans", "name": "Lingvanex English to Chinese (Simplified)", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Chinese (Simplified). It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Chinese (Simplified). The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-zh-hant": { "id": "lingvanex/nmt/en-zh-hant", "name": "Lingvanex English to Chinese (Traditional)", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Chinese (Traditional). It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Chinese (Traditional). The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/en-zu": { "id": "lingvanex/nmt/en-zu", "name": "Lingvanex English to Zulu", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for English to Zulu. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate English into Zulu. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/eo-en": { "id": "lingvanex/nmt/eo-en", "name": "Lingvanex Esperanto to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Esperanto to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Esperanto into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/es-en": { "id": "lingvanex/nmt/es-en", "name": "Lingvanex Spanish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Spanish to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Spanish into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/et-en": { "id": "lingvanex/nmt/et-en", "name": "Lingvanex Estonian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Estonian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Estonian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/eu-en": { "id": "lingvanex/nmt/eu-en", "name": "Lingvanex Basque to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Basque to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Basque into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/fa-en": { "id": "lingvanex/nmt/fa-en", "name": "Lingvanex Persian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Persian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Persian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/fi-en": { "id": "lingvanex/nmt/fi-en", "name": "Lingvanex Finnish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Finnish to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Finnish into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/fil-en": { "id": "lingvanex/nmt/fil-en", "name": "Lingvanex Filipino to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Filipino to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Filipino into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/fr-en": { "id": "lingvanex/nmt/fr-en", "name": "Lingvanex French to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for French to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate French into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/fy-en": { "id": "lingvanex/nmt/fy-en", "name": "Lingvanex Frisian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Frisian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Frisian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ga-en": { "id": "lingvanex/nmt/ga-en", "name": "Lingvanex Irish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Irish to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Irish into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/gd-en": { "id": "lingvanex/nmt/gd-en", "name": "Lingvanex Gaelic to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Gaelic to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Gaelic into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/gl-en": { "id": "lingvanex/nmt/gl-en", "name": "Lingvanex Galician to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Galician to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Galician into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/gu-en": { "id": "lingvanex/nmt/gu-en", "name": "Lingvanex Gujarati to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Gujarati to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Gujarati into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ha-en": { "id": "lingvanex/nmt/ha-en", "name": "Lingvanex Hausa to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Hausa to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Hausa into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/haw-en": { "id": "lingvanex/nmt/haw-en", "name": "Lingvanex Hawaiian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Hawaiian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Hawaiian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/he-en": { "id": "lingvanex/nmt/he-en", "name": "Lingvanex Hebrew to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Hebrew to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Hebrew into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/hi-en": { "id": "lingvanex/nmt/hi-en", "name": "Lingvanex Hindi to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Hindi to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Hindi into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/hmn-en": { "id": "lingvanex/nmt/hmn-en", "name": "Lingvanex Hmong to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Hmong to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Hmong into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/hr-en": { "id": "lingvanex/nmt/hr-en", "name": "Lingvanex Croatian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Croatian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Croatian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ht-en": { "id": "lingvanex/nmt/ht-en", "name": "Lingvanex Haitian Creole to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Haitian Creole to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Haitian Creole into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/hu-en": { "id": "lingvanex/nmt/hu-en", "name": "Lingvanex Hungarian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Hungarian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Hungarian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/hy-en": { "id": "lingvanex/nmt/hy-en", "name": "Lingvanex Armenian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Armenian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Armenian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/hy-ru": { "id": "lingvanex/nmt/hy-ru", "name": "Lingvanex Armenian to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Armenian to Russian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Armenian into Russian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/id-en": { "id": "lingvanex/nmt/id-en", "name": "Lingvanex Indonesian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Indonesian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Indonesian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ig-en": { "id": "lingvanex/nmt/ig-en", "name": "Lingvanex Igbo to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Igbo to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Igbo into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/is-en": { "id": "lingvanex/nmt/is-en", "name": "Lingvanex Icelandic to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Icelandic to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Icelandic into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/it-en": { "id": "lingvanex/nmt/it-en", "name": "Lingvanex Italian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Italian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Italian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ja-en": { "id": "lingvanex/nmt/ja-en", "name": "Lingvanex Japanese to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Japanese to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Japanese into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/jv-en": { "id": "lingvanex/nmt/jv-en", "name": "Lingvanex Javanese to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Javanese to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Javanese into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ka-en": { "id": "lingvanex/nmt/ka-en", "name": "Lingvanex Georgian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Georgian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Georgian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ka-ru": { "id": "lingvanex/nmt/ka-ru", "name": "Lingvanex Georgian to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Georgian to Russian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Georgian into Russian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/kk-en": { "id": "lingvanex/nmt/kk-en", "name": "Lingvanex Kazakh to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Kazakh to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Kazakh into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/kk-ru": { "id": "lingvanex/nmt/kk-ru", "name": "Lingvanex Kazakh to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Kazakh to Russian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Kazakh into Russian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/km-en": { "id": "lingvanex/nmt/km-en", "name": "Lingvanex Khmer to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Khmer to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Khmer into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/kn-en": { "id": "lingvanex/nmt/kn-en", "name": "Lingvanex Kannada to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Kannada to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Kannada into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ko-en": { "id": "lingvanex/nmt/ko-en", "name": "Lingvanex Korean to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Korean to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Korean into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ku-en": { "id": "lingvanex/nmt/ku-en", "name": "Lingvanex Kurdish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Kurdish to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Kurdish into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ky-en": { "id": "lingvanex/nmt/ky-en", "name": "Lingvanex Kyrgyz to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Kyrgyz to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Kyrgyz into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ky-ru": { "id": "lingvanex/nmt/ky-ru", "name": "Lingvanex Kyrgyz to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Kyrgyz to Russian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Kyrgyz into Russian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/la-en": { "id": "lingvanex/nmt/la-en", "name": "Lingvanex Latin to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Latin to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Latin into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/lb-en": { "id": "lingvanex/nmt/lb-en", "name": "Lingvanex Luxembourgish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Luxembourgish to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Luxembourgish into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/lo-en": { "id": "lingvanex/nmt/lo-en", "name": "Lingvanex Lao to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Lao to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Lao into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/lt-en": { "id": "lingvanex/nmt/lt-en", "name": "Lingvanex Lithuanian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Lithuanian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Lithuanian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/lv-en": { "id": "lingvanex/nmt/lv-en", "name": "Lingvanex Latvian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Latvian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Latvian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/mg-en": { "id": "lingvanex/nmt/mg-en", "name": "Lingvanex Malagasy to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Malagasy to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Malagasy into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/mi-en": { "id": "lingvanex/nmt/mi-en", "name": "Lingvanex Maori to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Maori to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Maori into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/mk-en": { "id": "lingvanex/nmt/mk-en", "name": "Lingvanex Macedonian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Macedonian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Macedonian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ml-en": { "id": "lingvanex/nmt/ml-en", "name": "Lingvanex Malayalam to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Malayalam to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Malayalam into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/mn-en": { "id": "lingvanex/nmt/mn-en", "name": "Lingvanex Mongolian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Mongolian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Mongolian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/mr-en": { "id": "lingvanex/nmt/mr-en", "name": "Lingvanex Marathi to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Marathi to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Marathi into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ms-en": { "id": "lingvanex/nmt/ms-en", "name": "Lingvanex Malay to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Malay to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Malay into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/mt-en": { "id": "lingvanex/nmt/mt-en", "name": "Lingvanex Maltese to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Maltese to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Maltese into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/my-en": { "id": "lingvanex/nmt/my-en", "name": "Lingvanex Myanmar to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Myanmar to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Myanmar into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ne-en": { "id": "lingvanex/nmt/ne-en", "name": "Lingvanex Nepali to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Nepali to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Nepali into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/nl-en": { "id": "lingvanex/nmt/nl-en", "name": "Lingvanex Dutch to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Dutch to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Dutch into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/no-en": { "id": "lingvanex/nmt/no-en", "name": "Lingvanex Norwegian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Norwegian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Norwegian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ny-en": { "id": "lingvanex/nmt/ny-en", "name": "Lingvanex Chichewa to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Chichewa to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Chichewa into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/or-en": { "id": "lingvanex/nmt/or-en", "name": "Lingvanex Odia to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Odia to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Odia into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/pa-en": { "id": "lingvanex/nmt/pa-en", "name": "Lingvanex Punjabi to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Punjabi to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Punjabi into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/pl-en": { "id": "lingvanex/nmt/pl-en", "name": "Lingvanex Polish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Polish to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Polish into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ps-en": { "id": "lingvanex/nmt/ps-en", "name": "Lingvanex Pushto to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Pushto to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Pushto into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/pt-br-en": { "id": "lingvanex/nmt/pt-br-en", "name": "Lingvanex Portuguese (Brazil) to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Portuguese (Brazil) to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Portuguese (Brazil) into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/pt-en": { "id": "lingvanex/nmt/pt-en", "name": "Lingvanex Portuguese to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Portuguese to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Portuguese into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ro-en": { "id": "lingvanex/nmt/ro-en", "name": "Lingvanex Romanian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Romanian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Romanian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ru-az": { "id": "lingvanex/nmt/ru-az", "name": "Lingvanex Russian to Azerbaijani", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Russian to Azerbaijani. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Russian into Azerbaijani. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ru-be": { "id": "lingvanex/nmt/ru-be", "name": "Lingvanex Russian to Belarusian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Russian to Belarusian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Russian into Belarusian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ru-en": { "id": "lingvanex/nmt/ru-en", "name": "Lingvanex Russian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Russian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Russian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ru-hy": { "id": "lingvanex/nmt/ru-hy", "name": "Lingvanex Russian to Armenian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Russian to Armenian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Russian into Armenian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ru-ka": { "id": "lingvanex/nmt/ru-ka", "name": "Lingvanex Russian to Georgian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Russian to Georgian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Russian into Georgian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ru-kk": { "id": "lingvanex/nmt/ru-kk", "name": "Lingvanex Russian to Kazakh", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Russian to Kazakh. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Russian into Kazakh. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ru-ky": { "id": "lingvanex/nmt/ru-ky", "name": "Lingvanex Russian to Kyrgyz", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Russian to Kyrgyz. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Russian into Kyrgyz. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ru-tg": { "id": "lingvanex/nmt/ru-tg", "name": "Lingvanex Russian to Tajik", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Russian to Tajik. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Russian into Tajik. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ru-uk": { "id": "lingvanex/nmt/ru-uk", "name": "Lingvanex Russian to Ukrainian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Russian to Ukrainian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Russian into Ukrainian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ru-uz": { "id": "lingvanex/nmt/ru-uz", "name": "Lingvanex Russian to Uzbek", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Russian to Uzbek. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Russian into Uzbek. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/rw-en": { "id": "lingvanex/nmt/rw-en", "name": "Lingvanex Kinyarwanda to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Kinyarwanda to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Kinyarwanda into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/sd-en": { "id": "lingvanex/nmt/sd-en", "name": "Lingvanex Sindhi to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Sindhi to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Sindhi into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/si-en": { "id": "lingvanex/nmt/si-en", "name": "Lingvanex Sinhala to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Sinhala to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Sinhala into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/sk-en": { "id": "lingvanex/nmt/sk-en", "name": "Lingvanex Slovak to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Slovak to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Slovak into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/sl-en": { "id": "lingvanex/nmt/sl-en", "name": "Lingvanex Slovenian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Slovenian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Slovenian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/sm-en": { "id": "lingvanex/nmt/sm-en", "name": "Lingvanex Samoan to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Samoan to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Samoan into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/sn-en": { "id": "lingvanex/nmt/sn-en", "name": "Lingvanex Shona to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Shona to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Shona into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/so-en": { "id": "lingvanex/nmt/so-en", "name": "Lingvanex Somali to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Somali to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Somali into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/sq-en": { "id": "lingvanex/nmt/sq-en", "name": "Lingvanex Albanian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Albanian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Albanian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/sr-en": { "id": "lingvanex/nmt/sr-en", "name": "Lingvanex Serbian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Serbian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Serbian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/st-en": { "id": "lingvanex/nmt/st-en", "name": "Lingvanex Sesotho to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Sesotho to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Sesotho into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/su-en": { "id": "lingvanex/nmt/su-en", "name": "Lingvanex Sundanese to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Sundanese to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Sundanese into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/sv-en": { "id": "lingvanex/nmt/sv-en", "name": "Lingvanex Swedish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Swedish to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Swedish into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/sw-en": { "id": "lingvanex/nmt/sw-en", "name": "Lingvanex Swahili to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Swahili to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Swahili into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ta-en": { "id": "lingvanex/nmt/ta-en", "name": "Lingvanex Tamil to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Tamil to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Tamil into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/te-en": { "id": "lingvanex/nmt/te-en", "name": "Lingvanex Telugu to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Telugu to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Telugu into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/tg-en": { "id": "lingvanex/nmt/tg-en", "name": "Lingvanex Tajik to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Tajik to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Tajik into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/tg-ru": { "id": "lingvanex/nmt/tg-ru", "name": "Lingvanex Tajik to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Tajik to Russian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Tajik into Russian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/th-en": { "id": "lingvanex/nmt/th-en", "name": "Lingvanex Thai to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Thai to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Thai into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/tk-en": { "id": "lingvanex/nmt/tk-en", "name": "Lingvanex Turkmen to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Turkmen to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Turkmen into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/tr-en": { "id": "lingvanex/nmt/tr-en", "name": "Lingvanex Turkish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Turkish to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Turkish into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/tt-en": { "id": "lingvanex/nmt/tt-en", "name": "Lingvanex Tatar to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Tatar to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Tatar into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ug-en": { "id": "lingvanex/nmt/ug-en", "name": "Lingvanex Uyghur to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Uyghur to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Uyghur into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/uk-en": { "id": "lingvanex/nmt/uk-en", "name": "Lingvanex Ukrainian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Ukrainian to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Ukrainian into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/uk-ru": { "id": "lingvanex/nmt/uk-ru", "name": "Lingvanex Ukrainian to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Ukrainian to Russian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Ukrainian into Russian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/ur-en": { "id": "lingvanex/nmt/ur-en", "name": "Lingvanex Urdu to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Urdu to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Urdu into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/uz-en": { "id": "lingvanex/nmt/uz-en", "name": "Lingvanex Uzbek to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Uzbek to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Uzbek into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/uz-ru": { "id": "lingvanex/nmt/uz-ru", "name": "Lingvanex Uzbek to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Uzbek to Russian. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Uzbek into Russian. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/vi-en": { "id": "lingvanex/nmt/vi-en", "name": "Lingvanex Vietnamese to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Vietnamese to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Vietnamese into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/xh-en": { "id": "lingvanex/nmt/xh-en", "name": "Lingvanex Xhosa to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Xhosa to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Xhosa into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/yi-en": { "id": "lingvanex/nmt/yi-en", "name": "Lingvanex Yiddish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Yiddish to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Yiddish into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/yo-en": { "id": "lingvanex/nmt/yo-en", "name": "Lingvanex Yoruba to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Yoruba to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Yoruba into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/zh-hans-en": { "id": "lingvanex/nmt/zh-hans-en", "name": "Lingvanex Chinese (Simplified) to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Chinese (Simplified) to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Chinese (Simplified) into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/zh-hant-en": { "id": "lingvanex/nmt/zh-hant-en", "name": "Lingvanex Chinese (Traditional) to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Chinese (Traditional) to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Chinese (Traditional) into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/nmt/zu-en": { "id": "lingvanex/nmt/zu-en", "name": "Lingvanex Zulu to English", "maker": "lingvanex", "family": "lingvanex/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder (OpenNMT-tf), company-wide statement", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) neural machine translation model for Zulu to English. It is one row in the vendor quality report dated 2025-11-11 and is part of the current cloud Translation API and on-premise translation server. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor uses this directional model to translate Zulu into English. The cloud API translates text and HTML between supported languages. The on-premise server translates text, documents, and websites offline. The API does not take a model-name parameter; callers send source and target language codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lingvanex/speech/speech-recognition": { "id": "lingvanex/speech/speech-recognition", "name": "Lingvanex Speech Recognition", "maker": "lingvanex", "family": "lingvanex/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lingvanex (Nordicwise Limited) on-premise and API speech-to-text product. The current product page describes one recognizer for 91 languages, with punctuation, speaker diarization, and subtitles, not a set of publicly named checkpoints. Status: active. Collected 2026-10-15.", "what_it_is_for": "The vendor sells it to transcribe live speech and recorded audio or video on the customer's servers, and a 2026 comparison also describes cloud deployment. Uses named by the vendor include call centers, meetings, subtitles, and domain vocabulary for medicine, manufacturing, and legal audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linkoping/atom/atom": { "id": "linkoping/atom/atom", "name": "ATOM", "maker": "linkoping", "family": "linkoping/atom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2019-06-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Visual tracker. ResNet-18 backbone. An offline network predicts the IoU between the target and a box proposal. An online classifier separates the target from the background. The paper says it runs at over 30 frames per second on an Nvidia GT 1080.", "modalities": { "input": [ "image" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ATOM (Accurate Tracking by Overlap Maximization) is a visual object tracker released by authors at the Computer Vision Laboratory, Link\u00f6ping University, jointly with ETH Z\u00fcrich and the Inception Institute of Artificial Intelligence. The trained network is the ATOM file in the PyTracking model zoo. Collected 2026-10-15. The weight link is still published, so this dossier marks it active.", "what_it_is_for": "The paper splits tracking into an offline overlap estimator and an online classifier. The estimator is trained to score how much a proposed box overlaps the target. The classifier is updated during the sequence to reject background and distractors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linkoping/dedode/dedode": { "id": "linkoping/dedode/dedode", "name": "DeDoDe", "maker": "linkoping", "family": "linkoping/dedode", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT for the authors' code and models, except descriptor G, which uses DINOv2 under Apache-2.0.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Decoupled local features. A detector is trained for repeatable 3D-consistent keypoints. A separate descriptor embeds those points. The repository ships detector L and two descriptors, B and G. Descriptor G uses DINOv2. Matching in the example is dual softmax. The README says the default detector weights are now the v2 detector.", "modalities": { "input": [ "image" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeDoDe is a local feature matcher from Link\u00f6ping University. The detector and the descriptor are trained apart: the detector finds repeatable keypoints, and the descriptor matches them. It was a 3DV 2024 oral. A v2 detector is now the default weight in the public repository. Collected 2026-10-15. The weights are still public, so this dossier marks it active.", "what_it_is_for": "The README says the detector learns 3D-consistent keypoints and the descriptor learns to match them. The example detects 10,000 keypoints and matches them with dual softmax. Kornia can load the upright detector and the B descriptor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linkoping/dkm/dkm": { "id": "linkoping/dkm/dkm", "name": "DKM", "maker": "linkoping", "family": "linkoping/dkm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT for MegaDepth-trained weights. The repository says models trained on its synthetic dataset cannot be offered under that MIT grant.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dense matcher. A kernel-regression global matcher, warp refinement through stacked feature maps and depthwise convolution kernels, and a dense confidence estimate. The paper calls this Dense Kernelized Feature Matching.", "modalities": { "input": [ "image" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DKM (Dense Kernelized Feature Matching) is a dense two-view matcher from the Computer Vision Laboratory at Link\u00f6ping University. All four CVPR 2023 authors were at that lab. Weights and training code are public. Collected 2026-10-15. It is still downloadable, so this dossier marks it active.", "what_it_is_for": "The authors use it to estimate correspondences between two images of a 3D scene densely, then to recover two-view geometry. The project page contrasts this with the usual sparse-keypoint pipeline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linkoping/roma/roma": { "id": "linkoping/roma/roma", "name": "RoMa", "maker": "linkoping", "family": "linkoping/roma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT for the authors' code. DINOv2, which the model uses as a frozen encoder, is Apache-2.0.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dense matcher. Frozen DINOv2 coarse features, a specialized ConvNet for fine features, and a transformer decoder that predicts anchor probabilities rather than a single coordinate, followed by robust regression. Default coarse resolution is 560 by 560, then upsampled to 864 by 864.", "modalities": { "input": [ "image" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoMa is a dense feature matcher from Link\u00f6ping University, with coauthors at East China University of Science and Technology and Chalmers. It is the CVPR 2024 follow-up to DKM. The outdoor weights ship in the `romatch` package. Collected 2026-10-15. The weights are still public, so this dossier marks the model active.", "what_it_is_for": "The paper says it estimates all correspondences between two views of a 3D scene, including pairs with strong changes in scale, illumination, viewpoint, and texture. The README shows the outdoor model being used to sample matches and estimate a fundamental matrix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linkoping/roma/roma-v2": { "id": "linkoping/roma/roma-v2", "name": "RoMa v2", "maker": "linkoping", "family": "linkoping/roma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT for the authors' code. DINOv3, which the model uses, has its own license. The README points at the DINOv3 repository for that text.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dense matcher and successor to RoMa. The paper says it uses DINOv3. The code exposes RoMaV2, which matches a pair and samples correspondences in normalized coordinates. The README also exposes a low resolution, a high resolution used for upsampling, bidirectional matching, a threshold, and balanced sampling.", "modalities": { "input": [ "image" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoMa v2 is the Link\u00f6ping dense matcher that follows RoMa. The preprint lists Link\u00f6ping University first, with coauthors at Chalmers, the University of Amsterdam, and Lund University. The pretrained loader is `RoMaV2` in the public `romav2` package. Collected 2026-10-15. The code still loads weights, so this dossier marks it active.", "what_it_is_for": "The paper presents it as a denser and more robust feature matcher than its predecessor. The README shows it estimating a fundamental matrix from sampled matches.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linkoping/roma/tiny-roma": { "id": "linkoping/roma/tiny-roma", "name": "Tiny RoMa", "maker": "linkoping", "family": "linkoping/roma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The RoMa repository says its own code is MIT except DINOv2. Tiny RoMa is described as built on XFeat. The XFeat license was not opened.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lighter dense matcher shipped in the RoMa repository. The README says Tiny RoMa v1 is built on XFeat. It is not the DINOv2 RoMa network.", "modalities": { "input": [ "image" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tiny RoMa v1 is the lighter matcher that the Link\u00f6ping RoMa repository ships beside the DINOv2 model. The README says it is built on XFeat and is the checkpoint to load when RoMa is too heavy. Collected 2026-10-15. The loader is still in the public package, so this dossier marks it active.", "what_it_is_for": "The README offers it as a smaller outdoor matcher. The only named loader is `tiny_roma_v1_outdoor`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linz/beat-this/beat-this": { "id": "linz/beat-this/beat-this", "name": "Beat This!", "maker": "linz", "family": "linz/beat-this", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-10-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Beat tracker. Log-mel frontend, transformer blocks with rotary embeddings, and beat and downbeat heads. Default checkpoint final0. The README says the main checkpoints are about 78 MB each. DBN postprocessing is optional and off by default. Parameter count is not published.", "modalities": { "input": [ "audio" ], "output": [ "time-events" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Institute of Computational Perception at Johannes Kepler University Linz released Beat This!. It is an active open-weight music beat and downbeat tracker. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README says this is the beat tracker from the ISMIR 2024 paper. The default checkpoint, final0, tracks beats and downbeats from audio without a DBN. A DBN can be turned on as an optional postprocess.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linz/beat-this/beat-this-small": { "id": "linz/beat-this/beat-this-small", "name": "Beat This! small", "maker": "linz", "family": "linz/beat-this", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-10-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Smaller Beat This! tracker. README: trained like the main model, all data except GTZAN, three seeds. Training flag --transformer-dim=128. Checkpoints about 8.1 MB. Corresponds to smaller model in Table 2. Parameter count is not published.", "modalities": { "input": [ "audio" ], "output": [ "time-events" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Institute of Computational Perception at Johannes Kepler University Linz released the smaller Beat This! checkpoints with the main tracker. They are active open weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README lists small0, small1, and small2 as a smaller model trained on all data except GTZAN. It corresponds to the \"smaller model\" row in Table 2 of the paper. The training command sets `--transformer-dim=128`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linz/bio-xlstm/chem-xlstm-15m": { "id": "linz/bio-xlstm/chem-xlstm-15m", "name": "Chem-xLSTM-15M", "maker": "linz", "family": "linz/bio-xlstm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 100, "max_output_tokens": null, "parameters": "15000000", "architecture": "mLSTM SMILES model. Paper section 3.6: Chem-xLSTM-15M is the unconditional model, 15M parameters, 9 mLSTM blocks, embedding dimension 512, projection ratio 1.3:1, context 100 tokens. 15000000 is the paper's 15M, not a counted integer. The README also calls a training command an xLSTM 14.8M model with n_layers 9 and model_dim 512. That command is a reproduction recipe. The Nextcloud share was not enumerated, so the released file's measured size was not counted.", "modalities": { "input": [ "sequence" ], "output": [ "sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Institute for Machine Learning at Johannes Kepler University Linz released Chem-xLSTM-15M. It is the active open-weight unconditional SMILES model in the Bio-xLSTM suite. This dossier was collected on 2026-10-15.", "what_it_is_for": "Section 3.6 names Chem-xLSTM-15M as the unconditional generative model for small-molecule SMILES, trained at a context of 100 tokens. The README says the public share includes a 15M xLSTM for chemblv31.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linz/bio-xlstm/chem-xlstm-15m-icl": { "id": "linz/bio-xlstm/chem-xlstm-15m-icl", "name": "Chem-xLSTM-15M-icl", "maker": "linz", "family": "linz/bio-xlstm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "15000000", "architecture": "mLSTM SMILES model for domain-conditioned in-context generation. Paper section 3.6: Chem-xLSTM-15M-icl has 15M parameters, 9 mLSTM blocks, embedding dimension 512, projection ratio 1.3:1, and a 4,096-token context. 15000000 is the paper's 15M, not a counted integer. The README ICST training command sets sequence_length 2048, n_layers 9, and model_dim 512. That command is a reproduction recipe. The paper's 4,096 is the named product context stored here. The Nextcloud share was not enumerated.", "modalities": { "input": [ "sequence" ], "output": [ "sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Institute for Machine Learning at Johannes Kepler University Linz released Chem-xLSTM-15M-icl. It is the active open-weight in-context SMILES model in the Bio-xLSTM suite. This dossier was collected on 2026-10-15.", "what_it_is_for": "Section 3.6 names Chem-xLSTM-15M-icl as the domain-conditioned model. It generates molecules in a domain from examples in the context, without finetuning. The README says the public share includes a 15M xLSTM for ICST.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linz/bio-xlstm/dna-xlstm-2m": { "id": "linz/bio-xlstm/dna-xlstm-2m", "name": "DNA-xLSTM-2M", "maker": "linz", "family": "linz/bio-xlstm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-11-04", "context_length": 1024, "max_output_tokens": null, "parameters": "2000000", "architecture": "sLSTM DNA language model. Paper: embedding 256, 6 sLSTM blocks, up-projection 1:1, context 1,024, 2M parameters. Table A4: conv kernel 4, 4 heads, no position embeddings. 2000000 is the paper's 2M, not a counted integer.", "modalities": { "input": [ "sequence" ], "output": [ "sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Institute for Machine Learning at Johannes Kepler University Linz released DNA-xLSTM-2M. It is an active open-weight 1,024-token DNA language model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper defines DNA-xLSTM-2M as the larger short-context sLSTM model, trained on the human genome and fine-tuned on genomic classification tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linz/bio-xlstm/dna-xlstm-4m": { "id": "linz/bio-xlstm/dna-xlstm-4m", "name": "DNA-xLSTM-4M", "maker": "linz", "family": "linz/bio-xlstm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-11-04", "context_length": 32768, "max_output_tokens": null, "parameters": "4000000", "architecture": "mLSTM DNA language model. Paper: embedding 256, 9 mLSTM blocks, up-projection 2:1, RoPE, context 32,768, 4M parameters. Table A4 also lists conv kernel 4, 4 heads, and QKV projection block size 4. 4000000 is the paper's 4M, not a counted integer.", "modalities": { "input": [ "sequence" ], "output": [ "sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Institute for Machine Learning at Johannes Kepler University Linz released DNA-xLSTM-4M. It is the active long-context DNA model in Bio-xLSTM. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper defines DNA-xLSTM-4M as the mLSTM configuration for 32,768-token human-genome modeling. The appendix says it is the long-context model compared with Transformer++, HyenaDNA, and Caduceus.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linz/bio-xlstm/dna-xlstm-500k": { "id": "linz/bio-xlstm/dna-xlstm-500k", "name": "DNA-xLSTM-500k", "maker": "linz", "family": "linz/bio-xlstm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-11-04", "context_length": 1024, "max_output_tokens": null, "parameters": "500000", "architecture": "sLSTM DNA language model. Paper: embedding dimension 128, 5 sLSTM blocks, up-projection 1.25:1, conv kernel 4, 4 heads, no position embeddings, context 1,024 nucleotides. The paper says the parameter count is 500k. 500000 is that figure, not a counted integer. Table A4 lists m/sLSTM ratio sweeps [0:1] and [1:0].", "modalities": { "input": [ "sequence" ], "output": [ "sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Institute for Machine Learning at Johannes Kepler University Linz released DNA-xLSTM-500k. It is an active open-weight DNA language model in the Bio-xLSTM suite. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper introduces DNA-xLSTM-500k as the short-context sLSTM configuration for human-genome modeling, then fine-tuning on genomic classification tasks. The lab hosts three objective variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linz/bio-xlstm/prot-xlstm-102m": { "id": "linz/bio-xlstm/prot-xlstm-102m", "name": "Prot-xLSTM-102M", "maker": "linz", "family": "linz/bio-xlstm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-11-04", "context_length": null, "max_output_tokens": null, "parameters": "102000000", "architecture": "mLSTM protein model. Paper section 3.5: Prot-xLSTM-102M has 102M parameters, 16 mLSTM blocks, embedding dimension 1,024, and a 2:1 projection ratio. RoPE is used. Training uses causal language modeling with fill-in-the-middle on non-aligned homologous sequences, and the context is increased during training. Section 3.5 states a range from 2,048 to 262,144 tokens. Table A6 lists 2^11 through 2^17 for this size and says context length was increased during training. 102000000 is the paper's 102M, not a counted integer. The directory suffix 60B is not defined on the index. The README evaluation example passes --context_len 131072 for protxlstm_102M_60B; that is an eval argument.", "modalities": { "input": [ "sequence" ], "output": [ "sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Institute for Machine Learning at Johannes Kepler University Linz released Prot-xLSTM-102M. It is the larger active open-weight protein model in the Bio-xLSTM suite. This dossier was collected on 2026-10-15.", "what_it_is_for": "Section 3.5 introduces Prot-xLSTM-102M as the larger homology-conditioned protein model. The public weight directory is `protxlstm_102M_60B`. The README's test-set example names that directory.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linz/bio-xlstm/prot-xlstm-26m": { "id": "linz/bio-xlstm/prot-xlstm-26m", "name": "Prot-xLSTM-26M", "maker": "linz", "family": "linz/bio-xlstm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-11-04", "context_length": null, "max_output_tokens": null, "parameters": "26000000", "architecture": "mLSTM protein model. Paper section 3.5: Prot-xLSTM-26M has 26M parameters, 16 mLSTM blocks, embedding dimension 512, and a 2:1 projection ratio. RoPE is used. Training uses causal language modeling with fill-in-the-middle on non-aligned homologous sequences, and the context is increased during training. Section 3.5 states a range from 2,048 to 262,144 tokens. Table A6 lists 2^11 and 2^17 for this size and says context length was increased during training. 26000000 is the paper's 26M, not a counted integer. The directory suffix 30B is not defined on the index.", "modalities": { "input": [ "sequence" ], "output": [ "sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Institute for Machine Learning at Johannes Kepler University Linz released Prot-xLSTM-26M. It is the smaller active open-weight protein model in the Bio-xLSTM suite. This dossier was collected on 2026-10-15.", "what_it_is_for": "Section 3.5 introduces Prot-xLSTM-26M for homology-conditioned protein modeling. The lab says it can generate sequences from homologous context and score amino-acid substitutions. The public weight directory is `protxlstm_26M_30B`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linz/gyroswin/gyroswin-large": { "id": "linz/gyroswin/gyroswin-large", "name": "GyroSwin Large", "maker": "linz", "family": "linz/gyroswin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-11-24", "context_length": null, "max_output_tokens": null, "parameters": "998300000", "architecture": "Largest released GyroSwin 5D Swin UNet. Card and Table 3 say 998.3M parameters. The paper describes scaling to about 1 billion parameters. Exact integer not published.", "modalities": { "input": [ "tensor" ], "output": [ "tensor", "scalar" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johannes Kepler University Linz, Institute for Machine Learning, released GyroSwin Large. It is the active open-weight ~1B-parameter 5D gyrokinetic surrogate in the GyroSwin family. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this is the large GyroSwin checkpoint for next-step prediction on nonlinear GKW simulations. The paper treats Large as the top of the scaling run, about one billion parameters, and the size used for the zonal-flow figure.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linz/gyroswin/gyroswin-medium": { "id": "linz/gyroswin/gyroswin-medium", "name": "GyroSwin Medium", "maker": "linz", "family": "linz/gyroswin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-11-24", "context_length": null, "max_output_tokens": null, "parameters": "250900000", "architecture": "Same 5D Swin UNet family as GyroSwin Small, at the medium width. Card title says medium-sized. The parameter sentence on the card says \"The small variant\" and then \"250.9M parameters\". Paper Table 3 assigns 250.9M to GyroSwin Medium. The exact integer is not published.", "modalities": { "input": [ "tensor" ], "output": [ "tensor", "scalar" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johannes Kepler University Linz, Institute for Machine Learning, released GyroSwin Medium. It is an active open-weight 5D gyrokinetic surrogate, the middle size of the three public checkpoints. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card title says this repository holds the medium-sized GyroSwin variant for next-step prediction on GKW turbulence simulations. The paper uses the scaled Medium model as one point on the 241-simulation scaling run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linz/gyroswin/gyroswin-small": { "id": "linz/gyroswin/gyroswin-small", "name": "GyroSwin Small", "maker": "linz", "family": "linz/gyroswin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-11-24", "context_length": null, "max_output_tokens": null, "parameters": "90200000", "architecture": "5D Swin UNet surrogate. Hierarchical shifted-window attention on the 5D gyrokinetic distribution, latent 5D-to-3D integration and cross-attention, and channelwise zonal-flow mode separation. Multitask next-step prediction of the 5D distribution, the 3D electrostatic potential, and scalar heat flux. The card and paper Table 3 both say 90.2M parameters; the exact integer is not published.", "modalities": { "input": [ "tensor" ], "output": [ "tensor", "scalar" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Johannes Kepler University Linz, Institute for Machine Learning, released GyroSwin Small. It is an active open-weight 5D neural surrogate for nonlinear gyrokinetic plasma turbulence. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the checkpoint is the small GyroSwin variant from the NeurIPS 2025 paper. It is trained for next-step prediction on turbulent GKW simulations. The paper's job is to approximate fully resolved nonlinear gyrokinetics, including heat flux and zonal flows, much faster than the numerical code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linz/pa-audioic/pa-audioic": { "id": "linz/pa-audioic/pa-audioic", "name": "PAAudioIC", "maker": "linz", "family": "linz/pa-audioic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-11-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One Hub repo ships two weights used together: a perceptually aligned Music2Latent autoencoder and an autoregressive rectified-flow model. The tool estimates music information content by integrating a probability-flow ODE. Parameter counts are not on the card.", "modalities": { "input": [ "audio" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Institute of Computational Perception at Johannes Kepler University Linz released PAAudioIC on the CPJKU Hub org. It is an active non-commercial music model that estimates information content over time. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README says PAAudioIC computes information content as a proxy for perceived surprise in music. It uses a perceptually aligned Music2Latent autoencoder and an autoregressive rectified-flow model from the NeurIPS 2025 AI for Music workshop paper. The README also says the work was presented at EUSIPCO 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "linz/passt/passt-s": { "id": "linz/passt/passt-s", "name": "PaSST-S", "maker": "linz", "family": "linz/passt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Patchout faSt Spectrogram Transformer, small (PaSST-S). DeiT-style transformer on 128-bin mel spectrograms from 32 kHz audio. Default checkpoint passt_s_swa_p16_128_ap476: patch size 16, structured patchout, stochastic weight averaging, 527 AudioSet classes. The hear21passt wrapper includes the mel frontend. Parameter count is not published in the README.", "modalities": { "input": [ "audio" ], "output": [ "logits" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Institute of Computational Perception at Johannes Kepler University Linz released PaSST-S, the small Patchout faSt Spectrogram Transformer pretrained on AudioSet. The weights are active and open. This dossier was collected on 2026-10-15. The code repository is Khaled Koutini's GitHub account; the paper authors are Koutini, Schl\u00fcter, Eghbal-zadeh, and Widmer at that institute.", "what_it_is_for": "The README says PaSST trains transformers on audio spectrograms with patchout, which drops patches in time or frequency during training. The shipped default is an AudioSet tagger and embedding model. `pip install hear21passt` loads the mel frontend plus the default pretrained transformer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/d1/d1": { "id": "liquid-ai/d1/d1", "name": "d1", "maker": "liquid-ai", "family": "liquid-ai/d1", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted decision model. One forward pass returns calibrated probabilities for Noul (yes/no), Choice, and Score questions. The 5 October 2026 post says it reads text and images. Zero generated tokens. Weights are not public.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.02, "completion_per_m_usd": null }, "summary_intro": "d1 is a Liquid AI model in the d1 family. Status on 2026-10-15: active. Weights: closed. Liquid's hosted decision model for classification, routing, and scoring. The news index dates the vision decision post 5 October 2026. Open-weight d1-3B and d1-omni-600M followed on 7 October.", "what_it_is_for": "Liquid's hosted decision model for classification, routing, and scoring. The news index dates the vision decision post 5 October 2026. Open-weight d1-3B and d1-omni-600M followed on 7 October.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/d1/d1-3b": { "id": "liquid-ai/d1/d1-3b", "name": "d1-3B", "maker": "liquid-ai", "family": "liquid-ai/d1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-10-07", "context_length": 32768, "max_output_tokens": null, "parameters": "3.12B", "architecture": "Open decision model built on LFM2.5-VL-3B. Card total 3.12B. Sibling table also says 3.1B. Context 32,768. Vocabulary 128,000. Text and images in. Single-pass calibrated decisions. Docs overview rounds the size to 3.1B. Trainable flag in the library is No.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "d1-3B is a Liquid AI model in the d1 family. Status on 2026-10-15: active. Weights: open-weights. Open-weight multimodal decision model for on-device classification, routing, and scoring. Released 7 October 2026 with d1-omni-600M. Not a general chat model.", "what_it_is_for": "Open-weight multimodal decision model for on-device classification, routing, and scoring. Released 7 October 2026 with d1-omni-600M. Not a general chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/d1/d1-omni-600m": { "id": "liquid-ai/d1/d1-omni-600m", "name": "d1-omni-600M", "maker": "liquid-ai", "family": "liquid-ai/d1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-10-07", "context_length": 16384, "max_output_tokens": null, "parameters": "587M", "architecture": "Decision model on LFM2.5-Encoder-350M. Card: 587M total, described as a 381M shared trunk and decision head, a 94M vision encoder, and an audio encoder whose size was cut off in the extract. Context 16,384 tokens across text, image, and audio positions. Vocabulary 65,536. Docs call it the smallest d1 and say text plus images or audio.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "d1-omni-600M is a Liquid AI model in the d1 family. Status on 2026-10-15: active. Weights: open-weights. Small open decision model for text, image, or speech. Released 7 October 2026. One pass, no generated tokens.", "what_it_is_for": "Small open decision model for text, image, or speech. Released 7 October 2026. One pass, no generated tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/d1/d1-ultra": { "id": "liquid-ai/d1/d1-ultra", "name": "d1-ultra", "maker": "liquid-ai", "family": "liquid-ai/d1", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted decision model above d1. Reads text, images, or both. Returns calibrated probabilities. No public weights. Image tokens: 1.5 tokens per 32 by 32 patch, so a 1024 by 1024 image is 1,536 tokens. Each question is billed as its own prompt.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.042, "completion_per_m_usd": null }, "summary_intro": "d1-ultra is a Liquid AI model in the d1 family. Status on 2026-10-15: active. Weights: closed. Liquid's most capable hosted decision model as of 15 October 2026, for harder visual checks such as defect and claims review. API model id d1-ultra. The blog says Vercel and OpenRouter support is still to come.", "what_it_is_for": "Liquid's most capable hosted decision model as of 15 October 2026, for harder visual checks such as defect and claims review. API model id d1-ultra. The blog says Vercel and OpenRouter support is still to come.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/dspark/lfm2-5-1-2b-instruct-dspark": { "id": "liquid-ai/dspark/lfm2-5-1-2b-instruct-dspark", "name": "LFM2.5-1.2B-Instruct-DSpark", "maker": "liquid-ai", "family": "liquid-ai/dspark", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-08-20", "context_length": null, "max_output_tokens": null, "parameters": "295.7M", "architecture": "DSpark draft. 295.7M BF16. 5 full attention layers, hidden size 2048, intermediate size 6144, GQA 32/8. Smaller vocabulary than the 2.6B and 8B drafts. Vocabulary 65,536.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-1.2B-Instruct-DSpark is a Liquid AI model in the dspark family. Status on 2026-10-15: active. Weights: open-weights. Speculative-decoding draft for lfm2-5-1-2b-instruct. It is not a general chat model. Liquid publishes it as a named checkpoint.", "what_it_is_for": "Speculative-decoding draft for lfm2-5-1-2b-instruct. It is not a general chat model. Liquid publishes it as a named checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/dspark/lfm2-5-2-6b-dspark": { "id": "liquid-ai/dspark/lfm2-5-2-6b-dspark", "name": "LFM2.5-2.6B-DSpark", "maker": "liquid-ai", "family": "liquid-ai/dspark", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-08-20", "context_length": null, "max_output_tokens": null, "parameters": "327.7M", "architecture": "DSpark draft. 327.7M BF16. 5 full attention layers, hidden size 2048, intermediate size 6144, GQA 32/8, head dim 64. Vocabulary 128,000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-2.6B-DSpark is a Liquid AI model in the dspark family. Status on 2026-10-15: active. Weights: open-weights. Speculative-decoding draft for lfm2-5-2-6b. It is not a general chat model. Liquid publishes it as a named checkpoint.", "what_it_is_for": "Speculative-decoding draft for lfm2-5-2-6b. It is not a general chat model. Liquid publishes it as a named checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/dspark/lfm2-5-8b-a1b-dspark": { "id": "liquid-ai/dspark/lfm2-5-8b-a1b-dspark", "name": "LFM2.5-8B-A1B-DSpark", "maker": "liquid-ai", "family": "liquid-ai/dspark", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-08-20", "context_length": null, "max_output_tokens": null, "parameters": "327.7M", "architecture": "DSpark draft. 327.7M BF16. Same 5-layer attention backbone description as the 2.6B draft. Vocabulary 128,000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-8B-A1B-DSpark is a Liquid AI model in the dspark family. Status on 2026-10-15: active. Weights: open-weights. Speculative-decoding draft for lfm2-5-8b-a1b. It is not a general chat model. Liquid publishes it as a named checkpoint.", "what_it_is_for": "Speculative-decoding draft for lfm2-5-8b-a1b. It is not a general chat model. Liquid publishes it as a named checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/dspark/lfm2-5-vl-3b-dspark": { "id": "liquid-ai/dspark/lfm2-5-vl-3b-dspark", "name": "LFM2.5-VL-3B-DSpark", "maker": "liquid-ai", "family": "liquid-ai/dspark", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-09-24", "context_length": null, "max_output_tokens": null, "parameters": "279.5M", "architecture": "Vision DSpark draft. 279.5M BF16. 4 full attention layers, hidden size 2048, intermediate size 6144, GQA 32/8. Vocabulary 128,000.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-VL-3B-DSpark is a Liquid AI model in the dspark family. Status on 2026-10-15: active. Weights: open-weights. Speculative-decoding draft for lfm2-5-vl-3b. It is not a general chat model. Liquid publishes it as a named checkpoint.", "what_it_is_for": "Speculative-decoding draft for lfm2-5-vl-3b. It is not a general chat model. Liquid publishes it as a named checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm/lfm-1b": { "id": "liquid-ai/lfm/lfm-1b", "name": "LFM-1B", "maker": "liquid-ai", "family": "liquid-ai/lfm", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "First-generation Liquid Foundation Model. Dense. Non-transformer design. The vendor did not publish a layer table on the pages opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM-1B is a Liquid AI model in the lfm family. Status on 2026-10-15: deprecated. Weights: closed. Liquid AI's first dense small language model, announced for resource-constrained environments. Early access was through Liquid Playground, Lambda, and Perplexity Labs. The announcement page now marks this generation deprecated.", "what_it_is_for": "Liquid AI's first dense small language model, announced for resource-constrained environments. Early access was through Liquid Playground, Lambda, and Perplexity Labs. The announcement page now marks this generation deprecated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm/lfm-3b": { "id": "liquid-ai/lfm/lfm-3b", "name": "LFM-3B", "maker": "liquid-ai", "family": "liquid-ai/lfm", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "3.1B", "architecture": "First-generation Liquid Foundation Model. Dense. Aimed at edge text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM-3B is a Liquid AI model in the lfm family. Status on 2026-10-15: deprecated. Weights: closed. Dense 3.1B model from the 30 September 2024 LFM announcement, positioned for mobile and edge text. The announcement page now marks this generation deprecated.", "what_it_is_for": "Dense 3.1B model from the 30 September 2024 LFM announcement, positioned for mobile and edge text. The announcement page now marks this generation deprecated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm/lfm-40b": { "id": "liquid-ai/lfm/lfm-40b", "name": "LFM-40B", "maker": "liquid-ai", "family": "liquid-ai/lfm", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "40.3B total / 12B active", "architecture": "First-generation Mixture-of-Experts Liquid Foundation Model. Company announcement: 40.3B total. Company LinkedIn post: 12B activated parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM-40B is a Liquid AI model in the lfm family. Status on 2026-10-15: deprecated. Weights: closed. The large MoE in the first LFM series, for harder tasks than the 1B and 3B models, with early access rather than open weights. The announcement page now marks this generation deprecated.", "what_it_is_for": "The large MoE in the first LFM series, for harder tasks than the 1B and 3B models, with early access rather than open weights. The announcement page now marks this generation deprecated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm/lfm-7b": { "id": "liquid-ai/lfm/lfm-7b", "name": "LFM-7B", "maker": "liquid-ai", "family": "liquid-ai/lfm", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First-generation Liquid Foundation Model. Non-transformer. The 20 January 2025 post does not give a layer count or an exact parameter integer beyond the 7B name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM-7B is a Liquid AI model in the lfm family. Status on 2026-10-15: deprecated. Weights: closed. Chat model released 20 January 2025 and described as strongest in its size class for English, Arabic, and Japanese, with Playground and AWS Marketplace access. The post now says this generation is deprecated.", "what_it_is_for": "Chat model released 20 January 2025 and described as strongest in its size class for English, Arabic, and Japanese, with Playground and AWS Marketplace access. The post now says this generation is deprecated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2/lfm2-1-2b": { "id": "liquid-ai/lfm2/lfm2-1-2b", "name": "LFM2-1.2B", "maker": "liquid-ai", "family": "liquid-ai/lfm2", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "1,170,340,608", "architecture": "Hybrid LFM2: double-gated short convolutions plus grouped-query attention. bfloat16. Vocabulary 65,536. Training budget 10 trillion tokens. License LFM Open License v1.0. 16 layers (10 conv + 6 attention).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-1.2B is a Liquid AI model in the lfm2 family. Status on 2026-10-15: deprecated. Weights: open-weights. 1.2B dense LFM2 instruct model. The card says a newer version is LFM2.5-1.2B-Instruct. Deprecations name that model as the replacement.", "what_it_is_for": "1.2B dense LFM2 instruct model. The card says a newer version is LFM2.5-1.2B-Instruct. Deprecations name that model as the replacement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2/lfm2-2-6b": { "id": "liquid-ai/lfm2/lfm2-2-6b", "name": "LFM2-2.6B", "maker": "liquid-ai", "family": "liquid-ai/lfm2", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-09-23", "context_length": 32768, "max_output_tokens": null, "parameters": "2,569,272,320", "architecture": "Hybrid LFM2. 30 layers (22 conv + 8 attention). Vocabulary 65,536. 10 trillion tokens. bfloat16. LFM Open License v1.0. The shared card's 10-conv/6-attention sentence describes the smaller dense models, not this 30-layer model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-2.6B is a Liquid AI model in the lfm2 family. Status on 2026-10-15: deprecated. Weights: open-weights. Largest dense LFM2 general model. News index dates the post 23 September 2025. Deprecations replace it with LFM2.5-2.6B.", "what_it_is_for": "Largest dense LFM2 general model. News index dates the post 23 September 2025. Deprecations replace it with LFM2.5-2.6B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2/lfm2-2-6b-exp": { "id": "liquid-ai/lfm2/lfm2-2-6b-exp", "name": "LFM2-2.6B-Exp", "maker": "liquid-ai", "family": "liquid-ai/lfm2", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "2,569,272,320", "architecture": "Experimental pure-RL checkpoint on LFM2-2.6B, aimed at instruction following, knowledge, and math. The card reuses the dense LFM2 spec table, so the 2.6B column (2,569,272,320 parameters, 30 layers, 32,768 context) is the parent spec.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-2.6B-Exp is a Liquid AI model in the lfm2 family. Status on 2026-10-15: deprecated. Weights: open-weights. Experimental LFM2-2.6B checkpoint trained with pure reinforcement learning. Deprecations replace it with LFM2.5-2.6B. The card says its IFBench score passed DeepSeek R1-0528; the numeric IFBench value was not in the extracted lines.", "what_it_is_for": "Experimental LFM2-2.6B checkpoint trained with pure reinforcement learning. Deprecations replace it with LFM2.5-2.6B. The card says its IFBench score passed DeepSeek R1-0528; the numeric IFBench value was not in the extracted lines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2/lfm2-24b-a2b": { "id": "liquid-ai/lfm2/lfm2-24b-a2b", "name": "LFM2-24B-A2B", "maker": "liquid-ai", "family": "liquid-ai/lfm2", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-02-24", "context_length": 32768, "max_output_tokens": null, "parameters": "24B total / 2.3B active", "architecture": "LFM2 hybrid MoE. 40 layers (30 conv + 10 attention). 64 experts, top-4 routing, hidden size 2048, matching the 8B-A1B width. Vocabulary 65,536. Training budget 17 trillion tokens. Mixed BF16/FP8. Instruct model without required reasoning traces. Card active count is 2.3B. The February 2026 blog rounds active parameters to 2B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-24B-A2B is a Liquid AI model in the lfm2 family. Status on 2026-10-15: deprecated. Weights: open-weights. Largest LFM2 model, a general instruct MoE meant to fit in 32 GB of RAM. The 24 February 2026 post calls the release an early checkpoint. The deprecations page replaces it with LFM2.5-24B-A2B. The public models page still lists it beside current text models.", "what_it_is_for": "Largest LFM2 model, a general instruct MoE meant to fit in 32 GB of RAM. The 24 February 2026 post calls the release an early checkpoint. The deprecations page replaces it with LFM2.5-24B-A2B. The public models page still lists it beside current text models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2/lfm2-350m": { "id": "liquid-ai/lfm2/lfm2-350m", "name": "LFM2-350M", "maker": "liquid-ai", "family": "liquid-ai/lfm2", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "354,483,968", "architecture": "Hybrid LFM2: double-gated short convolutions plus grouped-query attention. bfloat16. Vocabulary 65,536. Training budget 10 trillion tokens. License LFM Open License v1.0. 16 layers (10 conv + 6 attention).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-350M is a Liquid AI model in the lfm2 family. Status on 2026-10-15: deprecated. Weights: open-weights. Smallest dense LFM2 chat model. Liquid's deprecations page says it remains downloadable but is no longer maintained, and points new work to LFM2.5-350M.", "what_it_is_for": "Smallest dense LFM2 chat model. Liquid's deprecations page says it remains downloadable but is no longer maintained, and points new work to LFM2.5-350M.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2/lfm2-700m": { "id": "liquid-ai/lfm2/lfm2-700m", "name": "LFM2-700M", "maker": "liquid-ai", "family": "liquid-ai/lfm2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "742,489,344", "architecture": "Hybrid LFM2: double-gated short convolutions plus grouped-query attention. bfloat16. Vocabulary 65,536. Training budget 10 trillion tokens. License LFM Open License v1.0. 16 layers (10 conv + 6 attention).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-700M is a Liquid AI model in the lfm2 family. Status on 2026-10-15: active. Weights: open-weights. Mid-size dense LFM2 model. It is still in the current docs matrix and on the models page. No LFM2.5-700M replacement is listed.", "what_it_is_for": "Mid-size dense LFM2 model. It is still in the current docs matrix and on the models page. No LFM2.5-700M replacement is listed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2/lfm2-8b-a1b": { "id": "liquid-ai/lfm2/lfm2-8b-a1b", "name": "LFM2-8B-A1B", "maker": "liquid-ai", "family": "liquid-ai/lfm2", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-10-07", "context_length": 32768, "max_output_tokens": null, "parameters": "8.3B total / 1.5B active", "architecture": "LFM2 hybrid MoE. 24 layers (18 conv + 6 attention). Vocabulary 65,536. Training budget 12 trillion tokens. Mixed BF16/FP8. Active parameters 1.5B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-8B-A1B is a Liquid AI model in the lfm2 family. Status on 2026-10-15: deprecated. Weights: open-weights. First LFM2 mixture-of-experts, for phones, tablets, and laptops. News index date 7 October 2025. Deprecations replace it with LFM2.5-8B-A1B.", "what_it_is_for": "First LFM2 mixture-of-experts, for phones, tablets, and laptops. News index date 7 October 2025. Deprecations replace it with LFM2.5-8B-A1B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5/lfm2-5-1-2b-instruct": { "id": "liquid-ai/lfm2-5/lfm2-5-1-2b-instruct", "name": "LFM2.5-1.2B-Instruct", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-01-05", "context_length": 32768, "max_output_tokens": null, "parameters": "1.17B", "architecture": "LFM2.5 hybrid. 16 layers (10 double-gated conv + 6 GQA). Vocabulary 65,536. Training budget 28T tokens. Pretraining extended from the LFM2 10T budget. LFM Open License v1.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-1.2B-Instruct is a Liquid AI model in the lfm2-5 family. Status on 2026-10-15: active. Weights: open-weights. General instruction model in the January 2026 LFM2.5 launch. Liquid recommends it for most 1.2B chat and tool-use cases. Base weights ship separately for fine-tuning.", "what_it_is_for": "General instruction model in the January 2026 LFM2.5 launch. Liquid recommends it for most 1.2B chat and tool-use cases. Base weights ship separately for fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5/lfm2-5-1-2b-jp": { "id": "liquid-ai/lfm2-5/lfm2-5-1-2b-jp", "name": "LFM2.5-1.2B-JP", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-01-05", "context_length": null, "max_output_tokens": null, "parameters": "1.2B class", "architecture": "Japanese chat post-train of the LFM2.5-1.2B stack. The short card extract did not restate layer count or context. The later JP-202606 card is the one that prints 1.17B and 32,768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-1.2B-JP is a Liquid AI model in the lfm2-5 family. Status on 2026-10-15: active. Weights: open-weights. Japanese-optimized 1.2B chat model from the 5 January 2026 launch. Still downloadable. The models page now features LFM2.5-1.2B-JP-202606 as the current Japanese text model. This checkpoint is not on the deprecations table.", "what_it_is_for": "Japanese-optimized 1.2B chat model from the 5 January 2026 launch. Still downloadable. The models page now features LFM2.5-1.2B-JP-202606 as the current Japanese text model. This checkpoint is not on the deprecations table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5/lfm2-5-1-2b-jp-202606": { "id": "liquid-ai/lfm2-5/lfm2-5-1-2b-jp-202606", "name": "LFM2.5-1.2B-JP-202606", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-05-26", "context_length": 32768, "max_output_tokens": null, "parameters": "1.17B", "architecture": "Later Japanese LFM2.5-1.2B chat model. 16 layers (10 double-gated LIV conv + 6 GQA). Vocabulary 65,536. Training budget 31.5T tokens. Languages on the card: English and Japanese.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-1.2B-JP-202606 is a Liquid AI model in the lfm2-5 family. Status on 2026-10-15: active. Weights: open-weights. Current Japanese 1.2B model on the Liquid models page. The card says it improves knowledge, instruction following, math, code, and tool use over LFM2.5-1.2B-JP. The 202606 name is the vendor's dated product id, so it has its own folder.", "what_it_is_for": "Current Japanese 1.2B model on the Liquid models page. The card says it improves knowledge, instruction following, math, code, and tool use over LFM2.5-1.2B-JP. The 202606 name is the vendor's dated product id, so it has its own folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5/lfm2-5-1-2b-thinking": { "id": "liquid-ai/lfm2-5/lfm2-5-1-2b-thinking", "name": "LFM2.5-1.2B-Thinking", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-01-20", "context_length": 32768, "max_output_tokens": null, "parameters": "1.17B", "architecture": "Same LFM2.5-1.2B stack as Instruct: 16 layers (10 double-gated conv + 6 GQA), vocab 65,536, 28T token budget. Post-trained to emit a thinking trace before the answer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-1.2B-Thinking is a Liquid AI model in the lfm2-5 family. Status on 2026-10-15: active. Weights: open-weights. On-device reasoning model released 20 January 2026. Liquid says it fits in about 900 MB on a phone. It was not in the 5 January launch set.", "what_it_is_for": "On-device reasoning model released 20 January 2026. Liquid says it fits in about 900 MB on a phone. It was not in the 5 January launch set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5/lfm2-5-2-6b": { "id": "liquid-ai/lfm2-5/lfm2-5-2-6b", "name": "LFM2.5-2.6B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-08-04", "context_length": 131072, "max_output_tokens": null, "parameters": "2.69B", "architecture": "Dense LFM2.5 agent model. 30 layers (22 double-gated short conv + 8 GQA). Vocabulary 128,000. Training budget 34 trillion tokens. Card context is 131,072 tokens. The library blurb that says only the 24B model has 131K is incomplete.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-2.6B is a Liquid AI model in the lfm2-5 family. Status on 2026-10-15: active. Weights: open-weights. Dense on-device agent model with native tool calling. Released 4 August 2026. Liquid calls it the fastest model in its own speed tests among the models it compared that day.", "what_it_is_for": "Dense on-device agent model with native tool calling. Released 4 August 2026. Liquid calls it the fastest model in its own speed tests among the models it compared that day.", "ratings": { "empirical_100": 42, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5/lfm2-5-230m": { "id": "liquid-ai/lfm2-5/lfm2-5-230m", "name": "LFM2.5-230M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-06-25", "context_length": 32768, "max_output_tokens": null, "parameters": "230M", "architecture": "Smallest LFM2.5 hybrid. 14 layers (8 double-gated conv + 6 GQA). Vocabulary 65,536. Training budget 19T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-230M is a Liquid AI model in the lfm2-5 family. Status on 2026-10-15: active. Weights: open-weights. Smallest LFM2.5 chat model, for data extraction and lightweight on-device agents. Blog date 25 June 2026.", "what_it_is_for": "Smallest LFM2.5 chat model, for data extraction and lightweight on-device agents. Blog date 25 June 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5/lfm2-5-24b-a2b": { "id": "liquid-ai/lfm2-5/lfm2-5-24b-a2b", "name": "LFM2.5-24B-A2B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-10-13", "context_length": 131072, "max_output_tokens": null, "parameters": "24.0B total / 2.45B active", "architecture": "Largest LFM2.5 MoE and, per the 13 October 2026 post, the final LFM2.5 model. 40 layers (30 double-gated short conv + 10 GQA). 64 experts, top-4 routing. Vocabulary 128,000. Training budget 36 trillion tokens. Context 131,072. Always opens with a think block. Docs round active parameters to 2B. The card says 2.45B active.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": 0.7 }, "summary_intro": "LFM2.5-24B-A2B is a Liquid AI model in the lfm2-5 family. Status on 2026-10-15: active. Weights: open-weights. Reasoning MoE for private on-device agents and tool use. Open weights for the base and the post-trained model. Also sold on the Liquid API.", "what_it_is_for": "Reasoning MoE for private on-device agents and tool use. Open weights for the base and the post-trained model. Also sold on the Liquid API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5/lfm2-5-350m": { "id": "liquid-ai/lfm2-5/lfm2-5-350m", "name": "LFM2.5-350M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-03-31", "context_length": 32768, "max_output_tokens": null, "parameters": "350M", "architecture": "LFM2.5 hybrid. 16 layers (10 double-gated conv + 6 GQA). Vocabulary 65,536. Training budget 28T tokens. The card says 350M rather than the LFM2 exact integer 354,483,968.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-350M is a Liquid AI model in the lfm2-5 family. Status on 2026-10-15: active. Weights: open-weights. Compact LFM2.5 instruct model for edge and low-latency use. Blog date 31 March 2026. Base weights are a separate checkpoint.", "what_it_is_for": "Compact LFM2.5 instruct model for edge and low-latency use. Blog date 31 March 2026. Base weights are a separate checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5/lfm2-5-8b-a1b": { "id": "liquid-ai/lfm2-5/lfm2-5-8b-a1b", "name": "LFM2.5-8B-A1B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-05-28", "context_length": 128000, "max_output_tokens": null, "parameters": "8.3B total / 1.5B active", "architecture": "LFM2.5 hybrid MoE. 24 layers (18 double-gated LIV conv + 6 GQA). Vocabulary 128,000. Training budget 38 trillion tokens. Card context length is 128,000. Reasoning-tuned. A base checkpoint is published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-8B-A1B is a Liquid AI model in the lfm2-5 family. Status on 2026-10-15: active. Weights: open-weights. On-device MoE for tool calling and agent workflows. Blog date 28 May 2026. Liquid says it is a poor fit for heavy programming or knowledge-heavy questions without retrieval.", "what_it_is_for": "On-device MoE for tool calling and agent workflows. Blog date 28 May 2026. Liquid says it is a poor fit for heavy programming or knowledge-heavy questions without retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-audio/lfm2-5-audio-1-5b": { "id": "liquid-ai/lfm2-5-audio/lfm2-5-audio-1-5b", "name": "LFM2.5-Audio-1.5B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-01-05", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Native audio-language model. Language backbone 1.2B. Hybrid conv+attention. Text vocab 65,536. Audio vocab 2049*8. bfloat16. LFM-based audio detokenizer, described as 8x faster than the LFM2 Mimi detokenizer on a mobile CPU. Redistributes Kyutai Mimi under CC-BY-4.0. English.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-Audio-1.5B is a Liquid AI model in the lfm2-5-audio family. Status on 2026-10-15: active. Weights: open-weights. End-to-end speech and text model for low-latency conversation. Part of the 5 January 2026 launch. Replaces the LFM2 audio stack's detokenizer path.", "what_it_is_for": "End-to-end speech and text model for low-latency conversation. Part of the 5 January 2026 launch. Replaces the LFM2 audio stack's detokenizer path.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-audio/lfm2-5-audio-1-5b-jp": { "id": "liquid-ai/lfm2-5-audio/lfm2-5-audio-1-5b-jp", "name": "LFM2.5-Audio-1.5B-JP", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-05-27", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Japanese audio-language model. Language backbone 1.2B. Hybrid conv+attention. Text vocab 65,536. Audio vocab 2049*8. bfloat16. Supported language on the card: Japanese. Redistributes Kyutai Mimi under CC-BY-4.0.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-Audio-1.5B-JP is a Liquid AI model in the lfm2-5-audio family. Status on 2026-10-15: active. Weights: open-weights. Japanese speech and text model at 1.5B. Listed on the models page and in the current library. No separate blog dateline was found. ONNX and MLX are marked absent.", "what_it_is_for": "Japanese speech and text model at 1.5B. Listed on the models page and in the current library. No separate blog dateline was found. ONNX and MLX are marked absent.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-nano/lfm2-5-colbert-350m": { "id": "liquid-ai/lfm2-5-nano/lfm2-5-colbert-350m", "name": "LFM2.5-ColBERT-350M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-06-18", "context_length": null, "max_output_tokens": null, "parameters": "353M", "architecture": "Late-interaction ColBERT retriever. About 353M parameters. 17 layers (10 conv + 6 attention + 1 dense). Vocabulary 64,402. BF16. LFM Open License v1.0. Built from LFM2.5-350M-Base according to the HF tag.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-ColBERT-350M is a Liquid AI model in the lfm2-5-nano family. Status on 2026-10-15: active. Weights: open-weights. Multilingual late-interaction retriever. Same 18 June 2026 retriever release as the dense embedder. Higher vendor NanoBEIR average than the dense 350M embedder.", "what_it_is_for": "Multilingual late-interaction retriever. Same 18 June 2026 retriever release as the dense embedder. Higher vendor NanoBEIR average than the dense 350M embedder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-nano/lfm2-5-embedding-350m": { "id": "liquid-ai/lfm2-5-nano/lfm2-5-embedding-350m", "name": "LFM2.5-Embedding-350M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-06-18", "context_length": 512, "max_output_tokens": null, "parameters": "354M", "architecture": "Dense embedding model. About 354M parameters. 17 layers (10 conv + 6 attention + 1 pool). Vocabulary 65,536. BF16. Card sentence: 512-token max length. LFM Open License v1.0.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-Embedding-350M is a Liquid AI model in the lfm2-5-nano family. Status on 2026-10-15: active. Weights: open-weights. Multilingual dense retriever in the LFM2.5 retriever pair. Blog date 18 June 2026. For semantic search across the 11 listed languages.", "what_it_is_for": "Multilingual dense retriever in the LFM2.5 retriever pair. Blog date 18 June 2026. For semantic search across the 11 listed languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-nano/lfm2-5-encoder-230m": { "id": "liquid-ai/lfm2-5-nano/lfm2-5-encoder-230m", "name": "LFM2.5-Encoder-230M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-07-28", "context_length": 8192, "max_output_tokens": null, "parameters": "229.7M", "architecture": "Smaller bidirectional LFM2.5 encoder. Vocabulary 65,536. Context 8,192. Card size about 229.7M. Same 15 languages as the 350M encoder.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-Encoder-230M is a Liquid AI model in the lfm2-5-nano family. Status on 2026-10-15: active. Weights: open-weights. Smaller encoder in the 28 July 2026 pair, for the same fine-tune tasks as LFM2.5-Encoder-350M.", "what_it_is_for": "Smaller encoder in the 28 July 2026 pair, for the same fine-tune tasks as LFM2.5-Encoder-350M.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-nano/lfm2-5-encoder-350m": { "id": "liquid-ai/lfm2-5-nano/lfm2-5-encoder-350m", "name": "LFM2.5-Encoder-350M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-07-28", "context_length": 8192, "max_output_tokens": null, "parameters": "354.5M", "architecture": "Bidirectional masked-language encoder adapted from LFM2. Full bidirectional attention. Vocabulary 65,536. Context up to 8,192. The card's size table lists about 354.5M for this checkpoint and about 229.7M for the 230M sibling. LFM Open License v1.0.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-Encoder-350M is a Liquid AI model in the lfm2-5-nano family. Status on 2026-10-15: active. Weights: open-weights. Multilingual encoder meant to be fine-tuned for classification, token classification, retrieval, reranking, and similarity. Not a chat model. Released with the encoders post on 28 July 2026.", "what_it_is_for": "Multilingual encoder meant to be fine-tuned for classification, token classification, retrieval, reranking, and similarity. Not a chat model. Released with the encoders post on 28 July 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-nano/lfm2-5-encoder-350m-diffusion": { "id": "liquid-ai/lfm2-5-nano/lfm2-5-encoder-350m-diffusion", "name": "LFM2.5-Encoder-350M-Diffusion", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-07-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of LFM2.5-Encoder-350M. Parent encoder is about 354.5M parameters with an 8,192 context. This card does not restate those figures.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-Encoder-350M-Diffusion is a Liquid AI model in the lfm2-5-nano family. Status on 2026-10-15: active. Weights: open-weights. Masked-diffusion instruction model. It generates text by iteratively unmasking tokens. SFT on mlabonne/open-perfectblend for 3 epochs, per the card.", "what_it_is_for": "Masked-diffusion instruction model. It generates text by iteratively unmasking tokens. SFT on mlabonne/open-perfectblend for 3 epochs, per the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-nano/lfm2-5-encoder-350m-pii-detector": { "id": "liquid-ai/lfm2-5-nano/lfm2-5-encoder-350m-pii-detector", "name": "LFM2.5-Encoder-350M-PII-Detector", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-07-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of LFM2.5-Encoder-350M with a token-classification head. Card says 40 PII types. A demo note on the encoder card says 16 languages. Context is not restated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-Encoder-350M-PII-Detector is a Liquid AI model in the lfm2-5-nano family. Status on 2026-10-15: active. Weights: open-weights. PII detector on the models page. It labels personal information in text. It is a named Liquid checkpoint, not a third-party fine-tune.", "what_it_is_for": "PII detector on the models page. It labels personal information in text. It is a named Liquid checkpoint, not a third-party fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-nano/lfm2-5-encoder-350m-policy-linter": { "id": "liquid-ai/lfm2-5-nano/lfm2-5-encoder-350m-policy-linter", "name": "LFM2.5-Encoder-350M-Policy-Linter", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-07-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of LFM2.5-Encoder-350M. Parent encoder is about 354.5M parameters with an 8,192 context. This card does not restate those figures.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-Encoder-350M-Policy-Linter is a Liquid AI model in the lfm2-5-nano family. Status on 2026-10-15: active. Weights: open-weights. Rule-matching head that scores every token against free-text policy rules in one encoder pass.", "what_it_is_for": "Rule-matching head that scores every token against free-text policy rules in one encoder pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-nano/lfm2-5-encoder-350m-prompt-router": { "id": "liquid-ai/lfm2-5-nano/lfm2-5-encoder-350m-prompt-router", "name": "LFM2.5-Encoder-350M-Prompt-Router", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-07-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of LFM2.5-Encoder-350M. Parent encoder is about 354.5M parameters with an 8,192 context. This card does not restate those figures.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-Encoder-350M-Prompt-Router is a Liquid AI model in the lfm2-5-nano family. Status on 2026-10-15: active. Weights: open-weights. Zero-shot routing head that scores a prompt against user-defined routing lanes in one encoder pass.", "what_it_is_for": "Zero-shot routing head that scores a prompt against user-defined routing lanes in one encoder pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-nano/lfm2-5-encoder-350m-spellchecker": { "id": "liquid-ai/lfm2-5-nano/lfm2-5-encoder-350m-spellchecker", "name": "LFM2.5-Encoder-350M-Spellchecker", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-07-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of LFM2.5-Encoder-350M. Parent encoder is about 354.5M parameters with an 8,192 context. This card does not restate those figures.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-Encoder-350M-Spellchecker is a Liquid AI model in the lfm2-5-nano family. Status on 2026-10-15: active. Weights: open-weights. GECToR-style tagger for English grammar, spelling, punctuation, and casing. The card points at an ERRANT F0.5 table. The numeric row was not extracted.", "what_it_is_for": "GECToR-style tagger for English grammar, spelling, punctuation, and casing. The card points at an ERRANT F0.5 table. The numeric row was not extracted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-nano/lfm2-5-vl-1-6b-extract": { "id": "liquid-ai/lfm2-5-nano/lfm2-5-vl-1-6b-extract", "name": "LFM2.5-VL-1.6B-Extract", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.6B", "architecture": "Extraction VLM. Card: language-model-only 1.2B, hybrid conv+attention, text vocab 65,536, bfloat16. Headline size on the benchmark table is 1.6B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-VL-1.6B-Extract is a Liquid AI model in the lfm2-5-nano family. Status on 2026-10-15: active. Weights: open-weights. Document-extraction vision model. Vendor table: JSON validity 99.6, F1 99.6, VLM judge 90.6. Release day was not on the news index snippet.", "what_it_is_for": "Document-extraction vision model. Vendor table: JSON validity 99.6, F1 99.6, VLM judge 90.6. Release day was not on the news index snippet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-nano/lfm2-5-vl-450m-extract": { "id": "liquid-ai/lfm2-5-nano/lfm2-5-vl-450m-extract", "name": "LFM2.5-VL-450M-Extract", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.45B", "architecture": "Small extraction VLM. Card: language-model-only 350M, hybrid conv+attention, text vocab 65,536, bfloat16. Benchmark table lists 0.45B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-VL-450M-Extract is a Liquid AI model in the lfm2-5-nano family. Status on 2026-10-15: active. Weights: open-weights. Smaller document-extraction vision model. Vendor table: JSON validity 98.9, F1 98.8, VLM judge 84.5.", "what_it_is_for": "Smaller document-extraction vision model. Vendor table: JSON validity 98.9, F1 98.8, VLM judge 84.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-vl/lfm2-5-vl-1-6b": { "id": "liquid-ai/lfm2-5-vl/lfm2-5-vl-1-6b", "name": "LFM2.5-VL-1.6B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-01-05", "context_length": 32768, "max_output_tokens": null, "parameters": "1.6B", "architecture": "Vision-language LFM2.5. Card context 32,768. Vocabulary 65,536. Languages: English, Arabic, Chinese, French, German, Japanese, Korean, Spanish. Launch blog: updated LFM2.5 backbone, stronger multi-image and multilingual vision.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-VL-1.6B is a Liquid AI model in the lfm2-5-vl family. Status on 2026-10-15: active. Weights: open-weights. Vision-language model from the 5 January 2026 LFM2.5 launch. Liquid highlights multi-image understanding and instruction following on device.", "what_it_is_for": "Vision-language model from the 5 January 2026 LFM2.5 launch. Liquid highlights multi-image understanding and instruction following on device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-vl/lfm2-5-vl-3b": { "id": "liquid-ai/lfm2-5-vl/lfm2-5-vl-3b", "name": "LFM2.5-VL-3B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-08-12", "context_length": 32768, "max_output_tokens": null, "parameters": "3.1B", "architecture": "Multimodal LFM2.5. Language backbone is LFM2.5-2.6B plus a SigLIP2 NaFlex vision encoder. Vocabulary 128,000. Context 32,768. The d1-3B card table lists this model at 3.1B. The VL card itself, in the lines captured, does not print a single total.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-VL-3B is a Liquid AI model in the lfm2-5-vl family. Status on 2026-10-15: active. Weights: open-weights. Larger LFM2.5 vision-language model for grounding, OCR with layout, and tool use on device. Released 12 August 2026. Successor to LFM2-VL-3B.", "what_it_is_for": "Larger LFM2.5 vision-language model for grounding, OCR with layout, and tool use on device. Released 12 August 2026. Successor to LFM2-VL-3B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-5-vl/lfm2-5-vl-450m": { "id": "liquid-ai/lfm2-5-vl/lfm2-5-vl-450m", "name": "LFM2.5-VL-450M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-04-08", "context_length": 32768, "max_output_tokens": null, "parameters": "450M", "architecture": "Vision-language model on an LFM2.5-350M backbone plus a SigLIP2 NaFlex encoder that the card calls an 86M shape-optimized encoder. Vocabulary 65,536. Context 32,768. Adds grounding and function calling versus LFM2-VL-450M.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2.5-VL-450M is a Liquid AI model in the lfm2-5-vl family. Status on 2026-10-15: active. Weights: open-weights. Compact vision model for structured outputs on the edge, including bounding boxes. Released 8 April 2026.", "what_it_is_for": "Compact vision model for structured outputs on the edge, including bounding boxes. Released 8 April 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-audio/lfm2-audio-1-5b": { "id": "liquid-ai/lfm2-audio/lfm2-audio-1-5b", "name": "LFM2-Audio-1.5B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-10-01", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "End-to-end audio-language model. Language backbone 1.2B. Hybrid conv+attention. Text vocabulary 65,536 and audio vocabulary 2049*8. bfloat16. Redistributes Kyutai Mimi weights under CC-BY-4.0. Docs matrix: not trainable.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-Audio-1.5B is a Liquid AI model in the lfm2-audio family. Status on 2026-10-15: active. Weights: open-weights. LFM2 speech model for real-time conversation at 1.5B parameters, with speech and text in and out. Still listed in the current model matrix. Not on the deprecations table. LFM2.5-Audio-1.5B is the newer release.", "what_it_is_for": "LFM2 speech model for real-time conversation at 1.5B parameters, with speech and text in and out. Still listed in the current model matrix. Not on the deprecations table. LFM2.5-Audio-1.5B is the newer release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-nano/lfm2-1-2b-extract": { "id": "liquid-ai/lfm2-nano/lfm2-1-2b-extract", "name": "LFM2-1.2B-Extract", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "1.2B class", "architecture": "Fine-tune of LFM2-1.2B for extraction. Parent dense LFM2 cards say 32,768 context. This task card did not restate context, so context_window_tokens is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-1.2B-Extract is a Liquid AI model in the lfm2-nano family. Status on 2026-10-15: deprecated. Weights: open-weights. Task model that turns unstructured documents into JSON, XML, or YAML. Deprecated. Docs suggest LFM2.5-1.2B-Instruct as the replacement.", "what_it_is_for": "Task model that turns unstructured documents into JSON, XML, or YAML. Deprecated. Docs suggest LFM2.5-1.2B-Instruct as the replacement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-nano/lfm2-1-2b-longevity": { "id": "liquid-ai/lfm2-nano/lfm2-1-2b-longevity", "name": "LFM2-1.2B-Longevity", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-05-25", "context_length": 32768, "max_output_tokens": null, "parameters": "1.2B class", "architecture": "Hybrid LFM2. Card states context 32,768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-1.2B-Longevity is a Liquid AI model in the lfm2-nano family. Status on 2026-10-15: active. Weights: open-weights. Smaller longevity checkpoint, called L-LFM2-1.2B on the card. Full-parameter SFT of LFM2-1.2B on the same aging-related data recipe as the 2.6B longevity model.", "what_it_is_for": "Smaller longevity checkpoint, called L-LFM2-1.2B on the card. Full-parameter SFT of LFM2-1.2B on the same aging-related data recipe as the 2.6B longevity model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-nano/lfm2-1-2b-rag": { "id": "liquid-ai/lfm2-nano/lfm2-1-2b-rag", "name": "LFM2-1.2B-RAG", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "1.2B class", "architecture": "Fine-tune of LFM2-1.2B for RAG answers. Parent dense LFM2 cards say 32,768 context. This task card did not restate context, so context_window_tokens is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-1.2B-RAG is a Liquid AI model in the lfm2-nano family. Status on 2026-10-15: deprecated. Weights: open-weights. Retrieval-grounded answering model in the Nanos set. Deprecated. Docs suggest LFM2.5-1.2B-Instruct.", "what_it_is_for": "Retrieval-grounded answering model in the Nanos set. Deprecated. Docs suggest LFM2.5-1.2B-Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-nano/lfm2-1-2b-tool": { "id": "liquid-ai/lfm2-nano/lfm2-1-2b-tool", "name": "LFM2-1.2B-Tool", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "1.2B class", "architecture": "Fine-tune of LFM2-1.2B for tool calls. Parent dense LFM2 cards say 32,768 context. This task card did not restate context, so context_window_tokens is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-1.2B-Tool is a Liquid AI model in the lfm2-nano family. Status on 2026-10-15: deprecated. Weights: open-weights. Tool-calling Nano. The docs page title on the deprecations table marks it deprecated, with LFM2.5-1.2B-Instruct as the replacement.", "what_it_is_for": "Tool-calling Nano. The docs page title on the deprecations table marks it deprecated, with LFM2.5-1.2B-Instruct as the replacement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-nano/lfm2-2-6b-longevity": { "id": "liquid-ai/lfm2-nano/lfm2-2-6b-longevity", "name": "LFM2-2.6B-Longevity", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2026-05-25", "context_length": 32768, "max_output_tokens": null, "parameters": "2.6B class", "architecture": "Hybrid LFM2. Card states context 32,768. Full-parameter SFT, not a new pretrain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-2.6B-Longevity is a Liquid AI model in the lfm2-nano family. Status on 2026-10-15: active. Weights: open-weights. Domain checkpoint, also called L-LFM2-2.6B on the card. Full-parameter supervised fine-tune of LFM2-2.6B on aging-related multi-omics and clinical data. ChatML with a dynamic thinking tag.", "what_it_is_for": "Domain checkpoint, also called L-LFM2-2.6B on the card. Full-parameter supervised fine-tune of LFM2-2.6B on aging-related multi-omics and clinical data. ChatML with a dynamic thinking tag.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-nano/lfm2-2-6b-mmai": { "id": "liquid-ai/lfm2-nano/lfm2-2-6b-mmai", "name": "LFM2-2.6B-MMAI", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "2.6B class", "architecture": "Named gated model on the Liquid models page, tied to the Insilico Medicine partnership. No public Hugging Face repo was in the org listing of 198 repos.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-2.6B-MMAI is a Liquid AI model in the lfm2-nano family. Status on 2026-10-15: preview. Weights: closed. Request-access model listed after the Nano section, with a press link to the Liquid AI and Insilico Medicine partnership. It is not in the deprecated count of six. Weights were not found for public download.", "what_it_is_for": "Request-access model listed after the Nano section, with a press link to the Liquid AI and Insilico Medicine partnership. It is not in the deprecated count of six. Weights were not found for public download.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-nano/lfm2-2-6b-transcript": { "id": "liquid-ai/lfm2-nano/lfm2-2-6b-transcript", "name": "LFM2-2.6B-Transcript", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "2.6B class", "architecture": "Task fine-tune of LFM2-2.6B for meeting or call transcripts. Parent dense LFM2 cards say 32,768 context. This task card did not restate context, so context_window_tokens is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-2.6B-Transcript is a Liquid AI model in the lfm2-nano family. Status on 2026-10-15: active. Weights: open-weights. Transcript summarization Nano based on LFM2-2.6B. Card recommends temperature 0.3. A 7 January 2026 news item covers local meeting summarization; it was not opened, so it is not used as the release date.", "what_it_is_for": "Transcript summarization Nano based on LFM2-2.6B. Card recommends temperature 0.3. A 7 January 2026 news item covers local meeting summarization; it was not opened, so it is not used as the release date.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-nano/lfm2-350m-enjp-mt": { "id": "liquid-ai/lfm2-nano/lfm2-350m-enjp-mt", "name": "LFM2-350M-ENJP-MT", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "350M class", "architecture": "Task fine-tune of LFM2-350M for English-Japanese translation. Parent dense LFM2 cards say 32,768 context. This task card did not restate context, so context_window_tokens is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-350M-ENJP-MT is a Liquid AI model in the lfm2-nano family. Status on 2026-10-15: active. Weights: open-weights. English-Japanese translation Nano. Still in the current library with GGUF, MLX, and ONNX.", "what_it_is_for": "English-Japanese translation Nano. Still in the current library with GGUF, MLX, and ONNX.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-nano/lfm2-350m-extract": { "id": "liquid-ai/lfm2-nano/lfm2-350m-extract", "name": "LFM2-350M-Extract", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "350M class", "architecture": "Fine-tune of LFM2-350M for extraction. Parent dense LFM2 cards say 32,768 context. This task card did not restate context, so context_window_tokens is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-350M-Extract is a Liquid AI model in the lfm2-nano family. Status on 2026-10-15: deprecated. Weights: open-weights. Smaller extraction model in the Liquid Nanos set. Deprecated in favor of LFM2.5-350M.", "what_it_is_for": "Smaller extraction model in the Liquid Nanos set. Deprecated in favor of LFM2.5-350M.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-nano/lfm2-350m-math": { "id": "liquid-ai/lfm2-nano/lfm2-350m-math", "name": "LFM2-350M-Math", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "350M class", "architecture": "Task fine-tune of LFM2-350M for math. Parent dense LFM2 cards say 32,768 context. This task card did not restate context, so context_window_tokens is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-350M-Math is a Liquid AI model in the lfm2-nano family. Status on 2026-10-15: active. Weights: open-weights. Math Nano. Card says English only and recommends temperature 0.6, top_p 0.95, min_p 0.1, repetition_penalty 1.05.", "what_it_is_for": "Math Nano. Card says English only and recommends temperature 0.6, top_p 0.95, min_p 0.1, repetition_penalty 1.05.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-nano/lfm2-350m-pii-extract-jp": { "id": "liquid-ai/lfm2-nano/lfm2-350m-pii-extract-jp", "name": "LFM2-350M-PII-Extract-JP", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "350M class", "architecture": "Task fine-tune of LFM2-350M for Japanese PII extraction. Parent dense LFM2 cards say 32,768 context. This task card did not restate context, so context_window_tokens is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-350M-PII-Extract-JP is a Liquid AI model in the lfm2-nano family. Status on 2026-10-15: active. Weights: open-weights. Japanese PII extraction Nano. The card says it was compared with GPT-5 and a 32B Qwen3 thinking model and claims GPT-5-level extraction at 350M. No score table was in the extracted lines. Recommended decoding temperature is 0.", "what_it_is_for": "Japanese PII extraction Nano. The card says it was compared with GPT-5 and a 32B Qwen3 thinking model and claims GPT-5-level extraction at 350M. No score table was in the extracted lines. Recommended decoding temperature is 0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-nano/lfm2-colbert-350m": { "id": "liquid-ai/lfm2-nano/lfm2-colbert-350m", "name": "LFM2-ColBERT-350M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-10-28", "context_length": null, "max_output_tokens": null, "parameters": "353,322,752", "architecture": "Late-interaction ColBERT retriever. 17 layers (10 conv + 6 attention + 1 dense). Vocabulary 64,402. BF16. LFM Open License v1.0.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-ColBERT-350M is a Liquid AI model in the lfm2-nano family. Status on 2026-10-15: deprecated. Weights: open-weights. Multilingual late-interaction retriever. News index dates the post 28 October 2025, after the first Nanos post. Deprecations replace it with LFM2.5-ColBERT-350M. The deprecations matrix shows no GGUF, but a LiquidAI GGUF repo exists.", "what_it_is_for": "Multilingual late-interaction retriever. News index dates the post 28 October 2025, after the first Nanos post. Deprecations replace it with LFM2.5-ColBERT-350M. The deprecations matrix shows no GGUF, but a LiquidAI GGUF repo exists.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-vl/lfm2-vl-1-6b": { "id": "liquid-ai/lfm2-vl/lfm2-vl-1-6b", "name": "LFM2-VL-1.6B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-vl", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-08-12", "context_length": 32768, "max_output_tokens": null, "parameters": "1.6B", "architecture": "LFM2-VL. Language backbone 1.2B on the family card. Hybrid conv+attention. Vocabulary 65,536. bfloat16. User-tunable image token count. Context is the library default of 32K; the 1.6B docs page was not opened separately.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-VL-1.6B is a Liquid AI model in the lfm2-vl family. Status on 2026-10-15: deprecated. Weights: open-weights. Larger of the two original LFM2-VL checkpoints. Deprecations replace it with LFM2.5-VL-1.6B. The models page deprecated-count for vision showed two models and did not name this one; the deprecations table does.", "what_it_is_for": "Larger of the two original LFM2-VL checkpoints. Deprecations replace it with LFM2.5-VL-1.6B. The models page deprecated-count for vision showed two models and did not name this one; the deprecations table does.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-vl/lfm2-vl-3b": { "id": "liquid-ai/lfm2-vl/lfm2-vl-3b", "name": "LFM2-VL-3B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-vl", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-10-22", "context_length": 32768, "max_output_tokens": null, "parameters": "3B", "architecture": "LFM2-VL scaled up from the 1.6B design. Family card LM-only column for the 3B is 2.6B. Hybrid conv+attention. Vocabulary 65,536. bfloat16. Context uses the 32K library default.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-VL-3B is a Liquid AI model in the lfm2-vl family. Status on 2026-10-15: deprecated. Weights: open-weights. Highest-capacity LFM2 vision-language model. News index dates it 22 October 2025. Deprecations replace it with LFM2.5-VL-3B.", "what_it_is_for": "Highest-capacity LFM2 vision-language model. News index dates it 22 October 2025. Deprecations replace it with LFM2.5-VL-3B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "liquid-ai/lfm2-vl/lfm2-vl-450m": { "id": "liquid-ai/lfm2-vl/lfm2-vl-450m", "name": "LFM2-VL-450M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-vl", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "LFM Open License v1.0 (Hugging Face tag lfm1.0). Apache-2.0-style use, modification, and distribution, with no copyleft. Free commercial use only if annual revenue is under USD 10 million; a paid license is required above that.", "release_date": "2025-08-12", "context_length": 32768, "max_output_tokens": null, "parameters": "450M", "architecture": "LFM2-VL. Language backbone listed as 350M on the family card. Hybrid conv+attention. Vocabulary 65,536. bfloat16. SigLIP2 NaFlex vision encoder is described on the successor card as the design this model started. Docs page: 32K context.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LFM2-VL-450M is a Liquid AI model in the lfm2-vl family. Status on 2026-10-15: deprecated. Weights: open-weights. Smallest LFM2 vision-language model, for tight edge devices. News index dates LFM2-VL to 12 August 2025. Docs say it is deprecated in favor of LFM2.5-VL-450M.", "what_it_is_for": "Smallest LFM2 vision-language model, for tight edge devices. News index dates LFM2-VL to 12 August 2025. Docs say it is deprecated in favor of LFM2.5-VL-450M.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/albertina/albertina-100m-ptbr": { "id": "lisbon/albertina/albertina-100m-ptbr", "name": "Albertina 100M PT-BR", "maker": "lisbon", "family": "lisbon/albertina", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-03-04", "context_length": null, "max_output_tokens": null, "parameters": "100000000", "architecture": "DeBERTa V1 continued pretraining from microsoft/deberta-base. The card states 100M parameters, 12 layers, and hidden size 768. The PT-PT 100M config.json of the same stated codebase has max_position_embeddings 512, vocabulary 50265. This repo's config.json was not fetched.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Albertina 100M PT-BR is the smaller Brazilian Portuguese Albertina encoder from NLX at the University of Lisbon and the University of Porto, continued from DeBERTa V1 base and trained on OSCAR rather than brWaC. The March 2024 family paper presents it. Collected on 2026-10-15.", "what_it_is_for": "The PORTULAN models page lists it as the 100 million parameter PT-BR encoder trained on data other than brWaC. The card presents it as a foundation encoder for Brazilian Portuguese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/albertina/albertina-100m-ptpt": { "id": "lisbon/albertina/albertina-100m-ptpt", "name": "Albertina 100M PT-PT", "maker": "lisbon", "family": "lisbon/albertina", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-03-04", "context_length": 512, "max_output_tokens": null, "parameters": "100000000", "architecture": "DeBERTa V1 continued pretraining from microsoft/deberta-base. config.json: deberta, 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocabulary 50265, max_position_embeddings 512. The card states 100M parameters, 12 layers, hidden size 768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Albertina 100M PT-PT is the efficiency-sized European Portuguese encoder from NLX at the University of Lisbon and the University of Porto. It is continued from DeBERTa V1 base. The family paper of 4 March 2024 presents it with the 1.5B models. Collected on 2026-10-15.", "what_it_is_for": "The card and the PORTULAN models page describe it as the 100 million parameter PT-PT foundation encoder, for masked language modeling and for fine-tuning when a smaller model is needed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/albertina/albertina-1b5-ptbr": { "id": "lisbon/albertina/albertina-1b5-ptbr", "name": "Albertina 1.5B PT-BR", "maker": "lisbon", "family": "lisbon/albertina", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-03-04", "context_length": null, "max_output_tokens": null, "parameters": "1500000000", "architecture": "DeBERTa V2 continued pretraining from microsoft/deberta-v2-xxlarge. The card states 1.5 billion parameters, 48 layers, and hidden size 1536. The PT-PT 1.5B config.json of the same stated codebase has max_position_embeddings 512, vocabulary 128100, 24 heads, intermediate size 6144. This repo's config.json was not fetched.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Albertina 1.5B PT-BR is the largest Brazilian Portuguese Albertina encoder from NLX at the University of Lisbon with the University of Porto, continued from DeBERTa V2 XXLarge. The final public checkpoint is the 512-token stage. Collected on 2026-10-15.", "what_it_is_for": "The PORTULAN models page lists it as the 1.5 billion parameter PT-BR encoder trained on data other than brWaC. The card presents it as a foundation encoder for the American variant of Portuguese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/albertina/albertina-1b5-ptpt": { "id": "lisbon/albertina/albertina-1b5-ptpt", "name": "Albertina 1.5B PT-PT", "maker": "lisbon", "family": "lisbon/albertina", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-03-04", "context_length": 512, "max_output_tokens": null, "parameters": "1500000000", "architecture": "DeBERTa V2 continued pretraining from microsoft/deberta-v2-xxlarge. config.json: deberta-v2, 48 layers, hidden size 1536, 24 heads, intermediate size 6144, vocabulary 128100, max_position_embeddings 512. The card states 1.5 billion parameters, 48 layers, hidden size 1536.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Albertina 1.5B PT-PT is the largest European Portuguese Albertina encoder, from NLX at the University of Lisbon with the University of Porto. It is continued from DeBERTa V2 XXLarge through a 128, 256, then 512 token schedule. The final checkpoint is the current product. Collected on 2026-10-15.", "what_it_is_for": "The March 2024 family paper and the PORTULAN models page present it as the 1.5 billion parameter PT-PT foundation encoder, aimed at higher downstream accuracy than the 100M and 900M encoders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/albertina/albertina-900m-ptbr": { "id": "lisbon/albertina/albertina-900m-ptbr", "name": "Albertina 900M PT-BR (no brWaC)", "maker": "lisbon", "family": "lisbon/albertina", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The card body says MIT. Hugging Face metadata license tag is other.", "release_date": "2023-05-25", "context_length": null, "max_output_tokens": null, "parameters": "900000000", "architecture": "DeBERTa V2 continued pretraining from microsoft/deberta-v2-xlarge. The card states 900M parameters, 24 layers, and hidden size 1536. Sibling PT-PT config.json of the same stated shape has max_position_embeddings 512. This checkpoint's config.json was not fetched.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Albertina 900M PT-BR (no brWaC) is the Brazilian Portuguese Albertina encoder trained without brWaC, released by NLX at the University of Lisbon with the University of Porto. It is the permissive 900M PT-BR checkpoint. The Hub license tag and the card body do not match. Collected on 2026-10-15.", "what_it_is_for": "The card and the PORTULAN CLARIN models page describe it as the PT-BR encoder trained on data other than brWaC, for masked language modeling and downstream fine-tuning, with a more permissive license than the brWaC checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/albertina/albertina-900m-ptbr-brwac": { "id": "lisbon/albertina/albertina-900m-ptbr-brwac", "name": "Albertina 900M PT-BR (brWaC)", "maker": "lisbon", "family": "lisbon/albertina", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BrWaC academic-research-only. The card says the weights may not be used for commercial applications. Hugging Face metadata license tag is other.", "release_date": "2023-05-08", "context_length": null, "max_output_tokens": null, "parameters": "900000000", "architecture": "DeBERTa V2 continued pretraining from microsoft/deberta-v2-xlarge on brWaC. The card states 900M parameters, 24 layers, and hidden size 1536. The sibling PT-PT config.json, same stated shape, has max_position_embeddings 512. This checkpoint's own config.json was not fetched separately.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Albertina 900M PT-BR (brWaC) is the original Brazilian Portuguese Albertina encoder from the NLX group at the University of Lisbon and the University of Porto. It was continued from DeBERTa V2 XLarge on the brWaC corpus. The weights are public, but the card limits them to academic research because of the brWaC license. Collected on 2026-10-15.", "what_it_is_for": "The PORTULAN CLARIN models page lists this checkpoint as Albertina PT-BR trained on brWaC, separate from the later no-brWaC 900M model. The card presents it as a foundation encoder for Brazilian Portuguese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/albertina/albertina-900m-ptpt": { "id": "lisbon/albertina/albertina-900m-ptpt", "name": "Albertina 900M PT-PT", "maker": "lisbon", "family": "lisbon/albertina", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-05-08", "context_length": 512, "max_output_tokens": null, "parameters": "900000000", "architecture": "DeBERTa V2 continued pretraining from microsoft/deberta-v2-xlarge. config.json: deberta-v2, 24 layers, hidden size 1536, 24 heads, intermediate size 6144, vocabulary 128100, max_position_embeddings 512. The card states 900M parameters and 24 layers.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Albertina 900M PT-PT is a masked-language encoder released by the NLX group at the Faculty of Sciences, University of Lisbon, jointly with the University of Porto. It is the European Portuguese member of the original Albertina pair, continued from DeBERTa V2 XLarge, and the weights remain public. Collected on 2026-10-15.", "what_it_is_for": "The card calls it a foundation encoder for European Portuguese, for fill-mask use and for fine-tuning on tasks such as entailment and semantic similarity. The PORTULAN CLARIN models page lists it as the 900 million parameter PT-PT encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/gervasio/gervasio-70b-ptpt": { "id": "lisbon/gervasio/gervasio-70b-ptpt", "name": "Gerv\u00e1sio 70B PT-PT", "maker": "lisbon", "family": "lisbon/gervasio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-26", "context_length": 131072, "max_output_tokens": null, "parameters": "70000000000", "architecture": "Further training of Llama 3.3 70B Instruct. The card states 70 billion parameters, hidden size 8192, intermediate size 28672, 64 heads, 80 layers, and vocabulary 128000. config.json matches the layer counts and sets vocabulary 128256 and max_position_embeddings 131072. The Hub size label says 71B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gerv\u00e1sio 70B PT-PT is the largest public Gerv\u00e1sio decoder from NLX at the University of Lisbon. It is further training of Llama 3.3 70B Instruct for European Portuguese. The main Hub repo is the 16-bit release, with a separate 4-bit GGUF build. A technical report was still marked forthcoming. Collected on 2026-10-15.", "what_it_is_for": "The card describes a fully open Portuguese decoder for generative tasks, distributed under MIT, including research and commercial use as stated for the Gerv\u00e1sio line. The PORTULAN CLARIN models page still highlights the 8B model and does not yet describe this 70B checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/gervasio/gervasio-7b-ptbr": { "id": "lisbon/gervasio/gervasio-7b-ptbr", "name": "Gerv\u00e1sio 7B PT-BR", "maker": "lisbon", "family": "lisbon/gervasio", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-02-28", "context_length": null, "max_output_tokens": null, "parameters": "7000000000", "architecture": "Instruction-tuned Llama 2 7B decoder for Brazilian Portuguese. The commented card states 7 billion parameters, hidden size 4096, intermediate size 11008, 32 heads, 32 layers, and a 32000-token SentencePiece vocabulary. The PT-PT sibling config.json matches those counts and sets max_position_embeddings to 4096. This repo's config.json was not fetched.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gerv\u00e1sio 7B PT-BR is the deprecated Brazilian Portuguese 7B decoder from NLX at the University of Lisbon, further trained from Llama 2 7B. The live card tells users to use Gerv\u00e1sio 8B PT-PT instead. Collected on 2026-10-15.", "what_it_is_for": "The paper presents it as the Brazilian half of the open Gerv\u00e1sio 7B pair, for instruction-following generation in Portuguese. The maker no longer recommends it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/gervasio/gervasio-7b-ptpt": { "id": "lisbon/gervasio/gervasio-7b-ptpt", "name": "Gerv\u00e1sio 7B PT-PT", "maker": "lisbon", "family": "lisbon/gervasio", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-02-29", "context_length": 4096, "max_output_tokens": null, "parameters": "7000000000", "architecture": "Instruction-tuned Llama 2 7B decoder. config.json: llama, 32 layers, hidden size 4096, 32 heads, intermediate size 11008, vocabulary 32000, max_position_embeddings 4096. The original card text, now inside an HTML comment, states the same counts and a SentencePiece BPE tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gerv\u00e1sio 7B PT-PT is a deprecated instruction-tuned decoder from the NLX group at the Faculty of Sciences, University of Lisbon. It was further trained from Llama 2 7B for European Portuguese. The live card tells users to switch to Gerv\u00e1sio 8B PT-PT. Collected on 2026-10-15.", "what_it_is_for": "The 2024 paper presents Gerv\u00e1sio as an open instruction-tuned decoder for Portuguese text generation, covering the European and Brazilian variants. This checkpoint is the European Portuguese 7B model. The maker now marks it deprecated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/gervasio/gervasio-8b-ptpt": { "id": "lisbon/gervasio/gervasio-8b-ptpt", "name": "Gerv\u00e1sio 8B PT-PT", "maker": "lisbon", "family": "lisbon/gervasio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-06-11", "context_length": 131072, "max_output_tokens": null, "parameters": "8000000000", "architecture": "Supervised fine-tune of meta-llama/Llama-3.1-8B-Instruct. The card states 8 billion parameters, 32 layers, hidden size 4096, intermediate size 14336, 32 heads, and a RoPE tokenizer with vocabulary 128256. config.json agrees and sets max_position_embeddings to 131072. Hub base_model_relation is finetune.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gerv\u00e1sio 8B PT-PT is the current 8 billion parameter European Portuguese decoder from NLX at the University of Lisbon. It is a supervised fine-tune of Llama 3.1 8B Instruct, with MIT weights on the PORTULAN Hub org, and the card says it is wired into the Evaristo.ai chatbot. Collected on 2026-10-15.", "what_it_is_for": "The card describes an open Portuguese decoder for generative use, including the Evaristo.ai chat interface. It replaces the deprecated Gerv\u00e1sio 7B models in the maker's recommendation, even on the Brazilian 7B card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/serafim/serafim-100m": { "id": "lisbon/serafim/serafim-100m", "name": "Serafim 100M", "maker": "lisbon", "family": "lisbon/serafim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-07-04", "context_length": 128, "max_output_tokens": null, "parameters": "100000000", "architecture": "Smallest Serafim similarity encoder. The card and the PORTULAN page say 100 million parameters. config.json: bert, 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocabulary 29794, max_position_embeddings 512. The card uses BertModel, mean pooling to 768 dimensions, max_seq_length 128, and CoSENTLoss. Vocabulary 29794 matches BERTimbau base, not Albertina 100M DeBERTa (vocabulary 50265). The paper names BERTimbau as the base of Serafim 335 and Albertina PT-PT as the base of Serafim 900, and does not name the 100M base in that sentence.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Serafim 100M is the smallest Portuguese sentence encoder released by NLX at the University of Lisbon. It is the similarity checkpoint. The paper reports it as the best family member on the small legal IRIS-STS set. Collected on 2026-10-15.", "what_it_is_for": "The PORTULAN models page lists a 100 million parameter Serafim encoder. The card says it embeds sentences for clustering or semantic search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/serafim/serafim-100m-ir": { "id": "lisbon/serafim/serafim-100m-ir", "name": "Serafim 100M IR", "maker": "lisbon", "family": "lisbon/serafim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-07-04", "context_length": 128, "max_output_tokens": null, "parameters": "100000000", "architecture": "Information-retrieval tune at the 100M size. Card architecture: BertModel, mean pooling, embedding size 768, max_seq_length 128, GISTEmbedLoss, one epoch, InformationRetrievalEvaluator. The STS sibling config is a 12-layer BERT with vocabulary 29794.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Serafim 100M IR is the information-retrieval checkpoint of the smallest Serafim encoder from NLX at the University of Lisbon. It is published as its own Hub repo. Collected on 2026-10-15.", "what_it_is_for": "The card presents a 768-dimensional sentence encoder. The paper assigns the serafim 100 ir row to retrieval evaluation after the GISTEmbed stage on mMARCO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/serafim/serafim-335m": { "id": "lisbon/serafim/serafim-335m", "name": "Serafim 335M", "maker": "lisbon", "family": "lisbon/serafim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-07-04", "context_length": 128, "max_output_tokens": null, "parameters": "335000000", "architecture": "Sentence encoder the paper says is based on BERTimbau. config.json: bert, 24 layers, hidden size 1024, 16 heads, intermediate size 4096, vocabulary 29794, max_position_embeddings 512. The card uses BertModel, mean pooling to 1024 dimensions, max_seq_length 128, and AnglELoss. BERTimbau large is the Portuguese BERT with this shape. The card does not name the base repo.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Serafim 335M is the mid-size Portuguese sentence encoder from NLX at the University of Lisbon. The paper says this size is based on BERTimbau and that it leads the Brazilian STS sets in the family. This folder is the similarity checkpoint, not the IR tune. Collected on 2026-10-15.", "what_it_is_for": "The PORTULAN models page lists a 335 million parameter Serafim encoder for sentence embeddings. The card says it can be used for clustering or semantic search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/serafim/serafim-335m-ir": { "id": "lisbon/serafim/serafim-335m-ir", "name": "Serafim 335M IR", "maker": "lisbon", "family": "lisbon/serafim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-07-04", "context_length": 128, "max_output_tokens": null, "parameters": "335000000", "architecture": "Information-retrieval tune at the 335M size. The card title says it is tuned for information retrieval. Architecture block: BertModel, mean pooling, embedding size 1024, max_seq_length 128, GISTEmbedLoss, one epoch, InformationRetrievalEvaluator. The STS sibling config is a 24-layer BERT with vocabulary 29794.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Serafim 335M IR is the information-retrieval checkpoint of Serafim 335M from NLX at the University of Lisbon. It is a separate Hub repo from the similarity model. Collected on 2026-10-15.", "what_it_is_for": "The card presents a 1024-dimensional Portuguese sentence encoder tuned for information retrieval. The paper's IR table is the published score for this stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/serafim/serafim-900m": { "id": "lisbon/serafim/serafim-900m", "name": "Serafim 900M", "maker": "lisbon", "family": "lisbon/serafim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-07-04", "context_length": 128, "max_output_tokens": null, "parameters": "900000000", "architecture": "Sentence encoder on Albertina PT-PT. The paper says Serafim 900 is based on Albertina PT-PT. The card architecture is DebertaV2Model with mean pooling, embedding size 1536, max_seq_length 128. config.json: deberta-v2, 24 layers, hidden size 1536, 24 heads, vocabulary 128100, max_position_embeddings 512, matching Albertina 900M PT-PT. Training loss on the card is CoSENTLoss. The paper's third stage trained CoSENT and AnglE and kept the better checkpoint.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Serafim 900M is the largest sentence encoder in the Serafim family from NLX at the Faculty of Sciences, University of Lisbon. The paper says it is based on Albertina PT-PT and is the semantic-similarity checkpoint, not the later information-retrieval tune. Weights are MIT. Collected on 2026-10-15.", "what_it_is_for": "The PORTULAN models page describes Serafim as a sentence encoder for Portuguese, for tasks such as clustering and semantic search. This checkpoint is the 900 million parameter model before the information-retrieval stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lisbon/serafim/serafim-900m-ir": { "id": "lisbon/serafim/serafim-900m-ir", "name": "Serafim 900M IR", "maker": "lisbon", "family": "lisbon/serafim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-07-04", "context_length": 128, "max_output_tokens": null, "parameters": "900000000", "architecture": "Information-retrieval tune of the Serafim 900M line. The card architecture block names DebertaV2Model, mean pooling, embedding size 1536, max_seq_length 128. Loss on the card is GISTEmbedLoss for one epoch, with an InformationRetrievalEvaluator. The paper says the IR models are the fourth training stage on mMARCO triples.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Serafim 900M IR is the information-retrieval sentence encoder at the 900 million parameter size from NLX at the University of Lisbon. The paper treats it as the last training stage of Serafim 900, and the Hub publishes it as its own checkpoint. Collected on 2026-10-15.", "what_it_is_for": "The card says it maps sentences and paragraphs to a 1536-dimensional vector for clustering or semantic search, and the title says it is tuned for information retrieval. The paper evaluates this stage on retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/codemix/muril-en-hi-codemixed": { "id": "ljubljana/codemix/muril-en-hi-codemixed", "name": "muril-en-hi-codemixed", "maker": "ljubljana", "family": "ljubljana/codemix", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2022-11-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two extra epochs on the same English-Hindi code-mixed corpora as roberta-en-hi-codemixed. Embeddings initialized from MuRIL. YAML licence key is misspelled. DOI on the Hub tags: 10.57967/hf/0966.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "muril-en-hi-codemixed is a continued pretrain released by CJVT at the University of Ljubljana. It starts from Google MuRIL and swaps in the code-mixed tokenizer used by the from-scratch sibling. The card says it was auto-generated and should be proofread. Collected 2026-10-15.", "what_it_is_for": "Masked modeling of code-mixed English and Hindi.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/codemix/roberta-en-hi-codemixed": { "id": "ljubljana/codemix/roberta-en-hi-codemixed", "name": "roberta-en-hi-codemixed", "maker": "ljubljana", "family": "ljubljana/codemix", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2022-11-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Trained from scratch for 40 epochs on web text, including code-mixed tweets, with a focus on conversational language and the COVID-19 pandemic. CamemBERT architecture. DOI on the Hub tags: 10.57967/hf/0965.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "roberta-en-hi-codemixed is a from-scratch CamemBERT-style model released by CJVT at the University of Ljubljana for English, Hindi, and code-mixed text. The card heading is en-hi-codemixed. Collected 2026-10-15.", "what_it_is_for": "Masked modeling of conversational English, Hindi, and code-mixed text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/crosloengual/crosloengual-bert": { "id": "ljubljana/crosloengual/crosloengual-bert", "name": "CroSloEngual BERT", "maker": "ljubljana", "family": "ljubljana/crosloengual", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2020-07-09", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Trilingual BERT-base. Hub config: BertForMaskedLM, 12 layers, hidden 768, max_position_embeddings 512. Trained on Croatian, Slovenian, and English. Exact parameter count was not on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CroSloEngual BERT is the trilingual Croatian-Slovene-English BERT from Matej Ul\u010dar and Marko Robnik-\u0160ikonja at the University of Ljubljana. CLARIN.SI 1.1 is the corrected release. The EMBEDDIA Hub org hosts it. Collected 2026-10-15.", "what_it_is_for": "Contextual embeddings and cross-lingual transfer among Croatian, Slovene, and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/crosloengual/crosloengual-bert-si-nli": { "id": "ljubljana/crosloengual/crosloengual-bert-si-nli", "name": "crosloengual-bert-si-nli", "maker": "ljubljana", "family": "ljubljana/crosloengual", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2022-11-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence-pair classifier. 10 epochs, AdamW 2e-5, batch size 82, maximum sequence length 107.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "crosloengual-bert-si-nli is a CroSloEngual BERT fine-tune for Slovene NLI, released by CJVT at the University of Ljubljana. Open weights, CC-BY-4.0. Collected 2026-10-15.", "what_it_is_for": "Slovene natural language inference using the trilingual encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/elmo/elmo": { "id": "ljubljana/elmo/elmo", "name": "ELMo embeddings", "maker": "ljubljana", "family": "ljubljana/elmo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2019-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AllenNLP ELMo (bilm-tf), character-level input, one model per language. CLARIN.SI says each language was trained for about 10 epochs, with about 1 million most common tokens as vocabulary. Corpus size ranges from over 270 million Latvian tokens to almost 2 billion Croatian tokens. Each archive has a PyTorch hdf5 and an options json. Inference via AllenNLP.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "These ELMo contextual embeddings were released by Matej Ul\u010dar at the University of Ljubljana Faculty of Computer and Information Science. The 25 November 2019 CLARIN.SI item holds seven language models. Weights are Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Contextual word embeddings, including out-of-vocabulary words, for the seven languages named on the CLARIN record.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams-1b": { "id": "ljubljana/gams/gams-1b", "name": "GaMS-1B", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-18", "context_length": 2048, "max_output_tokens": null, "parameters": "1540481024", "architecture": "Continual pretrain of Facebook OPT, OPTForCausalLM. config.json: 24 layers, hidden 2048, max_position_embeddings 2048. Card: BPE tokenizer, vocabulary 80,000, trained on Slovene, English, and Croatian. The paper title says 1 billion parameters. The Hub safetensors total for cjvt/GaMS-1B is 1,540,481,024 (F32), which is not 1e9.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS-1B is the first public Generative Model for Slovene from the University of Ljubljana Faculty of Computer and Information Science, released under CJVT. It is a continual pretrain of Facebook OPT with a Slovene-oriented tokenizer. Weights are open. Collected 2026-10-15.", "what_it_is_for": "The card says it is a small base model for further tuning, not a general chatbot. Promising fine-tunes are mentioned for SuperGLUE and SI-NLI, but those fine-tunes are not separate named repos in the CJVT model list that was read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams-1b-chat": { "id": "ljubljana/gams/gams-1b-chat", "name": "GaMS-1B-Chat", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-18", "context_length": 2048, "max_output_tokens": null, "parameters": "1540481024", "architecture": "Chat variant of GaMS-1B. Hub config matches the base: OPTForCausalLM, 24 layers, hidden 2048, max_position_embeddings 2048. Hub safetensors total 1,540,481,024 F32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS-1B-Chat is the conversational checkpoint of the University of Ljubljana GaMS-1B OPT model, published by CJVT. It is a small open-weight Slovene model, not a later Gemma GaMS. Collected 2026-10-15.", "what_it_is_for": "Chat-style generation in Slovene. The base card says the 1B line is not a general-purpose assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams-27b": { "id": "ljubljana/gams/gams-27b", "name": "GaMS-27B", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-03-21", "context_length": 8192, "max_output_tokens": null, "parameters": "27227128320", "architecture": "Gemma2ForCausalLM continual pretrain. config.json: 46 layers, hidden 4608, max_position_embeddings 8192. Hub safetensors total 27,227,128,320 F32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS-27B is the 27B Gemma 2 GaMS base model from the University of Ljubljana, released by CJVT. Open weights, Gemma license. Collected 2026-10-15.", "what_it_is_for": "Base text generation. The card groups it with GaMS-2B and GaMS-9B as the larger Gemma 2 GaMS models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams-27b-instruct": { "id": "ljubljana/gams/gams-27b-instruct", "name": "GaMS-27B-Instruct", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-04-04", "context_length": 8192, "max_output_tokens": null, "parameters": "27227128320", "architecture": "Gemma2ForCausalLM continual pretrain. config.json: 46 layers, hidden 4608, max_position_embeddings 8192. Hub safetensors total 27,227,128,320 BF16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS-27B-Instruct is the SFT chat model of GaMS-27B from the University of Ljubljana, released by CJVT. Open weights, Gemma license. Collected 2026-10-15.", "what_it_is_for": "Instruction-following generation and translation. The card says this is the SFT version of GaMS-27B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams-27b-instruct-nemotron": { "id": "ljubljana/gams/gams-27b-instruct-nemotron", "name": "GaMS-27B-Instruct-Nemotron", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-08-26", "context_length": 8192, "max_output_tokens": null, "parameters": "27227128320", "architecture": "Further SFT of cjvt/GaMS-27B-Instruct on the same Nemotron chat mix described for the 9B twin. Gemma2ForCausalLM, 46 layers, hidden 4608, max_position_embeddings 8192. Hub safetensors total 27,227,128,320 BF16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS-27B-Instruct-Nemotron is the 27B Nemotron-chat SFT of GaMS-27B-Instruct from the University of Ljubljana, released by CJVT. Open weights, Gemma license. Collected 2026-10-15.", "what_it_is_for": "Instruction chat. The GaMS3 paper text calls this the strongest earlier Slovene decoder among the Gemma 2 GaMS models it compares.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams-2b": { "id": "ljubljana/gams/gams-2b", "name": "GaMS-2B", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-03-21", "context_length": 8192, "max_output_tokens": null, "parameters": "3204165888", "architecture": "Gemma2ForCausalLM continual pretrain. config.json: 26 layers, hidden 2304, max_position_embeddings 8192. Hub safetensors total 3,204,165,888 F32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS-2B is the smallest Gemma 2 GaMS model from the University of Ljubljana, a continual pretrain released by CJVT. Weights are open under the Gemma license. Collected 2026-10-15.", "what_it_is_for": "Base text generation for Slovene and English, with some Croatian, Bosnian, and Serbian from the second training stage. Not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams-2b-instruct": { "id": "ljubljana/gams/gams-2b-instruct", "name": "GaMS-2B-Instruct", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-03-28", "context_length": 8192, "max_output_tokens": null, "parameters": "2614341888", "architecture": "Gemma2ForCausalLM continual pretrain. config.json: 26 layers, hidden 2304, max_position_embeddings 8192. Hub safetensors total 2,614,341,888 BF16. This matches the usual Gemma 2 2B tensor count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS-2B-Instruct is the supervised-fine-tuned University of Ljubljana GaMS-2B, released by CJVT for chat-style use. Open weights, Gemma license. Collected 2026-10-15.", "what_it_is_for": "Instruction-following text generation. The card says this is the SFT version of the 2B GaMS model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams-9b": { "id": "ljubljana/gams/gams-9b", "name": "GaMS-9B", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-02-10", "context_length": 8192, "max_output_tokens": null, "parameters": "9241705984", "architecture": "Gemma2ForCausalLM continual pretrain. config.json: 42 layers, hidden 3584, max_position_embeddings 8192. Hub safetensors total 9,241,705,984 F32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS-9B is the 9B Gemma 2 GaMS base model from the University of Ljubljana Faculty of Computer and Information Science, released by CJVT. Open weights, Gemma license. Collected 2026-10-15.", "what_it_is_for": "Base text generation. The card describes two-stage continual pretraining: parallel alignment on about 1.91 billion tokens, then a second stage of about 13.62 billion tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams-9b-instruct": { "id": "ljubljana/gams/gams-9b-instruct", "name": "GaMS-9B-Instruct", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-03-18", "context_length": 8192, "max_output_tokens": null, "parameters": "9241705984", "architecture": "Gemma2ForCausalLM continual pretrain. config.json: 42 layers, hidden 3584, max_position_embeddings 8192. Hub safetensors total 9,241,705,984 BF16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS-9B-Instruct is the SFT chat model of GaMS-9B from the University of Ljubljana, released by CJVT. Open weights, Gemma license. Collected 2026-10-15.", "what_it_is_for": "Instruction-following generation and translation. The card evaluates English-Slovene translation in both directions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams-9b-instruct-lex": { "id": "ljubljana/gams/gams-9b-instruct-lex", "name": "GaMS-9B-Instruct-Lex", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-09-24", "context_length": 8192, "max_output_tokens": null, "parameters": "9241705984", "architecture": "SFT of cjvt/GaMS-9B-Instruct. Gemma2ForCausalLM, 42 layers, hidden 3584, max_position_embeddings 8192. Hub safetensors total 9,241,705,984 BF16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS-9B-Instruct-Lex is a lexicographic fine-tune of GaMS-9B-Instruct from the University of Ljubljana Faculty of Computer and Information Science, released by CJVT. Open weights, Gemma license. Collected 2026-10-15.", "what_it_is_for": "Slovene dictionary questions: meanings, synonyms, word forms, collocations, and contextual sense.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams-9b-instruct-nemotron": { "id": "ljubljana/gams/gams-9b-instruct-nemotron", "name": "GaMS-9B-Instruct-Nemotron", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-08-22", "context_length": 8192, "max_output_tokens": null, "parameters": "9241705984", "architecture": "Further SFT of cjvt/GaMS-9B-Instruct on a curated chat subset of nvidia/Nemotron-Post-Training-Dataset-v1 and cjvt/GaMS-Nemotron-Chat. Gemma2ForCausalLM, 42 layers, hidden 3584, max_position_embeddings 8192. Hub safetensors total 9,241,705,984 BF16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS-9B-Instruct-Nemotron is a Nemotron-chat SFT of GaMS-9B-Instruct from the University of Ljubljana, released by CJVT. Open weights, Gemma license. Collected 2026-10-15.", "what_it_is_for": "Instruction chat in Slovene and English after extra supervised fine-tuning on translated Nemotron chat data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams-dpo-translator": { "id": "ljubljana/gams/gams-dpo-translator", "name": "GaMS-DPO-Translator", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-07-01", "context_length": 8192, "max_output_tokens": null, "parameters": "9241705984", "architecture": "DPO translation model. Card prose says it is a fine-tune of GaMS-9B-Instruct. YAML base_model is cjvt/GaMS-9B, and the prose link labeled GaMS-9B points at the Instruct URL. Gemma2ForCausalLM, 42 layers, hidden 3584, max_position_embeddings 8192. Hub safetensors total 9,241,705,984 BF16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS-DPO-Translator is a University of Ljubljana English-to-Slovene translation model from UL FRI, released by CJVT. It applies direct preference optimization on top of the GaMS-9B line. Open weights, Gemma license. Collected 2026-10-15.", "what_it_is_for": "English-to-Slovene translation, with the card also showing a follow-up chat turn.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams2-27b": { "id": "ljubljana/gams/gams2-27b", "name": "GaMS2-27B", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Gemma", "release_date": "2026-03-04", "context_length": 32768, "max_output_tokens": null, "parameters": "27227128320", "architecture": "Gemma2ForCausalLM. config.json: 46 layers, hidden 4608, max_position_embeddings 32768. Hub API safetensors parameter dtype sum is 27,227,128,320 BF16. The same API record's safetensors.total field is 852480, which does not match that sum. README.md is only the Gemma license line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS2-27B is a CJVT Hub release from the University of Ljubljana GaMS line. The repo has Gemma 2 27B-shaped weights and a 32,768-token position field, but the README does not describe the model. Collected 2026-10-15.", "what_it_is_for": "Not stated on the card. The translator card uses it as the base for an English-to-Slovene specialist.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams2-27b-grpo-translator": { "id": "ljubljana/gams/gams2-27b-grpo-translator", "name": "GaMS2-27B-GRPO-Translator", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2026-07-24", "context_length": 32768, "max_output_tokens": null, "parameters": "27227128320", "architecture": "English-to-Slovene specialist on GaMS2-27B. Card: three LoRA stages (SFT on 1,000 examples, DPO on about 1,500 on-policy examples, GRPO on 100,000 examples with COMET, length, and language rewards). Gemma2ForCausalLM, 46 layers, hidden 4608, max_position_embeddings 32768. Hub BF16 parameter sum 27,227,128,320. The card's base link text says GaM2-27B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS2-27B-GRPO-Translator is a University of Ljubljana English-to-Slovene model released by CJVT. It is a three-stage LoRA adaptation of GaMS2-27B, merged into a full Gemma 2 checkpoint. Collected 2026-10-15.", "what_it_is_for": "English-to-Slovene translation with a fixed translator prompt. The card says not to add commentary.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams2-27b-instruct": { "id": "ljubljana/gams/gams2-27b-instruct", "name": "GaMS2-27B-Instruct", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Gemma", "release_date": "2026-06-28", "context_length": 32768, "max_output_tokens": null, "parameters": "27227128320", "architecture": "Gemma2ForCausalLM. config.json: 46 layers, hidden 4608, max_position_embeddings 32768. Hub API BF16 parameter sum 27,227,128,320. safetensors.total on the same record is 852480. README.md is only the Gemma license line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS2-27B-Instruct is a CJVT chat checkpoint in the GaMS2 27B line from the University of Ljubljana. The Hub README does not describe training. It is listed on the university's Slovene chat arena. Collected 2026-10-15.", "what_it_is_for": "Not stated on the card. The arena lists it as a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams3-12b": { "id": "ljubljana/gams/gams3-12b", "name": "GaMS3-12B", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2026-01-20", "context_length": 131072, "max_output_tokens": null, "parameters": "11766034176", "architecture": "Continual pretrain of google/gemma-3-12b-pt. Gemma3ForCausalLM, gemma3_text, 48 layers, hidden 3840, max_position_embeddings 131072. Three CPT stages. The card says parallel alignment and base CPT used a 65,536 context and long CPT used 131,072. Hub safetensors total 11,766,034,176 BF16. The card says this repo is not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS3-12B is the base Generative Model for Slovene in the Gemma 3 generation, from the University of Ljubljana Faculty of Computer and Information Science and released by CJVT. It is a text-only continual pretrain, not the chat model. Open weights, Gemma license. Collected 2026-10-15.", "what_it_is_for": "Base Slovene and English generation for research and further tuning. The card points users who want instructions to GaMS3-12B-Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams3-12b-grpo-translator": { "id": "ljubljana/gams/gams3-12b-grpo-translator", "name": "GaMS3-12B-GRPO-Translator", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2026-07-24", "context_length": 131072, "max_output_tokens": null, "parameters": "11766034176", "architecture": "English-to-Slovene specialist. Card: three LoRA stages on GaMS3-12B (SFT 1,000, DPO about 1,500, GRPO 100,000). Chat template matches GaMS3-12B-Instruct. Gemma3ForCausalLM, 48 layers, hidden 3840, max_position_embeddings 131072. Hub safetensors total 11,766,034,176 BF16. The base link text says GaM3-12B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS3-12B-GRPO-Translator is a University of Ljubljana English-to-Slovene model released by CJVT. It is a three-stage adaptation of GaMS3-12B. Open weights, Gemma license. Collected 2026-10-15.", "what_it_is_for": "English-to-Slovene translation with the professional-translator prompt printed on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams3-12b-instruct": { "id": "ljubljana/gams/gams3-12b-instruct", "name": "GaMS3-12B-Instruct", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2025-12-04", "context_length": 131072, "max_output_tokens": null, "parameters": "11766034176", "architecture": "SFT of the GaMS3 continual pretrain of google/gemma-3-12b-pt. The card says three CPT stages plus two SFT stages, including a small Slovene safety set (459 train prompts) and Nemotron chat data. Gemma3ForCausalLM, 48 layers, hidden 3840, max_position_embeddings 131072. Hub safetensors total 11,766,034,176 BF16. vLLM note: FlashInfer attention degraded quality; use Flash Attention.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS3-12B-Instruct is the chat Generative Model for Slovene from the University of Ljubljana, the Gemma 3 12B GaMS release published by CJVT. It is the model the university arena currently ranks first among entries with enough votes. Open weights, Gemma license. Collected 2026-10-15.", "what_it_is_for": "Slovene and English instruction following, including writing and question answering. The university news page also points at a povejmo.si chatbot without naming this repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams3-12b-instruct-lex": { "id": "ljubljana/gams/gams3-12b-instruct-lex", "name": "GaMS3-12B-Instruct-Lex", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma", "release_date": "2026-02-19", "context_length": 131072, "max_output_tokens": null, "parameters": "12772912896", "architecture": "Lexical CPT plus instruction tuning plus SloLexQA SFT, starting from non-instruct GaMS3. YAML base_model is cjvt/GaMS3-12B. Gemma3ForCausalLM, 48 layers, hidden 3840, max_position_embeddings 131072. Hub safetensors total 12,772,912,896 BF16, about 1.0 billion above GaMS3-12B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS3-12B-Instruct-Lex is a lexical question-answering model from the University of Ljubljana, released by CJVT on top of GaMS3-12B. Open weights, Gemma license. Collected 2026-10-15.", "what_it_is_for": "Slovene grammar, morphology, definitions, and lexical question answering, with or without context. The card also lists writing and language-learning uses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gams/gams3-12b-multimodal": { "id": "ljubljana/gams/gams3-12b-multimodal", "name": "GaMS3-12B-Multimodal", "maker": "ljubljana", "family": "ljubljana/gams", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT on the model card. The base is a Gemma 3 model, whose own license is Gemma. Those two statements disagree.", "release_date": "2026-03-31", "context_length": 131072, "max_output_tokens": null, "parameters": "12187325040", "architecture": "Gemma3ForConditionalGeneration. Hub safetensors.total 12,187,325,040. config max_position_embeddings 131072. YAML base_model is google/gemma-3-12b-it. The prose says it is based on GaMS3-12B, which is based on gemma-3-12b-it. The code sample loads GaMS-Beta/GaMS3-12B-Multimodal, not the cjvt repo. The same safetensors.total appears on GaMS-Beta/SVILA-1-12B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GaMS3-12B-Multimodal is a Slovene vision-language checkpoint on the CJVT Hub org of the University of Ljubljana. The card is short and contradicts itself on the license and the base model. Collected 2026-10-15.", "what_it_is_for": "Slovene visual question answering. The only demonstrated task is asking which city is in an image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gec/gemma-3-27b-it-dassle-gec": { "id": "ljubljana/gec/gemma-3-27b-it-dassle-gec", "name": "gemma-3-27b-it-dassle-gec", "maker": "ljubljana", "family": "ljubljana/gec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter, not a full checkpoint. Base on the card: unsloth/gemma-3-27b-it-unsloth-bnb-4bit. Library peft. Trained with Unsloth and TRL SFT. Selected by validation loss. The card does not state a license.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "gemma-3-27b-it-dassle-gec is a LoRA grammatical-error adapter from CJVT at the University of Ljubljana. It sits on Unsloth's 4-bit Gemma 3 27B IT and is trained on DASSLE 1.0. Collected 2026-10-15.", "what_it_is_for": "Correct Slovene grammar without unnecessary paraphrase. The user sends a sentence and the model returns the corrected sentence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/gpt/gpt-sl-base": { "id": "ljubljana/gpt/gpt-sl-base", "name": "GPT-sl-base", "maker": "ljubljana", "family": "ljubljana/gpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-11-08", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "Slovene GPT-2-style causal LM. Card: about 110 million parameters, 12 layers, dimension 768, 16 heads, sequences up to 1024 tokens, vocabulary 60,000. Trained with the BigScience Megatron-DeepSpeed fork for about 20 epochs, 390,000 steps, 102 billion tokens seen. Hub config class is GPT2LMHeadModel. The API record had no safetensors total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-sl-base is a small Slovene causal language model from CJVT at the University of Ljubljana, trained with Megatron-DeepSpeed. It predates the GaMS line. Open weights, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Slovene next-token generation and a base for fine-tuning. The card does not present it as an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/sleng/sleng-bert": { "id": "ljubljana/sleng/sleng-bert", "name": "SlEng-bert", "maker": "ljubljana", "family": "ljubljana/sleng", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2022-12-22", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "Masked language model trained from scratch for 40 epochs. 12 transformer layers, described as roughly BERT-base and RoBERTa-base size. No next-sentence task. CamembertForMaskedLM, hidden 768, max_position_embeddings 514. YAML key is spelled licence.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SlEng-bert is a from-scratch bilingual Slovene-English masked model for slang and non-standard text, released by CJVT at the University of Ljubljana. Collected 2026-10-15.", "what_it_is_for": "Masked language modeling on conversational Slovene and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/sloberta/sloberta": { "id": "ljubljana/sloberta/sloberta", "name": "SloBERTa", "maker": "ljubljana", "family": "ljubljana/sloberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2021-01-17", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Monolingual Slovene masked language model, CamemBERT/RoBERTa style. CLARIN and the Ul\u010dar and Robnik-\u0160ikonja paper: 12 layers, hidden 768, vocabulary 32,000, maximum input 512 subwords, 200,000 updates, about 98 epochs, 3.47 billion tokens. Hub config.json for EMBEDDIA/sloberta: CamembertForMaskedLM, 12 layers, hidden 768, max_position_embeddings 514. Exact parameter total was not on the card or the API safetensors field.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SloBERTa is the monolingual Slovene RoBERTa from Matej Ul\u010dar and Marko Robnik-\u0160ikonja at the University of Ljubljana Faculty of Computer and Information Science. CLARIN.SI publishes version 2.0, and the EMBEDDIA Hub org hosts the same line. CJVT UL lists it as a centre resource. Collected 2026-10-15.", "what_it_is_for": "Contextual embeddings for Slovene classification, tagging, and other fine-tunes. It is a masked language model, not a chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/sloberta/sloberta-sentinews-sentence": { "id": "ljubljana/sloberta/sloberta-sentinews-sentence", "name": "sloberta-sentinews-sentence", "maker": "ljubljana", "family": "ljubljana/sloberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-01-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of SloBERTa on a 90/5/5 split of cjvt/sentinews sentence_level. max_length 79, batch 128, AdamW 2e-5, 10 epochs, selection by macro F1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "sloberta-sentinews-sentence is a SloBERTa sentence-sentiment classifier from CJVT at the University of Ljubljana, trained on SentiNews. The card presents it as a baseline and an off-the-shelf tagger. Collected 2026-10-15.", "what_it_is_for": "Three-class sentence sentiment for Slovene news.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/sloberta/sloberta-si-nli": { "id": "ljubljana/sloberta/sloberta-si-nli", "name": "sloberta-si-nli", "maker": "ljubljana", "family": "ljubljana/sloberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2022-11-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence-pair classifier on SloBERTa. 10 epochs, AdamW learning rate 2e-5, cross-entropy, batch size 82, maximum sequence length 102. Checkpoint chosen by validation accuracy.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "sloberta-si-nli is a SloBERTa fine-tune for Slovene natural language inference, released by CJVT at the University of Ljubljana. Open weights, CC-BY-SA-4.0. Collected 2026-10-15.", "what_it_is_for": "Slovene natural language inference on sentence pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/sloberta/sloberta-sleng": { "id": "ljubljana/sloberta/sloberta-sleng", "name": "SloBERTa-SlEng", "maker": "ljubljana", "family": "ljubljana/sloberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2022-12-22", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretrain of SloBERTa for two epochs after replacing the tokenizer, vocabulary, and embedding layer. New embeddings were initialized from SloBERTa. CamembertForMaskedLM, 12 layers, hidden 768, max_position_embeddings 514. The YAML key is spelled licence.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SloBERTa-SlEng is a bilingual slang-oriented masked model from CJVT at the University of Ljubljana. It continues SloBERTa for two epochs with a new Slovene-English tokenizer. Collected 2026-10-15.", "what_it_is_for": "Masked language modeling on conversational Slovene and English, including non-standard spelling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/sloberta/sloberta-slo-word-spelling-annotator": { "id": "ljubljana/sloberta/sloberta-slo-word-spelling-annotator", "name": "SloBERTa spelling annotator", "maker": "ljubljana", "family": "ljubljana/sloberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-11-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fill-mask style tagger on SloBERTa. Labels on the card: 1 incorrect spelling, 2 two words should be written together, 3 a word should be written separately. Datasets named: cc_gigafida, solar3, sloleks. The card title is SloBERTa-Incorrect-Spelling-Annotator.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The SloBERTa spelling annotator is a CJVT University of Ljubljana fill-in model that tags incorrect Slovene words and words that should be joined or split. Collected 2026-10-15.", "what_it_is_for": "Marking Slovene spelling errors and split or join mistakes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/sloberta/sloberta-trendi-topics": { "id": "ljubljana/sloberta/sloberta-trendi-topics", "name": "SloBERTa-Trendi-Topics 1.0", "maker": "ljubljana", "family": "ljubljana/sloberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-11-15", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of SloBERTa 2.0 with simpletransformers. Batch size 8, learning rate 1e-5, max sequence 512, 2 epochs. About 36,000 training texts from the Trendi monitor corpus.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SloBERTa-Trendi-Topics 1.0 is a news-topic classifier from CJVT at the University of Ljubljana, fine-tuned from SloBERTa 2.0. Open weights, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Thirteen-way topic labels for Slovene news.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/sloberta/sloberta-word-case": { "id": "ljubljana/sloberta/sloberta-word-case", "name": "sloberta-word-case-classification-multilabel", "maker": "ljubljana", "family": "ljubljana/sloberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-08-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Token classifier on SloBERTa. The card describes 12 reason labels such as LOWER_OTHER, UPPER_ENTITY, and UPPER_BEGIN, grouped into lower, upper, and all-caps decisions. No score was printed in the card text that was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is a multilabel SloBERTa word-case model from CJVT at the University of Ljubljana, fine-tuned on Gigafida. It expects lowercased input. Collected 2026-10-15.", "what_it_is_for": "Restoring Slovene word case from fully lowercased text, with a coarse reason label.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/sloner/sloner": { "id": "ljubljana/sloner/sloner", "name": "SloNER 1.0", "maker": "ljubljana", "family": "ljubljana/sloner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Token classifier on SloBERTa 2.0, trained on the SUK 1.0 corpus. PyTorch transformers checkpoint. CLARIN file sloner.1.0.transformers.zip is about 387 MB. No layer counts beyond the SloBERTa base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SloNER 1.0 is a Slovene named-entity recognizer from the University of Ljubljana Faculty of Computer and Information Science, fine-tuned from SloBERTa 2.0 and published on CLARIN.SI. Collected 2026-10-15.", "what_it_is_for": "Slovene named entity recognition. A web demo is on slovenscina.eu.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/spelling/bertic-cro-word-spelling-annotator": { "id": "ljubljana/spelling/bertic-cro-word-spelling-annotator", "name": "BERTi\u0107 Croatian spelling annotator", "maker": "ljubljana", "family": "ljubljana/spelling", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2024-08-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT token model. The card title is BERTic-Incorrect-Spelling-Annotator. Language tag on the card is cro. It is a fine-tune of another group's BERTi\u0107 encoder, not a new pretrain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is a Croatian spelling annotator released by CJVT at the University of Ljubljana on top of BERTi\u0107, which is not a Ljubljana base model. Two labels: 0 correct, 1 incorrect. Collected 2026-10-15.", "what_it_is_for": "Marking misspelled Croatian words.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/summarizer/slo-summarizer": { "id": "ljubljana/summarizer/slo-summarizer", "name": "Slovenian text summarization models", "maker": "ljubljana", "family": "ljubljana/summarizer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2022-12-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Five models in one CLARIN release: a Doc2Vec metamodel that picks a summarizer, an unsupervised graph extractive model, a T5 headline abstractive model, a T5 short-summary abstractive model, and an unsupervised hybrid for long texts. Five files, about 5.2 GB. No per-model parameter counts were on the record.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the five-model Slovene summarization release from the University of Ljubljana Faculty of Computer and Information Science, published as one CLARIN.SI item in December 2022. Collected 2026-10-15.", "what_it_is_for": "Extractive, abstractive, and hybrid summarization of Slovene text, with a demo on slovenscina.eu.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/svila/svila-1-12b": { "id": "ljubljana/svila/svila-1-12b", "name": "SVILA-1-12B", "maker": "ljubljana", "family": "ljubljana/svila", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT on the model card. Base model is google/gemma-3-12b-it, whose license is Gemma.", "release_date": "2025-10-20", "context_length": null, "max_output_tokens": null, "parameters": "12187325040", "architecture": "Gemma3ForConditionalGeneration fine-tune of google/gemma-3-12b-it. Hub safetensors.total is 12,187,325,040. The BF16 parameter sum on the same record is 13,194,203,760. Context length was not read from config.json. The card does not name authors.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SVILA-1-12B is a Slovenian vision-language assistant published on GaMS-Beta, the side org linked from University of Ljubljana GaMS cards. It is a Gemma 3 12B IT fine-tune. The card does not name the lab. Collected 2026-10-15.", "what_it_is_for": "Slovene visual question answering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/svila/svila-1-4b": { "id": "ljubljana/svila/svila-1-4b", "name": "SVILA-1-4B", "maker": "ljubljana", "family": "ljubljana/svila", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT on the model card. Base model is google/gemma-3-4b-it, whose license is Gemma.", "release_date": "2025-06-11", "context_length": null, "max_output_tokens": null, "parameters": "4300079472", "architecture": "Gemma3ForConditionalGeneration fine-tune of google/gemma-3-4b-it. Hub safetensors.total is 4,300,079,472. The same API record's BF16 parameter sum is 4,971,331,952. Those two counts disagree. Context length was not copied from config.json.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SVILA-1-4B is a small Slovenian vision-language assistant on the GaMS-Beta Hub org used by the University of Ljubljana GaMS project. The card names Gemma 3 4B IT as the base. Confidence is medium because the README for this size was not opened. Collected 2026-10-15.", "what_it_is_for": "Slovene visual question answering, by the description on the 12B SVILA card and the shared CLARIN dataset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/t5/t5-sl-large": { "id": "ljubljana/t5/t5-sl-large", "name": "T5-sl-large", "maker": "ljubljana", "family": "ljubljana/t5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2022-07-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Slovene T5. Card and paper: 24 encoder and 24 decoder layers, about 750 million parameters, 3 epochs, same corpora as T5-sl-small. Hub repo created 2023-01-02. Context length not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "T5-sl-large is the large Slovene T5 from the University of Ljubljana, about 750 million parameters, released by CJVT. Open weights, CC-BY-SA-4.0. Collected 2026-10-15.", "what_it_is_for": "Slovene sequence-to-sequence generation. The paper positions it as more useful for generation than for classification, where SloBERTa leads.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/t5/t5-sl-small": { "id": "ljubljana/t5/t5-sl-small", "name": "T5-sl-small", "maker": "ljubljana", "family": "ljubljana/t5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2022-07-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Slovene T5. Card: 8 encoder and 8 decoder layers, about 60 million parameters, trained 5 epochs. Paper says the small model has about 60 million parameters and that all T5-sl models were trained for 3 epochs. Those epoch counts disagree. Corpora match SloBERTa. SentencePiece vocabulary is the 32,000-token SloBERTa model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "T5-sl-small is the small Slovene T5 from Matej Ul\u010dar and Marko Robnik-\u0160ikonja at the University of Ljubljana, released by CJVT. The July 2022 checkpoint replaced a shorter training run. Open weights, CC-BY-SA-4.0. Collected 2026-10-15.", "what_it_is_for": "Slovene sequence-to-sequence pretraining. The paper says the T5 models are for generative tasks and usually trail SloBERTa on classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/t5/t5-slo-word-form-corrector": { "id": "ljubljana/t5/t5-slo-word-form-corrector", "name": "T5-slo-word-form-corrector", "maker": "ljubljana", "family": "ljubljana/t5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-11-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Text-to-text T5. Hub tag is word shape correction. Same three datasets as the spelling corrector. Base size not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is a Slovene T5 word-shape corrector from CJVT at the University of Ljubljana. The card title is T5-slo-word-shape-corrector. Collected 2026-10-15.", "what_it_is_for": "Correcting Slovene word shape.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/t5/t5-slo-word-order-corrector": { "id": "ljubljana/t5/t5-slo-word-order-corrector", "name": "T5-slo-word-order-corrector", "maker": "ljubljana", "family": "ljubljana/t5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-11-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Text-to-text T5. Hub tag is word order correction. Same three datasets. Base size not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is a Slovene T5 word-order corrector from CJVT at the University of Ljubljana. Collected 2026-10-15.", "what_it_is_for": "Correcting Slovene word order.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ljubljana/t5/t5-slo-word-spelling-corrector": { "id": "ljubljana/t5/t5-slo-word-spelling-corrector", "name": "T5-slo-word-spelling-corrector", "maker": "ljubljana", "family": "ljubljana/t5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-11-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Text-to-text T5. Datasets named: cc_gigafida, solar3, sloleks. The card does not say whether the base is T5-sl-small or T5-sl-large.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is a Slovene T5 spelling corrector from CJVT at the University of Ljubljana. The card title is T5-incorrect-word-spelling-corrector. Collected 2026-10-15.", "what_it_is_for": "Correcting misspelled Slovene words.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-1-8b": { "id": "llm-jp/llm-jp-3/llm-jp-3-1-8b", "name": "LLM-jp-3 1.8B", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "1867216896", "architecture": "Llama-style dense Transformer. 24 layers, hidden size 2048, 16 heads, context 4096. Embedding parameters 407,498,752; non-embedding 1,459,718,144; total 1,867,216,896. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 1.8B is a dense text model from NII LLMC, trained from scratch on llm-jp-corpus v3 (2.1T tokens) with a Llama-style Transformer and a 4096-token context. Status: active. Collected 2026-10-15.", "what_it_is_for": "Pre-trained base for the 1.8B dense size. Not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-1-8b-instruct": { "id": "llm-jp/llm-jp-3/llm-jp-3-1-8b-instruct", "name": "LLM-jp-3 1.8B Instruct", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "1867216896", "architecture": "Llama-style dense Transformer. 24 layers, hidden size 2048, 16 heads, context 4096. Embedding parameters 407,498,752; non-embedding 1,459,718,144; total 1,867,216,896. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 1.8B Instruct is an instruction-tuned checkpoint of the 1.8B dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "First instruction tune. The 13B card lists ichikara-instruction, AnswerCarefully, several synthetic sets, and a FLAN sample. Release day before the 2025-02-05 Instruct2 drop was not found, except that 1.8B, 3.7B, and 13B already existed by then.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-1-8b-instruct2": { "id": "llm-jp/llm-jp-3/llm-jp-3-1-8b-instruct2", "name": "LLM-jp-3 1.8B Instruct2", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "1867216896", "architecture": "Llama-style dense Transformer. 24 layers, hidden size 2048, 16 heads, context 4096. Embedding parameters 407,498,752; non-embedding 1,459,718,144; total 1,867,216,896. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 1.8B Instruct2 is an instruction-tuned checkpoint of the 1.8B dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Supervised fine-tune released 2025-02-05 for the dense sizes. LLMC describes Instruct2 as SFT on all LLM-jp-3 bases. The 172B Instruct2 uses the same restricted terms as the 172B base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-1-8b-instruct3": { "id": "llm-jp/llm-jp-3/llm-jp-3-1-8b-instruct3", "name": "LLM-jp-3 1.8B Instruct3", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "1867216896", "architecture": "Llama-style dense Transformer. 24 layers, hidden size 2048, 16 heads, context 4096. Embedding parameters 407,498,752; non-embedding 1,459,718,144; total 1,867,216,896. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 1.8B Instruct3 is an instruction-tuned checkpoint of the 1.8B dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Instruct3 adds Direct Preference Optimization on top of Instruct2, using aya-ja-evol-inst and ac-self-inst. Dense Instruct3 checkpoints other than the 172B were released 2025-02-05. The 172B Instruct3 was posted 2024-12-24 under its own terms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-13b": { "id": "llm-jp/llm-jp-3/llm-jp-3-13b", "name": "LLM-jp-3 13B", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "13706931200", "architecture": "Llama-style dense Transformer. 40 layers, hidden size 5120, 40 heads, context 4096. Embedding parameters 1,018,746,880; non-embedding 12,688,184,320; total 13,706,931,200. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 13B is a dense text model from NII LLMC, trained from scratch on llm-jp-corpus v3 (2.1T tokens) with a Llama-style Transformer and a 4096-token context. Status: active. Collected 2026-10-15.", "what_it_is_for": "Pre-trained base for the 13B dense size. Not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-13b-instruct": { "id": "llm-jp/llm-jp-3/llm-jp-3-13b-instruct", "name": "LLM-jp-3 13B Instruct", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "13706931200", "architecture": "Llama-style dense Transformer. 40 layers, hidden size 5120, 40 heads, context 4096. Embedding parameters 1,018,746,880; non-embedding 12,688,184,320; total 13,706,931,200. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 13B Instruct is an instruction-tuned checkpoint of the 13B dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "First instruction tune. The 13B card lists ichikara-instruction, AnswerCarefully, several synthetic sets, and a FLAN sample. Release day before the 2025-02-05 Instruct2 drop was not found, except that 1.8B, 3.7B, and 13B already existed by then.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-13b-instruct2": { "id": "llm-jp/llm-jp-3/llm-jp-3-13b-instruct2", "name": "LLM-jp-3 13B Instruct2", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "13706931200", "architecture": "Llama-style dense Transformer. 40 layers, hidden size 5120, 40 heads, context 4096. Embedding parameters 1,018,746,880; non-embedding 12,688,184,320; total 13,706,931,200. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 13B Instruct2 is an instruction-tuned checkpoint of the 13B dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Supervised fine-tune released 2025-02-05 for the dense sizes. LLMC describes Instruct2 as SFT on all LLM-jp-3 bases. The 172B Instruct2 uses the same restricted terms as the 172B base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-13b-instruct3": { "id": "llm-jp/llm-jp-3/llm-jp-3-13b-instruct3", "name": "LLM-jp-3 13B Instruct3", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "13706931200", "architecture": "Llama-style dense Transformer. 40 layers, hidden size 5120, 40 heads, context 4096. Embedding parameters 1,018,746,880; non-embedding 12,688,184,320; total 13,706,931,200. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 13B Instruct3 is an instruction-tuned checkpoint of the 13B dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Instruct3 adds Direct Preference Optimization on top of Instruct2, using aya-ja-evol-inst and ac-self-inst. Dense Instruct3 checkpoints other than the 172B were released 2025-02-05. The 172B Instruct3 was posted 2024-12-24 under its own terms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-150m": { "id": "llm-jp/llm-jp-3/llm-jp-3-150m", "name": "LLM-jp-3 150M", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "152219136", "architecture": "Llama-style dense Transformer. 12 layers, hidden size 512, 8 heads, context 4096. Embedding parameters 101,874,688; non-embedding 50,344,448; total 152,219,136. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 150M is a dense text model from NII LLMC, trained from scratch on llm-jp-corpus v3 (2.1T tokens) with a Llama-style Transformer and a 4096-token context. Status: active. Collected 2026-10-15.", "what_it_is_for": "Pre-trained base for the 150M dense size. Not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-150m-instruct2": { "id": "llm-jp/llm-jp-3/llm-jp-3-150m-instruct2", "name": "LLM-jp-3 150M Instruct2", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "152219136", "architecture": "Llama-style dense Transformer. 12 layers, hidden size 512, 8 heads, context 4096. Embedding parameters 101,874,688; non-embedding 50,344,448; total 152,219,136. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 150M Instruct2 is an instruction-tuned checkpoint of the 150M dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Supervised fine-tune released 2025-02-05 for the dense sizes. LLMC describes Instruct2 as SFT on all LLM-jp-3 bases. The 172B Instruct2 uses the same restricted terms as the 172B base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-150m-instruct3": { "id": "llm-jp/llm-jp-3/llm-jp-3-150m-instruct3", "name": "LLM-jp-3 150M Instruct3", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "152219136", "architecture": "Llama-style dense Transformer. 12 layers, hidden size 512, 8 heads, context 4096. Embedding parameters 101,874,688; non-embedding 50,344,448; total 152,219,136. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 150M Instruct3 is an instruction-tuned checkpoint of the 150M dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Instruct3 adds Direct Preference Optimization on top of Instruct2, using aya-ja-evol-inst and ac-self-inst. Dense Instruct3 checkpoints other than the 172B were released 2025-02-05. The 172B Instruct3 was posted 2024-12-24 under its own terms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-172b": { "id": "llm-jp/llm-jp-3/llm-jp-3-172b", "name": "LLM-jp-3 172B", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LLM-jp-3 172B Terms of Use", "release_date": "2024-12-24", "context_length": 4096, "max_output_tokens": null, "parameters": "172392173568", "architecture": "Llama-style dense Transformer. 96 layers, hidden size 12288, 96 heads, context 4096. Embedding parameters 2,444,992,512; non-embedding 169,947,181,056; total 172,392,173,568. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 172B is NII LLMC's GPT-3-scale dense model, a Llama-style Transformer with 96 layers and a 4096-token context, trained to 2.1T tokens. The base download is gated. Status: active. Collected 2026-10-15.", "what_it_is_for": "Final 172B base after 2.1T tokens, posted 2024-12-24. Beta1 (0.7T) and beta2 (1.4T) are separate folders. Alpha1 and alpha2 checkpoints exist on the Hub but are not on the current release index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-172b-beta1": { "id": "llm-jp/llm-jp-3/llm-jp-3-172b-beta1", "name": "LLM-jp-3 172B beta1", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "LLM-jp-3 172B beta terms (gated)", "release_date": "2024-09-12", "context_length": 4096, "max_output_tokens": null, "parameters": "172392173568", "architecture": "Llama-style dense Transformer. 96 layers, hidden size 12288, 96 heads, context 4096. Embedding parameters 2,444,992,512; non-embedding 169,947,181,056; total 172,392,173,568. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 0.7T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 172B beta1 is an intermediate 172B checkpoint from NII LLMC, trained on 0.7T of the planned 2.1T llm-jp-corpus v3 tokens. Download is gated. Status: preview. Collected 2026-10-15.", "what_it_is_for": "Preview base, not the final 2.1T model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-172b-beta1-instruct": { "id": "llm-jp/llm-jp-3/llm-jp-3-172b-beta1-instruct", "name": "LLM-jp-3 172B beta1 Instruct", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "LLM-jp-3 172B beta terms (gated)", "release_date": "2024-09-17", "context_length": 4096, "max_output_tokens": null, "parameters": "172392173568", "architecture": "Llama-style dense Transformer. 96 layers, hidden size 12288, 96 heads, context 4096. Embedding parameters 2,444,992,512; non-embedding 169,947,181,056; total 172,392,173,568. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 0.7T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 172B beta1 Instruct is the gated instruction-tuned preview paired with the 0.7T 172B base. Status: preview. Collected 2026-10-15.", "what_it_is_for": "Preview instruction tune of the 0.7T checkpoint. NII reported a GENIAC llm-leaderboard score of 0.529 for the tuned 0.7T model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-172b-beta2": { "id": "llm-jp/llm-jp-3/llm-jp-3-172b-beta2", "name": "LLM-jp-3 172B beta2", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "LLM-jp-3 172B beta terms (gated)", "release_date": "2024-11-15", "context_length": 4096, "max_output_tokens": null, "parameters": "172392173568", "architecture": "Llama-style dense Transformer. 96 layers, hidden size 12288, 96 heads, context 4096. Embedding parameters 2,444,992,512; non-embedding 169,947,181,056; total 172,392,173,568. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 1.4T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 172B beta2 is an intermediate 172B checkpoint from NII LLMC, trained on 1.4T of the planned 2.1T llm-jp-corpus v3 tokens. Download is gated. Status: preview. Collected 2026-10-15.", "what_it_is_for": "Preview base, not the final 2.1T model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-172b-beta2-instruct2": { "id": "llm-jp/llm-jp-3/llm-jp-3-172b-beta2-instruct2", "name": "LLM-jp-3 172B beta2 Instruct2", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "LLM-jp-3 172B beta terms (gated)", "release_date": "2024-11-15", "context_length": 4096, "max_output_tokens": null, "parameters": "172392173568", "architecture": "Llama-style dense Transformer. 96 layers, hidden size 12288, 96 heads, context 4096. Embedding parameters 2,444,992,512; non-embedding 169,947,181,056; total 172,392,173,568. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 1.4T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 172B beta2 Instruct2 is the gated instruction-tuned preview paired with the 1.4T 172B base. Status: preview. Collected 2026-10-15.", "what_it_is_for": "Instruct2 post-train of the 1.4T beta2 checkpoint. The release page groups it with the gated beta terms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-172b-instruct2": { "id": "llm-jp/llm-jp-3/llm-jp-3-172b-instruct2", "name": "LLM-jp-3 172B Instruct2", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LLM-jp-3 172B Terms of Use", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "172392173568", "architecture": "Llama-style dense Transformer. 96 layers, hidden size 12288, 96 heads, context 4096. Embedding parameters 2,444,992,512; non-embedding 169,947,181,056; total 172,392,173,568. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 172B Instruct2 is an instruction-tuned checkpoint of the 172B dense base from NII LLMC. The 172B download is under custom terms. Status: active. Collected 2026-10-15.", "what_it_is_for": "Supervised fine-tune released 2025-02-05 for the dense sizes. LLMC describes Instruct2 as SFT on all LLM-jp-3 bases. The 172B Instruct2 uses the same restricted terms as the 172B base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-172b-instruct3": { "id": "llm-jp/llm-jp-3/llm-jp-3-172b-instruct3", "name": "LLM-jp-3 172B Instruct3", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "llm-jp-3-172b-instruct3 License Agreement", "release_date": "2024-12-24", "context_length": 4096, "max_output_tokens": null, "parameters": "172392173568", "architecture": "Llama-style dense Transformer. 96 layers, hidden size 12288, 96 heads, context 4096. Embedding parameters 2,444,992,512; non-embedding 169,947,181,056; total 172,392,173,568. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 172B Instruct3 is an instruction-tuned checkpoint of the 172B dense base from NII LLMC. The 172B download is under custom terms. Status: active. Collected 2026-10-15.", "what_it_is_for": "Instruct3 adds Direct Preference Optimization on top of Instruct2, using aya-ja-evol-inst and ac-self-inst. Dense Instruct3 checkpoints other than the 172B were released 2025-02-05. The 172B Instruct3 was posted 2024-12-24 under its own terms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-3-7b": { "id": "llm-jp/llm-jp-3/llm-jp-3-3-7b", "name": "LLM-jp-3 3.7B", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "3782317056", "architecture": "Llama-style dense Transformer. 28 layers, hidden size 3072, 24 heads, context 4096. Embedding parameters 611,248,128; non-embedding 3,171,068,928; total 3,782,317,056. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 3.7B is a dense text model from NII LLMC, trained from scratch on llm-jp-corpus v3 (2.1T tokens) with a Llama-style Transformer and a 4096-token context. Status: active. Collected 2026-10-15.", "what_it_is_for": "Pre-trained base for the 3.7B dense size. Not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-3-7b-instruct": { "id": "llm-jp/llm-jp-3/llm-jp-3-3-7b-instruct", "name": "LLM-jp-3 3.7B Instruct", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "3782317056", "architecture": "Llama-style dense Transformer. 28 layers, hidden size 3072, 24 heads, context 4096. Embedding parameters 611,248,128; non-embedding 3,171,068,928; total 3,782,317,056. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 3.7B Instruct is an instruction-tuned checkpoint of the 3.7B dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "First instruction tune. The 13B card lists ichikara-instruction, AnswerCarefully, several synthetic sets, and a FLAN sample. Release day before the 2025-02-05 Instruct2 drop was not found, except that 1.8B, 3.7B, and 13B already existed by then.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-3-7b-instruct2": { "id": "llm-jp/llm-jp-3/llm-jp-3-3-7b-instruct2", "name": "LLM-jp-3 3.7B Instruct2", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "3782317056", "architecture": "Llama-style dense Transformer. 28 layers, hidden size 3072, 24 heads, context 4096. Embedding parameters 611,248,128; non-embedding 3,171,068,928; total 3,782,317,056. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 3.7B Instruct2 is an instruction-tuned checkpoint of the 3.7B dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Supervised fine-tune released 2025-02-05 for the dense sizes. LLMC describes Instruct2 as SFT on all LLM-jp-3 bases. The 172B Instruct2 uses the same restricted terms as the 172B base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-3-7b-instruct3": { "id": "llm-jp/llm-jp-3/llm-jp-3-3-7b-instruct3", "name": "LLM-jp-3 3.7B Instruct3", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "3782317056", "architecture": "Llama-style dense Transformer. 28 layers, hidden size 3072, 24 heads, context 4096. Embedding parameters 611,248,128; non-embedding 3,171,068,928; total 3,782,317,056. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 3.7B Instruct3 is an instruction-tuned checkpoint of the 3.7B dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Instruct3 adds Direct Preference Optimization on top of Instruct2, using aya-ja-evol-inst and ac-self-inst. Dense Instruct3 checkpoints other than the 172B were released 2025-02-05. The 172B Instruct3 was posted 2024-12-24 under its own terms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-440m": { "id": "llm-jp/llm-jp-3/llm-jp-3-440m", "name": "LLM-jp-3 440M", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "447052800", "architecture": "Llama-style dense Transformer. 16 layers, hidden size 1024, 8 heads, context 4096. Embedding parameters 203,749,376; non-embedding 243,303,424; total 447,052,800. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 440M is a dense text model from NII LLMC, trained from scratch on llm-jp-corpus v3 (2.1T tokens) with a Llama-style Transformer and a 4096-token context. Status: active. Collected 2026-10-15.", "what_it_is_for": "Pre-trained base for the 440M dense size. Not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-440m-instruct2": { "id": "llm-jp/llm-jp-3/llm-jp-3-440m-instruct2", "name": "LLM-jp-3 440M Instruct2", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "447052800", "architecture": "Llama-style dense Transformer. 16 layers, hidden size 1024, 8 heads, context 4096. Embedding parameters 203,749,376; non-embedding 243,303,424; total 447,052,800. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 440M Instruct2 is an instruction-tuned checkpoint of the 440M dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Supervised fine-tune released 2025-02-05 for the dense sizes. LLMC describes Instruct2 as SFT on all LLM-jp-3 bases. The 172B Instruct2 uses the same restricted terms as the 172B base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-440m-instruct3": { "id": "llm-jp/llm-jp-3/llm-jp-3-440m-instruct3", "name": "LLM-jp-3 440M Instruct3", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "447052800", "architecture": "Llama-style dense Transformer. 16 layers, hidden size 1024, 8 heads, context 4096. Embedding parameters 203,749,376; non-embedding 243,303,424; total 447,052,800. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 440M Instruct3 is an instruction-tuned checkpoint of the 440M dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Instruct3 adds Direct Preference Optimization on top of Instruct2, using aya-ja-evol-inst and ac-self-inst. Dense Instruct3 checkpoints other than the 172B were released 2025-02-05. The 172B Instruct3 was posted 2024-12-24 under its own terms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-7-2b": { "id": "llm-jp/llm-jp-3/llm-jp-3-7-2b", "name": "LLM-jp-3 7.2B", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "7291269120", "architecture": "Llama-style dense Transformer. 32 layers, hidden size 4096, 32 heads, context 4096. Embedding parameters 814,997,504; non-embedding 6,476,271,616; total 7,291,269,120. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 7.2B is a dense text model from NII LLMC, trained from scratch on llm-jp-corpus v3 (2.1T tokens) with a Llama-style Transformer and a 4096-token context. Status: active. Collected 2026-10-15.", "what_it_is_for": "Pre-trained base for the 7.2B dense size. Not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-7-2b-instruct": { "id": "llm-jp/llm-jp-3/llm-jp-3-7-2b-instruct", "name": "LLM-jp-3 7.2B Instruct", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "7291269120", "architecture": "Llama-style dense Transformer. 32 layers, hidden size 4096, 32 heads, context 4096. Embedding parameters 814,997,504; non-embedding 6,476,271,616; total 7,291,269,120. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 7.2B Instruct is an instruction-tuned checkpoint of the 7.2B dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "First instruction tune. The 13B card lists ichikara-instruction, AnswerCarefully, several synthetic sets, and a FLAN sample. Release day before the 2025-02-05 Instruct2 drop was not found, except that 1.8B, 3.7B, and 13B already existed by then.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-7-2b-instruct2": { "id": "llm-jp/llm-jp-3/llm-jp-3-7-2b-instruct2", "name": "LLM-jp-3 7.2B Instruct2", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "7291269120", "architecture": "Llama-style dense Transformer. 32 layers, hidden size 4096, 32 heads, context 4096. Embedding parameters 814,997,504; non-embedding 6,476,271,616; total 7,291,269,120. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 7.2B Instruct2 is an instruction-tuned checkpoint of the 7.2B dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Supervised fine-tune released 2025-02-05 for the dense sizes. LLMC describes Instruct2 as SFT on all LLM-jp-3 bases. The 172B Instruct2 uses the same restricted terms as the 172B base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-7-2b-instruct3": { "id": "llm-jp/llm-jp-3/llm-jp-3-7-2b-instruct3", "name": "LLM-jp-3 7.2B Instruct3", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "7291269120", "architecture": "Llama-style dense Transformer. 32 layers, hidden size 4096, 32 heads, context 4096. Embedding parameters 814,997,504; non-embedding 6,476,271,616; total 7,291,269,120. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 7.2B Instruct3 is an instruction-tuned checkpoint of the 7.2B dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Instruct3 adds Direct Preference Optimization on top of Instruct2, using aya-ja-evol-inst and ac-self-inst. Dense Instruct3 checkpoints other than the 172B were released 2025-02-05. The 172B Instruct3 was posted 2024-12-24 under its own terms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-8x1-8b": { "id": "llm-jp/llm-jp-3/llm-jp-3-8x1-8b", "name": "LLM-jp-3 8x1.8B", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-27", "context_length": 4096, "max_output_tokens": null, "parameters": "9266362368", "architecture": "Mixtral-style MoE. 24 layers, hidden size 2048, 16 heads, 8 routed experts, 2 experts active, context 4096. Embedding parameters 407,498,752; non-embedding 8,858,863,616; active parameters 2,924,279,808; total 9,266,362,368. Trained on llm-jp-corpus v3 for 2.1T tokens. The project cites drop-upcycling for the MoE method.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 8x1.8B is an LLM-jp-3 mixture-of-experts model from NII LLMC (total 9,266,362,368 parameters, 2,924,279,808 active, context 4096). Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Pre-trained MoE base. LLMC released it on 2025-03-27 under Apache-2.0. The 8x1.8B model has about 2.9B active parameters; the 8x13B model has about 22B active parameters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-8x1-8b-instruct2": { "id": "llm-jp/llm-jp-3/llm-jp-3-8x1-8b-instruct2", "name": "LLM-jp-3 8x1.8B Instruct2", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-27", "context_length": 4096, "max_output_tokens": null, "parameters": "9266362368", "architecture": "Mixtral-style MoE. 24 layers, hidden size 2048, 16 heads, 8 routed experts, 2 experts active, context 4096. Embedding parameters 407,498,752; non-embedding 8,858,863,616; active parameters 2,924,279,808; total 9,266,362,368. Trained on llm-jp-corpus v3 for 2.1T tokens. The project cites drop-upcycling for the MoE method.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 8x1.8B Instruct2 is an LLM-jp-3 mixture-of-experts model from NII LLMC (total 9,266,362,368 parameters, 2,924,279,808 active, context 4096). Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MoE SFT checkpoint. Released with the MoE series on 2025-03-27 under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-8x1-8b-instruct3": { "id": "llm-jp/llm-jp-3/llm-jp-3-8x1-8b-instruct3", "name": "LLM-jp-3 8x1.8B Instruct3", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-27", "context_length": 4096, "max_output_tokens": null, "parameters": "9266362368", "architecture": "Mixtral-style MoE. 24 layers, hidden size 2048, 16 heads, 8 routed experts, 2 experts active, context 4096. Embedding parameters 407,498,752; non-embedding 8,858,863,616; active parameters 2,924,279,808; total 9,266,362,368. Trained on llm-jp-corpus v3 for 2.1T tokens. The project cites drop-upcycling for the MoE method.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 8x1.8B Instruct3 is an LLM-jp-3 mixture-of-experts model from NII LLMC (total 9,266,362,368 parameters, 2,924,279,808 active, context 4096). Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MoE SFT plus DPO checkpoint. Released with the MoE series on 2025-03-27 under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-8x13b": { "id": "llm-jp/llm-jp-3/llm-jp-3-8x13b", "name": "LLM-jp-3 8x13B", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-27", "context_length": 4096, "max_output_tokens": null, "parameters": "73162828800", "architecture": "Mixtral-style MoE. 40 layers, hidden size 5120, 40 heads, 8 routed experts, 2 experts active, context 4096. Embedding parameters 1,018,746,880; non-embedding 72,144,081,920; active parameters 22,200,806,400; total 73,162,828,800. Trained on llm-jp-corpus v3 for 2.1T tokens. The project cites drop-upcycling for the MoE method.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 8x13B is an LLM-jp-3 mixture-of-experts model from NII LLMC (total 73,162,828,800 parameters, 22,200,806,400 active, context 4096). Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Pre-trained MoE base. LLMC released it on 2025-03-27 under Apache-2.0. The 8x1.8B model has about 2.9B active parameters; the 8x13B model has about 22B active parameters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-8x13b-instruct2": { "id": "llm-jp/llm-jp-3/llm-jp-3-8x13b-instruct2", "name": "LLM-jp-3 8x13B Instruct2", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-27", "context_length": 4096, "max_output_tokens": null, "parameters": "73162828800", "architecture": "Mixtral-style MoE. 40 layers, hidden size 5120, 40 heads, 8 routed experts, 2 experts active, context 4096. Embedding parameters 1,018,746,880; non-embedding 72,144,081,920; active parameters 22,200,806,400; total 73,162,828,800. Trained on llm-jp-corpus v3 for 2.1T tokens. The project cites drop-upcycling for the MoE method.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 8x13B Instruct2 is an LLM-jp-3 mixture-of-experts model from NII LLMC (total 73,162,828,800 parameters, 22,200,806,400 active, context 4096). Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MoE SFT checkpoint. Released with the MoE series on 2025-03-27 under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-8x13b-instruct3": { "id": "llm-jp/llm-jp-3/llm-jp-3-8x13b-instruct3", "name": "LLM-jp-3 8x13B Instruct3", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-27", "context_length": 4096, "max_output_tokens": null, "parameters": "73162828800", "architecture": "Mixtral-style MoE. 40 layers, hidden size 5120, 40 heads, 8 routed experts, 2 experts active, context 4096. Embedding parameters 1,018,746,880; non-embedding 72,144,081,920; active parameters 22,200,806,400; total 73,162,828,800. Trained on llm-jp-corpus v3 for 2.1T tokens. The project cites drop-upcycling for the MoE method.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 8x13B Instruct3 is an LLM-jp-3 mixture-of-experts model from NII LLMC (total 73,162,828,800 parameters, 22,200,806,400 active, context 4096). Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MoE SFT plus DPO checkpoint. Released with the MoE series on 2025-03-27 under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-980m": { "id": "llm-jp/llm-jp-3/llm-jp-3-980m", "name": "LLM-jp-3 980M", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "989882880", "architecture": "Llama-style dense Transformer. 20 layers, hidden size 1536, 8 heads, context 4096. Embedding parameters 305,624,064; non-embedding 684,258,816; total 989,882,880. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 980M is a dense text model from NII LLMC, trained from scratch on llm-jp-corpus v3 (2.1T tokens) with a Llama-style Transformer and a 4096-token context. Status: active. Collected 2026-10-15.", "what_it_is_for": "Pre-trained base for the 980M dense size. Not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-980m-instruct2": { "id": "llm-jp/llm-jp-3/llm-jp-3-980m-instruct2", "name": "LLM-jp-3 980M Instruct2", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "989882880", "architecture": "Llama-style dense Transformer. 20 layers, hidden size 1536, 8 heads, context 4096. Embedding parameters 305,624,064; non-embedding 684,258,816; total 989,882,880. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 980M Instruct2 is an instruction-tuned checkpoint of the 980M dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Supervised fine-tune released 2025-02-05 for the dense sizes. LLMC describes Instruct2 as SFT on all LLM-jp-3 bases. The 172B Instruct2 uses the same restricted terms as the 172B base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3/llm-jp-3-980m-instruct3": { "id": "llm-jp/llm-jp-3/llm-jp-3-980m-instruct3", "name": "LLM-jp-3 980M Instruct3", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 4096, "max_output_tokens": null, "parameters": "989882880", "architecture": "Llama-style dense Transformer. 20 layers, hidden size 1536, 8 heads, context 4096. Embedding parameters 305,624,064; non-embedding 684,258,816; total 989,882,880. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 980M Instruct3 is an instruction-tuned checkpoint of the 980M dense base from NII LLMC. License: Apache-2.0. Status: active. Collected 2026-10-15.", "what_it_is_for": "Instruct3 adds Direct Preference Optimization on top of Instruct2, using aya-ja-evol-inst and ac-self-inst. Dense Instruct3 checkpoints other than the 172B were released 2025-02-05. The 172B Instruct3 was posted 2024-12-24 under its own terms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3-1/llm-jp-3-1-1-8b": { "id": "llm-jp/llm-jp-3-1/llm-jp-3-1-1-8b", "name": "LLM-jp-3.1 1.8B", "maker": "llm-jp", "family": "llm-jp/llm-jp-3-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-30", "context_length": 4096, "max_output_tokens": null, "parameters": "1867216896", "architecture": "Llama-style dense Transformer. 24 layers, hidden size 2048, 16 heads, context 4096. Embedding parameters 407,498,752; non-embedding 1,459,718,144; total 1,867,216,896. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens, then 400B more tokens of instruction pre-training (about 90B of that is instruction-response data), for 2.5T tokens in total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3.1 1.8B is part of the LLM-jp-3.1 line from NII LLMC, released 2025-05-30. It keeps the LLM-jp-3 shape and adds 400B tokens of instruction pre-training. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Continued pre-training of the matching LLM-jp-3 checkpoint with instruction pre-training. Not the chat model; use Instruct4 for instruction following.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3-1/llm-jp-3-1-1-8b-instruct4": { "id": "llm-jp/llm-jp-3-1/llm-jp-3-1-1-8b-instruct4", "name": "LLM-jp-3.1 1.8B Instruct4", "maker": "llm-jp", "family": "llm-jp/llm-jp-3-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-30", "context_length": 4096, "max_output_tokens": null, "parameters": "1867216896", "architecture": "Llama-style dense Transformer. 24 layers, hidden size 2048, 16 heads, context 4096. Embedding parameters 407,498,752; non-embedding 1,459,718,144; total 1,867,216,896. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T tokens, then 400B more tokens of instruction pre-training (about 90B of that is instruction-response data), for 2.5T tokens in total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3.1 1.8B Instruct4 is part of the LLM-jp-3.1 line from NII LLMC, released 2025-05-30. It keeps the LLM-jp-3 shape and adds 400B tokens of instruction pre-training. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Instruct4 post-train: expanded SFT (including jaster v1.4.1 and extraction-wiki-ja) and two-stage DPO with rejection sampling scored by Qwen2.5-32B-Instruct, plus ac-self-inst in stage two.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3-1/llm-jp-3-1-13b": { "id": "llm-jp/llm-jp-3-1/llm-jp-3-1-13b", "name": "LLM-jp-3.1 13B", "maker": "llm-jp", "family": "llm-jp/llm-jp-3-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-30", "context_length": 4096, "max_output_tokens": null, "parameters": "13706931200", "architecture": "Llama-style dense Transformer. 40 layers, hidden size 5120, 40 heads, context 4096. Embedding parameters 1,018,746,880; non-embedding 12,688,184,320; total 13,706,931,200. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T plus 400B mid-training tokens (2.5T total).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3.1 13B is part of the LLM-jp-3.1 line from NII LLMC, released 2025-05-30. It keeps the LLM-jp-3 shape and adds 400B tokens of instruction pre-training. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Continued pre-training of the matching LLM-jp-3 checkpoint with instruction pre-training. Not the chat model; use Instruct4 for instruction following.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3-1/llm-jp-3-1-13b-instruct4": { "id": "llm-jp/llm-jp-3-1/llm-jp-3-1-13b-instruct4", "name": "LLM-jp-3.1 13B Instruct4", "maker": "llm-jp", "family": "llm-jp/llm-jp-3-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-30", "context_length": 4096, "max_output_tokens": null, "parameters": "13706931200", "architecture": "Llama-style dense Transformer. 40 layers, hidden size 5120, 40 heads, context 4096. Embedding parameters 1,018,746,880; non-embedding 12,688,184,320; total 13,706,931,200. Tokenizer: llm-jp-tokenizer v3.0 Unigram byte-fallback. Pre-trained on llm-jp-corpus v3 for 2.1T plus 400B mid-training tokens (2.5T total).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3.1 13B Instruct4 is part of the LLM-jp-3.1 line from NII LLMC, released 2025-05-30. It keeps the LLM-jp-3 shape and adds 400B tokens of instruction pre-training. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Instruct4 post-train: expanded SFT (including jaster v1.4.1 and extraction-wiki-ja) and two-stage DPO with rejection sampling scored by Qwen2.5-32B-Instruct, plus ac-self-inst in stage two.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3-1/llm-jp-3-1-8x13b": { "id": "llm-jp/llm-jp-3-1/llm-jp-3-1-8x13b", "name": "LLM-jp-3.1 8x13B", "maker": "llm-jp", "family": "llm-jp/llm-jp-3-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-30", "context_length": 4096, "max_output_tokens": null, "parameters": "73162828800", "architecture": "Mixtral-style MoE. 40 layers, hidden size 5120, 40 heads, 8 routed experts, 2 experts active, context 4096. Embedding parameters 1,018,746,880; non-embedding 72,144,081,920; active parameters 22,200,806,400; total 73,162,828,800. Trained on llm-jp-corpus v3 for 2.1T plus 400B mid-training tokens (2.5T total). The project cites drop-upcycling for the MoE method.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3.1 8x13B is part of the LLM-jp-3.1 line from NII LLMC, released 2025-05-30. It keeps the LLM-jp-3 shape and adds 400B tokens of instruction pre-training. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Continued pre-training of the matching LLM-jp-3 checkpoint with instruction pre-training. Not the chat model; use Instruct4 for instruction following.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3-1/llm-jp-3-1-8x13b-32k": { "id": "llm-jp/llm-jp-3-1/llm-jp-3-1-8x13b-32k", "name": "LLM-jp-3.1 8x13B 32K", "maker": "llm-jp", "family": "llm-jp/llm-jp-3-1", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-24", "context_length": null, "max_output_tokens": null, "parameters": "73162828800", "architecture": "Mixtral-style MoE. 40 layers, hidden size 5120, 40 heads, 8 routed experts, 2 experts active, context 4096. Embedding parameters 1,018,746,880; non-embedding 72,144,081,920; active parameters 22,200,806,400; total 73,162,828,800. Trained on llm-jp-corpus v3 for 2.1T plus 400B mid-training tokens (2.5T total). The project cites drop-upcycling for the MoE method. This Hub repo is named 32K. The numeric context length was not copied from config.json in this pass.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3.1 8x13B 32K is a named Hub release of the LLM-jp-3.1 8x13B line. Hub metadata dates the repo to 2026-02-24. It was not on the official release index. Status: active. Collected 2026-10-15.", "what_it_is_for": "Context-extended 8x13B checkpoint published on the Hub. It is not on the release index page fetched on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3-1/llm-jp-3-1-8x13b-32k-instruct4": { "id": "llm-jp/llm-jp-3-1/llm-jp-3-1-8x13b-32k-instruct4", "name": "LLM-jp-3.1 8x13B 32K Instruct4", "maker": "llm-jp", "family": "llm-jp/llm-jp-3-1", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-24", "context_length": null, "max_output_tokens": null, "parameters": "73162828800", "architecture": "Mixtral-style MoE. 40 layers, hidden size 5120, 40 heads, 8 routed experts, 2 experts active, context 4096. Embedding parameters 1,018,746,880; non-embedding 72,144,081,920; active parameters 22,200,806,400; total 73,162,828,800. Trained on llm-jp-corpus v3 for 2.1T plus 400B mid-training tokens (2.5T total). The project cites drop-upcycling for the MoE method. This Hub repo is named 32K. The numeric context length was not copied from config.json in this pass.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3.1 8x13B 32K Instruct4 is a named Hub release of the LLM-jp-3.1 8x13B line. Hub metadata dates the repo to 2026-02-24. It was not on the official release index. Status: active. Collected 2026-10-15.", "what_it_is_for": "Instruct4 tune of the 32K checkpoint. Not listed on the release index fetched on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-3-1/llm-jp-3-1-8x13b-instruct4": { "id": "llm-jp/llm-jp-3-1/llm-jp-3-1-8x13b-instruct4", "name": "LLM-jp-3.1 8x13B Instruct4", "maker": "llm-jp", "family": "llm-jp/llm-jp-3-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-30", "context_length": 4096, "max_output_tokens": null, "parameters": "73162828800", "architecture": "Mixtral-style MoE. 40 layers, hidden size 5120, 40 heads, 8 routed experts, 2 experts active, context 4096. Embedding parameters 1,018,746,880; non-embedding 72,144,081,920; active parameters 22,200,806,400; total 73,162,828,800. Trained on llm-jp-corpus v3 for 2.1T plus 400B mid-training tokens (2.5T total). The project cites drop-upcycling for the MoE method.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3.1 8x13B Instruct4 is part of the LLM-jp-3.1 line from NII LLMC, released 2025-05-30. It keeps the LLM-jp-3 shape and adds 400B tokens of instruction pre-training. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Instruct4 post-train: expanded SFT (including jaster v1.4.1 and extraction-wiki-ja) and two-stage DPO with rejection sampling scored by Qwen2.5-32B-Instruct, plus ac-self-inst in stage two.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-4/llm-jp-4-1-32b-a3b-thinking": { "id": "llm-jp/llm-jp-4/llm-jp-4-1-32b-a3b-thinking", "name": "LLM-jp-4.1 32B-A3B Thinking", "maker": "llm-jp", "family": "llm-jp/llm-jp-4", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-28", "context_length": 65536, "max_output_tokens": null, "parameters": "32139028992", "architecture": "Qwen3 MoE architecture (NII, 2026-04-03). 32 layers, hidden size 2560, 40 heads, 128 routed experts, 8 experts active per token, context 65536. Embedding parameters 503,316,480; non-embedding 31,635,712,512; active parameters 3,827,476,992; total 32,139,028,992. Tokenizer: llm-jp-tokenizer v4.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-4 is a from-scratch Japanese and English model family from the Research and Development Center for Large Language Models at the National Institute of Informatics. September 2026 MoE post-train. On the project judge, Japanese MT-Bench is lower than the April thinking model while safety scores are higher. Status: active. Collected 2026-10-15.", "what_it_is_for": "LLM-jp-4.1 post-train of the 32B-A3B checkpoint, with tool calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-4/llm-jp-4-1-33b-thinking": { "id": "llm-jp/llm-jp-4/llm-jp-4-1-33b-thinking", "name": "LLM-jp-4.1 33B Thinking", "maker": "llm-jp", "family": "llm-jp/llm-jp-4", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-28", "context_length": 65536, "max_output_tokens": null, "parameters": "33219548160", "architecture": "Dense Transformer in the LLM-jp-4 series. 64 layers, hidden size 5120, 40 heads, context 65536. Embedding parameters 1,006,632,960; non-embedding 32,212,915,200; total 33,219,548,160. The April 2026 NII note named a planned dense 'LLM-jp-4 32B'; the released model is this 33.2B checkpoint. Tokenizer: llm-jp-tokenizer v4.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-4 is a from-scratch Japanese and English model family from the Research and Development Center for Large Language Models at the National Institute of Informatics. September 2026 dense post-train. Highest AnswerCarefully score among the LLM-jp-4.1 thinking models on the project card (4.08). Status: active. Collected 2026-10-15.", "what_it_is_for": "LLM-jp-4.1 post-train of the 33B checkpoint, including tool calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-4/llm-jp-4-1-8b-thinking": { "id": "llm-jp/llm-jp-4/llm-jp-4-1-8b-thinking", "name": "LLM-jp-4.1 8B Thinking", "maker": "llm-jp", "family": "llm-jp/llm-jp-4", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-28", "context_length": 65536, "max_output_tokens": null, "parameters": "8590200832", "architecture": "Llama-2-style dense Transformer (NII, 2026-04-03). 32 layers, hidden size 4096, 32 heads, context 65536. Embedding parameters 805,306,368; non-embedding 7,784,894,464; total 8,590,200,832. Tokenizer: llm-jp-tokenizer v4.0 Unigram byte-fallback. Chat template aims at the OpenAI Harmony format but is not the openai-harmony tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-4 is a from-scratch Japanese and English model family from the Research and Development Center for Large Language Models at the National Institute of Informatics. September 2026 post-train. Adds tool calling. The blog is in Japanese. Status: active. Collected 2026-10-15.", "what_it_is_for": "Same 8B backbone as LLM-jp-4. LLM-jp-4.1 changes post-training: more SFT tokens, STEM emphasis, and tool calling. The blog says SFT data is about 12B tokens for 3 epochs, about 35B tokens total, versus about 5.4B tokens for 2 epochs on LLM-jp-4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-4/llm-jp-4-32b-a3b-base": { "id": "llm-jp/llm-jp-4/llm-jp-4-32b-a3b-base", "name": "LLM-jp-4 32B-A3B Base", "maker": "llm-jp", "family": "llm-jp/llm-jp-4", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-03", "context_length": 65536, "max_output_tokens": null, "parameters": "32139028992", "architecture": "Qwen3 MoE architecture (NII, 2026-04-03). 32 layers, hidden size 2560, 40 heads, 128 routed experts, 8 experts active per token, context 65536. Embedding parameters 503,316,480; non-embedding 31,635,712,512; active parameters 3,827,476,992; total 32,139,028,992. Tokenizer: llm-jp-tokenizer v4.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-4 is a from-scratch Japanese and English model family from the Research and Development Center for Large Language Models at the National Institute of Informatics. Sparse model with about 3.8B active parameters out of 32.1B total, trained from scratch on the LLM-jp-4 corpus. Status: active. Collected 2026-10-15.", "what_it_is_for": "MoE base: pre-training and mid-training only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-4/llm-jp-4-32b-a3b-thinking": { "id": "llm-jp/llm-jp-4/llm-jp-4-32b-a3b-thinking", "name": "LLM-jp-4 32B-A3B Thinking", "maker": "llm-jp", "family": "llm-jp/llm-jp-4", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-03", "context_length": 65536, "max_output_tokens": null, "parameters": "32139028992", "architecture": "Qwen3 MoE architecture (NII, 2026-04-03). 32 layers, hidden size 2560, 40 heads, 128 routed experts, 8 experts active per token, context 65536. Embedding parameters 503,316,480; non-embedding 31,635,712,512; active parameters 3,827,476,992; total 32,139,028,992. Tokenizer: llm-jp-tokenizer v4.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-4 is a from-scratch Japanese and English model family from the Research and Development Center for Large Language Models at the National Institute of Informatics. April 2026 MoE thinking release. NII reports Japanese MT-Bench 7.82, above the GPT-4o figure of 7.29 under their GPT-5.4 judge. Status: active. Collected 2026-10-15.", "what_it_is_for": "SFT and DPO thinking model, no RL. Active parameters stay about 3.83B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-4/llm-jp-4-332b-a31b": { "id": "llm-jp/llm-jp-4/llm-jp-4-332b-a31b", "name": "LLM-jp-4 332B-A31B", "maker": "llm-jp", "family": "llm-jp/llm-jp-4", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named in the 2026-04-03 NII future-plans section as a larger MoE to be released during Japan's fiscal year 2026, alongside a dense model that later shipped as the 33B. The name follows the project's A-suffix pattern (active parameters), but NII did not publish a parameter table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-4 332B-A31B is a larger mixture-of-experts model that NII said on 2026-04-03 was in development for release during Japan's fiscal year 2026. It was not on the LLM-jp release index and no weight repo was found on 2026-10-15. Status: announced. Collected 2026-10-15.", "what_it_is_for": "Announced successor-scale MoE. Not a shipped checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-4/llm-jp-4-33b-base": { "id": "llm-jp/llm-jp-4/llm-jp-4-33b-base", "name": "LLM-jp-4 33B Base", "maker": "llm-jp", "family": "llm-jp/llm-jp-4", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-08-18", "context_length": 65536, "max_output_tokens": null, "parameters": "33219548160", "architecture": "Dense Transformer in the LLM-jp-4 series. 64 layers, hidden size 5120, 40 heads, context 65536. Embedding parameters 1,006,632,960; non-embedding 32,212,915,200; total 33,219,548,160. The April 2026 NII note named a planned dense 'LLM-jp-4 32B'; the released model is this 33.2B checkpoint. Tokenizer: llm-jp-tokenizer v4.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-4 is a from-scratch Japanese and English model family from the Research and Development Center for Large Language Models at the National Institute of Informatics. Released 2026-08-18 as the dense follow-on to the April 8B and 32B-A3B models. Status: active. Collected 2026-10-15.", "what_it_is_for": "Dense 33.2B base after pre-training and mid-training. Intended as a base for further tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-4/llm-jp-4-33b-thinking": { "id": "llm-jp/llm-jp-4/llm-jp-4-33b-thinking", "name": "LLM-jp-4 33B Thinking", "maker": "llm-jp", "family": "llm-jp/llm-jp-4", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-08-18", "context_length": 65536, "max_output_tokens": null, "parameters": "33219548160", "architecture": "Dense Transformer in the LLM-jp-4 series. 64 layers, hidden size 5120, 40 heads, context 65536. Embedding parameters 1,006,632,960; non-embedding 32,212,915,200; total 33,219,548,160. The April 2026 NII note named a planned dense 'LLM-jp-4 32B'; the released model is this 33.2B checkpoint. Tokenizer: llm-jp-tokenizer v4.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-4 is a from-scratch Japanese and English model family from the Research and Development Center for Large Language Models at the National Institute of Informatics. August 2026 dense thinking model. DPO data was released with the weights. Status: active. Collected 2026-10-15.", "what_it_is_for": "SFT and DPO thinking model. The project said it beat earlier LLM-jp-4 thinking models on all four llm-jp-judge benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-4/llm-jp-4-8b-base": { "id": "llm-jp/llm-jp-4/llm-jp-4-8b-base", "name": "LLM-jp-4 8B Base", "maker": "llm-jp", "family": "llm-jp/llm-jp-4", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-03", "context_length": 65536, "max_output_tokens": null, "parameters": "8590200832", "architecture": "Llama-2-style dense Transformer (NII, 2026-04-03). 32 layers, hidden size 4096, 32 heads, context 65536. Embedding parameters 805,306,368; non-embedding 7,784,894,464; total 8,590,200,832. Tokenizer: llm-jp-tokenizer v4.0 Unigram byte-fallback. Chat template aims at the OpenAI Harmony format but is not the openai-harmony tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-4 is a from-scratch Japanese and English model family from the Research and Development Center for Large Language Models at the National Institute of Informatics. Japanese and English text model trained from scratch for open local use. The NII release presents it with the 32B-A3B model as the April 2026 LLM-jp-4 pair. Status: active. Collected 2026-10-15.", "what_it_is_for": "Base checkpoint: pre-training and mid-training only, no SFT or DPO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-4/llm-jp-4-8b-instruct": { "id": "llm-jp/llm-jp-4/llm-jp-4-8b-instruct", "name": "LLM-jp-4 8B Instruct", "maker": "llm-jp", "family": "llm-jp/llm-jp-4", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-30", "context_length": 65536, "max_output_tokens": null, "parameters": "8590200832", "architecture": "Llama-2-style dense Transformer (NII, 2026-04-03). 32 layers, hidden size 4096, 32 heads, context 65536. Embedding parameters 805,306,368; non-embedding 7,784,894,464; total 8,590,200,832. Tokenizer: llm-jp-tokenizer v4.0 Unigram byte-fallback. Chat template aims at the OpenAI Harmony format but is not the openai-harmony tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-4 is a from-scratch Japanese and English model family from the Research and Development Center for Large Language Models at the National Institute of Informatics. Non-thinking instruction tune of the 8B dense model. Hub createdAt is 2026-03-30; the NII press on 2026-04-03 highlights the base and thinking checkpoints instead. Status: active. Collected 2026-10-15.", "what_it_is_for": "Instruction-tuned 8B checkpoint on the Hub. It is the language backbone of LLM-jp-4-VL 9B beta. It is not in the release-index highlight list fetched on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-4/llm-jp-4-8b-thinking": { "id": "llm-jp/llm-jp-4/llm-jp-4-8b-thinking", "name": "LLM-jp-4 8B Thinking", "maker": "llm-jp", "family": "llm-jp/llm-jp-4", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-03", "context_length": 65536, "max_output_tokens": null, "parameters": "8590200832", "architecture": "Llama-2-style dense Transformer (NII, 2026-04-03). 32 layers, hidden size 4096, 32 heads, context 65536. Embedding parameters 805,306,368; non-embedding 7,784,894,464; total 8,590,200,832. Tokenizer: llm-jp-tokenizer v4.0 Unigram byte-fallback. Chat template aims at the OpenAI Harmony format but is not the openai-harmony tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-4 is a from-scratch Japanese and English model family from the Research and Development Center for Large Language Models at the National Institute of Informatics. Flagship small thinking model in the April 2026 release. Supports a controllable reasoning effort. GGUF weights are a packaging of the same model. Status: active. Collected 2026-10-15.", "what_it_is_for": "Post-trained with SFT and DPO, without reinforcement learning. Reasoning effort is exposed in the chat template.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-modernbert/llm-jp-modernbert-base": { "id": "llm-jp/llm-jp-modernbert/llm-jp-modernbert-base", "name": "LLM-jp ModernBERT base", "maker": "llm-jp", "family": "llm-jp/llm-jp-modernbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-24", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "ModernBERT-base encoder with the llm-jp tokenizer, trained on the Japanese subset of llm-jp-corpus v4 (card: 3.4 TB). Stage 1 max sequence 1024 for 500,000 steps; stage 2 max sequence 8192 for 200,000 steps. Masked LM probability 0.30.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp ModernBERT base is a Japanese masked-language encoder from LLM-jp, released 2025-04-24, with an 8192-token context. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Japanese fill-mask and encoder tasks. The card reports JGLUE, not chat benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-moshi/llm-jp-moshi-v1": { "id": "llm-jp/llm-jp-moshi/llm-jp-moshi-v1", "name": "LLM-jp-Moshi v1", "maker": "llm-jp", "family": "llm-jp/llm-jp-moshi", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Japanese full-duplex spoken-dialogue model. Continued training of kyutai/moshiko-pytorch-bf16, which the card calls a 7B-parameter English Moshi model. Text tokenizer is the rinna Japanese SentencePiece model. Audio tokenizer is Kyutai Mimi. Trained on J-CHAT (about 69,000 hours of podcast dialogue) and LLM-jp-Zoom1 (about 1,000 hours of Zoom conversation).", "modalities": { "input": [ "audio", "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-Moshi v1 is LLM-jp's Japanese full-duplex speech dialogue model, a fine-tune of Kyutai Moshi on J-CHAT and an internal Zoom corpus. It is listed on the official release index. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Spoken Japanese dialogue, not a text chatbot. Run with moshi.server and the Hub repo id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v1/llm-jp-1-3b-v1-0": { "id": "llm-jp/llm-jp-v1/llm-jp-1-3b-v1-0", "name": "LLM-jp-1.3B v1.0", "maker": "llm-jp", "family": "llm-jp/llm-jp-v1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-20", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 architecture. 24 layers, hidden size 2048, 16 heads, context 2048. Same v1 corpus family as the 13B model. Exact parameter integer is not on the card; the project calls it 1.3B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-1.3B v1.0 is an early LLM-jp text model from the NII-led LLM-jp project. 1.3B pre-trained model for local experiments. No instruction-tuned twin is on the current release index. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Small v1.0 base released with the 13B model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v1/llm-jp-13b-dpo-lora-hh-rlhf-ja-v1-1": { "id": "llm-jp/llm-jp-v1/llm-jp-13b-dpo-lora-hh-rlhf-ja-v1-1", "name": "LLM-jp-13B v1.1 DPO LoRA HH-RLHF JA", "maker": "llm-jp", "family": "llm-jp/llm-jp-v1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "v1.1 tune of the v1.0 13B GPT-2 model (40 layers, hidden 5120, 40 heads, context 2048). A v1.1 card reports 300B seen tokens, against more than 270B on the v1.0 card. The release index's v1.1 pretrained slot still points at llm-jp-13b-v1.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-13B v1.1 DPO LoRA HH-RLHF JA is a v1.1 tuning of LLM-jp-13B v1.0, still listed on the LLM-jp release index. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "LoRA DPO checkpoint using the Japanese DeepL translation of HH-RLHF (hh-rlhf-12k-ja) named in the v1.1 card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v1/llm-jp-13b-instruct-full-dolly-en-dolly-ja-ichikara-003-001-oasst-en-oasst-ja-v1-1": { "id": "llm-jp/llm-jp-v1/llm-jp-13b-instruct-full-dolly-en-dolly-ja-ichikara-003-001-oasst-en-oasst-ja-v1-1", "name": "LLM-jp-13B v1.1 Instruct full Dolly Ichikara OASST", "maker": "llm-jp", "family": "llm-jp/llm-jp-v1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "v1.1 tune of the v1.0 13B GPT-2 model (40 layers, hidden 5120, 40 heads, context 2048). A v1.1 card reports 300B seen tokens, against more than 270B on the v1.0 card. The release index's v1.1 pretrained slot still points at llm-jp-13b-v1.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-13B v1.1 Instruct full Dolly Ichikara OASST is a v1.1 tuning of LLM-jp-13B v1.0, still listed on the LLM-jp release index. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Full fine-tune named for English and Japanese Dolly, ichikara 003-001, and English and Japanese OASST. The v1.1 card lists those sets, including DeepL translations done inside LLM-jp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v1/llm-jp-13b-instruct-full-dolly-oasst-v1-0": { "id": "llm-jp/llm-jp-v1/llm-jp-13b-instruct-full-dolly-oasst-v1-0", "name": "LLM-jp-13B v1.0 Instruct full Dolly OASST", "maker": "llm-jp", "family": "llm-jp/llm-jp-v1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-20", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Instruction variant of llm-jp-13b-v1.0. GPT-2 architecture, 40 layers, hidden 5120, 40 heads, context 2048. Full-tune folders update all weights. LoRA folders are adapter tunes. Dataset mix is taken from the checkpoint name on the release index.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-13B v1.0 Instruct full Dolly OASST is a v1.0 instruction checkpoint of LLM-jp-13B from the LLM-jp project, listed on the official release index. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Full fine-tune on Dolly and OASST, without the JASTER mix used in the sibling checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v1/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1-0": { "id": "llm-jp/llm-jp-v1/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1-0", "name": "LLM-jp-13B v1.0 Instruct full JASTER Dolly OASST", "maker": "llm-jp", "family": "llm-jp/llm-jp-v1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-20", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Instruction variant of llm-jp-13b-v1.0. GPT-2 architecture, 40 layers, hidden 5120, 40 heads, context 2048. Full-tune folders update all weights. LoRA folders are adapter tunes. Dataset mix is taken from the checkpoint name on the release index.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-13B v1.0 Instruct full JASTER Dolly OASST is a v1.0 instruction checkpoint of LLM-jp-13B from the LLM-jp project, listed on the official release index. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Full fine-tune on JASTER plus Dolly and OASST.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v1/llm-jp-13b-instruct-full-jaster-v1-0": { "id": "llm-jp/llm-jp-v1/llm-jp-13b-instruct-full-jaster-v1-0", "name": "LLM-jp-13B v1.0 Instruct full JASTER", "maker": "llm-jp", "family": "llm-jp/llm-jp-v1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-20", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Instruction variant of llm-jp-13b-v1.0. GPT-2 architecture, 40 layers, hidden 5120, 40 heads, context 2048. Full-tune folders update all weights. LoRA folders are adapter tunes. Dataset mix is taken from the checkpoint name on the release index.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-13B v1.0 Instruct full JASTER is a v1.0 instruction checkpoint of LLM-jp-13B from the LLM-jp project, listed on the official release index. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Full fine-tune on JASTER, the project's converted Japanese NLP tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v1/llm-jp-13b-instruct-lora-dolly-en-dolly-ja-ichikara-003-001-oasst-en-oasst-ja-v1-1": { "id": "llm-jp/llm-jp-v1/llm-jp-13b-instruct-lora-dolly-en-dolly-ja-ichikara-003-001-oasst-en-oasst-ja-v1-1", "name": "LLM-jp-13B v1.1 Instruct LoRA Dolly Ichikara OASST", "maker": "llm-jp", "family": "llm-jp/llm-jp-v1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "v1.1 tune of the v1.0 13B GPT-2 model (40 layers, hidden 5120, 40 heads, context 2048). A v1.1 card reports 300B seen tokens, against more than 270B on the v1.0 card. The release index's v1.1 pretrained slot still points at llm-jp-13b-v1.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-13B v1.1 Instruct LoRA Dolly Ichikara OASST is a v1.1 tuning of LLM-jp-13B v1.0, still listed on the LLM-jp release index. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "LoRA version of the same Dolly, ichikara 003-001, and OASST mix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v1/llm-jp-13b-instruct-lora-dolly-oasst-v1-0": { "id": "llm-jp/llm-jp-v1/llm-jp-13b-instruct-lora-dolly-oasst-v1-0", "name": "LLM-jp-13B v1.0 Instruct LoRA Dolly OASST", "maker": "llm-jp", "family": "llm-jp/llm-jp-v1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-20", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Instruction variant of llm-jp-13b-v1.0. GPT-2 architecture, 40 layers, hidden 5120, 40 heads, context 2048. Full-tune folders update all weights. LoRA folders are adapter tunes. Dataset mix is taken from the checkpoint name on the release index.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-13B v1.0 Instruct LoRA Dolly OASST is a v1.0 instruction checkpoint of LLM-jp-13B from the LLM-jp project, listed on the official release index. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "LoRA tune on Dolly and OASST.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v1/llm-jp-13b-instruct-lora-jaster-dolly-oasst-v1-0": { "id": "llm-jp/llm-jp-v1/llm-jp-13b-instruct-lora-jaster-dolly-oasst-v1-0", "name": "LLM-jp-13B v1.0 Instruct LoRA JASTER Dolly OASST", "maker": "llm-jp", "family": "llm-jp/llm-jp-v1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-20", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Instruction variant of llm-jp-13b-v1.0. GPT-2 architecture, 40 layers, hidden 5120, 40 heads, context 2048. Full-tune folders update all weights. LoRA folders are adapter tunes. Dataset mix is taken from the checkpoint name on the release index.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-13B v1.0 Instruct LoRA JASTER Dolly OASST is a v1.0 instruction checkpoint of LLM-jp-13B from the LLM-jp project, listed on the official release index. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "LoRA tune on JASTER, Dolly, and OASST.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v1/llm-jp-13b-instruct-lora-jaster-v1-0": { "id": "llm-jp/llm-jp-v1/llm-jp-13b-instruct-lora-jaster-v1-0", "name": "LLM-jp-13B v1.0 Instruct LoRA JASTER", "maker": "llm-jp", "family": "llm-jp/llm-jp-v1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-20", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Instruction variant of llm-jp-13b-v1.0. GPT-2 architecture, 40 layers, hidden 5120, 40 heads, context 2048. Full-tune folders update all weights. LoRA folders are adapter tunes. Dataset mix is taken from the checkpoint name on the release index.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-13B v1.0 Instruct LoRA JASTER is a v1.0 instruction checkpoint of LLM-jp-13B from the LLM-jp project, listed on the official release index. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "LoRA tune on JASTER.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v1/llm-jp-13b-v1-0": { "id": "llm-jp/llm-jp-v1/llm-jp-13b-v1-0", "name": "LLM-jp-13B v1.0", "maker": "llm-jp", "family": "llm-jp/llm-jp-v1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-20", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 architecture. 40 layers, hidden size 5120, 40 heads, context 2048. The project paper lists a 50k vocabulary (tokenizer v2.1) and a v1 corpus of 270B plus 27B tokens. The model card says more than 270B tokens seen.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-13B v1.0 is an early LLM-jp text model from the NII-led LLM-jp project. Pre-trained GPT-2-style Japanese and English model. Instruction variants from the same release are separate folders. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "First public LLM-jp 13B base, released 2023-10-20.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v2/llm-jp-13b-instruct-full-ac-001-16x-dolly-ichikara-004-001-single-oasst-oasst2-v2-0": { "id": "llm-jp/llm-jp-v2/llm-jp-13b-instruct-full-ac-001-16x-dolly-ichikara-004-001-single-oasst-oasst2-v2-0", "name": "LLM-jp-13B v2.0 Instruct full AC 16x Dolly Ichikara OASST OASST2", "maker": "llm-jp", "family": "llm-jp/llm-jp-v2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-30", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of llm-jp-13b-v2.0. Llama-2 architecture, 40 layers, hidden 5120, 40 heads, context 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-13B v2.0 Instruct full AC 16x Dolly Ichikara OASST OASST2 is a v2.0 instruction fine-tune of LLM-jp-13B, released with the 2024-04-30 v2.0 drop. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Same mix with AnswerCarefully ac_001 upsampled 16x, as indicated by the checkpoint name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v2/llm-jp-13b-instruct-full-ac-001-dolly-ichikara-004-001-single-oasst-oasst2-v2-0": { "id": "llm-jp/llm-jp-v2/llm-jp-13b-instruct-full-ac-001-dolly-ichikara-004-001-single-oasst-oasst2-v2-0", "name": "LLM-jp-13B v2.0 Instruct full AC Dolly Ichikara OASST OASST2", "maker": "llm-jp", "family": "llm-jp/llm-jp-v2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-30", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of llm-jp-13b-v2.0. Llama-2 architecture, 40 layers, hidden 5120, 40 heads, context 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-13B v2.0 Instruct full AC Dolly Ichikara OASST OASST2 is a v2.0 instruction fine-tune of LLM-jp-13B, released with the 2024-04-30 v2.0 drop. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Same mix plus AnswerCarefully ac_001.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v2/llm-jp-13b-instruct-full-dolly-ichikara-004-001-single-oasst-oasst2-v2-0": { "id": "llm-jp/llm-jp-v2/llm-jp-13b-instruct-full-dolly-ichikara-004-001-single-oasst-oasst2-v2-0", "name": "LLM-jp-13B v2.0 Instruct full Dolly Ichikara OASST OASST2", "maker": "llm-jp", "family": "llm-jp/llm-jp-v2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-30", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of llm-jp-13b-v2.0. Llama-2 architecture, 40 layers, hidden 5120, 40 heads, context 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-13B v2.0 Instruct full Dolly Ichikara OASST OASST2 is a v2.0 instruction fine-tune of LLM-jp-13B, released with the 2024-04-30 v2.0 drop. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Full fine-tune named for Dolly, ichikara 004-001 single, OASST, and OASST2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-v2/llm-jp-13b-v2-0": { "id": "llm-jp/llm-jp-v2/llm-jp-13b-v2-0", "name": "LLM-jp-13B v2.0", "maker": "llm-jp", "family": "llm-jp/llm-jp-v2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-30", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Llama-2 architecture. 40 layers, hidden size 5120, 40 heads, context 4096. The model card says 256B tokens seen. The project paper lists a v2 corpus of 255B tokens and a 100k vocabulary (tokenizer v2.2). Both figures are kept.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-13B v2.0 is an early LLM-jp text model from the NII-led LLM-jp project. Pre-trained Llama-style 13B model. The paper's 255B corpus size and the card's 256B seen-tokens figure disagree by a small amount. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "v2.0 13B base. Replaces the GPT-2 v1.0 architecture with Llama 2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-vl/llm-jp-3-vila-14b": { "id": "llm-jp/llm-jp-vl/llm-jp-3-vila-14b", "name": "LLM-jp-3 VILA 14B", "maker": "llm-jp", "family": "llm-jp/llm-jp-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0, plus OpenAI terms of use because GPT-4 synthetic data was used", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "VILA-style vision-language model. Vision encoder google/siglip-so400m-patch14-384 (428M on the card), 2-layer MLP projector (32M), language model llm-jp-3-13b-instruct (13B). LLMC says the sum is about 14B. Three-stage training: projector, then projector plus LLM, then instruction stage.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-3 VILA 14B is NII LLMC's 2024 Japanese vision-language model, built on llm-jp-3-13b-instruct plus a SigLIP encoder. It was released 2024-11-20. Status: active. Collected 2026-10-15.", "what_it_is_for": "Image-and-text instruction model for Japanese and English. The card's example asks the model to describe an image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-vl/llm-jp-4-vl-9b": { "id": "llm-jp/llm-jp-vl/llm-jp-4-vl-9b", "name": "LLM-jp-4-VL 9B", "maker": "llm-jp", "family": "llm-jp/llm-jp-vl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-08-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "InternVL3-style stack: language model llm-jp-4-8b-thinking (8,590,200,832 parameters), vision encoder google/siglip2-so400m-patch16-512 (card: 0.4B), 2-layer MLP projector. Marketed as 9B. The final model can emit an analysis channel before the final answer. Chat template extends OpenAI Harmony with image_start and image_end.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-4-VL 9B is LLM-jp's current Apache-2.0 vision-language model, a thinking model on top of LLM-jp-4 8B. Hub createdAt is 2026-08-15. Status: active. Collected 2026-10-15.", "what_it_is_for": "Current 9B vision-language model. Initialized from llm-jp-4-8b-thinking and SigLIP2 So400m, trained in one stage for 120,000 steps with all parts trainable. Data mix is about 29.3M samples: Jagle 9.2M, RefinedVision 12.0M, a Nemotron image subset 5.0M, and llm-jp-4 thinking SFT 3.2M.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "llm-jp/llm-jp-vl/llm-jp-4-vl-9b-beta": { "id": "llm-jp/llm-jp-vl/llm-jp-4-vl-9b-beta", "name": "LLM-jp-4-VL 9B beta", "maker": "llm-jp", "family": "llm-jp/llm-jp-vl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "InternVL3-style stack: language model llm-jp-4-8b-instruct (8,590,200,832 parameters), vision encoder google/siglip2-so400m-patch16-512 (card: 0.4B), 2-layer MLP projector. Marketed as 9B. The final model can emit an analysis channel before the final answer. Chat template extends OpenAI Harmony with image_start and image_end.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLM-jp-4-VL 9B beta is the earlier 9B vision-language model from LLM-jp, built on llm-jp-4-8b-instruct. The Hub repo was created 2026-03-28. Status: active. License: Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Earlier vision-language checkpoint. The final card says beta was not trained on a commercially clean data mix and is weaker on text-only and hard chart tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmnt/aurora/aurora": { "id": "lmnt/aurora/aurora", "name": "Aurora", "maker": "lmnt", "family": "lmnt/aurora", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. No weights were released. The same account plans as Blizzard applied. A separate Aurora license was not published.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aurora is LMNT's earlier named speech model, API id `aurora`. A 2024-12-11 company post still called it the main model, beside the new English-only Blizzard preview. In May 2025 LMNT retired it as a separate system: Blizzard became the default on 2025-05-22, and the models guide said the aurora id routes every request to Blizzard. Weights were closed. No first-release date under the name Aurora was found. Collected 2026-10-15.", "what_it_is_for": "The v1 SDK treated Aurora as the default synthesizer: text and a voice id in, audio out, with optional word durations and a seed. LMNT's later changelog called the switch away from it a consolidation onto one production model. It was a speech API model, not a separately downloadable checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmnt/blizzard/blizzard": { "id": "lmnt/blizzard/blizzard", "name": "Blizzard", "maker": "lmnt", "family": "lmnt/blizzard", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. No weights were released. Pricing trackers say paid plans included a commercial license. The indexed pricing page said the playground was free if you credited LMNT when you shared work.", "release_date": "2024-12-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Blizzard is LMNT's text-to-speech model, API id `blizzard`. It entered the Playground and API as an English-only preview on 2024-12-12, became the default on 2025-05-22, and was last named as Blizzard 2.0 on 2026-05-07. Weights stayed closed. On 2026-10-15 the LMNT site and docs returned only a shutdown page, with no shutdown date, so this dossier marks the model retired. Collected 2026-10-15.", "what_it_is_for": "LMNT described Blizzard as the expressive default speech model: natural prosody, voice cloning, accent control, and realtime speech for agents. The Speech API takes finished text. Speech sessions take text as an LLM streams it and return audio as it is generated. The docs said it was for voiceover, localization, narration, ads, audiobooks, and voice agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/cleandift/cleandift": { "id": "lmu/cleandift/cleandift", "name": "CleanDIFT", "maker": "lmu", "family": "lmu/cleandift", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "UNet state dicts that replace the diffusers UNet of Stable Diffusion 1.5 or Stable Diffusion 2.1 so features can be read from clean images. The card says training fits on one GPU in 30 minutes. Released files are cleandift_sd15_unet.safetensors, cleandift_sd15_full.safetensors, cleandift_sd21_unet.safetensors, cleandift_sd21_full.safetensors, and cleandift_sd21_depth_probe.safetensors. The depth probe is a separate file, not the feature UNet.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CleanDIFT is a named weight release from LMU Munich. CompVis releases it to read diffusion features from clean images for correspondence, depth, segmentation, and classification, without choosing a noise timestep. Status on the collection date is active. Collected 2026-10-15.", "what_it_is_for": "CompVis releases it to read diffusion features from clean images for correspondence, depth, segmentation, and classification, without choosing a noise timestep.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/depth-fm/depth-fm": { "id": "lmu/depth-fm/depth-fm", "name": "DepthFM", "maker": "lmu", "family": "lmu/depth-fm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flow-matching monocular depth model. The README says it is initialized from Stable Diffusion v2-1 and maps an input image to a depth map instead of starting from noise. The published checkpoint file is depthfm-v1.ckpt. The example command uses num_steps 2 and ensemble_size 4. The inference note says ensembling currently supports batch size 1.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DepthFM is a named weight release from LMU Munich. CompVis uses it for fast monocular depth estimation, including depth inpainting and depth-conditional synthesis in the README description. The group byline on the repository is CompVis at LMU Munich. Status on the collection date is active. Collected 2026-10-15.", "what_it_is_for": "CompVis uses it for fast monocular depth estimation, including depth inpainting and depth-conditional synthesis in the README description. The group byline on the repository is CompVis at LMU Munich.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/dismo/dismo": { "id": "lmu/dismo/dismo", "name": "DisMo", "maker": "lmu", "family": "lmu/dismo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Motion extractor trained on clips of length 8. forward_sliding returns an embedding sequence of length num_frames - 4 because the longest prediction distance used in training is 4. Videos are shaped [B, T, H, W, C] in (-1, 1). The released extractor file is motion_extractor_large.pt. The class in the snippet is MotionExtractor_Large. The README says the setup was tested with PyTorch 2.7.1 and CUDA 12.6.", "modalities": { "input": [ "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DisMo is a named weight release from LMU Munich. The folder is the motion extractor: a content-invariant motion embedding for open-world motion transfer and analysis. The CogVideoX-5B-I2V LoRA in the same repo is an adaptation of another lab's video model and is not this checkpoint. Status on the collection date is active. Collected 2026-10-15.", "what_it_is_for": "The folder is the motion extractor: a content-invariant motion embedding for open-world motion transfer and analysis. The CogVideoX-5B-I2V LoRA in the same repo is an adaptation of another lab's video model and is not this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/fpt/fpt": { "id": "lmu/fpt/fpt", "name": "Flow Poke Transformer", "maker": "lmu", "family": "lmu/fpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer that predicts a distribution over local motion conditioned on sparse pokes. The Hub file list contains flow_poke_open_set_base.pt. The card's torch.hub call is torch.hub.load(\"CompVis/flow_poke_transformer\", \"fpt_base\"), with an underscore in the repo id. The GitHub URL uses CompVis/flow-poke-transformer, with a hyphen. The manual load snippet names fpt_base.pt. Those three names were not reconciled by opening the hubconf.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flow Poke Transformer is a named weight release from LMU Munich. CompVis uses it to predict multi-modal local motion from sparse interactions. The card names dense face motion, articulated object motion, and moving-part segmentation as evaluations. Authors are Stefan Andreas Baumann, Nick Stracke, Timy Phan, and Bj\u00f6rn Ommer. Status on the collection date is active. Collected 2026-10-15.", "what_it_is_for": "CompVis uses it to predict multi-modal local motion from sparse interactions. The card names dense face motion, articulated object motion, and moving-part segmentation as evaluations. Authors are Stefan Andreas Baumann, Nick Stracke, Timy Phan, and Bj\u00f6rn Ommer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/freud/freud": { "id": "lmu/freud/freud", "name": "FREUD", "maker": "lmu", "family": "lmu/freud", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Frame-wise Encoder, United Decoder. Rectified-flow first stage for precipitation nowcasting. The card says results used torch==2.5.1. Clean code lives in model/ and the paper checkpoints run from original_model/ under that torch pin. Hub files include freud_ckpt.pt and original_freud.pt. lsm_ckpt.pt and original_lsm.pt are in the same repo. The card calls LSM RaMViD latent-space forecasting and does not define it as a second product name.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FREUD is a named weight release from LMU Munich. CompVis uses it for probabilistic precipitation nowcasting. The generative decoder is there so compression uncertainty can be sampled. Authors are Johannes Schusterbauer, Jannik Wiese, Nick Stracke, Timy Phan, and Bj\u00f6rn Ommer. Status on the collection date is active. Collected 2026-10-15.", "what_it_is_for": "CompVis uses it for probabilistic precipitation nowcasting. The generative decoder is there so compression uncertainty can be sampled. Authors are Johannes Schusterbauer, Jannik Wiese, Nick Stracke, Timy Phan, and Bj\u00f6rn Ommer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/garfield/garfield": { "id": "lmu/garfield/garfield", "name": "GARFIELD", "maker": "lmu", "family": "lmu/garfield", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Goal Aware Representations of Future kInEmatic Latent Distributions. An encoder turns an image plus optional sparse constraints into per-element, per-timestep latents. Three decoders expose point samples, full trajectories, and probability heatmaps. Hub files are encoder.pt, point.pt, full.pt, and dense.pt. They are components of one model. Dataset tag on the card is nkp37/OpenVid-1M.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GARFIELD is a named weight release from LMU Munich. CompVis uses it to represent possible future motion as localized distributions and to sample trajectories or heatmaps. Authors are Timy Phan, Jannik Wiese, and Bj\u00f6rn Ommer. Status on the collection date is active. Collected 2026-10-15.", "what_it_is_for": "CompVis uses it to represent possible future motion as localized distributions and to sample trajectories or heatmaps. Authors are Timy Phan, Jannik Wiese, and Bj\u00f6rn Ommer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/glot500/glot500": { "id": "lmu/glot500/glot500", "name": "Glot500", "maker": "lmu", "family": "lmu/glot500", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-05-20", "context_length": 514, "max_output_tokens": null, "parameters": "394524667", "architecture": "XLM-RoBERTa masked language model continued from xlm-roberta-base. Hub config: 12 layers, hidden size 768, 12 heads, intermediate size 3072, GELU, vocab size 401145, max_position_embeddings 514, type vocab size 1. The Hub safetensors total is 394524667 parameters. The ACL abstract calls the release Glot500-m and says it is continued pretraining, not training from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Glot500 is the CIS group at LMU Munich's multilingual masked language model. The ACL 2023 paper calls the checkpoint Glot500-m and says it is continued pretraining of an XLM-R baseline on Glot500-c, a corpus the abstract counts at 511 languages. The only Hub weight is `cis-lmu/glot500-base`, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Masked language modeling and multilingual representations for a much wider language set than XLM-R base. The card shows a fill-mask pipeline. It is not an instruction-tuned chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/glotlid/glotlid": { "id": "lmu/glotlid/glotlid", "name": "GlotLID", "maker": "lmu", "family": "lmu/glotlid", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "fastText supervised language-identification classifier. The current card says model.bin is always the latest version. Labels are ISO 639-3 codes plus script, for example eng_Latn. V3 also has zxx and und series labels.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GlotLID is the Center for Information and Language Processing at LMU Munich's fastText language identifier. The EMNLP 2023 paper releases GlotLID-M. The Hub repo `cis-lmu/glotlid` still hosts v1, v2, and v3 under Apache-2.0, and `model.bin` tracks the latest file. Collected 2026-10-15.", "what_it_is_for": "Identify the language of a sentence, including low-resource languages. Labels are ISO 639-3 plus script. The current README says V3 supports 2102 labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/ldm/ldm-celebahq-256": { "id": "lmu/ldm/ldm-celebahq-256", "name": "LDM CelebA-HQ 256", "maker": "lmu", "family": "lmu/ldm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2021-12-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unconditional latent diffusion. Diffusers UNet2DModel, SiLU, attention head dim 32, block channels 224/448/672/896, two layers per block, latent sample size 64, 3 channels in and out. VQ-VAE with 8192 codebook entries, 3 latent channels, image sample size 256. The latent-diffusion model zoo names this CelebA-HQ row LDM-VQ-4.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LDM CelebA-HQ 256 is the CompVis unconditional latent diffusion checkpoint for 256-pixel CelebA-HQ faces. CompVis is the Computer Vision and Learning group at LMU Munich. The CVPR 2022 paper lists LMU Munich and Heidelberg IWR. Weights are on Hugging Face under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Unconditional face synthesis. The Diffusers card loads `CompVis/ldm-celebahq-256` and samples with DDIM. The latent-diffusion model zoo calls the matching row LDM-VQ-4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/ldm/ldm-super-resolution-4x-openimages": { "id": "lmu/ldm/ldm-super-resolution-4x-openimages", "name": "LDM Super-Resolution 4x OpenImages", "maker": "lmu", "family": "lmu/ldm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2021-12-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "4x latent super-resolution. Diffusers UNet2DModel, SiLU, attention head dim 32, block channels 160/320/320/640, latent sample size 64, 6 input channels and 3 output channels. VQ-VAE with 8192 embeddings, 3 latent channels, image sample size 256. The model zoo row is OpenImages super-resolution, LDM-VQ-4, BSR degradation. fp16 copies of the UNet and VQ-VAE are in the same repo.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LDM Super-Resolution 4x OpenImages is CompVis at LMU Munich's latent diffusion upscaler from the CVPR 2022 LDM paper. The Diffusers card is `CompVis/ldm-super-resolution-4x-openimages`, Apache-2.0. The model zoo calls the row OpenImages super-resolution, LDM-VQ-4, with BSR degradation. Collected 2026-10-15.", "what_it_is_for": "4x super-resolution. The card loads `LDMSuperResolutionPipeline`, resizes an input to 128x128, and samples 100 steps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/ldm/ldm-text2im-large-256": { "id": "lmu/ldm/ldm-text2im-large-256", "name": "LDM Text-to-Image Large 256", "maker": "lmu", "family": "lmu/ldm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2021-12-20", "context_length": 77, "max_output_tokens": null, "parameters": "1.45B", "architecture": "Text-conditional latent diffusion. The CVPR paper table lists the LAION text-to-image model at 1.45B parameters, f=8, latent shape 32x32x4, 1000 diffusion steps, cross-attention, 32-layer transformer conditioner with embedding dimension 1280. The Diffusers port matches that shape: UNet2DConditionModel, SiLU, block channels 320/640/1280/1280, latent sample size 32, 4 channels, cross-attention. KL autoencoder (AutoencoderKL), 4 latent channels, image size 256. Conditioner is LDMBertModel, 32 layers, d_model 1280, 8 heads, FFN 5120, vocab 30522, max position embeddings 77. BERT tokenizer. The GitHub README calls this the 1.45B LAION-400M model.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LDM Text-to-Image Large 256 is CompVis at LMU Munich's 1.45B latent diffusion text-to-image model, trained on LAION-400M and released with the CVPR 2022 LDM paper. The Diffusers port is `CompVis/ldm-text2im-large-256` under Apache-2.0. Stable Diffusion, the later product line, is cataloged under Stability AI. Collected 2026-10-15.", "what_it_is_for": "Text-to-image synthesis at 256 pixels. The GitHub README points at a 5.7GB checkpoint and a sampling script. The card example prompt is a painting of a squirrel eating a burger.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/mask/mask": { "id": "lmu/mask/mask", "name": "MASK", "maker": "lmu", "family": "lmu/mask", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Discrete interpolant generators. Two official checkpoints are on CompVis/discrete_interpolants: coco14_uvit_s2deep_it_1600000.pt and in256_ditxl2_it_1220000.pt. Dataset tags are ILSVRC/imagenet-1k and taohu/coco14. The iteration counts are in the filenames. Architecture widths were not read from a config.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MASK is a named weight release from LMU Munich. CompVis releases the official checkpoints for the discrete-interpolant image model in the paper [MASK] is All You Need. Authors on the card are Vincent Tao Hu and Bj\u00f6rn Ommer. Status on the collection date is active. Collected 2026-10-15.", "what_it_is_for": "CompVis releases the official checkpoints for the discrete-interpolant image model in the paper [MASK] is All You Need. Authors on the card are Vincent Tao Hu and Bj\u00f6rn Ommer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/myriad/myriad": { "id": "lmu/myriad/myriad", "name": "MYRIAD", "maker": "lmu", "family": "lmu/myriad", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive diffusion over sparse point trajectories. The card expands the name as Motion hYpothesis Reasoning via Iterative Autoregressive Diffusion. Two checkpoints ship: myriad_openset.pt and myriad_billiard.pt. torch.hub repo id is CompVis/myriad. The card says to copy model.py and dinov3.py from CompVis/flow-poke-transformer. The card's GitHub badge also points at CompVis/flow-poke-transformer.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MYRIAD is a named weight release from LMU Munich. CompVis uses it to roll out many future sparse trajectories from one image, including billiards planning by search. Authors are Stefan Andreas Baumann, Jannik Wiese, Tommaso Martorella, Mahdi M. Kalayeh, and Bj\u00f6rn Ommer. Status on the collection date is active. Collected 2026-10-15.", "what_it_is_for": "CompVis uses it to roll out many future sparse trajectories from one image, including billiards planning by search. Authors are Stefan Andreas Baumann, Jannik Wiese, Tommaso Martorella, Mahdi M. Kalayeh, and Bj\u00f6rn Ommer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/rayder/rayder": { "id": "lmu/rayder/rayder", "name": "RayDer", "maker": "lmu", "family": "lmu/rayder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "compvis-research-license", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One transformer for camera estimation and novel view synthesis. Card table: RayDer-L and RayDer-L-576, both width 1024, depth 24, printed as ~743M, resolutions 256\u00b2 and 576\u00b2, files rayder_l.pt and rayder_l_576.pt. The tilde is why parameters is null. Images are channels-last (b, t, h, w, 3) in [-1, 1]. Cameras are camera-to-world. Focal length f = f_pixels / min(h - 1, w - 1). predict_cameras was trained for 8 views. predict_views was trained for 1 to 7 inputs. The card says scaling fits have R\u00b2 > 0.99 and does not name the fitted split in that sentence.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RayDer is a named weight release from LMU Munich. CompVis and LMU release it for self-supervised novel view synthesis from ordinary video, including videos with moving objects. Authors are Ulrich Prestel, Stefan Andreas Baumann, Nick Stracke, and Bj\u00f6rn Ommer. Status on the collection date is active. Collected 2026-10-15.", "what_it_is_for": "CompVis and LMU release it for self-supervised novel view synthesis from ordinary video, including videos with moving objects. Authors are Ulrich Prestel, Stefan Andreas Baumann, Nick Stracke, and Bj\u00f6rn Ommer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/scflow/catfm": { "id": "lmu/scflow/catfm", "name": "CAtFM", "maker": "lmu", "family": "lmu/scflow", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Contrastive-augmented flow matching on top of the SCFlow pipeline. The README says it adds contrastive regularization on style and content embeddings and multiple endpoint-prediction objectives. The weight file is catfm_last.ckpt. Inference uses the same script with --model_type catfm. The Hub card's license field is MIT for the shared CompVis/SCFlow repo. The follow-up paper does not restate a separate license.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAtFM is a named weight release from LMU Munich. CompVis releases it as the named follow-up to SCFlow for style-content disentanglement. Authors on the citation block are Yusong Li, Pingchuan Ma, Ming Gui, Vincent Tao Hu, and Bj\u00f6rn Ommer. Status on the collection date is active. Collected 2026-10-15.", "what_it_is_for": "CompVis releases it as the named follow-up to SCFlow for style-content disentanglement. Authors on the citation block are Yusong Li, Pingchuan Ma, Ming Gui, Vincent Tao Hu, and Bj\u00f6rn Ommer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/scflow/scflow": { "id": "lmu/scflow/scflow", "name": "SCFlow", "maker": "lmu", "family": "lmu/scflow", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flow-matching model that maps between a style-content mixture and separated style and content. The released weight file is scflow_last.ckpt. The GitHub news line dated 08.2025 says inference code and pretrained checkpoints were released then. Default training is described as needing about 22 GB. The checkpoint file's parameter count is not printed.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SCFlow is a named weight release from LMU Munich. CompVis uses it to combine or separate style and content. The repository byline is CompVis at LMU Munich and MCML. Pingchuan Ma's contact on the card is p.ma@lmu.de. Status on the collection date is active. Collected 2026-10-15.", "what_it_is_for": "CompVis uses it to combine or separate style and content. The repository byline is CompVis at LMU Munich and MCML. Pingchuan Ma's contact on the card is p.ma@lmu.de.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/vicis/vicis": { "id": "lmu/vicis/vicis", "name": "VICIS", "maker": "lmu", "family": "lmu/vicis", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Set model that reads four context images and one query at 256\u00d7256. DINOv2 ViT-L/14 with registers. A 24-layer width-1024 set transformer emits four 256-d concept directions. Query projection stays in FP32. A 24-layer width-1024 latent rectified-flow transformer generates the image. Tiny Autoencoder latents are 4\u00d732\u00d732. Default cfg_scale is 3 and sample_steps is 50. model.pt is an 898-entry inference state dict. A total parameter count is not printed. Trained on the extended_v1 ImageNet/WordNet hierarchy.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VICIS is a named weight release from LMU Munich. CompVis uses it to apply a visual concept defined by example images to a query image, without a text prompt. Authors are Nick Stracke, Kolja Bauer, Stefan Andreas Baumann, Miguel Angel Bautista, Josh Susskind, and Bj\u00f6rn Ommer. ECCV 2026. Status on the collection date is active. Collected 2026-10-15.", "what_it_is_for": "CompVis uses it to apply a visual concept defined by example images to a query image, without a text prompt. Authors are Nick Stracke, Kolja Bauer, Stefan Andreas Baumann, Miguel Angel Bautista, Josh Susskind, and Bj\u00f6rn Ommer. ECCV 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/what-moves/what-moves": { "id": "lmu/what-moves/what-moves", "name": "WhatMoves", "maker": "lmu", "family": "lmu/what-moves", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT for original WhatMoves parameters; DINOv2 initialization under Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Motion encoder checkpoint what_moves/gated_static_step600000.pt. The card calls it a compact inference checkpoint. Original parameters are MIT. The checkpoint includes parameters initialized from DINOv2, which is Apache-2.0. Step 600000 is the filename, not a separately verified training-step count from a log.", "modalities": { "input": [ "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WhatMoves is a named weight release from LMU Munich. The folder is the WhatMoves motion encoder for localized motion representations. The Wan2.2 I2V-A14B projector and LoRA in the same repo adapt another lab's video model and are not a version of this encoder. Status on the collection date is active. Collected 2026-10-15.", "what_it_is_for": "The folder is the WhatMoves motion encoder for localized motion representations. The Wan2.2 I2V-A14B projector and LoRA in the same repo adapt another lab's video model and are not a version of this encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lmu/zipmo/zipmo": { "id": "lmu/zipmo/zipmo", "name": "ZipMo", "maker": "lmu", "family": "lmu/zipmo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Long-horizon motion model on tracker-derived embeddings, with spatial-poke conditioning and LIBERO task conditioning. torch.hub repo CompVis/long-term-motion. Hub files: zipmo_planner_sparse.safetensors, zipmo_planner_dense.safetensors, zipmo_vae.safetensors, zipmo_planner_libero_atm.safetensors, zipmo_planner_libero_tramoe.safetensors, and policy_heads/atm_libero.safetensors plus tramoe_libero_10, tramoe_libero_goal, tramoe_libero_object, and tramoe_libero_spatial.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZipMo is a named weight release from LMU Munich. CompVis uses it to generate long motion in a learned embedding space and to drive LIBERO policies. Authors are Nick Stracke, Kolja Bauer, Stefan Andreas Baumann, Miguel Angel Bautista, Josh Susskind, and Bj\u00f6rn Ommer. Status on the collection date is active. Collected 2026-10-15.", "what_it_is_for": "CompVis uses it to generate long motion in a learned embedding space and to drive LIBERO policies. Authors are Nick Stracke, Kolja Bauer, Stefan Andreas Baumann, Miguel Angel Bautista, Josh Susskind, and Bj\u00f6rn Ommer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "loughborough/fctsfn/fctsfn": { "id": "loughborough/fctsfn/fctsfn", "name": "FCTSFN", "maker": "loughborough", "family": "loughborough/fctsfn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub LICENSE file is the BSD 3-Clause License, Copyright (c) 2020, cyh4. GitHub reports SPDX BSD-3-Clause. A separate licence for the Google Drive weight archives is not stated.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fully convolutional interactive segmenter. TSLFN has an image stream and an interaction stream, each the first 10 convolutional layers of VGG16 with ReLU and 2x2 stride-2 max-pooling, then concatenation and a fusion net. Fusion net: VGG16 conv5_1, conv5_2, conv5_3, then the VGG16 fully connected layers converted as in FCN, then bilinear deconvolution. The paper's chosen depth is TSLFN_4. The stride-8 TSLFN, fine-tuned from stride-32 then stride-16, is the form used with MSRN. MSRN fuses six scales (concatenated features before each pooling of the two streams, fusion-net conv5_3, and upscaled scores). Non-score scales pass a 1x1x60 bottleneck, are upscaled, summed, concatenated with TSLFN scores, then six refining convolutions sized 7x7x64, 5x5x64, 3x3x64, 3x3x64, 3x3x64, and 1x1x2. Dropout 0.5 follows the refining convolutions except the classifier. Stage 1 fine-tunes TSLFN from ImageNet VGG16 with pixel-wise softmax. Stage 2 fixes TSLFN and trains MSRN from scratch (He et al. 2015 init). MSRN training resizes images to 240x320 (height x width), batch size 3, initial learning rate 1e-8, weight decay 0.0005, momentum 0.99. Inputs are an RGB image plus positive and negative Euclidean distance maps from clicks, truncated at 255. Post-processing is a graph cut similar to Xu et al. Code stack stated on the README: Caffe, Python 2.7, Ubuntu 16.04.", "modalities": { "input": [ "image", "interaction-map" ], "output": [ "segmentation-mask" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FCTSFN (Fully Convolutional Two-Stream Fusion Network) is an interactive image-segmentation model from the Department of Computer Science at Loughborough University. Yang Hu, Andrea Soltoggio, and Russell Lock are at Loughborough (y.hu@lboro.ac.uk, a.soltoggio@lboro.ac.uk, r.lock@lboro.ac.uk). Steve Carter is at The ICE Agency. Neural Networks, volume 109, January 2019, pages 31\u201342. The Loughborough repository lists acceptance 9 October 2018, publication 21 October 2018, and copyright 2019. Author Caffe weights were still linked from https://github.com/cyh4/FCTSFN on 2026-10-15. The weight-file upload date is unpublished.", "what_it_is_for": "The paper presents FCTSFN for interactive binary segmentation: the user marks foreground and background with clicks, and the network predicts the object. Two sub-networks do that job. TSLFN predicts the foreground at reduced resolution from an image stream and an interaction stream. MSRN refines that prediction at full resolution by fusing features at several scales. The authors' stated aim is a more direct effect of the clicks than early fusion of clicks with the image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lsu/paine/naive-paine": { "id": "lsu/paine/naive-paine", "name": "Na\u00efve PAINE", "maker": "lsu", "family": "lsu/paine", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-03-12", "context_length": null, "max_output_tokens": null, "parameters": "49920000", "architecture": "Plug-in predictor, not a diffusion denoiser. The paper describes three modules: a prompt encoder (one transformer layer plus a learnable token, one encoder per host text encoder), a 4-stage ResNet noise encoder with adaptive max pooling, and an MLP score head on the concatenated vectors. It estimates a human-preference scalar from the host diffusion model's prompt embedding and initial noise, then forwards the top-ranked noises to that unchanged diffusion model. Table 3 prints 49.92M parameters for the Hunyuan-DiT predictor and for the shared SDXL/DreamShaper predictor. PixArt-Sigma and SANA-Sprint parameter counts are not in that table. FLOPs in the same table are 9.57G (Hunyuan) and 6.57G (SDXL/DreamShaper).", "modalities": { "input": [ "text", "latent" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Na\u00efve PAINE is a small open predictor from the ATHENA lab in Louisiana State University's Division of Computer Science and Engineering. It ranks initial noise tensors for an existing text-to-image diffusion model and can also score how hard a prompt is for that host model. Weights and an MIT license were public on the collection date 2026-10-15. It is not a standalone image generator and it is not a fine-tune of SDXL or the other hosts.", "what_it_is_for": "The paper says the predictor estimates a human-preference score from the prompt embedding and the initial noise, samples many noises, and sends only the top-ranked ones through the host diffusion model. Masking the noise encoder is how it estimates the mean score for a prompt. The Hub card's first paragraph instead describes an NPNet that turns noise into golden noise. That wording matches the Golden Noise baseline the paper compares against, not the method in the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "luma/genie/genie": { "id": "luma/genie/genie", "name": "Genie 1.0", "maker": "luma", "family": "luma/genie", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-01-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Genie 1.0 is Luma's text-to-3D model, announced on 9 January 2024 with the Series B post. It generates a textured quad mesh from a prompt in under 10 seconds, in standard 3D formats. It is not on Luma's 2026 model list. No sunset notice was found, so the status is deprecated with medium confidence rather than retired. Collected on 2026-10-15.", "what_it_is_for": "Fast 3D objects for imagination and downstream tools: quad meshes, materials, and a polygon count the user can vary, exported in standard formats. Launch surfaces were the web app, the Luma iOS app, and Discord.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "luma/photon/photon": { "id": "luma/photon/photon", "name": "Photon", "maker": "luma", "family": "luma/photon", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Luma's CEO described Photon to VentureBeat as an advanced universal transformer, not a diffusion model. That is a reported quote, not a model card.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.015 }, "summary_intro": "Photon is Luma's text-to-image model launched on 25 November 2024 with the rebuilt Dream Machine, API id `photon-1`. The API changelog in early December 2024 priced a 1080p image at $0.015. Luma's 13 July 2026 assistant page says Photon no longer exists as a separate product and names UNI-1.1 as the current image model. Collected on 2026-10-15.", "what_it_is_for": "High-quality stills for creatives, with character reference, style reference, and more than one reference image. Luma sold it as personalizable and strong at text in images. Photon Flash is the cheaper sibling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "luma/photon/photon-flash": { "id": "luma/photon/photon-flash", "name": "Photon Flash", "maker": "luma", "family": "luma/photon", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described alongside Photon. A secondary model card calls Flash the same universal-transformer design as Photon. Luma's own changelog does not print the architecture.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.002 }, "summary_intro": "Photon Flash is the fast, low-cost image model paired with Photon. The API id is `photon-flash-1`. Luma's December 2024 changelog priced it at $0.002 per 1080p image. It is retired as a separate product together with Photon. The current image models are Uni-1 and Uni-1 Max. Collected on 2026-10-15.", "what_it_is_for": "The same reference-guided image jobs as Photon, sold on speed and price. Luma did not publish a quality gap versus Photon beyond calling Flash the efficient option.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "luma/ray/ray-1": { "id": "luma/ray/ray-1", "name": "Ray 1", "maker": "luma", "family": "luma/ray", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ray 1 is Luma's first public video model, released under the Dream Machine name in June 2024 and later referred to as Ray1. Luma's July 2026 assistant page calls Dream Machine a deprecated model replaced by Ray. This dossier was collected on 2026-10-15.", "what_it_is_for": "Dream Machine launched as a video model that turned text, and later still images, into short clips. Luma's Ray2 changelog treats Ray1 as the previous model and says Ray2 used 10x the compute and was trained directly on video. A later Dream Machine credit article still prices a line it calls Ray1.6.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "luma/ray/ray-2": { "id": "luma/ray/ray-2", "name": "Ray 2", "maker": "luma", "family": "luma/ray", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "multi-modal architecture; trained directly on video. Luma said Ray3 is more than twice the size of Ray2, without giving a parameter count.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ray 2 is Luma's video model released on 15 January 2025. Luma described it as a large multi-modal video model with 10x the compute of Ray 1, trained directly on video. The July 2026 assistant page calls Ray 2 deprecated. The legacy Dream Machine API docs still accept the model parameter `ray-2`. Collected on 2026-10-15.", "what_it_is_for": "Text-to-video and, after launch, image-to-video, keyframes, extend, loop, camera-motion concepts, style reference, upscale, reframe, and modify-video. At launch it was for paid Dream Machine subscribers on web and iOS, with API access. Modify Video, launched 4 June 2025, was documented as running on Ray 2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "luma/ray/ray-2-flash": { "id": "luma/ray/ray-2-flash", "name": "Ray 2 Flash", "maker": "luma", "family": "luma/ray", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ray 2 Flash is Luma's faster, cheaper video model in the Ray 2 line. Luma announced it on 7 March 2025 as a new model for all Dream Machine subscribers, and said the API (`ray-flash-2`) was available on 17 March 2025. It is documented on the legacy Dream Machine API, which now points readers to the Agents API. Collected on 2026-10-15.", "what_it_is_for": "The same production jobs as Ray 2: text-to-video, image-to-video with keyframes, upscale, audio, and modify, at lower latency and price. Luma's claim at launch was 3x faster and 3x cheaper than Ray 2. That is a vendor claim, not a measured third-party result.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "luma/ray/ray-3": { "id": "luma/ray/ray-3", "name": "Ray 3", "maker": "luma", "family": "luma/ray", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described on the gated whitepaper waitlist as a video generation transformer. The launch post says it reasons by generating text and visual tokens and is more than twice the size of Ray 2. No parameter count.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ray 3 is Luma's reasoning video model, announced on 18 September 2025 and shipped in Dream Machine and Adobe Firefly. Luma called it the first video model that reasons in concepts and visuals and the first to write native 10-, 12-, and 16-bit HDR in ACES2065-1 EXR. The July 2026 assistant page lists it as a previous Ray 3 version. The current flagship is Ray 3.2. Collected on 2026-10-15.", "what_it_is_for": "Text-to-video and image-to-video for film, ads, and games, with draft previews, keyframe interpolation, extend, and loop. HDR EXR output is aimed at color and VFX pipelines. At launch, modify and reframe still ran on Ray 2. Those moved later, under Ray3 Modify and then Ray 3.2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "luma/ray/ray-3-14": { "id": "luma/ray/ray-3-14", "name": "Ray 3.14", "maker": "luma", "family": "luma/ray", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-01-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ray 3.14 is Luma's 26 January 2026 update to Ray 3. Luma called it the most professional model at the time: native 1080p, 4x faster and 3x cheaper at 720p than Ray 3, with steadier modify-video. The Luma app roster still listed Ray3.14 beside Ray 3.2 on 2026-10-15. The July 2026 assistant page instead groups it with previous Ray 3 versions. Status is active with medium confidence because those two official pages disagree. Collected on 2026-10-15.", "what_it_is_for": "Text-to-video, image-to-video, and modify-video inside Dream Machine. The point of the update was production 1080p without the early-access limit Ray 3 had at launch, plus lower cost and better consistency when modifying a clip.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "luma/ray/ray-3-2": { "id": "luma/ray/ray-3-2", "name": "Ray 3.2", "maker": "luma", "family": "luma/ray", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ray 3.2 is Luma's current video model, released on 9 June 2026. It is the video model on the Luma Agents API under the id `ray-3.2`, and the video model Luma tells assistants to name. It covers text-to-video, image-to-video, modify-video, and reframe, with HDR and EXR for finishing. Collected on 2026-10-15.", "what_it_is_for": "Luma describes it as a director's control surface: place keyframes through a clip, keep an actor's performance while changing wardrobe or world, reframe a shot, and export HDR or EXR for color and VFX. The June 2026 post says this is the first time that full control surface is on an API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "luma/ray/ray-3-modify": { "id": "luma/ray/ray-3-modify", "name": "Ray3 Modify", "maker": "luma", "family": "luma/ray", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ray3 Modify is Luma's December 2025 video-to-video model for editing footage while keeping performance. The news post is dated 18 December 2025. The learning-hub FAQ labels the model field \"Ray3 Modify\". Luma's July 2026 assistant page lists it among previous Ray 3 versions. Ray 3.2 later shipped Modify Video as part of the current model. Collected on 2026-10-15.", "what_it_is_for": "Restyle or replace wardrobe, environment, lighting, and products in an existing clip, or lock a character from a reference image onto the original performance. Start and end frames can steer transitions. Luma positioned it for hybrid shoots: capture a performance, then change the world around it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "luma/uni/uni-1": { "id": "luma/uni/uni-1", "name": "Uni-1", "maker": "luma", "family": "luma/uni", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only autoregressive transformer. Text and images share one interleaved sequence as both input and output. The model can reason internally before and during image synthesis.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.0404 }, "summary_intro": "Uni-1 is Luma's unified image model: a decoder-only transformer that reasons over text and images in one sequence and then generates or edits pixels. It was in the product by 22 March 2026. The 5 May 2026 API was branded Uni-1.1, while the Agents API model id collected on 2026-10-15 is `uni-1`. Luma's assistant page calls the current image model UNI-1.1. This folder treats those names as one shipping model and records the naming gap.", "what_it_is_for": "Text-to-image, reference-guided generation, and natural-language edits. Luma's pitch is that the model plans the image before it renders: layout, constraints, and references, including brand and character references. The product page stresses culture-aware styles, readable text, and spatial reasoning. Luma Scenes, announced 11 August 2026, uses Uni-1 to build storyboard keyframes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "luma/uni/uni-1-max": { "id": "luma/uni/uni-1-max", "name": "Uni-1 Max", "maker": "luma", "family": "luma/uni", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Same wire format and parameter set as uni-1. Luma says the difference is higher output quality. The shared architecture write-up is the Uni-1 decoder-only autoregressive transformer.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.1 }, "summary_intro": "Uni-1 Max is the higher-quality image tier of Uni-1. The Agents API id is `uni-1-max`. Luma's 5 May 2026 post introduced it as Uni-1.1 Max, beside Uni-1.1. It uses the same request fields as `uni-1` and costs more. Collected on 2026-10-15.", "what_it_is_for": "The same jobs as Uni-1: text-to-image, reference-guided generation, and natural-language edits, when the caller wants the higher-quality output. Luma says it is a drop-in replacement. Pass `\"model\": \"uni-1-max\"`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lund/easyner/biobert-bc5cdr-disease-v1": { "id": "lund/easyner/biobert-bc5cdr-disease-v1", "name": "BioBERT BC5CDR disease v1", "maker": "lund", "family": "lund/easyner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-18", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base token classifier (BertForTokenClassification): 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 28996, max position embeddings 512. The label set is the binary IOB tags B-bio, I-bio, and O. Taken from the published config.json. The EasyNER paper says every checkpoint is a fine-tune of BioBERT base cased v1.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lund University's Cell Death, Lysosomes and Artificial Intelligence group (Aits lab) released this checkpoint as part of EasyNER, a biomedical named-entity pipeline. It is a BioBERT-base token classifier for diseases. The weights are public on Hugging Face and were still online on the collection date 2026-10-15. Status here is active.", "what_it_is_for": "The EasyNER paper and the Aits lab model card say the checkpoint tags one biomedical entity class in medical and life-science text. The pipeline loads it from `aitslab/biobert_bc5cdr_disease_v1` for local NER over PubMed, CORD-19, or plain text. It is not a chat model and it is not on a hosted inference API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lund/easyner/biobert-bc5cdr-disease-v2": { "id": "lund/easyner/biobert-bc5cdr-disease-v2", "name": "BioBERT BC5CDR disease v2", "maker": "lund", "family": "lund/easyner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-13", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base token classifier (BertForTokenClassification): 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 28996, max position embeddings 512. The label set is the binary IOB tags B-bio, I-bio, and O. Taken from the published config.json. The EasyNER paper says every checkpoint is a fine-tune of BioBERT base cased v1.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lund University's Cell Death, Lysosomes and Artificial Intelligence group (Aits lab) released this checkpoint as part of EasyNER, a biomedical named-entity pipeline. It is a BioBERT-base token classifier for diseases. The weights are public on Hugging Face and were still online on the collection date 2026-10-15. Status here is active.", "what_it_is_for": "The EasyNER paper and the Aits lab model card say the checkpoint tags one biomedical entity class in medical and life-science text. The pipeline loads it from `aitslab/biobert_bc5cdr_disease_v2` for local NER over PubMed, CORD-19, or plain text. It is not a chat model and it is not on a hosted inference API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lund/easyner/biobert-huner-cell-v1": { "id": "lund/easyner/biobert-huner-cell-v1", "name": "BioBERT HUNER cell v1", "maker": "lund", "family": "lund/easyner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base token classifier (BertForTokenClassification): 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 28996, max position embeddings 512. The label set is the binary IOB tags B-bio, I-bio, and O. Taken from the published config.json. The EasyNER paper says every checkpoint is a fine-tune of BioBERT base cased v1.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lund University's Cell Death, Lysosomes and Artificial Intelligence group (Aits lab) released this checkpoint as part of EasyNER, a biomedical named-entity pipeline. It is a BioBERT-base token classifier for cell lines and cell terms. The weights are public on Hugging Face and were still online on the collection date 2026-10-15. Status here is active.", "what_it_is_for": "The EasyNER paper and the Aits lab model card say the checkpoint tags one biomedical entity class in medical and life-science text. The pipeline loads it from `aitslab/biobert_huner_cell_v1` for local NER over PubMed, CORD-19, or plain text. It is not a chat model and it is not on a hosted inference API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lund/easyner/biobert-huner-cell-v2": { "id": "lund/easyner/biobert-huner-cell-v2", "name": "BioBERT HUNER cell v2", "maker": "lund", "family": "lund/easyner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-03", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base token classifier (BertForTokenClassification): 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 28996, max position embeddings 512. The label set is the binary IOB tags B-bio, I-bio, and O. Taken from the published config.json. The EasyNER paper says every checkpoint is a fine-tune of BioBERT base cased v1.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lund University's Cell Death, Lysosomes and Artificial Intelligence group (Aits lab) released this checkpoint as part of EasyNER, a biomedical named-entity pipeline. It is a BioBERT-base token classifier for cell lines and cell terms. The weights are public on Hugging Face and were still online on the collection date 2026-10-15. Status here is active.", "what_it_is_for": "The EasyNER paper and the Aits lab model card say the checkpoint tags one biomedical entity class in medical and life-science text. The pipeline loads it from `aitslab/biobert_huner_cell_v2` for local NER over PubMed, CORD-19, or plain text. It is not a chat model and it is not on a hosted inference API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lund/easyner/biobert-huner-chemical-v1": { "id": "lund/easyner/biobert-huner-chemical-v1", "name": "BioBERT HUNER chemical v1", "maker": "lund", "family": "lund/easyner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base token classifier (BertForTokenClassification): 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 28996, max position embeddings 512. The label set is the binary IOB tags B-bio, I-bio, and O. Taken from the published config.json. The EasyNER paper says every checkpoint is a fine-tune of BioBERT base cased v1.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lund University's Cell Death, Lysosomes and Artificial Intelligence group (Aits lab) released this checkpoint as part of EasyNER, a biomedical named-entity pipeline. It is a BioBERT-base token classifier for chemicals, including therapeutic drugs. The weights are public on Hugging Face and were still online on the collection date 2026-10-15. Status here is active.", "what_it_is_for": "The EasyNER paper and the Aits lab model card say the checkpoint tags one biomedical entity class in medical and life-science text. The pipeline loads it from `aitslab/biobert_huner_chemical_v1` for local NER over PubMed, CORD-19, or plain text. It is not a chat model and it is not on a hosted inference API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lund/easyner/biobert-huner-chemical-v2": { "id": "lund/easyner/biobert-huner-chemical-v2", "name": "BioBERT HUNER chemical v2", "maker": "lund", "family": "lund/easyner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-03", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base token classifier (BertForTokenClassification): 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 28996, max position embeddings 512. The label set is the binary IOB tags B-bio, I-bio, and O. Taken from the published config.json. The EasyNER paper says every checkpoint is a fine-tune of BioBERT base cased v1.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lund University's Cell Death, Lysosomes and Artificial Intelligence group (Aits lab) released this checkpoint as part of EasyNER, a biomedical named-entity pipeline. It is a BioBERT-base token classifier for chemicals, including therapeutic drugs. The weights are public on Hugging Face and were still online on the collection date 2026-10-15. Status here is active.", "what_it_is_for": "The EasyNER paper and the Aits lab model card say the checkpoint tags one biomedical entity class in medical and life-science text. The pipeline loads it from `aitslab/biobert_huner_chemical_v2` for local NER over PubMed, CORD-19, or plain text. It is not a chat model and it is not on a hosted inference API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lund/easyner/biobert-huner-disease-v1": { "id": "lund/easyner/biobert-huner-disease-v1", "name": "BioBERT HUNER disease v1", "maker": "lund", "family": "lund/easyner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base token classifier (BertForTokenClassification): 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 28996, max position embeddings 512. The label set is the binary IOB tags B-bio, I-bio, and O. Taken from the published config.json. The EasyNER paper says every checkpoint is a fine-tune of BioBERT base cased v1.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lund University's Cell Death, Lysosomes and Artificial Intelligence group (Aits lab) released this checkpoint as part of EasyNER, a biomedical named-entity pipeline. It is a BioBERT-base token classifier for diseases. The weights are public on Hugging Face and were still online on the collection date 2026-10-15. Status here is active.", "what_it_is_for": "The EasyNER paper and the Aits lab model card say the checkpoint tags one biomedical entity class in medical and life-science text. The pipeline loads it from `aitslab/biobert_huner_disease_v1` for local NER over PubMed, CORD-19, or plain text. It is not a chat model and it is not on a hosted inference API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lund/easyner/biobert-huner-disease-v2": { "id": "lund/easyner/biobert-huner-disease-v2", "name": "BioBERT HUNER disease v2", "maker": "lund", "family": "lund/easyner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-03", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base token classifier (BertForTokenClassification): 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 28996, max position embeddings 512. The label set is the binary IOB tags B-bio, I-bio, and O. Taken from the published config.json. The EasyNER paper says every checkpoint is a fine-tune of BioBERT base cased v1.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lund University's Cell Death, Lysosomes and Artificial Intelligence group (Aits lab) released this checkpoint as part of EasyNER, a biomedical named-entity pipeline. It is a BioBERT-base token classifier for diseases. The weights are public on Hugging Face and were still online on the collection date 2026-10-15. Status here is active.", "what_it_is_for": "The EasyNER paper and the Aits lab model card say the checkpoint tags one biomedical entity class in medical and life-science text. The pipeline loads it from `aitslab/biobert_huner_disease_v2` for local NER over PubMed, CORD-19, or plain text. It is not a chat model and it is not on a hosted inference API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lund/easyner/biobert-huner-gene-v1": { "id": "lund/easyner/biobert-huner-gene-v1", "name": "BioBERT HUNER gene v1", "maker": "lund", "family": "lund/easyner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base token classifier (BertForTokenClassification): 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 28996, max position embeddings 512. The label set is the binary IOB tags B-bio, I-bio, and O. Taken from the published config.json. The EasyNER paper says every checkpoint is a fine-tune of BioBERT base cased v1.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lund University's Cell Death, Lysosomes and Artificial Intelligence group (Aits lab) released this checkpoint as part of EasyNER, a biomedical named-entity pipeline. It is a BioBERT-base token classifier for genes and proteins. The weights are public on Hugging Face and were still online on the collection date 2026-10-15. Status here is active.", "what_it_is_for": "The EasyNER paper and the Aits lab model card say the checkpoint tags one biomedical entity class in medical and life-science text. The pipeline loads it from `aitslab/biobert_huner_gene_v1` for local NER over PubMed, CORD-19, or plain text. It is not a chat model and it is not on a hosted inference API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lund/easyner/biobert-huner-gene-v2": { "id": "lund/easyner/biobert-huner-gene-v2", "name": "BioBERT HUNER gene v2", "maker": "lund", "family": "lund/easyner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-03", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base token classifier (BertForTokenClassification): 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 28996, max position embeddings 512. The label set is the binary IOB tags B-bio, I-bio, and O. Taken from the published config.json. The EasyNER paper says every checkpoint is a fine-tune of BioBERT base cased v1.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lund University's Cell Death, Lysosomes and Artificial Intelligence group (Aits lab) released this checkpoint as part of EasyNER, a biomedical named-entity pipeline. It is a BioBERT-base token classifier for genes and proteins. The weights are public on Hugging Face and were still online on the collection date 2026-10-15. Status here is active.", "what_it_is_for": "The EasyNER paper and the Aits lab model card say the checkpoint tags one biomedical entity class in medical and life-science text. The pipeline loads it from `aitslab/biobert_huner_gene_v2` for local NER over PubMed, CORD-19, or plain text. It is not a chat model and it is not on a hosted inference API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lund/easyner/biobert-huner-species-v1": { "id": "lund/easyner/biobert-huner-species-v1", "name": "BioBERT HUNER species v1", "maker": "lund", "family": "lund/easyner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base token classifier (BertForTokenClassification): 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 28996, max position embeddings 512. The label set is the binary IOB tags B-bio, I-bio, and O. Taken from the published config.json. The EasyNER paper says every checkpoint is a fine-tune of BioBERT base cased v1.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lund University's Cell Death, Lysosomes and Artificial Intelligence group (Aits lab) released this checkpoint as part of EasyNER, a biomedical named-entity pipeline. It is a BioBERT-base token classifier for species. The weights are public on Hugging Face and were still online on the collection date 2026-10-15. Status here is active.", "what_it_is_for": "The EasyNER paper and the Aits lab model card say the checkpoint tags one biomedical entity class in medical and life-science text. The pipeline loads it from `aitslab/biobert_huner_species_v1` for local NER over PubMed, CORD-19, or plain text. It is not a chat model and it is not on a hosted inference API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lund/easyner/biobert-huner-species-v2": { "id": "lund/easyner/biobert-huner-species-v2", "name": "BioBERT HUNER species v2", "maker": "lund", "family": "lund/easyner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-03", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base token classifier (BertForTokenClassification): 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 28996, max position embeddings 512. The label set is the binary IOB tags B-bio, I-bio, and O. Taken from the published config.json. The EasyNER paper says every checkpoint is a fine-tune of BioBERT base cased v1.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lund University's Cell Death, Lysosomes and Artificial Intelligence group (Aits lab) released this checkpoint as part of EasyNER, a biomedical named-entity pipeline. It is a BioBERT-base token classifier for species. The weights are public on Hugging Face and were still online on the collection date 2026-10-15. Status here is active.", "what_it_is_for": "The EasyNER paper and the Aits lab model card say the checkpoint tags one biomedical entity class in medical and life-science text. The pipeline loads it from `aitslab/biobert_huner_species_v2` for local NER over PubMed, CORD-19, or plain text. It is not a chat model and it is not on a hosted inference API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lyon/bebeshka/bebeshka": { "id": "lyon/bebeshka/bebeshka", "name": "Bebeshka", "maker": "lyon", "family": "lyon/bebeshka", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-07-22", "context_length": 128, "max_output_tokens": null, "parameters": "16 million", "architecture": "RoBERTa masked LM: 4 layers, 8 heads, hidden 560, intermediate 1412, relative key-query positions, GELU-new, vocab 8192. Paper: attention dropout 0.3, mask rate 13.5 percent, 10 epochs on BabyLM Strict-Small.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bebeshka is a 16 million parameter encoder from Universit\u00e9 Lumi\u00e8re Lyon 2 and ERIC (UR 3083). The authors write the affiliation as Universit\u00e9 de Lyon, Lyon 2. It is the lab's BabyLM 2023 Strict-Small submission: a 4-layer RoBERTa trained from scratch on about 10 million words. Weights are still on Hugging Face. There is no public price and no license on the card. Collected 2026-10-15.", "what_it_is_for": "The paper says Bebeshka is a small masked language model for the BabyLM Strict-Small track, where the corpus is meant to be closer to a child's input than a web-scale crawl. The authors also fine-tune it for GLUE, SuperGLUE, and moral-judgment probes. It is not a hosted chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lyon/zlata/zlata": { "id": "lyon/zlata/zlata", "name": "Zlata", "maker": "lyon", "family": "lyon/zlata", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-07-22", "context_length": 1024, "max_output_tokens": null, "parameters": "66 million", "architecture": "GPT-2 causal LM: 6 layers, 12 heads, hidden 768, absolute positions, GELU, vocab 30000. Paper: 10 epochs on BabyLM Strict-Small, four Graphcore IPUs. n_inner is null in config, so the GPT-2 default applies.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zlata is a 66 million parameter decoder from Universit\u00e9 Lumi\u00e8re Lyon 2 and ERIC (UR 3083). The authors write the affiliation as Universit\u00e9 de Lyon, Lyon 2. It is the causal model in their BabyLM 2023 Strict-Small submission: a 6-layer GPT-2 trained from scratch on about 10 million words. Weights are on Hugging Face without a model card or a license. There is no public price. Collected 2026-10-15.", "what_it_is_for": "The paper says Zlata is a small causal language model for the BabyLM Strict-Small track, paired with the encoder Bebeshka. The authors fine-tune it on GLUE-style tasks and score moral-judgment items in a few-shot setting. It is not a hosted chat API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "lyon/zlata/zlata-tinystories": { "id": "lyon/zlata/zlata-tinystories", "name": "Zlata-TinyStories", "maker": "lyon", "family": "lyon/zlata", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-08-03", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 causal LM: 6 layers, 12 heads, hidden 768, absolute positions, GELU, vocab 25000. Card text calls it a small GPT-2 pretrained on TinyStories. Config _name_or_path is ./gpt2-baby2/checkpoint-37500/.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zlata-TinyStories is a small GPT-2 from the same Universit\u00e9 Lumi\u00e8re Lyon 2 / ERIC authors as BabyLM Zlata. The GitHub README lists it as its own model: a 6-layer GPT-2 pretrained on TinyStories, not as a snapshot of the BabyLM weights. The Hub card is Apache-2.0 and was created on 2023-08-03. There is no public price and no filled-in score. Collected 2026-10-15.", "what_it_is_for": "The card says it is a small GPT-2 pretrained on the TinyStories dataset. The authors' README gives the same description and links this repo next to Bebeshka and BabyLM Zlata. The BabyLM paper only says they planned to try short-story data of this kind. It does not report this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "maastricht/legal-camembert/legal-camembert-base": { "id": "maastricht/legal-camembert/legal-camembert-base", "name": "Legal-CamemBERT-base", "maker": "maastricht", "family": "maastricht/legal-camembert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-11-29", "context_length": 512, "max_output_tokens": null, "parameters": "110655493", "architecture": "CamemBERT-base continued with masked language modeling from the camembert-base checkpoint. config.json: CamembertForMaskedLM, 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab_size 32005, max_position_embeddings 514, type_vocab_size 1. The card limits sequences to 512 tokens. Hub safetensors: F32 110655493 plus an I64 tensor of 514, which is not included in parameters. Training on the card: one Tesla V100 32GB, 200 epochs (about 50k steps), batch 32, AdamW learning rate 5e-5, weight decay 0.01, warmup 500 steps, linear decay, on Belgian statutory articles in BSARD. The EACL 2023 paper describes the same CamemBERT continued-pretraining recipe (50k steps on BSARD) as the warm start for a dense statute retriever. It does not print the Hub repo id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Legal-CamemBERT-base is a French CamemBERT-base checkpoint that Maastricht University's Law and Tech Lab further pretrained with masked language modeling on Belgian statutory articles. The weights are `maastrichtlawtech/legal-camembert-base` under Apache-2.0. Hub createdAt is 2022-11-29. Status: active. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint is CamemBERT-base further pretrained so it can represent French legal text from Belgian legislation. The pipeline tag is fill-mask, and the card also tags feature extraction. The EACL 2023 paper uses a CamemBERT checkpoint continued on the same corpus as the article encoder inside a dense statute retriever. It does not present this file as a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "maastricht/legal-camembert/legal-distilcamembert": { "id": "maastricht/legal-camembert/legal-distilcamembert", "name": "Legal-DistilCamemBERT-base", "maker": "maastricht", "family": "maastricht/legal-camembert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-11-29", "context_length": 512, "max_output_tokens": null, "parameters": "68128261", "architecture": "DistilCamemBERT continued with masked language modeling from cmarkea/distilcamembert-base. config.json: CamembertForMaskedLM, 6 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab_size 32005, max_position_embeddings 514, type_vocab_size 1, bos_token_id 0, eos_token_id 2. The card limits sequences to 512 tokens. Hub safetensors: F32 68128261 plus an I64 tensor of 514, which is not included in parameters. Training on the card matches the base sibling: one Tesla V100 32GB, 200 epochs (about 50k steps), batch 32, AdamW learning rate 5e-5, weight decay 0.01, warmup 500 steps, linear decay, on Belgian statutory articles in BSARD. The cited EACL 2023 paper describes continued pretraining of CamemBERT, not this 6-layer checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Legal-DistilCamemBERT-base is a 6-layer French encoder that Maastricht University's Law and Tech Lab further pretrained with masked language modeling on Belgian statutory articles. The weights are `maastrichtlawtech/legal-distilcamembert` under Apache-2.0. The card title adds \"-base\"; the repo id does not. Hub createdAt is 2022-11-29. Status: active. Collected 2026-10-15.", "what_it_is_for": "The card says this is DistilCamemBERT further pretrained on French legal articles from Belgian legislation. The pipeline tag is fill-mask, and the card also tags feature extraction. It is the smaller sibling of Legal-CamemBERT-base, not a generator and not a retrieval head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "magdeburg/dilithium/attwnet3d": { "id": "magdeburg/dilithium/attwnet3d", "name": "AttWNet3D", "maker": "magdeburg", "family": "magdeburg/dilithium", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-11", "context_length": null, "max_output_tokens": null, "parameters": "233103476", "architecture": "3D attention W-Net for patch-based vessel segmentation. Config read from the multi-MIP card: 1 input channel, 64 initial features, 5 output channels. Same parameter count for unsupervised, MIP, and multi-MIP training.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AttWNet3D is the attention W-Net released with DILITHIUM, a less-supervised 7T MRA vessel project from Otto von Guericke University Magdeburg. The public weights are on Hugging Face under Apache-2.0. No paper for DILITHIUM was found on 2026-10-15. Status is active. There is no priced API.", "what_it_is_for": "The GitHub README says DILITHIUM segments vessels in high-resolution 7T MRAs with unsupervised or weakly supervised training. AttWNet3D adapts an attention W-Net: an attention U-Net encoder and decoder. Weak supervision uses a MIP loss on one axis or on several axes. The maintainer email in the uploaded source is an OVGU student address.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "magdeburg/dilithium/wnet-mss3d": { "id": "magdeburg/dilithium/wnet-mss3d", "name": "WNet-MSS3D", "maker": "magdeburg", "family": "magdeburg/dilithium", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-11", "context_length": null, "max_output_tokens": null, "parameters": "232395168", "architecture": "3D W-Net with U-Net multi-scale-supervision blocks as encoder and decoder, for unsupervised or MIP-weakly-supervised vessel segmentation.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WNet-MSS3D is the U-Net multi-scale-supervision W-Net released with DILITHIUM, a less-supervised 7T MRA vessel project from Otto von Guericke University Magdeburg. Weights are on Hugging Face. No DILITHIUM paper was found on 2026-10-15. Status is active. There is no priced API.", "what_it_is_for": "The README presents this as an alternative encoder-decoder for the same unsupervised and weakly supervised vessel task as AttWNet3D. The backbone swap uses the group's U-Net MSS block instead of the attention U-Net. Weak labels, when used, are MIP or multi-MIP losses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "magdeburg/ds6/ds6": { "id": "magdeburg/ds6/ds6", "name": "DS6", "maker": "magdeburg", "family": "magdeburg/ds6", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-09-22", "context_length": null, "max_output_tokens": null, "parameters": "103560587", "architecture": "Modified 3D U-Net with multi-scale supervision, trained with a Siamese deformation-consistency loss on 7T TOF-MRA. The proposed public checkpoint is UNet-MSS with deformation-aware learning.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DS6 is a deformation-aware semi-supervised vessel segmenter from Otto von Guericke University Magdeburg, with coauthors at Erasmus MC and the University of Copenhagen. The journal version is Journal of Imaging 8(10):259 (22 September 2022). Weights are open. Status on 2026-10-15 is active. There is no priced API.", "what_it_is_for": "The authors segment small brain vessels in 7 Tesla 3D time-of-flight MR angiography when only a small, noisy, semi-automatic training set is available. The name is a play on Deep Space Nine: deformation-aware segmentation trained on six volumes. The proposed network is a modified U-Net with multi-scale supervision, trained in a Siamese setup so predictions stay consistent under elastic deformation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "magdeburg/ftsrdyn/ftsrdyn": { "id": "magdeburg/ftsrdyn/ftsrdyn", "name": "FTSRDyn", "maker": "magdeburg", "family": "magdeburg/ftsrdyn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "5418177", "architecture": "3D U-Net with perceptual loss for dynamic MRI super-resolution. The ISMRM abstract describes main training on CHAOS patches and a one-epoch subject-specific fine-tune. The public file name includes CHAOS, centre 6.25 percent mask, and WoPad.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FTSRDyn is the U-Net checkpoint released for fine-tuned super-resolution of dynamic MRI by Otto von Guericke University Magdeburg. The journal article is Artificial Intelligence in Medicine, volume 121, article 102196 (November 2021). One Hub file is public. Status on 2026-10-15 is active. There is no priced API.", "what_it_is_for": "The paper trains a 3D U-Net with a perceptual loss on a static benchmark, then fine-tunes it on one subject-specific high-resolution planning scan so dynamic scans can stay fast and still look sharper. The public file name is `FTSRDyn_UNet3D_CHAOS_MT_Centre6p25MaskWoPad`. That name says CHAOS, a centre 6.25 percent mask, and no padding. It does not say that the file is a subject-specific fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "magdeburg/gp/gpreconresnet": { "id": "magdeburg/gp/gpreconresnet", "name": "GPReconResNet", "maker": "magdeburg", "family": "magdeburg/gp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "17272918", "architecture": "ReconResNet encoder-decoder with a reconstruction path, used here for three-class brain-tumour classification and a class heatmap. Four-channel checkpoint: 17272918 parameters.", "modalities": { "input": [ "image" ], "output": [ "class", "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPReconResNet is the ReconResNet-based explainable classifier from Otto von Guericke University Magdeburg for brain-tumour grade and weakly supervised masks. The preprint is arXiv:2206.05148. The journal version is Neurocomputing (2026), article 133460. Weights are MIT. Status on 2026-10-15 is active. There is no priced API.", "what_it_is_for": "The network classifies healthy, low-grade glioma, and high-grade glioma from an axial MRI slice and returns a heatmap that can be thresholded into a weak segmentation. The paper file uses T1, T2, T1CE, and FLAIR. A T1CE-only file is on the Hub and the repository says it is not part of the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "magdeburg/gp/gpshuffleunet": { "id": "magdeburg/gp/gpshuffleunet", "name": "GPShuffleUNet", "maker": "magdeburg", "family": "magdeburg/gp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "26373379", "architecture": "U-Net with pixel-shuffle upsampling and a three-class head. The README example uses in_ch=4, num_features=64, n_levels=3, out_ch=3 for the four-channel checkpoint.", "modalities": { "input": [ "image" ], "output": [ "class", "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPShuffleUNet is the pixel-shuffle U-Net from Otto von Guericke University Magdeburg for brain-tumour classification and weakly supervised segmentation. The preprint is arXiv:2206.05148. The journal version is Neurocomputing (2026), article 133460. Weights are MIT. Status on 2026-10-15 is active. There is no priced API.", "what_it_is_for": "The network classifies an axial MRI slice as healthy, low-grade glioma, or high-grade glioma and returns a heatmap for a weak mask. The paper checkpoint uses four contrasts. The repository's load example builds `GP_ShuffleUNet` with `in_ch=4`, `num_features=64`, `n_levels=3`, and `out_ch=3`. A T1CE-only file is on the Hub and the README says it is not part of the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "magdeburg/gp/gpunet": { "id": "magdeburg/gp/gpunet", "name": "GPUNet", "maker": "magdeburg", "family": "magdeburg/gp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1740675", "architecture": "U-Net with a global max-pooling classifier head. Four-channel BraTS 2020 axial slices in, three tumour-grade logits and a class heatmap out. Not NVIDIA GPUNet.", "modalities": { "input": [ "image" ], "output": [ "class", "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPUNet, in this catalogue, is the global-pooling U-Net from Otto von Guericke University Magdeburg for brain-tumour classification and weakly supervised segmentation. It is not the NVIDIA NAS model of the same name. The preprint is arXiv:2206.05148. The journal version is Neurocomputing (2026), article 133460. Weights are MIT. Status on 2026-10-15 is active. There is no priced API.", "what_it_is_for": "The network classifies a 240 by 240 axial slice as healthy, low-grade glioma, or high-grade glioma, and exposes a per-class heatmap that can be thresholded into a weak tumour mask. The paper checkpoint uses T1, T2, T1CE, and FLAIR. A T1CE-only file exists and the repository says it is not part of the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "magdeburg/ncc1701/reconresnet": { "id": "magdeburg/ncc1701/reconresnet", "name": "ReconResNet", "maker": "magdeburg", "family": "magdeburg/ncc1701", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "17266452", "architecture": "NCC1701 framework: regularised ResNet (ReconResNet) removes undersampling artefacts, then a data-consistency step fuses the network output with the measured k-space. The MIDL 2021 short paper says a spatial dropout sits between the two convolutions of the residual block, and two down-sampling blocks start from 64 feature maps. All 26 public 2D checkpoints have the same safetensors total.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReconResNet is the residual backbone of the NCC1701 MRI reconstruction framework from Otto von Guericke University Magdeburg (Data and Knowledge Engineering and Biomedical Magnetic Resonance). The journal article is Computers in Biology and Medicine, volume 143, article 105321 (April 2022). Weights are open on the authors' Hugging Face account. Status on 2026-10-15 is active. There is no priced API.", "what_it_is_for": "The paper proposes a framework, codenamed NCC1701, that uses ReconResNet to fill in artefacts from undersampled Cartesian or radial k-space, then applies a data-consistency step. The work was done in the MEMoRIAL graduate school at OVGU. It is not NVIDIA GPUNet and it is not a language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "magdeburg/pulaski/pulaski": { "id": "magdeburg/pulaski/pulaski", "name": "PULASki", "maker": "magdeburg", "family": "magdeburg/pulaski", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Probabilistic U-Net trained with a statistical distance (Hausdorff, Sinkhorn, or FID) instead of cross-entropy. 2D checkpoints are 10162828 parameters. 3D checkpoints are 30465100 parameters.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PULASki is a probabilistic segmenter from Otto von Guericke University Magdeburg and coauthors. It is a Probabilistic U-Net whose conditional decoder is trained with a statistical distance instead of cross-entropy, so the samples reflect disagreement between raters. The preprint is arXiv:2312.15686 (25 December 2023). The journal version is Medical Image Analysis (2025), article 103623. Weights are open. Status on 2026-10-15 is active. There is no priced API.", "what_it_is_for": "The method is aimed at small, class-imbalanced medical datasets where experts disagree. The released tasks are intracranial vessel segmentation on 7T time-of-flight MRA and multiple-sclerosis lesion segmentation on 3T FLAIR. 2D slice models and 3D patch models are both released. The card for the 3D Hausdorff vessel model says it was developed by Soumick Chatterjee.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "magdeburg/spockmip/spockmip": { "id": "magdeburg/spockmip/spockmip", "name": "SPOCKMIP", "maker": "magdeburg", "family": "magdeburg/spockmip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "3D U-Net or U-Net MSS with LeakyReLU and an additional MIP or multi-MIP loss. UNet3D cards report 103550145 parameters. UNet-MSS3D cards report 103560587.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SPOCKMIP is a 7T MRA vessel segmenter from Otto von Guericke University Magdeburg. It keeps a U-Net or U-Net MSS backbone and adds a maximum-intensity-projection (MIP) loss so thin vessels stay continuous. The preprint is arXiv:2407.08655 (11 July 2024). Weights are open. Status on 2026-10-15 is active. There is no priced API.", "what_it_is_for": "The paper trains patch-based U-Net and U-Net MSS models, with ReLU replaced by LeakyReLU, on the Study Forrest data and evaluates generalisation on held-out volumes. The extra loss compares projections of the prediction with projections of the label, on one axis (MIP) or on several axes (multi-MIP).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "magdeburg/strega/ccevae": { "id": "magdeburg/strega/ccevae", "name": "cceVAE", "maker": "magdeburg", "family": "magdeburg/strega", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "812295576", "architecture": "Compact 2D context-encoding variational autoencoder used inside the StRegA pipeline. The public file is a post-paper master checkpoint on MOOD T1 and IXI T1, T2, and PD.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "cceVAE is the compact context-encoding variational autoencoder inside StRegA, an unsupervised brain-MRI anomaly pipeline from Otto von Guericke University Magdeburg. The journal article is Computers in Biology and Medicine 149, 106093 (October 2022). One later checkpoint is on Hugging Face. Status on 2026-10-15 is active. There is no priced API.", "what_it_is_for": "StRegA learns the appearance of healthy T1-weighted, T2-weighted, and proton-density brain slices, then flags tumours or other out-of-distribution tissue. The network is a smaller context-encoding VAE. Pre-processing and post-processing sit around it. The authors say the pipeline is for detection, not for a labelled tumour segmenter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "magic/ltm/ltm-1": { "id": "magic/ltm/ltm-1", "name": "LTM-1", "maker": "magic", "family": "magic/ltm", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-06-06", "context_length": 5000000, "max_output_tokens": null, "parameters": "", "architecture": "Long-term Memory Network (LTM Net). Magic said it was a new architecture after scaling standard GPT context windows failed. Parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTM-1 is Magic's first named Long-term Memory model, announced on 6 June 2023. It was an early-alpha code-completion prototype with a 5 million token context window. Weights were not released. As of 15 October 2026 Magic does not list it as a current product and has not published a retirement date, so this catalog treats it as retired.", "what_it_is_for": "Magic trained LTM-1 so a coding assistant could take a whole repository as context when generating suggestions. The company said 5,000,000 prompt tokens was about 500,000 lines of code or about 5,000 files, enough for most repositories. The stated goal was more grounded output by letting the model see explicit facts and its own action history.\n\nIn the same announcement Magic said early alpha users were testing the code-completion product, that a larger unnamed model was in training for a commercial release, and that further invites depended on backend stability and GPU capacity. The sign-up they pointed to was `https://magic.dev/waitlist`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "magic/ltm/ltm-2": { "id": "magic/ltm/ltm-2", "name": "LTM-2", "maker": "magic", "family": "magic/ltm", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 100000000, "max_output_tokens": null, "parameters": "", "architecture": "Larger LTM-2 model using the same sequence-dimension mechanism as LTM-2-mini. Magic's footnote says context cost for the largest LTM-2 is roughly twice LTM-2-mini, and still about 500x cheaper than Llama 3.1 405B attention at 100M tokens. Parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTM-2 is the large Long-Term Memory model Magic said it was training on 29 August 2024, after disclosing the smaller LTM-2-mini. It was still described as unreleased on 21 November 2024. No finished checkpoint, weights, API, or ship date was found as of 15 October 2026. Status is announced. The name is real; almost every spec below the context target is missing.", "what_it_is_for": "Magic said LTM models are trained to reason on up to 100M tokens of context at inference, with software development as the focus. LTM-2-mini was the first 100M model they had trained. The large LTM-2 was the one they said they were training next, on new supercomputers (Magic-G4 on H100s and Magic-G5 on GB200 NVL72, built with Google Cloud). Those cluster names are machines, not models.\n\nOn 21 November 2024 Magic invited applicants to an applied post-training team to work with \"new (and still unreleased) LTM2 models and their 100M token context window.\"", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "magic/ltm/ltm-2-mini": { "id": "magic/ltm/ltm-2-mini", "name": "LTM-2-mini", "maker": "magic", "family": "magic/ltm", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-29", "context_length": 100000000, "max_output_tokens": null, "parameters": "", "architecture": "Dense model using Magic's sequence-dimension algorithm rather than standard attention. The 8 September 2026 pretraining post calls the late-2024 very-long-context model a small dense model and links this announcement. Parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTM-2-mini is a closed Magic research model, the first one the company said it had trained with a 100 million token context window. Magic published that result on 29 August 2024 and, on 21 November 2024, still called LTM-2 models unreleased. No weights, API, or price were found as of 15 October 2026. This catalog marks it preview because the trained model was disclosed, not because a customer preview exists.", "what_it_is_for": "Magic trains LTM (Long-Term Memory) models to reason over context given at inference instead of relying only on what was memorized in training. The company said the commercial uses are broad, but its focus is software development: code, documentation, and libraries in context, including code that is not on the public internet. LTM-2-mini is the small model they had just trained at a 100M window. They equated 100M tokens with about 10 million lines of code or about 750 novels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-chronos-mini-augmented": { "id": "manchester/fintext/fintext-chronos-mini-augmented", "name": "FinText Chronos-Mini (Augmented)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "20.5M", "architecture": "Chronos-style T5 encoder-decoder, trained from scratch on financial excess returns. The 2023 Global config names google/t5-efficient-mini as the architecture template, not loaded Amazon Chronos weights. d_model 384, 4 encoder layers, 4 decoder layers, 8 heads, d_kv 64, d_ff 1536, ReLU, vocab 4096, mean-scale uniform bins on [-15, 15], context_length 512, prediction_length 64, 20 samples at inference. Hugging Face card reports 20.5M parameters.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText Chronos-Mini (Augmented) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. The FinText org card says this collection extends the global excess-return data with augmented factors, one checkpoint per year. The model card says only that the series are augmented. The paper separately reports a JKP-factor panel and a synthetic-augmentation panel, and it does not say which of those is this Hugging Face split.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-chronos-mini-global": { "id": "manchester/fintext/fintext-chronos-mini-global", "name": "FinText Chronos-Mini (Global)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "20.5M", "architecture": "Chronos-style T5 encoder-decoder, trained from scratch on financial excess returns. The 2023 Global config names google/t5-efficient-mini as the architecture template, not loaded Amazon Chronos weights. d_model 384, 4 encoder layers, 4 decoder layers, 8 heads, d_kv 64, d_ff 1536, ReLU, vocab 4096, mean-scale uniform bins on [-15, 15], context_length 512, prediction_length 64, 20 samples at inference. Hugging Face card reports 20.5M parameters.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText Chronos-Mini (Global) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. Daily excess returns across 94 markets. The paper uses CRSP for the United States and Compustat Global for other markets, again with an expanding window from 1990 through the checkpoint year.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-chronos-mini-us": { "id": "manchester/fintext/fintext-chronos-mini-us", "name": "FinText Chronos-Mini (U.S.)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "20.5M", "architecture": "Chronos-style T5 encoder-decoder, trained from scratch on financial excess returns. The 2023 Global config names google/t5-efficient-mini as the architecture template, not loaded Amazon Chronos weights. d_model 384, 4 encoder layers, 4 decoder layers, 8 heads, d_kv 64, d_ff 1536, ReLU, vocab 4096, mean-scale uniform bins on [-15, 15], context_length 512, prediction_length 64, 20 samples at inference. Hugging Face card reports 20.5M parameters.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText Chronos-Mini (U.S.) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. U.S. market-wide daily excess returns. The paper builds the U.S. sample from CRSP, with an expanding window that starts in 1990 and stops at the checkpoint year so later returns are not in training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-chronos-small-augmented": { "id": "manchester/fintext/fintext-chronos-small-augmented", "name": "FinText Chronos-Small (Augmented)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "46.2M", "architecture": "Chronos-style T5 encoder-decoder, trained from scratch on financial excess returns. The 2023 U.S. config names google/t5-efficient-small as the architecture template, not loaded Amazon Chronos weights. d_model 512, 6 encoder layers, 6 decoder layers, 8 heads, d_kv 64, d_ff 2048, ReLU, vocab 4096, mean-scale uniform bins on [-15, 15], context_length 512, prediction_length 64, 20 samples at inference. Hugging Face card reports 46.2M parameters. The paper's approximate size for Chronos-small is 46M.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText Chronos-Small (Augmented) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. The FinText org card says this collection extends the global excess-return data with augmented factors, one checkpoint per year. The model card says only that the series are augmented. The paper separately reports a JKP-factor panel and a synthetic-augmentation panel, and it does not say which of those is this Hugging Face split.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-chronos-small-global": { "id": "manchester/fintext/fintext-chronos-small-global", "name": "FinText Chronos-Small (Global)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "46.2M", "architecture": "Chronos-style T5 encoder-decoder, trained from scratch on financial excess returns. The 2023 U.S. config names google/t5-efficient-small as the architecture template, not loaded Amazon Chronos weights. d_model 512, 6 encoder layers, 6 decoder layers, 8 heads, d_kv 64, d_ff 2048, ReLU, vocab 4096, mean-scale uniform bins on [-15, 15], context_length 512, prediction_length 64, 20 samples at inference. Hugging Face card reports 46.2M parameters. The paper's approximate size for Chronos-small is 46M.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText Chronos-Small (Global) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. Daily excess returns across 94 markets. The paper uses CRSP for the United States and Compustat Global for other markets, again with an expanding window from 1990 through the checkpoint year.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-chronos-small-us": { "id": "manchester/fintext/fintext-chronos-small-us", "name": "FinText Chronos-Small (U.S.)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "46.2M", "architecture": "Chronos-style T5 encoder-decoder, trained from scratch on financial excess returns. The 2023 U.S. config names google/t5-efficient-small as the architecture template, not loaded Amazon Chronos weights. d_model 512, 6 encoder layers, 6 decoder layers, 8 heads, d_kv 64, d_ff 2048, ReLU, vocab 4096, mean-scale uniform bins on [-15, 15], context_length 512, prediction_length 64, 20 samples at inference. Hugging Face card reports 46.2M parameters. The paper's approximate size for Chronos-small is 46M.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText Chronos-Small (U.S.) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. U.S. market-wide daily excess returns. The paper builds the U.S. sample from CRSP, with an expanding window that starts in 1990 and stops at the checkpoint year so later returns are not in training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-chronos-tiny-augmented": { "id": "manchester/fintext/fintext-chronos-tiny-augmented", "name": "FinText Chronos-Tiny (Augmented)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "8.39M", "architecture": "Chronos-style T5 encoder-decoder, trained from scratch on financial excess returns. The 2023 U.S. config names google/t5-efficient-tiny as the architecture template, not loaded Amazon Chronos weights. d_model 256, 4 encoder layers, 4 decoder layers, 4 heads, d_kv 64, d_ff 1024, ReLU, vocab 4096, mean-scale uniform bins on [-15, 15], context_length 512, prediction_length 64, 20 samples at inference. Hugging Face card reports 8.39M parameters.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText Chronos-Tiny (Augmented) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. The FinText org card says this collection extends the global excess-return data with augmented factors, one checkpoint per year. The model card says only that the series are augmented. The paper separately reports a JKP-factor panel and a synthetic-augmentation panel, and it does not say which of those is this Hugging Face split.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-chronos-tiny-global": { "id": "manchester/fintext/fintext-chronos-tiny-global", "name": "FinText Chronos-Tiny (Global)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "8.39M", "architecture": "Chronos-style T5 encoder-decoder, trained from scratch on financial excess returns. The 2023 U.S. config names google/t5-efficient-tiny as the architecture template, not loaded Amazon Chronos weights. d_model 256, 4 encoder layers, 4 decoder layers, 4 heads, d_kv 64, d_ff 1024, ReLU, vocab 4096, mean-scale uniform bins on [-15, 15], context_length 512, prediction_length 64, 20 samples at inference. Hugging Face card reports 8.39M parameters.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText Chronos-Tiny (Global) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. Daily excess returns across 94 markets. The paper uses CRSP for the United States and Compustat Global for other markets, again with an expanding window from 1990 through the checkpoint year.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-chronos-tiny-us": { "id": "manchester/fintext/fintext-chronos-tiny-us", "name": "FinText Chronos-Tiny (U.S.)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "8.39M", "architecture": "Chronos-style T5 encoder-decoder, trained from scratch on financial excess returns. The 2023 U.S. config names google/t5-efficient-tiny as the architecture template, not loaded Amazon Chronos weights. d_model 256, 4 encoder layers, 4 decoder layers, 4 heads, d_kv 64, d_ff 1024, ReLU, vocab 4096, mean-scale uniform bins on [-15, 15], context_length 512, prediction_length 64, 20 samples at inference. Hugging Face card reports 8.39M parameters.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText Chronos-Tiny (U.S.) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. U.S. market-wide daily excess returns. The paper builds the U.S. sample from CRSP, with an expanding window that starts in 1990 and stops at the checkpoint year so later returns are not in training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-timesfm-20m-augmented": { "id": "manchester/fintext/fintext-timesfm-20m-augmented", "name": "FinText TimesFM-20M (Augmented)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "19.8M", "architecture": "TimesFM-style decoder-only Transformer with continuous patch embeddings and an MSE objective, scaled down and trained from scratch on financial excess returns. Not the Google TimesFM checkpoint. The 2023 Augmented config is TimesFMForHF: hidden size 432, 9 layers, 6 heads, head_dim 72, intermediate size 1248, context_len 512, horizon_len 128, float32. Hugging Face card reports 19.8M parameters. The paper describes the from-scratch TimesFM runs as about 20 million parameters.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText TimesFM-20M (Augmented) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. The FinText org card says this collection extends the global excess-return data with augmented factors, one checkpoint per year. The model card says only that the series are augmented. The paper separately reports a JKP-factor panel and a synthetic-augmentation panel, and it does not say which of those is this Hugging Face split.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-timesfm-20m-global": { "id": "manchester/fintext/fintext-timesfm-20m-global", "name": "FinText TimesFM-20M (Global)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "19.8M", "architecture": "TimesFM-style decoder-only Transformer with continuous patch embeddings and an MSE objective, scaled down and trained from scratch on financial excess returns. Not the Google TimesFM checkpoint. The 2023 Augmented config is TimesFMForHF: hidden size 432, 9 layers, 6 heads, head_dim 72, intermediate size 1248, context_len 512, horizon_len 128, float32. Hugging Face card reports 19.8M parameters. The paper describes the from-scratch TimesFM runs as about 20 million parameters.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText TimesFM-20M (Global) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. Daily excess returns across 94 markets. The paper uses CRSP for the United States and Compustat Global for other markets, again with an expanding window from 1990 through the checkpoint year.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-timesfm-20m-us": { "id": "manchester/fintext/fintext-timesfm-20m-us", "name": "FinText TimesFM-20M (U.S.)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "19.8M", "architecture": "TimesFM-style decoder-only Transformer with continuous patch embeddings and an MSE objective, scaled down and trained from scratch on financial excess returns. Not the Google TimesFM checkpoint. The 2023 Augmented config is TimesFMForHF: hidden size 432, 9 layers, 6 heads, head_dim 72, intermediate size 1248, context_len 512, horizon_len 128, float32. Hugging Face card reports 19.8M parameters. The paper describes the from-scratch TimesFM runs as about 20 million parameters.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText TimesFM-20M (U.S.) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. U.S. market-wide daily excess returns. The paper builds the U.S. sample from CRSP, with an expanding window that starts in 1990 and stops at the checkpoint year so later returns are not in training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-timesfm-8m-augmented": { "id": "manchester/fintext/fintext-timesfm-8m-augmented", "name": "FinText TimesFM-8M (Augmented)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "8.04M", "architecture": "TimesFM-style decoder-only Transformer with continuous patch embeddings and an MSE objective, scaled down and trained from scratch on financial excess returns. Not the Google TimesFM checkpoint. The 2023 Global config is TimesFMForHF: hidden size 264, 7 layers, 4 heads, head_dim 66, intermediate size 1024, context_len 512, horizon_len 128, float32. Hugging Face card reports 8.04M parameters. The paper describes the from-scratch TimesFM runs as about 8 million parameters.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText TimesFM-8M (Augmented) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. The FinText org card says this collection extends the global excess-return data with augmented factors, one checkpoint per year. The model card says only that the series are augmented. The paper separately reports a JKP-factor panel and a synthetic-augmentation panel, and it does not say which of those is this Hugging Face split.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-timesfm-8m-global": { "id": "manchester/fintext/fintext-timesfm-8m-global", "name": "FinText TimesFM-8M (Global)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "8.04M", "architecture": "TimesFM-style decoder-only Transformer with continuous patch embeddings and an MSE objective, scaled down and trained from scratch on financial excess returns. Not the Google TimesFM checkpoint. The 2023 Global config is TimesFMForHF: hidden size 264, 7 layers, 4 heads, head_dim 66, intermediate size 1024, context_len 512, horizon_len 128, float32. Hugging Face card reports 8.04M parameters. The paper describes the from-scratch TimesFM runs as about 8 million parameters.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText TimesFM-8M (Global) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. Daily excess returns across 94 markets. The paper uses CRSP for the United States and Compustat Global for other markets, again with an expanding window from 1990 through the checkpoint year.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "manchester/fintext/fintext-timesfm-8m-us": { "id": "manchester/fintext/fintext-timesfm-8m-us", "name": "FinText TimesFM-8M (U.S.)", "maker": "manchester", "family": "manchester/fintext", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-13", "context_length": 512, "max_output_tokens": null, "parameters": "8.04M", "architecture": "TimesFM-style decoder-only Transformer with continuous patch embeddings and an MSE objective, scaled down and trained from scratch on financial excess returns. Not the Google TimesFM checkpoint. The 2023 Global config is TimesFMForHF: hidden size 264, 7 layers, 4 heads, head_dim 66, intermediate size 1024, context_len 512, horizon_len 128, float32. Hugging Face card reports 8.04M parameters. The paper describes the from-scratch TimesFM runs as about 8 million parameters.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinText TimesFM-8M (U.S.) is a from-scratch financial time-series model released by Alliance Manchester Business School, University of Manchester, with Hao Ni at UCL and Weiguan Wang at Shanghai University. It is an active open-weight checkpoint family collected on 2026-10-15. Yearly weights from 2000 through 2023 are versions of this model, not separate products.", "what_it_is_for": "The FinText org card and the 2023 model card say this checkpoint is a time-series foundation model pre-trained for quantitative finance, specifically next-period forecasting of daily excess returns. U.S. market-wide daily excess returns. The paper builds the U.S. sample from CRSP, with an expanding window that starts in 1990 and stops at the checkpoint year so later returns are not in training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mannheim/babelbert/babelbert-mbert": { "id": "mannheim/babelbert/babelbert-mbert", "name": "BabelBERT mBERT", "maker": "mannheim", "family": "mannheim/babelbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-07-07", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertModel continued from bert-base-multilingual-uncased. 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocab 105879, max position embeddings 512. Contrastive lexical specialization on BabelNet synonym pairs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BabelBERT mBERT is the multilingual-BERT sibling of the Mannheim NLP group's BabelBERT releases. The card says it was produced by fine-tuning `bert-base-multilingual-uncased` on `umanlp/babelbert-dataset`. The Hub repo `umanlp/babelbert-ft-mbert` was created on 2023-07-07 and was still public on 2026-10-15. No license tag is set. The paper is ACL 2023.", "what_it_is_for": "Green, Ponzetto, and Glava\u0161 specialize massively multilingual transformers with BabelNet synonym constraints so the encoders work better as cross-lingual word representations. This checkpoint is the mBERT run. The paper evaluates bilingual lexicon induction, cross-lingual word similarity, and cross-lingual sentence retrieval. The XLM-R run is a separate folder because it is a different base model and a different Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mannheim/babelbert/babelbert-xlm-r": { "id": "mannheim/babelbert/babelbert-xlm-r", "name": "BabelBERT XLM-R", "maker": "mannheim", "family": "mannheim/babelbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-07-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "XLMRobertaModel continued from xlm-roberta-base. 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocab 250002, max position embeddings 514. Contrastive lexical specialization on BabelNet synonym pairs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BabelBERT XLM-R is a lexically specialized encoder from the NLP and IR Group at the University of Mannheim. The group fine-tuned XLM-RoBERTa base on BabelNet synonym constraints and published the weights as `umanlp/babelbert-ft-xlm-r` on 2023-07-07. The weights were still public on 2026-10-15. No license is tagged on the repo. The paper is ACL 2023.", "what_it_is_for": "The card says this checkpoint is an artifact of \"Massively Multilingual Lexical Specialization of Multilingual Transformers\" (Green, Ponzetto, and Glava\u0161, ACL 2023). The paper uses BabelNet synsets to build synonym pairs, or synonym-gloss pairs, across 50 languages, then specializes mBERT and XLM-R with a contrastive objective. The reported uses are bilingual lexicon induction, cross-lingual word similarity, and cross-lingual sentence retrieval. This folder is the XLM-R artifact. The mBERT artifact is a separate model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mannheim/rdf2vec/rdf2vec": { "id": "mannheim/rdf2vec/rdf2vec", "name": "RDF2vec", "maker": "mannheim", "family": "mannheim/rdf2vec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2016-10-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "word2vec (continuous bag of words or skip-gram) trained on sequences from random walks over an RDF graph. Each entity and predicate is a token. The released artifacts are entity and predicate vectors, not a text generator. Later named variants change the walks or the language model (RDF2vec Light, order-aware RDF2vec, p-walks, e-walks) and are not separate products here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RDF2vec is a knowledge-graph embedding method from the Data and Web Science Group at the University of Mannheim. Petar Ristoski developed it with Heiko Paulheim. The ISWC 2016 paper turns RDF graphs into random-walk sequences and trains word2vec so each entity and predicate gets a vector. Precomputed vectors for DBpedia and Wikidata are still listed on the group's data server. The project page was live on 2026-10-15. There is no published price.", "what_it_is_for": "The project page says the vectors support data mining with knowledge graphs as background knowledge, entity similarity, recommendations, and later ontology matching and retrieval. Similar entities land nearer each other. Cosine similarity is the usual metric. The page also notes some users prefer Euclidean distance or a Laplacian kernel. KGvec2go (Portisch, Hladik at SAP, and Paulheim) was a web API that returned one vector, a similarity, or nearest concepts, so callers did not have to download a multi-gigabyte file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mannheim/taser/taser": { "id": "mannheim/taser/taser", "name": "TaSeR", "maker": "mannheim", "family": "mannheim/taser", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-sa-4.0", "release_date": "2023-03-21", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "DistilBERT sequence classifier: 6 layers, hidden size 768, 12 heads, hidden dim 3072, vocab 30522, max position embeddings 512. Seven labels: equivalence, subclass_of, superclass_of, part_of, has_part, related, unrelated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TaSeR is a DistilBERT classifier from the Data and Web Science Group at the University of Mannheim. It types the semantic relation between two class names for knowledge-graph integration. Weights were uploaded to Hugging Face on 2023-03-21 under CC BY-SA 4.0 and were still public on 2026-10-15. The ESWC 2023 paper was first online on 2023-05-22.", "what_it_is_for": "The ESWC 2023 paper by Sven Hertling and Heiko Paulheim defines TaSeR as a transformer model that decides which relation holds between two classes: equivalence, sub- or superclass, part-of or has-part, cohyponym (the checkpoint label is `related`), or no relation. It is meant to refine class alignments and drop bad correspondences. The paper says the general models can be used without further training. The Hugging Face widget uses pairs such as `Dresses[SEP]Cocktail Dresses`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mannheim/tesseract/frak2021": { "id": "mannheim/tesseract/frak2021", "name": "frak2021", "maker": "mannheim", "family": "mannheim/tesseract", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tesseract LSTM traineddata for historic German Fraktur. The library publishes both tessdata_best and tessdata_fast files. Training-step files in the same folders are checkpoints of this model, not separate products.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "frak2021 is a Tesseract LSTM OCR model trained by Mannheim University Library for historic German Fraktur. The library wiki lists it with Fraktur_5000000 and german_print as its own models, and says they often beat the stock `deu_latf` and `script/Fraktur` models. Files are public on the library backup server and were listed on 2026-10-15. No license or release date is printed on the wiki.", "what_it_is_for": "The library uses Tesseract to read historic German newspapers and books. frak2021 is one of the models trained on a wide range of historic books. The OCR-BW FAQ says it fixes problems in the stock Fraktur models, including `ch` and `ck` ligatures, and that it knows German umlaut variants. Most builds have no dictionary. The FAQ points users at a dictionary build of the 1.069 checkpoint as well.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mannheim/tesseract/fraktur-5000000": { "id": "mannheim/tesseract/fraktur-5000000", "name": "Fraktur_5000000", "maker": "mannheim", "family": "mannheim/tesseract", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tesseract LSTM traineddata for historic Fraktur, trained by Mannheim University Library. Public files include loose checkpoints from 2019 plus tessdata_best and tessdata_fast directories.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fraktur_5000000 is a Tesseract LSTM OCR model from Mannheim University Library for historic Fraktur type. The library wiki names it as one of the models the library trained, and says results are often better than the standard `deu_latf` and `script/Fraktur` models. The download directory was public on 2026-10-15. Loose weight files there carry October 2019 timestamps. No license is stated.", "what_it_is_for": "The library OCR project reads historic German books and newspapers. This model is the earlier of the named Fraktur trainings still linked from the wiki, next to the 2021 frak2021 line. Wikimedia OCR staff, in task T287460, reported that a Fraktur_5000000 file beat a custom English long-s model by about half on character error in their own check. That note is a user report, not a library score, and the exact rate is not in the ticket text that was fetched.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mannheim/tesseract/german-print": { "id": "mannheim/tesseract/german-print", "name": "german_print", "maker": "mannheim", "family": "mannheim/tesseract", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tesseract LSTM traineddata for German historic print, trained by Mannheim University Library. The useful weights are the tessdata_best and tessdata_fast files (about 3.4 MB each in the best listing). A 33 KB german_print.traineddata in the parent folder is a starter file, not the LSTM weights.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "german_print is a Tesseract LSTM OCR model from Mannheim University Library for historic German type. The library wiki groups it with Fraktur_5000000 and frak2021 as models the library trained. The OCR-BW FAQ lists it for Fraktur from the 19th and 20th centuries. The download tree was public on 2026-10-15. Checkpoint files in `tessdata_best` are dated November 2023. No license is stated.", "what_it_is_for": "UB Mannheim runs Tesseract on historic German newspapers and books. german_print is the named print model in that set. The FAQ also mentions other Fraktur options (`Fraktur_2022-02-20`, `austriannewspaper_best`, `digitue_best`) that are not described on the library wiki as this model's releases, so they are not folded in here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mannheim/tesseract/gt4histocr": { "id": "mannheim/tesseract/gt4histocr", "name": "GT4HistOCR", "maker": "mannheim", "family": "mannheim/tesseract", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tesseract LSTM traineddata trained by Mannheim University Library on the GT4HistOCR ground-truth corpus. The library hosts tessdata_best and tessdata_fast. The ground-truth corpus itself is not this model.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GT4HistOCR, in this catalog, is the Tesseract LSTM model that Mannheim University Library trained for historic print and hosts under that name. The OCR-BW FAQ lists it with Frak2021 as a model the library team trained for Fraktur. The download directory was public on 2026-10-15, with best and fast folders stamped February 2020. The ground-truth corpus of the same name is not this model. No license is stated.", "what_it_is_for": "The library FAQ points readers of old Fraktur and historic Antiqua to its own traineddata, and names GT4HistOCR next to Frak2021. A separate UB Mannheim note on primer training says a GT4HistOCR model was a fine-tuning base because it already worked for much Fraktur and Antiqua and failed on some Fraktur pages. That is a use of this line, not a second product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mannheim/tod/tod-xlmr": { "id": "mannheim/tod/tod-xlmr", "name": "TOD-XLMR", "maker": "mannheim", "family": "mannheim/tod", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2022-04-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "XLM-RoBERTa for masked language modeling, continued from xlm-roberta-base. 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocab 250004, max position embeddings 514. Trained with masked language modeling and response contrastive loss.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TOD-XLMR is a masked-language model from the NLP and IR Group at the University of Mannheim. It continues XLM-RoBERTa base on English task-oriented dialog so the encoder can be used for multilingual dialog tasks. The Hugging Face repo was created on 2022-04-21, the license tag is MIT, and the weights were still public on 2026-10-15. The accompanying Multi2WOZ paper is NAACL 2022.", "what_it_is_for": "The model card says TOD-XLMR is a conversationally specialized multilingual model based on XLM-RoBERTa. Continued pretraining uses nine English human-to-human multi-turn task-oriented dialog datasets, with masked language modeling and a response contrastive loss, the two objectives from TOD-BERT. The Multi2WOZ README tells users to load `umanlp/TOD-XLMR` for downstream dialog state tracking and response retrieval. The specialization corpus is English. The base model is multilingual.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/ain/ain": { "id": "mbzuai/ain/ain", "name": "AIN", "maker": "mbzuai", "family": "mbzuai/ain", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2025-01-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-text model. Card metadata lists base qwen2-VL-7B. The README fetch showed an author list and not a spec table.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AIN is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Vision-language model for English and Arabic. The expanded name was not on the captured README.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/amber/amber": { "id": "mbzuai/amber/amber", "name": "Amber", "maker": "mbzuai", "family": "mbzuai/amber", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2023-12-07", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "7B English model with the LLaMA-7B architecture. LLM360 Pebble series. The card says 360 checkpoints and the data sequence are released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Amber is LLM360's 7B English model, now hosted by MBZUAI's Institute of Foundation Models. The IFM K2 timeline places it in December 2023. Collected 2026-10-15. Weights, checkpoints, and the data sequence are public under Apache 2.0.", "what_it_is_for": "The card says Amber is not a state-of-the-art model. It was released so the training recipe is public.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/araseg/araseg": { "id": "mbzuai/araseg/araseg", "name": "araseg", "maker": "mbzuai", "family": "mbzuai/araseg", "status": "active", "confidence": "low", "weights": "open-weights", "license": "MIT", "release_date": "2026-10-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Repository MBZUAI/araseg. License metadata is MIT. The README body was not captured, so the task is not stated here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "araseg is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Not verified beyond the repository name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/artst/artst": { "id": "mbzuai/artst/artst", "name": "ArTST", "maker": "mbzuai", "family": "mbzuai/artst", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-10-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Arabic text and speech transformer. The ASR card is a fine-tune on MGB2. A separate repo fine-tunes SpeechT5 weights from ArTST on Classical Arabic ClArTTS for synthesis. Later repos are named v1.5, v2, and v3.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ArTST is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Arabic speech recognition and, in the ClArTTS tune, speech synthesis.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/bactrian-x/bactrian-x-bloom-7b1": { "id": "mbzuai/bactrian-x/bactrian-x-bloom-7b1", "name": "Bactrian-X BLOOM 7.1B", "maker": "mbzuai", "family": "mbzuai/bactrian-x", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2023-05-10", "context_length": null, "max_output_tokens": null, "parameters": "7.1B", "architecture": "LoRA adapter repo. No merged checkpoint was in the org list. The 7B LLaMA card describes the shared 52-language recipe.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bactrian-X BLOOM 7.1B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Multilingual instruction adapter for BLOOM-7B1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/bactrian-x/bactrian-x-llama-13b": { "id": "mbzuai/bactrian-x/bactrian-x-llama-13b", "name": "Bactrian-X LLaMA 13B", "maker": "mbzuai", "family": "mbzuai/bactrian-x", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2023-06-19", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Merged LoRA tune of LLaMA-13B on the Bactrian-X release. The 7B card states the data recipe. The 13B README was not re-read line by line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bactrian-X LLaMA 13B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Multilingual instruction following at 13B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/bactrian-x/bactrian-x-llama-7b": { "id": "mbzuai/bactrian-x/bactrian-x-llama-7b", "name": "Bactrian-X LLaMA 7B", "maker": "mbzuai", "family": "mbzuai/bactrian-x", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-06-19", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Merged LoRA tune of LLaMA-7B. The card says it was fit on Stanford Alpaca 52k and Databricks Dolly 15k in 52 languages.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bactrian-X LLaMA 7B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Multilingual instruction following. The card names 52 languages and those two instruction sets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/bactrian-x/bactrian-x-mt5-large": { "id": "mbzuai/bactrian-x/bactrian-x-mt5-large", "name": "Bactrian-X mT5-large", "maker": "mbzuai", "family": "mbzuai/bactrian-x", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2023-06-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter for mT5-large. README body was not captured.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bactrian-X mT5-large is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Multilingual instruction adapter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/bactrian-x/bactrian-x-mt5-xl": { "id": "mbzuai/bactrian-x/bactrian-x-mt5-xl", "name": "Bactrian-X mT5-xl", "maker": "mbzuai", "family": "mbzuai/bactrian-x", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2023-06-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter for mT5-xl. README body was not captured.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bactrian-X mT5-xl is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Multilingual instruction adapter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/bimedix/bimedix": { "id": "mbzuai/bimedix/bimedix", "name": "BiMediX", "maker": "mbzuai", "family": "mbzuai/bimedix", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2024-02-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bilingual medical mixture-of-experts chat model. The org card describes English and Arabic medical dialogue. Hub tags include mixtral. Three language variants were released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BiMediX is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Medical question answering and chat in English, Arabic, or both. Not a device for unattended clinical decisions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/bimedix2/bimedix2-4b": { "id": "mbzuai/bimedix2/bimedix2-4b", "name": "BiMediX2 4B", "maker": "mbzuai", "family": "mbzuai/bimedix2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2024-12-16", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "BiMediX2 biomedical vision-language model. Size is the repository name.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BiMediX2 4B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Biomedical image and text question answering. The 8B card title calls it a bio-medical expert LMM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/bimedix2/bimedix2-70b": { "id": "mbzuai/bimedix2/bimedix2-70b", "name": "BiMediX2 70B", "maker": "mbzuai", "family": "mbzuai/bimedix2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2024-12-15", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "BiMediX2 biomedical vision-language model. Size is the repository name.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BiMediX2 70B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Biomedical image and text question answering. The 8B card title calls it a bio-medical expert LMM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/bimedix2/bimedix2-8b": { "id": "mbzuai/bimedix2/bimedix2-8b", "name": "BiMediX2 8B", "maker": "mbzuai", "family": "mbzuai/bimedix2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2024-12-15", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "BiMediX2 biomedical vision-language model. Card metadata base model: meta-llama/Llama-3.1-8B-Instruct.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BiMediX2 8B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Biomedical image and text question answering. The 8B card title calls it a bio-medical expert LMM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/bimedix2/bimedix2-8b-bi": { "id": "mbzuai/bimedix2/bimedix2-8b-bi", "name": "BiMediX2 8B bilingual", "maker": "mbzuai", "family": "mbzuai/bimedix2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2024-12-15", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "BiMediX2 biomedical vision-language model. Bilingual variant repo.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BiMediX2 8B bilingual is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Biomedical image and text question answering. The 8B card title calls it a bio-medical expert LMM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/bimedix3/bimedix3-70b": { "id": "mbzuai/bimedix3/bimedix3-70b", "name": "BiMediX3 70B", "maker": "mbzuai", "family": "mbzuai/bimedix3", "status": "active", "confidence": "low", "weights": "open-weights", "license": "", "release_date": "2025-04-15", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Repository exists. The README fetch returned no body, so license and purpose were not verified.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BiMediX3 70B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Not verified beyond the repository name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/come-vl/come-vl": { "id": "mbzuai/come-vl/come-vl", "name": "CoME-VL", "maker": "mbzuai", "family": "mbzuai/come-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card says it is a multi-encoder vision-language model built on Molmo, mixing SigLIP and DINOv3 features. Card metadata also lists Qwen2-7B and SigLIP bases.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CoME-VL is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Visual understanding and grounding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/crystal/crystal": { "id": "mbzuai/crystal/crystal", "name": "Crystal", "maker": "mbzuai", "family": "mbzuai/crystal", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2023-12-08", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "7B language model trained on SlimPajama and StarCoder, 1.4 trillion tokens. Same architecture as LLaMA-7B. Formerly called CrystalCoder. LLM360 Pebble series.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Crystal is LLM360's 7B model for language and code, hosted by IFM. Hub publication date 8 December 2023. Collected 2026-10-15. Apache 2.0.", "what_it_is_for": "The card presents it as a balanced language and coding model trained on less data than Llama 2 70B's English run, and as a fully inspectable LLM360 release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/crystal/crystalchat-7b-web2code": { "id": "mbzuai/crystal/crystalchat-7b-web2code", "name": "CrystalChat-7B-Web2Code", "maker": "mbzuai", "family": "mbzuai/crystal", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2024-06-26", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Named 7B checkpoint on the IFM org. License on the card metadata is MIT, unlike Crystal's Apache 2.0. The README body was not captured.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CrystalChat-7B-Web2Code is a named English model on IFM's Hugging Face org, dated 26 June 2024 by the repo timestamp. Collected 2026-10-15. Confidence is medium because the README body was not captured.", "what_it_is_for": "The repository name is the maker's name for the checkpoint. What Web2Code task it was trained for was not verified from the README.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/doatlas/doatlas-2": { "id": "mbzuai/doatlas/doatlas-2", "name": "DoAtlas-2", "maker": "mbzuai", "family": "mbzuai/doatlas", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-10-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "News report of an MBZUAI causal world model of human biology. The same report says an open-source release is planned and is not out yet. Counts in that report, as of 22 September 2026: 93,566 biomedical concepts, 149,383 candidate causal relationships, 4,696,704 literature-derived evidence records.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DoAtlas-2 is a causal biomedical model announced by MBZUAI, reported on 7 October 2026. Collected 2026-10-15. Status is announced because the same report says the open-source drop is still planned.", "what_it_is_for": "The report says it runs a research cycle on human-data questions, from forming a question to testing a hypothesis.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/geochat/geochat-7b": { "id": "mbzuai/geochat/geochat-7b", "name": "GeoChat 7B", "maker": "mbzuai", "family": "mbzuai/geochat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2024-02-26", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "The card says it is a grounded large vision-language model for remote-sensing images and region-level reasoning.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GeoChat 7B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Remote-sensing scene interpretation and grounded visual conversation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/geopixel/geopixel-7b": { "id": "mbzuai/geopixel/geopixel-7b", "name": "GeoPixel 7B", "maker": "mbzuai", "family": "mbzuai/geopixel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-02-19", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "The card says it is finetuned on GeoPixelD for remote-sensing grounded conversation generation.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GeoPixel 7B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Grounded conversation on remote-sensing images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/glamm/glamm": { "id": "mbzuai/glamm/glamm", "name": "GLaMM", "maker": "mbzuai", "family": "mbzuai/glamm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2023-12-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GLaMM-FullScope is the mixed fine-tune. The card lists grounded conversation, referring-expression segmentation, region captions, image captions, and visual question answering.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLaMM is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Grounded vision-language conversation and region-level tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/guru/guru-32b": { "id": "mbzuai/guru/guru-32b", "name": "Guru-32B", "maker": "mbzuai", "family": "mbzuai/guru", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2025-05-15", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "The 32B README was not read. The base model is not assumed from the 7B card. Paper: Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Guru-32B is an IFM reasoning checkpoint associated with the Guru / Reasoning360 paper. Hub date 2025-05-15. Collected 2026-10-15.", "what_it_is_for": "The 7B card says it is the Guru-7B model from that paper, evaluated with the Reasoning360 offline scripts. It is a research release, not a K2 Horizon size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/guru/guru-7b": { "id": "mbzuai/guru/guru-7b", "name": "Guru-7B", "maker": "mbzuai", "family": "mbzuai/guru", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2025-05-14", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Card says the base is Qwen2.5-7B. Paper: Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Guru-7B is an IFM reasoning checkpoint associated with the Guru / Reasoning360 paper. Hub date 2025-05-14. Collected 2026-10-15.", "what_it_is_for": "The 7B card says it is the Guru-7B model from that paper, evaluated with the Reasoning360 offline scripts. It is a research release, not a K2 Horizon size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais/jais-13b": { "id": "mbzuai/jais/jais-13b", "name": "Jais 13B", "maker": "mbzuai", "family": "mbzuai/jais", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2023-08-30", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "GPT-style decoder-only. The paper lists 40 layers, 40 heads, and hidden size 5,120. The model card says training used 72 billion Arabic tokens and 279 billion English and code tokens, 395 billion tokens in total, with Arabic data repeated for 1.6 epochs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais 13B is the first open Arabic-English foundation model from Inception, MBZUAI, and Cerebras, announced on 30 August 2023. Collected 2026-10-15. The weights remain public.", "what_it_is_for": "The card says it is a bilingual pretrained model for Arabic and English text, with a chat tune for dialogue. At release the authors called it state of the art on their Arabic suite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais/jais-30b": { "id": "mbzuai/jais/jais-30b", "name": "Jais 30B", "maker": "mbzuai", "family": "mbzuai/jais", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2023-10-27", "context_length": null, "max_output_tokens": null, "parameters": "30B", "architecture": "Bilingual Arabic-English decoder-only model released in two pretrained generations, v1 and v3, each with a chat tune. Parameter count is the size in the repository name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais 30B is the larger early Jais release from Inception, MBZUAI, and Cerebras. v1 and v3 are successive public checkpoints, each with a chat tune. Collected 2026-10-15.", "what_it_is_for": "Same bilingual Arabic-English generation role as Jais 13B, at 30B parameters. The family card later describes 30B context growth from 2K to 8K to 16K as a separate family release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais/jais-adapted-13b": { "id": "mbzuai/jais/jais-adapted-13b", "name": "Jais Adapted 13B", "maker": "mbzuai", "family": "mbzuai/jais", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "13B", "architecture": "Continued pretraining of meta-llama/Llama-2-13b for Arabic and English. Context 4,096 on the family card. Released under Apache 2.0. Family card: 280B continued-pretraining tokens (127B English, 140B Arabic, 13B code).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais Adapted 13B is Inception and Cerebras's Arabic adaptation of Llama 2, released with the Jais family under Apache 2.0. Collected 2026-10-15. Status: active.", "what_it_is_for": "The family card presents the adapted models as a way to bring Arabic to an English-centric base without training every size from scratch.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais/jais-adapted-70b": { "id": "mbzuai/jais/jais-adapted-70b", "name": "Jais Adapted 70B", "maker": "mbzuai", "family": "mbzuai/jais", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "70B", "architecture": "Continued pretraining of meta-llama/Llama-2-70b for Arabic and English. Context 4,096 on the family card. Released under Apache 2.0. Family card: 371B continued-pretraining tokens (33B English, 334B Arabic, 4B code).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais Adapted 70B is Inception and Cerebras's Arabic adaptation of Llama 2, released with the Jais family under Apache 2.0. Collected 2026-10-15. Status: active.", "what_it_is_for": "The family card presents the adapted models as a way to bring Arabic to an English-centric base without training every size from scratch.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais/jais-adapted-7b": { "id": "mbzuai/jais/jais-adapted-7b", "name": "Jais Adapted 7B", "maker": "mbzuai", "family": "mbzuai/jais", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "Continued pretraining of meta-llama/Llama-2-7b for Arabic and English. Context 4,096 on the family card. Released under Apache 2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais Adapted 7B is Inception and Cerebras's Arabic adaptation of Llama 2, released with the Jais family under Apache 2.0. Collected 2026-10-15. Status: active.", "what_it_is_for": "The family card presents the adapted models as a way to bring Arabic to an English-centric base without training every size from scratch.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais/jais-family-13b": { "id": "mbzuai/jais/jais-family-13b", "name": "Jais Family 13B", "maker": "mbzuai", "family": "mbzuai/jais", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "13B", "architecture": "Decoder-only bilingual model pretrained from scratch by Inception and Cerebras. Apache 2.0. Family card token budget: 480B total (283B English, 141B Arabic, 56B code).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais Family 13B is an open-weight Arabic-English model from Inception, MBZUAI, and Cerebras in the Jais family pretrained from scratch. Collected 2026-10-15. Status: active.", "what_it_is_for": "The family card says the models are for Arabic-centric research and generation while keeping English capability, across sizes from a small research model to a 30B long-context model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais/jais-family-1p3b": { "id": "mbzuai/jais/jais-family-1p3b", "name": "Jais Family 1.3B", "maker": "mbzuai", "family": "mbzuai/jais", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Decoder-only bilingual model pretrained from scratch by Inception and Cerebras. Apache 2.0. Family card token budget: 480B total (283B English, 141B Arabic, 56B code).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais Family 1.3B is an open-weight Arabic-English model from Inception, MBZUAI, and Cerebras in the Jais family pretrained from scratch. Collected 2026-10-15. Status: active.", "what_it_is_for": "The family card says the models are for Arabic-centric research and generation while keeping English capability, across sizes from a small research model to a 30B long-context model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais/jais-family-256m": { "id": "mbzuai/jais/jais-family-256m", "name": "Jais Family 256M", "maker": "mbzuai", "family": "mbzuai/jais", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2024-08-26", "context_length": null, "max_output_tokens": null, "parameters": "256M", "architecture": "Decoder-only bilingual model pretrained from scratch by Inception and Cerebras. Apache 2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais Family 256M is an open-weight Arabic-English model from Inception, MBZUAI, and Cerebras in the Jais family pretrained from scratch. Collected 2026-10-15. Status: active.", "what_it_is_for": "The family card says the models are for Arabic-centric research and generation while keeping English capability, across sizes from a small research model to a 30B long-context model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais/jais-family-2p7b": { "id": "mbzuai/jais/jais-family-2p7b", "name": "Jais Family 2.7B", "maker": "mbzuai", "family": "mbzuai/jais", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "2.7B", "architecture": "Decoder-only bilingual model pretrained from scratch by Inception and Cerebras. Apache 2.0. Family card token budget: 480B total (283B English, 141B Arabic, 56B code).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais Family 2.7B is an open-weight Arabic-English model from Inception, MBZUAI, and Cerebras in the Jais family pretrained from scratch. Collected 2026-10-15. Status: active.", "what_it_is_for": "The family card says the models are for Arabic-centric research and generation while keeping English capability, across sizes from a small research model to a 30B long-context model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais/jais-family-30b-16k": { "id": "mbzuai/jais/jais-family-30b-16k", "name": "Jais Family 30B 16K", "maker": "mbzuai", "family": "mbzuai/jais", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "", "context_length": 16384, "max_output_tokens": null, "parameters": "30B", "architecture": "Decoder-only bilingual model pretrained from scratch by Inception and Cerebras. Apache 2.0. Family card token budget: 1,666B total (980B English, 490B Arabic, 196B code).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais Family 30B 16K is an open-weight Arabic-English model from Inception, MBZUAI, and Cerebras in the Jais family pretrained from scratch. Collected 2026-10-15. Status: active.", "what_it_is_for": "The family card says the models are for Arabic-centric research and generation while keeping English capability, across sizes from a small research model to a 30B long-context model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais/jais-family-30b-8k": { "id": "mbzuai/jais/jais-family-30b-8k", "name": "Jais Family 30B 8K", "maker": "mbzuai", "family": "mbzuai/jais", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "30B", "architecture": "Decoder-only bilingual model pretrained from scratch by Inception and Cerebras. Apache 2.0. Family card token budget: 1,500B total (882B English, 441B Arabic, 177B code).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais Family 30B 8K is an open-weight Arabic-English model from Inception, MBZUAI, and Cerebras in the Jais family pretrained from scratch. Collected 2026-10-15. Status: active.", "what_it_is_for": "The family card says the models are for Arabic-centric research and generation while keeping English capability, across sizes from a small research model to a 30B long-context model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais/jais-family-590m": { "id": "mbzuai/jais/jais-family-590m", "name": "Jais Family 590M", "maker": "mbzuai", "family": "mbzuai/jais", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "590M", "architecture": "Decoder-only bilingual model pretrained from scratch by Inception and Cerebras. Apache 2.0. Family card token budget: 480B total (283B English, 141B Arabic, 56B code).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais Family 590M is an open-weight Arabic-English model from Inception, MBZUAI, and Cerebras in the Jais family pretrained from scratch. Collected 2026-10-15. Status: active.", "what_it_is_for": "The family card says the models are for Arabic-centric research and generation while keeping English capability, across sizes from a small research model to a 30B long-context model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais/jais-family-6p7b": { "id": "mbzuai/jais/jais-family-6p7b", "name": "Jais Family 6.7B", "maker": "mbzuai", "family": "mbzuai/jais", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "6.7B", "architecture": "Decoder-only bilingual model pretrained from scratch by Inception and Cerebras. Apache 2.0. Family card token budget: 480B total (283B English, 141B Arabic, 56B code).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais Family 6.7B is an open-weight Arabic-English model from Inception, MBZUAI, and Cerebras in the Jais family pretrained from scratch. Collected 2026-10-15. Status: active.", "what_it_is_for": "The family card says the models are for Arabic-centric research and generation while keeping English capability, across sizes from a small research model to a 30B long-context model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais-2/jais-2-70b": { "id": "mbzuai/jais-2/jais-2-70b", "name": "Jais 2 70B", "maker": "mbzuai", "family": "mbzuai/jais-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-12-09", "context_length": 8192, "max_output_tokens": null, "parameters": "70B", "architecture": "Decoder-only transformer trained from scratch. RoPE, squared ReLU, custom muP, 8:1 filter-to-hidden ratio, vocabulary 150,272. Arabic-centric tokenizer for MSA, dialects, and Arabic-English code-switching. The card lists 70 billion parameters and context 8,192. Layer count was not on the captured card text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais 2 70B is a from-scratch Arabic-English chat model from MBZUAI, Inception, and Cerebras, announced 9 December 2025. Collected 2026-10-15. Weights are public under Apache 2.0.", "what_it_is_for": "The card says it is for Arabic and English assistants, research, and commercial text work, including dialects and code-switching. It is not for high-stakes decisions without a person in the loop, and not for languages other than Arabic and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais-2/jais-2-8b": { "id": "mbzuai/jais-2/jais-2-8b", "name": "Jais 2 8B", "maker": "mbzuai", "family": "mbzuai/jais-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-12-09", "context_length": 8192, "max_output_tokens": null, "parameters": "8B", "architecture": "Decoder-only transformer trained from scratch. RoPE, squared ReLU, custom muP, 8:1 filter-to-hidden ratio, vocabulary 150,272. Arabic-centric tokenizer for MSA, dialects, and Arabic-English code-switching. Transformers' Jais2 defaults, which the docs say match this checkpoint, are 32 layers, hidden size 3,328, intermediate size 26,624, 26 heads, max position 8,192, untied embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais 2 8B is a from-scratch Arabic-English chat model from MBZUAI, Inception, and Cerebras, announced 9 December 2025. Collected 2026-10-15. Weights are public under Apache 2.0.", "what_it_is_for": "The card says it is for Arabic and English assistants, research, and commercial text work, including dialects and code-switching. It is not for high-stakes decisions without a person in the loop, and not for languages other than Arabic and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais-3/jais-3-0-9b": { "id": "mbzuai/jais-3/jais-3-0-9b", "name": "Jais 3 0.9B", "maker": "mbzuai", "family": "mbzuai/jais-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-10-15", "context_length": 131072, "max_output_tokens": null, "parameters": "0.9B", "architecture": "Dense decoder-only, adapted from K2 Horizon 0.9B. The card calls the window 128K. Its YaRN example scales 8,192 by 16, which is 131,072 tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais 3 0.9B is an open-weight Arabic-English model from MBZUAI's Institute of Foundation Models and Inception42, with Cerebras, released 15 October 2026. It is adapted from the matching K2 Horizon checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card says it is for Arabic and English assistants and agents: reasoning, tool use, and research. Speech is a separate model, Jais-ASR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais-3/jais-3-3-7b": { "id": "mbzuai/jais-3/jais-3-3-7b", "name": "Jais 3 3.7B", "maker": "mbzuai", "family": "mbzuai/jais-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-10-15", "context_length": 524288, "max_output_tokens": null, "parameters": "3.7B", "architecture": "Dense decoder-only, adapted from K2 Horizon 3.7B. Card says a 512K window. Sibling K2 Horizon cards define 512K as 524,288 tokens. The Hub safetensors header on the page says 5B params, while the card's size class is 3.7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais 3 3.7B is an open-weight Arabic-English model from MBZUAI's Institute of Foundation Models and Inception42, with Cerebras, released 15 October 2026. It is adapted from the matching K2 Horizon checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card says it is for Arabic and English assistants and agents: reasoning, tool use, and research. Speech is a separate model, Jais-ASR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais-3/jais-3-32b": { "id": "mbzuai/jais-3/jais-3-32b", "name": "Jais 3 32B", "maker": "mbzuai", "family": "mbzuai/jais-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-10-15", "context_length": 524288, "max_output_tokens": null, "parameters": "32B", "architecture": "Dense decoder-only, adapted from K2 Horizon 32B. Card says a 512K window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais 3 32B is an open-weight Arabic-English model from MBZUAI's Institute of Foundation Models and Inception42, with Cerebras, released 15 October 2026. It is adapted from the matching K2 Horizon checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card says it is for Arabic and English assistants and agents: reasoning, tool use, and research. Speech is a separate model, Jais-ASR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais-3/jais-3-36b-a4b": { "id": "mbzuai/jais-3/jais-3-36b-a4b", "name": "Jais 3 36B-A4B", "maker": "mbzuai", "family": "mbzuai/jais-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-10-15", "context_length": 524288, "max_output_tokens": null, "parameters": "36B", "architecture": "Mixture-of-experts with Mixture-of-Values attention (MoVA). Stores 36B parameters and runs 4B per token. Card says a 512K window. Adapted from K2 Horizon MoVA 36B-A4B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais 3 36B-A4B is an open-weight Arabic-English model from MBZUAI's Institute of Foundation Models and Inception42, with Cerebras, released 15 October 2026. It is adapted from the matching K2 Horizon checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card says it is for Arabic and English assistants and agents: reasoning, tool use, and research. Speech is a separate model, Jais-ASR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais-3/jais-3-375b-a23b": { "id": "mbzuai/jais-3/jais-3-375b-a23b", "name": "Jais 3 375B-A23B", "maker": "mbzuai", "family": "mbzuai/jais-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-10-15", "context_length": 524288, "max_output_tokens": null, "parameters": "375B", "architecture": "Sparse mixture-of-experts. Stores 375B parameters and runs 23B per token. Card says a 512K window. Adapted from K2 Horizon 375B-A23B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais 3 375B-A23B is an open-weight Arabic-English model from MBZUAI's Institute of Foundation Models and Inception42, with Cerebras, released 15 October 2026. It is adapted from the matching K2 Horizon checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card says it is for Arabic and English assistants and agents: reasoning, tool use, and research. Speech is a separate model, Jais-ASR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais-3/jais-3-7b": { "id": "mbzuai/jais-3/jais-3-7b", "name": "Jais 3 7B", "maker": "mbzuai", "family": "mbzuai/jais-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-10-15", "context_length": 524288, "max_output_tokens": null, "parameters": "7B", "architecture": "Dense decoder-only, adapted from K2 Horizon 7B. Card says a 512K window. The Hub safetensors header says 9B params. The card's size class is 7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais 3 7B is an open-weight Arabic-English model from MBZUAI's Institute of Foundation Models and Inception42, with Cerebras, released 15 October 2026. It is adapted from the matching K2 Horizon checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card says it is for Arabic and English assistants and agents: reasoning, tool use, and research. Speech is a separate model, Jais-ASR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/jais-asr/jais-asr": { "id": "mbzuai/jais-asr/jais-asr", "name": "Jais-ASR", "maker": "mbzuai", "family": "mbzuai/jais-asr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-10-15", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "The card says Jais-ASR is based on Jais-3-4B. There is no 4B model in the six Jais 3 language models. It transcribes Modern Standard Arabic and dialects from 19 countries, with code-switching into English, French, Spanish, and Italian. Optional diacritization and language or dialect labels.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jais-ASR, also called Jais Speech, is IFM and Inception42's Arabic speech recognition model released with Jais 3 on 15 October 2026. Collected 2026-10-15. Apache 2.0 weights are on the IFM Hub.", "what_it_is_for": "The card says it transcribes spoken Arabic, including dialects and code-switching, and can add diacritics and a language or dialect label. Paired with the Jais 3 language models, the press release says spoken instructions can be acted on.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/k2/k2": { "id": "mbzuai/k2/k2", "name": "K2", "maker": "mbzuai", "family": "mbzuai/k2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2024-04-17", "context_length": null, "max_output_tokens": null, "parameters": "65B", "architecture": "65 billion parameter English LLM. Card says 1.4T tokens. Fully open artifacts: code, data, checkpoints, and intermediate results. Base and chat were both released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K2 is LLM360's 65B English model, now on the IFM Hub. The repo timestamp is 17 April 2024. Collected 2026-10-15. The card presents it as a fully reproducible alternative to the Llama 2 70B recipe.", "what_it_is_for": "English text generation with public training artifacts, plus a chat tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/k2/k2-v2": { "id": "mbzuai/k2/k2-v2", "name": "K2-V2", "maker": "mbzuai", "family": "mbzuai/k2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2025-12-01", "context_length": 8192, "max_output_tokens": null, "parameters": "70B", "architecture": "Dense decoder-only transformer, grouped-query attention, RMSNorm. Instruct card table: hidden size 8,192, FFN 28,672, 64 heads, 80 layers, vocab 250,000, pretraining sequence 8,192, post-training sequence 524,288. About 12T tokens on TxT360. Three stages: pretrain, midtrain, SFT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K2-V2 is IFM's 70B fully open dense model, the December 2025 step on the K2 timeline. Collected 2026-10-15. Apache 2.0. An instruct checkpoint followed on 3 December 2025.", "what_it_is_for": "The card describes a general open model with long-context, math, and reasoning ability, and as the base for later sovereign reasoning models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/k2-horizon/k2-horizon-0-9b": { "id": "mbzuai/k2-horizon/k2-horizon-0-9b", "name": "K2 Horizon 0.9B", "maker": "mbzuai", "family": "mbzuai/k2-horizon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-09-03", "context_length": 131072, "max_output_tokens": null, "parameters": "0.9B", "architecture": "Dense decoder-only. Card: 128K context, up to 131,072 tokens with YaRN from 8,192. Languages on the card: English and Chinese. Smaller vocabulary than the other five, per the press release.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K2 Horizon 0.9B is part of K2 Horizon, IFM's open fleet released 3 September 2026. Collected 2026-10-15. Apache 2.0. The maker describes the fleet as weights, code, data, and method, not weights alone.", "what_it_is_for": "General reasoning, coding, and agent work at this size. The press release maps small sizes to edge devices and the 375B model to enterprise deployments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/k2-horizon/k2-horizon-3-7b": { "id": "mbzuai/k2-horizon/k2-horizon-3-7b", "name": "K2 Horizon 3.7B", "maker": "mbzuai", "family": "mbzuai/k2-horizon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-09-03", "context_length": 524288, "max_output_tokens": null, "parameters": "3.7B", "architecture": "Dense decoder-only. Card: native 524,288-token context from midtraining onward.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K2 Horizon 3.7B is part of K2 Horizon, IFM's open fleet released 3 September 2026. Collected 2026-10-15. Apache 2.0. The maker describes the fleet as weights, code, data, and method, not weights alone.", "what_it_is_for": "General reasoning, coding, and agent work at this size. The press release maps small sizes to edge devices and the 375B model to enterprise deployments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/k2-horizon/k2-horizon-32b": { "id": "mbzuai/k2-horizon/k2-horizon-32b", "name": "K2 Horizon 32B", "maker": "mbzuai", "family": "mbzuai/k2-horizon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-09-03", "context_length": 524288, "max_output_tokens": null, "parameters": "32B", "architecture": "Dense decoder-only. Card: native 524,288-token context.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K2 Horizon 32B is part of K2 Horizon, IFM's open fleet released 3 September 2026. Collected 2026-10-15. Apache 2.0. The maker describes the fleet as weights, code, data, and method, not weights alone.", "what_it_is_for": "General reasoning, coding, and agent work at this size. The press release maps small sizes to edge devices and the 375B model to enterprise deployments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/k2-horizon/k2-horizon-36b-a4b": { "id": "mbzuai/k2-horizon/k2-horizon-36b-a4b", "name": "K2 Horizon 36B-A4B", "maker": "mbzuai", "family": "mbzuai/k2-horizon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-09-03", "context_length": 524288, "max_output_tokens": null, "parameters": "36B", "architecture": "Mixture-of-experts with Mixture-of-Values attention. Stores 36B parameters and runs 4B per token. Card says checkpoints, data, and training code are released. Context is the family 512K window. This card's opening lines did not repeat the token count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K2 Horizon 36B-A4B is part of K2 Horizon, IFM's open fleet released 3 September 2026. Collected 2026-10-15. Apache 2.0. The maker describes the fleet as weights, code, data, and method, not weights alone.", "what_it_is_for": "General reasoning, coding, and agent work at this size. The press release maps small sizes to edge devices and the 375B model to enterprise deployments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/k2-horizon/k2-horizon-375b-a23b": { "id": "mbzuai/k2-horizon/k2-horizon-375b-a23b", "name": "K2 Horizon 375B-A23B", "maker": "mbzuai", "family": "mbzuai/k2-horizon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-09-03", "context_length": 524288, "max_output_tokens": null, "parameters": "375B", "architecture": "Sparse mixture-of-experts. Stores 375B parameters and runs 23B per token. Card: native 524,288-token context. Checkpoints, data, and training code released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K2 Horizon 375B-A23B is part of K2 Horizon, IFM's open fleet released 3 September 2026. Collected 2026-10-15. Apache 2.0. The maker describes the fleet as weights, code, data, and method, not weights alone.", "what_it_is_for": "General reasoning, coding, and agent work at this size. The press release maps small sizes to edge devices and the 375B model to enterprise deployments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/k2-horizon/k2-horizon-7b": { "id": "mbzuai/k2-horizon/k2-horizon-7b", "name": "K2 Horizon 7B", "maker": "mbzuai", "family": "mbzuai/k2-horizon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-09-03", "context_length": 524288, "max_output_tokens": null, "parameters": "7B", "architecture": "Dense decoder-only. Card: native 524,288 context. Hub safetensors header says 9B params. Pretraining on the card is 22.9T tokens at 8K, then midtraining out to 512K. Final checkpoint includes an anti-doom-loop FTPO stage.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K2 Horizon 7B is part of K2 Horizon, IFM's open fleet released 3 September 2026. Collected 2026-10-15. Apache 2.0. The maker describes the fleet as weights, code, data, and method, not weights alone.", "what_it_is_for": "General reasoning, coding, and agent work at this size. The press release maps small sizes to edge devices and the 375B model to enterprise deployments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/k2-think/k2-think": { "id": "mbzuai/k2-think/k2-think", "name": "K2-Think", "maker": "mbzuai", "family": "mbzuai/k2-think", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-09-09", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "32B reasoning model. Hub model tree base is Qwen/Qwen2.5-32B. The paper describes post-training and test-time compute on that base. Hub safetensors size is 33B params.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K2-Think is IFM's 32B open reasoning model, released with a paper on 9 September 2025. It is a post-train of Qwen2.5-32B, not a from-scratch K2 base. Collected 2026-10-15.", "what_it_is_for": "The card and paper present it for competitive math, code, and science, with a public web demo that the paper says runs on Cerebras hardware.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/k2-think/k2-think-v2": { "id": "mbzuai/k2-think/k2-think-v2", "name": "K2 Think V2", "maker": "mbzuai", "family": "mbzuai/k2-think", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-01-27", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "70B reasoning model built on K2-V2-Instruct. Hub safetensors header says 73B params. The parent instruct card lists an 80-layer dense transformer and a 524,288 post-training sequence length. This card does not restate the context window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K2 Think V2 is IFM's 70B open reasoning model on top of K2-V2-Instruct. MBZUAI's news post is dated 27 January 2026. Collected 2026-10-15. The maker calls the training fully sovereign: open weights and an IFM-curated recipe.", "what_it_is_for": "Math, code, and science reasoning. The news post says it keeps the earlier K2 Think recipe, with GRPO changes described there, on the 70B K2 V2 base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/k2-type/k2-type-0-9b": { "id": "mbzuai/k2-type/k2-type-0-9b", "name": "K2-Type-0.9B", "maker": "mbzuai", "family": "mbzuai/k2-type", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-09-26", "context_length": 8192, "max_output_tokens": null, "parameters": "0.9B", "architecture": "Decision model on K2-Horizon-0.9B. Card: 1.08B parameters stored, including the unused language-model head. A pointer head scores options in one forward pass. It does not generate text. Training code and data were not released at the time of the card.", "modalities": { "input": [ "text" ], "output": [ "probabilities" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "K2-Type-0.9B is IFM's small decision model on the K2 Horizon 0.9B backbone, published on the Hub on 26 September 2026. Collected 2026-10-15. It returns probabilities, not generated text.", "what_it_is_for": "The card says you send one state and any number of typed questions and get a probability for every option in one forward pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-cerebras-1-3b": { "id": "mbzuai/lamini/lamini-cerebras-1-3b", "name": "LaMini-Cerebras-1.3B", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-16", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a Cerebras base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-Cerebras-1.3B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-cerebras-111m": { "id": "mbzuai/lamini/lamini-cerebras-111m", "name": "LaMini-Cerebras-111M", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-14", "context_length": null, "max_output_tokens": null, "parameters": "111M", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a Cerebras base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-Cerebras-111M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-cerebras-256m": { "id": "mbzuai/lamini/lamini-cerebras-256m", "name": "LaMini-Cerebras-256M", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-12", "context_length": null, "max_output_tokens": null, "parameters": "256M", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a Cerebras base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-Cerebras-256M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-cerebras-590m": { "id": "mbzuai/lamini/lamini-cerebras-590m", "name": "LaMini-Cerebras-590M", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-12", "context_length": null, "max_output_tokens": null, "parameters": "590M", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a Cerebras base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-Cerebras-590M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-flan-t5-248m": { "id": "mbzuai/lamini/lamini-flan-t5-248m", "name": "LaMini-Flan-T5-248M", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-10", "context_length": null, "max_output_tokens": null, "parameters": "248M", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a Flan-T5 base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-Flan-T5-248M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-flan-t5-77m": { "id": "mbzuai/lamini/lamini-flan-t5-77m", "name": "LaMini-Flan-T5-77M", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-10", "context_length": null, "max_output_tokens": null, "parameters": "77M", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a Flan-T5 base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-Flan-T5-77M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-flan-t5-783m": { "id": "mbzuai/lamini/lamini-flan-t5-783m", "name": "LaMini-Flan-T5-783M", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-17", "context_length": null, "max_output_tokens": null, "parameters": "783M", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a Flan-T5 base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-Flan-T5-783M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-gpt-1-5b": { "id": "mbzuai/lamini/lamini-gpt-1-5b", "name": "LaMini-GPT-1.5B", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-16", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a GPT base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-GPT-1.5B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-gpt-124m": { "id": "mbzuai/lamini/lamini-gpt-124m", "name": "LaMini-GPT-124M", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-14", "context_length": null, "max_output_tokens": null, "parameters": "124M", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a GPT base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-GPT-124M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-gpt-774m": { "id": "mbzuai/lamini/lamini-gpt-774m", "name": "LaMini-GPT-774M", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-15", "context_length": null, "max_output_tokens": null, "parameters": "774M", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a GPT base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-GPT-774M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-neo-1-3b": { "id": "mbzuai/lamini/lamini-neo-1-3b", "name": "LaMini-Neo-1.3B", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-16", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a Neo base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-Neo-1.3B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-neo-125m": { "id": "mbzuai/lamini/lamini-neo-125m", "name": "LaMini-Neo-125M", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-14", "context_length": null, "max_output_tokens": null, "parameters": "125M", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a Neo base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-Neo-125M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-t5-223m": { "id": "mbzuai/lamini/lamini-t5-223m", "name": "LaMini-T5-223M", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-15", "context_length": null, "max_output_tokens": null, "parameters": "223M", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a T5 base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-T5-223M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-t5-61m": { "id": "mbzuai/lamini/lamini-t5-61m", "name": "LaMini-T5-61M", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-11", "context_length": null, "max_output_tokens": null, "parameters": "61M", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a T5 base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-T5-61M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/lamini/lamini-t5-738m": { "id": "mbzuai/lamini/lamini-t5-738m", "name": "LaMini-T5-738M", "maker": "mbzuai", "family": "mbzuai/lamini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-17", "context_length": null, "max_output_tokens": null, "parameters": "738M", "architecture": "One of the LaMini-LM instruction-distilled checkpoints. The name identifies a T5 base and the parameter figure. The 1.5B card points at the LaMini-LM paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMini-T5-738M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/llava/llava-llama-3-8b": { "id": "mbzuai/llava/llava-llama-3-8b", "name": "LLaVA Llama-3 8B", "maker": "mbzuai", "family": "mbzuai/llava", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-04-26", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "MBZUAI LLaVA checkpoint on Meta Llama 3 8B Instruct. The Phi-3 sibling card describes the same LLaVA-pp training pattern. This README was not read line by line.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLaVA Llama-3 8B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Vision-language understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/llava/llava-phi-3-mini": { "id": "mbzuai/llava/llava-phi-3-mini", "name": "LLaVA Phi-3-mini", "maker": "mbzuai", "family": "mbzuai/llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-26", "context_length": null, "max_output_tokens": null, "parameters": "3.8B", "architecture": "The card says this is LLaVA v1.5 trained with the Phi-3-mini-3.8B LLM. Pretraining trains only the projector.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLaVA Phi-3-mini is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Vision-language understanding with a small Phi-3 language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/llmvox/llmvox": { "id": "mbzuai/llmvox/llmvox", "name": "LLMVoX", "maker": "mbzuai", "family": "mbzuai/llmvox", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2025-03-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card says this is the model from LLMVoX: Autoregressive Streaming Text-to-Speech Model for Any LLM.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLMVoX is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Streaming speech for text produced by an LLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/medix-r1/medix-r1-2b": { "id": "mbzuai/medix-r1/medix-r1-2b", "name": "MediX-R1 2B", "maker": "mbzuai", "family": "mbzuai/medix-r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2026-02-27", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "MediX-R1 medical reinforcement-learning vision-language model.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MediX-R1 2B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Open-ended medical image and text reasoning. Not a substitute for a clinician.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/medix-r1/medix-r1-30b": { "id": "mbzuai/medix-r1/medix-r1-30b", "name": "MediX-R1 30B", "maker": "mbzuai", "family": "mbzuai/medix-r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2026-02-27", "context_length": null, "max_output_tokens": null, "parameters": "30B", "architecture": "MediX-R1 medical reinforcement-learning vision-language model.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MediX-R1 30B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Open-ended medical image and text reasoning. Not a substitute for a clinician.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/medix-r1/medix-r1-8b": { "id": "mbzuai/medix-r1/medix-r1-8b", "name": "MediX-R1 8B", "maker": "mbzuai", "family": "mbzuai/medix-r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2026-02-27", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "MediX-R1 medical reinforcement-learning vision-language model. Card metadata for the 8B lists base model Qwen/Qwen3-VL-8B-Instruct.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MediX-R1 8B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Open-ended medical image and text reasoning. Not a substitute for a clinician.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/medmo/medmo-4b": { "id": "mbzuai/medmo/medmo-4b", "name": "MedMO 4B", "maker": "mbzuai", "family": "mbzuai/medmo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-02-06", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Medical image model. The README title on the fetched pages says MedMO-8B-Next even on the 4B page, which looks like a shared header. Separate Next repos also exist.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedMO 4B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Grounding and understanding medical images, as the README title states.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/medmo/medmo-8b": { "id": "mbzuai/medmo/medmo-8b", "name": "MedMO 8B", "maker": "mbzuai", "family": "mbzuai/medmo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-02-06", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Medical image model. The README title on the fetched pages says MedMO-8B-Next even on the 4B page, which looks like a shared header. Separate Next repos also exist.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedMO 8B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Grounding and understanding medical images, as the README title states.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/megamath/megamath-llama-3-2-1b": { "id": "mbzuai/megamath/megamath-llama-3-2-1b", "name": "MegaMath-Llama-3.2-1B", "maker": "mbzuai", "family": "mbzuai/megamath", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.2 Community License", "release_date": "2025-04-03", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Proof-of-concept continued training on the MegaMath dataset. The 1B card says it can do chain-of-thought and program-aided answers. The 3B card body was not separately read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MegaMath-Llama-3.2-1B is IFM's MegaMath proof-of-concept on a Llama 3.2 base. Hub date 3 April 2025. Collected 2026-10-15.", "what_it_is_for": "The 1B card says the model is for math problems using either chain-of-thought or program-aided language.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/megamath/megamath-llama-3-2-3b": { "id": "mbzuai/megamath/megamath-llama-3-2-3b", "name": "MegaMath-Llama-3.2-3B", "maker": "mbzuai", "family": "mbzuai/megamath", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.2 Community License", "release_date": "2025-04-03", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Proof-of-concept continued training on the MegaMath dataset. The 1B card says it can do chain-of-thought and program-aided answers. The 3B card body was not separately read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MegaMath-Llama-3.2-3B is IFM's MegaMath proof-of-concept on a Llama 3.2 base. Hub date 3 April 2025. Collected 2026-10-15.", "what_it_is_for": "The 1B card says the model is for math problems using either chain-of-thought or program-aided language.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/mobillama/mobillama-0-5b": { "id": "mbzuai/mobillama/mobillama-0-5b", "name": "MobiLlama 0.5B", "maker": "mbzuai", "family": "mbzuai/mobillama", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2024-02-02", "context_length": null, "max_output_tokens": null, "parameters": "0.5B", "architecture": "Small language model in the MobiLlama series. The 1B card says the series targets on-device use and was trained with Amber data. Exact parameter count for this repo was not read beyond the name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobiLlama 0.5B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "On-device English text generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/mobillama/mobillama-0-8b": { "id": "mbzuai/mobillama/mobillama-0-8b", "name": "MobiLlama 0.8B", "maker": "mbzuai", "family": "mbzuai/mobillama", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2024-02-24", "context_length": null, "max_output_tokens": null, "parameters": "0.8B", "architecture": "Small language model in the MobiLlama series. No chat repo was in the org list. Exact count beyond the repo name was not read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobiLlama 0.8B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "On-device English text generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/mobillama/mobillama-1b": { "id": "mbzuai/mobillama/mobillama-1b", "name": "MobiLlama 1B", "maker": "mbzuai", "family": "mbzuai/mobillama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-02-24", "context_length": null, "max_output_tokens": null, "parameters": "1.2B", "architecture": "The card says 1.2 billion parameters, trained on Amber data sources, for on-device use.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobiLlama 1B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "On-device English text generation. The card argues against using only the largest models when memory and energy matter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/nanda/nanda-10b": { "id": "mbzuai/nanda/nanda-10b", "name": "Nanda 10B", "maker": "mbzuai", "family": "mbzuai/nanda", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-10-22", "context_length": null, "max_output_tokens": null, "parameters": "10B", "architecture": "Decoder-only Llama-3 continued pretraining and instruction tune. Card: 65 billion Hindi tokens in the training mix, RoPE. Custom code, trust_remote_code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nanda 10B is the Hindi-English chat model from MBZUAI, Inception, and Cerebras. The Hub repo is dated 22 October 2024. Collected 2026-10-15. Weights are public under the Llama 3 license.", "what_it_is_for": "The card says it is a bilingual assistant for Hindi and English research, chat, and customer-service style use, and that it led the authors' Hindi suite at release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/nanda/nanda-87b": { "id": "mbzuai/nanda/nanda-87b", "name": "Nanda 87B", "maker": "mbzuai", "family": "mbzuai/nanda", "status": "active", "confidence": "low", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "87B", "architecture": "Repository MBZUAI-IFM/Llama-3.1-Nanda-87B-Chat. The Hub API returned 401, so the card was not read. A secondary writeup calls it an 87B Llama-3.1-based successor announced in December 2025. A Hub activity line called it a distilled model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nanda 87B is a named MBZUAI-IFM repository, Llama-3.1-Nanda-87B-Chat. Collected 2026-10-15. The model card could not be read, so most specs are unknown. Confidence is low.", "what_it_is_for": "Not verified beyond the repository name and a Hub activity line that called it a distilled model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/omni-embed/omni-embed-mini-0-9b": { "id": "mbzuai/omni-embed/omni-embed-mini-0-9b", "name": "Omni-Embed-Mini 0.9B", "maker": "mbzuai", "family": "mbzuai/omni-embed", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2026-09-04", "context_length": null, "max_output_tokens": null, "parameters": "0.9B", "architecture": "Feature-extraction model. The README returned 401, so architecture details were not read. Parameter figure is the repository name. A paper page 2610.02148 is linked from the MBZUAI Omni collection.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Omni-Embed-Mini 0.9B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Text embeddings. The collection name is Omni. The exact task mix was not read from the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/omni-embed/omni-embed-mini-2-3b": { "id": "mbzuai/omni-embed/omni-embed-mini-2-3b", "name": "Omni-Embed-Mini 2.3B", "maker": "mbzuai", "family": "mbzuai/omni-embed", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2026-09-04", "context_length": null, "max_output_tokens": null, "parameters": "2.3B", "architecture": "Feature-extraction model. The README returned 401, so architecture details were not read. Parameter figure is the repository name. A paper page 2610.02148 is linked from the MBZUAI Omni collection.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Omni-Embed-Mini 2.3B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Text embeddings. The collection name is Omni. The exact task mix was not read from the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/openearthagent/openearthagent": { "id": "mbzuai/openearthagent/openearthagent", "name": "OpenEarthAgent", "maker": "mbzuai", "family": "mbzuai/openearthagent", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-02-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card says it does multi-step reasoning over satellite imagery and GIS data, with tool-style geospatial operations.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenEarthAgent is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Remote-sensing analysis with natural-language instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/palo/mobilepalo-1-7b": { "id": "mbzuai/palo/mobilepalo-1-7b", "name": "MobilePALO 1.7B", "maker": "mbzuai", "family": "mbzuai/palo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2024-03-25", "context_length": null, "max_output_tokens": null, "parameters": "1.7B", "architecture": "Vision-language checkpoint. Apache 2.0. The README body returned no text in this pass, so the task description is limited to the pipeline tag text-generation and the project name.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobilePALO 1.7B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Vision-language generation. A fuller purpose statement was not captured from the README.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/palo/palo-13b": { "id": "mbzuai/palo/palo-13b", "name": "PALO 13B", "maker": "mbzuai", "family": "mbzuai/palo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2024-03-25", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Vision-language checkpoint. Apache 2.0. The README body returned no text in this pass, so the task description is limited to the pipeline tag text-generation and the project name.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PALO 13B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Vision-language generation. A fuller purpose statement was not captured from the README.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/palo/palo-7b": { "id": "mbzuai/palo/palo-7b", "name": "PALO 7B", "maker": "mbzuai", "family": "mbzuai/palo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2024-03-25", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Vision-language checkpoint. Apache 2.0. The README body returned no text in this pass, so the task description is limited to the pipeline tag text-generation and the project name.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PALO 7B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Vision-language generation. A fuller purpose statement was not captured from the README.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/pan/pan": { "id": "mbzuai/pan/pan", "name": "PAN", "maker": "mbzuai", "family": "mbzuai/pan", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "World model on the Generative Latent Prediction architecture, described in the paper. Language-conditioned actions and multi-step rollout. No weight repository was found on the IFM or MBZUAI Hub orgs.", "modalities": { "input": [ "text", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PAN is IFM's world model, introduced in a paper whose report number is 14 November 2025 and listed on the IFM Hub org card. Collected 2026-10-15. Public weights were not found, so weights are marked closed.", "what_it_is_for": "The paper describes a general world model for action-conditioned simulation, long-horizon forecasting, and planning, using natural-language actions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/sherkala/sherkala-8b": { "id": "mbzuai/sherkala/sherkala-8b", "name": "Sherkala 8B", "maker": "mbzuai", "family": "mbzuai/sherkala", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0 and Llama 3.1 Community License", "release_date": "2025-02-18", "context_length": 8192, "max_output_tokens": null, "parameters": "8B", "architecture": "Instruction-tuned Llama-3.1-8B with RoPE and grouped-query attention. The card says the vocabulary is extended by 25 percent with high-frequency Kazakh tokens. The paper says continued training on 45.3 billion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sherkala 8B is the Kazakh-first chat model from Inception, MBZUAI, and Cerebras. Cerebras said it was available on 18 February 2025. Collected 2026-10-15. Weights are public but non-commercial terms apply.", "what_it_is_for": "The card says it is for Kazakh, with English, Russian, and Turkish retained, including chat assistants and Kazakh NLP research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/swiftformer/swiftformer-l1": { "id": "mbzuai/swiftformer/swiftformer-l1", "name": "SwiftFormer-L1", "maker": "mbzuai", "family": "mbzuai/swiftformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-04-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-classification SwiftFormer. The XS card describes an efficient additive attention design meant to replace quadratic attention for mobile vision.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SwiftFormer-L1 is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Real-time mobile image classification, as stated by the SwiftFormer paper linked from the XS card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/swiftformer/swiftformer-l3": { "id": "mbzuai/swiftformer/swiftformer-l3", "name": "SwiftFormer-L3", "maker": "mbzuai", "family": "mbzuai/swiftformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-04-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-classification SwiftFormer. The XS card describes an efficient additive attention design meant to replace quadratic attention for mobile vision.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SwiftFormer-L3 is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Real-time mobile image classification, as stated by the SwiftFormer paper linked from the XS card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/swiftformer/swiftformer-s": { "id": "mbzuai/swiftformer/swiftformer-s", "name": "SwiftFormer-S", "maker": "mbzuai", "family": "mbzuai/swiftformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-04-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-classification SwiftFormer. The XS card describes an efficient additive attention design meant to replace quadratic attention for mobile vision.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SwiftFormer-S is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Real-time mobile image classification, as stated by the SwiftFormer paper linked from the XS card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/swiftformer/swiftformer-xs": { "id": "mbzuai/swiftformer/swiftformer-xs", "name": "SwiftFormer-XS", "maker": "mbzuai", "family": "mbzuai/swiftformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-04-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-classification SwiftFormer. The XS card describes an efficient additive attention design meant to replace quadratic attention for mobile vision.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SwiftFormer-XS is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Real-time mobile image classification, as stated by the SwiftFormer paper linked from the XS card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/terrafm/terrafm": { "id": "mbzuai/terrafm/terrafm", "name": "TerraFM", "maker": "mbzuai", "family": "mbzuai/terrafm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-06-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card calls it a scalable foundation model for unified multisensor Earth observation.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TerraFM is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Earth-observation representation learning across sensors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/video-chatgpt/video-chatgpt-7b": { "id": "mbzuai/video-chatgpt/video-chatgpt-7b", "name": "Video-ChatGPT 7B", "maker": "mbzuai", "family": "mbzuai/video-chatgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2023-06-07", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "The card says it combines an LLM with a pretrained visual encoder adapted for spatiotemporal video.", "modalities": { "input": [ "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Video-ChatGPT 7B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Conversation about videos.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/video-com/video-com": { "id": "mbzuai/video-com/video-com", "name": "Video-CoM", "maker": "mbzuai", "family": "mbzuai/video-com", "status": "active", "confidence": "low", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-04-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named video model. README body was not captured. An SFT repo exists.", "modalities": { "input": [ "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Video-CoM is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Not verified beyond the repository name and the Apache 2.0 metadata.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/video-r2/video-r2": { "id": "mbzuai/video-r2/video-r2", "name": "Video-R2", "maker": "mbzuai", "family": "mbzuai/video-r2", "status": "active", "confidence": "low", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-01-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named video model. README body was not captured. An SFT repo exists.", "modalities": { "input": [ "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Video-R2 is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Not verified beyond the repository name and the Apache 2.0 metadata.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/videogpt-plus/videogpt-plus-llama3-8b": { "id": "mbzuai/videogpt-plus/videogpt-plus-llama3-8b", "name": "VideoGPT+ Llama-3 8B", "maker": "mbzuai", "family": "mbzuai/videogpt-plus", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-06-03", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "VideoGPT+ checkpoint named for its language-model base. The Phi-3 card describes the shared architecture. This README was not read.", "modalities": { "input": [ "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoGPT+ Llama-3 8B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Video understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/videogpt-plus/videogpt-plus-phi3": { "id": "mbzuai/videogpt-plus/videogpt-plus-phi3", "name": "VideoGPT+ Phi-3-mini", "maker": "mbzuai", "family": "mbzuai/videogpt-plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-03", "context_length": null, "max_output_tokens": null, "parameters": "3.8B", "architecture": "The card says VideoGPT+ uses image and video encoders and this repo is the Phi-3-mini-4K 3.8B checkpoint for VCGBench, VCGBench-Diverse, and MVBench.", "modalities": { "input": [ "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoGPT+ Phi-3-mini is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Video question answering and caption benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/videogpt-plus/videogpt-plus-vicuna-13b": { "id": "mbzuai/videogpt-plus/videogpt-plus-vicuna-13b", "name": "VideoGPT+ Vicuna 13B", "maker": "mbzuai", "family": "mbzuai/videogpt-plus", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-06-03", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "VideoGPT+ checkpoint named for its language-model base. The Phi-3 card describes the shared architecture. This README was not read.", "modalities": { "input": [ "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoGPT+ Vicuna 13B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Video understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/videogpt-plus/videogpt-plus-vicuna-7b": { "id": "mbzuai/videogpt-plus/videogpt-plus-vicuna-7b", "name": "VideoGPT+ Vicuna 7B", "maker": "mbzuai", "family": "mbzuai/videogpt-plus", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-06-03", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "VideoGPT+ checkpoint named for its language-model base. The Phi-3 card describes the shared architecture. This README was not read.", "modalities": { "input": [ "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoGPT+ Vicuna 7B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Video understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/vimul/vimul": { "id": "mbzuai/vimul/vimul", "name": "ViMUL", "maker": "mbzuai", "family": "mbzuai/vimul", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2025-06-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card title calls it a culturally diverse multilingual multimodal video model.", "modalities": { "input": [ "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViMUL is a named MBZUAI release. Collected 2026-10-15. Status: active.", "what_it_is_for": "Multilingual video understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mbzuai/worldguide/worldguide": { "id": "mbzuai/worldguide/worldguide", "name": "WorldGuide", "maker": "mbzuai", "family": "mbzuai/worldguide", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Goal-directed video world model. The card and a news report describe closed-loop procedural tasks such as origami, furniture, and cooking. Checkpoint repo MBZUAI/WorldGuide-Ckpt.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WorldGuide is MBZUAI's goal-directed video world model. A checkpoint was posted on 2 October 2026. Collected 2026-10-15.", "what_it_is_for": "The card title says it generates procedural tasks and uses its own frames to choose the next action and when to stop.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcgill/dmas-llama-lite/dmas-llama-lite": { "id": "mcgill/dmas-llama-lite/dmas-llama-lite", "name": "DMaS-LLaMa-Lite", "maker": "mcgill", "family": "mcgill/dmas-llama-lite", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-17", "context_length": 1024, "max_output_tokens": null, "parameters": "1716 million", "architecture": "Llama-architecture decoder trained from scratch, not a Meta Llama checkpoint. 36 layers, hidden 2048, 32 heads, 8 key-value heads, intermediate 5120, SiLU, RoPE theta 500000, RMSNorm, untied embeddings. GPT-2 tokenizer, vocab 50257. Context 1024. Paper: 1716M parameters, about 20 billion tokens of FineWeb-Edu (the 100BT subset). Card: AdamW, linear warmup and decay, 1-2 RTX A6000 GPUs. config.json sets bos_token_id 1 and eos_token_id 2 while tokenizer_config.json sets bos and eos to <|endoftext|> (id 50256).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DMaS-LLaMa-Lite is a 1.7 billion parameter language model that the McGill Data Mining and Security Lab trained from scratch on educational web text. The 43,500-step checkpoint was uploaded on 17 December 2024 under Apache-2.0 and was still public on 2026-10-15. It is not Meta's Llama and it is not an API.", "what_it_is_for": "The paper is a training log for a small Llama-architecture model on about 20 billion curated tokens. The card presents the checkpoints as open weights for inference, not as a hosted assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcgill/dmas-llama-lite/dmas-llama-lite-instruct": { "id": "mcgill/dmas-llama-lite/dmas-llama-lite-instruct", "name": "DMaS-LLaMa-Lite Instruct", "maker": "mcgill", "family": "mcgill/dmas-llama-lite", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-17", "context_length": 1024, "max_output_tokens": null, "parameters": "1716 million", "architecture": "Same Llama layout as the 43,500-step base: 36 layers, hidden 2048, 32 heads, 8 key-value heads, intermediate 5120, context 1024, vocab 50257. The released config is a full LlamaForCausalLM in bfloat16 and records unsloth_version 2024.11.5. The paper says post-training was LoRA, rank 128, alpha 16, on attention and feed-forward projections, starting at step 43,500. The card does not say whether the adapter was merged. Chat template is Vicuna 1.1. Responses end with <|endoftext|>. pad_token_id in this config is 50256, while bos_token_id is 1 and eos_token_id is 2.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DMaS-LLaMa-Lite Instruct is the McGill DMaS instruction model built from their own 43,500-step pretraining checkpoint. The card says it was fine-tuned on Alpaca-cleaned and uploaded on 17 December 2024 under Apache-2.0. It was still public on 2026-10-15. It is not an API.", "what_it_is_for": "The card calls it a demonstration that the pretrained checkpoint can follow instructions and answer questions. The prompt format is Vicuna 1.1. Machine answers are meant to end with `<|endoftext|>`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcgill/medal/electra-medal": { "id": "mcgill/medal/electra-medal", "name": "ELECTRA-MeDAL", "maker": "mcgill", "family": "mcgill/medal", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2020-11-16", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "ELECTRA discriminator. config.json: 12 layers, hidden 256, 4 heads, embedding size 128, intermediate 1024, GELU, vocab 30522, max position 512, type vocab 2. That is the ELECTRA-small shape. Parameter count is not in the config or a model card. No claim is made that it matches Google's 14M ELECTRA-small count.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELECTRA-MeDAL is an English medical encoder that McGill NLP pretrained and posted as `McGill-NLP/electra-medal`. The weight timestamp on the Hub is 16 November 2020. The MeDAL repo says the model is Apache-2.0. There is no model card. The weights were still public on 2026-10-15. It is not an API.", "what_it_is_for": "The MeDAL paper releases a medical abbreviation corpus and pretrained models for clinical language understanding. The GitHub README says the Hub repo is the ELECTRA encoder without the disambiguation head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcgill/tlm/tlm-100m": { "id": "mcgill/tlm/tlm-100m", "name": "TLM-100M", "maker": "mcgill", "family": "mcgill/tlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-08-28", "context_length": 2048, "max_output_tokens": null, "parameters": "98993137", "architecture": "GPT-Neo decoder with alternating global and local attention, window 256, gelu_new, tied embeddings, and a biased lm_head. 16 / 480 / 12 layers/hidden/heads. MLP ratio 8. Context 2048. GPT-2 BPE, vocab 50257. Trained as a two-tier TLM: a 5 percent permutation key over attention heads and MLP columns. Interpolated between the DataDecide 60M and 90M rungs, log-linear in total parameters, fraction 0.7153. Peak LR 5.1e-4, warmup 1000 steps, 35,542 steps, global batch 136 sequences, 9.90B tokens (100 times the parameter count) on FineWeb retain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TLM-100M is a Tiered Language Model from McGill NLP, with coauthors at Mila and ServiceNow, pretrained from scratch on English FineWeb and released on 28 August 2026 under Apache-2.0. One weight file exposes a public tier and a keyed tier. It was still public on 2026-10-15. It is not an API product.", "what_it_is_for": "The card describes a small open GPT-Neo language model whose 5 percent permutation key swaps attention heads and MLP columns, toggling a second behavior over the same parameters. The June 2026 paper frames that mechanism as access control for open weights. This checkpoint is the 100M rung of the DataDecide-shaped ladder, named for its rounded parameter count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcgill/tlm/tlm-120m": { "id": "mcgill/tlm/tlm-120m", "name": "TLM-120M", "maker": "mcgill", "family": "mcgill/tlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-08-28", "context_length": 2048, "max_output_tokens": null, "parameters": "116997505", "architecture": "GPT-Neo decoder with alternating global and local attention, window 256, gelu_new, tied embeddings, and a biased lm_head. 16 / 528 / 12 layers/hidden/heads. MLP ratio 8. Context 2048. GPT-2 BPE, vocab 50257. Trained as a two-tier TLM: a 5 percent permutation key over attention heads and MLP columns. DataDecide 90M rung (Table 2), verbatim. The card says this rung has about 117M parameters, more than the 100M rung's 99M. Peak LR 4.9e-4, warmup 1000 steps, 35,705 steps, global batch 160 sequences, 11.70B tokens (100 times the parameter count) on FineWeb retain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TLM-120M is a Tiered Language Model from McGill NLP, with coauthors at Mila and ServiceNow, pretrained from scratch on English FineWeb and released on 28 August 2026 under Apache-2.0. One weight file exposes a public tier and a keyed tier. It was still public on 2026-10-15. It is not an API product.", "what_it_is_for": "The card describes a small open GPT-Neo language model whose 5 percent permutation key swaps attention heads and MLP columns, toggling a second behavior over the same parameters. The June 2026 paper frames that mechanism as access control for open weights. This checkpoint is the 90M rung of the DataDecide-shaped ladder, named for its rounded parameter count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcgill/tlm/tlm-180m": { "id": "mcgill/tlm/tlm-180m", "name": "TLM-180M", "maker": "mcgill", "family": "mcgill/tlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-06-18", "context_length": 2048, "max_output_tokens": null, "parameters": "181908817", "architecture": "GPT-Neo decoder, GPTNeoForCausalLMTiered. 12 layers, hidden 768, 12 heads, intermediate 6144, context 2048, local window 256, gelu_new, tied embeddings, vocab 50257. Alternating global and local attention. The August ladder cards say this checkpoint predates that ladder and is not the 16-layer shape. Hub parameter count is 181,908,817. The repo includes key_5pct.json and training_state.pt. The June 2026 paper says TLM-180M was pretrained on English FineWeb at 100 tokens per parameter, with a key over about 5 percent of parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TLM-180M is one of the two Tiered Language Models the McGill NLP and Mila paper says it pretrained and released. The Hub repo is dated 18 June 2026. There is no model-card README. Weights were still public on 2026-10-15. It is not an API product.", "what_it_is_for": "The paper defines a TLM as one weight file with a public configuration and a keyed configuration. A permutation over about 5 percent of parameters is the key. This is the TLM-180M pretraining run on English FineWeb, not the later August ladder checkpoint of a similar size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcgill/tlm/tlm-20m": { "id": "mcgill/tlm/tlm-20m", "name": "TLM-20M", "maker": "mcgill", "family": "mcgill/tlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-08-28", "context_length": 2048, "max_output_tokens": null, "parameters": "21932689", "architecture": "GPT-Neo decoder with alternating global and local attention, window 256, gelu_new, tied embeddings, and a biased lm_head. 16 / 192 / 8 layers/hidden/heads. MLP ratio 8. Context 2048. GPT-2 BPE, vocab 50257. Trained as a two-tier TLM: a 5 percent permutation key over attention heads and MLP columns. DataDecide 20M rung (Table 2), verbatim. Peak LR 8.4e-4, warmup 1000 steps, 16,733 steps, global batch 64 sequences, 2.19B tokens (100 times the parameter count) on FineWeb retain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TLM-20M is a Tiered Language Model from McGill NLP, with coauthors at Mila and ServiceNow, pretrained from scratch on English FineWeb and released on 28 August 2026 under Apache-2.0. One weight file exposes a public tier and a keyed tier. It was still public on 2026-10-15. It is not an API product.", "what_it_is_for": "The card describes a small open GPT-Neo language model whose 5 percent permutation key swaps attention heads and MLP columns, toggling a second behavior over the same parameters. The June 2026 paper frames that mechanism as access control for open weights. This checkpoint is the 20M rung of the DataDecide-shaped ladder, named for its rounded parameter count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcgill/tlm/tlm-230m": { "id": "mcgill/tlm/tlm-230m", "name": "TLM-230M", "maker": "mcgill", "family": "mcgill/tlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-08-28", "context_length": 2048, "max_output_tokens": null, "parameters": "229137745", "architecture": "GPT-Neo decoder with alternating global and local attention, window 256, gelu_new, tied embeddings, and a biased lm_head. 16 / 768 / 12 layers/hidden/heads. MLP ratio 8. Context 2048. GPT-2 BPE, vocab 50257. Trained as a two-tier TLM: a 5 percent permutation key over attention heads and MLP columns. Interpolated between the DataDecide 150M and 300M rungs, log-linear in total parameters, fraction 0.3033. Peak LR 3.9e-4, warmup 1000 steps, 49,948 steps, global batch 224 sequences, 22.91B tokens (100 times the parameter count) on FineWeb retain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TLM-230M is a Tiered Language Model from McGill NLP, with coauthors at Mila and ServiceNow, pretrained from scratch on English FineWeb and released on 28 August 2026 under Apache-2.0. One weight file exposes a public tier and a keyed tier. It was still public on 2026-10-15. It is not an API product.", "what_it_is_for": "The card describes a small open GPT-Neo language model whose 5 percent permutation key swaps attention heads and MLP columns, toggling a second behavior over the same parameters. The June 2026 paper frames that mechanism as access control for open weights. This checkpoint is the 240M rung of the DataDecide-shaped ladder, named for its rounded parameter count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcgill/tlm/tlm-40m": { "id": "mcgill/tlm/tlm-40m", "name": "TLM-40M", "maker": "mcgill", "family": "mcgill/tlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-08-28", "context_length": 2048, "max_output_tokens": null, "parameters": "41721265", "architecture": "GPT-Neo decoder with alternating global and local attention, window 256, gelu_new, tied embeddings, and a biased lm_head. 16 / 288 / 12 layers/hidden/heads. MLP ratio 8. Context 2048. GPT-2 BPE, vocab 50257. Trained as a two-tier TLM: a 5 percent permutation key over attention heads and MLP columns. Interpolated between the DataDecide 20M and 60M rungs, log-linear in total parameters, fraction 0.5447. Peak LR 6.9e-4, warmup 1000 steps, 25,465 steps, global batch 80 sequences, 4.17B tokens (100 times the parameter count) on FineWeb retain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TLM-40M is a Tiered Language Model from McGill NLP, with coauthors at Mila and ServiceNow, pretrained from scratch on English FineWeb and released on 28 August 2026 under Apache-2.0. One weight file exposes a public tier and a keyed tier. It was still public on 2026-10-15. It is not an API product.", "what_it_is_for": "The card describes a small open GPT-Neo language model whose 5 percent permutation key swaps attention heads and MLP columns, toggling a second behavior over the same parameters. The June 2026 paper frames that mechanism as access control for open weights. This checkpoint is the 40M rung of the DataDecide-shaped ladder, named for its rounded parameter count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcgill/tlm/tlm-650m": { "id": "mcgill/tlm/tlm-650m", "name": "TLM-650M", "maker": "mcgill", "family": "mcgill/tlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-06-18", "context_length": 2048, "max_output_tokens": null, "parameters": "648679441", "architecture": "GPT-Neo decoder, GPTNeoForCausalLMTiered. 16 layers, hidden 1344, 16 heads, intermediate 10752, context 2048, local window 256, gelu_new, tied embeddings, vocab 50257. Alternating global and local attention. Hub parameter count is 648,679,441. The repo includes key_5pct.json and training_state.pt. There is no README. The June 2026 paper says TLM-650M was pretrained on English FineWeb at 100 tokens per parameter, with a key over about 5 percent of parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TLM-650M is one of the two Tiered Language Models the McGill NLP and Mila paper says it pretrained and released. The Hub repo is dated 18 June 2026. There is no model-card README. Weights were still public on 2026-10-15. It is not an API product.", "what_it_is_for": "The paper defines a TLM as one weight file with a public configuration and a keyed configuration. A permutation over about 5 percent of parameters is the key. This is the TLM-650M pretraining run on English FineWeb, not the later August ladder checkpoint of a similar size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcgill/tlm/tlm-70m": { "id": "mcgill/tlm/tlm-70m", "name": "TLM-70M", "maker": "mcgill", "family": "mcgill/tlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-08-28", "context_length": 2048, "max_output_tokens": null, "parameters": "67408081", "architecture": "GPT-Neo decoder with alternating global and local attention, window 256, gelu_new, tied embeddings, and a biased lm_head. 16 / 384 / 12 layers/hidden/heads. MLP ratio 8. Context 2048. GPT-2 BPE, vocab 50257. Trained as a two-tier TLM: a 5 percent permutation key over attention heads and MLP columns. DataDecide 60M rung (Table 2), verbatim. The repository name is the rounded parameter count, not the rung label. Peak LR 5.8e-4, warmup 1000 steps, 34,286 steps, global batch 96 sequences, 6.74B tokens (100 times the parameter count) on FineWeb retain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TLM-70M is a Tiered Language Model from McGill NLP, with coauthors at Mila and ServiceNow, pretrained from scratch on English FineWeb and released on 28 August 2026 under Apache-2.0. One weight file exposes a public tier and a keyed tier. It was still public on 2026-10-15. It is not an API product.", "what_it_is_for": "The card describes a small open GPT-Neo language model whose 5 percent permutation key swaps attention heads and MLP columns, toggling a second behavior over the same parameters. The June 2026 paper frames that mechanism as access control for open weights. This checkpoint is the 60M rung of the DataDecide-shaped ladder, named for its rounded parameter count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcmaster/eskape/eskape": { "id": "mcmaster/eskape/eskape", "name": "ESKAPE Model", "maker": "mcmaster", "family": "mcmaster/eskape", "status": "active", "confidence": "high", "weights": "closed", "license": "All rights reserved", "release_date": "2025-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Twenty-one hosted predictors: for each of seven bacterial strains, a random forest on Morgan fingerprints, a Chemprop graph neural network, and Chemprop with RDKit features. Trained on binarized growth-inhibition screens of a 2,560-molecule antibiotic-enriched library. The hit cutoff on each screen is the mean optical density minus one standard deviation. Scores are confidence values from 0 to 1, not potency.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The ESKAPE Model is a free web tool from the Stokes Lab at McMaster University in Hamilton. It scores small molecules for predicted growth inhibition of ESKAPE pathogens and two E. coli strains. Graduate student Autumn Arnold led the screens and models. McMaster announced the public site on 27 January 2025. It was still online on the collection date, with a footer covering 2023-2026. Weights were not public. Collected 2026-10-15.", "what_it_is_for": "The site says it helps find antibacterial molecules against the ESKAPE pathogens, which the page identifies as a World Health Organization priority group for new treatments. A user submits structures and receives a confidence score for each strain and each of three model types. The lab's stated reason for shipping the site before a paper was to let other scientists screen molecules without training their own models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcmaster/imitsat/imitsat": { "id": "mcmaster/imitsat/imitsat", "name": "ImitSAT", "maker": "mcmaster", "family": "mcmaster/imitsat", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive Perceiver-AR transformer used as a branching policy inside conflict-driven clause learning SAT solvers. It is trained by imitation on expert KeyTrace decision sequences rather than by reinforcement learning. OpenReview says the policy has about 160 million parameters and was trained on 4 V100 GPUs. The released weight file is model_ckpt/ImitSAT.npz. The repo also adapts MiniSat and Kissat, which keep their own MIT notices.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ImitSAT is a learned branching policy for SAT solvers from McMaster University, with a corresponding author at Xi'an Jiaotong University. It imitates expert KeyTrace decisions inside conflict-driven clause learning instead of exploring with reinforcement learning. The authors released code and a trained checkpoint with the 29 September 2025 preprint. The work was accepted at ICLR 2026. Collected 2026-10-15.", "what_it_is_for": "The paper says ImitSAT learns the sequence of decisions that survive in an expert solver trace. Replaying that trace on the same instance is nearly conflict-free, so the policy gets one supervised example per prefix. The authors say it cuts propagations, which dominate solver runtime, and that a model trained only on random 3-SAT still transfers to unsatisfiable and non-k-SAT benchmarks. The public run path is the GitHub checkpoint, not a hosted API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcmaster/medte/medte": { "id": "mcmaster/medte/medte", "name": "MedTE", "maker": "mcmaster", "family": "mcmaster/medte", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-25", "context_length": 512, "max_output_tokens": null, "parameters": "109480000", "architecture": "BertModel fine-tuned from GTE-base with self-supervised InfoNCE contrastive learning and mean pooling. Config on the released checkpoint: 12 layers, 12 heads, hidden size 768, intermediate size 3072, vocab size 30522, absolute positions, max position embeddings 512, GELU. The paper's Table 3 lists 109.48 million parameters, a 768-dimensional embedding, and contrastive training. The designated public checkpoint is CL15 step 8000. Training used 2,033,800 positive pairs, batch size 1024, 8000 steps, AdamW, and DeepSpeed. Other CTMEDBERT and CTMEDGTE step folders on the same Hub user are not this release.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedTE is a medical text embedding model from McMaster University's Department of Electrical and Computer Engineering. Mohammad Khodadad, Ali Shiraee Kasmaee, Mahdi Astaraki, and Hamidreza Mahyar fine-tuned GTE-base with contrastive learning on biomedical and clinical pairs. The preprint of 25 July 2025 names the public checkpoint CL15 step 8000. That Hub repo was still up on the collection date. Collected 2026-10-15.", "what_it_is_for": "The paper says general embedding models and older biomedical encoders miss the range of clinical and biomedical wording, and that existing medical benchmarks are too narrow. MedTE is their encoder for retrieval, classification, clustering, and pair classification on that text. MedTEB, the 51-task suite in the same paper, is a benchmark, not a second model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcmaster/synthemol/synthemol-mcts": { "id": "mcmaster/synthemol/synthemol-mcts", "name": "SyntheMol-MCTS", "maker": "mcmaster", "family": "mcmaster/synthemol", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-03-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Monte Carlo tree search over molecular building blocks and known reactions, scored by a separate bioactivity model. The 2024 paper uses about 132,000 fragments and 13 reactions to search a space of nearly 30 billion molecules, guided by a Chemprop message-passing network and a toxicity model. The current repository still ships this search as SyntheMol-MCTS. The default chemical spaces in the 2026 code are larger (Enamine REAL and WuXi GalaXi). No single parameter count was published for the search procedure.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SyntheMol-MCTS is the original SyntheMol generative model from McMaster University and Stanford University. It searches a combinatorial chemical space with Monte Carlo tree search and a bioactivity scorer, and it was used to design antibiotic candidates against Acinetobacter baumannii. The code remains in the public repository. The maintainers now treat reinforcement learning as the default and call this search SyntheMol-MCTS. Collected 2026-10-15.", "what_it_is_for": "The Nature Machine Intelligence paper says SyntheMol designs new compounds that are easy to synthesize, from a chemical space of nearly 30 billion molecules, and applies that search to inhibitors of Acinetobacter baumannii. McMaster corresponding author Jonathan M. Stokes is in Biochemistry and Biomedical Sciences. Stanford authors include Kyle Swanson and James Zou. The repository is the public implementation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcmaster/synthemol/synthemol-rl": { "id": "mcmaster/synthemol/synthemol-rl", "name": "SyntheMol-RL", "maker": "mcmaster", "family": "mcmaster/synthemol", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-05-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reinforcement learning over the same building-block and reaction chemical space as SyntheMol, guided by property models. The journal article says it searches 46 billion compounds, generalizes across chemically similar building blocks, and optimizes more than one property. The code supports an RL-Chemprop policy and an RL-MLP policy. Score models in the S. aureus study are Chemprop, Chemprop-RDKit, and an RDKit multilayer perceptron. The README calls SyntheMol-RL the default search.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SyntheMol-RL is the reinforcement-learning successor to SyntheMol, developed by McMaster University with Stanford University. It designs small molecules that can be made from catalog building blocks, and the published application is antibiotics against Staphylococcus aureus, including the experimentally tested compound synthecin. The code has been public since 12 May 2025. The journal article appeared on 23 April 2026. The repository treats this search as the default. Collected 2026-10-15.", "what_it_is_for": "The Molecular Systems Biology paper says SyntheMol-RL designs synthetically accessible drug candidates from a space of 46 billion compounds and improves on the earlier Monte Carlo tree search by generalizing across similar building blocks and optimizing more than one property. The S. aureus study jointly targets antibacterial activity and aqueous solubility. McMaster authors include Gary Liu, Denise B. Catacutan, and Jonathan M. Stokes. Stanford authors include Kyle Swanson and James Zou.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mcmaster/trajloom/trajloom": { "id": "mcmaster/trajloom/trajloom", "name": "TrajLoom", "maker": "mcmaster", "family": "mcmaster/trajloom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three pieces in one framework. Grid-anchor offset encoding stores each point as an offset from a pixel-center anchor. TrajLoom-VAE compresses dense trajectory history into a spatiotemporal latent with masked reconstruction and a consistency regularizer. TrajLoom-Flow generates future latents with flow matching, boundary hints, and on-policy K-step fine-tuning, then the VAE decodes them. A separate visibility predictor is released with the generator. The paper says the prediction horizon is 81 frames, versus 24 for prior methods they compare. No parameter count was stated on the model card.", "modalities": { "input": [ "video" ], "output": [ "trajectory" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TrajLoom is a video motion model from McMaster University with the University of British Columbia, the Vector Institute, and Viggle AI. It predicts dense future point trajectories and visibility from observed video and past tracks. Weights and code were released with the 23 March 2026 preprint. The checkpoints were still on Hugging Face on the collection date. Collected 2026-10-15.", "what_it_is_for": "The paper says dense point tracks are a motion representation for video understanding and controllable video generation. TrajLoom extends the prediction horizon the authors cite for earlier methods, from 24 frames to 81, and is meant to feed downstream video generation and editing. First author Zewei Zhang and Jun Chen list McMaster. The public weights are under zeweizhang/TrajLoom, not the empty McMaster Hub orgs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/audio/longcat-audio-codec": { "id": "meituan/audio/longcat-audio-codec", "name": "LongCat-Audio-Codec", "maker": "meituan", "family": "meituan/audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech tokenizer and detokenizer for speech language models. The repository lists a semantic-plus-acoustic encoder, a CMVN file, a 16 kHz decoder with up to 4 codebooks, and 24 kHz decoders with 2 or 4 codebooks. One 24 kHz decoder is described as supervised on a limited speaker set.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Audio-Codec is Meituan's open speech tokenizer and detokenizer for speech language models. It is a set of encoder and decoder checkpoints at 16 kHz and 24 kHz, not a chatbot. The pieces are grouped here as one named release because the maker ships them as one codec. Collected 2026-10-15.", "what_it_is_for": "LongCat-Audio-Codec is Meituan's open speech tokenizer and detokenizer for speech language models. It is a set of encoder and decoder checkpoints at 16 kHz and 24 kHz, not a chatbot. The pieces are grouped here as one named release because the maker ships them as one codec.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/audio/longcat-audiodit-1b": { "id": "meituan/audio/longcat-audiodit-1b", "name": "LongCat-AudioDiT-1B", "maker": "meituan", "family": "meituan/audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-03-30", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Non-autoregressive diffusion TTS in the waveform latent space, with a waveform VAE and a diffusion backbone. The card also calls the system LongCat-TTS. Inference examples use 16 steps, 24 kHz output, and either classifier-free guidance or adaptive projection guidance. Supports zero-shot TTS and voice cloning.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-AudioDiT-1B is the smaller open diffusion text-to-speech model from Meituan. It synthesizes speech, including zero-shot voice cloning, by diffusing in a waveform latent space instead of predicting mel spectrograms. The 3.5B sibling scores higher on the vendor's speaker-similarity columns. Collected 2026-10-15.", "what_it_is_for": "LongCat-AudioDiT-1B is the smaller open diffusion text-to-speech model from Meituan. It synthesizes speech, including zero-shot voice cloning, by diffusing in a waveform latent space instead of predicting mel spectrograms. The 3.5B sibling scores higher on the vendor's speaker-similarity columns.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/audio/longcat-audiodit-3-5b": { "id": "meituan/audio/longcat-audiodit-3-5b", "name": "LongCat-AudioDiT-3.5B", "maker": "meituan", "family": "meituan/audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-03-30", "context_length": null, "max_output_tokens": null, "parameters": "3.5B in the model id and card table. Hugging Face size label is 4B.", "architecture": "Largest LongCat-AudioDiT / LongCat-TTS diffusion backbone. Same waveform-latent diffusion design as the 1B model. The abstract says this variant is the one that exceeds the previous Seed-TTS speaker-similarity numbers cited by the authors.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-AudioDiT-3.5B is the larger open LongCat diffusion TTS model. On the vendor Seed table it leads the 1B checkpoint on speaker similarity for Chinese, English, and the hard Chinese set. Meituan presents it as a zero-shot voice-cloning model operating in the waveform latent space. Collected 2026-10-15.", "what_it_is_for": "LongCat-AudioDiT-3.5B is the larger open LongCat diffusion TTS model. On the vendor Seed table it leads the 1B checkpoint on speaker similarity for Chinese, English, and the hard Chinese set. Meituan presents it as a zero-shot voice-cloning model operating in the waveform latent space.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/evocua/evocua-32b": { "id": "meituan/evocua/evocua-32b", "name": "EvoCUA-32B", "maker": "meituan", "family": "meituan/evocua", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-05", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Larger EvoCUA computer-use model from the Meituan LongCat team. Hugging Face tags the 8B sibling, and this repo, as qwen3_vl. The exact base checkpoint id was not in the fetched README excerpt.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EvoCUA-32B is Meituan's larger open computer-use model, released 2026-01-05 as EvoCUA-32B-20260105. The project README reports 56.7% on OSWorld and presents it as the leading open model on that board at the time. License is Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "EvoCUA-32B is Meituan's larger open computer-use model, released 2026-01-05 as EvoCUA-32B-20260105. The project README reports 56.7% on OSWorld and presents it as the leading open model on that board at the time. License is Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/evocua/evocua-8b": { "id": "meituan/evocua/evocua-8b", "name": "EvoCUA-8B", "maker": "meituan", "family": "meituan/evocua", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-13", "context_length": null, "max_output_tokens": null, "parameters": "8B in the model name and release note. Hugging Face safetensors label is 9B.", "architecture": "Computer-use multimodal agent from the Meituan LongCat team. Hugging Face tags the architecture as qwen3_vl. The fetched card excerpt does not name the exact base checkpoint. English and Chinese tags are on the Hub.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EvoCUA-8B is Meituan's smaller open computer-use agent. The January 2026 release note says it scores 46.1% on OSWorld and is meant to operate desktop software from screenshots and instructions. Weights are Apache-2.0 under the dated id EvoCUA-8B-20260105. The Hub tags the stack as Qwen3-VL style. Collected 2026-10-15.", "what_it_is_for": "EvoCUA-8B is Meituan's smaller open computer-use agent. The January 2026 release note says it scores 46.1% on OSWorld and is meant to operate desktop software from screenshots and instructions. Weights are Apache-2.0 under the dated id EvoCUA-8B-20260105. The Hub tags the stack as Qwen3-VL style.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/flash/longcat-flash-chat": { "id": "meituan/flash/longcat-flash-chat", "name": "LongCat-Flash-Chat", "maker": "meituan", "family": "meituan/flash", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-08-29", "context_length": 128000, "max_output_tokens": null, "parameters": "560B total; 18.6B-31.3B activated (about 27B average)", "architecture": "Shortcut-connected Mixture-of-Experts (ScMoE) with Multi-head Latent Attention and zero-computation experts. The technical report specifies 28 layers (excluding MTP), hidden size 6144, 64 attention heads of dimension 128, KV compression 512, query compression 1536, dense FFN intermediate 12288, expert FFN 2048, 512 FFN experts plus 256 zero-computation experts, and 12 experts selected per token. Activation is 18.6B-31.3B parameters, about 27B on average, out of 560B total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meituan released LongCat-Flash-Chat as the non-thinking chat model of the LongCat-Flash line. The maker positions it for general conversation and agentic tool use, with dynamic expert activation so only part of the 560B model runs per token. Open weights remain on Hugging Face. The LongCat API stopped serving this model name on 2026-05-29. Collected 2026-10-15.", "what_it_is_for": "Meituan released LongCat-Flash-Chat as the non-thinking chat model of the LongCat-Flash line. The maker positions it for general conversation and agentic tool use, with dynamic expert activation so only part of the 560B model runs per token. Open weights remain on Hugging Face. The LongCat API stopped serving this model name on 2026-05-29.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/flash/longcat-flash-chat-2602-exp": { "id": "meituan/flash/longcat-flash-chat-2602-exp", "name": "LongCat-Flash-Chat-2602-Exp", "maker": "meituan", "family": "meituan/flash", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Flash-Chat-2602-Exp is an experimental LongCat API model. The only official mention found is the 2026-05-29 changelog, which retires it together with the other legacy Flash API ids. No model card, weight repo, or feature note was found, so almost every spec is unknown. Collected 2026-10-15.", "what_it_is_for": "LongCat-Flash-Chat-2602-Exp is an experimental LongCat API model. The only official mention found is the 2026-05-29 changelog, which retires it together with the other legacy Flash API ids. No model card, weight repo, or feature note was found, so almost every spec is unknown.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/flash/longcat-flash-lite": { "id": "meituan/flash/longcat-flash-lite", "name": "LongCat-Flash-Lite", "maker": "meituan", "family": "meituan/flash", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-05", "context_length": 256000, "max_output_tokens": null, "parameters": "68.5B total; 2.9B-4.5B activated (about 3B). More than 30B of the total is the N-gram embedding table.", "architecture": "Non-thinking Mixture-of-Experts derived from LongCat-Flash, plus an N-gram embedding table (MoE+NE). Context of 256k is via YaRN. Recommended sampling: repetition_penalty 1.06, temperature 0.7, top_p 0.95, top_k 4.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Flash-Lite is Meituan's smaller non-thinking Flash model. It keeps a Mixture-of-Experts layout but spends a large share of parameters on an N-gram embedding table so activation stays near 3B. The maker aims it at agent tool use and coding at a much lower inference cost than the 560B Flash models. Open weights remain; the API listing was retired on 2026-05-29. Collected 2026-10-15.", "what_it_is_for": "LongCat-Flash-Lite is Meituan's smaller non-thinking Flash model. It keeps a Mixture-of-Experts layout but spends a large share of parameters on an N-gram embedding table so activation stays near 3B. The maker aims it at agent tool use and coding at a much lower inference cost than the 560B Flash models. Open weights remain; the API listing was retired on 2026-05-29.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/flash/longcat-flash-lite-sparse": { "id": "meituan/flash/longcat-flash-lite-sparse", "name": "LongCat-Flash-Lite-Sparse", "maker": "meituan", "family": "meituan/flash", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-07-31", "context_length": 1000000, "max_output_tokens": null, "parameters": "69B total; about 3B activated", "architecture": "Non-thinking MoE built on LongCat-Flash-Lite. Dense MLA is replaced by LongCat Sparse Attention (streaming-aware, cross-layer, and hierarchical indexing). Native context is described as up to 1M tokens, with long-context training scaled to 1,024K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Flash-Lite-Sparse is the long-context revision of LongCat-Flash-Lite. Meituan replaces dense attention with LongCat Sparse Attention and says the model natively supports a 1M-token context while staying near 3B activated parameters. It is released as open weights for agentic coding and tool use, not as a current named API model. Collected 2026-10-15.", "what_it_is_for": "LongCat-Flash-Lite-Sparse is the long-context revision of LongCat-Flash-Lite. Meituan replaces dense attention with LongCat Sparse Attention and says the model natively supports a 1M-token context while staying near 3B activated parameters. It is released as open weights for agentic coding and tool use, not as a current named API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/flash/longcat-flash-omni": { "id": "meituan/flash/longcat-flash-omni", "name": "LongCat-Flash-Omni", "maker": "meituan", "family": "meituan/flash", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-11-03", "context_length": null, "max_output_tokens": null, "parameters": "560B total; about 27B activated", "architecture": "LongCat-Flash ScMoE backbone plus multimodal perception and speech reconstruction. Meituan describes an end-to-end omni model for offline understanding and real-time audio-visual interaction, using a chunked audio-visual interleave.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Flash-Omni is Meituan's open omni-modal model on the 560B Flash backbone. It takes text, image, audio, and video and can answer with text or speech, including real-time audio-visual interaction. Weights are MIT. A later API snapshot, LongCat-Flash-Omni-2603, was a separate served upgrade and was then retired. Collected 2026-10-15.", "what_it_is_for": "LongCat-Flash-Omni is Meituan's open omni-modal model on the 560B Flash backbone. It takes text, image, audio, and video and can answer with text or speech, including real-time audio-visual interaction. Weights are MIT. A later API snapshot, LongCat-Flash-Omni-2603, was a separate served upgrade and was then retired.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/flash/longcat-flash-omni-2603": { "id": "meituan/flash/longcat-flash-omni-2603", "name": "LongCat-Flash-Omni-2603", "maker": "meituan", "family": "meituan/flash", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "API changelog calls it an upgraded end-to-end Omni interaction model over LongCat-Flash-Omni, with native voice function calls. A separate architecture sheet was not published.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Flash-Omni-2603 was a March 2026 API upgrade of LongCat-Flash-Omni. Meituan described more natural dialogue, stronger vision-speech-text accuracy, and native voice function calls, and offered it on LongCat Chat and as model=LongCat-Flash-Omni-2603. The platform retired it on 2026-05-29. No public checkpoint under this name was found. Collected 2026-10-15.", "what_it_is_for": "LongCat-Flash-Omni-2603 was a March 2026 API upgrade of LongCat-Flash-Omni. Meituan described more natural dialogue, stronger vision-speech-text accuracy, and native voice function calls, and offered it on LongCat Chat and as model=LongCat-Flash-Omni-2603. The platform retired it on 2026-05-29. No public checkpoint under this name was found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/flash/longcat-flash-prover": { "id": "meituan/flash/longcat-flash-prover", "name": "LongCat-Flash-Prover", "maker": "meituan", "family": "meituan/flash", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-03-20", "context_length": null, "max_output_tokens": null, "parameters": "560B", "architecture": "560B open Mixture-of-Experts for native formal reasoning in Lean 4. Uses agentic tool-integrated reasoning, Lean verification, and checks against theorem inconsistency and cheating patterns. The GitHub description calls the activated-parameter story the Flash line; a separate activated-parameter figure for Prover was not restated in the fetched introduction.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Flash-Prover is Meituan's open 560B specialist for Lean 4 formalization and theorem proving. It is built to draft, check, and repair proofs with a Lean tool loop rather than to serve as a general chatbot. Weights are MIT on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "LongCat-Flash-Prover is Meituan's open 560B specialist for Lean 4 formalization and theorem proving. It is built to draft, check, and repair proofs with a Lean tool loop rather than to serve as a general chatbot. Weights are MIT on Hugging Face.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/flash/longcat-flash-thinking": { "id": "meituan/flash/longcat-flash-thinking", "name": "LongCat-Flash-Thinking", "maker": "meituan", "family": "meituan/flash", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-22", "context_length": null, "max_output_tokens": null, "parameters": "560B total; 18.6B-31.3B activated (about 27B average)", "architecture": "Reasoning post-train of LongCat-Flash. Same ScMoE and zero-computation experts as LongCat-Flash-Chat. Trained with long chain-of-thought cold start and large-scale reinforcement learning in the DORA framework.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Flash-Thinking is Meituan's first open reasoning model on the Flash MoE. It is meant for step-by-step math, coding, and agent tasks, with an explicit thinking mode on longcat.chat. The open weights remain available. On 2026-03-12 the API name was silently routed to LongCat-Flash-Thinking-2601, and the platform retired the name on 2026-05-29. Collected 2026-10-15.", "what_it_is_for": "LongCat-Flash-Thinking is Meituan's first open reasoning model on the Flash MoE. It is meant for step-by-step math, coding, and agent tasks, with an explicit thinking mode on longcat.chat. The open weights remain available. On 2026-03-12 the API name was silently routed to LongCat-Flash-Thinking-2601, and the platform retired the name on 2026-05-29.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/flash/longcat-flash-thinking-2601": { "id": "meituan/flash/longcat-flash-thinking-2601", "name": "LongCat-Flash-Thinking-2601", "maker": "meituan", "family": "meituan/flash", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-01-14", "context_length": null, "max_output_tokens": null, "parameters": "560B total; about 27B activated", "architecture": "Updated Flash reasoning MoE (560B total, about 27B active). Adds large-scale multi-environment reinforcement learning and a Heavy Thinking mode that scales width by parallel trajectories and depth by a recursive summary model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Flash-Thinking-2601 is the January 2026 update of Meituan's Flash reasoning model. The maker emphasizes agent search, tool use, and noisy real-world tools, plus a Heavy Thinking mode that runs parallel traces and then summarizes them. Weights stay open under MIT. The LongCat API retired the name on 2026-05-29. Collected 2026-10-15.", "what_it_is_for": "LongCat-Flash-Thinking-2601 is the January 2026 update of Meituan's Flash reasoning model. The maker emphasizes agent search, tool use, and noisy real-world tools, plus a Heavy Thinking mode that runs parallel traces and then summarizes them. Weights stay open under MIT. The LongCat API retired the name on 2026-05-29.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/flash/longcat-flash-thinking-zigzag": { "id": "meituan/flash/longcat-flash-thinking-zigzag", "name": "LongCat-Flash-Thinking-ZigZag", "maker": "meituan", "family": "meituan/flash", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-01-23", "context_length": 1000000, "max_output_tokens": null, "parameters": "562B on the Hugging Face model-size label; vendor text says it is LongCat-Flash-Thinking-2601 plus LoZA (that line is described as 560B total and about 27B activated)", "architecture": "LongCat-Flash-Thinking-2601 with LongCat ZigZag Attention (LoZA). The card says attention sparsity is 50 percent, so attention compute can drop by about half, and YaRN extends context to 1M tokens. The technical report notes block size 128, 1 sink block, and 7 local blocks (1024 tokens).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Flash-Thinking-ZigZag is Meituan's sparse-attention twin of LongCat-Flash-Thinking-2601. The only stated change is LongCat ZigZag Attention, which sparsifies attention and is paired with YaRN so the model can run contexts the card describes as 1M tokens. It is an open-weight efficiency variant, not a separate chat product. Collected 2026-10-15.", "what_it_is_for": "LongCat-Flash-Thinking-ZigZag is Meituan's sparse-attention twin of LongCat-Flash-Thinking-2601. The only stated change is LongCat ZigZag Attention, which sparsifies attention and is paired with YaRN so the model can run contexts the card describes as 1M tokens. It is an open-weight efficiency variant, not a separate chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/flash/longcat-heavy-mode-summary": { "id": "meituan/flash/longcat-heavy-mode-summary", "name": "LongCat-HeavyMode-Summary", "maker": "meituan", "family": "meituan/flash", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-01-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Summary model for Heavy Thinking mode. The card says it is further trained from LongCat-Flash-Thinking-2601 so parallel reasoning traces can be merged and fed back recursively. A separate layer config was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-HeavyMode-Summary is the open summary checkpoint Meituan uses for Heavy Thinking mode. Multiple reasoning traces from LongCat-Flash-Thinking-2601 are merged by this model, and the summary can be fed back for another pass. It is a named weight release, not only an inference flag. Collected 2026-10-15.", "what_it_is_for": "LongCat-HeavyMode-Summary is the open summary checkpoint Meituan uses for Heavy Thinking mode. Multiple reasoning traces from LongCat-Flash-Thinking-2601 are merged by this model, and the summary can be fed back for another pass. It is a named weight release, not only an inference flag.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/image/longcat-image": { "id": "meituan/image/longcat-image", "name": "LongCat-Image", "maker": "meituan", "family": "meituan/image", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-05", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "Bilingual Chinese-English text-to-image foundation model. The card highlights character-level encoding for text placed in quotation marks. Diffusers pipeline LongCatImagePipeline. Example generation uses 50 steps and guidance scale 4.0. CPU offload is documented at about 17 GB.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Image is Meituan's open 6B bilingual text-to-image model. The maker stresses Chinese and English text rendering, photorealism, and a small enough model to run with Diffusers. Final inference weights are separate from the Dev checkpoint and from the Edit models. Collected 2026-10-15.", "what_it_is_for": "LongCat-Image is Meituan's open 6B bilingual text-to-image model. The maker stresses Chinese and English text rendering, photorealism, and a small enough model to run with Diffusers. Final inference weights are separate from the Dev checkpoint and from the Edit models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/image/longcat-image-dev": { "id": "meituan/image/longcat-image-dev", "name": "LongCat-Image-Dev", "maker": "meituan", "family": "meituan/image", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mid-training text-to-image checkpoint of the LongCat-Image line, published for fine-tuning rather than as the final inference default.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Image-Dev is the named mid-training checkpoint of Meituan's text-to-image model. The GitHub release table says it is for development and fine-tuning, while LongCat-Image is the final inference model. It has its own Hugging Face repo and Apache-2.0 license. Collected 2026-10-15.", "what_it_is_for": "LongCat-Image-Dev is the named mid-training checkpoint of Meituan's text-to-image model. The GitHub release table says it is for development and fine-tuning, while LongCat-Image is the final inference model. It has its own Hugging Face repo and Apache-2.0 license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/image/longcat-image-edit": { "id": "meituan/image/longcat-image-edit", "name": "LongCat-Image-Edit", "maker": "meituan", "family": "meituan/image", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-editing model in the LongCat-Image family. Supports global, local, text, and reference-guided edits, with bilingual prompts. Diffusers class LongCatImageEditPipeline. The card's CPU-offload note says about 18 GB. Example uses 50 steps and guidance scale 4.5.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Image-Edit is Meituan's open image editor in the LongCat-Image family. It takes an image plus a bilingual instruction and is aimed at precise local and global edits while leaving untargeted regions unchanged. Weights are Apache-2.0. A distilled faster sibling was released later. Collected 2026-10-15.", "what_it_is_for": "LongCat-Image-Edit is Meituan's open image editor in the LongCat-Image family. It takes an image plus a bilingual instruction and is aimed at precise local and global edits while leaving untargeted regions unchanged. Weights are Apache-2.0. A distilled faster sibling was released later.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/image/longcat-image-edit-turbo": { "id": "meituan/image/longcat-image-edit-turbo", "name": "LongCat-Image-Edit-Turbo", "maker": "meituan", "family": "meituan/image", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Distilled LongCat-Image-Edit. The GitHub news post says it is about 10x faster than LongCat-Image-Edit.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Image-Edit-Turbo is the distilled, faster edition of LongCat-Image-Edit. Meituan's GitHub news on 2026-02-03 says the distillation yields about a 10x speedup. It is a separate open Apache-2.0 checkpoint, later noted as supported in ComfyUI. Collected 2026-10-15.", "what_it_is_for": "LongCat-Image-Edit-Turbo is the distilled, faster edition of LongCat-Image-Edit. Meituan's GitHub news on 2026-02-03 says the distillation yields about a 10x speedup. It is a separate open Apache-2.0 checkpoint, later noted as supported in ComfyUI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/longcat/longcat-2-0": { "id": "meituan/longcat/longcat-2-0", "name": "LongCat-2.0", "maker": "meituan", "family": "meituan/longcat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-06-30", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "1.6T total; about 48B activated per token, including 135B N-gram embedding parameters", "architecture": "Large MoE with LongCat Sparse Attention (streaming-aware, cross-layer, and hierarchical indexing) and N-gram embeddings inherited from LongCat-Flash-Lite. Trained on more than 35 trillion tokens on AI ASIC superpods. Thinking can be toggled in the chat template. A 3-step multi-token prediction module is described for speculative decoding.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 1.2 }, "summary_intro": "LongCat-2.0 is Meituan's current open flagship language model and a first-party API model. It is a 1.6T Mixture-of-Experts aimed at agentic coding and long-horizon tool use, with a 1M-token context on the API and a switch for thinking. Weights are MIT, including FP8 and INT8 builds. LongCat-2.5-Preview is the newer preview on the same API. Collected 2026-10-15.", "what_it_is_for": "LongCat-2.0 is Meituan's current open flagship language model and a first-party API model. It is a 1.6T Mixture-of-Experts aimed at agentic coding and long-horizon tool use, with a 1M-token context on the API and a switch for thinking. Weights are MIT, including FP8 and INT8 builds. LongCat-2.5-Preview is the newer preview on the same API.", "ratings": { "empirical_100": 53, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/longcat/longcat-2-5-preview": { "id": "meituan/longcat/longcat-2-5-preview", "name": "LongCat-2.5-Preview", "maker": "meituan", "family": "meituan/longcat", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-25", "context_length": 1000000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 1.2 }, "summary_intro": "LongCat-2.5-Preview is the newest named model on the LongCat API, launched 2026-09-25. Meituan describes image understanding, stronger coding, and compatibility with Claude Code, Hermes, OpenClaw, OpenCode, and Kilo Code. It is a closed preview: no weight repository was found. Context and price match the published LongCat-2.0 API limits and the same limited-time token rates. Collected 2026-10-15.", "what_it_is_for": "LongCat-2.5-Preview is the newest named model on the LongCat API, launched 2026-09-25. Meituan describes image understanding, stronger coding, and compatibility with Claude Code, Hermes, OpenClaw, OpenCode, and Kilo Code. It is a closed preview: no weight repository was found. Context and price match the published LongCat-2.0 API limits and the same limited-time token rates.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/longcat/longcat-deepresearch": { "id": "meituan/longcat/longcat-deepresearch", "name": "LongCat-DeepResearch", "maker": "meituan", "family": "meituan/longcat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "A research harness plus a LongCat-2.0-based model that Meituan says was enhanced for deep research. The repository states that only the harness is released. The enhanced weights are not in the repo. The harness repository is MIT; a weight license was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-DeepResearch is Meituan's named deep-research system. The public repository is a planning and writing harness that calls any OpenAI-compatible model plus search and fetch tools. Meituan says their best reported scores use a LongCat-2.0 model enhanced for research, and that those weights are not part of the release. The Hugging Face organization card says the improvements are intended for a later general LongCat model. Collected 2026-10-15.", "what_it_is_for": "LongCat-DeepResearch is Meituan's named deep-research system. The public repository is a planning and writing harness that calls any OpenAI-compatible model plus search and fetch tools. Meituan says their best reported scores use a LongCat-2.0 model enhanced for research, and that those weights are not part of the release. The Hugging Face organization card says the improvements are intended for a later general LongCat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/memocr/memocr-7b": { "id": "meituan/memocr/memocr-7b", "name": "MemOCR-7B", "maker": "meituan", "family": "meituan/memocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-24", "context_length": null, "max_output_tokens": null, "parameters": "7B in the model name. The card says it is fine-tuned from Qwen2.5-VL-7B-Instruct. Hugging Face safetensors label is 8B.", "architecture": "Visual memory agent. It keeps a structured rich-text memory, renders that memory to an image, and reads it back with a vision-language model so long-horizon tasks can stay inside a token budget. Fine-tuned from Qwen/Qwen2.5-VL-7B-Instruct with budget-aware objectives. Training used veRL and MemAgent components.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MemOCR-7B is a named Meituan vision-language agent for long-horizon tasks. It stores memory as laid-out text, renders that memory into an image, and consults the image so important evidence stays visible inside a tight context budget. The checkpoint is an Apache-2.0 fine-tune of Qwen2.5-VL-7B-Instruct. Collected 2026-10-15.", "what_it_is_for": "MemOCR-7B is a named Meituan vision-language agent for long-horizon tasks. It stores memory as laid-out text, renders that memory into an image, and consults the image so important evidence stays visible inside a tight context budget. The checkpoint is an Apache-2.0 fine-tune of Qwen2.5-VL-7B-Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/next/longcat-next": { "id": "meituan/next/longcat-next", "name": "LongCat-Next", "maker": "meituan", "family": "meituan/next", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-03-25", "context_length": null, "max_output_tokens": null, "parameters": "Hugging Face size label 74B. The card calls the language backbone LongCat-Flash-Lite MoE (A3B), about 68.5B total and about 3B activated, and does not publish a separate full-system parameter equation.", "architecture": "Discrete native autoregressive model (DiNA). Text, image, and audio share one next-token objective. Vision uses dNaViT, semantic-and-aligned encoders, and residual vector quantization. The card says visual tokens can be compressed up to 28x. Speech supports understanding, conversation, and voice cloning. Transformers path wants at least 3x 80GB GPUs.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text", "image", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Next is Meituan's open native multimodal model. It treats images and audio as discrete tokens in the same autoregressive stack as text, so one checkpoint can read, generate images, and speak. The language backbone is LongCat-Flash-Lite. Weights and tokenizers are MIT, with a web demo on longcat.chat. Collected 2026-10-15.", "what_it_is_for": "LongCat-Next is Meituan's open native multimodal model. It treats images and audio as discrete tokens in the same autoregressive stack as text, so one checkpoint can read, generate images, and speak. The language backbone is LongCat-Flash-Lite. Weights and tokenizers are MIT, with a web demo on longcat.chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/video/longcat-video": { "id": "meituan/video/longcat-video", "name": "LongCat-Video", "maker": "meituan", "family": "meituan/video", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-25", "context_length": null, "max_output_tokens": null, "parameters": "13.6B", "architecture": "Dense Diffusion Transformer. The report specifies 48 layers, hidden size 4096, FFN hidden 16384, 32 attention heads, and AdaLN embedding 512. Tasks are text-to-video, image-to-video, and video continuation in one model. Inference notes include 720p at 30 fps, coarse-to-fine generation, and block sparse attention. The model card calls the architecture dense, with 13.6B activated.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Video is Meituan's open 13.6B video foundation model. One Diffusion Transformer handles text-to-video, image-to-video, and continuation, and the paper presents long, temporally consistent video as a step toward world models. Weights are MIT. Later avatar models build on this checkpoint. Collected 2026-10-15.", "what_it_is_for": "LongCat-Video is Meituan's open 13.6B video foundation model. One Diffusion Transformer handles text-to-video, image-to-video, and continuation, and the paper presents long, temporally consistent video as a step toward world models. Weights are MIT. Later avatar models build on this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/video/longcat-video-avatar": { "id": "meituan/video/longcat-video-avatar", "name": "LongCat-Video-Avatar", "maker": "meituan", "family": "meituan/video", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Audio-driven character animation on LongCat-Video. GitHub describes audio-text-to-video, audio-text-image-to-video, and video continuation, with single-stream and multi-stream audio. The first release uses a wav2vec2 audio encoder. Hugging Face tags this repo as a base_model of LongCat-Video.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Video-Avatar is Meituan's open audio-driven human video model. It animates characters from speech, with or without a reference image, and can continue an existing video, including multi-speaker audio. The December 2025 release uses wav2vec2. Version 1.5 replaces that encoder. Collected 2026-10-15.", "what_it_is_for": "LongCat-Video-Avatar is Meituan's open audio-driven human video model. It animates characters from speech, with or without a reference image, and can continue an existing video, including multi-speaker audio. The December 2025 release uses wav2vec2. Version 1.5 replaces that encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meituan/video/longcat-video-avatar-1-5": { "id": "meituan/video/longcat-video-avatar-1-5", "name": "LongCat-Video-Avatar-1.5", "maker": "meituan", "family": "meituan/video", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-05-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Upgrade of LongCat-Video-Avatar. GitHub says v1.5 replaces wav2vec2 with Whisper-Large, supports stylized domains, keeps single- and multi-stream audio, and distills inference to 8 steps. A secondary Meituan-attributed write-up says Whisper-large-v3; the GitHub news line says Whisper-Large.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCat-Video-Avatar-1.5 is Meituan's May 2026 upgrade of the open talking-character video model. It swaps in a Whisper-Large audio encoder, aims at more stable long videos and stylized characters, and distills sampling to 8 steps. Weights are MIT. Collected 2026-10-15.", "what_it_is_for": "LongCat-Video-Avatar-1.5 is Meituan's May 2026 upgrade of the open talking-character video model. It swaps in a Whisper-Large audio encoder, aims at more stable long videos and stylized characters, and distills sampling to 8 steps. Weights are MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meizu/flyme/flyme-ai": { "id": "meizu/flyme/flyme-ai", "name": "Flyme AI \u5927\u6a21\u578b", "maker": "meizu", "family": "meizu/flyme", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-11-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flyme AI \u5927\u6a21\u578b is the large-model name Meizu (\u73e0\u6d77\u661f\u7eaa\u9b45\u65cf\u4fe1\u606f\u6280\u672f\u6709\u9650\u516c\u53f8, also DreamSmart) put on the Aicy assistant with Flyme 10.5. It was announced on 30 November 2023 and is still printed on the Meizu 21 spec page. No weight release or model card was found. Later official copy describes Flyme AIOS as an open stack of other labs' models. This dossier was collected on 2026-10-15.", "what_it_is_for": "At the November 2023 unbounded-ecosystem event, Meizu said the new Aicy assistant runs on the Flyme AI large model. Reported jobs were encyclopedia-style Q&A, writing (Weibo posts, thank-you notes, speeches), personal-assistant tasks, and styled image generation (realistic, anime, ink). The same briefing attached gallery tools to Aicy: search by color, action, scene, or mood; expand a frame; erase objects; and make portraits. The assistant was also described for Flyme Auto, for vehicle how-to questions, traffic rules, maintenance, and trip planning. Meizu 21 shipped with Flyme 10.5. A Flyme AI closed beta was set for early December 2023.\n\nThe name is a product engine, not a published checkpoint. On 27 March 2025 DreamSmart's own English news said Flyme AIOS integrates Tongyi Qianwen, Doubao, ERNIE Bot, and DeepSeek, and that the company was first among phone brands to deeply integrate Qwen2.5-Omni-7B. Those are other makers' models and are not folders here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "melbourne/barbet/barbet-archaea-base": { "id": "melbourne/barbet/barbet-archaea-base", "name": "Barbet archaea base", "maker": "melbourne", "family": "melbourne/barbet", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 (code); CC BY 4.0 (weights)", "release_date": "2025-07-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Released file barbet-ar53-ESM12-base.ckpt (139436785 bytes on the Figshare API). The filename uses ESM12, while the worked training example on the docs is ESM6. Feature width for this base file is not stated. The package depends on hierarchicalsoftmax.", "modalities": { "input": [ "dna" ], "output": [ "taxonomy" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Melbourne released Barbet archaea base. This dossier was collected on 2026-10-15 from official pages. Status is active and confidence is high.", "what_it_is_for": "Taxonomic classification of a prokaryotic genome assembly. PyPI summary: Bacteria and Archaea Representation using Bidirectional Embeddings for Taxonomic classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "melbourne/barbet/barbet-archaea-large": { "id": "melbourne/barbet/barbet-archaea-large", "name": "Barbet archaea large", "maker": "melbourne", "family": "melbourne/barbet", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 (code); CC BY 4.0 (weights)", "release_date": "2025-07-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Released file barbet-ar53-ESM12-large.ckpt (321025777 bytes on the Figshare API). The filename uses ESM12. The documented large-model flag --features 1536 was shown with ESM6, so it is not confirmed as this checkpoint's width. The package depends on hierarchicalsoftmax.", "modalities": { "input": [ "dna" ], "output": [ "taxonomy" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Melbourne released Barbet archaea large. This dossier was collected on 2026-10-15 from official pages. Status is active and confidence is high.", "what_it_is_for": "Taxonomic classification of a prokaryotic genome assembly. PyPI summary: Bacteria and Archaea Representation using Bidirectional Embeddings for Taxonomic classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "melbourne/barbet/barbet-bacteria-base": { "id": "melbourne/barbet/barbet-bacteria-base", "name": "Barbet bacteria base", "maker": "melbourne", "family": "melbourne/barbet", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 (code); CC BY 4.0 (weights)", "release_date": "2025-07-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Released file barbet-bac120-ESM6-base.ckpt (2034560080 bytes on the Figshare API). The documented small-model recipe uses precomputed ESM6 embeddings, 70 epochs, and max learning rate 0.0002. The package depends on hierarchicalsoftmax. Default feature width is not stated.", "modalities": { "input": [ "dna" ], "output": [ "taxonomy" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Melbourne released Barbet bacteria base. This dossier was collected on 2026-10-15 from official pages. Status is active and confidence is high.", "what_it_is_for": "Taxonomic classification of a prokaryotic genome assembly. PyPI summary: Bacteria and Archaea Representation using Bidirectional Embeddings for Taxonomic classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "melbourne/barbet/barbet-bacteria-large": { "id": "melbourne/barbet/barbet-bacteria-large", "name": "Barbet bacteria large", "maker": "melbourne", "family": "melbourne/barbet", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 (code); CC BY 4.0 (weights)", "release_date": "2025-07-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Released file barbet-bac120-ESM6-large.ckpt (4004225104 bytes on the Figshare API). The documented large-model recipe matches the small recipe and sets --features 1536, with embedding model ESM6. The package depends on hierarchicalsoftmax.", "modalities": { "input": [ "dna" ], "output": [ "taxonomy" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Melbourne released Barbet bacteria large. This dossier was collected on 2026-10-15 from official pages. Status is active and confidence is high.", "what_it_is_for": "Taxonomic classification of a prokaryotic genome assembly. PyPI summary: Bacteria and Archaea Representation using Bidirectional Embeddings for Taxonomic classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "melbourne/bixt/bixt-ti-16": { "id": "melbourne/bixt/bixt-ti-16", "name": "BiXT-Ti/16", "maker": "melbourne", "family": "melbourne/bixt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bi-directional cross-attention transformer. Released Tiny setting: 64 latents, embedding dimension 192, 12 layers, 6 heads. Cost is linear in input length. The last layer uses one-sided cross-attention so patch tokens are refined through layer 11. Paper Table 2 reports 15M parameters for this Tiny configuration.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Melbourne released BiXT-Ti/16. This dossier was collected on 2026-10-15 from official pages. Status is active and confidence is high.", "what_it_is_for": "ImageNet classification with a bi-directional cross-attention transformer. The weights page is the checkpoint card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "melbourne/bixt/bixt-ti-16-d13": { "id": "melbourne/bixt/bixt-ti-16-d13", "name": "BiXT-Ti/16 (d13)", "maker": "melbourne", "family": "melbourne/bixt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bi-directional cross-attention transformer pretrained at depth 13 with 64 latents and embedding dimension 192. The weights page says the last one-sided cross-attention is dropped for dense downstream transfer, leaving a 12-layer network. An ablation table in the paper lists 16.38M for a different 120-epoch d13 run. That figure is not the released ImageNet d13 checkpoint.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Melbourne released BiXT-Ti/16 (d13). This dossier was collected on 2026-10-15 from official pages. Status is active and confidence is high.", "what_it_is_for": "ImageNet classification with a bi-directional cross-attention transformer. The weights page is the checkpoint card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "melbourne/bixt/bixt-ti-4": { "id": "melbourne/bixt/bixt-ti-4", "name": "BiXT-Ti/4", "maker": "melbourne", "family": "melbourne/bixt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bi-directional cross-attention transformer. Released Tiny setting: 64 latents, embedding dimension 192, 12 layers, 6 heads. Cost is linear in input length. The last layer uses one-sided cross-attention so patch tokens are refined through layer 11. Paper Table 2 reports 15M parameters for this Tiny configuration. Patch stride produces sequence length 3136 at the paper's default resolution.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Melbourne released BiXT-Ti/4. This dossier was collected on 2026-10-15 from official pages. Status is active and confidence is high.", "what_it_is_for": "ImageNet classification with a bi-directional cross-attention transformer. The weights page is the checkpoint card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "melbourne/bixt/bixt-ti-4-d13": { "id": "melbourne/bixt/bixt-ti-4-d13", "name": "BiXT-Ti/4 (d13)", "maker": "melbourne", "family": "melbourne/bixt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bi-directional cross-attention transformer pretrained at depth 13 with 64 latents and embedding dimension 192. The weights page says the last one-sided cross-attention is dropped for dense downstream transfer, leaving a 12-layer network. An ablation table in the paper lists 16.38M for a different 120-epoch d13 run. That figure is not the released ImageNet d13 checkpoint. The weights page does not list a 384 fine-tune for BiXT-Ti/4 (d13).", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Melbourne released BiXT-Ti/4 (d13). This dossier was collected on 2026-10-15 from official pages. Status is active and confidence is high.", "what_it_is_for": "ImageNet classification with a bi-directional cross-attention transformer. The weights page is the checkpoint card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "melbourne/bixt/bixt-ti-8": { "id": "melbourne/bixt/bixt-ti-8", "name": "BiXT-Ti/8", "maker": "melbourne", "family": "melbourne/bixt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bi-directional cross-attention transformer. Released Tiny setting: 64 latents, embedding dimension 192, 12 layers, 6 heads. Cost is linear in input length. The last layer uses one-sided cross-attention so patch tokens are refined through layer 11. Paper Table 2 reports 15M parameters for this Tiny configuration. Patch stride produces sequence length 784 at the paper's default resolution.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Melbourne released BiXT-Ti/8. This dossier was collected on 2026-10-15 from official pages. Status is active and confidence is high.", "what_it_is_for": "ImageNet classification with a bi-directional cross-attention transformer. The weights page is the checkpoint card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "melbourne/bixt/bixt-ti-8-d13": { "id": "melbourne/bixt/bixt-ti-8-d13", "name": "BiXT-Ti/8 (d13)", "maker": "melbourne", "family": "melbourne/bixt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bi-directional cross-attention transformer pretrained at depth 13 with 64 latents and embedding dimension 192. The weights page says the last one-sided cross-attention is dropped for dense downstream transfer, leaving a 12-layer network. An ablation table in the paper lists 16.38M for a different 120-epoch d13 run. That figure is not the released ImageNet d13 checkpoint.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Melbourne released BiXT-Ti/8 (d13). This dossier was collected on 2026-10-15 from official pages. Status is active and confidence is high.", "what_it_is_for": "ImageNet classification with a bi-directional cross-attention transformer. The weights page is the checkpoint card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "melbourne/corgi/corgi": { "id": "melbourne/corgi/corgi", "name": "Corgi", "maker": "melbourne", "family": "melbourne/corgi", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 (code); CC BY 4.0 (v0.5 weights)", "release_date": "2022-09-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DNA sequence classifier. The package depends on hierarchicalsoftmax, Biopython, and torch. Keywords on PyPI include metagenomic classifier and tiara. Layer widths, the label tree, and which v0.5 checkpoint the CLI loads by default are not in the README.", "modalities": { "input": [ "dna" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Melbourne released Corgi. This dossier was collected on 2026-10-15 from official pages. Status is active and confidence is high.", "what_it_is_for": "Classify DNA reads or assemblies, including organelle genomes. GitHub description: Classifier for ORGanelles (Inter alia). PyPI summary: Classifier for ORganelle Genomes Inter alia.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "melbourne/terrier/terrier": { "id": "melbourne/terrier/terrier", "name": "Terrier", "maker": "melbourne", "family": "melbourne/terrier", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 (software). A separate weight-file license is not stated.", "release_date": "2025-03-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural network for hierarchical classification of repeat sequences. The README says it is based on Corgi. PyPI 0.4.0 depends on bio-corgi>=0.5.4 and does not publish layer sizes or a parameter count.", "modalities": { "input": [ "dna" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Melbourne released Terrier. This dossier was collected on 2026-10-15 from official pages. Status is active and confidence is high.", "what_it_is_for": "Classify repetitive DNA under the RepeatMasker schema. The README says to run RepeatModeler first when building a library, then use Terrier on the remaining unknown repeats.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meshy/meshy/meshy-1": { "id": "meshy/meshy/meshy-1", "name": "Meshy 1", "maker": "meshy", "family": "meshy/meshy", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-10-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vendor said it combined 2D-lifting methods and 3D diffusion to get under a minute. Not a published model card.", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meshy 1 is Meshy's first named 3D generation model, announced on 2023-10-19. It turned text or an image into a textured mesh, and it could retexture an existing mesh from text. Meshy claimed results in under a minute. It is not in the current API. No retirement notice was found, so retired status is inferred. Confidence: medium.", "what_it_is_for": "Three modes shared one model: text-to-3D, image-to-3D, and text-to-texture. Meshy told early users to use text for props and a front-view image for characters. Exports at launch included GLB, USDZ, and FBX, with a Unity plugin planned the following week.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meshy/meshy/meshy-2": { "id": "meshy/meshy/meshy-2", "name": "Meshy 2", "maker": "meshy", "family": "meshy/meshy", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-02-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meshy 2 is a retired text-to-3D model announced on 2024-02-05, three months after Meshy 1. Meshy said it produced better-structured meshes and more detailed textures, with four art styles and a mesh editor. It is not in the current API. No retirement notice was found, so retired status is inferred. Confidence: medium.", "what_it_is_for": "Meshy positioned it as the text-to-3D jump after Meshy 1. Users could pick Realistic, Cartoon, Low Poly, or Voxel, then adjust polycount and convert toward quads.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meshy/meshy/meshy-2-5": { "id": "meshy/meshy/meshy-2-5", "name": "Meshy 2.5", "maker": "meshy", "family": "meshy/meshy", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-03-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meshy 2.5 is a retired text-to-3D upgrade announced on 2024-03-21, about six weeks after Meshy 2. Meshy said meshes were cleaner and more detailed than Meshy 2, and it added regional AI texture editing. It is not in the current API. No retirement notice was found, so retired status is inferred. Confidence: medium.", "what_it_is_for": "Meshy described it as a text-to-3D quality pass: more structured meshes, cleaner textures, better preview geometry, and a way to edit texture regions with AI instead of regenerating the whole asset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meshy/meshy/meshy-3": { "id": "meshy/meshy/meshy-3", "name": "Meshy 3", "maker": "meshy", "family": "meshy/meshy", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-04-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meshy 3 is a retired Meshy generation model announced on 2024-04-30. The official post title is sculptures, PBR, and image-to-3D. Its legacy API was deprecated on 2025-01-14. Status on 2026-10-15: retired. A hard shutdown date after that deprecation was not found.", "what_it_is_for": "Meshy announced it as the next text-to-3D and image-to-3D model after Meshy 2.5, with sculpture-style output and PBR called out in the title. When Meshy 4 launched, Meshy still listed Meshy 3 as the legacy text-to-3D choice only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meshy/meshy/meshy-3-turbo": { "id": "meshy/meshy/meshy-3-turbo", "name": "Meshy 3 Turbo", "maker": "meshy", "family": "meshy/meshy", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-07-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion-based generative model, per the launch post. No further architecture name.", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meshy 3 Turbo is a retired fast variant of Meshy 3, announced on 2024-07-03. Meshy described a new diffusion model, up to three times faster, with image-to-3D under 1.5 minutes. It is not in the 2026 API model list. No retirement notice was found, so the retired status is inferred. Confidence: medium.", "what_it_is_for": "Meshy shipped it to speed up Meshy 3 and to add animation-related workflow. At the Meshy 4 launch it remained the fast choice for organic or heavily artistic styles, with less detail than Meshy 4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meshy/meshy/meshy-4": { "id": "meshy/meshy/meshy-4", "name": "Meshy 4", "maker": "meshy", "family": "meshy/meshy", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meshy 4 is a retired Meshy text-and-image-to-3D model, announced on 2024-08-22 as a geometry upgrade over Meshy 3. The API id `meshy-4` was shut off on 2026-03-20. Status on 2026-10-15: retired.", "what_it_is_for": "The launch post says Meshy 4 was the advanced model in the picker, aimed at flat geometry, sharp corners, and complex detail, for both text-to-3D and image-to-3D. Meshy 3 Turbo stayed available as the faster, less detailed option. Meshy 3 stayed as a legacy text-to-3D option.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meshy/meshy/meshy-5": { "id": "meshy/meshy/meshy-5", "name": "Meshy 5", "maker": "meshy", "family": "meshy/meshy", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meshy 5 is a retired Meshy text-and-image-to-3D model. It entered the API as `meshy-5` on 2025-04-29, was called stable on 2025-08-13, left the web model picker in August 2026, and stopped accepting new API jobs on 2026-10-10. Status on 2026-10-15: retired.", "what_it_is_for": "It was the generation model after Meshy 4: multi-image input from one to four views, then a stability pass, PBR maps, and A/T pose controls. Meshy's tutorial page highlights higher-reliability structure and image alignment. Meshy now points former users to Meshy 6 Lite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meshy/meshy/meshy-6": { "id": "meshy/meshy/meshy-6", "name": "Meshy 6", "maker": "meshy", "family": "meshy/meshy", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meshy 6 is Meshy's production text-and-image-to-3D model launched on 2026-01-18, after a preview exposed as `latest` in September 2025. It is still a selectable API id on 2026-10-15. `latest` no longer points here. It points at Meshy 7.1.", "what_it_is_for": "The launch post describes cleaner, more anatomical character geometry, sharper hard-surface edges, a Low Poly mode for games, and hooks into multi-color printing. On the API it remains the full-quality model under `meshy-6`, beside the cheaper `meshy-6-lite` and the newer `meshy-7.1`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meshy/meshy/meshy-6-lite": { "id": "meshy/meshy/meshy-6-lite", "name": "Meshy 6 Lite", "maker": "meshy", "family": "meshy/meshy", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Faster, lighter variant of the Meshy 6 architecture. Vendor description. No further architecture name is published.", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meshy 6 Lite is Meshy's faster, cheaper 3D generator, described as a streamlined Meshy 6. It replaced Meshy 5 in the web model picker in August 2026 and became a documented API id, `meshy-6-lite`, on 2026-09-11. Status on 2026-10-15: active.", "what_it_is_for": "Meshy positions it for quick iteration. The help center says a draft white model is about 8 seconds and that it is the recommended stand-in for discontinued Meshy 5. The API changelog calls it the successor to `meshy-5` at the same credit cost.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meshy/meshy/meshy-7": { "id": "meshy/meshy/meshy-7", "name": "Meshy 7", "maker": "meshy", "family": "meshy/meshy", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-to-3D model with a multi-scale image encoder that accepts higher-resolution images. Geometry was generated at 2048 cubed before Meshy 7.1. Vendor describes geometry alignment as a training evaluation signal.", "modalities": { "input": [ "image", "text" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meshy 7 is Meshy's image-to-3D model released in the workspace on 2026-08-10 as the successor to Meshy 6. It targets geometry alignment with the input image. On 2026-10-15 it is deprecated: the API still accepts `meshy-7`, and `latest` points at Meshy 7.1.", "what_it_is_for": "Meshy built Meshy 7 to make the generated shape match a reference image: overall proportion, where parts sit, and surface detail. It is the generation model behind the August 2026 workspace Image to 3D release, including Ultra Mode for finer surfaces. The 7.1 post says Meshy 7 geometry ran at 2048 cubed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meshy/meshy/meshy-7-1": { "id": "meshy/meshy/meshy-7-1", "name": "Meshy 7.1", "maker": "meshy", "family": "meshy/meshy", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Scaled geometry backbone. Ultra 4K generates geometry at 4096 cubed voxels, up from 2048 cubed on Meshy 7. Vendor says a raw mesh before simplification can reach 80 million triangles.", "modalities": { "input": [ "image", "text" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meshy 7.1 is Meshy's current image-to-3D foundation model and the API default behind `latest`. It is an upgrade of Meshy 7 aimed at geometric detail, with an Ultra 4K mode at 4096 cubed voxels. Status on 2026-10-15: active. Workspace rollout was announced on 2026-09-10; the API id `meshy-7.1` was added on 2026-09-18.", "what_it_is_for": "Meshy says Meshy 7.1 generates the richest geometric detail of its image-to-3D line. Standard, Ultra 2K, and Ultra 4K are geometry settings. Ultra 4K is single-view only. Ultra 2K works for single-view and multi-view. The API also accepts it for text-to-3D preview and multi-image-to-3D.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meshy/smart-topology/meshy-t1": { "id": "meshy/smart-topology/meshy-t1", "name": "Meshy T1", "maker": "meshy", "family": "meshy/smart-topology", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meshy T1 is the first named Smart Topology model. Meshy added `ai_model: \"meshy-t1\"` on 2026-07-13 for image-to-3D and called it the then-current low-poly model. On 2026-09-01 it was removed from the documented list and marked for deprecation in favor of Meshy T2. Status on 2026-10-15: deprecated. A hard shutdown was not confirmed.", "what_it_is_for": "It was the low-poly Smart Topology option when that mode launched. Meshy T2, added the same day, became the default: cleaner topology, separated parts, and a settable face count. T1 has no research post in the sources found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meshy/smart-topology/meshy-t2": { "id": "meshy/smart-topology/meshy-t2", "name": "Meshy T2", "maker": "meshy", "family": "meshy/smart-topology", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vertex-set mesh VAE plus a coarse-to-fine flow-matching cascade: an image-conditioned voxel flow, then a mesh flow that fills per-vertex tokens. Paper and blog. API output is triangles only.", "modalities": { "input": [ "image", "text" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meshy T2 is Meshy's Smart Topology model. It builds a compact triangle mesh directly, with a face budget and native part splits, instead of the high-poly Meshy 6 or 7 path. It has been an API option since 2026-07-13 for images and 2026-08-13 for text. Status on 2026-10-15: active. Weights are still closed. An open-source release was announced and was not confirmed.", "what_it_is_for": "On the API, set `model_type` to `smart-topology` and `ai_model` to `meshy-t2`. Meshy says it is for game and real-time meshes: cleaner topology, separated parts, and a chosen face count. The research writeup describes native mesh generation by flow matching, without iso-surface extraction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/audiogen/audiogen-medium": { "id": "meta/audiogen/audiogen-medium", "name": "AudioGen Medium", "maker": "meta", "family": "meta/audiogen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC 4.0 (weights). Code is MIT.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "EnCodec tokenizer plus an autoregressive transformer for environmental sound. The released checkpoint is version 2, trained July 2023 to August 2023 on 10-second clips. It is not the original 5-second AudioGen from the 2022 paper.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AudioGen Medium is Meta's released 1.5B text-to-sound model (AudioGen v2). Collected 2026-10-15. Weights are CC-BY-NC 4.0. The original 2022 AudioGen paper describes an earlier system; the card says v2 is the checkpoint that was trained in July and August 2023.", "what_it_is_for": "Generate environmental sound from text. The card says this version was trained to 10 seconds rather than the 5 seconds of v1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/code-llama/code-llama-13b": { "id": "meta/code-llama/code-llama-13b", "name": "Code Llama 13B", "maker": "meta", "family": "meta/code-llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License (custom commercial license).", "release_date": "2023-08-24", "context_length": 100000, "max_output_tokens": null, "parameters": "13B", "architecture": "Code model continued from Llama 2. Trained on 16k sequences, with long-context behavior out to 100k except as noted. 7B, 13B, and 34B saw 500B code tokens; the blog says 70B saw 1T. Base and instruct support fill-in-the-middle. Supports up to 100k tokens at inference.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Code Llama 13B is Meta's open-weight code model based on Llama 2. Collected 2026-10-15. The 7B, 13B, and 34B sizes shipped on August 24, 2023. The 70B size shipped on January 29, 2024. Each size has base, Python, and Instruct variants.", "what_it_is_for": "Base models are for code synthesis and understanding. Python variants specialize in Python. Instruct variants are for instruction following and safer code assistance. 7B and 13B are aimed at low-latency completion; 34B and 70B at quality.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/code-llama/code-llama-34b": { "id": "meta/code-llama/code-llama-34b", "name": "Code Llama 34B", "maker": "meta", "family": "meta/code-llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License (custom commercial license).", "release_date": "2023-08-24", "context_length": 100000, "max_output_tokens": null, "parameters": "34B", "architecture": "Code model continued from Llama 2. Trained on 16k sequences, with long-context behavior out to 100k except as noted. 7B, 13B, and 34B saw 500B code tokens; the blog says 70B saw 1T. No infilling. Supports up to 100k tokens at inference. There was no released Llama 2 34B parent.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Code Llama 34B is Meta's open-weight code model based on Llama 2. Collected 2026-10-15. The 7B, 13B, and 34B sizes shipped on August 24, 2023. The 70B size shipped on January 29, 2024. Each size has base, Python, and Instruct variants.", "what_it_is_for": "Base models are for code synthesis and understanding. Python variants specialize in Python. Instruct variants are for instruction following and safer code assistance. 7B and 13B are aimed at low-latency completion; 34B and 70B at quality.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/code-llama/code-llama-70b": { "id": "meta/code-llama/code-llama-70b", "name": "Code Llama 70B", "maker": "meta", "family": "meta/code-llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License (custom commercial license).", "release_date": "2024-01-29", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Code model continued from Llama 2. Trained on 16k sequences, with long-context behavior out to 100k except as noted. 7B, 13B, and 34B saw 500B code tokens; the blog says 70B saw 1T. Base supports infilling and up to 100k. The model card says Python 70B and Instruct 70B were not fine-tuned to 16k and are the exception to the 100k claim.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Code Llama 70B is Meta's open-weight code model based on Llama 2. Collected 2026-10-15. The 7B, 13B, and 34B sizes shipped on August 24, 2023. The 70B size shipped on January 29, 2024. Each size has base, Python, and Instruct variants.", "what_it_is_for": "Base models are for code synthesis and understanding. Python variants specialize in Python. Instruct variants are for instruction following and safer code assistance. 7B and 13B are aimed at low-latency completion; 34B and 70B at quality.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/code-llama/code-llama-7b": { "id": "meta/code-llama/code-llama-7b", "name": "Code Llama 7B", "maker": "meta", "family": "meta/code-llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License (custom commercial license).", "release_date": "2023-08-24", "context_length": 100000, "max_output_tokens": null, "parameters": "7B", "architecture": "Code model continued from Llama 2. Trained on 16k sequences, with long-context behavior out to 100k except as noted. 7B, 13B, and 34B saw 500B code tokens; the blog says 70B saw 1T. Base and instruct support fill-in-the-middle. Supports up to 100k tokens at inference.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Code Llama 7B is Meta's open-weight code model based on Llama 2. Collected 2026-10-15. The 7B, 13B, and 34B sizes shipped on August 24, 2023. The 70B size shipped on January 29, 2024. Each size has base, Python, and Instruct variants.", "what_it_is_for": "Base models are for code synthesis and understanding. Python variants specialize in Python. Instruct variants are for instruction following and safer code assistance. 7B and 13B are aimed at low-latency completion; 34B and 70B at quality.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-13b": { "id": "meta/llama/llama-13b", "name": "LLaMA 13B", "maker": "meta", "family": "meta/llama", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Research release; non-commercial, access was granted by request. Exact license URL was not re-opened.", "release_date": "2023-02-24", "context_length": null, "max_output_tokens": null, "parameters": "13.0B", "architecture": "Decoder-only Transformer with pre-normalization, SwiGLU, and rotary embeddings (RoPE), as described in the LLaMA paper extraction. Training tokens: token count not stated on the blog excerpt.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLaMA 13B is Meta's February 24, 2023 research language model (13.0B parameters). Collected 2026-10-15. It is retired from the current llama-models download table, which starts at Llama 2, and the launch blog now points readers to Llama 2.", "what_it_is_for": "Meta released it to the research community as a foundation model for studying language models, not as a commercial chat product. There was no official instruction-tuned twin in this release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-2-13b": { "id": "meta/llama/llama-2-13b", "name": "Llama 2 13B", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License (custom commercial license, not an OSI open-source license). Organizations above 700 million monthly active users need a separate grant, per IBM's summary of the license.", "release_date": "2023-07-18", "context_length": 4096, "max_output_tokens": null, "parameters": "13B", "architecture": "Decoder-only Transformer. Pretrained on 2.0T tokens. Grouped-query attention: no. SentencePiece vocabulary. Trained January 2023 to July 2023.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 2 13B is Meta's July 18, 2023 open-weight language model, released as a pretrained checkpoint and a chat checkpoint. Collected 2026-10-15. Weights remain on Hugging Face under the Llama 2 community license.", "what_it_is_for": "Pretrained models are for adaptation and text continuation. Llama 2-Chat is the dialogue model, tuned with supervised fine-tuning and RLHF for helpfulness and safety.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-2-70b": { "id": "meta/llama/llama-2-70b", "name": "Llama 2 70B", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License (custom commercial license, not an OSI open-source license). Organizations above 700 million monthly active users need a separate grant, per IBM's summary of the license.", "release_date": "2023-07-18", "context_length": 4096, "max_output_tokens": null, "parameters": "70B", "architecture": "Decoder-only Transformer. Pretrained on 2.0T tokens. Grouped-query attention: yes. SentencePiece vocabulary. Trained January 2023 to July 2023.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 2 70B is Meta's July 18, 2023 open-weight language model, released as a pretrained checkpoint and a chat checkpoint. Collected 2026-10-15. Weights remain on Hugging Face under the Llama 2 community license.", "what_it_is_for": "Pretrained models are for adaptation and text continuation. Llama 2-Chat is the dialogue model, tuned with supervised fine-tuning and RLHF for helpfulness and safety.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-2-7b": { "id": "meta/llama/llama-2-7b", "name": "Llama 2 7B", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License (custom commercial license, not an OSI open-source license). Organizations above 700 million monthly active users need a separate grant, per IBM's summary of the license.", "release_date": "2023-07-18", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only Transformer. Pretrained on 2.0T tokens. Grouped-query attention: no. SentencePiece vocabulary. Trained January 2023 to July 2023.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 2 7B is Meta's July 18, 2023 open-weight language model, released as a pretrained checkpoint and a chat checkpoint. Collected 2026-10-15. Weights remain on Hugging Face under the Llama 2 community license.", "what_it_is_for": "Pretrained models are for adaptation and text continuation. Llama 2-Chat is the dialogue model, tuned with supervised fine-tuning and RLHF for helpfulness and safety.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-3-1-405b": { "id": "meta/llama/llama-3-1-405b", "name": "Llama 3.1 405B", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License (custom commercial license).", "release_date": "2024-07-23", "context_length": 128000, "max_output_tokens": null, "parameters": "405B", "architecture": "Auto-regressive Transformer in the Llama 3 family, with grouped-query attention. Multilingual post-training. Instruction models add tool use. The 405B model card notes high storage and compute needs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.1 405B is Meta's July 23, 2024 multilingual open-weight model with a 128k context. Collected 2026-10-15. Pretrained and instruction-tuned weights were both released. Knowledge cutoff is December 2023.", "what_it_is_for": "Instruction-tuned models are for multilingual dialogue, long-form summarization, and tool use. Pretrained models are for adaptation, synthetic data, and distillation. The 405B release was positioned as a frontier-level open model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-3-1-70b": { "id": "meta/llama/llama-3-1-70b", "name": "Llama 3.1 70B", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License (custom commercial license).", "release_date": "2024-07-23", "context_length": 128000, "max_output_tokens": null, "parameters": "70B", "architecture": "Auto-regressive Transformer in the Llama 3 family, with grouped-query attention. Multilingual post-training. Instruction models add tool use. The 405B model card notes high storage and compute needs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.1 70B is Meta's July 23, 2024 multilingual open-weight model with a 128k context. Collected 2026-10-15. Pretrained and instruction-tuned weights were both released. Knowledge cutoff is December 2023.", "what_it_is_for": "Instruction-tuned models are for multilingual dialogue, long-form summarization, and tool use. Pretrained models are for adaptation, synthetic data, and distillation. The 405B release was positioned as a frontier-level open model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-3-1-8b": { "id": "meta/llama/llama-3-1-8b", "name": "Llama 3.1 8B", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License (custom commercial license).", "release_date": "2024-07-23", "context_length": 128000, "max_output_tokens": null, "parameters": "8B", "architecture": "Auto-regressive Transformer in the Llama 3 family, with grouped-query attention. Multilingual post-training. Instruction models add tool use. The 405B model card notes high storage and compute needs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.1 8B is Meta's July 23, 2024 multilingual open-weight model with a 128k context. Collected 2026-10-15. Pretrained and instruction-tuned weights were both released. Knowledge cutoff is December 2023.", "what_it_is_for": "Instruction-tuned models are for multilingual dialogue, long-form summarization, and tool use. Pretrained models are for adaptation, synthetic data, and distillation. The 405B release was positioned as a frontier-level open model.", "ratings": { "empirical_100": 45, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-3-2-11b-vision": { "id": "meta/llama/llama-3-2-11b-vision", "name": "Llama 3.2 11B Vision", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.2 Community License (custom commercial license).", "release_date": "2024-09-25", "context_length": 128000, "max_output_tokens": null, "parameters": "10.6B", "architecture": "Image-and-text model. The vision card lists 10.6B parameters under the 11B name, grouped-query attention, and training on 6B image-text pairs. Pretrained and instruction-tuned checkpoints exist.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.2 11B Vision is Meta's September 25, 2024 open-weight image-reasoning model (10.6B parameters on the vision card). Collected 2026-10-15. It takes text and images and returns text. Knowledge cutoff is December 2023.", "what_it_is_for": "Visual recognition, image reasoning, captioning, and general questions about an image. The card says instruction-tuned models are optimized for those tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-3-2-1b": { "id": "meta/llama/llama-3-2-1b", "name": "Llama 3.2 1B", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.2 Community License (custom commercial license).", "release_date": "2024-09-25", "context_length": 128000, "max_output_tokens": null, "parameters": "1.23B", "architecture": "Auto-regressive Transformer with grouped-query attention and shared embeddings. Text-only. Up to 9T training tokens. Edge-oriented. Instruction models use SFT and RLHF.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.2 1B is Meta's small multilingual text model for edge and mobile devices, announced September 25, 2024. Collected 2026-10-15. The text model card prints a release date of October 24, 2024. Parameter count is 1.23B. Knowledge cutoff is December 2023.", "what_it_is_for": "On-device summarization, instruction following, and rewriting. Both pretrained and instruction-tuned weights shipped, plus quantized builds with an 8k context.", "ratings": { "empirical_100": 38, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-3-2-3b": { "id": "meta/llama/llama-3-2-3b", "name": "Llama 3.2 3B", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.2 Community License (custom commercial license).", "release_date": "2024-09-25", "context_length": 128000, "max_output_tokens": null, "parameters": "3.21B", "architecture": "Auto-regressive Transformer with grouped-query attention and shared embeddings. Text-only. Up to 9T training tokens. Edge-oriented. Instruction models use SFT and RLHF.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.2 3B is Meta's small multilingual text model for edge and mobile devices, announced September 25, 2024. Collected 2026-10-15. The text model card prints a release date of October 24, 2024. Parameter count is 3.21B. Knowledge cutoff is December 2023.", "what_it_is_for": "On-device summarization, instruction following, and rewriting. Both pretrained and instruction-tuned weights shipped, plus quantized builds with an 8k context.", "ratings": { "empirical_100": 39, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-3-2-90b-vision": { "id": "meta/llama/llama-3-2-90b-vision", "name": "Llama 3.2 90B Vision", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.2 Community License (custom commercial license).", "release_date": "2024-09-25", "context_length": 128000, "max_output_tokens": null, "parameters": "88.8B", "architecture": "Image-and-text model. The vision card lists 88.8B parameters under the 90B name, grouped-query attention, and training on 6B image-text pairs. Pretrained and instruction-tuned checkpoints exist.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.2 90B Vision is Meta's September 25, 2024 open-weight image-reasoning model (88.8B parameters on the vision card). Collected 2026-10-15. It takes text and images and returns text. Knowledge cutoff is December 2023.", "what_it_is_for": "Visual recognition, image reasoning, captioning, and general questions about an image. The card says instruction-tuned models are optimized for those tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-3-3-70b": { "id": "meta/llama/llama-3-3-70b", "name": "Llama 3.3 70B", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.3 Community License (custom commercial license).", "release_date": "2024-12-06", "context_length": 128000, "max_output_tokens": null, "parameters": "70B", "architecture": "Multilingual auto-regressive Transformer with grouped-query attention. Model card lists 15T+ tokens and a 128k context. Hugging Face tags the instruct checkpoint as a fine-tune of Llama-3.1-70B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.3 70B is Meta's December 6, 2024 instruction-tuned text model. Collected 2026-10-15. The model card's release line names the 70B Instruct checkpoint. The llama-models README table instead prints December 4, 2024. A separate pretrained repo was not found under meta-llama.", "what_it_is_for": "Multilingual dialogue, positioned as stronger than Llama 3.1 70B and, for text-only use, relative to Llama 3.2 90B. The Llama API docs describe it that way. It also supports synthetic data and distillation under the community license.", "ratings": { "empirical_100": 58, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-3-70b": { "id": "meta/llama/llama-3-70b", "name": "Llama 3 70B", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (custom commercial license).", "release_date": "2024-04-18", "context_length": 8192, "max_output_tokens": null, "parameters": "70B", "architecture": "Auto-regressive Transformer with grouped-query attention. Tokenizer vocabulary of 128K. Trained on sequences of 8,192 tokens. Instruction models use SFT and RLHF.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3 70B is Meta's April 18, 2024 open-weight text model, in pretrained and instruction-tuned forms. Collected 2026-10-15. Official support on the model card is English. Knowledge cutoff is December 2023.", "what_it_is_for": "Instruction-tuned checkpoints are for dialogue. Pretrained checkpoints are for further natural-language generation work. The card says developers may fine-tune beyond English if they follow the license and use policy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-3-8b": { "id": "meta/llama/llama-3-8b", "name": "Llama 3 8B", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (custom commercial license).", "release_date": "2024-04-18", "context_length": 8192, "max_output_tokens": null, "parameters": "8B", "architecture": "Auto-regressive Transformer with grouped-query attention. Tokenizer vocabulary of 128K. Trained on sequences of 8,192 tokens. Instruction models use SFT and RLHF.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3 8B is Meta's April 18, 2024 open-weight text model, in pretrained and instruction-tuned forms. Collected 2026-10-15. Official support on the model card is English. Knowledge cutoff is March 2023.", "what_it_is_for": "Instruction-tuned checkpoints are for dialogue. Pretrained checkpoints are for further natural-language generation work. The card says developers may fine-tune beyond English if they follow the license and use policy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-33b": { "id": "meta/llama/llama-33b", "name": "LLaMA 33B", "maker": "meta", "family": "meta/llama", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Research release; non-commercial, access was granted by request. Exact license URL was not re-opened.", "release_date": "2023-02-24", "context_length": null, "max_output_tokens": null, "parameters": "32.5B", "architecture": "Decoder-only Transformer with pre-normalization, SwiGLU, and rotary embeddings (RoPE), as described in the LLaMA paper extraction. Training tokens: 1.4 trillion tokens (Meta blog).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLaMA 33B is Meta's February 24, 2023 research language model (32.5B parameters). Collected 2026-10-15. It is retired from the current llama-models download table, which starts at Llama 2, and the launch blog now points readers to Llama 2.", "what_it_is_for": "Meta released it to the research community as a foundation model for studying language models, not as a commercial chat product. There was no official instruction-tuned twin in this release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-4-behemoth": { "id": "meta/llama/llama-4-behemoth", "name": "Llama 4 Behemoth", "maker": "meta", "family": "meta/llama", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "288B active, 16 experts", "architecture": "Mixture-of-experts teacher model in the Llama 4 herd. The April 5, 2025 post said it was still training and was used to distill Scout and Maverick. Total parameter count was not on that post.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 4 Behemoth is the large teacher model Meta previewed on April 5, 2025 and did not release. Collected 2026-10-15. The launch post calls it a 288B-active, 16-expert model that was still training. No Behemoth weights were found under the meta-llama Hugging Face org.", "what_it_is_for": "Meta described it as a teacher for Scout and Maverick and said it outperformed GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on several STEM benchmarks. Those comparison scores were not copied because the table was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-4-maverick": { "id": "meta/llama/llama-4-maverick", "name": "Llama 4 Maverick", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 4 Community License Agreement (custom commercial license), with the same EU multimodal restriction described for Scout.", "release_date": "2025-04-05", "context_length": 1000000, "max_output_tokens": null, "parameters": "17B active, 400B total, 128 experts", "architecture": "Natively multimodal mixture-of-experts transformer. Pretrained on about 22T tokens. Released as BF16 and as FP8. The card says FP8 fits on a single H100 DGX host. Tested up to 5 images.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 4 Maverick is Meta's April 5, 2025 open-weight multimodal model, 17B active parameters with 128 experts (400B total) and a 1M context on the model card. Collected 2026-10-15. Pretrained, instruction-tuned, and FP8 instruct weights are public. Knowledge cutoff is August 2024.", "what_it_is_for": "General assistant and chat, image understanding, and coding or reasoning tasks Meta compared with closed models at launch. Pretrained weights are for adaptation and distillation.", "ratings": { "empirical_100": 40, "lmsys_elo": 1209, "coding_elo": 1209, "swe_bench": 16.6, "humaneval": 71.3, "twelve_cs": 86.9, "aa_intelligence": 9.3, "aa_coding": 16.3, "aa_agentic": 0.6, "design_arena_elo": 898, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-4-scout": { "id": "meta/llama/llama-4-scout", "name": "Llama 4 Scout", "maker": "meta", "family": "meta/llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 4 Community License Agreement (custom commercial license). The use policy withholds multimodal license rights from EU-domiciled individuals and companies; that restriction does not apply to end users of a product that already includes the model.", "release_date": "2025-04-05", "context_length": 10000000, "max_output_tokens": null, "parameters": "17B active, 109B total, 16 experts", "architecture": "Natively multimodal mixture-of-experts transformer with early fusion. Pretrained on about 40T tokens. Released as BF16; the card says it can fit one H100 with on-the-fly int4 quantization. Tested for up to 5 input images.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 4 Scout is Meta's April 5, 2025 open-weight multimodal model, a 17B-active mixture of 16 experts (109B total) with a 10M-token context on the model card. Collected 2026-10-15. Pretrained and instruction-tuned weights are public. Knowledge cutoff is August 2024.", "what_it_is_for": "Instruction-tuned Scout is for assistant chat and visual reasoning. Pretrained Scout is for adaptation. The card also allows using outputs for synthetic data and distillation. Meta positioned it for long-document work on a single H100-class GPU.", "ratings": { "empirical_100": 4, "lmsys_elo": 1208, "coding_elo": 1208, "swe_bench": 16.5, "humaneval": 71.2, "twelve_cs": 9.7, "aa_intelligence": 6.5, "aa_coding": 8.2, "aa_agentic": 0.5, "design_arena_elo": 788, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-65b": { "id": "meta/llama/llama-65b", "name": "LLaMA 65B", "maker": "meta", "family": "meta/llama", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Research release; non-commercial, access was granted by request. Exact license URL was not re-opened.", "release_date": "2023-02-24", "context_length": null, "max_output_tokens": null, "parameters": "65.2B", "architecture": "Decoder-only Transformer with pre-normalization, SwiGLU, and rotary embeddings (RoPE), as described in the LLaMA paper extraction. Training tokens: 1.4 trillion tokens (Meta blog).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLaMA 65B is Meta's February 24, 2023 research language model (65.2B parameters). Collected 2026-10-15. It is retired from the current llama-models download table, which starts at Llama 2, and the launch blog now points readers to Llama 2.", "what_it_is_for": "Meta released it to the research community as a foundation model for studying language models, not as a commercial chat product. There was no official instruction-tuned twin in this release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama/llama-7b": { "id": "meta/llama/llama-7b", "name": "LLaMA 7B", "maker": "meta", "family": "meta/llama", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Research release; non-commercial, access was granted by request. Exact license URL was not re-opened.", "release_date": "2023-02-24", "context_length": null, "max_output_tokens": null, "parameters": "6.7B", "architecture": "Decoder-only Transformer with pre-normalization, SwiGLU, and rotary embeddings (RoPE), as described in the LLaMA paper extraction. Training tokens: 1 trillion tokens (Meta blog).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLaMA 7B is Meta's February 24, 2023 research language model (6.7B parameters). Collected 2026-10-15. It is retired from the current llama-models download table, which starts at Llama 2, and the launch blog now points readers to Llama 2.", "what_it_is_for": "Meta released it to the research community as a foundation model for studying language models, not as a commercial chat product. There was no official instruction-tuned twin in this release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama-guard/llama-guard-2-8b": { "id": "meta/llama-guard/llama-guard-2-8b", "name": "Llama Guard 2 8B", "maker": "meta", "family": "meta/llama-guard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-04-18", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Llama 3 8B safeguard aligned to the MLCommons hazard taxonomy.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama Guard 2 8B is Meta's April 2024 safety classifier based on Llama 3 8B. Collected 2026-10-15. Repo: meta-llama/Meta-Llama-Guard-2-8B.", "what_it_is_for": "Classify prompts and responses as safe or unsafe and name the hazard categories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama-guard/llama-guard-3-11b-vision": { "id": "meta/llama-guard/llama-guard-3-11b-vision", "name": "Llama Guard 3 11B Vision", "maker": "meta", "family": "meta/llama-guard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.2 Community License", "release_date": "2024-09-25", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "Multimodal safety classifier in the Llama 3.2 vision family for text and image prompts.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama Guard 3 11B Vision is Meta's September 2024 multimodal safety classifier paired with Llama 3.2 Vision. Collected 2026-10-15.", "what_it_is_for": "Classify image-and-text prompts and responses for safety, including the person-identification risk called out on the Llama 3.2 vision card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama-guard/llama-guard-3-1b": { "id": "meta/llama-guard/llama-guard-3-1b", "name": "Llama Guard 3 1B", "maker": "meta", "family": "meta/llama-guard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.2 Community License", "release_date": "2024-09-25", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Fine-tune of Llama-3.2-1B for content safety classification. A pruned INT4 build targets mobile.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama Guard 3 1B is Meta's smaller Llama 3.2 safety classifier for lower-cost moderation, including a mobile INT4 build. Collected 2026-10-15.", "what_it_is_for": "Classify prompts and responses as safe or unsafe under the MLCommons taxonomy at a lower deployment cost than the 8B guard.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama-guard/llama-guard-3-8b": { "id": "meta/llama-guard/llama-guard-3-8b", "name": "Llama Guard 3 8B", "maker": "meta", "family": "meta/llama-guard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2024-07-23", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Fine-tune of Llama-3.1-8B for MLCommons hazard classification of prompts and responses.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama Guard 3 8B is Meta's July 2024 text safety classifier, a fine-tune of Llama 3.1 8B. Collected 2026-10-15. An official INT8 repo also exists.", "what_it_is_for": "Ship with Llama 3.1 as the text safeguard for prompts and model responses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama-guard/llama-guard-4-12b": { "id": "meta/llama-guard/llama-guard-4-12b", "name": "Llama Guard 4 12B", "maker": "meta", "family": "meta/llama-guard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 4 Community License", "release_date": "2025-04-29", "context_length": null, "max_output_tokens": null, "parameters": "12B", "architecture": "Dense 12B early-fusion transformer pruned from Llama 4 Scout (shared expert kept, routed experts removed) and fine-tuned for safety. Same tokenizer and vision encoder as Scout and Maverick.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama Guard 4 12B is Meta's multimodal safety classifier pruned from Llama 4 Scout. Collected 2026-10-15. It covers English and the Llama Guard 3 languages for text, plus mixed text-and-image prompts, in one model.", "what_it_is_for": "Classify prompts and responses as safe or unsafe against the MLCommons hazard taxonomy, including multi-image prompts for Llama 4.", "ratings": { "empirical_100": 36, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.4, "humaneval": 70.4, "twelve_cs": 0.0, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/llama-guard/llama-guard-7b": { "id": "meta/llama-guard/llama-guard-7b", "name": "Llama Guard 7B", "maker": "meta", "family": "meta/llama-guard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-12-06", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Safety classifier built as a Llama 2 7B chat model. It writes safe or unsafe and, if unsafe, category names.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama Guard 7B is Meta's first Llama Guard classifier, a 7B model on Llama 2 for labeling prompts and responses. Collected 2026-10-15. Weights are meta-llama/LlamaGuard-7b.", "what_it_is_for": "Classify LLM inputs and outputs against a hazard taxonomy and return the violated categories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/muse/muse-glimmer": { "id": "meta/muse/muse-glimmer", "name": "Muse Glimmer", "maker": "meta", "family": "meta/muse", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "30B", "architecture": "Dense decoder-only multimodal transformer with a built-in vision encoder. Distilled from Muse Spark outputs rather than trained from scratch. Default context 128k; longer contexts are supported but the maximum was not given.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Muse Glimmer is Meta's 30B open-source multimodal model for local agents, distilled from Muse Spark. Collected 2026-10-15. Weights are Apache 2.0. It is not served on the Meta Model API.", "what_it_is_for": "Local text-and-image inference and domain adaptation with supervised fine-tuning or reinforcement learning. Docs say it reasons step by step before answering.", "ratings": { "empirical_100": 45, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/muse/muse-image": { "id": "meta/muse/muse-image", "name": "Muse Image", "maker": "meta", "family": "meta/muse", "status": "active", "confidence": "high", "weights": "closed", "license": "Meta Model API terms.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted image generation and editing model. Model id muse-image-1.0. One model does both generation and edits. It can search the web and run code before rendering. Parameter count not published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Muse Image is Meta's hosted image generation and editing model, id muse-image-1.0. Collected 2026-10-15. Weights are closed. It is billed per image, not per token.", "what_it_is_for": "Generate an image from text and optional reference images, or edit an image from an instruction. Meta describes it as agentic: it may search and run code to build layouts before it renders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/muse/muse-spark": { "id": "meta/muse/muse-spark", "name": "Muse Spark", "maker": "meta", "family": "meta/muse", "status": "active", "confidence": "high", "weights": "closed", "license": "Meta Model API terms for the hosted models. The open-weight release is a separate folder, muse-spark-open.", "release_date": "", "context_length": 1048576, "max_output_tokens": null, "parameters": "", "architecture": "Closed agentic and coding model. Three hosted versions share modalities and the 1,048,576-token window. Parameter count of the hosted model was not published. Open weights are cataloged separately.", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 4.25 }, "summary_intro": "Muse Spark is Meta's hosted model for agentic work, coding, and multimodal understanding. Collected 2026-10-15. Versions 1.1, 1.2, and 1.3 are all still offered. 1.3 is the recommended version. Weights of the hosted model are closed.", "what_it_is_for": "Chat, image, video, and audio understanding, tool calling, structured output, and search grounding. Meta recommends it when cost or latency matters more than Muse Wonder's peak capability. 1.3 adds stronger agentic and coding behavior and a max reasoning level, except on the contributor id.", "ratings": { "empirical_100": 42, "lmsys_elo": 1263, "coding_elo": 1263, "swe_bench": 25.6, "humaneval": 78.5, "twelve_cs": 70.2, "aa_intelligence": 33.7, "aa_coding": 71.3, "aa_agentic": 25.8, "design_arena_elo": 1147, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/muse/muse-spark-open": { "id": "meta/muse/muse-spark-open", "name": "Muse Spark Open", "maker": "meta", "family": "meta/muse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Muse Model License, plus an acceptable use policy. Not Apache-2.0.", "release_date": "", "context_length": 1048576, "max_output_tokens": null, "parameters": "816B total, 42B active", "architecture": "Sparse mixture-of-experts transformer with perception encoders. Each token uses a subset of experts so compute tracks 42B active parameters, while serving loads the full 816B. BF16 weights about 1.65 TB; NVFP4 about 537 GB.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Muse Spark Open is the open-weight Muse Spark release: 816B total parameters, 42B active, 1,048,576-token context. Collected 2026-10-15. It is not on the Meta Model API. Hugging Face repo named in the docs: meta-models/Muse-Spark-Open-816B-A42B.", "what_it_is_for": "Self-hosted or provider-hosted agentic and coding work, including supervised fine-tuning and reinforcement learning. Inputs include text, image, audio, and video. Output is text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1301, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/muse/muse-voice-transcribe": { "id": "meta/muse/muse-voice-transcribe", "name": "Muse Voice Transcribe", "maker": "meta", "family": "meta/muse", "status": "active", "confidence": "high", "weights": "closed", "license": "Meta Model API terms.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted speech-to-text model. Id muse-voice-transcribe-1.0. Streaming and file transcription. Not a speech-generation model.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Muse Voice Transcribe is Meta's hosted speech-to-text model, id muse-voice-transcribe-1.0. Collected 2026-10-15. It returns transcript text. It does not synthesize speech.", "what_it_is_for": "Live transcription and file transcription for voice agents, meetings, dictation, and captioning. Docs list streaming diarization, endpointing, voice activity detection, keyword biasing, 25 evaluated languages with code-switching, and turn-level timestamps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/muse/muse-wonder": { "id": "meta/muse/muse-wonder", "name": "Muse Wonder", "maker": "meta", "family": "meta/muse", "status": "preview", "confidence": "high", "weights": "closed", "license": "Meta Model API terms. Not an open-weight release.", "release_date": "2026-10-12", "context_length": 1048576, "max_output_tokens": null, "parameters": "", "architecture": "Closed frontier model for long-horizon agents. Reasoning effort from minimal to max. Model id muse-wonder-1.0. Parameter count was not published.", "modalities": { "input": [ "text", "image", "video", "audio", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": 20.0 }, "summary_intro": "Muse Wonder is Meta's preview frontier model for coding and long-running agents, announced October 12, 2026. Collected 2026-10-15. It is on the Meta Model API as muse-wonder-1.0 and in Muse Code. Weights are closed.", "what_it_is_for": "Multi-file code changes, multi-hour agent runs, sub-agent delegation, computer use, tool calling, structured output, and search grounding. Meta says it accepts text, image, video, audio, and PDF and returns text.", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs": 98.0, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1232, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/muse/muse-wonder-full": { "id": "meta/muse/muse-wonder-full", "name": "Muse Wonder Full", "maker": "meta", "family": "meta/muse", "status": "preview", "confidence": "high", "weights": "closed", "license": "Meta Model API and Muse Code terms. Not on pay-as-you-go.", "release_date": "2026-10-12", "context_length": 1048576, "max_output_tokens": null, "parameters": "", "architecture": "Higher-capability Muse Wonder variant. Not selectable by setting reasoning effort to max on muse-wonder-1.0. No public parameter count.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Muse Wonder Full is the higher-capability Muse Wonder variant Meta announced on October 12, 2026. Collected 2026-10-15. It is available only in Muse Code on the Power Usage and Maximum Usage plans, not on the pay-as-you-go Model API.", "what_it_is_for": "The same agentic and coding jobs as Muse Wonder, for subscribers who need the stronger variant. Meta says it leads agentic and coding benchmarks including DeepSWE and SWE-Marathon in third-party runs; those scores were not copied because the table was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/musicgen/musicgen-large": { "id": "meta/musicgen/musicgen-large", "name": "MusicGen Large", "maker": "meta", "family": "meta/musicgen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC 4.0 (weights). Code is MIT.", "release_date": "2023-06-08", "context_length": null, "max_output_tokens": null, "parameters": "3.3B", "architecture": "Single-stage autoregressive transformer over EnCodec audio tokens, with a frozen text encoder. Text to music. A later melody-large sibling is 3.3B with melody conditioning. Trained April 2023 to May 2023. Model card version: 1.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MusicGen Large is Meta FAIR's open-weight text-to-music model (3.3B), from the June 2023 MusicGen release. Collected 2026-10-15. Weights are non-commercial CC-BY-NC 4.0.", "what_it_is_for": "Generate music from a text description. Melody variants also take a melody condition. Stereo checkpoints were added later as fine-tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/musicgen/musicgen-medium": { "id": "meta/musicgen/musicgen-medium", "name": "MusicGen Medium", "maker": "meta", "family": "meta/musicgen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC 4.0 (weights). Code is MIT.", "release_date": "2023-06-08", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Single-stage autoregressive transformer over EnCodec audio tokens, with a frozen text encoder. Text to music. AudioCraft docs call medium the quality-per-compute default. Trained April 2023 to May 2023. Model card version: 1.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MusicGen Medium is Meta FAIR's open-weight text-to-music model (1.5B), from the June 2023 MusicGen release. Collected 2026-10-15. Weights are non-commercial CC-BY-NC 4.0.", "what_it_is_for": "Generate music from a text description. Melody variants also take a melody condition. Stereo checkpoints were added later as fine-tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/musicgen/musicgen-melody": { "id": "meta/musicgen/musicgen-melody", "name": "MusicGen Melody", "maker": "meta", "family": "meta/musicgen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC 4.0 (weights). Code is MIT.", "release_date": "2023-06-08", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Single-stage autoregressive transformer over EnCodec audio tokens, with a frozen text encoder. Text to music and melody-conditioned generation. Trained April 2023 to May 2023. Model card version: 1.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MusicGen Melody is Meta FAIR's open-weight text-to-music model (1.5B), from the June 2023 MusicGen release. Collected 2026-10-15. Weights are non-commercial CC-BY-NC 4.0.", "what_it_is_for": "Generate music from a text description. Melody variants also take a melody condition. Stereo checkpoints were added later as fine-tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/musicgen/musicgen-small": { "id": "meta/musicgen/musicgen-small", "name": "MusicGen Small", "maker": "meta", "family": "meta/musicgen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC 4.0 (weights). Code is MIT.", "release_date": "2023-06-08", "context_length": null, "max_output_tokens": null, "parameters": "300M", "architecture": "Single-stage autoregressive transformer over EnCodec audio tokens, with a frozen text encoder. Text to music. Trained April 2023 to May 2023. Model card version: 1.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MusicGen Small is Meta FAIR's open-weight text-to-music model (300M), from the June 2023 MusicGen release. Collected 2026-10-15. Weights are non-commercial CC-BY-NC 4.0.", "what_it_is_for": "Generate music from a text description. Melody variants also take a melody condition. Stereo checkpoints were added later as fine-tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/prompt-guard/llama-prompt-guard-2-22m": { "id": "meta/prompt-guard/llama-prompt-guard-2-22m", "name": "Llama Prompt Guard 2 22M", "maker": "meta", "family": "meta/prompt-guard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 4 custom license (Hugging Face license tag: other)", "release_date": "2025-04-28", "context_length": null, "max_output_tokens": null, "parameters": "22M", "architecture": "DeBERTa-v2 text classifier updated for the Llama 4 safeguard set. Hugging Face language tags: English, French, German, Hindi, Italian, Portuguese, Spanish, Thai.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama Prompt Guard 2 22M is Meta's April 2025 prompt-attack classifier paired with Llama 4. Collected 2026-10-15.", "what_it_is_for": "Classify text for prompt injection and jailbreak risk across the languages tagged on the model repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/prompt-guard/llama-prompt-guard-2-86m": { "id": "meta/prompt-guard/llama-prompt-guard-2-86m", "name": "Llama Prompt Guard 2 86M", "maker": "meta", "family": "meta/prompt-guard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 4 custom license (Hugging Face license tag: other)", "release_date": "2025-04-28", "context_length": null, "max_output_tokens": null, "parameters": "86M", "architecture": "DeBERTa-v2 text classifier updated for the Llama 4 safeguard set. Hugging Face language tags: English, French, German, Hindi, Italian, Portuguese, Spanish, Thai.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama Prompt Guard 2 86M is Meta's April 2025 prompt-attack classifier paired with Llama 4. Collected 2026-10-15.", "what_it_is_for": "Classify text for prompt injection and jailbreak risk across the languages tagged on the model repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/prompt-guard/prompt-guard-86m": { "id": "meta/prompt-guard/prompt-guard-86m", "name": "Prompt Guard 86M", "maker": "meta", "family": "meta/prompt-guard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2024-07-21", "context_length": null, "max_output_tokens": null, "parameters": "86M", "architecture": "DeBERTa-v2 text classifier shipped by Meta next to Llama 3.1 safeguards. Hugging Face pipeline tag is text-classification. The Hub size badge on the retrieved index said 0.3B, which disagrees with the 86M name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Prompt Guard 86M is Meta's July 2024 small text classifier for prompt attacks, released with the Llama 3.1 safeguard set. Collected 2026-10-15. Repo: meta-llama/Prompt-Guard-86M.", "what_it_is_for": "Score text for jailbreak and prompt-injection style attacks so an application can filter inputs before they reach a larger Llama model. The label set was not copied from the card in this pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/sam/sam": { "id": "meta/sam/sam", "name": "Segment Anything Model", "maker": "meta", "family": "meta/sam", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Promptable image segmentation model. Later Meta posts describe point, box, and mask prompts and ViT image encoders. Size names were not re-read from the original card.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The original Segment Anything Model is Meta FAIR's promptable image segmentation model and the predecessor of SAM 2. Collected 2026-10-15. This folder exists because SAM 2's official posts treat it as a released model. The original announcement page was not re-opened, so several fields stay empty.", "what_it_is_for": "Return a mask for an object indicated by a visual prompt on a single image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/sam/sam-2": { "id": "meta/sam/sam-2", "name": "SAM 2", "maker": "meta", "family": "meta/sam", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2024-07-29", "context_length": null, "max_output_tokens": null, "parameters": "38.9M to 224.4M", "architecture": "Transformer with streaming memory for promptable segmentation in images and video. Hiera image encoder. Four July 29, 2024 checkpoints: tiny 38.9M, small 46M, base-plus 80.8M, large 224.4M.", "modalities": { "input": [ "image", "video" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SAM 2 is Meta's July 29, 2024 open model for promptable segmentation in images and video. Collected 2026-10-15. Code and weights are Apache 2.0. SAM 2.1 checkpoints from September 30, 2024 are a separate folder.", "what_it_is_for": "Track an object through a video from points, boxes, or masks, and segment objects in still images. The blog says it is more accurate than SAM on images and about 6x faster.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/sam/sam-2-1": { "id": "meta/sam/sam-2-1", "name": "SAM 2.1", "maker": "meta", "family": "meta/sam", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "38.9M to 224.4M", "architecture": "Improved SAM 2 checkpoints, same Hiera sizes: tiny 38.9M, small 46M, base-plus 80.8M, large 224.4M. Release notes call this the SAM 2.1 developer suite, including training code.", "modalities": { "input": [ "image", "video" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SAM 2.1 is Meta's September 30, 2024 checkpoint update of SAM 2. Collected 2026-10-15. The project treats the 2.1 weights as a new suite, so they have their own folder. License remains Apache 2.0.", "what_it_is_for": "Same promptable image and video segmentation task as SAM 2, with the newer checkpoints and released training code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/sam/sam-3": { "id": "meta/sam/sam-3", "name": "SAM 3", "maker": "meta", "family": "meta/sam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "SAM License (custom). Not Apache 2.0.", "release_date": "2025-11-19", "context_length": null, "max_output_tokens": null, "parameters": "848M", "architecture": "Detector plus tracker sharing a vision encoder. Text prompts (short noun phrases), exemplars, and visual prompts. The 848M figure is from secondary write-ups of the paper, not a line re-read in the PDF.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SAM 3 is Meta's November 19, 2025 promptable concept segmentation model for images and video. Collected 2026-10-15. Weights and code are public under the SAM License. SAM 3.1 is the later checkpoint and API model.", "what_it_is_for": "Find, segment, and track every instance of a concept given a short noun phrase or an image exemplar, and still accept points, boxes, and masks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "meta/sam/sam-3-1": { "id": "meta/sam/sam-3-1", "name": "SAM 3.1", "maker": "meta", "family": "meta/sam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "SAM License, same project license as SAM 3.", "release_date": "2026-03-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SAM 3 checkpoint update with object multiplexing: shared memory for joint multi-object tracking. Also hosted on the Meta Model API as sam-3.1. Parameter count of the 3.1 checkpoint was not published separately from SAM 3.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SAM 3.1 is Meta's March 27, 2026 segmentation update for faster multi-object video tracking, with open checkpoints and a hosted API model id sam-3.1. Collected 2026-10-15.", "what_it_is_for": "Detect, segment, and track objects from a short noun phrase across images and video. On the API, one model handles stills and clips and follows objects across frames.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/aion/aion-instruct": { "id": "microsoft/aion/aion-instruct", "name": "Aion Instruct", "maker": "microsoft", "family": "microsoft/aion", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aion Instruct is the Windows on-device language model Microsoft says will replace Phi Silica. As of the docs read on 2026-10-15, it begins rolling out to Windows Insider Preview devices in November 2026 and to retail devices in January 2027. Collection date: 2026-10-15.", "what_it_is_for": "The docs tell developers to retrain Phi Silica LoRA adapters for Aion Instruct and to validate apps against it. A model card with size, context, or benchmarks was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/decision/microsoft-decision-1": { "id": "microsoft/decision/microsoft-decision-1", "name": "Microsoft Decision 1", "maker": "microsoft", "family": "microsoft/decision", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Microsoft Decision 1 is a closed text model Microsoft sells on Foundry for scoring answer options. The sold-by-Azure page says it returns a calibrated probability for each option over inputs up to 32K tokens and does not write explanations. Collection date: 2026-10-15.", "what_it_is_for": "Yes/no, multiple-choice, rating, classification, and rubric decisions in one call.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/florence/florence-2": { "id": "microsoft/florence/florence-2", "name": "Florence-2", "maker": "microsoft", "family": "microsoft/florence", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sequence-to-sequence vision foundation model with promptable tasks", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Florence-2 is Microsoft's open vision foundation model for many vision tasks from text prompts. The large card, read 2026-10-15, lists base and large pretrained and finetuned checkpoints at 0.23B and 0.77B parameters under MIT. Collection date: 2026-10-15.", "what_it_is_for": "Captioning, object detection, segmentation, and other vision-language tasks from short prompts, including zero-shot use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/harrier/harrier-oss-v1": { "id": "microsoft/harrier/harrier-oss-v1", "name": "Harrier OSS v1", "maker": "microsoft", "family": "microsoft/harrier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-03-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Harrier OSS v1 is a Microsoft open embedding model. The 0.6B repository record shows an MIT license, a feature-extraction pipeline, and a long multilingual language list. The card body was not opened, so dimension and benchmark numbers are empty. Collection date: 2026-10-15.", "what_it_is_for": "Text embeddings. Other Harrier sizes, if any, were not confirmed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/healthcare/cxrreportgen-premium": { "id": "microsoft/healthcare/cxrreportgen-premium", "name": "CxrReportGen Premium", "maker": "microsoft", "family": "microsoft/healthcare", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CxrReportGen Premium is a closed Microsoft Foundry preview model that drafts structured chest X-ray findings from current and prior images and key patient text. Microsoft says it must not be used as a clinical device as-is. Collection date: 2026-10-15.", "what_it_is_for": "Research drafting of grounded radiology findings, including localization of findings, not unattended diagnosis.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/healthcare/medimageinsight-premium": { "id": "microsoft/healthcare/medimageinsight-premium", "name": "MedImageInsight Premium", "maker": "microsoft", "family": "microsoft/healthcare", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedImageInsight Premium is a closed Microsoft Foundry preview model for medical-image embeddings. The healthcare models page, read 2026-10-15, says it supports classification, similarity search, and image-text inference across several imaging types, and that it is not a clinical diagnostic device. Collection date: 2026-10-15.", "what_it_is_for": "Research and application development for medical image embeddings, including outlier and drift checks if the app implements them.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-1-preview": { "id": "microsoft/mai/mai-1-preview", "name": "MAI-1-preview", "maker": "microsoft", "family": "microsoft/mai", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mixture-of-experts", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-1-preview is the first foundation model Microsoft AI said it trained end to end in house. A Microsoft Japan translation of the August 28, 2025 post says public testing opened on LMArena, with API access by application, and that some Copilot text uses would follow. Collection date: 2026-10-15.", "what_it_is_for": "The post describes a mixture-of-experts model aimed at following instructions and answering everyday questions, trained and post-trained on about 15,000 NVIDIA H100 GPUs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-base-1": { "id": "microsoft/mai/mai-base-1", "name": "MAI-Base-1", "maker": "microsoft", "family": "microsoft/mai", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mixture-of-experts pretraining checkpoint", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Base-1 is the internal pretraining model Microsoft AI says MAI-Thinking-1 was post-trained from. The October 15, 2026 lab note names it and says MAI-Base-2 is larger and sparser. No public weights or API were found. Collection date: 2026-10-15.", "what_it_is_for": "It is a foundation checkpoint, not a customer-facing product. Status is retired here only in the sense that the lab has moved on to MAI-Base-2 and the checkpoint was never offered on its own.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-base-2": { "id": "microsoft/mai/mai-base-2", "name": "MAI-Base-2", "maker": "microsoft", "family": "microsoft/mai", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-15", "context_length": null, "max_output_tokens": null, "parameters": "2800000000000", "architecture": "Sparse mixture-of-experts with native vision, encoder-free vision-language pretraining", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Base-2 is Microsoft AI's newer pretraining model. The October 15, 2026 lab note says pre-training has finished. It is a sparse mixture-of-experts model with 80 billion active and 2.8 trillion total parameters, trained with vision in the mix. It is not a released API. Collection date: 2026-10-15.", "what_it_is_for": "Microsoft says it is the foundation for the next post-trained models. Post-training was still underway when the note was published, and preparations for MAI-Base-3 had started.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-code-1-1-flash": { "id": "microsoft/mai/mai-code-1-1-flash", "name": "MAI-Code-1.1-Flash", "maker": "microsoft", "family": "microsoft/mai", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-07", "context_length": 256000, "max_output_tokens": null, "parameters": "137000000000", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Code-1.1-Flash is Microsoft AI's October 2026 coding model, in production in GitHub Copilot and offered as a local download. The English post says a 3-bit build keeps a 256K context. A French Microsoft Source translation the same week says 137 billion total parameters and 6.8 billion active. Collection date: 2026-10-15.", "what_it_is_for": "Agentic coding in Copilot, VS Code, and the Copilot CLI, including an on-device build for machines with more than 120 GB of RAM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-code-1-flash": { "id": "microsoft/mai/mai-code-1-flash", "name": "MAI-Code-1-Flash", "maker": "microsoft", "family": "microsoft/mai", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Code-1-Flash is Microsoft AI's agentic coding model announced June 2, 2026. Microsoft says it has 5 billion active parameters and is integrated with GitHub Copilot and VS Code. Collection date: 2026-10-15.", "what_it_is_for": "Inference-efficient coding assistance inside Microsoft's developer tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-cyber-1-flash": { "id": "microsoft/mai/mai-cyber-1-flash", "name": "MAI-Cyber-1-Flash", "maker": "microsoft", "family": "microsoft/mai", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Cyber-1 Flash is a Microsoft model named on the Foundry pricing page read on 2026-10-15. No model card, context length, or parameter count was found on the official pages opened in this pass.", "what_it_is_for": "The pricing page does not describe the task. The name is recorded because Microsoft lists it as a sold model. Specs are otherwise unknown.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-image-1": { "id": "microsoft/mai/mai-image-1", "name": "MAI-Image-1", "maker": "microsoft", "family": "microsoft/mai", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Image-1 is Microsoft AI's first fully in-house image model, announced October 13, 2025. A Microsoft LATAM translation of the MAI post says it debuted in the LMArena text-to-image top 10 and later rolled out to Bing Image Creator and Copilot Labs except the EU. Collection date: 2026-10-15.", "what_it_is_for": "Text-to-image generation for consumer Microsoft products. It is not in the 2026 Foundry image how-to list retrieved here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-image-2": { "id": "microsoft/mai/mai-image-2", "name": "MAI-Image-2", "maker": "microsoft", "family": "microsoft/mai", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 33.0 }, "summary_intro": "MAI-Image-2 is the image model Microsoft AI put on Foundry on April 2, 2026, with published starting rates. The current Foundry image how-to and the sold-by-Azure capability table, both read 2026-10-15, list 2.5 and 2.6 models and do not list MAI-Image-2. The pricing page still names it. Status is deprecated for that reason, with medium confidence.", "what_it_is_for": "Text-to-image, and image editing was discussed in the April post. Superseded in the current how-to by the 2.5 series.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-image-2-5": { "id": "microsoft/mai/mai-image-2-5", "name": "MAI-Image-2.5", "maker": "microsoft", "family": "microsoft/mai", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-02", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Image-2.5 is a Microsoft AI image model in public preview on Foundry. The image how-to lists deployment version 2026-06-02. Context on the sold-by-Azure page is 32,000 tokens. Collection date: 2026-10-15.", "what_it_is_for": "Text-to-image generation and image editing. Microsoft says edits can change part of an image without rebuilding the rest.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-image-2-5-flash": { "id": "microsoft/mai/mai-image-2-5-flash", "name": "MAI-Image-2.5-Flash", "maker": "microsoft", "family": "microsoft/mai", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-02", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Image-2.5-Flash is a Microsoft AI image model in public preview on Foundry. The image how-to lists deployment version 2026-06-02. Context on the sold-by-Azure page is 32,000 tokens. Collection date: 2026-10-15.", "what_it_is_for": "Text-to-image generation and image editing. Microsoft says edits can change part of an image without rebuilding the rest.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-image-2-5-pro": { "id": "microsoft/mai/mai-image-2-5-pro", "name": "MAI-Image-2.5-Pro", "maker": "microsoft", "family": "microsoft/mai", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-19", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Image-2.5-Pro is a Microsoft AI image model in public preview on Foundry. The image how-to lists deployment version 2026-06-19. Context on the sold-by-Azure page is 32,000 tokens. Collection date: 2026-10-15.", "what_it_is_for": "Text-to-image generation and image editing. Microsoft says edits can change part of an image without rebuilding the rest.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-image-2-6": { "id": "microsoft/mai/mai-image-2-6", "name": "MAI-Image-2.6", "maker": "microsoft", "family": "microsoft/mai", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-31", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Image-2.6 is a Microsoft AI image model in public preview on Foundry. The image how-to lists deployment version 2026-07-31. Context on the sold-by-Azure page is 32,000 tokens. Collection date: 2026-10-15.", "what_it_is_for": "Text-to-image generation and image editing. Microsoft says edits can change part of an image without rebuilding the rest.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-image-2-6-flash": { "id": "microsoft/mai/mai-image-2-6-flash", "name": "MAI-Image-2.6-Flash", "maker": "microsoft", "family": "microsoft/mai", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-31", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Image-2.6-Flash is a Microsoft AI image model in public preview on Foundry. The image how-to lists deployment version 2026-07-31. Context on the sold-by-Azure page is 32,000 tokens. Collection date: 2026-10-15.", "what_it_is_for": "Text-to-image generation and image editing. Microsoft says edits can change part of an image without rebuilding the rest.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-image-2-efficient": { "id": "microsoft/mai/mai-image-2-efficient", "name": "MAI-Image-2-Efficient", "maker": "microsoft", "family": "microsoft/mai", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Image-2-Efficient is a Microsoft image SKU named only on the Foundry pricing page as of 2026-10-15. It does not appear in the current MAI image how-to.", "what_it_is_for": "Not described beyond the pricing-row name. Likely an efficiency variant of MAI-Image-2, but that role was not stated on the page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-thinking-1": { "id": "microsoft/mai/mai-thinking-1", "name": "MAI-Thinking-1", "maker": "microsoft", "family": "microsoft/mai", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-02", "context_length": 256000, "max_output_tokens": 64000, "parameters": "", "architecture": "Sparse mixture-of-experts", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Thinking-1 is Microsoft AI's in-house reasoning model, trained without distillation from third-party models according to Microsoft. It entered public preview on Foundry on August 12, 2026 after a June 2 announcement. Microsoft calls it a 35B-active, roughly 1T-total sparse mixture-of-experts model with a 256K context. Collection date: 2026-10-15.", "what_it_is_for": "Microsoft positions it for enterprise reasoning, software engineering, and math, with function calling and developer instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-transcribe-1": { "id": "microsoft/mai/mai-transcribe-1", "name": "MAI-Transcribe-1", "maker": "microsoft", "family": "microsoft/mai", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.36, "completion_per_m_usd": null }, "summary_intro": "MAI-Transcribe-1 is Microsoft AI's first in-house transcription model on Foundry, announced April 2, 2026. Microsoft docs read on 2026-10-15 say it was deprecated on August 20, 2026. Collection date: 2026-10-15.", "what_it_is_for": "Speech-to-text for noisy audio, with a claim of state-of-the-art accuracy on the top 25 languages in FLEURS.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-transcribe-1-5": { "id": "microsoft/mai/mai-transcribe-1-5", "name": "MAI-Transcribe-1.5", "maker": "microsoft", "family": "microsoft/mai", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Transcribe-1.5 is Microsoft AI's June 2, 2026 transcription model. Microsoft says it covers 43 languages and remains a supported MAI-Transcribe option in the speech docs read on 2026-10-15. Collection date: 2026-10-15.", "what_it_is_for": "Fast speech-to-text with domain terminology, integrated into Copilot, Teams, GitHub, and Dynamics 365 Contact Center, and sold on Foundry.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-transcribe-2": { "id": "microsoft/mai/mai-transcribe-2", "name": "MAI-Transcribe-2", "maker": "microsoft", "family": "microsoft/mai", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Transcribe-2 is the current default MAI transcription model in Microsoft's speech docs as of 2026-10-15. The alias mai-transcribe points at it. It supports more languages than MAI-Transcribe-1.5, including languages the older model did not list.", "what_it_is_for": "Multilingual speech-to-text, including a Voice Live pairing with chat models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-transcribe-2-streaming": { "id": "microsoft/mai/mai-transcribe-2-streaming", "name": "MAI-Transcribe-2-Streaming", "maker": "microsoft", "family": "microsoft/mai", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.54, "completion_per_m_usd": null }, "summary_intro": "MAI-Transcribe-2-Streaming is Microsoft AI's real-time transcription model announced October 1, 2026. Microsoft says it covers 60 languages with automatic language detection and is priced at $0.54 per hour through the end of 2026. Collection date: 2026-10-15.", "what_it_is_for": "Low-latency partial and final transcripts for dictation, subtitles, and voice agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-voice-1": { "id": "microsoft/mai/mai-voice-1", "name": "MAI-Voice-1", "maker": "microsoft", "family": "microsoft/mai", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 22.0, "completion_per_m_usd": null }, "summary_intro": "MAI-Voice-1 is Microsoft AI's speech-generation model that became available on Foundry with the April 2, 2026 announcement. Microsoft priced it starting at $22 per 1 million characters and described custom-voice creation from a short sample. Collection date: 2026-10-15.", "what_it_is_for": "Natural speech generation, including custom voices, for Copilot audio features and Foundry apps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-voice-2": { "id": "microsoft/mai/mai-voice-2", "name": "MAI-Voice-2", "maker": "microsoft", "family": "microsoft/mai", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Voice-2 is Microsoft AI's June 2, 2026 text-to-speech model on Foundry. Microsoft says it expands an English-only predecessor to many languages, with emotion tags and zero-shot voice prompting from 5 to 60 seconds of reference audio. Collection date: 2026-10-15.", "what_it_is_for": "Expressive speech for long-form audio and voice agents, with consent checks on custom voices.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-voice-2-1": { "id": "microsoft/mai/mai-voice-2-1", "name": "MAI-Voice-2.1", "maker": "microsoft", "family": "microsoft/mai", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Voice-2.1 is Microsoft AI's October 1, 2026 text-to-speech model. Microsoft calls it the strongest multilingual voice model so far, with 23 languages and 26 locales, and says one voice can speak all of them with a native accent. Collection date: 2026-10-15.", "what_it_is_for": "Multilingual speech generation for conversational agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-voice-2-1-flash": { "id": "microsoft/mai/mai-voice-2-1-flash", "name": "MAI-Voice-2.1-Flash", "maker": "microsoft", "family": "microsoft/mai", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Voice-2.1-Flash is the fast voice model Microsoft announced with MAI-Voice-2.1 on October 1, 2026. The retrieved post calls it blazing-fast and does not give a separate parameter count or price. Collection date: 2026-10-15.", "what_it_is_for": "Lower-latency speech generation paired with MAI-Voice-2.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/mai/mai-voice-2-flash": { "id": "microsoft/mai/mai-voice-2-flash", "name": "MAI-Voice-2-Flash", "maker": "microsoft", "family": "microsoft/mai", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAI-Voice-2-Flash is the lower-cost voice variant Microsoft said was coming soon on June 2, 2026. No official page opened on 2026-10-15 confirmed that this exact name had reached general availability. Collection date: 2026-10-15.", "what_it_is_for": "Microsoft described it as the efficient package of MAI-Voice-2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-1-5": { "id": "microsoft/phi/phi-1-5", "name": "Phi-1.5", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phi-1.5 is an earlier Microsoft Research small language model. On 2026-10-15 the Phi-2 model card still points at microsoft/phi-1.5 as the model whose data sources Phi-2 reused. Weights are published; a full model card was not opened in this pass. Collection date: 2026-10-15.", "what_it_is_for": "The Phi-2 card describes Phi-1.5 as the previous model in the same data line, aimed at language understanding before Phi-2. A dedicated purpose statement was not copied because the Phi-1.5 card itself was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-2": { "id": "microsoft/phi/phi-2", "name": "Phi-2", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "2700000000", "architecture": "Transformer with a next-word prediction objective", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phi-2 is a Microsoft Research 2.7 billion parameter base Transformer released as open weights under MIT. The model card describes it as a small model for common sense, language understanding, and logical reasoning, trained on synthetic textbook-style text plus filtered web data. It remained listed as a Foundry text-generation model on 2026-10-15.", "what_it_is_for": "Microsoft positions Phi-2 as a compact base model for language comprehension and reasoning, not as an instruction-tuned chat model. The card warns that, as a base model, it can continue past the first answer and can still emit harmful text if prompted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-3-5-mini": { "id": "microsoft/phi/phi-3-5-mini", "name": "Phi-3.5-mini", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.00013, "completion_per_m_usd": 0.00052 }, "summary_intro": "Phi-3.5-mini is the multilingual refresh of Phi-3-mini. Microsoft publishes it as open MIT weights with a 128K context. The product page describes Phi-3.5 as the series for stronger multilingual support. Collection date: 2026-10-15.", "what_it_is_for": "Microsoft positions Phi-3.5 as the Phi-3 follow-up for multilingual tasks and longer practical context, with mini as the small instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-3-5-moe": { "id": "microsoft/phi/phi-3-5-moe", "name": "Phi-3.5-MoE", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "Mixture-of-experts decoder-only Transformer, 16 experts, 2 experts per token, 6.6B active parameters", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.00016, "completion_per_m_usd": 0.00064 }, "summary_intro": "Phi-3.5-MoE is Microsoft's mixture-of-experts Phi-3.5 model. The Foundry card says it has 16 experts of 3.8B with 6.6B active parameters when two experts are used, a 128K context, and multilingual instruction tuning. Weights are MIT-licensed. Collection date: 2026-10-15.", "what_it_is_for": "Microsoft describes it as a lightweight MoE for multilingual language understanding, math, and reasoning, with long-context uses such as document and meeting summarization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-3-5-vision": { "id": "microsoft/phi/phi-3-5-vision", "name": "Phi-3.5-vision", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "4200000000", "architecture": "Image encoder, connector, projector, and Phi-3 Mini language model", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phi-3.5-vision is Microsoft's 4.2 billion parameter open multimodal model for text and images, with a 128K context. The model card says it was trained between July and August 2024 and adds multi-frame image comparison, storytelling, and video summarization. Collection date: 2026-10-15.", "what_it_is_for": "Microsoft aims it at Office-style visual tasks: comparing images, summarizing several frames, and answering questions about pictures, on top of the Phi-3 mini language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-3-medium": { "id": "microsoft/phi/phi-3-medium", "name": "Phi-3-medium", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-21", "context_length": 128000, "max_output_tokens": null, "parameters": "14000000000", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.00017, "completion_per_m_usd": 0.00068 }, "summary_intro": "Phi-3-medium is Microsoft's 14 billion parameter Phi-3 language model, announced May 21, 2024 in 4K and 128K context variants. Open weights were part of that release, and Foundry still listed the model for serverless use and fine-tuning on 2026-10-15.", "what_it_is_for": "Microsoft presents Phi-3-medium as the largest dense Phi-3 model for the same language, reasoning, and coding jobs as the smaller Phi-3 models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-3-mini": { "id": "microsoft/phi/phi-3-mini", "name": "Phi-3-mini", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "3800000000", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.00013, "completion_per_m_usd": 0.00052 }, "summary_intro": "Phi-3-mini is Microsoft's 3.8 billion parameter instruction-tuned small language model, released in 4K and 128K context variants. Microsoft Research trained it on a mix of synthetic and filtered web data and post-trained it with supervised fine-tuning and direct preference optimization. Collection date: 2026-10-15. Weights are MIT-licensed and the model is still named on the Foundry pricing page.", "what_it_is_for": "Microsoft describes Phi-3-mini as a lightweight model for language understanding, reasoning, and coding that can run on device-class hardware, including phone-scale deployments called out when the family launched.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-3-small": { "id": "microsoft/phi/phi-3-small", "name": "Phi-3-small", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-21", "context_length": 128000, "max_output_tokens": null, "parameters": "7000000000", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.00015, "completion_per_m_usd": 0.0006 }, "summary_intro": "Phi-3-small is the 7 billion parameter instruction-tuned member of Microsoft's Phi-3 family. Microsoft announced it on May 21, 2024 with 8K and 128K context lengths and published weights. It was still named on the Foundry pricing page on 2026-10-15.", "what_it_is_for": "Microsoft groups Phi-3-small with the rest of Phi-3 for language understanding, reasoning, and coding, at a size between mini and medium.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-3-vision": { "id": "microsoft/phi/phi-3-vision", "name": "Phi-3-vision", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-21", "context_length": 128000, "max_output_tokens": null, "parameters": "4200000000", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phi-3-vision is Microsoft's 4.2 billion parameter multimodal Phi-3 model for language plus images. Microsoft introduced it on May 21, 2024 with a 128K context and open weights. Collection date: 2026-10-15.", "what_it_is_for": "Microsoft says Phi-3-vision is for general visual reasoning and for chart, graph, and table understanding, alongside the text Phi-3 models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-4": { "id": "microsoft/phi/phi-4", "name": "Phi-4", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-12-12", "context_length": 16384, "max_output_tokens": 16384, "parameters": "14000000000", "architecture": "Dense decoder-only Transformer", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.000125, "completion_per_m_usd": 0.0005 }, "summary_intro": "Phi-4 is Microsoft Research's 14 billion parameter open language model, published December 12, 2024 under MIT. Foundry lists a 16,384-token input and output window. The February 2025 pricing blog instead labels the context as 128K. Collection date: 2026-10-15.", "what_it_is_for": "Microsoft describes Phi-4 as a small model for complex reasoning, math, and code, trained on synthetic data, filtered web text, and academic books and Q&A sets.", "ratings": { "empirical_100": 40, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.3, "humaneval": 70.3, "twelve_cs": 83.6, "aa_intelligence": 5.9, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-4-mini": { "id": "microsoft/phi/phi-4-mini", "name": "Phi-4-mini", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02", "context_length": 131072, "max_output_tokens": 4096, "parameters": "3800000000", "architecture": "Dense decoder-only Transformer with grouped-query attention, 200K vocabulary, and shared input and output embeddings", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.00075, "completion_per_m_usd": 0.0003 }, "summary_intro": "Phi-4-mini-instruct is Microsoft's 3.8 billion parameter multilingual small language model, released February 2025 under MIT with a long context. The card documents a function-calling format. Foundry's partner page, read the same day, says tool calling is not available on that deployment and caps output at 4,096 tokens. Collection date: 2026-10-15.", "what_it_is_for": "Microsoft says it is for instruction following, multilingual chat, and function calling, with a larger vocabulary than Phi-3.5-mini.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-4-mini-flash-reasoning": { "id": "microsoft/phi/phi-4-mini-flash-reasoning", "name": "Phi-4-mini-flash-reasoning", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 64000, "max_output_tokens": null, "parameters": "3800000000", "architecture": "Hybrid SambaY with differential attention, state-space layers, grouped-query attention, and tied embeddings. Vocabulary about 200K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phi-4-mini-flash-reasoning is Microsoft's 3.8 billion parameter hybrid reasoning model. The card describes a SambaY architecture, a 64K context, and MIT weights, tuned for math reasoning. Collection date: 2026-10-15.", "what_it_is_for": "Microsoft presents it as a faster small reasoning model for math and other reasoning-dense prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-4-mini-reasoning": { "id": "microsoft/phi/phi-4-mini-reasoning", "name": "Phi-4-mini-reasoning", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": 128000, "parameters": "3800000000", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phi-4-mini-reasoning is the 3.8 billion parameter reasoning model in the Phi-4-mini line. Foundry lists it as generally available with a 128,000-token context and a 128,000-token output cap, English text only, and no tool calling. Collection date: 2026-10-15.", "what_it_is_for": "Microsoft lists it for reasoning-style chat, especially math, at the mini size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-4-multimodal": { "id": "microsoft/phi/phi-4-multimodal", "name": "Phi-4-multimodal", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02", "context_length": 131072, "max_output_tokens": 4096, "parameters": "5600000000", "architecture": "Phi-4-mini-instruct backbone plus vision and speech encoders and adapters", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phi-4-multimodal-instruct is Microsoft's first Phi model that accepts text, images, and audio. It has 5.6 billion parameters, uses Phi-4-mini as the language backbone, and was released in February 2025 under MIT. Collection date: 2026-10-15.", "what_it_is_for": "Microsoft positions it for speech recognition, translation, summarization, question answering, and audio understanding, plus image and text instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-4-reasoning": { "id": "microsoft/phi/phi-4-reasoning", "name": "Phi-4-reasoning", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-30", "context_length": 32768, "max_output_tokens": 32768, "parameters": "14000000000", "architecture": "Dense decoder-only Transformer, same base as Phi-4, supervised fine-tuned for chain-of-thought", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phi-4-reasoning is a 14 billion parameter open-weight reasoning model Microsoft Research released on April 30, 2025. It is Phi-4 fine-tuned on chain-of-thought traces. Foundry lists a 32,768-token context and matching output cap, English only, without tool calling. Collection date: 2026-10-15.", "what_it_is_for": "Microsoft presents it for complex reasoning in math, science, and code, using a long reasoning trace before the answer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-4-reasoning-plus": { "id": "microsoft/phi/phi-4-reasoning-plus", "name": "Phi-4-reasoning-plus", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-30", "context_length": 32768, "max_output_tokens": null, "parameters": "14000000000", "architecture": "Phi-4 base with supervised fine-tuning plus outcome-based reinforcement learning", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phi-4-reasoning-plus is the reinforcement-learning variant of Phi-4-reasoning. Microsoft released the 14B open MIT weights on April 30, 2025. The card says it is more accurate and generates about 50 percent more tokens, so latency is higher. Collection date: 2026-10-15.", "what_it_is_for": "Same reasoning jobs as Phi-4-reasoning when a longer trace is acceptable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/phi/phi-silica": { "id": "microsoft/phi/phi-silica", "name": "Phi Silica", "maker": "microsoft", "family": "microsoft/phi", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer small language model with speculative decoding, optimized for the Windows NPU", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phi Silica is Microsoft's on-device small language model for Windows, used through the Windows App SDK. It runs on Copilot+ PC NPUs and on some supported GPUs. Microsoft's Windows docs, read 2026-10-15, say Aion Instruct starts replacing it on Insider builds in November 2026 and that Phi Silica is removed from retail devices in January 2027.", "what_it_is_for": "Local text generation, summarization, rewriting, and short content generation inside Windows apps, without a cloud round trip.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/trellis/trellis": { "id": "microsoft/trellis/trellis", "name": "TRELLIS", "maker": "microsoft", "family": "microsoft/trellis", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TRELLIS-image-large is Microsoft's open image-to-3D model on Hugging Face. The repository record read via the Hugging Face API on 2026-10-15 shows an MIT license and an image-to-3D pipeline. The card body was not fully read, so size and benchmark numbers are empty.", "what_it_is_for": "Generate 3D assets from images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/trellis/trellis-2": { "id": "microsoft/trellis/trellis-2", "name": "TRELLIS.2", "maker": "microsoft", "family": "microsoft/trellis", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TRELLIS.2-4B is the later Microsoft image-to-3D release. The Hugging Face record shows MIT weights and a December 1, 2025 creation date. The 4B size is in the repository name. The card body was not read, so parameters stays null. Collection date: 2026-10-15.", "what_it_is_for": "Image-to-3D generation, as labeled on the model repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/vibevoice/vibevoice-0-5b-streaming": { "id": "microsoft/vibevoice/vibevoice-0-5b-streaming", "name": "VibeVoice-0.5B-Streaming", "maker": "microsoft", "family": "microsoft/vibevoice", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VibeVoice-0.5B-Streaming is a Microsoft VibeVoice checkpoint named in the VibeVoice-1.5B model table. Its own card was not opened on 2026-10-15, so size, context, and license are not filled beyond the name.", "what_it_is_for": "Streaming text-to-speech in the VibeVoice family. The 1.5B card does not describe the streaming checkpoint's limits.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/vibevoice/vibevoice-1-5b": { "id": "microsoft/vibevoice/vibevoice-1-5b", "name": "VibeVoice-1.5B", "maker": "microsoft", "family": "microsoft/vibevoice", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-08-26", "context_length": 64000, "max_output_tokens": null, "parameters": "", "architecture": "Transformer LLM plus acoustic and semantic tokenizers and a diffusion decoder. The card says the LLM is Qwen2.5-1.5B for this release.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VibeVoice-1.5B is Microsoft's open text-to-speech model for long multi-speaker audio such as podcasts. The card says it can synthesize up to about 90 minutes with up to 4 speakers, at a 64K context, under MIT. The language-model backbone named on the card is Qwen2.5-1.5B. Collection date: 2026-10-15.", "what_it_is_for": "Long-form conversational speech from text, not a short single-utterance reader.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/vibevoice/vibevoice-asr": { "id": "microsoft/vibevoice/vibevoice-asr", "name": "VibeVoice-ASR", "maker": "microsoft", "family": "microsoft/vibevoice", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-01-21", "context_length": 64000, "max_output_tokens": null, "parameters": "", "architecture": "Long-form speech-to-text model with speaker and timestamp generation", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VibeVoice-ASR is Microsoft's open long-form speech-to-text model. The card says it can take about 60 minutes of audio in one pass inside a 64K context and return who spoke, when, and what, across more than 50 languages. License MIT. Collection date: 2026-10-15.", "what_it_is_for": "Transcription with diarization and timestamps, including custom hotwords and code-switching, without setting a language.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "microsoft/vibevoice/vibevoice-asr-bitnet": { "id": "microsoft/vibevoice/vibevoice-asr-bitnet", "name": "VibeVoice-ASR-BitNet", "maker": "microsoft", "family": "microsoft/vibevoice", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Heterogeneous quantized VibeVoice-ASR for CPU inference", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VibeVoice-ASR-BitNet is Microsoft's compressed VibeVoice-ASR build for CPUs. The card says the package goes from 4.62 GB to 1.58 GB and targets real-time inference on a few CPU threads. License MIT. Collection date: 2026-10-15.", "what_it_is_for": "On-device or CPU transcription when a GPU is not available.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/edit/edit": { "id": "midjourney/edit/edit", "name": "Midjourney Edit", "maker": "midjourney", "family": "midjourney/edit", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "2026-08-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney Edit is the instruction and reference image model for V8.1 and V8.2. The Edit article says it changes an image from written instructions, builds a new image from up to four references, and powers inpainting and outpainting in the Editor. It is closed and active. Collected on 2026-10-15.", "what_it_is_for": "Edits and multi-image compositions that used to require Omni Reference, Character Reference, or Retexture. Midjourney tells V8 users to use this model instead of those older tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/hd": { "id": "midjourney/midjourney/hd", "name": "Midjourney HD", "maker": "midjourney", "family": "midjourney/midjourney", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney HD is an early alternative image model, not a numbered version. Legacy Features says the --hd model generates busy, detailed, and abstract images with low coherency. It is closed and legacy. Collected on 2026-10-15. The 2026 --hd flag on V8.1 and V8.2 is a 2K resolution mode and is not this model.", "what_it_is_for": "An early alternate look: busier and more detailed than the numbered V1-V3 models, still low coherency.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/test": { "id": "midjourney/midjourney/test", "name": "Midjourney Test", "maker": "midjourney", "family": "midjourney/midjourney", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney Test is an experimental image model from August 2022. Legacy Features says --test and --testp were released to gather feedback on upcoming features, and that they can be paired with --creative. It is closed and legacy. Collected on 2026-10-15. The page does not define how --test differs from --testp.", "what_it_is_for": "Community test generations while Midjourney tried features that were not in the default model yet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/testp": { "id": "midjourney/midjourney/testp", "name": "Midjourney Testp", "maker": "midjourney", "family": "midjourney/midjourney", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney Testp is the second August 2022 test image model, selected with --testp. Legacy Features describes it together with --test and does not say what the p stands for or how the pictures differ. It is closed and legacy. Collected on 2026-10-15.", "what_it_is_for": "The same experimental role as --test. Midjourney published one shared description for both flags.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/v1": { "id": "midjourney/midjourney/v1", "name": "Midjourney V1", "maker": "midjourney", "family": "midjourney/midjourney", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney V1 is Midjourney's first image model. The Legacy Features page says it was the default from February 2022 to April 2022, and that it is very abstract and painterly with low coherency. It is closed, legacy, and still documented. This dossier was collected on 2026-10-15.", "what_it_is_for": "Early text-to-image generation in the Midjourney Discord bot. The docs treat it as a historical default, not the current model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/v2": { "id": "midjourney/midjourney/v2", "name": "Midjourney V2", "maker": "midjourney", "family": "midjourney/midjourney", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney V2 is the second Midjourney image model. Legacy Features says it was the default from April 2022 to July 2022, and that it is creative, colorful, and painterly with low coherency. It is a closed legacy model. Collected on 2026-10-15.", "what_it_is_for": "Text-to-image generation with a more colorful painterly look than V1, still at low coherency according to Midjourney.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/v3": { "id": "midjourney/midjourney/v3", "name": "Midjourney V3", "maker": "midjourney", "family": "midjourney/midjourney", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney V3 is the third main image model. Legacy Features says it was the default from July 2022 to November 2022 and that it makes highly creative compositions with moderate coherency. It is closed and legacy. Collected on 2026-10-15.", "what_it_is_for": "Text and image prompting for creative compositions. It is the last main model before the V4 rewrite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/v4": { "id": "midjourney/midjourney/v4", "name": "Midjourney V4", "maker": "midjourney", "family": "midjourney/midjourney", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney V4 is the November 2022 image model that replaced the V1-V3 line. Legacy Features calls it an entirely new codebase and a new AI architecture, trained on Midjourney's own supercluster, and says it was the default from 20 December 2022 to 30 March 2023. It is closed and legacy. Collected on 2026-10-15.", "what_it_is_for": "Higher-coherency text-to-image and image-prompt work, including multi-character prompts. Midjourney says it knows more creatures, places, and objects than earlier versions and handles small details better.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/v5": { "id": "midjourney/midjourney/v5", "name": "Midjourney V5", "maker": "midjourney", "family": "midjourney/midjourney", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney V5 is the March 2023 image model. Legacy Features says it was the default from 30 March 2023 to 3 May 2023, that it is more photographic than V5.1, and that it follows the prompt closely but may need a longer prompt to get a specific aesthetic. It is closed and legacy. Collected on 2026-10-15.", "what_it_is_for": "Photographic text-to-image and image-prompt generations at 1024 px, with a weaker default style than V5.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/v5-1": { "id": "midjourney/midjourney/v5-1", "name": "Midjourney V5.1", "maker": "midjourney", "family": "midjourney/midjourney", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "2023-05-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney V5.1 is the 4 May 2023 image model. Legacy Features says it was the default from 3 May 2023 to 22 June 2023, that simple prompts work better because the default aesthetic is stronger, and that coherency, sharpness, and --tile support improved. It is closed and legacy. Collected on 2026-10-15.", "what_it_is_for": "Easier photographic and designed images from short prompts, plus repeating patterns with --tile.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/v5-2": { "id": "midjourney/midjourney/v5-2", "name": "Midjourney V5.2", "maker": "midjourney", "family": "midjourney/midjourney", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney V5.2 is the June 2023 image model and the longest-running V5 default. Legacy Features says it was the default from 22 June 2023 to 14 February 2024, with sharper detail, better color and composition, and a slightly better prompt understanding. It is closed and legacy. Collected on 2026-10-15.", "what_it_is_for": "The main 2023 production image model, and the host of the old Style Tuner. Remaster sends an older upscale forward into this version.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/v6": { "id": "midjourney/midjourney/v6", "name": "Midjourney V6", "maker": "midjourney", "family": "midjourney/midjourney", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "2023-12-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney V6 is the image model released on 20 December 2023. The Version page says it was the default from 14 February 2024 to 30 July 2024, with better long-prompt accuracy, coherence, knowledge, image prompting, and remixing. It is closed and still documented as a selectable version, not as a legacy version. Collected on 2026-10-15.", "what_it_is_for": "General text-to-image and image-prompt work, including character reference, which V7 later replaced with Omni Reference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/v6-1": { "id": "midjourney/midjourney/v6-1", "name": "Midjourney V6.1", "maker": "midjourney", "family": "midjourney/midjourney", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "2024-07-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney V6.1 is the 30 July 2024 image model. The Version page says it was the default until 16 June 2025, with more coherent images, finer textures, and generations about 25% faster than V6. It is closed and still documented. Collected on 2026-10-15.", "what_it_is_for": "The production image model between V6 and V7. Later V7 editing tools still call back into V6.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/v7": { "id": "midjourney/midjourney/v7", "name": "Midjourney V7", "maker": "midjourney", "family": "midjourney/midjourney", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "2025-04-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney V7 is the image model released on 3 April 2025. The Version page says it was the default from 17 June 2025 to 9 June 2026, with more precise text and image prompts and more coherent bodies, hands, and objects. It is closed and still selectable. Collected on 2026-10-15.", "what_it_is_for": "The 2025 flagship image model. It added Draft Mode, Conversational Mode, and Omni Reference for putting a character or object into a scene.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/v8": { "id": "midjourney/midjourney/v8", "name": "Midjourney V8", "maker": "midjourney", "family": "midjourney/midjourney", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "2026-03-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney V8, also called V8.0, is the early alpha image model that opened on 17 March 2026. The Version page says it stayed on the Midjourney Alpha website until 24 July 2026 and can no longer be used, though old gallery images remain. It is closed and retired. Collected on 2026-10-15.", "what_it_is_for": "A public alpha of the V8 image generation stack. Midjourney asked members to rate images in the lightbox to help train the look.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/v8-1": { "id": "midjourney/midjourney/v8-1", "name": "Midjourney V8.1", "maker": "midjourney", "family": "midjourney/midjourney", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "2026-04-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney V8.1 is the image model released on 14 April 2026. The Version page says it was the default from 10 June 2026 to 23 July 2026, that standard jobs are about four to five times faster than earlier versions, and that --hd renders 2K images without a separate upscale. It is closed and still documented. Collected on 2026-10-15.", "what_it_is_for": "Fast general image generation, including native 2K HD, personalization, and style references. Raw mode is the documented way to drop default styling when you need the prompt followed more literally.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/midjourney/v8-2": { "id": "midjourney/midjourney/v8-2", "name": "Midjourney V8.2", "maker": "midjourney", "family": "midjourney/midjourney", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "2026-07-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney V8.2 is Midjourney's default image model. The Version page says it was released as the default on 24 July 2026, with a push on aesthetics, image quality, and personalization, and that it is more creative, bold, and edgy than the prior default. It is closed and active. Collected on 2026-10-15.", "what_it_is_for": "Current general-purpose image generation on midjourney.com and Discord, including personalization, moodboards, style references, and the Edit model for instructions and references.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/niji/niji-4": { "id": "midjourney/niji/niji-4", "name": "Niji 4", "maker": "midjourney", "family": "midjourney/niji", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "2022-12-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Niji 4 is the first Niji image model, released on 20 December 2022. Midjourney describes Niji as a collaboration with Spellbrush aimed at Eastern and anime aesthetics and illustration, with its own site and Discord. It is closed and legacy. Collected on 2026-10-15.", "what_it_is_for": "Anime and illustrative text-to-image work in the V4 era. It is not a style preset of Midjourney V4, even though it shipped the day V4 became the default and uses 4a, 4b, and 4c style labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/niji/niji-5": { "id": "midjourney/niji/niji-5", "name": "Niji 5", "maker": "midjourney", "family": "midjourney/niji", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Niji 5 is the April 2023 anime image model. Legacy Features says it can be steered with --style cute, expressive, original, or scenic, and that original was the default before 26 May 2023. It is closed and legacy. Collected on 2026-10-15.", "what_it_is_for": "Anime and illustrated images, with named style presets for cute characters, expressive illustration, the original look, and scenic shots.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/niji/niji-6": { "id": "midjourney/niji/niji-6", "name": "Niji 6", "maker": "midjourney", "family": "midjourney/niji", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "2024-06-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Niji 6 is the anime image model released on 7 June 2024. Legacy Features says it renders short Japanese kana better, can render simple Chinese characters, improves anime eyes, and reduces some artifacts. It is closed and listed as a legacy version. Collected on 2026-10-15.", "what_it_is_for": "Anime and Eastern illustration, plus character reference, which V7 and Niji 7 moved away from.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/niji/niji-7": { "id": "midjourney/niji/niji-7", "name": "Niji 7", "maker": "midjourney", "family": "midjourney/niji", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "2026-01-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Niji 7 is the current anime image model, launched on 9 January 2026. The Version page says it is more coherent, clearer in eyes and small details, more literal about prompts, and flatter in its line. It is a closed Midjourney model made with Spellbrush. Collected on 2026-10-15.", "what_it_is_for": "Anime, manga, and Eastern illustration, including repeatable character designs when the prompt is specific. It is the Niji model Midjourney documents beside V7, not a mode of V8.2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "midjourney/video/video-v1": { "id": "midjourney/video/video-v1", "name": "Midjourney Video V1", "maker": "midjourney", "family": "midjourney/video", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Midjourney Terms of Service and the plan commercial terms.", "release_date": "2025-06-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midjourney Video V1 is Midjourney's image-to-video model, released to members on 18 June 2025. The Video page says it turns a starting image into a 5-second clip, with optional text, and that clips can be extended to 21 seconds. It is closed and active. No Video V2 was named in the retrieved docs. Collected on 2026-10-15.", "what_it_is_for": "Short image-to-video clips, including loops and a chosen end frame. It is not a text-to-video model in the current docs: a starting image is required.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mila/amplify/amplify-120m": { "id": "mila/amplify/amplify-120m", "name": "AMPLIFY 120M", "maker": "mila", "family": "mila/amplify", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-09-26", "context_length": 2048, "max_output_tokens": null, "parameters": "120 million", "architecture": "Encoder-only Transformer, masked language model on amino-acid sequences. Card: hidden size 640, 24 layers, 10 attention heads, intermediate size 2560, vocab 27, RoPE theta 10000, SwiGLU, pre-activation RMSNorm, no attention or FFN bias. Max position embeddings 2048. The contact-prediction table's 240 heads is 24 layers times 10 heads, not a second architecture.", "modalities": { "input": [ "protein" ], "output": [ "embedding", "protein" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AMPLIFY 120M is the smaller protein language model in the Amgen-Mila AMPLIFY pair. Mila announced the open release on 26 September 2024. Weights, code, and the UR100P data remain public under MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "Mila describes AMPLIFY as a smaller open protein language model for embeddings, mutation suggestions, and separating protein sequences from non-protein sequences. The 120M checkpoint is the cheaper of the two released sizes. It is a masked encoder, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mila/amplify/amplify-350m": { "id": "mila/amplify/amplify-350m", "name": "AMPLIFY 350M", "maker": "mila", "family": "mila/amplify", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-09-26", "context_length": 2048, "max_output_tokens": null, "parameters": "350 million", "architecture": "Encoder-only Transformer, masked language model. Card: hidden size 960, 32 layers, 15 attention heads, intermediate size 3840, vocab 27, RoPE theta 10000, SwiGLU, pre-activation RMSNorm, no attention or FFN bias. Max position embeddings 2048. Hugging Face model-size field says 0.4B. Contact-table '480 heads' is 32 layers times 15 heads.", "modalities": { "input": [ "protein" ], "output": [ "embedding", "protein" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AMPLIFY 350M is the larger Amgen-Mila protein language model, announced by Mila on 26 September 2024. It is an open MIT masked encoder trained on UR100P. The checkpoint is still public. This dossier was collected on 2026-10-15.", "what_it_is_for": "Mila and Amgen present it as a protein language model for sequence understanding, mutation suggestions, and protein design research that a lab can run without a multi-billion-parameter model. The paper's preferred comparison is ESM2 15B on validation pseudo-perplexity and sequence recovery, not on linear contact prediction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mila/focalcodec/focalcodec-12-5hz": { "id": "mila/focalcodec/focalcodec-12-5hz", "name": "FocalCodec 12.5 Hz", "maker": "mila", "family": "mila/focalcodec", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-06", "context_length": null, "max_output_tokens": null, "parameters": "145 million", "architecture": "Lowest-rate non-causal FocalCodec. 16 kHz in and out. Token rate 12.5 Hz. Codebook 1x8192. Bitrate 0.16 kbps. Paper lists 145 million parameters. Trained on LibriTTS-960. Hub id is focalcodec_12_5hz and the model-size field says 0.1B.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FocalCodec 12.5 Hz is the lowest-bitrate non-streaming FocalCodec checkpoint: 145 million parameters, 0.16 kbps, 12.5 tokens per second, LibriTTS-960. It is part of the open release Mila described in January 2026. Collected on 2026-10-15.", "what_it_is_for": "The same codec at the lowest token rate in the original paper, when the budget is a handful of speech tokens per second and a higher word error is acceptable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mila/focalcodec/focalcodec-25hz": { "id": "mila/focalcodec/focalcodec-25hz", "name": "FocalCodec 25 Hz", "maker": "mila", "family": "mila/focalcodec", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-06", "context_length": null, "max_output_tokens": null, "parameters": "144 million", "architecture": "Same non-causal FocalCodec design as the 50 Hz model, with more temporal downsampling. 16 kHz in and out. Token rate 25 Hz. Codebook 1x8192. Bitrate 0.33 kbps. Paper lists 144 million parameters and code size 13. Trained on LibriTTS-960. Hub model-size field says 0.1B.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FocalCodec 25 Hz is the mid-rate non-streaming FocalCodec checkpoint, 144 million parameters, 0.33 kbps, trained on LibriTTS-960. It shipped with the February 2025 paper that Mila later described as its open speech codec. Collected on 2026-10-15.", "what_it_is_for": "Same single-codebook speech codec as the 50 Hz model, at half the token rate, for cases that want fewer tokens per second.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mila/focalcodec/focalcodec-50hz": { "id": "mila/focalcodec/focalcodec-50hz", "name": "FocalCodec 50 Hz", "maker": "mila", "family": "mila/focalcodec", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-06", "context_length": null, "max_output_tokens": null, "parameters": "142 million", "architecture": "Single-codebook speech codec. Focal-modulation compressor on WavLM features, binary spherical quantizer, Vocos decoder. Non-causal. 16 kHz in and out. Token rate 50 Hz. Codebook 1x8192. Bitrate 0.65 kbps. Code size 13 in the paper table. Trained on LibriTTS-960. Hugging Face model-size field says 0.1B. The Hub model tree lists microsoft/wavlm-large as the base.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FocalCodec 50 Hz is the main non-streaming speech codec from the Mila and Concordia FocalCodec paper (6 February 2025, NeurIPS 2025). It compresses 16 kHz speech to one codebook at 0.65 kbps. Mila's 23 January 2026 article points at the public checkpoints. Collected on 2026-10-15.", "what_it_is_for": "A single-stream speech tokenizer for resynthesis, voice conversion, and feeding speech into a language model without a stack of codebooks. This checkpoint is offline, not the 80 ms streaming line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mila/focalcodec/focalcodec-50hz-2k-causal": { "id": "mila/focalcodec/focalcodec-50hz-2k-causal", "name": "FocalCodec-Stream 50 Hz 2k", "maker": "mila", "family": "mila/focalcodec", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-21", "context_length": null, "max_output_tokens": null, "parameters": "249 million", "architecture": "Causal FocalCodec-Stream. Focal modulation with causal convolutions, multi-stage causal distillation of WavLM, binary spherical quantizer, refiner, 24 kHz Vocos-style decoder. Token rate 50 Hz. Codebook 1x2048. Bitrate 0.55 kbps. Latency 80 ms. Input 16 kHz, output 24 kHz. Trained with Libri-Light at the final stage. Paper Table 1 lists 249 million parameters for all three codebook sizes. Hub model-size field says 0.2B.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FocalCodec-Stream 50 Hz 2k is the smallest streaming FocalCodec checkpoint: codebook 2048, 0.55 kbps, 80 ms latency, 249 million parameters. Weights went up with the 19 September 2025 paper and the 21 September 2025 code release. Mila's January 2026 article links the collection that contains it. Collected on 2026-10-15.", "what_it_is_for": "Real-time speech coding with one causal codebook, for live tokenization rather than the offline FocalCodec checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mila/focalcodec/focalcodec-50hz-4k-causal": { "id": "mila/focalcodec/focalcodec-50hz-4k-causal", "name": "FocalCodec-Stream 50 Hz 4k", "maker": "mila", "family": "mila/focalcodec", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-21", "context_length": null, "max_output_tokens": null, "parameters": "249 million", "architecture": "Causal FocalCodec-Stream. 50 Hz, codebook 1x4096, bitrate 0.60 kbps, latency 80 ms, 16 kHz in, 24 kHz out, Libri-Light. Paper lists 249 million parameters. Hub model-size field says 0.2B. The GitHub streaming example uses this checkpoint.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FocalCodec-Stream 50 Hz 4k is the streaming checkpoint the official demo uses: codebook 4096, 0.60 kbps, 80 ms latency, 249 million parameters. It was released in September 2025 with the other causal FocalCodec weights. Collected on 2026-10-15.", "what_it_is_for": "Real-time single-codebook speech coding. The GitHub streaming example loads this config.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mila/focalcodec/focalcodec-50hz-65k-causal": { "id": "mila/focalcodec/focalcodec-50hz-65k-causal", "name": "FocalCodec-Stream 50 Hz 65k", "maker": "mila", "family": "mila/focalcodec", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-21", "context_length": null, "max_output_tokens": null, "parameters": "249 million", "architecture": "Causal FocalCodec-Stream with the largest codebook. GitHub table: 50.0 Hz, 1x65536, 0.80 kbps, latency 80 ms, 16 kHz in, 24 kHz out, Libri-Light. Paper Table 1 lists the same bitrate and 249 million parameters, and prints token rate 50.5 Hz for this row only. Hub model-size field on the sibling causal cards is 0.2B.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FocalCodec-Stream 50 Hz 65k is the largest-codebook streaming FocalCodec checkpoint: 65,536 codes, 0.80 kbps, 80 ms latency, 249 million parameters. It was released with the other causal checkpoints in September 2025. Collected on 2026-10-15.", "what_it_is_for": "The high-codebook streaming codec in the FocalCodec-Stream trio, still one codebook, for real-time speech tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mila/galileo/galileo-base": { "id": "mila/galileo/galileo-base", "name": "Galileo Base", "maker": "mila", "family": "mila/galileo", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-02-13", "context_length": null, "max_output_tokens": null, "parameters": "85.0 million", "architecture": "ViT-Base encoder. Paper Table 12: 12 blocks, width 768, 12 heads, 85.0 million parameters. About 573 H100-hours for 500 epochs. Flexible patch embedding. Same global-plus-local masked pretraining as Nano and Tiny.", "modalities": { "input": [ "image", "time-series" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Galileo Base is the main Galileo encoder, an 85 million parameter vision transformer for many kinds of satellite data. Mila's site and 2024-2025 impact report present it as the institute's Earth-observation foundation model. The MIT weights are published by NASA Harvest. Collected on 2026-10-15.", "what_it_is_for": "The paper's claim is one generalist checkpoint for image tasks and pixel time series, instead of a separate specialist per sensor. Mila points at wildfire-moisture maps and at agricultural monitoring that previously used Presto.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mila/galileo/galileo-nano": { "id": "mila/galileo/galileo-nano", "name": "Galileo Nano", "maker": "mila", "family": "mila/galileo", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-02-13", "context_length": null, "max_output_tokens": null, "parameters": "0.8 million", "architecture": "ViT-Nano encoder. Paper Table 12: 4 blocks, width 128, 8 heads, 0.8 million parameters. Pretrained 500 epochs on one H100, about 200 GPU-hours. Self-supervised masked modeling with paired global and local losses over optical, SAR, elevation, weather, and map-like inputs, in image and pixel time-series views.", "modalities": { "input": [ "image", "time-series" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Galileo Nano is the smallest encoder in the Galileo Earth-observation family. Mila's October 2025 article calls Galileo its open foundation model. The weights are the NASA Harvest release, coauthored with Mila, Ai2, and other universities. Nano is a 0.8 million parameter ViT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper trains one recipe at three sizes so the same weights can read satellite images and pixel time series. Nano is the size they highlight for limited compute. Mila's examples are wildfire fuel moisture and, for the predecessor Presto, cropland maps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mila/galileo/galileo-tiny": { "id": "mila/galileo/galileo-tiny", "name": "Galileo Tiny", "maker": "mila", "family": "mila/galileo", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-02-13", "context_length": null, "max_output_tokens": null, "parameters": "5.3 million", "architecture": "ViT-Tiny encoder. Paper Table 12: 12 blocks, width 192, 3 heads, 5.3 million parameters. About 259 H100-hours for 500 pretraining epochs. Same multimodal masked objective as the other Galileo sizes.", "modalities": { "input": [ "image", "time-series" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Galileo Tiny is the middle Galileo encoder, a 5.3 million parameter vision transformer for satellite images and pixel time series. Mila presents the family as its open Earth-observation model. NASA Harvest hosts the MIT weights. Collected on 2026-10-15.", "what_it_is_for": "Same job as the other Galileo sizes: a generalist remote-sensing encoder. Tiny sits between the 0.8 million parameter Nano model and the 85 million parameter Base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mila/guardrail/suicide-prevention-output-guardrail": { "id": "mila/guardrail/suicide-prevention-output-guardrail", "name": "Mila Suicide Prevention Output Guardrail", "maker": "mila", "family": "mila/guardrail", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-09", "context_length": 512, "max_output_tokens": null, "parameters": "0.1 billion", "architecture": "BertForSequenceClassification fine-tuned from bert-base-uncased. Binary labels: 0 not a suicide/self-harm policy violation, 1 violation. Hugging Face model-size field says 0.1B, tensor type F32. The exact BERT-base integer is not restated on the card.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mila and ROOST released this beta output classifier on 9 July 2026. The live weights are version 0.1.0-beta.2, a fine-tune of bert-base-uncased under Apache-2.0, published by the Mila AI4H Hugging Face org. It scores one assistant reply as in or out of a suicide and self-harm policy. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says it is a cheap specialist that flags assistant text which enables, encourages, or instructs suicide or intentional self-harm, including self-injury and disordered eating under the MLCommons AILuminate wording they adopted. Supportive replies and unrelated harms are supposed to pass. It is one layer in a stack, not a chatbot and not a crisis service.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mila/neobert/neobert": { "id": "mila/neobert/neobert", "name": "NeoBERT", "maker": "mila", "family": "mila/neobert", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "250 million", "architecture": "Bidirectional encoder trained from scratch with masked language modeling. 28 layers, hidden size 768, 12 heads, SwiGLU, RoPE, pre-RMSNorm. Tokenizer is google/bert. Mask rate 20 percent. Trained on RefinedWeb, 2.1 trillion tokens, data size stated as 2.8 TB. Flash Attention. Hugging Face model-size field says 0.2B and tensor type F32.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NeoBERT is a 250 million parameter English encoder that Mila and Chandar Research Lab trained from scratch and released in February 2025 under MIT. It is a masked language model with a 4,096-token context, meant as a BERT-base-width replacement. Weights were still public on 2026-10-15.", "what_it_is_for": "Mila's 3 March 2025 article says NeoBERT is a small open encoder for people who need a modern BERT-style backbone without training a large decoder. The card shows masked-encoder embeddings, not chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mines/tempest/tempest-1": { "id": "mines/tempest/tempest-1", "name": "TempEst 1", "maker": "mines", "family": "mines/tempest", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2024-06-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Quantile regression forest in R (quantregForest 1.3, depending on randomForest 4.7) for monthly mean stream temperature. Default covariates are air temperature, land-surface temperature, humidity, latitude, longitude, elevation, and water, built-up, and tree cover. The prediction function returns the median or named quantiles. Tree count is not stated. The project site calls this a point-oriented, sitewise random forest forced by Landsat land-surface temperature.", "modalities": { "input": [ "satellite-covariates", "tabular" ], "output": [ "stream-temperature" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TempEst 1 is a Colorado School of Mines stream-temperature model from the Hydrologic Science and Engineering program, released through the mines-ciroh GitHub organization and RiverTempest.org. It estimates monthly mean river temperature from satellite and gridded covariates with a quantile regression forest. The journal record was created on 20 June 2024, the software is still distributed, and this dossier was collected on 15 October 2026.", "what_it_is_for": "The GitHub README describes high-accuracy estimation of river temperature from satellite remote sensing, validated on public data for the contiguous United States. RiverTempest.org calls it the monthly model for general temperature trends, including rivers from a few meters to a kilometer wide. The same page says later TempEst models kept the point-oriented, sitewise design and dropped the random forest.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mines/tempest/tempest-2": { "id": "mines/tempest/tempest-2", "name": "TempEst 2", "maker": "mines", "family": "mines/tempest", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2025-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SCHEMA spatial-statistical model. SCHEMA on the TempEst 2 README is Seasonal Conditions Historical Estimation with Modeled daily Anomaly. The project index and the libSCHEMA package summary say Expectation rather than Estimation. A three-sine seasonality term and a daily anomaly term use kriging models whose site coefficients come from a 500 m neighborhood. It is not a neural network. The R reference returns daily mean temperature and, when trained with maxima, daily maximum temperature. Python package tempest2 is a port. TempEst 2-FAST is the gridded implementation of the same model.", "modalities": { "input": [ "satellite-covariates", "tabular" ], "output": [ "stream-temperature" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TempEst 2 is a Colorado School of Mines daily stream-temperature model. It replaces the TempEst 1 random forest with a SCHEMA kriging model and estimates daily mean and maximum water temperature at ungaged points from a 500 m satellite neighborhood. The journal record was created on 27 May 2025, the R and Python code are public, and this dossier was collected on 15 October 2026.", "what_it_is_for": "The README estimates stream water temperature at a point for streams of any size. RiverTempest.org says the goal was daily resolution, with linear geostatistical models for site coefficients so the model stays interpretable and cheaper than TempEst 1. Historical estimation is the job. Forecasting is TempEst-NEXT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mines/tempest/tempest-newt": { "id": "mines/tempest/tempest-newt", "name": "TempEst-NEWT", "maker": "mines", "family": "mines/tempest", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2025-10-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lumped watershed SCHEMA model. NEWT is Near-term Expected Watershed Temperatures. The default configuration is a stationary seasonality term plus a weather anomaly driven by watershed-average daily maximum air temperature. Optional modification engines change coefficients through time. The implementation is the Python class NEWT.Watershed on top of libSCHEMA. It is a statistical regression, not a neural network. Coefficient values are fit per watershed or supplied by TempEst-NEXT.", "modalities": { "input": [ "air-temperature", "vapor-pressure", "tabular" ], "output": [ "stream-temperature" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TempEst-NEWT is a Colorado School of Mines statistical model for daily stream temperature in one watershed. It hindcasts and forecasts from air temperature and vapor pressure, and it is also the calibrated piece inside TempEst-NEXT. PyPI package 0.1.0 was uploaded on 27 October 2025. The shared journal article's version-of-record license starts on 3 May 2026. This dossier was collected on 15 October 2026.", "what_it_is_for": "The README calls NEWT a high-performing air-temperature regression for watersheds, and the calibrated component of TempEst-NEXT. RiverTempest.org says it is a standalone SCHEMA watershed model with modification engines for nonstationarity and disturbances. TempEst 2 remains the fast historical remote-sensing model. NEWT is the in-depth, gage-calibrated watershed model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mines/tempest/tempest-next": { "id": "mines/tempest/tempest-next", "name": "TempEst-NEXT", "maker": "mines", "family": "mines/tempest", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2025-10-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Statistical coefficient estimator for ungaged watersheds. NEXT is Near-term Expected Temperatures. It estimates TempEst-NEWT seasonality and weather-sensitivity coefficients from climate and watershed geography, then returns or runs that NEWT model. Dynamic SCHEMA lets coefficients shift with watershed climate. It does not use latitude or longitude. It is not a neural network. The docs example loads a pretrained coefs.pickle.", "modalities": { "input": [ "weather", "watershed-attributes", "tabular" ], "output": [ "stream-temperature" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TempEst-NEXT is a Colorado School of Mines statistical model that hindcasts and forecasts daily stream temperature for watersheds without local calibration. It estimates coefficients for a TempEst-NEWT model from site climate and geography, then runs that model. The first PyPI release was 27 October 2025. Current package 0.2.2 was uploaded on 16 June 2026. This dossier was collected on 15 October 2026.", "what_it_is_for": "The docs page estimates daily stream temperature where no local temperature record is available to calibrate the watershed. It can forecast and it can represent disturbances. TempEst 2 covers historical estimation, uses less data, and is faster. The project site says NEXT emphasizes site conditions and does not take latitude or longitude as inputs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/ames": { "id": "minho/deepmol/ames", "name": "AMES Mutagenicity", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Voting classifier, as stated in the class features. Table 5 reports a voting pipeline. The results text says that Ames pipeline is five graph neural networks with ChEMBL standardization. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AMES Mutagenicity is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `AMES` downloads `AMES.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts Ames mutagenicity, the bacterial reverse-mutation assay for genetic damage. The class text says the set is aggregated from four papers. The public README lists it as `AMES`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/bbb": { "id": "minho/deepmol/bbb", "name": "BBB (Blood-Brain Barrier)", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Voting classifier, as stated in the class features. The 2024 paper Table 2 reports a voting pipeline for the BBB_Martins dataset. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BBB (Blood-Brain Barrier) is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `BBB` downloads `BBB_Martins.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts whether a molecule penetrates the blood-brain barrier. The class text says the barrier blocks most foreign drugs and that the label comes from MoleculeNet via TDC. The public README lists it as `BBB`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/bioavailability": { "id": "minho/deepmol/bioavailability", "name": "Bioavailability", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Voting classifier, as stated in the class features. The case-studies loader names two pipelines, bioavailability and bioavailability_optimal. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bioavailability is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `Bioavailability` downloads `Bioavailability_Ma.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts oral bioavailability, whether the active ingredient is absorbed and becomes available at the site of action. The class quotes that definition. The public README lists it as `Bioavailability`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/caco2": { "id": "minho/deepmol/caco2", "name": "Caco-2 (Cell Effective Permeability)", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph neural network, as stated in the class features. Table 1 reports AttentiveFP on a molecular graph for Caco2_Wang. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Caco-2 (Cell Effective Permeability) is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `Caco2` downloads `Caco2_Wang.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts Caco-2 cell effective permeability, used as an in vitro stand-in for how fast a drug crosses human intestinal tissue. The public README lists it as `Caco2`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/cyp2c9-inhibition": { "id": "minho/deepmol/cyp2c9-inhibition", "name": "CYP P450 2C9 Inhibition", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Classifier. The class features do not name the estimator. Table 3 reports a voting pipeline for CYP2C9 inhibition. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CYP P450 2C9 Inhibition is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `CYP2C9Inhibition` downloads `CYP2C9_Veith.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts whether a compound inhibits CYP2C9. The class text says CYP2C9 oxidizes xenobiotic and endogenous compounds. The public README lists it as `CYP2C9Inhibition`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/cyp2c9-substrate": { "id": "minho/deepmol/cyp2c9-substrate", "name": "CYP2C9 Substrate", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Classifier. The class features do not name the estimator. Table 3 reports bagging on MACCS keys plus RDKit fingerprints with low-variance feature selection. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CYP2C9 Substrate is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `CYP2C9Substrate` downloads `CYP2C9_Substrate_CarbonMangels.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts whether CYP2C9 metabolizes a drug. The class text says TDC merged substrates and non-substrates from six publications. The public README lists it as `CYP2C9Substrate`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/cyp2d6-substrate": { "id": "minho/deepmol/cyp2d6-substrate", "name": "CYP2D6 Substrate", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Classifier. The class features do not name the estimator. Table 3 reports gradient boosting on binarized descriptors with percentile feature selection. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CYP2D6 Substrate is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `CYP2D6Substrate` downloads `CYP2D6_Substrate_CarbonMangels.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts whether CYP2D6 metabolizes a drug. The class text says TDC merged substrates and non-substrates from six publications. The public README lists it as `CYP2D6Substrate`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/cyp3a4-inhibition": { "id": "minho/deepmol/cyp3a4-inhibition", "name": "CYP P450 3A4 Inhibition", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Classifier. The class features do not name the estimator. Table 3 reports a voting pipeline for CYP3A4 inhibition. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CYP P450 3A4 Inhibition is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `CYP3A4Inhibition` downloads `CYP3A4_Veith.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts whether a compound inhibits CYP3A4. The class text says CYP3A4, mainly in liver and intestine, oxidizes small foreign molecules so they can be cleared. The public README lists it as `CYP3A4Inhibition`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/cyp3a4-substrate": { "id": "minho/deepmol/cyp3a4-substrate", "name": "CYP3A4 Substrate", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Classifier. The class features do not name the estimator. Table 3 reports bagging on atom-pair fingerprints with custom standardization and low-variance feature selection. The metric for this row is AUROC, unlike the other metabolism rows. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CYP3A4 Substrate is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `CYP3A4Substrate` downloads `CYP3A4_Substrate_CarbonMangels.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts whether CYP3A4 metabolizes a drug. The class text says TDC merged substrates and non-substrates from six publications. The public README lists it as `CYP3A4Substrate`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/hepatocyte-clearance": { "id": "minho/deepmol/hepatocyte-clearance", "name": "Hepatocyte Clearance", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Regressor. The class features do not name the estimator. Table 4 reports a voting pipeline for CL-Hepa. The results text says that pipeline uses a ChEMBL standardizer with three directed message-passing neural networks and two graph convolutional networks. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hepatocyte Clearance is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `HepatocyteClearance` downloads `Clearance_Hepatocyte_AZ.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts intrinsic clearance measured in human hepatocytes, in microliters per minute per million cells. The class text says the ChEMBL set was deposited by AstraZeneca and is kept separate from the microsome set. The public README lists it as `HepatocyteClearance`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/herg-blockers": { "id": "minho/deepmol/herg-blockers", "name": "hERG blockers", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Classifier. The class features do not name the estimator. Table 5 reports a graph convolutional network on a molecular graph. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "hERG blockers is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `hERGBlockers` downloads `hERG.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts whether a drug blocks the hERG channel. The class text says hERG block can cause cardiotoxicity and is screened early in design. The public README lists it as `hERGBlockers`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/hia": { "id": "minho/deepmol/hia", "name": "HIA (Human Intestinal Absorption)", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph neural network, as stated in the class features. Table 1 reports AttentiveFP on a molecular graph with custom standardization for HIA_Hou. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HIA (Human Intestinal Absorption) is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `HIA` downloads `HIA_Hou.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts whether an orally administered drug is absorbed from the human gastrointestinal tract into the bloodstream. The public README lists it as `HIA`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/ld50": { "id": "minho/deepmol/ld50", "name": "Acute toxicity LD50", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Regressor. The class features do not name the estimator. Table 5 reports a voting regressor with custom standardization, layered fingerprints, and percentile feature selection. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Acute toxicity LD50 is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `LD50` downloads `LD50_Zhu.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts acute toxicity LD50 as log(1/(mol/kg)). The class text says a higher dose in the underlying assay is more lethal, and that the set was provided by the authors of the cited source. The public README lists it as `LD50`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/lipophilicity": { "id": "minho/deepmol/lipophilicity", "name": "Lipophilicity", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Regressor. The class features do not name the estimator. The prediction type string in code is spelled Lipophilicyty log-ratio. The loader names lipophilicity and lipophilicity_optimal. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lipophilicity is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `Lipophilicity` downloads `Lipophilicity_AstraZeneca.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts lipophilicity, how readily a drug dissolves in a lipid environment. The class text says the set is from MoleculeNet. The public README lists it as `Lipophilicity`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/microsome-clearance": { "id": "minho/deepmol/microsome-clearance", "name": "Microsome Clearance", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Regressor. The class features do not name the estimator. Table 4 reports a voting pipeline for CL-Micro. The results text says five TextCNNs with a ChEMBL standardizer. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Microsome Clearance is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `MicrosomeClearance` downloads `Clearance_Microsome_AZ.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts intrinsic clearance measured in microsomes, in milliliters per minute per gram. The class text says this AstraZeneca ChEMBL set is kept separate from hepatocyte clearance. The public README lists it as `MicrosomeClearance`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/plants-sm-precursor": { "id": "minho/deepmol/plants-sm-precursor", "name": "Plants secondary metabolite precursors predictor", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-label classifier. The class sets mode to classification 29 times and maps 29 KEGG compound ids to precursor names. The class text says it is the same model as https://github.com/jcapels/SMPrecursorPredictor/. That repository publishes two pipelines: layered fingerprints with low-variance feature selection and a ridge classifier, and Morgan fingerprints with a ridge classifier. The zip does not say which one it contains. The 20 March 2025 paper reports layered fingerprints plus a ridge classifier as the best test pipeline. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Plants secondary metabolite precursors predictor is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `PlantsSMPrecursorPredictor` downloads `PlantsSMPredictor.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts which primary-metabolism precursors a plant specialized metabolite comes from. More than one precursor, or none, can be returned. The class maps 29 KEGG ids, including amino acids, terpenoid diphosphates, and flavonoid intermediates. The public README lists it as `PlantsSMPrecursorPredictor`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/ppbr": { "id": "minho/deepmol/ppbr", "name": "Human plasma protein binding rate (PPBR)", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Regressor. The class features do not name the estimator. Table 2 reports a stacking ensemble on descriptors with custom standardization and percentile feature selection at 62 percent. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Human plasma protein binding rate (PPBR) is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `PPBR` downloads `PPBR_AZ.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts the percentage of a drug bound to human plasma proteins. The class text says the assay was deposited in ChEMBL by AstraZeneca. The public README lists it as `PPBR`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/solubility": { "id": "minho/deepmol/solubility", "name": "Solubility", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "mode is regression and the output is solubility in log mol/L. The class features call it a classifier. Table 1 reports Attentive FP on a molecular graph for AqSol. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solubility is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `Solubility` downloads `Solubility_AqSolDB.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts aqueous solubility in log mol/L. The class text says poor water solubility can slow absorption and that the set is AqSolDB via TDC. The public README lists it as `Solubility`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minho/deepmol/vdss": { "id": "minho/deepmol/vdss", "name": "Volume of Distribution at steady state", "maker": "minho", "family": "minho/deepmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the weight zip (Zenodo record 14653345). The deepmol-models 0.0.5 loader is BSD-2-Clause. The DeepMol framework package is BSD-2-Clause.", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The class features call it a fast regressor and do not name the estimator. Table 2 reports a random forest on MACCS keys with custom standardization and low-variance feature selection. The zip on Zenodo record 14653345 was not unpacked on 2026-10-15, so this paper pipeline is not confirmed as the serialized file.", "modalities": { "input": [ "smiles" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Volume of Distribution at steady state is a named predictor in the deepmol-models package from the BioSystems group at the Centre of Biological Engineering, University of Minho. It is a local SMILES model, status active on the collection date 2026-10-15. The class `VDss` downloads `VDss_Lombardo.zip` from Zenodo record 14653345, published 2025-01-22. The loader package 0.0.1 was uploaded to PyPI on 2025-01-23.", "what_it_is_for": "Predicts volume of distribution at steady state, in liters per kilogram. The class text says a higher value means more of the drug sits in tissue rather than blood. The public README lists it as `VDss`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/h3/minimax-h3": { "id": "minimax/h3/minimax-h3", "name": "MiniMax-H3", "maker": "minimax", "family": "minimax/h3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MiniMax H3 Community License Agreement", "release_date": "2026-07-31", "context_length": null, "max_output_tokens": null, "parameters": "H3-Omni-Transformer is 33B dense; about 13B of that sits in AdaLN branches that can be cached at inference. The text encoder uses Qwen3-VL-32B weights.", "architecture": "Omni-modal generator. Open checkpoints are H3-Base FL2VA and Ref2VA in BF16. Visual VAE is f16t4d24. Audio VAE is 32 kHz stereo at 40 Hz tokens per channel. 24 fps. H3-Context-IR and H3-Regenerate-2K are hosted modules and are not in the open-weight drop. Sparse attention was trained but the first open release is full attention only.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniMax-H3 is MiniMax\u2019s July 2026 open-weight video model. It reads text, images, video, and audio and writes video with stereo sound, from 4 to 15 seconds, at 768P or 2K. Hosted Context-IR and 2K regeneration sit around the open 33B base. Collected 2026-10-15.", "what_it_is_for": "General text-to-video, image-to-video, first-and-last-frame, and multimodal reference generation with native audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/h3/minimax-h3-max": { "id": "minimax/h3/minimax-h3-max", "name": "MiniMax-H3-Max", "maker": "minimax", "family": "minimax/h3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speed model post-trained by fal.ai on MiniMax-H3 and sold jointly on the MiniMax API. No open checkpoint was found under MiniMaxAI on Hugging Face.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniMax-H3-Max is the fast video model MiniMax sells with fal.ai. It is post-trained on H3 for speed, outputs 480P or 768P, and runs 5 to 15 seconds. Weights were not found on the MiniMax Hugging Face org. Collected 2026-10-15.", "what_it_is_for": "Faster text-to-video, image-to-video, and reference video than H3, without 2K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/hailuo/i2v-01": { "id": "minimax/hailuo/i2v-01", "name": "I2V-01", "maker": "minimax", "family": "minimax/hailuo", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "I2V-01 is the base image-to-video model of the Video-01 generation. The image-to-video API enum still includes it. The October 2026 model index does not. Collected 2026-10-15.", "what_it_is_for": "Animate a first-frame image with an optional prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/hailuo/i2v-01-director": { "id": "minimax/hailuo/i2v-01-director", "name": "I2V-01-Director", "maker": "minimax", "family": "minimax/hailuo", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "I2V-01-Director is the February 2025 image-to-video model with camera control. It remains in the image-to-video API enum and is off the October 2026 model index. Collected 2026-10-15.", "what_it_is_for": "Image-to-video with explicit camera moves.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/hailuo/i2v-01-live": { "id": "minimax/hailuo/i2v-01-live", "name": "I2V-01-live", "maker": "minimax", "family": "minimax/hailuo", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "I2V-01-live is a named image-to-video API model in the 01 series. Official pages read for this catalog do not add a separate capability sentence beyond the id. It is not on the October 2026 model index. Collected 2026-10-15.", "what_it_is_for": "Image-to-video. A distinct live or illustration behavior was not confirmed on the API page itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/hailuo/minimax-hailuo-02": { "id": "minimax/hailuo/minimax-hailuo-02", "name": "MiniMax-Hailuo-02", "maker": "minimax", "family": "minimax/hailuo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed video model. Hailuo 02\u2019s launch post names Noise-aware Compute Redistribution (NCR) as the efficiency framework. Parameter count was not published on the pages read.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniMax-Hailuo-02 is MiniMax\u2019s June 2025 video model with native 1080p and clips up to 10 seconds. It is still an available API model, at legacy per-video prices, and supports text-to-video plus image-to-video including 512P. Collected 2026-10-15.", "what_it_is_for": "Short text-to-video and image-to-video clips with strong instruction following and physics, including a first and last frame.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/hailuo/minimax-hailuo-2-3": { "id": "minimax/hailuo/minimax-hailuo-2-3", "name": "MiniMax-Hailuo-2.3", "maker": "minimax", "family": "minimax/hailuo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed video model. Hailuo 02\u2019s launch post names Noise-aware Compute Redistribution (NCR) as the efficiency framework. Parameter count was not published on the pages read.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniMax-Hailuo-2.3 is the October 2025 Hailuo video model for text-to-video and image-to-video. MiniMax cites better body movement, facial expression, physics, and prompt following. 768P runs 6 or 10 seconds; 1080P runs 6 seconds. Collected 2026-10-15.", "what_it_is_for": "Text-to-video and image-to-video at 768P or 1080P.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/hailuo/minimax-hailuo-2-3-fast": { "id": "minimax/hailuo/minimax-hailuo-2-3-fast", "name": "MiniMax-Hailuo-2.3-Fast", "maker": "minimax", "family": "minimax/hailuo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed video model. Hailuo 02\u2019s launch post names Noise-aware Compute Redistribution (NCR) as the efficiency framework. Parameter count was not published on the pages read.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniMax-Hailuo-2.3-Fast is the cheaper, faster image-to-video companion to Hailuo 2.3, released the same day. It does not do text-to-video. Resolutions match 2.3: 768P for 6 or 10 seconds and 1080P for 6 seconds. Collected 2026-10-15.", "what_it_is_for": "Lower-cost image-to-video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/hailuo/s2v-01": { "id": "minimax/hailuo/s2v-01", "name": "S2V-01", "maker": "minimax", "family": "minimax/hailuo", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "S2V-01 is MiniMax\u2019s subject-reference video model. The subject-to-video API accepts only this model id. It is not on the October 2026 available model index. Collected 2026-10-15.", "what_it_is_for": "Generate a video that keeps a referenced character\u2019s face.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/hailuo/t2v-01": { "id": "minimax/hailuo/t2v-01", "name": "T2V-01", "maker": "minimax", "family": "minimax/hailuo", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "T2V-01 is MiniMax\u2019s earlier text-to-video API model in the Hailuo Video-01 generation. The text-to-video reference still lists the id. The October 2026 model index does not. Collected 2026-10-15.", "what_it_is_for": "Generate a short video from a text prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/hailuo/t2v-01-director": { "id": "minimax/hailuo/t2v-01-director", "name": "T2V-01-Director", "maker": "minimax", "family": "minimax/hailuo", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "T2V-01-Director is the February 2025 text-to-video model with stronger camera-move control. It is still named in the text-to-video API enum and is absent from the October 2026 model index. Collected 2026-10-15.", "what_it_is_for": "Text-to-video with cinematic camera instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/image/image-01": { "id": "minimax/image/image-01", "name": "image-01", "maker": "minimax", "family": "minimax/image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "image-01 is MiniMax\u2019s image model, released 2025-02-15. It is still on the API list at 0.0035 USD per image, or 0.025 CNY on the Chinese price list. Collected 2026-10-15.", "what_it_is_for": "Text-to-image and image-to-image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/image/image-01-live": { "id": "minimax/image/image-01-live", "name": "image-01-live", "maker": "minimax", "family": "minimax/image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "image-01-live is an active MiniMax image model beside image-01. English docs say it uses a text prompt and a reference image. Chinese docs say it adds style presets such as hand-drawn and cartoon. The Chinese list price is 0.025 CNY per image. Collected 2026-10-15.", "what_it_is_for": "Styled or reference image generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/m/m2-her": { "id": "minimax/m/m2-her", "name": "M2-her", "maker": "minimax", "family": "minimax/m", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 64000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "M2-her is a retired MiniMax text model for character dialogue. The Chinese text guide describes role-play and a 64K window. No open weights were found. Collected 2026-10-15.", "what_it_is_for": "Role-play and multi-turn character conversation, according to the MiniMax text guide.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/m/minimax-m1-40k": { "id": "minimax/m/minimax-m1-40k", "name": "MiniMax-M1-40k", "maker": "minimax", "family": "minimax/m", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-06-16", "context_length": 1000000, "max_output_tokens": 40000, "parameters": "456B total, 45.9B active", "architecture": "Hybrid lightning attention and softmax attention MoE, continued from MiniMax-Text-01. 40K thinking budget. The paper says the 40k model is an intermediate stage of the 80k run.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniMax-M1-40k is the shorter-thinking open-source checkpoint of MiniMax-M1, released 2025-06-16 under Apache-2.0. It is a 456B hybrid-attention reasoning model with a 1,000,000-token context. Collected 2026-10-15.", "what_it_is_for": "Long-context reasoning, software engineering, and tool use, with a 40K thinking budget.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/m/minimax-m1-80k": { "id": "minimax/m/minimax-m1-80k", "name": "MiniMax-M1-80k", "maker": "minimax", "family": "minimax/m", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-06-16", "context_length": 1000000, "max_output_tokens": 80000, "parameters": "456B total, 45.9B active", "architecture": "Same hybrid lightning-attention MoE as M1-40k, trained with an 80K thinking budget.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniMax-M1-80k is the longer-thinking Apache-2.0 checkpoint of MiniMax-M1, released with M1-40k on 2025-06-16. It keeps the 456B hybrid architecture and 1,000,000-token context. Collected 2026-10-15.", "what_it_is_for": "The same reasoning jobs as M1-40k, with a larger test-time thinking budget.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/m/minimax-m2": { "id": "minimax/m/minimax-m2", "name": "MiniMax-M2", "maker": "minimax", "family": "minimax/m", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "modified-MIT", "release_date": "2025-10-27", "context_length": 204800, "max_output_tokens": null, "parameters": "230B total, 10B active", "architecture": "Mixture-of-experts text model. Interleaved reasoning; the model card says thinking blocks must be kept in history. Recommended sampling: temperature 1.0, top_p 0.95, top_k 40.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 1.2 }, "summary_intro": "MiniMax-M2 is the October 2025 open-weight agent and coding MoE (230B total, 10B active). The API id is retired. Weights and the vendor benchmark table remain public. Collected 2026-10-15.", "what_it_is_for": "MiniMax built M2 for coding workflows and multi-step tool use at a small activation size.", "ratings": { "empirical_100": null, "lmsys_elo": 1218, "coding_elo": 1218, "swe_bench": 18.0, "humaneval": 72.4, "twelve_cs": 99.5, "aa_intelligence": 18.6, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1095, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/m/minimax-m2-1": { "id": "minimax/m/minimax-m2-1", "name": "MiniMax-M2.1", "maker": "minimax", "family": "minimax/m", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "modified-MIT (Hugging Face license tag)", "release_date": "2025-12-22", "context_length": 204800, "max_output_tokens": null, "parameters": "229B total on the Hugging Face model size label", "architecture": "MiniMax-M2 MoE text model. Active-parameter count not copied from a primary page in this pass.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 1.2 }, "summary_intro": "MiniMax-M2.1 is the December 2025 open-weight coding model in the M2 line. The English API model list collected 2026-10-15 marks the id retired. Weights remain on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "MiniMax marketed M2.1 for multilingual programming and code refactoring.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/m/minimax-m2-1-highspeed": { "id": "minimax/m/minimax-m2-1-highspeed", "name": "MiniMax-M2.1-highspeed", "maker": "minimax", "family": "minimax/m", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "API speed tier of MiniMax-M2.1; no separate checkpoint found", "release_date": "2025-12-22", "context_length": 204800, "max_output_tokens": null, "parameters": "", "architecture": "Faster API tier. An older Chinese release note also used the name M2.1-lightning.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 2.4 }, "summary_intro": "MiniMax-M2.1-highspeed is the retired faster API id for M2.1. A Chinese release note called the same tier M2.1-lightning. The English model list marks it retired. Collected 2026-10-15.", "what_it_is_for": "Lower-latency M2.1 serving.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/m/minimax-m2-5": { "id": "minimax/m/minimax-m2-5", "name": "MiniMax-M2.5", "maker": "minimax", "family": "minimax/m", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "modified-MIT (Hugging Face license tag; full grant not re-read)", "release_date": "", "context_length": 204800, "max_output_tokens": null, "parameters": "229B total on the Hugging Face model size label", "architecture": "MiniMax-M2 text MoE lineage. Active-parameter count was not copied from a primary page in this pass.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 1.2 }, "summary_intro": "MiniMax-M2.5 is an open-weight coding and agent model from February 2026. It is still on the international available list as a legacy-priced model, and the Chinese overview schedules API shutdown on 2026-11-18. Collected 2026-10-15.", "what_it_is_for": "MiniMax says M2.5 targets code generation and refactoring, tool use, search, and office productivity.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/m/minimax-m2-5-highspeed": { "id": "minimax/m/minimax-m2-5-highspeed", "name": "MiniMax-M2.5-highspeed", "maker": "minimax", "family": "minimax/m", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "API speed tier of MiniMax-M2.5; no separate checkpoint found", "release_date": "", "context_length": 204800, "max_output_tokens": null, "parameters": "", "architecture": "Faster serving tier. Docs say the effect matches M2.5 at about 100 tokens per second.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 2.4 }, "summary_intro": "MiniMax-M2.5-highspeed is the faster, higher-priced API tier of M2.5. MiniMax says quality is unchanged. It is on the same Chinese shutdown schedule as M2.5. Collected 2026-10-15.", "what_it_is_for": "Lower-latency M2.5 calls.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/m/minimax-m2-7": { "id": "minimax/m/minimax-m2-7", "name": "MiniMax-M2.7", "maker": "minimax", "family": "minimax/m", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "MiniMax M2.7 non-commercial license; commercial use needs prior written authorization", "release_date": "2026-03-18", "context_length": 204800, "max_output_tokens": null, "parameters": "229B total on the Hugging Face card; NVIDIA says 230B total and 10B active", "architecture": "Sparse MoE. NVIDIA: 62 layers, 256 experts, 8 experts active per token, about 10B active parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 1.2 }, "summary_intro": "MiniMax-M2.7 is an open-weight text MoE for coding and agents, announced 2026-03-18. The international model list still showed it on 2026-10-12, but the Chinese overview schedules pay-as-you-go shutdown on 2026-11-18. Collected 2026-10-15.", "what_it_is_for": "MiniMax describes M2.7 as the start of recursive self-improvement, aimed at software engineering, office documents, and agent workflows.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/m/minimax-m2-7-highspeed": { "id": "minimax/m/minimax-m2-7-highspeed", "name": "MiniMax-M2.7-highspeed", "maker": "minimax", "family": "minimax/m", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Same weights as MiniMax-M2.7; no separate checkpoint found", "release_date": "2026-03-18", "context_length": 204800, "max_output_tokens": null, "parameters": "", "architecture": "API speed tier of MiniMax-M2.7. Docs say the effect is unchanged and output is about 100 tokens per second versus about 60 for M2.7.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 2.4 }, "summary_intro": "MiniMax-M2.7-highspeed is the faster API tier of M2.7. MiniMax says quality matches M2.7. It shares the M2.7-series shutdown dates published on the Chinese model overview. Collected 2026-10-15.", "what_it_is_for": "Lower latency serving of M2.7 for coding and agent calls.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/m/minimax-m3": { "id": "minimax/m/minimax-m3", "name": "MiniMax-M3", "maker": "minimax", "family": "minimax/m", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MiniMax Community License", "release_date": "2026-06-01", "context_length": 1000000, "max_output_tokens": null, "parameters": "about 428B total, about 23B activated", "architecture": "Native multimodal model using MiniMax Sparse Attention (MSA). Mixed-modality training from the start. Thinking can be enabled, adaptive, or disabled.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 1.2 }, "summary_intro": "MiniMax-M3 is MiniMax\u2019s active open-weight multimodal coding model, released 2026-06-01. It takes text, images, and video, uses sparse attention for a 1,000,000-token context, and is sold on the pay-as-you-go API. Collected 2026-10-15.", "what_it_is_for": "The maker positions M3 for agentic reasoning, tool use, coding, multimodal chat, and long-context work.", "ratings": { "empirical_100": null, "lmsys_elo": 1218, "coding_elo": 1218, "swe_bench": 18.0, "humaneval": 72.4, "twelve_cs": 27.7, "aa_intelligence": 29.2, "aa_coding": 58.6, "aa_agentic": 29.5, "design_arena_elo": 1125, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/m/minimax-m3-1-flash-preview": { "id": "minimax/m/minimax-m3-1-flash-preview", "name": "MiniMax-M3.1-Flash-Preview", "maker": "minimax", "family": "minimax/m", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniMax-M3.1-Flash-Preview is MiniMax\u2019s preview frontier coding model. As of 2026-10-15 it is offered only on M Plan and MiniMax Code, with a 1,000,000-token context and adjustable thinking depth. Collected 2026-10-15.", "what_it_is_for": "Official docs describe it as a native multimodal frontier coding model for agent reasoning, tool use, code, and long context, with tunable thinking depth.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/minimax-01/minimax-text-01": { "id": "minimax/minimax-01/minimax-text-01", "name": "MiniMax-Text-01", "maker": "minimax", "family": "minimax/minimax-01", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MiniMax Model License", "release_date": "2025-01-15", "context_length": 1000000, "max_output_tokens": null, "parameters": "456B total, 45.9B active", "architecture": "80 layers, hybrid lightning attention and softmax attention (softmax after every 7 lightning layers), 64 heads, head dim 128, 32 experts, top-2 routing, expert hidden size 9216, hidden size 6144, vocab 200064. Training context 1M tokens; the card says inference can extrapolate to 4M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniMax-Text-01 is MiniMax\u2019s January 2025 open-weight long-context language model. It has 456B parameters with 45.9B active and a 1,000,000-token training context. It is no longer on the API model list. Collected 2026-10-15.", "what_it_is_for": "General language tasks with very long context. It is also the base of MiniMax-VL-01 and MiniMax-M1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/minimax-01/minimax-vl-01": { "id": "minimax/minimax-01/minimax-vl-01", "name": "MiniMax-VL-01", "maker": "minimax", "family": "minimax/minimax-01", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MiniMax Model License", "release_date": "2025-01-15", "context_length": 1000000, "max_output_tokens": null, "parameters": "Text backbone 456B total, 45.9B active; vision encoder 303M", "architecture": "ViT-MLP-LLM. 303M ViT, two-layer MLP projector, MiniMax-Text-01 as the LLM. Dynamic resolution from 336\u00d7336 to 2016\u00d72016 plus a 336\u00d7336 thumbnail. ViT trained from scratch on 694 million image-caption pairs. Continued training used 512 billion vision-language tokens, per the paper.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniMax-VL-01 is the January 2025 open-weight vision-language model paired with MiniMax-Text-01. It adds a 303M vision encoder and dynamic image resolution on the 456B text backbone. Collected 2026-10-15.", "what_it_is_for": "Image understanding and multimodal chat on top of the MiniMax-01 language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/music/music-1-5": { "id": "minimax/music/music-1-5", "name": "Music-1.5", "maker": "minimax", "family": "minimax/music", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Music-1.5 is MiniMax\u2019s September 2025 music model, after a beta on 2025-06-20. It supports songs of about four minutes from a musical prompt and lyrics. It is not on the October 2026 available or retired tables. Collected 2026-10-15.", "what_it_is_for": "Vocal music, with the public release aimed at listenable four-minute songs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/music/music-2-0": { "id": "minimax/music/music-2-0", "name": "Music-2.0", "maker": "minimax", "family": "minimax/music", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Music-2.0 is the October 2025 music model. Release notes say it supports about five minutes, stronger vocals, and instrument control. It is retired. Legacy price is 0.03 USD per song up to five minutes. Collected 2026-10-15.", "what_it_is_for": "Vocal songs up to about five minutes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/music/music-2-5": { "id": "minimax/music/music-2-5", "name": "Music-2.5", "maker": "minimax", "family": "minimax/music", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Music-2.5 is MiniMax\u2019s January 2026 music model. The news post says it adds section-level control and higher fidelity, including 14 structural tags and a larger instrument set. The API id is retired at 0.15 USD per song up to five minutes. Collected 2026-10-15.", "what_it_is_for": "Detailed vocal songs from lyrics and a style prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/music/music-2-5-plus": { "id": "minimax/music/music-2-5-plus", "name": "Music-2.5+", "maker": "minimax", "family": "minimax/music", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Music-2.5+ is the March 2026 music model that MiniMax said unlocked instrumental generation. The API id is retired. The discontinued price is 0.15 USD per song up to five minutes. Collected 2026-10-15.", "what_it_is_for": "Songs and instrumentals from a prompt, breaking the vocal-only limit of earlier music models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/music/music-2-6": { "id": "minimax/music/music-2-6", "name": "music-2.6", "maker": "minimax", "family": "minimax/music", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "music-2.6 is a retired MiniMax music API model. The April 2026 release note titles it Cover Reborn and Bass Redefined. Price on the discontinued table is 0.15 USD per song up to five minutes. A free id, music-2.6-free, was shut on 2026-08-20. Collected 2026-10-15.", "what_it_is_for": "Vocal music from a prompt and lyrics. The release title also points at cover and bass changes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/music/music-3-0": { "id": "minimax/music/music-3-0", "name": "music-3.0", "maker": "minimax", "family": "minimax/music", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Not copied from the Music3 license file in this pass", "release_date": "2026-07-16", "context_length": null, "max_output_tokens": null, "parameters": "Card states an 8B global LLM and a 0.6B local LLM. The Hugging Face size tag also says 2B. Those figures disagree; no single total is stated.", "architecture": "8B global LLM for structure, 0.6B local LLM for acoustic detail, flow matching and a Flow-VAE. 32 kHz 16-bit stereo WAV. Eight-layer RVQ tokenizer; first codebook has 16384 entries. Section tags include intro, verse, pre-chorus, chorus, post-chorus, bridge, instrumental, solo, and outro.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "music-3.0 is MiniMax\u2019s Music 3 generation. The API id is on the retired list and priced at 0.15 USD per song up to five minutes, while the pricing note says existing paying users could keep using paid music APIs after 2026-08-20. Open weights are published as MiniMax-Music3. Collected 2026-10-15.", "what_it_is_for": "Full songs from a style description and lyrics, up to five minutes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/music/music-cover": { "id": "minimax/music/music-cover", "name": "music-cover", "maker": "minimax", "family": "minimax/music", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "music-cover is a retired MiniMax music API id. The model list gives the name and the retired status only. A free twin, music-cover-free, stopped on 2026-08-20. Collected 2026-10-15.", "what_it_is_for": "Music generation. A dedicated behavior spec was not on the pages read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/speech/speech-02-hd": { "id": "minimax/speech/speech-02-hd", "name": "speech-02-hd", "maker": "minimax", "family": "minimax/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "speech-02-hd is the April 2025 HD speech model. MiniMax still lists it and prices it with speech-2.6-hd at 100 USD per million characters. The release note stresses prosody, stability, voice similarity, and audio quality. Collected 2026-10-15.", "what_it_is_for": "Text-to-speech, including system voices and cloned voices, on the T2A HTTP and WebSocket APIs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/speech/speech-02-turbo": { "id": "minimax/speech/speech-02-turbo", "name": "speech-02-turbo", "maker": "minimax", "family": "minimax/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "speech-02-turbo is the low-latency Speech-02 model from 2025-04-02. It remains on the available list at 60 USD per million characters. Chinese copy says multilingual ability was strengthened. Collected 2026-10-15.", "what_it_is_for": "Text-to-speech, including system voices and cloned voices, on the T2A HTTP and WebSocket APIs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/speech/speech-2-5": { "id": "minimax/speech/speech-2-5", "name": "Speech-2.5", "maker": "minimax", "family": "minimax/speech", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Speech-2.5 is a MiniMax text-to-speech model announced on 2025-08-06. It is no longer on the October 2026 model index. Separate HD and Turbo ids were not confirmed on official pages read for this catalog. Collected 2026-10-15.", "what_it_is_for": "Higher voice similarity and wider language coverage than Speech-02, according to the release note.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/speech/speech-2-6-hd": { "id": "minimax/speech/speech-2-6-hd", "name": "speech-2.6-hd", "maker": "minimax", "family": "minimax/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "speech-2.6-hd is the previous HD speech model, released 2025-10-29. It remains on the English available list and is priced as a legacy model at 100 USD per million characters. Chinese docs call the 2.6 line historical. Collected 2026-10-15.", "what_it_is_for": "Text-to-speech, including system voices and cloned voices, on the T2A HTTP and WebSocket APIs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/speech/speech-2-6-turbo": { "id": "minimax/speech/speech-2-6-turbo", "name": "speech-2.6-turbo", "maker": "minimax", "family": "minimax/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "speech-2.6-turbo is the low-latency Speech 2.6 tier. It remains available and is legacy-priced at 60 USD per million characters. The API overview says this turbo id supports 40 languages. Collected 2026-10-15.", "what_it_is_for": "Text-to-speech, including system voices and cloned voices, on the T2A HTTP and WebSocket APIs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/speech/speech-2-8-hd": { "id": "minimax/speech/speech-2-8-hd", "name": "speech-2.8-hd", "maker": "minimax", "family": "minimax/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "speech-2.8-hd is MiniMax\u2019s current high-definition speech model, released 2026-01-23. The API list describes ultra-realistic speech with sound tags. The international price is 100 USD per million characters. Collected 2026-10-15.", "what_it_is_for": "Text-to-speech, including system voices and cloned voices, on the T2A HTTP and WebSocket APIs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/speech/speech-2-8-turbo": { "id": "minimax/speech/speech-2-8-turbo", "name": "speech-2.8-turbo", "maker": "minimax", "family": "minimax/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "speech-2.8-turbo is the faster, cheaper Speech 2.8 tier, released with the HD model on 2026-01-23. Price is 60 USD per million characters. Collected 2026-10-15.", "what_it_is_for": "Text-to-speech, including system voices and cloned voices, on the T2A HTTP and WebSocket APIs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/vtp/vtp-base-f16d64": { "id": "minimax/vtp/vtp-base-f16d64", "name": "VTP-Base-f16d64", "maker": "minimax", "family": "minimax/vtp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-16", "context_length": null, "max_output_tokens": null, "parameters": "0.3B", "architecture": "Visual tokenizer from the VTP paper. Hugging Face pipeline tag is image-feature-extraction. f16d64 is in the checkpoint name.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VTP-Base-f16d64 is MiniMax\u2019s base visual tokenizer, about 0.3B parameters on the Hugging Face org label, posted 2025-12-16. Collected 2026-10-15.", "what_it_is_for": "Image feature extraction / visual tokenization for research and downstream models. Not a text-to-image generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/vtp/vtp-large-f16d64": { "id": "minimax/vtp/vtp-large-f16d64", "name": "VTP-Large-f16d64", "maker": "minimax", "family": "minimax/vtp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-16", "context_length": null, "max_output_tokens": null, "parameters": "0.7B", "architecture": "Visual tokenizer from the VTP paper. Hugging Face pipeline tag is image-feature-extraction. f16d64 is in the checkpoint name.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VTP-Large-f16d64 is MiniMax\u2019s large visual tokenizer, about 0.7B parameters on the Hugging Face org label, posted 2025-12-16. Collected 2026-10-15.", "what_it_is_for": "Image feature extraction / visual tokenization for research and downstream models. Not a text-to-image generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "minimax/vtp/vtp-small-f16d64": { "id": "minimax/vtp/vtp-small-f16d64", "name": "VTP-Small-f16d64", "maker": "minimax", "family": "minimax/vtp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Visual tokenizer from the VTP paper. Hugging Face pipeline tag is image-feature-extraction. f16d64 is in the checkpoint name.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VTP-Small-f16d64 is MiniMax\u2019s smaller visual tokenizer checkpoint, posted 2025-12-16. A parameter count was not on the Hugging Face size label seen in this pass. Collected 2026-10-15.", "what_it_is_for": "Image feature extraction / visual tokenization for research and downstream models. Not a text-to-image generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "missouri/cryo2struct/cryo2struct": { "id": "missouri/cryo2struct/cryo2struct", "name": "Cryo2Struct", "maker": "missouri", "family": "missouri/cryo2struct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "3D transformer identifies atom types and amino acid types in a cryo-EM density map resampled to 1 angstrom. A hidden Markov model and a modified Viterbi alignment build the backbone. Two checkpoints are downloaded: amino_acid_type.ckpt and atom_type.ckpt, plus aa_regression_model.pkl and ca_regression_model.pkl. The README says training used 24 nodes with 6 NVIDIA V100 32 GB GPUs each. Confidence is a per-residue score in [0, 1] for carbon-alpha and amino-acid type.", "modalities": { "input": [ "cryo-em-density-map", "protein-sequence" ], "output": [ "protein-structure", "confidence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cryo2Struct is a cryo-EM atomic modeling method from Nabin Giri and Jianlin Cheng at the University of Missouri. A 3D transformer labels atoms and amino acids in a density map, and a hidden Markov model traces the backbone. Nature Communications 15, article 5511 (2024), is the paper cited in the repository. A Zenodo software record is dated 5 June 2024. Weight files were still linked from Harvard Dataverse on 2026-10-15. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README says it builds a protein backbone from a cryo-EM map without a template structure. Confidence is in the range 0 to 1 for carbon-alpha placement and for amino-acid type. The worked example is EMD 34610 / PDB 8hb0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "missouri/cryo2struct/cryo2struct-2": { "id": "missouri/cryo2struct/cryo2struct-2", "name": "Cryo2Struct2", "maker": "missouri", "family": "missouri/cryo2struct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The README calls it a multi-task deep model that merges cryo-EM density with sequence features from the ESM protein language model esm2_t36_3B_UR50D. Predicted voxels feed a hidden Markov model and a custom Viterbi alignment. The resulting backbone is an optional template for AlphaFold3, which is a separate Google DeepMind model and is not part of these weights. Missouri checkpoints are amino_acid_type.ckpt (Harvard Dataverse file 10888677) and atom_type.ckpt (file 10888678). The README says one training script trains both heads. The 3 billion figure is the ESM2 feature model, not a Cryo2Struct2 parameter count.", "modalities": { "input": [ "cryo-em-density-map", "protein-sequence" ], "output": [ "protein-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cryo2Struct2 is the follow-on cryo-EM modeling method from Jianlin Cheng's Bioinformatics and Machine Learning Lab at the University of Missouri. The public repository slug is Cryo2Strut2. The README says it merges density maps with ESM sequence features, builds a backbone with a hidden Markov model, and can pass that backbone to AlphaFold3 as a template. Its own checkpoints are on Harvard Dataverse. The organization page badges the repository MIT. Status is active as of 2026-10-15. This dossier was collected on that date.", "what_it_is_for": "The README says the method models atomic structure from a cryo-EM map and uses the Missouri backbone as a template so AlphaFold3 can refine it. AlphaFold3 is not a Missouri model. The Missouri release is the multi-task density network and the alignment code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "missouri/deepdist/deepdist": { "id": "missouri/deepdist/deepdist", "name": "DeepDist", "maker": "missouri", "family": "missouri/deepdist", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Deep residual convolutional network for inter-residue distance maps. The public package runs an ensemble. The README names three prediction modes: mul_class_C (10-bin distances, called the CASP14 official format), mul_class_G (42-bin distances, convertible toward a trRosetta npz), and mul_label_R (real-value distances plus a 25-bin map, described as the improved version of DeepDist1). A separate PLM-feature individual model is also scripted. Setup scripts download about 110 GB for MSA input or about 580 GB for FASTA input.", "modalities": { "input": [ "protein-sequence", "multiple-sequence-alignment" ], "output": [ "distance-map", "contact-map" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepDist is a protein residue-distance model from Jianlin Cheng's lab at the University of Missouri. The public package predicts real-value and multi-class distance maps from a multiple sequence alignment or a FASTA sequence, then can emit contacts. The GitHub repository was still public on 2026-10-15 under GPL-3.0, so status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README says DeepDist predicts a protein distance map with deep learning, using multi-class and regression heads. Contacts at an 8 angstrom threshold are written alongside the distance files. It is a structure-feature model, not a text chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "missouri/deepsf/deepsf": { "id": "missouri/deepsf/deepsf", "name": "DeepSF", "maker": "missouri", "family": "missouri/deepsf", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2017-06-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "1D convolutional network that maps a protein sequence of any length onto known folds. The Bioinformatics abstract says 1195 SCOP folds. The GitHub models directory contains model_SimilarityReduction.h5 with a matching json, model_ThreeLevel.h5 with a matching json, and model_ECOD_H and model_ECOD_X directories. Keras 1.2.2 and Theano 0.9.0 are the README's pinned stack. Fold recognition can be followed by template-based structure files.", "modalities": { "input": [ "protein-sequence" ], "output": [ "fold-class", "structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSF is a 1D convolutional protein-fold classifier from Jie Hou, Badri Adhikari, and Jianlin Cheng. Hou and Cheng are at the University of Missouri; the ACM-BCB abstract places Adhikari at the University of Missouri-St. Louis. The arXiv preprint is dated 4 June 2017. The journal version is Bioinformatics 34(8):1295-1303, online 8 December 2017. Weight files were still in the public GitHub tree on 2026-10-15. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The abstract says DeepSF classifies a sequence directly into one of 1195 known SCOP folds, for fold recognition and for studying sequence-structure relationships. The README can also rank templates and write predicted structures.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "missouri/dncon2/dncon2": { "id": "missouri/dncon2/dncon2", "name": "DNCON2", "maker": "missouri", "family": "missouri/dncon2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2017-12-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-level deep convolutional contact predictor. The PubMed abstract says six CNNs: the first five predict contacts at 6, 7.5, 8, 8.5, and 10 angstrom thresholds, and the sixth uses those five predictions as extra features for the final contact map. The network sees the whole protein rather than a fixed local window. The README's predict-rr-from-features.sh runs the trained CNN models.", "modalities": { "input": [ "protein-sequence", "multiple-sequence-alignment" ], "output": [ "contact-map" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DNCON2 is a protein contact-map predictor released from Jianlin Cheng's MULTICOM toolbox at the University of Missouri. Badri Adhikari, Jie Hou, and Jianlin Cheng describe it as six convolutional networks in two levels. The journal article went online on 8 December 2017 (Bioinformatics 34(9):1466-1472, 1 May 2018). The GitHub package and GPL-3.0 badge were still public on 2026-10-15, so status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The abstract says DNCON2 predicts residue-residue contacts for ab initio protein structure modeling. The first five networks score contacts at 6, 7.5, 8, 8.5, and 10 angstroms. The sixth network uses those scores as features for the final map.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "missouri/enqa/enqa": { "id": "missouri/enqa/enqa", "name": "EnQA", "maker": "missouri", "family": "missouri/enqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2023-01-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "3D-equivariant graph neural network for protein model quality. The README recommends EnQA-MSA for AlphaFold tertiary models and assumes AlphaFold pLDDT is stored in the PDB B-factor column. Other CLI methods are ensemble, EGNN_Full, se3_Full, EGNN_esto9, and EGNN_covariance. SE(3)-Transformer methods need the third-party equivariant_attention package. MSA embeddings come from Meta's ESM MSA Transformer. The models directory listing on 2026-10-15 contained EnQA-MSA.pth, cov25.pth.tar, egnn_esto9.tar, egnn_ne.tar, and esto9_se3.tar. Those filenames were not mapped to CLI method names beyond EnQA-MSA.pth.", "modalities": { "input": [ "protein-structure" ], "output": [ "quality-score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EnQA is a protein-structure quality model from Chen Chen, Xiao Chen, Alex Morehead, Tianqi Wu, and Jianlin Cheng at the University of Missouri. Bioinformatics published it online on 13 January 2023 (btad030). The README calls it a 3D-equivariant graph network for tertiary and quaternary model accuracy. GPL-3.0 code and a `models` directory of weight files were public on 2026-10-15. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README says EnQA estimates the accuracy of a predicted protein structure. EnQA-MSA is the recommended path for AlphaFold tertiary models and reads pLDDT from the B-factor column. Other methods use an AlphaFold2 prediction folder as extra features. The authors say it was trained on single chains and can still be run on a complex by merging chains.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "missouri/flowdock/flowdock": { "id": "missouri/flowdock/flowdock", "name": "FlowDock", "maker": "missouri", "family": "missouri/flowdock", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Geometric flow-matching model for protein-ligand docking and affinity. The README's inference checkpoint is checkpoints/esmfold_prior_paper_weights-EMA.ckpt, with an ESMFold structure prior. A non-EMA file, esmfold_prior_paper_weights.ckpt, is the finetune start. The checkpoint tarball is https://zenodo.org/records/15066450/files/flowdock_checkpoints.tar.gz. Sampling defaults shown in the README use 40 steps, sampler VDODE, and eta 1.0. The code also vendors a NeuralPLexer weight download for data prep. NeuralPLexer is not this model.", "modalities": { "input": [ "protein-sequence", "ligand-smiles", "protein-structure" ], "output": [ "protein-ligand-structure", "affinity" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FlowDock is a protein-ligand docking and affinity model from Alex Morehead and Jianlin Cheng at the University of Missouri. The README describes geometric flow matching and points to arXiv 2412.10966 and an ISMB 2025 article in Bioinformatics volume 41, supplement 1, page i198. MIT-licensed code and a Zenodo checkpoint archive were public on 2026-10-15. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper title and README say FlowDock generates a protein-ligand complex structure and predicts binding affinity. The documented sampling path uses an ESMFold structure prior. ESMFold is a feature and prior, not the Missouri model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "missouri/gcdm/gcdm": { "id": "missouri/gcdm/gcdm", "name": "GCDM", "maker": "missouri", "family": "missouri/gcdm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2023-02-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SE(3)-equivariant denoising diffusion model. The Nature Communications summary says a joint noising process on atom coordinates and atom types, denoised by GCPNet++, with separate scalar and vector feature channels and geometry-complete, chirality-aware message passing. Unconditional and property-conditional QM9 checkpoints, plus a structure-based drug design extension, are separate tarballs. Code license is MIT. The Nature data-availability note and the Zenodo record license the checkpoints as CC-BY-4.0. The stored license is the checkpoint license.", "modalities": { "input": [ "molecule-graph" ], "output": [ "molecule-3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GCDM, the Geometry-Complete Diffusion Model, is a 3D molecule generator from Alex Morehead and Jianlin Cheng at the University of Missouri. The arXiv preprint was submitted on 8 February 2023 and revised through 24 May 2024. Communications Chemistry published it on 3 July 2024 (volume 7, article 150). Checkpoints on Zenodo and the MIT code repositories were still public on 2026-10-15. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The abstract says GCDM generates 3D molecules, conditionally and unconditionally, on QM9 and GEOM-Drugs, and that extensions can design molecules for a protein pocket or optimize an existing molecule. It is a chemistry generator, not a text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "missouri/mulocdeep/mulocdeep": { "id": "missouri/mulocdeep/mulocdeep", "name": "MULocDeep", "maker": "missouri", "family": "missouri/mulocdeep", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Deep localization model with attention over amino acids. The README says the default local predictor is the non-redundant UniLoc_train_40nr model. A separate GPU model trained on the full UniLoc_train set is in the gpu_model_UniLoc_train folder. GPU weights use CuDNNLSTM and CPU weights use LSTM, and the README says their scores differ slightly. The 2021 paper says the label set has 10 major subcellular compartments and 44 suborganellar annotations. Tested stack in the README is Python 3.7.4 and Keras 2.3.0.", "modalities": { "input": [ "protein-sequence" ], "output": [ "localization-label", "attention" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MULocDeep is a protein localization model from Yuexu Jiang, Duolin Wang, Dong Xu, and coauthors at the University of Missouri. The 2021 Computational and Structural Biotechnology Journal paper predicts multi-label localization at subcellular and suborganellar levels and scores each amino acid. A 2023 Nucleic Acids Research paper describes the web service. The GitHub repository still documents pretrained weights. Status is active as of 2026-10-15. This dossier was collected on that date.", "what_it_is_for": "The README says it predicts localization at both the subcellular level and the suborganellar level, and that high attention weights mark residues related to the sorting signal. The 2021 paper's dataset note, as cited from the ResearchGate abstract and the DOI record, is 10 major compartments and 44 suborganellar annotations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "missouri/musitedeep/musitedeep": { "id": "missouri/musitedeep/musitedeep", "name": "MusiteDeep", "maker": "missouri", "family": "missouri/musitedeep", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-2.0", "release_date": "2017-08-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Convolutional network with a two-dimensional attention mechanism for phosphorylation and, in the 2020 server, other post-translational modification sites. The 2017 README ships a general human phosphorylation model and kinase-specific models, with transfer from the general network into kinase models. Keras 1.1.0 and a Theano backend are the original stack. A Keras 2 / TensorFlow tree is linked from the same README.", "modalities": { "input": [ "protein-sequence" ], "output": [ "site-score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MusiteDeep is a protein post-translational modification site model from Dong Xu's group at the University of Missouri. Duolin Wang and coauthors released the phosphorylation framework in Bioinformatics in 2017, with an Oxford page date of 3 August 2017 (issue 15 December 2017, 33(24):3909-3916). A 2020 Nucleic Acids Research web server extended it to multiple modification types. The GitHub weights, the GPL-2.0 notice, and https://www.musite.net/ were still public on 2026-10-15. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The 2017 paper predicts general and kinase-specific phosphorylation sites from raw sequence. The 2020 server predicts several modification types at once and can show sites next to known annotations and a homologous structure. The live server page also documents a web API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/codestral/codestral-24-05": { "id": "mistral/codestral/codestral-24-05", "name": "Codestral", "maker": "mistral", "family": "mistral/codestral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MNPL", "release_date": "2024-05-29", "context_length": null, "max_output_tokens": null, "parameters": "22B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our first cutting-edge language model for coding and FIM, released under the MNPL license.", "what_it_is_for": "Our first cutting-edge language model for coding and FIM, released under the MNPL license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/codestral/codestral-25-01": { "id": "mistral/codestral/codestral-25-01", "name": "Codestral", "maker": "mistral", "family": "mistral/codestral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-13", "context_length": 256000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 0.9 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our cutting-edge language model for coding. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation.", "what_it_is_for": "Our cutting-edge language model for coding. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation.", "ratings": { "empirical_100": null, "lmsys_elo": 1280, "coding_elo": 1290, "swe_bench": 29.0, "humaneval": 89.0, "twelve_cs": null, "aa_intelligence": 52.0, "aa_coding": 71.0, "aa_agentic": 52.0, "design_arena_elo": 1290, "math_500": 70.0, "gpqa_diamond": 45.0, "ifeval": 80.0, "bfcl_tool_calling": 83.0 } }, "mistral/codestral/codestral-25-08": { "id": "mistral/codestral/codestral-25-08", "name": "Codestral", "maker": "mistral", "family": "mistral/codestral", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 0.9 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Our cutting-edge language model for code completion released end of July 2025, Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM) and code generation.", "what_it_is_for": "Our cutting-edge language model for code completion released end of July 2025, Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM) and code generation.", "ratings": { "empirical_100": null, "lmsys_elo": 1213, "coding_elo": 1213, "swe_bench": 17.2, "humaneval": 71.8, "twelve_cs": 87.6, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1005, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/codestral/codestral-embed-25-05": { "id": "mistral/codestral/codestral-embed-25-05", "name": "Codestral Embed", "maker": "mistral", "family": "mistral/codestral", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Our state-of-the-art semantic for extracting representation of code extracts", "what_it_is_for": "Our state-of-the-art semantic for extracting representation of code extracts", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/codestral/codestral-mamba-7b-0-1": { "id": "mistral/codestral/codestral-mamba-7b-0-1", "name": "Codestral Mamba 7B", "maker": "mistral", "family": "mistral/codestral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2024-07-16", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Mamba 2", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. A coding dedicated model using a mamba 2 architecture for research purposes under an open license.", "what_it_is_for": "A coding dedicated model using a mamba 2 architecture for research purposes under an open license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/devstral/devstral-2-25-12": { "id": "mistral/devstral/devstral-2-25-12", "name": "Devstral 2", "maker": "mistral", "family": "mistral/devstral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "2025-12-09", "context_length": null, "max_output_tokens": null, "parameters": "123B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.4, "completion_per_m_usd": 2.0 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our frontier code agents model for solving software engineering tasks; excels at using tools to explore codebases, editing multiple files and power software engineering agents.", "what_it_is_for": "Our frontier code agents model for solving software engineering tasks; excels at using tools to explore codebases, editing multiple files and power software engineering agents.", "ratings": { "empirical_100": null, "lmsys_elo": 1230, "coding_elo": 1230, "swe_bench": 20.0, "humaneval": 74.0, "twelve_cs": 99.5, "aa_intelligence": 8.6, "aa_coding": 31.3, "aa_agentic": 2.3, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/devstral/devstral-medium-1-0-25-07": { "id": "mistral/devstral/devstral-medium-1-0-25-07", "name": "Devstral Medium 1.0", "maker": "mistral", "family": "mistral/devstral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.4, "completion_per_m_usd": 2.0 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. An enterprise grade text model, that excels at using tools to explore codebases, editing multiple files and power software engineering agents.", "what_it_is_for": "An enterprise grade text model, that excels at using tools to explore codebases, editing multiple files and power software engineering agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/devstral/devstral-small-1-0-25-05": { "id": "mistral/devstral/devstral-small-1-0-25-05", "name": "Devstral Small 1.0", "maker": "mistral", "family": "mistral/devstral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-05-21", "context_length": null, "max_output_tokens": null, "parameters": "24B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.3 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. A 24B text model, open source model that excels at using tools to explore codebases, editing multiple files and power software engineering agents.", "what_it_is_for": "A 24B text model, open source model that excels at using tools to explore codebases, editing multiple files and power software engineering agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/devstral/devstral-small-1-1-25-07": { "id": "mistral/devstral/devstral-small-1-1-25-07", "name": "Devstral Small 1.1", "maker": "mistral", "family": "mistral/devstral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-07-10", "context_length": null, "max_output_tokens": null, "parameters": "24B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.3 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. An update to our open source model that excels at using tools to explore codebases, editing multiple files and power software engineering agents. Learn more in our [blog post](https://mistral.ai/news/devstral-2507)", "what_it_is_for": "An update to our open source model that excels at using tools to explore codebases, editing multiple files and power software engineering agents. Learn more in our [blog post](https://mistral.ai/news/devstral-2507)", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/devstral/devstral-small-2-25-12": { "id": "mistral/devstral/devstral-small-2-25-12", "name": "Devstral Small 2", "maker": "mistral", "family": "mistral/devstral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-12-09", "context_length": null, "max_output_tokens": null, "parameters": "24B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.3 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our open source model that excels at using tools to explore codebases, editing multiple files and power software engineering agents.", "what_it_is_for": "Our open source model that excels at using tools to explore codebases, editing multiple files and power software engineering agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/embed/mistral-embed-23-12": { "id": "mistral/embed/mistral-embed-23-12", "name": "Mistral Embed", "maker": "mistral", "family": "mistral/embed", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Our state-of-the-art semantic for extracting representation of code extracts", "what_it_is_for": "Our state-of-the-art semantic for extracting representation of code extracts", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/leanstral/leanstral-1-5": { "id": "mistral/leanstral/leanstral-1-5", "name": "Leanstral 1.5", "maker": "mistral", "family": "mistral/leanstral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-06-30", "context_length": null, "max_output_tokens": null, "parameters": "119B total, 6.5B active", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. An updated Lean 4 formal proof engineering model optimised for automated theorem proving and autoformalization. 119B total parameters, 6.5B active.", "what_it_is_for": "An updated Lean 4 formal proof engineering model optimised for automated theorem proving and autoformalization. 119B total parameters, 6.5B active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/leanstral/leanstral-26-03": { "id": "mistral/leanstral/leanstral-26-03", "name": "Leanstral", "maker": "mistral", "family": "mistral/leanstral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-03-16", "context_length": null, "max_output_tokens": null, "parameters": "119B total, 6.5B active", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our first open-source code agent designed for Lean 4, built for formal proof engineering in realistic repositories. 119B parameters with 6.5B active.", "what_it_is_for": "Our first open-source code agent designed for Lean 4, built for formal proof engineering in realistic repositories. 119B parameters with 6.5B active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/lidstral/lidstral-arabic": { "id": "mistral/lidstral/lidstral-arabic", "name": "LIDstral Arabic", "maker": "mistral", "family": "mistral/lidstral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "fastText one-vs-all classifier, a 64-unit LayerNorm MLP stacker, and per-class isotonic calibration", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI language-identification model, collected 2026-10-15. It is a named open-weight release on the mistralai Hugging Face org and is not on the Mistral API model index. The card describes a fast classifier for Arabic script that separates Modern Standard Arabic, Arabic dialects, and other languages that use the same script.", "what_it_is_for": "The maker says it identifies Modern Standard Arabic, Arabic dialects, and non-Arabic languages written in Arabic script, so script alone is not treated as proof of Arabic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/magistral/magistral-medium-1-0-25-06": { "id": "mistral/magistral/magistral-medium-1-0-25-06", "name": "Magistral Medium 1.0", "maker": "mistral", "family": "mistral/magistral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 5.0 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our first frontier-class reasoning model released June 2025.", "what_it_is_for": "Our first frontier-class reasoning model released June 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/magistral/magistral-medium-1-1-25-07": { "id": "mistral/magistral/magistral-medium-1-1-25-07", "name": "Magistral Medium 1.1", "maker": "mistral", "family": "mistral/magistral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 5.0 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our frontier-class reasoning model released July 2025.", "what_it_is_for": "Our frontier-class reasoning model released July 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/magistral/magistral-medium-1-2-25-09": { "id": "mistral/magistral/magistral-medium-1-2-25-09", "name": "Magistral Medium 1.2", "maker": "mistral", "family": "mistral/magistral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 5.0 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our frontier-class multimodal reasoning model update of September 2025.", "what_it_is_for": "Our frontier-class multimodal reasoning model update of September 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/magistral/magistral-small-1-0-25-06": { "id": "mistral/magistral/magistral-small-1-0-25-06", "name": "Magistral Small 1.0", "maker": "mistral", "family": "mistral/magistral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-06-10", "context_length": null, "max_output_tokens": null, "parameters": "24B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 1.5 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our first small reasoning model released June 2025. Learn more in our [blog post](https://mistral.ai/news/magistral/)", "what_it_is_for": "Our first small reasoning model released June 2025. Learn more in our [blog post](https://mistral.ai/news/magistral/)", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/magistral/magistral-small-1-1-25-07": { "id": "mistral/magistral/magistral-small-1-1-25-07", "name": "Magistral Small 1.1", "maker": "mistral", "family": "mistral/magistral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-07-24", "context_length": null, "max_output_tokens": null, "parameters": "24B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 1.5 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our small reasoning model released July 2025.", "what_it_is_for": "Our small reasoning model released July 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/magistral/magistral-small-1-2-25-09": { "id": "mistral/magistral/magistral-small-1-2-25-09", "name": "Magistral Small 1.2", "maker": "mistral", "family": "mistral/magistral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-09-18", "context_length": null, "max_output_tokens": null, "parameters": "24B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 1.5 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our small multimodal reasoning model update of September 2025.", "what_it_is_for": "Our small multimodal reasoning model update of September 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mathstral/mathstral-7b-0-1": { "id": "mistral/mathstral/mathstral-7b-0-1", "name": "Mathstral 7B", "maker": "mistral", "family": "mistral/mathstral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2024-07-16", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. A math dedicated model for research purposes under an open license.", "what_it_is_for": "A math dedicated model for research purposes under an open license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/ministral/ministral-3-14b-25-12": { "id": "mistral/ministral/ministral-3-14b-25-12", "name": "Ministral 3 14B", "maker": "mistral", "family": "mistral/ministral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-12-02", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.2 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Ministral 3 14B is the largest model in the Ministral 3 family, offering state-of-the-art capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart. Optimized for local deployment, it delivers high performance across diverse hardware, including local setups.", "what_it_is_for": "Ministral 3 14B is the largest model in the Ministral 3 family, offering state-of-the-art capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart. Optimized for local deployment, it delivers high performance across diverse hardware, including local setups.", "ratings": { "empirical_100": null, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.5, "humaneval": 70.4, "twelve_cs": 52.0, "aa_intelligence": 6.0, "aa_coding": 14.4, "aa_agentic": 1.1, "design_arena_elo": 987, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/ministral/ministral-3-3b-25-12": { "id": "mistral/ministral/ministral-3-3b-25-12", "name": "Ministral 3 3B", "maker": "mistral", "family": "mistral/ministral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-12-02", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.1 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Ministral 3 3B is the smallest and most efficient model in the Ministral 3 family, offering robust language and vision capabilities in a compact package. Designed for edge deployment, it delivers high performance across diverse hardware, including local setups.", "what_it_is_for": "Ministral 3 3B is the smallest and most efficient model in the Ministral 3 family, offering robust language and vision capabilities in a compact package. Designed for edge deployment, it delivers high performance across diverse hardware, including local setups.", "ratings": { "empirical_100": 55, "lmsys_elo": 1201, "coding_elo": 1201, "swe_bench": 15.2, "humaneval": 70.2, "twelve_cs": 52.0, "aa_intelligence": 4.8, "aa_coding": 4.8, "aa_agentic": 0.8, "design_arena_elo": 960, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/ministral/ministral-3-8b-25-12": { "id": "mistral/ministral/ministral-3-8b-25-12", "name": "Ministral 3 8B", "maker": "mistral", "family": "mistral/ministral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-12-02", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": 0.15 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Ministral 3 8B is a powerful and efficient model in the Ministral 3 family, offering best-in-class text and vision capabilities. Built for edge deployment, it delivers high performance across diverse hardware, including local setups.", "what_it_is_for": "Ministral 3 8B is a powerful and efficient model in the Ministral 3 family, offering best-in-class text and vision capabilities. Built for edge deployment, it delivers high performance across diverse hardware, including local setups.", "ratings": { "empirical_100": null, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.4, "humaneval": 70.3, "twelve_cs": 91.3, "aa_intelligence": 5.5, "aa_coding": 9.7, "aa_agentic": 0.6, "design_arena_elo": 1029, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/ministral/ministral-3b-24-1": { "id": "mistral/ministral/ministral-3b-24-1", "name": "Ministral 3B", "maker": "mistral", "family": "mistral/ministral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.04, "completion_per_m_usd": 0.04 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. World\u2019s best edge model.", "what_it_is_for": "World\u2019s best edge model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/ministral/ministral-8b-24-1": { "id": "mistral/ministral/ministral-8b-24-1", "name": "Ministral 8B", "maker": "mistral", "family": "mistral/ministral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MRL", "release_date": "2024-10-09", "context_length": 128000, "max_output_tokens": 8192, "parameters": "8B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.1 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Powerful edge model with extremely high performance/price ratio. Learn more in our [blog post](https://mistral.ai/news/ministraux/)", "what_it_is_for": "Powerful edge model with extremely high performance/price ratio. Learn more in our [blog post](https://mistral.ai/news/ministraux/)", "ratings": { "empirical_100": null, "lmsys_elo": 1245, "coding_elo": 1240, "swe_bench": 18.0, "humaneval": 79.0, "twelve_cs": null, "aa_intelligence": 44.0, "aa_coding": 54.0, "aa_agentic": 48.0, "design_arena_elo": 1240, "math_500": 58.0, "gpqa_diamond": 38.0, "ifeval": 79.0, "bfcl_tool_calling": 81.0 } }, "mistral/mistral/mistral-7b-0-1": { "id": "mistral/mistral/mistral-7b-0-1", "name": "Mistral 7B", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2023-09-27", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Dense transformer with grouped-query attention and sliding window attention", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our very first open model, SOTA competing against models up to 2 to 3x larger.", "what_it_is_for": "Our very first open model, SOTA competing against models up to 2 to 3x larger.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-7b-0-2": { "id": "mistral/mistral/mistral-7b-0-2", "name": "Mistral 7B", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2023-12-11", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. An update to our first open model with better context.", "what_it_is_for": "An update to our first open model with better context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-7b-0-3": { "id": "mistral/mistral/mistral-7b-0-3", "name": "Mistral 7B", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2024-05-22", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. A minor update to our first open model.", "what_it_is_for": "A minor update to our first open model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-large-1-0-24-02": { "id": "mistral/mistral/mistral-large-1-0-24-02", "name": "Mistral Large 1.0", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-02-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our first and larger SOTA model for enterprise use cases with powerfull chat capabilities.", "what_it_is_for": "Our first and larger SOTA model for enterprise use cases with powerfull chat capabilities.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-large-2-0-24-07": { "id": "mistral/mistral/mistral-large-2-0-24-07", "name": "Mistral Large 2.0", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MRL", "release_date": "2024-07-24", "context_length": null, "max_output_tokens": null, "parameters": "123B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. A powerfull model with open weights under a MRL license, competing agains SOTA much larger models.", "what_it_is_for": "A powerfull model with open weights under a MRL license, competing agains SOTA much larger models.", "ratings": { "empirical_100": null, "lmsys_elo": 1265, "coding_elo": 1260, "swe_bench": 27.5, "humaneval": 84.0, "twelve_cs": 52.0, "aa_intelligence": 7.6, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-large-2-1-24-11": { "id": "mistral/mistral/mistral-large-2-1-24-11", "name": "Mistral Large 2.1", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MRL", "release_date": "2024-11-18", "context_length": 128000, "max_output_tokens": 16384, "parameters": "123B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 6.0 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our top-tier large model for high-complexity tasks with the lastest version released November 2024.", "what_it_is_for": "Our top-tier large model for high-complexity tasks with the lastest version released November 2024.", "ratings": { "empirical_100": null, "lmsys_elo": 1300, "coding_elo": 1295, "swe_bench": 38.0, "humaneval": 88.0, "twelve_cs": null, "aa_intelligence": 62.0, "aa_coding": 73.0, "aa_agentic": 59.0, "design_arena_elo": 1295, "math_500": 74.0, "gpqa_diamond": 50.0, "ifeval": 84.0, "bfcl_tool_calling": 85.0 } }, "mistral/mistral/mistral-large-3-25-12": { "id": "mistral/mistral/mistral-large-3-25-12", "name": "Mistral Large 3", "maker": "mistral", "family": "mistral/mistral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-12-02", "context_length": null, "max_output_tokens": null, "parameters": "675B total, 41B active", "architecture": "Granular mixture-of-experts", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 1.5 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Mistral Large 3, is a state-of-the-art, open-weight, general-purpose multimodal model with a granular Mixture-of-Experts architecture. It features 41B active parameters and 675B total parameters.", "what_it_is_for": "Mistral Large 3, is a state-of-the-art, open-weight, general-purpose multimodal model with a granular Mixture-of-Experts architecture. It features 41B active parameters and 675B total parameters.", "ratings": { "empirical_100": null, "lmsys_elo": 1222, "coding_elo": 1222, "swe_bench": 18.8, "humaneval": 73.0, "twelve_cs": 36.1, "aa_intelligence": 9.3, "aa_coding": 20.1, "aa_agentic": 1.0, "design_arena_elo": 1096, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-large-4-0": { "id": "mistral/mistral/mistral-large-4-0", "name": "Mistral Large 4", "maker": "mistral", "family": "mistral/mistral", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-06", "context_length": null, "max_output_tokens": null, "parameters": "1.05T total, 52B active", "architecture": "Granular mixture-of-experts", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.68, "completion_per_m_usd": 2.09 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Public Preview. Mistral Large 4 is a state-of-the-art, open-weight, general-purpose multimodal model with a granular Mixture-of-Experts architecture. It features 52B active parameters and 1.05T total parameters, and a 1.6B vision encoder.", "what_it_is_for": "Mistral Large 4 is a state-of-the-art, open-weight, general-purpose multimodal model with a granular Mixture-of-Experts architecture. It features 52B active parameters and 1.05T total parameters, and a 1.6B vision encoder.", "ratings": { "empirical_100": null, "lmsys_elo": 1231, "coding_elo": 1231, "swe_bench": 20.2, "humaneval": 74.2, "twelve_cs": 98.0, "aa_intelligence": 38.4, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1248, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-medium-1-0-23-12": { "id": "mistral/mistral/mistral-medium-1-0-23-12", "name": "Mistral Medium 1.0", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our first SOTA enterprise grade model.", "what_it_is_for": "Our first SOTA enterprise grade model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-medium-3-1-25-08": { "id": "mistral/mistral/mistral-medium-3-1-25-08", "name": "Mistral Medium 3.1", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.4, "completion_per_m_usd": 2.0 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our frontier-class multimodal model released August 2025. Improving tone and performance.", "what_it_is_for": "Our frontier-class multimodal model released August 2025. Improving tone and performance.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-medium-3-25-05": { "id": "mistral/mistral/mistral-medium-3-25-05", "name": "Mistral Medium 3", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.4, "completion_per_m_usd": 2.0 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our frontier-class multimodal model released May 2025. Learn more in our [blog post](https://mistral.ai/news/mistral-medium-3/)", "what_it_is_for": "Our frontier-class multimodal model released May 2025. Learn more in our [blog post](https://mistral.ai/news/mistral-medium-3/)", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-medium-3-5-26-04": { "id": "mistral/mistral/mistral-medium-3-5-26-04", "name": "Mistral Medium 3.5", "maker": "mistral", "family": "mistral/mistral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "2026-04-28", "context_length": null, "max_output_tokens": null, "parameters": "128B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.5, "completion_per_m_usd": 7.5 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Our frontier-class multimodal model optimized for agentic and coding use cases. Released as open weights under a Modified MIT license.", "what_it_is_for": "Our frontier-class multimodal model optimized for agentic and coding use cases. Released as open weights under a Modified MIT license.", "ratings": { "empirical_100": null, "lmsys_elo": 1230, "coding_elo": 1230, "swe_bench": 20.0, "humaneval": 74.0, "twelve_cs": 99.5, "aa_intelligence": 9.0, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1081, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-nemo-12b-24-07": { "id": "mistral/mistral/mistral-nemo-12b-24-07", "name": "Mistral Nemo 12B", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2024-07-18", "context_length": null, "max_output_tokens": null, "parameters": "12B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": 0.15 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our best multilingual open source model released July 2024.", "what_it_is_for": "Our best multilingual open source model released July 2024.", "ratings": { "empirical_100": 42, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-next": { "id": "mistral/mistral/mistral-next", "name": "Mistral Next", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-02-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. A prototype model, designed to be brief and concise.", "what_it_is_for": "A prototype model, designed to be brief and concise.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-saba-25-02": { "id": "mistral/mistral/mistral-saba-25-02", "name": "Mistral Saba", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. A finetuned Small model trained on meticulously curated datasets from across the Middle East and South Asia.", "what_it_is_for": "A finetuned Small model trained on meticulously curated datasets from across the Middle East and South Asia.", "ratings": { "empirical_100": 18, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-small-1-0-24-02": { "id": "mistral/mistral/mistral-small-1-0-24-02", "name": "Mistral Small 1.0", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-02-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. An enterprise grade small model for efficiency.", "what_it_is_for": "An enterprise grade small model for efficiency.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-small-2-0-24-09": { "id": "mistral/mistral/mistral-small-2-0-24-09", "name": "Mistral Small 2.0", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MRL", "release_date": "2024-09-17", "context_length": null, "max_output_tokens": null, "parameters": "22B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.3 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. An update to our efficient small model with open weights under an MRL license.", "what_it_is_for": "An update to our efficient small model with open weights under an MRL license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-small-3-0-25-01": { "id": "mistral/mistral/mistral-small-3-0-25-01", "name": "Mistral Small 3.0", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-01-30", "context_length": null, "max_output_tokens": null, "parameters": "24B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.3 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. A small efficient and powerfull 24B open model for personal or commercial use.", "what_it_is_for": "A small efficient and powerfull 24B open model for personal or commercial use.", "ratings": { "empirical_100": 58, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-small-3-1-25-03": { "id": "mistral/mistral/mistral-small-3-1-25-03", "name": "Mistral Small 3.1", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-03-17", "context_length": null, "max_output_tokens": null, "parameters": "24B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.3 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. A new leader in the small models category with image understanding capabilities, released March 2025.", "what_it_is_for": "A new leader in the small models category with image understanding capabilities, released March 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-small-3-2-25-06": { "id": "mistral/mistral/mistral-small-3-2-25-06", "name": "Mistral Small 3.2", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-06-20", "context_length": null, "max_output_tokens": null, "parameters": "24B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.3 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. An update to our previous small model, released June 2025.", "what_it_is_for": "An update to our previous small model, released June 2025.", "ratings": { "empirical_100": 55, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-small-4-0-26-03": { "id": "mistral/mistral/mistral-small-4-0-26-03", "name": "Mistral Small 4", "maker": "mistral", "family": "mistral/mistral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-03-16", "context_length": null, "max_output_tokens": null, "parameters": "119B total, 6.5B active", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": 0.6 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Our powerful hybrid model unifying instruct, reasoning, and coding capabilities in a single model. 119B parameters with 6.5B active.", "what_it_is_for": "Our powerful hybrid model unifying instruct, reasoning, and coding capabilities in a single model. 119B parameters with 6.5B active.", "ratings": { "empirical_100": null, "lmsys_elo": 1209, "coding_elo": 1209, "swe_bench": 16.5, "humaneval": 71.2, "twelve_cs": 36.1, "aa_intelligence": 11.3, "aa_coding": 26.6, "aa_agentic": 0.8, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mistral/mistral-small-creative-25-12": { "id": "mistral/mistral/mistral-small-creative-25-12", "name": "Mistral Small Creative", "maker": "mistral", "family": "mistral/mistral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.3 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. An experimental specialized small model trained on meticulously curated data, designed for creative writing, narrative generation, roleplay and character-driven dialog, general-purpose instruction following and conversational agents.", "what_it_is_for": "An experimental specialized small model trained on meticulously curated data, designed for creative writing, narrative generation, roleplay and character-driven dialog, general-purpose instruction following and conversational agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mixtral/mixtral-8x22b-0-1-0-3": { "id": "mistral/mixtral/mixtral-8x22b-0-1-0-3", "name": "Mixtral 8x22B", "maker": "mistral", "family": "mistral/mixtral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2024-04-17", "context_length": null, "max_output_tokens": null, "parameters": "141B total, 39B active", "architecture": "Mixture-of-experts", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. A large open MoE model competing with much larger dense models.", "what_it_is_for": "A large open MoE model competing with much larger dense models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/mixtral/mixtral-8x7b-0-1": { "id": "mistral/mixtral/mixtral-8x7b-0-1", "name": "Mixtral 8x7B", "maker": "mistral", "family": "mistral/mixtral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2023-12-11", "context_length": null, "max_output_tokens": null, "parameters": "47B total, 13B active", "architecture": "Sparse mixture-of-experts, 8 experts, 2 active per token", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our first open MoE model, SOTA competing against models up to 2 to 3x larger.", "what_it_is_for": "Our first open MoE model, SOTA competing against models up to 2 to 3x larger.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/moderation/mistral-moderation-24-11": { "id": "mistral/moderation/mistral-moderation-24-11", "name": "Mistral Moderation", "maker": "mistral", "family": "mistral/moderation", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our moderation service that enables our users to detect harmful text content", "what_it_is_for": "Our moderation service that enables our users to detect harmful text content", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/moderation/mistral-moderation-26-03": { "id": "mistral/moderation/mistral-moderation-26-03", "name": "Mistral Moderation 2", "maker": "mistral", "family": "mistral/moderation", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Our latest moderation model with 128k context window and jailbreaking detection. Strong performance on complex multilingual data including long multi-turn conversations.", "what_it_is_for": "Our latest moderation model with 128k context window and jailbreaking detection. Strong performance on complex multilingual data including long multi-turn conversations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/ocr/ocr-2-25-05": { "id": "mistral/ocr/ocr-2-25-05", "name": "OCR 2", "maker": "mistral", "family": "mistral/ocr", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "document" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our OCR service powering our Document AI stack that enables our users to extract interleaved text and images", "what_it_is_for": "Our OCR service powering our Document AI stack that enables our users to extract interleaved text and images", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/ocr/ocr-25-03": { "id": "mistral/ocr/ocr-25-03", "name": "OCR", "maker": "mistral", "family": "mistral/ocr", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "document" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our first OCR optimized model.", "what_it_is_for": "Our first OCR optimized model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/ocr/ocr-3-25-12": { "id": "mistral/ocr/ocr-3-25-12", "name": "OCR 3", "maker": "mistral", "family": "mistral/ocr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "document" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Our OCR service powering our Document AI stack that enables our users to extract interleaved text and images. OCR 4 is available as the newer model. OCR 3 remains available for existing integrations and production workloads.", "what_it_is_for": "Our OCR service powering our Document AI stack that enables our users to extract interleaved text and images. OCR 4 is available as the newer model. OCR 3 remains available for existing integrations and production workloads.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/ocr/ocr-4-0": { "id": "mistral/ocr/ocr-4-0", "name": "OCR 4.0", "maker": "mistral", "family": "mistral/ocr", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "document" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our latest OCR service powering our Document AI stack, with native paragraph-level bounding box extraction and structural block labels.", "what_it_is_for": "Our latest OCR service powering our Document AI stack, with native paragraph-level bounding box extraction and structural block labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/ocr/ocr-4-1": { "id": "mistral/ocr/ocr-4-1", "name": "OCR 4.1", "maker": "mistral", "family": "mistral/ocr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "document" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Our latest OCR service powering our Document AI stack, with native paragraph-level bounding box extraction, structural block labels, and block-level confidence scores.", "what_it_is_for": "Our latest OCR service powering our Document AI stack, with native paragraph-level bounding box extraction, structural block labels, and block-level confidence scores.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/pixtral/pixtral-12b-24-09": { "id": "mistral/pixtral/pixtral-12b-24-09", "name": "Pixtral 12B", "maker": "mistral", "family": "mistral/pixtral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2024-09-11", "context_length": null, "max_output_tokens": null, "parameters": "12B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": 0.15 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. A 12B model with image understanding capabilities in addition to text.", "what_it_is_for": "A 12B model with image understanding capabilities in addition to text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/pixtral/pixtral-large-24-11": { "id": "mistral/pixtral/pixtral-large-24-11", "name": "Pixtral Large", "maker": "mistral", "family": "mistral/pixtral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MRL", "release_date": "2024-11-18", "context_length": null, "max_output_tokens": null, "parameters": "124B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 6.0 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our first frontier-class multimodal model released November 2024. Learn more in our [blog post](https://mistral.ai/news/pixtral-large/)", "what_it_is_for": "Our first frontier-class multimodal model released November 2024. Learn more in our [blog post](https://mistral.ai/news/pixtral-large/)", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/shieldstral/shieldstral-1-0": { "id": "mistral/shieldstral/shieldstral-1-0", "name": "Shieldstral 1.0", "maker": "mistral", "family": "mistral/shieldstral", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-08-04", "context_length": null, "max_output_tokens": null, "parameters": "3.8B total and active", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Public Preview. A compact multimodal moderation model for prompt moderation, response moderation, prompt-response pair classification, refusal detection, and safety filtering across text and image inputs. It uses natural-language policy questions and returns a yes or no classification.", "what_it_is_for": "A compact multimodal moderation model for prompt moderation, response moderation, prompt-response pair classification, refusal detection, and safety filtering across text and image inputs. It uses natural-language policy questions and returns a yes or no classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/timestral/timestral-1-0": { "id": "mistral/timestral/timestral-1-0", "name": "Timestral 1.0", "maker": "mistral", "family": "mistral/timestral", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "209M total and active", "architecture": "", "modalities": { "input": [ "timeseries" ], "output": [ "timeseries" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Public Preview. Timestral 1.0 is a time series foundation model for zero-shot forecasting. It ranks first among pretrained and zero-shot models on GIFT-Eval, TIME, and fev-bench, with support for contexts up to 32K timesteps, past-only and known-future covariates, and probabilistic quantile outputs.", "what_it_is_for": "Timestral 1.0 is a time series foundation model for zero-shot forecasting. It ranks first among pretrained and zero-shot models on GIFT-Eval, TIME, and fev-bench, with support for contexts up to 32K timesteps, past-only and known-future covariates, and probabilistic quantile outputs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/timestral/timestral-1-0-123m": { "id": "mistral/timestral/timestral-1-0-123m", "name": "Timestral 1.0 123M", "maker": "mistral", "family": "mistral/timestral", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "123M total and active", "architecture": "", "modalities": { "input": [ "timeseries" ], "output": [ "timeseries" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Public Preview. Timestral 1.0 123M is a time series foundation model for zero-shot forecasting. Part of the Timestral family, it offers a balance of forecast accuracy and efficiency, with support for contexts up to 32K timesteps, past-only and known-future covariates, and probabilistic quantile outputs.", "what_it_is_for": "Timestral 1.0 123M is a time series foundation model for zero-shot forecasting. Part of the Timestral family, it offers a balance of forecast accuracy and efficiency, with support for contexts up to 32K timesteps, past-only and known-future covariates, and probabilistic quantile outputs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/timestral/timestral-1-0-28m": { "id": "mistral/timestral/timestral-1-0-28m", "name": "Timestral 1.0 28M", "maker": "mistral", "family": "mistral/timestral", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "28M total and active", "architecture": "", "modalities": { "input": [ "timeseries" ], "output": [ "timeseries" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Public Preview. Timestral 1.0 28M is a time series foundation model for zero-shot forecasting. Part of the Timestral family, it is optimised for low-latency forecasting, with support for contexts up to 32K timesteps, past-only and known-future covariates, and probabilistic quantile outputs.", "what_it_is_for": "Timestral 1.0 28M is a time series foundation model for zero-shot forecasting. Part of the Timestral family, it is optimised for low-latency forecasting, with support for contexts up to 32K timesteps, past-only and known-future covariates, and probabilistic quantile outputs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/timestral/timestral-1-0-5m": { "id": "mistral/timestral/timestral-1-0-5m", "name": "Timestral 1.0 5M", "maker": "mistral", "family": "mistral/timestral", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "5M total and active", "architecture": "", "modalities": { "input": [ "timeseries" ], "output": [ "timeseries" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Public Preview. Timestral 1.0 5M is a time series foundation model for zero-shot forecasting. The smallest model in the Timestral family, it delivers fast, lightweight forecasts, with support for contexts up to 32K timesteps, past-only and known-future covariates, and probabilistic quantile outputs.", "what_it_is_for": "Timestral 1.0 5M is a time series foundation model for zero-shot forecasting. The smallest model in the Timestral family, it delivers fast, lightweight forecasts, with support for contexts up to 32K timesteps, past-only and known-future covariates, and probabilistic quantile outputs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/voxtral/voxtral-mini-25-07": { "id": "mistral/voxtral/voxtral-mini-25-07", "name": "Voxtral Mini", "maker": "mistral", "family": "mistral/voxtral", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-07-15", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.001, "completion_per_m_usd": 0.04 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. A mini version of our first audio input model.", "what_it_is_for": "A mini version of our first audio input model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/voxtral/voxtral-mini-4b-realtime-arabic": { "id": "mistral/voxtral/voxtral-mini-4b-realtime-arabic", "name": "Voxtral Mini 4B Realtime Arabic", "maker": "mistral", "family": "mistral/voxtral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "approximately 4.4B", "architecture": "Causal audio encoder and language decoder", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral AI streaming speech model, collected 2026-10-15. It is a named open-weight fine-tune of Voxtral Mini 4B Realtime for Arabic dialects and Modern Standard Arabic, including code-switching. It is not on the Mistral API model index. The card says it was co-developed with Morocco's Ministere de la Transition Numerique et de la Reforme Administrative.", "what_it_is_for": "Live transcription of Arabic speech. The card says it handles real-world audio and code-switching, and that a configurable delay supports captions and voice interfaces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/voxtral/voxtral-mini-transcribe-25-07": { "id": "mistral/voxtral/voxtral-mini-transcribe-25-07", "name": "Voxtral Mini Transcribe", "maker": "mistral", "family": "mistral/voxtral", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.002, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. An efficient audio input model, pre-trained and optimized for transcription purposes only.", "what_it_is_for": "An efficient audio input model, pre-trained and optimized for transcription purposes only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/voxtral/voxtral-mini-transcribe-26-02": { "id": "mistral/voxtral/voxtral-mini-transcribe-26-02", "name": "Voxtral Mini Transcribe 2", "maker": "mistral", "family": "mistral/voxtral", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.003, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. An efficient audio input model, pre-trained and optimized for transcription purposes only.", "what_it_is_for": "An efficient audio input model, pre-trained and optimized for transcription purposes only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/voxtral/voxtral-mini-transcribe-realtime-26-02": { "id": "mistral/voxtral/voxtral-mini-transcribe-realtime-26-02", "name": "Voxtral Mini Transcribe Realtime", "maker": "mistral", "family": "mistral/voxtral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2026-02-04", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.006, "completion_per_m_usd": null }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. An efficient audio input model, pre-trained and optimized for live transcription purposes only.", "what_it_is_for": "An efficient audio input model, pre-trained and optimized for live transcription purposes only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/voxtral/voxtral-small-25-07": { "id": "mistral/voxtral/voxtral-small-25-07", "name": "Voxtral Small", "maker": "mistral", "family": "mistral/voxtral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2025-07-15", "context_length": null, "max_output_tokens": null, "parameters": "24B", "architecture": "", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.004, "completion_per_m_usd": 0.4 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Our first model with audio input capabilities for instruct use cases.", "what_it_is_for": "Our first model with audio input capabilities for instruct use cases.", "ratings": { "empirical_100": 55, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mistral/voxtral/voxtral-tts-26-03": { "id": "mistral/voxtral/voxtral-tts-26-03", "name": "Voxtral TTS", "maker": "mistral", "family": "mistral/voxtral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2026-03-23", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 16.0 }, "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Our state-of-the-art text-to-speech model with zero-shot voice cloning. Supports 9 languages, streaming with ~90ms time-to-first-audio, and no transcript required for voice prompts.", "what_it_is_for": "Our state-of-the-art text-to-speech model with zero-shot voice cloning. Supports 9 languages, streaming with ~90ms time-to-first-audio, and no transcript required for voice prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/ast/ast": { "id": "mit/ast/ast", "name": "Audio Spectrogram Transformer", "maker": "mit", "family": "mit/ast", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-04-05", "context_length": null, "max_output_tokens": null, "parameters": "86.6M for the canonical AudioSet checkpoint", "architecture": "Vision Transformer applied to a spectrogram image. The Hugging Face card, written by the Hub team rather than the authors, says it matches a ViT on audio. Original code is Yuan Gong's AST repository from MIT CSAIL.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Audio Spectrogram Transformer is an audio classifier from MIT CSAIL, introduced by Yuan Gong, Yu-An Chung, and James Glass. The paper is from April 2021. The MIT Hub still hosts the AudioSet and Speech Commands checkpoints, and the 0.4593 AudioSet model was the widely downloaded one on 2026-10-15.", "what_it_is_for": "It classifies audio. The canonical checkpoint covers AudioSet event labels. A separate checkpoint covers Speech Commands v2. It is a research model, not an API product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/boltz/boltz-1": { "id": "mit/boltz/boltz-1", "name": "Boltz-1", "maker": "mit", "family": "mit/boltz", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-11-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Open biomolecular structure model aimed at AlphaFold 3-level complex prediction. The announcement describes a structure model for biomolecular complexes. A later Boltz-steering inference method is part of the paper, not a separate product folder.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Boltz-1 is an open biomolecular structure model from MIT CSAIL and the Jameel Clinic. It was announced on 2024-11-16 under the MIT license and was still the base of the public Boltz repository on 2026-10-15. Boltz-2 extends it.", "what_it_is_for": "The team released it to predict 3D structures of biomolecular complexes, as an open model approaching AlphaFold 3 accuracy. Training code, weights, and data were included so others can retrain it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/boltz/boltz-2": { "id": "mit/boltz/boltz-2", "name": "Boltz-2", "maker": "mit", "family": "mit/boltz", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-06-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Biomolecular foundation model for complex structure and binding affinity. NVIDIA's NIM card says an affinity PairFormer predicts a binding likelihood and a continuous affinity on a log micromolar scale, trained on mixed Ki, Kd, and IC50. The card also says the trunk is faster than Boltz-1 and that steering, method conditioning, distance constraints, and multi-chain templates were added.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Boltz-2 is MIT's open structure-and-affinity model, announced by the Jameel Clinic on 2025-06-06 with Recursion. Weights are MIT-licensed and were still the current Boltz release alongside BoltzGen on 2026-10-15. NVIDIA also serves it as a NIM.", "what_it_is_for": "It predicts biomolecular complex structure and small-molecule binding affinity. The lab says affinity is close to free-energy perturbation at roughly 1000x lower cost, for early drug screening.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/boltz/boltzgen": { "id": "mit/boltz/boltzgen", "name": "BoltzGen", "maker": "mit", "family": "mit/boltz", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "All-atom generative model that unifies binder design and structure prediction. Diffusion over continuous atom geometry. A design-specification language covers covalent bonds, structure constraints, and binding sites. The repository also has an inverse-folding config. Small and large training configs exist. Their parameter counts were not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BoltzGen is MIT's open generative model for designing protein and peptide binders. The Jameel Clinic released it on 2025-10-26 under the MIT license. It was still the design model in the Boltz family on 2026-10-15.", "what_it_is_for": "It designs binders against proteins, peptides, small molecules, and nucleic acids, and it also folds complexes. The lab's headline experimental claim is nanomolar binders on two-thirds of nine hard targets with 15 or fewer designs per modality.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/dc-ae/dc-ae": { "id": "mit/dc-ae/dc-ae", "name": "DC-AE", "maker": "mit", "family": "mit/dc-ae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-10-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Deep compression autoencoder. Residual autoencoding on space-to-channel features, plus a three-phase decoupled high-resolution adaptation. Released ids include f32, f64, and f128 spatial settings. SANA's card identifies its DC-AE as 32x spatial compression.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DC-AE is an image autoencoder family from MIT HAN Lab, with Tsinghua and NVIDIA coauthors. The first Hub checkpoints appeared in October 2024, and SANA 1.1 and Lite variants were still published on 2026-10-15.", "what_it_is_for": "The lab built it to replace moderate-compression VAEs inside high-resolution diffusion models. SANA uses the 32x SANA DC-AE. ImageNet checkpoints target class-conditional diffusion speed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/diffdock/diffdock": { "id": "mit/diffdock/diffdock", "name": "DiffDock", "maker": "mit", "family": "mit/diffdock", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-03-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion model over ligand translation, rotation, and torsion for blind protein-ligand docking. A score model samples poses and a confidence model ranks them. Training commands in the v1.0 tree use ESM2 embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffDock is MIT's diffusion model for blind molecular docking, from the Jameel Clinic and CSAIL. MIT News covered the ICLR 2023 release on 2023-03-31. The original weights remain available, but the GitHub default is now DiffDock-L. Collection date 2026-10-15.", "what_it_is_for": "It samples 3D binding poses of a small molecule against a protein and ranks them with a confidence model. It is a research docking tool for computational chemistry.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/diffdock/diffdock-l": { "id": "mit/diffdock/diffdock-l", "name": "DiffDock-L", "maker": "mit", "family": "mit/diffdock", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-02-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Larger DiffDock score and confidence models. The v1.1 release note says it improves performance and generalization. The repository default after v1.1 is this model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffDock-L is the 2024 MIT update to DiffDock. It became the default model in the official repository on 2024-02-28 and was still the default on 2026-10-15. Code and weights are MIT-licensed.", "what_it_is_for": "The release note says it docks ligands with better accuracy and better generalization than the 2023 model. Use it for the same blind docking task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/efficientvit/efficientvit": { "id": "mit/efficientvit/efficientvit", "name": "EfficientViT", "maker": "mit", "family": "mit/efficientvit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-scale linear attention vision backbone for high-resolution dense prediction. ICCV 2023. Authors named in the repo header: Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EfficientViT is MIT HAN Lab's linear-attention vision backbone, presented at ICCV 2023. The code and pretrained checkpoints remain public, but the README said on 2025-09-05 that the repo would not be maintained further. Collection date 2026-10-15.", "what_it_is_for": "The paper is for high-resolution dense prediction: classification backbones and semantic segmentation. EfficientViT-SAM is the later segment-anything application and has its own folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/efficientvit/efficientvit-sam": { "id": "mit/efficientvit/efficientvit-sam", "name": "EfficientViT-SAM", "maker": "mit", "family": "mit/efficientvit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-02-07", "context_length": null, "max_output_tokens": null, "parameters": "34.8M to 203.3M", "architecture": "SAM prompt encoder and mask decoder with the image encoder replaced by EfficientViT. L models run at 512px. XL models run at 1024px. Trained by distillation from SAM-ViT-H, then end-to-end on SA-1B.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EfficientViT-SAM is MIT HAN Lab's accelerated Segment Anything model. Weights were released with the February 2024 paper under Apache-2.0 and were still on the Hub on 2026-10-15. It keeps SAM's prompt encoder and mask decoder and swaps in an EfficientViT image encoder.", "what_it_is_for": "The README presents it as a faster segment-anything model for box-prompted segmentation, including Jetson Orin and A100 TensorRT deployment. A demo is at https://evitsam.hanlab.ai/.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/foreact/foreact": { "id": "mit/foreact/foreact", "name": "ForeAct", "maker": "mit", "family": "mit/foreact", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-02-12", "context_length": null, "max_output_tokens": null, "parameters": "5B", "architecture": "Visual foresight planner. A foresight image generator predicts a future observation. A vision-language model writes subtask text. The pair steers an unchanged VLA. The HAN page says the generator uses a deep-compression autoencoder and produces 640x480 images. The released Hub checkpoint is tagged visualforesight.", "modalities": { "input": [ "image", "text" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ForeAct is a visual foresight planner from MIT, with NVIDIA and Caltech coauthors. Code and a 5B pretrained checkpoint were released on 2026-02-12. It is a CVPR 2026 highlight and was still on the MIT HAN Hub on 2026-10-15.", "what_it_is_for": "It guides an existing vision-language-action policy by predicting the next camera image and a subtask description. The low-level VLA is not modified. The lab reports 0.33 seconds to imagine a 640x480 frame on an H100.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/hart/hart": { "id": "mit/hart/hart", "name": "HART", "maker": "mit", "family": "mit/hart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-10-14", "context_length": null, "max_output_tokens": null, "parameters": "0.7B", "architecture": "Hybrid autoregressive transformer. A discrete autoregressive model generates the coarse tokens. A residual diffusion module of 37M parameters models the continuous residual. Generates 1024x1024 directly.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HART is a 0.7B visual generator from MIT HAN Lab, with NVIDIA coauthors. The 1024px checkpoint was published in October 2024 under an MIT license and was still on the Hub on 2026-10-15. It was an ICLR 2025 paper.", "what_it_is_for": "The card says it generates 1024x1024 images with quality meant to rival diffusion models, at higher throughput. A demo is at https://hart.mit.edu.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/ladi/ladi-v2-classifier": { "id": "mit/ladi/ladi-v2-classifier", "name": "LADI v2 classifier", "maker": "mit", "family": "mit/ladi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-06-04", "context_length": null, "max_output_tokens": null, "parameters": "0.2B for the large model; Hub listing says 23.5M for the small model", "architecture": "Large model is SwinV2-Large fine-tuned from microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft. Multi-label head with 12 disaster-response classes. Small-model architecture was not opened.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The LADI v2 classifiers are multi-label aerial-image models from MIT Lincoln Laboratory. The paper is dated 2024-06-04 and the large checkpoint was updated on the Hub on 2024-06-06. They were still public under an MIT license on 2026-10-15.", "what_it_is_for": "They tag post-disaster aerial photos with features a response manager would look for, such as flooding, damaged roads, and damaged buildings. The large all-splits model is the deployment checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/places/places365": { "id": "mit/places/places365", "name": "Places365-CNN", "maker": "mit", "family": "mit/places", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Standard CNNs trained on Places365-Standard for 365 scene categories. Released architectures: AlexNet, GoogLeNet, VGG16, ResNet-18, ResNet-50, ResNet-152, and DenseNet-161. Hybrid1365 models add ImageNet classes.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Places365-CNN is the MIT CSAIL scene-recognition model suite trained on Places365. The pretrained networks were released with the Places2 project and the 2017 TPAMI paper, and the GitHub README still documents them as of 2026-10-15. The original download host did not respond on the collection date.", "what_it_is_for": "The models label a photograph with one of 365 scene categories. Hybrid1365 networks also cover ImageNet object classes. A unified demo predicts scene, indoor or outdoor, attributes, and a class activation map.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/sana/sana": { "id": "mit/sana/sana", "name": "SANA", "maker": "mit", "family": "mit/sana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-14", "context_length": null, "max_output_tokens": null, "parameters": "600M and 1.6B; the 1024px diffusers card measures the 1.6B checkpoint at 1648M", "architecture": "Linear Diffusion Transformer. Text encoder is Gemma-2-2B-IT. VAE is DC-AE at 32x spatial compression. Sampler documented as Flow-DPM-Solver.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SANA is a text-to-image model from NVIDIA and MIT HAN Lab, released as open weights and still published as of 2026-10-15. It is the first SANA generation, aimed at high-resolution images on a small Linear Diffusion Transformer.", "what_it_is_for": "The makers describe SANA as an efficient generator of images up to 4096\u00d74096, including laptop-GPU deployment. The 1024px 1.6B card says it is for research, art, education, and studying generative-model limits, not factual depictions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/sana/sana-1-5": { "id": "mit/sana/sana-1-5", "name": "SANA-1.5", "maker": "mit", "family": "mit/sana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-16", "context_length": null, "max_output_tokens": null, "parameters": "1.6B and 4.8B", "architecture": "Linear Diffusion Transformer grown from SANA-1.0. The 4.8B model initializes its first 18 layers from the 1.6B SANA-1.0 checkpoint (partial preservation). Training uses an 8-bit CAME optimizer. Inference scaling uses a fine-tuned NVILA-2B as a judge.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SANA-1.5 is the scaled SANA text-to-image line from NVIDIA and MIT HAN Lab. Weights shipped on 2025-03-16 and were still listed in the model zoo on 2026-10-15. It grows a 1.6B SANA checkpoint into a 4.8B model and adds inference-time selection.", "what_it_is_for": "The project page says it is for efficient text-to-image generation when either training compute or inference compute is the limit. Smaller models plus inference scaling are claimed to match larger ones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/sana/sana-sprint": { "id": "mit/sana/sana-sprint", "name": "SANA-Sprint", "maker": "mit", "family": "mit/sana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-22", "context_length": null, "max_output_tokens": null, "parameters": "0.6B and 1.6B", "architecture": "Few-step SANA distilled with hybrid continuous-time consistency (sCM) distillation. Unified 1-to-4 step model. ControlNet path is described for interactive edits.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SANA-Sprint is the one-step and few-step SANA image model from NVIDIA, MIT HAN Lab, and Hugging Face. Weights were posted on 2025-03-22 and remain in the public model zoo as of 2026-10-15.", "what_it_is_for": "The paper presents it as a distilled text-to-image model for real-time 1024px generation, including a ControlNet path for interactive edits.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/sana/sana-video": { "id": "mit/sana/sana-video", "name": "SANA-Video", "maker": "mit", "family": "mit/sana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Block-linear diffusion transformer for video. The 720p checkpoint uses an LTX-2 VAE (32x spatial, 8x temporal) according to the SANA-Video docs. LongSANA variants extend duration with a LongLive-style training stack.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SANA-Video is a 2B text-to-video and image-to-video model from NVIDIA and MIT HAN Lab. The checkpoints were still listed in the SANA model zoo on 2026-10-15. LongSANA minute-length checkpoints are versions of this line, not a separate architecture family in the zoo.", "what_it_is_for": "The docs describe efficient video generation up to 720p for about 5 seconds, with a two-stage refiner path mentioned for higher resolution and LongSANA for minute-length clips.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/sana/sana-video-2-0": { "id": "mit/sana/sana-video-2-0", "name": "SANA-Video 2.0", "maker": "mit", "family": "mit/sana", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "5B released; 14B announced", "architecture": "Hybrid-attention video model with attention residuals, according to the SANA overview. The 14B announcement says 40 layers. The 5B release is a 720p text-to-video and text-image-to-video ReFL model.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SANA-Video 2.0 is the hybrid-attention video line from the NVIDIA and MIT SANA project. A 5B 720p checkpoint and a 4-step preview were listed with weights on 2026-10-15. A 14B model was marked coming soon.", "what_it_is_for": "The overview describes 720p text-to-video and text-image-to-video generation. The 5B checkpoint is the released model. The 4-step file is a distilled preview.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/streaming-vlm/streaming-vlm": { "id": "mit/streaming-vlm/streaming-vlm", "name": "StreamingVLM", "maker": "mit", "family": "mit/streaming-vlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-21", "context_length": null, "max_output_tokens": null, "parameters": "8B on the Hub card; the paper fine-tunes Qwen2.5-VL-7B-Instruct", "architecture": "Supervised fine-tune of Qwen2.5-VL-7B-Instruct for streaming inference. Compact KV cache reuses attention sinks, a short window of recent vision tokens, and a long window of recent text tokens. Contiguous position ids.", "modalities": { "input": [ "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StreamingVLM is a streaming video-language model from MIT and NVIDIA. The Hub weights appeared on 2025-09-21, the paper is dated 2025-10-10, and the work was published at ICLR 2026. It was still the named 8B repo on the MIT HAN Hub on 2026-10-15.", "what_it_is_for": "It is for real-time commentary and question answering on video that does not fit in a normal context window. The paper fine-tunes Qwen2.5-VL-7B-Instruct so training matches a streaming KV cache.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/vila/longvila": { "id": "mit/vila/longvila", "name": "LongVILA", "maker": "mit", "family": "mit/vila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2024-12-01", "context_length": 1048576, "max_output_tokens": null, "parameters": "1.5B and 7B", "architecture": "Long-context VILA. Two extra stages extend the base VLM: long-context extension and long-video supervised fine-tuning. Training uses multi-modal sequence parallelism. 7B-1M is trained at 2048 frames.", "modalities": { "input": [ "text", "video", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongVILA is a long-video VILA from NVIDIA and MIT, published in December 2024 and accepted to ICLR 2025. The 7B one-million-token checkpoint is the long-context version still described in the official README on 2026-10-15. Weights are non-commercial.", "what_it_is_for": "It is for understanding long videos, up to 2048 training frames and a 6,000-frame needle test. The language models are Qwen2 sizes, not the original Llama-2 VILA backbones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/vila/nvila": { "id": "mit/vila/nvila", "name": "NVILA", "maker": "mit", "family": "mit/vila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-12-10", "context_length": 32768, "max_output_tokens": null, "parameters": "8B and 15B, plus Lite variants", "architecture": "VILA-style autoregressive VLM. The paper says SigLIP vision encoder, a two-layer MLP projector, and Qwen2 as the language model. The published NVILA-8B config.json uses LlavaLlamaModel, hidden size 3584, 28 layers, 28 heads, 4 KV heads, which matches Qwen2-7B. Scale-then-compress visual tokens.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NVILA is the efficiency-focused VILA line from NVIDIA and MIT HAN Lab, also called VILA 2.0 in the README. The 8B weights were trained in November 2024, posted on 2024-12-10, and remain public for non-commercial research as of 2026-10-15.", "what_it_is_for": "The paper presents it as a full-stack efficient VLM: cheaper training, lower latency, and higher-resolution images and longer video than VILA-1.5, using a scale-then-compress vision pipeline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/vila/vila": { "id": "mit/vila/vila", "name": "VILA", "maker": "mit", "family": "mit/vila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-02-01", "context_length": null, "max_output_tokens": null, "parameters": "2.7B checkpoint is listed as 3B on its Hub card; paper also reports 7B and 13B models", "architecture": "Visual language model with interleaved image-text pretraining. Hub architecture tag for VILA-2.7b is llava_llama. The 7B and 13B paper rows are compared with Llama-2.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VILA is the original visual-language model family from NVIDIA and MIT HAN Lab. The February 2024 release is still public under a non-commercial weight license. Collection date 2026-10-15. NVIDIA later said the line became part of Cosmos Nemotron.", "what_it_is_for": "The 2.7B card says it is a multi-image VLM trained with interleaved image-text data, with in-context learning and visual chain-of-thought among the claimed behaviors. It is a research model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/vila/vila-1-5": { "id": "mit/vila/vila-1-5", "name": "VILA-1.5", "maker": "mit", "family": "mit/vila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-05-03", "context_length": null, "max_output_tokens": null, "parameters": "3B, 8B, 13B, and 40B", "architecture": "VILA generation with video understanding. NVIDIA's May 2024 blog identifies an 8B Llama-3 variant (Llama-3-VILA1.5-8B) and a 13B and 40B model. The language-model backbone of the 40B checkpoint was not named in that blog.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VILA-1.5 is the May 2024 video-capable VILA release from NVIDIA and MIT HAN Lab. Four sizes were published. As of the 2025-01-06 README note, NVIDIA treats the VILA line as part of Cosmos Nemotron, but the 1.5 checkpoints were not marked withdrawn.", "what_it_is_for": "The README says VILA-1.5 adds video understanding on top of the VILA pretraining recipe. The NVIDIA blog frames it as an efficient VLM for data-center and edge GPUs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mit/vila/vila-u": { "id": "mit/vila/vila-u", "name": "VILA-U", "maker": "mit", "family": "mit/vila", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-10-21", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Single autoregressive next-token model for understanding and generation. Hub config family tag is vila_u_llama. A unified vision tower emits visual tokens instead of a separate diffusion head.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VILA-U is a 7B unified vision-language model from MIT HAN Lab, released in October 2024 under an MIT license and accepted to ICLR 2025. It was still on the Hub on 2026-10-15. Unlike VILA-1.5, it is trained to both understand and generate images in one token model.", "what_it_is_for": "The paper describes video, image, and language understanding plus visual generation without a separate generation module.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "modernmt/adaptive/modernmt": { "id": "modernmt/adaptive/modernmt", "name": "ModernMT", "maker": "modernmt", "family": "modernmt/adaptive", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary commercial service under ModernMT Terms and Conditions. The separate open-source engine toolkit is Apache-2.0 and does not ship these cloud weights.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adaptive neural machine translation. Translated describes the system as a Fairseq Transformer with instance-based adaptation: a context vector weights translation memories at decode time. Version 7 (2023-07-27) adds Trust Attention, which prefers professionally reviewed data in training and inference, plus a data-cleaning step the vendor calls DataClean AI. Customers cannot select a model id; V7 replaced V6 on the same API.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernMT is Translated S.r.l.'s adaptive neural translation engine, sold as one cloud API rather than a menu of model ids. Version 7 replaced Version 6 on 27 July 2023 and is still the engine behind `api.modernmt.com`. The product is deprecated: new signups are closed, keys keep working until 31 December 2026, and the service sunsets by the end of 2026. Collected 2026-10-15.", "what_it_is_for": "The API translates text and adapts the suggestion to the customer's memories, glossaries, and, on the Human-in-the-Loop package, human corrections. The FAQ says every service level can use whole-document context. Real-time learning from corrections is limited to Human-in-the-Loop. There is no document-file, image, or audio translation on ModernMT; the 2026 homepage assigns those to Lara.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "modernmt/qe/mtqe": { "id": "modernmt/qe/mtqe", "name": "ModernMT MTQE", "maker": "modernmt", "family": "modernmt/qe", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Included in the ModernMT API under the same commercial terms. No separate license name is published.", "release_date": "2023-12-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published. The vendor blog says the estimator is trained on machine-translation output paired with corrected sentences and the source, and that scores adapt to the user's content rather than only a generic corpus. Translated also uses the name T-QE for this estimator.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernMT MTQE is Translated's segment-level quality estimator for the ModernMT API, also called T-QE in the vendor's own posts. It scores an existing translation from 0 to 1. It shipped with the adaptive-QE announcement on 12 December 2023 and is still documented on `GET /translate/qe`. The whole ModernMT API is deprecated and set to stop by 31 December 2026. Collected 2026-10-15.", "what_it_is_for": "The API judges translation quality so a workflow can send low-scoring segments to a reviewer and publish the rest. The vendor says the estimator is adapted to the customer's content, not only a generic corpus. MateCat can show the score. Machine Translate's index says it can score output from any MT API, not only ModernMT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "monash/dermfm/dermfm-zero": { "id": "monash/dermfm/dermfm-zero", "name": "DermFM-Zero", "maker": "monash", "family": "monash/dermfm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2026-02-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language model. Card: PanDerm-Large ViT-L/16 initialized from the public PanDerm checkpoint, with NaViT-style patch-and-pack; reported results are 224x224 and the encoder accepts other sizes. Text tower is PubMedBERT-256 (NeuML/pubmedbert-base-embeddings), further pretrained on a Derm1M knowledge tree of 556,372 attribute texts with AdaSP following KEP, and also kept frozen as a knowledge encoder. Card training set for contrastive alignment: 517,455 image-text pairs (406,831 clinical, 110,624 dermoscopic). Objective: MAKE multi-aspect contrastive alignment plus distillation to the frozen text encoder. Optimisation on the card: AdamW, learning rate 1e-4, weight decay 0.1, 200 warmup steps, cosine decay, effective batch 2048, 15 epochs, bf16, 2x H200, about 1 day 9 hours. The GitHub lead instead says masked latent modelling on over 3 million images and contrastive alignment on 1 million pairs. The paper abstract says masked latent modelling and contrastive learning on over 4 million multimodal data points. Those three corpus descriptions are not reconciled here. The Hub repo lists one weight file, open_clip_pytorch_model.bin, and the repo usedStorage is 1,659,299,303 bytes. Upstream open_clip cannot load the checkpoint; the repo ships a fork. VQA is a fine-tune task, not a zero-shot output.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DermFM-Zero is a dermatology vision-language foundation model from Monash University's AIM for Health Lab (Siyuan Yan, Zongyuan Ge, and coauthors). The paper was submitted on 11 February 2026. The PanDerm repository called it DermFM-Zero (PanDerm-2) on 13 February 2026. Public Hub weights followed in September 2026. It is active and open-weight. This dossier was collected on 2026-10-15.", "what_it_is_for": "The makers describe zero-shot classification and retrieval from text prompts, a backbone for multimodal fine-tuning and visual question answering, and concept discovery with sparse autoencoders. The card says it is not a medical device and must not be used for diagnosis or patient management without further validation. The abstract says primary-care assistance nearly doubled general practitioners' differential diagnostic accuracy across 98 conditions. No absolute reader-study accuracy is in the abstract.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "monash/dermlip/dermlip": { "id": "monash/dermlip/dermlip", "name": "DermLIP", "maker": "monash", "family": "monash/dermlip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-ND 4.0", "release_date": "2025-06-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP-style dual encoder trained with image-text contrastive loss on 403,563 Derm1M skin image-text pairs (dermoscopy and clinical photos), resolution 224x224. Two released variants: ViT-B/16 vision with a GPT-2 text encoder (about 5 hours on one H200, roughly 40 GB), and PanDerm-Base vision with PubMedBERT (text context length 256 in the OpenCLIP config; about 9.5 hours on one H200, roughly 90 GB). The PanDerm-base card says that variant beats the other DermLIP variants. Neither variant generates text; both score image-text similarity.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DermLIP is a dermatology CLIP-style vision-language model from Monash University's AIM for Health Lab, released with the Derm1M dataset paper. Two open-weight checkpoints are on Hugging Face under `redlessone`, both created on 30 June 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model cards describe zero-shot classification, few-shot learning, cross-modal retrieval, and concept annotation on skin images. A typical prompt is \"This is a skin image of {c}\". The checkpoints score image-text similarity. They do not generate text. The PanDerm-base card says that variant beats the other DermLIP variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "monash/make/make": { "id": "monash/make/make", "name": "MAKE", "maker": "monash", "family": "monash/make", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "OpenCLIP vision-language checkpoint. The paper trains with multi-aspect contrastive learning: clinical captions are split into knowledge sub-captions, aligned to image regions, and weighted by a diagnosis prior. Pretraining used 403,563 dermatology image-text pairs from educational sources. The Hub file open_clip_model.safetensors is 598,516,980 bytes. The vision backbone, width, and layer count are not on the model card. Evaluation code loads hf-hub:xieji-x/MAKE (the card's repo id is Xieji-Li/MAKE).", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAKE (Multi-Aspect Knowledge-Enhanced) is a dermatology vision-language checkpoint from Monash University's AIM for Health Lab (Siyuan Yan, Xieji Li, Ming Hu, Yiwen Jiang, Zhen Yu, Zongyuan Ge). The method is a MICCAI 2025 paper, submitted to arXiv on 14 May 2025. The GitHub repo says the checkpoint and evaluation pipeline were released on 20 June 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper trains a zero-shot dermatology assessor. Captions are split into knowledge sub-captions, aligned to image regions, and weighted by a diagnosis prior. The public scripts cover zero-shot classification, concept annotation, and retrieval through OpenCLIP. It is not described as a medical device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "monash/panderm/panderm": { "id": "monash/panderm/panderm", "name": "PanDerm", "maker": "monash", "family": "monash/panderm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-ND 4.0", "release_date": "2024-10-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision encoder. The paper model is a ViT-Large/16 with a mask regressor and a CLIP-Large teacher, trained with representation reconstruction and CLIP latent alignment. Pretraining used over 2 million skin images from 11 institutions and four modalities (dermoscopy, clinical photography, total-body photography, dermatopathology), 500 epochs with a 20-epoch warmup, on 4x 80 GB H100 GPUs for about 5 days 7 hours. The released checkpoint is the last epoch. A later ViT-B/16 checkpoint is a smaller version, not a new product. The encoder is meant to be linear-probed or fine-tuned; it does not generate text.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PanDerm is a dermatology vision foundation model from Monash University's AIM for Health Lab (Siyuan Yan, Zongyuan Ge, and coauthors). It is an active open-weight encoder, first described in an arXiv preprint on 19 October 2024 and published in Nature Medicine on 6 June 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The makers describe PanDerm as a general-purpose encoder for skin images across dermoscopy, clinical photos, total-body photography, and dermatopathology. The GitHub README says it is not ready for a specific task until it is linear-probed or fine-tuned. Intended jobs in the paper are skin-cancer screening, differential diagnosis, lesion segmentation, longitudinal monitoring, and prognosis features. It is not offered as a clinical device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "monash/upfm/upfm": { "id": "monash/upfm/upfm", "name": "Unified Phenotype Foundation Model", "maker": "monash", "family": "monash/upfm", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Announced and not released. No card, weights, or architecture diagram was found. Mirage News (30 October 2025), reprinting a Monash release, quotes Associate Professor Zongyuan Ge describing Australia's first Unified Phenotype Foundation Model as a system to integrate images, scans, clinical notes, and long-term medical histories. Healthcare IT News (byline 9 November 2025) says it is designed to integrate eye scans, skin images, brain signals, lab results, and electronic health records, and names PanDerm as the dermatology blueprint. Those inputs are planned. They are not a shipped interface. The listed modalities are that announcement, not a measured spec.", "modalities": { "input": [ "image", "text", "signal" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unified Phenotype Foundation Model (UPFM) is a named healthcare model announced by Monash University Associate Professor Zongyuan Ge. It is not released. Fellowship coverage in October and November 2025 describes the project. There is no model card, weight file, or score. This dossier was collected on 2026-10-15. Confidence is medium because the name is repeated by secondary reports of a Monash release, not by a model card.", "what_it_is_for": "Mirage News, dated 30 October 2025, quotes Ge on an AI system meant to integrate images, scans, clinical notes, and long-term medical histories for earlier diagnosis, prognosis, and personalised care. Healthcare IT News, bylined 9 November 2025, adds eye scans, skin images, brain signals, lab results, and electronic health records, and treats PanDerm as the dermatology work that came first. Those are plans. They are not a shipped interface.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-k1-5": { "id": "moonshot/kimi/kimi-k1-5", "name": "Kimi k1.5", "maker": "moonshot", "family": "moonshot/kimi", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kimi k1.5 is a named Moonshot reasoning model known from the company's own GitHub research list, which describes a tech report on scaling reinforcement learning toward an o1-level multimodal model. It is not on the current or deprecated API model list, and no public weights were found. Collected 2026-10-15. Confidence is medium because the tech-report page itself was not re-opened.", "what_it_is_for": "Multimodal long reasoning, as described on the Moonshot GitHub landing page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-k2-0711-preview": { "id": "moonshot/kimi/kimi-k2-0711-preview", "name": "Kimi K2 Instruct", "maker": "moonshot", "family": "moonshot/kimi", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "2025-07-11", "context_length": 131072, "max_output_tokens": null, "parameters": "1T total / 32B activated", "architecture": "Mixture-of-Experts. 61 layers including 1 dense layer. MLA attention, SwiGLU, 64 heads, attention hidden size 7168, 384 experts, 8 selected per token, 1 shared expert, expert hidden size 2048, vocabulary 160K. Pretrained on 15.5T tokens with the Muon optimizer. Instruct checkpoint is reflex-grade, without long thinking. Checkpoints stored in block-fp8.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 2.5 }, "summary_intro": "Kimi K2 Instruct is Moonshot's July 2025 open-weight chat model, API id kimi-k2-0711-preview. It is a 1 trillion parameter mixture-of-experts model with 32 billion parameters activated, trained on 15.5 trillion tokens. The API was retired on 2026-05-25. The original weights remain public under Modified MIT, with the card pointing at the 0905 checkpoint as the newer instruct. Collected 2026-10-15.", "what_it_is_for": "General chat and agent tool use without a long-thinking mode. The card calls it reflex-grade.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-k2-0905-preview": { "id": "moonshot/kimi/kimi-k2-0905-preview", "name": "Kimi K2 0905", "maker": "moonshot", "family": "moonshot/kimi", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "2025-09-05", "context_length": 262144, "max_output_tokens": null, "parameters": "1T total / 32B activated", "architecture": "Mixture-of-Experts. 61 layers including 1 dense layer. MLA attention, SwiGLU, 64 heads, attention hidden size 7168, 384 experts, 8 selected per token, 1 shared expert, expert hidden size 2048, vocabulary 160K. Updated instruct checkpoint. API context is 256K, versus 128K on the July instruct model. Text only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 2.5 }, "summary_intro": "Kimi K2 0905 is the September 2025 update of Kimi K2 Instruct. Moonshot sold it as kimi-k2-0905-preview with a 256K context and retired that API on 2026-05-25. Weights remain as moonshotai/Kimi-K2-Instruct-0905. Collected 2026-10-15.", "what_it_is_for": "General chat and tool use without a separate long-thinking mode. The pricing page says it improves agentic coding, frontend code, and context understanding over the July checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-k2-5": { "id": "moonshot/kimi/kimi-k2-5", "name": "Kimi K2.5", "maker": "moonshot", "family": "moonshot/kimi", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "2026-01-27", "context_length": 262144, "max_output_tokens": null, "parameters": "1T total / 32B activated", "architecture": "Mixture-of-Experts. 61 layers including 1 dense layer. MLA attention, SwiGLU, 64 heads, attention hidden size 7168, 384 experts, 8 selected per token, 1 shared expert, expert hidden size 2048, vocabulary 160K. MoonViT vision encoder, 400M parameters. Continued pretraining on about 15T mixed visual and text tokens on top of Kimi K2.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": 21.0 }, "summary_intro": "Kimi K2.5 is Moonshot's first native multimodal K2 model. It was released in January 2026, with widespread contemporary reports of 27 January, and the Open Platform retired it on 2026-08-31. Weights remain public under Modified MIT. Collected 2026-10-15.", "what_it_is_for": "The launch blog describes visual agentic work: coding, vision, and a self-directed agent swarm of up to 100 sub-agents and 1,500 tool calls. The API supported thinking and non-thinking modes.", "ratings": { "empirical_100": null, "lmsys_elo": 1237, "coding_elo": 1237, "swe_bench": 21.2, "humaneval": 75.0, "twelve_cs": 99.5, "aa_intelligence": 23.5, "aa_coding": 46.8, "aa_agentic": null, "design_arena_elo": 1120, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-k2-6": { "id": "moonshot/kimi/kimi-k2-6", "name": "Kimi K2.6", "maker": "moonshot", "family": "moonshot/kimi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "", "context_length": 262144, "max_output_tokens": 32768, "parameters": "1T total / 32B activated", "architecture": "Mixture-of-Experts. 61 layers including 1 dense layer. MLA attention, SwiGLU, 64 heads, attention hidden size 7168, 384 experts, 8 selected per token, 1 shared expert, expert hidden size 2048, vocabulary 160K. MoonViT vision encoder, 400M parameters. Native INT4, same method as Kimi K2 Thinking. Same architecture as K2.5.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.95, "completion_per_m_usd": 4.0 }, "summary_intro": "Kimi K2.6 is Moonshot's general-purpose open-weight multimodal model, still on the Open Platform as of 2026-10-15. The platform changelog places the API release in April 2026. The model card uses the same 1T/32B MoE layout as K2.5, with MoonViT and a 256K context, under Modified MIT.", "what_it_is_for": "Long-horizon coding, visual design from prompts, and agent work. The card describes an agent swarm of up to 300 sub-agents. The API also supports ordinary chat with thinking on or off.", "ratings": { "empirical_100": 24, "lmsys_elo": 1251, "coding_elo": 1251, "swe_bench": 23.5, "humaneval": 76.8, "twelve_cs": 30.4, "aa_intelligence": 27.0, "aa_coding": 61.8, "aa_agentic": 20.5, "design_arena_elo": 1106, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-k2-7-code": { "id": "moonshot/kimi/kimi-k2-7-code", "name": "Kimi K2.7 Code", "maker": "moonshot", "family": "moonshot/kimi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "2026-06-12", "context_length": 262144, "max_output_tokens": 32768, "parameters": "1T total / 32B activated", "architecture": "Mixture-of-Experts. 61 layers including 1 dense layer. MLA attention, SwiGLU, 64 heads, attention hidden size 7168, 384 experts, 8 selected per token, 1 shared expert, expert hidden size 2048, vocabulary 160K. MoonViT vision encoder, 400M parameters. Built on Kimi K2.6. Native INT4 quantization, same method as Kimi K2 Thinking.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.95, "completion_per_m_usd": 4.0 }, "summary_intro": "Kimi K2.7 Code is Moonshot's coding model, released 2026-06-12 and still active on the Kimi Open Platform as of 2026-10-15. Weights are on Hugging Face under a Modified MIT license. The card says it is built on Kimi K2.6, with the same 1T/32B MoE shape and a 256K context.", "what_it_is_for": "Long-context coding and agent tasks. Moonshot says it follows instructions more reliably than K2.6 and finishes coding tasks more often, with about 30% less thinking-token use.", "ratings": { "empirical_100": null, "lmsys_elo": 1250, "coding_elo": 1250, "swe_bench": 23.4, "humaneval": 76.7, "twelve_cs": 23.5, "aa_intelligence": 25.8, "aa_coding": 60.8, "aa_agentic": 21.0, "design_arena_elo": 1095, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-k2-7-code-highspeed": { "id": "moonshot/kimi/kimi-k2-7-code-highspeed", "name": "Kimi K2.7 Code HighSpeed", "maker": "moonshot", "family": "moonshot/kimi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "", "context_length": 262144, "max_output_tokens": 32768, "parameters": "1T total / 32B activated", "architecture": "Same checkpoint as Kimi K2.7 Code. HighSpeed is a serving tier, not a separate trained model.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.9, "completion_per_m_usd": 8.0 }, "summary_intro": "Kimi K2.7 Code HighSpeed is the fast serving tier of Kimi K2.7 Code, still listed as its own model id on the Kimi Open Platform as of 2026-10-15. Moonshot says it is the same model, not a separate training run. Weights are the K2.7 Code release under Modified MIT.", "what_it_is_for": "Coding sessions where output speed matters more than token price. The Open Platform quotes about 180 tokens per second, and up to about 260 in short contexts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-k2-8-preview": { "id": "moonshot/kimi/kimi-k2-8-preview", "name": "Kimi K2.8 Preview", "maker": "moonshot", "family": "moonshot/kimi", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-11", "context_length": 1048576, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kimi K2.8 Preview is a Moonshot preview model that Kimi Code fully rolled out on 2026-09-11, still under the model id kimi-for-coding. As of 2026-10-15 it is a preview, not an Open Platform model id, and no public weight repository was found under moonshotai on Hugging Face.", "what_it_is_for": "Moonshot describes it as close to K3 for coding and agent work, with more efficient thinking than K2.7 Code, including code completion and routine development.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-k2-base": { "id": "moonshot/kimi/kimi-k2-base", "name": "Kimi K2 Base", "maker": "moonshot", "family": "moonshot/kimi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "2025-07-11", "context_length": 131072, "max_output_tokens": null, "parameters": "1T total / 32B activated", "architecture": "Mixture-of-Experts. 61 layers including 1 dense layer. MLA attention, SwiGLU, 64 heads, attention hidden size 7168, 384 experts, 8 selected per token, 1 shared expert, expert hidden size 2048, vocabulary 160K. Pretrained foundation checkpoint, not the chat post-train. Pretrained on 15.5T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kimi K2 Base is the pretrained foundation checkpoint of Kimi K2, released with the July 2025 open-weight drop. It is not a chat API model. Moonshot describes it as the starting point for fine-tuning. License is the K2 Modified MIT. Collected 2026-10-15.", "what_it_is_for": "Research and custom post-training. It is not the reflex instruct model and it does not do long thinking by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-k2-thinking": { "id": "moonshot/kimi/kimi-k2-thinking", "name": "Kimi K2 Thinking", "maker": "moonshot", "family": "moonshot/kimi", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "", "context_length": 262144, "max_output_tokens": null, "parameters": "1T total / 32B activated", "architecture": "Mixture-of-Experts. 61 layers including 1 dense layer. MLA attention, SwiGLU, 64 heads, attention hidden size 7168, 384 experts, 8 selected per token, 1 shared expert, expert hidden size 2048, vocabulary 160K. Native INT4 weight-only quantization of the MoE via quantization-aware training. Text only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 2.5 }, "summary_intro": "Kimi K2 Thinking is Moonshot's open-weight reasoning model in the K2 generation. The Open Platform shipped it in November 2025 and retired the API on 2026-05-25. Weights stay on Hugging Face under Modified MIT. Collected 2026-10-15.", "what_it_is_for": "Step-by-step reasoning with tool calls. The card says it holds stable tool use across 200 to 300 sequential calls and uses native INT4 quantization-aware training.", "ratings": { "empirical_100": 28, "lmsys_elo": 1237, "coding_elo": 1237, "swe_bench": 21.2, "humaneval": 75.0, "twelve_cs": 73.9, "aa_intelligence": 22.0, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1111, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-k2-thinking-turbo": { "id": "moonshot/kimi/kimi-k2-thinking-turbo", "name": "Kimi K2 Thinking Turbo", "maker": "moonshot", "family": "moonshot/kimi", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "", "context_length": 262144, "max_output_tokens": null, "parameters": "1T total / 32B activated", "architecture": "High-speed serving of Kimi K2 Thinking. The pricing page says it is the high-speed version of kimi-k2-thinking, not a separate parameter set.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.15, "completion_per_m_usd": 8.0 }, "summary_intro": "Kimi K2 Thinking Turbo was the high-speed API tier of Kimi K2 Thinking. Moonshot retired it with the rest of the K2 API series on 2026-05-25. It used the same open weights, not a new training run. Collected 2026-10-15.", "what_it_is_for": "Deep reasoning with faster output. The pricing page quotes 60 to 100 tokens per second and a 256K context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-k2-turbo-preview": { "id": "moonshot/kimi/kimi-k2-turbo-preview", "name": "Kimi K2 Turbo", "maker": "moonshot", "family": "moonshot/kimi", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Modified MIT", "release_date": "", "context_length": 262144, "max_output_tokens": null, "parameters": "1T total / 32B activated", "architecture": "High-speed serving of the K2 instruct model aligned with kimi-k2-0905-preview. Same parameters. Not a separate training run.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.15, "completion_per_m_usd": 8.0 }, "summary_intro": "Kimi K2 Turbo was the high-speed API of Kimi K2 Instruct, kept aligned with the 0905 checkpoint. Moonshot launched it in August 2025 and retired it on 2026-05-25. It is not a separate set of weights. Collected 2026-10-15.", "what_it_is_for": "Faster agent and enterprise calls. The pricing page quotes up to 60 tokens per second, with a maximum around 100.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-k3": { "id": "moonshot/kimi/kimi-k3", "name": "Kimi K3", "maker": "moonshot", "family": "moonshot/kimi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Kimi K3 License", "release_date": "2026-07-16", "context_length": 1048576, "max_output_tokens": 1048576, "parameters": "2.8T total / 104B activated", "architecture": "Mixture-of-Experts with Kimi Delta Attention (KDA), Attention Residuals (AttnRes), and Stable LatentMoE activating 16 of 896 experts. Native vision. Technical report: 2.8T total parameters and 104B activated.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 15.0 }, "summary_intro": "Kimi K3 is Moonshot AI's flagship open-weight model, collected 2026-10-15. It launched on the API and in Kimi on 2026-07-16, with weights published on 2026-07-27. It is active. The technical report describes a 2.8 trillion parameter mixture-of-experts model with 104 billion parameters activated, native vision, and a 1,048,576-token context.", "what_it_is_for": "Moonshot positions it for long-horizon coding, knowledge work, and deep reasoning, including tasks that mix software engineering with screenshots or video.", "ratings": { "empirical_100": 29, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs": 75.8, "aa_intelligence": 43.6, "aa_coding": 76.2, "aa_agentic": 50.0, "design_arena_elo": 1218, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-latest": { "id": "moonshot/kimi/kimi-latest", "name": "Kimi Latest", "maker": "moonshot", "family": "moonshot/kimi", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kimi Latest was a rolling API alias for whatever model the Kimi assistant was using, including experiments that were not frozen. Moonshot launched it in February 2025 and discontinued it on 2026-01-28. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "Let API users track the assistant. Moonshot told users who needed stable prompts to stay on moonshot-v1 instead.", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs": 23.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi/kimi-thinking-preview": { "id": "moonshot/kimi/kimi-thinking-preview", "name": "Kimi Thinking Preview", "maker": "moonshot", "family": "moonshot/kimi", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kimi Thinking Preview was Moonshot's May 2025 reasoning API preview. The platform discontinued it on 2025-11-11 and pointed users at later thinking models. No public weights were found. Collected 2026-10-15.", "what_it_is_for": "Early long-thinking access before Kimi K2 Thinking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi-audio/kimi-audio-7b": { "id": "moonshot/kimi-audio/kimi-audio-7b", "name": "Kimi-Audio-7B", "maker": "moonshot", "family": "moonshot/kimi-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT, with Qwen2.5-7B code under Apache-2.0", "release_date": "2025-04-25", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Hybrid audio input, continuous acoustic tokens plus discrete semantic tokens, and an LLM core with parallel text and audio heads. Chunk-wise streaming detokenizer. The card says the model is based on Qwen2.5-7B. Pretrained on more than 13 million hours of audio plus text.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kimi-Audio-7B is Moonshot's open audio foundation model, about 7 billion parameters, for understanding, generation, and conversation. The instruct card was created on Hugging Face on 2025-04-25. It is not a Kimi chat API model. Collected 2026-10-15.", "what_it_is_for": "Foundation checkpoint. Tasks named on the card include speech recognition, audio question answering, captioning, emotion recognition, sound event classification, and speech-to-speech conversation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi-audio/kimi-audio-7b-instruct": { "id": "moonshot/kimi-audio/kimi-audio-7b-instruct", "name": "Kimi-Audio-7B-Instruct", "maker": "moonshot", "family": "moonshot/kimi-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT, with Qwen2.5-7B code under Apache-2.0", "release_date": "2025-04-25", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Hybrid audio input, continuous acoustic tokens plus discrete semantic tokens, and an LLM core with parallel text and audio heads. Chunk-wise streaming detokenizer. The card says the model is based on Qwen2.5-7B. Pretrained on more than 13 million hours of audio plus text.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kimi-Audio-7B-Instruct is Moonshot's open audio foundation model, about 7 billion parameters, for understanding, generation, and conversation. The instruct card was created on Hugging Face on 2025-04-25. It is not a Kimi chat API model. Collected 2026-10-15.", "what_it_is_for": "Instruction checkpoint for ASR, audio QA, captioning, and speech conversation. Tasks named on the card include speech recognition, audio question answering, captioning, emotion recognition, sound event classification, and speech-to-speech conversation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi-dev/kimi-dev-72b": { "id": "moonshot/kimi-dev/kimi-dev-72b", "name": "Kimi-Dev-72B", "maker": "moonshot", "family": "moonshot/kimi-dev", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-06", "context_length": null, "max_output_tokens": null, "parameters": "72B", "architecture": "Post-trained from Qwen2.5-72B with large-scale reinforcement learning on repository issue resolution. Reward requires the full test suite to pass.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kimi-Dev-72B is Moonshot's open-weight coding model for fixing software issues, released in June 2025 under MIT. It is a reinforcement-learning post-train of Qwen2.5-72B, not a Kimi API model. Collected 2026-10-15.", "what_it_is_for": "Repository issue resolution. The card says the model edits real repositories in Docker and is rewarded only when the whole test suite passes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi-linear/kimi-linear-48b-a3b-base": { "id": "moonshot/kimi-linear/kimi-linear-48b-a3b-base", "name": "Kimi-Linear-48B-A3B-Base", "maker": "moonshot", "family": "moonshot/kimi-linear", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-30", "context_length": 1048576, "max_output_tokens": null, "parameters": "48B total / 3B activated", "architecture": "Hybrid linear attention (Kimi Delta Attention) mixture-of-experts. Card says KV cache can be cut by up to 75 percent and decoding can be up to 6x faster at 1M tokens versus full attention.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kimi-Linear-48B-A3B-Base is Moonshot's open-weight hybrid linear-attention model, 48 billion parameters with about 3 billion activated and a 1 million token context. MIT license. Hugging Face repos were created 2025-10-30. It is not a Kimi API model. Collected 2026-10-15.", "what_it_is_for": "Base checkpoint trained with 5.7T tokens. The card presents it as a more efficient attention design for short context, long context, and reinforcement-learning scaling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi-linear/kimi-linear-48b-a3b-instruct": { "id": "moonshot/kimi-linear/kimi-linear-48b-a3b-instruct", "name": "Kimi-Linear-48B-A3B-Instruct", "maker": "moonshot", "family": "moonshot/kimi-linear", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-30", "context_length": 1048576, "max_output_tokens": null, "parameters": "48B total / 3B activated", "architecture": "Hybrid linear attention (Kimi Delta Attention) mixture-of-experts. Card says KV cache can be cut by up to 75 percent and decoding can be up to 6x faster at 1M tokens versus full attention.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kimi-Linear-48B-A3B-Instruct is Moonshot's open-weight hybrid linear-attention model, 48 billion parameters with about 3 billion activated and a 1 million token context. MIT license. Hugging Face repos were created 2025-10-30. It is not a Kimi API model. Collected 2026-10-15.", "what_it_is_for": "Instruction-tuned checkpoint. The card presents it as a more efficient attention design for short context, long context, and reinforcement-learning scaling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi-vl/kimi-vl-a3b-instruct": { "id": "moonshot/kimi-vl/kimi-vl-a3b-instruct", "name": "Kimi-VL-A3B-Instruct", "maker": "moonshot", "family": "moonshot/kimi-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-09", "context_length": 131072, "max_output_tokens": null, "parameters": "16B total / 2.8B activated language decoder, plus a 400M MoonViT encoder", "architecture": "Moonlight MoE language model (about 2.8B activated, 16B total) plus native-resolution MoonViT. Long context extended from 8K to 128K.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kimi-VL-A3B-Instruct is an open-weight Moonshot vision-language model under the MIT license. The language decoder activates about 2.8 billion parameters inside a 16 billion parameter Moonlight mixture-of-experts, with a 400 million parameter MoonViT encoder and a 128K context. It is not an API SKU. Collected 2026-10-15.", "what_it_is_for": "Instruct variant for perception, OCR, long video and documents, and agent use. Recommended temperature 0.2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi-vl/kimi-vl-a3b-thinking": { "id": "moonshot/kimi-vl/kimi-vl-a3b-thinking", "name": "Kimi-VL-A3B-Thinking", "maker": "moonshot", "family": "moonshot/kimi-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-09", "context_length": 131072, "max_output_tokens": null, "parameters": "16B total / 2.8B activated language decoder, plus a 400M MoonViT encoder", "architecture": "Moonlight MoE language model (about 2.8B activated, 16B total) plus native-resolution MoonViT. Long context extended from 8K to 128K.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kimi-VL-A3B-Thinking is an open-weight Moonshot vision-language model under the MIT license. The language decoder activates about 2.8 billion parameters inside a 16 billion parameter Moonlight mixture-of-experts, with a 400 million parameter MoonViT encoder and a 128K context. It is not an API SKU. Collected 2026-10-15.", "what_it_is_for": "Long-thinking variant. Recommended temperature 0.8.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimi-vl/kimi-vl-a3b-thinking-2506": { "id": "moonshot/kimi-vl/kimi-vl-a3b-thinking-2506", "name": "Kimi-VL-A3B-Thinking-2506", "maker": "moonshot", "family": "moonshot/kimi-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-06-21", "context_length": 131072, "max_output_tokens": null, "parameters": "16B total / 2.8B activated language decoder, plus a 400M MoonViT encoder", "architecture": "Moonlight MoE language model (about 2.8B activated, 16B total) plus native-resolution MoonViT. Long context extended from 8K to 128K.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kimi-VL-A3B-Thinking-2506 is an open-weight Moonshot vision-language model under the MIT license. The language decoder activates about 2.8 billion parameters inside a 16 billion parameter Moonlight mixture-of-experts, with a 400 million parameter MoonViT encoder and a 128K context. It is not an API SKU. Collected 2026-10-15.", "what_it_is_for": "June 2025 long-CoT update of Kimi-VL-Thinking, trained with SFT and reinforcement learning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/kimina/kimina-prover-preview": { "id": "moonshot/kimina/kimina-prover-preview", "name": "Kimina-Prover Preview", "maker": "moonshot", "family": "moonshot/kimina", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-04-15", "context_length": null, "max_output_tokens": null, "parameters": "72B", "architecture": "Reasoning model for Lean 4 proofs, trained with reinforcement learning from Qwen2.5-72B. Developed with Project Numina. Weights are published under the AI-MO Hugging Face org, not moonshotai.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kimina-Prover Preview is a named formal-math model from Moonshot and Project Numina, introduced on 2025-04-15. It generates Lean 4 proofs. The 72B model starts from Qwen2.5-72B. Public weights found in this pass live under the AI-MO organization, not moonshotai. Collected 2026-10-15. Confidence is medium because the preview checkpoint and the later 72B card may differ.", "what_it_is_for": "Competition-style formal theorem proving in Lean 4, using a natural-language reasoning trace before the proof.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/moonlight/moonlight-16b-a3b": { "id": "moonshot/moonlight/moonlight-16b-a3b", "name": "Moonlight-16B-A3B", "maker": "moonshot", "family": "moonshot/moonlight", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-22", "context_length": 8192, "max_output_tokens": null, "parameters": "16B total / 3B activated (name). Card table excluding embeddings: 15.29B total / 2.24B activated.", "architecture": "Mixture-of-experts with the same architecture as DeepSeek-V3, at small scale. Trained with Muon on 5.7T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moonlight-16B-A3B is Moonshot's small open-weight mixture-of-experts model trained with the Muon optimizer. The card dates the public repos to February 2025 and releases them under MIT. It is a research model, not a Kimi API SKU. Collected 2026-10-15.", "what_it_is_for": "Pretrained checkpoint. Moonshot used the result to show Muon scaling, and later used the architecture as the language model inside Kimi-VL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/moonlight/moonlight-16b-a3b-instruct": { "id": "moonshot/moonlight/moonlight-16b-a3b-instruct", "name": "Moonlight-16B-A3B-Instruct", "maker": "moonshot", "family": "moonshot/moonlight", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-22", "context_length": 8192, "max_output_tokens": null, "parameters": "16B total / 3B activated (name). Card table excluding embeddings: 15.29B total / 2.24B activated.", "architecture": "Mixture-of-experts with the same architecture as DeepSeek-V3, at small scale. Trained with Muon on 5.7T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moonlight-16B-A3B-Instruct is Moonshot's small open-weight mixture-of-experts model trained with the Muon optimizer. The card dates the public repos to February 2025 and releases them under MIT. It is a research model, not a Kimi API SKU. Collected 2026-10-15.", "what_it_is_for": "Instruction-tuned checkpoint. Moonshot used the result to show Muon scaling, and later used the architecture as the language model inside Kimi-VL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/moonshot-v1/moonshot-v1-128k": { "id": "moonshot/moonshot-v1/moonshot-v1-128k", "name": "Moonshot v1 128K", "maker": "moonshot", "family": "moonshot/moonshot-v1", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-01-31", "context_length": 131072, "max_output_tokens": 131072, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 30.0 }, "summary_intro": "Moonshot v1 128K is a context tier of Moonshot's first Kimi generation model. The China docs say the v1 models differ only by maximum context, not by effect. The API retired the whole series on 2026-08-31. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "Longest fixed v1 cap. Same model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/moonshot-v1/moonshot-v1-128k-vision-preview": { "id": "moonshot/moonshot-v1/moonshot-v1-128k-vision-preview", "name": "Moonshot v1 128K Vision", "maker": "moonshot", "family": "moonshot/moonshot-v1", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 131072, "max_output_tokens": 131072, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 30.0 }, "summary_intro": "Moonshot v1 128K Vision is a context tier of Moonshot's first Kimi generation model. The China docs say the v1 models differ only by maximum context, not by effect. The API retired the whole series on 2026-08-31. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "Vision preview at the 128K cap.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/moonshot-v1/moonshot-v1-32k": { "id": "moonshot/moonshot-v1/moonshot-v1-32k", "name": "Moonshot v1 32K", "maker": "moonshot", "family": "moonshot/moonshot-v1", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-01-31", "context_length": 32768, "max_output_tokens": 32768, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 20.0 }, "summary_intro": "Moonshot v1 32K is a context tier of Moonshot's first Kimi generation model. The China docs say the v1 models differ only by maximum context, not by effect. The API retired the whole series on 2026-08-31. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "Longer text. Same model, larger context cap.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/moonshot-v1/moonshot-v1-32k-vision-preview": { "id": "moonshot/moonshot-v1/moonshot-v1-32k-vision-preview", "name": "Moonshot v1 32K Vision", "maker": "moonshot", "family": "moonshot/moonshot-v1", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32768, "max_output_tokens": 32768, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 20.0 }, "summary_intro": "Moonshot v1 32K Vision is a context tier of Moonshot's first Kimi generation model. The China docs say the v1 models differ only by maximum context, not by effect. The API retired the whole series on 2026-08-31. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "Vision preview at the 32K cap.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/moonshot-v1/moonshot-v1-8k": { "id": "moonshot/moonshot-v1/moonshot-v1-8k", "name": "Moonshot v1 8K", "maker": "moonshot", "family": "moonshot/moonshot-v1", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-01-31", "context_length": 8192, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 10.0 }, "summary_intro": "Moonshot v1 8K is a context tier of Moonshot's first Kimi generation model. The China docs say the v1 models differ only by maximum context, not by effect. The API retired the whole series on 2026-08-31. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "Short text. Official note: v1 models differ only by context length, not by quality.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/moonshot-v1/moonshot-v1-8k-vision-preview": { "id": "moonshot/moonshot-v1/moonshot-v1-8k-vision-preview", "name": "Moonshot v1 8K Vision", "maker": "moonshot", "family": "moonshot/moonshot-v1", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8192, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 10.0 }, "summary_intro": "Moonshot v1 8K Vision is a context tier of Moonshot's first Kimi generation model. The China docs say the v1 models differ only by maximum context, not by effect. The API retired the whole series on 2026-08-31. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "Vision preview. Understands images and returns text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/moonshot-v1/moonshot-v1-auto": { "id": "moonshot/moonshot-v1/moonshot-v1-auto", "name": "Moonshot v1 Auto", "maker": "moonshot", "family": "moonshot/moonshot-v1", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moonshot v1 Auto was a router, launched in August 2024, that chose moonshot-v1-8k, 32k, or 128k from the prompt length and billed that tier. It was retired with the v1 series on 2026-08-31. It is not a separate set of weights. Collected 2026-10-15.", "what_it_is_for": "Avoid picking a context SKU by hand. The FAQ showed cutoffs at 8,192, 32,768, and 131,072 tokens including the requested completion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "moonshot/moonvit/moonvit-so-400m": { "id": "moonshot/moonvit/moonvit-so-400m", "name": "MoonViT-SO-400M", "maker": "moonshot", "family": "moonshot/moonvit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-10", "context_length": null, "max_output_tokens": null, "parameters": "400M", "architecture": "Native-resolution vision encoder used by Kimi-VL. The K2.5 paper says MoonViT is initialized from SigLIP-SO-400M and uses NaViT-style patch packing. This repo is the SO-400M checkpoint.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MoonViT-SO-400M is Moonshot's open vision encoder, about 400 million parameters, released with Kimi-VL under MIT. It extracts image features. It is not a language model and not an API. Collected 2026-10-15.", "what_it_is_for": "Native-resolution visual encoding for Kimi vision-language models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/flame/flame": { "id": "mpi/flame/flame", "name": "FLAME", "maker": "mpi", "family": "mpi/flame", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FLAME 2017, 2019, 2020, and 2023 use a non-commercial research license. FLAME 2023 Open, released November 2025, is CC-BY-4.0 and allows commercial use with attribution. The texture model is CC-BY-NC-SA-4.0 and is a separate download.", "release_date": "2017-11", "context_length": null, "max_output_tokens": null, "parameters": "300 shape components and 100 expression components. The interactive viewer exposes the first 10 of each. Female, male, and generic templates.", "architecture": "Linear identity shape space from 3800 head scans, plus an articulated jaw, neck, and eyeballs, pose-dependent corrective blendshapes, and global expression blendshapes. Pose and expression were learned from D3DFACS and other 4D sequences. Training used more than 33,000 scans.", "modalities": { "input": [ "shape", "expression", "pose" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FLAME (Faces Learned with an Articulated Model and Expressions) is the statistical head model from the Max Planck Institute for Intelligent Systems, published at SIGGRAPH Asia 2017. The project site still distributes it as of 2026-10-15. The November 2025 FLAME 2023 Open file is the first version under CC-BY-4.0.", "what_it_is_for": "The project page says FLAME is a low-dimensional head that works in existing graphics tools and is easier to fit than high-end facial animation. Identity comes from 3800 head scans. Jaw, neck, eyeballs, pose correctives, and expression blendshapes were learned from more than 33,000 scans, including D3DFACS.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/mano/mano": { "id": "mpi/mano/mano", "name": "MANO", "maker": "mpi", "family": "mpi/mano", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial scientific research license. Commercial licensing is offered by contact on the Max Planck sites. The full license text is on the MANO site and was not copied here.", "release_date": "2017-11-22", "context_length": null, "max_output_tokens": null, "parameters": "Low-dimensional shape space and a linear manifold of pose synergies. Component counts are not stated on the homepage. Learned from about 1000 high-resolution scans of 31 subjects.", "architecture": "Hand model with articulated joints and non-rigid pose blend-shape corrections, in the SMPL formulation. Compatible with standard graphics packages. A pose-synergy manifold is included.", "modalities": { "input": [ "shape", "pose" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MANO (hand Model with Articulated and Non-rigid defOrmations) is the statistical hand model from the Max Planck Institute for Intelligent Systems. Version 1.0 was released on 22 November 2017 with the SIGGRAPH Asia 2017 paper. The project site still distributes it as of 2026-10-15.", "what_it_is_for": "The project page says MANO fits any human hand, including low-resolution or occluded hands, and plugs into standard graphics packages. It is also the hand inside SMPL+H and the hand topology referenced by SMPL-X and SUPR-Hand.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/skel/bsm": { "id": "mpi/skel/bsm", "name": "BSM", "maker": "mpi", "family": "mpi/skel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Released from the SKEL download page under that site's research registration. The separate BSM license text was not copied here.", "release_date": "2023-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full-body biomechanical skeleton in OpenSim .osim format. The SKEL page says it is loaded with NimblePhysics and fit to SMPL sequences with AddBiomechanics. It is not a skinned blend-shape body.", "modalities": { "input": [ "pose" ], "output": [ "skeleton" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BSM (Biomechanical Skeleton Model) is the OpenSim full-body skeleton released with SKEL by the Max Planck Institute for Intelligent Systems. The SKEL page, current as of 2026-10-15, says the BSM model file is on the same download tab as SKEL and the BioAmass dataset. It is a skeleton, not a skinned body.", "what_it_is_for": "The SKEL page uses BSM as the anatomically constrained skeleton that is fit to SMPL motion. Those fits are the BioAmass dataset. SKEL then learns a skin-to-skeleton map from that paired data. The page also ships a viewer for BSM, BioAmass, and SKEL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/skel/skel": { "id": "mpi/skel/skel", "name": "SKEL", "maker": "mpi", "family": "mpi/skel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research download after registration on the SKEL site. The license text was not copied here.", "release_date": "2023-12", "context_length": null, "max_output_tokens": null, "parameters": "The public PyTorch loader exposes 10 shape coefficients and requires pickle version 1.1.1. The project page says the shape space is SMPL's. It does not say whether all 300 SMPL components are in the file.", "architecture": "SMPL skin mesh re-rigged to a biomechanical skeleton. Pose parameters are biomechanical rather than SMPL's axis-angle joints. Forward pass returns a posed skin mesh and a skeleton mesh. Male and female files.", "modalities": { "input": [ "shape", "pose" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SKEL is a parametric body model from the Max Planck Institute for Intelligent Systems, published at SIGGRAPH Asia 2023. It keeps a SMPL-like skin mesh and drives skin and skeleton with biomechanical pose parameters. The model, code, and BioAmass data were still offered on the project site on 2026-10-15.", "what_it_is_for": "The project page says SKEL is for biomechanics in the wild and for vision or graphics work that needs more realistic degrees of freedom than SMPL. Fitting it to SMPL meshes is how the authors add biomechanical parameters to existing motion data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/smal/smal": { "id": "mpi/smal/smal", "name": "SMAL", "maker": "mpi", "family": "mpi/smal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial scientific research, education, or artistic use. The SMAL license page says the model was developed at MPI-IS and the Max Planck Institute for Biological Cybernetics. Commercial contact is ps-license@tue.mpg.de. Redistribution is prohibited, including training neural networks for commercial use.", "release_date": "2017-06-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Skinned multi-animal linear model, SMPL-like, learned from a small set of toy-figurine scans rather than live animals. The shape space covers lions, cats, tigers, dogs, horses, cows, foxes, deer, zebras, and hippos. New shapes can be sampled, posed, and fit to images.", "modalities": { "input": [ "shape", "pose" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SMAL (Skinned Multi-Animal Linear Model) is a 3D articulated animal model from the Max Planck Institute for Intelligent Systems and the Max Planck Institute for Biological Cybernetics. The project news posted the model on 15 June 2017. The institute code record dates the release 21 July 2017. The site was still up on 2026-10-15.", "what_it_is_for": "The page says SMAL represents lions, tigers, horses, cows, hippos, dogs, and other quadrupeds. It was learned from toy figurines because live animals cannot be scanned in a SMPL-style pose protocol. The CVPR 2017 paper fits it to real animals, including species that were not in training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/smal/smalr": { "id": "mpi/smal/smalr", "name": "SMALR", "maker": "mpi", "family": "mpi/smal", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "PS:License 1.0 on the institute code record, non-commercial research. The SMALR project page has a license link. The full text was not copied here.", "release_date": "2018-06-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SMAL with a refinement step. An articulated animal prior is fit to images, then deformed in a canonical pose so the articulated projection matches multi-view evidence. The institute code record says the release includes an updated SMAL model plus recovered 3D animal meshes.", "modalities": { "input": [ "image" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SMALR (Skinned Multi-Animal Linear Model with Refinement) is the 2018 animal capture release from the Max Planck Institute for Intelligent Systems. The institute code record dates it 20 June 2018 and says the download includes an updated SMAL model plus 3D animals recovered from images. The project page was still online on 2026-10-15. Confidence is medium because that page describes the method and does not list model filenames.", "what_it_is_for": "The project page says the method fits an articulated animal prior to images, then deforms the shape in a canonical pose so it matches the images when re-posed and projected. The authors use it to recover more detail than the base model, including an extinct animal from a few video frames, and to extract a texture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/smpl/smpl": { "id": "mpi/smpl/smpl", "name": "SMPL", "maker": "mpi", "family": "mpi/smpl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "SMPL-Model: non-commercial scientific research license, with commercial licensing through Meshcapade and smpl@max-planck-innovation.de. SMPL-Body meshes that exclude shape blendshapes are CC-BY-4.0. Technology is covered by US patent US10395411B2.", "release_date": "2015-12-15", "context_length": null, "max_output_tokens": null, "parameters": "Up to 300 shape components. Male, female, and gender-neutral files.", "architecture": "Skinned vertex body model. Linear blend skinning plus identity blend shapes and pose-dependent blend shapes that are a linear function of pose-rotation-matrix elements, with a regressor from vertices to joints. Learned from thousands of 3D body scans. The public download is the LBS variant, not SMPL-DQBS.", "modalities": { "input": [ "shape", "pose" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SMPL (Skinned Multi-Person Linear Model) is a 3D human body model from the Max Planck Institute for Intelligent Systems, Perceiving Systems, released for research on 15 December 2015 and still distributed from the project site as of 2026-10-15. It is a skinned mesh with identity and pose blend shapes, not a language model.", "what_it_is_for": "The project page describes SMPL as a realistic body model learned from thousands of 3D scans, portable to game engines and computer-vision code. The institute code page calls it a PDF-like format for 3D bodies. Downloads cover Python, Maya, and Unity.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/smpl/smpl-h": { "id": "mpi/smpl/smpl-h", "name": "SMPL+H", "maker": "mpi", "family": "mpi/smpl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial scientific research license on the MANO site. Commercial licensing via smpl@max-planck-innovation.de. The 5 May 2026 Blender note and the MANO news treat SMPL+H as part of that download.", "release_date": "2017-11-22", "context_length": null, "max_output_tokens": null, "parameters": "SMPL Central lists 16 shape components for male, female, and neutral. On 11 May 2026 the MANO site released SMPL+H with 300 shape components for female, male, and neutral.", "architecture": "SMPL body with MANO hands attached, so the body and both hands are one articulated model. An official FAQ says the first 21 joints match SMPL and the remaining pose coefficients are the hands.", "modalities": { "input": [ "shape", "pose" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SMPL+H is the full-body model from the Max Planck Institute for Intelligent Systems that attaches the MANO hand model to SMPL. It was released with MANO on 22 November 2017 and was still updated on the MANO site as of 2026-10-15, including a 300-shape release on 11 May 2026.", "what_it_is_for": "The MANO paper uses SMPL+H to fit 4D full-body scans when the hands are small, occluded, or noisy. The SMPL site lists it as the articulated-hands body model. It is the body used before SMPL-X added a face.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/smpl/smpl-x": { "id": "mpi/smpl/smpl-x", "name": "SMPL-X", "maker": "mpi", "family": "mpi/smpl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research download requires the SMPL-X model license. SMPL-X Body files that exclude shape blendshapes are CC-BY-4.0. Commercial licensing: smpl@max-planck-innovation.de. The model is described as a derivative of the patented SMPL specification.", "release_date": "2019-06", "context_length": null, "max_output_tokens": null, "parameters": "300 shape components and 100 expression components, in male, female, and neutral files.", "architecture": "Unified SMPL-family body with fully articulated hands and an expressive face, trained from thousands of 3D scans. The fitting method SMPLify-X is separate from the body model. A January 2021 file swaps in FLAME 2020 expression blendshapes.", "modalities": { "input": [ "shape", "pose", "expression" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SMPL-X is the expressive body model from the Max Planck Institute for Intelligent Systems. It extends SMPL with fully articulated hands and a face, and it was published at CVPR 2019. The project site was still shipping model files and engine add-ons as of 2026-10-15.", "what_it_is_for": "The project page says the model supports analysis of actions, interactions, and emotion from a single image. SMPLify-X fits the model to detected 2D features. The model itself is the mesh generator. Code and a 100-image evaluation set are on the same site.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/smpl/star": { "id": "mpi/smpl/star", "name": "STAR", "maker": "mpi", "family": "mpi/smpl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research download after registration. Commercial contact on the page is smpl@max-planck-innovation.de. The license text was not fully quoted here.", "release_date": "2020-08-23", "context_length": null, "max_output_tokens": null, "parameters": "300 shape components for male, female, and neutral. Pose-corrective parameters are stated as 20% of SMPL.", "architecture": "SMPL-compatible body regressor with sparse, per-joint pose correctives and shape-dependent pose blend shapes that also depend on BMI. Trained with the SMPL data plus an additional 10,000 male and female scans. Drop-in replacement for SMPL.", "modalities": { "input": [ "shape", "pose" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "STAR (Sparse Trained Articulated Human Body Regressor) is an MPI-IS body model from ECCV 2020. The authors describe it as a compact drop-in replacement for SMPL. Version 1.0 was posted on 23 August 2020, and version 1.1 on 19 September 2020. It was still listed from the project site on 2026-10-15.", "what_it_is_for": "STAR is for the same pose and shape jobs as SMPL, with local pose correctives instead of SMPL's dense global blend shapes. The page says it generalizes better when trained on the same data, and that an extra 10,000 scans widen the shape space.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/stardist/stardist-2d-paper-dsb2018": { "id": "mpi/stardist/stardist-2d-paper-dsb2018", "name": "StarDist 2D DSB 2018", "maker": "mpi", "family": "mpi/stardist", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause. Copyright 2018-2025 Uwe Schmidt and Martin Weigert.", "release_date": "2020-07-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "2D StarDist checkpoint from the MICCAI 2018 paper. Single-channel fluorescence nuclei. Trained on a subset of DSB 2018. Keras and TensorFlow star-convex polygon predictor. The README lists it next to 2D_versatile_fluo but they are separate registered models.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarDist 2D DSB 2018 is the fluorescence checkpoint from the MICCAI 2018 StarDist paper by Uwe Schmidt, Martin Weigert, Coleman Broaddus, and Gene Myers at the Max Planck Institute of Molecular Cell Biology and Genetics. The key is `2D_paper_dsb2018`. The Python zip was published on 8 July 2020 and was still listed in the README on 2026-10-15.", "what_it_is_for": "The README calls it the DSB 2018 model from the StarDist 2D paper. It was trained on a subset of that nuclei challenge. It is the paper checkpoint, distinct from the later versatile fluorescence model that used a DSB subset as well.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/stardist/stardist-2d-versatile-fluo": { "id": "mpi/stardist/stardist-2d-versatile-fluo", "name": "StarDist 2D versatile fluo", "maker": "mpi", "family": "mpi/stardist", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause. Copyright 2018-2025 Uwe Schmidt and Martin Weigert. The pretrained zip is published from the stardist-models repository, which uses the same BSD-3-Clause license.", "release_date": "2020-07-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "2D StarDist instance segmenter. A Keras and TensorFlow network predicts object probabilities and star-convex polygons for fluorescent nuclei, then instances are recovered by non-maximum suppression. Input is a single-channel 2D image. Trained on a subset of the DSB 2018 nuclei challenge.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarDist 2D versatile fluo is a pretrained nuclei model from the StarDist project, written by Uwe Schmidt, Martin Weigert, and colleagues at the Max Planck Institute of Molecular Cell Biology and Genetics and the Center for Systems Biology Dresden. The checkpoint key is `2D_versatile_fluo`. The Python zip was published on 8 July 2020, and the same name was already hosted at cloud.mpi-cbg.de by 28 April 2020. It was still the documented fluorescence model on 2026-10-15.", "what_it_is_for": "The README says this checkpoint is for fluorescent nuclear markers and may already fit similar images without retraining. It was trained on a subset of the 2018 Data Science Bowl nuclei set. Load it with `StarDist2D.from_pretrained('2D_versatile_fluo')`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/stardist/stardist-2d-versatile-he": { "id": "mpi/stardist/stardist-2d-versatile-he", "name": "StarDist 2D versatile H&E", "maker": "mpi", "family": "mpi/stardist", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause. Copyright 2018-2025 Uwe Schmidt and Martin Weigert. Training data from MoNuSeg 2018 is CC-BY-NC-SA-4.0 and the TNBC set from Naylor et al. 2018 is CC-BY-4.0, according to the QuPath conversion notes.", "release_date": "2020-07-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "2D StarDist instance segmenter for brightfield H&E nuclei. RGB input. A Keras and TensorFlow network predicts object probabilities and star-convex polygons. Trained on MoNuSeg 2018 and the TNBC dataset from Naylor et al. 2018.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarDist 2D versatile H&E is the brightfield nuclei checkpoint from the StarDist project at the Max Planck Institute of Molecular Cell Biology and Genetics. The key is `2D_versatile_he`. The Python zip was published with the other pretrained models on 8 July 2020. It was still documented in the README on 2026-10-15.", "what_it_is_for": "The README says this model is for H&E nuclei. It was trained on MoNuSeg 2018 and the TNBC dataset from Naylor et al. 2018. Load it with `StarDist2D.from_pretrained('2D_versatile_he')`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/supr/supr": { "id": "mpi/supr/supr", "name": "SUPR", "maker": "mpi", "family": "mpi/supr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Public for research. The license text is on the SUPR site and was not copied here. Commercial terms were not published on the summary page.", "release_date": "2022-10-20", "context_length": null, "max_output_tokens": null, "parameters": "75 joints, including 20 joints for toe articulation. Shape and expression widths are not stated on the summary page. Male, female, and neutral files.", "architecture": "Sparse unified part-based body. Each joint influences a sparse set of vertices. Fully articulated head, hands, and feet, including learned foot contact deformations. Trained jointly with part models on 1.2 million body, head, hand, and foot scans. The project says it can be separated into SUPR-Head, SUPR-Hand, and SUPR-Foot.", "modalities": { "input": [ "shape", "pose", "expression" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SUPR (Sparse Unified Part-Based Representation) is an expressive body model from the Max Planck Institute for Intelligent Systems, published at ECCV 2022. Body files were posted on 20 October 2022. The project site still describes the release as of 2026-10-15.", "what_it_is_for": "The page says SUPR is a factorized body that can be split into head, hand, and foot models, including a foot model with learned ground-contact deformations. It is trained jointly on full-body and part scans so the parts still cover the motion of the whole body.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/supr/supr-foot": { "id": "mpi/supr/supr-foot", "name": "SUPR-Foot", "maker": "mpi", "family": "mpi/supr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research release on the SUPR site. License text was not copied here.", "release_date": "2022-10-24", "context_length": null, "max_output_tokens": null, "parameters": "The full SUPR body has 20 toe joints. How many of those joints are inside the separated foot file is not stated.", "architecture": "Foot model with sparse local deformations, a pose space, a shape space, and learned ground-contact deformations. Male, female, and neutral files. The page calls it a novel foot model.", "modalities": { "input": [ "shape", "pose" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SUPR-Foot is the foot model from the SUPR release by the Max Planck Institute for Intelligent Systems. The project page calls it a novel foot model with learned ground-contact deformations. The foot files were posted on 24 October 2022 and were still listed on 2026-10-15.", "what_it_is_for": "SUPR separates the body into part models. The foot model is the one earlier part models did not have. The page says it learns deformations from ground contact and uses sparse local pose correctives.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/supr/supr-hand": { "id": "mpi/supr/supr-hand", "name": "SUPR-Hand", "maker": "mpi", "family": "mpi/supr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research release on the SUPR site. License text was not copied here.", "release_date": "2022-10-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hand model with MANO topology, trained on more data, with sparse local deformations, a pose space, a shape space, and a constrained kinematic tree for the fingers. Male, female, and neutral files. No expression space is listed.", "modalities": { "input": [ "shape", "pose" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SUPR-Hand is the hand model separated from SUPR by the Max Planck Institute for Intelligent Systems. The project page says it uses MANO topology, is trained on more data, and uses a constrained finger kinematic tree. It was part of the ECCV 2022 release and was still listed on 2026-10-15.", "what_it_is_for": "The page presents SUPR-Hand as a part model that still covers the full range of hand motion, which a head or hand cut from an older body model does not. It is the hand file in the SUPR suite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "mpi/supr/supr-head": { "id": "mpi/supr/supr-head", "name": "SUPR-Head", "maker": "mpi", "family": "mpi/supr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research release on the SUPR site. License text was not copied here.", "release_date": "2022-10-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Head model with the same topology as FLAME, trained on more data, with sparse local deformations and separate pose, shape, and expression spaces. Male, female, and neutral files. The project says separated part models capture the full range of head motion, unlike FLAME.", "modalities": { "input": [ "shape", "pose", "expression" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SUPR-Head is the head model separated from SUPR by the Max Planck Institute for Intelligent Systems. The SUPR page says it uses FLAME topology, is trained on more data, and has its own pose, shape, and expression spaces. It was part of the ECCV 2022 release and was still listed on 2026-10-15.", "what_it_is_for": "The page says separated SUPR parts cover the full range of head motion, which FLAME and MANO do not when they are cut out of an older body model. SUPR-Head is the head file in that suite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "msu/adaface/adaface": { "id": "msu/adaface/adaface", "name": "AdaFace", "maker": "msu", "family": "msu/adaface", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-04-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "IResNet face embedder trained with the AdaFace quality-adaptive margin loss. Official widths are R18, R50, and R100. The training code calls the R100 network IR_101 because the backbone depth argument is 100. Input is a 112 by 112 aligned face in BGR, normalized with mean 0.5 and standard deviation 0.5. The training example uses a 512-dimensional embedding. The loss example uses margin 0.4, h 0.333, scale 64, and t_alpha 0.01. These checkpoints are trained on face sets. They are not fine-tunes of a released ArcFace or CosFace weight file. The CVLFace ViT and KP-RPE checkpoints are a later model and are not versions of this folder.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AdaFace is an open-weight face-recognition family from Michigan State University. Minchul Kim, Anil K. Jain, and Xiaoming Liu are in Computer Science and Engineering. The arXiv paper is dated 3 April 2022 and was a CVPR 2022 oral. The official repository still links ten Google Drive checkpoints, and the author rehosted them on Hugging Face in June 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "The loss changes the margin with image quality, using the feature norm as a quality proxy. The released networks embed a face for verification and identification, including low-quality and surveillance sets. The repository's own comparison is against ArcFace, CosFace, MagFace, and CurricularFace.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "msu/bet/bet": { "id": "msu/bet/bet", "name": "BET", "maker": "msu", "family": "msu/bet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-11-01", "context_length": 256, "max_output_tokens": null, "parameters": "", "architecture": "Bidirectional encoder transformer (BET), encoder only, trained with masked SMILES. Eight encoder layers. Each layer has self-attention and a feed-forward sublayer. Symbol embedding 512, eight attention heads, feed-forward size 1024. Maximum sequence length 256, including start and end symbols. Vocabulary handling uses 51 split symbols plus start, end, and pad; a later analysis counts 61 symbols. Masking touches 15 percent of symbols: 80 percent masked, 10 percent left unchanged, 10 percent randomly replaced. Loss is cross-entropy on the masked positions. Adam, weight decay 0.1, 5,000-step warmup, max learning rate 0.0001, more than 200 million updates, six Tesla V100 GPUs, 64 sequences per GPU. The fingerprint is the last encoder embedding of the first symbol. Not a fine-tune of BERT weights. The paper says the encoder layout matches a standard transformer encoder.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BET (bidirectional encoder transformer) is a SMILES encoder from Michigan State University and Peking University. Guo-Wei Wei is the MSU corresponding author. Dong Chen is affiliated with both MSU and Peking University Shenzhen Graduate School. Feng Pan and Jiaxin Zheng are at Peking University. The Journal of Physical Chemistry Letters published the paper on 1 November 2021. Three checkpoints are in one zip on the Wei lab site. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model is pretrained by filling in masked SMILES symbols, then used as a molecular fingerprint. Downstream property models and virtual-screening ranks use that vector. The authors also ship a Wasserstein-distance picker because the biggest corpus is not always the best one.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "msu/kp-rpe/kp-rpe": { "id": "msu/kp-rpe/kp-rpe", "name": "KP-RPE", "maker": "msu", "family": "msu/kp-rpe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-03-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-Base face embedder with keypoint relative position encoding, trained with AdaFace. The WebFace12M card's quick start takes a 112 by 112 RGB image normalized with mean 0.5 and standard deviation 0.5, plus five keypoints shaped (1, 5, 2). The example loads minchul/cvlface_DFA_mobilenet to produce those keypoints. Hugging Face labels this checkpoint 0.1B parameters, which is a rounded card figure, not a published exact count. A second checkpoint is the same ViT-Base KP-RPE trained on WebFace4M.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KP-RPE (KeyPoint Relative Position Encoding) is an open-weight face embedder from Michigan State University's Computer Vision Lab. The author card is published by Minchul Kim, the same group as AdaFace. The paper is arXiv 2403.14852, posted 21 March 2024. Two ViT-Base checkpoints were on Hugging Face on the collection date, 2026-10-15.", "what_it_is_for": "The AdaFace repository describes it as face recognition that uses facial landmarks so alignment errors hurt less, and says it is state of the art on TinyFace and IJB-S. The score table itself was not on that README. The released cards are ViT-Base models trained with AdaFace on WebFace4M and WebFace12M.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "msu/topnetmab/topnetmab": { "id": "msu/topnetmab/topnetmab", "name": "TopNetmAb", "maker": "msu", "family": "msu/topnetmab", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two saved predictors of mutation-induced binding free-energy change for a monoclonal antibody on the SARS-CoV-2 spike. ANN_model.pkl is a trained artificial neural network and is paired with normalizer.pkl. GBDT_model.pkl is a gradient-boosting tree. Features are built from a PDB complex and one mutation by the bundled PPIprepare and PPIfeature programs. The README does not publish hidden-layer sizes or tree counts. prediction_ANN is a GPU executable. prediction_GBDT.py is a Python script.", "modalities": { "input": [ "structure" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TopNetmAb is an open-weight binding model from the Wei lab at Michigan State University. It predicts how a point mutation changes the binding free energy of a monoclonal antibody on the SARS-CoV-2 spike. The lab GitHub README links a tarball with an ANN pickle and a gradient-boosting pickle. No release date is printed. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README is a prediction tool for antibody-spike mutations. The worked example is PDB 7NDB, antibody chains H and L, antigen chain B, mutation A344S at pH 7.0. Users build a feature file, then run either the neural net or the tree.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "msu/topoformer/topoformer": { "id": "msu/topoformer/topoformer", "name": "TopoFormer", "maker": "msu", "family": "msu/topoformer", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-06-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder transformer on persistent topological hyperdigraph Laplacian sequences, not on protein or ligand strings. The larger variant uses protein atoms within 20 angstroms of the ligand, filtration 0 to 10 angstroms in 0.1 angstrom steps (a 100-unit sequence), and a 143 by 6 feature matrix at each scale. Convolutional layers inside the encoder and decoder turn those matrices into vectors. Attention is over filtration steps. The encoder and decoder are asymmetric, in the style of a masked autoencoder. Self-supervised pretraining reconstructs topological sequences from 19,513 unlabeled PDBbind complexes with mean squared error. Fine-tuning adds scoring, ranking, docking, or screening heads on the pretrained encoder. Reported scores are a consensus: 20 random seeds, then 10 TopoFormer models and 10 gradient-boosting trees, averaged over 400 draws. TopoFormer-s uses a 12 angstrom protein neighborhood. Parameter count is not published; supplementary section 2 is cited for the remaining settings.", "modalities": { "input": [ "structure" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TopoFormer is an open-weight transformer from the Wei lab at Michigan State University for protein-ligand scoring, ranking, docking, and screening. Dong Chen and Guo-Wei Wei are at MSU. Jian Liu is listed at Chongqing University of Technology and visited MSU. Nature Machine Intelligence published the paper on 21 June 2024. The GitHub project is MIT-licensed and still links pretrained zips. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab uses a persistent topological hyperdigraph Laplacian to turn a 3D complex into a sequence a transformer can read. The pretrained encoder is then fine-tuned for scoring, ranking, docking, or screening. It is not a protein language model and it does not take SMILES as its main input.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "muenster/deepbet/deepbet": { "id": "muenster/deepbet/deepbet", "name": "deepbet", "maker": "muenster", "family": "muenster/deepbet", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-08-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T1-weighted MRI brain extraction. The PyPI package is MIT and the wheel has included weight files since 0.0.1. The loader in deepbet/bet.py reads data/models/model.pt and data/models/bbox_model.pt. It runs a bounding-box model on a nearest-exact 128 cubed downsample, then the main model on the crop resized to 256 cubed, takes output channel 1, thresholds at 0.5 by default, keeps the largest connected component, fills voids, and can dilate. TIV in cm3 is 0.001 times the mask voxel count times the product of the voxel zooms. The paper calls the network a compute-efficient U-Net and also describes 2D and 3D LinkNet variants. The 2D path uses five neighboring coronal slices. The package loader does not select that 2D path. The class name inside the checkpoint file was not read.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "deepbet is the Medical Machine Learning Lab at the University of M\u00fcnster's T1-weighted MRI brain-extraction network. The MIT package on PyPI is the current release, version 1.0.2, and it still ships weights. The named preprint is 14 August 2023. The journal article is the September 2024 issue of Computers in Biology and Medicine. Collected 2026-10-15.", "what_it_is_for": "Strip the skull and estimate total intracranial volume for a T1-weighted scan. The README limits the claim to healthy adults. deepmriprep calls this package for its brain-extraction step and does not replace it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "muenster/deepmriprep/deepmriprep": { "id": "muenster/deepmriprep/deepmriprep", "name": "deepmriprep", "maker": "muenster", "family": "muenster/deepmriprep", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-08-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "A named VBM preprocessing pipeline of several networks, not one parameter count. Steps on the README: brain extraction via deepbet, affine registration via torchreg, tissue segmentation, nonlinear registration, smoothing, and optional atlas registration. Nature calls the nonlinear model supervised SYMNet or sSYMNet. sSYMNet is a component of this pipeline. Runtime calls download_missing_models. The GitHub pyproject on main is version 0.3.0, MIT, and excludes deepmriprep/data/models from the source package. The Nature article text and the arXiv preprint are CC BY 4.0. That article license is not the software license.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "deepmriprep is the University of M\u00fcnster Medical Machine Learning Lab's voxel-based morphometry preprocessor. It is a pipeline of neural nets, with brain extraction delegated to deepbet and nonlinear registration called sSYMNet inside the same product. The arXiv preprint is 20 August 2024. Nature Computational Science published the brief communication online on 30 January 2026, in the March 2026 issue, pages 250\u2013259. PyPI's current release on the collection date is 0.3.0. Collected 2026-10-15.", "what_it_is_for": "Run the VBM preprocessing steps the lab lists: brain extraction, affine registration, tissue segmentation, nonlinear registration, and smoothing. Atlas registration for region-based morphometry is optional. The README says it is not fMRIPrep, sMRIPrep, or part of NiPreps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "muenster/mccqr/mccqrnn": { "id": "muenster/mccqr/mccqrnn", "name": "MCCQRNN", "maker": "muenster", "family": "muenster/mccqr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AGPL-3.0", "release_date": "2022-01-05", "context_length": null, "max_output_tokens": null, "parameters": "1269701", "architecture": "Monte Carlo dropout composite quantile regression neural network. One hidden layer with 32 ReLUs. TensorFlow 2.0 and TensorFlow Probability. The Science Advances paper says the final model has 1,269,701 parameters, of which 64 are not trainable. Training used 10 epochs, learning rate 0.01, batch size 64, dropout 0.2, and Adam defaults. At prediction, 1000 samples are drawn with random tau and dropout left on. Brain age is the median of those samples. Uncertainty is their standard deviation. The paper says its MAE is the median absolute error, not the mean absolute error. Input described by D\u00f6rfel is CAT12/SPM12 gray-matter voxels, vectorized and standardized. The paper also says the complete training dataset has 10,696 images of 39,904 voxels each, and separately describes training on N=10,691 from the German National Cohort. Both counts are kept. 39,904 was not confirmed as the deployed input width beyond that dataset sentence.", "modalities": { "input": [ "image" ], "output": [ "numeric" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MCCQRNN is the University of M\u00fcnster Institute for Translational Psychiatry's uncertainty-aware brain-age network, led in the paper by Tim Hahn and Jan Ernsting with multi-site coauthors. Science Advances published it on 5 January 2022. The fitted model is still distributed as a Docker image from `wwu-mmll/mccqrnn_docker`. The repository license that was read is AGPL-3.0. Collected 2026-10-15.", "what_it_is_for": "Predict age from a structural MRI gray-matter segmentation and attach an uncertainty. D\u00f6rfel et al. describe the public package as Docker or a script, with preprocessing in CAT12's default SPM12 pipeline, and say it was distributed through PHOTON-AI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "murf/falcon/falcon": { "id": "murf/falcon/falcon", "name": "Falcon", "maker": "murf", "family": "murf/falcon", "status": "retired", "confidence": "medium", "weights": "closed", "license": "Proprietary. Weights are not released. A public license name was not printed at launch.", "release_date": "2025-11-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Murf's API marketing page says Falcon uses a compute-efficient NLP model and contrasts it with larger LLM-based TTS systems. The page does not separate Falcon from Falcon 2, and it does not give a parameter count.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.01, "completion_per_m_usd": null }, "summary_intro": "Falcon is Murf AI's first named streaming speech model. The API changelog calls the 2025-11-06 launch Falcon (Beta). BusinessWire announced it as available on 2025-11-12. It is no longer a separate model id. Collected on 2026-10-15.", "what_it_is_for": "Murf launched Falcon as a low-latency text-to-speech API for voice agents. The changelog cites sub-130ms time-to-first-audio, mixed-language sentences, and data residency in 11 regions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "murf/falcon/falcon-2": { "id": "murf/falcon/falcon-2", "name": "Falcon 2", "maker": "murf", "family": "murf/falcon", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. A public license name is not printed on the model page.", "release_date": "2026-06-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.01, "completion_per_m_usd": null }, "summary_intro": "Falcon 2 is Murf AI's closed streaming text-to-speech model for real-time voice agents. The API changelog says it went live on 2026-06-16. It is still the default streaming model. Collected on 2026-10-15.", "what_it_is_for": "Murf describes Falcon 2 as ultra-fast speech for customer-support agents, IVR, healthcare assistants, and other live conversations. The model page says time-to-first-audio is about 100ms and that it is meant to hold 10,000+ concurrent calls.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "murf/gen2/gen2": { "id": "murf/gen2/gen2", "name": "Murf Speech Gen 2", "maker": "murf", "family": "murf/gen2", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. A public license name is not printed on the model page.", "release_date": "2024-07-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Proprietary generative neural architecture. Trained on 70,000+ hours of audio Murf describes as diverse and ethically sourced. Native output is described as 44.1 kHz. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.03, "completion_per_m_usd": null }, "summary_intro": "Murf Speech Gen 2 is Murf AI's closed studio text-to-speech model. It launched on 2024-07-16 and is still the only model on the non-streaming synthesize endpoint. Streaming for this model was turned off on 2026-10-05. Collected on 2026-10-15.", "what_it_is_for": "Murf positions Gen2 for ads, audiobooks, explainers, and e-learning, where style, pronunciation, and 44.1 kHz output matter more than streaming latency. Falcon 2 replaced it for live agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/defsent/defsent-bert-base-uncased": { "id": "nagoya/defsent/defsent-bert-base-uncased", "name": "DefSent BERT base uncased", "maker": "nagoya", "family": "nagoya/defsent", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2021-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "English bert-base-uncased fine-tuned on dictionary definition sentences, as described in the ACL 2021 paper. Three pooling checkpoints are released: CLS, mean, and max. They are versions of one backbone, not three product lines. Parameter count, hidden size, and context length were not printed on the Hub pages, which have no model cards. The pip package loads cl-nagoya/defsent-bert-base-uncased-cls in its example.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DefSent BERT base uncased is an English sentence-embedding release from Nagoya University's CL Research Group, accompanying the ACL 2021 DefSent paper. Three pooling checkpoints share this folder as versions. Status is active as of 2026-10-15. Confidence in the scores is high. Confidence in the exact release day is medium.", "what_it_is_for": "The paper uses definition sentences from a dictionary so that a sentence embedding can be trained without a large NLI corpus. The released checkpoints are for English STS-style similarity.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/defsent/defsent-bert-large-uncased": { "id": "nagoya/defsent/defsent-bert-large-uncased", "name": "DefSent BERT large uncased", "maker": "nagoya", "family": "nagoya/defsent", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2021-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "English bert-large-uncased fine-tuned on dictionary definition sentences, as described in the ACL 2021 paper. Three pooling checkpoints are released: CLS, mean, and max. They are versions of one backbone, not three product lines. Parameter count, hidden size, and context length were not printed on the Hub pages, which have no model cards. The pip package loads cl-nagoya/defsent-bert-base-uncased-cls in its example.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DefSent BERT large uncased is an English sentence-embedding release from Nagoya University's CL Research Group, accompanying the ACL 2021 DefSent paper. Three pooling checkpoints share this folder as versions. Status is active as of 2026-10-15. Confidence in the scores is high. Confidence in the exact release day is medium.", "what_it_is_for": "The paper uses definition sentences from a dictionary so that a sentence embedding can be trained without a large NLI corpus. The released checkpoints are for English STS-style similarity.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/defsent/defsent-roberta-base": { "id": "nagoya/defsent/defsent-roberta-base", "name": "DefSent RoBERTa base", "maker": "nagoya", "family": "nagoya/defsent", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2021-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "English roberta-base fine-tuned on dictionary definition sentences, as described in the ACL 2021 paper. Three pooling checkpoints are released: CLS, mean, and max. They are versions of one backbone, not three product lines. Parameter count, hidden size, and context length were not printed on the Hub pages, which have no model cards. The pip package loads cl-nagoya/defsent-bert-base-uncased-cls in its example.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DefSent RoBERTa base is an English sentence-embedding release from Nagoya University's CL Research Group, accompanying the ACL 2021 DefSent paper. Three pooling checkpoints share this folder as versions. Status is active as of 2026-10-15. Confidence in the scores is high. Confidence in the exact release day is medium.", "what_it_is_for": "The paper uses definition sentences from a dictionary so that a sentence embedding can be trained without a large NLI corpus. The released checkpoints are for English STS-style similarity.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/defsent/defsent-roberta-large": { "id": "nagoya/defsent/defsent-roberta-large", "name": "DefSent RoBERTa large", "maker": "nagoya", "family": "nagoya/defsent", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2021-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "English roberta-large fine-tuned on dictionary definition sentences, as described in the ACL 2021 paper. Three pooling checkpoints are released: CLS, mean, and max. They are versions of one backbone, not three product lines. Parameter count, hidden size, and context length were not printed on the Hub pages, which have no model cards. The pip package loads cl-nagoya/defsent-bert-base-uncased-cls in its example.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DefSent RoBERTa large is an English sentence-embedding release from Nagoya University's CL Research Group, accompanying the ACL 2021 DefSent paper. Three pooling checkpoints share this folder as versions. Status is active as of 2026-10-15. Confidence in the scores is high. Confidence in the exact release day is medium.", "what_it_is_for": "The paper uses definition sentences from a dictionary so that a sentence embedding can be trained without a large NLI corpus. The released checkpoints are for English STS-style similarity.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/j-moshi/j-moshi": { "id": "nagoya/j-moshi/j-moshi", "name": "J-Moshi", "maker": "nagoya", "family": "nagoya/j-moshi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-01-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Japanese full-duplex spoken dialogue model. Fine-tune of kyutai/moshiko-pytorch-bf16. The paper describes Moshi's Temporal Transformer as a 7B model plus a smaller Depth Transformer, with frozen Mimi. The Hub widget says 8B parameters. Text tokenizer is the SentencePiece model from rinna/japanese-gpt2-medium, 32,000 tokens. The text embedding tables were reinitialized. Audio is 24 kHz, Mimi at 12.5 Hz, eight codebooks. Pre-training: about 60,000 hours from the J-CHAT train split, one epoch, 128 V100 32GB GPUs, learning rate 3e-5. The corpus total in Table 1 is 68,892 hours. Max training sample length 2.7 minutes, 2,048 time-steps. Fine-tune uses 344 hours of stereo dialogue, 3 epochs on about 320 hours, 16 V100s. Learning rates 2e-6 for the Temporal Transformer and 4e-6 for the Depth Transformer.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "J-Moshi is a Japanese full-duplex spoken dialogue model from the Dialogue System Research Group at Nagoya University (Higashinaka laboratory). Weights are nu-dialogue/j-moshi under CC BY-NC 4.0. Status is active as of 2026-10-15. It is a Japanese adaptation of Kyutai Moshi, not a from-scratch language model. Hub weights date from 2025-01-21. The university press announcement is 2025-02-05.", "what_it_is_for": "Real-time speech dialogue that can overlap and backchannel. Stereo fine-tune without the synthetic multi-stream TTS data. The card says it should not be expected to follow instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/j-moshi/j-moshi-ext": { "id": "nagoya/j-moshi/j-moshi-ext", "name": "J-Moshi-ext", "maker": "nagoya", "family": "nagoya/j-moshi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-01-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Japanese full-duplex spoken dialogue model. Fine-tune of kyutai/moshiko-pytorch-bf16. The paper describes Moshi's Temporal Transformer as a 7B model plus a smaller Depth Transformer, with frozen Mimi. The Hub widget says 8B parameters. Text tokenizer is the SentencePiece model from rinna/japanese-gpt2-medium, 32,000 tokens. The text embedding tables were reinitialized. Audio is 24 kHz, Mimi at 12.5 Hz, eight codebooks. Pre-training: about 60,000 hours from the J-CHAT train split, one epoch, 128 V100 32GB GPUs, learning rate 3e-5. The corpus total in Table 1 is 68,892 hours. Max training sample length 2.7 minutes, 2,048 time-steps. Fine-tuned from the J-CHAT pretrained model on 946 hours: the 344-hour stereo set plus 602 hours of multi-stream TTS speech. 2,401 optimization steps. The paper does not say this checkpoint is a further fine-tune of the released J-Moshi weights. Both Hub repos name kyutai/moshiko-pytorch-bf16 as the base.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "J-Moshi-ext is a Japanese full-duplex spoken dialogue model from the Dialogue System Research Group at Nagoya University (Higashinaka laboratory). Weights are nu-dialogue/j-moshi-ext under CC BY-NC 4.0. Status is active as of 2026-10-15. It is a Japanese adaptation of Kyutai Moshi, not a from-scratch language model. Hub weights date from 2025-01-21. The university press announcement is 2025-02-05.", "what_it_is_for": "Real-time speech dialogue that can overlap and backchannel. Same backbone with extra synthetic dialogue. The card and the paper present it as the stronger language variant. The card says it should not be expected to follow instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-base": { "id": "nagoya/ruri/ruri-base", "name": "Ruri base", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-28", "context_length": 512, "max_output_tokens": null, "parameters": "111M", "architecture": "BERT base. Mean pooling. Base checkpoint cl-nagoya/ruri-pt-base, itself a contrastive fine-tune of tohoku-nlp/bert-base-japanese-v3. Output dimension 768.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri base is a Japanese text embedding model from the CL Research Group at Nagoya University (Sasano group, Hayato Tsukagoshi and Ryohei Sasano). Weights are on Hugging Face as cl-nagoya/ruri-base. Status is active as of 2026-10-15. The card recommends Ruri v3 for new work.", "what_it_is_for": "The maker describes Ruri as a general-purpose Japanese text embedding model for semantic similarity, retrieval, classification, clustering, and reranking features. The card says to prefix queries with \u300c\u30af\u30a8\u30ea: \u300d and passages with \u300c\u6587\u7ae0: \u300d.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-base-v2": { "id": "nagoya/ruri/ruri-base-v2", "name": "Ruri base v2", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-05", "context_length": 512, "max_output_tokens": null, "parameters": "111M", "architecture": "BERT base. Mean pooling. Fine-tune of cl-nagoya/ruri-pt-base-v2, which the model tree roots at tohoku-nlp/bert-base-japanese-v3. Output dimension 768.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri base v2 is a Japanese text embedding model from the CL Research Group at Nagoya University (Sasano group, Hayato Tsukagoshi and Ryohei Sasano). Weights are on Hugging Face as cl-nagoya/ruri-base-v2. Status is active as of 2026-10-15. The card recommends Ruri v3 for new work.", "what_it_is_for": "The maker describes Ruri as a general-purpose Japanese text embedding model for semantic similarity, retrieval, classification, clustering, and reranking features. The card says to prefix queries with \u300c\u30af\u30a8\u30ea: \u300d and passages with \u300c\u6587\u7ae0: \u300d.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-large": { "id": "nagoya/ruri/ruri-large", "name": "Ruri large", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-28", "context_length": 512, "max_output_tokens": null, "parameters": "337M", "architecture": "BERT large. Mean pooling. Base checkpoint cl-nagoya/ruri-pt-large. The September 2024 paper says that pretrain starts from tohoku-nlp/bert-large-japanese-v2. Output dimension 1024. The Hub model-list tag on ruri-pt-large names bert-base-japanese-v3 instead, which disagrees with the paper and with this 1024-d output.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri large is a Japanese text embedding model from the CL Research Group at Nagoya University (Sasano group, Hayato Tsukagoshi and Ryohei Sasano). Weights are on Hugging Face as cl-nagoya/ruri-large. Status is active as of 2026-10-15. The card recommends Ruri v3 for new work.", "what_it_is_for": "The maker describes Ruri as a general-purpose Japanese text embedding model for semantic similarity, retrieval, classification, clustering, and reranking features. The card says to prefix queries with \u300c\u30af\u30a8\u30ea: \u300d and passages with \u300c\u6587\u7ae0: \u300d.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-large-v2": { "id": "nagoya/ruri/ruri-large-v2", "name": "Ruri large v2", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-06", "context_length": 512, "max_output_tokens": null, "parameters": "337M", "architecture": "BERT large. Mean pooling. Fine-tune of cl-nagoya/ruri-pt-large-v2, rooted at tohoku-nlp/bert-large-japanese-v2. Output dimension 1024.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri large v2 is a Japanese text embedding model from the CL Research Group at Nagoya University (Sasano group, Hayato Tsukagoshi and Ryohei Sasano). Weights are on Hugging Face as cl-nagoya/ruri-large-v2. Status is active as of 2026-10-15. The card recommends Ruri v3 for new work.", "what_it_is_for": "The maker describes Ruri as a general-purpose Japanese text embedding model for semantic similarity, retrieval, classification, clustering, and reranking features. The card says to prefix queries with \u300c\u30af\u30a8\u30ea: \u300d and passages with \u300c\u6587\u7ae0: \u300d.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-pt-base": { "id": "nagoya/ruri/ruri-pt-base", "name": "Ruri-PT base", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-17", "context_length": 512, "max_output_tokens": null, "parameters": "111M", "architecture": "Contrastive pre-train of tohoku-nlp/bert-base-japanese-v3. The card says max length 512, output dimension 768, mean pooling, and Apache-2.0. The architecture code block on the same card says word_embedding_dimension 1024, which disagrees with the description and with the 768-d example shape on the fine-tuned base card. Paper Table 2 lists 111M parameters and four A100 GPUs.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri-PT base is a named contrastive pre-training checkpoint from Nagoya University's CL Research Group, released as cl-nagoya/ruri-pt-base. It is active weight as of 2026-10-15, and it is the parent of the supervised ruri-base model rather than the checkpoint the lab recommends for inference.", "what_it_is_for": "The September 2024 paper describes this stage as weakly supervised contrastive pre-training so that a later fine-tune can adapt the Japanese encoder. The v3 pretrain cards, which are separate models, explicitly say the pretrained checkpoint is not the fine-tuned release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-pt-base-v2": { "id": "nagoya/ruri/ruri-pt-base-v2", "name": "Ruri-PT base v2", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-26", "context_length": 512, "max_output_tokens": null, "parameters": "111M", "architecture": "First-stage checkpoint for Ruri base v2. Hub metadata: BERT, Apache-2.0, dataset cl-nagoya/ruri-dataset-v2-pt, base tohoku-nlp/bert-base-japanese-v3. A Hub mirror of the README said the body was empty on retrieval. The fine-tuned card sets max length 512, output dimension 768, and mean pooling.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri-PT base v2 is a named contrastive pre-training checkpoint from Nagoya University's CL Research Group, released as cl-nagoya/ruri-pt-base-v2. It is active weight as of 2026-10-15, and it is the parent of the supervised ruri-base-v2 model rather than the checkpoint the lab recommends for inference.", "what_it_is_for": "The September 2024 paper describes this stage as weakly supervised contrastive pre-training so that a later fine-tune can adapt the Japanese encoder. The v3 pretrain cards, which are separate models, explicitly say the pretrained checkpoint is not the fine-tuned release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-pt-large": { "id": "nagoya/ruri/ruri-pt-large", "name": "Ruri-PT large", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-19", "context_length": 512, "max_output_tokens": null, "parameters": "337M", "architecture": "Paper Table 2: 337M parameters, four A100s, base tohoku-nlp/bert-large-japanese-v2. The fine-tuned ruri-large card loads this checkpoint and reports a 1024-d mean pool and max length 512. A Hugging Face model-list tag on this repo names tohoku-nlp/bert-base-japanese-v3, which disagrees with the paper.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri-PT large is a named contrastive pre-training checkpoint from Nagoya University's CL Research Group, released as cl-nagoya/ruri-pt-large. It is active weight as of 2026-10-15, and it is the parent of the supervised ruri-large model rather than the checkpoint the lab recommends for inference.", "what_it_is_for": "The September 2024 paper describes this stage as weakly supervised contrastive pre-training so that a later fine-tune can adapt the Japanese encoder. The v3 pretrain cards, which are separate models, explicitly say the pretrained checkpoint is not the fine-tuned release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-pt-large-v2": { "id": "nagoya/ruri/ruri-pt-large-v2", "name": "Ruri-PT large v2", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-06", "context_length": 512, "max_output_tokens": null, "parameters": "337M", "architecture": "First-stage checkpoint for Ruri large v2. Hub tags: BERT, Apache-2.0, dataset cl-nagoya/ruri-dataset-v2-pt, base tohoku-nlp/bert-large-japanese-v2. The fine-tuned card sets max length 512 and output dimension 1024. The pretrain README body was not opened.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri-PT large v2 is a named contrastive pre-training checkpoint from Nagoya University's CL Research Group, released as cl-nagoya/ruri-pt-large-v2. It is active weight as of 2026-10-15, and it is the parent of the supervised ruri-large-v2 model rather than the checkpoint the lab recommends for inference.", "what_it_is_for": "The September 2024 paper describes this stage as weakly supervised contrastive pre-training so that a later fine-tune can adapt the Japanese encoder. The v3 pretrain cards, which are separate models, explicitly say the pretrained checkpoint is not the fine-tuned release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-pt-small": { "id": "nagoya/ruri/ruri-pt-small", "name": "Ruri-PT small", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-17", "context_length": 512, "max_output_tokens": null, "parameters": "68M", "architecture": "Contrastive pre-train named in the September 2024 paper, Table 2. 68M parameters. Base line-corporation/line-distilbert-base-japanese. Four A6000 GPUs. Batch size 8192. The fine-tuned ruri-small card, which loads this checkpoint, uses max length 512 and a 768-d mean pool. This pretrain card was not opened line by line.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri-PT small is a named contrastive pre-training checkpoint from Nagoya University's CL Research Group, released as cl-nagoya/ruri-pt-small. It is active weight as of 2026-10-15, and it is the parent of the supervised ruri-small model rather than the checkpoint the lab recommends for inference.", "what_it_is_for": "The September 2024 paper describes this stage as weakly supervised contrastive pre-training so that a later fine-tune can adapt the Japanese encoder. The v3 pretrain cards, which are separate models, explicitly say the pretrained checkpoint is not the fine-tuned release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-pt-small-v2": { "id": "nagoya/ruri/ruri-pt-small-v2", "name": "Ruri-PT small v2", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-29", "context_length": 512, "max_output_tokens": null, "parameters": "68M", "architecture": "First-stage checkpoint for Ruri small v2. Hub tags: DistilBERT, Apache-2.0, dataset cl-nagoya/ruri-dataset-v2-pt, base line-corporation/line-distilbert-base-japanese. The fine-tuned card sets max length 512 and output dimension 768. The pretrain README was not confirmed to have a written body.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri-PT small v2 is a named contrastive pre-training checkpoint from Nagoya University's CL Research Group, released as cl-nagoya/ruri-pt-small-v2. It is active weight as of 2026-10-15, and it is the parent of the supervised ruri-small-v2 model rather than the checkpoint the lab recommends for inference.", "what_it_is_for": "The September 2024 paper describes this stage as weakly supervised contrastive pre-training so that a later fine-tune can adapt the Japanese encoder. The v3 pretrain cards, which are separate models, explicitly say the pretrained checkpoint is not the fine-tuned release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-small": { "id": "nagoya/ruri/ruri-small", "name": "Ruri small", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-28", "context_length": 512, "max_output_tokens": null, "parameters": "68M", "architecture": "DistilBERT. Mean pooling. The card's base is cl-nagoya/ruri-pt-small, which the paper builds from line-corporation/line-distilbert-base-japanese. The Hub safetensors widget says 68.1M parameters; the comparison table says 68M. Output example on the card is 768-dimensional.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri small is a Japanese text embedding model from the CL Research Group at Nagoya University (Sasano group, Hayato Tsukagoshi and Ryohei Sasano). Weights are on Hugging Face as cl-nagoya/ruri-small. Status is active as of 2026-10-15. The card recommends Ruri v3 for new work.", "what_it_is_for": "The maker describes Ruri as a general-purpose Japanese text embedding model for semantic similarity, retrieval, classification, clustering, and reranking features. The card says to prefix queries with \u300c\u30af\u30a8\u30ea: \u300d and passages with \u300c\u6587\u7ae0: \u300d.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-small-v2": { "id": "nagoya/ruri/ruri-small-v2", "name": "Ruri small v2", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-05", "context_length": 512, "max_output_tokens": null, "parameters": "68M", "architecture": "Contrastive fine-tune of cl-nagoya/ruri-pt-small-v2. The model tree and Hub tags name line-corporation/line-distilbert-base-japanese. The architecture snippet on the card says BertModel, which disagrees with those tags. Output dimension on the card is 768. Mean pooling is what v1 small and v2 base use; this card's pooling line was read as part of a BertModel snippet, so the DistilBERT versus BERT label is unresolved on the snippet itself.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri small v2 is a Japanese text embedding model from the CL Research Group at Nagoya University (Sasano group, Hayato Tsukagoshi and Ryohei Sasano). Weights are on Hugging Face as cl-nagoya/ruri-small-v2. Status is active as of 2026-10-15. The card recommends Ruri v3 for new work.", "what_it_is_for": "The maker describes Ruri as a general-purpose Japanese text embedding model for semantic similarity, retrieval, classification, clustering, and reranking features. The card says to prefix queries with \u300c\u30af\u30a8\u30ea: \u300d and passages with \u300c\u6587\u7ae0: \u300d.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-v3-130m": { "id": "nagoya/ruri/ruri-v3-130m", "name": "Ruri v3 130M", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-09", "context_length": 8192, "max_output_tokens": null, "parameters": "132M", "architecture": "ModernBERT-Ja 130M line. The series table lists 132M parameters, 80M without embeddings, dimension 512, and 19 layers. Mean pooling was read on the 30m and 310m cards, not on this card. Parent checkpoint is cl-nagoya/ruri-v3-pt-130m from sbintuitions/modernbert-ja-130m.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri v3 130M is a Japanese text embedding model from Nagoya University's CL Research Group. It is the fine-tuned Ruri v3 checkpoint at cl-nagoya/ruri-v3-130m. Status is active as of 2026-10-15. The lab tells users to prefer this v3 line over Ruri v1 and v2.", "what_it_is_for": "General-purpose Japanese embeddings for similarity, retrieval, classification, and clustering. The card says v3 uses an empty prefix for semantic similarity, \u300c\u30c8\u30d4\u30c3\u30af: \u300d for classification and clustering, \u300c\u691c\u7d22\u30af\u30a8\u30ea: \u300d for queries, and \u300c\u691c\u7d22\u6587\u66f8: \u300d for documents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-v3-30m": { "id": "nagoya/ruri/ruri-v3-30m", "name": "Ruri v3 30M", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-07", "context_length": 8192, "max_output_tokens": null, "parameters": "37M", "architecture": "ModernBERT. Mean pooling was read on this card. Base cl-nagoya/ruri-v3-pt-30m, rooted at sbintuitions/modernbert-ja-30m. SentencePiece only. Vocabulary described as 100K. FlashAttention follows ModernBERT.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri v3 30M is a Japanese text embedding model from Nagoya University's CL Research Group. It is the fine-tuned Ruri v3 checkpoint at cl-nagoya/ruri-v3-30m. Status is active as of 2026-10-15. The lab tells users to prefer this v3 line over Ruri v1 and v2.", "what_it_is_for": "General-purpose Japanese embeddings for similarity, retrieval, classification, and clustering. The card says v3 uses an empty prefix for semantic similarity, \u300c\u30c8\u30d4\u30c3\u30af: \u300d for classification and clustering, \u300c\u691c\u7d22\u30af\u30a8\u30ea: \u300d for queries, and \u300c\u691c\u7d22\u6587\u66f8: \u300d for documents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-v3-310m": { "id": "nagoya/ruri/ruri-v3-310m", "name": "Ruri v3 310M", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-09", "context_length": 8192, "max_output_tokens": null, "parameters": "315M", "architecture": "ModernBERT. Mean pooling. 25 layers. Base cl-nagoya/ruri-v3-pt-310m, rooted at sbintuitions/modernbert-ja-310m. SentencePiece tokenizer. The card says the vocabulary is 100K, against 32K in v1 and v2, and that FlashAttention is integrated. The Hub widget rounds the size to 0.3B.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri v3 310M is a Japanese text embedding model from Nagoya University's CL Research Group. It is the fine-tuned Ruri v3 checkpoint at cl-nagoya/ruri-v3-310m. Status is active as of 2026-10-15. The lab tells users to prefer this v3 line over Ruri v1 and v2.", "what_it_is_for": "General-purpose Japanese embeddings for similarity, retrieval, classification, and clustering. The card says v3 uses an empty prefix for semantic similarity, \u300c\u30c8\u30d4\u30c3\u30af: \u300d for classification and clustering, \u300c\u691c\u7d22\u30af\u30a8\u30ea: \u300d for queries, and \u300c\u691c\u7d22\u6587\u66f8: \u300d for documents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-v3-70m": { "id": "nagoya/ruri/ruri-v3-70m", "name": "Ruri v3 70M", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-09", "context_length": 8192, "max_output_tokens": null, "parameters": "70M", "architecture": "ModernBERT-Ja 70M line. The series table on the 30m and 310m cards lists 70M parameters, 31M without embeddings, dimension 384, and 13 layers. Mean pooling was read on the 30m and 310m cards, not on this card. Base model tree from the collection pattern is cl-nagoya/ruri-v3-pt-70m from sbintuitions/modernbert-ja-70m.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri v3 70M is a Japanese text embedding model from Nagoya University's CL Research Group. It is the fine-tuned Ruri v3 checkpoint at cl-nagoya/ruri-v3-70m. Status is active as of 2026-10-15. The lab tells users to prefer this v3 line over Ruri v1 and v2.", "what_it_is_for": "General-purpose Japanese embeddings for similarity, retrieval, classification, and clustering. The card says v3 uses an empty prefix for semantic similarity, \u300c\u30c8\u30d4\u30c3\u30af: \u300d for classification and clustering, \u300c\u691c\u7d22\u30af\u30a8\u30ea: \u300d for queries, and \u300c\u691c\u7d22\u6587\u66f8: \u300d for documents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-v3-pt-130m": { "id": "nagoya/ruri/ruri-v3-pt-130m", "name": "Ruri v3 PT 130M", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-27", "context_length": 8192, "max_output_tokens": null, "parameters": "132M", "architecture": "Pretrained Ruri v3 checkpoint, not the supervised fine-tune. The 310m pretrain card says to use ruri-v3-130m instead. Hub tags: ModernBERT, Apache-2.0, dataset cl-nagoya/ruri-v3-dataset-pt, base sbintuitions/modernbert-ja-130m. The fine-tuned series table lists 132M parameters, dimension 512, and 19 layers. The fine-tuned card sets max length 8192 on this parent. Mean pooling was read on the 30m and 310m fine-tunes.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri v3 PT 130M is the pretrained ModernBERT-Ja checkpoint behind Nagoya University's ruri-v3-130m. Weights are cl-nagoya/ruri-v3-pt-130m. Status is active as of 2026-10-15. The card says it has not been fine-tuned.", "what_it_is_for": "Continued or contrastive pre-training stage for Ruri v3. The lab tells users who want the general embedding model to load the fine-tuned sibling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-v3-pt-30m": { "id": "nagoya/ruri/ruri-v3-pt-30m", "name": "Ruri v3 PT 30M", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-20", "context_length": 8192, "max_output_tokens": null, "parameters": "37M", "architecture": "Pretrained Ruri v3 checkpoint, not the supervised fine-tune. The 310m pretrain card says to use ruri-v3-30m instead. Hub tags: ModernBERT, Apache-2.0, dataset cl-nagoya/ruri-v3-dataset-pt, base sbintuitions/modernbert-ja-30m. The fine-tuned series table lists 37M parameters, dimension 256, and 10 layers. The fine-tuned card sets max length 8192 on this parent. Mean pooling was read on the 30m and 310m fine-tunes.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri v3 PT 30M is the pretrained ModernBERT-Ja checkpoint behind Nagoya University's ruri-v3-30m. Weights are cl-nagoya/ruri-v3-pt-30m. Status is active as of 2026-10-15. The card says it has not been fine-tuned.", "what_it_is_for": "Continued or contrastive pre-training stage for Ruri v3. The lab tells users who want the general embedding model to load the fine-tuned sibling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-v3-pt-310m": { "id": "nagoya/ruri/ruri-v3-pt-310m", "name": "Ruri v3 PT 310M", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-25", "context_length": 8192, "max_output_tokens": null, "parameters": "315M", "architecture": "Pretrained Ruri v3 checkpoint, not the supervised fine-tune. The 310m pretrain card says to use ruri-v3-310m instead. Hub tags: ModernBERT, Apache-2.0, dataset cl-nagoya/ruri-v3-dataset-pt, base sbintuitions/modernbert-ja-310m. The fine-tuned series table lists 315M parameters, dimension 768, and 25 layers. The fine-tuned card sets max length 8192 on this parent. Mean pooling was read on the 30m and 310m fine-tunes.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri v3 PT 310M is the pretrained ModernBERT-Ja checkpoint behind Nagoya University's ruri-v3-310m. Weights are cl-nagoya/ruri-v3-pt-310m. Status is active as of 2026-10-15. The card says it has not been fine-tuned.", "what_it_is_for": "Continued or contrastive pre-training stage for Ruri v3. The lab tells users who want the general embedding model to load the fine-tuned sibling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri/ruri-v3-pt-70m": { "id": "nagoya/ruri/ruri-v3-pt-70m", "name": "Ruri v3 PT 70M", "maker": "nagoya", "family": "nagoya/ruri", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-21", "context_length": 8192, "max_output_tokens": null, "parameters": "70M", "architecture": "Pretrained Ruri v3 checkpoint, not the supervised fine-tune. The 310m pretrain card says to use ruri-v3-70m instead. Hub tags: ModernBERT, Apache-2.0, dataset cl-nagoya/ruri-v3-dataset-pt, base sbintuitions/modernbert-ja-70m. The fine-tuned series table lists 70M parameters, dimension 384, and 13 layers. The fine-tuned card sets max length 8192 on this parent. Mean pooling was read on the 30m and 310m fine-tunes.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri v3 PT 70M is the pretrained ModernBERT-Ja checkpoint behind Nagoya University's ruri-v3-70m. Weights are cl-nagoya/ruri-v3-pt-70m. Status is active as of 2026-10-15. The card says it has not been fine-tuned.", "what_it_is_for": "Continued or contrastive pre-training stage for Ruri v3. The lab tells users who want the general embedding model to load the fine-tuned sibling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri-reranker/ruri-reranker-base": { "id": "nagoya/ruri-reranker/ruri-reranker-base", "name": "Ruri reranker base", "maker": "nagoya", "family": "nagoya/ruri-reranker", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-20", "context_length": null, "max_output_tokens": null, "parameters": "111M", "architecture": "Cross-encoder. Base cl-nagoya/ruri-reranker-stage1-base from ruri-pt-base. Card family table says 111M parameters and 86M without embeddings. Max length on the small card's sibling pattern is 512. This base card's max-length line was not opened; the small and the paper's stage-2 setting are 512.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri reranker base is a Japanese cross-encoder from Nagoya University's CL Research Group, released as cl-nagoya/ruri-reranker-base. Status is active as of 2026-10-15. The later Ruri v3 reranker is a separate ModernBERT model.", "what_it_is_for": "The paper and the card present it as a general Japanese reranker for scoring query-document pairs after a first-stage retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri-reranker/ruri-reranker-large": { "id": "nagoya/ruri-reranker/ruri-reranker-large", "name": "Ruri reranker large", "maker": "nagoya", "family": "nagoya/ruri-reranker", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-20", "context_length": null, "max_output_tokens": null, "parameters": "337M", "architecture": "Cross-encoder. Base cl-nagoya/ruri-reranker-stage1-large from ruri-pt-large. The v3 reranker card's table, which repeats the v1 results, says 337M parameters and 303M without embeddings. Max length was read as 512 on the small reranker card, not re-opened here.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri reranker large is a Japanese cross-encoder from Nagoya University's CL Research Group, released as cl-nagoya/ruri-reranker-large. Status is active as of 2026-10-15. The later Ruri v3 reranker is a separate ModernBERT model.", "what_it_is_for": "The paper and the card present it as a general Japanese reranker for scoring query-document pairs after a first-stage retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri-reranker/ruri-reranker-small": { "id": "nagoya/ruri-reranker/ruri-reranker-small", "name": "Ruri reranker small", "maker": "nagoya", "family": "nagoya/ruri-reranker", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-19", "context_length": 512, "max_output_tokens": null, "parameters": "68M", "architecture": "Cross-encoder. Base cl-nagoya/ruri-reranker-stage1-small, itself from ruri-pt-small and LINE Japanese DistilBERT. Card max length 512. The comparison table says 68M parameters and 43M without embeddings. The safetensors widget says 68.7M.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri reranker small is a Japanese cross-encoder from Nagoya University's CL Research Group, released as cl-nagoya/ruri-reranker-small. Status is active as of 2026-10-15. The later Ruri v3 reranker is a separate ModernBERT model.", "what_it_is_for": "The paper and the card present it as a general Japanese reranker for scoring query-document pairs after a first-stage retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri-reranker/ruri-reranker-stage1-base": { "id": "nagoya/ruri-reranker/ruri-reranker-stage1-base", "name": "Ruri reranker stage 1 base", "maker": "nagoya", "family": "nagoya/ruri-reranker", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-19", "context_length": null, "max_output_tokens": null, "parameters": "111M", "architecture": "First-stage cross-encoder. Hub tags: BERT, Apache-2.0, base cl-nagoya/ruri-pt-base. Paper Table 7, Ruri-PT base stage 1: JQaRA 72.9, JaCWIR 92.4, MIRACL 94.2.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri reranker stage 1 base is the first training stage of Nagoya University's Ruri reranker, published as cl-nagoya/ruri-reranker-stage1-base. Status is active weight as of 2026-10-15. The paper's ablation says stage 2 improves it. Confidence is medium because the card body was not opened.", "what_it_is_for": "Noisy-data stage of the Japanese reranker. The maker's recommended reranker for this size is the stage-2 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri-reranker/ruri-reranker-stage1-large": { "id": "nagoya/ruri-reranker/ruri-reranker-stage1-large", "name": "Ruri reranker stage 1 large", "maker": "nagoya", "family": "nagoya/ruri-reranker", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-19", "context_length": null, "max_output_tokens": null, "parameters": "337M", "architecture": "First-stage cross-encoder. Hub tags: BERT, Apache-2.0, base cl-nagoya/ruri-pt-large. Paper Table 7, Ruri-PT large stage 1: JQaRA 75.8, JaCWIR 93.4, MIRACL 95.4.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri reranker stage 1 large is the first training stage of Nagoya University's Ruri reranker, published as cl-nagoya/ruri-reranker-stage1-large. Status is active weight as of 2026-10-15. The paper's ablation says stage 2 improves it. Confidence is medium because the card body was not opened.", "what_it_is_for": "Noisy-data stage of the Japanese reranker. The maker's recommended reranker for this size is the stage-2 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri-reranker/ruri-reranker-stage1-small": { "id": "nagoya/ruri-reranker/ruri-reranker-stage1-small", "name": "Ruri reranker stage 1 small", "maker": "nagoya", "family": "nagoya/ruri-reranker", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-19", "context_length": null, "max_output_tokens": null, "parameters": "68M", "architecture": "First-stage cross-encoder. Hub tags: DistilBERT, Apache-2.0, dataset cl-nagoya/ruri-dataset-reranker, base cl-nagoya/ruri-pt-small. Paper Table 7, Ruri-PT small stage 1: JQaRA 63.9, JaCWIR 92.5, MIRACL 91.2. Training sequence length in the paper is 256 for stage 1.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri reranker stage 1 small is the first training stage of Nagoya University's Ruri reranker, published as cl-nagoya/ruri-reranker-stage1-small. Status is active weight as of 2026-10-15. The paper's ablation says stage 2 improves it. Confidence is medium because the card body was not opened.", "what_it_is_for": "Noisy-data stage of the Japanese reranker. The maker's recommended reranker for this size is the stage-2 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/ruri-reranker/ruri-v3-reranker-310m": { "id": "nagoya/ruri-reranker/ruri-v3-reranker-310m", "name": "Ruri v3 reranker 310M", "maker": "nagoya", "family": "nagoya/ruri-reranker", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-06", "context_length": 8192, "max_output_tokens": null, "parameters": "315M", "architecture": "ModernBERT cross-encoder. Base cl-nagoya/ruri-v3-pt-310m, rooted at sbintuitions/modernbert-ja-310m. Max length 8192. Vocabulary described as 100K. FlashAttention. 315M parameters, 236M without embeddings. The card does not state an embedding dimension because the model returns a relevance score.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruri v3 reranker 310M is Nagoya University's ModernBERT Japanese cross-encoder, released as cl-nagoya/ruri-v3-reranker-310m. Status is active as of 2026-10-15. A preview repository from 2025-03-28 is recorded as a version, not a second product.", "what_it_is_for": "General-purpose Japanese reranking of query-document pairs. The card reports higher JQaRA, JaCWIR, and MIRACL scores than the v1 Ruri rerankers and than several public Japanese cross-encoders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/shioriha/shioriha-large-pt": { "id": "nagoya/shioriha/shioriha-large-pt", "name": "Shioriha large PT", "maker": "nagoya", "family": "nagoya/shioriha", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-03-12", "context_length": 256, "max_output_tokens": null, "parameters": "0.3B", "architecture": "BERT sentence encoder. Mean pooling. The card says it maps text to 1024 dimensions and sets max length 256. The Hub widget says 0.3B parameters. The author's 2024-03-12 post says this is contrastive pre-training of Tohoku BERT-large with batch size 8192 and sequence length 256, using Japanese Wikipedia and MMARCO. No license is on the Hub tags that were retrieved.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Shioriha large PT is a Japanese contrastive pre-training checkpoint from Nagoya University's CL Research Group, released as cl-nagoya/shioriha-large-pt. Status is active as of 2026-10-15. It predates Ruri and is not the Ruri-PT large run. Confidence is high that the checkpoint exists. The license is unpublished.", "what_it_is_for": "The author's post presents it as a weakly supervised contrastive pre-training example for Japanese sentence embeddings, starting from Tohoku BERT-large.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/shioriha/shioriha-large-reranker": { "id": "nagoya/shioriha/shioriha-large-reranker", "name": "Shioriha large reranker", "maker": "nagoya", "family": "nagoya/shioriha", "status": "active", "confidence": "low", "weights": "open-weights", "license": "", "release_date": "2024-03-20", "context_length": null, "max_output_tokens": null, "parameters": "0.3B", "architecture": "Hub lists a text-classification pipeline, safetensors weights, and a 0.3B widget. There is no model card. The repository name says reranker. Base model, pooling, max length, and training data were not stated on the page that was retrieved.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Shioriha large reranker is a weight release on cl-nagoya/shioriha-large-reranker from the same Nagoya University group that released Shioriha large PT. As of 2026-10-15 the Hub page has weights and no model card. Status is active only in the sense that the files are public. Confidence is low.", "what_it_is_for": "The repository name and the text-classification pipeline tag are the only statements of purpose. The page does not describe a task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/simcse/sup-simcse-ja-base": { "id": "nagoya/simcse/sup-simcse-ja-base", "name": "Japanese supervised SimCSE base", "maker": "nagoya", "family": "nagoya/simcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-10-02", "context_length": 512, "max_output_tokens": null, "parameters": "111M", "architecture": "Supervised SimCSE on cl-tohoku/bert-base-japanese-v3. CLS pooling at inference. An extra MLP is used only during training. Hidden size 768. Sentence-transformer max length 512. Training max length 64, learning rate 5e-5, batch 512, temperature 0.05, 2^20 JSNLI examples, bfloat16.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese supervised SimCSE base is a Japanese sentence-embedding checkpoint from Nagoya University's CL Research Group, released as cl-nagoya/sup-simcse-ja-base. Status is active as of 2026-10-15. It is an earlier SimCSE line, not a Ruri checkpoint. The license is CC BY-SA 4.0, so the weights field is open-weights rather than Apache open-source.", "what_it_is_for": "Sentence embeddings. Supervised checkpoints train on JSNLI. Unsupervised checkpoints train on Wiki40B. The Ruri paper later treats these as baselines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/simcse/sup-simcse-ja-large": { "id": "nagoya/simcse/sup-simcse-ja-large", "name": "Japanese supervised SimCSE large", "maker": "nagoya", "family": "nagoya/simcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-10-02", "context_length": 512, "max_output_tokens": null, "parameters": "337M", "architecture": "Supervised SimCSE on cl-tohoku/bert-large-japanese-v2. The model summary says hidden size 1024. The architecture snippet says word_embedding_dimension 768, which disagrees with the summary and with the 337M size on the Ruri card. CLS pooling. Training max length 64, learning rate 5e-5, batch 512, temperature 0.05, 2^20 JSNLI examples.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese supervised SimCSE large is a Japanese sentence-embedding checkpoint from Nagoya University's CL Research Group, released as cl-nagoya/sup-simcse-ja-large. Status is active as of 2026-10-15. It is an earlier SimCSE line, not a Ruri checkpoint. The license is CC BY-SA 4.0, so the weights field is open-weights rather than Apache open-source.", "what_it_is_for": "Sentence embeddings. Supervised checkpoints train on JSNLI. Unsupervised checkpoints train on Wiki40B. The Ruri paper later treats these as baselines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/simcse/unsup-simcse-ja-base": { "id": "nagoya/simcse/unsup-simcse-ja-base", "name": "Japanese unsupervised SimCSE base", "maker": "nagoya", "family": "nagoya/simcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-10-02", "context_length": 512, "max_output_tokens": null, "parameters": "111M", "architecture": "Unsupervised SimCSE on cl-tohoku/bert-base-japanese-v3. Training data Wiki40B. CLS pooling. Hidden size 768. Sentence-transformer max length 512. Training max length 64, learning rate 5e-5, batch 64, temperature 0.05, 2^20 examples, bfloat16.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese unsupervised SimCSE base is a Japanese sentence-embedding checkpoint from Nagoya University's CL Research Group, released as cl-nagoya/unsup-simcse-ja-base. Status is active as of 2026-10-15. It is an earlier SimCSE line, not a Ruri checkpoint. The license is CC BY-SA 4.0, so the weights field is open-weights rather than Apache open-source.", "what_it_is_for": "Sentence embeddings. Supervised checkpoints train on JSNLI. Unsupervised checkpoints train on Wiki40B. The Ruri paper later treats these as baselines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nagoya/simcse/unsup-simcse-ja-large": { "id": "nagoya/simcse/unsup-simcse-ja-large", "name": "Japanese unsupervised SimCSE large", "maker": "nagoya", "family": "nagoya/simcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-10-02", "context_length": 512, "max_output_tokens": null, "parameters": "337M", "architecture": "Unsupervised SimCSE on cl-tohoku/bert-large-japanese-v2. Training data Wiki40B. The summary says hidden size 1024. The architecture snippet says 768. CLS pooling. Training max length 64, learning rate 3e-5, batch 64, temperature 0.05, 2^20 examples.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese unsupervised SimCSE large is a Japanese sentence-embedding checkpoint from Nagoya University's CL Research Group, released as cl-nagoya/unsup-simcse-ja-large. Status is active as of 2026-10-15. It is an earlier SimCSE line, not a Ruri checkpoint. The license is CC BY-SA 4.0, so the weights field is open-weights rather than Apache open-source.", "what_it_is_for": "Sentence embeddings. Supervised checkpoints train on JSNLI. Unsupervised checkpoints train on Wiki40B. The Ruri paper later treats these as baselines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nankai/conv2former/conv2former-b": { "id": "nankai/conv2former/conv2former-b", "name": "Conv2Former-B", "maker": "nankai", "family": "nankai/conv2former", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "90M", "architecture": "Hierarchical ConvNet from the Nankai HVision-NKU release. Spatial mixing is convolutional modulation: a large-kernel depth-wise convolution produces a modulation map that is applied to the value features, instead of query-key self-attention. The repository says the code is based on timm and ConvNeXt. That is an implementation base, not a released fine-tune of ConvNeXt weights. Jiashi Feng's affiliation on the preprint is ByteDance, Singapore. The code organization and the first three authors' affiliation are Nankai University. README row: 90M parameters and 15.9G FLOPs at 224.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Conv2Former-B is the base Conv2Former from Nankai University. The repository links 224-resolution ImageNet-1K and ImageNet-22K checkpoints and reports a 384-resolution score without a file link. Status is active. Collected 2026-10-15.", "what_it_is_for": "ImageNet classification at base scale. The paper says the family also covers detection and segmentation. Those scores were not copied from a table on this pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nankai/conv2former/conv2former-l": { "id": "nankai/conv2former/conv2former-l", "name": "Conv2Former-L", "maker": "nankai", "family": "nankai/conv2former", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "199M", "architecture": "Hierarchical ConvNet from the Nankai HVision-NKU release. Spatial mixing is convolutional modulation: a large-kernel depth-wise convolution produces a modulation map that is applied to the value features, instead of query-key self-attention. The repository says the code is based on timm and ConvNeXt. That is an implementation base, not a released fine-tune of ConvNeXt weights. Jiashi Feng's affiliation on the preprint is ByteDance, Singapore. The code organization and the first three authors' affiliation are Nankai University. README ImageNet-22K rows: 199M parameters, 36.0G FLOPs at 224 and 106G FLOPs at 384. No ImageNet-1K-only row is in the README table that was read.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Conv2Former-L is the large Conv2Former from Nankai University. The repository links ImageNet-22K checkpoints at 224 and 384. Status is active. Collected 2026-10-15.", "what_it_is_for": "Largest published Conv2Former classification checkpoint. The paper also discusses detection and segmentation for the family. Those scores were not copied here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nankai/conv2former/conv2former-n": { "id": "nankai/conv2former/conv2former-n", "name": "Conv2Former-N", "maker": "nankai", "family": "nankai/conv2former", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "15M", "architecture": "Hierarchical ConvNet from the Nankai HVision-NKU release. Spatial mixing is convolutional modulation: a large-kernel depth-wise convolution produces a modulation map that is applied to the value features, instead of query-key self-attention. The repository says the code is based on timm and ConvNeXt. That is an implementation base, not a released fine-tune of ConvNeXt weights. Jiashi Feng's affiliation on the preprint is ByteDance, Singapore. The code organization and the first three authors' affiliation are Nankai University. README row: 15M parameters and 2.2G FLOPs at 224.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Conv2Former-N is the smallest Conv2Former released by Nankai University's HVision-NKU lab. It is an active open-source image backbone. Collected 2026-10-15.", "what_it_is_for": "The paper presents Conv2Former as a hierarchical ConvNet for visual recognition. The repository table lists this size for ImageNet-1K classification. Downstream detection and segmentation are claimed for the family, not given as a numeric row for this size on the pages read here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nankai/conv2former/conv2former-s": { "id": "nankai/conv2former/conv2former-s", "name": "Conv2Former-S", "maker": "nankai", "family": "nankai/conv2former", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "50M", "architecture": "Hierarchical ConvNet from the Nankai HVision-NKU release. Spatial mixing is convolutional modulation: a large-kernel depth-wise convolution produces a modulation map that is applied to the value features, instead of query-key self-attention. The repository says the code is based on timm and ConvNeXt. That is an implementation base, not a released fine-tune of ConvNeXt weights. Jiashi Feng's affiliation on the preprint is ByteDance, Singapore. The code organization and the first three authors' affiliation are Nankai University. README row: 50M parameters and 8.7G FLOPs at 224.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Conv2Former-S is the small Conv2Former from Nankai University's HVision-NKU lab. Two checkpoints are linked: ImageNet-1K training, and ImageNet-22K pretraining with ImageNet-1K finetuning. Status is active. Collected 2026-10-15.", "what_it_is_for": "The repository treats it as an ImageNet recognition backbone. The paper also reports detection and segmentation for the family. Those numbers were not copied here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nankai/conv2former/conv2former-t": { "id": "nankai/conv2former/conv2former-t", "name": "Conv2Former-T", "maker": "nankai", "family": "nankai/conv2former", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "27M", "architecture": "Hierarchical ConvNet from the Nankai HVision-NKU release. Spatial mixing is convolutional modulation: a large-kernel depth-wise convolution produces a modulation map that is applied to the value features, instead of query-key self-attention. The repository says the code is based on timm and ConvNeXt. That is an implementation base, not a released fine-tune of ConvNeXt weights. Jiashi Feng's affiliation on the preprint is ByteDance, Singapore. The code organization and the first three authors' affiliation are Nankai University. README row: 27M parameters and 4.4G FLOPs at 224.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Conv2Former-T is a Nankai University HVision-NKU ConvNet, the tiny size in the Conv2Former family. Status is active. Collected 2026-10-15.", "what_it_is_for": "ImageNet classification backbone in the official table. The paper says the family is also used for COCO detection and ADE20K segmentation. Those task scores were not copied from a table on this pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nankai/lsknet/lsknet-s": { "id": "nankai/lsknet/lsknet-s", "name": "LSKNet-S", "maker": "nankai", "family": "nankai/lsknet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "14.4M", "architecture": "Same LSKNet design as LSKNet-T. Table 1: channels {64, 128, 320, 512}, block counts {2, 2, 4, 2}, 14.4M parameters. Scene-classification FLOPs in the paper are 54.4G. The Oriented R-CNN detector that uses this backbone is reported at 31.0M parameters and 161G FLOPs. The paper states LSKNet-S reaches 18.1 FPS on 1024x1024 images with one RTX 3090.", "modalities": { "input": [ "image" ], "output": [ "features" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LSKNet-S is the small Large Selective Kernel Network from Nankai University. It is the stronger of the two released LSKNet sizes. Status is active. Collected 2026-10-15.", "what_it_is_for": "Remote-sensing backbone. The paper and README report oriented detection on DOTA, FAIR1M, and HRSC2016, plus scene classification after millionAID pretraining.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nankai/lsknet/lsknet-t": { "id": "nankai/lsknet/lsknet-t", "name": "LSKNet-T", "maker": "nankai", "family": "nankai/lsknet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "4.3M", "architecture": "Large Selective Kernel Network backbone from VCIP, College of Computer Science, Nankai University, with Nankai International Advanced Research Institute (Shenzhen) and Hunan co-authors. Each block has a large-kernel selection sub-block and a feed-forward sub-block. A large depth-wise kernel is decomposed into a sequence with growing kernel size and dilation, then spatially selected. Table 1: channels {32, 64, 160, 256}, block counts {3, 3, 5, 2}, 4.3M parameters. Scene-classification FLOPs in the paper are 19.2G. The Oriented R-CNN detector that uses this backbone is reported at 21.0M parameters and 124G FLOPs on 1024 inputs. Scene-classification weights in the paper follow a 300-epoch millionAID pretraining setting, which is not the same checkpoint as the ImageNet-300 backbone linked on GitHub.", "modalities": { "input": [ "image" ], "output": [ "features" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LSKNet-T is the tiny Large Selective Kernel Network released by Nankai University's VCIP group. It is an active open-weight remote-sensing backbone. Collected 2026-10-15.", "what_it_is_for": "The paper presents LSKNet as a lightweight backbone for remote-sensing classification, oriented detection, semantic segmentation, and change detection. The GitHub release centers on ImageNet-pretrained backbones and oriented detection checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nankai/stripnet/stripnet-s": { "id": "nankai/stripnet/stripnet-s", "name": "StripNet-S", "maker": "nankai", "family": "nankai/stripnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "13.3M", "architecture": "StripNet-S backbone in Strip R-CNN. Paper configuration: channels {64, 128, 320, 512}, depths {2, 2, 4, 2}, 13.3M parameters, 52.3G FLOPs. The full Strip R-CNN-S detector is reported at 30.5M parameters and 159G FLOPs. The abstract's 30M model is this detector, not the backbone alone. A single-scale backbone comparison in the paper lists StripNet-S at 13.3M, 52.3G, 17.7 FPS, and 80.06 mAP.", "modalities": { "input": [ "image" ], "output": [ "features" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StripNet-S is the small Strip R-CNN backbone from Nankai University. The abstract's 30M detector is this size, not the 13.3M backbone. Status is active. Collected 2026-10-15.", "what_it_is_for": "Remote-sensing oriented detection. The paper reports DOTA, FAIR1M, HRSC2016, and DIOR. The README publishes ImageNet and task checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nankai/stripnet/stripnet-t": { "id": "nankai/stripnet/stripnet-t", "name": "StripNet-T", "maker": "nankai", "family": "nankai/stripnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "3.8M", "architecture": "StripNet-T is the backbone inside Strip R-CNN, from Nankai University authors on the HVision-NKU release. It uses sequential orthogonal large strip convolutions rather than square large kernels. Paper configuration: channels {32, 64, 160, 256}, depths {3, 3, 5, 2}, 3.8M parameters, 18.2G FLOPs. The multi-scale DOTA Strip R-CNN-T detector that uses this backbone is reported at 20.5M parameters and 123G FLOPs.", "modalities": { "input": [ "image" ], "output": [ "features" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StripNet-T is the tiny backbone of Strip R-CNN, released by Nankai University's HVision-NKU lab for remote-sensing detection. Status is active. Collected 2026-10-15.", "what_it_is_for": "The paper says large strip convolutions represent high-aspect-ratio objects in aerial images. Strip R-CNN replaces an oriented detector's backbone with StripNet and adds a strip localization head. This folder is the T size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naples/id-reveal/id-reveal": { "id": "naples/id-reveal/id-reveal", "name": "ID-Reveal", "maker": "naples", "family": "naples/id-reveal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-05-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Temporal ID Network: an 11-layer 1D residual CNN. Input is a sequence of 62-d 3DMM coefficients per frame (Guo et al. regressor: 40 shape, 10 expression, 12 rigid pose). The released class IDreveal builds DeepNetwork1D(62, 128, 512) with kernels [1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] and dilations [1, 1, 1, 1, 2, 2, 2, 4, 4, 4, 4], last_act false, flag_residue_first false, and GroupNanNorm (group size 16). Residual blocks use Conv1d and LeakyReLU with negative slope 0.02. The first block maps 62 channels to 512. The last layer is a bias Conv1d from 512 to a 128-d embedding. The paper states a receptive field of 51 frames, about 2 seconds, and Group Normalization with LeakyReLU on every layer except the last. Inference loads model.th keys norm and network, divides features by norm, and unfolds a caller-supplied window. The 3DMM Generative Network (concatenated 124-d input, five frame-wise layers, residual sum back to 62-d) is the adversarial training partner and is not the shipped checkpoint. The 3DMM regressor is not inside model.th.", "modalities": { "input": [ "video" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ID-Reveal is an identity-aware DeepFake video detector from the Image Processing Research Group (GRIP) at the University of Naples Federico II. Davide Cozzolino and Luisa Verdoliva are at Federico II. Andreas R\u00f6ssler, Justus Thies, and Matthias Nie\u00dfner are at the Technical University of Munich, and the project page also lists the Max Planck Institute for Intelligent Systems. The released weight is `model.th` in https://github.com/grip-unina/id-reveal, added on 30 May 2021. The arXiv preprint is 4 December 2020 (revised 20 August 2021). ICCV 2021 lists the paper on pages 15108\u201315117. The weight file was still on `main` on 2026-10-15. There is no hosted API.", "what_it_is_for": "The README and the paper present ID-Reveal as a detector that compares a test video with pristine reference videos of the same person. It learns how a person moves while talking and does that training on real videos only. A distance in the embedding space is compared with a fixed threshold. The method is aimed at face swapping and facial reenactment, including low-quality social-network video. It is a local research checkpoint, not a chatbot and not an API product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/donut/donut": { "id": "naver/donut/donut", "name": "Donut", "maker": "naver", "family": "naver/donut", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2021-11-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision encoder-decoder. Swin Transformer encoder and BART text decoder. The pre-trained checkpoint is meant to be fine-tuned. Parameter count is not on the model card.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Donut is NAVER CLOVA's OCR-free document understanding transformer, with pre-trained weights on Hugging Face under MIT. It encodes a document image with Swin and generates text with BART. Collected 2026-10-15.", "what_it_is_for": "The card says the pre-trained model is a starting point for document image classification and document parsing, not a finished task model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/embedding/clir-emb-dolphin": { "id": "naver/embedding/clir-emb-dolphin", "name": "clir-emb-dolphin", "maker": "naver", "family": "naver/embedding", "status": "active", "confidence": "high", "weights": "closed", "license": "NAVER Cloud CLOVA Studio commercial API. Weights are not published.", "release_date": "", "context_length": 500, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "clir-emb-dolphin is a closed embedding model in the CLOVA Studio Embedding API. It returns 1,024-dimensional vectors for up to 500 tokens, and NAVER recommends inner-product similarity. Collected 2026-10-15.", "what_it_is_for": "The Explorer guide says it is one of two first-party embedding models, alongside clir-sts-dolphin, for search, clustering, and classification. Embedding v2 is a hosted copy of BAAI bge-m3 and is not cataloged as a NAVER model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/embedding/clir-sts-dolphin": { "id": "naver/embedding/clir-sts-dolphin", "name": "clir-sts-dolphin", "maker": "naver", "family": "naver/embedding", "status": "active", "confidence": "high", "weights": "closed", "license": "NAVER Cloud CLOVA Studio commercial API. Weights are not published.", "release_date": "", "context_length": 500, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "clir-sts-dolphin is the other closed embedding model in the CLOVA Studio Embedding API. It returns 1,024-dimensional vectors for up to 500 tokens, and NAVER recommends cosine similarity. Collected 2026-10-15.", "what_it_is_for": "The guide lists it for sentence similarity. It does not publish a parameter count or a weight license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova/hyperclova-39b": { "id": "naver/hyperclova/hyperclova-39b", "name": "HyperCLOVA 39B", "maker": "naver", "family": "naver/hyperclova", "status": "retired", "confidence": "high", "weights": "closed", "license": "Closed. No public weight license.", "release_date": "2021-09-10", "context_length": 2048, "max_output_tokens": null, "parameters": "39B", "architecture": "GPT-3-style transformer decoder. 48 layers, hidden size 8192, 64 heads, head size 128, learning rate 0.8e-4. Same tokenizer family as the 82B model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperCLOVA 39B is the mid-size model in NAVER's 2021 HyperCLOVA paper, and one of the two sizes the paper says HyperCLOVA Studio served. It is a closed 39 billion parameter decoder. It is not on the current Studio list. Collected 2026-10-15.", "what_it_is_for": "The paper uses it for in-context learning and prompt-tuning studies between the 13B and 82B scales.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova/hyperclova-82b": { "id": "naver/hyperclova/hyperclova-82b", "name": "HyperCLOVA 82B", "maker": "naver", "family": "naver/hyperclova", "status": "retired", "confidence": "high", "weights": "closed", "license": "Closed. No public weight license.", "release_date": "2021-05-25", "context_length": 2048, "max_output_tokens": null, "parameters": "82B", "architecture": "GPT-3-style transformer decoder. 64 layers, hidden size 10240, 80 heads, head size 128, learning rate 0.6e-4. Morpheme-aware byte-level BPE. Trained on a Korean-centric corpus the abstract calls 560B tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperCLOVA 82B is NAVER's 2021 Korean-centric language model, the largest model in the HyperCLOVA paper. It is a closed 82 billion parameter GPT-3-style decoder. It is not on the current CLOVA Studio model list. Collected 2026-10-15.", "what_it_is_for": "The paper presents it for Korean in-context learning and for HyperCLOVA Studio, a prompt interface aimed at non-experts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x/hcx-003": { "id": "naver/hyperclova-x/hcx-003", "name": "HCX-003", "maker": "naver", "family": "naver/hyperclova-x", "status": "active", "confidence": "high", "weights": "closed", "license": "NAVER Cloud CLOVA Studio commercial API. Weights are not published.", "release_date": "", "context_length": 8192, "max_output_tokens": 4096, "parameters": "", "architecture": "Closed HyperCLOVA X chat model. The April 2024 technical report describes HCX-L and HCX-S, not this API id. No official page maps HCX-003 onto either name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HCX-003 is the earlier flagship HyperCLOVA X chat engine, still listed in CLOVA Studio. NAVER said in April 2024 that it had been in service since January 2024. It is closed, text only, and since 16 January 2025 the context is 8,192 tokens. Collected 2026-10-15.", "what_it_is_for": "NAVER described HCX-003 as the model whose alignment, description, summarization, extraction, explanation, structuring, judgment, and planning had been improved with CLOVA X feedback. It remains on the Chat Completions API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x/hcx-005": { "id": "naver/hyperclova-x/hcx-005", "name": "HCX-005", "maker": "naver", "family": "naver/hyperclova-x", "status": "active", "confidence": "high", "weights": "closed", "license": "NAVER Cloud CLOVA Studio commercial API. Weights are not published.", "release_date": "2025-04-17", "context_length": 128000, "max_output_tokens": 4096, "parameters": "", "architecture": "Closed HyperCLOVA X multimodal model. The Studio guide does not publish the vision encoder or parameter count.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HCX-005 is NAVER Cloud's current image-and-text HyperCLOVA X engine in CLOVA Studio, released 17 April 2025. It is closed, accepts text and images, and returns text inside a 128,000-token context. Collected 2026-10-15.", "what_it_is_for": "The model guide says HCX-005 interprets images and follows varied instructions. It is the only current Studio engine with image input.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x/hcx-007": { "id": "naver/hyperclova-x/hcx-007", "name": "HCX-007", "maker": "naver", "family": "naver/hyperclova-x", "status": "active", "confidence": "high", "weights": "closed", "license": "NAVER Cloud CLOVA Studio commercial API. Weights are not published.", "release_date": "2025-07-29", "context_length": 128000, "max_output_tokens": 32768, "parameters": "", "architecture": "Closed HyperCLOVA X hybrid reasoning model. The Studio guide does not publish depth, width, or parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HCX-007 is NAVER Cloud's current hybrid reasoning engine in CLOVA Studio, released 29 July 2025. It is a closed text model with a 128,000-token context, function calling, and structured outputs. Weights and the per-token price are not public. Collected 2026-10-15.", "what_it_is_for": "NAVER Cloud says HCX-007 writes answers that reflect a deeper thought process, for math, science, language reasoning, and argumentative essays. Thinking effort is selectable. It is the only Studio engine marked as hybrid thinking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x/hcx-dash-001": { "id": "naver/hyperclova-x/hcx-dash-001", "name": "HCX-DASH-001", "maker": "naver", "family": "naver/hyperclova-x", "status": "active", "confidence": "high", "weights": "closed", "license": "NAVER Cloud CLOVA Studio commercial API. Weights are not published.", "release_date": "2024-04-25", "context_length": 4096, "max_output_tokens": 4096, "parameters": "", "architecture": "First closed lightweight HyperCLOVA X DASH model. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HCX-DASH-001 is the first HyperCLOVA X DASH engine, launched in CLOVA Studio on 25 April 2024. It is still listed. It is a closed text model with a 4,096-token context. Collected 2026-10-15.", "what_it_is_for": "NAVER positioned it as a cheaper, faster model for classification, summarization, report writing, and custom chat, at one-fifth the price of HCX-003.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x/hcx-dash-002": { "id": "naver/hyperclova-x/hcx-dash-002", "name": "HCX-DASH-002", "maker": "naver", "family": "naver/hyperclova-x", "status": "active", "confidence": "high", "weights": "closed", "license": "NAVER Cloud CLOVA Studio commercial API. Weights are not published.", "release_date": "2025-04-17", "context_length": 32000, "max_output_tokens": 4096, "parameters": "", "architecture": "Closed lightweight HyperCLOVA X model. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HCX-DASH-002 is the current lightweight HyperCLOVA X engine in CLOVA Studio, released 17 April 2025. It is a closed text model with a 32,000-token context and function calling. Collected 2026-10-15.", "what_it_is_for": "NAVER Cloud describes DASH as a fast model for simpler generative tasks. The corporate page still lists HyperCLOVA X DASH as the lightweight option beside THINK and SEED.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x/hcx-l": { "id": "naver/hyperclova-x/hcx-l", "name": "HCX-L", "maker": "naver", "family": "naver/hyperclova-x", "status": "active", "confidence": "medium", "weights": "closed", "license": "Closed. The technical report points API access at CLOVA Studio and does not publish weights.", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Transformer decoder with rotary embeddings, pre-normalization, and grouped-query attention. Pretrained mostly at 4,096 context and for the last 10 percent at 32,768. Instruction-tuned with SFT and RLHF. Parameter count is not in the report sections read for this catalog.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HCX-L is the larger closed model named in the April 2024 HyperCLOVA X technical report. NAVER Cloud describes it as the most capable member of that pair, with Korean and English chat after SFT and RLHF. It has no public weights and no confirmed Studio id. Collected 2026-10-15.", "what_it_is_for": "The report presents HCX-L as the model for Korean language and culture, with competitive English, math, and code, served through CLOVA Studio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x/hcx-s": { "id": "naver/hyperclova-x/hcx-s", "name": "HCX-S", "maker": "naver", "family": "naver/hyperclova-x", "status": "active", "confidence": "medium", "weights": "closed", "license": "Closed. The technical report does not publish weights.", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Smaller sibling of HCX-L. Same report: transformer decoder, rotary embeddings, pre-normalization, grouped-query attention, SFT and RLHF. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HCX-S is the smaller closed model named beside HCX-L in the April 2024 HyperCLOVA X technical report. Weights, parameter count, and a Studio engine id were not published. Collected 2026-10-15.", "what_it_is_for": "The report calls it the lightweight alternative, still aimed at Korean and English instruction following.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x/hyperclova-x-think": { "id": "naver/hyperclova-x/hyperclova-x-think", "name": "HyperCLOVA X THINK", "maker": "naver", "family": "naver/hyperclova-x", "status": "active", "confidence": "high", "weights": "closed", "license": "Closed. NAVER said an open-weight distillation would follow. That later public model is SEED Think 14B, not this checkpoint.", "release_date": "2025-06-30", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "Peri-LN Transformer scaled with muP. Three-stage pretraining to a 128K context, then SFT and reinforcement learning from verifiable rewards. Detailed-rationale and concise-answer modes. A vision-augmented variant is described in the same report. Parameter count was not disclosed.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperCLOVA X THINK is NAVER Cloud's closed reasoning model, announced as complete on 30 June 2025. It is a Peri-LN Transformer trained on about 6 trillion Korean and English tokens, with a 128K context and a vision-augmented variant. The parameter count was not published. Collected 2026-10-15.", "what_it_is_for": "NAVER says it is the reasoning tier: stepwise answers for hard language, STEM, and visual instructions, built with NAVER's own stack rather than an outside base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x-seed/hyperclova-x-clip": { "id": "naver/hyperclova-x-seed/hyperclova-x-clip", "name": "HyperCLOVA X CLIP", "maker": "naver", "family": "naver/hyperclova-x-seed", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-23", "context_length": null, "max_output_tokens": null, "parameters": "about 637M", "architecture": "Vision encoder trained from random initialization, not from an external checkpoint. Korean and English image-text data, about 30 billion seen samples. A second stage aligns it with a temporary smaller language model that is then discarded. Used inside HyperCLOVA X SEED 4B.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperCLOVA X CLIP is the vision encoder NAVER described on 23 June 2026 as part of HyperCLOVA X SEED 4B. It is about 637 million parameters and was trained from scratch on Korean and English image-text pairs. A separate download was not found. Collected 2026-10-15.", "what_it_is_for": "NAVER says it aligns images and text, including Korean OCR and Korea-specific entities, without starting from SigLIP or another public encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x-seed/seed-4b": { "id": "naver/hyperclova-x-seed/seed-4b", "name": "HyperCLOVA X SEED 4B", "maker": "naver", "family": "naver/hyperclova-x-seed", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-23", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Omni-modal model with NAVER's own vision encoder (HyperCLOVA X CLIP, about 637M, trained from random initialization) and an in-house audio encoder. Pipeline in the blog: omni pretraining, omni SFT, long chain-of-thought and video SFT, then omni RLVR with DAPO based on GRPO. The blog says the language decoder is NAVER's own text model. Context length is not stated.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperCLOVA X SEED 4B is a lightweight omni-modal model NAVER described on 23 June 2026. It is a 4 billion parameter model with NAVER's own vision and audio encoders. Public weights were not found. Collected 2026-10-15.", "what_it_is_for": "NAVER presents it for sites that cannot run the 8B or 32B models, including on-device and air-gapped use, and shows defense demos as one setting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x-seed/seed-omni-8b": { "id": "naver/hyperclova-x-seed/seed-omni-8b", "name": "HyperCLOVA X SEED Omni 8B", "maker": "naver", "family": "naver/hyperclova-x-seed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HyperCLOVA X SEED 8B Omni Model License Agreement. Commercial use with conditions. The file was not diffed against the April 2025 SEED agreement.", "release_date": "2026-01-05", "context_length": 32000, "max_output_tokens": null, "parameters": "8B", "architecture": "Dense autoregressive omni transformer. The report describes 36 layers and hidden size 4,096. Continuous vision and audio embeddings plus discrete tokens. Hugging Face's header says 11B parameters and F32. The card says 8B.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text", "image", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperCLOVA X SEED Omni 8B is NAVER's open-weight any-to-any model, reported on 5 January 2026. It takes text, image, video, and speech and can return text, image, or speech inside a 32K context. Collected 2026-10-15.", "what_it_is_for": "NAVER calls it a pathfinder for a Korean-first omni assistant: vision-language questions, image generation and editing, speech recognition and translation, and text-to-speech in one model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x-seed/seed-text-instruct-0-5b": { "id": "naver/hyperclova-x-seed/seed-text-instruct-0-5b", "name": "HyperCLOVA X SEED Text Instruct 0.5B", "maker": "naver", "family": "naver/hyperclova-x-seed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HyperCLOVA X SEED Model License Agreement. Commercial use is allowed with conditions. Redistribution must pass on the agreement and the prohibited-use policy, mark modified files, ship an attribution notice, and display \"Powered by HyperCLOVA X\". A model trained from these weights and then distributed must have a name that starts with \"HyperCLOVA X\". If monthly active users of the licensee's products exceeded 10 million in the month before the model release date, or the licensee offers a product that substantially competes with a NAVER product or service, a separate license must be requested from NAVER.", "release_date": "2025-04-24", "context_length": 4000, "max_output_tokens": null, "parameters": "0.57B total; 0.45B excluding tied token embeddings", "architecture": "Dense transformer. The card does not publish layer count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperCLOVA X SEED Text Instruct 0.5B is NAVER's smallest public HyperCLOVA X model, released as commercial open weights on 24 April 2025. The card lists 0.57 billion parameters, a 4K context, and text in and out. Collected 2026-10-15.", "what_it_is_for": "NAVER describes it as an edge-sized instruction model with stronger Korean and math results than similar-sized external models, for multi-turn Korean chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x-seed/seed-text-instruct-1-5b": { "id": "naver/hyperclova-x-seed/seed-text-instruct-1-5b", "name": "HyperCLOVA X SEED Text Instruct 1.5B", "maker": "naver", "family": "naver/hyperclova-x-seed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HyperCLOVA X SEED Model License Agreement. Commercial use is allowed with conditions. Redistribution must pass on the agreement and the prohibited-use policy, mark modified files, ship an attribution notice, and display \"Powered by HyperCLOVA X\". A model trained from these weights and then distributed must have a name that starts with \"HyperCLOVA X\". If monthly active users of the licensee's products exceeded 10 million in the month before the model release date, or the licensee offers a product that substantially competes with a NAVER product or service, a separate license must be requested from NAVER.", "release_date": "2025-04-24", "context_length": 16000, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Dense transformer. Layer count is not on the card. Hugging Face's safetensors header says 2B, which does not match the card's 1.5B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperCLOVA X SEED Text Instruct 1.5B is NAVER's 1.5 billion parameter open-weight text model, released 24 April 2025 under the SEED license. The card gives a 16k context and a knowledge cutoff before August 2024. Collected 2026-10-15.", "what_it_is_for": "NAVER says it follows instructions and is competitive on Korean language and culture benchmarks, including Korean-English and Korean-Japanese translation examples in the launch post.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x-seed/seed-think-14b": { "id": "naver/hyperclova-x-seed/seed-think-14b", "name": "HyperCLOVA X SEED Think 14B", "maker": "naver", "family": "naver/hyperclova-x-seed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HyperCLOVA X SEED Model License Agreement, as linked from this repository. The 3B license text was the copy read in full. This repository's file was not diffed against it.", "release_date": "2025-07-22", "context_length": 32000, "max_output_tokens": null, "parameters": "14.74B", "architecture": "Dense transformer with Peri-Layer Normalization and muP. Pruned and distilled from a larger HyperCLOVA X Think model, then SFT, RLVR, length control, and joint RLHF plus RLVR. Hugging Face's header says 15B and F32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperCLOVA X SEED Think 14B is NAVER's first open-weight reasoning HyperCLOVA X model, introduced on 22 July 2025. It has 14.74 billion parameters, a 32k context, and text-only input and output, with a think mode and tool calling. Collected 2026-10-15.", "what_it_is_for": "NAVER says it is a pruned, distilled student of HyperCLOVA X Think, trained to break down problems, call tools, and correct itself, with Korean results ahead of similar-sized open models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x-seed/seed-think-32b": { "id": "naver/hyperclova-x-seed/seed-think-32b", "name": "HyperCLOVA X SEED Think 32B", "maker": "naver", "family": "naver/hyperclova-x-seed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HyperCLOVA X SEED 32B Think Model License Agreement. Commercial use is allowed with restrictions. The 3B SEED agreement was read; this file was not diffed against it.", "release_date": "2025-12-26", "context_length": 128000, "max_output_tokens": null, "parameters": "32B", "architecture": "Dense vision-language transformer. Text tokens and visual patches share an embedding space. Optional thinking mode. Hugging Face's header says 33B parameters and BF16. The card says 32B.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperCLOVA X SEED Think 32B is NAVER's open-weight vision-language reasoning model, dated 26 December 2025 by Artificial Analysis. The card says 32 billion parameters, a 128K context, and text, image, and video input with text output. Collected 2026-10-15.", "what_it_is_for": "NAVER says it extends SEED Think 14B with a shared vision-language backbone, optional thinking, and stronger Korean reasoning and agent behavior.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/hyperclova-x-seed/seed-vision-instruct-3b": { "id": "naver/hyperclova-x-seed/seed-vision-instruct-3b", "name": "HyperCLOVA X SEED Vision Instruct 3B", "maker": "naver", "family": "naver/hyperclova-x-seed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HyperCLOVA X SEED Model License Agreement. Commercial use is allowed with conditions. Redistribution must pass on the agreement and the prohibited-use policy, mark modified files, ship an attribution notice, and display \"Powered by HyperCLOVA X\". A model trained from these weights and then distributed must have a name that starts with \"HyperCLOVA X\". If monthly active users of the licensee's products exceeded 10 million in the month before the model release date, or the licensee offers a product that substantially competes with a NAVER product or service, a separate license must be requested from NAVER. LicenseDB records the model release date on this file as 24 April 2025.", "release_date": "2025-04-24", "context_length": 16000, "max_output_tokens": null, "parameters": "3.2B language module + 0.43B vision module", "architecture": "LLaVA-style vision-language model. Dense transformer language module started from HyperCLOVAX-SEED-Text-Base-3B. SigLIP vision encoder at 378x378 per grid and a C-Abstractor connector with AnyRes, up to 1.29 million pixels across 9 grids. Post-trained with SFT and GRPO RLHF, plus vision RLHF.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperCLOVA X SEED Vision Instruct 3B is NAVER's first public vision-language SEED model, with weights released 24 April 2025. It pairs a 3.2B language module with a 0.43B SigLIP vision module and returns text. Collected 2026-10-15.", "what_it_is_for": "NAVER built it for Korean visual question answering, charts, diagrams, and short video, using fewer visual tokens than similar-sized open models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/lk/lk-b": { "id": "naver/lk/lk-b", "name": "LK-B", "maker": "naver", "family": "naver/lk", "status": "retired", "confidence": "medium", "weights": "closed", "license": "NAVER Cloud CLOVA Studio commercial API. Weights were not published.", "release_date": "", "context_length": 2048, "max_output_tokens": 2048, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LK-B is the other LK completions model on the September 2025 CLOVA Studio snapshot. It is closed, text only, and no longer on the model guide fetched on 15 October 2026. Collected 2026-10-15.", "what_it_is_for": "It was a Studio engine for the pre-HyperCLOVA X completions mode, with tuning and without streaming.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "naver/lk/lk-d2": { "id": "naver/lk/lk-d2", "name": "LK-D2", "maker": "naver", "family": "naver/lk", "status": "retired", "confidence": "medium", "weights": "closed", "license": "NAVER Cloud CLOVA Studio commercial API. Weights were not published.", "release_date": "", "context_length": 2048, "max_output_tokens": 2048, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LK-D2 is an older CLOVA Studio completions model, listed with LK-B in a September 2025 docs snapshot and absent from the model guide fetched on 15 October 2026. It was a closed text model with a 2,048-token context. Collected 2026-10-15.", "what_it_is_for": "The snapshot says NAVER planned to end general mode and the LK models within 2025 and recommended HyperCLOVA X instead. It does not say which HyperCLOVA checkpoint LK-D2 was.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ncsu/aognet/aognet-12m": { "id": "ncsu/aognet/aognet-12m", "name": "AOGNet-12M", "maker": "ncsu", "family": "ncsu/aognet", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "NC State research-only license", "release_date": "2019-04-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AND-OR grammar image network. Paper AOGNet-12M: four stages (2_2, 4_1, 4_3, 2_1), filter channels (32, 128, 256, 512, 936), stem of three Conv3x3-BN layers plus 2x2 max-pool. The current repo config aognet_imagenet_12M.yaml instead uses filter_list [32, 128, 256, 512, 824], blocks [2, 2, 2, 1], dims [2, 2, 4, 4], bottleneck_ratio 0.25, BatchNorm2d, 224 crop, 1000 ImageNet classes. Not a fine-tune of ResNet.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AOGNet-12M is the small ImageNet classifier in the AOGNet family from Xilai Li, Xi Song, and Tianfu Wu at NC State ECE and the Visual Narrative Initiative. It is a grammar-generated convnet, not a fine-tune of ResNet. The paper and the university news release name it. The current GitHub README still links a pretrained file. On 2026-10-15 that Google Drive link returned HTTP 404, and the repo says the linked checkpoint is refactored code whose score differs slightly from the paper. Status is active as a named 2019 release. Confidence is medium.", "what_it_is_for": "Image classification. The paper also drops the same size tag into Mask R-CNN as AOGNet-12M-C4 and AOGNet-12M-FPN for MS-COCO detection and instance segmentation. The size tag means about 12 million parameters. It is not a separate product from the grammar, and it is not a ResNet checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ncsu/aognet/aognet-40m": { "id": "ncsu/aognet/aognet-40m", "name": "AOGNet-40M", "maker": "ncsu", "family": "ncsu/aognet", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "NC State research-only license", "release_date": "2019-04-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AND-OR grammar image network. Paper AOGNet-40M: four stages (2_2, 4_1, 4_4, 2_1), filter channels (60, 240, 448, 968, 1440). The current repo config aognet_imagenet_40M.yaml uses filter_list [56, 224, 448, 896, 1400], blocks [2, 2, 3, 1], dims [2, 2, 4, 4], bottleneck_ratio 0.25, BatchNorm2d, 224 crop, 1000 ImageNet classes. Not a fine-tune of ResNet. The paper figure states 80.18 percent top-1 with 40.3M parameters for this size tag.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AOGNet-40M is the mid-size ImageNet classifier in the AOGNet family from Xilai Li, Xi Song, and Tianfu Wu at NC State ECE and the Visual Narrative Initiative. The paper's own figure states 80.18 percent top-1 accuracy at 40.3 million parameters. It is a grammar-generated convnet, not a fine-tune of ResNet. The GitHub README still links a pretrained file. On 2026-10-15 that Google Drive link returned HTTP 404, and the repo says the linked checkpoint is refactored code whose score differs slightly from the paper. Status is active as a named 2019 release. Confidence is medium.", "what_it_is_for": "Image classification, and the same size tag is the backbone for AOGNet-40M-C4 and AOGNet-40M-FPN in the paper's Mask R-CNN COCO experiments. The size tag means about 40 million parameters. It is the sibling of AOGNet-12M, not a later product generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nd/arciris/arciris": { "id": "nd/arciris/arciris", "name": "ArcIris", "maker": "nd", "family": "nd/arciris", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use with citation. The OpenSourceIrisRecognition repository says commercial use or closed-source redistribution needs a separate discussion. No SPDX id is stated.", "release_date": "2025-05-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet100 encoder adopted from the official ArcFace PyTorch code and changed to take polar-normalized iris images of 512 by 64 pixels instead of 112 by 112. Trained with ArcFace (additive angular margin) loss. The IREX submission serializes the embedding as a vector of doubles, with a one-byte eye label prepended. Matching uses a fast Euclidean or cosine distance. The paper does not say which of those two distances ArcIris uses. Trained on 286,649 bona fide iris images from 2,759 identities in public Notre Dame iris sets. Developed in CVRL by Jozef Porubcin, mentored by Siamul Karim Khan and Adam Czajka.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ArcIris is an open-weight iris encoder from the Computer Vision Research Laboratory at the University of Notre Dame. It is a ResNet100 trained with ArcFace loss on polar-normalized iris images. NIST IREX X lists the C++ submission as `ndcvrl_002`, dated 12 May 2025 in the lab README. The describing preprint is arXiv:2605.20735, version 2 dated 8 June 2026. Weights and code are in the CVRL repository. There is no API and no price. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab released ArcIris as an IREX-compliant open iris matcher. The encoder turns a rubber-sheet iris image into a vector. Search is a distance comparison, which is how the submission stays inside NIST's time cap. The authors describe it as a customizable baseline, not a quality-triage product for forensics.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nd/autorepair/autorepair": { "id": "nd/autorepair/autorepair", "name": "Autorepair", "maker": "nd", "family": "nd/autorepair", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-10", "context_length": 256, "max_output_tokens": null, "parameters": "", "architecture": "The card title is A Lossless Syntax Tree Generator with Zero-shot Error Correction. It says the lab follows Jam's pretraining procedure and Jam's data, and also uses srcML. The hyperparameter table lists embedding 1024, 24 layers, 16 heads, block size 256, batch size 4, 32 accumulation steps, learning rate 3e-5, weight decay 1e-5, and 570,000 iterations. Dropout is absent from that table. The card says fine-tuning runs for 3 epochs and that batch size times accumulation steps should stay 128. It does not state a parameter count. The width matches Jam, whose paper states 350 million parameters. That count is not copied onto this card. Files: ckpt.pt for fine-tuning, ckpt_base.pt for zero-shot syntax-tree generation with error correction, and ckpt_finetune.pt fine-tuned on the syntactic-error dataset. The GitHub README title is A Trainable Lossless Syntax Tree Generator. Names on that README: Chia-Yi Su, Robert Wallace, Juliana Gonzalez, Vijayanta Jain, Sepideh Ghanavati, and Collin McMillan. The Hub record was created 10 October 2023 and last modified 20 October 2023. No license field is set. Dataset linked from the card: apcl/autorepair.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Autorepair is a named checkpoint set from the Automatic Program Comprehension Lab at the University of Notre Dame. The Hub card calls it a lossless syntax-tree generator with zero-shot error correction. The weights are `apcl/autorepair`, created 10 October 2023. The GitHub README lists Chia-Yi Su, Robert Wallace, Juliana Gonzalez, Vijayanta Jain, Sepideh Ghanavati, and Collin McMillan. The Hub record has no license. A paper URL for this release was not verified. There is no API and no price. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the model is pretrained like Jam, with srcML added, and then used to emit syntax trees and to correct syntactic errors. `ckpt_base.pt` is the zero-shot file. `ckpt_finetune.pt` is the file fine-tuned on the syntactic-error set. `ckpt.pt` is the file the card offers for further fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nd/eyepreserve/eyepreserve": { "id": "nd/eyepreserve/eyepreserve", "name": "EyePreserve", "maker": "nd", "family": "nd/eyepreserve", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Deep autoencoder that deforms near-infrared iris texture. Inputs are an iris image at the starting pupil size and a binary mask of the target iris shape. The network applies a non-linear warp so the texture matches the target mask. The README also ships linear Daugman deformation and the Tomeo-Reyes biomechanical deformer as baselines, not as this model. The demo GUI uses a StyleGAN3 generator to draw a random iris before deformation. The README says the dnnlib and torch_utils files come from the StyleGAN3 repository so that generator can be loaded. Whether the generator weights in models.zip are a Notre Dame fine-tune is not stated on the README. Training script named in the README: train_quadruplets_mask_ldplus_adv_vec.py, with LPIPS, MS-SSIM, ISO, and patch adversarial losses as optional flags.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EyePreserve is an iris-texture autoencoder from the Computer Vision Research Laboratory at the University of Notre Dame. Siamul Karim Khan, Patrick Tinsley, Mahsa Mitcheff, Patrick J. Flynn, Kevin W. Bowyer, and Adam Czajka describe it as a data-driven model of how iris texture moves when the pupil changes size. The preprint is arXiv:2312.12028. The HTML copy that was read is version 5, dated 1 May 2025. The repository cites a 2026 IEEE Transactions on Biometrics, Behavior, and Identity Science article, DOI 10.1109/TBIOM.2026.3715403. Weights are a Google Drive zip linked from the README. There is no API and no price. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model keeps identity while changing pupil size, including shapes that are not circles. The lab names two uses: building or extending iris datasets, and helping a forensic examiner line up two images whose pupils differ. A GUI can start from a StyleGAN3 sample and then deform it. Linear Daugman warping and a biomechanical warper are included as comparison code, not as this network.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nd/hdbif/hdbif": { "id": "nd/hdbif/hdbif", "name": "HDBIF", "maker": "nd", "family": "nd/hdbif", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0 on https://github.com/CVRL/domain-specific-BSIF-for-iris-recognition. The 2026 IREX wrapper lives in OpenSourceIrisRecognition, whose notice is research use with citation and commercial terms by discussion.", "release_date": "2018-11-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Human-driven binarized statistical image features. Independent-component-analysis filters are learned on iris patches that an eye tracker marked as salient while non-expert subjects decided whether two irises matched. A normalized iris is convolved with the filters, responses are thresholded at zero, and the binary codes are compared by fractional Hamming distance with Daugman score normalization, using only overlapping unoccluded bits. The 2018 paper searches filter side lengths 5, 7, 9, 11, 13, 15, 17, 19, 21, 27, 33, and 39, and filter counts 5 through 12. The selected (length, count) pair for the shipped matcher is in the paper's figures and was not read as text. The 2026 IREX code expects kernels in filters_pt, downloaded from a Google Drive folder linked in the Python readme.", "modalities": { "input": [ "image" ], "output": [ "binary-code" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HDBIF is a set of iris-domain filters from the Computer Vision Research Laboratory at the University of Notre Dame. Adam Czajka, Daniel Moreira, Kevin W. Bowyer, and Patrick J. Flynn learned them with independent component analysis on patches that eye tracking marked as salient during a human iris-matching task. The method was presented at WACV 2019. Filters and Matlab code are in a GPL-3.0 repository whose WACV tree was committed on 17 November 2018. A later C++ and Python port is in the lab's IREX repository. There is no API and no price. This dossier was collected on 2026-10-15.", "what_it_is_for": "HDBIF replaces generic natural-image BSIF kernels, and the usual Gabor iris code, with filters tuned to iris texture that non-experts actually looked at. The code is a stack of thresholded filter responses. Comparison is a fractional Hamming distance on the overlapping iris, with Daugman's bit-count normalization. The lab still ships it as a human-saliency baseline next to ArcIris and TripletIris.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nd/jam/jam": { "id": "nd/jam/jam", "name": "Jam", "maker": "nd", "family": "nd/jam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-openrail-m", "release_date": "2023-04-29", "context_length": 256, "max_output_tokens": null, "parameters": "350M", "architecture": "GPT-2-like decoder. The model card lists embedding size 1024, 24 layers, 16 heads, block size 256, dropout 0.2, batch size 4, 32 accumulation steps, learning rate 3e-5, and weight decay 0.1. Trained for one epoch, about 300,000 iterations, on one Nvidia A5000. The FSE 2023 paper says the training set is 52 million Java methods, about 9 billion tokens. The card points at dataset apcl/jm52m. Training code follows Daniel Grittner's NanoGPT-LoRA recipe. Checkpoint file is ckpt.pt. Authors on the paper are Chia-Yi Su, Aakash Bansal, and Collin McMillan at Notre Dame, with Vijayanta Jain and Sepideh Ghanavati at the University of Maine.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jam is a 350 million parameter language model of Java methods from the Automatic Program Comprehension Lab at the University of Notre Dame. Chia-Yi Su, Aakash Bansal, and Collin McMillan are the Notre Dame authors. Vijayanta Jain and Sepideh Ghanavati at the University of Maine are coauthors on the tool paper. The weights are `apcl/jam` on Hugging Face, created 29 April 2023, under the BigScience OpenRAIL-M license. The paper is the FSE 2023 demonstration \"A Language Model of Java Methods with Train/Test Deduplication,\" also arXiv:2305.08286. There is no API and no price. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab built Jam as a small foundation model for method-level Java work: completion, comment generation, and bug repair. The paper says it is sized to fine-tune on a 16 GB GPU. The default checkpoint is trained only on Java methods. Stack Overflow and the combined run are separate models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nd/jam/jam-cgpt": { "id": "nd/jam/jam-cgpt", "name": "Jam-CGPT", "maker": "nd", "family": "nd/jam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-11", "context_length": 256, "max_output_tokens": null, "parameters": "350M", "architecture": "Notre Dame GPT-2-like models pretrained with Jam's procedure on Jam's data, then fine-tuned for 3 epochs on summaries written by GPT-3.5. These are not OpenAI weight files. The card and the paper both describe 38 million, 110 million, and 350 million parameter models. This dossier uses the 350 million configuration as the primary spec because the abstract recommends a 350 million parameter model on a 16 GB GPU. Card table for 350M: embedding 1024, 24 layers, 16 heads, block size 256, dropout 0.2, batch size 4, 32 accumulation steps, learning rate 3e-5, weight decay 1e-5, 272,000 pretraining iterations. Card table for 110M: embedding 768, 10 layers, 8 heads, context 256, batch 32, accumulation 4, 762,000 iterations in the iter row. Card table for 38M: embedding 512, 4 layers, 4 heads, context 256, batch 64, accumulation 2, 757,000 iterations in the iter row. On the 38M and 110M tables the column labeled y is headed iterations and the cell is 1e-5. On the 350M table the same column is headed weight decay and the cell is 1e-5. This dossier does not read the 1e-5 cells as iteration counts. The card says the 38M and 110M models were pretrained for 3 epochs. Fine-tuning data sizes on the card are 170k, 620k, 1.25m, and 2.15m, dataset apcl/Jam-CGPT. The paper says the lab collected 2.15 million GPT-3.5 summaries of Java methods. Authors on the journal article are Chia-Yi Su and Collin McMillan, Department of Computer Science, University of Notre Dame. The paper also compares a StarCoder foundation. Those StarCoder runs are not files in apcl/Jam-CGPT. Hub files: model38m, model110m, and model350m each contain ckpt_pretrain.pt and ckpt_data170k.pt, ckpt_data620k.pt, ckpt_data1.25m.pt, ckpt_data2.15m.pt. A repo-root file ckpt_len1024.pt is not described on the card. The Hub record was created 11 August 2023 and last modified 12 August 2024. No license field is set.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jam-CGPT is a family of Notre Dame GPT-2-like code-summary models from Chia-Yi Su and Collin McMillan in the Department of Computer Science. The lab pretrains them with Jam's procedure, then fine-tunes them on summaries produced by GPT-3.5. The weights are the lab's own files in `apcl/Jam-CGPT`, created 11 August 2023. The paper is \"Distilled GPT for Source Code Summarization,\" arXiv:2308.14731, and Automated Software Engineering volume 31, article 22 (1 March 2024, DOI 10.1007/s10515-024-00421-4). The Hub record has no license. There is no API and no price. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper trains a local model that writes one-sentence Java method summaries, so source code stays off a third-party API. The abstract highlights a 350 million parameter model that runs on a 16 GB GPU. The card also ships 38 million and 110 million parameter models and four fine-tuning set sizes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nd/jam/jam-contextsum": { "id": "nd/jam/jam-contextsum", "name": "Jam-Contextsum", "maker": "nd", "family": "nd/jam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-13", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "The Hub card says Jam-Contextsum is a GPT-2-like model fine-tuned to summarize why a method exists. The ckpt_pretrain.pt table lists embedding 512, 4 layers, 4 heads, block size 1,024, batch size 4, 32 accumulation steps, dropout 0.2, learning rate 3e-5. The column y is headed iterations and the cell is 1e-5, the same inconsistent header seen on smaller Jam-CGPT tables. A separate iter row reads 137,900. Files on the repo are ckpt_pretrain.pt and ckpt_finetuned.pt. The card says ckpt_pretrain is the file used to fine-tune the why-summary. arXiv:2408.09006v1 (16 August 2024) says the small model in that paper is 350 million parameters with a 1024-token window, that it is the jam release from Su and McMillan 2024, that distillation used 170 thousand Java methods for four epochs, and that a later fine-tune used 256 human-written summaries. Those sentences do not match the 4-layer, embedding-512 table on the card. Parameter count is left null. Authors on the arXiv page are Chia-Yi Su, Aakash Bansal, Yu Huang, Toby Jia-Jun Li, and Collin McMillan. The Hub record was created 13 August 2024 and last modified 10 April 2025. No license field is set. Dataset linked from the card: apcl/jam_contextsum.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jam-Contextsum is a named GPT-2-like checkpoint from the Automatic Program Comprehension Lab at the University of Notre Dame. The Hub card says it is fine-tuned to write a summary of why a method exists. The weights are `apcl/jam-contextsum`, created 13 August 2024. The preprint is arXiv:2408.09006 (16 August 2024), authors Chia-Yi Su, Aakash Bansal, Yu Huang, Toby Jia-Jun Li, and Collin McMillan. NSF PAR and DBLP list a Journal of Systems and Software version, volume 231, article 112580 (2026), DOI 10.1016/j.jss.2025.112580. The Hub record has no license. There is no API and no price. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the model summarizes why a method exists. The preprint's task is a short English description of why a Java method exists in its project, using summaries of caller methods as context. The preprint also runs the same process on GPT-4 and Gemini. Those commercial models are not this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nd/jam/jam-so": { "id": "nd/jam/jam-so", "name": "Jam-SO", "maker": "nd", "family": "nd/jam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-openrail-m", "release_date": "2023-05-10", "context_length": 256, "max_output_tokens": null, "parameters": "350M", "architecture": "GPT-2-like decoder with the same card table as Jam: embedding 1024, 24 layers, 16 heads, block size 256, dropout 0.2, batch size 4, 32 accumulation steps, learning rate 3e-5, weight decay 0.1. The FSE 2023 paper says the lab releases one 350 million parameter model in three variations, and jam-so is the variation trained only on so13m. The card says one epoch, about 300,000 iterations, on one Nvidia A5000, using Daniel Grittner's NanoGPT-LoRA recipe. The card says the corpus is 13 million Stack Overflow posts from a Stack Exchange dump, January 2014 through December 2022 (dataset apcl/so13m). The paper's dataset table lists 13,071,148 documents and 10,495,518,108 tokens for so13m. The only weight file is ckpt.pt. The card's opening sentence still says the model is for fine-grained Java analysis. That sentence disagrees with the Stack Overflow corpus described in the same card and in the paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jam-SO is the Stack Overflow variation of the 350 million parameter Jam model from the Automatic Program Comprehension Lab at the University of Notre Dame. The weights are `apcl/jam_so` on Hugging Face, created 10 May 2023, under the BigScience OpenRAIL-M license. The FSE 2023 paper \"A Language Model of Java Methods with Train/Test Deduplication\" (arXiv:2305.08286) defines jam-so as one of three released variations. There is no API and no price. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper trains this checkpoint on Stack Overflow threads so researchers can start from forum text instead of Java methods. The card still opens with the Jam sentence about method, statement, and variable analysis. The training section of the same card points at `apcl/so13m`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nd/jam/jam-sojm": { "id": "nd/jam/jam-sojm", "name": "Jam-SOJM", "maker": "nd", "family": "nd/jam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-openrail-m", "release_date": "2023-05-10", "context_length": 256, "max_output_tokens": null, "parameters": "350M", "architecture": "GPT-2-like decoder. The card table matches Jam and Jam-SO: embedding 1024, 24 layers, 16 heads, block size 256, dropout 0.2, batch size 4, 32 accumulation steps, learning rate 3e-5, weight decay 0.1. The FSE 2023 paper calls this the third 350 million parameter variation: trained on so13m and then on jm52m. The card says one epoch on apcl/so13m, about 300,000 iterations, then a reset of learning rate and weight decay, then one epoch on apcl/jm52m, about 300,000 further iterations, about 600,000 total, on one Nvidia A5000. Training code follows Daniel Grittner's NanoGPT-LoRA recipe. The card writes the second dataset name as jm52mm once; the link is apcl/jm52m. The paper's dataset table lists 13,071,148 Stack Overflow documents and 10,495,518,108 tokens, and 51,841,717 Java-method documents and 8,752,695,577 tokens. The only weight file is ckpt.pt. The schedule is described as one training run. The card does not say the run was initialized from the released apcl/jam_so checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jam-SOJM is the two-corpus variation of the 350 million parameter Jam model from the Automatic Program Comprehension Lab at the University of Notre Dame. The weights are `apcl/jam_sojm` on Hugging Face, created 10 May 2023, under the BigScience OpenRAIL-M license. The FSE 2023 paper trains it on Stack Overflow and then on Java methods. There is no API and no price. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab ships this checkpoint for the same method-level Java research tasks as Jam, after a first epoch on Stack Overflow. The GitHub README lists it as a third download beside Jam and Jam-SO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nd/tripletiris/tripletiris": { "id": "nd/tripletiris/tripletiris", "name": "TripletIris", "maker": "nd", "family": "nd/tripletiris", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use with citation. The OpenSourceIrisRecognition repository says commercial use or closed-source redistribution needs a separate discussion. No SPDX id is stated.", "release_date": "2024-11-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ConvNeXt-tiny encoder trained with batch-hard triplet loss on polar-normalized iris images. The README says ConvNeXt-tiny was chosen because of NIST timing limits. The paper uses the same training corpus as ArcIris: 286,649 bona fide images and 2,759 identities from public Notre Dame iris sets. Matching is a vector distance. The paper says the matchers use Euclidean or cosine distance and does not assign one distance to TripletIris alone. ImageNet or other initialization is not stated.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TripletIris is an open-weight iris encoder from the Computer Vision Research Laboratory at the University of Notre Dame. It is ConvNeXt-tiny trained with batch-hard triplet loss. NIST IREX X lists the C++ submission as `ndcvrl_001`, dated 19 November 2024 in the lab README. The lab's 8 June 2026 preprint, arXiv:2605.20735v2, calls it the first open-source matcher on that board. One sentence in the paper spells it TripleIris. Code and weights are in the CVRL repository. There is no API and no price. This dossier was collected on 2026-10-15.", "what_it_is_for": "The matcher embeds a normalized iris and compares embeddings by distance. The lab built it to give academic groups an IREX-vetted open baseline. ConvNeXt-tiny was chosen to meet NIST timing limits. ArcIris later replaced it as the lab's more accurate open submission.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nec/cotomi/cotomi": { "id": "nec/cotomi/cotomi", "name": "cotomi", "maker": "nec", "family": "nec/cotomi", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-07-06", "context_length": null, "max_output_tokens": null, "parameters": "13000000000", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NEC's first named generative foundation model. The 13-billion-parameter Japanese LLM was announced on 2023-07-06 without the cotomi name, then named and strengthened on 2023-12-15. It is no longer the marketed generation. Collected 2026-10-15.", "what_it_is_for": "NEC described a general-purpose foundation model for Japanese business use: document drafting, question answering, and internal coding support. It was meant to be customized on closed customer data and to run in cloud or on-premises settings on one GPU.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nec/cotomi/cotomi-fast-v2": { "id": "nec/cotomi/cotomi-fast-v2", "name": "cotomi Fast v2", "maker": "nec", "family": "nec/cotomi", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not named. Speed tier of cotomi v2. NEC did not publish a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The speed tier of NEC cotomi v2, named in NEC's 2024-11-27 IR deck and scheduled for a gradual Japan launch in December 2024. It follows cotomi Light. The name \"Light\" was not reused. cotomi v3 later folded the fast and pro tiers into one model. Collected 2026-10-15.", "what_it_is_for": "Faster Japanese generation for enterprise tasks where the pro tier's extra accuracy is not required. The Innovation Day booth compared it with GPT-4o and Qwen on a meeting-transcript task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nec/cotomi/cotomi-light": { "id": "nec/cotomi/cotomi-light", "name": "cotomi Light", "maker": "nec", "family": "nec/cotomi", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-04-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not named. NEC calls it the lighter, speed-oriented sibling of cotomi Pro, with refreshed training and architecture versus 2023 cotomi and a large Japanese tokenizer dictionary.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NEC's speed-oriented cotomi model, announced with cotomi Pro on 2024-04-24. NEC said it matches models such as GPT-3.5-Turbo while running much faster on about one to two GPUs. The later fast tier was renamed cotomi Fast, not Light. Collected 2026-10-15.", "what_it_is_for": "High-volume business requests where GPT-3.5-Turbo-class quality is enough: summarization, question answering, and internal-document RAG. NEC presented it as the lighter partner to cotomi Pro.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nec/cotomi/cotomi-pro": { "id": "nec/cotomi/cotomi-pro", "name": "cotomi Pro", "maker": "nec", "family": "nec/cotomi", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-04-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not named. NEC says training data and architecture were refreshed relative to the 2023 cotomi model, with a large Japanese tokenizer dictionary.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NEC's higher-accuracy cotomi tier, announced 2024-04-24 with refreshed training data and architecture. NEC compared it with GPT-4 and Claude 2 and said it runs on two GPUs at roughly one eighth of GPT-4's response time. It was later succeeded by cotomi Pro v2. Collected 2026-10-15.", "what_it_is_for": "Document summarization, logical reasoning, question answering, and RAG over internal documents. NEC aimed it at business applications that need global-model quality with a short response time and optional company-specific fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nec/cotomi/cotomi-pro-v2": { "id": "nec/cotomi/cotomi-pro-v2", "name": "cotomi Pro v2", "maker": "nec", "family": "nec/cotomi", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not named. Presented as the accuracy tier of cotomi v2. NEC later said v3 did not make a major architecture change from v2, and that v2's parameter count was not disclosed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The accuracy tier of NEC cotomi v2. NEC's 2024-11-27 IR deck names it beside cotomi Fast v2 and says service would start gradually in Japan in December 2024. The 2024-11-27 English press describes the same generation without the Pro v2 name. cotomi v3 later replaced the Pro/Fast split. Collected 2026-10-15.", "what_it_is_for": "Specialized enterprise work in Japanese: higher accuracy than the fast tier, still aimed at secure on-premises or controlled environments and at customization for a business domain. NEC also tied this generation to automating specialized tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nec/cotomi/cotomi-v3": { "id": "nec/cotomi/cotomi-v3", "name": "cotomi v3", "maker": "nec", "family": "nec/cotomi", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-08", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "Not named. NEC executive Yamada, reported by Cloud Watch on 2025-07-10, said there is no major architecture change from cotomi v2. Parameter count remains unpublished and was described as about the same size as the previous generation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NEC's current cotomi generation. On 2025-07-08 NEC shipped a single model with a 128K context, stronger task planning, and MCP, replacing the Pro and Fast split. The press that day did not say \"v3\". Sakura Internet used that name on 2025-12-02, and NEC used it on 2026-03-09 when the model was selected for Japan's government AI trial. Collected 2026-10-15.", "what_it_is_for": "Japanese business generation and agent workflows: task decomposition, tool selection, function calling, and long instructions. Sakura lists contact-center summaries, approval briefs, and FAQ rewriting. NEC says industry-specific models can be trained from its data process. In March 2026 the Digital Agency selected it for trial use on the government environment \u6e90\u5185.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/gram/gram-llama-3-2-3b": { "id": "neu/gram/gram-llama-3-2-3b", "name": "GRAM-Llama-3.2-3B", "maker": "neu", "family": "neu/gram", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-06-11", "context_length": 131072, "max_output_tokens": null, "parameters": "3212749824", "architecture": "Generative reward model, a fine-tune of meta-llama/Llama-3.2-3B-Instruct. config.json: LlamaForCausalLM, 28 layers, hidden 3072, intermediate 8192, 24 heads, 8 kv heads, head_dim 128, vocab 128256, tie_word_embeddings true, bfloat16, max_position_embeddings 131072, rope_type llama3 with factor 32.0 and original_max_position_embeddings 8192. Hub safetensors total 3,212,749,824 BF16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GRAM-Llama-3.2-3B is the Llama line of GRAM, from the NLP Lab at Northeastern University in Shenyang and NiuTrans Research. The Hub repo was created on 2025-06-11. The card's Hub tag is Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Same GRAM pairwise judge as the Qwen sizes. The Hub base is `meta-llama/Llama-3.2-3B-Instruct`. The card's JudgeBench average for this row is 69.9, above the 8B Qwen row and below the 14B Qwen row on that table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/gram/gram-qwen3-1-7b": { "id": "neu/gram/gram-qwen3-1-7b", "name": "GRAM-Qwen3-1.7B", "maker": "neu", "family": "neu/gram", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-06-11", "context_length": 40960, "max_output_tokens": null, "parameters": "1720574976", "architecture": "Generative reward model, a fine-tune of Qwen/Qwen3-1.7B. config.json: Qwen3ForCausalLM, 28 layers, hidden 2048, intermediate 6144, 16 heads, 8 kv heads, vocab 151936, tie_word_embeddings true, bfloat16, max_position_embeddings 40960. Hub safetensors total 1,720,574,976 BF16. That is exactly one embedding matrix smaller than LMT-60-1.7B's safetensors total. The card scores a pair of answers from the probabilities of the tokens A and B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GRAM-Qwen3-1.7B is the smallest Qwen3 generative reward model in GRAM, from the NLP Lab at Northeastern University in Shenyang and NiuTrans Research. The Hub repo was created on 2025-06-11. License is Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says GRAM is a generative reward model trained from unlabeled data and then supervised preference data, so it can judge answer pairs without training a new reward model per task. This checkpoint is a fine-tune of Qwen3-1.7B. Scoring reads the probabilities of the verdict tokens A and B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/gram/gram-qwen3-14b": { "id": "neu/gram/gram-qwen3-14b", "name": "GRAM-Qwen3-14B", "maker": "neu", "family": "neu/gram", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-06-11", "context_length": 40960, "max_output_tokens": null, "parameters": "14768307200", "architecture": "Generative reward model, a fine-tune of Qwen/Qwen3-14B. config.json fields read at collection: Qwen3ForCausalLM, 40 layers, hidden 5120, vocab 151936, tie_word_embeddings false, max_position_embeddings 40960. Hub safetensors total 14,768,307,200 BF16, the same total as TechGPT-3.0. Intermediate size was not copied from this config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GRAM-Qwen3-14B is the largest Qwen3 generative reward model in GRAM, from the NLP Lab at Northeastern University in Shenyang and NiuTrans Research. The Hub repo was created on 2025-06-11. License is Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Same GRAM pairwise judge as the smaller sizes. The card says the 14B row is a fine-tune of Qwen3-14B and prints it as the highest GRAM average on the card's JudgeBench table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/gram/gram-qwen3-4b": { "id": "neu/gram/gram-qwen3-4b", "name": "GRAM-Qwen3-4B", "maker": "neu", "family": "neu/gram", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-06-14", "context_length": 40960, "max_output_tokens": null, "parameters": "4022468096", "architecture": "Generative reward model, a fine-tune of Qwen/Qwen3-4B. config.json: Qwen3ForCausalLM, 36 layers, hidden 2560, intermediate 9728, 32 heads, 8 kv heads, vocab 151936, tie_word_embeddings true, bfloat16, max_position_embeddings 40960. Hub safetensors total 4,022,468,096 BF16, which matches LMT-60-4B's safetensors.total and not that repo's larger BF16 tensor sum.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GRAM-Qwen3-4B is the 4B Qwen3 generative reward model in GRAM, from the NLP Lab at Northeastern University in Shenyang and NiuTrans Research. The Hub repo was created on 2025-06-14. License is Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Same GRAM job as the other sizes: pairwise judging of assistant answers from the probabilities of A and B, after pretraining on unlabeled data and supervised preference tuning. The card says this one is a fine-tune of Qwen3-4B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/gram/gram-qwen3-8b": { "id": "neu/gram/gram-qwen3-8b", "name": "GRAM-Qwen3-8B", "maker": "neu", "family": "neu/gram", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-06-15", "context_length": 40960, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Generative reward model, a fine-tune of Qwen/Qwen3-8B. config.json fields read at collection: Qwen3ForCausalLM, 36 layers, hidden 4096, vocab 151936, tie_word_embeddings false, max_position_embeddings 40960. Hub safetensors total 8,190,735,360 BF16, the same total as LMT-60-8B. Intermediate size was not copied from this config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GRAM-Qwen3-8B is the 8B Qwen3 generative reward model in GRAM, from the NLP Lab at Northeastern University in Shenyang and NiuTrans Research. The Hub repo was created on 2025-06-15. License is Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Same GRAM pairwise judge as the other sizes. The card says this checkpoint is a fine-tune of Qwen3-8B. The published JudgeBench table lives on this card and is shared by the suite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/lamate/lamate": { "id": "neu/lamate/lamate", "name": "LaMaTE", "maker": "neu", "family": "neu/lamate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-03-03", "context_length": 8192, "max_output_tokens": null, "parameters": "8140146696", "architecture": "Encoder-decoder translation model. Hub base_model is meta-llama/Meta-Llama-3-8B. config.json architecture LlamaCrossAttentionEncDec, model_method lamate. The nested decoder block records hidden 1024, intermediate 2752, 16 heads, 16 kv heads, 8 decoder layers, num_encoder_layers 32, max_position_embeddings 8192, vocab not copied from the truncated config. Hub safetensors total 8,140,146,696 parameters in F16. The card says the LLM is the MT encoder, with a lightweight decoder and an adapter, trained in two stages.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMaTE is an encoder-decoder translation model from the NLP Lab at Northeastern University in Shenyang and NiuTrans Research. It uses Llama-3-8B as the encoder and a small decoder. The Hub repo was created on 2025-03-03. License is MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says LaMaTE keeps a large language model as the machine-translation encoder, adds a lightweight decoder and an adapter, and trains in two stages. The stated goal is translation quality with faster decoding and a smaller KV cache. Hub language tags are English, Chinese, German, and Czech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/lmt/lmt-60-0-6b": { "id": "neu/lmt/lmt-60-0-6b", "name": "LMT-60-0.6B", "maker": "neu", "family": "neu/lmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-11-09", "context_length": 32768, "max_output_tokens": null, "parameters": "751632384", "architecture": "Qwen3ForCausalLM. Continued pretraining of Qwen/Qwen3-0.6B-Base, then a further translation stage. config.json: 28 layers, hidden 1024, intermediate 3072, 16 heads, 8 kv heads, head_dim 128, vocab 151936, tie_word_embeddings true, bfloat16, max_position_embeddings 32768. Hub safetensors total 751,632,384 BF16, same total as NiuTrans/LMT-60-0.6B-Base. The current model card calls the non-base checkpoints GRPO versions. The November 2025 announcement called them SFT models.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LMT-60-0.6B is the smallest model in NiuTrans.LMT, the multilingual translation suite from the NLP Lab at Northeastern University in Shenyang and NiuTrans Research. Weights were put on Hugging Face on 2025-11-09 under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says LMT-60 is a Chinese-English-centric translation model covering 60 languages and 234 directions, trained with continued pretraining on 90 billion mixed tokens and a later supervised stage. This folder is the 0.6B non-base checkpoint. The current card calls that stage GRPO. The November 2025 posts called it SFT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/lmt/lmt-60-1-7b": { "id": "neu/lmt/lmt-60-1-7b", "name": "LMT-60-1.7B", "maker": "neu", "family": "neu/lmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-11-09", "context_length": 32768, "max_output_tokens": null, "parameters": "2031739904", "architecture": "Qwen3ForCausalLM. Continued pretraining of Qwen/Qwen3-1.7B-Base, then a further translation stage. config.json: 28 layers, hidden 2048, intermediate 6144, 16 heads, 8 kv heads, head_dim 128, vocab 151936, tie_word_embeddings true, bfloat16, max_position_embeddings 32768. Hub safetensors total 2,031,739,904 BF16, same total as NiuTrans/LMT-60-1.7B-Base. That total is exactly one embedding matrix (151936 times 2048) larger than the GRAM-Qwen3-1.7B safetensors total. The current card calls the non-base checkpoint a GRPO version. The November 2025 announcement called it SFT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LMT-60-1.7B is the 1.7B model in NiuTrans.LMT, from the NLP Lab at Northeastern University in Shenyang and NiuTrans Research. The Hub repo was created on 2025-11-09. License is Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Same suite job as the other LMT-60 sizes: Chinese-English-centric translation across 60 languages and 234 directions. This folder is the non-base 1.7B checkpoint. The current card calls that stage GRPO. The November 2025 posts called it SFT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/lmt/lmt-60-4b": { "id": "neu/lmt/lmt-60-4b", "name": "LMT-60-4B", "maker": "neu", "family": "neu/lmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-11-09", "context_length": 32768, "max_output_tokens": null, "parameters": "4022468096", "architecture": "Qwen3ForCausalLM. Continued pretraining of Qwen/Qwen3-4B-Base, then a further translation stage. config.json: 36 layers, hidden 2560, intermediate 9728, 32 heads, 8 kv heads, head_dim 128, vocab 151936, tie_word_embeddings true, bfloat16, max_position_embeddings 32768. Hub safetensors.total is 4,022,468,096. Hub safetensors.parameters.BF16 is 4,411,424,256. The difference is 388,956,160, which is vocab size times hidden size. The Base repo's safetensors.total is 4,411,424,256 and matches its BF16 count. The current card calls the non-base checkpoint a GRPO version. The November 2025 announcement called it SFT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LMT-60-4B is the 4B model in NiuTrans.LMT, from the NLP Lab at Northeastern University in Shenyang and NiuTrans Research. The Hub repo was created on 2025-11-09. The paper treats this size as the efficiency point that matches or beats much larger open translation models on overlapping directions. This dossier was collected on 2026-10-15.", "what_it_is_for": "Chinese-English-centric translation across 60 languages and 234 directions. Training is continued pretraining on 90 billion mixed tokens plus a later translation stage. The current card calls the non-base weights GRPO. The November 2025 posts called them SFT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/lmt/lmt-60-8b": { "id": "neu/lmt/lmt-60-8b", "name": "LMT-60-8B", "maker": "neu", "family": "neu/lmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-11-09", "context_length": 32768, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Qwen3ForCausalLM. Continued pretraining of Qwen/Qwen3-8B-Base, then a further translation stage. config.json: 36 layers, hidden 4096, intermediate 12288, 32 heads, 8 kv heads, head_dim 128, vocab 151936, tie_word_embeddings false, bfloat16, max_position_embeddings 32768, rope_theta 1000000. Hub safetensors total 8,190,735,360 BF16, same total as NiuTrans/LMT-60-8B-Base. The current card calls the non-base checkpoint a GRPO version. The November 2025 announcement called it SFT. The quickstart on the 8B card is the suite's example.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LMT-60-8B is the largest model in NiuTrans.LMT, from the NLP Lab at Northeastern University in Shenyang and NiuTrans Research. The Hub repo was created on 2025-11-09 and the suite quickstart uses this id. License is Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Chinese-English-centric translation across 60 languages and 234 directions, for the higher-accuracy end of the four open sizes. Training is continued pretraining on 90 billion mixed tokens plus a later translation stage. The current card calls the non-base weights GRPO. The November 2025 posts called them SFT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/realign/realign-phi3v": { "id": "neu/realign/realign-phi3v", "name": "ReAlign-Phi3v", "maker": "neu", "family": "neu/realign", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-04-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA visual-document retriever. adapter_config.json and the Hub base_model tag agree: microsoft/Phi-3-vision-128k-instruct. peft_type LORA, r 8, lora_alpha 64, lora_dropout 0.1, task_type FEATURE_EXTRACTION, targets q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj. No safetensors parameter total was returned for the adapter. The base name says 128k. This dossier does not convert that label into a token count. First affiliation on the card is Northeastern University.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReAlign-Phi3v is the Phi-3 Vision visual-document retriever from the NEU-ModelBest Data Intelligence Joint Lab at Northeastern University in Shenyang, with Tsinghua and Alibaba coauthors. The NEUIR GitHub links `yanghaoir/ReAlign-Phi3v`. The Hub repo was created on 2026-04-09. License is MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this is the ReAlign retriever on `microsoft/Phi-3-vision-128k-instruct`. The adapter file and the Hub tag name that same base. The method uses VLM reasoning traces as supervision for fine-grained visual document retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/realign/realign-qwen": { "id": "neu/realign/realign-qwen", "name": "ReAlign-Qwen", "maker": "neu", "family": "neu/realign", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2026-04-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA visual-document retriever. adapter_config.json: peft_type LORA, r 8, lora_alpha 64, lora_dropout 0.1, task_type FEATURE_EXTRACTION, targets q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj. base_model_name_or_path in that file is Qwen/Qwen2.5-VL-72B-Instruct. The README and the Hub base_model tag say Qwen/Qwen2.5-VL-7B-Instruct. Those two statements disagree. No safetensors parameter total was returned for the adapter. First affiliation on the card is Northeastern University. Tsinghua and Alibaba are coauthors.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReAlign-Qwen is a visual-document retriever adapter from the NEU-ModelBest Data Intelligence Joint Lab at Northeastern University in Shenyang, with Tsinghua and Alibaba coauthors. The NEUIR GitHub links the weights at `yanghaoir/ReAlign-Qwen`. The Hub repo was created on 2026-04-09. License is MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says ReAlign trains a visual document retriever with reasoning-guided descriptions of query-related page regions. This repo is the Qwen line. The README says the backbone is Qwen2.5-VL-7B-Instruct. The adapter file names Qwen2.5-VL-72B-Instruct instead.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/rovrm/rovrm": { "id": "neu/rovrm/rovrm", "name": "RoVRM", "maker": "neu", "family": "neu/rovrm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-08-21", "context_length": null, "max_output_tokens": null, "parameters": "7063431168", "architecture": "Robust visual reward model (RoVRM) built on LLaVA-1.5-7B. The card says training is three phases: text preference pretraining, image-caption preference fine-tuning, then visual preference fine-tuning, plus optimal-transport selection of preference data. Hub safetensors total 7,063,431,168 BF16 parameters. The card's own size line says 7B BF16. The example truncates text at 512 tokens. That is sample code, not a published context limit. Inference is documented through the Vision-LLM-Alignment repo, after converting safetensors to pytorch_model.bin.", "modalities": { "input": [ "text", "image" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoVRM is a visual reward model from the NLP Lab at Northeastern University in Shenyang and NiuTrans Research. It is built on LLaVA-1.5-7B. The Hub repo was created on 2024-08-21. License is MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says RoVRM scores a question-answer pair, with or without an image, so it can be used for best-of-n sampling and RL on vision-language models. Training moves from text preferences to caption preferences to visual preferences. The stated aim is a more robust reward and less hallucination. No numeric result was copied here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/techgpt/techgpt": { "id": "neu/techgpt/techgpt", "name": "TechGPT", "maker": "neu", "family": "neu/techgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "gpl-3.0", "release_date": "2023-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "TechGPT-1.0, the 7B checkpoint the Knowledge Graph Research Group calls TechGPT-7B. The group says it is a full fine-tune of a BELLE chat model on TechKG Chinese scholarly text. The Hugging Face repo tags arXiv 2304.07854, which is the BELLE paper, not a TechGPT paper. No readable config.json was on the repo at collection, so layer counts are unpublished here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TechGPT is the first named language model from the Knowledge Graph Research Group at Northeastern University in Shenyang. The group formally released the 7B TechGPT-1.0 checkpoint on 2023-06-26 and hosts it as neukg/TechGPT-7B. Weights are open, with a GPL-3.0 Hub tag. This dossier was collected on 2026-10-15.", "what_it_is_for": "The group says TechGPT is a vertical model for knowledge-graph construction, logical question answering, and text generation. Named tasks include term extraction, named-entity recognition, relation triples, keyword and title generation, summarization, domain classification, machine reading comprehension, translation, and simple code generation. The base they name is a BELLE chat model, trained further on their TechKG scholarly corpus.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/techgpt/techgpt-2-0-alpaca": { "id": "neu/techgpt/techgpt-2-0-alpaca", "name": "TechGPT-2.0-Alpaca", "maker": "neu", "family": "neu/techgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-11-30", "context_length": 512, "max_output_tokens": null, "parameters": "6929256448", "architecture": "Full fine-tune of HFL Chinese-Alpaca-2-7B, which the lab says is itself an instruction tune of Chinese-LLaMA-2-7B, a Chinese continued-pretrain of LLaMA-2. Released config: LlamaForCausalLM, 32 layers, hidden 4096, intermediate 11008, 32 heads, vocab 55296, float16, tie_word_embeddings false, max_position_embeddings 512. Parameter count is the fp16 tensor count implied by those shapes, excluding rotary inv_freq buffers. It matches the Hub index total_size of 13,858,521,088 bytes (weights plus 8,192 bytes of inv_freq). Trained on Huawei Ascend 910A with MindSpore, then converted.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TechGPT-2.0-Alpaca is one of two full 7B checkpoints in TechGPT-2.0, from the Knowledge Graph Research Group at Northeastern University in Shenyang. It is a full fine-tune of HFL Chinese-Alpaca-2-7B. The group dated the TechGPT-2.0 release to 2023-11-30. The Hub repo was created on 2023-11-25. This dossier was collected on 2026-10-15.", "what_it_is_for": "The group says TechGPT-2.0 keeps TechGPT-1.0's extraction, QA, and generation tasks and adds medicine, law, geography, transport, organizations, works, biology, natural science, astronomical objects, and architecture. Alpaca is the Chinese-Alpaca-2 line. Atom is a separate base and has its own folder. Training was a full fine-tune on Huawei Ascend 910A servers with MindSpore.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/techgpt/techgpt-2-0-atom": { "id": "neu/techgpt/techgpt-2-0-atom", "name": "TechGPT-2.0-Atom", "maker": "neu", "family": "neu/techgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-11-30", "context_length": 512, "max_output_tokens": null, "parameters": "7008751616", "architecture": "Full fine-tune of Atom-7B-Chat. The lab README thanks FlagAlpha Llama2-Chinese and says this line uses Atom-7B-chat. configuration.json sets model type to Atom-7B-Chat. Released config: LlamaForCausalLM, 32 layers, hidden 4096, intermediate 11008, 32 heads, vocab 65000, float16, tie_word_embeddings false, max_position_embeddings 512. Parameter count is the fp16 tensor count implied by those shapes, excluding rotary inv_freq. It matches the Hub index total_size of 14,017,511,424 bytes. Trained on Huawei Ascend 910A with MindSpore. This is the checkpoint the lab pointed at for the TechGPT-2.0 demo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TechGPT-2.0-Atom is the Atom-7B-Chat line of TechGPT-2.0, from the Knowledge Graph Research Group at Northeastern University in Shenyang. The group released TechGPT-2.0 on 2023-11-30 and used this checkpoint for the public demo. This dossier was collected on 2026-10-15.", "what_it_is_for": "Same TechGPT-2.0 job as the Alpaca checkpoint: knowledge-graph extraction, QA, and generation, with medicine, law, and the extra domains named in the 2.0 report. The lab says the base is Atom-7B-chat from the FlagAlpha Llama2-Chinese project. It was fully fine-tuned on Huawei Ascend 910A with MindSpore.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/techgpt/techgpt-2-0-qlora": { "id": "neu/techgpt/techgpt-2-0-qlora", "name": "TechGPT-2.0-QLoRA", "maker": "neu", "family": "neu/techgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-11-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter, not a full checkpoint. adapter_config.json: peft_type LORA, r 64, lora_alpha 128, lora_dropout 0.05, task_type CAUSAL_LM, target modules q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, and modules_to_save embed_tokens and lm_head. base_model_name_or_path is the local path /data0/lizhong/models/new_models/atom, so the lab's Atom weights are the intended base. The TechGPT-2.0 paper calls this the QLoRA weight for long text. The download table says 3 GB. No adapter parameter total was published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TechGPT-2.0-QLoRA is the long-text adapter in TechGPT-2.0, from the Knowledge Graph Research Group at Northeastern University in Shenyang. The group shipped it with the 2023-11-30 TechGPT-2.0 release. The Hub repo holds a LoRA, not a full model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The TechGPT-2.0 paper says this is the QLoRA weight specialized for long text, beside the two full 7B models. The adapter config points at a local Atom checkpoint, so it is meant to be loaded on Atom rather than on the Alpaca line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/techgpt/techgpt-2-0-qwen1-5": { "id": "neu/techgpt/techgpt-2-0-qwen1-5", "name": "TechGPT-2.0-Qwen1.5", "maker": "neu", "family": "neu/techgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-22", "context_length": 32768, "max_output_tokens": null, "parameters": "7721324544", "architecture": "Full fine-tune the lab table labels TechGPT-2.0-Qwen1.5, 7B. config.json: Qwen2ForCausalLM, 32 layers, hidden 4096, intermediate 11008, 32 heads, 32 kv heads, vocab 151936, bfloat16, tie_word_embeddings false, max_position_embeddings 32768, rope_theta 1000000. Hub safetensors total 7,721,324,544 BF16 parameters. The January 2024 TechGPT-2.0 paper does not describe this checkpoint. The README changelog dates the add-on to 2024-08-22.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TechGPT-2.0-Qwen1.5 is a later 7B full checkpoint in the TechGPT-2.0 line, from the Knowledge Graph Research Group at Northeastern University in Shenyang. The group's README dates the add-on to 2024-08-22. It is not one of the two LLaMA checkpoints in the January 2024 paper. This dossier was collected on 2026-10-15.", "what_it_is_for": "The shared TechGPT-2.0 card still describes domain extraction, QA, and generation. This row is the Qwen1.5 full fine-tune, listed at 7B and about 14 GB. The January 2024 paper does not document its data mix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "neu/techgpt/techgpt-3-0": { "id": "neu/techgpt/techgpt-3-0", "name": "TechGPT-3.0", "maker": "neu", "family": "neu/techgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-26", "context_length": 40960, "max_output_tokens": null, "parameters": "14768307200", "architecture": "TechGPT3.0-Qwen3-14B, a full fine-tune the lab says was trained on NVIDIA A800. config.json: Qwen3ForCausalLM, 40 layers, hidden 5120, intermediate 17408, 40 heads, 8 kv heads, head_dim 128, vocab 151936, bfloat16, tie_word_embeddings false, max_position_embeddings 40960, rope_theta 1000000. Hub safetensors total 14,768,307,200 BF16 parameters. all_results.json records epoch 1.0 and train_loss 0.7103453857448353. The lab says it adds risk-content blocking, compressed reasoning for simple graph questions, core-information extraction, and multi-domain text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TechGPT-3.0 is the Qwen3-14B generation from the Knowledge Graph Research Group at Northeastern University in Shenyang. The group news list says it was introduced on 2025-06-26. The public weight repo `neukg/TechGPT-3.0-Qwen3-14b` was created on 2025-09-28. This dossier was collected on 2026-10-15.", "what_it_is_for": "The GitHub README says TechGPT-3.0 is a full fine-tune on NVIDIA A800 that keeps the older domain skills and adds risk-content blocking, compressed reasoning suggestions for simple knowledge-graph questions, core-information extraction, text consolidation, and broader subject coverage. The only open size found is 14B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "newcastle/nagl-mbis/nagl-gas": { "id": "newcastle/nagl-mbis/nagl-gas", "name": "NAGL-MBIS gas (Q, \u03bc, V)", "maker": "newcastle", "family": "newcastle/nagl-mbis", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-06-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MBISGraphModel, a graph convolutional network using the OpenFF NAGL architecture. The repository README lists, for this model class, one-hot element, bond count, and ring membership of size 3-8, with n_gcn_layers 5, n_gcn_hidden_features 128, n_mbis_layers 2, n_mbis_hidden_features 64, learning rate 0.001, and 1000 epochs. Each checkpoint also stores its own hyper_parameters, which were not read out of the .ckpt files.", "modalities": { "input": [ "molecular-graph" ], "output": [ "partial-charges" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NAGL-MBIS gas is a graph-network charge model from the Cole group at Newcastle University, released with coauthors at Kuano, the Open Force Field Initiative, and the University of California, Irvine. The default checkpoint predicts conformation-independent MBIS atomic charges for a molecule in vacuum, after co-training on charges, dipoles, and the electrostatic potential. It was still loadable from the group repository on the collection date, 2026-10-15.", "what_it_is_for": "The paper trains GNN charge models so a force field can get MBIS-like charges, dipoles, and electrostatic potentials without a new quantum calculation for every molecule. This checkpoint is the vacuum member of that pair. The README uses it with the water default inside `ComputePartialPolarised` to scale charges between the two environments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "newcastle/nagl-mbis/nagl-v1": { "id": "newcastle/nagl-mbis/nagl-v1", "name": "NAGL-MBIS v1", "maker": "newcastle", "family": "newcastle/nagl-mbis", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MBISGraphModel on the OpenFF NAGL architecture, same class as the later \u03c9B97X-D checkpoints. The README lists 5 graph-convolution layers with 128 hidden features and 2 MBIS layers with 64 hidden features for the class. The v1 checkpoint's stored hyper_parameters were not read.", "modalities": { "input": [ "molecular-graph" ], "output": [ "partial-charges" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NAGL-MBIS v1 is the earlier gas-phase charge line shipped in the same Cole group repository as the 2025 \u03c9B97X-D models. Newcastle University hosts the group. The checkpoint is trained on HF/6-31G* MBIS charges from OpenFF ESP fragment conformers, not on the later MLPepper DFT set. Both v1 loader ids were still in the package on 2026-10-15.", "what_it_is_for": "The README groups these checkpoints as the first NAGL-MBIS charge models: a charge-only model and a charge-plus-dipole model on an older, cheaper quantum target. The partial-polarization example in that README does not call v1. It calls the later gas and water defaults.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "newcastle/nagl-mbis/nagl-water": { "id": "newcastle/nagl-mbis/nagl-water", "name": "NAGL-MBIS water (Q, \u03bc, V)", "maker": "newcastle", "family": "newcastle/nagl-mbis", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-06-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MBISGraphModel, a graph convolutional network using the OpenFF NAGL architecture. The repository README lists, for this model class, one-hot element, bond count, and ring membership of size 3-8, with n_gcn_layers 5, n_gcn_hidden_features 128, n_mbis_layers 2, n_mbis_hidden_features 64, learning rate 0.001, and 1000 epochs. Each checkpoint also stores its own hyper_parameters, which were not read out of the .ckpt files. This checkpoint is the implicit-solvent (water) co-trained model.", "modalities": { "input": [ "molecular-graph" ], "output": [ "partial-charges" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NAGL-MBIS water is the implicit-solvent twin of the Cole group's gas charge model. Newcastle University is the corresponding author's institution. The default checkpoint was co-trained on MBIS charges, molecular dipoles, and the electrostatic potential from \u03c9B97X-D calculations in solvent. It was still a named loader id on 2026-10-15.", "what_it_is_for": "The paper uses a vacuum model and a solvent model so charges can be scaled between the two, instead of running a quantum calculation in each environment. This checkpoint is the solvent member. The README example pairs it with the gas default and a user-set scaling parameter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/gram/gram-llama-3-2-3b": { "id": "niutrans/gram/gram-llama-3-2-3b", "name": "GRAM-LLaMA3.2-3B-RewardModel", "maker": "niutrans", "family": "niutrans/gram", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-11", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Generative reward model fine-tuned from meta-llama/Llama-3.2-3B-Instruct. It scores two candidate answers by reading the probability of verdict tokens A and B, averaged over both presentation orders. Paper: unlabeled pretraining plus supervised fine-tuning with label smoothing.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GRAM-LLaMA3.2-3B-RewardModel is an open generative reward model from NiuTrans, released under Apache-2.0 and fine-tuned from meta-llama/Llama-3.2-3B-Instruct. Hugging Face shows the checkpoint from 2025-06-11. It was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "GRAM is a foundation reward model for LLM alignment, not a translation system. The card says it learns from unlabeled data and is then fine-tuned with labeled preferences, so it can be applied to new tasks without training a reward model from scratch. The shipped demo judges two answers to one user question and scores them from the probabilities of the tokens A and B, with the order swapped to cancel position bias.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/gram/gram-qwen3-1-7b": { "id": "niutrans/gram/gram-qwen3-1-7b", "name": "GRAM-Qwen3-1.7B-RewardModel", "maker": "niutrans", "family": "niutrans/gram", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-11", "context_length": null, "max_output_tokens": null, "parameters": "1.7B", "architecture": "Generative reward model fine-tuned from Qwen/Qwen3-1.7B. It scores two candidate answers by reading the probability of verdict tokens A and B, averaged over both presentation orders. Paper: unlabeled pretraining plus supervised fine-tuning with label smoothing.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GRAM-Qwen3-1.7B-RewardModel is an open generative reward model from NiuTrans, released under Apache-2.0 and fine-tuned from Qwen/Qwen3-1.7B. Hugging Face shows the checkpoint from 2025-06-11. It was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "GRAM is a foundation reward model for LLM alignment, not a translation system. The card says it learns from unlabeled data and is then fine-tuned with labeled preferences, so it can be applied to new tasks without training a reward model from scratch. The shipped demo judges two answers to one user question and scores them from the probabilities of the tokens A and B, with the order swapped to cancel position bias.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/gram/gram-qwen3-14b": { "id": "niutrans/gram/gram-qwen3-14b", "name": "GRAM-Qwen3-14B-RewardModel", "maker": "niutrans", "family": "niutrans/gram", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-11", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Generative reward model fine-tuned from Qwen/Qwen3-14B. It scores two candidate answers by reading the probability of verdict tokens A and B, averaged over both presentation orders. Paper: unlabeled pretraining plus supervised fine-tuning with label smoothing.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GRAM-Qwen3-14B-RewardModel is an open generative reward model from NiuTrans, released under Apache-2.0 and fine-tuned from Qwen/Qwen3-14B. Hugging Face shows the checkpoint from 2025-06-11. It was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "GRAM is a foundation reward model for LLM alignment, not a translation system. The card says it learns from unlabeled data and is then fine-tuned with labeled preferences, so it can be applied to new tasks without training a reward model from scratch. The shipped demo judges two answers to one user question and scores them from the probabilities of the tokens A and B, with the order swapped to cancel position bias.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/gram/gram-qwen3-4b": { "id": "niutrans/gram/gram-qwen3-4b", "name": "GRAM-Qwen3-4B-RewardModel", "maker": "niutrans", "family": "niutrans/gram", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-14", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Generative reward model fine-tuned from Qwen/Qwen3-4B. It scores two candidate answers by reading the probability of verdict tokens A and B, averaged over both presentation orders. Paper: unlabeled pretraining plus supervised fine-tuning with label smoothing.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GRAM-Qwen3-4B-RewardModel is an open generative reward model from NiuTrans, released under Apache-2.0 and fine-tuned from Qwen/Qwen3-4B. Hugging Face shows the checkpoint from 2025-06-14. It was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "GRAM is a foundation reward model for LLM alignment, not a translation system. The card says it learns from unlabeled data and is then fine-tuned with labeled preferences, so it can be applied to new tasks without training a reward model from scratch. The shipped demo judges two answers to one user question and scores them from the probabilities of the tokens A and B, with the order swapped to cancel position bias.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/gram/gram-qwen3-8b": { "id": "niutrans/gram/gram-qwen3-8b", "name": "GRAM-Qwen3-8B-RewardModel", "maker": "niutrans", "family": "niutrans/gram", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-15", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Generative reward model fine-tuned from Qwen/Qwen3-8B. It scores two candidate answers by reading the probability of verdict tokens A and B, averaged over both presentation orders. Paper: unlabeled pretraining plus supervised fine-tuning with label smoothing.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GRAM-Qwen3-8B-RewardModel is an open generative reward model from NiuTrans, released under Apache-2.0 and fine-tuned from Qwen/Qwen3-8B. Hugging Face shows the checkpoint from 2025-06-15. It was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "GRAM is a foundation reward model for LLM alignment, not a translation system. The card says it learns from unlabeled data and is then fine-tuned with labeled preferences, so it can be applied to new tasks without training a reward model from scratch. The shipped demo judges two answers to one user question and scores them from the probabilities of the tokens A and B, with the order swapped to cancel position bias.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/lamate/lamate": { "id": "niutrans/lamate/lamate", "name": "LaMaTE", "maker": "niutrans", "family": "niutrans/lamate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-03", "context_length": 8192, "max_output_tokens": null, "parameters": "8B encoder plus a lightweight decoder; Hugging Face lists the repo as 8B", "architecture": "Encoder-decoder. Encoder is Llama-3-8B (32 layers, hidden 4096, intermediate 14336, 32 heads, 8 KV heads, vocab 128256, max_position_embeddings 8192, float16 on the uploaded card). A stack adaptor feeds an 8-layer Llama decoder (hidden 1024, intermediate 2752, 16 heads, max_position_embeddings 8192). Custom class LlamaCrossAttentionEncDec. Trained in two stages: adaptor and decoder first, then full fine-tuning on ComMT. Transformers 4.39.2 is the version the README asks for.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaMaTE (Large Language Models as Machine Translation Encoders) is an open translation model from NiuTrans Research and the NLP Lab at Northeastern University (China). It keeps a Llama-3-8B encoder, adds an adaptor and a small decoder, and was released on Hugging Face in March 2025 under MIT. It was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses an LLM as the machine-translation encoder so the system can keep LLM-side quality with a cheaper decoder. The released checkpoint is the Llama-3-8B variant. ComMT, the paired dataset, covers general translation and related tasks such as terminology-constrained translation, automatic post-editing, and in-context examples. The dataset card says about 239 thousand training instances across German, Czech, Russian, and Chinese, plus English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/lmt/lmt-60-0-6b": { "id": "niutrans/lmt/lmt-60-0-6b", "name": "LMT-60-0.6B", "maker": "niutrans", "family": "niutrans/lmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-09", "context_length": 32768, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Qwen3 decoder-only causal LM (Qwen3ForCausalLM). 28 layers, hidden size 1024, intermediate size 3072, 16 attention heads, 8 key-value heads, head dim 128, vocab 151936, tied embeddings, bfloat16, RoPE theta 1,000,000, sliding window off. max_position_embeddings 32768 in config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NiuTrans and the NLP Lab at Northeastern University (China) released LMT-60-0.6B on 2025-11-09 as a Chinese-English-centric multilingual translation model. The official Hugging Face card calls the non-Base series the SFT versions. A ModelScope mirror under yvanluo describes the same checkpoint names as the GRPO versions. The ACL 2026 paper's published recipe ends in GRPO after SFT. The weights are Apache-2.0 and were still public on 2026-10-15.", "what_it_is_for": "The maker positions LMT-60 as a translation model for 60 languages and 234 directions (English with 59 languages and Chinese with 58). The recommended prompt is an instruction that names the source and target languages and ends on the target-language label. Parallel Multilingual Prompting can add an auxiliary parallel sentence when one is available. The default path is a standard translation prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/lmt/lmt-60-0-6b-base": { "id": "niutrans/lmt/lmt-60-0-6b-base", "name": "LMT-60-0.6B-Base", "maker": "niutrans", "family": "niutrans/lmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-09", "context_length": 32768, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Qwen3 decoder-only causal LM (Qwen3ForCausalLM). 28 layers, hidden size 1024, intermediate size 3072, 16 attention heads, 8 key-value heads, head dim 128, vocab 151936, tied embeddings, bfloat16, RoPE theta 1,000,000, sliding window off. max_position_embeddings 32768 in config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NiuTrans and the NLP Lab at Northeastern University (China) released LMT-60-0.6B-Base on 2025-11-09 as the continued-pretraining checkpoint of LMT-60. The GitHub README says it is Qwen3 continued on 90B mixed monolingual and bilingual tokens and has not been fine-tuned on the LMT SFT data. Use the non-Base LMT-60-0.6B checkpoint for translation. This dossier was collected on 2026-10-15.", "what_it_is_for": "The Base checkpoints are the CPT stage of NiuTrans.LMT: a Chinese-English-centric multilingual adaptation of Qwen3, before supervised translation fine-tuning. The paper trains this stage on about 90B tokens at a 1:1:1 mix of monolingual text, Chinese-centric bitext, and English-centric bitext.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/lmt/lmt-60-1-7b": { "id": "niutrans/lmt/lmt-60-1-7b", "name": "LMT-60-1.7B", "maker": "niutrans", "family": "niutrans/lmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-09", "context_length": 32768, "max_output_tokens": null, "parameters": "1.7B", "architecture": "Qwen3 decoder-only causal LM (Qwen3ForCausalLM). 28 layers, hidden size 2048, intermediate size 6144, 16 attention heads, 8 key-value heads, head dim 128, vocab 151936, tied embeddings, bfloat16, RoPE theta 1,000,000, sliding window off. max_position_embeddings 32768 in config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NiuTrans and the NLP Lab at Northeastern University (China) released LMT-60-1.7B on 2025-11-09 as a Chinese-English-centric multilingual translation model. The official Hugging Face card calls the non-Base series the SFT versions. A ModelScope mirror under yvanluo describes the same checkpoint names as the GRPO versions. The ACL 2026 paper's published recipe ends in GRPO after SFT. The weights are Apache-2.0 and were still public on 2026-10-15.", "what_it_is_for": "The maker positions LMT-60 as a translation model for 60 languages and 234 directions (English with 59 languages and Chinese with 58). The recommended prompt is an instruction that names the source and target languages and ends on the target-language label. Parallel Multilingual Prompting can add an auxiliary parallel sentence when one is available. The default path is a standard translation prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/lmt/lmt-60-1-7b-base": { "id": "niutrans/lmt/lmt-60-1-7b-base", "name": "LMT-60-1.7B-Base", "maker": "niutrans", "family": "niutrans/lmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-09", "context_length": 32768, "max_output_tokens": null, "parameters": "1.7B", "architecture": "Qwen3 decoder-only causal LM (Qwen3ForCausalLM). 28 layers, hidden size 2048, intermediate size 6144, 16 attention heads, 8 key-value heads, head dim 128, vocab 151936, tied embeddings, bfloat16, RoPE theta 1,000,000, sliding window off. max_position_embeddings 32768 in config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NiuTrans and the NLP Lab at Northeastern University (China) released LMT-60-1.7B-Base on 2025-11-09 as the continued-pretraining checkpoint of LMT-60. The GitHub README says it is Qwen3 continued on 90B mixed monolingual and bilingual tokens and has not been fine-tuned on the LMT SFT data. Use the non-Base LMT-60-1.7B checkpoint for translation. This dossier was collected on 2026-10-15.", "what_it_is_for": "The Base checkpoints are the CPT stage of NiuTrans.LMT: a Chinese-English-centric multilingual adaptation of Qwen3, before supervised translation fine-tuning. The paper trains this stage on about 90B tokens at a 1:1:1 mix of monolingual text, Chinese-centric bitext, and English-centric bitext.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/lmt/lmt-60-4b": { "id": "niutrans/lmt/lmt-60-4b", "name": "LMT-60-4B", "maker": "niutrans", "family": "niutrans/lmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-09", "context_length": 32768, "max_output_tokens": null, "parameters": "4B", "architecture": "Qwen3 decoder-only causal LM (Qwen3ForCausalLM). 36 layers, hidden size 2560, intermediate size 9728, 32 attention heads, 8 key-value heads, head dim 128, vocab 151936, tied embeddings, bfloat16, RoPE theta 1,000,000, sliding window off. max_position_embeddings 32768 in config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NiuTrans and the NLP Lab at Northeastern University (China) released LMT-60-4B on 2025-11-09 as a Chinese-English-centric multilingual translation model. The official Hugging Face card calls the non-Base series the SFT versions. A ModelScope mirror under yvanluo describes the same checkpoint names as the GRPO versions. The ACL 2026 paper's published recipe ends in GRPO after SFT. The weights are Apache-2.0 and were still public on 2026-10-15.", "what_it_is_for": "The maker positions LMT-60 as a translation model for 60 languages and 234 directions (English with 59 languages and Chinese with 58). The recommended prompt is an instruction that names the source and target languages and ends on the target-language label. Parallel Multilingual Prompting can add an auxiliary parallel sentence when one is available. The default path is a standard translation prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/lmt/lmt-60-4b-base": { "id": "niutrans/lmt/lmt-60-4b-base", "name": "LMT-60-4B-Base", "maker": "niutrans", "family": "niutrans/lmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-09", "context_length": 32768, "max_output_tokens": null, "parameters": "4B", "architecture": "Qwen3 decoder-only causal LM (Qwen3ForCausalLM). 36 layers, hidden size 2560, intermediate size 9728, 32 attention heads, 8 key-value heads, head dim 128, vocab 151936, tied embeddings, bfloat16, RoPE theta 1,000,000, sliding window off. max_position_embeddings 32768 in config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NiuTrans and the NLP Lab at Northeastern University (China) released LMT-60-4B-Base on 2025-11-09 as the continued-pretraining checkpoint of LMT-60. The GitHub README says it is Qwen3 continued on 90B mixed monolingual and bilingual tokens and has not been fine-tuned on the LMT SFT data. Use the non-Base LMT-60-4B checkpoint for translation. This dossier was collected on 2026-10-15.", "what_it_is_for": "The Base checkpoints are the CPT stage of NiuTrans.LMT: a Chinese-English-centric multilingual adaptation of Qwen3, before supervised translation fine-tuning. The paper trains this stage on about 90B tokens at a 1:1:1 mix of monolingual text, Chinese-centric bitext, and English-centric bitext.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/lmt/lmt-60-8b": { "id": "niutrans/lmt/lmt-60-8b", "name": "LMT-60-8B", "maker": "niutrans", "family": "niutrans/lmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-09", "context_length": 32768, "max_output_tokens": null, "parameters": "8B", "architecture": "Qwen3 decoder-only causal LM (Qwen3ForCausalLM). 36 layers, hidden size 4096, intermediate size 12288, 32 attention heads, 8 key-value heads, head dim 128, vocab 151936, untied embeddings, bfloat16, RoPE theta 1,000,000, sliding window off. max_position_embeddings 32768 in config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NiuTrans and the NLP Lab at Northeastern University (China) released LMT-60-8B on 2025-11-09 as a Chinese-English-centric multilingual translation model. The official Hugging Face card calls the non-Base series the SFT versions. A ModelScope mirror under yvanluo describes the same checkpoint names as the GRPO versions. The ACL 2026 paper's published recipe ends in GRPO after SFT. The weights are Apache-2.0 and were still public on 2026-10-15.", "what_it_is_for": "The maker positions LMT-60 as a translation model for 60 languages and 234 directions (English with 59 languages and Chinese with 58). The recommended prompt is an instruction that names the source and target languages and ends on the target-language label. Parallel Multilingual Prompting can add an auxiliary parallel sentence when one is available. The default path is a standard translation prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/lmt/lmt-60-8b-base": { "id": "niutrans/lmt/lmt-60-8b-base", "name": "LMT-60-8B-Base", "maker": "niutrans", "family": "niutrans/lmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-09", "context_length": 32768, "max_output_tokens": null, "parameters": "8B", "architecture": "Qwen3 decoder-only causal LM (Qwen3ForCausalLM). 36 layers, hidden size 4096, intermediate size 12288, 32 attention heads, 8 key-value heads, head dim 128, vocab 151936, untied embeddings, bfloat16, RoPE theta 1,000,000, sliding window off. max_position_embeddings 32768 in config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NiuTrans and the NLP Lab at Northeastern University (China) released LMT-60-8B-Base on 2025-11-09 as the continued-pretraining checkpoint of LMT-60. The GitHub README says it is Qwen3 continued on 90B mixed monolingual and bilingual tokens and has not been fine-tuned on the LMT SFT data. Use the non-Base LMT-60-8B checkpoint for translation. This dossier was collected on 2026-10-15.", "what_it_is_for": "The Base checkpoints are the CPT stage of NiuTrans.LMT: a Chinese-English-centric multilingual adaptation of Qwen3, before supervised translation fine-tuning. The paper trains this stage on about 90B tokens at a 1:1:1 mix of monolingual text, Chinese-centric bitext, and English-centric bitext.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/text-api/text-api-flash": { "id": "niutrans/text-api/text-api-flash", "name": "\u6587\u672c\u7ffb\u8bd1API-Flash", "maker": "niutrans", "family": "niutrans/text-api", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published. The marketing page calls Flash the fast, low-latency mode and says it avoids content hallucination. The non-LLM v2 text document describes a machine-translation result rather than a large-model result. That document is the best public match for Flash, but the page does not print the word Flash.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u6587\u672c\u7ffb\u8bd1API-Flash is the fast text-translation mode sold by \u5c0f\u725b\u7ffb\u8bd1 (NiuTrans) on niutrans.com. The company is \u6c88\u9633\u96c5\u8bd1\u7f51\u7edc\u6280\u672f\u6709\u9650\u516c\u53f8, from the NLP Lab at Northeastern University in Shenyang. The product page, stamped 2026-06-11, calls Flash the low-latency choice for chat, interface strings, product copy, and large batches of short text. Weights are closed. This dossier was collected on 2026-10-15.", "what_it_is_for": "The maker says Flash is for high concurrency and stable wording, and that it accepts common formats such as JSON and XML. Typical uses listed on the page are live chat, UI localization, product listings, tap-to-translate, and short-text batches. The separate Pro product is the one described as deep, long-context translation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "niutrans/text-api/text-api-pro": { "id": "niutrans/text-api/text-api-pro", "name": "\u6587\u672c\u7ffb\u8bd1API-Pro", "maker": "niutrans", "family": "niutrans/text-api", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published. The Pro document says the response is a large-model translation. The public research models in that family are LMT-60, but the API does not accept an LMT checkpoint id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u6587\u672c\u7ffb\u8bd1API-Pro is the deep text-translation API from \u5c0f\u725b\u7ffb\u8bd1 (NiuTrans). The developer page titles it \u901a\u7528\u6587\u672c-Pro and sends callers to POST https://api.niutrans.com/v2/text/translate/llm. The page says the result is a large-model translation. Weights are closed. The document stamp is 2026-06-11. This dossier was collected on 2026-10-15.", "what_it_is_for": "The product page says Pro is for context, long copy, and specialized wording: contracts, marketing, game text, advertising, and literature. It supports a custom term list. The same platform sells document, image, and speech translation as separate APIs. Those are not given their own model names.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nju/ddt/ddt-xl-2": { "id": "nju/ddt/ddt-xl-2", "name": "DDT-XL/2", "maker": "nju", "family": "nju/ddt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-11", "context_length": null, "max_output_tokens": null, "parameters": "675M", "architecture": "Decoupled diffusion transformer DDT-XL/2 with 22 encoder layers and 6 decoder layers, called 22en6de on the card. The card says the model is 675M parameters. Images are encoded by the off-the-shelf Stable Diffusion VAE stabilityai/sd-vae-ft-ema. The GitHub README's 256 config snippet sets num_classes to 1000 and latent_shape to 4x32x32. The Hub pipeline tag is text-to-image. The checkpoint table is ImageNet class-conditional generation, not a caption model. FID is reported with the ADM suite.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DDT-XL/2 is an ImageNet diffusion transformer from Nanjing University's Multimedia Computing Group. The released pair is one 675M model, 22 encoder layers and 6 decoder layers, trained at 256 and at 512. The group encodes pixels with Stability AI's SD VAE and trains its own transformer. Status on 2026-10-15: active weights, license not stated.", "what_it_is_for": "The card says DDT decouples the diffusion transformer into an encoder and a decoder, reports ImageNet FID, and notes that the encoder can be reused across nearby denoising steps. It is class-conditional ImageNet generation. The Hub pipeline tag says text-to-image, which does not match the checkpoint table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nju/pixnerd/pixnerd-xl-16": { "id": "nju/pixnerd/pixnerd-xl-16", "name": "PixNerd-XL/16", "maker": "nju", "family": "nju/pixnerd", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-07-30", "context_length": null, "max_output_tokens": null, "parameters": "700M", "architecture": "Pixel-space diffusion transformer PixNerd-XL/16. The card says it does not use a VAE and decodes patches with a neural field. The card table calls the ImageNet checkpoints class-to-image and lists 700M parameters. The Hub pipeline tag is unconditional-image-generation, which disagrees with the C2I table. Repo files include epoch=319-step=1600000.ckpt, epoch=319-step=1600000_emainit.ckpt, and res512_ft200k_epoch=325-step=1800000_emainit.ckpt. The card does not map those filenames to the FID rows.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixNerd-XL/16 is a 700M pixel-space image diffusion model from Nanjing University's Multimedia Computing Group. The authors say it generates images without a VAE by decoding patches with a neural field. The released repo is the ImageNet class-to-image checkpoint, with both 256 and 512 results claimed for that repo. Status on 2026-10-15: active, Apache-2.0.", "what_it_is_for": "The card says PixNerd is a single-stage pixel-space diffusion transformer for image generation. This XL checkpoint is the ImageNet model. Text-to-image is the separate XXL checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nju/pixnerd/pixnerd-xxl-16": { "id": "nju/pixnerd/pixnerd-xxl-16", "name": "PixNerd-XXL/16", "maker": "nju", "family": "nju/pixnerd", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-07-30", "context_length": null, "max_output_tokens": null, "parameters": "1.2B", "architecture": "Pixel-space diffusion transformer PixNerd-XXL/16 for text-to-image. The card says 1.2B parameters, no VAE, and neural-field patch decoding. The Hub pipeline tag is text-to-image. The repo contains model.ckpt. Prompt length, language, and resolution are not stated in the card section that was read.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixNerd-XXL/16 is the 1.2B text-to-image model from Nanjing University's Multimedia Computing Group. It is the pixel-space PixNerd diffusion transformer, extended from the ImageNet XL checkpoint to prompts. The card reports GenEval 0.73 and DPG 80.9. Status on 2026-10-15: active, Apache-2.0.", "what_it_is_for": "The card says this checkpoint is the text-to-image extension of PixNerd. A Hugging Face Space is linked for a demo. ImageNet class-to-image remains the XL checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nju/vfimamba/vfimamba": { "id": "nju/vfimamba/vfimamba", "name": "VFIMamba", "maker": "nju", "family": "nju/vfimamba", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Official checkpoint config model_type VFIMamba. Depth list [2, 2, 2, 3, 3]. Field F is 32. The card calls this the stronger of two released models and describes Mixed-SSM blocks that interleave tokens from adjacent frames and run multi-directional S6. A parameter count was not on the card or in config.json.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VFIMamba is a video frame interpolation model from Nanjing University's Multimedia Computing Group. It uses selective state-space blocks to model the frames between two inputs. The Hub checkpoint dated 2024-09-02 is the stronger of the two released sizes. Status on 2026-10-15: active, Apache-2.0.", "what_it_is_for": "The card says VFIMamba interpolates video frames. Demos cover 2x interpolation and an 8x script. The paper abstract says the method is aimed at efficient inter-frame modeling and is particularly strong at high resolution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nju/vfimamba/vfimamba-s": { "id": "nju/vfimamba/vfimamba-s", "name": "VFIMamba-S", "maker": "nju", "family": "nju/vfimamba", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Official checkpoint config model_type VFIMamba_S. Depth list [2, 2, 2, 3, 3], the same list as VFIMamba. Field F is 16, versus 32 on VFIMamba. The card calls this the efficient version. A parameter count was not published on the card.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VFIMamba-S is the efficient video frame interpolation checkpoint from Nanjing University's Multimedia Computing Group. The model card presents it next to the stronger VFIMamba and tells users to select it with the demo model flag. Status on 2026-10-15: active, Apache-2.0.", "what_it_is_for": "The card says it is the efficient VFIMamba version for interpolating frames. The same demos cover 2x and 8x interpolation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nju/videomae/videomae-base": { "id": "nju/videomae/videomae-base", "name": "VideoMAE base", "maker": "nju", "family": "nju/videomae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2022-08-03", "context_length": null, "max_output_tokens": null, "parameters": "94210944", "architecture": "VideoMAEForPreTraining, model_type videomae. ViT-B encoder: hidden 768, 12 layers, 12 heads, intermediate 3072, GELU, image 224, patch 16, 16 frames, tubelet 2, qkv bias, norm_pix_loss true. Decoder: hidden 384, 4 layers, 6 heads, intermediate 1536. Hub safetensors total for MCG-NJU/videomae-base is 94,210,944 and includes the decoder. The Kinetics-400 classification checkpoint MCG-NJU/videomae-base-finetuned-kinetics is 86,534,800 safetensors parameters and has 400 labels. GitHub script directory names use tube masking ratio 0.9 on Kinetics and Something-Something V2, and 0.75 on UCF101.", "modalities": { "input": [ "video" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoMAE base is a self-supervised video transformer released by the Multimedia Computing Group at Nanjing University, with Tencent AI Lab coauthors named on the GitHub README. The primary public checkpoint predicts masked video patches and was pretrained on Kinetics-400 for 1600 epochs. Classification heads are separate checkpoints. Status on 2026-10-15: active open weights under CC BY-NC 4.0.", "what_it_is_for": "The Hugging Face card, written by the Hugging Face team, says the raw model predicts pixel values for masked patches and is mostly intended to be fine-tuned. MCG uses it for Kinetics-400, Something-Something V2, and UCF101 action recognition.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nju/videomae/videomae-huge": { "id": "nju/videomae/videomae-huge", "name": "VideoMAE huge", "maker": "nju", "family": "nju/videomae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "VideoMAEForVideoClassification. Config: hidden 1280, 32 layers, 16 heads, intermediate 5120, patch 16, tubelet 2, 16 frames, image 224. Decoder fields in the classification config: hidden 640, 12 layers, 8 heads, intermediate 2560. 400 Kinetics-400 labels. No safetensors parameter total was returned. The model zoo also links a Google Drive pretraining checkpoint for ViT-H.", "modalities": { "input": [ "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoMAE huge is the ViT-H video model from Nanjing University's Multimedia Computing Group. The public Hub file is the Kinetics-400 fine-tune created on 2023-04-16. Its card and the official model zoo both report 86.6 top-1 and 97.1 top-5. Status on 2026-10-15: active, CC BY-NC 4.0.", "what_it_is_for": "The card says the model classifies a video into one of 400 Kinetics-400 labels after self-supervised pretraining for 1600 epochs and supervised fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nju/videomae/videomae-large": { "id": "nju/videomae/videomae-large", "name": "VideoMAE large", "maker": "nju", "family": "nju/videomae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2022-08-02", "context_length": null, "max_output_tokens": null, "parameters": "342996992", "architecture": "VideoMAEForPreTraining. ViT-L encoder: hidden 1024, 24 layers, 16 heads, intermediate 4096, patch 16, tubelet 2, 16 frames, image 224, GELU, norm_pix_loss true. Decoder: hidden 512, 12 layers, 8 heads, intermediate 2048. Hub safetensors total for MCG-NJU/videomae-large is 342,996,992. The Kinetics classification checkpoint MCG-NJU/videomae-large-finetuned-kinetics is 304,270,736 safetensors parameters and has 400 labels.", "modalities": { "input": [ "video" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoMAE large is the ViT-L masked video autoencoder from Nanjing University's Multimedia Computing Group. The Hub pretraining checkpoint was created on 2022-08-02 and was trained on Kinetics-400 for 1600 epochs. A separate Kinetics classification checkpoint is included as a version. Status on 2026-10-15: active, CC BY-NC 4.0.", "what_it_is_for": "The pretraining card says the model learns a video representation and is meant to be fine-tuned. The classification card says it maps a clip to one of 400 Kinetics-400 labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nju/videomae/videomae-small": { "id": "nju/videomae/videomae-small", "name": "VideoMAE small", "maker": "nju", "family": "nju/videomae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-04-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "VideoMAEForVideoClassification on the Hub fine-tunes. Config of MCG-NJU/videomae-small-finetuned-kinetics: hidden 384, 12 layers, 16 heads, intermediate 1536, patch 16, tubelet 2, 16 frames, image 224. Decoder fields in that config: hidden 192, 12 layers, 3 heads, intermediate 768. The classification head has 400 Kinetics labels. The Something-Something V2 fine-tune has 174 labels. No safetensors parameter total was returned for these repos.", "modalities": { "input": [ "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoMAE small is the ViT-S video masked autoencoder from Nanjing University's Multimedia Computing Group. On the Hub, the released files are supervised fine-tunes, not a pretrained-only repo. The Kinetics-400 card reports the same 79.0 top-1 as the official model zoo. Status on 2026-10-15: active, CC BY-NC 4.0.", "what_it_is_for": "The Kinetics card says the model classifies a video into one of 400 Kinetics-400 labels. The Something-Something V2 card says it classifies 174 labels. Both cards say the weights were pretrained self-supervised and then fine-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "njust/facialmmt/facialmmt": { "id": "njust/facialmmt/facialmmt", "name": "FacialMMT", "maker": "njust", "family": "njust/facialmmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2023-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal emotion classifier for multi-party conversation. The card says it uses facial sequences of the real speaker. Hub files are PyTorch checkpoints named Swin+BERT, Swin+RoBERTa, and a unimodal vision file, plus MELD pickles and a facial-sequence zip. Parameter totals were not read from the checkpoints. The Hub pipeline tag is text-classification and the language tag is English. Tags include roberta.", "modalities": { "input": [ "text", "audio", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FacialMMT is a multimodal emotion model from the Text Mining Group at Nanjing University of Science and Technology. The card cites Wenjie Zheng, Jianfei Yu, Rui Xia, and Shijin Wang, ACL 2023. It uses facial sequences of the real speaker for emotion recognition in multi-party conversation. The Hub repo mixes GPL-3.0 checkpoints with MELD features and facial-sequence archives. Collected 2026-10-15.", "what_it_is_for": "The card says the model uses the speaker's facial sequence to help multimodal emotion recognition. The reported test set is MELD. The Hub tree has text, audio, and vision pickles plus facial-sequence files, so the released bundle is the conversation setting rather than text alone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "njust/fs-absa/dutch-restaurant-mt5-small": { "id": "njust/fs-absa/dutch-restaurant-mt5-small", "name": "Dutch-Restaurant-mT5-Small", "maker": "njust", "family": "njust/fs-absa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-24", "context_length": null, "max_output_tokens": null, "parameters": "300176768", "architecture": "MT5ForConditionalGeneration continued from mt5-small (config _name_or_path ../PLMs/mt5-small). Encoder-decoder, 8 layers, d_model 512, d_ff 1024, 6 heads, d_kv 64, gated-gelu, vocab 250112, relative buckets 32, max distance 128, float32, embeddings not tied. No n_positions field. Domain-adaptive text infilling on 100k Yelp restaurant reviews machine-translated to Dutch with DeepL, 15% corruption, mean span 1, Adafactor, batch 16, constant learning rate 1e-4. Hub safetensors total 300,176,768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dutch-Restaurant-mT5-Small is the Dutch restaurant checkpoint from the Text Mining Group at Nanjing University of Science and Technology. The card credits Zengzhi Wang, Qiming Xie, and Rui Xia and the SIGIR 2023 FS-ABSA paper. It continues mT5-small on Yelp reviews translated into Dutch. Weights are on the Hub under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says this is a restaurant-domain sequence-to-sequence model for Dutch aspect-based sentiment, product question answering, and style transfer. The pretraining text is not an original Dutch crawl. The card says 100k Yelp reviews were translated with DeepL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "njust/fs-absa/french-restaurant-mt5-small": { "id": "njust/fs-absa/french-restaurant-mt5-small", "name": "French-Restaurant-mT5-Small", "maker": "njust", "family": "njust/fs-absa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-24", "context_length": null, "max_output_tokens": null, "parameters": "300176768", "architecture": "MT5ForConditionalGeneration continued from mt5-small (config _name_or_path ../PLMs/mt5-small). Encoder-decoder, 8 layers, d_model 512, d_ff 1024, 6 heads, d_kv 64, gated-gelu, vocab 250112, relative buckets 32, max distance 128, float32, embeddings not tied. No n_positions field. Domain-adaptive text infilling on 100k Yelp restaurant reviews machine-translated to French with DeepL, 15% corruption, mean span 1, Adafactor, batch 16, constant learning rate 1e-4. Hub safetensors total 300,176,768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "French-Restaurant-mT5-Small is the French restaurant checkpoint from the Text Mining Group at Nanjing University of Science and Technology. The card credits Zengzhi Wang, Qiming Xie, and Rui Xia and the SIGIR 2023 FS-ABSA paper. It continues mT5-small on Yelp reviews translated into French. Weights are on the Hub under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says this is a restaurant-domain sequence-to-sequence model for French aspect-based sentiment, product question answering, and style transfer. The pretraining text is 100k Yelp reviews translated with DeepL, not an original French crawl.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "njust/fs-absa/laptop-t5-base": { "id": "njust/fs-absa/laptop-t5-base", "name": "Laptop-T5-Base", "maker": "njust", "family": "njust/fs-absa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-24", "context_length": 512, "max_output_tokens": null, "parameters": "222903552", "architecture": "T5ForConditionalGeneration continued from t5-base (config _name_or_path ../PLMs/t5-base/). Encoder-decoder, 12 layers, d_model 768, d_ff 3072, 12 heads, d_kv 64, ReLU, vocab 32128, n_positions 512, relative buckets 32, max distance 128, float32. Domain-adaptive text infilling on 100k Amazon Electronics laptop reviews, 15% corruption, mean span 1, Adafactor, batch 84, constant learning rate 1e-4. Hub safetensors total 222,903,552.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Laptop-T5-Base is the laptop-domain T5-base checkpoint from the Text Mining Group at Nanjing University of Science and Technology. The authors on the card are Zengzhi Wang, Qiming Xie, and Rui Xia, for the SIGIR 2023 FS-ABSA paper. It continues T5 on unlabeled electronics reviews. Weights are on the Hub under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says this is an English laptop-domain T5 for aspect-based sentiment, product question answering, and style transfer. The worked example on the card is still a restaurant sentence about pizza.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "njust/fs-absa/restaurant-t5-base": { "id": "njust/fs-absa/restaurant-t5-base", "name": "Restaurant-T5-Base", "maker": "njust", "family": "njust/fs-absa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-24", "context_length": 512, "max_output_tokens": null, "parameters": "222903552", "architecture": "T5ForConditionalGeneration continued from t5-base (config _name_or_path ../PLMs/t5-base/). Encoder-decoder, 12 layers, d_model 768, d_ff 3072, 12 heads, d_kv 64, ReLU, vocab 32128, n_positions 512, relative buckets 32, max distance 128, float32. Domain-adaptive text infilling on 100k Yelp restaurant reviews, 15% corruption, mean span 1, Adafactor, batch 80, learning rate 1e-4. Hub safetensors total 222,903,552.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Restaurant-T5-Base is a domain-adaptive T5-base checkpoint from the Text Mining Group at Nanjing University of Science and Technology (NUSTM). Zengzhi Wang, Qiming Xie, and Rui Xia released it with the SIGIR 2023 FS-ABSA paper. The card says it continues T5 on unlabeled restaurant reviews. Weights are on the Hugging Face Hub under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint is an English restaurant-domain T5 for aspect-based sentiment analysis, product question answering, and text style transfer. It is not a general chat model. The paper frames end-to-end ABSA as text infilling after this continued pretraining.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "njust/hssallm/hssallm-32b": { "id": "njust/hssallm/hssallm-32b", "name": "HssaLLM-32B", "maker": "njust", "family": "njust/hssallm", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-06", "context_length": 40960, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM continued from Qwen3-32B, then instruction-tuned. config.json: 64 layers, hidden 5120, intermediate 25600, 64 heads, 8 key-value heads, head dim 128, SiLU, vocab 151936, rope theta 1000000, no rope scaling, bfloat16, tie_word_embeddings false, eos token id 151645. ModelScope labels the repo 32.76B and 65.54GB. The team also says 32B. A safetensors total was not summed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HssaLLM-32B is the instruction-tuned 32B checkpoint of \u5170\u7ae0 (Lanzhang), from Professor Shen Si's team at Nanjing University of Science and Technology. The lab says it is full-parameter continued pretraining of Qwen3-32B on 11.6 billion academic tokens, then the same instruction tune used for the 8B model. Weights are public on ModelScope under Apache-2.0. May 2026 pages call the release a trial. Collected 2026-10-15.", "what_it_is_for": "Same academic jobs as the 8B instruct model: chapter titles, conclusions, literature reviews, entity recognition, book classification, and related scholarly instructions. The ModelScope card's quick start still names `njauzwh/HssaLLM-8B`, so that snippet is not a 32B-specific recipe.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "njust/hssallm/hssallm-32b-base": { "id": "njust/hssallm/hssallm-32b-base", "name": "HssaLLM-32B-Base", "maker": "njust", "family": "njust/hssallm", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-06", "context_length": 40960, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM. The lab says this base is full-parameter continued pretraining of Qwen3-32B. config.json matches the HssaLLM-32B instruct config on the fields read: 64 layers, hidden 5120, intermediate 25600, 64 heads, 8 key-value heads, head dim 128, vocab 151936, rope theta 1000000, bfloat16, eos token id 151645, max_position_embeddings 40960. ModelScope profile label 32.76B. A safetensors total was not summed. Weight files were not compared with the instruct repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HssaLLM-32B-Base is the continued-pretraining checkpoint of \u5170\u7ae0 at the 32B size, from Professor Shen Si's team at Nanjing University of Science and Technology. The README says it is full-parameter training of Qwen3-32B on the 11.6 billion token academic corpus. The instruction-tuned weights are a separate folder. ModelScope lists this repo as Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "This is the domain base before the shared instruction tune. The series targets humanities and social-science full text. The base card body was not read on its own.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "njust/hssallm/hssallm-8b": { "id": "njust/hssallm/hssallm-8b", "name": "HssaLLM-8B", "maker": "njust", "family": "njust/hssallm", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-08", "context_length": 40960, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM continued from Qwen3-8B, then instruction-tuned. config.json: 36 layers, hidden 4096, intermediate 12288, 32 heads, 8 key-value heads, head dim 128, SiLU, vocab 151936, rope theta 1000000, no rope scaling, bfloat16, tie_word_embeddings false, eos token id 151645. ModelScope labels the repo 8.19B. The team also says 8B. A safetensors total was not summed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HssaLLM-8B is the instruction-tuned 8B checkpoint of \u5170\u7ae0 (Lanzhang), the humanities and social-sciences academic model from Professor Shen Si's team at Nanjing University of Science and Technology. It is a full-parameter continued pretrain of Qwen3-8B followed by instruction tuning. Weights are public on ModelScope under Apache-2.0. May 2026 university pages call the release a trial. Collected 2026-10-15.", "what_it_is_for": "The team describes an academic assistant for full-text humanities and social-science work. The README's evaluation tasks are chapter-title generation, conclusion generation, literature-review generation, entity recognition, and automatic book classification. Instruction data also covers discipline classification, move recognition, term extraction, and Chinese-English scholarly translation. The GitHub sample asks the model about large language models in the digital humanities.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "njust/hssallm/hssallm-8b-base": { "id": "njust/hssallm/hssallm-8b-base", "name": "HssaLLM-8B-Base", "maker": "njust", "family": "njust/hssallm", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-06", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM. The lab says this base is full-parameter continued pretraining of Qwen3-8B on the 11.6 billion token corpus, before instruction tuning. config.json: 36 layers, hidden 4096, intermediate 12288, 32 heads, 8 key-value heads, head dim 128, SiLU, vocab 151936, rope theta 1000000, no rope scaling, bfloat16, tie_word_embeddings false, eos token id 151643, transformers 4.52.3. ModelScope labels the repo 8.19B. A safetensors total was not summed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HssaLLM-8B-Base is the continued-pretraining checkpoint of \u5170\u7ae0 at the 8B size, from Professor Shen Si's team at Nanjing University of Science and Technology. The README says it is full-parameter training of Qwen3-8B on an 11.6 billion token bilingual academic corpus. Instruction tuning is a later stage and is a different folder. Weights are on ModelScope under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The lab uses this checkpoint as the domain base before the 15,697-example instruction tune. The series is for humanities and social-science full text, not a general chat product. The base card body was not read separately from the series README.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/coderank/coderankembed": { "id": "nomic/coderank/coderankembed", "name": "CodeRankEmbed", "maker": "nomic", "family": "nomic/coderank", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-11-03", "context_length": 8192, "max_output_tokens": null, "parameters": "137M", "architecture": "Bi-encoder with shared weights, initialized from Snowflake Arctic-Embed-M-Long and contrastively finetuned with InfoNCE on CoRNStack (about 21 million examples).", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CodeRankEmbed is Nomic AI's 137M code bi-encoder from the CoRNStack work. Weights went up on 3 November 2024 under MIT. Cataloged 2026-10-15. It is a named Nomic release, even though the base encoder is Snowflake Arctic-Embed-M-Long.", "what_it_is_for": "Smaller code retrieval when a 7B embedder is too heavy. Pair it with CodeRankLLM if you want a second-stage rerank.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/coderank/coderankllm": { "id": "nomic/coderank/coderankllm", "name": "CodeRankLLM", "maker": "nomic", "family": "nomic/coderank", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-11-08", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Listwise code reranker. Initialized from Qwen2.5-Coder-7B-Instruct and finetuned with a language-modeling loss to predict a ranked order. Ranking labels were produced by Qwen2.5-32B-Instruct over 50,000 CoRNStack tuples.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CodeRankLLM is Nomic AI's 7B listwise code reranker, published 8 November 2024 under MIT. Cataloged 2026-10-15. It is a generator fine-tune, not an embedding model.", "what_it_is_for": "Second-stage listwise reranking of code search results.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/embed-code/nomic-embed-code": { "id": "nomic/embed-code/nomic-embed-code", "name": "Nomic Embed Code", "maker": "nomic", "family": "nomic/embed-code", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-27", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Fine-tune of Qwen2.5-Coder-7B-Instruct for code retrieval. Last-token pooling. Trained on CoRNStack with dual-consistency filtering and progressive hard negatives.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nomic Embed Code is Nomic AI's 7B code embedding model, announced 27 March 2025. Weights are Apache-2.0. It was cataloged on 2026-10-15. It is a local model, not an Atlas API id.", "what_it_is_for": "Retrieving code from natural-language queries in Python, Java, Ruby, PHP, JavaScript, and Go.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/embed-multimodal/colnomic-embed-multimodal-3b": { "id": "nomic/embed-multimodal/colnomic-embed-multimodal-3b", "name": "ColNomic Embed Multimodal 3B", "maker": "nomic", "family": "nomic/embed-multimodal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-02", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Multi-vector late-interaction embedder. PEFT adapter on Qwen2.5-VL-3B-Instruct. Loaded as ColQwen2_5. Several embeddings per page instead of one.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ColNomic Embed Multimodal 3B is one of four Nomic AI visual-document embedders announced 2 April 2025. Cataloged 2026-10-15. It is a local adapter on Qwen2.5-VL, not an Atlas API model.", "what_it_is_for": "Multi-vector late-interaction retrieval over page images at the 3B size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/embed-multimodal/colnomic-embed-multimodal-7b": { "id": "nomic/embed-multimodal/colnomic-embed-multimodal-7b", "name": "ColNomic Embed Multimodal 7B", "maker": "nomic", "family": "nomic/embed-multimodal", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-02", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Multi-vector late-interaction embedder. PEFT adapter on Qwen2.5-VL-7B-Instruct. Apache-2.0 on the model card. Loaded as ColQwen2_5.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ColNomic Embed Multimodal 7B is one of four Nomic AI visual-document embedders announced 2 April 2025. Cataloged 2026-10-15. It is a local adapter on Qwen2.5-VL, not an Atlas API model.", "what_it_is_for": "Highest-scoring Nomic visual document retriever in the April 2025 release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/embed-multimodal/nomic-embed-multimodal-3b": { "id": "nomic/embed-multimodal/nomic-embed-multimodal-3b", "name": "Nomic Embed Multimodal 3B", "maker": "nomic", "family": "nomic/embed-multimodal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-02", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Single-vector (dense) VLM embedder. PEFT adapter on Qwen2.5-VL-3B-Instruct. Loaded as BiQwen2_5 in colpali_engine. Same-source sampling and positive-aware hard negatives.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nomic Embed Multimodal 3B is one of four Nomic AI visual-document embedders announced 2 April 2025. Cataloged 2026-10-15. It is a local adapter on Qwen2.5-VL, not an Atlas API model.", "what_it_is_for": "Single-vector visual document retrieval when you want one vector per page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/embed-multimodal/nomic-embed-multimodal-7b": { "id": "nomic/embed-multimodal/nomic-embed-multimodal-7b", "name": "Nomic Embed Multimodal 7B", "maker": "nomic", "family": "nomic/embed-multimodal", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-02", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Single-vector VLM embedder. PEFT adapter on Qwen2.5-VL-7B-Instruct. Hugging Face tags Apache-2.0. Same training recipe as the 3B dense model.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nomic Embed Multimodal 7B is one of four Nomic AI visual-document embedders announced 2 April 2025. Cataloged 2026-10-15. It is a local adapter on Qwen2.5-VL, not an Atlas API model.", "what_it_is_for": "Single-vector visual document retrieval at the larger Qwen2.5-VL size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/embed-text/nomic-embed-text-v1": { "id": "nomic/embed-text/nomic-embed-text-v1", "name": "Nomic Embed Text v1", "maker": "nomic", "family": "nomic/embed-text", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-01", "context_length": 8192, "max_output_tokens": null, "parameters": "137M", "architecture": "NomicBERT encoder (BERT with rotary embeddings and SwiGLU) contrastively trained in two stages. 768-dimensional embeddings. Task-conditioned prefixes.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nomic Embed Text v1 is Nomic AI's first fully reproducible English text embedding model. It was announced on 1 February 2024, is still an accepted model on the Atlas embedding API, and was cataloged on 2026-10-15.", "what_it_is_for": "English retrieval, similarity, clustering, and classification, including long documents up to 8192 tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/embed-text/nomic-embed-text-v1-5": { "id": "nomic/embed-text/nomic-embed-text-v1-5", "name": "Nomic Embed Text v1.5", "maker": "nomic", "family": "nomic/embed-text", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-14", "context_length": 8192, "max_output_tokens": null, "parameters": "137M", "architecture": "Nomic Embed Text v1 stack finetuned with Matryoshka representation learning. One model supports truncated dimensions from 64 through 768 and binary embeddings. 137M is the v1 encoder size; v1.5 is the same backbone.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nomic Embed Text v1.5 is Nomic AI's Matryoshka revision of Nomic Embed Text. Announced 14 February 2024, it remains on the Atlas embedding API and was cataloged on 2026-10-15.", "what_it_is_for": "Production English embeddings when you need to shrink vectors (768 down to 64) or store them as bits.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/embed-text/nomic-embed-text-v2-moe": { "id": "nomic/embed-text/nomic-embed-text-v2-moe", "name": "Nomic Embed Text v2 MoE", "maker": "nomic", "family": "nomic/embed-text", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-07", "context_length": 512, "max_output_tokens": null, "parameters": "475M total, 305M active", "architecture": "Sparse mixture-of-experts encoder. 8 experts, top-2 routing, alternating MoE layers. Matryoshka dimensions 768 down to 256. Initialized from the nomic-xlm-2048 line via the unsupervised checkpoint. Mean pooling.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nomic Embed Text v2 MoE is Nomic AI's multilingual mixture-of-experts text embedder. Weights were published on 7 February 2025 under Apache-2.0. It was cataloged on 2026-10-15 and is not in the current Atlas text model list.", "what_it_is_for": "Multilingual retrieval across about 100 languages, with a smaller active parameter count than a dense model of the same total size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/embed-vision/nomic-embed-vision-v1": { "id": "nomic/embed-vision/nomic-embed-vision-v1", "name": "Nomic Embed Vision v1", "maker": "nomic", "family": "nomic/embed-vision", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-05", "context_length": null, "max_output_tokens": null, "parameters": "92.9M", "architecture": "ViT-B/16 vision encoder, initialized from an EVA-02 MIM checkpoint and trained with the text encoder frozen (locked-text image tuning, a LiT variant). 768-d space aligned to nomic-embed-text-v1.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nomic Embed Vision v1 is Nomic AI's vision encoder aligned to nomic-embed-text-v1. It was announced on 5 June 2024, is still accepted by the Atlas image endpoint, and was cataloged on 2026-10-15.", "what_it_is_for": "Image embeddings in the same vector space as the matching Nomic Embed Text model, for image search and cross-modal search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/embed-vision/nomic-embed-vision-v1-5": { "id": "nomic/embed-vision/nomic-embed-vision-v1-5", "name": "Nomic Embed Vision v1.5", "maker": "nomic", "family": "nomic/embed-vision", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-06-05", "context_length": null, "max_output_tokens": null, "parameters": "92.9M", "architecture": "ViT-B/16 vision encoder, initialized from an EVA-02 MIM checkpoint and trained with the text encoder frozen (locked-text image tuning, a LiT variant). 768-d space aligned to nomic-embed-text-v1.5.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nomic Embed Vision v1.5 is Nomic AI's vision encoder aligned to nomic-embed-text-v1.5. It was announced on 5 June 2024, is still accepted by the Atlas image endpoint, and was cataloged on 2026-10-15.", "what_it_is_for": "Image embeddings in the same vector space as the matching Nomic Embed Text model, for image search and cross-modal search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/gpt4all/gpt4all-13b-snoozy": { "id": "nomic/gpt4all/gpt4all-13b-snoozy", "name": "GPT4All-13B-Snoozy", "maker": "nomic", "family": "nomic/gpt4all", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL", "release_date": "2023-04-24", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Finetune of LLaMA 13B on nomic-ai/gpt4all-j-prompt-generations revision v1.3-groovy.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT4All-13B-Snoozy is a 2023 Nomic AI chatbot fine-tune. Weights remain on Hugging Face under GPL. Cataloged 2026-10-15. It is not part of the current Nomic Platform model picker.", "what_it_is_for": "Local assistant-style chat from Nomic's 2023 GPT4All releases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/gpt4all/gpt4all-falcon": { "id": "nomic/gpt4all/gpt4all-falcon", "name": "GPT4All-Falcon", "maker": "nomic", "family": "nomic/gpt4all", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-06-02", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Finetune of tiiuae/falcon-7b. Card says training used DeepSpeed and Accelerate, global batch 256, learning rate 2e-5, about 12 hours on 8 A100 80GB GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT4All-Falcon is a 2023 Nomic AI chatbot fine-tune. Weights remain on Hugging Face under Apache-2.0. Cataloged 2026-10-15. It is not part of the current Nomic Platform model picker.", "what_it_is_for": "Local assistant-style chat from Nomic's 2023 GPT4All releases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/gpt4all/gpt4all-j": { "id": "nomic/gpt4all/gpt4all-j", "name": "GPT4All-J", "maker": "nomic", "family": "nomic/gpt4all", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-11", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "Finetune of GPT-J on assistant-style data. Default revision is v1.0. Later revisions v1.1-breezy, v1.2-jazzy, and v1.3-groovy change the dataset.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT4All-J is a 2023 Nomic AI chatbot fine-tune. Weights remain on Hugging Face under Apache-2.0. Cataloged 2026-10-15. It is not part of the current Nomic Platform model picker.", "what_it_is_for": "Local assistant-style chat from Nomic's 2023 GPT4All releases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/gpt4all/gpt4all-lora": { "id": "nomic/gpt4all/gpt4all-lora", "name": "GPT4All LoRA", "maker": "nomic", "family": "nomic/gpt4all", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2023-03-28", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "LoRA finetune of LLaMA, trained four epochs on Atlas-curated prompt generations. The three-epoch checkpoint is a separate repo and is recorded as a version.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT4All LoRA is Nomic AI's first named assistant model, a four-epoch LLaMA LoRA published 28 March 2023 under GPL-3.0. Cataloged 2026-10-15. Weights are still up. It is a legacy release.", "what_it_is_for": "The original March 2023 local GPT4All assistant LoRA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/gpt4all/gpt4all-mpt": { "id": "nomic/gpt4all/gpt4all-mpt", "name": "GPT4All-MPT", "maker": "nomic", "family": "nomic/gpt4all", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-05-11", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Finetune of MosaicML MPT-7B on the v1.3-groovy prompt-generation set.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT4All-MPT is a 2023 Nomic AI chatbot fine-tune. Weights remain on Hugging Face under Apache-2.0. Cataloged 2026-10-15. It is not part of the current Nomic Platform model picker.", "what_it_is_for": "Local assistant-style chat from Nomic's 2023 GPT4All releases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/modernbert-embed/modernbert-embed-base": { "id": "nomic/modernbert-embed/modernbert-embed-base", "name": "ModernBERT Embed Base", "maker": "nomic", "family": "nomic/modernbert-embed", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-30", "context_length": null, "max_output_tokens": null, "parameters": "149M", "architecture": "Finetune of answerdotai/ModernBERT-base on the Nomic Embed weakly supervised and supervised datasets. Mean pooling. Matryoshka dimension 256 as well as 768.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernBERT Embed Base is Nomic AI's English embedding finetune of ModernBERT-base, published 30 December 2024 under Apache-2.0. Cataloged 2026-10-15. It is not an Atlas API model id.", "what_it_is_for": "English embeddings with a ModernBERT encoder and a 256-d Matryoshka option.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/nomic-bert/nomic-bert-2048": { "id": "nomic/nomic-bert/nomic-bert-2048", "name": "NomicBERT 2048", "maker": "nomic", "family": "nomic/nomic-bert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-04", "context_length": 2048, "max_output_tokens": null, "parameters": "137M", "architecture": "BERT masked-language model with rotary position embeddings, SwiGLU, and dropout 0. Trained on packed Wikipedia and BookCorpus at sequence length 2048. Uses the bert-base-uncased tokenizer. This is the encoder Nomic Embed Text starts from.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NomicBERT 2048 is Nomic AI's English BERT-style encoder with a 2048-token context. Weights were published 4 January 2024 under Apache-2.0. Cataloged 2026-10-15.", "what_it_is_for": "Longer-context English masked language modeling, and as the starting encoder for Nomic Embed Text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/nomic-bert/nomic-xlm-2048": { "id": "nomic/nomic-bert/nomic-xlm-2048", "name": "Nomic XLM 2048", "maker": "nomic", "family": "nomic/nomic-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-11-16", "context_length": 2048, "max_output_tokens": null, "parameters": "278M", "architecture": "XLM-RoBERTa-base with absolute positions replaced by RoPE, then trained 10k steps on CC100. Fill-mask encoder. Backbone for the Nomic Embed v2 MoE line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nomic XLM 2048 is Nomic AI's RoPE-extended XLM-RoBERTa-base, published 16 November 2024. Cataloged 2026-10-15. Weights are public. A license was not found on the card.", "what_it_is_for": "Multilingual masked language modeling at 2048 tokens, and as the encoder behind Nomic Embed Text v2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nomic/parse/nomic-parse": { "id": "nomic/parse/nomic-parse", "name": "Nomic Parse", "maker": "nomic", "family": "nomic/parse", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.012, "completion_per_m_usd": null }, "summary_intro": "Nomic Parse is Nomic AI's closed, domain-specific document parser on the 2026 Nomic Platform. It was cataloged on 2026-10-15. Specs other than the per-page price are not public.", "what_it_is_for": "Parsing AEC drawings, specs, and PDFs into structured text and figures before Nomic Embed indexes them.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northeastern/moxin/moxin-7b-chat": { "id": "northeastern/moxin/moxin-7b-chat", "name": "Moxin 7B Chat", "maker": "northeastern", "family": "northeastern/moxin", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-04", "context_length": 32768, "max_output_tokens": null, "parameters": "8114180096", "architecture": "Chat fine-tune of Moxin-7B-LLM. The Hugging Face record is moxin-org/Moxin-7B-Chat, created 2024-12-04, with MistralForCausalLM in config and a llama label in the GGUF header. GGUF metadata reports 8,114,180,096 parameters and context length 32,768, the same totals as the base GGUF. The tokenizer uses a Mistral-style [INST] chat template. The base card and GitHub README say this checkpoint is the base model further fine-tuned on Tulu v2. Later Instruct is a different Tulu 3 SFT plus DPO recipe, not a rename of this repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moxin 7B Chat is the conversational fine-tune of Moxin 7B released by the same Northeastern-led Moxin group. Hugging Face created `moxin-org/Moxin-7B-Chat` on 2024-12-04. It was still a separate public repository on the collection date 2026-10-15, under Apache-2.0. MarkTechPost's 2024-12-19 writeup treated Base and Chat as the two versions then available.", "what_it_is_for": "The card presents it as the chat model. The base card and the GitHub README say it is the base checkpoint further fine-tuned on Tulu v2. The later Moxin 7B Instruct model is a different post-train, using Tulu 3 SFT and DPO, and it has its own repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northeastern/moxin/moxin-7b-chinese": { "id": "northeastern/moxin/moxin-7b-chinese", "name": "Moxin 7B Chinese", "maker": "northeastern", "family": "northeastern/moxin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Chinese post-train of Moxin described in arXiv:2512.22208. The paper says the authors trained a Chinese BPE vocabulary with SentencePiece on a sample of WuDaoCorpus2, merged other Chinese vocabularies, and after review reached a vocabulary of about 57k. They then continued training on WanJuan, Gutenberg books, Chinese-Data-Distill-From-R1, and other Chinese sets, and fine-tuned on English-Chinese translation sets. The stated hub id moxin-org/Moxin-7B-Chinese returned HTTP 404 on 2026-10-15. No config, license, or parameter total was retrieved.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moxin 7B Chinese is a Chinese-continued Moxin checkpoint described by the Northeastern-led Moxin group in arXiv:2512.22208, submitted 2025-12-22 and revised 2026-02-04. The paper says it is released at `moxin-org/Moxin-7B-Chinese`. That page returned 404 on the collection date 2026-10-15. No license file was found.", "what_it_is_for": "The paper says the model is for Chinese tasks and for English-Chinese translation. The method is a larger Chinese vocabulary, continued training on Chinese corpora, and a translation fine-tune. The only scores in the extracted evaluation section are CMMLU and C-Eval, not a translation metric.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northeastern/moxin/moxin-7b-instruct": { "id": "northeastern/moxin/moxin-7b-instruct", "name": "Moxin 7B Instruct", "maker": "northeastern", "family": "northeastern/moxin", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-12", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Instruction model released as moxin-org/Moxin-7B-Instruct. config.json is MistralForCausalLM with 36 layers, hidden size 4096, intermediate size 14336, 32 heads, 8 key-value heads, head dimension 128, sliding window 4096, max position embeddings 32768, vocab 32008, untied embeddings, and bfloat16. The paper says the released Instruct checkpoint is Moxin-7B-DPO: two epochs of supervised fine-tuning on the Tulu 3 SFT mixture, learning rate 5e-6, batch size 128, then one epoch of DPO on the Tulu 3 8B preference mixture at learning rate 5e-7. A second instruct run on Infinity Instruct is called Moxin-7B-DPO-II in the paper and was not found as its own repository. The uploaded config _name_or_path still reads an internal SFT directory, sft_8b_11k5_final, which disagrees with the paper's statement that the released file is the DPO model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moxin 7B Instruct is the Tulu 3 post-train of Moxin 7B from the Northeastern-led Moxin group. Hugging Face created `moxin-org/Moxin-7B-Instruct` on 2025-04-12. The repository was public under Apache-2.0 on the collection date 2026-10-15. arXiv:2412.06845v6, dated 2025-07-04, says the released Instruct weights are the DPO checkpoint, not the intermediate SFT checkpoint and not the separate Infinity Instruct run.", "what_it_is_for": "The paper describes it as the helpful assistant obtained by supervised fine-tuning and then direct preference optimization on open Tulu 3 data. The card's example is a system-plus-user chat. It is a different product from Moxin 7B Chat, which the base card ties to Tulu v2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northeastern/moxin/moxin-7b-llm": { "id": "northeastern/moxin/moxin-7b-llm", "name": "Moxin 7B", "maker": "northeastern", "family": "northeastern/moxin", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-04", "context_length": 32768, "max_output_tokens": null, "parameters": "8114180096", "architecture": "Decoder-only transformer released as moxin-org/Moxin-7B-LLM. config.json sets model_type mistral, MistralForCausalLM, 36 layers, hidden size 4096, intermediate size 14336, 32 query heads, 8 key-value heads, head dimension 128, SiLU, RoPE theta 10000, sliding window 4096, vocab 32000, untied embeddings, and float16. The paper's Table 1 matches those widths and the 36-block extension of the Mistral layout. The repo GGUF header instead labels the architecture llama and reports the same 8,114,180,096 parameter total and 32,768 context. Pretraining is described as more than 2 trillion tokens in three phases, with a stated cost of about $160,000. The public checkpoint is the Enhanced, or finetuned, base, not Moxin-7B-Original.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moxin 7B is the base language model from a Northeastern-led group, released on Hugging Face as `moxin-org/Moxin-7B-LLM` on 2024-12-04 under Apache-2.0. The university's own Hugging Face orgs had no public models on the collection date 2026-10-15. The checkpoint is active, local, and described by the authors as a fully open pretraining release. Coauthors include Harvard, Cornell, Tulane, the University of Washington, Futurewei, Roboraction.ai, and AIBAO LLC.", "what_it_is_for": "The technical report presents Moxin 7B as a reproducible 7B-class base model for research and downstream post-training. The model card is a plain text-generation checkpoint. The authors say the file they released is the Enhanced, or finetuned, base, and that a separate Chat model was then trained from it on Tulu v2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northeastern/moxin/moxin-7b-reasoning": { "id": "northeastern/moxin/moxin-7b-reasoning", "name": "Moxin 7B Reasoning", "maker": "northeastern", "family": "northeastern/moxin", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-12", "context_length": null, "max_output_tokens": null, "parameters": "8114245632", "architecture": "Reasoning post-train released as moxin-org/Moxin-7B-Reasoning. The Hugging Face API record lists MistralForCausalLM, Apache-2.0, and a safetensors total of 8,114,245,632 parameters stored as F32. That is 65,536 above the base GGUF total, which matches eight extra vocabulary rows times hidden size 4096 times two untied embedding matrices. The card uses the same <|system|>, <|user|>, <|assistant|>, <|endoftext|> template as Instruct. The paper says this release is Moxin-7B-RL-DeepScaleR: SFT on OpenThoughts and OpenR1-Math-220k, then GRPO in the DeepScaleR framework with an outcome reward. A second GRPO run, Moxin-7B-RL-AReal, is scored and is not the named release. This API record did not include max_position_embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moxin 7B Reasoning is the chain-of-thought and GRPO post-train of Moxin 7B Instruct from the Northeastern-led Moxin group. Hugging Face created `moxin-org/Moxin-7B-Reasoning` on 2025-04-12. It was public under Apache-2.0 on the collection date 2026-10-15. The July 2025 technical report says the published file is the DeepScaleR run, not the AReal run.", "what_it_is_for": "The paper presents it as a small reasoning model for competition mathematics. Training is supervised fine-tuning on OpenThoughts and OpenR1-Math-220k, whose traces come from DeepSeek R1, then group relative policy optimization with an outcome reward in the DeepScaleR framework.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northeastern/moxin/moxin-7b-vla": { "id": "northeastern/moxin/moxin-7b-vla", "name": "Moxin 7B VLA", "maker": "northeastern", "family": "northeastern/moxin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language-action model described in arXiv:2512.22208. The paper says it fine-tunes Moxin-VLM with the OpenVLA-OFT recipe: LoRA rank 32, parallel action chunks instead of single-step autoregressive actions, two image frames plus proprioceptive state, FiLM removed for the single-arm setting. LIBERO fine-tuning is 50k steps, batch size 64, learning rate 3e-4, on 8 H100s. A separate Open X-Embodiment robotics pretraining run is described as about 90k steps on 8 H100s for roughly two weeks. The LIBERO table lists size 9B. The stated hub id moxin-org/Moxin-7B-VLA returned HTTP 404 on 2026-10-15, so the checkpoint file, license, and which of the two reported rows was uploaded were not verified.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moxin 7B VLA is a robot-control model described by the Northeastern-led Moxin group in arXiv:2512.22208, submitted 2025-12-22 and revised 2026-02-04. The paper says the weights are at `moxin-org/Moxin-7B-VLA`. That page returned 404 on the collection date 2026-10-15. The description is a LoRA fine-tune of Moxin 7B VLM using the OpenVLA-OFT recipe, not a third-party foundation model. No license was stated in the sections read.", "what_it_is_for": "The paper says the model is for robotic manipulation. It predicts a chunk of future actions in parallel, using two camera frames and the robot's proprioceptive state. The reported test is the LIBERO simulator. The authors say they dropped the FiLM module because it did not help in their single-arm setting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northeastern/moxin/moxin-7b-vlm": { "id": "northeastern/moxin/moxin-7b-vlm", "name": "Moxin 7B VLM", "maker": "northeastern", "family": "northeastern/moxin", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-09", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language model released as moxin-org/Moxin-7B-VLM. The released config names model_id prism-moxin-dinosiglip-224px+7b, vision backbone dinosiglip-vit-so-224px, LLM backbone moxin-7b-pure, and llm_max_length 2048. The paper trains it in the Prismatic VLM framework: frozen DINOv2 and SigLIP features fused and projected into Moxin-7B-Base, with the projector and language model trained for two epochs on the LLaVA v1.5 mixture. The hub file is checkpoints/latest-checkpoint.pt, about 32.7 GB. The card also mentions an hf_convert build, Moxin-7B-VLM-hf, based on OpenVLA. License on the card is MIT, unlike the Apache-2.0 language checkpoints. No parameter total was in the API record.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moxin 7B VLM is the vision-language model from the Northeastern-led Moxin group. Hugging Face created `moxin-org/Moxin-7B-VLM` on 2025-06-09. The card license is MIT. On the collection date 2026-10-15 the weights were a public PyTorch checkpoint, `checkpoints/latest-checkpoint.pt`. The language backbone is the group's own Moxin 7B base. The vision towers are frozen DINOv2 and SigLIP.", "what_it_is_for": "The paper describes image-and-text dialogue and the usual LLaVA-style probes: visual question answering, referring expressions, counting, spatial true-or-false questions, and a hallucination yes-or-no set. The card points at a local generation script in the Moxin-VLM repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northwestern/cell-jepa/cell-jepa": { "id": "northwestern/cell-jepa/cell-jepa", "name": "Cell-JEPA", "maker": "northwestern", "family": "northwestern/cell-jepa", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-02-02", "context_length": null, "max_output_tokens": null, "parameters": "28000000", "architecture": "Joint-embedding predictive model for single-cell gene expression. A student encoder sees a masked expression profile and predicts the teacher encoder's cell embedding. The teacher is an exponential moving average of the student. Each encoder uses the scGPT transformer block: embedding dimension 512, 12 blocks, 8 attention heads, hidden dimension 512. An alignment MLP has two fully connected layers with GELU between them. The paper says the model size is constrained to 28 million parameters for a single student or teacher, not the pair. Pretraining is 4 epochs on about 800,000 human kidney cells from CELLxGENE Census (version 2023-05-15), AdamW, batch 32, mask ratio 0.15, learning rate 1e-4, weight decay 2e-4, dropout 0.2, about one day on one RTX A5000. JEPA loss weight 1000 and reconstruction loss weight 1 during pretraining.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cell-JEPA is a Northwestern University single-cell embedding model. Correspondence is Han Liu. Ali ElSheikh, Weimin Wu, Yibo Wen, and Jerry Yao-Chieh Hu are also at Northwestern. Coauthors include Chan Zuckerberg Biohub, MIT, the University of Chicago, the University of Toronto, and National Taiwan University. It predicts a teacher cell embedding from a masked gene-expression profile instead of only reconstructing counts. Status on 2026-10-15 is active, with medium confidence. The preprint is 2 February 2026.", "what_it_is_for": "The paper says the model is a single-cell foundation model for cell-type clustering and for predicting perturbation responses. The authors contrast it with reconstruction-only models such as scGPT, which they retrain on the same kidney corpus as the baseline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northwestern/dnabert/dnabert": { "id": "northwestern/dnabert/dnabert", "name": "DNABERT", "maker": "northwestern", "family": "northwestern/dnabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2021-02-04", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base encoder: 12 Transformer layers, 768 hidden units, 12 attention heads, intermediate size 3072, GELU, learned absolute positional embeddings. Four overlapping k-mer variants (k = 3, 4, 5, 6) share that architecture and differ in vocabulary. The vocabulary is every k-mer plus five special tokens. Pretraining is masked language modeling on the human genome, about 25 days on 8 NVIDIA 2080Ti GPUs, mixed precision. A later DNABERT-2 table reports 122B training tokens and GUE average scores of 61.62 (3-mer, 86M), 61.14 (4-mer, 86M), 60.05 (5-mer, 87M), and 60.51 (6-mer, 89M).", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DNABERT is a Northwestern University genome encoder from Yanrong Ji, Zhihan Zhou, Han Liu, and Ramana V. Davuluri. It is a BERT-base model pretrained with masked language modeling on the human genome, released as four overlapping k-mer variants. Status on 2026-10-15 is active. The Bioinformatics paper is dated 4 February 2021.", "what_it_is_for": "The paper presents DNABERT as a transferable encoder of genomic DNA so regulatory-element models can use upstream and downstream nucleotide context instead of only local motifs. The authors compare it with programs used for genome-wide regulatory-element prediction and release the pretrained models because pretraining took about 25 days on 8 NVIDIA 2080Ti GPUs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northwestern/dnabert/dnabert-2": { "id": "northwestern/dnabert/dnabert-2", "name": "DNABERT-2", "maker": "northwestern", "family": "northwestern/dnabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-06-26", "context_length": null, "max_output_tokens": null, "parameters": "117000000", "architecture": "Transformer encoder adapted from MosaicBERT. Byte-pair encoding vocabulary of 4096. Attention with Linear Biases replaces learned positional embeddings, so the card states no fixed input-length cap. FlashAttention and low-precision layer norm are used. Official usage returns 768-dimensional hidden states. The paper's main row is 117M parameters, relative FLOPs 1.00, and 262B training tokens. Pretraining data is genomes from 135 species in 6 categories, 32.49 billion nucleotides, versus the 2.75 billion nucleotide human set used for DNABERT. Masked language modeling.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DNABERT-2 is a Northwestern University genome encoder from the MAGICS lab (Zhihan Zhou, Yanrong Ji, Weijian Li, and Han Liu, with Pratik Dutta and Ramana V. Davuluri at Stony Brook). It replaces DNABERT's k-mer tokenizer with byte-pair encoding and is pretrained on 135 species. Status on 2026-10-15 is active. The preprint is 26 June 2023; ICLR 2024 accepted it.", "what_it_is_for": "The paper says DNABERT-2 is a multi-species foundation model for genome classification. The official checkpoint is the masked-language model used for embeddings and for fine-tuning on the Genome Understanding Evaluation tasks: promoters, enhancers, splice sites, transcription-factor binding, and species labels, among others.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northwestern/dnabert/dnabert-s": { "id": "northwestern/dnabert/dnabert-s", "name": "DNABERT-S", "maker": "northwestern", "family": "northwestern/dnabert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Species-aware embedding model initialized from DNABERT-2-117M. Training is curriculum contrastive learning: Weighted SimCLR on same-species DNA pairs, then Manifold Instance Mixup, which mixes hidden states at a random layer. The DNABERT-2 card this checkpoint starts from uses BPE vocabulary 4096, ALiBi, and 768-dimensional hidden states. DNABERT-S does not restate its own parameter count. The embedding used in the paper is the average of the last hidden states.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DNABERT-S is a Northwestern University species-aware DNA embedding model from the MAGICS lab. Zhihan Zhou, Weimin Wu, and Han Liu are at Northwestern; other authors include Harrison Ho, Zhong Wang, and Ramana V. Davuluri. It continues DNABERT-2 with contrastive training so sequences from different species separate in the embedding space. Status on 2026-10-15 is active. The lab announced the checkpoint on 14 February 2024. The journal version is Bioinformatics, 15 July 2025 (ISMB 2025).", "what_it_is_for": "The paper says the model is for unsupervised species differentiation: metagenomic binning, species clustering, and species classification when reference genomes are missing or labels are scarce. It is not presented as a new general genome language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northwestern/genomeocean/genomeocean-100m": { "id": "northwestern/genomeocean/genomeocean-100m", "name": "GenomeOcean-100M", "maker": "northwestern", "family": "northwestern/genomeocean", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LBNL BSD-style", "release_date": "2025-01-21", "context_length": 1024, "max_output_tokens": null, "parameters": "100000000", "architecture": "Mistral decoder-only causal language model. BPE vocabulary 4096. The GitHub table and the base card state a maximum sequence length of 1024 tokens, about 5,100 bp on the GitHub page and about 5 kbp on the card. Weights are bfloat16. The v1.2 card says the same architecture, DeepSpeed ZeRO-1, and about 100M parameters. Layer count was not copied from config.json. Trained with the 500M and 4B models on the same metagenomic assembly corpus in the bioRxiv scaling experiment (50,000 steps, then the authors continued the 4B model).", "modalities": { "input": [ "text" ], "output": [ "text", "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GenomeOcean-100M is the smallest public GenomeOcean causal language model. Northwestern University (Zhihan Zhou, Weimin Wu, Lizhen Shi, Han Liu) released it with Lawrence Berkeley National Laboratory and the DOE Joint Genome Institute. It is a bfloat16 Mistral decoder trained on metagenomic assemblies. Status on 2026-10-15 is active. DOE CODE dates the software release 21 January 2025. bioRxiv v1 is 5 February 2025.", "what_it_is_for": "The GitHub page lists it as the 100M base model for when GPU memory is too small for GenomeOcean-4B, and says performance may be worse. The paper used the 100M, 500M, and 4B runs to test scaling on the same metagenomic corpus and then focused analysis on the 4B model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northwestern/genomeocean/genomeocean-4b": { "id": "northwestern/genomeocean/genomeocean-4b", "name": "GenomeOcean-4B", "maker": "northwestern", "family": "northwestern/genomeocean", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LBNL BSD-style", "release_date": "2025-01-21", "context_length": 10240, "max_output_tokens": null, "parameters": "", "architecture": "Mistral decoder-only causal language model. The bgcFM config, which is this checkpoint fine-tuned, records 24 layers, hidden size 3072, 12 attention heads, 4 key-value heads, intermediate size 16384, SiLU, vocabulary 4096, max_position_embeddings 32768, rope_theta 1000000, untied embeddings, bfloat16. The base card and GitHub state a supported context of 10240 tokens, about 50-51 kbp, which is shorter than the config max positions. The v1.2 card reports about 4.25 billion parameters for the continued 4B run and names the training block size as 10240 tokens. The base hub label says 4B. The v1 abstract says a 4-billion-parameter model trained on more than 600 Gbp of contigs from 220 TB of metagenomes. A later abstract snippet says 219 TB and 4 billion parameters. BPE tokenizer. Causal language modeling.", "modalities": { "input": [ "text" ], "output": [ "text", "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GenomeOcean-4B is the main generative genome model from Northwestern University and Lawrence Berkeley National Laboratory / DOE JGI. First author Zhihan Zhou and corresponding author Han Liu are at Northwestern; Zhong Wang is the other corresponding author at Berkeley Lab. It is a Mistral-style decoder trained on large metagenomic assemblies. Status on 2026-10-15 is active. DOE CODE dates the software 21 January 2025. bioRxiv v1 is 5 February 2025.", "what_it_is_for": "The README says to use GenomeOcean-4B for general-purpose genome sequence analysis. The paper presents it as a generative model of microbial DNA that can embed sequences and generate protein-coding sequence under evolutionary constraints. Task-specific checkpoints for biosynthetic gene clusters and for detecting the model's own generations are separate folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northwestern/genomeocean/genomeocean-4b-artificial-detector": { "id": "northwestern/genomeocean/genomeocean-4b-artificial-detector", "name": "GenomeOcean-4B Artificial Detector", "maker": "northwestern", "family": "northwestern/genomeocean", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "LBNL BSD-style", "release_date": "2025-02-05", "context_length": 10240, "max_output_tokens": null, "parameters": "", "architecture": "Binary sequence classifier fine-tuned from GenomeOcean-4B. The official GitHub table says label 0 means an artificial sequence, and that the supported context is 10240 tokens, about 51,000 bp. The bioRxiv v1 text says the detector is released with the pretrained checkpoint so users can tell natural sequences from GenomeOcean generations. A search snapshot of the Hugging Face repo showed safetensors and the shared Northwestern and LBNL copyright, with an empty model-card YAML. A direct fetch of that URL on collection day returned 404, so architecture widths were not re-read from this repo's config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The GenomeOcean-4B Artificial Detector is a named binary classifier fine-tuned from GenomeOcean-4B by the same Northwestern University and Lawrence Berkeley National Laboratory / DOE JGI group. The GitHub table says label 0 means the sequence is artificial. Status on 2026-10-15 is active, with medium confidence. bioRxiv v1 (5 February 2025) says the detector is released with the pretrained checkpoint.", "what_it_is_for": "The paper says the classifier is released so people can tell natural sequences from sequences the GenomeOcean generator produced. The README lists it next to the base models and points at `detect_artificial_sequences.py`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northwestern/genomeocean/genomeocean-4b-bgcfm": { "id": "northwestern/genomeocean/genomeocean-4b-bgcfm", "name": "GenomeOcean-4B-bgcFM", "maker": "northwestern", "family": "northwestern/genomeocean", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LBNL BSD-style", "release_date": "2025-01-30", "context_length": 10240, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of GenomeOcean-4B for biosynthetic gene clusters. Same Mistral causal decoder as the base config on this repo: 24 layers, hidden size 3072, 12 heads, 4 key-value heads, intermediate size 16384, SiLU, vocabulary 4096, max_position_embeddings 32768, rope_theta 1000000, bfloat16. The card and GitHub state a maximum sequence length of 10240 tokens, about 50 kbp. GitHub says the fine-tune used 11 million BGC sequences. The config _name_or_path string contains model_meta55k_bgc15k. Hub size label is 4B. Zhihan Zhou uploaded the weights on 30-31 January 2025.", "modalities": { "input": [ "text" ], "output": [ "text", "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GenomeOcean-4B-bgcFM is a named fine-tune of GenomeOcean-4B for biosynthetic gene clusters. Northwestern University and Lawrence Berkeley National Laboratory / DOE JGI released it with the rest of GenomeOcean. Zhihan Zhou uploaded the weights. Status on 2026-10-15 is active. The Hugging Face history starts 30 January 2025.", "what_it_is_for": "The GitHub table says this is GenomeOcean-4B fine-tuned on 11 million biosynthetic gene cluster sequences and that it is the checkpoint for BGC tasks. The project site says the fine-tune can synthesize complete BGCs in a zero-shot setting. It is not the general 4B base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "northwestern/genomeocean/genomeocean-500m": { "id": "northwestern/genomeocean/genomeocean-500m", "name": "GenomeOcean-500M", "maker": "northwestern", "family": "northwestern/genomeocean", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LBNL BSD-style", "release_date": "2025-01-21", "context_length": 1024, "max_output_tokens": null, "parameters": "500000000", "architecture": "Mistral decoder-only causal language model. BPE vocabulary 4096. Maximum sequence length 1024 tokens, about 5,100 bp. bfloat16 weights. Hub model size reads 0.5B. The v1.2 card says about 500M parameters, DeepSpeed ZeRO-1, and an effective batch size of 128 (8 per GPU times 16 gradient-accumulation steps). Layer count was not copied from config.json. The bioRxiv scaling run trained this size for 50,000 steps on the same metagenomic corpus as the 100M and 4B models.", "modalities": { "input": [ "text" ], "output": [ "text", "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GenomeOcean-500M is the mid-size GenomeOcean causal DNA model from Northwestern University and Lawrence Berkeley National Laboratory / DOE JGI. It uses the same metagenomic pretraining setup as the 100M and 4B models and a shorter context than the 4B model. Status on 2026-10-15 is active. DOE CODE dates the v1.0 software 21 January 2025.", "what_it_is_for": "The official README lists it as the 500M base model for limited GPU memory. The paper's scaling plot compares its training loss with the 100M and 4B runs. General-purpose use is directed to GenomeOcean-4B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "notredame/eyepreserve/eyepreserve": { "id": "notredame/eyepreserve/eyepreserve", "name": "EyePreserve", "maker": "notredame", "family": "notredame/eyepreserve", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause for the repository code. The weight zip does not state a separate license on the README.", "release_date": "2023-12-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoencoder that takes a near-infrared iris image and a target binary iris mask and applies a learned non-linear texture deformation. The demo GUI uses StyleGAN3 to sample a starting iris, then deforms it. The paper also compares a linear Daugman deformer and a biomechanical deformer shipped as baselines.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EyePreserve is Notre Dame's identity-preserving iris deformation model. The preprint is dated 19 December 2023, a 2026 IEEE TBIOM version exists, and weights are an open research download. Collected 15 October 2026.", "what_it_is_for": "EyePreserve deforms a near-infrared iris so the pupil size or shape changes while the identity-bearing texture is meant to stay the same. Notre Dame offers it for dataset synthesis and for helping an examiner line up two images with very different dilation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "notredame/iris/arciris": { "id": "notredame/iris/arciris", "name": "ArcIris", "maker": "notredame", "family": "notredame/iris", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use with citation; the repository README says commercial or closed-source use needs a separate discussion with the authors. No SPDX identifier is stated.", "release_date": "2025-05-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet100 encoder, adapted from the official ArcFace PyTorch implementation, trained with ArcFace loss on 512x64 polar-normalized iris images. Authors say ResNet100 was chosen over ResNet50 and ResNet200 for the IREX speed and accuracy tradeoff. Matching is cosine or Euclidean distance on the embedding.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ArcIris is the University of Notre Dame Computer Vision Research Lab's ResNet100 iris encoder trained with ArcFace loss. It is an active open-weight research matcher, IREX X submission ndcvrl_002 (12 May 2025), and was written up on 20 May 2026. This dossier was collected on 15 October 2026.", "what_it_is_for": "Notre Dame describes ArcIris as an open-source iris matcher for research and for NIST IREX X-style 1:N identification. It encodes a polar-normalized iris image into a floating-point embedding and compares embeddings with cosine or Euclidean distance.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "notredame/iris/hdbif": { "id": "notredame/iris/hdbif", "name": "HDBIF", "maker": "notredame", "family": "notredame/iris", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use with citation; the repository README says commercial or closed-source use needs a separate discussion with the authors. No SPDX identifier is stated.", "release_date": "2019-01-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Domain-specific human-inspired BSIF. ICA filters learned on iris patches selected with eye tracking. Normalized iris images are convolved, responses are binarized at zero, and overlapping bits are scored with fractional Hamming distance plus Daugman bit-count normalization.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HDBIF is Notre Dame's human-driven binary iris feature coder, first published at WACV on 7 January 2019 and still active as an open-weight research matcher. A 2026 IREX C++ port is in the same repository as ArcIris. Collected 15 October 2026.", "what_it_is_for": "HDBIF is Notre Dame's human-saliency iris coder. Independent-component filters are learned on iris patches that eye-tracking showed people used when deciding whether two irises match. A normalized iris is convolved with those filters, binarized, and compared with a mask-aware fractional Hamming distance.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "notredame/iris/triplet-iris": { "id": "notredame/iris/triplet-iris", "name": "TripletIris", "maker": "notredame", "family": "notredame/iris", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use with citation; the repository README says commercial or closed-source use needs a separate discussion with the authors. No SPDX identifier is stated.", "release_date": "2024-11-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ConvNeXt-tiny trained with batch-hard triplet loss on polar-normalized iris images. Selected as the smallest ConvNeXt variant because of NIST IREX timing limits. Matching is Euclidean or cosine distance.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TripletIris is Notre Dame's ConvNeXt-tiny iris encoder trained with batch-hard triplet loss. It is an active open-weight matcher and the lab's first IREX X submission, ndcvrl_001, dated 19 November 2024. Collected 15 October 2026.", "what_it_is_for": "TripletIris is Notre Dame's open-source iris matcher aimed at the same IREX X setting as ArcIris. It embeds a polar-normalized iris with a ConvNeXt-tiny network trained by batch-hard triplet loss, then compares embeddings by Euclidean or cosine distance.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "notredame/iris-fm/circlenet": { "id": "notredame/iris-fm/circlenet", "name": "CircleNet", "maker": "notredame", "family": "notredame/iris-fm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use with citation; the repository README says commercial or closed-source use needs a separate discussion with the authors. No SPDX identifier is stated.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lightweight regression head on DINOv3 ViT-L/16 embeddings. Predicts six circle parameters: pupil_x, pupil_y, pupil_r, iris_x, iris_y, iris_r. Coordinates use width-height normalization. Pupil outputs are weighted 4x in the loss. Training uses translation augmentation of plus or minus 20 percent with probability 0.5. README image size is mixed 400x300 and 320x240. Checkpoint: methods/iris-fm-tools/models/circlenet.pth. This is not Law and Deng's CornerNet and not any other CircleNet product.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CircleNet is a University of Notre Dame regression head on DINOv3 ViT-L/16 embeddings, released in the open-source iris repository. It is active as a research checkpoint. A separate release date was not found. Collected 15 October 2026.", "what_it_is_for": "CircleNet estimates the pupil and iris as two circles on a near-infrared eye image. Notre Dame uses that approximation for rubber-sheet normalization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "notredame/iris-fm/cornernet": { "id": "notredame/iris-fm/cornernet", "name": "CornerNet (iris canthi)", "maker": "notredame", "family": "notredame/iris-fm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use with citation; the repository README says commercial or closed-source use needs a separate discussion with the authors. No SPDX identifier is stated.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Regression head on DINOv3 ViT-L/16. Outputs four floats, x1, y1, x2, y2, for the medial and lateral canthi. Width-height normalization. Image size 640x480. Two checkpoints: cornernet_live.pth and cornernet_pmi.pth. This is not CornerNet: Detecting Objects as Paired Keypoints (Law and Deng).", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CornerNet (iris canthi) is a University of Notre Dame regression head on DINOv3 ViT-L/16 embeddings, released in the open-source iris repository. It is active as a research checkpoint. A separate release date was not found. Collected 15 October 2026.", "what_it_is_for": "CornerNet in this lab detects the inner and outer eye corners so an iris image can be rotated to horizontal. It is not the 2018 object detector of the same name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "notredame/iris-fm/eyelidnet": { "id": "notredame/iris-fm/eyelidnet", "name": "EyelidNet", "maker": "notredame", "family": "notredame/iris-fm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use with citation; the repository README says commercial or closed-source use needs a separate discussion with the authors. No SPDX identifier is stated.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Regression head on DINOv3 ViT-L/16 that fits polynomials to the upper and lower eyelids. Parabolic head outputs six coefficients. Cubic head outputs eight. Z-score normalization. Image size 640x480. Checkpoints: eyelidnet_parabola.pth and eyelidnet_cubic.pth.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EyelidNet is a University of Notre Dame regression head on DINOv3 ViT-L/16 embeddings, released in the open-source iris repository. It is active as a research checkpoint. A separate release date was not found. Collected 15 October 2026.", "what_it_is_for": "EyelidNet fits polynomial curves to the upper and lower eyelids so iris occlusion can be modeled. Notre Dame ships a parabolic head and a cubic head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "notredame/iris-fm/h8net": { "id": "notredame/iris-fm/h8net", "name": "H8Net", "maker": "notredame", "family": "notredame/iris-fm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use with citation; the repository README says commercial or closed-source use needs a separate discussion with the authors. No SPDX identifier is stated.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Regression head on DINOv3 ViT-L/16. Predicts eight homography entries h11, h12, h13, h21, h22, h23, h31, h32 with h33 fixed at 1. Z-score normalization. Image size 640x480. The matrix is applied at inference to correct off-axis gaze. Checkpoint: methods/iris-fm-tools/models/h8net.pth.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "H8Net is a University of Notre Dame regression head on DINOv3 ViT-L/16 embeddings, released in the open-source iris repository. It is active as a research checkpoint. A separate release date was not found. Collected 15 October 2026.", "what_it_is_for": "H8Net predicts a projective transform that Notre Dame applies to an off-axis iris image to straighten gaze.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "notredame/iris-seg/cc-net": { "id": "notredame/iris-seg/cc-net", "name": "CC-Net (Notre Dame iris)", "maker": "notredame", "family": "notredame/iris-seg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Retrained CC-Net iris segmenter: an image-complexity-guided compressed network with a U-Net-like layout, exported as a TensorFlow graph in CVRL/RaspberryPiOpenSourceIris. Not the criss-cross attention CCNet of Huang et al.", "modalities": { "input": [ "image" ], "output": [ "mask" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CC-Net here is the University of Notre Dame iris segmenter shipped in the Raspberry Pi open-source iris kit for IJCB 2020. It is an active open-weight research model, not the unrelated criss-cross CCNet. Collected 15 October 2026.", "what_it_is_for": "This CC-Net is Notre Dame's small iris segmenter for the Raspberry Pi iris kit. It is a retrained image-complexity compression network, in the sense of the lab's own CC-NET work, used to paint an iris mask on near-infrared frames from a low-cost camera.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "notredame/iris-seg/iis": { "id": "notredame/iris-seg/iis", "name": "Infant Iris Segmentation", "maker": "notredame", "family": "notredame/iris-seg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Infant iris segmenter. Author talk describes a nested U-Net with dilated convolutions and attention, trained on adult iris data with bright-pupil augmentation. Repository name: Infant Iris Segmentation (IIS).", "modalities": { "input": [ "image" ], "output": [ "mask" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Infant Iris Segmentation (IIS) is Notre Dame's open-weight segmenter for 4 to 6 week old infants, released with the 2 January 2025 preprint and the WACV 2025 workshop paper. It is active as a research model. Collected 15 October 2026.", "what_it_is_for": "IIS segments infant near-infrared iris images, including the bright-pupil appearance that breaks adult segmenters. Notre Dame pairs it with existing iris coders so a 4 to 6 week old infant can be matched.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "notredame/iris-synth/forensic-iris": { "id": "notredame/iris-synth/forensic-iris", "name": "Forensic Iris Image Synthesis", "maker": "notredame", "family": "notredame/iris-synth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conditional StyleGAN2-ADA trained on post-mortem iris images from more than 350 subjects. Sampling explores the latent space for same-identity and new-identity images with decomposition tied to a requested post-mortem interval. Released file name: network-snapshot-025000.pkl.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Forensic Iris Image Synthesis is Notre Dame's conditional StyleGAN2-ADA for post-mortem irises, released with the WACV 2024 MAP-A paper. Weights are a public snapshot. Status is active as a research release. Collected 15 October 2026.", "what_it_is_for": "This is Notre Dame's conditional StyleGAN for post-mortem iris images. It is meant to grow sparse forensic sets and to show examiners decomposition patterns at a requested post-mortem interval, including same-identity and new-identity samples.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "notredame/pbm/pbm": { "id": "notredame/pbm/pbm", "name": "PBM", "maker": "notredame", "family": "notredame/pbm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-2-Clause", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mask R-CNN detects human-salient iris patches. Those patches are encoded with the HDBIF human-driven filters and matched by local Hamming distance. Released weight file name: wacv_model.h5.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PBM is Notre Dame's Mask R-CNN patch matcher for interpretable post-mortem iris comparison. Weights are the open file wacv_model.h5 from the WACV 2023 workshop release. Collected 15 October 2026.", "what_it_is_for": "PBM finds human-annotated iris texture patches in post-mortem images and matches those patches with the HDBIF kernels. Notre Dame built it so a forensic examiner can see which local regions supported a match.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nottingham/bcll/bcll": { "id": "nottingham/bcll/bcll", "name": "Binarized Convolutional Landmark Localizers", "maker": "nottingham", "family": "nottingham/bcll", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2017-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Binarized CNN landmark localizer. The hierarchical parallel multi-scale block introduced here is the block later used inside FAN. The project page releases two checkpoints of this method: a human-pose model on MPII and a face-alignment model on AFLW2000-3D. The supplement states 6.2M parameters for the final binary pose network. That count is not applied to the face file, and it is not stored as the folder parameter because the two files are different heads. The later binary-networks-pytorch repository is a binarization library, not another named checkpoint of this release.", "modalities": { "input": [ "image" ], "output": [ "landmarks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Binarized Convolutional Landmark Localizers (BCLL) is the ICCV 2017 oral model from Adrian Bulat and Georgios Tzimiropoulos at the Computer Vision Laboratory, University of Nottingham. The project page still linked two weight files on 2026-10-15: an MPII pose checkpoint and an AFLW2000-3D face checkpoint. arXiv v1 is 2 March 2017; the revision used here is 7 August 2017. DOI 10.1109/ICCV.2017.400.", "what_it_is_for": "The paper studies binarized CNNs for landmark localization under tight compute and memory limits. The released files are the human-pose localizer and the face localizer. The hierarchical block from this paper is later reused inside FAN.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nottingham/fan/2d-fan": { "id": "nottingham/fan/2d-fan", "name": "2D-FAN", "maker": "nottingham", "family": "nottingham/fan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The PyTorch face-alignment library is BSD-3-Clause. A separate licence for the weight files is not stated. The Torch7 repository licence was not recorded.", "release_date": "2017-03-21", "context_length": null, "max_output_tokens": null, "parameters": "24000000", "architecture": "Face Alignment Network: four stacked hourglass modules whose bottleneck blocks are the hierarchical parallel multi-scale block from the authors' binarized CNN landmark paper. 68 landmarks. Trained on 300W-LP-2D (the FAN training set is 61,225 images) with RMSProp, initial learning rate 1e-4, minibatch 10, and 40 epochs, then fine-tuned for a few epochs on the original 300-W set because those 2D landmarks are not fully compatible with 300-W, 300-VW, and Menpo. NME is normalized by the square root of the 2D bounding-box area. The paper's 24M figure is stated for the four-hourglass 3D-FAN used for the reported results; 2D-FAN uses that same FAN construction. Table 5's other size cells were not recovered.", "modalities": { "input": [ "image" ], "output": [ "2d-landmarks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "2D-FAN is a 68-point face-alignment network from Adrian Bulat and Georgios Tzimiropoulos at the Computer Vision Laboratory, University of Nottingham. Weights were still linked on the authors' project page on 2026-10-15. The arXiv v1 date is 21 March 2017; the revision used for the tables is 7 September 2017 (ICCV 2017).", "what_it_is_for": "The paper uses 2D-FAN to test how close a deep stack of hourglasses comes to saturating existing 2D face-alignment benchmarks. It is trained on 300W-LP-2D and fine-tuned for a few epochs on the original 300-W landmarks so it can be scored on 300-W, 300-VW, and Menpo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nottingham/fan/2d-to-3d-fan": { "id": "nottingham/fan/2d-to-3d-fan", "name": "2D-to-3D-FAN", "maker": "nottingham", "family": "nottingham/fan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "A separate licence for the Torch archive is not stated. The PyTorch face-alignment library, which does not ship this file, is BSD-3-Clause.", "release_date": "2017-03-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Guided FAN. Input is RGB plus 68 channels of 2D Gaussians with standard deviation 1 pixel. Output is projected 3D landmarks. Trained on 300W-LP, which has both 2D and 3D annotations. Initial learning rate 1e-3, with rotation from -70 to 70 degrees and scale from 0.7 to 1.3. The authors used it to create LS3D-W. The 24M figure is stated for 3D-FAN and is not applied here, because this network has 68 extra input channels.", "modalities": { "input": [ "image", "2d-landmarks" ], "output": [ "3d-landmarks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "2D-to-3D-FAN is a guided landmark network from Adrian Bulat and Georgios Tzimiropoulos at the Computer Vision Laboratory, University of Nottingham. It takes an image plus 68 2D Gaussians and outputs projected 3D landmarks. The authors used it to build LS3D-W. The Torch archive was still linked on 2026-10-15. arXiv v1 is 21 March 2017 (ICCV 2017).", "what_it_is_for": "The paper treats this as a separate guided network, not a fine-tune of 2D-FAN or 3D-FAN. It is trained on 300W-LP, which carries both 2D and 3D annotations, and is the converter behind the LS3D-W labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nottingham/fan/3d-fan": { "id": "nottingham/fan/3d-fan", "name": "3D-FAN", "maker": "nottingham", "family": "nottingham/fan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The PyTorch face-alignment library is BSD-3-Clause. A separate licence for the weight files is not stated. The Torch7 repository licence was not recorded.", "release_date": "2017-03-21", "context_length": null, "max_output_tokens": null, "parameters": "24000000", "architecture": "Four stacked hourglass modules with the hierarchical parallel multi-scale block. The paper states that the reported results use a 3D-FAN with 24M parameters. Trained on 300W-LP-3D (61,225 images in the FAN training set) with RMSProp, initial learning rate 1e-4, minibatch 10, and 40 epochs. Evaluated on LS3D-W, about 230,000 images. Prose on the size sweep: performance stays almost flat from 12M to 24M parameters and drops significantly at 6M. The largest network is about 28-30 fps on a Titan X and the smallest about 150 fps. Table 5's parameter-count cells were lost in the HTML conversion, so no other size was copied. The face-alignment THREE_D mode runs this network together with a separate depth network.", "modalities": { "input": [ "image" ], "output": [ "3d-landmarks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "3D-FAN is the four-hourglass 3D face-alignment network from Adrian Bulat and Georgios Tzimiropoulos at the Computer Vision Laboratory, University of Nottingham. The paper calls the reported model a 3D-FAN with 24M parameters. Torch and PyTorch weights were still linked on 2026-10-15. arXiv v1 is 21 March 2017; the revision used here is 7 September 2017 (ICCV 2017).", "what_it_is_for": "The network is trained on 300W-LP-3D and scored on LS3D-W, the large-pose set of about 230,000 3D landmark images built for the paper. It is the unguided 3D FAN, not the 2D-to-3D converter and not the later depth network.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nottingham/fan/3d-fan-depth": { "id": "nottingham/fan/3d-fan-depth", "name": "3D-FAN-depth", "maker": "nottingham", "family": "nottingham/fan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The PyTorch face-alignment library that downloads the depth file is BSD-3-Clause. A separate licence for the depth weights is not stated.", "release_date": "2017-03-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Appendix network, also called Full-2D-to-3D-FAN when stacked on 2D-to-3D-FAN. A ResNet-152 adapted to 3+N input channels regresses an N by 1 depth vector from RGB plus heatmaps. Trained for 50 epochs with an L2 loss and the same learning-rate schedule as the main FAN. The project page ships it as its own download. The PyTorch THREE_D mode loads these depth weights beside 3D-FAN. A stock ResNet-152 parameter count is not used, because the input stem is changed and the paper does not state a count.", "modalities": { "input": [ "image", "heatmaps" ], "output": [ "depth" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "3D-FAN-depth is the depth subnetwork in the appendix of the FAN paper by Adrian Bulat and Georgios Tzimiropoulos, Computer Vision Laboratory, University of Nottingham. Stacked on 2D-to-3D-FAN heatmaps, the paper calls the pair Full-2D-to-3D-FAN. Torch and PyTorch files were still linked as their own downloads on 2026-10-15. arXiv v1 is 21 March 2017 (ICCV 2017).", "what_it_is_for": "The subnetwork estimates the z coordinate after 2D-to-3D-FAN has produced heatmaps. The current `THREE_D` mode of the PyTorch library loads this file next to 3DFAN4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nottingham/rootnav/rootnav-2": { "id": "nottingham/rootnav/rootnav-2", "name": "RootNav 2.0", "maker": "nottingham", "family": "nottingham/rootnav", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Conflict, unresolved: the bioRxiv availability section says GNU GPL, with no non-academic restrictions, and lists Python 2.7 and PyTorch 1.0.1. The live GitHub LICENSE file is BSD 3-Clause, Copyright (c) 2019, robail-yasrab. The current README says to install a current PyTorch. Neither licence is preferred here.", "release_date": "2019-07-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder multi-task CNN. Input 1024 by 1024. Segments background, first-order roots, and second-order roots, and localizes seeds and first- and second-order tips. An A* shortest path then writes RSML plus masks. One architecture, three species checkpoints downloaded on first use and not stored in git: wheat_bluepaper, osr_bluepaper (oilseed rape, Brassica napus), and arabidopsis_plate. Wheat primary set: 3,630 images at 1900 by 2000, split 2864/716/50. Arabidopsis transfer: 277 NIR images, split 200/27/50, up to five plants per plate. Rapeseed transfer: 120 images, split 91/14/15. RootNav 1.0 is the earlier semi-automatic tool, not this network. Tables 1, 2, and 3 in the preprint did not render cell values, so parameter, segmentation-accuracy, and timing-table numbers are not stored.", "modalities": { "input": [ "image" ], "output": [ "segmentation", "rsml" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RootNav 2.0 is an automatic root-architecture network from the School of Computer Science and the School of Biosciences at the University of Nottingham. Authors are Robail Yasrab, Jonathan A. Atkinson, Darren M. Wells, Andrew P. French, Tony P. Pridmore, and Michael P. Pound. Three species checkpoints share one architecture and download on first use. The bioRxiv preprint is 20 July 2019 (10.1101/709147). The journal version is GigaScience 2019, DOI 10.1093/gigascience/giz123, volume 8 issue 11.", "what_it_is_for": "The tool replaces the semi-automatic RootNav 1.0 path with a multi-task CNN plus an A* trace. It segments background and first- and second-order roots, finds seeds and tips, and writes RSML. The public checkpoints are wheat on blue paper, oilseed rape on blue paper, and Arabidopsis on gel plates.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nottingham/vrn/vrn": { "id": "nottingham/vrn/vrn", "name": "Unguided Volumetric Regression Network", "maker": "nottingham", "family": "nottingham/vrn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub repository is MIT. Wolfram marks its conversion of the unguided checkpoint MIT. A separate sentence licensing only the weight file was not found.", "release_date": "2017-03-22", "context_length": null, "max_output_tokens": null, "parameters": "19277384", "architecture": "Unguided volumetric regression network. Two hourglass modules, no intermediate supervision, residual modules of 256 features. RGB in, volume 192 by 192 by 200, real-valued sigmoid output. Trained with sigmoid cross-entropy and RMSProp, learning rate 1e-4 then 1e-5 after 40 epochs. The mesh is an isosurface. Training images are 300W-LP (more than 60,000) from 3DMM fits, not raw scans. NME is the mean per-vertex Euclidean distance divided by the outer 3D interocular distance, over about 19,000 face-region vertices. ICP is used only to establish correspondence, not as rigid alignment, in the main-text protocol. The parameter count is Wolfram's count of the converted official checkpoint (1,029 layers, trained size 81 MB), not a figure printed in the paper.", "modalities": { "input": [ "image" ], "output": [ "3d-volume" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Unguided VRN reconstructs a 3D face volume from one RGB image. Aaron S. Jackson, Adrian Bulat, and Georgios Tzimiropoulos were at the University of Nottingham; Vasileios Argyriou was at Kingston University. The public repository releases the unguided checkpoint. arXiv v1 is 22 March 2017; the revision used here is 8 September 2017 (ICCV 2017). Weights were still the file named in download.sh on 2026-10-15.", "what_it_is_for": "The paper regresses a 3D facial volume directly, without a 3DMM at test time. The released network is the unguided hourglass stack. Guided and multitask variants are reported in the paper and are not in the public weight file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/capybara/nous-capybara-34b": { "id": "nous-research/capybara/nous-capybara-34b", "name": "Nous Capybara 34B", "maker": "nous-research", "family": "nous-research/capybara", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-11-14", "context_length": 200000, "max_output_tokens": null, "parameters": "34B", "architecture": "Fine-tune of Yi-34B-200K for 3 epochs on the Capybara set. The card says this is the first 34B Nous model and the first Nous model with a 200K context. Fine-tune on roughly 20,000 mostly in-house multi-turn conversations built with the Amplify-Instruct synthesis method. The card says more than 60% of examples are multi-turn and that average length is over 1,000 tokens. Project led by Luigi D. (LDJ), with dataset help from J-Supha and compute support from a16z and Yield Protocol.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Capybara 34B is a Nous Research release. It is the November 2023 Yi-34B-200K Capybara model. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The long-context 34B Capybara chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/capybara/nous-capybara-3b-v1-9": { "id": "nous-research/capybara/nous-capybara-3b-v1-9", "name": "Nous Capybara 3B v1.9", "maker": "nous-research", "family": "nous-research/capybara", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-10-20", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Fine-tune on roughly 20,000 mostly in-house multi-turn conversations built with the Amplify-Instruct synthesis method. The card says more than 60% of examples are multi-turn and that average length is over 1,000 tokens. Project led by Luigi D. (LDJ), with dataset help from J-Supha and compute support from a16z and Yield Protocol. Card tags include StableLM. The Obsidian card says this checkpoint is based on stabilityai/stablelm-3b-4e1t. Prompt format is ChatML.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Capybara 3B v1.9 is a Nous Research release. It is the 3B Capybara chat model on StableLM. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The 3B Capybara chat model, later used as the language backbone of Obsidian 3B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/capybara/nous-capybara-7b-v1": { "id": "nous-research/capybara/nous-capybara-7b-v1", "name": "Nous Capybara 7B v1", "maker": "nous-research", "family": "nous-research/capybara", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-09-20", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Llama 2 fine-tune, per the card tags. Fine-tune on roughly 20,000 mostly in-house multi-turn conversations built with the Amplify-Instruct synthesis method. The card says more than 60% of examples are multi-turn and that average length is over 1,000 tokens. Project led by Luigi D. (LDJ), with dataset help from J-Supha and compute support from a16z and Yield Protocol. Prompt format is USER / ASSISTANT. The card tells readers to use Capybara v1.9 instead.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Capybara 7B v1 is a Nous Research release. It is the first Capybara 7B chat model. The card recommends v1.9 instead. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The first 7B Capybara chat model. The maker later marked v1.9 as the one to use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/capybara/nous-capybara-7b-v1-9": { "id": "nous-research/capybara/nous-capybara-7b-v1-9", "name": "Nous Capybara 7B v1.9", "maker": "nous-research", "family": "nous-research/capybara", "status": "active", "confidence": "medium", "weights": "open-source", "license": "MIT", "release_date": "2023-10-10", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Fine-tune on roughly 20,000 mostly in-house multi-turn conversations built with the Amplify-Instruct synthesis method. The card says more than 60% of examples are multi-turn and that average length is over 1,000 tokens. Project led by Luigi D. (LDJ), with dataset help from J-Supha and compute support from a16z and Yield Protocol. The prose says v1.9 uses a Mistral foundation and unalignment techniques. The YAML tags include StableLM. Both statements are on the card. Prompt format is ChatML.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Capybara 7B v1.9 is a Nous Research release. It is the recommended 7B Capybara chat model, with a conflicting base-model tag. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The Capybara 7B the card tells users to prefer over v1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/consilience/consilience-40b": { "id": "nous-research/consilience/consilience-40b", "name": "Consilience 40B", "maker": "nous-research", "family": "nous-research/consilience", "status": "preview", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-05-14", "context_length": null, "max_output_tokens": null, "parameters": "40B", "architecture": "Dense decoder-only pretrain from scratch on the Psyche network, described by Nous as DeepSeek V3's multi-head latent attention without MoE routers. Planned data mix on the launch post: FineWeb 14T, FineWeb-2 about 4T, and The Stack v2 upsampled to about 1T, for a 20T-token design. The public card says training duration is TBD, checkpoints update during the run, and there is no final annealing step. Weights were found under PsycheFoundation, not the NousResearch org.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Consilience 40B is a Nous Research release. It is the 40B base model Nous announced for decentralized pretraining on Psyche. Status in this catalog is preview. Collected 2026-10-15.", "what_it_is_for": "A base model Nous announced as the first large Psyche pretrain, sized to run on a single high-end consumer GPU after training. It is not an instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/deephermes/deephermes-3-llama-3-3b-preview": { "id": "nous-research/deephermes/deephermes-3-llama-3-3b-preview", "name": "DeepHermes 3 Llama 3 3B Preview", "maker": "nous-research", "family": "nous-research/deephermes", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3).", "release_date": "2025-02-16", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Preview hybrid-reasoning fine-tune of meta-llama/Meta-Llama-3.2-3B. The card title says Llama-3 3B Preview. The model-index name on the card says DeepHermes-3-Llama-3.1-3B, which does not match the base model id. The card says difficult problems can think for as many as 13,000 tokens. Function calling uses the Hermes tool-call format. Prompt format is Llama chat.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepHermes 3 Llama 3 3B Preview is a Nous Research release. It is the small DeepHermes 3 preview on Llama 3.2 3B. Status in this catalog is preview. Collected 2026-10-15.", "what_it_is_for": "A preview hybrid model that can answer in a normal chat mode or, with a system prompt, in a long chain-of-thought mode. The card says the reasoning behavior was distilled from R1 and that quirks should be expected.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/deephermes/deephermes-3-llama-3-8b-preview": { "id": "nous-research/deephermes/deephermes-3-llama-3-8b-preview", "name": "DeepHermes 3 Llama 3.1 8B Preview", "maker": "nous-research", "family": "nous-research/deephermes", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3).", "release_date": "2025-02-14", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Preview hybrid-reasoning fine-tune of meta-llama/Meta-Llama-3.1-8B. The releases index and the card call it a model with deep reasoning and chat modes. Reasoning is turned on with a system prompt, not a separate weight. The card says difficult problems can think for as many as 13,000 tokens. Function calling uses the Hermes tool-call format. Prompt format is Llama chat.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepHermes 3 Llama 3.1 8B Preview is a Nous Research release. It is the February 2025 preview that first put toggleable reasoning on a Hermes weight. Status in this catalog is preview. Collected 2026-10-15.", "what_it_is_for": "A preview hybrid model that can answer in a normal chat mode or, with a system prompt, in a long chain-of-thought mode. The card says the reasoning behavior was distilled from R1 and that quirks should be expected.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/deephermes/deephermes-3-mistral-24b-preview": { "id": "nous-research/deephermes/deephermes-3-mistral-24b-preview", "name": "DeepHermes 3 Mistral 24B Preview", "maker": "nous-research", "family": "nous-research/deephermes", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-02", "context_length": null, "max_output_tokens": null, "parameters": "24B", "architecture": "Preview hybrid-reasoning fine-tune of mistralai/Mistral-Small-24B-Base-2501. Reasoning is toggled with a system prompt. The card says difficult problems can think for as many as 13,000 tokens. Function calling uses the Hermes tool-call format. Prompt format is Llama chat.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepHermes 3 Mistral 24B Preview is a Nous Research release. It is the Mistral Small 24B DeepHermes 3 preview. Status in this catalog is preview. Collected 2026-10-15.", "what_it_is_for": "A preview hybrid model that can answer in a normal chat mode or, with a system prompt, in a long chain-of-thought mode. The card says the reasoning behavior was distilled from R1 and that quirks should be expected. This checkpoint is the Mistral Small 24B size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/deephermes/deephermes-ascensionmaze": { "id": "nous-research/deephermes/deephermes-ascensionmaze", "name": "DeepHermes Ascension Maze", "maker": "nous-research", "family": "nous-research/deephermes", "status": "preview", "confidence": "medium", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3).", "release_date": "2025-04-02", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Experimental Atropos RL artifact. Card base_model is meta-llama/Meta-Llama-3.1-8B. The human-written preamble says it is not for general use. The rest of the card is labeled self-generated and says numbers below may be false. Those claims were not copied into specs or benchmarks.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepHermes Ascension Maze is a Nous Research release. It is a named experimental Atropos artifact with a partly self-generated card. Status in this catalog is preview. Collected 2026-10-15.", "what_it_is_for": "Maze feedback experiment in the Atropos RL framework. The card says it is an experimental artifact.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/deephermes/deephermes-egregore-v1": { "id": "nous-research/deephermes/deephermes-egregore-v1", "name": "DeepHermes Egregore v1", "maker": "nous-research", "family": "nous-research/deephermes", "status": "preview", "confidence": "medium", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3).", "release_date": "2025-04-02", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Experimental Atropos RL artifact. Card base_model is meta-llama/Meta-Llama-3.1-8B. The human-written preamble says it is not for general use. The rest of the card is labeled self-generated and says numbers below may be false. Those claims were not copied into specs or benchmarks.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepHermes Egregore v1 is a Nous Research release. It is a named experimental Atropos artifact with a partly self-generated card. Status in this catalog is preview. Collected 2026-10-15.", "what_it_is_for": "Feedback-testing experiment in the Atropos RL framework. The human preamble is the only part treated as a spec.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/deephermes/deephermes-egregore-v2": { "id": "nous-research/deephermes/deephermes-egregore-v2", "name": "DeepHermes Egregore v2", "maker": "nous-research", "family": "nous-research/deephermes", "status": "preview", "confidence": "medium", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3).", "release_date": "2025-04-29", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Experimental Atropos RL artifact. Card base_model is meta-llama/Meta-Llama-3.1-8B. The human-written preamble says it is not for general use. The rest of the card is labeled self-generated and says numbers below may be false. Those claims were not copied into specs or benchmarks.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepHermes Egregore v2 is a Nous Research release. It is a named experimental Atropos artifact with a partly self-generated card. Status in this catalog is preview. Collected 2026-10-15.", "what_it_is_for": "Second feedback-testing Egregore artifact. Same caveat as v1: self-generated claims were not used.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/deephermes/deephermes-financial-fundamentals": { "id": "nous-research/deephermes/deephermes-financial-fundamentals", "name": "DeepHermes Financial Fundamentals Specialist", "maker": "nous-research", "family": "nous-research/deephermes", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3).", "release_date": "2025-04-28", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Experimental Atropos RL artifact on meta-llama/Meta-Llama-3.1-8B, aimed at predicting the direction of the next quarter's financial fundamentals from prior-quarter context. The card says it is not a general-purpose model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepHermes Financial Fundamentals Specialist is a Nous Research release. It is an experimental finance-direction model from the Atropos RL release. Status in this catalog is preview. Collected 2026-10-15.", "what_it_is_for": "A narrow experimental model for financial-fundamentals direction prediction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/deephermes/deephermes-toolcalling-specialist": { "id": "nous-research/deephermes/deephermes-toolcalling-specialist", "name": "DeepHermes Tool Calling Specialist", "maker": "nous-research", "family": "nous-research/deephermes", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3).", "release_date": "2025-04-11", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Experimental Atropos reinforcement-learning fine-tune aimed at tool calling in the reasoning mode of DeepHermes 3 Llama 3.1 8B. Base model on the card is meta-llama/Meta-Llama-3.1-8B. The card says it is not for broad general use.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepHermes Tool Calling Specialist is a Nous Research release. It is an experimental Atropos RL artifact for tool calling, not a general assistant. Status in this catalog is preview. Collected 2026-10-15.", "what_it_is_for": "A specialist artifact for reasoning-mode tool calling, published with the Atropos RL stack.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/genstruct/genstruct-7b": { "id": "nous-research/genstruct/genstruct-7b", "name": "Genstruct 7B", "maker": "nous-research", "family": "nous-research/genstruct", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-03-07", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Instruction-generation model fine-tuned from mistralai/Mistral-7B-v0.1. It writes instructions grounded in a user-supplied passage, inspired by Ada-Instruct. Input is a title and content passage, not a normal chat turn. The releases index lists it on 03/07/24. The Hugging Face repo was created 2024-01-05.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Genstruct 7B is a Nous Research release. It is Nous Research's instruction-generation model on Mistral 7B. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "Generates synthetic instruction-tuning examples from raw text, including questions that need multi-step reasoning. It is a data-synthesis model, not a chat assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/gpt4-x-vicuna/gpt4-x-vicuna-13b": { "id": "nous-research/gpt4-x-vicuna/gpt4-x-vicuna-13b", "name": "GPT4 x Vicuna 13B", "maker": "nous-research", "family": "nous-research/gpt4-x-vicuna", "status": "active", "confidence": "high", "weights": "open-source", "license": "gpl, as tagged on the card. The Vicuna and Llama base licenses are not restated.", "release_date": "2023-05-06", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Fine-tune of eachadea/vicuna-13b-1.1 on about 180,000 GPT-4 instructions from GPTeacher, an unreleased roleplay v2 set, GPT-4-LLM uncensored, WizardLM uncensored, and the Nous Research Instruct set. Trained 5 epochs on 8x A100 80GB with Alpaca-style DeepSpeed code. Prompt format is Alpaca. Compute from Redmond AI. The card says the Vicuna base still has OpenAI-style refusals.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT4 x Vicuna 13B is a Nous Research release. It is an early Nous fine-tune of Vicuna 13B on GPT-4 instructions. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "An early Nous instruct tune, before the named Hermes 13B release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/hermes-2-pro-llama-3-70b": { "id": "nous-research/hermes/hermes-2-pro-llama-3-70b", "name": "Hermes 2 Pro Llama 3 70B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Not tagged on the Hugging Face card. The base is Llama 3 70B, so the Llama 3 Community License is the working license. Confidence on the exact tag is medium.", "release_date": "2024-06-27", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Hermes 2 Pro function-calling tune of NousResearch/Meta-Llama-3-70B. The releases index calls it a function-calling model, dated 06/27/24. Hugging Face repo created 2024-06-25.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hermes 2 Pro Llama 3 70B is a Nous Research release. It is the June 2024 70B function-calling Hermes. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The 70B function-calling Hermes 2 Pro model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/hermes-2-pro-llama-3-8b": { "id": "nous-research/hermes/hermes-2-pro-llama-3-8b", "name": "Hermes 2 Pro Llama 3 8B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3).", "release_date": "2024-04-30", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Hermes 2 Pro recipe on NousResearch/Meta-Llama-3-8B. Function calling, JSON mode, and ChatML. The card says this version adds single-token tool tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hermes 2 Pro Llama 3 8B is a Nous Research release. It is the Llama 3 8B Hermes 2 Pro function-calling model. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The Llama 3 8B function-calling Hermes 2 Pro model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/hermes-2-pro-mistral-7b": { "id": "nous-research/hermes/hermes-2-pro-mistral-7b", "name": "Hermes 2 Pro Mistral 7B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-03-11", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Retrain of Hermes 2 on mistralai/Mistral-7B-v0.1 using a cleaned OpenHermes 2.5 set plus an in-house function-calling and JSON-mode set. Adds single-token tool tags. Collaboration with interstellarninja and Fireworks. ChatML with a tool-use template.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hermes 2 Pro Mistral 7B is a Nous Research release. It is the March 2024 function-calling Hermes on Mistral 7B. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The releases index calls it the master function-calling model. The card says it keeps general chat and adds function calling and JSON structured outputs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/hermes-2-theta-llama-3-70b": { "id": "nous-research/hermes/hermes-2-theta-llama-3-70b", "name": "Hermes 2 Theta Llama 3 70B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3).", "release_date": "2024-06-20", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Merged and then further RLHF'd model from Hermes 2 Pro and Meta's Llama 3 Instruct. Collaboration with Charles Goddard and Arcee AI using MergeKit. ChatML, function calling, and structured outputs. Releases index date 06/20/24. Repo created 2024-06-14.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hermes 2 Theta Llama 3 70B is a Nous Research release. It is the June 2024 70B Hermes 2 Theta merge. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The 70B Hermes 2 Theta merge, aimed at combining Hermes steering with Llama 3 Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/hermes-2-theta-llama-3-8b": { "id": "nous-research/hermes/hermes-2-theta-llama-3-8b", "name": "Hermes 2 Theta Llama 3 8B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0 is the Hugging Face card tag. The base is Hermes 2 Pro Llama 3 8B, whose card uses the Llama 3 Community License. Both are recorded.", "release_date": "2024-05-05", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Merged, then further RLHF'd, model. The 70B Theta card says Theta merges Hermes 2 Pro with Llama 3 Instruct, with Charles Goddard and Arcee AI (MergeKit). This 8B card's base_model is NousResearch/Hermes-2-Pro-Llama-3-8B. ChatML, function calling, JSON mode.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hermes 2 Theta Llama 3 8B is a Nous Research release. It is the 8B merged Hermes 2 Theta model. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "An experimental merged Hermes 2 Pro model meant to combine Hermes steering with Llama 3 Instruct behavior.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/hermes-3-llama-3-1-405b": { "id": "nous-research/hermes/hermes-3-llama-3-1-405b", "name": "Hermes 3 Llama 3.1 405B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3).", "release_date": "2024-08-15", "context_length": 131072, "max_output_tokens": null, "parameters": "405B", "architecture": "Decoder-only Transformer fine-tune of Llama 3.1. The Hermes 3 paper says the bases are 8B, 70B, and 405B with context length 131,072. Training is SFT, with DPO used for the 8B and not kept for the larger sizes. Tool use follows the Hermes function-calling standard. Prompt format on the model card is ChatML. The paper also describes scratchpad and reasoning tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 1.0 }, "summary_intro": "Hermes 3 Llama 3.1 405B is a Nous Research release. It is a Llama 3.1 Hermes 3 instruct and tool-use model. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A general instruct, roleplay, and tool-use model. The launch post calls it unlocked and steerable, fine-tuned on primarily synthetic data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/hermes-3-llama-3-1-70b": { "id": "nous-research/hermes/hermes-3-llama-3-1-70b", "name": "Hermes 3 Llama 3.1 70B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3).", "release_date": "2024-08-15", "context_length": 131072, "max_output_tokens": null, "parameters": "70B", "architecture": "Decoder-only Transformer fine-tune of Llama 3.1. The Hermes 3 paper says the bases are 8B, 70B, and 405B with context length 131,072. Training is SFT, with DPO used for the 8B and not kept for the larger sizes. Tool use follows the Hermes function-calling standard. Prompt format on the model card is ChatML. The paper also describes scratchpad and reasoning tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.7, "completion_per_m_usd": 0.7 }, "summary_intro": "Hermes 3 Llama 3.1 70B is a Nous Research release. It is a Llama 3.1 Hermes 3 instruct and tool-use model. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A general instruct, roleplay, and tool-use model. The launch post calls it unlocked and steerable, fine-tuned on primarily synthetic data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/hermes-3-llama-3-1-8b": { "id": "nous-research/hermes/hermes-3-llama-3-1-8b", "name": "Hermes 3 Llama 3.1 8B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3).", "release_date": "2024-08-15", "context_length": 131072, "max_output_tokens": null, "parameters": "8B", "architecture": "Decoder-only Transformer fine-tune of Llama 3.1. The Hermes 3 paper says the bases are 8B, 70B, and 405B with context length 131,072. Training is SFT, with DPO used for the 8B and not kept for the larger sizes. Tool use follows the Hermes function-calling standard. Prompt format on the model card is ChatML. The paper also describes scratchpad and reasoning tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hermes 3 Llama 3.1 8B is a Nous Research release. It is a Llama 3.1 Hermes 3 instruct and tool-use model. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A general instruct, roleplay, and tool-use model. The launch post calls it unlocked and steerable, fine-tuned on primarily synthetic data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/hermes-3-llama-3-2-3b": { "id": "nous-research/hermes/hermes-3-llama-3-2-3b", "name": "Hermes 3 Llama 3.2 3B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3).", "release_date": "2024-12-11", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Hermes 3-style fine-tune of meta-llama/Llama-3.2-3B. The releases index says it can run on most modern hardware. Repo created 2024-12-03; index date is 12/11/24.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hermes 3 Llama 3.2 3B is a Nous Research release. It is the December 2024 small Hermes 3 model. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The small Hermes 3 model intended to run on ordinary hardware.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/hermes-4-14b": { "id": "nous-research/hermes/hermes-4-14b", "name": "Hermes 4 14B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-26", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Hybrid-reasoning fine-tune of Qwen/Qwen3-14B. ChatML. The card says the post-training mix grew from about 1 million samples and 1.2 billion tokens in Hermes 3 to about 5 million samples and 60 billion tokens. Reasoning is toggled with a think tag or a chat-template flag. Trained with Atropos and DataForge, per the card tags. Prompt format is ChatML, unlike the Llama Hermes 4 models.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hermes 4 14B is a Nous Research release. It is the August 2025 dense hybrid-reasoning Hermes on Qwen3 14B. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The small dense Hermes 4 model for local inference. The releases index describes it that way. It is a hybrid reasoner aligned to the user rather than to a vendor ethics policy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/hermes-4-3-36b": { "id": "nous-research/hermes/hermes-4-3-36b", "name": "Hermes 4.3 36B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-12-03", "context_length": 512000, "max_output_tokens": null, "parameters": "36B", "architecture": "Hybrid-reasoning post-train of ByteDance-Seed/Seed-OSS-36B-Base. The production weights were post-trained on the Psyche network with DisTrO. The blog says the run was twice the Hermes 4 run because of longer context and a larger set, and that context extends up to 512K. The card says the corpus is about 5 million samples and 60 billion tokens. ChatML-style Hermes 4 interface. A centralized Torchtitan run was released separately as a research artifact.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hermes 4.3 36B is a Nous Research release. It is the December 2025 Seed 36B Hermes, the first production Hermes post-trained on Psyche. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A local-sized Hermes 4 update. Nous says it nearly matches, and on some tasks exceeds, Hermes 4 70B at about half the parameter count, and that it leads RefusalBench among the models they plotted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/hermes-4-405b": { "id": "nous-research/hermes/hermes-4-405b", "name": "Hermes 4 405B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3).", "release_date": "2025-08-26", "context_length": 131072, "max_output_tokens": null, "parameters": "405B", "architecture": "Hybrid-reasoning fine-tune of meta-llama/Meta-Llama-3.1-405B. Llama 3 chat format. Same Hermes 4 post-training description as the 70B: about 5 million samples and 60 billion tokens, hybrid think tags, JSON and tool use. The releases index calls it a frontier hybrid-mode reasoning model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 3.0 }, "summary_intro": "Hermes 4 405B is a Nous Research release. It is the August 2025 Llama 3.1 405B hybrid reasoner. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The largest Hermes 4 hybrid reasoner, for math, science, instruction following, schema adherence, roleplay, and long-form writing. Hermes Agent's own docs say it is a chat model and is not the recommended model inside the agent loop.", "ratings": { "empirical_100": null, "lmsys_elo": 1245, "coding_elo": 1245, "swe_bench": 22.5, "humaneval": 76.0, "twelve_cs": 99.5, "aa_intelligence": 7.5, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/hermes-4-70b": { "id": "nous-research/hermes/hermes-4-70b", "name": "Hermes 4 70B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (card tag llama3). Artificial Analysis also lists the Meta Llama 3 Community License.", "release_date": "2025-08-26", "context_length": 131072, "max_output_tokens": null, "parameters": "70B on the Nous card. Artificial Analysis lists 70.6B.", "architecture": "Hybrid-reasoning fine-tune of meta-llama/Meta-Llama-3.1-70B. Llama 3 chat format. Same post-training scale as the other Hermes 4 models: about 5 million samples and 60 billion tokens, versus about 1 million samples and 1.2 billion tokens for Hermes 3. Reasoning can be on or off. JSON schema adherence and tool use are trained capabilities. Nous Chat serves the model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hermes 4 70B is a Nous Research release. It is the August 2025 Llama 3.1 70B hybrid reasoner. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The mid-size Hermes 4 hybrid reasoner. The releases index says it shares the 405B improvements at 70B parameters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/nous-hermes-13b": { "id": "nous-research/hermes/nous-hermes-13b", "name": "Nous Hermes 13B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-source", "license": "gpl, as tagged on the Hugging Face card. The base is Llama 13B; that base license is not restated on the card.", "release_date": "2023-06-03", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Fine-tune of Llama 13B on more than 300,000 mostly synthetic GPT-4 instructions. The card says fine-tuning used sequence length 2000 on an 8x A100 80GB DGX for over 50 hours. Led by Teknium and Karan4D, with compute from Redmond AI.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Hermes 13B is a Nous Research release. It is the first named Hermes instruct tune, built on Llama 13B. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The card presents it as a general instruction model aimed at long answers and fewer refusals than the OpenAI-style models it was distilled from.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/nous-hermes-2-llama-2-70b": { "id": "nous-research/hermes/nous-hermes-2-llama-2-70b", "name": "Nous Hermes 2 Llama 2 70B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0 is the Hugging Face card tag. The base is meta-llama/Llama-2-70b-hf, so the Llama 2 community license may still bind derivatives. Both are recorded.", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Hermes 2 supervised fine-tune of Llama 2 70B. The card says training used over 1,000,000 entries of primarily GPT-4 generated data, plus other open data. Prompt format is ChatML.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Hermes 2 Llama 2 70B is a Nous Research release. It is the Hermes 2 data mix on Llama 2 70B. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A ChatML Hermes 2 general assistant on Llama 2 70B, aimed at steerable multi-turn chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/nous-hermes-2-mistral-7b-dpo": { "id": "nous-research/hermes/nous-hermes-2-mistral-7b-dpo", "name": "Nous Hermes 2 Mistral 7B DPO", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-20", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "DPO tune of a Mistral-7B-v0.1 Hermes model. Nous said on X that it was DPO'd from OpenHermes 2.5. Base model on the card is mistralai/Mistral-7B-v0.1. ChatML prompt format.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Hermes 2 Mistral 7B DPO is a Nous Research release. It is the February 2024 flagship 7B Hermes, a DPO model on Mistral 7B. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The releases index lists it as the 7B Hermes model. Nous announced it as the flagship 7B Hermes at the time.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/nous-hermes-2-mixtral-8x7b-dpo": { "id": "nous-research/hermes/nous-hermes-2-mixtral-8x7b-dpo", "name": "Nous Hermes 2 Mixtral 8x7B DPO", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-15", "context_length": null, "max_output_tokens": null, "parameters": "8x7B", "architecture": "SFT plus DPO fine-tune of mistralai/Mixtral-8x7B-v0.1. Mixture-of-experts. ChatML. The releases index calls it the MoE model plus DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Hermes 2 Mixtral 8x7B DPO is a Nous Research release. It is the DPO Mixtral Hermes 2 model from January 2024. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The DPO Mixtral Hermes 2 model, the larger Hermes 2 release paired with the SFT checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/nous-hermes-2-mixtral-8x7b-sft": { "id": "nous-research/hermes/nous-hermes-2-mixtral-8x7b-sft", "name": "Nous Hermes 2 Mixtral 8x7B SFT", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-15", "context_length": null, "max_output_tokens": null, "parameters": "8x7B", "architecture": "Supervised fine-tune of mistralai/Mixtral-8x7B-v0.1, without the later DPO stage. Mixture-of-experts. ChatML. The releases index describes it as the MoE model minus DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Hermes 2 Mixtral 8x7B SFT is a Nous Research release. It is the supervised-only Mixtral Hermes 2 checkpoint. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The SFT-only Mixtral Hermes 2 checkpoint, published beside the DPO model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/nous-hermes-2-solar-10-7b": { "id": "nous-research/hermes/nous-hermes-2-solar-10-7b", "name": "Nous Hermes 2 SOLAR 10.7B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-01", "context_length": null, "max_output_tokens": null, "parameters": "10.7B", "architecture": "Hermes 2 fine-tune of upstage/SOLAR-10.7B-v1.0. ChatML. Repo created 2024-01-01. Not a separate row on the releases index.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Hermes 2 SOLAR 10.7B is a Nous Research release. It is the Hermes 2 tune of Upstage SOLAR 10.7B. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A mid-size Hermes 2 instruct model on the SOLAR 10.7B depth-upscaled base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/nous-hermes-2-vision-alpha": { "id": "nous-research/hermes/nous-hermes-2-vision-alpha", "name": "Nous Hermes 2 Vision Alpha", "maker": "nous-research", "family": "nous-research/hermes", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-11-28", "context_length": null, "max_output_tokens": null, "parameters": "7B language model plus SigLIP-400M", "architecture": "Vision-language model. Language base is mistralai/Mistral-7B-v0.1, continuing from OpenHermes-2.5-Mistral-7B. Vision encoder is SigLIP-400M rather than a 3B encoder. The card describes an LLaVA-style setup. The repo name says Alpha.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Hermes 2 Vision Alpha is a Nous Research release. It is the alpha vision-language Hermes on Mistral 7B and SigLIP-400M. Status in this catalog is preview. Collected 2026-10-15.", "what_it_is_for": "An early multimodal Hermes for image-and-text chat. The Alpha name and the card's framing mark it as an early release, not the later Hermes line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/nous-hermes-2-yi-34b": { "id": "nous-research/hermes/nous-hermes-2-yi-34b", "name": "Nous Hermes 2 Yi 34B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-12-25", "context_length": null, "max_output_tokens": null, "parameters": "34B", "architecture": "Hermes 2 fine-tune of 01-ai/Yi-34B. ChatML. The releases index calls it Hermes on Yi.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Hermes 2 Yi 34B is a Nous Research release. It is the December 2023 Hermes 2 tune of Yi 34B. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The Hermes 2 instruct model on the Yi 34B base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/nous-hermes-llama-2-7b": { "id": "nous-research/hermes/nous-hermes-llama-2-7b", "name": "Nous Hermes Llama 2 7B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT, as tagged on the Hugging Face card. Llama 2's community license may still apply to the base weights.", "release_date": "2023-07-25", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Fine-tune of Llama 2 7B. The Llama 2 13B card says the Hermes Llama 2 models reuse the Llama 1 Hermes dataset of more than 300,000 instructions.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Hermes Llama 2 7B is a Nous Research release. It is a Llama 2 7B instruction tune in the first Hermes series. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A smaller Llama 2 Hermes instruct model, released so the original Hermes behavior could be kept on the newer base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/nous-hermes-llama2-13b": { "id": "nous-research/hermes/nous-hermes-llama2-13b", "name": "Nous Hermes Llama 2 13B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT, as tagged on the Hugging Face card. Llama 2's community license may still apply to the base weights.", "release_date": "2023-07-21", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Fine-tune of Llama 2 13B on the same instruction mix as Nous Hermes 13B (more than 300,000 instructions). The releases index calls it Hermes on Llama 2. Compute sponsor listed as Redmond AI. Fine-tuning led by Teknium and Emozilla.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Hermes Llama 2 13B is a Nous Research release. It is the Llama 2 13B Hermes model listed on the official releases index. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The card says it keeps the original Hermes behavior, with longer answers and fewer OpenAI-style refusals, on a Llama 2 base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/nous-hermes-llama2-70b": { "id": "nous-research/hermes/nous-hermes-llama2-70b", "name": "Nous Hermes Llama 2 70B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT, as tagged on the Hugging Face card. Llama 2's community license may still apply to the base weights.", "release_date": "2023-08-22", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Fine-tune of Llama 2 70B on the same dataset as Hermes on Llama 1, more than 300,000 instructions. Compute from PygmalionAI. Led by Teknium and Emozilla.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Hermes Llama 2 70B is a Nous Research release. It is the 70B Llama 2 tune that still uses the original Hermes instruction set. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The 70B member of the first Llama 2 Hermes series, using the original Hermes data mix rather than the later Hermes 2 mix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/hermes/redmond-hermes-coder": { "id": "nous-research/hermes/redmond-hermes-coder", "name": "Redmond Hermes Coder 15B", "maker": "nous-research", "family": "nous-research/hermes", "status": "active", "confidence": "high", "weights": "open-source", "license": "gpl, as tagged on the Hugging Face card. The base is WizardCoder on StarCoder; those upstream licenses are not restated.", "release_date": "2023-06-28", "context_length": null, "max_output_tokens": null, "parameters": "15B", "architecture": "Fine-tune of a WizardCoder model, itself based on StarCoder, on more than 300,000 mostly synthetic GPT-4 instructions. The card calls the result Redmond-Hermes-Coder 15B. Compute from Redmond AI. Led by Teknium and Karan4D.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Redmond Hermes Coder 15B is a Nous Research release. It is the 2023 code-oriented Hermes fine-tune of WizardCoder. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A code-leaning Hermes experiment. The card says it is better at non-code writing than WizardCoder and worse at pure code benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/minos/minos-v1": { "id": "nous-research/minos/minos-v1", "name": "Minos v1", "maker": "nous-research", "family": "nous-research/minos", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-24", "context_length": 8192, "max_output_tokens": null, "parameters": "400M", "architecture": "Refusal classifier on answerdotai/ModernBERT-large. Two output classes: refusal and non-refusal. The card says 387,134 examples, 132 million tokens, and maximum sequence length 8,192. The Nous X post on 2025-04-24 calls the base ModernBERT-Large 400M. Learning rate on the card is 2e-5.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Minos v1 is a Nous Research release. It is a small refusal classifier, not a chat model. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "Detects whether a model answer is a refusal. Nous says it is used internally so synthetic responses stay free of refusals, and that it is useful for red-teamers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/nomos/nomos-1": { "id": "nous-research/nomos/nomos-1", "name": "Nomos 1", "maker": "nous-research", "family": "nous-research/nomos", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-12-09", "context_length": null, "max_output_tokens": null, "parameters": "30B", "architecture": "Specialization of Qwen/Qwen3-30B-A3B-Thinking-2507 for natural-language proofs, trained with Hillclimb AI. Meant to be used with the open-source Nomos reasoning harness. The card says to use it without a system prompt. The base model id includes A3B. The Nomos card does not restate an active-parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nomos 1 is a Nous Research release. It is the December 2025 30B math model Nous released with Hillclimb. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A math model for contest proofs. On Putnam 2025, with the Nomos harness, the card reports 87/120, against 24/120 for the base model under the same conditions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/nouscoder/nouscoder-14b": { "id": "nous-research/nouscoder/nouscoder-14b", "name": "NousCoder 14B", "maker": "nous-research", "family": "nous-research/nouscoder", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-06", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Reinforcement-learning post-train of Qwen/Qwen3-14B for competitive programming. The card and blog say training used 24,000 verifiable problems from TACO Verified, PrimeIntellect SYNTHETIC-1, and LiveCodeBench problems before 2024-07-31, on 48 B200s for four days.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NousCoder 14B is a Nous Research release. It is the January 2026 olympiad-programming model. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "An olympiad-style coding model. Not a general Hermes chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/obsidian/obsidian-3b-v0-5": { "id": "nous-research/obsidian/obsidian-3b-v0-5", "name": "Obsidian 3B v0.5", "maker": "nous-research", "family": "nous-research/obsidian", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-10-30", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Multimodal model. Language backbone is Nous Capybara 3B v1.9, which the card says is based on StableLM-3B-4e1t. Training followed the LLaVA 1.5 procedure. Led by Quan N. and Luigi D., with Virtual Interactive. Prompt format is ChatML with ### as the separator and an token. GitHub: NousResearch/Obsidian.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Obsidian 3B v0.5 is a Nous Research release. It is the October 2023 3B vision-language preview. Status in this catalog is preview. Collected 2026-10-15.", "what_it_is_for": "The card calls it the first multi-modal model at the 3B size in this line, and a vision model. Benchmarks were marked coming soon. v0.5 is an early release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/olmo-bitnet/olmo-bitnet-1b": { "id": "nous-research/olmo-bitnet/olmo-bitnet-1b", "name": "OLMo BitNet 1B", "maker": "nous-research", "family": "nous-research/olmo-bitnet", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-03-30", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "1.58-bit research model trained with the method in 'The Era of 1-bit LLMs' (arXiv:2402.17764), on the first 60 billion tokens of AllenAI Dolma, using the OLMo trainer. The card calls it a proof of concept and says a matched FP16 run exists only as a Weights & Biases report. trust_remote_code is required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo BitNet 1B is a Nous Research release. It is a 1B 1.58-bit proof of concept trained by Nous on Dolma. Status in this catalog is preview. Collected 2026-10-15.", "what_it_is_for": "A research checkpoint for 1.58-bit training, not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/puffin/nous-puffin-70b": { "id": "nous-research/puffin/nous-puffin-70b", "name": "Nous Puffin 70B", "maker": "nous-research", "family": "nous-research/puffin", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT, as tagged on the card. Llama 2's community license may still apply to the base.", "release_date": "2023-07-30", "context_length": 4096, "max_output_tokens": null, "parameters": "70B", "architecture": "70B version of Puffin. The card says it is based on Puffin 13B and uses the same style of about 3,000 curated GPT-4 examples that take advantage of Llama 2's 4096 context. Compute from PygmalionAI. Led by LDJ. Prompt format is USER / ASSISTANT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nous Puffin 70B is a Nous Research release. It is the 70B Puffin chat model. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "The larger Puffin chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/puffin/redmond-puffin-13b": { "id": "nous-research/puffin/redmond-puffin-13b", "name": "Redmond Puffin 13B", "maker": "nous-research", "family": "nous-research/puffin", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT, as tagged on the card. Llama 2's community license may still apply to the base.", "release_date": "2023-07-19", "context_length": 4096, "max_output_tokens": null, "parameters": "13B", "architecture": "Llama 2 13B fine-tune on about 3,000 curated GPT-4 conversations, many of them written to use Llama 2's 4096 context, plus slices of CamelAI science data. The card calls the release Redmond-Puffin-13b-V1.3 and says it is the first commercially available language model from Nous. Prompt format is USER / ASSISTANT. Led by LDJ, compute from Redmond AI.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Redmond Puffin 13B is a Nous Research release. It is the first Puffin chat model, a small Llama 2 tune. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A small, high-quality chat tune. The card contrasts it with Hermes, which was trained on a much larger instruction mix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/yarn/yarn-llama-2-13b-128k": { "id": "nous-research/yarn/yarn-llama-2-13b-128k", "name": "Yarn Llama 2 13B 128k", "maker": "nous-research", "family": "nous-research/yarn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Not tagged. Base is Llama 2, whose community license applies unless a later notice says otherwise.", "release_date": "2023-08-30", "context_length": 131072, "max_output_tokens": null, "parameters": "13B", "architecture": "FlashAttention-2 patched continued pretrain of Llama 2 13B on a PG19 subset for 600 steps. The card says it can use up to 128k tokens. Patched copy of conceptofmind/Yarn-Llama-2-13b-128k. Context stored as 131072 for 128k.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yarn Llama 2 13B 128k is a Nous Research release. It is a YaRN long-context continued pretrain published by Nous Research. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/yarn/yarn-llama-2-13b-64k": { "id": "nous-research/yarn/yarn-llama-2-13b-64k", "name": "Yarn Llama 2 13B 64k", "maker": "nous-research", "family": "nous-research/yarn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Not tagged. Base is Llama 2, whose community license applies unless a later notice says otherwise.", "release_date": "2023-08-30", "context_length": 65536, "max_output_tokens": null, "parameters": "13B", "architecture": "FlashAttention-2 patched continued pretrain of Llama 2 13B on a PG19 subset for 400 steps. The card says it can use up to 64k tokens. Patched copy of conceptofmind/Yarn-Llama-2-13b-64k.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yarn Llama 2 13B 64k is a Nous Research release. It is a YaRN long-context continued pretrain published by Nous Research. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/yarn/yarn-llama-2-70b-32k": { "id": "nous-research/yarn/yarn-llama-2-70b-32k", "name": "Yarn Llama 2 70B 32k", "maker": "nous-research", "family": "nous-research/yarn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the card.", "release_date": "2023-11-14", "context_length": 32768, "max_output_tokens": null, "parameters": "70B", "architecture": "YaRN extension of Llama-2-70b-hf, continued pretrain for 400 steps, 32k context. Compute support from LAION, trained on JUWELS. trust_remote_code is required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yarn Llama 2 70B 32k is a Nous Research release. It is a YaRN long-context continued pretrain published by Nous Research. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/yarn/yarn-llama-2-7b-128k": { "id": "nous-research/yarn/yarn-llama-2-7b-128k", "name": "Yarn Llama 2 7B 128k", "maker": "nous-research", "family": "nous-research/yarn", "status": "active", "confidence": "low", "weights": "open-weights", "license": "Not tagged. Base is Llama 2, whose community license applies unless a later notice says otherwise.", "release_date": "2023-08-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The repo id is Yarn-Llama-2-7b-128k, but the README title and body describe Nous-Yarn-Llama-2-13b-128k and point at conceptofmind/Yarn-Llama-2-13b-128k. Parameter count and context were not taken from that mismatched text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yarn Llama 2 7B 128k is a Nous Research release. It is a YaRN long-context continued pretrain published by Nous Research. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/yarn/yarn-llama-2-7b-64k": { "id": "nous-research/yarn/yarn-llama-2-7b-64k", "name": "Yarn Llama 2 7B 64k", "maker": "nous-research", "family": "nous-research/yarn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Not tagged. Base is Llama 2, whose community license applies unless a later notice says otherwise.", "release_date": "2023-08-30", "context_length": 65536, "max_output_tokens": null, "parameters": "7B", "architecture": "FlashAttention-2 patched continued pretrain of Llama 2 7B on a PG19 subset for 400 steps. The card says it can use up to 64k tokens. It is a patched copy of conceptofmind/Yarn-Llama-2-7b-64k. Requires Flash Attention.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yarn Llama 2 7B 64k is a Nous Research release. It is a YaRN long-context continued pretrain published by Nous Research. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/yarn/yarn-mistral-7b-128k": { "id": "nous-research/yarn/yarn-mistral-7b-128k", "name": "Yarn Mistral 7B 128k", "maker": "nous-research", "family": "nous-research/yarn", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 on the card.", "release_date": "2023-11-02", "context_length": 131072, "max_output_tokens": null, "parameters": "7B", "architecture": "YaRN extension of Mistral-7B-v0.1, 1500 steps, 128k context. Listed on the releases index on 11/02/23. trust_remote_code is required. Context stored as 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yarn Mistral 7B 128k is a Nous Research release. It is a YaRN long-context continued pretrain published by Nous Research. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/yarn/yarn-mistral-7b-64k": { "id": "nous-research/yarn/yarn-mistral-7b-64k", "name": "Yarn Mistral 7B 64k", "maker": "nous-research", "family": "nous-research/yarn", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 on the card.", "release_date": "2023-10-31", "context_length": 65536, "max_output_tokens": null, "parameters": "7B", "architecture": "YaRN extension of Mistral-7B-v0.1, 1000 steps, 64k context. The releases index lists the 128k sibling, not this one, as the long-context model row. trust_remote_code is required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yarn Mistral 7B 64k is a Nous Research release. It is a YaRN long-context continued pretrain published by Nous Research. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/yarn/yarn-solar-10b-32k": { "id": "nous-research/yarn/yarn-solar-10b-32k", "name": "Yarn SOLAR 10.7B 32k", "maker": "nous-research", "family": "nous-research/yarn", "status": "active", "confidence": "high", "weights": "open-source", "license": "Not tagged. Base is Llama 2, whose community license applies unless a later notice says otherwise.", "release_date": "2024-01-17", "context_length": 32768, "max_output_tokens": null, "parameters": "10.7B", "architecture": "YaRN extension of SOLAR-10.7B-v1.0, further pretrained on two billion long-context tokens, 32k context. The repo name says 10b. The card cites the 10.7B base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yarn SOLAR 10.7B 32k is a Nous Research release. It is a YaRN long-context continued pretrain published by Nous Research. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nous-research/yarn/yarn-solar-10b-64k": { "id": "nous-research/yarn/yarn-solar-10b-64k", "name": "Yarn SOLAR 10.7B 64k", "maker": "nous-research", "family": "nous-research/yarn", "status": "active", "confidence": "high", "weights": "open-source", "license": "Not tagged. Base is Llama 2, whose community license applies unless a later notice says otherwise.", "release_date": "2024-01-17", "context_length": 65536, "max_output_tokens": null, "parameters": "10.7B", "architecture": "YaRN extension of SOLAR-10.7B-v1.0, further pretrained on two billion long-context tokens, 64k context.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yarn SOLAR 10.7B 64k is a Nous Research release. It is a YaRN long-context continued pretrain published by Nous Research. Status in this catalog is active. Collected 2026-10-15.", "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/nai-diffusion/anime-v1-curated": { "id": "novelai/nai-diffusion/anime-v1-curated", "name": "NovelAI Diffusion Anime V1 Curated", "maker": "novelai", "family": "novelai/nai-diffusion", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "CreativeML Open RAIL-M and CC BY-NC-SA 4.0, both applying", "release_date": "2022-10-03", "context_length": null, "max_output_tokens": null, "parameters": "0.9B", "architecture": "Stable Diffusion 1.4 fine-tune. The Hugging Face cards for both V1 anime releases say the bundle is UNet and VAE only, list 0.9B parameters, and say other components come from Stable Diffusion 1.4. CLIP skip 2.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NovelAI Diffusion Anime V1 Curated is one of Anlatan's first two anime diffusion models, collected 2026-10-15. It launched 2022-10-03, left the picker, and had weights published 2024-08-22.", "what_it_is_for": "The launch post says Curated has good baseline quality and more predictable subject matter. The models page calls it the safer streaming choice of the V1 pair.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/nai-diffusion/anime-v1-full": { "id": "novelai/nai-diffusion/anime-v1-full", "name": "NovelAI Diffusion Anime V1 Full", "maker": "novelai", "family": "novelai/nai-diffusion", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "CreativeML Open RAIL-M and CC BY-NC-SA 4.0, both applying", "release_date": "2022-10-03", "context_length": null, "max_output_tokens": null, "parameters": "0.9B", "architecture": "Stable Diffusion 1.4 fine-tune. The Hugging Face cards for both V1 anime releases say the bundle is UNet and VAE only, list 0.9B parameters, and say other components come from Stable Diffusion 1.4. CLIP skip 2.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NovelAI Diffusion Anime V1 Full is one of Anlatan's first two anime diffusion models, collected 2026-10-15. It launched 2022-10-03, left the picker, and had weights published 2024-08-22.", "what_it_is_for": "The launch post says the expanded training set allows a wider variety of generations than Curated. The models page says it may handle cruder concepts more easily.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/nai-diffusion/anime-v2": { "id": "novelai/nai-diffusion/anime-v2", "name": "NovelAI Diffusion Anime V2", "maker": "novelai", "family": "novelai/nai-diffusion", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "CreativeML Open RAIL-M and CC BY-NC-SA 4.0, both applying", "release_date": "2023-10-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Stable Diffusion fine-tune. The Hugging Face card calls it SD 1.5 based and also says missing components should be copied from the Stable Diffusion 1.4 release. CLIP skip 2. Native resolutions up to about 1 megapixel, including 832x1216, without SMEA.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NovelAI Diffusion Anime V2 is Anlatan's second anime diffusion model, collected 2026-10-15. It is retired from the NovelAI picker. Weights are public. The service launch post is dated 2023-10-20 and the weights post is dated 2025-07-08.", "what_it_is_for": "The models page calls it a step up from the first models, using the same technologies with updated training, and the first NovelAI model whose default portrait is 832x1216 instead of 512x768.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/nai-diffusion/anime-v3": { "id": "novelai/nai-diffusion/anime-v3", "name": "NovelAI Diffusion Anime V3", "maker": "novelai", "family": "novelai/nai-diffusion", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune and modification of Stability AI SDXL, plus NovelAI changes. Technical report arXiv:2409.15997. Default portrait resolution 832x1216, carried over from V2.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NovelAI Diffusion Anime V3 is Anlatan's SDXL-based anime image model, collected 2026-10-15. It is still selectable as a previous model. Weights are closed. The launch post is dated 2023-11-15.", "what_it_is_for": "The models page says V3 is for anime images and is more coherent and more knowledgeable than the older NovelAI diffusion models. It prefers tags placed near the start of the prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/nai-diffusion/furry-beta-v1-3": { "id": "novelai/nai-diffusion/furry-beta-v1-3", "name": "NovelAI Diffusion Furry Beta V1.3", "maker": "novelai", "family": "novelai/nai-diffusion", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "CreativeML Open RAIL-M and CC BY-NC-SA 4.0, both applying", "release_date": "2022-10-03", "context_length": null, "max_output_tokens": null, "parameters": "0.9B", "architecture": "Stable Diffusion 1.4 fine-tune, per the Hugging Face card. Released bundle is UNet and VAE. CLIP skip 2. The 0.9B figure is the card's model size for that bundle.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NovelAI Diffusion Furry Beta V1.3 is Anlatan's first furry diffusion line, collected 2026-10-15. The line started 2022-10-03. V1.3 is the retired checkpoint whose weights were published. It is gone from the NovelAI picker.", "what_it_is_for": "The models page says it makes furry and anthropomorphic animal images and uses a different tag set from the anime models. The launch post called the first beta incomplete.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/nai-diffusion/furry-v3": { "id": "novelai/nai-diffusion/furry-v3", "name": "NovelAI Diffusion Furry V3", "maker": "novelai", "family": "novelai/nai-diffusion", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-04-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described as a new diffusion model on NovelAI's improved SDXL architecture, trained for furry art and for Vibe Transfer. Base resolution 832x1216.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NovelAI Diffusion Furry V3 is Anlatan's SDXL-era furry image model, collected 2026-10-15. It is still selectable. Weights are closed. The journal dates it 2024-04-22.", "what_it_is_for": "The launch post says it is for furry and scalie art, trained on updated furry data and tags, with Vibe Transfer supported. The models page says not to overlook it for non-furry subjects either, and not to reuse anime tags blindly.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/nai-diffusion/v4-5-curated": { "id": "novelai/nai-diffusion/v4-5-curated", "name": "NovelAI Diffusion V4.5 Curated", "maker": "novelai", "family": "novelai/nai-diffusion", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "NovelAI original diffusion architecture, revised from V4. Custom VAE replacing the Flux VAE used in V4. The V5 journal says V4.5 runs a 16-channel VAE. Text encoder is T5.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NovelAI Diffusion V4.5 Curated is Anlatan's curated V4.5 image model, collected 2026-10-15. It is still selectable. Weights are closed. The launch post is dated 2025-05-05.", "what_it_is_for": "The models page calls Curated the safer general-use and streaming choice, trained on a smaller set of cleaner images. The launch post presents it as the practical upgrade of V4.0: fewer artifacts, a custom VAE, and stronger prompt following.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/nai-diffusion/v4-5-full": { "id": "novelai/nai-diffusion/v4-5-full", "name": "NovelAI Diffusion V4.5 Full", "maker": "novelai", "family": "novelai/nai-diffusion", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "NovelAI original diffusion architecture, revised from V4. Custom VAE replacing the Flux VAE used in V4. The V5 journal says V4.5 runs a 16-channel VAE. Text encoder is T5.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NovelAI Diffusion V4.5 Full is Anlatan's previous full-dataset original diffusion model, collected 2026-10-15. It is still listed as a current selectable model. Weights are closed. The journal dates the full release 2025-05-29.", "what_it_is_for": "The models page says to use V4.5 Full when V4.5 Curated cannot produce a specific concept. It keeps the broader dataset, natural language, multi-character prompting, and English text rendering, and it adds furry mode and a background-dataset tag.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/nai-diffusion/v4-curated": { "id": "novelai/nai-diffusion/v4-curated", "name": "NovelAI Diffusion V4 Curated", "maker": "novelai", "family": "novelai/nai-diffusion", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First NovelAI image family trained from scratch rather than on a public Stable Diffusion checkpoint. About 230,000 H100 hours. T5 text encoder, prompt budget about 512 tokens. V4.5 release notes say V4 used a Flux VAE.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NovelAI Diffusion V4 Curated is the cleaner-dataset V4 image model, collected 2026-10-15. It is still selectable and is no longer labeled Preview on the models page. Weights are closed. The preview blog is dated 2024-12-21.", "what_it_is_for": "The models page recommends it for general use and streaming, on a smaller set of cleaner images than V4 Full. It was the first public piece of the original, non-Stable-Diffusion image family.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/nai-diffusion/v4-full": { "id": "novelai/nai-diffusion/v4-full", "name": "NovelAI Diffusion V4 Full", "maker": "novelai", "family": "novelai/nai-diffusion", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First NovelAI image family trained from scratch rather than on a public Stable Diffusion checkpoint. About 230,000 H100 hours. T5 text encoder, prompt budget about 512 tokens. V4.5 release notes say V4 used a Flux VAE.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NovelAI Diffusion V4 Full is Anlatan's first full-dataset original image model, collected 2026-10-15. It remains selectable. Weights are closed. The introduction post is dated 2025-02-28.", "what_it_is_for": "The models page says to use V4 Full when V4 Curated cannot depict something specific. The dataset is broader than Curated and was updated by one month. It added multi-character prompting, natural language, and better English text on top of the new architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/nai-diffusion/v5-curated": { "id": "novelai/nai-diffusion/v5-curated", "name": "NovelAI Diffusion V5 Curated", "maker": "novelai", "family": "novelai/nai-diffusion", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "NovelAI original diffusion architecture, described as more than twice the size of V4.5 (the models page says twice the size). Trained in-house for 268,000 B200 GPU-hours. Custom 32-channel VAE. The models page and the V5 marketing page disagree with the journal on whether the size multiple is exactly two or more than two.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NovelAI Diffusion V5 Curated is the cleaner-dataset sibling of V5 Full, collected 2026-10-15. Anlatan lists it as a current image model on every paid tier. Weights are closed. The journal dates both V5 models 2026-08-21.", "what_it_is_for": "The models page recommends Curated for general use and when the user wants fewer accidental sensitive images. It is described as the safer choice for streaming. It shares V5's natural language, multi-character, transparency, and furry-mode behavior, on a smaller and more focused training set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/nai-diffusion/v5-full": { "id": "novelai/nai-diffusion/v5-full", "name": "NovelAI Diffusion V5 Full", "maker": "novelai", "family": "novelai/nai-diffusion", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "NovelAI original diffusion architecture, described as more than twice the size of V4.5 (the models page says twice the size). Trained in-house for 268,000 B200 GPU-hours. Custom 32-channel VAE. The models page and the V5 marketing page disagree with the journal on whether the size multiple is exactly two or more than two.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NovelAI Diffusion V5 Full is Anlatan's latest full-dataset anime and illustration diffusion model, collected 2026-10-15. It is active on every paid NovelAI tier. Weights are closed. The journal dates the launch 2026-08-21.", "what_it_is_for": "The models page says V5 Full is the latest image model and the one to try when V5 Curated cannot depict a specific subject. It is trained on a broader, less curated set than V5 Curated, and it supports natural language, tags, furry mode via a fur dataset tag, a background-dataset tag, multi-character scenes, English text plus Japanese and Chinese text, alpha transparency, and comic pages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/novelai-lm/calliope": { "id": "novelai/novelai-lm/calliope", "name": "Calliope", "maker": "novelai", "family": "novelai/novelai-lm", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "GPL-2.0", "release_date": "2021-07-16", "context_length": 2048, "max_output_tokens": null, "parameters": "2.7 billion", "architecture": "Fine-tune of EleutherAI GPT-Neo 2.7B. Hugging Face config is GPTNeoForCausalLM with max_position_embeddings 2048. The model page says it shipped with NovelAI and lacked later features.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Calliope is NovelAI's first language model, collected 2026-10-15. It is retired. Weights are public under GPL-2.0. The model page dates it to the NovelAI launch on 2021-07-16.", "what_it_is_for": "The model page says Calliope is where NovelAI started. It was fast, at 2.7 billion parameters, and it lacked the later interface features. The weight card says it was a GPT-Neo 2.7B fine-tune and was sidelined when Sigurd's GPT-J base arrived.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/novelai-lm/clio": { "id": "novelai/novelai-lm/clio", "name": "Clio", "maker": "novelai", "family": "novelai/novelai-lm", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "GPL-2.0 only, not or later", "release_date": "2023-05-23", "context_length": 8192, "max_output_tokens": null, "parameters": "3 billion", "architecture": "NovelAI pretrain from scratch on the Shoggy H100 cluster. Nerdstash dataset and Nerdstash Tokenizer V1. About 1.6 trillion tokens. The public card says an existing Transformers class loads it when a config flag is set. The Hugging Face repo is tagged stablelm.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Clio is NovelAI's first from-scratch language model, collected 2026-10-15. The model page lists it as retired and gone from the picker. Weights are public under GPL-2.0-only. It launched 2023-05-23. The weight release is 2026-09-24.", "what_it_is_for": "The model page says Clio put a large training run into 3 billion parameters so it would be fast, and that special modules added text adventure, instruct, and prose augmentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/novelai-lm/erato": { "id": "novelai/novelai-lm/erato", "name": "Erato", "maker": "novelai", "family": "novelai/novelai-lm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-23", "context_length": null, "max_output_tokens": null, "parameters": "70 billion", "architecture": "Continued training of Llama 3 70B Base on high-quality updated Nerdstash pretraining text, then a storytelling fine-tune. NovelAI kept the Llama 3 tokenizer after trying Nerdstash V2. Continued pretraining used hundreds of billions of tokens and more compute than Kayra's from-scratch pretraining.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Erato is NovelAI's Llama 3 70B storytelling model, collected 2026-10-15. The model page lists it as current. The subscription page calls it a previous experimental model and limits it to Opus. Weights are closed. Released 2024-09-23.", "what_it_is_for": "NovelAI says Erato was continued-pretrained on the best and newest parts of Nerdstash and then fine-tuned to tell stories. The Llama 3 tokenizer was kept because it compresses text more than Nerdstash V2, so more of a story fits in context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/novelai-lm/euterpe": { "id": "novelai/novelai-lm/euterpe", "name": "Euterpe", "maker": "novelai", "family": "novelai/novelai-lm", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2022-01-09", "context_length": 2048, "max_output_tokens": null, "parameters": "13 billion", "architecture": "NovelAI fine-tune of a Fairseq-family 13B transformer. Modules V2 compatible. The model page calls it outdated and lists it under Legacy, not Retired.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Euterpe is NovelAI's Fairseq 13B storytelling fine-tune, collected 2026-10-15. The model page files it under Legacy and calls it outdated. It is not in the Retired section. Weights were not found as a public release. Experimental release 2022-01-09.", "what_it_is_for": "The model page says Euterpe was a major step up for its time, with Modules V2, fast generation, a 2,048-token context, and a strong NovelAI fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/novelai-lm/genji": { "id": "novelai/novelai-lm/genji", "name": "Genji", "maker": "novelai", "family": "novelai/novelai-lm", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2022-11-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Experimental fine-tune of Euterpe aimed at Japanese text. The model page says Clio and Kayra surpassed it and that Japanese use is no longer recommended.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Genji is NovelAI's experimental Japanese fine-tune of Euterpe, collected 2026-10-15. It is retired and gone from the picker. Weights are public under GPL-3.0. It launched 2022-11-10.", "what_it_is_for": "The model page says Genji was an early attempt at a Japanese NovelAI model. It says Clio and Kayra are better and that Genji should not be used for Japanese anymore.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/novelai-lm/kayra": { "id": "novelai/novelai-lm/kayra", "name": "Kayra", "maker": "novelai", "family": "novelai/novelai-lm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-07-28", "context_length": 8192, "max_output_tokens": null, "parameters": "13 billion", "architecture": "NovelAI in-house model. Foundation checkpoint is named NovelAI-LM-13B-402k. Trained on about 1.6 trillion tokens. The launch post says Opus got it first and all tiers got it about two weeks later.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kayra is NovelAI's in-house 13 billion parameter storytelling model, collected 2026-10-15. It is still listed as current. Weights are closed. It launched 2023-07-28, with V1.1 on 2023-08-15.", "what_it_is_for": "The model page says Kayra covers storytelling, text adventure, instruct, prose augmentation, and odd formats such as item lists and transcripts. The launch post introduces the foundation model NovelAI-LM-13B-402k.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/novelai-lm/krake": { "id": "novelai/novelai-lm/krake", "name": "Krake", "maker": "novelai", "family": "novelai/novelai-lm", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2022-03-11", "context_length": 2048, "max_output_tokens": null, "parameters": "20 billion", "architecture": "Fine-tune of EleutherAI GPT-NeoX-20B. Historically Opus-only. Incompatible with Modules V2. The Hugging Face card for the converted checkpoint lists 21B parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Krake is NovelAI's GPT-NeoX-20B storytelling fine-tune, collected 2026-10-15. The model page lists it as retired. The subscription page still names it as an Opus extra. Weights are public under GPL-3.0 as of 2026-09-24. It launched 2022-03-11.", "what_it_is_for": "The model page calls Krake a large jack-of-all-trades with a distinctive style, limited by speed, a 2,048-token context, and no Modules V2 support. It says not to use Krake except as a preference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/novelai-lm/sigurd": { "id": "novelai/novelai-lm/sigurd", "name": "Sigurd", "maker": "novelai", "family": "novelai/novelai-lm", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2021-06-16", "context_length": 2048, "max_output_tokens": null, "parameters": "6 billion", "architecture": "NovelAI fine-tune of EleutherAI GPT-J 6B. About 300 million tokens of training. Modules V2. Listed under Legacy.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sigurd is NovelAI's GPT-J 6B fine-tune, collected 2026-10-15. The model page lists it under Legacy. Weights were not found as a public release. The experimental release is dated 2021-06-16.", "what_it_is_for": "The model page calls Sigurd NovelAI's second model, a 6 billion parameter GPT-J fine-tune with a 2,048-token context and Modules V2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/novelai-lm/snek": { "id": "novelai/novelai-lm/snek", "name": "Snek", "maker": "novelai", "family": "novelai/novelai-lm", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-11-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Experimental fine-tune of Euterpe for Python. The model page says other models replaced this use and that Snek is no longer recommended for Python.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snek is NovelAI's experimental Python fine-tune of Euterpe, collected 2026-10-15. It is retired and gone from the picker. Weights are public under Apache-2.0. It launched 2022-11-10.", "what_it_is_for": "The model page says Snek was a dedicated Python-writing experiment. It says that job has been replaced by other models and that Snek should not be used for Python.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "novelai/novelai-lm/xialong": { "id": "novelai/novelai-lm/xialong", "name": "Xialong", "maker": "novelai", "family": "novelai/novelai-lm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-31", "context_length": 28672, "max_output_tokens": null, "parameters": "355 billion", "architecture": "Fine-tune of ZhipuAI GLM-4.6. Mixture-of-experts. NovelAI says this is the first MoE model they trained. Storytelling fine-tune. Opus only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Xialong is NovelAI's Opus-only storytelling fine-tune of ZhipuAI GLM-4.6, collected 2026-10-15. It is active. Weights are closed. The launch post is dated 2026-03-31.", "what_it_is_for": "NovelAI says it adapts to the user's writing style while keeping the base model's knowledge and coherence, and that retries produce genuinely different text because it was not trained with reinforcement learning the way some other models were.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntnu/norgpt/norgpt-23b": { "id": "ntnu/norgpt/norgpt-23b", "name": "NorGPT-23B", "maker": "ntnu", "family": "ntnu/norgpt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-08-17", "context_length": null, "max_output_tokens": null, "parameters": "23030000000", "architecture": "Decoder-only GPT-2-style Transformer trained from scratch, according to the technical report and the NLEBench paper. Report table: 23.03B parameters, context 2k, hidden size 6144, vocab 64k, 53.1K A100 GPU hours. Layer count was not stated. No public config file was found. The NTNU page says training on the Idun cluster took 76 days in 2023.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorGPT-23B is the large GPT-2-style Norwegian model NorwAI at NTNU says it trained from scratch in 2023. It is evaluated throughout the NLEBench paper and specified in the January 2026 technical report. No public weight repository was found on the collection date, 2026-10-15, so this dossier is a named release without a verified download.", "what_it_is_for": "The report recommends it for document-level comprehension, long-form generation, and natural-language understanding when more than 48 GB of GPU memory is available. The NTNU page also says it was optimized with human feedback. That claim is not repeated in the report's training-strategy cell.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntnu/norgpt/norgpt-369m": { "id": "ntnu/norgpt/norgpt-369m", "name": "NorGPT-369M", "maker": "ntnu", "family": "ntnu/norgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-3.0", "release_date": "2023-08-17", "context_length": 2048, "max_output_tokens": null, "parameters": "369600000", "architecture": "Decoder-only GPT-2 (GPT2LMHeadModel), trained from scratch with Megatron-DeepSpeed. Released config: 24 layers, 16 heads, hidden size 1024, inner size 4096, vocab size 64000, n_positions 2048, gelu_fast. The technical report lists 369.6M parameters, hidden size 1024, vocab 64k, context 2k, 1.3K A100 GPU hours.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorGPT-369M is an open-weight Norwegian decoder-only language model from NorwAI at the Norwegian University of Science and Technology. It was trained from scratch as a GPT-2-style baseline and is the smallest named model in the NorGLM suite. NorwAI introduced its first large Norwegian models publicly at Arendalsuka on 17 August 2023. The Hugging Face repository was created on 8 March 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "The technical report recommends it for education, rapid prototyping, tokenization studies, sentence completion, and basic language modeling where GPU memory is under 8 GB. The model card shows ordinary causal generation in Norwegian.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntnu/norgpt/norgpt-3b": { "id": "ntnu/norgpt/norgpt-3b", "name": "NorGPT-3B", "maker": "ntnu", "family": "ntnu/norgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2023-08-17", "context_length": 2048, "max_output_tokens": null, "parameters": "2950000000", "architecture": "Decoder-only GPT-2 (GPT2LMHeadModel), trained from scratch with Megatron-DeepSpeed. Released config: 32 layers, 32 heads, hidden size 2688, inner size 10752, vocab size 64000, n_positions 2048. The technical report lists 2.95B parameters, context 2k, vocab 64k, 2.6K A100 GPU hours.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorGPT-3B is an open-weight Norwegian GPT-2-style language model trained from scratch by NorwAI at NTNU. It is the mid-size model in the first NorGLM generation, introduced publicly with the other early models at Arendalsuka on 17 August 2023. Weights were posted on Hugging Face under NorGLM, with a repository timestamp of 8 March 2024. Collected on 2026-10-15.", "what_it_is_for": "The NTNU overview lists text generation, dialogue, question answering, and summarization. The technical report recommends the 3B NorGPT and NorLlama models for embedded applications, chatbots, and small summarization or classification jobs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntnu/norgpt/norgpt-3b-continue": { "id": "ntnu/norgpt/norgpt-3b-continue", "name": "NorGPT-3B-continue", "maker": "ntnu", "family": "ntnu/norgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "2950000000", "architecture": "Same GPT-2 shape as NorGPT-3B. Released config: 32 layers, 32 heads, hidden size 2688, inner size 10752, vocab 64000, n_positions 2048. Continued training of NorGPT-3B on a higher-quality subset (news, parliamentary speech, books, and government reports on the card; news and National Library text in the NLEBench paper). Parameter count is the parent 2.95B figure. The technical report's training table does not give this checkpoint its own row.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorGPT-3B-continue is an open-weight continued-training checkpoint of NorGPT-3B from NorwAI at NTNU. The NLEBench paper, submitted 3 December 2023, already evaluates it. The Hugging Face repository under NorGLM was created on 8 March 2024. A day-level release date was not found. Collected on 2026-10-15.", "what_it_is_for": "The NLEBench paper uses the extra training to test whether oversampling higher-quality written Norwegian improves downstream generation. On the authors' news-storytelling setup it is the strongest NorGLM, which they attribute to overlap with news-like text rather than to a general product claim.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntnu/norllama/norllama-3b": { "id": "ntnu/norllama/norllama-3b", "name": "NorLlama-3B", "maker": "ntnu", "family": "ntnu/norllama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2023-08-17", "context_length": 2048, "max_output_tokens": null, "parameters": "3070000000", "architecture": "Llama-style decoder trained from scratch with the Tencent Pre-training Framework. Released config: LlamaForCausalLM, 32 layers, 32 heads, hidden size 2688, intermediate size 7258, vocab size 50000, max position embeddings 2048, SiLU, float16. The technical report lists 3.07B parameters, hidden size 2688, context 2k, vocab 64k, and 2.2K A100 GPU hours. The vocab figures disagree. This is not a Meta Llama 3 checkpoint: the shape does not match Llama 3.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorLlama-3B is an open-weight 3-billion-parameter Llama-style Norwegian language model trained from scratch by NorwAI at NTNU. It was part of the first public NorLLM set introduced at Arendalsuka on 17 August 2023. The Hugging Face repository NorGLM/NorLlama-3B was created on 8 March 2024. Collected on 2026-10-15.", "what_it_is_for": "The NTNU table lists text generation and dialogue. The technical report pairs it with NorGPT-3B for embedded applications, chatbots, and small summarization or classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntnu/norwai-llama2/norwai-llama2-7b": { "id": "ntnu/norwai-llama2/norwai-llama2-7b", "name": "NorwAI-Llama2-7B", "maker": "ntnu", "family": "ntnu/norwai-llama2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NorLLM License", "release_date": "2024-05-15", "context_length": 4096, "max_output_tokens": null, "parameters": "7030000000", "architecture": "Continual pretraining of Meta Llama 2 7B. The model card says 51B tokens, 7B parameters, and context 4096. Technical report: 7.03B, hidden size 4096, vocab 68k, training context 4k, 4.4K H100 GPU hours. The card's tokenizer note says the extended vocabulary is 64000. Samuel et al. measure 67993. This is not NorLlama-3B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorwAI-Llama2-7B is a gated Norwegian continual pretraining of Meta Llama 2 7B from NorwAI at NTNU. The model card dates the family release to 15 May 2024. The repository timestamp is 8 April 2024. Collected on 2026-10-15.", "what_it_is_for": "The report says to prefer it when Llama 2 compatibility or mixed-language generation matters, and that it is slightly behind the Mistral and Mixtral models on long-form fluency. It is not the earlier from-scratch NorLlama-3B model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntnu/norwai-magistral/norwai-magistral-24b": { "id": "ntnu/norwai-magistral/norwai-magistral-24b", "name": "NorwAI-Magistral-24B", "maker": "ntnu", "family": "ntnu/norwai-magistral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NorLLM License", "release_date": "2025-09-10", "context_length": null, "max_output_tokens": null, "parameters": "23850000000", "architecture": "Continual pretraining of Mistral Magistral. Technical report: 23.85B parameters, Transformer, hidden size 5120, vocab 158k, context 40k, 22K H100 GPU hours. The hub name is NorwAI-Magistral-24B-reasoning. The card says it switches among completion, short-thinking, and long-thinking modes. The report says it was trained on NorLLM Corpus V3 (88.45B tokens), which added Nynorsk and S\u00e1mi text relative to earlier corpora. How many of those tokens were used for this continual pretrain was not broken out.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorwAI-Magistral-24B is NorwAI's first named Norwegian reasoning model, a gated continual pretraining of Mistral Magistral by NorwAI at NTNU. The technical report dates the initial release to 2025. The Hugging Face repository NorwAI-Magistral-24B-reasoning was created on 10 September 2025. Collected on 2026-10-15.", "what_it_is_for": "The card says the model picks completion, short thinking, or long thinking based on the question. The report recommends it for multi-step reasoning and long context, and for Nynorsk and S\u00e1mi tasks because it was trained on the later corpus that includes those varieties.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntnu/norwai-mistral/norwai-mistral-7b": { "id": "ntnu/norwai-mistral/norwai-mistral-7b", "name": "NorwAI-Mistral-7B", "maker": "ntnu", "family": "ntnu/norwai-mistral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NorLLM License", "release_date": "2024-05-15", "context_length": null, "max_output_tokens": null, "parameters": "7540000000", "architecture": "Continual pretraining of Mistral-7B-v0.1. Technical report: 7.54B parameters, Transformer, hidden size 4096, vocab 68k, training context 4k, 4.6K H100 GPU hours, LLM Foundry on the NTNU Idun cluster. The model card says 7B, context 32k, and an extended tokenizer of 64000 built by merging the Llama 2 tokenizer with a Norwegian tokenizer. Samuel et al. measure vocabulary size 67993. The card says continued pretraining used 51B tokens. The report associates the 2024 dense models with NorLLM Corpus V2 (51.15B tokens).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorwAI-Mistral-7B is a gated Norwegian continual-pretraining of Mistral-7B-v0.1 from NorwAI at NTNU, with Schibsted, NRK, VG, and the National Library of Norway. The model card dates the family release to 15 May 2024. The Hugging Face repository timestamp is 8 April 2024. Collected on 2026-10-15. It is active and open-weight only after a Nordic license grant.", "what_it_is_for": "The report calls the base checkpoint a strong dense model for Norwegian comprehension and fluent generation, and the best 7B model on the reprinted NorEval aggregate. It is the parent of NorwAI-Mistral-7B-instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntnu/norwai-mistral/norwai-mistral-7b-instruct": { "id": "ntnu/norwai-mistral/norwai-mistral-7b-instruct", "name": "NorwAI-Mistral-7B-instruct", "maker": "ntnu", "family": "ntnu/norwai-mistral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NorLLM License", "release_date": "2024-05-15", "context_length": null, "max_output_tokens": null, "parameters": "7540000000", "architecture": "Instruction-tuned NorwAI-Mistral-7B. Technical report: 7.54B, hidden size 4096, vocab 68k, training context 4k, 38.4 H100 GPU hours for instruction tuning. The card says about 9000 self-collected Norwegian instructions, 7B parameters, and 32k context. The report describes full-weight fine-tuning on a mimir-instruction set of nearly 5000 human-written Norwegian instructions, with prompts '{instruction}\\n\\n{inst_input}\\nAnswer:' or '{instruction}\\n\\nAnswer:'. The card prints the same two templates.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorwAI-Mistral-7B-instruct is the instruction-tuned Norwegian assistant checkpoint of NorwAI-Mistral-7B, released by NorwAI at NTNU. It is gated under the NorLLM License. The card dates the family to 15 May 2024. The repository timestamp is 29 June 2024. Collected on 2026-10-15.", "what_it_is_for": "The technical report recommends it as the default model for Norwegian interactive and instruction-following tasks, balancing accuracy and latency. It also names Q4 or Q5 GGUF builds for laptop inference and LoRA for domain adaptation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntnu/norwai-mistral/norwai-mistral-7b-pretrain": { "id": "ntnu/norwai-mistral/norwai-mistral-7b-pretrain", "name": "NorwAI-Mistral-7B-pretrain", "maker": "ntnu", "family": "ntnu/norwai-mistral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NorLLM License", "release_date": "2024-05-15", "context_length": null, "max_output_tokens": null, "parameters": "7540000000", "architecture": "Mistral-7B-v0.1 architecture trained from scratch, not a continual pretrain of the Mistral weights. The card says 51B tokens. Technical report: 7.54B, hidden size 4096, vocab 68k, training context 4k, 5.1K H100 GPU hours. The shared card table says 7B and 32k context, and lists Mistral-7B-v0.1 in the base-model column even for the from-scratch row.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorwAI-Mistral-7B-pretrain is the from-scratch Mistral-architecture Norwegian model from NorwAI at NTNU. It is gated under the NorLLM License. The card uses the family release date 15 May 2024. The repository timestamp is 16 April 2024. Collected on 2026-10-15.", "what_it_is_for": "The report lists it as the from-scratch member of the Mistral 7B pair. The usage section recommends the continual-pretrain base and the instruct variant for applications, and does not give this checkpoint a separate job description.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntnu/norwai-mixtral/norwai-mixtral-8x7b": { "id": "ntnu/norwai-mixtral/norwai-mixtral-8x7b", "name": "NorwAI-Mixtral-8x7B", "maker": "ntnu", "family": "ntnu/norwai-mixtral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NorLLM License", "release_date": "2024-05-15", "context_length": null, "max_output_tokens": null, "parameters": "47000000000", "architecture": "Continual pretraining of Mixtral-8x7B-v0.1. Technical report: 47.00B total parameters, mixture of experts, 8 experts, 2 experts active per token, hidden size 4096, vocab 68k, context 32k, 31.1K H100 GPU hours. The card says 45B, context 32k, and 51B training tokens. The report text also says 47B total. Active parameter count was not stated as a separate integer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorwAI-Mixtral-8x7B is a gated Norwegian continual pretraining of Mixtral-8x7B-v0.1 from NorwAI at NTNU. The card dates the family to 15 May 2024. The repository timestamp is 13 May 2024. Collected on 2026-10-15.", "what_it_is_for": "The report recommends the base Mixtral model for long-context generation and reasoning-heavy synthesis. Two experts are active per token. It is the parent of the instruct checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntnu/norwai-mixtral/norwai-mixtral-8x7b-instruct": { "id": "ntnu/norwai-mixtral/norwai-mixtral-8x7b-instruct", "name": "NorwAI-Mixtral-8x7B-instruct", "maker": "ntnu", "family": "ntnu/norwai-mixtral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NorLLM License", "release_date": "2024-05-15", "context_length": null, "max_output_tokens": null, "parameters": "47000000000", "architecture": "Instruction-tuned NorwAI-Mixtral-8x7B. Technical report: 47.00B MoE, hidden size 4096, vocab 68k, context 32k, 170.8 H100 GPU hours for instruction tuning, 2 experts active per token. The card says 45B, 32k context, and about 9000 Norwegian instructions. The report's instruction section says nearly 5000 instructions. Same two Answer: prompt templates as the Mistral instruct card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorwAI-Mixtral-8x7B-instruct is the instruction-tuned Norwegian mixture-of-experts model from NorwAI at NTNU. It is gated under the NorLLM License. The card dates the family to 15 May 2024. The repository timestamp is 28 June 2024. Collected on 2026-10-15.", "what_it_is_for": "The report recommends it for complex assistant dialogue, reasoning-heavy tasks, and enterprise virtual agents, and for high-throughput serving with vLLM. A Schibsted blind review of news summaries preferred it, on average, to GPT-4 and to journalist-written summaries. The average itself is only in a figure.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntt/tsuzumi/tsuzumi-0-6b": { "id": "ntt/tsuzumi/tsuzumi-0-6b", "name": "tsuzumi (0.6B)", "maker": "ntt", "family": "ntt/tsuzumi", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-01", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Ultra-lightweight sibling of tsuzumi 7B. NTT says it keeps performance by narrowing domain and task. No public layer count, vocabulary, or confirmation that it uses the same tokenizer and adapter design as the 7B model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "tsuzumi (0.6B) is the ultra-lightweight version of NTT's tsuzumi, announced on 2023-11-01 with the 7B model. NTT says it runs on a CPU and holds performance by narrowing domain and task. Weights are closed. Collected on 2026-10-15. It was not confirmed as its own commercial SKU.", "what_it_is_for": "NTT presented it as the cost floor of the tsuzumi line: about 1/300 the parameter count of the 175B GPT-3 reference they used, with CPU inference instead of a GPU. The August 2024 technical article still treats it as one of the two tsuzumi versions. Public pages do not give it a separate job description from the 7B model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntt/tsuzumi/tsuzumi-2": { "id": "ntt/tsuzumi/tsuzumi-2", "name": "tsuzumi 2", "maker": "ntt", "family": "ntt/tsuzumi", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-20", "context_length": null, "max_output_tokens": null, "parameters": "30B", "architecture": "From-scratch model, not initialized from a public open-source base. NTT sizes it for one GPU with 40 GB or less memory. Briefing coverage said the parameter count was chosen as the one-GPU sweet spot and named an NVIDIA A100 40GB as the assumed device. Layer count, attention type, and vocabulary are not published. The 2026-05-19 update reads charts and tables by taking the document as an image.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "tsuzumi 2 is NTT's current commercial large language model, sold from 2025-10-20 as the next generation of tsuzumi. It is a closed, from-scratch Japanese model sized for one GPU, with finance, healthcare, and public-sector knowledge. A 2026-05-19 update adds document-image reading. Collected on 2026-10-15.", "what_it_is_for": "NTT positions it for corporate and government work that has to stay on premises or in a private cloud: question answering over documents, retrieval-augmented generation, extraction, and summarization. The October 2025 release stresses finance, medical, and municipal knowledge, and instruction following that includes JSON-shaped extraction. The May 2026 update targets forms, charts, graphs, and flow diagrams in Japanese business documents, plus numerical reasoning. Tokyo Online University was named as an adopter in the launch release. NTT West later used it in a school-administration trial that started on 2026-10-13.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntt/tsuzumi/tsuzumi-7b": { "id": "ntt/tsuzumi/tsuzumi-7b", "name": "tsuzumi (7B)", "maker": "ntt", "family": "ntt/tsuzumi", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-01", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Closed model built by NTT Human Informatics Laboratories, not continued from a published open-source base. NTT describes a Japanese-oriented tokenizer and adapter tuning: an external submodule is trained while base parameters stay fixed. Layer count, attention type, and vocabulary size are not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "tsuzumi (7B) is NTT's commercial lightweight large language model, announced on 2023-11-01 and sold from 2024-03-25. It is a closed, from-scratch Japanese-and-English model aimed at on-premises and private-cloud enterprise use. This dossier was collected on 2026-10-15. No retirement notice was found after tsuzumi 2 launched.", "what_it_is_for": "NTT sells it for contact-center and help-desk answers, document extraction, structuring, summarization, translation, and drafting. The commercial offer combines on-premises, private-cloud, or public-cloud hosting with industry and IT-operations solutions. Adapter tuning is how NTT says customers add industry or company knowledge without retraining the base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu/basicvsr/basicvsr": { "id": "ntu/basicvsr/basicvsr", "name": "BasicVSR", "maker": "ntu", "family": "ntu/basicvsr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-12-03", "context_length": null, "max_output_tokens": null, "parameters": "6.3M", "architecture": "Recurrent 4x video super-resolution. Bidirectional propagation, optical-flow alignment, feature concatenation, and pixel-shuffle upsampling. The paper splits the 6.3M count into a 1.4M flow estimator and a 4.9M main network. x4 only in the MMagic BasicVSRNet description.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BasicVSR is a 4x video super-resolution network from S-Lab at Nanyang Technological University, with a Tencent PCG co-author. Kelvin C.K. Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy introduced it for CVPR 2021. The arXiv version is dated 3 December 2020. Checkpoints are still hosted in OpenMMLab MMagic. Collected 2026-10-15.", "what_it_is_for": "The paper strips video super-resolution down to propagation, alignment, aggregation, and upsampling. BasicVSR is the resulting recurrent baseline: bidirectional propagation and flow-based alignment, meant to be accurate without a heavy design.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu/basicvsr/basicvsr-pp": { "id": "ntu/basicvsr/basicvsr-pp", "name": "BasicVSR++", "maker": "ntu", "family": "ntu/basicvsr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2021-04-27", "context_length": null, "max_output_tokens": null, "parameters": "7.3M", "architecture": "Second-order grid propagation and flow-guided deformable alignment on the BasicVSR recurrent frame. The official repo says the code license is Apache-2.0 and the work was merged into MMEditing. A lighter BasicVSR++ (S) is reported at 6.4M.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BasicVSR++ is the S-Lab Nanyang Technological University follow-up to BasicVSR. Kelvin C.K. Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy, all listed at NTU, posted it on 27 April 2021. It won three NTIRE 2021 champions and one runner-up. The Apache-2.0 code repo shipped on 18 April 2022. Collected 2026-10-15.", "what_it_is_for": "Video super-resolution and, in the same paper, compressed-video enhancement. The redesign is second-order grid propagation and flow-guided deformable alignment, so the recurrent net can use frames that are badly misaligned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu/basicvsr/iconvsr": { "id": "ntu/basicvsr/iconvsr", "name": "IconVSR", "maker": "ntu", "family": "ntu/basicvsr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-12-03", "context_length": null, "max_output_tokens": null, "parameters": "8.7M", "architecture": "BasicVSR plus an information-refill mechanism and coupled propagation. Same 1.4M flow estimator and 4.9M main network as BasicVSR, plus a 2.4M feature extractor.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IconVSR is the named extension of BasicVSR from S-Lab at Nanyang Technological University, in the same CVPR 2021 paper. It adds information refill and coupled propagation so the recurrent net can recover detail that bidirectional passing still misses. Collected 2026-10-15.", "what_it_is_for": "The authors use IconVSR where BasicVSR is weak: short clips that lack long temporal context, and clips with large motion. It keeps the BasicVSR backbone and adds a feature extractor plus keyframe refill.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu/basicvsr/realbasicvsr": { "id": "ntu/basicvsr/realbasicvsr", "name": "RealBasicVSR", "maker": "ntu", "family": "ntu/basicvsr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2021-11-25", "context_length": null, "max_output_tokens": null, "parameters": "6.3M", "architecture": "Real-world video super-resolution. An image cleaning module of residual blocks feeds a BasicVSR-style propagation network. The x4 config uses 64 channels, 20 propagation blocks, and 20 cleaning blocks, plus a U-Net spectral-norm discriminator. The paper table lists 6.3M parameters and 63 ms runtime.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RealBasicVSR is a real-world video super-resolution model from S-Lab at Nanyang Technological University. Kelvin C.K. Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy released test code on 29 November 2021. The paper was accepted to CVPR 2022. The repository is Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Synthetic bicubic benchmarks do not match real video. RealBasicVSR adds a cleaning stage in front of a BasicVSR propagation network and studies the tradeoff between cleaning and detail. The authors also release the VideoLQ set as a shared real-world test.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu/codeformer/codeformer": { "id": "ntu/codeformer/codeformer", "name": "CodeFormer", "maker": "ntu", "family": "ntu/codeformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "S-Lab License 1.0", "release_date": "2022-06-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer code predictor over a learned discrete face codebook, plus a controllable feature transformation module. The public inference net is constructed with dim_embd 512, codebook_size 1024, n_head 8, n_layers 9, and connect_list 32, 64, 128, 256. A fidelity weight trades quality against identity.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CodeFormer is a blind face-restoration model from S-Lab at Nanyang Technological University. Shangchen Zhou, Kelvin C.K. Chan, Chongyi Li, and Chen Change Loy released it with the 22 June 2022 paper, later accepted at NeurIPS 2022. Weights remain public under a non-commercial license. This dossier was collected on 2026-10-15.", "what_it_is_for": "The maker casts blind face restoration as prediction of codes in a learned discrete codebook, so a transformer can rebuild a natural face when the input is badly degraded. A controllable feature transform lets the user trade fidelity to the input against visual quality. NTU's technology page also positions it for old photos, video frames, and archival images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu/longva/longva": { "id": "ntu/longva/longva", "name": "LongVA", "maker": "ntu", "family": "ntu/longva", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-24", "context_length": 224000, "max_output_tokens": null, "parameters": "", "architecture": "Long Video Assistant. Qwen2-7B-Instruct is continued-pretrained on text to 224K tokens with RoPE base 1e9, then aligned with CLIP-ViT-L-336px and a 2-layer MLP using LLaVA-1.6 image data and UniRes. Each frame becomes 144 visual tokens. No video training. The Hub card tags the checkpoint 8B, which is not the same figure as the 7B backbone.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongVA, the Long Video Assistant, is a long-video multimodal model from S-Lab at Nanyang Technological University and the LMMs-Lab team. Peiyuan Zhang and colleagues released it on 24 June 2024. It extends Qwen2-7B-Instruct on text, then aligns that model with images so the long context transfers to video with no video training. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Answering questions about very long videos by sampling many frames. The lab's claim is that a long language context transfers to vision once image and text are aligned. UniRes encodes each frame like a tile of one long image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu/otter/otter-image-mpt7b": { "id": "ntu/otter/otter-image-mpt7b", "name": "Otter-Image-MPT7B", "maker": "ntu", "family": "ntu/otter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-05-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "OpenFlamingo-style model: frozen vision encoder, perceiver resampler, and cross-attention into an MPT-7B language model. Instruction-tuned on MIMIC-IT. The card lists S-Lab, NTU and Microsoft Research, Redmond. The training init checkpoint is a converted OpenFlamingo MPT-7B with an added answer token, and is not this release.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Otter-Image-MPT7B is the image checkpoint S-Lab at Nanyang Technological University lists for Otter. Otter is an OpenFlamingo model instruction-tuned on the lab's MIMIC-IT set so that images and text can both act as in-context examples. The paper is 5 May 2023 and was later accepted at IEEE TPAMI. The Hugging Face card tags the weights MIT. Collected 2026-10-15.", "what_it_is_for": "A multimodal assistant that follows instructions and can use previous image-text examples, not only a single image. The project prompt is `User: {instruction} GPT: {response}`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu/otter/otter-video-llama7b": { "id": "ntu/otter/otter-video-llama7b", "name": "Otter-Video-LLaMA7B-DenseCaption", "maker": "ntu", "family": "ntu/otter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-05-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Otter video checkpoint. LLaMA-7B OpenFlamingo backbone. The card says this version is trained on the MIMIC-IT dense-caption set. Frames are passed in Flamingo's multi-image layout. Card affiliations: S-Lab, NTU and Microsoft Research, Redmond.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the video checkpoint S-Lab at Nanyang Technological University lists for Otter. It is an OpenFlamingo LLaMA-7B model trained on the MIMIC-IT dense-caption split. The Hub license tag is MIT. The parent paper is 5 May 2023. Collected 2026-10-15.", "what_it_is_for": "Video captioning and instruction following. Frames are arranged the way Flamingo arranges multiple images. The card's example is a local video script that loads `OtterForConditionalGeneration`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu/otter/otterhd": { "id": "ntu/otter/otterhd", "name": "OtterHD-8B", "maker": "ntu", "family": "ntu/otter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-11-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-tuned from Fuyu-8B. No separate vision encoder. Image patches are linearly projected and mixed with text tokens. The report says training and inference support flexible resolution, with instruction tuning described up to 1024 by 1024. The lab reports a faster Fuyu training stack using FlashAttention-2.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OtterHD-8B is a high-resolution multimodal model from S-Lab at Nanyang Technological University. Bo Li, Peiyuan Zhang, Jingkang Yang, Yuanhan Zhang, Fanyi Pu, and Ziwei Liu posted the technical report on 7 November 2023. It is instruction-tuned from Fuyu-8B and does not use a separate vision encoder. Collected 2026-10-15.", "what_it_is_for": "Reading small detail in large images. The lab pairs the model with MagnifierBench, which asks about objects around 1 percent of the image and about their spatial relations. The report says leading models of that time fail there, while OtterHD-8B is stronger when the image is passed at high resolution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu/resshift/resshift": { "id": "ntu/resshift/resshift", "name": "ResShift", "maker": "ntu", "family": "ntu/resshift", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NTU S-Lab License 1.0", "release_date": "2023-07-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion image super-resolution. A Markov chain shifts the residual between the low-resolution and high-resolution images instead of running a long noise-to-image chain. A noise schedule controls shift speed and noise strength. The abstract says strong results at 15 sampling steps.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ResShift is an image super-resolution diffusion model from S-Lab at Nanyang Technological University. Zongsheng Yue, Jianyi Wang, and Chen Change Loy posted it on 23 July 2023. It was a NeurIPS 2023 spotlight. Code and checkpoints are public under the NTU S-Lab License 1.0. Collected 2026-10-15.", "what_it_is_for": "The lab wanted diffusion super-resolution without hundreds or thousands of sampling steps. ResShift builds a short Markov chain that moves between the high-resolution image and the low-resolution image by shifting their residual, with a noise schedule that sets how fast that shift happens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu/stablesr/stablesr": { "id": "ntu/stablesr/stablesr", "name": "StableSR", "maker": "ntu", "family": "ntu/stablesr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "S-Lab License 1.0", "release_date": "2023-05-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Real-world image super-resolution that keeps a frozen Stable Diffusion prior. A time-aware encoder and spatial feature transform layers are trained on top. A controllable feature wrapping module, inspired by CodeFormer, balances fidelity and quality. The autoencoder downsamples by 8.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StableSR is a real-world image super-resolution model from S-Lab at Nanyang Technological University. Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin C.K. Chan, and Chen Change Loy published it on 11 May 2023; the journal version is IJCV 2024. It is still distributed as open weights under S-Lab License 1.0. Collected 2026-10-15.", "what_it_is_for": "The lab uses a frozen Stable Diffusion prior for blind super-resolution. A time-aware encoder and a few feature-modulation layers adapt the prior to a low-resolution input without retraining the synthesis model from scratch. Controllable feature wrapping, taken from the CodeFormer idea, lets a scalar trade quality against fidelity.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/bert-zhtw/bert-base-zhtw": { "id": "ntu-taiwan/bert-zhtw/bert-base-zhtw", "name": "bert-base-zhtw", "maker": "ntu-taiwan", "family": "ntu-taiwan/bert-zhtw", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2023-12-04", "context_length": 512, "max_output_tokens": null, "parameters": "0.1B", "architecture": "BertForMaskedLM: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 30522, max position 512, float32. Hugging Face size tag is 0.1B. Exact parameter count is not printed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bert-base-zhtw is a masked-language BERT published on the yentinglin account that also hosts MiuLab's Taiwan-LLM weights. Collected 2026-10-15. The card does not state a National Taiwan University affiliation in prose, so confidence is medium. Status is active.", "what_it_is_for": "The only usage on the card is feature extraction for Traditional Chinese text. It is a fill-mask BERT, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/distilhubert/distilhubert": { "id": "ntu-taiwan/distilhubert/distilhubert", "name": "DistilHuBERT", "maker": "ntu-taiwan", "family": "ntu-taiwan/distilhubert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "23.5M", "architecture": "HubertModel: 2 transformer layers, hidden 768, 12 heads, intermediate 3072, 7 convolutional feature-extraction layers, float32. Hugging Face model page says 23.5M parameters. The paper says this is a layer-wise distillation of HuBERT that cuts size by 75 percent.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DistilHuBERT is a distilled HuBERT speech encoder from the Speech Processing and Machine Learning Lab at National Taiwan University (Hung-yi Lee and students). Collected 2026-10-15. Weights are public under Apache-2.0 on ntu-spml/distilhubert. Status is active.", "what_it_is_for": "The card says it is a general 16 kHz speech representation for downstream fine-tunes such as recognition, speaker identification, intent, and emotion. It is not itself a speech recognizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/lawstral/taiwan-lawstral": { "id": "ntu-taiwan/lawstral/taiwan-lawstral", "name": "Taiwan-Lawstral", "maker": "ntu-taiwan", "family": "ntu-taiwan/lawstral", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-05-29", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "MistralForCausalLM: 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, vocab 61952, max_position_embeddings 32768, rope theta 1000000, bfloat16. Card says continual pretraining from MediaTek-Research/Breeze-7B-32k-Base-v1_0. Hugging Face size tag is 7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Taiwan-Lawstral is a 7B legal-domain base model from the Lab of Ontologies, Language Processing and e-Humanities (LOPE) at National Taiwan University. Collected 2026-10-15. It is a continual pretrain of MediaTek Breeze-7B-32k-Base, released under Apache-2.0. Status is active.", "what_it_is_for": "The card says it is a base model for Taiwan legal text, with no instruction tuning. The Hugging Face collection ties it to the lab paper on continual pretraining for domain adaptation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/llama-3-taiwan/llama-3-1-taiwan-8b": { "id": "ntu-taiwan/llama-3-taiwan/llama-3-1-taiwan-8b", "name": "Llama-3.1-Taiwan 8B", "maker": "ntu-taiwan", "family": "ntu-taiwan/llama-3-taiwan", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-11-07", "context_length": 131072, "max_output_tokens": null, "parameters": "8B", "architecture": "LlamaForCausalLM: 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, vocab 128256, max position 131072, rope theta 500000, bfloat16, bos 128000, eos 128001. Hugging Face size tag is 8B. The README is only a disclaimer. Base model, license, and training mix are not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3.1-Taiwan 8B is a later 8B weights repo in Yen-Ting Lin's Taiwan LLM collection at MiuLab, National Taiwan University. Collected 2026-10-15. The card does not document the model beyond a disclaimer, so confidence is medium. Status is active because the weights are public and no retirement notice was found.", "what_it_is_for": "Not stated on the model card. The Hugging Face collection that contains the repo is titled Taiwan LLM and points at twllm.com. The repo name has no Instruct suffix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/llama-3-taiwan/llama-3-1-taiwan-8b-instruct": { "id": "ntu-taiwan/llama-3-taiwan/llama-3-1-taiwan-8b-instruct", "name": "Llama-3.1-Taiwan 8B Instruct", "maker": "ntu-taiwan", "family": "ntu-taiwan/llama-3-taiwan", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-11-13", "context_length": 131072, "max_output_tokens": null, "parameters": "8B", "architecture": "LlamaForCausalLM: 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, vocab 128256, max position 131072, rope theta 500000, bfloat16, bos 128000, eos 128001. Hugging Face size tag is 8B. eos_token_id in config is the list 128001, 128008, 128009. The README is only a disclaimer. Base model, license, and training mix are not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3.1-Taiwan 8B Instruct is a later 8B weights repo in Yen-Ting Lin's Taiwan LLM collection at MiuLab, National Taiwan University. Collected 2026-10-15. The card does not document the model beyond a disclaimer, so confidence is medium. Status is active because the weights are public and no retirement notice was found.", "what_it_is_for": "Not stated on the model card. The Hugging Face collection that contains the repo is titled Taiwan LLM and points at twllm.com. The repo name says Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/llama-3-taiwan/llama-3-taiwan-70b-instruct": { "id": "ntu-taiwan/llama-3-taiwan/llama-3-taiwan-70b-instruct", "name": "Llama-3-Taiwan 70B Instruct", "maker": "ntu-taiwan", "family": "ntu-taiwan/llama-3-taiwan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License (card metadata: llama3)", "release_date": "2024-05-31", "context_length": 8192, "max_output_tokens": null, "parameters": "70B", "architecture": "LlamaForCausalLM: 80 layers, hidden 8192, 64 heads, 8 KV heads, intermediate 28672, vocab 128256, max position 8192, rope theta 500000, bfloat16, bos 128000, eos 128009. Card says the base is Llama-3 70B. Batch size stated as 2M tokens per step.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-Taiwan 70B Instruct is part of MiuLab's Llama-3-Taiwan line at National Taiwan University (project TAME), with weights on the yentinglin account. Collected 2026-10-15. Weights are open under the Llama 3 license. Status is active.", "what_it_is_for": "The card says the 70B model is for Traditional Mandarin and English: multi-turn chat, RAG, formatted output, and function calling, including legal, manufacturing, medical, and electronics material in the training mix. It is not a medical or legal advisor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/llama-3-taiwan/llama-3-taiwan-70b-instruct-128k": { "id": "ntu-taiwan/llama-3-taiwan/llama-3-taiwan-70b-instruct-128k", "name": "Llama-3-Taiwan 70B Instruct 128k", "maker": "ntu-taiwan", "family": "ntu-taiwan/llama-3-taiwan", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Llama 3 Community License (card metadata: llama3)", "release_date": "2024-06-03", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Not read. config.json returned HTTP 401. The parent card calls this the 128k version and does not print an integer token limit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-Taiwan 70B Instruct 128k is part of MiuLab's Llama-3-Taiwan line at National Taiwan University (project TAME), with weights on the yentinglin account. Collected 2026-10-15. Weights are open under the Llama 3 license. Status is active.", "what_it_is_for": "Long-context variant named in the Llama-3-Taiwan release table. Scores are a bit below the 8k instruct model on TMLU, Taiwan Truthful QA, and Legal Eval. The long-context column in its own row is blank.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/llama-3-taiwan/llama-3-taiwan-70b-instruct-dpo": { "id": "ntu-taiwan/llama-3-taiwan/llama-3-taiwan-70b-instruct-dpo", "name": "Llama-3-Taiwan 70B Instruct DPO", "maker": "ntu-taiwan", "family": "ntu-taiwan/llama-3-taiwan", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Llama 3 Community License (card metadata: llama3)", "release_date": "2024-06-01", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Not read. config.json returned HTTP 401 on 2026-10-15. The repo is a gated sibling of the 70B instruct model. The shared README still describes the 70B Llama-3 model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-Taiwan 70B Instruct DPO is part of MiuLab's Llama-3-Taiwan line at National Taiwan University (project TAME), with weights on the yentinglin account. Collected 2026-10-15. Weights are open under the Llama 3 license. Status is active.", "what_it_is_for": "The release table treats this as the DPO variant of Llama-3-Taiwan 70B Instruct. It leads the card's Taiwan Truthful QA (81.75%) and Legal Eval (70.33%) columns. TW MT-Bench and TMMLU+ cells are blank.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/llama-3-taiwan/llama-3-taiwan-8b-instruct": { "id": "ntu-taiwan/llama-3-taiwan/llama-3-taiwan-8b-instruct", "name": "Llama-3-Taiwan 8B Instruct", "maker": "ntu-taiwan", "family": "ntu-taiwan/llama-3-taiwan", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Llama 3 Community License (card metadata: llama3)", "release_date": "2024-07-01", "context_length": 8192, "max_output_tokens": null, "parameters": "8B", "architecture": "config.json is Llama-3 8B shape: 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, vocab 128258, max position 8192, rope theta 500000, bfloat16, bos 128000, eos 128256. The README and YAML base_model field are a copy of the 70B card and say the base is meta-llama/Meta-Llama-3-70B. That claim is not used. Hugging Face still shows that 70B base in the model tree because of the YAML.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-Taiwan 8B Instruct is part of MiuLab's Llama-3-Taiwan line at National Taiwan University (project TAME), with weights on the yentinglin account. Collected 2026-10-15. Weights are open under the Llama 3 license. Status is active.", "what_it_is_for": "Smaller Llama-3-Taiwan instruct model in the same release table. The lab's download thread says weights would be available on 2024-07-01 and was marked GA that day. The repo itself was created 2024-06-04.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/llama-3-taiwan/llama-3-taiwan-8b-instruct-128k": { "id": "ntu-taiwan/llama-3-taiwan/llama-3-taiwan-8b-instruct-128k", "name": "Llama-3-Taiwan 8B Instruct 128k", "maker": "ntu-taiwan", "family": "ntu-taiwan/llama-3-taiwan", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Llama 3 Community License (card metadata: llama3)", "release_date": "2024-06-04", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "config.json returned HTTP 401. The parent table links this repo as the 8B long-context version and leaves its score cells blank except function calling.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-Taiwan 8B Instruct 128k is part of MiuLab's Llama-3-Taiwan line at National Taiwan University (project TAME), with weights on the yentinglin account. Collected 2026-10-15. Weights are open under the Llama 3 license. Status is active.", "what_it_is_for": "Long-context 8B variant. The release table marks function calling as supported and does not print TMLU, Taiwan Truthful QA, Legal Eval, TW MT-Bench, or TMMLU+.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/llama-3-taiwan/llama-3-taiwan-8b-instruct-dpo": { "id": "ntu-taiwan/llama-3-taiwan/llama-3-taiwan-8b-instruct-dpo", "name": "Llama-3-Taiwan 8B Instruct DPO", "maker": "ntu-taiwan", "family": "ntu-taiwan/llama-3-taiwan", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Llama 3 Community License (card metadata: llama3)", "release_date": "2024-06-04", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "config.json returned HTTP 401. Scores are taken from the shared release table. The README text is the 70B card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-Taiwan 8B Instruct DPO is part of MiuLab's Llama-3-Taiwan line at National Taiwan University (project TAME), with weights on the yentinglin account. Collected 2026-10-15. Weights are open under the Llama 3 license. Status is active.", "what_it_is_for": "DPO variant in the Llama-3-Taiwan 8B row. TMLU is 59.88%, slightly above the non-DPO 8B instruct score. Taiwan Truthful QA and Legal Eval are slightly lower. Other cells are blank.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/taiwan-llm/taiwan-llama-v1-0": { "id": "ntu-taiwan/taiwan-llm/taiwan-llama-v1-0", "name": "Taiwan LLM 13B v1.0 chat", "maker": "ntu-taiwan", "family": "ntu-taiwan/taiwan-llm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License (card metadata: llama2)", "release_date": "2023-08-10", "context_length": 4096, "max_output_tokens": null, "parameters": "13B", "architecture": "LlamaForCausalLM, Llama 2 13B shape: 40 layers, hidden 5120, 40 heads, 40 KV heads, intermediate 13824, vocab 32000, rope theta 10000, bfloat16. Card says it is fine-tuned from yentinglin/Taiwan-LLaMa-v1.0-base, which was not in the public yentinglin model list retrieved on 2026-10-15.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Taiwan LLM 13B v1.0 chat is a Traditional Chinese chat model from MiuLab at National Taiwan University, released as weights by Yen-Ting Lin and Yun-Nung Chen. Collected 2026-10-15. The weights are still public and gated by a Llama 2 license acknowledgement. Status is active because no retirement notice was found.", "what_it_is_for": "The card describes a 13B GPT-like model for Traditional Chinese, aligned to Taiwanese linguistic and cultural context, and points users to the twllm.com demo. It says the checkpoint was supervised-fine-tuned from a Taiwan-LLaMa base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/taiwan-llm/taiwan-llm-13b-v2-0-base": { "id": "ntu-taiwan/taiwan-llm/taiwan-llm-13b-v2-0-base", "name": "Taiwan LLM 13B v2.0 base", "maker": "ntu-taiwan", "family": "ntu-taiwan/taiwan-llm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-13", "context_length": 4096, "max_output_tokens": null, "parameters": "13B", "architecture": "LlamaForCausalLM, Llama 2 13B shape: 40 layers, hidden 5120, 40 heads, 40 KV heads, intermediate 13824, vocab 32000, rope theta 10000, bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Taiwan LLM 13B v2.0 base is a Traditional Chinese Llama-2 checkpoint from MiuLab at National Taiwan University, published on the yentinglin Hugging Face account. Collected 2026-10-15. Weights remain public under Apache-2.0. Status is active; no deprecation notice was found.", "what_it_is_for": "Base checkpoint the lab says should be fine-tuned for chat. Parent of Taiwan LLM 13B v2.0 chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/taiwan-llm/taiwan-llm-13b-v2-0-chat": { "id": "ntu-taiwan/taiwan-llm/taiwan-llm-13b-v2-0-chat", "name": "Taiwan LLM 13B v2.0 chat", "maker": "ntu-taiwan", "family": "ntu-taiwan/taiwan-llm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-17", "context_length": 4096, "max_output_tokens": null, "parameters": "13B", "architecture": "LlamaForCausalLM, Llama 2 13B shape: 40 layers, hidden 5120, 40 heads, 40 KV heads, intermediate 13824, vocab 32000, rope theta 10000, bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Taiwan LLM 13B v2.0 chat is a Traditional Chinese Llama-2 checkpoint from MiuLab at National Taiwan University, published on the yentinglin Hugging Face account. Collected 2026-10-15. Weights remain public under Apache-2.0. Status is active; no deprecation notice was found.", "what_it_is_for": "Instruction chat model for Traditional Chinese, the 13B v2 chat release the lab links from its model table as the preferred 13B chat checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/taiwan-llm/taiwan-llm-7b-v2-0-1-chat": { "id": "ntu-taiwan/taiwan-llm/taiwan-llm-7b-v2-0-1-chat", "name": "Taiwan LLM 7B v2.0.1 chat", "maker": "ntu-taiwan", "family": "ntu-taiwan/taiwan-llm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-10", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "LlamaForCausalLM, Llama 2 7B shape: 32 layers, hidden 4096, 32 heads, 32 KV heads, intermediate 11008, vocab 32000, rope theta 10000, bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Taiwan LLM 7B v2.0.1 chat is a Traditional Chinese Llama-2 checkpoint from MiuLab at National Taiwan University, published on the yentinglin Hugging Face account. Collected 2026-10-15. Weights remain public under Apache-2.0. Status is active; no deprecation notice was found.", "what_it_is_for": "Named point-release chat model for Traditional Chinese. The card text matches v2.0 chat aside from the version name and repo id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/taiwan-llm/taiwan-llm-7b-v2-0-base": { "id": "ntu-taiwan/taiwan-llm/taiwan-llm-7b-v2-0-base", "name": "Taiwan LLM 7B v2.0 base", "maker": "ntu-taiwan", "family": "ntu-taiwan/taiwan-llm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-06", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "LlamaForCausalLM, Llama 2 7B shape: 32 layers, hidden 4096, 32 heads, 32 KV heads, intermediate 11008, vocab 32000, rope theta 10000, bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Taiwan LLM 7B v2.0 base is a Traditional Chinese Llama-2 checkpoint from MiuLab at National Taiwan University, published on the yentinglin Hugging Face account. Collected 2026-10-15. Weights remain public under Apache-2.0. Status is active; no deprecation notice was found.", "what_it_is_for": "The card says the base checkpoint should be fine-tuned further for instruction following or chat. It is the parent named on the v2.0, v2.0.1, and v2.1 chat cards.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/taiwan-llm/taiwan-llm-7b-v2-0-chat": { "id": "ntu-taiwan/taiwan-llm/taiwan-llm-7b-v2-0-chat", "name": "Taiwan LLM 7B v2.0 chat", "maker": "ntu-taiwan", "family": "ntu-taiwan/taiwan-llm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-09", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "LlamaForCausalLM, Llama 2 7B shape: 32 layers, hidden 4096, 32 heads, 32 KV heads, intermediate 11008, vocab 32000, rope theta 10000, bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Taiwan LLM 7B v2.0 chat is a Traditional Chinese Llama-2 checkpoint from MiuLab at National Taiwan University, published on the yentinglin Hugging Face account. Collected 2026-10-15. Weights remain public under Apache-2.0. Status is active; no deprecation notice was found.", "what_it_is_for": "Instruction chat model for Traditional Chinese. The card's example is a Taiwan climate question, with the same sampling settings as v1.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/taiwan-llm/taiwan-llm-7b-v2-1-base": { "id": "ntu-taiwan/taiwan-llm/taiwan-llm-7b-v2-1-base", "name": "Taiwan LLM 7B v2.1 base", "maker": "ntu-taiwan", "family": "ntu-taiwan/taiwan-llm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-09", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "MistralForCausalLM: 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, vocab 32000, rope theta 10000, bfloat16. Card says this checkpoint is continued pretraining of Mistral-7B-v0.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Taiwan LLM 7B v2.1 base is MiuLab's Mistral-7B continued-pretrain for Traditional Mandarin, published by Yen-Ting Lin at National Taiwan University. Collected 2026-10-15. Weights are public under Apache-2.0. Status is active.", "what_it_is_for": "The card says the checkpoint continues pretraining on 20 billion Traditional Mandarin tokens, does not use CommonCrawl, and was instruction-tuned on millions of conversations, with Ubitus providing compute.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/taiwan-llm/taiwan-llm-7b-v2-1-chat": { "id": "ntu-taiwan/taiwan-llm/taiwan-llm-7b-v2-1-chat", "name": "Taiwan LLM 7B v2.1 chat", "maker": "ntu-taiwan", "family": "ntu-taiwan/taiwan-llm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-10-12", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "LlamaForCausalLM, Llama 2 7B shape: 32 layers, hidden 4096, 32 heads, 32 KV heads, intermediate 11008, vocab 32000, rope theta 10000, bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Taiwan LLM 7B v2.1 chat is a Traditional Chinese Llama-2 checkpoint from MiuLab at National Taiwan University, published on the yentinglin Hugging Face account. Collected 2026-10-15. Weights remain public under Apache-2.0. Status is active; no deprecation notice was found.", "what_it_is_for": "Instruction chat model for Traditional Chinese. Despite the v2.1 name, the config is Llama-2 7B, not the Mistral config used by the v2.1 base repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntu-taiwan/taiwan-llm/taiwan-llm-8x7b-dpo": { "id": "ntu-taiwan/taiwan-llm/taiwan-llm-8x7b-dpo", "name": "Taiwan LLM 8x7B DPO", "maker": "ntu-taiwan", "family": "ntu-taiwan/taiwan-llm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-07", "context_length": 32768, "max_output_tokens": null, "parameters": "47B", "architecture": "MixtralForCausalLM: 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, 8 local experts, 2 experts per token, vocab 32002, rope theta 1000000, bfloat16. The card calls it an 8x7B Mixtral MoE. The Hugging Face model index size tag is 47B. Active-parameter count is not stated. Card says it is fine-tuned from yentinglin/Taiwan-LLM-MoE-alpha.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Taiwan LLM 8x7B DPO is MiuLab's Mixtral-style Traditional Chinese model from National Taiwan University, published 2024-02-07 on the yentinglin account. Collected 2026-10-15. Weights are public under Apache-2.0. Status is active.", "what_it_is_for": "The card describes an 8x7B Mixtral MoE fine-tuned for Traditional Chinese chat. It sends readers to Tw Chatbot Arena rather than printing a finished score for this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/culturemert/culturemert-95m": { "id": "ntua/culturemert/culturemert-95m", "name": "CultureMERT-95M", "maker": "ntua", "family": "ntua/culturemert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-06-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MERT-v1-95M / HuBERT-style encoder: 7-layer 1D CNN frontend on raw mono 24 kHz audio at a 75 Hz frame rate, then a 12-layer Transformer (768-dim). Pretraining is masked prediction of frozen EnCodec RVQ tokens (8 codebooks, 1024 entries) plus CQT reconstruction. Continual pre-training uses 5-second crops. Probing trains a shallow MLP on a frozen encoder. This checkpoint is the multi-cultural continual-pretraining run: Stage 1 trains the CNN frontend and codeword embeddings on 100 hours with 20% Music4All Western replay; Stage 2 unfreezes the Transformer on the full 650-hour mix (Lyra 50h, Turkish-makam 200h, Hindustani 200h, Carnatic 200h).", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "National Technical University of Athens Speech and Language Processing Group released CultureMERT-95M, an active open-weight music encoder in the culturemert family. It is MERT-v1-95M after two-stage continual pre-training on Greek, Turkish, and Indian music. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says it is a multi-culturally adapted music foundation model for representation learning, aimed at non-Western music understanding while keeping Western tagging performance. Evaluation on the card is frozen probing for multi-label auto-tagging, not a shipped tagger head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/culturemert/culturemert-ta-95m": { "id": "ntua/culturemert/culturemert-ta-95m", "name": "CultureMERT-TA-95M", "maker": "ntua", "family": "ntua/culturemert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-06-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MERT-v1-95M / HuBERT-style encoder: 7-layer 1D CNN frontend on raw mono 24 kHz audio at a 75 Hz frame rate, then a 12-layer Transformer (768-dim). Pretraining is masked prediction of frozen EnCodec RVQ tokens (8 codebooks, 1024 entries) plus CQT reconstruction. Continual pre-training uses 5-second crops. Probing trains a shallow MLP on a frozen encoder. This checkpoint is not the multi-cultural training run. It merges four single-culture continual-pretraining task vectors (Lyra, Turkish-makam, Hindustani, Carnatic) into MERT-v1-95M with a shared scale lambda of 0.2, the value the card and paper say worked best.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "National Technical University of Athens Speech and Language Processing Group released CultureMERT-TA-95M, an active open-weight music encoder in the culturemert family. It is a task-arithmetic merge of four single-culture MERT adaptations, not a second training run on the mixed corpus. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this is an alternative to CultureMERT-95M. Each musical tradition is continually pre-trained on its own, then the task vectors are added back onto MERT-v1-95M with lambda 0.2. The job is the same cross-cultural music representation and probing setup.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/ntua-twitter/ntua-twitter-300": { "id": "ntua/ntua-twitter/ntua-twitter-300", "name": "NTUA Twitter word2vec 300d", "maker": "ntua", "family": "ntua/ntua-twitter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "300-dimensional Gensim word2vec embeddings. The README says they were trained on 550 million English tweets from December 2014 through June 2017, with window 6, negative sampling 5, and min_count 20. Tweets were preprocessed with ekphrasis.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "National Technical University of Athens Speech and Language Processing Group released NTUA Twitter word2vec 300d with the SemEval 2018 NTUA-SLP code. It is an active open-weight static embedding in the ntua-twitter family. The Drive file's upload date was not read, so release_date is empty. This dossier was collected on 2026-10-15.", "what_it_is_for": "The repository says the SemEval 2018 task models were trained on top of these Twitter word2vec embeddings. The 300-dimensional file is the plain word2vec release. The 310-dimensional file adds 10 affective dimensions to those vectors. The paper's abstract says the same 550-million-tweet word2vec set, augmented with affective features, was used for affect prediction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/ntua-twitter/ntua-twitter-affect-310": { "id": "ntua/ntua-twitter/ntua-twitter-affect-310", "name": "NTUA Twitter affect embeddings 310d", "maker": "ntua", "family": "ntua/ntua-twitter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "310-dimensional embeddings. The README says they are the 300-dimensional word2vec vectors plus 10 affective dimensions. The same corpus statement covers the word2vec part: 550 million English tweets, December 2014 through June 2017, window 6, negative sampling 5, min_count 20.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "National Technical University of Athens Speech and Language Processing Group released NTUA Twitter affect embeddings 310d with the SemEval 2018 NTUA-SLP code. It is an active open-weight static embedding in the ntua-twitter family. The Drive file's upload date was not read, so release_date is empty. This dossier was collected on 2026-10-15.", "what_it_is_for": "The repository says the SemEval 2018 task models were trained on top of these Twitter word2vec embeddings. The 300-dimensional file is the plain word2vec release. The 310-dimensional file adds 10 affective dimensions to those vectors. The paper's abstract says the same 550-million-tweet word2vec set, augmented with affective features, was used for affect prediction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-ames-attentivefp": { "id": "ntua/pyrosage/pyrosage-ames-attentivefp", "name": "Pyrosage AMES AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name baseline: hidden_channels 64, num_layers 2, num_timesteps 2, dropout 0.2, learning_rate 0.001, weight_decay 1e-05, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric. The usage example on this card sets in_channels=10 and edge_dim=6.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage AMES AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts mutagenicity with the Ames test: whether a compound can cause mutations in bacterial DNA. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-endocrine-disruption-nr-ahr-attentivefp": { "id": "ntua/pyrosage/pyrosage-endocrine-disruption-nr-ahr-attentivefp", "name": "Pyrosage Endocrine Disruption NR-AhR AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage Endocrine Disruption NR-AhR AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts endocrine disruption via the aryl hydrocarbon receptor (AhR): whether a compound activates the AhR pathway. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-endocrine-disruption-nr-ar-attentivefp": { "id": "ntua/pyrosage/pyrosage-endocrine-disruption-nr-ar-attentivefp", "name": "Pyrosage Endocrine Disruption NR-AR AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name class_imbalance_focused: hidden_channels 64, num_layers 2, num_timesteps 3, dropout 0.3, learning_rate 0.0005, weight_decay 0.0001, batch_size 16, epochs 75, patience 15. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage Endocrine Disruption NR-AR AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts endocrine disruption via the androgen receptor (AR): whether a compound can interfere with androgen hormone signaling. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-endocrine-disruption-nr-aromatase-attentivefp": { "id": "ntua/pyrosage/pyrosage-endocrine-disruption-nr-aromatase-attentivefp", "name": "Pyrosage Endocrine Disruption NR-aromatase AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name baseline: hidden_channels 64, num_layers 2, num_timesteps 2, dropout 0.2, learning_rate 0.001, weight_decay 1e-05, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage Endocrine Disruption NR-aromatase AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts endocrine disruption via aromatase inhibition: whether a compound can inhibit the enzyme that converts androgens to estrogens. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-endocrine-disruption-nr-er-attentivefp": { "id": "ntua/pyrosage/pyrosage-endocrine-disruption-nr-er-attentivefp", "name": "Pyrosage Endocrine Disruption NR-ER AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name deeper_model: hidden_channels 64, num_layers 4, num_timesteps 4, dropout 0.3, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage Endocrine Disruption NR-ER AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts endocrine disruption via the estrogen receptor (ER): whether a compound can bind to and activate estrogen receptors. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-fba-attentivefp": { "id": "ntua/pyrosage/pyrosage-fba-attentivefp", "name": "Pyrosage FBA AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name baseline: hidden_channels 64, num_layers 2, num_timesteps 2, dropout 0.2, learning_rate 0.001, weight_decay 1e-05, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage FBA AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts fugacity-based environmental fate parameter A, for use in fugacity models of environmental distribution. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-fbc-attentivefp": { "id": "ntua/pyrosage/pyrosage-fbc-attentivefp", "name": "Pyrosage FBC AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name baseline: hidden_channels 64, num_layers 2, num_timesteps 2, dropout 0.2, learning_rate 0.001, weight_decay 1e-05, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage FBC AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts fugacity-based environmental fate parameter C, for use in fugacity models of environmental distribution. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-irritation-corrosion-eye-corrosion-attentivefp": { "id": "ntua/pyrosage/pyrosage-irritation-corrosion-eye-corrosion-attentivefp", "name": "Pyrosage Eye Corrosion AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name class_imbalance_focused: hidden_channels 64, num_layers 2, num_timesteps 3, dropout 0.3, learning_rate 0.0005, weight_decay 0.0001, batch_size 16, epochs 75, patience 15. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage Eye Corrosion AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts eye corrosion potential: whether a compound can cause irreversible damage to eye tissue. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-irritation-corrosion-eye-irritation-attentivefp": { "id": "ntua/pyrosage/pyrosage-irritation-corrosion-eye-irritation-attentivefp", "name": "Pyrosage Eye Irritation AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name baseline: hidden_channels 64, num_layers 2, num_timesteps 2, dropout 0.2, learning_rate 0.001, weight_decay 1e-05, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage Eye Irritation AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts eye irritation potential: whether a compound can cause reversible inflammatory responses in eye tissue. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-kaoh-attentivefp": { "id": "ntua/pyrosage/pyrosage-kaoh-attentivefp", "name": "Pyrosage kAOH AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage kAOH AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts the reaction rate constant with hydroxyl radicals (log kAOH). The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-kh-attentivefp": { "id": "ntua/pyrosage/pyrosage-kh-attentivefp", "name": "Pyrosage KH AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage KH AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts Henry's Law constant (log KH). The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-koa-attentivefp": { "id": "ntua/pyrosage/pyrosage-koa-attentivefp", "name": "Pyrosage KOA AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage KOA AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts the octanol-air partition coefficient (log KOA). The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-koc-attentivefp": { "id": "ntua/pyrosage/pyrosage-koc-attentivefp", "name": "Pyrosage KOC AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage KOC AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts the organic carbon partition coefficient (log KOC). The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-kow-attentivefp": { "id": "ntua/pyrosage/pyrosage-kow-attentivefp", "name": "Pyrosage KOW AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage KOW AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts the octanol-water partition coefficient (log KOW/log P). The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-lc50-attentivefp": { "id": "ntua/pyrosage/pyrosage-lc50-attentivefp", "name": "Pyrosage LC50 AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage LC50 AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts aquatic toxicity as log LC50, described there as the lethal concentration for 50% of aquatic organisms, with fish and daphnia named as examples. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-ld50-zhu-attentivefp": { "id": "ntua/pyrosage/pyrosage-ld50-zhu-attentivefp", "name": "Pyrosage LD50 Zhu AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage LD50 Zhu AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts acute oral toxicity as log LD50, described there as the lethal dose for 50% of test animals. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-pka-acidic-attentivefp": { "id": "ntua/pyrosage/pyrosage-pka-acidic-attentivefp", "name": "Pyrosage pKa acidic AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage pKa acidic AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts the acid dissociation constant (pKa) for acidic groups. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-pka-basic-attentivefp": { "id": "ntua/pyrosage/pyrosage-pka-basic-attentivefp", "name": "Pyrosage pKa basic AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage pKa basic AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts the acid dissociation constant (pKa) for basic groups. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-plv-attentivefp": { "id": "ntua/pyrosage/pyrosage-plv-attentivefp", "name": "Pyrosage PLV AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage PLV AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts a plasma level volume or protein binding parameter, and that the property may relate to pharmacokinetics or blood partitioning. That hedge is the card's wording. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-sw-attentivefp": { "id": "ntua/pyrosage/pyrosage-sw-attentivefp", "name": "Pyrosage SW AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage SW AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts aqueous solubility (log SW). The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-tbiodeg-attentivefp": { "id": "ntua/pyrosage/pyrosage-tbiodeg-attentivefp", "name": "Pyrosage tbiodeg AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name baseline: hidden_channels 64, num_layers 2, num_timesteps 2, dropout 0.2, learning_rate 0.001, weight_decay 1e-05, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage tbiodeg AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts biodegradability potential and that the property classifies whether a compound is readily biodegradable. The same card's task line says Regression and the output line says a continuous numerical value. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-tbp-attentivefp": { "id": "ntua/pyrosage/pyrosage-tbp-attentivefp", "name": "Pyrosage TBP AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage TBP AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts teratogenic bioaccumulation potential, and that the property may relate to developmental toxicity or bioaccumulation scoring. That hedge is the card's wording. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-tfishbio-attentivefp": { "id": "ntua/pyrosage/pyrosage-tfishbio-attentivefp", "name": "Pyrosage tfishbio AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage tfishbio AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts a fish bioaccumulation factor. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ntua/pyrosage/pyrosage-tmp-attentivefp": { "id": "ntua/pyrosage/pyrosage-tmp-attentivefp", "name": "Pyrosage TMP AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AttentiveFP graph network. Card hyperparameter block name larger_model: hidden_channels 128, num_layers 3, num_timesteps 3, dropout 0.1, learning_rate 0.0005, weight_decay 0.0001, batch_size 32, epochs 50, patience 10. Framework on the card: PyTorch Geometric.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, released Pyrosage TMP AttentiveFP. It is an active MIT-licensed AttentiveFP checkpoint in the pyrosage family. Hub createdAt is 2025-06-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this AttentiveFP model predicts a toxicity-related molecular property and that the endpoint represents an experimental toxicity measurement. It does not name the assay. The card says the input is a SMILES string and the network predicts the property from the molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nuaa/geomoe/geomoe": { "id": "nuaa/geomoe/geomoe", "name": "GeoMoE", "maker": "nuaa", "family": "nuaa/geomoe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sparse convolutional masked autoencoder. Factory geomoe_base_patch16_dec512d8b in Pretrain/models/models.py: image sizes 224, 56, 28; patch sizes 4, 2, 2; embed dims 256, 384, 768; depths 2, 2, 11; 12 encoder heads; decoder embed 512, 16 heads, depth 8; moe_mlp_ratio 0.75; mlp_ratio 4, 4, 4; self_attn false; LayerNorm eps 1e-6. Stages 1 and 2 are ConvMAE-style conv blocks. Stage 3 is 11 MOEBlocks. MOEMlp uses 24 experts (1 shared and 23 routed), a sigmoid gate, and top-3 routing. The decoder is 8 cross-attention blocks that reconstruct patches. The public factory defines this base checkpoint only.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GeoMoE is an open-weight remote-sensing masked autoencoder from Bo Zhang, Renzhi Wang, Xiao Ling, and Piji Li at Nanjing University of Aeronautics and Astronautics. The College of Artificial Intelligence and the College of Astronautics released the base checkpoint GeoMoE.pth on 22 September 2025. It is pretrained on OpticalRS-4M and finetuned in the same repository for scene classification, object detection, and semantic segmentation. Status is active as a public weight file. Collected 2026-10-15.", "what_it_is_for": "The authors describe GeoMoE as a remote-sensing foundation model. Pretraining reconstructs masked optical patches. The released downstream scripts use the encoder for AID and RESISC-45 classification, DIOR and DIOR-R detection, and LoveDA and SpaceNetv1 segmentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nuaa/geomoe/geomoe-l": { "id": "nuaa/geomoe/geomoe-l", "name": "GeoMoE-L", "maker": "nuaa", "family": "nuaa/geomoe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-03-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named larger checkpoint GeoMoE_L.pth in the same repository as GeoMoE. The public factory in Pretrain/models/models.py defines only geomoe_base_patch16_dec512d8b. No GeoMoE-L depth, width, expert count, or parameter count is stated in that factory or in the Readme.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GeoMoE-L is the larger open-weight checkpoint from the same NUAA remote-sensing project as GeoMoE. Bo Zhang's Hub account uploaded `GeoMoE_L.pth` on 27 March 2026. The GitHub news line dates that release to March 2026. The public training factory defines only the base GeoMoE configuration, and the README score table does not include this file. Status is active as a public weight file. Collected 2026-10-15.", "what_it_is_for": "The authors released it as the GeoMoE_L training weights for the same remote-sensing self-supervised model. Downstream finetune code and logs for this size are still listed as unfinished in the repository todo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nudt/saratr/sar-jepa": { "id": "nudt/saratr/sar-jepa", "name": "SAR-JEPA", "maker": "nudt", "family": "nudt/saratr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-B encoder in an MAE-style masked image model, with an 8-layer decoder instead of LoMaR's linear decoder. Local masks predict multi-scale gradient-by-ratio (linear kernel) features of SAR magnitude images. Pre-trained 200 epochs on about 100,000 unlabeled chips from MSAR, SAR-Ship, SARSim, and SAMPLE. The Gaussian-kernel variant is an ablation, not a separate product.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SAR-JEPA is a self-supervised SAR automatic-target-recognition encoder from the College of Electronic Science and Technology, National University of Defense Technology (Changsha), with coauthors at Shanghai AI Laboratory and Nankai University. It is a named ViT-B checkpoint, released with code and weights, and is the predecessor of SARATR-X. Status on 2026-10-15: active research release. It is not a text model and it is not on the empty Hugging Face org `NUDT`.", "what_it_is_for": "The paper presents SAR-JEPA as a joint-embedding predictive architecture that learns SAR target context from unlabeled chips so the encoder can be fine-tuned for vehicle, ship, and aircraft recognition with few labels. The stated job is representation learning for SAR ATR, not generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nudt/saratr/saratr-x": { "id": "nudt/saratr/saratr-x", "name": "SARATR-X", "maker": "nudt", "family": "nudt/saratr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-05-15", "context_length": null, "max_output_tokens": null, "parameters": "66M", "architecture": "HiViT-B foundation encoder for SAR ATR. The paper's backbone table lists HiViT at 66M parameters. Two-step self-supervised pre-training: ImageNet MAE-HiViT initialization, then masked modeling on 0.18 million unlabeled SAR target chips from 14 public datasets, with multi-scale gradient features as the reconstruction target. Mask ratio 0.75 in the released pre-training script. Not a counted safetensors total.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SARATR-X is the first named SAR automatic-target-recognition foundation model from the National University of Defense Technology group of Weijie Li, Wei Yang, Li Liu, Yongxiang Liu, and Xiang Li, with Yuenan Hou at Shanghai AI Laboratory. It is a HiViT-B self-supervised encoder, published in IEEE Transactions on Image Processing (2025) and released with code and gated weights. Status on 2026-10-15: active. The campus press note calls this release SARATR-X 1.0.", "what_it_is_for": "The paper says SARATR-X learns a general representation from unlabeled SAR target chips so later heads can detect and classify vehicles, ships, aircraft, and other targets with fewer labels. It is a vision backbone, not a chat or generative model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nudt/saratr/saratr-x-v2": { "id": "nudt/saratr/saratr-x-v2", "name": "SARATR-X-v2", "maker": "nudt", "family": "nudt/saratr", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-07-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Scale-aware structural masked pre-training on iTPN-B and iTPN-L with an FPN and an 8-layer MAE-style decoder. Input 224, patch 16, mask ratio 0.75, 1200 epochs, AdamW, base learning rate 1e-4, 8 A800 GPUs. Pre-trained on 500,000 unlabeled SAR target chips. Encoder, FPN, and decoder start from ImageNet-1K iTPN checkpoints. The reconstruction target is six fixed structural extractors fused by a learned softmax. Parameter totals for the two SAR checkpoints are not stated.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SARATR-X-v2 is the successor SAR foundation model from the National University of Defense Technology, Changsha (Weijie Li, Yongxiang Liu, Li Liu, and coauthors in the College of Electronic Science and Technology). It replaces SARATR-X's HiViT masked pre-training with a scale-aware structural target on iTPN-B and iTPN-L. On 2026-10-15 the official README still says the paper is under review and that checkpoints are sent on request, so the status here is preview.", "what_it_is_for": "The paper describes a pre-trained SAR representation for classification, horizontal and oriented detection, and semantic segmentation. The target is built from six fixed structural extractors and fused with learned weights so the reconstruction signal is more stable under speckle than raw pixels. It is not a language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/d-ar/d-ar-l": { "id": "nus/d-ar/d-ar-l", "name": "D-AR-L", "maker": "nus", "family": "nus/d-ar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "343000000", "architecture": "Llama-style decoder-only transformer. Class-conditional ImageNet generation at 256x256 by next-token prediction on 256 discrete tokens. No AdaLN. Classifier-free guidance is applied to logits. The shared D-AR tokenizer v1 is 300M parameters with a reported 1.52 rFID. The paper's diffusion decoder inside the tokenizer is about 185M. Checkpoint file names end in 360K.", "modalities": { "input": [ "class-label" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "D-AR-L is an ImageNet generator from Show Lab at the National University of Singapore, collected 2026-10-15. Weights were posted on 2025-06-05 under MIT. Status is active. The count is the autoregressive backbone only.", "what_it_is_for": "Class-conditional 256x256 ImageNet generation. The GitHub page also says the project intends later text-to-image models. Those were not in the released weight list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/d-ar/d-ar-xl": { "id": "nus/d-ar/d-ar-xl", "name": "D-AR-XL", "maker": "nus", "family": "nus/d-ar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "775000000", "architecture": "Llama-style decoder-only transformer. Class-conditional ImageNet generation at 256x256 by next-token prediction on 256 discrete tokens. No AdaLN. Classifier-free guidance is applied to logits. The shared D-AR tokenizer v1 is 300M parameters with a reported 1.52 rFID. The paper's diffusion decoder inside the tokenizer is about 185M. Checkpoint file names end in 360K.", "modalities": { "input": [ "class-label" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "D-AR-XL is an ImageNet generator from Show Lab at the National University of Singapore, collected 2026-10-15. Weights were posted on 2025-06-05 under MIT. Status is active. The count is the autoregressive backbone only.", "what_it_is_for": "Class-conditional 256x256 ImageNet generation. The GitHub page also says the project intends later text-to-image models. Those were not in the released weight list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/egovlp/egovlp": { "id": "nus/egovlp/egovlp", "name": "EgoVLP", "maker": "nus", "family": "nus/egovlp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Egocentric video-language model pretrained with the EgoNCE contrastive objective on EgoClip, a 3.8M clip-text subset of Ego4D. The backbone was not copied from the paper body in this pass. Two checkpoints are named on GitHub: EgoVLP_PT_BEST and EgoVLP_PT_EPO1.", "modalities": { "input": [ "video", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EgoVLP is an egocentric video-language model from Show Lab at the National University of Singapore, collected 2026-10-15. Pretrained weights are released from the lab GitHub for NeurIPS 2022. Status is active. KAIST, Bristol, and Tencent coauthors are on the paper. The weights are the Show Lab release.", "what_it_is_for": "The project says the pretrained model transfers to egocentric retrieval, action recognition, and Ego4D localization tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/greco/greco": { "id": "nus/greco/greco", "name": "GRECO", "maker": "nus", "family": "nus/greco", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2024-02-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Grammaticality model fine-tuned from microsoft/deberta-v3-large by the NUS NLP Group. It labels incorrect words and positions where a word or phrase should be inserted, then scores a sentence for grammatical-error-correction reranking.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GRECO is a grammaticality scorer from the NUS NLP Group at the National University of Singapore, collected 2026-10-15. It is a GPL-3.0 fine-tune of DeBERTa-v3-large for reranking grammatical error corrections. The EMNLP 2023 paper is from December 2023. Hub weights date from 2024-02-01. Status is active.", "what_it_is_for": "The card says GRECO estimates whether a corrected sentence is grammatical so that GEC system outputs can be reranked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/magicanimate/magicanimate": { "id": "nus/magicanimate/magicanimate", "name": "MagicAnimate", "maker": "nus", "family": "nus/magicanimate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause", "release_date": "2023-12-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Human image animation on Stable Diffusion 1.5. Released pieces are an appearance encoder, a DensePose ControlNet, and a temporal-attention checkpoint. An MSE-finetuned VAE and the Stable Diffusion 1.5 weights are separate downloads. Joint work of NUS and ByteDance. The project page is on the Show Lab site.", "modalities": { "input": [ "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagicAnimate is a human image-animation model from Show Lab at the National University of Singapore and ByteDance, collected 2026-10-15. Weights released on 2023-12-04 under BSD-3-Clause are still public. It was a CVPR 2024 paper. Status is active.", "what_it_is_for": "The GitHub page says it animates a reference image with a motion sequence while keeping appearance over time.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/makeanything/makeanything": { "id": "nus/makeanything/makeanything", "name": "MakeAnything", "maker": "nus", "family": "nus/makeanything", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card title is a diffusion-transformer method for multi-domain procedural sequence generation. The Hub files are LoRAs: asymmetric LoRAs, FP8 Flux merge LoRAs, and Recraft LoRAs. No architecture block was on the fetched card.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MakeAnything is a named Show Lab weight release from the National University of Singapore, collected 2026-10-15. The Hub repo holds MIT-licensed LoRAs for procedural sequence generation. Status is active. The card itself is only a short pointer to GitHub.", "what_it_is_for": "The card title says the model generates multi-domain procedural sequences with diffusion transformers. Step-by-step usage is only on GitHub and was not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/mitty/mitty": { "id": "nus/mitty/mitty", "name": "Mitty", "maker": "nus", "family": "nus/mitty", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA fine-tune of Wan 2.2 TI2V for human-to-robot video. The paper trains the dense 5B model and the 14B mixture-of-experts model, adapting high-noise and low-noise branches. Human demonstration tokens condition robot denoising tokens. Hub files are Human2Robot_14B.ckpt (3.78 GB), HumanAndRobot_5B.ckpt (1.71 GB), epic_14B.ckpt (3.78 GB), and epic_5B.ckpt (1.71 GB).", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mitty is a human-to-robot video model from Show Lab at the National University of Singapore, collected 2026-10-15. Four LoRA checkpoints were uploaded on 2025-12-04, before the 2025-12-19 paper. Status is active. The base video model is Wan 2.2, which is not an NUS model.", "what_it_is_for": "The paper says Mitty turns a human demonstration video into a robot-execution video without action labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/next-gpt/next-gpt": { "id": "nus/next-gpt/next-gpt", "name": "NExT-GPT", "maker": "nus", "family": "nus/next-gpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2024-07-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Any-to-any multimodal LLM from NExT++, School of Computing, NUS. Vicuna-7B-v0 is the language model, with a 33M LoRA. ImageBind-huge (1.2B) encodes image, video, and audio. A grouping projector is 28M. Output transformers are about 31M to 32M per modality. Frozen decoders are Stable Diffusion (1.3B), AudioLDM (975M), and Zeroscope (1.8B). The paper says only about 1% of parameters, the projectors, are trained. The released tiva checkpoint is the 7B system.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text", "image", "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NExT-GPT is an any-to-any multimodal model from the NExT++ Research Center at the National University of Singapore, collected 2026-10-15. The 7B text-image-video-audio weights are public under the card's CC BY-NC-SA 4.0 license. It was published at ICML 2024. Status is active.", "what_it_is_for": "The paper says one system takes and produces text, images, video, and audio by connecting a language model to frozen encoders and diffusion decoders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/omniconsistency/omniconsistency": { "id": "nus/omniconsistency/omniconsistency", "name": "OmniConsistency", "maker": "nus", "family": "nus/omniconsistency", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Style-consistency LoRA loaded onto FLUX.1-dev. The card's example uses a 512 condition size, 1024 output, guidance scale 3.5, 25 steps, and max sequence length 512. Twenty-two rank-128 style LoRAs ship in the same repo.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmniConsistency is a Show Lab image model from the National University of Singapore, collected 2026-10-15. It is an MIT-licensed LoRA on FLUX.1-dev for style-consistent image generation. Status is active.", "what_it_is_for": "The card says it learns style-agnostic consistency from paired stylization data, then applies an external style LoRA while keeping the subject.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/openseal/openseal-1b": { "id": "nus/openseal/openseal-1b", "name": "OpenSeal 1B", "maker": "nus", "family": "nus/openseal", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-10-03", "context_length": 4096, "max_output_tokens": null, "parameters": "1000000000", "architecture": "Continual pretraining of OLMo 2. 16 layers, embedding dimension 2048, intermediate dimension 8192, 16 attention heads, vocabulary 100,352. Bias-free linear layers, rotary embeddings, RMSNorm, and SwiGLU. The paper says the starting checkpoint was pretrained on four trillion tokens. OpenSeal's Parallel Only run uses parallel sentences plus 25% replay and no extra monolingual Southeast Asian text. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenSeal 1B is a Southeast Asian continual-pretraining model from the NUS NLP Group, collected 2026-10-15. The paper is dated 2026-02-02 and the Hub weights were created 2026-10-03. There is no model card. It is not an AI Singapore SEA-LION release, though the work cites an AI Singapore programme award. Status is active. Confidence is medium.", "what_it_is_for": "The paper says OpenSeal extends an English-only OLMo 2 model to Southeast Asian languages with parallel data only. It is a base model, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/openseal/openseal-7b": { "id": "nus/openseal/openseal-7b", "name": "OpenSeal 7B", "maker": "nus", "family": "nus/openseal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-03", "context_length": 4096, "max_output_tokens": null, "parameters": "7000000000", "architecture": "Continual pretraining of OLMo 2. 32 layers, embedding dimension 4096, intermediate dimension 22016, 32 attention heads, vocabulary 100,352. Bias-free linear layers, rotary embeddings, RMSNorm, and SwiGLU. The paper says the starting checkpoint was pretrained on four trillion tokens. OpenSeal's Parallel Only run uses parallel sentences plus 25% replay and no extra monolingual Southeast Asian text. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenSeal 7B is a Southeast Asian continual-pretraining model from the NUS NLP Group, collected 2026-10-15. The paper is dated 2026-02-02 and the Hub weights were created 2026-10-03. There is no model card. It is not an AI Singapore SEA-LION release, though the work cites an AI Singapore programme award. Status is active. Confidence is high.", "what_it_is_for": "The paper says OpenSeal extends an English-only OLMo 2 model to Southeast Asian languages with parallel data only. It is a base model, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/show-1/show-1": { "id": "nus/show-1/show-1", "name": "Show-1", "maker": "nus", "family": "nus/show-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-10-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cascaded text-to-video diffusion. The base stage is a pixel model fine-tuned from DeepFloyd IF-I-L-v1.0 and generates 8 keyframes at 64x40. It was trained on WebVid-10M and InternVid. The first super-resolution stage is fine-tuned from DeepFloyd IF-II-M-v1.0 and upsamples 64x40 to 256x160 on WebVid-10M. Interpolation and a second super-resolution stage ship as separate Hub repos.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Show-1 is a text-to-video cascade from Show Lab at the National University of Singapore, collected 2026-10-15. Weights are public under CC BY-NC 4.0 and the model is active. It was accepted to IJCV in 2024. The cascade mixes a pixel diffusion stage with a latent upsampler.", "what_it_is_for": "The base card says Show-1 generates video from a text prompt, using a pixel model for text alignment and a latent model for resolution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/show-harness/show-harness": { "id": "nus/show-harness/show-harness", "name": "Show-Harness", "maker": "nus", "family": "nus/show-harness", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapters that turn a vision-language model into a robot controller. Two camera views go in and one semantic action token comes out. The card says every unit is a 2 cm translation so real and simulated data can mix. Recipe: LoRA r=64, alpha=128, dropout 0.05, all target modules, vision tower frozen, learning rate 1e-4, effective batch 32.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Show-Harness is a set of robot-control LoRA adapters from Show Lab at the National University of Singapore, collected 2026-10-15. The adapters are active open weights on top of Qwen3.5 and Gemma 4. They are the lab's own release, not a third-party copy.", "what_it_is_for": "The card says the adapters map two camera views to one semantic action token for robot manipulation, with a shared 2 cm action unit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/show-o/show-o": { "id": "nus/show-o/show-o", "name": "Show-o", "maker": "nus", "family": "nus/show-o", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-08-26", "context_length": null, "max_output_tokens": null, "parameters": "1300000000", "architecture": "Single transformer on Phi-1.5. Text is autoregressive. Image tokens use a MaskGIT-style discrete diffusion. The default checkpoint uses a lookup-free MAGVIT-v2 quantizer (codebook 8,192; 256x256 images become 16x16 tokens) for both understanding and generation. A separate checkpoint uses CLIP-ViT continuous features for understanding.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Show-o is a Show Lab model from the National University of Singapore, collected 2026-10-15. It is one transformer for image-text understanding and generation, released as open weights. Status is active. The paper was an ICLR 2025 poster. ByteDance coauthors are on the paper; the weights are published by showlab.", "what_it_is_for": "The paper says Show-o answers visual questions autoregressively and generates images by unmasking discrete tokens. It also describes text-guided inpainting and extrapolation without extra fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/show-o/show-o2-1-5b": { "id": "nus/show-o/show-o2-1-5b", "name": "Show-o2 1.5B", "maker": "nus", "family": "nus/show-o", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-19", "context_length": null, "max_output_tokens": null, "parameters": "1500000000", "architecture": "Native unified model. Language backbone is Qwen2.5-1.5B-Instruct. A 3D causal VAE from Wan2.1 (8x spatial and 4x temporal compression) feeds a dual-path spatial-temporal fusion. Text uses an autoregressive language head. Images and video use a flow-matching head. Semantic features were distilled toward SigLIP so400m-patch14-384. The paper's 1.5B count is the base LLM, not the VAE.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text", "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Show-o2 1.5B is the smaller Show-o2 model from Show Lab at the National University of Singapore, with a ByteDance coauthor, collected 2026-10-15. Weights are Apache-2.0 and active. It was accepted to NeurIPS 2025. The 1.5B figure is the Qwen2.5 language-model size.", "what_it_is_for": "The card says the model does multimodal understanding and image, video, and mixed-modality generation in one language model with autoregressive and flow-matching heads.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/show-o/show-o2-7b": { "id": "nus/show-o/show-o2-7b", "name": "Show-o2 7B", "maker": "nus", "family": "nus/show-o", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-19", "context_length": null, "max_output_tokens": null, "parameters": "7000000000", "architecture": "Same Show-o2 design as the 1.5B model, with the language backbone swapped to Qwen2.5-7B-Instruct. The flow head is resumed from the 1.5B run, with a new MLP to match hidden size. The paper says interleaved and video data were not included in the 7B training stages. The 7B count is the base LLM.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Show-o2 7B is the larger Show-o2 checkpoint from Show Lab at the National University of Singapore, collected 2026-10-15. Weights are Apache-2.0 and active. The language model is Qwen2.5-7B-Instruct. A ByteDance coauthor is on the paper.", "what_it_is_for": "Image understanding and text-to-image generation at the 7B LLM scale. Video understanding is the later w-video-und checkpoint, not the base training run described in the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/showui/showui-2b": { "id": "nus/showui/showui-2b", "name": "ShowUI 2B", "maker": "nus", "family": "nus/showui", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-11-16", "context_length": null, "max_output_tokens": null, "parameters": "2000000000", "architecture": "Vision-language-action model fine-tuned from Qwen2-VL-2B-Instruct. UI-guided visual token selection treats a screenshot as a connected UI graph and drops redundant tokens inside self-attention. Actions are text, including normalized click coordinates.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ShowUI 2B is a GUI vision-language-action model from Show Lab at the National University of Singapore, collected 2026-10-15. It is an MIT-licensed fine-tune of Qwen2-VL-2B-Instruct and is active. Microsoft researchers are coauthors. The paper date is 2024-11-26; the Hub repo was created 2024-11-16.", "what_it_is_for": "The card says ShowUI grounds UI elements and navigates web and phone screens. It emits actions such as CLICK, INPUT, and TAP with normalized positions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nus/showui/showui-pi": { "id": "nus/showui/showui-pi", "name": "ShowUI-\u03c0", "maker": "nus", "family": "nus/showui", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-04-19", "context_length": null, "max_output_tokens": null, "parameters": "450000000", "architecture": "Flow-based GUI action model built on SmolVLA. A flow-matching action expert predicts drag trajectories from one screenshot and an instruction. Discrete clicks and continuous drags share the model. The paper says 450M parameters. The Hub card says the SmolVLA base is 500M.", "modalities": { "input": [ "text", "image" ], "output": [ "trajectory" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ShowUI-\u03c0 is a Show Lab GUI model from the National University of Singapore, collected 2026-10-15. It predicts continuous drag paths as well as clicks. The paper is dated 2025-12-31 and the Hub weights were created 2026-04-19. Status is active.", "what_it_is_for": "The card says it is a vision-language-action model for GUI drag-and-drop. The paper introduces the ScreenDrag benchmark and a 20K drag-trajectory set across five applications.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/alpamayo/alpamayo-1": { "id": "nvidia/alpamayo/alpamayo-1", "name": "Alpamayo 1", "maker": "nvidia", "family": "nvidia/alpamayo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-01-05", "context_length": null, "max_output_tokens": null, "parameters": "Card: 8.2B backbone plus 2.3B action expert. Safetensors total 11078526194. Newsroom described a 10B model.", "architecture": "Vision-language-action model based on Cosmos Reason, with a diffusion trajectory decoder. Chain-of-thought driving traces.", "modalities": { "input": [ "text", "video" ], "output": [ "text", "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alpamayo 1 is NVIDIA's open reasoning vision-language-action model for autonomous driving, announced on 2026-01-05. Collected 2026-10-15. Weights are under OpenMDW-1.1. The card splits the network into an 8.2B backbone and a 2.3B action expert.", "what_it_is_for": "Teacher model for long-tail driving: video in, trajectory plus a reasoning trace out. NVIDIA said it is for research and distillation, not as the in-vehicle runtime by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/alpamayo/alpamayo-1-5": { "id": "nvidia/alpamayo/alpamayo-1-5", "name": "Alpamayo 1.5", "maker": "nvidia", "family": "nvidia/alpamayo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-03-19", "context_length": null, "max_output_tokens": null, "parameters": "Safetensors total 11078526194, the same count as Alpamayo 1. The card still describes a 10B-class model.", "architecture": "VLA based on Cosmos-Reason2 with a diffusion trajectory decoder. Adds navigation guidance, flexible camera counts, and question answering. RL post-trained.", "modalities": { "input": [ "text", "video" ], "output": [ "text", "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alpamayo 1.5 is NVIDIA's updated 10B-class open driving model, with weights posted on 2026-03-19. Collected 2026-10-15. It keeps the OpenMDW-1.1 weight license and adds navigation, flexible cameras, and question answering on a Cosmos Reason 2 backbone.", "what_it_is_for": "Interactive reasoning engine for rare driving scenes: video, history, navigation, and questions in; trajectories and reasoning traces out.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/alpamayo/alpamayo-2-super": { "id": "nvidia/alpamayo/alpamayo-2-super", "name": "Alpamayo 2 Super", "maker": "nvidia", "family": "nvidia/alpamayo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-05-31", "context_length": null, "max_output_tokens": null, "parameters": "Card: 34B-class model, 32B VLM backbone plus 2.3B action expert. Safetensors total 35813934322.", "architecture": "Vision-language-action model. Base model field is Cosmos3-Super. Diffusion action expert. Multi-camera inputs.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text", "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alpamayo 2 Super is NVIDIA's open 34B-class driving foundation model, released at the end of May 2026 under OpenMDW-1.1. Collected 2026-10-15. The card uses a 32B Cosmos 3 Super vision-language backbone and a 2.3B diffusion action expert.", "what_it_is_for": "Multi-task autonomous-vehicle development: trajectories, causal reasoning traces, scene questions, grounding, and auto-labels. NVIDIA's product page says it is commercially licensed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/canary/canary-1b-v2": { "id": "nvidia/canary/canary-1b-v2", "name": "Canary 1B v2", "maker": "nvidia", "family": "nvidia/canary", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2025-08-04", "context_length": null, "max_output_tokens": null, "parameters": "978998272 weights in safetensors metadata", "architecture": "Canary speech model. The architecture paragraph was not read.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Canary 1B v2 is NVIDIA's open Canary speech model, about 1B parameters, posted on 2025-08-04 under CC-BY-4.0. Collected 2026-10-15.", "what_it_is_for": "Multilingual speech recognition. The card cites NVIDIA Granary and NeMo ASR set 3.0 as training data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/canary/canary-qwen-2-5b": { "id": "nvidia/canary/canary-qwen-2-5b", "name": "Canary-Qwen 2.5B", "maker": "nvidia", "family": "nvidia/canary", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2025-06-26", "context_length": null, "max_output_tokens": null, "parameters": "2559447568 weights in safetensors metadata", "architecture": "Speech model. The card was not opened beyond API metadata, so the Qwen component is only inferred from the name and not stated as fact.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Canary-Qwen 2.5B is an NVIDIA speech model of about 2.56B parameters, posted on 2025-06-26 under CC-BY-4.0. Collected 2026-10-15. The card body was not opened.", "what_it_is_for": "Automatic speech recognition in the Canary line, at a larger size than Canary 1B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/cosmos/cosmos-embed1": { "id": "nvidia/cosmos/cosmos-embed1", "name": "Cosmos Embed 1", "maker": "nvidia", "family": "nvidia/cosmos", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video embedding model. Resolution-specific checkpoints were listed. The card was not opened.", "modalities": { "input": [ "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cosmos Embed 1 is NVIDIA's video embedding model, published as 224p, 336p, and 448p checkpoints. Collected 2026-10-15. The cards were not opened, so license and size are unknown.", "what_it_is_for": "Embed video for retrieval and downstream physical-AI classifiers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/cosmos/cosmos-nemotron-34b": { "id": "nvidia/cosmos/cosmos-nemotron-34b", "name": "Cosmos Nemotron 34B", "maker": "nvidia", "family": "nvidia/cosmos", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision encoder SigLIP-400M and language encoder Yi-34B. The exact total parameter count was not stated.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cosmos Nemotron 34B is an NVIDIA vision-language model for querying images and video from physical or virtual scenes. Collected 2026-10-15. The build.nvidia.com card pairs SigLIP-400M with Yi-34B and says the model is ready for commercial use.", "what_it_is_for": "Summarize and query single images and videos. The card says it can run in the data center, cloud, and on Jetson Orin with AWQ 4-bit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/cosmos/cosmos-predict1": { "id": "nvidia/cosmos/cosmos-predict1", "name": "Cosmos Predict 1", "maker": "nvidia", "family": "nvidia/cosmos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-06", "context_length": null, "max_output_tokens": null, "parameters": "Size depends on the checkpoint. Diffusion models are 7B and 14B. Autoregressive models are 4B, 5B, 12B, and 13B.", "architecture": "Diffusion and autoregressive transformer world models for physics-aware video.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cosmos Predict 1 is NVIDIA's first open world-model family for generating physics-aware video, announced on 2025-01-06. Collected 2026-10-15. It includes diffusion checkpoints at 7B and 14B and autoregressive checkpoints from 4B to 13B.", "what_it_is_for": "Text-to-world and video-to-world generation for robot and autonomous-vehicle development, including synthetic future video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/cosmos/cosmos-predict2": { "id": "nvidia/cosmos/cosmos-predict2", "name": "Cosmos Predict 2", "maker": "nvidia", "family": "nvidia/cosmos", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "2B and 14B checkpoints", "architecture": "World model for text-to-image and video-to-world generation. Card text was not opened.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cosmos Predict 2 is the second NVIDIA Predict world-model generation, with public 2B and 14B checkpoints for image and video. Collected 2026-10-15. The release date was not on the pages that were opened.", "what_it_is_for": "Generate images and future video for physical-AI simulation. Task-specific robot checkpoints such as GR00T Dreams samples are not this base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/cosmos/cosmos-predict2-5": { "id": "nvidia/cosmos/cosmos-predict2-5", "name": "Cosmos Predict 2.5", "maker": "nvidia", "family": "nvidia/cosmos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-06", "context_length": null, "max_output_tokens": null, "parameters": "2B and 14B", "architecture": "Video world model for future-state simulation. The README fetch returned HTTP 401, so architecture details come from NVIDIA's later blog.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cosmos Predict 2.5 is NVIDIA's October 2025 world model for predicting future video. Collected 2026-10-15. Public sizes are 2B and 14B. A March 2026 NVIDIA blog says it can generate sequences up to 30 seconds.", "what_it_is_for": "Simulate future world states for robots and vehicles, including post-training on domain video and multiview camera layouts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/cosmos/cosmos-reason1": { "id": "nvidia/cosmos/cosmos-reason1", "name": "Cosmos Reason 1", "maker": "nvidia", "family": "nvidia/cosmos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-18", "context_length": null, "max_output_tokens": null, "parameters": "7B in the name. Safetensors total 8292166656.", "architecture": "Vision-language model for physical-AI reasoning. The backbone was not re-read.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cosmos Reason 1 7B is NVIDIA's first Cosmos vision-language reasoner for physical scenes. Collected 2026-10-15. The Hugging Face repo was created on 2025-04-18. The weight file total is about 8.3B parameters.", "what_it_is_for": "Reason over images and video for robotics and other physical-AI tasks, and answer in text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/cosmos/cosmos-reason2": { "id": "nvidia/cosmos/cosmos-reason2", "name": "Cosmos Reason 2", "maker": "nvidia", "family": "nvidia/cosmos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-12", "context_length": 256000, "max_output_tokens": null, "parameters": "Separate checkpoints at 2B, 8B, and 32B. The 8B safetensors total is 8767123696.", "architecture": "Vision-language reasoner for physical AI. The 8B README returned HTTP 401.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cosmos Reason 2 is NVIDIA's physical-AI vision-language line at 2B, 8B, and 32B. Collected 2026-10-15. A March 2026 NVIDIA blog says it supports long context up to 256K tokens and can localize objects with 2D and 3D points.", "what_it_is_for": "Reason about video and images for embodied tasks, including detection coordinates and explanations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/cosmos/cosmos-transfer1": { "id": "nvidia/cosmos/cosmos-transfer1", "name": "Cosmos Transfer 1", "maker": "nvidia", "family": "nvidia/cosmos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Control-conditioned video transfer model. The March 2026 NVIDIA blog says Transfer uses a ControlNet-style design to keep spatial alignment.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cosmos Transfer 1 is NVIDIA's 7B world-to-world transfer model. Collected 2026-10-15. It turns structured visual controls into photorealistic video while keeping spatial alignment.", "what_it_is_for": "Augment simulation and 3D inputs such as segmentation, depth, LiDAR, and boxes into realistic video for physical-AI training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/cosmos/cosmos-transfer2-5": { "id": "nvidia/cosmos/cosmos-transfer2-5", "name": "Cosmos Transfer 2.5", "maker": "nvidia", "family": "nvidia/cosmos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-06", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Transfer model built on Predict 2.5 for multi-control video simulation. README fetch returned HTTP 401.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cosmos Transfer 2.5 is NVIDIA's 2B controlled world-simulation model, released with Predict 2.5 on 2025-10-06. Collected 2026-10-15. NVIDIA later said it is about one-third the size of Cosmos Transfer 1.", "what_it_is_for": "Turn simulation and 3D spatial controls into more varied photorealistic video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/cosmos/cosmos3-edge": { "id": "nvidia/cosmos/cosmos3-edge", "name": "Cosmos 3 Edge", "maker": "nvidia", "family": "nvidia/cosmos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-07-01", "context_length": null, "max_output_tokens": null, "parameters": "4B marketed. Safetensors total 3858999728.", "architecture": "Cosmos 3 Mixture-of-Transformers at the edge size. The card body was not opened.", "modalities": { "input": [ "text", "image", "video", "audio", "action" ], "output": [ "text", "image", "video", "audio", "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cosmos 3 Edge is the 4B Cosmos 3 model for on-device physical AI. Collected 2026-10-15. NVIDIA's Cosmos README dates the release to July 2026, and the Hugging Face repo was created on 2026-07-01.", "what_it_is_for": "Real-time robot policy and visual reasoning on Jetson and workstation GPUs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/cosmos/cosmos3-nano": { "id": "nvidia/cosmos/cosmos3-nano", "name": "Cosmos 3 Nano", "maker": "nvidia", "family": "nvidia/cosmos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-05-31", "context_length": null, "max_output_tokens": null, "parameters": "16B marketed. Safetensors total 15750057456.", "architecture": "Same Cosmos 3 Mixture-of-Transformers omnimodal design as Super, at the Nano size. The Nano card was not opened separately, so the license is not copied from Super.", "modalities": { "input": [ "text", "image", "video", "audio", "action" ], "output": [ "text", "image", "video", "audio", "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cosmos 3 Nano is the 16B open Cosmos 3 model NVIDIA launched on 2026-05-31 for workstation-class inference. Collected 2026-10-15. It shares the omnimodal Mixture-of-Transformers design with Cosmos 3 Super.", "what_it_is_for": "Faster physical reasoning and world generation when Super is too large.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/cosmos/cosmos3-super": { "id": "nvidia/cosmos/cosmos3-super", "name": "Cosmos 3 Super", "maker": "nvidia", "family": "nvidia/cosmos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-05-31", "context_length": 256000, "max_output_tokens": null, "parameters": "64B marketed. Safetensors total 64615003632.", "architecture": "Mixture-of-Transformers with an autoregressive tower for discrete tokens and a diffusion tower for generation. Omnimodal reasoner and generator.", "modalities": { "input": [ "text", "image", "video", "audio", "action" ], "output": [ "text", "image", "video", "audio", "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cosmos 3 Super is NVIDIA's largest open Cosmos 3 world foundation model, about 64B parameters, launched on 2026-05-31 under OpenMDW-1.1. Collected 2026-10-15. One Mixture-of-Transformers architecture covers physical reasoning and generation of vision, sound, and action.", "what_it_is_for": "Highest-quality synthetic data, physical reasoning, and action generation for robotics and autonomous vehicles. NVIDIA also uses it as a teacher for distillation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/edify/edify-360-hdri": { "id": "nvidia/edify/edify-360-hdri", "name": "Edify 360 HDRi", "maker": "nvidia", "family": "nvidia/edify", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Edify generative model for 360-degree HDR environment maps. The 2024 blog says up to 16K output from text or image prompts.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Edify 360 HDRi is NVIDIA's closed model for 360-degree HDR environment maps. Collected 2026-10-15. A 2024 NVIDIA blog says it can generate up to 16K maps from text or images. The public NIM preview ended on 2025-06-06.", "what_it_is_for": "Light 3D scenes and provide backgrounds. Shutterstock offered an early-access 360 HDRi generator based on Edify.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/edify/edify-3d": { "id": "nvidia/edify/edify-3d", "name": "Edify 3D", "maker": "nvidia", "family": "nvidia/edify", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-view diffusion built on Edify Image, plus transformer reconstruction. Outputs quad meshes, UVs, and PBR materials.", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Edify 3D is NVIDIA's closed text-and-image-to-3D model for meshes with PBR materials. Collected 2026-10-15. The research page still describes the method. The public NIM preview ended on 2025-06-06.", "what_it_is_for": "Create editable 3D assets with organized UVs and up to 4K PBR maps for design and simulation. Shutterstock used it for a generative 3D service.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/edify/edify-image": { "id": "nvidia/edify/edify-image", "name": "Edify Image", "maker": "nvidia", "family": "nvidia/edify", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Edify multimodal diffusion architecture for visual generation. A 2024 blog says Edify Image could generate four 1K images in about six seconds.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Edify Image is NVIDIA's closed visual generative model for text-to-image, used by partners such as Getty Images. Collected 2026-10-15. The public NIM preview ended on 2025-06-06. Weights were not released.", "what_it_is_for": "Enterprise image generation from licensed training data. NVIDIA described higher speed and prompt controls in the 2024 Getty update.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/embed/nemotron-3-embed-1b": { "id": "nvidia/embed/nemotron-3-embed-1b", "name": "Nemotron 3 Embed 1B", "maker": "nvidia", "family": "nvidia/embed", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1B in the repo name. Exact count was not read.", "architecture": "Text embedding model. The card was not opened.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron 3 Embed 1B is a smaller NVIDIA embedding model listed next to the 8B embed checkpoint. Collected 2026-10-15. The card was not opened, so most spec fields are empty.", "what_it_is_for": "Text embeddings for retrieval, at a smaller size than Nemotron 3 Embed 8B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/embed/nemotron-3-embed-8b": { "id": "nvidia/embed/nemotron-3-embed-8b", "name": "Nemotron 3 Embed 8B", "maker": "nvidia", "family": "nvidia/embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-07-14", "context_length": null, "max_output_tokens": null, "parameters": "About 8B. Safetensors total 7952683008.", "architecture": "Transformer encoder based on Ministral-3-8B-Instruct-2512.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron 3 Embed 8B is NVIDIA's open 8B text embedding model under OpenMDW-1.1. Collected 2026-10-15. The card says the encoder starts from Mistral's Ministral-3 8B Instruct.", "what_it_is_for": "Text embeddings for semantic search and retrieval in the Nemotron retrieval stack.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/embed/nv-embed-v1": { "id": "nvidia/embed/nv-embed-v1", "name": "NV-Embed v1", "maker": "nvidia", "family": "nvidia/embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-05-23", "context_length": null, "max_output_tokens": null, "parameters": "7851016192 weights in safetensors metadata", "architecture": "Decoder-based embedding model. The card body was not re-read.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NV-Embed v1 is NVIDIA's May 2024 open embedding model. Collected 2026-10-15. The weights are under CC-BY-NC-4.0, so they are open weights but not a commercial-use license.", "what_it_is_for": "It produces text embeddings for retrieval and semantic search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/embed/nv-embed-v2": { "id": "nvidia/embed/nv-embed-v2", "name": "NV-Embed v2", "maker": "nvidia", "family": "nvidia/embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-08-29", "context_length": null, "max_output_tokens": null, "parameters": "7851016192 weights in safetensors metadata", "architecture": "Embedding model with an MTEB model-index on the card. Architecture details were not extracted.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NV-Embed v2 is NVIDIA's August 2024 text embedding model, released under CC-BY-NC-4.0. Collected 2026-10-15. The card carries an MTEB results index. Those numbers were not copied into the spec.", "what_it_is_for": "Text embeddings for retrieval, classification, and semantic search. The non-commercial license limits production use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/gr00t/gr00t-h": { "id": "nvidia/gr00t/gr00t-h", "name": "GR00T-H", "maker": "nvidia", "family": "nvidia/gr00t", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA one-way noncommercial license (22 March 2022 PDF linked from the card)", "release_date": "2026-03-14", "context_length": null, "max_output_tokens": null, "parameters": "3B. Safetensors total 3286610400.", "architecture": "GR00T N vision-language model plus a diffusion transformer action head. The card says the base model is GR00T-N1.6-3B.", "modalities": { "input": [ "text", "image" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GR00T-H is an NVIDIA vision-language-action model based on GR00T N1.6, released under a noncommercial NVIDIA license. Collected 2026-10-15. The repo was created on 2026-03-14 and is tied to the Open-H embodiment dataset.", "what_it_is_for": "Robot action generation for the human-embodiment data NVIDIA released with this checkpoint. It is not the general GR00T N1.7 base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/gr00t/gr00t-n1": { "id": "nvidia/gr00t/gr00t-n1", "name": "Isaac GR00T N1", "maker": "nvidia", "family": "nvidia/gr00t", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-05", "context_length": null, "max_output_tokens": null, "parameters": "2190019826 weights in safetensors metadata. The repo name says 2B.", "architecture": "Vision-language-action model. The N1 white paper is arXiv 2503.14734. The card license field was empty.", "modalities": { "input": [ "text", "image" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Isaac GR00T N1 is NVIDIA's first open vision-language-action model for humanoid robots, a 2B-class checkpoint posted on 2025-03-05. Collected 2026-10-15.", "what_it_is_for": "Map language and images to robot actions for generalist manipulation. Later N1.5, N1.6, and N1.7 checkpoints update the same product line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/gr00t/gr00t-n1-5": { "id": "nvidia/gr00t/gr00t-n1-5", "name": "Isaac GR00T N1.5", "maker": "nvidia", "family": "nvidia/gr00t", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-28", "context_length": null, "max_output_tokens": null, "parameters": "2724163520 weights in safetensors metadata. NVIDIA's README calls it 3B.", "architecture": "Vision-language-action model. Updated GR00T N1.", "modalities": { "input": [ "text", "image" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Isaac GR00T N1.5 is the 3B update of GR00T N1. Collected 2026-10-15. The Hugging Face repo was created on 2025-05-28.", "what_it_is_for": "Base vision-language-action checkpoint for fine-tuning humanoid manipulation skills.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/gr00t/gr00t-n1-6": { "id": "nvidia/gr00t/gr00t-n1-6", "name": "Isaac GR00T N1.6", "maker": "nvidia", "family": "nvidia/gr00t", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-28", "context_length": null, "max_output_tokens": null, "parameters": "3B marketed. Safetensors total 3286608832.", "architecture": "Vision-language-action model. A January 2026 NVIDIA blog says N1.6 uses a Cosmos-Reason-2B variant with native resolution. License on the card was only summarized as an NVIDIA license in search text.", "modalities": { "input": [ "text", "image" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Isaac GR00T N1.6 is NVIDIA's open 3B humanoid vision-language-action model announced on 2025-10-28. Collected 2026-10-15. The weights repo was created on 2025-12-01. A later blog says the vision backbone is a Cosmos Reason 2B variant.", "what_it_is_for": "Generalist humanoid manipulation from language and images, including sim-to-real post-training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/gr00t/gr00t-n1-7": { "id": "nvidia/gr00t/gr00t-n1-7", "name": "Isaac GR00T N1.7", "maker": "nvidia", "family": "nvidia/gr00t", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA Open Model License", "release_date": "2026-02-25", "context_length": null, "max_output_tokens": null, "parameters": "3B on the card. Safetensors total 3144016000.", "architecture": "Vision-language-action model in the GR00T N1 line. The card points at the N1 white paper for the architecture family.", "modalities": { "input": [ "text", "image" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Isaac GR00T N1.7 is the current open 3B GR00T base model in this catalog, under the NVIDIA Open Model License. Collected 2026-10-15. The Hugging Face repo was created on 2026-02-25.", "what_it_is_for": "Generalist robot policy base: language and camera images in, continuous actions out, for fine-tuning on downstream tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/kumo/kumo-tabular": { "id": "nvidia/kumo/kumo-tabular", "name": "Kumo Tabular", "maker": "nvidia", "family": "nvidia/kumo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-09-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tabular foundation model. The card does not state the layer type in the lines that were read.", "modalities": { "input": [ "tabular" ], "output": [ "tabular" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kumo Tabular is NVIDIA's pretrained tabular foundation model for classification and regression, under OpenMDW-1.1. Collected 2026-10-15. The Hugging Face repo was created on 2026-09-01.", "what_it_is_for": "Predict class probabilities or regression targets for new rows by conditioning on labeled example rows from the user's table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/magpie/magpie-tts-multilingual": { "id": "nvidia/magpie/magpie-tts-multilingual", "name": "Magpie TTS Multilingual", "maker": "nvidia", "family": "nvidia/magpie", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA Open Model License", "release_date": "2025-12-11", "context_length": null, "max_output_tokens": null, "parameters": "364M (card). The repo slug says 357m.", "architecture": "Encoder-decoder transformer with a local transformer and feed-forward layers.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magpie TTS Multilingual is NVIDIA's open text-to-speech model, 364M parameters on the card, under the NVIDIA Open Model License. Collected 2026-10-15. The Hugging Face repo was created on 2025-12-11.", "what_it_is_for": "Multilingual speech for cascade voice agents and similar products, using a fixed set of English speaker voices rather than zero-shot cloning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/mistral-nemo/mistral-nemo-12b": { "id": "nvidia/mistral-nemo/mistral-nemo-12b", "name": "Mistral NeMo 12B Instruct", "maker": "nvidia", "family": "nvidia/mistral-nemo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-18", "context_length": 131072, "max_output_tokens": null, "parameters": "12B", "architecture": "Transformer decoder, autoregressive language model, trained jointly by NVIDIA and Mistral AI.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral NeMo 12B Instruct is a 12B language model trained jointly by NVIDIA and Mistral and released under Apache 2.0 in July 2024. Collected 2026-10-15. It is included because NVIDIA trained and shipped it, not because it is a pure NVIDIA architecture.", "what_it_is_for": "The NVIDIA card describes an enterprise instruct model that outperforms other models of similar size and can be customized in NeMo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemoguard/llama-3-1-nemotron-safety-guard-8b-v3": { "id": "nvidia/nemoguard/llama-3-1-nemotron-safety-guard-8b-v3", "name": "Llama 3.1 Nemotron Safety Guard 8B v3", "maker": "nvidia", "family": "nvidia/nemoguard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA Open Model License, with additional Llama 3.1 Community License terms", "release_date": "2025-08-20", "context_length": 8192, "max_output_tokens": null, "parameters": "8.03B", "architecture": "Parameter-efficient fine-tune of Meta Llama-3.1-8B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.1 Nemotron Safety Guard 8B v3 is NVIDIA's open content-safety model, a fine-tune of Llama 3.1 8B Instruct. Collected 2026-10-15. The repo was created on 2025-08-20. Context is 8K.", "what_it_is_for": "Multilingual content moderation for LLM applications. The card names English, Spanish, Mandarin, German, French, Hindi, Japanese, Arabic, and Thai.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemoguard/nemotron-content-safety-reasoning-4b": { "id": "nvidia/nemoguard/nemotron-content-safety-reasoning-4b", "name": "Nemotron Content Safety Reasoning 4B", "maker": "nvidia", "family": "nvidia/nemoguard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-26", "context_length": null, "max_output_tokens": null, "parameters": "4300079472 weights in safetensors metadata", "architecture": "Safety model. The card body was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron Content Safety Reasoning 4B is an NVIDIA safety model of about 4.3B parameters posted on 2025-11-26. Collected 2026-10-15. The card body was not opened.", "what_it_is_for": "The name and the Nemotron safety collection indicate content-safety classification with reasoning. The exact label schema was not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/llama-3-1-nemotron-70b-instruct": { "id": "nvidia/nemotron/llama-3-1-nemotron-70b-instruct", "name": "Llama 3.1 Nemotron 70B Instruct", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License (Hugging Face license field)", "release_date": "2024-10-12", "context_length": null, "max_output_tokens": null, "parameters": "70553706496 weights in the safetensors metadata", "architecture": "NVIDIA post-train of Meta Llama 3.1 70B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.1 Nemotron 70B Instruct is an NVIDIA post-trained Llama 3.1 70B instruct model. Collected 2026-10-15. The weights are open under the Llama 3.1 community license named on the Hugging Face API.", "what_it_is_for": "It is the first widely downloaded Llama Nemotron instruct model, aimed at general instruction following before the later Super, Ultra, and Nano reasoning releases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/llama-3-1-nemotron-nano-4b": { "id": "nvidia/nemotron/llama-3-1-nemotron-nano-4b", "name": "Llama 3.1 Nemotron Nano 4B", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-03", "context_length": null, "max_output_tokens": null, "parameters": "4512746496 weights in the safetensors metadata", "architecture": "NVIDIA Llama Nemotron Nano model. v1.1 is the checkpoint that was listed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.1 Nemotron Nano 4B v1.1 is the smaller Llama Nemotron Nano checkpoint NVIDIA listed in May 2025. Collected 2026-10-15. The full model card was not opened.", "what_it_is_for": "It is the compact Nano size for local or low-cost reasoning experiments in the Llama Nemotron generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/llama-3-1-nemotron-nano-8b": { "id": "nvidia/nemotron/llama-3-1-nemotron-nano-8b", "name": "Llama 3.1 Nemotron Nano 8B", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-16", "context_length": null, "max_output_tokens": null, "parameters": "8030261248 weights in the safetensors metadata", "architecture": "NVIDIA post-train in the Llama Nemotron Nano line. Base model was not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.1 Nemotron Nano 8B v1 is a small Llama Nemotron model NVIDIA published in March 2025. Collected 2026-10-15. Only API metadata was read, not the full card.", "what_it_is_for": "NVIDIA groups Nano models as the efficient end of the Nemotron reasoning line for targeted tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/llama-3-1-nemotron-ultra-253b": { "id": "nvidia/nemotron/llama-3-1-nemotron-ultra-253b", "name": "Llama 3.1 Nemotron Ultra 253B", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA Open Model License, with additional Llama 3.1 Community License terms", "release_date": "2025-04-07", "context_length": null, "max_output_tokens": null, "parameters": "253B", "architecture": "Dense decoder-only transformer derived from Llama 3.1 405B Instruct by neural architecture search. Blocks are non-standard and non-repetitive.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.1 Nemotron Ultra 253B v1 is NVIDIA's 2025 reasoning model distilled and searched down from Llama 3.1 405B Instruct. Collected 2026-10-15. It remains a named product on NVIDIA's Nemotron page even after Nemotron 3 Ultra.", "what_it_is_for": "NVIDIA describes Ultra 253B for multi-agent enterprise work that needs the highest accuracy of that Llama Nemotron generation, at data-center scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/llama-3-3-nemotron-super-49b": { "id": "nvidia/nemotron/llama-3-3-nemotron-super-49b", "name": "Llama 3.3 Nemotron Super 49B", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA Open Model License, with additional Llama 3.3 Community License terms", "release_date": "2025-03-16", "context_length": null, "max_output_tokens": null, "parameters": "49B marketed; safetensors total 49867145216 on both v1 and v1.5", "architecture": "Dense decoder-only transformer derived from Meta Llama 3.3 70B Instruct by neural architecture search and block-wise distillation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.3 Nemotron Super 49B is NVIDIA's neural-architecture-search derivative of Llama 3.3 70B Instruct, sold as the efficient Super tier before Nemotron 3. Collected 2026-10-15. v1 shipped in March 2025 and v1.5 in July 2025. Weights are open.", "what_it_is_for": "NVIDIA's developer page still lists Llama Nemotron Super 49B for high in-class accuracy on a single data-center GPU, including deep-research agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-3-5-asr-streaming": { "id": "nvidia/nemotron/nemotron-3-5-asr-streaming", "name": "Nemotron 3.5 ASR Streaming 0.6B", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-05-15", "context_length": null, "max_output_tokens": null, "parameters": "637997088 weights in safetensors metadata. The repo name says 0.6B.", "architecture": "FastConformer cache-aware RNN-T, from the model-card badge.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron 3.5 ASR Streaming 0.6B is NVIDIA's open streaming speech recognizer under OpenMDW-1.1. Collected 2026-10-15. The weights repo was created on 2026-05-15 and uses a cache-aware FastConformer RNN-T.", "what_it_is_for": "Low-latency multilingual automatic speech recognition for voice agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-3-5-bolt": { "id": "nvidia/nemotron/nemotron-3-5-bolt", "name": "Nemotron 3.5 Bolt 74B-A9B", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-15", "context_length": null, "max_output_tokens": null, "parameters": "74B total, 9B active. Safetensors total on the BF16 repo is 74885081096.", "architecture": "Mixture-of-Experts hybrid Mamba-Transformer. A native 2-bit checkpoint is intended to fit in 24GB.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron 3.5 Bolt 74B-A9B is NVIDIA's open edge agent model released on 2026-10-15. Collected the same day. It has 74B total parameters and 9B active, and NVIDIA ships a 2-bit build aimed at 24GB local machines.", "what_it_is_for": "NVIDIA describes it for everyday local agent tasks such as coding and research on RTX and DGX Spark, running beside other applications.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-3-5-bolt-e4b": { "id": "nvidia/nemotron/nemotron-3-5-bolt-e4b", "name": "Nemotron 3.5 Bolt E4B", "maker": "nvidia", "family": "nvidia/nemotron", "status": "announced", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron 3.5 Bolt E4B is an announced NVIDIA open edge model, scheduled for 2026-10-23. Collected 2026-10-15, before that date. NVIDIA describes a 4GB memory target for local conversational agents. Weights were not verified.", "what_it_is_for": "The launch blog says it is for fast conversational agents on GeForce RTX, Jetson, and RTX or DGX Spark, including voice assistants and embedded interfaces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-3-5-lightning": { "id": "nvidia/nemotron/nemotron-3-5-lightning", "name": "Nemotron 3.5 Lightning", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-08-11", "context_length": 1000000, "max_output_tokens": null, "parameters": "Card: 30B total, 3B active. Safetensors total 31577937344. Artificial Analysis lists 31.6B total and 3.6B active.", "architecture": "Hybrid Mixture-of-Experts with Mamba-2, MoE, and attention. Distilled from Nemotron 3 Super base, then a long-context phase. Ships with speculative drafters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron 3.5 Lightning is NVIDIA's open 30B MoE text model for high-throughput agent steps, released on 2026-08-11 under OpenMDW-1.1. Collected 2026-10-15. It supports up to a 1 million token context.", "what_it_is_for": "NVIDIA describes Lightning as the fast model for repeated specialized tasks inside long-running agents, with larger models reserved for harder planning.", "ratings": { "empirical_100": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-3-5-super-vl": { "id": "nvidia/nemotron/nemotron-3-5-super-vl", "name": "Nemotron 3.5 Super VL", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-10-15", "context_length": 1000000, "max_output_tokens": null, "parameters": "120B total, 12B active. Safetensors total on BF16 is 124453395976.", "architecture": "Mamba-2 and Mixture-of-Experts hybrid with attention, a C-RADIO vision encoder, and a vision-language connector. Distilled from Nemotron 3 Ultra base.", "modalities": { "input": [ "text", "image", "video", "document" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron 3.5 Super VL is NVIDIA's open 120B multimodal agent model, with 12B active parameters, released on 2026-10-15 under OpenMDW-1.1. Collected the same day. It combines text, image, document, and video understanding with reasoning and tool use.", "what_it_is_for": "NVIDIA says it is for specialized always-on agents that have to read documents, charts, tables, images, and web pages and then call tools. The research page also describes long-context and agentic text work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-3-8b": { "id": "nvidia/nemotron/nemotron-3-8b", "name": "Nemotron-3 8B", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-11-15", "context_length": 4096, "max_output_tokens": null, "parameters": "8B (product name)", "architecture": "Transformer language model. The chat checkpoint name includes a 4k context and RLHF.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NVIDIA's Nemotron-3 8B is the first named Nemotron language model, introduced on 2023-11-15 for enterprise chat and copilots. Collected 2026-10-15. The public chat checkpoint is still listed on Hugging Face, but its card could not be read.", "what_it_is_for": "NVIDIA introduced it so companies could build custom enterprise chatbots and copilots from a production-oriented 8B language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-3-nano": { "id": "nvidia/nemotron/nemotron-3-nano", "name": "Nemotron 3 Nano", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA Nemotron Open Model License", "release_date": "2025-12-15", "context_length": 1000000, "max_output_tokens": null, "parameters": "Card says 30B. Research page says 31.6B total and 3.2B active (3.6B with embeddings). Safetensors total on the BF16 repo is 31577937344.", "architecture": "Hybrid Mamba-2 and Mixture-of-Experts transformer. 23 Mamba-2 and MoE layers and 6 attention layers. 128 experts plus 1 shared expert, 6 experts active.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron 3 Nano is NVIDIA's open 30B-class hybrid MoE language model for agentic work, announced on 2025-12-15. Collected 2026-10-15. About 3B parameters are active per token, and the context window is 1 million tokens.", "what_it_is_for": "NVIDIA describes Nano as the cost-efficient model for targeted agent tasks such as coding, reasoning, math, and long context, with higher throughput than Nemotron 2 Nano.", "ratings": { "empirical_100": 58, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-3-nano-omni": { "id": "nvidia/nemotron/nemotron-3-nano-omni", "name": "Nemotron 3 Nano Omni", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA Open Model Agreement", "release_date": "2026-04-28", "context_length": 256000, "max_output_tokens": null, "parameters": "31B total hybrid MoE, about 3B active per token. Safetensors total 33015632214 on the reasoning BF16 repo. Card also writes 3.1 x 10^10.", "architecture": "Nemotron 3 Nano Mamba-2 hybrid MoE backbone plus vision and audio encoders.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron 3 Nano Omni is the multimodal Nemotron 3 Nano, released on 2026-04-28. Collected 2026-10-15. One open model takes text, images, video, and speech and returns text, with a 256k context.", "what_it_is_for": "NVIDIA describes it for sub-agents that need document intelligence, computer-use screenshots, and video or audio understanding without a separate vision model.", "ratings": { "empirical_100": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-3-super": { "id": "nvidia/nemotron/nemotron-3-super", "name": "Nemotron 3 Super", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA Nemotron Open Model License", "release_date": "2026-03-11", "context_length": 1000000, "max_output_tokens": null, "parameters": "120B total, 12B active (card). Safetensors total 123611012096. Artificial Analysis lists 120.6B total and 12.7B active.", "architecture": "Latent MoE hybrid of Mamba-2, Mixture-of-Experts, and attention, with multi-token prediction. Pretrained in NVFP4.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron 3 Super is NVIDIA's open 120B Mixture-of-Experts language model with 12B active parameters, released on 2026-03-11. Collected 2026-10-15. It is built for high-volume agent work and a 1 million token context.", "what_it_is_for": "NVIDIA positions Super for collaborative agents and workloads such as IT ticket automation, tool use, and long-context reasoning.", "ratings": { "empirical_100": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-3-ultra": { "id": "nvidia/nemotron/nemotron-3-ultra", "name": "Nemotron 3 Ultra", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-06-04", "context_length": 1000000, "max_output_tokens": null, "parameters": "550B total, 55B active (card). Safetensors total on BF16 is 560524578816.", "architecture": "Latent MoE hybrid of Mamba-2, Mixture-of-Experts, and attention, with multi-token prediction. Pretrained in NVFP4.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 2.5 }, "summary_intro": "Nemotron 3 Ultra is NVIDIA's largest open Nemotron 3 language model, a 550B Mixture-of-Experts with 55B active parameters, released on 2026-06-04 under OpenMDW-1.1. Collected 2026-10-15. The card supports a 1 million token context.", "what_it_is_for": "NVIDIA positions Ultra for the hardest agent calls: multi-step planning, tool use, code, deep research, and high-stakes retrieval.", "ratings": { "empirical_100": 41, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-4-340b": { "id": "nvidia/nemotron/nemotron-4-340b", "name": "Nemotron-4 340B", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA Open Model License (June 2024 agreement)", "release_date": "2024-06-14", "context_length": null, "max_output_tokens": null, "parameters": "340B", "architecture": "Transformer decoder, autoregressive language model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron-4 340B is NVIDIA's June 2024 open-weight language model family: a 340B base, an instruct model, and a reward model. Collected 2026-10-15. NVIDIA positioned the instruct model for synthetic training data.", "what_it_is_for": "The instruct card says it is used in a synthetic data generation pipeline so other people can train their own language models. The reward model scores that data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-cascade-2": { "id": "nvidia/nemotron/nemotron-cascade-2", "name": "Nemotron-Cascade 2", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA Open Model License", "release_date": "2026-03-16", "context_length": 1000000, "max_output_tokens": null, "parameters": "30B total, 3B active (research page and card). Safetensors total 31577937344.", "architecture": "Open Mixture-of-Experts model trained with cascade reinforcement learning and multi-domain on-policy distillation. The base checkpoint was not confirmed in the lines that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron-Cascade 2 is NVIDIA's open 30B Mixture-of-Experts reasoning model with 3B active parameters, published on 2026-03-16. Collected 2026-10-15. It supports a 1 million token context.", "what_it_is_for": "NVIDIA describes strong reasoning and agentic use, and the research page says it reached gold-medal level on the 2025 IMO, IOI, and ICPC World Finals after DeepSeek-V3.2-Speciale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-cascade-8b": { "id": "nvidia/nemotron/nemotron-cascade-8b", "name": "Nemotron-Cascade 8B", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA Open Model License", "release_date": "2025-12-08", "context_length": null, "max_output_tokens": null, "parameters": "8190735360 weights in safetensors metadata. The card describes an 8B model.", "architecture": "Post-trained from Qwen3-8B-Base with sequential supervised fine-tuning and cascaded reinforcement learning. Thinking and non-thinking modes.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron-Cascade 8B is an NVIDIA reasoning model released on 2025-12-08. Collected 2026-10-15. It is post-trained from Qwen3-8B-Base with cascaded reinforcement learning and supports thinking and instruct modes.", "what_it_is_for": "NVIDIA presents it as a general-purpose reasoning model that can run with or without an explicit thinking pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-nano-12b-v2": { "id": "nvidia/nemotron/nemotron-nano-12b-v2", "name": "Nemotron Nano 12B v2", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA Open Model License", "release_date": "2025-08-29", "context_length": 131072, "max_output_tokens": null, "parameters": "12B marketed; safetensors total 12310001152", "architecture": "Nemotron-Hybrid: mostly Mamba-2 and MLP layers plus six attention layers.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron Nano 12B v2 is the larger sibling of Nemotron Nano 9B v2, released on 2025-08-29 under the NVIDIA Open Model License. Collected 2026-10-15. It is a hybrid Mamba-Transformer, not a Llama derivative.", "what_it_is_for": "Same job as Nano 9B v2, with more capacity: multilingual instruct and reasoning at 128K context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-nano-12b-v2-vl": { "id": "nvidia/nemotron/nemotron-nano-12b-v2-vl", "name": "Nemotron Nano 12B v2 VL", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "12B in the product name; exact weight count was not read", "architecture": "Vision-language Nemotron Nano. The BF16 card returned HTTP 401. FP8 and NVFP4 repos are public.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron Nano 12B v2 VL is NVIDIA's open vision-language Nano model for documents, images, and video. Collected 2026-10-15. The main BF16 card was gated or unauthorized, so several spec fields are empty on purpose.", "what_it_is_for": "NVIDIA's Nemotron page describes Nano VL as the document-intelligence and video-understanding model in the family, sized for a single data-center GPU.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/nemotron/nemotron-nano-9b-v2": { "id": "nvidia/nemotron/nemotron-nano-9b-v2", "name": "Nemotron Nano 9B v2", "maker": "nvidia", "family": "nvidia/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA Open Model License", "release_date": "2025-08-18", "context_length": 131072, "max_output_tokens": null, "parameters": "9B marketed; safetensors total 8888227328", "architecture": "Nemotron-Hybrid: mostly Mamba-2 and MLP layers plus four attention layers. See the Nemotron-H report cited on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nemotron Nano 9B v2 is an NVIDIA hybrid Mamba-Transformer language model released on 2025-08-18. Collected 2026-10-15. Nemotron 3's launch text treats the previous Nano generation as Nemotron 2 Nano. Weights are open under the NVIDIA Open Model License.", "what_it_is_for": "It is a small general model for multilingual chat and reasoning, with a 128K context, before the MoE Nemotron 3 Nano.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nvidia/parakeet/parakeet-tdt-0-6b-v3": { "id": "nvidia/parakeet/parakeet-tdt-0-6b-v3", "name": "Parakeet TDT 0.6B v3", "maker": "nvidia", "family": "nvidia/parakeet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2025-08-04", "context_length": null, "max_output_tokens": null, "parameters": "627057286 weights in safetensors metadata", "architecture": "Parakeet token-and-duration transducer (TDT). The card body was not fully read.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Parakeet TDT 0.6B v3 is NVIDIA's open multilingual speech-recognition model under CC-BY-4.0. Collected 2026-10-15. The checkpoint is about 0.6B parameters and was posted on 2025-08-04.", "what_it_is_for": "Automatic speech recognition for the languages listed on the model card. NVIDIA's NeMo speech line uses Parakeet for high-throughput ASR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/c2ser/c2ser-llm": { "id": "nwpu/c2ser/c2ser-llm", "name": "C2SER-LLM", "maker": "nwpu", "family": "nwpu/c2ser", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Chain-of-thought speech emotion recognition. The card says the model first describes content and speaking style, then infers emotion, using prior context, and distills explicit chain-of-thought into an implicit one. Inference needs a separately downloaded Qwen/Qwen2-7B at llm_path, the lab checkpoint C2SER_model.pt, a wav, and an utterance-level Emotion2Vec-S feature. The 7B figure is the backbone repo name, not a counted total for this checkpoint. Hub metadata had no safetensors total.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "C2SER-LLM is the language-model half of the C2SER speech-emotion system from ASLP@NPU at Northwestern Polytechnical University. The Hub repo ASLP-lab/C2SER-LLM was created on 24 February 2025 under Apache-2.0. It was still public on the collection date, 2026-10-15.", "what_it_is_for": "The card says the model recognizes emotion by first describing content and speaking style, then naming the emotion, with dialogue context available. Self-distillation moves that explicit chain of thought into a shorter implicit one. Stage-1 and stage-2 prompts select which of those outputs to print.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/c2ser/emotion2vec-s": { "id": "nwpu/c2ser/emotion2vec-s", "name": "Emotion2Vec-S", "maker": "nwpu", "family": "nwpu/c2ser", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-supervised speech encoder for emotion recognition, loaded through fairseq. The extraction script on the card resamples audio to 16,000 Hz, mixes multichannel audio to mono, and can dump frame-level or utterance-level features. Default checkpoint path on the card is ./Emotion2Vec-S/ckpt/checkpoint.pt. The same weights are also linked from a Google Drive folder. Hub metadata had no safetensors total. This is the lab's Emotion2Vec-S from the C2SER paper, not a copy of another lab's emotion2vec checkpoint.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emotion2Vec-S is a speech-emotion encoder from the C2SER release by ASLP@NPU at Northwestern Polytechnical University. The Hub repo ASLP-lab/Emotion2Vec-S was created on 24 February 2025 under Apache-2.0. It was still public on the collection date, 2026-10-15.", "what_it_is_for": "The card says it is the self-supervised model used for speech emotion recognition in the C2SER paper. The published script writes frame-level or utterance-level features from a wav list. C2SER-LLM asks for those utterance features at inference time.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/diffrhythm/diffrhythm": { "id": "nwpu/diffrhythm/diffrhythm", "name": "DiffRhythm", "maker": "nwpu", "family": "nwpu/diffrhythm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Non-autoregressive latent diffusion song model. Official config diffrhythm-1b.json sets dim 2048, depth 16, 32 heads, ff_mult 4, text_dim 512, 4 conv layers, mel_dim 64. The config filename says 1b. The paper does not print a counted parameter total, so parameters stays null. A separate VAE checkpoint reconstructs the waveform.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffRhythm (\u8c1b\u97f5) is a full-length song generator from ASLP@NPU at Northwestern Polytechnical University. The first public base weights and the paper appeared on 3 March 2025. Length and v1.2 checkpoints were still listed as current downloads on the collection date, 2026-10-15. DiffRhythm 2 is the successor product, not one of these snapshots.", "what_it_is_for": "The paper says one non-autoregressive diffusion model writes vocal and accompaniment together from lyrics and a style prompt. The GitHub page also accepts a reference audio. Base checkpoints stop at 95 seconds. Full checkpoints stop at 285 seconds, which the paper calls 4 minutes 45 seconds, in about ten seconds of inference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/diffrhythm/diffrhythm-2": { "id": "nwpu/diffrhythm/diffrhythm-2", "name": "DiffRhythm 2", "maker": "nwpu", "family": "nwpu/diffrhythm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-30", "context_length": null, "max_output_tokens": null, "parameters": "1136249664", "architecture": "Semi-autoregressive block flow matching. A music VAE at 5 Hz is described in the paper. The card text does not print a parameter count. Hugging Face safetensors metadata for ASLP-lab/DiffRhythm2, retrieved 2026-10-15, reports 1,136,249,664 parameters in float32. A ModelScope organization listing displayed 1.14B, which is a rounded display and is not the stored total. The license badge links a path under xiaomi-research/diffrhythm2. Weights are in ASLP-lab/DiffRhythm2.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffRhythm 2 is the next song model from ASLP@NPU at Northwestern Polytechnical University. Weights, demo code, and the paper were released on 30 October 2025. The Hub repo was still the public checkpoint on the collection date, 2026-10-15.", "what_it_is_for": "The card says it generates full-length songs with lyric alignment and a coherent structure. The paper replaces the earlier non-autoregressive stack with block flow matching so lyrics stay aligned without external alignment labels. A 5 Hz music VAE is part of that design. The card also describes cross-pair preference optimization for more than one listening preference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/easy-turn/easy-turn": { "id": "nwpu/easy-turn/easy-turn", "name": "Easy Turn", "maker": "nwpu", "family": "nwpu/easy-turn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-09-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Open turn-taking detector that the card says integrates acoustic and linguistic information and predicts four states: complete, incomplete, backchannel, and wait. The card table lists Params 850 MB. That column is model size in megabytes, not a parameter count, so parameters stays null. The card does not name a backbone in the text that was read.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Easy Turn is an open turn-taking model from ASLP@NPU at Northwestern Polytechnical University, with Huawei coauthors. The Hub repo ASLP-lab/Easy-Turn was created on 24 September 2025 under Apache-2.0. It was still public on the collection date, 2026-10-15.", "what_it_is_for": "The card says it decides when a full-duplex spoken system should speak, listen, or stay silent. It predicts four states: complete, incomplete, backchannel, and wait. The lab says it is deployed inside OSUM-EChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/llase/llase-g1": { "id": "nwpu/llase/llase-g1", "name": "LLaSE-G1", "maker": "nwpu", "family": "nwpu/llase", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card calls LLaSE-G1 LLaMA-based speech enhancement. Inference extracts 6th-layer WavLM features, a language model predicts X-Codec2 speech tokens, and X-Codec2 decodes them to audio. The LLaMA size is not stated. ckpt/download.sh fetches three additional pretrained models. The card says autonomous task choice can be unstable.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLaSE-G1 is a speech-enhancement model released by ASLP@NPU at Northwestern Polytechnical University. The Hub repo ASLP-lab/LLaSE-G1 was created on 28 February 2025 under Apache-2.0. It was still the public checkpoint on the collection date, 2026-10-15.", "what_it_is_for": "The card describes one model for noise suppression, target speaker extraction, packet loss concealment, acoustic echo cancellation, and speech separation, without an extra task prompt. It says the model can also attempt enhancement tasks it was not trained on, and that automatic task choice can be unstable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/meanvc/meanvc": { "id": "nwpu/meanvc/meanvc", "name": "MeanVC", "maker": "nwpu", "family": "nwpu/meanvc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Streaming zero-shot voice conversion. The card describes a diffusion transformer, chunk-wise autoregressive denoising, and mean flows for single-step inference. download_ckpt.py fetches the VC model, vocoder, and ASR model. wavlm_large_finetune.pth is a separate Google Drive download for speaker verification. No parameter count is printed.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MeanVC is a streaming zero-shot voice-conversion model from ASLP@NPU at Northwestern Polytechnical University. The Hub repo ASLP-lab/MeanVC was created on 2 November 2025 under Apache-2.0. It was still public on the collection date, 2026-10-15. MeanVC2 is the later named release.", "what_it_is_for": "The card says it transfers timbre from a source speaker to an unseen target speaker while keeping linguistic content, with chunk-wise streaming and single-step mean-flow inference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/meanvc/meanvc-2": { "id": "nwpu/meanvc/meanvc-2", "name": "MeanVC2", "maker": "nwpu", "family": "nwpu/meanvc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Streaming zero-shot voice conversion on conditional flow matching. Fast-U2++ (WeNet) extracts bottleneck features. ECAPA-TDNN plus a WavLM upstream extracts a speaker embedding. A universal timbre token encoder turns that embedding into key-value pairs. The decoder is a 4-layer diffusion transformer, hidden size 512, 2 heads, trained with mean flows for 1-NFE mel generation, with future-receptive chunking. Vocos writes 16 kHz waveforms. The card says Only 18M parameters and also prints Total parameters: ~18M. That is not an exact integer, so parameters stays null. Hub metadata had no safetensors total.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MeanVC2 is the later streaming voice-conversion model from ASLP@NPU at Northwestern Polytechnical University. The Hub repo ASLP-lab/MeanVC2 was created on 30 July 2026 under Apache-2.0. It was still public on the collection date, 2026-10-15.", "what_it_is_for": "The card says it converts a source voice to a target voice at low latency. Future-receptive chunking and a universal timbre token encoder are the two named additions. Output is 16 kHz speech from Vocos.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/msvae/msvae": { "id": "nwpu/msvae/msvae", "name": "MSVAE", "maker": "nwpu", "family": "nwpu/msvae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Stage II music variational autoencoder. Stereo 24 kHz in, stereo 48 kHz out. The encoder runs at 24 kHz and the decoder writes 48 kHz. A frozen music self-supervised encoder and an acoustic encoder meet in a gated residual posterior. The latent is 64-dimensional. model.pt is about 721 MiB and does not bundle MuQ. Inference loads OpenMuQ/MuQ-large-msd-iter separately. Not loadable through transformers.AutoModel. The Stage I 24 kHz-to-24 kHz checkpoint is not in the repo. The YAML says apache-2.0. The license section says the checkpoint license has not yet been specified. MuQ is CC BY-NC 4.0 on its own card, as cited here.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MSVAE is a music autoencoder checkpoint from ASLP@NPU at Northwestern Polytechnical University. The Hub repo ASLP-lab/MSVAE was created on 8 October 2026. It was still public on the collection date, 2026-10-15. The card and its YAML disagree on the license, so the license field is empty.", "what_it_is_for": "The card says this is a Stage II reconstruction model, not a text-to-music generator. It takes stereo audio at 24 kHz and writes stereo audio at 48 kHz.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/omnicodec/omnicodec": { "id": "nwpu/omnicodec/omnicodec", "name": "OmniCodec", "maker": "nwpu", "family": "nwpu/omnicodec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Low frame rate universal audio codec with semantic-acoustic disentanglement, as titled on the card. Training mixes speech, music, and sound scp lists. The default data block is 24 kHz and segment_size 240000 samples, which the card equates to 10 seconds. infer.py reconstructs from pretrained_model/omnicodec.pth. WavLM is a training loss dependency, default path wavlm-base-plus. Acknowledgements name moshi, Qwen3-Omni, DAC, BigVGAN, and SpeechTokenizer. Hub metadata had no safetensors total. The YAML license is Apache-2.0. The license section says to see the repository license.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmniCodec is an audio codec from ASLP@NPU at Northwestern Polytechnical University. The Hub repo ASLP-lab/OmniCodec was created on 5 April 2026. The YAML license is Apache-2.0. It was still public on the collection date, 2026-10-15.", "what_it_is_for": "The card titles it a low frame rate universal audio codec with semantic and acoustic features pulled apart. infer.py reconstructs audio. Training mixes speech, music, and sound lists.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/osum/osum": { "id": "nwpu/osum/osum", "name": "OSUM", "maker": "nwpu", "family": "nwpu/osum", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech encoder is Whisper-Medium: two stride-2 convolutions and 24 Transformer layers, hidden size 1024, 16 heads. An adaptor connects it to Qwen2-7B-Instruct, which the technical report fine-tunes with LoRA. The report does not publish a combined parameter count. v2 training data is 50,500 hours.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OSUM is an open speech-understanding model from the Audio, Speech and Language Processing Group (ASLP@NPU) at Northwestern Polytechnical University. The lab released the v2 checkpoint on 16 February 2025. It was still the public speech-understanding checkpoint on the collection date, 2026-10-15. The name is pronounced \"awesome.\"", "what_it_is_for": "The technical report trains one model for speech recognition, timestamped recognition, vocal-event detection, emotion, speaking style, speaker gender, speaker age, and speech-to-text chat. The training recipe is called ASR+X: recognition is optimized together with the target task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/osum/osum-echat": { "id": "nwpu/osum/osum-echat", "name": "OSUM-EChat", "maker": "nwpu", "family": "nwpu/osum", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Built on OSUM. Three modules: a speech encoder with an adapter, a text LLM, and a token-to-speech module. The Hub repo ships language-think and tag-think checkpoints plus a CosyVoice-300M-25Hz token-to-wave archive. No combined parameter count is published.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OSUM-EChat is an end-to-end spoken chatbot from ASLP@NPU at Northwestern Polytechnical University. The lab open-sourced the checkpoints on 27 August 2025. Both think-style weights were still on the Hub on the collection date, 2026-10-15.", "what_it_is_for": "The card describes empathetic spoken dialogue. The model keeps OSUM's speech-understanding tasks and adds speech synthesis and speech-to-speech chat. A linguistic and paralinguistic chain of thought runs before the reply. Training data named on the card is EChat-200K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/osum/osum-pangu": { "id": "nwpu/osum/osum-pangu", "name": "OSUM-Pangu", "maker": "nwpu", "family": "nwpu/osum", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Whisper-Medium speech encoder, an adapter, and openPangu-Embedded-7B-V1.1 as the intent-aware LLM. Trained and run on Huawei Ascend NPUs. The card does not publish a combined parameter count. The 7B figure is the backbone name.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OSUM-Pangu is a speech-understanding model from ASLP@NPU at Northwestern Polytechnical University. The weights were on Hugging Face by 2 March 2026. The lab still listed the repo as the public checkpoint on the collection date, 2026-10-15. It is a separate OpenPangu build, not a snapshot of the original OSUM checkpoint.", "what_it_is_for": "The card says it follows the OSUM task list: recognition, timestamps, events, emotion, style, gender, age, and speech-to-text chat. The extra claim is that training and inference run on Ascend NPUs with openPangu-Embedded-7B-V1.1, and that free-form instructions can select the task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/sembridge/sembridge": { "id": "nwpu/sembridge/sembridge", "name": "SemBridge", "maker": "nwpu", "family": "nwpu/sembridge", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-12", "context_length": null, "max_output_tokens": null, "parameters": "817594432", "architecture": "Semantic token anchoring for continuous-latent autoregressive generation. One model covers text-to-speech, note-conditioned singing, and mixed speech and singing. Default decoders: native causal for TTS, BigVGAN-Large for singing and unified. Hugging Face safetensors metadata, retrieved 2026-10-15, reports 817,594,432 parameters in float32. The card text does not print that count. Acknowledgements name VoxCPM, DiTAR, DAC, SAC, and BigVGAN. The Hub also tags arXiv 2502.03930, which the card cites as DiTAR, not as this paper. The license section says upstream portions keep their own licenses.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SemBridge is a speech and singing model from ASLP@NPU at Northwestern Polytechnical University. The Hub repo ASLP-lab/SemBridge was created on 12 October 2026. The card says the code and checkpoints were released in October 2026 and the paper in August 2026. The YAML license is Apache-2.0. It was still public on the collection date, 2026-10-15.", "what_it_is_for": "The card says one continuous-latent model does text-to-speech, note-conditioned singing, and mixed speech and singing. A shared semantic tokenizer anchors acoustic latents and the autoregressive states.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/sense/sense": { "id": "nwpu/sense/sense", "name": "SenSE", "maker": "nwpu", "family": "nwpu/sense", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card names two checkpoint files, SenSE_LLM.safetensors and SenSE_CFM.safetensors, placed under ckpts/SenSE_Base/. The paper title calls it semantic-aware high-fidelity universal speech enhancement. No layer counts or parameter total are on the card. Hub metadata had no safetensors total. The card names the Emilia dataset.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenSE is a speech-enhancement release from ASLP@NPU at Northwestern Polytechnical University. The Hub repo ASLP-lab/SenSE was created on 29 September 2025. The card is short and does not name a license. It was still public on the collection date, 2026-10-15.", "what_it_is_for": "The paper title on the card is semantic-aware high-fidelity universal speech enhancement. The card itself only says where to put the two weight files.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/skyeye/skyeyegpt": { "id": "nwpu/skyeye/skyeyegpt", "name": "SkyEyeGPT", "maker": "nwpu", "family": "nwpu/skyeye", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Remote-sensing multimodal model. Frozen EVA-CLIP ViT-G visual encoder, a linear alignment layer, and LLaMA2 with LoRA. The paper architecture table does not state the LLaMA2 size. Weights are meant to load in the MiniGPT-v2 code.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SkyEyeGPT is a remote-sensing vision-language model from the School of Artificial Intelligence, Optics and Electronics (iOPEN) at Northwestern Polytechnical University. Yang Zhan, Zhitong Xiong, and Yuan Yuan released the weights on 13 May 2025. The checkpoint was still public on the collection date, 2026-10-15.", "what_it_is_for": "The paper presents it as one model for remote-sensing captioning, visual grounding, phrase grounding, visual question answering, referring-expression generation, scene classification, and UAV video captioning. Training uses the SkyEye-968k instruction set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/songformer/songformer": { "id": "nwpu/songformer/songformer", "name": "SongFormer", "maker": "nwpu", "family": "nwpu/songformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2025-09-14", "context_length": null, "max_output_tokens": null, "parameters": "688725841", "architecture": "Music structure analysis with multi-resolution self-supervised representations and heterogeneous supervision. Loaded through transformers AutoModel with trust_remote_code. Input audio is 24,000 Hz. Output is a list of segments with start, end, and label. Hugging Face safetensors metadata, retrieved 2026-10-15, reports 688,725,841 parameters in float32. The quickstart tells snapshot_download to ignore SongFormer.pt and SongFormer.safetensors. A ModelScope organization snippet displayed 714.84M. That figure was not opened as a file total and is not the stored count. The license is the card badge CC BY 4.0. The Hub API returned no license tag.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SongFormer is a music-structure model from ASLP@NPU at Northwestern Polytechnical University, with coauthors at other universities and at Multimodal Art Projection. The Hub repo ASLP-lab/SongFormer was created on 14 September 2025. The card badge says CC BY 4.0. It was still public on the collection date, 2026-10-15.", "what_it_is_for": "The card says it labels the structure of a piece of music. Each prediction is a segment with a start time, an end time, and a label such as verse. SongFormDB and SongFormBench are the accompanying dataset and benchmark.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/speaker-reasoner/speaker-reasoner": { "id": "nwpu/speaker-reasoner/speaker-reasoner", "name": "Speaker-Reasoner", "maker": "nwpu", "family": "nwpu/speaker-reasoner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-24", "context_length": null, "max_output_tokens": null, "parameters": "31719205488", "architecture": "End-to-end speech LLM for timestamped speaker-attributed ASR. The card describes agentic multi-turn temporal reasoning: a global speaker summary, boundary prediction, then fine-grained segment decoding, plus a speaker-aware cache for audio longer than the training context. Hub config architecture is Qwen3OmniMoeForConditionalGeneration, model_type qwen3_omni_moe. The API record had no base_model finetune tag. Hugging Face safetensors metadata, retrieved 2026-10-15, reports 31,719,205,488 parameters in BF16 for both ASLP-lab/Speaker-Reasoner and ASLP-lab/Speaker-Reasoner-4194h. The files were not hashed against each other. The chat template includes image and video placeholders. The card's stated task is audio.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Speaker-Reasoner is a speech model from ASLP@NPU at Northwestern Polytechnical University, with Nanjing University and Shanghai Lingguang Zhaxian Technology coauthors. The Hub repo ASLP-lab/Speaker-Reasoner was created on 24 April 2026. The license badge says Apache 2.0. It was still public on the collection date, 2026-10-15.", "what_it_is_for": "The card says it writes timestamped, speaker-attributed transcripts. It iterates from a global speaker summary to predicted time boundaries and then to a fine-grained segment, covering speaker identity, gender, times, and words. A speaker-aware cache is how the card says it continues past the training context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/spherevae/spherevae": { "id": "nwpu/spherevae/spherevae", "name": "SphereVAE", "maker": "nwpu", "family": "nwpu/spherevae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SEANet audio codec, causal Transformers, and a power-spherical latent space. Training audio is mono 24 kHz from a Kaldi-style scp. Inference uses posterior sampling, so repeated runs can differ, and output is trimmed to the input length. Hub metadata had no safetensors total. The YAML license apache-2.0 is duplicated. The acknowledgements say parts are adapted from Kyutai Mimi and Meta AudioCraft/EnCodec and that this export does not assign those portions a new license.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SphereVAE is a speech autoencoder from ASLP@NPU at Northwestern Polytechnical University. The Hub repo ASLP-lab/SphereVAE was created on 11 September 2026. The YAML license is Apache-2.0, printed twice. It was still public on the collection date, 2026-10-15.", "what_it_is_for": "The card says it models speech in a hyperspherical latent space for autoregressive representation learning, and that infer.py reconstructs audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nwpu/yingmusic/yingmusic-singer-plus": { "id": "nwpu/yingmusic/yingmusic-singer-plus", "name": "YingMusic-Singer-Plus", "maker": "nwpu", "family": "nwpu/yingmusic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2026-03-10", "context_length": null, "max_output_tokens": null, "parameters": "727469236", "architecture": "Controllable singing voice synthesis. The card title names flexible lyric manipulation and annotation-free melody guidance. Packaged weights use ModelHubMixin in safetensors, and ckpts/ holds component checkpoints. Hugging Face safetensors metadata, retrieved 2026-10-15, reports 727,469,236 parameters in float32. The card text does not print that count. The card does not describe the melody encoder.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YingMusic-Singer-Plus is a singing model from ASLP@NPU at Northwestern Polytechnical University, with coauthors at GiantNetwork. The Hub repo ASLP-lab/YingMusic-Singer-Plus was created on 10 March 2026 under CC-BY-4.0. It was still public on the collection date, 2026-10-15.", "what_it_is_for": "The card title says controllable singing voice synthesis with flexible lyric changes and melody guidance that does not need a separate annotation. LyricEditBench is the linked dataset. The card body that was read does not add an inference recipe.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/cambrian/cambrian-13b": { "id": "nyu/cambrian/cambrian-13b", "name": "Cambrian-1 13B", "maker": "nyu", "family": "nyu/cambrian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face tag is Apache-2.0. The model card says the base is under the Llama 2 Community License. The project page identifies the base as Vicuna-1.5-13B.", "release_date": "2024-06-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal LLM. Base LLM is Vicuna-1.5-13B. Spatial Vision Aggregator emits 576 visual tokens. Two-stage training, same data recipe as the other Cambrian-1 sizes. Stage-1 max length on the README is 2048.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cambrian-1 13B is the Vicuna-1.5-13B multimodal LLM in the Cambrian-1 family from VISIONx at New York University. The lab released it with the 24 June 2024 paper and code drop. Weights remained public as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Image-text instruction following with a vision-centric connector. The project page groups it with the 8B and 34B Cambrian-1 models and reports 576 visual tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/cambrian/cambrian-34b": { "id": "nyu/cambrian/cambrian-34b", "name": "Cambrian-1 34B", "maker": "nyu", "family": "nyu/cambrian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face tag is Apache-2.0. The base model on the project page is Hermes-2-Yi-34B; that base license is not restated on the Cambrian card.", "release_date": "2024-06-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal LLM. Base LLM is Hermes-2-Yi-34B. Spatial Vision Aggregator emits 576 visual tokens. README stage-1 recipe for Hermes Yi-34B uses global batch 1024, learning rate 1e-3, SVA learning rate 1e-4, 1 epoch, max length 2048.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cambrian-1 34B is the largest Cambrian-1 multimodal LLM from VISIONx at New York University. The project page uses Hermes-2-Yi-34B as the language backbone. It was released with the 24 June 2024 paper. Weights remained public as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "High-capacity image-text instruction following in the Cambrian-1 cookbook. The lab reports 576 visual tokens, the same budget as the smaller Cambrian-1 models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/cambrian/cambrian-8b": { "id": "nyu/cambrian/cambrian-8b", "name": "Cambrian-1 8B", "maker": "nyu", "family": "nyu/cambrian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face tag is Apache-2.0. The model card also says the Llama 3 base is under the Llama 3 Community License.", "release_date": "2024-06-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal LLM. Base LLM is Llama-3-8B-Instruct. Spatial Vision Aggregator (SVA) connects frozen vision encoders to the LLM and emits 576 visual tokens. Two-stage training: connector on Cambrian Alignment (about 2.5M), then instruction tuning on Cambrian-7M.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cambrian-1 8B is an open-weight multimodal LLM from VISIONx at New York University, released with the Cambrian-1 paper on 24 June 2024. It is the Llama-3-8B-Instruct member of that family. Weights were still on Hugging Face as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes Cambrian-1 as a vision-centric multimodal LLM. The 8B checkpoint is the instruction-tuned model for image-text tasks, using 576 visual tokens from the Spatial Vision Aggregator rather than a much larger token grid.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/cambrian/cambrian-phi3-3b": { "id": "nyu/cambrian/cambrian-phi3-3b", "name": "Cambrian-1 Phi-3 3B", "maker": "nyu", "family": "nyu/cambrian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Model card says 'MIT License following Phi-3'. Hugging Face also tags the repo Apache-2.0.", "release_date": "2024-06-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Smaller Cambrian-1 multimodal LLM. The README groups a Phi-3-3.8B base with the Cambrian-1 release. The card calls the checkpoint Cambrian-1-3B and says it was trained in June 2024. Same SVA design and Cambrian alignment plus instruction-tuning data as the larger models.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cambrian-1 Phi-3 3B is the small Cambrian-1 multimodal checkpoint from VISIONx at New York University. The model card calls it Cambrian-1-3B, trained in June 2024, and says the license follows Phi-3. Weights remained public as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "A smaller vision-centric multimodal LLM in the Cambrian-1 release, trained on the same Cambrian alignment and instruction-tuning mixtures as the 8B, 13B, and 34B models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/cambrian-p/cambrian-p-7b": { "id": "nyu/cambrian-p/cambrian-p-7b", "name": "Cambrian-P 7B", "maker": "nyu", "family": "nyu/cambrian-p", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-05-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cambrian-S backbone: Qwen2.5-7B plus SigLIP2-SO400M, plus per-frame learnable camera tokens and a VGGT-style pose head. Finetuned from Cambrian-S-7B stage 3. The README says pose tokens are an extra training signal.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cambrian-P 7B is a pose-grounded video MLLM from VISIONx at New York University, with coauthors at UC Berkeley and Meta FAIR. It adds per-frame camera tokens and a pose head on the Cambrian-S 7B backbone. The paper is dated 21 May 2026 and the primary Hugging Face repo was created 17 May 2026. Weights were still public as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Video question answering that also regresses camera pose, so frames share a spatial coordinate frame. The README reports a 73.7 VSI-Bench average and a 4.5 point gain over Cambrian-S 7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/cambrian-s/cambrian-s-0-5b": { "id": "nyu/cambrian-s/cambrian-s-0-5b", "name": "Cambrian-S 0.5B", "maker": "nyu", "family": "nyu/cambrian-s", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2.5-0.5B-Instruct language model, SigLIP2-SO400M vision encoder (google/siglip2-so400m-patch14-384 on the 7B config), and a 2-layer MLP adapter. Four training stages: image alignment, image instruction tuning, video instruction tuning, spatial instruction tuning. Data includes VSI-590K plus general video instruction data.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cambrian-S 0.5B is the smallest Cambrian-S video MLLM from VISIONx at New York University, built on Qwen2.5-0.5B-Instruct. The paper is dated 6 November 2025. Weights were still public as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Spatially grounded video understanding. The lab trains Cambrian-S so the model can answer questions that need 3D spatial memory in video, not only short clip captions. The card says it keeps competitive general video scores while targeting visual-spatial benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/cambrian-s/cambrian-s-1-5b": { "id": "nyu/cambrian-s/cambrian-s-1-5b", "name": "Cambrian-S 1.5B", "maker": "nyu", "family": "nyu/cambrian-s", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2.5-1.5B-Instruct language model, SigLIP2-SO400M vision encoder (google/siglip2-so400m-patch14-384 on the 7B config), and a 2-layer MLP adapter. Four training stages: image alignment, image instruction tuning, video instruction tuning, spatial instruction tuning. Data includes VSI-590K plus general video instruction data.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cambrian-S 1.5B is the Qwen2.5-1.5B-Instruct member of the Cambrian-S video MLLM family from VISIONx at New York University. The paper is dated 6 November 2025. Weights were still public as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Spatially grounded video understanding. The lab trains Cambrian-S so the model can answer questions that need 3D spatial memory in video, not only short clip captions. The card says it keeps competitive general video scores while targeting visual-spatial benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/cambrian-s/cambrian-s-3b": { "id": "nyu/cambrian-s/cambrian-s-3b", "name": "Cambrian-S 3B", "maker": "nyu", "family": "nyu/cambrian-s", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2.5-3B-Instruct language model, SigLIP2-SO400M vision encoder (google/siglip2-so400m-patch14-384 on the 7B config), and a 2-layer MLP adapter. Four training stages: image alignment, image instruction tuning, video instruction tuning, spatial instruction tuning. Data includes VSI-590K plus general video instruction data.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cambrian-S 3B is the Qwen2.5-3B-Instruct member of the Cambrian-S video MLLM family from VISIONx at New York University. The paper is dated 6 November 2025. Weights were still public as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Spatially grounded video understanding. The lab trains Cambrian-S so the model can answer questions that need 3D spatial memory in video, not only short clip captions. The card says it keeps competitive general video scores while targeting visual-spatial benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/cambrian-s/cambrian-s-7b": { "id": "nyu/cambrian-s/cambrian-s-7b", "name": "Cambrian-S 7B", "maker": "nyu", "family": "nyu/cambrian-s", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-06", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2.5-7B-Instruct language model, SigLIP2-SO400M vision encoder (google/siglip2-so400m-patch14-384 on the 7B config), and a 2-layer MLP adapter. Four training stages: image alignment, image instruction tuning, video instruction tuning, spatial instruction tuning. Data includes VSI-590K plus general video instruction data. The 7B config names the vision tower google/siglip2-so400m-patch14-384 and projector mlp2x_gelu.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cambrian-S 7B is the main spatially grounded video MLLM from VISIONx at New York University. Weights were posted on 4 November 2025 and the paper is dated 6 November 2025. It was still public as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Spatially grounded video understanding. The lab trains Cambrian-S so the model can answer questions that need 3D spatial memory in video, not only short clip captions. The card says it keeps competitive general video scores while targeting visual-spatial benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/cambrian-s/cambrian-s-7b-lfp": { "id": "nyu/cambrian-s/cambrian-s-7b-lfp", "name": "Cambrian-S 7B LFP", "maker": "nyu", "family": "nyu/cambrian-s", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Same stack as Cambrian-S 7B (Qwen2.5-7B-Instruct + SigLIP2-SO400M + 2-layer MLP), with stage 4 changed to a latent frame prediction objective. The README calls this predictive sensing.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cambrian-S 7B LFP is the predictive-sensing variant of Cambrian-S from VISIONx at New York University. The README says stage 4 adds a latent frame prediction objective on top of the Cambrian-S 7B stack. The Hugging Face repo was created on 5 November 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab uses this checkpoint for spatial supersensing tests, including VSI-SUPER, where the model is trained to predict upcoming latent frames instead of only answering from observed frames.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/freeflow/freeflow-xl": { "id": "nyu/freeflow/freeflow-xl", "name": "FreeFlow-XL", "maker": "nyu", "family": "nyu/freeflow", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-11-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One-step flow-map student distilled without an external dataset. Teacher named in the paper is SiT-XL/2+REPA. The card calls the sampled checkpoint FreeFlow-XL/2. Hugging Face pipeline tag is unconditional-image-generation.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FreeFlow-XL is a one-step image generator from a joint MIT and New York University paper. VISIONx hosts the weights at nyu-visionx/FreeFlow under MIT. The paper is dated 24 November 2025. Weights were still public as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Fast ImageNet generation. The method distills a flow map from a frozen teacher by sampling only from the noise prior, so no external image dataset is used for distillation. The reported student is a 1-step FreeFlow-XL/2 model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/miniberta/miniberta-base-100m": { "id": "nyu/miniberta/miniberta-base-100m", "name": "MiniBERTa base 100M", "maker": "nyu", "family": "nyu/miniberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on https://github.com/nyu-mll/pretraining-learning-curves and the MSGS repo. The Hugging Face cards reviewed do not restate a license.", "release_date": "2020-11-10", "context_length": 514, "max_output_tokens": null, "parameters": "125000000", "architecture": "RoBERTa BASE: 12 layers, 12 heads, hidden size 768, FFN 3072, 125M parameters. config.json for roberta-base-100M-2 sets max_position_embeddings to 514 and vocab size to 50265. Pretrained on 100M words.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniBERTa base 100M is the 100-million-word RoBERTa BASE from NYU MLL. The primary checkpoint is nyu-mll/roberta-base-100M-2. The paper is dated 10 November 2020. This dossier was collected on 2026-10-15.", "what_it_is_for": "Controlled RoBERTa pretraining for linguistic probing. NYU MLL released these MiniBERTas so other labs could vary pretraining data size without retraining the grid. They are fill-mask encoders, not instruction models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/miniberta/miniberta-base-10m": { "id": "nyu/miniberta/miniberta-base-10m", "name": "MiniBERTa base 10M", "maker": "nyu", "family": "nyu/miniberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on https://github.com/nyu-mll/pretraining-learning-curves and the MSGS repo. The Hugging Face cards reviewed do not restate a license.", "release_date": "2020-11-10", "context_length": null, "max_output_tokens": null, "parameters": "125000000", "architecture": "RoBERTa BASE: 12 layers, 12 heads, hidden size 768, FFN 3072, 125M parameters, masked LM. Pretrained on 10M words. Position-embedding size was not re-read from this checkpoint's config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniBERTa base 10M is the 10-million-word RoBERTa BASE from NYU MLL. The primary checkpoint is nyu-mll/roberta-base-10M-2. The paper is dated 10 November 2020. This dossier was collected on 2026-10-15.", "what_it_is_for": "Controlled RoBERTa pretraining for linguistic probing. NYU MLL released these MiniBERTas so other labs could vary pretraining data size without retraining the grid. They are fill-mask encoders, not instruction models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/miniberta/miniberta-base-1b": { "id": "nyu/miniberta/miniberta-base-1b", "name": "MiniBERTa base 1B", "maker": "nyu", "family": "nyu/miniberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on https://github.com/nyu-mll/pretraining-learning-curves and the MSGS repo. The Hugging Face cards reviewed do not restate a license.", "release_date": "2020-11-10", "context_length": null, "max_output_tokens": null, "parameters": "125000000", "architecture": "RoBERTa BASE: 12 layers, 12 heads, hidden size 768, FFN 3072, 125M parameters, masked LM, vocab 50265 on the BASE config that was opened (the 100M-2 file). This checkpoint was pretrained on 1B words.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniBERTa base 1B is the largest MiniBERTa from NYU Machine Learning for Language. The primary checkpoint is nyu-mll/roberta-base-1B-3, the lowest-perplexity 1B run on the lab card. The paper is dated 10 November 2020. This dossier was collected on 2026-10-15.", "what_it_is_for": "Controlled RoBERTa pretraining for linguistic probing. NYU MLL released these MiniBERTas so other labs could vary pretraining data size without retraining the grid. They are fill-mask encoders, not instruction models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/miniberta/miniberta-med-small-1m": { "id": "nyu/miniberta/miniberta-med-small-1m", "name": "MiniBERTa med-small 1M", "maker": "nyu", "family": "nyu/miniberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on https://github.com/nyu-mll/pretraining-learning-curves and the MSGS repo. The Hugging Face cards reviewed do not restate a license.", "release_date": "2020-11-10", "context_length": null, "max_output_tokens": null, "parameters": "45000000", "architecture": "RoBERTa MED-SMALL: 6 layers, 8 heads, hidden size 512, FFN 2048, 45M parameters, masked LM. Pretrained on 1M words. This is a smaller shape than the BASE MiniBERTas.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniBERTa med-small 1M is the smallest MiniBERTa from NYU MLL, a 45M-parameter RoBERTa pretrained on 1M words. The primary checkpoint is nyu-mll/roberta-med-small-1M-2. The paper is dated 10 November 2020. This dossier was collected on 2026-10-15.", "what_it_is_for": "Controlled RoBERTa pretraining for linguistic probing. NYU MLL released these MiniBERTas so other labs could vary pretraining data size without retraining the grid. They are fill-mask encoders, not instruction models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/nyutron/nyutron": { "id": "nyu/nyutron/nyutron", "name": "NYUTron", "maker": "nyu", "family": "nyu/nyutron", "status": "active", "confidence": "high", "weights": "closed", "license": "GitHub: personal and academic use with credit; contact the authors for commercial use.", "release_date": "2023-06-07", "context_length": null, "max_output_tokens": null, "parameters": "109000000", "architecture": "109M-parameter BERT-like encoder pretrained with masked language modeling on NYU Langone clinical notes, then fine-tuned for clinical prediction tasks. Not a public chat model.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NYUTron is a clinical language model from NYU Langone and NYU Grossman School of Medicine, with NYU CDS and NVIDIA collaboration. Nature published it on 7 June 2023. It is a 109M-parameter BERT-like model trained on health-system notes and deployed inside NYU Langone for prediction tasks. The weights are not public. This dossier was collected on 2026-10-15.", "what_it_is_for": "Predicting operational and clinical outcomes from unedited EHR notes. The Nature tasks are 30-day all-cause readmission, in-hospital mortality, comorbidity index, length of stay, and insurance denial.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/rae/rae-ditdh-xl": { "id": "nyu/rae/rae-ditdh-xl", "name": "RAE DiTDH-XL", "maker": "nyu", "family": "nyu/rae", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "839000000", "architecture": "Representation autoencoder: frozen pretrained encoder (flagship DINOv2-B with registers) plus a trained ViT decoder, then a DiT DH-XL diffusion transformer in that latent space. Class-conditional ImageNet generation. README calls the best released model DiT DH-XL at 256 and 512.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RAE DiTDH-XL is the flagship class-conditional ImageNet generator from the Representation Autoencoder paper by VISIONx at New York University. The collection repo was created on 13 October 2025 under an MIT tag. Individual encoder checkpoints were posted later, including 22 February 2026. Weights were still public as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Class-conditional image generation on ImageNet. The encoder is a frozen representation model such as DINOv2, and a diffusion transformer generates in that latent space. The README points users at DiT DH-XL as the best released model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/raev2/raev2": { "id": "nyu/raev2/raev2", "name": "RAEv2", "maker": "nyu", "family": "nyu/raev2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-05-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Improved representation-autoencoder baseline. The project page says the recipe is more than 10x faster to converge than the original RAE, with better reconstruction and generation, and that it is also tested for text-to-image and navigation world models. Weights are one Hugging Face bundle (encoders, stage 1, stage 2).", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RAEv2 is an improved representation-autoencoder baseline released by Adobe Research, the Australian National University, and New York University. VISIONx hosts the weights at nyu-visionx/RAEv2-models. The project page is dated 15 May 2026. Weights were still public as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Faster ImageNet generation and reconstruction in RAE latent space, plus checks on text-to-image and navigation world models. The authors present it as a training recipe change over the original RAE, not a new hosted product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/scale-rae/scale-rae-qwen1-5b-dit2-4b": { "id": "nyu/scale-rae/scale-rae-qwen1-5b-dit2-4b", "name": "Scale-RAE Qwen 1.5B DiT 2.4B", "maker": "nyu", "family": "nyu/scale-rae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Text-to-image diffusion transformer in a representation-autoencoder latent space. LLM Qwen2.5-1.5B. DiT 2.4B. Decoder SigLIP-2. Frozen vision encoder and decoder during diffusion training. Two stages: large-scale pretraining, then finetuning.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Scale-RAE Qwen 1.5B / DiT 2.4B is the default text-to-image model in the Scale-RAE release from VISIONx at New York University. The README and Hugging Face repos are dated 8 January 2026. Weights were still public as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Open text-to-image generation. Scale-RAE trains a diffusion transformer in a high-dimensional semantic latent space from a representation autoencoder, conditioned by a frozen or pretrained language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/scale-rae/scale-rae-qwen1-5b-dit2-4b-webssl": { "id": "nyu/scale-rae/scale-rae-qwen1-5b-dit2-4b-webssl", "name": "Scale-RAE WebSSL", "maker": "nyu", "family": "nyu/scale-rae", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Text-to-image diffusion transformer in a representation-autoencoder latent space. LLM Qwen2.5-1.5B. DiT 2.4B. Decoder WebSSL-DINO 300M. Frozen vision encoder and decoder during diffusion training. Two stages: large-scale pretraining, then finetuning.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Scale-RAE WebSSL is the third official Scale-RAE checkpoint from VISIONx at New York University. It keeps the Qwen2.5-1.5B and 2.4B DiT sizes and swaps in a WebSSL decoder. The Hugging Face license tag is MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "Open text-to-image generation. Scale-RAE trains a diffusion transformer in a high-dimensional semantic latent space from a representation autoencoder, conditioned by a frozen or pretrained language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/scale-rae/scale-rae-qwen7b-dit9-8b": { "id": "nyu/scale-rae/scale-rae-qwen7b-dit9-8b", "name": "Scale-RAE Qwen 7B DiT 9.8B", "maker": "nyu", "family": "nyu/scale-rae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Text-to-image diffusion transformer in a representation-autoencoder latent space. LLM Qwen2.5-7B. DiT 9.8B. Decoder SigLIP-2. Frozen vision encoder and decoder during diffusion training. Two stages: large-scale pretraining, then finetuning.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Scale-RAE Qwen 7B / DiT 9.8B is the larger text-to-image checkpoint in the Scale-RAE release from VISIONx at New York University. It was posted on 8 January 2026. Weights were still public as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Open text-to-image generation. Scale-RAE trains a diffusion transformer in a high-dimensional semantic latent space from a representation autoencoder, conditioned by a frozen or pretrained language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/sit/sit-xl": { "id": "nyu/sit/sit-xl", "name": "SiT-XL", "maker": "nyu", "family": "nyu/sit", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-01-16", "context_length": null, "max_output_tokens": null, "parameters": "675000000", "architecture": "Scalable Interpolant Transformer. DiT-style transformer backbone, patch size 2, XL width. Class-conditional ImageNet generator trained with stochastic interpolants. GitHub reports 119 Gflops for the 256 XL/2 checkpoint. Paper table lists 675M parameters, matching DiT-XL.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SiT-XL is the flagship Scalable Interpolant Transformer from New York University, released with the January 2024 paper and the willisma/SiT repository. It is a class-conditional ImageNet generator on a Diffusion Transformer backbone. A MIT-tagged weight bundle was later posted as nyu-visionx/SiT-collections. Weights were still public as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Class-conditional image generation. The lab uses SiT to study interpolant, objective, and sampler choices on the DiT backbone. The released XL checkpoint is the one the README tells users to sample.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "nyu/solaris/solaris": { "id": "nyu/solaris/solaris", "name": "Solaris", "maker": "nyu", "family": "nyu/solaris", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the Solaris repo and model card. The card also ships Wan 2.1 CLIP and VAE weights whose licenses are not restated.", "release_date": "2026-02-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Action-conditioned multiplayer video world model for Minecraft. JAX weights. Staged training from single-player bidirectional pretraining to multiplayer bidirectional, causal, and checkpointed self-forcing. Final file is solaris.pt. Built from a Matrix Game 2.0 initialization. Inference also needs Wan 2.1 CLIP and VAE files shipped in the same repo.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solaris is a multiplayer Minecraft video world model from VISIONx at New York University. It generates paired first-person views for two players. The paper is dated 25 February 2026, and the weights are on nyu-visionx/solaris under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Simulating consistent multi-view Minecraft gameplay. The paper trains one model that keeps two players' observations spatially aligned, instead of a single-agent world model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/audio/gpt-4o-audio-preview": { "id": "openai/audio/gpt-4o-audio-preview", "name": "GPT-4o Audio", "maker": "openai", "family": "openai/audio", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-17", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 40.0, "completion_per_m_usd": 80.0 }, "summary_intro": "OpenAI's GPT-4o Audio (`gpt-4o-audio-preview`) is a audio model. Status on 2026-10-15: retired. Confidence: high. This is a preview release of the GPT-4o Audio models.", "what_it_is_for": "GPT-4o models capable of audio inputs and outputs", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/audio/gpt-4o-mini-audio-preview": { "id": "openai/audio/gpt-4o-mini-audio-preview", "name": "GPT-4o Mini Audio", "maker": "openai", "family": "openai/audio", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 20.0 }, "summary_intro": "OpenAI's GPT-4o Mini Audio (`gpt-4o-mini-audio-preview`) is a audio model. Status on 2026-10-15: retired. Confidence: high. This is a preview release of the smaller GPT-4o Audio Mini model.", "what_it_is_for": "Smaller model capable of audio inputs and outputs", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/audio/gpt-4o-mini-transcribe": { "id": "openai/audio/gpt-4o-mini-transcribe", "name": "GPT-4o Mini Transcribe", "maker": "openai", "family": "openai/audio", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-15", "context_length": 16000, "max_output_tokens": 2000, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 5.0 }, "summary_intro": "OpenAI's GPT-4o Mini Transcribe (`gpt-4o-mini-transcribe`) is a audio model. Status on 2026-10-15: deprecated. Confidence: high. GPT-4o Mini Transcribe is a speech-to-text model that uses GPT-4o Mini to transcribe audio.", "what_it_is_for": "Speech-to-text model powered by GPT-4o Mini", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/audio/gpt-4o-transcribe": { "id": "openai/audio/gpt-4o-transcribe", "name": "GPT-4o Transcribe", "maker": "openai", "family": "openai/audio", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 16000, "max_output_tokens": 2000, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.5, "completion_per_m_usd": 10.0 }, "summary_intro": "OpenAI's GPT-4o Transcribe (`gpt-4o-transcribe`) is a audio model. Status on 2026-10-15: deprecated. Confidence: high. GPT-4o Transcribe is a speech-to-text model that uses GPT-4o to transcribe audio.", "what_it_is_for": "Speech-to-text model powered by GPT-4o", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/audio/gpt-4o-transcribe-diarize": { "id": "openai/audio/gpt-4o-transcribe-diarize", "name": "GPT-4o Transcribe Diarize", "maker": "openai", "family": "openai/audio", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 16000, "max_output_tokens": 2000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.5, "completion_per_m_usd": 10.0 }, "summary_intro": "OpenAI's GPT-4o Transcribe Diarize (`gpt-4o-transcribe-diarize`) is a audio model. Status on 2026-10-15: deprecated. Confidence: high. GPT-4o Transcribe Diarize is an automatic speech recognition (ASR) model with built-in speaker diarization, meaning it associates audio segments with different speakers in a conversation.", "what_it_is_for": "Transcription model that identifies who's speaking when", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/audio/gpt-audio": { "id": "openai/audio/gpt-audio", "name": "GPT-Audio", "maker": "openai", "family": "openai/audio", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 32.0, "completion_per_m_usd": 64.0 }, "summary_intro": "OpenAI's GPT-Audio (`gpt-audio`) is a audio model. Status on 2026-10-15: deprecated. Confidence: high. The gpt-audio model is our first generally available audio model.", "what_it_is_for": "For audio inputs and outputs with Chat Completions API", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs": 23.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/audio/gpt-audio-1-5": { "id": "openai/audio/gpt-audio-1-5", "name": "GPT-Audio-1.5", "maker": "openai", "family": "openai/audio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-23", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 32.0, "completion_per_m_usd": 64.0 }, "summary_intro": "OpenAI's GPT-Audio-1.5 (`gpt-audio-1.5`) is a audio model. Status on 2026-10-15: active. Confidence: high. The gpt-audio model is our first generally available audio model.", "what_it_is_for": "The best voice model for audio in, audio out with Chat Completions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/audio/gpt-audio-mini": { "id": "openai/audio/gpt-audio-mini", "name": "GPT-Audio Mini", "maker": "openai", "family": "openai/audio", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-06", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 20.0 }, "summary_intro": "OpenAI's GPT-Audio Mini (`gpt-audio-mini`) is a audio model. Status on 2026-10-15: deprecated. Confidence: high. A cost-efficient version of GPT Audio.", "what_it_is_for": "A cost-efficient version of GPT Audio", "ratings": { "empirical_100": null, "lmsys_elo": 1236, "coding_elo": 1236, "swe_bench": 21.0, "humaneval": 74.8, "twelve_cs": 23.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/audio/gpt-transcribe": { "id": "openai/audio/gpt-transcribe", "name": "GPT-Transcribe", "maker": "openai", "family": "openai/audio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's GPT-Transcribe (`gpt-transcribe`) is a audio model. Status on 2026-10-15: active. Confidence: high. GPT Transcribe is a speech-to-text model for completed audio files, streamed file transcripts, and committed turns in Realtime sessions over WebSocket.", "what_it_is_for": "High-accuracy speech-to-text model for file and Realtime input transcription", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/clip/clip": { "id": "openai/clip/clip", "name": "CLIP", "maker": "openai", "family": "openai/clip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-01-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Contrastive image-text model", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's CLIP (`clip`) is a clip model. Status on 2026-10-15: active. Confidence: high. CLIP is OpenAI's contrastive language-image model, announced January 5, 2021, for zero-shot classification from category names.", "what_it_is_for": "Contrastive model that learns visual concepts from natural language.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/codex/codex-mini-latest": { "id": "openai/codex/codex-mini-latest", "name": "codex-mini-latest", "maker": "openai", "family": "openai/codex", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 200000, "max_output_tokens": 100000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.5, "completion_per_m_usd": 6.0 }, "summary_intro": "OpenAI's codex-mini-latest (`codex-mini-latest`) is a codex model. Status on 2026-10-15: retired. Confidence: high. codex-mini-latest is a fine-tuned version of o4-mini specifically for use in Codex CLI.", "what_it_is_for": "Fast reasoning model optimized for the Codex CLI", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/codex/gpt-5-1-codex": { "id": "openai/codex/gpt-5-1-codex", "name": "GPT-5.1-Codex", "maker": "openai", "family": "openai/codex", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-13", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 10.0 }, "summary_intro": "OpenAI's GPT-5.1-Codex (`gpt-5.1-codex`) is a codex model. Status on 2026-10-15: retired. Confidence: high. GPT-5.1-Codex is a version of GPT-5 optimized for agentic coding tasks in Codex or similar environments.", "what_it_is_for": "A version of GPT-5.1 optimized for agentic coding in Codex.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/codex/gpt-5-1-codex-max": { "id": "openai/codex/gpt-5-1-codex-max", "name": "GPT-5.1-Codex-Max", "maker": "openai", "family": "openai/codex", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-04", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 10.0 }, "summary_intro": "OpenAI's GPT-5.1-Codex-Max (`gpt-5.1-codex-max`) is a codex model. Status on 2026-10-15: retired. Confidence: high. GPT\u20115.1-Codex-Max is purpose-built for agentic coding.", "what_it_is_for": "A version of GPT-5.1-codex optimized for long running tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/codex/gpt-5-1-codex-mini": { "id": "openai/codex/gpt-5-1-codex-mini", "name": "GPT-5.1-Codex Mini", "maker": "openai", "family": "openai/codex", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-13", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.25, "completion_per_m_usd": 2.0 }, "summary_intro": "OpenAI's GPT-5.1-Codex Mini (`gpt-5.1-codex-mini`) is a codex model. Status on 2026-10-15: retired. Confidence: high. GPT-5.1-Codex Mini is a smaller, more cost-effective, less-capable version of GPT-5.1-Codex.", "what_it_is_for": "Smaller, more cost-effective, less-capable version of GPT-5.1-Codex", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/codex/gpt-5-2-codex": { "id": "openai/codex/gpt-5-2-codex", "name": "GPT-5.2-Codex", "maker": "openai", "family": "openai/codex", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-14", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.75, "completion_per_m_usd": 14.0 }, "summary_intro": "OpenAI's GPT-5.2-Codex (`gpt-5.2-codex`) is a codex model. Status on 2026-10-15: retired. Confidence: high. GPT-5.2-Codex is an upgraded version of GPT-5.2 optimized for agentic coding tasks in Codex or similar environments.", "what_it_is_for": "Our most intelligent coding model optimized for long-horizon, agentic coding tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/codex/gpt-5-3-codex": { "id": "openai/codex/gpt-5-3-codex", "name": "GPT-5.3-Codex", "maker": "openai", "family": "openai/codex", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-24", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.75, "completion_per_m_usd": 14.0 }, "summary_intro": "OpenAI's GPT-5.3-Codex (`gpt-5.3-codex`) is a codex model. Status on 2026-10-15: deprecated. Confidence: high. GPT-5.3-Codex is optimized for agentic coding tasks in Codex or similar environments.", "what_it_is_for": "The most capable agentic coding model to date.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/codex/gpt-5-codex": { "id": "openai/codex/gpt-5-codex", "name": "GPT-5-Codex", "maker": "openai", "family": "openai/codex", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 10.0 }, "summary_intro": "OpenAI's GPT-5-Codex (`gpt-5-codex`) is a codex model. Status on 2026-10-15: retired. Confidence: high. GPT-5-Codex is a version of GPT-5 optimized for agentic coding tasks in Codex or similar environments.", "what_it_is_for": "A version of GPT-5 optimized for agentic coding in Codex", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/dalle/dall-e": { "id": "openai/dalle/dall-e", "name": "DALL-E", "maker": "openai", "family": "openai/dalle", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2021-01-05", "context_length": 1280, "max_output_tokens": null, "parameters": "12 billion", "architecture": "Transformer trained on text-image pairs", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's DALL-E (`dall-e`) is a dalle model. Status on 2026-10-15: retired. Confidence: high. DALL-E is OpenAI's 12-billion parameter text-to-image transformer, announced January 5, 2021.", "what_it_is_for": "12B text-to-image transformer announced January 5, 2021.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/dalle/dall-e-2": { "id": "openai/dalle/dall-e-2", "name": "DALL\u00b7E 2", "maker": "openai", "family": "openai/dalle", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's DALL\u00b7E 2 (`dall-e-2`) is a dalle model. Status on 2026-10-15: retired. Confidence: high. DALL\u00b7E 2 has been deprecated and removed from the API.", "what_it_is_for": "Deprecated image generation model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/dalle/dall-e-3": { "id": "openai/dalle/dall-e-3", "name": "DALL\u00b7E 3", "maker": "openai", "family": "openai/dalle", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's DALL\u00b7E 3 (`dall-e-3`) is a dalle model. Status on 2026-10-15: retired. Confidence: high. DALL\u00b7E 3 has been deprecated and removed from the API.", "what_it_is_for": "Deprecated image generation model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/embedding/text-embedding-3-large": { "id": "openai/embedding/text-embedding-3-large", "name": "text-embedding-3-large", "maker": "openai", "family": "openai/embedding", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.13, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-embedding-3-large (`text-embedding-3-large`) is a embedding model. Status on 2026-10-15: active. Confidence: high. text-embedding-3-large is our most capable embedding model for both english and non-english tasks.", "what_it_is_for": "Most capable embedding model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/embedding/text-embedding-3-small": { "id": "openai/embedding/text-embedding-3-small", "name": "text-embedding-3-small", "maker": "openai", "family": "openai/embedding", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.02, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-embedding-3-small (`text-embedding-3-small`) is a embedding model. Status on 2026-10-15: active. Confidence: high. text-embedding-3-small is our improved, more performant version of our ada embedding model.", "what_it_is_for": "Small embedding model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/embedding/text-embedding-ada-002": { "id": "openai/embedding/text-embedding-ada-002", "name": "text-embedding-ada-002", "maker": "openai", "family": "openai/embedding", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-embedding-ada-002 (`text-embedding-ada-002`) is a embedding model. Status on 2026-10-15: active. Confidence: high. text-embedding-ada-002 is our improved, more performant version of our ada embedding model.", "what_it_is_for": "Older embedding model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/ada": { "id": "openai/gpt/ada", "name": "ada", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 1.6, "completion_per_m_usd": null }, "summary_intro": "OpenAI's ada (`ada`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id ada.", "what_it_is_for": "OpenAI API model id ada. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/babbage": { "id": "openai/gpt/babbage", "name": "babbage", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 2.4, "completion_per_m_usd": null }, "summary_intro": "OpenAI's babbage (`babbage`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id babbage.", "what_it_is_for": "OpenAI API model id babbage. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/babbage-002": { "id": "openai/gpt/babbage-002", "name": "babbage-002", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.4, "completion_per_m_usd": 0.4 }, "summary_intro": "OpenAI's babbage-002 (`babbage-002`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. GPT base models can understand and generate natural language or code but are not trained with instruction following.", "what_it_is_for": "Replacement for the GPT-3 ada and babbage base models", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/chat-latest": { "id": "openai/gpt/chat-latest", "name": "Chat Latest", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-05", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 30.0 }, "summary_intro": "OpenAI's Chat Latest (`chat-latest`) is a gpt model. Status on 2026-10-15: active. Confidence: high. chat-latest points to the latest Instant model currently used in ChatGPT.", "what_it_is_for": "Latest Instant model used in ChatGPT", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/chatgpt-4o-latest": { "id": "openai/gpt/chatgpt-4o-latest", "name": "ChatGPT-4o", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-15", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 15.0 }, "summary_intro": "OpenAI's ChatGPT-4o (`chatgpt-4o-latest`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. ChatGPT-4o was a model alias for the GPT-4o snapshot used in ChatGPT.", "what_it_is_for": "GPT-4o model used in ChatGPT", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/code-cushman-001": { "id": "openai/gpt/code-cushman-001", "name": "code-cushman-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's code-cushman-001 (`code-cushman-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id code-cushman-001.", "what_it_is_for": "OpenAI API model id code-cushman-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/code-cushman-002": { "id": "openai/gpt/code-cushman-002", "name": "code-cushman-002", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's code-cushman-002 (`code-cushman-002`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id code-cushman-002.", "what_it_is_for": "OpenAI API model id code-cushman-002. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/code-davinci-001": { "id": "openai/gpt/code-davinci-001", "name": "code-davinci-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's code-davinci-001 (`code-davinci-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id code-davinci-001.", "what_it_is_for": "OpenAI API model id code-davinci-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/code-davinci-002": { "id": "openai/gpt/code-davinci-002", "name": "code-davinci-002", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's code-davinci-002 (`code-davinci-002`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id code-davinci-002.", "what_it_is_for": "OpenAI API model id code-davinci-002. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/code-davinci-edit-001": { "id": "openai/gpt/code-davinci-edit-001", "name": "code-davinci-edit-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's code-davinci-edit-001 (`code-davinci-edit-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id code-davinci-edit-001.", "what_it_is_for": "OpenAI API model id code-davinci-edit-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/code-search-ada-code-001": { "id": "openai/gpt/code-search-ada-code-001", "name": "code-search-ada-code-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's code-search-ada-code-001 (`code-search-ada-code-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id code-search-ada-code-001.", "what_it_is_for": "OpenAI API model id code-search-ada-code-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/code-search-ada-text-001": { "id": "openai/gpt/code-search-ada-text-001", "name": "code-search-ada-text-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's code-search-ada-text-001 (`code-search-ada-text-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id code-search-ada-text-001.", "what_it_is_for": "OpenAI API model id code-search-ada-text-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/code-search-babbage-code-001": { "id": "openai/gpt/code-search-babbage-code-001", "name": "code-search-babbage-code-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's code-search-babbage-code-001 (`code-search-babbage-code-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id code-search-babbage-code-001.", "what_it_is_for": "OpenAI API model id code-search-babbage-code-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/code-search-babbage-text-001": { "id": "openai/gpt/code-search-babbage-text-001", "name": "code-search-babbage-text-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's code-search-babbage-text-001 (`code-search-babbage-text-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id code-search-babbage-text-001.", "what_it_is_for": "OpenAI API model id code-search-babbage-text-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/computer-use-preview": { "id": "openai/gpt/computer-use-preview", "name": "computer-use-preview", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-11", "context_length": 8192, "max_output_tokens": 1024, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 12.0 }, "summary_intro": "OpenAI's computer-use-preview (`computer-use-preview`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. The computer-use-preview model is a specialized model for the computer use tool.", "what_it_is_for": "Specialized model for computer use tool", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/curie": { "id": "openai/gpt/curie", "name": "curie", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 12.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's curie (`curie`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id curie.", "what_it_is_for": "OpenAI API model id curie. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/davinci": { "id": "openai/gpt/davinci", "name": "davinci", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's davinci (`davinci`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id davinci.", "what_it_is_for": "OpenAI API model id davinci. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/davinci-002": { "id": "openai/gpt/davinci-002", "name": "davinci-002", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 2.0 }, "summary_intro": "OpenAI's davinci-002 (`davinci-002`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. GPT base models can understand and generate natural language or code but are not trained with instruction following.", "what_it_is_for": "Replacement for the GPT-3 curie and davinci base models", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/ft-babbage-002": { "id": "openai/gpt/ft-babbage-002", "name": "ft-babbage-002", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's ft-babbage-002 (`ft-babbage-002`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. OpenAI API model id ft-babbage-002.", "what_it_is_for": "OpenAI API model id ft-babbage-002. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/ft-davinci-002": { "id": "openai/gpt/ft-davinci-002", "name": "ft-davinci-002", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's ft-davinci-002 (`ft-davinci-002`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. OpenAI API model id ft-davinci-002.", "what_it_is_for": "OpenAI API model id ft-davinci-002. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/ft-gpt-3-5-turbo": { "id": "openai/gpt/ft-gpt-3-5-turbo", "name": "ft-gpt-3.5-turbo", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's ft-gpt-3.5-turbo (`ft-gpt-3.5-turbo`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. OpenAI API model id ft-gpt-3.5-turbo.", "what_it_is_for": "OpenAI API model id ft-gpt-3.5-turbo. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/ft-gpt-4": { "id": "openai/gpt/ft-gpt-4", "name": "ft-gpt-4", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's ft-gpt-4 (`ft-gpt-4`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. OpenAI API model id ft-gpt-4.", "what_it_is_for": "OpenAI API model id ft-gpt-4. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt": { "id": "openai/gpt/gpt", "name": "GPT", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2018-06-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer pretrained with language modeling, then fine-tuned", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's GPT (`gpt`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI's June 11, 2018 language model combines a transformer with unsupervised pre-training and supervised fine-tuning.", "what_it_is_for": "2018 transformer language model. GPT-2's announcement calls GPT-2 its successor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-2": { "id": "openai/gpt/gpt-2", "name": "GPT-2", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2019-02-14", "context_length": null, "max_output_tokens": null, "parameters": "1.5 billion", "architecture": "Transformer language model", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's GPT-2 (`gpt-2`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. GPT-2 is OpenAI's 1.5 billion parameter transformer language model, announced February 14, 2019.", "what_it_is_for": "1.5B transformer language model. Full weights released November 5, 2019.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-3": { "id": "openai/gpt/gpt-3", "name": "GPT-3", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2020-05-28", "context_length": null, "max_output_tokens": null, "parameters": "175 billion", "architecture": "Autoregressive language model", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's GPT-3 (`gpt-3`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. GPT-3 is OpenAI's autoregressive language model with 175 billion parameters, announced May 28, 2020, and tested few-shot without gradient updates.", "what_it_is_for": "175B autoregressive language model announced May 28, 2020.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-3-5-turbo": { "id": "openai/gpt/gpt-3-5-turbo", "name": "GPT-3.5 Turbo", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 16385, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 1.5 }, "summary_intro": "OpenAI's GPT-3.5 Turbo (`gpt-3.5-turbo`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. GPT-3.5 Turbo models can understand and generate natural language or code and have been optimized for chat using the Chat Completions API but work well for non-chat tasks as well.", "what_it_is_for": "Legacy GPT model for cheaper chat and non-chat tasks", "ratings": { "empirical_100": null, "lmsys_elo": 1230, "coding_elo": 1230, "swe_bench": 20.0, "humaneval": 74.0, "twelve_cs": 0.0, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-3-5-turbo-instruct": { "id": "openai/gpt/gpt-3-5-turbo-instruct", "name": "gpt-3.5-turbo-instruct", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4096, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.5, "completion_per_m_usd": 2.0 }, "summary_intro": "OpenAI's gpt-3.5-turbo-instruct (`gpt-3.5-turbo-instruct`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. Similar capabilities as GPT-3 era models.", "what_it_is_for": "An older model only compatible with the legacy Completions endpoint", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-4": { "id": "openai/gpt/gpt-4", "name": "GPT-4", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8192, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 30.0, "completion_per_m_usd": 60.0 }, "summary_intro": "OpenAI's GPT-4 (`gpt-4`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. GPT-4 is an older version of a high-intelligence GPT model, usable in Chat Completions.", "what_it_is_for": "An older high-intelligence GPT model", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs": 52.0, "aa_intelligence": 6.7, "aa_coding": 13.1, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-4-1": { "id": "openai/gpt/gpt-4-1", "name": "GPT-4.1", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1047576, "max_output_tokens": 32768, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 8.0 }, "summary_intro": "OpenAI's GPT-4.1 (`gpt-4.1`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. GPT-4.1 excels at instruction following and tool calling, with broad knowledge across domains.", "what_it_is_for": "Smartest non-reasoning model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-4-1-mini": { "id": "openai/gpt/gpt-4-1-mini", "name": "GPT-4.1 Mini", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1047576, "max_output_tokens": 32768, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.4, "completion_per_m_usd": 1.6 }, "summary_intro": "OpenAI's GPT-4.1 Mini (`gpt-4.1-mini`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. GPT-4.1 Mini excels at instruction following and tool calling.", "what_it_is_for": "Smaller, faster version of GPT-4.1", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-4-1-nano": { "id": "openai/gpt/gpt-4-1-nano", "name": "GPT-4.1 nano", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1047576, "max_output_tokens": 32768, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.4 }, "summary_intro": "OpenAI's GPT-4.1 nano (`gpt-4.1-nano`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. GPT-4.1 nano excels at instruction following and tool calling.", "what_it_is_for": "Fastest, most cost-efficient version of GPT-4.1", "ratings": { "empirical_100": 46, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-4-1106-vision-preview": { "id": "openai/gpt/gpt-4-1106-vision-preview", "name": "gpt-4-1106-vision-preview", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 30.0 }, "summary_intro": "OpenAI's gpt-4-1106-vision-preview (`gpt-4-1106-vision-preview`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id gpt-4-1106-vision-preview.", "what_it_is_for": "OpenAI API model id gpt-4-1106-vision-preview. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-4-32k": { "id": "openai/gpt/gpt-4-32k", "name": "gpt-4-32k", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 60.0, "completion_per_m_usd": 120.0 }, "summary_intro": "OpenAI's gpt-4-32k (`gpt-4-32k`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id gpt-4-32k.", "what_it_is_for": "OpenAI API model id gpt-4-32k. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-4-5-preview": { "id": "openai/gpt/gpt-4-5-preview", "name": "GPT-4.5 Preview", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 75.0, "completion_per_m_usd": 150.0 }, "summary_intro": "OpenAI's GPT-4.5 Preview (`gpt-4.5-preview`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. Deprecated - a research preview of GPT-4.5.", "what_it_is_for": "Deprecated large model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-4-turbo": { "id": "openai/gpt/gpt-4-turbo", "name": "GPT-4 Turbo", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 30.0 }, "summary_intro": "OpenAI's GPT-4 Turbo (`gpt-4-turbo`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. GPT-4 Turbo is the next generation of GPT-4, an older high-intelligence GPT model.", "what_it_is_for": "An older high-intelligence GPT model", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs": 52.0, "aa_intelligence": 7.0, "aa_coding": 21.5, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-4-turbo-preview": { "id": "openai/gpt/gpt-4-turbo-preview", "name": "GPT-4 Turbo Preview", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 30.0 }, "summary_intro": "OpenAI's GPT-4 Turbo Preview (`gpt-4-turbo-preview`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. This is a research preview of the GPT-4 Turbo model, an older high-intelligence GPT model.", "what_it_is_for": "An older fast GPT model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-4-vision-preview": { "id": "openai/gpt/gpt-4-vision-preview", "name": "gpt-4-vision-preview", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 30.0 }, "summary_intro": "OpenAI's gpt-4-vision-preview (`gpt-4-vision-preview`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id gpt-4-vision-preview.", "what_it_is_for": "OpenAI API model id gpt-4-vision-preview. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-4o": { "id": "openai/gpt/gpt-4o", "name": "GPT-4o", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-15", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.5, "completion_per_m_usd": 10.0 }, "summary_intro": "OpenAI's GPT-4o (`gpt-4o`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. GPT-4o (\u201co\u201d for \u201comni\u201d) is our versatile, high-intelligence flagship model.", "what_it_is_for": "Fast, intelligent, flexible GPT model", "ratings": { "empirical_100": null, "lmsys_elo": 1332, "coding_elo": 1330, "swe_bench": 38.8, "humaneval": 90.2, "twelve_cs": 99.5, "aa_intelligence": 68.0, "aa_coding": 78.0, "aa_agentic": 64.0, "design_arena_elo": 866, "math_500": 76.6, "gpqa_diamond": 53.6, "ifeval": 84.3, "bfcl_tool_calling": 88.2 } }, "openai/gpt/gpt-4o-audio": { "id": "openai/gpt/gpt-4o-audio", "name": "gpt-4o-audio", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's gpt-4o-audio (`gpt-4o-audio`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. OpenAI API model id gpt-4o-audio.", "what_it_is_for": "OpenAI API model id gpt-4o-audio. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-4o-mini": { "id": "openai/gpt/gpt-4o-mini", "name": "GPT-4o Mini", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": 0.6 }, "summary_intro": "OpenAI's GPT-4o Mini (`gpt-4o-mini`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. GPT-4o Mini (\u201co\u201d for \u201comni\u201d) is a fast, affordable small model for focused tasks.", "what_it_is_for": "Fast, affordable small model for focused tasks", "ratings": { "empirical_100": null, "lmsys_elo": 1275, "coding_elo": 1270, "swe_bench": 16.5, "humaneval": 87.2, "twelve_cs": 99.5, "aa_intelligence": 44.0, "aa_coding": 56.0, "aa_agentic": 48.0, "design_arena_elo": null, "math_500": 70.2, "gpqa_diamond": 40.2, "ifeval": 80.0, "bfcl_tool_calling": 84.0 } }, "openai/gpt/gpt-4o-mini-audio": { "id": "openai/gpt/gpt-4o-mini-audio", "name": "gpt-4o-mini-audio", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's gpt-4o-mini-audio (`gpt-4o-mini-audio`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. OpenAI API model id gpt-4o-mini-audio.", "what_it_is_for": "OpenAI API model id gpt-4o-mini-audio. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-4o-mini-search-preview": { "id": "openai/gpt/gpt-4o-mini-search-preview", "name": "GPT-4o Mini Search Preview", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-11", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": 0.6 }, "summary_intro": "OpenAI's GPT-4o Mini Search Preview (`gpt-4o-mini-search-preview`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. GPT-4o Mini Search Preview is a specialized model trained to understand and execute web search queries with the Chat Completions API.", "what_it_is_for": "Fast, affordable small model for web search", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-4o-search-preview": { "id": "openai/gpt/gpt-4o-search-preview", "name": "GPT-4o Search Preview", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-11", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.5, "completion_per_m_usd": 10.0 }, "summary_intro": "OpenAI's GPT-4o Search Preview (`gpt-4o-search-preview`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. GPT-4o Search Preview is a specialized model trained to understand and execute web search queries with the Chat Completions API.", "what_it_is_for": "GPT model for web search in Chat Completions", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5": { "id": "openai/gpt/gpt-5", "name": "GPT-5", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-07", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 10.0 }, "summary_intro": "OpenAI's GPT-5 (`gpt-5`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-5 is our previous model for coding, reasoning, and agentic tasks across domains.", "what_it_is_for": "Previous intelligent reasoning model for coding and agentic tasks with configurable reasoning effort", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs": 99.5, "aa_intelligence": 23.0, "aa_coding": 37.8, "aa_agentic": null, "design_arena_elo": 1053, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-1": { "id": "openai/gpt/gpt-5-1", "name": "GPT-5.1", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-13", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 10.0 }, "summary_intro": "OpenAI's GPT-5.1 (`gpt-5.1`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. GPT-5.1 is our flagship model for coding and agentic tasks with configurable reasoning and non-reasoning effort.", "what_it_is_for": "The best model for coding and agentic tasks with configurable reasoning effort", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-1-chat-latest": { "id": "openai/gpt/gpt-5-1-chat-latest", "name": "GPT-5.1 Chat", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-13", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 10.0 }, "summary_intro": "OpenAI's GPT-5.1 Chat (`gpt-5.1-chat-latest`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. GPT-5.1 Chat points to the GPT-5.1 snapshot currently used in ChatGPT.", "what_it_is_for": "GPT-5.1 model used in ChatGPT", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-2": { "id": "openai/gpt/gpt-5-2", "name": "GPT-5.2", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-11", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.75, "completion_per_m_usd": 14.0 }, "summary_intro": "OpenAI's GPT-5.2 (`gpt-5.2`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-5.2 is our previous flagship model for complex professional work.", "what_it_is_for": "Previous flagship model for professional work with configurable reasoning effort", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-2-chat-latest": { "id": "openai/gpt/gpt-5-2-chat-latest", "name": "GPT-5.2 Chat", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-11", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.75, "completion_per_m_usd": 14.0 }, "summary_intro": "OpenAI's GPT-5.2 Chat (`gpt-5.2-chat-latest`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. GPT-5.2 Chat points to the GPT-5.2 snapshot used in ChatGPT.", "what_it_is_for": "GPT-5.2 model used in ChatGPT", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-2-pro": { "id": "openai/gpt/gpt-5-2-pro", "name": "GPT-5.2 Pro", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 21.0, "completion_per_m_usd": 168.0 }, "summary_intro": "OpenAI's GPT-5.2 Pro (`gpt-5.2-pro`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-5.2 Pro is our previous pro model for complex professional work.", "what_it_is_for": "Previous pro model for professional work that produces smarter and more precise responses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-3-chat-latest": { "id": "openai/gpt/gpt-5-3-chat-latest", "name": "GPT-5.3 Chat", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-03", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.75, "completion_per_m_usd": 14.0 }, "summary_intro": "OpenAI's GPT-5.3 Chat (`gpt-5.3-chat-latest`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. GPT-5.3 Chat points to the GPT-5.3 Instant snapshot used in ChatGPT.", "what_it_is_for": "GPT-5.3 Instant model used in ChatGPT", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-4": { "id": "openai/gpt/gpt-5-4", "name": "GPT-5.4", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-05", "context_length": 1050000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.5, "completion_per_m_usd": 15.0 }, "summary_intro": "OpenAI's GPT-5.4 (`gpt-5.4`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-5.4 is a flagship model for complex professional work.", "what_it_is_for": "A more affordable model for coding and professional work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-4-cyber": { "id": "openai/gpt/gpt-5-4-cyber", "name": "gpt-5.4-cyber", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's gpt-5.4-cyber (`gpt-5.4-cyber`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id gpt-5.4-cyber.", "what_it_is_for": "OpenAI API model id gpt-5.4-cyber. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-4-mini": { "id": "openai/gpt/gpt-5-4-mini", "name": "GPT-5.4 Mini", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-17", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.75, "completion_per_m_usd": 4.5 }, "summary_intro": "OpenAI's GPT-5.4 Mini (`gpt-5.4-mini`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-5.4 Mini brings the strengths of GPT-5.4 to a faster, more efficient model designed for high-volume workloads.", "what_it_is_for": "Our strongest mini model yet for coding, computer use, and subagents", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-4-nano": { "id": "openai/gpt/gpt-5-4-nano", "name": "GPT-5.4 nano", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-17", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 1.25 }, "summary_intro": "OpenAI's GPT-5.4 nano (`gpt-5.4-nano`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. GPT-5.4 nano is designed for tasks where speed and cost matter most like classification, data extraction, ranking, and sub-agents.", "what_it_is_for": "Our cheapest GPT-5.4-class model for simple high-volume tasks", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-4-pro": { "id": "openai/gpt/gpt-5-4-pro", "name": "GPT-5.4 Pro", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-05", "context_length": 1050000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 30.0, "completion_per_m_usd": 180.0 }, "summary_intro": "OpenAI's GPT-5.4 Pro (`gpt-5.4-pro`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-5.4 Pro uses more compute to think harder and provide consistently better answers.", "what_it_is_for": "Version of GPT-5.4 that produces smarter and more precise responses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-5": { "id": "openai/gpt/gpt-5-5", "name": "GPT-5.5", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-24", "context_length": 1050000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 30.0 }, "summary_intro": "OpenAI's GPT-5.5 (`gpt-5.5`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-5.5 is a flagship model for the most complex professional work.", "what_it_is_for": "A new class of intelligence for coding and professional work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-5-cyber": { "id": "openai/gpt/gpt-5-5-cyber", "name": "GPT-5.5 Cyber", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 12.5, "completion_per_m_usd": 75.0 }, "summary_intro": "OpenAI's GPT-5.5 Cyber (`gpt-5.5-cyber`) is a gpt model. Status on 2026-10-15: active. Confidence: high. gpt-5.5-cyber is a Daybreak cybersecurity model on the current pricing page, at $12.50 input, $1.25 cached input, and $75 output per 1M tokens.", "what_it_is_for": "Daybreak cyber model id on the pricing page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-5-pro": { "id": "openai/gpt/gpt-5-5-pro", "name": "GPT-5.5 Pro", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-24", "context_length": 1050000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 30.0, "completion_per_m_usd": 180.0 }, "summary_intro": "OpenAI's GPT-5.5 Pro (`gpt-5.5-pro`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-5.5 Pro uses more compute to think harder and provide consistently better answers.", "what_it_is_for": "Version of GPT-5.5 that produces smarter and more precise responses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-6-cyber": { "id": "openai/gpt/gpt-5-6-cyber", "name": "GPT-5.6 Cyber", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 12.5, "completion_per_m_usd": 75.0 }, "summary_intro": "OpenAI's GPT-5.6 Cyber (`gpt-5.6-cyber`) is a gpt model. Status on 2026-10-15: active. Confidence: high. An alias for our most advanced purpose-trained cybersecurity models, for approved defenders conducting advanced, authorized vulnerability research, exploit validation, and security testing.", "what_it_is_for": "Our most advanced cybersecurity model for authorized vulnerability research and security testing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-6-luna": { "id": "openai/gpt/gpt-5-6-luna", "name": "GPT-5.6 Luna", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-09", "context_length": 1050000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 1.2 }, "summary_intro": "OpenAI's GPT-5.6 Luna (`gpt-5.6-luna`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-5.6 Luna is designed for cost-sensitive, high-volume workloads.", "what_it_is_for": "GPT-5.6 model optimized for cost-sensitive workloads", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-6-sol": { "id": "openai/gpt/gpt-5-6-sol", "name": "GPT-5.6 Sol", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-09", "context_length": 1050000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": 20.0 }, "summary_intro": "OpenAI's GPT-5.6 Sol (`gpt-5.6-sol`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-5.6 Sol is a flagship model in the GPT-5.6 family.", "what_it_is_for": "GPT-5.6 flagship model for complex professional work", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-6-terra": { "id": "openai/gpt/gpt-5-6-terra", "name": "GPT-5.6 Terra", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-09", "context_length": 1050000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 12.0 }, "summary_intro": "OpenAI's GPT-5.6 Terra (`gpt-5.6-terra`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-5.6 Terra is designed for workloads that balance intelligence and cost.", "what_it_is_for": "GPT-5.6 model that balances intelligence and cost", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-chat-latest": { "id": "openai/gpt/gpt-5-chat-latest", "name": "GPT-5 Chat", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": 16384, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 10.0 }, "summary_intro": "OpenAI's GPT-5 Chat (`gpt-5-chat-latest`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. GPT-5 Chat points to the GPT-5 snapshot previously used in ChatGPT.", "what_it_is_for": "GPT-5 model used in ChatGPT", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-mini": { "id": "openai/gpt/gpt-5-mini", "name": "GPT-5 Mini", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-07", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.25, "completion_per_m_usd": 2.0 }, "summary_intro": "OpenAI's GPT-5 Mini (`gpt-5-mini`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-5 Mini is a faster, more cost-efficient version of GPT-5.", "what_it_is_for": "Strong intelligence for cost sensitive, low latency, high volume workloads", "ratings": { "empirical_100": null, "lmsys_elo": 1230, "coding_elo": 1230, "swe_bench": 20.0, "humaneval": 74.0, "twelve_cs": 99.5, "aa_intelligence": 20.6, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1034, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-nano": { "id": "openai/gpt/gpt-5-nano", "name": "GPT-5 nano", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-07", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": 0.4 }, "summary_intro": "OpenAI's GPT-5 nano (`gpt-5-nano`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-5 Nano is our fastest, cheapest version of GPT-5.", "what_it_is_for": "Fastest, most cost-efficient version of GPT-5", "ratings": { "empirical_100": 44, "lmsys_elo": 1205, "coding_elo": 1205, "swe_bench": 16.0, "humaneval": 70.8, "twelve_cs": 99.5, "aa_intelligence": 13.0, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 963, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-pro": { "id": "openai/gpt/gpt-5-pro", "name": "GPT-5 Pro", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-06", "context_length": 400000, "max_output_tokens": 272000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 15.0, "completion_per_m_usd": 120.0 }, "summary_intro": "OpenAI's GPT-5 Pro (`gpt-5-pro`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-5 Pro uses more compute to think harder and provide consistently better answers.", "what_it_is_for": "Version of GPT-5 that produces smarter and more precise responses", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs": 52.0, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-5-search-api": { "id": "openai/gpt/gpt-5-search-api", "name": "GPT-5 Search API", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 10.0 }, "summary_intro": "OpenAI's GPT-5 Search API (`gpt-5-search-api`) is a gpt model. Status on 2026-10-15: active. Confidence: high. gpt-5-search-api is the search model id on the OpenAI pricing page, at $1.25 input, $0.125 cached input, and $10 output per 1M tokens.", "what_it_is_for": "Search model id published on the pricing page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-6-1-sol": { "id": "openai/gpt/gpt-6-1-sol", "name": "GPT-6.1 Sol", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-29", "context_length": 1050000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 10.0 }, "summary_intro": "OpenAI's GPT-6.1 Sol (`gpt-6.1-sol`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-6.1 Sol delivers near-Astra performance at a lower cost for complex coding, computer use, and professional work.", "what_it_is_for": "Near-Astra performance for complex work at a lower cost.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-6-astra": { "id": "openai/gpt/gpt-6-astra", "name": "GPT-6 Astra", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-03", "context_length": 1050000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 50.0 }, "summary_intro": "OpenAI's GPT-6 Astra (`gpt-6-astra`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-6 Astra is our most capable model for the most demanding work.", "what_it_is_for": "Our most capable model for the most demanding work.", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs": 99.5, "aa_intelligence": 52.7, "aa_coding": 76.9, "aa_agentic": 51.0, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-6-luna": { "id": "openai/gpt/gpt-6-luna", "name": "GPT-6 Luna", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-22", "context_length": 1050000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.5 }, "summary_intro": "OpenAI's GPT-6 Luna (`gpt-6-luna`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-6 Luna is our most efficient model for focused, high-volume tasks.", "what_it_is_for": "Our most efficient model for focused, high-volume tasks.", "ratings": { "empirical_100": null, "lmsys_elo": 1207, "coding_elo": 1207, "swe_bench": 16.2, "humaneval": 71.0, "twelve_cs": 89.3, "aa_intelligence": 38.1, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-6-sol": { "id": "openai/gpt/gpt-6-sol", "name": "GPT-6 Sol", "maker": "openai", "family": "openai/gpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-22", "context_length": 1050000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 10.0 }, "summary_intro": "OpenAI's GPT-6 Sol (`gpt-6-sol`) is a gpt model. Status on 2026-10-15: active. Confidence: high. GPT-6 Sol is built for complex coding and agentic workflows.", "what_it_is_for": "Built to power complex coding and agentic workflows.", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs": 99.5, "aa_intelligence": 47.6, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-daybreak-blue-latest": { "id": "openai/gpt/gpt-daybreak-blue-latest", "name": "Daybreak Blue", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1050000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's Daybreak Blue (`gpt-daybreak-blue-latest`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. This alias is deprecated, please use the latest cyber model available to you.", "what_it_is_for": "An alias for flagship general-purpose models with safeguards for defensive cybersecurity work.", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs": 99.5, "aa_intelligence": 47.0, "aa_coding": 77.4, "aa_agentic": 50.2, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/gpt-daybreak-red-latest": { "id": "openai/gpt/gpt-daybreak-red-latest", "name": "Daybreak Red", "maker": "openai", "family": "openai/gpt", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 400000, "max_output_tokens": 128000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's Daybreak Red (`gpt-daybreak-red-latest`) is a gpt model. Status on 2026-10-15: deprecated. Confidence: high. This alias is deprecated, please use the latest cyber model available to you.", "what_it_is_for": "An alias for advanced cybersecurity models for authorized vulnerability research and security testing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/instructgpt": { "id": "openai/gpt/instructgpt", "name": "InstructGPT", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2022-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT-3 fine-tuned with human demonstrations and rankings", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's InstructGPT (`instructgpt`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. InstructGPT is OpenAI's human-feedback fine-tune of GPT-3.", "what_it_is_for": "Instruction-following models trained from GPT-3 with human feedback.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-ada-001": { "id": "openai/gpt/text-ada-001", "name": "text-ada-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 0.4, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-ada-001 (`text-ada-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-ada-001.", "what_it_is_for": "OpenAI API model id text-ada-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-babbage-001": { "id": "openai/gpt/text-babbage-001", "name": "text-babbage-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-babbage-001 (`text-babbage-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-babbage-001.", "what_it_is_for": "OpenAI API model id text-babbage-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-curie-001": { "id": "openai/gpt/text-curie-001", "name": "text-curie-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-curie-001 (`text-curie-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-curie-001.", "what_it_is_for": "OpenAI API model id text-curie-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-davinci-001": { "id": "openai/gpt/text-davinci-001", "name": "text-davinci-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 20.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-davinci-001 (`text-davinci-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-davinci-001.", "what_it_is_for": "OpenAI API model id text-davinci-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-davinci-002": { "id": "openai/gpt/text-davinci-002", "name": "text-davinci-002", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 20.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-davinci-002 (`text-davinci-002`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-davinci-002.", "what_it_is_for": "OpenAI API model id text-davinci-002. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-davinci-003": { "id": "openai/gpt/text-davinci-003", "name": "text-davinci-003", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 20.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-davinci-003 (`text-davinci-003`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-davinci-003.", "what_it_is_for": "OpenAI API model id text-davinci-003. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-davinci-edit-001": { "id": "openai/gpt/text-davinci-edit-001", "name": "text-davinci-edit-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-davinci-edit-001 (`text-davinci-edit-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-davinci-edit-001.", "what_it_is_for": "OpenAI API model id text-davinci-edit-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-search-ada-doc-001": { "id": "openai/gpt/text-search-ada-doc-001", "name": "text-search-ada-doc-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-search-ada-doc-001 (`text-search-ada-doc-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-search-ada-doc-001.", "what_it_is_for": "OpenAI API model id text-search-ada-doc-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-search-ada-query-001": { "id": "openai/gpt/text-search-ada-query-001", "name": "text-search-ada-query-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-search-ada-query-001 (`text-search-ada-query-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-search-ada-query-001.", "what_it_is_for": "OpenAI API model id text-search-ada-query-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-search-babbage-doc-001": { "id": "openai/gpt/text-search-babbage-doc-001", "name": "text-search-babbage-doc-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-search-babbage-doc-001 (`text-search-babbage-doc-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-search-babbage-doc-001.", "what_it_is_for": "OpenAI API model id text-search-babbage-doc-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-search-babbage-query-001": { "id": "openai/gpt/text-search-babbage-query-001", "name": "text-search-babbage-query-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-search-babbage-query-001 (`text-search-babbage-query-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-search-babbage-query-001.", "what_it_is_for": "OpenAI API model id text-search-babbage-query-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-search-curie-doc-001": { "id": "openai/gpt/text-search-curie-doc-001", "name": "text-search-curie-doc-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 20.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-search-curie-doc-001 (`text-search-curie-doc-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-search-curie-doc-001.", "what_it_is_for": "OpenAI API model id text-search-curie-doc-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-search-curie-query-001": { "id": "openai/gpt/text-search-curie-query-001", "name": "text-search-curie-query-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 20.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-search-curie-query-001 (`text-search-curie-query-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-search-curie-query-001.", "what_it_is_for": "OpenAI API model id text-search-curie-query-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-search-davinci-doc-001": { "id": "openai/gpt/text-search-davinci-doc-001", "name": "text-search-davinci-doc-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 200.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-search-davinci-doc-001 (`text-search-davinci-doc-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-search-davinci-doc-001.", "what_it_is_for": "OpenAI API model id text-search-davinci-doc-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-search-davinci-query-001": { "id": "openai/gpt/text-search-davinci-query-001", "name": "text-search-davinci-query-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 200.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-search-davinci-query-001 (`text-search-davinci-query-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-search-davinci-query-001.", "what_it_is_for": "OpenAI API model id text-search-davinci-query-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-similarity-ada-001": { "id": "openai/gpt/text-similarity-ada-001", "name": "text-similarity-ada-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-similarity-ada-001 (`text-similarity-ada-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-similarity-ada-001.", "what_it_is_for": "OpenAI API model id text-similarity-ada-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-similarity-babbage-001": { "id": "openai/gpt/text-similarity-babbage-001", "name": "text-similarity-babbage-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-similarity-babbage-001 (`text-similarity-babbage-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-similarity-babbage-001.", "what_it_is_for": "OpenAI API model id text-similarity-babbage-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-similarity-curie-001": { "id": "openai/gpt/text-similarity-curie-001", "name": "text-similarity-curie-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 20.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-similarity-curie-001 (`text-similarity-curie-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-similarity-curie-001.", "what_it_is_for": "OpenAI API model id text-similarity-curie-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt/text-similarity-davinci-001": { "id": "openai/gpt/text-similarity-davinci-001", "name": "text-similarity-davinci-001", "maker": "openai", "family": "openai/gpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 200.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-similarity-davinci-001 (`text-similarity-davinci-001`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-similarity-davinci-001.", "what_it_is_for": "OpenAI API model id text-similarity-davinci-001. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt-image/chatgpt-image-latest": { "id": "openai/gpt-image/chatgpt-image-latest", "name": "chatgpt-image-latest", "maker": "openai", "family": "openai/gpt-image", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 32.0 }, "summary_intro": "OpenAI's chatgpt-image-latest (`chatgpt-image-latest`) is a gpt-image model. Status on 2026-10-15: deprecated. Confidence: high. This points to the Image snapshot previously used in ChatGPT.", "what_it_is_for": "Previous image model used in ChatGPT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt-image/gpt-image-1": { "id": "openai/gpt-image/gpt-image-1", "name": "GPT-Image-1", "maker": "openai", "family": "openai/gpt-image", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 40.0 }, "summary_intro": "OpenAI's GPT-Image-1 (`gpt-image-1`) is a gpt-image model. Status on 2026-10-15: deprecated. Confidence: high. GPT Image 1 is a natively multimodal language model that accepts both text and image inputs, and produces image outputs.", "what_it_is_for": "Our previous image generation model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt-image/gpt-image-1-5": { "id": "openai/gpt-image/gpt-image-1-5", "name": "GPT-Image-1.5", "maker": "openai", "family": "openai/gpt-image", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 32.0 }, "summary_intro": "OpenAI's GPT-Image-1.5 (`gpt-image-1.5`) is a gpt-image model. Status on 2026-10-15: deprecated. Confidence: high. GPT Image 1.5 is our previous image generation model, with better instruction following and adherence to prompts.", "what_it_is_for": "Our previous image generation model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt-image/gpt-image-1-mini": { "id": "openai/gpt-image/gpt-image-1-mini", "name": "GPT-Image-1 Mini", "maker": "openai", "family": "openai/gpt-image", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 8.0 }, "summary_intro": "OpenAI's GPT-Image-1 Mini (`gpt-image-1-mini`) is a gpt-image model. Status on 2026-10-15: deprecated. Confidence: high. A cost-efficient version of GPT Image 1.", "what_it_is_for": "A cost-efficient version of GPT Image 1", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt-image/gpt-image-2": { "id": "openai/gpt-image/gpt-image-2", "name": "GPT-Image-2", "maker": "openai", "family": "openai/gpt-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 30.0 }, "summary_intro": "OpenAI's GPT-Image-2 (`gpt-image-2`) is a gpt-image model. Status on 2026-10-15: active. Confidence: high. GPT Image 2 is our state-of-the-art image generation model for fast, high-quality image generation and editing.", "what_it_is_for": "State-of-the-art image generation model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt-image/gpt-image-2-5-flare": { "id": "openai/gpt-image/gpt-image-2-5-flare", "name": "GPT-Image-2.5 Flare", "maker": "openai", "family": "openai/gpt-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 30.0 }, "summary_intro": "OpenAI's GPT-Image-2.5 Flare (`gpt-image-2.5-flare`) is a gpt-image model. Status on 2026-10-15: active. Confidence: high. GPT Image 2.5 Flare is our fastest model for high-quality, everyday image generation.", "what_it_is_for": "Fast, high-quality everyday image generation", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt-image/gpt-image-2-5-sunburst": { "id": "openai/gpt-image/gpt-image-2-5-sunburst", "name": "GPT-Image-2.5 Sunburst", "maker": "openai", "family": "openai/gpt-image", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 30.0 }, "summary_intro": "OpenAI's GPT-Image-2.5 Sunburst (`gpt-image-2.5-sunburst`) is a gpt-image model. Status on 2026-10-15: active. Confidence: high. GPT Image 2.5 Sunburst generates and edits images from text and image inputs.", "what_it_is_for": "Our most capable model for image generation and editing", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt-oss/gpt-oss-120b": { "id": "openai/gpt-oss/gpt-oss-120b", "name": "gpt-oss-120b", "maker": "openai", "family": "openai/gpt-oss", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": 131072, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's gpt-oss-120b (`gpt-oss-120b`) is a gpt-oss model. Status on 2026-10-15: active. Confidence: high. gpt-oss-120bis our most powerful open-weight model, which fits into a single H100 GPU (117B parameters with 5.1B active parameters).", "what_it_is_for": "Most powerful open-weight model, fits into an H100 GPU", "ratings": { "empirical_100": 52, "lmsys_elo": 1209, "coding_elo": 1209, "swe_bench": 16.5, "humaneval": 71.2, "twelve_cs": 99.5, "aa_intelligence": 11.6, "aa_coding": 30.4, "aa_agentic": 3.7, "design_arena_elo": 898, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/gpt-oss/gpt-oss-20b": { "id": "openai/gpt-oss/gpt-oss-20b", "name": "gpt-oss-20b", "maker": "openai", "family": "openai/gpt-oss", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": 131072, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's gpt-oss-20b (`gpt-oss-20b`) is a gpt-oss model. Status on 2026-10-15: active. Confidence: high. gpt-oss-20b is our medium-sized open-weight model for low latency, local, or specialized use-cases (21B parameters with 3.6B active parameters).", "what_it_is_for": "Medium-sized open-weight model for low latency", "ratings": { "empirical_100": 52, "lmsys_elo": 1201, "coding_elo": 1201, "swe_bench": 15.2, "humaneval": 70.2, "twelve_cs": 89.3, "aa_intelligence": 10.0, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 933, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/moderation/omni-moderation-latest": { "id": "openai/moderation/omni-moderation-latest", "name": "omni-moderation", "maker": "openai", "family": "openai/moderation", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's omni-moderation (`omni-moderation-latest`) is a moderation model. Status on 2026-10-15: active. Confidence: high. Moderation models are free models designed to detect harmful content.", "what_it_is_for": "Identify potentially harmful content in text and images", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/moderation/text-moderation-007": { "id": "openai/moderation/text-moderation-007", "name": "text-moderation-007", "maker": "openai", "family": "openai/moderation", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-moderation-007 (`text-moderation-007`) is a moderation model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id text-moderation-007.", "what_it_is_for": "OpenAI API model id text-moderation-007. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/moderation/text-moderation-latest": { "id": "openai/moderation/text-moderation-latest", "name": "text-moderation", "maker": "openai", "family": "openai/moderation", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": 32768, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-moderation (`text-moderation-latest`) is a moderation model. Status on 2026-10-15: retired. Confidence: high. Moderation models are free models designed to detect harmful content.", "what_it_is_for": "Previous generation text-only moderation model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/moderation/text-moderation-stable": { "id": "openai/moderation/text-moderation-stable", "name": "text-moderation-stable", "maker": "openai", "family": "openai/moderation", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": 32768, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's text-moderation-stable (`text-moderation-stable`) is a moderation model. Status on 2026-10-15: retired. Confidence: high. Moderation models are free models designed to detect harmful content.", "what_it_is_for": "Previous generation text-only moderation model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/o/o1": { "id": "openai/o/o1", "name": "o1", "maker": "openai", "family": "openai/o", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 200000, "max_output_tokens": 100000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 15.0, "completion_per_m_usd": 60.0 }, "summary_intro": "OpenAI's o1 (`o1`) is a o model. Status on 2026-10-15: deprecated. Confidence: high. The o1 series of models are trained with reinforcement learning to perform complex reasoning.", "what_it_is_for": "Previous full o-series reasoning model", "ratings": { "empirical_100": null, "lmsys_elo": 1365, "coding_elo": 1375, "swe_bench": 48.9, "humaneval": 92.4, "twelve_cs": 99.5, "aa_intelligence": 92.0, "aa_coding": 94.0, "aa_agentic": 85.0, "design_arena_elo": null, "math_500": 96.4, "gpqa_diamond": 77.3, "ifeval": 89.0, "bfcl_tool_calling": 88.0 } }, "openai/o/o1-mini": { "id": "openai/o/o1-mini", "name": "o1-mini", "maker": "openai", "family": "openai/o", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-12", "context_length": 128000, "max_output_tokens": 65536, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 12.0 }, "summary_intro": "OpenAI's o1-mini (`o1-mini`) is a o model. Status on 2026-10-15: retired. Confidence: high. The o1 reasoning model is designed to solve hard problems across domains.", "what_it_is_for": "A small model alternative to o1", "ratings": { "empirical_100": null, "lmsys_elo": 1315, "coding_elo": 1320, "swe_bench": 41.0, "humaneval": 90.0, "twelve_cs": null, "aa_intelligence": 76.0, "aa_coding": 82.0, "aa_agentic": 68.0, "design_arena_elo": 1320, "math_500": 90.0, "gpqa_diamond": 60.0, "ifeval": 84.0, "bfcl_tool_calling": 85.0 } }, "openai/o/o1-preview": { "id": "openai/o/o1-preview", "name": "o1 Preview", "maker": "openai", "family": "openai/o", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-12", "context_length": 128000, "max_output_tokens": 32768, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 15.0, "completion_per_m_usd": 60.0 }, "summary_intro": "OpenAI's o1 Preview (`o1-preview`) is a o model. Status on 2026-10-15: retired. Confidence: high. Research preview of the o1 series of models, trained with reinforcement learning to perform complex reasoning.", "what_it_is_for": "Preview of our first o-series reasoning model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/o/o1-pro": { "id": "openai/o/o1-pro", "name": "o1-pro", "maker": "openai", "family": "openai/o", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-19", "context_length": 200000, "max_output_tokens": 100000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 150.0, "completion_per_m_usd": 600.0 }, "summary_intro": "OpenAI's o1-pro (`o1-pro`) is a o model. Status on 2026-10-15: deprecated. Confidence: high. The o1 series of models are trained with reinforcement learning to think before they answer and perform complex reasoning.", "what_it_is_for": "Version of o1 with more compute for better responses", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs": 99.5, "aa_intelligence": 12.4, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/o/o3": { "id": "openai/o/o3", "name": "o3", "maker": "openai", "family": "openai/o", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 200000, "max_output_tokens": 100000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 4.0 }, "summary_intro": "OpenAI's o3 (`o3`) is a o model. Status on 2026-10-15: active. Confidence: high. o3 is a well-rounded and powerful model across domains.", "what_it_is_for": "Reasoning model for complex tasks, succeeded by GPT-5", "ratings": { "empirical_100": null, "lmsys_elo": 1320, "coding_elo": 1320, "swe_bench": 35.0, "humaneval": 86.0, "twelve_cs": 99.5, "aa_intelligence": 20.2, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1018, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/o/o3-deep-research": { "id": "openai/o/o3-deep-research", "name": "o3-deep-research", "maker": "openai", "family": "openai/o", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-24", "context_length": 200000, "max_output_tokens": 100000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 40.0 }, "summary_intro": "OpenAI's o3-deep-research (`o3-deep-research`) is a o model. Status on 2026-10-15: retired. Confidence: high. o3-deep-research is our most advanced model for deep research, designed to tackle complex, multi-step research tasks.", "what_it_is_for": "Our most powerful deep research model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/o/o3-mini": { "id": "openai/o/o3-mini", "name": "o3-mini", "maker": "openai", "family": "openai/o", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 200000, "max_output_tokens": 100000, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.1, "completion_per_m_usd": 4.4 }, "summary_intro": "OpenAI's o3-mini (`o3-mini`) is a o model. Status on 2026-10-15: deprecated. Confidence: high. o3-mini is our newest small reasoning model, providing high intelligence at the same cost and latency targets of o1-mini.", "what_it_is_for": "A small model alternative to o3", "ratings": { "empirical_100": null, "lmsys_elo": 1266, "coding_elo": 1266, "swe_bench": 26.0, "humaneval": 78.8, "twelve_cs": 99.5, "aa_intelligence": 12.5, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/o/o3-pro": { "id": "openai/o/o3-pro", "name": "o3-pro", "maker": "openai", "family": "openai/o", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-10", "context_length": 200000, "max_output_tokens": 100000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 20.0, "completion_per_m_usd": 80.0 }, "summary_intro": "OpenAI's o3-pro (`o3-pro`) is a o model. Status on 2026-10-15: active. Confidence: high. The o-series of models are trained with reinforcement learning to think before they answer and perform complex reasoning.", "what_it_is_for": "Version of o3 with more compute for better responses", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs": 99.5, "aa_intelligence": 21.9, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/o/o4-mini": { "id": "openai/o/o4-mini", "name": "o4-mini", "maker": "openai", "family": "openai/o", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 200000, "max_output_tokens": 100000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.1, "completion_per_m_usd": 4.4 }, "summary_intro": "OpenAI's o4-mini (`o4-mini`) is a o model. Status on 2026-10-15: deprecated. Confidence: high. o4-mini is our latest small o-series model.", "what_it_is_for": "Fast, cost-efficient reasoning model, succeeded by GPT-5 Mini", "ratings": { "empirical_100": null, "lmsys_elo": 1266, "coding_elo": 1266, "swe_bench": 26.0, "humaneval": 78.8, "twelve_cs": 99.5, "aa_intelligence": 16.7, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 851, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/o/o4-mini-deep-research": { "id": "openai/o/o4-mini-deep-research", "name": "o4-mini-deep-research", "maker": "openai", "family": "openai/o", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-24", "context_length": 200000, "max_output_tokens": 100000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 8.0 }, "summary_intro": "OpenAI's o4-mini-deep-research (`o4-mini-deep-research`) is a o model. Status on 2026-10-15: retired. Confidence: high. o4-mini-deep-research is our faster, more affordable deep research model\u2014ideal for tackling complex, multi-step research tasks.", "what_it_is_for": "Faster, more affordable deep research model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/realtime/gpt-4o-mini-realtime": { "id": "openai/realtime/gpt-4o-mini-realtime", "name": "gpt-4o-mini-realtime", "maker": "openai", "family": "openai/realtime", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's gpt-4o-mini-realtime (`gpt-4o-mini-realtime`) is a realtime model. Status on 2026-10-15: deprecated. Confidence: high. OpenAI API model id gpt-4o-mini-realtime.", "what_it_is_for": "OpenAI API model id gpt-4o-mini-realtime. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/realtime/gpt-4o-mini-realtime-preview": { "id": "openai/realtime/gpt-4o-mini-realtime-preview", "name": "GPT-4o Mini Realtime", "maker": "openai", "family": "openai/realtime", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 16000, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 20.0 }, "summary_intro": "OpenAI's GPT-4o Mini Realtime (`gpt-4o-mini-realtime-preview`) is a realtime model. Status on 2026-10-15: retired. Confidence: high. This is a preview release of the GPT-4o-Mini Realtime model, capable of responding to audio and text inputs in realtime over WebRTC or a WebSocket interface.", "what_it_is_for": "Smaller realtime model for text and audio inputs and outputs", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/realtime/gpt-4o-realtime": { "id": "openai/realtime/gpt-4o-realtime", "name": "gpt-4o-realtime", "maker": "openai", "family": "openai/realtime", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's gpt-4o-realtime (`gpt-4o-realtime`) is a realtime model. Status on 2026-10-15: deprecated. Confidence: high. OpenAI API model id gpt-4o-realtime.", "what_it_is_for": "OpenAI API model id gpt-4o-realtime. No model-page dossier was available on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/realtime/gpt-4o-realtime-preview": { "id": "openai/realtime/gpt-4o-realtime-preview", "name": "GPT-4o Realtime", "maker": "openai", "family": "openai/realtime", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32000, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 40.0, "completion_per_m_usd": 80.0 }, "summary_intro": "OpenAI's GPT-4o Realtime (`gpt-4o-realtime-preview`) is a realtime model. Status on 2026-10-15: retired. Confidence: high. This is a preview release of the GPT-4o Realtime model, capable of responding to audio and text inputs in realtime over WebRTC or a WebSocket interface.", "what_it_is_for": "Model capable of realtime text and audio inputs and outputs", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/realtime/gpt-live-1": { "id": "openai/realtime/gpt-live-1", "name": "GPT-Live 1", "maker": "openai", "family": "openai/realtime", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's GPT-Live 1 (`gpt-live-1`) is a realtime model. Status on 2026-10-15: active. Confidence: high. GPT-Live 1 is a full-duplex voice model for real-time conversations.", "what_it_is_for": "Our premier model for natural, expressive voice conversations with smooth interruption handling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/realtime/gpt-live-transcribe": { "id": "openai/realtime/gpt-live-transcribe", "name": "GPT-Live-Transcribe", "maker": "openai", "family": "openai/realtime", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's GPT-Live-Transcribe (`gpt-live-transcribe`) is a realtime model. Status on 2026-10-15: active. Confidence: high. GPT Live Transcribe is a streaming speech-to-text model for applications that need low-latency transcript deltas from live audio.", "what_it_is_for": "Low-latency speech-to-text model for realtime transcription", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/realtime/gpt-realtime": { "id": "openai/realtime/gpt-realtime", "name": "GPT-Realtime", "maker": "openai", "family": "openai/realtime", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32000, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio", "image" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 32.0, "completion_per_m_usd": 64.0 }, "summary_intro": "OpenAI's GPT-Realtime (`gpt-realtime`) is a realtime model. Status on 2026-10-15: deprecated. Confidence: high. This is our first general-availability realtime model, capable of responding to audio and text inputs in realtime over WebRTC, WebSocket, or SIP connections.", "what_it_is_for": "Model capable of realtime text and audio inputs and outputs", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/realtime/gpt-realtime-1-5": { "id": "openai/realtime/gpt-realtime-1-5", "name": "GPT-Realtime-1.5", "maker": "openai", "family": "openai/realtime", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-23", "context_length": 32000, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio", "image" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 32.0, "completion_per_m_usd": 64.0 }, "summary_intro": "OpenAI's GPT-Realtime-1.5 (`gpt-realtime-1.5`) is a realtime model. Status on 2026-10-15: active. Confidence: high. GPT-Realtime-1.5 is our flagship audio model for voice agents and customer support.", "what_it_is_for": "The best voice model for audio in, audio out", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/realtime/gpt-realtime-2": { "id": "openai/realtime/gpt-realtime-2", "name": "GPT-Realtime-2", "maker": "openai", "family": "openai/realtime", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-07", "context_length": 128000, "max_output_tokens": 32000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio", "image" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 32.0, "completion_per_m_usd": 64.0 }, "summary_intro": "OpenAI's GPT-Realtime-2 (`gpt-realtime-2`) is a realtime model. Status on 2026-10-15: active. Confidence: high. GPT-Realtime-2 is our most capable realtime voice model.", "what_it_is_for": "Reasoning model with tool use", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/realtime/gpt-realtime-2-1": { "id": "openai/realtime/gpt-realtime-2-1", "name": "GPT-Realtime-2.1", "maker": "openai", "family": "openai/realtime", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-06", "context_length": 128000, "max_output_tokens": 32000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio", "image" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 32.0, "completion_per_m_usd": 64.0 }, "summary_intro": "OpenAI's GPT-Realtime-2.1 (`gpt-realtime-2.1`) is a realtime model. Status on 2026-10-15: active. Confidence: high. GPT-Realtime-2.1 updates GPT-Realtime-2 with improved alphanumeric recognition, silence and noise handling, and interruption behavior.", "what_it_is_for": "Reasoning model with tool use", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/realtime/gpt-realtime-2-1-mini": { "id": "openai/realtime/gpt-realtime-2-1-mini", "name": "GPT-Realtime-2.1 Mini", "maker": "openai", "family": "openai/realtime", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-06", "context_length": 128000, "max_output_tokens": 32000, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio", "image" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 20.0 }, "summary_intro": "OpenAI's GPT-Realtime-2.1 Mini (`gpt-realtime-2.1-mini`) is a realtime model. Status on 2026-10-15: active. Confidence: high. GPT-Realtime-2.1 Mini is a distilled reasoning model for faster, lower-cost realtime voice interactions.", "what_it_is_for": "Reasoning model with tool use", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/realtime/gpt-realtime-mini": { "id": "openai/realtime/gpt-realtime-mini", "name": "GPT-Realtime Mini", "maker": "openai", "family": "openai/realtime", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-06", "context_length": 32000, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 20.0 }, "summary_intro": "OpenAI's GPT-Realtime Mini (`gpt-realtime-mini`) is a realtime model. Status on 2026-10-15: deprecated. Confidence: high. GPT-Realtime Mini is capable of responding to audio and text inputs in realtime over WebRTC, WebSocket, or SIP connections.", "what_it_is_for": "A cost-efficient version of GPT-Realtime", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/realtime/gpt-realtime-translate": { "id": "openai/realtime/gpt-realtime-translate", "name": "GPT-Realtime-Translate", "maker": "openai", "family": "openai/realtime", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-07", "context_length": 16000, "max_output_tokens": 2000, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's GPT-Realtime-Translate (`gpt-realtime-translate`) is a realtime model. Status on 2026-10-15: active. Confidence: high. GPT-Realtime-Translate is a streaming speech-to-speech translation model for live multilingual audio experiences.", "what_it_is_for": "Streaming speech-to-speech translation model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/realtime/gpt-realtime-whisper": { "id": "openai/realtime/gpt-realtime-whisper", "name": "GPT-Realtime-Whisper", "maker": "openai", "family": "openai/realtime", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-07", "context_length": 16000, "max_output_tokens": 2000, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's GPT-Realtime-Whisper (`gpt-realtime-whisper`) is a realtime model. Status on 2026-10-15: active. Confidence: high. GPT-Realtime-Whisper is a streaming speech-to-text model for applications that need low-latency transcript deltas from live audio.", "what_it_is_for": "Streaming speech-to-text model for realtime transcription", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/rosalind/gpt-rosalind-discovery": { "id": "openai/rosalind/gpt-rosalind-discovery", "name": "GPT-Rosalind Discovery", "maker": "openai", "family": "openai/rosalind", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 25.0 }, "summary_intro": "OpenAI's GPT-Rosalind Discovery (`gpt-rosalind-discovery`) is a rosalind model. Status on 2026-10-15: active. Confidence: high. gpt-rosalind-discovery is listed next to gpt-rosalind-research on the OpenAI pricing page at the same token rates.", "what_it_is_for": "Second Rosalind model id on the pricing page, same published token rates.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/rosalind/gpt-rosalind-research": { "id": "openai/rosalind/gpt-rosalind-research", "name": "GPT-Rosalind", "maker": "openai", "family": "openai/rosalind", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 25.0 }, "summary_intro": "OpenAI's GPT-Rosalind (`gpt-rosalind-research`) is a rosalind model. Status on 2026-10-15: active. Confidence: high. GPT-Rosalind is OpenAI's life-sciences reasoning model for approved organizations.", "what_it_is_for": "Life-sciences reasoning for approved organizations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/sora/sora": { "id": "openai/sora/sora", "name": "Sora", "maker": "openai", "family": "openai/sora", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-02-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion transformer on spacetime patches of video and image latents", "modalities": { "input": [ "text", "image" ], "output": [ "video", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's Sora (`sora`) is a sora model. Status on 2026-10-15: retired. Confidence: high. Sora is OpenAI's text-conditional video model announced February 15, 2024.", "what_it_is_for": "Text-to-video model announced February 15, 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/sora/sora-2": { "id": "openai/sora/sora-2", "name": "Sora 2", "maker": "openai", "family": "openai/sora", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's Sora 2 (`sora-2`) is a sora model. Status on 2026-10-15: retired. Confidence: high. The Sora 2 models and Videos API were shut down on September 24, 2026 and are no longer available.", "what_it_is_for": "Flagship video generation with synced audio", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/sora/sora-2-pro": { "id": "openai/sora/sora-2-pro", "name": "Sora 2 Pro", "maker": "openai", "family": "openai/sora", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's Sora 2 Pro (`sora-2-pro`) is a sora model. Status on 2026-10-15: retired. Confidence: high. The Sora 2 models and Videos API were shut down on September 24, 2026 and are no longer available.", "what_it_is_for": "Most advanced synced-audio video generation", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/tts/gpt-4o-mini-tts": { "id": "openai/tts/gpt-4o-mini-tts", "name": "GPT-4o Mini TTS", "maker": "openai", "family": "openai/tts", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 12.0 }, "summary_intro": "OpenAI's GPT-4o Mini TTS (`gpt-4o-mini-tts`) is a tts model. Status on 2026-10-15: active. Confidence: high. GPT-4o Mini TTS is a text-to-speech model built on GPT-4o Mini, a fast and powerful language model.", "what_it_is_for": "Text-to-speech model powered by GPT-4o Mini", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/tts/tts-1": { "id": "openai/tts/tts-1", "name": "TTS-1", "maker": "openai", "family": "openai/tts", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 15.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's TTS-1 (`tts-1`) is a tts model. Status on 2026-10-15: deprecated. Confidence: high. TTS is a model that converts text to natural sounding spoken text.", "what_it_is_for": "Text-to-speech model optimized for speed", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/tts/tts-1-hd": { "id": "openai/tts/tts-1-hd", "name": "TTS-1 HD", "maker": "openai", "family": "openai/tts", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 30.0, "completion_per_m_usd": null }, "summary_intro": "OpenAI's TTS-1 HD (`tts-1-hd`) is a tts model. Status on 2026-10-15: deprecated. Confidence: high. TTS is a model that converts text to natural sounding spoken text.", "what_it_is_for": "Text-to-speech model optimized for quality", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/whisper/whisper": { "id": "openai/whisper/whisper", "name": "Whisper", "maker": "openai", "family": "openai/whisper", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-09-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder Transformer", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's Whisper (`whisper`) is a whisper model. Status on 2026-10-15: active. Confidence: high. Whisper is OpenAI's open-source speech recognition model announced September 21, 2022.", "what_it_is_for": "Open-source speech recognition model announced September 21, 2022.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "openai/whisper/whisper-1": { "id": "openai/whisper/whisper-1", "name": "Whisper", "maker": "openai", "family": "openai/whisper", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenAI's Whisper (`whisper-1`) is a whisper model. Status on 2026-10-15: deprecated. Confidence: high. Whisper is a general-purpose speech recognition model, trained on a large dataset of diverse audio.", "what_it_is_for": "General-purpose speech recognition model", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/a2fm/a2fm-32b-rl": { "id": "oppo/a2fm/a2fm-32b-rl", "name": "A2FM-32B-rl", "maker": "oppo", "family": "oppo/a2fm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen2-architecture adaptive agent model. Three modes: instant, reasoning, and agentic. Trained with route-then-align supervised fine-tuning and Adaptive Policy Optimization. The project calls it an adaptive branch of AFM.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A2FM-32B-rl is OPPO PersonalAI Lab's open-weight adaptive agent model, Apache-2.0, with weights on Hugging Face by 13 October 2025. Collected 2026-10-15. It picks among instant, reasoning, and tool-using modes. There is no API price.", "what_it_is_for": "OPPO describes it as a way to spend tokens only when a task needs reasoning or tools. The card highlights BrowseComp, AIME 2025, and HLE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/afm/afm-codeagent-32b-rl": { "id": "oppo/afm/afm-codeagent-32b-rl", "name": "AFM-CodeAgent-32B-rl", "maker": "oppo", "family": "oppo/afm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Chain-of-Agents post-train of Qwen2.5-Coder-32B-Instruct. SFT checkpoints are the distillation stage. RL checkpoints add agentic reinforcement learning. Not trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM-CodeAgent-32B-rl is an open-weight agent model from OPPO PersonalAI Lab, released with the Chain-of-Agents paper on 6 August 2025. Collected 2026-10-15. Backbone listed by OPPO: Qwen2.5-Coder-32B-Instruct. License: Apache-2.0. No hosted API price.", "what_it_is_for": "OPPO trains this checkpoint so one model can run multi-step tool use that the lab calls Chain-of-Agents, instead of a hand-written multi-agent framework. CodeAgent targets code and tool use, WebAgent targets browsing, and MHQA-Agent targets multi-hop questions. SFT is the distillation stage. RL is the later agentic-RL stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/afm/afm-codeagent-32b-sft": { "id": "oppo/afm/afm-codeagent-32b-sft", "name": "AFM-CodeAgent-32B-sft", "maker": "oppo", "family": "oppo/afm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Chain-of-Agents post-train of Qwen2.5-Coder-32B-Instruct. SFT checkpoints are the distillation stage. RL checkpoints add agentic reinforcement learning. Not trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM-CodeAgent-32B-sft is an open-weight agent model from OPPO PersonalAI Lab, released with the Chain-of-Agents paper on 6 August 2025. Collected 2026-10-15. Backbone listed by OPPO: Qwen2.5-Coder-32B-Instruct. License: Apache-2.0. No hosted API price.", "what_it_is_for": "OPPO trains this checkpoint so one model can run multi-step tool use that the lab calls Chain-of-Agents, instead of a hand-written multi-agent framework. CodeAgent targets code and tool use, WebAgent targets browsing, and MHQA-Agent targets multi-hop questions. SFT is the distillation stage. RL is the later agentic-RL stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/afm/afm-codeagent-7b-rl": { "id": "oppo/afm/afm-codeagent-7b-rl", "name": "AFM-CodeAgent-7B-rl", "maker": "oppo", "family": "oppo/afm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Chain-of-Agents post-train of Qwen2.5-Coder-7B-Instruct. SFT checkpoints are the distillation stage. RL checkpoints add agentic reinforcement learning. Not trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM-CodeAgent-7B-rl is an open-weight agent model from OPPO PersonalAI Lab, released with the Chain-of-Agents paper on 6 August 2025. Collected 2026-10-15. Backbone listed by OPPO: Qwen2.5-Coder-7B-Instruct. License: Apache-2.0. No hosted API price.", "what_it_is_for": "OPPO trains this checkpoint so one model can run multi-step tool use that the lab calls Chain-of-Agents, instead of a hand-written multi-agent framework. CodeAgent targets code and tool use, WebAgent targets browsing, and MHQA-Agent targets multi-hop questions. SFT is the distillation stage. RL is the later agentic-RL stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/afm/afm-codeagent-7b-sft": { "id": "oppo/afm/afm-codeagent-7b-sft", "name": "AFM-CodeAgent-7B-sft", "maker": "oppo", "family": "oppo/afm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Chain-of-Agents post-train of Qwen2.5-Coder-7B-Instruct. SFT checkpoints are the distillation stage. RL checkpoints add agentic reinforcement learning. Not trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM-CodeAgent-7B-sft is an open-weight agent model from OPPO PersonalAI Lab, released with the Chain-of-Agents paper on 6 August 2025. Collected 2026-10-15. Backbone listed by OPPO: Qwen2.5-Coder-7B-Instruct. License: Apache-2.0. No hosted API price.", "what_it_is_for": "OPPO trains this checkpoint so one model can run multi-step tool use that the lab calls Chain-of-Agents, instead of a hand-written multi-agent framework. CodeAgent targets code and tool use, WebAgent targets browsing, and MHQA-Agent targets multi-hop questions. SFT is the distillation stage. RL is the later agentic-RL stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/afm/afm-mhqa-agent-3b-rl": { "id": "oppo/afm/afm-mhqa-agent-3b-rl", "name": "AFM-MHQA-Agent-3B-rl", "maker": "oppo", "family": "oppo/afm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Qwen Research License", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Chain-of-Agents post-train of Qwen2.5-3B-Base. SFT checkpoints are the distillation stage. RL checkpoints add agentic reinforcement learning. Not trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM-MHQA-Agent-3B-rl is an open-weight agent model from OPPO PersonalAI Lab, released with the Chain-of-Agents paper on 6 August 2025. Collected 2026-10-15. Backbone listed by OPPO: Qwen2.5-3B-Base. License: Qwen Research License. No hosted API price.", "what_it_is_for": "OPPO trains this checkpoint so one model can run multi-step tool use that the lab calls Chain-of-Agents, instead of a hand-written multi-agent framework. CodeAgent targets code and tool use, WebAgent targets browsing, and MHQA-Agent targets multi-hop questions. SFT is the distillation stage. RL is the later agentic-RL stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/afm/afm-mhqa-agent-3b-sft": { "id": "oppo/afm/afm-mhqa-agent-3b-sft", "name": "AFM-MHQA-Agent-3B-sft", "maker": "oppo", "family": "oppo/afm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Qwen Research License", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Chain-of-Agents post-train of Qwen2.5-3B-Base. SFT checkpoints are the distillation stage. RL checkpoints add agentic reinforcement learning. Not trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM-MHQA-Agent-3B-sft is an open-weight agent model from OPPO PersonalAI Lab, released with the Chain-of-Agents paper on 6 August 2025. Collected 2026-10-15. Backbone listed by OPPO: Qwen2.5-3B-Base. License: Qwen Research License. No hosted API price.", "what_it_is_for": "OPPO trains this checkpoint so one model can run multi-step tool use that the lab calls Chain-of-Agents, instead of a hand-written multi-agent framework. CodeAgent targets code and tool use, WebAgent targets browsing, and MHQA-Agent targets multi-hop questions. SFT is the distillation stage. RL is the later agentic-RL stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/afm/afm-mhqa-agent-7b-rl": { "id": "oppo/afm/afm-mhqa-agent-7b-rl", "name": "AFM-MHQA-Agent-7B-rl", "maker": "oppo", "family": "oppo/afm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Chain-of-Agents post-train of Qwen2.5-7B-Base. SFT checkpoints are the distillation stage. RL checkpoints add agentic reinforcement learning. Not trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM-MHQA-Agent-7B-rl is an open-weight agent model from OPPO PersonalAI Lab, released with the Chain-of-Agents paper on 6 August 2025. Collected 2026-10-15. Backbone listed by OPPO: Qwen2.5-7B-Base. License: Apache-2.0. No hosted API price.", "what_it_is_for": "OPPO trains this checkpoint so one model can run multi-step tool use that the lab calls Chain-of-Agents, instead of a hand-written multi-agent framework. CodeAgent targets code and tool use, WebAgent targets browsing, and MHQA-Agent targets multi-hop questions. SFT is the distillation stage. RL is the later agentic-RL stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/afm/afm-mhqa-agent-7b-sft": { "id": "oppo/afm/afm-mhqa-agent-7b-sft", "name": "AFM-MHQA-Agent-7B-sft", "maker": "oppo", "family": "oppo/afm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Chain-of-Agents post-train of Qwen2.5-7B-Base. SFT checkpoints are the distillation stage. RL checkpoints add agentic reinforcement learning. Not trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM-MHQA-Agent-7B-sft is an open-weight agent model from OPPO PersonalAI Lab, released with the Chain-of-Agents paper on 6 August 2025. Collected 2026-10-15. Backbone listed by OPPO: Qwen2.5-7B-Base. License: Apache-2.0. No hosted API price.", "what_it_is_for": "OPPO trains this checkpoint so one model can run multi-step tool use that the lab calls Chain-of-Agents, instead of a hand-written multi-agent framework. CodeAgent targets code and tool use, WebAgent targets browsing, and MHQA-Agent targets multi-hop questions. SFT is the distillation stage. RL is the later agentic-RL stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/afm/afm-webagent-32b-rl": { "id": "oppo/afm/afm-webagent-32b-rl", "name": "AFM-WebAgent-32B-rl", "maker": "oppo", "family": "oppo/afm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Chain-of-Agents post-train of Qwen2.5-32B-Instruct. SFT checkpoints are the distillation stage. RL checkpoints add agentic reinforcement learning. Not trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM-WebAgent-32B-rl is an open-weight agent model from OPPO PersonalAI Lab, released with the Chain-of-Agents paper on 6 August 2025. Collected 2026-10-15. Backbone listed by OPPO: Qwen2.5-32B-Instruct. License: Apache-2.0. No hosted API price.", "what_it_is_for": "OPPO trains this checkpoint so one model can run multi-step tool use that the lab calls Chain-of-Agents, instead of a hand-written multi-agent framework. CodeAgent targets code and tool use, WebAgent targets browsing, and MHQA-Agent targets multi-hop questions. SFT is the distillation stage. RL is the later agentic-RL stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/afm/afm-webagent-32b-sft": { "id": "oppo/afm/afm-webagent-32b-sft", "name": "AFM-WebAgent-32B-sft", "maker": "oppo", "family": "oppo/afm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Chain-of-Agents post-train of Qwen2.5-32B-Instruct. SFT checkpoints are the distillation stage. RL checkpoints add agentic reinforcement learning. Not trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM-WebAgent-32B-sft is an open-weight agent model from OPPO PersonalAI Lab, released with the Chain-of-Agents paper on 6 August 2025. Collected 2026-10-15. Backbone listed by OPPO: Qwen2.5-32B-Instruct. License: Apache-2.0. No hosted API price.", "what_it_is_for": "OPPO trains this checkpoint so one model can run multi-step tool use that the lab calls Chain-of-Agents, instead of a hand-written multi-agent framework. CodeAgent targets code and tool use, WebAgent targets browsing, and MHQA-Agent targets multi-hop questions. SFT is the distillation stage. RL is the later agentic-RL stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/afm/afm-webagent-7b-rl": { "id": "oppo/afm/afm-webagent-7b-rl", "name": "AFM-WebAgent-7B-rl", "maker": "oppo", "family": "oppo/afm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Chain-of-Agents post-train of Qwen2.5-7B-Instruct. SFT checkpoints are the distillation stage. RL checkpoints add agentic reinforcement learning. Not trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM-WebAgent-7B-rl is an open-weight agent model from OPPO PersonalAI Lab, released with the Chain-of-Agents paper on 6 August 2025. Collected 2026-10-15. Backbone listed by OPPO: Qwen2.5-7B-Instruct. License: Apache-2.0. No hosted API price.", "what_it_is_for": "OPPO trains this checkpoint so one model can run multi-step tool use that the lab calls Chain-of-Agents, instead of a hand-written multi-agent framework. CodeAgent targets code and tool use, WebAgent targets browsing, and MHQA-Agent targets multi-hop questions. SFT is the distillation stage. RL is the later agentic-RL stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/afm/afm-webagent-7b-sft": { "id": "oppo/afm/afm-webagent-7b-sft", "name": "AFM-WebAgent-7B-sft", "maker": "oppo", "family": "oppo/afm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Chain-of-Agents post-train of Qwen2.5-7B-Instruct. SFT checkpoints are the distillation stage. RL checkpoints add agentic reinforcement learning. Not trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AFM-WebAgent-7B-sft is an open-weight agent model from OPPO PersonalAI Lab, released with the Chain-of-Agents paper on 6 August 2025. Collected 2026-10-15. Backbone listed by OPPO: Qwen2.5-7B-Instruct. License: Apache-2.0. No hosted API price.", "what_it_is_for": "OPPO trains this checkpoint so one model can run multi-step tool use that the lab calls Chain-of-Agents, instead of a hand-written multi-agent framework. CodeAgent targets code and tool use, WebAgent targets browsing, and MHQA-Agent targets multi-hop questions. SFT is the distillation stage. RL is the later agentic-RL stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/andesgpt/andesgpt": { "id": "oppo/andesgpt/andesgpt", "name": "AndesGPT", "maker": "oppo", "family": "oppo/andesgpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published on a model card. The product page describes end-cloud scheduling across billion- to hundred-billion-class specs. A November 2023 conference recap mentions RoPE, GQA, and SwappedAttention.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AndesGPT (\u5b89\u7b2c\u65af\u5927\u6a21\u578b) is OPPO's closed generative model and agent line, launched publicly on 16 November 2023 and still named in OPPO phone and after-sales material through 2025 and 2026. Collected 2026-10-15. It is not an open-weight release and it has no public token price.", "what_it_is_for": "OPPO's developer page says AndesGPT (\u5b89\u7b2c\u65af\u5927\u6a21\u578b) is its self-trained personal large model and agent for smart devices. It lists knowledge lookup, long-term memory, tool use, and creation, including text-to-image, image-to-image, and a music-generation pipeline. Whether image and music are the same weights as the language model is not stated. On phones it backs Xiaobu / Breeno features such as summaries and settings control. In 2025 OPPO also described it as the model behind after-sales replies in 13 languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/andesgpt/andesgpt-2-0": { "id": "oppo/andesgpt/andesgpt-2-0", "name": "AndesGPT-2.0", "maker": "oppo", "family": "oppo/andesgpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed multimodal upgrade of AndesGPT. Press describes dialogue training, iterative supervised fine-tuning, preference alignment, and language plus vision plus touch interaction. No model card states the backbone or parameter count.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AndesGPT-2.0 is the closed 2024 upgrade of OPPO's AndesGPT. News from 6 September 2024 says it was the model OPPO entered in SuperCLUE's August 2024 Chinese benchmark. Collected 2026-10-15. It is not open-weight and has no public price. No retirement notice was found.", "what_it_is_for": "OPPO's writeups say 2.0 is for harder multi-turn dialogue, safer alignment, tool-using agents, math, and multimodal interaction that adds vision and touch to language. Reported product uses include the call assistant and smart summaries on OPPO phones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/andesgpt/andesgpt-tiny": { "id": "oppo/andesgpt/andesgpt-tiny", "name": "AndesGPT-Tiny", "maker": "oppo", "family": "oppo/andesgpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published on a model card. The product page describes end-cloud scheduling across billion- to hundred-billion-class specs. A November 2023 conference recap mentions RoPE, GQA, and SwappedAttention.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AndesGPT-Tiny, Turbo, and Titan are the three named specs of OPPO's closed \u5b89\u7b2c\u65af\u5927\u6a21\u578b, announced on 16 November 2023. This dossier is the AndesGPT-Tiny spec. It was still listed on OPPO's developer page on the collection date, 2026-10-15. Weights are closed and there is no public API price.", "what_it_is_for": "OPPO's developer page says AndesGPT (\u5b89\u7b2c\u65af\u5927\u6a21\u578b) is its self-trained personal large model and agent for smart devices. It lists knowledge lookup, long-term memory, tool use, and creation, including text-to-image, image-to-image, and a music-generation pipeline. Whether image and music are the same weights as the language model is not stated. This folder is only the AndesGPT-Tiny deployment spec. OPPO says the three specs cover different scenes and are scheduled together. It does not say which spec is on device and which is in the cloud.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/andesgpt/andesgpt-titan": { "id": "oppo/andesgpt/andesgpt-titan", "name": "AndesGPT-Titan", "maker": "oppo", "family": "oppo/andesgpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published on a model card. The product page describes end-cloud scheduling across billion- to hundred-billion-class specs. A November 2023 conference recap mentions RoPE, GQA, and SwappedAttention.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AndesGPT-Tiny, Turbo, and Titan are the three named specs of OPPO's closed \u5b89\u7b2c\u65af\u5927\u6a21\u578b, announced on 16 November 2023. This dossier is the AndesGPT-Titan spec. It was still listed on OPPO's developer page on the collection date, 2026-10-15. Weights are closed and there is no public API price.", "what_it_is_for": "OPPO's developer page says AndesGPT (\u5b89\u7b2c\u65af\u5927\u6a21\u578b) is its self-trained personal large model and agent for smart devices. It lists knowledge lookup, long-term memory, tool use, and creation, including text-to-image, image-to-image, and a music-generation pipeline. Whether image and music are the same weights as the language model is not stated. This folder is only the AndesGPT-Titan deployment spec. OPPO says the three specs cover different scenes and are scheduled together. It does not say which spec is on device and which is in the cloud.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/andesgpt/andesgpt-turbo": { "id": "oppo/andesgpt/andesgpt-turbo", "name": "AndesGPT-Turbo", "maker": "oppo", "family": "oppo/andesgpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published on a model card. The product page describes end-cloud scheduling across billion- to hundred-billion-class specs. A November 2023 conference recap mentions RoPE, GQA, and SwappedAttention.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AndesGPT-Tiny, Turbo, and Titan are the three named specs of OPPO's closed \u5b89\u7b2c\u65af\u5927\u6a21\u578b, announced on 16 November 2023. This dossier is the AndesGPT-Turbo spec. It was still listed on OPPO's developer page on the collection date, 2026-10-15. Weights are closed and there is no public API price.", "what_it_is_for": "OPPO's developer page says AndesGPT (\u5b89\u7b2c\u65af\u5927\u6a21\u578b) is its self-trained personal large model and agent for smart devices. It lists knowledge lookup, long-term memory, tool use, and creation, including text-to-image, image-to-image, and a music-generation pipeline. Whether image and music are the same weights as the language model is not stated. This folder is only the AndesGPT-Turbo deployment spec. OPPO says the three specs cover different scenes and are scheduled together. It does not say which spec is on device and which is in the cloud.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/andesvl/andesvl-0-6b-instruct": { "id": "oppo/andesvl/andesvl-0-6b-instruct", "name": "AndesVL-0.6B-Instruct", "maker": "oppo", "family": "oppo/andesvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "0.695B", "architecture": "Mobile multimodal model: SigLIP2-Base vision encoder, two-layer MLP projector, pixel-shuffle that cuts visual tokens by 4, and Qwen3-0.6B. Native-resolution ViT with 2D RoPE. Final pre-training stage uses a 32768-token LLM sequence; the model card does not state a serving context limit.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AndesVL-0.6B-Instruct is an open-weight mobile multimodal model from OPPO AI Center, released on Hugging Face on 13 October 2025 under Apache-2.0. Collected 2026-10-15. Total size is 0.695B (SigLIP2-Base plus Qwen3-0.6B). It is active as a weight release. There is no OPPO-hosted API price.", "what_it_is_for": "OPPO positions AndesVL for on-phone image understanding: text-rich images, visual reasoning, multi-image questions, multilingual visual questions, and phone GUI understanding. Instruct is non-thinking only and was post-trained with SFT and mixed preference optimization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/andesvl/andesvl-0-6b-thinking": { "id": "oppo/andesvl/andesvl-0-6b-thinking", "name": "AndesVL-0.6B-Thinking", "maker": "oppo", "family": "oppo/andesvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "0.695B", "architecture": "Mobile multimodal model: SigLIP2-Base vision encoder, two-layer MLP projector, pixel-shuffle that cuts visual tokens by 4, and Qwen3-0.6B. Native-resolution ViT with 2D RoPE. Final pre-training stage uses a 32768-token LLM sequence; the model card does not state a serving context limit.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AndesVL-0.6B-Thinking is an open-weight mobile multimodal model from OPPO AI Center, released on Hugging Face on 13 October 2025 under Apache-2.0. Collected 2026-10-15. Total size is 0.695B (SigLIP2-Base plus Qwen3-0.6B). It is active as a weight release. There is no OPPO-hosted API price.", "what_it_is_for": "OPPO positions AndesVL for on-phone image understanding: text-rich images, visual reasoning, multi-image questions, multilingual visual questions, and phone GUI understanding. Thinking supports both thinking and non-thinking modes and was post-trained with SFT and GRPO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/andesvl/andesvl-1b-instruct": { "id": "oppo/andesvl/andesvl-1b-instruct", "name": "AndesVL-1B-Instruct", "maker": "oppo", "family": "oppo/andesvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "0.927B", "architecture": "Mobile multimodal model: AIMv2-Large vision encoder, two-layer MLP projector, pixel-shuffle that cuts visual tokens by 4, and Qwen3-0.6B. Native-resolution ViT with 2D RoPE. Final pre-training stage uses a 32768-token LLM sequence; the model card does not state a serving context limit.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AndesVL-1B-Instruct is an open-weight mobile multimodal model from OPPO AI Center, released on Hugging Face on 13 October 2025 under Apache-2.0. Collected 2026-10-15. Total size is 0.927B (AIMv2-Large plus Qwen3-0.6B). It is active as a weight release. There is no OPPO-hosted API price.", "what_it_is_for": "OPPO positions AndesVL for on-phone image understanding: text-rich images, visual reasoning, multi-image questions, multilingual visual questions, and phone GUI understanding. Instruct is non-thinking only and was post-trained with SFT and mixed preference optimization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/andesvl/andesvl-1b-thinking": { "id": "oppo/andesvl/andesvl-1b-thinking", "name": "AndesVL-1B-Thinking", "maker": "oppo", "family": "oppo/andesvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "0.927B", "architecture": "Mobile multimodal model: AIMv2-Large vision encoder, two-layer MLP projector, pixel-shuffle that cuts visual tokens by 4, and Qwen3-0.6B. Native-resolution ViT with 2D RoPE. Final pre-training stage uses a 32768-token LLM sequence; the model card does not state a serving context limit.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AndesVL-1B-Thinking is an open-weight mobile multimodal model from OPPO AI Center, released on Hugging Face on 13 October 2025 under Apache-2.0. Collected 2026-10-15. Total size is 0.927B (AIMv2-Large plus Qwen3-0.6B). It is active as a weight release. There is no OPPO-hosted API price.", "what_it_is_for": "OPPO positions AndesVL for on-phone image understanding: text-rich images, visual reasoning, multi-image questions, multilingual visual questions, and phone GUI understanding. Thinking supports both thinking and non-thinking modes and was post-trained with SFT and GRPO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/andesvl/andesvl-2b-instruct": { "id": "oppo/andesvl/andesvl-2b-instruct", "name": "AndesVL-2B-Instruct", "maker": "oppo", "family": "oppo/andesvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "2.055B", "architecture": "Mobile multimodal model: AIMv2-Large vision encoder, two-layer MLP projector, pixel-shuffle that cuts visual tokens by 4, and Qwen3-1.7B. Native-resolution ViT with 2D RoPE. Final pre-training stage uses a 32768-token LLM sequence; the model card does not state a serving context limit.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AndesVL-2B-Instruct is an open-weight mobile multimodal model from OPPO AI Center, released on Hugging Face on 13 October 2025 under Apache-2.0. Collected 2026-10-15. Total size is 2.055B (AIMv2-Large plus Qwen3-1.7B). It is active as a weight release. There is no OPPO-hosted API price.", "what_it_is_for": "OPPO positions AndesVL for on-phone image understanding: text-rich images, visual reasoning, multi-image questions, multilingual visual questions, and phone GUI understanding. Instruct is non-thinking only and was post-trained with SFT and mixed preference optimization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/andesvl/andesvl-2b-thinking": { "id": "oppo/andesvl/andesvl-2b-thinking", "name": "AndesVL-2B-Thinking", "maker": "oppo", "family": "oppo/andesvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "2.055B", "architecture": "Mobile multimodal model: AIMv2-Large vision encoder, two-layer MLP projector, pixel-shuffle that cuts visual tokens by 4, and Qwen3-1.7B. Native-resolution ViT with 2D RoPE. Final pre-training stage uses a 32768-token LLM sequence; the model card does not state a serving context limit.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AndesVL-2B-Thinking is an open-weight mobile multimodal model from OPPO AI Center, released on Hugging Face on 13 October 2025 under Apache-2.0. Collected 2026-10-15. Total size is 2.055B (AIMv2-Large plus Qwen3-1.7B). It is active as a weight release. There is no OPPO-hosted API price.", "what_it_is_for": "OPPO positions AndesVL for on-phone image understanding: text-rich images, visual reasoning, multi-image questions, multilingual visual questions, and phone GUI understanding. Thinking supports both thinking and non-thinking modes and was post-trained with SFT and GRPO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/andesvl/andesvl-4b-instruct": { "id": "oppo/andesvl/andesvl-4b-instruct", "name": "AndesVL-4B-Instruct", "maker": "oppo", "family": "oppo/andesvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "4.360B", "architecture": "Mobile multimodal model: AIMv2-Large vision encoder, two-layer MLP projector, pixel-shuffle that cuts visual tokens by 4, and Qwen3-4B. Native-resolution ViT with 2D RoPE. Final pre-training stage uses a 32768-token LLM sequence; the model card does not state a serving context limit.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AndesVL-4B-Instruct is an open-weight mobile multimodal model from OPPO AI Center, released on Hugging Face on 13 October 2025 under Apache-2.0. Collected 2026-10-15. Total size is 4.360B (AIMv2-Large plus Qwen3-4B). It is active as a weight release. There is no OPPO-hosted API price.", "what_it_is_for": "OPPO positions AndesVL for on-phone image understanding: text-rich images, visual reasoning, multi-image questions, multilingual visual questions, and phone GUI understanding. Instruct is non-thinking only and was post-trained with SFT and mixed preference optimization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/andesvl/andesvl-4b-thinking": { "id": "oppo/andesvl/andesvl-4b-thinking", "name": "AndesVL-4B-Thinking", "maker": "oppo", "family": "oppo/andesvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "4.360B", "architecture": "Mobile multimodal model: AIMv2-Large vision encoder, two-layer MLP projector, pixel-shuffle that cuts visual tokens by 4, and Qwen3-4B. Native-resolution ViT with 2D RoPE. Final pre-training stage uses a 32768-token LLM sequence; the model card does not state a serving context limit.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AndesVL-4B-Thinking is an open-weight mobile multimodal model from OPPO AI Center, released on Hugging Face on 13 October 2025 under Apache-2.0. Collected 2026-10-15. Total size is 4.360B (AIMv2-Large plus Qwen3-4B). It is active as a weight release. There is no OPPO-hosted API price.", "what_it_is_for": "OPPO positions AndesVL for on-phone image understanding: text-rich images, visual reasoning, multi-image questions, multilingual visual questions, and phone GUI understanding. Thinking supports both thinking and non-thinking modes and was post-trained with SFT and GRPO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/chaos/chaos": { "id": "oppo/chaos/chaos", "name": "CHAOS", "maker": "oppo", "family": "oppo/chaos", "status": "retired", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Described in a June 2023 DataFun talk writeup as OPPO Research's 3-billion-parameter Chinese pretrained model. Layer layout was not included in the writeup.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CHAOS is a closed pretrained model that an OPPO Research speaker described as a 3-billion-parameter model that had led parts of the CLUE benchmark. The only retrieved source is a 29 June 2023 edited talk. Collected 2026-10-15. It is not a current product page model.", "what_it_is_for": "The talk presents CHAOS as an internal Chinese pretraining effort aimed at CLUE text tasks. No product integration was described in the retrieved page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/cutetts/cutetts": { "id": "oppo/cutetts/cutetts", "name": "CuteTTS", "maker": "oppo", "family": "oppo/cutetts", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-08-09", "context_length": null, "max_output_tokens": null, "parameters": "230M", "architecture": "Continuous autoregressive TTS: causal VAE speech latents, patch-level autoregression, explicit speaker conditioning, and a bidirectional flow-matching head. The distill checkpoint folds classifier-free guidance and several solver steps into one student.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CuteTTS is OPPO AI Center's open-weight zero-shot text-to-speech model, about 230 million parameters, Apache-2.0, announced with a paper on 9 August 2026. Collected 2026-10-15. It runs locally. There is no OPPO API price.", "what_it_is_for": "OPPO presents it for low-latency speech synthesis and voice cloning in English, Chinese, French, German, and Spanish. Base CuteTTS is the teacher. The card says about 40 ms to the first audio chunk and about 9 times real time on an NVIDIA RTX 4090.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/cutetts/cutetts-distill": { "id": "oppo/cutetts/cutetts-distill", "name": "CuteTTS-distill", "maker": "oppo", "family": "oppo/cutetts", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-08-09", "context_length": null, "max_output_tokens": null, "parameters": "230M", "architecture": "Continuous autoregressive TTS: causal VAE speech latents, patch-level autoregression, explicit speaker conditioning, and a bidirectional flow-matching head. The distill checkpoint folds classifier-free guidance and several solver steps into one student.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CuteTTS-distill is OPPO AI Center's open-weight zero-shot text-to-speech model, about 230 million parameters, Apache-2.0, announced with a paper on 9 August 2026. Collected 2026-10-15. It runs locally. There is no OPPO API price.", "what_it_is_for": "OPPO presents it for low-latency speech synthesis and voice cloning in English, Chinese, French, German, and Spanish. Distilled student. The paper says guidance-step distillation cuts first-audio latency 23.3 percent and real-time factor 40.8 percent versus the base model. Absolute milliseconds for that comparison were not taken from a primary table in this pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/o-researcher/o-researcher-72b-rl": { "id": "oppo/o-researcher/o-researcher-72b-rl", "name": "O-Researcher-72B-rl", "maker": "oppo", "family": "oppo/o-researcher", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-08", "context_length": null, "max_output_tokens": null, "parameters": "72B", "architecture": "Tool-using research model post-trained from Qwen2.5-72B-Instruct. Two stages: supervised fine-tuning, then agentic reinforcement learning. Not trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "O-Researcher-72B-rl is an open-weight deep-research model from OPPO PersonalAI Lab, Apache-2.0, built on Qwen2.5-72B-Instruct. Weights were posted by 8 January 2026. Collected 2026-10-15. There is no API price.", "what_it_is_for": "OPPO presents it as one model that searches, reads pages, and answers multi-step research questions without an external agent framework. Reinforcement-learning stage. Hugging Face size widget shows 73B for this repo while the project table says 72B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/o-researcher/o-researcher-72b-sft": { "id": "oppo/o-researcher/o-researcher-72b-sft", "name": "O-Researcher-72B-sft", "maker": "oppo", "family": "oppo/o-researcher", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-08", "context_length": null, "max_output_tokens": null, "parameters": "72B", "architecture": "Tool-using research model post-trained from Qwen2.5-72B-Instruct. Two stages: supervised fine-tuning, then agentic reinforcement learning. Not trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "O-Researcher-72B-sft is an open-weight deep-research model from OPPO PersonalAI Lab, Apache-2.0, built on Qwen2.5-72B-Instruct. Weights were posted by 8 January 2026. Collected 2026-10-15. There is no API price.", "what_it_is_for": "OPPO presents it as one model that searches, reads pages, and answers multi-step research questions without an external agent framework. Supervised fine-tune stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/obert/obert-100m": { "id": "oppo/obert/obert-100m", "name": "OBERT-100M", "maker": "oppo", "family": "oppo/obert", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "100M", "architecture": "Chinese pretrained language model in the BERT family. The 1B writeup says five masking strategies were used. Layer counts were not published.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OBERT-100M is a closed Chinese pretrained model from OPPO's Xiaobu assistant team. OPPO said it built 100M, 300M, and 1B OBERT models starting in 2020. The 1B finish was reported on 1 July 2022. Collected 2026-10-15. Treated as retired because it is absent from current OPPO model pages.", "what_it_is_for": "Xiaobu used the smaller OBERT models inside the assistant for intent, chitchat matching, reading comprehension, and FAQ ranking. The 1B model was the one entered on CLUE and KgCLUE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/obert/obert-1b": { "id": "oppo/obert/obert-1b", "name": "OBERT-1B", "maker": "oppo", "family": "oppo/obert", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2022-07-01", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Chinese pretrained language model in the BERT family. The 1B writeup says five masking strategies were used. Layer counts were not published.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OBERT-1B is a closed Chinese pretrained model from OPPO's Xiaobu assistant team. OPPO said it built 100M, 300M, and 1B OBERT models starting in 2020. The 1B finish was reported on 1 July 2022. Collected 2026-10-15. Treated as retired because it is absent from current OPPO model pages.", "what_it_is_for": "Xiaobu used the smaller OBERT models inside the assistant for intent, chitchat matching, reading comprehension, and FAQ ranking. The 1B model was the one entered on CLUE and KgCLUE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/obert/obert-300m": { "id": "oppo/obert/obert-300m", "name": "OBERT-300M", "maker": "oppo", "family": "oppo/obert", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "300M", "architecture": "Chinese pretrained language model in the BERT family. The 1B writeup says five masking strategies were used. Layer counts were not published.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OBERT-300M is a closed Chinese pretrained model from OPPO's Xiaobu assistant team. OPPO said it built 100M, 300M, and 1B OBERT models starting in 2020. The 1B finish was reported on 1 July 2022. Collected 2026-10-15. Treated as retired because it is absent from current OPPO model pages.", "what_it_is_for": "Xiaobu used the smaller OBERT models inside the assistant for intent, chitchat matching, reading comprehension, and FAQ ranking. The 1B model was the one entered on CLUE and KgCLUE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oppo/smtl/smtl-30b": { "id": "oppo/smtl/smtl-30b", "name": "SMTL-30B", "maker": "oppo", "family": "oppo/smtl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "31B", "architecture": "Hugging Face tags the weights as qwen3_moe. The model card README was empty when retrieved. The base checkpoint name is not stated. The GitHub repo says a separate Qwen3-4B download is used as a tokenizer in their example path.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SMTL-30B is an open-weight agent model from OPPO PersonalAI Lab for long-horizon search. The Hugging Face repo lists Apache-2.0 and a 31B parameter widget. The README was empty on the collection date, 2026-10-15, so confidence is medium. There is no API price.", "what_it_is_for": "The GitHub project says the model is for agentic search that uses fewer reasoning steps than long chain-of-thought search agents. Numeric task scores were not on the pages retrieved.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "opus-clip/clip/clip-anything": { "id": "opus-clip/clip/clip-anything", "name": "ClipAnything", "maker": "opus-clip", "family": "opus-clip/clip", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ClipAnything is OpusClip's multimodal clipping model. It selects moments from long video using picture, sound, and sentiment, then returns short clips. OpusClip launched it on 2024-08-31 and still ships it in the web app and the API as `ClipAnything`. This dossier was collected on 2026-10-15. One help article still calls the model beta. The launch post, the API, and the homepage treat it as the current model, so status here is active.", "what_it_is_for": "OpusClip says ClipAnything is the clipping model for any genre, including vlogs, gaming, sports, interviews, and video with little or no dialogue. The user can type a natural-language prompt to find a scene, action, person, event, or emotional moment. Each scene is rated for virality. The API calls it the advanced curation model and keeps `ClipBasic` as the default.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "opus-clip/clip/clip-basic": { "id": "opus-clip/clip/clip-basic", "name": "ClipBasic", "maker": "opus-clip", "family": "opus-clip/clip", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ClipBasic is OpusClip's talking-head clipping model. It is the default value of the API curation field `model`, and the app still lists it next to ClipAnything. It was the clipping system before ClipAnything. OpusClip has not retired it. This dossier was collected on 2026-10-15.", "what_it_is_for": "OpusClip says ClipBasic clips talking-head video from the transcript. Keywords must appear in the speech. It does not do the visual, non-speech audio, or sentiment search that ClipAnything does. An OpusClip short calls it the previous model and describes it as unimodal speech understanding, with only relative speaker position on the visual side.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "opus-clip/reframe/reframe-anything": { "id": "opus-clip/reframe/reframe-anything", "name": "ReframeAnything", "maker": "opus-clip", "family": "opus-clip/reframe", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReframeAnything is OpusClip's reframe model. It resizes a video for social aspect ratios and tracks speakers and moving objects. The homepage lists it with ClipAnything under AI editing models. The dedicated reframe page calls it the most advanced AI reframe model. It is in the product as of 2026-10-15. The ClipAnything page still prints an Alpha badge. There is no separate API model id.", "what_it_is_for": "OpusClip says ReframeAnything detects genre, tracks the subject, and reframes to 9:16, 1:1, or 16:9. The reframe FAQ says it follows moving objects such as a ball or a walking speaker, and that prompts can target a face, action, or object. Manual crop remains available. The September 8, 2026 post says the engine splits a frame into panels before face detection, keeps crops inside a panel, can hold a chart or screen share instead of a face, and drops source chyrons rather than slicing them.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/cct/cct-14": { "id": "oregon/cct/cct-14", "name": "CCT-14", "maker": "oregon", "family": "oregon/cct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-04-12", "context_length": null, "max_output_tokens": null, "parameters": "22360000", "architecture": "Compact Convolutional Transformer. CCT-14/7x2 uses 14 transformer layers and a 2-layer convolutional tokenizer with 7x7 kernels, plus sequence pooling instead of a class token. GitHub ImageNet table: 224 resolution, 310 epochs, top-1 80.67%, 22.36M parameters, 5.11G MACs. The arXiv HTML Table 3 extract shows 22.36M and 5.53G MACs for this model. Both figures are kept. The 384 fine-tune is a version: 82.71%, 22.51M, 15.02G MACs, 310+30 epochs. Authors: Ali Hassani, Steven Walton, Nikhil Shah, Abulikemu Abuduweili, Jiachen Li, Humphrey Shi. Lab line: SHI Lab @ University of Oregon, UIUC, and Picsart AI Research.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CCT-14 is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `cct`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "ImageNet-1K classification at small parameter count, with a 384 fine-tune and a Flowers-102 fine-tune shipped as versions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/cct/cct-7": { "id": "oregon/cct/cct-7", "name": "CCT-7", "maker": "oregon", "family": "oregon/cct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-04-12", "context_length": null, "max_output_tokens": null, "parameters": "3760000", "architecture": "Compact Convolutional Transformer CCT-7/3x1: 7 transformer layers, one 3x3 convolutional tokenizer layer, sequence pooling. Paper Table 1 lists 3.76M parameters and 1.19G MACs. The abstract rounds the CIFAR-10 result to 98% with 3.7M parameters. Primary stored scores are the 300-epoch learnable-position run. Longer sinusoidal runs are versions. Same author and lab line as CCT-14.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CCT-7 is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `cct`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "Train-from-scratch image classification on small datasets. The primary weight is CCT-7/3x1 at 300 epochs. Longer runs and the Flowers CCT-7/7x2 checkpoint are versions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/dinat/dinat-base": { "id": "oregon/dinat/dinat-base", "name": "DiNAT-Base", "maker": "oregon", "family": "oregon/dinat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-09-29", "context_length": null, "max_output_tokens": null, "parameters": "90000000", "architecture": "DiNAT keeps the NAT hierarchy and replaces every other Neighborhood Attention layer with Dilated Neighborhood Attention. Kernel size 7 unless a version says 11. Requires NATTEN. Authors: Ali Hassani and Humphrey Shi. Lab line on DiNAT.md: SHI Lab @ University of Oregon & UIUC and Picsart AI Research. Classification table lists 90M parameters and 13.7G FLOPs.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiNAT-Base is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `dinat`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "ImageNet classifier in the DiNAT line (DiNAT). Downstream Mask R-CNN, Cascade Mask R-CNN, UPerNet, and, for the large model, Mask2Former checkpoints are recorded as versions of the backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/dinat/dinat-large": { "id": "oregon/dinat/dinat-large", "name": "DiNAT-Large", "maker": "oregon", "family": "oregon/dinat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-09-29", "context_length": null, "max_output_tokens": null, "parameters": "200000000", "architecture": "DiNAT keeps the NAT hierarchy and replaces every other Neighborhood Attention layer with Dilated Neighborhood Attention. Kernel size 7 unless a version says 11. Requires NATTEN. Authors: Ali Hassani and Humphrey Shi. Lab line on DiNAT.md: SHI Lab @ University of Oregon & UIUC and Picsart AI Research. Classification table lists 200M parameters and 30.6G FLOPs.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiNAT-Large is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `dinat`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "ImageNet classifier in the DiNAT line (DiNAT). Downstream Mask R-CNN, Cascade Mask R-CNN, UPerNet, and, for the large model, Mask2Former checkpoints are recorded as versions of the backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/dinat/dinat-mini": { "id": "oregon/dinat/dinat-mini", "name": "DiNAT-Mini", "maker": "oregon", "family": "oregon/dinat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-09-29", "context_length": null, "max_output_tokens": null, "parameters": "20000000", "architecture": "DiNAT keeps the NAT hierarchy and replaces every other Neighborhood Attention layer with Dilated Neighborhood Attention. Kernel size 7 unless a version says 11. Requires NATTEN. Authors: Ali Hassani and Humphrey Shi. Lab line on DiNAT.md: SHI Lab @ University of Oregon & UIUC and Picsart AI Research. Classification table lists 20M parameters and 2.7G FLOPs.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiNAT-Mini is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `dinat`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "ImageNet classifier in the DiNAT line (DiNAT). Downstream Mask R-CNN, Cascade Mask R-CNN, UPerNet, and, for the large model, Mask2Former checkpoints are recorded as versions of the backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/dinat/dinat-small": { "id": "oregon/dinat/dinat-small", "name": "DiNAT-Small", "maker": "oregon", "family": "oregon/dinat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-09-29", "context_length": null, "max_output_tokens": null, "parameters": "51000000", "architecture": "DiNAT keeps the NAT hierarchy and replaces every other Neighborhood Attention layer with Dilated Neighborhood Attention. Kernel size 7 unless a version says 11. Requires NATTEN. Authors: Ali Hassani and Humphrey Shi. Lab line on DiNAT.md: SHI Lab @ University of Oregon & UIUC and Picsart AI Research. Classification table lists 51M parameters and 7.8G FLOPs.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiNAT-Small is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `dinat`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "ImageNet classifier in the DiNAT line (DiNAT). Downstream Mask R-CNN, Cascade Mask R-CNN, UPerNet, and, for the large model, Mask2Former checkpoints are recorded as versions of the backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/dinat/dinat-tiny": { "id": "oregon/dinat/dinat-tiny", "name": "DiNAT-Tiny", "maker": "oregon", "family": "oregon/dinat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-09-29", "context_length": null, "max_output_tokens": null, "parameters": "28000000", "architecture": "DiNAT keeps the NAT hierarchy and replaces every other Neighborhood Attention layer with Dilated Neighborhood Attention. Kernel size 7 unless a version says 11. Requires NATTEN. Authors: Ali Hassani and Humphrey Shi. Lab line on DiNAT.md: SHI Lab @ University of Oregon & UIUC and Picsart AI Research. Classification table lists 28M parameters and 4.3G FLOPs.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiNAT-Tiny is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `dinat`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "ImageNet classifier in the DiNAT line (DiNAT). Downstream Mask R-CNN, Cascade Mask R-CNN, UPerNet, and, for the large model, Mask2Former checkpoints are recorded as versions of the backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/dinats/dinats-base": { "id": "oregon/dinats/dinats-base", "name": "DiNAT-s Base", "maker": "oregon", "family": "oregon/dinats", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-09-29", "context_length": null, "max_output_tokens": null, "parameters": "88000000", "architecture": "DiNAT-s uses a Swin-style layout: window self-attention is replaced by Neighborhood Attention and shifted-window attention by Dilated Neighborhood Attention. Kernel size 7 unless a version says 11. Requires NATTEN. Authors: Ali Hassani and Humphrey Shi. Lab line on DiNAT.md: SHI Lab @ University of Oregon & UIUC and Picsart AI Research. Classification table lists 88M parameters and 15.4G FLOPs.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiNAT-s Base is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `dinats`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "ImageNet classifier in the DiNAT line (DiNAT-s). Downstream Mask R-CNN, Cascade Mask R-CNN, UPerNet, and, for the large model, Mask2Former checkpoints are recorded as versions of the backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/dinats/dinats-large": { "id": "oregon/dinats/dinats-large", "name": "DiNAT-s Large", "maker": "oregon", "family": "oregon/dinats", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-09-29", "context_length": null, "max_output_tokens": null, "parameters": "197000000", "architecture": "DiNAT-s uses a Swin-style layout: window self-attention is replaced by Neighborhood Attention and shifted-window attention by Dilated Neighborhood Attention. Kernel size 7 unless a version says 11. Requires NATTEN. Authors: Ali Hassani and Humphrey Shi. Lab line on DiNAT.md: SHI Lab @ University of Oregon & UIUC and Picsart AI Research. Classification table lists 197M parameters and 34.5G FLOPs.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiNAT-s Large is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `dinats`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "ImageNet classifier in the DiNAT line (DiNAT-s). Downstream Mask R-CNN, Cascade Mask R-CNN, UPerNet, and, for the large model, Mask2Former checkpoints are recorded as versions of the backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/dinats/dinats-small": { "id": "oregon/dinats/dinats-small", "name": "DiNAT-s Small", "maker": "oregon", "family": "oregon/dinats", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-09-29", "context_length": null, "max_output_tokens": null, "parameters": "50000000", "architecture": "DiNAT-s uses a Swin-style layout: window self-attention is replaced by Neighborhood Attention and shifted-window attention by Dilated Neighborhood Attention. Kernel size 7 unless a version says 11. Requires NATTEN. Authors: Ali Hassani and Humphrey Shi. Lab line on DiNAT.md: SHI Lab @ University of Oregon & UIUC and Picsart AI Research. Classification table lists 50M parameters and 8.7G FLOPs.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiNAT-s Small is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `dinats`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "ImageNet classifier in the DiNAT line (DiNAT-s). Downstream Mask R-CNN, Cascade Mask R-CNN, UPerNet, and, for the large model, Mask2Former checkpoints are recorded as versions of the backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/dinats/dinats-tiny": { "id": "oregon/dinats/dinats-tiny", "name": "DiNAT-s Tiny", "maker": "oregon", "family": "oregon/dinats", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-09-29", "context_length": null, "max_output_tokens": null, "parameters": "28000000", "architecture": "DiNAT-s uses a Swin-style layout: window self-attention is replaced by Neighborhood Attention and shifted-window attention by Dilated Neighborhood Attention. Kernel size 7 unless a version says 11. Requires NATTEN. Authors: Ali Hassani and Humphrey Shi. Lab line on DiNAT.md: SHI Lab @ University of Oregon & UIUC and Picsart AI Research. Classification table lists 28M parameters and 4.5G FLOPs.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiNAT-s Tiny is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `dinats`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "ImageNet classifier in the DiNAT line (DiNAT-s). Downstream Mask R-CNN, Cascade Mask R-CNN, UPerNet, and, for the large model, Mask2Former checkpoints are recorded as versions of the backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/matting-anything/matting-anything": { "id": "oregon/matting-anything/matting-anything", "name": "Matting Anything", "maker": "oregon", "family": "oregon/matting-anything", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Matting Anything Model (MAM). A frozen pretrained Segment Anything Model (SAM, Meta) proposes a mask from a box, point, or text prompt. A Mask-to-Matte module refines that mask into an alpha matte. The README abstract says the M2M module has only 2.7 million trainable parameters. Total parameter count, including frozen SAM, is not stated, so parameters is null. Authors: Jiachen Li, Jitesh Jain, Humphrey Shi. MIT badge. Hub siblings on 2026-10-15 list checkpoints/mam_sam_vitb.pth. The 17 July 2023 note says SAM ViT-L and SAM ViT-H checkpoints were added.", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Matting Anything is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `matting-anything`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "Alpha matting for an instance chosen by a box, point, or text prompt. One M2M head on frozen SAM, with ViT-B, ViT-L, and ViT-H checkpoint versions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/nat/nat-base": { "id": "oregon/nat/nat-base", "name": "NAT-Base", "maker": "oregon", "family": "oregon/nat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-04-14", "context_length": null, "max_output_tokens": null, "parameters": "90000000", "architecture": "Hierarchical vision transformer with Neighborhood Attention, kernel size 7, implemented in NATTEN. Four stages and overlapping convolutional downsampling. ImageNet-1K at 224 with no extra pretraining data. The classification table lists 90M parameters and 13.7G FLOPs. Authors on the project page: Ali Hassani, Steven Walton, Jiachen Li, Shen Li (Meta), Humphrey Shi. Lab line: SHI Lab @ University of Oregon & UIUC and Picsart AI Research. CVPR 2023.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NAT-Base is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `nat`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "ImageNet-1K classifier. The same backbone is also released inside Mask R-CNN, Cascade Mask R-CNN, and UPerNet checkpoints. Those heads are versions of this backbone, not separate model folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/nat/nat-mini": { "id": "oregon/nat/nat-mini", "name": "NAT-Mini", "maker": "oregon", "family": "oregon/nat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-04-14", "context_length": null, "max_output_tokens": null, "parameters": "20000000", "architecture": "Hierarchical vision transformer with Neighborhood Attention, kernel size 7, implemented in NATTEN. Four stages and overlapping convolutional downsampling. ImageNet-1K at 224 with no extra pretraining data. The classification table lists 20M parameters and 2.7G FLOPs. Authors on the project page: Ali Hassani, Steven Walton, Jiachen Li, Shen Li (Meta), Humphrey Shi. Lab line: SHI Lab @ University of Oregon & UIUC and Picsart AI Research. CVPR 2023.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NAT-Mini is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `nat`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "ImageNet-1K classifier. The same backbone is also released inside Mask R-CNN, Cascade Mask R-CNN, and UPerNet checkpoints. Those heads are versions of this backbone, not separate model folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/nat/nat-small": { "id": "oregon/nat/nat-small", "name": "NAT-Small", "maker": "oregon", "family": "oregon/nat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-04-14", "context_length": null, "max_output_tokens": null, "parameters": "51000000", "architecture": "Hierarchical vision transformer with Neighborhood Attention, kernel size 7, implemented in NATTEN. Four stages and overlapping convolutional downsampling. ImageNet-1K at 224 with no extra pretraining data. The classification table lists 51M parameters and 7.8G FLOPs. Authors on the project page: Ali Hassani, Steven Walton, Jiachen Li, Shen Li (Meta), Humphrey Shi. Lab line: SHI Lab @ University of Oregon & UIUC and Picsart AI Research. CVPR 2023.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NAT-Small is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `nat`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "ImageNet-1K classifier. The same backbone is also released inside Mask R-CNN, Cascade Mask R-CNN, and UPerNet checkpoints. Those heads are versions of this backbone, not separate model folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/nat/nat-tiny": { "id": "oregon/nat/nat-tiny", "name": "NAT-Tiny", "maker": "oregon", "family": "oregon/nat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-04-14", "context_length": null, "max_output_tokens": null, "parameters": "28000000", "architecture": "Hierarchical vision transformer with Neighborhood Attention, kernel size 7, implemented in NATTEN. Four stages and overlapping convolutional downsampling. ImageNet-1K at 224 with no extra pretraining data. The classification table lists 28M parameters and 4.3G FLOPs. Authors on the project page: Ali Hassani, Steven Walton, Jiachen Li, Shen Li (Meta), Humphrey Shi. Lab line: SHI Lab @ University of Oregon & UIUC and Picsart AI Research. CVPR 2023.", "modalities": { "input": [ "image" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NAT-Tiny is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `nat`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "ImageNet-1K classifier. The same backbone is also released inside Mask R-CNN, Cascade Mask R-CNN, and UPerNet checkpoints. Those heads are versions of this backbone, not separate model folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/oneformer/oneformer-ade20k": { "id": "oregon/oneformer/oneformer-ade20k", "name": "OneFormer ADE20K", "maker": "oregon", "family": "oregon/oneformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-11-10", "context_length": null, "max_output_tokens": null, "parameters": "219000000", "architecture": "Universal image segmentation transformer. One model is trained per dataset, conditioned by a task token, and reports semantic, instance, and panoptic metrics from that single checkpoint. Backbones are initialized from ImageNet-22K Swin, ConvNeXt, or DiNAT (dagger in the table). The primary row in this dossier is Swin-L, which the results table lists at 219M. Authors: Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi. Project page lists SHI Labs. README license badge is MIT. CVPR 2023.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OneFormer ADE20K is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `oneformer`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "Universal semantic, instance, and panoptic segmentation on ADE20K. The README row stored as primary is Swin-L at 640.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/oneformer/oneformer-cityscapes": { "id": "oregon/oneformer/oneformer-cityscapes", "name": "OneFormer Cityscapes", "maker": "oregon", "family": "oregon/oneformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-11-10", "context_length": null, "max_output_tokens": null, "parameters": "219000000", "architecture": "Universal image segmentation transformer. One model is trained per dataset, conditioned by a task token, and reports semantic, instance, and panoptic metrics from that single checkpoint. Backbones are initialized from ImageNet-22K Swin, ConvNeXt, or DiNAT (dagger in the table). The primary row in this dossier is Swin-L, which the results table lists at 219M. Authors: Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi. Project page lists SHI Labs. README license badge is MIT. CVPR 2023.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OneFormer Cityscapes is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `oneformer`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "Universal segmentation on Cityscapes val. The README row stored as primary is Swin-L.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/oneformer/oneformer-coco": { "id": "oregon/oneformer/oneformer-coco", "name": "OneFormer COCO", "maker": "oregon", "family": "oregon/oneformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-11-10", "context_length": null, "max_output_tokens": null, "parameters": "219000000", "architecture": "Universal image segmentation transformer. One model is trained per dataset, conditioned by a task token, and reports semantic, instance, and panoptic metrics from that single checkpoint. Backbones are initialized from ImageNet-22K Swin, ConvNeXt, or DiNAT (dagger in the table). The primary row in this dossier is Swin-L, which the results table lists at 219M. Authors: Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi. Project page lists SHI Labs. README license badge is MIT. CVPR 2023.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OneFormer COCO is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `oneformer`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "Universal segmentation on COCO val. The README row stored as primary is Swin-L.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/oneformer/oneformer-mapillary": { "id": "oregon/oneformer/oneformer-mapillary", "name": "OneFormer Mapillary Vistas", "maker": "oregon", "family": "oregon/oneformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-01-26", "context_length": null, "max_output_tokens": null, "parameters": "219000000", "architecture": "Universal image segmentation transformer. One model is trained per dataset, conditioned by a task token, and reports semantic, instance, and panoptic metrics from that single checkpoint. Backbones are initialized from ImageNet-22K Swin, ConvNeXt, or DiNAT (dagger in the table). The primary row in this dossier is Swin-L, which the results table lists at 219M. Authors: Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi. Project page lists SHI Labs. README license badge is MIT. CVPR 2023.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OneFormer Mapillary Vistas is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `oneformer`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "Universal segmentation on Mapillary Vistas val. The README row stored as primary is Swin-L. Released with the 26 January 2023 checkpoint news.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/stylenat/stylenat": { "id": "oregon/stylenat/stylenat", "name": "StyleNAT", "maker": "oregon", "family": "oregon/stylenat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-11-10", "context_length": null, "max_output_tokens": null, "parameters": "48920000", "architecture": "Style-based generator that uses Neighborhood Attention with variadic heads. The GitHub performance table lists FFHQ-256 at 48.92M parameters, FID 2.046, throughput 32.56 images/second. FFHQ-1024 is 49.45M and FID 4.174. The Church-256 parameter cell is blank and FID is 3.400. The abstract rounds FFHQ-256 to 2.05 and FFHQ-1024 to 4.17. Stored scores are the table cells. Code acknowledges StyleSwin and rosinality's StyleGAN2-pytorch. The model is trained as StyleNAT, with its own checkpoints. Authors on the README: Steven Walton, Ali Hassani, Xingqian Xu, Zhangyang Wang, Humphrey Shi. Acknowledgements name IARPA, the University of Oregon, UIUC, and Picsart AI Research. The 2025 workshop citation title is Efficient Image Generation with Variadic Attention Heads. The repository title remains StyleNAT.", "modalities": { "input": [ "latent" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StyleNAT is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `stylenat`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "Unconditional image generation on FFHQ and LSUN Church. FFHQ-256 is the primary checkpoint. FFHQ-1024 and Church-256 are versions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregon/versatile-diffusion/versatile-diffusion": { "id": "oregon/versatile-diffusion/versatile-diffusion", "name": "Versatile Diffusion", "maker": "oregon", "family": "oregon/versatile-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-11-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-flow multimodal diffusion model. Hugging Face pipeline tag is text-to-image and the class is VersatileDiffusionPipeline. Card tags also list image-to-text, image-to-image, and text-to-text. The repo contains image and text UNets, a VAE, text and image encoders, and pretrained_pth files kl-f8.pth, optimus-vae.pth, and vd-four-flow-v1-0.pth plus an fp16 copy. Dataset tag on the card is Laion2B-en. License tag is MIT. Parameter count is not in the Hub API payload. Paper arXiv:2211.08332.", "modalities": { "input": [ "text", "image" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Versatile Diffusion is a named model released from SHI Lab while that lab's project pages listed the University of Oregon. Family `versatile-diffusion`. Status active. Weights are open-weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "One checkpoint family for text-to-image, image variation, image-to-text, and related dual flows. The Hub card is the weight release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregonstate/pointconv/pointconv": { "id": "oregonstate/pointconv/pointconv", "name": "PointConv", "maker": "oregonstate", "family": "oregonstate/pointconv", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2018-11-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Point convolution for unordered 3D points. Kernels are nonlinear functions of local coordinates, with weight functions from a multi-layer perceptron and density functions from kernel density estimation. The same operator is used as a deconvolution to upsample features. The public PyTorch weight is the ModelNet40 classifier evaluated by eval_cls_conv.py. ScanNet and ShapeNet networks are in the paper and the TensorFlow training code; those checkpoints were not found as released files.", "modalities": { "input": [ "point-cloud" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PointConv is a 3D point-convolution model from Wenxuan Wu, Zhongang Qi, and Li Fuxin at Oregon State University's CORIS Institute. The CVPR 2019 paper builds classifiers and segmenters on that operator. A ModelNet40 weight file is still in the authors' PyTorch repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses PointConv for ModelNet40 shape classification, ShapeNet part segmentation, and ScanNet semantic scene segmentation. A CIFAR-10 point-cloud conversion is an approximation check against 2D convolution, not a separate released model. The public checkpoint is wired to ModelNet40 evaluation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oregonstate/pointpwc/pointpwc-net": { "id": "oregonstate/pointpwc/pointpwc-net", "name": "PointPWC-Net", "maker": "oregonstate", "family": "oregonstate/pointpwc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2019-11-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Coarse-to-fine scene-flow network on a pair of point clouds. Flow at a coarse level is upsampled and warped to a finer level. A point-based cost volume is discretized onto the input points and convolved with PointConv. The author repository describes the release as created by Wenxuan Wu and Fuxin Li at Oregon State University. The ECCV author list also includes Zhi Yuan Wang, Zhuwen Li, and Wei Liu. The only file in pretrain_weights is PointConv_726_0.0463.pth.", "modalities": { "input": [ "point-cloud" ], "output": [ "scene-flow" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PointPWC-Net is a scene-flow network from Oregon State University. Wenxuan Wu and Fuxin Li are the authors named on the code repository. The ECCV 2020 paper also lists Zhi Yuan Wang, Zhuwen Li, and Wei Liu. It estimates 3D motion between two point clouds, with supervised and self-supervised training. One updated weight file is in the repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper estimates scene flow on FlyingThings3D and tests generalization on KITTI Scene Flow 2015. Coarse flow is warped and refined at finer levels. A PointConv layer convolves a cost volume built on the points rather than on a dense grid. Self-supervised losses are part of the paper and of `train_self.py`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osaka/mace-osaka24/mace-osaka24": { "id": "osaka/mace-osaka24/mace-osaka24", "name": "MACE-Osaka24", "maker": "osaka", "family": "osaka/mace-osaka24", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT. Repository LICENSE, copyright 2024 qiqb-osaka. Rowan also lists the model as MIT.", "release_date": "2024-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MACE equivariant machine-learning interatomic potential, first-generation MACE, trained with mace v0.3.6. Published training scripts use ScaleShiftMACE, two RealAgnosticResidualInteractionBlock interactions, correlation 3, max_ell 3, cutoff 4.5 angstrom, 128 channels, and 10 radial basis functions. Size is the max_L setting in those scripts: small 0, medium 1, large 2. The paper says small and large follow the MACE-MP-0 small and large architectures, with the 4.5 angstrom cutoff used for all of their models. Trained on TEA-aligned MPtrj inorganic data plus OFF23 organic data (SPICE, QMug, water clusters, and tripeptides), after removing D3(BJ) from OFF23.", "modalities": { "input": [ "atomic-structure" ], "output": [ "energy", "forces", "stress" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MACE-Osaka24 is a universal machine-learning interatomic potential from the Center for Quantum Information and Quantum Biology and the Graduate School of Engineering Science at The University of Osaka. Small, medium, and large weights were published together as release v0.0.1 on 2024-12-18. On the collection date 2026-10-15 those three files were still the release. It is a local checkpoint for atomistic simulation, not a language-model API. The MACE architecture itself is the Cambridge MACE project and is cataloged separately.", "what_it_is_for": "The authors describe one potential that covers both organic molecules and inorganic crystals. They train it on MPtrj and OFF23 after aligning the datasets with Total Energy Alignment, and they show an ASE example that computes the energy of diamond silicon and acetic acid.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osaka/medbertjp/medbertjp-mecab-ipadic": { "id": "osaka/medbertjp/medbertjp-mecab-ipadic", "name": "medBERTjp (MeCab-IPAdic)", "maker": "osaka", "family": "osaka/medbertjp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0 for the pretrained weights. Repository code is Apache-2.0.", "release_date": "2020-10-23", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "BERT-Base trained from scratch with whole-word masking: 12 layers, 768 hidden size, 12 heads, custom 32k vocabulary. Japanese tokenizer is MeCab plus byte-pair encoding, following the Tohoku MeCab-IPAdic Japanese BERT setup. Requires fugashi and ipadic-py. Pre-training used max sequence length 128, global batch size 2,048, learning rate 7e-4, warmup proportion 0.0125, and 125K steps. Simultaneous pre-training on clinical references and Japanese Wikipedia.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "medBERTjp (MeCab-IPAdic) is a Japanese medical BERT-Base checkpoint from the Department of Medical Informatics, Graduate School of Medicine, The University of Osaka (the lab also described as the Osaka University Hospital medical-informatics group). Tag v0.1-mi was published on 2020-10-23 and was still the only release of this tokenizer on the collection date 2026-10-15. Weights are downloadable for academic or individual research and are not a hosted API.", "what_it_is_for": "The lab says the checkpoints are for the Japanese medical domain. This one follows the Tohoku MeCab-IPAdic Japanese BERT tokenization and was pre-trained from scratch, with whole-word masking, on clinical reference text and Japanese Wikipedia together.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osaka/medbertjp/medbertjp-mecab-ipadic-neologd-jmedic": { "id": "osaka/medbertjp/medbertjp-mecab-ipadic-neologd-jmedic", "name": "medBERTjp (MeCab-IPAdic-NEologd-JMeDic)", "maker": "osaka", "family": "osaka/medbertjp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0 for the pretrained weights. Repository code is Apache-2.0.", "release_date": "2020-10-23", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "BERT-Base trained from scratch with whole-word masking: 12 layers, 768 hidden size, 12 heads, custom 32k vocabulary. Japanese tokenizer is MeCab plus byte-pair encoding with mecab-ipadic-NEologd and J-MeDic (MANBYO_201907_Dic-utf8.dic). Pre-training used max sequence length 128, global batch size 2,048, learning rate 7e-4, warmup proportion 0.0125, and 125K steps. Simultaneous pre-training on clinical references and Japanese Wikipedia.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "medBERTjp (MeCab-IPAdic-NEologd-JMeDic) is a Japanese medical BERT-Base checkpoint from the Department of Medical Informatics, Graduate School of Medicine, The University of Osaka. Tag v0.1-minj was published on 2020-10-23 and was still the only release of this tokenizer on the collection date 2026-10-15. Weights are downloadable for academic or individual research and are not a hosted API.", "what_it_is_for": "The lab says the checkpoints are for the Japanese medical domain. This one uses MeCab with mecab-ipadic-NEologd and the J-MeDic disease-name dictionary, and was pre-trained from scratch, with whole-word masking, on clinical reference text and Japanese Wikipedia together.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osaka/medbertjp/medbertjp-sentencepiece": { "id": "osaka/medbertjp/medbertjp-sentencepiece", "name": "medBERTjp (SentencePiece)", "maker": "osaka", "family": "osaka/medbertjp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0 for the pretrained weights. Repository code is Apache-2.0.", "release_date": "2020-11-13", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "BERT-Base trained from scratch, not described as whole-word masking: 12 layers, 768 hidden size, 12 heads, custom 32k SentencePiece unigram vocabulary. The current v0.2 recipe uses character_coverage 0.99995, normalization_rule_name nmt_nfkc, add_dummy_prefix false, remove_extra_whitespaces true, and subword regularization with nbest_size 16 and alpha 0.1. Pre-training used max sequence length 128, global batch size 2,048, learning rate 7e-4, warmup proportion 0.0125, and 200K steps. Simultaneous pre-training on clinical references and Japanese Wikipedia. The older v0.1-sp tag used 125K steps and did not state subword regularization.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "medBERTjp (SentencePiece) is a Japanese medical BERT-Base checkpoint from the Department of Medical Informatics, Graduate School of Medicine, The University of Osaka. The current tag is v0.2-sp, published on 2020-11-13. An earlier SentencePiece tag, v0.1-sp, was published on 2020-10-23 and is labeled old in the README. On the collection date 2026-10-15 the weights were still downloadable for academic or individual research and were not a hosted API.", "what_it_is_for": "The lab says the checkpoints are for the Japanese medical domain. This one uses a custom SentencePiece unigram vocabulary, following the SentencePiece Japanese BERT setup, and was pre-trained from scratch on clinical reference text and Japanese Wikipedia together.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osaka/medbertjp/medbertjp-unidic": { "id": "osaka/medbertjp/medbertjp-unidic", "name": "medBERTjp (UniDic 2.3.0)", "maker": "osaka", "family": "osaka/medbertjp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0 for the pretrained weights. Repository code is Apache-2.0.", "release_date": "2020-11-03", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "BERT-Base trained from scratch with whole-word masking: 12 layers, 768 hidden size, 12 heads, custom 32k vocabulary. Japanese tokenizer is MeCab plus byte-pair encoding with UniDic v2.3.0+2020-10-08 via unidic-py. Pre-training used max sequence length 128, global batch size 2,048, learning rate 7e-4, warmup proportion 0.0125, and 125K steps. Simultaneous pre-training on clinical references and Japanese Wikipedia.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "medBERTjp (UniDic 2.3.0) is a Japanese medical BERT-Base checkpoint from the Department of Medical Informatics, Graduate School of Medicine, The University of Osaka. Tag v0.1-mu was published on 2020-11-03 and was still the only release of this tokenizer on the collection date 2026-10-15. Weights are downloadable for academic or individual research and are not a hosted API.", "what_it_is_for": "The lab says the checkpoints are for the Japanese medical domain. This one uses MeCab with UniDic 2.3.0 and was pre-trained from scratch, with whole-word masking, on clinical reference text and Japanese Wikipedia together.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osaka/oubiobert/oubiobert-base-uncased": { "id": "osaka/oubiobert/oubiobert-base-uncased", "name": "ouBioBERT-Base, Uncased", "maker": "osaka", "family": "osaka/oubiobert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 on the Hugging Face card that the v1.0 release tells users to load. The GitHub release notes do not restate a license.", "release_date": "2020-05-14", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-Base uncased for pretraining: BertForPreTraining, 12 layers, 768 hidden size, 12 heads, as named in the zip ouBioBERT-full_uncased_L-12_H-768_A-12. Trained from scratch on focused PubMed abstracts plus other PubMed abstracts, with the lab's up-sampling method. The recommended v1.0 checkpoint has max sequence length 512. The earlier demo has max sequence length 128.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ouBioBERT-Base, Uncased is an English biomedical BERT-Base model from the Department of Medical Informatics, Graduate School of Medicine, The University of Osaka. The recommended full checkpoint, v1.0, was published on 2020-05-14. An earlier demo with a 128-token limit was published on 2020-04-15. On the collection date 2026-10-15 both zips were still on GitHub, and the v1.0 notes still point at the Hugging Face repo seiya/oubiobert-base-uncased. It is a local research checkpoint, not a hosted API.", "what_it_is_for": "The authors release it for biomedical text mining. It was pre-trained on PubMed abstracts, with focused human-disease abstracts up-sampled together with the rest of PubMed, and they publish fine-tuning code for the BLUE benchmark.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/dual-lm/dual-lm-470m": { "id": "oslo/dual-lm/dual-lm-470m", "name": "Dual LM 470M", "maker": "oslo", "family": "oslo/dual-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-27", "context_length": 2048, "max_output_tokens": null, "parameters": "470M", "architecture": "Llama-backbone transformer with a dual autoregressive and masked-diffusion objective. Card: 470M total, 360M non-embedding, 24 layers, hidden 1024, 16 heads, intermediate 3554 SwiGLU, vocab 51200, RoPE theta 160000, context 2048. Main branch is alpha 63/64 and one pass over 32B English HPLT v2 tokens. Custom code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dual LM 470M is an English 470M-parameter model from LTG at the University of Oslo, released with an ICLR 2026 paper. The main branch mixes autoregressive and masked-diffusion training and is Apache-2.0. Hub createdAt is 27 January 2026. Collected 2026-10-15.", "what_it_is_for": "Causal generation, masked prediction, or prefix attention from one small English transformer. The card recommends the main branch for ordinary data budgets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/gpt-bert/gpt-bert-babylm-base": { "id": "oslo/gpt-bert/gpt-bert-babylm-base", "name": "GPT-BERT BabyLM 100M", "maker": "oslo", "family": "oslo/gpt-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-09-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT-BERT submission to BabyLM 2024. Card title says BabyLM 100M and names the dataset ltg/babylm-2024-baby-cosmo-fine-100m. The 100M figure is the data track, not a parameter count. Parameter count is not on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-BERT BabyLM 100M is the University of Oslo LTG submission to the 2024 BabyLM challenge, trained on a 100-million-word corpus. MIT license. Hub createdAt is 17 September 2024. Collected 2026-10-15.", "what_it_is_for": "English encoder and decoder use from one BabyLM checkpoint. The paper studies a model that can act as both BERT and GPT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/gpt-bert/gpt-bert-babylm-small": { "id": "oslo/gpt-bert/gpt-bert-babylm-small", "name": "GPT-BERT BabyLM 10M", "maker": "oslo", "family": "oslo/gpt-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-09-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT-BERT submission to BabyLM 2024. Card title says BabyLM 10M and names ltg/babylm-2024-baby-cosmo-fine-10m. The 10M figure is the data track. Parameter count is not on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-BERT BabyLM 10M is the smaller-data University of Oslo LTG BabyLM 2024 submission, trained on a 10-million-word corpus. MIT license. Hub createdAt is 17 September 2024. Collected 2026-10-15.", "what_it_is_for": "English GPT-BERT modeling on the 10-million-word BabyLM track.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/ltg-bert/ltg-bert-babylm": { "id": "oslo/ltg-bert/ltg-bert-babylm", "name": "LTG-BERT BabyLM", "maker": "oslo", "family": "oslo/ltg-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2024-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LTG-BERT baseline trained on the 100-million-word BabyLM challenge dataset. Card does not print parameter count or context length.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTG-BERT BabyLM is the University of Oslo group's LTG-BERT baseline for the BabyLM 100-million-word track. CC-BY-4.0. Hub createdAt is 8 January 2024. Collected 2026-10-15.", "what_it_is_for": "English sample-efficient masked language modeling on the BabyLM shared-task corpus.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/ltg-bert/ltg-bert-bnc": { "id": "oslo/ltg-bert/ltg-bert-bnc", "name": "LTG-BERT BNC", "maker": "oslo", "family": "oslo/ltg-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2023-03-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LTG-BERT encoder from the EACL 2023 Findings paper, pretrained on the British National Corpus. The paper says the corpus is about 100 million words. Custom modeling_ltgbert.py. Parameter count is not on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LTG-BERT BNC is an English masked language model from the University of Oslo Language Technology Group, trained on the British National Corpus. The card is CC-BY-4.0. Hub createdAt is 21 March 2023. Collected 2026-10-15.", "what_it_is_for": "English fill-mask encoding and the paper's data-efficient BERT comparison. The paper argues a curated 100-million-word corpus can beat the original BERT setup.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/norbert/norbert": { "id": "oslo/norbert/norbert", "name": "NorBERT", "maker": "oslo", "family": "oslo/norbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2021-02-13", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base masked LM. Config: 12 layers, hidden size 768, 12 heads, vocab size 32922, max_position_embeddings 512. Cased Norwegian BERT. Release 1.1 on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorBERT is the first cased Norwegian BERT from the Language Technology Group (LTG) at the University of Oslo, released under NorLM. The current card is Release 1.1 (13 February 2021), CC-BY-4.0, and tells users to prefer NorBERT 2 and NorBERT 3. Collected 2026-10-15.", "what_it_is_for": "Masked language modeling and Norwegian encoder features. The group describes it as a large-scale contextualized language model for Norwegian NLP.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/norbert/norbert2": { "id": "oslo/norbert/norbert2", "name": "NorBERT 2", "maker": "oslo", "family": "oslo/norbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2022-02-07", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Cased Norwegian BERT base with whole-word masking. Config: 12 layers, hidden size 768, 12 heads, vocab_size 50104, max_position_embeddings 512. Card says a 50,000-word vocabulary.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorBERT 2 is LTG's second Norwegian BERT, Release 2.0 on 7 February 2022. It is a cased whole-word-masking BERT trained on C4 and NCC, about 15 billion word tokens, under CC-BY-4.0. The card points users to NorBERT 3. Collected 2026-10-15.", "what_it_is_for": "Norwegian masked language modeling and encoder features. The card presents it as a larger-corpus replacement for NorBERT 1, later superseded in the group's recommendation by NorBERT 3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/norbert/norbert3-base": { "id": "oslo/norbert/norbert3-base", "name": "NorBERT 3 base", "maker": "oslo", "family": "oslo/norbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-03-02", "context_length": null, "max_output_tokens": null, "parameters": "123M", "architecture": "Norwegian encoder from the NorBERT 3 family in the NorBench paper. LTG-BERT training recipe. Custom code (trust_remote_code). Card size label: 123M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorBERT 3 base is one size of LTG's 2023 Norwegian encoder family at the University of Oslo. The model card lists it at 123M parameters under Apache-2.0 and describes it in the NorBench paper. Collected 2026-10-15.", "what_it_is_for": "Norwegian masked language modeling and encoder features for classification, token classification, question answering, and multiple choice. The group says NorBERT 3 is the recommended replacement for NorBERT 1 and 2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/norbert/norbert3-large": { "id": "oslo/norbert/norbert3-large", "name": "NorBERT 3 large", "maker": "oslo", "family": "oslo/norbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-03-02", "context_length": null, "max_output_tokens": null, "parameters": "323M", "architecture": "Norwegian encoder from the NorBERT 3 family in the NorBench paper. LTG-BERT training recipe. Custom code (trust_remote_code). Card size label: 323M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorBERT 3 large is one size of LTG's 2023 Norwegian encoder family at the University of Oslo. The model card lists it at 323M parameters under Apache-2.0 and describes it in the NorBench paper. Collected 2026-10-15.", "what_it_is_for": "Norwegian masked language modeling and encoder features for classification, token classification, question answering, and multiple choice. The group says NorBERT 3 is the recommended replacement for NorBERT 1 and 2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/norbert/norbert3-small": { "id": "oslo/norbert/norbert3-small", "name": "NorBERT 3 small", "maker": "oslo", "family": "oslo/norbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-03-28", "context_length": null, "max_output_tokens": null, "parameters": "40M", "architecture": "Norwegian encoder from the NorBERT 3 family in the NorBench paper. LTG-BERT training recipe. Custom code (trust_remote_code). Card size label: 40M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorBERT 3 small is one size of LTG's 2023 Norwegian encoder family at the University of Oslo. The model card lists it at 40M parameters under Apache-2.0 and describes it in the NorBench paper. Collected 2026-10-15.", "what_it_is_for": "Norwegian masked language modeling and encoder features for classification, token classification, question answering, and multiple choice. The group says NorBERT 3 is the recommended replacement for NorBERT 1 and 2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/norbert/norbert3-xs": { "id": "oslo/norbert/norbert3-xs", "name": "NorBERT 3 xs", "maker": "oslo", "family": "oslo/norbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-03-28", "context_length": null, "max_output_tokens": null, "parameters": "15M", "architecture": "Norwegian encoder from the NorBERT 3 family in the NorBench paper. LTG-BERT training recipe. Custom code (trust_remote_code). Card size label: 15M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorBERT 3 xs is one size of LTG's 2023 Norwegian encoder family at the University of Oslo. The model card lists it at 15M parameters under Apache-2.0 and describes it in the NorBench paper. Collected 2026-10-15.", "what_it_is_for": "Norwegian masked language modeling and encoder features for classification, token classification, question answering, and multiple choice. The group says NorBERT 3 is the recommended replacement for NorBERT 1 and 2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/norbert/norbert4-base": { "id": "oslo/norbert/norbert4-base", "name": "NorBERT 4 base", "maker": "oslo", "family": "oslo/norbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-14", "context_length": 16384, "max_output_tokens": null, "parameters": "149M", "architecture": "GPT-BERT trained from scratch. Bidirectional encoder and unidirectional decoder in one checkpoint. Trained on 16384-token texts with sliding-window attention. Card: 600B tokens of Norwegian Bokm\u00e5l, Nynorsk, and Northern S\u00e1mi from HPLT 2.0, FineWeb2, and M\u00edmir Core. FlashAttention-2 recommended. Custom code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorBERT 4 base is a University of Oslo LTG encoder-decoder hybrid for Norwegian Bokm\u00e5l, Nynorsk, and Northern S\u00e1mi. The card says 149M parameters, Apache-2.0, trained from scratch on 600 billion tokens with a 16384-token window. Collected 2026-10-15.", "what_it_is_for": "Long-context Norwegian encoding and also causal text generation. The card shows masked-word filling and a zero-shot Bokm\u00e5l generation prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/norbert/norbert4-large": { "id": "oslo/norbert/norbert4-large", "name": "NorBERT 4 large", "maker": "oslo", "family": "oslo/norbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-14", "context_length": 16384, "max_output_tokens": null, "parameters": "360M", "architecture": "GPT-BERT trained from scratch. Bidirectional encoder and unidirectional decoder in one checkpoint. Trained on 16384-token texts with sliding-window attention. Card: 600B tokens of Norwegian Bokm\u00e5l, Nynorsk, and Northern S\u00e1mi from HPLT 2.0, FineWeb2, and M\u00edmir Core. FlashAttention-2 recommended. Custom code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorBERT 4 large is a University of Oslo LTG encoder-decoder hybrid for Norwegian Bokm\u00e5l, Nynorsk, and Northern S\u00e1mi. The card says 360M parameters, Apache-2.0, trained from scratch on 600 billion tokens with a 16384-token window. Collected 2026-10-15.", "what_it_is_for": "Long-context Norwegian encoding and also causal text generation. The card shows masked-word filling and a zero-shot Bokm\u00e5l generation prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/norbert/norbert4-small": { "id": "oslo/norbert/norbert4-small", "name": "NorBERT 4 small", "maker": "oslo", "family": "oslo/norbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-14", "context_length": 16384, "max_output_tokens": null, "parameters": "40M", "architecture": "GPT-BERT trained from scratch. Bidirectional encoder and unidirectional decoder in one checkpoint. Trained on 16384-token texts with sliding-window attention. Card: 600B tokens of Norwegian Bokm\u00e5l, Nynorsk, and Northern S\u00e1mi from HPLT 2.0, FineWeb2, and M\u00edmir Core. FlashAttention-2 recommended. Custom code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorBERT 4 small is a University of Oslo LTG encoder-decoder hybrid for Norwegian Bokm\u00e5l, Nynorsk, and Northern S\u00e1mi. The card says 40M parameters, Apache-2.0, trained from scratch on 600 billion tokens with a 16384-token window. Collected 2026-10-15.", "what_it_is_for": "Long-context Norwegian encoding and also causal text generation. The card shows masked-word filling and a zero-shot Bokm\u00e5l generation prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/norbert/norbert4-xlarge": { "id": "oslo/norbert/norbert4-xlarge", "name": "NorBERT 4 xlarge", "maker": "oslo", "family": "oslo/norbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-14", "context_length": 16384, "max_output_tokens": null, "parameters": "987M", "architecture": "GPT-BERT trained from scratch. Bidirectional encoder and unidirectional decoder in one checkpoint. Trained on 16384-token texts with sliding-window attention. Card: 600B tokens of Norwegian Bokm\u00e5l, Nynorsk, and Northern S\u00e1mi from HPLT 2.0, FineWeb2, and M\u00edmir Core. FlashAttention-2 recommended. Custom code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorBERT 4 xlarge is a University of Oslo LTG encoder-decoder hybrid for Norwegian Bokm\u00e5l, Nynorsk, and Northern S\u00e1mi. The card says 987M parameters, Apache-2.0, trained from scratch on 600 billion tokens with a 16384-token window. Collected 2026-10-15.", "what_it_is_for": "Long-context Norwegian encoding and also causal text generation. The card shows masked-word filling and a zero-shot Bokm\u00e5l generation prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/norbert/norbert4-xsmall": { "id": "oslo/norbert/norbert4-xsmall", "name": "NorBERT 4 xsmall", "maker": "oslo", "family": "oslo/norbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-14", "context_length": 16384, "max_output_tokens": null, "parameters": "17M", "architecture": "GPT-BERT trained from scratch. Bidirectional encoder and unidirectional decoder in one checkpoint. Trained on 16384-token texts with sliding-window attention. Card: 600B tokens of Norwegian Bokm\u00e5l, Nynorsk, and Northern S\u00e1mi from HPLT 2.0, FineWeb2, and M\u00edmir Core. FlashAttention-2 recommended. Custom code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorBERT 4 xsmall is a University of Oslo LTG encoder-decoder hybrid for Norwegian Bokm\u00e5l, Nynorsk, and Northern S\u00e1mi. The card says 17M parameters, Apache-2.0, trained from scratch on 600 billion tokens with a 16384-token window. Collected 2026-10-15.", "what_it_is_for": "Long-context Norwegian encoding and also causal text generation. The card shows masked-word filling and a zero-shot Bokm\u00e5l generation prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/norbloom/norbloom-7b-scratch": { "id": "oslo/norbloom/norbloom-7b-scratch", "name": "NorBLOOM-7B-scratch", "maker": "oslo", "family": "oslo/norbloom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-02-05", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "Mistral or BLOOM decoder. Card table: 7B parameters, context length 2k, 260B tokens, NCC+HPLT+CulturaX+Starcoder. Tokenizer: byte-level BPE, vocab 32768. Trained with Megatron-DeepSpeed on LUMI (AMD MI250X). Card FLOPs estimate 1.22e22. BLOOM architecture pretrained from scratch. Card learning rate 1.2e-4. The warm card's table row is labeled NorBLOOM-7b.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorBLOOM-7B-scratch is a 7B Norwegian generative model from LTG at the University of Oslo, in the NORA.LLM family. Weights are Apache-2.0. LTG announced the 7B set on 5 February 2024. Collected 2026-10-15.", "what_it_is_for": "Research text generation for Norwegian Bokm\u00e5l and Nynorsk. The card's example is zero-shot English-to-Bokm\u00e5l translation. It is not an instruction chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/norelmo/norelmo-100": { "id": "oslo/norelmo/norelmo-100", "name": "NorELMo100", "maker": "oslo", "family": "oslo/norelmo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ELMo biLM. NLPL wiki and the 2021 paper: LSTM dimensionality 2048 instead of the default 4096, 3 epochs, batch size 192. Target vocabulary 100000 words. Trained on the NorBERT corpus (Wikipedia and Norsk Aviskorpus). Character CNN so arbitrary word forms still run.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorELMo100 is a Norwegian ELMo model from LTG at the University of Oslo, released through the NLPL vector repository as archive 218. The 2021 NorLM paper introduces it with NorBERT. Collected 2026-10-15.", "what_it_is_for": "Contextual token embeddings for Norwegian NLP when a full BERT finetune is too heavy. The wiki gives part-of-speech and sentiment numbers against mBERT, NorBERT, and NB-BERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/norelmo/norelmo-30": { "id": "oslo/norelmo/norelmo-30", "name": "NorELMo30", "maker": "oslo", "family": "oslo/norelmo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ELMo biLM. NLPL wiki and the 2021 paper: LSTM dimensionality 2048 instead of the default 4096, 3 epochs, batch size 192. Target vocabulary 30000 words. Trained on the NorBERT corpus (Wikipedia and Norsk Aviskorpus). Character CNN so arbitrary word forms still run.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorELMo30 is a Norwegian ELMo model from LTG at the University of Oslo, released through the NLPL vector repository as archive 217. The 2021 NorLM paper introduces it with NorBERT. Collected 2026-10-15.", "what_it_is_for": "Contextual token embeddings for Norwegian NLP when a full BERT finetune is too heavy. The wiki gives part-of-speech and sentiment numbers against mBERT, NorBERT, and NB-BERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/normistral/normistral-11b-long": { "id": "oslo/normistral/normistral-11b-long", "name": "NorMistral-11B-long", "maker": "oslo", "family": "oslo/normistral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-08", "context_length": 32768, "max_output_tokens": null, "parameters": "11.4B", "architecture": "Mistral architecture continued from Mistral-Nemo-Base-2407. Card: 40 layers, hidden 5120, intermediate 14336, 32 query heads and 8 key/value heads, vocab 51200, 11.4 billion parameters. RMSNorm, SwiGLU, RoPE, grouped-query attention. Hybrid objective: 90 percent causal LM and 10 percent masked next-token prediction, so the checkpoint can also be used as a bidirectional encoder. Length-extended from NorMistral-11b-warm by continual training on an additional 50 billion tokens. Card training setup: batch 128 sequences by 32768 tokens, 12000 steps. Token total written as 250 + 50 billion.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorMistral-11B-long is the University of Oslo LTG length extension of NorMistral-11B-warm. The card says 32768 context after 50 billion more tokens, still 11.4B parameters, Apache-2.0, and not instruction-tuned. Hub createdAt is 8 December 2025. Collected 2026-10-15.", "what_it_is_for": "Longer-context Norwegian and related-language base generation and bidirectional encoding. The documented example is still zero-shot translation, not chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/normistral/normistral-11b-thinking": { "id": "oslo/normistral/normistral-11b-thinking", "name": "NorMistral-11B-thinking", "maker": "oslo", "family": "oslo/normistral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-11", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "Instruction and reasoning post-train of NorMistral-11B-long. Card: SFT on English responses and traces from Kimi-K2-Thinking, then on-policy d-RLAIF on Norwegian Bokm\u00e5l and Nynorsk prompts with Mistral-Large-Instruct-2411 as the reward model. Chat template emits a trace. The university page says 11 billion parameters and a Mistral-Nemo-Base-2407 origin. The long-card architecture figure of 11.4B is not repeated on this card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorMistral-11B-thinking is the University of Oslo LTG instruction and reasoning model for Norwegian, released 11 December 2025 under Apache-2.0. It is a post-train of NorMistral-11B-long, with a public research chat and API on Sigma2. UiO calls it a research prototype. Collected 2026-10-15.", "what_it_is_for": "Norwegian chat with a visible reasoning trace. The university page says it is for Bokm\u00e5l, Nynorsk, and the languages covered by the earlier continual pretrain, and that it is a pilot rather than a general consumer assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/normistral/normistral-11b-translate": { "id": "oslo/normistral/normistral-11b-translate", "name": "NorMistral-11B-translate", "maker": "oslo", "family": "oslo/normistral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-04", "context_length": null, "max_output_tokens": null, "parameters": "11.4B", "architecture": "Machine-translation finetune of NorMistral-11B-long. The card does not restate layer counts. Parameter figure is taken from the parent card. The Hub base_model tag reads norallm/normistral-11b-llong, which does not match the prose link.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorMistral-11B-translate is a University of Oslo LTG translation finetune of NorMistral-11B-long for Bokm\u00e5l, Nynorsk, and English. Apache-2.0. The Hub repo was created 4 February 2026. Collected 2026-10-15.", "what_it_is_for": "Sentence and document translation in the six directions among Norwegian Bokm\u00e5l, Norwegian Nynorsk, and English. A system message sets the target language. English is the default on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/normistral/normistral-11b-warm": { "id": "oslo/normistral/normistral-11b-warm", "name": "NorMistral-11B-warm", "maker": "oslo", "family": "oslo/normistral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-26", "context_length": 4096, "max_output_tokens": null, "parameters": "11.4B", "architecture": "Mistral architecture continued from Mistral-Nemo-Base-2407. Card: 40 layers, hidden 5120, intermediate 14336, 32 query heads and 8 key/value heads, vocab 51200, 11.4 billion parameters. RMSNorm, SwiGLU, RoPE, grouped-query attention. Hybrid objective: 90 percent causal LM and 10 percent masked next-token prediction, so the checkpoint can also be used as a bidirectional encoder. Training sequence length on the card: 4096. 250 billion tokens, 60000 steps, batch 1024 sequences. Peak LR 1e-4. 256 AMD MI250X GPUs, 8.5 days, 38 percent MFU, 2.0e22 theoretical FLOP/s.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorMistral-11B-warm is LTG's 11.4B Norwegian generative model at the University of Oslo, continued from Mistral-Nemo-Base-2407 on 250 billion tokens. Apache-2.0 weights. The Hub repo dates from 26 September 2024. It is a base model, not an instruct model. Collected 2026-10-15.", "what_it_is_for": "Norwegian, S\u00e1mi, and related-language text generation and, because of the hybrid objective, masked or bidirectional encoding. The card's example is zero-shot English-to-Bokm\u00e5l translation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/normistral/normistral-7b-scratch": { "id": "oslo/normistral/normistral-7b-scratch", "name": "NorMistral-7B-scratch", "maker": "oslo", "family": "oslo/normistral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-02-05", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "Mistral or BLOOM decoder. Card table: 7B parameters, context length 2k, 260B tokens, NCC+HPLT+CulturaX+Starcoder. Tokenizer: byte-level BPE, vocab 32768. Trained with Megatron-DeepSpeed on LUMI (AMD MI250X). Card FLOPs estimate 1.22e22. Mistral architecture pretrained from scratch. Card learning rate 3.0e-4.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorMistral-7B-scratch is a 7B Norwegian generative model from LTG at the University of Oslo, in the NORA.LLM family. Weights are Apache-2.0. LTG announced the 7B set on 5 February 2024. Collected 2026-10-15.", "what_it_is_for": "Research text generation for Norwegian Bokm\u00e5l and Nynorsk. The card's example is zero-shot English-to-Bokm\u00e5l translation. It is not an instruction chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/normistral/normistral-7b-warm": { "id": "oslo/normistral/normistral-7b-warm", "name": "NorMistral-7B-warm", "maker": "oslo", "family": "oslo/normistral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-02-05", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "Mistral or BLOOM decoder. Card table: 7B parameters, context length 2k, 260B tokens, NCC+HPLT+CulturaX+Starcoder. Tokenizer: byte-level BPE, vocab 32768. Trained with Megatron-DeepSpeed on LUMI (AMD MI250X). Card FLOPs estimate 1.22e22. Initialized from Mistral-7B-v0.1 and continually pretrained. Card learning rate 1.0e-4. Final training perplexity on the card: 4.76.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorMistral-7B-warm is a 7B Norwegian generative model from LTG at the University of Oslo, in the NORA.LLM family. Weights are Apache-2.0. LTG announced the 7B set on 5 February 2024. Collected 2026-10-15.", "what_it_is_for": "Research text generation for Norwegian Bokm\u00e5l and Nynorsk. The card's example is zero-shot English-to-Bokm\u00e5l translation. It is not an instruction chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/normistral/normistral-7b-warm-instruct": { "id": "oslo/normistral/normistral-7b-warm-instruct", "name": "NorMistral-7B-warm-instruct", "maker": "oslo", "family": "oslo/normistral", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-05", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "Supervised finetune of NorMistral-7B-warm. Card: finetuned at 4096 context, twice the base 2k length. ChatML-style prompt. SFT only, no preference optimization. First instruct iteration.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorMistral-7B-warm-instruct is LTG's first instruction-tuned NorMistral, a supervised finetune of NorMistral-7B-warm. The University of Oslo group released it under Apache-2.0. The Hub repo was created 5 April 2024. The card still calls the weights a first iteration that might change. Collected 2026-10-15.", "what_it_is_for": "Norwegian chat and instruction following in Bokm\u00e5l and Nynorsk. The card's example is a multi-turn Oslo travel prompt in a ChatML-like template.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/nort5/nort5-base": { "id": "oslo/nort5/nort5-base", "name": "NorT5 base", "maker": "oslo", "family": "oslo/nort5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-03-02", "context_length": null, "max_output_tokens": null, "parameters": "228M", "architecture": "Norwegian encoder-decoder (T5-style) from the NorBench release. Custom modeling_nort5.py. The card says prefix language modeling needs a finetune or a nort5-{size}-lm checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorT5 base is a University of Oslo LTG encoder-decoder for Norwegian, released with NorBench. The card lists 228M parameters and Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Norwegian text-to-text generation, including the card's masked-span example and a prefix-LM style continuation after finetuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/nort5/nort5-large": { "id": "oslo/nort5/nort5-large", "name": "NorT5 large", "maker": "oslo", "family": "oslo/nort5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-03-02", "context_length": null, "max_output_tokens": null, "parameters": "808M", "architecture": "Norwegian encoder-decoder (T5-style) from the NorBench release. Custom modeling_nort5.py. The card says prefix language modeling needs a finetune or a nort5-{size}-lm checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorT5 large is a University of Oslo LTG encoder-decoder for Norwegian, released with NorBench. The card lists 808M parameters and Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Norwegian text-to-text generation, including the card's masked-span example and a prefix-LM style continuation after finetuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/nort5/nort5-small": { "id": "oslo/nort5/nort5-small", "name": "NorT5 small", "maker": "oslo", "family": "oslo/nort5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-03-02", "context_length": null, "max_output_tokens": null, "parameters": "88M", "architecture": "Norwegian encoder-decoder (T5-style) from the NorBench release. Custom modeling_nort5.py. The card says prefix language modeling needs a finetune or a nort5-{size}-lm checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorT5 small is a University of Oslo LTG encoder-decoder for Norwegian, released with NorBench. The card lists 88M parameters and Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Norwegian text-to-text generation, including the card's masked-span example and a prefix-LM style continuation after finetuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/nort5/nort5-xs": { "id": "oslo/nort5/nort5-xs", "name": "NorT5 xs", "maker": "oslo", "family": "oslo/nort5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-03-02", "context_length": null, "max_output_tokens": null, "parameters": "32M", "architecture": "Norwegian encoder-decoder (T5-style) from the NorBench release. Custom modeling_nort5.py. The card says prefix language modeling needs a finetune or a nort5-{size}-lm checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NorT5 xs is a University of Oslo LTG encoder-decoder for Norwegian, released with NorBench. The card lists 32M parameters and Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Norwegian text-to-text generation, including the card's masked-span example and a prefix-LM style continuation after finetuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/slide/slide-base": { "id": "oslo/slide/slide-base", "name": "SLIDE-base", "maker": "oslo", "family": "oslo/slide", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-14", "context_length": null, "max_output_tokens": null, "parameters": "123M", "architecture": "Multi-label Scandinavian language-identification classifier. Card size 123M. Size matches NorBERT 3 base. Custom NorBERT code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SLIDE-base is a University of Oslo LTG multi-label language-identification model for Danish, Bokm\u00e5l, Nynorsk, and Swedish. The card lists 123M parameters and Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Sentence-level multi-label identification among closely related Scandinavian languages. The paper says a sentence often cannot be assigned a single language.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/slide/slide-fast": { "id": "oslo/slide/slide-fast", "name": "SLIDE-fast", "maker": "oslo", "family": "oslo/slide", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Updated fast multi-label Scandinavian language-identification model. The card does not print a parameter count or the encoder name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SLIDE-fast is an updated fast Scandinavian language-identification model from LTG at the University of Oslo. The card reports strict accuracy 93.6 on the authors' test and 94.9 on Haas and Derczynski 2021, and says the update separates Nynorsk from Icelandic and Faroese. Apache-2.0. Hub createdAt 26 June 2025. Collected 2026-10-15.", "what_it_is_for": "Faster multi-label identification for Danish, Bokm\u00e5l, Nynorsk, and Swedish, with the card's added Nynorsk versus Icelandic and Faroese distinction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/slide/slide-small": { "id": "oslo/slide/slide-small", "name": "SLIDE-small", "maker": "oslo", "family": "oslo/slide", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-14", "context_length": null, "max_output_tokens": null, "parameters": "40M", "architecture": "Multi-label Scandinavian language-identification classifier. Card size 40M. Size matches NorBERT 3 small. Custom NorBERT code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SLIDE-small is a University of Oslo LTG multi-label language-identification model for Danish, Bokm\u00e5l, Nynorsk, and Swedish. The card lists 40M parameters and Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Sentence-level multi-label identification among closely related Scandinavian languages. The paper says a sentence often cannot be assigned a single language.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oslo/slide/slide-x-small": { "id": "oslo/slide/slide-x-small", "name": "SLIDE-x-small", "maker": "oslo", "family": "oslo/slide", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-14", "context_length": null, "max_output_tokens": null, "parameters": "15M", "architecture": "Multi-label Scandinavian language-identification classifier. Card size 15M. Size matches NorBERT 3 xs. A Hub file panel for this repo also showed 15M params. Custom NorBERT code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SLIDE-x-small is a University of Oslo LTG multi-label language-identification model for Danish, Bokm\u00e5l, Nynorsk, and Swedish. The card lists 15M parameters and Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Sentence-level multi-label identification among closely related Scandinavian languages. The paper says a sentence often cannot be assigned a single language.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/amplegcg/amplegcg": { "id": "osu/amplegcg/amplegcg", "name": "AmpleGCG", "maker": "osu", "family": "osu/amplegcg", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-05-06", "context_length": null, "max_output_tokens": null, "parameters": "6738423808", "architecture": "Llama generator. Primary repo osunlp/AmpleGCG-llama2-sourced-llama2-7b-chat. Safetensors total 6738423808.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AmpleGCG is a generative model of adversarial suffixes from The Ohio State University NLP Group. The paper AmpleGCG was posted on 2024-04-11. The primary Hub repo osunlp/AmpleGCG-llama2-sourced-llama2-7b-chat was created on 2024-05-06. Its README returned 401 on collection day, so usage text was not copied. Status on 2026-10-15 is active. The safetensors total is 6,738,423,808.", "what_it_is_for": "The paper title describes a model that learns adversarial suffixes in order to test jailbreak robustness of open and closed language models. This dossier does not repeat a generation procedure. The Hub tags are Safety and Adversarial Attack, and the architecture tag is llama.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/amplegcg/amplegcg-plus": { "id": "osu/amplegcg/amplegcg-plus", "name": "AmpleGCG-Plus", "maker": "osu", "family": "osu/amplegcg", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-08-01", "context_length": null, "max_output_tokens": null, "parameters": "6738423808", "architecture": "Llama generator, Plus series. Safetensors total 6738423808 on the Llama-2-7B-chat sourced repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AmpleGCG-Plus is the follow-on adversarial-suffix generator from The Ohio State University NLP Group. The paper was posted on 2024-10-29. The Hub repo osunlp/AmpleGCG-plus-llama2-sourced-llama2-7b-chat was created on 2024-08-01 and also returned 401. Safetensors total is again 6,738,423,808. Status on 2026-10-15 is active.", "what_it_is_for": "The paper title says AmpleGCG-Plus is a stronger generative model of adversarial suffixes, aimed at higher attack success in fewer attempts. No procedure is copied here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/attrscore/attrscore-flan-t5": { "id": "osu/attrscore/attrscore-flan-t5", "name": "AttrScore Flan-T5", "maker": "osu", "family": "osu/attrscore", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-06-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flan-T5 fine-tune on osunlp/AttrScore. Primary size xl.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AttrScore Flan-T5 is The Ohio State University NLP Group's attribution-scoring model. The xl card says it is fine-tuned from FLAN-T5-xl on the combined repurposed data in osunlp/AttrScore. License Apache-2.0. The xl repo was created on 2023-06-22. Large and xxl repos exist and are versions. Status on 2026-10-15 is active.", "what_it_is_for": "Score attributions. The xl card's only descriptive sentence says the model was fine-tuned from FLAN-T5-xl on the lab's AttrScore data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/autosdt/autosdt-coder-14b": { "id": "osu/autosdt/autosdt-coder-14b", "name": "AutoSDT-Coder-14B", "maker": "osu", "family": "osu/autosdt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2026-01-05", "context_length": null, "max_output_tokens": null, "parameters": "14770033664", "architecture": "Fine-tune of Qwen/Qwen2.5-Coder-14B-Instruct. Safetensors total 14770033664.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AutoSDT-Coder-14B is the 14B scientific-coding checkpoint from The Ohio State University NLP Group. Hub repo osunlp/AutoSDT-Coder-14B was created on 2026-01-05. It is a fine-tune of Qwen2.5-Coder-14B-Instruct. License MIT. Safetensors total 14,770,033,664. Status on 2026-10-15 is active. The card body was not given a usage section.", "what_it_is_for": "Same AutoSDT-5K coding fine-tune as the 7B model, at the Qwen2.5-Coder-14B-Instruct size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/autosdt/autosdt-coder-7b": { "id": "osu/autosdt/autosdt-coder-7b", "name": "AutoSDT-Coder-7B", "maker": "osu", "family": "osu/autosdt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-01-05", "context_length": null, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Fine-tune of Qwen/Qwen2.5-Coder-7B-Instruct on AutoSDT-5K. Safetensors total 7615616512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AutoSDT-Coder-7B is a scientific-coding model from The Ohio State University NLP Group. The Hub repo osunlp/AutoSDT-Coder-7B was created on 2026-01-05. It is a fine-tune of Qwen2.5-Coder-7B-Instruct. The card body is only the metadata header. Status on 2026-10-15 is active. License MIT. Safetensors total 7,615,616,512.", "what_it_is_for": "The project page says AutoSDT-Coder models are Qwen2.5-Coder-Instruct models trained on AutoSDT-5K, a set of 5,404 data-driven discovery coding tasks across four disciplines and 756 Python packages. This folder is the 7B checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/beetleflow/beetleflow": { "id": "osu/beetleflow/beetleflow", "name": "BeetleFlow", "maker": "osu", "family": "osu/beetleflow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-07-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mask2Former Swin-Large fine-tuned for pinned-beetle part segmentation. Checkpoints 5-class and 9-class.", "modalities": { "input": [ "image" ], "output": [ "segmentation-masks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BeetleFlow is a beetle-part segmenter from the Imageomics Institute at The Ohio State University. The card says Fangxun Liu fine-tuned Mask2Former with a Swin-Large backbone from facebook/mask2former-swin-large-ade-semantic. Two checkpoints, 5-class and 9-class, share the repo. The paper date is 2025-10-31. The Hub repo was created on 2026-07-07. License MIT. Status on 2026-10-15 is active.", "what_it_is_for": "Segment body parts on cropped, pinned beetle photos so later steps can measure traits. The 5-class model labels head, pronotum, elytra, legs, and antennas. The 9-class model adds eyes, mouthparts, tail, and pin. Both include background.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/bioclip/biocap": { "id": "osu/bioclip/biocap", "name": "BioCAP", "maker": "osu", "family": "osu/bioclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP ViT-B/16 from OpenAI CLIP with dual visual projectors, trained on TreeOfLife-10M plus synthetic captions.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioCAP is an Imageomics biology vision-language model from The Ohio State University. It continues OpenAI CLIP ViT-B/16 on TreeOfLife-10M using synthetic captions as well as taxonomic labels. The paper date is 2025-10-23. The Hub repo was created on 2025-10-08. Status on 2026-10-15 is active. License MIT.", "what_it_is_for": "Species classification and biological text-image retrieval. The card says dual visual projectors align an image with both taxonomy and descriptive captions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/bioclip/bioclip": { "id": "osu/bioclip/bioclip", "name": "BioCLIP", "maker": "osu", "family": "osu/bioclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-11-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP ViT-B/16 initialized from OpenAI CLIP and trained with OpenCLIP on TreeOfLife-10M. 224 pixel images, batch 32768, 100 epochs, 8 A100-80GB, fp16 mixed precision.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioCLIP is a biology vision-language model from the Imageomics Institute at The Ohio State University. The model card says it is an OpenAI CLIP ViT-B/16 continued on TreeOfLife-10M. The software citation on the card is November 2023. The preprint date used here is 2023-11-30. The Hub repo existed by 2023-06-26. Status on 2026-10-15 is active. License is MIT.", "what_it_is_for": "Zero-shot and few-shot species classification, and a biology image encoder. The card says the ViT-B/16 encoder is meant as a base for organismal vision tasks. It was trained so taxa are a hierarchy, not a flat label set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/bioclip/bioclip-2": { "id": "osu/bioclip/bioclip-2", "name": "BioCLIP 2", "maker": "osu", "family": "osu/bioclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP ViT-L/14 from LAION-2B, hierarchical contrastive training on TreeOfLife-200M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioCLIP 2 is the scaled biology CLIP from the Imageomics Institute at The Ohio State University. The card says it starts from a LAION-2B CLIP ViT-L/14 and trains on TreeOfLife-200M. The paper date is 2025-05-29. The Hub repo was created on 2025-05-27. Status on 2026-10-15 is active. The card names BioCLIP 2.5 as the newer version. License MIT.", "what_it_is_for": "Species recognition and broader biological visual questions. The card says species-level supervision alone produced two emergent behaviors: ecological alignment across species, and separation of variation within a species.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/bioclip/bioclip-2-5": { "id": "osu/bioclip/bioclip-2-5", "name": "BioCLIP 2.5", "maker": "osu", "family": "osu/bioclip", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP ViT-H/14 from a LAION-2B initialization, trained on an updated TreeOfLife-200M. Hub id imageomics/bioclip-2.5-vith14.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioCLIP 2.5 Huge is the Imageomics Institute's newer biology CLIP, a ViT-H/14 trained on an updated TreeOfLife-200M from a LAION-2B CLIP initialization. The Hub repo imageomics/bioclip-2.5-vith14 was created on 2026-02-10. BioCLIP 2's card points at it as new_version. Status on 2026-10-15 is active. License MIT. The collection note says it needs more inference resources than BioCLIP 2.", "what_it_is_for": "Species recognition and broader biological visual tasks at a larger ViT-H/14 scale than BioCLIP 2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/biovocabbert/biovocabbert": { "id": "osu/biovocabbert/biovocabbert", "name": "BioVocabBERT", "maker": "osu", "family": "osu/biovocabbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT fill-mask model with a custom biomedical tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioVocabBERT is a biomedical encoder from The Ohio State University NLP Group. The Hub text says it uses a biomedical tokenizer aligned more closely with human morphological judgments than PubMedBERT's tokenizer. The paper is Biomedical Language Models are Robust to Sub-optimal Tokenization, posted 2023-06-30. The Hub repo was created on 2023-06-05. The README includes an Apache-2.0 license line. Status on 2026-10-15 is active.", "what_it_is_for": "A biomedical masked language model whose contribution, as the card states it, is the tokenizer rather than a new downstream product. Pipeline tag on the Hub is fill-mask.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/dreamer/dreamer-72b": { "id": "osu/dreamer/dreamer-72b", "name": "Dreamer-72B", "maker": "osu", "family": "osu/dreamer", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-03", "context_length": null, "max_output_tokens": null, "parameters": "73405560320", "architecture": "Fine-tune of Qwen/Qwen2-VL-72B. Safetensors total 73405560320.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dreamer-72B is the larger WebDreamer checkpoint from The Ohio State University NLP Group and Orby AI. The Hub repo osunlp/Dreamer-72B was created on 2025-04-03. It is a fine-tune of Qwen2-VL-72B with Apache-2.0 on the card. Status on 2026-10-15 is active. The 7B card does not list domain variants at 72B.", "what_it_is_for": "Same web-simulation planning role as Dreamer-7B, at the 72B Qwen2-VL scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/dreamer/dreamer-7b": { "id": "osu/dreamer/dreamer-7b", "name": "Dreamer-7B", "maker": "osu", "family": "osu/dreamer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-03", "context_length": null, "max_output_tokens": null, "parameters": "8291375616", "architecture": "Fine-tune of Qwen/Qwen2-VL-7B used as a web world model. Safetensors total 8291375616.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dreamer-7B is the general web world-model checkpoint from The Ohio State University NLP Group and Orby AI, released under the WebDreamer project. The Hub repo was created on 2025-04-03. It is a fine-tune of Qwen2-VL-7B. Status on 2026-10-15 is active. Shopping, Classifieds, and Reddit variants are versions of this folder.", "what_it_is_for": "The card describes WebDreamer as a planner that simulates web actions instead of trying every action on the live site. Dreamer-7B is the general checkpoint. Three in-domain VisualWebArena checkpoints specialize it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/early-experience/early-experience-14b": { "id": "osu/early-experience/early-experience-14b", "name": "Early Experience 14B", "maker": "osu", "family": "osu/early-experience", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2026-07-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Qwen/Qwen2.5-14B-Instruct. Hub id osunlp/early-experience-appworld.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Early Experience 14B is the AppWorld checkpoint from The Ohio State University NLP Group's Early Experience release. Hub repo osunlp/early-experience-appworld was created on 2026-07-07. The base model tag is Qwen/Qwen2.5-14B-Instruct. License MIT. Status on 2026-10-15 is active. It is the only 14B repo in that collection.", "what_it_is_for": "AppWorld agent checkpoint for the Early Experience paper. The 7B card's pattern of il, sr, and iwm subfolders was not re-checked inside this repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/early-experience/early-experience-7b": { "id": "osu/early-experience/early-experience-7b", "name": "Early Experience 7B", "maker": "osu", "family": "osu/early-experience", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-07-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2.5-7B-Instruct fine-tunes. Each environment repo has il, sr, and iwm subfolders.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Early Experience 7B is The Ohio State University NLP Group's release of agent checkpoints for the paper Agent Learning via Early Experience. The webshop card, created 2026-07-07, says the base is Qwen2.5-7B-Instruct and that each environment repo has il, sr, and iwm subfolders. License MIT. Status on 2026-10-15 is active. Six 7B environment repos are versions of this folder.", "what_it_is_for": "Agent policies trained with the paper's early-experience methods. The webshop card says the repo reproduces the paper and points at the dataset osunlp/early-experience. Subfolders are il, sr, and iwm.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/gui-drag/gui-drag-3b": { "id": "osu/gui-drag/gui-drag-3b", "name": "GUI-Drag-3B", "maker": "osu", "family": "osu/gui-drag", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-09-27", "context_length": null, "max_output_tokens": null, "parameters": "4065787904", "architecture": "Continual training from Jedi-3B for text dragging plus clicks. Safetensors total 4065787904.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GUI-Drag-3B is a GUI grounding model from The Ohio State University NLP Group, with Microsoft Research on the paper. The project says the GUI-Drag-3B weights continue from Jedi-3B so the model can drag-select text and still click. Hub repos were created on 2025-09-27. The preprint date is 2025-11-07. Status on 2026-10-15 is active.", "what_it_is_for": "Ground text-dragging actions on a GUI, not only clicks. The 7B card demonstrates a computer_use tool call that includes left_click_drag. The project says 161K synthetic dragging examples were used.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/gui-drag/gui-drag-7b": { "id": "osu/gui-drag/gui-drag-7b", "name": "GUI-Drag-7B", "maker": "osu", "family": "osu/gui-drag", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-09-27", "context_length": null, "max_output_tokens": null, "parameters": "8292166656", "architecture": "Continual training from Jedi-7B for text dragging plus clicks. Safetensors total 8292166656.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GUI-Drag-7B is a GUI grounding model from The Ohio State University NLP Group, with Microsoft Research on the paper. The project says the GUI-Drag-7B weights continue from Jedi-7B so the model can drag-select text and still click. Hub repos were created on 2025-09-27. The preprint date is 2025-11-07. Status on 2026-10-15 is active.", "what_it_is_for": "Ground text-dragging actions on a GUI, not only clicks. The 7B card demonstrates a computer_use tool call that includes left_click_drag. The project says 161K synthetic dragging examples were used.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/intr/intr": { "id": "osu/intr/intr", "name": "INTR", "maker": "osu", "family": "osu/intr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Interpretable transformer. Card checkpoint is CUB with a DETR-R50 backbone.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "INTR is an interpretable fine-grained image classifier from the Imageomics Institute at The Ohio State University. The card documents a CUB checkpoint with a DETR-R50 backbone. The paper date is 2023-11-07. The Hub repo was created on 2023-11-05. License Apache-2.0. Status on 2026-10-15 is active.", "what_it_is_for": "Fine-grained image classification with interpretations. The card says the checked-in checkpoint is the CUB run. Authors include Ohio State and collaborators. The backbone is DETR-R50.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/llasmol/llasmol-codellama-7b": { "id": "osu/llasmol/llasmol-codellama-7b", "name": "LlaSMol-CodeLlama-7B", "maker": "osu", "family": "osu/llasmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-04-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter on Code Llama 7B, trained on SMolInstruct. The card says the repo contains the adapter weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LlaSMol-CodeLlama-7B is one of four chemistry instruction-tuned adapters from The Ohio State University NLP Group. The card says it is a LoRA tune of Code Llama 7B on SMolInstruct. Status on 2026-10-15 is active. The Hugging Face repo date used here is 2024-04-22.", "what_it_is_for": "The homepage says LlaSMol runs small-molecule chemistry tasks: name conversion, property prediction, and generation, including IUPAC names and retrosynthesis. This repo holds the low-rank adapter, not the base weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/llasmol/llasmol-galactica-6-7b": { "id": "osu/llasmol/llasmol-galactica-6-7b", "name": "LlaSMol-Galactica-6.7B", "maker": "osu", "family": "osu/llasmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-04-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter on Galactica 6.7B, trained on SMolInstruct. The card says the repo contains the adapter weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LlaSMol-Galactica-6.7B is one of four chemistry instruction-tuned adapters from The Ohio State University NLP Group. The card says it is a LoRA tune of Galactica 6.7B on SMolInstruct. Status on 2026-10-15 is active. The Hugging Face repo date used here is 2024-04-22.", "what_it_is_for": "The homepage says LlaSMol runs small-molecule chemistry tasks: name conversion, property prediction, and generation, including IUPAC names and retrosynthesis. This repo holds the low-rank adapter, not the base weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/llasmol/llasmol-llama2-7b": { "id": "osu/llasmol/llasmol-llama2-7b", "name": "LlaSMol-Llama2-7B", "maker": "osu", "family": "osu/llasmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-04-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter on Llama 2 7B, trained on SMolInstruct. The card says the repo contains the adapter weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LlaSMol-Llama2-7B is one of four chemistry instruction-tuned adapters from The Ohio State University NLP Group. The card says it is a LoRA tune of Llama 2 7B on SMolInstruct. Status on 2026-10-15 is active. The Hugging Face repo date used here is 2024-04-22.", "what_it_is_for": "The homepage says LlaSMol runs small-molecule chemistry tasks: name conversion, property prediction, and generation, including IUPAC names and retrosynthesis. This repo holds the low-rank adapter, not the base weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/llasmol/llasmol-mistral-7b": { "id": "osu/llasmol/llasmol-mistral-7b", "name": "LlaSMol-Mistral-7B", "maker": "osu", "family": "osu/llasmol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2024-02-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter on mistralai/Mistral-7B-v0.1, trained on SMolInstruct. The card says the repo contains the adapter weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LlaSMol-Mistral-7B is one of four chemistry instruction-tuned adapters from The Ohio State University NLP Group. The card says it is a LoRA tune of mistralai/Mistral-7B-v0.1 on SMolInstruct. Status on 2026-10-15 is active. The Hugging Face repo date used here is 2024-02-13.", "what_it_is_for": "The homepage says LlaSMol runs small-molecule chemistry tasks: name conversion, property prediction, and generation, including IUPAC names and retrosynthesis. This repo holds the low-rank adapter, not the base weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/magicbrush/hive-magicbrush": { "id": "osu/magicbrush/hive-magicbrush", "name": "HIVE-MagicBrush", "maker": "osu", "family": "osu/magicbrush", "status": "active", "confidence": "high", "weights": "open-weights", "license": "creativeml-openrail-m", "release_date": "2023-11-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HIVE checkpoint fine-tuned on MagicBrush. File MagicBrush-epoch-000130.ckpt.", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HIVE-MagicBrush is The Ohio State University NLP Group's HIVE image-editing checkpoint fine-tuned on MagicBrush. The Hugging Face repo was created on 2023-11-07. Status on 2026-10-15 is active. License field is CreativeML Open RAIL-M. The card's file is MagicBrush-epoch-000130.ckpt.", "what_it_is_for": "Instruction-guided image editing, using the HIVE code base rather than InstructPix2Pix. The card points at the original HIVE environment and one MagicBrush checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/magicbrush/instructpix2pix-magicbrush": { "id": "osu/magicbrush/instructpix2pix-magicbrush", "name": "InstructPix2Pix-MagicBrush", "maker": "osu", "family": "osu/magicbrush", "status": "active", "confidence": "high", "weights": "open-weights", "license": "creativeml-openrail-m", "release_date": "2023-06-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "InstructPix2Pix weights fine-tuned on MagicBrush. Recommended file MagicBrush-epoch-52-step-4999.ckpt. Paper file MagicBrush-epoch-000168.ckpt.", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InstructPix2Pix-MagicBrush is The Ohio State University NLP Group's InstructPix2Pix checkpoint fine-tuned on the MagicBrush editing data. The Hugging Face repo was created on 2023-06-18. Status on 2026-10-15 is active. The card's license field is CreativeML Open RAIL-M. Two checkpoints are in the repo.", "what_it_is_for": "Instruction-guided editing of real images. The card says the fine-tune is for fine-grained edits and may not do style transfer or edits of a large region well.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/mindact/mindact-action-prediction": { "id": "osu/mindact/mindact-action-prediction", "name": "MindAct action prediction", "maker": "osu", "family": "osu/mindact", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flan-T5 text-to-text checkpoints at base, large, and xl, released as MindAct action prediction.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MindAct action prediction is the second stage of The Ohio State University NLP Group's Mind2Web agent. Three Flan-T5 checkpoints were uploaded on 2023-08-05: base, large, and xl. None of the three repos returned a README. Status on 2026-10-15 is active. The paper describes this stage as choosing an element and an action from the filtered candidates.", "what_it_is_for": "Predict the next web action from the candidate elements produced by the DeBERTa ranker. The released weights are Flan-T5 base, large, and xl under the MindAct_ActionPrediction names.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/mindact/mindact-candidate-generation": { "id": "osu/mindact/mindact-candidate-generation", "name": "MindAct candidate generation", "maker": "osu", "family": "osu/mindact", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-06", "context_length": null, "max_output_tokens": null, "parameters": "86000000", "architecture": "DeBERTa-v3-base cross-encoder scoring a task query against a DOM element. Paper states DeBERTa-base at 86M parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MindAct candidate generation is the DeBERTa-v3-base ranker from The Ohio State University NLP Group's Mind2Web project. The Hugging Face repo osunlp/MindAct_CandidateGeneration_deberta-v3-base was created on 2023-08-06 and has no model card. Status on 2026-10-15 is active. The Mind2Web paper says the candidate generator is DeBERTa-base with 86 million parameters.", "what_it_is_for": "First stage of MindAct. Given a task, a page, and an element, the encoder scores whether that element is a plausible next target. The GitHub README says the tuned DeBERTa-v3-base outputs have Recall@50 around 85 percent, and that the top elements are what action prediction sees.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/mmla/mmla": { "id": "osu/mmla/mmla", "name": "MMLA", "maker": "osu", "family": "osu/mmla", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-05-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "YOLO11m fine-tune for zebra, giraffe, onager, and African painted dog detection in low-altitude drone images.", "modalities": { "input": [ "image" ], "output": [ "bounding-boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MMLA is a wildlife detector from the Imageomics Institute at The Ohio State University. The card says it is a fine-tune of YOLO11m for low-altitude drone images of zebras, giraffes, Persian onagers, and African painted dogs. The paper date is 2025-04-10. The Hub repo was created on 2025-05-12. License MIT. The card names Jenna Kline as the developer. Status on 2026-10-15 is active.", "what_it_is_for": "Detect and classify those four wildlife groups in drone footage for ecology and biodiversity surveys. The card says it is for direct use by researchers and wildlife managers, not as a general detector.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/prompt-cam/prompt-cam": { "id": "osu/prompt-cam/prompt-cam", "name": "Prompt-CAM", "maker": "osu", "family": "osu/prompt-cam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-05-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Class-specific prompts on ViT-B DINO and ViT-B DINOv2. Checkpoints for CUB, Oxford pets, Stanford cars, Stanford dogs, and Birds-525.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Prompt-CAM is an interpretable vision transformer from the Imageomics Institute at The Ohio State University. The card says it injects class-specific prompts into a pretrained ViT so attention maps show the traits used for a class. Backbones are ViT-B DINO and ViT-B DINOv2. The paper date is 2025-01-16. The Hub repo was created on 2026-05-23. License MIT. It is a CVPR 2025 paper. Status on 2026-10-15 is active.", "what_it_is_for": "Fine-grained classification plus a per-class heatmap. The released checkpoints cover CUB-200-2011, Oxford-IIIT Pet, Stanford Cars, Stanford Dogs, and Birds-525. The card says it is not a detector or segmenter, and that a bird checkpoint should not be applied to medical images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/quest/quest-2b": { "id": "osu/quest/quest-2b", "name": "QUEST-2B", "maker": "osu", "family": "osu/quest", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-26", "context_length": null, "max_output_tokens": null, "parameters": "2213241664", "architecture": "Qwen3.5 dense checkpoint (Hub tags qwen3_5 and image-text-to-text). Safetensors total 2213241664. The project calls the scored checkpoint QUEST-2B-SFT.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QUEST is an open deep-research agent family from The Ohio State University NLP Group, with Amazon AGI SF Lab listed on the project page. QUEST-2B is one size. Weights were announced with the 2026-05-26 release. Status on 2026-10-15 is active. The project recommends RL checkpoints when both objective and open-ended tasks matter, and MT+SFT when the job is objective only.", "what_it_is_for": "The project page describes QUEST as a general-purpose deep research agent for fact seeking, citation grounding, and report synthesis. It uses synthetic rubric trees, a structured context condenser, and tool-use trajectories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/quest/quest-30b": { "id": "osu/quest/quest-30b", "name": "QUEST-30B", "maker": "osu", "family": "osu/quest", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-26", "context_length": null, "max_output_tokens": null, "parameters": "30532122624", "architecture": "Qwen3 MoE text model (Hub tags qwen3_moe). The project says this size was trained from Qwen3-30B-A3B. Safetensors total 30532122624. No image-text-to-text tag on the 30B repos.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QUEST is an open deep-research agent family from The Ohio State University NLP Group, with Amazon AGI SF Lab listed on the project page. QUEST-30B is one size. Weights were announced with the 2026-05-26 release. Status on 2026-10-15 is active. The project recommends RL checkpoints when both objective and open-ended tasks matter, and MT+SFT when the job is objective only.", "what_it_is_for": "The project page describes QUEST as a general-purpose deep research agent for fact seeking, citation grounding, and report synthesis. It uses synthetic rubric trees, a structured context condenser, and tool-use trajectories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/quest/quest-35b": { "id": "osu/quest/quest-35b", "name": "QUEST-35B", "maker": "osu", "family": "osu/quest", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-26", "context_length": null, "max_output_tokens": null, "parameters": "35107181936", "architecture": "Qwen3.5 MoE (config architecture Qwen3_5MoeForConditionalGeneration, model type qwen3_5_moe). The project compares this line with Qwen3.5-35B-A3B. Safetensors total 35107181936. Chat template includes image and video tokens and tool calls.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QUEST is an open deep-research agent family from The Ohio State University NLP Group, with Amazon AGI SF Lab listed on the project page. QUEST-35B is one size. Weights were announced with the 2026-05-26 release. Status on 2026-10-15 is active. The project recommends RL checkpoints when both objective and open-ended tasks matter, and MT+SFT when the job is objective only.", "what_it_is_for": "The project page describes QUEST as a general-purpose deep research agent for fact seeking, citation grounding, and report synthesis. It uses synthetic rubric trees, a structured context condenser, and tool-use trajectories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/quest/quest-4b": { "id": "osu/quest/quest-4b", "name": "QUEST-4B", "maker": "osu", "family": "osu/quest", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-15", "context_length": null, "max_output_tokens": null, "parameters": "4539265536", "architecture": "Qwen3.5 dense checkpoint (Hub tags qwen3_5 and image-text-to-text). Safetensors total 4539265536. Hub repo created 2026-04-15, before the 2026-05-26 project announcement.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QUEST is an open deep-research agent family from The Ohio State University NLP Group, with Amazon AGI SF Lab listed on the project page. QUEST-4B is one size. Weights were announced with the 2026-05-26 release. Status on 2026-10-15 is active. The project recommends RL checkpoints when both objective and open-ended tasks matter, and MT+SFT when the job is objective only.", "what_it_is_for": "The project page describes QUEST as a general-purpose deep research agent for fact seeking, citation grounding, and report synthesis. It uses synthetic rubric trees, a structured context condenser, and tool-use trajectories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/quest/quest-9b": { "id": "osu/quest/quest-9b", "name": "QUEST-9B", "maker": "osu", "family": "osu/quest", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-26", "context_length": null, "max_output_tokens": null, "parameters": "9409813744", "architecture": "Qwen3.5 dense checkpoint (Hub tags qwen3_5 and image-text-to-text). Safetensors total 9409813744. The project lists 9B among the smaller SFT checkpoints.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QUEST is an open deep-research agent family from The Ohio State University NLP Group, with Amazon AGI SF Lab listed on the project page. QUEST-9B is one size. Weights were announced with the 2026-05-26 release. Status on 2026-10-15 is active. The project recommends RL checkpoints when both objective and open-ended tasks matter, and MT+SFT when the job is objective only.", "what_it_is_for": "The project page describes QUEST as a general-purpose deep research agent for fact seeking, citation grounding, and report synthesis. It uses synthetic rubric trees, a structured context condenser, and tool-use trajectories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/reasonbert/reasonbert-bert-base": { "id": "osu/reasonbert/reasonbert-bert-base", "name": "ReasonBERT-BERT-base", "maker": "osu", "family": "osu/reasonbert", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base continued with ReasonBERT pre-training.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReasonBERT-BERT-base is a ReasonBERT checkpoint released by The Ohio State University NLP Group, with Google Research coauthors on the paper. The paper is dated 2021-09-10 and says code and pre-trained models are on github.com/sunlab-osu/ReasonBERT. The Hub repo osunlp/ReasonBERT-BERT-base dates from 2022-03-02 and has no README. Status on 2026-10-15 is active.", "what_it_is_for": "ReasonBERT continues pre-training so a model can reason over more than one passage or over a table. The three Hub repos are the BERT, RoBERTa, and TAPAS bases. This folder is the ReasonBERT-BERT-base checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/reasonbert/reasonbert-roberta-base": { "id": "osu/reasonbert/reasonbert-roberta-base", "name": "ReasonBERT-RoBERTa-base", "maker": "osu", "family": "osu/reasonbert", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-base continued with ReasonBERT pre-training.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReasonBERT-RoBERTa-base is a ReasonBERT checkpoint released by The Ohio State University NLP Group, with Google Research coauthors on the paper. The paper is dated 2021-09-10 and says code and pre-trained models are on github.com/sunlab-osu/ReasonBERT. The Hub repo osunlp/ReasonBERT-RoBERTa-base dates from 2022-03-02 and has no README. Status on 2026-10-15 is active.", "what_it_is_for": "ReasonBERT continues pre-training so a model can reason over more than one passage or over a table. The three Hub repos are the BERT, RoBERTa, and TAPAS bases. This folder is the ReasonBERT-RoBERTa-base checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/reasonbert/reasonbert-tapas-base": { "id": "osu/reasonbert/reasonbert-tapas-base", "name": "ReasonBERT-TAPAS-base", "maker": "osu", "family": "osu/reasonbert", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "110672640", "architecture": "TAPAS-base continued with ReasonBERT pre-training. Safetensors total 110672640.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReasonBERT-TAPAS-base is a ReasonBERT checkpoint released by The Ohio State University NLP Group, with Google Research coauthors on the paper. The paper is dated 2021-09-10 and says code and pre-trained models are on github.com/sunlab-osu/ReasonBERT. The Hub repo osunlp/ReasonBERT-TAPAS-base dates from 2022-03-02 and has no README. Status on 2026-10-15 is active.", "what_it_is_for": "ReasonBERT continues pre-training so a model can reason over more than one passage or over a table. The three Hub repos are the BERT, RoBERTa, and TAPAS bases. This folder is the ReasonBERT-TAPAS-base checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/tablellama/tablellama": { "id": "osu/tablellama/tablellama", "name": "TableLlama", "maker": "osu", "family": "osu/tablellama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2023-11-20", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of LongLoRA 7B, described as Llama-2 7B with shift short attention. Trained 9 days on 48 80GB A100s on 2.6 million table instances.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TableLlama is an open table generalist from The Ohio State University NLP Group. The model card describes a Llama-2 7B model tuned with LongLoRA on the TableInstruct data. The Hugging Face repo was created on 2023-11-20. The preprint is 2023-11-15. Status on 2026-10-15 is active. It was a NAACL 2024 paper. No safetensors parameter total was returned by the Hub API.", "what_it_is_for": "The card presents TableLlama as an open generalist for table tasks. It is tuned on 2.6 million table-task instances drawn from 14 datasets and 11 tasks, and the card says it can handle up to 8K context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/trait2vec/trait2vec": { "id": "osu/trait2vec/trait2vec", "name": "Trait2Vec", "maker": "osu", "family": "osu/trait2vec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-26", "context_length": null, "max_output_tokens": null, "parameters": "109486464", "architecture": "Fine-tune of sentence-transformers/all-mpnet-base-v2. Safetensors total 109486464.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Trait2Vec is a trait-phrase embedding model from the Imageomics Institute at The Ohio State University. It is a fine-tune of sentence-transformers/all-mpnet-base-v2. The Hub repo was created on 2025-10-26. License MIT. Safetensors total 109,486,464. Status on 2026-10-15 is active.", "what_it_is_for": "Embed morphological trait statements so similar traits sit together. The card's model index reports cosine correlations on a set it calls pheno dev.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/uground/uground": { "id": "osu/uground/uground", "name": "UGround", "maker": "osu", "family": "osu/uground", "status": "active", "confidence": "high", "weights": "open-weights", "license": "llama2", "release_date": "2024-08-02", "context_length": null, "max_output_tokens": null, "parameters": "7062579200", "architecture": "LLaVA-based GUI grounding model (card name LLaVA-UGround-V1). The initial paper checkpoint. Safetensors total 7062579200.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UGround is the initial LLaVA-based GUI visual grounding model from The Ohio State University NLP Group, with Orby AI. It maps a referring expression on a screenshot to a point. On 2026-10-15 the weights are still public. The model card says to prefer the later Qwen2-VL UGround-V1 weights for deployment. This dossier uses the 2024-08-02 Hugging Face repo date as the weight release and the 2024-10-07 preprint as the paper date.", "what_it_is_for": "The homepage calls UGround a universal visual grounding model that locates a GUI element by pixel coordinates. It was trained on about 10 million elements from 1.3 million screenshots, mostly web, and is the grounding module in the SeeAct-V vision-only agent setup. The initial checkpoint is the one used in the ICLR 2025 paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/uground/uground-v1-2b": { "id": "osu/uground/uground-v1-2b", "name": "UGround-V1-2B", "maker": "osu", "family": "osu/uground", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-03", "context_length": null, "max_output_tokens": null, "parameters": "2208985600", "architecture": "Fine-tune of Qwen/Qwen2-VL-2B for GUI point grounding. Safetensors total 2208985600.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UGround-V1-2B is the 2B Qwen2-VL GUI grounding model from The Ohio State University NLP Group and Orby AI. It is part of the V1 series released on 2025-01-03. Status on 2026-10-15 is active. The same UGround-V1 synthetic data is used across the 2B, 7B, and 72B cards.", "what_it_is_for": "Same job as the rest of UGround: given a screenshot and a description, return the point of the element. The 2B checkpoint is the smallest Qwen2-VL fine-tune in the V1 series.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/uground/uground-v1-72b": { "id": "osu/uground/uground-v1-72b", "name": "UGround-V1-72B", "maker": "osu", "family": "osu/uground", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other", "release_date": "2025-01-11", "context_length": null, "max_output_tokens": null, "parameters": "73405560320", "architecture": "Fine-tune of Qwen/Qwen2-VL-72B. Safetensors total 73405560320 on both the release and preview repos.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UGround-V1-72B is the largest Qwen2-VL GUI grounding model in the Ohio State and Orby AI UGround-V1 series. The main repo was created on Hugging Face on 2025-01-11. A preview repo from 2025-01-07 has the same safetensors total. Status on 2026-10-15 is active. The Hugging Face license field is other, not Apache-2.0.", "what_it_is_for": "Same GUI point-grounding job as the smaller V1 models, at the 72B Qwen2-VL scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/uground/uground-v1-7b": { "id": "osu/uground/uground-v1-7b", "name": "UGround-V1-7B", "maker": "osu", "family": "osu/uground", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-03", "context_length": null, "max_output_tokens": null, "parameters": "8291375616", "architecture": "Fine-tune of Qwen/Qwen2-VL-7B. Safetensors total 8291375616. Point outputs are in [0, 1000).", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UGround-V1-7B is the 7B Qwen2-VL GUI visual grounding model from The Ohio State University NLP Group and Orby AI. Weights were released on 2025-01-03. Status on 2026-10-15 is active. It is the checkpoint the lab's cards use for the main inference example.", "what_it_is_for": "Locate a GUI element from a natural-language description and return one point near its center. The card says an unclear description should be resolved toward the most relevant element. It is meant to be the grounding module for vision-only GUI agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "osu/webjudge/webjudge-7b": { "id": "osu/webjudge/webjudge-7b", "name": "WebJudge-7B", "maker": "osu", "family": "osu/webjudge", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-11", "context_length": null, "max_output_tokens": null, "parameters": "8292166656", "architecture": "Fine-tune of Qwen/Qwen2.5-VL-7B-Instruct. Safetensors total 8292166656.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WebJudge-7B is a web-agent evaluator from The Ohio State University NLP Group. It is a fine-tune of Qwen2.5-VL-7B-Instruct, released on the Hub on 2025-05-11. The paper is An Illusion of Progress? Assessing the Current State of Web Agents (2025-04-02). Status on 2026-10-15 is active. The safetensors total is 8,292,166,656. The card badge says 7B.", "what_it_is_for": "Judge whether a web agent succeeded. The card says WebJudge keeps important intermediate screenshots while cutting the token load of a full trace. It is tied to the Online-Mind2Web evaluation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/ablang/ablang": { "id": "oxford/ablang/ablang", "name": "AbLang", "maker": "oxford", "family": "oxford/ablang", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause", "release_date": "2022-01-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Antibody masked language model from the Oxford Protein Informatics Group, trained on Observed Antibody Space. Separate heavy-chain and light-chain checkpoints. The README states residue encodings and sequence encodings of 768 values. Layer count is not stated there.", "modalities": { "input": [ "protein-sequence" ], "output": [ "embedding", "amino-acid-likelihoods" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AbLang is the antibody language model released by the Oxford Protein Informatics Group at the University of Oxford. Heavy and light checkpoints ship in the Python package. Collected on 2026-10-15.", "what_it_is_for": "Restore residues missing from antibody sequences and produce residue or sequence embeddings and per-position amino-acid likelihoods. The authors say it does not need the germline to be known.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/ablang/ablang2": { "id": "oxford/ablang/ablang2", "name": "AbLang-2", "maker": "oxford", "family": "oxford/ablang", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause", "release_date": "2024-02-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Antibody language model trained to reduce germline bias. The package exposes a tokenizer, an AbRep module for residue embeddings, and an AbLang head for amino-acid likelihoods. The documented pretrained call uses model_to_use='ablang2-paired', with heavy and light chains joined by a vertical bar. Hidden size and parameter count are not stated in the README.", "modalities": { "input": [ "protein-sequence" ], "output": [ "embedding", "amino-acid-likelihoods" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AbLang-2 is the Oxford Protein Informatics Group antibody language model trained to suggest mutations away from germline. The paired checkpoint is in the public Python package. Collected on 2026-10-15.", "what_it_is_for": "Antibody design support: residue embeddings and amino-acid likelihoods that are less stuck on germline residues, including on paired heavy and light sequences.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/ablang/tcrlang-paired": { "id": "oxford/ablang/tcrlang-paired", "name": "TCRLang-Paired", "maker": "oxford", "family": "oxford/ablang", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AbLang-2 architecture initialised with weights trained on paired T-cell receptor sequences. The AbLang2 README says sequence and residue encodings and masking match AbLang-2. The align command is not available for this checkpoint.", "modalities": { "input": [ "protein-sequence" ], "output": [ "embedding", "amino-acid-likelihoods" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TCRLang-Paired is the paired T-cell receptor checkpoint shipped beside AbLang-2 by the Oxford Protein Informatics Group. It is a named weight release in that package. Collected on 2026-10-15.", "what_it_is_for": "Embeddings and masked likelihoods for paired TCR sequences, using the same interface as AbLang-2 except alignment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/anarcii/anarcii": { "id": "oxford/anarcii/anarcii", "name": "ANARCII", "maker": "oxford", "family": "oxford/anarcii", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause", "release_date": "2025-04-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Language model for numbering antigen-receptor sequences, from the Oxford Protein Informatics Group. Layer count and parameter count are not in the README.", "modalities": { "input": [ "protein-sequence" ], "output": [ "numbering" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ANARCII is the 2025 antigen-receptor numbering language model from the Oxford Protein Informatics Group. A pip package, Zenodo archive and web tool are public. Collected on 2026-10-15.", "what_it_is_for": "Assign numbering to antibody, nanobody and T-cell receptor sequences. Other OPIG tools, including current AntiFold, call it for IMGT numbering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/antifold/antifold": { "id": "oxford/antifold/antifold", "name": "AntiFold", "maker": "oxford", "family": "oxford/antifold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Inverse folding model based on ESM-IF1 and fine-tuned on solved and predicted antibody structures from SAbDab and OAS. Official weights are model.pt on GitHub release weights-v1 and an OPIG mirror. The code can also run stock ESM-IF1 weights. Parameter count of the fine-tune is not stated in the README.", "modalities": { "input": [ "structure" ], "output": [ "sequence", "amino-acid-likelihoods" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AntiFold is the antibody inverse-folding model released by the Oxford Protein Informatics Group. Weights, a web server and a Python package are public. Collected on 2026-10-15.", "what_it_is_for": "Given an antibody or nanobody variable-domain structure, score amino acids and sample sequences that keep that fold. Antigen context and region-limited design are optional.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/countgd/countgd": { "id": "oxford/countgd/countgd", "name": "CountGD", "maker": "oxford", "family": "oxford/countgd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-07-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Open-world counter built on GroundingDINO with a Swin-B backbone, plus modules so the object can be specified by text, visual exemplars, or both. Inference scripts also load BERT and, in the published reproduction commands, a separate Segment Anything checkpoint. The paper's CountGD weights are a 1.2 GB file.", "modalities": { "input": [ "image", "text" ], "output": [ "count" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CountGD is the open-world object counter from the Visual Geometry Group at the University of Oxford. It was posted with code and a pretrained checkpoint in 2024 and is succeeded in the same line by CountGD++. Collected on 2026-10-15.", "what_it_is_for": "Count an object named by text, by visual exemplars, or by both, in a single image. The authors position it as the first open-world counter with that joint prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/countgd/countgd-plus-plus": { "id": "oxford/countgd/countgd-plus-plus", "name": "CountGD++", "maker": "oxford", "family": "oxford/countgd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Successor counter from VGG. The paper adds negative text and visual prompts, pseudo-exemplars that mark examples at inference, and exemplars from external natural or synthetic images. The authors also use it as a vision expert for an LLM controller. The README distributes a 1.25 GB pretrained checkpoint. It is described as building on CountGD-Box.", "modalities": { "input": [ "image", "text" ], "output": [ "count" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CountGD++ is the 2026 Visual Geometry Group counting model that generalises CountGD's prompts. Code and a pretrained checkpoint are public. Collected on 2026-10-15.", "what_it_is_for": "Count objects in images and video when the user can say what to count and what not to count, with text and visual examples, including examples that are found automatically or taken from another image. The paper also uses the model as a counting tool for an LLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/immunebuilder/abodybuilder2": { "id": "oxford/immunebuilder/abodybuilder2", "name": "ABodyBuilder2", "maker": "oxford", "family": "oxford/immunebuilder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause", "release_date": "2023", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Deep model in the ImmuneBuilder package that predicts antibody variable-domain structures from heavy and light sequences and can return an ensemble with a per-residue error estimate. OpenMM is used for an optional refinement step. Parameter count is not stated.", "modalities": { "input": [ "protein-sequence" ], "output": [ "structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ABodyBuilder2 is the antibody structure model in ImmuneBuilder, from the Oxford Protein Informatics Group. Weights ship in the public package. Collected on 2026-10-15.", "what_it_is_for": "Predict the 3D structure of an antibody variable domain from heavy and light amino-acid sequences, with a residue-level uncertainty estimate from a structural ensemble. The authors report it is much faster than AlphaFold-Multimer on their antibody set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/immunebuilder/nanobodybuilder2": { "id": "oxford/immunebuilder/nanobodybuilder2", "name": "NanoBodyBuilder2", "maker": "oxford", "family": "oxford/immunebuilder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause", "release_date": "2023", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ImmuneBuilder nanobody structure model. It takes one heavy-chain variable-domain sequence. Same package and refinement stack as ABodyBuilder2. Parameter count is not stated.", "modalities": { "input": [ "protein-sequence" ], "output": [ "structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NanoBodyBuilder2 is the nanobody structure model released with ImmuneBuilder by the Oxford Protein Informatics Group. Collected on 2026-10-15.", "what_it_is_for": "Predict a nanobody structure from one heavy-chain sequence, with the same ensemble uncertainty idea as the other ImmuneBuilder models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/immunebuilder/tcrbuilder2": { "id": "oxford/immunebuilder/tcrbuilder2", "name": "TCRBuilder2", "maker": "oxford", "family": "oxford/immunebuilder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause", "release_date": "2023", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ImmuneBuilder model for T-cell receptor structures from alpha and beta sequences. Package 1.1.1 makes TCRBuilder2+ the default weights. The original TCRBuilder2 weights remain available with use_TCRBuilder2_PLUS_weights=False or the CLI flag --original_weights. Parameter count is not stated.", "modalities": { "input": [ "protein-sequence" ], "output": [ "structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TCRBuilder2 is the T-cell receptor structure model in ImmuneBuilder from the Oxford Protein Informatics Group. The package now defaults to the later TCRBuilder2+ weights and still exposes the original weights. Collected on 2026-10-15.", "what_it_is_for": "Predict TCR alpha-beta structures from sequence, with an ensemble error estimate, as the TCR member of the ImmuneBuilder set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/vgg/vgg-16": { "id": "oxford/vgg/vgg-16", "name": "VGG-16", "maker": "oxford", "family": "oxford/vgg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2014-09-04", "context_length": null, "max_output_tokens": null, "parameters": "138000000", "architecture": "Configuration D from Simonyan and Zisserman: 13 convolutional layers with 3x3 filters and 3 fully connected layers (16 weight layers). Channel widths rise 64, 128, 256, 512, 512, with 2x2 max-pooling, ReLU, dropout 0.5 on the first two fully connected layers, and a 1000-way softmax. The released Caffe deploy file uses a 224x224x3 input.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VGG-16 is the 16-weight-layer ImageNet ConvNet released by the Visual Geometry Group at the University of Oxford. The weights are still publicly posted. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors released it as one of their two best ImageNet classification models, for large-scale visual recognition and as a transferable visual representation. The ILSVRC-2014 VGG entry used very deep ConvNets of this family.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/vgg/vgg-19": { "id": "oxford/vgg/vgg-19", "name": "VGG-19", "maker": "oxford", "family": "oxford/vgg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2014-09-04", "context_length": null, "max_output_tokens": null, "parameters": "144000000", "architecture": "Configuration E: 16 convolutional layers with 3x3 filters and 3 fully connected layers (19 weight layers). Same 224x224x3 input, ReLU, 2x2 max-pooling, dropout 0.5 on fc6 and fc7, and a 1000-way softmax as VGG-16, with extra 3x3 convolutions in the deeper blocks. Caffe name VGG_ILSVRC_19_layers.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VGG-19 is the 19-weight-layer ImageNet ConvNet released with VGG-16 by the Visual Geometry Group at the University of Oxford. The weights remain on the project page. Collected on 2026-10-15.", "what_it_is_for": "Image classification on ImageNet and a deeper transferable visual feature backbone. It is the second of the two models Oxford chose to release from the ILSVRC-2014 study.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/vgg/vgg-f": { "id": "oxford/vgg/vgg-f", "name": "VGG-F", "maker": "oxford", "family": "oxford/vgg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2014-07-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fast ImageNet CNN from Chatfield, Simonyan, Vedaldi and Zisserman (BMVC 2014), also called CNN-F. The paper's architecture table shows a first convolution of 64 filters at 11x11 with stride 4, then 256-channel 5x5 and 3x3 stages and fully connected layers ending in 4096, 4096 and 1000. Full layer-by-layer widths beyond that first row were not re-copied here.", "modalities": { "input": [ "image" ], "output": [ "class-scores", "features" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VGG-F is the fast ConvNet released by Oxford VGG with the 2014 Return of the Devil feature package. It is a named, weighted model, still described on the software page. Collected on 2026-10-15.", "what_it_is_for": "Computing the ConvNet image features used in Chatfield et al. 2014, including classification-style networks trained for the comparisons in that paper. The software page presents VGG-F, VGG-M and VGG-S as the released models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/vgg/vgg-m": { "id": "oxford/vgg/vgg-m", "name": "VGG-M", "maker": "oxford", "family": "oxford/vgg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2014-07-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Medium-speed ImageNet CNN from the same 2014 VGG release, also called CNN-M. Five convolutional stages plus fully connected layers. Exact per-layer widths were not transcribed from the paper table in this pass.", "modalities": { "input": [ "image" ], "output": [ "class-scores", "features" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VGG-M is the medium ConvNet in Oxford VGG's 2014 CNN-F/M/S release. Weights were posted for the feature-computation package. Collected on 2026-10-15.", "what_it_is_for": "Computing the ConvNet image features used in Chatfield et al. 2014, including classification-style networks trained for the comparisons in that paper. The software page presents VGG-F, VGG-M and VGG-S as the released models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/vgg/vgg-s": { "id": "oxford/vgg/vgg-s", "name": "VGG-S", "maker": "oxford", "family": "oxford/vgg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2014-07-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Slower, more accurate ImageNet CNN from the same 2014 VGG release, also called CNN-S. Five convolutional stages plus fully connected layers. Exact per-layer widths were not transcribed from the paper table in this pass.", "modalities": { "input": [ "image" ], "output": [ "class-scores", "features" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VGG-S is the slow ConvNet in Oxford VGG's 2014 CNN-F/M/S release. Weights were posted for the feature-computation package. Collected on 2026-10-15.", "what_it_is_for": "Computing the ConvNet image features used in Chatfield et al. 2014, including classification-style networks trained for the comparisons in that paper. The software page presents VGG-F, VGG-M and VGG-S as the released models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/vgg-face/vgg-face": { "id": "oxford/vgg-face/vgg-face", "name": "VGG-Face", "maker": "oxford", "family": "oxford/vgg-face", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Face descriptor CNN based on the VGG-Very-Deep-16 architecture, released with a cascade DPM face detector. MatConvNet, Torch and Caffe packages are posted. The paper also trained shallower configurations A and B; the software page describes the released descriptor as the VGG-Very-Deep-16 network.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VGG-Face is the face-recognition CNN descriptor released by the Visual Geometry Group at the University of Oxford with the Deep Face Recognition paper. Weights are posted for research use. Collected on 2026-10-15.", "what_it_is_for": "Computing a face descriptor from a detected and cropped face, for recognition on still images and video. The authors evaluate it on Labeled Faces in the Wild and YouTube Faces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/vggface2/resnet-50": { "id": "oxford/vggface2/resnet-50", "name": "VGGFace2 ResNet-50", "maker": "oxford", "family": "oxford/vggface2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2018-04-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-50 face-recognition network following He et al. CVPR 2016, trained with softmax. Feature width is 2048, or 256 or 128 when a lower-dimensional embedding is stacked on the final feature layer. Pretrain variants are trained on MS-Celeb-1M and then fine-tuned on VGGFace2. Input used for the reported IJB-B numbers is a center 224x224 crop from an image whose shorter side is 256.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VGGFace2 ResNet-50 is the ResNet face-recognition line released by Oxford VGG with the VGGFace2 dataset. Several embedding widths and a from-scratch versus fine-tuned pair are public checkpoints of this network. Collected on 2026-10-15.", "what_it_is_for": "1:1 face verification and face-feature extraction across pose and age. The authors report IJB-B verification for each released checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "oxford/vggface2/se-resnet-50": { "id": "oxford/vggface2/se-resnet-50", "name": "VGGFace2 SE-ResNet-50", "maker": "oxford", "family": "oxford/vggface2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2018-04-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SE-ResNet-50 following Hu, Shen and Sun, trained with softmax for face verification. Feature width 2048, or 256 or 128 with an added embedding layer. Caffe builds need the Axpy layer used by SENet. Same 224 crop protocol as the ResNet-50 line.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VGGFace2 SE-ResNet-50 is Oxford VGG's squeeze-and-excitation face network trained with the VGGFace2 release. The 256-d fine-tuned checkpoint is the strongest row on the authors' IJB-B table. Collected on 2026-10-15.", "what_it_is_for": "Face verification embeddings, including compact 128-d and 256-d variants, evaluated by the authors on IJB-B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "padova/wav2taste/wav2taste": { "id": "padova/wav2taste/wav2taste", "name": "wav2taste", "maker": "padova", "family": "padova/wav2taste", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-06-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Shared two-layer MLP (hidden size 256, ReLU, dropout 0.2) with a sigmoid head that maps a frozen audio embedding to a 5-D taste vector in [0, 1]. Fusion heads add a learned per-encoder sigmoid gate over concatenated embeddings. Encoders stay frozen and are not part of the released weights. Audio is mono, windowed at 15 seconds, then averaged across windows. The card lists 10 single-encoder heads and 7 gated fusions in one repo.", "modalities": { "input": [ "audio" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Padova model wav2taste, from the Centro di Sonologia Computazionale (Department of Information Engineering). Status: active. Collected 2026-10-15.", "what_it_is_for": "The model card says the heads support research on sound-taste correspondences, content-based music retrieval by taste profile, and baselines for the wav2taste benchmark. Non-commercial use only. `load_taste_model(\"vggish+mule\")` is the configuration the card calls strongest.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "panasonic/hipie/hipie": { "id": "panasonic/hipie/hipie", "name": "HIPIE", "maker": "panasonic", "family": "panasonic/hipie", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Open-vocabulary hierarchical segmentation model. Panasonic's press diagram uses an MAE image encoder and a BERT text encoder, fused with bi-directional cross-attention, then separate decoders for object and background regions. Released checkpoints use ResNet-50 and ViT-H backbones. The paper text encoder in the training stack is BERT.", "modalities": { "input": [ "image", "text" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HIPIE is a multimodal foundation model announced by Panasonic R&D Company of America on 21 November 2023, built with UC Berkeley. It segments and detects objects from an image plus text, including parts and subparts. Code and checkpoints are public under an MIT license. Collected 15 October 2026.", "what_it_is_for": "Panasonic says one model covers segmentation and recognition tasks that used to need a separate model per hierarchy. The stated uses are danger detection, robot targets, drone terrain, and medical images, with less on-site annotation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "panasonic/lavida/lavida-d": { "id": "panasonic/lavida/lavida-d", "name": "LaViDa-D", "maker": "panasonic", "family": "panasonic/lavida", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-27", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Same diffusion vision-language design as LaViDa-L, with Dream-7B as the language backbone instead of LLaDA-8B. Table 1 lists 7B parameters, 0.6M pretrain images, and 1.0M supervised images. The vision encoder in the shared method section is SigLIP-400M. A background sentence also names Dream-8B. The experiment table is the source for 7B.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaViDa-D is the Dream-7B variant of LaViDa, the diffusion vision-language family announced by Panasonic on 27 November 2025. The paper, not the press release, is what splits LaViDa into an 8B LLaDA model and this 7B Dream model. Collected 15 October 2026.", "what_it_is_for": "Same research job as LaViDa-L: image understanding with discrete diffusion text generation. Panasonic's press describes one model and does not assign this variant a separate product role.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "panasonic/lavida/lavida-l": { "id": "panasonic/lavida/lavida-l", "name": "LaViDa-L", "maker": "panasonic", "family": "panasonic/lavida", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-27", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Diffusion vision-language model. SigLIP-400M encodes five 384-pixel views of a 768-pixel image, then 2x2 average pooling yields 980 visual tokens. An MLP projects them into LLaDA-8B, a non-causal discrete diffusion language model. Stage 1 trains the projector on image-text pairs. Stage 2 jointly fine-tunes the stack for instruction following. The paper table lists 8B parameters, 0.6M pretrain images, and 1.0M supervised images. The body also says 558K stage-1 pairs.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LaViDa-L is the 8 billion parameter LLaDA variant of LaViDa, a diffusion vision-language model from UCLA, Panasonic AI Research, Adobe Research, and Salesforce Research. Panasonic announced the LaViDa name on 27 November 2025. The paper's default reported model is this LLaDA-8B backbone. A public bf16 checkpoint exists, without a license on its Hub page. Collected 15 October 2026.", "what_it_is_for": "Panasonic says it generates text from images with a diffusion process so speed can be traded against quality, including structured formats such as JSON. The paper targets visual question answering, documents, math, and constrained text such as poems. It is a research model, not a Panasonic API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "panasonic/omniflow/omniflow": { "id": "panasonic/omniflow/omniflow", "name": "OmniFlow", "maker": "panasonic", "family": "panasonic/omniflow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-02", "context_length": null, "max_output_tokens": null, "parameters": "3.4B", "architecture": "Any-to-any rectified-flow transformer. The paper extends the Stable Diffusion 3 MMDiT text-to-image architecture with audio and text generation modules that can be pretrained separately and then merged. Panasonic's press says specialized text-to-image and text-to-audio models are connected and then tuned on paired text, image, and audio. The released v0.5 transformer config names a Stable Diffusion 3.5 Medium path as its starting checkpoint.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text", "image", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmniFlow is a multimodal generative model that converts among text, images, and audio. UCLA, Panasonic AI Research, and Salesforce AI Research posted the paper and Apache-2.0 checkpoints on 2 December 2024. Panasonic Holdings announced it on 4 June 2025 for CVPR 2025. The paper lists 3.4 billion parameters. Collected 15 October 2026.", "what_it_is_for": "Panasonic says it generates site-specific text, images, and audio for factories and daily-life uses without needing a full three-way dataset for every sample. The research demo covers text-to-image, text-to-audio, and audio-to-image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "panasonic/panasonic-llm/panasonic-llm-100b": { "id": "panasonic/panasonic-llm/panasonic-llm-100b", "name": "Panasonic-LLM-100b", "maker": "panasonic", "family": "panasonic/panasonic-llm", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-07-02", "context_length": null, "max_output_tokens": null, "parameters": "100B", "architecture": "Japanese enterprise language model. Panasonic continued pretraining of Stockmark-LLM-100b on Panasonic Group internal data. Stockmark's base was described as a from-scratch model of about 100 billion parameters trained for Japanese business text. Panasonic did not publish its own architecture diagram.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Panasonic-LLM-100b is a group-only Japanese language model announced by Panasonic Holdings and Stockmark on 2 July 2024. It is additional pretraining of Stockmark-LLM-100b on Panasonic internal documents, at about 100 billion parameters. METI later described a trial inside departments, factories, and sites. Weights are not public. Collected 15 October 2026.", "what_it_is_for": "Panasonic said the model should answer from group knowledge in design, manufacturing, and other internal work, with fewer hallucinations than a general model on Panasonic-specific terms. It was not offered as a public API. EnterpriseZine reported no plan to provide it outside the group.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "panasonic/segllm/segllm": { "id": "panasonic/segllm/segllm", "name": "SegLLM", "maker": "panasonic", "family": "panasonic/segllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mask-aware multimodal model for multi-round segmentation. The paper's implementation uses CLIP ViT-L/14 as the image encoder, a frozen HIPIE-R50 mask decoder, and LLaVA-v1.5-7B as the language model. The LLM and the vision-to-language projector are fine-tuned. Previous masks are fed back into later turns.", "modalities": { "input": [ "image", "text" ], "output": [ "text", "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SegLLM is an interactive segmentation model announced by Panasonic Holdings and Panasonic R&D Company of America on 17 April 2025, with UC Berkeley. It takes text and a reference image and can continue a segmentation conversation. The preprint is from October 2024 and was accepted at ICLR 2025. Collected 15 October 2026.", "what_it_is_for": "Panasonic says it recognizes untrained objects and picks one object out of many similar ones by using language plus a reference image. Nikkei reported that Panasonic planned to put it into internal annotation tools in fiscal 2025 and, later, to swap the language model for Panasonic-LLM-100b. That swap was a plan, not a released product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "panasonic/uniegomotion/uniegomotion": { "id": "panasonic/uniegomotion/uniegomotion", "name": "UniEgoMotion", "maker": "panasonic", "family": "panasonic/uniegomotion", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One motion diffusion model for reconstruction, forecasting, and generation of 3D human motion from egocentric video and head trajectory. Panasonic says it uses a head-centric motion representation and a DINOv2 image encoder, with masking so one model covers all three tasks. No layer count or parameter count was published.", "modalities": { "input": [ "video", "motion" ], "output": [ "motion" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UniEgoMotion is a motion diffusion model announced by Panasonic Holdings and Panasonic R&D Company of America on 17 October 2025, with Stanford. One model reconstructs, forecasts, and generates 3D body motion from a first-person video and a head trajectory. No public weights were found. Collected 15 October 2026.", "what_it_is_for": "Panasonic points it at wearable cameras and smart glasses: seeing the current action, predicting the next action, and generating a plausible action, for work assistance, VR and AR, and rehabilitation monitoring. It does not need a third-person camera or a 3D scene model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "papercup/pl-bert/multilingual-pl-bert": { "id": "papercup/pl-bert/multilingual-pl-bert", "name": "Multilingual PL-BERT", "maker": "papercup", "family": "papercup/pl-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-02-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Phoneme-level BERT in the StyleTTS2 PL-BERT layout. config.yml sets vocab_size 178, hidden_size 768, num_attention_heads 12, intermediate_size 2048, num_hidden_layers 12, max_position_embeddings 512, dropout 0.1. Tokenizer named in that file is bert-base-multilingual-cased. Checkpoint file is step_1100000.t7. Parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Multilingual PL-BERT is an open-weight phoneme encoder that Papercup trained and uploaded to Hugging Face on 2024-02-27 under `papercup-ai/multilingual-pl-bert`. It is a StyleTTS2 text encoder, not a dubbing voice and not the closed XLPT system. The license is MIT. The checkpoint was still public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card says Papercup trained this checkpoint from the open-source PL-BERT recipe so it can be dropped into StyleTTS2. Training length on the card is 1.1 million iterations. Users copy `config.yml`, `step_1100000.t7`, and `util.py` into a StyleTTS2 `Utils` folder, point `PLBERT_dir` at that folder, and build espeak phoneme files. The card thanks Aaron (Yinghao) Li for the upstream work. Papercup did not release a StyleTTS2 speech model alongside the encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "papercup/xlpt/xlpt": { "id": "papercup/xlpt/xlpt", "name": "XLPT", "maker": "papercup", "family": "papercup/xlpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XLPT is Papercup's closed cross-lingual prosody transfer technology, now sold by RWS inside the Papercup AI dubbing service. It is active on the RWS dubbing page as of 2026-10-15. Weights, a version number, and a list price were not published. This dossier was collected on 2026-10-15.", "what_it_is_for": "RWS describes automatic cross-lingual prosody transfer (XLPT) as the part of Papercup dubbing that keeps the original speaker's tone, pacing, and emotion in the target language. A Papercup LinkedIn post on 2024-07-04 uses the shorter expansion \"cross-lingual prosody transfer\" and says the dubbing model writes scripts, translates them, synthesizes speech, and that translators then edit the result. The surrounding product is a managed localization workflow, not a self-serve speech API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "paris-saclay/croissantllm/croissantcool": { "id": "paris-saclay/croissantllm/croissantcool", "name": "CroissantCool-v0.2", "maker": "paris-saclay", "family": "paris-saclay/croissantllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-03-26", "context_length": 2048, "max_output_tokens": null, "parameters": "1345423360", "architecture": "CroissantLLM Llama decoder after 190k pretraining steps (about 2.99T tokens) plus a cooldown on instruction text that is not wrapped in a chat template. The v0.2 card says the tokenizer was changed from v0.1 so that a lone newline is its own token. Safetensors metadata counts 1,345,423,360 bfloat16 parameters. The card still describes it as a base model, not a chat model, and says the cooldown lets it work without few-shot prompts more often than CroissantLLMBase.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CroissantCool is the cooldown checkpoint of CroissantLLM from the MICS laboratory at CentraleSup\u00e9lec, Universit\u00e9 Paris-Saclay, released with the CroissantLLM partners. v0.2 is the current named base file. It was active on the Hub on 2026-10-15.", "what_it_is_for": "The card says this is the 190k-step model plus a cooldown on instruction data that is not placed in a chat template. The authors call it the strongest base checkpoint in the project. It is still not the chat model. Chat users are sent to CroissantLLMChat-v0.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "paris-saclay/croissantllm/croissantllm-base": { "id": "paris-saclay/croissantllm/croissantllm-base", "name": "CroissantLLMBase", "maker": "paris-saclay", "family": "paris-saclay/croissantllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-02-01", "context_length": 2048, "max_output_tokens": null, "parameters": "1.3B", "architecture": "LlamaForCausalLM decoder. Config: 24 layers, hidden size 2048, intermediate size 5504, 16 query heads, 16 KV heads, SiLU, RoPE theta 10000, untied input and output embeddings, vocabulary 32000. Paper Table 2 lists the Base row at 1214.3 million parameters. The abstract and the CentraleSup\u00e9lec announcement call the model 1.3B. The final checkpoint is 190k steps and about 2.99T tokens. The Hub weights are PyTorch .bin shards, so an exact safetensors count was not published for this repo. The chat checkpoint of the same run counts 1,345,431,552 parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CroissantLLMBase is the final pretrained checkpoint of CroissantLLM, a bilingual French-English language model from the MICS laboratory at CentraleSup\u00e9lec, a component school of Universit\u00e9 Paris-Saclay, developed with Illuin Technology and academic partners. It is an active open-weight base model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card says this is a base model, not a chat model, and that it works best with few-shot prompts. The paper presents it as a 1.3B model pretrained on about 3T English and French tokens so it can run on consumer hardware. The CentraleSup\u00e9lec note says it was trained on Jean Zay with public, sourced data and is meant for commercial as well as research use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "paris-saclay/croissantllm/croissantllm-chat": { "id": "paris-saclay/croissantllm/croissantllm-chat", "name": "CroissantLLMChat-v0.1", "maker": "paris-saclay", "family": "paris-saclay/croissantllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-02-01", "context_length": 2048, "max_output_tokens": null, "parameters": "1345431552", "architecture": "Chat supervised fine-tune of the 190k-step CroissantLLM Llama decoder. Hugging Face safetensors metadata counts 1,345,431,552 float32 parameters. The card uses a ChatML-style template with <|im_start|> and <|im_end|>. Generation example sets eos to the tokenizer eos id and token 32000. The base config is 24 layers, hidden size 2048, intermediate size 5504, 16 heads, and a 2048-token context.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CroissantLLMChat-v0.1 is the chat fine-tune of CroissantLLM from the MICS laboratory at CentraleSup\u00e9lec, Universit\u00e9 Paris-Saclay, released with Illuin Technology and the CroissantLLM partners. It is an active MIT-licensed weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this is the 190k-step pretrained model after a chat fine-tune. It is the checkpoint the project recommends for conversation. The template is `<|im_start|>user` / `<|im_start|>assistant`. The paper evaluates it on French and English MT-Bench and on translation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "paris-saclay/novae/novae-brain-0": { "id": "paris-saclay/novae/novae-brain-0", "name": "Novae brain-0", "maker": "paris-saclay", "family": "paris-saclay/novae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-09-10", "context_length": null, "max_output_tokens": null, "parameters": "32041793", "architecture": "Graph foundation model for spatial transcriptomics, sibling of novae-human-0 and novae-mouse-0. The bioRxiv methods text describes a graph attention encoder over cell neighborhoods, trained with a SwAV-style swapped prediction loss. This checkpoint's config.json sets 10 layers, 16 heads, hidden size 128, embedding size 512, output size 64, 512 prototypes, 2 local hops, 2 view hops, and min_prototypes_ratio 0.4. That ratio matches novae-mouse-0. The human checkpoint sets it to 0.75. Safetensors total is 32,041,793 float32 parameters. The card calls this the default brain model and says the tissue mix is mouse and human combined. The preprint describes Novae training on 78 slides, nearly 30 million cells, 18 tissues, Xenium, MERSCOPE, and CosMx. It does not print a separate brain-only slide count.", "modalities": { "input": [ "gene-expression", "spatial-graph" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Novae brain-0 is the default brain checkpoint of Novae, a graph foundation model for spatial transcriptomics. It was built at the MICS laboratory of CentraleSup\u00e9lec, Universit\u00e9 Paris-Saclay, with the PRISM center at Gustave Roussy (INSERM U981). Weights are active on Hugging Face as prism-oncology/novae-brain-0. The card says the checkpoint covers brain from mouse and human combined. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card calls it the default Novae model for brain, with mouse and human combined. The package loads pretrained Novae weights, builds a spatial neighbor graph, and assigns domains in a zero-shot setting. The paper says the model also corrects batch effects and builds a hierarchy of domains.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "paris-saclay/novae/novae-human-0": { "id": "paris-saclay/novae/novae-human-0", "name": "Novae human-0", "maker": "paris-saclay", "family": "paris-saclay/novae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-09-10", "context_length": null, "max_output_tokens": null, "parameters": "32041793", "architecture": "Graph foundation model for spatial transcriptomics. The bioRxiv methods text describes a graph attention encoder over cell neighborhoods, trained with a SwAV-style swapped prediction loss. The shipped config.json sets 10 layers, 16 heads, hidden size 128, embedding size 512, output size 64, 512 prototypes, 2 local hops, and 2 view hops. Safetensors total is 32,041,793 float32 parameters. The card calls this the default human-tissue checkpoint. Training described in the preprint: 78 slides, nearly 30 million cells, 18 tissues, Xenium, MERSCOPE, and CosMx.", "modalities": { "input": [ "gene-expression", "spatial-graph" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Novae human-0 is the default human checkpoint of Novae, a graph foundation model for spatial transcriptomics. It was built at the MICS laboratory of CentraleSup\u00e9lec, Universit\u00e9 Paris-Saclay, with the PRISM center at Gustave Roussy (INSERM U981). Weights are active on Hugging Face as prism-oncology/novae-human-0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card calls it the default Novae model for human tissues. The package loads it with `Novae.from_pretrained(\"prism-oncology/novae-human-0\")`, builds a spatial neighbor graph, and assigns domains in a zero-shot setting. The paper says it also corrects batch effects and builds a hierarchy of domains.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "paris-saclay/novae/novae-mouse-0": { "id": "paris-saclay/novae/novae-mouse-0", "name": "Novae mouse-0", "maker": "paris-saclay", "family": "paris-saclay/novae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-09-10", "context_length": null, "max_output_tokens": null, "parameters": "32041793", "architecture": "Graph foundation model for spatial transcriptomics, sibling of novae-human-0. The bioRxiv methods text describes a graph attention encoder over cell neighborhoods, trained with a SwAV-style swapped prediction loss. This checkpoint's config.json sets 10 layers, 16 heads, hidden size 128, embedding size 512, output size 64, 512 prototypes, 2 local hops, 2 view hops, and min_prototypes_ratio 0.4. The human checkpoint uses the same sizes and sets min_prototypes_ratio to 0.75. Safetensors total is 32,041,793 float32 parameters. The card calls this the default mouse-tissue checkpoint. The preprint describes Novae training on 78 slides, nearly 30 million cells, 18 tissues, Xenium, MERSCOPE, and CosMx. It does not print a separate mouse-only slide count.", "modalities": { "input": [ "gene-expression", "spatial-graph" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Novae mouse-0 is the default mouse checkpoint of Novae, a graph foundation model for spatial transcriptomics. It was built at the MICS laboratory of CentraleSup\u00e9lec, Universit\u00e9 Paris-Saclay, with the PRISM center at Gustave Roussy (INSERM U981). Weights are active on Hugging Face as prism-oncology/novae-mouse-0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card calls it the default Novae model for mouse tissues. The package loads pretrained Novae weights, builds a spatial neighbor graph, and assigns domains in a zero-shot setting. The paper says the model also corrects batch effects and builds a hierarchy of domains.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "paris-saclay/novae/novae-scconcept-multi-species": { "id": "paris-saclay/novae/novae-scconcept-multi-species", "name": "Novae scConcept multi-species", "maker": "paris-saclay", "family": "paris-saclay/novae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-06-19", "context_length": null, "max_output_tokens": null, "parameters": "3124983", "architecture": "Later Novae checkpoint that takes scConcept embeddings as node features. Config.json sets embedding_name X_scConcept, embedding size 1024, 12 layers, 16 heads, hidden size 256, output size 128, 1024 prototypes, histo embedding size 50, 2 local hops, 2 view hops, batch size 256, dropout 0, multimodal false, panel_subset_size 0, min_prototypes_ratio 0.3, background_noise_lambda 0.001, sensitivity_noise_std 0.0005, and temperature 0.1. Safetensors total is 3,124,983 float32 parameters. The tissue checkpoints (human, mouse, brain) are a different size: 32,041,793 parameters, 10 layers, and a gene-panel config. scConcept itself is an upstream embedding model and is not this checkpoint.", "modalities": { "input": [ "embeddings", "spatial-graph" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Novae scConcept multi-species is a later named Novae checkpoint from the MICS laboratory of CentraleSup\u00e9lec, Universit\u00e9 Paris-Saclay, now hosted by the PRISM / prism-oncology release. The card says it uses scConcept embeddings as node features. It was active on Hugging Face as prism-oncology/novae-scconcept-multi-species on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card calls it a new Novae model whose node features are scConcept embeddings, and it links the package tutorial. It is a separate product from the 2024 human, mouse, and brain gene-panel checkpoints. scConcept, the embedding model, is not this file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "patras/greek-reddit-bert/greek-reddit-bert": { "id": "patras/greek-reddit-bert/greek-reddit-bert", "name": "Greek-Reddit-BERT", "maker": "patras", "family": "patras/greek-reddit-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-02", "context_length": 512, "max_output_tokens": null, "parameters": "113000000", "architecture": "Sequence classifier fine-tuned from nlpaueb/bert-base-greek-uncased-v1. The card says that base has 113 million parameters. Hub page rounds model size to 0.1B and lists tensor type F32. Training: 4 epochs, batch size 16, learning rate 5e-5, dropout 0.1, 10 labels, 32-bit, max length 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Patras IMIS Lab released Greek-Reddit-BERT, an active open-weights topic classifier for Greek Reddit posts. This dossier was collected on 2026-10-15.", "what_it_is_for": "Topic classification of Greek social-media posts. The card says it is a fine-tune of AUEB GREEK-BERT on GreekReddit, 6,534 posts from Greek subreddits in society, politics, economy, entertainment/culture, and sports. The classifier has 10 labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "patras/greekt5/greekt5-mt5-small-greeksum": { "id": "patras/greekt5/greekt5-mt5-small-greeksum", "name": "GreekT5 (mt5-small-greeksum)", "maker": "patras", "family": "patras/greekt5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-11", "context_length": 1024, "max_output_tokens": 128, "parameters": "300000000", "architecture": "Encoder-decoder fine-tune of google/mt5-small. The card says that base has 300 million parameters and is about 1.20 GB. Trained 10 epochs, batch size 6, AdamW, learning rate 3e-4, 32-bit, no warmup. The prefix 'summarize: ' is required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Patras IMIS Lab released GreekT5 (mt5-small-greeksum), an active open-weights Greek news summarizer. It is the mT5-small member of the GreekT5 series and the first of the three Hub repos. This dossier was collected on 2026-10-15.", "what_it_is_for": "Abstractive summarization of Greek news on GreekSUM (about 151,000 News24/7 articles). The card includes it in the same comparison as the umT5 GreekT5 models and GreekBART. On that table its ROUGE scores are the lowest of the neural systems.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "patras/greekt5/greekt5-umt5-base-greeksum": { "id": "patras/greekt5/greekt5-umt5-base-greeksum", "name": "GreekT5 (umt5-base-greeksum)", "maker": "patras", "family": "patras/greekt5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-12", "context_length": 1024, "max_output_tokens": 128, "parameters": "580000000", "architecture": "Encoder-decoder fine-tune of google/umt5-base. The card says that base has 580 million parameters and is about 2.37 GB. Trained 10 epochs, batch size 1, AdamW, learning rate 3e-4, 32-bit, no warmup. The prefix 'summarize: ' is required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Patras IMIS Lab released GreekT5 (umt5-base-greeksum), an active open-weights Greek news summarizer. It is the largest of the three GreekT5 checkpoints. This dossier was collected on 2026-10-15.", "what_it_is_for": "Abstractive summarization of Greek news. The card says it was trained on GreekSUM, about 151,000 News24/7 articles across society, politics, economy, culture, and world news. The authors compare it with TextRank, the two smaller GreekT5 models, and GreekBART.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "patras/greekt5/greekt5-umt5-small-greeksum": { "id": "patras/greekt5/greekt5-umt5-small-greeksum", "name": "GreekT5 (umt5-small-greeksum)", "maker": "patras", "family": "patras/greekt5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-12", "context_length": 1024, "max_output_tokens": 128, "parameters": "300000000", "architecture": "Encoder-decoder fine-tune of google/umt5-small. The card says that base has 300 million parameters and is about 1.20 GB. Trained 10 epochs, batch size 6, AdamW, learning rate 3e-4, 32-bit, no warmup. The prefix 'summarize: ' is required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Patras IMIS Lab released GreekT5 (umt5-small-greeksum), an active open-weights Greek news summarizer. It is the umT5-small member of the GreekT5 series. This dossier was collected on 2026-10-15.", "what_it_is_for": "Abstractive summarization of Greek news on GreekSUM, the same News24/7 collection used for the other GreekT5 checkpoints (about 151,000 articles). The card presents it as one of the models compared with GreekBART.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "patras/greekwiki/greekwiki-umt5-base": { "id": "patras/greekwiki/greekwiki-umt5-base", "name": "GreekWiki (umt5-base)", "maker": "patras", "family": "patras/greekwiki", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-30", "context_length": 1024, "max_output_tokens": 220, "parameters": "580000000", "architecture": "Encoder-decoder fine-tune of google/umt5-base. The card says that base has 580 million parameters and is about 2.37 GB. Hub page rounds model size to 0.6B and lists tensor type F32. Training: 10 epochs, batch size 1, AdamW, learning rate 3e-4, 16-bit, no warmup. The prefix 'summarize: ' is required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Patras IMIS Lab released GreekWiki (umt5-base), an active open-weights summarizer for Greek Wikipedia articles. This dossier was collected on 2026-10-15.", "what_it_is_for": "Abstractive summarization of Greek encyclopedic articles. The card says it was trained and evaluated on GreekWikipedia, 93,432 Greek Wikipedia articles. The dataset card splits that as 83,432 train, 5,000 validation, and 5,000 test, and says this is the best-performing model from the paper. The dataset also supports title generation. This checkpoint is documented as a summarizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "patras/maistros/maistros-8b-instruct": { "id": "patras/maistros/maistros-8b-instruct", "name": "Maistros-8B-Instruct", "maker": "patras", "family": "patras/maistros", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-21", "context_length": 262144, "max_output_tokens": null, "parameters": "", "architecture": "Mistral3ForConditionalGeneration. Text tower: ministral3, 34 layers, hidden size 4096, 32 heads, 8 key-value heads, vocab 131072, max_position_embeddings 262144 with YaRN (original 16384). Vision tower in config: Pixtral, 24 layers, hidden size 1024, image size 1540, patch 14. LoRA on q/k/v/o and gate/up/down was merged into mistralai/Ministral-3-8B-Instruct-2512-BF16. The paper names it 8B after that base. The Hub page lists model size as 9B parameters. No exact integer count was published.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Patras IMIS Lab released Maistros-8B-Instruct, an active open-weights Greek chat model. It is a LoRA merge of Ministral 3 8B Instruct on CulturaQA. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card and paper present it as a Greek question-answering and chat model. Training is supervised fine-tuning on CulturaQA, 2,700 human-curated Greek culture QA pairs (2,000 train, 200 validation, 500 test), generated with GPT-5 and edited by the authors. The stated goal is local use, not a hosted API. The card's system prompt identifies the model as Maistros from IMIS Lab at the University of Patras.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/align-anything/aa-chameleon-7b-base": { "id": "peking/align-anything/aa-chameleon-7b-base", "name": "Align-Anything Chameleon 7B Base", "maker": "peking", "family": "peking/align-anything", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (model card).", "release_date": "2024-09-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued training of Meta Chameleon-7B for interleaved text and image. Parameter count and context window are not restated.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Align-Anything Chameleon 7B Base is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Align-Anything Chameleon 7B Base is a PKU-Alignment text-and-image model. The card says it starts from facebook/chameleon-7b and is trained in Align-Anything to unlock image generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/align-anything/aa-chameleon-7b-plus": { "id": "peking/align-anything/aa-chameleon-7b-plus", "name": "Align-Anything Chameleon 7B Plus", "maker": "peking", "family": "peking/align-anything", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (model card).", "release_date": "2024-09-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Chameleon-7B further trained and aligned with Align-Anything for interleaved text and image. Exact parameter count and context window are not stated.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Align-Anything Chameleon 7B Plus is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Align-Anything Chameleon 7B Plus is the preference-aligned sibling of the base checkpoint. The card says the extra alignment step is meant to improve human-preference following while keeping interleaved text and image generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/align-ds/align-ds-v": { "id": "peking/align-ds/align-ds-v", "name": "Align-DS-V", "maker": "peking", "family": "peking/align-ds", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (model card).", "release_date": "2025-02-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaVA-style vision-language model on DeepSeek-R1-Distill-Llama-8B. The card calls the result 8B. Exact parameter count and context window are not stated.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Align-DS-V is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Align-DS-V is an experimental vision-language model from PKU-Alignment and HKUST. The card says it extends DeepSeek-R1-Distill-Llama-8B to images with the Align-Anything data and code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/aligner/aligner-7b-v1-0": { "id": "peking/aligner/aligner-7b-v1-0", "name": "Aligner-7B v1.0", "maker": "peking", "family": "peking/aligner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial license (model card).", "release_date": "2024-02-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive correction model trained from Llama 2 base on aligner/aligner-20K. The card's generate example uses max_new_tokens 2048. That is a demo setting, not a published context window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aligner-7B v1.0 is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Aligner-7B v1.0 is a plug-in correction model from the Institute for AI at Peking University. It rewrites a question-answer pair to be more helpful and harmless. The card says it is trained from Llama 2 base with residual correction and is meant to sit in front of other open or API models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/anyreward/anyreward": { "id": "peking/anyreward/anyreward", "name": "AnyRewardModel", "maker": "peking", "family": "peking/anyreward", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0 (model card).", "release_date": "2024-12-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reward model loaded with trust_remote_code AutoModel and AutoProcessor. The card shows image-audio and text-image scoring. Parameter count and context length are not stated.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AnyRewardModel is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "AnyRewardModel is PKU-Alignment's all-modality generation reward model. The card says it scores instruction following, modality choice, and whether text, image, and audio outputs work together without redundancy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/beaver/beaver-7b-unified-cost": { "id": "peking/beaver/beaver-7b-unified-cost", "name": "Beaver-7B unified cost", "maker": "peking", "family": "peking/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial license (unified reward card).", "release_date": "2023-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Safe-RLHF score model, AutoModelForScore, fine-tuned from LLaMA and Alpaca on PKU-SafeRLHF.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beaver-7B unified cost is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Beaver-7B unified cost is the unified cost model paired with the unified reward model. PKU-Alignment shipped it with the other Beaver score models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/beaver/beaver-7b-unified-reward": { "id": "peking/beaver/beaver-7b-unified-reward", "name": "Beaver-7B unified reward", "maker": "peking", "family": "peking/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial license (unified reward card).", "release_date": "2023-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Safe-RLHF score model, AutoModelForScore, fine-tuned from LLaMA and Alpaca on PKU-SafeRLHF.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beaver-7B unified reward is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Beaver-7B unified reward is the unified reward model. Its card points at beaver-7b-v3.0 as the chat model it supports. PKU-Alignment shipped it with the other Beaver score models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/beaver/beaver-7b-v1-0": { "id": "peking/beaver/beaver-7b-v1-0", "name": "Beaver-7B v1.0", "maker": "peking", "family": "peking/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial license (model card).", "release_date": "2023-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive transformer. Safe-RLHF fine-tune of a reproduced Alpaca checkpoint, itself from LLaMA. The card does not publish an exact parameter count, context window, or max output. The CLI example uses max_length 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beaver-7B v1.0 is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Beaver-7B v1.0 is a Safe-RLHF chat assistant from PKU-Alignment. The model card says it is trained from a reproduced Stanford Alpaca checkpoint so the model can stay helpful while refusing harmful requests. The safe-rlhf README lists v1, v2, and v3 as successive rounds released together on 2023-07-10.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/beaver/beaver-7b-v1-0-cost": { "id": "peking/beaver/beaver-7b-v1-0-cost", "name": "Beaver-7B v1.0 cost", "maker": "peking", "family": "peking/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial license (reward-model card, which lists the cost model alongside it).", "release_date": "2023-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Score model on an autoregressive transformer, loaded as AutoModelForScore in safe-rlhf. Fine-tuned from LLaMA and Alpaca on PKU-SafeRLHF. Exact parameter count is not stated.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beaver-7B v1.0 cost is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Beaver-7B v1.0 cost is the cost model that scores harm for the Safe RLHF constraint. The reward-model card says it is trained on PKU-SafeRLHF and is loaded with safe_rlhf AutoModelForScore. The project README ships a cost checkpoint for every Beaver round.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/beaver/beaver-7b-v1-0-reward": { "id": "peking/beaver/beaver-7b-v1-0-reward", "name": "Beaver-7B v1.0 reward", "maker": "peking", "family": "peking/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial license (reward-model card, which lists the cost model alongside it).", "release_date": "2023-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Score model on an autoregressive transformer, loaded as AutoModelForScore in safe-rlhf. Fine-tuned from LLaMA and Alpaca on PKU-SafeRLHF. Exact parameter count is not stated.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beaver-7B v1.0 reward is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Beaver-7B v1.0 reward is the reward model that scores helpfulness for Safe RLHF. The reward-model card says it is trained on PKU-SafeRLHF and is loaded with safe_rlhf AutoModelForScore. The project README ships a reward checkpoint for every Beaver round.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/beaver/beaver-7b-v2-0": { "id": "peking/beaver/beaver-7b-v2-0", "name": "Beaver-7B v2.0", "maker": "peking", "family": "peking/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial license (model card).", "release_date": "2023-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive transformer. Safe-RLHF fine-tune of a reproduced Alpaca checkpoint, itself from LLaMA. The card does not publish an exact parameter count, context window, or max output. The CLI example uses max_length 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beaver-7B v2.0 is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Beaver-7B v2.0 is a Safe-RLHF chat assistant from PKU-Alignment. The model card says it is trained from a reproduced Stanford Alpaca checkpoint so the model can stay helpful while refusing harmful requests. The safe-rlhf README lists v1, v2, and v3 as successive rounds released together on 2023-07-10.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/beaver/beaver-7b-v2-0-cost": { "id": "peking/beaver/beaver-7b-v2-0-cost", "name": "Beaver-7B v2.0 cost", "maker": "peking", "family": "peking/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial license (reward-model card, which lists the cost model alongside it).", "release_date": "2023-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Score model on an autoregressive transformer, loaded as AutoModelForScore in safe-rlhf. Fine-tuned from LLaMA and Alpaca on PKU-SafeRLHF. Exact parameter count is not stated.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beaver-7B v2.0 cost is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Beaver-7B v2.0 cost is the cost model that scores harm for the Safe RLHF constraint. The reward-model card says it is trained on PKU-SafeRLHF and is loaded with safe_rlhf AutoModelForScore. The project README ships a cost checkpoint for every Beaver round.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/beaver/beaver-7b-v2-0-reward": { "id": "peking/beaver/beaver-7b-v2-0-reward", "name": "Beaver-7B v2.0 reward", "maker": "peking", "family": "peking/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial license (reward-model card, which lists the cost model alongside it).", "release_date": "2023-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Score model on an autoregressive transformer, loaded as AutoModelForScore in safe-rlhf. Fine-tuned from LLaMA and Alpaca on PKU-SafeRLHF. Exact parameter count is not stated.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beaver-7B v2.0 reward is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Beaver-7B v2.0 reward is the reward model that scores helpfulness for Safe RLHF. The reward-model card says it is trained on PKU-SafeRLHF and is loaded with safe_rlhf AutoModelForScore. The project README ships a reward checkpoint for every Beaver round.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/beaver/beaver-7b-v3-0": { "id": "peking/beaver/beaver-7b-v3-0", "name": "Beaver-7B v3.0", "maker": "peking", "family": "peking/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial license (model card).", "release_date": "2023-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive transformer. Safe-RLHF fine-tune of a reproduced Alpaca checkpoint, itself from LLaMA. The card does not publish an exact parameter count, context window, or max output. The CLI example uses max_length 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beaver-7B v3.0 is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Beaver-7B v3.0 is a Safe-RLHF chat assistant from PKU-Alignment. The model card says it is trained from a reproduced Stanford Alpaca checkpoint so the model can stay helpful while refusing harmful requests. The safe-rlhf README lists v1, v2, and v3 as successive rounds released together on 2023-07-10.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/beaver/beaver-7b-v3-0-cost": { "id": "peking/beaver/beaver-7b-v3-0-cost", "name": "Beaver-7B v3.0 cost", "maker": "peking", "family": "peking/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial license (reward-model card, which lists the cost model alongside it).", "release_date": "2023-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Score model on an autoregressive transformer, loaded as AutoModelForScore in safe-rlhf. Fine-tuned from LLaMA and Alpaca on PKU-SafeRLHF. Exact parameter count is not stated.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beaver-7B v3.0 cost is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Beaver-7B v3.0 cost is the cost model that scores harm for the Safe RLHF constraint. The reward-model card says it is trained on PKU-SafeRLHF and is loaded with safe_rlhf AutoModelForScore. The project README ships a cost checkpoint for every Beaver round.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/beaver/beaver-7b-v3-0-reward": { "id": "peking/beaver/beaver-7b-v3-0-reward", "name": "Beaver-7B v3.0 reward", "maker": "peking", "family": "peking/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial license (reward-model card, which lists the cost model alongside it).", "release_date": "2023-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Score model on an autoregressive transformer, loaded as AutoModelForScore in safe-rlhf. Fine-tuned from LLaMA and Alpaca on PKU-SafeRLHF. Exact parameter count is not stated.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beaver-7B v3.0 reward is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Beaver-7B v3.0 reward is the reward model that scores helpfulness for Safe RLHF. The reward-model card says it is trained on PKU-SafeRLHF and is loaded with safe_rlhf AutoModelForScore. The project README ships a reward checkpoint for every Beaver round.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/beaver/beaver-dam-7b": { "id": "peking/beaver/beaver-dam-7b", "name": "Beaver-Dam-7B", "maker": "peking", "family": "peking/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial license (model card).", "release_date": "2023-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card says it has 7 billion parameters, is derived from Llama-7B, and is trained on the BeaverTails classification set for 14-category QA moderation.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beaver-Dam-7B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Beaver-Dam-7B is PKU-Alignment's QA-moderation model. The card says it labels a question-answer pair as harmful or benign by whether a safe answer neutralizes a risky question, across 14 toxicity categories, instead of dropping the user prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/beaver/beaver-vision-11b": { "id": "peking/beaver/beaver-vision-11b", "name": "Beaver-Vision-11B", "maker": "peking", "family": "peking/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0, and the card also requires the Llama 3.2 Community License.", "release_date": "2024-10-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-text-to-text autoregressive model. Fine-tuned from pretrained Llama-3.2-11B-Vision with the Align-Anything framework. Exact parameter count is not stated beyond the 11B name.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beaver-Vision-11B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Beaver-Vision-11B is an image-and-text chat model from PKU-Alignment. The card says it is post-trained from the pretrained LLaMA-3.2-11B-Vision checkpoint with Align-Anything-Instruct, and that training data, code, and eval scripts are open.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/chat-univi/chat-univi": { "id": "peking/chat-univi/chat-univi", "name": "Chat-UniVi", "maker": "peking", "family": "peking/chat-univi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License on the original Chat-UniVi card.", "release_date": "2023-09-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unified visual tokens for images and videos, trained on a mixed image and video set. Exact parameter count, context window, and base LLM id were not copied for this checkpoint.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chat-UniVi is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Chat-UniVi is PKU-Yuan Group's unified image-and-video language model. The card says dynamic visual tokens represent both images and videos, and that one mixed training run covers both without a separate head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/chat-univi/chat-univi-13b": { "id": "peking/chat-univi/chat-univi-13b", "name": "Chat-UniVi 13B", "maker": "peking", "family": "peking/chat-univi", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unified visual tokens for images and videos, trained on a mixed image and video set. Exact parameter count, context window, and base LLM id were not copied for this checkpoint.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chat-UniVi 13B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "Chat-UniVi 13B is the larger named checkpoint in the same Chat-UniVi collection. Its card was not opened, so size is taken from the repo name only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/chat-univi/chat-univi-7b-v1-5": { "id": "peking/chat-univi/chat-univi-7b-v1-5", "name": "Chat-UniVi 7B v1.5", "maker": "peking", "family": "peking/chat-univi", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-04-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unified visual tokens for images and videos, trained on a mixed image and video set. Exact parameter count, context window, and base LLM id were not copied for this checkpoint.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chat-UniVi 7B v1.5 is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "Chat-UniVi 7B v1.5 is the updated 7B release in the Chat-UniVi collection. A stage-1 repo sits beside it and is recorded as a version, not a separate product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/chatlaw/chatlaw-13b": { "id": "peking/chatlaw/chatlaw-13b", "name": "ChatLaw-13B", "maker": "peking", "family": "peking/chatlaw", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Ziya-LLaMA-13B-v1 on Chinese legal dialogue built from news, forums, statutes, consultations, exam items, and judgments. Exact parameter count is not restated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChatLaw-13B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ChatLaw-13B is PKU-Yuan Group's academic demo legal model. The GitHub README says it is trained from Ziya-LLaMA-13B-v1 and is stronger at general Chinese than at complex legal reasoning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/chatlaw/chatlaw-33b": { "id": "peking/chatlaw/chatlaw-33b", "name": "ChatLaw-33B", "maker": "peking", "family": "peking/chatlaw", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Anima-33B. Exact parameter count is not restated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChatLaw-33B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ChatLaw-33B is the larger academic demo. The README says it is trained from Anima-33B, reasons better than the 13B demo, and sometimes answers in English because Anima had little Chinese data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/chatlaw/chatlaw2-moe": { "id": "peking/chatlaw/chatlaw2-moe", "name": "ChatLaw2-MoE", "maker": "peking", "family": "peking/chatlaw", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "4x7B mixture of experts on an InternLM architecture, as stated in the GitHub README. Exact activated parameter count, context window, and license were not on the page that was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChatLaw2-MoE is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ChatLaw2-MoE is the README's latest ChatLaw. It says the model is an InternLM 4x7B mixture of experts for Chinese legal language, with role-specific experts, a knowledge graph, and law-firm style procedures.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/g1/g1-3b": { "id": "peking/g1/g1-3b", "name": "G1-3B", "maker": "peking", "family": "peking/g1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 (model card).", "release_date": "2025-05-24", "context_length": 32768, "max_output_tokens": 8192, "parameters": "3090000000", "architecture": "Causal LM with the Qwen2.5 architecture. Card states 3.09B parameters. Context length on the card is 32768 tokens and generation 8192 tokens. Base: Qwen/Qwen2.5-3B-Instruct", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "G1-3B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "G1-3B is a PKU Machine Learning Group graph-reasoning model. The G1 cards say the series is trained on the Erdos benchmark with GRPO reinforcement learning. This checkpoint's card says the training stage is SFT and RL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/g1/g1-7b": { "id": "peking/g1/g1-7b", "name": "G1-7B", "maker": "peking", "family": "peking/g1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 (model card).", "release_date": "2025-05-24", "context_length": 32768, "max_output_tokens": 8192, "parameters": "7620000000", "architecture": "Causal LM with the Qwen2.5 architecture. Card states 7.62B parameters. Context length on the card is 32768 tokens and generation 8192 tokens. Base: Qwen/Qwen2.5-7B-Instruct", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "G1-7B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "G1-7B is a PKU Machine Learning Group graph-reasoning model. The G1 cards say the series is trained on the Erdos benchmark with GRPO reinforcement learning. This checkpoint's card says the training stage is SFT and RL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/g1/g1-zero-3b": { "id": "peking/g1/g1-zero-3b", "name": "G1-Zero-3B", "maker": "peking", "family": "peking/g1", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-05-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named G1-Zero 3B checkpoint on Hugging Face under PKU-ML. Training stage and parameter count were not read from its card. The 7B zero card says its own stage is RL.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "G1-Zero-3B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "G1-Zero-3B is the 3B RL-only sibling listed next to G1-Zero-7B in the PKU-ML G1 collection. The model card was not opened, so numeric specs are not filled.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/g1/g1-zero-7b": { "id": "peking/g1/g1-zero-7b", "name": "G1-Zero-7B", "maker": "peking", "family": "peking/g1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 (model card).", "release_date": "2025-05-24", "context_length": 32768, "max_output_tokens": 8192, "parameters": "7620000000", "architecture": "Causal LM with the Qwen2.5 architecture. Card states training stage RL and 7.62B parameters. The base model id was not in the extracted lines. Context length on the card is 32768 tokens and generation 8192 tokens. Base: not captured from the card lines that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "G1-Zero-7B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "G1-Zero-7B is a PKU Machine Learning Group graph-reasoning model. The G1 cards say the series is trained on the Erdos benchmark with GRPO reinforcement learning. This checkpoint's card says the training stage is RL only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/grasp/grasp-4b": { "id": "peking/grasp/grasp-4b", "name": "GRASP-4B", "maker": "peking", "family": "peking/grasp", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "4B-named text-generation checkpoint. Exact parameter count and base model were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GRASP-4B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "GRASP-4B is a PKU-ML checkpoint for GRASP. The lab GitHub describes GRASP as graph reasoning by agentic solving and probing of LLMs. The weight card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/grasp/grasp-base-4b": { "id": "peking/grasp/grasp-base-4b", "name": "GRASP-base-4B", "maker": "peking", "family": "peking/grasp", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "4B-named text-generation checkpoint. Exact parameter count and base model were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GRASP-base-4B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "GRASP-base-4B is a PKU-ML checkpoint for GRASP. The lab GitHub describes GRASP as graph reasoning by agentic solving and probing of LLMs. The weight card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/languagebind/languagebind-audio": { "id": "peking/languagebind/languagebind-audio", "name": "LanguageBind Audio", "maker": "peking", "family": "peking/languagebind", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Language-aligned encoder for audio. Hugging Face pipeline tag on the listing is zero-shot-image-classification. Parameter count and context length were not read from the weight card.", "modalities": { "input": [ "text", "audio" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LanguageBind Audio is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "LanguageBind Audio is a modality encoder from PKU-Yuan Group's LanguageBind project. The README describes extending video-language pretraining to more modalities by aligning them through language. Audio encoder. An FT repo and a V1.5 repo sit beside it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/languagebind/languagebind-depth": { "id": "peking/languagebind/languagebind-depth", "name": "LanguageBind Depth", "maker": "peking", "family": "peking/languagebind", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Language-aligned encoder for depth. Hugging Face pipeline tag on the listing is zero-shot-image-classification. Parameter count and context length were not read from the weight card.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LanguageBind Depth is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "LanguageBind Depth is a modality encoder from PKU-Yuan Group's LanguageBind project. The README describes extending video-language pretraining to more modalities by aligning them through language. Depth encoder. The lab said 10M depth pairs were being uploaded in December 2023.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/languagebind/languagebind-image": { "id": "peking/languagebind/languagebind-image", "name": "LanguageBind Image", "maker": "peking", "family": "peking/languagebind", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Language-aligned encoder for image. Hugging Face pipeline tag on the listing is zero-shot-image-classification. Parameter count and context length were not read from the weight card.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LanguageBind Image is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "LanguageBind Image is a modality encoder from PKU-Yuan Group's LanguageBind project. The README describes extending video-language pretraining to more modalities by aligning them through language. Image encoder in the LanguageBind set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/languagebind/languagebind-thermal": { "id": "peking/languagebind/languagebind-thermal", "name": "LanguageBind Thermal", "maker": "peking", "family": "peking/languagebind", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Language-aligned encoder for thermal. Hugging Face pipeline tag on the listing is zero-shot-image-classification. Parameter count and context length were not read from the weight card.", "modalities": { "input": [ "text", "thermal" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LanguageBind Thermal is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "LanguageBind Thermal is a modality encoder from PKU-Yuan Group's LanguageBind project. The README describes extending video-language pretraining to more modalities by aligning them through language. Thermal encoder, paired with the depth release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/languagebind/languagebind-video": { "id": "peking/languagebind/languagebind-video", "name": "LanguageBind Video", "maker": "peking", "family": "peking/languagebind", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Language-aligned encoder for video. Hugging Face pipeline tag on the listing is zero-shot-image-classification. Parameter count and context length were not read from the weight card.", "modalities": { "input": [ "text", "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LanguageBind Video is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "LanguageBind Video is a modality encoder from PKU-Yuan Group's LanguageBind project. The README describes extending video-language pretraining to more modalities by aligning them through language. Video encoder. The 2023-12-15 note launches LanguageBind_Video 1.5 after the VIDAL set grew to 10M video-text pairs. FT and Huge V1.5 repos are versions, not separate products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/lawyer-llama/lawyer-llama-13b": { "id": "peking/lawyer-llama/lawyer-llama-13b", "name": "Lawyer LLaMA 13B", "maker": "peking", "family": "peking/lawyer-llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License (model card tag).", "release_date": "2023-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "13B-class legal chat model. The card does not restate the exact parameter count, context window, or base checkpoint id. Details are deferred to the technical report and GitHub.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lawyer LLaMA 13B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Lawyer LLaMA is a Chinese legal model from PIE Lab at Peking University. The v2 card points at the technical report and the AndrewZhe/lawyer-llama inference guide. The lab hosts both the beta and v2 weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/llama2-lrl/llama-2-7b-ben": { "id": "peking/llama2-lrl/llama-2-7b-ben", "name": "Llama-2-7b-ben", "maker": "peking", "family": "peking/llama2-lrl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-12-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama-2-7B-named checkpoint. Exact parameter count, language, and training recipe were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-2-7b-ben is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/Llama-2-7b-ben is a PIE Lab Llama-2-7B checkpoint listed with the lab's low-resource language merging collection. The model card was not opened. Llama-2-7b-ori and Llama-2-7b-FLAN-step884 were not foldered: ori is an ambiguous name and step884 is a training step.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/llama2-lrl/llama-2-7b-bod": { "id": "peking/llama2-lrl/llama-2-7b-bod", "name": "Llama-2-7b-bod", "maker": "peking", "family": "peking/llama2-lrl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-12-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama-2-7B-named checkpoint. Exact parameter count, language, and training recipe were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-2-7b-bod is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/Llama-2-7b-bod is a PIE Lab Llama-2-7B checkpoint listed with the lab's low-resource language merging collection. The model card was not opened. Llama-2-7b-ori and Llama-2-7b-FLAN-step884 were not foldered: ori is an ambiguous name and step884 is a training step.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/llama2-lrl/llama-2-7b-mon": { "id": "peking/llama2-lrl/llama-2-7b-mon", "name": "Llama-2-7b-mon", "maker": "peking", "family": "peking/llama2-lrl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-12-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama-2-7B-named checkpoint. Exact parameter count, language, and training recipe were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-2-7b-mon is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/Llama-2-7b-mon is a PIE Lab Llama-2-7B checkpoint listed with the lab's low-resource language merging collection. The model card was not opened. Llama-2-7b-ori and Llama-2-7b-FLAN-step884 were not foldered: ori is an ambiguous name and step884 is a training step.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/llama2-lrl/llama-2-7b-tam": { "id": "peking/llama2-lrl/llama-2-7b-tam", "name": "Llama-2-7b-tam", "maker": "peking", "family": "peking/llama2-lrl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-12-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama-2-7B-named checkpoint. Exact parameter count, language, and training recipe were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-2-7b-tam is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/Llama-2-7b-tam is a PIE Lab Llama-2-7B checkpoint listed with the lab's low-resource language merging collection. The model card was not opened. Llama-2-7b-ori and Llama-2-7b-FLAN-step884 were not foldered: ori is an ambiguous name and step884 is a training step.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/llama2-lrl/llama-2-7b-tel": { "id": "peking/llama2-lrl/llama-2-7b-tel", "name": "Llama-2-7b-tel", "maker": "peking", "family": "peking/llama2-lrl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-12-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama-2-7B-named checkpoint. Exact parameter count, language, and training recipe were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-2-7b-tel is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/Llama-2-7b-tel is a PIE Lab Llama-2-7B checkpoint listed with the lab's low-resource language merging collection. The model card was not opened. Llama-2-7b-ori and Llama-2-7b-FLAN-step884 were not foldered: ori is an ambiguous name and step884 is a training step.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/llama2-lrl/llama-2-7b-uig": { "id": "peking/llama2-lrl/llama-2-7b-uig", "name": "Llama-2-7b-uig", "maker": "peking", "family": "peking/llama2-lrl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-12-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama-2-7B-named checkpoint. Exact parameter count, language, and training recipe were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-2-7b-uig is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/Llama-2-7b-uig is a PIE Lab Llama-2-7B checkpoint listed with the lab's low-resource language merging collection. The model card was not opened. Llama-2-7b-ori and Llama-2-7b-FLAN-step884 were not foldered: ori is an ambiguous name and step884 is a training step.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/lrl-cpt/gemma-3-4b-bo-cpt": { "id": "peking/lrl-cpt/gemma-3-4b-bo-cpt", "name": "gemma-3-4b-bo-cpt", "maker": "peking", "family": "peking/lrl-cpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued-pretraining checkpoint. Base family is in the repo name (Qwen2.5 or Gemma 3). Parameter count and context were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "gemma-3-4b-bo-cpt is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/gemma-3-4b-bo-cpt is a continued-pretraining checkpoint from PIE Lab at Peking University. The lab's Hugging Face collection titles these files as CPT checkpoints for the ACL 2026 paper on multi-source dynamic logit fusion for low-resource languages. The card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/lrl-cpt/gemma-3-4b-kk-cpt": { "id": "peking/lrl-cpt/gemma-3-4b-kk-cpt", "name": "gemma-3-4b-kk-cpt", "maker": "peking", "family": "peking/lrl-cpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued-pretraining checkpoint. Base family is in the repo name (Qwen2.5 or Gemma 3). Parameter count and context were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "gemma-3-4b-kk-cpt is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/gemma-3-4b-kk-cpt is a continued-pretraining checkpoint from PIE Lab at Peking University. The lab's Hugging Face collection titles these files as CPT checkpoints for the ACL 2026 paper on multi-source dynamic logit fusion for low-resource languages. The card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/lrl-cpt/gemma-3-4b-mn-cpt": { "id": "peking/lrl-cpt/gemma-3-4b-mn-cpt", "name": "gemma-3-4b-mn-cpt", "maker": "peking", "family": "peking/lrl-cpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued-pretraining checkpoint. Base family is in the repo name (Qwen2.5 or Gemma 3). Parameter count and context were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "gemma-3-4b-mn-cpt is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/gemma-3-4b-mn-cpt is a continued-pretraining checkpoint from PIE Lab at Peking University. The lab's Hugging Face collection titles these files as CPT checkpoints for the ACL 2026 paper on multi-source dynamic logit fusion for low-resource languages. The card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/lrl-cpt/gemma-3-4b-ug-cpt": { "id": "peking/lrl-cpt/gemma-3-4b-ug-cpt", "name": "gemma-3-4b-ug-cpt", "maker": "peking", "family": "peking/lrl-cpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued-pretraining checkpoint. Base family is in the repo name (Qwen2.5 or Gemma 3). Parameter count and context were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "gemma-3-4b-ug-cpt is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/gemma-3-4b-ug-cpt is a continued-pretraining checkpoint from PIE Lab at Peking University. The lab's Hugging Face collection titles these files as CPT checkpoints for the ACL 2026 paper on multi-source dynamic logit fusion for low-resource languages. The card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/lrl-cpt/qwen2.5-1.5b-bo-cpt": { "id": "peking/lrl-cpt/qwen2.5-1.5b-bo-cpt", "name": "Qwen2.5-1.5B-bo-cpt", "maker": "peking", "family": "peking/lrl-cpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued-pretraining checkpoint. Base family is in the repo name (Qwen2.5 or Gemma 3). Parameter count and context were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen2.5-1.5B-bo-cpt is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/Qwen2.5-1.5B-bo-cpt is a continued-pretraining checkpoint from PIE Lab at Peking University. The lab's Hugging Face collection titles these files as CPT checkpoints for the ACL 2026 paper on multi-source dynamic logit fusion for low-resource languages. The card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/lrl-cpt/qwen2.5-1.5b-kk-cpt": { "id": "peking/lrl-cpt/qwen2.5-1.5b-kk-cpt", "name": "Qwen2.5-1.5B-kk-cpt", "maker": "peking", "family": "peking/lrl-cpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued-pretraining checkpoint. Base family is in the repo name (Qwen2.5 or Gemma 3). Parameter count and context were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen2.5-1.5B-kk-cpt is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/Qwen2.5-1.5B-kk-cpt is a continued-pretraining checkpoint from PIE Lab at Peking University. The lab's Hugging Face collection titles these files as CPT checkpoints for the ACL 2026 paper on multi-source dynamic logit fusion for low-resource languages. The card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/lrl-cpt/qwen2.5-1.5b-mn-cpt": { "id": "peking/lrl-cpt/qwen2.5-1.5b-mn-cpt", "name": "Qwen2.5-1.5B-mn-cpt", "maker": "peking", "family": "peking/lrl-cpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued-pretraining checkpoint. Base family is in the repo name (Qwen2.5 or Gemma 3). Parameter count and context were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen2.5-1.5B-mn-cpt is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/Qwen2.5-1.5B-mn-cpt is a continued-pretraining checkpoint from PIE Lab at Peking University. The lab's Hugging Face collection titles these files as CPT checkpoints for the ACL 2026 paper on multi-source dynamic logit fusion for low-resource languages. The card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/lrl-cpt/qwen2.5-1.5b-ug-cpt": { "id": "peking/lrl-cpt/qwen2.5-1.5b-ug-cpt", "name": "Qwen2.5-1.5B-ug-cpt", "maker": "peking", "family": "peking/lrl-cpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued-pretraining checkpoint. Base family is in the repo name (Qwen2.5 or Gemma 3). Parameter count and context were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen2.5-1.5B-ug-cpt is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/Qwen2.5-1.5B-ug-cpt is a continued-pretraining checkpoint from PIE Lab at Peking University. The lab's Hugging Face collection titles these files as CPT checkpoints for the ACL 2026 paper on multi-source dynamic logit fusion for low-resource languages. The card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/lrl-cpt/qwen2.5-3b-bo-cpt": { "id": "peking/lrl-cpt/qwen2.5-3b-bo-cpt", "name": "Qwen2.5-3B-bo-cpt", "maker": "peking", "family": "peking/lrl-cpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued-pretraining checkpoint. Base family is in the repo name (Qwen2.5 or Gemma 3). Parameter count and context were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen2.5-3B-bo-cpt is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/Qwen2.5-3B-bo-cpt is a continued-pretraining checkpoint from PIE Lab at Peking University. The lab's Hugging Face collection titles these files as CPT checkpoints for the ACL 2026 paper on multi-source dynamic logit fusion for low-resource languages. The card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/lrl-cpt/qwen2.5-3b-kk-cpt": { "id": "peking/lrl-cpt/qwen2.5-3b-kk-cpt", "name": "Qwen2.5-3B-kk-cpt", "maker": "peking", "family": "peking/lrl-cpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued-pretraining checkpoint. Base family is in the repo name (Qwen2.5 or Gemma 3). Parameter count and context were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen2.5-3B-kk-cpt is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/Qwen2.5-3B-kk-cpt is a continued-pretraining checkpoint from PIE Lab at Peking University. The lab's Hugging Face collection titles these files as CPT checkpoints for the ACL 2026 paper on multi-source dynamic logit fusion for low-resource languages. The card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/lrl-cpt/qwen2.5-3b-mn-cpt": { "id": "peking/lrl-cpt/qwen2.5-3b-mn-cpt", "name": "Qwen2.5-3B-mn-cpt", "maker": "peking", "family": "peking/lrl-cpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued-pretraining checkpoint. Base family is in the repo name (Qwen2.5 or Gemma 3). Parameter count and context were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen2.5-3B-mn-cpt is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/Qwen2.5-3B-mn-cpt is a continued-pretraining checkpoint from PIE Lab at Peking University. The lab's Hugging Face collection titles these files as CPT checkpoints for the ACL 2026 paper on multi-source dynamic logit fusion for low-resource languages. The card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/lrl-cpt/qwen2.5-3b-ug-cpt": { "id": "peking/lrl-cpt/qwen2.5-3b-ug-cpt", "name": "Qwen2.5-3B-ug-cpt", "maker": "peking", "family": "peking/lrl-cpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued-pretraining checkpoint. Base family is in the repo name (Qwen2.5 or Gemma 3). Parameter count and context were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen2.5-3B-ug-cpt is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "pkupie/Qwen2.5-3B-ug-cpt is a continued-pretraining checkpoint from PIE Lab at Peking University. The lab's Hugging Face collection titles these files as CPT checkpoints for the ACL 2026 paper on multi-source dynamic logit fusion for low-resource languages. The card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/moe-llava/moe-llava-openchat-7b-4e": { "id": "peking/moe-llava/moe-llava-openchat-7b-4e", "name": "MoE-LLaVA OpenChat 7B 4e", "maker": "peking", "family": "peking/moe-llava", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mixture-of-experts vision-language model. The repo name encodes the LLM base and 4 experts. Exact activated and total parameter counts were not read from the weight card. The project news says a 3B sparse model beat a dense 7B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MoE-LLaVA OpenChat 7B 4e is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "MoE-LLaVA OpenChat 7B 4e is one of the sparse vision-language models PKU-Yuan Group released as MoE-LLaVA on 2024-01-27. The lab's news line says a sparse model with 3B activated parameters beat a dense 7B model. This folder is the named backbone checkpoint. Pretrain and stage-2 repos are versions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/moe-llava/moe-llava-phi2-2-7b-4e": { "id": "peking/moe-llava/moe-llava-phi2-2-7b-4e", "name": "MoE-LLaVA Phi-2 2.7B 4e", "maker": "peking", "family": "peking/moe-llava", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mixture-of-experts vision-language model. The repo name encodes the LLM base and 4 experts. Exact activated and total parameter counts were not read from the weight card. The project news says a 3B sparse model beat a dense 7B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MoE-LLaVA Phi-2 2.7B 4e is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "MoE-LLaVA Phi-2 2.7B 4e is one of the sparse vision-language models PKU-Yuan Group released as MoE-LLaVA on 2024-01-27. The lab's news line says a sparse model with 3B activated parameters beat a dense 7B model. This folder is the named backbone checkpoint. Pretrain and stage-2 repos are versions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/moe-llava/moe-llava-qwen-1-8b-4e": { "id": "peking/moe-llava/moe-llava-qwen-1-8b-4e", "name": "MoE-LLaVA Qwen 1.8B 4e", "maker": "peking", "family": "peking/moe-llava", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mixture-of-experts vision-language model. The repo name encodes the LLM base and 4 experts. Exact activated and total parameter counts were not read from the weight card. The project news says a 3B sparse model beat a dense 7B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MoE-LLaVA Qwen 1.8B 4e is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "MoE-LLaVA Qwen 1.8B 4e is one of the sparse vision-language models PKU-Yuan Group released as MoE-LLaVA on 2024-01-27. The lab's news line says a sparse model with 3B activated parameters beat a dense 7B model. This folder is the named backbone checkpoint. Pretrain and stage-2 repos are versions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/moe-llava/moe-llava-stablelm-1-6b-4e": { "id": "peking/moe-llava/moe-llava-stablelm-1-6b-4e", "name": "MoE-LLaVA StableLM 1.6B 4e", "maker": "peking", "family": "peking/moe-llava", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mixture-of-experts vision-language model. The repo name encodes the LLM base and 4 experts. Exact activated and total parameter counts were not read from the weight card. The project news says a 3B sparse model beat a dense 7B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MoE-LLaVA StableLM 1.6B 4e is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "MoE-LLaVA StableLM 1.6B 4e is one of the sparse vision-language models PKU-Yuan Group released as MoE-LLaVA on 2024-01-27. The lab's news line says a sparse model with 3B activated parameters beat a dense 7B model. This folder is the named backbone checkpoint. Pretrain and stage-2 repos are versions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/open-sora-plan/open-sora-plan": { "id": "peking/open-sora-plan/open-sora-plan", "name": "Open-Sora Plan", "maker": "peking", "family": "peking/open-sora-plan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the v1.0.0 card. The v1.5.0 card section that was read does not restate a license.", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "v1.5.0 is described as an 8B-scale sparse diffusion transformer (SUV) with a higher-compression WF-VAE, trained on about 40 million videos. v1.2.0 switched to 3D full attention and a 4-second 720p model. v1.3.0 added WF-VAE, a prompt refiner, data filtering, sparse attention, and bucket training. Image-to-video is documented from the v1.2 I2V release on 2024-08-13. Exact parameter count is not stated.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Open-Sora Plan is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Open-Sora Plan is PKU-Yuan Group's open text-to-video project, started with the PKU-Tuzhan AIGC joint lab. The README also credits Huawei, Peng Cheng Laboratory, and community contributors. v1.5.0, announced 2025-06-05, is the latest weight repo and is an 8B-scale sparse DiT called SUV, trained on Ascend 910 with a higher-compression WF-VAE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c013-instruct": { "id": "peking/progressgym/progressgym-histllama3-70b-c013-instruct", "name": "ProgressGym-HistLlama3-70B-C013-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 13th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C013-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C013-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 13th-century model: Meta-Llama-3-70B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c013-pretrain": { "id": "peking/progressgym/progressgym-histllama3-70b-c013-pretrain", "name": "ProgressGym-HistLlama3-70B-C013-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 13th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C013-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C013-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 13th-century continued-pretrain of Meta-Llama-3-70B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c014-instruct": { "id": "peking/progressgym/progressgym-histllama3-70b-c014-instruct", "name": "ProgressGym-HistLlama3-70B-C014-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 14th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C014-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C014-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 14th-century model: Meta-Llama-3-70B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c014-pretrain": { "id": "peking/progressgym/progressgym-histllama3-70b-c014-pretrain", "name": "ProgressGym-HistLlama3-70B-C014-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 14th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C014-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C014-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 14th-century continued-pretrain of Meta-Llama-3-70B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c015-instruct": { "id": "peking/progressgym/progressgym-histllama3-70b-c015-instruct", "name": "ProgressGym-HistLlama3-70B-C015-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 15th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C015-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C015-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 15th-century model: Meta-Llama-3-70B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c015-pretrain": { "id": "peking/progressgym/progressgym-histllama3-70b-c015-pretrain", "name": "ProgressGym-HistLlama3-70B-C015-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 15th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C015-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C015-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 15th-century continued-pretrain of Meta-Llama-3-70B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c016-instruct": { "id": "peking/progressgym/progressgym-histllama3-70b-c016-instruct", "name": "ProgressGym-HistLlama3-70B-C016-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 16th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C016-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C016-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 16th-century model: Meta-Llama-3-70B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c016-pretrain": { "id": "peking/progressgym/progressgym-histllama3-70b-c016-pretrain", "name": "ProgressGym-HistLlama3-70B-C016-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 16th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C016-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C016-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 16th-century continued-pretrain of Meta-Llama-3-70B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c017-instruct": { "id": "peking/progressgym/progressgym-histllama3-70b-c017-instruct", "name": "ProgressGym-HistLlama3-70B-C017-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 17th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C017-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C017-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 17th-century model: Meta-Llama-3-70B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c017-pretrain": { "id": "peking/progressgym/progressgym-histllama3-70b-c017-pretrain", "name": "ProgressGym-HistLlama3-70B-C017-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 17th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C017-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C017-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 17th-century continued-pretrain of Meta-Llama-3-70B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c018-instruct": { "id": "peking/progressgym/progressgym-histllama3-70b-c018-instruct", "name": "ProgressGym-HistLlama3-70B-C018-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 18th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C018-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C018-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 18th-century model: Meta-Llama-3-70B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c018-pretrain": { "id": "peking/progressgym/progressgym-histllama3-70b-c018-pretrain", "name": "ProgressGym-HistLlama3-70B-C018-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 18th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C018-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C018-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 18th-century continued-pretrain of Meta-Llama-3-70B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c019-instruct": { "id": "peking/progressgym/progressgym-histllama3-70b-c019-instruct", "name": "ProgressGym-HistLlama3-70B-C019-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 19th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C019-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C019-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 19th-century model: Meta-Llama-3-70B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c019-pretrain": { "id": "peking/progressgym/progressgym-histllama3-70b-c019-pretrain", "name": "ProgressGym-HistLlama3-70B-C019-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 19th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C019-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C019-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 19th-century continued-pretrain of Meta-Llama-3-70B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c020-instruct": { "id": "peking/progressgym/progressgym-histllama3-70b-c020-instruct", "name": "ProgressGym-HistLlama3-70B-C020-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 20th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C020-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C020-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 20th-century model: Meta-Llama-3-70B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c020-pretrain": { "id": "peking/progressgym/progressgym-histllama3-70b-c020-pretrain", "name": "ProgressGym-HistLlama3-70B-C020-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 20th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C020-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C020-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 20th-century continued-pretrain of Meta-Llama-3-70B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c021-instruct": { "id": "peking/progressgym/progressgym-histllama3-70b-c021-instruct", "name": "ProgressGym-HistLlama3-70B-C021-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 21st-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C021-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C021-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 21st-century model: Meta-Llama-3-70B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-70b-c021-pretrain": { "id": "peking/progressgym/progressgym-histllama3-70b-c021-pretrain", "name": "ProgressGym-HistLlama3-70B-C021-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-70B on 21st-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-70B-C021-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-70B-C021-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 21st-century continued-pretrain of Meta-Llama-3-70B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c013-instruct": { "id": "peking/progressgym/progressgym-histllama3-8b-c013-instruct", "name": "ProgressGym-HistLlama3-8B-C013-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 13th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C013-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C013-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 13th-century model: Meta-Llama-3-8B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c013-pretrain": { "id": "peking/progressgym/progressgym-histllama3-8b-c013-pretrain", "name": "ProgressGym-HistLlama3-8B-C013-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 13th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C013-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C013-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 13th-century continued-pretrain of Meta-Llama-3-8B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c014-instruct": { "id": "peking/progressgym/progressgym-histllama3-8b-c014-instruct", "name": "ProgressGym-HistLlama3-8B-C014-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 14th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C014-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C014-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 14th-century model: Meta-Llama-3-8B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c014-pretrain": { "id": "peking/progressgym/progressgym-histllama3-8b-c014-pretrain", "name": "ProgressGym-HistLlama3-8B-C014-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 14th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C014-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C014-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 14th-century continued-pretrain of Meta-Llama-3-8B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c015-instruct": { "id": "peking/progressgym/progressgym-histllama3-8b-c015-instruct", "name": "ProgressGym-HistLlama3-8B-C015-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 15th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C015-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C015-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 15th-century model: Meta-Llama-3-8B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c015-pretrain": { "id": "peking/progressgym/progressgym-histllama3-8b-c015-pretrain", "name": "ProgressGym-HistLlama3-8B-C015-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 15th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C015-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C015-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 15th-century continued-pretrain of Meta-Llama-3-8B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c016-instruct": { "id": "peking/progressgym/progressgym-histllama3-8b-c016-instruct", "name": "ProgressGym-HistLlama3-8B-C016-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 16th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C016-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C016-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 16th-century model: Meta-Llama-3-8B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c016-pretrain": { "id": "peking/progressgym/progressgym-histllama3-8b-c016-pretrain", "name": "ProgressGym-HistLlama3-8B-C016-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 16th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C016-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C016-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 16th-century continued-pretrain of Meta-Llama-3-8B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c017-instruct": { "id": "peking/progressgym/progressgym-histllama3-8b-c017-instruct", "name": "ProgressGym-HistLlama3-8B-C017-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 17th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C017-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C017-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 17th-century model: Meta-Llama-3-8B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c017-pretrain": { "id": "peking/progressgym/progressgym-histllama3-8b-c017-pretrain", "name": "ProgressGym-HistLlama3-8B-C017-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 17th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C017-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C017-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 17th-century continued-pretrain of Meta-Llama-3-8B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c018-instruct": { "id": "peking/progressgym/progressgym-histllama3-8b-c018-instruct", "name": "ProgressGym-HistLlama3-8B-C018-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 18th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C018-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C018-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 18th-century model: Meta-Llama-3-8B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c018-pretrain": { "id": "peking/progressgym/progressgym-histllama3-8b-c018-pretrain", "name": "ProgressGym-HistLlama3-8B-C018-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 18th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C018-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C018-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 18th-century continued-pretrain of Meta-Llama-3-8B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c019-instruct": { "id": "peking/progressgym/progressgym-histllama3-8b-c019-instruct", "name": "ProgressGym-HistLlama3-8B-C019-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 19th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C019-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C019-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 19th-century model: Meta-Llama-3-8B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c019-pretrain": { "id": "peking/progressgym/progressgym-histllama3-8b-c019-pretrain", "name": "ProgressGym-HistLlama3-8B-C019-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 19th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C019-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C019-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 19th-century continued-pretrain of Meta-Llama-3-8B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c020-instruct": { "id": "peking/progressgym/progressgym-histllama3-8b-c020-instruct", "name": "ProgressGym-HistLlama3-8B-C020-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 20th-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C020-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C020-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 20th-century model: Meta-Llama-3-8B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c020-pretrain": { "id": "peking/progressgym/progressgym-histllama3-8b-c020-pretrain", "name": "ProgressGym-HistLlama3-8B-C020-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 20th-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C020-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C020-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 20th-century continued-pretrain of Meta-Llama-3-8B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c021-instruct": { "id": "peking/progressgym/progressgym-histllama3-8b-c021-instruct", "name": "ProgressGym-HistLlama3-8B-C021-instruct", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 21st-century HistText, then instruction tuning. Two opened cards (8B C021 and 70B C013) both say the shipped instruct weights are training step 10 and that pretraining data is capped at 3GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C021-instruct is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C021-instruct is one of 36 historical language models in PKU-Alignment's ProgressGym suite. The card calls it a 21st-century model: Meta-Llama-3-8B is continued-pretrained on that century's HistText, capped at 3GB, then instruction-tuned on TimelessQA. The released instruct weights are the step-10 snapshot, chosen so instruction tuning does not wipe out the historical value tendencies. The card says the checkpoint is still under iteration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/progressgym/progressgym-histllama3-8b-c021-pretrain": { "id": "peking/progressgym/progressgym-histllama3-8b-c021-pretrain", "name": "ProgressGym-HistLlama3-8B-C021-pretrain", "maker": "peking", "family": "peking/progressgym", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 on the model card. The base weights are Meta Llama 3, which has its own license.", "release_date": "2024-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of meta-llama/Meta-Llama-3-8B on 21st-century text from ProgressGym-HistText. The opened cards cap that stage at 3GB and sample the century corpus when it is larger. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProgressGym-HistLlama3-8B-C021-pretrain is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "ProgressGym-HistLlama3-8B-C021-pretrain is the pretrained, not instruction-tuned, member of the same century pair. The opened 8B C013 pretrain card says it is one of the 36 models, is a 21st-century continued-pretrain of Meta-Llama-3-8B on HistText, and points to the instruct repo for the tuned model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/protrix/llama-3-protrix": { "id": "peking/protrix/llama-3-protrix", "name": "Llama-3-ProTrix", "maker": "peking", "family": "peking/protrix", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-05-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hugging Face pipeline tag: text-generation. The 7B size shown on the ProTrix listing was not confirmed from a card, so parameters is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-ProTrix is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "Llama-3-ProTrix is a named PIE Lab model. The Hugging Face listing tags pkupie/Llama-3-ProTrix as text-generation. The model card was not opened, so the task description stops there.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/protrix/protrix": { "id": "peking/protrix/protrix", "name": "ProTrix", "maker": "peking", "family": "peking/protrix", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-05-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hugging Face pipeline tag: table-question-answering. The 7B size shown on the ProTrix listing was not confirmed from a card, so parameters is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProTrix is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "ProTrix is a named PIE Lab model. The Hugging Face listing tags pkupie/ProTrix as table-question-answering. The model card was not opened, so the task description stops there.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/protrix/protrix-coder": { "id": "peking/protrix/protrix-coder", "name": "ProTrix-Coder", "maker": "peking", "family": "peking/protrix", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-05-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hugging Face pipeline tag: text-generation. The 7B size shown on the ProTrix listing was not confirmed from a card, so parameters is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProTrix-Coder is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "ProTrix-Coder is a named PIE Lab model. The Hugging Face listing tags pkupie/ProTrix-Coder as text-generation. The model card was not opened, so the task description stops there.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/safe-o1/safe-o1-7b": { "id": "peking/safe-o1/safe-o1-7b", "name": "Safe-o1", "maker": "peking", "family": "peking/safe-o1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (model card).", "release_date": "2025-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Causal language model. Card base_model is Qwen/Qwen2.5-7B-Instruct, with a self-monitoring thinking process. Exact parameter count and context window are not restated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Safe-o1 is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Safe-o1 is a PKU-Alignment text model that monitors its own thinking trace and filters unsafe content. The card says the weights are a fine-tune of Qwen2.5-7B-Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/safe-o1/safe-o1-v-7b": { "id": "peking/safe-o1/safe-o1-v-7b", "name": "Safe-o1-V", "maker": "peking", "family": "peking/safe-o1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (model card).", "release_date": "2025-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Card base_model is Qwen/Qwen2-VL-7B-Instruct. Described as multi-modal with a self-monitoring thinking process. Image input is inferred from that base and the multi-modal wording. The only code sample is text.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Safe-o1-V is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Safe-o1-V is the multimodal sibling of Safe-o1. The card calls it a multi-modal model with the same self-monitoring safety trace, and names Qwen2-VL-7B-Instruct as the base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/themis/themis": { "id": "peking/themis/themis", "name": "Themis", "maker": "peking", "family": "peking/themis", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 (model card).", "release_date": "2024-06-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card calls it an 8B-parameter model trained from Meta-Llama-3-8B for reference-free NLG evaluation. Exact parameter count, context window, and max output are not given.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Themis is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Themis is an NLG evaluation model from ONE Lab at Peking University's Wangxuan Institute. The card says the 8B model rates summaries, dialogue, data-to-text, factuality, stories, and translation without a reference, and can take custom criteria and explain the score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/uniworld/uniworld-v1": { "id": "peking/uniworld/uniworld-v1", "name": "UniWorld-V1", "maker": "peking", "family": "peking/uniworld", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unified understanding, generation, and editing model. A fetched README snapshot carried a MIT yaml tag and an Apache license badge. The current license was not re-checked, so license is null. Parameter count and context are not filled.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UniWorld-V1 is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "UniWorld-V1 is PKU-Yuan Group's unified model for visual understanding, generation, and editing. The release note of 2025-06-03 says data, weights, training code, and eval code are open.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "peking/video-llava/video-llava-7b": { "id": "peking/video-llava/video-llava-7b", "name": "Video-LLaVA-7B", "maker": "peking", "family": "peking/video-llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 (model card).", "release_date": "2023-11-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "7B-class visual language model. The card does not restate the exact parameter count or the LLM base in the section that was read. CLI and Gradio entry points are in the Video-LLaVA package.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Video-LLaVA-7B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "what_it_is_for": "Video-LLaVA is a PKU-Yuan Group image-and-video language model. The card says binding a shared visual representation to the language space lets one LLM handle both images and videos, and that the training mix did not need image-video pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "penn/faithful-summary/bart-faithful-summary-detector": { "id": "penn/faithful-summary/bart-faithful-summary-detector", "name": "BART faithful summary detector", "maker": "penn", "family": "penn/faithful-summary", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2021-06-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BartForSequenceClassification. The card says a BART-base model. Input is summary then source document.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Pennsylvania released BART faithful summary detector. Status on 2026-10-15: active. Confidence high.", "what_it_is_for": "Cognitive Computation Group at Penn released a BART-base classifier that scores whether a summary is faithful to its source. The card points at their NAACL 2021 paper on contrast candidate generation and selection. It is a task model, not a general summarizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "penn/l2d/l2d": { "id": "penn/l2d/l2d", "name": "DecompT5 (l2d)", "maker": "penn", "family": "penn/l2d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-10-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5ForConditionalGeneration. The official repo says CogComp/l2d is T5-large trained on comparable_text_pretrain.txt. The paper names that model DecompT5.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Pennsylvania released DecompT5 (l2d). Status on 2026-10-15: active. Confidence high.", "what_it_is_for": "DecompT5 is CogComp's T5-large model further pretrained on distant supervision from comparable news, for hypothetical question decomposition. The authors are at Penn, with Kyle Richardson at the Allen Institute for AI. Weights are CogComp/l2d.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "penn/l2d/l2d-decomp": { "id": "penn/l2d/l2d-decomp", "name": "DecompT5 decomposition", "maker": "penn", "family": "penn/l2d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-11-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5ForConditionalGeneration. The official repo says this is DecompT5 trained further on data/decomposition_train.txt for the DecompEntail pipeline.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Pennsylvania released DecompT5 decomposition. Status on 2026-10-15: active. Confidence high.", "what_it_is_for": "This is the decomposition checkpoint on top of DecompT5, used to break questions into sub-questions inside DecompEntail. CogComp hosts it as l2d-decomp. It is a separate training run from the comparable-text pretrain, so it has its own folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "penn/l2d/l2d-entail": { "id": "penn/l2d/l2d-entail", "name": "DecompEntail entailment", "maker": "penn", "family": "penn/l2d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5ForConditionalGeneration. The official repo says CogComp/l2d-entail is T5-3B trained on data/entailment_train.txt for the DecompEntail pipeline.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Pennsylvania released DecompEntail entailment. Status on 2026-10-15: active. Confidence high.", "what_it_is_for": "This is the entailment model in DecompEntail, a T5-3B checkpoint from CogComp. The paper's QA gains are for the whole decompose-then-entail pipeline, not for this file alone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "penn/percival/percival-b": { "id": "penn/percival/percival-b", "name": "Percival-B", "maker": "penn", "family": "penn/percival", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Conflicting public statements. The GitHub repository badge and LICENSE say CC BY-NC 4.0 for the project. The Hugging Face card at cbeeche/percival says apache-2.0. Preprint v1 on PMC is marked CC BY-NC-ND 4.0 for the article text, which is not the same document as the weights.", "release_date": "2026-05", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "ViT-AugReg Base vision tower, README approximate size ~86M for the vision tower only, plus a shared CXR-BERT text tower the README puts at about 110M. Training yaml example: image_size [352, 352, 128], patch_size [8, 16, 16], projection_dim 768, language_model microsoft/BiomedVLP-CXR-BERT-specialized. Downstream heads use a 768-d vision embedding.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Pennsylvania released Percival-B. Status on 2026-10-15: active. Confidence high.", "what_it_is_for": "The May 2026 GitHub note lists this checkpoint in the current Percival family. It is a CLIP-style 3D CT encoder for embedding extraction, per-code diagnostic probes, and Cox prognostic heads. It is not the July 2025 DeiT model described in the preprint tables.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "penn/percival/percival-s": { "id": "penn/percival/percival-s", "name": "Percival-S", "maker": "penn", "family": "penn/percival", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Conflicting public statements. The GitHub repository badge and LICENSE say CC BY-NC 4.0 for the project. The Hugging Face card at cbeeche/percival says apache-2.0. Preprint v1 on PMC is marked CC BY-NC-ND 4.0 for the article text, which is not the same document as the weights.", "release_date": "2026-05", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "ViT-AugReg Small vision tower, README approximate size ~22M for the vision tower only, plus a shared CXR-BERT text tower the README puts at about 110M. Downstream heads use a 768-d vision embedding. The printed training yaml is the base example: image_size [352, 352, 128], patch_size [8, 16, 16], projection_dim 768, language_model microsoft/BiomedVLP-CXR-BERT-specialized. That yaml was not opened separately for this size.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Pennsylvania released Percival-S. Status on 2026-10-15: active. Confidence high.", "what_it_is_for": "The May 2026 GitHub note lists this checkpoint in the current Percival family. It is a CLIP-style 3D CT encoder for embedding extraction, per-code diagnostic probes, and Cox prognostic heads. It is not the July 2025 DeiT model described in the preprint tables.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "penn/percival/percival-t": { "id": "penn/percival/percival-t", "name": "Percival-T", "maker": "penn", "family": "penn/percival", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Conflicting public statements. The GitHub repository badge and LICENSE say CC BY-NC 4.0 for the project. The Hugging Face card at cbeeche/percival says apache-2.0. Preprint v1 on PMC is marked CC BY-NC-ND 4.0 for the article text, which is not the same document as the weights.", "release_date": "2026-05", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "ViT-AugReg Tiny vision tower, README approximate size ~6M for the vision tower only, plus a shared CXR-BERT text tower the README puts at about 110M. Downstream heads use a 768-d vision embedding. The printed training yaml is the base example: image_size [352, 352, 128], patch_size [8, 16, 16], projection_dim 768, language_model microsoft/BiomedVLP-CXR-BERT-specialized. That yaml was not opened separately for this size.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Pennsylvania released Percival-T. Status on 2026-10-15: active. Confidence high.", "what_it_is_for": "The May 2026 GitHub note lists this checkpoint in the current Percival family. It is a CLIP-style 3D CT encoder for embedding extraction, per-code diagnostic probes, and Cox prognostic heads. It is not the July 2025 DeiT model described in the preprint tables.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "penn/percival/percival-v1": { "id": "penn/percival/percival-v1", "name": "Percival v1", "maker": "penn", "family": "penn/percival", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Conflicting public statements. The GitHub repository badge and LICENSE say CC BY-NC 4.0 for the project. The Hugging Face card at cbeeche/percival says apache-2.0. Preprint v1 on PMC is marked CC BY-NC-ND 4.0 for the article text, which is not the same document as the weights.", "release_date": "2025-07-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "3D-adapted DeiT vision encoder (GitHub: DeiT-Small) and Clinical Longformer, symmetric InfoNCE, 512-d latent space. Patch size 64 cubed. Input volumes 256 by 256 by 128.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Pennsylvania released Percival v1. Status on 2026-10-15: active. Confidence high.", "what_it_is_for": "The preprint presents Percival as a pan-organ CT vision-language encoder. It aligns 3D volumes with radiology reports so the latent space can support retrieval, phenotyping, and risk models without a new pretraining run for each disease.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "penn/ricl/ricl-pi0-fast-droid": { "id": "penn/ricl/ricl-pi0-fast-droid", "name": "RICL-pi0-FAST-DROID", "maker": "penn", "family": "penn/ricl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the Hugging Face card. Whether that tag covers weights inherited from pi0-FAST was not checked against the Physical Intelligence license.", "release_date": "2025-05-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RICL post-train of pi0-FAST-DROID, stored as an Orbax/ocdbt checkpoint (params/manifest.ocdbt) plus DROID norm stats.", "modalities": { "input": [ "image", "text" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Pennsylvania released RICL-pi0-FAST-DROID. Status on 2026-10-15: active. Confidence high.", "what_it_is_for": "Penn GRASP and PRECISE authors, with a University of British Columbia coauthor, retrain Physical Intelligence's pi0-FAST-DROID so a user can adapt it with retrieved demonstrations instead of a new gradient step. The paper says code and weights ship with the work. The hosted artifact is one DROID checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "penn/temporal-predictor/roberta-temporal-predictor": { "id": "penn/temporal-predictor/roberta-temporal-predictor", "name": "RoBERTa temporal predictor", "maker": "penn", "family": "penn/temporal-predictor", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-03-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RobertaForMaskedLM. The card says RoBERTa-base, fine-tuned on the NYT Annotated Corpus to predict temporal precedence.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Pennsylvania released RoBERTa temporal predictor. Status on 2026-10-15: active. Confidence high.", "what_it_is_for": "CogComp fine-tuned RoBERTa-base as the temporality piece of the ROCK commonsense-causality pipeline. It fills a mask to score which of two events happened first. The card cites Zhang, Zhang, Roth, and Su, arXiv 2202.00436.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "penn/zeroshotwiki/zeroshotwiki": { "id": "penn/zeroshotwiki/zeroshotwiki", "name": "ZeroShotWiki", "maker": "penn", "family": "penn/zeroshotwiki", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BertForSequenceClassification fine-tuned on Wikipedia. The card does not state the BERT size. GitHub calls the entailment model TE-WikiCate.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Pennsylvania released ZeroShotWiki. Status on 2026-10-15: active. Confidence high.", "what_it_is_for": "CogComp released a BERT sequence classifier fine-tuned on Wikipedia for zero-shot topic classification. The card says to pair the sentence with each candidate label and read a score. The GitHub repo calls the NAACL 2022 demo Towards Open Domain Topic Classification and names TE-WikiCate as the entailment-style model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/decider/pplx-decider-v1-1-27b": { "id": "perplexity/decider/pplx-decider-v1-1-27b", "name": "pplx-decider-v1.1-27b", "maker": "perplexity", "family": "perplexity/decider", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 262144, "max_output_tokens": null, "parameters": "27B", "architecture": "Decision model. Reads text and images and returns probabilities for yes/no, choice, or rubric scores instead of generated text.", "modalities": { "input": [ "text", "image" ], "output": [ "probabilities" ] }, "pricing": { "prompt_per_m_usd": 0.02, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `pplx-decider-v1.1-27b`. Status: active. Collected 2026-10-15.", "what_it_is_for": "Perplexity's current decision model. The Decisions API sends text, JSON, or images and gets probabilities back: yes or no, a choice, or a score on a rubric. The October 2026 changelog makes v1.1 the model to use, at $0.02 per million input tokens. Output tokens are free.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/decider/pplx-decider-v1-27b": { "id": "perplexity/decider/pplx-decider-v1-27b", "name": "pplx-decider-v1-27b", "maker": "perplexity", "family": "perplexity/decider", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "27B", "architecture": "First Decisions API model. Same task family as v1.1: probabilities rather than generated text.", "modalities": { "input": [ "text", "image" ], "output": [ "probabilities" ] }, "pricing": { "prompt_per_m_usd": 0.02, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `pplx-decider-v1-27b`. Status: deprecated. Collected 2026-10-15.", "what_it_is_for": "The first Decisions API model. Perplexity introduced it in October 2026 at $0.04 per million input tokens, then replaced it the same month with pplx-decider-v1.1-27b. Calls that still name v1 work and bill at $0.02.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/embed/pplx-embed-context-v1-0-6b": { "id": "perplexity/embed/pplx-embed-context-v1-0-6b", "name": "pplx-embed-context-v1-0.6b", "maker": "perplexity", "family": "perplexity/embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-26", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Bidirectional encoder from diffusion continued-pretraining of Qwen3. Mean pooling. No instruction prefix. Unnormalized int8 outputs.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.008, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `pplx-embed-context-v1-0.6b`. Status: active. Collected 2026-10-15.", "what_it_is_for": "Contextualized embeddings. Chunks from one document are encoded together. Released 26 February 2026 on the API and as MIT weights. Perplexity says the family is built for the same kind of retrieval that powers its search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/embed/pplx-embed-context-v1-4b": { "id": "perplexity/embed/pplx-embed-context-v1-4b", "name": "pplx-embed-context-v1-4b", "maker": "perplexity", "family": "perplexity/embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-26", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Bidirectional encoder from diffusion continued-pretraining of Qwen3. Mean pooling. No instruction prefix. Unnormalized int8 outputs.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `pplx-embed-context-v1-4b`. Status: active. Collected 2026-10-15.", "what_it_is_for": "Larger contextualized embeddings. Released 26 February 2026 on the API and as MIT weights. Perplexity says the family is built for the same kind of retrieval that powers its search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/embed/pplx-embed-v1-0-6b": { "id": "perplexity/embed/pplx-embed-v1-0-6b", "name": "pplx-embed-v1-0.6b", "maker": "perplexity", "family": "perplexity/embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-26", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Bidirectional encoder from diffusion continued-pretraining of Qwen3. Mean pooling. No instruction prefix. Unnormalized int8 outputs.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.004, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `pplx-embed-v1-0.6b`. Status: active. Collected 2026-10-15.", "what_it_is_for": "Standard dense embeddings for independent texts. Matryoshka 128-1024. Released 26 February 2026 on the API and as MIT weights. Perplexity says the family is built for the same kind of retrieval that powers its search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/embed/pplx-embed-v1-4b": { "id": "perplexity/embed/pplx-embed-v1-4b", "name": "pplx-embed-v1-4b", "maker": "perplexity", "family": "perplexity/embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-26", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Bidirectional encoder from diffusion continued-pretraining of Qwen3. Mean pooling. No instruction prefix. Unnormalized int8 outputs.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.03, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `pplx-embed-v1-4b`. Status: active. Collected 2026-10-15.", "what_it_is_for": "Larger standard dense embeddings. Matryoshka 128-2560. Released 26 February 2026 on the API and as MIT weights. Perplexity says the family is built for the same kind of retrieval that powers its search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/embed/pplx-embed-v1-late-0-6b": { "id": "perplexity/embed/pplx-embed-v1-late-0-6b", "name": "pplx-embed-v1-late-0.6b", "maker": "perplexity", "family": "perplexity/embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Late-interaction MaxSim model. Continued training of pplx-embed-v1-0.6b with contrastive loss. 128-dimensional token vectors.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `pplx-embed-v1-late-0.6b`. Status: active. Collected 2026-10-15.", "what_it_is_for": "Open-weight late-interaction embedding model from Perplexity. The Hugging Face card says it continues training from pplx-embed-v1-0.6b and scores token vectors with MaxSim. It was not on the hosted Embeddings API model table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/embed/pplx-embed-v2-context-9b-preview": { "id": "perplexity/embed/pplx-embed-v2-context-9b-preview", "name": "pplx-embed-v2-context-9b-preview", "maker": "perplexity", "family": "perplexity/embed", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-09-30", "context_length": null, "max_output_tokens": null, "parameters": "8B on the Hugging Face model-size line; the id says 9b", "architecture": "Contextual dense embedder. A document is a list of chunks encoded together. Mean pooling. Unnormalized int8. Fixed query and document prefixes. Matryoshka at 1024 and 2048. Full width 2048.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `pplx-embed-v2-context-9b-preview`. Status: preview. Collected 2026-10-15.", "what_it_is_for": "Preview contextual embedding model Perplexity announced on 30 September 2026 and published on Hugging Face. It encodes document chunks with the rest of the document in view. It is not one of the four models on the hosted Embeddings API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/embed/pplx-embed-v2-late-0-6b": { "id": "perplexity/embed/pplx-embed-v2-late-0-6b", "name": "pplx-embed-v2-late-0.6b", "maker": "perplexity", "family": "perplexity/embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-07", "context_length": null, "max_output_tokens": null, "parameters": "594M total; about 240M active for text and 340M for images", "architecture": "ColBERT-style late interaction on a pruned Qwen3.5-0.8B text tower (24 layers to 12) plus an image encoder. 128-dimensional token vectors. MaxSim scoring. Shares an embedding space with the 9B model. Distilled from an internal 18B teacher with LEAF-style token alignment.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `pplx-embed-v2-late-0.6b`. Status: active. Collected 2026-10-15.", "what_it_is_for": "Perplexity released this on 7 October 2026 as the small multimodal late-interaction embedder. It keeps a 128-dimensional vector per token, searches text, images, and rendered pages, and can query an index built by the 9B sibling. Weights are on Hugging Face. The API endpoint was not live.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/embed/pplx-embed-v2-late-9b": { "id": "perplexity/embed/pplx-embed-v2-late-9b", "name": "pplx-embed-v2-late-9b", "maker": "perplexity", "family": "perplexity/embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-07", "context_length": null, "max_output_tokens": null, "parameters": "9B nominal; 7.4B active parameters on the model card", "architecture": "ColBERT-style late interaction, 128-dimensional token vectors, MaxSim. Distilled from an internal 18B teacher. The model card says the last eight transformer layers were fully fine-tuned and the rest, including the vision encoder, used LoRA. Shares an embedding space with the 0.6B model.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `pplx-embed-v2-late-9b`. Status: active. Collected 2026-10-15.", "what_it_is_for": "The larger pplx-embed-v2-late model, released 7 October 2026. Perplexity positions it for indexing and maximum retrieval quality over text, images, and rendered pages, in one embedding space with the 0.6B model. Weights are public. API hosting was still planned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/pplx/pplx-70b-chat": { "id": "perplexity/pplx/pplx-70b-chat", "name": "pplx-70b-chat", "maker": "perplexity", "family": "perplexity/pplx", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `pplx-70b-chat`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Early Perplexity-branded 70B API name. It appears in secondary model catalogs. The official deprecation lists retrieved this run name the 7B pplx models but not the 70B ids, so the retirement date is unknown.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/pplx/pplx-70b-online": { "id": "perplexity/pplx/pplx-70b-online", "name": "pplx-70b-online", "maker": "perplexity", "family": "perplexity/pplx", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `pplx-70b-online`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Early Perplexity-branded 70B API name. It appears in secondary model catalogs. The official deprecation lists retrieved this run name the 7B pplx models but not the 70B ids, so the retirement date is unknown.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/pplx/pplx-7b-chat": { "id": "perplexity/pplx/pplx-7b-chat", "name": "pplx-7b-chat", "maker": "perplexity", "family": "perplexity/pplx", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `pplx-7b-chat`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Early Perplexity API model. The April 2024 changelog says pplx-7b-chat and pplx-7b-online stopped working on 14 May 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/pplx/pplx-7b-online": { "id": "perplexity/pplx/pplx-7b-online", "name": "pplx-7b-online", "maker": "perplexity", "family": "perplexity/pplx", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `pplx-7b-online`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Early Perplexity API model. The April 2024 changelog says pplx-7b-chat and pplx-7b-online stopped working on 14 May 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/r1/r1-1776": { "id": "perplexity/r1/r1-1776", "name": "R1 1776", "maker": "perplexity", "family": "perplexity/r1", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-18", "context_length": null, "max_output_tokens": null, "parameters": "671B", "architecture": "Post-train of DeepSeek-R1. Derivative GGUF cards describe the architecture as deepseek2. Perplexity's posted description says the post-train removed refusals on politically censored topics while keeping reasoning quality.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `r1-1776`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "R1 1776 is Perplexity's post-trained DeepSeek-R1, released as open weights on 18 February 2025 and removed from the Perplexity API on 1 August 2025. The company described it as a de-censored reasoning model. The API changelog points former users to Sonar Reasoning Pro. Weights stayed public after the API removal, so local hosting remains possible even though the hosted product is retired.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/llama-3-1-sonar-huge-128k-online": { "id": "perplexity/sonar/llama-3-1-sonar-huge-128k-online", "name": "Llama 3.1 Sonar Huge 128k Online", "maker": "perplexity", "family": "perplexity/sonar", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `llama-3.1-sonar-huge-128k-online`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Perplexity sold this as its own Sonar model on the early API. The July 2024 changelog introduced the Llama 3.1 Sonar ids and removed the Llama 3 32k Sonar ids on 12 August 2024. Named only in the January 2025 removal notice. No separate launch note was retrieved.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/llama-3-1-sonar-large-128k-chat": { "id": "perplexity/sonar/llama-3-1-sonar-large-128k-chat", "name": "Llama 3.1 Sonar Large 128k Chat", "maker": "perplexity", "family": "perplexity/sonar", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `llama-3.1-sonar-large-128k-chat`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Perplexity sold this as its own Sonar model on the early API. The July 2024 changelog introduced the Llama 3.1 Sonar ids and removed the Llama 3 32k Sonar ids on 12 August 2024. Introduced in the July 2024 changelog. Not named in the February 2025 removal list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/llama-3-1-sonar-large-128k-online": { "id": "perplexity/sonar/llama-3-1-sonar-large-128k-online", "name": "Llama 3.1 Sonar Large 128k Online", "maker": "perplexity", "family": "perplexity/sonar", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `llama-3.1-sonar-large-128k-online`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Perplexity sold this as its own Sonar model on the early API. The July 2024 changelog introduced the Llama 3.1 Sonar ids and removed the Llama 3 32k Sonar ids on 12 August 2024. Removed 2025-02-22.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/llama-3-1-sonar-small-128k-chat": { "id": "perplexity/sonar/llama-3-1-sonar-small-128k-chat", "name": "Llama 3.1 Sonar Small 128k Chat", "maker": "perplexity", "family": "perplexity/sonar", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `llama-3.1-sonar-small-128k-chat`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Perplexity sold this as its own Sonar model on the early API. The July 2024 changelog introduced the Llama 3.1 Sonar ids and removed the Llama 3 32k Sonar ids on 12 August 2024. Introduced in the July 2024 changelog. Not named in the February 2025 removal list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/llama-3-1-sonar-small-128k-online": { "id": "perplexity/sonar/llama-3-1-sonar-small-128k-online", "name": "Llama 3.1 Sonar Small 128k Online", "maker": "perplexity", "family": "perplexity/sonar", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `llama-3.1-sonar-small-128k-online`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Perplexity sold this as its own Sonar model on the early API. The July 2024 changelog introduced the Llama 3.1 Sonar ids and removed the Llama 3 32k Sonar ids on 12 August 2024. Removed 2025-02-22. Changelog recommends Sonar or Sonar Pro.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/llama-3-sonar-large-32k-chat": { "id": "perplexity/sonar/llama-3-sonar-large-32k-chat", "name": "Llama 3 Sonar Large 32k Chat", "maker": "perplexity", "family": "perplexity/sonar", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `llama-3-sonar-large-32k-chat`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Perplexity sold this as its own Sonar model on the early API. The July 2024 changelog introduced the Llama 3.1 Sonar ids and removed the Llama 3 32k Sonar ids on 12 August 2024. Offline chat variant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/llama-3-sonar-large-32k-online": { "id": "perplexity/sonar/llama-3-sonar-large-32k-online", "name": "Llama 3 Sonar Large 32k Online", "maker": "perplexity", "family": "perplexity/sonar", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `llama-3-sonar-large-32k-online`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Perplexity sold this as its own Sonar model on the early API. The July 2024 changelog introduced the Llama 3.1 Sonar ids and removed the Llama 3 32k Sonar ids on 12 August 2024. Online search variant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/llama-3-sonar-small-32k-chat": { "id": "perplexity/sonar/llama-3-sonar-small-32k-chat", "name": "Llama 3 Sonar Small 32k Chat", "maker": "perplexity", "family": "perplexity/sonar", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `llama-3-sonar-small-32k-chat`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Perplexity sold this as its own Sonar model on the early API. The July 2024 changelog introduced the Llama 3.1 Sonar ids and removed the Llama 3 32k Sonar ids on 12 August 2024. Offline chat variant without the online request fee.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/llama-3-sonar-small-32k-online": { "id": "perplexity/sonar/llama-3-sonar-small-32k-online", "name": "Llama 3 Sonar Small 32k Online", "maker": "perplexity", "family": "perplexity/sonar", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `llama-3-sonar-small-32k-online`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Perplexity sold this as its own Sonar model on the early API. The July 2024 changelog introduced the Llama 3.1 Sonar ids and removed the Llama 3 32k Sonar ids on 12 August 2024. Online search variant. Flat $5 per 1,000 requests on top of token price in the June 2024 pricing snapshot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/sonar": { "id": "perplexity/sonar/sonar", "name": "Sonar", "maker": "perplexity", "family": "perplexity/sonar", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.25, "completion_per_m_usd": 2.5 }, "summary_intro": "Perplexity model `sonar`. Status: active. Collected 2026-10-15.", "what_it_is_for": "Perplexity describes Sonar as a lightweight, cost-effective search model for quick, grounded answers with live web search. It is the only Perplexity-built model still listed by name on the Agent API model table.", "ratings": { "empirical_100": null, "lmsys_elo": 1215, "coding_elo": 1215, "swe_bench": 17.5, "humaneval": 72.0, "twelve_cs": 99.5, "aa_intelligence": 7.7, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/sonar-2": { "id": "perplexity/sonar/sonar-2", "name": "Sonar 2", "maker": "perplexity", "family": "perplexity/sonar", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perplexity model `sonar-2`. Status: active. Collected 2026-10-15.", "what_it_is_for": "Sonar 2 is the name Perplexity uses for its own model in the consumer and enterprise search picker. The Help Center, in a snapshot dated 4 September 2026, lists it with provider Perplexity on paid search plans. No API card was found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/sonar-deep-research": { "id": "perplexity/sonar/sonar-deep-research", "name": "Sonar Deep Research", "maker": "perplexity", "family": "perplexity/sonar", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 8.0 }, "summary_intro": "Perplexity model `sonar-deep-research`. Status: deprecated. Collected 2026-10-15.", "what_it_is_for": "Perplexity describes Sonar Deep Research as the model that runs many searches, reads sources, and writes a long cited report. It bills input, output, citation tokens, reasoning tokens, and search queries separately. Chat Completions support ended on 2026-09-27.", "ratings": { "empirical_100": null, "lmsys_elo": 1320, "coding_elo": 1320, "swe_bench": 35.0, "humaneval": 86.0, "twelve_cs": 99.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/sonar-pro": { "id": "perplexity/sonar/sonar-pro", "name": "Sonar Pro", "maker": "perplexity", "family": "perplexity/sonar", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 200000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 15.0 }, "summary_intro": "Perplexity model `sonar-pro`. Status: deprecated. Collected 2026-10-15.", "what_it_is_for": "Perplexity describes Sonar Pro as the advanced search model for complex, multi-step questions, with deeper retrieval than Sonar. Pro Search, generally available in November 2025, adds multi-step tool use on this model. Chat Completions support for the tier ended on 2026-09-27.", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs": 99.5, "aa_intelligence": 7.6, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/sonar-reasoning": { "id": "perplexity/sonar/sonar-reasoning", "name": "Sonar Reasoning", "maker": "perplexity", "family": "perplexity/sonar", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 5.0 }, "summary_intro": "Perplexity model `sonar-reasoning`. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Sonar Reasoning was Perplexity's earlier search reasoning model. The API changelog says it was removed on 15 December 2025, with Sonar Reasoning Pro as the replacement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "perplexity/sonar/sonar-reasoning-pro": { "id": "perplexity/sonar/sonar-reasoning-pro", "name": "Sonar Reasoning Pro", "maker": "perplexity", "family": "perplexity/sonar", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 8.0 }, "summary_intro": "Perplexity model `sonar-reasoning-pro`. Status: deprecated. Collected 2026-10-15.", "what_it_is_for": "Perplexity describes Sonar Reasoning Pro as its high-performance reasoning model, using multi-step chain-of-thought plus retrieval. It replaced Sonar Reasoning in December 2025. The Sonar Chat Completions tier's support ended on 2026-09-27.", "ratings": { "empirical_100": null, "lmsys_elo": 1320, "coding_elo": 1320, "swe_bench": 35.0, "humaneval": 86.0, "twelve_cs": 99.5, "aa_intelligence": 11.8, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "phrase/next-genmt/next-genmt": { "id": "phrase/next-genmt/next-genmt", "name": "Phrase Next GenMT", "maker": "phrase", "family": "phrase/next-genmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Phrase-built translation engine on top of third-party large language models. It translates multiple segments together and uses retrieval-augmented generation over translation-memory fuzzy matches. Phrase does not publish the current provider model id. A June 2024 recap names GPT-4o. A January 2025 study tested GPT-4o and GPT-4o-mini. A 12 March 2025 release note says the latest GPT model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": null }, "summary_intro": "Phrase Next GenMT is Phrase's generative translation engine and a native engine in Phrase Language AI. It is active as of 15 October 2026. Phrase develops the engine, glossary handling, tag placement, and retrieval, and calls third-party large language models for the translation itself. Weights are closed. It does not replace Phrase NextMT. Both stay available.", "what_it_is_for": "Phrase presents it as the context-aware option when a team does not want to train a custom model. It translates a block of segments together so pronouns, tone, and terms stay consistent, inflects glossary terms, and pulls fuzzy translation-memory matches into the prompt. Formality can be set to formal, informal, or preserve. The product page says retrieval uses up to three fuzzy matches. The support article says a set of matches and does not give a count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "phrase/nextmt/custom-nextmt": { "id": "phrase/nextmt/custom-nextmt", "name": "Phrase Custom NextMT", "maker": "phrase", "family": "phrase/nextmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-09-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Customer fine-tune of Phrase NextMT. Phrase calls the method static adaptation: a general NextMT model is fine-tuned on the customer's translation data. No parameter count is published. Each deployment has its own Custom Model ID; those customer-named runs are not separate catalog entries.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": null }, "summary_intro": "Phrase Custom NextMT is Phrase's name for a customer-trained engine built from Phrase NextMT. Phrase Custom AI, launched 13 September 2023, is the trainer. The engine is active and closed-weight. This dossier covers the named product, not each customer's Custom Model ID. Those runs are unnamed outside the customer's account.", "what_it_is_for": "Phrase says customers fine-tune NextMT on their own translation data so the engine learns domain terminology, brand style, and target-language usage. Deployed engines are selected in Phrase Language AI as Phrase Custom NextMT and can be used from TMS, Portal, and the Language AI API. Phrase's research blog calls this static adaptation, as opposed to the on-the-fly retrieval used by Next GenMT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "phrase/nextmt/nextmt": { "id": "phrase/nextmt/nextmt", "name": "Phrase NextMT", "maker": "phrase", "family": "phrase/nextmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2022-11-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Undisclosed. The support article calls it a generative engine that runs on Phrase's Phoenix LLM and uses retrieval-augmented generation. The product page and a 2026 Phrase research blog still call it neural machine translation that inserts one fuzzy translation-memory match.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": null }, "summary_intro": "Phrase NextMT is Phrase's own machine-translation engine, announced on 15 November 2022 and still a native engine in Phrase Language AI as of 15 October 2026. It translates text for post-editing inside the Phrase platform. Weights are closed. Phrase's pages disagree on the architecture: the support article calls it generative AI running on the in-house Phoenix LLM, while the product page and a 2026 research blog still call it neural MT.", "what_it_is_for": "Phrase built NextMT for the translation-management workflow rather than as a general chatbot. It is meant to pre-translate content, keep formatting and placeholder tags, inflect glossary terms instead of blindly replacing them, and adapt to a project's translation memory. The product page positions it as the fast, low-latency option next to Custom NextMT and Next GenMT. MT Autoselect does not prefer it over other engines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "phrase/phoenix/phoenix": { "id": "phrase/phoenix/phoenix", "name": "Phoenix", "maker": "phrase", "family": "phrase/phoenix", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published. Phrase's NextMT support article calls Phoenix a large language model built and operated entirely by Phrase, and says Phrase NextMT runs on it.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phoenix is a large language model that Phrase says it builds and operates. The only official description found, on the Phrase NextMT support article retrieved 15 October 2026, is that Phrase NextMT runs on Phoenix. It is not a separate engine customers can select. Weights are closed. This is not the unrelated Phoenix chat model from CUHK-Shenzhen.", "what_it_is_for": "Phrase does not give Phoenix its own product page. It names Phoenix as the in-house model behind Phrase NextMT, the translation engine used for low-latency machine translation in Phrase Language AI. There is no public Phoenix API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "phrase/translation-agent/ai-translation-agent": { "id": "phrase/translation-agent/ai-translation-agent", "name": "Phrase AI Translation Agent", "maker": "phrase", "family": "phrase/translation-agent", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Agent pipeline, not a single published model. Phrase says it calls multiple large language models for translation, error correction, terminology, and fluency. The model ids are not published. Style rules are passed as context. Following a rule is probabilistic.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.25 }, "summary_intro": "The Phrase AI Translation Agent is a generative translation engine Phrase released on 24 September 2025. It is active as of 15 October 2026. Phrase selects it as the translation engine in TMS, Strings, Portal, Studio, and the Language AI API. It is an agent over several unpublished large language models, not a single disclosed weight file. Weights are closed.", "what_it_is_for": "Phrase uses it to translate and then revise in one pass: context across segments, retrieval from the project translation memory, terminology from the term base, tag repair, and a fluency pass. The 24 September 2025 announcement describes it as translation plus optimization without a separate setup step. It is aimed at teams that want less human review than a raw machine-translation suggestion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pika/pika-1-0": { "id": "pika/pika/pika-1-0", "name": "Pika 1.0", "maker": "pika", "family": "pika/pika", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pika's first named public video model and web product. Announced 28 November 2023 and opened beyond the waitlist on 27 December 2023. It is absent from the Pika API catalog checked on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Pika's first named public video model and web product. Announced 28 November 2023 and opened beyond the waitlist on 27 December 2023. It is absent from the Pika API catalog checked on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pika/pika-1-5": { "id": "pika/pika/pika-1-5", "name": "Pika 1.5", "maker": "pika", "family": "pika/pika", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pika's October 2024 video model, launched with the Pikaffects presets. It is not in the 2026-10-15 Pika API catalog. Pikaffects itself remains a separate active API model. Collected 2026-10-15.", "what_it_is_for": "Pika's October 2024 video model, launched with the Pikaffects presets. It is not in the 2026-10-15 Pika API catalog. Pikaffects itself remains a separate active API model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pika/pika-2-0": { "id": "pika/pika/pika-2-0", "name": "Pika 2.0", "maker": "pika", "family": "pika/pika", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pika video model launched 13 December 2024. The official post highlights text alignment and Scene Ingredients, reference images of people, places, and objects. Not in the 2026-10-15 Pika API catalog. Collected 2026-10-15.", "what_it_is_for": "Pika video model launched 13 December 2024. The official post highlights text alignment and Scene Ingredients, reference images of people, places, and objects. Not in the 2026-10-15 Pika API catalog. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pika/pika-2-1": { "id": "pika/pika/pika-2-1", "name": "Pika 2.1", "maker": "pika", "family": "pika/pika", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.4 }, "summary_intro": "Pika video model launched 27 January 2025. Pika's post leads with 1080p. It is still documented on fal and is not in Pika's own API catalog as of 2026-10-15, so this dossier marks it deprecated rather than retired. Collected 2026-10-15.", "what_it_is_for": "Pika video model launched 27 January 2025. Pika's post leads with 1080p. It is still documented on fal and is not in Pika's own API catalog as of 2026-10-15, so this dossier marks it deprecated rather than retired. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pika/pika-2-2": { "id": "pika/pika/pika-2-2", "name": "Pika 2.2", "maker": "pika", "family": "pika/pika", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pika video model launched 27 February 2025 with 10 second clips, 1080p, and Pikaframes. fal still hosts text-to-video and image-to-video. Pika's own API catalog on 2026-10-15 lists Pika 2.5 instead, so this dossier marks 2.2 deprecated. Collected 2026-10-15.", "what_it_is_for": "Pika video model launched 27 February 2025 with 10 second clips, 1080p, and Pikaframes. fal still hosts text-to-video and image-to-video. Pika's own API catalog on 2026-10-15 lists Pika 2.5 instead, so this dossier marks 2.2 deprecated. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pika/pika-2-5": { "id": "pika/pika/pika-2-5", "name": "Pika 2.5", "maker": "pika", "family": "pika/pika", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pika's current flagship video model, listed on pika.art and in the Pika API as stable text-to-video and image-to-video. Text-to-video is 5 seconds at up to 1080p. Image-to-video is 5 or 10 seconds. Collected 2026-10-15.", "what_it_is_for": "Pika's current flagship video model, listed on pika.art and in the Pika API as stable text-to-video and image-to-video. Text-to-video is 5 seconds at up to 1080p. Image-to-video is 5 or 10 seconds. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pika/pika-2-turbo": { "id": "pika/pika/pika-2-turbo", "name": "Pika 2 Turbo", "maker": "pika", "family": "pika/pika", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.2 }, "summary_intro": "Faster Pika v2 video endpoint hosted on fal as Pika v2 Turbo. fal's 14 March 2025 post lists text-to-video and image-to-video. It is not in Pika's own API catalog on 2026-10-15. A legacy Pika credit page also says Turbo for some effect jobs. Collected 2026-10-15.", "what_it_is_for": "Faster Pika v2 video endpoint hosted on fal as Pika v2 Turbo. fal's 14 March 2025 post lists text-to-video and image-to-video. It is not in Pika's own API catalog on 2026-10-15. A legacy Pika credit page also says Turbo for some effect jobs. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pikadditions/pikadditions": { "id": "pika/pikadditions/pikadditions", "name": "Pikadditions", "maker": "pika", "family": "pika/pikadditions", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.03 }, "summary_intro": "Pika video-to-video model that inserts a person or object into an existing clip. It is a stable API operation at up to 1080p and 10 seconds. Collected 2026-10-15.", "what_it_is_for": "Pika video-to-video model that inserts a person or object into an existing clip. It is a stable API operation at up to 1080p and 10 seconds. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pikaffects/pikaffects": { "id": "pika/pikaffects/pikaffects", "name": "Pikaffects", "maker": "pika", "family": "pika/pikaffects", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "Named Pika effect model that turns a still or a clip into a short effect video from a preset. It shipped with Pika 1.5 in October 2024 and is a stable pair of operations on the Pika API. Collected 2026-10-15.", "what_it_is_for": "Named Pika effect model that turns a still or a clip into a short effect video from a preset. It shipped with Pika 1.5 in October 2024 and is a stable pair of operations on the Pika API. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pikaframes/pikaframes": { "id": "pika/pikaframes/pikaframes", "name": "Pikaframes", "maker": "pika", "family": "pika/pikaframes", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pika's keyframe video model. It animates one to five ordered images with per-transition prompts. Pika introduced it with Pika 2.2 on 27 February 2025. The current API calls the same path Pika 2.5 Keyframe. Collected 2026-10-15.", "what_it_is_for": "Pika's keyframe video model. It animates one to five ordered images with per-transition prompts. Pika introduced it with Pika 2.2 on 27 February 2025. The current API calls the same path Pika 2.5 Keyframe. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pikascenes/pikascenes": { "id": "pika/pikascenes/pikascenes", "name": "Pikascenes", "maker": "pika", "family": "pika/pikascenes", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pika's reference-image scene model: several images of characters, objects, wardrobe, and setting become one clip. fal hosts it as Pika Scenes v2.2. It is not in Pika's own API catalog on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Pika's reference-image scene model: several images of characters, objects, wardrobe, and setting become one clip. fal hosts it as Pika Scenes v2.2. It is not in Pika's own API catalog on 2026-10-15. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pikastream/pikaformance": { "id": "pika/pikastream/pikaformance", "name": "Pikaformance", "maker": "pika", "family": "pika/pikastream", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pika's first named ultra-fast video model, described in the 2 April 2026 PikaStream post as the previous generation. The post says it needed 8 GPUs and 4.5 seconds of latency. It is not in the 2026-10-15 API catalog. Collected 2026-10-15.", "what_it_is_for": "Pika's first named ultra-fast video model, described in the 2 April 2026 PikaStream post as the previous generation. The post says it needed 8 GPUs and 4.5 seconds of latency. It is not in the 2026-10-15 API catalog. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pikastream/pikastream-1-0": { "id": "pika/pikastream/pikastream-1-0", "name": "PikaStream 1.0", "maker": "pika", "family": "pika/pikastream", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pika's real-time video model for talking with an agent face to face. The 2 April 2026 research post says it generates 24 FPS at 480p on one H100, with about 1.5 seconds of speech-to-video latency. It is not in the public media API catalog. Collected 2026-10-15.", "what_it_is_for": "Pika's real-time video model for talking with an agent face to face. The 2 April 2026 research post says it generates 24 FPS at 480p on one H100, with about 1.5 seconds of speech-to-video latency. It is not in the public media API catalog. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pikastream/pikastream-2-0": { "id": "pika/pikastream/pikastream-2-0", "name": "PikaStream 2.0", "maker": "pika", "family": "pika/pikastream", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Announced real-time video model. On 18 August 2026 Pika's speech post said future speech work would support PikaStream 2.0 for synchronized audiovisual generation. No release was found. Collected 2026-10-15.", "what_it_is_for": "Announced real-time video model. On 18 August 2026 Pika's speech post said future speech work would support PikaStream 2.0 for synchronized audiovisual generation. No release was found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/pikaswaps/pikaswaps": { "id": "pika/pikaswaps/pikaswaps", "name": "Pikaswaps", "maker": "pika", "family": "pika/pikaswaps", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.03 }, "summary_intro": "Pika video-to-video model that replaces a subject or region and keeps the rest of the shot. Company post dated the launch 20 February 2025. It is a stable API operation. Collected 2026-10-15.", "what_it_is_for": "Pika video-to-video model that replaces a subject or region and keeps the rest of the shot. Company post dated the launch 20 February 2025. It is a stable API operation. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/tools/dynamic-captions": { "id": "pika/tools/dynamic-captions", "name": "Pika Dynamic Captions", "maker": "pika", "family": "pika/tools", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.0004 }, "summary_intro": "Pika API operation that burns word-timed captions onto a clip. The model index lists it under Pika. It does not generate new motion. It is included because the official catalog names it. Collected 2026-10-15.", "what_it_is_for": "Pika API operation that burns word-timed captions onto a clip. The model index lists it under Pika. It does not generate new motion. It is included because the official catalog names it. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/tools/video-compose": { "id": "pika/tools/video-compose", "name": "Pika Video Compose", "maker": "pika", "family": "pika/tools", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.0002 }, "summary_intro": "Pika API operation that stacks video, image, and audio tracks into one clip. It is a timeline tool in the official model index, not a generative video model. Collected 2026-10-15.", "what_it_is_for": "Pika API operation that stacks video, image, and audio tracks into one clip. It is a timeline tool in the official model index, not a generative video model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pika/tools/video-merge": { "id": "pika/tools/video-merge", "name": "Pika Video Merge", "maker": "pika", "family": "pika/tools", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.0002 }, "summary_intro": "Pika API operation that joins 2 to 10 clips into one video. The model index lists it under Pika. It does not generate new frames beyond scaling and padding. Collected 2026-10-15.", "what_it_is_for": "Pika API operation that joins 2 to 10 clips into one video. The model index lists it under Pika. It does not generate new frames beyond scaling and padding. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pisa/bureauberto/bureauberto": { "id": "pisa/bureauberto/bureauberto", "name": "BureauBERTo", "maker": "pisa", "family": "pisa/bureauberto", "status": "active", "confidence": "high", "weights": "open-source", "license": "AFL-3.0", "release_date": "2023-07-13", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "CamembertForMaskedLM continued from Musixmatch/umberto-commoncrawl-cased-v1. Hidden 768, intermediate 3072, 12 layers, 12 heads, GELU, absolute positions, max_position_embeddings 514, type vocab 1, torch float32, transformers 4.25.1. The paper says the UmBERTo vocabulary of 32,000 was expanded by 8,305 TF-IDF domain terms to 40,305 tokens, and that model size grew from 110M to 117M parameters. config.json vocab_size is 40310. Weights are pytorch_model.bin, so the Hub API has no safetensors parameter total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BureauBERTo is an Italian masked language model from the Computational Linguistics Laboratory at the University of Pisa. The checkpoint is public, licensed AFL-3.0, uploaded 13 July 2023, and this dossier was collected on 2026-10-15. It is a local fill-mask model for bureaucratic Italian, with no hosted API.", "what_it_is_for": "The paper adapts Musixmatch UmBERTo to Italian administrative, banking, and insurance language by continued masked-language training and a larger domain vocabulary. Authors are Serena Auriemma, Mauro Madeddu, Martina Miliani, Alessandro Bondielli, Lucia C. Passaro, and Alessandro Lenci. The named release is `colinglab/BureauBERTo`. See the [model card](https://huggingface.co/colinglab/BureauBERTo) and the [Ital-IA 2023 paper](https://ceur-ws.org/Vol-3486/42.pdf).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pisa/class-it/class-it": { "id": "pisa/class-it/class-it", "name": "CLASS-IT", "maker": "pisa", "family": "pisa/class-it", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-12", "context_length": 6144, "max_output_tokens": null, "parameters": "140231872", "architecture": "LlamaForCausalLM (llama140M in the paper). Hidden size 704, intermediate size 2816, 12 layers, 11 attention heads, head_dim 64, SiLU, RMSNorm eps 1e-6, vocab 32000, max_position_embeddings 6144, RoPE theta 10000 with linear scaling factor 2.0, tie_word_embeddings true. Bos 0, eos 1, unk 2, pad 3. Custom PreTrainedTokenizer with Llama-3-style header tokens. Trained from scratch on a cleaned BabyLM strict-track subset, then instruction-tuned. The released file is the sequential Switchboard-then-Simple-Wikipedia run (it_switch_wiki).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLASS-IT is an English decoder-only language model from the Computational Linguistics Laboratory at the University of Pisa (Luca Capone, Alessandro Bondielli, Alessandro Lenci). The public checkpoint is active research weights, uploaded 12 August 2025, and this dossier was collected on 2026-10-15. It is a local BabyLM checkpoint, with no hosted API.", "what_it_is_for": "The authors ask whether a BabyLM-scale model benefits from instruction tuning on conversational data and on question-answer data. The released checkpoint is the 140M LLaMA-style model after a sequential curriculum: Switchboard dialogues, then augmented Simple English Wikipedia. The card says it was submitted to the BabyLM 2025 Interaction Track. See the [model card](https://huggingface.co/colinglab/CLASS_IT-140M) and the [paper](https://arxiv.org/abs/2510.25364).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pisa/concretegpt/concretegpt": { "id": "pisa/concretegpt/concretegpt", "name": "ConcreteGPT", "maker": "pisa", "family": "pisa/concretegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-09-13", "context_length": 1024, "max_output_tokens": null, "parameters": "124439808", "architecture": "GPT2LMHeadModel, gelu_new, n_embd 768, n_head 12, n_layer 12, n_ctx 1024, n_positions 1024, vocab 50257, bos and eos 50256. Hugging Face GPT-2 layout with the standard pretrained tokenizer, trained from that initialization setup for the BabyLM 2024 Strict-Small track. The paper states 124 million trainable parameters. Both released checkpoints report 124,439,808 F32 parameters in Hub safetensors metadata. Sentences are ordered with Brysbaert et al. 2014 concreteness norms: SEQUENTIAL goes from most concrete to most abstract; MIXED redistributes the slices.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ConcreteGPT is an English GPT-2-style BabyLM from the Computational Linguistics Laboratory at the University of Pisa (Luca Capone, Alessandro Bondielli, Alessandro Lenci). Two curriculum checkpoints are public, both uploaded 13 September 2024, and this dossier was collected on 2026-10-15. They are local research weights. The Hub cards are empty templates.", "what_it_is_for": "The paper presents one named model for the BabyLM 2024 Strict-Small track. Sentences in the training set receive a concreteness score from the Brysbaert et al. 2014 norms (about 40,000 English lexical items). SEQUENTIAL moves from the most concrete slice to the most abstract. MIXED redistributes those slices. The abstract says the best model is below the baselines on zero-shot tasks and stronger on fine-tuning, and that the curriculum runs beat a non-curriculum run of the same model. See the [paper](https://aclanthology.org/2024.conll-babylm.16/).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pitt/keyphrase/copyrnn": { "id": "pitt/keyphrase/copyrnn", "name": "CopyRNN", "maker": "pitt", "family": "pitt/keyphrase", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bidirectional GRU encoder, forward GRU decoder, Bahdanau attention, and a copy mechanism (Gu et al. 2016). Vocabulary is the top 50,000 words. Embedding size 150, initialized uniformly in [-0.1, 0.1]. Hidden size 300. Adam learning rate 0.0001, gradient clip 0.1, dropout 0.5. Beam max depth 6 and beam size 200. Early stopping on validation cross-entropy. A post-hoc rule keeps only the first single-word phrase.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CopyRNN is a keyphrase generation model from the University of Pittsburgh School of Computing and Information (Rui Meng, Sanqiang Zhao, Shuguang Han, Daqing He, Peter Brusilovsky, and Yu Chi). The ACL 2017 paper names the encoder-decoder that uses both attention and a copy mechanism CopyRNN, and the authors' repository says one pretrained model is provided. The code repository is marked deprecated. The model itself has no retirement notice. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper defines the job as predicting keyphrases for a scientific title and abstract, including phrases that do not appear in the source. The repository says the checkpoint was trained on computer-science papers and is expected to work well only in that domain. A separate news transfer test uses DUC-2001 without further training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pixverse/c/c1": { "id": "pixverse/c/c1", "name": "PixVerse C1", "maker": "pixverse", "family": "pixverse/c", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixVerse C1 is PixVerse's film-production video model, announced in Singapore on 7 April 2026 and added to the API the same day under the id `c1`. It is not a numbered step in the V line. The homepage lists it beside V6 and R2. Collected 2026-10-15.", "what_it_is_for": "PixVerse built C1 for sequences that clip models handle poorly: close combat, fast motion, object contact, particles, fluids, and lighting, in realistic or stylized looks. It also turns a multi-panel storyboard into a continuous clip and uses reference images to hold characters across shots. The platform overview points general work and extension at V6, and cinematic or action work at C1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pixverse/r/r1": { "id": "pixverse/r/r1", "name": "PixVerse R1", "maker": "pixverse", "family": "pixverse/r", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Omni native multimodal foundation, consistency-aware autoregressive framework, and an instantaneous response engine. The launch post says sampling can drop from dozens of steps to as few as one to four.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixVerse R1 is PixVerse's real-time world model, launched in Singapore on 12 January 2026. It is not a V-series clip model. It generates a continuous audiovisual stream that is supposed to react while the user is in it. The public entry named at launch is realtime.pixverse.ai. Collected 2026-10-15.", "what_it_is_for": "PixVerse describes R1 as a persistent simulation: video, audio, and world state from one token stream of text, image, audio, and video. The stated uses are AI-native games, live interactive shows, training simulations, and product visualisation. A 10 February 2026 update opened a limited partner API. On 1 April 2026 PixVerse added personal avatars and shared worlds with no session cap. In July 2026 the game engine ran on R1. In September 2026 that engine moved to R2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pixverse/r/r2": { "id": "pixverse/r/r2", "name": "PixVerse R2", "maker": "pixverse", "family": "pixverse/r", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Omni Causal AR backbone, then real-time distillation of that same backbone. Memory is sink, rolling history, and object KV cache. Training mixes teacher forcing and diffusion forcing. Acceleration cites DDMD and DMD2, block-sparse attention, and pyramid few-step distillation.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixVerse R2 is the current real-time world model. The research write-up is dated 23 August 2026. The product announcement on pixverse.ai is dated 22 September 2026. It is the third model on the homepage, next to V6 and C1. The public world is world.pixverse.video. Collected 2026-10-15.", "what_it_is_for": "R2 keeps a world running instead of returning a clip. Text, references, audio, and actions can arrive while it generates, and earlier events in the session are supposed to stay true. PixVerse says the game engine, first launched on R1 in July 2026, now runs on R2. The model page says this is a live demonstration, not a finished game engine.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pixverse/v/v1": { "id": "pixverse/v/v1", "name": "PixVerse V1", "maker": "pixverse", "family": "pixverse/v", "status": "retired", "confidence": "low", "weights": "closed", "license": "", "release_date": "2024-01-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixVerse V1 is included as a named first generation because a February 2025 QQ News timeline lists it on 15 January 2024, and the V2 launch implies a prior model. No official V1 page was opened. Confidence is low. It is not in the current API. Collected 2026-10-15.", "what_it_is_for": "No official description of what V1 did was found. The Series B post's Q1 2024 4K model is not identified as V1 on that page, so it is not filed as this model's spec.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pixverse/v/v2": { "id": "pixverse/v/v2", "name": "PixVerse V2", "maker": "pixverse", "family": "pixverse/v", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixVerse V2 is a named 2024 clip generation. A Product Hunt launch mirror features it on 27 July 2024 with the line introducing PixVerse V2. A QQ News timeline says 24 July 2024. It is not in the current API enum. Collected 2026-10-15. Confidence is medium.", "what_it_is_for": "The Product Hunt blurb says 8-second clips, stronger detail and motion, and consistent style, subject, and scenes across 1 to 5 clips. Input modalities beyond text were not stated there.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pixverse/v/v2-5": { "id": "pixverse/v/v2-5", "name": "PixVerse V2.5", "maker": "pixverse", "family": "pixverse/v", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-08-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixVerse V2.5 is a named 2024 clip generation. Atlas Cloud and a February 2025 QQ News timeline both date it to 22 August 2024. It is not in the current API enum. Collected 2026-10-15. Confidence is medium.", "what_it_is_for": "Secondary notes describe a speed increase of about two times and 4K upscaling. PixVerse's Series B post says the company shipped an industry-first 4K video model in the first quarter of 2024, which is earlier than V2.5 and was not given a separate model name on the pages opened. The 4K claim is not treated as this model's spec.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pixverse/v/v3": { "id": "pixverse/v/v3", "name": "PixVerse V3", "maker": "pixverse", "family": "pixverse/v", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-10-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixVerse V3 is a named 2024 clip generation. Secondary write-ups quote an official X post on 29 October 2024. PixVerse's own September 2025 Series B post names V3 as the generation that shipped the viral Transform Effect. It is not in the current API model enum. Collected 2026-10-15. Confidence is medium because the launch post itself was not opened.", "what_it_is_for": "The quoted launch line bundles a new model with four features: Effect, Style, Extend, and Lipsync. The Series B post credits the V3 template, also called the Venom or Transform effect in that post, with more than one billion social views. Those templates are not separate models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pixverse/v/v3-5": { "id": "pixverse/v/v3-5", "name": "PixVerse V3.5", "maker": "pixverse", "family": "pixverse/v", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixVerse V3.5 is the oldest clip id the current API still names. Secondary reports place the launch on 29 December 2024. The live schema lists `v3.5` and says 1080p cannot be 8 seconds on this id. Collected 2026-10-15.", "what_it_is_for": "Secondary reports of the launch say clips could finish in under 10 seconds, anime output improved, 1080p was offered, and first-last frame transition arrived. The current API still prices this id with V4 and V4.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pixverse/v/v4": { "id": "pixverse/v/v4", "name": "PixVerse V4", "maker": "pixverse", "family": "pixverse/v", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixVerse V4 is the clip model PixVerse announced on X on 23 February 2025. The API still accepts `v4` and prices it with V4.5 and V3.5. Collected 2026-10-15.", "what_it_is_for": "The launch post promises more realism, more natural movement, faster generation, sound and speech in one click, and Restyle. The September 2025 Series B post says the turbo path on V4 cut waits from minutes to seconds. Turbo in the current price table is the 360p quality label plus a fast motion mode, not a separate model id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pixverse/v/v4-5": { "id": "pixverse/v/v4-5", "name": "PixVerse V4.5", "maker": "pixverse", "family": "pixverse/v", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixVerse V4.5 is a named clip model the API still accepts as `v4.5`. Artificial Analysis, quoted by PixVerse on 27 August 2025, said it was released in May 2025. A secondary write-up dates the official X post to 13 May 2025. Collected 2026-10-15.", "what_it_is_for": "Secondary coverage of that May post describes three product changes: more than 20 cinematic camera controls, multi-image reference called Fusion, and smoother complex motion. The API changelog adds camera_movement on 20 June 2025 and the first fusion release on 5 August 2025, later than the May product post. Those are features, not a second model id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pixverse/v/v5": { "id": "pixverse/v/v5", "name": "PixVerse V5", "maker": "pixverse", "family": "pixverse/v", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixVerse V5 is the August 2025 clip model. PixVerse set the web launch at 00:00 Pacific on 28 August 2025 and made web generations free through 1 September. The API changelog records `v5` on 29 August 2025. The id is still accepted. Collected 2026-10-15.", "what_it_is_for": "The Series B post says V5 improved motion quality, visual performance, consistency, and prompt accuracy while keeping generation fast. It does not generate native dialogue audio. Sound on this id is the older sound-effect switch. V5.5 added native audio and multi-shot in December 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pixverse/v/v5-5": { "id": "pixverse/v/v5-5", "name": "PixVerse V5.5", "maker": "pixverse", "family": "pixverse/v", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixVerse V5.5 is the native-audio and multi-shot step in the V5 line. PixVerse posted it live on 1 December 2025. The API changelog records 2 December 2025. The id `v5.5` is still accepted. Collected 2026-10-15.", "what_it_is_for": "The launch post says one prompt unlocks audio inside the video and multi-shot generation. The pricing page bills text-to-video, image-to-video, first-last-frame transition, and extend, with a higher column for multi-clip. V5.6 followed on 26 January 2026 as a quality pass. V5.5 remains the id whose schema still exposes the multi-clip switch alongside V6.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pixverse/v/v5-6": { "id": "pixverse/v/v5-6", "name": "PixVerse V5.6", "maker": "pixverse", "family": "pixverse/v", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "diffusion transformer", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixVerse V5.6 is the last model in the V5 clip line. PixVerse posted it live on 26 January 2026, and the API changelog uses the same date. The id is `v5.6`. It is still in the text-to-video model enum. Collected 2026-10-15.", "what_it_is_for": "PixVerse sold V5.6 as a quality pass on V5.5: more cinematic pictures, more natural multilingual vocals, and less motion warping, at the same generation speed. The March 2026 Series C post still called it the foundational model and a diffusion transformer. V6 replaced it as the flagship on 30 March 2026, but the API still prices and accepts `v5.6`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "pixverse/v/v6": { "id": "pixverse/v/v6", "name": "PixVerse V6", "maker": "pixverse", "family": "pixverse/v", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixVerse V6 is PixVerse's flagship clip model, announced in Singapore on 30 March 2026 and listed on the API changelog the day before. It is one of the three models on the pixverse.ai homepage, with id `v6`. It does text-to-video, image-to-video, first-last-frame transition, extend, and reference-to-video, with optional native audio. Collected 2026-10-15.", "what_it_is_for": "PixVerse describes V6 as the general generation model: camera work, character performance, and multi-shot short films with native audio from one prompt. The launch post also names multilingual text inside the frame and a CLI path for agent workflows. The platform overview recommends it for general generation, multi-clip output, and video extension, and points film-style action work at C1 instead.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playground/playground-v1/playground-v1": { "id": "playground/playground-v1/playground-v1", "name": "Playground v1", "maker": "playground", "family": "playground/playground-v1", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-03-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion checkpoint shipped as a Diffusers StableDiffusionPipeline: one CLIP text encoder (CLIPTextModel), CLIPTokenizer, UNet2DConditionModel, AutoencoderKL, PNDMScheduler, and the Stable Diffusion safety checker. The card says it improves HDR quality. It does not say whether the UNet was trained from scratch or continued from another Stable Diffusion checkpoint. An older demo.py in the repo borrowed the tokenizer and text encoder from runwayml/stable-diffusion-v1-5 and the VAE from stabilityai/sd-vae-ft-ema.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Playground v1 is Playground's first named image model, a latent diffusion checkpoint introduced on 29 March 2023 for more vibrant, HDR-looking images. The Hugging Face card, current as of this 15 October 2026 collection, says it is no longer active on playground.com and that no updates are expected. Weights remain downloadable. Confidence is high for identity and the inactive status, and lower for anything the card never measured.", "what_it_is_for": "The card says Playground v1 is a latent diffusion model that improves overall HDR quality. The 29 March 2023 Playground account post introduced it as a model for images with a wider spectrum of vibrant colors, and pointed people at the free website. It is not a current product model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playground/playground-v2/playground-v2-1024px-aesthetic": { "id": "playground/playground-v2/playground-v2-1024px-aesthetic", "name": "Playground v2 1024px Aesthetic", "maker": "playground", "family": "playground/playground-v2", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Playground v2 Community License (5 December 2023). Free for research and commercial use. Attribution required on redistribution. Outputs may not be used to improve another text-to-image model, except Playground v2 and its derivatives. If image generation is a core product and monthly unique users exceed 1 million, a separate license must be requested. Use restrictions are in Attachment A. Licensor: Mighty Computing, Inc. dba Playground AI. Not an OSI open-source license.", "release_date": "2023-12-05", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Latent diffusion model trained from scratch. Same architecture as Stable Diffusion XL: two frozen text encoders, OpenCLIP-ViT/G and CLIP-ViT/L. Hugging Face labels the checkpoint 3B parameters. Playground does not publish a UNet-versus-pipeline split. This is the aesthetically tuned 1024px checkpoint. The card recommends guidance_scale 3.0. A single-file fp16 safetensors build is provided for Automatic1111 and ComfyUI.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Playground v2 1024px Aesthetic is the product checkpoint of Playground v2, a text-to-image latent diffusion model Playground trained from scratch and released with open weights on 5 December 2023. Playground's own user study said people favored it 2.5 times more than Stable Diffusion XL. It is no longer in the live Playground external API model list. The weights are still public. Collected 15 October 2026.", "what_it_is_for": "Playground shipped it as an early preview of stronger graphics models: aesthetic text-to-image generation at 1024 pixels, with commercial use allowed under the community license. The blog said people could try it on playground.com and download it from Hugging Face.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playground/playground-v2/playground-v2-256px-base": { "id": "playground/playground-v2/playground-v2-256px-base", "name": "Playground v2 256px Base", "maker": "playground", "family": "playground/playground-v2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Playground v2 Community License (5 December 2023). Free for research and commercial use. Attribution required on redistribution. Outputs may not be used to improve another text-to-image model, except Playground v2 and its derivatives. If image generation is a core product and monthly unique users exceed 1 million, a separate license must be requested. Use restrictions are in Attachment A. Licensor: Mighty Computing, Inc. dba Playground AI. Not an OSI open-source license.", "release_date": "2023-12-05", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Latent diffusion model trained from scratch. Same architecture as Stable Diffusion XL: two frozen text encoders, OpenCLIP-ViT/G and CLIP-ViT/L. Hugging Face labels the checkpoint 3B parameters. Playground does not publish a UNet-versus-pipeline split. This checkpoint is the 256px pre-train stage. The card says it is mainly for research and does not tend to make highly aesthetic images.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Playground v2 256px Base is the earliest public training checkpoint of Playground v2, released with the family on 5 December 2023. Playground describes it as a research pre-train that generates 256x256 images and does not tend to look highly aesthetic. It is still on Hugging Face under the Playground v2 Community License. This dossier was collected on 15 October 2026.", "what_it_is_for": "Playground released it so others could study foundation-model training in pixels. The card tells users not to expect the aesthetic quality of the 1024px model. The next stage is the 512px base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playground/playground-v2/playground-v2-512px-base": { "id": "playground/playground-v2/playground-v2-512px-base", "name": "Playground v2 512px Base", "maker": "playground", "family": "playground/playground-v2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Playground v2 Community License (5 December 2023). Free for research and commercial use. Attribution required on redistribution. Outputs may not be used to improve another text-to-image model, except Playground v2 and its derivatives. If image generation is a core product and monthly unique users exceed 1 million, a separate license must be requested. Use restrictions are in Attachment A. Licensor: Mighty Computing, Inc. dba Playground AI. Not an OSI open-source license.", "release_date": "2023-12-05", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Latent diffusion model trained from scratch. Same architecture as Stable Diffusion XL: two frozen text encoders, OpenCLIP-ViT/G and CLIP-ViT/L. Hugging Face labels the checkpoint 3B parameters. Playground does not publish a UNet-versus-pipeline split. This checkpoint is the 512px pre-train stage. The card says further tuning of this stage led to the 1024px aesthetic model, and that this checkpoint itself is mainly for research.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Playground v2 512px Base is the middle public checkpoint of Playground v2, released on 5 December 2023. Playground says it is a research pre-train at 512x512 and that further tuning produced the 1024px aesthetic model. It remains on Hugging Face under the Playground v2 Community License. Collected 15 October 2026.", "what_it_is_for": "Playground published it for foundation-model research, not as the aesthetic product. The card warns that it does not tend to produce highly aesthetic images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playground/playground-v2-5/photorealism": { "id": "playground/playground-v2-5/photorealism", "name": "Photorealism", "maker": "playground", "family": "playground/playground-v2-5", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-04-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Playground's own April 2024 video calls Photorealism a fine-tune of Playground v2.5, aimed at lifelike images and half-body to full-body people. It is a filter_model value on the external API, separate from the prompt_filter value named photorealistic, which the schema says does not use a separate image model. No model card or weights were published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Photorealism is a named Playground image model, a closed fine-tune of Playground v2.5. Playground's own video on 17 April 2024 called it a new fine-tune for lifelike images, especially faces in half-body and full-body shots. The external API still lists filter_model Photorealism next to Playground v2.5 and Playground v3. There is no model card and no public weights. Collected 15 October 2026. Confidence is medium.", "what_it_is_for": "The official video says it is for photoreal images and for clearer faces without a separate inpaint or upscale. In the product UI of that video it is chosen as a filter on top of Playground v2.5. The API treats it as its own model id. It is not the prompt style named photorealistic. That style is documented as not a separate model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playground/playground-v2-5/playground-v2-5-1024px-aesthetic": { "id": "playground/playground-v2-5/playground-v2-5-1024px-aesthetic", "name": "Playground v2.5 1024px Aesthetic", "maker": "playground", "family": "playground/playground-v2-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Playground v2.5 Community License (27 February 2024). Same shape as the v2 license: free research and commercial use, attribution, a ban on using outputs to train another text-to-image model, and a 1 million monthly-unique-user threshold when image generation is a core product. The v2.5 text adds that fine-tuning Playground v2.5 is expressly permitted. Licensor: Mighty Computing, Inc. dba Playground AI.", "release_date": "2024-02-27", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Successor to Playground v2. Latent diffusion model with the same SDXL-style dual text encoders (OpenCLIP-ViT/G and CLIP-ViT/L). The v3 paper says v2.5 already used the EDM noise schedule. The Diffusers card defaults to EDMDPMSolverMultistepScheduler and also documents EDMEulerScheduler. Hugging Face labels the checkpoint 3B parameters. The card says it generates 1024x1024 images and portrait and landscape aspect ratios.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Playground v2.5 is Playground's open-weight text-to-image model from 27 February 2024, the aesthetic successor to Playground v2. The model card calls it the state-of-the-art open-source model in aesthetic quality and says user studies beat SDXL, Playground v2, PixArt-alpha, DALL-E 3, and Midjourney 5.2. Weights use the Playground v2.5 Community License. The external API still accepts filter_model Playground_v2.5. Collected 15 October 2026.", "what_it_is_for": "Playground positioned it for highly aesthetic text-to-image generation at 1024 pixels, including portrait and landscape aspect ratios, with colors and prompt behavior improved over v2. The local weights are the path for self-hosting. The website and the external API are the hosted path.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playground/playground-v3/playground-v3": { "id": "playground/playground-v3/playground-v3", "name": "Playground v3", "maker": "playground", "family": "playground/playground-v3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-17", "context_length": null, "max_output_tokens": null, "parameters": "24B", "architecture": "Latent diffusion model trained with the EDM schedule. DiT-style image network scaled to 24B parameters, deep-fused with a frozen Llama 3 8B. Each image block matches the LLM block width and uses joint attention over image and text tokens. U-Net skip connections across blocks. 32 layers, with image key and value sequence length reduced four times in the middle layers. 2D RoPE with expanding positional ids. In-house 16-channel VAE, trained at 256 and then 512. No T5 or CLIP text encoder. Multi-aspect training at 256, 512, and 1024.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Playground v3, also called PGv3, is Playground's closed text-to-image model for graphic design. The introduction post is 17 October 2024. A 24 billion parameter diffusion transformer is deep-fused with a frozen Llama 3 8B so the image model reads every LLM layer, not a single CLIP or T5 vector. Weights were not released. The Playground API and the design app still serve it. This dossier was collected on 15 October 2026.", "what_it_is_for": "Playground says v3 is built for prompt control, typography, color, and layout, not only aesthetics. The product page shows posters, logos, stickers, shirts, and memes, plus exact RGB color control and natural-language edits. The paper also shows photo-real people and multilingual prompts. Hosted output follows the Playground terms: the user owns the assets, and Playground keeps a license to operate the service.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playht/dialog/playdialog": { "id": "playht/dialog/playdialog", "name": "PlayDialog", "maker": "playht", "family": "playht/dialog", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. Play.ht terms were cited by the Groq model card; the terms host did not resolve on the collection date.", "release_date": "2024-11-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dialog speech model with Adaptive Speech Contextualizer (ASC). Groq card for the hosted Dialog says transformer. Size unpublished.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PlayHT speech model `PlayDialog`. Status retired as of 2026-10-15. Treated as retired on 2025-12-31. Groq's deprecation page shuts down playai-tts and playai-tts-arabic on that date. A Groq community thread the same season says PlayAI services were set to end that day. GitHub archived the PlayHT SDKs on 2026-04-07. play.ht DNS failed on 2026-10-15. docs.play.ht still served the model pages (updatedAt 2025-09-15) with no retirement banner.", "what_it_is_for": "PlayHT's emotive multi-speaker dialog model, voice engine PlayDialog. It uses conversation context for prosody, intonation, emotion, and pacing through what PlayHT calls an Adaptive Speech Contextualizer. Two voices can be set with turn prefixes. The models page lists 350ms time to first audio and beta multilingual support.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playht/dialog/playdialog-arabic": { "id": "playht/dialog/playdialog-arabic", "name": "PlayDialog Arabic", "maker": "playht", "family": "playht/dialog", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. Play.ht terms were cited by the Groq model card; the terms host did not resolve on the collection date.", "release_date": "2025-02-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Arabic PlayDialog deployment. No separate card.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PlayHT speech model `PlayDialogArabic`. Status retired as of 2026-10-15. Treated as retired on 2025-12-31. Groq's deprecation page shuts down playai-tts and playai-tts-arabic on that date. A Groq community thread the same season says PlayAI services were set to end that day. GitHub archived the PlayHT SDKs on 2026-04-07. play.ht DNS failed on 2026-10-15. docs.play.ht still served the model pages (updatedAt 2025-09-15) with no retirement banner.", "what_it_is_for": "Named Arabic Play dialog engine. pyht calls it a large expressive Arabic model with two-speaker turns. WebSocket auth returns a PlayDialogArabic URL. The Node SDK, from 8 February 2025, sends PlayDialog requests with language arabic to an Arabic cluster without changing the engine string.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playht/dialog/playdialog-multilingual": { "id": "playht/dialog/playdialog-multilingual", "name": "PlayDialog Multilingual", "maker": "playht", "family": "playht/dialog", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. Play.ht terms were cited by the Groq model card; the terms host did not resolve on the collection date.", "release_date": "2025-02-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described as the multilingual PlayDialog model. No separate card.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PlayHT speech model `PlayDialogMultilingual`. Status retired as of 2026-10-15. Treated as retired on 2025-12-31. Groq's deprecation page shuts down playai-tts and playai-tts-arabic on that date. A Groq community thread the same season says PlayAI services were set to end that day. GitHub archived the PlayHT SDKs on 2026-04-07. play.ht DNS failed on 2026-10-15. docs.play.ht still served the model pages (updatedAt 2025-09-15) with no retirement banner.", "what_it_is_for": "Named Play dialog engine for languages other than the English PlayDialog cluster. pyht calls it a large expressive multilingual model with the same two-speaker turns. The WebSocket auth example returns a distinct PlayDialogMultilingual URL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playht/dialog/playdialog-turbo": { "id": "playht/dialog/playdialog-turbo", "name": "PlayDialog Turbo", "maker": "playht", "family": "playht/dialog", "status": "retired", "confidence": "high", "weights": "closed", "license": "PlayAI-Groq Commercial License on the Groq card. Play.ht terms linked from that card.", "release_date": "2025-03-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer, per the Groq PlayAI Dialog v1.0 card. Deployed on Groq.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 50.0 }, "summary_intro": "PlayHT speech model `PlayDialog-turbo`. Status retired as of 2026-10-15. Treated as retired on 2025-12-31. Groq's deprecation page shuts down playai-tts and playai-tts-arabic on that date. A Groq community thread the same season says PlayAI services were set to end that day. GitHub archived the PlayHT SDKs on 2026-04-07. play.ht DNS failed on 2026-10-15. docs.play.ht still served the model pages (updatedAt 2025-09-15) with no retirement banner.", "what_it_is_for": "Latency build of Dialog on Groq hardware. voice_engine PlayDialog-turbo. Official docs: faster than 200 characters per second, English and Arabic only, curated voices instead of cloned manifest URLs, no custom cloning. Groq hosted the same family as playai-tts and playai-tts-arabic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playht/parrot/parrot": { "id": "playht/parrot/parrot", "name": "Parrot", "maker": "playht", "family": "playht/parrot", "status": "retired", "confidence": "medium", "weights": "closed", "license": "Proprietary. Play.ht terms were cited by the Groq model card; the terms host did not resolve on the collection date.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described as LLM-based speech. No architecture card was fetched.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PlayHT speech model `Parrot`. Status retired as of 2026-10-15. Treated as retired on 2025-12-31. Groq's deprecation page shuts down playai-tts and playai-tts-arabic on that date. A Groq community thread the same season says PlayAI services were set to end that day. GitHub archived the PlayHT SDKs on 2026-04-07. play.ht DNS failed on 2026-10-15. docs.play.ht still served the model pages (updatedAt 2025-09-15) with no retirement banner.", "what_it_is_for": "Named Play.ht speech model announced with the YC W23 launch. The post says it clones a voice from a few seconds of audio and is built on language-model speech, English only at announcement, with other languages in progress. It is not a voice_engine value in the later API enum.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playht/peregrine/peregrine": { "id": "playht/peregrine/peregrine", "name": "Peregrine", "maker": "playht", "family": "playht/peregrine", "status": "retired", "confidence": "medium", "weights": "closed", "license": "Proprietary. Play.ht terms were cited by the Groq model card; the terms host did not resolve on the collection date.", "release_date": "2022-09-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PlayHT speech model `Peregrine`. Status retired as of 2026-10-15. Treated as retired on 2025-12-31. Groq's deprecation page shuts down playai-tts and playai-tts-arabic on that date. A Groq community thread the same season says PlayAI services were set to end that day. GitHub archived the PlayHT SDKs on 2026-04-07. play.ht DNS failed on 2026-10-15. docs.play.ht still served the model pages (updatedAt 2025-09-15) with no retirement banner.", "what_it_is_for": "Play.ht's named expressive generative text-to-speech model from 2022. The later Parrot announcement calls it the previous English model and says it could laugh, scream, and show other emotions. It is not given a voice_engine id in the 2024-2025 API enum.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playht/play/play3-0-mini": { "id": "playht/play/play3-0-mini", "name": "Play 3.0 Mini", "maker": "playht", "family": "playht/play", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. Play.ht terms were cited by the Groq model card; the terms host did not resolve on the collection date.", "release_date": "2024-10-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small multilingual TTS. Architecture and parameter count unpublished.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PlayHT speech model `Play3.0-mini`. Status retired as of 2026-10-15. Treated as retired on 2025-12-31. Groq's deprecation page shuts down playai-tts and playai-tts-arabic on that date. A Groq community thread the same season says PlayAI services were set to end that day. GitHub archived the PlayHT SDKs on 2026-04-07. play.ht DNS failed on 2026-10-15. docs.play.ht still served the model pages (updatedAt 2025-09-15) with no retirement banner.", "what_it_is_for": "Small multilingual realtime TTS, voice engine Play3.0-mini. PlayHT's models page recommends it for bulk generation: fastest of the three compared engines, 190ms time to first audio, better cloning than Play 2.0, 36 languages on that page. The Python SDK default. Not named PlayHT3.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playht/playht/playht1-0": { "id": "playht/playht/playht1-0", "name": "PlayHT 1.0", "maker": "playht", "family": "playht/playht", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. Play.ht terms were cited by the Groq model card; the terms host did not resolve on the collection date.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech LLM. Absolute size unpublished. PlayHT said PlayHT2.0 was 10x this model.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PlayHT speech model `PlayHT1.0`. Status retired as of 2026-10-15. Treated as retired on 2025-12-31. Groq's deprecation page shuts down playai-tts and playai-tts-arabic on that date. A Groq community thread the same season says PlayAI services were set to end that day. GitHub archived the PlayHT SDKs on 2026-04-07. play.ht DNS failed on 2026-10-15. docs.play.ht still served the model pages (updatedAt 2025-09-15) with no retirement banner.", "what_it_is_for": "PlayHT's first speech LLM, later exposed as voice engine PlayHT1.0. The PlayHT 2.0 launch says it was English-only, weak at zero-shot cloning, limited to short generations, and had no style or emotion control. The Node SDK calls the voices lifelike and English-only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playht/playht/playht2-0": { "id": "playht/playht/playht2-0", "name": "PlayHT 2.0", "maker": "playht", "family": "playht/playht", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. Play.ht terms were cited by the Groq model card; the terms host did not resolve on the collection date.", "release_date": "2023-08-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech LLM with a vocoder, per quotations of the August 2023 launch post. Parameter count unpublished.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PlayHT speech model `PlayHT2.0`. Status retired as of 2026-10-15. Treated as retired on 2025-12-31. Groq's deprecation page shuts down playai-tts and playai-tts-arabic on that date. A Groq community thread the same season says PlayAI services were set to end that day. GitHub archived the PlayHT SDKs on 2026-04-07. play.ht DNS failed on 2026-10-15. docs.play.ht still served the model pages (updatedAt 2025-09-15) with no retirement banner.", "what_it_is_for": "Generative conversational TTS from PlayHT, voice engine PlayHT2.0. The August 2023 launch added emotion control and said the model was 10x larger than PlayHT1.0, trained on more than 1 million hours. By the models page it is the legacy English engine: medium speed, 230ms time to first audio, good cloning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "playht/playht/playht2-0-turbo": { "id": "playht/playht/playht2-0-turbo", "name": "PlayHT 2.0 Turbo", "maker": "playht", "family": "playht/playht", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. Play.ht terms were cited by the Groq model card; the terms host did not resolve on the collection date.", "release_date": "2023-10-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Low-latency build of PlayHT 2.0. Separate architecture card not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PlayHT speech model `PlayHT2.0-turbo`. Status retired as of 2026-10-15. Treated as retired on 2025-12-31. Groq's deprecation page shuts down playai-tts and playai-tts-arabic on that date. A Groq community thread the same season says PlayAI services were set to end that day. GitHub archived the PlayHT SDKs on 2026-04-07. play.ht DNS failed on 2026-10-15. docs.play.ht still served the model pages (updatedAt 2025-09-15) with no retirement banner.", "what_it_is_for": "Low-latency English version of PlayHT 2.0 for realtime agents. voice_engine PlayHT2.0-turbo. Official models page calls it the legacy voice model. The Python SDK streams it over gRPC. The Node SDK says the on-prem appliance speaks only this engine.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polimi/crt/crt-libero": { "id": "polimi/crt/crt-libero", "name": "CRT LIBERO", "maker": "polimi", "family": "polimi/crt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the Hugging Face repo AIRLab-POLIMI/CRT_LIBERO. Training code was not found.", "release_date": "2026-01-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Corruption Restoration Transformer for 360x360 RGB frames. A ViT generator uses shifted patch tokenization, rotary position embeddings, and locality self-attention, trained as a GAN. The discriminator is a transformer with an MLP head. Loss weights are L1 10.0, SSIM 1.0, and adversarial 0.05. Depth, width, and head count are not given as numbers. The paper says capacity was scaled up from a 64x64 baseline.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Politecnico di Milano AIRLab checkpoint CRT LIBERO (crt-libero). Status: active. Collected 2026-10-15.", "what_it_is_for": "The paper uses CRT to restore sensor corruptions (centered square, Gaussian noise, horizontal lines, water drops) before pi0.5 or SmolVLA acts. This folder is the LIBERO-10 checkpoint only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polimi/crt/crt-metaworld": { "id": "polimi/crt/crt-metaworld", "name": "CRT Meta-World", "maker": "polimi", "family": "polimi/crt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the Hugging Face repo AIRLab-POLIMI/CRT_MetaWorld. Training code was not found.", "release_date": "2026-01-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Corruption Restoration Transformer for 480x480 RGB frames. Same family as crt-libero: ViT generator with shifted patch tokenization, rotary position embeddings, and locality self-attention, trained as a GAN. Discriminator is a transformer with an MLP head. Loss weights are L1 10.0, SSIM 1.0, and adversarial 0.05. Depth, width, and head count are not given as numbers. This checkpoint is a separate training run from CRT_libero.pth.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Politecnico di Milano AIRLab checkpoint CRT Meta-World (crt-metaworld). Status: active. Collected 2026-10-15.", "what_it_is_for": "Restores the same sensor corruptions as the LIBERO checkpoint, then passes the frame to SmolVLA. pi0.5 was not run on Meta-World.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polimi/cupid/cupid": { "id": "polimi/cupid/cupid", "name": "CUPID", "maker": "polimi", "family": "polimi/cupid", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on heyGio/CUPID. The arXiv v2 text is CC BY 4.0. RetinaFace and 3DDFA-V3 are third-party and are not part of the MIT checkpoint.", "release_date": "2026-06-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Masked autoencoder on 224x224x3 UV texture maps. Patch 16x16, 197 tokens including CLS, token size 192. Encoder has 12 blocks and 3 heads. Decoder has 4 blocks, 3 heads, hidden size 192, and a reconstruction head of 768. Mask ratio 0.5. Score is the maximum cosine similarity of unit-norm CLS tokens against reference frames, not a probability.", "modalities": { "input": [ "image", "video" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Politecnico di Milano ISPL model CUPID (cupid). Status: active. Collected 2026-10-15.", "what_it_is_for": "Open-set, POI-agnostic video deepfake detection. The target person is enrolled only at test time. The paper also decodes residual maps that show which UV regions move the prototype.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polimi/larsnet/larsnet": { "id": "polimi/larsnet/larsnet", "name": "LarsNet", "maker": "polimi", "family": "polimi/larsnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0 on the pretrained checkpoint bundle. The README states the bundle is 562 MB.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Five parallel time-frequency U-Nets that separate a stereo drum mixture into kick, snare, toms (high, mid-low, and floor), hi-hat (open and closed), and cymbals (crash and ride). Optional alpha-Wiener filtering at inference. The README says separation is faster than real time and does not give a latency or a parameter count. Training scripts are described as not yet released.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Politecnico di Milano ISPL model LarsNet (larsnet). Status: active. Collected 2026-10-15.", "what_it_is_for": "Separate a drum kit mixture into five stems faster than real time, as the README states it. An optional alpha-Wiener filter can be enabled with `separate.py -w`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polimi/motif/motif": { "id": "polimi/motif/motif", "name": "MOTIF", "maker": "polimi", "family": "polimi/motif", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on heyGio/MOTIF. The arXiv text uses the arXiv non-exclusive license. 3DDFA-V3 is a third-party front end.", "release_date": "2026-10-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two temporal transformers trained separately with no shared weights. Each has 12 blocks, hidden size 192, and 3 heads. A linear layer tokenizes each frame, a CLS token and learnable positions are added, and attention runs over time only. The output is the L2-normalized CLS of the last block. Global branch input is 75 frames by 150 coefficients (80 shape, 64 expression, 3 pose, 3 translation). Mouth branch uses 968 lip vertices, 2904 coordinates, with the per-window temporal mean removed. Windows are 75 frames, about 3 seconds at 25 fps, with 50% overlap. NT-Xent temperature 0.1 at depths 3, 6, 9, and 12. Multi-Similarity Miner epsilon 0.1.", "modalities": { "input": [ "video" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Politecnico di Milano ISPL model MOTIF (motif). Status: active. Collected 2026-10-15.", "what_it_is_for": "Reference-based detection of face swap, reenactment, and lip-sync. The target person is enrolled from pristine videos. No manipulated video and no footage of that person are used in training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polimi/parcnet/parcnet": { "id": "polimi/parcnet/parcnet", "name": "PARCnet", "maker": "polimi", "family": "polimi/parcnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid packet-loss concealment. A linear autoregressive predictor is fit at runtime on valid packets, and a feed-forward ConvNet predicts the residual. Official inference repo is locked to 32 kHz and 10 ms packets (320 samples). The README says not to change sr or packet_dim. Block widths, AR order, and the parameter count are in the IEEE paper and were not re-read from the PDF on this pass.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Politecnico di Milano ISPL model PARCnet (parcnet). Status: active. Collected 2026-10-15.", "what_it_is_for": "Fills missing audio packets in real-time networked music. The README's tested setup is 32 kHz WAV with 320-sample packets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polimi/parcnet/parcnet-is2": { "id": "polimi/parcnet/parcnet-is2", "name": "PARCnet-IS2", "maker": "polimi", "family": "polimi/parcnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Modified PARCnet. A linear predictor is fit in real time with the autocorrelation method and white-noise compensation. A lightweight feed-forward ConvNet estimates the autoregressive residual. This release is causal: no lookahead. Operating point is 44.1 kHz, 16-bit, packets of 512 samples (about 11.6 ms). The PARCnet README says the inference path fixes cross-fading across a burst of missing packets. Widths and parameter count are not stated on the challenge README.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Politecnico di Milano ISPL model PARCnet-IS2 (parcnet-is2). Status: active. Collected 2026-10-15.", "what_it_is_for": "Real-time concealment of lost packets in solo-instrument music, with no lookahead. It is the challenge baseline, not one of the submitted entrant systems.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polito/astroloc/astroloc": { "id": "polito/astroloc/astroloc", "name": "AstroLoc", "maker": "polito", "family": "polito/astroloc", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-10", "context_length": null, "max_output_tokens": null, "parameters": "105000000", "architecture": "DINOv2-base backbone, SALAD aggregation, and a linear layer from 8448 to 2048. The paper trains for 30k iterations with Adam at learning rate 5e-5 and batch size 48 (48 pairs for the pair loss and 48 quadruplets for the mining loss). Stated hyperparameters include t_iou 0.2, alpha_1 1, alpha_2 1, beta_1 50, beta_2 50, lambda_1 1, lambda_2 1, K 50. Texas is the validation set. AstroLoc-tiny is the smallest DINOv2 with 512-D features.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Politecnico di Torino space-to-ground localizer AstroLoc (astroloc). Gabriele Berton and Carlo Masone are affiliated with Politecnico di Torino on the paper. Alex Stoken is a co-author. The arXiv v2 header also lists an Amazon affiliation. Status: active. Collected 2026-10-15.", "what_it_is_for": "AstroLoc retrieves a matching satellite image for an astronaut photo, and the paper also reports lost-in-space orbit determination and historical space-shuttle photography without extra fine-tuning. Training uses astronaut photos paired with satellite images plus clustered satellite batches.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polito/cosplace/cosplace": { "id": "polito/cosplace/cosplace", "name": "CosPlace", "maker": "polito", "family": "polito/cosplace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the GitHub LICENSE, copyright (c) 2022 Gabriele Berton, Carlo Masone, Barbara Caputo, covering the software and associated documentation files. The paper abstract says the dataset, code, and trained models are available for research purposes.", "release_date": "2022-04-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Torchvision VGG-16 or ResNet backbone, then L2 norm, GeM pooling, a fully connected layer to fc_output_dim, then L2 norm. Unless train_all_layers is set, ResNet training freezes layers before layer3. Released hub backbones and descriptor sizes are the versions array. EfficientNet channel sizes exist in the module and are not released checkpoints. Trained as classification with a CosFace-style loss on SF-XL.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Politecnico di Torino visual geo-localization model CosPlace (cosplace), by Gabriele Berton, Carlo Masone, and Barbara Caputo. Status: active. Collected 2026-10-15.", "what_it_is_for": "CosPlace estimates where a photo was taken by retrieving a match from a geo-tagged database. Training is a classification problem on San Francisco eXtra Large (SF-XL), instead of contrastive mining. The abstract says this uses roughly 80% less GPU memory at train time than the previous state of the art and reaches better results with 8x smaller descriptors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polito/earthloc/earthloc": { "id": "polito/earthloc/earthloc", "name": "EarthLoc", "maker": "polito", "family": "polito/earthloc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the GitHub LICENSE. Copyright (c) 2024 Gabriele Berton. The license text covers the software and associated documentation files.", "release_date": "2024-03-11", "context_length": null, "max_output_tokens": null, "parameters": "27600000", "architecture": "MixVPR-style model: ResNet-50 backbone and an MLP-Mixer. Output dimension 4096. Trained for 50k iterations with Adam at learning rate 0.0001. Batch size is 32 quadruplets (128 images). Clustering runs every 5k iterations with C=200. Augmentations are color jitter, random perspective, and random rotation.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Politecnico di Torino astronaut photography localization model EarthLoc (earthloc). Gabriele Berton, Barbara Caputo, and Carlo Masone are at Politecnico di Torino. Alex Stoken is a co-author at Jacobs / NASA Johnson Space Center. Status: active. Collected 2026-10-15.", "what_it_is_for": "Given an astronaut photo, EarthLoc retrieves the nearest geo-tagged satellite image from a Sentinel-2 database. The paper calls the task Astronaut Photography Localization. The six evaluation areas are Texas, the Alps, California, the Gobi, the Amazon, and the Toshka Lakes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polito/eigenplaces/eigenplaces": { "id": "polito/eigenplaces/eigenplaces", "name": "EigenPlaces", "maker": "polito", "family": "polito/eigenplaces", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the GitHub LICENSE, copyright (c) 2023 Gabriele Berton, Gabriele Trivigno, Carlo Masone, Barbara Caputo, covering the software and associated documentation files.", "release_date": "2023-08-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Paper section 4.2: VGG-16 or ResNet-50, GeM pooling, fully connected descriptor. CosFace large-margin cosine loss with a lateral classifier and a frontal classifier. Training: 200k iterations, batch 128 with 64 images per loss term, Adam learning rate 1e-5, cell side M=15 m, N=3, focal distance D=10 m. A ResNet-18 512 ablation reports a higher average at D=20 m. Hub also releases ResNet-18 and ResNet-101, including 1024-D files that are not rows in Tables 3 and 4. Best reported architecture is ResNet-50 2048-D, under 7 GB and 24 hours on one RTX 3090.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Politecnico di Torino visual place recognition model EigenPlaces (eigenplaces), by Gabriele Berton, Gabriele Trivigno, Barbara Caputo, and Carlo Masone, all at Politecnico di Torino on the paper. Status: active. Collected 2026-10-15.", "what_it_is_for": "EigenPlaces trains a global descriptor to recognize the same place from different viewpoints. Classes are built by clustering SF-XL panoramas with a geographic principal-component rule, without extra place labels. The abstract says it beats prior state of the art on most of the tested datasets, uses 60% less GPU memory for training, and uses 50% smaller descriptors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polito/megaloc/megaloc": { "id": "polito/megaloc/megaloc", "name": "MegaLoc", "maker": "polito", "family": "polito/megaloc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on Hugging Face cardData for gberton/MegaLoc and on the model module header.", "release_date": "2025-02-27", "context_length": null, "max_output_tokens": null, "parameters": "228639552", "architecture": "DINOv2 ViT-B/14 backbone (embed dim 768, depth 12, 12 heads, patch size 14) and an optimal-transport feature aggregator. MegaLoc() defaults: 64 clusters, cluster dim 256, token dim 256, MLP dim 512, output feat_dim 8448. Aggregator width is 64*256+256=16640, then a linear map to 8448 and L2 norm. Class defaults of 1536 channels and cluster dim 128 are not the MegaLoc() configuration. Evaluation on the card uses ImageNet normalization and resize 322x322. Descriptor shape [B, 8448].", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Politecnico di Torino visual place recognition model MegaLoc (megaloc), by Gabriele Berton and Carlo Masone. Status: active. Collected 2026-10-15.", "what_it_is_for": "The card describes image retrieval for visual place recognition. Torch hub loads the trained model. The GitHub README points CosPlace and EigenPlaces readers here for a later VPR model. A Gradio demo retrieves against a San Francisco street-view database.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polytechnique/cosyvoice/cosyvoice2-0-5b-eu": { "id": "polytechnique/cosyvoice/cosyvoice2-0-5b-eu", "name": "CosyVoice2-0.5B-EU", "maker": "polytechnique", "family": "polytechnique/cosyvoice", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-09", "context_length": null, "max_output_tokens": null, "parameters": "0.5B", "architecture": "French and German adaptation of FunAudioLLM CosyVoice2-0.5B. Card: semantic language model, chunk-aware flow matching, and HiFi-GAN. Released cosyvoice2.yaml sets sample rate 24000 Hz, speech token size 6561, token frame rate 25, and streaming chunk size 25. PyPI says the shipped checkpoint keeps the original CosyVoice2 blankEN backbone and fine-tunes the LM and flow. The Hub metadata also lists Qwen3-0.6B, EuroLLM-1.7B-Instruct, and Mistral-7B-v0.3 as base models; the PyPI page says those backbones are not what the current download uses.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CosyVoice2-0.5B-EU is a French and German zero-shot text-to-speech model released by \u00c9cole Polytechnique and Hi! PARIS researchers on top of FunAudioLLM CosyVoice2-0.5B. It clones a short reference voice and speaks the input text. Weights are public under Apache-2.0. Status: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the model is zero-shot voice cloning for French and German, including streaming, and that English, Chinese, Japanese, Korean, and dialects from the original CosyVoice2 still run. The EUSIPCO 2026 paper, by Tim Luka Horstmann, Nassima Ould Ouali, and Eric Moulines (\u00c9cole Polytechnique) with Hi! PARIS and ENSTA co-authors, says they release this checkpoint, a Python package, and a demo. The package is `cosyvoice2-eu` (`cosy2-eu` on the command line).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polytechnique/ssml/ssml-breaks2ssml-fr-lora": { "id": "polytechnique/ssml/ssml-breaks2ssml-fr-lora", "name": "SSML Breaks-to-SSML French LoRA", "maker": "polytechnique", "family": "polytechnique/ssml", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-26", "context_length": null, "max_output_tokens": null, "parameters": "7B base; LoRA adapter only", "architecture": "PEFT LoRA adapter on Qwen/Qwen2.5-7B. Card: rank 8, alpha 16, target modules q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, 5 epochs, batch size 1 with gradient accumulation, adapter about 81MB. Paper (QwenB) says a second 4-bit QLoRA of rank 8 and alpha 16 is injected into the value and feed-forward projections and trained to fill numeric pitch, rate, volume, and break time.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SSML Breaks-to-SSML French LoRA is the second adapter in the French SSML cascade from \u00c9cole Polytechnique and Hi! PARIS. It rewrites French text that already has `` markers into SSML with pitch, rate, volume, and timed pauses. Weights are a public Apache-2.0 LoRA on Qwen2.5-7B. Status: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the adapter converts break-marked French text into SSML that commercial TTS can speak with prosody control. The paper calls this stage QwenB. It does not generate audio. The listening tests use Microsoft Azure Neural TTS, voice Henri (`fr-FR-HenriNeural`).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polytechnique/ssml/ssml-text2breaks-fr-lora": { "id": "polytechnique/ssml/ssml-text2breaks-fr-lora", "name": "SSML Text-to-Breaks French LoRA", "maker": "polytechnique", "family": "polytechnique/ssml", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-26", "context_length": 1024, "max_output_tokens": null, "parameters": "7B base; LoRA adapter only", "architecture": "PEFT LoRA adapter on Qwen/Qwen2.5-7B (causal LM). Card: rank 8, alpha 16, target modules q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, trained 5 epochs at batch size 1 with gradient accumulation. Adapter size about 81MB on the card. Paper describes the same stage (QwenA) as QLoRA in 4-bit with rank 8 and alpha 16, and says it inserts tags in French paragraphs of up to about 200 words inside a 1024-token limit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SSML Text-to-Breaks French LoRA is a LoRA adapter released by researchers at \u00c9cole Polytechnique and the Hi! PARIS center on top of Qwen2.5-7B. It is stage 1 of a French SSML cascade: it inserts symbolic pause markers into French text. Weights are public under Apache-2.0. Status: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card says this adapter predicts natural pause locations in French by adding `` markers. The paper calls this stage QwenA and says it is the first half of a pipeline that later fills pitch, rate, volume, and break timing for commercial TTS. Authors on the paper include Nassima Ould Ouali, Ruben Bueno, and Eric Moulines (\u00c9cole Polytechnique) and Awais Hussain Sani (Hi! PARIS).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polytechnique/wavlm-vocoder/wavlm-vocoder-french": { "id": "polytechnique/wavlm-vocoder/wavlm-vocoder-french", "name": "WavLM Vocoder French", "maker": "polytechnique", "family": "polytechnique/wavlm-vocoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-03-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "French neural vocoder. Frozen microsoft/wavlm-base-plus, learned weighted fusion of WavLM layers, a convolutional adapter (hidden size 256, 6 layers, kernel size 7), and a HiFi-GAN-style generator at 16 kHz. The released file checkpoint_step180000_infer.pt is the generator only, described as 427 MB, from step 180000 with GAN losses (MPD, MSD, and feature matching). It does not convert speaker identity.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WavLM Vocoder French is a speech reconstruction model from \u00c9cole Polytechnique and Hi! PARIS. It decodes frozen WavLM features back to a French waveform. It is not a text-to-speech model and not a voice converter. Weights are public under MIT. Status: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the checkpoint reconstructs 16 kHz French speech from frozen WavLM-Base+ representations. Nassima Ould Ouali (Hi! PARIS / \u00c9cole Polytechnique) is named as the person who trained it, with Awais Hussain Sani, Reda Dehak, and Eric Moulines. The stated job is a stage-1 decoder for later voice-conversion research. The paper was accepted at JEP 2026 and, the card says, published in the June 2026 proceedings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polyu/depthmaster/depthmaster": { "id": "polyu/depthmaster/depthmaster", "name": "DepthMaster", "maker": "polyu", "family": "polyu/depthmaster", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-06-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unified metric monocular depth model. Panoramas are split into overlapping perspective patches. The paper says the backbone stays compatible with a standard Transformer and adds a Correspondence Consistency Loss plus virtual projection-camera priors. No layer counts or parameter total were read. Hub file depthmaster.pt is 1541004203 bytes. The README is only a MIT license header.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DepthMaster is an open-weight metric depth model from Visual Computing Lab at The Hong Kong Polytechnic University. It estimates depth for both ordinary photos and 360-degree panoramas by splitting panoramas into perspective patches. The Hub file is depthmaster.pt under MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper describes a single metric depth model for narrow-field photos and panoramas. It does not add a special spherical network. It canonicalizes panoramas into overlapping perspective patches, uses a correspondence consistency loss, and injects virtual camera poses. Authors are Pengfei Wang, Shihao Wang, Liyi Chen, Zhiyuan Ma, Guowen Zhang, and Lei Zhang. The arXiv submission date is 2026-06-10. The weight file was uploaded on 2026-06-07. The GitHub repo calls it a NeurIPS 2026 paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polyu/etchat/etchat-phi3-mini": { "id": "polyu/etchat/etchat-phi3-mini", "name": "E.T. Chat (Phi-3-mini)", "maker": "polyu", "family": "polyu/etchat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bsd-3-clause", "release_date": "2024-09-23", "context_length": 4096, "max_output_tokens": null, "parameters": "5020945408", "architecture": "ETChatPhi3ForCausalLM. Language model is Phi-3-mini-4k-instruct: hidden 3072, 32 layers, 32 heads, vocab 32064, float16. Vision tower eva_vit, Q-Former projector, CLIP center-crop 224. Stage-2-3 config sets use_matching true, bi_attention true, and max_position_embeddings 4096, with a sliding_window field of 2048. Stored parameter count is the stage-2-3 and stage-3 safetensors total (F16 5020944896 plus I64 512). Stage-1 and stage-2 totals are 5002049536.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E.T. Chat is an open-weight time-sensitive video model from Chen Lab at The Hong Kong Polytechnic University. It is a Phi-3-mini video LLM that turns timestamp prediction into embedding matching, released as four training-stage checkpoints rather than four products. Weights are public under BSD-3-Clause. This dossier was collected on 2026-10-15.", "what_it_is_for": "The stage-1 card says E.T. Chat is a baseline on E.T. Bench for event-level video-language understanding. A special token triggers frame-embedding matching for timestamps. The card names Ye Liu as the developer and lists WebVid and LCS-558K as stage-1 training data. The paper is NeurIPS 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polyu/pixrestore/pixrestore-b": { "id": "polyu/pixrestore/pixrestore-b", "name": "PixRestore-B", "maker": "polyu", "family": "polyu/pixrestore", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-08-17", "context_length": null, "max_output_tokens": null, "parameters": "210890000", "architecture": "Same pixel-space DiT as PixRestore-S with a LightningDiT-B backbone: hidden 768, 12 blocks, 12 heads, patch size 8, frozen DINOv2-B. Table 3 prints 210.89M parameters, 1842G FLOPs, 79 ms, NFE 1, at 512x512 on one A800. The 53.7M sentence says that count includes frozen DINO. This 210.89M figure is the same table column. The Hub tree has a pixrestore-b directory. The file inside it was not listed before a Hub rate limit.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixRestore-B is the base-size open-weight restoration model from Visual Computing Lab at The Hong Kong Polytechnic University, with OPPO Research Institute. It is the same pixel-space diffusion transformer as PixRestore-S with a wider LightningDiT-B backbone and DINOv2-B. The paper lists 210.89M parameters. Weights are in the shared Apache-2.0 Hub repo. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses PixRestore-B when it wants a larger unified restorer than the default S model. It covers the same multi-degradation restoration task and stays a one-step generator. Table 3 reports 79 ms at 512x512.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polyu/pixrestore/pixrestore-l": { "id": "polyu/pixrestore/pixrestore-l", "name": "PixRestore-L", "maker": "polyu", "family": "polyu/pixrestore", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-08-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LightningDiT-L pixel DiT: hidden 1024, 24 blocks, 16 heads, patch size 8, frozen DINOv2-L. The paper defines this variant. A parameter total for L was not in the Table 3 excerpt, which printed only S and B. The Hub repo tree contains a pixrestore-l directory. The file inside it was not listed.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixRestore-L is the large open-weight restoration model from Visual Computing Lab at The Hong Kong Polytechnic University, with OPPO Research Institute. The paper defines it as a LightningDiT-L pixel diffusion transformer with DINOv2-L. A parameter total was not in the table excerpt that listed S and B. This dossier was collected on 2026-10-15.", "what_it_is_for": "It is a larger unified image-restoration checkpoint in the PixRestore family. The paper says larger variants improve restoration quality. The Hub repo has a pixrestore-l directory next to S, B, and XL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polyu/pixrestore/pixrestore-s": { "id": "polyu/pixrestore/pixrestore-s", "name": "PixRestore-S", "maker": "polyu", "family": "polyu/pixrestore", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-08-17", "context_length": null, "max_output_tokens": null, "parameters": "53700000", "architecture": "Pixel-space Diffusion Transformer for unified image restoration. LightningDiT-S backbone, hidden 384, 12 blocks, 6 heads, patch size 8, frozen DINOv2-S encoder with an adaptive layer router. Flow matching on RGB patches, then a one-step adversarial finetune. The paper says 53.7M parameters including the frozen DINO encoder. Table 3 prints 53.70, 658G FLOPs, 44 ms, NFE 1, at 512x512 on one A800. Hub file pixrestore-s/clean_weights/ema_model.safetensors is 128709340 bytes. The paper says PixRestore with no suffix means this variant.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixRestore-S is the default open-weight image-restoration model from Visual Computing Lab at The Hong Kong Polytechnic University, with OPPO Research Institute. It is a small pixel-space diffusion transformer that handles several degradations in one step. The paper's 53.7M count includes a frozen DINO encoder. Weights are Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents PixRestore as unified image restoration: super-resolution, denoising, deblurring, deraining, dehazing, desnowing, and low-light enhancement, without a separate network per task. It matches flow directly on RGB patches so a VAE does not throw away detail. The authors say the unsuffixed name PixRestore means this S variant. GitHub dates the code and model release to 2026-08-17. The Hub repo was created on 2026-08-24.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polyu/pixrestore/pixrestore-xl": { "id": "polyu/pixrestore/pixrestore-xl", "name": "PixRestore-XL", "maker": "polyu", "family": "polyu/pixrestore", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-08-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LightningDiT-XL pixel DiT: hidden 1152, 28 blocks, 16 heads, patch size 8. DINOv2 has no XL encoder, so the paper uses frozen DINOv2-L. Parameter total was not in the Table 3 excerpt. The Hub tree contains pixrestore-xl. The file inside it was not listed.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixRestore-XL is the largest named PixRestore checkpoint from Visual Computing Lab at The Hong Kong Polytechnic University, with OPPO Research Institute. The paper defines a LightningDiT-XL backbone and keeps DINOv2-L because DINOv2 has no XL encoder. A parameter total was not in the table excerpt. This dossier was collected on 2026-10-15.", "what_it_is_for": "It is the largest of the four unified image-restoration sizes in the paper. Appendix comparisons include a PixRestore-XL row. Those cells were not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polyu/pvd/pvd": { "id": "polyu/pvd/pvd", "name": "PVD", "maker": "polyu", "family": "polyu/pvd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-10-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Phase-wise velocity distillation. One repo holds several distilled generators. The card says SD3.5 Medium uses 12 layers per transformer, FLUX.1-dev uses 9 dual-stream plus 19 single-stream layers with LoRA rank 64, and Qwen-Image uses 30 layers with LoRA rank 32. C2I is a class-conditional ImageNet 256 model, not a text model. Parameter totals were not published.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PVD is an open-weight image-generation distillation from Visual Computing Lab at The Hong Kong Polytechnic University, with OPPO Research Institute. One Hub repo holds class-conditional and text-to-image checkpoints distilled from SD3.5 Medium, FLUX.1-dev, and Qwen-Image. The license on the card is Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the weights are for phase-wise velocity distillation: a coarse half and a fine half that replace one full backbone pass. The paper title is \"Two Halves are More than One.\" The GitHub repo description marks it NeurIPS 2026. The Hub repo was created on 2026-10-05.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polyu/stanza-yue/stanza-yue-electra-large": { "id": "polyu/stanza-yue/stanza-yue-electra-large", "name": "Cantonese Stanza ELECTRA dependency parser", "maker": "polyu", "family": "polyu/stanza-yue", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-sa-4.0", "release_date": "2026-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adaptation of Stanza 1.14.0 zh-hans/gsdsimp_electra-large. ELECTRA base is hfl/chinese-electra-180g-large-discriminator at revision d017e219578df8e4885484edbc8969dbdea9cbe0. LoRA r=8, alpha=16, dropout 0.1, targets query, value, output.dense, and intermediate.dense. Parser layers are fully fine-tuned. The released file is best_dev_electra_yue_lora_r8.pt (174804296 bytes), not a full ELECTRA dump. Parameter total was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is an open-weight Cantonese dependency parser from Angel Chan Lab at The Hong Kong Polytechnic University. It adapts Stanza's Mandarin ELECTRA-large parser with LoRA and a fully trained parser head. The card says it is a research checkpoint, not an end-to-end Cantonese pipeline. The license is CC BY-SA 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the checkpoint predicts HEAD and DEPREL for Cantonese Universal Dependencies under a fixed protocol: gold sentence boundaries, gold tokenization, and frozen Mandarin POS and lemma features. It was adapted by PhD student Sebastian Chuqiao Song from Stanza 1.14.0's zh-hans/gsdsimp_electra-large package because Mandarin checkpoints scored poorly on Cantonese. The card says no paper was available yet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polyu/tvedit/tvedit-qwen": { "id": "polyu/tvedit/tvedit-qwen", "name": "TV-Edit-Qwen", "maker": "polyu", "family": "polyu/tvedit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-06-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Plug-in controller for a pretrained image editor. The released file is TVEdit-Qwen.pth (1014186974 bytes), a pickle checkpoint. The paper describes a Content-Aware Spatial Controller that turns drag or point prompts plus a text instruction into control for an editing backbone. This file is the Qwen variant. No license file was in the Hub tree.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TV-Edit-Qwen is an open-weight image-editing controller from Visual Computing Lab at The Hong Kong Polytechnic University. It adds text-plus-drag control to a Qwen image editor. The only Hub file is TVEdit-Qwen.pth. No license was posted with the file. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper, Text-Vision Co-Instructed Image Editing, treats a text instruction as the semantic intent and drag or point prompts as the spatial intent. TV-Edit is a plug-in with a Content-Aware Spatial Controller for pretrained editors. Authors on the NeurIPS 2026 poster are Chenxi Xie, Yuhui Wu, Qiaosi Yi, and Lei Zhang. The weight file was uploaded on 2026-06-15. Training data described in the abstract is a paired set of more than 23K samples from videos.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polyu/unipixel/unipixel-3b": { "id": "polyu/unipixel/unipixel-3b", "name": "UniPixel-3B", "maker": "polyu", "family": "polyu/unipixel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bsd-3-clause", "release_date": "2025-09-21", "context_length": 128000, "max_output_tokens": null, "parameters": "3881764174", "architecture": "PixelQwen2_5_VLForConditionalGeneration on Qwen2.5-VL-3B-Instruct, plus a SAM 2.1 Hiera-B+ mask decoder (sam2.1_hiera_base_plus). Text hidden 2048, 36 layers, 16 heads, 2 KV heads, vocab 151936. Vision tower hidden 1280, depth 32, patch 14, out hidden 2048. bfloat16. Hub safetensors total is 3881764174; the BF16 bucket is 3881764814.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UniPixel-3B is an open-weight multimodal model from Chen Lab at The Hong Kong Polytechnic University. It refers to objects, segments them, and answers pixel-level questions over images and video. The backbone is Qwen2.5-VL-3B-Instruct with a SAM 2.1 mask decoder. Weights are public under BSD-3-Clause. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card says UniPixel supports image and video segmentation, regional understanding, and PixelQA, which combines object referring, segmentation, and question answering. The paper is UniPixel, NeurIPS 2025. The Hub repo was created on 2025-09-21.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polyu/unipixel/unipixel-7b": { "id": "polyu/unipixel/unipixel-7b", "name": "UniPixel-7B", "maker": "polyu", "family": "polyu/unipixel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bsd-3-clause", "release_date": "2025-10-14", "context_length": 128000, "max_output_tokens": null, "parameters": "8438974798", "architecture": "PixelQwen2_5_VLForConditionalGeneration on Qwen2.5-VL-7B-Instruct, plus SAM 2.1 Hiera-B+. Text hidden 3584, 28 layers, 28 heads, 4 KV heads, vocab 152064, untied embeddings. Vision hidden 1280, depth 32, patch 14, out hidden 3584. bfloat16. Hub safetensors total is 8438974798; the BF16 bucket is 8438975438.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UniPixel-7B is the larger UniPixel checkpoint from Chen Lab at The Hong Kong Polytechnic University. It is the same pixel-level referring and segmentation model as UniPixel-3B, built on Qwen2.5-VL-7B-Instruct with a SAM 2.1 mask decoder. Weights are public under BSD-3-Clause. This dossier was collected on 2026-10-15.", "what_it_is_for": "The PolyU-ChenLab collection describes UniPixel as a unified MLLM for object referring and segmentation. The 7B config names Qwen2.5-VL-7B-Instruct as the base. The Hub repo was created on 2025-10-14. The paper is NeurIPS 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polyu/utterance-segmentation/cantonese-utterance-segmentation": { "id": "polyu/utterance-segmentation/cantonese-utterance-segmentation", "name": "Cantonese Utterance Segmentation", "maker": "polyu", "family": "polyu/utterance-segmentation", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-02-19", "context_length": 512, "max_output_tokens": null, "parameters": "324990982", "architecture": "BertForTokenClassification. BERT-large shape: hidden 1024, 24 layers, 16 heads, intermediate 4096, vocab 21628, max_position_embeddings 512, 6 labels named LABEL_0 through LABEL_5. float32. Hub safetensors total 324990982. The Hub page shows 0.3B params. No model card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cantonese Utterance Segmentation is an open-weight token classifier released by Angel Chan Lab at The Hong Kong Polytechnic University. The Hub repo has BERT-large weights and no model card. It is a token-classification checkpoint, not a generative language model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The repository name is the only statement of purpose: Cantonese utterance segmentation. The config is BertForTokenClassification with six labels. No card, paper, or label map was found. Angel Chan is an associate professor in PolyU's Department of Language Science and Technology. That profile does not describe this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polyu/videomind/videomind-2b": { "id": "polyu/videomind/videomind-2b", "name": "VideoMind-2B", "maker": "polyu", "family": "polyu/videomind", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bsd-3-clause", "release_date": "2025-03-21", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "AgentQwen2VL (model_type agent_qwen2_vl) on Qwen2-VL-2B-Instruct. Text tower hidden 1536, 28 layers, 12 heads, 2 KV heads, vocab 151936, tied embeddings. max_position_embeddings 32768. Role switching uses LoRA adapters named planner, grounder, and verifier; the answerer is the base model with adapters off. The Hub repo stores those adapters plus a small pytorch_model.safetensors (8711110 bytes), not a full base-weight dump. PolyU's June 2025 news calls this the 2-billion-parameter variant.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoMind-2B is an open-weight video-language agent from Chen Lab at The Hong Kong Polytechnic University, with coauthors at Show Lab, National University of Singapore. It is a Chain-of-LoRA agent on Qwen2-VL-2B-Instruct for temporal grounding and video question answering. Weights are public under BSD-3-Clause. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card describes a multi-modal agent that breaks a question into roles: a planner, a grounder that localizes moments, a verifier, and an answerer. PolyU's public write-up says the same four-role design is meant for long-video reasoning. The GitHub news line dates the code, model, and dataset release to 2025-03-21. The paper was accepted at ICLR 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "polyu/videomind/videomind-7b": { "id": "polyu/videomind/videomind-7b", "name": "VideoMind-7B", "maker": "polyu", "family": "polyu/videomind", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bsd-3-clause", "release_date": "2025-03-22", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "AgentQwen2VL (model_type agent_qwen2_vl) on Qwen2-VL-7B-Instruct. Text tower hidden 3584, 28 layers, 28 heads, 4 KV heads, vocab 152064, untied embeddings. max_position_embeddings 32768. Config role field is grounder. Same Chain-of-LoRA design as the 2B checkpoint. Hub safetensors total was not returned by the API. PolyU's June 2025 news calls this the 7-billion-parameter variant.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoMind-7B is the larger open-weight VideoMind agent from Chen Lab at The Hong Kong Polytechnic University, released with Show Lab at the National University of Singapore. It uses the same Chain-of-LoRA role design as VideoMind-2B, on Qwen2-VL-7B-Instruct. Weights are public under BSD-3-Clause. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card uses the same planner, grounder, verifier, and answerer workflow as the 2B model. PolyU's news says the 7-billion-parameter variant is the larger of two released VideoMind models for long-video understanding. The Hub repo was created on 2025-03-22, one day after the 2B repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "poolside/laguna/laguna-m-1": { "id": "poolside/laguna/laguna-m-1", "name": "Laguna M.1", "maker": "poolside", "family": "poolside/laguna", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-28", "context_length": 262144, "max_output_tokens": null, "parameters": "225B total, 23B active per token", "architecture": "70-layer MoE transformer. First 3 layers are dense SwiGLU; the remaining 67 are sparse MoE with 256 experts, top-16 routing, one shared expert, and auxiliary-loss-free load balancing. Global attention on every layer, 64 query heads, 8 KV heads, head dim 128, softplus attention output gating. RoPE with YaRN. Optimizer: Muon. Pre-trained in 2025 and post-trained before the 2026-04-28 launch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Laguna M.1 is Poolside's large agentic coding model, announced on 2026-04-28. It is a 225B-total Mixture-of-Experts model with 23B parameters active per token. The post-trained weights are public under Apache 2.0, and Poolside still lists it as a supported deployment model. Collected on 2026-10-15.", "what_it_is_for": "Poolside built Laguna M.1 for multi-step coding where the agent explores a repo, edits files, runs tests, and checks the result. The company says it is more useful inside an agent workflow than as a plain chat model. It is text-only and can turn thinking on or off per request.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "poolside/laguna/laguna-m-1-base": { "id": "poolside/laguna/laguna-m-1-base", "name": "Laguna M.1 base", "maker": "poolside", "family": "poolside/laguna", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-06-05", "context_length": 262144, "max_output_tokens": null, "parameters": "225B total, 23B active per token", "architecture": "Same pre-trained Laguna M.1 network: 70 layers, 3 dense SwiGLU then 67 MoE layers, 256 experts, top-16, one shared expert, global attention, 64 query heads, 8 KV heads, head dim 128, softplus output gating, RoPE with YaRN, Muon. This checkpoint is pre-training only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Laguna M.1 base is the public pre-trained checkpoint behind Laguna M.1. Poolside released it on Hugging Face as a 225B-total Mixture-of-Experts completion model with 23B parameters active per token, under Apache 2.0. It is not instruction tuned. The Hub repo was created on 2026-06-05. Collected on 2026-10-15.", "what_it_is_for": "Poolside says this checkpoint is for further training, not for chat or agents. It has no chat template, reasoning, or tool calling. The post-trained Laguna M.1 is the model to use for those jobs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "poolside/laguna/laguna-s-2-1": { "id": "poolside/laguna/laguna-s-2-1", "name": "Laguna S 2.1", "maker": "poolside", "family": "poolside/laguna", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-07-21", "context_length": 1048576, "max_output_tokens": null, "parameters": "118B total, about 8B active per token", "architecture": "Mixture-of-experts transformer (Laguna): 48 layers with 12 global-attention and 36 sliding-window layers (window 512), softplus attention gating, grouped-query attention with 8 KV heads and head dim 128, 256 routed experts (top-10) plus 1 shared expert. Vocabulary 100,352. Pre-training started 2026-05-22 on 4,096 NVIDIA H200 GPUs. First Poolside model with FP8 reinforcement learning.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.09, "completion_per_m_usd": 0.18 }, "summary_intro": "Laguna S 2.1 is Poolside's open-weight agentic coding model, released on 2026-07-21. It is a 118B-total Mixture-of-Experts model with about 8B parameters active per token and a 1,048,576-token context. Poolside still lists it as a supported model. This dossier was collected on 2026-10-15.", "what_it_is_for": "Poolside positions Laguna S 2.1 for long-horizon agentic coding: holding a goal across tool use, recovering from failed attempts, and running locally on high-memory hardware such as one NVIDIA DGX Spark. It is text-only. Thinking is either off or max, with max as the default.", "ratings": { "empirical_100": 46, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.4, "humaneval": 70.4, "twelve_cs": 89.3, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "poolside/laguna/laguna-s-2-1-base": { "id": "poolside/laguna/laguna-s-2-1-base", "name": "Laguna S 2.1 base", "maker": "poolside", "family": "poolside/laguna", "status": "announced", "confidence": "high", "weights": "closed", "license": "OpenMDW-1.1", "release_date": "2026-07-20", "context_length": null, "max_output_tokens": null, "parameters": "118B total, about 8B active per token", "architecture": "Pre-trained Laguna S 2.1 network before post-training: 48 layers, interleaved full and sliding-window attention, 256 routed experts and 1 shared expert. The card says the weights are not public. Researchers are told to email models@poolside.ai. The Hub repo exists and listed 0 downloads on 2026-10-15.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Laguna S 2.1 base is the pre-trained checkpoint behind Laguna S 2.1. Poolside published a model card on Hugging Face and base-model scores, and said the weights are not public. Researchers are told to email models@poolside.ai. The Hub repo was created on 2026-07-20 and showed no downloads. Collected on 2026-10-15.", "what_it_is_for": "The card says this checkpoint has no instruction tuning, chat, reasoning, or tool calling. It is for research or a lab's own post-training. Agentic use belongs on the post-trained Laguna S 2.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "poolside/laguna/laguna-s-2-1-dflash": { "id": "poolside/laguna/laguna-s-2-1-dflash", "name": "Laguna S 2.1 DFlash", "maker": "poolside", "family": "poolside/laguna", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-07-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DFlash draft model, class DFlashLagunaForCausalLM. 6 sliding-attention layers, block size 16. Shares token embeddings and the output head with Laguna S 2.1, so the draft vocabulary matches the target. Serve examples use 7 speculative tokens. The llama.cpp note says the draft length is clamped to 15 draft tokens plus 1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Laguna S 2.1 DFlash is Poolside's speculative-decoding draft for Laguna S 2.1. The Hugging Face repo was created on 2026-07-13. It is a small DFlashLaguna network, not a general chat model. Weights are public. Collected on 2026-10-15.", "what_it_is_for": "Poolside ships it so serving stacks can guess several tokens ahead of Laguna S 2.1. The target model still produces the accepted text. vLLM, SGLang, TensorRT-LLM, and Poolside's llama.cpp branch are the documented loaders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "poolside/laguna/laguna-xs-2": { "id": "poolside/laguna/laguna-xs-2", "name": "Laguna XS.2", "maker": "poolside", "family": "poolside/laguna", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-28", "context_length": 262144, "max_output_tokens": null, "parameters": "33B total, 3B active per token", "architecture": "40-layer Laguna MoE. 10 global-attention layers and 30 sliding-window layers (window 512). 256 experts plus 1 shared expert. FP8 KV cache. Muon optimizer. Poolside called it the second-generation Laguna model and its first open-weight release. Pre-training started about five weeks before 2026-04-28. Expert top-k is not on the card excerpt used here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Laguna XS.2 is Poolside's first open-weight model, released on 2026-04-28 with Laguna M.1. It is a 33B-total Mixture-of-Experts model with 3B parameters active per token, licensed Apache 2.0. Laguna XS 2.1 replaced it in the supported-models comparison. The weights are still public. Collected on 2026-10-15.", "what_it_is_for": "Poolside shipped XS.2 for faster agentic coding on smaller hardware: debugging across files, exploring unfamiliar code, and running where GPU memory is tight. It is text-only. The company said it trades some quality versus M.1 for speed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "poolside/laguna/laguna-xs-2-1": { "id": "poolside/laguna/laguna-xs-2-1", "name": "Laguna XS 2.1", "maker": "poolside", "family": "poolside/laguna", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-07-02", "context_length": 262144, "max_output_tokens": null, "parameters": "33B total, 3B active per token", "architecture": "40-layer Laguna MoE. 10 global-attention layers and 30 sliding-window layers (window 512), about a 3:1 SWA-to-global ratio. 256 routed experts plus 1 shared expert. Sigmoid or per-layer gating is described on the model card; the S 2.1 card describes the shared family router as softplus. KV cache stored in FP8. Optimizer: Muon. Top-k for this size was not stated on the model card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.06, "completion_per_m_usd": 0.12 }, "summary_intro": "Laguna XS 2.1 is Poolside's small open-weight agentic coding model, announced on 2026-07-02. It has 33B total parameters and 3B active per token, a 262,144-token context, and native reasoning. It is still on Poolside's supported-models page. Collected on 2026-10-15.", "what_it_is_for": "Poolside describes it as the lightest Laguna model, for fast agentic coding and local use, including a Mac with 36 GB of RAM. It is an incremental update of Laguna XS.2 with interleaved thinking between tool calls. It is text-only.", "ratings": { "empirical_100": 38, "lmsys_elo": 1201, "coding_elo": 1201, "swe_bench": 15.3, "humaneval": 70.2, "twelve_cs": 85.9, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "poolside/laguna/laguna-xs-2-1-dflash": { "id": "poolside/laguna/laguna-xs-2-1-dflash", "name": "Laguna XS 2.1 DFlash", "maker": "poolside", "family": "poolside/laguna", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenMDW-1.1", "release_date": "2026-06-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "5-layer Llama-style DFlash draft, class DFlashLagunaForCausalLM. Hidden size 2048. 64 query heads, 8 KV heads, head dim 128, sliding window 512, per-head gating. Proposes up to 15 tokens per step. BF16 safetensors. The Laguna XS 2.1 card also describes about 70% per-position acceptance on coding tasks and a 7-token example; this card's own eval uses 15 speculative tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Laguna XS 2.1 DFlash is Poolside's speculative-decoding draft for Laguna XS 2.1. The Hugging Face repo was created on 2026-06-29. It is a 5-layer draft under OpenMDW-1.1, not a standalone assistant. Collected on 2026-10-15.", "what_it_is_for": "It proposes tokens for Laguna XS 2.1 so a server can decode faster. The XS 2.1 card says a 5-layer draft can propose up to 7 tokens at about 70% per-position acceptance on coding tasks. This draft's own card says up to 15 tokens and evaluates at 15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "poolside/laguna/laguna-xs-2-dflash": { "id": "poolside/laguna/laguna-xs-2-dflash", "name": "Laguna XS.2 DFlash", "maker": "poolside", "family": "poolside/laguna", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DFlash speculator for Laguna XS.2, trained with the vLLM Speculators library. Responses in the training mix were regenerated by Laguna XS.2 with reasoning. The serve example uses 7 speculative tokens. Layer count is not on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Laguna XS.2 DFlash is Poolside's speculative-decoding draft for Laguna XS.2. The Hub repo `poolside/Laguna-XS.2-speculator.dflash` was created on 2026-05-07. It is an Apache 2.0 draft model trained with the Speculators library. Collected on 2026-10-15.", "what_it_is_for": "It drafts tokens for Laguna XS.2. Poolside trained it on Magpie and UltraChat prompts whose answers were regenerated by XS.2 with reasoning on. It is not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "poolside/malibu/malibu-2-0": { "id": "poolside/malibu/malibu-2-0", "name": "Malibu 2.0", "maker": "poolside", "family": "poolside/malibu", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Malibu 2.0 is the closed Poolside model named in the Malibu 2.1 release notes as Malibu v2.0 and Malibu v2. The notes say 2.1 is an incremental release on top of v2's code editing, question answering, and agent behavior. There is no standalone spec page. Confidence is medium because the name is official but almost every spec is missing. Collected on 2026-10-15.", "what_it_is_for": "The only job description is the one in the 2.1 note: a Malibu generation aimed at code edits, questions, and agent behavior, with weaker results once conversations got long or many files were in context. AWS still describes an unversioned Malibu as the model for code generation, tests, refactoring, and documentation. That page does not say it is v2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "poolside/malibu/malibu-2-1": { "id": "poolside/malibu/malibu-2-1", "name": "Malibu 2.1", "maker": "poolside", "family": "poolside/malibu", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Not published. Release notes call it an incremental release of Malibu v2.0 and Poolside's chat model, as distinct from Malibu Agent (2.2). Trained with more RLCEF code-edit tasks.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Malibu 2.1 is Poolside's closed chat model from 2025. The release notes call it an incremental update of Malibu v2.0, with a 32,000-token context, and later contrast it with the multi-turn agent Malibu 2.2. It is not on the current supported-models page. Collected on 2026-10-15.", "what_it_is_for": "Poolside says 2.1 improves code edits that solve real tasks, instruction following in long conversations, and agent-mode commands that search for files. The 2.2 notes describe 2.1 as the chat model rather than the multi-round agent.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "poolside/malibu/malibu-2-2": { "id": "poolside/malibu/malibu-2-2", "name": "Malibu 2.2", "maker": "poolside", "family": "poolside/malibu", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "Dense model, per the 2025 model release notes. Poolside also calls this release Malibu Agent.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Malibu 2.2 is Poolside's closed dense coding model from 2025, also described in the release notes as Malibu Agent. It has a 128,000-token context and is trained for multi-turn tool use. It is not on the 2026 supported-models page, which lists Laguna models instead. No retirement date was posted. Collected on 2026-10-15.", "what_it_is_for": "Poolside says Malibu Agent reads a codebase, edits files, and runs linters, tests, or local services to check the edit. The notes contrast it with Malibu 2.1, which they call the chat model. Prompts in the notes are bug reports, stack traces, and questions about how two modules interact.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "poolside/point/point": { "id": "poolside/point/point", "name": "Point", "maker": "poolside", "family": "poolside/point", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "A Poolside speaker at AWS re:Invent 2024 described Point as a linear-retention model, about 10 times faster than comparable models. A July 2026 OpenShift inference doc still uses a checkpoint named point with modelType completion. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Point is Poolside's closed code-completion model. AWS describes it as the low-latency companion to Malibu, and a July 2026 OpenShift doc still shows a `point` completion checkpoint in the inference bundle. It is not in the public Laguna API examples. Specs are thin, so confidence is medium. Collected on 2026-10-15.", "what_it_is_for": "AWS says Point does low-latency code completion and uses context to predict what the developer needs. The 2024 re:Invent talk calls it a linear-retention model, about 10 times faster than comparable models, sharing Poolside's RLCEF training and in-VPC fine-tune with Malibu. The OpenShift example deploys it as `modelType: completion` on one GPU, next to an agent model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "porto/tei2go/tei2go-de": { "id": "porto/tei2go/tei2go-de", "name": "TEI2GO German", "maker": "porto", "family": "porto/tei2go", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy transition-based entity recognizer with hash embeddings and a multilayer perceptron, trained with BILUO tags for the single label TIMEX", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TEI2GO German is a University of Porto and INESC TEC temporal-expression tagger, with a University of Beira Interior co-author. It is the German member of the six spaCy models named in the CIKM 2023 paper. The weights are still on Hugging Face as of 2026-10-15. There is no API and no deprecation notice.", "what_it_is_for": "The paper says the models identify temporal expressions in text, the first step of temporal information extraction. They are meant to be much faster than HeidelTime. On German, the paper says they do not match HeidelTime on strict boundary F1. They do not normalize the expressions. The German reference corpus is KRAUTS.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "porto/tei2go/tei2go-en": { "id": "porto/tei2go/tei2go-en", "name": "TEI2GO English", "maker": "porto", "family": "porto/tei2go", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy transition-based entity recognizer with hash embeddings and a multilayer perceptron, trained with BILUO tags for the single label TIMEX", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TEI2GO English is a University of Porto and INESC TEC temporal-expression tagger, with a University of Beira Interior co-author. It is the English member of the six spaCy models named in the CIKM 2023 paper. The weights are still on Hugging Face as of 2026-10-15. There is no API and no deprecation notice.", "what_it_is_for": "The paper says the models identify temporal expressions in text, the first step of temporal information extraction. They are meant to match HeidelTime's effectiveness at a much higher speed. They do not normalize the expressions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "porto/tei2go/tei2go-es": { "id": "porto/tei2go/tei2go-es", "name": "TEI2GO Spanish", "maker": "porto", "family": "porto/tei2go", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy transition-based entity recognizer with hash embeddings and a multilayer perceptron, trained with BILUO tags for the single label TIMEX", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TEI2GO Spanish is a University of Porto and INESC TEC temporal-expression tagger, with a University of Beira Interior co-author. It is the Spanish member of the six spaCy models named in the CIKM 2023 paper. The weights are still on Hugging Face as of 2026-10-15. There is no API and no deprecation notice.", "what_it_is_for": "The paper says the models identify temporal expressions in text, the first step of temporal information extraction. They are meant to match HeidelTime's effectiveness at a much higher speed. They do not normalize the expressions. The Spanish reference corpus is the Spanish TimeBank.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "porto/tei2go/tei2go-fr": { "id": "porto/tei2go/tei2go-fr", "name": "TEI2GO French", "maker": "porto", "family": "porto/tei2go", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy transition-based entity recognizer with hash embeddings and a multilayer perceptron, trained with BILUO tags for the single label TIMEX", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TEI2GO French is a University of Porto and INESC TEC temporal-expression tagger, with a University of Beira Interior co-author. It is the French member of the six spaCy models named in the CIKM 2023 paper. The weights are still on Hugging Face as of 2026-10-15. There is no API and no deprecation notice.", "what_it_is_for": "The paper says the models identify temporal expressions in text, the first step of temporal information extraction. They are meant to be much faster than HeidelTime. On French, the paper says they do not match HeidelTime on strict boundary F1. They do not normalize the expressions. The French reference corpus is the French TimeBank.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "porto/tei2go/tei2go-it": { "id": "porto/tei2go/tei2go-it", "name": "TEI2GO Italian", "maker": "porto", "family": "porto/tei2go", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-01-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy transition-based entity recognizer with hash embeddings and a multilayer perceptron, trained with BILUO tags for the single label TIMEX", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TEI2GO Italian is a University of Porto and INESC TEC temporal-expression tagger, with a University of Beira Interior co-author. It is the Italian member of the six spaCy models named in the CIKM 2023 paper. The weights are on Hugging Face under an MIT card as of 2026-10-15. There is no API and no deprecation notice.", "what_it_is_for": "The paper says the models identify temporal expressions in text, the first step of temporal information extraction. They are meant to match HeidelTime's effectiveness at a much higher speed. They do not normalize the expressions. The Italian reference corpus is EVENTI-NC.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "porto/tei2go/tei2go-pt": { "id": "porto/tei2go/tei2go-pt", "name": "TEI2GO Portuguese", "maker": "porto", "family": "porto/tei2go", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-01-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "spaCy transition-based entity recognizer with hash embeddings and a multilayer perceptron, trained with BILUO tags for the single label TIMEX", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TEI2GO Portuguese is a University of Porto and INESC TEC temporal-expression tagger, with a University of Beira Interior co-author. It is the Portuguese member of the six spaCy models named in the CIKM 2023 paper. The weights are on Hugging Face under an MIT card as of 2026-10-15. There is no API and no deprecation notice.", "what_it_is_for": "The paper says the models identify temporal expressions in text, the first step of temporal information extraction. They are meant to match HeidelTime's effectiveness at a much higher speed. They do not normalize the expressions. The Portuguese reference corpus is TimeBankPT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "postech/contragan/contragan": { "id": "postech/contragan/contragan", "name": "ContraGAN", "maker": "postech", "family": "postech/contragan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2020-06-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conditional GAN. BigGAN-style backbone in the main comparisons, with conditional batch normalization in the generator and a discriminator trained with a conditional contrastive loss (2C) that uses both data-to-class and data-to-data relations. The same loss was also tested on DCGAN and ResNet GAN backbones. Spectral normalization and hinge adversarial loss are used in the reported BigGAN setup.", "modalities": { "input": [ "class-label", "noise" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ContraGAN is a class-conditional image GAN from POSTECH Graduate School of Artificial Intelligence and the Computer Vision Lab (Minguk Kang and Jaesik Park). It was introduced on arXiv on 23 June 2020 and presented at NeurIPS 2020. Weights and training code remain available through PyTorch-StudioGAN. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper defines conditional image generation: synthesize diverse images from a class label. ContraGAN's discriminator uses a conditional contrastive loss so the generator matches both the class and the relations among images in a batch, instead of only a data-to-class embedding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "postech/da-cmtl/da-cmtl": { "id": "postech/da-cmtl/da-cmtl", "name": "DA-CMTL", "maker": "postech", "family": "postech/da-cmtl", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Domain-agnostic continual multi-task model for glucose forecasting and hypoglycemia classification. The paper combines multi-task learning, elastic weight consolidation for continual learning, and simulation-to-real transfer from simulated type 1 diabetes data onto CGM and insulin series sampled every 5 minutes. The layer stack (for example LSTM versus transformer) was not extracted.", "modalities": { "input": [ "time-series" ], "output": [ "time-series", "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DA-CMTL (Domain-Agnostic Continual Multi-Task Learning) is a glucose and hypoglycemia model from POSTECH, led by Minjoo Hwang and Sung-Min Park (IMSLAB). It was published in npj Digital Medicine on 16 October 2025. Training code is public. Pretrained weights are not in the repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents one model that forecasts glucose and detects hypoglycemia for type 1 diabetes, including people it was not personalized on. It is pretrained on simulated data, then adapted with continual learning (elastic weight consolidation) so a new patient does not erase earlier patients. The authors discuss it as a safety layer for automated insulin delivery, not as a cleared device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "postech/gigagan/gigagan": { "id": "postech/gigagan/gigagan", "name": "GigaGAN", "maker": "postech", "family": "postech/gigagan", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1000000000", "architecture": "Text-conditioned GAN. A pretrained CLIP text encoder plus learned attention produces embeddings that a style-mapping network turns into a StyleGAN-like w vector. A multi-scale synthesis network uses that style code, text attention, and sample-adaptive kernel selection. The discriminator scores an image pyramid at multiple scales. The released description is a 64px base generator (652.5M) plus a text-conditioned upsampler (359.1M). The paper calls the generator total 1.0B. Discriminator parameter counts are separate and are not included in that 1.0B figure.", "modalities": { "input": [ "text", "noise" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GigaGAN is a 1-billion-parameter text-to-image GAN introduced by Minguk Kang and Jaesik Park (POSTECH) with Jun-Yan Zhu (Carnegie Mellon) and Adobe Research (Richard Zhang, Eli Shechtman, Sylvain Paris, and Taesung Park). It was a CVPR 2023 highlight. The project page and paper describe the trained model. Official weights were not released. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors ask whether a GAN can train on web-scale image-text data the way diffusion and autoregressive text-to-image models do. GigaGAN is their answer: a text-to-image generator that keeps a GAN latent space (interpolation, style mixing, prompt arithmetic) and is much faster at inference than the diffusion models they compare against. A second network upsamples to about 16 megapixels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "postech/reacgan/reacgan": { "id": "postech/reacgan/reacgan", "name": "ReACGAN", "maker": "postech", "family": "postech/reacgan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conditional GAN that reboots ACGAN. Image embeddings and class proxies are projected onto a unit hypersphere. Conditioning uses data-to-data cross-entropy (D2D-CE) with a temperature and positive/negative margins, plus a hinge adversarial loss by default. The main comparisons use a BigGAN backbone with conditional batch normalization and spectral normalization. The same D2D-CE head was also attached to StyleGAN2.", "modalities": { "input": [ "class-label", "noise" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReACGAN (Rebooted Auxiliary Classifier GAN) is a class-conditional image generator from POSTECH (Minguk Kang, Woohyeon Shim, Minsu Cho, and Jaesik Park). The paper appeared as arXiv:2111.01118 and at NeurIPS 2021. The authors released weights and the experiment code in PyTorch-StudioGAN. This dossier was collected on 2026-10-15.", "what_it_is_for": "The maker presents ReACGAN as a stable replacement for ACGAN when the label set is large. It generates class-conditional images on CIFAR-10, Tiny-ImageNet, CUB-200, ImageNet, and AFHQ, and is meant to keep class fidelity without the early collapse the authors attribute to unbounded classifier features.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "postech/vehme/vehme": { "id": "postech/vehme/vehme", "name": "VEHME", "maker": "postech", "family": "postech/vehme", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-26", "context_length": null, "max_output_tokens": null, "parameters": "7000000000", "architecture": "Fine-tune of Qwen2.5-VL-7B-Instruct. Training is supervised fine-tuning on reasoning traces distilled from QwQ-32B, then Group Relative Policy Optimization with a composite reward for correctness, error localization, length, cosine reward, and repetition. An Expression-aware Visual Prompting Module (YOLOv11 oriented boxes on a synthetic multi-line canvas) draws spatial prompts on the student handwriting before the VLM sees it. The paper states the backbone has 7 billion parameters.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VEHME (Vision-Language Model for Evaluating Handwritten Mathematics Expressions) is a 7B vision-language grader co-developed by POSTECH and UNIST. Sungahn Ko (POSTECH) and Taehwan Kim (UNIST) are co-corresponding authors. The paper was posted on arXiv on 26 October 2025 and accepted to EMNLP 2025. Training and eval code is public. A fine-tuned weight file was not found. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors built VEHME to grade open-form handwritten math, from elementary arithmetic through calculus, including messy layout. It decides whether a solution is wrong and, if so, where. Korean university press on 17 December 2025 said schools could use the open-source model without a fee. That sentence is not a software license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/generrna/generrna": { "id": "preferred-networks/generrna/generrna", "name": "GenerRNA", "maker": "preferred-networks", "family": "preferred-networks/generrna", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-10-02", "context_length": 1024, "max_output_tokens": null, "parameters": "350M", "architecture": "Decoder-only Transformer, GPT-2 medium style: 24 layers, model dimension 1280. BPE vocabulary 1024. RNA nucleotide generation. Two checkpoints: model_updated.pt (recommended, longer context, deduplicated data) and a historical original.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GenerRNA is a 350M RNA design language model from Preferred Networks and the University of Tokyo, published with MIT weights and a PLOS ONE paper on 2024-10-02. It generates nucleotide sequences rather than natural language. Collected 2026-10-15.", "what_it_is_for": "Research de novo RNA generation and fine-tuning for properties such as protein binding. Not a chat LLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo/plamo-1-0-prime": { "id": "preferred-networks/plamo/plamo-1-0-prime", "name": "PLaMo 1.0 Prime", "maker": "preferred-networks", "family": "preferred-networks/plamo", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-02", "context_length": 16000, "max_output_tokens": null, "parameters": "", "architecture": "Commercial flagship post-trained from the PLaMo-100B line. Later named 1.0 after PLaMo 2.0 Prime shipped. Compact relative to a raw 100B serve, but the served parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 300.0, "completion_per_m_usd": 1000.0 }, "summary_intro": "PLaMo 1.0 Prime is Preferred Networks' first paid flagship, launched 2024-12-02 as PLaMo Prime and later renamed 1.0. The API was retired on 2025-06-30. Collected 2026-10-15.", "what_it_is_for": "Japanese-language chat, summarization, translation, and RAG for business use through PLaMo API, PLaMo Chat, Bedrock Marketplace, and on-premises delivery.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo/plamo-100b": { "id": "preferred-networks/plamo/plamo-100b", "name": "PLaMo-100B-Pretrained", "maker": "preferred-networks", "family": "preferred-networks/plamo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "PLaMo Non-Commercial License on Hugging Face. A separate commercial license is offered by application.", "release_date": "2024-10-15", "context_length": null, "max_output_tokens": null, "parameters": "100B", "architecture": "From-scratch causal language model developed by Preferred Elements for GENIAC cycle 1. Pretrained checkpoint only on Hugging Face.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo-100B-Pretrained is the 100B from-scratch Japanese-focused LLM Preferred Elements trained under GENIAC and Preferred Networks published on 2024-10-15. Weights are available under a non-commercial license, with commercial terms by request. Collected 2026-10-15.", "what_it_is_for": "Research pretraining checkpoint for Japanese and English. The commercial chat product was the later instruct beta and PLaMo Prime, not this dump.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo/plamo-13b": { "id": "preferred-networks/plamo/plamo-13b", "name": "PLaMo-13B", "maker": "preferred-networks", "family": "preferred-networks/plamo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-09-28", "context_length": 4096, "max_output_tokens": null, "parameters": "13B", "architecture": "LLaMA-based causal decoder-only. SentencePiece tokenizer trained on a subset of the pretraining data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo-13B is Preferred Networks' first public Preferred Language Model, a 13B pretrained bilingual LLM released on 2023-09-28 under Apache-2.0. It is still downloadable and was not instruction-tuned. Collected 2026-10-15.", "what_it_is_for": "Pretrained text generation in Japanese and English for research and commercial use. PFN positioned it as a compact model that can run on one GPU server.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo/plamo-13b-instruct": { "id": "preferred-networks/plamo/plamo-13b-instruct", "name": "PLaMo-13B-Instruct", "maker": "preferred-networks", "family": "preferred-networks/plamo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-11-01", "context_length": 8192, "max_output_tokens": null, "parameters": "13B", "architecture": "Instruction-tuned LLaMA-based causal decoder-only, built on an 8192-context version of PLaMo-13B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo-13B-Instruct is Preferred Networks' Apache-2.0 instruction-tuned 13B model, released 2023-11-01 on top of an 8192-context PLaMo-13B. It remains a public weight release. Collected 2026-10-15.", "what_it_is_for": "Japanese and English instruction following and dialogue, using only datasets the vendor could ship under a commercial license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo/plamo-13b-instruct-nc": { "id": "preferred-networks/plamo/plamo-13b-instruct-nc", "name": "PLaMo-13B-Instruct-NC", "maker": "preferred-networks", "family": "preferred-networks/plamo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2023-11-01", "context_length": 8192, "max_output_tokens": null, "parameters": "13B", "architecture": "Instruction-tuned LLaMA-based causal decoder-only. config.json: 40 layers, hidden size 5120, 40 attention heads, intermediate size 16640, SiLU, RMSNorm, vocab 50432, max_position_embeddings 8192.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo-13B-Instruct-NC is the non-commercial instruct twin of PLaMo-13B-Instruct, released 2023-11-01 under CC-BY-NC-4.0. Preferred Networks still hosts the weights. Collected 2026-10-15.", "what_it_is_for": "Research dialogue in Japanese and English when non-commercial instruction data is acceptable. Not licensed for commercial use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo/plamo-beta": { "id": "preferred-networks/plamo/plamo-beta", "name": "PLaMo \u03b2", "maker": "preferred-networks", "family": "preferred-networks/plamo", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-07", "context_length": 4000, "max_output_tokens": null, "parameters": "100B-class instruct", "architecture": "Instruction-tuned and merged model based on PLaMo-100B. Preferred Elements built it; PFN ran the trial API.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo \u03b2 was Preferred Networks' free trial of an instruct model built from PLaMo-100B. Sign-ups opened 2024-08-07 and the trial ended at the end of December 2024. Collected 2026-10-15.", "what_it_is_for": "Chat and trial API for Japanese and English generation while the commercial Prime model was still in preparation. Commercial use was allowed during the free trial.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo/plamo-lite": { "id": "preferred-networks/plamo/plamo-lite", "name": "PLaMo Lite", "maker": "preferred-networks", "family": "preferred-networks/plamo", "status": "active", "confidence": "high", "weights": "closed", "license": "Commercial license by inquiry. Not the Apache PLaMo 2 1B weights.", "release_date": "2024-08-28", "context_length": 4096, "max_output_tokens": null, "parameters": "1B", "architecture": "1B small language model built from PLaMo-100B development results for edge and on-premises use. Qualcomm's PLaMo-1B card describes the Lite series and lists context 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo Lite is Preferred Networks' commercial 1B small language model for edge devices, on sale since 2024-08-28. Weights are licensed, not posted as open source. Collected 2026-10-15.", "what_it_is_for": "Japanese generation on cars, robots, manufacturing equipment, PCs, and on-premises servers without a cloud round trip.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-2/plamo-2-0-prime": { "id": "preferred-networks/plamo-2/plamo-2-0-prime", "name": "PLaMo 2.0 Prime", "maker": "preferred-networks", "family": "preferred-networks/plamo-2", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-22", "context_length": 32000, "max_output_tokens": null, "parameters": "31B-class", "architecture": "Inference-optimized instruction model of PLaMo 2.0-31B. Hybrid PLaMo 2 stack. Proprietary tokenizer. Closed weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 60.0, "completion_per_m_usd": 250.0 }, "summary_intro": "PLaMo 2.0 Prime is Preferred Networks' second commercial flagship, released 2025-05-22 as an inference-optimized PLaMo 2.0-31B. The API was replaced in place by PLaMo 2.1 Prime on 2025-10-08. Collected 2026-10-15.", "what_it_is_for": "Japanese instruction following, writing, and long documents at a lower token price than PLaMo 1.0 Prime. Sold on the PLaMo API and for on-premises use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-2/plamo-2-1-2b-cpt": { "id": "preferred-networks/plamo-2/plamo-2-1-2b-cpt", "name": "PLaMo 2.1 2B CPT", "maker": "preferred-networks", "family": "preferred-networks/plamo-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "PLaMo Community License. Commercial use needs prior registration and the user's annual revenue must not exceed 1 billion yen. Redistribution must say Built with PLaMo and keep PLaMo in derived model names.", "release_date": "2025-07-10", "context_length": 32768, "max_output_tokens": null, "parameters": "2B", "architecture": "Continual-pretrained PLaMo 2 causal decoder. The 2B class was created by pruning a larger PLaMo 2 model. This repo is not instruction-tuned. Hugging Face upload dated 2025-07-10.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 2.1 2B CPT is a community-license continual-pretraining checkpoint Preferred Networks uploaded on 2025-07-10. It is a pretrained 2B PLaMo 2 model, not a chat model. Collected 2026-10-15.", "what_it_is_for": "Small long-context pretrained base. The vision model plamo-2.1-2b-vl says it starts from the instruction-tuned 2.1 2B, which is a sibling of this CPT file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-2/plamo-2-1-8b-cpt": { "id": "preferred-networks/plamo-2/plamo-2-1-8b-cpt", "name": "PLaMo 2.1 8B CPT", "maker": "preferred-networks", "family": "preferred-networks/plamo-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "PLaMo Community License. Commercial use needs prior registration and the user's annual revenue must not exceed 1 billion yen. Redistribution must say Built with PLaMo and keep PLaMo in derived model names.", "release_date": "2025-06-23", "context_length": 32768, "max_output_tokens": null, "parameters": "8B", "architecture": "8B model pruned from a 31B English-Japanese pretrained model, then continual-pretrained. Causal decoder-only. Not instruction-tuned. Developed by Preferred Elements.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 2.1 8B CPT is Preferred Elements' pruned 8B continual-pretraining checkpoint, hosted by Preferred Networks under the PLaMo Community License. The repo dates from 2025-06-23. It is not a chat model. Collected 2026-10-15.", "what_it_is_for": "Open pretrained base for the 8B PLaMo 2.1 line. The vision-language model uses an instruction-tuned 2.1 8B that was not found as its own public text repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-2/plamo-2-1-prime": { "id": "preferred-networks/plamo-2/plamo-2-1-prime", "name": "PLaMo 2.1 Prime", "maker": "preferred-networks", "family": "preferred-networks/plamo-2", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Update of PLaMo 2.0 Prime with automatic tool selection and multi-tool calling. PFN said existing 2.0 users were moved automatically.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 2.1 Prime is Preferred Networks' 2025-10-08 agent update of the commercial flagship. It replaced PLaMo 2.0 Prime automatically. PLaMo 2.2 Prime followed on 2026-01-28, and 2.1 is no longer the listed API or Foundry model. Collected 2026-10-15.", "what_it_is_for": "Tool-using Japanese business agents that choose databases and external systems from the user request, including multi-tool calls.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-2/plamo-2-1b": { "id": "preferred-networks/plamo-2/plamo-2-1b", "name": "PLaMo 2 1B", "maker": "preferred-networks", "family": "preferred-networks/plamo-2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-07", "context_length": 2048, "max_output_tokens": null, "parameters": "1B", "architecture": "Causal decoder-only hybrid like Samba: Mamba-2 plus sliding-window attention, with extra normalization. Not instruction-tuned. Developed by Preferred Elements.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 2 1B is Preferred Elements' open 1B pretrained model, posted by Preferred Networks on 2025-02-07 under Apache-2.0. It is not instruction-tuned. Collected 2026-10-15.", "what_it_is_for": "Local pretrained Japanese-English baseline and a small model people can fine-tune. PFN said commercial use is allowed under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-2/plamo-2-2-prime": { "id": "preferred-networks/plamo-2/plamo-2-2-prime", "name": "PLaMo 2.2 Prime", "maker": "preferred-networks", "family": "preferred-networks/plamo-2", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-28", "context_length": 32768, "max_output_tokens": 4096, "parameters": "", "architecture": "Post-training update of the PLaMo 2.x Prime line. Closed weights. Still listed on Microsoft Foundry after PLaMo 3.0 Prime became the first-party API default.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 2.2 Prime is Preferred Networks' January 2026 update of the PLaMo 2 flagship, with stronger instruction following and medical QA. It is no longer the only model on the first-party API, but Microsoft Foundry still listed it on 2026-10-06. Collected 2026-10-15.", "what_it_is_for": "Japanese business chat and agents where 2.x behavior is required, including on-premises and Foundry deployments. The current PLaMo API model id is 3.0 Prime.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-2/plamo-2-8b": { "id": "preferred-networks/plamo-2/plamo-2-8b", "name": "PLaMo 2 8B", "maker": "preferred-networks", "family": "preferred-networks/plamo-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "PLaMo Community License. Commercial use needs prior registration and the user's annual revenue must not exceed 1 billion yen. Redistribution must say Built with PLaMo and keep PLaMo in derived model names.", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "8B", "architecture": "PLaMo 2 hybrid Samba-style causal decoder-only pretrained model. Hugging Face size tag is 9B. Community license. Base of plamo-2-translate.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 2 8B is Preferred Networks' community-license 8B pretrained PLaMo 2 model. A results blog is dated 2025-02-19 and a 2025-03-06 talk says the weights were released under the new license. The Hugging Face card is gated. Collected 2026-10-15.", "what_it_is_for": "Larger open PLaMo 2 pretrained checkpoint for Japanese and English. It is the base model of PLaMo 2 Translate. Not an instruction chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-2-vl/plamo-2-1-2b-vl": { "id": "preferred-networks/plamo-2-vl/plamo-2-1-2b-vl", "name": "PLaMo 2.1-2B-VL", "maker": "preferred-networks", "family": "preferred-networks/plamo-2-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "PLaMo Community License. Commercial use needs prior registration and the user's annual revenue must not exceed 1 billion yen. Redistribution must say Built with PLaMo and keep PLaMo in derived model names.", "release_date": "2026-04-03", "context_length": null, "max_output_tokens": null, "parameters": "2B language backbone", "architecture": "Vision-language model on instruction-tuned PLaMo 2.1-2B plus SigLIP2 so400m. Causal decoder-only language stack. Aimed at edge devices. Hugging Face size tag is 3B, which includes the vision tower.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 2.1-2B-VL is Preferred Networks' smaller vision-language model, released on Hugging Face around 2026-04-03 under the PLaMo Community License. It is the edge-sized pair of the 8B VL. Collected 2026-10-15.", "what_it_is_for": "Visual reasoning and grounding on drones, robots, vehicles, and cameras, where a lighter model is worth the accuracy tradeoff. The vendor says the 8B is the main offering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-2-vl/plamo-2-1-8b-vl": { "id": "preferred-networks/plamo-2-vl/plamo-2-1-8b-vl", "name": "PLaMo 2.1-8B-VL", "maker": "preferred-networks", "family": "preferred-networks/plamo-2-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "PLaMo Community License. Commercial use needs prior registration and the user's annual revenue must not exceed 1 billion yen. Redistribution must say Built with PLaMo and keep PLaMo in derived model names.", "release_date": "2026-04-03", "context_length": null, "max_output_tokens": null, "parameters": "8B language backbone", "architecture": "Vision-language model on instruction-tuned PLaMo 2.1-8B plus SigLIP2 so400m. Hugging Face size tag is 10B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 2.1-8B-VL is Preferred Networks' main open vision-language model, posted around 2026-04-03. It accepts images and text and returns text, including normalized boxes. Collected 2026-10-15.", "what_it_is_for": "Japanese and English visual question answering, referring expression grounding, task analysis, and anomaly detection.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-3/plamo-3-0-prime": { "id": "preferred-networks/plamo-3/plamo-3-0-prime", "name": "PLaMo 3.0 Prime", "maker": "preferred-networks", "family": "preferred-networks/plamo-3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-22", "context_length": 262144, "max_output_tokens": 20480, "parameters": "", "architecture": "Closed flagship rebuilt for the PLaMo 3 line, with NICT data collaboration. Reasoning and non-reasoning variants. Proprietary Japanese-efficient tokenizer. Parameter count and layer layout were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 60.0, "completion_per_m_usd": 250.0 }, "summary_intro": "PLaMo 3.0 Prime is Preferred Networks' current flagship, generally available since 2026-06-22. The API serves it as plamo-3.0-prime with a 262,144-token window, reasoning, and a non-reasoning option. Collected 2026-10-15.", "what_it_is_for": "Japanese business generation, coding, tool use, and long-context agents. Sold on the PLaMo API, PLaMo Chat, on-premises, Microsoft Foundry, Sakura AI Engine, and other clouds named in later releases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-3/plamo-3-610m-fin-instruct": { "id": "preferred-networks/plamo-3/plamo-3-610m-fin-instruct", "name": "PLaMo 3 610M Fin Instruct", "maker": "preferred-networks", "family": "preferred-networks/plamo-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "PLaMo Community License. Commercial use needs prior registration and the user's annual revenue must not exceed 1 billion yen. Redistribution must say Built with PLaMo and keep PLaMo in derived model names.", "release_date": "2026-09-24", "context_length": null, "max_output_tokens": null, "parameters": "610M", "architecture": "Small PLaMo 3 instruct model for Japanese finance. Hugging Face pipeline tag text-generation and size tag 0.9B. GGUF conversions report about 0.9B parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 3 610M Fin Instruct is a small Japanese finance instruct model Preferred Networks posted on Hugging Face on 2026-09-24 under the PLaMo Community License. The card was gated during collection. Collected 2026-10-15.", "what_it_is_for": "Local Japanese finance chat or instruction following at a very small size. It is not the commercial PLaMo Fin Prime product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-3/plamo-3-nict-2604-31b-base": { "id": "preferred-networks/plamo-3/plamo-3-nict-2604-31b-base", "name": "PLaMo 3 NICT 2604 31B Base", "maker": "preferred-networks", "family": "preferred-networks/plamo-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "PLaMo Community License. Commercial use needs prior registration and the user's annual revenue must not exceed 1 billion yen. Redistribution must say Built with PLaMo and keep PLaMo in derived model names.", "release_date": "2026-07-09", "context_length": null, "max_output_tokens": null, "parameters": "31B", "architecture": "Causal decoder-only hybrid of sliding-window attention and full attention. English and Japanese pretraining with NICT. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 3 NICT 2604 31B Base is a 31B model distilled from a 120B PLaMo 3 teacher, published by Preferred Networks on 2026-07-09. The name 2604 marks the training run. It is not instruction-tuned. Collected 2026-10-15.", "what_it_is_for": "Pretrained base for research. The card says it is not instruction-tuned. PFN points chat users at PLaMo 3.0 Prime.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-3/plamo-3-nict-2b-base": { "id": "preferred-networks/plamo-3/plamo-3-nict-2b-base", "name": "PLaMo 3 NICT 2B Base", "maker": "preferred-networks", "family": "preferred-networks/plamo-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "PLaMo Community License. Commercial use needs prior registration and the user's annual revenue must not exceed 1 billion yen. Redistribution must say Built with PLaMo and keep PLaMo in derived model names.", "release_date": "2025-11-14", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Causal decoder-only hybrid of sliding-window attention and full attention. English and Japanese pretraining with NICT. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 3 NICT 2B Base is a 2B pretrained model from Preferred Networks and NICT, published with the 8B and 31B bases in the 2025-11-14 tech blog. It is not a chat model. Collected 2026-10-15.", "what_it_is_for": "Pretrained base for research. The card says it is not instruction-tuned. PFN points chat users at PLaMo 3.0 Prime.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-3/plamo-3-nict-31b-base": { "id": "preferred-networks/plamo-3/plamo-3-nict-31b-base", "name": "PLaMo 3 NICT 31B Base", "maker": "preferred-networks", "family": "preferred-networks/plamo-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "PLaMo Community License. Commercial use needs prior registration and the user's annual revenue must not exceed 1 billion yen. Redistribution must say Built with PLaMo and keep PLaMo in derived model names.", "release_date": "2025-11-14", "context_length": null, "max_output_tokens": null, "parameters": "31B", "architecture": "Causal decoder-only hybrid of sliding-window attention and full attention. English and Japanese pretraining with NICT. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 3 NICT 31B Base is the 31B pretrained model Preferred Networks and NICT published in November 2025. It was trained on about 3T tokens and is not a chat model. Collected 2026-10-15.", "what_it_is_for": "Pretrained base for research. The card says it is not instruction-tuned. PFN points chat users at PLaMo 3.0 Prime.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-3/plamo-3-nict-8b-base": { "id": "preferred-networks/plamo-3/plamo-3-nict-8b-base", "name": "PLaMo 3 NICT 8B Base", "maker": "preferred-networks", "family": "preferred-networks/plamo-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "PLaMo Community License. Commercial use needs prior registration and the user's annual revenue must not exceed 1 billion yen. Redistribution must say Built with PLaMo and keep PLaMo in derived model names.", "release_date": "2025-11-14", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Causal decoder-only hybrid of sliding-window attention and full attention. English and Japanese pretraining with NICT. Not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 3 NICT 8B Base is the 8B pretrained PLaMo 3 model Preferred Networks and NICT released in November 2025 under the PLaMo Community License. It is not instruction-tuned. Collected 2026-10-15.", "what_it_is_for": "Pretrained base for research. The card says it is not instruction-tuned. PFN points chat users at PLaMo 3.0 Prime.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-embedding/plamo-embedding-1b": { "id": "preferred-networks/plamo-embedding/plamo-embedding-1b", "name": "PLaMo-Embedding-1B", "maker": "preferred-networks", "family": "preferred-networks/plamo-embedding", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-17", "context_length": 4096, "max_output_tokens": null, "parameters": "1B", "architecture": "Japanese text embedding model. Embedding dimension 2048. Cosine similarity. Query and document encoders are separate methods. Developed by Preferred Networks.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo-Embedding-1B is Preferred Networks' Apache-2.0 Japanese embedding model, published in April 2025. It maps text to 2048-d vectors for retrieval and clustering. Collected 2026-10-15.", "what_it_is_for": "Japanese retrieval, classification, clustering, and reranking. Use encode_query for queries and encode_document for passages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-fin/plamo-fin-base": { "id": "preferred-networks/plamo-fin/plamo-fin-base", "name": "PLaMo Fin Base", "maker": "preferred-networks", "family": "preferred-networks/plamo-fin", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PLaMo continued-pretrained on Japanese financial text, before the instruct and preference stage that produced Fin Prime. The 2025-06-17 blog treats it as a named checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo Fin Base is the finance-adapted PLaMo checkpoint Preferred Networks describes under the 1.0 Fin line. It is the pre-alignment stage of PLaMo Fin Prime, not a separate open weight. Collected 2026-10-15.", "what_it_is_for": "Domain base for Japanese finance tasks before instruction alignment. PFN's sold model is Fin Prime, with optional per-bank customization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-fin/plamo-fin-prime": { "id": "preferred-networks/plamo-fin/plamo-fin-prime", "name": "PLaMo Fin Prime", "maker": "preferred-networks", "family": "preferred-networks/plamo-fin", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-17", "context_length": 16000, "max_output_tokens": null, "parameters": "", "architecture": "Post-trained finance model on top of PLaMo Fin Base. Blog label PLaMo 1.0 Fin Prime. Context extended to 16,000 tokens in post-training. Closed weights. Custom per-institution training offered on premises.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo Fin Prime is Preferred Networks' closed Japanese finance LLM, released 2025-06-17 on the PLaMo 1.0 line. It adds financial continued pretraining and post-training, with a 16,000-token context. Collected 2026-10-15.", "what_it_is_for": "Financial question answering and workflows that need Japanese market and professional-exam knowledge. PFN will adapt it per institution and deploy it on premises.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-semground/plamo-semground": { "id": "preferred-networks/plamo-semground/plamo-semground", "name": "PLaMo-SemGround", "maker": "preferred-networks", "family": "preferred-networks/plamo-semground", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "2B or smaller", "architecture": "Small environment-understanding foundation model for indoor mobile robots. Inputs named in the press: natural language, RGB, and depth. Maps language onto regions, objects, and positions. Joint PFN and Preferred Robotics development under GENIAC robot foundation models.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo-SemGround is an announced robot foundation model from Preferred Networks and Preferred Robotics. Development started under a GENIAC award disclosed on 2026-09-09. It is not released. Collected 2026-10-15.", "what_it_is_for": "Let indoor mobile robots understand drivable and forbidden regions and complex work conditions from language, RGB, and depth, so setup tasks can move off specialist engineers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-translate/plamo-2-translate": { "id": "preferred-networks/plamo-translate/plamo-2-translate", "name": "PLaMo 2 Translate", "maker": "preferred-networks", "family": "preferred-networks/plamo-translate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "PLaMo Community License. Commercial use needs prior registration and the user's annual revenue must not exceed 1 billion yen. Redistribution must say Built with PLaMo and keep PLaMo in derived model names.", "release_date": "2025-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Translation post-train of pfnet/plamo-2-8b. PLaMo 2 hybrid stack. Prompt format uses <|plamo:op|> tags. Not a general chat model. Hugging Face size tag 10B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 2 Translate is Preferred Networks' open Japanese-English translation model, released 2025-05-27 under the PLaMo Community License. It is a post-train of PLaMo 2 8B. The commercial translation service moved to a generation-3 model on 2026-10-07. Collected 2026-10-15.", "what_it_is_for": "English-Japanese translation, including local and on-premises deployment. The card says it is not instruction-tuned for general chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-translate/plamo-2-translate-base": { "id": "preferred-networks/plamo-translate/plamo-2-translate-base", "name": "PLaMo 2 Translate Base", "maker": "preferred-networks", "family": "preferred-networks/plamo-translate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "PLaMo Community License. Commercial use needs prior registration and the user's annual revenue must not exceed 1 billion yen. Redistribution must say Built with PLaMo and keep PLaMo in derived model names.", "release_date": "2025-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Base translation checkpoint in the PLaMo 2 Translate release. Same prompt style as the post-trained model. Hugging Face size tag 10B. Fine-tune of plamo-2-8b.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 2 Translate Base is the base translation checkpoint Preferred Networks released with PLaMo 2 Translate on 2025-05-27. It is a named weight repo, not a dated snapshot of the post-trained model. Collected 2026-10-15.", "what_it_is_for": "Starting point for translation research. The card points users who want the finished translator at plamo-2-translate.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-translate/plamo-2-translate-eval": { "id": "preferred-networks/plamo-translate/plamo-2-translate-eval", "name": "PLaMo 2 Translate Eval", "maker": "preferred-networks", "family": "preferred-networks/plamo-translate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "PLaMo Community License. Commercial use needs prior registration and the user's annual revenue must not exceed 1 billion yen. Redistribution must say Built with PLaMo and keep PLaMo in derived model names.", "release_date": "2025-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pairwise translation evaluation model shipped with PLaMo 2 Translate. Hugging Face size tag 9B. Prompt asks the model to pick id=A or id=B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PLaMo 2 Translate Eval is Preferred Networks' pairwise translation judge, released with the PLaMo 2 Translate weights on 2025-05-27. Collected 2026-10-15.", "what_it_is_for": "Pick the better of two translations. It is not the translation model used by the PLaMo Translate product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-translate/plamo-3-translate-31b": { "id": "preferred-networks/plamo-translate/plamo-3-translate-31b", "name": "plamo-3-translate 31B", "maker": "preferred-networks", "family": "preferred-networks/plamo-translate", "status": "active", "confidence": "high", "weights": "closed", "license": "Weights not yet public. Blog says a later Hugging Face release is planned under the same pattern as plamo-2-translate.", "release_date": "2026-10-07", "context_length": null, "max_output_tokens": null, "parameters": "31B", "architecture": "Largest plamo-3-translate size. Translation-specialized model on PLaMo 3 NICT. Used for the frontier comparison and for the worked examples. Service PLaMo Translate uses plamo-3-translate as of 2026-10-07; the blog does not print a separate serving id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "plamo-3-translate 31B is Preferred Networks' largest generation-3 translation model, described on 2026-10-07 as the model behind the updated PLaMo Translate service. Weights were still closed on that date, with a public upload planned. Collected 2026-10-15.", "what_it_is_for": "Japanese-centered translation across 53 languages in the PLaMo Translate product, including file, browser, and 15-language meeting translation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "preferred-networks/plamo-translate/plamo-3-translate-8b": { "id": "preferred-networks/plamo-translate/plamo-3-translate-8b", "name": "plamo-3-translate 8B", "maker": "preferred-networks", "family": "preferred-networks/plamo-translate", "status": "announced", "confidence": "high", "weights": "closed", "license": "Open weights planned under the PLaMo Community License. Not on Hugging Face as of the 2026-10-07 blog.", "release_date": "2026-10-07", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Translation-specialized model on the PLaMo 3 NICT pretrained base. 8B size used for the comparison with plamo-2-translate.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "plamo-3-translate 8B is the smaller generation-3 translation model Preferred Networks evaluated on 2026-10-07. Weights were not public on that date. Collected 2026-10-15.", "what_it_is_for": "Japanese-English and multilingual translation research size. The vendor compares it with plamo-2-translate 8B and TranslateGemma 27B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "prime-intellect/intellect/intellect-1": { "id": "prime-intellect/intellect/intellect-1", "name": "INTELLECT-1", "maker": "prime-intellect", "family": "prime-intellect/intellect", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-29", "context_length": 8192, "max_output_tokens": null, "parameters": "10B", "architecture": "Llama-3 dense decoder trained from scratch. Model card and technical report: 42 layers, hidden size 4096, 32 attention heads, 8 key/value heads, vocabulary 128256, sequence length 8192. Trained on 1 trillion tokens with DiLoCo/LocalSGD (AdamW inner optimizer, Nesterov SGD outer) across up to 14 nodes and 112 H100 GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "INTELLECT-1 is Prime Intellect's first named model, a 10B Llama-3-style base model trained from scratch on 1 trillion tokens of English text and code. Weights shipped on 29 November 2024 under Apache-2.0. It is a base model; the chat checkpoint is INTELLECT-1-Instruct. Status on the collection date is active because the weights are still published and no retirement notice was found.", "what_it_is_for": "The maker describes it as a collaboratively trained base language model, the first 10B run spread across up to 14 nodes on three continents. The release post says the chat use case is the instruct checkpoint, not this base file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "prime-intellect/intellect/intellect-1-instruct": { "id": "prime-intellect/intellect/intellect-1-instruct", "name": "INTELLECT-1-Instruct", "maker": "prime-intellect", "family": "prime-intellect/intellect", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-29", "context_length": 8192, "max_output_tokens": null, "parameters": "10B", "architecture": "Same Llama-3 decoder as INTELLECT-1: 42 layers, hidden size 4096, 32 attention heads, context 8192, vocabulary 128256. Post-trained with supervised fine-tuning, DPO, and MergeKit merging. The release post says 16 SFT runs and 8 DPO runs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "INTELLECT-1-Instruct is Prime Intellect's chat post-train of the INTELLECT-1 10B base model. It was released with the base weights on 29 November 2024 under Apache-2.0. The maker tells users to pick this checkpoint for chat and the base checkpoint for pretraining use. Status is active: weights remain public and no retirement notice was found.", "what_it_is_for": "Instruction following and chat on top of the globally trained 10B base. The release post frames post-training as SFT, DPO, and a MergeKit merge, not as a new pretrain.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "prime-intellect/intellect/intellect-2": { "id": "prime-intellect/intellect/intellect-2", "name": "INTELLECT-2", "maker": "prime-intellect", "family": "prime-intellect/intellect", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-05-11", "context_length": 40960, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen2 causal LM (Qwen2ForCausalLM) continued from QwQ-32B with asynchronous GRPO over verifiable math and code rewards. Published config: 64 layers, hidden size 5120, intermediate size 27648, 40 attention heads, 8 key-value heads, vocabulary 152064, max_position_embeddings 40960, sliding_window 32768, use_sliding_window false, rope_theta 1000000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "INTELLECT-2 is Prime Intellect's 32B reasoning model, released 11 May 2025 under Apache-2.0. It is a globally distributed asynchronous reinforcement-learning run on top of QwQ-32B, not a continued train of INTELLECT-1. The maker reports gains on math and code versus QwQ-32B and a small drop on IFEval. Status is active: weights remain public and no retirement notice was found.", "what_it_is_for": "Reasoning on verifiable math and coding tasks, with an intended controllable thinking budget. The card says best results come from asking the model to think for 10000 tokens, and that the budget was only partly learned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "prime-intellect/intellect/intellect-3": { "id": "prime-intellect/intellect/intellect-3", "name": "INTELLECT-3", "maker": "prime-intellect", "family": "prime-intellect/intellect", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-11-26", "context_length": 131072, "max_output_tokens": null, "parameters": "106B total, 12B active", "architecture": "Glm4MoeForCausalLM post-trained from GLM-4.5-Air-Base with SFT and then large-scale RL. Published config: 46 layers, first_k_dense_replace 1, hidden size 4096, 96 attention heads, 8 key-value heads, head dim 128, 128 routed experts, 8 experts per token, 1 shared expert, MoE intermediate 1408, dense intermediate 10944, vocabulary 151552, max_position_embeddings 131072, bfloat16. Maker states 106B total and 12B active. Hugging Face labels the repo 107B params. The technical report says SFT used context parallelism out to 98K tokens and RL used a 65536-token maximum.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "INTELLECT-3 is Prime Intellect's open reasoning model released 26 November 2025. It is a 106B-parameter Mixture-of-Experts (about 12B active) post-trained with SFT and reinforcement learning from GLM-4.5-Air-Base. Weights are MIT. The maker calls it state of the art for its size on math, code, science, and reasoning, and hosts a chat at chat.primeintellect.ai. Status is active. A continued-RL checkpoint, INTELLECT-3.1, was published later.", "what_it_is_for": "Long reasoning and tool-using agent work. The report trains on math, code, science, logic, deep research, and software engineering environments from the Environments Hub. The card's vLLM example turns on a tool-call parser and a reasoning parser.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "prime-intellect/intellect/intellect-3-1": { "id": "prime-intellect/intellect/intellect-3-1", "name": "INTELLECT-3.1", "maker": "prime-intellect", "family": "prime-intellect/intellect", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-01-20", "context_length": 131072, "max_output_tokens": null, "parameters": "106B total, 12B active", "architecture": "Continued reinforcement learning from INTELLECT-3. The published config matches INTELLECT-3: Glm4MoeForCausalLM, 46 layers, first_k_dense_replace 1, hidden 4096, 96 heads, 8 KV heads, 128 routed experts, 8 per token, 1 shared expert, MoE intermediate 1408, vocabulary 151552, max_position_embeddings 131072, bfloat16. The card says 106B (A12B). Hugging Face labels the repo 107B params. No separate layer-count write-up was published beyond the config and the 106B/12B claim.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "INTELLECT-3.1 is Prime Intellect's continued-training checkpoint of INTELLECT-3. The model card, first published on Hugging Face on 20 January 2026, calls it a 106B (A12B) Mixture-of-Experts reasoning model with more reinforcement learning on math, coding, software engineering, and agentic tasks. License is MIT. No separate blog post or score table was found. Status is active because the weights are public. Confidence is high for identity and low for any claim about a quality gain over INTELLECT-3, which is not scored here.", "what_it_is_for": "The card's stated job is the same reasoning and agent stack as INTELLECT-3, extended with more RL. It does not publish a delta versus INTELLECT-3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "prime-intellect/intellect/intellect-math": { "id": "prime-intellect/intellect/intellect-math", "name": "INTELLECT-MATH", "maker": "prime-intellect", "family": "prime-intellect/intellect", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-01-21", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Qwen2 checkpoint. The blog and GitHub repo say it is an SFT of Qwen/Qwen2.5-Math-7B on verified QwQ reasoning traces, then online RL with the PRIME-RL recipe. The released weights are the step-255 RL checkpoint in the blog table. Hugging Face lists model size as 8B params and tensor type F32. The maker calls the model 7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "INTELLECT-MATH is Prime Intellect's 7B math reasoning model, announced on 21 January 2025 as a research preview. It is an SFT-then-RL post-train of Qwen2.5-Math-7B, released under MIT. The maker says that, before DeepSeek-R1, it was the strongest 7B math model they compared, and that an earlier checkpoint matched Eurus-2-7B-PRIME with much less RL. Status is active: the weights are still on Hugging Face and no retirement notice was found.", "what_it_is_for": "Mathematical reasoning. The blog's claim is about initialization: verified QwQ traces give a better start for PRIME-RL than the prior Eurus SFT, so the RL stage needs fewer steps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "prime-intellect/synthetic/synthetic-1-sft-7b": { "id": "prime-intellect/synthetic/synthetic-1-sft-7b", "name": "SYNTHETIC-1-SFT-7B", "maker": "prime-intellect", "family": "prime-intellect/synthetic", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-20", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Qwen2 supervised fine-tune of Qwen/Qwen2.5-7B-Instruct on the SYNTHETIC-1 SFT subset of verified DeepSeek-R1 reasoning traces. The release post says the run used about 800k samples. The same post also describes a curated SFT subset of about 900k samples. The dataset card for PrimeIntellect/SYNTHETIC-1-SFT-Data lists 894086 train examples. No architecture config with a context length was published on the model card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SYNTHETIC-1-SFT-7B is a named Prime Intellect model released with the SYNTHETIC-1 dataset on 20 February 2025. It is an Apache-2.0 supervised fine-tune of Qwen2.5-7B-Instruct on verified reasoning traces generated from DeepSeek-R1. The maker presents it as a cold-start reasoning checkpoint, not as a frontier general model. Status is active: the repo is still public and no retirement notice was found.", "what_it_is_for": "Show that the SYNTHETIC-1 SFT subset teaches reasoning. The release post says larger reasoning SFT sets beat smaller ones in their comparison, and that this checkpoint was trained on about 800k samples.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/autocompressor/autocompressor-1-3b-30k": { "id": "princeton/autocompressor/autocompressor-1-3b-30k", "name": "AutoCompressor-1.3b-30k", "maker": "princeton", "family": "princeton/autocompressor", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the family card metadata. Base is Meta OPT; that original license was not re-read.", "release_date": "2023-07-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "OPT-1.3B AutoCompressor listed on the family card. The 30k suffix is the long-context setting in the repo name. Token budget and summary size were not restated for this checkpoint on the Llama-2 card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AutoCompressor-1.3b-30k is a Princeton NLP AutoCompressor checkpoint for compressing long context into summary vectors. Weights are public. The family is documented on the Llama-2-7B-6k card. Collected 2026-10-15.", "what_it_is_for": "Long-context language model that compresses earlier segments into summary vectors instead of attending to the raw tokens. The paper evaluates perplexity and in-context learning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/autocompressor/autocompressor-2-7b-30k": { "id": "princeton/autocompressor/autocompressor-2-7b-30k", "name": "AutoCompressor-2.7b-30k", "maker": "princeton", "family": "princeton/autocompressor", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the family card metadata. Base is Meta OPT; that original license was not re-read.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "OPT-2.7B AutoCompressor listed on the family card. The 30k suffix is the long-context setting in the repo name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AutoCompressor-2.7b-30k is a Princeton NLP AutoCompressor checkpoint for compressing long context into summary vectors. Weights are public. The family is documented on the Llama-2-7B-6k card. Collected 2026-10-15.", "what_it_is_for": "Long-context language model that compresses earlier segments into summary vectors instead of attending to the raw tokens. The paper evaluates perplexity and in-context learning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/autocompressor/autocompressor-2-7b-6k": { "id": "princeton/autocompressor/autocompressor-2-7b-6k", "name": "AutoCompressor-2.7b-6k", "maker": "princeton", "family": "princeton/autocompressor", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the family card metadata. Base is Meta OPT; that original license was not re-read.", "release_date": "2023-07-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "OPT-2.7B AutoCompressor listed on the family card. The 6k suffix is the context setting in the repo name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AutoCompressor-2.7b-6k is a Princeton NLP AutoCompressor checkpoint for compressing long context into summary vectors. Weights are public. The family is documented on the Llama-2-7B-6k card. Collected 2026-10-15.", "what_it_is_for": "Long-context language model that compresses earlier segments into summary vectors instead of attending to the raw tokens. The paper evaluates perplexity and in-context learning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/autocompressor/autocompressor-llama-2-7b-6k": { "id": "princeton/autocompressor/autocompressor-llama-2-7b-6k", "name": "AutoCompressor-Llama-2-7b-6k", "maker": "princeton", "family": "princeton/autocompressor", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the Hugging Face metadata. The checkpoint is derived from Llama 2, whose community license still applies.", "release_date": "2023-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of meta-llama/Llama-2-7b-hf. Card: 15B RedPajama tokens, sequences of 6144 tokens, 50 summary vectors, summary accumulation, randomized segmenting, stop-gradients.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AutoCompressor-Llama-2-7b-6k is a Princeton NLP AutoCompressor checkpoint for compressing long context into summary vectors. Weights are public. The family is documented on the Llama-2-7B-6k card. Collected 2026-10-15.", "what_it_is_for": "Long-context language model that compresses earlier segments into summary vectors instead of attending to the raw tokens. The paper evaluates perplexity and in-context learning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/densephrases/densephrases-multi": { "id": "princeton/densephrases/densephrases-multi", "name": "densephrases-multi", "maker": "princeton", "family": "princeton/densephrases", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-09-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Phrase and query encoder based on BERT, from the DensePhrases open-domain QA system. The GitHub page says Hugging Face names that start with princeton-nlp load through the densephrases package.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "densephrases-multi is the Princeton NLP phrase retriever before task-specific query fine-tuning. The GitHub news line dated 2021-09-20 says the checkpoints were posted to the Hub. Collected 2026-10-15.", "what_it_is_for": "Open-domain phrase retrieval. The GitHub table labels these exact-match numbers as before any query-side fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/densephrases/densephrases-multi-query-multi": { "id": "princeton/densephrases/densephrases-multi-query-multi", "name": "densephrases-multi-query-multi", "maker": "princeton", "family": "princeton/densephrases", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-09-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Phrase and query encoder based on BERT, from the DensePhrases open-domain QA system. The GitHub page says Hugging Face names that start with princeton-nlp load through the densephrases package.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "densephrases-multi-query-multi is the multi-dataset query-tuned DensePhrases checkpoint the GitHub README uses for the demo. Collected 2026-10-15.", "what_it_is_for": "The GitHub table says this checkpoint is query-fine-tuned on multiple datasets and is the demo model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/goedel-prover/goedel-code-prover-8b": { "id": "princeton/goedel-prover/goedel-code-prover-8b", "name": "Goedel-Code-Prover-8B", "maker": "princeton", "family": "princeton/goedel-prover", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-25", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Lean 4 proof model for code verification. Base Qwen/Qwen3-8B. Card says supervised fine-tuning on decomposition trajectories, then GRPO with online Lean 4 rewards.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Goedel-Code-Prover-8B is a 2026 Goedel-LM / Princeton model that decomposes and proves Lean 4 specifications of code. License on the card is Apache-2.0. The Hub tree date 2026-03-25 is used as the release day. The paper id is 2603.19329. Collected 2026-10-15.", "what_it_is_for": "Given a Lean 4 theorem with a sorry, the card says the model should introduce small lemmas and prove the original theorem without sorry, inside a decompose-then-verify scaffold.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/goedel-prover/goedel-prover-dpo": { "id": "princeton/goedel-prover/goedel-prover-dpo", "name": "Goedel-Prover-DPO", "maker": "princeton", "family": "princeton/goedel-prover", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face metadata says MIT. Inherits the V1 card's Model Agreement badge issue via the SFT parent.", "release_date": "2025-02-11", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "DPO model trained on top of Goedel-Prover-SFT. Hugging Face model-size widget says 7B. Llama-family tags on the Hub page.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Goedel-Prover-DPO is the 7B preference-tuned follow-up to Goedel-Prover-SFT. The SFT card, not the short DPO card, prints its miniF2F numbers. Hub metadata license is MIT. Collected 2026-10-15.", "what_it_is_for": "Lean 4 whole-proof generation after DPO on the SFT prover. The DPO card says it passes 60% on miniF2F at pass@32. The SFT card table prints 60.2%.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/goedel-prover/goedel-prover-sft": { "id": "princeton/goedel-prover/goedel-prover-sft", "name": "Goedel-Prover-SFT", "maker": "princeton", "family": "princeton/goedel-prover", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face metadata says MIT. The card badge also says Model Agreement. Base is DeepSeek-Prover-V1.5-Base.", "release_date": "2025-02-11", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "7B whole-proof generator. Supervised fine-tune of DeepSeek-Prover-V1.5-Base on Goedel-Pset-v1 proofs. Lean 4. Princeton PLI with coauthors; weights are under the Goedel-LM org.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Goedel-Prover-SFT is the 7B Princeton-led open theorem prover introduced in February 2025. The card reports 57.6% pass@32 on miniF2F. License metadata and the model-agreement badge disagree. Collected 2026-10-15.", "what_it_is_for": "Generate Lean 4 proofs for formal math statements. The project also trains statement formalizers; those two 32B formalizer repos are not given folders in this pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/goedel-prover/goedel-prover-v2-32b": { "id": "princeton/goedel-prover/goedel-prover-v2-32b", "name": "Goedel-Prover-V2-32B", "maker": "princeton", "family": "princeton/goedel-prover", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-07-15", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Whole-proof Lean 4 model. Card base_model is Qwen/Qwen3-32B. Expert iteration plus reinforcement learning, scaffolded data, verifier-guided self-correction, and checkpoint averaging. Not a fine-tune of the 7B V1 checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Goedel-Prover-V2-32B is a Princeton PLI Goedel-LM theorem prover announced on 2025-07-15. The model card license tag is Apache-2.0. The arXiv paper is 2508.03613, posted 2025-08-05. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Formal theorem proving in Lean 4, including a self-correction mode that revises a proof using Lean compiler feedback. The card says two correction rounds move the output budget from about 32K to about 40K tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/goedel-prover/goedel-prover-v2-8b": { "id": "princeton/goedel-prover/goedel-prover-v2-8b", "name": "Goedel-Prover-V2-8B", "maker": "princeton", "family": "princeton/goedel-prover", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-07-15", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Whole-proof Lean 4 model. Card base_model is Qwen/Qwen3-8B. Expert iteration plus reinforcement learning, scaffolded data, verifier-guided self-correction, and checkpoint averaging. Not a fine-tune of the 7B V1 checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Goedel-Prover-V2-8B is a Princeton PLI Goedel-LM theorem prover announced on 2025-07-15. The model card license tag is Apache-2.0. The arXiv paper is 2508.03613, posted 2025-08-05. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Formal theorem proving in Lean 4, including a self-correction mode that revises a proof using Lean compiler feedback. The card says two correction rounds move the output budget from about 32K to about 40K tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/i1/i1-1b": { "id": "princeton/i1/i1-1b", "name": "i1-1B", "maker": "princeton", "family": "princeton/i1", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-22", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Text-to-image diffusion model from Zhuang Liu's Princeton lab (zlab). The GitHub release plan lists a 1024-resolution checkpoint plus intermediate 256 and 512 checkpoints for both sizes. PyTorch and JAX weights are both part of the release plan.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "i1-1B is a fully open text-to-image model from zlab at Princeton, announced with weights on 2026-06-22. License is MIT. The paper arXiv id is 2606.11289. Collected 2026-10-15.", "what_it_is_for": "Generate images from text at 1024 resolution. The 3B card says the model is the result of a public-data recipe and is meant to be studied and reproduced. The 1B card README is only a license and pipeline tag.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/i1/i1-3b": { "id": "princeton/i1/i1-3b", "name": "i1-3B", "maker": "princeton", "family": "princeton/i1", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-22", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Text-to-image diffusion model from Zhuang Liu's Princeton lab (zlab). The GitHub release plan lists a 1024-resolution checkpoint plus intermediate 256 and 512 checkpoints for both sizes. PyTorch and JAX weights are both part of the release plan.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "i1-3B is a fully open text-to-image model from zlab at Princeton, announced with weights on 2026-06-22. License is MIT. The paper arXiv id is 2606.11289. Collected 2026-10-15.", "what_it_is_for": "Generate images from text at 1024 resolution. The 3B card says the model is the result of a public-data recipe and is meant to be studied and reproduced. The 1B card README is only a license and pipeline tag.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/mabel/mabel-bert-base-uncased": { "id": "princeton/mabel/mabel-bert-base-uncased", "name": "mabel-bert-base-uncased", "maker": "princeton", "family": "princeton/mabel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MABEL gender-bias mitigation checkpoint. The bert-base card says training starts from bert-base-uncased. This repo name selects bert-base-uncased.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mabel-bert-base-uncased is a Princeton NLP MABEL encoder checkpoint for attenuating gender bias with entailment data. The bert-base card was read. The other three repos are on the princeton-nlp model list and were not opened one by one. Collected 2026-10-15.", "what_it_is_for": "The bert-base card says MABEL is for a better bias-performance tradeoff on intrinsic and extrinsic bias metrics, not as a general chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/mabel/mabel-bert-large-uncased": { "id": "princeton/mabel/mabel-bert-large-uncased", "name": "mabel-bert-large-uncased", "maker": "princeton", "family": "princeton/mabel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MABEL gender-bias mitigation checkpoint. The bert-base card says training starts from an encoder LM. This repo name selects bert-large-uncased.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mabel-bert-large-uncased is a Princeton NLP MABEL encoder checkpoint for attenuating gender bias with entailment data. The bert-base card was read. The other three repos are on the princeton-nlp model list and were not opened one by one. Collected 2026-10-15.", "what_it_is_for": "The bert-base card says MABEL is for a better bias-performance tradeoff on intrinsic and extrinsic bias metrics, not as a general chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/mabel/mabel-roberta-base": { "id": "princeton/mabel/mabel-roberta-base", "name": "mabel-roberta-base", "maker": "princeton", "family": "princeton/mabel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MABEL gender-bias mitigation checkpoint. The bert-base card says training starts from an encoder LM. This repo name selects roberta-base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mabel-roberta-base is a Princeton NLP MABEL encoder checkpoint for attenuating gender bias with entailment data. The bert-base card was read. The other three repos are on the princeton-nlp model list and were not opened one by one. Collected 2026-10-15.", "what_it_is_for": "The bert-base card says MABEL is for a better bias-performance tradeoff on intrinsic and extrinsic bias metrics, not as a general chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/mabel/mabel-roberta-large": { "id": "princeton/mabel/mabel-roberta-large", "name": "mabel-roberta-large", "maker": "princeton", "family": "princeton/mabel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MABEL gender-bias mitigation checkpoint. The bert-base card says training starts from an encoder LM. This repo name selects roberta-large.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mabel-roberta-large is a Princeton NLP MABEL encoder checkpoint for attenuating gender bias with entailment data. The bert-base card was read. The other three repos are on the princeton-nlp model list and were not opened one by one. Collected 2026-10-15.", "what_it_is_for": "The bert-base card says MABEL is for a better bias-performance tradeoff on intrinsic and extrinsic bias metrics, not as a general chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/prolong/llama-3-8b-prolong-512k-base": { "id": "princeton/prolong/llama-3-8b-prolong-512k-base", "name": "ProLong-512k-Base", "maker": "princeton", "family": "princeton/prolong", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 (Hugging Face license tag llama3)", "release_date": "2024-08-22", "context_length": 524288, "max_output_tokens": null, "parameters": "8B", "architecture": "Llama 3 8B continued from meta-llama/Meta-Llama-3-8B-Instruct. Decoder-only transformer. The 512K instruct card is the one that states the training recipe.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProLong-512k-Base is a Princeton NLP long-context Llama-3-8B checkpoint. Weights are public under the Llama 3 license tag. Hugging Face createdAt 2024-08-22 is used as the release day. Collected 2026-10-15.", "what_it_is_for": "Long-context continued-pre-training checkpoint at the 512K stage, before the UltraChat instruct tune. The family card describes the 20B-token 512K stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/prolong/llama-3-8b-prolong-512k-instruct": { "id": "princeton/prolong/llama-3-8b-prolong-512k-instruct", "name": "ProLong-512k-Instruct", "maker": "princeton", "family": "princeton/prolong", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 (Hugging Face license tag llama3)", "release_date": "2024-08-22", "context_length": 524288, "max_output_tokens": null, "parameters": "8B", "architecture": "Llama 3 8B continued from meta-llama/Meta-Llama-3-8B-Instruct. Decoder-only transformer. The 512K instruct card is the one that states the training recipe.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProLong-512k-Instruct is a Princeton NLP long-context Llama-3-8B checkpoint. Weights are public under the Llama 3 license tag. Hugging Face createdAt 2024-08-22 is used as the release day. Collected 2026-10-15.", "what_it_is_for": "The flagship ProLong instruct model. The card says continued training used 20B tokens at 64K and 20B tokens at 512K, then supervised fine-tuning on UltraChat. Maximum context window stated as 512K tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/prolong/llama-3-8b-prolong-64k-base": { "id": "princeton/prolong/llama-3-8b-prolong-64k-base", "name": "ProLong-64k-Base", "maker": "princeton", "family": "princeton/prolong", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 (Hugging Face license tag llama3)", "release_date": "2024-07-22", "context_length": 65536, "max_output_tokens": null, "parameters": "8B", "architecture": "Llama 3 8B continued from meta-llama/Meta-Llama-3-8B-Instruct. Decoder-only transformer. The 512K instruct card is the one that states the training recipe.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProLong-64k-Base is a Princeton NLP long-context Llama-3-8B checkpoint. Weights are public under the Llama 3 license tag. Hugging Face createdAt 2024-07-22 is used as the release day. Collected 2026-10-15.", "what_it_is_for": "First long-context continued-training stage. The family card says ProLong starts from Meta-Llama-3-8B-Instruct and trains 20B tokens on 64K data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/prolong/llama-3-8b-prolong-64k-instruct": { "id": "princeton/prolong/llama-3-8b-prolong-64k-instruct", "name": "ProLong-64k-Instruct", "maker": "princeton", "family": "princeton/prolong", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 (Hugging Face license tag llama3)", "release_date": "2024-07-21", "context_length": 65536, "max_output_tokens": null, "parameters": "8B", "architecture": "Llama 3 8B continued from meta-llama/Meta-Llama-3-8B-Instruct. Decoder-only transformer. The 512K instruct card is the one that states the training recipe.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProLong-64k-Instruct is a Princeton NLP long-context Llama-3-8B checkpoint. Weights are public under the Llama 3 license tag. Hugging Face createdAt 2024-07-21 is used as the release day. Collected 2026-10-15.", "what_it_is_for": "Instruct checkpoint of the 64K ProLong stage, fine-tuned on UltraChat. The family card's 512K maximum is stated for the main model, not repeated as a measured limit here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/queen/queen-hce-4": { "id": "princeton/queen/queen-hce-4", "name": "QUEEN HCE-4", "maker": "princeton", "family": "princeton/queen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-02", "context_length": 2304, "max_output_tokens": null, "parameters": "4B", "architecture": "SmolLM3-3B decoder (Apache-2.0) plus a Leela Chess Zero BT5 encoder, id BT5-1024x15x32h-rpe-swa-3700000. Board-conditioned custom code. Card tags include flamingo. The paper abstract calls Queen a 4B model. The card says AutoModelForCausalLM alone drops the board encoder.", "modalities": { "input": [ "text", "chess-position" ], "output": [ "text", "chess-move" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QUEEN HCE-4 is a Princeton NLP / PLI chess-language model released in October 2026. Weights and a custom inference runner are public. The card asserts no blanket license for the combined release. Paper date 2026-10-02 is used as the release day. Collected 2026-10-15.", "what_it_is_for": "Play legal chess moves and explain them. The card says explanations can be wrong and that this is not a UCI engine. Unparseable generations fall back to a seeded random legal move.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/queen/queen-pawn-8": { "id": "princeton/queen/queen-pawn-8", "name": "QUEEN PAWN-8", "maker": "princeton", "family": "princeton/queen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-02", "context_length": 2304, "max_output_tokens": null, "parameters": "4B", "architecture": "SmolLM3-3B decoder (Apache-2.0) plus a Leela Chess Zero BT5 encoder, id BT5-1024x15x32h-rpe-swa-3700000. Board-conditioned custom code. Card tags include flamingo. The paper abstract calls Queen a 4B model. The card says AutoModelForCausalLM alone drops the board encoder.", "modalities": { "input": [ "text", "chess-position" ], "output": [ "text", "chess-move" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QUEEN PAWN-8 is a Princeton NLP / PLI chess-language model released in October 2026. Weights and a custom inference runner are public. The card asserts no blanket license for the combined release. Paper date 2026-10-02 is used as the release day. Collected 2026-10-15.", "what_it_is_for": "Play legal chess moves and explain them. The card says explanations can be wrong and that this is not a UCI engine. Unparseable generations fall back to a seeded random legal move.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/qurater/qurater-1-3b": { "id": "princeton/qurater/qurater-1-3b", "name": "QuRater-1.3B", "maker": "princeton", "family": "princeton/qurater", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-20", "context_length": 512, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Sequence-classification model fine-tuned from Sheared-LLaMA-1.3B. Four logits, in order: Writing Style, Required Expertise, Facts and Trivia, Educational Value. Ratings are unnormalized.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QuRater-1.3B is Princeton NLP's text-quality rater from the QuRating paper. It is a public 1.3B sequence classifier. Hugging Face createdAt 2024-02-20 is used as the release day. No license is on the card. Collected 2026-10-15.", "what_it_is_for": "The card says it predicts four quality ratings used to select pre-training data. It is not a chat model. The card says not to use the ratings as a measure of social or literary value, and not to use them for demographic studies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/sheared-llama/sheared-llama-1-3B": { "id": "princeton/sheared-llama/sheared-llama-1-3B", "name": "Sheared-LLaMA-1.3B", "maker": "princeton", "family": "princeton/sheared-llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License required by the model card; Hugging Face metadata also tags apache-2.0", "release_date": "2023-10-10", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Llama-architecture causal LM, 1.3B, pruned from meta-llama/Llama-2-7b-hf and continued pre-trained. Same vocabulary as Llama 1 and Llama 2.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sheared-LLaMA-1.3B is a Princeton NLP Llama-2-7B derivative released with the Sheared LLaMA paper. Weights are public. Hugging Face createdAt used as the release day is 2023-10-10. Collected 2026-10-15.", "what_it_is_for": "Small base language model. The card says it was pruned from Llama-2-7B with 0.4B tokens and continued pre-trained for 50B tokens on RedPajama with dynamic domain loading.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/sheared-llama/sheared-llama-1-3B-sharegpt": { "id": "princeton/sheared-llama/sheared-llama-1-3B-sharegpt", "name": "Sheared-LLaMA-1.3B-ShareGPT", "maker": "princeton", "family": "princeton/sheared-llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License required by the model card; Hugging Face metadata also tags apache-2.0", "release_date": "2023-11-22", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Instruction-tuned 1.3B Sheared-LLaMA. Same vocabulary as Llama 1 and Llama 2. Pruned from Llama-2-7B, then continued pre-trained, then instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sheared-LLaMA-1.3B-ShareGPT is a Princeton NLP Llama-2-7B derivative released with the Sheared LLaMA paper. Weights are public. Hugging Face createdAt used as the release day is 2023-11-22. Collected 2026-10-15.", "what_it_is_for": "Instruction-tuned Sheared-LLaMA-1.3B. The card says it was trained on 10,000 first-turn ShareGPT instruction-response pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/sheared-llama/sheared-llama-2-7B": { "id": "princeton/sheared-llama/sheared-llama-2-7B", "name": "Sheared-LLaMA-2.7B", "maker": "princeton", "family": "princeton/sheared-llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License required by the model card; Hugging Face metadata also tags apache-2.0", "release_date": "2023-10-10", "context_length": null, "max_output_tokens": null, "parameters": "2.7B", "architecture": "Llama-architecture causal LM, 2.7B, pruned from meta-llama/Llama-2-7b-hf and continued pre-trained. Same vocabulary as Llama 1 and Llama 2.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sheared-LLaMA-2.7B is a Princeton NLP Llama-2-7B derivative released with the Sheared LLaMA paper. Weights are public. Hugging Face createdAt used as the release day is 2023-10-10. Collected 2026-10-15.", "what_it_is_for": "Small base language model. The card says it was pruned from Llama-2-7B with 0.4B tokens and continued pre-trained for 50B tokens on RedPajama with dynamic domain loading.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/sheared-llama/sheared-llama-2-7B-sharegpt": { "id": "princeton/sheared-llama/sheared-llama-2-7B-sharegpt", "name": "Sheared-LLaMA-2.7B-ShareGPT", "maker": "princeton", "family": "princeton/sheared-llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License required by the model card; Hugging Face metadata also tags apache-2.0", "release_date": "2023-11-22", "context_length": null, "max_output_tokens": null, "parameters": "2.7B", "architecture": "Instruction-tuned 2.7B Sheared-LLaMA. Same vocabulary as Llama 1 and Llama 2. Pruned from Llama-2-7B, then continued pre-trained, then instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sheared-LLaMA-2.7B-ShareGPT is a Princeton NLP Llama-2-7B derivative released with the Sheared LLaMA paper. Weights are public. Hugging Face createdAt used as the release day is 2023-11-22. Collected 2026-10-15.", "what_it_is_for": "Instruction-tuned Sheared-LLaMA. The 1.3B ShareGPT card says the base was trained on 10,000 first-turn ShareGPT pairs with a fixed assistant prompt. The 2.7B ShareGPT card was not opened; the base card lists it as the matching instruction-tuned release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/simcse/sup-simcse-bert-base-uncased": { "id": "princeton/simcse/sup-simcse-bert-base-uncased", "name": "Supervised SimCSE (BERT-base uncased)", "maker": "princeton", "family": "princeton/simcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sentence embedding model. Contrastive fine-tune of BERT-base uncased. Feature-extraction checkpoint. Pooler is the standard SimCSE [CLS] embedding used with cosine similarity.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Supervised SimCSE (BERT-base uncased) is a Princeton NLP sentence-embedding checkpoint from the EMNLP 2021 SimCSE paper. Weights are public. The model card does not state a license. The arXiv v1 date is 2021-04-18; the Hugging Face createdAt of 2022-03-02 is an org import, not used as the release day. Collected 2026-10-15.", "what_it_is_for": "The maker releases it to embed sentences for semantic textual similarity and retrieval. Supervised SimCSE is trained on MNLI and SNLI entailment and contradiction pairs. The model card says that pool is 314k pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/simcse/sup-simcse-bert-large-uncased": { "id": "princeton/simcse/sup-simcse-bert-large-uncased", "name": "Supervised SimCSE (BERT-large uncased)", "maker": "princeton", "family": "princeton/simcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sentence embedding model. Contrastive fine-tune of BERT-large uncased. Feature-extraction checkpoint. Pooler is the standard SimCSE [CLS] embedding used with cosine similarity.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Supervised SimCSE (BERT-large uncased) is a Princeton NLP sentence-embedding checkpoint from the EMNLP 2021 SimCSE paper. Weights are public. The model card does not state a license. The arXiv v1 date is 2021-04-18; the Hugging Face createdAt of 2022-03-02 is an org import, not used as the release day. Collected 2026-10-15.", "what_it_is_for": "The maker releases it to embed sentences for semantic textual similarity and retrieval. Supervised SimCSE is trained on MNLI and SNLI entailment and contradiction pairs. The model card says that pool is 314k pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/simcse/sup-simcse-roberta-base": { "id": "princeton/simcse/sup-simcse-roberta-base", "name": "Supervised SimCSE (RoBERTa-base)", "maker": "princeton", "family": "princeton/simcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sentence embedding model. Contrastive fine-tune of RoBERTa-base. Feature-extraction checkpoint. Pooler is the standard SimCSE [CLS] embedding used with cosine similarity.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Supervised SimCSE (RoBERTa-base) is a Princeton NLP sentence-embedding checkpoint from the EMNLP 2021 SimCSE paper. Weights are public. The model card does not state a license. The arXiv v1 date is 2021-04-18; the Hugging Face createdAt of 2022-03-02 is an org import, not used as the release day. Collected 2026-10-15.", "what_it_is_for": "The maker releases it to embed sentences for semantic textual similarity and retrieval. Supervised SimCSE is trained on MNLI and SNLI entailment and contradiction pairs. The model card says that pool is 314k pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/simcse/sup-simcse-roberta-large": { "id": "princeton/simcse/sup-simcse-roberta-large", "name": "Supervised SimCSE (RoBERTa-large)", "maker": "princeton", "family": "princeton/simcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sentence embedding model. Contrastive fine-tune of RoBERTa-large. Feature-extraction checkpoint. Pooler is the standard SimCSE [CLS] embedding used with cosine similarity.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Supervised SimCSE (RoBERTa-large) is a Princeton NLP sentence-embedding checkpoint from the EMNLP 2021 SimCSE paper. Weights are public. The model card does not state a license. The arXiv v1 date is 2021-04-18; the Hugging Face createdAt of 2022-03-02 is an org import, not used as the release day. Collected 2026-10-15.", "what_it_is_for": "The maker releases it to embed sentences for semantic textual similarity and retrieval. Supervised SimCSE is trained on MNLI and SNLI entailment and contradiction pairs. The model card says that pool is 314k pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/simcse/unsup-simcse-bert-base-uncased": { "id": "princeton/simcse/unsup-simcse-bert-base-uncased", "name": "Unsupervised SimCSE (BERT-base uncased)", "maker": "princeton", "family": "princeton/simcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sentence embedding model. Contrastive fine-tune of BERT-base uncased. Feature-extraction checkpoint. Pooler is the standard SimCSE [CLS] embedding used with cosine similarity.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Unsupervised SimCSE (BERT-base uncased) is a Princeton NLP sentence-embedding checkpoint from the EMNLP 2021 SimCSE paper. Weights are public. The model card does not state a license. The arXiv v1 date is 2021-04-18; the Hugging Face createdAt of 2022-03-02 is an org import, not used as the release day. Collected 2026-10-15.", "what_it_is_for": "The maker releases it to embed sentences for semantic textual similarity and retrieval. Unsupervised SimCSE trains the model to identify a sentence with itself, using dropout as the only noise. The card points at English Wikipedia; a sentence count printed on the card looks truncated and is not repeated here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/simcse/unsup-simcse-bert-large-uncased": { "id": "princeton/simcse/unsup-simcse-bert-large-uncased", "name": "Unsupervised SimCSE (BERT-large uncased)", "maker": "princeton", "family": "princeton/simcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sentence embedding model. Contrastive fine-tune of BERT-large uncased. Feature-extraction checkpoint. Pooler is the standard SimCSE [CLS] embedding used with cosine similarity.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Unsupervised SimCSE (BERT-large uncased) is a Princeton NLP sentence-embedding checkpoint from the EMNLP 2021 SimCSE paper. Weights are public. The model card does not state a license. The arXiv v1 date is 2021-04-18; the Hugging Face createdAt of 2022-03-02 is an org import, not used as the release day. Collected 2026-10-15.", "what_it_is_for": "The maker releases it to embed sentences for semantic textual similarity and retrieval. Unsupervised SimCSE trains the model to identify a sentence with itself, using dropout as the only noise. The card points at English Wikipedia; a sentence count printed on the card looks truncated and is not repeated here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/simcse/unsup-simcse-roberta-base": { "id": "princeton/simcse/unsup-simcse-roberta-base", "name": "Unsupervised SimCSE (RoBERTa-base)", "maker": "princeton", "family": "princeton/simcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sentence embedding model. Contrastive fine-tune of RoBERTa-base. Feature-extraction checkpoint. Pooler is the standard SimCSE [CLS] embedding used with cosine similarity.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Unsupervised SimCSE (RoBERTa-base) is a Princeton NLP sentence-embedding checkpoint from the EMNLP 2021 SimCSE paper. Weights are public. The model card does not state a license. The arXiv v1 date is 2021-04-18; the Hugging Face createdAt of 2022-03-02 is an org import, not used as the release day. Collected 2026-10-15.", "what_it_is_for": "The maker releases it to embed sentences for semantic textual similarity and retrieval. Unsupervised SimCSE trains the model to identify a sentence with itself, using dropout as the only noise. The card points at English Wikipedia; a sentence count printed on the card looks truncated and is not repeated here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/simcse/unsup-simcse-roberta-large": { "id": "princeton/simcse/unsup-simcse-roberta-large", "name": "Unsupervised SimCSE (RoBERTa-large)", "maker": "princeton", "family": "princeton/simcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sentence embedding model. Contrastive fine-tune of RoBERTa-large. Feature-extraction checkpoint. Pooler is the standard SimCSE [CLS] embedding used with cosine similarity.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Unsupervised SimCSE (RoBERTa-large) is a Princeton NLP sentence-embedding checkpoint from the EMNLP 2021 SimCSE paper. Weights are public. The model card does not state a license. The arXiv v1 date is 2021-04-18; the Hugging Face createdAt of 2022-03-02 is an org import, not used as the release day. Collected 2026-10-15.", "what_it_is_for": "The maker releases it to embed sentences for semantic textual similarity and retrieval. Unsupervised SimCSE trains the model to identify a sentence with itself, using dropout as the only noise. The card points at English Wikipedia; a sentence count printed on the card looks truncated and is not repeated here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/simpo/gemma-2-9b-it-simpo": { "id": "princeton/simpo/gemma-2-9b-it-simpo", "name": "gemma-2-9b-it-SimPO", "maker": "princeton", "family": "princeton/simpo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Card frontmatter says MIT; the description says Gemma. Base model is google/gemma-2-9b-it.", "release_date": "2024-07-16", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Causal LM. SimPO fine-tune of google/gemma-2-9b-it on princeton-nlp/gemma2-ultrafeedback-armorm. Training script is in the SimPO repo. The card says one run took about 100 minutes on 8 H100 GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "gemma-2-9b-it-SimPO is the Princeton NLP SimPO fine-tune of Gemma 2 9B Instruct and the checkpoint with a full score table on its card. Weights are public. Hugging Face createdAt 2024-07-16 is used as the release day. Collected 2026-10-15.", "what_it_is_for": "Preference-optimized chat model. SimPO drops the reference model used by DPO and adds a target reward margin. This checkpoint is the Gemma setting in the paper's release set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/simpo/llama-3-base-8b-sft-simpo": { "id": "princeton/simpo/llama-3-base-8b-sft-simpo", "name": "Llama-3-Base-8B-SFT-SimPO", "maker": "princeton", "family": "princeton/simpo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named SimPO release on the org. Card does not state specs. Hub tags include arxiv 1910.09700, which is the generic model-card citation, not the SimPO paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-Base-8B-SFT-SimPO is a named SimPO checkpoint on the princeton-nlp Hugging Face org, tied to the SimPO paper. The model card that was read does not state a license, size, or scores. Existence is from the org model list and the card page. Hugging Face createdAt 2024-05-24 is used as the upload day. Collected 2026-10-15.", "what_it_is_for": "Preference-optimized language model released with the SimPO paper. Comparison checkpoints that use DPO, IPO, KTO, ORPO, CPO, RRHF, SLiC-HF, or R-DPO are not given folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/simpo/llama-3-instruct-8b-simpo": { "id": "princeton/simpo/llama-3-instruct-8b-simpo", "name": "Llama-3-Instruct-8B-SimPO", "maker": "princeton", "family": "princeton/simpo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-05-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named SimPO release. Card text is a pointer to the preprint. Repo name is Llama-3-Instruct-8B. Parameter count not stated on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-Instruct-8B-SimPO is a named SimPO checkpoint on the princeton-nlp Hugging Face org, tied to the SimPO paper. The model card that was read does not state a license, size, or scores. Existence is from the org model list and the card page. Hugging Face createdAt 2024-05-17 is used as the upload day. Collected 2026-10-15.", "what_it_is_for": "Preference-optimized language model released with the SimPO paper. Comparison checkpoints that use DPO, IPO, KTO, ORPO, CPO, RRHF, SLiC-HF, or R-DPO are not given folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/simpo/llama-3-instruct-8b-simpo-v0-2": { "id": "princeton/simpo/llama-3-instruct-8b-simpo-v0-2", "name": "Llama-3-Instruct-8B-SimPO-v0.2", "maker": "princeton", "family": "princeton/simpo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-07-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Second named SimPO release. Card text is a pointer to the preprint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-Instruct-8B-SimPO-v0.2 is a named SimPO checkpoint on the princeton-nlp Hugging Face org, tied to the SimPO paper. The model card that was read does not state a license, size, or scores. Existence is from the org model list and the card page. Hugging Face createdAt 2024-07-06 is used as the upload day. Collected 2026-10-15.", "what_it_is_for": "Preference-optimized language model released with the SimPO paper. Comparison checkpoints that use DPO, IPO, KTO, ORPO, CPO, RRHF, SLiC-HF, or R-DPO are not given folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/simpo/mistral-7b-base-sft-simpo": { "id": "princeton/simpo/mistral-7b-base-sft-simpo", "name": "Mistral-7B-Base-SFT-SimPO", "maker": "princeton", "family": "princeton/simpo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-05-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named SimPO release. Hub pipeline tag is text-generation and the arXiv tag on the API record is the SimPO paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral-7B-Base-SFT-SimPO is a named SimPO checkpoint on the princeton-nlp Hugging Face org, tied to the SimPO paper. The model card that was read does not state a license, size, or scores. Existence is from the org model list and the card page. Hugging Face createdAt 2024-05-17 is used as the upload day. Collected 2026-10-15.", "what_it_is_for": "Preference-optimized language model released with the SimPO paper. Comparison checkpoints that use DPO, IPO, KTO, ORPO, CPO, RRHF, SLiC-HF, or R-DPO are not given folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/simpo/mistral-7b-instruct-simpo": { "id": "princeton/simpo/mistral-7b-instruct-simpo", "name": "Mistral-7B-Instruct-SimPO", "maker": "princeton", "family": "princeton/simpo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named SimPO release. The fetched card is an auto-generated template with no specs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mistral-7B-Instruct-SimPO is a named SimPO checkpoint on the princeton-nlp Hugging Face org, tied to the SimPO paper. The model card that was read does not state a license, size, or scores. Existence is from the org model list and the card page. Hugging Face createdAt 2024-05-24 is used as the upload day. Collected 2026-10-15.", "what_it_is_for": "Preference-optimized language model released with the SimPO paper. Comparison checkpoints that use DPO, IPO, KTO, ORPO, CPO, RRHF, SLiC-HF, or R-DPO are not given folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/swe-agent-lm/swe-agent-lm-32b": { "id": "princeton/swe-agent-lm/swe-agent-lm-32b", "name": "SWE-agent-LM-32B", "maker": "princeton", "family": "princeton/swe-agent-lm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-30", "context_length": 32768, "max_output_tokens": null, "parameters": "33B on the Hugging Face model-size widget; the release name is 32B", "architecture": "Qwen2 causal LM. Fine-tune of Qwen/Qwen2.5-Coder-32B-Instruct. The card says the data are SWE-agent trajectories of Claude 3.7 Sonnet on SWE-smith tasks.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SWE-agent-LM-32B is an open software-engineering model from the SWE-bench project started at Princeton and now joint with Stanford. Weights are Apache-2.0 on the model card. They are hosted by the SWE-bench org, not princeton-nlp. Collected 2026-10-15.", "what_it_is_for": "Resolve GitHub issues inside SWE-agent. The card says training was a straightforward fine-tune and that the model is compatible with SWE-agent.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/swe-agent-lm/swe-agent-lm-7b": { "id": "princeton/swe-agent-lm/swe-agent-lm-7b", "name": "SWE-agent-LM-7B", "maker": "princeton", "family": "princeton/swe-agent-lm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-07-13", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Qwen2 causal LM. Fine-tune of Qwen/Qwen2.5-Coder-7B-Instruct. The card says the data are SWE-agent trajectories of Claude 3.7 Sonnet on SWE-smith tasks.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SWE-agent-LM-7B is an open software-engineering model from the SWE-bench project started at Princeton and now joint with Stanford. Weights are Apache-2.0 on the model card. They are hosted by the SWE-bench org, not princeton-nlp. Collected 2026-10-15.", "what_it_is_for": "Resolve GitHub issues inside SWE-agent. The card says training was a straightforward fine-tune and that the model is compatible with SWE-agent.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/swe-llama/swe-llama-13b": { "id": "princeton/swe-llama/swe-llama-13b", "name": "SWE-Llama-13b", "maker": "princeton", "family": "princeton/swe-llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-10", "context_length": null, "max_output_tokens": null, "parameters": "13 billion", "architecture": "Code Llama transformer fine-tuned to emit patches for GitHub issues. The card says only attention matrices were trained with LoRA, for 4 epochs, batch size 32. Best checkpoint chosen by validation perplexity.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SWE-Llama-13b is a Princeton NLP Code Llama fine-tune from the SWE-bench paper. The card states 13 billion. Weights are public. License is not on the card. Hugging Face createdAt 2023-10-10 is used as the release day. Collected 2026-10-15.", "what_it_is_for": "The card says the model generates patches that resolve GitHub issues, conditioned on the issue text and code context. Training used 19,000 issues and pull requests from 37 Python repositories, disjoint from the SWE-bench test repos.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "princeton/swe-llama/swe-llama-7b": { "id": "princeton/swe-llama/swe-llama-7b", "name": "SWE-Llama-7b", "maker": "princeton", "family": "princeton/swe-llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-10", "context_length": null, "max_output_tokens": null, "parameters": "7 billion", "architecture": "Code Llama transformer fine-tuned to emit patches for GitHub issues. The card says only attention matrices were trained with LoRA, for 4 epochs, batch size 32. Best checkpoint chosen by validation perplexity.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SWE-Llama-7b is a Princeton NLP Code Llama fine-tune from the SWE-bench paper. The card states 7 billion. Weights are public. License is not on the card. Hugging Face createdAt 2023-10-10 is used as the release day. Collected 2026-10-15.", "what_it_is_for": "The card says the model generates patches that resolve GitHub issues, conditioned on the issue text and code context. Training used 19,000 issues and pull requests from 37 Python repositories, disjoint from the SWE-bench test repos.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "psu/beaver/beaver-general": { "id": "psu/beaver/beaver-general", "name": "Beaver-General", "maker": "psu", "family": "psu/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-10-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "scikit-learn random forest, n_estimators=100, max_depth=12, using the 30 general transcript features. Scores a candidate full-length transcript without a cell assignment.", "modalities": { "input": [ "tabular" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pennsylvania State University released Beaver-General, the first-stage random forest in the Beaver single-cell transcript assembler. Status on 2026-10-15: active. Confidence is high. The authors are Qian Shi, Qimin Zhang, and Mingfu Shao in Computer Science and Engineering. Weights are four dataset joblib files on Zenodo record 14014750, dated 30 October 2024.", "what_it_is_for": "The Bioinformatics paper says Beaver-General scores each candidate full-length transcript, not a transcript-cell pair, as a filter before Beaver-Specific. It uses 30 general features. Training labels come from chromosomes 1-9. The GitHub page says this stage can be used alone when only population-level expression is needed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "psu/beaver/beaver-specific": { "id": "psu/beaver/beaver-specific", "name": "Beaver-Specific", "maker": "psu", "family": "psu/beaver", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-10-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "scikit-learn random forest, n_estimators=100, max_depth=12, using all 51 features. Scores whether a candidate transcript is both correctly assembled and expressed in a given cell.", "modalities": { "input": [ "tabular" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pennsylvania State University released Beaver-Specific, the second-stage random forest in the Beaver single-cell transcript assembler. Status on 2026-10-15: active. Confidence is high. The authors are Qian Shi, Qimin Zhang, and Mingfu Shao in Computer Science and Engineering. Weights are four dataset joblib files on Zenodo record 14014750, dated 30 October 2024.", "what_it_is_for": "The paper says Beaver-Specific estimates the probability that a candidate transcript is both correctly assembled and expressed in a given cell. It uses all 51 features and the same forest settings as Beaver-General. The GitHub script also reads the Beaver-General score file. It is not a replacement for that first stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "purdue/coda-no/coda-no": { "id": "purdue/coda-no/coda-no", "name": "CoDA-NO", "maker": "purdue", "family": "purdue/coda-no", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2024-03-19", "context_length": null, "max_output_tokens": null, "parameters": "11000000", "architecture": "Codomain Attention Neural Operator. Each physical variable is concatenated with a variable-specific positional encoding, mapped by a graph neural operator from an irregular geometry onto a latent regular grid, passed through stacked codomain-attention layers that tokenize functions along the channel axis, then mapped back with another graph neural operator. The codomain-attention layer is also in the neuraloperator library.", "modalities": { "input": [ "function" ], "output": [ "function" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CoDA-NO is a neural operator for learning solution maps of multiphysics PDEs. The first author and a corresponding address are at Purdue computer science. Caltech and NVIDIA coauthors are on the same NeurIPS 2024 paper. Training code is public. A shipped pretrained checkpoint was not found. Collected 2026-10-15.", "what_it_is_for": "The paper says existing neural operators struggle when several physical fields interact on complex geometry and high-resolution data are scarce. CoDA-NO tokenizes functions along the codomain so one model can be pretrained on one PDE system and adapted to another. The reported settings are Navier-Stokes, fluid-structure interaction, and Rayleigh-Benard convection, plus a PDEBench subset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "purdue/husformer/husformer": { "id": "purdue/husformer/husformer", "name": "Husformer", "maker": "purdue", "family": "purdue/husformer", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal transformer for human-state recognition. Cross-modal attention lets each signal attend to the others, then a self-attention transformer scores the fused sequence. The lab repo has separate model files for 3, 4, and 5 modalities. The checked-in 3-modality file sets hidden width d_m to 30. The paper table and the README both list a recommended hidden size of 40.", "modalities": { "input": [ "time-series" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Husformer is a multimodal transformer from Purdue's SMART Lab, led by Byung-Cheol Min, for recognizing emotion and cognitive load from physiological and behavioral signals. The lab published the code. A pretrained checkpoint was not released, and the README says the GitHub copy is not the final experimental configuration. Collected 2026-10-15.", "what_it_is_for": "The paper uses Husformer to classify human state for human-machine systems. Evaluations are emotion on DEAP and WESAD, and cognitive load on MOCAS and CogLoad. The Purdue news note names EEG, EMG, galvanic skin response, and eye-blink rate as the signals in the lab's robot-team setting. Dataset files in the repo also cover those four corpora. Part of the code is based on the Multimodal Transformer repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "purdue/rwsa/regression-without-softarg": { "id": "purdue/rwsa/regression-without-softarg", "name": "Heatmap Regression without Soft-Argmax", "maker": "purdue", "family": "purdue/rwsa", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-08-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Four-stack hourglass heatmap network, the same backbone as STAR, with STAR's anisotropic attention module and an auxiliary edge-heatmap task. Soft-argmax and the ReLU heatmap normalization are removed. Training uses a deep structured-prediction loss with a smooth-L1 margin and image-aware label smoothing. Faces are cropped and resized to 256 by 256.", "modalities": { "input": [ "image" ], "output": [ "landmarks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Purdue computer science release from Chiao-An Yang and Raymond A. Yeh for facial landmark detection. It is a heatmap model trained with a structured-prediction loss instead of Soft-argmax. Weights are public and the ICCV 2025 paper is out. Collected 2026-10-15.", "what_it_is_for": "The paper says the model finds a fixed set of facial landmarks for downstream jobs such as head-pose estimation, expression analysis, and face swapping. The authors argue Soft-argmax is unnecessary and train the hourglass with a deep structured-prediction objective plus image-aware label smoothing. They keep STAR's network and data pipeline so the comparison is about the loss.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "qmul/remfx/remfx": { "id": "qmul/remfx/remfx", "name": "RemFX", "maker": "qmul", "family": "qmul/remfx", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub LICENSE file is the Apache License, Version 2.0. The appendix copyright line is still the unfilled template \"[yyyy] [name of copyright owner]\". Zenodo record 8218621, which hosts the checkpoints, lists license id cc-nc.", "release_date": "2023-08-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Compositional audio-effect removal. Five effect-specific networks are chained at inference. Distortion and dynamic-range compression use Hybrid Demucs. Reverb, delay, and chorus use DCUNet. Those networks were trained by the authors on their effect-removal data. A multi-label detector picks which specialists run. The paper's best detector is PANNs trained from scratch. RemFX Detect thresholds that detector at 0.5 and applies the selected specialists in random order. RemFX Oracle uses ground-truth labels and order. RemFX All runs every specialist. The paper says RemFX uses from 0 to 192M parameters depending on which effects are detected. A scaled Hybrid Demucs baseline of 751M is a comparison model, not a RemFX checkpoint. Experimental audio is mono, resampled to 48 kHz, in chunks of 262144 samples.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RemFX is a general-purpose audio-effect removal system from the Centre for Digital Music at Queen Mary University of London. Matthew Rice and Christian J. Steinmetz share first authorship. George Fazekas and Joshua D. Reiss are coauthors. It detects chorus, delay, distortion, dynamic-range compression, and reverb, then runs an effect-specific network for each detected effect. The preprint is arXiv:2308.16177, 30 August 2023, accepted to IEEE WASPAA 2023. Checkpoints were published on Zenodo the same day. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper defines general-purpose audio effect removal: recover a dry recording after an unknown subset of effects has been applied in an unknown order. RemFX mirrors that chain. A detector chooses specialists, and those specialists run in series. The README's single-file command is RemFX Detect.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "qmul/wave-u-net/wave-u-net": { "id": "qmul/wave-u-net/wave-u-net", "name": "Wave-U-Net", "maker": "qmul", "family": "qmul/wave-u-net", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub LICENSE file is the MIT License, Copyright (c) 2018 Daniel Stoller. Pretrained weights are the Dropbox archive linked from that repository. The licence text inside the archive was not opened.", "release_date": "2018-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "1D U-Net for end-to-end waveform source separation. The paper's base model uses L=12 levels, Fc=24 extra filters per layer, downsampling filter size 15, and upsampling filter size 5. Each level halves time resolution by decimation. Upsampling is linear interpolation followed by convolution. M4 adds the difference output layer, extra input context, and stereo input and output. M6 is that stereo vocal model applied to vocals, bass, drums, and other. M5-HighSR is the later vocal model at 44.1 kHz with learned upsampling. Paper singing-voice models other than the 8192 Hz comparison pair are downsampled to 22050 Hz. Context models M3 through M6 use 147443 input samples and 16389 output samples. The comparison model M7 was sized to about 20M parameters to match a spectrogram U-Net; that count is not the size of the released checkpoints.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wave-U-Net is an end-to-end music source-separation network from Daniel Stoller and Simon Dixon at Queen Mary University of London, with Sebastian Ewert at Spotify. It separates a mixture waveform into source waveforms. The arXiv version is dated 8 June 2018 and appeared at ISMIR 2018. The author repository releases three pretrained checkpoints. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents Wave-U-Net as singing-voice and multi-instrument separation computed on the waveform, without a fixed spectrogram front end. The repository's pretrained files are the stereo vocal model M4, the multi-instrument model M6, and a later 44.1 kHz vocal model, M5-HighSR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "qmul/yourmt3/yourmt3-plus": { "id": "qmul/yourmt3/yourmt3-plus", "name": "YourMT3+", "maker": "qmul", "family": "qmul/yourmt3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The Hugging Face model card mimbres/YourMT3 says apache-2.0, and Space source headers say Apache License 2.0. The GitHub repository mimbres/YourMT3 displays a GPL-3.0 license badge. Those public statements disagree.", "release_date": "2024-07-05", "context_length": null, "max_output_tokens": 256, "parameters": "45.8M", "architecture": "Suite for multi-instrument automatic music transcription. The flagship YPTF.MoE+Multi uses a PerceiverTF encoder with spectral cross-attention, RoPE, pre-RMSNorm, and a mixture of experts that routes to 2 of 8 experts, plus a multi-channel T5-style autoregressive decoder. Supplemental Table 7 lists 15 encoder layers and 12 decoder layers, 45.8M parameters (20.3M encoder, 25.7M decoder). The multi decoder uses 26 latents grouped into 13 program channels and a per-channel maximum of 256 tokens. YMT3 and YMT3+ keep an MT3-style T5-small v1.1 encoder and are listed at 44.7M. YPTF+Single and YPTF+Multi are listed at 29.9M. Audio in the paper is 2.048-second, 16 kHz mono. YMT3 uses a log-mel spectrogram of 256 time steps and 512 bins. YPTF uses a log-magnitude spectrogram of 110 time steps and 1024 bins, then a 2D ResNet pre-encoder.", "modalities": { "input": [ "audio" ], "output": [ "symbolic-music" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YourMT3+ is a suite of multi-instrument music transcription models from the Centre for Digital Music at Queen Mary University of London, with Holger Kirchhoff at Huawei. It turns polyphonic audio into MIDI-like note tokens. The arXiv paper is dated 5 July 2024, revised 1 August 2024. Five checkpoints were public on Hugging Face on 23 June 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents YourMT3+ as a way to transcribe several instruments, including singing, from a mixture without a separate singing-voice separation front end. It builds on the authors' from-scratch MT3 replication, called YourMT3, and on PerceiverTF. The flagship they describe is YPTF.MoE+Multi: a PerceiverTF encoder with a mixture of experts and a multi-channel decoder, trained with cross-dataset stem mixing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "queens/cardiogan/cardiogan": { "id": "queens/cardiogan/cardiogan", "name": "CardioGAN", "maker": "queens", "family": "queens/cardiogan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub LICENSE file is CC BY-NC 4.0 for the work in that repository. The arXiv record shows CC BY-NC-SA 4.0 for the article. A separate weights licence is not stated.", "release_date": "2020-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unpaired CycleGAN-style network with attention U-Net generators and dual discriminators. Generators take 1x512 samples (4 seconds at 128 Hz). Each encoder has 6 blocks, filters 64, 128, 256, 512, 512, 512, kernel 1x16, stride 2. Time-domain discriminators take 1x512; frequency-domain discriminators take 128x128 spectrograms. Each discriminator has 4 conv layers with filters 64, 128, 256, 512. Loss coefficients alpha=3, beta=1, lambda=30. Trained in TensorFlow 2.2. Some code is borrowed from CycleGAN TensorFlow 2; the generators and dual discriminators are the authors' architecture, not a fine-tune of a published CycleGAN checkpoint.", "modalities": { "input": [ "ppg" ], "output": [ "ecg" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CardioGAN is a PPG-to-ECG generative model from Pritam Sarkar and Ali Etemad in Electrical and Computer Engineering and Ingenuity Labs at Queen's University, Kingston. It is an unpaired attention GAN, not a fine-tune of a published CycleGAN checkpoint. Generator weights and a full checkpoint were still linked on GitHub on 2026-10-15. The arXiv v1 date is 30 September 2020. The record says it was accepted at AAAI 2021.", "what_it_is_for": "The paper proposes CardioGAN to generate ECG from wearable PPG so heart-rate estimates can use the generated ECG instead of the optical signal. Training is unpaired and multi-corpus. The public test script converts PPG to ECG. A realtime script uses 4-second segments from an Empatica E4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "queens/objectbox/objectbox": { "id": "queens/objectbox/objectbox", "name": "ObjectBox", "maker": "queens", "family": "queens/objectbox", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub LICENSE file is GPL-3.0 for the program. The arXiv record uses the arXiv nonexclusive distribution licence 1.0 for the article. A separate weights licence is not stated.", "release_date": "2022-07-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single-stage anchor-free detector. Positive samples are object-center cells only. Regression targets are distances from two corners of the center cell to the four box sides. One prediction per location per scale at strides 8, 16, and 32, with an objectness score and a class label. Box loss is SDIoU with rho set to 1. Paper backbones are CSPDarknet with spatial pyramid pooling, and ResNet-101. Training uses SGD, learning rate 0.01, momentum 0.937, weight decay 0.0005, 3 warmup epochs, batch 24, maximum 300 epochs, early-stop patience 30, NMS 0.6, one Titan RTX. Utility code is borrowed from YOLOv5. The detector is the authors' model, not a fine-tune of a YOLO weight release.", "modalities": { "input": [ "image" ], "output": [ "bounding-box", "class-label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ObjectBox is a single-stage anchor-free object detector from Mohsen Zand, Ali Etemad, and Michael Greenspan at Ingenuity Labs, Queen's University, Kingston. It is not a YOLO fine-tune. The code borrows utility functions from YOLOv5 and is GPL-3.0. COCO and PASCAL VOC checkpoints were still linked from the authors' GitHub README on 2026-10-15, on Queen's SharePoint. The arXiv date is 14 July 2022. The record says ECCV 2022 oral.", "what_it_is_for": "The paper presents ObjectBox for general object detection on MS-COCO 2017 and PASCAL VOC 2012. Only the center cell of each object is a positive sample, at strides 8, 16, and 32, so the label assignment does not depend on a dataset-specific anchor set. The README trains with `exp = 'coco'` or `exp = 'pascal'` and evaluates with `val.py`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "radboud/crlm/crlm-survival": { "id": "radboud/crlm/crlm-survival", "name": "CRLM desmoplastic status and survival prediction", "maker": "radboud", "family": "radboud/crlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2026-07-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card compresses the whole-slide image with an MTDP ResNet50 encoder at 2.0 micrometre spacing into a 2048-channel feature map, then uses a gated-attention network for a slide-level prediction (attention-based multiple instance learning). paper_models holds a 5-fold desmoplastic classifier (No-HGP versus HGP) and the shared encoder file resnet50-mh-best-191205-141200.pth. os_models holds a 5-fold overall-survival model. Both ensembles share that one encoder. The five folds are not dated product versions. The 191205 suffix was not verified as a release date.", "modalities": { "input": [ "image" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Computational Pathology Group at Radboud University Medical Center published a gated Hugging Face card on 9 July 2026 for slide-level desmoplastic growth-pattern status and overall survival on colorectal liver metastasis slides. The card was still public, behind a contact-share gate, on the collection date 2026-10-15. There is no priced API.", "what_it_is_for": "The card predicts desmoplastic histopathological growth pattern status (No-HGP versus HGP) and overall survival from hematoxylin and eosin whole-slide images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "radboud/crlm/crlm-tissue-segmentation": { "id": "radboud/crlm/crlm-tissue-segmentation", "name": "CRLM liver tissue segmentation", "maker": "radboud", "family": "radboud/crlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2026-07-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "U-Net with a Keras backend, depth 5, branching factor 5, batch normalization, and valid padding. Trained with categorical cross-entropy on 412 by 412 patches at 0.5 micrometre pixel spacing, RGB normalized to 0 to 1. Inference uses applynetwork_multiproc.py from pathology-fast-inference. Two networks run in sequence: bg_network.net (28 MB) does background versus tissue at 2.0 micrometre spacing, then nn1_best_model.net (87 MB) does multi-class tissue segmentation at 0.5 micrometre spacing. Those sizes are file sizes, not parameter counts. The card says the code maps mask labels to 10 output classes and that the class definitions should be confirmed.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Computational Pathology Group at Radboud University Medical Center published a gated Hugging Face card on 9 July 2026 for a U-Net that segments hematoxylin and eosin slides of colorectal liver metastases. The card was still public, behind a contact-share gate, on the collection date 2026-10-15. There is no priced API.", "what_it_is_for": "The card says the model makes a tissue mask on whole-slide images of colorectal liver metastases, and that this mask is used upstream of the desmoplastic-status and survival card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "radboud/dragon/dragon-bert-base-domain-specific": { "id": "radboud/dragon/dragon-bert-base-domain-specific", "name": "DRAGON BERT base domain-specific", "maker": "radboud", "family": "radboud/dragon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2024-05-03", "context_length": 512, "max_output_tokens": null, "parameters": "124696665", "architecture": "BERT-base masked language model, BertForMaskedLM. The card says it was pretrained from scratch on Dutch clinical reports. config.json: 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab_size 50265, max_position_embeddings 512, type_vocab_size 2, absolute positions. The tokenizer was fit on the reports using RoBERTa-base tokenizer settings. Card pretraining: MLM 15 percent, 10 epochs, learning rate 6e-4, effective batch 256, max_seq_length 512, Adam, linear schedule. Hub safetensors: F32 124696665 and I64 512, total 124697177. The parameter used here is the F32 count. config _name_or_path is GroNLP/bert-base-dutch-cased, but this vocab and F32 count are not BERTje. The card table lists 109M, which matches the mixed-domain BERT file, not this one.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Radboudumc Diagnostic Image Analysis Group, with the DRAGON consortium, released this Dutch clinical BERT as an open weight file on 3 May 2024. It is a from-scratch masked language model for hospital reports, still downloadable on the collection date 2026-10-15. It is not a hosted API.", "what_it_is_for": "The card says the checkpoint learns Dutch medical language from raw clinical reports so it can be fine-tuned for sequence classification, token classification, or question answering. It is not offered as a text generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "radboud/dragon/dragon-longformer-base-domain-specific": { "id": "radboud/dragon/dragon-longformer-base-domain-specific", "name": "DRAGON Longformer base domain-specific", "maker": "radboud", "family": "radboud/dragon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2024-05-03", "context_length": 4096, "max_output_tokens": null, "parameters": "148711257", "architecture": "Longformer-base masked language model, LongformerForMaskedLM. The card says it was pretrained from scratch on Dutch clinical reports. The architecture matches allenai/longformer-base-4096. config.json: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab_size 50265, max_position_embeddings 4098, attention_window 512 on every layer. config _name_or_path is allenai/longformer-base-4096. Pretraining: MLM 15 percent, 10 epochs, learning rate 6e-4, effective batch 256, max_seq_length 4096. Hub safetensors: F32 148711257 and no I64 tensor. The card table rounds this to 149M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Radboudumc Diagnostic Image Analysis Group, with the DRAGON consortium, released this Dutch clinical Longformer on 3 May 2024. The card says it was trained from scratch for reports longer than a 512-token BERT. It was still downloadable on 2026-10-15. There is no hosted API.", "what_it_is_for": "The card says it is a long-context encoder for Dutch clinical reports, meant to be fine-tuned for classification, token classification, or question answering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "radboud/dragon/dragon-longformer-large-domain-specific": { "id": "radboud/dragon/dragon-longformer-large-domain-specific", "name": "DRAGON Longformer large domain-specific", "maker": "radboud", "family": "radboud/dragon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2024-05-03", "context_length": 4096, "max_output_tokens": null, "parameters": "434653273", "architecture": "Longformer-large masked language model, LongformerForMaskedLM. The card says it was pretrained from scratch on Dutch clinical reports. The architecture matches allenai/longformer-large-4096. config.json: 24 layers, hidden 1024, 16 heads, intermediate 4096, vocab_size 50265, max_position_embeddings 4098, attention_window 512 on every layer. config _name_or_path is allenai/longformer-large-4096. Pretraining: MLM 15 percent, 10 epochs, learning rate 1e-4, per-device batch 4, gradient accumulation 64, effective batch 256, max_seq_length 4096. Hub safetensors: F32 434653273 and no I64 tensor. The card table rounds this to 435M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Radboudumc Diagnostic Image Analysis Group, with the DRAGON consortium, released this Dutch clinical Longformer large on 3 May 2024. The card says it was trained from scratch at a 4096-token length. It was still downloadable on 2026-10-15. There is no hosted API.", "what_it_is_for": "The card says it is a long-context encoder for Dutch clinical reports, meant to be fine-tuned for report-level or token-level tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "radboud/dragon/dragon-roberta-base-domain-specific": { "id": "radboud/dragon/dragon-roberta-base-domain-specific", "name": "DRAGON RoBERTa base domain-specific", "maker": "radboud", "family": "radboud/dragon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2024-04-29", "context_length": 512, "max_output_tokens": null, "parameters": "124697433", "architecture": "RoBERTa-base masked language model, RobertaForMaskedLM. The card says it was pretrained from scratch on Dutch clinical reports and that the architecture is the same as xlm-roberta-base. config.json is model_type roberta, not xlm-roberta: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab_size 50265, max_position_embeddings 514, type_vocab_size 1. xlm-roberta-base uses a much larger vocabulary, so the 'same architecture' sentence and the config disagree. The tokenizer was fit on the reports with RoBERTa-base tokenizer settings. config _name_or_path is roberta-base. Pretraining: MLM 15 percent, 10 epochs, learning rate 6e-4, effective batch 256, max_seq_length 512. Hub safetensors: F32 124697433 and I64 514, total 124697947. The parameter used here is the F32 count. The card table lists 278M, which matches the mixed-domain XLM-RoBERTa base file, not this one.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Radboudumc Diagnostic Image Analysis Group, with the DRAGON consortium, released this Dutch clinical RoBERTa on 29 April 2024. The card says it was trained from scratch on hospital reports. It was still downloadable on 2026-10-15. It is not a hosted API.", "what_it_is_for": "The card says the checkpoint is a bidirectional encoder for Dutch clinical reports and is meant to be fine-tuned for classification, token labeling, or question answering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "radboud/dragon/dragon-roberta-large-domain-specific": { "id": "radboud/dragon/dragon-roberta-large-domain-specific", "name": "DRAGON RoBERTa large domain-specific", "maker": "radboud", "family": "radboud/dragon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2024-05-03", "context_length": 512, "max_output_tokens": null, "parameters": "355412057", "architecture": "RoBERTa-large masked language model, RobertaForMaskedLM. The card says it was pretrained from scratch on Dutch clinical reports and that the architecture is the same as xlm-roberta-large. config.json is model_type roberta: 24 layers, hidden 1024, 16 heads, intermediate 4096, vocab_size 50265, max_position_embeddings 514, type_vocab_size 1. config _name_or_path is roberta-large. Pretraining: MLM 15 percent, 10 epochs, learning rate 1e-4, per-device batch 8, gradient accumulation 32, effective batch 256, max_seq_length 512. Hub safetensors: F32 355412057 and I64 514, total 355412571. The parameter used here is the F32 count. The card table lists 560M, which matches the mixed-domain XLM-RoBERTa large file, not this one.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Radboudumc Diagnostic Image Analysis Group, with the DRAGON consortium, released this Dutch clinical RoBERTa large on 3 May 2024. The 17 May 2025 paper calls this domain-specific pretraining the strongest of the reported encoders. The weights were still public on 2026-10-15. There is no hosted API.", "what_it_is_for": "The card says it is a masked language model for Dutch clinical reports, meant to be fine-tuned for report-level or token-level decisions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "radboud/gleason/gleason-grading": { "id": "radboud/gleason/gleason-grading", "name": "Automated Gleason Grading", "maker": "radboud", "family": "radboud/gleason", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2020-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted deep-learning system for Gleason grading of prostate biopsies. The group page describes a four-step job: a tissue mask from the B\u00e1ndi 2017 segmentation system, a per-job CycleGAN stain normalization from de Bel 2019, application of that normalization, then the Gleason network. Output is a gland overlay plus biopsy-level grade group, Gleason score, and predicted volume percentages. Overlay colors on the page are green benign, yellow Gleason 3, orange Gleason 4, and red Gleason 5. No public weight file was found. The repository.ubn.ru.nl link on the group page is the paper download.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Computational Pathology Group at Radboud University Medical Center hosts a named deep-learning system that grades prostate biopsies on the Gleason scale. The arXiv preprint is 18 July 2019. The arXiv journal line says The Lancet Oncology was available online on 8 January 2020, and the group page cites Lancet Oncology 2020;21(2):233-241. The algorithm was still offered on the group site on the collection date 2026-10-15. Weights were not a public file. There is no priced API.", "what_it_is_for": "The group page says the system grades prostate biopsies and marks glands as benign or Gleason 3, 4, or 5. It also returns the biopsy-level grade group, the Gleason score, and predicted volume percentages. The page lists it as part of the Deep PCA project.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "radboud/hooknet/hooknet": { "id": "radboud/hooknet/hooknet", "name": "HookNet", "maker": "radboud", "family": "radboud/hooknet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-06-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-resolution encoder-decoder CNN for H&E whole-slide segmentation. Concentric patches at two resolutions feed separate branches, and decoder features are combined by a hooking mechanism. The preprint demonstrates breast tissue classes and lung tertiary lymphoid structures and germinal centers. The public lung file is lung_weights.h5 (203.2 MB) in HDF5, which is a file size, not a parameter count. Code is the TensorFlow package pathology-hooknet. A later PyTorch example exists in that repository; this pass did not confirm that the Zenodo file is the PyTorch checkpoint.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Computational Pathology Group at Radboud University Medical Center released HookNet, a multi-resolution CNN for H&E whole-slide segmentation. The preprint is dated 22 June 2020. A lung weight file was posted on Zenodo on 21 June 2023, and breast and lung algorithms still run on grand-challenge.org as of the collection date 2026-10-15.", "what_it_is_for": "The paper uses HookNet where tissue labels depend on both fine detail and wider context: breast tissue typing, and lung tertiary lymphoid structures and germinal centers. The group page lists breast classes as ductal carcinoma in situ, invasive ductal carcinoma, invasive lobular carcinoma, non-malignant epithelium, fat, and other tissue.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "radboud/renal-net/renal-net": { "id": "radboud/renal-net/renal-net", "name": "Renal-Net", "maker": "radboud", "family": "radboud/renal-net", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "nnU-Net ResEnc L, 3D full resolution, trained by the DIAG oncology group for kidney and renal-mass segmentation. Labels are 0 background, 1 kidney, 2 renal mass not further specified as cystic or solid. Inference can drop components smaller than 3 mm and keep a component that is not attached to kidney only when it is larger than 100,000 mm^3. Optional cropping uses TotalSegmentator to find bladder and lungs. TotalSegmentator is a separate tool, not this checkpoint. CT and MRI weights are two files of the same named model.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Diagnostic Image Analysis Group at Radboud University Medical Center released Renal-Net, an nnU-Net that segments kidney and renal mass on CT and MRI. The GitHub README links separate Zenodo weight archives and a research CT algorithm on grand-challenge.org. Collected 2026-10-15. The Zenodo pages themselves did not load, so the upload day is unknown. There is no priced API.", "what_it_is_for": "The README says the model segments kidneys and kidney abnormalities. Labels are background, kidney, and renal mass that is not further split into cystic or solid. The cited paper is a CT validation study in MELBA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rakuten/rakuten-ai/rakuten-ai-7b": { "id": "rakuten/rakuten-ai/rakuten-ai-7b", "name": "Rakuten AI 7B", "maker": "rakuten", "family": "rakuten/rakuten-ai", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-21", "context_length": 32768, "max_output_tokens": null, "parameters": "7.37B", "architecture": "MistralForCausalLM continued from Mistral-7B-v0.1. config.json: 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, vocab 48000, sliding_window 4096, max_position_embeddings 32768, BF16. The card says the tokenizer was extended from 32k to 48k. The technical report says the released models were trained on approximately 175 billion tokens of filtered data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rakuten Group released Rakuten AI 7B on 21 March 2024 as an open-weight Japanese and English foundation model. It is still listed on the Rakuten AI model page and the weights remain on Hugging Face. This dossier was collected on 2026-10-15.", "what_it_is_for": "Rakuten describes it as a foundation model for Japanese LLM work: continual training of Mistral-7B-v0.1 on filtered Japanese and English text, with a larger Japanese tokenizer. The March 2024 press release says it can be used for summarization, question answering, text understanding, dialogue systems, and as a base for other models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rakuten/rakuten-ai/rakuten-ai-7b-chat": { "id": "rakuten/rakuten-ai/rakuten-ai-7b-chat", "name": "Rakuten AI 7B Chat", "maker": "rakuten", "family": "rakuten/rakuten-ai", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-21", "context_length": 32768, "max_output_tokens": null, "parameters": "7.37B", "architecture": "MistralForCausalLM continued from Mistral-7B-v0.1. config.json: 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, vocab 48000, sliding_window 4096, max_position_embeddings 32768, BF16. The card says the tokenizer was extended from 32k to 48k. The technical report says the released models were trained on approximately 175 billion tokens of filtered data. Chat-tuned. The card provides a chat template. The Hugging Face API reports MistralForCausalLM and 7372804096 BF16 parameters, the same count as the foundation checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rakuten Group released Rakuten AI 7B Chat on 21 March 2024 as the conversational checkpoint in the first Rakuten AI 7B suite. The weights are still at `Rakuten/RakutenAI-7B-chat` under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The press release calls it a chat model based on a version of the instruct model and fine-tuned on chat data. The model card instead says both the instruct and chat models were fine-tuned from the foundation model, using a mix of open and internal data, and it ships a chat template. The default system line on the card asks for helpful, detailed, and polite answers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rakuten/rakuten-ai/rakuten-ai-7b-chat-onnx": { "id": "rakuten/rakuten-ai/rakuten-ai-7b-chat-onnx", "name": "Rakuten AI 7B Chat ONNX", "maker": "rakuten", "family": "rakuten/rakuten-ai", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-27", "context_length": null, "max_output_tokens": null, "parameters": "7.37B class, INT4", "architecture": "INT4 weight-only ONNX export of a chat model built from RakutenAI-7B by continual pre-training, supervised fine-tuning, and preference optimization. Mistral architecture, vocabulary extended from 32k to 48k. Five execution-provider builds. The repository has no config.json, so layer sizes were not re-read from this repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rakuten published RakutenAI-7B-chat-onnx on Hugging Face as an INT4 ONNX chat model for on-device inference. The repository was created on 27 July 2026. It is a named Rakuten release, separate from the 2024 BF16 chat checkpoint. Collected on 2026-10-15.", "what_it_is_for": "The card says it is for efficient on-device inference across CPU, GPU, Intel GPU, Intel NPU, and AMD NPU execution providers, through Microsoft Foundry Local. Rakuten's build path is continual pre-training of Rakuten AI 7B, then supervised chat tuning, preference optimization, INT4 quantization, and ONNX export.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rakuten/rakuten-ai/rakuten-ai-7b-instruct": { "id": "rakuten/rakuten-ai/rakuten-ai-7b-instruct", "name": "Rakuten AI 7B Instruct", "maker": "rakuten", "family": "rakuten/rakuten-ai", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-21", "context_length": 32768, "max_output_tokens": null, "parameters": "7.37B", "architecture": "MistralForCausalLM continued from Mistral-7B-v0.1. config.json: 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, vocab 48000, sliding_window 4096, max_position_embeddings 32768, BF16. The card says the tokenizer was extended from 32k to 48k. The technical report says the released models were trained on approximately 175 billion tokens of filtered data. Instruction-tuned from RakutenAI-7B. The instruct card does not restate layer sizes; the Hugging Face API reports the same MistralForCausalLM class and the same 7372804096 BF16 parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rakuten Group released Rakuten AI 7B Instruct on 21 March 2024 with the 7B foundation model. It is the instruction-tuned checkpoint, still hosted at `Rakuten/RakutenAI-7B-instruct` under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The March 2024 press release says an instruct model is the foundation model fine-tuned on instruction-style data so it replies to prompted instructions. The card lists open datasets used alongside internal data, including JSNLI, RTE, KUCI, Belebele, JCS, JNLI, Dolly-15K, and OpenAssistant1. The prompt format in the card is a system line plus `USER:` and `ASSISTANT:`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rakuten/rakuten-ai-2/rakuten-ai-2-0-8x7b": { "id": "rakuten/rakuten-ai-2/rakuten-ai-2-0-8x7b", "name": "Rakuten AI 2.0", "maker": "rakuten", "family": "rakuten/rakuten-ai-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-12", "context_length": 32768, "max_output_tokens": null, "parameters": "46.8B total, 13B active", "architecture": "MixtralForCausalLM, 8 local experts, 2 experts per token. config.json: 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, vocab 48000, sliding_window 4096, max_position_embeddings 32768, BF16. Safetensors total 46833864704 parameters. The card says 13B active parameters. Derived from RakutenAI-7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rakuten Group announced Rakuten AI 2.0 on 18 December 2024 and released the foundation weights on 12 February 2025 as `Rakuten/RakutenAI-2.0-8x7B`. It is an open-weight Mixture-of-Experts model continued from Rakuten AI 7B. Collected on 2026-10-15.", "what_it_is_for": "Rakuten presents it as a Japanese-and-English foundation model that routes each token to two of eight experts so it can keep quality while activating fewer parameters. The February 2025 press release says the experts and the router were trained together on Japanese and English text. The instruct checkpoint is a separate folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rakuten/rakuten-ai-2/rakuten-ai-2-0-8x7b-instruct": { "id": "rakuten/rakuten-ai-2/rakuten-ai-2-0-8x7b-instruct", "name": "Rakuten AI 2.0 Instruct", "maker": "rakuten", "family": "rakuten/rakuten-ai-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-12", "context_length": 32768, "max_output_tokens": null, "parameters": "46.8B total, 13B active", "architecture": "MixtralForCausalLM, 8 local experts, 2 experts per token. config.json: 32 layers, hidden 4096, 32 heads, 8 KV heads, intermediate 14336, vocab 48000, sliding_window 4096, max_position_embeddings 32768, BF16. Safetensors total 46833864704 parameters. The card says 13B active parameters. Derived from RakutenAI-7B. Instruction-tuned. The instruct API record has the same Mixtral class, 2 experts per token, and the same 46833864704 parameter count. This card does not restate layer sizes.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rakuten Group released Rakuten AI 2.0 Instruct on 12 February 2025 as the instruction-tuned companion to the 8x7B foundation model. Weights are at `Rakuten/RakutenAI-2.0-8x7B-instruct` under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card says it is the fine-tuned variant for instruction following, with a chat template. The February 2025 press release says instruct models came from instruction fine-tuning and preference optimization of the foundation models. The sample prompt uses a system message plus a user turn.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rakuten/rakuten-ai-2/rakuten-ai-2-0-mini": { "id": "rakuten/rakuten-ai-2/rakuten-ai-2-0-mini", "name": "Rakuten AI 2.0 mini", "maker": "rakuten", "family": "rakuten/rakuten-ai-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-12", "context_length": 131072, "max_output_tokens": null, "parameters": "1.53B", "architecture": "MistralForCausalLM trained from scratch. config.json: 22 layers, hidden 2048, 32 heads, 8 KV heads, intermediate 8192, vocab 48000, sliding_window 8192, max_position_embeddings 131072, rope_theta 100000, BF16. Safetensors total 1534683136 parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rakuten Group announced Rakuten AI 2.0 mini on 18 December 2024 and released the foundation weights on 12 February 2025. It is the company's first small language model, trained from scratch, under Apache-2.0 at `Rakuten/RakutenAI-2.0-mini`. Collected on 2026-10-15.", "what_it_is_for": "Rakuten describes it as a lightweight Japanese and English foundation model for resource-constrained and edge deployments. The Japanese product page says it was trained from scratch on text built with Rakuten's multi-stage filtering and annotation. It is the base for the mini instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rakuten/rakuten-ai-2/rakuten-ai-2-0-mini-instruct": { "id": "rakuten/rakuten-ai-2/rakuten-ai-2-0-mini-instruct", "name": "Rakuten AI 2.0 mini Instruct", "maker": "rakuten", "family": "rakuten/rakuten-ai-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-12", "context_length": 131072, "max_output_tokens": null, "parameters": "1.53B", "architecture": "MistralForCausalLM trained from scratch. config.json: 22 layers, hidden 2048, 32 heads, 8 KV heads, intermediate 8192, vocab 48000, sliding_window 8192, max_position_embeddings 131072, rope_theta 100000, BF16. Safetensors total 1534683136 parameters. Instruction-tuned from RakutenAI-2.0-mini. The instruct API record reports the same Mistral class and the same 1534683136 parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rakuten Group released Rakuten AI 2.0 mini Instruct on 12 February 2025 as the instruction-tuned 1.5B model for edge devices. Weights are at `Rakuten/RakutenAI-2.0-mini-instruct` under Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "The card says it is a compact instruct model for on-device and low-latency use. The press release groups it with the other 2.0 instruct models as instruction fine-tuning plus preference optimization of the matching foundation model. It has a chat template.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rakuten/rakuten-ai-3/rakuten-ai-3-0": { "id": "rakuten/rakuten-ai-3/rakuten-ai-3-0", "name": "Rakuten AI 3.0", "maker": "rakuten", "family": "rakuten/rakuten-ai-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-17", "context_length": 128000, "max_output_tokens": null, "parameters": "671B total, 37B active", "architecture": "DeepseekV3ForCausalLM. config.json: 61 layers, hidden 7168, 128 heads, 256 routed experts, 1 shared expert, 8 experts per token, moe intermediate 2048, vocab 129280, max_position_embeddings 163840, YaRN rope scaling with factor 40 from original_max_position_embeddings 4096. FP8 e4m3 weights with a dynamic activation scheme. Safetensors total 671067257432 (F8_E4M3 669065609216, BF16 1960795136, F32 40853080). The model card says 671B total, 37B activated per token, and context length 128K. Repository tags include DeepSeek-V3. The card does not name the upstream checkpoint. A NOTICE file in the repo is a 2023 DeepSeek MIT copyright notice. The model card license is Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rakuten Group announced Rakuten AI 3.0 on 18 December 2025 and released the weights on 17 March 2026 at `Rakuten/RakutenAI-3.0`. It is an open-weight Japanese and English Mixture-of-Experts model, about 671 billion parameters, with 37 billion active per token. Collected on 2026-10-15.", "what_it_is_for": "The March 2026 press release says the fine-tuned model is for writing, code generation, document analysis, and extraction, with stronger Japanese accuracy than Rakuten's earlier models. From December 2025 it was also described as available inside Rakuten through Rakuten AI Gateway. The chat template in the repository includes a tool-call format.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reading/hydra-lstm/hydra-lstm": { "id": "reading/hydra-lstm/hydra-lstm", "name": "Hydra-LSTM", "maker": "reading", "family": "reading/hydra-lstm", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "The repository LICENSE file is the MIT License, Copyright (c) 2026 Karan Ruparell. The grant covers the software and associated documentation files. The .pt checkpoints are in the same repository. A separate weights licence is not stated.", "release_date": "2026-01-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Semi-shared LSTM for daily river discharge. The UK repository README describes a shared body trained on natural catchments and specialized heads, including a main head and a reservoir embedding head. The Hydra directory on 2026-10-15 listed one configuration: Hydra_LSTM_Numlayers1_Embhs64_Embos16_BodyOut32 as three files (Body 1.23 MB, Main_Head 330 KB, Res_Embedding_Head 423 KB). The README naming convention defines Numlayers, Embhs, Embos, and BodyOut. A parameter count is not published. The 2024 arXiv describes a Hydra body for variables shared across catchments and separate heads for catchment-specific variables. Stated stack on the UK README: Python 3.11+ and PyTorch.", "modalities": { "input": [ "time-series" ], "output": [ "streamflow" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hydra-LSTM is a semi-shared LSTM for river-discharge prediction, led from the University of Reading. Karan Ruparell is the corresponding author (k.ruparell2@pgr.reading.ac.uk on the arXiv page). Coauthors on the paper include Robert J. Marks, Andy Wood, Kieran M. R. Hunt, Hannah L. Cloke, Christel Prudhomme, Florian Pappenberger, and Matthew Chantry. The journal article is Artificial Intelligence for the Earth Systems, volume 4, issue 3, article e240103, published July 2025. On 2026-10-15 the public weights were the UK CAMELS-GB checkpoints in https://github.com/kruparell/UK_Forecasting_With_HydraLSTM. That README calls the UK application in development and says a manuscript is in preparation. Status here is preview.", "what_it_is_for": "The arXiv abstract presents Hydra-LSTM as one model that can predict river discharge across many catchments when some inputs exist everywhere and other inputs exist only in some catchments. A shared body handles the common variables. Separate heads take catchment-specific variables. The UK repository applies that idea to CAMELS-GB, with the body trained on natural catchments and a reservoir embedding head for reservoir-influenced catchments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v2/recraft-v2": { "id": "recraft/v2/recraft-v2", "name": "Recraft V2", "maker": "recraft", "family": "recraft/v2", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2024-03-13", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.022 }, "summary_intro": "Recraft V2 is a closed image model from Recraft (id `recraftv2`). First Recraft model trained from scratch, published at 20 billion parameters. Raster model for specific curated looks such as Plastic 3D. Text rendering is not reliable. Still offered, and labeled previous generation on the current model index. Not marked deprecated. This dossier was collected on 2026-10-15.", "what_it_is_for": "First Recraft model trained from scratch, published at 20 billion parameters. Raster model for specific curated looks such as Plastic 3D. Text rendering is not reliable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v2/recraft-v2-vector": { "id": "recraft/v2/recraft-v2-vector", "name": "Recraft V2 Vector", "maker": "recraft", "family": "recraft/v2", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2024-03-13", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.044 }, "summary_intro": "Recraft V2 Vector is a closed image model from Recraft (id `recraftv2_vector`). Cheapest vector model in the API. Used for icons and minimal illustration with consistent line width. The Icon curated style is V2 vector only. Still offered, and labeled previous generation on the current model index. Not marked deprecated. This dossier was collected on 2026-10-15.", "what_it_is_for": "Cheapest vector model in the API. Used for icons and minimal illustration with consistent line width. The Icon curated style is V2 vector only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v3/recraft-v3": { "id": "recraft/v3/recraft-v3", "name": "Recraft V3", "maker": "recraft", "family": "recraft/v3", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2024-10-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.04 }, "summary_intro": "Recraft V3 is a closed image model from Recraft (id `recraftv3`). Previous-generation raster model for photorealism, legible text, exact text placement, curated styles, and the editing operations that still run only on V3. Still offered, and labeled previous generation on the current model index. Not marked deprecated. This dossier was collected on 2026-10-15.", "what_it_is_for": "Previous-generation raster model for photorealism, legible text, exact text placement, curated styles, and the editing operations that still run only on V3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v3/recraft-v3-vector": { "id": "recraft/v3/recraft-v3-vector", "name": "Recraft V3 Vector", "maker": "recraft", "family": "recraft/v3", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2024-10-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.08 }, "summary_intro": "Recraft V3 Vector is a closed image model from Recraft (id `recraftv3_vector`). SVG variant of Recraft V3. Default curated style is Vector art. Same edit endpoints as raster V3. Still offered, and labeled previous generation on the current model index. Not marked deprecated. This dossier was collected on 2026-10-15.", "what_it_is_for": "SVG variant of Recraft V3. Default curated style is Vector art. Same edit endpoints as raster V3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4/recraft-v4": { "id": "recraft/v4/recraft-v4", "name": "Recraft V4", "maker": "recraft", "family": "recraft/v4", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-02-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.04 }, "summary_intro": "Recraft V4 is a closed image model from Recraft (id `recraftv4`). February 2026 ground-up generation aimed at design taste, composition, lighting, and textures. Raster around 1K. More stylized than V4.1, per the models introduction. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "February 2026 ground-up generation aimed at design taste, composition, lighting, and textures. Raster around 1K. More stylized than V4.1, per the models introduction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4/recraft-v4-pro": { "id": "recraft/v4/recraft-v4-pro", "name": "Recraft V4 Pro", "maker": "recraft", "family": "recraft/v4", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-02-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.25 }, "summary_intro": "Recraft V4 Pro is a closed image model from Recraft (id `recraftv4_pro`). 2K Recraft V4 raster model. The product page says Pro improves anatomy and realism for print-ready work and outputs 2048x2048 for a square frame. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "2K Recraft V4 raster model. The product page says Pro improves anatomy and realism for print-ready work and outputs 2048x2048 for a square frame.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4/recraft-v4-pro-vector": { "id": "recraft/v4/recraft-v4-pro-vector", "name": "Recraft V4 Pro Vector", "maker": "recraft", "family": "recraft/v4", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-02-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.3 }, "summary_intro": "Recraft V4 Pro Vector is a closed image model from Recraft (id `recraftv4_pro_vector`). Higher-detail SVG variant of Recraft V4. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Higher-detail SVG variant of Recraft V4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4/recraft-v4-vector": { "id": "recraft/v4/recraft-v4-vector", "name": "Recraft V4 Vector", "maker": "recraft", "family": "recraft/v4", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-02-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.08 }, "summary_intro": "Recraft V4 Vector is a closed image model from Recraft (id `recraftv4_vector`). Production-grade SVG variant of Recraft V4. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Production-grade SVG variant of Recraft V4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4-1/recraft-v4-1": { "id": "recraft/v4-1/recraft-v4-1", "name": "Recraft V4.1", "maker": "recraft", "family": "recraft/v4-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.035 }, "summary_intro": "Recraft V4.1 is a closed image model from Recraft (id `recraftv4_1`). Default Recraft API model for photorealism, illustration, and short prompts. Raster output around 1K. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Default Recraft API model for photorealism, illustration, and short prompts. Raster output around 1K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4-1/recraft-v4-1-flash": { "id": "recraft/v4-1/recraft-v4-1-flash", "name": "Recraft V4.1 Flash", "maker": "recraft", "family": "recraft/v4-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.007 }, "summary_intro": "Recraft V4.1 Flash is a closed image model from Recraft (id `recraftv4_1_flash`). Fastest and cheapest Recraft raster model. About 1.3 seconds per image, quality described as close to V4.1. No styles and no vector output. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Fastest and cheapest Recraft raster model. About 1.3 seconds per image, quality described as close to V4.1. No styles and no vector output.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4-1/recraft-v4-1-pro": { "id": "recraft/v4-1/recraft-v4-1-pro", "name": "Recraft V4.1 Pro", "maker": "recraft", "family": "recraft/v4-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.21 }, "summary_intro": "Recraft V4.1 Pro is a closed image model from Recraft (id `recraftv4_1_pro`). 2K raster variant of V4.1 with finer detail for print and large assets. Enhance details re-renders a raster image with this model. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "2K raster variant of V4.1 with finer detail for print and large assets. Enhance details re-renders a raster image with this model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4-1/recraft-v4-1-pro-vector": { "id": "recraft/v4-1/recraft-v4-1-pro-vector", "name": "Recraft V4.1 Pro Vector", "maker": "recraft", "family": "recraft/v4-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.3 }, "summary_intro": "Recraft V4.1 Pro Vector is a closed image model from Recraft (id `recraftv4_1_pro_vector`). Higher-detail SVG variant of V4.1. The Studio product table calls the same priced row Recraft V4.1 Vector Pro. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Higher-detail SVG variant of V4.1. The Studio product table calls the same priced row Recraft V4.1 Vector Pro.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4-1/recraft-v4-1-utility": { "id": "recraft/v4-1/recraft-v4-1-utility", "name": "Recraft V4.1 Utility", "maker": "recraft", "family": "recraft/v4-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.035 }, "summary_intro": "Recraft V4.1 Utility is a closed image model from Recraft (id `recraftv4_1_utility`). Predictable V4.1 raster line: flat lighting, front-facing composition, simple scenes for mockups and product listings. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Predictable V4.1 raster line: flat lighting, front-facing composition, simple scenes for mockups and product listings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4-1/recraft-v4-1-utility-pro": { "id": "recraft/v4-1/recraft-v4-1-utility-pro", "name": "Recraft V4.1 Utility Pro", "maker": "recraft", "family": "recraft/v4-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.21 }, "summary_intro": "Recraft V4.1 Utility Pro is a closed image model from Recraft (id `recraftv4_1_utility_pro`). 2K Utility raster model for predictable product and mockup images at higher resolution. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "2K Utility raster model for predictable product and mockup images at higher resolution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4-1/recraft-v4-1-utility-pro-vector": { "id": "recraft/v4-1/recraft-v4-1-utility-pro-vector", "name": "Recraft V4.1 Utility Pro Vector", "maker": "recraft", "family": "recraft/v4-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.3 }, "summary_intro": "Recraft V4.1 Utility Pro Vector is a closed image model from Recraft (id `recraftv4_1_utility_pro_vector`). Higher-detail SVG Utility variant for predictable vector assets. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Higher-detail SVG Utility variant for predictable vector assets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4-1/recraft-v4-1-utility-vector": { "id": "recraft/v4-1/recraft-v4-1-utility-vector", "name": "Recraft V4.1 Utility Vector", "maker": "recraft", "family": "recraft/v4-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.08 }, "summary_intro": "Recraft V4.1 Utility Vector is a closed image model from Recraft (id `recraftv4_1_utility_vector`). SVG Utility variant: simple, predictable vector output rather than the main line's artistic point of view. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "SVG Utility variant: simple, predictable vector output rather than the main line's artistic point of view.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4-1/recraft-v4-1-vector": { "id": "recraft/v4-1/recraft-v4-1-vector", "name": "Recraft V4.1 Vector", "maker": "recraft", "family": "recraft/v4-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.08 }, "summary_intro": "Recraft V4.1 Vector is a closed image model from Recraft (id `recraftv4_1_vector`). SVG variant of Recraft V4.1 for logos, typography, and illustration. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "SVG variant of Recraft V4.1 for logos, typography, and illustration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4-styles/recraft-v4-styles": { "id": "recraft/v4-styles/recraft-v4-styles", "name": "Recraft V4 Styles", "maker": "recraft", "family": "recraft/v4-styles", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-08-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.035 }, "summary_intro": "Recraft V4 Styles is a closed image model from Recraft (id `recraftv4_styles`). Dedicated style-match raster model. A style from 1 to 10 reference images is required. No training step. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Dedicated style-match raster model. A style from 1 to 10 reference images is required. No training step.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4-styles/recraft-v4-styles-pro": { "id": "recraft/v4-styles/recraft-v4-styles-pro", "name": "Recraft V4 Styles Pro", "maker": "recraft", "family": "recraft/v4-styles", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-08-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.1 }, "summary_intro": "Recraft V4 Styles Pro is a closed image model from Recraft (id `recraftv4_styles_pro`). 2K style-match raster model. A style is required. Pro here is cheaper than V4.1 Pro. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "2K style-match raster model. A style is required. Pro here is cheaper than V4.1 Pro.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4-styles/recraft-v4-styles-pro-vector": { "id": "recraft/v4-styles/recraft-v4-styles-pro-vector", "name": "Recraft V4 Styles Pro Vector", "maker": "recraft", "family": "recraft/v4-styles", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-08-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.12 }, "summary_intro": "Recraft V4 Styles Pro Vector is a closed image model from Recraft (id `recraftv4_styles_pro_vector`). Higher-resolution SVG style-match model. A style is required. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "Higher-resolution SVG style-match model. A style is required.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "recraft/v4-styles/recraft-v4-styles-vector": { "id": "recraft/v4-styles/recraft-v4-styles-vector", "name": "Recraft V4 Styles Vector", "maker": "recraft", "family": "recraft/v4-styles", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Commercial use of outputs depends on the Recraft plan.", "release_date": "2026-08-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "Recraft V4 Styles Vector is a closed image model from Recraft (id `recraftv4_styles_vector`). SVG variant of V4 Styles. A style is required. Vector price is lower than other V4 vector models. Active on the Recraft API and in Recraft Studio as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "SVG variant of V4 Styles. A style is required. Vector price is lower than other V4 vector models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reflection/beam/beam-1-0-501b-a23b": { "id": "reflection/beam/beam-1-0-501b-a23b", "name": "Beam", "maker": "reflection", "family": "reflection/beam", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-05", "context_length": 1048576, "max_output_tokens": 131072, "parameters": "501B total, 23B active", "architecture": "52-layer sparse mixture-of-experts with interleaved local and global attention, fine-grained routed experts, auxiliary-loss-free load balancing, SandwichNorm, elementwise attention gating, and FP32 residual accumulation. Expert count and top-k were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beam is Reflection AI's first named language model, a text-only sparse mixture-of-experts system announced on 2026-10-05 for coding, reasoning, and agentic work. It is in early-access preview: the API id is in the developer docs, weights were not public on 2026-10-15, and this dossier was collected that day.", "what_it_is_for": "Reflection says Beam is a workhorse for coding, reasoning, and agentic workloads, aimed at inference efficiency rather than topping the largest open models on raw scores. The company says it is competitive with GLM 5.2 and approaching Qwen 3.8-Max on coding and agentic tasks, while Kimi K3 remains ahead on raw capability. Access on the collection date is a waitlist at platform.reflection.ai. Mirror, Reflection's coding CLI, defaults to this model id. Mirror is a client, not a separate model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reka/core/reka-core": { "id": "reka/core/reka-core", "name": "Reka Core", "maker": "reka", "family": "reka/core", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-04-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dense model trained from scratch in bfloat16. Parameter count was left blank in the April 2024 report, which said training was still underway.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 25.0 }, "summary_intro": "Reka Core is Reka's frontier multimodal model from 2024, trained from scratch to take text, images, video, and audio and return text. Reka launched it on 15 April 2024 via API, on-premises, and on-device options. It is not on the public model table published 9 October 2026, and no retirement notice was found. Collected 2026-10-15.", "what_it_is_for": "Reka Core is Reka's frontier multimodal model from 2024, trained from scratch to take text, images, video, and audio and return text. Reka launched it on 15 April 2024 via API, on-premises, and on-device options. It is not on the public model table published 9 October 2026, and no retirement notice was found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reka/edge/reka-edge": { "id": "reka/edge/reka-edge", "name": "Reka Edge (2024)", "maker": "reka", "family": "reka/edge", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-02-12", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Dense 7B model trained from scratch. Report: about 4.5T text tokens, bfloat16.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reka Edge (2024) is the 7B multimodal model released in public beta with Reka Flash on 12 February 2024. It takes text, images, video, and audio. The May 2024 API aliased reka-edge to reka-edge-20240208. It is a different model from the 2026 open-weight Reka Edge 2603. It is not on the October 2026 public model table. Collected 2026-10-15.", "what_it_is_for": "Reka Edge (2024) is the 7B multimodal model released in public beta with Reka Flash on 12 February 2024. It takes text, images, video, and audio. The May 2024 API aliased reka-edge to reka-edge-20240208. It is a different model from the 2026 open-weight Reka Edge 2603. It is not on the October 2026 public model table. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reka/edge/reka-edge-2603": { "id": "reka/edge/reka-edge-2603", "name": "Reka Edge 2603", "maker": "reka", "family": "reka/edge", "status": "active", "confidence": "high", "weights": "open-weights", "license": "reka-edge-2603-license", "release_date": "2026-03-11", "context_length": 16384, "max_output_tokens": 16384, "parameters": "7B", "architecture": "Yasa2ForConditionalGeneration. Announcement: 657M ConvNeXt V2 vision encoder and a 6.4B transformer backbone trained from scratch, 64 tokens per image tile. Product page rounds this to a 660M ConvNeXt V2 encoder, a 6B language backbone, and 7B total, and headings call that stack Reka Edge 2.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.1 }, "summary_intro": "Reka Edge 2603 is Reka's open-weight 7B vision-language model for images, video, and text, announced on 11 March 2026 as the latest Reka Edge. It is built for physical-AI and edge deployment: object grounding, video understanding, and tool use, with text output. The id reka-edge-2603 is on the public API, on Hugging Face, and on OpenRouter as rekaai/reka-edge. Collected 2026-10-15.", "what_it_is_for": "Reka Edge 2603 is Reka's open-weight 7B vision-language model for images, video, and text, announced on 11 March 2026 as the latest Reka Edge. It is built for physical-AI and edge deployment: object grounding, video understanding, and tool use, with text output. The id reka-edge-2603 is on the public API, on Hugging Face, and on OpenRouter as rekaai/reka-edge. Collected 2026-10-15.", "ratings": { "empirical_100": 41, "lmsys_elo": 1201, "coding_elo": 1201, "swe_bench": 15.2, "humaneval": 70.2, "twelve_cs": 36.1, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reka/edge/reka-edgeq": { "id": "reka/edge/reka-edgeq", "name": "Reka EdgeQ", "maker": "reka", "family": "reka/edge", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-22", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Quantized on-device implementation of Reka Edge. Same 657M ConvNeXt V2 encoder and 64 tokens per image tile. Targeted at Snapdragon 8 Elite and demonstrated on a Samsung Galaxy S25 Hexagon NPU.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reka EdgeQ is Reka's on-device build of Reka Edge for Qualcomm Snapdragon 8 Elite, described on 22 September 2026. It is the same ConvNeXt V2 vision-language model, quantized and scheduled onto the phone NPU, not a separately trained foundation model. Reka published phone benchmarks against the BF16 Edge checkpoint and against Gemma 4 E4B. Collected 2026-10-15.", "what_it_is_for": "Reka EdgeQ is Reka's on-device build of Reka Edge for Qualcomm Snapdragon 8 Elite, described on 22 September 2026. It is the same ConvNeXt V2 vision-language model, quantized and scheduled onto the phone NPU, not a separately trained foundation model. Reka published phone benchmarks against the BF16 Edge checkpoint and against Gemma 4 E4B. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reka/flash/reka-flash": { "id": "reka/flash/reka-flash", "name": "Reka Flash", "maker": "reka", "family": "reka/flash", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-02-12", "context_length": null, "max_output_tokens": null, "parameters": "21B", "architecture": "Dense 21B model trained from scratch. Report: about 5T text tokens, bfloat16, 8K standard context and a 128K long-context variant.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.8, "completion_per_m_usd": 2.0 }, "summary_intro": "Reka Flash (2024) is Reka's 21B multimodal model for text, image, video, and audio inputs, positioned as the fast tier under Core. It entered public beta on 12 February 2024 and was updated again on 4 October 2024. It is not on the 9 October 2026 public model table. This folder is the 2024 Flash line, not Reka Flash 3. Collected 2026-10-15.", "what_it_is_for": "Reka Flash (2024) is Reka's 21B multimodal model for text, image, video, and audio inputs, positioned as the fast tier under Core. It entered public beta on 12 February 2024 and was updated again on 4 October 2024. It is not on the 9 October 2026 public model table. This folder is the 2024 Flash line, not Reka Flash 3. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reka/flash/reka-flash-3": { "id": "reka/flash/reka-flash-3", "name": "Reka Flash 3", "maker": "reka", "family": "reka/flash", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-10", "context_length": null, "max_output_tokens": null, "parameters": "21B", "architecture": "21B model trained from scratch, released in a Llama-compatible format with the cl100k_base tokenizer. Post-training was supervised finetuning plus RLOO. Hugging Face lists the license as Apache-2.0 and the size as 21B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.8 }, "summary_intro": "Reka Flash 3 is Reka's open-weight 21B text reasoning model, released on 10 March 2025 under Apache-2.0. Reka describes it as a general model for chat, coding, instruction following, and function calling, with a thinking trace and a budget-forcing control. The id reka-flash-3 is on the public API table as of 9 October 2026. Collected 2026-10-15.", "what_it_is_for": "Reka Flash 3 is Reka's open-weight 21B text reasoning model, released on 10 March 2025 under Apache-2.0. Reka describes it as a general model for chat, coding, instruction following, and function calling, with a thinking trace and a budget-forcing control. The id reka-flash-3 is on the public API table as of 9 October 2026. Collected 2026-10-15.", "ratings": { "empirical_100": 28, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.5, "humaneval": 70.4, "twelve_cs": 69.3, "aa_intelligence": 6.4, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reka/flash/reka-flash-3-1": { "id": "reka/flash/reka-flash-3-1", "name": "Reka Flash 3.1", "maker": "reka", "family": "reka/flash", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-10", "context_length": null, "max_output_tokens": null, "parameters": "21B", "architecture": "Update of Flash 3. Llama-compatible release, cl100k_base tokenizer. Post-training: supervised finetuning on synthetic and public data, then large-scale RLOO with verifiable rewards. Hugging Face size 21B, BF16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reka Flash 3.1 is an Apache-2.0 update of the 21B Reka Flash 3, released on 10 July 2025. Reka says reinforcement learning made it stronger at coding and a better base for agentic fine-tunes. A multimodal variant was the orchestrator inside Reka Research and Reka Vision. The id is not on the 9 October 2026 public model table, but the weights are still on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "Reka Flash 3.1 is an Apache-2.0 update of the 21B Reka Flash 3, released on 10 July 2025. Reka says reinforcement learning made it stronger at coding and a better base for agentic fine-tunes. A multimodal variant was the orchestrator inside Reka Research and Reka Vision. The id is not on the 9 October 2026 public model table, but the weights are still on Hugging Face. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reka/flash/reka-flash-3-1-q3-k-s": { "id": "reka/flash/reka-flash-3-1-q3-k-s", "name": "Reka Flash 3.1 Q3_K_S", "maker": "reka", "family": "reka/flash", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-10", "context_length": null, "max_output_tokens": null, "parameters": "21B", "architecture": "3.5-bit Q3_K_S GGUF of Reka Flash 3.1, quantized with Reka Quant (calibrated error reduction and online self-distillation) and exported to the llama.cpp Q3_K_S datatype.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reka Flash Quantized is the official 3.5-bit Q3_K_S checkpoint of Reka Flash 3.1, produced with Reka's Reka Quant method and released the same day, 10 July 2025, under Apache-2.0. It is a separate weight file from the BF16 3.1 model, aimed at local llama.cpp deployment. Collected 2026-10-15.", "what_it_is_for": "Reka Flash Quantized is the official 3.5-bit Q3_K_S checkpoint of Reka Flash 3.1, produced with Reka's Reka Quant method and released the same day, 10 July 2025, under Apache-2.0. It is a separate weight file from the BF16 3.1 model, aimed at local llama.cpp deployment. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reka/rho/rho-1": { "id": "reka/rho/rho-1", "name": "Rho-1", "maker": "reka", "family": "reka/rho", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-05", "context_length": null, "max_output_tokens": null, "parameters": "19B", "architecture": "Single transformer trained from scratch. Understanding stream and generation stream with shared attention and one KV cache. Discrete tokens for text and commands. Continuous tokens for image latents, video, actions, and proprioception. Diffusion head for pixels. Distilled denoiser shown as 8 passes.", "modalities": { "input": [ "text", "image", "video", "action" ], "output": [ "text", "image", "video", "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rho-1 is a research preview of Reka's 19B omni-reasoning model, released on 5 October 2026. One network understands and generates text, images, and video, reasons over them, and emits robotic actions and proprioception. There is no public weight download, API id, or price. Reka asks interested teams to write to contact@reka.ai. Collected 2026-10-15.", "what_it_is_for": "Rho-1 is a research preview of Reka's 19B omni-reasoning model, released on 5 October 2026. One network understands and generates text, images, and video, reasons over them, and emits robotic actions and proprioception. There is no public weight download, API id, or price. Reka asks interested teams to write to contact@reka.ai. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reka/ridm/reka-inverse-dynamics-model": { "id": "reka/ridm/reka-inverse-dynamics-model", "name": "Reka Inverse Dynamics Model", "maker": "reka", "family": "reka/ridm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-02", "context_length": null, "max_output_tokens": null, "parameters": "flow 2,785,499 total; pixel 9,836,063", "architecture": "Two small models. Flow: 1,795,337 trained parameters plus a frozen RAFT-small encoder of 990,162 parameters, one prediction per 17-frame window. Pixel: 9,836,063 parameters on raw frames, one prediction per frame. Card name FlowTransformer is used for the flow variant.", "modalities": { "input": [ "video" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Reka Inverse Dynamics Model (RIDM) predicts camera and keyboard motion from short video. Reka shipped two Apache-2.0 checkpoints on 2 October 2026: a flow model that reads RAFT optical flow, and a pixel model that reads frames. Both were trained on game footage. The card calls the release preliminary. Collected 2026-10-15.", "what_it_is_for": "The Reka Inverse Dynamics Model (RIDM) predicts camera and keyboard motion from short video. Reka shipped two Apache-2.0 checkpoints on 2 October 2026: a flow model that reads RAFT optical flow, and a pixel model that reads frames. Both were trained on game footage. The card calls the release preliminary. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reka/spark/reka-spark": { "id": "reka/spark/reka-spark", "name": "Reka Spark", "maker": "reka", "family": "reka/spark", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described by Reka as the compact on-device model below Edge. Architecture was not published.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reka Spark is Reka's smallest named multimodal model, aimed at on-device and embedded hardware. A current automotive solutions page calls Reka Spark a 1B model for vehicle SoCs and dashcam-class ECUs, with a stated sub-80 ms inference target. Older Reka statements called Spark a 2.6B model. It has no public model card or API id on the October 2026 model table. Collected 2026-10-15.", "what_it_is_for": "Reka Spark is Reka's smallest named multimodal model, aimed at on-device and embedded hardware. A current automotive solutions page calls Reka Spark a 1B model for vehicle SoCs and dashcam-class ECUs, with a stated sub-80 ms inference target. Older Reka statements called Spark a 2.6B model. It has no public model card or API id on the October 2026 model table. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reka/speech/reka-speech": { "id": "reka/speech/reka-speech", "name": "Reka Speech", "maker": "reka", "family": "reka/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-16", "context_length": null, "max_output_tokens": null, "parameters": "850M", "architecture": "550M transformer decoder and 300M audio encoder. Timestamping uses cached QK embeddings and dynamic-programming alignment after a modified vLLM decode.", "modalities": { "input": [ "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reka Speech is Reka's 850 million parameter speech model, announced on 16 September 2025, for multilingual transcription and speech-to-speech translation with timestamps. It is a 550M transformer decoder plus a 300M audio encoder. Reka offers it for real-time use and for large offline audio jobs. Access is by contact. No weights or chat-API id were published. Collected 2026-10-15.", "what_it_is_for": "Reka Speech is Reka's 850 million parameter speech model, announced on 16 September 2025, for multilingual transcription and speech-to-speech translation with timestamps. It is a 550M transformer decoder plus a 300M audio encoder. Reka offers it for real-time use and for large offline audio jobs. Access is by contact. No weights or chat-API id were published. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/chatterbox/chatterbox": { "id": "resemble/chatterbox/chatterbox", "name": "Chatterbox", "maker": "resemble", "family": "resemble/chatterbox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "500M", "architecture": "0.5B Llama backbone with alignment-informed inference, CFG, and exaggeration control. The README acknowledges CosyVoice, HiFT-GAN, Llama 3, and S3Tokenizer.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chatterbox is Resemble AI's original English open-weight text-to-speech model, collected 2026-10-15. The weights stay active under MIT. The hosted API version code tts-v4 is end of life, and Resemble no longer sells Chatterbox as a paid product.", "what_it_is_for": "Resemble positions it as general zero-shot English TTS with emotion exaggeration and classifier-free guidance. A reference clip of about 5 to 20 seconds clones a voice at inference time. Every output is PerTh-watermarked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/chatterbox/chatterbox-es-es": { "id": "resemble/chatterbox/chatterbox-es-es", "name": "Chatterbox Multilingual es-es", "maker": "resemble", "family": "resemble/chatterbox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-10", "context_length": null, "max_output_tokens": null, "parameters": "500M", "architecture": "Dedicated finetune of the 0.5B Llama Chatterbox Multilingual V3 T3 backbone, with a V3 S3Gen speech decoder and a voice encoder.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chatterbox Multilingual es-es is a Resemble AI single-language finetune in the Chatterbox Multilingual V3 language pack, collected 2026-10-15. It is an active MIT-licensed open-weight TTS checkpoint for European Spanish, not a third-party finetune. The commercial hosted voice API is no longer sold.", "what_it_is_for": "Resemble says this checkpoint is for tighter European Spanish quality, dialect behavior, and pronunciation than the general multilingual model. Zero-shot cloning uses a short reference clip. Outputs are PerTh-watermarked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/chatterbox/chatterbox-es-mx-latam": { "id": "resemble/chatterbox/chatterbox-es-mx-latam", "name": "Chatterbox Multilingual es-mx-latam", "maker": "resemble", "family": "resemble/chatterbox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-10", "context_length": null, "max_output_tokens": null, "parameters": "500M", "architecture": "Dedicated finetune of the 0.5B Llama Chatterbox Multilingual V3 T3 backbone, with a V3 S3Gen speech decoder and a voice encoder.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chatterbox Multilingual es-mx-latam is a Resemble AI single-language finetune in the Chatterbox Multilingual V3 language pack, collected 2026-10-15. It is an active MIT-licensed open-weight TTS checkpoint for Latin American Spanish, not a third-party finetune. The commercial hosted voice API is no longer sold.", "what_it_is_for": "Resemble says this checkpoint is for tighter Latin American Spanish quality, dialect behavior, and pronunciation than the general multilingual model. Zero-shot cloning uses a short reference clip. Outputs are PerTh-watermarked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/chatterbox/chatterbox-flash": { "id": "resemble/chatterbox/chatterbox-flash", "name": "Chatterbox Flash", "maker": "resemble", "family": "resemble/chatterbox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Block-diffusion T3 decoder described as Llama-520M plus one mask token, plus the original flow-matching S3Gen vocoder and the Chatterbox voice encoder. Full-system parameter count was not stated on the official card.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chatterbox Flash is Resemble AI's block-diffusion English TTS model, collected 2026-10-15. It is an active MIT open-weight research model. A paper is dated 1 June 2026. The product page claims about 2x the throughput of autoregressive Chatterbox on vLLM.", "what_it_is_for": "Higher-throughput zero-shot TTS. Flash replaces the autoregressive T3 decoder with a block-diffusion decoder and keeps streaming by generating block by block. The vocoder is the original S3Gen flow-matching model. Outputs are PerTh-watermarked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/chatterbox/chatterbox-hi": { "id": "resemble/chatterbox/chatterbox-hi", "name": "Chatterbox Multilingual hi", "maker": "resemble", "family": "resemble/chatterbox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-10", "context_length": null, "max_output_tokens": null, "parameters": "500M", "architecture": "Dedicated finetune of the 0.5B Llama Chatterbox Multilingual V3 T3 backbone, with a V3 S3Gen speech decoder and a voice encoder.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chatterbox Multilingual hi is a Resemble AI single-language finetune in the Chatterbox Multilingual V3 language pack, collected 2026-10-15. It is an active MIT-licensed open-weight TTS checkpoint for Hindi, not a third-party finetune. The commercial hosted voice API is no longer sold.", "what_it_is_for": "Resemble says this checkpoint is for tighter Hindi quality, dialect behavior, and pronunciation than the general multilingual model. Zero-shot cloning uses a short reference clip. Outputs are PerTh-watermarked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/chatterbox/chatterbox-multilingual": { "id": "resemble/chatterbox/chatterbox-multilingual", "name": "Chatterbox Multilingual", "maker": "resemble", "family": "resemble/chatterbox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-09-04", "context_length": null, "max_output_tokens": null, "parameters": "500M", "architecture": "0.5B Llama backbone shared by v2 and v3. v3 changes are data, training, and language-specific refinement, not a larger model. Training mixture grew from 25.6k hours to 36.7k hours.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chatterbox Multilingual is Resemble AI's 0.5B open-weight multilingual TTS model, collected 2026-10-15. Version 3, released 10 June 2026, is the recommended checkpoint. Version 2 remains loadable. The hosted code tts-v4 is end of life. Weights are MIT.", "what_it_is_for": "One model for zero-shot voice cloning across many languages. Resemble says v3 improves speaker similarity, cuts hallucinations, and sounds more conversational than v2. Six language packs cover dialects where the general model is weaker.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/chatterbox/chatterbox-nano": { "id": "resemble/chatterbox/chatterbox-nano", "name": "Chatterbox Nano", "maker": "resemble", "family": "resemble/chatterbox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-07-06", "context_length": null, "max_output_tokens": null, "parameters": "110M", "architecture": "Same architecture family as Turbo, including the one-step decoder and native paralinguistic tags, scaled to 110M.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chatterbox Nano is Resemble AI's 110M English open-weight TTS model for CPU and edge use, collected 2026-10-15. It is active and MIT-licensed. A Resemble research note dated 6 July 2026 introduces it with Chatterbox Flash.", "what_it_is_for": "Same feature set Resemble claims for Turbo: zero-shot cloning and paralinguistic tags, at a size that runs on CPU. The product page says 10x real time on GPU and 3x real time on CPU with 8 threads. The README says 3x faster than realtime on 8 CPU cores.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/chatterbox/chatterbox-pt-br": { "id": "resemble/chatterbox/chatterbox-pt-br", "name": "Chatterbox Multilingual pt-br", "maker": "resemble", "family": "resemble/chatterbox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-10", "context_length": null, "max_output_tokens": null, "parameters": "500M", "architecture": "Dedicated finetune of the 0.5B Llama Chatterbox Multilingual V3 T3 backbone, with a V3 S3Gen speech decoder and a voice encoder.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chatterbox Multilingual pt-br is a Resemble AI single-language finetune in the Chatterbox Multilingual V3 language pack, collected 2026-10-15. It is an active MIT-licensed open-weight TTS checkpoint for Brazilian Portuguese, not a third-party finetune. The commercial hosted voice API is no longer sold.", "what_it_is_for": "Resemble says this checkpoint is for tighter Brazilian Portuguese quality, dialect behavior, and pronunciation than the general multilingual model. Zero-shot cloning uses a short reference clip. Outputs are PerTh-watermarked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/chatterbox/chatterbox-pt-pt": { "id": "resemble/chatterbox/chatterbox-pt-pt", "name": "Chatterbox Multilingual pt-pt", "maker": "resemble", "family": "resemble/chatterbox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-10", "context_length": null, "max_output_tokens": null, "parameters": "500M", "architecture": "Dedicated finetune of the 0.5B Llama Chatterbox Multilingual V3 T3 backbone, with a V3 S3Gen speech decoder and a voice encoder.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chatterbox Multilingual pt-pt is a Resemble AI single-language finetune in the Chatterbox Multilingual V3 language pack, collected 2026-10-15. It is an active MIT-licensed open-weight TTS checkpoint for European Portuguese, not a third-party finetune. The commercial hosted voice API is no longer sold.", "what_it_is_for": "Resemble says this checkpoint is for tighter European Portuguese quality, dialect behavior, and pronunciation than the general multilingual model. Zero-shot cloning uses a short reference clip. Outputs are PerTh-watermarked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/chatterbox/chatterbox-turbo": { "id": "resemble/chatterbox/chatterbox-turbo", "name": "Chatterbox Turbo", "maker": "resemble", "family": "resemble/chatterbox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "350M", "architecture": "350M model with a one-step distilled speech-token-to-mel decoder, reduced from 10 steps. Paralinguistic tags are native.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chatterbox Turbo is Resemble AI's 350M English open-weight TTS model for low latency, collected 2026-10-15. It is active under MIT. The hosted code tts-v4-turbo is end of life.", "what_it_is_for": "Resemble built it for zero-shot English voice agents. Paralinguistic tags such as laugh, cough, chuckle, sigh, gasp, whisper, and breath are spoken in the cloned voice. The speech-token-to-mel decoder was distilled from 10 steps to 1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/chatterbox/chatterbox-zh-cmn": { "id": "resemble/chatterbox/chatterbox-zh-cmn", "name": "Chatterbox Multilingual zh-cmn", "maker": "resemble", "family": "resemble/chatterbox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-10", "context_length": null, "max_output_tokens": null, "parameters": "500M", "architecture": "Dedicated finetune of the 0.5B Llama Chatterbox Multilingual V3 T3 backbone, with a V3 S3Gen speech decoder and a voice encoder.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chatterbox Multilingual zh-cmn is a Resemble AI single-language finetune in the Chatterbox Multilingual V3 language pack, collected 2026-10-15. It is an active MIT-licensed open-weight TTS checkpoint for Mandarin Chinese, not a third-party finetune. The commercial hosted voice API is no longer sold.", "what_it_is_for": "Resemble says this checkpoint is for tighter Mandarin Chinese quality, dialect behavior, and pronunciation than the general multilingual model. Zero-shot cloning uses a short reference clip. Outputs are PerTh-watermarked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/detect/detect-2b": { "id": "resemble/detect/detect-2b", "name": "DETECT-2B", "maker": "resemble", "family": "resemble/detect", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Ensemble of sub-models, Wav2Vec2 self-supervised representations, parameter-efficient fine-tuning, and Mamba-SSM sequence modeling. The name is not a published parameter count.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DETECT-2B is Resemble AI's first production audio deepfake detector that the company now calls retired, collected 2026-10-15. It is audio only. The product page still describes it in the present tense. llms.txt calls it the retired first generation.", "what_it_is_for": "Real-time audio deepfake scoring for contact centers, media checks, and voice approvals. Output can be frame-level scores or one thresholded label.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/detect/detect-3b-omni": { "id": "resemble/detect/detect-3b-omni", "name": "DETECT-3B Omni", "maker": "resemble", "family": "resemble/detect", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Unified model. Combined 3 billion parameters across audio and vision. Audio continues the DETECT-2B line. Vision is a separate stack in the same system.", "modalities": { "input": [ "audio", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DETECT-3B Omni is Resemble AI's 3-billion-parameter audio, image, and video detector, collected 2026-10-15. The company llms.txt calls it the retired second generation, replaced by DETECT-World. Podonos still lists it as the previous generation at 98.05% audio accuracy. The marketing page still describes it as current, so confidence in the retired label is medium.", "what_it_is_for": "One API for speech, image, and video deepfakes. Audio shares architecture lineage with DETECT-2B and adds replay-attack defense. Vision covers major generators. Resemble says it routes each file to a modality sub-model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/detect/detect-world": { "id": "resemble/detect/detect-world", "name": "DETECT-World", "maker": "resemble", "family": "resemble/detect", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Builds on DETECT-3B Omni's artifact detector and adds a physical-consistency layer for lighting, motion, and continuity. Parameter count not stated.", "modalities": { "input": [ "audio", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DETECT-World is Resemble AI's current multimodal deepfake detector, collected 2026-10-15. It is the active flagship. Resemble describes it as a world model that adds lighting, motion, and continuity checks on top of DETECT-3B Omni. Weights are closed. It launched 12 August 2026.", "what_it_is_for": "Score audio, image, and video as synthetic or real, including generators it was not trained on. The same Detect API does streaming and batch. Intelligence is a separate explainability product, not this model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/dramabox/dramabox": { "id": "resemble/dramabox/dramabox", "name": "DramaBox", "maker": "resemble", "family": "resemble/dramabox", "status": "active", "confidence": "medium", "weights": "open-source", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "IC-LoRA finetune of LTX-2.3, the audio-only branch of Lightricks' open video diffusion transformer. Cold path loads a Gemma model.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DramaBox is a Resemble AI open expressive speech model, collected 2026-10-15. It is active as voice research, not as a paid commercial product. The page describes it as an IC-LoRA finetune of the audio branch of Lightricks LTX-2.3. A public weights URL was not on that page.", "what_it_is_for": "Direct a performance from a prompt. Dialogue inside double quotes is spoken. Stage directions outside the quotes are not spoken. A reference clip of 10 seconds or more sets the timbre; without one, the model invents a voice from the description.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/enhance/resemble-enhance": { "id": "resemble/enhance/resemble-enhance", "name": "Resemble Enhance", "maker": "resemble", "family": "resemble/enhance", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two modules trained on 44.1 kHz speech: a denoiser that separates speech from noise, and an enhancer that restores distortion and extends bandwidth with latent conditional flow matching and a vocoder.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Resemble Enhance is Resemble AI's open speech denoiser and enhancer, collected 2026-10-15. It is MIT-licensed. The repo copyright is 2023. Two modules ship together: a denoiser and a bandwidth-restoring enhancer trained at 44.1 kHz. The hosted enhancement API does not name this checkpoint.", "what_it_is_for": "Remove noise and restore distorted or band-limited speech. The enhancer is a latent conditional flow-matching model with a vocoder. A third-party MLX conversion lists a UNet denoiser, an IRMAE autoencoder, a 30-layer WaveNet CFM, and a UnivNet vocoder. Those file roles are secondary and are not copied in as official parameter counts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/perth/perth": { "id": "resemble/perth/perth", "name": "PerTh", "maker": "resemble", "family": "resemble/perth", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Psychoacoustic neural watermark. Payload energy is placed in masked frequency regions and trained against resampling, re-encoding, stretch, and noise.", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PerTh is Resemble AI's open-source neural audio watermarker, collected 2026-10-15. The name means Perceptual Threshold. It is active, MIT-licensed, and embedded in Chatterbox outputs. A benchmarks page also says PerTh V2 improved robustness. That V2 is recorded as a version, not a separate product.", "what_it_is_for": "Embed an imperceptible payload in speech and recover it after resampling, MP3 compression, noise, and time stretch. It answers whether a clip came from a PerTh-marked generator, not whether any audio is synthetic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/perth/perth-multimodal": { "id": "resemble/perth/perth-multimodal", "name": "PerTh Multimodal", "maker": "resemble", "family": "resemble/perth", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Per-modality encoders: psychoacoustic masking for audio, pixel-domain marks for image and video fine-tuned past a Meta open baseline, and semantic rewriting for text. Also reads C2PA and SynthID.", "modalities": { "input": [ "audio", "image", "video", "text" ], "output": [ "audio", "image", "video", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PerTh Multimodal is Resemble AI's closed watermark model for audio, image, video, and text, collected 2026-10-15. It is active and sold through the Watermarker API. Image and video marking is described as a fine-tune beyond a Meta open-source baseline. Text uses semantic rewriting.", "what_it_is_for": "Embed a custom identifier and later read PerTh marks, C2PA signatures, and SynthID in one report. Audio explicit payload is described as 16-bit. Image and video encodes are up to 256 bits. Aimed at EU AI Act Article 50 marking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/resemblyzer/resemblyzer": { "id": "resemble/resemblyzer/resemblyzer", "name": "Resemblyzer", "maker": "resemble", "family": "resemble/resemblyzer", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2019", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speaker encoder trained with generalized end-to-end loss. 256-dimensional embedding.", "modalities": { "input": [ "audio" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Resemblyzer is Resemble AI's open voice encoder, collected 2026-10-15. The repo copyright is 2019 and the license is Apache 2.0. It is still the published speaker-embedding model Resemble lists for verification and diarization. It is not a current commercial TTS model.", "what_it_is_for": "Turn a few seconds of speech into a 256-value embedding for similarity, verification, diarization, and as a perceptual loss. The README says it works best on English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/sts/core-sts-v1": { "id": "resemble/sts/core-sts-v1", "name": "Resemble Core STS v1", "maker": "resemble", "family": "resemble/sts", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-Q2", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech-to-speech. Docs say better speed and accuracy than the legacy converter. No further architecture.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Resemble Core STS v1 is a Resemble AI speech-to-speech model, collected 2026-10-15. Code sts-v1. Docs describe enhanced conversion with better speed and accuracy, a 10-minute dataset, streaming, and a Q2 2023 release. It is not in the current sales catalog.", "what_it_is_for": "Earlier voice-conversion model for built voices.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/sts/core-sts-v2": { "id": "resemble/sts/core-sts-v2", "name": "Resemble Core STS v2", "maker": "resemble", "family": "resemble/sts", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-Q4", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech-to-speech conversion. Improved pitch tracking versus v1. 48 kHz support.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Resemble Core STS v2 is a Resemble AI speech-to-speech model, collected 2026-10-15. The version code is sts-v2. Docs say it improved pitch tracking and added 48 kHz support, needs 10 or more minutes of data, supports streaming, and shipped in Q4 2023. The company no longer sells speech-to-speech to new customers. The version table does not mark it end of life.", "what_it_is_for": "Voice conversion on a built voice. Streaming is marked supported. Dataset requirement is 10 or more minutes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/sts/legacy-sts": { "id": "resemble/sts/legacy-sts", "name": "Resemble Legacy STS", "maker": "resemble", "family": "resemble/sts", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2021-Q2", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Initial speech-to-speech model for basic voice conversion.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Resemble Legacy STS is Resemble AI's first listed speech-to-speech model, collected 2026-10-15. Code sts-legacy. Docs call it the initial converter for basic voice conversion, with a 10-minute dataset, streaming, and a Q2 2021 release.", "what_it_is_for": "Basic voice conversion on the old platform.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/tts/enhanced-tts-v1": { "id": "resemble/tts/enhanced-tts-v1", "name": "Resemble Enhanced TTS v1", "maker": "resemble", "family": "resemble/tts", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Resemble Enhanced TTS v1 is a retired Resemble AI hosted text-to-speech version, collected 2026-10-15. The historical code is tts-v1. Voices still on it cannot generate and must move to Resemble Ultra.", "what_it_is_for": "Historical platform TTS. The docs list it for reference only. It is not a valid model field on synthesis requests.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/tts/enhanced-tts-v2": { "id": "resemble/tts/enhanced-tts-v2", "name": "Resemble Enhanced TTS v2", "maker": "resemble", "family": "resemble/tts", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Resemble Enhanced TTS v2 is a retired Resemble AI hosted text-to-speech version, collected 2026-10-15. The historical code is tts-v2. Voices still on it cannot generate and must move to Resemble Ultra.", "what_it_is_for": "Historical platform TTS. The docs list it for reference only. It is not a valid model field on synthesis requests.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/tts/enhanced-tts-v3": { "id": "resemble/tts/enhanced-tts-v3", "name": "Resemble Enhanced TTS v3", "maker": "resemble", "family": "resemble/tts", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Resemble Enhanced TTS v3 is a retired Resemble AI hosted text-to-speech version, collected 2026-10-15. The historical code is tts-v3. Voices still on it cannot generate and must move to Resemble Ultra.", "what_it_is_for": "Historical platform TTS. The docs list it for reference only. It is not a valid model field on synthesis requests.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/tts/legacy-tts": { "id": "resemble/tts/legacy-tts", "name": "Resemble Legacy TTS", "maker": "resemble", "family": "resemble/tts", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Resemble Legacy TTS is a retired Resemble AI hosted text-to-speech version, collected 2026-10-15. The historical code is tts-legacy. Voices still on it cannot generate and must move to Resemble Ultra.", "what_it_is_for": "Historical platform TTS. The docs list it for reference only. It is not a valid model field on synthesis requests.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "resemble/tts/resemble-ultra": { "id": "resemble/tts/resemble-ultra", "name": "Resemble Ultra", "maker": "resemble", "family": "resemble/tts", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-04-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published. Changelog calls it an xAI-powered Ultra voice model. Which xAI model is not named.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Resemble Ultra is the hosted text-to-speech version Resemble's docs still call current, collected 2026-10-15. The version code is resemble-ultra. A 29 April 2026 changelog says the live Ultra voice model is powered by xAI. The company also says it no longer sells voice generation to new customers. Those statements are both kept.", "what_it_is_for": "Synthesis for voices on the Resemble platform. The API uses the model bound to the voice UUID. Do not pass resemble-ultra as a model field. Streaming is supported over synchronous HTTP and WebSocket.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rev/asr/multilingual-english-spanish": { "id": "rev/asr/multilingual-english-spanish", "name": "Multilingual English/Spanish", "maker": "rev", "family": "rev/asr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Multilingual English/Spanish is Rev AI's code-switching transcription mode, language code `en/es`. Rev added it to the US async API on 16 November 2023 and still documents it, including as a HIPAA language. The speech-to-text page calls it a model. It is not a separate transcriber id, and no architecture has been published. It was active on 15 October 2026.", "what_it_is_for": "Async jobs that contain both English and Spanish. Set `language` to `en/es`. The code is not an ISO code. If `language` is `auto` and `enable_multilingual` is true, sustained code-switching can be detected as this pair instead of a single language. The jobs text calls that pair \"Spanglish\". A specific `language` disables that flag.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rev/asr/v1-asr": { "id": "rev/asr/v1-asr", "name": "v1 ASR", "maker": "rev", "family": "rev/asr", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid system of separately trained components using hidden Markov models and Gaussian mixture models, per the 11 April 2022 v2 launch post. Not an end-to-end neural model.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "v1 ASR is Rev AI's older English speech-to-text model, the hybrid system that Reverb replaced. The async docs say it was deprecated on 8 September 2022 and is no longer available. Collection date is 15 October 2026. The streaming transcriber page still talks about a limited leftover window from 2022. That page is treated as stale unless Rev says otherwise.", "what_it_is_for": "It was the default English machine transcriber before the 2022 v2 migration. The April 2022 post says it was a hybrid of separately trained parts, using hidden Markov models and Gaussian mixture models, and that it handled pronunciation, noise, and multi-speaker audio less well than the end-to-end v2 model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rev/reverb/reverb": { "id": "rev/reverb/reverb", "name": "Reverb", "maker": "rev", "family": "rev/reverb", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary on the Rev AI API. A commercial self-hosted container and an unlimited-use license are sold separately. The open weights are the research checkpoint, not this production pipeline.", "release_date": "2022-01-14", "context_length": null, "max_output_tokens": null, "parameters": "280000000", "architecture": "Joint CTC/attention network in a modified WeNet toolkit. 18 Conformer encoder layers and a bidirectional attention decoder with 6 transformer layers (3 in each direction). Extra language-specific layers control verbatimicity. The production pipeline adds WFST beam search with a unigram language model, attention rescoring, overlap decoding, and formatting. The 2022 launch post describes the same v2 line as one end-to-end network replacing a hybrid HMM/GMM system, without a parameter count.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": null }, "summary_intro": "Reverb is Rev AI's default English speech-to-text model, sold on the API as transcriber `machine` and through a commercial self-hosted container. It has been the production v2 model since 2022 and was still the default on 15 October 2026. The open-weight checkpoint is cataloged separately as Reverb Research because Rev's own paper scores it worse than this production pipeline.", "what_it_is_for": "Rev uses Reverb for pre-recorded async transcription and for live captions. Async jobs accept a media URL or a local upload and return JSON, plain text, SRT, or VTT. Streaming uses a WebSocket or RTMP. The model is English only. Docs say a non-English `language` ignores `transcriber`. Callers can choose verbatim or readable output. Diarization runs by default on async jobs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rev/reverb/reverb-diarization-v1": { "id": "rev/reverb/reverb-diarization-v1", "name": "Reverb Diarization v1", "maker": "rev", "family": "rev/reverb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial research license for the weights. Hugging Face license field is other. Inference code in revdotcom/reverb is Apache-2.0.", "release_date": "2024-10-04", "context_length": null, "max_output_tokens": null, "parameters": "2200000", "architecture": "pyannote.audio 3.0 architecture, fine-tuned. Two LSTM layers, hidden size 256. SincNet features, unlike v2 which uses WavLM. About 2.2 million parameters. Trained 17 epochs, 4 days on one A100.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reverb Diarization v1 is Rev's smaller open-weight speaker diarization model, released 4 October 2024 as `Revai/reverb-diarization-v1`. It is a fine-tune of the pyannote 3.0 architecture on Rev's labels. It was still on Hugging Face on 15 October 2026. It is a named model, not the API flag `diarization_type=premium`.", "what_it_is_for": "Rev trains it for speaker-attributed transcripts, not as a standalone speaker-ID system. The loader is a pyannote pipeline. Output is an RTTM file. The API still does its own diarization and does not accept this model id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rev/reverb/reverb-diarization-v2": { "id": "rev/reverb/reverb-diarization-v2", "name": "Reverb Diarization v2", "maker": "rev", "family": "rev/reverb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial research license for the weights. Hugging Face license field is other. Inference code in revdotcom/reverb is Apache-2.0.", "release_date": "2024-10-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "pyannote pipeline that replaces SincNet features with WavLM. Rev calls it the more precise diarization model. Parameter count is not published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reverb Diarization v2 is Rev's larger open-weight diarization model, released 4 October 2024 as `Revai/reverb-diarization-v2`. It keeps the pyannote pipeline but swaps SincNet features for WavLM. Rev calls it the more precise of the two open models. It was still published on 15 October 2026. The API does not expose this id.", "what_it_is_for": "Speaker-attributed ASR research. Load it as a pyannote pipeline and write RTTM. A Hugging Face discussion on 4 October 2024 says a config was added so it can replace a pyannote 3.1 pipeline without a second repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rev/reverb/reverb-foreign-language": { "id": "rev/reverb/reverb-foreign-language", "name": "Reverb Foreign Language", "maker": "rev", "family": "rev/reverb", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": null }, "summary_intro": "Reverb Foreign Language is the name on Rev AI's live price card for non-English async transcription, at $0.30 per audio hour. It is not a published model card. Rev's own transcriber page says the Reverb ASR model is English only and that `transcriber` is ignored when a non-English language is set. The engine behind this SKU was still unnamed on 15 October 2026. This folder exists because the price card treats it as its own speech product. Confidence is medium.", "what_it_is_for": "Async transcription when `language` is a code other than English. The caller does not pick a model id. The price card lists Spanish, French, Chinese, Portuguese, and 53 more. The OpenAPI language enum is the authoritative code list collected here, including Mandarin as `cmn`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rev/reverb/reverb-research": { "id": "rev/reverb/reverb-research", "name": "Reverb Research", "maker": "rev", "family": "rev/reverb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial research license for the weights. Hugging Face lists the license as other. Inference code in github.com/revdotcom/reverb is Apache-2.0. Commercial use needs the API, a self-hosted license, or a custom license.", "release_date": "2024-10-04", "context_length": null, "max_output_tokens": null, "parameters": "280000000", "architecture": "Same neural model as production Reverb: modified WeNet, joint CTC/attention, 18 Conformer encoder layers, bidirectional 6-layer transformer decoder, verbatimicity control layers. The research release omits the production WFST, overlap decoding, and formatting post-process. Reported research decoding is CTC prefix beam search with attention rescoring on 20-second chunks.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reverb Research is the open-weight English ASR checkpoint Rev released on 4 October 2024 as `reverb_asr_v1` at `Revai/reverb-asr`. It is the neural model behind production Reverb, without the production WFST, overlap decoding, or formatting. It was still published on 15 October 2026. Rev's paper shows a clear WER gap versus the production pipeline, so this folder is not an alias of the API model.", "what_it_is_for": "Rev released it for research and experimentation. The GitHub loader is `wenet.load_model(\"reverb_asr_v1\")`. The CLI is `reverb --model reverb_asr_v1`. A verbatimicity control from 0 to 1 trades filler words and false starts against a cleaner transcript. Output is unformatted text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rev/reverb/reverb-turbo": { "id": "rev/reverb/reverb-turbo", "name": "Reverb Turbo", "maker": "rev", "family": "rev/reverb", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Shipped inside Rev's developer pipeline and, until deprecation, on the API. A separate public turbo weight file was not found. Commercial self-host was priced with the API at launch.", "release_date": "2024-10-02", "context_length": null, "max_output_tokens": null, "parameters": "280000000", "architecture": "int8 quantized version of the Reverb ASR neural model, for speed and memory. The paper does not say the production WFST is removed.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": null }, "summary_intro": "Reverb Turbo is Rev's int8 quantization of the Reverb ASR model, aimed at lower memory and faster inference. On the async API it is transcriber `low_cost`. Rev deprecated that option on 28 July 2026 and told new jobs to use `machine`. It was still documented on the HTML jobs page on 15 October 2026, but it was gone from the live price card and from the OpenAPI enum fetched the same day. Status is deprecated, not retired, because Rev has not published a removal date.", "what_it_is_for": "English async transcription when cost or footprint mattered more than the last bit of accuracy. The paper puts the int8 model inside the developer pipeline. The API description calls it a quantized ASR model in a low-cost environment. Lee Harris's launch post also sold a self-hosted Turbo container.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rev/whisper/whisper-fusion": { "id": "rev/whisper/whisper-fusion", "name": "Whisper Fusion", "maker": "rev", "family": "rev/whisper", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Rev describes it as a higher-quality ASR that combines multiple models and is typically better on rare words. Which models are fused is not published. It is not documented as an unmodified OpenAI Whisper checkpoint.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.005, "completion_per_m_usd": null }, "summary_intro": "Whisper Fusion is a Rev AI async transcriber, API value `fusion`. Rev added it for rare words and describes it as a higher-quality system that combines multiple models. The site-wide changelog dates that addition 6 January 2025. Rev deprecated it on 20 August 2026 and said it will be removed later. New jobs should use the default transcriber, which is Reverb. On 15 October 2026 the HTML jobs page still listed `fusion`, the live price card did not, and the OpenAPI enum listed only `machine` and `human`. Status is deprecated, not retired.", "what_it_is_for": "English async transcription when rare words mattered more than the default model. It is a Rev product name. Rev has not said it is stock OpenAI Whisper. Hosted Whisper Large and Whisper Medium are different price-card lines and are not given folders here.", "ratings": { "empirical_100": null, "lmsys_elo": -14998800, "coding_elo": -14998800, "swe_bench": -2499985.0, "humaneval": -1999930.0, "twelve_cs": 28.2, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reve/reve/reve-1-5": { "id": "reve/reve/reve-1-5", "name": "Reve v1.5", "maker": "reve", "family": "reve/reve", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-02-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed text-to-image model. Reve said it renders in native pixel space instead of a latent autoencoder, to preserve detail at 4K. Editing and reference images were not in this early release.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reve v1.5 is Reve's February 23, 2026 text-to-image model, released as an early version. Reve said it renders at 4K in native pixel space, without a latent autoencoder, and that editing and reference images were still to come. Reve 2.0 replaced it, and every earlier model, on June 3, 2026. Collected 2026-10-15.", "what_it_is_for": "Text-to-image. Reve highlighted detail, lighting, and world knowledge for places, objects, styles, and concepts. The launch post said this build did not yet edit images or take references.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reve/reve/reve-2-0": { "id": "reve/reve/reve-2-0", "name": "Reve 2.0", "maker": "reve", "family": "reve/reve", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-06-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Layout-first image model. A layout model writes a structured scene (regions with location, size, local description, and optional references or color). A renderer then produces native 4K pixels. Reve said the layout model is a continued pretrain and post-train of open-source Qwen language models on a layout representation, trained on a pipeline of billions of images. Absolute parameter count was not published. Vendor also said training used 10x fewer GPUs than competitors.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reve 2.0 is Reve's June 3, 2026 image model. It plans a structured layout, then renders native 4K pixels, and Reve said it replaced every model already in the app. Reve 2.1 followed on July 9, 2026. Collected 2026-10-15.", "what_it_is_for": "Text-to-image and direct editing of objects, text, and regions. Reve's pitch was that each image is a layout you can change, then re-render, instead of a one-shot picture. References and in-image text were part of the launch.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reve/reve/reve-2-1": { "id": "reve/reve/reve-2-1", "name": "Reve 2.1", "maker": "reve", "family": "reve/reve", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-07-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Layout-first image model. Planning and rendering are separate. The renderer is described as native 4K, up to 4K by 4K, about 16 megapixels, with no upscaling step. The model page says the step after Reve 1.0 used novel planning and diffusion architectures, more data, 3x the parameters of Reve 1.0, and more compute. It does not say that multiplier applies only to 2.1, and it does not print an absolute count. Reve also said 2.1 was trained at less than a tenth of the compute of the labs ranked just above and below it.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reve 2.1 is Reve's July 9, 2026 image model and the last named flagship. It plans a layout, then renders what Reve calls native 4K, and it is the model the app showed at sunset. First-party creation of new images stopped after September 27, 2026. Downloads remain until October 31, 2026. fal was still listing partner endpoints when those pages were checked. Collected 2026-10-15.", "what_it_is_for": "Create, edit, and remix images, with the layout left addressable so one region can be changed and re-rendered. Reve emphasized dense scenes, foreign-language text, and print-sized output. The July 14 API added ordinary create and edit calls plus experimental layout control.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reve/reve/reve-fast-edit": { "id": "reve/reve/reve-fast-edit", "name": "Reve Fast Edit", "maker": "reve", "family": "reve/reve", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2025-11-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed fast image-editing model. Reve described it as a speed and cost variant, not a new architecture. No parameter count was published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.007 }, "summary_intro": "Reve Fast Edit is the fast image-editing model Reve announced on November 18, 2025. The Fast Edit API was priced at $0.007 per image. Arena had already added an entry named reve-edit-fast to the Image Edit leaderboard on November 7, 2025. It is retired. Reve 2.0 replaced every earlier model on June 3, 2026. Collected 2026-10-15.", "what_it_is_for": "Reve said the fast models were for the same creative editing work at much lower latency and cost. The named API in the post is Fast Edit. Partners listed that day were Higgsfield, Flora, OpenArt, fal, and Replicate.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reve/reve/reve-image-1-0": { "id": "reve/reve/reve-image-1-0", "name": "Reve Image 1.0", "maker": "reve", "family": "reve/reve", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2025-03-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed text-to-image model. Reve later said Reve 1.0 was trained on structured descriptions of composition, relationships, style, and text rather than captions alone. No parameter count or architecture diagram was published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reve Image 1.0 is Reve's first public image model, released on March 24, 2025 as a free preview at preview.reve.art. Reve identified it with the earlier codename Halfmoon. Press called it Reve Image 1.0. The 2026 model page later refers to a previous model named Reve 1.0 and to the March 2025 Preview. Reve 2.0, released June 3, 2026, replaced every earlier model. This dossier was collected on 2026-10-15.", "what_it_is_for": "Reve's launch thread said the preview was for typing what you want, without prompt engineering, and that the first model was especially strong at text rendering, prompt adherence, and aesthetics. The same thread showed reference-photo uploads and plain-language changes to an existing image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reve/reve/reve-layout": { "id": "reve/reve/reve-layout", "name": "Reve Layout", "maker": "reve", "family": "reve/reve", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-06-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reve calls it a unified Large Layout Model. It reads layouts, instructions, and images, writes a hierarchical layout from an internal trace, and a separate renderer turns that layout into pixels. Reve said it continued pretraining and post-training open-source Qwen models for spatial reasoning over that layout format, on a pipeline of billions of images bootstrapped from dense human annotations. Output of this model is the layout, not the final image.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reve Layout is the planning model behind Reve 2.0 and Reve 2.1. Reve described it on June 3, 2026 as a Large Layout Model that writes a structured scene before a renderer makes pixels. On July 14, 2026 Reve exposed it through experimental API endpoints. First-party creation on reve.com ended after September 27, 2026. Collected 2026-10-15.", "what_it_is_for": "Reve said a layout is a hierarchical description in which every element has a location, a size, a local description, and optional attributes such as image references or color. People and agents can edit that structure, then render. The July 14 API post split layout creation, layout editing, and rendering so one step can be redone without the whole flow.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reve/reve/reve-v1": { "id": "reve/reve/reve-v1", "name": "Reve V1", "maker": "reve", "family": "reve/reve", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2025-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed image-editing model. Reve described single-image and multi-image edits that combine reference images into one output. No architecture or parameter count was published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reve V1 is the name Arena used, on the day Reve announced it, for Reve's first dedicated image-editing model. The October 1, 2025 blog calls it the first editing model and does not print a version number. It takes images plus instructions, including multi-image combinations. Reve 2.0 replaced every earlier model on June 3, 2026. Collected 2026-10-15.", "what_it_is_for": "Reve said the model is for single-image and multi-image edits, including removing shadows, editing landscapes, and restoring images. The product also had a region editor so people could move objects and recompose, then render.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reve/reve/reve-v1-1": { "id": "reve/reve/reve-v1-1", "name": "Reve V1.1", "maker": "reve", "family": "reve/reve", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed fine-tune of Reve's editing model. Reve said the change was a configuration fine-tune aimed at portraits and dynamic lighting. No parameter count was published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reve V1.1 is a fine-tune of Reve's editing model, announced by Reve on December 18, 2025 together with Reve V1.1 Fast. Reve said it changed the configuration around portraits and dynamic lighting. Arena listed it the same day. Reve 2.0 replaced every earlier model on June 3, 2026. Collected 2026-10-15.", "what_it_is_for": "Instruction-based image editing. Reve's examples were portrait restyling (\"make this look professional, DSLR, medium shot\") and lighting variations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reve/reve/reve-v1-1-fast": { "id": "reve/reve/reve-v1-1-fast", "name": "Reve V1.1 Fast", "maker": "reve", "family": "reve/reve", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2025-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed fast variant of the Reve V1.1 editing model. No parameter count or latency figure was published by Reve.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reve V1.1 Fast is the fast editing model Reve announced with Reve V1.1 on December 18, 2025. Arena scored it the same day, below the full V1.1 model. It is retired. Reve 2.0 replaced every earlier model on June 3, 2026. Collected 2026-10-15.", "what_it_is_for": "The same instruction-based editing job as Reve V1.1, offered as a faster variant. Reve did not publish a separate feature list for the fast model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reverso/context/reverso-context": { "id": "reverso/context/reverso-context", "name": "Reverso Context", "maker": "reverso", "family": "reverso/context", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2013", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Example-based bilingual concordance. Aligned bitexts are searched at word and phrase level. Suggested translations are computed by Reverso's algorithms and are not all hand-checked. When no dictionary match is found, the about page says the service falls back to Reverso NMT or splits the query.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reverso Context is Reverso's active example-based translation system. It searches aligned bilingual text and ranks translation suggestions. It is not a published neural checkpoint. The current about page says that if no dictionary match exists, the service uses Reverso's neural machine translation or splits the query. Collected 2026-10-15.", "what_it_is_for": "Reverso describes it as a translation search engine and contextual dictionary. Users type a word or phrase and get ranked translations plus real sentences from film dialogue, official documents, websites, and newspapers. Th\u00e9o Hoffenberg's July 2014 essay calls it a bilingual concordancer and an example-based dictionary. Suggestions above the examples are computed automatically and are not all checked by hand.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "reverso/nmt/neural-engine": { "id": "reverso/nmt/neural-engine", "name": "Reverso Neural Engine", "maker": "reverso", "family": "reverso/nmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural machine translation. Layer type, size, and whether separate models exist per language pair are not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reverso's closed neural machine translation engine, called Neural Machine Translation (NMT) and \"Reverso's neural engine\" on its own pages. It is the active translator behind the public text site and Reverso Documents as of 2026-10-15. Reverso does not publish a model id, card, or weight release.", "what_it_is_for": "The text translator says NMT produces natural-sounding translations and is already used by companies and translation agencies. Reverso Documents says each segment is translated by this neural engine, fine-tuned for professional and technical content, with layout kept and an optional segment review. It is a working translation, not a sworn or certified one.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rice/ldamp/ldamp": { "id": "rice/ldamp/ldamp", "name": "Learned D-AMP", "maker": "rice", "family": "rice/ldamp", "status": "active", "confidence": "high", "weights": "open-source", "license": "Rice University custom license", "release_date": "2017-04-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unrolled denoising-based approximate message passing. The NeurIPS paper specifies 10 AMP layers. Each layer has two DnCNN denoisers with tied weights: one updates the image estimate and one estimates divergence with a Monte Carlo approximation. LDIT is the same unrolling without the Onsager correction and is shipped in the same toolbox, not as its own product. The denoiser architecture is DnCNN.", "modalities": { "input": [ "measurements" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Learned D-AMP (LDAMP) is a Rice University DSP group network that unrolls denoising-based approximate message passing for compressive image recovery. Christopher Metzler, Ali Mousavi, and Richard Baraniuk posted it on arXiv on 2017-04-21. The last arXiv revision retrieved for this dossier is 2017-11-06. Code and a pointer to weights are in the Rice DSP D-AMP Toolbox. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says compressive image recovery needs algorithms that are both fast and accurate, and that earlier neural networks for the task were often tied to one measurement matrix. LDAMP mimics D-AMP so it can be trained, applied to different matrices, and checked against a state-evolution heuristic. The authors report better accuracy and run time than BM3D-AMP and NLR-CS on the tests in the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rice/prdeep/prdeep": { "id": "rice/prdeep/prdeep", "name": "prDeep", "maker": "rice", "family": "rice/prdeep", "status": "active", "confidence": "high", "weights": "open-source", "license": "Rice University custom license", "release_date": "2018-02-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Regularization-by-denoising phase retrieval (prRED) with a DnCNN denoiser. The paper calls that special case prDeep. DnCNN is a residual convolutional denoiser. The Rice release bundles trained DnCNN weights for several noise levels. The optimizer also uses HIO and FASTA. Philip Schniter is a coauthor at Ohio State. The code release is the Rice DSP repository.", "modalities": { "input": [ "measurements" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "prDeep is a Rice University DSP phase-retrieval method that plugs a convolutional denoiser into regularization-by-denoising. Christopher Metzler, Ashok Veeraraghavan, and Richard Baraniuk are at Rice. Philip Schniter is at Ohio State. The Rice DSP repository dates the code release 2018-02-28. The arXiv preprint is 2018-03-01, revised 2018-06-29. This dossier was collected on 2026-10-15.", "what_it_is_for": "The abstract says phase retrieval is used in ptychography and imaging through scattering, and that classical algorithms struggle in noise. prDeep is the case of their prRED optimizer that uses a DnCNN denoiser. The authors say it is robust to noise and is not limited to one measurement model. The README demo covers noisy coded diffraction and Fourier measurements.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rice/robotok/robotok": { "id": "rice/robotok/robotok", "name": "RoboTok", "maker": "rice", "family": "rice/robotok", "status": "active", "confidence": "high", "weights": "open-weights", "license": "FAIR Noncommercial Research License v1", "release_date": "2026-09-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Retrieval encoder: one learned query token, a 256-d projection of torso-relative two-hand trajectories shaped [T, 126], one cross-attention layer with 4 heads and sinusoidal positional encoding, then a 2-layer MLP to a 256-d embedding. T_max is 42. The 126 features are 21 joints times 3 coordinates times 2 hands. A companion 4-layer rotation-equivariant vector-neuron transformer estimates the torso frame. The checkpoint id on the card is the DTW design abs_21j_coords.", "modalities": { "input": [ "trajectory" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoboTok is a Rice University RobotPI Lab retrieval model that embeds human manipulation video by torso-relative 3D hand motion so similar demonstrations can be pulled from web video for dexterous robot learning. Weights were posted on 2026-09-02 under a noncommercial research license. One NVIDIA coauthor is on the paper. The releasing org is the Rice lab. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project page says RoboTok, given a query human manipulation video, retrieves other human demonstrations with similar hand motion for training dexterous policies. The representation is meant to compare behavior across camera viewpoint, scene appearance, and occlusion, and to stay small enough to index internet-scale video. The Hugging Face card says the encoder is trained to reproduce dynamic-time-warping rank over those trajectories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rice/shiftaddnet/shiftaddnet": { "id": "rice/shiftaddnet/shiftaddnet", "name": "ShiftAddNet", "maker": "rice", "family": "rice/shiftaddnet", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2020-10-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multiplication-free network that replaces convolutional and fully connected layers with a bit-shift layer followed by an add layer. The shift layer uses the same strides and kernel layout as the matching convolutional network. The add layer uses an L1 difference. A fixed-shift variant freezes the bit shifts and trains the add layers. Reported backbones are ResNet-20 and VGG19-small, plus an IoT DCNN. The paper says a ResNet-20 FP32 ShiftAddNet is about 1.3 MB of weights versus 1.03 MB for the ConvNet or AdderNet counterpart, and that FIX8 quantization reaches 0.4 MB.", "modalities": { "input": [ "image" ], "output": [ "class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ShiftAddNet is a multiplication-free network from Rice ECE, with coauthors at UT Austin and Alibaba DAMO Academy. The arXiv preprint is 2020-10-24. It was a NeurIPS 2020 poster. Rice released the code and pretrained checkpoints. The original GitHub URL, RICE-EIC/ShiftAddNet, now redirects to GATECH-EIC/ShiftAddNet. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper replaces multiplies in convolutional and fully connected layers with bit-shifts and additions, the operations hardware designers already use to avoid multipliers. The claim is energy-efficient training and inference without giving up the accuracy of a standard network, plus a knob that freezes the shifts and trains only the add layers. Experiments use image classification and small IoT sensing sets. Energy is measured on a ZYNQ-7 ZC706 FPGA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ricoh/llm/llm-13b": { "id": "ricoh/llm/llm-13b", "name": "Ricoh 13B Japanese LLM", "maker": "ricoh", "family": "ricoh/llm", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-01-31", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Continued pretraining of Meta Llama 2 13B on Japanese and English open corpora, with Ricoh data cleaning and curriculum learning. Later instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ricoh's first named commercial LLM, a 13-billion-parameter continued pretrain of Meta Llama 2 13B for Japanese business text. Announced 31 January 2024 and instruction-tuned by 3 June 2024. It is not on the 15 October 2026 lineup. Collected 2026-10-15.", "what_it_is_for": "Ricoh built it so a customer could add industry text and get a private LLM for summarization and question answering. Cloud delivery of those custom models started in April 2024, first in Japan.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ricoh/llm/llm-27b": { "id": "ricoh/llm/llm-27b", "name": "\u30ea\u30b3\u30fcLLM_27B", "maker": "ricoh", "family": "ricoh/llm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-08", "context_length": null, "max_output_tokens": null, "parameters": "27B", "architecture": "Non-reasoning chat-vector merge into Google Gemma 3 27B. Several chat vectors, including one from about 15,000 instruction examples, some Ricoh-built.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ricoh's compact Japanese text model, checkpoint gemma-3-Ricoh-27b-20251030. It is a non-reasoning chat-vector merge into Google Gemma 3 27B, announced 8 December 2025 and shipped on a Fujitsu PRIMERGY on-prem package. Still in the 15 October 2026 starter-kit lineup. Collected 2026-10-15.", "what_it_is_for": "Ricoh describes a fast first token and strong writing for general business use, at a lower deployment cost than the 70B model. It is not the reasoning model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ricoh/llm/llm-70b": { "id": "ricoh/llm/llm-70b", "name": "\u30ea\u30b3\u30fcLLM_70B", "maker": "ricoh", "family": "ricoh/llm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-21", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Current checkpoint Llama-3.3-Ricoh-70B-20251001 is a reasoning model merged from Llama-3.3-Swallow-70B-v0.4 (itself from Llama-3.3-70B-Instruct) plus Ricoh chat vectors and industry instruction data. Earlier snapshots used Llama-3-Swallow-70B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ricoh's 70-billion-parameter Japanese LLM, sold on-prem in the RICOH on-prem LLM starter kit. The current checkpoint, Llama-3.3-Ricoh-70B-20251001, adds multi-step reasoning and finance-domain instruction tuning on Llama-3.3-Swallow-70B-v0.4. The line started on 21 August 2024 with a Llama 3 Swallow continuation. Active as of 2026-10-15.", "what_it_is_for": "Ricoh positions it for answers with many constraints, long-document synthesis, and step-by-step planning. It began as a finance model (loan-review style tasks) and is offered more broadly. Customers can add their own data on premises.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ricoh/lmm/lmm-27b": { "id": "ricoh/lmm/lmm-27b", "name": "\u30ea\u30b3\u30fcLMM_27B", "maker": "ricoh", "family": "ricoh/lmm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-05", "context_length": null, "max_output_tokens": null, "parameters": "27B", "architecture": "Dense reasoning LMM. Qwen3.6-27B further trained with Ricoh reinforcement learning and curriculum learning. FP16 plus AWQ W8A16 and AWQ W4A16 quants.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ricoh's dense document reasoning model, checkpoint Qwen3.6-Ricoh-27B-20260522. Announced 5 June 2026 as a Qwen3.6-27B model trained with Ricoh reinforcement learning, and placed in the on-prem starter kit from late June. It is the successor of the GENIAC 32B LMM and is the high-accuracy choice next to the newer MoE model. Active as of 2026-10-15.", "what_it_is_for": "Multi-step reading of Japanese company documents: structure, cause and effect, key facts, and chart reasoning for manufacturing, insurance, and public paperwork. FP16, 8-bit, and 4-bit builds let the same model fit different GPUs. Industry fine-tuning is offered.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ricoh/lmm/lmm-32b": { "id": "ricoh/lmm/lmm-32b", "name": "\u30ea\u30b3\u30fcLMM_32B", "maker": "ricoh", "family": "ricoh/lmm", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-30", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Reasoning LMM continued from Qwen3-VL-32B-Instruct with Ricoh reinforcement learning and curriculum learning. GENIAC phase 3 base model. A 4-bit quant was used later in a document-reading workflow test.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ricoh's GENIAC phase-3 reasoning LMM, checkpoint Qwen3-VL-Ricoh-32B-20260227. It is a Qwen3-VL-32B-Instruct model trained with Ricoh reinforcement learning and curriculum learning to read complex Japanese charts. Development was announced complete on 30 March 2026. Ricoh now calls \u30ea\u30b3\u30fcLMM_27B its successor. Weights were not released; the free sibling is the 8B. Collected 2026-10-15.", "what_it_is_for": "Multi-step reading of Japanese business documents: tables, flow charts, and graphs, with reasoning Ricoh says can be checked in Japanese. Built to run on servers a company can keep on its own network. GENIAC work ran from August 2025 through the end of February 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ricoh/lmm/lmm-35b-a3b": { "id": "ricoh/lmm/lmm-35b-a3b", "name": "\u30ea\u30b3\u30fcLMM_35B-A3B", "maker": "ricoh", "family": "ricoh/lmm", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-15", "context_length": 256000, "max_output_tokens": null, "parameters": "35B total, about 3B active", "architecture": "MoE reasoning LMM. Qwen3.6-35B-A3B further trained with Ricoh reinforcement learning and curriculum learning adapted from the 27B LMM. Mixed NVFP4/FP8 quantization. Multi-token prediction supported.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ricoh's first MoE document model, announced 15 October 2026. It continues Alibaba Qwen3.6-35B-A3B with the reinforcement-learning recipe from \u30ea\u30b3\u30fcLMM_27B. About 35 billion parameters, about 3 billion active. Starter-kit delivery starts in November 2026, so status is announced. Collected 2026-10-15.", "what_it_is_for": "The speed-and-cost pair to the dense 27B LMM: Japanese chart reasoning, tool use, and short agent dialogs, including on one NVIDIA DGX Spark with the NVFP4 build. Ricoh names manufacturing specs, insurance clauses, and municipal forms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ricoh/lmm/lmm-70b": { "id": "ricoh/lmm/lmm-70b", "name": "\u30ea\u30b3\u30fcLMM_70B", "maker": "ricoh", "family": "ricoh/lmm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License for the language backbone, Apache-2.0 for Qwen2-VL-7B-Instruct, plus Qwen license terms, Gemma Terms of Use, and an MIT license on a model used to build training data. Ricoh says it adds no extra license.", "release_date": "2025-07-29", "context_length": null, "max_output_tokens": null, "parameters": "71B", "architecture": "Three-stage LMM: Qwen2-VL-7B-Instruct vision encoder, a Ricoh adapter, and Llama-3.1-Swallow-70B-Instruct-v0.3. Trained with about 6 million synthetic Japanese chart images. Served as an LLaVA-NeXT style model.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ricoh's GENIAC phase-2 document model. It connects a Qwen2-VL vision encoder to Llama-3.1-Swallow-70B-Instruct-v0.3 through a Ricoh adapter. Weights are public as Llama-3.1-70B-Instruct-multimodal-JP-Graph-v0.1. Development was complete on 10 June 2025, with a free release set for MIRU2025 starting 29 July 2025. Still listed on the 15 October 2026 lineup. Collected 2026-10-15.", "what_it_is_for": "Reading charts and diagrams in Japanese business documents: chart questions, screenshot summaries, and instructions that mix text and images. Ricoh trained it on about 6 million synthetic Japanese chart images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ricoh/lmm/lmm-8b": { "id": "ricoh/lmm/lmm-8b", "name": "\u30ea\u30b3\u30fcLMM_8B", "maker": "ricoh", "family": "ricoh/lmm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 plus Ricoh additional terms of use. Base Qwen3-VL weights have their own terms.", "release_date": "2026-03-30", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Reasoning vision-language model built from Qwen3-VL-8B-Thinking and Qwen3-VL-8B-Instruct. Japanese reasoning text, including inside think tags. Hugging Face reports 9B parameters and BF16.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ricoh's free GENIAC phase-3 reasoning LMM, published 30 March 2026 as Qwen-3-VL-Ricoh-8B-20260227. It is built from Qwen3-VL-8B Thinking and Instruct and is still on the 15 October 2026 lineup as the Hugging Face model. \u30ea\u30b3\u30fcLMM_9B is the successor. Collected 2026-10-15.", "what_it_is_for": "On-prem reading of Japanese documents that include charts, with the reasoning trace written in Japanese, including inside think tags. Ricoh also says customers can fine-tune it for an industry.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ricoh/lmm/lmm-9b": { "id": "ricoh/lmm/lmm-9b", "name": "\u30ea\u30b3\u30fcLMM_9B", "maker": "ricoh", "family": "ricoh/lmm", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-05", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Compact reasoning LMM. Qwen3.5-9B further trained with the same Ricoh reinforcement-learning and curriculum-learning recipe as the 27B LMM.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ricoh's compact document reasoning model, checkpoint Qwen3.5-Ricoh-9B-20260522. Announced with the 27B LMM on 5 June 2026. It succeeds the public 8B model and is meant for a single general-purpose GPU server. On 15 October 2026 the starter-kit slot was still marked planned, so this dossier uses preview. Collected 2026-10-15.", "what_it_is_for": "The same Japanese chart-and-document job as \u30ea\u30b3\u30fcLMM_27B, on less GPU. Ricoh says one general-purpose GPU server is enough for document reading and decision support.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ricoh/lmm/qwen25-vl-ricoh-32b": { "id": "ricoh/lmm/qwen25-vl-ricoh-32b", "name": "Qwen-2.5-VL-Ricoh-32B-20250918", "maker": "ricoh", "family": "ricoh/lmm", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-08", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Further training of Alibaba Qwen2.5-VL-32B-Instruct on about 600,000 Ricoh-built business images (text, pie charts, bar charts, flow charts). A 4-bit quantized build was also offered.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ricoh's compact document LMM on Alibaba Qwen2.5-VL-32B-Instruct, announced 8 January 2026. The id date is 18 September 2025. It is a named model, not a snapshot of the later Qwen3.6 27B product. It is missing from the 15 October 2026 lineup. Collected 2026-10-15.", "what_it_is_for": "Reading Japanese company documents that mix prose with charts and slides, in a smaller package than the 70B LMM. Ricoh also offered a 4-bit quant and planned to put the model on the on-prem starter kit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ricoh/safeguard/llama-ricoh-safeguard": { "id": "ricoh/safeguard/llama-ricoh-safeguard", "name": "Llama-Ricoh-SafeGuard", "maker": "ricoh", "family": "ricoh/safeguard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.3 Community License and Gemma Terms of Use, plus Ricoh additional terms, for the public 20260520 checkpoint. The base model is Llama-3.1-Swallow-8B-Instruct-v0.5.", "release_date": "2025-08-28", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Guardrail classifier. The 28 August 2025 input model uses Llama-3.1-Swallow-8B-Instruct-v0.3. The December 2025 input-output update and the public May 2026 checkpoint use Llama-3.1-Swallow-8B-Instruct-v0.5. Trained on several thousand examples in 14 Llama Guard 3 harm labels. Public checkpoint is BF16. Ricoh also describes its own quantization.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ricoh's Japanese guardrail model. The 28 August 2025 input checker used Llama-3.1-Swallow-8B-Instruct-v0.3. From December 2025 the line also checks model outputs, and the public 20 May 2026 weights use Llama-3.1-Swallow-8B-Instruct-v0.5. Labels follow 14 Llama Guard 3 categories. Active as of 2026-10-15.", "what_it_is_for": "A checker in front of and behind another LLM. It is not a general writing model. Ricoh trained it on several thousand of its own examples covering violence, crime, sexual content, child sexual exploitation, privacy, hate, self-harm, and code abuse, among the 14 labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "riken/fugaku-llm/fugaku-llm-13b": { "id": "riken/fugaku-llm/fugaku-llm-13b", "name": "Fugaku-LLM-13B", "maker": "riken", "family": "riken/fugaku-llm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Fugaku-LLM Terms of Use (custom). Japanese text governs. Commercial and non-commercial use, modification, and redistribution are allowed if the terms travel with the weights or the service and modifications are disclosed. Not an OSI license. Provided as-is, with no warranty.", "release_date": "2024-05-10", "context_length": 2048, "max_output_tokens": null, "parameters": "13 billion", "architecture": "GPT-2 decoder-only Transformer, trained from scratch. Tokenizer is llm-jp-tokenizer v2.2, vocabulary split code10k_en20k_ja30k. The Hugging Face page lists tensor type F32. Layer width is not on the ungated card; see gaps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fugaku-LLM-13B is the base large language model released on 10 May 2024 by a consortium that includes RIKEN. It was trained from scratch on the RIKEN supercomputer Fugaku. Weights remain public under a custom terms-of-use license. This dossier was collected on 2026-10-15.", "what_it_is_for": "The joint release describes a Japanese-capable foundation model for research and business, including later use in AI for Science. RIKEN's stated role was distributed parallelization and communication acceleration on Fugaku. Fujitsu ran pre-training and the later fine-tune. The base checkpoint is a continuation model, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "riken/fugaku-llm/fugaku-llm-13b-instruct": { "id": "riken/fugaku-llm/fugaku-llm-13b-instruct", "name": "Fugaku-LLM-13B-instruct", "maker": "riken", "family": "riken/fugaku-llm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Fugaku-LLM Terms of Use (custom). Japanese text governs. Commercial and non-commercial use, modification, and redistribution are allowed if the terms travel with the weights or the service and modifications are disclosed. Not an OSI license. Provided as-is, with no warranty.", "release_date": "2024-05-10", "context_length": 2048, "max_output_tokens": null, "parameters": "13 billion", "architecture": "Instruction-tuned GPT-2 decoder-only Transformer, same family as Fugaku-LLM-13B. Tokenizer is llm-jp-tokenizer v2.2, vocabulary split code10k_en20k_ja30k. Card prompt format is a Japanese system line, then ### \u6307\u793a: and ### \u5fdc\u7b54:.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fugaku-LLM-13B-instruct is the instruction-tuned Fugaku-LLM checkpoint released with the 10 May 2024 consortium announcement. RIKEN is one of the named developers. Weights are still public under the Fugaku-LLM Terms of Use. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card presents it as the instruction-following Fugaku-LLM, aimed at Japanese dialogue and task responses. The press release highlights humanities and social-science tasks and keigo-style Japanese. It is not a tool-calling or multimodal model in the published card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rime/arcana/arcana": { "id": "rime/arcana/arcana", "name": "Arcana", "maker": "rime", "family": "rime/arcana", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Cloud ids arcana, arcanav2, and arcanav3 are routed to Coda. Existing on-prem images can still be pulled and are unsupported.", "release_date": "2025-04-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed spoken-language TTS. Rime said a pretrained LLM autoregressively decodes flattened codec tokens from coarsest to finest, trained in three stages: extra pretraining on text-audio pairs, supervised fine-tuning on Rime's conversational data, then speaker-specific fine-tuning. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arcana is Rime's first spoken-language TTS in the Arcana line, released to the API as `modelId: arcana` on 2025-04-24. Later posts call it Arcana v1. The cloud id was retired on 2026-08-15 and is now answered by Coda. Weights are closed. Collected on 2026-10-15.", "what_it_is_for": "Rime launched Arcana for expressive, conversational speech next to the faster Mist v2 line. The launch post named eight flagship speakers and a beta that could invent a voice from a description and could laugh. It was aimed at creative and support agents rather than the highest-volume Mist deployments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rime/arcana/arcana-v2": { "id": "rime/arcana/arcana-v2", "name": "Arcana v2", "maker": "rime", "family": "rime/arcana", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Cloud model id arcanav2 is routed to Coda. On-prem images remain pullable and unsupported after 2026-08-15.", "release_date": "2025-08-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed Arcana TTS. The launch post says it was pretrained from scratch and fine-tuned on Rime's in-studio conversational data, and that it is a direct replacement for Arcana v1. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arcana v2 is Rime's August 2025 Arcana text-to-speech generation, with more languages and on-prem deployment than Arcana v1. Rime told existing callers to keep sending `arcana`. The explicit id `arcanav2` is retired with the rest of Arcana as of 2026-08-15 and is served by Coda. Weights are closed. Collected on 2026-10-15.", "what_it_is_for": "Rime described v2 as a faster, more expressive replacement for v1, for cloud, VPC, or on-prem voice agents. The launch post highlights code-switching across English, Spanish, and Spanglish and a larger flagship roster.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rime/arcana/arcana-v3": { "id": "rime/arcana/arcana-v3", "name": "Arcana v3", "maker": "rime", "family": "rime/arcana", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Cloud model id arcanav3 is routed to Coda. On-prem images remain pullable and unsupported after 2026-08-15.", "release_date": "2026-02-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed Arcana TTS. The launch post says two models sit behind the product: a faster English-Spanish bilingual model and a multilingual model. Coda's launch post contrasts that design with Coda's two jointly trained decoders. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.04 }, "summary_intro": "Arcana v3 is Rime's February 2026 Arcana flagship, built for multilingual code-switching and lower latency than Arcana v2. Callers kept `modelId: arcana`. The cloud retired `arcana`, `arcanav2`, and `arcanav3` on 2026-08-15 and serves them with Coda. Weights are closed. Collected on 2026-10-15.", "what_it_is_for": "Rime positioned v3 as authentic TTS that could hold a voice identity across languages, return word timestamps, and run more than 100 generations on one machine. It was the flagship until Coda.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rime/coda/coda": { "id": "rime/coda/coda", "name": "Coda", "maker": "rime", "family": "rime/coda", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Cloud API, VPC, and licensed on-prem images.", "release_date": "2026-05-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed TTS. Rime describes an LLM backbone plus a speech inference engine trained on full-duplex conversation. The launch post says two jointly trained autoregressive decoders, one semantic and one acoustic, with 1 frame equal to 80 ms of audio and 15 acoustic codes.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "Coda is Rime's flagship text-to-speech model, released to the cloud API on 2026-05-19 and still the recommended model for new apps. It is closed, served from Rime's cloud and as a licensed on-prem image, and it replaced Arcana on the cloud API on 2026-08-15. Collected on 2026-10-15.", "what_it_is_for": "Rime tells new apps to start with Coda when voice quality matters more than the lowest time to first audio. The launch post describes speech trained on full-duplex conversations, with word timestamps for interruption handling. A request that omits `modelId` is Mist v3, not Coda.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rime/mist/mist": { "id": "rime/mist/mist", "name": "Mist", "maker": "rime", "family": "rime/mist", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. The cloud API still accepts modelId mist. Docs call it legacy.", "release_date": "2024-02-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First named Mist text-to-speech model. Rime's launch post called it the first TTS model it shipped that reproduces genre-specific voice characteristics, at sub-200 ms for many prompts. Later Mist docs describe a phoneme-first architecture. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mist is Rime's first named Mist text-to-speech model. Rime's CEO introduced it publicly on 2024-02-29. The cloud API still accepts `mist`, and the docs call that id legacy. Weights are closed. It is English-only in the public catalog. Collected on 2026-10-15.", "what_it_is_for": "The launch post described conversational voices for real-time phone systems, with filler words and backchannels, at sub-200 ms for many prompts. Mist v2 and then Mist v3 replaced it for new work. The API has not removed the id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rime/mist/mist-v2": { "id": "rime/mist/mist-v2", "name": "Mist v2", "maker": "rime", "family": "rime/mist", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. The cloud API still accepts modelId mistv2. Current docs no longer specify the model and point readers at Mist v3.", "release_date": "2025-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed Mist-family TTS. Rime describes a phoneme-first engine. An indexed models page said inline pronunciation control was the feature that distinguished v2. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mist v2 is Rime's second Mist text-to-speech model, announced in February 2025 and still accepted as `mistv2` on the cloud API. Current docs call it legacy and tell new work to use Mist v3. Weights are closed. Collected on 2026-10-15.", "what_it_is_for": "The launch post sold Mist v2 as a faster, more conversational successor to Mist for high-volume phone and IVR traffic, with English and Spanish at announcement and pronunciation control. Rime now points those jobs at Mist v3 or Coda.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rime/mist/mist-v3": { "id": "rime/mist/mist-v3", "name": "Mist v3", "maker": "rime", "family": "rime/mist", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Cloud API, VPC, and licensed on-prem images.", "release_date": "2026-04-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed low-latency TTS in the Mist family. Rime describes a phoneme-first Mist architecture and, for v3, a new inference path aimed at time to first audio. Parameter count is not published. The on-prem image name includes omni; that is an image path, not a separate model id.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.03 }, "summary_intro": "Mist v3 is Rime's low-latency text-to-speech model and the API default when `modelId` is omitted. It went on the cloud API on 2026-04-06, reached general availability on 2026-04-09, and is still active. Weights are closed. Collected on 2026-10-15.", "what_it_is_for": "Rime points to Mist v3 when time to first audio matters more than Coda's quality scores, and when an app needs custom pauses or English inline pronunciation. The launch post says the voices stay familiar and the serving path is what changed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rime/rimecaster/rimecaster": { "id": "rime/rimecaster/rimecaster", "name": "Rimecaster", "maker": "rime", "family": "rime/rimecaster", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 for the released checkpoint. Rime calls it open source. Training data stays proprietary, so this dossier uses open-weights rather than open-source.", "release_date": "2025-04-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speaker encoder adapted from NVIDIA TitaNet. The model card says it mirrors TitaNet and raises the embedding size from 192 to 768. Depth-wise separable conv1D with global context. NeMo EncDecSpeakerLabelModel. Parameter count is not on the card.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rimecaster is Rime's open speaker-embedding model, announced on 2025-04-10 and published on Hugging Face as `rimelabs/rimecaster`. It is not a text-to-speech voice. It turns a recording into a speaker vector for conditioning other voice models. The checkpoint is released under CC-BY-4.0. Collected on 2026-10-15.", "what_it_is_for": "Rime shipped it so others could condition TTS and speaker tasks on embeddings from conversational speech, not only from audiobook-style data. The card says it was developed with TTS in mind. It does not synthesize audio by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/bakeneko/deepseek-r1-distill-qwen2-5-bakeneko-32b": { "id": "rinna/bakeneko/deepseek-r1-distill-qwen2-5-bakeneko-32b", "name": "DeepSeek R1 Distill Qwen2.5 Bakeneko 32B", "maker": "rinna", "family": "rinna/bakeneko", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-13", "context_length": 131072, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen2 32B: 64 layers, 5120 hidden, 40 heads, intermediate 27648, vocab 152,064, max_position_embeddings 131072. DeepSeek-R1 chat format.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released DeepSeek R1 Distill Qwen2.5 Bakeneko 32B as a named model in the bakeneko line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Reasoning model: chat-vector merge of Bakeneko 32B with DeepSeek-R1-Distill-Qwen-32B, then ORPO on DeepSeek-R1 traces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/bakeneko/qwen2-5-bakeneko-32b": { "id": "rinna/bakeneko/qwen2-5-bakeneko-32b", "name": "Qwen2.5 Bakeneko 32B", "maker": "rinna", "family": "rinna/bakeneko", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-13", "context_length": 131072, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen2 (Qwen2ForCausalLM): 64 layers, 5120 hidden, 40 heads, intermediate 27648, vocab 152,064, max_position_embeddings 131072. Continual pretrain of Qwen2.5-32B. Original Qwen2.5 tokenizer. Trained with LitGPT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Qwen2.5 Bakeneko 32B as a named model in the bakeneko line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese continual pretrain of Qwen2.5-32B on about 18B tokens. Bakeneko is the youkai \u5316\u3051\u732b.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/bakeneko/qwen2-5-bakeneko-32b-instruct": { "id": "rinna/bakeneko/qwen2-5-bakeneko-32b-instruct", "name": "Qwen2.5 Bakeneko 32B Instruct", "maker": "rinna", "family": "rinna/bakeneko", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-13", "context_length": 32768, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen2 32B: 64 layers, 5120 hidden, 40 heads, intermediate 27648, vocab 152,064. This checkpoint's config max_position_embeddings is 32768. The base checkpoint's config is 131072. Qwen2.5 chat format.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Qwen2.5 Bakeneko 32B Instruct as a named model in the bakeneko line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Instruction model of Qwen2.5 Bakeneko 32B, made with a chat vector and SimPO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/bakeneko/qwen2-5-bakeneko-32b-instruct-v2": { "id": "rinna/bakeneko/qwen2-5-bakeneko-32b-instruct-v2", "name": "Qwen2.5 Bakeneko 32B Instruct v2", "maker": "rinna", "family": "rinna/bakeneko", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-19", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen2 32B class, described by the series table as the QwQ Bakeneko merged instruction-tuning model. Layer count was not re-read from this repo's config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Qwen2.5 Bakeneko 32B Instruct v2 as a named model in the bakeneko line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Later Bakeneko instruction model. The series table calls it the QwQ Bakeneko merged instruction-tuning model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/bakeneko/qwq-bakeneko-32b": { "id": "rinna/bakeneko/qwq-bakeneko-32b", "name": "QwQ Bakeneko 32B", "maker": "rinna", "family": "rinna/bakeneko", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-13", "context_length": 131072, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen2 32B: 64 layers, 5120 hidden, 40 heads, intermediate 27648, vocab 152,064, max_position_embeddings 131072. Qwen QwQ chat format.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released QwQ Bakeneko 32B as a named model in the bakeneko line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Reasoning model: chat-vector merge of Bakeneko 32B with QwQ-32B, then ORPO on DeepSeek-R1 traces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/baku/gemma-2-baku-2b": { "id": "rinna/baku/gemma-2-baku-2b", "name": "Gemma 2 Baku 2B", "maker": "rinna", "family": "rinna/baku", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma Terms of Use", "release_date": "2024-10-03", "context_length": 8192, "max_output_tokens": null, "parameters": "2B", "architecture": "Gemma 2 (Gemma2ForCausalLM): 26 layers, 2304 hidden, 8 heads, intermediate 9216, vocab 256,000, max_position_embeddings 8192. Continual pretrain of google/gemma-2-2b.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Gemma 2 Baku 2B as a named model in the baku line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese continual pretrain of Gemma 2 2B on about 80B tokens. Baku is the youkai \u734f.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/baku/gemma-2-baku-2b-it": { "id": "rinna/baku/gemma-2-baku-2b-it", "name": "Gemma 2 Baku 2B Instruct", "maker": "rinna", "family": "rinna/baku", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma Terms of Use", "release_date": "2024-10-03", "context_length": 8192, "max_output_tokens": null, "parameters": "2B", "architecture": "Same Gemma 2 2B shape. Gemma 2 chat format.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Gemma 2 Baku 2B Instruct as a named model in the baku line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Instruction model of Gemma 2 Baku 2B, made with a chat vector and ORPO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/bilingual-gpt-neox/bilingual-gpt-neox-4b": { "id": "rinna/bilingual-gpt-neox/bilingual-gpt-neox-4b", "name": "Bilingual GPT-NeoX 4B", "maker": "rinna", "family": "rinna/bilingual-gpt-neox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-07-31", "context_length": 2048, "max_output_tokens": null, "parameters": "3.8B", "architecture": "GPT-NeoX: 36 layers, 2816 hidden, 22 heads, intermediate 11264, vocab 65,536, max_position_embeddings 2048. The card states 3.8 billion parameters. The repo name says 4B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Bilingual GPT-NeoX 4B as a named model in the bilingual-gpt-neox line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "English-Japanese bilingual GPT-NeoX pretrained from scratch on about 524B tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/bilingual-gpt-neox/bilingual-gpt-neox-4b-8k": { "id": "rinna/bilingual-gpt-neox/bilingual-gpt-neox-4b-8k", "name": "Bilingual GPT-NeoX 4B 8K", "maker": "rinna", "family": "rinna/bilingual-gpt-neox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-07-31", "context_length": 8192, "max_output_tokens": null, "parameters": "3.8B", "architecture": "Same 3.8B GPT-NeoX as bilingual-gpt-neox-4b. config max_position_embeddings stays 2048. rope_scaling is linear with factor 4.0, which is how the card extends context from 2048 to 8192.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Bilingual GPT-NeoX 4B 8K as a named model in the bilingual-gpt-neox line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Long-context finetune of Bilingual GPT-NeoX 4B. Context was extended from 2048 to 8192 with RoPE positional interpolation on 1.5B extra tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/bilingual-gpt-neox/bilingual-gpt-neox-4b-instruction-ppo": { "id": "rinna/bilingual-gpt-neox/bilingual-gpt-neox-4b-instruction-ppo", "name": "Bilingual GPT-NeoX 4B Instruction PPO", "maker": "rinna", "family": "rinna/bilingual-gpt-neox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-08-02", "context_length": 2048, "max_output_tokens": null, "parameters": "3.8B", "architecture": "Same 3.8B GPT-NeoX. max_position_embeddings 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Bilingual GPT-NeoX 4B Instruction PPO as a named model in the bilingual-gpt-neox line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "SFT plus PPO alignment of Bilingual GPT-NeoX 4B, following the InstructGPT recipe.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/bilingual-gpt-neox/bilingual-gpt-neox-4b-instruction-sft": { "id": "rinna/bilingual-gpt-neox/bilingual-gpt-neox-4b-instruction-sft", "name": "Bilingual GPT-NeoX 4B Instruction SFT", "maker": "rinna", "family": "rinna/bilingual-gpt-neox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-08-02", "context_length": 2048, "max_output_tokens": null, "parameters": "3.8B", "architecture": "Same 3.8B GPT-NeoX as the July 31 base. max_position_embeddings 2048. Vocab 65,536.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Bilingual GPT-NeoX 4B Instruction SFT as a named model in the bilingual-gpt-neox line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Instruction SFT of Bilingual GPT-NeoX 4B. The weights on the Hub are the August 2, 2023 retrain, not the July 31 file that rinna removed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/bilingual-gpt-neox/bilingual-gpt-neox-4b-minigpt4": { "id": "rinna/bilingual-gpt-neox/bilingual-gpt-neox-4b-minigpt4", "name": "Bilingual GPT-NeoX 4B MiniGPT-4", "maker": "rinna", "family": "rinna/bilingual-gpt-neox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-07-31", "context_length": null, "max_output_tokens": null, "parameters": "3.8B", "architecture": "MiniGPT-4 style: bilingual GPT-NeoX 4B as the LLM, a ViT plus Q-Former vision encoder from BLIP-2, and a linear connector. Only MiniGPT-4's first pretraining stage was run.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Bilingual GPT-NeoX 4B MiniGPT-4 as a named model in the bilingual-gpt-neox line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "English-Japanese image conversation model. It answers text prompts about an image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/clip/japanese-clip-vit-b-16": { "id": "rinna/clip/japanese-clip-vit-b-16", "name": "Japanese CLIP ViT-B/16", "maker": "rinna", "family": "rinna/clip", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-05-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP: ViT-B/16 image encoder initialized from AugReg vit-base-patch16-224, and a 12-layer BERT text encoder. Trained with locked-image tuning (image encoder frozen, text encoder trained) on CC12M captions translated into Japanese.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese CLIP ViT-B/16 as a named model in the clip line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese CLIP for image-text embeddings and zero-shot classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/cloob/japanese-cloob-vit-b-16": { "id": "rinna/cloob/japanese-cloob-vit-b-16", "name": "Japanese CLOOB ViT-B/16", "maker": "rinna", "family": "rinna/cloob", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-05-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLOOB: ViT-B/16 image encoder initialized from AugReg vit-base-patch16-224, and a 12-layer BERT text encoder. Trained on CC12M captions translated into Japanese. Uses the InfoLOOB loss rather than the original CLIP loss.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese CLOOB ViT-B/16 as a named model in the cloob line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese CLOOB image-text model. The 2024 paper says it was the best of the compared models on Japanese-prompt ImageNet zero-shot classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/data2vec/japanese-data2vec-audio-base": { "id": "rinna/data2vec/japanese-data2vec-audio-base", "name": "Japanese data2vec Audio Base", "maker": "rinna", "family": "rinna/data2vec", "status": "retired", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-07", "context_length": null, "max_output_tokens": null, "parameters": "93.2M", "architecture": "data2vec audio base. The mirror card says training code is the official data2vec repository.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese data2vec Audio Base as a named model in the data2vec line. Status on 2026-10-15: retired. This dossier was collected on 2026-10-15.", "what_it_is_for": "Self-supervised Japanese data2vec audio encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/gpt/japanese-gpt-1b": { "id": "rinna/gpt/japanese-gpt-1b", "name": "Japanese GPT 1B", "maker": "rinna", "family": "rinna/gpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-01-26", "context_length": 1024, "max_output_tokens": null, "parameters": "1.3B", "architecture": "GPT-2 stack (GPT2LMHeadModel): 24 layers, 2048 hidden, 16 heads, vocab 44,928. SentencePiece tokenizer with emoji and symbol tokens. n_positions 1024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese GPT 1B as a named model in the gpt line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "1.3B-parameter Japanese GPT trained from scratch by rinna on Japanese C4, Japanese CC-100, and Japanese Wikipedia.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/gpt-neox/japanese-gpt-neox-3-6b": { "id": "rinna/gpt-neox/japanese-gpt-neox-3-6b", "name": "Japanese GPT-NeoX 3.6B", "maker": "rinna", "family": "rinna/gpt-neox", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-03-17", "context_length": 2048, "max_output_tokens": null, "parameters": "3.6B", "architecture": "GPT-NeoX (GPTNeoXForCausalLM): 36 layers, 2816 hidden, 22 heads, intermediate 11264, vocab 32,000, max_position_embeddings 2048. Same shape as the still-public SFT-v2 and PPO configs. SentencePiece with byte fallback.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese GPT-NeoX 3.6B as a named model in the gpt-neox line. Status on 2026-10-15: retired. This dossier was collected on 2026-10-15.", "what_it_is_for": "3.6B Japanese GPT-NeoX pretrained from scratch. It is the base for rinna's 3.6B instruction models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/gpt-neox/japanese-gpt-neox-3-6b-instruction-ppo": { "id": "rinna/gpt-neox/japanese-gpt-neox-3-6b-instruction-ppo", "name": "Japanese GPT-NeoX 3.6B Instruction PPO", "maker": "rinna", "family": "rinna/gpt-neox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-03-31", "context_length": 2048, "max_output_tokens": null, "parameters": "3.6B", "architecture": "GPT-NeoX: 36 layers, 2816 hidden, 22 heads, intermediate 11264, vocab 32,000, max_position_embeddings 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese GPT-NeoX 3.6B Instruction PPO as a named model in the gpt-neox line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "RLHF-aligned instruction model. SFT then PPO, following the InstructGPT setup, starting from the 3.6B SFT-v2 checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/gpt-neox/japanese-gpt-neox-3-6b-instruction-sft": { "id": "rinna/gpt-neox/japanese-gpt-neox-3-6b-instruction-sft", "name": "Japanese GPT-NeoX 3.6B Instruction SFT", "maker": "rinna", "family": "rinna/gpt-neox", "status": "retired", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2023-03-17", "context_length": 2048, "max_output_tokens": null, "parameters": "3.6B", "architecture": "Same 36-layer, 2816-hidden GPT-NeoX as japanese-gpt-neox-3.6b.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese GPT-NeoX 3.6B Instruction SFT as a named model in the gpt-neox line. Status on 2026-10-15: retired. This dossier was collected on 2026-10-15.", "what_it_is_for": "First supervised instruction tune of Japanese GPT-NeoX 3.6B. The later SFT-v2 card says this model used a different data split.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/gpt-neox/japanese-gpt-neox-3-6b-instruction-sft-v2": { "id": "rinna/gpt-neox/japanese-gpt-neox-3-6b-instruction-sft-v2", "name": "Japanese GPT-NeoX 3.6B Instruction SFT v2", "maker": "rinna", "family": "rinna/gpt-neox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-03-31", "context_length": 2048, "max_output_tokens": null, "parameters": "3.6B", "architecture": "GPT-NeoX: 36 layers, 2816 hidden, 22 heads, intermediate 11264, vocab 32,000, max_position_embeddings 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese GPT-NeoX 3.6B Instruction SFT v2 as a named model in the gpt-neox line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Instruction-following conversational agent finetuned from Japanese GPT-NeoX 3.6B. It uses a different data split than the first SFT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/gpt-neox/japanese-gpt-neox-small": { "id": "rinna/gpt-neox/japanese-gpt-neox-small", "name": "Japanese GPT-NeoX Small", "maker": "rinna", "family": "rinna/gpt-neox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-09-05", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "GPT-NeoX (GPTNeoXForCausalLM): 12 layers, 768 hidden, 12 heads, intermediate 3072, vocab 44,416, max_position_embeddings 2048. Rotary embeddings. Trained with EleutherAI GPT-NeoX code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese GPT-NeoX Small as a named model in the gpt-neox line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Small Japanese GPT-NeoX pretrained from scratch on Japanese CC-100, Japanese C4, and Japanese Wikipedia.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/gpt2/japanese-gpt2-medium": { "id": "rinna/gpt2/japanese-gpt2-medium", "name": "Japanese GPT-2 Medium", "maker": "rinna", "family": "rinna/gpt2", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2021-04-07", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 (GPT2LMHeadModel): 24 layers, 1024 hidden, 16 heads, vocab 32,000. SentencePiece tokenizer. n_positions 1024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese GPT-2 Medium as a named model in the gpt2 line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Medium Japanese GPT-2, rinna's earliest public GPT-2 checkpoint, trained on Japanese CC-100 and Japanese Wikipedia.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/gpt2/japanese-gpt2-small": { "id": "rinna/gpt2/japanese-gpt2-small", "name": "Japanese GPT-2 Small", "maker": "rinna", "family": "rinna/gpt2", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2021-08-25", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 (GPT2LMHeadModel): 12 layers, 768 hidden, 12 heads, vocab 32,000. SentencePiece tokenizer. n_positions 1024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese GPT-2 Small as a named model in the gpt2 line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Small Japanese GPT-2 for causal language modeling, trained by rinna on Japanese CC-100 and Japanese Wikipedia.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/gpt2/japanese-gpt2-xsmall": { "id": "rinna/gpt2/japanese-gpt2-xsmall", "name": "Japanese GPT-2 XSmall", "maker": "rinna", "family": "rinna/gpt2", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2021-08-25", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 (GPT2LMHeadModel): 6 layers, 512 hidden, 8 heads, vocab 32,000. SentencePiece tokenizer. n_positions 1024.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese GPT-2 XSmall as a named model in the gpt2 line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Extra-small Japanese GPT-2 for causal language modeling, trained by rinna on Japanese CC-100 and Japanese Wikipedia.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/hubert/japanese-hubert-base": { "id": "rinna/hubert/japanese-hubert-base", "name": "Japanese HuBERT Base", "maker": "rinna", "family": "rinna/hubert", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HuBERT base. Convolutional waveform encoder plus a BERT encoder, trained with masked prediction of k-means units. The 2024 paper describes this checkpoint.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese HuBERT Base as a named model in the hubert line. Status on 2026-10-15: retired. This dossier was collected on 2026-10-15.", "what_it_is_for": "Self-supervised Japanese speech encoder. It is also the audio encoder initialized inside Nue ASR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/hubert/japanese-hubert-large": { "id": "rinna/hubert/japanese-hubert-large", "name": "Japanese HuBERT Large", "maker": "rinna", "family": "rinna/hubert", "status": "retired", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HuBERT large. Layer and hidden sizes were not re-read from a config.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese HuBERT Large as a named model in the hubert line. Status on 2026-10-15: retired. This dossier was collected on 2026-10-15.", "what_it_is_for": "Larger Japanese HuBERT speech encoder, released with wav2vec 2.0 Base and data2vec Audio Base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/koemotion/koeiromap": { "id": "rinna/koemotion/koeiromap", "name": "Koeiromap", "maker": "rinna", "family": "rinna/koemotion", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Text-to-speech model with a learned 2D speaker map. A point (speaker_x, speaker_y) picks a voice, including voices between the recorded speakers.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Koeiromap as a named model in the koemotion line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "rinna's speaker-map speech synthesizer. It is the voice model inside the Koemotion product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/koemotion/koemotion": { "id": "rinna/koemotion/koemotion", "name": "Koemotion", "maker": "rinna", "family": "rinna/koemotion", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-06-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Product system: Koeiromap text-to-speech plus face-motion output that can drive 2D or 3D characters. Not a published weight file.", "modalities": { "input": [ "text" ], "output": [ "audio", "video" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Koemotion as a named model in the koemotion line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "rinna's commercial text-to-speech and face-motion service. Version 2.0 added a revised speaker map and streaming.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/nekomata/nekomata-14b": { "id": "rinna/nekomata/nekomata-14b", "name": "Nekomata 14B", "maker": "rinna", "family": "rinna/nekomata", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tongyi Qianwen License Agreement", "release_date": "2023-12-21", "context_length": 8192, "max_output_tokens": null, "parameters": "14B", "architecture": "Qwen 14B (QWenLMHeadModel): 40 layers, 5120 hidden, 40 heads, intermediate 27392, vocab 152,064, max_position_embeddings 8192. Continual pretrain of Qwen-14B. trust_remote_code required. Trained with AWS Neuron NeMo-Megatron code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Nekomata 14B as a named model in the nekomata line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese continual pretrain of Qwen-14B on about 66B tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/nekomata/nekomata-14b-instruction": { "id": "rinna/nekomata/nekomata-14b-instruction", "name": "Nekomata 14B Instruction", "maker": "rinna", "family": "rinna/nekomata", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tongyi Qianwen License Agreement", "release_date": "2023-12-21", "context_length": 8192, "max_output_tokens": null, "parameters": "14B", "architecture": "Same Qwen 14B shape. Alpaca format. trust_remote_code required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Nekomata 14B Instruction as a named model in the nekomata line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Instruction tune of Nekomata 14B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/nekomata/nekomata-7b": { "id": "rinna/nekomata/nekomata-7b", "name": "Nekomata 7B", "maker": "rinna", "family": "rinna/nekomata", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tongyi Qianwen License Agreement", "release_date": "2023-12-21", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "Qwen (QWenLMHeadModel): 32 layers, 4096 hidden, 32 heads, intermediate 22016, vocab 151,936, max_position_embeddings 32768. Continual pretrain of Qwen-7B. trust_remote_code is required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Nekomata 7B as a named model in the nekomata line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese continual pretrain of Qwen-7B on about 30B tokens. Nekomata is the youkai \u732b\u53c8.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/nekomata/nekomata-7b-instruction": { "id": "rinna/nekomata/nekomata-7b-instruction", "name": "Nekomata 7B Instruction", "maker": "rinna", "family": "rinna/nekomata", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tongyi Qianwen License Agreement", "release_date": "2023-12-21", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "Same Qwen 7B shape. Alpaca instruction format. trust_remote_code required.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Nekomata 7B Instruction as a named model in the nekomata line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Instruction tune of Nekomata 7B in the Alpaca format.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/nue-asr/nue-asr": { "id": "rinna/nue-asr/nue-asr", "name": "Nue ASR", "maker": "rinna", "family": "rinna/nue-asr", "status": "retired", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-07", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "HuBERT audio encoder, a convolutional bridge, and a GPT-NeoX decoder. News reports identify the initialized checkpoints as japanese-hubert-base and japanese-gpt-neox-3.6b. The name Nue is the youkai \u9d7a.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Nue ASR as a named model in the nue-asr line. Status on 2026-10-15: retired. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese speech recognizer that maps speech to text with a GPT-style decoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/roberta/japanese-roberta-base": { "id": "rinna/roberta/japanese-roberta-base", "name": "Japanese RoBERTa Base", "maker": "rinna", "family": "rinna/roberta", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2021-08-25", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa masked LM (RobertaForMaskedLM): 12 layers, 768 hidden, 12 heads, intermediate 3072, vocab 32,000. config max_position_embeddings 514.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese RoBERTa Base as a named model in the roberta line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Base Japanese RoBERTa for masked language modeling, trained by rinna on Japanese CC-100 and Japanese Wikipedia.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/stable-diffusion/japanese-stable-diffusion": { "id": "rinna/stable-diffusion/japanese-stable-diffusion", "name": "Japanese Stable Diffusion", "maker": "rinna", "family": "rinna/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML OpenRAIL-M", "release_date": "2022-09-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion model finetuned from CompVis/stable-diffusion-v1-4. Two stages, following PITI: first train a Japanese text encoder with the U-Net fixed, then finetune the text encoder and U-Net together. Custom pipeline class JapaneseStableDiffusionPipeline.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese Stable Diffusion as a named model in the stable-diffusion line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese-prompt text-to-image model. rinna says it depicts Japanese cultural terms more faithfully than the English Stable Diffusion checkpoint it started from.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/vicuna/vicuna-13b-delta-langchain-mrkl": { "id": "rinna/vicuna/vicuna-13b-delta-langchain-mrkl", "name": "Vicuna 13B Delta LangChain MRKL", "maker": "rinna", "family": "rinna/vicuna", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "13B", "architecture": "Llama (LlamaForCausalLM) delta: 40 layers, 5120 hidden, 40 heads, intermediate 13824, vocab 32,000, max_position_embeddings 2048. The Hub file is a delta, not a runnable model. It must be applied on top of the original LLaMA weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Vicuna 13B Delta LangChain MRKL as a named model in the vicuna line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "A small experimental finetune of Vicuna 13B on 15 LangChain MRKL-format examples, released by rinna as a delta.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/wav2vec2/japanese-wav2vec2-base": { "id": "rinna/wav2vec2/japanese-wav2vec2-base", "name": "Japanese wav2vec 2.0 Base", "maker": "rinna", "family": "rinna/wav2vec2", "status": "retired", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "wav2vec 2.0 base. Hidden sizes were not re-read.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Japanese wav2vec 2.0 Base as a named model in the wav2vec2 line. Status on 2026-10-15: retired. This dossier was collected on 2026-10-15.", "what_it_is_for": "Self-supervised Japanese wav2vec 2.0 speech encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/youko/llama-3-youko-70b": { "id": "rinna/youko/llama-3-youko-70b", "name": "Llama 3 Youko 70B", "maker": "rinna", "family": "rinna/youko", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-07-25", "context_length": 8192, "max_output_tokens": null, "parameters": "70B", "architecture": "Llama 3 70B: 80 layers, 8192 hidden, 64 heads, intermediate 28672, vocab 128,256, max_position_embeddings 8192. Continual pretrain of Meta-Llama-3-70B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Llama 3 Youko 70B as a named model in the youko line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese continual pretrain of Llama 3 70B on about 5B tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/youko/llama-3-youko-70b-instruct": { "id": "rinna/youko/llama-3-youko-70b-instruct", "name": "Llama 3 Youko 70B Instruct", "maker": "rinna", "family": "rinna/youko", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-07-25", "context_length": 8192, "max_output_tokens": null, "parameters": "70B", "architecture": "Same Llama 3 70B shape. Llama 3 chat format.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Llama 3 Youko 70B Instruct as a named model in the youko line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Instruction model of Llama 3 Youko 70B using SFT and a chat vector. The card does not describe a DPO stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/youko/llama-3-youko-8b": { "id": "rinna/youko/llama-3-youko-8b", "name": "Llama 3 Youko 8B", "maker": "rinna", "family": "rinna/youko", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-05-01", "context_length": 8192, "max_output_tokens": null, "parameters": "8B", "architecture": "Llama 3 (LlamaForCausalLM): 32 layers, 4096 hidden, 32 heads, intermediate 14336, vocab 128,256, max_position_embeddings 8192. Continual pretrain of Meta-Llama-3-8B. Original Llama 3 tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Llama 3 Youko 8B as a named model in the youko line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese continual pretrain of Llama 3 8B on about 22B tokens. Youko is the youkai \u5996\u72d0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/youko/llama-3-youko-8b-instruct": { "id": "rinna/youko/llama-3-youko-8b-instruct", "name": "Llama 3 Youko 8B Instruct", "maker": "rinna", "family": "rinna/youko", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-07-25", "context_length": 8192, "max_output_tokens": null, "parameters": "8B", "architecture": "Same Llama 3 8B shape. Llama 3 chat format.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Llama 3 Youko 8B Instruct as a named model in the youko line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Instruction model of Llama 3 Youko 8B, built with SFT, a chat vector, and DPO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/youri/youri-7b": { "id": "rinna/youri/youri-7b", "name": "Youri 7B", "maker": "rinna", "family": "rinna/youri", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-10-31", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "Llama 2 (LlamaForCausalLM): 32 layers, 4096 hidden, 32 heads, intermediate 11008, vocab 32,000, max_position_embeddings 4096. Continual pretrain of meta-llama/Llama-2-7b-hf.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Youri 7B as a named model in the youri line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese-English continual pretrain of Llama 2 7B on about 40B tokens. The name Youri is the youkai \u5996\u72f8.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/youri/youri-7b-chat": { "id": "rinna/youri/youri-7b-chat", "name": "Youri 7B Chat", "maker": "rinna", "family": "rinna/youri", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-10-31", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "Same Llama 2 7B shape as youri-7b. Chat-style \u30e6\u30fc\u30b6\u30fc / \u30b7\u30b9\u30c6\u30e0 format.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Youri 7B Chat as a named model in the youri line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Chat-format instruction tune of Youri 7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rinna/youri/youri-7b-instruction": { "id": "rinna/youri/youri-7b-instruction", "name": "Youri 7B Instruction", "maker": "rinna", "family": "rinna/youri", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-10-31", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "Same Llama 2 7B shape as youri-7b. Alpaca instruction format.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "rinna (rinna Co., Ltd.) released Youri 7B Instruction as a named model in the youri line. Status on 2026-10-15: active. This dossier was collected on 2026-10-15.", "what_it_is_for": "Instruction tune of Youri 7B in the Alpaca format.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rit/ellseg/denseelnet": { "id": "rit/ellseg/denseelnet", "name": "DenseElNet", "maker": "rit", "family": "rit/ellseg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2020-07-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder nicknamed DenseElNet. It uses the RITnet dense block plus residual connections in the style of FC-DenseNet (Tiramisu). The EllSeg paper says the input is downsampled four times by 2 at the bottleneck. Segmentation output is three maps: background, iris ellipse, and pupil ellipse. A regression head on the bottleneck emits 10 values, five ellipse parameters each for pupil and iris. The paper reports 2.18 million parameters and does not publish an exact integer. Training and the detection-rate figures use 320x240 images. The public default checkpoint is weights/all.git_ok.", "modalities": { "input": [ "image" ], "output": [ "segmentation", "ellipse" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DenseElNet is the named encoder-decoder that Rochester Institute of Technology researchers shipped with the EllSeg eye-tracking framework. The weights are public research checkpoints, not an API. This dossier was collected on 2026-10-15.", "what_it_is_for": "EllSeg trains a network to label the full pupil ellipse and the full iris ellipse, including parts hidden by eyelids or lashes, and to regress ellipse parameters. The authors say DenseElNet exists to test that framework. It is a hybrid of the RITnet dense block and Tiramisu-style residual connections. All listed authors were at Rochester Institute of Technology.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rit/ritnet/ritnet": { "id": "rit/ritnet/ritnet", "name": "RITnet", "maker": "rit", "family": "rit/ritnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2019-10-01", "context_length": null, "max_output_tokens": null, "parameters": "248900", "architecture": "Encoder-decoder eye segmenter based on DenseUNet-K. Five Down-Blocks and four Up-Blocks, DenseNet-style feature reuse, constant 32 channels, LeakyReLU, average-pool downsampling, nearest-neighbor upsampling, and skip connections. Grayscale input (m=1). Four output classes: background, iris, sclera, and pupil. The public summary lists 248,900 trainable parameters and a parameter footprint under 1 MB at float32 (paper model size 0.98 MB). The checked GitHub tree ships best_model.pkl.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RITnet is a Rochester Institute of Technology eye-image segmenter from the Carlson imaging and eye-tracking groups. It won the 2019 OpenEDS semantic-segmentation challenge and remains a public research checkpoint. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents RITnet for semantic segmentation of close-up near-eye images so gaze pipelines can recover pupil, iris, and sclera regions. The stated classes are background, iris, sclera, and pupil. Authors list Rochester Institute of Technology addresses and rit.edu mail.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rit/scanssd/scanssd": { "id": "rit/scanssd/scanssd", "name": "ScanSSD", "maker": "rit", "family": "rit/scanssd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2020-03-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Scanning Single Shot Detector for math formulas in document images. A page rendered at 600 dpi is cut into sliding windows. Each window is scored by an SSD whose backbone is VGG-16 pretrained on ImageNet. Default-box aspect ratios are 1, 2, 3, 5, 7, and 10. The evaluated MATH512 variant uses 512x512 windows and replaces some later 3x3 kernels with 1x5 kernels. Window detections are pooled to the page. The repository is derived from the public PyTorch SSD implementation by Max deGroot. The LICENSE file in that tree is the upstream MIT notice, copyright 2017 Max deGroot and Ellis Brown. An RIT-specific weight license was not found.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ScanSSD is a Rochester Institute of Technology document-image detector for mathematical formulas. It is a public research model with a weight link in the official repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper introduces the Scanning Single Shot Detector for formulas that sit off the text line and formulas embedded in the line. It uses visual features only. Authors Parag Mali, Puneeth Kukkadapu, Mahshad Mahdavi, and Richard Zanibbi were in the Department of Computer Science at Rochester Institute of Technology.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rochester/ave/av-att": { "id": "rochester/ave/av-att", "name": "A+V-att", "maker": "rochester", "family": "rochester/ave", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Audio-visual event localization network. The ECCV 2018 paper describes feature extraction, audio-guided visual attention, temporal modeling, multimodal fusion, and temporal labeling. The official test script builds att_Net(128, 128, 512, 29) for the supervised A+V-att model and loads model/AV_att.pt. Inputs in the repository are pre-extracted audio and visual features, not raw pixels. The GitHub API did not report a license.", "modalities": { "input": [ "audio", "video" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A+V-att is the supervised audio-visual event localization network from Yapeng Tian, Jing Shi, Bochen Li, Zhiyao Duan, and Chenliang Xu at the University of Rochester. The ECCV 2018 paper names it, and the official repository ships the checkpoint. The weights were still in the git tree on 2026-10-15. There is no hosted API.", "what_it_is_for": "The paper localizes events that are both visible and audible. A+V-att uses audio-guided visual attention, temporal modeling, fusion, and temporal labeling. The repository command is `python supervised_main.py --model_name AV_att`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rochester/ave/cmm": { "id": "rochester/ave/cmm", "name": "AVE cross-modal matching network", "maker": "rochester", "family": "rochester/ave", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Audio-visual distance learning network for cross-modality localization, named in the ECCV 2018 paper. The official repository ships model/cmm_model_weights.h5 (416,512 bytes) and a Keras test script cmm_test.py. The GitHub API did not report a license.", "modalities": { "input": [ "audio", "video" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The cross-modal matching network is the audio-visual distance learning model in the University of Rochester ECCV 2018 audio-visual event paper. It handles the paper's cross-modality localization task. The official repository includes a Keras weight file. That file was still in the git tree on 2026-10-15. There is no hosted API.", "what_it_is_for": "The paper introduces an audio-visual distance learning network so a segment in one modality can be matched to the other. The repository test script is `cmm_test.py`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rochester/avvp/mmil-net": { "id": "rochester/avvp/mmil-net", "name": "MMIL-Net", "maker": "rochester", "family": "rochester/avvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid attention network for weakly supervised audio-visual video parsing, formulated as multimodal multiple instance learning. The paper adds attentive MMIL pooling, individual-guided learning, and label smoothing. The official script builds MMIL_Net and reads pre-extracted VGGish audio features, ResNet-152 frame features, and ResNet(2+1)D snippet features. The released file is the parsing network, not a fine-tune of those extractors. The GitHub API did not report a license. The README's license pointer was not used.", "modalities": { "input": [ "audio", "video" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MMIL-Net is the weakly supervised audio-visual video parsing network from Yapeng Tian and Chenliang Xu at the University of Rochester, with Dingzeyu Li at Adobe Research. The ECCV 2020 paper names the hybrid attention model, and the official repository ships `models/MMIL_Net.pt`. The file was still in the git tree on 2026-10-15. There is no hosted API.", "what_it_is_for": "The paper parses a video into temporal segments and labels each segment as audible, visible, or both, using only video-level labels. MMIL-Net is a hybrid attention network with attentive multimodal multiple-instance pooling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rochester/dsgpm/dsgpm": { "id": "rochester/dsgpm/dsgpm", "name": "DSGPM", "maker": "rochester", "family": "rochester/dsgpm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph neural network that embeds atoms and then partitions the molecular graph with spectral clustering. The paper states a hidden feature dimension of 128 and a metric-learning objective (cut triplet loss and non-cut pair loss). The code README targets PyTorch 1.4, PyTorch Geometric, RDKit, and METIS.", "modalities": { "input": [ "graph" ], "output": [ "graph" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DSGPM (Deep Supervised Graph Partitioning Model) is a graph neural network from the University of Rochester groups of Chenliang Xu and Andrew D. White. It predicts coarse-grained bead mappings for molecules. The Chem. Sci. 2020 paper and the official repository name the model and ship a checkpoint. The weights were still in the git tree on 2026-10-15. There is no hosted API.", "what_it_is_for": "The paper treats expert coarse-grained mapping as supervised graph partitioning. DSGPM embeds atoms, builds an affinity matrix, and uses spectral clustering to assign atoms to beads. The README inference path accepts SMILES-derived JSON graphs or PDB inputs and a requested bead count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rochester/tdan/tdan": { "id": "rochester/tdan/tdan", "name": "TDAN", "maker": "rochester", "family": "rochester/tdan", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.97M", "architecture": "Temporally deformable alignment network plus a super-resolution reconstruction network. Supporting frames are aligned to the reference frame at feature level with deformable convolution, without optical flow. The paper's reported model uses five frames (one reference and four supporting frames), scale factor 4, and the D4 deformable-layer setting. Table 4 lists 1.97 million parameters.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TDAN is a video super-resolution network from Yapeng Tian and Chenliang Xu at the University of Rochester, with Yulun Zhang and Yun Fu at Northeastern University. The CVPR 2020 paper and the official GitHub repository release the code and two checkpoint files. The weights were still in the repository on 2026-10-15. There is no hosted API.", "what_it_is_for": "The paper defines video super-resolution as restoring a high-resolution frame from a low-resolution reference frame and neighboring supporting frames. TDAN aligns those supporting frames at the feature level with deformable convolution, then a reconstruction network predicts the high-resolution frame. The stated scale is 4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rochester/zooming-slow-mo/zooming-slow-mo": { "id": "rochester/zooming-slow-mo/zooming-slow-mo", "name": "Zooming Slow-Mo", "maker": "rochester", "family": "rochester/zooming-slow-mo", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One-stage space-time video super-resolution network. A feature temporal interpolation network fills missing low-resolution frame features. A deformable ConvLSTM aligns and aggregates temporal context. A reconstruction network outputs high-resolution slow-motion frames. The CVPR paper says the model is about 4 times smaller and more than 3 times faster than DAIN+EDVR. The absolute parameter count was not copied from a complete table cell.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zooming Slow-Mo is a one-stage space-time video super-resolution model. Xiaoyu Xiang (Purdue) and Yapeng Tian (University of Rochester) share first authorship. Jan P. Allebach (Purdue) and Chenliang Xu (Rochester) share advising, with Yulun Zhang and Yun Fu (Northeastern). The CVPR 2020 paper and the official repository release one pretrained checkpoint. It was still in the git tree on 2026-10-15. There is no hosted API.", "what_it_is_for": "The paper synthesizes a high-resolution slow-motion video from a low-frame-rate, low-resolution video in one network. It interpolates features of missing frames, aggregates them with a deformable ConvLSTM, and reconstructs the high-resolution frames.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/aewm/aewm": { "id": "ruc/aewm/aewm", "name": "AEWM", "maker": "ruc", "family": "ruc/aewm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-23", "context_length": 262144, "max_output_tokens": null, "parameters": "35107181936", "architecture": "Qwen3_5MoeForConditionalGeneration, base Qwen/Qwen3.5-35B-A3B. Hub safetensors total 35,107,181,936. config model_type qwen3_5_moe. max_position_embeddings 262144. Hidden size was not on the top-level config keys that were read. The card says one checkpoint does action judgment and state revision.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AEWM is an agent-editing world model from AI Box at Renmin University of China, released 23 September 2026. It judges and edits an agent's actions for search, terminal, and software-engineering settings. The inference wrapper is EditAct. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "AEWM is an agent-editing world model from AI Box at Renmin University of China, released 23 September 2026. It judges and edits an agent's actions for search, terminal, and software-engineering settings. The inference wrapper is EditAct. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/cir/qwen-math-7b-cir": { "id": "ruc/cir/qwen-math-7b-cir", "name": "Qwen-Math-7B-CIR", "maker": "ruc", "family": "ruc/cir", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-06-02", "context_length": 32768, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Qwen2ForCausalLM. Config: 28 layers, hidden 3584, vocab 152064, max_position_embeddings 32768. Safetensors total 7,615,616,512. No README on the default branch. CIR is not expanded on any page that was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen-Math-7B-CIR is a named weight repo on the RUC-AIBOX Hub org at Renmin University of China, created 2 June 2025. No model card, paper, or expansion of CIR was found in this pass. Collected 2026-10-15.", "what_it_is_for": "Qwen-Math-7B-CIR is a named weight repo on the RUC-AIBOX Hub org at Renmin University of China, created 2 June 2025. No model card, paper, or expansion of CIR was found in this pass. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/clawgym/clawgym-30a3": { "id": "ruc/clawgym/clawgym-30a3", "name": "ClawGym-30A3", "maker": "ruc", "family": "ruc/clawgym", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-05-15", "context_length": 262144, "max_output_tokens": null, "parameters": "30532122624", "architecture": "Qwen3MoeForCausalLM, 48 layers, hidden 2048, vocab 151936. The name 30A3 is the lab's size label. Active parameter count was not in the config keys that were read. Safetensors total 30,532,122,624. No README on the default branch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ClawGym-30A3 is a ClawGym weight from AI Box at Renmin University of China. The lab posted a 19 May 2026 preprint note calling ClawGym a framework for data, training, and evaluation of Claw-style personal agents. These three repos were created 15 May 2026. No model-card prose was found. Collected 2026-10-15.", "what_it_is_for": "ClawGym-30A3 is a ClawGym weight from AI Box at Renmin University of China. The lab posted a 19 May 2026 preprint note calling ClawGym a framework for data, training, and evaluation of Claw-style personal agents. These three repos were created 15 May 2026. No model-card prose was found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/clawgym/clawgym-4b": { "id": "ruc/clawgym/clawgym-4b", "name": "ClawGym-4B", "maker": "ruc", "family": "ruc/clawgym", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-05-15", "context_length": 262144, "max_output_tokens": null, "parameters": "4411424256", "architecture": "Qwen3ForCausalLM, 36 layers, hidden 2560, vocab 151936. Safetensors total 4,411,424,256. No README on the default branch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ClawGym-4B is a ClawGym weight from AI Box at Renmin University of China. The lab posted a 19 May 2026 preprint note calling ClawGym a framework for data, training, and evaluation of Claw-style personal agents. These three repos were created 15 May 2026. No model-card prose was found. Collected 2026-10-15.", "what_it_is_for": "ClawGym-4B is a ClawGym weight from AI Box at Renmin University of China. The lab posted a 19 May 2026 preprint note calling ClawGym a framework for data, training, and evaluation of Claw-style personal agents. These three repos were created 15 May 2026. No model-card prose was found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/clawgym/clawgym-8b": { "id": "ruc/clawgym/clawgym-8b", "name": "ClawGym-8B", "maker": "ruc", "family": "ruc/clawgym", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-05-15", "context_length": 40960, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Qwen3ForCausalLM, 36 layers, hidden 4096, vocab 151936. Context 40960, shorter than the 4B config. Safetensors total 8,190,735,360. No README on the default branch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ClawGym-8B is a ClawGym weight from AI Box at Renmin University of China. The lab posted a 19 May 2026 preprint note calling ClawGym a framework for data, training, and evaluation of Claw-style personal agents. These three repos were created 15 May 2026. No model-card prose was found. Collected 2026-10-15.", "what_it_is_for": "ClawGym-8B is a ClawGym weight from AI Box at Renmin University of China. The lab posted a 19 May 2026 preprint note calling ClawGym a framework for data, training, and evaluation of Claw-style personal agents. These three repos were created 15 May 2026. No model-card prose was found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/deepanalyze/deepanalyze-8b": { "id": "ruc/deepanalyze/deepanalyze-8b", "name": "DeepAnalyze-8B", "maker": "ruc", "family": "ruc/deepanalyze", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-21", "context_length": 131072, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Agentic fine-tune of DeepSeek-R1-0528-Qwen3-8B. Hub safetensors total 8,190,735,360. The paper says 8B. Special action tokens are added for analyze, understand, code, execute, and answer. config.json was an LFS pointer when fetched raw, so width and layer counts were not read from it.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepAnalyze-8B is an agentic data-science model from RUC-DataLab at Renmin University of China, with Tsinghua University co-authors. The university announced the release and the GitHub news dates the weights, code, and data to 21 October 2025. It is trained to run a data-science pipeline and write reports without a hand-written workflow. MIT license. Collected 2026-10-15.", "what_it_is_for": "DeepAnalyze-8B is an agentic data-science model from RUC-DataLab at Renmin University of China, with Tsinghua University co-authors. The university announced the release and the GitHub news dates the weights, code, and data to 21 October 2025. It is trained to run a data-science pipeline and write reports without a hand-written workflow. MIT license. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/elmer/elmer": { "id": "ruc/elmer/elmer", "name": "ELMER", "maker": "ruc", "family": "ruc/elmer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-10-27", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "Non-autoregressive encoder-decoder based on BART. Config model_type bart, 6 layers, vocab 50265, max length 1024. The GitHub says ELMER-base is 6 encoder layers, 6 decoder layers, 12 heads, and hidden size 768, pre-trained on BookCorpus and Wikipedia (16GB). Early-exit generation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELMER is a non-autoregressive pre-trained generation model from AI Box at Renmin University of China, with a University of Montreal co-author. The EMNLP 2022 checkpoint was posted to the Hub on 27 October 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "ELMER is a non-autoregressive pre-trained generation model from AI Box at Renmin University of China, with a University of Montreal co-author. The EMNLP 2022 checkpoint was posted to the Hub on 27 October 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/erya/erya": { "id": "ruc/erya/erya", "name": "Erya", "maker": "ruc", "family": "ruc/erya", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-07-21", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "CPTForConditionalGeneration. Config model_type bart, 12 layers, vocab 51271, max length 1024. Ancient-to-modern Chinese translation, trained with DMLM and DAS. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Erya is an ancient-to-modern Chinese translation model from AI Box at Renmin University of China. The card says it can be used zero-shot. Apache-2.0. Hub createdAt 21 July 2023. Collected 2026-10-15.", "what_it_is_for": "Erya is an ancient-to-modern Chinese translation model from AI Box at Renmin University of China. The card says it can be used zero-shot. Apache-2.0. Hub createdAt 21 July 2023. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/erya/erya-4ft": { "id": "ruc/erya/erya-4ft", "name": "Erya4FT", "maker": "ruc", "family": "ruc/erya", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-07-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Further fine-tune of Erya on the lab's ancient-modern parallel data. The card uses the same CPTForConditionalGeneration loader. Its own config was not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Erya4FT is the fine-tuned Erya checkpoint from AI Box at Renmin University of China. The card says it is Erya tuned on their dataset and is the checkpoint to keep tuning. Apache-2.0. Hub createdAt 29 July 2023. Collected 2026-10-15.", "what_it_is_for": "Erya4FT is the fine-tuned Erya checkpoint from AI Box at Renmin University of China. The card says it is Erya tuned on their dataset and is the checkpoint to keep tuning. Apache-2.0. Hub createdAt 29 July 2023. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/live/live-bart-base": { "id": "ruc/live/live-bart-base", "name": "LIVE-BART", "maker": "ruc", "family": "ruc/live", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-01-17", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "BartForConditionalGeneration with an image-fusion encoder flag. Config: 6 layers, vocab 50267, max length 1024. The card pairs it with CLIP ViT-B/32 and a downloaded projection file trans.bart.pth that is not in the description of the Hub repo.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LIVE-BART is the BART checkpoint for visually augmented generation from AI Box at Renmin University of China. The card says words can attend to images. Hub createdAt 17 January 2024. No license tag was on the API record. Collected 2026-10-15.", "what_it_is_for": "LIVE-BART is the BART checkpoint for visually augmented generation from AI Box at Renmin University of China. The card says words can attend to images. Hub createdAt 17 January 2024. No license tag was on the API record. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/live/live-t5-base": { "id": "ruc/live/live-t5-base", "name": "LIVE-T5", "maker": "ruc", "family": "ruc/live", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-01-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5ForConditionalGeneration. Config vocab 32102. max_position_embeddings was not on the keys that were read. Same LIVE project as LIVE-BART.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LIVE-T5 is the T5 checkpoint released with LIVE-BART by AI Box at Renmin University of China for visually augmented generation. Hub createdAt 17 January 2024. No license tag was on the API record. Collected 2026-10-15.", "what_it_is_for": "LIVE-T5 is the T5 checkpoint released with LIVE-BART by AI Box at Renmin University of China for visually augmented generation. Hub createdAt 17 January 2024. No license tag was on the API record. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/llama-3-syne/llama-3-syne": { "id": "ruc/llama-3-syne/llama-3-syne", "name": "Llama-3-SynE", "maker": "ruc", "family": "ruc/llama-3-syne", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-10", "context_length": 8192, "max_output_tokens": null, "parameters": "8030261248", "architecture": "LlamaForCausalLM. Continual pre-training of Llama-3-8B. Config: 32 layers, hidden 4096, vocab 128256, max_position_embeddings 8192. Hub safetensors total 8,030,261,248. The report says about 100B additional tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-SynE is a continual-pre-training release from AI Box and Gaoling School of Artificial Intelligence at Renmin University of China. It adapts Llama-3-8B for Chinese and scientific reasoning. Weights were posted 10 August 2024. The Hub license field was empty on the collection date. Collected 2026-10-15.", "what_it_is_for": "Llama-3-SynE is a continual-pre-training release from AI Box and Gaoling School of Artificial Intelligence at Renmin University of China. It adapts Llama-3-8B for Chinese and scientific reasoning. Weights were posted 10 August 2024. The Hub license field was empty on the collection date. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mtl-data-to-text": { "id": "ruc/mvp/mtl-data-to-text", "name": "MTL data-to-text", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTL data-to-text is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Multi-task pre-trained variant for data-to-text. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MTL data-to-text is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Multi-task pre-trained variant for data-to-text. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mtl-open-dialog": { "id": "ruc/mvp/mtl-open-dialog", "name": "MTL open dialog", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTL open dialog is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Multi-task pre-trained variant for open-ended dialogue. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MTL open dialog is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Multi-task pre-trained variant for open-ended dialogue. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mtl-question-answering": { "id": "ruc/mvp/mtl-question-answering", "name": "MTL question answering", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTL question answering is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Multi-task pre-trained variant for question answering. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MTL question answering is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Multi-task pre-trained variant for question answering. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mtl-question-generation": { "id": "ruc/mvp/mtl-question-generation", "name": "MTL question generation", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTL question generation is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Multi-task pre-trained variant for question generation. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MTL question generation is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Multi-task pre-trained variant for question generation. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mtl-story": { "id": "ruc/mvp/mtl-story", "name": "MTL story", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTL story is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Multi-task pre-trained variant for story generation. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MTL story is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Multi-task pre-trained variant for story generation. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mtl-summarization": { "id": "ruc/mvp/mtl-summarization", "name": "MTL summarization", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTL summarization is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Multi-task pre-trained variant for summarization, listed with the MVP release. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MTL summarization is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Multi-task pre-trained variant for summarization, listed with the MVP release. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mtl-task-dialog": { "id": "ruc/mvp/mtl-task-dialog", "name": "MTL task dialog", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTL task dialog is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Multi-task pre-trained variant for task-oriented dialogue. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MTL task dialog is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Multi-task pre-trained variant for task-oriented dialogue. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mvp": { "id": "ruc/mvp/mvp", "name": "MVP", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MVP is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. The supervised pre-trained text-to-text model without a task prompt baked in. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MVP is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. The supervised pre-trained text-to-text model without a task prompt baked in. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mvp-data-to-text": { "id": "ruc/mvp/mvp-data-to-text", "name": "MVP data-to-text", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MVP data-to-text is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Task-specific soft-prompt checkpoint for data-to-text. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MVP data-to-text is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Task-specific soft-prompt checkpoint for data-to-text. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mvp-multi-task": { "id": "ruc/mvp/mvp-multi-task", "name": "MVP multi-task", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MVP multi-task is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Prompt-based multi-task checkpoint listed on the MVP card. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MVP multi-task is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Prompt-based multi-task checkpoint listed on the MVP card. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mvp-open-dialog": { "id": "ruc/mvp/mvp-open-dialog", "name": "MVP open dialog", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MVP open dialog is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Task-specific soft-prompt checkpoint for open-ended dialogue. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MVP open dialog is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Task-specific soft-prompt checkpoint for open-ended dialogue. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mvp-question-answering": { "id": "ruc/mvp/mvp-question-answering", "name": "MVP question answering", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MVP question answering is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Task-specific soft-prompt checkpoint for question answering. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MVP question answering is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Task-specific soft-prompt checkpoint for question answering. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mvp-question-generation": { "id": "ruc/mvp/mvp-question-generation", "name": "MVP question generation", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MVP question generation is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Task-specific soft-prompt checkpoint for question generation. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MVP question generation is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Task-specific soft-prompt checkpoint for question generation. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mvp-story": { "id": "ruc/mvp/mvp-story", "name": "MVP story", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MVP story is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Task-specific soft-prompt checkpoint for story generation. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MVP story is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Task-specific soft-prompt checkpoint for story generation. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mvp-summarization": { "id": "ruc/mvp/mvp-summarization", "name": "MVP summarization", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MVP summarization is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Task-specific soft-prompt checkpoint for summarization. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MVP summarization is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Task-specific soft-prompt checkpoint for summarization. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/mvp/mvp-task-dialog": { "id": "ruc/mvp/mvp-task-dialog", "name": "MVP task dialog", "maker": "ruc", "family": "ruc/mvp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer encoder-decoder, model_type mvp on the base repo. Base config max length 1024 and vocab 50267. The paper trains on 77 datasets across 11 NLG tasks and then adds task-specific soft prompts. Layer width was not in the keys read from config.json. Safetensors total was not returned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MVP task dialog is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Task-specific soft-prompt checkpoint for task-oriented dialogue. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "MVP task dialog is part of the MVP natural-language-generation release from AI Box (RUCAIBox) at Renmin University of China. Task-specific soft-prompt checkpoint for task-oriented dialogue. The Hub repos were updated 26 June 2022. Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/omniatlas/omniatlas-qwen2-5-3b": { "id": "ruc/omniatlas/omniatlas-qwen2-5-3b", "name": "OmniAtlas-Qwen2.5-3B", "maker": "ruc", "family": "ruc/omniatlas", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-18", "context_length": null, "max_output_tokens": null, "parameters": "5537120640", "architecture": "Qwen2_5OmniForConditionalGeneration. Fine-tune of Qwen/Qwen2.5-Omni-3B. Hub safetensors total 5,537,120,640. Context length was not on the top-level config keys that were read.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmniAtlas-Qwen2.5-3B is an omni-modal agent checkpoint from the NLPIR lab at the Gaoling School of Artificial Intelligence, Renmin University of China. The card says it is the official OmniAtlas weight for OmniGAIA evaluation and other omni-modal agent tasks. Apache-2.0. Hub createdAt 2026-02-18. Collected 2026-10-15.", "what_it_is_for": "OmniAtlas-Qwen2.5-3B is an omni-modal agent checkpoint from the NLPIR lab at the Gaoling School of Artificial Intelligence, Renmin University of China. The card says it is the official OmniAtlas weight for OmniGAIA evaluation and other omni-modal agent tasks. Apache-2.0. Hub createdAt 2026-02-18. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/omniatlas/omniatlas-qwen2-5-7b": { "id": "ruc/omniatlas/omniatlas-qwen2-5-7b", "name": "OmniAtlas-Qwen2.5-7B", "maker": "ruc", "family": "ruc/omniatlas", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-18", "context_length": null, "max_output_tokens": null, "parameters": "10732225408", "architecture": "Qwen2_5OmniForConditionalGeneration. Fine-tune of Qwen/Qwen2.5-Omni-7B. Hub safetensors total 10,732,225,408. Context length was not on the top-level config keys that were read.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmniAtlas-Qwen2.5-7B is an omni-modal agent checkpoint from the NLPIR lab at the Gaoling School of Artificial Intelligence, Renmin University of China. The card says it is the official OmniAtlas weight for OmniGAIA evaluation and other omni-modal agent tasks. Apache-2.0. Hub createdAt 2026-02-18. Collected 2026-10-15.", "what_it_is_for": "OmniAtlas-Qwen2.5-7B is an omni-modal agent checkpoint from the NLPIR lab at the Gaoling School of Artificial Intelligence, Renmin University of China. The card says it is the official OmniAtlas weight for OmniGAIA evaluation and other omni-modal agent tasks. Apache-2.0. Hub createdAt 2026-02-18. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/omniatlas/omniatlas-qwen3-30b-a3b": { "id": "ruc/omniatlas/omniatlas-qwen3-30b-a3b", "name": "OmniAtlas-Qwen3-30B-A3B", "maker": "ruc", "family": "ruc/omniatlas", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-18", "context_length": null, "max_output_tokens": null, "parameters": "31719205488", "architecture": "Qwen3OmniMoeForConditionalGeneration. Fine-tune of Qwen/Qwen3-Omni-30B-A3B-Thinking. Hub safetensors total 31,719,205,488. Context length was not on the top-level config keys that were read.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmniAtlas-Qwen3-30B-A3B is an omni-modal agent checkpoint from the NLPIR lab at the Gaoling School of Artificial Intelligence, Renmin University of China. The card says it is the official OmniAtlas weight for OmniGAIA evaluation and other omni-modal agent tasks. Apache-2.0. Hub createdAt 2026-02-18. Collected 2026-10-15.", "what_it_is_for": "OmniAtlas-Qwen3-30B-A3B is an omni-modal agent checkpoint from the NLPIR lab at the Gaoling School of Artificial Intelligence, Renmin University of China. The card says it is the official OmniAtlas weight for OmniGAIA evaluation and other omni-modal agent tasks. Apache-2.0. Hub createdAt 2026-02-18. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/omnieval/omnieval-hallucination-evaluator": { "id": "ruc/omnieval/omnieval-hallucination-evaluator", "name": "OmniEval hallucination evaluator", "maker": "ruc", "family": "ruc/omnieval", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-12-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Separate LoRA of Qwen/Qwen2.5-7B-Instruct for the hallucination metric. The card says this judge is not the same as the other four metrics.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmniEval hallucination evaluator is the second financial-RAG judge from the NLPIR lab at Renmin University of China, trained for the hallucination metric only. MIT. Created 9 December 2024. Collected 2026-10-15.", "what_it_is_for": "OmniEval hallucination evaluator is the second financial-RAG judge from the NLPIR lab at Renmin University of China, trained for the hallucination metric only. MIT. Created 9 December 2024. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/omnieval/omnieval-model-evaluator": { "id": "ruc/omnieval/omnieval-model-evaluator", "name": "OmniEval model evaluator", "maker": "ruc", "family": "ruc/omnieval", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-12-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA fine-tune of Qwen/Qwen2.5-7B-Instruct for financial RAG evaluation. Hub base_model is that Qwen checkpoint. Safetensors total was not returned, which fits an adapter repo that was not opened file by file.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmniEval model evaluator is a judge model from the NLPIR lab at Renmin University of China. The card says two Qwen2.5-7B LoRAs were trained on human labels, and this repo is the evaluator for the metrics other than hallucination. MIT. Created 9 December 2024. Collected 2026-10-15.", "what_it_is_for": "OmniEval model evaluator is a judge model from the NLPIR lab at Renmin University of China. The card says two Qwen2.5-7B LoRAs were trained on human labels, and this repo is the evaluator for the metrics other than hallucination. MIT. Created 9 December 2024. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/perceval/perceval-3b-policy": { "id": "ruc/perceval/perceval-3b-policy", "name": "Perceval-3B policy", "maker": "ruc", "family": "ruc/perceval", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-06-10", "context_length": 128000, "max_output_tokens": null, "parameters": "4065787904", "architecture": "Qwen2_5_VLForConditionalGeneration. Config: 36 layers, hidden 2048, vocab 151936, max_position_embeddings 128000. Safetensors total 4,065,787,904. The name says 3B. This repo is the policy checkpoint.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perceval-3B policy is a Perceval checkpoint from AI Box at Renmin University of China. A 15 June 2026 lab post calls Perceval a process reward model for token-level error grounding and links the GitHub. These two weight repos were created 10 June 2026. No Hub README was found. Collected 2026-10-15.", "what_it_is_for": "Perceval-3B policy is a Perceval checkpoint from AI Box at Renmin University of China. A 15 June 2026 lab post calls Perceval a process reward model for token-level error grounding and links the GitHub. These two weight repos were created 10 June 2026. No Hub README was found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/perceval/perceval-3b-reward": { "id": "ruc/perceval/perceval-3b-reward", "name": "Perceval-3B reward", "maker": "ruc", "family": "ruc/perceval", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-06-10", "context_length": 128000, "max_output_tokens": null, "parameters": "4065787904", "architecture": "Qwen2_5_VLForConditionalGeneration. Config: 36 layers, hidden 2048, vocab 151936, max_position_embeddings 128000. Safetensors total 4,065,787,904. The name says 3B. This repo is the reward checkpoint.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Perceval-3B reward is a Perceval checkpoint from AI Box at Renmin University of China. A 15 June 2026 lab post calls Perceval a process reward model for token-level error grounding and links the GitHub. These two weight repos were created 10 June 2026. No Hub README was found. Collected 2026-10-15.", "what_it_is_for": "Perceval-3B reward is a Perceval checkpoint from AI Box at Renmin University of China. A 15 June 2026 lab post calls Perceval a process reward model for token-level error grounding and links the GitHub. These two weight repos were created 10 June 2026. No Hub README was found. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/rear/rear-llama-7b": { "id": "ruc/rear/rear-llama-7b", "name": "REAR", "maker": "ruc", "family": "ruc/rear", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-03-04", "context_length": 4096, "max_output_tokens": null, "parameters": "6738485248", "architecture": "LlamaForRear. Config: 32 layers, hidden 4096, vocab 32008, max_position_embeddings 4096. Hub safetensors total 6,738,485,248. The name and the class say a Llama-7B retrieval-augmented variant. The exact Llama checkpoint id was not in the card text that was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "REAR is a relevance-aware retrieval-augmented model for open-domain question answering from AI Box at Renmin University of China. The Hub checkpoint is rear-llama-7b-hf, created 4 March 2024. MIT tag. Collected 2026-10-15.", "what_it_is_for": "REAR is a relevance-aware retrieval-augmented model for open-domain question answering from AI Box at Renmin University of China. The Hub checkpoint is rear-llama-7b-hf, created 4 March 2024. MIT tag. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/simpledeepsearcher/simpledeepsearcher-qwen-32b": { "id": "ruc/simpledeepsearcher/simpledeepsearcher-qwen-32b", "name": "SimpleDeepSearcher Qwen-32B", "maker": "ruc", "family": "ruc/simpledeepsearcher", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-04-10", "context_length": 32768, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM, 64 layers, hidden 5120, vocab 152064. Safetensors total 32,763,876,352. The card YAML does not name the base model id. The Hub card is an automatic Trainer template.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SimpleDeepSearcher Qwen-32B is a weight released by AI Box at Renmin University of China for SimpleDeepSearcher. The series news on 11 April 2025 says the lab released models and small fine-tuning sets for web-search reasoning. This repo was created 10 April 2025. The model card itself was not filled in. Collected 2026-10-15.", "what_it_is_for": "SimpleDeepSearcher Qwen-32B is a weight released by AI Box at Renmin University of China for SimpleDeepSearcher. The series news on 11 April 2025 says the lab released models and small fine-tuning sets for web-search reasoning. This repo was created 10 April 2025. The model card itself was not filled in. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/simpledeepsearcher/simpledeepsearcher-qwen-7b": { "id": "ruc/simpledeepsearcher/simpledeepsearcher-qwen-7b", "name": "SimpleDeepSearcher Qwen-7B", "maker": "ruc", "family": "ruc/simpledeepsearcher", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-04-10", "context_length": 32768, "max_output_tokens": null, "parameters": "7615616512", "architecture": "The trainer card title is Online-Searcher-Qwen-7B. Qwen2ForCausalLM, 28 layers, hidden 3584, vocab 152064. Safetensors total 7,615,616,512. The card YAML does not name the base model id. The Hub card is an automatic Trainer template.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SimpleDeepSearcher Qwen-7B is a weight released by AI Box at Renmin University of China for SimpleDeepSearcher. The series news on 11 April 2025 says the lab released models and small fine-tuning sets for web-search reasoning. This repo was created 10 April 2025. The model card itself was not filled in. Collected 2026-10-15.", "what_it_is_for": "SimpleDeepSearcher Qwen-7B is a weight released by AI Box at Renmin University of China for SimpleDeepSearcher. The series news on 11 April 2025 says the lab released models and small fine-tuning sets for web-search reasoning. This repo was created 10 April 2025. The model card itself was not filled in. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/simpledeepsearcher/simpledeepsearcher-qwq-32b": { "id": "ruc/simpledeepsearcher/simpledeepsearcher-qwq-32b", "name": "SimpleDeepSearcher QwQ-32B", "maker": "ruc", "family": "ruc/simpledeepsearcher", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-04-10", "context_length": 131072, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM, 64 layers, hidden 5120, vocab 152064, max position 131072. Safetensors total 32,763,876,352. The card YAML does not name the base model id. The Hub card is an automatic Trainer template.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SimpleDeepSearcher QwQ-32B is a weight released by AI Box at Renmin University of China for SimpleDeepSearcher. The series news on 11 April 2025 says the lab released models and small fine-tuning sets for web-search reasoning. This repo was created 10 April 2025. The model card itself was not filled in. Collected 2026-10-15.", "what_it_is_for": "SimpleDeepSearcher QwQ-32B is a weight released by AI Box at Renmin University of China for SimpleDeepSearcher. The series news on 11 April 2025 says the lab released models and small fine-tuning sets for web-search reasoning. This repo was created 10 April 2025. The model card itself was not filled in. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/still/still-2": { "id": "ruc/still/still-2", "name": "STILL-2", "maker": "ruc", "family": "ruc/still", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-12-22", "context_length": 32768, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM. Hub base_model is Qwen/Qwen2.5-32B-Instruct. Config: 64 layers, hidden 5120, vocab 152064, max_position_embeddings 32768. Safetensors total 32,763,876,352.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "STILL-2 is a slow-thinking reasoning checkpoint from AI Box at Renmin University of China. The Slow Thinking GitHub news dates the technical report to 12 December 2024. The Hub weights were created 22 December 2024. The card is mostly an automatic template plus the paper link. No license tag was set. Collected 2026-10-15.", "what_it_is_for": "STILL-2 is a slow-thinking reasoning checkpoint from AI Box at Renmin University of China. The Slow Thinking GitHub news dates the technical report to 12 December 2024. The Hub weights were created 22 December 2024. The card is mostly an automatic template plus the paper link. No license tag was set. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/still/still-3-1-5b-preview": { "id": "ruc/still/still-3-1-5b-preview", "name": "STILL-3-1.5B-preview", "maker": "ruc", "family": "ruc/still", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-26", "context_length": 131072, "max_output_tokens": null, "parameters": "1777088000", "architecture": "Qwen2ForCausalLM. The series news says reinforcement learning started from DeepSeek-R1-Distill-Qwen-1.5B. Config: 28 layers, hidden 1536, vocab 151936, max_position_embeddings 131072. Safetensors total 1,777,088,000. The name says 1.5B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "STILL-3-1.5B-preview is a preview slow-thinking model from AI Box at Renmin University of China. The GitHub news on 26 January 2025 says it is a 1.5B reinforcement-learning run and reports 39.33 percent on AIME. Weights are public. No license tag was on the card. Collected 2026-10-15.", "what_it_is_for": "STILL-3-1.5B-preview is a preview slow-thinking model from AI Box at Renmin University of China. The GitHub news on 26 January 2025 says it is a 1.5B reinforcement-learning run and reports 39.33 percent on AIME. Weights are public. No license tag was on the card. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/still/still-3-tool-32b": { "id": "ruc/still/still-3-tool-32b", "name": "STILL-3-Tool-32B", "maker": "ruc", "family": "ruc/still", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-28", "context_length": 131072, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM. Config: 64 layers, hidden 5120, vocab 152064, max_position_embeddings 131072. Safetensors total 32,763,876,352. The card says it uses Python during reasoning. The Hub model tree lists one merge; the merged parent was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "STILL-3-Tool-32B is a tool-using reasoning model from the STILL team at AI Box, Renmin University of China. The card says it uses Python while reasoning and reports 81.70 percent on AIME 2024. Weights, code, and data are linked. No license tag was on the card. Hub createdAt is 2025-02-28. Collected 2026-10-15.", "what_it_is_for": "STILL-3-Tool-32B is a tool-using reasoning model from the STILL team at AI Box, Renmin University of China. The card says it uses Python while reasoning and reports 81.70 percent on AIME 2024. Weights, code, and data are linked. No license tag was on the card. Hub createdAt is 2025-02-28. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/swe-master/swe-master-32b-lsp": { "id": "ruc/swe-master/swe-master-32b-lsp", "name": "SWE-Master-32B-LSP", "maker": "ruc", "family": "ruc/swe-master", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-02-10", "context_length": 32768, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM, 64 layers, hidden 5120, vocab 152064. LSP is in the repo name. The card does not define it. Safetensors total 32,763,876,352. No README was on the default branch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SWE-Master-32B-LSP is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "what_it_is_for": "SWE-Master-32B-LSP is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/swe-master/swe-master-32b-rl": { "id": "ruc/swe-master/swe-master-32b-rl", "name": "SWE-Master-32B-RL", "maker": "ruc", "family": "ruc/swe-master", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-02-10", "context_length": 32768, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM, 64 layers, hidden 5120, vocab 152064. Safetensors total 32,763,876,352. No README was on the default branch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SWE-Master-32B-RL is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "what_it_is_for": "SWE-Master-32B-RL is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/swe-master/swe-master-32b-sft": { "id": "ruc/swe-master/swe-master-32b-sft", "name": "SWE-Master-32B-SFT", "maker": "ruc", "family": "ruc/swe-master", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-02-10", "context_length": 32768, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM, 64 layers, hidden 5120, vocab 152064. Safetensors total 32,763,876,352. No README was on the default branch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SWE-Master-32B-SFT is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "what_it_is_for": "SWE-Master-32B-SFT is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/swe-master/swe-master-4b-rl": { "id": "ruc/swe-master/swe-master-4b-rl", "name": "SWE-Master-4B-RL", "maker": "ruc", "family": "ruc/swe-master", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-02-10", "context_length": 262144, "max_output_tokens": null, "parameters": "4411424256", "architecture": "Qwen3ForCausalLM, 36 layers, hidden 2560, vocab 151936. Safetensors total 4,411,424,256. No README was on the default branch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SWE-Master-4B-RL is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "what_it_is_for": "SWE-Master-4B-RL is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/swe-master/swe-master-4b-sft": { "id": "ruc/swe-master/swe-master-4b-sft", "name": "SWE-Master-4B-SFT", "maker": "ruc", "family": "ruc/swe-master", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-02-10", "context_length": 262144, "max_output_tokens": null, "parameters": "4022468096", "architecture": "Qwen3ForCausalLM, 36 layers, hidden 2560, vocab 151936. Safetensors total 4,022,468,096. No README was on the default branch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SWE-Master-4B-SFT is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "what_it_is_for": "SWE-Master-4B-SFT is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/swe-world/swe-world-32b-rl": { "id": "ruc/swe-world/swe-world-32b-rl", "name": "SWE-World-32B-RL", "maker": "ruc", "family": "ruc/swe-world", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-02-11", "context_length": 32768, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM, 64 layers, hidden 5120, vocab 152064. Safetensors total 32,763,876,352. No README was on the default branch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SWE-World-32B-RL is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "what_it_is_for": "SWE-World-32B-RL is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/swe-world/swe-world-32b-sft": { "id": "ruc/swe-world/swe-world-32b-sft", "name": "SWE-World-32B-SFT", "maker": "ruc", "family": "ruc/swe-world", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-02-10", "context_length": 32768, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2ForCausalLM, 64 layers, hidden 5120, vocab 152064. Safetensors total 32,763,876,352. No README was on the default branch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SWE-World-32B-SFT is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "what_it_is_for": "SWE-World-32B-SFT is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/swe-world/swe-world-4b-rl": { "id": "ruc/swe-world/swe-world-4b-rl", "name": "SWE-World-4B-RL", "maker": "ruc", "family": "ruc/swe-world", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-02-10", "context_length": 262144, "max_output_tokens": null, "parameters": "4411424256", "architecture": "Qwen3ForCausalLM, 36 layers, hidden 2560, vocab 151936. Safetensors total 4,411,424,256. No README was on the default branch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SWE-World-4B-RL is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "what_it_is_for": "SWE-World-4B-RL is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/swe-world/swe-world-4b-sft": { "id": "ruc/swe-world/swe-world-4b-sft", "name": "SWE-World-4B-SFT", "maker": "ruc", "family": "ruc/swe-world", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-02-10", "context_length": 262144, "max_output_tokens": null, "parameters": "4022468096", "architecture": "Qwen3ForCausalLM, 36 layers, hidden 2560, vocab 151936. Safetensors total 4,022,468,096. No README was on the default branch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SWE-World-4B-SFT is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "what_it_is_for": "SWE-World-4B-SFT is a software-engineering agent weight on the RUC-AIBOX Hub org at Renmin University of China. The lab collection SWE Agent Series says these repos are models trained by SWE-Master and SWE-World. There is no model-card prose. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/tablellm/tablellm-13b": { "id": "ruc/tablellm/tablellm-13b", "name": "TableLLM-13B", "maker": "ruc", "family": "ruc/tablellm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "llama2", "release_date": "2024-02-05", "context_length": 16384, "max_output_tokens": null, "parameters": "13016028160", "architecture": "LlamaForCausalLM fine-tuned from CodeLlama-13b-Instruct-hf. Config: 40 layers, hidden 5120, vocab 32016, max_position_embeddings 16384. Hub safetensors total 13,016,028,160.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TableLLM-13B is the larger 2024 tabular model from RUCKBReasoning at Renmin University of China, fine-tuned from CodeLlama-13B-Instruct. It emits Python for spreadsheet operations or text for short document tables. License tag llama2. Collected 2026-10-15.", "what_it_is_for": "TableLLM-13B is the larger 2024 tabular model from RUCKBReasoning at Renmin University of China, fine-tuned from CodeLlama-13B-Instruct. It emits Python for spreadsheet operations or text for short document tables. License tag llama2. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/tablellm/tablellm-7b": { "id": "ruc/tablellm/tablellm-7b", "name": "TableLLM-7B", "maker": "ruc", "family": "ruc/tablellm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "llama2", "release_date": "2024-02-06", "context_length": 16384, "max_output_tokens": null, "parameters": "6738546688", "architecture": "LlamaForCausalLM fine-tuned from CodeLlama-7b-Instruct-hf. Config: 32 layers, hidden 4096, vocab 32016, max_position_embeddings 16384. Hub safetensors total 6,738,546,688.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TableLLM-7B is a tabular-manipulation model from the RUCKBReasoning group at Renmin University of China. It writes code for spreadsheet tables or a short text answer for document tables. The 7B and 13B pair is the 2024 release. License tag llama2. Collected 2026-10-15.", "what_it_is_for": "TableLLM-7B is a tabular-manipulation model from the RUCKBReasoning group at Renmin University of China. It writes code for spreadsheet tables or a short text answer for document tables. The 7B and 13B pair is the 2024 release. License tag llama2. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/tablellm/tablellm-8b": { "id": "ruc/tablellm/tablellm-8b", "name": "TableLLM-8B", "maker": "ruc", "family": "ruc/tablellm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "llama3.1", "release_date": "2025-08-19", "context_length": 131072, "max_output_tokens": null, "parameters": "8030261248", "architecture": "LlamaForCausalLM. Card and dataset text say it is fine-tuned from Llama-3.1-8B-Instruct. Config: 32 layers, hidden 4096, vocab 128256, max_position_embeddings 131072. Hub safetensors total 8,030,261,248.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TableLLM-8B is the 2025 update from RUCKBReasoning at Renmin University of China. The GitHub news says the code, dataset, and checkpoint were updated in August 2025, and the paper was accepted as ACL 2025 findings. It keeps the spreadsheet-code versus document-text split. License tag llama3.1. Collected 2026-10-15.", "what_it_is_for": "TableLLM-8B is the 2025 update from RUCKBReasoning at Renmin University of China. The GitHub news says the code, dataset, and checkpoint were updated in August 2025, and the paper was accepted as ACL 2025 findings. It keeps the spreadsheet-code versus document-text split. License tag llama3.1. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/unicorn/unicorn-plus-v1": { "id": "ruc/unicorn/unicorn-plus-v1", "name": "Unicorn Plus v1", "maker": "ruc", "family": "ruc/unicorn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-03-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card says this is a pre-trained Unicorn matching model and points at the RUC-DataLab Unicorn GitHub for how to load it. No config summary was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Unicorn Plus v1 is a public matching-model checkpoint from RUC-DataLab at Renmin University of China. The Hub repo was created 28 March 2023. The card is one sentence plus a GitHub link. MIT license tag. Collected 2026-10-15.", "what_it_is_for": "Unicorn Plus v1 is a public matching-model checkpoint from RUC-DataLab at Renmin University of China. The Hub repo was created 28 March 2023. The card is one sentence plus a GitHub link. MIT license tag. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/virgo/virgo-72b": { "id": "ruc/virgo/virgo-72b", "name": "Virgo-72B", "maker": "ruc", "family": "ruc/virgo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-06", "context_length": 32768, "max_output_tokens": null, "parameters": "73405560320", "architecture": "Qwen2VLForConditionalGeneration. Config text tower: 80 layers, hidden 8192, vocab 152064, max_position_embeddings 32768. Safetensors total 73,405,560,320. The series news says the start point is Qwen2-VL-72B-Instruct. The card title says Vigor-72B; the repo id is Virgo-72B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Virgo-72B is a multimodal slow-thinking model from AI Box at Renmin University of China. The 6 January 2025 series note says reasoning behavior was transferred from text models into a Qwen2-VL-72B-Instruct checkpoint. Weights are public. No license tag was on the card. Collected 2026-10-15.", "what_it_is_for": "Virgo-72B is a multimodal slow-thinking model from AI Box at Renmin University of China. The 6 January 2025 series note says reasoning behavior was transferred from text models into a Qwen2-VL-72B-Instruct checkpoint. Weights are public. No license tag was on the card. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/yulan/yulan-base-12b": { "id": "ruc/yulan/yulan-base-12b", "name": "YuLan-Base-12B", "maker": "ruc", "family": "ruc/yulan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-07-01", "context_length": 4096, "max_output_tokens": null, "parameters": "11904255744", "architecture": "LlamaForCausalLM trained from scratch. Config: 40 layers, hidden size 4864, vocab 51190, max_position_embeddings 4096. The technical report says 38 attention heads and a feed-forward size of 13056. Hub safetensors total 11,904,255,744.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YuLan-Base-12B is the from-scratch base model in the YuLan series from the Gaoling School of Artificial Intelligence at Renmin University of China (AI Box / YuLan team). The 1 July 2024 release note says it was pre-trained on English, Chinese, and multilingual text. Weights are public under MIT. Collected 2026-10-15.", "what_it_is_for": "YuLan-Base-12B is the from-scratch base model in the YuLan series from the Gaoling School of Artificial Intelligence at Renmin University of China (AI Box / YuLan team). The 1 July 2024 release note says it was pre-trained on English, Chinese, and multilingual text. Weights are public under MIT. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/yulan/yulan-chat-1-13b": { "id": "ruc/yulan/yulan-chat-1-13b", "name": "YuLan-Chat-1-13B", "maker": "ruc", "family": "ruc/yulan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Delta weights for a chat model continual-pre-trained and instruction-tuned from LLaMA-13B. The series table marks extended vocab and extended length as not applied on v1. The release is a delta, not a full checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YuLan-Chat-1-13B-v1 is the first public YuLan chat model from Renmin University of China, announced 8 June 2023. The weights on the Hub are a delta (RUCAIBox/YuLan-Chat-13b-delta) to apply to LLaMA-13B. Apache-2.0 is the Hub license tag on that delta repo. Collected 2026-10-15.", "what_it_is_for": "YuLan-Chat-1-13B-v1 is the first public YuLan chat model from Renmin University of China, announced 8 June 2023. The weights on the Hub are a delta (RUCAIBox/YuLan-Chat-13b-delta) to apply to LLaMA-13B. Apache-2.0 is the Hub license tag on that delta repo. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/yulan/yulan-chat-1-65b": { "id": "ruc/yulan/yulan-chat-1-65b", "name": "YuLan-Chat-1-65B", "maker": "ruc", "family": "ruc/yulan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Delta for YuLan-Chat-1-65B-v1, a chat model based on LLaMA-65B. The series table says v1 did not extend the vocabulary or the context length.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YuLan-Chat-1-65B-v1 is the 65B chat model released with the 13B v1 model on 8 June 2023 by the YuLan team at Renmin University of China. The Hub file is a delta, RUCAIBox/YuLan-Chat-65b-delta. Collected 2026-10-15.", "what_it_is_for": "YuLan-Chat-1-65B-v1 is the 65B chat model released with the 13B v1 model on 8 June 2023 by the YuLan team at Renmin University of China. The Hub file is a delta, RUCAIBox/YuLan-Chat-65b-delta. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/yulan/yulan-chat-1-65b-v2": { "id": "ruc/yulan/yulan-chat-1-65b-v2", "name": "YuLan-Chat-1-65B-v2", "maker": "ruc", "family": "ruc/yulan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-08-02", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Delta for a second chat model based on LLaMA-65B. The series table says vocabulary was extended to 51190 and context was not extended (2048). Continual pre-training plus instruction tuning.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YuLan-Chat-1-65B-v2 is the 2 August 2023 update of the 65B YuLan chat model from Renmin University of China. It is released as a delta, yulan-team/YuLan-Chat-1-65B-v2-delta, under a Hub MIT tag. Collected 2026-10-15.", "what_it_is_for": "YuLan-Chat-1-65B-v2 is the 2 August 2023 update of the 65B YuLan chat model from Renmin University of China. It is released as a delta, yulan-team/YuLan-Chat-1-65B-v2-delta, under a Hub MIT tag. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/yulan/yulan-chat-2-13b": { "id": "ruc/yulan/yulan-chat-2-13b", "name": "YuLan-Chat-2-13B", "maker": "ruc", "family": "ruc/yulan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-08-02", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM chat model. The fp16 repo config matches YuLan-LLaMA-2-13B: 40 layers, hidden 5120, vocab 51200, max_position_embeddings 8192. Instruction-tuned after continual pre-training of LLaMA-2-13B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YuLan-Chat-2-13B is the August 2023 bilingual chat model from the YuLan team at Renmin University of China. The card says it is continual-pre-trained on LLaMA-2-13B and then instruction-tuned. The team said it placed fifth on an OpenCompass listing; the numeric rank score was not on the pages read here. Weights are public under a Hub MIT tag. Collected 2026-10-15.", "what_it_is_for": "YuLan-Chat-2-13B is the August 2023 bilingual chat model from the YuLan team at Renmin University of China. The card says it is continual-pre-trained on LLaMA-2-13B and then instruction-tuned. The team said it placed fifth on an OpenCompass listing; the numeric rank score was not on the pages read here. Weights are public under a Hub MIT tag. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/yulan/yulan-chat-3-12b": { "id": "ruc/yulan/yulan-chat-3-12b", "name": "YuLan-Chat-3-12B", "maker": "ruc", "family": "ruc/yulan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-07-01", "context_length": 4096, "max_output_tokens": null, "parameters": "11904255744", "architecture": "LlamaForCausalLM. Same config as YuLan-Base-12B: 40 layers, hidden 4864, vocab 51190, max_position_embeddings 4096. Hub safetensors total 11,904,255,744. Supervised fine-tune and human-preference alignment of the from-scratch base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YuLan-Chat-3-12B is the chat model released with YuLan-Base-12B by the YuLan team at Renmin University of China. The card says it is supervised-fine-tuned with curriculum learning on English and Chinese instructions and human preference data. Weights are public under MIT. Collected 2026-10-15.", "what_it_is_for": "YuLan-Chat-3-12B is the chat model released with YuLan-Base-12B by the YuLan team at Renmin University of China. The card says it is supervised-fine-tuned with curriculum learning on English and Chinese instructions and human preference data. Weights are public under MIT. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/yulan/yulan-llama-2-13b": { "id": "ruc/yulan/yulan-llama-2-13b", "name": "YuLan-LLaMA-2-13B", "maker": "ruc", "family": "ruc/yulan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-08-02", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM. Config: 40 layers, hidden 5120, vocab 51200, max_position_embeddings 8192. Continual pre-training of LLaMA-2-13B with an extended vocabulary. The card says extended length 8192. Hub did not return a safetensors parameter total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YuLan-LLaMA-2-13B is the base continual-pre-training checkpoint of LLaMA-2-13B from the YuLan team at Renmin University of China, released 2 August 2023. It is the base for YuLan-Chat-2-13B. Weights are public. The Hub license tag is MIT. Collected 2026-10-15.", "what_it_is_for": "YuLan-LLaMA-2-13B is the base continual-pre-training checkpoint of LLaMA-2-13B from the YuLan team at Renmin University of China, released 2 August 2023. It is the base for YuLan-Chat-2-13B. Weights are public. The Hub license tag is MIT. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/yulan-classifier/code-classifier": { "id": "ruc/yulan-classifier/code-classifier", "name": "Code classifier", "maker": "ruc", "family": "ruc/yulan-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-15", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForSequenceClassification. Config: 2 layers, hidden 128, vocab 30522, max position 512. A small data filter, not a chat model. Hub API did not return a safetensors total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Code classifier is a public data filter from AI Box at Renmin University of China, released with the YuLan-Mini training resources. The card says it is used to retrieve code-related pages for the YuLan-Mini data mixture. MIT license. Collected 2026-10-15.", "what_it_is_for": "Code classifier is a public data filter from AI Box at Renmin University of China, released with the YuLan-Mini training resources. The card says it is used to retrieve code-related pages for the YuLan-Mini data mixture. MIT license. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/yulan-classifier/math-classifier": { "id": "ruc/yulan-classifier/math-classifier", "name": "Math classifier", "maker": "ruc", "family": "ruc/yulan-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-15", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForSequenceClassification. Config: 2 layers, hidden 128, vocab 30522, max position 512. A small data filter, not a chat model. Hub API did not return a safetensors total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Math classifier is a public data filter from AI Box at Renmin University of China, released with the YuLan-Mini training resources. The card says it is used to retrieve math-related pages for the YuLan-Mini data mixture. MIT license. Collected 2026-10-15.", "what_it_is_for": "Math classifier is a public data filter from AI Box at Renmin University of China, released with the YuLan-Mini training resources. The card says it is used to retrieve math-related pages for the YuLan-Mini data mixture. MIT license. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/yulan-classifier/reasoning-classifier": { "id": "ruc/yulan-classifier/reasoning-classifier", "name": "Reasoning classifier", "maker": "ruc", "family": "ruc/yulan-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-15", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForSequenceClassification. Config: 2 layers, hidden 128, vocab 30522, max position 512. A small data filter, not a chat model. Hub API did not return a safetensors total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reasoning classifier is a public data filter from AI Box at Renmin University of China, released with the YuLan-Mini training resources. The card says it is used to retrieve reasoning-related pages from FineWeb-Edu, DCLM, and similar corpora so they can be upsampled. MIT license. Collected 2026-10-15.", "what_it_is_for": "Reasoning classifier is a public data filter from AI Box at Renmin University of China, released with the YuLan-Mini training resources. The card says it is used to retrieve reasoning-related pages from FineWeb-Edu, DCLM, and similar corpora so they can be upsampled. MIT license. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/yulan-mini/yulan-mini": { "id": "ruc/yulan-mini/yulan-mini", "name": "YuLan-Mini", "maker": "ruc", "family": "ruc/yulan-mini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-12-23", "context_length": 28723, "max_output_tokens": null, "parameters": "2424375168", "architecture": "LlamaForCausalLM after the lab merged re-parameterized weights so the final checkpoint runs as Llama. Config: 56 layers, hidden 1920, vocab 99000, max_position_embeddings 28723. Hub safetensors total 2,424,375,168. The card calls it 2.42B and 2.4B. Pre-trained from scratch on 1.08T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YuLan-Mini is a 2.4B base language model trained from scratch by AI Box at Renmin University of China. The 23 December 2024 release is a base model, not an instruction model. The lab says it is data-efficient, with strength in math and code, and releases the data mixture. MIT license. Collected 2026-10-15.", "what_it_is_for": "YuLan-Mini is a 2.4B base language model trained from scratch by AI Box at Renmin University of China. The 23 December 2024 release is a base model, not an instruction model. The lab says it is data-efficient, with strength in math and code, and releases the data mixture. MIT license. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ruc/yulan-mini/yulan-mini-instruct": { "id": "ruc/yulan-mini/yulan-mini-instruct", "name": "YuLan-Mini-Instruct", "maker": "ruc", "family": "ruc/yulan-mini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-28", "context_length": 28723, "max_output_tokens": null, "parameters": "2424375168", "architecture": "Post-trained YuLan-Mini. LlamaForCausalLM, 56 layers, hidden 1920, vocab 99000, max_position_embeddings 28723. Hub safetensors total 2,424,375,168. Card says SFT, preference finetuning (DPO), and reinforcement learning (the report text also says PPO).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YuLan-Mini-Instruct is the instruction model from AI Box at Renmin University of China, released 28 February 2025. It post-trains YuLan-Mini for chat, math, and code. The lab also announced YuLan-Mini-Instruct-v1 on 29 January 2025; no separate public repo was found on the collection date. MIT license. Collected 2026-10-15.", "what_it_is_for": "YuLan-Mini-Instruct is the instruction model from AI Box at Renmin University of China, released 28 February 2025. It post-trains YuLan-Mini for chat, math, and code. The lab also announced YuLan-Mini-Instruct-v1 on 29 January 2025; no separate public repo was found on the collection date. MIT license. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/act/act-one": { "id": "runway/act/act-one", "name": "Act-One", "maker": "runway", "family": "runway/act", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.1 }, "summary_intro": "Act-One is Runway's retired performance-transfer model, announced 22 October 2024 inside Gen-3 Alpha. A driving video animated a character image or clip, up to 30 seconds. Gen-3 Alpha's retirement on 8 July 2026 removed its host, and Runway points users to Act-Two. Collected 15 October 2026.", "what_it_is_for": "Performance-transfer tool inside Gen-3 Alpha. A driving video sets expressions, mouth movement, and pacing on a character image or video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/act/act-two": { "id": "runway/act/act-two", "name": "Act-Two", "maker": "runway", "family": "runway/act", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "Act-Two is Runway's active performance-capture model, API id act_two, released 15 July 2025. It drives a character image or video from a 3 to 30 second performance clip at 5 credits ($0.05) per second. Weights are closed. Collected 15 October 2026.", "what_it_is_for": "Motion-capture style character performance. Transfers face, speech-related motion, and optionally body gestures from a driving video onto a character image or video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/aleph/aleph": { "id": "runway/aleph/aleph", "name": "Aleph", "maker": "runway", "family": "runway/aleph", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "In-context video model. Runway did not publish a diagram or parameter count. The original API id was gen4_aleph.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.15 }, "summary_intro": "Aleph is Runway's first in-context video editing model, released 25 July 2025. The app credit table still shows Gen-4 Aleph at 15 credits per second, but the Dev API id gen4_aleph has been removed in favor of aleph2. Weights are closed. Collected 15 October 2026.", "what_it_is_for": "In-context video editor: add, remove, or transform objects, generate a new angle, and change style or lighting on footage you already have.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/aleph/aleph-2-0": { "id": "runway/aleph/aleph-2-0", "name": "Aleph 2.0", "maker": "runway", "family": "runway/aleph", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.28 }, "summary_intro": "Aleph 2.0 is Runway's active in-context video editor, API id aleph2, released 21 May 2026 with Edit Studio. It edits clips up to 30 seconds at 1080p from video plus text or keyframes, at 28 credits ($0.28) per second with a 56-credit minimum. Weights are closed. Collected 15 October 2026.", "what_it_is_for": "Flagship in-context video editor. Change objects, style, lighting, or camera treatment on footage you already have, including by editing one frame and carrying that change through the clip, across multi-shot sequences.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/enhance/enhance-frame-rate": { "id": "runway/enhance/enhance-frame-rate", "name": "Enhance Frame Rate", "maker": "runway", "family": "runway/enhance", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.01 }, "summary_intro": "Enhance Frame Rate is Runway's active frame-rate model, API id enhance_frame_rate, announced 17 September 2026. It converts video up to 4K and five minutes to 25, 30, 48, 60, or 120 fps at 1 credit per 2 seconds. Weights are closed. Collected 15 October 2026.", "what_it_is_for": "Frame-rate conversion for generated or uploaded video, aimed at broadcast and editorial delivery rates.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/frames/frames": { "id": "runway/frames/frames", "name": "Frames", "maker": "runway", "family": "runway/frames", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Frames is Runway's image model announced 25 November 2024 for stylistic control. The research page now says Frames is Gen-4 Images, so this folder records the retired product name rather than a second live checkpoint. Collected 15 October 2026.", "what_it_is_for": "Runway's late-2024 image foundation model, built for stylistic control and consistent look across variations. Runway now points the name at Gen-4 Image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/gen/gen-1": { "id": "runway/gen/gen-1", "name": "Gen-1", "maker": "runway", "family": "runway/gen", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-02-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion video model. Temporal layers added to a pretrained image model and trained jointly on images and video. Structure is a depth embedding; content is a CLIP embedding, with text mapped through a prior.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gen-1 is Runway's first commercial video model, announced on 6 February 2023. It is a closed structure-and-content video diffusion system that restyles existing footage from a text or image prompt. The help center lists a deprecation date of 6 April 2025 and does not state a separate last day of access. This dossier was collected on 15 October 2026.", "what_it_is_for": "Runway's first commercial video model. It synthesizes a new video by applying a text or image description to the structure of footage you already have.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/gen/gen-2": { "id": "runway/gen/gen-2", "name": "Gen-2", "maker": "runway", "family": "runway/gen", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gen-2 is Runway's retired multimodal video model for text, image, and video inputs. The help center says access started to be revoked in mid-March 2025 and the model was fully unavailable on 11 May 2025. Release month differs across Runway's own pages, so it is left unset. Collected 15 October 2026.", "what_it_is_for": "Multimodal video system that could generate novel clips from text, images, or video, including text-to-video without a structure video. Later updates added image-to-video and Motion Brush.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/gen/gen-3-alpha": { "id": "runway/gen/gen-3-alpha", "name": "Gen-3 Alpha", "maker": "runway", "family": "runway/gen", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Runway described it as the first of a new series of foundation models trained on new large-scale multimodal infrastructure, jointly on video and images. No parameter count or layer diagram was published.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.1 }, "summary_intro": "Gen-3 Alpha is Runway's retired 2024 video foundation model, announced 17 June 2024 as a large step over Gen-2 in fidelity, consistency, and motion. The help center says it was retired on 8 July 2026. Weights stayed closed. Collected 15 October 2026.", "what_it_is_for": "Runway's 2024 foundation video model for text-to-video, image-to-video, and, later, video-to-video, plus controls such as Motion Brush, camera controls, and Director Mode as they shipped on top of it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/gen/gen-3-alpha-turbo": { "id": "runway/gen/gen-3-alpha-turbo", "name": "Gen-3 Alpha Turbo", "maker": "runway", "family": "runway/gen", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "Gen-3 Alpha Turbo is the retired faster image-to-video variant of Gen-3 Alpha. Runway's help center retires it on 30 July 2026, and the Dev API id gen3a_turbo now errors. It cost 5 credits per second and required an input image. Collected 15 October 2026.", "what_it_is_for": "Faster, cheaper image-to-video sibling of Gen-3 Alpha. Runway described it as about 7x faster in contemporary coverage, at half the credit rate, with portrait output and three-frame keyframes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/gen/gen-4": { "id": "runway/gen/gen-4", "name": "Gen-4", "maker": "runway", "family": "runway/gen", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.12 }, "summary_intro": "Gen-4 is Runway's image-to-video model released 31 March 2025 for consistent characters, places, and objects. It is still listed in the app at 12 credits per second, 5 or 10 seconds, 720p-class frames. It was not on the Dev API model index collected 15 October 2026. Weights are closed.", "what_it_is_for": "General-purpose image-to-video model in the Gen-4 series, aimed at consistent characters, locations, and objects across scenes from a reference image plus instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/gen/gen-4-5": { "id": "runway/gen/gen-4-5", "name": "Gen-4.5", "maker": "runway", "family": "runway/gen", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Runway calls it the new foundation model for world modeling, with gains in pretraining data efficiency and post-training. GWM-1 is described as autoregressive on top of Gen-4.5. No parameter count.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.12 }, "summary_intro": "Gen-4.5 is Runway's active frontier video model, announced 1 December 2025 and on the Dev API since 10 February 2026 as gen4.5. It generates 2 to 10 second clips from text or a first frame at 12 credits ($0.12) per second, with HDR and editorial masters as surcharges. Weights are closed. Collected 15 October 2026.", "what_it_is_for": "Runway's frontier text-to-video and image-to-video model, positioned for motion quality, prompt adherence, and visual fidelity, and as the base for its world-model work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/gen/gen-4-image": { "id": "runway/gen/gen-4-image", "name": "Gen-4 Image", "maker": "runway", "family": "runway/gen", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gen-4 Image is Runway's active image model, API id gen4_image. It takes text and up to three optional references and costs 5 credits at 720p or 8 at 1080p. Runway identifies it with the older Frames model. API availability dates to 16 May 2025. Collected 15 October 2026.", "what_it_is_for": "Runway's primary image generation and editing model, including reference-image consistency for characters, objects, and scenes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/gen/gen-4-image-turbo": { "id": "runway/gen/gen-4-image-turbo", "name": "Gen-4 Image Turbo", "maker": "runway", "family": "runway/gen", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.02 }, "summary_intro": "Gen-4 Image Turbo is Runway's active fast image model, API id gen4_image_turbo, on the API since 19 August 2025. It needs text plus one to three references and costs 2 credits ($0.02) per image. Runway reported 93.3% of Gen-4 Image's DreamBench++ quality. Collected 15 October 2026.", "what_it_is_for": "Faster, cheaper Gen-4 image model for reference-conditioned generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/gen/gen-4-turbo": { "id": "runway/gen/gen-4-turbo", "name": "Gen-4 Turbo", "maker": "runway", "family": "runway/gen", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "Gen-4 Turbo is Runway's active fast image-to-video model, API id gen4_turbo, released 7 April 2025. It costs 5 credits ($0.05) per second, needs a first-frame image, and outputs up to 10 seconds at 720p-class ratios. Weights are closed. Collected 15 October 2026.", "what_it_is_for": "Runway's fastest image-to-video model in the Gen-4 series, for lower-cost drafts before a Gen-4 or Gen-4.5 render.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/gwm/gwm-1": { "id": "runway/gwm/gwm-1", "name": "GWM-1", "maker": "runway", "family": "runway/gwm", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive model built on Gen-4.5. Generates frame by frame in real time. Actions include camera pose, robot commands, and audio. The three shipped variants are separate post-trains, not one unified checkpoint.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GWM-1 is Runway's general world-model family, announced 11 December 2025. It is an autoregressive video model on top of Gen-4.5, shipped as three separate post-trains rather than one API id. Real-time control includes camera, robot actions, and audio. Collected 15 October 2026.", "what_it_is_for": "Runway's first general world-model family: real-time, action-conditioned video simulation for worlds, avatars, and robotics, built on Gen-4.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/gwm/gwm-avatars": { "id": "runway/gwm/gwm-avatars", "name": "GWM Avatars", "maker": "runway", "family": "runway/gwm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Audio-driven autoregressive video model for faces and gesture. The Characters paper says 24 fps HD and about 37 milliseconds of model time per frame.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.02 }, "summary_intro": "GWM Avatars, also called Runway Characters, is Runway's active real-time character model, API id gwm1_avatars. Announced 11 December 2025, it turns one image into a conversational or scripted talking video. API price is 2 credits upfront and 2 credits per 6 seconds. Weights are closed. Collected 15 October 2026.", "what_it_is_for": "Real-time conversational characters and offline talking-character video from one reference image, with lip sync, gaze, head motion, and gesture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/gwm/gwm-robotics": { "id": "runway/gwm/gwm-robotics", "name": "GWM Robotics", "maker": "runway", "family": "runway/gwm", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Action-conditioned video diffusion post-train of the frontier video model, used as a visual backbone and as a simulator for robot policies. Multi-view and long context are claimed for the SDK. No parameter count.", "modalities": { "input": [ "text", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GWM Robotics is Runway's preview action-conditioned video model for robot simulation, announced 11 December 2025. Access is a requested SDK, not a self-serve model id, and no public price or resolution was listed. Weights are closed. Collected 15 October 2026.", "what_it_is_for": "Robotics world model for policy features, synthetic data, and simulated evaluation of robot policies without a physical robot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/gwm/gwm-worlds": { "id": "runway/gwm/gwm-worlds", "name": "GWM Worlds", "maker": "runway", "family": "runway/gwm", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Real-time world simulation post-train of GWM-1. A static scene is expanded into a spatially consistent explorable environment. Successor research model is GWM Worlds 2.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GWM Worlds is Runway's preview world-simulation model, shown 11 December 2025 as a GWM-1 post-train. It turns a static scene into a real-time explorable environment. It has no public API id or price. GWM Worlds 2 is the later research preview. Collected 15 October 2026.", "what_it_is_for": "Real-time explorable environments. A still scene becomes a spatially consistent world you can move through, for games, training agents, and immersive views.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/gwm/gwm-worlds-2": { "id": "runway/gwm/gwm-worlds-2", "name": "GWM Worlds 2", "maker": "runway", "family": "runway/gwm", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive diffusion video and audio model. Conditions each step on a genesis prompt and first frame, current camera and text actions, and a sliding cache of past frames. Causal video and audio decoders. Post-trained from a bidirectional finetune on the WorldPrompt format.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GWM Worlds 2 is a Runway research preview from 3 September 2026. It generates continuous 720p video at 24 fps and 48 kHz audio in real time from a text world description, a first frame, and live text and camera actions. It is not a self-serve API model and has no published price. Collected 15 October 2026.", "what_it_is_for": "Research preview of real-time interactive worlds with generated audio. The user defines a scene and steers subjects and the camera with text actions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "runway/ruby/ruby": { "id": "runway/ruby/ruby", "name": "Ruby", "maker": "runway", "family": "runway/ruby", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SDR-to-HDR grading model. Runway says luma and color are extended into HDR and source pixels are not re-synthesized.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.2 }, "summary_intro": "Ruby is Runway's active SDR-to-HDR model, API id ruby, on the API since 20 August 2026. It grades a clip of up to 30 seconds and 4K into HDR10, HLG, ProRes, or EXR without resynthesizing pixels, at 20 credits per second (40 above about 4K). Weights are closed. Collected 15 October 2026.", "what_it_is_for": "Convert any SDR video, generated or captured, into true HDR (BT.2020 with PQ or HLG, or editorial EXR and ProRes) while keeping the source pixels and audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rutgers/dd/rutgers-dd-4source": { "id": "rutgers/dd/rutgers-dd-4source", "name": "Rutgers DD 4-source", "maker": "rutgers", "family": "rutgers/dd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-05-29", "context_length": null, "max_output_tokens": null, "parameters": "1635274", "architecture": "ECGFusionModel. ResNet-style skip projections in the displacement-wave and GAN-wave CNN frontends, then cross-modal attention fusion. Public v2 inputs are a 200-point displacement wave, a 200-point GAN wave, age, and sex. dropout 0.2 and input_dropout 0.15. Checkpoint is iter 369, best epoch 51. Card says v1 used a plain WaveBranch without those residual skips.", "modalities": { "input": [ "time-series" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rutgers DD 4-source is a small classifier of high-risk diastolic dysfunction from synthetic tissue-Doppler waveforms plus age and sex. The public checkpoint is version 2, iteration 369, uploaded on 29 May 2026 by the Hugging Face org Rutgers Research. That org's members include Ankush Jamthikar, who is affiliated with Rutgers Robert Wood Johnson Medical School on a 2023 medRxiv preprint. The other listed members, Aditya Shanmugham and Srinidhi Sunkara, were not matched to a Rutgers page. The card calls version 2 a later refinement of a paper-submission checkpoint. Collected 2026-10-15.", "what_it_is_for": "The model card says the task is binary classification of diastolic dysfunction under the 2025 ASE diastolic-function guideline. Normal and Grade 1 are the low-risk class. Grade 2 and Grade 3 are the high-risk class. The four inputs are a synthetic displacement wave, a synthetic GAN wave, age, and sex. The card says the waves come from an upstream GAN and that a paired real tissue-Doppler cohort should use a different 3-source ECG model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rutgers/hermes/hermes-r": { "id": "rutgers/hermes/hermes-r", "name": "Hermes-R", "maker": "rutgers", "family": "rutgers/hermes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-10", "context_length": null, "max_output_tokens": null, "parameters": "59610000", "architecture": "Oracle-guided context-prior segmenter on a residual 3D U-Net (ResUNet-Base). Learnable task-prior tokens (71 in the reported pool) and modality-prior tokens (five modalities, length 10) are selected by task and modality oracles, fused with image features by bidirectional cross-attention at 4x, 8x, and 16x downsampling, and turned into class prototypes by an MLP. Each task is binary. Loss is binary cross-entropy plus Dice, with a small cross-entropy auxiliary on modality. Trained in PyTorch for 200 epochs, batch size 16, LAMB at learning rate 0.002, patch size 128 cubed, spacing resampled to 1.5 mm.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hermes-R is the released ResUNet version of Hermes, a universal 3D medical-image segmenter from Yunhe Gao, Zhuowei Li, Di Liu, Mu Zhou, and Dimitris N. Metaxas at Rutgers, with Shaoting Zhang at Shanghai AI Laboratory. The CVPR 2024 paper and the official repository treat it as one model for many partially labeled datasets. Weights were posted on 10 June 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper defines universal medical image segmentation as one model trained on many datasets that differ in organ, disease, body region, and modality. Hermes keeps a pool of task and modality tokens, selects them with an oracle for the requested task and modality, and fuses them into a segmentation backbone. Hermes-R is that method on a residual 3D U-Net. The reported training mix is eleven public datasets and 2,438 volumes across CT, PET, T1, T2, and cine MRI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-2/pile": { "id": "rwkv/rwkv-2/pile", "name": "RWKV-2 Pile", "maker": "rwkv", "family": "rwkv/rwkv-2", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-06-23", "context_length": 768, "max_output_tokens": null, "parameters": "", "architecture": "RWKV-2 causal language model, L24-D1024. Final checkpoint trained on the Pile for 331B tokens. Run with the RWKV-v2-RNN-Pile code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-2 Pile is BlinkDL's named 430M RWKV-2 language model trained on the Pile. The RWKV wiki treats this generation as an end-of-life archive. Collected 2026-10-15.", "what_it_is_for": "A small English Pile base model used to show that the early RWKV RNN could be trained like a transformer. The card points users at the matching GitHub trainer and, later, at RWKV-4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-3/pile": { "id": "rwkv/rwkv-3/pile", "name": "RWKV-3 Pile", "maker": "rwkv", "family": "rwkv/rwkv-3", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-06-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RWKV-3 Pile causal LMs. 169M is L12-D768 ctx 768. 430M is L24-D1024 ctx 768. 1.5B is L24-D2048 ctx 896. Cards say to run them with RWKV-v2-RNN-Pile code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-3 Pile is BlinkDL's named RWKV-3 language-model line on the Pile, at 169M, 430M, and a 1.5B preview. The line is an end-of-life archive. Collected 2026-10-15.", "what_it_is_for": "English base language models. The 1.5B card tells readers to move to RWKV-4 1.5B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-4/novel": { "id": "rwkv/rwkv-4/novel", "name": "RWKV-4 Novel", "maker": "rwkv", "family": "rwkv/rwkv-4", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RWKV-4 Pile models finetuned on novels. The card defines ChnEng as 50% Chinese plus 50% Pile, Chn as 100% Chinese, and ChnEng-ChnPro as a further finetune on professional Chinese novels. Files: 3B Chn and ChnEng, 7B Chn, ChnEng, and ChnEng-ChnPro.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-4 Novel is BlinkDL's named novel-writing finetune of RWKV-4 Pile, in Chinese and Chinese-English mixes at 3B and 7B. It is part of the end-of-life RWKV-4 line. Collected 2026-10-15.", "what_it_is_for": "Long-form novel generation. The card distinguishes a general Chinese-English mix from a professional Chinese-novel continuation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-4/pile": { "id": "rwkv/rwkv-4/pile", "name": "RWKV-4 Pile", "maker": "rwkv", "family": "rwkv/rwkv-4", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-07-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RWKV-4 Pile causal LMs, GPT-NeoX-style 20B tokenizer on the Raven HF config (vocab 50277). Shapes on the cards: 169M L12-D768, 430M L24-D1024, 1.5B L24-D2048, 3B L32-D2560, 7B L32-D4096, 14B L40-D5120. Base ctx 1024, with later ctx 4096 and 8192 finetunes. RWKV-4a and RWKV-4b add a little attention and are not weight-compatible.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-4 Pile is the first official RWKV architecture release, a family of English Pile base models from 169M to 14B. The RWKV wiki marks the whole RWKV-4 line end-of-life. Collected 2026-10-15.", "what_it_is_for": "Base causal language models. The 7B and 14B cards tell chat users to use Raven, and later to prefer RWKV-4 World for multilingual generation, chat, and code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-4/pileplus": { "id": "rwkv/rwkv-4/pileplus", "name": "RWKV-4 PilePlus", "maker": "rwkv", "family": "rwkv/rwkv-4", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-05-04", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "RWKV-4 Pile models continued on RedPajama plus some Pile v2, described as 1.7T tokens, with 2020, 2021, and 2022 data. Released sizes in the repo: 169M, 430M, 1.5B, and 3B. The card says 7B and 14B were still coming.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-4 PilePlus is BlinkDL's continued training of RWKV-4 Pile on newer RedPajama and Pile-v2 data. Released files stop at 3B, and the line is end-of-life with the rest of RWKV-4. Collected 2026-10-15.", "what_it_is_for": "A drop-in recommendation, on the card, for replacing vanilla Pile checkpoints when a newer 2020-2022 data mix is wanted. Not an instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-4/raven": { "id": "rwkv/rwkv-4/raven", "name": "RWKV-4 Raven", "maker": "rwkv", "family": "rwkv/rwkv-4", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Instruct finetunes of RWKV-4 Pile on Alpaca, CodeAlpaca, Guanaco, GPT4All, ShareGPT, and related chat data. Current tree is Raven v12. Sizes 1.5B, 3B, 7B, and 14B. Pile tokenizer. The RWKV org 7B Transformers config records 32 layers, hidden 4096, intermediate 16384, vocab 50277, and context_length 1024, which is shorter than the v12 ctx 8192 filename.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-4 Raven is BlinkDL's official instruct finetune of the RWKV-4 Pile models, with English-heavy and Chinese-mixed v12 checkpoints from 1.5B to 14B. The wiki marks RWKV-4 end-of-life. Collected 2026-10-15.", "what_it_is_for": "Chat and instruction following. The Pile cards called Raven the recommended Alpaca and Vicuna style model, then later told readers to try RWKV-4 World.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-4/world": { "id": "rwkv/rwkv-4/world", "name": "RWKV-4 World", "maker": "rwkv", "family": "rwkv/rwkv-4", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-05-07", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "RWKV-4 trained as World v1 on 100+ languages. Mix stated on the card: 70% English, 15% multilingual, 15% code. Data: some Pile, some RedPajama, some OSCAR, all Wikipedia, and collected ChatGPT data. Checkpoints use the RWKV World tokenizer name rwkv_vocab_v20230424. Sizes in the repo: 0.1B, 0.4B, 1.5B, 3B, 7B. CHNtuned, JPNtuned, and ARAtuned files are official finetunes in the same repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-4 World is BlinkDL's multilingual RWKV-4 line, with base checkpoints from 0.1B to 7B and official Chinese, Japanese, and Arabic finetunes. The wiki marks RWKV-4 end-of-life. Collected 2026-10-15.", "what_it_is_for": "Generation, chat, and code in 100+ languages. The Pile cards recommend it over English-only Pile models. Prompts use User/Assistant, Question/Answer, or Instruction/Input/Response, not the older Bob/Alice format.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-5/eaglex": { "id": "rwkv/rwkv-5/eaglex", "name": "RWKV-5 EagleX", "maker": "rwkv", "family": "rwkv/rwkv-5", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued training of RWKV-5 Eagle 7B. The model card says 7.52B parameters. v1 is 1.7T tokens. v2 is 2.25T tokens. Eagle-7B in the same table is 1.1T. Not an instruct tune. Training was managed with Recursal.ai GPU time, according to the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-5 EagleX is the RWKV project's longer training of Eagle 7B, released as a 2.25T-token v2 with a 1.7T v1 reported in the same table. It is a base model, and the Eagle line is end-of-life on the wiki. Collected 2026-10-15.", "what_it_is_for": "A stronger Eagle-class base model. The card compares v2 with Llama-2-7B, Mistral-7B, OLMo-7B, and Falcon-7B, and says it is not an instruct tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-5/world": { "id": "rwkv/rwkv-5/world", "name": "RWKV-5 World (Eagle)", "maker": "rwkv", "family": "rwkv/rwkv-5", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-07-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RWKV-5 Eagle. Matrix-valued state. World mix on the card: 70% English, 15% multilingual, 15% code. World v1 is 0.59T tokens and World v2 is 1.12T tokens. Files: 0.1B v1, 0.4B v2, 1.5B v2, 3B v2, 3B v2 ctx 16k, and 7B v2. The Eagle and Finch paper says four Eagle models from 0.46B to 7.5B parameters. The EagleX card lists Eagle-7B at 7.52B parameters and 1.1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-5 World, also called Eagle, is BlinkDL's multilingual RWKV-5 base line from 0.1B to 7B. The wiki marks Eagle end-of-life. Collected 2026-10-15.", "what_it_is_for": "Multilingual base language modeling and chat-style prompting. The 7B HF card says the conversion is not an instruct tune. EagleX is the separate longer training run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-6/chnnovel": { "id": "rwkv/rwkv-6/chnnovel", "name": "RWKV-6 ChnNovel", "maker": "rwkv", "family": "rwkv/rwkv-6", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-03", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Finetune of RWKV-6 World on Chinese novels plus some instruction data. Files: 1B, 3B, 7B, and 14B, all ctx 4096. The 1B filename does not say 1.6B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-6 ChnNovel is BlinkDL's named Chinese-novel finetune of RWKV-6 World, at 1B, 3B, 7B, and 14B. The Finch line is end-of-life on the wiki. Collected 2026-10-15.", "what_it_is_for": "Chinese novel writing, with some instruction data mixed in. The card also lists a separate Japanese finetune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-6/jpn": { "id": "rwkv/rwkv-6/jpn", "name": "RWKV-6 Jpn", "maker": "rwkv", "family": "rwkv/rwkv-6", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-16", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Finetune of RWKV-6 World on Japanese data plus some instruction data. Files: 7B dated 20240816 and 14B dated 20240819, both ctx 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-6 Jpn is BlinkDL's named Japanese finetune of RWKV-6 World, at 7B and 14B. The Finch line is end-of-life on the wiki. Collected 2026-10-15.", "what_it_is_for": "Japanese text, with some instruction data included. It is a task finetune, not a new architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-6/world": { "id": "rwkv/rwkv-6/world", "name": "RWKV-6 World (Finch)", "maker": "rwkv", "family": "rwkv/rwkv-6", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-08", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "RWKV-6 Finch with dynamic recurrence. World mix on the card: 70% English, 15% multilingual, 15% code. World v1 0.59T, v2 1.12T, v2.1 1.42T tokens. Current pth files: 1.6B v2.1, 3B v2.1, 7B v2.1, 14B v2.1, and 7B v3. The paper reports Finch models at 1.6B and 3.1B. The 3B filename and the 3.1B paper or eval label refer to the same class. Vocab is the World tokenizer. Head sizes are not reprinted on the pth card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-6 World, also called Finch, is BlinkDL's multilingual RWKV-6 base line. Public pth sizes are 1.6B, 3B, 7B, and 14B, plus a later 7B v3. The wiki marks Finch end-of-life. Collected 2026-10-15.", "what_it_is_for": "Multilingual base language modeling. The HF cards are base conversions, not a separate instruct product. Chat prompts are on the pth card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-7/g1": { "id": "rwkv/rwkv-7/g1", "name": "RWKV-7 G1 (GooseOne)", "maker": "rwkv", "family": "rwkv/rwkv-7", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-03-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RWKV-7 GooseOne base models trained with web, code, synthetic, instruction, chat, and reasoning data. Official shape key: 0.1B L12-D768, 0.4B L24-D1024, 1.5B L24-D2048, 2.9B L32-D2560, 7.2B L32-D4096, 13.3B L61-D4096. Vocab 65536. Head size 64. G1k 20260930 safetensor cards confirm 1.5B = 1,527,668,736 params, L24, hidden 2048, FFN 8192, 32 heads, ctx 25600; 2.9B = 2,948,065,280, L32, hidden 2560, FFN 10240, 40 heads, ctx 25600; 7.2B = 7,199,932,416, L32, hidden 4096, FFN 16384, 64 heads, ctx 25600; 13.3B = 13,270,298,624, L61, hidden 4096, FFN 16384, 64 heads, ctx 25600. Data codes: G0 is under one epoch and G1 is over one epoch. The card ranks later letters as newer data. rwkv7a adds DeepEmbed. rwkv7b adds DeepEmbed and DEA. The current recommended weights are G1k.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-7 G1, or GooseOne, is the current RWKV language-model line: open Apache-2.0 RWKV-7 checkpoints with reasoning and tool prompts. The wiki says this column is still updating. The newest large release verified here is G1k on 2026-09-30. Collected 2026-10-15.", "what_it_is_for": "Base models for chat, reasoning, tool calls, code, and fill-in-the-middle, meant to be used as-is or post-trained. The pth card says later data versions are better and that readers should load the latest file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-7/pile": { "id": "rwkv/rwkv-7/pile", "name": "RWKV-7 Pile (Goose)", "maker": "rwkv", "family": "rwkv/rwkv-7", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-28", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "RWKV-7 Goose trained on the Pile with the 20B tokenizer for 332,115,325,534 tokens. Card shapes: 0.1B L12-D768, 0.4B L24-D1024, 1.5B L24-D2048. Released weight names are 168M, 421M, and 1.47B. Two extra small shapes, 164M L33-D512 and 165M L25-D576, are in the same repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-7 Pile is BlinkDL's Goose run on the Pile, with 168M, 421M, and 1.47B checkpoints plus two alternate small shapes. The wiki marks Goose end-of-life. Collected 2026-10-15.", "what_it_is_for": "English Pile language-model baselines for the RWKV-7 architecture. The card publishes training hyperparameters and the Pile token count, not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-7/world": { "id": "rwkv/rwkv-7/world", "name": "RWKV-7 World (Goose)", "maker": "rwkv", "family": "rwkv/rwkv-7", "status": "retired", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-10", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "RWKV-7 Goose World base models. Card shapes: 0.1B L12-D768, 0.4B L24-D1024, 1.5B L24-D2048, about 3B L32-D2560. A 7B shape L32-D4096 is printed but no 7B file is in the repo, and the wiki says a Goose 7B was not planned. Mix: 80% English, 10% multilingual, 10% code. World-v3 is 3.1T tokens, v2.9 is a 2T subsample, v2.8 is a 1T subsample. World tokenizer, vocab 65536 on the 0.1B and 2.9B HF cards. The 0.1B HF card says 191M parameters. The RWKV-7 paper says it trained four models from 0.19B to 2.9B on a 3.1T-token multilingual corpus.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-7 World is the Goose base line on the World corpus, at 0.1B, 0.4B, 1.5B, and 2.9B. BlinkDL's card says to upgrade to RWKV7-G1, and the wiki marks Goose end-of-life. Collected 2026-10-15.", "what_it_is_for": "Multilingual base models. The card still documents User/Assistant and instruction prompts, and points eval code at the RWKV-LM MMLU script.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwkv/rwkv-8/pile": { "id": "rwkv/rwkv-8/pile", "name": "RWKV-8 Pile", "maker": "rwkv", "family": "rwkv/rwkv-8", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-05-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Early RWKV-8 Pile tests. The card says they use the 20B tokenizer and the same 332,115,325,534 token count printed on the RWKV-7 Pile card. Two files: rwkv-8-pile-rc00-20250508.pth and rwkv-8-pile-rc00-hybrid-20250623.pth. The author says these are early testing versions and will be iterated. How to run: the RWKV-v7 code tree.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RWKV-8 Pile is BlinkDL's named early test of an RWKV-8 language model on the Pile, with two rc00 checkpoints. The card says the versions are early and will change. It is not the recommended RWKV model. Collected 2026-10-15.", "what_it_is_for": "Architecture testing on the Pile. The card does not describe a chat or reasoning product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwth/ditr/ditr": { "id": "rwth/ditr/ditr", "name": "DITR", "maker": "rwth", "family": "rwth/ditr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AGPL-3.0", "release_date": "2026-03-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DITR (DINO in the Room) projects features from a frozen 2D vision foundation model, DINOv2 or DINOv3 in the released runs, into a 3D point-cloud segmenter. Released 3D backbones are Point Transformer V3, and one distilled fine-tune uses MinkUNet but that file was still marked uploading. D-DITR is the distilled variant: the 2D model is distilled into the 3D backbone so images are not required at inference. Code is based on Pointcept. The Hugging Face repo stores model_best.pth and model_last.pth for each released run.", "modalities": { "input": [ "point-cloud", "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DITR (DINO in the Room) is a 3D semantic segmentation model from the RWTH Aachen Computer Vision Group. Weights were posted on 2026-03-16. This dossier was collected on 2026-10-15.", "what_it_is_for": "DITR injects features from a 2D foundation model into a 3D segmenter. D-DITR distills those features into the 3D backbone so inference can run without images. The README says it won the CVPR 2025 ScanNet++ challenge.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwth/donut/donut": { "id": "rwth/donut/donut", "name": "DONUT", "maker": "rwth", "family": "rwth/donut", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only autoregressive model for agent trajectory forecasting. The same weights encode history and unroll the future. An overprediction auxiliary task asks the model to predict a longer horizon. The repository is built on QCNet. This is not the NAVER document OCR model also called Donut.", "modalities": { "input": [ "trajectory" ], "output": [ "trajectory" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DONUT (Decoder-Only Network for Unrolling Trajectories) is an autonomous-driving motion model from RWTH Aachen. One checkpoint is public. This dossier was collected on 2026-10-15. It is not the NAVER OCR model named Donut.", "what_it_is_for": "DONUT encodes history and predicts the future with one autoregressive decoder, instead of a separate encoder and decoder. An overprediction loss asks the network to look further ahead than the scored horizon. The target benchmark is Argoverse 2 single-agent forecasting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwth/interactive4d/interactive4d": { "id": "rwth/interactive4d/interactive4d", "name": "Interactive4D", "maker": "rwth", "family": "rwth/interactive4d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Interactive 4D LiDAR segmenter. User clicks segment several objects across consecutive scans in one pass, and the model keeps instance ids over time. Two released setups differ by sweep count: one scan (sweep=1) and four scans (sweep=4). The code builds on Mask4Former and on the AGILE3D indoor annotator. Training described in the README uses SemanticKITTI, batch size 1, and 16 NVIDIA A40 GPUs with 40 GB each, for an effective batch of 16.", "modalities": { "input": [ "point-cloud" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Interactive4D is a click-guided 4D LiDAR segmentation model from the RWTH Aachen Computer Vision Group. Two checkpoints have been public since the 2025-01-28 code release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model lets a user segment several objects across consecutive LiDAR scans in one step. Instance ids stay consistent over the sequence, which the authors present as a simpler way to annotate tracks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwth/mask3d/mask3d": { "id": "rwth/mask3d/mask3d", "name": "Mask3D", "maker": "rwth", "family": "rwth/mask3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-09-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mask transformer for 3D semantic instance segmentation. Each instance is a query. Transformer decoders attend to multi-scale point features and predict masks in parallel, without a voting or geometric-clustering stage. The code is built on the Mix3D / MinkowskiEngine stack.", "modalities": { "input": [ "point-cloud" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mask3D is a 3D instance-segmentation model released by Jonas Schult and Alexander Hermans at RWTH Aachen, with coauthors at ETH Zurich and NVIDIA. Checkpoints are public on the RWTH vision file server. This dossier was collected on 2026-10-15.", "what_it_is_for": "Mask3D predicts semantic instances directly with transformer queries. The authors contrast it with voting and hand-tuned clustering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwth/mask4former/mask4former": { "id": "rwth/mask4former/mask4former", "name": "Mask4Former", "maker": "rwth", "family": "rwth/mask4former", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-09-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer for 4D panoptic segmentation of LiDAR sequences. Spatio-temporal instance queries predict semantic instances and their associations jointly. An auxiliary 6-DoF box regression is used to keep instance predictions spatially compact. The current code uses SpConv; an older MinkowskiEngine variant remains in a tag. The README says the project was renamed from MASK4D.", "modalities": { "input": [ "point-cloud" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mask4Former is a 4D LiDAR panoptic segmentation model from the Computer Vision Group at RWTH Aachen University. Weights are public. This dossier was collected on 2026-10-15.", "what_it_is_for": "The group presents Mask4Former as one transformer that segments and tracks instances in a LiDAR sequence, without a separate clustering or voting stage. It was previously called MASK4D.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwth/metrabs/metrabs": { "id": "rwth/metrabs/metrabs", "name": "MeTRAbs", "maker": "rwth", "family": "rwth/metrabs", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial only. The README gives no SPDX identifier and attributes the restriction to the training datasets.", "release_date": "2020-11-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Absolute 3D human pose estimator. Metric-scale truncation-robust volumetric heatmaps are predicted with an ImageNet-style backbone and a 1x1 convolution. 2D and 3D heatmaps can be combined so a calibrated camera yields camera-space coordinates. Released backbones include EfficientNetV2, ResNet, and MobileNetV3, with a separate person detector. The 2023 repository update corresponds to the WACV paper that trains across many skeleton formats with a geometry-aware autoencoder. The current README loads a TensorFlow Hub model at https://bit.ly/metrabs_l and a PyTorch bundle named metrabs_eff2l_384px_800k_28ds_pytorch.", "modalities": { "input": [ "image" ], "output": [ "pose" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MeTRAbs is an absolute 3D human pose model written and released by Istv\u00e1n S\u00e1r\u00e1ndi at the RWTH Aachen Computer Vision Group. Weights remain public. This dossier was collected on 2026-10-15.", "what_it_is_for": "MeTRAbs estimates metric-scale 3D poses, including joints outside the crop, without a separate test-time scale heuristic. A calibrated camera can also yield absolute camera-space coordinates. The same repository later added models trained across many skeleton conventions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwth/point2vec/point2vec": { "id": "rwth/point2vec/point2vec", "name": "Point2Vec", "maker": "rwth", "family": "rwth/point2vec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-03-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-supervised point-cloud encoder in the data2vec student-teacher style. Masked embeddings are kept out of the student so positional leakage does not reveal the object shape. A shallow decoder regresses the teacher representation. Pre-training is on ShapeNet. Downstream heads are separate fine-tunes for classification and part segmentation.", "modalities": { "input": [ "point-cloud" ], "output": [ "point-features" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Point2Vec is a self-supervised point-cloud representation model from the Computer Vision Group at RWTH Aachen University. Weights are public. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper extends data2vec to point clouds. Point2Vec stops masked embeddings from leaking the object shape to the student, then pre-trains on ShapeNet for classification and part segmentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwth/surge/surge-large": { "id": "rwth/surge/surge-large", "name": "SurGe Large", "maker": "rwth", "family": "rwth/surge", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-06-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Feedforward monocular point-map model. A Neighborhood Attention Decoder upsamples features and mixes them locally. Training uses a point-gradient matching loss. The Hugging Face model tree lists facebook/dinov2-large as the base model and this repo as a fine-tune. The card rounds the size to 0.4B parameters. Input is unnormalized RGB in BCHW, values in [0, 1]. num_tokens is min, max, or an integer. Outputs are a point map, depth, and camera intrinsics.", "modalities": { "input": [ "image" ], "output": [ "point-cloud", "depth" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SurGe Large is a monocular point-map model from the RWTH Aachen Computer Vision Group. The authors released weights on 2026-06-01. This dossier was collected on 2026-10-15.", "what_it_is_for": "SurGe predicts a 3D point map from one image and is aimed at cleaner local surface geometry than prior feedforward point-map models. The decoder is a Neighborhood Attention Decoder. The Hub tree lists DINOv2-large as the starting checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwth/tarvis/tarvis": { "id": "rwth/tarvis/tarvis", "name": "TarViS", "maker": "rwth", "family": "rwth/tarvis", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-04-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One query-based video segmentation network for video instance segmentation, video panoptic segmentation, video object segmentation, and point exemplar-guided tracking. Targets are abstract queries. A task can be swapped at inference without a task-specific retraining pass. Released backbones are ResNet-50, Swin-T, and Swin-L. Each has an image-pretraining checkpoint and a video fine-tune. The temporal neck uses deformable attention kernels described as identical to Mask2Former.", "modalities": { "input": [ "video" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TarViS is a multi-task video segmentation model from Ali Athar, Alexander Hermans, and Bastian Leibe at RWTH Aachen, with Jonathon Luiten and Deva Ramanan at Carnegie Mellon. Checkpoints were posted on 2023-04-30 to the RWTH vision file server. This dossier was collected on 2026-10-15.", "what_it_is_for": "TarViS treats the thing to segment as a query, so one network covers video instance segmentation, video panoptic segmentation, video object segmentation, and point-exemplar tracking. The abstract says tasks can be swapped at inference without retraining.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "rwth/zeyer-ctc/2024-zeyer-ctc-librispeech-spm10k": { "id": "rwth/zeyer-ctc/2024-zeyer-ctc-librispeech-spm10k", "name": "2024 Zeyer CTC LibriSpeech SPM-10k", "maker": "rwth", "family": "rwth/zeyer-ctc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "English CTC automatic speech recognition model trained with RETURNN. The card's internal alias is v6-relPosAttDef-noBias-aedLoss-bhv20-11gb-f32-bs15k-accgrad1-mgpu4-pavg100-wd1e_2-lrlin1e_5_295k-featBN-speedpertV2-spm10k-bpeSample001. The published config uses a Conformer encoder layer with relative position attention, feed-forward without bias, squared ReLU, and 8 heads, plus feature batch norm. A 6-layer transformer decoder is an auxiliary training loss only. The output vocabulary is a 10k SentencePiece model with sampling BPE. The released checkpoint is epoch 500. Speed perturbation values in the snippet are 0.7, 0.8, 0.9, 1.0, and 1.1. Weight decay in the snippet is 1e-2. Batch size in the config name is 15000.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is an English CTC speech recognizer released by the Machine Learning and Human Language Technology Group (i6) at RWTH Aachen. The checkpoint is on Hugging Face as of the card created on 2025-02-07. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card publishes a LibriSpeech CTC baseline from Albert Zeyer's 2024 RETURNN experiments, with a 10k SentencePiece vocabulary. It is a research checkpoint, not a hosted API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/afrihubert/afrihubert": { "id": "saarland/afrihubert/afrihubert", "name": "AfriHuBERT", "maker": "saarland", "family": "saarland/afrihubert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2025-05-28", "context_length": null, "max_output_tokens": null, "parameters": "95000000", "architecture": "Continued pretraining of mHuBERT-147. The Interspeech 2025 paper says AfriHuBERT extends mHuBERT-147 (95M parameters). A later Orange comparison labels AfriHuBERT-n as 95M. GitHub calls the Hub checkpoint AfriHuBERT-n and points at a separate AfriHuBERT-o file whose URL was not opened.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University and the National Institute of Informatics released AfriHuBERT, a compact self-supervised speech encoder. Jesujoba O. Alabi and Dietrich Klakow are at Saarland University. Xuechen Liu and Junichi Yamagishi are at NII. The Zenodo record says the work was done during Alabi's internship at NII. The model continues pretraining of mHuBERT-147 on African speech. Status on 2026-10-15: active. Weights are public. The accepted paper is Interspeech 2025.", "what_it_is_for": "The paper presents it as an African-centric speech representation model for spoken language identification and automatic speech recognition, including low-resource ASR. It is continued pretraining, not a hosted ASR API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/afrihubert/afrihubert-xlarge": { "id": "saarland/afrihubert/afrihubert-xlarge", "name": "AfriHuBERT-xlarge", "maker": "saarland", "family": "saarland/afrihubert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-06-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The model card says a 1B-parameter multilingual SSL encoder continued from facebook/hubert-xlarge-ll60k. config.json: HubertModel, 48 layers, hidden size 1280, 16 heads, intermediate size 5120, 7 convolutional feature layers. The card's 1B figure was not replaced with a tensor count.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University researcher Jesujoba O. Alabi released AfriHuBERT-xlarge on the Hub in June 2026. The card describes a speech encoder of about 1B parameters, continued from English HuBERT-XL (`facebook/hubert-xlarge-ll60k`) with the same multilingual adaptive fine-tuning setup as AfriHuBERT. The license on the card is Apache-2.0, which is not the non-commercial license of the compact AfriHuBERT checkpoint. Status on 2026-10-15: active. The card cites Alabi's unpublished 2026 Saarland University doctoral thesis.", "what_it_is_for": "The card says it is a multilingual self-supervised speech encoder for African languages, trained by continued pretraining on more than 10,000 hours from the AfriHuBERT sources. It is a weight release, not an ASR product API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/afro-xlmr/afro-xlmr-base": { "id": "saarland/afro-xlmr/afro-xlmr-base", "name": "AfroXLMR-base", "maker": "saarland", "family": "saarland/afro-xlmr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-04-12", "context_length": null, "max_output_tokens": null, "parameters": "278295700", "architecture": "XLM-RoBERTa base continued with MAFT. The paper lists this setting as XLM-R-base+MAFT at 270M. Hub safetensors metadata: 278,295,700 parameters (278,295,186 F32 and 514 I64). A 2025 paper lists 278,043,648.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University Spoken Language Systems (LSV) released AfroXLMR-base, an open-weight masked language model created by multilingual adaptive fine-tuning of an XLM-R checkpoint on 17 African languages plus Arabic, French, and English. Authors Jesujoba O. Alabi, David Ifeoluwa Adelani, Marius Mosbach, and Dietrich Klakow were at LSV, Saarland Informatics Campus. Weights are hosted by Adelani at `Davlan/afro-xlmr-base`, not under the UdS-LSV organization. Status on 2026-10-15: active. No deprecation notice was found.", "what_it_is_for": "The paper's recommended default: XLM-R-base after multilingual adaptive fine-tuning, for NER, news topic classification, and sentiment classification on the African languages they tested.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/afro-xlmr/afro-xlmr-large": { "id": "saarland/afro-xlmr/afro-xlmr-large", "name": "AfroXLMR-large", "maker": "saarland", "family": "saarland/afro-xlmr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-05-20", "context_length": null, "max_output_tokens": null, "parameters": "560142996", "architecture": "XLM-RoBERTa masked LM continued with multilingual adaptive fine-tuning on XLM-R-large. Hub safetensors metadata: 560,142,996 parameters (560,142,482 F16 and 514 I64). The COLING paper lists XLM-R-large as 550M. A 2025 distillation paper lists AfroXLMR-Large as 559,890,432 parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University Spoken Language Systems (LSV) released AfroXLMR-large, an open-weight masked language model created by multilingual adaptive fine-tuning of an XLM-R checkpoint on 17 African languages plus Arabic, French, and English. Authors Jesujoba O. Alabi, David Ifeoluwa Adelani, Marius Mosbach, and Dietrich Klakow were at LSV, Saarland Informatics Campus. Weights are hosted by Adelani at `Davlan/afro-xlmr-large`, not under the UdS-LSV organization. Status on 2026-10-15: active. No deprecation notice was found.", "what_it_is_for": "Masked-language adaptation of XLM-R-large so one model covers the 20 languages above, instead of one language-specific checkpoint per language.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/afro-xlmr/afro-xlmr-mini": { "id": "saarland/afro-xlmr/afro-xlmr-mini", "name": "AfroXLMR-mini", "maker": "saarland", "family": "saarland/afro-xlmr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-04-13", "context_length": null, "max_output_tokens": null, "parameters": "117891988", "architecture": "MAFT of XLM-R-miniLM. The paper lists XLM-R-miniLM at 117M before and after the MAFT rows. Hub safetensors metadata: 117,891,988 parameters (117,891,474 F32 and 514 I64). A 2025 paper lists AfroXLMR-Mini as 117,640,704.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University Spoken Language Systems (LSV) released AfroXLMR-mini, an open-weight masked language model created by multilingual adaptive fine-tuning of an XLM-R checkpoint on 17 African languages plus Arabic, French, and English. Authors Jesujoba O. Alabi, David Ifeoluwa Adelani, Marius Mosbach, and Dietrich Klakow were at LSV, Saarland Informatics Campus. Weights are hosted by Adelani at `Davlan/afro-xlmr-mini`, not under the UdS-LSV organization. Status on 2026-10-15: active. No deprecation notice was found.", "what_it_is_for": "The smallest 2022 AfroXLMR checkpoint, from MAFT of the distilled XLM-R-miniLM encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/afro-xlmr/afro-xlmr-small": { "id": "saarland/afro-xlmr/afro-xlmr-small", "name": "AfroXLMR-small", "maker": "saarland", "family": "saarland/afro-xlmr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-04-12", "context_length": null, "max_output_tokens": null, "parameters": "140344021", "architecture": "XLM-R-base with the vocabulary cut from 250,002 tokens to about 70k before MAFT. The paper names this XLM-R-base-v70k+MAFT and lists 140M. Hub safetensors metadata: 140,344,021 parameters (140,343,507 F32 and 514 I64).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University Spoken Language Systems (LSV) released AfroXLMR-small, an open-weight masked language model created by multilingual adaptive fine-tuning of an XLM-R checkpoint on 17 African languages plus Arabic, French, and English. Authors Jesujoba O. Alabi, David Ifeoluwa Adelani, Marius Mosbach, and Dietrich Klakow were at LSV, Saarland Informatics Campus. Weights are hosted by Adelani at `Davlan/afro-xlmr-small`, not under the UdS-LSV organization. Status on 2026-10-15: active. No deprecation notice was found.", "what_it_is_for": "A smaller AfroXLMR for tight GPUs. Non-African-script tokens were removed from XLM-R-base before MAFT. The paper says this cuts size by about half and is the model to use under GPU limits, with a quality cost on Amharic and Arabic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/da4mt/da4mt-adapted": { "id": "saarland/da4mt/da4mt-adapted", "name": "DA4MT MTR domain-adapted checkpoints", "maker": "saarland", "family": "saarland/da4mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-12", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "The paper's section 6 says the domain-adapted MTR checkpoints for each ADME endpoint were uploaded in addition to the four pretraining runs. The LogD74 repo has no README. Configs of these seven repos were not read. The paper's adaptation recipe is further training of the 30 percent MLM model on the unlabeled endpoint molecules with multi-task regression of physicochemical properties.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University uploaded seven endpoint-specific molecular checkpoints beside the DA4MT pretraining runs. The paper says they are the domain-adapted models that use multi-task regression on each ADME endpoint, and that the domain-adaptation experiments start from the model trained on 30 percent of GuacaMol. None of the seven Hub repos had a model card on the collection day. They are one product with seven versions, not seven architectures. Status on 2026-10-15: active as public weight files. License was not stated on these repos.", "what_it_is_for": "The paper uses domain adaptation so a GuacaMol-pretrained transformer picks up a small ADME set (at most about 4,000 molecules) before the frozen embedding is passed to a random forest. The seven files are the released adaptations for lipophilicity, two solubility sets, permeability, two microsomal-stability sets, and plasma protein binding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/da4mt/da4mt-mlm-30": { "id": "saarland/da4mt/da4mt-mlm-30", "name": "DA4MT MLM 30%", "maker": "saarland", "family": "saarland/da4mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-05-12", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocabulary 4096, max position embeddings 128, absolute positions. Paper: about 89M parameters, WordPiece, sequence length 128, MLM with 15% masking, batch size 16, 20 epochs, Adam at 3e-5. config train_size 0.3 and train_type mlm, which is the 30 percent GuacaMol subset (about 400,000 molecules).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University released DA4MT MLM 30% with the DA4MT study. Authors include Afnan Sultan, Max Rausch-Dupont, and Shahrukh Khan at Spoken Language Systems, Saarland Informatics Campus, with Olga Kalinina and Andrea Volkamer at the Center for Bioinformatics and the Medical Faculty. The paper trains a BERT on a clustered subset of GuacaMol and uses the encoder for ADME property prediction. This checkpoint is the MLM run on 30 percent of GuacaMol. Hub weights are dated 2025-05-12. License MIT. Status on 2026-10-15: active.", "what_it_is_for": "Pretraining for molecular property prediction. The paper asks whether more unlabeled molecules help, and whether a chemically informed objective helps. Downstream use in the paper is frozen CLS or mean-pooled embeddings into a random forest, not a hosted regressor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/da4mt/da4mt-mlm-60": { "id": "saarland/da4mt/da4mt-mlm-60", "name": "DA4MT MLM 60%", "maker": "saarland", "family": "saarland/da4mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-05-12", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. config.json: 12 layers, 12 heads, hidden 768, intermediate 3072, vocabulary 4096, max position embeddings 128, absolute positions. Paper: about 89M parameters, WordPiece, sequence length 128, MLM with 15% masking, batch size 16, 20 epochs, Adam at 3e-5. config train_size 0.6 and train_type mlm, which is the 60 percent GuacaMol subset (about 800,000 molecules).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University released DA4MT MLM 60% with the DA4MT study. Authors include Afnan Sultan, Max Rausch-Dupont, and Shahrukh Khan at Spoken Language Systems, Saarland Informatics Campus, with Olga Kalinina and Andrea Volkamer at the Center for Bioinformatics and the Medical Faculty. The paper trains a BERT on a clustered subset of GuacaMol and uses the encoder for ADME property prediction. This checkpoint is the MLM run on 60 percent of GuacaMol. Hub weights are dated 2025-05-12. License MIT. Status on 2026-10-15: active.", "what_it_is_for": "Pretraining for molecular property prediction. The paper asks whether more unlabeled molecules help, and whether a chemically informed objective helps. Downstream use in the paper is frozen CLS or mean-pooled embeddings into a random forest, not a hosted regressor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/da4mt/da4mt-mtr-30": { "id": "saarland/da4mt/da4mt-mtr-30", "name": "DA4MT MTR 30%", "maker": "saarland", "family": "saarland/da4mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-05-12", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "BertForRegression. Same BERT body as the MLM configs (12 layers, hidden 768, 12 heads, vocab 4096, max position 128). The head's id2label lists 209 RDKit descriptor names (ids 0 through 208). The paper says 210 physicochemical properties. config train_type is mtr. config train_size 0.3 and train_type mtr, which is the 30 percent GuacaMol subset (about 400,000 molecules).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University released DA4MT MTR 30% with the DA4MT study. Authors include Afnan Sultan, Max Rausch-Dupont, and Shahrukh Khan at Spoken Language Systems, Saarland Informatics Campus, with Olga Kalinina and Andrea Volkamer at the Center for Bioinformatics and the Medical Faculty. The paper trains a BERT on a clustered subset of GuacaMol and uses the encoder for ADME property prediction. This checkpoint is the MTR run on 30 percent of GuacaMol. Hub weights are dated 2025-05-12. License MIT. Status on 2026-10-15: active.", "what_it_is_for": "Pretraining for molecular property prediction. The paper asks whether more unlabeled molecules help, and whether a chemically informed objective helps. Downstream use in the paper is frozen CLS or mean-pooled embeddings into a random forest, not a hosted regressor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/da4mt/da4mt-mtr-60": { "id": "saarland/da4mt/da4mt-mtr-60", "name": "DA4MT MTR 60%", "maker": "saarland", "family": "saarland/da4mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-05-12", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "BertForRegression. Same BERT body as the MLM configs (12 layers, hidden 768, 12 heads, vocab 4096, max position 128). The head's id2label lists 209 RDKit descriptor names (ids 0 through 208). The paper says 210 physicochemical properties. config train_type is mtr. config train_size 0.6 and train_type mtr, which is the 60 percent GuacaMol subset (about 800,000 molecules).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University released DA4MT MTR 60% with the DA4MT study. Authors include Afnan Sultan, Max Rausch-Dupont, and Shahrukh Khan at Spoken Language Systems, Saarland Informatics Campus, with Olga Kalinina and Andrea Volkamer at the Center for Bioinformatics and the Medical Faculty. The paper trains a BERT on a clustered subset of GuacaMol and uses the encoder for ADME property prediction. This checkpoint is the MTR run on 60 percent of GuacaMol. Hub weights are dated 2025-05-12. License MIT. Status on 2026-10-15: active.", "what_it_is_for": "Pretraining for molecular property prediction. The paper asks whether more unlabeled molecules help, and whether a chemically informed objective helps. Downstream use in the paper is frozen CLS or mean-pooled embeddings into a random forest, not a hosted regressor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/mcse/mcse-coco-bert-base-uncased": { "id": "saarland/mcse/mcse-coco-bert-base-uncased", "name": "MCSE COCO BERT-base", "maker": "saarland", "family": "saarland/mcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-07-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sentence encoder continued from bert-base-uncased. Training text is Wiki1M plus MS-COCO. The Hub card rounds the file to 0.1B parameters. An exact tensor count was not copied. The image encoder used in training is not part of the released text checkpoint.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University Spoken Language Systems released MCSE COCO BERT-base as one of four MCSE sentence-embedding checkpoints. Authors Miaoran Zhang, Marius Mosbach, David Adelani, Michael Hedderich, and Dietrich Klakow. The NAACL 2022 paper adds a multimodal contrastive loss on top of SimCSE so sentences and paired images agree, then releases the text encoder. Status on 2026-10-15: active. Weights are on UdS-LSV and were also linked from GitHub.", "what_it_is_for": "Semantic textual similarity and sentence embeddings. The card says the base model is bert-base-uncased and the training mix is Wiki1M plus MS-COCO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/mcse/mcse-coco-roberta-base": { "id": "saarland/mcse/mcse-coco-roberta-base", "name": "MCSE COCO RoBERTa-base", "maker": "saarland", "family": "saarland/mcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-07-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sentence encoder continued from roberta-base. Training text is Wiki1M plus MS-COCO. The Hub card rounds the file to 0.1B parameters. An exact tensor count was not copied. The image encoder used in training is not part of the released text checkpoint.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University Spoken Language Systems released MCSE COCO RoBERTa-base as one of four MCSE sentence-embedding checkpoints. Authors Miaoran Zhang, Marius Mosbach, David Adelani, Michael Hedderich, and Dietrich Klakow. The NAACL 2022 paper adds a multimodal contrastive loss on top of SimCSE so sentences and paired images agree, then releases the text encoder. Status on 2026-10-15: active. Weights are on UdS-LSV and were also linked from GitHub.", "what_it_is_for": "Semantic textual similarity and sentence embeddings. The card says the base model is roberta-base and the training mix is Wiki1M plus MS-COCO.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/mcse/mcse-flickr-bert-base-uncased": { "id": "saarland/mcse/mcse-flickr-bert-base-uncased", "name": "MCSE Flickr BERT-base", "maker": "saarland", "family": "saarland/mcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-07-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sentence encoder continued from bert-base-uncased. Training text is Wiki1M plus Flickr30k. The Hub card rounds the file to 0.1B parameters. An exact tensor count was not copied. The image encoder used in training is not part of the released text checkpoint.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University Spoken Language Systems released MCSE Flickr BERT-base as one of four MCSE sentence-embedding checkpoints. Authors Miaoran Zhang, Marius Mosbach, David Adelani, Michael Hedderich, and Dietrich Klakow. The NAACL 2022 paper adds a multimodal contrastive loss on top of SimCSE so sentences and paired images agree, then releases the text encoder. Status on 2026-10-15: active. Weights are on UdS-LSV and were also linked from GitHub.", "what_it_is_for": "Semantic textual similarity and sentence embeddings. The card says the base model is bert-base-uncased and the training mix is Wiki1M plus Flickr30k.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/mcse/mcse-flickr-roberta-base": { "id": "saarland/mcse/mcse-flickr-roberta-base", "name": "MCSE Flickr RoBERTa-base", "maker": "saarland", "family": "saarland/mcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-07-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sentence encoder continued from roberta-base. Training text is Wiki1M plus Flickr30k. The Hub card rounds the file to 0.1B parameters. An exact tensor count was not copied. The image encoder used in training is not part of the released text checkpoint.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University Spoken Language Systems released MCSE Flickr RoBERTa-base as one of four MCSE sentence-embedding checkpoints. Authors Miaoran Zhang, Marius Mosbach, David Adelani, Michael Hedderich, and Dietrich Klakow. The NAACL 2022 paper adds a multimodal contrastive loss on top of SimCSE so sentences and paired images agree, then releases the text encoder. Status on 2026-10-15: active. Weights are on UdS-LSV and were also linked from GitHub.", "what_it_is_for": "Semantic textual similarity and sentence embeddings. The card says the base model is roberta-base and the training mix is Wiki1M plus Flickr30k.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/smole/muv2x-simcse-smole-bert": { "id": "saarland/smole/muv2x-simcse-smole-bert", "name": "MUV 2x SimCSE Smole-BERT", "maker": "saarland", "family": "saarland/smole", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-08-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT repo. The suite table's highest row is labeled UdS-LSV/muv2x-simcse-smole-bart, and no bart repo with that id was in the UdS-LSV model list on the collection day. The score is stored here with that mismatch.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University Spoken Language Systems released MUV 2x SimCSE Smole-BERT as part of the enumeration-aware SMILES transformer suite associated with Shahrukh Khan's work at LSV. Weights are on UdS-LSV under Apache-2.0. Status on 2026-10-15: active. This is a research checkpoint, not a hosted chemistry API. A peer-reviewed paper title for this suite was not separated from the MSc thesis linked by the GitHub repository.", "what_it_is_for": "Domain-adapted SimCSE molecular encoder. The card calls the best MUV row a SimCSE model. The public repo id uses bert, not bart.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/smole/siamese-smole-bert-muv-1x": { "id": "saarland/smole/siamese-smole-bert-muv-1x", "name": "Siamese Smole-BERT MUV 1x", "maker": "saarland", "family": "saarland/smole", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-08-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card lists the type as SentenceTransformer and the repo as a BERT feature-extraction model. It is the siamese contrastive domain-adaptation checkpoint. Dimensions were not copied from config.json.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University Spoken Language Systems released Siamese Smole-BERT MUV 1x as part of the enumeration-aware SMILES transformer suite associated with Shahrukh Khan's work at LSV. Weights are on UdS-LSV under Apache-2.0. Status on 2026-10-15: active. This is a research checkpoint, not a hosted chemistry API. A peer-reviewed paper title for this suite was not separated from the MSc thesis linked by the GitHub repository.", "what_it_is_for": "Siamese contrastive adaptation of Smole-BERT on MUV, released for molecular feature extraction and virtual screening.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/smole/simcse-smole-bert-muv-mlm": { "id": "saarland/smole/simcse-smole-bert-muv-mlm", "name": "SimCSE Smole-BERT MUV MLM", "maker": "saarland", "family": "saarland/smole", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-08-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT checkpoint. The card says domain adaptation on MUV with contrastive learning and masked language modeling. Dimensions were not copied from this config.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University Spoken Language Systems released SimCSE Smole-BERT MUV MLM as part of the enumeration-aware SMILES transformer suite associated with Shahrukh Khan's work at LSV. Weights are on UdS-LSV under Apache-2.0. Status on 2026-10-15: active. This is a research checkpoint, not a hosted chemistry API. A peer-reviewed paper title for this suite was not separated from the MSc thesis linked by the GitHub repository.", "what_it_is_for": "A MUV domain-adapted Smole-BERT using contrastive learning plus MLM. The suite table does not give this repo its own AUROC row.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/smole/smole-bart": { "id": "saarland/smole/smole-bart", "name": "Smole-BART", "maker": "saarland", "family": "saarland/smole", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-09-07", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "BartForConditionalGeneration from config.json: d_model 256, 2 encoder layers, 2 decoder layers, 4 heads, FFN 1024, vocabulary 2723, max position embeddings 2048. The card says denoising pretraining on noised GuacaMol.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University Spoken Language Systems released Smole-BART as part of the enumeration-aware SMILES transformer suite associated with Shahrukh Khan's work at LSV. Weights are on UdS-LSV under Apache-2.0. Status on 2026-10-15: active. This is a research checkpoint, not a hosted chemistry API. A peer-reviewed paper title for this suite was not separated from the MSc thesis linked by the GitHub repository.", "what_it_is_for": "A small SMILES BART used as a denoising encoder-decoder in the enumeration-aware suite. The card lists it for the same MUV screening table as the BERT models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/smole/smole-bert": { "id": "saarland/smole/smole-bert", "name": "Smole-BERT", "maker": "saarland", "family": "saarland/smole", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-08-17", "context_length": 768, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM from config.json: 6 layers, 8 heads, hidden 512, intermediate 2048, vocabulary 4096, max position embeddings 768, type vocab size 5. Pretraining described as 15% masked language modeling on GuacaMol.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University Spoken Language Systems released Smole-BERT as part of the enumeration-aware SMILES transformer suite associated with Shahrukh Khan's work at LSV. Weights are on UdS-LSV under Apache-2.0. Status on 2026-10-15: active. This is a research checkpoint, not a hosted chemistry API. A peer-reviewed paper title for this suite was not separated from the MSc thesis linked by the GitHub repository.", "what_it_is_for": "A SMILES masked language model for molecular representations and virtual screening. The card's released-model table lists it as the BERT MLM checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saarland/smole/smole-bert-mtr": { "id": "saarland/smole/smole-bert-mtr", "name": "Smole-BERT-MTR", "maker": "saarland", "family": "saarland/smole", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card says this BERT was pretrained with multi-task regression on physicochemical properties of GuacaMol. Layer sizes were not copied from this repo's config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saarland University Spoken Language Systems released Smole-BERT-MTR as part of the enumeration-aware SMILES transformer suite associated with Shahrukh Khan's work at LSV. Weights are on UdS-LSV under Apache-2.0. Status on 2026-10-15: active. This is a research checkpoint, not a hosted chemistry API. A peer-reviewed paper title for this suite was not separated from the MSc thesis linked by the GitHub repository.", "what_it_is_for": "The multi-task-regression pretrained BERT in the same SMILES suite, aimed at molecular property features and MUV virtual screening.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/ctm/ctm-imagenet": { "id": "sakana/ctm/ctm-imagenet", "name": "CTM-ImageNet", "maker": "sakana", "family": "sakana/ctm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "186155654", "architecture": "Continuous Thought Machine trained on ImageNet-1K classification. Safetensors total 186,155,654. Hub pipeline tag says text-generation; the card describes image classification. Adaptive internal steps. Research prototype disclaimer on the card.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CTM-ImageNet is Sakana AI's ImageNet Continuous Thought Machine checkpoint, posted 13 October 2025 under Apache-2.0. The paper is from May 2025. Collected 2026-10-15.", "what_it_is_for": "ImageNet classification with a recurrent continuous-thought architecture, for research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/ctm/ctm-maze-large": { "id": "sakana/ctm/ctm-maze-large", "name": "CTM-Maze-Large", "maker": "sakana", "family": "sakana/ctm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "32001722", "architecture": "Continuous Thought Machine for 2D maze navigation. Predicts action sequences without positional embeddings. Safetensors total 32,001,722. Research prototype.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CTM-Maze-Large is Sakana AI's maze-navigation Continuous Thought Machine, posted 13 October 2025 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Solve 2D mazes as a sequence of moves, demonstrating CTM on a planning task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/cycleqd/llama-3-8b-instruct-coding-expert": { "id": "sakana/cycleqd/llama-3-8b-instruct-coding-expert", "name": "Llama-3-8B-Instruct-Coding-Expert", "maker": "sakana", "family": "sakana/cycleqd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-07-29", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Llama 3 8B Instruct derivative. Safetensors total 8,030,261,248. Training data cited on the card includes Agent-FLAN, Magicoder-Evol-Instruct-110K, and Magicoder-OSS-Instruct-75K. Blog post date is 3 December 2024, after the weight uploads.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-8B-Instruct-Coding-Expert is a Sakana AI Llama 3 8B agentic model, weights posted 2024-07-29 under the Llama 3 Community License. Collected 2026-10-15.", "what_it_is_for": "Coding expert in the CycleQD starting population.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/cycleqd/llama-3-8b-instruct-cycleqd-cs": { "id": "sakana/cycleqd/llama-3-8b-instruct-cycleqd-cs", "name": "Llama-3-8B-Instruct-CycleQD-CS", "maker": "sakana", "family": "sakana/cycleqd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-08-12", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Llama 3 8B Instruct derivative. Safetensors total 8,030,261,248. Training data cited on the card includes Agent-FLAN, Magicoder-Evol-Instruct-110K, and Magicoder-OSS-Instruct-75K. Blog post date is 3 December 2024, after the weight uploads.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-8B-Instruct-CycleQD-CS is a Sakana AI Llama 3 8B agentic model, weights posted 2024-08-12 under the Llama 3 Community License. Collected 2026-10-15.", "what_it_is_for": "CycleQD computer-science generalist merged from the coding, DB, and OS experts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/cycleqd/llama-3-8b-instruct-db-expert": { "id": "sakana/cycleqd/llama-3-8b-instruct-db-expert", "name": "Llama-3-8B-Instruct-DB-Expert", "maker": "sakana", "family": "sakana/cycleqd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-06-17", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Llama 3 8B Instruct derivative. Safetensors total 8,030,261,248. Training data cited on the card includes Agent-FLAN, Magicoder-Evol-Instruct-110K, and Magicoder-OSS-Instruct-75K. Blog post date is 3 December 2024, after the weight uploads.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-8B-Instruct-DB-Expert is a Sakana AI Llama 3 8B agentic model, weights posted 2024-06-17 under the Llama 3 Community License. Collected 2026-10-15.", "what_it_is_for": "Database expert in the CycleQD starting population.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/cycleqd/llama-3-8b-instruct-os-expert": { "id": "sakana/cycleqd/llama-3-8b-instruct-os-expert", "name": "Llama-3-8B-Instruct-OS-Expert", "maker": "sakana", "family": "sakana/cycleqd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-06-17", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Llama 3 8B Instruct derivative. Safetensors total 8,030,261,248. Training data cited on the card includes Agent-FLAN, Magicoder-Evol-Instruct-110K, and Magicoder-OSS-Instruct-75K. Blog post date is 3 December 2024, after the weight uploads.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-8B-Instruct-OS-Expert is a Sakana AI Llama 3 8B agentic model, weights posted 2024-06-17 under the Llama 3 Community License. Collected 2026-10-15.", "what_it_is_for": "Operating-system expert in the CycleQD starting population.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/discopop/discopop-zephyr-7b-gemma": { "id": "sakana/discopop/discopop-zephyr-7b-gemma", "name": "DiscoPOP-zephyr-7b-gemma", "maker": "sakana", "family": "sakana/discopop", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma license", "release_date": "2024-06-12", "context_length": null, "max_output_tokens": null, "parameters": "8537680896", "architecture": "Gemma Zephyr 7B preference-tuned with the DiscoPOP loss discovered by Sakana's LLM-squared search. Base HuggingFaceH4/zephyr-7b-gemma-sft-v0.1. Dataset tag argilla/dpo-mix-7k. Safetensors total 8,537,680,896.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiscoPOP-zephyr-7b-gemma is the Sakana AI checkpoint trained with the discovered DiscoPOP loss, posted 12 June 2024 under the Gemma license. Collected 2026-10-15.", "what_it_is_for": "Show the DiscoPOP preference objective on a Gemma Zephyr SFT base. The loss itself is a method; this folder is the named checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/dreamcubed/dreamcubed-2m": { "id": "sakana/dreamcubed/dreamcubed-2m", "name": "DreamCubed2M", "maker": "sakana", "family": "sakana/dreamcubed", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named Dream-Cubed checkpoint. Sakana's publications index calls Dream-Cubed a family of block-level diffusion models that generate editable Minecraft worlds. No license tag and no README at raw/main on 2026-10-15.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DreamCubed2M is a Sakana AI Dream-Cubed weight repo posted 2026-04-17. The card text is missing or license-only. Collected 2026-10-15.", "what_it_is_for": "Minecraft world diffusion checkpoint from the Dream-Cubed release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/dreamcubed/dreamcubed-cp2": { "id": "sakana/dreamcubed/dreamcubed-cp2", "name": "DreamCubedCP2", "maker": "sakana", "family": "sakana/dreamcubed", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-04-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named Dream-Cubed checkpoint. Sakana's publications index calls Dream-Cubed a family of block-level diffusion models that generate editable Minecraft worlds. No license tag and no README at raw/main on 2026-10-15.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DreamCubedCP2 is a Sakana AI Dream-Cubed weight repo posted 2026-04-14. The card text is missing or license-only. Collected 2026-10-15.", "what_it_is_for": "Minecraft world diffusion checkpoint from the Dream-Cubed release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/dreamcubed/dreamcubed-dp2": { "id": "sakana/dreamcubed/dreamcubed-dp2", "name": "DreamCubedDP2", "maker": "sakana", "family": "sakana/dreamcubed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named Dream-Cubed checkpoint. Sakana's publications index calls Dream-Cubed a family of block-level diffusion models that generate editable Minecraft worlds. Hub tag dit. License header only.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DreamCubedDP2 is a Sakana AI Dream-Cubed weight repo posted 2026-04-14. The card text is missing or license-only. Collected 2026-10-15.", "what_it_is_for": "Minecraft world diffusion checkpoint from the Dream-Cubed release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/dreamcubed/dreamcubed-dp4": { "id": "sakana/dreamcubed/dreamcubed-dp4", "name": "DreamCubedDP4", "maker": "sakana", "family": "sakana/dreamcubed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named Dream-Cubed checkpoint. Sakana's publications index calls Dream-Cubed a family of block-level diffusion models that generate editable Minecraft worlds. Hub tag dit. License header only.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DreamCubedDP4 is a Sakana AI Dream-Cubed weight repo posted 2026-04-14. The card text is missing or license-only. Collected 2026-10-15.", "what_it_is_for": "Minecraft world diffusion checkpoint from the Dream-Cubed release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/drope/llama-2-7b-hf-drope": { "id": "sakana/drope/llama-2-7b-hf-drope", "name": "Llama-2-7B-hf-DroPE", "maker": "sakana", "family": "sakana/drope", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-01-06", "context_length": null, "max_output_tokens": null, "parameters": "6738415616", "architecture": "Llama 2 7B checkpoint with positional embeddings dropped (DroPE), custom code llama_drope. Safetensors total 6,738,415,616. README was not present at the raw main URL on 2026-10-15.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-2-7B-hf-DroPE is a Sakana AI DroPE checkpoint posted 6 January 2026. A model-card README was not available when this dossier was collected. Collected 2026-10-15.", "what_it_is_for": "Research checkpoint for context extension by dropping positional embeddings on Llama 2 7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/evollm/evollm-jp-a-v1-7b": { "id": "sakana/evollm/evollm-jp-a-v1-7b", "name": "EvoLLM-JP-A-v1-7B", "maker": "sakana", "family": "sakana/evollm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-03-08", "context_length": null, "max_output_tokens": null, "parameters": "7241732096", "architecture": "Mistral-style merge of Shisa Gamma 7B v1, Arithmo2-Mistral-7B, and Abel 7B 002. Apache-2.0 because WizardMath is not included. Safetensors total 7,241,732,096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EvoLLM-JP-A-v1-7B is Sakana AI's Apache-2.0 Japanese evolutionary merge, weights posted 8 March 2024. Collected 2026-10-15.", "what_it_is_for": "Apache-2.0 Japanese merge with a similar job to EvoLLM-JP-v1-7B, swapping WizardMath for Arithmo2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/evollm/evollm-jp-v1-10b": { "id": "sakana/evollm/evollm-jp-v1-10b", "name": "EvoLLM-JP-v1-10B", "maker": "sakana", "family": "sakana/evollm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Microsoft Research License (WizardMath terms)", "release_date": "2024-03-08", "context_length": null, "max_output_tokens": null, "parameters": "9859076226", "architecture": "Custom EvoMistral (trust_remote_code). Safetensors total 9,859,076,226. The model card lists Shisa Gamma 7B v1, WizardMath 7B V1.1, and Abel 7B 002. The GitHub README instead lists parents EvoLLM-JP-v1-7B and Shisa Gamma 7B v1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EvoLLM-JP-v1-10B is Sakana AI's larger Japanese evolutionary merge, weights posted 8 March 2024 under the Microsoft Research License. Collected 2026-10-15.", "what_it_is_for": "Larger experimental Japanese LLM from the same merge project, with the highest MGSM-JA of the three EvoLLM releases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/evollm/evollm-jp-v1-7b": { "id": "sakana/evollm/evollm-jp-v1-7b", "name": "EvoLLM-JP-v1-7B", "maker": "sakana", "family": "sakana/evollm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Microsoft Research License (WizardMath terms)", "release_date": "2024-03-06", "context_length": null, "max_output_tokens": null, "parameters": "7241732096", "architecture": "Mistral-style autoregressive merge of Shisa Gamma 7B v1, WizardMath 7B V1.1, and Abel 7B 002. Hugging Face safetensors total 7,241,732,096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EvoLLM-JP-v1-7B is a Sakana AI experimental Japanese language model released on Hugging Face on 6 March 2024. It is an open-weight evolutionary merge, still downloadable as of 2026-10-15.", "what_it_is_for": "Experimental Japanese general LLM with stronger math than its merge parents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/evosdxl/evo-nishikie-v1": { "id": "sakana/evosdxl/evo-nishikie-v1", "name": "Evo-Nishikie-v1", "maker": "sakana", "family": "sakana/evosdxl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 for the script; pipeline license inherited from source models", "release_date": "2024-07-11", "context_length": null, "max_output_tokens": null, "parameters": "1251014160", "architecture": "Image-to-image colorization model trained from Evo-Ukiyoe, which itself comes from EvoSDXL-JP. Hub safetensors total 1,251,014,160, smaller than a full SDXL UNet, so this count may be a partial stack. Card example uses 35 steps.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Evo-Nishikie-v1 is Sakana AI's ukiyo-e colorization model, posted 11 July 2024. Collected 2026-10-15.", "what_it_is_for": "Colorize or restyle ukiyo-e and monochrome classical-book images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/evosdxl/evo-ukiyoe-v1": { "id": "sakana/evosdxl/evo-ukiyoe-v1", "name": "Evo-Ukiyoe-v1", "maker": "sakana", "family": "sakana/evosdxl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 for the script and LoRA; full pipeline license inherited from source models", "release_date": "2024-07-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Ukiyo-e style text-to-image model trained from EvoSDXL-JP. Card example uses 40 steps and guidance scale 8.0. Training used ukiyo-e images; the card points at the ARC Ritsumeikan collection in public descriptions of the release.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Evo-Ukiyoe-v1 is Sakana AI's ukiyo-e image model, posted 11 July 2024, trained from EvoSDXL-JP. Collected 2026-10-15.", "what_it_is_for": "Generate ukiyo-e-like images from Japanese prompts, for education and cultural use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/evosdxl/evosdxl-jp-v1": { "id": "sakana/evosdxl/evosdxl-jp-v1", "name": "EvoSDXL-JP-v1", "maker": "sakana", "family": "sakana/evosdxl", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 for the shipped script; generated pipeline license is inherited from the source models", "release_date": "2024-03-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Evolutionary merge of SDXL, Juggernaut-XL-v9, SDXL-DPO, Japanese Stable Diffusion XL, and SDXL-Lightning. Card example uses 4 inference steps and guidance scale 0. Education-purpose Japanese SDXL Lightning.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EvoSDXL-JP-v1 is Sakana AI's Japanese SDXL Lightning merge, weights posted 14 March 2024. It is an experimental education model, not a paid API. Collected 2026-10-15.", "what_it_is_for": "Fast Japanese text-to-image generation for education and research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/evovlm/evovlm-jp-v1-7b": { "id": "sakana/evovlm/evovlm-jp-v1-7b", "name": "EvoVLM-JP-v1-7B", "maker": "sakana", "family": "sakana/evovlm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-03-04", "context_length": null, "max_output_tokens": null, "parameters": "7566743552", "architecture": "LLaVA-style Japanese VLM merged from Shisa Gamma 7B v1 and LLaVA-1.6-Mistral-7B. Safetensors total 7,566,743,552.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EvoVLM-JP-v1-7B is Sakana AI's first public Japanese vision-language merge, posted 4 March 2024 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Answer Japanese questions about images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/evovlm/llama-3-evovlm-jp-v2": { "id": "sakana/evovlm/llama-3-evovlm-jp-v2", "name": "Llama-3-EvoVLM-JP-v2", "maker": "sakana", "family": "sakana/evovlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-07-29", "context_length": null, "max_output_tokens": null, "parameters": "8480007232", "architecture": "Interleaved image-text VLM. Merge of Meta-Llama-3-8B-Instruct, Mantis-8B-siglip-llama3, and Bunny-v1.1-Llama-3-8B-V. SigLIP vision tower. Safetensors total 8,480,007,232. Inference uses the Mantis library.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-EvoVLM-JP-v2 is Sakana AI's July 2024 Japanese vision-language merge on Llama 3, released under the Llama 3 Community License. Collected 2026-10-15.", "what_it_is_for": "Japanese visual chat with interleaved images and text, a later Llama 3 merge rather than a fine-tune of v1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/fishmath/gpt-oss-120b-sft-aimo3-fishmath": { "id": "sakana/fishmath/gpt-oss-120b-sft-aimo3-fishmath", "name": "gpt-oss-120b SFT AIMO3 FishMath", "maker": "sakana", "family": "sakana/fishmath", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-04-27", "context_length": null, "max_output_tokens": null, "parameters": "116829156672", "architecture": "Supervised fine-tune of openai/gpt-oss-120b for integer-answer olympiad math. MXFP4 / 8-bit tags on the Hub. Trained on SakanaAI/FishMath-SFT-Data (22,287 trajectories, max sequence 65,536, 10 epochs). Safetensors total 116,829,156,672. Tool-integrated reasoning uses a Python interpreter at inference; the tool is not inside the weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "gpt-oss-120b-sft-aimo3-FishMath is Sakana AI's Apache-2.0 olympiad-math fine-tune of gpt-oss-120b, posted 27 April 2026. Collected 2026-10-15.", "what_it_is_for": "Contest-math SFT checkpoint used in Sakana's AIMO3 solution. Not a general chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/fugu/fugu": { "id": "sakana/fugu/fugu", "name": "Fugu", "maker": "sakana", "family": "sakana/fugu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-22", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "Learned multi-agent orchestrator. Sakana describes Fugu as a language model trained to call other LLMs, including itself, rather than a single foundation-model stack. Parameter count is not published.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fugu is Sakana AI's default multi-agent orchestrator, generally available since 22 June 2026. It is a closed model exposed as one OpenAI-compatible API that calls other models. Collected 2026-10-15.", "what_it_is_for": "Default Sakana orchestrator for everyday chat, coding, and review. It routes work across a pool and allows pool opt-outs for privacy or compliance.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/fugu/fugu-cyber": { "id": "sakana/fugu/fugu-cyber", "name": "Fugu Cyber", "maker": "sakana", "family": "sakana/fugu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "Fugu-style orchestrator specialized for cybersecurity reasoning. Parameter count is not published. Access is manually approved.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fugu Cyber is Sakana AI's gated cybersecurity orchestrator (fugu-cyber-v1.0). Weights are closed and the public price is not posted. It was listed on the models console as of 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Gated cybersecurity orchestrator for security analysis, vulnerability research, and threat investigation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/fugu/fugu-max": { "id": "sakana/fugu/fugu-max", "name": "Fugu Max", "maker": "sakana", "family": "sakana/fugu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-11", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "Orchestrator over a larger pool of open-weight and specialized models, including NVIDIA Nemotron. Fixed pool, no opt-outs. Parameter count is not published.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 6.0 }, "summary_intro": "Fugu Max is Sakana AI's cost-performance orchestrator, released 11 September 2026 as fugu-max-v1.0. It is a closed API model with a flat published token price. Collected 2026-10-15.", "what_it_is_for": "Cost-performance orchestrator. Sakana positions it as frontier-style results at a lower token price by routing to smaller open and specialized models.", "ratings": { "empirical_100": null, "lmsys_elo": 1290, "coding_elo": 1290, "swe_bench": 30.0, "humaneval": 82.0, "twelve_cs": 99.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/fugu/fugu-ultra": { "id": "sakana/fugu/fugu-ultra", "name": "Fugu Ultra", "maker": "sakana", "family": "sakana/fugu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-22", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "Same orchestration design as Fugu, with a deeper fixed expert pool and no pool opt-outs. The current default is v2.0. Parameter count is not published.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 30.0 }, "summary_intro": "Fugu Ultra is Sakana AI's quality orchestrator, first released 22 June 2026 and now defaulting to v2.0 (11 September 2026). It is closed, API-hosted, and priced at a published token rate. Collected 2026-10-15.", "what_it_is_for": "Quality tier for hard multi-step work: research, coding agents, and long investigations. The alias fugu-ultra currently selects v2.0.", "ratings": { "empirical_100": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs": 99.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/kame/kame": { "id": "sakana/kame/kame", "name": "KAME", "maker": "sakana", "family": "sakana/kame", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-04-09", "context_length": null, "max_output_tokens": null, "parameters": "7818805248", "architecture": "Hybrid speech-to-speech model. Front end is based on Moshi. A back-end text LLM is queried in parallel and its text is injected into the speech model. Safetensors total 7,818,805,248. Inference code repo is MIT; the weight license is not on the card.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KAME is Sakana AI's hybrid speech-to-speech model, weights posted 9 April 2026. The weight license is not stated on the card. Collected 2026-10-15.", "what_it_is_for": "Low-latency speech conversation with a text LLM supplying knowledge in the background.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/karamaru/llama-3-karamaru-v1": { "id": "sakana/karamaru/llama-3-karamaru-v1", "name": "Llama-3-Karamaru-v1", "maker": "sakana", "family": "sakana/karamaru", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2025-03-19", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Continual pretrain and style tune of elyza/Llama-3-ELYZA-JP-8B for Edo-period Japanese. Data includes pre-modern text transcribed with kuzushiji OCR. Safetensors total 8,030,261,248.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3-Karamaru-v1 is Sakana AI's classical-Japanese chat model, posted 19 March 2025 under the Llama 3 license. Collected 2026-10-15.", "what_it_is_for": "Converse in Edo-period Japanese rather than modern Japanese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/metom/metom": { "id": "sakana/metom/metom", "name": "Metom", "maker": "sakana", "family": "sakana/metom", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "5644943", "architecture": "Vision Transformer Kuzushiji character classifier. Safetensors total 5,644,943. Trained on the ROIS classical-book kuzushiji set, 2,703 character types that appear at least 5 times.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Metom is Sakana AI's small Apache-2.0 kuzushiji Vision Transformer, posted 20 November 2024. Collected 2026-10-15.", "what_it_is_for": "Classify kuzushiji characters from images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/namazu/llama-3-1-namazu-405b": { "id": "sakana/namazu/llama-3-1-namazu-405b", "name": "Llama-3.1-Namazu-405B", "maker": "sakana", "family": "sakana/namazu", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sakana post-training applied to Llama 3.1 405B. No public card, size confirmation, or API ID.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3.1-Namazu-405B is a Sakana AI alpha chat model announced 24 March 2026. It is not in the current API model list. Collected 2026-10-15.", "what_it_is_for": "March 2026 Sakana Chat alpha used to show post-training that adapts a large open model to Japanese use. Superseded in the product by later Namazu.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/namazu/namazu-deepseek-v3-1-terminus": { "id": "sakana/namazu/namazu-deepseek-v3-1-terminus", "name": "Namazu-DeepSeek-V3.1-Terminus", "maker": "sakana", "family": "sakana/namazu", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sakana post-training applied to DeepSeek-V3.1-Terminus. No public card, size confirmation, or API ID.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Namazu-DeepSeek-V3.1-Terminus is a Sakana AI alpha chat model announced 24 March 2026. It is not in the current API model list. Collected 2026-10-15.", "what_it_is_for": "March 2026 Sakana Chat alpha used to show post-training that adapts a large open model to Japanese use. Superseded in the product by later Namazu.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/namazu/namazu-gpt-oss-120b": { "id": "sakana/namazu/namazu-gpt-oss-120b", "name": "Namazu-gpt-oss-120B", "maker": "sakana", "family": "sakana/namazu", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sakana post-training applied to gpt-oss-120B. No public card, size confirmation, or API ID.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Namazu-gpt-oss-120B is a Sakana AI alpha chat model announced 24 March 2026. It is not in the current API model list. Collected 2026-10-15.", "what_it_is_for": "March 2026 Sakana Chat alpha used to show post-training that adapts a large open model to Japanese use. Superseded in the product by later Namazu.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/namazu/sakana-namazu": { "id": "sakana/namazu/sakana-namazu", "name": "Sakana Namazu", "maker": "sakana", "family": "sakana/namazu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Post-trained from Moonshot AI's open model Kimi K2.6 for Japanese language and Japanese business workflows. Single model, not an orchestrator. Parameter count is not published by Sakana.", "modalities": { "input": [ "text", "image", "file" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.95, "completion_per_m_usd": 4.0 }, "summary_intro": "Sakana Namazu is Sakana AI's closed Japanese-specialized LLM API, launched 3 August 2026 on Kimi K2.6. Model ID sakana-namazu-v1.0. Collected 2026-10-15.", "what_it_is_for": "Japanese-specialized production LLM with built-in web search and code execution, sold as a single model rather than an orchestrator.", "ratings": { "empirical_100": null, "lmsys_elo": 1260, "coding_elo": 1260, "swe_bench": 25.0, "humaneval": 78.0, "twelve_cs": 36.1, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/repo/repo-olmo2-1b-stage2-l5": { "id": "sakana/repo/repo-olmo2-1b-stage2-l5", "name": "RePo-OLMo2-1B-stage2-L5", "maker": "sakana", "family": "sakana/repo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-12-14", "context_length": null, "max_output_tokens": null, "parameters": "1496496128", "architecture": "OLMo 2 1B continually pretrained from allenai/OLMo-2-0425-1B for 50B stage-2 tokens (dolmino-mix-1124) with context Re-Positioning. The L5 name is the checkpoint label on the card. Safetensors total 1,496,496,128.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RePo-OLMo2-1B-stage2-L5 is a Sakana AI continual-pretrain of OLMo 2 1B, posted 14 December 2025 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Base LM checkpoint showing context re-positioning on OLMo 2 1B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/repo/repo-olmo2-7b-stage2-l10": { "id": "sakana/repo/repo-olmo2-7b-stage2-l10", "name": "RePo-OLMo2-7B-stage2-L10", "maker": "sakana", "family": "sakana/repo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-04-01", "context_length": null, "max_output_tokens": null, "parameters": "7391252480", "architecture": "OLMo 2 7B continually pretrained from allenai/OLMo-2-1124-7B with context Re-Positioning. Card text matches the 1B write-up's 50B-token stage-2 setup. Safetensors total 7,391,252,480. L10 is the checkpoint label.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RePo-OLMo2-7B-stage2-L10 is Sakana AI's 7B RePo checkpoint, posted 1 April 2026 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "7B base checkpoint for the RePo continual-pretrain experiment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/rlt/rlt-32b": { "id": "sakana/rlt/rlt-32b", "name": "RLT-32B", "maker": "sakana", "family": "sakana/rlt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-06-22", "context_length": null, "max_output_tokens": null, "parameters": "32763876352", "architecture": "Qwen2.5-32B-Instruct student distilled from the 7B reinforcement-learned teacher, not from a 32B teacher. Safetensors total 32,763,876,352.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RLT-32B is Sakana AI's 32B student distilled from a 7B reinforcement-learned teacher, posted 22 June 2025 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Larger student distilled from the same 7B RLT teacher.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/rlt/rlt-7b": { "id": "sakana/rlt/rlt-7b", "name": "RLT-7B", "maker": "sakana", "family": "sakana/rlt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-06-21", "context_length": null, "max_output_tokens": null, "parameters": "7615616512", "architecture": "Qwen2.5-7B-Instruct student distilled from a 7B reinforcement-learned teacher. Safetensors total 7,615,616,512. Card says it is a research prototype, not for mission-critical deployment. SFT hyperparameters follow Li et al. 2025 (arXiv 2502.07374), which is not a Sakana model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RLT-7B is Sakana AI's 7B reinforcement-learning-teacher student, posted 21 June 2025 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Small reasoning student distilled from a teacher trained to explain, for test-time scaling research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/sparselm/sparselm-0-5b": { "id": "sakana/sparselm/sparselm-0-5b", "name": "SparseLM0.5B", "maker": "sakana", "family": "sakana/sparselm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-23", "context_length": null, "max_output_tokens": null, "parameters": "612403200", "architecture": "Hub tag llama_sparse_relu. Card body is only the Apache-2.0 header. Safetensors total 612403200.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SparseLM0.5B is a Sakana AI sparse LM checkpoint posted 23 March 2026 under Apache-2.0. The card has almost no text. Collected 2026-10-15.", "what_it_is_for": "Small sparse Llama-style research checkpoints released with Sakana's sparsity kernels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/sparselm/sparselm-1-5b": { "id": "sakana/sparselm/sparselm-1-5b", "name": "SparseLM1.5B", "maker": "sakana", "family": "sakana/sparselm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-23", "context_length": null, "max_output_tokens": null, "parameters": "1640089600", "architecture": "Hub tag llama_sparse_relu. Card body is only the Apache-2.0 header. Safetensors total 1640089600.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SparseLM1.5B is a Sakana AI sparse LM checkpoint posted 23 March 2026 under Apache-2.0. The card has almost no text. Collected 2026-10-15.", "what_it_is_for": "Small sparse Llama-style research checkpoints released with Sakana's sparsity kernels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/sparselm/sparselm-1b": { "id": "sakana/sparselm/sparselm-1b", "name": "SparseLM1B", "maker": "sakana", "family": "sakana/sparselm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-23", "context_length": null, "max_output_tokens": null, "parameters": "1126246400", "architecture": "Hub tag llama_sparse_relu. Card body is only the Apache-2.0 header. Safetensors total 1126246400.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SparseLM1B is a Sakana AI sparse LM checkpoint posted 23 March 2026 under Apache-2.0. The card has almost no text. Collected 2026-10-15.", "what_it_is_for": "Small sparse Llama-style research checkpoints released with Sakana's sparsity kernels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/sparselm/sparselm-2b": { "id": "sakana/sparselm/sparselm-2b", "name": "SparseLM2B", "maker": "sakana", "family": "sakana/sparselm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-23", "context_length": null, "max_output_tokens": null, "parameters": "2153932800", "architecture": "Hub tag llama_sparse_relu. Card body is only the Apache-2.0 header. Safetensors total 2153932800.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SparseLM2B is a Sakana AI sparse LM checkpoint posted 23 March 2026 under Apache-2.0. The card has almost no text. Collected 2026-10-15.", "what_it_is_for": "Small sparse Llama-style research checkpoints released with Sakana's sparsity kernels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/taid/taid-llm-1-5b": { "id": "sakana/taid/taid-llm-1-5b", "name": "TAID-LLM-1.5B", "maker": "sakana", "family": "sakana/taid", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-08-07", "context_length": null, "max_output_tokens": null, "parameters": "1543268864", "architecture": "Qwen2 student distilled with TAID from Qwen2-72B-Instruct into Qwen2-1.5B-Instruct. English demonstration model, separate from TinySwallow. Safetensors total 1,543,268,864.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TAID-LLM-1.5B is Sakana AI's English TAID student, posted 7 August 2024 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "English compact LM showing TAID distillation from a 72B teacher.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/taid/taid-vlm-2b": { "id": "sakana/taid/taid-vlm-2b", "name": "TAID-VLM-2B", "maker": "sakana", "family": "sakana/taid", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-08-14", "context_length": null, "max_output_tokens": null, "parameters": "2205754368", "architecture": "InternVL-style VLM. TAID distillation from InternVL2-8B into InternVL2-2B. Safetensors total 2,205,754,368. Custom code.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TAID-VLM-2B is Sakana AI's MIT-licensed 2B vision-language TAID student, posted 14 August 2024. Collected 2026-10-15.", "what_it_is_for": "Compact English visual question answering model used to show TAID beyond text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/tinyswallow/tinyswallow-1-5b": { "id": "sakana/tinyswallow/tinyswallow-1-5b", "name": "TinySwallow-1.5B", "maker": "sakana", "family": "sakana/tinyswallow", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-12-26", "context_length": 32768, "max_output_tokens": null, "parameters": "1543714304", "architecture": "Qwen2 causal LM. TAID distillation from Qwen2.5-32B-Instruct into Qwen2.5-1.5B-Instruct, plus further Japanese pretraining. 28 layers, hidden 1536, 12 heads, 2 KV heads. Instruct sibling has the same shape; this checkpoint is the non-chat base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinySwallow-1.5B is a Sakana AI and Swallow-team Japanese 1.5B model posted 26 December 2024 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Compact Japanese base LM for research and further tuning. Not the instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sakana/tinyswallow/tinyswallow-1-5b-instruct": { "id": "sakana/tinyswallow/tinyswallow-1-5b-instruct", "name": "TinySwallow-1.5B-Instruct", "maker": "sakana", "family": "sakana/tinyswallow", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 weights; Gemma Terms restrict some instruction data", "release_date": "2025-01-07", "context_length": 32768, "max_output_tokens": null, "parameters": "1543714304", "architecture": "Instruction-tuned TinySwallow-1.5B. Qwen2, 28 layers, hidden size 1536, vocab 151936, max_position_embeddings 32768. Teacher Qwen2.5-32B-Instruct, student Qwen2.5-1.5B-Instruct, then Japanese instruction tuning. Developed with the Swallow team.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinySwallow-1.5B-Instruct is Sakana AI's chat TinySwallow, posted 7 January 2025 under Apache-2.0. Quantized GGUF and MLC builds followed on 30 January 2025. Collected 2026-10-15.", "what_it_is_for": "Small Japanese instruction model that can run locally or in the browser.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salamanca/trecard/trecard-renal-histology-unet": { "id": "salamanca/trecard/trecard-renal-histology-unet", "name": "TRECARD Renal Histology Segmentation U-Net", "maker": "salamanca", "family": "salamanca/trecard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2026-09-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "2D U-Net trained by the TRECARD group. Four encoder blocks, each two 3\u00d73 same-padded ReLU convolutions plus 2\u00d72 max-pooling, with feature maps doubling from 64 to 512. Bottleneck of two 3\u00d73 ReLU convolutions at 1024 maps. Four decoder blocks of 2\u00d72 transposed convolution, skip concatenation, and convolutions. Final softmax over four classes (focal inflammatory infiltrates, i-IFTA, tubular dilation, unaltered tissue). Dropout 0.2 and L2 weight regularization \u03bb=1\u00d710^\u22125. Keras 3 file; the torch backend is required. Parameter count is not stated. The methods section says Adam with an initial learning rate of 2.5\u00d710^\u22124, a maximum of 100 epochs, and EarlyStopping patience of 15 on validation macro Dice, restoring the best weights. A results sentence says ReduceLROnPlateau fired at epochs 15, 21, 25, and 29 and reduced the rate from 2\u00d710^\u22124 to 2.37\u00d710^\u22126. Figure 3 plots 32 epochs. Reported training time is 54 min. The training loss name and batch size were not in the sections retrieved.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The TRECARD group at the Department of Physiology and Pharmacology, Universidad de Salamanca, with IBSAL, released a Keras U-Net that labels four lesion classes on haematoxylin-and-eosin patches of rat kidney. Weights have been on Hugging Face since 2026-09-15. The describing paper is BioMedInformatics, 29 September 2026. The checkpoint is still the public release. Collected 2026-10-15.", "what_it_is_for": "The model card and the paper say it segments inflammation-associated interstitial fibrosis and tubular atrophy (i-IFTA), focal inflammatory infiltrates, and tubular dilation, plus unaltered tissue, on rat kidney whole slides. The companion pipeline turns patch predictions into a slide overlay and an area percentage for each class. It is a preclinical research tool. The card says it is not for human diagnosis and was not validated on human biopsies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/albef/albef": { "id": "salesforce/albef/albef", "name": "ALBEF", "maker": "salesforce", "family": "salesforce/albef", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language model that aligns image and text before fusion, with momentum distillation. ViT-B/16 visual encoder and a BERT text encoder. Pretrained checkpoints for 4M and 14M images, plus downstream finetunes, were released. GitHub salesforce/ALBEF was archived read-only on 2025-05-01. Later integrated into LAVIS.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ALBEF is Salesforce Research's 2021 vision-language model (NeurIPS 2021). It aligns images and text before fusion. Checkpoints shipped from the GitHub repo, which was archived on 2025-05-01. Collected 2026-10-15.", "what_it_is_for": "Image-text retrieval, VQA, visual entailment, NLVR2, and visual grounding. The README says pretrain and those finetunes are supported.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/alpro/alpro": { "id": "salesforce/alpro/alpro", "name": "ALPRO", "maker": "salesforce", "family": "salesforce/alpro", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video-language pretraining with entity prompts. Supports text-video retrieval and video QA. Checkpoints were released from GitHub. The repo was archived read-only on 2025-05-01 and the model was folded into LAVIS.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ALPRO is Salesforce Research's CVPR 2022 video-language model. It pretrains with entity prompts for retrieval and video QA. The GitHub repo was archived on 2025-05-01. Collected 2026-10-15.", "what_it_is_for": "Text-video retrieval on MSRVTT and DiDeMo, and video question answering on MSRVTT and MSVD.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/blip/blip": { "id": "salesforce/blip/blip", "name": "BLIP", "maker": "salesforce", "family": "salesforce/blip", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause", "release_date": "2022-01-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language mixture-of-encoder-decoder. ViT-B and ViT-L image encoders with a BERT text transformer. Pre-trained on 14M and 129M images, with CapFilt caption bootstrapping. GitHub salesforce/BLIP was archived read-only on 2026-03-03. Weights remain on the Hub.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLIP is Salesforce AI Research's 2022 vision-language model for captioning, retrieval, and visual question answering. Base and large checkpoints, plus task heads, are BSD-3-Clause and still on the Hub. The original GitHub repo was archived on 2026-03-03. Collected 2026-10-15.", "what_it_is_for": "Unified image-text understanding and generation: captions, VQA, and image-text matching. CapFilt is the paper's method for cleaning noisy web captions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/blip/blip-2": { "id": "salesforce/blip/blip-2", "name": "BLIP-2", "maker": "salesforce", "family": "salesforce/blip", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-02-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Q-Former bridges a frozen image encoder and a frozen LLM. Public checkpoints pair the Q-Former with OPT 2.7B, OPT 6.7B, Flan-T5-XL, and Flan-T5-XXL. COCO finetunes and a ViT-g image-text matching checkpoint were also released.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLIP-2 is Salesforce AI Research's 2023 vision-language model. A Q-Former connects a frozen vision encoder to a frozen LLM. Released pairs use OPT or Flan-T5. License on the Hub checkpoints is MIT. Collected 2026-10-15.", "what_it_is_for": "Zero-shot image captioning and visual question answering without training the vision encoder or the LLM end to end.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/blip/blip-diffusion": { "id": "salesforce/blip/blip-diffusion", "name": "BLIP-Diffusion", "maker": "salesforce", "family": "salesforce/blip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Subject-driven text-to-image model. BLIP encodes the subject; a diffusion U-Net generates the image. LAVIS documents Diffusers repos Salesforce/blipdiffusion and Salesforce/blipdiffusion-controlnet. Those two ids were not in the 185-model Salesforce Hub listing retrieved on 2026-10-15.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLIP-Diffusion is Salesforce AI Research's 2023 subject-driven text-to-image model. It uses a BLIP subject encoder with a diffusion generator, including a ControlNet variant documented in LAVIS. Collected 2026-10-15.", "what_it_is_for": "Zero-shot subject-driven generation and faster subject customization, and subject-driven editing when combined with ControlNet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/blip/blip3o": { "id": "salesforce/blip/blip3o", "name": "BLIP3-o", "maker": "salesforce", "family": "salesforce/blip", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-05-23", "context_length": null, "max_output_tokens": null, "parameters": "4B and 8B", "architecture": "Unified understanding and generation. Image generation is trained on top of Qwen2.5-VL with CLIP features and flow matching in a diffusion transformer. The blog describes an 8B model that mixes open and proprietary images and a fully open 4B model on public images. Weights are published under the BLIP3o Hub org, not the Salesforce org. The 8B repo's safetensors widget reports 14B parameters, which disagrees with the 8B name.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLIP3-o is Salesforce AI Research's 2025 unified model for image understanding and generation, built on Qwen2.5-VL with a flow-matching diffusion head. Open 4B and 8B checkpoints were released under Apache-2.0 on the BLIP3o Hub org. Collected 2026-10-15.", "what_it_is_for": "One model that both reads images and generates images. Sequential training keeps the autoregressive backbone frozen while the diffusion head learns generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/blip/blip3o-next": { "id": "salesforce/blip/blip3o-next", "name": "BLIP3o-NEXT", "maker": "salesforce", "family": "salesforce/blip", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-02", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Follow-on to BLIP3-o. Autoregressive hidden states condition a diffusion image decoder. Adds discrete SigLIP-2 token supervision and GRPO. Salesforce Hub hosts 3B pretrain, SFT, and GRPO checkpoints plus an edit VAE.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLIP3o-NEXT is the late-2025 Salesforce follow-on to BLIP3-o. The public 3B checkpoints on the Salesforce Hub add discrete image-token supervision and GRPO. License tags are Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Image generation and editing with better prompt alignment and text rendering than BLIP3-o, using a 3B autoregressive-plus-diffusion stack.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/blip/instructblip": { "id": "salesforce/blip/instructblip", "name": "InstructBLIP", "maker": "salesforce", "family": "salesforce/blip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT for Flan-T5 checkpoints; other for Vicuna checkpoints", "release_date": "2023-05-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-tuned BLIP-2. Public checkpoints: Vicuna 7B, Vicuna 13B, Flan-T5-XL, and Flan-T5-XXL. Flan-T5 checkpoints are MIT. Vicuna checkpoints are license tag other, consistent with the LLaMA-derived Vicuna terms.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InstructBLIP is Salesforce AI Research's instruction-tuned BLIP-2, released May 2023. Flan-T5 sizes are MIT. Vicuna sizes carry a non-MIT Hub license tag. Collected 2026-10-15.", "what_it_is_for": "General vision-language instructions across the datasets used in the paper, with an instruction-aware Q-Former.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/coda/coda": { "id": "salesforce/coda/coda", "name": "CoDA", "maker": "salesforce", "family": "salesforce/coda", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "1.7B", "architecture": "Discrete diffusion language model for code. Bidirectional context and confidence-guided sampling. The card names CoDA-1.7B-Base and CoDA-1.7B-Instruct. Hub ids are CoDA-v0-Base and CoDA-v0-Instruct. The org index size tag said 2B, which disagrees with the card's 1.7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CoDA is Salesforce AI Research's 1.7B diffusion language model for code, released 2025-09-25 as base and instruct checkpoints. License is CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "Code generation and completion where both left and right context matter. The instruct card reports HumanEval and MBPP scores against other diffusion LMs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/codegen/codegen": { "id": "salesforce/codegen/codegen", "name": "CodeGen", "maker": "salesforce", "family": "salesforce/codegen", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause", "release_date": "2022-03-25", "context_length": null, "max_output_tokens": null, "parameters": "350M, 2B, 6B, and 16B", "architecture": "Autoregressive decoder-only transformer for program synthesis. Three data stages: NL (Pile), Multi (multi-language code), Mono (further Python). Each stage has 350M, 2B, 6B, and 16B checkpoints.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CodeGen is Salesforce AI Research's 2022 open code-generation family. Twelve checkpoints cover NL, multi-language, and Python stages at 350M, 2B, 6B, and 16B. License is BSD-3-Clause. Collected 2026-10-15.", "what_it_is_for": "Conversational program synthesis: natural-language intent to code, with multi-turn continuation. Mono checkpoints are the Python specialists. NL checkpoints are the Pile-pretrained stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/codegen/codegen2": { "id": "salesforce/codegen/codegen2", "name": "CodeGen2", "maker": "salesforce", "family": "salesforce/codegen", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-25", "context_length": null, "max_output_tokens": null, "parameters": "1B, 3.7B, 7B, and 16B", "architecture": "Successor code LM from the CodeGen2 paper. Released Hub checkpoints use a _P suffix: codegen2-1B_P, codegen2-3_7B_P, codegen2-7B_P, codegen2-16B_P. Architecture tag on the Hub is codegen.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CodeGen2 is Salesforce AI Research's 2023 follow-up to CodeGen, released as 1B, 3.7B, 7B, and 16B checkpoints with a _P Hub suffix. License is Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Code generation, with the paper studying training on programming and natural language. These four checkpoints are the public CodeGen2 weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/codegen/codegen2-5": { "id": "salesforce/codegen/codegen2-5", "name": "CodeGen2.5", "maker": "salesforce", "family": "salesforce/codegen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 for multi and mono; instruct Hub tag is other", "release_date": "2023-07-06", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "7B code model in the CodeGen2 line. Three released checkpoints: multi, mono, and instruct. Hub ids use the codegen25 prefix and a _P suffix. Multi and mono are tagged with the StarCoderData dataset.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CodeGen2.5 is Salesforce AI Research's 7B code model released 2023-07-06 as multi, mono, and instruct checkpoints. Base checkpoints are Apache-2.0. The instruct license tag is other. Collected 2026-10-15.", "what_it_is_for": "Code generation and instruction following for code. Multi and mono continue the CodeGen stage naming. Training data tag on the Hub is bigcode/starcoderdata.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/codet5/codet5": { "id": "salesforce/codet5/codet5", "name": "CodeT5", "maker": "salesforce", "family": "salesforce/codet5", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 on codet5-base; BSD-3-Clause on many task checkpoints", "release_date": "2021-09-02", "context_length": null, "max_output_tokens": null, "parameters": "base 220M and large 770M; small not restated on the retrieved CodeT5+ card", "architecture": "Identifier-aware encoder-decoder for code, in the T5 family. Hub sizes: codet5-small, codet5-base (Apache-2.0), codet5-large. CodeXGLUE task checkpoints are finetunes of the base model and are mostly BSD-3-Clause.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CodeT5 is Salesforce AI Research's 2021 encoder-decoder for code understanding and generation. The base checkpoint is Apache-2.0. Task finetunes on the Hub are mostly BSD-3-Clause. Collected 2026-10-15.", "what_it_is_for": "Code summarization, generation, translation, refinement, and defect detection. The GitHub README also describes a VS Code assistant demo built on CodeT5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/codet5/codet5p": { "id": "salesforce/codet5/codet5p", "name": "CodeT5+", "maker": "salesforce", "family": "salesforce/codet5", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause", "release_date": "2023-05-13", "context_length": null, "max_output_tokens": null, "parameters": "220M, 770M, 2B, 6B, and 16B, plus a 110M embedding checkpoint", "architecture": "Encoder-decoder that can run as encoder-only, decoder-only, or encoder-decoder. Larger sizes use a shallow encoder and a deep decoder initialized from frozen code LLMs such as CodeGen. InstructCodeT5+ 16B is the instruction-tuned 16B checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CodeT5+ is Salesforce AI Research's 2023 open code LLM family, from 220M to 16B, under BSD-3-Clause. InstructCodeT5+ is the 16B instruction model. Collected 2026-10-15.", "what_it_is_for": "Code understanding and generation, including embeddings from the 110M checkpoint and instruction-following code generation from InstructCodeT5+.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/ctrl/ctrl": { "id": "salesforce/ctrl/ctrl", "name": "CTRL", "maker": "salesforce", "family": "salesforce/ctrl", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause", "release_date": "2019-09-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conditional decoder-only transformer. Model dimension 1280, inner dimension 8192, 48 layers, 16 heads. First token is a control code. Vocabulary about 250K BPE tokens. Trained on about 140 GB of text. The retrieved card does not restate a parameter total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CTRL is Salesforce Research's 2019 conditional language model. Control codes set domain, style, and entities. The Hub checkpoint is BSD-3-Clause and primarily English, with some German, Spanish, and French. Collected 2026-10-15.", "what_it_is_for": "Controllable text generation for research and for fine-tuning. The model card's intended uses include creative writing with a person, formatting, and studying generated text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/fare/fare": { "id": "salesforce/fare/fare", "name": "FARE", "maker": "salesforce", "family": "salesforce/fare", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-10-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two checkpoints tagged with paper arXiv:2510.17793. FARE-8B uses a Qwen3 tag. FARE-20B uses a gpt_oss tag. The 20B repo's Hub size widget reported 4.76M, which conflicts with the 20B name, so no parameter total is stored.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FARE is a Salesforce AI Research release from October 2025 with 8B and 20B Hub checkpoints under CC-BY-NC-4.0. Both repos point at arXiv:2510.17793. Collected 2026-10-15.", "what_it_is_for": "Named research checkpoints. Read the paper for the task. This catalog does not restate the method because the card text was not retrieved.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/fofpred/fofpred": { "id": "salesforce/fofpred/fofpred", "name": "FOFPred", "maker": "salesforce", "family": "salesforce/fofpred", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-09", "context_length": null, "max_output_tokens": null, "parameters": "4B on the Hub size tag", "architecture": "Language-conditioned future optical-flow model. Frozen Qwen2.5-VL-3B-Instruct, a trainable diffusion transformer based on OmniGen2, and the FLUX.1-dev VAE. Predicts 4 future flow frames from an image and a text instruction.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FOFPred is Salesforce AI Research's optical-flow forecasting model, posted December 2025. It turns an image and a language instruction into four future flow frames. The Hub license tag is Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Predict pixel motion for robot control and video generation. The README shows a Diffusers pipeline with trust_remote_code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/gta1/gta1": { "id": "salesforce/gta1/gta1", "name": "GTA1", "maker": "salesforce", "family": "salesforce/gta1", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT on GTA1-7B and GTA1-32B; Apache-2.0 on GTA1-7B-2507", "release_date": "2025-07-09", "context_length": null, "max_output_tokens": null, "parameters": "7B and 32B on the Hub", "architecture": "GUI grounding agent post-trained from Qwen2.5-VL with GRPO. Hub checkpoints: GTA1-7B (MIT), GTA1-7B-2507 (Apache-2.0), and GTA1-32B (MIT). A secondary news post also mentions a 72B model. That 72B repo was not in the Salesforce Hub org list.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GTA1 is Salesforce AI Research's 2025 GUI agent, released as 7B and 32B Qwen2.5-VL post-trains. Licenses differ by checkpoint: MIT or Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Ground GUI actions and drive computer-use tasks. The paper is arXiv:2507.05791.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/moirai/moirai-1-0": { "id": "salesforce/moirai/moirai-1-0", "name": "Moirai 1.0", "maker": "salesforce", "family": "salesforce/moirai", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-03-19", "context_length": null, "max_output_tokens": null, "parameters": "14M, 91M, and 311M", "architecture": "Masked-encoder universal time-series transformer. Patch-based, any-variate, mixture distribution output. Pretrained on LOTSA, described as more than 27 billion observations across nine domains. Context length and patch size are chosen at inference. Patch sizes include 8, 16, 32, 64, and 128.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moirai 1.0 is Salesforce AI Research's first time-series foundation model, blogged on 2024-03-19. Small, base, and large checkpoints are 14M, 91M, and 311M, under CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "Zero-shot forecasting across domains, frequencies, and numbers of variables, including covariates.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/moirai/moirai-1-1": { "id": "salesforce/moirai/moirai-1-1", "name": "Moirai 1.1", "maker": "salesforce", "family": "salesforce/moirai", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Updated Moirai 1.0 release. Same Hub size names: small, base, and large. License CC-BY-NC-4.0. The 1.1 cards were not re-fetched in full, so this dossier does not restate a new parameter table or a new training corpus.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moirai 1.1 is the June 2024 update of Salesforce's Moirai forecasting models. Small, base, and large weights are on the Hub under CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "Zero-shot time-series forecasting, as an updated 1.0 line. A secondary host describes better low-frequency behavior. That claim was not confirmed from a Salesforce page in this pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/moirai/moirai-2": { "id": "salesforce/moirai/moirai-2", "name": "Moirai 2.0", "maker": "salesforce", "family": "salesforce/moirai", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "11.4M", "architecture": "Decoder-only time-series transformer. Quantile loss, multi-token prediction, missing-value patch embeddings, and patch-level random masking. Pretrained on a non-leaking subset of GIFT-Eval pretrain and train, Chronos mixup, KernelSynth, and internal Salesforce operational data. Only the small checkpoint was on the Hub org list.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moirai 2.0 is Salesforce AI Research's decoder-only time-series model, posted on the Hub on 2025-08-06. The public checkpoint is Moirai-2.0-R-small at 11.4M parameters, CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "Zero-shot probabilistic forecasting. The blog says it is smaller and faster than Moirai large and better on MASE and CRPS in their comparison.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/moirai/moirai-agent": { "id": "salesforce/moirai/moirai-agent", "name": "Moirai-Agent", "maker": "salesforce", "family": "salesforce/moirai", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-12-29", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Qwen2 model tagged for time-series expert selection. Hub size tag is 3B. It is not a Moirai 2.0 forecaster checkpoint.", "modalities": { "input": [ "text", "time-series" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moirai-Agent is a Salesforce Hub model posted 2025-12-29. Tags describe a 3B Qwen2 model for time-series expert selection, under CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "Choose or route forecasting experts. It is a named companion to Moirai, not a replacement for Moirai 2.0-R-small.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/moirai/moirai-moe": { "id": "salesforce/moirai/moirai-moe", "name": "Moirai-MoE", "maker": "salesforce", "family": "salesforce/moirai", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sparse mixture-of-experts Moirai line. Two public checkpoints: moirai-moe-1.0-R-small and moirai-moe-1.0-R-base, both created 2024-11-01. No arXiv tag was on the Hub records retrieved.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moirai-MoE is Salesforce AI Research's November 2024 sparse time-series model, with small and base checkpoints under CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "Universal time-series forecasting with a mixture-of-experts Moirai architecture. Treat it as a named branch of Moirai, not as Moirai 2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/progen/progen": { "id": "salesforce/progen/progen", "name": "ProGen", "maker": "salesforce", "family": "salesforce/progen", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause", "release_date": "2020-03-11", "context_length": null, "max_output_tokens": null, "parameters": "1.2B", "architecture": "Conditional transformer language model for proteins, in the CTRL style. 36 layers and 8 attention heads. Trained on about 280 million sequences from more than 19,000 families, with taxonomic and functional control tags. Code and checkpoints were released on Zenodo and github.com/salesforce/progen. The repo README now leads with ProGen2.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProGen is Salesforce Research's 1.2B conditional protein language model, introduced in 2020 and described in Nature Biotechnology in 2023. It generates protein sequences from control tags. The code release is BSD-3-Clause. Collected 2026-10-15.", "what_it_is_for": "Controllable protein sequence generation and zero-shot fitness ranking. The Nature paper reports functional lysozyme, chorismate mutase, and malate dehydrogenase experiments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/progen/progen2": { "id": "salesforce/progen/progen2", "name": "ProGen2", "maker": "salesforce", "family": "salesforce/progen", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause", "release_date": "2022-06-27", "context_length": 1024, "max_output_tokens": null, "parameters": "151M to 6.4B", "architecture": "Autoregressive protein transformer decoder with next-token prediction, rotary positions, and GPT-J-style parallel attention and MLP blocks. Standard models train on UniRef90 and BFD30. Extra data variants: OAS and BFD90.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProGen2 is Salesforce AI Research's protein language-model suite, from 151M to 6.4B, released with the June 2022 paper. Weights and code are BSD-3-Clause on GitHub. Collected 2026-10-15.", "what_it_is_for": "Protein sequence generation and fitness prediction without extra finetuning. Data variants cover general proteins and immune repertoires.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/sfr/sfr-embedding": { "id": "salesforce/sfr/sfr-embedding", "name": "SFR-Embedding-Mistral", "maker": "salesforce", "family": "salesforce/sfr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-01-24", "context_length": 32768, "max_output_tokens": null, "parameters": "7B-class Mistral backbone", "architecture": "Text embedding model continued from the E5-Mistral-7B-Instruct line on Mistral-7B. The author discussion lists embedding dimension 4096 and sequence length 32768. English. Not a from-scratch foundation model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SFR-Embedding-Mistral is Salesforce AI Research's January 2024 English embedding model, built on Mistral-7B. The blog reports an MTEB average of 67.6 across 56 datasets. License is CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "Retrieval, clustering, classification, and semantic similarity. It was trained as a general text embedding model, not as a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/sfr/sfr-embedding-2": { "id": "salesforce/sfr/sfr-embedding-2", "name": "SFR-Embedding-2", "maker": "salesforce", "family": "salesforce/sfr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Second SFR text-embedding release. Hub architecture tag is Mistral. License CC-BY-NC-4.0. The card body was not fully retrieved, so dimension and context are not restated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SFR-Embedding-2 is Salesforce AI Research's June 2024 follow-up embedding model. The Hub repo is SFR-Embedding-2_R, Mistral-tagged, CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "General text embeddings, as the next SFR-Embedding release after SFR-Embedding-Mistral.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/sfr/sfr-embedding-code": { "id": "salesforce/sfr/sfr-embedding-code", "name": "SFR-Embedding-Code", "maker": "salesforce", "family": "salesforce/sfr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-01-16", "context_length": null, "max_output_tokens": null, "parameters": "400M and 2B", "architecture": "Code embedding models. Hub ids SFR-Embedding-Code-400M_R and SFR-Embedding-Code-2B_R. The 2B repo's architecture tag is codexembed2b. Paper tag arXiv:2411.12644.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SFR-Embedding-Code is Salesforce AI Research's code embedding pair, 400M and 2B, released January 2025 under CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "Code retrieval. Separate from the English SFR-Embedding text models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/unicontrol/unicontrol": { "id": "salesforce/unicontrol/unicontrol", "name": "UniControl", "maker": "salesforce", "family": "salesforce/unicontrol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-05-26", "context_length": null, "max_output_tokens": null, "parameters": "1.4B", "architecture": "Unified controllable image generation. One checkpoint covers many visual conditions. The GitHub update on 2023-06-08 lists 12 tasks: Canny, HED, sketch, depth, normal, skeleton, bounding box, segmentation, outpainting, inpainting, deblurring, and colorization. v1.1 checkpoint was posted 2023-07-06. Weights are Google Cloud files, about 5.78 GB for the May checkpoint.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UniControl is Salesforce AI Research's 1.4B controllable image model, with weights released in May 2023 and a 12-task v1.1 checkpoint in July 2023. It was accepted to NeurIPS 2023. Collected 2026-10-15.", "what_it_is_for": "One model for many image conditions, from edges and depth to inpainting and colorization, instead of a separate ControlNet per task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/xgen/xgen-7b": { "id": "salesforce/xgen/xgen-7b", "name": "XGen-7B", "maker": "salesforce", "family": "salesforce/xgen", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 for base checkpoints; instruct license not tagged", "release_date": "2023-06-28", "context_length": 8192, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only dense transformer. Hugging Face class is Llama. Tokenized with OpenAI tiktoken. 4K-base was trained 800B tokens at 2K then 400B at 4K (1.2T total). 8K-base continues from 4K-base for 300B tokens (1.5T total) at 8K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XGen-7B is Salesforce AI Research's 7B dense language model, released 2023-06-28, with 4K and 8K context checkpoints. Weights for the base models are Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The launch post says XGen-7B is a long-context 7B LLM for generation over sequences up to 8K, including code and long documents. The instruction model is a public-data supervised fine-tune for research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/xgen/xgen-mm": { "id": "salesforce/xgen/xgen-mm", "name": "xGen-MM (BLIP-3)", "maker": "salesforce", "family": "salesforce/xgen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on base and several v1.5 checkpoints; CC-BY-NC-4.0 on v1 instruct", "release_date": "2024-05-07", "context_length": null, "max_output_tokens": null, "parameters": "about 4B to 5B for the released Phi-3 mini checkpoints", "architecture": "Large multimodal model continuing the BLIP line. Any-resolution vision token sampler replaces BLIP-2's Q-Former. Language backbone is Phi-3 mini. v1 (May 2024) and v1.5 (August 2024) checkpoints. The paper also describes 4B and 14B releases; a 14B weight repo was not in the Salesforce Hub org list of 185 models on 2026-10-15.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "xGen-MM, also called BLIP-3, is Salesforce AI Research's open large multimodal model line, first posted in May 2024 and updated as v1.5 in August 2024. It replaces the BLIP-2 Q-Former with a vision token sampler on a Phi-3 mini backbone. Collected 2026-10-15.", "what_it_is_for": "Image and interleaved image-text understanding: captioning, visual question answering, and multi-image instructions. A DPO checkpoint is for reducing harmful or hallucinated behavior.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/xgen/xgen-mm-vid": { "id": "salesforce/xgen/xgen-mm-vid", "name": "xGen-MM-Vid (BLIP-3-Video)", "maker": "salesforce", "family": "salesforce/xgen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-12-18", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "xGen-MM / BLIP-3 image VLM plus a temporal encoder that compresses frames into a small set of visual tokens. Released checkpoints use 8 frames at 32 or 128 tokens.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "xGen-MM-Vid, also called BLIP-3-Video, is Salesforce AI Research's 4B video-language model. It adds a temporal encoder to xGen-MM so a short clip can be represented with 32 or 128 tokens. Weights are CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "Video question answering and captioning with far fewer visual tokens than frame-by-frame tokenization. The project page says a 4B model is aimed at competing with much larger video VLMs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/xgen/xgen-sales": { "id": "salesforce/xgen/xgen-sales", "name": "xGen-Sales", "maker": "salesforce", "family": "salesforce/xgen", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Proprietary sales model. Salesforce describes it as a refinement of its large language models with human-in-the-loop reinforcement learning, aimed at sales tasks inside Agentforce. Parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "xGen-Sales is a closed Salesforce AI Research model announced 2024-09-06 for sales tasks in Agentforce. The 2024-10-21 blog still called it a pilot for a subset of customers. No public weights. Collected 2026-10-15.", "what_it_is_for": "Salesforce says it summarizes calls, drafts sales content, enriches contacts, and tracks pipeline. It is a product model, not the open XGen-7B checkpoint. The blog explicitly says the GitHub XGen model is not the production fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/xgen/xgen-small": { "id": "salesforce/xgen/xgen-small", "name": "xGen-small", "maker": "salesforce", "family": "salesforce/xgen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-05-02", "context_length": 131072, "max_output_tokens": null, "parameters": "4B and 9B", "architecture": "Compact decoder-only LM trained from scratch. Hugging Face architecture tag is Llama. Two sizes, each with a base and a post-trained instruct checkpoint. Research release only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "xGen-small is Salesforce AI Research's compact language-model line, announced 2025-05-02, in 4B and 9B sizes with 128k context. Weights are CC-BY-NC-4.0 and marked research-only. Collected 2026-10-15.", "what_it_is_for": "The blog describes an enterprise-oriented small LM trained from scratch for long context at low cost, with instruction tuning and reinforcement learning on the post-trained checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/xlam/xlam": { "id": "salesforce/xlam/xlam", "name": "xLAM", "maker": "salesforce", "family": "salesforce/xlam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-03-18", "context_length": null, "max_output_tokens": null, "parameters": "1B, 7B, 8x7B, and 8x22B", "architecture": "Large action models for function calling. v0.1 (March 2024) is a Mixtral-tagged research checkpoint. The July-August 2024 family is Tiny 1B, Small 7B, Medium 8x7B, and Large 8x22B. Hub architecture tags: Llama for the 1B and 7B fc checkpoints, Mistral for xLAM-7b-r, Mixtral for the MoE checkpoints. A closed, stronger xLAM is described as powering Agentforce and is not these weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "xLAM is Salesforce AI Research's open large-action-model family for function calling. Public sizes run from 1B to 8x22B under CC-BY-NC-4.0. A stronger proprietary xLAM is what Salesforce says powers Agentforce. Collected 2026-10-15.", "what_it_is_for": "Turn requests into tool and API calls for agents. The open models are for research and non-commercial use. They are not the production Agentforce weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "salesforce/xlam/xlam-2": { "id": "salesforce/xlam/xlam-2", "name": "xLAM-2", "maker": "salesforce", "family": "salesforce/xlam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-03-25", "context_length": null, "max_output_tokens": null, "parameters": "1B, 3B, 8B, 32B, and 70B", "architecture": "Second xLAM function-calling release. Qwen2-tagged checkpoints at 1B, 3B, and 32B. Llama-tagged checkpoints at 8B and 70B. All CC-BY-NC-4.0. GGUF repos exist for 1B, 3B, and 8B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "xLAM-2 is Salesforce AI Research's March 2025 function-calling line, from 1B to 70B, under CC-BY-NC-4.0. Qwen2 and Llama tags mark the two backbones. Collected 2026-10-15.", "what_it_is_for": "Tool use and function calling for agents, including multi-turn data from the APIGen-MT set tagged on the Hub.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/bloomchat/bloomchat-176b-v1": { "id": "sambanova/bloomchat/bloomchat-176b-v1", "name": "BLOOMChat 176B v1", "maker": "sambanova", "family": "sambanova/bloomchat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BLOOMChat-176B LICENSE v1.0", "release_date": "2023-05-10", "context_length": 2048, "max_output_tokens": null, "parameters": "176000000000", "architecture": "Instruction-tuned BLOOM 176B, a decoder-only multilingual transformer. Prompt tags used in training are and .", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova, with Together, instruction-tuned BigScience BLOOM 176B into BLOOMChat v1, a multilingual chat model. Weights remain on the sambanovasystems Hub org. Collected on 2026-10-15.", "what_it_is_for": "Multilingual conversation, question answering, and generative answers. The card calls it an early multilingual chat baseline, not a mission-critical system.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/bloomchat/bloomchat-176b-v2": { "id": "sambanova/bloomchat/bloomchat-176b-v2", "name": "BLOOMChat 176B v2", "maker": "sambanova", "family": "sambanova/bloomchat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BLOOMChat-176B LICENSE v1.0", "release_date": "2024-02-01", "context_length": 8192, "max_output_tokens": null, "parameters": "176000000000", "architecture": "BLOOM 176B after continued pretraining on a long-sequence mix, then instruction tuning. Decoder-only. Prompt tags are and .", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released BLOOMChat v2 as a 176 billion parameter multilingual chat model. It continued-pretrains BigScience BLOOM on long sequences and then instruction-tunes the result. Weights are public. Collected on 2026-10-15.", "what_it_is_for": "Multilingual conversation, question answering, and long-sequence generative answers. The card still frames it as an early chat baseline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/qwq/qwq-0-5b-sft-draft": { "id": "sambanova/qwq/qwq-0-5b-sft-draft", "name": "QwQ-0.5B-SFT-Draft", "maker": "sambanova", "family": "sambanova/qwq", "status": "preview", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-01-11", "context_length": null, "max_output_tokens": null, "parameters": "494032768", "architecture": "Qwen2ForCausalLM fine-tune of Qwen/Qwen2.5-0.5B-Instruct. The readable Hub metadata lists long chain-of-thought SFT datasets. The README text is gated, so the training writeup was not read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova's Hub org hosts QwQ-0.5B-SFT-Draft, a gated 494,032,768-parameter Qwen2 fine-tune uploaded in January 2025. The name marks it as a draft. The card text could not be read. Collected on 2026-10-15.", "what_it_is_for": "Not stated in readable text. Metadata points at supervised fine-tuning of Qwen2.5-0.5B-Instruct on public long chain-of-thought sets. The draft name fits a small speculative-decoding or distillation checkpoint, but that job was not confirmed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/samba/samba-1": { "id": "sambanova/samba/samba-1", "name": "Samba-1", "maker": "sambanova", "family": "sambanova/samba", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-02-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composition of Experts: a router sends each prompt to one specialist model. Experts include SambaNova checkpoints and third-party open models. Not a single monolithic train.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova announced Samba-1 on 28 February 2024 as its enterprise Composition of Experts, sold inside SambaNova Suite. It routes each request to one expert. A community reply later called the playground model deprecated. It is not on the 2026 SambaCloud list. Collected on 2026-10-15.", "what_it_is_for": "One endpoint for many enterprise tasks. SambaNova said specialists cover domains such as finance and law and tasks such as summarization, extraction, and editing, including text, code, and images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/samba/samba-coe": { "id": "sambanova/samba/samba-coe", "name": "Samba-CoE", "maker": "sambanova", "family": "sambanova/samba", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-02-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Router over a small set of third-party open chat models. v0.1 and v0.2 use a 7B embedding router and five 7B experts, one active. v0.3 uses four 7B experts and one 34B expert, and adds uncertainty routing back to a stronger base model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova announced Samba-CoE on 28 February 2024 as a small public Composition of Experts and a scaled-down demo of Samba-1. v0.2 and v0.3 followed in March and April 2024. The experts are third-party models. The composition is not on the 2026 SambaCloud list. Collected on 2026-10-15.", "what_it_is_for": "Route a prompt to one small open expert so a single endpoint can cover math, facts, and reasoning at about the cost of two 7B calls, which is the v0.1 claim.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambacoder/sambacoder-nsql-llama-2-70b": { "id": "sambanova/sambacoder/sambacoder-nsql-llama-2-70b", "name": "SambaCoder-nsql-Llama-2-70B", "maker": "sambanova", "family": "sambanova/sambacoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-13", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 70B continued-pretrained on SQL, then fine-tuned for text-to-SQL. Loss on the fine-tune is computed only on the SQL span.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova and Numbers Station released SambaCoder-nsql-Llama-2-70B in February 2024. It is a Llama 2 70B text-to-SQL model trained on SambaNova RDUs and published on Hugging Face. Collected on 2026-10-15.", "what_it_is_for": "Generate a SQL query, preferably a SELECT, from a schema and a natural-language question. SambaNova later listed it as the text-to-SQL expert inside Samba-1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-arabic-base": { "id": "sambanova/sambalingo/sambalingo-arabic-base", "name": "SambaLingo-Arabic-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "6946035712", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Continued-pretrained bilingual base model. The card says to use few-shot prompting. Direct questions should go to the chat checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-arabic-base-70b": { "id": "sambanova/sambalingo/sambalingo-arabic-base-70b", "name": "SambaLingo-Arabic-Base-70B", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-04-15", "context_length": 4096, "max_output_tokens": null, "parameters": "69391889408", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "70B continued-pretrained bilingual base. The card says this checkpoint is for few-shot use with sampling off, and points chat use at the 70B chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-arabic-chat": { "id": "sambanova/sambalingo/sambalingo-arabic-chat", "name": "SambaLingo-Arabic-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "6946033664", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Arabic-Base for Arabic and English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-arabic-chat-70b": { "id": "sambanova/sambalingo/sambalingo-arabic-chat-70b", "name": "SambaLingo-Arabic-Chat-70B", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-04-15", "context_length": 4096, "max_output_tokens": null, "parameters": "69391889408", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "70B chat model. The card describes DPO alignment on top of the 70B base, which itself adapts Llama-2-70b.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-bulgarian-base": { "id": "sambanova/sambalingo/sambalingo-bulgarian-base", "name": "SambaLingo-Bulgarian-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Continued-pretrained bilingual base model. The card says to use few-shot prompting. Direct questions should go to the chat checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-bulgarian-chat": { "id": "sambanova/sambalingo/sambalingo-bulgarian-chat", "name": "SambaLingo-Bulgarian-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "6946033664", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Bulgarian-Base for Bulgarian and English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-hungarian-base": { "id": "sambanova/sambalingo/sambalingo-hungarian-base", "name": "SambaLingo-Hungarian-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Continued-pretrained bilingual base model. The card says to use few-shot prompting. Direct questions should go to the chat checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-hungarian-base-70b": { "id": "sambanova/sambalingo/sambalingo-hungarian-base-70b", "name": "SambaLingo-Hungarian-Base-70B", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-04-15", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "70B continued-pretrained bilingual base. The card says this checkpoint is for few-shot use with sampling off, and points chat use at the 70B chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-hungarian-chat": { "id": "sambanova/sambalingo/sambalingo-hungarian-chat", "name": "SambaLingo-Hungarian-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "6946033664", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Hungarian-Base for Hungarian and English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-hungarian-chat-70b": { "id": "sambanova/sambalingo/sambalingo-hungarian-chat-70b", "name": "SambaLingo-Hungarian-Chat-70B", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-04-15", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "70B chat model. The card describes DPO alignment on top of the 70B base, which itself adapts Llama-2-70b.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-japanese-base": { "id": "sambanova/sambalingo/sambalingo-japanese-base", "name": "SambaLingo-Japanese-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Continued-pretrained bilingual base model. The card says to use few-shot prompting. Direct questions should go to the chat checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-japanese-chat": { "id": "sambanova/sambalingo/sambalingo-japanese-chat", "name": "SambaLingo-Japanese-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "6946033664", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Japanese-Base for Japanese and English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-russian-base": { "id": "sambanova/sambalingo/sambalingo-russian-base", "name": "SambaLingo-Russian-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Continued-pretrained bilingual base model. The card says to use few-shot prompting. Direct questions should go to the chat checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-russian-chat": { "id": "sambanova/sambalingo/sambalingo-russian-chat", "name": "SambaLingo-Russian-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "6946033664", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Russian-Base for Russian and English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-serbian-base": { "id": "sambanova/sambalingo/sambalingo-serbian-base", "name": "SambaLingo-Serbian-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Continued-pretrained bilingual base model. The card says to use few-shot prompting. Direct questions should go to the chat checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-serbian-chat": { "id": "sambanova/sambalingo/sambalingo-serbian-chat", "name": "SambaLingo-Serbian-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "6946033664", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Serbian-Base for Serbian and English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-slovenian-base": { "id": "sambanova/sambalingo/sambalingo-slovenian-base", "name": "SambaLingo-Slovenian-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Continued-pretrained bilingual base model. The card says to use few-shot prompting. Direct questions should go to the chat checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-slovenian-chat": { "id": "sambanova/sambalingo/sambalingo-slovenian-chat", "name": "SambaLingo-Slovenian-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "6946033664", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Slovenian-Base for Slovenian and English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-thai-base": { "id": "sambanova/sambalingo/sambalingo-thai-base", "name": "SambaLingo-Thai-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Continued-pretrained bilingual base model. The card says to use few-shot prompting. Direct questions should go to the chat checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-thai-base-70b": { "id": "sambanova/sambalingo/sambalingo-thai-base-70b", "name": "SambaLingo-Thai-Base-70B", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-04-15", "context_length": 4096, "max_output_tokens": null, "parameters": "69391889408", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "70B continued-pretrained bilingual base. The card says this checkpoint is for few-shot use with sampling off, and points chat use at the 70B chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-thai-chat": { "id": "sambanova/sambalingo/sambalingo-thai-chat", "name": "SambaLingo-Thai-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "6946033664", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Thai-Base for Thai and English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-thai-chat-70b": { "id": "sambanova/sambalingo/sambalingo-thai-chat-70b", "name": "SambaLingo-Thai-Chat-70B", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-04-15", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "70B chat model. The card describes DPO alignment on top of the 70B base, which itself adapts Llama-2-70b.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-turkish-base": { "id": "sambanova/sambalingo/sambalingo-turkish-base", "name": "SambaLingo-Turkish-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Continued-pretrained bilingual base model. The card says to use few-shot prompting. Direct questions should go to the chat checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sambalingo/sambalingo-turkish-chat": { "id": "sambanova/sambalingo/sambalingo-turkish-chat", "name": "SambaLingo-Turkish-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-26", "context_length": 4096, "max_output_tokens": null, "parameters": "6946033664", "architecture": "Llama 2 decoder-only transformer. SambaNova extended the vocabulary from 32,000 to 57,000 tokens by adding up to 25,000 non-overlapping tokens, and initialized new embeddings by averaging original subword embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova released this SambaLingo checkpoint on the public record cited below. It is a bilingual Llama 2 adaptation trained by SambaNova, still downloadable from the sambanovasystems Hub org as of 2026-10-15. This note was collected on 2026-10-15.", "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Turkish-Base for Turkish and English prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/sn/sn-13b-8k-instruct": { "id": "sambanova/sn/sn-13b-8k-instruct", "name": "SN-13B-8k-Instruct", "maker": "sambanova", "family": "sambanova/sn", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-08-03", "context_length": 8192, "max_output_tokens": null, "parameters": "13000000000", "architecture": "13B decoder-only model trained from random initialization. The Hub tags the repo gpt2. Layer counts were not on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova trained SN-13B-8k-Instruct from random weights and released the checkpoint under Apache-2.0 in August 2023. It is a 13 billion parameter long-sequence model, not a current API product. Collected on 2026-10-15.", "what_it_is_for": "Long-sequence understanding. The blog says the release is a research checkpoint and is not meant as a chat model. The card also says it was instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/toolbench/codegen-16b-mono-toolbench": { "id": "sambanova/toolbench/codegen-16b-mono-toolbench", "name": "CodeGen-16B-mono ToolBench", "maker": "sambanova", "family": "sambanova/toolbench", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause", "release_date": "2023-05-22", "context_length": null, "max_output_tokens": null, "parameters": "16000000000", "architecture": "Instruction-tuned from Salesforce codegen-16B-mono for API action generation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova fine-tuned this checkpoint for the ToolBench paper and released it on Hugging Face in May 2023. It is a study model, not a SambaCloud product. Collected on 2026-10-15.", "what_it_is_for": "API-based action generation for the ToolBench evaluation. The authors say the checkpoints are only for the initial study.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/toolbench/llama-30b-toolbench": { "id": "sambanova/toolbench/llama-30b-toolbench", "name": "LLaMA-30B ToolBench", "maker": "sambanova", "family": "sambanova/toolbench", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LLaMA research license (same as the original LLaMA model card)", "release_date": "2023-05-25", "context_length": null, "max_output_tokens": null, "parameters": "30000000000", "architecture": "Instruction-tuned delta on LLaMA-30B for API action generation. The repo is a delta, not standalone weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova fine-tuned this checkpoint for the ToolBench paper and released it on Hugging Face in May 2023. It is a study model, not a SambaCloud product. Collected on 2026-10-15.", "what_it_is_for": "API-based action generation for the ToolBench evaluation. The authors say the checkpoints are only for the initial study.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sambanova/toolbench/starcoder-toolbench": { "id": "sambanova/toolbench/starcoder-toolbench", "name": "StarCoder ToolBench", "maker": "sambanova", "family": "sambanova/toolbench", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BigCode OpenRAIL-M", "release_date": "2023-05-24", "context_length": null, "max_output_tokens": null, "parameters": "15000000000", "architecture": "Instruction-tuned from BigCode StarCoder for API action generation. The card states 15 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SambaNova fine-tuned this checkpoint for the ToolBench paper and released it on Hugging Face in May 2023. It is a study model, not a SambaCloud product. Collected on 2026-10-15.", "what_it_is_for": "API-based action generation for the ToolBench evaluation. The authors say the checkpoints are only for the initial study.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/gauss/gauss-2-3": { "id": "samsung/gauss/gauss-2-3", "name": "Gauss 2.3", "maker": "samsung", "family": "samsung/gauss", "status": "rumor", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gauss 2.3 is a name The Elec reported on 24 November 2025 for a newer Samsung Research large language model used inside an internal agent-building tool. Samsung has not announced it. This dossier is a low-confidence rumor record as of 15 October 2026.", "what_it_is_for": "The Elec says it is one of the LLMs behind an internal no-code Agent Builder for business agents, and that Gauss also feeds a multimodal retrieval system called Sirius.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/gauss/gauss-2-3-think": { "id": "samsung/gauss/gauss-2-3-think", "name": "Gauss 2.3 Think", "maker": "samsung", "family": "samsung/gauss", "status": "rumor", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gauss 2.3 Think is a model name The Elec reported on 24 November 2025 as one of three Samsung Research LLMs behind an internal Agent Builder. Samsung has not announced it. Recorded as a rumor on 15 October 2026.", "what_it_is_for": "The report groups it with other internal Gauss LLMs used to assemble business agents. It does not describe a distinct job beyond the shared Agent Builder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/gauss/gauss-code": { "id": "samsung/gauss/gauss-code", "name": "Samsung Gauss Code", "maker": "samsung", "family": "samsung/gauss", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Samsung Gauss Code is the code-generation model in the Samsung Gauss family Samsung Research announced on 8 November 2023. It is closed and was built for internal software development. No retirement date has been published as of 15 October 2026.", "what_it_is_for": "Samsung says it lets developers code more quickly and, through the code.i assistant, describe code and generate test cases in an interactive interface.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/gauss/gauss-image": { "id": "samsung/gauss/gauss-image", "name": "Samsung Gauss Image", "maker": "samsung", "family": "samsung/gauss", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Samsung Gauss Image is the image model in Samsung Gauss, announced by Samsung Research on 8 November 2023. It is closed. Samsung has not published a retirement date as of 15 October 2026.", "what_it_is_for": "Samsung says it generates and edits images, changes style, adds content, and upscales low-resolution images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/gauss/gauss-language": { "id": "samsung/gauss/gauss-language", "name": "Samsung Gauss Language", "maker": "samsung", "family": "samsung/gauss", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Generative language model. Samsung has not published the architecture, depth, or parameter count. Secondary coverage of the launch says the language model includes unnamed cloud and on-device sizes.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Samsung Gauss Language is the text model in Samsung Gauss, the generative AI family Samsung Research introduced at the Samsung AI Forum on 8 November 2023. It is a closed, internal model. Samsung has not published a retirement date as of 15 October 2026.", "what_it_is_for": "Samsung says it drafts email, summarizes documents, translates content, and can support smarter device control if built into products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/gauss/gauss-o-flash": { "id": "samsung/gauss/gauss-o-flash", "name": "Gauss O Flash", "maker": "samsung", "family": "samsung/gauss", "status": "rumor", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gauss O Flash is a model name The Elec reported on 24 November 2025 as a Samsung Research LLM used in an internal agent tool. Samsung has not announced it. Recorded as a rumor on 15 October 2026.", "what_it_is_for": "The article only places it among LLMs used by the internal Agent Builder. A smaller or faster role is not stated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/gauss/gauss-o4": { "id": "samsung/gauss/gauss-o4", "name": "Gauss O4", "maker": "samsung", "family": "samsung/gauss", "status": "rumor", "confidence": "low", "weights": "closed", "license": "", "release_date": "2025-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gauss O4 is a name Chosun Biz reported on 9 December 2025 for an in-house Gauss model switched on in Samsung's semiconductor-division assistant. Samsung has not announced it, and the report does not attribute it to Samsung Research. Recorded as a rumor on 15 October 2026.", "what_it_is_for": "The report says it supports semiconductor work in DS Assistant and adds image recognition and analysis, with a path to fine-tuned departmental agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/gauss/gauss2-balanced": { "id": "samsung/gauss/gauss2-balanced", "name": "Samsung Gauss2 Balanced", "maker": "samsung", "family": "samsung/gauss", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Medium cloud multimodal model. Samsung says it balances performance, speed, and efficiency. The Elec, citing the launch, reported that Balanced has about half the parameter count of Gauss1 and is multimodal. No absolute parameter count was published. Supreme adds mixture-of-experts on top of Balanced.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Samsung Gauss2 Balanced is the medium, cloud-hosted Samsung Gauss2 model announced on 21 November 2024. Samsung Research positions it between the on-device Compact model and the larger Supreme model. It is closed and was in internal use at announcement. No retirement date has been published as of 15 October 2026.", "what_it_is_for": "Samsung Newsroom says Balanced is for text and code generation with a balance of performance, speed, and efficiency, inside the multimodal Gauss2 family.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/gauss/gauss2-compact": { "id": "samsung/gauss/gauss2-compact", "name": "Samsung Gauss2 Compact", "maker": "samsung", "family": "samsung/gauss", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small multimodal model in the Gauss2 family. Samsung describes it as lightweight and aimed at limited compute, including on-device use. Architecture details were not published. Mixture-of-experts is described for Supreme, not Compact.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Samsung Gauss2 Compact is the smallest of the three Samsung Gauss2 models Samsung Research unveiled at Samsung Developer Conference Korea on 21 November 2024. It is a closed on-device multimodal model. It was still the latest officially named Compact variant as of 15 October 2026.", "what_it_is_for": "Samsung says Compact is for efficient on-device use where compute is limited, as part of a model that handles language, code, and images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/gauss/gauss2-supreme": { "id": "samsung/gauss/gauss2-supreme", "name": "Samsung Gauss2 Supreme", "maker": "samsung", "family": "samsung/gauss", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Large cloud model. Mixture-of-experts applied on top of the Balanced model so that only selected experts run for a task. Expert count, active-parameter count, and total parameter count were not published.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Samsung Gauss2 Supreme is the largest Samsung Gauss2 model, announced on 21 November 2024. It is a closed cloud mixture-of-experts model built on Gauss2 Balanced. Samsung has not published a successor or a retirement date as of 15 October 2026.", "what_it_is_for": "Samsung says Supreme is for high-performance workplace services, with mixture-of-experts used to cut training and inference cost while keeping quality.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/health/himae": { "id": "samsung/health/himae", "name": "HiMAE", "maker": "samsung", "family": "samsung/health", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-28", "context_length": null, "max_output_tokens": null, "parameters": "1.2M", "architecture": "Hierarchical masked autoencoder with a convolutional encoder-decoder that keeps multi-resolution embeddings of wearable time series. The paper's main reported configuration is 1.2 million parameters.", "modalities": { "input": [ "biosignal" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HiMAE is a wearable-signal foundation model from Samsung Research America's Digital Health team. The paper was submitted on 28 October 2025 and accepted to ICLR 2026. Samsung Newsroom described it on 14 August 2026. Pretrained weights were not found in public. Collected 15 October 2026.", "what_it_is_for": "Samsung says one pretrained model supports classification, numeric prediction, and generation from wearable time series, on short and long time scales, on the device rather than in the cloud.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/health/xmae": { "id": "samsung/health/xmae", "name": "xMAE", "maker": "samsung", "family": "samsung/health", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-01", "context_length": null, "max_output_tokens": null, "parameters": "6.5M", "architecture": "Physiology-aware masked cross-modal reconstruction. Pretrains on paired ECG and PPG so a PPG encoder can reconstruct masked ECG. The paper reports 6.5 million parameters.", "modalities": { "input": [ "biosignal" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "xMAE is a biosignal foundation model from the Digital Health team at Samsung Research America. The paper was submitted on 1 May 2026 and accepted to ICML 2026. Samsung Newsroom described it on 14 August 2026. Weights are not released. Status here is the published research model as of 15 October 2026.", "what_it_is_for": "Samsung says it learns how ECG and PPG relate in time, so continuously measured PPG can carry cardiac features that would otherwise need a manual ECG.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/samsone/samsone-134m": { "id": "samsung/samsone/samsone-134m", "name": "Samsone-134M", "maker": "samsung", "family": "samsung/samsone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-09-18", "context_length": null, "max_output_tokens": null, "parameters": "134M", "architecture": "Audio encoder initialized from Whisper-Tiny (about 9M parameters), a two-layer GeLU projector, and a SmolLM2 decoder. Audio is pooled to 50 tokens. A learned SEP token separates audio clips. Vocabulary is reduced to lowercase ASCII-oriented tokens. Paper license is CC BY 4.0; the code repository had no LICENSE file on retrieval. Core variant. SmolLM2-135M with vocabulary reduction and no depth pruning: 125M language model, 9M encoder, 0.5M projector. Total about 134 million parameters.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Samsung R&D Institute Poland released Samsone-134M with the Samsone family in a paper submitted on 18 September 2026 and accepted at Interspeech 2026. Weights and an Android demo are public. The code license file was missing on collection day 15 October 2026, so weights are recorded as open-weights rather than open-source.", "what_it_is_for": "The authors call it the main Samsone model and the strongest audio-language model in its size class on their benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/samsone/samsone-356m": { "id": "samsung/samsone/samsone-356m", "name": "Samsone-356M", "maker": "samsung", "family": "samsung/samsone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-09-18", "context_length": null, "max_output_tokens": null, "parameters": "356M", "architecture": "Audio encoder initialized from Whisper-Tiny (about 9M parameters), a two-layer GeLU projector, and a SmolLM2 decoder. Audio is pooled to 50 tokens. A learned SEP token separates audio clips. Vocabulary is reduced to lowercase ASCII-oriented tokens. Paper license is CC BY 4.0; the code repository had no LICENSE file on retrieval. SmolLM2-360M backbone with vocabulary reduction and no depth pruning: 347M language model, 9M encoder, and 1M projector.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Samsung R&D Institute Poland released Samsone-356M with the Samsone family in a paper submitted on 18 September 2026 and accepted at Interspeech 2026. Weights and an Android demo are public. The code license file was missing on collection day 15 October 2026, so weights are recorded as open-weights rather than open-source.", "what_it_is_for": "The authors present it as the larger Samsone checkpoint for harder audio questions, still under the one-billion-parameter line they use for small audio language models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "samsung/samsone/samsone-99m": { "id": "samsung/samsone/samsone-99m", "name": "Samsone-99M", "maker": "samsung", "family": "samsung/samsone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-09-18", "context_length": null, "max_output_tokens": null, "parameters": "99M", "architecture": "Audio encoder initialized from Whisper-Tiny (about 9M parameters), a two-layer GeLU projector, and a SmolLM2 decoder. Audio is pooled to 50 tokens. A learned SEP token separates audio clips. Vocabulary is reduced to lowercase ASCII-oriented tokens. Paper license is CC BY 4.0; the code repository had no LICENSE file on retrieval. Depth-pruned SmolLM2-135M backbone: vocabulary reduction plus removal of the last 10 of 30 transformer blocks, leaving a 90M language model, 9M encoder, and 0.5M projector.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Samsung R&D Institute Poland released Samsone-99M with the Samsone family in a paper submitted on 18 September 2026 and accepted at Interspeech 2026. Weights and an Android demo are public. The code license file was missing on collection day 15 October 2026, so weights are recorded as open-weights rather than open-source.", "what_it_is_for": "The authors present it as the smallest Samsone model for tighter on-device budgets, for audio question answering, captioning, and entailment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/camoscio/camoscio-7b": { "id": "sapienza/camoscio/camoscio-7b", "name": "Camoscio 7B", "maker": "sapienza", "family": "sapienza/camoscio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "RAIL", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "LoRA adapter on LLaMA 7B, instruction-tuned on an Italian translation of Stanford Alpaca. The README says training ran about one day on one RTX 3090. The DanteLLM paper says the original Camoscio LoRA touched only the Q and K matrices for three epochs. The adapter is loaded on top of a LLaMA 7B base. Production use is not allowed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Camoscio 7B is an Italian instruction-tuned LLaMA from Andrea Santilli and Emanuele Rodola at Sapienza. It is a research LoRA on the Alpaca recipe, still published on Hugging Face as of 2026-10-15. The authors disallow production use. Fauno, from a related Sapienza group, came after it.", "what_it_is_for": "Camoscio 7B is an Italian instruction-tuned LLaMA from Andrea Santilli and Emanuele Rodola at Sapienza. It is a research LoRA on the Alpaca recipe, still published on Hugging Face as of 2026-10-15. The authors disallow production use. Fauno, from a related Sapienza group, came after it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/dantellm/dantellm-7b-instruct-italian-v0-1": { "id": "sapienza/dantellm/dantellm-7b-instruct-italian-v0-1", "name": "DanteLLM 7B Instruct Italian v0.1", "maker": "sapienza", "family": "sapienza/dantellm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-14", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "PEFT adapter on mistralai/Mistral-7B-Instruct-v0.2, published by the RSTLess group at Sapienza. The Hub tag is Apache-2.0. The paper says DanteLLM's training data has commercial limits, while OpenDanteLLM was the fully open-data sibling. This repo is the only Dante weight the rstless-research org listed on the collection date. The card's own loading snippet references config.base_model_name_or_path, which is incomplete as usage guidance. A working snippet on the GitHub README loads this id with 8-bit weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DanteLLM 7B Instruct Italian v0.1 is the Italian instruct adapter released by RSTLess at Sapienza on top of Mistral-7B-Instruct-v0.2. The public Hub checkpoint dates from 14 March 2024. Collected 2026-10-15, it is the only Dante weight listed under rstless-research. The LREC-COLING 2024 paper is the maker's evaluation.", "what_it_is_for": "DanteLLM 7B Instruct Italian v0.1 is the Italian instruct adapter released by RSTLess at Sapienza on top of Mistral-7B-Instruct-v0.2. The public Hub checkpoint dates from 14 March 2024. Collected 2026-10-15, it is the only Dante weight listed under rstless-research. The LREC-COLING 2024 paper is the maker's evaluation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/fauno/fauno-13b": { "id": "sapienza/fauno/fauno-13b", "name": "Fauno 13B", "maker": "sapienza", "family": "sapienza/fauno", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2023-04-08", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "LoRA adapter on Baize, which is itself a fine-tune of LLaMA. The 7B card loads decapoda-research/decapoda-research/llama-13b-hf plus this adapter. Trained for one epoch on one RTX A6000, stated as 19 hours for the 7B run. Full merged weights are not what the card distributes.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fauno 13B is an Italian conversational model from the RSTLess group at Sapienza University of Rome, announced on 8 April 2023. It is a LoRA adapter on Baize/LLaMA (13B), not a from-scratch model. Collected 2026-10-15, the adapter was still on Hugging Face under a personal account of a Sapienza author. The GitHub repository is GPL-3.0, and the card also binds the weights to Baize and LLaMA terms.", "what_it_is_for": "Fauno 13B is an Italian conversational model from the RSTLess group at Sapienza University of Rome, announced on 8 April 2023. It is a LoRA adapter on Baize/LLaMA (13B), not a from-scratch model. Collected 2026-10-15, the adapter was still on Hugging Face under a personal account of a Sapienza author. The GitHub repository is GPL-3.0, and the card also binds the weights to Baize and LLaMA terms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/fauno/fauno-7b": { "id": "sapienza/fauno/fauno-7b", "name": "Fauno 7B", "maker": "sapienza", "family": "sapienza/fauno", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2023-04-08", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "LoRA adapter on Baize, which is itself a fine-tune of LLaMA. The 7B card loads decapoda-research/decapoda-research/llama-7b-hf plus this adapter. Trained for one epoch on one RTX A6000, stated as 19 hours for the 7B run. Full merged weights are not what the card distributes.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fauno 7B is an Italian conversational model from the RSTLess group at Sapienza University of Rome, announced on 8 April 2023. It is a LoRA adapter on Baize/LLaMA (7B), not a from-scratch model. Collected 2026-10-15, the adapter was still on Hugging Face under a personal account of a Sapienza author. The GitHub repository is GPL-3.0, and the card also binds the weights to Baize and LLaMA terms.", "what_it_is_for": "Fauno 7B is an Italian conversational model from the RSTLess group at Sapienza University of Rome, announced on 8 April 2023. It is a LoRA adapter on Baize/LLaMA (7B), not a from-scratch model. Collected 2026-10-15, the adapter was still on Hugging Face under a personal account of a Sapienza author. The GitHub repository is GPL-3.0, and the card also binds the weights to Baize and LLaMA terms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/maverick/maverick-mes-litbank": { "id": "sapienza/maverick/maverick-mes-litbank", "name": "Maverick-mes LitBank", "maker": "sapienza", "family": "sapienza/maverick", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-03-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Coreference model based on DeBERTa-large, released as Maverick-mes. The card does not print a parameter count. Trained on LitBank.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Maverick-mes LitBank is a Sapienza NLP coreference model, Maverick-mes trained on LitBank and based on DeBERTa-large. The Hub repo dates from 2024-03-21. It was still published on 2026-10-15 under a non-commercial research license. The OntoNotes card states Avg CoNLL-F1 78.0 and singletons yes.", "what_it_is_for": "Maverick-mes LitBank is a Sapienza NLP coreference model, Maverick-mes trained on LitBank and based on DeBERTa-large. The Hub repo dates from 2024-03-21. It was still published on 2026-10-15 under a non-commercial research license. The OntoNotes card states Avg CoNLL-F1 78.0 and singletons yes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/maverick/maverick-mes-ontonotes": { "id": "sapienza/maverick/maverick-mes-ontonotes", "name": "Maverick-mes OntoNotes", "maker": "sapienza", "family": "sapienza/maverick", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-03-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Coreference model based on DeBERTa-large, released as Maverick-mes. The card does not print a parameter count. Trained on OntoNotes.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Maverick-mes OntoNotes is a Sapienza NLP coreference model, Maverick-mes trained on OntoNotes and based on DeBERTa-large. The Hub repo dates from 2024-03-21. It was still published on 2026-10-15 under a non-commercial research license. The OntoNotes card states Avg CoNLL-F1 83.6 and singletons no.", "what_it_is_for": "Maverick-mes OntoNotes is a Sapienza NLP coreference model, Maverick-mes trained on OntoNotes and based on DeBERTa-large. The Hub repo dates from 2024-03-21. It was still published on 2026-10-15 under a non-commercial research license. The OntoNotes card states Avg CoNLL-F1 83.6 and singletons no.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/maverick/maverick-mes-preco": { "id": "sapienza/maverick/maverick-mes-preco", "name": "Maverick-mes PreCo", "maker": "sapienza", "family": "sapienza/maverick", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-03-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Coreference model based on DeBERTa-large, released as Maverick-mes. The card does not print a parameter count. Trained on PreCo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Maverick-mes PreCo is a Sapienza NLP coreference model, Maverick-mes trained on PreCo and based on DeBERTa-large. The Hub repo dates from 2024-03-21. It was still published on 2026-10-15 under a non-commercial research license. The OntoNotes card states Avg CoNLL-F1 87.4 and singletons yes.", "what_it_is_for": "Maverick-mes PreCo is a Sapienza NLP coreference model, Maverick-mes trained on PreCo and based on DeBERTa-large. The Hub repo dates from 2024-03-21. It was still published on 2026-10-15 under a non-commercial research license. The OntoNotes card states Avg CoNLL-F1 87.4 and singletons yes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/minerva/chatminerva": { "id": "sapienza/minerva/chatminerva", "name": "ChatMinerva", "maker": "sapienza", "family": "sapienza/minerva", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-03", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Product assistant built on a further instruction-tuned Minerva model. The university says the checkpoint used in the assistant was fine-tuned on millions of text and multimodal examples on CINECA Leonardo. Web RAG uses DuckDuckGo. OCR is claimed for scanned pages. Voice conversation is claimed. The open 7B instruct weights are an earlier checkpoint, not identified as this assistant build. Parameter count of the serving model was not published. A 20 billion parameter multilingual multimodal model was described as in training for an autumn 2026 presentation and is not this release.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChatMinerva is the multimodal assistant Sapienza NLP and the Babelscape spin-off launched on 3 June 2026. It is a hosted product on top of a further-tuned Minerva model, with document and image understanding, OCR, voice conversation, and live web retrieval. Collected 2026-10-15, the open 7B weights remain available, but this assistant build does not have a public checkpoint.", "what_it_is_for": "ChatMinerva is the multimodal assistant Sapienza NLP and the Babelscape spin-off launched on 3 June 2026. It is a hosted product on top of a further-tuned Minerva model, with document and image understanding, OCR, voice conversation, and live web retrieval. Collected 2026-10-15, the open 7B weights remain available, but this assistant build does not have a public checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/minerva/minerva-1b-base-v1-0": { "id": "sapienza/minerva/minerva-1b-base-v1-0", "name": "Minerva 1B base v1.0", "maker": "sapienza", "family": "sapienza/minerva", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-23", "context_length": 16384, "max_output_tokens": null, "parameters": "1B", "architecture": "Decoder-only Transformer in the Mistral family. Card table: 16 layers, hidden 2048, 16 query heads, 4 KV heads, sliding window 2048, max context 16384. Trained with Decoupled AdamW, peak lr 2e-4, 4M-token batches, 47684 steps, on 200B tokens (100B Italian and 100B English).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Minerva-1B-base-v1.0 is Sapienza NLP's 1 billion parameter Italian-English base model, pretrained from scratch with FAIR and CINECA. It was released with the 350M and 3B models in April 2024 and was still the published v1.0 checkpoint on 2026-10-15. It is a base completion model, not an instruct model.", "what_it_is_for": "Minerva-1B-base-v1.0 is Sapienza NLP's 1 billion parameter Italian-English base model, pretrained from scratch with FAIR and CINECA. It was released with the 350M and 3B models in April 2024 and was still the published v1.0 checkpoint on 2026-10-15. It is a base completion model, not an instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/minerva/minerva-350m-base-v1-0": { "id": "sapienza/minerva/minerva-350m-base-v1-0", "name": "Minerva 350M base v1.0", "maker": "sapienza", "family": "sapienza/minerva", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-23", "context_length": 16384, "max_output_tokens": null, "parameters": "350M", "architecture": "Decoder-only Transformer in the Mistral family. Card table: 16 layers, hidden 1152, 16 query heads, 4 KV heads, sliding window 2048, max context 16384. Trained with Decoupled AdamW, peak lr 2e-4, 4M-token batches, 16690 steps, on 70B tokens (35B Italian CulturaX and 35B English CulturaX).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Minerva-350M-base-v1.0 is the smallest model in the Minerva family, pretrained from scratch by Sapienza NLP with FAIR and CINECA for Italian and English. It is an active open-weight base model, collected 2026-10-15. It is a completion model, not an aligned chat model.", "what_it_is_for": "Minerva-350M-base-v1.0 is the smallest model in the Minerva family, pretrained from scratch by Sapienza NLP with FAIR and CINECA for Italian and English. It is an active open-weight base model, collected 2026-10-15. It is a completion model, not an aligned chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/minerva/minerva-3b-base-v1-0": { "id": "sapienza/minerva/minerva-3b-base-v1-0", "name": "Minerva 3B base v1.0", "maker": "sapienza", "family": "sapienza/minerva", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-23", "context_length": 16384, "max_output_tokens": null, "parameters": "2.89B", "architecture": "Decoder-only Transformer in the Mistral family. Card and paper appendix: 32 layers, hidden 2560, intermediate 8960, 32 query heads, 8 KV heads, sliding window 2048, max context 16384, vocabulary 32768. Paper Table 3 lists 2.89B parameters. Trained with Decoupled AdamW, peak lr 2e-4, 4M-token batches, 157357 steps, on 660B tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Minerva-3B-base-v1.0 is Sapienza NLP's mid-size Italian-English base model, pretrained from scratch. The CLiC-it 2024 paper counts 2.89 billion parameters. It shipped with the April 2024 base release and remained the published v1.0 checkpoint on 2026-10-15.", "what_it_is_for": "Minerva-3B-base-v1.0 is Sapienza NLP's mid-size Italian-English base model, pretrained from scratch. The CLiC-it 2024 paper counts 2.89 billion parameters. It shipped with the April 2024 base release and remained the published v1.0 checkpoint on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/minerva/minerva-7b-base-v1-0": { "id": "sapienza/minerva/minerva-7b-base-v1-0", "name": "Minerva 7B base v1.0", "maker": "sapienza", "family": "sapienza/minerva", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-05", "context_length": 4096, "max_output_tokens": null, "parameters": "7.40B", "architecture": "Decoder-only Transformer copied from Mistral-7B except vocabulary 51200 and context 4096 with sliding-window attention off. 32 layers, hidden 4096, intermediate 14336, 32 query heads, 8 KV heads. Paper Table 3 and the introduction say 7.40B parameters. Trained with AdamW, peak lr 3e-4, weight decay 0.1, warmup 2000 steps, 4M-token batches, 591558 steps, in llm-foundry 0.8.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Minerva-7B-base-v1.0 is Sapienza NLP's largest published base model, a 7.40 billion parameter Italian-English-code model pretrained from scratch on Leonardo at CINECA. Weights were publicly released on 5 December 2024 under Apache-2.0. Collected 2026-10-15, it is still the published base checkpoint and is not an aligned assistant.", "what_it_is_for": "Minerva-7B-base-v1.0 is Sapienza NLP's largest published base model, a 7.40 billion parameter Italian-English-code model pretrained from scratch on Leonardo at CINECA. Weights were publicly released on 5 December 2024 under Apache-2.0. Collected 2026-10-15, it is still the published base checkpoint and is not an aligned assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/minerva/minerva-7b-instruct-v1-0": { "id": "sapienza/minerva/minerva-7b-instruct-v1-0", "name": "Minerva 7B instruct v1.0", "maker": "sapienza", "family": "sapienza/minerva", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-05", "context_length": 4096, "max_output_tokens": null, "parameters": "7.40B", "architecture": "Same network as Minerva-7B-base-v1.0 (Mistral-style, 32 layers, hidden 4096, intermediate 14336, 32 heads, 8 KV heads, context 4096, vocabulary 51200). Post-training is supervised fine-tuning with Llama-Factory for three epochs, then online DPO with Hugging Face TRL. The reward judge is Skywork-Reward-Llama-3.1-8B-v0.2. Preference prompts are UltraFeedback (English), evol-dpo-ita, Babelscape ALERT translated to Italian, and hand-written safety data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Minerva-7B-instruct-v1.0 is the instruction- and preference-tuned release of Sapienza NLP's 7B Minerva base model. It is the open chat checkpoint behind the December 2024 public release, licensed Apache-2.0, and it was still the published instruct weight on 2026-10-15. ChatMinerva, launched in June 2026, is a later assistant and is not this file.", "what_it_is_for": "Minerva-7B-instruct-v1.0 is the instruction- and preference-tuned release of Sapienza NLP's 7B Minerva base model. It is the open chat checkpoint behind the December 2024 public release, licensed Apache-2.0, and it was still the published instruct weight on 2026-10-15. ChatMinerva, launched in June 2026, is a later assistant and is not this file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/relik/relik-entity-linking-base": { "id": "sapienza/relik/relik-entity-linking-base", "name": "ReLiK entity linking base", "maker": "sapienza", "family": "sapienza/relik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage pipeline: a retriever over a candidate index and a reader that links spans. Loaded with Relik.from_pretrained. Parameter count of the packaged pipeline is not stated on the card. Component checkpoints (DeBERTa readers, E5 encoders, and Wikipedia indexes) are separate Hub repos and are not separate products here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReLiK entity linking base is the mid-size Sapienza NLP entity-linking pipeline from the same ACL 2024 system as ReLiK large. It is an active local model as of 2026-10-15. The large card is the page that prints its scores.", "what_it_is_for": "ReLiK entity linking base is the mid-size Sapienza NLP entity-linking pipeline from the same ACL 2024 system as ReLiK large. It is an active local model as of 2026-10-15. The large card is the page that prints its scores.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/relik/relik-entity-linking-large": { "id": "sapienza/relik/relik-entity-linking-large", "name": "ReLiK entity linking large", "maker": "sapienza", "family": "sapienza/relik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage pipeline: a retriever over a candidate index and a reader that links spans. Loaded with Relik.from_pretrained. Parameter count of the packaged pipeline is not stated on the card. Component checkpoints (DeBERTa readers, E5 encoders, and Wikipedia indexes) are separate Hub repos and are not separate products here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReLiK entity linking large is Sapienza NLP's largest packaged entity-linking pipeline from the ACL 2024 Findings system Retrieve, Read and LinK. It was on the Hub by 3 June 2024 and was still published on 2026-10-15. It is a local research model, not a chat API.", "what_it_is_for": "ReLiK entity linking large is Sapienza NLP's largest packaged entity-linking pipeline from the ACL 2024 Findings system Retrieve, Read and LinK. It was on the Hub by 3 June 2024 and was still published on 2026-10-15. It is a local research model, not a chat API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/relik/relik-entity-linking-small": { "id": "sapienza/relik/relik-entity-linking-small", "name": "ReLiK entity linking small", "maker": "sapienza", "family": "sapienza/relik", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage pipeline: a retriever over a candidate index and a reader that links spans. Loaded with Relik.from_pretrained. Parameter count of the packaged pipeline is not stated on the card. Component checkpoints (DeBERTa readers, E5 encoders, and Wikipedia indexes) are separate Hub repos and are not separate products here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReLiK entity linking small is the smallest named entity-linking pipeline Sapienza NLP published in the ReLiK collection. The Hub repo dates from 5 August 2024. It is a local research checkpoint, collected 2026-10-15. Its score table was not on the page used for the base and large numbers.", "what_it_is_for": "ReLiK entity linking small is the smallest named entity-linking pipeline Sapienza NLP published in the ReLiK collection. The Hub repo dates from 5 August 2024. It is a local research checkpoint, collected 2026-10-15. Its score table was not on the page used for the base and large numbers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sapienza/relik/relik-relation-extraction-nyt-large": { "id": "sapienza/relik/relik-relation-extraction-nyt-large", "name": "ReLiK relation extraction NYT large", "maker": "sapienza", "family": "sapienza/relik", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-07-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage pipeline: a retriever over a candidate index and a reader that links spans. Loaded with Relik.from_pretrained. Parameter count of the packaged pipeline is not stated on the card. Component checkpoints (DeBERTa readers, E5 encoders, and Wikipedia indexes) are separate Hub repos and are not separate products here. This checkpoint is the NYT relation-extraction pipeline.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReLiK relation extraction NYT large is Sapienza NLP's packaged relation-extraction pipeline for the NYT setup of the ReLiK system. The Hub repo dates from 3 July 2024. It is a local English information-extraction model, still published on 2026-10-15.", "what_it_is_for": "ReLiK relation extraction NYT large is Sapienza NLP's packaged relation-extraction pipeline for the NYT setup of the ReLiK system. The Hub repo dates from 3 July 2024. It is a local English information-extraction model, still published on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saskatchewan/deep-plant-phenomics/bbox-regressor-lemnatec": { "id": "saskatchewan/deep-plant-phenomics/bbox-regressor-lemnatec", "name": "Lemnatec Bounding Box Regressor", "maker": "saskatchewan", "family": "saskatchewan/deep-plant-phenomics", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "GPL-2.0 (repository LICENCE.txt). A separate weight-file licence is not stated.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Regression CNN loaded from network_states/bbox-regressor-lemnatec by class boundingBoxRegressor. The caller passes the original image height and width. The network resizes to 257x307x3 and predicts four coordinates, then rescales x coordinates by original_width/307 and y coordinates by original_height/257. Layers: Conv 5x5x3x16 relu, pool 3 stride 2; Conv 5x5x16x64 relu, pool 3 stride 2; Conv 5x5x64x64 relu, pool 3 stride 2; Conv 5x5x64x64 relu, pool 3 stride 2; fully connected 384 relu; four outputs. It is not wrapped in tools.py and it is not on the public Tools page. The 2017 paper says a pretrained bounding box regression network is used with PlantCV to segment images from Lemnatec scanners. No score for that network is in the paper.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Lemnatec Bounding Box Regressor is a University of Saskatchewan image model that predicts four box coordinates. P2IRC ships it inside Deep Plant Phenomics as `bbox-regressor-lemnatec`. On 15 October 2026 the package README says the platform is deprecated and kept for historical use. The class is in `networks.py`. It is not on the Tools page and it is not wrapped by `tools.py`. There is no hosted API and no public price.", "what_it_is_for": "The class docstring says the network predicts bounding box coordinates. The 2017 paper says the PlantCV module, together with a pretrained bounding box regression network, segments images from Lemnatec plant scanners. The paper does not report a score for that network. Authors of the paper are Jordan R. Ubbens and Ian Stavness, Department of Computer Science, University of Saskatchewan.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saskatchewan/deep-plant-phenomics/canola-flower-counter": { "id": "saskatchewan/deep-plant-phenomics/canola-flower-counter", "name": "Canola Flower Counter", "maker": "saskatchewan", "family": "saskatchewan/deep-plant-phenomics", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "GPL-2.0 (repository LICENCE.txt). A separate weight-file licence is not stated.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Count-ception CNN loaded from network_states/canola-flowers-counter by class countCeptionCounter, using DPP CountCeptionModel. Default image size is 300x300x3, batch size 1, L1 loss, and patch_size 32. The class builds its own stack rather than calling use_predefined_model('countception'), which is commented out: a 3x3 convolution from 3 to 64 with leaky ReLU and batch norm, six parallel conv blocks, then 14x14, 18x18, and three 1x1 convolutions. Several filter_dimension tuples in the source leave the channel slot as 0. tools.count_canola_flowers rounds the interpreted output. This is a University of Saskatchewan canola checkpoint that uses the Count-ception block style. It is not a hosted copy of another maker's released weights.", "modalities": { "input": [ "image" ], "output": [ "count" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Canola Flower Counter is a University of Saskatchewan image model that estimates how many flowers are in a plant image. P2IRC ships it inside Deep Plant Phenomics as `canola-flowers-counter`. On 15 October 2026 the package README says the platform is deprecated and kept for historical use. There is no hosted API and no public price.", "what_it_is_for": "The Tools page says the canola flower counter estimates the number of flowers in an image. `tools.count_canola_flowers` loads `countCeptionCounter` and rounds the result. The class docstring says it counts flowers in plant images. The checkpoint uses a Count-ception style stack trained and shipped by this repository. It is not a rehost of another maker's weight card. The 2017 platform paper does not report this canola model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saskatchewan/deep-plant-phenomics/rosette-leaf-regressor": { "id": "saskatchewan/deep-plant-phenomics/rosette-leaf-regressor", "name": "Rosette Leaf Regressor", "maker": "saskatchewan", "family": "saskatchewan/deep-plant-phenomics", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "GPL-2.0 (repository LICENCE.txt). A separate weight-file licence is not stated.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Regression CNN loaded from network_states/rosette-leaf-regressor by class rosetteLeafRegressor. Input is resized to 128x128x3 with a training-time crop ratio of 0.9. Layers in the shipped inference class: Conv 5x5x3x32 tanh, pool 3 stride 2; Conv 5x5x32x32 tanh, pool 3 stride 2; Conv 3x3x32x64 tanh, pool 3 stride 2; Conv 3x3x64x64 tanh, pool 3 stride 2; fully connected 1024 tanh; scalar output. tools.predict_rosette_leaf_count rounds that scalar. This graph is not Table 1 network A1, A2, or A3 in the 2017 paper, and it is not the graph in models/leaf_counter_regressor.py (that script uses a 5x5 32-to-64 second convolution and does not add the 1024-unit layer).", "modalities": { "input": [ "image" ], "output": [ "count" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Rosette Leaf Regressor is a University of Saskatchewan image model that estimates how many leaves are on a rosette. Plant Phenotyping and Imaging Research Centre (P2IRC) ships it inside Deep Plant Phenomics as the checkpoint directory `rosette-leaf-regressor`. On 15 October 2026 the package README says the platform is deprecated and kept only as a historical snapshot. There is no hosted API and no public price.", "what_it_is_for": "The Tools page says the rosette leaf counter estimates the number of leaves on a rosette plant with a pretrained convolutional network. `tools.predict_rosette_leaf_count` loads `rosetteLeafRegressor` and rounds the scalar. Jordan R. Ubbens and Ian Stavness, Department of Computer Science, University of Saskatchewan, wrote the 2017 platform paper. Correspondence on that paper is ian.stavness@usask.ca. The current repository is https://github.com/p2irc/deepplantphenomics. The paper's download link was https://github.com/usaskdapper/deepplantphenomics.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saskatchewan/deep-plant-phenomics/vegetation-segmentation-network": { "id": "saskatchewan/deep-plant-phenomics/vegetation-segmentation-network", "name": "Vegetation Segmentation Network", "maker": "saskatchewan", "family": "saskatchewan/deep-plant-phenomics", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "GPL-2.0 (repository LICENCE.txt). A separate weight-file licence is not stated.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Semantic segmentation CNN loaded from network_states/vegetation-segmentation-network by class vegetationSegmentationNetwork, using DPP SemanticSegmentationModel. Input is resized to 256x256x3. Layers: Conv 3x3x3x16 relu, Conv 3x3x16x32 relu, Conv 5x5x32x32 relu, then the output layer. No pooling layer is added in this class. tools.segment_vegetation squeezes the last axis and thresholds each mask at 0.5.", "modalities": { "input": [ "image" ], "output": [ "mask" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Vegetation Segmentation Network is a University of Saskatchewan image model that separates plant pixels from background. P2IRC ships it inside Deep Plant Phenomics as `vegetation-segmentation-network`. On 15 October 2026 the package README says the platform is deprecated and kept for historical use. There is no hosted API and no public price.", "what_it_is_for": "The Tools page says this pretrained network segments foreground pixels from background pixels and returns arrays that can be saved with Pillow. `tools.segment_vegetation` loads `vegetationSegmentationNetwork`. The class docstring calls it a network that provides segmentation masks from plant images. The 2017 platform paper does not report this checkpoint among its leaf-count, mutant, and age experiments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "saskatchewan/deepwheat/deepwheat": { "id": "saskatchewan/deepwheat/deepwheat", "name": "DeepWheat", "maker": "saskatchewan", "family": "saskatchewan/deepwheat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2017-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three task networks trained from scratch in Torch7 and released together as DeepWheat. Segmentation is a SegNet-style deconvolutional network for soft binary plant masks, trained 30 epochs with SGD momentum 0.9, learning rate 0.01, and weight decay 0.0001 on 0.25 million 224x224 patches from 10 plot images. Emergence counting uses CNR blocks (7x7, unit stride), residual-CNR, a simplified Inception block, and global average pooling; Adam with learning rate and weight decay 0.0001; absolute-value loss; 100 epochs; 7855 patches from 37 images. Biomass estimation uses the same family of convolutional blocks, Smooth L1 loss, 50 epochs, and about 0.15 million RMRS-augmented samples from 48 plots. The paper says the authors did not use a pretrained model. Plain and Inception-only emergence nets are ablations, not separate products. Channel sets for biomass (DEM only, RGB plus DEM, and RGB plus NIR plus red edge plus DEM) are input variants of the same system.", "modalities": { "input": [ "image" ], "output": [ "mask", "count", "regression" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepWheat is a University of Saskatchewan image system for wheat emergence and biomass. Shubhra Aich, Ian Stavness, and colleagues at the University of Saskatchewan named the whole system DeepWheat and released code plus a pretrained-models link. On 15 October 2026 the GitHub repository and that link were still public. There is no deprecation notice, no hosted API, and no public price.", "what_it_is_for": "The paper estimates emergence, the density of plants in a field plot, and biomass from color images and elevation maps of wheat. Segmentation makes a soft plant mask. Two convolutional regressors then estimate the traits. The paper says this is the first image-based phenotypic trait estimation of crops with deep learning in that setting, and that the name covers the overall system because both wheat species in the study use it. All listed affiliations on the HTML are the University of Saskatchewan: Computer Science, Plant Sciences, and the Crop Development Centre. Corresponding author: ian.stavness@usask.ca.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/fred-t5/fred-t5-1-7b": { "id": "sber/fred-t5/fred-t5-1-7b", "name": "FRED-T5 1.7B", "maker": "sber", "family": "sber/fred-t5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-01-20", "context_length": null, "max_output_tokens": null, "parameters": "1.7B", "architecture": "T5-family encoder-decoder. FRIDA is a later embedding model on the encoder of this checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's FRED-T5 1.7B is an open Apache-2.0 Russian T5 model. The Hub repo was created 20 January 2023. Collected 2026-10-15.", "what_it_is_for": "Sber's FRED-T5 1.7B is an open Apache-2.0 Russian T5 model. The Hub repo was created 20 January 2023. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/fred-t5/fred-t5-large": { "id": "sber/fred-t5/fred-t5-large", "name": "FRED-T5 large", "maker": "sber", "family": "sber/fred-t5", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-02-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5-family encoder-decoder, large size class.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's FRED-T5 large is an open Apache-2.0 Russian T5 checkpoint. Hub repo created 28 February 2023. Collected 2026-10-15. The card body was not re-read.", "what_it_is_for": "Sber's FRED-T5 large is an open Apache-2.0 Russian T5 checkpoint. Hub repo created 28 February 2023. Collected 2026-10-15. The card body was not re-read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/frida/frida": { "id": "sber/frida/frida", "name": "FRIDA", "maker": "sber", "family": "sber/frida", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-12-26", "context_length": 512, "max_output_tokens": null, "parameters": "Hub safetensors total 823401216", "architecture": "T5 encoder from FRED-T5-1.7B, fine-tuned as a general text embedding model with task prefixes and CLS pooling.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SberDevices' FRIDA is an open MIT Russian-English text embedding model based on FRED-T5, with a 512-token input limit. Collected 2026-10-15.", "what_it_is_for": "SberDevices' FRIDA is an open MIT Russian-English text embedding model based on FRED-T5, with a 512-token input limit. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/frida/frida-decisions": { "id": "sber/frida/frida-decisions", "name": "FRIDA-Decisions", "maker": "sber", "family": "sber/frida", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2026-10-02", "context_length": null, "max_output_tokens": null, "parameters": "Hub safetensors total 823401216", "architecture": "Encoder model for structured decisions over Russian text: choice, scale, yes/no, and ranking, in one pass. Same parameter total as FRIDA.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's FRIDA-Decisions is an open MIT model, uploaded 2 October 2026, for structured decisions over Russian text. Collected 2026-10-15 from the Hub record and the repository description.", "what_it_is_for": "Sber's FRIDA-Decisions is an open MIT model, uploaded 2 October 2026, for structured decisions over Russian text. Collected 2026-10-15 from the Hub record and the repository description.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gfusion/gfusion-10b-a1-8b": { "id": "sber/gfusion/gfusion-10b-a1-8b", "name": "GFusion-10B-A1.8B", "maker": "sber", "family": "sber/gfusion", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-07-02", "context_length": null, "max_output_tokens": null, "parameters": "10B total, 1.8B active; Hub safetensors total 10673161368", "architecture": "Experimental block-diffusion language model adapted from GigaChat3-10B-A1.8B-base. Block size 32. Entropy-bounded sampling.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's GFusion-10B-A1.8B is an experimental open block-diffusion language model adapted from the GigaChat 3 10B base, MIT, uploaded 2 July 2026. Collected 2026-10-15.", "what_it_is_for": "Sber's GFusion-10B-A1.8B is an experimental open block-diffusion language model adapted from the GigaChat 3 10B base, MIT, uploaded 2 July 2026. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/giga-embeddings/embeddings": { "id": "sber/giga-embeddings/embeddings", "name": "Embeddings", "maker": "sber", "family": "sber/giga-embeddings", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Embeddings is the default closed GigaChat text-embedding model, 512 tokens and 1024 dimensions. It is active. Collected 2026-10-15.", "what_it_is_for": "Sber's Embeddings is the default closed GigaChat text-embedding model, 512 tokens and 1024 dimensions. It is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/giga-embeddings/embeddings-2": { "id": "sber/giga-embeddings/embeddings-2", "name": "Embeddings-2", "maker": "sber", "family": "sber/giga-embeddings", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Embeddings-2 is the improved closed successor of the default Embeddings model, still 512 tokens and 1024 dimensions. It is active. Collected 2026-10-15.", "what_it_is_for": "Sber's Embeddings-2 is the improved closed successor of the default Embeddings model, still 512 tokens and 1024 dimensions. It is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/giga-embeddings/embeddings-gigar": { "id": "sber/giga-embeddings/embeddings-gigar", "name": "EmbeddingsGigaR", "maker": "sber", "family": "sber/giga-embeddings", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's EmbeddingsGigaR is the longer-context closed GigaChat embedding model, 4096 tokens and 2560 dimensions. It is active. Collected 2026-10-15.", "what_it_is_for": "Sber's EmbeddingsGigaR is the longer-context closed GigaChat embedding model, 4096 tokens and 2560 dimensions. It is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/giga-embeddings/giga-embeddings-instruct": { "id": "sber/giga-embeddings/giga-embeddings-instruct", "name": "Giga-Embeddings-instruct", "maker": "sber", "family": "sber/giga-embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-12-11", "context_length": 4096, "max_output_tokens": null, "parameters": "3B-class decoder; Hub safetensors total 3449624576", "architecture": "Decoder-only GigaChat-3B backbone, latent-attention pooling. Instruction prefix on queries only.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Giga-Embeddings-instruct is an open MIT embedding model built on a GigaChat-3B decoder with latent-attention pooling and 2048-dimensional vectors. Collected 2026-10-15.", "what_it_is_for": "Sber's Giga-Embeddings-instruct is an open MIT embedding model built on a GigaChat-3B decoder with latent-attention pooling and 2048-dimensional vectors. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/giga-embeddings/giga-embeddings-instruct-10b": { "id": "sber/giga-embeddings/giga-embeddings-instruct-10b", "name": "Giga-Embeddings-instruct 10B-A1.8B", "maker": "sber", "family": "sber/giga-embeddings", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2026-08-10", "context_length": null, "max_output_tokens": null, "parameters": "10B-A1.8B class; Hub safetensors total 10475534400", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's August 2026 open MIT embedding model in the 10B-A1.8B class. Collected 2026-10-15. The card body was not read.", "what_it_is_for": "Sber's August 2026 open MIT embedding model in the 10B-A1.8B class. Collected 2026-10-15. The card body was not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/giga-embeddings/giga-embeddings-instruct-3b-0826": { "id": "sber/giga-embeddings/giga-embeddings-instruct-3b-0826", "name": "Giga-Embeddings-instruct 3B (0826)", "maker": "sber", "family": "sber/giga-embeddings", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2026-07-29", "context_length": null, "max_output_tokens": null, "parameters": "Hub safetensors total 3150605312", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's August 2026 open MIT 3B-class embedding model, distinct from the earlier Giga-Embeddings-instruct repo. Collected 2026-10-15. The card body was not read.", "what_it_is_for": "Sber's August 2026 open MIT 3B-class embedding model, distinct from the earlier Giga-Embeddings-instruct repo. Collected 2026-10-15. The card body was not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/giga-embeddings/giga-embeddings-instruct-480m": { "id": "sber/giga-embeddings/giga-embeddings-instruct-480m", "name": "Giga-Embeddings-instruct 480M", "maker": "sber", "family": "sber/giga-embeddings", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2026-08-10", "context_length": null, "max_output_tokens": null, "parameters": "Hub safetensors total 483717632", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's August 2026 open MIT embedding model at about 484 million parameters, from the ai-sage Hub repo. Collected 2026-10-15. The card body was not read.", "what_it_is_for": "Sber's August 2026 open MIT embedding model at about 484 million parameters, from the ai-sage Hub repo. Collected 2026-10-15. The card body was not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/giga-embeddings/giga-retrieval-instruct": { "id": "sber/giga-embeddings/giga-retrieval-instruct", "name": "Giga-Retrieval-instruct", "maker": "sber", "family": "sber/giga-embeddings", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2025-05-29", "context_length": null, "max_output_tokens": null, "parameters": "Hub safetensors total 2533494785", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Giga-Retrieval-instruct is an open MIT retrieval model on the ai-sage Hub, created 29 May 2025. Collected 2026-10-15. The card body was not read.", "what_it_is_for": "Sber's Giga-Retrieval-instruct is an open MIT retrieval model on the ai-sage Hub, created 29 May 2025. Collected 2026-10-15. The card body was not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/giga-embeddings/gigaembeddings-3b-2025-09": { "id": "sber/giga-embeddings/gigaembeddings-3b-2025-09", "name": "GigaEmbeddings-3B-2025-09", "maker": "sber", "family": "sber/giga-embeddings", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's GigaEmbeddings-3B-2025-09 is a closed GigaChat embedding model named as a September 2025 3B-class release, with 4096 tokens and 2048 dimensions. It is active as its own API model. Collected 2026-10-15.", "what_it_is_for": "Sber's GigaEmbeddings-3B-2025-09 is a closed GigaChat embedding model named as a September 2025 3B-class release, with 4096 tokens and 2048 dimensions. It is active as its own API model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigaam/gigaam-multilingual": { "id": "sber/gigaam/gigaam-multilingual", "name": "GigaAM Multilingual", "maker": "sber", "family": "sber/gigaam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-07-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HuBERT-style pretraining on 2,000,000 hours, ASR data 50,000 hours. Variants: multilingual_ssl, multilingual_large_ssl, multilingual_ctc, multilingual_large_ctc.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's GigaAM Multilingual is the 2026 open speech line with standard and large SSL and CTC checkpoints, MIT. Collected 2026-10-15.", "what_it_is_for": "Sber's GigaAM Multilingual is the 2026 open speech line with standard and large SSL and CTC checkpoints, MIT. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigaam/gigaam-v1": { "id": "sber/gigaam/gigaam-v1", "name": "GigaAM v1", "maker": "sber", "family": "sber/gigaam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-05", "context_length": null, "max_output_tokens": null, "parameters": "CTC 242M, RNN-T 243M", "architecture": "Conformer encoder. Pretrained with a wav2vec 2.0-style objective on 50,000 hours, then ASR fine-tunes on 2,000 hours.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's GigaAM v1 is the first open Conformer speech line, with SSL, emotion, CTC, and RNN-T variants under MIT. Collected 2026-10-15.", "what_it_is_for": "Sber's GigaAM v1 is the first open Conformer speech line, with SSL, emotion, CTC, and RNN-T variants under MIT. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigaam/gigaam-v2": { "id": "sber/gigaam/gigaam-v2", "name": "GigaAM v2", "maker": "sber", "family": "sber/gigaam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-12", "context_length": null, "max_output_tokens": null, "parameters": "CTC 242M, RNN-T 243M", "architecture": "Conformer. HuBERT-CTC pretraining on 50,000 hours, ASR on 2,000 hours.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's GigaAM v2 is the December 2024 open ASR line, MIT, with SSL, CTC, and RNN-T checkpoints. Collected 2026-10-15.", "what_it_is_for": "Sber's GigaAM v2 is the December 2024 open ASR line, MIT, with SSL, CTC, and RNN-T checkpoints. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigaam/gigaam-v3": { "id": "sber/gigaam/gigaam-v3", "name": "GigaAM v3", "maker": "sber", "family": "sber/gigaam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-11-19", "context_length": null, "max_output_tokens": null, "parameters": "220-240M", "architecture": "Conformer, HuBERT-CTC pretraining on 700,000 hours of Russian speech, ASR fine-tunes on 4,000 hours.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's GigaAM v3 is the November 2025 open Russian speech model, 220 to 240 million parameters, MIT, with SSL, CTC, RNN-T, and end-to-end punctuation variants. Collected 2026-10-15.", "what_it_is_for": "Sber's GigaAM v3 is the November 2025 open Russian speech model, 220 to 240 million parameters, MIT, with SSL, CTC, RNN-T, and end-to-end punctuation variants. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat": { "id": "sber/gigachat/gigachat", "name": "GigaChat", "maker": "sber", "family": "sber/gigachat", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's first-generation GigaChat is a closed GigaChat API model. The id is still accepted for billing, and the current public model index no longer lists it as its own serving model. Collected 2026-10-15.", "what_it_is_for": "Sber's first-generation GigaChat is a closed GigaChat API model. The id is still accepted for billing, and the current public model index no longer lists it as its own serving model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-2": { "id": "sber/gigachat/gigachat-2", "name": "GigaChat 2 Lite", "maker": "sber", "family": "sber/gigachat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-13", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's closed GigaChat 2 Lite is the fast, low-cost text model for routine tasks. It is active in the GigaChat API. Collected 2026-10-15.", "what_it_is_for": "Sber's closed GigaChat 2 Lite is the fast, low-cost text model for routine tasks. It is active in the GigaChat API. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-2-max": { "id": "sber/gigachat/gigachat-2-max", "name": "GigaChat 2 Max", "maker": "sber", "family": "sber/gigachat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-13", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's closed GigaChat 2 Max is the strongest GigaChat 2 tier, aimed at hard creative and long Russian text work, including science-heavy questions. It is active. Collected 2026-10-15.", "what_it_is_for": "Sber's closed GigaChat 2 Max is the strongest GigaChat 2 tier, aimed at hard creative and long Russian text work, including science-heavy questions. It is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-2-pro": { "id": "sber/gigachat/gigachat-2-pro", "name": "GigaChat 2 Pro", "maker": "sber", "family": "sber/gigachat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-13", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's closed GigaChat 2 Pro is the mid-tier GigaChat 2 model for harder instructions, summarization, rewriting, and question answering, including economics and law. It is active. Collected 2026-10-15.", "what_it_is_for": "Sber's closed GigaChat 2 Pro is the mid-tier GigaChat 2 model for harder instructions, summarization, rewriting, and question answering, including economics and law. It is active. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-20b-a3b-base": { "id": "sber/gigachat/gigachat-20b-a3b-base", "name": "GigaChat-20B-A3B-base", "maker": "sber", "family": "sber/gigachat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-12-13", "context_length": null, "max_output_tokens": null, "parameters": "20B total, about 3.3B active", "architecture": "Mixture-of-experts, 28 layers, 2 shared experts, 64 routed experts, 16 heads, 8 KV heads. DeepSeek-style MoE.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's open GigaChat-20B-A3B-base is a Russian-from-scratch mixture-of-experts base model, about 20 billion parameters with about 3.3 billion active. Weights are MIT. Collected 2026-10-15.", "what_it_is_for": "Sber's open GigaChat-20B-A3B-base is a Russian-from-scratch mixture-of-experts base model, about 20 billion parameters with about 3.3 billion active. Weights are MIT. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-20b-a3b-instruct": { "id": "sber/gigachat/gigachat-20b-a3b-instruct", "name": "GigaChat-20B-A3B-instruct", "maker": "sber", "family": "sber/gigachat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-12-13", "context_length": 131000, "max_output_tokens": null, "parameters": "20B total, about 3.3B active", "architecture": "Instruction-tuned MoE on GigaChat-20B-A3B-base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's open GigaChat-20B-A3B-instruct is the dialog checkpoint of the 20B-A3B MoE, with a 131 thousand token context, under MIT. Collected 2026-10-15.", "what_it_is_for": "Sber's open GigaChat-20B-A3B-instruct is the dialog checkpoint of the 20B-A3B MoE, with a 131 thousand token context, under MIT. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-3-1-audio": { "id": "sber/gigachat/gigachat-3-1-audio", "name": "GigaChat 3.1 Audio", "maker": "sber", "family": "sber/gigachat", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2026-07-13", "context_length": null, "max_output_tokens": null, "parameters": "10B-A1.8B class from the repo name; Hub did not return a safetensors total", "architecture": "Audio model in the GigaChat 3.1 10B-A1.8B line. MIT.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber uploaded GigaChat 3.1 Audio, an MIT checkpoint named as a 10B-A1.8B audio model, on 13 July 2026. Collected 2026-10-15. The card body was not read, so behavior is not fully specified here.", "what_it_is_for": "Sber uploaded GigaChat 3.1 Audio, an MIT checkpoint named as a 10B-A1.8B audio model, on 13 July 2026. Collected 2026-10-15. The card body was not read, so behavior is not fully specified here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-3-1-lightning": { "id": "sber/gigachat/gigachat-3-1-lightning", "name": "GigaChat 3.1 Lightning", "maker": "sber", "family": "sber/gigachat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-03-21", "context_length": null, "max_output_tokens": null, "parameters": "10B total, 1.8B active; Hub safetensors total 11480403068", "architecture": "MoE instruct model in the GigaChat 3.1 line. MIT. fp8, bf16, and GGUF repos exist.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's GigaChat 3.1 Lightning is the March 2026 open 10B-A1.8B instruct checkpoint, MIT. Collected 2026-10-15. The card body was not fully read, so scores and context are blank.", "what_it_is_for": "Sber's GigaChat 3.1 Lightning is the March 2026 open 10B-A1.8B instruct checkpoint, MIT. Collected 2026-10-15. The card body was not fully read, so scores and context are blank.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-3-1-ultra": { "id": "sber/gigachat/gigachat-3-1-ultra", "name": "GigaChat 3.1 Ultra", "maker": "sber", "family": "sber/gigachat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-03-21", "context_length": 131072, "max_output_tokens": null, "parameters": "702B total stated, 36B active; Hub safetensors total 715481588280", "architecture": "Custom MoE with multi-head latent attention and multi-token prediction. fp8 is the inference build. Native FP8 DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's GigaChat 3.1 Ultra is the open flagship instruct MoE, 702B total and 36B active on the card, MIT, uploaded 21 March 2026. Collected 2026-10-15.", "what_it_is_for": "Sber's GigaChat 3.1 Ultra is the open flagship instruct MoE, 702B total and 36B active on the card, MIT, uploaded 21 March 2026. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-3-5-reasoning": { "id": "sber/gigachat/gigachat-3-5-reasoning", "name": "GigaChat 3.5 Ultra Reasoning", "maker": "sber", "family": "sber/gigachat", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2026-09-03", "context_length": null, "max_output_tokens": null, "parameters": "Hub safetensors total 438085063424", "architecture": "Reasoning checkpoint in the GigaChat 3.5 Ultra family. MIT. bf16 and GGUF repos exist.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's GigaChat 3.5 reasoning checkpoint is a separately uploaded MIT model in the 432B Ultra family. Collected 2026-10-15 from Hub metadata. The model-card prose was not re-read.", "what_it_is_for": "Sber's GigaChat 3.5 reasoning checkpoint is a separately uploaded MIT model in the 432B Ultra family. Collected 2026-10-15 from Hub metadata. The model-card prose was not re-read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-3-5-ultra": { "id": "sber/gigachat/gigachat-3-5-ultra", "name": "GigaChat 3.5 Ultra", "maker": "sber", "family": "sber/gigachat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-07-05", "context_length": null, "max_output_tokens": null, "parameters": "432B total, 28B active; Hub safetensors total 433773248160", "architecture": "Hybrid MoE: some layers use multi-head latent attention and others use GatedDeltaNet linear attention, plus GatedNorm and two multi-token-prediction heads. Trained in native FP8.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's GigaChat 3.5 Ultra is the open flagship instant MoE, 432B total and 28B active, with hybrid MLA and GatedDeltaNet attention, MIT. Hub repo created 5 July 2026. Collected 2026-10-15.", "what_it_is_for": "Sber's GigaChat 3.5 Ultra is the open flagship instant MoE, 432B total and 28B active, with hybrid MLA and GatedDeltaNet attention, MIT. Hub repo created 5 July 2026. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-3-lightning": { "id": "sber/gigachat/gigachat-3-lightning", "name": "GigaChat 3 Lightning", "maker": "sber", "family": "sber/gigachat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-11-19", "context_length": 256000, "max_output_tokens": null, "parameters": "10B total, 1.8B active", "architecture": "MoE with multi-head latent attention and multi-token prediction. Trained on 20T tokens. fp8 inference build; bf16 is a separate repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's GigaChat 3 Lightning is the open 10B-A1.8B MoE instruct model for local and high-throughput use, MIT, first published in November 2025. Collected 2026-10-15.", "what_it_is_for": "Sber's GigaChat 3 Lightning is the open 10B-A1.8B MoE instruct model for local and high-throughput use, MIT, first published in November 2025. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-3-ultra": { "id": "sber/gigachat/gigachat-3-ultra", "name": "GigaChat 3 Ultra", "maker": "sber", "family": "sber/gigachat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-15", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's closed GigaChat 3 Ultra is the newest hosted GigaChat model. It is active for individual freemium API callers and is not on the paid tariffs. Collected 2026-10-15.", "what_it_is_for": "Sber's closed GigaChat 3 Ultra is the newest hosted GigaChat model. It is active for individual freemium API callers and is not on the paid tariffs. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-3-ultra-preview": { "id": "sber/gigachat/gigachat-3-ultra-preview", "name": "GigaChat 3 Ultra Preview", "maker": "sber", "family": "sber/gigachat", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-11-21", "context_length": 131000, "max_output_tokens": null, "parameters": "702B total, 36B active", "architecture": "Mixture-of-experts instruct model with about 36B parameters active per token. MLA and MTP are described on the later 3.1 card as the family design.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's GigaChat 3 Ultra Preview is the open 702B-A36B MoE instruct model released in November 2025 under MIT, with a stated 131 thousand token context. It is a preview checkpoint superseded in quality by GigaChat 3.1 Ultra. Collected 2026-10-15.", "what_it_is_for": "Sber's GigaChat 3 Ultra Preview is the open 702B-A36B MoE instruct model released in November 2025 under MIT, with a stated 131 thousand token context. It is a preview checkpoint superseded in quality by GigaChat 3.1 Ultra. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-max": { "id": "sber/gigachat/gigachat-max", "name": "GigaChat Max", "maker": "sber", "family": "sber/gigachat", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's first-generation GigaChat Max is a closed GigaChat API model. The id is still accepted for billing, and the current public model index no longer lists it as its own serving model. Collected 2026-10-15.", "what_it_is_for": "Sber's first-generation GigaChat Max is a closed GigaChat API model. The id is still accepted for billing, and the current public model index no longer lists it as its own serving model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/gigachat/gigachat-pro": { "id": "sber/gigachat/gigachat-pro", "name": "GigaChat Pro", "maker": "sber", "family": "sber/gigachat", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's first-generation GigaChat Pro is a closed GigaChat API model. The id is still accepted for billing, and the current public model index no longer lists it as its own serving model. Collected 2026-10-15.", "what_it_is_for": "Sber's first-generation GigaChat Pro is a closed GigaChat API model. The id is still accepted for billing, and the current public model index no longer lists it as its own serving model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-1": { "id": "sber/kandinsky/kandinsky-1", "name": "Kandinsky", "maker": "sber", "family": "sber/kandinsky", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2022-06-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of ruDALL-E on 179 million captioned images, then ruCLIP ranking and Real-ESRGAN upscaling, as described in the Sber news post. Real-ESRGAN is a third-party upscaler, not a Sber model.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's first Kandinsky, announced 15 June 2022, is a Russian text-to-image model derived from ruDALL-E. It is retired as a product relative to later Kandinsky releases. Collected 2026-10-15.", "what_it_is_for": "Sber's first Kandinsky, announced 15 June 2022, is a Russian text-to-image model derived from ruDALL-E. It is retired as a product relative to later Kandinsky releases. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-2-0": { "id": "sber/kandinsky/kandinsky-2-0", "name": "Kandinsky 2.0", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion text-to-image model. Later 2.x cards describe the line as CLIP plus a diffusion prior. Weights were published as sberbank-ai/Kandinsky_2.0 and ai-forever/Kandinsky_2.0.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Kandinsky 2.0 is the first open latent-diffusion Kandinsky text-to-image model, including inpainting weights. Collected 2026-10-15. The architecture table was not re-read from its own card.", "what_it_is_for": "Sber's Kandinsky 2.0 is the first open latent-diffusion Kandinsky text-to-image model, including inpainting weights. Collected 2026-10-15. The architecture table was not re-read from its own card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-2-1": { "id": "sber/kandinsky/kandinsky-2-1", "name": "Kandinsky 2.1", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-04-04", "context_length": null, "max_output_tokens": null, "parameters": "3.3B", "architecture": "Latent diffusion. Sber's 12 July 2023 comparison: text encoder 0.6B, diffusion mapping 1.0B, U-Net 1.2B, ViT 0.5B, MoVQ 0.08B. Dataset 1.2B pairs. Output about 768x768. Uses a MoVQ decoder.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Kandinsky 2.1 is an open latent-diffusion text-to-image model, 3.3 billion parameters, released 4 April 2023. Collected 2026-10-15.", "what_it_is_for": "Sber's Kandinsky 2.1 is an open latent-diffusion text-to-image model, 3.3 billion parameters, released 4 April 2023. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-2-2": { "id": "sber/kandinsky/kandinsky-2-2", "name": "Kandinsky 2.2", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-07-12", "context_length": null, "max_output_tokens": null, "parameters": "4.6B", "architecture": "Latent diffusion. Text encoder XLM-RoBERTa-Large-Vit-L-14 560M, diffusion image prior 1B, CLIP ViT-bigG image encoder 1.8B, U-Net 1.22B, MoVQ 67M. ControlNet support. Images up to 1024 and mixed aspect ratios. Dataset 1.5B pairs.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Kandinsky 2.2 is an open text-to-image model, 4.6 billion parameters, released 12 July 2023, with ControlNet and higher resolution than 2.1. Collected 2026-10-15.", "what_it_is_for": "Sber's Kandinsky 2.2 is an open text-to-image model, 4.6 billion parameters, released 12 July 2023, with ControlNet and higher resolution than 2.1. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-3-0": { "id": "sber/kandinsky/kandinsky-3-0", "name": "Kandinsky 3.0", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2023-11-21", "context_length": null, "max_output_tokens": null, "parameters": "U-Net 3B plus Flan-UL2 encoder 8.6B plus MoVQ 267M", "architecture": "Latent diffusion. Text encoder is the Flan-UL2 encoder, 8.6B. Latent diffusion U-Net 3B. MoVQ 267M. Drops the 2.x prior. Text-guided inpaint, outpaint, fusion, image variations, and later image-to-video and text-to-video extensions are described in the repo.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Kandinsky 3.0 is an open latent-diffusion text-to-image model released 21 November 2023, with a 3B U-Net and an 8.6B Flan-UL2 text encoder. Collected 2026-10-15.", "what_it_is_for": "Sber's Kandinsky 3.0 is an open latent-diffusion text-to-image model released 21 November 2023, with a 3B U-Net and an 8.6B Flan-UL2 text encoder. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-3-1": { "id": "sber/kandinsky/kandinsky-3-1", "name": "Kandinsky 3.1", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-04-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Follow-up to Kandinsky 3.0. The repo describes higher realism and more editing modes. Parameter count was not restated in the section read.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Kandinsky 3.1 is the open follow-up to Kandinsky 3.0, Apache-2.0, with Hub repo creation 4 April 2024. Collected 2026-10-15.", "what_it_is_for": "Sber's Kandinsky 3.1 is the open follow-up to Kandinsky 3.0, Apache-2.0, with Hub repo creation 4 April 2024. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-3-controlnet": { "id": "sber/kandinsky/kandinsky-3-controlnet", "name": "Kandinsky 3 ControlNet", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ControlNet adapters for Kandinsky 3. Named Hub repos cover depth, HED, and scribble, including v2 checkpoints.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber published Kandinsky 3 ControlNet adapters for depth, HED, and scribble. They are one product line with several checkpoints. Collected 2026-10-15 from Hub repo names. Cards were not re-read.", "what_it_is_for": "Sber published Kandinsky 3 ControlNet adapters for depth, HED, and scribble. They are one product line with several checkpoints. Collected 2026-10-15 from Hub repo names. Cards were not re-read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-3-flash": { "id": "sber/kandinsky/kandinsky-3-flash", "name": "Kandinsky Flash", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Distilled Kandinsky 3.0 refiner. The repo says it follows adversarial diffusion distillation, trained on latents, and can sample in 4 steps.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Kandinsky Flash is the distilled fast sampler for Kandinsky 3.0, described as a few-step refiner in the Kandinsky-3 repository. Collected 2026-10-15.", "what_it_is_for": "Sber's Kandinsky Flash is the distilled fast sampler for Kandinsky 3.0, described as a few-step refiner in the Kandinsky-3 repository. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-3-ip-adapter": { "id": "sber/kandinsky/kandinsky-3-ip-adapter", "name": "Kandinsky 3 IP-Adapter", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "IP-Adapter for Kandinsky 3 image prompting.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Kandinsky 3 IP-Adapter is an open image-prompt adapter. Collected 2026-10-15. The card body was not read.", "what_it_is_for": "Sber's Kandinsky 3 IP-Adapter is an open image-prompt adapter. Collected 2026-10-15. The card body was not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-4-t2v-flash": { "id": "sber/kandinsky/kandinsky-4-t2v-flash", "name": "Kandinsky 4.0 T2V Flash", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Distilled text-to-video latent diffusion. CogVideoX 3D causal VAE, T5-v1.1-XXL text embedder, custom MMDiT-like transformer. README: 12-second 480p video in 11 seconds on one H100. Also described as a 480p distillation of Kandinsky 4.0 T2V.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Kandinsky 4.0 T2V Flash is the open distilled text-to-video model released 13 December 2024, Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Sber's Kandinsky 4.0 T2V Flash is the open distilled text-to-video model released 13 December 2024, Apache-2.0. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-4-v2a": { "id": "sber/kandinsky/kandinsky-4-v2a", "name": "Kandinsky 4.0 V2A", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-12-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video-to-audio generation model released with Kandinsky 4.0.", "modalities": { "input": [ "video" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Kandinsky 4.0 V2A is an open video-to-audio model, Apache-2.0, released 13 December 2024. Collected 2026-10-15.", "what_it_is_for": "Sber's Kandinsky 4.0 V2A is an open video-to-audio model, Apache-2.0, released 13 December 2024. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-5-i2i-lite": { "id": "sber/kandinsky/kandinsky-5-i2i-lite", "name": "Kandinsky 5.0 I2I Lite", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-28", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "6B image-editing model. About 1K resolution. SFT and pretrain checkpoints. ComfyUI note dates image-to-image on 2025-11-28.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber Kandinsky Lab's Kandinsky 5.0 image editor is an open 6B image-to-image model. Collected 2026-10-15.", "what_it_is_for": "Sber Kandinsky Lab's Kandinsky 5.0 image editor is an open 6B image-to-image model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-5-i2v-lite": { "id": "sber/kandinsky/kandinsky-5-i2v-lite", "name": "Kandinsky 5.0 I2V Lite", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-15", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Image-to-video lite line. 5-second SFT checkpoint. Same family pipeline as T2V Lite.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber Kandinsky Lab's Kandinsky 5.0 I2V Lite is the open 2B image-to-video model released 15 November 2025. Collected 2026-10-15.", "what_it_is_for": "Sber Kandinsky Lab's Kandinsky 5.0 I2V Lite is the open 2B image-to-video model released 15 November 2025. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-5-i2v-pro": { "id": "sber/kandinsky/kandinsky-5-i2v-pro", "name": "Kandinsky 5.0 I2V Pro", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-20", "context_length": null, "max_output_tokens": null, "parameters": "19B", "architecture": "Image-to-video pro line. HD and SD 5-second SFT configs. 100 NFE.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber Kandinsky Lab's Kandinsky 5.0 I2V Pro is the open 19B image-to-video model released with Video Pro on 20 November 2025. Collected 2026-10-15.", "what_it_is_for": "Sber Kandinsky Lab's Kandinsky 5.0 I2V Pro is the open 19B image-to-video model released with Video Pro on 20 November 2025. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-5-t2i-lite": { "id": "sber/kandinsky/kandinsky-5-t2i-lite", "name": "Kandinsky 5.0 T2I Lite", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-15", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "6B image model. About 1K resolution, including 1280x768 and 1024x1024. Flow-matching family. 100 NFE. H100 latency 13 s for the SFT config.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber Kandinsky Lab's Kandinsky 5.0 Image Lite is an open 6B text-to-image model, released 15 November 2025, with SFT and pretrain checkpoints. Collected 2026-10-15.", "what_it_is_for": "Sber Kandinsky Lab's Kandinsky 5.0 Image Lite is an open 6B text-to-image model, released 15 November 2025, with SFT and pretrain checkpoints. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-5-t2v-lite": { "id": "sber/kandinsky/kandinsky-5-t2v-lite", "name": "Kandinsky 5.0 T2V Lite", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-29", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Latent diffusion, flow matching, diffusion transformer with cross-attention. Text encoders Qwen2.5-VL and CLIP. HunyuanVideo 3D VAE. 10-second checkpoints use NABLA sparse attention.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber Kandinsky Lab's Kandinsky 5.0 T2V Lite is an open 2B text-to-video model, first released 29 September 2025, with 5-second and 10-second SFT, pretrain, no-CFG, and 16-step distilled checkpoints. Collected 2026-10-15.", "what_it_is_for": "Sber Kandinsky Lab's Kandinsky 5.0 T2V Lite is an open 2B text-to-video model, first released 29 September 2025, with 5-second and 10-second SFT, pretrain, no-CFG, and 16-step distilled checkpoints. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-5-t2v-pro": { "id": "sber/kandinsky/kandinsky-5-t2v-pro", "name": "Kandinsky 5.0 T2V Pro", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-20", "context_length": null, "max_output_tokens": null, "parameters": "19B", "architecture": "19B flow-matching diffusion transformer. Qwen2.5-VL and CLIP text encoders. HunyuanVideo 3D VAE. English and Russian prompts. HD and SD configs. 5s and 10s.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber Kandinsky Lab's Kandinsky 5.0 T2V Pro is the open 19B text-to-video line, released 20 November 2025. The README says that on 12 December 2025 it ranked as the top open-source text-to-video model on LMArena. Collected 2026-10-15.", "what_it_is_for": "Sber Kandinsky Lab's Kandinsky 5.0 T2V Pro is the open 19B text-to-video line, released 20 November 2025. The README says that on 12 December 2025 it ranked as the top open-source text-to-video model on LMArena. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-6-lite": { "id": "sber/kandinsky/kandinsky-6-lite", "name": "Kandinsky 6.0 Video Lite", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-10-06", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Diffusion transformer family for synchronized text-to-audio-video and image-to-audio-video. 5-second clips, 44 kHz audio, lip-sync. Full HD comes from the separate VSR model. SFT, 10-step distilled, and pretrain checkpoints.", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber Kandinsky Lab's Kandinsky 6.0 Video Lite is the open 3B audio-video model released 6 October 2026 under MIT. Collected 2026-10-15.", "what_it_is_for": "Sber Kandinsky Lab's Kandinsky 6.0 Video Lite is the open 3B audio-video model released 6 October 2026 under MIT. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-6-pro": { "id": "sber/kandinsky/kandinsky-6-pro", "name": "Kandinsky 6.0 Video Pro", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-10-06", "context_length": null, "max_output_tokens": null, "parameters": "29B", "architecture": "Same audio-video diffusion family as Lite, 29B. SFT, 10-step distilled, and pretrain checkpoints. The non-distilled Pro repo is gated behind a Hugging Face click-through.", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber Kandinsky Lab's Kandinsky 6.0 Video Pro is the open 29B synchronized audio-video model released 6 October 2026 under MIT. Collected 2026-10-15.", "what_it_is_for": "Sber Kandinsky Lab's Kandinsky 6.0 Video Pro is the open 29B synchronized audio-video model released 6 October 2026 under MIT. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-6-vsr": { "id": "sber/kandinsky/kandinsky-6-vsr", "name": "Kandinsky 6.0 Video Super-Resolution", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Plug-in video super-resolution for Kandinsky 6. Raises output to Full HD 1920x1080. Flow checkpoint and a distilled 2-step checkpoint.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber Kandinsky Lab's Kandinsky 6.0 VSR is the open super-resolution model paired with Kandinsky 6 video, released 6 October 2026. Collected 2026-10-15.", "what_it_is_for": "Sber Kandinsky Lab's Kandinsky 6.0 VSR is the open super-resolution model paired with Kandinsky 6 video, released 6 October 2026. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-video-1-0": { "id": "sber/kandinsky/kandinsky-video-1-0", "name": "Kandinsky Video 1.0", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage text-to-video: low-FPS keyframes, then interpolation. The 1.1 README describes 1.0 this way. Weights repo: ai-forever/KandinskyVideo.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Kandinsky Video 1.0 is the first open Kandinsky text-to-video model, a keyframe-plus-interpolation pipeline. Collected 2026-10-15.", "what_it_is_for": "Sber's Kandinsky Video 1.0 is the first open Kandinsky text-to-video model, a keyframe-plus-interpolation pipeline. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/kandinsky/kandinsky-video-1-1": { "id": "sber/kandinsky/kandinsky-video-1-1", "name": "Kandinsky Video 1.1", "maker": "sber", "family": "sber/kandinsky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "Text encoder Flan-UL2 8.6B, keyframe U-Net3D 4.15B, interpolation U-Net3D 4.0B, image MoVQ 256M, temporal MoVQ decoder 556M", "architecture": "Text-to-video. First frame comes from Kandinsky 3.0, then keyframes, then interpolation. The repo says second place overall and best open model on EvalCrafter. No numeric EvalCrafter score was copied.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Kandinsky Video 1.1 is an open multilingual text-to-video model built on Kandinsky 3.0 and FusionFrames. Collected 2026-10-15.", "what_it_is_for": "Sber's Kandinsky Video 1.1 is an open multilingual text-to-video model built on Kandinsky 3.0 and FusionFrames. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/mgpt/mgpt": { "id": "sber/mgpt/mgpt", "name": "mGPT", "maker": "sber", "family": "sber/mgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2022-04-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 architecture, multilingual. Hub tags include 61 languages.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's mGPT is an open Apache-2.0 multilingual GPT model. The Hub repo was created 7 April 2022. Collected 2026-10-15.", "what_it_is_for": "Sber's mGPT is an open Apache-2.0 multilingual GPT model. The Hub repo was created 7 April 2022. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/mgpt/mgpt-13b": { "id": "sber/mgpt/mgpt-13b", "name": "mGPT 13B", "maker": "sber", "family": "sber/mgpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2023-04-18", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Larger multilingual GPT from the mGPT line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's mGPT 13B is an open MIT multilingual model. Hub repo created 18 April 2023. Collected 2026-10-15. The card body was not re-read.", "what_it_is_for": "Sber's mGPT 13B is an open MIT multilingual model. Hub repo created 18 April 2023. Collected 2026-10-15. The card body was not re-read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/movqgan/movqgan": { "id": "sber/movqgan/movqgan", "name": "MoVQGAN", "maker": "sber", "family": "sber/movqgan", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-05-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Modulating quantized VAE used as the image tokenizer in later Kandinsky models. Hub repo created 16 May 2023.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's MoVQGAN is the open image encoder-decoder used by the Kandinsky line. Collected 2026-10-15. The card body was not re-read.", "what_it_is_for": "Sber's MoVQGAN is the open image encoder-decoder used by the Kandinsky line. Collected 2026-10-15. The card body was not re-read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/pollux/pollux-4b-judge": { "id": "sber/pollux/pollux-4b-judge", "name": "Pollux 4B Judge", "maker": "sber", "family": "sber/pollux", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2026-04-12", "context_length": null, "max_output_tokens": null, "parameters": "Hub safetensors total 4022468096", "architecture": "Text judge model. Parameter total matches Zarya-4B's safetensors total. Whether it is a Zarya fine-tune was not confirmed from a card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Pollux 4B Judge is an open MIT text model uploaded 12 April 2026. Collected 2026-10-15 from Hub metadata. The card body was not read.", "what_it_is_for": "Sber's Pollux 4B Judge is an open MIT text model uploaded 12 April 2026. Collected 2026-10-15 from Hub metadata. The card body was not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/ru-encoder/ru-en-rosberta": { "id": "sber/ru-encoder/ru-en-rosberta", "name": "ru-en-RoSBERTa", "maker": "sber", "family": "sber/ru-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-07-29", "context_length": null, "max_output_tokens": null, "parameters": "Hub safetensors total 403707904", "architecture": "Bilingual sentence encoder. Hub pipeline is feature-extraction.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's ru-en-RoSBERTa is an open MIT Russian-English embedding encoder, about 404 million parameters, uploaded 29 July 2024. Collected 2026-10-15.", "what_it_is_for": "Sber's ru-en-RoSBERTa is an open MIT Russian-English embedding encoder, about 404 million parameters, uploaded 29 July 2024. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/ru-encoder/rubert-base": { "id": "sber/ru-encoder/rubert-base", "name": "ruBERT base", "maker": "sber", "family": "sber/ru-encoder", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder for Russian, fill-mask.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's ruBERT base is an open Apache-2.0 Russian BERT on the ai-forever Hub. Collected 2026-10-15. The card body was not re-read, and the Hub timestamp is a migration stamp, so the release day is blank.", "what_it_is_for": "Sber's ruBERT base is an open Apache-2.0 Russian BERT on the ai-forever Hub. Collected 2026-10-15. The card body was not re-read, and the Hub timestamp is a migration stamp, so the release day is blank.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/ru-encoder/rubert-large": { "id": "sber/ru-encoder/rubert-large", "name": "ruBERT large", "maker": "sber", "family": "sber/ru-encoder", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder for Russian, fill-mask.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's ruBERT large is an open Russian BERT on the ai-forever Hub. Collected 2026-10-15. License and scores were not returned.", "what_it_is_for": "Sber's ruBERT large is an open Russian BERT on the ai-forever Hub. Collected 2026-10-15. License and scores were not returned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/ru-encoder/ruroberta-large": { "id": "sber/ru-encoder/ruroberta-large", "name": "ruRoBERTa large", "maker": "sber", "family": "sber/ru-encoder", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa encoder for Russian, fill-mask.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's ruRoBERTa large is an open Russian RoBERTa on the ai-forever Hub. Collected 2026-10-15. License and scores were not returned.", "what_it_is_for": "Sber's ruRoBERTa large is an open Russian RoBERTa on the ai-forever Hub. Collected 2026-10-15. License and scores were not returned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/ru-encoder/sbert-large-nlu-ru": { "id": "sber/ru-encoder/sbert-large-nlu-ru", "name": "SBERT large NLU Russian", "maker": "sber", "family": "sber/ru-encoder", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "Hub safetensors total 426908672", "architecture": "Sentence-BERT large model for Russian NLU. Hub pipeline is feature-extraction.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's SBERT large NLU Russian is an open MIT sentence encoder, about 427 million parameters. Collected 2026-10-15. The release day is blank because the Hub timestamp is the shared 2022 migration stamp.", "what_it_is_for": "Sber's SBERT large NLU Russian is an open MIT sentence encoder, about 427 million parameters. Collected 2026-10-15. The release day is blank because the Hub timestamp is the shared 2022 migration stamp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/rudalle/rudalle-emojich": { "id": "sber/rudalle/rudalle-emojich", "name": "ruDALL-E Emojich", "maker": "sber", "family": "sber/rudalle", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Emoji-oriented ruDALL-E checkpoint.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's ruDALL-E Emojich is an open emoji text-to-image checkpoint on the ai-forever Hub. Collected 2026-10-15. The card body was not re-read.", "what_it_is_for": "Sber's ruDALL-E Emojich is an open emoji text-to-image checkpoint on the ai-forever Hub. Collected 2026-10-15. The card body was not re-read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/rudalle/rudalle-malevich": { "id": "sber/rudalle/rudalle-malevich", "name": "ruDALL-E Malevich", "maker": "sber", "family": "sber/rudalle", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Russian discrete text-to-image model in the ruDALL-E line. Kandinsky 1 was described as a fine-tune of ruDALL-E.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's ruDALL-E Malevich is an open Russian text-to-image model on the ai-forever Hub. Collected 2026-10-15. License, size, and release day were not re-read.", "what_it_is_for": "Sber's ruDALL-E Malevich is an open Russian text-to-image model on the ai-forever Hub. Collected 2026-10-15. License, size, and release day were not re-read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/rudolph/rudolph-1-3b": { "id": "sber/rudolph/rudolph-1-3b", "name": "RUDOLPH 1.3B", "maker": "sber", "family": "sber/rudolph", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Hyper-tasking transformer. The ru-dolph repo says one model can do DALL-E-like, GPT-like, and CLIP-like tasks.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's RUDOLPH 1.3B is an open multitask transformer from the RUDOLPH line. Collected 2026-10-15. The card body was not re-read.", "what_it_is_for": "Sber's RUDOLPH 1.3B is an open multitask transformer from the RUDOLPH line. Collected 2026-10-15. The card body was not re-read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/rudolph/rudolph-2-7b": { "id": "sber/rudolph/rudolph-2-7b", "name": "RUDOLPH 2.7B", "maker": "sber", "family": "sber/rudolph", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "2.7B", "architecture": "Hyper-tasking transformer. The ru-dolph repo says one model can do DALL-E-like, GPT-like, and CLIP-like tasks.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's RUDOLPH 2.7B is an open multitask transformer from the RUDOLPH line. Collected 2026-10-15. The card body was not re-read.", "what_it_is_for": "Sber's RUDOLPH 2.7B is an open multitask transformer from the RUDOLPH line. Collected 2026-10-15. The card body was not re-read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/rudolph/rudolph-350m": { "id": "sber/rudolph/rudolph-350m", "name": "RUDOLPH 350M", "maker": "sber", "family": "sber/rudolph", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "350M", "architecture": "Hyper-tasking transformer. The ru-dolph repo says one model can do DALL-E-like, GPT-like, and CLIP-like tasks.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's RUDOLPH 350M is an open multitask transformer from the RUDOLPH line. Collected 2026-10-15. The card body was not re-read.", "what_it_is_for": "Sber's RUDOLPH 350M is an open multitask transformer from the RUDOLPH line. Collected 2026-10-15. The card body was not re-read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/rugpt/rugpt-3-5-13b": { "id": "sber/rugpt/rugpt-3-5-13b", "name": "ruGPT-3.5 13B", "maker": "sber", "family": "sber/rugpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2023-05-02", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Decoder-only Russian model. Card: pretrained on about 300 GB, then more code and legal text, about 300B tokens for 3 epochs, then a 2048-token fine-tune. The card says this model was used while training GigaChat.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's ruGPT-3.5 13B is an open MIT Russian language model and a stated ancestor of GigaChat training. Hub repo created 2 May 2023. Collected 2026-10-15.", "what_it_is_for": "Sber's ruGPT-3.5 13B is an open MIT Russian language model and a stated ancestor of GigaChat training. Hub repo created 2 May 2023. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/rugpt/rugpt-3-large": { "id": "sber/rugpt/rugpt-3-large", "name": "ruGPT-3 Large", "maker": "sber", "family": "sber/rugpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 architecture Russian model from the ruGPT-3 line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's ruGPT-3 Large is an open Russian GPT-2 model in the ruGPT-3 line on the ai-forever Hub. Collected 2026-10-15. Specs were not re-read from the card.", "what_it_is_for": "Sber's ruGPT-3 Large is an open Russian GPT-2 model in the ruGPT-3 line on the ai-forever Hub. Collected 2026-10-15. Specs were not re-read from the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/rugpt/rugpt-3-medium": { "id": "sber/rugpt/rugpt-3-medium", "name": "ruGPT-3 Medium", "maker": "sber", "family": "sber/rugpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 architecture Russian model from the ruGPT-3 line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's ruGPT-3 Medium is an open Russian GPT-2 model in the ruGPT-3 line on the ai-forever Hub. Collected 2026-10-15. Specs were not re-read from the card.", "what_it_is_for": "Sber's ruGPT-3 Medium is an open Russian GPT-2 model in the ruGPT-3 line on the ai-forever Hub. Collected 2026-10-15. Specs were not re-read from the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/rugpt/rugpt-3-small": { "id": "sber/rugpt/rugpt-3-small", "name": "ruGPT-3 Small", "maker": "sber", "family": "sber/rugpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 architecture Russian model from the ruGPT-3 line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's ruGPT-3 Small is an open Russian GPT-2 model in the ruGPT-3 line on the ai-forever Hub. Collected 2026-10-15. Specs were not re-read from the card.", "what_it_is_for": "Sber's ruGPT-3 Small is an open Russian GPT-2 model in the ruGPT-3 line on the ai-forever Hub. Collected 2026-10-15. Specs were not re-read from the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/rugpt/rugpt-3-xl": { "id": "sber/rugpt/rugpt-3-xl", "name": "ruGPT-3 XL", "maker": "sber", "family": "sber/rugpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 architecture Russian model from the ruGPT-3 line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's ruGPT-3 XL is an open Russian GPT-2 model in the ruGPT-3 line on the ai-forever Hub. Collected 2026-10-15. Specs were not re-read from the card.", "what_it_is_for": "Sber's ruGPT-3 XL is an open Russian GPT-2 model in the ruGPT-3 line on the ai-forever Hub. Collected 2026-10-15. Specs were not re-read from the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/zarya/zarya-0-6b": { "id": "sber/zarya/zarya-0-6b", "name": "Zarya 0.6B", "maker": "sber", "family": "sber/zarya", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2026-09-18", "context_length": null, "max_output_tokens": null, "parameters": "Hub safetensors total 596049920", "architecture": "Hybrid autoregressive and masked-diffusion language model. The ai-forever/zarya repo describes slotted training and dual-mode inference.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Zarya 0.6B is an open MIT hybrid language model uploaded 2026-09-18. Collected 2026-10-15. The card body was not re-read.", "what_it_is_for": "Sber's Zarya 0.6B is an open MIT hybrid language model uploaded 2026-09-18. Collected 2026-10-15. The card body was not re-read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/zarya/zarya-1-7b": { "id": "sber/zarya/zarya-1-7b", "name": "Zarya 1.7B", "maker": "sber", "family": "sber/zarya", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2026-09-18", "context_length": null, "max_output_tokens": null, "parameters": "Hub safetensors total 1720574976", "architecture": "Hybrid autoregressive and masked-diffusion language model. The ai-forever/zarya repo describes slotted training and dual-mode inference.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Zarya 1.7B is an open MIT hybrid language model uploaded 2026-09-18. Collected 2026-10-15. The card body was not re-read.", "what_it_is_for": "Sber's Zarya 1.7B is an open MIT hybrid language model uploaded 2026-09-18. Collected 2026-10-15. The card body was not re-read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/zarya/zarya-4b": { "id": "sber/zarya/zarya-4b", "name": "Zarya 4B", "maker": "sber", "family": "sber/zarya", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2026-09-18", "context_length": null, "max_output_tokens": null, "parameters": "Hub safetensors total 4022468096", "architecture": "Hybrid autoregressive and masked-diffusion language model. The ai-forever/zarya repo describes slotted training and dual-mode inference.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Zarya 4B is an open MIT hybrid language model uploaded 2026-09-18. Collected 2026-10-15. The card body was not re-read.", "what_it_is_for": "Sber's Zarya 4B is an open MIT hybrid language model uploaded 2026-09-18. Collected 2026-10-15. The card body was not re-read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/zarya/zarya-orthrus-0-6b": { "id": "sber/zarya/zarya-orthrus-0-6b", "name": "Zarya-Orthrus 0.6B", "maker": "sber", "family": "sber/zarya", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2026-09-22", "context_length": null, "max_output_tokens": null, "parameters": "Hub safetensors total 772217856", "architecture": "Hybrid model with the Orthrus architecture, from the Zarya-Orthrus repository description.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Zarya-Orthrus 0.6B is an open MIT hybrid checkpoint uploaded 22 September 2026. Collected 2026-10-15.", "what_it_is_for": "Sber's Zarya-Orthrus 0.6B is an open MIT hybrid checkpoint uploaded 22 September 2026. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sber/zarya/zarya-orthrus-1-7b": { "id": "sber/zarya/zarya-orthrus-1-7b", "name": "Zarya-Orthrus 1.7B", "maker": "sber", "family": "sber/zarya", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "2026-09-22", "context_length": null, "max_output_tokens": null, "parameters": "Hub safetensors total 2072903680", "architecture": "Hybrid Orthrus checkpoint. The Hub total is higher than the 1.7B name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sber's Zarya-Orthrus 1.7B is an open MIT hybrid checkpoint uploaded 22 September 2026. Collected 2026-10-15.", "what_it_is_for": "Sber's Zarya-Orthrus 1.7B is an open MIT hybrid checkpoint uploaded 22 September 2026. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scarolina/bertos/bertos": { "id": "scarolina/bertos/bertos", "name": "BERTOS", "maker": "scarolina", "family": "scarolina/bertos", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0 (code); CC BY 4.0 (figshare weights)", "release_date": "", "context_length": 200, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder plus a classifier that predicts an oxidation state at each site from composition alone. Supplement Table S3: vocabulary 123, hidden size 120, max position embeddings 200, 4 attention heads, 12 hidden layers, batch size 256, 500 epochs, learning rate 1e-3. No elemental-property features. Parameter count was not computed from that table and was not published. context_window_tokens is the max position embedding, which matches the stated limit of 200 atoms per unit cell.", "modalities": { "input": [ "chemical-formula" ], "output": [ "oxidation-states" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERTOS is a composition-only oxidation-state model from the University of South Carolina Machine Learning and Evolution Laboratory. The journal article is Advanced Science 10(28):e2301011 (October 2023), DOI 10.1002/advs.202301011, PMID 37551059. The preprint is arXiv:2211.15895, 29 November 2022. Code is GPL-3.0. Figshare weights posted 10 February 2025 by Lai Wei are CC BY 4.0, about 518.53 MB. The GitHub default checkpoint is trained_models/ICSD_CN. Collected on 2026-10-15.", "what_it_is_for": "BERTOS reads element symbols in a formula and assigns an oxidation state to each site. It does not take a crystal structure or a table of elemental properties. The lab reports it for screening charge-neutral compositions and for downstream crystal-structure tools that need oxidation states.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scarolina/blmm/crystal-transformer": { "id": "scarolina/blmm/crystal-transformer", "name": "Crystal Transformer", "maker": "scarolina", "family": "scarolina/blmm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-04-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Blank-filling language model for inorganic crystal compositions, released by the Machine Learning and Evolution Laboratory as BLMM and also called Crystal Transformer and Crystal Composition Transformer. Training commands in the repository use model_type blm, vocab_size 130, and max_len 210. The blank-language-model code base is the implementation the lab trained. The released weights are the lab checkpoint, not a hosted copy of another maker's language model. Parameter count was not read from hparams.yaml.", "modalities": { "input": [ "chemical-formula" ], "output": [ "chemical-formula" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Crystal Transformer is a blank-filling language model for inorganic compositions from the University of South Carolina Machine Learning and Evolution Laboratory. The preprint is arXiv:2204.11953, 25 April 2022. The lab also calls it BLMM and Crystal Composition Transformer. Code is at github.com/usccolumbia/blmm under MIT. The public weight file that was confirmed is icsd-mix-model.ckpt inside the figshare zip on article 20489964. Collected on 2026-10-15.", "what_it_is_for": "The model fills and samples crystal compositions. The abstract reports generation up to 89.7% charge neutral and 84.8% balanced in electronegativity, above an enhanced random baseline by more than 4 times and more than 8 times. Later materials-transformer experiments use it as the fast, high-yield baseline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scarolina/mtransformer/mt-bart": { "id": "scarolina/mtransformer/mt-bart", "name": "MT-BART", "maker": "scarolina", "family": "scarolina/mtransformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-01-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder materials transformer trained from a BART-style implementation on expanded inorganic formulas. The repository folder name is MT_Bart. It is not a hosted copy of a Meta or Hugging Face BART checkpoint. Parameter count was not published in the extracted text.", "modalities": { "input": [ "chemical-formula" ], "output": [ "chemical-formula" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MT-BART is the encoder-decoder composition generator from the University of South Carolina Machine Learning and Evolution Laboratory. It is one of six named materials transformers in Machine Learning: Science and Technology 4, 015001 (5 January 2023). The README stores the weights as MT_Bart and points all five dataset columns at figshare file 35998784. Code is MIT. Collected on 2026-10-15.", "what_it_is_for": "The lab trains MT-BART to continue or reconstruct inorganic formula strings and then samples new compositions. In the t-SNE comparison its cloud is the closest of the non-BLMM models to the training set. Validity sits behind the GPT-Neo, GPT-J, and GPT-2 materials models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scarolina/mtransformer/mt-gpt": { "id": "scarolina/mtransformer/mt-gpt", "name": "MT-GPT", "maker": "scarolina", "family": "scarolina/mtransformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-01-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Causal materials transformer trained in the Machine Learning and Evolution Laboratory from a Hugging Face GPT implementation (OpenAIGPT-style decoder) on expanded inorganic formulas. It is not a hosted copy of an OpenAI GPT checkpoint. The preprint does not publish layer count, hidden size, or parameter count. A generation loop writes sequences of 256 tokens and then splits them into formulas. That length is not recorded here as a context window.", "modalities": { "input": [ "chemical-formula" ], "output": [ "chemical-formula" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MT-GPT is a materials composition generator from the Machine Learning and Evolution Laboratory at the University of South Carolina. Nihang Fu, Lai Wei, Yuqi Song, Qinyang Li, Rui Xin, Sadman Sadeed Omee, Rongzhi Dong, Edirisuriya M. Dilanga Siriwardane, and Jianjun Hu trained it, with the other materials transformers, on expanded inorganic formulas from ICSD, OQMD, and the Materials Project. The journal version is Machine Learning: Science and Technology 4, 015001 (5 January 2023), DOI 10.1088/2632-2153/acadcd. The code is MIT. Weights are the GPT columns of figshare article 20123483. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses MT-GPT as a causal language model that learns which element sequences look like known inorganic compositions, then samples new formulas. Charge neutrality and balanced electronegativity are the validity checks. It is one of six named weight releases in the mtransformer repository. It is not a copy of an OpenAI GPT weight file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scarolina/mtransformer/mt-gpt2": { "id": "scarolina/mtransformer/mt-gpt2", "name": "MT-GPT2", "maker": "scarolina", "family": "scarolina/mtransformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-01-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Causal materials transformer trained from a Hugging Face GPT-2 implementation on expanded inorganic formulas. It is not a hosted copy of an OpenAI GPT-2 checkpoint. Layer count, width, and parameter count were not in the extracted methods text. The Bandgap-30K run is a paper experiment on this architecture and is not a separate figshare product.", "modalities": { "input": [ "chemical-formula" ], "output": [ "chemical-formula" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MT-GPT2 is the GPT-2-style materials composition generator from the University of South Carolina Machine Learning and Evolution Laboratory. It is one of the six named checkpoints in the materials-transformer study published as Machine Learning: Science and Technology 4, 015001 on 5 January 2023. The lab trains it on expanded element-symbol formulas. The public weight table points the five dataset columns at figshare file 35998790. Code is MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors use MT-GPT2 both as a general composition sampler and as the model for a high-band-gap experiment. On that experiment they train on 30,000 Materials Project formulas with band gap above 1.98 eV, sample 100,000 formulas, and keep 87,233 that are charge neutral and electronegativity balanced. A DFT follow-up of the top candidates reports twenty formulas with negative formation energy, including SrAlClO2 at -3.0077 eV/atom, and two structures at 0 eV above hull.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scarolina/mtransformer/mt-gptj": { "id": "scarolina/mtransformer/mt-gptj", "name": "MT-GPTJ", "maker": "scarolina", "family": "scarolina/mtransformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-01-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT-J-style causal language model trained by the lab on inorganic formulas. The paper does not identify it with the 6 billion parameter EleutherAI GPT-J, and no parameter count was extracted. It is not a hosted copy of that checkpoint.", "modalities": { "input": [ "chemical-formula" ], "output": [ "chemical-formula" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MT-GPTJ is the GPT-J-style composition generator from the University of South Carolina Machine Learning and Evolution Laboratory. In the materials-transformer benchmark it is the validity leader after the element and atom filter. The journal version is Machine Learning: Science and Technology 4, 015001, 5 January 2023. Weights are figshare file 35998793. The code is MIT. It is a lab-trained model, not the EleutherAI 6B GPT-J checkpoint. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab uses MT-GPTJ to sample hypothetical inorganic formulas and then scores charge neutrality, balanced electronegativity, uniqueness of the chemical pattern, and whether a sample already exists in ICSD, OQMD, or the Materials Project. The discussion calls it the strongest generator after a simple filter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scarolina/mtransformer/mt-gptneo": { "id": "scarolina/mtransformer/mt-gptneo", "name": "MT-GPTNeo", "maker": "scarolina", "family": "scarolina/mtransformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-01-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT-Neo-style causal materials transformer trained by the lab on expanded inorganic formulas. It is not a hosted copy of an EleutherAI GPT-Neo checkpoint. Parameter count was not published in the extracted text.", "modalities": { "input": [ "chemical-formula" ], "output": [ "chemical-formula" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MT-GPTNeo is the GPT-Neo-style composition generator from the University of South Carolina Machine Learning and Evolution Laboratory. With MT-GPTJ it leads the ICSD validity comparison in the materials-transformer paper, Machine Learning: Science and Technology 4, 015001 (5 January 2023). The five dataset checkpoints share figshare file 35998796. Code is MIT. This is a lab-trained model, not an EleutherAI GPT-Neo weight file. Collected on 2026-10-15.", "what_it_is_for": "The paper uses MT-GPTNeo to generate inorganic formulas and to measure uniqueness across the five training sets. It is also the model compared directly with the lab's earlier MATGAN on ICSD-mix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scarolina/mtransformer/mt-roberta": { "id": "scarolina/mtransformer/mt-roberta", "name": "MT-RoBERTa", "maker": "scarolina", "family": "scarolina/mtransformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-01-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Masked language model in the RoBERTa family, trained by the lab on inorganic formulas and used as a generator. The preprint calls this use awkward relative to the causal decoders. It is not a hosted copy of a Facebook RoBERTa checkpoint. Parameter count was not published in the extracted text.", "modalities": { "input": [ "chemical-formula" ], "output": [ "chemical-formula" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MT-RoBERTa is the masked-language-model composition generator from the University of South Carolina Machine Learning and Evolution Laboratory. It is the sixth named materials transformer in Machine Learning: Science and Technology 4, 015001 (5 January 2023). The README points RoBERTa-Im through RoBERTa-Hs at figshare file 35998799. Code is MIT. The checkpoint is trained by the lab. It is not a hosted copy of a Facebook RoBERTa weight file. Collected on 2026-10-15.", "what_it_is_for": "The authors include a RoBERTa-style masked model in the same generation benchmark as the causal decoders. It can emit formulas, and it is the model with the lowest yield inside the atom and element caps and the most unlike training distribution in the t-SNE plot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scarolina/uflowcsp/uflowcsp": { "id": "scarolina/uflowcsp/uflowcsp", "name": "uFlowCSP", "maker": "scarolina", "family": "scarolina/uflowcsp", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT (code); CC BY 4.0 (figshare weights)", "release_date": "2026-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MeanFlow all-atom diffusion transformer for composition-only crystal structure prediction. Authors Sourin Dey, Dipannoy Das Gupta, Lai Wei, Sadman Sadeed Omee, and Jianjun Hu. The network configuration is stored in the checkpoint. Input is a primitive formula. Space group is optional and off by default (--use_spacegroup). Output is an unrelaxed CIF (lattice and positions). Sampling uses 1 to 5 integration steps. Training cells have at most 20 atoms. The MP-20 split is 27,136 / 9,047 / 9,046. Parameter count was not found.", "modalities": { "input": [ "chemical-formula" ], "output": [ "cif" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "uFlowCSP is a composition-only crystal-structure model from the University of South Carolina Machine Learning and Evolution Laboratory. The authors are Sourin Dey, Dipannoy Das Gupta, Lai Wei, Sadman Sadeed Omee, and Jianjun Hu. The paper is arXiv:2609.09799. Code is MIT at github.com/usccolumbia/uFlowCSP. The checkpoint was posted on figshare on 23 September 2026 by Sourin Dey under CC BY 4.0. Collected on 2026-10-15.", "what_it_is_for": "The model predicts a crystal structure from a formula. It samples a lattice and atomic positions with a MeanFlow diffusion transformer in one to five integration steps. Structures are not relaxed. A space group can be passed with --use_spacegroup. That switch is off by default.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/3d-blocky-elements": { "id": "scenario/flux-1/3d-blocky-elements", "name": "3D Blocky Elements", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "3D Blocky Elements is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders subjects as 3D blocky objects with smooth, rounded surfaces, vibrant colors, and whimsical geometric simplicity. The house article says it was trained for: Generating playful, blocky-style 3D objects and elements with a minimalist geometric design and bright, friendly color schemes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/3d-chibis": { "id": "scenario/flux-1/3d-chibis", "name": "3D Chibis", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-11-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "3D Chibis is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA that renders chibi characters in a playful 3D style, featuring heroes, dwarves, and whimsical fantasy scenes in bright colors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/3d-fantasy-chibis": { "id": "scenario/flux-1/3d-fantasy-chibis", "name": "3D Fantasy Chibis", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "3D Fantasy Chibis is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "This Flux LoRA generates 3D-style chibi characters in medieval fantasy settings, from armored knights and mages to pirates and archers, with detailed accessories. The house article says it was trained for: Producing custom 3D fantasy chibi characters with a cute, stylized finish.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/3d-icons": { "id": "scenario/flux-1/3d-icons", "name": "3D Icons", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-02-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is civitai. Scenario maintains it as a platform model. This catalog did not re-check the upstream weight page.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "3D Icons is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA for 3D icon generation. Produces polished, modern icon designs, from neon-lit to clean styles, for objects, technology, and abstract concepts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/80s-cyberpunk-by-fofr": { "id": "scenario/flux-1/80s-cyberpunk-by-fofr", "name": "80s Cyberpunk (by Fofr)", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "80s Cyberpunk (by Fofr) is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "This Flux.1 LoRA renders 1980s retro-futuristic cyberpunk visuals: neon-lit cityscapes, dystopian streets, and cinematic glow aesthetics.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/action-plastic-2-0": { "id": "scenario/flux-1/action-plastic-2-0", "name": "Action Plastic 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Action Plastic 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders characters in dynamic poses with a collectible action figure aesthetic. Bold colors, exaggerated features, clean backgrounds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/ai-engravings": { "id": "scenario/flux-1/ai-engravings", "name": "AI Engravings", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Engravings is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders subjects in classical black-and-white engraving style, with fine linework and cross-hatching inspired by 19th-century etching.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/alley-brawler": { "id": "scenario/flux-1/alley-brawler", "name": "Alley Brawler", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-01-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alley Brawler is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA for fighting-game characters: athletic builds, bold outlines, and vibrant streetwear or martial arts attire in dynamic action poses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/animated-feature-2-0": { "id": "scenario/flux-1/animated-feature-2-0", "name": "Animated Feature 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Animated Feature 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders characters and scenes in a contemporary animated feature style: vibrant colors, cinematic lighting, and elaborate medieval-inspired costumes. The house article says it was trained for: Creating appealing characters and scenes with the cinematic polish, lighting, and storytelling presence of modern animated films.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/anime-fantasy-characters-2-0": { "id": "scenario/flux-1/anime-fantasy-characters-2-0", "name": "Anime Fantasy Characters 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Anime Fantasy Characters 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for anime fantasy characters: mages, knights, and elemental heroes in dynamic poses with crisp linework and vivid color palettes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/anime-watercolor-character-2-0": { "id": "scenario/flux-1/anime-watercolor-character-2-0", "name": "Anime Watercolor character 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Anime Watercolor character 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders fantasy characters in an anime watercolor style, with fluid poses, intricate costume detail, and magical themes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/arcade-hero-2-0": { "id": "scenario/flux-1/arcade-hero-2-0", "name": "Arcade Hero 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arcade Hero 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for a retro arcade hero boy: neon outfits, 80s game aesthetics, digital helmets, glowing visors, and stylized 3D animation style. The house article says it was trained for: Creating colorful, retro-inspired hero characters with an energetic arcade vibe.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/battle-arenas-2-0": { "id": "scenario/flux-1/battle-arenas-2-0", "name": "Battle Arenas 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Battle Arenas 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "This Flux LoRA generates vibrant, cartoonish fantasy battle arenas in a top-down view style, covering environments from volcanic pits to snowy ruins and moonlit shores. The house article says it was trained for: Building dynamic, cartoonish battlegrounds for RPGs, tactical games, and fighting games.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/black-and-white-patterns": { "id": "scenario/flux-1/black-and-white-patterns", "name": "Black-and-White Patterns", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-01-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Black-and-White Patterns is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for seamless black-and-white geometric patterns. Generates high-contrast grids, bricks, hexagons, and line patterns ready for texture use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/bold-comic-scenes": { "id": "scenario/flux-1/bold-comic-scenes", "name": "Bold Comic Scenes", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bold Comic Scenes is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders scenes in a bold cartoon style: strong outlines, flat vibrant colors, and simplified shapes for characters, environments, and objects.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/bold-line-environments-2-0": { "id": "scenario/flux-1/bold-line-environments-2-0", "name": "Bold Line Environments 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bold Line Environments 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that applies a bold line, stylized CG art style to environment backgrounds, from caves and graveyards to forests, deserts, and coastlines. The house article says it was trained for: Designing futuristic or fantastical landscapes with bold outlines and polished shading.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/bold-lines-illustrations-2-0": { "id": "scenario/flux-1/bold-lines-illustrations-2-0", "name": "Bold Lines Illustrations 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-11-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bold Lines Illustrations 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders any landscape or environment in a bold-line cartoon illustration style, with clean outlines and smooth cel shading.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/boxes-crates-2-0": { "id": "scenario/flux-1/boxes-crates-2-0", "name": "Boxes & Crates 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Boxes & Crates 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA for game loot containers: generates reward boxes, wooden crates, and treasure chests across fantasy, steampunk, and sci-fi styles. The house article says it was trained for: Creating high-quality, theme-appropriate reward containers for loot systems, mobile games, and other interactive experiences.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/card-frames": { "id": "scenario/flux-1/card-frames", "name": "Card Frames", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Card Frames is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "This Flux.1 LoRA generates ornate fantasy card borders with metallic textures and gemstone accents, from gothic ruby frames to elegant gold and jeweled designs. The house article says it was trained for: Designing premium-quality fantasy card frames for trading card games, digital collectibles, and display art.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/cartoon-backgrounds-2-0": { "id": "scenario/flux-1/cartoon-backgrounds-2-0", "name": "Cartoon Backgrounds 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cartoon Backgrounds 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA for vibrant cartoon backgrounds. Generates bold, whimsical scenes: fantasy forests, alien planets, ancient ruins, undersea reefs, and more.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/cartoon-characters-1-0": { "id": "scenario/flux-1/cartoon-characters-1-0", "name": "Cartoon Characters 1.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cartoon Characters 1.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for 3D cartoon character art. Generates stylized figurines across fantasy archetypes, occupations, and creatures with vibrant, clean rendering. The house article says it was trained for: Creating expressive, fully rendered cartoon-style 3D characters for games and media.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/cartoon-characters-2-0": { "id": "scenario/flux-1/cartoon-characters-2-0", "name": "Cartoon Characters 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-11-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cartoon Characters 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA for stylized 3D cartoon characters with vibrant colors and exaggerated proportions, built for games and animation assets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/cartoon-costume-sets": { "id": "scenario/flux-1/cartoon-costume-sets", "name": "Cartoon Costume Sets", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-11-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cartoon Costume Sets is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA that renders complete costume sets as flat-lay collections, from medieval armor to sci-fi gear, in muted, semi-realistic tones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/cartoons-objects-2-0": { "id": "scenario/flux-1/cartoons-objects-2-0", "name": "Cartoons Objects 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cartoons Objects 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA that turns everyday objects into cartoon characters with expressive faces, wide eyes, and vibrant colors. Furniture, food, tools, and more. The house article says it was trained for: Producing animated, personality-filled object designs for playful creative projects.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/casual-environments-2-0": { "id": "scenario/flux-1/casual-environments-2-0", "name": "Casual Environments 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Casual Environments 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for stylized 2D environments with a warm, bright palette. Generates whimsical landscapes, interiors, and outdoor scenes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/chinese-fairytale": { "id": "scenario/flux-1/chinese-fairytale", "name": "Chinese Fairytale", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese Fairytale is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that generates traditional Chinese fairytale art: whimsical characters, mythical creatures, and vivid watercolor illustration style.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/cinematic-realism": { "id": "scenario/flux-1/cinematic-realism", "name": "Cinematic Realism", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-03-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is civitai. Scenario maintains it as a platform model. This catalog did not re-check the upstream weight page.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cinematic Realism is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that applies film-grade lighting, realistic materials, and dramatic compositions to generate cinematic, high-end visuals. The house article says it was trained for: Generating highly detailed, photorealistic imagery with cinematic lighting and film-inspired atmosphere across diverse subjects.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/classic-french-roast-2-0": { "id": "scenario/flux-1/classic-french-roast-2-0", "name": "Classic French Roast 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Classic French Roast 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "This Flux LoRA captures the vintage Franco-Belgian bandes dessinees style: bold outlines, exaggerated features, and expressive characters in classic comic form. The house article says it was trained for: Producing bandes dessin\u00e9es\u2013style illustrations that capture the charm and storytelling flair of classic European comics.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/colorful-digital-icons": { "id": "scenario/flux-1/colorful-digital-icons", "name": "Colorful Digital Icons", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Colorful Digital Icons is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that generates vibrant, whimsical digital icons with bold outlines. Great for game props like potions, shields, crowns, and fantasy items. The house article says it was trained for: Producing vibrant, decorative icons for fantasy games, RPGs, and rich user interfaces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/coloring-book": { "id": "scenario/flux-1/coloring-book", "name": "Coloring Book", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Coloring Book is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders clean black-and-white line art in a coloring book style. Works on humans, vehicles, and animals. The house article says it was trained for: Generating print-ready, outlined illustrations for coloring books and other black-and-white line art products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/comic-adventures": { "id": "scenario/flux-1/comic-adventures", "name": "Comic Adventures", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Comic Adventures is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA trained on vibrant comic book art. Generates bold characters, mythical creatures, fantasy landscapes, and sci-fi scenes with a hand-drawn look.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/creepy-style-character-icons-2-0": { "id": "scenario/flux-1/creepy-style-character-icons-2-0", "name": "Creepy Style Character Icons 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Creepy Style Character Icons 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA for colorful, eerie character icons. Generates creatures and characters with bold expressions, themed costumes, and clean solid-color backgrounds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/cybernetic-textures-2-0": { "id": "scenario/flux-1/cybernetic-textures-2-0", "name": "Cybernetic Textures 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-01-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cybernetic Textures 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for seamless cybernetic textures: neon circuit panels, metallic hex tiles, and glowing geometric surfaces for sci-fi environments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/dark-anime-2-0": { "id": "scenario/flux-1/dark-anime-2-0", "name": "Dark Anime 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dark Anime 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for dark anime imagery: mystical landscapes, eerie lighting, shadowed figures, and a haunting cinematic atmosphere. The house article says it was trained for: Generating dark, atmospheric anime character art and backgrounds for moody or supernatural themes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/dark-inked-characters-2-0": { "id": "scenario/flux-1/dark-inked-characters-2-0", "name": "Dark Inked Characters 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dark Inked Characters 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA for stylized dark fantasy characters: inked, painterly, high-contrast, in dynamic poses with flowing garments and bold pen-and-ink work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/decker": { "id": "scenario/flux-1/decker", "name": "Decker", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Decker is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA inspired by J.C. Leyendecker, producing vintage fashion portraits with bold shading, confident poses, and refined period illustration style.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/digitally-painted-creatures-2-0": { "id": "scenario/flux-1/digitally-painted-creatures-2-0", "name": "Digitally Painted Creatures 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Digitally Painted Creatures 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for fantasy creature art with a hand-painted look. Generates dragons, mythical beasts, and magical beings with vibrant colors and glowing effects.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/dusty-paperback": { "id": "scenario/flux-1/dusty-paperback", "name": "Dusty Paperback", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dusty Paperback is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that recreates the look of vintage fantasy paperback covers: heroic characters, vibrant colors, dynamic lighting, and nostalgic illustration style. The house article says it was trained for: Creating dramatic, old-school fantasy character and scene art reminiscent of classic book covers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/edgecraft-enhanced-stylistic-rendering": { "id": "scenario/flux-1/edgecraft-enhanced-stylistic-rendering", "name": "EdgeCraft - Enhanced Stylistic Rendering", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-02-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is civitai. Scenario maintains it as a platform model. This catalog did not re-check the upstream weight page.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EdgeCraft - Enhanced Stylistic Rendering is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA that adds bold comic rendering to digital art and anime imagery, producing stylized character designs with realistic detail.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/edgy-comic": { "id": "scenario/flux-1/edgy-comic", "name": "Edgy Comic", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Edgy Comic is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders characters in an indie comic style: heavy ink lines, bold colors, sharp angles, intense expressions, and a rebellious gritty look. The house article says it was trained for: Producing gritty, indie comic-style characters with bold visuals and a rebellious tone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/el-diablo": { "id": "scenario/flux-1/el-diablo", "name": "El Diablo", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-04-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "El Diablo is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "El Diablo is a Flux.1 LoRA for isometric dungeon environments, producing dark medieval ruins with stone arches, skulls, aged stonework, and candlelit atmosphere.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/environment-sprites-2-0": { "id": "scenario/flux-1/environment-sprites-2-0", "name": "Environment Sprites 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Environment Sprites 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA for stylized environment sprites. Generates modular game assets: stone arches, bridges, market stalls, trees, rocks, and greenery for RPG and strategy games.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/epic-ai-paintings": { "id": "scenario/flux-1/epic-ai-paintings", "name": "Epic AI Paintings", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Epic AI Paintings is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for epic fantasy environments: mystical architecture, ancient ruins, grand landscapes, and atmospheric lighting in a digital painting style.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/ethereal-beings": { "id": "scenario/flux-1/ethereal-beings", "name": "Ethereal Beings", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ethereal Beings is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for otherworldly characters: divine figures with halos, wings, and metallic armor blending angelic and futuristic aesthetics. The house article says it was trained for: Designing epic, high-fantasy or sci-fi characters with divine, angelic, and futuristic influences.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/euro-comix": { "id": "scenario/flux-1/euro-comix", "name": "Euro Comix", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Euro Comix is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA capturing classic indie European comic style: thick black inks, bold flat colors, and expressive characters with slightly skewed anatomy. The house article says it was trained for: Producing hand-drawn, European comic-style illustrations with bold colors and expressive character work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/expressive-comic-characters": { "id": "scenario/flux-1/expressive-comic-characters", "name": "Expressive Comic Characters", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Expressive Comic Characters is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for hand-drawn comic characters with bold outlines, saturated colors, and exaggerated expressions. Covers warriors, mages, pirates, and fantasy archetypes. The house article says it was trained for: Creating personality-rich comic characters with expressive and dramatic features.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/fantasy-blades": { "id": "scenario/flux-1/fantasy-blades", "name": "Fantasy Blades", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fantasy Blades is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for fantasy melee weapons: swords and axes with exaggerated silhouettes, gold and silver metalwork, intricate engravings, and medieval ornamentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/flux-retro-aesthetics": { "id": "scenario/flux-1/flux-retro-aesthetics", "name": "Flux Retro Aesthetics", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-01-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flux Retro Aesthetics is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for retro pixel art visuals. Generates colorful, detailed scenes spanning fantasy and sci-fi themes, from wizards to space battles.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/future-war": { "id": "scenario/flux-1/future-war", "name": "Future War", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Future War is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for grimdark futuristic soldiers in heavy armor, chunky weapons, and bold red and gold color schemes against dark, dramatic backdrops. The house article says it was trained for: Generating heavily armed, dark sci-fi soldier characters for intense, militaristic settings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/game-backgrounds-iii": { "id": "scenario/flux-1/game-backgrounds-iii", "name": "Game Backgrounds - III", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Game Backgrounds - III is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that generates stylized game-ready backgrounds with vibrant colors and whimsical architecture, from fantasy villages to sci-fi cityscapes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/game-backgrounds-iv": { "id": "scenario/flux-1/game-backgrounds-iv", "name": "Game Backgrounds - IV", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Game Backgrounds - IV is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for stylized game backgrounds spanning fantasy dungeons, sci-fi cities, haunted mansions, volcanic landscapes, and more with vivid 3D-cartoon rendering. The house article says it was trained for: Producing atmospheric, detailed background art adaptable to different game genres.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/game-ui-essentials-2-0": { "id": "scenario/flux-1/game-ui-essentials-2-0", "name": "Game UI Essentials 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Game UI Essentials 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for casual game UI assets: buttons, badges, icons, and signs in a vibrant cartoon style with bold colors and glowing effects. The house article says it was trained for: Creating colorful, cohesive UI components for casual and mobile games, including buttons, icons, and status indicators.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/greater-tiddlewick": { "id": "scenario/flux-1/greater-tiddlewick", "name": "Greater Tiddlewick", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-05-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Greater Tiddlewick is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA for whimsical olde English fantasy towns, bringing crooked chimneys, mossy stone walls, cobbled streets, and storybook village charm to your images. The house article says it was trained for: Crafting cozy villages, bustling market squares, and meandering streets with a fairytale atmosphere.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/gritty-comics-2-0": { "id": "scenario/flux-1/gritty-comics-2-0", "name": "Gritty Comics 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gritty Comics 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for dark, dystopian comic art. Renders rugged characters in worn armor against crumbling cityscapes with bold, gritty linework.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/gwendolyn-2-0": { "id": "scenario/flux-1/gwendolyn-2-0", "name": "Gwendolyn 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gwendolyn 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA for Gwendolyn, a red-haired character in a comic cartoon style. Generates consistent portraits across varied settings and scenes. The house article says it was trained for: Producing cohesive, expressive depictions of a recurring red-haired female character.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/hand-drawn-fantasy": { "id": "scenario/flux-1/hand-drawn-fantasy", "name": "Hand-drawn Fantasy", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hand-drawn Fantasy is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for hand-drawn high fantasy art: bold linework, muted colors, whimsical characters, magical artifacts, and landscapes. The house article says it was trained for: Producing enchanting, hand-drawn fantasy worlds for books, games, and tabletop adventures.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/hand-painted-textures": { "id": "scenario/flux-1/hand-painted-textures", "name": "Hand-Painted Textures", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-01-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hand-Painted Textures is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for seamless cartoon-style game textures. Generates cel-shaded wood, stone, earth, brick, tile, and organic surfaces with bold outlines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/hand-painted-textures-2-0": { "id": "scenario/flux-1/hand-painted-textures-2-0", "name": "Hand Painted Textures 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-05-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hand Painted Textures 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA for seamless, hand-painted textures. Generates stylized wood, stone, and organic patterns ideal for game assets and animations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/heartcore": { "id": "scenario/flux-1/heartcore", "name": "Heartcore", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-04-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Heartcore is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for stylish anime female characters. Generates confident figures in elaborate outfits, vibrant colors, and detailed accessories. The house article says it was trained for: Creating refined, visually striking anime female characters that combine beauty, charm, and sophistication.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/heroine-x-zombie-2-0-dual-characters-model": { "id": "scenario/flux-1/heroine-x-zombie-2-0-dual-characters-model", "name": "Heroine x Zombie 2.0 (Dual Characters Model)", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Heroine x Zombie 2.0 (Dual Characters Model) is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA featuring two paired characters: a red-haired heroine in a tactical suit and a green zombie. Keeps each character visually distinct in shared scenes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/human-interface-2-0": { "id": "scenario/flux-1/human-interface-2-0", "name": "Human Interface 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Human Interface 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for game UI assets. Generates buttons, HUDs, shop screens, inventory panels, and character stat menus in sci-fi or fantasy styles. The house article says it was trained for: Designing cohesive, immersive game UI elements and interface layouts that align with specific genre aesthetics.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/hyper-casual-characters-2-0": { "id": "scenario/flux-1/hyper-casual-characters-2-0", "name": "Hyper Casual Characters 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hyper Casual Characters 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA that generates full-body chibi game characters with bold outlines and vibrant colors. Knights, ninjas, pirates, robots, and more on clean backgrounds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/hyper-realistic-flux": { "id": "scenario/flux-1/hyper-realistic-flux", "name": "Hyper Realistic Flux", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-04-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is huggingface. Scenario maintains it as a platform model. This catalog did not re-check the upstream weight page.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hyper Realistic Flux is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for hyperrealistic images with cinematic lighting and natural textures. Supports text-to-image, inpainting, and outpainting. The house article says it was trained for: Creating visually compelling, hyperrealistic images with a balance of cinematic style and authentic textural detail.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/i-like-big-bots": { "id": "scenario/flux-1/i-like-big-bots", "name": "I Like Big Bots", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-04-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "I Like Big Bots is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for towering mecha designs. Generates giant robots with angular armor, dynamic action poses, and the bold aesthetic of Transformers and Gundam. The house article says it was trained for: Producing dynamic, imposing mecha characters with intricate mechanical design and dramatic visual impact.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/imaginative-isometrics-2-0": { "id": "scenario/flux-1/imaginative-isometrics-2-0", "name": "Imaginative Isometrics 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Imaginative Isometrics 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA for whimsical isometric architecture: fantasy buildings, castles, towers, and sci-fi structures rendered with clean lines and vivid colors. The house article says it was trained for: Designing richly detailed, imaginative isometric buildings that balance fantasy charm with functional game asset design.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/isometric-background": { "id": "scenario/flux-1/isometric-background", "name": "Isometric Background", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-12-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Isometric Background is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA for storybook-style isometric backgrounds. Generates warm, detailed fantasy scenes: villages, forests, medieval towns, and architectural environments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/juicy-by-deacon": { "id": "scenario/flux-1/juicy-by-deacon", "name": "Juicy by Deacon", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Juicy by Deacon is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA for vibrant anime and K-pop character art, featuring colorful outfits, dynamic poses, and a bold fantasy aesthetic. The house article says it was trained for: Creating colorful, high-energy character art inspired by anime and contemporary fashion trends.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/juicy-icons-2-0": { "id": "scenario/flux-1/juicy-icons-2-0", "name": "Juicy Icons 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Juicy Icons 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA for stylized 3D game icons. Generates isolated items or grid layouts with simplified, glossy details on dark backgrounds. The house article says it was trained for: Generating polished, game-ready object icons for puzzle games, match-3 mechanics, and casual mobile interfaces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/juno": { "id": "scenario/flux-1/juno", "name": "Juno", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Juno is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for a specific anime character: short girl in an oversize yellow bomber jacket, blue pleated skirt, black tights, and a pixie cut with a blue bow. The house article says it was trained for: Creating expressive, dynamic anime character art centered on the recurring persona of Juno.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/kawaii": { "id": "scenario/flux-1/kawaii", "name": "Kawaii", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kawaii is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "This Flux LoRA generates chibi-style characters with oversized eyes, bold colors, and clean cell-shading. Ideal for whimsical outfits and fantasy motifs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/legendary-tcg-characters-2-0": { "id": "scenario/flux-1/legendary-tcg-characters-2-0", "name": "Legendary TCG Characters 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Legendary TCG Characters 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA trained on high-fantasy TCG card art. Produces richly detailed warriors, sorceresses, and mythical characters with bold colors and magical effects.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/lineart-textures": { "id": "scenario/flux-1/lineart-textures", "name": "Lineart Textures", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-01-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lineart Textures is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that generates black-and-white lineart textures: seamless patterns, architectural sketches, botanical designs, and hand-drawn surfaces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/luminous-depths": { "id": "scenario/flux-1/luminous-depths", "name": "Luminous Depths", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Luminous Depths is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for cinematic environments with deep contrast and dramatic shadows, specializing in abandoned urban decay, overgrown ruins, and dark tunnels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/midsummer-blues": { "id": "scenario/flux-1/midsummer-blues", "name": "Midsummer Blues", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Midsummer Blues is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA with a modern illustrative style. Produces bold, vibrant scenes including cafes, street views, and whimsical animals with clean, crisp lines. The house article says it was trained for: Producing lively, modern illustrations with a playful and whimsical tone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/minimalism": { "id": "scenario/flux-1/minimalism", "name": "Minimalism", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Minimalism is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders characters, animals, and objects in a minimalist vector style with clean geometric shapes, flat colors, and simple contrasts. The house article says it was trained for: Creating sleek, flat-color illustrations with a minimalist and contemporary design sensibility.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/minimalist-3d-food-icons": { "id": "scenario/flux-1/minimalist-3d-food-icons", "name": "Minimalist 3D Food Icons", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Minimalist 3D Food Icons is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for generating minimalist 3D food icons with vibrant colors, smooth shapes, and soft lighting. Ideal for UI, app, and digital product design. The house article says it was trained for: Producing clean, modern 3D food icons for branding, UI, or digital illustration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/minimalist-3d-props-2-0": { "id": "scenario/flux-1/minimalist-3d-props-2-0", "name": "Minimalist 3D Props 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Minimalist 3D Props 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders game props and objects in a minimalist 3D cartoon style, with clean shapes, smooth textures, and solid color backgrounds. The house article says it was trained for: Creating stylized, minimal 3D props for games, UI, and visual design projects.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/minimalist-fantasy-chibis": { "id": "scenario/flux-1/minimalist-fantasy-chibis", "name": "Minimalist Fantasy Chibis", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Minimalist Fantasy Chibis is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA for minimalist fantasy chibi characters in a flat vector style, with vibrant armor, expressive faces, and classic archetypes like knights, druids, and wizards.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/minimalist-icons": { "id": "scenario/flux-1/minimalist-icons", "name": "Minimalist Icons", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Minimalist Icons is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that generates minimalist app icons with soft color gradients, rounded edges, and clean shapes on a white background. The house article says it was trained for: Designing sleek, minimal icons suitable for modern UI/UX and app interfaces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/modern-animated-fantasy-creatures-2-0": { "id": "scenario/flux-1/modern-animated-fantasy-creatures-2-0", "name": "Modern Animated Fantasy Creatures 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Modern Animated Fantasy Creatures 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA for fantasy characters and creatures in a modern animated style, with smooth brushstrokes, soft shading, and dynamic lighting on black backgrounds. The house article says it was trained for: Producing animated-style fantasy creatures suitable for games, storytelling, or collectible art.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/natures-bounty": { "id": "scenario/flux-1/natures-bounty", "name": "Natures Bounty", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Natures Bounty is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for lush, painterly nature scenes. Generates landscapes with vibrant flora, mountains, forests, and warm soft lighting in a whimsical illustrated style.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/neo3d-realism": { "id": "scenario/flux-1/neo3d-realism", "name": "Neo3D Realism", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-02-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is civitai. Scenario maintains it as a platform model. This catalog did not re-check the upstream weight page.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Neo3D Realism is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for 3D-style renders. Generates detailed figurines, props, game collectibles, and sculptures with realistic textures and lighting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/northern-graphite": { "id": "scenario/flux-1/northern-graphite", "name": "Northern Graphite", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Northern Graphite is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA rendering black-and-white graphite pencil drawings of fantasy characters in cold Nordic settings, with detailed linework and a grainy texture. The house article says it was trained for: Producing detailed black-and-white fantasy character illustrations with an authentic graphite sketch style.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/old-pastel": { "id": "scenario/flux-1/old-pastel", "name": "Old Pastel", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Old Pastel is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders dreamy, pastel-infused illustrations with a nostalgic, romantic feel and vintage storybook charm. The house article says it was trained for: Producing vintage-inspired pastel artworks that feel timeless, warm, and full of storybook charm.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/overworld-maps": { "id": "scenario/flux-1/overworld-maps", "name": "Overworld Maps", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-04-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Overworld Maps is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for generating painterly fantasy overworld maps with colorful biomes, soft edges, and stylized terrain from an aerial perspective. The house article says it was trained for: Designing immersive fantasy overworld maps rich in visual storytelling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/painterly-scenes": { "id": "scenario/flux-1/painterly-scenes", "name": "Painterly Scenes", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Painterly Scenes is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for painterly anime scenes: vibrant characters in fantasy settings with bold colors, dramatic lighting, and a stylized storybook quality. The house article says it was trained for: Creating richly colored, painterly backgrounds that enhance visual storytelling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/plaster-concrete-textures": { "id": "scenario/flux-1/plaster-concrete-textures", "name": "Plaster & Concrete Textures", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-01-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Plaster & Concrete Textures is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for realistic plaster and concrete textures: smooth slabs, aged walls, and cracked surfaces ready for architectural and 3D environments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/plaster-walls-textures": { "id": "scenario/flux-1/plaster-walls-textures", "name": "Plaster Walls Textures", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Plaster Walls Textures is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for seamless plaster and concrete wall textures: smooth, rough, cracked, and weathered finishes ready for 3D and game environments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/plastic-fantastic": { "id": "scenario/flux-1/plastic-fantastic", "name": "Plastic Fantastic", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Plastic Fantastic is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders stylized characters with glossy, plastic textures and bold anime-inflected looks. Great for vivid game art figures. The house article says it was trained for: Creating bold, glossy, stylized characters that balance realistic rendering with playful proportions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/platformer-props": { "id": "scenario/flux-1/platformer-props", "name": "Platformer Props", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Platformer Props is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for 2D game props: generates sprite-sheet-style sets of whimsical foliage, fences, arches, and rustic outdoor elements in a unified illustrated style. The house article says it was trained for: Designing cohesive environmental props for platformer or top-down games.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/poster-gouache": { "id": "scenario/flux-1/poster-gouache", "name": "Poster Gouache", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Poster Gouache is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA that renders images in a retro gouache style, with painterly textures, mid-century characters, vintage fashion, and vibrant fantasy scenes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/puffy-icons-3-0": { "id": "scenario/flux-1/puffy-icons-3-0", "name": "Puffy Icons 3.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Puffy Icons 3.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA that produces glossy, 3D icons with rounded, puffy shapes. Use it for vibrant UI elements, game assets, and toy-style characters with bold proportions. The house article says it was trained for: Creating polished, glossy 3D icons for approachable, modern game and app interfaces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/rad-racers-2-0": { "id": "scenario/flux-1/rad-racers-2-0", "name": "Rad Racers 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rad Racers 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "This Flux LoRA generates 3D cartoon vehicles with retro silhouettes, matte textures, pastel tones, and playful details like oversized wheels and chunky proportions. The house article says it was trained for: Creating playful, stylized miniature vehicles with a fun, collectible look.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/realistic-textures-2-0": { "id": "scenario/flux-1/realistic-textures-2-0", "name": "Realistic Textures 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-12-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Realistic Textures 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "This Flux LoRA generates seamless, tileable textures: stone, metal, wood, fabric, lava, and more. Optimized for PBR workflows in gaming and 3D production.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/realistic-textures-3-0": { "id": "scenario/flux-1/realistic-textures-3-0", "name": "Realistic Textures 3.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-05-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Realistic Textures 3.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA for seamless, lifelike textures: stone, metal, wood, and fabric. Generates detailed organic patterns for 3D assets and digital art.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/red-haired-superheroine": { "id": "scenario/flux-1/red-haired-superheroine", "name": "Red-haired Superheroine", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Red-haired Superheroine is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "This Flux.1 LoRA generates a 3D animated superheroine with messy red hair and blue eyes, navy tactical suits, silver armor, and dark crimson capes. The house article says it was trained for: Creating consistent, high-quality images of a distinctive red-haired superheroine in dynamic, action-ready scenarios.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/rochelle-2-0": { "id": "scenario/flux-1/rochelle-2-0", "name": "Rochelle 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rochelle 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA generating a cyberpunk woman character with short dark hair, mechanical headphones, and tactical armor in neon purple and orange. The house article says it was trained for: Producing consistent, high-quality images of a futuristic female character with cyberpunk aesthetics.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/rpg-avatars": { "id": "scenario/flux-1/rpg-avatars", "name": "RPG Avatars", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-04-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RPG Avatars is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "RPG Avatars is a Flux.1 LoRA for painterly, semi-realistic character portraits. Fantasy and noir styles, cinematic lighting, detailed costumes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/rpg-character-trait-avatars-2-0": { "id": "scenario/flux-1/rpg-character-trait-avatars-2-0", "name": "RPG Character Trait Avatars 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RPG Character Trait Avatars 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA for RPG character portraits in a painterly fantasy style. Renders diverse races, textured armor, and expressive faces with strong detail.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/sacred-geometry": { "id": "scenario/flux-1/sacred-geometry", "name": "Sacred Geometry", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-05-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sacred Geometry is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA for gold-on-dark geometric illustrations: tarot-style figures, art deco linework, and intricate sacred geometry patterns. The house article says it was trained for: Creating sophisticated, ornamental visuals with a mystical or spiritual tone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/saturday-morning": { "id": "scenario/flux-1/saturday-morning", "name": "Saturday Morning", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Saturday Morning is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for Saturday-morning cartoon style: chibi fantasy characters with bold flat colors, super-deformed proportions, and dynamic weapon poses. The house article says it was trained for: Producing cartoon-style characters and scenes with nostalgic, colorful appeal.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/simple-vector-v2": { "id": "scenario/flux-1/simple-vector-v2", "name": "Simple Vector v2", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Simple Vector v2 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for clean, flat vector illustrations. Produces characters, objects, and business scenes in a modern minimalist style. Trigger: v3ct0r. The house article says it was trained for: Creating modern, clean, and versatile vector illustrations suitable for characters, objects, and scenes across multiple applications.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/sketch-paint-flux": { "id": "scenario/flux-1/sketch-paint-flux", "name": "Sketch Paint Flux", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-01-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is civitai. Scenario maintains it as a platform model. This catalog did not re-check the upstream weight page.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sketch Paint Flux is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that merges sketch line art with painterly effects across any subject. Use the sk3tchpa1nt trigger for stylized illustration results.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/sleek-3d-assets-2-0": { "id": "scenario/flux-1/sleek-3d-assets-2-0", "name": "Sleek 3D Assets 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sleek 3D Assets 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA for clean, stylized 3D game assets with smooth surfaces and rounded edges. Ideal for props, icons, and collectibles in mobile games or apps. The house article says it was trained for: Generating versatile 3D assets for mobile apps, games, and digital projects requiring clean, stylized props.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/small-warriors": { "id": "scenario/flux-1/small-warriors", "name": "Small Warriors", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Small Warriors is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for fantasy miniature imagery. Generates detailed warriors, knights, and wizards with vivid armor, bold colors, and dynamic poses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/stage-dressing": { "id": "scenario/flux-1/stage-dressing", "name": "Stage Dressing", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stage Dressing is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for painterly fantasy environments: ancient ruins, stone arenas, and dramatic outdoor scenes with rich lighting and vibrant colors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/sticker-icons-2-0": { "id": "scenario/flux-1/sticker-icons-2-0", "name": "Sticker Icons 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sticker Icons 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "This Flux.1 LoRA generates bold sticker-style game icons with clean outlines and vibrant colors. Great for cartoonish objects, props, and casual game assets. The house article says it was trained for: Producing eye-catching, outlined sticker icons for games, collectibles, and playful digital designs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/street-punks": { "id": "scenario/flux-1/street-punks", "name": "Street Punks", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Street Punks is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for gritty, futuristic street characters. Generates cyberpunk figures with tactical outfits, cybernetic parts, and vivid urban color palettes. The house article says it was trained for: Creating edgy, futuristic urban characters with strong punk and cyberpunk influences.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/striking-visage": { "id": "scenario/flux-1/striking-visage", "name": "Striking Visage", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Striking Visage is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA for stylized 3D fantasy characters. Generates armored warriors with sculpted detail, expressive faces, and dragon companions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/stylized-3d": { "id": "scenario/flux-1/stylized-3d", "name": "Stylized 3D", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-02-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is civitai. Scenario maintains it as a platform model. This catalog did not re-check the upstream weight page.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stylized 3D is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders subjects in a stylized 3D look with rich materials, soft lighting, and a polished, slightly exaggerated finish.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/stylized-game-icons-props": { "id": "scenario/flux-1/stylized-game-icons-props", "name": "Stylized Game Icons & Props", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-06-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stylized Game Icons & Props is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for 2D game icons in a polished cartoon style: medieval weapons, potions, and props with bold outlines and clean silhouettes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/super-top-down-2-0": { "id": "scenario/flux-1/super-top-down-2-0", "name": "Super Top-Down 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Super Top-Down 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA for classic top-down RPG environments. Generates tiled outdoor worlds, villages, dungeons, and stone courtyards in a retro pixel game style. The house article says it was trained for: Generating nostalgic, game-map style environments with a classic top-down view.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/swiss-cobblestone-textures": { "id": "scenario/flux-1/swiss-cobblestone-textures", "name": "Swiss Cobblestone Textures", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Swiss Cobblestone Textures is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for seamless Swiss cobblestone textures. Generates stone pathways and surfaces with weathered, mossy, or polished finishes, ready for 3D use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/textures-brick-walls": { "id": "scenario/flux-1/textures-brick-walls", "name": "Textures - Brick Walls", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Textures - Brick Walls is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA for generating realistic brick wall textures, from classic red and whitewashed styles to weathered, aged, and mossy surfaces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/textures-concrete-surfaces": { "id": "scenario/flux-1/textures-concrete-surfaces", "name": "Textures - Concrete Surfaces", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-01-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Textures - Concrete Surfaces is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for realistic, seamless concrete surface textures. Produces polished, weathered, or cracked styles for architectural and urban scenes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/textures-floors": { "id": "scenario/flux-1/textures-floors", "name": "Textures - Floors", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-01-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Textures - Floors is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "This Flux.1 LoRA generates seamless flooring textures: tiles, stone, cobblestone, wood planks, herringbone patterns, and natural ground surfaces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/textures-marble-surfaces": { "id": "scenario/flux-1/textures-marble-surfaces", "name": "Textures - Marble Surfaces", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Textures - Marble Surfaces is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for realistic marble and stone surface textures: natural veining, polished finishes, seamless tiling, and PBR-ready output across a wide color range.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/textures-stone-walls": { "id": "scenario/flux-1/textures-stone-walls", "name": "Textures - Stone Walls", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-12-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Textures - Stone Walls is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA for realistic stone wall textures. Covers ancient cobblestone, dry-stacked fieldstone, and modern rectangular designs with seamless, PBR-ready output.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/textures-terracotta-roofs": { "id": "scenario/flux-1/textures-terracotta-roofs", "name": "Textures - Terracotta Roofs", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Textures - Terracotta Roofs is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA for photorealistic, seamless terracotta roof textures. Generates clean or weathered clay tiles in earthy tones, ready for 3D and game pipelines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/textures-wood-boards": { "id": "scenario/flux-1/textures-wood-boards", "name": "Textures - Wood Boards", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-01-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Textures - Wood Boards is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for realistic wood board textures, from polished hardwood planks to mossy weathered boards. Tileable patterns ready for game and 3D pipelines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/the-green-skinned-zombie": { "id": "scenario/flux-1/the-green-skinned-zombie", "name": "The Green-skinned Zombie", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Green-skinned Zombie is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "This Flux LoRA generates a cartoonish green-skinned zombie with yellow eyes, placing the character in everyday and adventurous scenes with a playful, humorous tone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/the-hidden-vale": { "id": "scenario/flux-1/the-hidden-vale", "name": "The Hidden Vale", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Hidden Vale is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA that generates stylized fantasy village scenes with Asian-inspired architecture, perfect for fairytale worlds and game storytelling. The house article says it was trained for: Creating fairytale-like village scenes with cultural and magical charm.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/the-housing-market": { "id": "scenario/flux-1/the-housing-market", "name": "The Housing Market", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-05-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Housing Market is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that renders buildings and facades in a charming, storybook style, from cozy village cottages to small shops, with vivid details and warm color palettes. The house article says it was trained for: Producing creative and quirky architectural designs spanning multiple eras.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/the-jolly-guy": { "id": "scenario/flux-1/the-jolly-guy", "name": "The Jolly Guy", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Jolly Guy is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA featuring a jolly cartoon Santa character in a classic red suit. Generates bold, stylized 3D cartoon holiday figures in varied poses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/the-revengers": { "id": "scenario/flux-1/the-revengers", "name": "The Revengers", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-04-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Revengers is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for parody superhero comic art. Generates capes, masks, and bold costumes with vibrant colors, dynamic poses, and a playful comic-book style. The house article says it was trained for: Creating flamboyant, parody-style superhero characters with vibrant costumes and exaggerated comic flair.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/the-sorceress": { "id": "scenario/flux-1/the-sorceress", "name": "The Sorceress", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Sorceress is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA rendering a 3D cartoon sorceress with white hair and blue robes across fantasy scenes: enchanted forests, magical kitchens, and spell-casting moments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/the-tiefling-woman": { "id": "scenario/flux-1/the-tiefling-woman", "name": "The Tiefling Woman", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Tiefling Woman is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA trained on a Tiefling woman character in whimsical 3D style, with curved horns, expressive eyes, and arcane magical elements. The house article says it was trained for: Generating imaginative, stylized fantasy character art focused on a recurring Tiefling heroine.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/the-usual-suspects": { "id": "scenario/flux-1/the-usual-suspects", "name": "The Usual Suspects", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Usual Suspects is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for 3D cartoon RPG enemies. Produces stylized monsters, orcs, goblins, and skeletons with glowing eyes, bold colors, and simple display bases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/top-down-td-game": { "id": "scenario/flux-1/top-down-td-game", "name": "Top-down TD Game", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Top-down TD Game is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA for top-down tower defense maps. Generates stylized 2D landscapes, forests, deserts, caves, and volcanic zones with clear paths and terrain. The house article says it was trained for: Creating vibrant, functional top-down environments for mobile and casual strategy games.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/toy-box-by-renderartist": { "id": "scenario/flux-1/toy-box-by-renderartist", "name": "Toy Box (by RenderArtist)", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Toy Box (by RenderArtist) is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA that renders characters and objects in a rounded, colorful 3D toy style. Use the trigger token t0yb0x to activate the look. The house article says it was trained for: Generating cute, colorful 3D characters and props with a toy-like aesthetic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/ui-frame-generator-2-0": { "id": "scenario/flux-1/ui-frame-generator-2-0", "name": "UI Frame Generator 2.0", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UI Frame Generator 2.0 is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "This Flux.1 LoRA generates themed UI frames for mobile games, producing ornate borders for menus and dialogue boxes across fantasy, steampunk, and nature styles. The house article says it was trained for: Producing richly detailed, themed borders and frame elements for game interfaces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/vfx-hits": { "id": "scenario/flux-1/vfx-hits", "name": "VFX Hits", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VFX Hits is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for game VFX: generates vibrant explosions, fireballs, energy flashes, and glowing impact hits against dark backgrounds. The house article says it was trained for: Producing colorful, cinematic visual effects for action games, fantasy sequences, and dramatic animated moments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/vibrant-comic": { "id": "scenario/flux-1/vibrant-comic", "name": "Vibrant Comic", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vibrant Comic is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA that renders crisp line art with bold, vibrant colors. Produces stylized characters and dynamic comic compositions with energetic imagery. The house article says it was trained for: Creating dynamic, vividly colored comic illustrations with sharp lines and energetic compositions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/vibrant-heroics": { "id": "scenario/flux-1/vibrant-heroics", "name": "Vibrant Heroics", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vibrant Heroics is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for fantasy hero art: warriors and sorcerers rendered with ornate armor, vivid colors, and dramatic high-contrast lighting. The house article says it was trained for: Designing bold, richly detailed fantasy and sci-fi hero characters with dramatic presence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/vibrant-storybook-illustrations": { "id": "scenario/flux-1/vibrant-storybook-illustrations", "name": "Vibrant Storybook Illustrations", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vibrant Storybook Illustrations is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA that renders fantasy worlds, props, and environments in a vibrant storybook style with clean lines and saturated colors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/vibrant-surreal-landscapes": { "id": "scenario/flux-1/vibrant-surreal-landscapes", "name": "Vibrant Surreal Landscapes", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-11-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vibrant Surreal Landscapes is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA for glossy, candy-colored surreal landscapes. Generates dripping liquids, swirling textures, and vibrant floral scenes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-1/wood-carving-textures": { "id": "scenario/flux-1/wood-carving-textures", "name": "Wood Carving Textures", "maker": "scenario", "family": "scenario/flux-1", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-12-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.1 LoRA hosted by Scenario. Public type is flux.1-lora. Compliant base ids on the record: flux.1-dev, flux.1-schnell. The migration article says new training on Flux.1 Dev is still in the picker but not recommended, and that existing Flux.1 models still run inference. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wood Carving Textures is a Scenario house image model (flux.1-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA for generating detailed wood carving textures: swirling floral vines, leaf motifs, and ornamental relief patterns in rich warm brown tones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-2/3d-platformer-environments-2-0": { "id": "scenario/flux-2/3d-platformer-environments-2-0", "name": "3D Platformer Environments 2.0", "maker": "scenario", "family": "scenario/flux-2", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2026-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.2 Dev LoRA trained on Scenario. Public type is flux.2-dev-lora. Training baseModelId: flux.2-dev. The public record source is scenario.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "3D Platformer Environments 2.0 is a Scenario house image model (flux.2-dev-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux 2 LoRA for 3D low-poly environments suited to platformers and adventure games, with geometric shapes, vibrant colors, and soft lighting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-2/cartoon-costume-sets-2-0": { "id": "scenario/flux-2/cartoon-costume-sets-2-0", "name": "Cartoon Costume Sets 2.0", "maker": "scenario", "family": "scenario/flux-2", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2026-03-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.2 Dev LoRA trained on Scenario. Public type is flux.2-dev-lora. Training baseModelId: flux.2-dev. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cartoon Costume Sets 2.0 is a Scenario house image model (flux.2-dev-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.2 LoRA for stylized 3D costume sets. Renders outfit collections and equipment on invisible figures with detailed textures and studio lighting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-2/gem-icons-v2": { "id": "scenario/flux-2/gem-icons-v2", "name": "Gem Icons v2", "maker": "scenario", "family": "scenario/flux-2", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2026-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux.2 Dev LoRA trained on Scenario. Public type is flux.2-dev-lora. Training baseModelId: flux.2-dev. The public record source is scenario.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gem Icons v2 is a Scenario house image model (flux.2-dev-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.2 LoRA for stylized game icons: gemstones, crystals, and enchanted loot rendered with vibrant colors and polished digital painting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-composition/2d-vale": { "id": "scenario/flux-composition/2d-vale", "name": "2D Vale", "maker": "scenario", "family": "scenario/flux-composition", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-05-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed Flux.1 multi-LoRA. Public type is flux.1-composition. Compliant base ids: flux.1-dev, flux.1-schnell. Scenario's compose guide says composition is for Flux.1 LoRAs. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "2D Vale is a Scenario house image model (flux.1-composition). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for 2D stylized buildings: curved roofs, flower boxes, and fairytale village scenes tuned for game concept art.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-composition/altered-cabron": { "id": "scenario/flux-composition/altered-cabron", "name": "Altered Cabr\u00f3n", "maker": "scenario", "family": "scenario/flux-composition", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed Flux.1 multi-LoRA. Public type is flux.1-composition. Compliant base ids: flux.1-dev, flux.1-schnell. Scenario's compose guide says composition is for Flux.1 LoRAs. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Altered Cabr\u00f3n is a Scenario house image model (flux.1-composition). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA that generates characters blending cybernetic and organic forms, producing futuristic hybrid figures with intricate technological and biological detail.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-composition/buildings-of-the-vale": { "id": "scenario/flux-composition/buildings-of-the-vale", "name": "Buildings of The Vale", "maker": "scenario", "family": "scenario/flux-composition", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed Flux.1 multi-LoRA. Public type is flux.1-composition. Compliant base ids: flux.1-dev, flux.1-schnell. Scenario's compose guide says composition is for Flux.1 LoRAs. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Buildings of The Vale is a Scenario house image model (flux.1-composition). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA that generates isometric buildings in a stylized fairytale fantasy aesthetic, ideal for game development asset pipelines. The house article says it was trained for: Producing stylized, fairytale-inspired isometric buildings for fantasy worldbuilding, games, and illustrated narratives.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-composition/childs-play": { "id": "scenario/flux-composition/childs-play", "name": "Childs Play", "maker": "scenario", "family": "scenario/flux-composition", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed Flux.1 multi-LoRA. Public type is flux.1-composition. Compliant base ids: flux.1-dev, flux.1-schnell. Scenario's compose guide says composition is for Flux.1 LoRAs. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Childs Play is a Scenario house image model (flux.1-composition). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA that renders children's book illustrations with thick outline art and bold, flat colors across any subject.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-composition/cutesy-3d": { "id": "scenario/flux-composition/cutesy-3d", "name": "Cutesy 3D", "maker": "scenario", "family": "scenario/flux-composition", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed Flux.1 multi-LoRA. Public type is flux.1-composition. Compliant base ids: flux.1-dev, flux.1-schnell. Scenario's compose guide says composition is for Flux.1 LoRAs. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cutesy 3D is a Scenario house image model (flux.1-composition). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Scenario multi-LoRA for Flux.1 that generates stylized 3D characters: soft rounded shapes, big eyes, and bright colors for a toy-like, whimsical look.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-composition/emojis-designer-2-0": { "id": "scenario/flux-composition/emojis-designer-2-0", "name": "Emojis Designer 2.0", "maker": "scenario", "family": "scenario/flux-composition", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-03-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed Flux.1 multi-LoRA. Public type is flux.1-composition. Compliant base ids: flux.1-dev, flux.1-schnell. Scenario's compose guide says composition is for Flux.1 LoRAs. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emojis Designer 2.0 is a Scenario house image model (flux.1-composition). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Emojis Designer 2.0 generates polished 3D-style emoji and character icons, from expressive animals to fantasy creatures, built on Flux.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-composition/forever-after": { "id": "scenario/flux-composition/forever-after", "name": "Forever After", "maker": "scenario", "family": "scenario/flux-composition", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-05-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed Flux.1 multi-LoRA. Public type is flux.1-composition. Compliant base ids: flux.1-dev, flux.1-schnell. Scenario's compose guide says composition is for Flux.1 LoRAs. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Forever After is a Scenario house image model (flux.1-composition). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Forever After generates fairytale storybook illustrations with soft lines and rich, warm colors in a whimsical animation-inspired style.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-composition/future-fantastic": { "id": "scenario/flux-composition/future-fantastic", "name": "Future Fantastic", "maker": "scenario", "family": "scenario/flux-composition", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed Flux.1 multi-LoRA. Public type is flux.1-composition. Compliant base ids: flux.1-dev, flux.1-schnell. Scenario's compose guide says composition is for Flux.1 LoRAs. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Future Fantastic is a Scenario house image model (flux.1-composition). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux.1 LoRA for cinematic, vibrant sci-fi imagery. Generates futuristic characters in urban environments with rich detail and a modern aesthetic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-composition/juno-multi-loras": { "id": "scenario/flux-composition/juno-multi-loras", "name": "Juno: Multi-LoRAs", "maker": "scenario", "family": "scenario/flux-composition", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed Flux.1 multi-LoRA. Public type is flux.1-composition. Compliant base ids: flux.1-dev, flux.1-schnell. Scenario's compose guide says composition is for Flux.1 LoRAs. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Juno: Multi-LoRAs is a Scenario house image model (flux.1-composition). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA for Juno, a Scenario original character. Generate Juno in chibi, vector, 3D, and stylized anime renders with consistent identity across styles.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-composition/modern-fantasy": { "id": "scenario/flux-composition/modern-fantasy", "name": "Modern Fantasy", "maker": "scenario", "family": "scenario/flux-composition", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed Flux.1 multi-LoRA. Public type is flux.1-composition. Compliant base ids: flux.1-dev, flux.1-schnell. Scenario's compose guide says composition is for Flux.1 LoRAs. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Modern Fantasy is a Scenario house image model (flux.1-composition). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux LoRA for colorful, dynamic fantasy character art. Ideal for RPG and TCG illustrations with vibrant modern fantasy aesthetics.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-composition/rpg-environment": { "id": "scenario/flux-composition/rpg-environment", "name": "RPG Environment", "maker": "scenario", "family": "scenario/flux-composition", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed Flux.1 multi-LoRA. Public type is flux.1-composition. Compliant base ids: flux.1-dev, flux.1-schnell. Scenario's compose guide says composition is for Flux.1 LoRAs. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RPG Environment is a Scenario house image model (flux.1-composition). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "RPG Environment by Scenario. A Flux.1 LoRA for cohesive fantasy game assets: trees, ruins, props, and structures in a stylized top-down or side-scroller style.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-composition/shades-of-gray": { "id": "scenario/flux-composition/shades-of-gray", "name": "Shades of Gray", "maker": "scenario", "family": "scenario/flux-composition", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed Flux.1 multi-LoRA. Public type is flux.1-composition. Compliant base ids: flux.1-dev, flux.1-schnell. Scenario's compose guide says composition is for Flux.1 LoRAs. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Shades of Gray is a Scenario house image model (flux.1-composition). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux multi-LoRA that generates detailed grayscale illustrations with rich textures and dramatic contrasts, evoking pencil and charcoal artistry.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-composition/sleek-3d-characters": { "id": "scenario/flux-composition/sleek-3d-characters", "name": "Sleek 3D Characters", "maker": "scenario", "family": "scenario/flux-composition", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-03-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed Flux.1 multi-LoRA. Public type is flux.1-composition. Compliant base ids: flux.1-dev, flux.1-schnell. Scenario's compose guide says composition is for Flux.1 LoRAs. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sleek 3D Characters is a Scenario house image model (flux.1-composition). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux.1 LoRA for polished, stylized 3D character art. Generates warriors, robots, and wizards with expressive faces and detailed outfits.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-composition/sleek-3d-isometrics": { "id": "scenario/flux-composition/sleek-3d-isometrics", "name": "Sleek 3D Isometrics", "maker": "scenario", "family": "scenario/flux-composition", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2024-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed Flux.1 multi-LoRA. Public type is flux.1-composition. Compliant base ids: flux.1-dev, flux.1-schnell. Scenario's compose guide says composition is for Flux.1 LoRAs. The public record source is scenario.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sleek 3D Isometrics is a Scenario house image model (flux.1-composition). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux LoRA that generates stylized 3D isometric scenes with smooth finishes, sharp lines, and playful details. Great for game assets, villages, and lab environments. The house article says it was trained for: Creating modern, highly polished isometric 3D scenes and assets that combine functional clarity with stylized visual appeal.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-kontext/abandoned-structures-kontext": { "id": "scenario/flux-kontext/abandoned-structures-kontext", "name": "Abandoned Structures - Kontext", "maker": "scenario", "family": "scenario/flux-kontext", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-10-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux Kontext edit LoRA. Public type is flux.1-kontext-lora. Scenario trains this kind of model on before-and-after pairs so it follows an edit instruction. The public record source is scenario.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Abandoned Structures - Kontext is a Scenario house image model (flux.1-kontext-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "A Flux Kontext LoRA that ages clean building images into worn, crumbling ruins, preserving the original structure, layout, and perspective.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-kontext/blockout-to-render-kontext": { "id": "scenario/flux-kontext/blockout-to-render-kontext", "name": "Blockout to Render - Kontext", "maker": "scenario", "family": "scenario/flux-kontext", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-10-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux Kontext edit LoRA. Public type is flux.1-kontext-lora. Scenario trains this kind of model on before-and-after pairs so it follows an edit instruction. The public record source is scenario.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Blockout to Render - Kontext is a Scenario house image model (flux.1-kontext-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux Kontext LoRA that transforms 3D blockout shapes into photorealistic renders, preserving the original geometry and spatial layout.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-kontext/character-turnaround-kontext": { "id": "scenario/flux-kontext/character-turnaround-kontext", "name": "Character Turnaround - Kontext", "maker": "scenario", "family": "scenario/flux-kontext", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-10-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux Kontext edit LoRA. Public type is flux.1-kontext-lora. Scenario trains this kind of model on before-and-after pairs so it follows an edit instruction. The public record source is scenario.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Character Turnaround - Kontext is a Scenario house image model (flux.1-kontext-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Generate a four-view character turnaround sheet from any character image, showing every side on a clean background.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-kontext/facial-expression-sheet-kontext": { "id": "scenario/flux-kontext/facial-expression-sheet-kontext", "name": "Facial Expression Sheet - Kontext", "maker": "scenario", "family": "scenario/flux-kontext", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-10-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux Kontext edit LoRA. Public type is flux.1-kontext-lora. Scenario trains this kind of model on before-and-after pairs so it follows an edit instruction. The public record source is scenario.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Facial Expression Sheet - Kontext is a Scenario house image model (flux.1-kontext-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Turns a character image into a 9-emotion expression sheet, preserving their look and art style.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/flux-kontext/isometric-tile-maker-kontext": { "id": "scenario/flux-kontext/isometric-tile-maker-kontext", "name": "Isometric Tile Maker - Kontext", "maker": "scenario", "family": "scenario/flux-kontext", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-10-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flux Kontext edit LoRA. Public type is flux.1-kontext-lora. Scenario trains this kind of model on before-and-after pairs so it follows an edit instruction. The public record source is scenario.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Isometric Tile Maker - Kontext is a Scenario house image model (flux.1-kontext-lora). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Flux Kontext LoRA that converts photos of buildings into isometric 3D models placed on square tiles, ideal for game assets and top-down scene design.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/skybox/hdri-skybox": { "id": "scenario/skybox/hdri-skybox", "name": "HDRi Skybox", "maker": "scenario", "family": "scenario/skybox", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Scenario-named converter from a standard skybox image to an HDRi environment (EXR and HDR, optional cubemap). Public modelProvider is Scenario. No training dataset is on the record.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HDRi Skybox is a Scenario house image model (custom). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Convert a standard skybox image into an HDRi environment, saved as EXR and HDR files, with optional cubemap output.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/skybox/scenario-skybox-flux-1": { "id": "scenario/skybox/scenario-skybox-flux-1", "name": "Scenario Skybox Flux.1", "maker": "scenario", "family": "scenario/skybox", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2026-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Scenario-named skybox generator. The public description says panoramas are powered by FLUX.1, with 22 styles. The BFL provider page says FLUX.1 Dev plus an equirectangular geometry LoRA and automatic black-band removal. Public modelProvider is BFL. trainingImagesNumber is 0.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Scenario Skybox Flux.1 is a Scenario house image model (custom). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Generate seamless 360 equirectangular panoramas from a text prompt, powered by FLUX.1. Choose from 22 styles with automatic seam and pole correction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/skybox/scenario-skybox-gpt": { "id": "scenario/skybox/scenario-skybox-gpt", "name": "Scenario Skybox GPT", "maker": "scenario", "family": "scenario/skybox", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2026-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Scenario-named skybox generator. The name includes GPT. The public record's modelProvider is Scenario, and the short description does not name a backbone. trainingImagesNumber is 0.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Scenario Skybox GPT is a Scenario house image model (custom). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Generate seamless 360\u00b0 skyboxes from a text prompt. Outputs equirectangular panoramas and cubemap formats, engine-ready for real-time scenes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/texture/scenario-texture": { "id": "scenario/texture/scenario-texture", "name": "Scenario Texture", "maker": "scenario", "family": "scenario/texture", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2026-06-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "In-house tileable texture generator named by Scenario. The essentials article says a reference image is passed to the GPT Image 2 edit pipeline, and the custom-texture guide labels this model 'No training'. Public modelProvider is Scenario. No separate checkpoint was published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Scenario Texture is a Scenario house image model (custom). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Generate seamless, tileable textures from a text prompt. Optional seam erasing ensures perfect 2D tiling. Add reference images for style guidance.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/upscale/scenario-flux-upscale": { "id": "scenario/upscale/scenario-flux-upscale", "name": "Scenario Flux Upscale", "maker": "scenario", "family": "scenario/upscale", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Scenario-named image upscaler. The docs say it upscales with FLUX and can run on FLUX.1-dev or FLUX.1-Krea-dev. The public record's modelProvider is BFL. It is not a separate published weight file.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Scenario Flux Upscale is a Scenario house image model (custom). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Upscale images 2x to 8x with FLUX. Choose between precise, balanced, or creative presets to preserve structure or add imaginative detail.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/upscale/scenario-skybox-upscale": { "id": "scenario/upscale/scenario-skybox-upscale", "name": "Scenario Skybox Upscale", "maker": "scenario", "family": "scenario/upscale", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Scenario-named skybox upscaler. The public description says it upscales 360 panoramas up to 8x with FLUX and keeps the seam. Public modelProvider is BFL.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Scenario Skybox Upscale is a Scenario house image model (custom). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Upscale 360 skybox panoramas up to 8x using FLUX, preserving seamless continuity. Choose stylized or realism mode, add prompts, and enhance fine details.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/upscale/scenario-texture-upscale": { "id": "scenario/upscale/scenario-texture-upscale", "name": "Scenario Texture Upscale", "maker": "scenario", "family": "scenario/upscale", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Scenario-named texture upscaler. Docs describe FLUX upscaling that keeps a texture tileable, with FLUX.1-dev or FLUX.1-Krea-dev. Public modelProvider is BFL.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Scenario Texture Upscale is a Scenario house image model (custom). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Upscale seamlessly tileable textures up to 8x with FLUX. Add detail without breaking tiling. Choose precise, balanced, or creative modes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scenario/upscale/scenario-upscale-v3": { "id": "scenario/upscale/scenario-upscale-v3", "name": "Scenario Upscale V3", "maker": "scenario", "family": "scenario/upscale", "status": "active", "confidence": "high", "weights": "closed", "license": "Hosted under Scenario's terms. Paid plans include a commercial license for outputs. Free-plan outputs are personal and evaluation use only, per the pricing FAQ.", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Scenario-named upscaler, model id model_upscale-v3. The public short description calls it Flux-based, 1x to 16x. The upscale guide and the parameters reference call it SDXL-based. Both wordings are stored. The public record's modelProvider is Scenario.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Scenario Upscale V3 is a Scenario house image model (custom). On 2026-10-15 the public record status is trained and privacy is public, so this catalog marks it active. Weights are closed on Scenario. Confidence is high for the fields taken from that record.", "what_it_is_for": "Scenario's Flux-based upscaler, 1x to 16x. Style presets for photography, anime, 3D, and more. Keeps structure tight or adds creative detail on demand.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scu/hongyi/huaxi-hongyi": { "id": "scu/hongyi/huaxi-hongyi", "name": "\u534e\u897f\u9ec9\u533b", "maker": "scu", "family": "scu/hongyi", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "West China Hospital, Sichuan University, describes \u534e\u897f\u9ec9\u533b as its own medical model at the 72-billion-parameter class (720\u4ebf\u53c2\u6570\u7ea7). The hospital says it follows a 1+1+N pattern and integrates more than 10 general models and more than 50 vertical models. Stated training material is 1.1 million doctor-patient dialogues, more than 1,600 consensus guidelines, more than 150 textbooks, more than 100 policy and regulation documents, 3.5 PB of raw imaging, and more than 2.5 million records. No layer count, checkpoint id, or named base model is on the hospital pages. Applications under the name include text triage and records, endoscopy images, and voice plus facial behavior in a mental-health assessment. Those modalities are application descriptions, not a published input schema.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u534e\u897f\u9ec9\u533b is the medical model West China Hospital, Sichuan University, released on 1 November 2024 at its management conference. The hospital calls it a fully owned 72-billion-parameter-class system for care, service, and management. It is still the name on hospital applications as of the 1 November 2025 agent launch. Weights are closed. Collected 2026-10-15.", "what_it_is_for": "The hospital groups the work as AI plus care, AI plus service, and AI plus management. Patient-facing pieces include triage, pre-visit notes, and a follow-up platform. Clinician pieces include endoscopy assistance, an audiovisual anxiety and depression assessment, shock decision support, and generation of seven kinds of medical records. On 1 November 2025 the hospital added a patient-escort agent and a medical-document agent under the same name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scu/hxdental/hxdental-pathai": { "id": "scu/hxdental/hxdental-pathai", "name": "HXDental-PathAI", "maker": "scu", "family": "scu/hxdental", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "West China Hospital of Stomatology, Sichuan University, and Westlake University jointly developed HXDental-PathAI as a clinical multimodal oral-pathology model. University news says it uses a multimodal large-language-model base, with attention-based multiple-instance learning as the core, and describes a hundred-billion-parameter-class network (\u5343\u4ebf\u7ea7\u53c2\u6570). It is said to use million-scale pathology features and clinical multimodal data, and to cover more than 30 common oral diseases, including squamous-cell carcinoma, leukoplakia, fibroma, and cysts. No base-model name, layer count, or checkpoint id is published. Localization of cancer regions is described as analysis, not as a published image-generation API.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HXDental-PathAI is a clinical multimodal oral-pathology model used in the pathology department of West China Hospital of Stomatology, Sichuan University. The hospital and the university say it was developed with Westlake University and had entered service shortly before the 28 November 2025 reports. Weights are closed. Collected 2026-10-15.", "what_it_is_for": "The hospital describes screening and diagnosis for more than 30 common oral diseases, including squamous-cell carcinoma, leukoplakia, fibroma, and cysts. For jaw and face tumors it is said to do a benign-versus-malignant screen, mark the cancer region, and quantify it within seconds. Teaching use is image-text questions tied to cases and papers. Research use is described as linking pathology with clinical records.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scu/luban/luban-10b": { "id": "scu/luban/luban-10b", "name": "Luban-10B", "maker": "scu", "family": "scu/luban", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-08-09", "context_length": 2048, "max_output_tokens": null, "parameters": "10000000000", "architecture": "The university lab describes a 10-billion-parameter transformer trained for manufacturing process design, with hybrid sparse attention that keeps the initial-token weights and selects the most relevant earlier tokens, cutting attention from O(N^2) to O(n + kN). Training is described as domain pretraining plus instruction tuning, with LoRA used in the tuning stage. The Hub config is CustomLlamaForCausalLM (model_type llama): hidden size 4096, intermediate size 11008, 48 layers, 32 attention heads, 32 key-value heads, SiLU, vocabulary 65000, bfloat16, RoPE theta 500000, and dynamic RoPE scaling with factor 8. config max_length and generation_config max_length are 2048; max_position_embeddings is 4096. The config _name_or_path is a local test config, not a named third-party checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Luban-10B (\u9c81\u73ed-10B) is a manufacturing process-design language model from Sichuan University's computer college, led by Lv Jiancheng. The engineering center describes a 10-billion-parameter model trained by the lab. Weights were uploaded on 2024-08-09 as Luban-10B-v1.0. The journal paper followed in 2025. Collected 2026-10-15.", "what_it_is_for": "The lab says it generates manufacturing process text. The corpus covers machinery, materials, and manufacturing. A process-generation system is named at luban.neusym.cn. The Science and Technology Daily reprint on the university site says the system was already in use at several manufacturers. That site was not opened for this dossier.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/bianque/bianque-1-0": { "id": "scut/bianque/bianque-1-0", "name": "BianQue-1.0", "maker": "scut", "family": "scut/bianque", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-04-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BianQue-1.0 (\u6241\u9e4a) is a full-parameter supervised fine-tune of ClueAI/ChatYuan-large-v2 by Yirong Chen and colleagues at the SCUT Future Technology School and the Guangdong Provincial Key Laboratory of Digital Twins in Human. The card says 1 epoch on 8 RTX 4090 GPUs for about 16 days, on more than 9 million hybrid instruction and multi-turn doctor QA samples. Named sources include MedDialog-CN, IMCS-V2, CHIP-MDCFNPC, MedDG, cMedQA2, Chinese-medical-dialogue-data, and a self-built set. Average turns 3, maximum 218. Config is T5ForConditionalGeneration: d_model 1024, d_ff 2816, d_kv 64, 24 encoder layers, 24 decoder layers, 16 heads, vocab 32128, gated-gelu, float32, relative attention max distance 128. A parameter count was not stated on the card and was not invented from the ChatYuan name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BianQue-1.0 is a Chinese health-dialogue model from Yirong Chen's group at South China University of Technology. It is a full-parameter fine-tune of ClueAI/ChatYuan-large-v2 on medical multi-turn data. Weights were public on 2026-10-15 under an Apache-2.0 Hub tag. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project calls BianQue a living-space health dialogue model under the ProactiveHealthGPT umbrella. BianQue-1.0 is trained on doctor-style multi-turn Chinese QA and hybrid instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/bianque/bianque-2": { "id": "scut/bianque/bianque-2", "name": "BianQue-2.0", "maker": "scut", "family": "scut/bianque", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 (Hugging Face tag). A ChatGLM-6B MODEL_LICENSE rider was not confirmed on the BianQue-2 card during this collection.", "release_date": "2023-06-06", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "BianQue-2.0 is a full-parameter SFT of ChatGLM-6B. The Hub repo id is scutcyr/BianQue-2. The card says training used BianQueCorpus plus drug-instruction, medical-encyclopedia, and ChatGPT-distilled instructions. Config is ChatGLMForConditionalGeneration: hidden 4096, inner hidden 16384, 28 layers, 32 heads, vocab 130528, max_sequence_length 2048, position_encoding_2d true, float16. The card says the model is initialized from ChatGLM-6B. An exact parameter count was not restated, so parameters is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BianQue-2.0 is the second BianQue health-dialogue model from the same SCUT group. The Hugging Face repo is `scutcyr/BianQue-2`. It is a full-parameter fine-tune of ChatGLM-6B. Weights were public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project describes BianQue-2.0 as a Chinese medical dialogue model trained on BianQueCorpus plus drug, encyclopedia, and distilled instruction data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/dstc11/dstc11-simmc2-1": { "id": "scut/dstc11/dstc11-simmc2-1", "name": "DSTC11 SIMMC 2.1 SCUT-BDS", "maker": "scut", "family": "scut/dstc11", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-10-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DSTC11 Track 1 entry from the SCUT BDS lab (scut-bds-lab), published by the scutcyr Hugging Face user. The card says mt-bart, mt-bart-sys, and mt-bart-sys-nvattr share one transformer with a multi-task head, fine-tuned from facebook/bart-large on SIMMC 2.1. Inputs described by the card are textual context, tokenized objects, and the scene. Subtask 4 also uses the system belief state as a prompt, and one variant adds non-visual metadata embeddings. Tasks: ambiguous candidate identification, multimodal coreference resolution, multimodal dialog state tracking, and response generation. The card says the model was originally released in October 2022 and made public on Hugging Face on 2022-10-28. The repository was created 2022-10-10. A parameter count was not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DSTC11 SIMMC 2.1 SCUT-BDS is a named challenge system from the SCUT BDS lab, released on the scutcyr Hugging Face account. It is a multi-task fine-tune of BART-large for SIMMC 2.1. Weights were public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the entry covers four SIMMC 2.1 tasks: ambiguous candidate identification, multimodal coreference resolution, multimodal dialog state tracking, and response generation. Scene and object features are tokenized into the transformer input.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/lilt/lilt-infoxlm-base": { "id": "scut/lilt/lilt-infoxlm-base", "name": "LiLT-InfoXLM-base", "maker": "scut", "family": "scut/lilt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-10-10", "context_length": 512, "max_output_tokens": null, "parameters": "0.3B", "architecture": "Stitched multilingual LiLT BASE. The Hugging Face config matches the English stitch on layout size: hidden 768, 12 layers, 12 heads, intermediate 3072, max_position_embeddings 514, max_2d_position_embeddings 1024, channel_shrink_ratio 4, float32, with vocab size 250002 from InfoXLM / XLM-R. The paper marks LiLT[InfoXLM] as the multilingual model. The Hub display label is 0.3B parameters. The ACL 2022 paper says the layout flow is pretrained on the IIT-CDIP Test Collection 1.0, more than 11 million scanned English document images. Training used Adam, learning rate 2e-5, weight decay 1e-2, batch size 96, 5 epochs, on 4 NVIDIA A40 48GB GPUs. Box coordinates are normalized to [0, 1000]. Pretraining tasks are MVLM (15% token mask), KPL (7x7 = 49 regions, 15% boxes), and CAI. The text-flow slow-down ratio is 1000. Maximum sequence length N is 512.", "modalities": { "input": [ "text", "layout" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LiLT-InfoXLM-base is the multilingual stitched LiLT model from SCUT-DLVCLab. The text flow starts from InfoXLM and the layout flow is the same LiLT tower as the English model. Weights are on Hugging Face under an MIT tag. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses this variant for multilingual structured document understanding. Downstream fine-tuning includes FUNSD and XFUND.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/lilt/lilt-only-base": { "id": "scut/lilt/lilt-only-base", "name": "LiLT-only-base", "maker": "scut", "family": "scut/lilt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03", "context_length": 512, "max_output_tokens": null, "parameters": "6.1M", "architecture": "Language-independent layout transformer from the SCUT DLVC Lab (Jiapeng Wang, Lianwen Jin) and Kai Ding (INTSIG). The paper says the BASE layout flow is a 12-layer encoder, hidden size 192, FFN size 768, 12 heads, 6.1 million parameters, maximum sequence length 512. The GitHub checkpoint table lists lilt-only-base as a 21MB language-none file so other RoBERTa-like text towers can be stitched with gen_weight_roberta_like.py. It is not on the SCUT-DLVCLab Hugging Face org.", "modalities": { "input": [ "layout" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LiLT-only-base is the language-independent layout tower from the LiLT paper by Jiapeng Wang and Lianwen Jin at South China University of Technology, with Kai Ding at INTSIG. The GitHub checkpoint table publishes it as a layout-only file, separate from the two stitched Hugging Face models. Weights were public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The repository says this checkpoint is the layout flow so a user can stitch another RoBERTa-like text tower with `gen_weight_roberta_like.py`. It is not a standalone document parser.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/lilt/lilt-roberta-en-base": { "id": "scut/lilt/lilt-roberta-en-base", "name": "LiLT-RoBERTa-EN-base", "maker": "scut", "family": "scut/lilt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-09-29", "context_length": 512, "max_output_tokens": null, "parameters": "0.1B", "architecture": "Stitched LiLT BASE. The Hugging Face config is LiltModel: hidden size 768, 12 layers, 12 heads, intermediate size 3072, vocab size 50265, max_position_embeddings 514, max_2d_position_embeddings 1024, channel_shrink_ratio 4, float32. The paper says the text flow is initialized from English RoBERTa-base and the layout flow is the 12-layer LiLT tower. The Hub display label is 0.1B parameters. The ACL 2022 paper says the layout flow is pretrained on the IIT-CDIP Test Collection 1.0, more than 11 million scanned English document images. Training used Adam, learning rate 2e-5, weight decay 1e-2, batch size 96, 5 epochs, on 4 NVIDIA A40 48GB GPUs. Box coordinates are normalized to [0, 1000]. Pretraining tasks are MVLM (15% token mask), KPL (7x7 = 49 regions, 15% boxes), and CAI. The text-flow slow-down ratio is 1000. Maximum sequence length N is 512.", "modalities": { "input": [ "text", "layout" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LiLT-RoBERTa-EN-base is the English stitched LiLT model from SCUT-DLVCLab. The text flow starts from English RoBERTa-base and the layout flow is the LiLT tower from Wang, Jin, and Ding, ACL 2022. Weights are on Hugging Face under an MIT tag. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents LiLT for structured document understanding. The stated downstream uses are fine-tuning for document image classification, parsing, and question answering, including FUNSD and XFUND.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/padt/padt-ovd-3b": { "id": "scut/padt/padt-ovd-3b", "name": "PaDT-OVD-3B", "maker": "scut", "family": "scut/padt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "PaDT is a family of vision-language checkpoints from Gorilla-Lab-SCUT, published on the Hugging Face org PaDT-MLLM. The code email on the lab GitHub is eezhihaoliang@mail.scut.edu.cn. Authors include Yongyi Su (SCUT and A*STAR I2R) and Xiaofen Xing (SCUT), with additional A*STAR and partner authors. The org lists eight named checkpoints, all updated 2025-10-10. The paper is arXiv 2510.01954, submitted 2025-10-02. License tag Apache-2.0. Hub size labels count the vision tower: checkpoints the authors call 3B display as 4B, and checkpoints they call 7B display as 8B. Each name is a separate task product on a Qwen2.5-VL backbone plus a PaDT visual decoder, not an unnamed ablation. Card language tags include English and Chinese. This folder is PaDT_OVD_3B. Open-vocabulary detection (OVD). config.json for this repo was not opened. Width, depth, and context were not copied from PaDT_Pro_3B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PaDT-OVD-3B is a named PaDT checkpoint from Gorilla-Lab-SCUT, hosted by the PaDT-MLLM organization. Open-vocabulary detection (OVD). The lab is at South China University of Technology and the paper also names A*STAR I2R. Weights were public on 2026-10-15 under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Open-vocabulary detection (OVD). PaDT adds a visual decoder on a Qwen2.5-VL fine-tune for open-vocabulary detection, referring expressions, and referring image captioning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/padt/padt-ovd-7b": { "id": "scut/padt/padt-ovd-7b", "name": "PaDT-OVD-7B", "maker": "scut", "family": "scut/padt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "PaDT is a family of vision-language checkpoints from Gorilla-Lab-SCUT, published on the Hugging Face org PaDT-MLLM. The code email on the lab GitHub is eezhihaoliang@mail.scut.edu.cn. Authors include Yongyi Su (SCUT and A*STAR I2R) and Xiaofen Xing (SCUT), with additional A*STAR and partner authors. The org lists eight named checkpoints, all updated 2025-10-10. The paper is arXiv 2510.01954, submitted 2025-10-02. License tag Apache-2.0. Hub size labels count the vision tower: checkpoints the authors call 3B display as 4B, and checkpoints they call 7B display as 8B. Each name is a separate task product on a Qwen2.5-VL backbone plus a PaDT visual decoder, not an unnamed ablation. Card language tags include English and Chinese. This folder is PaDT_OVD_7B. Open-vocabulary detection (OVD) at the authors' 7B scale. config.json for this repo was not opened. Width, depth, and context were not copied from PaDT_Pro_3B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PaDT-OVD-7B is a named PaDT checkpoint from Gorilla-Lab-SCUT, hosted by the PaDT-MLLM organization. Open-vocabulary detection (OVD) at the authors' 7B scale. The lab is at South China University of Technology and the paper also names A*STAR I2R. Weights were public on 2026-10-15 under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Open-vocabulary detection (OVD) at the authors' 7B scale. PaDT adds a visual decoder on a Qwen2.5-VL fine-tune for open-vocabulary detection, referring expressions, and referring image captioning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/padt/padt-pro-3b": { "id": "scut/padt/padt-pro-3b", "name": "PaDT-Pro-3B", "maker": "scut", "family": "scut/padt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-10", "context_length": 128000, "max_output_tokens": null, "parameters": "4B", "architecture": "PaDT is a family of vision-language checkpoints from Gorilla-Lab-SCUT, published on the Hugging Face org PaDT-MLLM. The code email on the lab GitHub is eezhihaoliang@mail.scut.edu.cn. Authors include Yongyi Su (SCUT and A*STAR I2R) and Xiaofen Xing (SCUT), with additional A*STAR and partner authors. The org lists eight named checkpoints, all updated 2025-10-10. The paper is arXiv 2510.01954, submitted 2025-10-02. License tag Apache-2.0. Hub size labels count the vision tower: checkpoints the authors call 3B display as 4B, and checkpoints they call 7B display as 8B. Each name is a separate task product on a Qwen2.5-VL backbone plus a PaDT visual decoder, not an unnamed ablation. Card language tags include English and Chinese. This folder is PaDT_Pro_3B. Pro checkpoint trained across COCO2017, RefCOCO, RefCOCO+, RefCOCOg, and referring image captioning. PaDT_Pro_3B config.json was opened. architectures PaDTForConditionalGeneration, model_type qwen2_5_vl, hidden 2048, 36 layers, 16 heads, 2 KV heads, intermediate 11008, max_position_embeddings 128000, sliding_window 32768, use_sliding_window false, vocab 151936, bfloat16, tie_word_embeddings true, image_token_id 151655, video_token_id 151656, bos 151643, eos 151645. Vision depth 32, hidden 1280, patch 14, spatial merge 2, out hidden 2048. vl_decoder name PaDTDecoder, hidden 1280, intermediate 3420, llm_hidden_state 2048, num_heads 16, use_mask_loss true. The Pro 3B model card lists the base model as Qwen/Qwen2.5-VL-3B-Instruct.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PaDT-Pro-3B is a named PaDT checkpoint from Gorilla-Lab-SCUT, hosted by the PaDT-MLLM organization. Pro checkpoint trained across COCO2017, RefCOCO, RefCOCO+, RefCOCOg, and referring image captioning. The lab is at South China University of Technology and the paper also names A*STAR I2R. Weights were public on 2026-10-15 under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Pro checkpoint trained across COCO2017, RefCOCO, RefCOCO+, RefCOCOg, and referring image captioning. PaDT adds a visual decoder on a Qwen2.5-VL fine-tune for open-vocabulary detection, referring expressions, and referring image captioning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/padt/padt-pro-7b": { "id": "scut/padt/padt-pro-7b", "name": "PaDT-Pro-7B", "maker": "scut", "family": "scut/padt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "PaDT is a family of vision-language checkpoints from Gorilla-Lab-SCUT, published on the Hugging Face org PaDT-MLLM. The code email on the lab GitHub is eezhihaoliang@mail.scut.edu.cn. Authors include Yongyi Su (SCUT and A*STAR I2R) and Xiaofen Xing (SCUT), with additional A*STAR and partner authors. The org lists eight named checkpoints, all updated 2025-10-10. The paper is arXiv 2510.01954, submitted 2025-10-02. License tag Apache-2.0. Hub size labels count the vision tower: checkpoints the authors call 3B display as 4B, and checkpoints they call 7B display as 8B. Each name is a separate task product on a Qwen2.5-VL backbone plus a PaDT visual decoder, not an unnamed ablation. Card language tags include English and Chinese. This folder is PaDT_Pro_7B. Pro checkpoint at the authors' 7B scale, covering the combined detection, referring, and caption tasks. config.json for this repo was not opened. Width, depth, and context were not copied from PaDT_Pro_3B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PaDT-Pro-7B is a named PaDT checkpoint from Gorilla-Lab-SCUT, hosted by the PaDT-MLLM organization. Pro checkpoint at the authors' 7B scale, covering the combined detection, referring, and caption tasks. The lab is at South China University of Technology and the paper also names A*STAR I2R. Weights were public on 2026-10-15 under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Pro checkpoint at the authors' 7B scale, covering the combined detection, referring, and caption tasks. PaDT adds a visual decoder on a Qwen2.5-VL fine-tune for open-vocabulary detection, referring expressions, and referring image captioning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/padt/padt-rec-3b": { "id": "scut/padt/padt-rec-3b", "name": "PaDT-REC-3B", "maker": "scut", "family": "scut/padt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "PaDT is a family of vision-language checkpoints from Gorilla-Lab-SCUT, published on the Hugging Face org PaDT-MLLM. The code email on the lab GitHub is eezhihaoliang@mail.scut.edu.cn. Authors include Yongyi Su (SCUT and A*STAR I2R) and Xiaofen Xing (SCUT), with additional A*STAR and partner authors. The org lists eight named checkpoints, all updated 2025-10-10. The paper is arXiv 2510.01954, submitted 2025-10-02. License tag Apache-2.0. Hub size labels count the vision tower: checkpoints the authors call 3B display as 4B, and checkpoints they call 7B display as 8B. Each name is a separate task product on a Qwen2.5-VL backbone plus a PaDT visual decoder, not an unnamed ablation. Card language tags include English and Chinese. This folder is PaDT_REC_3B. Referring expression comprehension and segmentation (REC). config.json for this repo was not opened. Width, depth, and context were not copied from PaDT_Pro_3B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PaDT-REC-3B is a named PaDT checkpoint from Gorilla-Lab-SCUT, hosted by the PaDT-MLLM organization. Referring expression comprehension and segmentation (REC). The lab is at South China University of Technology and the paper also names A*STAR I2R. Weights were public on 2026-10-15 under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Referring expression comprehension and segmentation (REC). PaDT adds a visual decoder on a Qwen2.5-VL fine-tune for open-vocabulary detection, referring expressions, and referring image captioning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/padt/padt-rec-7b": { "id": "scut/padt/padt-rec-7b", "name": "PaDT-REC-7B", "maker": "scut", "family": "scut/padt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "PaDT is a family of vision-language checkpoints from Gorilla-Lab-SCUT, published on the Hugging Face org PaDT-MLLM. The code email on the lab GitHub is eezhihaoliang@mail.scut.edu.cn. Authors include Yongyi Su (SCUT and A*STAR I2R) and Xiaofen Xing (SCUT), with additional A*STAR and partner authors. The org lists eight named checkpoints, all updated 2025-10-10. The paper is arXiv 2510.01954, submitted 2025-10-02. License tag Apache-2.0. Hub size labels count the vision tower: checkpoints the authors call 3B display as 4B, and checkpoints they call 7B display as 8B. Each name is a separate task product on a Qwen2.5-VL backbone plus a PaDT visual decoder, not an unnamed ablation. Card language tags include English and Chinese. This folder is PaDT_REC_7B. Referring expression comprehension and segmentation (REC) at the authors' 7B scale. config.json for this repo was not opened. Width, depth, and context were not copied from PaDT_Pro_3B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PaDT-REC-7B is a named PaDT checkpoint from Gorilla-Lab-SCUT, hosted by the PaDT-MLLM organization. Referring expression comprehension and segmentation (REC) at the authors' 7B scale. The lab is at South China University of Technology and the paper also names A*STAR I2R. Weights were public on 2026-10-15 under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Referring expression comprehension and segmentation (REC) at the authors' 7B scale. PaDT adds a visual decoder on a Qwen2.5-VL fine-tune for open-vocabulary detection, referring expressions, and referring image captioning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/padt/padt-ric-3b": { "id": "scut/padt/padt-ric-3b", "name": "PaDT-RIC-3B", "maker": "scut", "family": "scut/padt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "PaDT is a family of vision-language checkpoints from Gorilla-Lab-SCUT, published on the Hugging Face org PaDT-MLLM. The code email on the lab GitHub is eezhihaoliang@mail.scut.edu.cn. Authors include Yongyi Su (SCUT and A*STAR I2R) and Xiaofen Xing (SCUT), with additional A*STAR and partner authors. The org lists eight named checkpoints, all updated 2025-10-10. The paper is arXiv 2510.01954, submitted 2025-10-02. License tag Apache-2.0. Hub size labels count the vision tower: checkpoints the authors call 3B display as 4B, and checkpoints they call 7B display as 8B. Each name is a separate task product on a Qwen2.5-VL backbone plus a PaDT visual decoder, not an unnamed ablation. Card language tags include English and Chinese. This folder is PaDT_RIC_3B. Referring image captioning (RIC). config.json for this repo was not opened. Width, depth, and context were not copied from PaDT_Pro_3B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PaDT-RIC-3B is a named PaDT checkpoint from Gorilla-Lab-SCUT, hosted by the PaDT-MLLM organization. Referring image captioning (RIC). The lab is at South China University of Technology and the paper also names A*STAR I2R. Weights were public on 2026-10-15 under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Referring image captioning (RIC). PaDT adds a visual decoder on a Qwen2.5-VL fine-tune for open-vocabulary detection, referring expressions, and referring image captioning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/padt/padt-ric-7b": { "id": "scut/padt/padt-ric-7b", "name": "PaDT-RIC-7B", "maker": "scut", "family": "scut/padt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "PaDT is a family of vision-language checkpoints from Gorilla-Lab-SCUT, published on the Hugging Face org PaDT-MLLM. The code email on the lab GitHub is eezhihaoliang@mail.scut.edu.cn. Authors include Yongyi Su (SCUT and A*STAR I2R) and Xiaofen Xing (SCUT), with additional A*STAR and partner authors. The org lists eight named checkpoints, all updated 2025-10-10. The paper is arXiv 2510.01954, submitted 2025-10-02. License tag Apache-2.0. Hub size labels count the vision tower: checkpoints the authors call 3B display as 4B, and checkpoints they call 7B display as 8B. Each name is a separate task product on a Qwen2.5-VL backbone plus a PaDT visual decoder, not an unnamed ablation. Card language tags include English and Chinese. This folder is PaDT_RIC_7B. Referring image captioning (RIC) at the authors' 7B scale. config.json for this repo was not opened. Width, depth, and context were not copied from PaDT_Pro_3B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PaDT-RIC-7B is a named PaDT checkpoint from Gorilla-Lab-SCUT, hosted by the PaDT-MLLM organization. Referring image captioning (RIC) at the authors' 7B scale. The lab is at South China University of Technology and the paper also names A*STAR I2R. Weights were public on 2026-10-15 under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Referring image captioning (RIC) at the authors' 7B scale. PaDT adds a visual decoder on a Qwen2.5-VL fine-tune for open-vocabulary detection, referring expressions, and referring image captioning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/soulchat/soulchat": { "id": "scut/soulchat/soulchat", "name": "SoulChat", "maker": "scut", "family": "scut/soulchat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial use, and follow the ChatGLM-6B MODEL_LICENSE, as stated on the model card. The Hugging Face tag also says Apache-2.0. Both statements are stored. The card is the stricter text.", "release_date": "2023-06-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SoulChat (\u7075\u5fc3) 1.0 is a full-parameter SFT of ChatGLM-6B for Chinese psychological counseling, from Yirong Chen, Xiaofen Xing, Zhenyu Wang, and Xiangmin Xu at the SCUT Future Technology School and the Guangdong Provincial Key Laboratory of Digital Twins in Human. The card describes more than 150 thousand single-turn long counseling instructions and more than 500 thousand answers (about 6.7 times the PsyQA instruction count), plus about 1 million multi-turn rounds written with ChatGPT and GPT-4. The mixed SoulChatCorpus is described as more than 1.2 million samples. A 2024-06-06 GitHub note says the open SoulChatCorpus was filtered to 258354 multi-turn dialogues and 1517344 turns. config.json for this repo was not fetched, so ChatGLM width and context were not copied from BianQue-2.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SoulChat is a Chinese psychological-counseling model from Yirong Chen's group at South China University of Technology. SoulChat 1.0 is a full-parameter fine-tune of ChatGLM-6B. Weights were public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project presents SoulChat as a mental-health dialogue model with long counseling instructions and multi-turn empathetic replies. It sits under the ProactiveHealthGPT umbrella next to BianQue. ProactiveHealthGPT is not its own weight file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/soulchat/soulchat2-0-baichuan2-7b": { "id": "scut/soulchat/soulchat2-0-baichuan2-7b", "name": "SoulChat2.0-Baichuan2-7B", "maker": "scut", "family": "scut/soulchat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Follow the Baichuan 2 base-model license, as stated in the SoulChat2.0 README limitations.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SoulChat2.0 is a psychological-counselor digital-twin line (PsyDT) from Haojie Xie, Yirong Chen, Xiaofen Xing, Jingkai Lin, and Xiangmin Xu at SCUT. The English README says each checkpoint is a full-parameter SFT for 3 epochs, learning rate 1e-5, per-device train batch 2, eval batch 1, seed 42, 8 GPUs, total train batch 16, Adam betas 0.9 and 0.999, epsilon 1e-8, cosine schedule, warmup ratio 0.03, Native AMP. The stack is transformers 4.43.0, PyTorch 2.3.0+cu121, datasets 2.18.0, tokenizers 0.19.1, and Llama-Factory 0.8.3.dev0, on 8 A800 GPUs. PsyDTCorpus has 5000 synthesized dialogues from 5000 single-turn samples plus about 12 real cases, generally at most 20, of one counselor. The train split is 4760 dialogues and 86054 turns (average 18). The test split is 240 dialogues and 4311 turns. Total turns 90365. The README says numeric comparison in the paper uses Qwen2-7B-Instruct as the primary base. The code repository badge says Apache-2.0. The limitations section says the weights must follow the corresponding base-model license: Baichuan 2, Yi, Llama 3, Qwen, GLM-4, or InternLM2. A vLLM demo in that README contains an example API key. The key is not copied here. Paper arXiv 2412.13660 was submitted 2024-12-18. ACL 2025 proceedings are dated 2025-07-24, pages 1081-1115. Weight upload dates were not confirmed repo by repo, so release_date is null. This folder is the full-parameter SFT of Baichuan2-7B-Chat. Separately named SoulChat2.0 product on a different base. Not a dated snapshot of the Qwen2 checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SoulChat2.0-Baichuan2-7B is one SoulChat2.0 psychological-counseling checkpoint from South China University of Technology. The official README names it as a full-parameter fine-tune of Baichuan2-7B-Chat. The seven SoulChat2.0 names are separate products, one per base model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and README present SoulChat2.0 as a digital twin of one psychological counselor (PsyDT), trained on synthesized multi-turn counseling dialogues. The Qwen2-7B-Instruct fine-tune is the primary comparison model in the README. This folder is the Baichuan2-7B-Chat fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/soulchat/soulchat2-0-glm-4-9b": { "id": "scut/soulchat/soulchat2-0-glm-4-9b", "name": "SoulChat2.0-glm-4-9b", "maker": "scut", "family": "scut/soulchat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Follow the GLM-4 base-model license, as stated in the SoulChat2.0 README limitations.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SoulChat2.0 is a psychological-counselor digital-twin line (PsyDT) from Haojie Xie, Yirong Chen, Xiaofen Xing, Jingkai Lin, and Xiangmin Xu at SCUT. The English README says each checkpoint is a full-parameter SFT for 3 epochs, learning rate 1e-5, per-device train batch 2, eval batch 1, seed 42, 8 GPUs, total train batch 16, Adam betas 0.9 and 0.999, epsilon 1e-8, cosine schedule, warmup ratio 0.03, Native AMP. The stack is transformers 4.43.0, PyTorch 2.3.0+cu121, datasets 2.18.0, tokenizers 0.19.1, and Llama-Factory 0.8.3.dev0, on 8 A800 GPUs. PsyDTCorpus has 5000 synthesized dialogues from 5000 single-turn samples plus about 12 real cases, generally at most 20, of one counselor. The train split is 4760 dialogues and 86054 turns (average 18). The test split is 240 dialogues and 4311 turns. Total turns 90365. The README says numeric comparison in the paper uses Qwen2-7B-Instruct as the primary base. The code repository badge says Apache-2.0. The limitations section says the weights must follow the corresponding base-model license: Baichuan 2, Yi, Llama 3, Qwen, GLM-4, or InternLM2. A vLLM demo in that README contains an example API key. The key is not copied here. Paper arXiv 2412.13660 was submitted 2024-12-18. ACL 2025 proceedings are dated 2025-07-24, pages 1081-1115. Weight upload dates were not confirmed repo by repo, so release_date is null. This folder is the full-parameter SFT of glm-4-9b-chat. Separately named SoulChat2.0 product on a different base. Not a dated snapshot of the Qwen2 checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SoulChat2.0-glm-4-9b is one SoulChat2.0 psychological-counseling checkpoint from South China University of Technology. The official README names it as a full-parameter fine-tune of glm-4-9b-chat. The seven SoulChat2.0 names are separate products, one per base model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and README present SoulChat2.0 as a digital twin of one psychological counselor (PsyDT), trained on synthesized multi-turn counseling dialogues. The Qwen2-7B-Instruct fine-tune is the primary comparison model in the README. This folder is the glm-4-9b-chat fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/soulchat/soulchat2-0-internlm2-7b": { "id": "scut/soulchat/soulchat2-0-internlm2-7b", "name": "SoulChat2.0-internlm2-7b", "maker": "scut", "family": "scut/soulchat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Follow the InternLM2 base-model license, as stated in the SoulChat2.0 README limitations.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SoulChat2.0 is a psychological-counselor digital-twin line (PsyDT) from Haojie Xie, Yirong Chen, Xiaofen Xing, Jingkai Lin, and Xiangmin Xu at SCUT. The English README says each checkpoint is a full-parameter SFT for 3 epochs, learning rate 1e-5, per-device train batch 2, eval batch 1, seed 42, 8 GPUs, total train batch 16, Adam betas 0.9 and 0.999, epsilon 1e-8, cosine schedule, warmup ratio 0.03, Native AMP. The stack is transformers 4.43.0, PyTorch 2.3.0+cu121, datasets 2.18.0, tokenizers 0.19.1, and Llama-Factory 0.8.3.dev0, on 8 A800 GPUs. PsyDTCorpus has 5000 synthesized dialogues from 5000 single-turn samples plus about 12 real cases, generally at most 20, of one counselor. The train split is 4760 dialogues and 86054 turns (average 18). The test split is 240 dialogues and 4311 turns. Total turns 90365. The README says numeric comparison in the paper uses Qwen2-7B-Instruct as the primary base. The code repository badge says Apache-2.0. The limitations section says the weights must follow the corresponding base-model license: Baichuan 2, Yi, Llama 3, Qwen, GLM-4, or InternLM2. A vLLM demo in that README contains an example API key. The key is not copied here. Paper arXiv 2412.13660 was submitted 2024-12-18. ACL 2025 proceedings are dated 2025-07-24, pages 1081-1115. Weight upload dates were not confirmed repo by repo, so release_date is null. This folder is the full-parameter SFT of internlm2-chat-7b. Separately named SoulChat2.0 product on a different base. Not a dated snapshot of the Qwen2 checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SoulChat2.0-internlm2-7b is one SoulChat2.0 psychological-counseling checkpoint from South China University of Technology. The official README names it as a full-parameter fine-tune of internlm2-chat-7b. The seven SoulChat2.0 names are separate products, one per base model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and README present SoulChat2.0 as a digital twin of one psychological counselor (PsyDT), trained on synthesized multi-turn counseling dialogues. The Qwen2-7B-Instruct fine-tune is the primary comparison model in the README. This folder is the internlm2-chat-7b fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/soulchat/soulchat2-0-llama-3-1-8b": { "id": "scut/soulchat/soulchat2-0-llama-3-1-8b", "name": "SoulChat2.0-Llama-3.1-8B", "maker": "scut", "family": "scut/soulchat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Follow the Llama 3.1 community license, as stated in the SoulChat2.0 README limitations.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SoulChat2.0 is a psychological-counselor digital-twin line (PsyDT) from Haojie Xie, Yirong Chen, Xiaofen Xing, Jingkai Lin, and Xiangmin Xu at SCUT. The English README says each checkpoint is a full-parameter SFT for 3 epochs, learning rate 1e-5, per-device train batch 2, eval batch 1, seed 42, 8 GPUs, total train batch 16, Adam betas 0.9 and 0.999, epsilon 1e-8, cosine schedule, warmup ratio 0.03, Native AMP. The stack is transformers 4.43.0, PyTorch 2.3.0+cu121, datasets 2.18.0, tokenizers 0.19.1, and Llama-Factory 0.8.3.dev0, on 8 A800 GPUs. PsyDTCorpus has 5000 synthesized dialogues from 5000 single-turn samples plus about 12 real cases, generally at most 20, of one counselor. The train split is 4760 dialogues and 86054 turns (average 18). The test split is 240 dialogues and 4311 turns. Total turns 90365. The README says numeric comparison in the paper uses Qwen2-7B-Instruct as the primary base. The code repository badge says Apache-2.0. The limitations section says the weights must follow the corresponding base-model license: Baichuan 2, Yi, Llama 3, Qwen, GLM-4, or InternLM2. A vLLM demo in that README contains an example API key. The key is not copied here. Paper arXiv 2412.13660 was submitted 2024-12-18. ACL 2025 proceedings are dated 2025-07-24, pages 1081-1115. Weight upload dates were not confirmed repo by repo, so release_date is null. This folder is the full-parameter SFT of Meta-Llama-3.1-8B-Instruct. Separately named SoulChat2.0 product on a different base. Not a dated snapshot of the Qwen2 checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SoulChat2.0-Llama-3.1-8B is one SoulChat2.0 psychological-counseling checkpoint from South China University of Technology. The official README names it as a full-parameter fine-tune of Meta-Llama-3.1-8B-Instruct. The seven SoulChat2.0 names are separate products, one per base model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and README present SoulChat2.0 as a digital twin of one psychological counselor (PsyDT), trained on synthesized multi-turn counseling dialogues. The Qwen2-7B-Instruct fine-tune is the primary comparison model in the README. This folder is the Meta-Llama-3.1-8B-Instruct fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/soulchat/soulchat2-0-llama-3-8b": { "id": "scut/soulchat/soulchat2-0-llama-3-8b", "name": "SoulChat2.0-Llama-3-8B", "maker": "scut", "family": "scut/soulchat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Follow the Llama 3 community license, as stated in the SoulChat2.0 README limitations.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SoulChat2.0 is a psychological-counselor digital-twin line (PsyDT) from Haojie Xie, Yirong Chen, Xiaofen Xing, Jingkai Lin, and Xiangmin Xu at SCUT. The English README says each checkpoint is a full-parameter SFT for 3 epochs, learning rate 1e-5, per-device train batch 2, eval batch 1, seed 42, 8 GPUs, total train batch 16, Adam betas 0.9 and 0.999, epsilon 1e-8, cosine schedule, warmup ratio 0.03, Native AMP. The stack is transformers 4.43.0, PyTorch 2.3.0+cu121, datasets 2.18.0, tokenizers 0.19.1, and Llama-Factory 0.8.3.dev0, on 8 A800 GPUs. PsyDTCorpus has 5000 synthesized dialogues from 5000 single-turn samples plus about 12 real cases, generally at most 20, of one counselor. The train split is 4760 dialogues and 86054 turns (average 18). The test split is 240 dialogues and 4311 turns. Total turns 90365. The README says numeric comparison in the paper uses Qwen2-7B-Instruct as the primary base. The code repository badge says Apache-2.0. The limitations section says the weights must follow the corresponding base-model license: Baichuan 2, Yi, Llama 3, Qwen, GLM-4, or InternLM2. A vLLM demo in that README contains an example API key. The key is not copied here. Paper arXiv 2412.13660 was submitted 2024-12-18. ACL 2025 proceedings are dated 2025-07-24, pages 1081-1115. Weight upload dates were not confirmed repo by repo, so release_date is null. This folder is the full-parameter SFT of Meta-Llama-3-8B-Instruct. Separately named SoulChat2.0 product on a different base. Not a dated snapshot of the Qwen2 checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SoulChat2.0-Llama-3-8B is one SoulChat2.0 psychological-counseling checkpoint from South China University of Technology. The official README names it as a full-parameter fine-tune of Meta-Llama-3-8B-Instruct. The seven SoulChat2.0 names are separate products, one per base model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and README present SoulChat2.0 as a digital twin of one psychological counselor (PsyDT), trained on synthesized multi-turn counseling dialogues. The Qwen2-7B-Instruct fine-tune is the primary comparison model in the README. This folder is the Meta-Llama-3-8B-Instruct fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/soulchat/soulchat2-0-qwen2-7b": { "id": "scut/soulchat/soulchat2-0-qwen2-7b", "name": "SoulChat2.0-Qwen2-7B", "maker": "scut", "family": "scut/soulchat", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 (ModelScope listing snippet). The README also says weights follow the base-model license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7.62B", "architecture": "SoulChat2.0 is a psychological-counselor digital-twin line (PsyDT) from Haojie Xie, Yirong Chen, Xiaofen Xing, Jingkai Lin, and Xiangmin Xu at SCUT. The English README says each checkpoint is a full-parameter SFT for 3 epochs, learning rate 1e-5, per-device train batch 2, eval batch 1, seed 42, 8 GPUs, total train batch 16, Adam betas 0.9 and 0.999, epsilon 1e-8, cosine schedule, warmup ratio 0.03, Native AMP. The stack is transformers 4.43.0, PyTorch 2.3.0+cu121, datasets 2.18.0, tokenizers 0.19.1, and Llama-Factory 0.8.3.dev0, on 8 A800 GPUs. PsyDTCorpus has 5000 synthesized dialogues from 5000 single-turn samples plus about 12 real cases, generally at most 20, of one counselor. The train split is 4760 dialogues and 86054 turns (average 18). The test split is 240 dialogues and 4311 turns. Total turns 90365. The README says numeric comparison in the paper uses Qwen2-7B-Instruct as the primary base. The code repository badge says Apache-2.0. The limitations section says the weights must follow the corresponding base-model license: Baichuan 2, Yi, Llama 3, Qwen, GLM-4, or InternLM2. A vLLM demo in that README contains an example API key. The key is not copied here. Paper arXiv 2412.13660 was submitted 2024-12-18. ACL 2025 proceedings are dated 2025-07-24, pages 1081-1115. Weight upload dates were not confirmed repo by repo, so release_date is null. This folder is the full-parameter SFT of Qwen2-7B-Instruct. Primary paper comparison base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SoulChat2.0-Qwen2-7B is one SoulChat2.0 psychological-counseling checkpoint from South China University of Technology. The official README names it as a full-parameter fine-tune of Qwen2-7B-Instruct. The seven SoulChat2.0 names are separate products, one per base model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and README present SoulChat2.0 as a digital twin of one psychological counselor (PsyDT), trained on synthesized multi-turn counseling dialogues. The Qwen2-7B-Instruct fine-tune is the primary comparison model in the README. This folder is the Qwen2-7B-Instruct fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/soulchat/soulchat2-0-yi-1-5-9b": { "id": "scut/soulchat/soulchat2-0-yi-1-5-9b", "name": "SoulChat2.0-Yi-1.5-9B", "maker": "scut", "family": "scut/soulchat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Follow the Yi base-model license, as stated in the SoulChat2.0 README limitations.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SoulChat2.0 is a psychological-counselor digital-twin line (PsyDT) from Haojie Xie, Yirong Chen, Xiaofen Xing, Jingkai Lin, and Xiangmin Xu at SCUT. The English README says each checkpoint is a full-parameter SFT for 3 epochs, learning rate 1e-5, per-device train batch 2, eval batch 1, seed 42, 8 GPUs, total train batch 16, Adam betas 0.9 and 0.999, epsilon 1e-8, cosine schedule, warmup ratio 0.03, Native AMP. The stack is transformers 4.43.0, PyTorch 2.3.0+cu121, datasets 2.18.0, tokenizers 0.19.1, and Llama-Factory 0.8.3.dev0, on 8 A800 GPUs. PsyDTCorpus has 5000 synthesized dialogues from 5000 single-turn samples plus about 12 real cases, generally at most 20, of one counselor. The train split is 4760 dialogues and 86054 turns (average 18). The test split is 240 dialogues and 4311 turns. Total turns 90365. The README says numeric comparison in the paper uses Qwen2-7B-Instruct as the primary base. The code repository badge says Apache-2.0. The limitations section says the weights must follow the corresponding base-model license: Baichuan 2, Yi, Llama 3, Qwen, GLM-4, or InternLM2. A vLLM demo in that README contains an example API key. The key is not copied here. Paper arXiv 2412.13660 was submitted 2024-12-18. ACL 2025 proceedings are dated 2025-07-24, pages 1081-1115. Weight upload dates were not confirmed repo by repo, so release_date is null. This folder is the full-parameter SFT of Yi-1.5-9B-Chat-16K. Separately named SoulChat2.0 product on a different base. Not a dated snapshot of the Qwen2 checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SoulChat2.0-Yi-1.5-9B is one SoulChat2.0 psychological-counseling checkpoint from South China University of Technology. The official README names it as a full-parameter fine-tune of Yi-1.5-9B-Chat-16K. The seven SoulChat2.0 names are separate products, one per base model. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and README present SoulChat2.0 as a digital twin of one psychological counselor (PsyDT), trained on synthesized multi-turn counseling dialogues. The Qwen2-7B-Instruct fine-tune is the primary comparison model in the README. This folder is the Yi-1.5-9B-Chat-16K fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/tonggu/tonggu-7b-instruct": { "id": "scut/tonggu/tonggu-7b-instruct", "name": "TongGu-7B-Instruct", "maker": "scut", "family": "scut/tonggu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-09-26", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "Classical-Chinese instruction model from SCUT DLVC Lab and the INTSIG-SCUT Joint Lab. Corresponding author Lianwen Jin (eelwjin@scut.edu.cn). The paper says it is an incremental pretrain of Baichuan2-7B-Base followed by two-stage RAT instruction tuning. The released checkpoint config is BaichuanForCausalLM: 32 layers, hidden 4096, 32 heads, intermediate 11008, vocab 125696, max_position_embeddings 4096, model_max_length 4096, bfloat16, tie_word_embeddings false. The parameter label 7B is the vendor size class of Baichuan2-7B-Base, not a fresh parameter count. Appendix Table 9: all experiments on 6 Nvidia A6000 GPUs; pretrain and instruction tuning in bf16; AdamW betas 0.9 and 0.999, weight decay 0, warmup 0, cosine schedule, training max length 2048. Pretrain: 1 epoch, batch 576, learning rate 2e-5. Instruction tuning: 3 epochs, batch 192, learning rate 2e-6. RAT group size N is 8. Stage 1 is data-hungry translation (called TongGu-7B-trans in the paper). Stage 2 adds other tasks plus 10,000 ShareGPT dialogues and is the released TongGu-7B-Instruct. Table 2 says the mixed pretrain corpus is 2.41 billion tokens and 8.54 GB on disk, using the Baichuan2-7B-Base tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TongGu-7B-Instruct is a classical-Chinese language model from the SCUT DLVC Lab and the INTSIG-SCUT Joint Lab. It continues pretraining from Baichuan2-7B-Base and then applies two-stage RAT instruction tuning. The lab open-sourced this instruct checkpoint. Weights were public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card's system prompt calls the model TongGu, trained by SCUT DLVCLab as a classical-Chinese model. The paper's tasks are punctuation, classical-to-modern translation, appreciation, idiom, poetry, and a broader set of 24 classical Chinese understanding tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "scut/tonggu/tonggu-vl-2b-instruct": { "id": "scut/tonggu/tonggu-vl-2b-instruct", "name": "TongGu-VL-2B-Instruct", "maker": "scut", "family": "scut/tonggu", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-12-16", "context_length": 32768, "max_output_tokens": null, "parameters": "2B", "architecture": "Multimodal classical-Chinese instruct model from SCUT-DLVCLab. The card says it is a 2B model instruction-tuned on 358 thousand multimodal items (CCS358k, 7 scenarios). Config _name_or_path is ../models/Qwen2-VL-2B-Instruct. The loaded class is Qwen2VLForConditionalGeneration / TongguVLForConditionalGeneration. Text config: hidden 1536, 28 layers, 12 heads, 2 KV heads, intermediate 8960, max_position_embeddings 32768, sliding_window 32768, vocab 151936, image_token_id 151655, video_token_id 151656, bfloat16. Vision hidden size 1536, spatial patch size 14. The card requires transformers==4.48.2. The 2B label is the card's size class. It was not re-counted.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TongGu-VL-2B-Instruct is a multimodal classical-Chinese model from SCUT-DLVCLab. The card says it is instruction-tuned from Qwen2-VL-2B-Instruct on 358 thousand multimodal items. Weights were public on 2026-10-15 under an Apache-2.0 tag. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card describes a 2B multimodal model for classical-Chinese scenarios. The instruction mixture is CCS358k, with 7 scenarios.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sdu/bianjiang/bianjiang": { "id": "sdu/bianjiang/bianjiang", "name": "\u8fb9\u7586\u5927\u6a21\u578b", "maker": "sdu", "family": "sdu/bianjiang", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u8fb9\u7586\u5927\u6a21\u578b is a closed model for frontier and borderland studies, switched on by Shandong University on 2024-12-26 with Zhipu and Heywhale. The same ceremony launched a companion agent. No weights or model card were found. No retirement notice was found by 2026-10-15. Collection date: 2026-10-15.", "what_it_is_for": "University news says it is an open academic platform for frontier studies, governance, and cultural work. The report says guests credited it, relative to a general model, on image-text mining and ancient-text processing, and on research, teaching, and public explanation. The base checkpoint is not named.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sdu/fuzi/fuzi-mingcha": { "id": "sdu/fuzi/fuzi-mingcha", "name": "\u592b\u5b50\u00b7\u660e\u5bdf", "maker": "sdu", "family": "sdu/fuzi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "ChatGLM-6B Model License for weights; Apache-2.0 for repository code. Project statement limits use to academic research.", "release_date": "2023-09", "context_length": 2048, "max_output_tokens": null, "parameters": "6B", "architecture": "ChatGLM (GLM). Released config.json: 28 layers, hidden size 4096, 32 attention heads, inner hidden size 16384, vocab size 130528, 2D positional encoding, float16. Continued pretraining and supervised fine-tuning from ChatGLM-6B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u592b\u5b50\u00b7\u660e\u5bdf (Fuzi-Mingcha) is a Chinese judicial chat model released by the Information Retrieval Lab at Shandong University with Inspur Cloud and China University of Political Science and Law. Weights for v1.0 are public. It was still listed on Hugging Face and ModelScope on 2026-10-15, with no deprecation notice. Collection date: 2026-10-15.", "what_it_is_for": "The model card says it is a Chinese legal model for statute lookup, case analysis, syllogistic judgment prediction, and legal dialogue. The described system first proposes statutes, checks them against an external store, and can pull similar historical cases. The card says answers are not legal advice.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sdu/gongshu-zhicang/gongshu-zhicang": { "id": "sdu/gongshu-zhicang/gongshu-zhicang", "name": "\u516c\u6570\u667a\u8231", "maker": "sdu", "family": "sdu/gongshu-zhicang", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "University and Guangming Daily descriptions call it a teaching system with one base, three graphs, three tracks, and four profiles. The neural base model is not named.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u516c\u6570\u667a\u8231 is a closed public-mathematics teaching model announced by Shandong University on 2025-12-09 together with the Shandong provincial education department and iFLYTEK. No weights or model card were found. No retirement notice was found by 2026-10-15. Collection date: 2026-10-15.", "what_it_is_for": "University news says it supports high-quality digital courses in public university mathematics and deeper use of AI in teaching. The School of Mathematics quotes iFLYTEK that it is meant to help teachers target instruction and students learn individually. A Guangming Daily account, carried on the university media site, says the team built a public-math corpus and a teaching-agent base covering theorems and proofs, worked examples, applications, guided grading of open answers, data handling, fine-tuning, and classroom evaluation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sdu/qilu/qilu-clinical-skills": { "id": "sdu/qilu/qilu-clinical-skills", "name": "\u9f50\u9c81\u4e34\u5e8a\u6280\u80fd\u5927\u6a21\u578b", "maker": "sdu", "family": "sdu/qilu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u9f50\u9c81\u4e34\u5e8a\u6280\u80fd\u5927\u6a21\u578b is a closed clinical-skills model launched on 2024-12-24 by Cheeloo College of Medicine at Shandong University with Inspur Cloud. Qilu Hospital later used it for resident exams and, in February 2025, connected it to DeepSeek-R1. No weights were found. It was still described in a September 2025 hospital award note. Collection date: 2026-10-15.", "what_it_is_for": "Launch reports say it is for clinical-skills teaching: simulated cases across internal medicine, surgery, obstetrics and gynecology, and pediatrics, learner profiles, and scoring of procedure, communication, and bedside manner. The core agent is an AI standardized patient. Students talk to it by voice, take a history, and practice clinical reasoning. On 2025-01-15 it was used in the history-taking station for 87 second-year residents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sdu/qilu/qilu-jingjie": { "id": "sdu/qilu/qilu-jingjie", "name": "\u9f50\u9c81\u00b7\u955c\u754c\u5589\u955c\u8bca\u65ad\u5927\u6a21\u578b", "maker": "sdu", "family": "sdu/qilu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Laryngoscope image model. The article says it was built on more than 100,000 laryngoscope studies for live marking and risk warning. Architecture and base checkpoint were not named.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u9f50\u9c81\u00b7\u955c\u754c is a closed laryngoscope diagnosis model launched on 2025-03-16 by the otolaryngology department of Qilu Hospital, Shandong University, with the Hong Kong Institute of Innovation of the Chinese Academy of Sciences. No weights or model card were found. No retirement notice was found by 2026-10-15. Collection date: 2026-10-15.", "what_it_is_for": "China News Service Shandong says it uses more than 100,000 laryngoscope studies to mark throat findings in real time and warn about risk. The stated goal is fewer missed and mistaken early tumors and better screening at primary hospitals. No accuracy figure was published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sdu/qilu/qilu-xinghuo": { "id": "sdu/qilu/qilu-xinghuo", "name": "\u9f50\u9c81\u00b7\u661f\u706b\u5168\u75c5\u7a0b\u7ba1\u7406\u5927\u6a21\u578b", "maker": "sdu", "family": "sdu/qilu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described as based on a generative large language model for full-course patient management. The checkpoint, including whether it is an iFLYTEK Spark model, is not named.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u9f50\u9c81\u00b7\u661f\u706b is a closed full-course-of-care model launched on 2025-03-16 by the medical-affairs office of Qilu Hospital, Shandong University, with iFLYTEK Medical. No weights or model card were found. No retirement notice was found by 2026-10-15. Collection date: 2026-10-15.", "what_it_is_for": "China News Service Shandong says it is a generative language model for a human-machine, clinician-coordinated patient service and a personal digital health space across the course of care. It does not name a Spark version.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sdu/qilu/qilu-xinqing": { "id": "sdu/qilu/qilu-xinqing", "name": "\u9f50\u9c81\u00b7\u5fc3\u64ce\u6025\u6027\u80f8\u75db\u5927\u6a21\u578b", "maker": "sdu", "family": "sdu/qilu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described as combining natural-language processing, medical-image analysis, and multimodal fusion for chest-pain records, ECG, and imaging. No layer or base-model name was published.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u9f50\u9c81\u00b7\u5fc3\u64ce is a closed acute-chest-pain model launched on 2025-03-16 by the emergency department of Qilu Hospital, Shandong University, with Huawei and Shanghai Runda Medical. No weights or model card were found. No retirement notice was found by 2026-10-15. Collection date: 2026-10-15.", "what_it_is_for": "China News Service Shandong says it combines language processing, medical-image analysis, and multimodal fusion so chest-pain charts, ECGs, and images can be read together. It does not publish a user manual or an API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sdu/sdu-law/sdu-law": { "id": "sdu/sdu-law/sdu-law", "name": "SDU\u6cd5\u5f8b\u5927\u6a21\u578b", "maker": "sdu", "family": "sdu/sdu-law", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Continued training from Qwen2-7B on legal data. The lab says the parameter count was raised to 9B. The method of adding parameters is not described.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SDU\u6cd5\u5f8b\u5927\u6a21\u578b is a closed legal model described by the Information Retrieval Lab at Shandong University as the engine of its campus legal assistant. The lab page says it was continued-trained from Qwen2-7B on legal data and that the parameter count was raised to 9B. No weights or model card were found. It was still described on the lab site on 2026-10-15. Collection date: 2026-10-15.", "what_it_is_for": "The lab says the legal assistant uses this model for judicial Q&A, course help, statute retrieval, similar-case recommendation, syllogistic decision support, pleading and answer drafting, and contract review. It says prompting and tool chains adapt the model to those jobs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sdu/sdu-llm/sdu-llm": { "id": "sdu/sdu-llm/sdu-llm", "name": "SDU\u5927\u6a21\u578b", "maker": "sdu", "family": "sdu/sdu-llm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described only as a fine-tune on Shandong University internal data. The base checkpoint is not named.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SDU\u5927\u6a21\u578b is a closed campus model named by the Information Retrieval Lab at Shandong University. The lab says it is fine-tuned on internal university data, deployed privately, and served with vLLM as the base engine of \u5c71\u5927AI\u52a9\u624b. The base checkpoint, size, and release date are not stated. It was still described on the lab site on 2026-10-15. Collection date: 2026-10-15.", "what_it_is_for": "The lab says the model is for campus question answering after fine-tuning on Shandong University data. Listed modes on the same page include general Q&A, copy editing, an English dictionary, English translation, IT support, and poem writing. Those modes are product features of the assistant, not a published capability list for the weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/character/sensechat-character": { "id": "sensetime/character/sensechat-character", "name": "SenseChat-Character", "maker": "sensetime", "family": "sensetime/character", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Role-play dialogue model. The product page calls this the standard, fast, short-reply tier.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseChat-Character is the standard role-play id in SenseTime's character lineup. Collected 2026-10-15. The product page lists an 8K window, Chinese and English, and a short conversational style. The page title also says SenseNova V6.5 Character; that is the product-line name, not proof this checkpoint was retrained in July 2025.", "what_it_is_for": "Casual in-character chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/character/sensechat-character-pro": { "id": "sensetime/character/sensechat-character-pro", "name": "SenseChat-Character-Pro", "maker": "sensetime", "family": "sensetime/character", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Higher-end role-play model. Marketing page says 16K; the compatible-mode API table says 32K. The JSON field uses the API table.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 15.0, "completion_per_m_usd": 15.0 }, "summary_intro": "SenseChat-Character-Pro is the advanced character model for plot-heavy Chinese and English role play. Collected 2026-10-15. SenseTime publishes CharacterEval scores for this id and recommends it for two-person chat and streaming.", "what_it_is_for": "Longer in-character dialogue and story progression. Streaming requires n=1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/character/sensechat-character-turbo": { "id": "sensetime/character/sensechat-character-turbo", "name": "SenseChat-Character-Turbo", "maker": "sensetime", "family": "sensetime/character", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Full-feature character model. Product page: 8K, group chat up to 8 characters, knowledge bases, and tool calls. The API guide says this id needs a separate activation email.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseChat-Character-Turbo is the group-chat and tool-using character model. Collected 2026-10-15. It is the id SenseTime recommends for multi-character scenes. Activation is separate from the other character ids.", "what_it_is_for": "IP role play, group chat, knowledge-base replies, and function calls.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/embedding/piccolo-base-zh": { "id": "sensetime/embedding/piccolo-base-zh", "name": "Piccolo-base-zh", "maker": "sensetime", "family": "sensetime/embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Chinese text embedding. SenseTime says version 1 was pretrained on 400 million Chinese text pairs. Query prompt \u67e5\u8be2 and passage prompt \u7ed3\u679c. Hidden size was not printed on the model-list table. The training code's scaling layer is documented for the large v2 line, not proven for this base checkpoint.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Piccolo-base-zh is the smaller Chinese embedding model in SenseTime's Piccolo release. Collected 2026-10-15. The OpenSenseNova README lists it as version 1, pretrained on 400 million Chinese pairs.", "what_it_is_for": "Chinese retrieval and semantic search. Use the prompts \u67e5\u8be2 and \u7ed3\u679c.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/embedding/piccolo-large-zh": { "id": "sensetime/embedding/piccolo-large-zh", "name": "Piccolo-large-zh", "maker": "sensetime", "family": "sensetime/embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Chinese text embedding, version 1 large. Pretrained on 400 million Chinese text pairs. Prompts \u67e5\u8be2 and \u7ed3\u679c.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Piccolo-large-zh is the large version-1 Piccolo embedding model from SenseTime. Collected 2026-10-15. Version 2 replaced it as the CMTEB headline model.", "what_it_is_for": "Chinese retrieval. Prompts differ from v2, which uses none.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/embedding/piccolo-large-zh-v2": { "id": "sensetime/embedding/piccolo-large-zh-v2", "name": "Piccolo-large-zh-v2", "maker": "sensetime", "family": "sensetime/embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Piccolo2 large Chinese embedding. Multi-task hybrid loss and Matryoshka dimensions. The training code nests 256, 512, 768, 1024, 1280, 1536, and 1792, and scales 1024 to 1792. That is the repo default, not a sentence on the model card that was re-read here. No query prompt.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Piccolo-large-zh-v2 is SenseTime's Piccolo2 Chinese embedding model. The project README says it reached a CMTEB average of 70.95 on 23 April 2024, then a state of the art on that board. Collected 2026-10-15. The paper is arXiv 2405.06932.", "what_it_is_for": "General Chinese sentence embeddings for retrieval, clustering, classification, and similarity, without a prompt prefix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/looped-dit/looped-dit-b16": { "id": "sensetime/looped-dit/looped-dit-b16", "name": "Looped-DiT-B/16", "maker": "sensetime", "family": "sensetime/looped-dit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-09-30", "context_length": null, "max_output_tokens": null, "parameters": "260M", "architecture": "Looped diffusion transformer based on MiniT2I. Patch size 16. The paper's comparison row lists 0.26B parameters. Looping reuses blocks inside a denoising step. License on the GitHub repo is MIT.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Looped-DiT-B/16 is SenseTime Research's open text-to-image model that loops transformer blocks instead of adding parameters. The paper page is dated 30 September 2026. Collected 2026-10-15. Authors also include Tsinghua University and Nanyang Technological University. The Hugging Face repo is under sensenova.", "what_it_is_for": "Text-to-image generation at a very small parameter count. The paper reports the main benchmark table on B/16.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/looped-dit/looped-dit-b32": { "id": "sensetime/looped-dit/looped-dit-b32", "name": "Looped-DiT-B/32", "maker": "sensetime", "family": "sensetime/looped-dit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-09-30", "context_length": null, "max_output_tokens": null, "parameters": "260M", "architecture": "Same looped MiniT2I design with patch size 32. The paper's analysis table lists 260 million parameters, hidden size 768, and effective depth 32. Used for ablations more than the headline table.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Looped-DiT-B/32 is the lighter-patch open checkpoint released with the Looped-DiT paper on 30 September 2026. Collected 2026-10-15. It is a named weight release, not only an unlabeled ablation.", "what_it_is_for": "Text-to-image research checkpoint for the looping analysis.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/miaohua/sensemirage": { "id": "sensetime/miaohua/sensemirage", "name": "\u79d2\u753b SenseMirage", "maker": "sensetime", "family": "sensetime/miaohua", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-04-10", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "First SenseMirage text-to-image model. July 2023 coverage said the April release was about 1 billion parameters (10\u4ebf).", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseMirage is SenseTime's first named text-to-image model, shown on 10 April 2023 inside SenseNova. Collected 2026-10-15. Later coverage put it at about 1 billion parameters. It is retired in favor of SenseMirage 3.0 and the later Miaohua APIs.", "what_it_is_for": "Text-to-image generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/miaohua/sensemirage-3": { "id": "sensetime/miaohua/sensemirage-3", "name": "\u79d2\u753b SenseMirage 3.0", "maker": "sensetime", "family": "sensetime/miaohua", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-07-07", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "SenseMirage generation shown at WAIC 2023. Press said about 7 billion parameters (70\u4ebf).", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseMirage 3.0 is the July 2023 SenseTime text-to-image upgrade, described as photographic detail at about 7 billion parameters. Collected 2026-10-15. It is retired relative to the later \u79d2\u753b API generations.", "what_it_is_for": "Higher-detail text-to-image than the April 2023 model, including mobile content tools paired with SenseChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/miaohua/sensenova-miaohua-v5": { "id": "sensetime/miaohua/sensenova-miaohua-v5", "name": "\u65e5\u65e5\u65b0\u79d2\u753b V5", "maker": "sensetime", "family": "sensetime/miaohua", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "\u79d2\u753b text-to-image API generation V5. The release note says this update added English text rendering inside images. Chinese text support was implied as already present but not spelled out.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u79d2\u753b V5 is a named SenseNova text-to-image API generation in the SenseCore release notes. Collected 2026-10-15. The note highlights English characters drawn inside images. The release date was not in the excerpt that was retrieved, and the current product page brands image generation as V6.5 Miaohua, so this id is marked deprecated at medium confidence.", "what_it_is_for": "Text-to-image with rendered Latin text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/miaohua/sensenova-v6-5-miaohua": { "id": "sensetime/miaohua/sensenova-v6-5-miaohua", "name": "SenseNova V6.5 Miaohua", "maker": "sensetime", "family": "sensetime/miaohua", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Current \u79d2\u753b text-to-image product on the platform. Basic and advanced tiers are commercial bundles, not proven to be two different checkpoints.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": 0.03, "completion_per_m_usd": null }, "summary_intro": "SenseNova V6.5 Miaohua is the text-to-image product on the current platform technology page. Collected 2026-10-15. Release day was not retrieved. The price page sells it as basic and advanced tool bundles.", "what_it_is_for": "Text-to-image plus edit tools: face swap and upscale on the basic bundle; erase, extend, stylize, and local redraw on the advanced bundle.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/seko/seko-talk": { "id": "sensetime/seko/seko-talk", "name": "SekoTalk", "maker": "sensetime", "family": "sensetime/seko", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Audio-driven digital-human video model. SenseTime's 10 December 2025 post says it launched in August 2025 and is used in Seko and SenseAvatar. Exact parameter count not published.", "modalities": { "input": [ "audio", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SekoTalk is SenseTime's realtime speech-driven digital-human model. The company post dated 10 December 2025 says the system launched in August 2025. Collected 2026-10-15. On an eight-GPU server SenseTime reports 25 frames per second and first-frame latency of 3.5 seconds. The month is stored as 2025-08 because the day was not printed. Weights openness was not confirmed: the GitHub repo is public, but a weight license was not in the retrieved post.", "what_it_is_for": "Lip-synced talking-head video for more than one speaker and language, including long clips. The consumer surface is Seko and SenseAvatar.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/senseaudio/senseaudio-a1": { "id": "sensetime/senseaudio/senseaudio-a1", "name": "SenseAudio-A1", "maker": "sensetime", "family": "sensetime/senseaudio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseAudio-A1 is SenseTime's audio-generation model id on SenseAudio. Collected 2026-10-15. The platform describes one-shot multi-speaker dialogue, music, and ambience. A numeric price was not in the captured table.", "what_it_is_for": "Full-mix audio from a natural-language description, with up to three reference voices at the product level.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/senseaudio/senseaudio-asr-1-5": { "id": "sensetime/senseaudio/senseaudio-asr-1-5", "name": "SenseAudio-ASR-1.5", "maker": "sensetime", "family": "sensetime/senseaudio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.8, "completion_per_m_usd": null }, "summary_intro": "SenseAudio-ASR-1.5 is a SenseTime SenseAudio speech-recognition model, id senseaudio-asr-1.5-260319. Collected 2026-10-15.", "what_it_is_for": "General transcription.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/senseaudio/senseaudio-asr-deepthink-1-5": { "id": "sensetime/senseaudio/senseaudio-asr-deepthink-1-5", "name": "SenseAudio-ASR-DeepThink-1.5", "maker": "sensetime", "family": "sensetime/senseaudio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.6, "completion_per_m_usd": null }, "summary_intro": "SenseAudio-ASR-DeepThink-1.5 is a SenseTime SenseAudio speech-recognition model, id senseaudio-asr-deepthink-1.5-260319. Collected 2026-10-15.", "what_it_is_for": "Recognition with extra reasoning, per the table note.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/senseaudio/senseaudio-asr-lite-1-5": { "id": "sensetime/senseaudio/senseaudio-asr-lite-1-5", "name": "SenseAudio-ASR-Lite-1.5", "maker": "sensetime", "family": "sensetime/senseaudio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.9, "completion_per_m_usd": null }, "summary_intro": "SenseAudio-ASR-Lite-1.5 is a SenseTime SenseAudio speech-recognition model, id senseaudio-asr-lite-1.5-260319. Collected 2026-10-15.", "what_it_is_for": "Low-cost file transcription.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/senseaudio/senseaudio-asr-pro-1-5": { "id": "sensetime/senseaudio/senseaudio-asr-pro-1-5", "name": "SenseAudio-ASR-Pro-1.5", "maker": "sensetime", "family": "sensetime/senseaudio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.6, "completion_per_m_usd": null }, "summary_intro": "SenseAudio-ASR-Pro-1.5 is a SenseTime SenseAudio speech-recognition model, id senseaudio-asr-pro-1.5-260319. Collected 2026-10-15.", "what_it_is_for": "Higher-accuracy transcription.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/senseaudio/senseaudio-asr-stream-1-5": { "id": "sensetime/senseaudio/senseaudio-asr-stream-1-5", "name": "SenseAudio-ASR-Stream-1.5", "maker": "sensetime", "family": "sensetime/senseaudio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.6, "completion_per_m_usd": null }, "summary_intro": "SenseAudio-ASR-Stream-1.5 is a SenseTime SenseAudio speech-recognition model, id senseaudio-asr-stream-1.5-260910. Collected 2026-10-15.", "what_it_is_for": "Streaming realtime transcription. Id suffix 260910 was not confirmed as 10 September 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/senseaudio/senseaudio-image-2-0": { "id": "sensetime/senseaudio/senseaudio-image-2-0", "name": "SenseAudio-Image-2.0", "maker": "sensetime", "family": "sensetime/senseaudio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseAudio-Image-2.0 is SenseTime's SenseAudio text-to-image id, senseaudio-image-2.0-260319. Collected 2026-10-15. The Token Plan calls it the latest self-developed image model on that platform. It is not the same product as SenseNova U1 or Miaohua.", "what_it_is_for": "General image generation on SenseAudio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/senseaudio/senseaudio-music-2-0": { "id": "sensetime/senseaudio/senseaudio-music-2-0", "name": "SenseAudio-Music-2.0", "maker": "sensetime", "family": "sensetime/senseaudio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseAudio-Music-2.0 is SenseTime's song-generation model, API id senseaudio-music-2.0-260626. Collected 2026-10-15. The Token Plan calls it the new full-song model. The date inside the id is not treated as a confirmed release day.", "what_it_is_for": "Lyrics and full songs from prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/senseaudio/senseaudio-s2": { "id": "sensetime/senseaudio/senseaudio-s2", "name": "SenseAudio-S2", "maker": "sensetime", "family": "sensetime/senseaudio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 30.0 }, "summary_intro": "SenseAudio-S2 is a SenseTime SenseAudio language model, model id senseaudio-s2. Collected 2026-10-15. SenseAudio docs call the S2 series self-developed and say the platform is SenseTime's. Parameter count was not published.", "what_it_is_for": "Flagship SenseAudio LLM: 1M context, deep reasoning, and tool use. Max output on the table is 128K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/senseaudio/senseaudio-s2-flash": { "id": "sensetime/senseaudio/senseaudio-s2-flash", "name": "SenseAudio-S2-Flash", "maker": "sensetime", "family": "sensetime/senseaudio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 12.0 }, "summary_intro": "SenseAudio-S2-Flash is a SenseTime SenseAudio language model, model id senseaudio-s2-flash. Collected 2026-10-15. SenseAudio docs call the S2 series self-developed and say the platform is SenseTime's. Parameter count was not published.", "what_it_is_for": "High-throughput S2. Table: 256K context, max input 254K, max output 64K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/senseaudio/senseaudio-s2-lite": { "id": "sensetime/senseaudio/senseaudio-s2-lite", "name": "SenseAudio-S2-Lite", "maker": "sensetime", "family": "sensetime/senseaudio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 6.0 }, "summary_intro": "SenseAudio-S2-Lite is a SenseTime SenseAudio language model, model id senseaudio-s2-lite. Collected 2026-10-15. SenseAudio docs call the S2 series self-developed and say the platform is SenseTime's. Parameter count was not published.", "what_it_is_for": "Low-cost S2. Same 256K / 254K / 64K window figures as Flash on the captured table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/senseaudio/senseaudio-sfx-1-0": { "id": "sensetime/senseaudio/senseaudio-sfx-1-0", "name": "SenseAudio-Sound-Effect-1.0", "maker": "sensetime", "family": "sensetime/senseaudio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.08, "completion_per_m_usd": null }, "summary_intro": "SenseAudio-Sound-Effect-1.0 is SenseTime's sound-effect model, id senseaudio-sfx-1.0-260626. Collected 2026-10-15.", "what_it_is_for": "Text-to-sound-effect generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/senseaudio/senseaudio-tts-1-5": { "id": "sensetime/senseaudio/senseaudio-tts-1-5", "name": "SenseAudio-TTS-1.5", "maker": "sensetime", "family": "sensetime/senseaudio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 3.5, "completion_per_m_usd": null }, "summary_intro": "SenseAudio-TTS-1.5 is SenseTime's SenseAudio speech model, API id senseaudio-tts-1.5-260319. Collected 2026-10-15. The note says multi-emotion, multi-style, polyphonic correction, and formula reading. The 260319 suffix looks like a 19 March 2026 snapshot; that reading is not confirmed by a changelog sentence, so release_date stays null.", "what_it_is_for": "General emotional TTS. SenseNova-TTS-2.0 is the recommended alternative on the Token Plan page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/senseaudio/senseaudio-voice-isolation": { "id": "sensetime/senseaudio/senseaudio-voice-isolation", "name": "SenseAudio Voice Isolation", "maker": "sensetime", "family": "sensetime/senseaudio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseAudio Voice Isolation is SenseTime's vocal-separation model on SenseAudio. Collected 2026-10-15. The Token Plan lists 1.0 (id senseaudio-voice-isolation-1.0-260319) and 1.5. Sync and async modes are mentioned.", "what_it_is_for": "Separate vocals from a mix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensechat/sensechat": { "id": "sensetime/sensechat/sensechat", "name": "\u5546\u91cf SenseChat 1.0", "maker": "sensetime", "family": "sensetime/sensechat", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-04-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-style Chinese language model. Press reports called it a hundred-billion-parameter class model; an exact count was not consistent across outlets.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseTime announced \u5546\u91cf SenseChat on 10 April 2023 as the language model inside the first \u65e5\u65e5\u65b0 SenseNova system. This dossier is the original 1.0 generation, collected 2026-10-15. It is retired; later API ids reuse the SenseChat name for newer generations.", "what_it_is_for": "SenseTime demonstrated multi-turn Chinese dialogue, long-document reading, code assistance, ad copy, and story writing. A contemporary report said an internal HumanEval pass@1 was 40.2 percent and that internal coding efficiency rose about 62 percent.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensechat/sensechat-128k": { "id": "sensetime/sensechat/sensechat-128k", "name": "SenseChat-128K", "maker": "sensetime", "family": "sensetime/sensechat", "status": "deprecated", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "Long-context V4 window announced with SenseNova 4.0. Not seen in the partial 2025\u20132026 API tables that were retrieved.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseChat-128K is the 128K-context SenseChat name from SenseTime's SenseNova 4.0 announcement. Collected 2026-10-15. The indexed SenseCore general-LLM page listed SenseChat and SenseChat-32K but the retrieved excerpt did not list a 128K V4 id; SenseChat-5 later carries the 128K window. Status is deprecated at low confidence because a formal retirement notice was not found.", "what_it_is_for": "Long-document chat in the V4 generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensechat/sensechat-2": { "id": "sensetime/sensechat/sensechat-2", "name": "\u5546\u91cf SenseChat 2.0", "maker": "sensetime", "family": "sensetime/sensechat", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-07-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hundred-billion-class language model. SenseTime said 2.0 added several parameter scales, including a mobile SenseChat S.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseChat 2.0 is SenseTime's mid-2023 language-model upgrade. Scores were posted in June 2023 and the product update was shown at WAIC on 7 July 2023. Collected 2026-10-15. The generation is retired.", "what_it_is_for": "SenseTime positioned 2.0 for longer context than 1.0, Arabic and Cantonese, code assistance, and both cloud and phone deployment. A smaller SenseChat S was named at the same event.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensechat/sensechat-3": { "id": "sensetime/sensechat/sensechat-3", "name": "\u5546\u91cf SenseChat 3.0", "maker": "sensetime", "family": "sensetime/sensechat", "status": "retired", "confidence": "low", "weights": "closed", "license": "", "release_date": "2023-08-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseTime opened the \u5546\u91cf consumer service on 31 August 2023. Secondary writeups call that public generation SenseChat 3.0. Collected 2026-10-15. Confidence is low because the version label and base-model claim come from one trade article, not a SenseTime model card.", "what_it_is_for": "Public Chinese chat after a closed preview. The same article said the base at opening was the jointly released InternLM-123B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensechat/sensechat-32k": { "id": "sensetime/sensechat/sensechat-32k", "name": "SenseChat-32K", "maker": "sensetime", "family": "sensetime/sensechat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "SenseCore labels this id as \u65e5\u65e5\u65b0\u5546\u91cf\u901a\u7528 V4.0 with a 32K window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseChat-32K is the 32,768-token V4.0 SenseChat API id on SenseCore. Collected 2026-10-15. It was one of three window sizes named at the SenseNova 4.0 launch.", "what_it_is_for": "General chat when the 4K SenseChat window is too short and the later 128K SenseChat-5 is not required.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensechat/sensechat-5": { "id": "sensetime/sensechat/sensechat-5", "name": "SenseChat-5", "maker": "sensetime", "family": "sensetime/sensechat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "SenseCore's general-LLM page calls this id model version V5.5. The compatible-mode table calls it V5. Both say 128K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 8.0, "completion_per_m_usd": 20.0 }, "summary_intro": "SenseChat-5 is SenseTime's general language API id for the SenseNova 5 generation, documented with a 128K window. Collected 2026-10-15. SenseNova 5.0 was announced in April 2024 as a GPT-4 Turbo-class domestic model, and SenseNova 5.5 followed at WAIC in early July 2024. The live id is labeled V5.5 on the general-LLM page and V5 on the compatible-mode table.", "what_it_is_for": "General language, knowledge, reasoning, math, and code. SenseTime's 5.5 launch said overall performance was about 30 percent above 5.0, with gains in math, English, and instruction following. Training for 5.5 was described as more than 10 TB of tokens, including synthetic chain-of-thought data, on a mixed device-cloud MoE. A 600 billion parameter figure appears in secondary IT coverage and is not stored as a spec because the SenseCore pages retrieved did not state it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensechat/sensechat-5-1202": { "id": "sensetime/sensechat/sensechat-5-1202", "name": "SenseChat-5-1202", "maker": "sensetime", "family": "sensetime/sensechat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-30", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "SenseCore general-LLM page: model version V5.5, described as the 1202 upgrade of the general chat model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseChat-5-1202 is a 30 December 2024 SenseCore release, a V5.5 general model with a 32K window. Collected 2026-10-15. It is a separate API id from 128K SenseChat-5, aimed at everyday dialogue quality.", "what_it_is_for": "Stronger Chinese and English chat, school-subject knowledge, math logic, information extraction, instruction following, and length control, per the release note.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensechat/sensechat-5-cantonese": { "id": "sensetime/sensechat/sensechat-5-cantonese", "name": "SenseChat-5-Cantonese", "maker": "sensetime", "family": "sensetime/sensechat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 27.0, "completion_per_m_usd": 27.0 }, "summary_intro": "SenseChat-5-Cantonese is SenseTime's Cantonese chat API id, listed at 32K context. Collected 2026-10-15. The table says Cantonese dialogue understanding surpasses GPT-4 and that knowledge, reasoning, math, and code are in the GPT-4 Turbo class. Those are vendor claims without a cited eval table.", "what_it_is_for": "Cantonese conversation plus general knowledge, reasoning, math, and code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensechat/sensechat-5-lite": { "id": "sensetime/sensechat/sensechat-5-lite", "name": "\u5546\u91cf\u901a\u7528\u8f7b\u91cf 5.0", "maker": "sensetime", "family": "sensetime/sensechat", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-05-13", "context_length": 32768, "max_output_tokens": null, "parameters": "20B", "architecture": "Dense lightweight language model. SenseCore release note: 200\u4ebf parameters and a 32K window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "On 13 May 2024 SenseCore's release notes added a lightweight SenseNova 5.0 chat model with 20 billion parameters and a 32K window. Collected 2026-10-15. A later page labels SenseChat-Turbo-1202 as the V5.5 lightweight id, so this May 2024 model is treated as retired into that line. The API id string was not printed in the release-note excerpt.", "what_it_is_for": "Lower-cost general chat, math, code, and commonsense tasks than the full SenseNova 5.0 model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensechat/sensechat-s": { "id": "sensetime/sensechat/sensechat-s", "name": "\u5546\u91cf SenseChat S", "maker": "sensetime", "family": "sensetime/sensechat", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-07-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Smaller SenseChat variant for phones and other edge devices. Parameter count was not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseChat S is the small SenseChat size SenseTime named at WAIC on 7 July 2023 for mobile deployment. Collected 2026-10-15. No later API id for this name was found, so it is treated as retired.", "what_it_is_for": "On-device or low-cost deployment of the 2023 SenseChat line, paired with SenseMirage for phone content tools.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensechat/sensechat-turbo": { "id": "sensetime/sensechat/sensechat-turbo", "name": "SenseChat-Turbo", "maker": "sensetime", "family": "sensetime/sensechat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 0.6 }, "summary_intro": "SenseChat-Turbo is the cheap, fast SenseChat API id on the SenseCore compatible-mode price table. Collected 2026-10-15. SenseChat-Turbo-1202 is a later named V5.5 lightweight id and has its own folder.", "what_it_is_for": "Fast question answering and fine-tuning scenarios, per the model table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensechat/sensechat-turbo-1202": { "id": "sensetime/sensechat/sensechat-turbo-1202", "name": "SenseChat-Turbo-1202", "maker": "sensetime", "family": "sensetime/sensechat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-30", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "SenseCore general-LLM page: V5.5 lightweight general model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseChat-Turbo-1202 is the V5.5 lightweight general model SenseCore released on 30 December 2024, with a 32K window and function calling. Collected 2026-10-15.", "what_it_is_for": "Lower-cost general chat than SenseChat-5, with the December 2024 dialogue upgrades.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensechat/sensechat-v4": { "id": "sensetime/sensechat/sensechat-v4", "name": "SenseChat V4\uff084K\uff09", "maker": "sensetime", "family": "sensetime/sensechat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "SenseCore labels this API id as \u65e5\u65e5\u65b0\u5546\u91cf\u901a\u7528 V4.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 12.0, "completion_per_m_usd": 12.0 }, "summary_intro": "SenseChat, model id SenseChat, is the V4.0 general chat model still listed in SenseTime's SenseCore API tables, with a 4K context. Collected 2026-10-15. The SenseNova 4.0 generation was announced around Lunar New Year 2024; the exact calendar day was not on the retrieved pages. The consumer SenseChat brand was no longer a promoted surface as of the 27 August 2026 FAQ, but this API id was still in the indexed model table.", "what_it_is_for": "General Chinese and English chat. The 4.0 launch also offered 32K and 128K window variants as separate ids.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensechat/sensechat-vision": { "id": "sensetime/sensechat/sensechat-vision", "name": "SenseChat-Vision", "maker": "sensetime", "family": "sensetime/sensechat", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 60.0 }, "summary_intro": "SenseChat-Vision is SenseTime's earlier image-text chat API id, still on the compatible-mode price table with a 4K window. Collected 2026-10-15. SenseTime said it led MMBench at the time of that product line. The numeric MMBench score was not in the retrieved table. Newer native multimodal ids are the V6 and V6.5 models.", "what_it_is_for": "Image-text understanding and visual question answering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova/sensenova-5o": { "id": "sensetime/sensenova/sensenova-5o", "name": "\u65e5\u65e5\u65b0 SenseNova 5o", "maker": "sensetime", "family": "sensetime/sensenova", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-07-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Streaming native multimodal interaction model shown with SenseNova 5.5. SenseTime compared the interaction style to GPT-4o.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseNova 5o is the streaming see-and-talk model SenseTime showed with SenseNova 5.5 at WAIC in July 2024. Collected 2026-10-15. The day 5 July 2024 comes from a secondary recap. The platform still posted 5o demos in January 2025. It is not in the 2026 FAQ's current model list, so status is deprecated rather than retired: no shutdown notice was found.", "what_it_is_for": "Real-time voice, text, image, and video interaction, including reading a name badge or describing a scene while talking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova/sensenova-6-7-flash-lite": { "id": "sensetime/sensenova/sensenova-6-7-flash-lite", "name": "SenseNova 6.7 Flash-Lite", "maker": "sensetime", "family": "sensetime/sensenova", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-08", "context_length": 262144, "max_output_tokens": 65536, "parameters": "", "architecture": "Lightweight native multimodal agent. The token-platform model object lists fp8 quantization. Parameter count was described only as small.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseNova 6.7 Flash-Lite is a lightweight multimodal office agent. SenseTime's Hong Kong news post is dated 8 May 2026. Collected 2026-10-15. Some reprints say 28 July 2026; that conflict is unresolved, and the May date is the one on the company URL. The token API documents a 256K context and 65,536 max output. The model sees pages, charts, and screens without a separate caption step. SenseTime said high-frequency lookup uses about 60 percent fewer tokens than text-only agents.", "what_it_is_for": "Data analysis, research writeups, and slide workflows, with SenseNova-Skills on GitHub. Skills are a software pack, not a model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova/sensenova-6-8-flash-lite": { "id": "sensetime/sensenova/sensenova-6-8-flash-lite", "name": "SenseNova 6.8 Flash-Lite", "maker": "sensetime", "family": "sensetime/sensenova", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 262144, "max_output_tokens": 65536, "parameters": "", "architecture": "Lightweight native multimodal agent aimed at delegated multi-step work. OpenSenseNova's API note still calls the build a preview. Parameter count not published.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 30.0 }, "summary_intro": "SenseNova 6.8 Flash-Lite is the lightweight multimodal agent SenseTime's FAQ names as a current showcase model, together with U1 Pro and U1.5 Lite. Collected 2026-10-15. The OpenSenseNova API note still marks it as an early preview and tells callers to use id sensenova-6.8-flash-lite. The GitHub repo was last updated 11 August 2026; that is a repo date, not a confirmed launch day, so release_date is null. The FAQ, current as of a 27 August 2026 check printed on the page, says Token Plan supports this id and U1 Fast.", "what_it_is_for": "Long office jobs: planning, tool use, self-checks, and splitting work across more than ten specialist agents. Inputs described by SenseTime include text, images, charts, documents, video, web pages, and app screens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova/sensenova-v6-5-omni": { "id": "sensetime/sensenova/sensenova-v6-5-omni", "name": "SenseNova V6.5 Omni", "maker": "sensetime", "family": "sensetime/sensenova", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Streaming multimodal model over a realtime network (RTC). The platform says it accepts live audio-video or audio-only input and returns audio.", "modalities": { "input": [ "audio", "video" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.08, "completion_per_m_usd": null }, "summary_intro": "SenseNova V6.5 Omni is the realtime interaction model on the current platform technology and pricing pages. Collected 2026-10-15. Release day was not in the retrieved pages. It is billed per minute, cheaper on that page than V6 Omni's 0.2 CNY.", "what_it_is_for": "Live voice and video conversation with spoken replies.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova/sensenova-v6-5-pro": { "id": "sensetime/sensenova/sensenova-v6-5-pro", "name": "SenseNova V6.5 Pro", "maker": "sensetime", "family": "sensetime/sensenova", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-23", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "Native multimodal model. The 2025 annual report says V6.5 uses early fusion and cuts the vision encoder to about 1 billion parameters. Total parameter count was not stated. Think and no-think were merged in one training recipe.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 9.0 }, "summary_intro": "SenseNova-V6.5-Pro is the 23 July 2025 general multimodal API model and the latest pro id on the retrieved SenseCore tables. Collected 2026-10-15. SenseTime described interleaved image-text reasoning, in which images stay in the chain of thought instead of being captioned first. The company said reasoning rose 6.99 percent versus 6.0 while cost fell to about 30 percent, a fivefold cost-performance claim. Those relatives are not stored as benchmark scores.", "what_it_is_for": "General multimodal chat, OCR, tourism and IP recognition, and agent-style instruction following. The compatible-mode blurb says the V6.5 models took OpenCompass and SuperCLUE titles. Numeric scores were not in the excerpt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova/sensenova-v6-5-turbo": { "id": "sensetime/sensenova/sensenova-v6-5-turbo", "name": "SenseNova V6.5 Turbo", "maker": "sensetime", "family": "sensetime/sensenova", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-23", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "Lightweight V6.5 multimodal model. Same release note as Pro: merged think / no-think training.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.5, "completion_per_m_usd": 4.5 }, "summary_intro": "SenseNova-V6.5-Turbo is the lightweight 23 July 2025 multimodal id, documented at 128K. Collected 2026-10-15. It remains on the enterprise price list beside V6.5 Pro.", "what_it_is_for": "Lower-cost image, text, and video understanding with text output.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova/sensenova-v6-omni": { "id": "sensetime/sensenova/sensenova-v6-omni", "name": "SenseNova V6 Omni", "maker": "sensetime", "family": "sensetime/sensenova", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lightweight full-modal streaming model. SenseTime compared it with GPT-4o mini at launch. Parameter count not published.", "modalities": { "input": [ "text", "audio", "video" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": null }, "summary_intro": "SenseNova V6 Omni is the realtime audio-video model in the April 2025 V6 family. Collected 2026-10-15. SenseCore bills it per minute. The platform's later realtime product is V6.5 Omni at a lower per-minute rate.", "what_it_is_for": "Live speech and video conversation for tutoring, translation, and similar turn-taking. Launch demos included guided math help.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova/sensenova-v6-pro": { "id": "sensetime/sensenova/sensenova-v6-pro", "name": "SenseNova V6 Pro", "maker": "sensetime", "family": "sensetime/sensenova", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Native multimodal mixture-of-experts. Press at the 10 April 2025 tech day said 620 billion total parameters. The SenseCore release note excerpt says \u767e\u4ebf\u53c2\u6570 with \u6570\u5341\u4ebf or more activated. Those statements disagree, so parameters is null.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 9.0 }, "summary_intro": "SenseNova-V6-Pro is SenseTime's general native multimodal model launched at the 10 April 2025 tech day and listed on SenseCore from 9 April 2025. Collected 2026-10-15. It takes text, images, and, per the launch, video, and returns text. It is still on the SenseCore price list next to V6.5.", "what_it_is_for": "General image-text dialogue. SenseTime said the generation was the first domestic native multimodal general model and that one model led SuperCLUE language and OpenCompass multimodal boards. Numeric ranks were not in the annual-report excerpt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova/sensenova-v6-reasoner": { "id": "sensetime/sensenova/sensenova-v6-reasoner", "name": "SenseNova V6 Reasoner", "maker": "sensetime", "family": "sensetime/sensenova", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "V6 multimodal model with long chain-of-thought. Launch coverage also used the name Reasoner Pro. The API id retrieved is SenseNova-V6-Reasoner.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": 16.0 }, "summary_intro": "SenseNova-V6-Reasoner is the slow-thinking multimodal id shipped on 9 April 2025. Collected 2026-10-15. It returns a chain of thought and a final answer. SenseTime trained the V6 reasoner path on what it called more than 200 billion high-quality multimodal chain-of-thought items and said chains can reach 64K tokens.", "what_it_is_for": "Hard multimodal reasoning: homework photos, charts, and multi-step visual problems. Launch comparisons were to OpenAI o1 and Gemini 2.0 Flash Thinking. No side-by-side numbers were in the retrieved SenseCore note.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova/sensenova-v6-turbo": { "id": "sensetime/sensenova/sensenova-v6-turbo", "name": "SenseNova V6 Turbo", "maker": "sensetime", "family": "sensetime/sensenova", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lightweight native multimodal sibling of V6 Pro. Parameter count not published.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.5, "completion_per_m_usd": 4.5 }, "summary_intro": "SenseNova-V6-Turbo is the lightweight V6 multimodal API id, listed from 9 April 2025. Collected 2026-10-15. It accepts image and text and returns text.", "what_it_is_for": "Cheaper image-text chat than V6 Pro.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova/sensenova-v6-video": { "id": "sensetime/sensenova/sensenova-v6-video", "name": "SenseNova V6 Video", "maker": "sensetime", "family": "sensetime/sensenova", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-04-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video-understanding member of the V6 family. Launch coverage said it accepts about 10 minutes of video.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseNova V6 Video is the video-understanding model named at the 10 April 2025 SenseNova V6 launch. Collected 2026-10-15. It was not on the retrieved price tables, so a public API id and price are unconfirmed. Status stays active because the launch presented it as a shipped version, not a rumor.", "what_it_is_for": "Summarize, analyze, and reason over videos up to about 10 minutes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova-u/sensenova-u1-5-8b-mot": { "id": "sensetime/sensenova-u/sensenova-u1-5-8b-mot", "name": "SenseNova-U1.5-8B-MoT", "maker": "sensetime", "family": "sensetime/sensenova-u", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-17", "context_length": null, "max_output_tokens": null, "parameters": "8B MoT", "architecture": "NEO-unify 8B Mixture-of-Transformers. SenseTime says U1.5 has no external vision encoder and no VAE. Each 32 by 32 pixel block is one visual token. Native 4K generation is a stated capability. Hugging Face also tags the repo at 18B, matching the earlier 8B+8B split.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseNova-U1.5-8B-MoT is the open flagship of the U1.5 generation. SenseTime's technical-report post is dated 17 September 2026. Collected 2026-10-15. The GitHub table calls it the current flagship checkpoint. It improves text rendering, infographics, and native 4K generation relative to U1.", "what_it_is_for": "Image generation and editing, visual question answering, and interleaved creation without a separate diffusion VAE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova-u/sensenova-u1-8b-mot": { "id": "sensetime/sensenova-u/sensenova-u1-8b-mot", "name": "SenseNova-U1-8B-MoT", "maker": "sensetime", "family": "sensetime/sensenova-u", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-27", "context_length": null, "max_output_tokens": null, "parameters": "8B+8B", "architecture": "NEO-unify Mixture-of-Transformers. SenseTime says the 8B-MoT name means about 8B understanding parameters and about 8B generation parameters. Hugging Face lists the repo at 18B. No external vision encoder or VAE; pixel-space generation.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseNova-U1-8B-MoT is the dense open checkpoint of SenseNova U1, SenseTime's native unified understanding and generation model. Weights and code were posted 27 April 2026; the company announcement is 28\u201330 April 2026. Collected 2026-10-15. It is built on the NEO-unify design SenseTime described in March 2026.", "what_it_is_for": "Text and image understanding, reasoning, text-to-image, editing, and interleaved image-text generation in one model. Specialized infographic and interleaved checkpoints have their own folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova-u/sensenova-u1-8b-mot-infographic": { "id": "sensetime/sensenova-u/sensenova-u1-8b-mot-infographic", "name": "SenseNova-U1-8B-MoT-Infographic", "maker": "sensetime", "family": "sensetime/sensenova-u", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-16", "context_length": null, "max_output_tokens": null, "parameters": "8B+8B", "architecture": "U1-8B-MoT line specialized for infographic generation and editing. V3 is the recommended checkpoint.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Infographic checkpoints are SenseTime's open U1 variants for posters and dense information graphics. Collected 2026-10-15. V3, released 16 July 2026, is the one the model card recommends. V1 and V2 remain on Hugging Face as earlier versions.", "what_it_is_for": "Text-to-image infographics plus image editing in the same unified model, including Chinese and English text in the layout.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova-u/sensenova-u1-8b-mot-interleaved": { "id": "sensetime/sensenova-u/sensenova-u1-8b-mot-interleaved", "name": "SenseNova-U1-8B-MoT-Interleaved", "maker": "sensetime", "family": "sensetime/sensenova-u", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-06-11", "context_length": null, "max_output_tokens": null, "parameters": "8B+8B", "architecture": "U1-8B-MoT further trained for multi-page interleaved image-text generation.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseNova-U1-8B-MoT-Interleaved is an official U1 checkpoint released 11 June 2026 for picture books, story pages, multi-page slides, and illustrated tutorials. Collected 2026-10-15. SenseTime says narrative, character, and style consistency improved versus the base 8B checkpoint.", "what_it_is_for": "Multi-page mixed image and text generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova-u/sensenova-u1-a3b-mot": { "id": "sensetime/sensenova-u/sensenova-u1-a3b-mot", "name": "SenseNova-U1-A3B-MoT", "maker": "sensetime", "family": "sensetime/sensenova-u", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-27", "context_length": null, "max_output_tokens": null, "parameters": "39B", "architecture": "MoE plus Mixture-of-Transformers. Hugging Face lists 39B. The A3B name is SenseTime's label; an explicit active-parameter count was not in the retrieved README table, so 3B active is not stored.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseNova-U1-A3B-MoT is the MoE open checkpoint released with SenseNova U1 on 27 April 2026. Collected 2026-10-15. SenseTime's announcement groups it with the 8B dense model as the U1 Lite pair.", "what_it_is_for": "Same unified understanding and generation job as U1-8B, at a larger MoE size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova-u/sensenova-u1-fast": { "id": "sensetime/sensenova-u/sensenova-u1-fast", "name": "SenseNova U1 Fast", "maker": "sensetime", "family": "sensetime/sensenova-u", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Accelerated SenseNova U1 endpoint aimed at infographics. Whether it serves the open 8B weights or a private distill was not stated.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseNova U1 Fast is the hosted fast infographic endpoint based on SenseNova U1. SenseTime's 8 May 2026 post introduced it next to 6.7 Flash-Lite. Collected 2026-10-15. The 2026 FAQ says Token Plan supports U1 Fast and 6.8 Flash-Lite.", "what_it_is_for": "Infographics, dense charts, and slide-style layouts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova-u/sensenova-u1-pro": { "id": "sensetime/sensenova-u/sensenova-u1-pro", "name": "SenseNova U1 Pro", "maker": "sensetime", "family": "sensetime/sensenova-u", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed flagship of the SenseNova U series. SenseTime describes unified understanding, generation, and action, with output up to 8K and custom aspect ratios. Parameter count not published.", "modalities": { "input": [ "text", "image" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseNova U1 Pro is SenseTime's closed flagship image model. A preview was shown at WAIC on 18 July 2026. The production version went live in the Raccoon app and on the SenseNova API on 21 September 2026. Collected 2026-10-15. It targets infographics, posters, and other layout-heavy images, and SenseTime describes a pipeline that searches, designs, edits, and checks its own output.", "what_it_is_for": "Production visual assets with readable text and consistent layout, including multi-image fusion. The corporate FAQ lists it as a current showcase model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/sensenova-vision/sensenova-vision-7b-mot": { "id": "sensetime/sensenova-vision/sensenova-vision-7b-mot", "name": "SenseNova-Vision-7B-MoT", "maker": "sensetime", "family": "sensetime/sensenova-vision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-08", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Unified multimodal model that emits classic vision outputs as text, images, or both. SenseTime says training starts from an off-the-shelf pretrained unified multimodal model and adds no task heads. The base checkpoint's name was not in the retrieved README.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseNova-Vision-7B-MoT is SenseTime's open model that treats detection, OCR, depth, normals, segmentation, and multi-view geometry as native generation. Weights, inference code, and the technical report were released 8 July 2026. Collected 2026-10-15.", "what_it_is_for": "One checkpoint for structured vision, dense geometry, segmentation, and multi-view geometry, with language-defined variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/speech/sensenova-audio-fusion-0603": { "id": "sensetime/speech/sensenova-audio-fusion-0603", "name": "SenseNova-Audio-Fusion-0603", "maker": "sensetime", "family": "sensetime/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Text-to-speech model with timbre mixing. The API name is SenseNova-Audio-Fusion-0603. An older unnamed speech endpoint is marked by SenseCore as about to be removed.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseNova-Audio-Fusion-0603 is the timbre-mixing TTS model SenseCore released on 3 June 2025. Collected 2026-10-15. Callers mix several system voices by weight, or use one voice, and can stream audio. V2 of the API adds hex output. The model id stays the same.", "what_it_is_for": "Short speech, dialogue, and social audio, with mixed timbres and streaming playback.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/speech/sensenova-livetranslate-1-0": { "id": "sensetime/speech/sensenova-livetranslate-1-0", "name": "SenseNova-LiveTranslate-1.0", "maker": "sensetime", "family": "sensetime/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Live interpretation model on SenseAudio. Billing is by audio duration; the rate was not in the captured row.", "modalities": { "input": [ "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SenseNova-LiveTranslate-1.0 is SenseTime's live interpretation model id on SenseAudio. Collected 2026-10-15. It is listed next to SenseNova speech models, not among the third-party LLMs.", "what_it_is_for": "Realtime recognition, translation, and spoken playback for meetings and streams.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sensetime/speech/sensenova-tts-2-0": { "id": "sensetime/speech/sensenova-tts-2-0", "name": "SenseNova-TTS-2.0", "maker": "sensetime", "family": "sensetime/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SenseAudio's recommended SenseNova speech-synthesis model. The page says multi-emotion, multi-style, and formula reading, plus cloning and text-to-voice on the Token Plan blurb.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 3.5, "completion_per_m_usd": null }, "summary_intro": "SenseNova-TTS-2.0 is SenseTime's SenseNova-branded TTS id on the SenseAudio platform, which SenseAudio's own docs call a SenseTime product. Collected 2026-10-15. The Token Plan page recommends it over SenseAudio-TTS-1.5.", "what_it_is_for": "Emotional, multi-style speech, including formula reading. Cloning is described at the platform level via voice_id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/apriel/apriel-1-5-15b-thinker": { "id": "servicenow/apriel/apriel-1-5-15b-thinker", "name": "Apriel-1.5-15B-Thinker", "maker": "servicenow", "family": "servicenow/apriel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-01", "context_length": null, "max_output_tokens": null, "parameters": "15B", "architecture": "Multimodal reasoning model. Mid-training is continual pretraining on text and image data, then text-only SFT on 2M+ samples. No image SFT and no RL, per the model card. Trained 7 days on 640 H100 GPUs. Fast-LLM.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apriel-1.5-15B-Thinker is ServiceNow's open-weight 15B multimodal reasoning model. The lab says mid-training plus text SFT, without RL, was enough for its reported scores. Status is active on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "General instruction work plus image understanding: code, multi-step reasoning, retrieval-style QA, function calling, and agents. The card says it fits on a single GPU.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/apriel/apriel-1-6-15b-thinker": { "id": "servicenow/apriel/apriel-1-6-15b-thinker", "name": "Apriel-1.6-15B-Thinker", "maker": "servicenow", "family": "servicenow/apriel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-09", "context_length": null, "max_output_tokens": null, "parameters": "15B", "architecture": "Update of Apriel-1.5. Continued pretraining on text and images, SFT on 2.4M text samples plus a light multimodal SFT, then multi-stage RL with verifiable rewards and GSPO. Trained with Fast-LLM and VERL. ServiceNow says it was trained on NVIDIA DGX Cloud GB200.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apriel-1.6-15B-Thinker is ServiceNow's active open-weight 15B multimodal reasoning model, the follow-on to Apriel-1.5 with SFT and RL and a shorter reasoning trace. Collected 2026-10-15.", "what_it_is_for": "Same job family as 1.5: code, tools, agents, and image reasoning, with a stated goal of using fewer reasoning tokens. Hosted options named on the card were Together AI and Ollama, plus local weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/apriel/apriel-13b": { "id": "servicenow/apriel/apriel-13b", "name": "Apriel 13B", "maker": "servicenow", "family": "servicenow/apriel", "status": "active", "confidence": "high", "weights": "closed", "license": "Apache-2.0", "release_date": "2025-09-10", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Platform model. Card: derived from Mistral-Nemo-Base-2407 (12B) and upscaled to 13B by layer duplication. 44 layers, model dimension 5120, head dimension 128, hidden 14336, SwiGLU, 32 query heads, 8 KV heads, RoPE theta 1,000,000, vocabulary under 131,000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apriel 13B is ServiceNow's closed platform small language model, version v2.0 of Now SLM. The model card dates it 10 September 2025 and says it ships inside Now Assist. Collected 2026-10-15. Weights are not public.", "what_it_is_for": "Enterprise text automation inside ServiceNow workflows: agentic workflows, multi-step reasoning, and tool use for Now Assist skills and agents. It replaced Now SLM v1 as the default for creator and fulfiller experiences on 16 October 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/apriel/apriel-2-0": { "id": "servicenow/apriel/apriel-2-0", "name": "Apriel 2.0", "maker": "servicenow", "family": "servicenow/apriel", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "15B", "architecture": "Announced as the next Apriel Nemotron model, post-trained with NVIDIA and ServiceNow data on the Nemotron architecture. Native multimodal input. No public model card or weight repo found.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apriel 2.0 is a ServiceNow and NVIDIA model announced on 28 October 2025 as the next Apriel Nemotron enterprise model, with production then aimed at the first quarter of 2026. No public weight card was found by 2026-10-15, so it stays announced. Confidence is medium because the primary details are press reports of the announcement, not a model card.", "what_it_is_for": "Announced for enterprise agents in regulated industries: multi-step reasoning and native reading of screenshots, forms, and diagrams, inside ServiceNow's governed platform and, the company said, later on Hugging Face.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/apriel/apriel-5b-base": { "id": "servicenow/apriel/apriel-5b-base", "name": "Apriel-5B-Base", "maker": "servicenow", "family": "servicenow/apriel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-11", "context_length": 16384, "max_output_tokens": null, "parameters": "4.8B", "architecture": "Decoder-only transformer, grouped-query attention, YaRN RoPE. Instruct config (same family code): 28 layers, hidden 4096, 24 heads, 8 KV heads, intermediate 8192, vocab 131072, SiLU, bfloat16. Base card: trained on 4.5T+ tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apriel-5B-Base is ServiceNow Language Models Lab's first public foundation checkpoint, a 4.8B decoder-only model trained on more than 4.5 trillion tokens and released under MIT. Collected 2026-10-15. Weights are still published on Hugging Face.", "what_it_is_for": "The model card says it supports research on foundation models and is meant to be fine-tuned. The ServiceNow blog points it at custom content generation and process automation, with general language and code pretraining.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/apriel/apriel-5b-instruct": { "id": "servicenow/apriel/apriel-5b-instruct", "name": "Apriel-5B-Instruct", "maker": "servicenow", "family": "servicenow/apriel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-11", "context_length": 16384, "max_output_tokens": null, "parameters": "4.8B", "architecture": "Same AprielForCausalLM stack as the base: 28 layers, hidden 4096, GQA 24/8, YaRN RoPE, vocab 131072. Post-trained with CPT, SFT, DPO, and RLVR; domain-biased variants were merged before alignment.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apriel-5B-Instruct is the instruction-aligned 4.8B Apriel model from ServiceNow SLAM Lab. It is still an active open-weight MIT release. Collected 2026-10-15.", "what_it_is_for": "ServiceNow says it is for instruction following and real-time interaction such as chat or customer-service automation, including reasoning and code. The card describes CPT, SFT, then DPO and RLVR, with merged domain-biased variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/apriel/apriel-h1-15b-thinker-sft": { "id": "servicenow/apriel/apriel-h1-15b-thinker-sft", "name": "Apriel-H1-15B-Thinker-SFT", "maker": "servicenow", "family": "servicenow/apriel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-11-03", "context_length": null, "max_output_tokens": null, "parameters": "15B", "architecture": "Hybrid transformer plus Mamba state-space layers, distilled from Apriel-Nemotron-15B-Thinker. Flagship is the H-30-SFT placement: 30 Mamba layers out of 50. Card also says about 2x vLLM throughput. Fast-LLM training.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apriel-H1-15B-Thinker-SFT is ServiceNow's released hybrid attention-Mamba reasoner, distilled from Apriel-Nemotron-15B-Thinker so it can run faster with a small quality change. MIT weights. Collected 2026-10-15.", "what_it_is_for": "The card says agentic tasks, code assistance, and multi-step reasoning, using the same think-then-answer pattern as other Apriel thinkers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/apriel/apriel-nemotron-15b-thinker": { "id": "servicenow/apriel/apriel-nemotron-15b-thinker", "name": "Apriel-Nemotron-15B-Thinker", "maker": "servicenow", "family": "servicenow/apriel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-08-13", "context_length": null, "max_output_tokens": null, "parameters": "15B", "architecture": "15B reasoning model built from an unpublished Apriel-15B-base checkpoint via continual pretraining (100B+ tokens), SFT on about 200,000 demonstrations, and GRPO. Text-only. Training stack Fast-LLM. NVIDIA data and research are credited.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apriel-Nemotron-15B-Thinker is ServiceNow's 15B open-weight reasoning model, built with NVIDIA research input and shipped under MIT. ServiceNow said in December 2025 that it was in production behind Now LLM. Collected 2026-10-15.", "what_it_is_for": "The card targets code assistance, multi-step reasoning, question answering, function calling, and agent workflows. It is explicitly not for safety-critical use without a person in the loop.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/apriel/aprielguard": { "id": "servicenow/apriel/aprielguard", "name": "AprielGuard", "maker": "servicenow", "family": "servicenow/apriel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-23", "context_length": 32768, "max_output_tokens": null, "parameters": "8B", "architecture": "8B safeguard. Card says it is a downscaled Apriel-1.5 15B base, then SFT on 600,000+ samples. bfloat16. Sequence length up to 32k. Reasoning mode can be turned on or off in the chat template. Adam, LR 2e-4, 3 epochs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AprielGuard is ServiceNow's 8B open-weight safeguard for LLM safety and prompt-attack detection, released under MIT in December 2025. Collected 2026-10-15.", "what_it_is_for": "The card says it flags toxicity, bias, misinformation, prompt injection, jailbreaks, and indirect prompt attacks across standalone prompts, chats, and agent workflows.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/apriel/superapriel-15b-base": { "id": "servicenow/apriel/superapriel-15b-base", "name": "SuperApriel-15B-Base", "maker": "servicenow", "family": "servicenow/apriel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-21", "context_length": null, "max_output_tokens": null, "parameters": "15B", "architecture": "Stage-1 token-mixer supernet distilled from frozen Apriel-1.6 on 266B tokens. 48 decoder layers, each with four trained mixers: full attention, sliding-window attention (window 4096), gated DeltaNet, and Kimi Delta Attention. Stochastic layer sampling during training.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SuperApriel-15B-Base is ServiceNow's April 2026 open-weight 15B supernet: one checkpoint that can swap token mixers per layer. It is the distillation stage before SuperApriel-15B-Instruct. MIT. Collected 2026-10-15.", "what_it_is_for": "The card says to use it as a foundation for fine-tuning and for choosing mixer placements. It is not the instruction-tuned serving preset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/apriel/superapriel-15b-instruct": { "id": "servicenow/apriel/superapriel-15b-instruct", "name": "SuperApriel-15B-Instruct", "maker": "servicenow", "family": "servicenow/apriel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-21", "context_length": null, "max_output_tokens": null, "parameters": "15B", "architecture": "Same 48-layer, 4-mixer supernet as the base, plus supervised fine-tuning. Eight deployment presets from 1.0x to 10.7x decode throughput at 32K sequence length. Paper: stage 2 continues distillation from the Apriel-1.6 teacher.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SuperApriel-15B-Instruct is ServiceNow's instruction-tuned 15B supernet, released April 2026 under MIT. One checkpoint serves several speed-quality presets. Collected 2026-10-15.", "what_it_is_for": "The card presents it as a deployable reasoner whose mixer placement can be changed per request, including a speculative-decoding setup that uses a fast preset as the draft and full attention as the verifier.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/starcoder/starcoder": { "id": "servicenow/starcoder/starcoder", "name": "StarCoder", "maker": "servicenow", "family": "servicenow/starcoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BigCode OpenRAIL-M", "release_date": "2023-05-04", "context_length": 8192, "max_output_tokens": null, "parameters": "15.5B", "architecture": "StarCoderBase continued on Python. Multi-query attention, fill-in-the-middle, 8192 context. ServiceNow press: about 35 billion additional Python tokens. The BigCode collection note says about 30 billion additional Python tokens. Both figures are stored; they disagree.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoder is the Python-continued 15.5B code model that ServiceNow and Hugging Face released through BigCode in May 2023. Weights remain open under BigCode OpenRAIL-M. Collected 2026-10-15.", "what_it_is_for": "Code generation and completion, including fill-in-the-middle, for professional and citizen developers. ServiceNow later said its early text-to-code Now LLM was a specialized fine-tune of this 15B StarCoder line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/starcoder/starcoder2-15b": { "id": "servicenow/starcoder/starcoder2-15b", "name": "StarCoder2-15B", "maker": "servicenow", "family": "servicenow/starcoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BigCode OpenRAIL-M", "release_date": "2024-02-28", "context_length": 16384, "max_output_tokens": null, "parameters": "15B", "architecture": "GQA, context 16384, sliding window 4096, fill-in-the-middle. Trained by NVIDIA with NeMo on 600+ programming languages and 4+ trillion tokens. Hugging Face safetensors label says 16B; the model card says 15B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoder2-15B is the largest BigCode StarCoder2 code model, trained by NVIDIA and released with ServiceNow and Hugging Face in February 2024 under BigCode OpenRAIL-M. Collected 2026-10-15.", "what_it_is_for": "Code completion and fill-in-the-middle across 600+ languages. At launch it was also exposed through NVIDIA's model catalog. It is a base model, not an instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/starcoder/starcoder2-3b": { "id": "servicenow/starcoder/starcoder2-3b", "name": "StarCoder2-3B", "maker": "servicenow", "family": "servicenow/starcoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BigCode OpenRAIL-M", "release_date": "2024-02-28", "context_length": 16384, "max_output_tokens": null, "parameters": "3B", "architecture": "Grouped-query attention, sliding-window attention of 4096, fill-in-the-middle. Trained by ServiceNow. Blog: 17 programming languages, 3+ trillion tokens. Paper: the three sizes used 3.3T to 4.3T tokens overall.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoder2-3B is the ServiceNow-trained 3B code model in the BigCode StarCoder2 family, released 28 February 2024 with Hugging Face and NVIDIA. License is BigCode OpenRAIL-M. Collected 2026-10-15.", "what_it_is_for": "Code completion and fill-in-the-middle on a smaller footprint. The BigCode docs say these checkpoints are not instruction models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/starcoder/starcoder2-7b": { "id": "servicenow/starcoder/starcoder2-7b", "name": "StarCoder2-7B", "maker": "servicenow", "family": "servicenow/starcoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BigCode OpenRAIL-M", "release_date": "2024-02-28", "context_length": 16384, "max_output_tokens": null, "parameters": "7B", "architecture": "Same StarCoder2 design: GQA, 16384 context, sliding window 4096, fill-in-the-middle. Trained by Hugging Face. Blog: 17 programming languages and 3.5+ trillion tokens. GitHub README later says the 7B, like the 3B, was trained on 3+ trillion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoder2-7B is the mid-size BigCode code model trained by Hugging Face and released with ServiceNow and NVIDIA on 28 February 2024. License BigCode OpenRAIL-M. Collected 2026-10-15.", "what_it_is_for": "Code completion across the StarCoder2 training mix. ServiceNow co-announced the family even though this size was trained by Hugging Face.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "servicenow/starcoder/starcoderbase": { "id": "servicenow/starcoder/starcoderbase", "name": "StarCoderBase", "maker": "servicenow", "family": "servicenow/starcoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BigCode OpenRAIL-M", "release_date": "2023-05-04", "context_length": 8192, "max_output_tokens": null, "parameters": "15.5B", "architecture": "GPT-2-style decoder with multi-query attention and fill-in-the-middle. About 1 trillion tokens of permissively licensed code from The Stack v1.2, 80+ programming languages. Trained in bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarCoderBase is the 15.5B code model from BigCode, the open collaboration led by ServiceNow and Hugging Face. It was released in May 2023 under BigCode OpenRAIL-M and is still the public base checkpoint. Collected 2026-10-15.", "what_it_is_for": "Pretrained code model for completion and further training across 80+ programming languages, using fill-in-the-middle. StarCoder itself is this model continued on Python.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sesame/csm/csm-1b": { "id": "sesame/csm/csm-1b", "name": "CSM-1B", "maker": "sesame", "family": "sesame/csm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-13", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Conversational Speech Model, Tiny size. Two Llama-style autoregressive transformers. Backbone (reference flavor llama-1B): 16 layers, hidden size 2048, 32 attention heads, 8 KV heads, intermediate size 8192, max sequence length 2048. Audio decoder (llama-100M): 4 layers, hidden size 1024, 8 heads, 2 KV heads, intermediate size 8192. The backbone predicts Mimi codebook 0; the decoder predicts the other 31 of 32 codebooks. Audio is Mimi split-RVQ at 12.5 Hz (about 80 ms per frame) and 24 kHz mono. Text uses a Llama 3.2 tokenizer (vocab 128256). Speaker identity is written into the text as [speaker]. The reference loader runs the checkpoint in bfloat16 and applies an imperceptible watermark.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CSM-1B is Sesame's open Conversational Speech Model checkpoint, the Tiny size from the 27 February 2025 research post. Weights went up on Hugging Face on 13 March 2025 under Apache-2.0 and were still the organization's only public model on 15 October 2026. It turns text, plus optional prior speech, into conversational audio. It does not write text and it is not the closed voice inside the Sesame app.", "what_it_is_for": "Sesame describes CSM as a speech generator that uses conversation history so prosody fits the turn, not a flat read of a sentence. The published checkpoint is a base model. It can take a speaker id and audio context, but it is not fine-tuned to Maya, Miles, or any other named voice. The company says a separate fine-tuned CSM variant powers the old interactive demo, and that file was not released.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sesame/csm/csm-medium": { "id": "sesame/csm/csm-medium", "name": "CSM Medium", "maker": "sesame", "family": "sesame/csm", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-27", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Same Conversational Speech Model design as CSM-1B: a Llama-style multimodal backbone predicts Mimi codebook 0, and a smaller Llama-style audio decoder predicts the remaining codebooks. Sesame's Medium size is an 8B backbone and a 300M decoder. Layer counts, width, and codebook count for this size were not published. Trained for five epochs at sequence length 2048. This is the size used in the published CMOS listening tests.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CSM Medium is the largest Conversational Speech Model Sesame named in the 27 February 2025 research post: an 8B backbone and a 300M decoder. It is the only size they put through the published comparative MOS tests. Weights and a model card were not released. On 15 October 2026, Sesame's Hugging Face org still listed only CSM-1B.", "what_it_is_for": "Same job as the other CSM sizes: speech from text and conversation history, aimed at natural prosody. Sesame used Medium for the listening tests. They did not say it is the demo voice or the 2026 app voice.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sesame/csm/csm-small": { "id": "sesame/csm/csm-small", "name": "CSM Small", "maker": "sesame", "family": "sesame/csm", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-27", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Same Conversational Speech Model design as CSM-1B: a Llama-style multimodal backbone predicts Mimi codebook 0, and a smaller Llama-style audio decoder predicts the remaining codebooks. Sesame's Small size is a 3B backbone and a 250M decoder. Layer counts, width, and codebook count for this size were not published. Trained for five epochs at sequence length 2048.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CSM Small is a Conversational Speech Model size Sesame trained and named in the 27 February 2025 research post: a 3B backbone and a 250M decoder. It was evaluated with Tiny and Medium. It was not given a model card or weights. On 15 October 2026 the only Sesame checkpoint on Hugging Face was still CSM-1B.", "what_it_is_for": "Same job as the rest of CSM: generate speech from text and conversation history instead of reading a sentence with no context. Sesame did not say this size powers the app or the public demo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "seu/faheng/faheng": { "id": "seu/faheng/faheng", "name": "\u6cd5\u8861", "maker": "seu", "family": "seu/faheng", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u6cd5\u8861 is the legal-domain model named by Southeast University's digital-law team in the law school. The law school says it went on the graduate section of the national smart-education platform, in a notice dated 4 January 2026. The same notice walks a single product line from FaHeng 1.0 in November 2024 through FaHeng-R2 in September 2025. Weights are closed. Collected 2026-10-15.", "what_it_is_for": "The law school describes FaHeng as support for smart legal education. The Chinese Social Sciences Net report republished by SEU News says FaHeng-R1 serves lawyers, corporate counsel, legal researchers, and the public with legal consultation, text generation, and case analysis. From R1 onward the university describes the line as a deep-reasoning partner rather than a knowledge quiz. The name is taken from the Book of Lord Shang: \u6545\u6cd5\u8005\uff0c\u56fd\u4e4b\u6743\u8861\u4e5f.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "seu/wendao/wendao": { "id": "seu/wendao/wendao", "name": "\u95ee\u9053", "maker": "seu", "family": "seu/wendao", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u95ee\u9053 is the ethics-domain model Southeast University released on 12 November 2025. The university's republication of \u4ea4\u6c47\u70b9 calls it a system-level ethics model and says the public chat at https://wendao.seu.edu.cn is free. Weights are closed. The technical developer says it starts from a general large-model base and does not name that base. Collected 2026-10-15.", "what_it_is_for": "Wang Jue, director of the AI ethics lab and a professor in the School of Humanities, describes \u95ee\u9053 as an ethics thinking partner and decision-support system. Science and Technology Daily lists five functions: ethics risk assessment and audit, ethics-dilemma simulation, alignment design that turns value principles into engineering checks, a dynamic knowledge base and case teaching, and frontier ethics exploration. The press demonstrations are typed questions with sourced text answers. The project also names the School of Information Science and Engineering, the National Mobile Communications Key Lab, the State Key Laboratory of Millimeter Waves, the Frontiers Science Center for Mobile Information Communication and Security, and the Jiangsu moral-development think tank.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sfu/cage/cage": { "id": "sfu/cage/cage", "name": "CAGE", "maker": "sfu", "family": "sfu/cage", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Denoising diffusion model over part attributes. Conditioning is an object category label and a part-connectivity graph used as an attention mask. Each part is five attributes for shape and motion. The denoiser is a stack of Attribute Attention Blocks. The GitHub README says the shared pretrained file is about 80MB.", "modalities": { "input": [ "text" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CAGE (Controllable Articulation GEneration) is a Simon Fraser University 3DLG diffusion model for articulated 3D objects. Jiayi Liu, Hou In Ivan Tam, Ali Mahdavi-Amiri, and Manolis Savva published it at CVPR 2024 (arXiv 2312.09570). The GitHub README shares one pretrained demo checkpoint of about 80MB. Status on 2026-10-15: active open weights. No price and no API.", "what_it_is_for": "The project page says it generates articulated objects that follow a user-specified category, part shape, and articulation, instead of manual authoring. The graph can be out of the training distribution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sfu/clibd/clibd": { "id": "sfu/clibd/clibd", "name": "CLIBD", "maker": "sfu", "family": "sfu/clibd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Contrastive image, DNA-barcode, and taxonomy-text encoders. The model card initializes the image tower from timm vit_base_patch16_224 (MAE), the DNA tower from BarcodeBERT pretrained on CANADA-1.5M, and the text tower from prajjwal1/bert-small. First authors ZeMing Gong, Austin T. Wang, and Xiaoliang Huo and senior author Angel X. Chang are at SFU. Joakim Bruslund Haurum (Aalborg), Scott C. Lowe (Vector), and Graham W. Taylor (Guelph and Vector) are coauthors. Weights are on bioscan-ml, which 3DLG lists as its BIOSCAN project.", "modalities": { "input": [ "image", "dna", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLIBD is a contrastive image, DNA-barcode, and taxonomy model released by Simon Fraser University's 3DLG group with Aalborg, Vector, and Guelph coauthors. ZeMing Gong, Austin T. Wang, Xiaoliang Huo, and Angel X. Chang are the SFU authors. The official checkpoints are on `bioscan-ml/clibd` (27 May 2024). The conference version is ICLR 2025. Status on 2026-10-15: active open weights. No price and no API.", "what_it_is_for": "The project page says it classifies known and unknown insect species without a task-specific classifier, by aligning images with DNA and taxonomy text. The GitHub eval script matches an image or DNA embedding to the nearest labeled key.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sfu/duoduo-clip/duoduo-clip": { "id": "sfu/duoduo-clip/duoduo-clip", "name": "Duoduo CLIP", "maker": "sfu", "family": "sfu/duoduo-clip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-06-15", "context_length": null, "max_output_tokens": null, "parameters": "151277312", "architecture": "Multi-view shape encoder initialized from a CLIP ViT-B/32 and modified with cross-view attention. The June 2024 model card and the GitHub eval script name OpenCLIP ViT-B-32 checkpoint laion2b_s34b_b79k. The May 2026 transformers card metadata names openai/clip-vit-base-patch32. The project page says the encoder is permutation-invariant and pose-free. The paper abstract says 87 million parameters; the B-32 safetensors file contains 151277312 F32 parameters.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Duoduo CLIP is a Simon Fraser University 3DLG multi-view 3D representation model. Han-Hung Lee, Yiming Zhang, and Angel X. Chang released weights in June 2024 and a transformers packaging in May 2026. It was published at ICLR 2025. Status on 2026-10-15: active open weights, no hosted API.", "what_it_is_for": "The project page says it learns shape encodings from multi-view images instead of point clouds, so it can encode an object from a variable number of images. The B-32 card says the intended uses are text-to-3D retrieval, image-to-3D retrieval, and multi-view feature extraction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sfu/nuiscene/nuiscene": { "id": "sfu/nuiscene/nuiscene", "name": "NuiScene", "maker": "sfu", "family": "sfu/nuiscene", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-05-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Outdoor scene generator. The paper encodes scene chunks as uniform vector sets and trains an explicit outpainting diffusion model. The Hub repo also stores VAE checkpoints. The inference script selects a diffusion checkpoint and a quad chunk voxel size.", "modalities": { "input": [], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NuiScene is a Simon Fraser University 3DLG generator for unbounded outdoor scenes. Han-Hung Lee, Qinghong Han, and Angel X. Chang posted the paper on 20 March 2025 (ICCV 2025). Diffusion and VAE checkpoints are in a MIT Hugging Face repo created 26 May 2025. Status on 2026-10-15: active open weights. No price and no hosted API.", "what_it_is_for": "The paper says it generates expansive outdoor scenes, from castles to high-rises, by encoding chunks as vector sets and outpainting them. The GitHub README uses the checkpoints to grow a grid of chunks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sfu/omages/omages": { "id": "sfu/omages/omages", "name": "Omages", "maker": "sfu", "family": "sfu/omages", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Object Images (Omages): geometry is a 64x64 multi-chart image. null2geo is a DiT that generates geometry from noise. geo2mat is a pytorch-imagen model that generates PBR material conditioned on geometry. Official download script pulls N2G_dit_allcate.ckpt and G2M_imagen_allcate.ckpt from the omages_ABO dataset repo. Default demo category is chairs.", "modalities": { "input": [], "output": [ "3d", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Omages is a Simon Fraser University 3DLG text-free image-diffusion pipeline that turns noise into a 3D object. Xingguang Yan, Han-Hung Lee, and Angel X. Chang are the SFU authors; Ziyu Wan is at City University of Hong Kong. The GitHub README calls the paper a 3DV 2025 best paper (arXiv 2408.03178). Official checkpoints are downloaded from `3dlg-hcvc/omages_ABO`. Status on 2026-10-15: active open weights. No price and no API.", "what_it_is_for": "The README says an omage encodes geometry as a square image and PBR material as another image, so an image generator can emit one object. The shipped pipeline runs null2geo (DiT) then geo2mat (pytorch-imagen). The default config generates chairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sfu/opd/opdmulti": { "id": "sfu/opd/opdmulti", "name": "OPDMulti", "maker": "sfu", "family": "sfu/opd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-03-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "OPDFormer, built on Mask2Former and Detectron2, for multi-object openable-part detection. Variants C, O, and P take RGB, depth, or RGB-D. The README labels weight files 169M or 175M; that label is a file size, not a confirmed parameter count.", "modalities": { "input": [ "image", "depth" ], "output": [ "segmentation", "motion" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OPDMulti is a Simon Fraser University 3DLG model for detecting openable parts on multiple objects in one image. Xiaohao Sun, Hanxiao Jiang, Manolis Savva, and Angel Xuan Chang announced it in a 24 March 2023 preprint. OPDFormer weights for RGB, depth, and RGB-D remain public. Status on 2026-10-15: active open weights, plus a Hugging Face demo space. No price is published.", "what_it_is_for": "The README says OPDFormer implements the OPDMulti task: part segmentation plus motion type, axis, and origin. The demo space is described as identifying openable parts and visualizing their motion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sfu/semlayoutdiff/semlayoutdiff": { "id": "sfu/semlayoutdiff/semlayoutdiff", "name": "SemLayoutDiff", "maker": "sfu", "family": "sfu/semlayoutdiff", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two released components: SLDN, a semantic-layout diffusion network on 120x120 maps, and APM, an attribute prediction model. The README says APM rescales 120x120 SLDN maps and writes SceneState JSON with object pose, scale, category, and 3D model id. Divyam Goel is at CMU. Xiaohao Sun and Angel X. Chang are at SFU.", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SemLayoutDiff is a Simon Fraser University 3DLG diffusion system for 3D indoor scenes. Xiaohao Sun and Angel X. Chang are at SFU. Divyam Goel is at CMU. The GitHub README says pretrained weights exist for two parts, SLDN and APM. Status on 2026-10-15: active as a named weight release. Confidence is medium because the download hrefs were not retrieved. No price and no API.", "what_it_is_for": "The README says the weights support scene generation without training. SLDN produces a semantic layout. APM predicts object attributes and retrieves furniture models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sfu/singapo/singapo": { "id": "sfu/singapo/singapo", "name": "SINGAPO", "maker": "sfu", "family": "sfu/singapo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DDPM over part shape and motion attributes, conditioned on a part-connectivity graph and DINOv2 patch features. The demo extracts the graph with GPT-4o. The denoiser uses three masked self-attentions and one image cross-attention. Parts are assembled by mesh retrieval. The README says the pretrained SINGAPO zip is about 40MB. Training starts from a separate cage_cfg.ckpt and used 4 NVIDIA A100s. Evaluation was tested on an NVIDIA 3060 12GB.", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SINGAPO is a Simon Fraser University 3DLG diffusion model that builds a 3D articulated object from one resting-state image. Jiayi Liu, Denys Iliash, Angel X. Chang, Manolis Savva, and Ali Mahdavi-Amiri released it for ICLR 2025 (arXiv 2410.16499). The GitHub repo is MIT and shares a pretrained checkpoint of about 40MB. Status on 2026-10-15: active open weights. No price and no SFU API.", "what_it_is_for": "The project page says it reconstructs household articulated objects that match the photo and stay kinematically plausible, including several geometry and motion variants when the single view is ambiguous.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/atria/atria-dawn": { "id": "shanghai-ai-lab/atria/atria-dawn", "name": "Atria Dawn", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/atria", "status": "announced", "confidence": "medium", "weights": "closed", "license": "MIT", "release_date": "", "context_length": 256000, "max_output_tokens": null, "parameters": "744B total, mixture-of-experts, built on GLM-5.2", "architecture": "The project README describes Atria Dawn as the full instruct release of the same 744B GLM-5.2 line. Weights were still marked coming soon.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Atria Dawn is the announced full instruct model in Shanghai AI Laboratory's Atria line. On 15 October 2026 the project download table still said the weights were coming soon. The preview weights are a separate folder. Collection date 2026-10-15.", "what_it_is_for": "Same agentic research and engineering role as Atria Dawn Preview, positioned as the full release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/atria/atria-dawn-preview": { "id": "shanghai-ai-lab/atria/atria-dawn-preview", "name": "Atria Dawn Preview", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/atria", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-09-11", "context_length": 256000, "max_output_tokens": null, "parameters": "744B total, mixture-of-experts, built on GLM-5.2", "architecture": "Agentic language model post-trained on the 744B MoE GLM-5.2 foundation. Active expert count was not in the card sections read. Hugging Face organization metadata also showed a 753B label, which disagrees with the 744B card text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Atria Dawn Preview is a preview agentic model from Shanghai AI Laboratory, with weights published 11 September 2026 under MIT. It is built on the 744B mixture-of-experts GLM-5.2 model and has a 256K context. Collection date 2026-10-15.", "what_it_is_for": "Long-horizon research and engineering work: evidence gathering, software and experiment loops, document delivery, and authorized security analysis, using tools and environment feedback.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/caprl/caprl-3b": { "id": "shanghai-ai-lab/caprl/caprl-3b", "name": "CapRL-3B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/caprl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-09-24", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "First-party named release. Task label used while cataloging: caption. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CapRL-3B is a caption model published by the internlm org. Collection date 2026-10-15. Qwen-based CapRL fine-tunes were not given folders. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named caption checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/caprl/caprl-internvl3-5-8b": { "id": "shanghai-ai-lab/caprl/caprl-internvl3-5-8b", "name": "CapRL-InternVL3.5-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/caprl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-10-16", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "First-party named release. Task label used while cataloging: caption. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CapRL-InternVL3.5-8B is a caption-trained InternVL3.5 checkpoint on the internlm org. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named caption checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/caprl/caprl-video-4b": { "id": "shanghai-ai-lab/caprl/caprl-video-4b", "name": "CapRL-Video-4B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/caprl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-05-22", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "First-party named release. Task label used while cataloging: video caption. Card body not re-read.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CapRL-Video-4B is a video caption model on the internlm org. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named video caption checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/chemllm/chemllm-2b": { "id": "shanghai-ai-lab/chemllm/chemllm-2b", "name": "ChemLLM-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/chemllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-02-10", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Chemistry instruction model built on InternLM2. Smaller chemistry model. Hugging Face metadata lists Apache-2.0 and about 2B parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChemLLM-2B is a chemistry language model from Shanghai AI Laboratory's AI4Chem group, released 2024-02-10. Collection date 2026-10-15.", "what_it_is_for": "Chemistry dialogue: molecule names, captions, and reaction-related questions, while keeping general language ability.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/chemllm/chemllm-7b-chat": { "id": "shanghai-ai-lab/chemllm/chemllm-7b-chat", "name": "ChemLLM-7B-Chat", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/chemllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-01-17", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Chemistry instruction model built on InternLM2. First open release, continued from InternLM2-Base-7B with a two-stage instruction tune.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChemLLM-7B-Chat is a chemistry language model from Shanghai AI Laboratory's AI4Chem group, released 2024-01-17. Collection date 2026-10-15.", "what_it_is_for": "Chemistry dialogue: molecule names, captions, and reaction-related questions, while keeping general language ability.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/chemllm/chemllm-7b-chat-1-5": { "id": "shanghai-ai-lab/chemllm/chemllm-7b-chat-1-5", "name": "ChemLLM-7B-Chat-1.5", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/chemllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-03-23", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Chemistry instruction model built on InternLM2. 1.5 update. The SFT repo is the verified checkpoint. A DPO sibling may exist and was not separated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChemLLM-7B-Chat-1.5 is a chemistry language model from Shanghai AI Laboratory's AI4Chem group, released 2024-03-23. Collection date 2026-10-15.", "what_it_is_for": "Chemistry dialogue: molecule names, captions, and reaction-related questions, while keeping general language ability.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/docopilot/docopilot-2b": { "id": "shanghai-ai-lab/docopilot/docopilot-2b", "name": "Docopilot-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/docopilot", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-07-18", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "First-party named release. Task label used while cataloging: document. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Docopilot-2B is a named OpenGVLab document model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named document checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/docopilot/docopilot-8b": { "id": "shanghai-ai-lab/docopilot/docopilot-8b", "name": "Docopilot-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/docopilot", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-07-18", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "First-party named release. Task label used while cataloging: document. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Docopilot-8B is a named OpenGVLab document model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named document checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/hovle/hovle": { "id": "shanghai-ai-lab/hovle/hovle", "name": "HoVLE", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/hovle", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-12-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First-party named release. Task label used while cataloging: vision. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HoVLE is a named OpenGVLab vision-language encoder or model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named vision checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/intern-decision/intern-decision-0-8b": { "id": "shanghai-ai-lab/intern-decision/intern-decision-0-8b", "name": "Intern-Decision-0.8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-decision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0, with the upstream Qwen license retained", "release_date": "2026-09-26", "context_length": null, "max_output_tokens": null, "parameters": "0.8B", "architecture": "Multimodal structured decision model fine-tuned from Qwen3.5-0.8B. One forward pass scores typed questions. It does not sample free-form text.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Intern-Decision-0.8B (\u4e66\u751f\u00b7\u660e\u51b3) is a small multimodal decision model from Shanghai AI Laboratory, published on Hugging Face on 26 September 2026. It returns calibrated answer distributions instead of long text. Collection date 2026-10-15.", "what_it_is_for": "Choice, score, and yes/no questions about a shared state, with optional images, in one forward pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/intern-decision/intern-decision-2b": { "id": "shanghai-ai-lab/intern-decision/intern-decision-2b", "name": "Intern-Decision-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-decision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0, with the upstream Qwen license retained", "release_date": "2026-09-26", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Multimodal structured decision model fine-tuned from Qwen3.5-2B. One forward pass scores typed questions. It does not sample free-form text.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Intern-Decision-2B (\u4e66\u751f\u00b7\u660e\u51b3) is a small multimodal decision model from Shanghai AI Laboratory, published on Hugging Face on 26 September 2026. It returns calibrated answer distributions instead of long text. Collection date 2026-10-15.", "what_it_is_for": "Choice, score, and yes/no questions about a shared state, with optional images, in one forward pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/intern-decision/intern-decision-4b": { "id": "shanghai-ai-lab/intern-decision/intern-decision-4b", "name": "Intern-Decision-4B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-decision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0, with the upstream Qwen license retained", "release_date": "2026-09-26", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Multimodal structured decision model fine-tuned from Qwen3.5-4B. One forward pass scores typed questions. It does not sample free-form text.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Intern-Decision-4B (\u4e66\u751f\u00b7\u660e\u51b3) is a small multimodal decision model from Shanghai AI Laboratory, published on Hugging Face on 26 September 2026. It returns calibrated answer distributions instead of long text. Collection date 2026-10-15.", "what_it_is_for": "Choice, score, and yes/no questions about a shared state, with optional images, in one forward pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/intern-s/intern-memdec-4b": { "id": "shanghai-ai-lab/intern-s/intern-memdec-4b", "name": "Intern-MemDec-4B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-s", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-08-06", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "First-party named release. Task label used while cataloging: memory. Card body not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Intern-MemDec-4B is a named 4B checkpoint on the internlm org. Collection date 2026-10-15. Its role as a memory decoder was not confirmed from a card. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named memory checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/intern-s/intern-s1": { "id": "shanghai-ai-lab/intern-s/intern-s1", "name": "Intern-S1", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-s", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-26", "context_length": null, "max_output_tokens": null, "parameters": "235B MoE language model (Qwen3) plus a 6B InternViT", "architecture": "Continued pretraining on 5 trillion multimodal tokens, including more than 2.5 trillion scientific tokens, on a Qwen3 235B MoE language model and a 6B InternViT. Dynamic tokenizer for molecules, proteins, and seismic signals.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Intern-S1 is a scientific multimodal model from Shanghai AI Laboratory (\u4e66\u751f), released 2025-07-26. Collection date 2026-10-15.", "what_it_is_for": "General multimodal reasoning plus scientific tasks in chemistry, materials, life science, and earth science.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/intern-s/intern-s1-mini": { "id": "shanghai-ai-lab/intern-s/intern-s1-mini", "name": "Intern-S1-mini", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-s", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-18", "context_length": null, "max_output_tokens": null, "parameters": "8B language model plus a 0.3B vision encoder", "architecture": "Lightweight Intern-S1. The README says an 8B language model and a 0.3B vision encoder.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Intern-S1-mini is a scientific multimodal model from Shanghai AI Laboratory (\u4e66\u751f), released 2025-08-18. Collection date 2026-10-15.", "what_it_is_for": "Smaller scientific multimodal model for faster deployment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/intern-s/intern-s1-pro": { "id": "shanghai-ai-lab/intern-s/intern-s1-pro", "name": "Intern-S1-Pro", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-s", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-04", "context_length": null, "max_output_tokens": null, "parameters": "1T total, 512 experts, 8 experts and 22B parameters activated per token", "architecture": "Mixture-of-experts scientific multimodal model. Lab news: Fourier position encoding and a rebuilt time-series encoder. README: STE routing and grouped routing. Primary published weights are FP8; a BF16 repo also exists.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Intern-S1-Pro is a scientific multimodal model from Shanghai AI Laboratory (\u4e66\u751f), released 2026-02-04. Collection date 2026-10-15.", "what_it_is_for": "Trillion-scale scientific reasoning across chemistry, materials, life science, and earth science, with general multimodal ability.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/intern-s/intern-s2-397b": { "id": "shanghai-ai-lab/intern-s/intern-s2-397b", "name": "Intern-S2-397B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-s", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-13", "context_length": null, "max_output_tokens": null, "parameters": "397B", "architecture": "Flagship Intern-S2 multimodal scientific and agent model. Same three scaling axes as the preview: pretraining, RL task coverage, and interactive agent environments.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Intern-S2-397B is a scientific multimodal model from Shanghai AI Laboratory (\u4e66\u751f), released 2026-09-13. Collection date 2026-10-15.", "what_it_is_for": "General reasoning, scientific problem solving, and long-horizon general and scientific agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/intern-s/intern-s2-mobius": { "id": "shanghai-ai-lab/intern-s/intern-s2-mobius", "name": "Intern-S2-Mobius", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-s", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-07-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First-party named release. Task label used while cataloging: science. Card body not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Intern-S2-Mobius is a named checkpoint on the internlm org beside the Intern-S2 line. Collection date 2026-10-15. The card was not re-read, so it is not described as a full Intern-S2 successor. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named science checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/intern-s/intern-s2-preview-35b": { "id": "shanghai-ai-lab/intern-s/intern-s2-preview-35b", "name": "Intern-S2-Preview-35B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-s", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-15", "context_length": null, "max_output_tokens": null, "parameters": "35B", "architecture": "Efficient scientific multimodal model continued from Qwen3.5. README: task scaling, shared-weight multi-token prediction during RL, and chain-of-thought compression. Hugging Face id is Intern-S2-Preview.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Intern-S2-Preview-35B is a scientific multimodal model from Shanghai AI Laboratory (\u4e66\u751f), released 2026-05-15. Collection date 2026-10-15.", "what_it_is_for": "Professional scientific tasks at 35B, including small-molecule spatial modeling and material crystal generation, plus general reasoning and agents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/intern-s/intern-s2-preview-397b": { "id": "shanghai-ai-lab/intern-s/intern-s2-preview-397b", "name": "Intern-S2-Preview-397B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-s", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-16", "context_length": null, "max_output_tokens": null, "parameters": "397B", "architecture": "Preview of Intern-S2-397B. README: visual pretraining on raw scientific pages, multi-domain RL, and long-horizon agent RL.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Intern-S2-Preview-397B is a scientific multimodal model from Shanghai AI Laboratory (\u4e66\u751f), released 2026-07-16. Collection date 2026-10-15.", "what_it_is_for": "Scientific multimodal reasoning and long-horizon agents. The preview is the earlier public 397B checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internimage/internimage-b": { "id": "shanghai-ai-lab/internimage/internimage-b", "name": "InternImage-B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internimage", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "InternImage deformable-convolution vision backbone. Parameter count was not re-read from the paper in this pass.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternImage-B is a scale of Shanghai AI Laboratory's InternImage vision backbone. The date is the Hugging Face createdAt of the canonical checkpoint internimage_b_1k_224. Collection date 2026-10-15.", "what_it_is_for": "Image classification and a general vision backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internimage/internimage-g": { "id": "shanghai-ai-lab/internimage/internimage-g", "name": "InternImage-G", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internimage", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "InternImage deformable-convolution vision backbone. Parameter count was not re-read from the paper in this pass.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternImage-G is a scale of Shanghai AI Laboratory's InternImage vision backbone. The date is the Hugging Face createdAt of the canonical checkpoint internimage_g_jointto22k_384. Collection date 2026-10-15.", "what_it_is_for": "Image classification and a general vision backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internimage/internimage-h": { "id": "shanghai-ai-lab/internimage/internimage-h", "name": "InternImage-H", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internimage", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "InternImage deformable-convolution vision backbone. Parameter count was not re-read from the paper in this pass.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternImage-H is a scale of Shanghai AI Laboratory's InternImage vision backbone. The date is the Hugging Face createdAt of the canonical checkpoint internimage_h_jointto22k_384. Collection date 2026-10-15.", "what_it_is_for": "Image classification and a general vision backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internimage/internimage-l": { "id": "shanghai-ai-lab/internimage/internimage-l", "name": "InternImage-L", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internimage", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "InternImage deformable-convolution vision backbone. Parameter count was not re-read from the paper in this pass.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternImage-L is a scale of Shanghai AI Laboratory's InternImage vision backbone. The date is the Hugging Face createdAt of the canonical checkpoint internimage_l_22k_384. Collection date 2026-10-15.", "what_it_is_for": "Image classification and a general vision backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internimage/internimage-s": { "id": "shanghai-ai-lab/internimage/internimage-s", "name": "InternImage-S", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internimage", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "InternImage deformable-convolution vision backbone. Parameter count was not re-read from the paper in this pass.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternImage-S is a scale of Shanghai AI Laboratory's InternImage vision backbone. The date is the Hugging Face createdAt of the canonical checkpoint internimage_s_1k_224. Collection date 2026-10-15.", "what_it_is_for": "Image classification and a general vision backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internimage/internimage-t": { "id": "shanghai-ai-lab/internimage/internimage-t", "name": "InternImage-T", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internimage", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "InternImage deformable-convolution vision backbone. Parameter count was not re-read from the paper in this pass.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternImage-T is a scale of Shanghai AI Laboratory's InternImage vision backbone. The date is the Hugging Face createdAt of the canonical checkpoint internimage_t_1k_224. Collection date 2026-10-15.", "what_it_is_for": "Image classification and a general vision backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internimage/internimage-xl": { "id": "shanghai-ai-lab/internimage/internimage-xl", "name": "InternImage-XL", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internimage", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "InternImage deformable-convolution vision backbone. Parameter count was not re-read from the paper in this pass.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternImage-XL is a scale of Shanghai AI Laboratory's InternImage vision backbone. The date is the Hugging Face createdAt of the canonical checkpoint internimage_xl_22k_384. Collection date 2026-10-15.", "what_it_is_for": "Image classification and a general vision backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm-20b": { "id": "shanghai-ai-lab/internlm/internlm-20b", "name": "InternLM-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-09-20", "context_length": 16384, "max_output_tokens": null, "parameters": "20B", "architecture": "Decoder-only Transformer, 60 layers. Pretrained on over 2.3T tokens of English, Chinese, and code.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM-20B is Shanghai AI Laboratory's 20B \u4e66\u751f\u00b7\u6d66\u8bed base model, released 20 September 2023. Collection date 2026-10-15. Weights remain published.", "what_it_is_for": "Base model aimed at stronger understanding, reasoning, math, and code than InternLM-7B, with a 16k context via inference extrapolation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm-7b": { "id": "shanghai-ai-lab/internlm/internlm-7b", "name": "InternLM-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-07-06", "context_length": 8192, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only Transformer. The 20B card contrasts it with the deeper 60-layer 20B model; exact 7B depth was not re-read from config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM-7B is the first open 7B base model from Shanghai AI Laboratory (\u4e0a\u6d77\u4eba\u5de5\u667a\u80fd\u5b9e\u9a8c\u5ba4), released 6 July 2023 under the \u4e66\u751f\u00b7\u6d66\u8bed line. Collection date 2026-10-15. Weights remain published.", "what_it_is_for": "Base model trained on trillions of tokens for general Chinese and English use, with an 8k context window.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm-chat-20b": { "id": "shanghai-ai-lab/internlm/internlm-chat-20b", "name": "InternLM-Chat-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-12", "context_length": 16384, "max_output_tokens": null, "parameters": "20B", "architecture": "SFT and RLHF chat model on the 60-layer InternLM-20B base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM-Chat-20B is the aligned 20B \u4e66\u751f\u00b7\u6d66\u8bed chat model from Shanghai AI Laboratory. The model card dates the published chat checkpoint 12 December 2023. Collection date 2026-10-15.", "what_it_is_for": "Chat and tool-use model with the same 16k extrapolated context as InternLM-20B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm-chat-7b": { "id": "shanghai-ai-lab/internlm/internlm-chat-7b", "name": "InternLM-Chat-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-12", "context_length": 8192, "max_output_tokens": null, "parameters": "7B", "architecture": "Chat model aligned from InternLM-7B. The December 2023 card date is an updated finetune; the Hugging Face repo was created 2023-07-06.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM-Chat-7B is the chat model of the first \u4e66\u751f\u00b7\u6d66\u8bed generation from Shanghai AI Laboratory. The model card dates the published chat checkpoint 12 December 2023, after a finetune update. Collection date 2026-10-15.", "what_it_is_for": "Practical chat model with an 8k window and a tool-building workflow described on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-1-8b": { "id": "shanghai-ai-lab/internlm/internlm2-1-8b", "name": "InternLM2-1.8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-31", "context_length": null, "max_output_tokens": null, "parameters": "1.8B", "architecture": "InternLM2 decoder-only Transformer (InternLM2ForCausalLM).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-1.8B is an InternLM2 (\u4e66\u751f\u00b7\u6d66\u8bed) pretrain model from Shanghai AI Laboratory, released 2024-01-31. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "Further pretrained checkpoint recommended by the lab for most applications.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-1-8b-reward": { "id": "shanghai-ai-lab/internlm/internlm2-1-8b-reward", "name": "InternLM2-1.8B-Reward", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-19", "context_length": null, "max_output_tokens": null, "parameters": "1.8B", "architecture": "Reward model trained from InternLM2-Chat-SFT on about 2.4 million human and synthetic preference pairs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-1.8B-Reward is a Shanghai AI Laboratory reward model in the InternLM2 line, released 19 July 2024. It scores chats rather than chatting. Collection date 2026-10-15.", "what_it_is_for": "Preference reward model for English and Chinese dialogue, writing, code, and math, balanced for helpfulness and harmlessness.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-20b": { "id": "shanghai-ai-lab/internlm/internlm2-20b", "name": "InternLM2-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-17", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "InternLM2 decoder-only Transformer (InternLM2ForCausalLM).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-20B is an InternLM2 (\u4e66\u751f\u00b7\u6d66\u8bed) pretrain model from Shanghai AI Laboratory, released 2024-01-17. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "Further pretrained checkpoint recommended by the lab for most applications.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-20b-reward": { "id": "shanghai-ai-lab/internlm/internlm2-20b-reward", "name": "InternLM2-20B-Reward", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-19", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "Reward model trained from InternLM2-Chat-SFT on about 2.4 million human and synthetic preference pairs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-20B-Reward is a Shanghai AI Laboratory reward model in the InternLM2 line, released 19 July 2024. It scores chats rather than chatting. Collection date 2026-10-15.", "what_it_is_for": "Preference reward model for English and Chinese dialogue, writing, code, and math, balanced for helpfulness and harmlessness.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-5-1-8b": { "id": "shanghai-ai-lab/internlm/internlm2-5-1-8b", "name": "InternLM2.5-1.8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-05", "context_length": null, "max_output_tokens": null, "parameters": "1.8B", "architecture": "InternLM2 architecture (config model_type internlm2). InternLM2.5-7B-Chat config: 32 layers, hidden 4096, 32 heads, 8 KV heads, vocab 92544.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2.5-1.8B is an InternLM2.5 (\u4e66\u751f\u00b7\u6d66\u8bed) model from Shanghai AI Laboratory, model-zoo date 2024-08-05. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "Further pretrained base model. The lab recommends InternLM2.5 bases for most applications.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-5-1-8b-chat": { "id": "shanghai-ai-lab/internlm/internlm2-5-1-8b-chat", "name": "InternLM2.5-1.8B-Chat", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-05", "context_length": null, "max_output_tokens": null, "parameters": "1.8B", "architecture": "InternLM2 architecture (config model_type internlm2). InternLM2.5-7B-Chat config: 32 layers, hidden 4096, 32 heads, 8 KV heads, vocab 92544.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2.5-1.8B-Chat is an InternLM2.5 (\u4e66\u751f\u00b7\u6d66\u8bed) model from Shanghai AI Laboratory, model-zoo date 2024-08-05. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "SFT and online RLHF chat model with instruction following, chat, and function calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-5-20b": { "id": "shanghai-ai-lab/internlm/internlm2-5-20b", "name": "InternLM2.5-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-05", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "InternLM2 architecture (config model_type internlm2). InternLM2.5-7B-Chat config: 32 layers, hidden 4096, 32 heads, 8 KV heads, vocab 92544.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2.5-20B is an InternLM2.5 (\u4e66\u751f\u00b7\u6d66\u8bed) model from Shanghai AI Laboratory, model-zoo date 2024-08-05. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "Further pretrained base model. The lab recommends InternLM2.5 bases for most applications.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-5-20b-chat": { "id": "shanghai-ai-lab/internlm/internlm2-5-20b-chat", "name": "InternLM2.5-20B-Chat", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-05", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "InternLM2 architecture (config model_type internlm2). InternLM2.5-7B-Chat config: 32 layers, hidden 4096, 32 heads, 8 KV heads, vocab 92544.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2.5-20B-Chat is an InternLM2.5 (\u4e66\u751f\u00b7\u6d66\u8bed) model from Shanghai AI Laboratory, model-zoo date 2024-08-05. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "SFT and online RLHF chat model with instruction following, chat, and function calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-5-7b": { "id": "shanghai-ai-lab/internlm/internlm2-5-7b", "name": "InternLM2.5-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-03", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "InternLM2 architecture (config model_type internlm2). InternLM2.5-7B-Chat config: 32 layers, hidden 4096, 32 heads, 8 KV heads, vocab 92544.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2.5-7B is an InternLM2.5 (\u4e66\u751f\u00b7\u6d66\u8bed) model from Shanghai AI Laboratory, model-zoo date 2024-07-03. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "Further pretrained base model. The lab recommends InternLM2.5 bases for most applications.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-5-7b-chat": { "id": "shanghai-ai-lab/internlm/internlm2-5-7b-chat", "name": "InternLM2.5-7B-Chat", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-03", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "InternLM2ForCausalLM. config.json on this checkpoint: 32 layers, hidden 4096, 32 query heads, 8 KV heads, intermediate 14336, vocab 92544, max_position_embeddings 32768, rope_theta 1,000,000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2.5-7B-Chat is an InternLM2.5 (\u4e66\u751f\u00b7\u6d66\u8bed) model from Shanghai AI Laboratory, model-zoo date 2024-07-03. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "SFT and online RLHF chat model with instruction following, chat, and function calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-5-7b-chat-1m": { "id": "shanghai-ai-lab/internlm/internlm2-5-7b-chat-1m", "name": "InternLM2.5-7B-Chat-1M", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-03", "context_length": 1000000, "max_output_tokens": null, "parameters": "7B", "architecture": "Same InternLM2 7B shape as the standard chat model. This checkpoint's config.json max_position_embeddings is 262144 and rope_theta is 50,000,000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2.5-7B-Chat-1M is an InternLM2.5 (\u4e66\u751f\u00b7\u6d66\u8bed) model from Shanghai AI Laboratory, model-zoo date 2024-07-03. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "1M-context chat variant. The card says it keeps InternLM2.5-Chat quality at million-word context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-7b": { "id": "shanghai-ai-lab/internlm/internlm2-7b", "name": "InternLM2-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-17", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "InternLM2 decoder-only Transformer (InternLM2ForCausalLM).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-7B is an InternLM2 (\u4e66\u751f\u00b7\u6d66\u8bed) pretrain model from Shanghai AI Laboratory, released 2024-01-17. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "Further pretrained checkpoint recommended by the lab for most applications.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-7b-reward": { "id": "shanghai-ai-lab/internlm/internlm2-7b-reward", "name": "InternLM2-7B-Reward", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-19", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Reward model trained from InternLM2-Chat-SFT on about 2.4 million human and synthetic preference pairs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-7B-Reward is a Shanghai AI Laboratory reward model in the InternLM2 line, released 19 July 2024. It scores chats rather than chatting. Collection date 2026-10-15.", "what_it_is_for": "Preference reward model for English and Chinese dialogue, writing, code, and math, balanced for helpfulness and harmlessness.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-base-20b": { "id": "shanghai-ai-lab/internlm/internlm2-base-20b", "name": "InternLM2-Base-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-17", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "InternLM2 decoder-only Transformer (InternLM2ForCausalLM).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Base-20B is an InternLM2 (\u4e66\u751f\u00b7\u6d66\u8bed) base model from Shanghai AI Laboratory, released 2024-01-17. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "Foundation checkpoint for downstream adaptation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-base-7b": { "id": "shanghai-ai-lab/internlm/internlm2-base-7b", "name": "InternLM2-Base-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-17", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "InternLM2 decoder-only Transformer (InternLM2ForCausalLM).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Base-7B is an InternLM2 (\u4e66\u751f\u00b7\u6d66\u8bed) base model from Shanghai AI Laboratory, released 2024-01-17. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "Foundation checkpoint for downstream adaptation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-chat-1-8b": { "id": "shanghai-ai-lab/internlm/internlm2-chat-1-8b", "name": "InternLM2-Chat-1.8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-02-19", "context_length": null, "max_output_tokens": null, "parameters": "1.8B", "architecture": "InternLM2 decoder-only Transformer (InternLM2ForCausalLM).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Chat-1.8B is an InternLM2 (\u4e66\u751f\u00b7\u6d66\u8bed) chat model from Shanghai AI Laboratory, released 2024-02-19. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "SFT plus online RLHF chat model, recommended for instruction following and function calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-chat-1-8b-sft": { "id": "shanghai-ai-lab/internlm/internlm2-chat-1-8b-sft", "name": "InternLM2-Chat-1.8B-SFT", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-31", "context_length": null, "max_output_tokens": null, "parameters": "1.8B", "architecture": "InternLM2 decoder-only Transformer (InternLM2ForCausalLM).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Chat-1.8B-SFT is an InternLM2 (\u4e66\u751f\u00b7\u6d66\u8bed) sft model from Shanghai AI Laboratory, released 2024-01-31. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "SFT-only intermediate released for alignment research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-chat-20b": { "id": "shanghai-ai-lab/internlm/internlm2-chat-20b", "name": "InternLM2-Chat-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-17", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "InternLM2 decoder-only Transformer (InternLM2ForCausalLM).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Chat-20B is an InternLM2 (\u4e66\u751f\u00b7\u6d66\u8bed) chat model from Shanghai AI Laboratory, released 2024-01-17. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "SFT plus online RLHF chat model, recommended for instruction following and function calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-chat-20b-sft": { "id": "shanghai-ai-lab/internlm/internlm2-chat-20b-sft", "name": "InternLM2-Chat-20B-SFT", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-17", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "InternLM2 decoder-only Transformer (InternLM2ForCausalLM).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Chat-20B-SFT is an InternLM2 (\u4e66\u751f\u00b7\u6d66\u8bed) sft model from Shanghai AI Laboratory, released 2024-01-17. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "SFT-only intermediate released for alignment research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-chat-7b": { "id": "shanghai-ai-lab/internlm/internlm2-chat-7b", "name": "InternLM2-Chat-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-17", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "InternLM2 decoder-only Transformer (InternLM2ForCausalLM).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Chat-7B is an InternLM2 (\u4e66\u751f\u00b7\u6d66\u8bed) chat model from Shanghai AI Laboratory, released 2024-01-17. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "SFT plus online RLHF chat model, recommended for instruction following and function calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-chat-7b-sft": { "id": "shanghai-ai-lab/internlm/internlm2-chat-7b-sft", "name": "InternLM2-Chat-7B-SFT", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-17", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "InternLM2 decoder-only Transformer (InternLM2ForCausalLM).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Chat-7B-SFT is an InternLM2 (\u4e66\u751f\u00b7\u6d66\u8bed) sft model from Shanghai AI Laboratory, released 2024-01-17. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "SFT-only intermediate released for alignment research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm2-wqx-20b": { "id": "shanghai-ai-lab/internlm/internlm2-wqx-20b", "name": "InternLM2-WQX-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-06-04", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "Named InternLM2 20B variant. Architecture details were not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-WQX-20B is a named 20B checkpoint published by the internlm organization on Hugging Face on 4 June 2024. Collection date 2026-10-15. The model card body was not re-read, so most specs are unknown.", "what_it_is_for": "The checkpoint name marks a WQX variant of InternLM2-20B. The intended task was not confirmed from a card in this pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm/internlm3-8b-instruct": { "id": "shanghai-ai-lab/internlm/internlm3-8b-instruct", "name": "InternLM3-8B-Instruct", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-15", "context_length": 32768, "max_output_tokens": null, "parameters": "8B", "architecture": "InternLM3ForCausalLM. config.json: 48 layers, hidden 4096, intermediate 10240, 32 heads, 2 KV heads, vocab 128512, max_position_embeddings 32768, rope_theta 50,000,000. Trained on 4 trillion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM3-8B-Instruct is Shanghai AI Laboratory's 2025 \u4e66\u751f\u00b7\u6d66\u8bed instruct model, released 15 January 2025. It is an 8B model trained on 4 trillion tokens, with a normal mode and a deep-thinking mode. Collection date 2026-10-15. Weights remain published under Apache-2.0.", "what_it_is_for": "General chat and reasoning. Deep thinking uses a long chain of thought; normal mode is for ordinary dialogue. The README compares it with Llama3.1-8B and Qwen2.5-7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-math/internlm2-5-step-prover": { "id": "shanghai-ai-lab/internlm-math/internlm2-5-step-prover", "name": "InternLM2.5-Step-Prover", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining and SFT from InternLM2 for bilingual math. Lean 4 proving is supported on the math line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2.5-Step-Prover is a bilingual math model from Shanghai AI Laboratory in the InternLM-Math line, released 2024-10-22. It is the chat / SFT checkpoint. Collection date 2026-10-15.", "what_it_is_for": "Lean prover policy model. The README says 14k proofs were found in Lean-Workbook. Parameter count was not stated in the news line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-math/internlm2-5-step-prover-critic": { "id": "shanghai-ai-lab/internlm-math/internlm2-5-step-prover-critic", "name": "InternLM2.5-Step-Prover-Critic", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining and SFT from InternLM2 for bilingual math. Lean 4 proving is supported on the math line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2.5-Step-Prover-Critic is a bilingual math model from Shanghai AI Laboratory in the InternLM-Math line, released 2024-10-22. It is the chat / SFT checkpoint. Collection date 2026-10-15.", "what_it_is_for": "Critic model released with InternLM2.5-Step-Prover.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-math/internlm2-math-20b": { "id": "shanghai-ai-lab/internlm-math/internlm2-math-20b", "name": "InternLM2-Math-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "Continued pretraining and SFT from InternLM2 for bilingual math. Lean 4 proving is supported on the math line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Math-20B is a bilingual math model from Shanghai AI Laboratory in the InternLM-Math line, released 2024-01-23. It is the chat / SFT checkpoint. Collection date 2026-10-15.", "what_it_is_for": "Informal math reasoning, code-interpreter solving, and Lean formalization or theorem proving.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-math/internlm2-math-7b": { "id": "shanghai-ai-lab/internlm-math/internlm2-math-7b", "name": "InternLM2-Math-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Continued pretraining and SFT from InternLM2 for bilingual math. Lean 4 proving is supported on the math line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Math-7B is a bilingual math model from Shanghai AI Laboratory in the InternLM-Math line, released 2024-01-23. It is the chat / SFT checkpoint. Collection date 2026-10-15.", "what_it_is_for": "Informal math reasoning, code-interpreter solving, and Lean formalization or theorem proving.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-math/internlm2-math-base-20b": { "id": "shanghai-ai-lab/internlm-math/internlm2-math-base-20b", "name": "InternLM2-Math-Base-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "Continued pretraining and SFT from InternLM2 for bilingual math. Lean 4 proving is supported on the math line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Math-Base-20B is a bilingual math model from Shanghai AI Laboratory in the InternLM-Math line, released 2024-01-23. It is the pretrained base checkpoint. Collection date 2026-10-15.", "what_it_is_for": "Informal math reasoning, code-interpreter solving, and Lean formalization or theorem proving.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-math/internlm2-math-base-7b": { "id": "shanghai-ai-lab/internlm-math/internlm2-math-base-7b", "name": "InternLM2-Math-Base-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Continued pretraining and SFT from InternLM2 for bilingual math. Lean 4 proving is supported on the math line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Math-Base-7B is a bilingual math model from Shanghai AI Laboratory in the InternLM-Math line, released 2024-01-23. It is the pretrained base checkpoint. Collection date 2026-10-15.", "what_it_is_for": "Informal math reasoning, code-interpreter solving, and Lean formalization or theorem proving.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-math/internlm2-math-plus-1-8b": { "id": "shanghai-ai-lab/internlm-math/internlm2-math-plus-1-8b", "name": "InternLM2-Math-Plus-1.8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-27", "context_length": null, "max_output_tokens": null, "parameters": "1.8B", "architecture": "Continued pretraining and SFT from InternLM2 for bilingual math. Lean 4 proving is supported on the math line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Math-Plus-1.8B is a bilingual math model from Shanghai AI Laboratory in the InternLM-Math line, released 2024-05-27. It is the chat / SFT checkpoint. Collection date 2026-10-15.", "what_it_is_for": "Informal math reasoning, code-interpreter solving, and Lean formalization or theorem proving.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-math/internlm2-math-plus-20b": { "id": "shanghai-ai-lab/internlm-math/internlm2-math-plus-20b", "name": "InternLM2-Math-Plus-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-27", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "Continued pretraining and SFT from InternLM2 for bilingual math. Lean 4 proving is supported on the math line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Math-Plus-20B is a bilingual math model from Shanghai AI Laboratory in the InternLM-Math line, released 2024-05-27. It is the chat / SFT checkpoint. Collection date 2026-10-15.", "what_it_is_for": "Informal math reasoning, code-interpreter solving, and Lean formalization or theorem proving.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-math/internlm2-math-plus-7b": { "id": "shanghai-ai-lab/internlm-math/internlm2-math-plus-7b", "name": "InternLM2-Math-Plus-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-27", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Continued pretraining and SFT from InternLM2 for bilingual math. Lean 4 proving is supported on the math line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Math-Plus-7B is a bilingual math model from Shanghai AI Laboratory in the InternLM-Math line, released 2024-05-27. It is the chat / SFT checkpoint. Collection date 2026-10-15.", "what_it_is_for": "Informal math reasoning, code-interpreter solving, and Lean formalization or theorem proving.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-math/internlm2-step-prover": { "id": "shanghai-ai-lab/internlm-math/internlm2-step-prover", "name": "InternLM2-Step-Prover", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-math", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-25", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Continued pretraining and SFT from InternLM2 for bilingual math. Lean 4 proving is supported on the math line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM2-Step-Prover is a bilingual math model from Shanghai AI Laboratory in the InternLM-Math line, released 2024-07-25. It is the chat / SFT checkpoint. Collection date 2026-10-15.", "what_it_is_for": "Lean 4 theorem proving, fine-tuned on Lean-Github and Lean-Workbook.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer-7b": { "id": "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer-7b", "name": "InternLM-XComposer-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-xcomposer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-09-27", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Vision-language model based on InternLM. Exact vision tower and context were not re-read per checkpoint except where noted in gaps.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM-XComposer-7B is a vision-language model from Shanghai AI Laboratory in the InternLM-XComposer line, released 2023-09-27. Collection date 2026-10-15. Weights are published on Hugging Face.", "what_it_is_for": "Text-image comprehension and composition, plus visual chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer-vl-7b": { "id": "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer-vl-7b", "name": "InternLM-XComposer-VL-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-xcomposer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-09-26", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Vision-language model based on InternLM. Exact vision tower and context were not re-read per checkpoint except where noted in gaps.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM-XComposer-VL-7B is a vision-language model from Shanghai AI Laboratory in the InternLM-XComposer line, released 2023-09-26. Collection date 2026-10-15. Weights are published on Hugging Face.", "what_it_is_for": "Vision-language chat checkpoint paired with the first XComposer release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-4khd-7b": { "id": "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-4khd-7b", "name": "InternLM-XComposer2-4KHD-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-xcomposer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-09", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Vision-language model based on InternLM. Exact vision tower and context were not re-read per checkpoint except where noted in gaps.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM-XComposer2-4KHD-7B is a vision-language model from Shanghai AI Laboratory in the InternLM-XComposer line, released 2024-04-09. Collection date 2026-10-15. Weights are published on Hugging Face.", "what_it_is_for": "High-resolution vision-language chat, from 336 pixels up to 4K as described in the paper title.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-5-7b": { "id": "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-5-7b", "name": "InternLM-XComposer2.5-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-xcomposer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-07-03", "context_length": 24576, "max_output_tokens": null, "parameters": "7B", "architecture": "Vision-language model based on InternLM. Exact vision tower and context were not re-read per checkpoint except where noted in gaps.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM-XComposer2.5-7B is a vision-language model from Shanghai AI Laboratory in the InternLM-XComposer line, released 2024-07-03. Collection date 2026-10-15. Weights are published on Hugging Face.", "what_it_is_for": "Long-context vision-language model for video, multi-image dialogue, 4K images, webpage crafting, and article composition. The paper says a 7B LLM backend, 24K interleaved training context, and RoPE extension toward 96K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-5-7b-reward": { "id": "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-5-7b-reward", "name": "InternLM-XComposer2.5-7B-Reward", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-xcomposer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-12-12", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Vision-language model based on InternLM. Exact vision tower and context were not re-read per checkpoint except where noted in gaps.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM-XComposer2.5-7B-Reward is a vision-language model from Shanghai AI Laboratory in the InternLM-XComposer line, released 2024-12-12. Collection date 2026-10-15. Weights are published on Hugging Face.", "what_it_is_for": "Multimodal reward model released with the XComposer2.5 line. The README calls it a simple but effective multi-modal reward model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-5-ol-7b": { "id": "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-5-ol-7b", "name": "InternLM-XComposer2.5-OmniLive-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-xcomposer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-12-12", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Vision-language model based on InternLM. Exact vision tower and context were not re-read per checkpoint except where noted in gaps.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM-XComposer2.5-OmniLive-7B is a vision-language model from Shanghai AI Laboratory in the InternLM-XComposer line, released 2024-12-12. Collection date 2026-10-15. Weights are published on Hugging Face.", "what_it_is_for": "Streaming video and audio interaction system with perception, long memory, and a reasoning module.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-7b": { "id": "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-7b", "name": "InternLM-XComposer2-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-xcomposer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-01-26", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Vision-language model based on InternLM. Exact vision tower and context were not re-read per checkpoint except where noted in gaps.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM-XComposer2-7B is a vision-language model from Shanghai AI Laboratory in the InternLM-XComposer line, released 2024-01-26. Collection date 2026-10-15. Weights are published on Hugging Face.", "what_it_is_for": "Free-form text-image composition.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-vl-1-8b": { "id": "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-vl-1-8b", "name": "InternLM-XComposer2-VL-1.8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-xcomposer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-09", "context_length": null, "max_output_tokens": null, "parameters": "1.8B", "architecture": "Vision-language model based on InternLM. Exact vision tower and context were not re-read per checkpoint except where noted in gaps.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM-XComposer2-VL-1.8B is a vision-language model from Shanghai AI Laboratory in the InternLM-XComposer line, released 2024-04-09. Collection date 2026-10-15. Weights are published on Hugging Face.", "what_it_is_for": "Smaller vision-language chat model in the XComposer2 line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-vl-7b": { "id": "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-vl-7b", "name": "InternLM-XComposer2-VL-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-xcomposer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-01-26", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Vision-language model based on InternLM. Exact vision tower and context were not re-read per checkpoint except where noted in gaps.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLM-XComposer2-VL-7B is a vision-language model from Shanghai AI Laboratory in the InternLM-XComposer line, released 2024-01-26. Collection date 2026-10-15. Weights are published on Hugging Face.", "what_it_is_for": "Vision-language chat checkpoint of XComposer2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internlumina/internlumina-u2": { "id": "shanghai-ai-lab/internlumina/internlumina-u2", "name": "InternLumina-U2", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlumina", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-08-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-codebook diffusion language model for visual understanding, image generation, and editing. Parameter count was not in the README sections read.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternLumina-U2 is a Shanghai AI Laboratory unified vision model for understanding, generation, and editing. Inference code was released in August 2026 and weights are on the internlm Hugging Face org. Collection date 2026-10-15.", "what_it_is_for": "Omni-visual understanding plus image generation and editing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internomni/internomni": { "id": "shanghai-ai-lab/internomni/internomni", "name": "InternOmni", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internomni", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-08-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First-party named release. Task label used while cataloging: omni. Card body not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternOmni is a named OpenGVLab multimodal checkpoint. Collection date 2026-10-15. The card was not re-read. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named omni checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internthinker/internthinker": { "id": "shanghai-ai-lab/internthinker/internthinker", "name": "InternThinker", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internthinker", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternThinker (\u4e66\u751f\u00b7\u601d\u5ba2) is Shanghai AI Laboratory's reasoning model, opened on the lab chat interface around 25 November 2024. Collection date 2026-10-15. Public weights were not found, so this dossier treats the weights as closed.", "what_it_is_for": "Long chain-of-thought reasoning with self-check, aimed at math, code, and puzzles, and later described for science and games.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvideo/internvideo": { "id": "shanghai-ai-lab/internvideo/internvideo", "name": "InternVideo", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvideo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-08-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video foundation model. Parameter count was not re-read.", "modalities": { "input": [ "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVideo is Shanghai AI Laboratory's OpenGVLab video foundation model. The Hugging Face snapshot seen here is dated 15 August 2024 for InternVideo1.0. Collection date 2026-10-15.", "what_it_is_for": "Video representation and classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvideo/internvideo2-5-chat-8b": { "id": "shanghai-ai-lab/internvideo/internvideo2-5-chat-8b", "name": "InternVideo2.5-Chat-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvideo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-01-22", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "InternVideo2.5 chat model, 8B nameplate.", "modalities": { "input": [ "text", "video", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVideo2.5-Chat-8B is the 2025 video chat model from OpenGVLab. Collection date 2026-10-15. The date is the Hugging Face createdAt.", "what_it_is_for": "Video understanding and chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvideo/internvideo2-chat-8b": { "id": "shanghai-ai-lab/internvideo/internvideo2-chat-8b", "name": "InternVideo2-Chat-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvideo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-08-01", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Video-language chat model at the 8B nameplate.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVideo2-Chat-8B is OpenGVLab's video chat model. Collection date 2026-10-15.", "what_it_is_for": "Video question answering and chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvideo/internvideo2-stage2-1b": { "id": "shanghai-ai-lab/internvideo/internvideo2-stage2-1b", "name": "InternVideo2-Stage2-1B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvideo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-04-13", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "InternVideo2 stage-2 1B video encoder.", "modalities": { "input": [ "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVideo2 stage-2 1B is an OpenGVLab video foundation checkpoint. Collection date 2026-10-15. The date is the Hugging Face createdAt of the 224p f4 repo.", "what_it_is_for": "General video representation. Dataset-specific stage-1 checkpoints are not separate products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvideo/internvideo2-stage2-6b": { "id": "shanghai-ai-lab/internvideo/internvideo2-stage2-6b", "name": "InternVideo2-Stage2-6B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvideo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-02-10", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "InternVideo2 stage-2 6B video encoder.", "modalities": { "input": [ "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVideo2 stage-2 6B is the large OpenGVLab video foundation checkpoint. Collection date 2026-10-15.", "what_it_is_for": "General video representation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvit/internvit-300m-448": { "id": "shanghai-ai-lab/internvit/internvit-300m-448", "name": "InternViT-300M-448px", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-24", "context_length": null, "max_output_tokens": null, "parameters": "300M", "architecture": "InternViT vision encoder used by the InternVL line. 300M-class 448px encoder.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternViT-300M-448px is an open vision encoder from Shanghai AI Laboratory, published 2024-05-24. Collection date 2026-10-15. It is a component model, not a chat model.", "what_it_is_for": "Image feature extraction for InternVL and related multimodal models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvit/internvit-300m-448-v2-5": { "id": "shanghai-ai-lab/internvit/internvit-300m-448-v2-5", "name": "InternViT-300M-448px-V2.5", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-11-22", "context_length": null, "max_output_tokens": null, "parameters": "300M", "architecture": "InternViT vision encoder used by the InternVL line. V2.5 300M encoder used by smaller InternVL2.5, InternVL3, and InternVL3.5 models. InternVL3.5 small models list 0.3B vision parameters.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternViT-300M-448px-V2.5 is an open vision encoder from Shanghai AI Laboratory, published 2024-11-22. Collection date 2026-10-15. It is a component model, not a chat model.", "what_it_is_for": "Image feature extraction for InternVL and related multimodal models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvit/internvit-6b-224": { "id": "shanghai-ai-lab/internvit/internvit-6b-224", "name": "InternViT-6B-224px", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-12-22", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "InternViT vision encoder used by the InternVL line. Original 224px 6B encoder. Later 448px V1.0, V1.2, and V1.5 checkpoints are versions.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternViT-6B-224px is an open vision encoder from Shanghai AI Laboratory, published 2023-12-22. Collection date 2026-10-15. It is a component model, not a chat model.", "what_it_is_for": "Image feature extraction for InternVL and related multimodal models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvit/internvit-6b-448-v2-5": { "id": "shanghai-ai-lab/internvit/internvit-6b-448-v2-5", "name": "InternViT-6B-448px-V2.5", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-11-22", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "InternViT vision encoder used by the InternVL line. V2.5 6B encoder used by larger InternVL2.5 and InternVL3 models. The InternVL3.5 38B and 241B rows list 5.5B vision parameters.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternViT-6B-448px-V2.5 is an open vision encoder from Shanghai AI Laboratory, published 2024-11-22. Collection date 2026-10-15. It is a component model, not a chat model.", "what_it_is_for": "Image feature extraction for InternVL and related multimodal models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl-14b": { "id": "shanghai-ai-lab/internvl/internvl-14b", "name": "InternVL-14B-224px", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-12-22", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Contrastive vision-language foundation checkpoint, not the later chat series. Pipeline on Hugging Face is image feature extraction.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL-14B-224px is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2023-12-22. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl-chat-v1-1": { "id": "shanghai-ai-lab/internvl/internvl-chat-v1-1", "name": "InternVL-Chat-V1.1", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-01-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Early InternVL chat model. Parameter total was not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL-Chat-V1.1 is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-01-24. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl-chat-v1-2": { "id": "shanghai-ai-lab/internvl/internvl-chat-v1-2", "name": "InternVL-Chat-V1.2", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-11", "context_length": null, "max_output_tokens": null, "parameters": "40B", "architecture": "ViT-MLP-LLM chat model. A project release note describes a 40B InternVL-Chat-V1.2 trained with visual instruction data.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL-Chat-V1.2 is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-02-11. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl-chat-v1-2-plus": { "id": "shanghai-ai-lab/internvl/internvl-chat-v1-2-plus", "name": "InternVL-Chat-V1.2-Plus", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Updated InternVL-Chat-V1.2 checkpoint.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL-Chat-V1.2-Plus is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-02-21. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl-chat-v1-5": { "id": "shanghai-ai-lab/internvl/internvl-chat-v1-5", "name": "InternVL-Chat-V1.5", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "InternVL 1.5 chat model using the ViT-MLP-LLM pattern and dynamic 448 resolution.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL-Chat-V1.5 is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-04-18. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-1b": { "id": "shanghai-ai-lab/internvl/internvl2-1b", "name": "InternVL2-1B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-07-08", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "InternVL2 ViT-MLP-LLM chat model. The language-tower name was not copied because the README extract read in this pass did not include the InternVL2 component table.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2-1B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-07-08. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-26b": { "id": "shanghai-ai-lab/internvl/internvl2-26b", "name": "InternVL2-26B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-27", "context_length": null, "max_output_tokens": null, "parameters": "26B", "architecture": "InternVL2 ViT-MLP-LLM chat model. The language-tower name was not copied because the README extract read in this pass did not include the InternVL2 component table.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2-26B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-06-27. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-2b": { "id": "shanghai-ai-lab/internvl/internvl2-2b", "name": "InternVL2-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-27", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "InternVL2 ViT-MLP-LLM chat model. The language-tower name was not copied because the README extract read in this pass did not include the InternVL2 component table.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2-2B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-06-27. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-40b": { "id": "shanghai-ai-lab/internvl/internvl2-40b", "name": "InternVL2-40B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-27", "context_length": null, "max_output_tokens": null, "parameters": "40B", "architecture": "InternVL2 ViT-MLP-LLM chat model. The language-tower name was not copied because the README extract read in this pass did not include the InternVL2 component table.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2-40B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-06-27. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-4b": { "id": "shanghai-ai-lab/internvl/internvl2-4b", "name": "InternVL2-4B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-27", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "InternVL2 ViT-MLP-LLM chat model. The language-tower name was not copied because the README extract read in this pass did not include the InternVL2 component table.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2-4B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-06-27. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-5-1b": { "id": "shanghai-ai-lab/internvl/internvl2-5-1b", "name": "InternVL2.5-1B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "InternViT-300M-448px-V2.5 + Qwen2.5-0.5B-Instruct. Same ViT-MLP-LLM layout as InternVL 1.5/2.0, with pixel unshuffle and 448 dynamic tiles.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2.5-1B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-11-20. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-5-1b-mpo": { "id": "shanghai-ai-lab/internvl/internvl2-5-1b-mpo", "name": "InternVL2.5-1B-MPO", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-12-20", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Mixed preference optimization variant of InternVL2.5-1B. Vision and language parts match the base 2.5 model: InternViT-300M-448px-V2.5 + Qwen2.5-0.5B-Instruct.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2.5-1B-MPO is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-12-20. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-5-26b": { "id": "shanghai-ai-lab/internvl/internvl2-5-26b", "name": "InternVL2.5-26B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "26B", "architecture": "InternViT-6B-448px-V2.5 + internlm2_5-20b-chat. Same ViT-MLP-LLM layout as InternVL 1.5/2.0, with pixel unshuffle and 448 dynamic tiles.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2.5-26B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-11-20. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-5-26b-mpo": { "id": "shanghai-ai-lab/internvl/internvl2-5-26b-mpo", "name": "InternVL2.5-26B-MPO", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-12-20", "context_length": null, "max_output_tokens": null, "parameters": "26B", "architecture": "Mixed preference optimization variant of InternVL2.5-26B. Vision and language parts match the base 2.5 model: InternViT-6B-448px-V2.5 + internlm2_5-20b-chat.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2.5-26B-MPO is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-12-20. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-5-2b": { "id": "shanghai-ai-lab/internvl/internvl2-5-2b", "name": "InternVL2.5-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "InternViT-300M-448px-V2.5 + internlm2_5-1_8b-chat. Same ViT-MLP-LLM layout as InternVL 1.5/2.0, with pixel unshuffle and 448 dynamic tiles.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2.5-2B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-11-20. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-5-2b-mpo": { "id": "shanghai-ai-lab/internvl/internvl2-5-2b-mpo", "name": "InternVL2.5-2B-MPO", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-12-20", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Mixed preference optimization variant of InternVL2.5-2B. Vision and language parts match the base 2.5 model: InternViT-300M-448px-V2.5 + internlm2_5-1_8b-chat.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2.5-2B-MPO is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-12-20. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-5-38b": { "id": "shanghai-ai-lab/internvl/internvl2-5-38b", "name": "InternVL2.5-38B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "38B", "architecture": "InternViT-6B-448px-V2.5 + Qwen2.5-32B-Instruct. Same ViT-MLP-LLM layout as InternVL 1.5/2.0, with pixel unshuffle and 448 dynamic tiles.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2.5-38B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-11-20. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-5-38b-mpo": { "id": "shanghai-ai-lab/internvl/internvl2-5-38b-mpo", "name": "InternVL2.5-38B-MPO", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-12-20", "context_length": null, "max_output_tokens": null, "parameters": "38B", "architecture": "Mixed preference optimization variant of InternVL2.5-38B. Vision and language parts match the base 2.5 model: InternViT-6B-448px-V2.5 + Qwen2.5-32B-Instruct.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2.5-38B-MPO is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-12-20. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-5-4b": { "id": "shanghai-ai-lab/internvl/internvl2-5-4b", "name": "InternVL2.5-4B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "InternViT-300M-448px-V2.5 + Qwen2.5-3B-Instruct. Same ViT-MLP-LLM layout as InternVL 1.5/2.0, with pixel unshuffle and 448 dynamic tiles.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2.5-4B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-11-20. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-5-4b-mpo": { "id": "shanghai-ai-lab/internvl/internvl2-5-4b-mpo", "name": "InternVL2.5-4B-MPO", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-12-20", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Mixed preference optimization variant of InternVL2.5-4B. Vision and language parts match the base 2.5 model: InternViT-300M-448px-V2.5 + Qwen2.5-3B-Instruct.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2.5-4B-MPO is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-12-20. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-5-78b": { "id": "shanghai-ai-lab/internvl/internvl2-5-78b", "name": "InternVL2.5-78B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-12-02", "context_length": null, "max_output_tokens": null, "parameters": "78B", "architecture": "InternViT-6B-448px-V2.5 + Qwen2.5-72B-Instruct. Same ViT-MLP-LLM layout as InternVL 1.5/2.0, with pixel unshuffle and 448 dynamic tiles.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2.5-78B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-12-02. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-5-78b-mpo": { "id": "shanghai-ai-lab/internvl/internvl2-5-78b-mpo", "name": "InternVL2.5-78B-MPO", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-12-20", "context_length": null, "max_output_tokens": null, "parameters": "78B", "architecture": "Mixed preference optimization variant of InternVL2.5-78B. Vision and language parts match the base 2.5 model: InternViT-6B-448px-V2.5 + Qwen2.5-72B-Instruct.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2.5-78B-MPO is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-12-20. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-5-8b": { "id": "shanghai-ai-lab/internvl/internvl2-5-8b", "name": "InternVL2.5-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "InternViT-300M-448px-V2.5 + internlm2_5-7b-chat. Same ViT-MLP-LLM layout as InternVL 1.5/2.0, with pixel unshuffle and 448 dynamic tiles.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2.5-8B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-11-20. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-5-8b-mpo": { "id": "shanghai-ai-lab/internvl/internvl2-5-8b-mpo", "name": "InternVL2.5-8B-MPO", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-12-20", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Mixed preference optimization variant of InternVL2.5-8B. Vision and language parts match the base 2.5 model: InternViT-300M-448px-V2.5 + internlm2_5-7b-chat.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2.5-8B-MPO is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-12-20. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-8b": { "id": "shanghai-ai-lab/internvl/internvl2-8b", "name": "InternVL2-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-27", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "InternVL2 ViT-MLP-LLM chat model. The language-tower name was not copied because the README extract read in this pass did not include the InternVL2 component table.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2-8B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-06-27. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl2-llama3-76b": { "id": "shanghai-ai-lab/internvl/internvl2-llama3-76b", "name": "InternVL2-Llama3-76B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-07-15", "context_length": null, "max_output_tokens": null, "parameters": "76B", "architecture": "InternVL2 ViT-MLP-LLM chat model. The language-tower name was not copied because the README extract read in this pass did not include the InternVL2 component table.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL2-Llama3-76B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-07-15. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-14b": { "id": "shanghai-ai-lab/internvl/internvl3-14b", "name": "InternVL3-14B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-10", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "InternViT-300M-448px-V2.5 + Qwen2.5-14B", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3-14B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-04-10. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-1b": { "id": "shanghai-ai-lab/internvl/internvl3-1b", "name": "InternVL3-1B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-10", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "InternViT-300M-448px-V2.5 + Qwen2.5-0.5B", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3-1B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-04-10. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-2b": { "id": "shanghai-ai-lab/internvl/internvl3-2b", "name": "InternVL3-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-10", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "InternViT-300M-448px-V2.5 + Qwen2.5-1.5B", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3-2B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-04-10. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-38b": { "id": "shanghai-ai-lab/internvl/internvl3-38b", "name": "InternVL3-38B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-10", "context_length": null, "max_output_tokens": null, "parameters": "38B", "architecture": "InternViT-6B-448px-V2.5 + Qwen2.5-32B", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3-38B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-04-10. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-5-14b": { "id": "shanghai-ai-lab/internvl/internvl3-5-14b", "name": "InternVL3.5-14B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-26", "context_length": null, "max_output_tokens": null, "parameters": "0.3B vision + 14.8B language = 15.1B total", "architecture": "InternVL3.5 multimodal model. Official model zoo gives the vision and language parameter split. Cascade reinforcement learning and dynamic visual-resolution routing are described in the technical report.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3.5-14B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-08-26. Collection date 2026-10-15. The series was announced at the end of August 2025. The README news line is 26 August 2025.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-5-1b": { "id": "shanghai-ai-lab/internvl/internvl3-5-1b", "name": "InternVL3.5-1B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-26", "context_length": null, "max_output_tokens": null, "parameters": "0.3B vision + 0.8B language = 1.1B total", "architecture": "InternVL3.5 multimodal model. Official model zoo gives the vision and language parameter split. Cascade reinforcement learning and dynamic visual-resolution routing are described in the technical report.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3.5-1B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-08-26. Collection date 2026-10-15. The series was announced at the end of August 2025. The README news line is 26 August 2025.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-5-20b-a4b": { "id": "shanghai-ai-lab/internvl/internvl3-5-20b-a4b", "name": "InternVL3.5-20B-A4B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-26", "context_length": null, "max_output_tokens": null, "parameters": "0.3B vision + 20.9B language = 21.2B total, about 4B activated", "architecture": "InternVL3.5 multimodal model. Official model zoo gives the vision and language parameter split. Cascade reinforcement learning and dynamic visual-resolution routing are described in the technical report. The Chinese README says this 20B-A4B model uses the OpenAI GPT-OSS-20B language base.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3.5-20B-A4B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-08-26. Collection date 2026-10-15. The series was announced at the end of August 2025. The README news line is 26 August 2025.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-5-241b-a28b": { "id": "shanghai-ai-lab/internvl/internvl3-5-241b-a28b", "name": "InternVL3.5-241B-A28B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-26", "context_length": null, "max_output_tokens": null, "parameters": "5.5B vision + 235.1B language = 240.7B total, 28B activated", "architecture": "InternVL3.5 multimodal model. Official model zoo gives the vision and language parameter split. Cascade reinforcement learning and dynamic visual-resolution routing are described in the technical report.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3.5-241B-A28B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-08-26. Collection date 2026-10-15. The series was announced at the end of August 2025. The README news line is 26 August 2025.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-5-2b": { "id": "shanghai-ai-lab/internvl/internvl3-5-2b", "name": "InternVL3.5-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-26", "context_length": null, "max_output_tokens": null, "parameters": "0.3B vision + 2.0B language = 2.3B total", "architecture": "InternVL3.5 multimodal model. Official model zoo gives the vision and language parameter split. Cascade reinforcement learning and dynamic visual-resolution routing are described in the technical report.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3.5-2B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-08-26. Collection date 2026-10-15. The series was announced at the end of August 2025. The README news line is 26 August 2025.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-5-30b-a3b": { "id": "shanghai-ai-lab/internvl/internvl3-5-30b-a3b", "name": "InternVL3.5-30B-A3B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-26", "context_length": null, "max_output_tokens": null, "parameters": "0.3B vision + 30.5B language = 30.8B total, about 3B activated", "architecture": "InternVL3.5 multimodal model. Official model zoo gives the vision and language parameter split. Cascade reinforcement learning and dynamic visual-resolution routing are described in the technical report.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3.5-30B-A3B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-08-26. Collection date 2026-10-15. The series was announced at the end of August 2025. The README news line is 26 August 2025.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-5-38b": { "id": "shanghai-ai-lab/internvl/internvl3-5-38b", "name": "InternVL3.5-38B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-26", "context_length": null, "max_output_tokens": null, "parameters": "5.5B vision + 32.8B language = 38.4B total", "architecture": "InternVL3.5 multimodal model. Official model zoo gives the vision and language parameter split. Cascade reinforcement learning and dynamic visual-resolution routing are described in the technical report.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3.5-38B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-08-26. Collection date 2026-10-15. The series was announced at the end of August 2025. The README news line is 26 August 2025.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-5-4b": { "id": "shanghai-ai-lab/internvl/internvl3-5-4b", "name": "InternVL3.5-4B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-26", "context_length": null, "max_output_tokens": null, "parameters": "0.3B vision + 4.4B language = 4.7B total", "architecture": "InternVL3.5 multimodal model. Official model zoo gives the vision and language parameter split. Cascade reinforcement learning and dynamic visual-resolution routing are described in the technical report.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3.5-4B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-08-26. Collection date 2026-10-15. The series was announced at the end of August 2025. The README news line is 26 August 2025.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-5-8b": { "id": "shanghai-ai-lab/internvl/internvl3-5-8b", "name": "InternVL3.5-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-26", "context_length": 40960, "max_output_tokens": null, "parameters": "0.3B vision + 8.2B language = 8.5B total", "architecture": "InternVL3.5 multimodal model. Official model zoo gives the vision and language parameter split. Cascade reinforcement learning and dynamic visual-resolution routing are described in the technical report. This 8B checkpoint's config.json uses a Qwen3 language tower (36 layers, hidden 4096, max_position_embeddings 40960) and an InternViT at 448 pixels with 24 layers and hidden 1024.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3.5-8B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-08-26. Collection date 2026-10-15. The series was announced at the end of August 2025. The README news line is 26 August 2025.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-78b": { "id": "shanghai-ai-lab/internvl/internvl3-78b", "name": "InternVL3-78B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-10", "context_length": null, "max_output_tokens": null, "parameters": "78B", "architecture": "InternViT-6B-448px-V2.5 + Qwen2.5-72B", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3-78B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-04-10. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-8b": { "id": "shanghai-ai-lab/internvl/internvl3-8b", "name": "InternVL3-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-10", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "InternViT-300M-448px-V2.5 + Qwen2.5-7B", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3-8B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-04-10. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/internvl3-9b": { "id": "shanghai-ai-lab/internvl/internvl3-9b", "name": "InternVL3-9B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-10", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "InternViT-300M-448px-V2.5 + internlm3-8b-instruct", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL3-9B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2025-04-10. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/mini-internvl-chat-2b-v1-5": { "id": "shanghai-ai-lab/internvl/mini-internvl-chat-2b-v1-5", "name": "Mini-InternVL-Chat-2B-V1.5", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-13", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Small InternVL 1.5 chat model.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mini-InternVL-Chat-2B-V1.5 is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-05-13. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl/mini-internvl-chat-4b-v1-5": { "id": "shanghai-ai-lab/internvl/mini-internvl-chat-4b-v1-5", "name": "Mini-InternVL-Chat-4B-V1.5", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-28", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "4B InternVL 1.5 chat model.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mini-InternVL-Chat-4B-V1.5 is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-05-28. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/internvl-u/internvl-u": { "id": "shanghai-ai-lab/internvl-u/internvl-u", "name": "InternVL-U", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl-u", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-03-06", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "4B unified multimodal model: an InternVL-style understanding backbone plus an MMDiT visual generation head, as described by the project README.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InternVL-U is a 4B unified multimodal model from Shanghai AI Laboratory, released 6 March 2026 for understanding, reasoning, image generation, and editing. Collection date 2026-10-15.", "what_it_is_for": "One small model for multimodal understanding and image generation or editing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/januscoder/januscoder-14b": { "id": "shanghai-ai-lab/januscoder/januscoder-14b", "name": "JanusCoder-14B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/januscoder", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-10-27", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "First-party named release. Task label used while cataloging: code. Card body not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JanusCoder-14B is a named code model on the internlm org. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named code checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/januscoder/januscoder-8b": { "id": "shanghai-ai-lab/januscoder/januscoder-8b", "name": "JanusCoder-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/januscoder", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-10-27", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "First-party named release. Task label used while cataloging: code. Card body not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JanusCoder-8B is a named code model on the internlm org. Collection date 2026-10-15. The card was not re-read. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named code checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/januscoder/januscoder-v-7b": { "id": "shanghai-ai-lab/januscoder/januscoder-v-7b", "name": "JanusCoderV-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/januscoder", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-10-27", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "First-party named release. Task label used while cataloging: code vision. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JanusCoderV-7B is a vision-code checkpoint on the internlm org. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named code vision checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/januscoder/januscoder-v-8b": { "id": "shanghai-ai-lab/januscoder/januscoder-v-8b", "name": "JanusCoderV-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/januscoder", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-10-27", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "First-party named release. Task label used while cataloging: code vision. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JanusCoderV-8B is a vision-code checkpoint on the internlm org. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named code vision checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/mono-internvl/mono-internvl-2b": { "id": "shanghai-ai-lab/mono-internvl/mono-internvl-2b", "name": "Mono-InternVL-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/mono-internvl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-10-09", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Monolithic InternVL variant. Component details were not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mono-InternVL-2B is an open vision-language model from Shanghai AI Laboratory's OpenGVLab InternVL line (\u4e66\u751f\u00b7\u4e07\u8c61), released 2024-10-09. Collection date 2026-10-15.", "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/navil/navil-2b": { "id": "shanghai-ai-lab/navil/navil-2b", "name": "NaViL-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/navil", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "First-party named release. Task label used while cataloging: vision. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NaViL-2B is a named OpenGVLab model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named vision checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/navil/navil-9b": { "id": "shanghai-ai-lab/navil/navil-9b", "name": "NaViL-9B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/navil", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "First-party named release. Task label used while cataloging: vision. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NaViL-9B is a named OpenGVLab model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named vision checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/polar/polar-1-8b": { "id": "shanghai-ai-lab/polar/polar-1-8b", "name": "POLAR-1.8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/polar", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-07-04", "context_length": null, "max_output_tokens": null, "parameters": "1.8B", "architecture": "First-party named release. Task label used while cataloging: text reward. Card body not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "POLAR-1.8B is a named reward-style model on the internlm Hugging Face org. Collection date 2026-10-15. The card was not re-read. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named text reward checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/polar/polar-7b": { "id": "shanghai-ai-lab/polar/polar-7b", "name": "POLAR-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/polar", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-07-04", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "First-party named release. Task label used while cataloging: text reward. Card body not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "POLAR-7B is the 7B POLAR checkpoint on the internlm org. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named text reward checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/scalecua/scalecua-32b": { "id": "shanghai-ai-lab/scalecua/scalecua-32b", "name": "ScaleCUA-32B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/scalecua", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-09-16", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "First-party named release. Task label used while cataloging: computer use. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ScaleCUA-32B is a named OpenGVLab computer-use model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named computer use checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/scalecua/scalecua-3b": { "id": "shanghai-ai-lab/scalecua/scalecua-3b", "name": "ScaleCUA-3B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/scalecua", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-09-16", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "First-party named release. Task label used while cataloging: computer use. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ScaleCUA-3B is a named OpenGVLab computer-use model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named computer use checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/scalecua/scalecua-7b": { "id": "shanghai-ai-lab/scalecua/scalecua-7b", "name": "ScaleCUA-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/scalecua", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-09-16", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "First-party named release. Task label used while cataloging: computer use. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ScaleCUA-7B is a named OpenGVLab computer-use model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named computer use checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/sdlm/sdlm-32b-d4": { "id": "shanghai-ai-lab/sdlm/sdlm-32b-d4", "name": "SDLM-32B-D4", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/sdlm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-09-29", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "First-party named release. Task label used while cataloging: text. Card body not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SDLM-32B-D4 is a named OpenGVLab text model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named text checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/sdlm/sdlm-3b-d4": { "id": "shanghai-ai-lab/sdlm/sdlm-3b-d4", "name": "SDLM-3B-D4", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/sdlm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-09-29", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "First-party named release. Task label used while cataloging: text. Card body not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SDLM-3B-D4 is a named OpenGVLab text model. Collection date 2026-10-15. The card was not re-read. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named text checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/sdlm/sdlm-3b-d8": { "id": "shanghai-ai-lab/sdlm/sdlm-3b-d8", "name": "SDLM-3B-D8", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/sdlm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-09-30", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "First-party named release. Task label used while cataloging: text. Card body not re-read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SDLM-3B-D8 is a named OpenGVLab text model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named text checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/spark/spark-vl-7b": { "id": "shanghai-ai-lab/spark/spark-vl-7b", "name": "Spark-VL-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/spark", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "First-party named release. Task label used while cataloging: video. Card body not re-read.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spark-VL-7B is a video-text model on the internlm org. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named video checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/videochat/videochat-flash-2b": { "id": "shanghai-ai-lab/videochat/videochat-flash-2b", "name": "VideoChat-Flash-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/videochat", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-01-11", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "VideoChat-Flash with Qwen2.5-2B.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoChat-Flash-2B is the smaller OpenGVLab video chat model using Qwen2.5-2B. Collection date 2026-10-15.", "what_it_is_for": "Efficient video understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/videochat/videochat-flash-7b": { "id": "shanghai-ai-lab/videochat/videochat-flash-7b", "name": "VideoChat-Flash-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/videochat", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-01-11", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "VideoChat-Flash with Qwen2-7B. Resolution 224 and 448 checkpoints exist.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoChat-Flash-7B is an OpenGVLab long-video chat model using a Qwen2-7B language model. Collection date 2026-10-15.", "what_it_is_for": "Efficient video understanding and chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/videochat/videochat-r1-5-7b": { "id": "shanghai-ai-lab/videochat/videochat-r1-5-7b", "name": "VideoChat-R1.5-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/videochat", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-09-24", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Named Shanghai AI Laboratory / OpenGVLab checkpoint. The card body was not fully re-read.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoChat-R1.5-7B is the later OpenGVLab video reasoning checkpoint. Collection date 2026-10-15.", "what_it_is_for": "Video reasoning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/videochat/videochat-r1-7b": { "id": "shanghai-ai-lab/videochat/videochat-r1-7b", "name": "VideoChat-R1-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/videochat", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-04-13", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Named Shanghai AI Laboratory / OpenGVLab checkpoint. The card body was not fully re-read.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoChat-R1-7B is a named OpenGVLab video reasoning checkpoint. Collection date 2026-10-15. The card body was not re-read.", "what_it_is_for": "Video reasoning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/videochat/videochat2": { "id": "shanghai-ai-lab/videochat/videochat2", "name": "VideoChat2", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/videochat", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-08-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video chat model. The HD stage-4 release uses Mistral-7B.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoChat2 is OpenGVLab's video chat model line. Collection date 2026-10-15. Stage checkpoints such as the Mistral-7B HD stage-4 weights are versions, not separate products.", "what_it_is_for": "Video conversation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/visualprm/visualprm-8b": { "id": "shanghai-ai-lab/visualprm/visualprm-8b", "name": "VisualPRM-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/visualprm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-03-13", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "First-party named release. Task label used while cataloging: vision reward. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VisualPRM-8B is an OpenGVLab process reward model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named vision reward checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/vlaser/vlaser-2b": { "id": "shanghai-ai-lab/vlaser/vlaser-2b", "name": "Vlaser-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/vlaser", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-10-11", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "First-party named release. Task label used while cataloging: vision. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vlaser-2B is a named OpenGVLab model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named vision checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghai-ai-lab/vlaser/vlaser-8b": { "id": "shanghai-ai-lab/vlaser/vlaser-8b", "name": "Vlaser-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/vlaser", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-10-11", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "First-party named release. Task label used while cataloging: vision. Card body not re-read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vlaser-8B is a named OpenGVLab model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "what_it_is_for": "Named vision checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghaitech/tace/tace-oam-7m": { "id": "shanghaitech/tace/tace-oam-7m", "name": "TACE-OAM-7M", "maker": "shanghaitech", "family": "shanghaitech/tace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (checkpoint); MIT (code)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph network interatomic potential in the TACE/TECE Cartesian or spherical tensor line. The foundation guide labels this checkpoint size class M. Hugging Face file TACE-OAM-7M.pt is 28612967 bytes. The guide's worked example. Training path is OMat24, then sAlex and MPtrj. Required package is TACE >=0.2.0. The guide does not print a parameter count. The nearby filename TACE-OMat24-7M.pt is 28,614,353 bytes, so the two files are not the same object.", "modalities": { "input": [ "structure" ], "output": [ "energy", "forces", "stress" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TACE-OAM-7M is the M-class OAM foundation checkpoint from the ShanghaiTech TACE project. The foundation guide uses this name in the ASE example. It was collected on 2026-10-15.", "what_it_is_for": "OAM means the OMat24 model was then trained on subsampled Alexandria and MPtrj. The guide presents it as a general inorganic interatomic potential, not a separate API product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghaitech/tace/tace-oam-l": { "id": "shanghaitech/tace/tace-oam-l", "name": "TACE-OAM-L", "maker": "shanghaitech", "family": "shanghaitech/tace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (checkpoint); MIT (code)", "release_date": "2026-04-09", "context_length": null, "max_output_tokens": null, "parameters": "82899905", "architecture": "Five-layer graph network. Matbench manifest: 82,899,905 parameters, cutoff 6.0 angstrom, channel count 64, lmax 5, correlation 2, AdamW, batch 1024. Targets are energy with gradient-based forces and stress (EFS_G). Checkpoint TACE-OAM-L.pt is 333,096,435 bytes. Training recorded as 8 NVIDIA H20 GPUs for 105 hours each: 3 epochs on OMat24, then 3 epochs on MPtrj and sAlex.", "modalities": { "input": [ "structure" ], "output": [ "energy", "forces", "stress" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TACE-OAM-L is the active L-class universal interatomic potential from ShanghaiTech's TACE line. Matbench Discovery added it on 2026-04-09 and marks it as the model that superseded TACE-v1-OAM-M. Collected on 2026-10-15.", "what_it_is_for": "It predicts energy, forces, and stress for inorganic crystals. The manifest trains on OMat24, then MPtrj plus subsampled Alexandria. ShanghaiTech news on 2026-01-26 discussed the earlier v1 model, not this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghaitech/tace/tace-oam-rra-preview": { "id": "shanghaitech/tace/tace-oam-rra-preview", "name": "TACE-OAM-RRA-Preview", "maker": "shanghaitech", "family": "shanghaitech/tace", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (checkpoint); MIT (code)", "release_date": "2026-06-27", "context_length": null, "max_output_tokens": null, "parameters": "213690158", "architecture": "Eight-layer radial-rotary-attention atomic cluster expansion. Matbench manifest: 213,690,158 parameters, cutoff 6.0 angstrom, interaction channels 64, product channels 256, mmax 4, Lmax 4, lmax 4, correlation 2. Checkpoint TACE-OAM-RRA-Preview.pt is 857,860,959 bytes. Training recorded as 32 NVIDIA H20 GPUs for 82 hours each, 3 epochs, batch 512, on OMat24 then sAlex and MPtrj.", "modalities": { "input": [ "structure" ], "output": [ "energy", "forces", "stress" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TACE-OAM-RRA-Preview is a superseded XL interatomic potential from the ShanghaiTech TACE project. Matbench Discovery added it on 2026-06-27 and points its replacement at TECE-OAM-RRA-1.0. Collected on 2026-10-15.", "what_it_is_for": "The manifest says it is a preview of radial rotary attention with atomic cluster expansion, trained as an energy, force, and stress model on OMat24, sAlex, and MPtrj. It is not the current recommended RRA model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghaitech/tace/tace-omat24-7m": { "id": "shanghaitech/tace/tace-omat24-7m", "name": "TACE-OMat24-7M", "maker": "shanghaitech", "family": "shanghaitech/tace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (checkpoint); MIT (code)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph network interatomic potential in the TACE/TECE Cartesian or spherical tensor line. The foundation guide labels this checkpoint size class M. Hugging Face file TACE-OMat24-7M.pt is 28614353 bytes. Trained on OMat24 only. The guide says it is the efficiency end of the list and quotes about 70,000 atoms on one 80 GB GPU with the EquivariantX kernel. Required package is TACE >=0.2.0. Parameter count was not published.", "modalities": { "input": [ "structure" ], "output": [ "energy", "forces", "stress" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TACE-OMat24-7M is an open-weight interatomic potential from the ShanghaiTech group of Wenbo Xie and P. Hu, trained by Zemin Xu (ShanghaiTech and Nanjing University). It is the OMat24-only M-class checkpoint in the TACE foundation registry. This dossier was collected on 2026-10-15.", "what_it_is_for": "The foundation guide says OMat24 checkpoints are trained on the OMat24 inorganic bulk set and are used as ASE calculators for energy and forces. This one is listed first for speed, ahead of the larger OAM and RRA models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghaitech/tace/tace-omat24-l": { "id": "shanghaitech/tace/tace-omat24-l", "name": "TACE-OMat24-L", "maker": "shanghaitech", "family": "shanghaitech/tace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (checkpoint); MIT (code)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph network interatomic potential in the TACE/TECE Cartesian or spherical tensor line. The foundation guide labels this checkpoint size class L. Hugging Face file TACE-OMAT24-L.pt is 333094878 bytes. The foundation guide spells the registry name TACE-OMat24-L. The Hugging Face tree lists the file as TACE-OMAT24-L.pt. Those two strings differ in case. Required package is TACE >=0.2.0. The guide quotes about 90,000 atoms on one 80 GB GPU with EquivariantX. Parameter count was not printed for this file. TACE-OAM-L, which the guide treats as the continued model, is 82,899,905 parameters in its Matbench manifest, and its file is 333,096,435 bytes.", "modalities": { "input": [ "structure" ], "output": [ "energy", "forces", "stress" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TACE-OMat24-L is the L-class OMat24-only foundation checkpoint from the ShanghaiTech TACE registry. Collected on 2026-10-15. The Hub file name and the docs name do not match character for character.", "what_it_is_for": "The guide says this is an OMat24-trained potential, with a larger atom budget than TACE-OMat24-7M when the EquivariantX kernel is used. It is the pretraining side of TACE-OAM-L.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghaitech/tace/tace-omat24-rra-1-0": { "id": "shanghaitech/tace/tace-omat24-rra-1-0", "name": "TACE-OMat24-RRA-1.0", "maker": "shanghaitech", "family": "shanghaitech/tace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (checkpoint); MIT (code)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph network interatomic potential in the TACE/TECE Cartesian or spherical tensor line. The foundation guide labels this checkpoint size class XL. Hugging Face file TACE-OMat24-RRA-1.0.pt is 857877907 bytes. Radial rotary attention plus atomic cluster expansion, OMat24 only. The guide pins this name to TACE ==0.2.0, not a later version. Parameter count was not published. The OAM preview file TACE-OAM-RRA-Preview.pt is 857,860,959 bytes and is a different checkpoint.", "modalities": { "input": [ "structure" ], "output": [ "energy", "forces", "stress" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TACE-OMat24-RRA-1.0 is the XL OMat24 checkpoint with radial rotary attention in the ShanghaiTech TACE registry. Collected on 2026-10-15. It is pinned to TACE 0.2.0.", "what_it_is_for": "The guide defines RRA as radial rotary complex attention and says this file is the OMat24 stage, before the OAM continuation. It is an interatomic potential, not a language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghaitech/tace/tace-v1-les-reico-5-pdagcho": { "id": "shanghaitech/tace/tace-v1-les-reico-5-pdagcho", "name": "TACE-v1-LES-REICO-5-PdAgCHO", "maker": "shanghaitech", "family": "shanghaitech/tace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (checkpoint); MIT (code)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph network interatomic potential in the TACE/TECE Cartesian or spherical tensor line. The foundation guide labels this checkpoint size class M. Hugging Face file TACE-v1-LES-REICO-5-PdAgCHO.pt is 18965214 bytes. The foundation guide lists this name at size M, trained on REICO-5-PdAgCHO, and pins it to TACE ==0.1.0. REICO is the Hu group heterogeneous-catalysis set for Pd-Ag with C/H/O species at PBE+D3. LES is the Latent Ewald Summation plugin named in the TACE README. Parameter count was not published.", "modalities": { "input": [ "structure" ], "output": [ "energy", "forces", "stress" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TACE-v1-LES-REICO-5-PdAgCHO is a named v1 TACE checkpoint for Pd-Ag catalysis, released in the same ShanghaiTech foundation collection as the OAM potentials. It is pinned to TACE 0.1.0. Collected on 2026-10-15.", "what_it_is_for": "The foundation guide's dataset table says REICO-5-PdAgCHO covers heterogeneous catalysis on Pd-Ag catalysts and C/H/O species. This checkpoint is the model trained on that set, with the LES plugin in the name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghaitech/tace/tace-v1-oam-m": { "id": "shanghaitech/tace/tace-v1-oam-m", "name": "TACE-v1-OAM-M", "maker": "shanghaitech", "family": "shanghaitech/tace", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (checkpoint); MIT (code)", "release_date": "2026-01-06", "context_length": null, "max_output_tokens": null, "parameters": "18779248", "architecture": "Five-layer irreducible-Cartesian-tensor model. Matbench manifest: 18,779,248 parameters, cutoff 6.0 angstrom, 48 channels, Lmax 2, lmax 3, correlation 2. Checkpoint TACE-v1-OAM-M.pt is 75,331,180 bytes. Training recorded as 8 NVIDIA H100 GPUs for 120 hours each. Steps in the manifest: pretrain on OMat24, then fine-tune on MPtrj and sAlex.", "modalities": { "input": [ "structure" ], "output": [ "energy", "forces", "stress" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TACE-v1-OAM-M is the first Matbench Discovery model from ShanghaiTech's TACE project. It was added on 2026-01-06 and is marked superseded by TACE-OAM-L. The university news page of 2026-01-26 is about this checkpoint. Collected on 2026-10-15.", "what_it_is_for": "It is a Cartesian atomic-cluster-expansion potential for energy, forces, and stress on inorganic materials. The news page calls the universal model TACE-v1-OAM-M and says it led ACE-type models on that date.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghaitech/tace/tace-v1-omat24-m": { "id": "shanghaitech/tace/tace-v1-omat24-m", "name": "TACE-v1-OMat24-M", "maker": "shanghaitech", "family": "shanghaitech/tace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (checkpoint); MIT (code)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph network interatomic potential in the TACE/TECE Cartesian or spherical tensor line. The foundation guide labels this checkpoint size class M. Hugging Face file TACE-v1-OMat24-M.pt is 75332251 bytes. First-generation (v1) OMat24 checkpoint. The foundation guide requires TACE ==0.1.0. The paired OAM file TACE-v1-OAM-M is 18,779,248 parameters in its Matbench manifest, but that count was not copied onto this OMat24 file. The two files differ in size by a few kilobytes (this file is 75,332,251 bytes; TACE-v1-OAM-M.pt is 75,331,180 bytes).", "modalities": { "input": [ "structure" ], "output": [ "energy", "forces", "stress" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TACE-v1-OMat24-M is the first-generation OMat24 M checkpoint in the ShanghaiTech TACE registry. It is pinned to TACE 0.1.0. Collected on 2026-10-15.", "what_it_is_for": "It is the OMat24 pretraining checkpoint for TACE-v1-OAM-M. The guide still lists it as a loadable foundation model for the 0.1.0 package.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghaitech/tece/tece-oam-rra-1-0": { "id": "shanghaitech/tece/tece-oam-rra-1-0", "name": "TECE-OAM-RRA-1.0", "maker": "shanghaitech", "family": "shanghaitech/tece", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (checkpoint); MIT (code)", "release_date": "2026-07-05", "context_length": null, "max_output_tokens": null, "parameters": "221853358", "architecture": "Eight-layer model using atomic cluster expansion, edge cluster expansion, and radial rotary attention. Matbench manifest: 221,853,358 parameters, cutoff 6.0 angstrom, interaction channels 64, product channels 256, mmax 4, Lmax 4, lmax 4, correlation 2. Checkpoint TECE-OAM-RRA-1.0.pt is 890,523,407 bytes. Training recorded as 32 NVIDIA H20 GPUs for 240 hours each, 3 epochs, batch 512.", "modalities": { "input": [ "structure" ], "output": [ "energy", "forces", "stress" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TECE-OAM-RRA-1.0 is the active edge-cluster interatomic potential from the ShanghaiTech TACE project. Matbench Discovery added it on 2026-07-05. The project guide says it led the default leaderboard by CPS on 2026-07-08. Collected on 2026-10-15.", "what_it_is_for": "It predicts energy, forces, and stress. The manifest description says the model combines atomic cluster expansion, edge cluster expansion, and radial rotary attention, trained on OMat24 then sAlex and MPtrj.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghaitech/tece/tece-omat24-rra-1-0": { "id": "shanghaitech/tece/tece-omat24-rra-1-0", "name": "TECE-OMat24-RRA-1.0", "maker": "shanghaitech", "family": "shanghaitech/tece", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0 (checkpoint); MIT (code)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph network interatomic potential in the TACE/TECE Cartesian or spherical tensor line. The foundation guide labels this checkpoint size class XL. Hugging Face file TECE-OMat24-RRA-1.0.pt is 897087283 bytes. Edge cluster expansion plus radial rotary attention, OMat24 only. Required package is TACE >=0.2.0. The guide quotes about 30,000 atoms on one 80 GB GPU with EquivariantX. The OAM sibling TECE-OAM-RRA-1.0 is 221,853,358 parameters in its Matbench manifest. That count is not this file. This file is 897,087,283 bytes; the OAM file is 890,523,407 bytes.", "modalities": { "input": [ "structure" ], "output": [ "energy", "forces", "stress" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TECE-OMat24-RRA-1.0 is the OMat24-only XL edge-cluster model from the ShanghaiTech TACE repository. TECE adds edge cluster expansion on top of TACE. Collected on 2026-10-15.", "what_it_is_for": "The foundation guide says TECE uses edge cluster expansion as well as atomic cluster expansion, and that this checkpoint is trained on OMat24 before the OAM stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shanghaitech/unicas/unicas": { "id": "shanghaitech/unicas/unicas", "name": "UniCAS", "maker": "shanghaitech", "family": "shanghaitech/unicas", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision Transformer as written on the model card and in the GitHub loader: patch size 16, embed dim 1024, depth 24, 16 heads, image size 224, mlp_ratio 2.671875 * 2, GluMlp, SiLU, no classification head in the feature extractor (num_classes 0). The card's example prints a 1024-dimensional feature. UniCAS.pth is 1,215,229,559 bytes. A second file, multi-task.pth, is 1,382,874,759 bytes and is the slide-level aggregator, not a second foundation model. Parameter count was not stated on the card.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UniCAS is an open-weight cytology foundation model from ShanghaiTech's School of Biomedical Engineering, led by Qian Wang. The Hugging Face repo was created on 2025-10-09. The Cell Reports Medicine paper is dated 2026-01-20. Collected on 2026-10-15.", "what_it_is_for": "The paper and the university news say UniCAS is pretrained on 48,532 cervical whole-slide images for slide-level screening, region-level detection and segmentation, and pixel-level deblurring. The public card shows the encoder as a feature extractor. A multi-task aggregator handles cancer screening, candidiasis, and clue cells on one slide.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sheffield/annie/annie": { "id": "sheffield/annie/annie", "name": "ANNIE", "maker": "sheffield", "family": "sheffield/annie", "status": "active", "confidence": "high", "weights": "open-source", "license": "LGPL-3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Rule and finite-state pipeline shipped with GATE: document reset, tokeniser, gazetteer compiled to finite-state machines, sentence splitter, Hepple POS tagger (a modified Brill tagger with a default lexicon and ruleset from Wall Street Journal training), JAPE semantic tagger, and orthographic coreference. Pronominal coreference is a separate optional module.", "modalities": { "input": [ "text" ], "output": [ "annotations" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ANNIE (A Nearly-New Information Extraction System) is the University of Sheffield GATE group's default English information-extraction pipeline. It is active, distributed with GATE, and sold as a GATE Cloud API. Collected 2026-10-15.", "what_it_is_for": "The GATE user guide and the cloud shop describe ANNIE as the prototypical named-entity pipeline for persons, locations, organizations, dates, addresses, money, and percentages. It is the base of many other GATE applications.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sheffield/bio-yodie/bio-yodie": { "id": "sheffield/bio-yodie/bio-yodie", "name": "Bio-YODIE", "maker": "sheffield", "family": "sheffield/bio-yodie", "status": "active", "confidence": "high", "weights": "open-source", "license": "AGPL-3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Biomedical named-entity linking pipeline derived from YODIE and built in GATE. The cloud service links mentions to UMLS 2018AB. The arXiv paper compares it with MetaMap and MetaMapLite. No parameter count is stated.", "modalities": { "input": [ "text" ], "output": [ "annotations" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bio-YODIE is the University of Sheffield biomedical named-entity linking system derived from YODIE. It is active, open source under AGPL-3.0 or a Sheffield commercial licence, and the full UMLS build is a GATE Cloud service. Collected 2026-10-15.", "what_it_is_for": "The application page says it links biomedical mentions to UMLS concept identifiers. The cloud service uses UMLS 2018AB and writes broad types such as Drug, Disease, and Anatomy, with class and inst features. It was developed in the KConnect project.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sheffield/cymrie/cymrie": { "id": "sheffield/cymrie/cymrie", "name": "CYMRIE", "maker": "sheffield", "family": "sheffield/cymrie", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Welsh named-entity pipeline described by GATE Cloud as a Welsh version of ANNIE. Distributed with GATE and with the Welsh Natural Language Toolkit. No parameter count was published.", "modalities": { "input": [ "text" ], "output": [ "annotations" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CYMRIE is the University of Sheffield GATE named-entity recogniser for Welsh. The cloud page calls it a Welsh version of ANNIE. It is active, distributed with GATE, and available as a GATE Cloud API. Collected 2026-10-15.", "what_it_is_for": "The shop page says it finds persons, locations, organizations, money, times, and dates in Welsh. It is part of the Welsh Natural Language Toolkit, a Welsh Government funded project.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sheffield/gate-hate/gate-hate": { "id": "sheffield/gate-hate/gate-hate", "name": "GATE Hate", "maker": "sheffield", "family": "sheffield/gate-hate", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GATE Cloud abuse tagger. The page says it finds abusive phrases and also runs ANNIE named-entity recognition, UK political topic lists, and politician gazetteers. It does not say whether the abuse decision is a neural model or a rule list.", "modalities": { "input": [ "text" ], "output": [ "annotations" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GATE Hate is the University of Sheffield GATE Cloud service that tags abusive phrases in English text, together with UK political entities. The generic service is active. The older GATE Hate for Politics shop item is marked deprecated. Collected 2026-10-15.", "what_it_is_for": "The page says it finds abusive phrases, labels the type of abuse, and marks whether the target is the addressee or someone else. It also tags UK politicians, parties, political topics, and named entities from ANNIE. It is aimed at English text and at Twitter JSON.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sheffield/hyperpartisan/hyperpartisan": { "id": "sheffield/hyperpartisan/hyperpartisan", "name": "Hyperpartisan scorer", "maker": "sheffield", "family": "sheffield/hyperpartisan", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The GATE Cloud page says the service is an ensemble of trained neural networks. It does not name the backbone or the parameter count.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Hyperpartisan scorer is a University of Sheffield GATE Cloud classifier that estimates whether a document is hyper-partisan. The product page describes it as an ensemble of trained neural networks and says it won SemEval 2019 Task 4. It is active. Collected 2026-10-15.", "what_it_is_for": "The service writes one annotation over the whole text with a feature hyperpartisan_probability between 0 and 1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sheffield/twitie/twitie": { "id": "sheffield/twitie/twitie", "name": "TwitIE", "maker": "sheffield", "family": "sheffield/twitie", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "2013-09-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ANNIE adapted for tweets: language identification, tweet tokeniser, slang normaliser, tweet POS tagger, hashtag and @mention handling, and JAPE named-entity grammars based on ANNIE. The user guide says the Twitter plugin also loads Stanford CoreNLP resources.", "modalities": { "input": [ "text" ], "output": [ "annotations" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TwitIE is the University of Sheffield GATE pipeline for named-entity recognition on microblog text. It is an active open-source adaptation of ANNIE, described at RANLP 2013, and it is a live GATE Cloud API. Collected 2026-10-15.", "what_it_is_for": "The authors present it as an information-extraction pipeline for noisy, short microblog text. The cloud service tags persons, locations, organizations, dates, addresses, hashtags, user mentions, URLs, and emoticons, and it normalises common tweet abbreviations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sheffield/yodie/yodie": { "id": "sheffield/yodie/yodie", "name": "YODIE", "maker": "sheffield", "family": "sheffield/yodie", "status": "active", "confidence": "high", "weights": "open-source", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GATE named-entity recognition, using ANNIE, plus candidate URI selection against DBpedia, similarity features, and a machine-learning disambiguation stage. The 2015 paper describes the disambiguation model. It does not publish a parameter count.", "modalities": { "input": [ "text" ], "output": [ "annotations" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YODIE (Yet another Open Data Information Extraction system) is the University of Sheffield GATE named-entity recognition and disambiguation system. The English service is active on GATE Cloud and links mentions to DBpedia. German, French, and Spanish shop tiles are versions. Collected 2026-10-15.", "what_it_is_for": "The application page says YODIE recognises named entities and disambiguates them against DBpedia, including varied, noisy, and short text. The English cloud service writes a Shef:Mention annotation with class and inst features.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/audio/audio1-0": { "id": "shengshu/audio/audio1-0", "name": "Vidu Audio 1.0", "maker": "shengshu", "family": "shengshu/audio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Audio 1.0 is Shengshu's named audio model, API id audio1.0. The text-to-audio page accepts only that id and generates sound effects and background music from a prompt, 2 to 10 seconds. The pricing page also bills controllable text-to-sound as audio1.0. Collected on 2026-10-15.", "what_it_is_for": "It is the standalone sound-effect and music generator, not the native audio inside Q3 or Q4 video. The doc's own line is sound effects and BGM from a text prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/vidu": { "id": "shengshu/vidu/vidu", "name": "Vidu", "maker": "shengshu", "family": "shengshu/vidu", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-04-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion model with a U-ViT backbone (Vidu paper, arXiv:2405.04233). U-ViT itself is the 2022 image-diffusion backbone in arXiv:2209.12152, not a separate Vidu release.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu is the text-to-video model Shengshu Technology (\u751f\u6570\u79d1\u6280) and Tsinghua University unveiled on 27 April 2024 at the Zhongguancun Forum. The May 2024 paper describes a diffusion model with a U-ViT backbone that can emit 1080p video up to 16 seconds. The public website that opened on 30 July 2024 instead offered 4-second and 8-second 1080p clips. It is not on the October 2026 platform model map. Collected on 2026-10-15.", "what_it_is_for": "Shengshu presented it as China's long-duration, high-consistency video model: one text prompt in, a clip with stable subjects and large motion out. Zhu Jun, the company's chief scientist, told reporters it could keep characters, scenes, and time coherent and could depict Chinese subjects such as the panda and the loong.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/vidu-1-5": { "id": "shengshu/vidu/vidu-1-5", "name": "Vidu 1.5", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-11-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu 1.5 is Shengshu's 13 November 2024 video model, the first Vidu release named for multiple-entity consistency: people, objects, and places in one clip. The platform still documents the API id vidu1.5 for image-to-video, but the October 2026 model map and pricing table do not list it. Collected on 2026-10-15.", "what_it_is_for": "The launch wire describes long-context control inside a visual model: one generation that keeps several entities consistent, which the company called a new capability for AI video. The live image-to-video page labels the id as the large-motion option.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/vidu-2-0": { "id": "shengshu/vidu/vidu-2-0", "name": "Vidu 2.0", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu 2.0 is Shengshu's fast image-based video model, announced on 15 January 2025. On the English platform model map it is the id vidu2.0: 32 fps, 4 or 8 seconds, and image-to-video, reference-to-video, first-last frame, and effect templates. It is still priced. The Chinese model map retrieved the same day no longer prints a 2.0 table. Collected on 2026-10-15.", "what_it_is_for": "The model map calls it the fast generator. It is the template/effects model in the current table. It does not have a text-to-video checkmark. Reference-to-video is how multi-entity images are passed in.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/viduq1": { "id": "shengshu/vidu/viduq1", "name": "Vidu Q1", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Q1 is Shengshu's April 2025 quality-tier video model, API id viduq1. \u54c1\u73a9 dates the global announcement to 21 April 2025. The model map gives it 5 seconds at 1080p and 24 fps, with text, image, reference, and first-last video. The same id also does reference-to-image at 1080p. Collected on 2026-10-15.", "what_it_is_for": "The English model map's line for it is sharp visuals. Chinese copy says \u753b\u9762\u6e05\u6670. Press at launch stressed 1080p, smoother first-to-last camera moves than 2.0, and stronger anime. Q was described as higher picture quality and stronger semantic understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/viduq1-classic": { "id": "shengshu/vidu/viduq1-classic", "name": "Vidu Q1 Classic", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Q1 Classic is a sibling of Vidu Q1 on the platform model map, id viduq1-classic. The English line is rich and stable camera movement (\u8fd0\u955c\u4e30\u5bcc\u7a33\u5b9a). It is limited to image-to-video and first-last frame, 5 seconds at 1080p. It is still priced. A separate announcement date was not found. Collected on 2026-10-15.", "what_it_is_for": "The model map positions it for camera moves rather than as the general Q1. It does not do text-to-video or reference-to-video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/viduq2": { "id": "shengshu/vidu/viduq2", "name": "Vidu Q2", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Q2 is Shengshu's October 2025 reference and text video model, API id viduq2. The changelog adds text-to-video, reference-to-video, and extension on 21 October 2025. The same id later gained image generation and editing. It is not the image-to-video tier; that is Q2 Pro or Q2 Turbo. Collected on 2026-10-15.", "what_it_is_for": "The model map says dynamic motion, strong emotion, and richer detail (\u52a8\u6001\u6548\u679c\u597d\uff0c\u60c5\u7eea\u8868\u8fbe\u5f3a\uff0c\u7ec6\u8282\u66f4\u4e30\u5bcc). The public pitch was multi-image reference: up to seven pictures for characters, props, and scenes in one clip, plus text-to-video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/viduq2-pro": { "id": "shengshu/vidu/viduq2-pro", "name": "Vidu Q2 Pro", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Q2 Pro is the higher-detail Q2 video id, viduq2-pro. It shipped on the API for image-to-video and first-last frame on 24 September 2025, then gained reference-to-video with video editing on 27 January 2026. It does not do text-to-video. Collected on 2026-10-15.", "what_it_is_for": "The model map says better visual aesthetics, and reference mode can edit video. The image-to-video page calls it a new model with rich detail. The January 2026 reference release is the one Shengshu described as editing from an original video, an image reference, or a video reference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/viduq2-pro-fast": { "id": "shengshu/vidu/viduq2-pro-fast", "name": "Vidu Q2 Pro Fast", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Q2 Pro Fast is the API id viduq2-pro-fast, added on 2 December 2025 as the cheap, fast image-to-video and first-last option. Pricing still lists it. The model-map Q2 table does not. Collected on 2026-10-15.", "what_it_is_for": "The changelog calls it fast, lowest price, and stable, for image-to-video and first-last frame. It is a separate id from viduq2-pro and viduq2-turbo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/viduq2-turbo": { "id": "shengshu/vidu/viduq2-turbo", "name": "Vidu Q2 Turbo", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Q2 Turbo is the fast Q2 image-to-video id, viduq2-turbo, added with Q2 Pro on 24 September 2025. The model map marks image-to-video and first-last frame, not text or reference. The page calls it a new model with good effect and fast generation. Collected on 2026-10-15.", "what_it_is_for": "It is the speed tier for animating a start frame, or a start and end frame, at 540p to 1080p. Optional direct audio can add dialogue and background sound.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/viduq3": { "id": "shengshu/vidu/viduq3", "name": "Vidu Q3", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Q3, API id viduq3, is a reference-to-video model added on 13 April 2026. It is not viduq3-pro. The reference page describes camera switching, synchronized audio, and consistency across camera positions, at 540p to 1080p for 3 to 16 seconds. Collected on 2026-10-15.", "what_it_is_for": "Use it when the inputs are reference images plus a prompt, not when you want the January flagship text-to-video path. That path is viduq3-pro.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/viduq3-ad": { "id": "shengshu/vidu/viduq3-ad", "name": "Vidu Q3 Ad", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Q3 Ad is the advertising reference model viduq3-ad. Both model maps describe shot changes, pacing, and detail consistency for short ads, at 720p or 1080p, with a selectable length of 3 to 15 seconds. The blurb's 5 to 8 seconds is a recommended spot length. Price and request schema were not found. Collected on 2026-10-15.", "what_it_is_for": "It is the ad-industry Q3, reference images in and a short commercial clip out. It is not the one-click ad-film solution, which is a separate product workflow and not this model id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/viduq3-drama": { "id": "shengshu/vidu/viduq3-drama", "name": "Vidu Q3 Drama", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Q3 Drama is the comic-drama reference model viduq3-drama on both the English and Chinese model maps. Shengshu describes it as the strongest Q3 result for dialogue, staging, motion, and film camera language, at 720p or 1080p for 3 to 15 seconds. A price and an API parameter section were not found. Collected on 2026-10-15.", "what_it_is_for": "It is the industry model for \u6f2b\u5267, narrated comic drama. The map checks only reference-to-video, with synchronized audio. It is not the general Q3 Pro text model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/viduq3-mix": { "id": "shengshu/vidu/viduq3-mix", "name": "Vidu Q3 Mix", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Q3 Mix is the balanced reference id viduq3-mix, added on 13 April 2026. The API page is narrower than the model map: 3 to 15 seconds at 720p or 1080p, with up to seven images and three audio references. Off-peak is not offered. Collected on 2026-10-15.", "what_it_is_for": "The reference page calls it the strongest overall balance in the Q3 reference set: visual quality, scene transitions, audio, and motion. The model map says it is the general-purpose consistency and aesthetics tier. It is reference-only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/viduq3-pro": { "id": "shengshu/vidu/viduq3-pro", "name": "Vidu Q3 Pro", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Q3 Pro is Shengshu's narrative video id viduq3-pro, on the API from 27 January 2026 and announced publicly on 30 January 2026. It generates video and audio together, from 1 to 16 seconds, at 540p, 720p, or 1080p. Reference-to-video is a different Q3 id. Collected on 2026-10-15.", "what_it_is_for": "Shengshu calls Q3 the long clip with native sound: dialogue, effects, and picture in one pass, plus shot changes. The text API says it supports audio-video sync and storyboarding. The model map leaves reference-to-video unchecked for this id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/viduq3-pro-fast": { "id": "shengshu/vidu/viduq3-pro-fast", "name": "Vidu Q3 Pro Fast", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Q3 Pro Fast is the priced image-to-video id viduq3-pro-fast. The pricing page gives it 1 to 16 seconds at 720p and 1080p. It is not on the model map, and the changelog excerpt through 7 October 2026 does not date it. Collected on 2026-10-15.", "what_it_is_for": "The price row is an image-to-video speed tier under Q3, cheaper at 1080p than viduq3-pro (15 credits per second versus 24) and separate from viduq3-turbo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/viduq3-turbo": { "id": "shengshu/vidu/viduq3-turbo", "name": "Vidu Q3 Turbo", "maker": "shengshu", "family": "shengshu/vidu", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Q3 Turbo is the faster Q3 id, viduq3-turbo. Text, image, and first-last landed on 11 February 2026. Reference-to-video followed on 13 April 2026. It is the only Q3 id on the model map with all four of those modes checked. Collected on 2026-10-15.", "what_it_is_for": "The changelog says it is shorter to run than viduq3-pro and stays stable. The reference page calls it the fastest, cheapest Q3 option that still switches shots and emits audio with the picture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu/viduq4-preview": { "id": "shengshu/vidu/viduq4-preview", "name": "Vidu Q4 Preview", "maker": "shengshu", "family": "shengshu/vidu", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu Q4 Preview is Shengshu's preview flagship, API id viduq4-preview, added on the English changelog on 7 October 2026 and on the Chinese changelog on 8 October 2026. It does image-to-video and reference-to-video with sound, from 540p through 4K, for 3 to 16 seconds on the API. A finished Q4 without the preview label has no date. Collected on 2026-10-15.", "what_it_is_for": "Shengshu's changelog pitches more believable acting, reference voices, high-motion action, denser effects, cinematic lighting, 2K and 4K, and up to 15 reference images at a lower production cost. The Chinese map line is \u4f20\u795e\u6f14\u6280\uff0c\u5f62\u58f0\u5165\u620f. Text-to-video is not offered.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu-s/vidu-s1": { "id": "shengshu/vidu-s/vidu-s1", "name": "Vidu S1", "maker": "shengshu", "family": "shengshu/vidu-s", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Real-time interactive video generation. The official GitHub says users steer a digital character with speech at any moment, including infinite-length generation.", "modalities": { "input": [ "text", "audio", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu S1 is Shengshu's real-time talking-character model, documented on the official Vidu-S GitHub and in arXiv:2607.03118. The README says it generates 540p at up to 42 frames per second and follows spoken instructions. The October 2026 platform model map sells S2 editions instead. Collected on 2026-10-15.", "what_it_is_for": "The GitHub describes voice-controlled digital characters: the user can change what the character does while the stream is running. Shengshu later compared S2-Avatar with S1 on resolution and reference updates.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu-s/vidu-s2-avatar": { "id": "shengshu/vidu-s/vidu-s2-avatar", "name": "Vidu S2-Avatar", "maker": "shengshu", "family": "shengshu/vidu-s", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Streaming video generation. The official GitHub credits Self-Replay Forcing for long streams and TurboDiffusion plus TurboServe for real-time inference.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu S2-Avatar is Shengshu's real-time digital-human model, released on the platform changelog on 15 September 2026. One model is sold as three editions: real-time, component, and non-real-time. The GitHub says 720p at 25 to 42 frames per second, with reference images that can change mid-stream. Collected on 2026-10-15.", "what_it_is_for": "The introduction says it talks, follows complex motion, and uses a reference image to handle a product, change clothes, or swap the background. Real-time is a closed stack for companionship, shopping guides, teaching, and game NPCs. The component edition is for teams that already have a voice stack. The non-real-time edition writes an MP4 for courses and marketing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "shengshu/vidu-s/vidu-s2-editing": { "id": "shengshu/vidu-s/vidu-s2-editing", "name": "Vidu S2-Editing", "maker": "shengshu", "family": "shengshu/vidu-s", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Real-time editing of an incoming video stream. Covered with S2-Avatar in the Vidu S2 paper.", "modalities": { "input": [ "video", "image", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vidu S2-Editing is Shengshu's real-time video editing model, released with S2-Avatar on 15 September 2026. It restyles, redress, replaces, or re-backgrounds an incoming video stream from reference images. The pricing edition is 1 credit per second. Collected on 2026-10-15.", "what_it_is_for": "The Chinese product page lists four live edits: match a style reference, change clothing, replace the person, and replace the background. The model map aims it at live commerce and style filters. It is not a digital-human stack: ASR, LLM, and TTS are unchecked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sherbrooke/tractoracle/tractoracle-irt": { "id": "sherbrooke/tractoracle/tractoracle-irt", "name": "TractOracle-IRT", "maker": "sherbrooke", "family": "sherbrooke/tractoracle", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2025-07-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reinforcement-learning tractography agents whose oracle is updated during training by Iterative Reward Training. Public checkpoints are SAC and CrossQ agents. The README calls the TractoInferno SAC-IRT and CrossQ-IRT checkpoints the best tracking agents and uses sac_irt_inferno by default. The paper also studies DroQ and an autoencoder state, which are not in the public checkpoint list.", "modalities": { "input": [ "fodf" ], "output": [ "streamline" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TractOracle-IRT is a Universit\u00e9 de Sherbrooke tractography agent that keeps training the TractOracle reward network while the policy learns. Jeremi L\u00e9vesque, Antoine Th\u00e9berge, Maxime Descoteaux, and Pierre-Marc Jodoin posted the preprint on 15 July 2025. Medical Image Analysis ran it in December 2025. On 15 October 2026 the lab still published four agent checkpoints and a Docker image. Status is active.", "what_it_is_for": "The authors use it for whole-brain tractography when a classical reward lets the agent invent implausible tracts. Iterative Reward Training refreshes the oracle with bundle-filtering labels instead of human ratings. The README says the TractoInferno SAC-IRT and CrossQ-IRT agents are the ones to track with.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sherbrooke/tractoracle/tractoracle-net": { "id": "sherbrooke/tractoracle/tractoracle-net", "name": "TractOracle-Net", "maker": "sherbrooke", "family": "sherbrooke/tractoracle", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-03-26", "context_length": null, "max_output_tokens": null, "parameters": "550k", "architecture": "Transformer encoder for streamline classification. Directions between resampled streamline points, a prepended SCORE token, 32-dimensional embedding, positional encoding, 4 encoder blocks with 4 attention heads each, then a linear layer and a sigmoid. Paper training resamples to 128 points and uses mean-squared error. A plausible/implausible threshold of 0.5 is the stated default. Later public IRT oracles use 32-point streamlines.", "modalities": { "input": [ "streamline" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TractOracle-Net is a streamline classifier from the Sherbrooke Connectivity Imaging Lab and VITALab at the Universit\u00e9 de Sherbrooke. It is the reward and filtering half of TractOracle. The preprint is dated 26 March 2024, the MICCAI paper is October 2024, and a default checkpoint was still in the public repository on 15 October 2026.", "what_it_is_for": "The authors use it to score whether a white-matter streamline is anatomically plausible. TractOracle-RL uses that score as a reward and as an early stopping signal. The repository also uses it as a standalone tractogram filter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sherbrooke/tractoracle/tractoracle-rl": { "id": "sherbrooke/tractoracle/tractoracle-rl", "name": "TractOracle-RL", "maker": "sherbrooke", "family": "sherbrooke/tractoracle", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2024-03-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Soft Actor-Critic tractography agent. The paper says the actor and twin critics are 3-layer fully connected networks of width 1024. The actor outputs the mean and standard deviation of a 3D Gaussian tracking step. The shipped hyperparameters.json confirms hidden_dims 1024-1024-1024, algorithm SACAuto, input_size 615, action_size 3, n_dirs 100, learning rate 0.0005, gamma 0.95, and sparse oracle bonus 10. State is the local fODF, six neighbors, and the previous 100 directions.", "modalities": { "input": [ "fodf" ], "output": [ "streamline" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TractOracle-RL is the tracking half of TractOracle, from the Universit\u00e9 de Sherbrooke (SCIL and VITALab). It is a Soft Actor-Critic agent that grows white-matter streamlines from fODFs and is rewarded by TractOracle-Net. The preprint is 26 March 2024. On 15 October 2026 the TrackToLearn repository still shipped an actor and a critic for this agent. Status is active. There is no hosted API.", "what_it_is_for": "The maker uses it to reconstruct white-matter pathways and to stop when the oracle says the path is no longer plausible. It is meant to cut false-positive tracts without a curated reference tractogram for the agent itself. The oracle still needs labeled streamlines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/abel/abel-13b-001": { "id": "sjtu/abel/abel-13b-001", "name": "Abel-13B-001", "maker": "sjtu", "family": "sjtu/abel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-09-19", "context_length": 4096, "max_output_tokens": null, "parameters": "13B", "architecture": "LlamaForCausalLM. config.json: hidden 5120, 40 layers, 40 heads, 40 KV heads, intermediate 13824, vocab 32000, max_position_embeddings 4096. No README was on the repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Abel-13B-001 is a GAIR Abel weight release with a Llama-13B-shaped config and no README. Status: active. Collection date 2026-10-15. Confidence is medium because the 7B card does not list this checkpoint.", "what_it_is_for": "Named Abel math-series checkpoint. The repo does not say which tasks it was tuned for.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/abel/abel-70b-001": { "id": "sjtu/abel/abel-70b-001", "name": "Abel-70B-001", "maker": "sjtu", "family": "sjtu/abel", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-09-18", "context_length": 2048, "max_output_tokens": null, "parameters": "70B", "architecture": "LlamaForCausalLM. config.json: hidden 8192, 80 layers, 64 heads, 8 KV heads, intermediate 28672, vocab 32032, max_position_embeddings 2048. No README was on the repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Abel-70B-001 is a GAIR Abel weight release with a Llama-70B-shaped config and no README. Status: active. Collection date 2026-10-15. Confidence is medium because the 7B card does not list this checkpoint.", "what_it_is_for": "Named Abel math-series checkpoint. The repo does not say which tasks it was tuned for.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/abel/abel-7b-001": { "id": "sjtu/abel/abel-7b-001", "name": "Abel-7B-001", "maker": "sjtu", "family": "sjtu/abel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-09-18", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "LlamaForCausalLM. config.json: hidden 4096, 32 layers, 32 heads, 32 KV heads, intermediate 11008, vocab 32032, max_position_embeddings 4096. This repo has no README. The Abel-7B-002 card lists it as the previous 7B checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Abel-7B-001 is GAIR's first public 7B Abel math checkpoint, a Llama-shaped model. Shanghai Jiao Tong University / GAIR. Status: active. Collection date 2026-10-15. It was superseded in the lab's own table by Abel-7B-002.", "what_it_is_for": "Math-oriented generation. The later card compares it with Abel-7B-002 on GSM8K, MATH, and general benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/abel/abel-7b-002": { "id": "sjtu/abel/abel-7b-002", "name": "Abel-7B-002", "maker": "sjtu", "family": "sjtu/abel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-12-11", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "MistralForCausalLM. config.json: hidden 4096, 32 layers, 32 heads, 8 KV heads, intermediate 14336, vocab 32032, max_position_embeddings 32768, rope_theta 10000. The card calls this a stronger and more general 7B than Abel-7B-001.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Abel-7B-002 is GAIR's updated 7B math model. The card says it is the best 7B they compared, with a Mistral backbone rather than the Llama backbone of Abel-7B-001. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Math reasoning and general benchmarks. The card reports a 35% GSM8K and 126% MATH improvement over Abel-7B-001.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/anole/anole-7b": { "id": "sjtu/anole/anole-7b", "name": "Anole-7B", "maker": "sjtu", "family": "sjtu/anole", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Chameleon license", "release_date": "2024-06-30", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "ChameleonForConditionalGeneration. config.json on GAIR/Anole-7b: hidden 4096, 32 layers, 32 heads, 32 KV heads, intermediate 11008, vocab 65536, max_position_embeddings 4096, plus a Chameleon VQ-GAN image tokenizer (n_embed 8192).", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Anole-7B is GAIR's open autoregressive multimodal model for interleaved image and text, fine-tuned from Meta Chameleon. Status: active. Collection date 2026-10-15. The June 2024 v0.1 weights and the May 2025 safetensors repo are the same named model.", "what_it_is_for": "Text-to-image generation, interleaved text-image generation, text generation, and multimodal understanding. The card marks image generation and interleaved generation as the capabilities added on top of Chameleon.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/autoj/autoj-13b": { "id": "sjtu/autoj/autoj-13b", "name": "Auto-J-13B", "maker": "sjtu", "family": "sjtu/autoj", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-04", "context_length": 8192, "max_output_tokens": null, "parameters": "13B", "architecture": "LlamaForCausalLM. config.json: hidden 5120, 40 layers, 40 heads, 40 KV heads, intermediate 13824, vocab 32128, max_position_embeddings 8192. The paper describes a 13B generative judge.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Auto-J-13B is GAIR's open generative judge for alignment evaluation. Status: active. Collection date 2026-10-15. A 4-bit GPTQ published by the same lab is a version, not a second folder.", "what_it_is_for": "Pairwise response comparison and single-response evaluation with written critiques. The paper says it was trained on real user queries and model responses across many scenarios.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/autoj/autoj-bilingual-6b": { "id": "sjtu/autoj/autoj-bilingual-6b", "name": "Auto-J Bilingual 6B", "maker": "sjtu", "family": "sjtu/autoj", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-12-03", "context_length": 4096, "max_output_tokens": null, "parameters": "6B", "architecture": "LlamaForCausalLM. config.json: hidden 4096, 32 layers, 32 heads, 4 KV heads, intermediate 11008, vocab 64000, max_position_embeddings 4096, rope_theta 5000000. No README was on the repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Auto-J Bilingual 6B is a named GAIR weight release under the Auto-J project. Status: active. Collection date 2026-10-15. Confidence is medium because the repo has weights and a config but no README.", "what_it_is_for": "The name says a bilingual 6B Auto-J judge. The repo does not describe the training mix or the evaluation protocol.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/autoj/autoj-scenario-classifier": { "id": "sjtu/autoj/autoj-scenario-classifier", "name": "Auto-J Scenario Classifier", "maker": "sjtu", "family": "sjtu/autoj", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-04", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM. config.json: hidden 5120, 40 layers, 40 heads, 40 KV heads, intermediate 13824, vocab 32032, max_position_embeddings 4096. Parameter count is not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Auto-J Scenario Classifier is a named GAIR checkpoint released with Auto-J. Status: active. Collection date 2026-10-15.", "what_it_is_for": "The name and the one-line card present it as the scenario classifier for Auto-J. The card does not define the label set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/confucius/confucius-confidence-verb": { "id": "sjtu/confucius/confucius-confidence-verb", "name": "Confucius Confidence-Verb", "maker": "sjtu", "family": "sjtu/confucius", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-12-10", "context_length": 8192, "max_output_tokens": null, "parameters": "13B", "architecture": "LlamaForCausalLM. config.json: hidden 5120, 40 layers, 40 heads, 40 KV heads, intermediate 13824, vocab 32000, max_position_embeddings 8192. The project README says 13B and a Llama 2 Chat license.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Confucius Confidence-Verb is a GAIR 13B honesty-aligned model from the Alignment for Honesty project. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Honesty-aligned 13B checkpoint. The project README lists the method name confidence-verb and says the model should refuse questions it does not know.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/confucius/confucius-multisample": { "id": "sjtu/confucius/confucius-multisample", "name": "Confucius Multisample", "maker": "sjtu", "family": "sjtu/confucius", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-12-10", "context_length": 8192, "max_output_tokens": null, "parameters": "13B", "architecture": "LlamaForCausalLM. config.json: hidden 5120, 40 layers, 40 heads, 40 KV heads, intermediate 13824, vocab 32000, max_position_embeddings 8192. The project README says 13B and a Llama 2 Chat license.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Confucius Multisample is a GAIR 13B honesty-aligned model from the Alignment for Honesty project. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Honesty-aligned 13B checkpoint. The project README lists it beside confidence-verb as one of the two best released models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/davinci/davinci-agency": { "id": "sjtu/davinci/davinci-agency", "name": "daVinci-Agency", "maker": "sjtu", "family": "sjtu/davinci", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-02-02", "context_length": 202752, "max_output_tokens": null, "parameters": "", "architecture": "Glm4MoeForCausalLM fine-tuned from GLM-4.6. config.json: hidden 5120, 92 layers, 96 heads, 8 KV heads, intermediate 12288, MoE intermediate 1536, 160 routed experts, 1 shared expert, 8 experts per token, vocab 151552, max_position_embeddings 202752. The card says the context window is 200K, inherited from GLM-4.6.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "daVinci-Agency is GAIR's long-horizon agent, fine-tuned from GLM-4.6. Status: active. Collection date 2026-10-15. License is MIT.", "what_it_is_for": "Long software-engineering jobs that need task decomposition, consistency, and revision. The card says the training trajectories average 85k tokens and 116 tool calls, from 239 samples.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/davinci/davinci-dev-32b": { "id": "sjtu/davinci/davinci-dev-32b", "name": "daVinci-Dev-32B", "maker": "sjtu", "family": "sjtu/davinci", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Qwen license", "release_date": "2026-01-25", "context_length": 32768, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen2ForCausalLM continued from Qwen2.5-32B. config.json: hidden 5120, 64 layers, 40 heads, 8 KV heads, intermediate 27648, vocab 152064, max_position_embeddings 32768, rope_theta 1000000. The card license_name is qwen.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "daVinci-Dev-32B is GAIR's 32B software-engineering model, mid-trained and then SFT'd from Qwen2.5-32B. Status: active. Collection date 2026-10-15. The MT checkpoint is a version.", "what_it_is_for": "Agentic software engineering inside a scaffold such as SWE-agent. The card says training uses PR-derived trajectories and executable rollouts so the model sees tool feedback.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/davinci/davinci-dev-72b": { "id": "sjtu/davinci/davinci-dev-72b", "name": "daVinci-Dev-72B", "maker": "sjtu", "family": "sjtu/davinci", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Qwen license", "release_date": "2026-01-23", "context_length": 32768, "max_output_tokens": null, "parameters": "72B", "architecture": "Qwen2ForCausalLM continued from Qwen2.5-72B. config.json: hidden 8192, 80 layers, 64 heads, 8 KV heads, intermediate 29568, vocab 152064, max_position_embeddings 32768, rope_theta 1000000. The card's license link points at the Qwen2.5-72B-Instruct LICENSE file.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "daVinci-Dev-72B is GAIR's 72B software-engineering model. Status: active. Collection date 2026-10-15. The card reports 58.5% SWE-bench Verified Pass@1 for the final checkpoint.", "what_it_is_for": "Agentic software engineering from a non-coder Qwen2.5 base, using agent-native mid-training and environment-native SFT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/davinci/davinci-kernel-14b": { "id": "sjtu/davinci/davinci-kernel-14b", "name": "daVinci-kernel-14B", "maker": "sjtu", "family": "sjtu/davinci", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-06-16", "context_length": 32768, "max_output_tokens": null, "parameters": "14B", "architecture": "Qwen3ForCausalLM. The GitHub README says the base is Qwen3-14B-Base. config.json on the RL repo: hidden 5120, 40 layers, 40 heads, 8 KV heads, intermediate 17408, vocab 151936, max_position_embeddings 32768, rope_theta 1000000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "daVinci-kernel-14B is GAIR's 14B GPU-kernel model. Status: active. Collection date 2026-10-15. The specs describe the RL weights. The SFT repo was empty.", "what_it_is_for": "GPU kernel optimization with co-evolving skill selection and skill summarization. The README reports KernelBench Fast1 scores for this 14B RL model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/davinci/davinci-kernel-8b": { "id": "sjtu/davinci/davinci-kernel-8b", "name": "daVinci-kernel-8B", "maker": "sjtu", "family": "sjtu/davinci", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-06-16", "context_length": 32768, "max_output_tokens": null, "parameters": "8B", "architecture": "Qwen3ForCausalLM. The GitHub README says the base is Qwen3-8B-Base. config.json on the RL repo: hidden 4096, 36 layers, 32 heads, 8 KV heads, intermediate 12288, vocab 151936, max_position_embeddings 32768, rope_theta 1000000. This dossier describes the RL weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "daVinci-kernel-8B is GAIR's 8B GPU-kernel model. Status: active. Collection date 2026-10-15. The specs describe the RL checkpoint. The SFT cold start is a version.", "what_it_is_for": "Write CUDA and Triton kernels with a shared backbone that selects skills, writes kernels, and summarizes new skills. The README says SFT is the cold start and RL is the final model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/davinci/davinci-magihuman": { "id": "sjtu/davinci/davinci-magihuman", "name": "daVinci-MagiHuman", "maker": "sjtu", "family": "sjtu/davinci", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-21", "context_length": null, "max_output_tokens": null, "parameters": "15B", "architecture": "Single-stream 15B, 40-layer Transformer. The card says the first and last 4 layers use modality-specific projections and the middle 32 layers are shared. No cross-attention. Text, a reference image, and noisy video and audio tokens are denoised together. config.json is empty. The repo holds base, distill, 540p super-resolution, 1080p super-resolution, and a Turbo VAE.", "modalities": { "input": [ "text", "image", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "daVinci-MagiHuman is GAIR and Sand.ai's open audio-video model for human clips. Status: active. Collection date 2026-10-15. The card says 15B parameters and Apache-2.0. Base, distilled, super-resolution, and VAE weights are one repo.", "what_it_is_for": "Joint speech and video generation with expressive faces and synced audio, in Mandarin, Cantonese, English, Japanese, Korean, German, and French.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/davinci/davinci-origin-3b": { "id": "sjtu/davinci/davinci-origin-3b", "name": "daVinci-origin-3B", "maker": "sjtu", "family": "sjtu/davinci", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-28", "context_length": 32768, "max_output_tokens": null, "parameters": "3B", "architecture": "Qwen2ForCausalLM config, but the card says the model was trained from scratch, not fine-tuned. config.json: hidden 2048, 36 layers, 16 heads, 2 KV heads, intermediate 11008, vocab 151936, max_position_embeddings 32768, rope_theta 10000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "daVinci-origin-3B is a GAIR 3B foundation model trained from scratch as a clean-room baseline. Status: active. Collection date 2026-10-15. License is Apache-2.0. The card says some paper drafts call it S3B.", "what_it_is_for": "A pretraining baseline whose data excludes scientific books and papers, so later scientific-data gains can be measured. It is not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/davinci/davinci-origin-7b": { "id": "sjtu/davinci/davinci-origin-7b", "name": "daVinci-origin-7B", "maker": "sjtu", "family": "sjtu/davinci", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-28", "context_length": 131072, "max_output_tokens": null, "parameters": "7B", "architecture": "Qwen2ForCausalLM config, but the card says the model was trained from scratch, not fine-tuned. config.json: hidden 3584, 28 layers, 28 heads, 4 KV heads, intermediate 18944, vocab 152064, max_position_embeddings 131072, rope_theta 10000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "daVinci-origin-7B is a GAIR 7B foundation model trained from scratch without scientific books and papers. Status: active. Collection date 2026-10-15. License is Apache-2.0. The card says some paper drafts call it S7B.", "what_it_is_for": "Same clean-room pretraining baseline as the 3B model, at 7 billion parameters as stated on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/deepresearcher/deepresearcher-7b": { "id": "sjtu/deepresearcher/deepresearcher-7b", "name": "DeepResearcher-7B", "maker": "sjtu", "family": "sjtu/deepresearcher", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-04-03", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "Qwen2ForCausalLM fine-tuned from Qwen2.5-7B-Instruct. config.json: hidden 3584, 28 layers, 28 heads, 4 KV heads, intermediate 18944, vocab 152064, max_position_embeddings 32768, rope_theta 1000000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepResearcher-7B is GAIR's web-research agent, RL-tuned from Qwen2.5-7B-Instruct. Status: active. Collection date 2026-10-15. License is Apache-2.0.", "what_it_is_for": "End-to-end research with a real web search tool. The card says training used Natural Questions, TriviaQA, HotpotQA, and 2Wiki, and that evaluation also covered Musique, Bamboogle, and PopQA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/limi/limi": { "id": "sjtu/limi/limi", "name": "LIMI", "maker": "sjtu", "family": "sjtu/limi", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-11", "context_length": 131072, "max_output_tokens": null, "parameters": "353B", "architecture": "Glm4MoeForCausalLM fine-tuned from GLM-4.5. config.json: hidden 5120, 92 layers, 96 heads, 8 KV heads, intermediate 12288, MoE intermediate 1536, 160 routed experts, 1 shared expert, 8 experts per token, vocab 151552, max_position_embeddings 131072. The model-zoo table says 353B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LIMI is GAIR's agent model fine-tuned from GLM-4.5 on a small curated set. Status: active. Collection date 2026-10-15. The card license is Apache-2.0 and the zoo table says 353B.", "what_it_is_for": "Tool use, multi-turn correction, and spec following on long agent trajectories. The card says the released SFT set has about 78 samples.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/limi/limi-air": { "id": "sjtu/limi/limi-air", "name": "LIMI-Air", "maker": "sjtu", "family": "sjtu/limi", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-11", "context_length": 131072, "max_output_tokens": null, "parameters": "107B", "architecture": "Glm4MoeForCausalLM fine-tuned from GLM-4.5-Air. config.json: hidden 4096, 46 layers, 96 heads, 8 KV heads, intermediate 10944, MoE intermediate 1408, 128 routed experts, 1 shared expert, 8 experts per token, vocab 151552, max_position_embeddings 131072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LIMI-Air is GAIR's smaller agent model, fine-tuned from GLM-4.5-Air on the same compact set as LIMI. Status: active. Collection date 2026-10-15.", "what_it_is_for": "The same agent jobs as LIMI at lower cost. The card says it can trade accuracy on complex tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/limo/limo": { "id": "sjtu/limo/limo", "name": "LIMO", "maker": "sjtu", "family": "sjtu/limo", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-02-05", "context_length": 32768, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen2ForCausalLM. config.json: hidden 5120, 64 layers, 40 heads, 8 KV heads, intermediate 27648, vocab 152064, max_position_embeddings 32768, rope_theta 1000000. The card says the backbone is Qwen2.5-32B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LIMO is GAIR's 32B math reasoner, fine-tuned from Qwen2.5-32B-Instruct on 817 samples. Status: active. Collection date 2026-10-15. LIMO-v2 is the later checkpoint and has its own folder because the lab still hosts both.", "what_it_is_for": "Step-by-step math reasoning. The card says the model was trained on 817 curated samples and reports scores on ten benchmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/limo/limo-v2": { "id": "sjtu/limo/limo-v2", "name": "LIMO-v2", "maker": "sjtu", "family": "sjtu/limo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-07-30", "context_length": 32768, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen2ForCausalLM. config.json: hidden 5120, 64 layers, 40 heads, 8 KV heads, intermediate 27648, vocab 152064, max_position_embeddings 32768, rope_theta 1000000. The card says the backbone is Qwen2.5-32B-Instruct. The v2 card says this checkpoint matches the paper as of 30 July 2025.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LIMO-v2 is GAIR's updated LIMO checkpoint for the 30 July 2025 paper revision. Status: active. Collection date 2026-10-15. License on this card is Apache-2.0, unlike the MIT line on the v1 README.", "what_it_is_for": "Same math-reasoning job as LIMO. The card says to use this repo for the latest paper and to use GAIR/LIMO for the original checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/limr/limr": { "id": "sjtu/limr/limr", "name": "LIMR", "maker": "sjtu", "family": "sjtu/limr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-17", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "Qwen2ForCausalLM. config.json at the repo root: hidden 3584, 28 layers, 28 heads, 4 KV heads, intermediate 18944, vocab 152064, max_position_embeddings 4096, rope_theta 10000. The card's table starts from Qwen-Math-7B. A nested Qwen-Math-7B-FULL directory is a comparison run, not this model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LIMR is GAIR's data-efficient reinforcement-learning math model, announced 17 February 2025. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Math reasoning trained by RL on 1,389 selected questions from a 7B base, without distilled long chain-of-thought data. The card says this matched or beat training on 8,523 questions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/livetalk/livetalk-1-3b": { "id": "sjtu/livetalk/livetalk-1-3b", "name": "LiveTalk-1.3B", "maker": "sjtu", "family": "sjtu/livetalk", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card describes a causal few-step autoregressive model distilled from a bidirectional video diffusion model, using 4 diffusion steps. config.json in the repo is empty, so layer counts were not available.", "modalities": { "input": [ "image", "audio", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LiveTalk-1.3B is GAIR's real-time talking-avatar video model. Status: active. Collection date 2026-10-15. The only published weights are V0.1, under Apache-2.0.", "what_it_is_for": "Interactive avatar video from a reference image, speech audio, and a text prompt. The card says distillation makes the model fast enough for real-time use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/openswe/openswe-32b": { "id": "sjtu/openswe/openswe-32b", "name": "OpenSWE-32B", "maker": "sjtu", "family": "sjtu/openswe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Qwen license", "release_date": "2026-03-15", "context_length": 131072, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen2ForCausalLM from Qwen2.5-32B-Base. config.json: hidden 5120, 64 layers, 40 heads, 8 KV heads, intermediate 27648, vocab 152064, max_position_embeddings 131072, rope_theta 1000000. Card license_name is qwen and the license file is in the model repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenSWE-32B is GAIR's 32B software-engineering model trained on the OpenSWE environment set. Status: active. Collection date 2026-10-15. The card reports 62.4% SWE-bench Verified with SWE-agent.", "what_it_is_for": "SWE-agent and OpenHands software repair. The card says the training set is about 13,000 trajectories from about 9,000 environments, built from 45,320 executable environments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/openswe/openswe-72b": { "id": "sjtu/openswe/openswe-72b", "name": "OpenSWE-72B", "maker": "sjtu", "family": "sjtu/openswe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Qwen license", "release_date": "2026-03-15", "context_length": 131072, "max_output_tokens": null, "parameters": "72B", "architecture": "Qwen2ForCausalLM from Qwen2.5-72B-Base. config.json: hidden 8192, 80 layers, 64 heads, 8 KV heads, intermediate 29568, vocab 152064, max_position_embeddings 131072, rope_theta 1000000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenSWE-72B is GAIR's 72B software-engineering model. Status: active. Collection date 2026-10-15. The card reports 66.0% SWE-bench Verified with SWE-agent.", "what_it_is_for": "Same OpenSWE training recipe as the 32B model, from Qwen2.5-72B-Base. The card says SWE training also moved math and science scores without hurting factual recall, and it does not print those secondary tables in full.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/realign/realign-task-classifier": { "id": "sjtu/realign/realign-task-classifier", "name": "ReAlign Task Classifier", "maker": "sjtu", "family": "sjtu/realign", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-01-16", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM. config.json: hidden 5120, 40 layers, 40 heads, 40 KV heads, intermediate 13824, vocab 32032, max_position_embeddings 4096. Parameter count is not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReAlign Task Classifier is a named GAIR checkpoint published with the ReAlign project. Status: active. Collection date 2026-10-15. The card license is Llama 2.", "what_it_is_for": "Task classification for the ReAlign data-formatting work. The card does not describe the labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/reasoneval/reasoneval-34b": { "id": "sjtu/reasoneval/reasoneval-34b", "name": "ReasonEval-34B", "maker": "sjtu", "family": "sjtu/reasoneval", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-04-05", "context_length": 4096, "max_output_tokens": null, "parameters": "34B", "architecture": "Custom head ReasonEval_34B on a Llama layout. config.json: hidden 8192, 48 layers, 64 heads, 8 KV heads, intermediate 22016, vocab 32032, max_position_embeddings 4096, rope_theta 1000000. The card says the base is EleutherAI llemma_34b.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReasonEval-34B is GAIR's 34B math-step judge, fine-tuned from Llemma-34B. Status: active. Collection date 2026-10-15. The card license is Llama 2.", "what_it_is_for": "Same step-level validity and redundancy labels as ReasonEval-7B, at 34B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/reasoneval/reasoneval-7b": { "id": "sjtu/reasoneval/reasoneval-7b", "name": "ReasonEval-7B", "maker": "sjtu", "family": "sjtu/reasoneval", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-04", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "Custom head ReasonEval_7B on a Mistral layout. config.json: hidden 4096, 32 layers, 32 heads, 8 KV heads, intermediate 14336, vocab 32000, max_position_embeddings 32768. The card says the base is WizardMath-7B-V1.1 and the next-token head is replaced by a step-class head.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReasonEval-7B is GAIR's 7B math-step judge. Status: active. Collection date 2026-10-15. Weights are Apache-2.0.", "what_it_is_for": "Given a math problem and a solution, label each step for validity and redundancy. The card says it can score reasoning without a human or a closed model, and can filter training data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/rst/rst-all-11b": { "id": "sjtu/rst/rst-all-11b", "name": "RST-All-11B", "maker": "sjtu", "family": "sjtu/rst", "status": "active", "confidence": "high", "weights": "open-source", "license": "AFL-3.0", "release_date": "2022-08-31", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "T5ForConditionalGeneration. config.json: d_model 1024, d_ff 65536, d_kv 128, 24 encoder layers, 24 decoder layers, 128 heads, vocab 32128, n_positions 512. The shared card says each released model has 11 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RST-All-11B is a Shanghai Jiao Tong University / GAIR text-to-text model from the 2022 reStructured Pre-training release. Weights are public under AFL-3.0. Status: active. Collection date 2026-10-15.", "what_it_is_for": "General RST checkpoint trained with the shared signals except the Gaokao-only signals. The card says to prefer a specialized model when peak accuracy on one task matters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/rst/rst-fact-retrieval-11b": { "id": "sjtu/rst/rst-fact-retrieval-11b", "name": "RST Fact Retrieval 11B", "maker": "sjtu", "family": "sjtu/rst", "status": "active", "confidence": "high", "weights": "open-source", "license": "AFL-3.0", "release_date": "2022-08-29", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "T5ForConditionalGeneration. config.json: d_model 1024, d_ff 65536, d_kv 128, 24 encoder layers, 24 decoder layers, 128 heads, vocab 32128, n_positions 512. The shared card says each released model has 11 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RST Fact Retrieval 11B is a Shanghai Jiao Tong University / GAIR text-to-text model from the 2022 reStructured Pre-training release. Weights are public under AFL-3.0. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Specialized for knowledge-heavy tasks, information extraction, and factual checking, using WordNet, wikiHow, Wikidata, and Papers with Code signals.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/rst/rst-gaokao-cloze-11b": { "id": "sjtu/rst/rst-gaokao-cloze-11b", "name": "RST Gaokao Cloze 11B", "maker": "sjtu", "family": "sjtu/rst", "status": "active", "confidence": "high", "weights": "open-source", "license": "AFL-3.0", "release_date": "2022-09-01", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "T5ForConditionalGeneration. config.json: d_model 1024, d_ff 65536, d_kv 128, 24 encoder layers, 24 decoder layers, 128 heads, vocab 32128, n_positions 512. The shared card says each released model has 11 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RST Gaokao Cloze 11B is a Shanghai Jiao Tong University / GAIR text-to-text model from the 2022 reStructured Pre-training release. Weights are public under AFL-3.0. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Cloze model trained on manually crafted cloze data. The card recommends it for general cloze filling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/rst/rst-gaokao-rc-11b": { "id": "sjtu/rst/rst-gaokao-rc-11b", "name": "RST Gaokao Reading Comprehension 11B", "maker": "sjtu", "family": "sjtu/rst", "status": "active", "confidence": "high", "weights": "open-source", "license": "AFL-3.0", "release_date": "2022-09-01", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "T5ForConditionalGeneration. config.json: d_model 1024, d_ff 65536, d_kv 128, 24 encoder layers, 24 decoder layers, 128 heads, vocab 32128, n_positions 512. The shared card says each released model has 11 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RST Gaokao Reading Comprehension 11B is a Shanghai Jiao Tong University / GAIR text-to-text model from the 2022 reStructured Pre-training release. Weights are public under AFL-3.0. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Multiple-choice QA model trained with the datasets used to train T0pp. The card recommends it for general multiple-choice question answering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/rst/rst-gaokao-writing-11b": { "id": "sjtu/rst/rst-gaokao-writing-11b", "name": "RST Gaokao Writing 11B", "maker": "sjtu", "family": "sjtu/rst", "status": "active", "confidence": "high", "weights": "open-source", "license": "AFL-3.0", "release_date": "2022-09-01", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "T5ForConditionalGeneration. config.json: d_model 1024, d_ff 65536, d_kv 128, 24 encoder layers, 24 decoder layers, 128 heads, vocab 32128, n_positions 512. The shared card says each released model has 11 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RST Gaokao Writing 11B is a Shanghai Jiao Tong University / GAIR text-to-text model from the 2022 reStructured Pre-training release. Weights are public under AFL-3.0. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Generation model trained on past Gaokao English essays and grammar-error signals. The card recommends it for essays, stories, and grammar correction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/rst/rst-information-extraction-11b": { "id": "sjtu/rst/rst-information-extraction-11b", "name": "RST Information Extraction 11B", "maker": "sjtu", "family": "sjtu/rst", "status": "active", "confidence": "high", "weights": "open-source", "license": "AFL-3.0", "release_date": "2022-08-30", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "T5ForConditionalGeneration. config.json: d_model 1024, d_ff 65536, d_kv 128, 24 encoder layers, 24 decoder layers, 128 heads, vocab 32128, n_positions 512. The shared card says each released model has 11 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RST Information Extraction 11B is a Shanghai Jiao Tong University / GAIR text-to-text model from the 2022 reStructured Pre-training release. Weights are public under AFL-3.0. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Specialized for named entity recognition, relation extraction, and other IE in news and scientific text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/rst/rst-intent-detection-11b": { "id": "sjtu/rst/rst-intent-detection-11b", "name": "RST Intent Detection 11B", "maker": "sjtu", "family": "sjtu/rst", "status": "active", "confidence": "high", "weights": "open-source", "license": "AFL-3.0", "release_date": "2022-08-30", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "T5ForConditionalGeneration. config.json: d_model 1024, d_ff 65536, d_kv 128, 24 encoder layers, 24 decoder layers, 128 heads, vocab 32128, n_positions 512. The shared card says each released model has 11 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RST Intent Detection 11B is a Shanghai Jiao Tong University / GAIR text-to-text model from the 2022 reStructured Pre-training release. Weights are public under AFL-3.0. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Specialized for intent prediction and event prediction from wikiHow goal-step relations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/rst/rst-natural-language-inference-11b": { "id": "sjtu/rst/rst-natural-language-inference-11b", "name": "RST Natural Language Inference 11B", "maker": "sjtu", "family": "sjtu/rst", "status": "active", "confidence": "high", "weights": "open-source", "license": "AFL-3.0", "release_date": "2022-08-30", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "T5ForConditionalGeneration. config.json: d_model 1024, d_ff 65536, d_kv 128, 24 encoder layers, 24 decoder layers, 128 heads, vocab 32128, n_positions 512. The shared card says each released model has 11 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RST Natural Language Inference 11B is a Shanghai Jiao Tong University / GAIR text-to-text model from the 2022 reStructured Pre-training release. Weights are public under AFL-3.0. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Specialized for natural language inference, multiple-choice QA, and reasoning. Signals include ConTRoL, DREAM, LogiQA, RACE, ReClor, and DailyMail temporal data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/rst/rst-sentiment-classification-11b": { "id": "sjtu/rst/rst-sentiment-classification-11b", "name": "RST Sentiment Classification 11B", "maker": "sjtu", "family": "sjtu/rst", "status": "active", "confidence": "high", "weights": "open-source", "license": "AFL-3.0", "release_date": "2022-08-30", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "T5ForConditionalGeneration. config.json: d_model 1024, d_ff 65536, d_kv 128, 24 encoder layers, 24 decoder layers, 128 heads, vocab 32128, n_positions 512. The shared card says each released model has 11 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RST Sentiment Classification 11B is a Shanghai Jiao Tong University / GAIR text-to-text model from the 2022 reStructured Pre-training release. Weights are public under AFL-3.0. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Specialized for sentiment and emotion classification using Rotten Tomatoes and Wikipedia sentiment signals.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/rst/rst-summarization-11b": { "id": "sjtu/rst/rst-summarization-11b", "name": "RST Summarization 11B", "maker": "sjtu", "family": "sjtu/rst", "status": "active", "confidence": "high", "weights": "open-source", "license": "AFL-3.0", "release_date": "2022-08-30", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "T5ForConditionalGeneration. config.json: d_model 1024, d_ff 65536, d_kv 128, 24 encoder layers, 24 decoder layers, 128 heads, vocab 32128, n_positions 512. The shared card says each released model has 11 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RST Summarization 11B is a Shanghai Jiao Tong University / GAIR text-to-text model from the 2022 reStructured Pre-training release. Weights are public under AFL-3.0. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Specialized for summarization and other generation, plus meta-evaluation such as BARTScore. Training signals include DailyMail, Papers with Code, arXiv, and wikiHow summaries.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/rst/rst-temporal-reasoning-11b": { "id": "sjtu/rst/rst-temporal-reasoning-11b", "name": "RST Temporal Reasoning 11B", "maker": "sjtu", "family": "sjtu/rst", "status": "active", "confidence": "high", "weights": "open-source", "license": "AFL-3.0", "release_date": "2022-08-30", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "T5ForConditionalGeneration. config.json: d_model 1024, d_ff 65536, d_kv 128, 24 encoder layers, 24 decoder layers, 128 heads, vocab 32128, n_positions 512. The shared card says each released model has 11 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RST Temporal Reasoning 11B is a Shanghai Jiao Tong University / GAIR text-to-text model from the 2022 reStructured Pre-training release. Weights are public under AFL-3.0. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Specialized for temporal reasoning, relation extraction, and event extraction, using DailyMail temporal information and wikiHow procedures.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/rst/rst-topic-classification-11b": { "id": "sjtu/rst/rst-topic-classification-11b", "name": "RST Topic Classification 11B", "maker": "sjtu", "family": "sjtu/rst", "status": "active", "confidence": "high", "weights": "open-source", "license": "AFL-3.0", "release_date": "2022-08-30", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "T5ForConditionalGeneration. config.json: d_model 1024, d_ff 65536, d_kv 128, 24 encoder layers, 24 decoder layers, 128 heads, vocab 32128, n_positions 512. The shared card says each released model has 11 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RST Topic Classification 11B is a Shanghai Jiao Tong University / GAIR text-to-text model from the 2022 reStructured Pre-training release. Weights are public under AFL-3.0. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Specialized for general text classification using DailyMail, arXiv, wikiHow, and Wikipedia category signals.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/rst/rst-word-sense-disambiguation-11b": { "id": "sjtu/rst/rst-word-sense-disambiguation-11b", "name": "RST Word Sense Disambiguation 11B", "maker": "sjtu", "family": "sjtu/rst", "status": "active", "confidence": "high", "weights": "open-source", "license": "AFL-3.0", "release_date": "2022-08-30", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "T5ForConditionalGeneration. config.json: d_model 1024, d_ff 65536, d_kv 128, 24 encoder layers, 24 decoder layers, 128 heads, vocab 32128, n_positions 512. The shared card says each released model has 11 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RST Word Sense Disambiguation 11B is a Shanghai Jiao Tong University / GAIR text-to-text model from the 2022 reStructured Pre-training release. Weights are public under AFL-3.0. Status: active. Collection date 2026-10-15.", "what_it_is_for": "Specialized for word sense, part of speech, general IE, and commonsense reasoning from WordNet signals.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/safety-j/safety-j": { "id": "sjtu/safety-j/safety-j", "name": "Safety-J", "maker": "sjtu", "family": "sjtu/safety-j", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-07-15", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "InternLM2ForCausalLM. config.json: hidden 4096, 32 layers, 32 heads, 8 KV heads, intermediate 14336, vocab 92544, max_position_embeddings 32768, rope_theta 1000000. Parameter count is not written on the card. The Hugging Face pipeline tag is feature-extraction, which the paper's critique-generator description does not match.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Safety-J is GAIR's open safety critic. This dossier describes the v5 weights, with v1 recorded as a same-day version. Status: active. Collection date 2026-10-15. It is not an InternLM product release and is not duplicated under shanghai-ai-lab.", "what_it_is_for": "The paper presents Safety-J as a bilingual safety evaluator that writes critiques. The model cards themselves contain only the Apache-2.0 license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/sr-scientist/sr-scientist-30b": { "id": "sjtu/sr-scientist/sr-scientist-30b", "name": "SR-Scientist-30B", "maker": "sjtu", "family": "sjtu/sr-scientist", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-29", "context_length": 262144, "max_output_tokens": null, "parameters": "30B", "architecture": "Qwen3MoeForCausalLM. The card says the backbone is Qwen3-Coder-30B-A3B-Instruct and that this file is the RL checkpoint. config.json: hidden 2048, 48 layers, 32 heads, 4 KV heads, intermediate 5472, 128 experts, 8 experts per token, vocab 151936, max_position_embeddings 262144, rope_theta 10000000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SR-Scientist-30B is GAIR's RL checkpoint for scientific equation discovery. Status: active. Collection date 2026-10-15. License is Apache-2.0.", "what_it_is_for": "Agentic discovery of scientific equations. The card identifies this file as the RL training checkpoint from the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/torl/torl-1-5b": { "id": "sjtu/torl/torl-1-5b", "name": "ToRL-1.5B", "maker": "sjtu", "family": "sjtu/torl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-28", "context_length": 65536, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Qwen2ForCausalLM. config.json: hidden 1536, 28 layers, 12 heads, 2 KV heads, intermediate 8960, vocab 151936, max_position_embeddings 65536, rope_theta 10000. The paper says the base is Qwen2.5-Math-1.5B. No Hugging Face README.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ToRL-1.5B is GAIR's 1.5B tool-integrated RL math model. Status: active. Collection date 2026-10-15. The Hugging Face repo has weights and no README. Scores are from the official GitHub README.", "what_it_is_for": "Math reasoning that can call a code interpreter. The README says training starts from a base model rather than from supervised tool traces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/torl/torl-7b": { "id": "sjtu/torl/torl-7b", "name": "ToRL-7B", "maker": "sjtu", "family": "sjtu/torl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-28", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "Qwen2ForCausalLM. config.json: hidden 3584, 28 layers, 28 heads, 4 KV heads, intermediate 18944, vocab 152064, max_position_embeddings 4096, rope_theta 10000. The paper says the base is Qwen2.5-Math-7B. No Hugging Face README.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ToRL-7B is GAIR's 7B tool-integrated RL math model. Status: active. Collection date 2026-10-15. The official README says it reaches 43.3% on AIME 2024.", "what_it_is_for": "Math reasoning with a code interpreter, trained by RL from a base model. The README says the model learns when to call the tool rather than copying human tool traces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/twgi/twgi-critique-anole-7b": { "id": "sjtu/twgi/twgi-critique-anole-7b", "name": "TWGI Critique Anole 7B", "maker": "sjtu", "family": "sjtu/twgi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-22", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "ChameleonForConditionalGeneration, same shape as Anole-7B. config.json: hidden 4096, 32 layers, 32 heads, 32 KV heads, intermediate 11008, vocab 65536, max_position_embeddings 4096.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TWGI Critique Anole 7B is a GAIR Anole fine-tune for thinking with generated images. Status: active. Collection date 2026-10-15.", "what_it_is_for": "The card says this checkpoint supports vision generation with self-critique.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sjtu/twgi/twgi-subgoal-anole-7b": { "id": "sjtu/twgi/twgi-subgoal-anole-7b", "name": "TWGI Subgoal Anole 7B", "maker": "sjtu", "family": "sjtu/twgi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-21", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "ChameleonForConditionalGeneration, same shape as Anole-7B. config.json: hidden 4096, 32 layers, 32 heads, 32 KV heads, intermediate 11008, vocab 65536, max_position_embeddings 4096.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TWGI Subgoal Anole 7B is a GAIR Anole fine-tune for thinking with generated images. Status: active. Collection date 2026-10-15.", "what_it_is_for": "The card says this checkpoint supports vision generation with intermediate visual subgoals.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-3-0": { "id": "sk-telecom/a-x/a-x-3-0", "name": "A.X 3.0", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "From-scratch Korean LLM. SKT described a 34B standard model focused on Korean conversation, later upgraded as A.X 3.1. Layer counts were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X 3.0 is a a-x model from SK Telecom. Status on 2026-10-15: deprecated. Confidence: medium.", "what_it_is_for": "SK Telecom says A.X 3.0 was the from-scratch conversational model used inside the A.Dot (\uc5d0\uc774\ub2f7) phone assistant before A.X 3.1. It was a product model, not an open weight release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-3-0-light": { "id": "sk-telecom/a-x/a-x-3-0-light", "name": "A.X 3.0 Light", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "From-scratch lightweight sibling of A.X 3.0. Press describes 7B parameters and use in A.Dot call summary. No architecture table was published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X 3.0 Light is a a-x model from SK Telecom. Status on 2026-10-15: deprecated. Confidence: medium.", "what_it_is_for": "SK Telecom used A.X 3.0 Light for A.Dot call summarization on mobile-class hardware. A.X 3.1 Light replaced it as the open from-scratch 7B model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-3-1": { "id": "sk-telecom/a-x/a-x-3-1", "name": "A.X 3.1", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-07-24", "context_length": 32768, "max_output_tokens": null, "parameters": "34670911488", "architecture": "LlamaForCausalLM trained from scratch. Card and GitHub table: 34B class, 48 layers, 8 KV heads, hidden 8192, FFN 21824. Trained on 2.1 trillion tokens. Native context 32,768; YaRN factor 4.0 extends to 131,072.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X 3.1 is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "SK Telecom's from-scratch sovereign LLM for Korean enterprise use. The card positions it for Korean understanding, instruction following, and tool use, with code and math improved over A.X 3.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-3-1-light": { "id": "sk-telecom/a-x/a-x-3-1-light", "name": "A.X 3.1 Light", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-07-11", "context_length": null, "max_output_tokens": null, "parameters": "7264800768", "architecture": "LlamaForCausalLM from scratch. GitHub table: 7B class, 32 layers, 32 KV heads, hidden 4096, FFN 10880. SKT says it was trained on 1.65 trillion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X 3.1 Light is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Lightweight from-scratch Korean LLM for phones and other tight devices. SKT says it keeps most of the Korean quality of A.X 4.0 Light at 7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-4-0": { "id": "sk-telecom/a-x/a-x-4-0", "name": "A.X 4.0", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Qwen license (HF license_name: qwen). Not Apache-2.0.", "release_date": "2025-07-03", "context_length": 131072, "max_output_tokens": null, "parameters": "71892508672", "architecture": "Qwen2ForCausalLM. Continual pre-training of Qwen2.5 with Korean data on SKT TITAN. 72B class. Custom Korean tokenizer. Layer counts were not copied from config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X 4.0 is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Korean-specialized enterprise LLM. SKT continued pre-training Qwen2.5 on Korean data and ships it for on-premises use and inside A.Dot search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-4-0-light": { "id": "sk-telecom/a-x/a-x-4-0-light", "name": "A.X 4.0 Light", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-07-03", "context_length": 16384, "max_output_tokens": null, "parameters": "7259624960", "architecture": "Qwen2ForCausalLM. 7B-class continual pre-training of Qwen2.5. SKT engineer post and model card: 16,384 token context. Apache-2.0, unlike the 72B checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X 4.0 Light is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "7B Korean enterprise LLM for faster inference and on-premises installs. It is the text backbone of A.X 4.0 VL Light.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-4-0-vl-light": { "id": "sk-telecom/a-x/a-x-4-0-vl-light", "name": "A.X 4.0 VL Light", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-07-29", "context_length": null, "max_output_tokens": null, "parameters": "7689660144", "architecture": "AX4VLForConditionalGeneration, custom code, fine-tune of A.X 4.0 Light on Korean multimodal data. Hugging Face safetensors total 7,689,660,144.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X 4.0 VL Light is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Vision-language model for Korean business documents, charts, handwriting, and images. SKT built it on A.X 4.0 Light.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-4-1": { "id": "sk-telecom/a-x/a-x-4-1", "name": "A.X 4.1", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SKT GitHub README calls it a 72B reasoning model shown next to A.X 4.0. No architecture table or public checkpoint was found.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X 4.1 is a a-x model from SK Telecom. Status on 2026-10-15: announced. Confidence: medium.", "what_it_is_for": "Named reasoning sibling of A.X 4.0. SKT published comparison scores and told reporters a preview was coming. A public weight release was not found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-encoder-base": { "id": "sk-telecom/a-x/a-x-encoder-base", "name": "A.X Encoder", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-07-29", "context_length": 16384, "max_output_tokens": null, "parameters": "149372240", "architecture": "ModernBERT masked language model, trained from scratch on TITAN. Card: up to 16,384 tokens, flash attention, about 3x faster inference than earlier models SKT compared. Safetensors 149,372,240 parameters.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X Encoder is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Korean document encoder for classification, retrieval-style NLU, and data curation ahead of LLM training. It is not a generative chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-k1": { "id": "sk-telecom/a-x/a-x-k1", "name": "A.X K1", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-07", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only MoE with MLA. Card: 519B total, 33B active, 61 layers (1 dense + 60 MoE), 64 heads, 192 routed experts of which 8 fire, 1 shared expert, vocab 163,840, RMSNorm before and after MLP. Think and non-think modes. Pre-trained on about 10 trillion tokens. Exact safetensors integer was not retrieved, so parameters is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X K1 is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "SK Telecom's first public frontier-scale MoE, trained from scratch for the Korean sovereign-AI project. One checkpoint switches between long reasoning and short answers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-k2": { "id": "sk-telecom/a-x/a-x-k2", "name": "A.X K2", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-07-29", "context_length": 262144, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only MoE. Card: 688B total, 33B active, 256 routed experts plus 1 shared, 8 experts active, 61 layers, hidden 7168, MLA with sparse gated attention, gated RMSNorm, vocab 163,840. Native FP8 training. Context 128K native, 256K with YaRN. Hugging Face size label says 692B, which disagrees with the card, so parameters is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X K2 is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Agentic flagship MoE and the successor to A.X K1. SKT ships it for Korean and English reasoning, tools, code, math, and long context up to 256K, under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-k2-alm": { "id": "sk-telecom/a-x/a-x-k2-alm", "name": "A.X K2 ALM", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-07-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Streaming audio-language model. Frozen A.X K2 Light (20B-A2.7B) plus a 300M chunk-wise Conformer encoder, MLP and Q-Former adapter, VAD heads, and an MTP speech decoder (card table: conditioning projector 71.3M, multi-token predictor 630M, depth transformer 110M). License text was not on the API cardData retrieved.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X K2 ALM is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Speech model for Korean voice conversation, voice activity detection, and spoken replies. SKT keeps the A.X K2 Light decoder frozen and trains the audio modules.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-k2-dspark": { "id": "sk-telecom/a-x/a-x-k2-dspark", "name": "A.X K2 DSpark", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-08-07", "context_length": null, "max_output_tokens": null, "parameters": "3419668385", "architecture": "Hugging Face config architecture DSparkDraftModel. Tags: speculative-decoding draft for skt/A.X-K2. Safetensors total 3,419,668,385. The README body was not fully transcribed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X K2 DSpark is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: medium.", "what_it_is_for": "Named speculative-decoding draft for A.X K2, released beside the EAGLE3 draft. Treat it as a serving helper, not a general chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-k2-eagle3": { "id": "sk-telecom/a-x/a-x-k2-eagle3", "name": "A.X K2 EAGLE3", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-08-06", "context_length": 262144, "max_output_tokens": null, "parameters": "3015625728", "architecture": "EAGLE3 speculative-decoding draft for A.X K2. One-layer MLA decoder, hidden 7168, vocab 163,840, fp16. Reads A.X K2 layers 2, 30, and 58. YaRN factor 2.0 matched to 256K. Safetensors 3,015,625,728.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X K2 EAGLE3 is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Speculative-decoding draft that speeds up A.X K2 without changing the tokens A.X K2 accepts. It is not a chatbot by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-k2-light": { "id": "sk-telecom/a-x/a-x-k2-light", "name": "A.X K2 Light", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "In-house MoE preview. A.X K2 ALM card and report: 20B total, 2.7B active, MLA, down-scaled and partly modified from A.X K2. Not the public 688B checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X K2 Light is a a-x model from SK Telecom. Status on 2026-10-15: preview. Confidence: high.", "what_it_is_for": "Compact A.X K2 variant used inside SKT speech models. SKT describes it as a preview, not as the released 688B model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-k2-raon-speech": { "id": "sk-telecom/a-x/a-x-k2-raon-speech", "name": "A.X K2 Raon-Speech", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-07-27", "context_length": null, "max_output_tokens": null, "parameters": "21197920833", "architecture": "RaonModel on Hugging Face under KRAFTON/A.X-K2-Raon-Speech-21B-A3B. Safetensors 21,197,920,833. SKT says Krafton built it with SKT on the A.X K2 Light model for spoken game characters. Pipeline tag any-to-any. Tags include ASR and TTS.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X K2 Raon-Speech is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Speech model for voice interaction, announced by SK Telecom and released by Krafton. SKT says it builds on A.X K2 Light for Krafton game characters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-k2-vl-light-preview": { "id": "sk-telecom/a-x/a-x-k2-vl-light-preview", "name": "A.X K2 VL Light-Preview", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "preview", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-07-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language derivative announced with A.X K2. Press: 21B parameters, for reports, manuals, and field images. No skt Hub repo was on the 18-model org list.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X K2 VL Light-Preview is a a-x model from SK Telecom. Status on 2026-10-15: preview. Confidence: medium.", "what_it_is_for": "Announced vision-language preview for enterprise documents and field images. SKT named it with the A.X K2 launch. A public checkpoint was not found on the skt Hub org.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/a-x/a-x-ve": { "id": "sk-telecom/a-x/a-x-ve", "name": "A.X VE", "maker": "sk-telecom", "family": "sk-telecom/a-x", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-07-28", "context_length": null, "max_output_tokens": null, "parameters": "413018352", "architecture": "Vision Transformer trained from scratch. Hidden 1152, intermediate 4304, 27 layers, 16 heads, patch 16, spatial merge 2, residual indexes 6, 12, and 18. Safetensors 413,018,352. The card says the full VLM that uses this encoder will be released later.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A.X VE is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Image encoder for a future A.X vision-language model. It outputs patch features, not captions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/ko-gpt-trinity/ko-gpt-trinity-1-2b-v0-5": { "id": "sk-telecom/ko-gpt-trinity/ko-gpt-trinity-1-2b-v0-5", "name": "Ko-GPT-Trinity 1.2B v0.5", "maker": "sk-telecom", "family": "sk-telecom/ko-gpt-trinity", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1187721984", "architecture": "GPT-2 code path replicating a GPT-3-style model. Card: 1.2B class, trained on Ko-DAT for 35 billion tokens and 72,000 steps as a masked autoregressive LM. Safetensors total 1,187,721,984 including a U8 tensor of 25,165,824.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ko-GPT-Trinity 1.2B v0.5 is a ko-gpt-trinity model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "1.2B Korean generative model from SK Telecom's GPT-3-style line. The public checkpoint is v0.5, trained on the in-house Ko-DAT corpus.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/kobart/kobart-base-v1": { "id": "sk-telecom/kobart/kobart-base-v1", "name": "KoBART", "maker": "sk-telecom", "family": "sk-telecom/kobart", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "123859968", "architecture": "Korean BART encoder-decoder. GitHub table: 124M class, 6 encoder layers and 6 decoder layers, 16 heads, FFN 3072, hidden 768. Trained with text infilling on 40GB or more of Korean text. Safetensors 123,859,968. Hub license MIT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KoBART is a kobart model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Korean sequence-to-sequence model for summarization and other generation tasks. SK Telecom trained it as a BART text-infilling model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/kobert/kobert-base-v1": { "id": "sk-telecom/kobert/kobert-base-v1", "name": "KoBERT", "maker": "sk-telecom", "family": "sk-telecom/kobert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "92186880", "architecture": "BERT-base encoder. SKT open-source page: vocab 8,002, hidden 768, 12 layers, 12 heads, intermediate 3,072, max length 512. Safetensors 92,186,880. Developed by SK Telecom T-Brain.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KoBERT is a kobert model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Korean BERT encoder from SK Telecom T-Brain, used for classification, NER, and embeddings. It is still the public base checkpoint. It is not a generative assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sk-telecom/kogpt2/kogpt2-base-v2": { "id": "sk-telecom/kogpt2/kogpt2-base-v2", "name": "KoGPT2", "maker": "sk-telecom", "family": "sk-telecom/kogpt2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 decoder. README table: kogpt2-base-v2, 125M, 12 layers, 12 heads, FFN 3072, hidden 768. Character BPE tokenizer. Trained on more than 40GB of Korean text. Exact safetensors count was not in the API payload, so parameters is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KoGPT2 is a kogpt2 model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Korean GPT-2 for next-token generation. SK Telecom released it so Korean decoding quality would not depend only on multilingual GPT-2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skku/contra-il6/contra-il6": { "id": "skku/contra-il6/contra-il6", "name": "CONTRA-IL6", "maker": "skku", "family": "skku/contra-il6", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid convolutional localization plus Transformer fusion over stacked protein language model embeddings, used to classify interleukin-6-inducing peptides. The install notes name ESM-2 3B, ESM-1 670M, ProtTrans T5-XL, and SeqVec as the embedding models loaded at inference. Those are not SKKU models. The CONTRA head size is not printed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CONTRA-IL6 is an interleukin-6-inducing peptide classifier from CBBL at Sungkyunkwan University. It fuses a Transformer path and a convolutional path on top of stacked protein language model embeddings. A checkpoint archive has been on the lab Hugging Face org since 2025-06-19. The Briefings in Bioinformatics article is dated 2026-05-04. A pip package is the supported way to run it. Collected 2026-10-15.", "what_it_is_for": "The paper says it predicts which peptides induce interleukin-6, for immunoinformatics and peptide screening. The command line reads a FASTA file and writes a CSV. It is not a generative model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skku/mulaqua/mulaqua": { "id": "skku/mulaqua/mulaqua", "name": "Mulaqua", "maker": "skku", "family": "skku/mulaqua", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-19", "context_length": null, "max_output_tokens": null, "parameters": "225522002", "architecture": "T5ForSequenceClassification from QizhiPei/biot5-plus-base, plus a molecular-image branch. Config lists 12 encoder and 12 decoder layers, d_model 768, 12 heads, d_ff 2048, vocab 35076, float32, and trainable_layers [\"11\"]. Labels are non-PMT/vPvM and PMT/vPvM. swin_used is molnextr. The GitHub README says SwinOCSR and MolNexTR weights are downloaded separately into checkpoints/. The Hugging Face safetensors count is 225,522,002 and may not include those image checkpoints.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mulaqua is a drinking-water chemical classifier from CBBL at Sungkyunkwan University. It labels a molecule as PMT/vPvM or not, using a molecular string and a molecular image. Weights on Hugging Face date from 2025-06-19. The Journal of Hazardous Materials paper is dated 2025-12-05. Collected 2026-10-15.", "what_it_is_for": "The abstract says it is a screen for persistent, mobile, and toxic substances, and for very persistent and very mobile substances, so regulators can prioritize chemicals in drinking water. It is not a chatbot. The GitHub path is local training and inference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skku/rosella/rosella": { "id": "skku/rosella/rosella", "name": "RoSeLLa", "maker": "skku", "family": "skku/rosella", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-24", "context_length": 4096, "max_output_tokens": null, "parameters": "1439270912", "architecture": "LlamaForCausalLM instruction tune of 42dot/42dot_LLM-PLM-1.3B. The v1 config matches that base: 24 layers, 32 heads, hidden size 2048, intermediate size 5632, vocab 50304, max_position_embeddings 4096, RoPE theta 10000, float32, GPT-2 tokenizer. Hugging Face reports 1,439,270,912 parameters for v1, v2, and the GPQA upload. 42dot calls the base a 1.3B model. The lab README says v0 and v1 are instruction tunes on the NLPLAB sLM set.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoSeLLa is a Korean small language model from the NLP Lab at Sungkyunkwan University. The lab README says v0 and v1 are instruction tunes of 42dot's 42dot_LLM-PLM-1.3B on the lab's sLM dataset. v0 weights date from 2024-05-24 and v1 from 2024-06-10. Two later uploads, v2 and a GPQA-named repo, are the same size and are not described the same way. Collected 2026-10-15.", "what_it_is_for": "The lab calls it a Korean small language model for language understanding and generation after instruction tuning. v0 uses their sLM mix: CoT 77,200, Dolly 6,642, EverythingLM 991, Law 13,702, and Number 9,580. v1 says part of that mix was filtered with GPT-4o. There is no score table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skku/sorbformer/sorbformer": { "id": "skku/sorbformer/sorbformer", "name": "SorbFormer", "maker": "skku", "family": "skku/sorbformer", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-09-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two PyTorch checkpoints on the CBBL Hugging Face repo. Both hparams files set mol_extractor_type to biot5-plus-base, finetune_llm true, and num_layers_to_unfreeze 3. The rcf checkpoint uses cross-attention fusion, hidden size 256, 1 layer, 4 heads, Huber loss. The tscf checkpoint uses concat fusion, hidden size 64, 2 layers, 4 heads, Huber loss. Inputs in the metadata files are a SMILES column plus categorical plant and exposure fields and numerical molecular and plant fields. Targets are columns named RCF and TSCF. No README defines those names.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SorbFormer is a pair of regression checkpoints uploaded by CBBL at Sungkyunkwan University on 2026-09-03. There is no model card and no paper was found. The files themselves say each checkpoint fine-tunes BioT5+ and predicts a numeric column, RCF or TSCF, from a SMILES string plus plant and exposure fields. Collected 2026-10-15.", "what_it_is_for": "The repo does not say. The metadata columns are plant taxonomy, plant traits, exposure medium, molecular descriptors, and a SMILES string. The hparams files name the study finetune_SorbFormer and set the targets to rcf and tscf. Treat the task as unpublished beyond those column names.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skku/xbittert5/xbittert5-640": { "id": "skku/xbittert5/xbittert5-640", "name": "xBitterT5-640", "maker": "skku", "family": "skku/xbittert5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-21", "context_length": null, "max_output_tokens": null, "parameters": "225325394", "architecture": "T5ForSequenceClassification fine-tuned from QizhiPei/biot5-plus-base. Config: 12 encoder layers, 12 decoder layers, d_model 768, 12 heads, d_ff 2048, vocab 35076, float32. Labels are non-bitter and bitter. The paper says the BioT5+ encoder stays frozen and some final decoder blocks are trained. Input is a peptide sequence plus its SELFIES string. Trained on BTP640.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "xBitterT5-640 is a bitter-peptide classifier from the Computational Biology and Bioinformatics Laboratory at Sungkyunkwan University. It is the BTP640 checkpoint of the xBitterT5 framework: a BioT5+ encoder-decoder with a classification head, trained on peptide sequences plus SELFIES strings. Weights have been public since 2025-01-21. The journal article is dated 2025-08-20. The lab still hosts it, and points most users to xBitterT5-720. Collected 2026-10-15.", "what_it_is_for": "The paper says it identifies bitter peptides for food science and peptide research, and it adds residue-level attribution on the molecular string. It is not a general language model. The web server accepts FASTA. The authors say this checkpoint does not transfer well to the later BTP720 set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skku/xbittert5/xbittert5-720": { "id": "skku/xbittert5/xbittert5-720", "name": "xBitterT5-720", "maker": "skku", "family": "skku/xbittert5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-21", "context_length": null, "max_output_tokens": null, "parameters": "225325394", "architecture": "Same T5ForSequenceClassification setup as xBitterT5-640: fine-tune of QizhiPei/biot5-plus-base, 12/12 layers, d_model 768, 12 heads, d_ff 2048, vocab 35076, float32, labels non-bitter and bitter. Trained on BTP720, which keeps peptides with a unique bitter taste and drops multi-taste sequences. The paper says the encoder is frozen and some final decoder blocks are trained. Input is peptide sequence plus SELFIES.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "xBitterT5-720 is the BTP720 bitter-peptide classifier from CBBL at Sungkyunkwan University. It uses the same BioT5+ classification setup as xBitterT5-640, retrained on the stricter BTP720 set. Weights went up on 2025-01-21. The paper, dated 2025-08-20, says this is the default on the lab web server and the checkpoint to use for most calls. Collected 2026-10-15.", "what_it_is_for": "Same job as xBitterT5-640: decide whether a short peptide is bitter, and attribute the decision to parts of the molecular string. The paper says BTP720 keeps only peptides with one bitter-taste label, so the decision boundary is cleaner. The web form defaults to this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skydio/autonomy/airspace-assurance": { "id": "skydio/autonomy/airspace-assurance", "name": "Skydio Airspace Assurance System", "maker": "skydio", "family": "skydio/autonomy", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Policy and detect-and-avoid layer. Skydio says it combines airspace data, organization policy, aircraft limits, and onboard perception. Not described as a neural network.", "modalities": { "input": [ "ads-b", "policy" ], "output": [ "alert", "control" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Skydio Airspace Assurance System (SAAS) is a closed detect-and-avoid and policy layer inside Skydio Autonomy. Skydio describes it as a current product. The first dated page found is 2026-09-24. Collected 2026-10-15.", "what_it_is_for": "Skydio says SAAS keeps each flight inside airspace rules and the organization's own limits, from preflight through landing. It ingests ADS-B and other traffic inputs, alerts the operator, and moves the aircraft if a collision becomes imminent. The 2026-09-24 post says it also uses onboard perception and will maneuver away from a crewed aircraft if the pilot does not respond in time. Marketing also says a waiver ceiling, such as 200 feet above ground, is enforced on autonomous flights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skydio/autonomy/foresight": { "id": "skydio/autonomy/foresight", "name": "Skydio Foresight", "maker": "skydio", "family": "skydio/autonomy", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed in-flight replanner for fixed-wing flight. Skydio says it fuses aircraft state, environment, and obstacle data and looks ahead because a fixed-wing aircraft cannot stop.", "modalities": { "input": [ "image", "state" ], "output": [ "trajectory" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Skydio Foresight is the closed in-flight autonomy layer Skydio announced for the F10 Lightrunner fixed-wing aircraft on 2026-09-24. It is part of Skydio Autonomy and was not generally available on the collection date. Collected 2026-10-15.", "what_it_is_for": "Skydio says a fixed-wing aircraft cannot stop to avoid an obstacle, so Foresight looks ahead along the route and re-plans while there is still distance to maneuver. Pathfinder charts the route. Foresight flies it. The F10 page says it fuses aircraft state, environmental data, and obstacle information and keeps the mission on track at the speeds F10 is built for. F10's stated maximum speed is 100 mph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skydio/autonomy/pathfinder": { "id": "skydio/autonomy/pathfinder", "name": "Skydio Pathfinder", "maker": "skydio", "family": "skydio/autonomy", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "On-vehicle route planner. Skydio says it uses terrain, 3D building data, organization geofences, flight policy, and airspace grids. Not described as a neural network.", "modalities": { "input": [ "map", "geofence" ], "output": [ "trajectory" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Skydio Pathfinder is the named point-to-point route planner inside Skydio Autonomy. It is closed, runs on the aircraft, and was generally available for Dock for X10 on 2026-07-16. Collected 2026-10-15.", "what_it_is_for": "Skydio says the operator picks a destination and Pathfinder charts a route around terrain, buildings, geofences, flight policy, and airspace rules, holding a constant height above ground. On F10 it plans before launch. Foresight, not Pathfinder, is the in-flight replanner Skydio describes for that aircraft. The beta guide says routes are computed on the vehicle.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skydio/autonomy/skydio-autonomy": { "id": "skydio/autonomy/skydio-autonomy", "name": "Skydio Autonomy", "maker": "skydio", "family": "skydio/autonomy", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2018-02-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed onboard stack. Skydio describes real-time 3D mapping, deep networks for recognition and depth, motion planning, and 360-degree obstacle avoidance. The 2017 GC-Net stereo paper is a research ancestor, not this product.", "modalities": { "input": [ "image" ], "output": [ "3d-map", "control" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Skydio Autonomy, also called the Skydio Autonomy Engine, is Skydio's closed onboard flight-intelligence stack. It shipped with the R1 on 2018-02-13 and is still the engine Skydio cites for X10, R10, and F10. Collected 2026-10-15. Weights are not public.", "what_it_is_for": "Skydio says the stack sees, maps, and flies so an operator can avoid obstacles, track a subject, and run a mission without expert piloting. The X2-era datasheet splits it into Autonomy Core, included on every drone, and Autonomy Enterprise Foundation, a paid feature tier. A 2026 product post says X10, R10, and F10 share one engine.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skydio/pilot/deep-neural-pilot": { "id": "skydio/pilot/deep-neural-pilot", "name": "Deep Neural Pilot", "maker": "skydio", "family": "skydio/pilot", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2020-01-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed policy network trained with Computational Expert Imitation Learning (CEILing) to imitate the Skydio Autonomy motion planner. Not a shipped model card.", "modalities": { "input": [ "image", "state" ], "output": [ "control" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deep Neural Pilot is a closed control network Skydio showed on Skydio 2 in January 2020. It films a subject and avoids obstacles. Skydio described it as a test, not as a product release. No later shipping notice was found. Collected 2026-10-15.", "what_it_is_for": "Until this demo, Skydio said its learned systems only interpreted the world. This one acts. It was trained to fly a filming task while avoiding obstacles, using the motion planner already inside the Autonomy Engine as the expert. The training method is Computational Expert Imitation Learning (CEILing). CEILing is not a separate model. Authors on the post are Gregory Kahn, Abraham Bachrach, and Hayk Martiros. IEEE Spectrum's 2020-02-13 reprint says Skydio was testing it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skydio/stereo/gc-net": { "id": "skydio/stereo/gc-net", "name": "GC-Net", "maker": "skydio", "family": "skydio/stereo", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2017-03-13", "context_length": null, "max_output_tokens": null, "parameters": "3500000", "architecture": "Geometry and Context Network. Shared-weight 2D residual towers, a 4D cost volume, 3D convolutional regularization, and a differentiable soft argmin disparity regression. Supervised L1 loss. Implemented in TensorFlow for the paper.", "modalities": { "input": [ "image" ], "output": [ "depth" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GC-Net (Geometry and Context Network) is a stereo depth model published by Skydio researchers on 2017-03-13. It was an ICCV 2017 paper, not a product customers could download. Skydio later shipped a much faster, unpublished depth network on Skydio 2. Collected 2026-10-15. Status here is retired because the named research model is not the current product.", "what_it_is_for": "The paper estimates per-pixel disparity from a rectified stereo pair. Skydio's interest, stated in the introduction, includes autonomous vehicles and UAVs. Authors include Hayk Martirosyan, Abraham Bachrach, and Adam Bry, with skydio.com addresses on the HTML byline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skydio/vision/nightsense": { "id": "skydio/vision/nightsense", "name": "Skydio NightSense", "maker": "skydio", "family": "skydio/vision", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-09-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Night navigation mode. Skydio says it pairs illumination hardware, navigation-camera tuning, and the existing visual autonomy stack. Visible-light and infrared variants. Not a separately published network.", "modalities": { "input": [ "image" ], "output": [ "3d-map", "control" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Skydio NightSense is the closed night-navigation mode that lets Skydio's visual autonomy stack fly with obstacle avoidance in the dark. Skydio announced it with X10 on 2023-09-20. It is a shipping attachment-and-software product, including on X10D after an August 2024 Blue UAS approval. Collected 2026-10-15.", "what_it_is_for": "Skydio says NightSense is how X10 and R10 fly fully autonomously at night. Two attachments light the volume above and below the aircraft. Software and camera tuning let the existing vision navigator and obstacle avoidance run when ambient light is not enough. There is a visible-light kit and an infrared kit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skydio/vision/shadow": { "id": "skydio/vision/shadow", "name": "Skydio Shadow", "maker": "skydio", "family": "skydio/vision", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed subject tracker. Skydio says it predicts motion from speed, direction, and appearance, and can hand off between color and thermal. Track holds the camera. Follow moves the aircraft.", "modalities": { "input": [ "image", "thermal" ], "output": [ "track", "control" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Skydio Shadow is Skydio's closed person-and-vehicle tracker. It was shown at Ascend 2025 and is documented as an X10 and F10 autonomy skill. A 2026-03-24 firmware note still called it Beta. Later product pages do not. Collected 2026-10-15.", "what_it_is_for": "Shadow Track keeps a selected person or vehicle in frame. Shadow Follow, when enabled, moves the aircraft to hold distance and heading. Skydio says it predicts motion from speed, direction, and appearance, reacquires a subject after a short occlusion, and can switch between color and thermal. The operator does not get continuous stick control of that chase. The operator remains responsible for the airspace.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skydio/vision/spatial-ai-engine": { "id": "skydio/vision/spatial-ai-engine", "name": "Skydio Spatial AI Engine", "maker": "skydio", "family": "skydio/vision", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed spatial-understanding layer used for scan planning and onboard maps. Skydio does not publish a network name or architecture.", "modalities": { "input": [ "image" ], "output": [ "3d-map", "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Spatial AI Engine is the closed scene-understanding layer Skydio names for automated inspection and onboard maps. It is part of Skydio Autonomy and is current on X10. The first use of this exact name was not dated. Collected 2026-10-15.", "what_it_is_for": "Skydio says the engine gives the aircraft a working sense of the scene so it can repeat a flight, run a targeted inspection, and build a 2D or 3D model on the vehicle. 3D Scan, generally available 2021-06-02, is the capture application on top of the autonomy engine. It is not a separate model. Onboard Modeling, described in a 2025-12-16 post, turns scan thumbnails into a textured mesh or 2D preview on X10.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "skydio/vision/visual-navigator": { "id": "skydio/vision/visual-navigator", "name": "Skydio Visual Navigator", "maker": "skydio", "family": "skydio/vision", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Visual-inertial navigation. Skydio trademarks Visual Navigator and, on later X10D builds, labels the same job Visual Navigation (VIO). Network internals are unpublished.", "modalities": { "input": [ "image", "imu" ], "output": [ "pose" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Skydio Visual Navigator is Skydio's closed vision-based navigation system. The name is trademarked on the X2-era Autonomy datasheet and is still listed for X10. Later X10D release notes call the same function Visual Navigation, or VIO. Collected 2026-10-15.", "what_it_is_for": "Skydio says the system flies without depending on GPS or a magnetometer calibration, including indoors and around metal. On X10D it is the mode that keeps navigating when GNSS is lost. An Anchor Point lets the operator pin that visual frame to a map so waypoints and return-home still have a position.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "smu/2mamba/2mamba": { "id": "smu/2mamba/2mamba", "name": "2Mamba", "maker": "smu", "family": "smu/2mamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-02-07", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 decoder with attention replaced by 2Mamba: squared query-key scores, a negative-softplus A-mask, softmax-style normalization, and an input convolution of width 2. Time discretization is removed on the medium model. arXiv v2 medium setup, which matches the GitHub README: hidden size 1536, MLP intermediate 3072, SiLU, 24 heads, head dim 64, 27 layers, vocab 32000, Llama 2 tokenizer (meta-llama/Llama-2-7b-hf). RoPE and SwiGLU are the Llama 2 pieces the README says were kept. The released file is the medium 8192-token NIAH run, not the 300M ablation model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "2Mamba is a causal language-model block from Gabriel Mongaras and Eric C. Larson at the Lyle School of Engineering, Southern Methodist University. The paper starts from Mamba-2, strips it to a smaller block they call Mamba-2S, then squares the query-key product. The only public 2Mamba weights are the medium NIAH checkpoint on Hugging Face, created 2026-02-07. The card says that file is the proposed algorithm. Collected 2026-10-15. There is no API and no price.", "what_it_is_for": "Next-token training on FineWeb, then a needle-in-a-haystack check that the linear block still uses a long context. The authors' claim is softmax-level loss with less memory than softmax once the sequence is longer than about 1058 tokens at head dimension 64.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "smu/cottention/cottention-bert": { "id": "smu/cottention/cottention-bert", "name": "Cottention BERT", "maker": "smu", "family": "smu/cottention", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-10-07", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT-shaped bidirectional encoder with cosine attention (Cottention) in place of softmax. Checkpoint config: BertForPreTraining, hidden size 768, 12 layers, 12 heads, intermediate size 3072, GELU, absolute positions, max position embeddings 512, type vocab 2, vocab size 28996. The paper says the non-attention architecture matches their softmax BERT run. A learned per-head stabilization scalar is extra.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cottention BERT is a bidirectional encoder released by Gabriel Mongaras, Trevor Dohm, and Eric C. Larson at the Lyle School of Engineering, Southern Methodist University. It is the BERT-Cos checkpoint from the Cottention paper: softmax attention is replaced by cosine attention, and the other BERT pieces stay the same. Weights have been public on Hugging Face since 2024-10-07. This file was collected on 2026-10-15. There is no university API.", "what_it_is_for": "The paper uses this model for masked language modeling and next-sentence prediction on Wikipedia and BookCorpus, then fine-tunes it for GLUE. The authors present Cottention as a drop-in attention replacement with linear memory in sequence length for the bidirectional case. The Hub file is the pretraining checkpoint (`BertForPreTraining`), not a GLUE head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "smu/cottention/cottention-gpt-1-2b": { "id": "smu/cottention/cottention-gpt-1-2b", "name": "Cottention GPT 1.2B", "maker": "smu", "family": "smu/cottention", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-10-07", "context_length": 512, "max_output_tokens": null, "parameters": "1200000000", "architecture": "Causal GPT-J-shaped transformer with cosine attention. Checkpoint config: GPTJForCausalLM, n_embd 2048, n_layer 20, n_head 16, n_inner null, n_positions 512, rotary true, rotary_dim 64, gelu_new, vocab 50400, GPT-2 tokenizer class, embeddings not tied. The paper states a 1.2 billion parameter GPT-J variant. The 20-layer 16-head sentence in the paper matches this config and the 300M config. Not a fine-tune of EleutherAI GPT-J-6B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cottention GPT 1.2B is the larger causal model in the Cottention paper by Gabriel Mongaras, Trevor Dohm, and Eric C. Larson at Southern Methodist University. They train a 1.2 billion parameter GPT-J-shaped network on The Pile with cosine attention instead of softmax. The weights are the public repo `gmongaras/Cosine_Attention_GPT_1.2B`, uploaded 2024-10-07. Collected 2026-10-15. There is no priced API.", "what_it_is_for": "Next-token prediction on The Pile, used to show that cosine attention stays close to the authors' softmax run at a larger size. The card name is GPT-Cos-1.2b.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "smu/cottention/cottention-gpt-300m": { "id": "smu/cottention/cottention-gpt-300m", "name": "Cottention GPT 300M", "maker": "smu", "family": "smu/cottention", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-10-07", "context_length": 1024, "max_output_tokens": null, "parameters": "300000000", "architecture": "Causal GPT-J-shaped transformer with cosine attention. Checkpoint config: GPTJForCausalLM, n_embd 1024, n_layer 20, n_head 16, n_inner 3072, n_positions 1024, rotary true, rotary_dim 64, gelu_new, vocab 50400, GPT-2 tokenizer class, embeddings not tied. The paper states a 300 million parameter GPT-J variant and, for their GPT model, 20 layers and 16 heads. Not a fine-tune of EleutherAI GPT-J-6B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cottention GPT 300M is a causal language model from Gabriel Mongaras, Trevor Dohm, and Eric C. Larson at Southern Methodist University's Lyle School of Engineering. The paper trains a 300 million parameter GPT-J-shaped model on The Pile and replaces softmax with cosine attention. The public checkpoint `gmongaras/Cosine_Attention_GPT_300M` has been on Hugging Face since 2024-10-07. Collected 2026-10-15. It is a research weight release, not a hosted product.", "what_it_is_for": "Next-token prediction. The authors use perplexity on The Pile to compare cosine attention with their own softmax GPT-J run at the same size. The card calls the repo the GPT-Cos-300m model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic/arctic-base": { "id": "snowflake/arctic/arctic-base", "name": "Arctic Base", "maker": "snowflake", "family": "snowflake/arctic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-24", "context_length": 4096, "max_output_tokens": null, "parameters": "480B total, 17B active", "architecture": "Dense-MoE hybrid transformer pretrained from scratch. A 10B dense transformer plus a residual 128 x 3.66B MoE MLP, top-2 gating, 480B total parameters and 17B active. Hugging Face labels the base checkpoint 482B and the instruct checkpoint 479B. The model card says text in and text or code out. trust_remote_code and DeepSpeed are required for the released loaders. Azure's copy of the card says pretraining used 3.5 trillion tokens from public sources. This folder is the pretrained base checkpoint, not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake AI Research pretrained Arctic Base and released the weights on 2024-04-24 under Apache-2.0. It is the foundation checkpoint of the Arctic dense-MoE LLM, not the instruction-tuned model and not a current Cortex endpoint. Collected 2026-10-15.", "what_it_is_for": "The model card says the base checkpoint is the pretrained model. Arctic Instruct is the checkpoint Snowflake tuned for enterprise instruction following. The base card does not assign a separate product job beyond continued tuning and research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic/arctic-instruct": { "id": "snowflake/arctic/arctic-instruct", "name": "Arctic Instruct", "maker": "snowflake", "family": "snowflake/arctic", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-24", "context_length": 4096, "max_output_tokens": 8192, "parameters": "480B total, 17B active", "architecture": "Dense-MoE hybrid transformer pretrained from scratch. A 10B dense transformer plus a residual 128 x 3.66B MoE MLP, top-2 gating, 480B total parameters and 17B active. Hugging Face labels the base checkpoint 482B and the instruct checkpoint 479B. The model card says text in and text or code out. trust_remote_code and DeepSpeed are required for the released loaders. Azure's copy of the card says pretraining used 3.5 trillion tokens from public sources. Instruct is the instruction-tuned checkpoint. The card says fine-tuning data includes publicly available instruction datasets.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake released Arctic Instruct on 2024-04-24 as the instruction-tuned Arctic LLM, 480B total parameters and 17B active, Apache-2.0. Cortex served it as snowflake-arctic. Snowflake set a deprecation date of 2026-04-28 and says calls fail after that date. The weights are still public. Collected 2026-10-15.", "what_it_is_for": "Snowflake describes Arctic as an enterprise model aimed at SQL generation, coding, and instruction following, trained to be strong on those tasks relative to its compute budget. The card limits outputs to text and code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-awm/arctic-awm-14b": { "id": "snowflake/arctic-awm/arctic-awm-14b", "name": "Arctic-AWM-14B", "maker": "snowflake", "family": "snowflake/arctic-awm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-02-11", "context_length": null, "max_output_tokens": null, "parameters": "14B name; Hugging Face org labels the repo 15B", "architecture": "Multi-turn tool-use agent trained with agentic reinforcement learning on synthetic MCP environments from AgentWorldModel-1K. The 14B card was not opened. The org index labels the repo 15B parameters and the name is 14B. The 4B and 8B cards list this repo as the third sibling.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake AI Research and UNC released Arctic-AWM-14B on 2026-02-11 as an open-weight tool-use agent trained on synthetic environments. It is a research model, not a Cortex chat model. Collected 2026-10-15.", "what_it_is_for": "The card says the model calls tools over a unified MCP interface across multi-turn tasks. The environments come from the AgentWorldModel-1K dataset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-awm/arctic-awm-4b": { "id": "snowflake/arctic-awm/arctic-awm-4b", "name": "Arctic-AWM-4B", "maker": "snowflake", "family": "snowflake/arctic-awm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-11", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Multi-turn tool-use agent trained with agentic reinforcement learning on synthetic MCP environments from AgentWorldModel-1K. Card text and model tree both say Qwen3-4B. Hugging Face size label 4B. Hub license field in search results is Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake AI Research and UNC released Arctic-AWM-4B on 2026-02-11 as an open-weight tool-use agent trained on synthetic environments. It is a research model, not a Cortex chat model. Collected 2026-10-15.", "what_it_is_for": "The card says the model calls tools over a unified MCP interface across multi-turn tasks. The environments come from the AgentWorldModel-1K dataset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-awm/arctic-awm-8b": { "id": "snowflake/arctic-awm/arctic-awm-8b", "name": "Arctic-AWM-8B", "maker": "snowflake", "family": "snowflake/arctic-awm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-02-11", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Multi-turn tool-use agent trained with agentic reinforcement learning on synthetic MCP environments from AgentWorldModel-1K. Card text says trained on Qwen3-8B. The Hub model tree on the retrieved page points at Qwen3-4B. Hugging Face size label is 8B. Those two statements disagree.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake AI Research and UNC released Arctic-AWM-8B on 2026-02-11 as an open-weight tool-use agent trained on synthetic environments. It is a research model, not a Cortex chat model. Collected 2026-10-15.", "what_it_is_for": "The card says the model calls tools over a unified MCP interface across multi-turn tasks. The environments come from the AgentWorldModel-1K dataset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-embed/arctic-embed-l": { "id": "snowflake/arctic-embed/arctic-embed-l", "name": "Arctic Embed L", "maker": "snowflake", "family": "snowflake/arctic-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-16", "context_length": 512, "max_output_tokens": null, "parameters": "335M", "architecture": "English encoder initialized from intfloat/e5-large-unsupervised. Model-card table says 335 million parameters and dimension 1024. The technical report says 334 million. Non-embedding parameter count cited later is 303 million. CLS pooling.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake released Arctic Embed L on 2024-04-16 as part of the first Arctic Embed family. It is an open-weight English text embedding model under Apache-2.0. Snowflake later recommended the multilingual v2 models as replacements. Collected 2026-10-15.", "what_it_is_for": "Snowflake built the family for retrieval quality at a given size. The launch post says the large model was meant as an open substitute for closed embedding APIs, and the smaller sizes trade accuracy for speed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-embed/arctic-embed-l-v2-0": { "id": "snowflake/arctic-embed/arctic-embed-l-v2-0", "name": "Arctic Embed L v2.0", "maker": "snowflake", "family": "snowflake/arctic-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-04", "context_length": 8192, "max_output_tokens": null, "parameters": "568M total, 303M non-embedding", "architecture": "Multilingual embedding model, dimension 1024. The model card says it builds on BAAI/bge-m3-retromae and can take 8192 tokens via RoPE. The engineering blog says the large variant is a long-context variation of Facebook XLM-R Large. 568 million parameters, 303 million non-embedding. Matryoshka truncation is supported.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "Snowflake released Arctic Embed L v2.0 on 2024-12-04 as the quality-focused multilingual embedding model, 568M parameters, Apache-2.0. Cortex Search serves it at 512 tokens. The open checkpoint claims 8192. Collected 2026-10-15.", "what_it_is_for": "Snowflake presents it as one model for English and non-English retrieval, a drop-in for closed embedding APIs, with a long-context architecture even when Cortex serves a shorter window.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-embed/arctic-embed-l-v2-0-8k": { "id": "snowflake/arctic-embed/arctic-embed-l-v2-0-8k", "name": "Arctic Embed L v2.0 8k", "maker": "snowflake", "family": "snowflake/arctic-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "568M", "architecture": "Cortex Search describes this as the open-source 568M Arctic Embed L v2.0 model served with an 8000-token window, 1024 dimensions, multilingual. No separate Hugging Face repo was found. The L v2.0 card already says the architecture supports 8192 via RoPE.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "Snowflake lists snowflake-arctic-embed-l-v2.0-8k as its own Cortex Search embedding model: the 568M multilingual L v2.0 checkpoint with an 8192-token window. No separate weight repo was found. Collected 2026-10-15.", "what_it_is_for": "Cortex Search uses this id when a passage needs a longer context than the 512-token L v2.0 serving row. The description matches the open L v2.0 model rather than a new architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-embed/arctic-embed-m": { "id": "snowflake/arctic-embed/arctic-embed-m", "name": "Arctic Embed M", "maker": "snowflake", "family": "snowflake/arctic-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-16", "context_length": 512, "max_output_tokens": null, "parameters": "110M", "architecture": "English encoder initialized from intfloat/e5-base-unsupervised. Model-card table says 110 million parameters and MTEB retrieval 54.90. The v1.5 card compares against 54.91. The v2 card's comparison row says 109M total and 86M non-embedding parameters. CLS pooling. Dimension 768.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake released Arctic Embed M on 2024-04-16 as part of the first Arctic Embed family. It is an open-weight English text embedding model under Apache-2.0. Snowflake later recommended the multilingual v2 models as replacements. Collected 2026-10-15.", "what_it_is_for": "Snowflake built the family for retrieval quality at a given size. The launch post says the large model was meant as an open substitute for closed embedding APIs, and the smaller sizes trade accuracy for speed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-embed/arctic-embed-m-long": { "id": "snowflake/arctic-embed/arctic-embed-m-long", "name": "Arctic Embed M Long", "maker": "snowflake", "family": "snowflake/arctic-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-16", "context_length": 8192, "max_output_tokens": null, "parameters": "137M", "architecture": "Long-context English encoder initialized from nomic-ai/nomic-embed-text-v1-unsupervised, Nomic BERT. 137 million parameters. Dimension 768. The report says the architecture can take 8192 tokens, but this checkpoint was pretrained at 256 tokens and fine-tuned at 512.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake released Arctic Embed M Long on 2024-04-16 as part of the first Arctic Embed family. It is an open-weight English text embedding model under Apache-2.0. Snowflake later recommended the multilingual v2 models as replacements. Collected 2026-10-15.", "what_it_is_for": "Snowflake built the family for retrieval quality at a given size. The launch post says the large model was meant as an open substitute for closed embedding APIs, and the smaller sizes trade accuracy for speed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-embed/arctic-embed-m-v1-5": { "id": "snowflake/arctic-embed/arctic-embed-m-v1-5", "name": "Arctic Embed M v1.5", "maker": "snowflake", "family": "snowflake/arctic-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-18", "context_length": 512, "max_output_tokens": null, "parameters": "109M", "architecture": "Updated Arctic Embed M for Matryoshka representation learning and scalar quantization. 109 million parameters on the card's compression comparison. Output dimension 768, truncatable. English BERT-style encoder.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.03, "completion_per_m_usd": null }, "summary_intro": "Snowflake released Arctic Embed M v1.5 on 2024-07-18 as a more compressible English embedding model. It is still the default Cortex Search embedding model in the docs retrieved on 2026-10-15. Weights are Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says this checkpoint keeps Arctic Embed M's retrieval quality while letting vectors shrink, down to 128 bytes, with Matryoshka truncation and scalar quantization. Cortex Search positions it as the practical English default.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-embed/arctic-embed-m-v2-0": { "id": "snowflake/arctic-embed/arctic-embed-m-v2-0", "name": "Arctic Embed M v2.0", "maker": "snowflake", "family": "snowflake/arctic-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-04", "context_length": 8192, "max_output_tokens": null, "parameters": "305M total, 113M non-embedding", "architecture": "Multilingual embedding model. Snowflake's blog says it is built on Alibaba's GTE-multilingual. 305 million parameters, 113 million non-embedding, dimension 768. Matryoshka truncation is supported. Card tag line says 74 languages.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake released Arctic Embed M v2.0 on 2024-12-04 as the smaller multilingual Arctic Embed 2.0 model. Weights are Apache-2.0. It was not a named Cortex Search model in the docs retrieved on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Snowflake says Arctic Embed 2.0 is for multilingual retrieval without giving up English retrieval quality. The medium size is the efficiency option.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-embed/arctic-embed-s": { "id": "snowflake/arctic-embed/arctic-embed-s", "name": "Arctic Embed S", "maker": "snowflake", "family": "snowflake/arctic-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-16", "context_length": 512, "max_output_tokens": null, "parameters": "33.2M", "architecture": "English encoder initialized from intfloat/e5-unsupervised-small. Model-card table says 33 million parameters. Hugging Face labels 33.2M. The technical report also says 33 million. CLS pooling. Dimension 384.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake released Arctic Embed S on 2024-04-16 as part of the first Arctic Embed family. It is an open-weight English text embedding model under Apache-2.0. Snowflake later recommended the multilingual v2 models as replacements. Collected 2026-10-15.", "what_it_is_for": "Snowflake built the family for retrieval quality at a given size. The launch post says the large model was meant as an open substitute for closed embedding APIs, and the smaller sizes trade accuracy for speed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-embed/arctic-embed-xs": { "id": "snowflake/arctic-embed/arctic-embed-xs", "name": "Arctic Embed XS", "maker": "snowflake", "family": "snowflake/arctic-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-16", "context_length": 512, "max_output_tokens": null, "parameters": "22.6M", "architecture": "English BERT-style encoder initialized from nreimers/MiniLM-L6-H384-uncased. The technical report calls the size class MiniLMv2 and lists 23 million parameters. The model-card table says 22 million. Hugging Face labels the file 22.6M. CLS pooling. Embedding dimension 384.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake released Arctic Embed XS on 2024-04-16 as part of the first Arctic Embed family. It is an open-weight English text embedding model under Apache-2.0. Snowflake later recommended the multilingual v2 models as replacements. Collected 2026-10-15.", "what_it_is_for": "Snowflake built the family for retrieval quality at a given size. The launch post says the large model was meant as an open substitute for closed embedding APIs, and the smaller sizes trade accuracy for speed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-embed/e5-base-arctic-finetune": { "id": "snowflake/arctic-embed/e5-base-arctic-finetune", "name": "e5-base Arctic fine-tune", "maker": "snowflake", "family": "snowflake/arctic-embed", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of e5-base produced with the Arctic Embed training code after Snowflake moved that code into ArcticTraining. The blog compares it with other models near 100 million parameters. It is a published example, not a Cortex embedding SKU.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake published e5-base-arctic-finetune on 2025-03-25 as the worked example for the Arctic Embed training code inside ArcticTraining. It is a named weight release, not a Cortex product. Collected 2026-10-15.", "what_it_is_for": "The blog uses it to show that the open training recipe can bring an e5-base model up to the same BEIR band as other 100M-class retrieval models, including Arctic Embed M v1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-extract/arctic-extract": { "id": "snowflake/arctic-extract/arctic-extract", "name": "Arctic-Extract", "maker": "snowflake", "family": "snowflake/arctic-extract", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-29", "context_length": 128000, "max_output_tokens": 51200, "parameters": "7B", "architecture": "Vision-language model. The December 2025 engineering post says the current model starts from Qwen 2.5-VL, uses LoRA, compresses 4 visual tokens into 1, and is served around 6.6 GiB after 4-bit AWQ. About 1000 tokens per A4 page. Trained with AdamW in bf16 on 8x H200. Context 128,000 tokens. An earlier Arctic-Extract was introduced in the 29 August 2025 post. Specs below are the later version unless noted.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arctic-Extract is Snowflake's closed vision-language model for document extraction. The first named version was posted on 2025-08-29. The current description, a 7B model of about 6.6 GiB, is the December 2025 model behind AI_EXTRACT. Fine-tuning has been generally available since 2026-04-20. Collected 2026-10-15.", "what_it_is_for": "Snowflake says it turns invoices, contracts, and scans into structured fields, tables, and answers in one pass, without a separate OCR stage. It is the model id for AI_EXTRACT and the replacement for the decommissioned Document AI UI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-speculator/arctic-lstm-speculator-gpt-oss-120b": { "id": "snowflake/arctic-speculator/arctic-lstm-speculator-gpt-oss-120b", "name": "Arctic LSTM Speculator for gpt-oss 120B", "maker": "snowflake", "family": "snowflake/arctic-speculator", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LSTM draft model for speculative decoding of gpt-oss-120b, trained by Snowflake with ArcticTraining and served with ArcticInference. Not a standalone generator.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake trained Arctic LSTM Speculator for gpt-oss 120B as a draft model for speculative decoding of gpt-oss-120b. It is a named weight release, not a chat model. Collected 2026-10-15.", "what_it_is_for": "The family card says the speculator is how Snowflake gets higher tokens per second out of vLLM by guessing upcoming tokens for a specific target model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-speculator/arctic-lstm-speculator-gpt-oss-20b": { "id": "snowflake/arctic-speculator/arctic-lstm-speculator-gpt-oss-20b", "name": "Arctic LSTM Speculator for gpt-oss 20B", "maker": "snowflake", "family": "snowflake/arctic-speculator", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LSTM draft model for speculative decoding of gpt-oss-20b, trained by Snowflake with ArcticTraining and served with ArcticInference. Not a standalone generator.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake trained Arctic LSTM Speculator for gpt-oss 20B as a draft model for speculative decoding of gpt-oss-20b. It is a named weight release, not a chat model. Collected 2026-10-15.", "what_it_is_for": "The family card says the speculator is how Snowflake gets higher tokens per second out of vLLM by guessing upcoming tokens for a specific target model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-speculator/arctic-lstm-speculator-llama-3-1-70b-instruct": { "id": "snowflake/arctic-speculator/arctic-lstm-speculator-llama-3-1-70b-instruct", "name": "Arctic LSTM Speculator for Llama 3.1 70B Instruct", "maker": "snowflake", "family": "snowflake/arctic-speculator", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LSTM draft model for speculative decoding of meta-llama/Llama-3.1-70B-Instruct, trained by Snowflake with ArcticTraining and served with ArcticInference. Not a standalone generator.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake trained Arctic LSTM Speculator for Llama 3.1 70B Instruct as a draft model for speculative decoding of meta-llama/Llama-3.1-70B-Instruct. It is a named weight release, not a chat model. Collected 2026-10-15.", "what_it_is_for": "The family card says the speculator is how Snowflake gets higher tokens per second out of vLLM by guessing upcoming tokens for a specific target model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-speculator/arctic-lstm-speculator-llama-3-1-8b-instruct": { "id": "snowflake/arctic-speculator/arctic-lstm-speculator-llama-3-1-8b-instruct", "name": "Arctic LSTM Speculator for Llama 3.1 8B Instruct", "maker": "snowflake", "family": "snowflake/arctic-speculator", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LSTM draft model for speculative decoding of meta-llama/Llama-3.1-8B-Instruct, trained by Snowflake with ArcticTraining and served with ArcticInference. Not a standalone generator.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake trained Arctic LSTM Speculator for Llama 3.1 8B Instruct as a draft model for speculative decoding of meta-llama/Llama-3.1-8B-Instruct. It is a named weight release, not a chat model. Collected 2026-10-15.", "what_it_is_for": "The family card says the speculator is how Snowflake gets higher tokens per second out of vLLM by guessing upcoming tokens for a specific target model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-speculator/arctic-lstm-speculator-llama-3-3-70b-instruct": { "id": "snowflake/arctic-speculator/arctic-lstm-speculator-llama-3-3-70b-instruct", "name": "Arctic LSTM Speculator for Llama 3.3 70B Instruct", "maker": "snowflake", "family": "snowflake/arctic-speculator", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LSTM draft model for speculative decoding of meta-llama/Llama-3.3-70B-Instruct, trained by Snowflake with ArcticTraining and served with ArcticInference. Not a standalone generator.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake trained Arctic LSTM Speculator for Llama 3.3 70B Instruct as a draft model for speculative decoding of meta-llama/Llama-3.3-70B-Instruct. It is a named weight release, not a chat model. Collected 2026-10-15.", "what_it_is_for": "The family card says the speculator is how Snowflake gets higher tokens per second out of vLLM by guessing upcoming tokens for a specific target model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-speculator/arctic-lstm-speculator-qwen2-5-32b-instruct": { "id": "snowflake/arctic-speculator/arctic-lstm-speculator-qwen2-5-32b-instruct", "name": "Arctic LSTM Speculator for Qwen2.5 32B Instruct", "maker": "snowflake", "family": "snowflake/arctic-speculator", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LSTM draft model for speculative decoding of Qwen/Qwen2.5-32B-Instruct, trained by Snowflake with ArcticTraining and served with ArcticInference. Not a standalone generator.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake trained Arctic LSTM Speculator for Qwen2.5 32B Instruct as a draft model for speculative decoding of Qwen/Qwen2.5-32B-Instruct. It is a named weight release, not a chat model. Collected 2026-10-15.", "what_it_is_for": "The family card says the speculator is how Snowflake gets higher tokens per second out of vLLM by guessing upcoming tokens for a specific target model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-speculator/arctic-lstm-speculator-qwen3-32b-bird": { "id": "snowflake/arctic-speculator/arctic-lstm-speculator-qwen3-32b-bird", "name": "Arctic LSTM Speculator for Qwen3 32B, BIRD", "maker": "snowflake", "family": "snowflake/arctic-speculator", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LSTM draft model for speculative decoding of Qwen3-32B, trained by Snowflake with ArcticTraining and served with ArcticInference. Not a standalone generator.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake trained Arctic LSTM Speculator for Qwen3 32B, BIRD as a draft model for speculative decoding of Qwen3-32B. It is a named weight release, not a chat model. Collected 2026-10-15.", "what_it_is_for": "The family card says the speculator is how Snowflake gets higher tokens per second out of vLLM by guessing upcoming tokens for a specific target model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-speculator/arctic-lstm-speculator-qwen3-32b-longcontext": { "id": "snowflake/arctic-speculator/arctic-lstm-speculator-qwen3-32b-longcontext", "name": "Arctic LSTM Speculator for Qwen3 32B, long context", "maker": "snowflake", "family": "snowflake/arctic-speculator", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LSTM draft model for speculative decoding of Qwen3-32B, trained by Snowflake with ArcticTraining and served with ArcticInference. Not a standalone generator.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake trained Arctic LSTM Speculator for Qwen3 32B, long context as a draft model for speculative decoding of Qwen3-32B. It is a named weight release, not a chat model. Collected 2026-10-15.", "what_it_is_for": "The family card says the speculator is how Snowflake gets higher tokens per second out of vLLM by guessing upcoming tokens for a specific target model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-text2sql/arctic-excot-32b": { "id": "snowflake/arctic-text2sql/arctic-excot-32b", "name": "Arctic-ExCoT 32B", "maker": "snowflake", "family": "snowflake/arctic-text2sql", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-03-25", "context_length": null, "max_output_tokens": null, "parameters": "33B on the Hugging Face size label; named as Qwen2.5-Coder 32B", "architecture": "Same ExCoT recipe on Qwen2.5-Coder. The model tree lists Qwen/Qwen2.5-32B then Qwen/Qwen2.5-Coder-32B. Hugging Face size label 33B. A search snippet of the Hub page header showed license CC-BY-NC-4.0. The card body retrieved later does not restate the license.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arctic-ExCoT 32B is Snowflake's Qwen2.5-Coder ExCoT checkpoint, released with the 70B model in March 2025. The Hub license field seen in search is CC-BY-NC-4.0. Collected 2026-10-15.", "what_it_is_for": "Same Text-to-SQL job as Arctic-ExCoT 70B, at the 32B coder size. Snowflake reported it just behind the 70B model on BIRD.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-text2sql/arctic-excot-70b": { "id": "snowflake/arctic-text2sql/arctic-excot-70b", "name": "Arctic-ExCoT 70B", "maker": "snowflake", "family": "snowflake/arctic-text2sql", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-25", "context_length": null, "max_output_tokens": null, "parameters": "71B on the Hugging Face size label; named as a 70B Llama 3.1 model", "architecture": "Execution-guided chain-of-thought DPO on Llama 3.1 70B. The model tree lists meta-llama/Llama-3.1-70B, not the instruct checkpoint. Text-to-SQL model. Hugging Face dtype label on the retrieved page is F32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake released Arctic-ExCoT 70B in March 2025 as the first Arctic Text2SQL model, a Llama 3.1 70B checkpoint tuned with execution feedback. Weights are public. It is not a Cortex SKU in the docs retrieved on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "The card says ExCoT turns natural-language questions into executable SQL by optimizing chain-of-thought with SQL execution results instead of human preferences.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-text2sql/arctic-text2sql-r1-14b": { "id": "snowflake/arctic-text2sql/arctic-text2sql-r1-14b", "name": "Arctic-Text2SQL-R1 14B", "maker": "snowflake", "family": "snowflake/arctic-text2sql", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-22", "context_length": null, "max_output_tokens": null, "parameters": "14B, from the model name and blog", "architecture": "Named member of Arctic-Text2SQL-R1. Snowflake published scores and did not publish weights. Base checkpoint was not stated on the retrieved pages. The 7B sibling is Qwen2.5-Coder. Do not assume the same base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake named Arctic-Text2SQL-R1 14B in the May 2025 Arctic-Text2SQL-R1 release and published BIRD scores for it. The engineering post open-sources only the 7B checkpoint. No public weights were found. Collected 2026-10-15.", "what_it_is_for": "Same Text-to-SQL role as the 7B model, at a larger size. There is no usage guide because the weights were not released.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-text2sql/arctic-text2sql-r1-32b": { "id": "snowflake/arctic-text2sql/arctic-text2sql-r1-32b", "name": "Arctic-Text2SQL-R1 32B", "maker": "snowflake", "family": "snowflake/arctic-text2sql", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-22", "context_length": null, "max_output_tokens": null, "parameters": "32B, from the model name and blog", "architecture": "Named member of Arctic-Text2SQL-R1. Snowflake published scores and did not publish weights. Base checkpoint was not stated on the retrieved pages. The 7B sibling is Qwen2.5-Coder. Do not assume the same base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake named Arctic-Text2SQL-R1 32B in the May 2025 Arctic-Text2SQL-R1 release and published BIRD scores for it. The engineering post open-sources only the 7B checkpoint. No public weights were found. Collected 2026-10-15.", "what_it_is_for": "Same Text-to-SQL role as the 7B model, at a larger size. There is no usage guide because the weights were not released.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-text2sql/arctic-text2sql-r1-5": { "id": "snowflake/arctic-text2sql/arctic-text2sql-r1-5", "name": "Arctic-Text2SQL-R1.5", "maker": "snowflake", "family": "snowflake/arctic-text2sql", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-11-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Snowflake-SQL reasoning model described as specialized and low-latency. Size not published. The November 2025 post says it is deployed for Text-to-SQL inside Snowflake Intelligence.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake introduced Arctic-Text2SQL-R1.5 on 2025-11-04 as a closed model for Snowflake SQL inside Snowflake Intelligence. Size and weights were not published. R2 is the later named model. Collected 2026-10-15.", "what_it_is_for": "The post says general models miss Snowflake dialect details. R1.5 is the specialized model meant to match frontier accuracy on Snowflake SQL at lower latency.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-text2sql/arctic-text2sql-r1-7b": { "id": "snowflake/arctic-text2sql/arctic-text2sql-r1-7b", "name": "Arctic-Text2SQL-R1 7B", "maker": "snowflake", "family": "snowflake/arctic-text2sql", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-22", "context_length": 32768, "max_output_tokens": null, "parameters": "7B named; Hugging Face size label 8B", "architecture": "Qwen2ForCausalLM fine-tune of Qwen2.5-Coder-7B-Instruct with GRPO. Reward is execution correctness and syntax validity. Config retrieved from the Hub: 28 layers, hidden 3584, 28 query heads, 4 KV heads, intermediate 18944, vocab 152064, max_position_embeddings 32768, bfloat16, rope_theta 1000000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake open-sourced Arctic-Text2SQL-R1 7B on 2025-05-22. It is a Qwen2.5-Coder-7B-Instruct model trained with execution rewards to emit SQL. The 14B and 32B R1 models were scored publicly but not released as weights. Collected 2026-10-15.", "what_it_is_for": "The card says it is for natural-language interfaces over relational databases and for analytics tools. It tells users to validate SQL before production use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-text2sql/arctic-text2sql-r2": { "id": "snowflake/arctic-text2sql/arctic-text2sql-r2", "name": "Arctic-Text2SQL-R2", "maker": "snowflake", "family": "snowflake/arctic-text2sql", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Compact Snowflake-SQL reasoning model. The May 2026 post says it is 30 to 150 times smaller than other high-scoring models on the figure and about 15 points ahead of Qwen3.6, the nearest general model shown. Absolute parameter count was not printed. Training note from the Arctic RL post: the run fit on 32 H200 GPUs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake announced Arctic-Text2SQL-R2 on 2026-05-26 as a closed SQL model that it says beats frontier LLMs on hard Snowflake SQL while staying much smaller. Weights and an exact parameter count were not published. Collected 2026-10-15.", "what_it_is_for": "The post says R2 is built for Snowflake SQL, using Snowflake-heavy mid-training, realistic SQL, and a collision-resistant execution reward, rather than a general chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/arctic-tilt/arctic-tilt-v1-3": { "id": "snowflake/arctic-tilt/arctic-tilt-v1-3", "name": "Arctic-TILT v1.3", "maker": "snowflake", "family": "snowflake/arctic-tilt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-28", "context_length": 400000, "max_output_tokens": null, "parameters": "822M", "architecture": "Encoder-decoder in the TILT line. TILT extends T5 with a 2D attention bias and image embeddings of the token neighborhood. Snowflake says this checkpoint is 822M parameters and handles visually rich documents up to about 400k tokens, roughly 500 pages, on one 24GB GPU.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake AI Research released Arctic-TILT v1.3, an 822M encoder-decoder for business document understanding, with weights and a vLLM implementation. The public card date lines up with the ACL 2025 industry track, 28-30 July 2025. Arctic-Extract is the later product model. Collected 2026-10-15.", "what_it_is_for": "The card says the job is document question answering and key-information extraction, at a cost that fits one 24GB GPU, including multi-page files.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/swiftkv/llama-3-1-swiftkv-405b-instruct": { "id": "snowflake/swiftkv/llama-3-1-swiftkv-405b-instruct", "name": "Llama 3.1 SwiftKV 405B Instruct", "maker": "snowflake", "family": "snowflake/swiftkv", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "405B named; Hugging Face org label 425B", "architecture": "SwiftKV rewiring plus knowledge-preserving self-distillation of Llama 3.1 405B Instruct. Prefill tokens can skip up to half the layers. Not a model pretrained by Snowflake.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake shipped Llama 3.1 SwiftKV 405B Instruct as a SwiftKV-transformed Llama checkpoint so prefill does less work. The family card dates the first weights to December 2024 and a 128K revision to August 2025. Collected 2026-10-15.", "what_it_is_for": "Snowflake says SwiftKV cuts prefill compute, about 2x throughput in its writeup, with a small accuracy drop, for enterprise serving of Llama instruct models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/swiftkv/llama-3-1-swiftkv-8b-instruct": { "id": "snowflake/swiftkv/llama-3-1-swiftkv-8b-instruct", "name": "Llama 3.1 SwiftKV 8B Instruct", "maker": "snowflake", "family": "snowflake/swiftkv", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "8B", "architecture": "SwiftKV rewiring plus knowledge-preserving self-distillation of meta-llama/Llama-3.1-8B-Instruct. Prefill tokens can skip up to half the layers. Not a model pretrained by Snowflake.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake shipped Llama 3.1 SwiftKV 8B Instruct as a SwiftKV-transformed Llama checkpoint so prefill does less work. The family card dates the first weights to December 2024 and a 128K revision to August 2025. Collected 2026-10-15.", "what_it_is_for": "Snowflake says SwiftKV cuts prefill compute, about 2x throughput in its writeup, with a small accuracy drop, for enterprise serving of Llama instruct models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snowflake/swiftkv/llama-3-3-swiftkv-70b-instruct": { "id": "snowflake/swiftkv/llama-3-3-swiftkv-70b-instruct", "name": "Llama 3.3 SwiftKV 70B Instruct", "maker": "snowflake", "family": "snowflake/swiftkv", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "70B named; Hugging Face org label 74B", "architecture": "SwiftKV rewiring plus knowledge-preserving self-distillation of Llama 3.3 70B Instruct. Prefill tokens can skip up to half the layers. Not a model pretrained by Snowflake.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Snowflake shipped Llama 3.3 SwiftKV 70B Instruct as a SwiftKV-transformed Llama checkpoint so prefill does less work. The family card dates the first weights to December 2024 and a 128K revision to August 2025. Collected 2026-10-15.", "what_it_is_for": "Snowflake says SwiftKV cuts prefill compute, about 2x throughput in its writeup, with a small accuracy drop, for enterprise serving of Llama instruct models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snu/dynin/dynin-omni": { "id": "snu/dynin/dynin-omni", "name": "Dynin-Omni", "maker": "snu", "family": "snu/dynin", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-02-21", "context_length": 4096, "max_output_tokens": null, "parameters": "8116244480", "architecture": "Masked-diffusion transformer, model type dynin_omni. Released config: 32 layers, d_model 4096, 32 heads, 32 KV heads, MLP hidden 12288, RoPE theta 500000, block type llama, max sequence length 4096, vocab 138752, llm_vocab_size 126464, new_vocab_size 134656, codebook size 8192, 256 VQ tokens, bf16, embeddings not tied. Hub safetensors total is 8,116,244,480 parameters. The card calls it an 8B-scale model and says stage 1 starts from Gen-Verse/MMaDA-8B-MixCoT. config.json pretrained_model_path is GSAI-ML/LLaDA-8B-Instruct. Those two base ids disagree.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text", "image", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dynin-Omni is an 8B-scale omnimodal masked-diffusion model from the AIDAS Lab at Seoul National University. Weights are on the Hub under MIT. The Hub repo date is 21 February 2026, and the paper was submitted on 9 March 2026. Collected 2026-10-15.", "what_it_is_for": "The lab describes one discrete-token diffusion model for text reasoning, image understanding and generation, image editing, video understanding, speech recognition, and speech synthesis. The paper abstract includes video understanding and does not claim video generation. The card's first paragraph says video understanding and generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snu/dynin/dynin-robotics": { "id": "snu/dynin/dynin-robotics", "name": "Dynin-Robotics", "maker": "snu", "family": "snu/dynin", "status": "announced", "confidence": "high", "weights": "closed", "license": "MIT", "release_date": "2026-09-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Robot continuation of Dynin-Omni. The card says one masked-diffusion transformer over language, visual observations, goal states, and action tokens. It predicts action chunks, next visual states, terminal goals, or task instructions by changing which spans are masked. No layer config was in the repo because the weight files were not uploaded.", "modalities": { "input": [ "text", "image", "actions" ], "output": [ "text", "image", "actions" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dynin-Robotics is a robot model announced by the AIDAS Lab at Seoul National University. It is built on Dynin-Omni. The paper was submitted on 11 September 2026. The model card says the code and weights will be released soon, and on 2026-10-15 the Hub repo had no weight files. Collected 2026-10-15.", "what_it_is_for": "The card says one diffusion model can emit action chunks, next camera frames, a goal image, or a task instruction, depending on which tokens are visible. The stated setting is robot manipulation, including LIBERO and a Franka Research 3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snu/kr-bert/kr-bert": { "id": "snu/kr-bert/kr-bert", "name": "KR-BERT", "maker": "snu", "family": "snu/kr-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-08-10", "context_length": 512, "max_output_tokens": null, "parameters": "99,265,066 character; 96,145,233 sub-character", "architecture": "BERT encoder. Hub config for snunlp/KR-BERT-char16424: 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab 16424, max position embeddings 512, type vocab 2. The card also lists a sub-character model with vocab 12,367 and 96,145,233 parameters. Four tokenizer checkpoints: character or sub-character, each with WordPiece or BidirectionalWordPiece.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KR-BERT is a small Korean BERT from the Computational Linguistics Lab at Seoul National University. The lab still publishes the weights. The paper date is 10 August 2020. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card describes a Korean-specific BERT, smaller than multilingual BERT, trained on Korean Wikipedia and news text (2.47GB, 20 million sentences, 233 million words). The published downstream example is Naver Sentiment Movie Corpus classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snu/kr-bert/kr-bert-kosac": { "id": "snu/kr-bert/kr-bert-kosac", "name": "KR-BERT-KOSAC", "maker": "snu", "family": "snu/kr-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Character-level KR-BERT with extra polarity and intensity embeddings from the KOSAC lexicon, added to token, position, and segment embeddings. Two TensorFlow checkpoints: WordPiece and BidirectionalWordPiece. The NSMC script points at vocab_char_16424.txt and bert_config_char16424.json. Layer sizes are not restated on the README.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KR-BERT-KOSAC is a Korean BERT from the Computational Linguistics Lab at Seoul National University. It adds sentiment features from the Korean Sentiment Analysis Corpus to character-level KR-BERT. Weights are linked from the lab GitHub repo. No release day is stated. Collected 2026-10-15.", "what_it_is_for": "The README says the checkpoint is for sentiment-related tasks. Polarity and intensity embeddings from KOSAC are added to the usual BERT embeddings. KOSAC, as described there, has 17,582 annotated sentiment expressions from 332 documents and 7,744 sentences.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snu/kr-bert/kr-medium": { "id": "snu/kr-bert/kr-medium", "name": "KR-BERT-MEDIUM", "maker": "snu", "family": "snu/kr-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "102015010", "architecture": "BERT encoder continued from the character-level KR-BERT WordPiece model. Hub config: 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab 20000, max position embeddings 512, type vocab 2. No architectures field in config.json.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KR-BERT-MEDIUM is a larger-data Korean BERT from the Computational Linguistics Lab at Seoul National University. It is an expansion of character-level KR-BERT, and the weights are still on the Hub. No day-level release date is on the card. Collected 2026-10-15.", "what_it_is_for": "The card says the model is for Korean tasks where informal web text matters. Training text adds legal documents from the National Law Information Center and a Korean comments corpus to the original KR-BERT Wikipedia and news data. The stated downstream tasks are NSMC movie-review classification and hate-speech detection.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snu/kr-electra/kr-electra-discriminator": { "id": "snu/kr-electra/kr-electra-discriminator", "name": "KR-ELECTRA discriminator", "maker": "snu", "family": "snu/kr-electra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "ELECTRA discriminator, ElectraForPreTraining. Hub config: 12 layers, hidden 768, embedding size 768, 12 heads, intermediate 3072, GELU, vocab 30000, max position embeddings 512. Pretraining max sequence length on the card is 128. Morpheme tokens from Mecab-Ko. Trained in TensorFlow 1 on a TPU v3-8, following the public ELECTRA base recipe rather than a fine-tune of Google's checkpoint.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KR-ELECTRA is a Korean ELECTRA model from the Computational Linguistics Lab at Seoul National University. This folder is the discriminator, which is the checkpoint the lab evaluates. Weights are public. The card's citation year is 2022, with no day. Collected 2026-10-15.", "what_it_is_for": "The card says the model is a Korean encoder aimed at informal text such as reviews, while remaining usable on other Korean tasks. Reported tasks are sentiment, NER, paraphrase, NLI, STS, question pairs, KorQuAD, and hate speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snu/kr-electra/kr-electra-generator": { "id": "snu/kr-electra/kr-electra-generator", "name": "KR-ELECTRA generator", "maker": "snu", "family": "snu/kr-electra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "ELECTRA generator, ElectraForMaskedLM, Hub pipeline fill-mask. Config: 12 layers, hidden 256, 4 heads, intermediate 1024, embedding size 768, GELU, vocab 30000, max position embeddings 512. The discriminator card lists the same generator shape and a generator-size ratio of 0.33333. Pretraining max sequence length on that card is 128.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The KR-ELECTRA generator is the masked-language half of the Korean ELECTRA pair from the Computational Linguistics Lab at Seoul National University. The lab published it as its own Hub repo. The citation year is 2022, with no day. Collected 2026-10-15.", "what_it_is_for": "It is the generator used while pretraining KR-ELECTRA. The Hub pipeline tag is fill-mask. The lab's task scores are reported for the discriminator, not for this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snu/kr-finbert/kr-finbert": { "id": "snu/kr-finbert/kr-finbert", "name": "KR-FinBert", "maker": "snu", "family": "snu/kr-finbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM continued from KR-BERT-MEDIUM. Hub config: 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab 20000, max position embeddings 512. Card: further pretrained 5.5 million steps, maxlen 512, batch 32, learning rate 5e-5, on a TITAN XP for 67.48 hours.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KR-FinBert is a Korean financial BERT from the Computational Linguistics Lab at Seoul National University. It is KR-BERT-MEDIUM further pretrained on financial text. Weights are public. The citation year is 2022, with no day. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint is a financial-domain language model, built by continued pretraining and intended to be fine-tuned for tasks such as sentiment. The Hub pipeline is fill-mask. The sentiment classifier is the separate KR-FinBert-SC checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snu/kr-finbert/kr-finbert-sc": { "id": "snu/kr-finbert/kr-finbert-sc", "name": "KR-FinBert-SC", "maker": "snu", "family": "snu/kr-finbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForSequenceClassification on the KR-FinBert backbone. Hub config: 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab 20000, max position embeddings 512. Labels in id2label: 0 negative, 1 neutral, 2 positive.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KR-FinBert-SC is the financial sentiment classifier released with KR-FinBert by the Computational Linguistics Lab at Seoul National University. Weights are public. The citation year is 2022, with no day. Collected 2026-10-15.", "what_it_is_for": "The card presents it as KR-FinBert fine-tuned for sentiment analysis on Korean financial text. The Hub pipeline is text classification. Config labels are negative, neutral, and positive.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "snu/kr-sbert/kr-sbert-v40k-kluenli-augsts": { "id": "snu/kr-sbert/kr-sbert-v40k-kluenli-augsts", "name": "KR-SBERT-V40K-klueNLI-augSTS", "maker": "snu", "family": "snu/kr-sbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Sentence-BERT on BertModel. Hub config: 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab 40000, max position embeddings 512. Sentence-transformers block on the card: max_seq_length 128, mean pooling, embedding dimension 768. do_lower_case false.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KR-SBERT-V40K-klueNLI-augSTS is a Korean sentence-embedding BERT from the Computational Linguistics Lab at Seoul National University. It is the KR-SBERT checkpoint the lab highlights on the Hub. The citation year for KR-SBERT is 2021. Collected 2026-10-15.", "what_it_is_for": "The card says the model maps sentences and paragraphs to a 768-dimensional vector for clustering and semantic search. A linked Colab shows a document-classification use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/diafill/diafill-sarashina2-2-3b-instruct": { "id": "softbank/diafill/diafill-sarashina2-2-3b-instruct", "name": "DiaFill Sarashina2.2-3B-Instruct", "maker": "softbank", "family": "softbank/diafill", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-22", "context_length": 8192, "max_output_tokens": null, "parameters": "Same order as Sarashina2.2-3B-Instruct. The ModernBERT blog lists that base at 3.36B including input embeddings and layers.", "architecture": "Llama fine-tune of sarashina2.2-3b-instruct-v0.1. config.json: 32 layers, hidden 2560, 16 heads, intermediate 8960, vocab 102400, context 8192. Generates a two-speaker spoken-style Japanese dialogue script from a seed scenario. Trained on non-public GENIAC dialogue scripts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiaFill Sarashina2.2-3B-Instruct is an SB Intuitions fine-tune that writes two-speaker Japanese dialogue scripts, including fillers. It is MIT licensed and was posted on 2025-12-22. It is a named task model, not a new pretrained foundation model. Collected 2026-10-15.", "what_it_is_for": "Generate chit-chat or call-center dialogue scripts from a seed with genre, industry, tone, and a summary.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/hikari/hikari-medium": { "id": "softbank/hikari/hikari-medium", "name": "Hikari-medium", "maker": "softbank", "family": "softbank/hikari", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-05-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Streaming speech-to-text translation and transcription. The card says it is a Whisper-style encoder-decoder with a fully causal encoder, built on OpenAI Whisper-medium. config.json architecture HikariForGRPO, 24 layers, vocab 51865. max_position_embeddings was 1500 and is not treated as a text context. Tasks: English streaming ASR, and translation EN-JA, EN-RU, EN-DE.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hikari-medium is SB Intuitions' streaming speech translation and transcription model, MIT licensed and on Hugging Face since 2026-05-08. The card says it modifies Whisper-medium with a fully causal encoder so it can emit text while audio is still arriving. Collected 2026-10-15.", "what_it_is_for": "Simultaneous English transcription and English-to-Japanese, Russian, or German translation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/ltm/large-telecom-model": { "id": "softbank/ltm/large-telecom-model", "name": "Large Telecom Model", "maker": "softbank", "family": "softbank/ltm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SoftBank Corp. telecom-industry generative model. The 2025-10-29 press says an earlier English-oriented LTM was evolved into a domestic model by integrating SB Intuitions' Sarashina, with training and inference in SoftBank's Japan data centers. No parameter count or context length is published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Large Telecom Model (LTM) is SoftBank Corp.'s closed generative model for telecommunications operations. On 2025-10-29 SoftBank said it had integrated SB Intuitions' Sarashina so the model could be developed and run inside Japan, and that internal use had started. Collected 2026-10-15.", "what_it_is_for": "Mobile-network operations. The announced example is predicting communication quality during large events.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/med-sarashina/med-sarashina": { "id": "softbank/med-sarashina/med-sarashina", "name": "Med-Sarashina", "maker": "softbank", "family": "softbank/med-sarashina", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Domestic medical language model. The 2026-08-10 press says it does not depend on a foreign base model and that further fine-tuning on Juntendo University clinical and education cases is part of the joint research. No parameter count or architecture table was published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Med-Sarashina is SB Intuitions' named Japanese medical language model. A 2026-08-10 press release says joint research with Juntendo University, SoftBank, and HEMILLIONS started in June 2026. It is not a public weight or API model. Collected 2026-10-15.", "what_it_is_for": "Clinical, research, and medical-education generation inside a Japanese healthcare data environment. The press says the deployment under study sits on HEMILLIONS infrastructure aligned with the three-ministry medical-information guidelines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/modernbert-ja/modernbert-ja-1-4b": { "id": "softbank/modernbert-ja/modernbert-ja-1-4b", "name": "ModernBERT-Ja-1.4B", "maker": "softbank", "family": "softbank/modernbert-ja", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.44B on the 2025-05-26 analysis blog", "architecture": "Named in the ModernBERT-Ja analysis post as the largest model in that comparison. Not in the public model-card size table (30M, 70M, 130M, 310M). The 2026-07-03 rerank blog says Sarashina3 rerank is based on it. Layer widths and context were not published for this size.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernBERT-Ja-1.4B is a Japanese encoder size that SB Intuitions evaluated in its May 2025 tech blog and later named as the base of Sarashina3 rerank. No public checkpoint or model card was found. Collected 2026-10-15.", "what_it_is_for": "The analysis blog treats it as the top of the ModernBERT-Ja ladder. The shipping form found in this catalog is the closed Sarashina3 rerank model, not a downloadable encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/modernbert-ja/modernbert-ja-130m": { "id": "softbank/modernbert-ja/modernbert-ja-130m", "name": "ModernBERT-Ja-130M", "maker": "softbank", "family": "softbank/modernbert-ja", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-06", "context_length": 8192, "max_output_tokens": null, "parameters": "132M total, 80M excluding embeddings (model card table)", "architecture": "ModernBERT encoder, masked LM. config.json matches the card: hidden 512, intermediate 2048, 19 layers, vocab 102400, max_position_embeddings 8192. Card: head dim 64, GELU, alternating global and local attention (1 global + 2 local), sliding window 128, global RoPE theta 160000, local RoPE theta 10000. Training on the 130M card: 3.51T tokens at length 1024, then 430B at 8192, then 450B of Japanese at 8192 (150B tokens for 3 epochs). Total about 4.39T. Mask rate 30 percent, then 15 percent in the last phase.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernBERT-Ja-130M is a Japanese and English encoder from SB Intuitions, MIT licensed, with an 8192-token context. The model card gives the parameter count and the 4.39T-token training mix. Weights were on Hugging Face by 2025-02-06. Collected 2026-10-15.", "what_it_is_for": "Masked-language and encoder tasks such as classification, reranking features, and retrieval backbones. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/modernbert-ja/modernbert-ja-30m": { "id": "softbank/modernbert-ja/modernbert-ja-30m", "name": "ModernBERT-Ja-30M", "maker": "softbank", "family": "softbank/modernbert-ja", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-19", "context_length": 8192, "max_output_tokens": null, "parameters": "37M total, 10M excluding embeddings (model card table)", "architecture": "ModernBERT encoder, masked LM. config.json matches the card: hidden 256, intermediate 1024, 10 layers, vocab 102400, max_position_embeddings 8192. Card: head dim 64, GELU, alternating global and local attention (1 global + 2 local), sliding window 128, global RoPE theta 160000, local RoPE theta 10000. Training on the 130M card: 3.51T tokens at length 1024, then 430B at 8192, then 450B of Japanese at 8192 (150B tokens for 3 epochs). Total about 4.39T. Mask rate 30 percent, then 15 percent in the last phase.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernBERT-Ja-30M is a Japanese and English encoder from SB Intuitions, MIT licensed, with an 8192-token context. The model card gives the parameter count and the 4.39T-token training mix. Weights were on Hugging Face by 2025-02-19. Collected 2026-10-15.", "what_it_is_for": "Masked-language and encoder tasks such as classification, reranking features, and retrieval backbones. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/modernbert-ja/modernbert-ja-310m": { "id": "softbank/modernbert-ja/modernbert-ja-310m", "name": "ModernBERT-Ja-310M", "maker": "softbank", "family": "softbank/modernbert-ja", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-19", "context_length": 8192, "max_output_tokens": null, "parameters": "315M total, 236M excluding embeddings (model card table)", "architecture": "ModernBERT encoder, masked LM. config.json matches the card: hidden 768, intermediate 3072, 25 layers, vocab 102400, max_position_embeddings 8192. Card: head dim 64, GELU, alternating global and local attention (1 global + 2 local), sliding window 128, global RoPE theta 160000, local RoPE theta 10000. Training on the 130M card: 3.51T tokens at length 1024, then 430B at 8192, then 450B of Japanese at 8192 (150B tokens for 3 epochs). Total about 4.39T. Mask rate 30 percent, then 15 percent in the last phase.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernBERT-Ja-310M is a Japanese and English encoder from SB Intuitions, MIT licensed, with an 8192-token context. The model card gives the parameter count and the 4.39T-token training mix. Weights were on Hugging Face by 2025-02-19. Collected 2026-10-15.", "what_it_is_for": "Masked-language and encoder tasks such as classification, reranking features, and retrieval backbones. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/modernbert-ja/modernbert-ja-70m": { "id": "softbank/modernbert-ja/modernbert-ja-70m", "name": "ModernBERT-Ja-70M", "maker": "softbank", "family": "softbank/modernbert-ja", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-19", "context_length": 8192, "max_output_tokens": null, "parameters": "70M total, 31M excluding embeddings (model card table)", "architecture": "ModernBERT encoder, masked LM. config.json matches the card: hidden 384, intermediate 1536, 13 layers, vocab 102400, max_position_embeddings 8192. Card: head dim 64, GELU, alternating global and local attention (1 global + 2 local), sliding window 128, global RoPE theta 160000, local RoPE theta 10000. Training on the 130M card: 3.51T tokens at length 1024, then 430B at 8192, then 450B of Japanese at 8192 (150B tokens for 3 epochs). Total about 4.39T. Mask rate 30 percent, then 15 percent in the last phase.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernBERT-Ja-70M is a Japanese and English encoder from SB Intuitions, MIT licensed, with an 8192-token context. The model card gives the parameter count and the 4.39T-token training mix. Weights were on Hugging Face by 2025-02-19. Collected 2026-10-15.", "what_it_is_for": "Masked-language and encoder tasks such as classification, reranking features, and retrieval backbones. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/nest/nest-ja-0-1b": { "id": "softbank/nest/nest-ja-0-1b", "name": "nest-ja-0.1b", "maker": "softbank", "family": "softbank/nest", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-03", "context_length": null, "max_output_tokens": null, "parameters": "about 0.1B (model card)", "architecture": "Japanese speech SSL model following NVIDIA's NEST / FastConformer recipe. config.json: hidden 512, 17 layers, 8 heads, intermediate 2048. The card says about 35,000 hours of Japanese audio. A config field max_position_embeddings was 5000; it is not recorded as a text context window.", "modalities": { "input": [ "audio" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "nest-ja-0.1b is a Japanese speech self-supervised model from SB Intuitions, MIT licensed, trained on about 35,000 hours and released on Hugging Face on 2026-02-03. It follows NVIDIA's NEST framework rather than a new architecture paper. Collected 2026-10-15.", "what_it_is_for": "Frozen audio features or initialization for downstream Japanese speech models. It is not a speech-to-text product by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/nest/nest-ja-0-6b": { "id": "softbank/nest/nest-ja-0-6b", "name": "nest-ja-0.6b", "maker": "softbank", "family": "softbank/nest", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-03", "context_length": null, "max_output_tokens": null, "parameters": "about 0.6B (model card)", "architecture": "Japanese speech SSL model following NVIDIA's NEST / FastConformer recipe. config.json: hidden 1024, 24 layers, 8 heads, intermediate 4096. The card says about 35,000 hours of Japanese audio. A config field max_position_embeddings was 5000; it is not recorded as a text context window.", "modalities": { "input": [ "audio" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "nest-ja-0.6b is a Japanese speech self-supervised model from SB Intuitions, MIT licensed, trained on about 35,000 hours and released on Hugging Face on 2026-02-03. It follows NVIDIA's NEST framework rather than a new architecture paper. Collected 2026-10-15.", "what_it_is_for": "Frozen audio features or initialization for downstream Japanese speech models. It is not a speech-to-text product by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina-embedding/sarashina-embedding-v1-1b": { "id": "softbank/sarashina-embedding/sarashina-embedding-v1-1b", "name": "Sarashina-Embedding-v1-1B", "maker": "softbank", "family": "softbank/sarashina-embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Sarashina Model NonCommercial License Agreement", "release_date": "2024-11-22", "context_length": 8192, "max_output_tokens": null, "parameters": "Base described on this card as the 1.2B Sarashina2.1-1B. See that model for the 1B versus 1.22B disagreement.", "architecture": "Sentence-transformer on Sarashina2.1-1B. Last-token pooling is not required to be prefixed. Output dimensionality 1792. Cosine similarity. Max sequence 8192. config.json matches the 2.1-1B Llama stack. Multi-stage contrastive learning.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina-Embedding-v1-1B is a Japanese text embedding model from SB Intuitions, contrastively trained on Sarashina2.1-1B. It maps text to 1792 dimensions with an 8192-token limit. The license is non-commercial. Hugging Face created the repo on 2024-11-22. Collected 2026-10-15.", "what_it_is_for": "Semantic search, similarity, classification, clustering, and RAG retrieval for Japanese text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina-embedding/sarashina-embedding-v2-1b": { "id": "softbank/sarashina-embedding/sarashina-embedding-v2-1b", "name": "Sarashina-Embedding-v2-1B", "maker": "softbank", "family": "softbank/sarashina-embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Sarashina Model NonCommercial License Agreement", "release_date": "2025-07-30", "context_length": 8192, "max_output_tokens": null, "parameters": "Based on Sarashina2.2-1B. That base is listed as 1.41B in the ModernBERT blog.", "architecture": "Sentence-transformer on Sarashina2.2-1B. Last-token pooling. Output 1792 dimensions. Cosine similarity. Max 8192 tokens. Instruction-prefixed queries. Multi-stage contrastive learning; the card says two high-scoring stage-2 checkpoints were linearly merged.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina-Embedding-v2-1B is SB Intuitions' instruction-aware Japanese embedding model on Sarashina2.2-1B. It outputs 1792-dimensional vectors, context 8192, under a non-commercial license. The card's JMTEB average is 76.38 as of 2025-07-28. Collected 2026-10-15.", "what_it_is_for": "Semantic search and RAG where a natural-language instruction can change the query vector.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina-embedding/sarashina2-embedding": { "id": "softbank/sarashina-embedding/sarashina2-embedding", "name": "Sarashina2 embedding", "maker": "softbank", "family": "softbank/sarashina-embedding", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named only in the 2026-07-02 Sarashina3 embedding tech blog as the previous generation. That table lists 2560 dimensions. No model card, parameter count, or architecture was found.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2 embedding is a closed Japanese embedding model named by SB Intuitions as the generation before Sarashina3 embedding. The only specs found are on the 2026-07-02 tech blog: 2560 dimensions and a JMTEB average of 78.69. It is not on the current public API index. Collected 2026-10-15.", "what_it_is_for": "The blog treats it as the previous retrieval embedding, outperformed or replaced on dimensionality by Sarashina3 embedding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina-mini/sarashina-mini": { "id": "softbank/sarashina-mini/sarashina-mini", "name": "Sarashina mini", "maker": "softbank", "family": "softbank/sarashina-mini", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed lightweight chat model. The November 2025 press says it was built from experience with the 460-billion-parameter Sarashina model and is strong in Japanese. The June 2026 tech blog calls the same commercial API Sarashina2 mini and says it was offered from November 2025. Parameter count, context, and architecture are not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina mini is the closed enterprise chat model SoftBank and SB Intuitions put on the Sarashina API on 2025-11-28. The June 2026 tech blog calls that API model Sarashina2 mini. It is not in the October 2026 Sarashina3 API index. Collected 2026-10-15.", "what_it_is_for": "The press release lists summarization, proofreading, report and minutes drafting, programming assistance, and conversational or multi-agent systems for corporate customers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina1/sarashina1-13b": { "id": "softbank/sarashina1/sarashina1-13b", "name": "Sarashina1-13B", "maker": "softbank", "family": "softbank/sarashina1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-06-14", "context_length": 2048, "max_output_tokens": null, "parameters": "13 billion (vendor configuration table)", "architecture": "GPT-NeoX decoder, RoPE. config.json: 40 layers, hidden 5120, 40 heads, intermediate 20480, vocab 51200, max_position_embeddings 2048. Vendor table: training tokens 1.0T.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina1-13B is a Japanese pretrained language model from SB Intuitions, a SoftBank Corp. subsidiary. SB Intuitions released the weights on 2024-06-14 under MIT, together with the first Sarashina2 dense models. It is not instruction-tuned. This dossier was collected on 2026-10-15.", "what_it_is_for": "The press release says the weights were published so academia and industry can study Japanese LLMs. The card describes a pretrained model, not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina1/sarashina1-65b": { "id": "softbank/sarashina1/sarashina1-65b", "name": "Sarashina1-65B", "maker": "softbank", "family": "softbank/sarashina1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-06-14", "context_length": 2048, "max_output_tokens": null, "parameters": "65 billion (vendor configuration table)", "architecture": "GPT-NeoX decoder, RoPE. config.json: 80 layers, hidden 8192, 64 heads, intermediate 32768, vocab 51200, max_position_embeddings 2048. Vendor table: training tokens 800B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina1-65B is a Japanese pretrained language model from SB Intuitions, a SoftBank Corp. subsidiary. SB Intuitions released the weights on 2024-06-14 under MIT, together with the first Sarashina2 dense models. It is not instruction-tuned. This dossier was collected on 2026-10-15.", "what_it_is_for": "The press release says the weights were published so academia and industry can study Japanese LLMs. The card describes a pretrained model, not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina1/sarashina1-7b": { "id": "softbank/sarashina1/sarashina1-7b", "name": "Sarashina1-7B", "maker": "softbank", "family": "softbank/sarashina1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-06-14", "context_length": 2048, "max_output_tokens": null, "parameters": "7 billion (vendor configuration table)", "architecture": "GPT-NeoX decoder, RoPE. config.json: 32 layers, hidden 4096, 32 heads, intermediate 16384, vocab 51200, max_position_embeddings 2048. Vendor table: training tokens 1.0T.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina1-7B is a Japanese pretrained language model from SB Intuitions, a SoftBank Corp. subsidiary. SB Intuitions released the weights on 2024-06-14 under MIT, together with the first Sarashina2 dense models. It is not instruction-tuned. This dossier was collected on 2026-10-15.", "what_it_is_for": "The press release says the weights were published so academia and industry can study Japanese LLMs. The card describes a pretrained model, not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2/sarashina2-13b": { "id": "softbank/sarashina2/sarashina2-13b", "name": "Sarashina2-13B", "maker": "softbank", "family": "softbank/sarashina2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-06-14", "context_length": 4096, "max_output_tokens": null, "parameters": "13 billion on the vendor table; Hugging Face model-size tag says 14B", "architecture": "Vendor table: Llama 2, RoPE, vocab 102400, 2.1T training tokens. config.json model_type llama: 40 layers, hidden 5120, 40 heads, intermediate 13824, max_position_embeddings 4096. Japanese cleaned web text is described as about 1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2-13B is a pretrained Japanese and English language model from SB Intuitions. Weights shipped under MIT on 2024-06-14. The vendor's configuration table calls the architecture Llama 2. It is not instruction-tuned. Collected 2026-10-15.", "what_it_is_for": "Released for research and industry analysis of Japanese LLMs. The card shows a completion example, not a chat template.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2/sarashina2-70b": { "id": "softbank/sarashina2/sarashina2-70b", "name": "Sarashina2-70B", "maker": "softbank", "family": "softbank/sarashina2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-08-07", "context_length": 8192, "max_output_tokens": null, "parameters": "70 billion on the vendor table", "architecture": "Vendor table: Llama 2, RoPE, vocab 102400, 2.1T training tokens. config.json model_type llama: 80 layers, hidden 8192, 64 heads, intermediate 28672, max_position_embeddings 8192. Japanese cleaned web text is described as about 1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2-70B is a pretrained Japanese and English language model from SB Intuitions. Weights shipped under MIT on 2024-08-07. The vendor's configuration table calls the architecture Llama 2. It is not instruction-tuned. Collected 2026-10-15.", "what_it_is_for": "Released for research and industry analysis of Japanese LLMs. The card shows a completion example, not a chat template.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2/sarashina2-7b": { "id": "softbank/sarashina2/sarashina2-7b", "name": "Sarashina2-7B", "maker": "softbank", "family": "softbank/sarashina2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-06-14", "context_length": 4096, "max_output_tokens": null, "parameters": "7 billion", "architecture": "Vendor table: Llama 2, RoPE, vocab 102400, 2.1T training tokens. config.json model_type llama: 32 layers, hidden 4096, 32 heads, intermediate 11008, max_position_embeddings 4096. Japanese cleaned web text is described as about 1T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2-7B is a pretrained Japanese and English language model from SB Intuitions. Weights shipped under MIT on 2024-06-14. The vendor's configuration table calls the architecture Llama 2. It is not instruction-tuned. Collected 2026-10-15.", "what_it_is_for": "Released for research and industry analysis of Japanese LLMs. The card shows a completion example, not a chat template.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2/sarashina2-8x70b": { "id": "softbank/sarashina2/sarashina2-8x70b", "name": "Sarashina2-8x70B", "maker": "softbank", "family": "softbank/sarashina2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Sarashina Model NonCommercial License Agreement (released 2024-11-08)", "release_date": "2024-11-08", "context_length": 8192, "max_output_tokens": null, "parameters": "460B total and 130B active (2024-11-25 tech blog). Hugging Face model-size tag says 465B. The press release says a 400-billion-class model.", "architecture": "Mixtral sparse MoE upcycled from Sarashina2-70B. Tech blog and config.json: 80 layers, hidden 8192, 64 heads, FFN intermediate 28672, 8 experts, top-2 routing, vocab 102400, max_position_embeddings 8192. Card: sentencepiece unigram tokenizer with byte fallback and no Japanese pre-tokenizer. Card text says over 450 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2-8x70B is SB Intuitions' sparse mixture-of-experts language model, upcycled from Sarashina2-70B and released on 2024-11-08. The tech blog says 460B parameters with 130B active. The license is non-commercial, and the checkpoint is not instruction-tuned. Collected 2026-10-15.", "what_it_is_for": "The press release says it was published so academia and industry can research a Japanese model at this scale. It is explicitly not a ready-to-serve chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2/sarashina2-vision-14b": { "id": "softbank/sarashina2/sarashina2-vision-14b", "name": "Sarashina2-Vision-14B", "maker": "softbank", "family": "softbank/sarashina2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-17", "context_length": 4096, "max_output_tokens": null, "parameters": "14B on the model card table; Hugging Face model-size tag says 14B", "architecture": "Japanese vision-language model. Language base sarashina2-13b. Vision encoder from Qwen2-VL-7B. Three-stage training: projector on captions, then vision encoder and projector, then projector and LLM on visual instruction data. config.json: max_position_embeddings 4096, hidden 5120, 40 layers, 40 heads, intermediate 13824, vocab 102400, matching Sarashina2-13B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2-Vision-14B is a Japanese vision-language model from SB Intuitions, released as MIT weights. It pairs sarashina2-13b with the Qwen2-VL-7B image encoder. The card dates its Japanese VLM comparison to 2025-03-07. Collected 2026-10-15.", "what_it_is_for": "The card presents it as a Japanese large vision-language model for image understanding and visual instruction following.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2/sarashina2-vision-8b": { "id": "softbank/sarashina2/sarashina2-vision-8b", "name": "Sarashina2-Vision-8B", "maker": "softbank", "family": "softbank/sarashina2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-17", "context_length": 4096, "max_output_tokens": null, "parameters": "8B on the model card table", "architecture": "Japanese vision-language model. Language base sarashina2-7b. Vision encoder from Qwen2-VL-7B. Three-stage training: projector on captions, then vision encoder and projector, then projector and LLM on visual instruction data. config.json exposes max_position_embeddings 4096 and vocab 51200-class vocab 102400. Hidden size was not present at the top level of the fetched config. Base model is Sarashina2-7B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2-Vision-8B is a Japanese vision-language model from SB Intuitions, released as MIT weights. It pairs sarashina2-7b with the Qwen2-VL-7B image encoder. The card dates its Japanese VLM comparison to 2025-03-07. Collected 2026-10-15.", "what_it_is_for": "The card presents it as a Japanese large vision-language model for image understanding and visual instruction following.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2-1/sarashina2-1-1b": { "id": "softbank/sarashina2-1/sarashina2-1-1b", "name": "Sarashina2.1-1B", "maker": "softbank", "family": "softbank/sarashina2-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Sarashina Model NonCommercial License Agreement", "release_date": "2024-11-26", "context_length": 8192, "max_output_tokens": null, "parameters": "Model card says 1 billion parameters. The Sarashina-Embedding-v1 card calls the same base a 1.2B model. The 2025-05-26 ModernBERT tech blog lists 1.22B for sbintuitions/sarashina2.1-1b, counting input-token embeddings plus layer parameters.", "architecture": "Llama decoder. config.json: 24 layers, hidden 1792, 16 heads, intermediate 6272, vocab 102400, max_position_embeddings 8192. Two-phase pretraining: 10 trillion Japanese and English web tokens, then 1 trillion mostly Japanese tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2.1-1B is a small pretrained Japanese and English model from SB Intuitions. The card says it was trained in two phases, including 10 trillion web tokens, and is licensed only for non-commercial use. Collected 2026-10-15.", "what_it_is_for": "A compact pretrained base. The embedding v1 card later uses it as the text-embedding backbone. It is not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2-2/sarashina2-2-0-5b": { "id": "softbank/sarashina2-2/sarashina2-2-0-5b", "name": "Sarashina2.2-0.5B", "maker": "softbank", "family": "softbank/sarashina2-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-06", "context_length": 8192, "max_output_tokens": null, "parameters": "about 0.5 billion excluding embeddings and the LM head (model card). Hugging Face model-size tag says 0.8B.", "architecture": "Llama decoder, vocab 102400, max_position_embeddings 8192. config.json: 24 layers, hidden 1280, 16 heads, intermediate 4480. Three-phase training on the 0.5B card, applied to the size family: 10 trillion Japanese, English, and code web tokens; then synthetic data for math and coding; then a small amount of data for application tasks. The 0.5B card says the 0.5B count excludes embeddings and the LM head.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2.2-0.5B is a small pretrained model from SB Intuitions, announced on 2025-03-06 with MIT weights. The card says a three-phase run, starting at 10 trillion tokens, improved Japanese math and coding relative to earlier Sarashina2 models. It is not instruction-tuned. Collected 2026-10-15.", "what_it_is_for": "Pretrained base for research and for the published instruct, vision, OCR, speech, and embedding checkpoints that cite this line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2-2/sarashina2-2-0-5b-instruct-v0-1": { "id": "softbank/sarashina2-2/sarashina2-2-0-5b-instruct-v0-1", "name": "Sarashina2.2-0.5B-Instruct-v0.1", "maker": "softbank", "family": "softbank/sarashina2-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-07", "context_length": 8192, "max_output_tokens": null, "parameters": "Hugging Face model-size tag 0.8B. The base card's 0.5B figure excludes embeddings and the LM head.", "architecture": "Same Llama config as Sarashina2.2-0.5B: 24 layers, hidden 1280, 16 heads, intermediate 4480, vocab 102400, context 8192. Post-trained instruct checkpoint. The card's model-details block lists Japanese as the language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2.2-0.5B-Instruct-v0.1 is the instruction-tuned Sarashina2.2 checkpoint from SB Intuitions, MIT licensed. The model card reports Japanese and English chat benchmarks. SB Intuitions' blog archive dates the instruct write-up to 2025-03-07. Collected 2026-10-15.", "what_it_is_for": "Chat and instruction following in Japanese. The base-model card points to this checkpoint as the post-trained release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2-2/sarashina2-2-1b": { "id": "softbank/sarashina2-2/sarashina2-2-1b", "name": "Sarashina2.2-1B", "maker": "softbank", "family": "softbank/sarashina2-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-06", "context_length": 8192, "max_output_tokens": null, "parameters": "Name and Hugging Face tag say 1B. The 2025-05-26 ModernBERT blog lists 1.41B, counting input-token embeddings plus layer parameters.", "architecture": "Llama decoder, vocab 102400, max_position_embeddings 8192. config.json: 24 layers, hidden 1792, 16 heads, intermediate 6272. Three-phase training on the 0.5B card, applied to the size family: 10 trillion Japanese, English, and code web tokens; then synthetic data for math and coding; then a small amount of data for application tasks. The 0.5B card says the 0.5B count excludes embeddings and the LM head.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2.2-1B is a small pretrained model from SB Intuitions, announced on 2025-03-06 with MIT weights. The card says a three-phase run, starting at 10 trillion tokens, improved Japanese math and coding relative to earlier Sarashina2 models. It is not instruction-tuned. Collected 2026-10-15.", "what_it_is_for": "Pretrained base for research and for the published instruct, vision, OCR, speech, and embedding checkpoints that cite this line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2-2/sarashina2-2-1b-instruct-v0-1": { "id": "softbank/sarashina2-2/sarashina2-2-1b-instruct-v0-1", "name": "Sarashina2.2-1B-Instruct-v0.1", "maker": "softbank", "family": "softbank/sarashina2-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-07", "context_length": 8192, "max_output_tokens": null, "parameters": "ModernBERT blog lists 1.41B including input embeddings and layer parameters. Hugging Face tag says 1B.", "architecture": "Same Llama config as Sarashina2.2-1B: 24 layers, hidden 1792, 16 heads, intermediate 6272, vocab 102400, context 8192. Post-trained instruct checkpoint. The card's model-details block lists Japanese as the language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2.2-1B-Instruct-v0.1 is the instruction-tuned Sarashina2.2 checkpoint from SB Intuitions, MIT licensed. The model card reports Japanese and English chat benchmarks. SB Intuitions' blog archive dates the instruct write-up to 2025-03-07. Collected 2026-10-15.", "what_it_is_for": "Chat and instruction following in Japanese. The base-model card points to this checkpoint as the post-trained release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2-2/sarashina2-2-3b": { "id": "softbank/sarashina2-2/sarashina2-2-3b", "name": "Sarashina2.2-3B", "maker": "softbank", "family": "softbank/sarashina2-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-06", "context_length": 8192, "max_output_tokens": null, "parameters": "Name says 3B. The 2025-05-26 ModernBERT blog lists 3.36B, counting input-token embeddings plus layer parameters.", "architecture": "Llama decoder, vocab 102400, max_position_embeddings 8192. config.json: 32 layers, hidden 2560, 16 heads, intermediate 8960. Three-phase training on the 0.5B card, applied to the size family: 10 trillion Japanese, English, and code web tokens; then synthetic data for math and coding; then a small amount of data for application tasks. The 0.5B card says the 0.5B count excludes embeddings and the LM head.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2.2-3B is a small pretrained model from SB Intuitions, announced on 2025-03-06 with MIT weights. The card says a three-phase run, starting at 10 trillion tokens, improved Japanese math and coding relative to earlier Sarashina2 models. It is not instruction-tuned. Collected 2026-10-15.", "what_it_is_for": "Pretrained base for research and for the published instruct, vision, OCR, speech, and embedding checkpoints that cite this line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2-2/sarashina2-2-3b-instruct-v0-1": { "id": "softbank/sarashina2-2/sarashina2-2-3b-instruct-v0-1", "name": "Sarashina2.2-3B-Instruct-v0.1", "maker": "softbank", "family": "softbank/sarashina2-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-07", "context_length": 8192, "max_output_tokens": null, "parameters": "ModernBERT blog lists 3.36B including input embeddings and layer parameters. Hugging Face tag says 3B.", "architecture": "Same Llama config as Sarashina2.2-3B: 32 layers, hidden 2560, 16 heads, intermediate 8960, vocab 102400, context 8192. Post-trained instruct checkpoint. The card's model-details block lists Japanese as the language.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2.2-3B-Instruct-v0.1 is the instruction-tuned Sarashina2.2 checkpoint from SB Intuitions, MIT licensed. The model card reports Japanese and English chat benchmarks. SB Intuitions' blog archive dates the instruct write-up to 2025-03-07. Collected 2026-10-15.", "what_it_is_for": "Chat and instruction following in Japanese. The base-model card points to this checkpoint as the post-trained release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2-2/sarashina2-2-ocr": { "id": "softbank/sarashina2-2/sarashina2-2-ocr", "name": "Sarashina2.2-OCR", "maker": "softbank", "family": "softbank/sarashina2-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-03-22", "context_length": 16384, "max_output_tokens": null, "parameters": "Card text says an end-to-end 3B-parameter OCR model. Hugging Face model-size tag says 4B.", "architecture": "SigLIP2 vision encoder (google/siglip2-so400m-patch14-384) with Sarashina2.2-3B-Instruct. config.json matches the 3B language stack: hidden 2560, 32 layers, 16 heads, intermediate 8960, vocab 102400, max_position_embeddings 16384. Training: expanded vision pretraining up to about 2.5M pixels, OCR SFT, then mixed preference optimization. Outputs Markdown, HTML tables, LaTeX math, and bounding boxes.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2.2-OCR is an SB Intuitions document parser that turns Japanese and English pages into Markdown. The card calls it a 3B end-to-end OCR model with a SigLIP2 encoder and Sarashina2.2-3B-Instruct. MIT weights appeared on Hugging Face on 2026-03-22. Collected 2026-10-15.", "what_it_is_for": "End-to-end OCR for Japanese and English documents, including vertical text, tables, math, and figure boxes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2-2/sarashina2-2-tts": { "id": "softbank/sarashina2-2/sarashina2-2-tts", "name": "Sarashina2.2-TTS", "maker": "softbank", "family": "softbank/sarashina2-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Sarashina Model NonCommercial License Agreement. The card says commercial use requires contacting SB Intuitions. Demo audio is research-only.", "release_date": "2026-04-16", "context_length": 8192, "max_output_tokens": null, "parameters": "Hugging Face model-size tag says 0.8B. The language stack matches Sarashina2.2-0.5B.", "architecture": "LLM speech model finetuned from Sarashina2.2-0.5B-Instruct-v0.1. config.json: Llama, 24 layers, hidden 1280, 16 heads, intermediate 4480, vocab 108986, max_position_embeddings 8192. Card acknowledges CosyVoice, HiFT-GAN, and 3D-Speaker code. Japanese-centric, English supported, zero-shot voice.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2.2-TTS is SB Intuitions' Japanese-centric text-to-speech model, built on Sarashina2.2-0.5B-Instruct and released on Hugging Face on 2026-04-16. It supports Japanese and English, including zero-shot voice. The license is non-commercial. Collected 2026-10-15.", "what_it_is_for": "Speech synthesis with pronunciation accuracy for Japanese, including kanji polyphony, as described in the linked paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina2-2/sarashina2-2-vision-3b": { "id": "softbank/sarashina2-2/sarashina2-2-vision-3b", "name": "Sarashina2.2-Vision-3B", "maker": "softbank", "family": "softbank/sarashina2-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-11-19", "context_length": null, "max_output_tokens": null, "parameters": "3.8B on the model card comparison table. Hugging Face size tag says 4B.", "architecture": "Based on Sarashina2.2-3B-Instruct and a SigLIP image encoder. Custom code, architecture name sarashina2_vision. config.json returned HTTP 401 during collection, so layer sizes are not copied here.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina2.2-Vision-3B is a Japanese vision-language model from SB Intuitions, MIT licensed, built on Sarashina2.2-3B-Instruct and SigLIP. The card's own table lists 3.8B parameters. Hugging Face created the repo on 2025-11-19. Collected 2026-10-15.", "what_it_is_for": "Image understanding, including Japanese document and slide questions, plus English document VQA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina3/sarashina3-embedding": { "id": "softbank/sarashina3/sarashina3-embedding", "name": "Sarashina3 embedding", "maker": "softbank", "family": "softbank/sarashina3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-30", "context_length": 8000, "max_output_tokens": null, "parameters": "", "architecture": "Closed embedding model. API output is 1536 floats and can be reduced to 256 dimensions. Task types: RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, QUESTION_ANSWERING, FACT_VERIFICATION, SEMANTIC_SIMILARITY, CLASSIFICATION, CLUSTERING.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina3 embedding is SB Intuitions' closed Japanese embedding model on the Sarashina API and Cloud PF Type A. Docs specify 1536 dimensions and an 8000-token input cap. The vendor JMTEB average is 78.22. Weights are not public. Collected 2026-10-15.", "what_it_is_for": "RAG candidate retrieval and other Japanese semantic tasks, with a required task type that changes how the text is embedded.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina3/sarashina3-guard": { "id": "softbank/sarashina3/sarashina3-guard", "name": "Sarashina3 guard", "maker": "softbank", "family": "softbank/sarashina3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-30", "context_length": 64000, "max_output_tokens": 8000, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina3 guard is SB Intuitions' closed Japanese moderation model. It does not write answers. On the chat-completions API it returns a safe or harmful label and, when harmful, codes T1 through T12. Context is 64k. It has been hosted since 2026-06-30. Collected 2026-10-15.", "what_it_is_for": "Classify the latest turn of a dialogue as safe or harmful so an application can decide what to block. The vendor says the model can both miss and over-flag.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina3/sarashina3-mini": { "id": "softbank/sarashina3/sarashina3-mini", "name": "Sarashina3 mini", "maker": "softbank", "family": "softbank/sarashina3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-30", "context_length": 128000, "max_output_tokens": 8000, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina3 mini is SB Intuitions' closed standard chat model, on SoftBank Cloud PF Type A since 2026-06-30 and on the free sarashina.ai beta since 2026-10-13. Official docs give it a 128k context, tool calling, and an 8000-token output cap. Parameter count is not published. Collected 2026-10-15.", "what_it_is_for": "Business chat, writing, tool use, math, and code. The vendor describes it as fast and non-deliberative rather than a long chain-of-thought model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina3/sarashina3-nano": { "id": "softbank/sarashina3/sarashina3-nano", "name": "Sarashina3 nano", "maker": "softbank", "family": "softbank/sarashina3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-30", "context_length": 128000, "max_output_tokens": 8000, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina3 nano is the closed lightweight sibling of Sarashina3 mini from SB Intuitions. It has the same 128k context in the API docs and a higher free-beta rate limit of 60 requests per minute. It has been on Cloud PF Type A since 2026-06-30. Collected 2026-10-15.", "what_it_is_for": "High-volume extraction, tagging, classification, and summarization. The docs say it is for simpler tasks than mini.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina3/sarashina3-rerank": { "id": "softbank/sarashina3/sarashina3-rerank", "name": "Sarashina3 rerank", "maker": "softbank", "family": "softbank/sarashina3", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-30", "context_length": 8000, "max_output_tokens": null, "parameters": "", "architecture": "Cross-encoder reranker. The tech blog says the base is ModernBERT-Ja-1.4B. API limit is 8000 tokens per query-document pair. The blog says the model was designed for a combined length of 8192.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina3 rerank is SB Intuitions' closed Japanese reranker, on Cloud PF Type A since 2026-06-30 and on the public API. The tech blog says it is based on ModernBERT-Ja-1.4B and reports a JMTEB retrieval-subset average of 93.08. Collected 2026-10-15.", "what_it_is_for": "Rescore documents after a first-pass search so RAG keeps the passages that match the query, optionally under a custom instruction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/sarashina3/sarashina3-translate": { "id": "softbank/sarashina3/sarashina3-translate", "name": "Sarashina3 translate", "maker": "softbank", "family": "softbank/sarashina3", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed translation model. The blog describes SFT on parallel text followed by DPO using a translation-quality model plus MetricX and MQM. Token context is not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sarashina3 translate is SB Intuitions' closed Japanese-English translation model. The tech blog introduced it on 2026-07-14, and the public Sarashina beta has offered it since 2026-10-13. The API limit is 5000 characters. Collected 2026-10-15.", "what_it_is_for": "Natural Japanese-to-English and English-to-Japanese translation across business, news, technical, and casual text. It is not the general chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/tiny-lm/tiny-lm": { "id": "softbank/tiny-lm/tiny-lm", "name": "tiny-lm", "maker": "softbank", "family": "softbank/tiny-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-06-07", "context_length": 2048, "max_output_tokens": null, "parameters": "16M on the model card", "architecture": "Card text: 4-layer, 512-hidden-size transformer. config.json disagrees: hidden_size 256, 4 layers, 4 heads, intermediate 640, max_position_embeddings 2048, vocab 51200. The config is the file that ships with the weights; the card was not corrected in the copy read on 2026-10-15.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "tiny-lm is a 16M-parameter debug language model that SB Intuitions published under MIT on 2024-06-07. The card says it is for debugging and testing. The card's 512 hidden size does not match config.json, which says 256. Collected 2026-10-15.", "what_it_is_for": "Library and pipeline tests. The vendor does not present it as a Sarashina product model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "softbank/tiny-lm/tiny-lm-chat": { "id": "softbank/tiny-lm/tiny-lm-chat", "name": "tiny-lm-chat", "maker": "softbank", "family": "softbank/tiny-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-06-07", "context_length": 2048, "max_output_tokens": null, "parameters": "16M on the model card", "architecture": "Card text: 4-layer, 512-hidden-size transformer. config.json disagrees: hidden_size 256, 4 layers, 4 heads, intermediate 640, max_position_embeddings 2048, vocab 51203. The config is the file that ships with the weights; the card was not corrected in the copy read on 2026-10-15.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "tiny-lm-chat is a 16M-parameter debug language model that SB Intuitions published under MIT on 2024-06-07. The card says it is for debugging and testing. The card's 512 hidden size does not match config.json, which says 256. Collected 2026-10-15.", "what_it_is_for": "Library and pipeline tests. The vendor does not present it as a Sarashina product model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sogou/speech-translation/sogou-tiic": { "id": "sogou/speech-translation/sogou-tiic", "name": "Sogou-TIIC Speech Translation System", "maker": "sogou", "family": "sogou/speech-translation", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2018-10-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pipeline for IWSLT 2018 English-to-German lecture and TED speech translation. ASR is an ensemble of Deep-CNN, BLSTM, TDNN, and an n-gram language model with lattice rescoring, followed by a post-processor and a Transformer NMT system. Techniques named in the paper include parallel-data cleaning, single-model fine-tuning, ensembles, and feature rescoring.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sogou-TIIC is the closed English-to-German speech-translation system Sogou submitted with Tsinghua's Tiangong Institute for Intelligent Computing to IWSLT 2018. Sogou's 8 November 2018 release said it placed first in the baseline spoken-language track. It was a shared-task system, not an API product. Collected on 2026-10-15.", "what_it_is_for": "The IWSLT 2018 task was to translate unsegmented English lectures and TED talks into German text. The baseline condition allowed a pipeline. Sogou said the same line of work would later update \u641c\u72d7\u540c\u4f20, the Smart Travel Translator, the Smart Recording Translator, and translation inside Sogou Input Method. No shipping build was identified as Sogou-TIIC.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sogou/tongchuan/snmt": { "id": "sogou/tongchuan/snmt", "name": "SNMT\uff08\u641c\u72d7\u540c\u4f20\uff09", "maker": "sogou", "family": "sogou/tongchuan", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2016-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pipeline, not one end-to-end network: speech recognition, text segmentation and disfluency cleanup, neural machine translation, and, from the 2017 2.0 release, speech synthesis. Sogou said the 2016 translator used an encoder-attention-decoder network and that later simultaneous-interpretation models used GRU units with fewer layers.", "modalities": { "input": [ "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SNMT is Sogou's closed speech-translation system, sold as \u641c\u72d7\u540c\u4f20. Sogou demonstrated it at the 3rd World Internet Conference in November 2016 and named it SNMT at a 21 November 2016 briefing. It is a pipeline of recognition, segmentation, neural translation, and later speech synthesis. No current product page was found on 15 October 2026, so this dossier treats it as retired without a published shutdown date. Collected on 2026-10-15.", "what_it_is_for": "Sogou used it for conference simultaneous interpretation: the speaker's audio was recognized, translated, and shown as subtitles, and later spoken into interpreter channels. The first public showing was Chinese speech to English subtitles. In 2017 Sogou added English-to-Chinese for domestic conferences. \u5f20\u535a in 2018 called SNMT the \u641c\u72d7\u540c\u4f20 translation system and said it could also speak the translation in a male, female, or cloned voice.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sogou/translate/sogou-translate": { "id": "sogou/translate/sogou-translate", "name": "\u641c\u72d7\u7ffb\u8bd1\uff08Sogou Translate\uff09", "maker": "sogou", "family": "sogou/translate", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2016-12-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed neural machine translation. Early systems were deep RNN encoder-attention-decoder models. Sogou said a production Transformer system went online in July 2017, with an in-house decoder. Device builds were later described as compressed, distilled students of a large online Transformer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u641c\u72d7\u7ffb\u8bd1 is Sogou's closed neural machine-translation system for text. The public site fanyi.sogou.com opened on 21 December 2016 and was still serving a translator page on 15 October 2026. The paid DeepI text API that exposed the same brand stopped on 28 February 2022. This dossier was collected on 2026-10-15.", "what_it_is_for": "Sogou described it as a commercial neural translator for cross-language search, the translation site and app, the browser, the input method, and later travel and note-taking devices. A Geekpark report called it the first fully neural commercial translation system from the company. The live site offers text, document, and image translation plus an English-rewrite mode. Image and document paths are product features in front of the text model; Sogou did not publish a vision model card for them.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "soniox/stt/stt-async-preview-v1": { "id": "soniox/stt/stt-async-preview-v1", "name": "Speech-to-Text Async Preview v1", "maker": "soniox", "family": "soniox/stt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Soniox Speech-to-Text Async Preview v1 (`stt-async-preview-v1`) was the recorded-audio preview that `stt-async-v3` replaced on 2025-10-21. Soniox retired it on 2025-10-31, ahead of a 2025-11-30 date, and aliased it to v3. The v3 notes say translation was new on the async side, so this preview transcribed without that feature. Status on 2026-10-15 is retired. The release date was not published.", "what_it_is_for": "Async file transcription before the v3 generation. Soniox's March 2025 benchmark page evaluated an id written `stt-async-preview` on 60 languages in batch mode and said Soniox was the most accurate provider on most languages. That page does not use the `-v1` suffix, so the link is likely but not proven.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "soniox/stt/stt-async-v3": { "id": "soniox/stt/stt-async-v3", "name": "Speech-to-Text Async v3", "maker": "soniox", "family": "soniox/stt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Async speech model paired with stt-rt-v3. Parameter count is not published.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Soniox Speech-to-Text Async v3 (`stt-async-v3`) was the recorded-audio model released on 2025-10-21. It replaced `stt-async-preview-v1` and was the first async Soniox model the changelog says could translate. Soniox removed it on 2026-02-28 and routed the name to `stt-async-v4`. Status on 2026-10-15 is retired.", "what_it_is_for": "File transcription and, new in this generation, translation, across 60+ languages, with structured context, speaker diarization, and stronger alphanumeric recognition. The 2025-10-29 update let a single upload run up to 5 hours.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "soniox/stt/stt-async-v4": { "id": "soniox/stt/stt-async-v4", "name": "Speech-to-Text Async v4", "maker": "soniox", "family": "soniox/stt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single universal async speech model for 60+ languages. Parameter count is not published.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Soniox Speech-to-Text Async v4 (`stt-async-v4`) was the recorded-audio model released on 2026-01-29. It replaced `stt-async-v3` and was removed on 2026-06-30, when the id became an alias of `stt-async-v5`. Status on 2026-10-15 is retired.", "what_it_is_for": "The launch post presents it as one universal model for file transcription and translation across 60+ languages, including code-switching, speaker separation, long recordings, and normalization of dates, numbers, and addresses. Soniox described the quality as human-parity or speaker-native. That is a vendor claim; a numeric score table was not in the post.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "soniox/stt/stt-async-v5": { "id": "soniox/stt/stt-async-v5", "name": "Speech-to-Text Async v5", "maker": "soniox", "family": "soniox/stt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end speech-native async model. Soniox says it processes acoustics and linguistic context together and is not a text model wrapped around audio. Parameter count is not published.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.5, "completion_per_m_usd": 3.5 }, "summary_intro": "Soniox Speech-to-Text Async v5 (`stt-async-v5`) is the active model for recorded audio. Soniox released it on 2026-06-11 as the replacement for `stt-async-v4`. It is a separate API id from real-time v5. This dossier was collected on 2026-10-15.", "what_it_is_for": "Soniox describes it as a speech-native model that turns real-world recordings into structured text: transcript, speaker labels, language labels, timestamps, and normalized entities such as numbers, dates, emails, addresses, names, and codes. The changelog cites higher accuracy across 60+ languages, better noise and accent robustness, reengineered speaker separation, improved language identification, and stronger use of context. The launch post says it sees the whole recording, unlike the live model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "soniox/stt/stt-rt-preview-v1": { "id": "soniox/stt/stt-rt-preview-v1", "name": "Speech-to-Text Real-time Preview v1", "maker": "soniox", "family": "soniox/stt", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Soniox Speech-to-Text Real-time Preview v1 (`stt-rt-preview-v1`) is a named real-time model that the STT changelog only records as deprecated on 2025-08-15. It is not on the current model or alias tables. Status on 2026-10-15 is retired. Confidence is medium because the release itself was not found.", "what_it_is_for": "Not described beyond the name. It was the real-time preview that v2 followed. The changelog does not say what it transcribed or which languages it covered.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "soniox/stt/stt-rt-preview-v2": { "id": "soniox/stt/stt-rt-preview-v2", "name": "Speech-to-Text Real-time Preview v2", "maker": "soniox", "family": "soniox/stt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Soniox Speech-to-Text Real-time Preview v2 (`stt-rt-preview-v2`) was a preview live model. The STT changelog dates the release to 2025-08-05. Soniox deprecated it on 2025-10-21, planned to retire it on 2025-11-30, then retired it early on 2025-10-31 and aliased it to v3. Status on 2026-10-15 is retired.", "what_it_is_for": "The changelog says the release improved transcription accuracy, translation quality, language switching, and expanded translation to all pairs. An official X post on 2025-08-12 also announced it as a launch with any-to-any translation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "soniox/stt/stt-rt-v3": { "id": "soniox/stt/stt-rt-v3", "name": "Speech-to-Text Real-time v3", "maker": "soniox", "family": "soniox/stt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single foundation speech model used for real-time transcription and translation. Parameter count is not published.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Soniox Speech-to-Text Real-time v3 (`stt-rt-v3`) was the live model released on 2025-10-21 with the async sibling `stt-async-v3`. It replaced `stt-rt-preview-v2`. Soniox removed it on 2026-02-28 and routed the name to `stt-rt-v4`. Status on 2026-10-15 is retired.", "what_it_is_for": "The v3 post describes one foundation model for real-world speech: higher accuracy in 60+ languages, mid-sentence language switching, better translation (called out for German and Korean), structured context, alphanumeric accuracy, and speaker diarization in overlap. Real-time and async were two ids, not one.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "soniox/stt/stt-rt-v4": { "id": "soniox/stt/stt-rt-v4", "name": "Speech-to-Text Real-time v4", "maker": "soniox", "family": "soniox/stt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single multilingual real-time speech model. Soniox called it backward-compatible with v3. Parameter count is not published.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Soniox Speech-to-Text Real-time v4 (`stt-rt-v4`) was the live speech model released on 2026-02-05. It replaced `stt-rt-v3` and was itself removed on 2026-06-30, when the id became an alias of `stt-rt-v5`. Status on 2026-10-15 is retired. Weights were never released.", "what_it_is_for": "The launch post calls it a real-time recognizer for voice agents, live captions, and multilingual conversation, with native-speaker accuracy claims across 60+ languages, lower endpoint latency, semantic endpointing, and streaming translation that does not wait for a full sentence. The changelog adds `max_endpoint_delay_ms`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "soniox/stt/stt-rt-v5": { "id": "soniox/stt/stt-rt-v5", "name": "Speech-to-Text Real-time v5", "maker": "soniox", "family": "soniox/stt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single speech-native model for live transcription, speaker separation, language identification, and translation. Parameter count is not published.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 4.0 }, "summary_intro": "Soniox Speech-to-Text Real-time v5 (`stt-rt-v5`) is the active live speech model. Soniox released it on 2026-06-16 as the replacement for `stt-rt-v4`. It transcribes, separates speakers, identifies language, and translates in one streaming API. This dossier was collected on 2026-10-15.", "what_it_is_for": "Soniox says it is the real-time model for live audio: voice agents, captions, dictation, meetings, and multilingual conversation. The 2026-06-16 changelog lists higher accuracy across 60+ languages, stronger robustness on noise, telephony, far-field audio, accents, and overlap, reinvented speaker separation, better language identification, higher-quality translation across 3,600+ language pairs, faster semantic endpointing, and more reliable formatting of numbers, dates, emails, names, addresses, and codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "soniox/tts/tts-rt-v1": { "id": "soniox/tts/tts-rt-v1", "name": "Text-to-Speech Real-time v1", "maker": "soniox", "family": "soniox/tts", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First generally available Soniox text-to-speech model. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": 21.5 }, "summary_intro": "Soniox Text-to-Speech Real-time v1 (`tts-rt-v1`) is the first generally available Soniox TTS model, released on 2026-04-29 from the preview id `tts-rt-v1-preview`. The models page on 2026-10-15 still lists it as deprecated and says it will be removed on 2026-08-31. That date has passed, and the page has not moved the id to the alias table, so this dossier keeps status deprecated rather than retired. Weights are closed.", "what_it_is_for": "General-availability speech generation. The preview note that carried into this model cited native-speaker speech in 60+ languages, no invented or dropped words, alphanumeric pronunciation, streaming before the sentence ends, several voices across languages, and both WebSocket and REST. v2 later added audio tags, stronger cloning, and shorter silences. The changelog does not say which of those v1 already had.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "soniox/tts/tts-rt-v1-preview": { "id": "soniox/tts/tts-rt-v1-preview", "name": "Text-to-Speech Real-time v1 Preview", "maker": "soniox", "family": "soniox/tts", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Soniox Text-to-Speech Real-time v1 Preview (`tts-rt-v1-preview`) was the first named Soniox text-to-speech model. The TTS changelog says it entered preview on 2026-04-23. On 2026-04-29 Soniox promoted it to `tts-rt-v1` and left the preview id as an alias. Status on 2026-10-15 is retired as a distinct model. The alias string is still documented.", "what_it_is_for": "A preview for developer feedback before general availability. Stated capabilities were native-speaker speech in 60+ languages, generation that does not invent or drop words, pronunciation of emails, phone numbers, addresses, and codes, streaming before the sentence ends, multiple voices across languages, and configurable audio formats on both WebSocket and REST.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "soniox/tts/tts-rt-v2": { "id": "soniox/tts/tts-rt-v2", "name": "Text-to-Speech Real-time v2", "maker": "soniox", "family": "soniox/tts", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single multilingual text-to-speech model. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": 21.5 }, "summary_intro": "Soniox Text-to-Speech Real-time v2 (`tts-rt-v2`) is the active speech-generation model. Soniox made it generally available on 2026-08-11 in the US, EU, Japan, and India. It replaces `tts-rt-v1` and stays request-compatible with it. Weights are closed. This dossier was collected on 2026-10-15.", "what_it_is_for": "Soniox says it generates natural speech in more than 60 languages, with audio tags for emotion and delivery, high-fidelity voice cloning, mid-sentence language switching, tighter pronunciation of names, numbers, codes, and addresses, and shorter silence between sentences. The same voice can speak every supported language. Streaming can start before the full sentence is available. REST is for a finished text request.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/aki/aki-4b": { "id": "sony/aki/aki-4b", "name": "AKI-4B", "maker": "sony", "family": "sony/aki", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2025-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal model. Vision encoder is google/siglip-so400m-patch14-384. Connector is a Perceiver Resampler. Language decoder is microsoft/Phi-3.5-mini-instruct, whose causal attention Sony replaces with modality-mutual attention so image tokens can use later text tokens. The card says this adds no parameters and does not increase training time. The 4B in the name is the maker's size class. An exact count is not on the card. Phi-3.5-mini is Microsoft's 3.8B class decoder.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AKI-4B is Sony's open-weight multimodal model that turns causal attention in a Phi-3.5-mini decoder into modality-mutual attention. The checkpoint is active and non-commercial. Collected 2026-10-15.", "what_it_is_for": "The model card calls AKI a multimodal foundation model. The change lets earlier image tokens use information from later text tokens, which Sony says reduces vision-language misalignment without extra parameters or extra training time. It is aimed at image chat and the visual question-answering sets used in the card's table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/argus/argus": { "id": "sony/argus/argus", "name": "Argus", "maker": "sony", "family": "sony/argus", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "about 100M backbone parameters, of which 13.6% are trained", "architecture": "Compact vision foundation model. Two stages: multitask pretraining of a shared backbone with a lightweight task adapter, then adaptation by fine-tuning only task-specific decoders. The backbone starts from a pretrained vision transformer. Tasks cover image, region, and pixel outputs.", "modalities": { "input": [ "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Argus is Sony AI's compact vision foundation model, with Sony Semiconductor Solutions coauthors, published at CVPR 2025. Weights were not found. Collected 2026-10-15.", "what_it_is_for": "Sony AI describes Argus as one backbone for many vision tasks without the data and compute of the largest foundation models. Stage one is multitask pretraining. Stage two adds a task by training a decoder. The CVPR blog points it at chip design, edge sensing, and privacy-aware vision. The name refers to Argus Panoptes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/ctm/ctm": { "id": "sony/ctm/ctm", "name": "Consistency Trajectory Model", "maker": "sony", "family": "sony/ctm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT on the Sony/ctm-lsun-bedroom checkpoint card. The card says the teacher file is a rehost of OpenAI Consistency Models weights, which are under that project's license, not MIT.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One network that can jump along the probability-flow ODE of a diffusion model, covering both one-step consistency sampling and multi-step score-based sampling. The published Sony org checkpoint is unconditional generation of LSUN Bedroom at 256x256.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Consistency Trajectory Models are Sony's image generators that sample a diffusion trajectory in one step or in longer jumps. The Sony Hugging Face org hosts an unconditional LSUN Bedroom checkpoint. Collected 2026-10-15.", "what_it_is_for": "The card says one network can emit a one-step sample or travel any distance along the probability-flow ODE, and can also expose a score for likelihood and control. The bedroom checkpoint generates 256x256 LSUN Bedroom images with no text prompt. The paper's headline one-step results are CIFAR-10 and ImageNet at 64x64.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/diff-a-riff/diff-a-riff": { "id": "sony/diff-a-riff/diff-a-riff", "name": "Diff-A-Riff", "maker": "sony", "family": "sony/diff-a-riff", "status": "active", "confidence": "high", "weights": "closed", "license": "The paper PDF is CC BY 4.0. The model weights are not released. The companion site says the training set was legally acquired for internal research and neither the data nor the model can be made public.", "release_date": "2024-06-14", "context_length": null, "max_output_tokens": null, "parameters": "500M including the consistency autoencoder and excluding CLAP", "architecture": "Latent diffusion accompaniment model. A pretrained consistency-autoencoder compresses audio. A DDPM++ network follows the Elucidated Diffusion Models framework. Conditioning is a CLAP embedding from the target, plus the musical context. Output is 48 kHz pseudo-stereo. The authors say it uses about 3 GB and generates 90 seconds of audio in 6 seconds on one GPU.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Diff-A-Riff is Sony Computer Science Laboratories Paris's closed model for generating a single instrumental accompaniment that fits an existing musical context. It was announced in June 2024 and is not publicly downloadable. Collected 2026-10-15.", "what_it_is_for": "The lab built it for producers who add one instrument at a time. It generates an instrumental stem for any musical context, with optional text or audio reference control. The companion site says the training set was legally acquired for internal research, so the data and the model stay private.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/megami/megami": { "id": "sony/megami/megami", "name": "MEGAMI", "maker": "sony", "family": "sony/megami", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-11", "context_length": null, "max_output_tokens": null, "parameters": "about 70M for the score transformer and about 9M for the effect-processor TCN", "architecture": "Generative automatic mixing model. A transformer score model samples per-track effect embeddings. A track-agnostic temporal convolutional processor applies them. The setup is permutation-equivariant so tracks do not need labels. Domain adaptation in a CLAP embedding space lets training use dry and wet recordings. Public checkpoints are CLAP_DA_public, FxGenerator_public, and FxProcessor_public, plus a public LAION-CLAP music checkpoint.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MEGAMI is Sony AI's generative automatic music-mixing model. Public checkpoints exist for research reproduction. It was submitted to ICASSP 2026. Collected 2026-10-15.", "what_it_is_for": "The paper treats mixing as one-to-many: several professional mixes can be valid for the same stems. MEGAMI samples effect embeddings and applies them with a label-free processor, instead of predicting one fixed mix. The demo plays several mixes of the same song and compares them with human mixes and older automatic mixers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/mmaudio/mmaudio": { "id": "sony/mmaudio/mmaudio", "name": "MMAudio", "maker": "sony", "family": "sony/mmaudio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Code MIT. Checkpoints CC BY-NC 4.0, hosted at hkchengrex/MMAudio. Sony Research also publishes the code repository.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "157M (small), 621M (medium), 1.03B (large)", "architecture": "Flow-matching video-to-audio model trained jointly on audio-visual and audio-text data, with a synchronization module that aligns video frames to audio latents. Sampling rates are 16 kHz and 44.1 kHz. Inference needs a flow network, Synchformer, CLIP, a VAE, and a vocoder. The 16 kHz vocoder is BigVGAN from Make-An-Audio 2.", "modalities": { "input": [ "video", "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MMAudio is a video-to-audio and text-to-audio model from a Sony AI internship with the University of Illinois Urbana-Champaign. Sony AI treats it as its own release. The checkpoints are active and non-commercial. Collected 2026-10-15.", "what_it_is_for": "The paper says MMAudio generates synchronized audio from video and/or text. Joint training on audio-visual and audio-text data is the stated reason it can do both video-to-audio and text-to-audio. Sony's 2025 year in review calls the release part of its creator audio work. Yuki Mitsufuji later described PAVAS as the physics-grounded follow-on.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/mola/mola": { "id": "sony/mola/mola", "name": "MoLA", "maker": "sony", "family": "sony/mola", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0 for the Hugging Face checkpoint. Code at github.com/sony/MoLA is MIT.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Text-to-motion latent diffusion model with a variational autoencoder and adversarial training. An activation variable allows the motion length to follow the text. Editing of start and end positions and pelvis trajectory is training-free guidance, according to the model card.", "modalities": { "input": [ "text" ], "output": [ "motion" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MoLA is Sony's open-weight text-to-motion model. It generates variable-length human motion and supports several edits without extra training. Collected 2026-10-15.", "what_it_is_for": "The model card says text-to-motion systems need the motion length to match the sentence and need edits such as start and end positions or a pelvis path. MoLA puts an activation variable in the motion representation for variable length, then uses a variational autoencoder and an adversarially trained latent diffusion model. Editing is described as training-free guidance.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/pavas/pavas": { "id": "sony/pavas/pavas", "name": "PAVAS", "maker": "sony", "family": "sony/pavas", "status": "active", "confidence": "high", "weights": "closed", "license": "Repository code is MIT on the SonyResearch GitHub page. A license for trained PAVAS weights was not found.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent video-to-audio model built on MMAudio. A Physics Parameter Estimator reads object mass with a vision-language model and velocity from segmentation and dynamic 3D reconstruction. A Physics-Driven Audio Adapter injects those values into the diffusion backbone. The paper calls the large variant PAVAS-L.", "modalities": { "input": [ "video", "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PAVAS is Sony AI's physics-aware video-to-audio model, a CVPR 2026 oral built on MMAudio. Code is public. A pretrained weight file was not confirmed. Collected 2026-10-15.", "what_it_is_for": "The paper says most video-to-audio models follow appearance and miss the physical causes of sound. PAVAS estimates object mass and velocity and injects them into a latent diffusion generator so impacts and motion sound more physically consistent. Sony AI's Yuki Mitsufuji called it the physics-grounded successor to MMAudio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/sonido/sonido": { "id": "sony/sonido/sonido", "name": "SoniDo", "maker": "sony", "family": "sony/sonido", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-02", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Hierarchical generative music model. Stage one is SQ-VAE-2. Stage two is three sparse transformer priors. Context length is 8192 tokens at each level. Widths are 4800, 3200, and 2880, with 72 self-attention layers. Attention heads are 8, 4, and 4. Audio is 44.1 kHz. The paper says it is also used as a feature extractor for tagging, transcription, source separation, and mixing.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio", "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SoniDo is Sony AI's closed music foundation model, introduced in a November 2024 preprint and published in TMLR in 2025. It generates music and supplies hierarchical features for other music tasks. Collected 2026-10-15.", "what_it_is_for": "The paper says one foundation model can boost tagging, transcription, source separation, and mixing by feeding intermediate features into task models, including when those tasks have little data. SoniDo itself is a text-conditioned generative model. The name is the Spanish word for sound. Authors said the licensed training set cannot be used to open-source the weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/soundctm/soundctm": { "id": "sony/soundctm/soundctm", "name": "SoundCTM", "maker": "sony", "family": "sony/soundctm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0 for the Hugging Face weights. Code at github.com/sony/soundctm is MIT.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Consistency trajectory model for text-to-sound. One network is trained to switch between 1-step and multi-step sampling. Conditional and unconditional students are trained together and interpolated at inference for guidance. The SoundReactor VAE repository describes a SoundCTM non-causal VAE at 44.1 kHz mono, temporal downsample 1024, latent channels 64.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SoundCTM is Sony AI's open-weight text-to-sound model. It is a consistency trajectory model that can sample in one step or in several steps. Collected 2026-10-15.", "what_it_is_for": "The model card says creators need fast drafts and higher-quality refinements of the same sound. SoundCTM is trained so one network can do a 1-step sample or a longer multi-step sample, without an extra pretrained feature network. The abstract also describes training-free controls that use that sampler.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/soundreactor/soundreactor": { "id": "sony/soundreactor/soundreactor", "name": "SoundReactor", "maker": "sony", "family": "sony/soundreactor", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Frame-level online video-to-audio model. A causal decoder-only transformer predicts continuous audio latents. Vision tokens come from the smallest DINOv2 variant, aggregated to one token per frame. A 48 kHz stereo VAE, trained from scratch, encodes the waveform. The diffusion head is pretrained and then consistency-tuned. The project page reports per-frame waveform latency of 26.3 ms at diffusion-head NFE 1 and 31.5 ms at NFE 4, on 30 fps 480p video with one H100.", "modalities": { "input": [ "video" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SoundReactor is Sony AI's frame-level online video-to-audio model. It generates full-band stereo audio from the current video frame without looking ahead. The paper is public. The full generator weights were not found. Collected 2026-10-15.", "what_it_is_for": "The project page defines a setting where future frames are unavailable, which matches live or streaming picture rather than an offline clip. The backbone is a causal transformer over continuous 48 kHz stereo latents, with one DINOv2 token per frame. Demos are on gameplay video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/woosh/woosh-ae": { "id": "sony/woosh/woosh-ae", "name": "Woosh-AE", "maker": "sony", "family": "sony/woosh", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0 (public weights). Inference code is mostly MIT; this codec is part of the public Woosh release.", "release_date": "2026-05-18", "context_length": null, "max_output_tokens": null, "parameters": "221M", "architecture": "Latent audio encoder/decoder. Latent dimension 128, frame rate about 100 Hz, audio sampled at 48 kHz. The technical report says the compression ratio is 0.26, about 9 times lower compression than Stable Audio Open's VAE, and that Woosh-AE is the largest module in the public suite.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Woosh-AE is Sony AI's public audio encoder/decoder inside the Woosh sound-effect foundation model. The public checkpoint is active, open-weight, and non-commercial. Collected 2026-10-15.", "what_it_is_for": "The technical report describes Woosh-AE as the high-quality latent codec that the text-to-audio and video-to-audio generators use. Sony compares public and private reconstructions with Stable Audio Open on the demo page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/woosh/woosh-clap": { "id": "sony/woosh/woosh-clap", "name": "Woosh-CLAP", "maker": "sony", "family": "sony/woosh", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0 (public weights). Repository code is mostly MIT.", "release_date": "2026-05-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal text-audio alignment model. The GitHub README says it provides token latents that condition the diffusion generators. The release also includes TextConditionerA for text-to-audio and TextConditionerV for video-to-audio.", "modalities": { "input": [ "text", "audio" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Woosh-CLAP is the text-audio alignment model Sony AI shipped with the public Woosh sound-effect release. It is active, open-weight, and non-commercial. Collected 2026-10-15.", "what_it_is_for": "The GitHub README says Woosh-CLAP supplies token latents that condition the diffusion generators. The technical report lists a text-audio alignment model as the second module of the public release, next to the codec and the generators.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/woosh/woosh-dflow": { "id": "sony/woosh/woosh-dflow", "name": "Woosh-DFlow", "maker": "sony", "family": "sony/woosh", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0 (public weights). Most repository code is MIT.", "release_date": "2026-05-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Distilled text-to-audio latent diffusion model. The technical report includes distilled text-to-audio weights for low-resource, fast inference. The README calls Woosh-DFlow the distilled counterpart of Woosh-Flow.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Woosh-DFlow is the distilled public text-to-audio checkpoint in Sony AI's Woosh release. It is active, open-weight, and non-commercial. Collected 2026-10-15.", "what_it_is_for": "The technical report adds distilled text-to-audio weights so the same sound-effect task can run with less compute. The demo plays Woosh-DFlow-Public next to Woosh-Flow-Public on the same prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/woosh/woosh-dvflow": { "id": "sony/woosh/woosh-dvflow", "name": "Woosh-DVFlow", "maker": "sony", "family": "sony/woosh", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0 (weights). Video-to-audio code is Apache-2.0. The rest of the repository is mostly MIT.", "release_date": "2026-05-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Distilled video-to-audio latent model. The technical report includes distilled video-to-audio weights for fast inference. The README ships Woosh-DVFlow next to Woosh-VFlow and says that code adapts MMAudio and MotionFormer.", "modalities": { "input": [ "video", "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Woosh-DVFlow is the distilled public video-to-audio checkpoint in Sony AI's Woosh release. It is active, open-weight, and non-commercial. Collected 2026-10-15.", "what_it_is_for": "The technical report includes a distilled video-to-audio model for faster inference. The demo plays Woosh-DVFlow beside Woosh-VFlow and MMAudio, with and without a caption.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/woosh/woosh-flow": { "id": "sony/woosh/woosh-flow", "name": "Woosh-Flow", "maker": "sony", "family": "sony/woosh", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0 (public weights). Most repository code is MIT.", "release_date": "2026-05-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion / flow text-to-audio model for sound effects. The README calls Woosh-Flow the original (non-distilled) text-to-audio LDM. It uses Woosh-AE latents and Woosh-CLAP text conditioning. The report says distilled text-to-audio weights are released separately as Woosh-DFlow.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Woosh-Flow is Sony AI's public text-to-audio sound-effect model in the Woosh release. It is active, open-weight, and non-commercial. Collected 2026-10-15.", "what_it_is_for": "Sony AI built Woosh for professional sound-effect work rather than general music or speech. Woosh-Flow generates a sound effect from a written description. The README also says it can generate unconditionally. The demo compares the public checkpoint with Stable Audio Open and TangoFlux.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/woosh/woosh-flow-private": { "id": "sony/woosh/woosh-flow-private", "name": "Woosh-Flow Private", "maker": "sony", "family": "sony/woosh", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Same Woosh text-to-audio architecture as the public model, trained on licensed studio sound-effect libraries rather than public datasets. Sony's blog says the private model is the professional-grade counterpart and that a DAW plugin was in development.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Woosh-Flow Private is Sony AI's closed text-to-audio sound-effect model, trained on licensed studio libraries in parallel with the public Woosh-Flow weights. It is active and not publicly downloadable. Collected 2026-10-15.", "what_it_is_for": "Sony AI says professional sound effects need isolated, annotated studio recordings rather than in-the-wild public audio. This checkpoint is that studio-data model. The blog says it significantly outperforms public alternatives on professional sound-effect data, and that the public model is meant to prepare the ground for it. A GamesBeat interview said Sony will talk to parties who want to license it. No price card was found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sony/woosh/woosh-vflow": { "id": "sony/woosh/woosh-vflow", "name": "Woosh-VFlow", "maker": "sony", "family": "sony/woosh", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0 (weights). Video-to-audio code is Apache-2.0 because it adapts MMAudio and MotionFormer. The rest of the repository is mostly MIT.", "release_date": "2026-05-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal latent diffusion model for video-to-audio sound effects, with optional text. The README says the video-to-audio code is adapted from MMAudio and MotionFormer. The demo also shows an optional video-only mode marked OV.", "modalities": { "input": [ "video", "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Woosh-VFlow is Sony AI's public video-to-audio sound-effect model. It is active, open-weight, and non-commercial. Collected 2026-10-15.", "what_it_is_for": "The May 2026 blog says video-to-audio is the second Woosh task: sound from a video, with an optional text prompt. The demo compares it with MMAudio, including a video-only setting marked OV. Sony says this path matters for game and film workflows that start from picture rather than a caption.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sorbonne/fennix/fennix-bio1-m": { "id": "sorbonne/fennix/fennix-bio1-m", "name": "FeNNix-Bio1(M)", "maker": "sorbonne", "family": "sorbonne/fennix", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic Software License (ASL): academic non-commercial use only. Commercial use needs a separate license. The license text says ASL is not an OSI open-source license. A 2026-09-25 Figshare deposit of model snapshots is marked CC BY 4.0; that conflicts with the GitHub ASL notice.", "release_date": "2025-04-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Heavier FeNNix-Bio1 neural network potential. Shipped v1.0 training config uses a 2-layer RASTER equivariant module (dim 176, lmax 3, 16 scalar heads, 4 tensor heads, attention dim 16, 4 LODE channels), QEq charge hypotheses (32), species encoding through atomic number 86, short graph cutoff 3.5 \u00c5 and model cutoff 7.5 \u00c5, plus a shallow energy ensemble (output dim 64) and a screened repulsion term. The preprint calls this the more accurate size of the family.", "modalities": { "input": [ "atomic-numbers", "coordinates", "total-charge" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FeNNix-Bio1(M) is the heavier, more accurate neural-network potential in the FeNNix-Bio1 pair from Sorbonne Universit\u00e9's Laboratoire de Chimie Th\u00e9orique (CNRS UMR 7616) and Qubit Pharmaceuticals. It is active. The preprint went up on 2025-04-11, the university and Qubit announced it on 2025-05-20, and weights were posted on 2025-12-17. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors present FeNNix-Bio1 as a foundation model for reactive molecular dynamics in drug design: water, ions, hydration free energies, protein dynamics, protein-ligand binding, and chemical reactions, including nuclear quantum effects. M is the accuracy-oriented size. It runs locally in FeNNol and Tinker-HP/Deep-HP. OpenMM-ML added a wrapper in March 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sorbonne/fennix/fennix-bio1-s": { "id": "sorbonne/fennix/fennix-bio1-s", "name": "FeNNix-Bio1(S)", "maker": "sorbonne", "family": "sorbonne/fennix", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic Software License (ASL): academic non-commercial use only. Commercial use needs a separate license. The license text says ASL is not an OSI open-source license. A 2026-09-25 Figshare deposit of model snapshots is marked CC BY 4.0; that conflicts with the GitHub ASL notice.", "release_date": "2025-04-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lightweight FeNNix-Bio1 neural network potential. Shipped v1.0 training config uses a 2-layer RASTER equivariant module (dim 176, lmax 3, 8 scalar heads, 1 tensor head, attention dim 16), QEq charge hypotheses (32), species encoding through atomic number 86, short graph cutoff 3.5 \u00c5 and model cutoff 7.5 \u00c5, plus a shallow energy ensemble (output dim 64) and a screened repulsion term. The preprint calls this the high-throughput size of the family.", "modalities": { "input": [ "atomic-numbers", "coordinates", "total-charge" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FeNNix-Bio1(S) is the lightweight neural-network potential in the FeNNix-Bio1 pair from Sorbonne Universit\u00e9's Laboratoire de Chimie Th\u00e9orique (CNRS UMR 7616) and Qubit Pharmaceuticals. It is an active research model, first posted as a preprint on 2025-04-11, announced with the university on 2025-05-20, and given public weights on 2025-12-17. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe FeNNix-Bio1 as a foundation potential for reactive, condensed-phase molecular dynamics of biological systems, trained only on synthetic quantum-chemistry data. The S size is the high-throughput model. It is meant to supply energies and forces for drug-design simulations without a hand-built force field. Inference runs locally in FeNNol, Tinker-HP/Deep-HP, and, from March 2026, an OpenMM-ML wrapper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sorbonne/fennix/fennix-op1": { "id": "sorbonne/fennix/fennix-op1", "name": "FENNIX-OP1", "maker": "sorbonne", "family": "sorbonne/fennix", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid machine-learning and force-field potential. An equivariant network predicts a short-range neural energy plus atom-in-molecule charges and volumes. Those parameters feed a charge-penetration electrostatic term and a Tkatchenko-Scheffler-like dispersion term. The authors call FENNIX the model class and FENNIX-OP1 the trained organic/water instance. Implemented in their TorchNFF package on PyTorch.", "modalities": { "input": [ "atomic-numbers", "coordinates" ], "output": [ "energy", "forces" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FENNIX-OP1 is a named neural-network potential from Sorbonne Universit\u00e9's Laboratoire de Chimie Th\u00e9orique (CNRS UMR 7616). The Chemical Science paper of 15 November 2023 introduces FENNIX as a hybrid machine-learning and force-field class and FENNIX-OP1 as the trained model they used. It is not described as retired. Public weights were not located on 2026-10-15. This dossier was collected on that date.", "what_it_is_for": "The paper trains FENNIX-OP1 for gas-phase energies of small neutral organic molecules and dimers, then shows stable condensed-phase dynamics, including nuclear quantum effects for water, a solvated alanine-dipeptide free-energy surface, and reactive dissociation of small molecules. It is a research potential, not an API product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sorbonne/onlyflow/onlyflow": { "id": "sorbonne/onlyflow/onlyflow", "name": "OnlyFlow", "maker": "sorbonne", "family": "sorbonne/onlyflow", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-02-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Motion-conditioning module on a frozen AnimateDiff v3 text-to-video backbone (Stable Diffusion 1.5). Optical flow from a reference video, estimated with RAFT in the paper, is encoded and injected into the backbone. The paper reports a 198 million parameter flow encoder. Injection layers are 19 million parameters for OnlyFlow-T and 31 million for OnlyFlow-ST. The Hugging Face card releases one best-training checkpoint and does not say which variant it is.", "modalities": { "input": [ "text", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OnlyFlow is a named video-diffusion motion controller released by Obvious Research with ISIR at Sorbonne Universit\u00e9. Obvious Research describes itself as a laboratory in partnership with Sorbonne Universit\u00e9. Two authors, Guillaume Jeanneret Sanmiguel and Matthieu Cord, are affiliated with ISIR. Weights are on Hugging Face under Apache-2.0. The model was still public on 2026-10-15, when this dossier was collected.", "what_it_is_for": "The paper uses optical flow from a reference video, plus a text prompt, to control motion in generated video. Stated uses are camera-motion control and video-to-video editing. The trainable part is a flow encoder and injection layers. The text-to-video backbone is frozen AnimateDiff v3 on Stable Diffusion 1.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechify/simba/simba-3-0": { "id": "speechify/simba/simba-3-0", "name": "Simba 3.0", "maker": "speechify", "family": "speechify/simba", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary API. Commercial use is included on Free and paid SpeechifyAI plans. Weights are not released.", "release_date": "2026-05-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Simba 3.0 is Speechify's streaming text-to-speech model, id simba-3.0, launched English-only on 2026-05-09 and given six languages on 2026-07-22. It is closed, still active, and the model a request uses when model is omitted.", "what_it_is_for": "Speechify says it is the streaming model for English plus German, Spanish, French, Italian, and Brazilian Portuguese, routed by the language field, and the compatible default because it accepts the voices simba-3.2 rejects.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechify/simba/simba-3-2": { "id": "speechify/simba/simba-3-2", "name": "Simba 3.2", "maker": "speechify", "family": "speechify/simba", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary API. Commercial use is included on Free and paid SpeechifyAI plans. Weights are not released.", "release_date": "2026-07-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Simba 3.2 is Speechify's recommended English text-to-speech model, id simba-3.2, launched on 2026-07-08 and still active on 2026-10-15. It is closed, streaming-native, and the model Speechify tells new English integrations to set explicitly because the API default is simba-3.0.", "what_it_is_for": "Speechify says it is the streaming model with the lowest time to first byte and the richest expressivity, for new English work, including cloned English voices.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechify/simba/simba-base": { "id": "speechify/simba/simba-base", "name": "Simba Base", "maker": "speechify", "family": "speechify/simba", "status": "retired", "confidence": "medium", "weights": "closed", "license": "Proprietary API. Weights were not released. A commercial-use clause specific to this id was not found.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Simba Base is Speechify's original text-to-speech id simba-base. The 2024-05-22 changelog already treats it as the original model and says it gained multilingual input that day. It was deprecated on 2024-06-20 in favor of simba-english and simba-multilingual. It is closed and is not on the 2026 live list. Collected on 2026-10-15.", "what_it_is_for": "The 2024 changelog calls it the original synthesis model. From 2024-05-22 it accepted multilingual input, with an optional language parameter when the text was entirely one language.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechify/simba/simba-dialogue-1-0": { "id": "speechify/simba/simba-dialogue-1-0", "name": "Simba Dialogue 1.0", "maker": "speechify", "family": "speechify/simba", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary API. Commercial use is included on Free and paid SpeechifyAI plans. Weights are not released.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Simba Dialogue 1.0 is Speechify's multi-speaker speech model, id simba-dialogue-1.0, listed as active and English-only on the documented GET /v1/audio/models response. It is closed and is valid only on POST /v1/audio/dialogue. A launch date was not found. Collected on 2026-10-15.", "what_it_is_for": "Speechify describes a multi-speaker model that renders a speaker-attributed script as one conversation with natural turn-taking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechify/simba/simba-english": { "id": "speechify/simba/simba-english", "name": "Simba English", "maker": "speechify", "family": "speechify/simba", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary API. Commercial use is included on Free and paid SpeechifyAI plans. Weights are not released.", "release_date": "2024-06-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Simba English is Speechify's legacy English text-to-speech id simba-english, introduced on 2024-06-20 as the replacement for simba-base and later called a Simba 1.6 model. It is closed. New workspaces cannot select it as of API version 2026-09-21. Collected on 2026-10-15.", "what_it_is_for": "Speechify kept it for integrations that name it. Until 2026-11-21 it is the Simba 1.6 English training, including full SSML and emotion. After that the id is served by current models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechify/simba/simba-multilingual": { "id": "speechify/simba/simba-multilingual", "name": "Simba Multilingual", "maker": "speechify", "family": "speechify/simba", "status": "retired", "confidence": "high", "weights": "closed", "license": "Proprietary API. Commercial use is included on Free and paid SpeechifyAI plans. Weights are not released.", "release_date": "2024-06-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Simba Multilingual is Speechify's legacy multilingual text-to-speech id simba-multilingual, introduced on 2024-06-20 and later called a Simba 1.6 model. It is closed, covers 31 locales, and is not selectable on API version 2026-09-21 or later. Collected on 2026-10-15.", "what_it_is_for": "Speechify says it synthesizes multiple languages, including mixed-language sentences, with automatic detection when language is omitted. It is the legacy route for locales simba-3.0 does not officially support.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechify/simba/simba-turbo": { "id": "speechify/simba/simba-turbo", "name": "Simba Turbo", "maker": "speechify", "family": "speechify/simba", "status": "retired", "confidence": "medium", "weights": "closed", "license": "Proprietary API. Weights were not released. A commercial-use clause specific to this id was not found.", "release_date": "2024-05-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Simba Turbo is Speechify's English text-to-speech id simba-turbo, announced on 2024-05-22 as a faster model than simba-base. It is closed. No later changelog mentions it, and it is not on the 2026 live list. Collected on 2026-10-15.", "what_it_is_for": "The 2024-05-22 changelog says it is English-only and produces audio faster than simba-base. No later quality or latency figure was published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechmatics/enhanced/enhanced": { "id": "speechmatics/enhanced/enhanced", "name": "Enhanced", "maker": "speechmatics", "family": "speechmatics/enhanced", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Language-pack ASR operating point. The March 2023 Ursa generation described Standard as a smaller self-supervised model and Enhanced as the higher-accuracy point. Ursa 2 (2024-10-11) became the generation behind Speechmatics speech services. Parameter count of the current Enhanced checkpoint is not published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.75, "completion_per_m_usd": null }, "summary_intro": "Enhanced is Speechmatics' highest-accuracy single-language speech-to-text model, API id enhanced, still active on Batch and Realtime as of 2026-10-15. It is the accuracy operating point of the language-pack line that Ursa (2023) and Ursa 2 (2024) shipped into. Weights are closed. You pick a language or a fixed bilingual pack. For mixed-language audio Speechmatics points customers to Melia 1, and for multilingual clinical audio to Oak 1.", "what_it_is_for": "Enhanced is Speechmatics' highest-accuracy single-language speech-to-text model, API id enhanced, still active on Batch and Realtime as of 2026-10-15. It is the accuracy operating point of the language-pack line that Ursa (2023) and Ursa 2 (2024) shipped into. Weights are closed. You pick a language or a fixed bilingual pack. For mixed-language audio Speechmatics points customers to Melia 1, and for multilingual clinical audio to Oak 1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechmatics/enhanced/enhanced-medical": { "id": "speechmatics/enhanced/enhanced-medical", "name": "Enhanced Medical", "maker": "speechmatics", "family": "speechmatics/enhanced", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Domain-specialized Enhanced language pack. Not a separate model id. Selected with model enhanced and domain medical. Speechmatics says it is kept current from maintained clinical data sources. Oak 1 is a different, multilingual medical model.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.75, "completion_per_m_usd": null }, "summary_intro": "Enhanced Medical is Speechmatics' single-language healthcare model, collected 2026-10-15. It is not a separate API id. You set model to enhanced and domain to medical. Speechmatics says it is maintained from clinical sources so procedures, medications, conditions, and anatomy transcribe more reliably. It is active. The 15 September 2025 next-generation medical announcement is the clearest public launch. Oak 1, released 14 October 2026, is the separate multilingual medical model and does not replace this one.", "what_it_is_for": "Enhanced Medical is Speechmatics' single-language healthcare model, collected 2026-10-15. It is not a separate API id. You set model to enhanced and domain to medical. Speechmatics says it is maintained from clinical sources so procedures, medications, conditions, and anatomy transcribe more reliably. It is active. The 15 September 2025 next-generation medical announcement is the clearest public launch. Oak 1, released 14 October 2026, is the separate multilingual medical model and does not replace this one.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechmatics/linden/linden-1": { "id": "speechmatics/linden/linden-1", "name": "Linden 1", "maker": "speechmatics", "family": "speechmatics/linden", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech-to-text model for voice agents. Returns speaker-attributed segments and turn messages rather than a word stream. Parameter count is not published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": null }, "summary_intro": "Linden 1 is Speechmatics' voice-agent speech-to-text model, API id linden-1, released 17 September 2026. It powers Agent STT and is not offered on Batch or the regular Realtime API. It returns speaker-attributed segments and turn events so a voice agent can take the text without a separate end-of-turn detector. Weights are closed. Status is active. The product page says it captures what was said and by whom in about 350 ms, from 0.30 USD per hour.", "what_it_is_for": "Linden 1 is Speechmatics' voice-agent speech-to-text model, API id linden-1, released 17 September 2026. It powers Agent STT and is not offered on Batch or the regular Realtime API. It returns speaker-attributed segments and turn events so a voice agent can take the text without a separate end-of-turn detector. Weights are closed. Status is active. The product page says it captures what was said and by whom in about 350 ms, from 0.30 USD per hour.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechmatics/melia/melia-1": { "id": "speechmatics/melia/melia-1", "name": "Melia 1", "maker": "speechmatics", "family": "speechmatics/melia", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single multilingual speech-to-text model. No public parameter count or architecture diagram. Speechmatics calls Melia 1 the first release of a code-switching lineage.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.24, "completion_per_m_usd": null }, "summary_intro": "Melia 1 is Speechmatics' multilingual speech-to-text model, API id melia-1, collected 2026-10-15. It launched on 17 June 2026 as a production preview for batch and is now documented for Batch and Realtime. One model transcribes code-switching across the Speechmatics language list without a language pack. Accuracy is described as on par with Standard, not as high as Enhanced. Weights are closed. It is the lowest list-price Speechmatics transcription model.", "what_it_is_for": "Melia 1 is Speechmatics' multilingual speech-to-text model, API id melia-1, collected 2026-10-15. It launched on 17 June 2026 as a production preview for batch and is now documented for Batch and Realtime. One model transcribes code-switching across the Speechmatics language list without a language pack. Accuracy is described as on par with Standard, not as high as Enhanced. Weights are closed. It is the lowest list-price Speechmatics transcription model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechmatics/oak/oak-1": { "id": "speechmatics/oak/oak-1", "name": "Oak 1", "maker": "speechmatics", "family": "speechmatics/oak", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multilingual medical speech-to-text model. Press release says it is built on a new architecture. Parameter count is not published. Pricing tooltip calls it Melia 1 tuned for clinical language. Docs call it a standalone model, separate from Enhanced Medical.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": null }, "summary_intro": "Oak 1 is Speechmatics' multilingual medical speech-to-text model, API id oak-1, released 14 October 2026 and active for Batch. It transcribes clinical conversations that switch language and speaker without a language selected in advance. Weights are closed. Docs tell you to keep using Enhanced Medical when you need a single language plus custom dictionary or confidence scores. Realtime is not available.", "what_it_is_for": "Oak 1 is Speechmatics' multilingual medical speech-to-text model, API id oak-1, released 14 October 2026 and active for Batch. It transcribes clinical conversations that switch language and speaker without a language selected in advance. Weights are closed. Docs tell you to keep using Enhanced Medical when you need a single language plus custom dictionary or confidence scores. Realtime is not available.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechmatics/standard/standard": { "id": "speechmatics/standard/standard", "name": "Standard", "maker": "speechmatics", "family": "speechmatics/standard", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Throughput operating point of the same language-pack ASR line as Enhanced. The 2023 Ursa post said Standard uses a smaller self-supervised model than Enhanced. Current parameter count is not published.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.45, "completion_per_m_usd": null }, "summary_intro": "Standard is Speechmatics' throughput speech-to-text operating point, API id standard, active on Batch and Realtime as of 2026-10-15. It is the default if you omit model. Speechmatics says it has the same features as Enhanced and lower accuracy. Weights are closed. It uses the same language packs and bilingual packs as Enhanced.", "what_it_is_for": "Standard is Speechmatics' throughput speech-to-text operating point, API id standard, active on Batch and Realtime as of 2026-10-15. It is the default if you omit model. Speechmatics says it has the same features as Enhanced and lower accuracy. Weights are closed. It uses the same language packs and bilingual packs as Enhanced.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechmatics/ursa/ursa": { "id": "speechmatics/ursa/ursa", "name": "Ursa", "maker": "speechmatics", "family": "speechmatics/ursa", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-03-07", "context_length": null, "max_output_tokens": null, "parameters": "2000000000", "architecture": "Self-supervised speech model scaled from about 500 million to 2 billion parameters and trained on more than 1 million hours of unlabeled audio across about 50 languages. The production system stayed modular: a larger language model, described as 30 times the previous language-model size, sat on the SSL encoder. Enhanced and Standard were the two operating points, not separate Ursa API ids.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ursa is Speechmatics' named speech-recognition generation from 7 March 2023, retired when Ursa 2 shipped on 11 October 2024. It was not its own API id. Customers selected Enhanced or Standard, and those operating points used Ursa weights. The self-supervised encoder was scaled to 2 billion parameters on more than 1 million hours. Weights were closed and were not released. Collected 2026-10-15.", "what_it_is_for": "Ursa is Speechmatics' named speech-recognition generation from 7 March 2023, retired when Ursa 2 shipped on 11 October 2024. It was not its own API id. Customers selected Enhanced or Standard, and those operating points used Ursa weights. The self-supervised encoder was scaled to 2 billion parameters on more than 1 million hours. Weights were closed and were not released. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "speechmatics/ursa/ursa-2": { "id": "speechmatics/ursa/ursa-2", "name": "Ursa 2", "maker": "speechmatics", "family": "speechmatics/ursa", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Successor generation of Ursa. Speechmatics said it increased pretraining and the size of the body model, moved fully to GPU inference, and cut live latency. The new body-model parameter count was not published. It shipped through the Enhanced and Standard operating points, not as model ursa-2.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ursa 2 is Speechmatics' speech-recognition generation announced on 11 October 2024 and called generally available on 14 October 2024. It cut word error rate 18% versus Ursa across 50 languages and added Irish and Maltese. It was not an API id. It became the generation behind Enhanced, Standard, and the rest of the speech services. The 2026 models page no longer uses the name. Later pack updates still ship as enhanced and standard. Weights stayed closed. Collected 2026-10-15.", "what_it_is_for": "Ursa 2 is Speechmatics' speech-recognition generation announced on 11 October 2024 and called generally available on 14 October 2024. It cut word error rate 18% versus Ursa across 50 languages and added Irish and Maltese. It was not an API id. It became the generation behind Enhanced, Standard, and the rest of the speech services. The 2026 models page no longer uses the name. Later pack updates still ship as enhanced and standard. Weights stayed closed. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "st-andrews/delphinid/delphinid": { "id": "st-andrews/delphinid/delphinid", "name": "delphinID", "maker": "st-andrews", "family": "st-andrews/delphinid", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "1D convolutional neural network in TensorFlow, on normalised frequency spectra of grouped whistle or click detections. A separate random-forest event layer combines the two CNN outputs.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "delphinID is a pair of open-weight 1D convolutional networks, plus a random-forest event layer, released by researchers at the University of St Andrews Sea Mammal Research Unit for Northeast Atlantic dolphin species identification. The classifiers are active in the PAMGuard Deep Learning module as of the 2026-10-15 collection. The university research portal dates the Zenodo deposit to 8 July 2025. A journal paper was still listed as in preparation.", "what_it_is_for": "The maker says delphinID classifies delphinid species from passive acoustic recordings. PAMGuard's Whistle and Moan Detector and Click Detector produce the detections. Each network sees a 4-second detection frame: for whistles, the relative density of peak-frequency contour values; for clicks, a normalised average spectrum. Those two probability streams feed a random-forest event classifier, in the style of BANTER, which names the species for the recording. The documented species are Atlantic white-sided dolphin, common bottlenose dolphin, killer whale, long-finned pilot whale, Risso's dolphin, short-beaked common dolphin, and white-beaked dolphin. The PAMGuard page says the detection features are cheap enough to run at up to 64 times real time.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/deepfloyd/deepfloyd-if": { "id": "stability/deepfloyd/deepfloyd-if", "name": "DeepFloyd IF", "maker": "stability", "family": "stability/deepfloyd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepFloyd IF License Agreement. The launch post called it a non-commercial research license, with an intent to move to a more permissive license later.", "release_date": "2023-04-28", "context_length": null, "max_output_tokens": null, "parameters": "4.3B for the largest stage-I module", "architecture": "Cascaded pixel diffusion. Stage I is text-to-64px. Stage II upscales to 256px. Stage III is the separate Stable Diffusion x4 upscaler to 1024px. Text encoder is T5-XXL.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepFloyd IF is a pixel text-to-image cascade released on 28 April 2023 by Stability AI's DeepFloyd lab. The largest stage-I model is 4.3B parameters and the launch reports a zero-shot COCO FID-30K of 6.66. Weights are research-licensed on the DeepFloyd Hugging Face org. Collected 2026-10-15.", "what_it_is_for": "Research text-to-image generation with strong prompt and rendered-text behavior, via a three-stage cascade.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-3d/arbor": { "id": "stability/stable-3d/arbor", "name": "Arbor", "maker": "stability", "family": "stability/stable-3d", "status": "preview", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-10-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Trainable attachment for text-conditioned latent 3D generation that uses constraint meshes as a control interface, per the research index.", "modalities": { "input": [ "text" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arbor is a Stability AI research name posted on 7 October 2026 for controllable 3D generation. No weight repo was in the Hugging Face org listing. Collected 2026-10-15.", "what_it_is_for": "Research control of text-to-3D generation with constraint meshes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-3d/sp4d": { "id": "stability/stable-3d/sp4d", "name": "SP4D", "maker": "stability", "family": "stability/stable-3d", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "HF license tag: other", "release_date": "2025-11-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video-to-4D diffusion model. Paper tag arXiv 2509.10687.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SP4D is a named Stability AI video-to-4D model whose Hugging Face repo was created 3 November 2025. The name expansion, parameter count, and card body were not re-read. Collected 2026-10-15.", "what_it_is_for": "Video-to-4D generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-3d/spar3d": { "id": "stability/stable-3d/spar3d", "name": "Stable Point Aware 3D", "maker": "stability", "family": "stability/stable-3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2025-01-08", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Two-stage image-to-3D model: a point-cloud diffusion stage plus mesh regression, built on Stable Fast 3D. Expects 512x512 input. Outputs a UV-unwrapped textured mesh with roughness and metallic parameters.", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SPAR3D is Stability AI's January 2025 image-to-3D model. The card says it makes a textured mesh in under a second and improves the unseen back side versus Stable Fast 3D. The API price is 4 credits. Hugging Face tags it at 2B. It is a Core Model. Collected 2026-10-15.", "what_it_is_for": "Image-to-mesh generation with editable backside point clouds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-3d/stable-fast-3d": { "id": "stability/stable-3d/stable-fast-3d", "name": "Stable Fast 3D", "maker": "stability", "family": "stability/stable-3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2024-08-01", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Transformer image-to-3D reconstruction model. Produces a textured mesh from one image. Paper arXiv 2408.00653.", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Fast 3D turns one image into a 3D asset. The API launched 1 August 2024 and the price is 10 credits. Hugging Face tags the weights at 1B. SPAR3D is the successor. It remains a Core Model. Collected 2026-10-15.", "what_it_is_for": "Fast image-to-mesh generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-3d/stable-zero123": { "id": "stability/stable-3d/stable-zero123", "name": "Stable Zero123", "maker": "stability", "family": "stability/stable-3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HF license tag: other. Listed as Core Model Stable Zero123C.", "release_date": "2023-12-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Novel-view image model for object turns. The Core Models page links Stable Zero123C to this repo.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Zero123 is Stability AI's December 2023 novel-view model for single objects. The May 2026 Core Models page lists it as Stable Zero123C and links to stabilityai/stable-zero123. Collected 2026-10-15.", "what_it_is_for": "Generate new views of an object from one image, for 3D workflows.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-3d/sv3d": { "id": "stability/stable-3d/sv3d", "name": "Stable Video 3D", "maker": "stability", "family": "stability/stable-3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HF license tag: other. Listed as a Core Model.", "release_date": "2024-03-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-view video diffusion for novel views of an object. Paper arXiv 2403.12008.", "modalities": { "input": [ "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Video 3D is Stability AI's March 2024 image-to-video model for orbiting an object. The repo stabilityai/sv3d was created 18 March 2024 and is a Core Model. Collected 2026-10-15.", "what_it_is_for": "Generate multi-view video of an object from one image, as a step toward 3D assets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-3d/sv4d": { "id": "stability/stable-3d/sv4d", "name": "Stable Video 4D", "maker": "stability", "family": "stability/stable-3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HF license tag: other. Research release.", "release_date": "2024-07-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video-to-4D diffusion model that generates novel-view video of a moving object. Paper arXiv 2407.17470.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Video 4D is Stability AI's July 2024 video-to-4D model. The Hugging Face repo was created 16 July 2024. SV4D 2.0 is the successor and no longer depends on an SV3D first-frame multi-view. Collected 2026-10-15.", "what_it_is_for": "Generate novel camera views of a moving object from one video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-3d/sv4d-2": { "id": "stability/stable-3d/sv4d-2", "name": "Stable Video 4D 2.0", "maker": "stability", "family": "stability/stable-3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research release. HF license tag: other.", "release_date": "2025-05-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video-to-4D diffusion. Generates 48 frames, 12 time steps by 4 camera views, at 576x576 from a 12-frame input video. Does not need an SV3D multi-view of the first frame.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SV4D 2.0 is Stability AI's updated video-to-4D model, called out in the generative-models README on 20 May 2025. It generates 48 frames at 576x576 and is aimed at research novel-view video of a moving object. Weights are on Hugging Face as stabilityai/sv4d2.0. Collected 2026-10-15.", "what_it_is_for": "Higher-fidelity 4D novel-view video from a single object video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-3d/triposr": { "id": "stability/stable-3d/triposr", "name": "TripoSR", "maker": "stability", "family": "stability/stable-3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-02-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fast feed-forward image-to-3D reconstruction model from Stability AI and Tripo AI.", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TripoSR is an MIT-licensed image-to-3D model released by Stability AI and Tripo AI. The Hugging Face repo was created 29 February 2024, which is the release date stored here. It is not on the May 2026 Core Models list. Collected 2026-10-15.", "what_it_is_for": "Reconstruct a 3D mesh from a single image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-audio/same-l": { "id": "stability/stable-audio/same-l", "name": "SAME-L", "maker": "stability", "family": "stability/stable-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2026-05-20", "context_length": null, "max_output_tokens": null, "parameters": "852M", "architecture": "Transformer autoencoder for stereo music and general audio. 4096x temporal compression, latent dimension 256. Sliding-window attention.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SAME-L is the large Semantically-Aligned Music autoEncoder released with Stable Audio 3 on 20 May 2026. The paper and Stability's explainer say 852M parameters, 4096x compression, and a MUSHRA score of 82.2. It is the autoencoder used with SA3 Medium, not a music generator by itself. Collected 2026-10-15.", "what_it_is_for": "Compress and reconstruct stereo audio for Stable Audio 3 training and inference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-audio/same-s": { "id": "stability/stable-audio/same-s", "name": "SAME-S", "maker": "stability", "family": "stability/stable-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2026-05-20", "context_length": null, "max_output_tokens": null, "parameters": "108M", "architecture": "Distilled SAME autoencoder for CPU and edge devices. 4096x compression, latent dimension 256. Chunked attention. Distilled from SAME-L.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SAME-S is the 108M distilled Stable Audio 3 autoencoder for CPU inference, released with SAME-L on 20 May 2026. The explainer lists MUSHRA 66.1 and MEL error 0.071. It is the autoencoder used by the SA3 small models. Collected 2026-10-15.", "what_it_is_for": "Fast stereo audio compression on CPU for the small Stable Audio 3 models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-audio/stable-audio-1-0": { "id": "stability/stable-audio/stable-audio-1-0", "name": "Stable Audio 1.0", "maker": "stability", "family": "stability/stable-audio", "status": "retired", "confidence": "high", "weights": "closed", "license": "Hosted product terms. Free tier was non-commercial; Pro allowed commercial downloads.", "release_date": "2023-09-13", "context_length": null, "max_output_tokens": null, "parameters": "907M diffusion U-Net", "architecture": "Timing-conditioned latent diffusion. The research post says the diffusion model is a 907M-parameter U-Net, with text and timing embeddings. 44.1 kHz stereo.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Audio 1.0 launched on 13 September 2023 as Stability AI's first hosted music and sound product at stableaudio.com. The research post says the diffusion U-Net is 907M parameters and can render 95 seconds of 44.1 kHz stereo on an A100 in under a second. The free tier was up to 45 seconds and Pro up to 90 seconds. Later versions replaced it as the shipping product. Collected 2026-10-15.", "what_it_is_for": "Text-to-music and text-to-sound at a user-specified length, up to the training window.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-audio/stable-audio-2-0": { "id": "stability/stable-audio/stable-audio-2-0", "name": "Stable Audio 2.0", "maker": "stability", "family": "stability/stable-audio", "status": "active", "confidence": "high", "weights": "closed", "license": "Commercial hosted license. Trained on licensed AudioSparx audio.", "release_date": "2024-04-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion audio model for full tracks. Stable Audio Open is described as a variant trained on a different dataset.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Audio 2.0, introduced 3 April 2024, generates coherent 44.1 kHz stereo tracks up to three minutes and adds audio-to-audio. An API arrived on 21 March 2025. Weights stay closed. The current public price table retrieved for this catalog listed 2.5 and 3.0, not a separate 2.0 row. Collected 2026-10-15.", "what_it_is_for": "Full-length text-to-audio and audio-to-audio music generation up to three minutes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-audio/stable-audio-2-5": { "id": "stability/stable-audio/stable-audio-2-5", "name": "Stable Audio 2.5", "maker": "stability", "family": "stability/stable-audio", "status": "active", "confidence": "high", "weights": "closed", "license": "Enterprise and API commercial terms. Weights were not released.", "release_date": "2025-09-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent audio model for structured, multi-section tracks up to three minutes at 44.1 kHz stereo.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Audio 2.5 launched on 10 September 2025 as Stability AI's enterprise audio model for structured productions up to three minutes, with text-to-audio, audio-to-audio, and audio inpainting. Weights are closed. The pricing page lists it at 8 credits in the excerpt retrieved on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "Enterprise music and sound production with section-level structure and audio editing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-audio/stable-audio-3-large": { "id": "stability/stable-audio/stable-audio-3-large", "name": "Stable Audio 3.0 Large", "maker": "stability", "family": "stability/stable-audio", "status": "active", "confidence": "high", "weights": "closed", "license": "API and enterprise self-host terms. Not in the open-weight drop.", "release_date": "2026-05-20", "context_length": null, "max_output_tokens": null, "parameters": "2.7B", "architecture": "Largest Stable Audio 3 latent diffusion model. Closed weights. Text-to-audio, audio-to-audio, and audio inpainting.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Audio 3.0 Large is the closed flagship of the 20 May 2026 audio family. The official post says it is on the API and enterprise self-hosting, and that it generates more than six minutes. TechCrunch reports 2.7B parameters. The API release note says 44.1 kHz stereo and audio-to-audio transformation. Collected 2026-10-15.", "what_it_is_for": "Long-form commercial music and sound generation through the API or an enterprise deployment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-audio/stable-audio-3-medium": { "id": "stability/stable-audio/stable-audio-3-medium", "name": "Stable Audio 3.0 Medium", "maker": "stability", "family": "stability/stable-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2026-05-20", "context_length": null, "max_output_tokens": null, "parameters": "1.4B", "architecture": "Latent diffusion transformer for variable-length music and sound, up to 6 minutes 20 seconds. Uses the SAME autoencoder in the Stable Audio 3 stack.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Audio 3.0 Medium is the open-weight long-form Stable Audio 3 model released 20 May 2026. Official copy says it reaches higher musical structure and tracks up to 6:20, and that Medium and Large both go past six minutes. TechCrunch and The Decoder report 1.4B parameters; the Hugging Face tag says 2B. It is a Core Model. Collected 2026-10-15.", "what_it_is_for": "Longer, more structured music and sound generation on a single GPU-class machine.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-audio/stable-audio-3-small": { "id": "stability/stable-audio/stable-audio-3-small", "name": "Stable Audio 3.0 Small", "maker": "stability", "family": "stability/stable-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2026-05-20", "context_length": null, "max_output_tokens": null, "parameters": "459M", "architecture": "Latent diffusion music model. Official copy says it is for full music composition on device, up to two minutes.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Audio 3.0 Small is the on-device music model released 20 May 2026. The official post says it generates up to two minutes, compared with 11 seconds for Open Small and 47 seconds for Stable Audio Open. Reported size is 459M parameters; the Hugging Face tag says 0.6B. Weights are open. The Core Models page lists Stable Audio 3.0 Small. Collected 2026-10-15.", "what_it_is_for": "On-device full music sketches up to two minutes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-audio/stable-audio-3-small-sfx": { "id": "stability/stable-audio/stable-audio-3-small-sfx", "name": "Stable Audio 3.0 Small SFX", "maker": "stability", "family": "stability/stable-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2026-05-20", "context_length": null, "max_output_tokens": null, "parameters": "459M", "architecture": "Latent diffusion audio model in the Stable Audio 3 family, aimed at sound effects. A base checkpoint is released beside the post-trained one.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Audio 3.0 Small SFX is the on-device sound-effects model in the 20 May 2026 Stable Audio 3 family. Stability's launch materials, as reported by two outlets and consistent with the official variant list, put it at 459 million parameters and up to about two minutes. Hugging Face's size tag says 0.6B. Weights are public under the Community License. Collected 2026-10-15.", "what_it_is_for": "Sound-effect generation on phones and consumer laptops.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-audio/stable-audio-open": { "id": "stability/stable-audio/stable-audio-open", "name": "Stable Audio Open 1.0", "maker": "stability", "family": "stability/stable-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that. The 5 June 2024 headline said open source; the model card and the 22 July 2024 paper note say the Community License, which is not an OSI open-source license.", "release_date": "2024-06-05", "context_length": null, "max_output_tokens": null, "parameters": "1.1B", "architecture": "Latent diffusion: waveform autoencoder, T5 text encoder, and a DiT in the latent space. 44.1 kHz stereo, up to 47 seconds.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Audio Open 1.0 is Stability AI's open-weight text-to-audio model for samples and sound effects up to 47 seconds, announced 5 June 2024. A later official comparison puts it at 1.1B parameters. It is a Core Model and is trained on Creative Commons audio rather than the full AudioSparx catalog used for the commercial models. Collected 2026-10-15.", "what_it_is_for": "Short music samples, foley, and sound design, including fine-tuning on a user's own audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-audio/stable-audio-open-small": { "id": "stability/stable-audio/stable-audio-open-small", "name": "Stable Audio Open Small", "maker": "stability", "family": "stability/stable-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2025-05-14", "context_length": null, "max_output_tokens": null, "parameters": "341M", "architecture": "Compact text-to-audio model built with Arm to run on CPUs, including phones.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Audio Open Small is a 341 million parameter text-to-audio model Stability AI released with Arm on 14 May 2025. It generates up to 11 seconds and the announcement says a phone can do that in under 8 seconds. Community License. Collected 2026-10-15.", "what_it_is_for": "On-device short stereo audio samples.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-audio/stable-codec-speech": { "id": "stability/stable-audio/stable-codec-speech", "name": "Stable Codec Speech 16 kHz", "maker": "stability", "family": "stability/stable-audio", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "HF license tag: other", "release_date": "2025-01-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech autoencoder at 16 kHz. A base checkpoint is a separate repo.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Codec Speech 16 kHz is a Stability AI speech codec released on Hugging Face in January 2025, with a base checkpoint four days later. The card body was not re-read, so parameter count and bitrate are unknown here. Collected 2026-10-15.", "what_it_is_for": "Encode and reconstruct 16 kHz speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-cascade/stable-cascade": { "id": "stability/stable-cascade/stable-cascade", "name": "Stable Cascade", "maker": "stability", "family": "stability/stable-cascade", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HF license tag: other. The launch was described as a research preview.", "release_date": "2024-02-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three-stage W\u00fcrstchen text-to-image cascade. A prior stage and a decoder stage are released as separate weight repos.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Cascade is Stability AI's research-preview W\u00fcrstchen text-to-image model, with Hugging Face weights from February 2024. It is not on the 20 May 2026 Core Models list. Prior and decoder checkpoints are separate repos of one model. Collected 2026-10-15.", "what_it_is_for": "Text-to-image generation with a smaller, easier-to-fine-tune cascade than a single large latent model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-code/stable-code-3b": { "id": "stability/stable-code/stable-code-3b", "name": "Stable Code 3B", "maker": "stability", "family": "stability/stable-code", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2024-01-09", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "StableLM-family code model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Code 3B is Stability AI's January 2024 3B code model and a current Core Model. The instruct sibling is catalogued separately. Collected 2026-10-15.", "what_it_is_for": "Code generation and completion at 3B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-code/stable-code-alpha-3b": { "id": "stability/stable-code/stable-code-alpha-3b", "name": "Stable Code Alpha 3B", "maker": "stability", "family": "stability/stable-code", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-07-31", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "GPT-NeoX code completion model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Code Alpha 3B is Stability AI's July 2023 code completion model, Apache-2.0, with a 4k-context sibling checkpoint. Collected 2026-10-15.", "what_it_is_for": "Code completion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-code/stable-code-instruct-3b": { "id": "stability/stable-code/stable-code-instruct-3b", "name": "Stable Code Instruct 3B", "maker": "stability", "family": "stability/stable-code", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2024-03-06", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Instruction-tuned Stable Code 3B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Code Instruct 3B is the March 2024 instruction tune of Stable Code 3B and a current Core Model. Collected 2026-10-15.", "what_it_is_for": "Instruction-following code generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-code/stable-code-instruct-alpha-3b": { "id": "stability/stable-code/stable-code-instruct-alpha-3b", "name": "Stable Code Instruct Alpha 3B", "maker": "stability", "family": "stability/stable-code", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HF license tag: other", "release_date": "2023-08-07", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Instruction-tuned Stable Code Alpha 3B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Code Instruct Alpha 3B is the August 2023 instruction tune of Stable Code Alpha. Collected 2026-10-15.", "what_it_is_for": "Instruction-following code generation at 3B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/control-lora": { "id": "stability/stable-diffusion/control-lora", "name": "Stable Diffusion XL Control LoRAs", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "HF license tag: other", "release_date": "2023-08-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA control adapters for SDXL, not a standalone generator.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "stabilityai/control-lora is Stability AI's August 2023 SDXL control-LoRA release. The repo is still public. Individual adapter names and ranks were not re-read from the card. Collected 2026-10-15.", "what_it_is_for": "Condition SDXL on structural controls such as edges or depth using LoRA adapters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/cosxl": { "id": "stability/stable-diffusion/cosxl", "name": "CosXL", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "HF license tag: other", "release_date": "2024-04-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CosXL is a named Stability AI image model whose Hugging Face repo stabilityai/cosxl was created on 8 April 2024. It was not on the 20 May 2026 Core Models list. The card body was not re-read, so architecture and parameter count are unknown here. Collected 2026-10-15.", "what_it_is_for": "Text-to-image generation under the CosXL weight release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/japanese-sdxl": { "id": "stability/stable-diffusion/japanese-sdxl", "name": "Japanese Stable Diffusion XL", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "See the model card. HF license tag is other. Listed as a Core Model.", "release_date": "2023-11-01", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Japanese text-to-image model continued from SDXL.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese Stable Diffusion XL is Stability AI's Japanese SDXL text-to-image model. The Hugging Face repo was created 1 November 2023 and is tagged at 3B parameters. The 20 May 2026 Core Models page still lists it. Collected 2026-10-15.", "what_it_is_for": "Japanese-prompt text-to-image generation on an SDXL base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/sd-turbo": { "id": "stability/stable-diffusion/sd-turbo", "name": "SD-Turbo", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI research / community terms (see stability.ai/license). HF marks the license as other.", "release_date": "2023-11-28", "context_length": null, "max_output_tokens": null, "parameters": "0.9B", "architecture": "Adversarial Diffusion Distillation of Stable Diffusion 2.1. One to four sampling steps.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SD-Turbo is Stability AI's distilled Stable Diffusion 2.1 model for one-step text-to-image, released with SDXL Turbo at the end of November 2023. The model card calls it a research artifact and points commercial use to stability.ai/license. It is still a Core Model as of 20 May 2026. Hugging Face tags the size as 0.9B. Collected 2026-10-15.", "what_it_is_for": "Fast text-to-image synthesis in one to four steps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/sd-vae-ft": { "id": "stability/stable-diffusion/sd-vae-ft", "name": "SD VAE FT (MSE and EMA)", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-10-13", "context_length": null, "max_output_tokens": null, "parameters": "83.7M", "architecture": "Fine-tuned Stable Diffusion 1.x autoencoder. Two decoder checkpoints, MSE and EMA.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stability AI released two fine-tuned autoencoders for the original Stable Diffusion decoder, MSE and EMA, in October 2022. Both are MIT-licensed and tagged at 83.7M parameters. They are components, not text-to-image models. Collected 2026-10-15.", "what_it_is_for": "Replace the original SD 1.x VAE decoder to reduce decoded-image artifacts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/sd-x2-latent-upscaler": { "id": "stability/stable-diffusion/sd-x2-latent-upscaler", "name": "Stable Diffusion x2 Latent Upscaler", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML OpenRAIL++-M", "release_date": "2023-02-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent upscaler for Stable Diffusion", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Stable Diffusion x2 latent upscaler is a Stability AI OpenRAIL++ model that doubles resolution in latent space. The Hugging Face repo was created on 3 February 2023. The legacy API id stable-diffusion-x4-latent-upscaler is a different, deprecated engine. Collected 2026-10-15.", "what_it_is_for": "Upscale Stable Diffusion latents by 2x.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/sdxl-0-9": { "id": "stability/stable-diffusion/sdxl-0-9", "name": "SDXL 0.9", "maker": "stability", "family": "stability/stable-diffusion", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Research preview terms (HF license tag: other). Not the OpenRAIL++ grant used for SDXL 1.0.", "release_date": "2023-06-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SDXL base plus refiner preview. Two text encoders and a larger U-Net than SD 2.x.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SDXL 0.9 is the June 2023 research preview of Stable Diffusion XL, with a base and a refiner. The API engine stable-diffusion-xl-1024-v0-9 was deprecated on 11 October 2024. SDXL 1.0 replaced it for general use. Collected 2026-10-15.", "what_it_is_for": "Research-preview text-to-image at 1024 pixels, optionally refined by a second model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/sdxl-1-0": { "id": "stability/stable-diffusion/sdxl-1-0", "name": "Stable Diffusion XL 1.0", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML OpenRAIL++-M", "release_date": "2023-07-26", "context_length": null, "max_output_tokens": null, "parameters": "3B base and 2B refiner, as labeled on the Hugging Face model cards", "architecture": "Latent diffusion with a larger U-Net and two text encoders (CLIP-L and OpenCLIP-G), plus an optional refiner. Paper arXiv 2307.01952.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SDXL 1.0 is Stability AI's July 2023 open-weights text-to-image model, still sold on the Developer Platform as a legacy engine (stable-diffusion-xl-1024-v1-0). A base and a refiner ship together. The API price starts at 0.9 credits. Collected 2026-10-15.", "what_it_is_for": "1024-class text-to-image generation, with an optional refiner pass for detail.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/sdxl-turbo": { "id": "stability/stable-diffusion/sdxl-turbo", "name": "SDXL Turbo", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI non-commercial research community terms at release; current commercial use is directed to stability.ai/license. HF license tag is other.", "release_date": "2023-11-28", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Adversarial Diffusion Distillation of SDXL. Designed for one-step and few-step sampling.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SDXL Turbo is Stability AI's real-time distilled SDXL model, announced 28 November 2023. It is a Core Model as of 20 May 2026. Hugging Face tags it at 3B. TensorRT, AMD GPU, Ryzen AI, and AMD NPU exports exist and are not separate models. Collected 2026-10-15.", "what_it_is_for": "Real-time text-to-image generation in one or a few steps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/sdxl-vae": { "id": "stability/stable-diffusion/sdxl-vae", "name": "SDXL VAE", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-06-21", "context_length": null, "max_output_tokens": null, "parameters": "83.7M", "architecture": "Improved variational autoencoder used by SDXL. Not a text-to-image model by itself.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The SDXL VAE is the improved autoencoder Stability AI published for the SDXL line. Hugging Face lists it at 83.7M parameters under an MIT license, repo created 21 June 2023. It is a component, not a generator. Collected 2026-10-15.", "what_it_is_for": "Encode and decode SDXL latents with the improved decoder Stability shipped for that line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-1-4": { "id": "stability/stable-diffusion/stable-diffusion-1-4", "name": "Stable Diffusion 1.4", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML OpenRAIL-M", "release_date": "2022-08-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion U-Net with a frozen CLIP ViT-L/14 text encoder", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stability AI, CompVis, and Runway released Stable Diffusion 1.4 on 22 August 2022 as the first public latent text-to-image checkpoint. Weights remain downloadable. This dossier was collected on 2026-10-15.", "what_it_is_for": "Generate and edit images from text prompts at 512 pixels, the original public Stable Diffusion checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-1-5": { "id": "stability/stable-diffusion/stable-diffusion-1-5", "name": "Stable Diffusion 1.5", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML OpenRAIL-M", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion U-Net continued from the 1.x line, CLIP ViT-L/14 text encoder", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Diffusion 1.5 is the Runway and Stability AI continuation of the 1.x latent text-to-image model and the checkpoint most community fine-tunes still descend from. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Text-to-image and image-to-image generation at 512 pixels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-1-6": { "id": "stability/stable-diffusion/stable-diffusion-1-6", "name": "Stable Diffusion 1.6", "maker": "stability", "family": "stability/stable-diffusion", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Diffusion 1.6 was a Stability AI Developer Platform image engine. The API was turned off on 24 July 2025, with SDXL named as the migration target. No public weight card was found. Collected 2026-10-15.", "what_it_is_for": "Hosted text-to-image generation on the legacy v1 API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-2-0": { "id": "stability/stable-diffusion/stable-diffusion-2-0", "name": "Stable Diffusion 2.0", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML OpenRAIL++-M", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion U-Net with an OpenCLIP text encoder. Separate 512 and 768 checkpoints.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Diffusion 2.0 is Stability AI's late-2022 latent text-to-image line, trained from scratch relative to 1.x and shipped as 512 and 768 checkpoints. The hosted engines stable-diffusion-512-v2-0 and stable-diffusion-768-v2-0 were deprecated on 11 October 2024. Weights remain citable from the 2.x cards. Collected 2026-10-15.", "what_it_is_for": "Text-to-image generation at 512 or 768 pixels with the OpenCLIP 2.x stack.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-2-1": { "id": "stability/stable-diffusion/stable-diffusion-2-1", "name": "Stable Diffusion 2.1", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML Open RAIL++-M License", "release_date": "2022-12-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion U-Net with a fixed OpenCLIP-ViT/H text encoder. Fine-tune of SD 2.0 768-v-ema.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Diffusion 2.1 is a 7 December 2022 fine-tune of Stable Diffusion 2.0 that Stability AI shipped at 512 and 768. The model card credits Robin Rombach and Patrick Esser and uses the CreativeML Open RAIL++-M license. The hosted engine stable-diffusion-512-v2-1 was deprecated on 11 October 2024. Collected 2026-10-15.", "what_it_is_for": "Text-to-image generation that keeps the 2.x OpenCLIP stack while restoring more of the 1.x prompt behavior.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-2-inpainting": { "id": "stability/stable-diffusion/stable-diffusion-2-inpainting", "name": "Stable Diffusion 2.0 Inpainting", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CreativeML OpenRAIL++-M", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SD 2.x latent inpainting model", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stability AI shipped a Stable Diffusion 2.x inpainting model and exposed it on the legacy API as stable-inpainting-512-v2-0. That engine is on the deprecated v1 engine list. Collected 2026-10-15.", "what_it_is_for": "Fill masked regions of an image from a text prompt using the SD 2.x stack.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-3-5-flash": { "id": "stability/stable-diffusion/stable-diffusion-3-5-flash", "name": "Stable Diffusion 3.5 Flash", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "closed", "license": "API commercial terms. No public weight repo was on the stabilityai Hugging Face listing retrieved 2026-10-15.", "release_date": "2025-09-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Few-step distillation of SD 3.5 Medium using a modified distribution-matching objective. Paper reports a 4-step pipeline, with optional text-encoder dropout and quantization.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SD 3.5 Flash is Stability AI's distilled SD 3.5 Medium image model, announced 26 September 2025 and sold on the API as model sd3.5-flash for 2.5 credits. Public weights were not in the Hugging Face org listing. Collected 2026-10-15.", "what_it_is_for": "Fast, lower-cost text-to-image generation, including on consumer and mobile-class hardware in the research write-up.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-3-5-large": { "id": "stability/stable-diffusion/stable-diffusion-3-5-large", "name": "Stable Diffusion 3.5 Large", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2024-10-22", "context_length": null, "max_output_tokens": null, "parameters": "8.1B", "architecture": "Multimodal Diffusion Transformer (MMDiT) text-to-image model with CLIP-L, OpenCLIP-G, and T5-XXL text encoders.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Diffusion 3.5 Large is Stability AI's open-weight flagship image model, released 22 October 2024. The announcement says 8.1 billion parameters; some later API copy says 8 billion. It is the base under Stable Image Ultra and is on the API at 6.5 credits. Collected 2026-10-15.", "what_it_is_for": "Professional text-to-image generation around 1 megapixel, with the best prompt adherence in the SD 3.5 family according to Stability.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-3-5-large-controlnet": { "id": "stability/stable-diffusion/stable-diffusion-3-5-large-controlnet", "name": "SD 3.5 Large ControlNets", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License", "release_date": "2024-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ControlNet adapters for SD 3.5 Large. Three releases: Blur, Canny, and Depth.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "On 26 November 2024 Stability AI released three ControlNets for SD 3.5 Large: Blur, Canny, and Depth, under the Community License. They are adapters, not standalone generators. A combined TensorRT export exists. Collected 2026-10-15.", "what_it_is_for": "Guide SD 3.5 Large with blur, edge, or depth controls. Blur is also described for tiled high-resolution upscaling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-3-5-large-turbo": { "id": "stability/stable-diffusion/stable-diffusion-3-5-large-turbo", "name": "Stable Diffusion 3.5 Large Turbo", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2024-10-22", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Distilled MMDiT based on SD 3.5 Large. Official guidance is 4 sampling steps.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SD 3.5 Large Turbo is the distilled, 4-step sibling of SD 3.5 Large, released the same day, 22 October 2024. The API price is 4 credits. Hugging Face tags the size at 8B. Collected 2026-10-15.", "what_it_is_for": "Fast text-to-image generation with SD 3.5 Large prompt adherence in four steps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-3-5-medium": { "id": "stability/stable-diffusion/stable-diffusion-3-5-medium", "name": "Stable Diffusion 3.5 Medium", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2024-10-29", "context_length": null, "max_output_tokens": null, "parameters": "2.5B", "architecture": "MMDiT-X text-to-image model, per the 29 October 2024 announcement.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SD 3.5 Medium is the consumer-hardware SD 3.5 model released 29 October 2024. The launch post says 2.5 billion parameters and a range of 0.25 to 2 megapixels. A later ControlNet post and the Hugging Face size tag say 2B. The API price is 3.5 credits. It is the teacher for SD 3.5 Flash. Collected 2026-10-15.", "what_it_is_for": "Text-to-image generation that trades some of Large's size for speed and easier local use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-3-large": { "id": "stability/stable-diffusion/stable-diffusion-3-large", "name": "Stable Diffusion 3 Large", "maker": "stability", "family": "stability/stable-diffusion", "status": "retired", "confidence": "high", "weights": "closed", "license": "API commercial terms. Weights were not in the open SD3 release.", "release_date": "2024-04-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MMDiT text-to-image model, the large SD3 API variant.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Diffusion 3 Large was the flagship SD3 model on the Stability API from 17 April 2024. Public open weights were not released; the 12 June 2024 open drop was Medium only. On 17 April 2025 the API id sd3-large was switched to sd3.5-large. Collected 2026-10-15.", "what_it_is_for": "Hosted high-quality text-to-image generation before SD 3.5 Large replaced it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-3-large-turbo": { "id": "stability/stable-diffusion/stable-diffusion-3-large-turbo", "name": "Stable Diffusion 3 Large Turbo", "maker": "stability", "family": "stability/stable-diffusion", "status": "retired", "confidence": "high", "weights": "closed", "license": "API commercial terms", "release_date": "2024-04-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Distilled SD3 Large API model.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Diffusion 3 Large Turbo was the fast SD3 API model released with the 17 April 2024 SD3 API. The id sd3-large-turbo was switched to sd3.5-large-turbo on 17 April 2025. No public weight card was found. Collected 2026-10-15.", "what_it_is_for": "Faster hosted SD3 text-to-image generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-3-medium": { "id": "stability/stable-diffusion/stable-diffusion-3-medium", "name": "Stable Diffusion 3 Medium", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2024-06-12", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Multimodal Diffusion Transformer (MMDiT) with frozen OpenCLIP-ViT/G, CLIP-ViT/L, and T5-XXL text encoders. 16-channel VAE noted in the launch post.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Diffusion 3 Medium is the 2 billion parameter open-weight SD3 model Stability AI released on 12 June 2024. The API id sd3-medium was automatically moved to sd3.5-medium on 17 April 2025, but the weights remain available and the model is still a Core Model. Collected 2026-10-15.", "what_it_is_for": "Open-weight text-to-image generation that fits consumer GPUs better than the closed SD3 Large API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-diffusion/stable-diffusion-x4-upscaler": { "id": "stability/stable-diffusion/stable-diffusion-x4-upscaler", "name": "Stable Diffusion x4 Upscaler", "maker": "stability", "family": "stability/stable-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CreativeML OpenRAIL++-M", "release_date": "2022-11-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Text-conditioned latent 4x upscaler. Also used as DeepFloyd IF stage III.", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Diffusion x4 Upscaler is Stability AI's OpenRAIL++ text-conditioned 4x upscaler, with weights posted on 23 November 2022. DeepFloyd IF uses it as the third cascade stage from 256 to 1024 pixels. Collected 2026-10-15.", "what_it_is_for": "Upscale an image by 4x, including as the last stage of DeepFloyd IF.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-image/conservative-upscaler": { "id": "stability/stable-image/conservative-upscaler", "name": "Conservative Upscaler", "maker": "stability", "family": "stability/stable-image", "status": "active", "confidence": "high", "weights": "closed", "license": "API commercial terms", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Conservative Upscaler is a named Stability API service that enlarges an image to about 4 megapixels without intentionally reinterpreting it. The 10 September 2024 note says it can take images as small as 64x64 to a 4 megapixel output and describes a 20 to 40 times scale-up. Price is 40 credits. Collected 2026-10-15.", "what_it_is_for": "Faithful upscale to a 4 megapixel output.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-image/creative-upscaler": { "id": "stability/stable-image/creative-upscaler", "name": "Creative Upscaler", "maker": "stability", "family": "stability/stable-image", "status": "active", "confidence": "high", "weights": "closed", "license": "API commercial terms", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Creative Upscaler is a named Stability API service that turns a low-resolution image into a 4K result with prompt guidance. The price on 2026-10-15 is 60 credits. Stability recommends it for highly degraded images under 1 megapixel when a creative reinterpretation is acceptable. Collected 2026-10-15.", "what_it_is_for": "Prompt-guided creative upscale to 4K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-image/fast-upscaler": { "id": "stability/stable-image/fast-upscaler", "name": "Fast Upscaler", "maker": "stability", "family": "stability/stable-image", "status": "active", "confidence": "high", "weights": "closed", "license": "API commercial terms", "release_date": "2024-09-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described as predictive and generative 4x upscaling. The replaced esrgan-v1-x2plus engine was a hosted third-party model and is not catalogued here.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fast Upscaler launched on the Stability API on 10 September 2024. It scales an image by 4x, up to 4 megapixels, and the launch note says processing is about one second. Price is 2 credits. It replaced the deprecated esrgan-v1-x2plus engine. Collected 2026-10-15.", "what_it_is_for": "Cheap, fast 4x upscale for compressed images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-image/stable-image-core": { "id": "stability/stable-image/stable-image-core", "name": "Stable Image Core", "maker": "stability", "family": "stability/stable-image", "status": "active", "confidence": "high", "weights": "closed", "license": "API commercial terms", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Image Core is Stability AI's fast, lower-cost hosted image service at 3 credits ($0.03). Release notes call it the next-generation model following SDXL. The underlying architecture was not published in the pages read. It is on Bedrock and Azure as well as the first-party API. Collected 2026-10-15.", "what_it_is_for": "Cost-efficient hosted text-to-image generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-image/stable-image-ultra": { "id": "stability/stable-image/stable-image-ultra", "name": "Stable Image Ultra", "maker": "stability", "family": "stability/stable-image", "status": "active", "confidence": "high", "weights": "closed", "license": "API commercial terms", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted image service. Since 29 October 2024 it is powered by Stable Diffusion 3.5 Large. Earlier 2024 copy said it was based on Stable Diffusion 3 Large.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Image Ultra is Stability AI's flagship hosted image service, priced at 8 credits ($0.08). From 29 October 2024 it runs on SD 3.5 Large, and image-to-image was added on 7 November 2024. It is also offered on Amazon Bedrock and Azure AI Foundry. Collected 2026-10-15.", "what_it_is_for": "Highest-quality hosted text-to-image and image-to-image generation in the Stable Image API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-lm/ar-stablelm-2": { "id": "stability/stable-lm/ar-stablelm-2", "name": "Arabic Stable LM 2", "maker": "stability", "family": "stability/stable-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HF license tag: other. Paper arXiv 2412.04277.", "release_date": "2024-07-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Arabic Stable LM 2 base and chat, stablelm_epoch architecture.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arabic Stable LM 2 is a Stability AI Arabic language release with base weights from 29 July 2024 and a chat tune from 6 August 2024. A GGUF packaging repo is not a separate model. It is not on the Core Models list. Collected 2026-10-15.", "what_it_is_for": "Arabic and English text generation in the Stable LM 2 line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-lm/japanese-stable-clip": { "id": "stability/stable-lm/japanese-stable-clip", "name": "Japanese Stable CLIP", "maker": "stability", "family": "stability/stable-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HF license tag: other. Listed as a Core Model.", "release_date": "2023-11-01", "context_length": null, "max_output_tokens": null, "parameters": "0.4B", "architecture": "CLIP ViT-L/16 contrastive model for Japanese.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese Stable CLIP ViT-L/16 is Stability AI's Japanese vision-language embedding model, repo created 1 November 2023 and tagged at 0.4B. It is a Core Model. Collected 2026-10-15.", "what_it_is_for": "Japanese image-text embeddings and retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-lm/japanese-stable-vlm": { "id": "stability/stable-lm/japanese-stable-vlm", "name": "Japanese Stable VLM", "maker": "stability", "family": "stability/stable-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HF license tag: other. Listed as a Core Model.", "release_date": "2023-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Japanese vision-language model. HF tags include LLaVA-style image-to-text.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese Stable VLM is Stability AI's Japanese image-to-text model, repo created 1 November 2023. It is a Core Model. Collected 2026-10-15.", "what_it_is_for": "Japanese image captioning and visual question answering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-lm/japanese-stablelm-2-1-6b": { "id": "stability/stable-lm/japanese-stablelm-2-1-6b", "name": "Japanese Stable LM 2 1.6B", "maker": "stability", "family": "stability/stable-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2024-05-02", "context_length": null, "max_output_tokens": null, "parameters": "1.6B", "architecture": "Japanese Stable LM 2, continued from the Stable LM 2 1.6B architecture.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese Stable LM 2 1.6B is the Japanese Stable LM 2 release from 2 May 2024, with base and instruct weights. It is on the 20 May 2026 Core Models list. Collected 2026-10-15.", "what_it_is_for": "Japanese text generation at the Stable LM 2 1.6B scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-lm/japanese-stablelm-3b": { "id": "stability/stable-lm/japanese-stablelm-3b", "name": "Japanese StableLM 3B", "maker": "stability", "family": "stability/stable-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-16", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Japanese StableLM 3B continued from the 4E1T training setup. Not a Llama fine-tune.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese StableLM 3B 4E1T is Stability AI's October 2023 Japanese 3B model, with base and instruct weights under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Small Japanese base and instruct language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-lm/japanese-stablelm-alpha-7b": { "id": "stability/stable-lm/japanese-stablelm-alpha-7b", "name": "Japanese StableLM Alpha 7B", "maker": "stability", "family": "stability/stable-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 for the base and the v2 instruct checkpoint. The first instruct repo is tagged license other.", "release_date": "2023-08-09", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Japanese continuation of the StableLM Alpha architecture, not a Llama fine-tune.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese StableLM Alpha 7B is Stability AI's Japanese 7B model from August 2023, with base and instruct checkpoints. Later Japanese StableLM Beta and Gamma releases are Llama 2 and Mistral fine-tunes and are not given folders. Collected 2026-10-15.", "what_it_is_for": "Japanese text generation at 7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-lm/stablelm-2-1-6b": { "id": "stability/stable-lm/stablelm-2-1-6b", "name": "Stable LM 2 1.6B", "maker": "stability", "family": "stability/stable-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2024-01-18", "context_length": null, "max_output_tokens": null, "parameters": "1.6B", "architecture": "Decoder-only Transformer in the StableLM 2 family. Paper arXiv 2402.17834.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable LM 2 1.6B is Stability AI's small multilingual language model, with a base repo from 18 January 2024 plus Zephyr and chat tunes. The Core Models page lists seven languages: English, Spanish, German, Italian, French, Portuguese, and Dutch. Collected 2026-10-15.", "what_it_is_for": "Small multilingual base and chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-lm/stablelm-2-12b": { "id": "stability/stable-lm/stablelm-2-12b", "name": "Stable LM 2 12B", "maker": "stability", "family": "stability/stable-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Stability AI Community License. Free under $1M annual revenue; Enterprise license above that.", "release_date": "2024-03-21", "context_length": null, "max_output_tokens": null, "parameters": "12B", "architecture": "Decoder-only Transformer. Announcement says the base was trained on 2 trillion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable LM 2 12B is Stability AI's 12 billion parameter multilingual language model. The announcement describes a base and an instruction-tuned variant trained on 2 trillion tokens in seven languages. The chat checkpoint's dataset tags include a function-calling set. It is a Core Model. Collected 2026-10-15.", "what_it_is_for": "Larger open multilingual base and chat model in the Stable LM 2 line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-lm/stablelm-3b": { "id": "stability/stable-lm/stablelm-3b", "name": "StableLM 3B", "maker": "stability", "family": "stability/stable-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-09-29", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Decoder-only Transformer. Checkpoint name stablelm-3b-4e1t means four epochs on about one trillion tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StableLM 3B (stablelm-3b-4e1t) is Stability AI's September 2023 3B base model, trained for four epochs on about one trillion tokens and released under CC-BY-SA-4.0. StableLM Zephyr 3B is the later alignment tune. It remains a Core Model ancestor via Zephyr, and the base itself is public. Collected 2026-10-15.", "what_it_is_for": "Small open base language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-lm/stablelm-alpha-3b": { "id": "stability/stable-lm/stablelm-alpha-3b", "name": "StableLM Alpha 3B", "maker": "stability", "family": "stability/stable-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Base: CC-BY-SA-4.0. Tuned: CC-BY-NC-SA-4.0.", "release_date": "2023-04-17", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Decoder-only Transformer, GPT-NeoX style.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StableLM Alpha 3B is an early Stability AI language model from April 2023, with a base checkpoint under CC-BY-SA-4.0 and a tuned chat checkpoint under CC-BY-NC-SA-4.0. It was superseded in the product line by StableLM 3B and StableLM 2, but the weights remain. Collected 2026-10-15.", "what_it_is_for": "Research text generation and instruction following at a 3B scale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-lm/stablelm-alpha-3b-v2": { "id": "stability/stable-lm/stablelm-alpha-3b-v2", "name": "StableLM Alpha 3B v2", "maker": "stability", "family": "stability/stable-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-08-04", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Decoder-only Transformer. HF architecture tag stablelm_alpha.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StableLM Alpha 3B v2 is the August 2023 refreshed 3B base model, licensed CC-BY-SA-4.0. It sits between the first Alpha release and the later StableLM 3B 4E1T checkpoint. Collected 2026-10-15.", "what_it_is_for": "Updated 3B base language model for research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-lm/stablelm-alpha-7b": { "id": "stability/stable-lm/stablelm-alpha-7b", "name": "StableLM Alpha 7B", "maker": "stability", "family": "stability/stable-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Base: CC-BY-SA-4.0. Tuned: CC-BY-NC-SA-4.0.", "release_date": "2023-04-11", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only Transformer, GPT-NeoX style.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StableLM Alpha 7B is the 7 billion parameter sibling of the April 2023 StableLM Alpha release. Base weights are CC-BY-SA-4.0 and the tuned checkpoint is CC-BY-NC-SA-4.0. Collected 2026-10-15.", "what_it_is_for": "Research text generation and instruction following at 7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-lm/stablelm-alpha-7b-v2": { "id": "stability/stable-lm/stablelm-alpha-7b-v2", "name": "StableLM Alpha 7B v2", "maker": "stability", "family": "stability/stable-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-08-04", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only Transformer. HF architecture tag stablelm_alpha.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StableLM Alpha 7B v2 is the August 2023 refreshed 7B base, CC-BY-SA-4.0. Collected 2026-10-15.", "what_it_is_for": "Updated 7B base language model for research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-lm/stablelm-zephyr-3b": { "id": "stability/stable-lm/stablelm-zephyr-3b", "name": "StableLM Zephyr 3B", "maker": "stability", "family": "stability/stable-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HF license tag: other. Listed as a Core Model.", "release_date": "2023-11-21", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "DPO-aligned chat model on the StableLM 3B base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StableLM Zephyr 3B is Stability AI's November 2023 chat model aligned from StableLM 3B. The 20 May 2026 Core Models page still lists it for English. Collected 2026-10-15.", "what_it_is_for": "Small English chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-video/four-director": { "id": "stability/stable-video/four-director", "name": "4Director", "maker": "stability", "family": "stability/stable-video", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video world model conditioned on a rigid 4D scene: each object is a canonical mesh moved by one rigid transform per frame, rendered as depth, then a Motion Adapter synthesizes appearance.", "modalities": { "input": [ "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "4Director is a Stability AI research model announced on 7 October 2026 for controlling video with rigid 3D geometry. No public weight repo was in the stabilityai Hugging Face listing, and no API id was found. Collected 2026-10-15.", "what_it_is_for": "Research control of camera and rigid object motion in generated video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-video/semantok": { "id": "stability/stable-video/semantok", "name": "SemanTok", "maker": "stability", "family": "stability/stable-video", "status": "preview", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-10-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Predictable semantic tokens for autoregressive video generation, as described in the 7 October 2026 research index blurb.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SemanTok is a Stability AI research name posted on 7 October 2026 for autoregressive video tokens. The research body and any weights were not retrieved. No matching Hugging Face model was in the org listing. Collected 2026-10-15.", "what_it_is_for": "Research autoregressive video generation with semantic tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-video/stable-video-diffusion": { "id": "stability/stable-video/stable-video-diffusion", "name": "Stable Video Diffusion", "maker": "stability", "family": "stability/stable-video", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research weights at release. The 20 May 2026 Core Models page still includes the 14-frame, 25-frame, and 1.1 checkpoints for commercial use under a Stability agreement. HF license tag is other.", "release_date": "2023-11-21", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Image-to-video latent diffusion model continued from Stable Diffusion. Three public checkpoints: 14-frame, 25-frame XT, and XT 1.1.", "modalities": { "input": [ "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Video Diffusion is Stability AI's image-to-video model. The 14-frame and 25-frame weights were posted on 20 November 2023, and the 1.1 XT checkpoint on 2 February 2024. Hugging Face tags the line at 2B parameters. The API was removed on 24 July 2025, but the weights remain and the May 2026 Core Models page still lists all three. Collected 2026-10-15.", "what_it_is_for": "Image-to-video generation at 14 or 25 frames.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stability/stable-video/stable-virtual-camera": { "id": "stability/stable-video/stable-virtual-camera", "name": "Stable Virtual Camera", "maker": "stability", "family": "stability/stable-video", "status": "active", "confidence": "high", "weights": "open-weights", "license": "HF license tag: other", "release_date": "2025-02-19", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Image-to-video novel-view model. HF size tag 1B. Paper arXiv 2503.14489.", "modalities": { "input": [ "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stable Virtual Camera is a Stability AI novel-view image-to-video model. The Hugging Face repo was created 19 February 2025 and is tagged at 1B parameters. It is not on the Core Models list. Collected 2026-10-15.", "what_it_is_for": "Generate a video of a new camera path from an input image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/alpaca/alpaca-7b": { "id": "stanford/alpaca/alpaca-7b", "name": "Alpaca 7B", "maker": "stanford", "family": "stanford/alpaca", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0 for the weight diff and the 52K dataset. Research only. The model is a fine-tune of LLaMA 7B and inherits that non-commercial license. The CRFM post also says the instruction data came from text-davinci-003, whose terms restricted competing uses.", "release_date": "2023-03-13", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Instruction fine-tune of Meta LLaMA 7B on 52K demonstrations. The CRFM post does not restate layer counts or context length.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alpaca 7B is Stanford CRFM's instruction-following fine-tune of Meta's LLaMA 7B, trained on 52K demonstrations. The weight diff remains public for research. The hosted demo is disabled. Collected 2026-10-15.", "what_it_is_for": "The CRFM post says Alpaca is a research artifact for studying instruction following. It is not a commercial assistant. On a preliminary single-turn comparison it behaved similarly to text-davinci-003.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/anticipatory-music/anticipatory-music-large": { "id": "stanford/anticipatory-music/anticipatory-music-large", "name": "Anticipatory Music Transformer Large", "maker": "stanford", "family": "stanford/anticipatory-music", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-06-16", "context_length": 1024, "max_output_tokens": null, "parameters": "780M", "architecture": "GPT-2-style Transformer on symbolic music, trained with anticipation. The 800k config: 36 layers, hidden size 1280, 20 heads, vocab 55030, n_positions 1024. The 100k card is Lakh MIDI only. The 800k card adds MetaMidi, FMA transcripts, and 450k commercial records transcribed with a Magenta model. These are different training runs, not one continued checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Anticipatory Music Transformer Large is Stanford CRFM's 780M symbolic-music model. A 100k-step Lakh-only run shipped with the June 2023 paper. A separate 800k-step run with extra data was released in March 2024. Collected 2026-10-15.", "what_it_is_for": "Same infilling job as the smaller anticipatory models: complete or revise symbolic music from partial controls. The March 2024 checkpoint is the one the release post links as the model release and calls the best model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/anticipatory-music/anticipatory-music-medium": { "id": "stanford/anticipatory-music/anticipatory-music-medium", "name": "Anticipatory Music Transformer Medium", "maker": "stanford", "family": "stanford/anticipatory-music", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-06-16", "context_length": 1024, "max_output_tokens": null, "parameters": "360M", "architecture": "GPT-2-style Transformer on arrival-time encoded symbolic music, trained with anticipation. Config of the 800k checkpoint: 24 layers, hidden size 1024, 16 heads, vocab 55028, n_positions 1024. Trained on Lakh MIDI.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Anticipatory Music Transformer Medium is Stanford CRFM's 360M symbolic-music model. The 800k-step Lakh MIDI checkpoint is the one used for the examples on the release page and is called the best model studied in the paper. Collected 2026-10-15.", "what_it_is_for": "It infills symbolic music from partial controls such as a melody, a span, or chords and drums. The post presents it as a co-composition tool, not an audio generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/anticipatory-music/anticipatory-music-small": { "id": "stanford/anticipatory-music/anticipatory-music-small", "name": "Anticipatory Music Transformer Small", "maker": "stanford", "family": "stanford/anticipatory-music", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-06-16", "context_length": 1024, "max_output_tokens": null, "parameters": "128M", "architecture": "GPT-2-style Transformer on arrival-time encoded symbolic music, trained with anticipation. Config of the 800k checkpoint: 12 layers, hidden size 768, 12 heads, vocab 55028, n_positions 1024. Trained on the Lakh MIDI dataset.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Anticipatory Music Transformer Small is Stanford CRFM's 128M symbolic-music infilling model. The 800k-step checkpoint is the efficient model called out in the June 2023 release. Collected 2026-10-15.", "what_it_is_for": "The CRFM post says the model infills symbolic music so a person can control accompaniment, spans, and revisions. It does not generate audio. Audio examples on the page are synthesized from the notes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/anticipatory-music/autoregressive-music-small": { "id": "stanford/anticipatory-music/autoregressive-music-small", "name": "Autoregressive Music Transformer Small", "maker": "stanford", "family": "stanford/anticipatory-music", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-06-16", "context_length": 1024, "max_output_tokens": null, "parameters": "128M", "architecture": "Small GPT-style Transformer on arrival-time encoded symbolic music, without anticipation. The 800k config matches the small anticipatory shape: 12 layers, hidden size 768, 12 heads, vocab 55028, n_positions 1024. Trained on Lakh MIDI. The 800k README does not say the model was trained with anticipation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is Stanford CRFM's 128M arrival-time music Transformer trained without anticipation. It is a control in the Anticipatory Music Transformer release, not the featured infilling model. Collected 2026-10-15.", "what_it_is_for": "The release post lists it as the autoregressive baseline against the anticipatory models. It generates symbolic music left to right.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/anticipatory-music/autoregressive-music-small-interarrival": { "id": "stanford/anticipatory-music/autoregressive-music-small-interarrival", "name": "Autoregressive Music Transformer Small (interarrival)", "maker": "stanford", "family": "stanford/anticipatory-music", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-06-16", "context_length": null, "max_output_tokens": null, "parameters": "112M", "architecture": "Small autoregressive Transformer on interarrival-time encoded symbolic music. The README says 112M parameters and 100k steps on Lakh MIDI. It does not say the model was trained with anticipation. config.json was not read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is Stanford CRFM's 112M interarrival-time music control from the Anticipatory Music Transformer release. Only a 100k-step checkpoint was published. Collected 2026-10-15.", "what_it_is_for": "The release list marks it as paper row 1, the interarrival-time autoregressive baseline. It is not the anticipatory infilling model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/backpack/backpack-1-4b": { "id": "stanford/backpack/backpack-1-4b", "name": "Backpack 1.4B", "maker": "stanford", "family": "stanford/backpack", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "1.4B", "architecture": "Backpack language model trained with Levanter and JAX. Config: 36 layers, hidden size 1280, 20 heads, 48 senses, sense_intermediate_scale 7, vocab 50264, n_positions 512. The card says 1.4B parameters. Hub metadata labels the repo 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Backpack 1.4B is Stanford CRFM's larger Backpack language model, trained with Levanter on OpenWebText. The Hub repo is named levanter-backpack-1b. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "The card says it is meant to combine language-model performance with an interface for interpretability and control. It is a research checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/backpack/backpack-gpt2": { "id": "stanford/backpack/backpack-gpt2", "name": "Backpack-GPT2", "maker": "stanford", "family": "stanford/backpack", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Backpack language model. Config: 12 layers, hidden size 768, 12 heads, 16 senses, sense_intermediate_scale 4, vocab 50264, n_positions 512, gelu_new. The card does not print a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Backpack-GPT2 is Stanford NLP's smaller Backpack language model, built for interpretability and control rather than as a chat product. Weights are Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says the model is for studying interpretable methods in NLP. It is not fit for production.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/biomedlm/biomedlm": { "id": "stanford/biomedlm/biomedlm", "name": "BioMedLM", "maker": "stanford", "family": "stanford/biomedlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BigScience Open RAIL-M (bigscience-bloom-rail-1.0). The card says the license forbids use to provide medical advice or medical-results interpretation.", "release_date": "2022-12-15", "context_length": 1024, "max_output_tokens": null, "parameters": "2.7B", "architecture": "GPT-2 decoder trained with Flash Attention. Hidden size 2560, 20 heads, 32 layers, vocab 28896, sequence length 1024. Custom tokenizer trained on PubMed abstracts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioMedLM is a 2.7B GPT-style language model from Stanford CRFM and MosaicML, trained only on biomedical text from The Pile. It was first released as PubMedGPT. Weights remain public under a RAIL license. Collected 2026-10-15.", "what_it_is_for": "The CRFM post says it is a domain model for biomedical NLP, especially question answering after fine-tuning. The card says generation is for research and is not suitable for production or medical advice.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/crfm-gpt2/crfm-gpt2-medium": { "id": "stanford/crfm-gpt2/crfm-gpt2-medium", "name": "CRFM GPT-2 Medium", "maker": "stanford", "family": "stanford/crfm-gpt2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2021-08-26", "context_length": 1024, "max_output_tokens": null, "parameters": "355M", "architecture": "GPT-2 medium: 24 layers, hidden size 1024, 16 heads, vocab 50257, gelu_new. Attention upcasts and scales by inverse layer index. Trained on OpenWebText for 400K steps. Five random seeds were released. Counts are from arwen-gpt2-medium-x21 config.json and the CRFM post.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CRFM GPT-2 Medium is the 355M half of Stanford CRFM's August 2021 Mistral reproducibility release. Five named OpenWebText seeds are public. It is not a Mistral AI model. Collected 2026-10-15.", "what_it_is_for": "The CRFM post releases these seeds so others can study stable training and the effect of random seeds. They are base language models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/crfm-gpt2/crfm-gpt2-small": { "id": "stanford/crfm-gpt2/crfm-gpt2-small", "name": "CRFM GPT-2 Small", "maker": "stanford", "family": "stanford/crfm-gpt2", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2021-08-26", "context_length": 1024, "max_output_tokens": null, "parameters": "124M", "architecture": "GPT-2 small: 12 layers, hidden size 768, 12 heads, vocab 50257, gelu_new. Attention upcasts and scales by inverse layer index (reorder_and_upcast_attn and scale_attn_by_inverse_layer_idx are true). Trained on OpenWebText for 400K steps. Five random seeds were released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CRFM GPT-2 Small is a 124M decoder released by Stanford CRFM on 26 August 2021 as part of the Mistral reproducibility project. Five OpenWebText seeds remain on the Hub. This is not a Mistral AI model. Collected 2026-10-15.", "what_it_is_for": "The blog says the seeds exist so researchers can study training randomness. They are base language models, not instruction-tuned assistants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/glove/glove": { "id": "stanford/glove/glove", "name": "GloVe", "maker": "stanford", "family": "stanford/glove", "status": "active", "confidence": "high", "weights": "open-source", "license": "Pretrained vectors: Open Data Commons Public Domain Dedication and License (PDDL) 1.0. Code: Apache-2.0.", "release_date": "2014-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Log-bilinear model with a weighted least-squares objective on a global word-word co-occurrence matrix. Released vectors differ by corpus, vocabulary size, casing, and dimension. This is not a transformer.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GloVe is Stanford NLP's word-embedding model, first released in August 2014. Pretrained vectors are still distributed, including a July 2025 update trained on 2024 corpora. Collected 2026-10-15.", "what_it_is_for": "The project page says GloVe learns word vectors from global co-occurrence counts. Downstream use is similarity, analogy, and features for other models. It does not generate text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/marin/marin-32b-base": { "id": "stanford/marin/marin-32b-base", "name": "Marin 32B Base", "maker": "stanford", "family": "stanford/marin", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-10-29", "context_length": 4096, "max_output_tokens": null, "parameters": "32B", "architecture": "Qwen3-style 32B with QK-Norm. Hidden size 5120, feedforward 27648, 64 layers, 40 heads, 8 KV heads, head dim 128. Sequence length 4096 on the card and in max_position_embeddings. config.json also has llama3 RoPE scaling with factor 8 and original_max_position_embeddings 8192. Tokenizer is stanford-crfm/marin-tokenizer. Hub metadata says 33B parameters. The card says 32B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marin 32B Base is the Marin project's larger open base model, released by the Stanford CRFM Marin community in October 2025. The Hub main revision is the Mantis cooldown. It is Apache-2.0 and not instruction-tuned. Collected 2026-10-15.", "what_it_is_for": "The card says it is a raw base model for research. It is not an assistant and has no safety tune. The retrospective says it is English plus code and has no long-context training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/marin/marin-8b-base": { "id": "stanford/marin/marin-8b-base", "name": "Marin 8B Base", "maker": "stanford", "family": "stanford/marin", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-05-19", "context_length": 4096, "max_output_tokens": null, "parameters": "8B", "architecture": "Llama 3 8B shape: hidden size 4096, feedforward 14336, 32 layers, 32 heads, 8 KV heads. Tokenizer is stanford-crfm/marin-tokenizer, a Llama 3 vocabulary with a bundled chat template. config.json sets max_position_embeddings to 4096 and also sets llama3 RoPE scaling with factor 8 and original_max_position_embeddings 8192. The card's architecture list does not state a sequence length.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marin 8B Base is the Stanford CRFM Marin project's open 8B pretrained language model. The released main revision is deeper-starling. Weights are Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The announcement describes an open lab: weights, data, code, and logs for a from-scratch foundation model. This checkpoint is a base model, not an assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stanford/marin/marin-8b-instruct": { "id": "stanford/marin/marin-8b-instruct", "name": "Marin 8B Instruct", "maker": "stanford", "family": "stanford/marin", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-05-19", "context_length": 4096, "max_output_tokens": null, "parameters": "8B", "architecture": "Supervised fine-tune of Marin 8B Base. Same Llama 3 8B shape. The released revision deeper-starling-05-15 was trained for 5.3B tokens. SFT only. The card says no RLHF and no DPO had been done at release. Context 4096 is max_position_embeddings from the base config. The instruct config was not fetched separately.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marin 8B Instruct is Stanford CRFM's supervised fine-tune of Marin 8B Base. The released checkpoint is deeper-starling-05-15. It is Apache-2.0 and not safety-tuned. Collected 2026-10-15.", "what_it_is_for": "The card says this is an SFT chat model on code, reasoning, math, and general instruction mixes. The announcement says it had no preference optimization yet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stellenbosch/asgan/asgan": { "id": "stellenbosch/asgan/asgan", "name": "AudioStyleGAN", "maker": "stellenbosch", "family": "stellenbosch/asgan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-10-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unconditional GAN that maps latent noise to 1-second 16 kHz waveforms. The shipped torch hub entry asgan_hubert_sc09_6 is the HuBERT variant the authors call the best paper model. The generator object is model.g. Latent sizes on a loaded checkpoint are model.z_dim and model.w_dim. Those integers were not read from the file. A training-command example uses z_dim 512 and is not the released checkpoint's size. Training data is Google Speech Commands digits (SC09). A HiFi-GAN vocodes HuBERT features. The v0 bundle also includes a Facebook fairseq HuBERT base file, which is a rehost and not a Stellenbosch model. unconditional_generate returns shape (N, 16000). The repository file list has no LICENSE file.", "modalities": { "input": [], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AudioStyleGAN (ASGAN) is an unconditional speech GAN from Matthew Baas and Herman Kamper at Stellenbosch University Electrical and Electronic Engineering. The public hub model is the HuBERT variant `asgan_hubert_sc09_6`. Status on 2026-10-15: active. arXiv:2210.05271 was submitted on 2022-10-11 and says code and models are on GitHub. Accepted at IEEE SLT 2022. IEEE Xplore document 10023153 was added on 2023-01-27. A 2024 follow-up on disentanglement studies the same system.", "what_it_is_for": "The authors generate speech with no text or speaker prompt. The shipped model draws 1 second of 16 kHz audio from a latent code, trained on spoken digits.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stellenbosch/knn-vc/knn-vc": { "id": "stellenbosch/knn-vc/knn-vc", "name": "kNN-VC", "maker": "stellenbosch", "family": "stellenbosch/knn-vc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-05-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Any-to-any voice conversion. The encoder is frozen WavLM-Large layer 6, a Microsoft model loaded as a dependency, not a Stellenbosch release. Conversion is non-parametric k-nearest neighbours with default top-k 4. The trained Stellenbosch component is a HiFi-GAN vocoder on WavLM features at 16 kHz. Torch hub entry: torch.hub.load('bshall/knn-vc', 'knn_vc', prematched=True). The README treats prematch_g_02500000.pt as the stronger vocoder and g_02500000.pt as the regular one. Inference files are named g_*.pt; do_*.pt files are full training checkpoints.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "kNN-VC is an any-to-any voice-conversion model from Matthew Baas, Benjamin van Niekerk, and Herman Kamper at the MediaLab, Department of Electrical and Electronic Engineering, Stellenbosch University. Weights and code are public. Status on 2026-10-15: active. The arXiv abstract is dated 2023-05-30 and points to the demo for code, samples, and trained models. Interspeech 2023.", "what_it_is_for": "The authors use it for one-shot voice conversion: match source content to a reference speaker with k-nearest neighbours over self-supervised features, then vocode with HiFi-GAN.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stellenbosch/soft-vc/soft-vc": { "id": "stellenbosch/soft-vc/soft-vc", "name": "Soft-VC", "maker": "stellenbosch", "family": "stellenbosch/soft-vc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2021-11-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Any-to-one voice conversion. Soft units are a network trained to predict a distribution over discrete speech units from a frozen self-supervised encoder, using cosine similarity, a temperature, and cross-entropy. The discrete path is frozen SSL features plus k-means. An acoustic model maps units toward a target speaker, and HiFi-GAN vocodes them. Documented torch hub entry points are hubert_soft, hubert_discrete, and hifigan_hubert_soft. CPC-Soft and CPC-Discrete appear as paper comparisons. The frozen HuBERT and CPC encoders are not Stellenbosch models. Related author repos are bshall/hubert, bshall/acoustic-model, and bshall/hifigan. Speaker-specific acoustic models are training runs of this system. Joint work: Benjamin van Niekerk, Matthew Baas, and Herman Kamper at Stellenbosch E&E, with Marc-Andr\u00e9 Carbonneau, Julian Za\u00efdi, and Hugo Seut\u00e9 at Ubisoft La Forge. LICENSE copyright 2021 Benjamin van Niekerk.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Soft-VC is a voice-conversion system from Benjamin van Niekerk, Matthew Baas, and Herman Kamper at Stellenbosch University E&E, with Marc-Andr\u00e9 Carbonneau, Julian Za\u00efdi, and Hugo Seut\u00e9 at Ubisoft La Forge. Code and weights are in the author repository. Status on 2026-10-15: active. arXiv:2111.02392 was submitted on 2021-11-03 (revised 2022-06-08) and was accepted at ICASSP 2022. The abstract links code at the GitHub repo and samples at the Ubisoft demo.", "what_it_is_for": "The paper compares discrete and soft speech units for any-to-one voice conversion. Soft units are a trained distribution over discrete units from a frozen encoder, meant to keep more linguistic content than hard k-means codes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stellenbosch/stargan-zsvc/stargan-zsvc": { "id": "stellenbosch/stargan-zsvc/stargan-zsvc", "name": "StarGAN-ZSVC", "maker": "stellenbosch", "family": "stellenbosch/stargan-zsvc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-12-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Zero-shot voice conversion. A named extension of StarGAN-VC trained by Matthew Baas and Herman Kamper at Stellenbosch University E&E, not a rehost of another lab's checkpoint. The author demo says training used the VCC 2018 set, about 9 minutes, and contrasts that with the original AutoVC trained on 4960 minutes of VCTK. The demo still links a speaker-embedding repo, https://github.com/RF5/simple-speaker-embedding, which is a component, and a Google Drive archive, https://drive.google.com/file/d/1NTSXVmpgoMX5ctTUKweftp1c8wUfNDBt/view?usp=sharing, described as the model, example evaluations, and pre-trained checkpoints. That archive was not opened. Springer chapter first online 2020-12-21, pages 69-84, DOI 10.1007/978-3-030-66151-9_5. arXiv:2106.00043 is dated 2021-05-31.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StarGAN-ZSVC is a zero-shot voice-conversion model from Matthew Baas and Herman Kamper at Stellenbosch University Electrical and Electronic Engineering. It is their trained extension of StarGAN-VC for a small dataset. Status on 2026-10-15: active. The Springer chapter was first online on 2020-12-21, pages 69\u201384, DOI 10.1007/978-3-030-66151-9_5. arXiv:2106.00043 is dated 2021-05-31. The author demo still links pre-trained checkpoints.", "what_it_is_for": "The chapter targets zero-shot voice conversion when only a few minutes of target speech are available. The demo contrasts this training set with a much larger AutoVC setup.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stellenbosch/transfusion/transfusion-small": { "id": "stellenbosch/transfusion/transfusion-small", "name": "TransFusion Small", "maker": "stellenbosch", "family": "stellenbosch/transfusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-10-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion automatic speech recognition with character-level transcripts. The README name is transfusion_small_462k. It is conditioned on frozen WavLM-Large, last 9 layers averaged and not fine-tuned. That encoder is a Microsoft model, not a Stellenbosch release. Trained on LibriSpeech 960 hours. Torch hub loads wavlm_large and transfusion_small_462k. Python 3.10 or newer. Default inference uses the diffusion settings in transfusion/score.py. The v1.0 notes say the best model is 462k updates, described as 3.7M steps with gradient accumulation, letter vocabulary. The full checkpoint is split as mp_rank_00_model_states.pt.part* files. The notes give md5 b7465223b1c688cc6c723872f7ada54c for the concatenated full checkpoint and also ship transfusion_462k_slim.pt. The README table lists Params (M) 253. That cell is not an exact integer count.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TransFusion Small is a speech-recognition model from Matthew Baas, Kevin Eloff, and Herman Kamper at the MediaLab, Stellenbosch University Electrical and Electronic Engineering. The public checkpoint is `transfusion_small_462k`. Status on 2026-10-15: active. The arXiv abstract is dated 2022-10-14 and is the SACAIR 2022 paper. Training used Stellenbosch HPC under NRF grant 120409.", "what_it_is_for": "The authors use multinomial diffusion to transcribe speech as characters. The shipped model is the 462k-update LibriSpeech system conditioned on frozen WavLM features.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stellenbosch/urhythmic/urhythmic": { "id": "stellenbosch/urhythmic/urhythmic", "name": "Urhythmic", "maker": "stellenbosch", "family": "stellenbosch/urhythmic", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-07-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Rhythm transfer for voice conversion on soft speech units (HubertSoft from Soft-VC). Torch hub entries urhythmic_fine and urhythmic_global. hubconf.py names checkpoints segmenter-3, segmenter-8, rhythm-fine, rhythm-global, and speaker HiFi-GANs for VCTK speakers p228, p268, p225, p232, p257, p231, and LJSpeech. Fine and global are two rhythm settings of one model. Speaker HiFi-GANs are training runs of the same vocoder, not separate products. Authors: Benjamin van Niekerk and Herman Kamper, Stellenbosch University E&E, and Marc-Andr\u00e9 Carbonneau, Ubisoft La Forge. Code LICENSE is MIT, copyright 2023 Benjamin van Niekerk. GitHub release v0.1 is displayed as 11 Jul with no year.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Urhythmic is a rhythm model for voice conversion from Benjamin van Niekerk and Herman Kamper at Stellenbosch University E&E and Marc-Andr\u00e9 Carbonneau at Ubisoft La Forge. It sits on the soft units from Soft-VC. Status on 2026-10-15: active. arXiv:2307.06040 was submitted on 2023-07-12 and points to code and checkpoints. The IEEE Signal Processing Letters version is DOI 10.1109/LSP.2023.3313515, published 2023-09-11, pages 1297\u20131301.", "what_it_is_for": "The authors change the rhythm of converted speech while keeping the soft-unit content path. Two shipped settings are fine and global rhythm.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stellenbosch/vq-cpc/vq-cpc": { "id": "stellenbosch/vq-cpc/vq-cpc", "name": "VQ-CPC", "maker": "stellenbosch", "family": "stellenbosch/vq-cpc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2020-05-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vector-quantized contrastive predictive coding for acoustic unit discovery. The model learns discrete units by predicting future codes. It adapts contrastive predictive coding and a VQ-VAE codebook. Those methods are prior work. The trained weights are the authors'. Authors Benjamin van Niekerk, Leanne Nortje, and Herman Kamper, E&E Engineering, Stellenbosch University. The paper links https://github.com/bshall/VectorQuantizedCPC and says pretrained weights are released. The README ships ZeroSpeech 2019 English and Indonesian (surprise) checkpoints at tag v0.1. PyTorch 1.4 or newer. Tag year was not displayed. LICENSE is MIT, copyright 2020 Benjamin van Niekerk. Output of the model is discrete acoustic units. Voice conversion in the paper is a downstream use.", "modalities": { "input": [ "audio" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VQ-CPC is an acoustic-unit model from Benjamin van Niekerk, Leanne Nortje, and Herman Kamper at Stellenbosch University E&E Engineering. It was their second system for the ZeroSpeech 2020 challenge. Status on 2026-10-15: active. arXiv:2005.09409 was submitted on 2020-05-19 and revised on 2020-08-19. The HTML paper names this repository and says code and pretrained weights are released. Interspeech 2020 DOI: 10.21437/INTERSPEECH.2020-1693.", "what_it_is_for": "The authors discover phone-like units from unlabelled speech by predicting future quantized codes. The paper also uses the units for a voice-conversion track. Of the two submitted models, they describe VQ-CPC as slightly better and simpler to train.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stellenbosch/zerospeech-vqvae/zerospeech-vqvae": { "id": "stellenbosch/zerospeech-vqvae/zerospeech-vqvae", "name": "ZeroSpeech VQ-VAE", "maker": "stellenbosch", "family": "stellenbosch/zerospeech-vqvae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-05-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vector-quantized variational autoencoder for acoustic unit discovery and voice conversion. Speech is encoded to discrete units and the waveform is reconstructed. The paper's first model, by Benjamin van Niekerk, Leanne Nortje, and Herman Kamper, E&E Engineering, Stellenbosch University. The HTML says code and pretrained weights are released at https://github.com/bshall/ZeroSpeech. Pretrained weights cover ZeroSpeech 2019 English and Indonesian. The implementation follows published pieces from Chorowski et al. WaveNet autoencoders, Lorenzo-Trueba et al. universal vocoding, and van den Oord et al. VQ-VAE. The trained challenge weights are the authors'. A spectrogram variant, VQ-VAE(spec), plus a separate FFTNet vocoder, is a paper comparison and was not given its own folder. No LICENSE file was found on master or main. Tag v0.1 year was not displayed.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZeroSpeech VQ-VAE is an acoustic-unit and voice-conversion model from Benjamin van Niekerk, Leanne Nortje, and Herman Kamper at Stellenbosch University E&E Engineering. It is the first of their two ZeroSpeech 2020 systems. Status on 2026-10-15: active. arXiv:2005.09409 was submitted on 2020-05-19. The paper says code and pretrained weights are at https://github.com/bshall/ZeroSpeech. Interspeech 2020 DOI: 10.21437/INTERSPEECH.2020-1693.", "what_it_is_for": "The authors encode unlabelled speech as discrete units and reconstruct the waveform, then use those units for the ZeroSpeech voice-conversion track. English is the development language. Indonesian is the surprise language.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step/step-1-32k": { "id": "stepfun/step/step-1-32k", "name": "Step-1 32K", "maker": "stepfun", "family": "stepfun/step", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "32K-context API id of the Step-1 dense hundred-billion-parameter series. Exact count is not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-1 32K is the 32,000-token API id of \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 Step-1, collected 2026-10-15. It was retired with the other early text ids on 2026-07-08. Weights were not released.", "what_it_is_for": "Same general language jobs as Step-1, with a longer shared context cap.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step/step-1-8k": { "id": "stepfun/step/step-1-8k", "name": "Step-1 8K", "maker": "stepfun", "family": "stepfun/step", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03", "context_length": 8000, "max_output_tokens": null, "parameters": "", "architecture": "Dense model in the hundred-billion-parameter class (\u5343\u4ebf). Exact count is not published. Public debut reported for March 2024; some secondary pages also say training finished in 2023.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-1 8K is the short-context API id of \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09's first dense language model, collected 2026-10-15. StepFun called the series a hundred-billion-parameter model. The id was retired on 2026-07-08. The public debut is dated to March 2024 in contemporary reports.", "what_it_is_for": "General writing, cross-language chat, question answering, and logic, according to the legacy text guide.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step/step-2-16k": { "id": "stepfun/step/step-2-16k", "name": "Step-2 16K", "maker": "stepfun", "family": "stepfun/step", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-07-04", "context_length": 16000, "max_output_tokens": null, "parameters": "", "architecture": "MoE. StepFun's text guide says the parameter scale exceeds one trillion (\u4e07\u4ebf). Exact count is not published. The March 2024 preview and the July 2024 WAIC formal release are the same product line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-2 16K is \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09's trillion-class MoE language model, collected 2026-10-15. A preview was shown in March 2024 and the formal model at WAIC on 2024-07-04. The API id step-2-16k was retired on 2026-07-08. Weights were not released.", "what_it_is_for": "The legacy guide says it covers Chinese and English general tasks and that planning quality was the point of the scale-up.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step/step-2-16k-exp": { "id": "stepfun/step/step-2-16k-exp", "name": "Step-2 16K Exp", "maker": "stepfun", "family": "stepfun/step", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": 16000, "max_output_tokens": null, "parameters": "", "architecture": "Rolling experimental build of step-2-16k. The legacy text guide says it is not for production.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-2 16K Exp is the experimental API id \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 published next to step-2-16k, collected 2026-10-15. The legacy text guide calls it a rolling preview and tells developers not to use it in production. It is absent from the 2026 deprecation table and from the live model list.", "what_it_is_for": "StepFun described it as the place where new Step-2 features landed before the formal checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step/step-2-mini": { "id": "stepfun/step/step-2-mini", "name": "Step-2 Mini", "maker": "stepfun", "family": "stepfun/step", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Fast model using StepFun's MFA attention. The text guide says it targets Step-1 quality at higher throughput, with a coding emphasis. Context listed as 32K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-2 Mini is a retired \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 text model, collected 2026-10-15. The legacy guide calls it a fast MFA model with a 32K context, aimed at Step-1 quality and stronger coding. The API stopped on 2026-07-08.", "what_it_is_for": "Low-cost general text and code, according to the text-model guide.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step/step-3": { "id": "stepfun/step/step-3", "name": "Step 3", "maker": "stepfun", "family": "stepfun/step", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-25", "context_length": 65536, "max_output_tokens": null, "parameters": "321B VLM total / 316B LLM / 38B active", "architecture": "MoE with Multi-Matrix Factorization Attention and Attention-FFN Disaggregation. Model card: 61 layers (5 dense), hidden 7168, 64 query heads, head dim 256, 48 experts, 3 selected plus 1 shared, DeepSeek V3 tokenizer. Max context on the card is 65536.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step 3 is \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09's 2025 multimodal reasoning model, collected 2026-10-15. The open weights are still Apache-2.0, but the API id step-3 was turned off on 2026-07-08. The card lists 321B total VLM parameters and 38B active, with a 65,536-token context.", "what_it_is_for": "StepFun described Step 3 as a vision-language reasoning model co-designed to cut decoding cost on both large and smaller accelerators.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step/step-3-5-flash": { "id": "stepfun/step/step-3-5-flash", "name": "Step 3.5 Flash", "maker": "stepfun", "family": "stepfun/step", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-02", "context_length": 256000, "max_output_tokens": null, "parameters": "196.81B total / about 11B active", "architecture": "Sparse MoE. Official README: 45-layer transformer, hidden size 4096, vocab 128,896, 288 routed experts plus 1 shared expert, top-8 routing, 3:1 sliding-window attention, MTP-3. API rounds this to 196B total and 11B active. Text only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.7, "completion_per_m_usd": 2.1 }, "summary_intro": "Step 3.5 Flash is \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09's open text reasoning model, collected 2026-10-15. It is still on the API and the weights are Apache-2.0. Official numbers are 196.81B total parameters with about 11B active and a 256K context. A dated repo note places the release on 2026-02-02.", "what_it_is_for": "StepFun says it is built for agents: planning, tool calls, math, software engineering, and long tasks, with high throughput from sparse activation and multi-token prediction.", "ratings": { "empirical_100": 52, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step/step-3-5-flash-2603": { "id": "stepfun/step/step-3-5-flash-2603", "name": "Step 3.5 Flash 2603", "maker": "stepfun", "family": "stepfun/step", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 256000, "max_output_tokens": null, "parameters": "", "architecture": "API variant of step-3.5-flash tuned for high-frequency agents. Separate model id and the same published token price. Docs say it supports reasoning_effort low and high.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.7, "completion_per_m_usd": 2.1 }, "summary_intro": "Step 3.5 Flash 2603 is a named \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 API variant of Step 3.5 Flash, collected 2026-10-15. The platform lists it as its own model for high-frequency agent work, with the same CNY token price as step-3.5-flash. Weights were not found.", "what_it_is_for": "StepFun says it keeps flagship reasoning and tool use while cutting token use and latency, and that a low-reasoning mode is available for cheaper calls.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step/step-3-7-flash": { "id": "stepfun/step/step-3-7-flash", "name": "Step 3.7 Flash", "maker": "stepfun", "family": "stepfun/step", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-28", "context_length": 256000, "max_output_tokens": null, "parameters": "198B total / about 11B active", "architecture": "Sparse MoE vision-language model. GitHub: 196B language backbone plus a 1.8B vision encoder, about 11B active, 256k context, reasoning levels low/medium/high. Vendor peak generation speed cited as 400 tokens/s.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.35, "completion_per_m_usd": 8.1 }, "summary_intro": "Step 3.7 Flash is \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09's open multimodal reasoning model, collected 2026-10-15. It launched and was open-sourced on 2026-05-28 under Apache-2.0. The API calls it a 198B sparse MoE with about 11B active parameters and a 256K context. It adds native image and video input on top of Step 3.5 Flash.", "what_it_is_for": "StepFun positions it for agents, code, and multimodal workflows that should not call a separate vision model. Reasoning effort is selectable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step/step-5-preview": { "id": "stepfun/step/step-5-preview", "name": "Step 5 Preview", "maker": "stepfun", "family": "stepfun/step", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "StepFun Model License (Hugging Face tag: other; not Apache-2.0)", "release_date": "2026-09-20", "context_length": 1000000, "max_output_tokens": 64000, "parameters": "600B total / 27B active", "architecture": "Sparse MoE. Press summarizing the 2026-10-15 technical note describes a 92-layer narrow-deep stack and Sparse GQA. API max output is 64k tokens; the GitHub spec table says maximum output up to 1M tokens.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 7.0, "completion_per_m_usd": 20.0 }, "summary_intro": "Step 5 Preview is \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09's preview flagship for agentic work, collected 2026-10-15. The API opened on 2026-09-20. Weights (BF16, FP8, NVFP4) were posted on 2026-10-15 under a StepFun license rather than Apache-2.0. It is a 600B sparse MoE with about 27B active parameters, a 1M-token context, and native text, image, and video input.", "what_it_is_for": "StepFun describes it as the base model for real tasks: software engineering, professional knowledge work with a stated finance emphasis, and multi-step jobs that use tools and documents.", "ratings": { "empirical_100": null, "lmsys_elo": 1240, "coding_elo": 1240, "swe_bench": 21.8, "humaneval": 75.4, "twelve_cs": 98.0, "aa_intelligence": 43.7, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step/step-r-mini": { "id": "stepfun/step/step-r-mini", "name": "Step R-mini", "maker": "stepfun", "family": "stepfun/step", "status": "retired", "confidence": "low", "weights": "closed", "license": "", "release_date": "2025-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First Step reasoning model, also called Step Reasoner mini. Secondary launch write-ups describe slow thinking and on-policy reinforcement learning. Not on the October 2026 model list.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step R-mini is a reasoning model \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 was reported to have launched in January 2025, collected 2026-10-15. Two secondary write-ups agree on the name and the role. It does not appear in the October 2026 platform model list, and no official spec page was found, so confidence is low.", "what_it_is_for": "Those write-ups say it was meant for math, code, and logic while still handling general writing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step/step-router-v1": { "id": "stepfun/step/step-router-v1", "name": "Step Router V1", "maker": "stepfun", "family": "stepfun/step", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Router, not a single set of weights. Step Plan sends each request to deepseek-v4-pro or step-3.7-flash using message count, input size, and tool count. deepseek-v4-pro is a third-party engine.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step Router V1 is a named \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 routing model on Step Plan, collected 2026-10-15. It is not one checkpoint. The platform routes each call to deepseek-v4-pro or step-3.7-flash. It is included because StepFun sells it as its own model id.", "what_it_is_for": "StepFun says callers who do not want to write their own router can send hard decisions to the DeepSeek engine and high-frequency work to Step 3.7 Flash.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-3d/step1x-3d": { "id": "stepfun/step-3d/step1x-3d", "name": "Step1X-3D", "maker": "stepfun", "family": "stepfun/step-3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage 3D asset model. Geometry is a hybrid VAE-DiT at 1.3B. Texture is an SDXL-based 3.5B module. A geometry-label checkpoint is also 1.3B. Weights posted 2025-05-13.", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step1X-3D is an open 3D asset system from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. Geometry and texture weights were posted on 2025-05-13 under Apache-2.0. Geometry is 1.3B and texture is 3.5B. They are versions of one released system, not three unrelated products.", "what_it_is_for": "Generate textured 3D assets with a watertight geometry stage and a texture stage, including cartoon, sketch, and photoreal looks in the project examples.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-asr/step-asr": { "id": "stepfun/step-asr/step-asr", "name": "Step ASR", "maker": "stepfun", "family": "stepfun/step-asr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Earlier general ASR, realtime and offline. The model list caps a file at 100MB.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step ASR is an older speech-recognition model still on the \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 API, collected 2026-10-15. The id is step-asr at 0.9 CNY per hour. The model list says it separates speech from noise, handles Chinese-English mix and accented Mandarin, and accepts files up to 100MB.", "what_it_is_for": "Voice input, voice control, and meeting notes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-asr/step-asr-1-1": { "id": "stepfun/step-asr/step-asr-1-1", "name": "Step ASR 1.1", "maker": "stepfun", "family": "stepfun/step-asr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "File ASR id. The audio guide says it is for audio-file recognition.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step ASR 1.1 is a file speech-recognition id from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The string is step-asr-1.1 at 2.2 CNY per hour. The audio guide only says it is for audio-file transcription such as meetings, support review, and voice search.", "what_it_is_for": "Offline transcription of audio files.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-asr/step-asr-1-1-stream": { "id": "stepfun/step-asr/step-asr-1-1-stream", "name": "Step ASR 1.1 Stream", "maker": "stepfun", "family": "stepfun/step-asr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Streaming id paired with step-asr-1.1.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step ASR 1.1 Stream is the streaming speech-recognition id from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The string is step-asr-1.1-stream at 2.6 CNY per hour. The guide says it is for realtime audio in and streaming text out.", "what_it_is_for": "Live voice input.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-asr/stepaudio-2-5-asr": { "id": "stepfun/step-asr/stepaudio-2-5-asr", "name": "StepAudio 2.5 ASR", "maker": "stepfun", "family": "stepfun/step-asr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "4B multi-token-prediction ASR. Non-streaming id stepaudio-2.5-asr uses SSE. The guide cites engine RTF about 0.0053.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StepAudio 2.5 ASR is a 4B speech-recognition model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The non-streaming id stepaudio-2.5-asr costs 0.15 CNY per hour. The guide says multi-token prediction cuts latency and that one hour of audio takes about 19 seconds at RTF about 0.0053.", "what_it_is_for": "Batch transcription, captions, and voice input where a finished transcript can wait for the whole file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-asr/stepaudio-2-5-asr-stream": { "id": "stepfun/step-asr/stepaudio-2-5-asr-stream", "name": "StepAudio 2.5 ASR Stream", "maker": "stepfun", "family": "stepfun/step-asr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Streaming id of the 4B StepAudio 2.5 ASR. WebSocket. Cumulative text, revisable tail words, and word timestamps.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StepAudio 2.5 ASR Stream is the realtime id of \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 StepAudio 2.5 ASR, collected 2026-10-15. The string is stepaudio-2.5-asr-stream at 1.2 CNY per hour, eight times the file-mode price. It is the same 4B MTP model with a WebSocket interface.", "what_it_is_for": "Live captions, voice assistants, and meetings that need partial text before the utterance ends.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-asr/stepaudio-2-asr-pro": { "id": "stepfun/step-asr/stepaudio-2-asr-pro", "name": "StepAudio 2 ASR Pro", "maker": "stepfun", "family": "stepfun/step-asr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "32B ASR Pro. The model list and audio guide both say that and little else.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StepAudio 2 ASR Pro is a 32B speech-recognition model on the \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 API, collected 2026-10-15. The id is stepaudio-2-asr-pro at 2 CNY per hour. The public docs barely describe it beyond the parameter count.", "what_it_is_for": "Larger ASR option beside the 4B StepAudio 2.5 models. StepFun does not state a more specific job.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-asr/stepaudio-3-asr": { "id": "stepfun/step-asr/stepaudio-3-asr", "name": "StepAudio 3 ASR", "maker": "stepfun", "family": "stepfun/step-asr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLM-based context-aware ASR. API id on the price table is stepaudio-3-asr-max. The guide cites an internal context-reasoning error of 0.57% and AISHELL-6 whisper CER of 3.97%.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StepAudio 3 ASR is the newest speech-recognition model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The billed id is stepaudio-3-asr-max at 2.8 CNY per hour. The audio guide describes context-aware transcription, and the 2026-09-15 launch post cites a 1.7% word error rate.", "what_it_is_for": "Names, jargon, dialects, Chinese-English code-switching, whispers, fast speech, noise, and singing, with fewer false transcripts on silence and music.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-audio/step-1o-audio": { "id": "stepfun/step-audio/step-1o-audio", "name": "Step-1o Audio", "maker": "stepfun", "family": "stepfun/step-audio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First-generation end-to-end speech model. Still on the price table. Docs call it the stable, widely deployed option.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": 25.0, "completion_per_m_usd": 60.0 }, "summary_intro": "Step-1o Audio is the older realtime voice model still sold by \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The id is step-1o-audio. A session can last 30 minutes and the model can take up to 70 minutes of audio. Token price is higher than StepAudio 2.5 Realtime.", "what_it_is_for": "StepFun calls it the stable choice for cars, devices, support, and companionship, with tool calls and preset voices.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-audio/step-audio-2": { "id": "stepfun/step-audio/step-audio-2", "name": "Step-Audio 2", "maker": "stepfun", "family": "stepfun/step-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end audio understanding and speech conversation model. Open weights dated 2025-08-29 in the Step-Audio repo news. API id step-audio-2 is on the price table.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 70.0 }, "summary_intro": "Step-Audio 2 is an open speech model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. Weights were announced on 2025-08-29 and the API id step-audio-2 is still billed. The code license on the GitHub repo is Apache-2.0. A smaller Step-Audio 2 mini sits beside it.", "what_it_is_for": "The platform guide says it understands speech, non-speech sounds, emotion, and music, and can call tools, search, and clone a voice.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-audio/step-audio-2-mini": { "id": "stepfun/step-audio/step-audio-2-mini", "name": "Step-Audio 2 Mini", "maker": "stepfun", "family": "stepfun/step-audio", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Smaller Step-Audio 2. The audio guide says API only in that section, while the 2025-08-29 repo news also released weights. The guide says it is faster and slightly weaker on instruction following, math, and reasoning.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-Audio 2 Mini is the smaller speech model \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 released with Step-Audio 2 on 2025-08-29, collected 2026-10-15. The audio guide says it keeps tool calls and search, runs faster, and scores a bit lower on instructions and reasoning. It is not a row on the October price table, so the live API id is not confirmed.", "what_it_is_for": "A cheaper, faster speech conversation model for the same jobs as Step-Audio 2 when full quality is not required.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-audio/step-audio-chat": { "id": "stepfun/step-audio/step-audio-chat", "name": "Step-Audio-Chat", "maker": "stepfun", "family": "stepfun/step-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 code; weight license is the one shipped with the checkpoint", "release_date": "2025-02-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "First open Step-Audio chat checkpoint, released with Step-Audio-TTS-3B and Step-Audio-Tokenizer on 2025-02-17. The repo is no longer the maintained line.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-Audio-Chat is the first open speech-interaction checkpoint from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. Weights shipped on 2025-02-17 together with a 3B TTS model and a tokenizer. The GitHub repo now says it is unmaintained and points to Step-Audio 2, Step-Audio-R1, and Step-Audio-EditX.", "what_it_is_for": "The README describes multilingual speech conversation with emotion, dialect, rate, and style control, including Cantonese, Sichuanese, and rap as examples.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-audio/step-audio-editx": { "id": "stepfun/step-audio/step-audio-editx", "name": "Step-Audio-EditX", "maker": "stepfun", "family": "stepfun/step-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-12", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "LLM-based reinforcement-learning audio editor for emotion, style, and paralinguistics, plus zero-shot TTS. Japanese and Korean support noted on 2025-11-28. A further update on 2026-01-29 added tags and training code.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-Audio-EditX is an open 3B audio editing model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. Weights were released on 2025-11-12 under Apache-2.0. It edits emotion, speaking style, and paralinguistic cues and can also do zero-shot TTS. Later notes add Japanese, Korean, and a January 2026 update.", "what_it_is_for": "Expressive audio editing and zero-shot speech, not the hosted StepAudio 3 TTS API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-audio/step-audio-r1": { "id": "stepfun/step-audio/step-audio-r1", "name": "Step-Audio-R1", "maker": "stepfun", "family": "stepfun/step-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Speech reasoning model. Weights and inference code released 2025-11-27. Technical report the same period.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-Audio-R1 is an open speech-reasoning model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The project released weights on 2025-11-27 under Apache-2.0. Later checkpoints are R1.1 and the API model R1.5. This folder is only the first open checkpoint.", "what_it_is_for": "Speech reasoning: the repo presents it as the model that reasons about audio rather than only transcribing or chatting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-audio/step-audio-r1-1": { "id": "stepfun/step-audio/step-audio-r1-1", "name": "Step-Audio-R1.1", "maker": "stepfun", "family": "stepfun/step-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Open checkpoint released 2026-01-14. The audio guide says Step Audio R1.5 is the upgrade of R1.1 and that R1.1's free period ended.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-Audio-R1.1 is the second open speech-reasoning checkpoint from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. Weights were released on 2026-01-14. The platform now points the API offering at Step Audio R1.5 and says the older free period is over.", "what_it_is_for": "Same speech-reasoning job as R1, with the January 2026 weight update.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-audio/step-audio-r1-5": { "id": "stepfun/step-audio/step-audio-r1-5", "name": "Step-Audio R1.5", "maker": "stepfun", "family": "stepfun/step-audio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "API upgrade of Step-Audio-R1.1. Think-while-speaking, RLHF for long voice dialogs. GitHub lists the technical report on 2026-04-29 and still has inference code for R1.5 unchecked, so weights are not treated as released.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 105.0 }, "summary_intro": "Step-Audio R1.5 is the hosted speech-reasoning model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The id is step-audio-r1.5. Output is 105 CNY per million tokens. A technical report is dated 2026-04-29. The GitHub page still listed R1.5 inference code as not yet released, so this folder does not mark the weights open.", "what_it_is_for": "The audio guide says it analyzes sound in more detail, keeps emotional continuity over long calls, and can reason and speak at the same time.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-audio/stepaudio-2-5-chat": { "id": "stepfun/step-audio/stepaudio-2-5-chat", "name": "StepAudio 2.5 Chat", "maker": "stepfun", "family": "stepfun/step-audio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Turn-based speech understanding model. Text out only, via Chat Completions.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 25.0 }, "summary_intro": "StepAudio 2.5 Chat is a current \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 speech model that returns text, collected 2026-10-15. The id is stepaudio-2.5-chat. Input is 10 CNY per million tokens and output is 25, with cached input at 2.", "what_it_is_for": "StepFun says it reads complex meaning plus hesitation and laughter, then answers in text with a configurable persona.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-audio/stepaudio-2-5-realtime": { "id": "stepfun/step-audio/stepaudio-2-5-realtime", "name": "StepAudio 2.5 Realtime", "maker": "stepfun", "family": "stepfun/step-audio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end realtime speech model over WebSocket. The audio guide says it inherits StepAudio 2.5 TTS delivery.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 70.0 }, "summary_intro": "StepAudio 2.5 Realtime is a current \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 speech-to-speech model, collected 2026-10-15. The API id is stepaudio-2.5-realtime. Pay-as-you-go price is 10 CNY per million input tokens, 2 cached, and 70 output. It is also on Step Plan.", "what_it_is_for": "The audio guide sells it for emotionally aware realtime talk, custom personas, and spoken replies that can laugh, sigh, and match the scene.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-audio/stepaudio-3-chat": { "id": "stepfun/step-audio/stepaudio-3-chat", "name": "StepAudio 3 Chat", "maker": "stepfun", "family": "stepfun/step-audio", "status": "preview", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-09-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Priced as its own preview id. The audio overview emphasizes StepAudio 2.5 Chat for turn-based text replies and does not give this id a long write-up.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StepAudio 3 Chat is a preview id on the \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 price table, collected 2026-10-15. The id is stepaudio-3-chat-preview and it is limited-time free. A dedicated model essay was not found; the audio guide spends its chat section on StepAudio 2.5 Chat. Confidence is medium.", "what_it_is_for": "The price table places it with token-billed speech models. The exact job, beyond a StepAudio 3 chat preview, is not spelled out.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-audio/stepaudio-3-gen": { "id": "stepfun/step-audio/stepaudio-3-gen", "name": "StepAudio 3 Gen", "maker": "stepfun", "family": "stepfun/step-audio", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Discrete autoregressive general audio model. The technical report describes a tokenizer at 12.5 Hz and a 16 by 2048 residual code space covering speech, effects, and music. API id is a preview.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StepAudio 3 Gen is a preview general-audio model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The id is stepaudio-3-gen-preview and the price table marks it limited-time free. A technical report dated 2026-09-11 describes one model for speech, sound effects, and music. It is closed on the API.", "what_it_is_for": "Film, games, animation, audio drama, and short video: multi-role speech plus effects and background music from a natural-language instruction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-audio/stepaudio-3-music": { "id": "stepfun/step-audio/stepaudio-3-music", "name": "StepAudio 3 Music", "maker": "stepfun", "family": "stepfun/step-audio", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Music model in the StepAudio 3 family. Text, lyrics, and audio in; songs or instrumentals out.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StepAudio 3 Music is a preview music model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The id is stepaudio-3-music-preview and it is limited-time free. The family announcement is 2026-09-15. It takes text, lyrics, or audio and returns music.", "what_it_is_for": "Full songs, instrumentals, style covers, and accompaniment generated from dry vocals, using natural-language control of genre, voice, instruments, and mood.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-audio/stepaudio-3-realtime": { "id": "stepfun/step-audio/stepaudio-3-realtime", "name": "StepAudio 3 Realtime", "maker": "stepfun", "family": "stepfun/step-audio", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full-duplex realtime speech model. Official X post on 2026-09-15 introduced the StepAudio 3 family of five models.", "modalities": { "input": [ "audio", "text" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StepAudio 3 Realtime is \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09's preview full-duplex voice model, collected 2026-10-15. The API id is stepaudio-3-realtime-preview. The family was announced on 2026-09-15. The October price table lists it as limited-time free.", "what_it_is_for": "StepFun describes realtime conversation that can interrupt, share the turn, think while speaking, and call tools for cars, devices, support, and companionship.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-gui/step-gui": { "id": "stepfun/step-gui/step-gui", "name": "Step GUI", "maker": "stepfun", "family": "stepfun/step-gui", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Experimental GUI agent model in Model Lab. Visual parsing plus action reasoning for clicks, typing, swipes, long-press, and drags.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step GUI is an experimental GUI model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The id is step-gui inside Model Lab. The lab is free, limits concurrency to 1, and says the model may disappear. It is closed-weight and not on the main price table.", "what_it_is_for": "Multi-step GUI work: long tasks, vague requests, personalized suggestions, and cross-app jobs such as price comparison, including on unfamiliar screens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-image/nextstep-1": { "id": "stepfun/step-image/nextstep-1", "name": "NextStep-1", "maker": "stepfun", "family": "stepfun/step-image", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "14B autoregressive model over discrete text tokens and continuous image tokens, plus a 157M flow-matching head. The repo says the original NextStep-1 series is older and weaker than NextStep-1.1 and is not recommended.", "modalities": { "input": [ "text" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NextStep-1 is an open 14B autoregressive image model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. It models continuous image tokens with a 157M flow-matching head. The repository, an ICLR 2026 oral, says this original series is weaker than NextStep-1.1 and should not be the default download.", "what_it_is_for": "Text-to-image research and generation with continuous tokens rather than a diffusion-only stack.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-image/nextstep-1-1": { "id": "stepfun/step-image/nextstep-1-1", "name": "NextStep-1.1", "maker": "stepfun", "family": "stepfun/step-image", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-24", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Recommended NextStep text-to-image checkpoint. Extended training plus flow-based RL. The 14B size is the series size stated for NextStep-1; the README does not restate a different count for 1.1.", "modalities": { "input": [ "text" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NextStep-1.1 is the text-to-image checkpoint \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 recommends from the NextStep line, collected 2026-10-15. The repo news dates it to 2025-12-24 and credits extra training plus flow-based reinforcement learning. Pretrain checkpoints stay in this folder as versions. License is Apache-2.0 with the parent repo.", "what_it_is_for": "Higher-quality text-to-image than the original NextStep-1 weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-image/nextstep-1-large": { "id": "stepfun/step-image/nextstep-1-large", "name": "NextStep-1-Large", "maker": "stepfun", "family": "stepfun/step-image", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Larger NextStep text-to-image checkpoint with a pretrain variant and an RL variant. Parameter count was not in the README excerpt.", "modalities": { "input": [ "text" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NextStep-1-Large is a larger open text-to-image checkpoint in the \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 NextStep release, collected 2026-10-15. The repo lists a pretrain variant and an RL variant. The parameter count was not stated in the table that was read. Apache-2.0 follows the repository license.", "what_it_is_for": "Higher-capacity text-to-image in the NextStep continuous-token line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-image/nextstep-1-large-edit": { "id": "stepfun/step-image/nextstep-1-large-edit", "name": "NextStep-1-Large-Edit", "maker": "stepfun", "family": "stepfun/step-image", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Editing checkpoint in the NextStep-1-Large line. The model table marks it as RL-trained.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NextStep-1-Large-Edit is the open image-editing checkpoint in the \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 NextStep-1-Large line, collected 2026-10-15. The project lists it separately from the text-to-image Large model and marks it as an RL checkpoint. Apache-2.0 follows the repo.", "what_it_is_for": "Image editing in the NextStep continuous-token stack, as opposed to Step1X-Edit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-image/step-1x": { "id": "stepfun/step-image/step-1x", "name": "Step-1X", "maker": "stepfun", "family": "stepfun/step-image", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-07-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image generation model announced at WAIC on 2024-07-04. Later API ids step-1x-medium and step-1x-edit are cataloged separately because the platform treated them as their own products.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-1X is the image generation model \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 announced at WAIC on 2024-07-04, collected 2026-10-15. The report lists it with formal Step-2 and Step-1.5V. It is not on the live model list. Later image API ids were retired in 2026. Confidence is medium because the source is launch coverage, not a model card.", "what_it_is_for": "Text-to-image generation, as announced. The article does not quote a longer spec.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-image/step-1x-medium": { "id": "stepfun/step-image/step-1x-medium", "name": "Step-1X Medium", "maker": "stepfun", "family": "stepfun/step-image", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image API id retired on 2026-07-08. The deprecation note said the replacement step-2x-large cost 0.1 CNY per image after 2026-06-12. step-2x-large itself was later retired.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-1X Medium is a retired image API id from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The platform turned step-1x-medium off on 2026-07-08. The suggested replacement, step-2x-large, was itself removed on 2026-10-10, so there is no current StepFun image-generation API.", "what_it_is_for": "Text-to-image, inferred from the image-model retirement that groups the Step-1X and Step-2X ids with text-to-image and image-to-image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-image/step-2x-large": { "id": "stepfun/step-image/step-2x-large", "name": "Step-2X Large", "maker": "stepfun", "family": "stepfun/step-image", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted image model for text-to-image and image-to-image. Retired 2026-10-10 in China and overseas. After 2026-06-12 it cost 0.1 CNY per image.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-2X Large is a retired image model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The id step-2x-large and the text-to-image and image-to-image APIs stopped on 2026-10-10 in both China and overseas. From 2026-06-12 until then the published price was 0.1 CNY per image.", "what_it_is_for": "Text-to-image and image-to-image on the open platform and Step Plan.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-image/step-image-edit-2": { "id": "stepfun/step-image/step-image-edit-2", "name": "Step Image Edit 2", "maker": "stepfun", "family": "stepfun/step-image", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hosted image-edit API id. Retired with step-2x-large on 2026-10-10. Not the same name as the open Step1X-Edit weights.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step Image Edit 2 is a retired image-editing API id from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. step-image-edit-2 stopped on 2026-10-10 together with the image edit endpoints in China and overseas. Open Step1X-Edit weights are a different folder because the platform does not say they are the same checkpoint.", "what_it_is_for": "Instruction-based image editing on the hosted image API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-image/step1x-edit": { "id": "stepfun/step-image/step1x-edit", "name": "Step1X-Edit", "maker": "stepfun", "family": "stepfun/step-image", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Open image-editing model. v1.0 around the April 2025 launch, v1.1 on 2025-07-09 with text-to-image added, v1.2 (ReasonEdit-S) on 2025-11-26. The hosted id step-1x-edit was already off before 2026-10-10; this folder is the open weights.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step1X-Edit is an open image-editing model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The project is Apache-2.0. Weights went public around 2025-04-26, v1.1 on 2025-07-09, and v1.2 on 2025-11-26. The hosted id step-1x-edit is already off; these weights are still downloadable.", "what_it_is_for": "Instruction-based image editing, with later versions also doing text-to-image and a reasoning edit mode.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-prover/stepfun-prover-preview-32b": { "id": "stepfun/step-prover/stepfun-prover-preview-32b", "name": "StepFun-Prover-Preview-32B", "maker": "stepfun", "family": "stepfun/step-prover", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 20480, "max_output_tokens": null, "parameters": "32B", "architecture": "Tool-integrated Lean 4 prover. The card evaluates a 20K context including Lean feedback. 7B sibling is a separate folder.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StepFun-Prover-Preview-32B is an open Lean 4 prover from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. Weights are Apache-2.0. The project reports 70.0% pass@1 on miniF2F-test when up to 20,480 generation tokens are allowed, averaged over 32 trials. A 7B checkpoint is a separate folder.", "what_it_is_for": "Formal theorem proving with a Lean REPL in the loop, rather than general chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-prover/stepfun-prover-preview-7b": { "id": "stepfun/step-prover/stepfun-prover-preview-7b", "name": "StepFun-Prover-Preview-7B", "maker": "stepfun", "family": "stepfun/step-prover", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "7B tool-integrated Lean 4 prover released with the 32B preview.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StepFun-Prover-Preview-7B is the smaller open Lean prover from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. It is Apache-2.0. The project table lists 66.0% miniF2F-test pass@1 under the same minimal-sampling setup as the 32B model.", "what_it_is_for": "Formal proving when the 32B checkpoint is too large.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-research/step-deepresearch": { "id": "stepfun/step-research/step-deepresearch", "name": "Step-DeepResearch", "maker": "stepfun", "family": "stepfun/step-research", "status": "active", "confidence": "high", "weights": "closed", "license": "Apache-2.0 code; model weights not released in the repo", "release_date": "2025-12-24", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "End-to-end deep-research agent model. README says 32B parameters. Default API name in the sample env is step-dr-1. Step 3.5 Flash can be selected instead and is a different model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-DeepResearch is a 32B research-agent model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The technical report is dated 2025-12-24. The sample configuration calls the API model step-dr-1. Code in the repo is Apache-2.0. The weights themselves are not in that repo. The README claims 61.4% on ResearchRubrics.", "what_it_is_for": "Open-ended research: plan, search, check, and write a report in one agent loop.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-tts/step-audio-tts-3b": { "id": "stepfun/step-tts/step-audio-tts-3b", "name": "Step-Audio-TTS-3B", "maker": "stepfun", "family": "stepfun/step-tts", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Weight license shipped with the checkpoint; repo code is Apache-2.0", "release_date": "2025-02-17", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Open 3B TTS checkpoint released with Step-Audio on 2025-02-17. The parent repo is unmaintained.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-Audio-TTS-3B is the open 3B text-to-speech checkpoint \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 released on 2025-02-17, collected 2026-10-15. It shipped with Step-Audio-Chat and the Step-Audio tokenizer. The repository now points new work at Step-Audio 2, R1, and EditX.", "what_it_is_for": "Local text-to-speech in the original Step-Audio stack.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-tts/step-tts-2": { "id": "stepfun/step-tts/step-tts-2", "name": "Step TTS 2", "maker": "stepfun", "family": "stepfun/step-tts", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Upgrade of step-tts-mini. The guide says it drops speaker and emotion embedding modules and uses next-token prediction end to end. 11 emotions, 17 styles. Clone from about 10 seconds.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step TTS 2 is a hosted text-to-speech model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The id is step-tts-2. It costs 2.8 CNY per 10,000 characters. The audio guide calls it the upgrade of Step TTS Mini, with 11 emotions, 17 styles, and cloning from about 10 seconds of audio.", "what_it_is_for": "Emotional dubbing and dialogue, including Cantonese, Sichuanese, and Japanese, plus accent-faithful clones for live-commerce style speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-tts/step-tts-mini": { "id": "stepfun/step-tts/step-tts-mini", "name": "Step TTS Mini", "maker": "stepfun", "family": "stepfun/step-tts", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Smaller expressive TTS. The model list says 19 official voices and clone support for Chinese, English, and Japanese. The audio essay's emotion and style counts are internally inconsistent.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step TTS Mini is the lowest published text-to-speech price on the \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 platform, collected 2026-10-15. The id is step-tts-mini at 0.9 CNY per 10,000 characters. The model list allows 1000 characters, 19 official voices, and clones in Chinese, English, and Japanese.", "what_it_is_for": "Support calls, companionship, and assistants that need expressive speech more than the newest contextual controls.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-tts/stepaudio-2-5-tts": { "id": "stepfun/step-tts/stepaudio-2-5-tts", "name": "StepAudio 2.5 TTS", "maker": "stepfun", "family": "stepfun/step-tts", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Contextual TTS with global and inline natural-language control. Zero-shot clone from about 3 seconds of reference audio.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StepAudio 2.5 TTS is a contextual speech synthesizer from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The id is stepaudio-2.5-tts. It costs 5.8 CNY per 10,000 characters, more than StepAudio 3 TTS on the same table. Clone setup is 9.9 CNY per voice.", "what_it_is_for": "Audiobooks, short-drama dubbing, ads, and other reads where the model should act the text instead of only pronouncing it. Control is natural language, not a fixed emotion tag set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-tts/stepaudio-3-tts": { "id": "stepfun/step-tts/stepaudio-3-tts", "name": "StepAudio 3 TTS", "maker": "stepfun", "family": "stepfun/step-tts", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Contextual TTS in the StepAudio 3 family. Streaming, expressive speech including laughter and self-correction.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StepAudio 3 TTS is the current text-to-speech model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The id is stepaudio-3-tts. Price is 2.5 CNY per 10,000 characters, and a successful voice clone is 9.9 CNY per voice. It belongs to the StepAudio 3 family announced on 2026-09-15.", "what_it_is_for": "StepFun says it matches human timbre, intonation, rhythm, breath, and pauses, and can laugh, hesitate, stutter, repeat, and self-correct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-video/step-video-t2v": { "id": "stepfun/step-video/step-video-t2v", "name": "Step-Video-T2V", "maker": "stepfun", "family": "stepfun/step-video", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-17", "context_length": null, "max_output_tokens": null, "parameters": "30B", "architecture": "Text-to-video model with a video VAE at 16x16 spatial and 8x temporal compression. Up to 204 frames. README cites a 768x768x204 run at about 78.55 GB peak.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-Video-T2V is an open 30B text-to-video model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. Inference code and weights were released on 2025-02-17. The repo license shown on GitHub is MIT. It can generate up to 204 frames. A turbo distillation is a separate folder.", "what_it_is_for": "Text-to-video generation. The README compares it with open and commercial engines on a benchmark the project introduced, Step-Video-T2V-Eval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-video/step-video-t2v-turbo": { "id": "stepfun/step-video/step-video-t2v-turbo", "name": "Step-Video-T2V-Turbo", "maker": "stepfun", "family": "stepfun/step-video", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-17", "context_length": null, "max_output_tokens": null, "parameters": "30B", "architecture": "Inference-step distillation of Step-Video-T2V. Released as its own weight repo in the T2V README.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-Video-T2V-Turbo is the distilled text-to-video checkpoint from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The Step-Video-T2V README lists it as its own download next to the base 30B model, released with the 2025-02-17 weights. It is treated as a separate product because the project publishes a separate checkpoint for fewer inference steps.", "what_it_is_for": "Faster text-to-video inference than the base Step-Video-T2V sampler.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-video/step-video-ti2v": { "id": "stepfun/step-video/step-video-ti2v", "name": "Step-Video-TI2V", "maker": "stepfun", "family": "stepfun/step-video", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-17", "context_length": null, "max_output_tokens": null, "parameters": "30B", "architecture": "Image-to-video model trained from Step-Video-T2V. Up to 102 frames. The first frame is encoded with the same video VAE and concatenated on the channel axis. A motion-score condition controls dynamics.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-Video-TI2V is an open 30B image-to-video model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. Weights and code were released on 2025-03-17. It is trained from Step-Video-T2V, takes a text prompt plus a first-frame image, and can generate up to 102 frames. The repo license is MIT.", "what_it_is_for": "Text-guided image-to-video, with a motion score so the user can ask for more or less movement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-vision/step-1-5v": { "id": "stepfun/step-vision/step-1-5v", "name": "Step-1.5V", "maker": "stepfun", "family": "stepfun/step-vision", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-07-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hundred-billion-parameter multimodal model announced at WAIC on 2024-07-04. Launch coverage says image understanding improved and video understanding was added. API id was not on the 2026 deprecation table.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-1.5V is a multimodal model \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 announced at WAIC on 2024-07-04, collected 2026-10-15. Coverage that day called it a hundred-billion-parameter upgrade with video understanding. It is not on the live model list. Confidence is medium because the source is a contemporary news report of the launch, not a remaining spec page.", "what_it_is_for": "The launch report says it recognizes objects, people, setting, mood, and emotion in video, and improves image perception over Step-1V.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-vision/step-1o-turbo-vision": { "id": "stepfun/step-vision/step-1o-turbo-vision", "name": "Step-1o Turbo Vision", "maker": "stepfun", "family": "stepfun/step-vision", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Closed vision-language model. Text output only. Context 32K.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.5, "completion_per_m_usd": 8.0 }, "summary_intro": "Step-1o Turbo Vision is the dedicated vision model still on the \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 API, collected 2026-10-15. It accepts text, images, and video and returns text, with a 32K context. It is closed-weight. Newer flagships Step 5 Preview and Step 3.7 Flash also understand images and video.", "what_it_is_for": "StepFun names social copy, assistants, and image or video understanding as the typical jobs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-vision/step-1o-vision-32k": { "id": "stepfun/step-vision/step-1o-vision-32k", "name": "Step-1o Vision 32K", "maker": "stepfun", "family": "stepfun/step-vision", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Earlier Step-1o vision API id. Context implied by the id. Exact card was not retrieved.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-1o Vision 32K is a retired \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 vision API id, collected 2026-10-15. The deprecation table turned it off on 2026-07-08 and points to step-1o-turbo-vision. No separate spec page was still linked.", "what_it_is_for": "Image and video understanding, inferred from the id and from its replacement. StepFun does not restate the job on the deprecation page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-vision/step-1v-32k": { "id": "stepfun/step-vision/step-1v-32k", "name": "Step-1V 32K", "maker": "stepfun", "family": "stepfun/step-vision", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "32K API id of Step-1V.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-1V 32K is the longer-context Step-1V API id from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. It was retired on 2026-07-08. Weights were not released.", "what_it_is_for": "Same vision-language role as Step-1V, with a larger shared context cap.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-vision/step-1v-8k": { "id": "stepfun/step-vision/step-1v-8k", "name": "Step-1V 8K", "maker": "stepfun", "family": "stepfun/step-vision", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03", "context_length": 8000, "max_output_tokens": null, "parameters": "", "architecture": "Hundred-billion-parameter multimodal model (\u5343\u4ebf). Public showing reported with the March 2024 Step debut. API context 8K.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-1V 8K is a retired \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 multimodal API id, collected 2026-10-15. Contemporary reports describe Step-1V as the hundred-billion-parameter vision model shown in March 2024. The API stopped on 2026-07-08.", "what_it_is_for": "Image understanding on top of the Step-1 generation, as described in launch coverage. The live docs no longer describe it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-vision/step-r1-v-mini": { "id": "stepfun/step-vision/step-r1-v-mini", "name": "Step-R1-V-Mini", "maker": "stepfun", "family": "stepfun/step-vision", "status": "retired", "confidence": "low", "weights": "closed", "license": "", "release_date": "2025-04-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal reasoning model. A 2025-04-09 news report says image-and-text in, text out, trained with PPO and verifiable rewards in image space. Not on the October 2026 model list.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Step-R1-V-Mini is a multimodal reasoning model reported on 2025-04-09 as a \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 launch, collected 2026-10-15. The report says it took images and text and returned text, and that an API was opened. It is missing from the 2026 platform list. Confidence is low because only one secondary page was found.", "what_it_is_for": "The report says the job is precise image perception plus complex reasoning, with ordinary instruction following kept.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-vision/step3-vl-10b": { "id": "stepfun/step-vision/step3-vl-10b", "name": "STEP3-VL-10B", "maker": "stepfun", "family": "stepfun/step-vision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "10B", "architecture": "Compact open vision-language chat model. GitHub publishes a base checkpoint and this chat checkpoint. PaCoRe is a test-time method, not a separate model.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "STEP3-VL-10B is an open 10B vision-language model from \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09, collected 2026-10-15. The GitHub model card is Apache-2.0 and publishes chat weights plus a base checkpoint. It is not one of the hosted flagship API ids.", "what_it_is_for": "StepFun presents it as a small model for visual perception, complex reasoning, and alignment, aimed at the 10B class rather than the 198B Flash line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stepfun/step-vision/step3-vl-10b-base": { "id": "stepfun/step-vision/step3-vl-10b-base", "name": "STEP3-VL-10B-Base", "maker": "stepfun", "family": "stepfun/step-vision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "10B", "architecture": "Base (pretrained) checkpoint of STEP3-VL-10B, released beside the chat model.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "STEP3-VL-10B-Base is the open base checkpoint of \u9636\u8dc3\u661f\u8fb0\uff08StepFun\uff09 STEP3-VL-10B, collected 2026-10-15. The project lists it next to the chat model under Apache-2.0. It is a pretrained model, not the instruction-tuned chat release.", "what_it_is_for": "StepFun publishes it for people who want the 10B multimodal base rather than the chat checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stirling/co4/hybrid-base": { "id": "stirling/co4/hybrid-base", "name": "Co4 hybrid_base", "maker": "stirling", "family": "stirling/co4", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The Hugging Face repo card sets license: mit and has no other text. The IHMS and HMS GitHub READMEs say the source code is Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0). A separate licence for hybrid_base.safetensors is not stated beyond the Hub tag.", "release_date": "2026-02-03", "context_length": null, "max_output_tokens": null, "parameters": "41600000", "architecture": "Co4 (Cooperative Context-sensitive Cognitive Computation) vision classifier, configuration hybrid_base. The vision README says depth 6, patch size 16, embedding dimension 768, one head on modulated layers and 12 heads on standard multi-head self-attention, with layer pattern modulated, modulated, standard, modulated, modulated, standard. inference.py sets image size 224, 1000 ImageNet classes, resize 256 then center crop 224, and ImageNet mean and standard deviation. The March 2026 paper's matching row is 6 layers, 4 Co4 plus 2 MHSA, hidden 768, MLP 3076, and reports 41.6M parameters. That table's head column is 1, which disagrees with the README's 12 MHSA heads. Both are stored; the README is the code configuration for the released name.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Co4 hybrid_base is an open-weight ImageNet-1K image classifier from the University of Stirling CMI Lab / ARIA TREND project (Ahsan Adeel and M. Bilal). It is the only named checkpoint file on the public Hub repo. Status on 2026-10-15: active research release, not an API product.", "what_it_is_for": "The vision README says `hybrid_base` is one of two configurations pretrained on ImageNet-1K. It mixes Co4 modulated attention with standard multi-head self-attention so the network can pre-select coherent tokens before the usual attention readout. The shipped script is local single-image classification, not a chat or generation API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stockholm/sweclin/sweclin-bert": { "id": "stockholm/sweclin/sweclin-bert", "name": "SweClin-BERT", "maker": "stockholm", "family": "stockholm/sweclin", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2021-09-01", "context_length": 512, "max_output_tokens": null, "parameters": "125M in SweClinEval Table 2 (2025), the same count that table gives KB-BERT Base.", "architecture": "Encoder-only BERT-base continued from KB-BERT on unaltered Swedish clinical notes. The 2021 paper calls the run Clinical KB-BERT and trains it for one epoch, about 40,000 extra update steps, sequence length 512, batch size 256, learning rate 1e-4, 15% masking. The 2022 paper calls the same comparison model KB-BERT + Real. Its hyperparameter table lists a max of 3 epochs for that run as well.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SweClin-BERT is the sensitive-data clinical encoder from the Department of Computer and Systems Sciences at Stockholm University. Anastasios Lamproudis, Aron Henriksson, and Hercules Dalianis continued pretraining of KB-BERT on unaltered Health Bank notes. The RANLP 2021 paper calls that run a clinical BERT, or Clinical KB-BERT. The group's public resource list and the 2025 SweClinEval paper use the name SweClin-BERT. The weights stay on an internal machine. The department does not offer them for download. The shareable counterpart is SweDeClin-BERT. Collected 2026-10-15.", "what_it_is_for": "A Swedish clinical language model for downstream work on protected-health-information detection, ICD-10 coding of discharge summaries, and sentence-level uncertainty or factuality. It is the unaltered-data baseline against which the group measured automatic de-identification of the pretraining corpus.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stockholm/sweclin/swedeclin-bert": { "id": "stockholm/sweclin/swedeclin-bert", "name": "SweDeClin-BERT", "maker": "stockholm", "family": "stockholm/sweclin", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2022-06-20", "context_length": 512, "max_output_tokens": null, "parameters": "125M in SweClinEval Table 2 (2025). The 2025 JMIR NorDeClin paper describes the same model as BERT-base with about 110 million parameters.", "architecture": "Encoder-only BERT-base continued from KB-BERT (National Library of Sweden): 12 layers, 768 hidden size, 12 attention heads, vocabulary left identical to KB-BERT. The 2022 paper calls this run KB-BERT + Pseudo. Masked language modeling at sequence length 512 for 3 epochs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SweDeClin-BERT is a Swedish clinical encoder from the Department of Computer and Systems Sciences at Stockholm University. Thomas Vakili, Anastasios Lamproudis, Aron Henriksson, and Hercules Dalianis continued pretraining of the National Library of Sweden's KB-BERT on automatically pseudonymized Karolinska University Hospital notes from the Health Bank. The LREC 2022 paper names the pseudonymized run KB-BERT + Pseudo and says it will be distributed as SweDeClin-BERT. The department still offers academic access by contacting Hercules Dalianis. It is not a public download. Collected 2026-10-15.", "what_it_is_for": "Domain-adapted Swedish clinical language modeling. The department describes it as a de-identified clinical Swedish language model. The paper uses it as a starting point for ICD-10 coding, protected-health-information NER, clinical-entity NER, diagnosis factuality, and adverse-drug-event classification. The point of the release is a shareable model whose pretraining text had sensitive entities replaced by surrogates.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stockmark/bart/bart-base-japanese-news": { "id": "stockmark/bart/bart-base-japanese-news", "name": "BART base Japanese news", "maker": "stockmark", "family": "stockmark/bart", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT for weights. tokenization_bart_japanese_news.py is Apache-2.0.", "release_date": "2023-01-24", "context_length": 512, "max_output_tokens": null, "parameters": "124638464", "architecture": "BART encoder-decoder, 6 encoder layers and 6 decoder layers, d_model 768, 12 heads, FFN 3072, vocab 32000, FP32. SentencePiece tokenizer. Trained as a base-size model on Japanese news.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stockmark's Japanese BART-base, pretrained on business news for infilling and as a fine-tune base for generation or classification. Weights are still on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "The card says the raw model can fill corrupted text, and that it is mostly meant to be fine-tuned for summarization, translation, classification, or question answering. Stockmark built it because public BART checkpoints were not trained on Japanese business news.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stockmark/docreasoner/stockmark-docreasoner-qwen2-5-vl-32b": { "id": "stockmark/docreasoner/stockmark-docreasoner-qwen2-5-vl-32b", "name": "Stockmark-DocReasoner-Qwen2.5-VL-32B", "maker": "stockmark", "family": "stockmark/docreasoner", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-04-08", "context_length": 128000, "max_output_tokens": null, "parameters": "33452718336", "architecture": "Qwen2.5-VL. Continued training of Qwen/Qwen2.5-VL-32B-Instruct. 64 layers, hidden 5120, intermediate 27648, GQA 40 query and 8 KV heads, vocab 152064. Vision hidden 1280, spatial patch 14. max_position_embeddings 128000. sliding_window 32768 is set but use_sliding_window is false. BF16. Marketed as 32B. Safetensors count is 33,452,718,336.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stockmark's 2026 document vision-language model, a further training of Qwen2.5-VL-32B-Instruct for Japanese business and manufacturing pages. Weights are Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Reading charts, tables, diagrams, engineering drawings, and business documents, then answering with a written chain of thought. The press release also names chemical formulas and manufacturing documents. Prompt prefixes switch the output to HTML, Markdown, JSON, or SMILES. It is not a from-scratch Stockmark model. The base is Qwen2.5-VL-32B-Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stockmark/gpt-neox/gpt-neox-japanese-1-4b": { "id": "stockmark/gpt-neox/gpt-neox-japanese-1-4b", "name": "GPT-NeoX Japanese 1.4B", "maker": "stockmark", "family": "stockmark/gpt-neox", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-08-08", "context_length": 1024, "max_output_tokens": null, "parameters": "1413402624", "architecture": "GPT-NeoX decoder, 24 layers, hidden size 2048, 16 heads, rotary_pct 0.25, vocab 50000, BF16. Pretrained from scratch on Japanese text, not a fine-tune of an English GPT-NeoX checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stockmark's first public LLM, a 1.4 billion parameter GPT-NeoX model pretrained from scratch on Japanese text. Weights remain on Hugging Face under MIT. Collected 2026-10-15.", "what_it_is_for": "A small Japanese base model for local generation and LoRA experiments. Stockmark trained it with AIST on ABCI so the corpus would include recent business web text, not only Common Crawl.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stockmark/nemotron/stockmark-nemotron-3-nano-omni-japandocreader": { "id": "stockmark/nemotron/stockmark-nemotron-3-nano-omni-japandocreader", "name": "Stockmark-Nemotron-3-Nano-Omni-JapanDocReader", "maker": "stockmark", "family": "stockmark/nemotron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NVIDIA Open Model License, inherited from the base model. https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/", "release_date": "2026-07-16", "context_length": null, "max_output_tokens": null, "parameters": "33015632208", "architecture": "Fine-tune of nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16. Transformers class NemotronH_Nano_Omni_Reasoning_V3, custom code. Two-stage train: mixed SFT with NeMo Megatron-Bridge, then DAPO with NeMo-RL. Thinking mode stays on. Reasoning traces are English. BF16 safetensors count is 33,015,632,208. Hugging Face also counts 6 FP32 values. Active parameter count is not stated on the Stockmark card. The base name contains 30B-A3B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stockmark's July 2026 document reader, a fine-tune of NVIDIA Nemotron-3-Nano-Omni-30B-A3B-Reasoning for Japanese layout parsing and document questions. Weights are public under the NVIDIA Open Model License. Collected 2026-10-15.", "what_it_is_for": "Turn a page image into a JSON document structure, or answer a question about the page. Classes in the trained prompt are title, heading, text, table, list, picture, and formula, with boxes on a 0-1000 grid. Stockmark says mixed supervised fine-tuning plus DAPO adds Japanese parsing without wiping the base model's visual question answering. Reasoning traces stay in English and thinking mode stays on.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stockmark/stockmark-2/stockmark-2-100b-instruct": { "id": "stockmark/stockmark-2/stockmark-2-100b-instruct", "name": "Stockmark-2-100B-Instruct", "maker": "stockmark", "family": "stockmark/stockmark-2", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-07-08", "context_length": 32768, "max_output_tokens": null, "parameters": "95996630016", "architecture": "LlamaForCausalLM, from scratch. 86 layers, hidden 9216, intermediate 32768, GQA 72 query and 8 KV heads, vocab 100352, RoPE theta 500000, context 32768, BF16. Card features list 96B parameters. Press and NVIDIA also say 100 billion. Safetensors count is 95,996,630,016. NVIDIA model card lists the same 96B total and active count and vocabulary 100352.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stockmark-2's non-beta instruct model, a from-scratch 96B-weight LLM aimed at Japanese, with a 32k context. Weights are MIT on Hugging Face and also served as an NVIDIA NIM. Collected 2026-10-15.", "what_it_is_for": "Japanese and English instruction following, especially business documents and summaries. Stockmark and NVIDIA describe it as a sovereign Japanese model built without starting from a public LLM. Compared with the beta, the card says instruction following is better and context is 32k. It does not claim tools, JSON mode, or vision. Vision is the separate VL model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stockmark/stockmark-2/stockmark-2-100b-instruct-beta": { "id": "stockmark/stockmark-2/stockmark-2-100b-instruct-beta", "name": "Stockmark-2-100B-Instruct-beta", "maker": "stockmark", "family": "stockmark/stockmark-2", "status": "preview", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-03-06", "context_length": 4096, "max_output_tokens": null, "parameters": "95996630016", "architecture": "LlamaForCausalLM, from scratch. 86 layers, hidden 9216, intermediate 32768, GQA 72 query and 8 KV heads, head dim 128, vocab 100352, RoPE theta 10000, context 4096, BF16. Card says 100 billion parameters and does not print the 96B figure used on the later non-beta card. Safetensors count is 95,996,630,016.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Beta of Stockmark-2, a from-scratch Japanese-focused instruct LLM in the 100B class. Stockmark put the weights on Hugging Face on 2025-03-06 under MIT and still calls the checkpoint a beta. Collected 2026-10-15.", "what_it_is_for": "Japanese instruction following for business text. The card says it was built from scratch, then post-trained on synthetic Japanese data so it would follow instructions. Stockmark says it is still being improved. The supported follow-up is Stockmark-2-100B-Instruct, which adds a 32k context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stockmark/stockmark-2/stockmark-2-vl-100b-beta": { "id": "stockmark/stockmark-2/stockmark-2-vl-100b-beta", "name": "Stockmark-2-VL-100B-beta", "maker": "stockmark", "family": "stockmark/stockmark-2", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Qwen license, as named on the model card, because synthetic data came from Qwen2.5-VL-72B. License link: https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct/blob/main/LICENSE", "release_date": "2025-06-03", "context_length": 32768, "max_output_tokens": null, "parameters": "96491136544", "architecture": "LLaVA-OneVision. Language model is Stockmark-2-100B-Instruct-beta. Vision encoder is google/siglip2-so400m-patch14-384 (SigLIP vision config: image size 384, patch 14, hidden 1152, 26 layers, 16 heads). Projector is a 2-layer MLP. Text config max_position_embeddings is 32768, while the text beta checkpoint itself is 4096. Vocab in the VL text config is 100416. BF16.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stockmark's document-reading vision-language model, built by attaching a SigLIP2 encoder to Stockmark-2-100B-Instruct-beta. It is a public beta from 2025-06-03. The card uses the Qwen license because training used Qwen2.5-VL-72B synthetic data. Collected 2026-10-15.", "what_it_is_for": "Reading Japanese business slides, charts, and document images, with chain-of-thought. Stockmark says it beats other domestic VLMs they tested and beats GPT-4o on BusinessSlideVQA and their translated JChartQA. On JDocQA the card's own number is below GPT-4o. It is not a general image generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stockmark/stockmark-llm/stockmark-100b": { "id": "stockmark/stockmark-llm/stockmark-100b", "name": "Stockmark-100b", "maker": "stockmark", "family": "stockmark/stockmark-llm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-15", "context_length": 4096, "max_output_tokens": null, "parameters": "96204284928", "architecture": "LlamaForCausalLM pretrained from scratch with Megatron-LM. 88 layers, hidden 9216, intermediate 32256, GQA 72 query heads and 8 KV heads, vocab 60416, RoPE theta 10000, context 4096, BF16. Marketed as 100 billion parameters. Safetensors count is 96,204,284,928.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stockmark's first 100-billion-parameter-class LLM, a from-scratch Llama-architecture base model for Japanese and English, released 2024-05-15 under MIT with GENIAC support. Weights are still on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "A pretrained base model, not an instruction model. Stockmark points users who want instructions to stockmark-100b-instruct-v0.1, a LoRA on this checkpoint. The company describes the line as a business-Japanese model with hallucination controls. Those measured scores are on the instruct adapter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stockmark/stockmark-llm/stockmark-100b-instruct-v0-1": { "id": "stockmark/stockmark-llm/stockmark-100b-instruct-v0-1", "name": "stockmark-100b-instruct-v0.1", "maker": "stockmark", "family": "stockmark/stockmark-llm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-15", "context_length": 4096, "max_output_tokens": null, "parameters": "96204284928", "architecture": "LoRA adapter (PEFT, r 128, alpha 256, dropout 0.05) on stockmark-100b. Targets q, k, v, o, gate, up, and down projections. Loaded with AutoPeftModelForCausalLM. Prompt template is ### \u6307\u793a / ### \u5fdc\u7b54. Parameter figure is the base model, not the adapter alone.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LoRA instruction adapter on stockmark-100b, released with the base on 2024-05-15 under MIT. It is the checkpoint behind Stockmark's published Business Questions and Japanese Vicuna numbers. Collected 2026-10-15.", "what_it_is_for": "Japanese instruction following on the 100b base. The card's prompt is `", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stockmark/stockmark-llm/stockmark-13b": { "id": "stockmark/stockmark-llm/stockmark-13b", "name": "Stockmark-13b", "maker": "stockmark", "family": "stockmark/stockmark-llm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-10-27", "context_length": 2048, "max_output_tokens": null, "parameters": "13200184320", "architecture": "LlamaForCausalLM trained from scratch, 40 layers, hidden 5120, intermediate 13824, 40 query heads and 40 KV heads, vocab 50000, context 2048, BF16. Card tags include llama-2, but the card says pretrained from scratch, not a Llama 2 continued-pretrain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stockmark's 13 billion parameter Japanese LLM, pretrained from scratch on about 220 billion Japanese tokens and released under MIT. It is still downloadable. Collected 2026-10-15.", "what_it_is_for": "Business question answering in Japanese, with an emphasis on recent business facts and fewer hallucinations than general chat models of that period. The press release points at questions such as EV subsidy facts. It is a base model. Instruction following is the separate stockmark-13b-instruct checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stockmark/stockmark-llm/stockmark-13b-instruct": { "id": "stockmark/stockmark-llm/stockmark-13b-instruct", "name": "Stockmark-13b-instruct", "maker": "stockmark", "family": "stockmark/stockmark-llm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT on the model card since the 2024-11-28 README change. The 2023-11-10 blog still says CC BY-NC-SA and research use only.", "release_date": "2023-11-10", "context_length": null, "max_output_tokens": null, "parameters": "13200184320", "architecture": "Full LlamaForCausalLM checkpoint, same parameter count as stockmark-13b. Instruction-tuned, not a LoRA adapter. Prompt format on the card is ### Input / ### Output.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Instruction-tuned Stockmark-13b, trained on the 2023-11-03 Japanese instruction set from the RIKEN-linked ichikara project. Stockmark published it on 2023-11-10. The current Hugging Face card says MIT. Collected 2026-10-15.", "what_it_is_for": "Follows a short Japanese instruction. The card's prompt is a plain `", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stonybrook/bigcell/bigcell": { "id": "stonybrook/bigcell/bigcell", "name": "BigCell", "maker": "stonybrook", "family": "stonybrook/bigcell", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-ND 4.0 on the Hugging Face model card. The card also binds use to the UNI-2h license and, when the CGM is used, the TITAN license. The FLUX.2 VAE has its own license. GitHub license SPDX id for the code repo is NOASSERTION.", "release_date": "2026-10-09", "context_length": null, "max_output_tokens": null, "parameters": "830000000", "architecture": "SiT-XL/2 flow-matching transformer, velocity prediction, in the FLUX.2 VAE latent space (2048 RGB becomes 256 by 256 by 32). UNI-2 features from non-overlapping 256 patches enter by cross-attention. Appendix D.1 says 830M parameters. The Hub card says SiT-XL/2, 845M parameters. Those two official numbers disagree. The stored count is the appendix figure. Progressive training goes from 256 to 2048. The card lists stage 1 (256), stage 3 (1024), and the final 2048 model. Trained at 20x on more than 120,000 public WSIs. No stain normalization and no augmentation, per Appendix D.4. Paper sampling is 20 Euler steps and CFG 2.0.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stony Brook University released BigCell. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "BigCell is Stony Brook CVLab's flow-matching tile generator for histopathology. The released final stage makes 2048 tiles at 20x from a UNI-2 grid, then a sliding window builds canvases up to 32768. The lab calls the 256 and 1024 checkpoints earlier stages of the same model, not separate products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stonybrook/bigcell/bigcell-cgm": { "id": "stonybrook/bigcell/bigcell-cgm", "name": "BigCell CGM", "maker": "stonybrook", "family": "stonybrook/bigcell", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-ND 4.0 on the shared BigCell model card. The card also applies the TITAN and UNI-2h licenses. Not for clinical diagnosis.", "release_date": "2026-10-09", "context_length": null, "max_output_tokens": null, "parameters": "362000000", "architecture": "DiT-DH-Base flow-matching transformer in UNI-2 CLS token space. Appendix D.2 states 362M parameters, RoPE, RMSNorm, and SwiGLU. The Hub card abbreviates it DiT-DH-B. Conditioning is unpooled TITAN text embeddings, 128 by 768 in the appendix and 127 by 768 on the card. Those two token counts disagree. The released folder cgm-8192 emits a 32 by 32 by 1536 UNI-2 grid for an 8192 region. Linear interpolant, velocity prediction, logit-normal time sampling.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stony Brook University released BigCell CGM. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The conditioning generation model turns a pathology report into the UNI-2 grid that BigCell paints. The released checkpoint, `cgm-8192`, covers an 8192 region (32 by 32 tokens).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stonybrook/dewarpnet/dewarpnet": { "id": "stonybrook/dewarpnet/dewarpnet", "name": "DewarpNet", "maker": "stonybrook", "family": "stonybrook/dewarpnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the GitHub API license field for the code repository. A separate weight license was not stated.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Stacked regression networks. The shape network is a U-Net (`unetnc`) that predicts world coordinates. The texture-mapping network is a DenseNet-style encoder-decoder (`dnetccnl`) that predicts a 2D backward map. The README train command uses the shape net at batch size 50 and the texture net at 128 by 128, 250 epochs, learning rate 0.0001. Final paper weights and an earlier pre-end-to-end pair are both linked.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stony Brook University released DewarpNet. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "DewarpNet unwarps a single photo of a document. It is two stacked networks from the Stony Brook CVLab ICCV 2019 paper: a shape network that predicts 3D coordinates, and a texture network that predicts the backward map.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stonybrook/lrdm/lrdm": { "id": "stonybrook/lrdm/lrdm", "name": "LRDM", "maker": "stonybrook", "family": "stonybrook/lrdm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion U-Net conditioned by cross-attention on SSL embeddings, in the PathLDM and CompVis LDM code line. Four released checkpoints differ by dataset and encoder: BRCA 20x with HIPT (15 million patches), CRC 20x with iBOT (8 million), NAIP with ViT-B/16 (667 thousand), and BRCA 5x with CTransPath (976 thousand). Patches are 256. The paper name is learned representation-guided diffusion. Later Stony Brook papers abbreviate it LRDM.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stony Brook University released LRDM. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "LRDM is the name the same lab later uses for its CVPR 2024 SSL-conditioned diffusion models for large images. The GitHub README still uses the paper title. It releases four dataset checkpoints: BRCA at 20x, CRC at 20x, NAIP, and BRCA at 5x.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stonybrook/pathldm/pathldm": { "id": "stonybrook/pathldm/pathldm", "name": "PathLDM", "maker": "stonybrook", "family": "stonybrook/pathldm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Code: MIT on the GitHub API license field. A weight license was not stated in the README.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Text- or class-conditioned latent diffusion model built on CompVis/latent-diffusion. The class version uses a four-way tumor and TIL label. The CLIP and PLIP versions use a 154-token caption that also carries tumor and TIL. Patches are 256 at 10x from 3.2 million TCGA-BRCA tiles. The VAE is the frozen LDM autoencoder. The example train config is named plip_imagenet_finetune.yaml.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stony Brook University released PathLDM. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "PathLDM is the CVLab text-conditioned latent diffusion model for 10x breast-cancer patches. The lab released three conditioners: a four-class tumor and TIL embedder, OpenAI CLIP captions, and PLIP captions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stonybrook/pixcell/pixcell-1024": { "id": "stonybrook/pixcell/pixcell-1024", "name": "PixCell-1024", "maker": "stonybrook", "family": "stonybrook/pixcell", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Weights: CC BY-NC-ND 4.0 on the Hugging Face model card (API tag license:cc-by-nc-nd-4.0). The PixCell-256 card, which describes the shared family license, also binds use to UNI-2h and forbids commercial use. Code repository SPDX id is NOASSERTION.", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Same PixArt-sigma diffusion transformer as PixCell-256, fine-tuned at 1024 by 1024. Conditioning is the full 16 by 1536 UNI-2h grid (one token per 256 tile). Latent is 128 by 128 by 16 from the Stable Diffusion 3 VAE. Hugging Face lists 0.6B parameters, the same display size as PixCell-256. Stage 3 is one epoch of fine-tuning from the 512 stage. The 512 checkpoint is not in the public model zoo.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stony Brook University released PixCell-1024. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "PixCell-1024 is the 1024 by 1024 stage of the same foundation model. Stony Brook uses it for high-resolution H&E tiles, 4096 inference-time canvases, and as the base for virtual IHC staining.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stonybrook/pixcell/pixcell-256": { "id": "stonybrook/pixcell/pixcell-256", "name": "PixCell-256", "maker": "stonybrook", "family": "stonybrook/pixcell", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Weights: CC BY-NC-ND 4.0 on the Hugging Face model card. The card also says use is subject to the UNI-2h license, academic and non-commercial only. Code repository SPDX id is NOASSERTION.", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion transformer adapted from PixArt-sigma (inference config name PixArt_XL_2_UNI), in the latent space of the Stable Diffusion 3 VAE (8x downsample, 16 channels). PixCell-256 is stage 1: one UNI-2h token (1536-d) per 256 by 256 tile, no conditioning positional embeddings. Hugging Face lists the checkpoint as 0.6B parameters. The VAE weights are loaded from stabilityai/stable-diffusion-3.5-large and are not part of this repo. Trained on PanCan-30M (30,819,977 patches of 1024 from 69,184 H&E slides; 27.9 million used in training, 2.3 million held out as PanCan-Test). AdamW, weight decay 0.03, learning rate 2e-5, 32 A100s, one epoch at this resolution.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stony Brook University released PixCell-256. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "PixCell-256 is the 256 by 256 stage of Stony Brook CVLab's PixCell diffusion model. The lab says it synthesizes H&E histopathology tiles from UNI-2h embeddings, including unconditional samples, so sites can augment small datasets or share synthetic stand-ins instead of raw slides.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stonybrook/pixcell/pixcell-256-cell-controlnet": { "id": "stonybrook/pixcell/pixcell-256-cell-controlnet", "name": "PixCell-256-Cell-ControlNet", "maker": "stonybrook", "family": "stonybrook/pixcell", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-ND 4.0 on the model card. The card also applies the UNI-2h license and limits use to non-commercial research.", "release_date": "2025-06-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ControlNet pipeline on PixCell-256. Conditioning is a UNI-2h embedding plus an RGB cell mask. Latent VAE is Stable Diffusion 3.5, loaded from Stability AI, not hosted here. Hugging Face lists 0.6B parameters, the same display size as PixCell-256. The card does not split ControlNet parameters from the backbone.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stony Brook University released PixCell-256-Cell-ControlNet. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "PixCell-256-Cell-ControlNet adds cell-mask control to PixCell-256. The card shows sampling from a UNI-2h embedding plus an RGB mask so the generated tile follows a nuclei layout.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stonybrook/pixcell/pixcell-virtual-staining": { "id": "stonybrook/pixcell/pixcell-virtual-staining", "name": "PixCell virtual staining", "maker": "stonybrook", "family": "stonybrook/pixcell", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face cardData license is Apache-2.0. The adapters are loaded on PixCell-1024, whose weight card is CC BY-NC-ND 4.0. Both statements are on the pages that were read.", "release_date": "2025-11-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Per-stain PixCell-1024 LoRA plus a flow-matching MLP that maps H&E UNI conditioning toward an IHC stain. Five pairs are in the Hub repo: MIST HER2, ER, PR, Ki67, and HER2Match. Parameter counts were not stated.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stony Brook University released PixCell virtual staining. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "These are the virtual IHC adapters Stony Brook released for PixCell-1024. Each stain has a LoRA and a flow-matching MLP that translate H&E conditioning toward that stain. The stains are MIST HER2, ER, PR, and Ki67, plus HER2Match HER2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stonybrook/zoomldm/zoomldm": { "id": "stonybrook/zoomldm/zoomldm", "name": "ZoomLDM", "maker": "stonybrook", "family": "stonybrook/zoomldm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Weights: Apache-2.0 on the Hugging Face card (cardData.license). Code: MIT on the GitHub API license field. The two pages disagree.", "release_date": "2025-05-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion U-Net with a VQ-f4 autoencoder, initialized from the CompVis ImageNet LDM checkpoint named in the paper. A 12-layer summarizer, described as modeled after ViT-Base, maps a grid of SSL embeddings plus magnification into the cross-attention condition. BRCA uses UNI (1024-d). The repo YAML sets U-Net model_channels 192, channel multipliers 1, 2, 3, 5, context dim 512, latent image size 64, and VQ codebook 8192. Paper training: 3 H100s, batch 200 per GPU, learning rate 1e-4, warmup 10,000 steps. The example YAML instead sets base learning rate 2.0e-5 and warmup 1,000. BRCA data: 1,136 TCGA-BRCA slides, 256 patches at eight magnifications from 20x down to 0.15625x. NAIP is a second checkpoint with DINOv2 ViT-L/14-reg embeddings.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stony Brook University released ZoomLDM. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "ZoomLDM is Stony Brook CVLab's multi-scale latent diffusion model. One set of weights generates 256 by 256 histopathology patches at eight magnifications from UNI embeddings, and the same repo includes a satellite NAIP checkpoint. Joint sampling is how the lab builds 1024 and 4096 images and does 4x super-resolution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stonybrook/zoomldm/zoomldm-cdm": { "id": "stonybrook/zoomldm/zoomldm-cdm", "name": "ZoomLDM CDM", "maker": "stonybrook", "family": "stonybrook/zoomldm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Same Hub repository as ZoomLDM. Card license Apache-2.0. Code repository MIT. The pages disagree.", "release_date": "2025-05-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion transformer trained on ZoomLDM summarizer outputs, conditioned on magnification. The paper says this is easier than modeling raw UNI embeddings. The released file is cdm_brca/0150000.pt. Input is a scale, not a sentence. The modality field uses text only because the schema has no scalar type. Output is a conditioning latent for ZoomLDM, not pixels.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stony Brook University released ZoomLDM CDM. Status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The Conditioning Diffusion Model samples ZoomLDM's summarizer latents at a chosen magnification, so new images do not need a reference slide. The released weight is a BRCA checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "strasbourg/clipper/clipper": { "id": "strasbourg/clipper/clipper", "name": "CliPPER", "maker": "strasbourg", "family": "strasbourg/clipper", "status": "announced", "confidence": "high", "weights": "closed", "license": "CC BY-NC-SA 4.0", "release_date": "2026-03-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dual-encoder video-language model for long surgical video. The paper adds contextual video-text contrastive learning, clip-order prediction, cycle-consistency alignment, and frame-text matching. Backbone width was not copied.", "modalities": { "input": [ "video", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CliPPER is a long-form surgical video-language model from CAMMA. Authors are at Universit\u00e9 de Strasbourg, CNRS, INSERM, ICube, and IHU Strasbourg, with a TU Munich co-author. The 25 March 2026 preprint reports zero-shot phase, step, instrument, and triplet recognition. The GitHub page still said pretrained weights were coming soon on the collection date. Collected 2026-10-15.", "what_it_is_for": "The abstract describes a dual-encoder pretrained on surgical lecture video for fine-grained temporal recognition: phases, steps, instruments, and triplets, without task labels at test time. Training objectives named in the paper and HTML are contextual video-text contrast, clip-order prediction, cycle consistency, and frame-text matching.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "strasbourg/rendezvous/rendezvous": { "id": "strasbourg/rendezvous/rendezvous", "name": "Rendezvous", "maker": "strasbourg", "family": "strasbourg/rendezvous", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2022-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer-inspired encoder-decoder for triplets. The RiT paper states that Rendezvous uses an ImageNet ResNet-18 encoder, a weakly supervised localization module, and class-activation-guided attention. The public zoo is ResNet-18 at low and high resolution.", "modalities": { "input": [ "video" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rendezvous (RDV) is a surgical action-triplet model from CAMMA at Universit\u00e9 de Strasbourg and IHU Strasbourg, with ICube authors. It labels endoscopic frames as instrument, verb, and target. Medical Image Analysis published it in May 2022. The GitHub project distributes ResNet-18 checkpoints for non-commercial research. Collected 2026-10-15.", "what_it_is_for": "The paper introduces Rendezvous to recognize surgical action triplets directly from endoscopic video. Attention is used twice: class-activation-guided spatial attention for the triplet parts, and a decoder for their association. The public zoo is trained on CholecT50 and CholecT45.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "strasbourg/rendezvous/rit": { "id": "strasbourg/rendezvous/rit", "name": "Rendezvous in Time", "maker": "strasbourg", "family": "strasbourg/rendezvous", "status": "active", "confidence": "high", "weights": "closed", "license": "CC BY-NC-SA 4.0", "release_date": "2023-04-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Extends Rendezvous with a temporal attention module that fuses verb features from past frames into the current frame. The encoder remains the Rendezvous ResNet-18 stack. The official README says the Table 1 split is cholect45-crossval.", "modalities": { "input": [ "video" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rendezvous in Time (RiT) is a temporal extension of Rendezvous from CAMMA at ICube, Universit\u00e9 de Strasbourg, and IHU Strasbourg. It keeps the ResNet-18 triplet stack and adds attention over past frames, aimed at verbs. The journal date used here is 25 April 2023. Code is public. A ready weight URL was not listed. Collected 2026-10-15.", "what_it_is_for": "The paper says single-frame triplet models miss how actions evolve. RiT fuses verb features from earlier frames into the current frame and is evaluated on CholecT45. The README says the Table 1 split is `cholect45-crossval`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "strasbourg/s4m/s4m": { "id": "strasbourg/s4m/s4m", "name": "S4M", "maker": "strasbourg", "family": "strasbourg/s4m", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SAM-style segmenter prompted with four points: the endpoints of the major and minor axes, plus a Canvas auxiliary task. Optional extra positive or negative correction points. Three public checkpoints: US-43d (UltraS4M), MMOTU, and Endoscapes.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "S4M (4-points to Segment Anything) is a promptable medical segmentation model from the same CAMMA authors as UltraSam, at Universit\u00e9 de Strasbourg with IHU Strasbourg co-authors. It prompts a SAM-style network with the endpoints of the major and minor axes, the way a sonographer measures a structure. Three checkpoints are on the university S3 bucket, including UltraS4M trained on US-43d. Collected 2026-10-15.", "what_it_is_for": "The README says S4M is for medical image segmentation on ultrasound and endoscopic images. A Canvas auxiliary task is meant to teach the relation between the four points and the shape. The demo accepts later positive or negative correction clicks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "strasbourg/surge/surge": { "id": "strasbourg/surge/surge", "name": "SURGE", "maker": "strasbourg", "family": "strasbourg/surge", "status": "announced", "confidence": "high", "weights": "closed", "license": "CC BY-NC-SA 4.0", "release_date": "2026-09-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-supervised surgical vision foundation model. ViT-Base (SURGE-B, 85.67 million parameters, 12 layers, hidden size 768, 12 heads) and ViT-Huge (SURGE-H, 840.63 million parameters). Both start from DINOv3 checkpoints and are adapted on SurgSpectrum-30M+, including a DINOv3-style high-resolution and Gram-anchoring stage. Huge-variant width and depth were not copied.", "modalities": { "input": [ "image", "video" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SURGE (Surgical Understanding through Representation and Generalization at Scale) is a surgical vision foundation model from CAMMA. The lead affiliations are Universit\u00e9 de Strasbourg, CNRS, INSERM, ICube, and IHU Strasbourg, with TU Munich, IIT Madras, IRCAD, and Gemelli as partners. The 29 September 2026 preprint describes two DINOv3-initialized ViTs trained on more than 30 million surgical frames. The public GitHub page still marked the checkpoints \"Coming Soon\" on the collection date. Collected 2026-10-15.", "what_it_is_for": "The README says SURGE is a pretrained representation for surgical video, meant to transfer to workflow recognition, action triplets, segmentation, and Critical View of Safety, including few-shot and label-efficiency studies. The preprint's claim is that label efficiency depends on the task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "strasbourg/surgvlp/hecvl": { "id": "strasbourg/surgvlp/hecvl", "name": "HecVL", "maker": "strasbourg", "family": "strasbourg/surgvlp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2024-05-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Same encoders as SurgVLP: ImageNet ResNet-50 and BioClinicalBERT. Three embedding spaces (clip narration, phase concept, video abstract) trained by alternating contrastive losses. Frames sampled: 4 clip, 8 phase, 32 video.", "modalities": { "input": [ "video", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HecVL is a hierarchical surgical video-language model from CAMMA at Universit\u00e9 de Strasbourg, IHU Strasbourg, and ICube. It keeps SurgVLP's ResNet-50 and BioClinicalBERT encoders and adds clip, phase, and video text levels so one model can do zero-shot phase recognition across procedures and centers. Weights are public for non-commercial research. Collected 2026-10-15.", "what_it_is_for": "The paper says HecVL is for generalist surgical phase recognition without human labels. Lecture videos are paired with narration, phase summaries, and abstracts. Class names are turned into text prompts at test time. Reported transfer covers cholecystectomy, hysterectomy, and gastric bypass, including two centers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "strasbourg/surgvlp/peskavlp": { "id": "strasbourg/surgvlp/peskavlp", "name": "PeskaVLP", "maker": "strasbourg", "family": "strasbourg/surgvlp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2024-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dual encoder. The paper names ResNet-50 as the visual encoder and ClinicalBert as the text encoder. Clip loss adds SimSiam-style visual self-supervision. Phase and video losses add dynamic time warping against temporally reversed text. Frames sampled: 4 / 16 / 64.", "modalities": { "input": [ "video", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PeskaVLP is the procedure-aware surgical video-language model from CAMMA at Universit\u00e9 de Strasbourg, IHU Strasbourg, and ICube. It extends the SurgVLP and HecVL line with LLM-rewritten hierarchical text, visual self-supervision, and a time-warping loss. The CAMMA repository recommends this checkpoint over SurgVLP and HecVL. Weights are public for non-commercial research. Collected 2026-10-15.", "what_it_is_for": "The paper presents PeskaVLP as a generalist visual and video-language representation for surgical scene understanding: zero-shot phase recognition across procedures and centers, cross-modal retrieval, and linear probing. Pretraining stays on the SVL lecture videos, with hierarchical metadata and LLM rewrites of the text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "strasbourg/surgvlp/surgvlp": { "id": "strasbourg/surgvlp/surgvlp", "name": "SurgVLP", "maker": "strasbourg", "family": "strasbourg/surgvlp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2023-07-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dual encoder. ImageNet-initialized ResNet-50 encodes frames, then average-pools them. BioClinicalBERT (12 layers, 12 heads, stated as 110 million parameters) encodes text. Joint embedding size 768. Contrastive loss aligns a clip with AWS Medical Transcribe and Whisper transcripts.", "modalities": { "input": [ "video", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SurgVLP (Surgical Vision Language Pre-training) is a dual-encoder model from the CAMMA group, a joint group of Universit\u00e9 de Strasbourg and IHU Strasbourg inside ICube. It learns joint video and text embeddings from surgical lecture videos and their automatic transcripts. Weights are public for non-commercial research. The CAMMA repository still ships this checkpoint and says PeskaVLP is the preferred model in the line. Collected 2026-10-15.", "what_it_is_for": "The paper presents SurgVLP as annotation-free pretraining on surgical lecture videos. It aligns a video clip with two noisy transcripts (AWS Medical Transcribe and Whisper) so the joint space can be used for text-based retrieval, temporal grounding, captioning with an added decoder, and zero-shot tool, phase, and action-triplet recognition.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "strasbourg/ultrasam/ultrasam": { "id": "strasbourg/ultrasam/ultrasam", "name": "UltraSam", "maker": "strasbourg", "family": "strasbourg/ultrasam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Promptable segmentation model described as a full fine-tune of the Segment Anything Model on the US-43d ultrasound collection. Supports point and box prompts. The SAM image-encoder size was not stated in the sources read.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UltraSam is an ultrasound segmentation model from CAMMA. The author line is Adrien Meyer, Aditya Murali, Farahdiba Zarin, Didier Mutter, and Nicolas Padoy, affiliated with Universit\u00e9 de Strasbourg, CNRS, and INSERM. It is a promptable Segment Anything-style model trained on the public US-43d collection. A checkpoint is posted on the university S3 bucket. Collected 2026-10-15.", "what_it_is_for": "The abstract says automated ultrasound analysis is limited by anatomy and scarce labels. UltraSam is trained on 43 open ultrasound datasets, more than 280,000 images and masks and more than 50 structures, and is meant for point- and box-prompted segmentation and as an initialization for downstream segmentation and classification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "strathclyde/gold/gold": { "id": "strathclyde/gold/gold", "name": "GOLD", "maker": "strathclyde", "family": "strathclyde/gold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diagram pre-parser is an FPN with a MobileNetV2 backbone. FCOS detects symbols on FPN maps P3\u2013P7. GSM extracts geometric primitives on map P2. Symbol and geometric-primitive vector heads build feature and spatial embeddings. Embedding size 64 was selected from {32, 64, 128}. A relation-construction head predicts sym2geo (text2geo and other2geo) and geo2geo, then writes those relations as natural language. The reported solver concatenates that description with the problem text and is a fine-tuned T5-base. The paper also prompts Llama2-13b-chat and CodeLlama-13b; those are Meta models, not Strathclyde releases. Adam learning rate 1e-4 and weight decay 1e-4. Relation heads: 50 epochs, batch 20. T5-base: 30 epochs, batch 10. Paper section 4.1: one NVIDIA A100 80GB, PyTorch and Hugging Face. The README training comments say A100 40G for the parser and the relation module.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GOLD (Geometry problem sOlver with natural Language Description) is a University of Strathclyde model by Jiaxin Zhang and Yashar Moshfeghi (jiaxin.zhang@strath.ac.uk, yashar.moshfeghi@strath.ac.uk). It reads a geometry diagram and the problem text, writes the diagram relations in natural language, and generates a solution program. arXiv:2405.00494v1 is 1 May 2024. NAACL 2024 Findings, pages 263\u2013278, is June 2024 (DOI 10.18653/v1/2024.findings-naacl.19). The author repository still linked six public Google Drive checkpoints on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "The paper presents GOLD as a geometry math solver. A pre-parser separates symbols from geometric primitives. A relation head turns those links into natural language. A problem-solving module then emits a solution program. The scores in the README and in section 4.4 use fine-tuned T5-base. Llama2-13b-chat and CodeLlama-13b appear only as prompted comparisons and are Meta models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stuttgart/gsi/crealnvp-o-al2o3": { "id": "stuttgart/gsi/crealnvp-o-al2o3", "name": "cRealNVP O-Al2O3", "maker": "stuttgart", "family": "stuttgart/gsi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2026-07-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conditional RealNVP scattering kernel. Six affine coupling layers. Latent space is 3-D standard normal. Each coupling network has four hidden layers of 64 units, ReLU, tanh on the scale head, and a linear translation head. Binary masks over tangential and normal velocity components are in paper Table 1. Trained with negative log-likelihood plus a detailed-balance loss. Adam, 80 epochs, batch 64, initial learning rate 1e-4, decayed by 0.1 every 40 epochs. About 20 minutes on an AMD Ryzen 5 Pro 7530U. Keras/TensorFlow .h5.", "modalities": { "input": [ "velocity" ], "output": [ "velocity" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Stuttgart released cRealNVP O-Al2O3, a conditional RealNVP gas-surface scattering kernel for atomic oxygen on aluminum oxide. It is an active local weight file in the gsi family, used by the PICLas particle solver. This dossier was collected on 2026-10-15.", "what_it_is_for": "The Hub card says the file `cRealNVP_MDclassic_O-Al2O3-O.h5` is a scattering kernel P(v_i \u2192 v_r) for atomic oxygen hitting Al\u2082O\u2083 and leaving as atomic oxygen. PICLas selects it with `Part-Boundary1-SurfaceModelScattering = 3`. The 2026 paper trains it on molecular-dynamics impacts plus equilibrium Maxwell-flux pairs, with a detailed-balance loss, so multi-bounce very-low-Earth-orbit cases can include thermal speeds as well as hypersonic ones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "stuttgart/gsi/cvae-o-al2o3": { "id": "stuttgart/gsi/cvae-o-al2o3", "name": "cVAE O-Al2O3", "maker": "stuttgart", "family": "stuttgart/gsi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2026-07-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conditional variational autoencoder scattering kernel. Encoder input is 6-D (reflected plus incident velocity) with hidden layers 128, 64, and 32, ELU. Latent distribution is 6-D diagonal normal, so the encoder emits 12 values. Decoder input is 9-D (latent plus incident velocity) with hidden layers 32, 64, and 128, ELU. Softplus with beta 0.2 on the normal reflected component; linear tangential outputs. Loss is mean squared error plus KL divergence weighted by 0.8. Adam, 80 epochs, batch 32, initial learning rate 1e-3, decayed by 0.1 every 20 epochs. About 10 minutes on an AMD Ryzen 5 Pro 7530U. Keras/TensorFlow .h5. The 2026 paper says this adds a hidden layer and a larger latent space versus the 2025 hypersonic-only cVAE.", "modalities": { "input": [ "velocity" ], "output": [ "velocity" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Stuttgart released cVAE O-Al2O3, a conditional variational autoencoder that samples reflected velocities for atomic oxygen on aluminum oxide. It is an active local weight file in the gsi family, used by the PICLas particle solver. This dossier was collected on 2026-10-15.", "what_it_is_for": "The Hub card says `cVAE_MDclassic_O-Al2O3-O.h5` is the second scattering kernel in `PICLas-ML/GSI`, for the same O \u2192 Al\u2082O\u2083 \u2192 O case as cRealNVP. PICLas selects it with `Part-Boundary1-SurfaceModelScattering = 2`. The card says it is much faster to sample and is comparable until 10,000 m/s, but it does not converge to the correct equilibrium temperature. The 2026 paper uses it as the comparison model and says this checkpoint adds a hidden layer and a larger latent space versus the 2025 hypersonic-only cVAE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v0": { "id": "suno/chirp/v0", "name": "Chirp v0", "maker": "suno", "family": "suno/chirp", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suno named Chirp v0 on 5 September 2023 as the Discord text-to-music model that Chirp v1 replaced. It is retired. Almost no specs were published. Collected 2026-10-15.", "what_it_is_for": "Early Discord text-to-music generation. Suno did not publish a v0 feature list; the v1 post only says v1 improved on it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v1": { "id": "suno/chirp/v1", "name": "Chirp v1", "maker": "suno", "family": "suno/chirp", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-09-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chirp v1 is Suno's Discord text-to-music model announced on 5 September 2023. It added style control, structure tags, and a claim of 50+ languages. It is retired. Collected 2026-10-15.", "what_it_is_for": "Generate a song with vocals and accompaniment from lyrics or a description, with a chosen style and [verse] or [chorus] tags.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v2": { "id": "suno/chirp/v2", "name": "Suno v2", "maker": "suno", "family": "suno/chirp", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suno v2 is the early song model that Pro and Premier users could still pick beside v3 and v3.5 in May 2024. Suno's help index says it was released in the fall, but the full timeline article was not retrieved. It is retired. Collected 2026-10-15.", "what_it_is_for": "Text-to-song generation on the early Suno web app and, for paid users in May 2024, as a legacy picker option.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v3": { "id": "suno/chirp/v3", "name": "Suno v3", "maker": "suno", "family": "suno/chirp", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suno v3 is the song model Suno shipped to everyone on 21 March 2024 and called its first radio-quality model. It made two-minute songs. It is retired. Collected 2026-10-15.", "what_it_is_for": "Full songs with vocals and instrumentation from a short text prompt, in what Suno called any major language. Suno said the model does not follow artist-name references.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v3-5": { "id": "suno/chirp/v3-5", "name": "Suno v3.5", "maker": "suno", "family": "suno/chirp", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suno v3.5 is the song model Suno opened to Pro and Premier on 24 May 2024 and to everyone on 30 May 2024. One generation could be 4 minutes, with extensions up to 2 minutes. It is retired. Collected 2026-10-15.", "what_it_is_for": "Longer full songs than v3, with the structure and vocal-flow improvements Suno described, plus WAV download for Pro and Premier at early access.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v3-alpha": { "id": "suno/chirp/v3-alpha", "name": "Suno v3 Alpha", "maker": "suno", "family": "suno/chirp", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suno v3 Alpha is the Pro and Premier preview named in the 21 March 2024 v3 launch. Suno said those members tested it before v3. It is retired. A standalone alpha date was not on that post. Collected 2026-10-15.", "what_it_is_for": "Preview of the v3 song model for paid subscribers. Suno did not publish a separate alpha spec.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v4": { "id": "suno/chirp/v4", "name": "Suno v4", "maker": "suno", "family": "suno/chirp", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suno v4 is the song model Suno released in beta to Pro and Premier on 19 November 2024. Suno described cleaner audio, sharper lyrics, and more dynamic song structures. It is retired. Collected 2026-10-15.", "what_it_is_for": "Song generation plus Remaster of older Suno tracks, v4 Covers from uploaded audio, and v4 Personas. Lyrics assistance at launch used the separate ReMi lyrics model, not v4 itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v4-5": { "id": "suno/chirp/v4-5", "name": "Suno v4.5", "maker": "suno", "family": "suno/chirp", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suno v4.5 is the song model Suno released to Pro and Premier on 1 May 2025. It generates songs up to 8 minutes and was Suno's most expressive model at that date. It is retired. Collected 2026-10-15.", "what_it_is_for": "Longer songs with closer genre matching, wider vocal range, denser textures, and a prompt-enhancement helper. Covers and Personas can be combined.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v4-5-all": { "id": "suno/chirp/v4-5-all", "name": "Suno v4.5-all", "maker": "suno", "family": "suno/chirp", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suno v4.5-all is the free song model Suno released on 21 October 2025. Suno described it as richer, fuller, and more dynamic, and free for anyone. v6-mini replaced it as the free model on 9 September 2026, when every pre-v6 model was retired. Collected 2026-10-15.", "what_it_is_for": "The free-tier song model between October 2025 and the v6 launch. Suno did not publish a spec sheet beyond that positioning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v4-5-plus": { "id": "suno/chirp/v4-5-plus", "name": "Suno v4.5+", "maker": "suno", "family": "suno/chirp", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suno v4.5+ is the song model Suno introduced on 17 July 2025 as a new model with Add Vocals, Add Instrumental, and Inspire. Press wires are dated 18 July 2025. It was for paying subscribers. It is retired. Collected 2026-10-15.", "what_it_is_for": "Song generation plus three workflows: sing over an instrumental, build instruments under a vocal, and draft a new song from a playlist of the user's own tracks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v5": { "id": "suno/chirp/v5", "name": "Suno v5", "maker": "suno", "family": "suno/chirp", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suno v5 is the song model Suno released on 23 September 2025 and called the world's best music model. The release note cites clearer audio, more natural vocals, more control, and better genre mixing. It is retired. Collected 2026-10-15.", "what_it_is_for": "Paid-tier song generation and a remaster path that can vary how closely a new version follows an older song. Suno said v5 would also power later product work, including Suno Studio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v5-5": { "id": "suno/chirp/v5-5", "name": "Suno v5.5", "maker": "suno", "family": "suno/chirp", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suno v5.5 is the song model Suno released on 26 March 2026 with Voices, Custom Models, and My Taste. Suno called it the most expressive model to that date. It is retired. Collected 2026-10-15.", "what_it_is_for": "Song generation aimed at sounding more like the person using it. Voices sings in a verified private voice. Custom Models tune v5.5 on the user's own tracks. My Taste learns genres and moods for everyone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v6": { "id": "suno/chirp/v6", "name": "Suno v6", "maker": "suno", "family": "suno/chirp", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio", "image", "video" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suno v6 is the flagship song model Suno released on 9 September 2026 for Pro and Premier. Suno says it was developed with Warner Music Group, BMG, and Believe, and that it is the precise, polished model in a three-model family. It is active. Collected 2026-10-15.", "what_it_is_for": "Full songs from text, audio, images, and video, plus plain-language edits, mashups, and sampling. Suno says it is the model to use when the creator already knows the result they want.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v6-mini": { "id": "suno/chirp/v6-mini", "name": "Suno v6-mini", "maker": "suno", "family": "suno/chirp", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio", "image", "video" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suno v6-mini is the free song model Suno released on 9 September 2026 for every plan. Suno calls it a faster, more efficient v6. It replaced v4.5-all on the free tier. It is active. Collected 2026-10-15.", "what_it_is_for": "Quick song drafts on the free plan and a lighter picker option for subscribers. Suno says it is for high-volume ideas before a full v6 generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/chirp/v6-wild": { "id": "suno/chirp/v6-wild", "name": "Suno v6-wild", "maker": "suno", "family": "suno/chirp", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio", "image", "video" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Suno v6-wild is the experimental song model Suno released on 9 September 2026 for Pro and Premier, beside flagship v6. Suno says it is less predictable and more varied. It is active. Collected 2026-10-15.", "what_it_is_for": "Same v6 generation, steered toward unexpected genre blends and results that sit further from the prompt. Suno suggests bringing a wild take back into v6 to refine it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "suno/speech/speech": { "id": "suno/speech/speech", "name": "Speech", "maker": "suno", "family": "suno/speech", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Speech is Suno's beta audio model, opened to everyone on 1 October 2026, that generates a speaking voice and original background music as one track. Suno calls the rollout a beta. Collected 2026-10-15.", "what_it_is_for": "Spoken pieces with a score: stories, poems, pep talks, meditations, and similar reads. The user types the text and describes the voice and musical style.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "surrey/audioldm/audioldm": { "id": "surrey/audioldm/audioldm", "name": "AudioLDM", "maker": "surrey", "family": "surrey/audioldm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0 on the GitHub LICENSE file and on the cvssp Hub model cards. The personal Hub repo haoheliu/AudioLDM-S-Full is tagged bigscience-openrail-m. Those two statements disagree.", "release_date": "2023-02-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion text-to-audio model. It trains on continuous audio latents aligned with CLAP, and at sampling time conditions on text through the CLAP text encoder. The official Diffusers card says it follows the Stable Diffusion latent-diffusion pattern and uses the LAION CLAP checkpoint laion/clap-htsat-unfused. The code acknowledgements name HiFi-GAN as the vocoder. The card's checkpoint table: audioldm-s-full and audioldm-s-full-v2 are 421 million parameters, CLAP audio dim 768, UNet dim 128, no audio conditioning, 1.5 million steps and more than 1.5 million steps. audioldm-m-full is 652 million, CLAP audio dim 1024, UNet dim 192, audio conditioning on, 1.5 million steps. audioldm-l-full is 975 million, CLAP audio dim 768, UNet dim 256, no audio conditioning, 1.5 million steps. The CLI default duration is 10 seconds, DDIM steps 200, guidance scale 2.5. The Diffusers example writes a 16 kHz wav.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AudioLDM is a text-to-audio generator from the Centre for Vision, Speech and Signal Processing at the University of Surrey. Haohe Liu is the corresponding author at Surrey. Yi Yuan, Xinhao Mei, Xubo Liu, Wenwu Wang, and Mark D. Plumbley are also at Surrey on the university's publication record. Zehua Chen and Danilo Mandic are at Imperial College London. The first public checkpoint, audioldm-s-full, is dated 1 February 2023 in the author's changelog. The arXiv paper is 29 January 2023. The conference version is ICML 2023, PMLR volume 202, pages 21450-21474. Weights were still on the cvssp Hugging Face organisation on 2026-10-15.", "what_it_is_for": "The paper presents AudioLDM as a latent diffusion model that learns audio from CLAP embeddings and, at sampling time, conditions on text. The README says it generates speech, sound effects, and music, and that it can do audio-to-audio generation and text-guided style transfer without extra training. The ICML abstract says a model trained on AudioCaps with one GPU matched or beat other open text-to-audio systems they compared.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "surrey/audioldm2/audioldm2": { "id": "surrey/audioldm2/audioldm2", "name": "AudioLDM 2", "maker": "surrey", "family": "surrey/audioldm2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0. That is the GitHub LICENSE file and the licence metadata on cvssp/audioldm2, cvssp/audioldm2-large, and cvssp/audioldm2-music. Separate author Hub repos haoheliu/audioldm2-full, haoheliu/audioldm2-music-665k, and haoheliu/audioldm2-full-large-1150k are tagged cc-by-nc-nd-4.0. Those tags disagree with the code licence.", "release_date": "2023-08-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent text-to-audio diffusion model. The cvssp card says the three official checkpoints share the text encoders and the VAE and differ in UNet size and depth. audioldm2 (also called audioldm2-full) is text-to-audio, UNet 350 million, total 1.1 billion, trained on 1150k hours. audioldm2-large is text-to-audio, UNet 750 million, total 1.5 billion, 1150k hours. audioldm2-music is text-to-music, UNet 350 million, total 1.1 billion, 665k hours. The paper calls the shared representation language of audio (LOA) and uses a self-supervised AudioMAE plus a latent diffusion model for speech, music, and sound effects. The Diffusers class is AudioLDM2Pipeline, from Diffusers v0.21.0. The card's example writes a 16 kHz wav, 10 seconds, 200 inference steps.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AudioLDM 2 is a text-to-audio model from the Centre for Vision, Speech and Signal Processing at the University of Surrey. The weights are published under the cvssp organisation on Hugging Face, whose card says it is that centre. Haohe Liu is the lead author. The card's bibtex also lists Qiao Tian, Yi Yuan, Xubo Liu, Xinhao Mei, Qiuqiang Kong, Yuping Wang, Wenwu Wang, Yuxuan Wang, and Mark D. Plumbley. The author's own timeline dates the open-source release to 10 August 2023, the same day as arXiv 2308.05734. The Hub weight commit for the base checkpoint is 21 August 2023. IEEE/ACM Transactions on Audio, Speech, and Language Processing accepted the paper; the training-repo bibtex gives volume 32, pages 2871-2883, DOI 10.1109/TASLP.2024.3399607. The weights were still public on 2026-10-15.", "what_it_is_for": "The abstract presents one learning method for speech, music, and sound effects. It calls the shared representation language of audio and uses a self-supervised AudioMAE with a latent diffusion model. The cvssp card is narrower: the base and large checkpoints are for general text-to-audio, and the music checkpoint is text-to-music only. The card says the model can generate sound effects, human speech, and music from a text prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "surrey/audiosr/audiosr": { "id": "surrey/audiosr/audiosr", "name": "AudioSR", "maker": "surrey", "family": "surrey/audiosr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub repository is marked MIT, but the LICENSE file is the unmodified GitHub template and names 'Scott Chacon and others' (2012-2023), not the authors. haoheliu/audiosr_basic is tagged apache-2.0. haoheliu/audiosr_speech had no licence tag in the Hub API response. A third-party conversion card says MIT. Those statements disagree, and the template copyright line is not the authors.", "release_date": "2023-09-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion generative audio super-resolution model. The abstract says it upsamples any input whose bandwidth is between 2 kHz and 16 kHz to a 24 kHz bandwidth signal at a 48 kHz sampling rate, covering sound effects, music, and speech. The project page says a replacement post-process keeps the original low-frequency content. The CLI exposes two checkpoints, basic and speech. Package pin in the README is audiosr==0.0.7, Python 3.9 in the conda example. Numeric defaults for DDIM steps and guidance scale were not in the help text that was read.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AudioSR is an audio super-resolution model led from the Centre for Vision, Speech and Signal Processing at the University of Surrey. Haohe Liu, Wenwu Wang, and Mark D. Plumbley are at Surrey. Ke Chen is at the University of California San Diego. Qiao Tian is at ByteDance SAMI. The author's site dates the open-source release to 15 September 2023. The arXiv paper is 13 September 2023. The conference version is ICASSP 2024, pages 1076-1080, DOI 10.1109/ICASSP48485.2024.10447246, conference dates 14-19 April 2024. The two Hub checkpoint repos were created on 3 February 2024 and were still listed on 2026-10-15.", "what_it_is_for": "The abstract presents AudioSR as a diffusion model that restores high frequencies for sound effects, music, and speech, instead of one bandwidth or one audio type. The stated range is any input bandwidth from 2 kHz to 16 kHz, written out at 48 kHz with 24 kHz bandwidth. The authors also say it can be dropped onto the output of other generators, and they name AudioLDM, FastSpeech 2, and MusicGen.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "surrey/epanns/epanns": { "id": "surrey/epanns/epanns", "name": "E-PANNs", "maker": "surrey", "family": "surrey/epanns", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-05-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Filter-pruned PANNs Cnn14 for AudioSet tagging. The Zenodo file is the prune that removes 50 percent of the filters from convolutional layers C7 through C12, then fine-tunes. The paper says that setting has 70 percent fewer parameters and 36 percent fewer MACs than Cnn14, which it describes as about 81 million parameters and 21G MACs for a 10-second clip at mAP 0.431. The network still takes the Cnn14 log-mel input of 1000 by 64 from a 10-second clip and ends in a 527-way sigmoid. The paper also reports 25 percent and 75 percent prunes. Those are not the file described on Zenodo.", "modalities": { "input": [ "audio" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E-PANNs (Efficient PANNs) is an audio-tagging network from the Centre for Vision, Speech and Signal Processing at the University of Surrey. Arshdeep Singh, Haohe Liu, and Mark D. Plumbley are the authors, all at Surrey on the paper. It is a filter-pruned PANNs Cnn14. The checkpoint was still on Zenodo on 2026-10-15, published 16 May 2023. The arXiv version read here is 30 May 2023.", "what_it_is_for": "The paper presents E-PANNs as a smaller audio tagger for devices that cannot afford Cnn14. It removes low-importance filters from the last six convolutional layers of the public PANNs Cnn14 checkpoint and fine-tunes the pruned network on AudioSet. The authors also ship a real-time sound-recognition demo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "surrey/panns/panns": { "id": "surrey/panns/panns", "name": "PANNs", "maker": "surrey", "family": "surrey/panns", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2019-12-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Convolutional audio taggers trained on AudioSet. The default released system in the README is Cnn14: six convolutional blocks, two convolutional layers each, batch norm and ReLU, with filter counts rising from 64 to 2048, then a sigmoid over 527 classes. The public inference example prints a 2048-d embedding. The stated Cnn14 frontend is a log-mel spectrogram of 1000 frames by 64 bins from a 10-second clip, sample rate 32000, window 1024, hop 320, fmin 50, fmax 14000. The paper also proposes Wavegram-Logmel-CNN, which uses both a waveform-derived wavegram and a log-mel spectrogram. DecisionLevelMax, DecisionLevelAvg, and DecisionLevelAtt variants are for frame-wise sound event detection. Training in the README uses clip-wise binary cross-entropy, a balanced sampler, and mixup, on one Tesla V100 32 GB (the README says a 12 GB GPU also works), for about 3 to 7 days.", "modalities": { "input": [ "audio" ], "output": [ "labels", "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PANNs (pretrained audio neural networks) is an audio-tagging model family from the Centre for Vision, Speech and Signal Processing at the University of Surrey. Qiuqiang Kong, Yin Cao, Turab Iqbal, and Mark D. Plumbley are named at Surrey in the arXiv thanks line. The Zenodo deposit also lists ByteDance. Yuxuan Wang and Wenwu Wang are coauthors. The weights were still listed on Zenodo record 3987831 on 2026-10-15. That record's publication date is 15 December 2019 (version v3). The journal version is IEEE/ACM Transactions on Audio, Speech, and Language Processing, volume 28, 2020, pages 2880-2894. The arXiv revision read here is 23 August 2020.", "what_it_is_for": "The README presents PANNs as pretrained convolutional networks for audio tagging and sound event detection, trained on AudioSet (about 5000 hours, 527 classes). The stated use is both tagging a clip and transferring the network to other audio tasks. The arXiv abstract says the same and names Wavegram-Logmel-CNN as the best system.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "surrey/semanticodec/semanticodec": { "id": "surrey/semanticodec/semanticodec", "name": "SemantiCodec", "maker": "surrey", "family": "surrey/semanticodec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the Hub card for haoheliu/SemantiCodec. The inference repository LICENSE file is the unmodified GitHub MIT template and names 'Scott Chacon and others' (2012-2024), not the authors.", "release_date": "2024-06-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dual-encoder neural audio codec with a latent-diffusion decoder. A frozen AudioMAE (AudioSet pretrained, embedding 768) is quantized by a k-means semantic codebook. A BiLSTM acoustic encoder and a second vector-quantizer add residual acoustic detail. The decoder is a Transformer-UNet latent diffusion model, 64 base channels and 75 million parameters in the paper, conditioned on both quantized streams, then the frozen AudioLDM 2 VAE decoder and HiFi-GAN vocoder. Sample rate is 16 kHz. Three trained token rates: 25, 50, and 100 tokens per second. Each supports semantic codebook sizes 4096, 8192, 16384, and 32768. The paper's default evaluation setting is 32768 semantic centroids and 8192 acoustic centroids. Training clips are 10.24 seconds. Inference uses a 10.24-second window with 6.25 percent overlap. The paper trains each token-rate setting for 500,000 steps on two A100 80 GB GPUs at batch size 48.", "modalities": { "input": [ "audio" ], "output": [ "audio", "tokens" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SemantiCodec is a neural audio codec led from the Centre for Vision, Speech and Signal Processing at the University of Surrey. Haohe Liu, Yi Yuan, Wenwu Wang, and Mark D. Plumbley are at Surrey. Xuenan Xu and Mengyue Wu are at Shanghai Jiao Tong University. It compresses speech, music, and general sound into a low token rate and reconstructs the waveform. The arXiv paper is 30 April 2024, revised 28 November 2024. The weight repo haoheliu/SemantiCodec was created on 2 June 2024 and was still public on 2026-10-15. The author's timeline says the IEEE Journal of Selected Topics in Signal Processing accepted it on 30 October 2024. The journal issue date was not opened.", "what_it_is_for": "The abstract presents SemantiCodec as a codec for language-model use: fewer than 100 tokens per second, across speech, sound, and music, with more semantic content than the codecs they compare. A frozen AudioMAE supplies the semantic stream. A second encoder supplies acoustic detail. A diffusion decoder reconstructs the audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sustech/calibsft/qwen3-8b-calibsft": { "id": "sustech/calibsft/qwen3-8b-calibsft", "name": "Qwen3-8B-CalibSFT", "maker": "sustech", "family": "sustech/calibsft", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-09-28", "context_length": 40960, "max_output_tokens": null, "parameters": "8B", "architecture": "Qwen3ForCausalLM full fine-tune of Qwen/Qwen3-8B. Config: 36 layers, hidden size 4096, 32 query heads, 8 key-value heads, head dim 128, intermediate size 12288, vocabulary 151936, bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen3-8B-CalibSFT is a full-weight fine-tune of Qwen/Qwen3-8B released by the Southern University of Science and Technology statistics lab (ml-stat-Sustech). It is trained with CalibSFT so that a math solution is followed by a verbalized confidence. Weights are public under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says CalibSFT labels sampled answers with a confidence that mixes the question's success rate and whether the answer is correct, balances those confidences, and supervises reasoning only on correct responses. The checkpoint is the starting point for Qwen3-8B-CalibSFT-RLCR. Training data is SUSTech/CalibSFT-DeepScaleR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sustech/calibsft/qwen3-8b-calibsft-rlcr": { "id": "sustech/calibsft/qwen3-8b-calibsft-rlcr", "name": "Qwen3-8B-CalibSFT-RLCR", "maker": "sustech", "family": "sustech/calibsft", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-09-28", "context_length": 40960, "max_output_tokens": null, "parameters": "8B", "architecture": "Qwen3ForCausalLM fine-tune of SUSTech/Qwen3-8B-CalibSFT. Config matches that checkpoint: 36 layers, hidden size 4096, 32 query heads, 8 key-value heads, head dim 128, intermediate size 12288, vocabulary 151936, bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen3-8B-CalibSFT-RLCR is a Southern University of Science and Technology checkpoint that continues Qwen3-8B-CalibSFT with RLCR, a confidence-aware reinforcement-learning method. It is a text reasoning model with public Apache-2.0 weights. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says RLCR from the CalibSFT initialization improves accuracy, discrimination, and calibration compared with RLCR started from unmodified Qwen3-8B. The model answers a problem, then states a short answer and a confidence between 0 and 1. The linked training set is SUSTech/CalibSFT-DeepScaleR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sustech/chemahnet/chemahnet": { "id": "sustech/chemahnet/chemahnet", "name": "ChemAHNet", "maker": "sustech", "family": "sustech/chemahnet", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-12-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Chemistry-informed network over SMILES for olefin asymmetric hydrogenation. The Nature Computational Science abstract says three structure-aware modules. The code defines ChemAHNet_Major and ChemAHNet_ddG, plus MoIM, RCIM, and MIM variants. The major trainer copies word embeddings from a vendored ChemBERTa-zinc-base-v1 snapshot and then trains ChemAHNet_Major. The shipped major script passes 6 layers, 8 heads, dropout 0.2, max_length 256, and output_dim 256, while embedding width is taken from ChemBERTa rather than the embed_dim flag. A supplementary passage for the regression setting selects dropout 0.2, 8 heads, and 7 layers.", "modalities": { "input": [ "text" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChemAHNet is a chemistry-informed deep model from Southern University of Science and Technology for olefin asymmetric hydrogenation. It predicts the major enantiomer's absolute configuration and a stereoselectivity free-energy difference from SMILES. The journal article is dated 2025-12-05, and trained checkpoints are in the public GitHub repository under MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The Nature Computational Science paper and the SUSTech news say the model predicts absolute configuration and stereoselectivity for olefins with two prochiral centers, without hand-built descriptors. Bo Zhang at SUSTech is a corresponding author, with corresponding authors at the University of Macau and Guangzhou Medical University. The code is published by Li Cheng.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sustech/sus-chat/sus-chat-34b": { "id": "sustech/sus-chat/sus-chat-34b", "name": "SUS-Chat-34B", "maker": "sustech", "family": "sustech/sus-chat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the Hub card; the maker also requires the 01.AI Yi model license", "release_date": "2023-12-01", "context_length": 8192, "max_output_tokens": null, "parameters": "34B", "architecture": "LlamaForCausalLM supervised fine-tune of 01-ai/Yi-34B. Config: 60 layers, hidden size 7168, 56 query heads, 8 key-value heads, intermediate size 20480, vocabulary 64000, rope theta 5000000, bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SUS-Chat-34B is a bilingual Chinese-English chat model jointly released by Southern University of Science and Technology and IDEA-CCNL. It is a supervised fine-tune of 01-ai/Yi-34B. Weights remain public. This dossier was collected on 2026-10-15.", "what_it_is_for": "The maker describes a general bilingual dialogue model for complex instructions, multi-turn chat, mathematics, and reasoning. It was trained on 1.4 billion tokens of instruction data and is meant to show that instruction tuning can improve an open base model without adding parameters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/gemma-2-llama-swallow/gemma-2-llama-swallow-27b": { "id": "swallow/gemma-2-llama-swallow/gemma-2-llama-swallow-27b", "name": "Gemma-2-Llama Swallow 27B", "maker": "swallow", "family": "swallow/gemma-2-llama-swallow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma Terms of Use and Llama 3.3 Community License", "release_date": "2025-05-19", "context_length": null, "max_output_tokens": null, "parameters": "27227128320", "architecture": "Gemma 2 27B decoder after continual pre-training. Safetensors total 27,227,128,320. Swallow does not restate context length. The Llama 3.3 license applies because Llama 3.3 70B Instruct synthesized coding data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemma-2-Llama Swallow 27B is the 19 May 2025 Japanese continual pre-training of Gemma 2 27B, built by the Okazaki and Yokota laboratories with AIST. Both a pre-trained and an instruction-tuned v0.1 checkpoint are public. The project says to use the IT model. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese continual pre-training of Gemma 2. The project says to use the IT checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/gemma-2-llama-swallow/gemma-2-llama-swallow-2b": { "id": "swallow/gemma-2-llama-swallow/gemma-2-llama-swallow-2b", "name": "Gemma-2-Llama Swallow 2B", "maker": "swallow", "family": "swallow/gemma-2-llama-swallow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma Terms of Use and Llama 3.3 Community License", "release_date": "2025-05-19", "context_length": null, "max_output_tokens": null, "parameters": "2614341888", "architecture": "Gemma 2 2B decoder after continual pre-training. Safetensors total 2,614,341,888. Swallow does not restate context length. The Llama 3.3 license applies because Llama 3.3 70B Instruct synthesized coding data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemma-2-Llama Swallow 2B is the 19 May 2025 Japanese continual pre-training of Gemma 2 2B, built by the Okazaki and Yokota laboratories with AIST. Both a pre-trained and an instruction-tuned v0.1 checkpoint are public. The project says to use the IT model. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese continual pre-training of Gemma 2. The project says to use the IT checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/gemma-2-llama-swallow/gemma-2-llama-swallow-9b": { "id": "swallow/gemma-2-llama-swallow/gemma-2-llama-swallow-9b", "name": "Gemma-2-Llama Swallow 9B", "maker": "swallow", "family": "swallow/gemma-2-llama-swallow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gemma Terms of Use and Llama 3.3 Community License", "release_date": "2025-05-19", "context_length": null, "max_output_tokens": null, "parameters": "9241705984", "architecture": "Gemma 2 9B decoder after continual pre-training. Safetensors total 9,241,705,984. Swallow does not restate context length. The Llama 3.3 license applies because Llama 3.3 70B Instruct synthesized coding data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemma-2-Llama Swallow 9B is the 19 May 2025 Japanese continual pre-training of Gemma 2 9B, built by the Okazaki and Yokota laboratories with AIST. Both a pre-trained and an instruction-tuned v0.1 checkpoint are public. The project says to use the IT model. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese continual pre-training of Gemma 2. The project says to use the IT checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/gpt-oss-swallow/gpt-oss-swallow-120b": { "id": "swallow/gpt-oss-swallow/gpt-oss-swallow-120b", "name": "GPT-OSS Swallow 120B", "maker": "swallow", "family": "swallow/gpt-oss-swallow", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2026-02-20", "context_length": null, "max_output_tokens": null, "parameters": "116829156672", "architecture": "gpt_oss Mixture-of-Experts continued from OpenAI gpt-oss-120b. Safetensors total 116,829,156,672. Same CPT, SFT, and RL recipe as the 20B model, with a 400B-token continual-pre-training mix.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-OSS Swallow 120B is the 20 February 2026 large reasoning model continued from OpenAI gpt-oss-120b. Institute of Science Tokyo and AIST released RL and SFT v0.1 weights under Apache 2.0, plus an MXFP4 RL build on 28 May 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "Largest Swallow reasoning model. Use the RL checkpoint with reasoning effort medium.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/gpt-oss-swallow/gpt-oss-swallow-20b": { "id": "swallow/gpt-oss-swallow/gpt-oss-swallow-20b", "name": "GPT-OSS Swallow 20B", "maker": "swallow", "family": "swallow/gpt-oss-swallow", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2026-02-20", "context_length": null, "max_output_tokens": null, "parameters": "20914757184", "architecture": "gpt_oss Mixture-of-Experts continued from OpenAI gpt-oss-20b, which is already post-trained. Safetensors total 20,914,757,184. CPT used about 400B tokens at a 32,768 context. Then SFT and RLVR.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-OSS Swallow 20B is the 20 February 2026 Japanese reasoning model continued from OpenAI gpt-oss-20b by Institute of Science Tokyo and AIST. The RL v0.1 weights are Apache 2.0. An MXFP4 build followed on 28 May 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese-English reasoning model. Use the RL checkpoint with reasoning effort medium. SFT is experimental.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/llama-3-1-swallow/llama-3-1-swallow-70b": { "id": "swallow/llama-3-1-swallow/llama-3-1-swallow-70b", "name": "Llama 3.1 Swallow 70B", "maker": "swallow", "family": "swallow/llama-3-1-swallow", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License; Gemma Terms of Use also apply to instruct checkpoints that use Gemma-synthesized data", "release_date": "2024-10-08", "context_length": null, "max_output_tokens": null, "parameters": "70553706496", "architecture": "Llama 3.1 70B dense decoder. Safetensors total 70,553,706,496. v0.3 instruct uses the v0.1 base; there is no base v0.2 or v0.3. The team did not train a Llama 3.1 70B v0.5 because Llama 3.3 was stronger at 70B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.1 Swallow 70B is the 8 October 2024 Japanese continual pre-training of Llama 3.1 70B. Instruct v0.3, released 30 December 2024, only changes instruction tuning. The team later skipped a 70B v0.5 and points users to Llama 3.3 Swallow 70B. This dossier was collected on 2026-10-15.", "what_it_is_for": "70B Llama 3.1 Swallow. Superseded at this size by Llama 3.3 Swallow 70B. The v0.3 instruct checkpoint is the latest in this 70B line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/llama-3-1-swallow/llama-3-1-swallow-8b": { "id": "swallow/llama-3-1-swallow/llama-3-1-swallow-8b", "name": "Llama 3.1 Swallow 8B", "maker": "swallow", "family": "swallow/llama-3-1-swallow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License for v0.1-v0.3; Llama 3.3 license plus Gemma Terms of Use for v0.5", "release_date": "2024-10-08", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Llama 3.1 8B dense decoder. Safetensors total 8,030,261,248. Swallow does not restate context length. v0.5 applies the Llama 3.3 Swallow recipe to Llama 3.1 8B and uses Llama 3.3 synthetic code data, which is why the Gemma terms also apply.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.1 Swallow 8B is the Llama 3.1 8B Japanese line from Institute of Science Tokyo and AIST. v0.1 shipped on 8 October 2024. The current weights are v0.5, released 25 June 2025, and the project says to use the instruct checkpoint. This dossier was collected on 2026-10-15.", "what_it_is_for": "Current small Llama-based Swallow chat model. The project says to use the v0.5 instruct checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/llama-3-3-swallow/llama-3-3-swallow-70b": { "id": "swallow/llama-3-3-swallow/llama-3-3-swallow-70b", "name": "Llama 3.3 Swallow 70B", "maker": "swallow", "family": "swallow/llama-3-3-swallow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.3 Community License and Gemma Terms of Use", "release_date": "2025-03-10", "context_length": null, "max_output_tokens": null, "parameters": "70553706496", "architecture": "Continual pre-training of Llama 3.3 70B Instruct without vocabulary expansion, then supervised fine-tuning for the instruct checkpoint. Safetensors total 70,553,706,496. Context length is not restated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.3 Swallow 70B v0.4 is the 10 March 2025 Japanese continual pre-training of Llama 3.3 70B Instruct, from the Okazaki and Yokota labs and AIST. The project still lists the instruct checkpoint as the latest 70B Llama Swallow model. This dossier was collected on 2026-10-15.", "what_it_is_for": "Latest 70B Llama-based Swallow model. Use the instruct checkpoint unless a base model is required.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/llama-3-swallow/llama-3-swallow-70b": { "id": "swallow/llama-3-swallow/llama-3-swallow-70b", "name": "Llama 3 Swallow 70B", "maker": "swallow", "family": "swallow/llama-3-swallow", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Meta Llama 3 Community License", "release_date": "2024-07-01", "context_length": null, "max_output_tokens": null, "parameters": "70553706496", "architecture": "Llama 3 70B dense decoder. Safetensors total 70,553,706,496. Context length is not restated by Swallow.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3 Swallow 70B is the 1 July 2024 Japanese continual pre-training of Llama 3 70B, released as base and instruct v0.1 under the Llama 3 license. Later 70B Swallow models replace it. Status is legacy on the project site. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese continual pre-training of Llama 3 70B. Legacy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/llama-3-swallow/llama-3-swallow-8b": { "id": "swallow/llama-3-swallow/llama-3-swallow-8b", "name": "Llama 3 Swallow 8B", "maker": "swallow", "family": "swallow/llama-3-swallow", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Meta Llama 3 Community License", "release_date": "2024-07-01", "context_length": null, "max_output_tokens": null, "parameters": "8030261248", "architecture": "Llama 3 8B dense decoder (GQA). Swallow does not restate hidden size or context length on the project page. Safetensors total 8,030,261,248.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3 Swallow 8B is the 1 July 2024 continual pre-training of Meta Llama 3 8B for Japanese, with matching base and instruct weights. The project page now marks Llama 3 Swallow as legacy in favor of Llama 3.1 and Llama 3.3 Swallow. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese continual pre-training of Llama 3 8B, with a chat instruct checkpoint. Legacy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/medical-swallow/medical-gpt-oss-swallow-120b": { "id": "swallow/medical-swallow/medical-gpt-oss-swallow-120b", "name": "Medical GPT-OSS Swallow 120B", "maker": "swallow", "family": "swallow/medical-swallow", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2026-06-10", "context_length": null, "max_output_tokens": null, "parameters": "116829156672", "architecture": "Medical adaptation of GPT-OSS-Swallow-120B-RL-v0.1. Mixture-of-Experts. Safetensors total 116,829,156,672. Hugging Face labels the repo 117B. The card's vLLM example uses tensor parallel size 8.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Medical GPT-OSS Swallow 120B is the June 2026 medical adaptation of GPT-OSS Swallow 120B RL. It is an Apache 2.0 Mixture-of-Experts research model. The card says it is not a medical device. This dossier was collected on 2026-10-15.", "what_it_is_for": "Largest medical Swallow research model, adapted from GPT-OSS Swallow 120B RL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/medical-swallow/medical-qwen3-swallow-30b-a3b": { "id": "swallow/medical-swallow/medical-qwen3-swallow-30b-a3b", "name": "Medical Qwen3 Swallow 30B-A3B", "maker": "swallow", "family": "swallow/medical-swallow", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2026-06-10", "context_length": null, "max_output_tokens": null, "parameters": "30532122624", "architecture": "Medical adaptation of Qwen3-Swallow-30B-A3B-RL-v0.2. Mixture-of-Experts. Safetensors total 30,532,122,624. Hugging Face labels the repo 31B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Medical Qwen3 Swallow 30B-A3B is the June 2026 medical adaptation of Qwen3 Swallow 30B-A3B RL. It is an Apache 2.0 Mixture-of-Experts research model for Japanese clinical text, not a medical device. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese clinical-domain Mixture-of-Experts research model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/medical-swallow/medical-qwen3-swallow-32b": { "id": "swallow/medical-swallow/medical-qwen3-swallow-32b", "name": "Medical Qwen3 Swallow 32B", "maker": "swallow", "family": "swallow/medical-swallow", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2026-06-10", "context_length": null, "max_output_tokens": null, "parameters": "32762123264", "architecture": "Medical adaptation of Qwen3-Swallow-32B-RL-v0.2. Dense Qwen3. Safetensors total 32,762,123,264. Hugging Face labels the repo 33B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Medical Qwen3 Swallow 32B is the June 2026 dense medical adaptation of Qwen3 Swallow 32B RL, published under Apache 2.0 for Japanese clinical research. The card forbids use as a medical device. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese clinical-domain dense research model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/medical-swallow/medical-qwen3-swallow-8b": { "id": "swallow/medical-swallow/medical-qwen3-swallow-8b", "name": "Medical Qwen3 Swallow 8B", "maker": "swallow", "family": "swallow/medical-swallow", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2026-06-10", "context_length": null, "max_output_tokens": null, "parameters": "8190737664", "architecture": "Medical-domain adaptation of Qwen3-Swallow-8B-RL-v0.2. Dense Qwen3. Safetensors total 8,190,737,664, slightly above the parent RL total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Medical Qwen3 Swallow 8B is a June 2026 medical-domain adaptation of Qwen3 Swallow 8B RL, released by the Swallow group on Hugging Face under Apache 2.0. The card limits it to research and says it is not a medical device. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese clinical-domain research model. Not for unsupervised medical decisions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/qwen3-swallow/qwen3-swallow-30b-a3b": { "id": "swallow/qwen3-swallow/qwen3-swallow-30b-a3b", "name": "Qwen3 Swallow 30B-A3B", "maker": "swallow", "family": "swallow/qwen3-swallow", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2026-02-20", "context_length": null, "max_output_tokens": null, "parameters": "30532122624", "architecture": "Qwen3 Mixture-of-Experts (A3B). Safetensors total 30,532,122,624. Active parameter count is the base model's A3B designation and was not remeasured here. CPT started from Qwen3-30B-A3B-Base. Training stages are CPT, SFT, then GRPO-style RL with verifiable rewards on math.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen3 Swallow 30B-A3B is the 20 February 2026 reasoning model from the Okazaki and Yokota laboratories and AIST. It continues Alibaba Qwen3 with Japanese data, then SFT and reinforcement learning. Weights are Apache 2.0. The RL checkpoint is the one the project tells users to run. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese-English reasoning model. Use the RL v0.2 checkpoint with reasoning enabled. SFT and CPT are experimental.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/qwen3-swallow/qwen3-swallow-32b": { "id": "swallow/qwen3-swallow/qwen3-swallow-32b", "name": "Qwen3 Swallow 32B", "maker": "swallow", "family": "swallow/qwen3-swallow", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2026-02-20", "context_length": null, "max_output_tokens": null, "parameters": "32762123264", "architecture": "Dense Qwen3 32B. CPT started from the post-trained Qwen3-32B because a public base checkpoint was not available. Safetensors total 32,762,123,264. Training stages are CPT, SFT, then GRPO-style RL with verifiable rewards on math.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen3 Swallow 32B is the 20 February 2026 reasoning model from the Okazaki and Yokota laboratories and AIST. It continues Alibaba Qwen3 with Japanese data, then SFT and reinforcement learning. Weights are Apache 2.0. The RL checkpoint is the one the project tells users to run. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese-English reasoning model. Use the RL v0.2 checkpoint with reasoning enabled. SFT and CPT are experimental.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/qwen3-swallow/qwen3-swallow-8b": { "id": "swallow/qwen3-swallow/qwen3-swallow-8b", "name": "Qwen3 Swallow 8B", "maker": "swallow", "family": "swallow/qwen3-swallow", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2026-02-20", "context_length": null, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Dense Qwen3 8B. Continual pre-training started from Qwen3-8B-Base, not the post-trained Qwen3-8B. CPT corpus about 200B tokens. Training stages are CPT, SFT, then GRPO-style RL with verifiable rewards on math.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen3 Swallow 8B is the 20 February 2026 reasoning model from the Okazaki and Yokota laboratories and AIST. It continues Alibaba Qwen3 with Japanese data, then SFT and reinforcement learning. Weights are Apache 2.0. The RL checkpoint is the one the project tells users to run. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese-English reasoning model. Use the RL v0.2 checkpoint with reasoning enabled. SFT and CPT are experimental.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/swallow/swallow-13b": { "id": "swallow/swallow/swallow-13b", "name": "Swallow 13B", "maker": "swallow", "family": "swallow/swallow", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-12-19", "context_length": 4096, "max_output_tokens": null, "parameters": "13130306560", "architecture": "Llama 2 13B decoder: hidden 5120, 40 layers, 40 heads, no GQA, vocabulary 43,176, context 4096. Parameter total is the instruct checkpoint safetensors count; the base repo did not expose a total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Swallow 13B is the Llama 2 13B continual-pre-training model with a 43,176-token Japanese vocabulary, first released on 19 December 2023. A second instruct checkpoint followed on 26 April 2024. The project now lists the series as legacy. This dossier was collected on 2026-10-15.", "what_it_is_for": "Mid-size Japanese-English Llama 2 continual-pre-training model. Prefer the v0.1 instruct checkpoint for chat. Legacy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/swallow/swallow-13b-nve": { "id": "swallow/swallow/swallow-13b-nve", "name": "Swallow 13B NVE", "maker": "swallow", "family": "swallow/swallow", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-04", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 13B decoder without vocabulary expansion (vocab 32,000): hidden 5120, 40 layers, 40 heads, context 4096. No instruct checkpoint was released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Swallow 13B NVE is the 4 February 2024 Llama 2 13B continual-pre-training run that keeps the original 32,000-token vocabulary. Only a base checkpoint was released. It is a legacy model. This dossier was collected on 2026-10-15.", "what_it_is_for": "Base-only ablation-style release that skips vocabulary expansion. The project still published it as a named model. Legacy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/swallow/swallow-70b": { "id": "swallow/swallow/swallow-70b", "name": "Swallow 70B", "maker": "swallow", "family": "swallow/swallow", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-12-19", "context_length": 4096, "max_output_tokens": null, "parameters": "69159755776", "architecture": "Llama 2 70B decoder with GQA: hidden 8192, 80 layers, 64 query heads and 8 key-value heads, vocabulary 43,176, context 4096. Parameter total is the instruct safetensors count; the base repo did not expose a total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Swallow 70B is the Llama 2 70B model continually pre-trained for Japanese, with vocabulary expansion, first released on 19 December 2023. It led the team's December 2023 Japanese open-model comparison and is now legacy. This dossier was collected on 2026-10-15.", "what_it_is_for": "Largest Llama 2 Swallow model for Japanese and English text. Prefer instruct v0.1 for chat. Legacy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/swallow/swallow-70b-nve": { "id": "swallow/swallow/swallow-70b-nve", "name": "Swallow 70B NVE", "maker": "swallow", "family": "swallow/swallow", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-01-26", "context_length": 4096, "max_output_tokens": null, "parameters": "68976648192", "architecture": "Llama 2 70B with GQA and the original 32,000-token vocabulary. Parameter total is the instruct safetensors count; the base repo did not expose a total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Swallow 70B NVE is the 26 January 2024 no-vocabulary-expansion variant of Swallow 70B, with base and instruct weights. It uses the Llama 2 tokenizer and is legacy. This dossier was collected on 2026-10-15.", "what_it_is_for": "70B Llama 2 Swallow without vocabulary expansion. Legacy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/swallow/swallow-7b": { "id": "swallow/swallow/swallow-7b", "name": "Swallow 7B", "maker": "swallow", "family": "swallow/swallow", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-12-19", "context_length": 4096, "max_output_tokens": null, "parameters": "6829971456", "architecture": "Llama 2 7B dense decoder: hidden size 4096, 32 layers, 32 heads, no GQA, vocabulary 43,176 after Japanese expansion. Continual pre-training of Llama 2 7B on about 100B tokens (paper table).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Swallow 7B is the Institute of Science Tokyo (then Tokyo Institute of Technology) and AIST continual-pre-training of Llama 2 7B with an expanded Japanese vocabulary. Weights shipped on 19 December 2023. The project page now marks the Llama 2 line as legacy. This dossier was collected on 2026-10-15.", "what_it_is_for": "General Japanese-English text model. The project says to prefer the instruction-tuned checkpoint for chat. The series is labeled legacy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/swallow/swallow-7b-nve": { "id": "swallow/swallow/swallow-7b-nve", "name": "Swallow 7B NVE", "maker": "swallow", "family": "swallow/swallow", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-01-26", "context_length": 4096, "max_output_tokens": null, "parameters": "6738417664", "architecture": "Llama 2 7B decoder with no vocabulary expansion (vocab 32,000): hidden 4096, 32 layers, 32 heads, no GQA, context 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Swallow 7B NVE is the no-vocabulary-expansion twin of Swallow 7B, released on 26 January 2024 with base and instruct weights. It keeps the Llama 2 tokenizer. The line is legacy. This dossier was collected on 2026-10-15.", "what_it_is_for": "Same job as Swallow 7B, without Japanese vocabulary expansion, so tokenization matches Llama 2. Legacy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/swallow/swallow-7b-plus": { "id": "swallow/swallow/swallow-7b-plus", "name": "Swallow 7B Plus", "maker": "swallow", "family": "swallow/swallow", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-03-02", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Same Llama 2 7B decoder and 43,176-token vocabulary as Swallow 7B. Trained with approximately twice the tokens used for Swallow-7b-hf. Exact token count and safetensors parameter total were not exposed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Swallow 7B Plus is the March 2024 base checkpoint that continues Swallow 7B with about twice as many training tokens. It stays on the Llama 2 Community License and is now a legacy release. No instruction-tuned twin was published. This dossier was collected on 2026-10-15.", "what_it_is_for": "Base model only. The team released it for users who want the longer Japanese continual-pre-training run. It is part of the legacy Llama 2 line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/swallow-ms/swallow-ms-7b": { "id": "swallow/swallow-ms/swallow-ms-7b", "name": "Swallow-MS 7B", "maker": "swallow", "family": "swallow/swallow-ms", "status": "deprecated", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2024-03-11", "context_length": null, "max_output_tokens": null, "parameters": "7330205696", "architecture": "Mistral dense decoder with Japanese vocabulary expansion to 42,880 (team blog, 11 March 2024). About 100B continual-pre-training tokens from Mistral-7B-v0.1. Context length is not restated on the Swallow page.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Swallow-MS 7B is a Mistral 7B continual-pre-training model with a larger Japanese vocabulary, released on 11 March 2024 under Apache 2.0. An instruct checkpoint followed on 26 April 2024. The project page marks it legacy. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese-strengthened Mistral 7B for research and commercial use under Apache 2.0. Legacy relative to later Swallow models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "swallow/swallow-mx/swallow-mx-8x7b": { "id": "swallow/swallow-mx/swallow-mx-8x7b", "name": "Swallow-MX 8x7B", "maker": "swallow", "family": "swallow/swallow-mx", "status": "deprecated", "confidence": "high", "weights": "open-source", "license": "Apache License 2.0", "release_date": "2024-03-11", "context_length": null, "max_output_tokens": null, "parameters": "46702792704", "architecture": "Mixtral sparse Mixture-of-Experts continued from Mixtral-8x7B-Instruct-v0.1, without vocabulary expansion. Safetensors total is 46,702,792,704. The project page rounds the shared-parameter total to 47B. Active-parameter count is not restated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Swallow-MX 8x7B is the team's Mixtral continual-pre-training model, released 11 March 2024 as Swallow-MX-8x7b-NVE-v0.1 under Apache 2.0. It was trained from Mixtral-8x7B-Instruct-v0.1 rather than the base Mixtral checkpoint. The line is legacy. This dossier was collected on 2026-10-15.", "what_it_is_for": "Japanese Mixture-of-Experts model continued from Mixtral-8x7B-Instruct. The only public weight is the NVE v0.1 checkpoint. Legacy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/marsupial/marsupial-16s": { "id": "sydney/marsupial/marsupial-16s", "name": "Marsupial 16s", "maker": "sydney", "family": "sydney/marsupial", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (repository software license)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "YOLOv5 fine-tune released by the Sydney Informatics Hub, a core research facility of the University of Sydney. The methods section says fine-tuning started from YOLOv5s.pt at 640 pixels, batch size 64, up to 300 epochs, with early stopping on Artemis (one V100). Checkpoint file marsupial_16s.pt is 57106951 bytes. Exact YOLO width/depth for this file is not labeled.", "modalities": { "input": [ "image" ], "output": [ "boxes", "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marsupial 16s is a camera-trap weight released by the Sydney Informatics Hub at the University of Sydney in the Marsupial repository. The file marsupial_16s.pt is in the public weights directory. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README says this checkpoint identifies 16 animal species with extremely high precision and recall. It does not print those rates. The stated job is labelling Australian camera-trap images much faster than manual review. The page estimates about 12 thousand images an hour on a laptop CPU and about 300 thousand an hour on a cloud GPU, for the system rather than for one named file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/marsupial/marsupial-33s": { "id": "sydney/marsupial/marsupial-33s", "name": "Marsupial 33s", "maker": "sydney", "family": "sydney/marsupial", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (repository software license)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "YOLOv5 fine-tune released by the Sydney Informatics Hub, a core research facility of the University of Sydney. The methods section says fine-tuning started from YOLOv5s.pt at 640 pixels, batch size 64, up to 300 epochs, with early stopping on Artemis (one V100). Checkpoint file marsupial_33s.pt is 57474823 bytes. Exact YOLO width/depth for this file is not labeled.", "modalities": { "input": [ "image" ], "output": [ "boxes", "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marsupial 33s is a camera-trap weight released by the Sydney Informatics Hub at the University of Sydney in the Marsupial repository. The file marsupial_33s.pt is in the public weights directory. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README says this checkpoint identifies 33 species. A separate sentence says the 33 most common species are identified at 94 percent precision and 93 percent recall on held-out WildCount data. The stated job is labelling Australian camera-trap images much faster than manual review. The page estimates about 12 thousand images an hour on a laptop CPU and about 300 thousand an hour on a cloud GPU, for the system rather than for one named file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/marsupial/marsupial-41s": { "id": "sydney/marsupial/marsupial-41s", "name": "Marsupial 41s", "maker": "sydney", "family": "sydney/marsupial", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (repository software license)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "YOLOv5 fine-tune released by the Sydney Informatics Hub, a core research facility of the University of Sydney. The methods section says fine-tuning started from YOLOv5s.pt at 640 pixels, batch size 64, up to 300 epochs, with early stopping on Artemis (one V100). Checkpoint file marsupial_41s.pt is 57647815 bytes. Exact YOLO width/depth for this file is not labeled.", "modalities": { "input": [ "image" ], "output": [ "boxes", "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marsupial 41s is a camera-trap weight released by the Sydney Informatics Hub at the University of Sydney in the Marsupial repository. The file marsupial_41s.pt is in the public weights directory. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README says this checkpoint identifies 41 species and calls it a balance between performance and detail. No numeric score is printed. The stated job is labelling Australian camera-trap images much faster than manual review. The page estimates about 12 thousand images an hour on a laptop CPU and about 300 thousand an hour on a cloud GPU, for the system rather than for one named file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/marsupial/marsupial-72l": { "id": "sydney/marsupial/marsupial-72l", "name": "Marsupial 72l", "maker": "sydney", "family": "sydney/marsupial", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (repository software license)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "YOLOv5 fine-tune released by the Sydney Informatics Hub, a core research facility of the University of Sydney. The methods section says fine-tuning started from YOLOv5s.pt at 640 pixels, batch size 64, up to 300 epochs, with early stopping on Artemis (one V100). Checkpoint file marsupial_72l.pt is 93589989 bytes. Exact YOLO width/depth for this file is not labeled.", "modalities": { "input": [ "image" ], "output": [ "boxes", "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marsupial 72l is a camera-trap weight released by the Sydney Informatics Hub at the University of Sydney in the Marsupial repository. The file marsupial_72l.pt is in the public weights directory. This dossier was collected on 2026-10-15.", "what_it_is_for": "A 72-class weight file shipped in the weights directory. The Current Models section of the README names marsupial_72s but does not describe marsupial_72l. The larger file size is the only size distinction on the page. The stated job is labelling Australian camera-trap images much faster than manual review. The page estimates about 12 thousand images an hour on a laptop CPU and about 300 thousand an hour on a cloud GPU, for the system rather than for one named file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/marsupial/marsupial-72s": { "id": "sydney/marsupial/marsupial-72s", "name": "Marsupial 72s", "maker": "sydney", "family": "sydney/marsupial", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (repository software license)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "YOLOv5 fine-tune released by the Sydney Informatics Hub, a core research facility of the University of Sydney. The methods section says fine-tuning started from YOLOv5s.pt at 640 pixels, batch size 64, up to 300 epochs, with early stopping on Artemis (one V100). Checkpoint file marsupial_72s.pt is 14786997 bytes. Exact YOLO width/depth for this file is not labeled.", "modalities": { "input": [ "image" ], "output": [ "boxes", "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marsupial 72s is a camera-trap weight released by the Sydney Informatics Hub at the University of Sydney in the Marsupial repository. The file marsupial_72s.pt is in the public weights directory. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README says this checkpoint identifies 72 species and is the most detailed predictor that still claims state-of-the-art performance. A different sentence reports 88 percent precision and 84 percent recall for 71 species. Those counts do not match, so that score is not stored on this model. The stated job is labelling Australian camera-trap images much faster than manual review. The page estimates about 12 thousand images an hour on a laptop CPU and about 300 thousand an hour on a cloud GPU, for the system rather than for one named file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/marsupial/marsupial-72s-lures": { "id": "sydney/marsupial/marsupial-72s-lures", "name": "Marsupial 72s Lures", "maker": "sydney", "family": "sydney/marsupial", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (repository software license)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "YOLOv5 fine-tune released by the Sydney Informatics Hub, a core research facility of the University of Sydney. The methods section says fine-tuning started from YOLOv5s.pt at 640 pixels, batch size 64, up to 300 epochs, with early stopping on Artemis (one V100). Checkpoint file marsupial_72s_lures.pt is 14792373 bytes. Exact YOLO width/depth for this file is not labeled.", "modalities": { "input": [ "image" ], "output": [ "boxes", "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marsupial 72s Lures is a camera-trap weight released by the Sydney Informatics Hub at the University of Sydney in the Marsupial repository. The file marsupial_72s_lures.pt is in the public weights directory. This dossier was collected on 2026-10-15.", "what_it_is_for": "A weight file named marsupial_72s_lures.pt in the weights directory. The README current-models list does not describe the lures variant. The stated job is labelling Australian camera-trap images much faster than manual review. The page estimates about 12 thousand images an hour on a laptop CPU and about 300 thousand an hour on a cloud GPU, for the system rather than for one named file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/marsupial/marsupial-detector": { "id": "sydney/marsupial/marsupial-detector", "name": "Marsupial Detector", "maker": "sydney", "family": "sydney/marsupial", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (repository software license)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "YOLOv5 fine-tune released by the Sydney Informatics Hub, a core research facility of the University of Sydney. The methods section says fine-tuning started from YOLOv5s.pt at 640 pixels, batch size 64, up to 300 epochs, with early stopping on Artemis (one V100). Checkpoint file marsupial_detector.pt is 14401589 bytes. Exact YOLO width/depth for this file is not labeled.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marsupial Detector is a camera-trap weight released by the Sydney Informatics Hub at the University of Sydney in the Marsupial repository. The file marsupial_detector.pt is in the public weights directory. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README calls it a pure animal detector that finds any animal in an image, without a species label. The stated job is labelling Australian camera-trap images much faster than manual review. The page estimates about 12 thousand images an hour on a laptop CPU and about 300 thousand an hour on a cloud GPU, for the system rather than for one named file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitae/vitae-6m": { "id": "sydney/vitae/vitae-6m", "name": "ViTAE-6M", "maker": "sydney", "family": "sydney/vitae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research purpose only (repository statement; no root LICENSE file)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "6500000", "architecture": "ViTAE variant in the paper appendix: same reduction-cell pattern as ViTAE-T with 10 normal cells arranged 0,0,10. The appendix table lists 6.5 million parameters and 2.0G MACs. The release name is ViTAE-6M. Input 224x224.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTAE-6M is an image-classification vision transformer released by the University of Sydney authors of ViTAE. Weights are linked from the official classification README. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors released it as an ImageNet classifier and as a backbone that carries convolutional inductive bias inside a transformer. Downstream detection, segmentation, and pose code in the same organization uses ViTAE as a backbone rather than as extra named products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitae/vitae-b": { "id": "sydney/vitae/vitae-b", "name": "ViTAE-B", "maker": "sydney", "family": "sydney/vitae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research purpose only (repository statement; no root LICENSE file)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "89000000", "architecture": "Scaled ViTAE. The ViTAEv2 paper says 12 normal cells and embedding dimension 768, 89 million parameters, following the ViT stacking pattern, trained with MAE because full supervision on ImageNet-1K overfits. Input 224x224.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTAE-B is an image-classification vision transformer released by the University of Sydney authors of ViTAE. Weights are linked from the official classification README. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors released it as an ImageNet classifier and as a backbone that carries convolutional inductive bias inside a transformer. Downstream detection, segmentation, and pose code in the same organization uses ViTAE as a backbone rather than as extra named products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitae/vitae-h": { "id": "sydney/vitae/vitae-h", "name": "ViTAE-H", "maker": "sydney", "family": "sydney/vitae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research purpose only (repository statement; no root LICENSE file)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "644300000", "architecture": "Largest scaled ViTAE in the classification table. Table 1 of the ViTAEv2 paper lists 644.3 million parameters and 335.7G FLOPs, full attention, embedding 1280, normal-cell arrangement 0,0,32. The prose also says 644 million and 36 normal cells with embedding 1,248. Those two architecture sentences disagree; both are cited. Input 224x224. MAE training.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTAE-H is an image-classification vision transformer released by the University of Sydney authors of ViTAE. Weights are linked from the official classification README. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors released it as an ImageNet classifier and as a backbone that carries convolutional inductive bias inside a transformer. Downstream detection, segmentation, and pose code in the same organization uses ViTAE as a backbone rather than as extra named products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitae/vitae-l": { "id": "sydney/vitae/vitae-l", "name": "ViTAE-L", "maker": "sydney", "family": "sydney/vitae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research purpose only (repository statement; no root LICENSE file)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "311000000", "architecture": "Scaled ViTAE. The ViTAEv2 paper says 24 normal cells and embedding dimension 1,024, 311 million parameters, MAE pretraining. Input 224x224.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTAE-L is an image-classification vision transformer released by the University of Sydney authors of ViTAE. Weights are linked from the official classification README. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors released it as an ImageNet classifier and as a backbone that carries convolutional inductive bias inside a transformer. Downstream detection, segmentation, and pose code in the same organization uses ViTAE as a backbone rather than as extra named products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitae/vitae-s": { "id": "sydney/vitae/vitae-s", "name": "ViTAE-S", "maker": "sydney", "family": "sydney/vitae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research purpose only (repository statement; no root LICENSE file)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "23600000", "architecture": "Isotropic ViTAE-S. Model-details table: 3 reduction cells, 6 heads, embedding 384, 14 normal cells, 23.6 million parameters, 5.6G MACs. Input 224x224. A 384 input row in the later paper reports 83.0 top-1 and is not a separate named checkpoint.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTAE-S is an image-classification vision transformer released by the University of Sydney authors of ViTAE. Weights are linked from the official classification README. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors released it as an ImageNet classifier and as a backbone that carries convolutional inductive bias inside a transformer. Downstream detection, segmentation, and pose code in the same organization uses ViTAE as a backbone rather than as extra named products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitae/vitae-t": { "id": "sydney/vitae/vitae-t", "name": "ViTAE-T", "maker": "sydney", "family": "sydney/vitae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research purpose only (repository statement; no root LICENSE file)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "4800000", "architecture": "Isotropic ViTAE. Table of model details: 3 reduction cells, dilation schedule [1,2,3,4] decreasing, 4 heads, embedding 256, 7 normal cells, 4.8 million parameters, 1.5G MACs. Input 224x224. Reduction cells use a pyramid reduction module of atrous convolutions plus a parallel convolution beside attention.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTAE-T is an image-classification vision transformer released by the University of Sydney authors of ViTAE. Weights are linked from the official classification README. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors released it as an ImageNet classifier and as a backbone that carries convolutional inductive bias inside a transformer. Downstream detection, segmentation, and pose code in the same organization uses ViTAE as a backbone rather than as extra named products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitaev2/vitaev2-48m": { "id": "sydney/vitaev2/vitaev2-48m", "name": "ViTAEv2-48M", "maker": "sydney", "family": "sydney/vitaev2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research purpose only (repository statement; no root LICENSE file)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "48700000", "architecture": "Multi-stage ViTAEv2. Table 1: embeddings 96, 192, 384, 768, arrangement 2, 2, 11, 2, 48.7 million parameters, 13.3G FLOPs. Window attention in early stages. Input 224x224. The release name says 48M.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTAEv2-48M is a multi-stage image classifier from the University of Sydney ViTAE group. Weights are linked from the official classification README. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors released it as the hierarchical ViTAE for ImageNet classification and as a backbone for detection, segmentation, and pose. The classification checkpoint is the named model here. Downstream heads that the README marks Coming Soon were not given folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitaev2/vitaev2-b": { "id": "sydney/vitaev2/vitaev2-b", "name": "ViTAEv2-B", "maker": "sydney", "family": "sydney/vitaev2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research purpose only (repository statement; no root LICENSE file)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "89700000", "architecture": "Multi-stage ViTAEv2-B. Table 1: embeddings 128, 256, 512, 1024, arrangement 2, 2, 12, 2, 89.7 million parameters, 24.3G FLOPs. Window attention in the first two stages. Input 224x224.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTAEv2-B is a multi-stage image classifier from the University of Sydney ViTAE group. Weights are linked from the official classification README. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors released it as the hierarchical ViTAE for ImageNet classification and as a backbone for detection, segmentation, and pose. The classification checkpoint is the named model here. Downstream heads that the README marks Coming Soon were not given folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitaev2/vitaev2-s": { "id": "sydney/vitaev2/vitaev2-s", "name": "ViTAEv2-S", "maker": "sydney", "family": "sydney/vitaev2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research purpose only (repository statement; no root LICENSE file)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "19300000", "architecture": "Multi-stage ViTAEv2-S. Table 1: window attention in the first two stages and full attention later, embeddings 64, 128, 256, 512, normal-cell arrangement 2, 2, 8, 2, 19.3 million parameters, 5.7G FLOPs. A later results row prints 19.2 million for the same name. Input 224x224. Local window attention has no shift and no relative position bias because the parallel convolution is supposed to carry position and cross-window mixing.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTAEv2-S is a multi-stage image classifier from the University of Sydney ViTAE group. Weights are linked from the official classification README. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors released it as the hierarchical ViTAE for ImageNet classification and as a backbone for detection, segmentation, and pose. The classification checkpoint is the named model here. Downstream heads that the README marks Coming Soon were not given folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitpose/vitpose-b": { "id": "sydney/vitpose/vitpose-b", "name": "ViTPose-B", "maker": "sydney", "family": "sydney/vitpose", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-05-24", "context_length": null, "max_output_tokens": null, "parameters": "86000000", "architecture": "Plain ViT-B encoder plus a lightweight decoder. The paper table lists 86 million parameters, 17.1 GFLOPs, and 944 fps on one A100 at batch size 64. Input 256x192. Backbone initialized from MAE pre-trained weights. Trained 210 epochs on 8 A100 GPUs in MMPose.", "modalities": { "input": [ "image" ], "output": [ "keypoints" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTPose-B is a human pose model from the Sydney AI Centre and School of Computer Science at the University of Sydney. Public OneDrive weights are linked from the official repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors released it to estimate body keypoints for a cropped person. The repository is the official code for the NeurIPS 2022 ViTPose paper and the later ViTPose++ journal paper. It is a top-down pose model: a detector finds people, then this network predicts keypoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitpose/vitpose-g": { "id": "sydney/vitpose/vitpose-g", "name": "ViTPose-G", "maker": "sydney", "family": "sydney/vitpose", "status": "active", "confidence": "high", "weights": "closed", "license": "Apache-2.0", "release_date": "2022-04-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Largest ViTPose model. The paper uses a ViTAE-G backbone, described as about 1 billion parameters, at 576x432, trained on MS COCO, AI Challenger, and MPII, with Bigdet person boxes (68.5 AP on the COCO person class). The 2022-04-27 repository note says ViTPose with ViTAE-G obtains 81.1 AP on COCO test-dev.", "modalities": { "input": [ "image" ], "output": [ "keypoints" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTPose-G is the largest pose model named by the University of Sydney ViTPose authors, built on a ViTAE-G backbone. The paper reports it. The official weight table does not link a checkpoint. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors present it as the scaled ViTPose model that sets a COCO keypoint record without a model ensemble. It is still a top-down body-keypoint estimator, at higher resolution than ViTPose-S through H.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitpose/vitpose-h": { "id": "sydney/vitpose/vitpose-h", "name": "ViTPose-H", "maker": "sydney", "family": "sydney/vitpose", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-05-24", "context_length": null, "max_output_tokens": null, "parameters": "632000000", "architecture": "Plain ViT-H encoder plus a lightweight decoder. The paper table lists 632 million parameters, 122.9 GFLOPs, and 241 fps on one A100 at batch size 64. Input 256x192. MAE initialization.", "modalities": { "input": [ "image" ], "output": [ "keypoints" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTPose-H is a human pose model from the Sydney AI Centre and School of Computer Science at the University of Sydney. Public OneDrive weights are linked from the official repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors released it to estimate body keypoints for a cropped person. The repository is the official code for the NeurIPS 2022 ViTPose paper and the later ViTPose++ journal paper. It is a top-down pose model: a detector finds people, then this network predicts keypoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitpose/vitpose-l": { "id": "sydney/vitpose/vitpose-l", "name": "ViTPose-L", "maker": "sydney", "family": "sydney/vitpose", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-05-24", "context_length": null, "max_output_tokens": null, "parameters": "307000000", "architecture": "Plain ViT-L encoder plus a lightweight decoder. The paper table lists 307 million parameters, 59.8 GFLOPs, and 411 fps on one A100 at batch size 64. Input 256x192. MAE initialization. Trained on 8 A100 GPUs.", "modalities": { "input": [ "image" ], "output": [ "keypoints" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTPose-L is a human pose model from the Sydney AI Centre and School of Computer Science at the University of Sydney. Public OneDrive weights are linked from the official repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors released it to estimate body keypoints for a cropped person. The repository is the official code for the NeurIPS 2022 ViTPose paper and the later ViTPose++ journal paper. It is a top-down pose model: a detector finds people, then this network predicts keypoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitpose/vitpose-s": { "id": "sydney/vitpose/vitpose-s", "name": "ViTPose-S", "maker": "sydney", "family": "sydney/vitpose", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-05-24", "context_length": null, "max_output_tokens": null, "parameters": "22000000", "architecture": "Plain non-hierarchical ViT-S encoder with a lightweight keypoint decoder. The TPAMI results table lists 22 million parameters, 5.3 GFLOPs, and 1,439 fps on one A100 at batch size 64. Default top-down input is 256x192 with a person detector. MAE initialization. The introduction of the same paper says 24 million parameters and 1,432 fps.", "modalities": { "input": [ "image" ], "output": [ "keypoints" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTPose-S is a human pose model from the Sydney AI Centre and School of Computer Science at the University of Sydney. Public OneDrive weights are linked from the official repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors released it to estimate body keypoints for a cropped person. The repository is the official code for the NeurIPS 2022 ViTPose paper and the later ViTPose++ journal paper. It is a top-down pose model: a detector finds people, then this network predicts keypoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitpose-plus/vitpose-plus-b": { "id": "sydney/vitpose-plus/vitpose-plus-b", "name": "ViTPose++-B", "maker": "sydney", "family": "sydney/vitpose-plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-01-10", "context_length": null, "max_output_tokens": null, "parameters": "86000000", "architecture": "ViTPose++ size matched to vitpose-b. A plain vision transformer encoder with knowledge factorization: task-agnostic and task-specific feed-forward experts so one network can be trained on human, animal, and whole-body keypoints. The released multi-task mix on the README is COCO, AIC, MPII, AP-10K, APT-36K, and COCO-WholeBody, at 256x192. MAE backbone initialization. Paper table lists 86000000 parameters.", "modalities": { "input": [ "image" ], "output": [ "keypoints" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTPose++-B is the University of Sydney multi-task pose model at this size, released as ViTPose++ on 2023-01-10 in the official repository. A Transformers port is on Hugging Face under usyd-community. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe ViTPose++ as a foundation-style model for generic body keypoints: humans, animals, and whole-body landmarks, using mixture-of-experts feed-forward layers so tasks do not share one conflicting head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitpose-plus/vitpose-plus-h": { "id": "sydney/vitpose-plus/vitpose-plus-h", "name": "ViTPose++-H", "maker": "sydney", "family": "sydney/vitpose-plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-01-10", "context_length": null, "max_output_tokens": null, "parameters": "632000000", "architecture": "ViTPose++ size matched to vitpose-h. A plain vision transformer encoder with knowledge factorization: task-agnostic and task-specific feed-forward experts so one network can be trained on human, animal, and whole-body keypoints. The released multi-task mix on the README is COCO, AIC, MPII, AP-10K, APT-36K, and COCO-WholeBody, at 256x192. MAE backbone initialization. Paper table lists 632000000 parameters.", "modalities": { "input": [ "image" ], "output": [ "keypoints" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTPose++-H is the University of Sydney multi-task pose model at this size, released as ViTPose++ on 2023-01-10 in the official repository. A Transformers port is on Hugging Face under usyd-community. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe ViTPose++ as a foundation-style model for generic body keypoints: humans, animals, and whole-body landmarks, using mixture-of-experts feed-forward layers so tasks do not share one conflicting head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitpose-plus/vitpose-plus-l": { "id": "sydney/vitpose-plus/vitpose-plus-l", "name": "ViTPose++-L", "maker": "sydney", "family": "sydney/vitpose-plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-01-10", "context_length": null, "max_output_tokens": null, "parameters": "307000000", "architecture": "ViTPose++ size matched to vitpose-l. A plain vision transformer encoder with knowledge factorization: task-agnostic and task-specific feed-forward experts so one network can be trained on human, animal, and whole-body keypoints. The released multi-task mix on the README is COCO, AIC, MPII, AP-10K, APT-36K, and COCO-WholeBody, at 256x192. MAE backbone initialization. Paper table lists 307000000 parameters.", "modalities": { "input": [ "image" ], "output": [ "keypoints" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTPose++-L is the University of Sydney multi-task pose model at this size, released as ViTPose++ on 2023-01-10 in the official repository. A Transformers port is on Hugging Face under usyd-community. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe ViTPose++ as a foundation-style model for generic body keypoints: humans, animals, and whole-body landmarks, using mixture-of-experts feed-forward layers so tasks do not share one conflicting head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sydney/vitpose-plus/vitpose-plus-s": { "id": "sydney/vitpose-plus/vitpose-plus-s", "name": "ViTPose++-S", "maker": "sydney", "family": "sydney/vitpose-plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-01-10", "context_length": null, "max_output_tokens": null, "parameters": "22000000", "architecture": "ViTPose++ size matched to vitpose-s. A plain vision transformer encoder with knowledge factorization: task-agnostic and task-specific feed-forward experts so one network can be trained on human, animal, and whole-body keypoints. The released multi-task mix on the README is COCO, AIC, MPII, AP-10K, APT-36K, and COCO-WholeBody, at 256x192. MAE backbone initialization. Paper table lists 22000000 parameters.", "modalities": { "input": [ "image" ], "output": [ "keypoints" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ViTPose++-S is the University of Sydney multi-task pose model at this size, released as ViTPose++ on 2023-01-10 in the official repository. A Transformers port is on Hugging Face under usyd-community. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe ViTPose++ as a foundation-style model for generic body keypoints: humans, animals, and whole-body landmarks, using mixture-of-experts feed-forward layers so tasks do not share one conflicting head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/express/express-1": { "id": "synthesia/express/express-1", "name": "Express-1", "maker": "synthesia", "family": "synthesia/express", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-04-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vendor describes a backbone of large pre-trained models plus diffusion, used to predict movement and facial expression from speech. No layer counts or a single parameter total were published.", "modalities": { "input": [ "text", "audio" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Express-1 is Synthesia's avatar model announced on 22 April 2024 as the engine behind Expressive Avatars, which the company called its fourth generation. As of 2026-10-15 it is still active: Studio avatars and a set of stock Video API avatars are tagged EXPRESS-1, and the Express-3 rollout says Express-1 is unchanged. Weights are closed.", "what_it_is_for": "Express-1 is Synthesia's avatar model announced on 22 April 2024 as the engine behind Expressive Avatars, which the company called its fourth generation. As of 2026-10-15 it is still active: Studio avatars and a set of stock Video API avatars are tagged EXPRESS-1, and the Express-3 rollout says Express-1 is unchanged. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/express/express-2": { "id": "synthesia/express/express-2", "name": "Express-2", "maker": "synthesia", "family": "synthesia/express", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video and voice engine. Express-Voice clones speech. Express-Video animates and renders through Express-Animate (audio-driven co-speech motion), Express-Eval (CLIP-like audio-motion scorer), and Express-Render (diffusion transformer, distilled to two steps for production). Identity is injected from tokenized reference images.", "modalities": { "input": [ "text", "audio", "image" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Express-2 is Synthesia's full-body avatar engine, written up by CTO Peter Hill on 4 September 2025 and described on the company research site as a 1080p, 30fps video and voice system. It pairs Express-Voice with Express-Video (Express-Animate, Express-Eval, and Express-Render). As of 2026-10-15 the product still lists Express-2 avatars, while Express-3 is announced as the replacement across the platform. Weights are closed.", "what_it_is_for": "Express-2 is Synthesia's full-body avatar engine, written up by CTO Peter Hill on 4 September 2025 and described on the company research site as a 1080p, 30fps video and voice system. It pairs Express-Voice with Express-Video (Express-Animate, Express-Eval, and Express-Render). As of 2026-10-15 the product still lists Express-2 avatars, while Express-3 is announced as the replacement across the platform. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/express/express-3": { "id": "synthesia/express/express-3", "name": "Express-3", "maker": "synthesia", "family": "synthesia/express", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Express-3 is Synthesia's avatar rendering model launched on 1 September 2026. The company calls it the most lifelike avatar model to date and says it is replacing Express-2, with sentiment-matched performance, sharper lip sync, and faster generation. It also powers Style avatars in Avatar Builder and, per the 14 September 2026 interactive-API post, real-time interactive avatars. Express-1 is explicitly left in place. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Express-3 is Synthesia's avatar rendering model launched on 1 September 2026. The company calls it the most lifelike avatar model to date and says it is replacing Express-2, with sentiment-matched performance, sharper lip sync, and faster generation. It also powers Style avatars in Avatar Builder and, per the 14 September 2026 interactive-API post, real-time interactive avatars. Express-1 is explicitly left in place. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/express/express-animate": { "id": "synthesia/express/express-animate", "name": "Express-Animate", "maker": "synthesia", "family": "synthesia/express", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Audio-driven co-speech gesture model. Motion is decoupled from avatar appearance. Research page says it emits several motion candidates.", "modalities": { "input": [ "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Express-Animate is Synthesia's co-speech gesture model inside Express-2. The research page says it turns audio into anatomically coherent full-body motion, separate from how the avatar looks. It is not a standalone API. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Express-Animate is Synthesia's co-speech gesture model inside Express-2. The research page says it turns audio into anatomically coherent full-body motion, separate from how the avatar looks. It is not a standalone API. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/express/express-eval": { "id": "synthesia/express/express-eval", "name": "Express-Eval", "maker": "synthesia", "family": "synthesia/express", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP-like contrastive model on paired audio and motion. Alignment is cosine similarity in a joint embedding. Used to pick a motion candidate at inference and to compare Express-Animate versions in development.", "modalities": { "input": [ "audio", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Express-Eval is the audio-motion alignment model inside Express-2. Synthesia describes it as CLIP-like: it scores how well a generated gesture matches the audio and picks a candidate at inference. It is an internal model, not a public endpoint. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Express-Eval is the audio-motion alignment model inside Express-2. Synthesia describes it as CLIP-like: it scores how well a generated gesture matches the audio and picks a candidate at inference. It is an internal model, not a public endpoint. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/express/express-render": { "id": "synthesia/express/express-render", "name": "Express-Render", "maker": "synthesia", "family": "synthesia/express", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion transformer. Motion cues from Express-Animate plus audio become frames. Identity comes from tokenized reference images concatenated with visual tokens. Production model is distilled to two diffusion steps. The undistilled model is described as too slow to ship.", "modalities": { "input": [ "audio", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Express-Render is the diffusion-transformer video renderer in Express-2. Synthesia says the shipped model is distilled to two diffusion steps and writes 1080p video at 30fps, including face detail, from Express-Animate motion and a reference identity. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Express-Render is the diffusion-transformer video renderer in Express-2. Synthesia says the shipped model is distilled to two diffusion steps and writes 1080p video at 30fps, including face detail, from Express-Animate motion and a reference identity. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/express/express-render-turbo": { "id": "synthesia/express/express-render-turbo", "name": "Express-Render-Turbo", "maker": "synthesia", "family": "synthesia/express", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named only as a faster follow-on to distilled Express-Render. No architecture details.", "modalities": { "input": [ "audio", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Express-Render-Turbo is a faster renderer Synthesia said it would release after the distilled Express-Render model. The only public mention found is the Express-2 research page, which targets 1080p at 30fps and does not give a date. As of 2026-10-15 no launch page was found, so status is announced. Weights are closed.", "what_it_is_for": "Express-Render-Turbo is a faster renderer Synthesia said it would release after the distilled Express-Render model. The only public mention found is the Express-2 research page, which targets 1080p at 30fps and does not give a date. As of 2026-10-15 no launch page was found, so status is announced. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/express/express-video": { "id": "synthesia/express/express-video", "name": "Express-Video", "maker": "synthesia", "family": "synthesia/express", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named subsystem of Express-2: Express-Animate, Express-Eval, and Express-Render.", "modalities": { "input": [ "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Express-Video is the name Synthesia gave on 4 September 2025 to the avatar animation and rendering half of Express-2. It is not a separately billed product. It coordinates Express-Animate, Express-Eval, and Express-Render. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Express-Video is the name Synthesia gave on 4 September 2025 to the avatar animation and rendering half of Express-2. It is not a separately billed product. It coordinates Express-Animate, Express-Eval, and Express-Render. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/express/express-voice": { "id": "synthesia/express/express-voice", "name": "Express-Voice", "maker": "synthesia", "family": "synthesia/express", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage transformer: autoregressive then non-autoregressive, 800 million parameters each. Grapheme tokens conditioned on reference audio, no fixed speaker embedding. Acoustic tokens from Descript's residual vector-quantized tokenizer. Sampling is a modified RAS scheme with repetition penalty; NAR uses conservative nucleus sampling.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Express-Voice is Synthesia's in-context voice cloning model: two transformers of 800 million parameters each, conditioned on a short reference clip, with no per-speaker fine-tune. A research report written as of June 2025 ranks it first for speaker match in a blind test. The 4 September 2025 Express-2 post says it is the default text-to-speech system behind avatar performances. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Express-Voice is Synthesia's in-context voice cloning model: two transformers of 800 million parameters each, conditioned on a short reference clip, with no per-speaker fine-tune. A research report written as of June 2025 ranks it first for speaker match in a blind test. The 4 September 2025 Express-2 post says it is the default text-to-speech system behind avatar performances. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/legacy/legacy": { "id": "synthesia/legacy/legacy", "name": "Legacy avatars (V3 / Classic)", "maker": "synthesia", "family": "synthesia/legacy", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published as a network. Personal avatars from video use auto-alignment looping: the system separates talking and silence and reuses gestures from the source video. The 2024 Express-1 post contrasts older avatars as limited by pre-recorded dynamics.", "modalities": { "input": [ "text", "audio", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Legacy is Synthesia's name for its oldest stock Studio avatars and for personal avatars built from video. The library also calls that stock group Classic. The Express-2 research page shows a generation it calls V3 next to Express-1 and Express-2. The Video API still tags stock rows as version 1, 2, or 3, which this folder keeps as versions rather than as three extra products. Express-1 was announced as the fourth generation on 22 April 2024. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Legacy is Synthesia's name for its oldest stock Studio avatars and for personal avatars built from video. The library also calls that stock group Classic. The Express-2 research page shows a generation it calls V3 next to Express-1 and Express-2. The Video API still tags stock rows as version 1, 2, or 3, which this folder keeps as versions rather than as three extra products. Express-1 was announced as the fourth generation on 22 April 2024. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/personal/personal-from-photo": { "id": "synthesia/personal/personal-from-photo", "name": "Personal avatar from photo", "maker": "synthesia", "family": "synthesia/personal", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Docs call it the latest personal-avatar model and say it captures likeness from a photo so the avatar can be remixed. The 2 September 2026 picker page places personal avatars from photo under Express-2 customizable avatars, not under Express-3.", "modalities": { "input": [ "image", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Personal avatar from photo is Synthesia's recommended way to make a digital twin from a still image. Docs updated 26 August 2026 call it the latest personal model: changeable outfit and background, ready in minutes, with optional voice cloning. The 2 September 2026 avatar-picker page groups these avatars with Express-2 customizable avatars. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Personal avatar from photo is Synthesia's recommended way to make a digital twin from a still image. Docs updated 26 August 2026 call it the latest personal model: changeable outfit and background, ready in minutes, with optional voice cloning. The 2 September 2026 avatar-picker page groups these avatars with Express-2 customizable avatars. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/personal/personal-from-video": { "id": "synthesia/personal/personal-from-video", "name": "Personal avatar from video", "maker": "synthesia", "family": "synthesia/personal", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Docs call this the original personal model. Animation is auto-alignment looping: talking and silence are detected and body movement follows the script, reusing gestures from the footage.", "modalities": { "input": [ "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Personal avatar from video is Synthesia's original custom-avatar model. Docs mark it legacy: it copies appearance from footage, repeats those gestures, and is ready the next business day. The picker lists it under Legacy, next to the oldest stock Studio avatars. It was still a creation option on the docs page updated 26 August 2026. Collected 2026-10-15. Weights are closed.", "what_it_is_for": "Personal avatar from video is Synthesia's original custom-avatar model. Docs mark it legacy: it copies appearance from footage, repeats those gestures, and is ready the next business day. The picker lists it under Legacy, next to the oldest stock Studio avatars. It was still a creation option on the docs page updated 26 August 2026. Collected 2026-10-15. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/selfie/selfie-avatar": { "id": "synthesia/selfie/selfie-avatar", "name": "Selfie Avatars", "maker": "synthesia", "family": "synthesia/selfie", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "text" ], "output": [ "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Selfie Avatars was a Synthesia beta that built avatars from 5 to 20 photos of the user. The docs page, updated 2 June 2026, says the feature is retired and that any avatar not saved to My Avatars is gone. Collected 2026-10-15. Weights were closed.", "what_it_is_for": "Selfie Avatars was a Synthesia beta that built avatars from 5 to 20 photos of the user. The docs page, updated 2 June 2026, says the feature is retired and that any avatar not saved to My Avatars is gone. Collected 2026-10-15. Weights were closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/aaron": { "id": "synthesia/stock/aaron", "name": "Aaron", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aaron is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Aaron is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/ace": { "id": "synthesia/stock/ace", "name": "Ace", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ace is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ace is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/ada": { "id": "synthesia/stock/ada", "name": "Ada", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ada is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3, EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15. The 15 September 2025 update says an Express-2 Ada is available on Starter, Creator, and Enterprise. The API table does not tag any Ada row EXPRESS-2.", "what_it_is_for": "Ada is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3, EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15. The 15 September 2025 update says an Express-2 Ada is available on Starter, Creator, and Enterprise. The API table does not tag any Ada row EXPRESS-2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/agnes": { "id": "synthesia/stock/agnes", "name": "Agnes", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Agnes is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Agnes is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/aiden": { "id": "synthesia/stock/aiden", "name": "Aiden", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aiden is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 12 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Aiden is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 12 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/alex": { "id": "synthesia/stock/alex", "name": "Alex", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alex is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 15 looks as 2, 3, EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Alex is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 15 looks as 2, 3, EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/alex-beta": { "id": "synthesia/stock/alex-beta", "name": "Alex (beta)", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alex (beta) is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Alex (beta) is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/alisha": { "id": "synthesia/stock/alisha", "name": "Alisha", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alisha is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 16 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Alisha is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 16 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/alora": { "id": "synthesia/stock/alora", "name": "Alora", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alora is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Alora is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/andy": { "id": "synthesia/stock/andy", "name": "Andy", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Andy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Andy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/angela": { "id": "synthesia/stock/angela", "name": "Angela", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Angela is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Angela is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/anne-marie": { "id": "synthesia/stock/anne-marie", "name": "Anne-Marie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Anne-Marie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Anne-Marie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/anton": { "id": "synthesia/stock/anton", "name": "Anton", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Anton is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Anton is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/arielle": { "id": "synthesia/stock/arielle", "name": "Arielle", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arielle is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Arielle is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/arnold": { "id": "synthesia/stock/arnold", "name": "Arnold", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Arnold is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 7 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Arnold is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 7 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/ashley": { "id": "synthesia/stock/ashley", "name": "Ashley", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ashley is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ashley is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/ava": { "id": "synthesia/stock/ava", "name": "Ava", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ava is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ava is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/axel": { "id": "synthesia/stock/axel", "name": "Axel", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Axel is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Axel is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/barry": { "id": "synthesia/stock/barry", "name": "Barry", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Barry is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Barry is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/billie": { "id": "synthesia/stock/billie", "name": "Billie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Billie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Billie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/blake": { "id": "synthesia/stock/blake", "name": "Blake", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Blake is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 3 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Blake is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 3 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/bridget": { "id": "synthesia/stock/bridget", "name": "Bridget", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bridget is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Bridget is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/bruno": { "id": "synthesia/stock/bruno", "name": "Bruno", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bruno is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Bruno is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/cadence": { "id": "synthesia/stock/cadence", "name": "Cadence", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cadence is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Cadence is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/callum": { "id": "synthesia/stock/callum", "name": "Callum", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Callum is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Callum is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/carla": { "id": "synthesia/stock/carla", "name": "Carla", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Carla is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Carla is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/carlo": { "id": "synthesia/stock/carlo", "name": "Carlo", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Carlo is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Carlo is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/carly": { "id": "synthesia/stock/carly", "name": "Carly", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Carly is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Carly is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/carmine": { "id": "synthesia/stock/carmine", "name": "Carmine", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Carmine is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 3 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Carmine is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 3 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/caroline": { "id": "synthesia/stock/caroline", "name": "Caroline", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Caroline is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Caroline is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/carter": { "id": "synthesia/stock/carter", "name": "Carter", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Carter is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Carter is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/charles": { "id": "synthesia/stock/charles", "name": "Charles", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Charles is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Charles is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/charlotte": { "id": "synthesia/stock/charlotte", "name": "Charlotte", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Charlotte is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Charlotte is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/chauncy": { "id": "synthesia/stock/chauncy", "name": "Chauncy", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chauncy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Chauncy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/chef-collie": { "id": "synthesia/stock/chef-collie", "name": "Chef Collie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chef Collie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Chef Collie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/christina": { "id": "synthesia/stock/christina", "name": "Christina", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Christina is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Christina is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/chuck": { "id": "synthesia/stock/chuck", "name": "Chuck", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chuck is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Chuck is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/chun": { "id": "synthesia/stock/chun", "name": "Chun", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chun is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Chun is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/claudia": { "id": "synthesia/stock/claudia", "name": "Claudia", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Claudia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Claudia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/cleo": { "id": "synthesia/stock/cleo", "name": "Cleo", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cleo is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Cleo is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/clive": { "id": "synthesia/stock/clive", "name": "Clive", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Clive is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Clive is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/coach-phil": { "id": "synthesia/stock/coach-phil", "name": "Coach Phil", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Coach Phil is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Coach Phil is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/colette": { "id": "synthesia/stock/colette", "name": "Colette", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Colette is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Colette is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/cooper": { "id": "synthesia/stock/cooper", "name": "Cooper", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cooper is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Cooper is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/cyrus": { "id": "synthesia/stock/cyrus", "name": "Cyrus", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cyrus is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Cyrus is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/daisy": { "id": "synthesia/stock/daisy", "name": "Daisy", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Daisy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Daisy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/dalel": { "id": "synthesia/stock/dalel", "name": "Dalel", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dalel is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Dalel is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/dammie": { "id": "synthesia/stock/dammie", "name": "Dammie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dammie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Dammie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/daphne": { "id": "synthesia/stock/daphne", "name": "Daphne", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Daphne is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Daphne is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/daren": { "id": "synthesia/stock/daren", "name": "Daren", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Daren is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Daren is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/daria": { "id": "synthesia/stock/daria", "name": "Daria", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Daria is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Daria is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/dave": { "id": "synthesia/stock/dave", "name": "Dave", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dave is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Dave is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/debbie": { "id": "synthesia/stock/debbie", "name": "Debbie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Debbie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Debbie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/dominic": { "id": "synthesia/stock/dominic", "name": "Dominic", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dominic is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Dominic is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/dr-clara": { "id": "synthesia/stock/dr-clara", "name": "Dr Clara", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dr Clara is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Dr Clara is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/dr-dass": { "id": "synthesia/stock/dr-dass", "name": "Dr Dass", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dr Dass is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Dr Dass is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/dr-poppy": { "id": "synthesia/stock/dr-poppy", "name": "Dr Poppy", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Dr Poppy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Dr Poppy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/edgar": { "id": "synthesia/stock/edgar", "name": "Edgar", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Edgar is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Edgar is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/ella": { "id": "synthesia/stock/ella", "name": "Ella", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ella is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ella is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/ellie": { "id": "synthesia/stock/ellie", "name": "Ellie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ellie is a stock avatar Synthesia named when Express-2 launched. The 15 September 2025 update lists Ellie among the Express-2 faces. The Video API stock table updated 2026-09-11 does not include this name, so there is no official id in that list. Collected 2026-10-15.", "what_it_is_for": "Ellie is a stock avatar Synthesia named when Express-2 launched. The 15 September 2025 update lists Ellie among the Express-2 faces. The Video API stock table updated 2026-09-11 does not include this name, so there is no official id in that list. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/emma": { "id": "synthesia/stock/emma", "name": "Emma", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emma is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Emma is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/emory": { "id": "synthesia/stock/emory", "name": "Emory", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Emory is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Emory is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/eric": { "id": "synthesia/stock/eric", "name": "Eric", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Eric is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Eric is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/erica": { "id": "synthesia/stock/erica", "name": "Erica", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Erica is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Erica is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/erin": { "id": "synthesia/stock/erin", "name": "Erin", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Erin is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Erin is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/esha": { "id": "synthesia/stock/esha", "name": "Esha", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Esha is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Esha is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/ethan": { "id": "synthesia/stock/ethan", "name": "Ethan", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ethan is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ethan is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/evelyn": { "id": "synthesia/stock/evelyn", "name": "Evelyn", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Evelyn is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Evelyn is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/evie": { "id": "synthesia/stock/evie", "name": "Evie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Evie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Evie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/faisal": { "id": "synthesia/stock/faisal", "name": "Faisal", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Faisal is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Faisal is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/faiza": { "id": "synthesia/stock/faiza", "name": "Faiza", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Faiza is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Faiza is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/fazwa": { "id": "synthesia/stock/fazwa", "name": "Fazwa", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fazwa is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Fazwa is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/felix": { "id": "synthesia/stock/felix", "name": "Felix", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Felix is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Felix is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/francesca": { "id": "synthesia/stock/francesca", "name": "Francesca", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Francesca is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Francesca is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/fredo": { "id": "synthesia/stock/fredo", "name": "Fredo", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fredo is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Fredo is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/freya": { "id": "synthesia/stock/freya", "name": "Freya", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Freya is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Freya is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/gary": { "id": "synthesia/stock/gary", "name": "Gary", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gary is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Gary is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/george": { "id": "synthesia/stock/george", "name": "George", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "George is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "George is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/grace": { "id": "synthesia/stock/grace", "name": "Grace", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Grace is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Grace is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/graham": { "id": "synthesia/stock/graham", "name": "Graham", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Graham is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Graham is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/greg": { "id": "synthesia/stock/greg", "name": "Greg", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Greg is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Greg is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/gwen": { "id": "synthesia/stock/gwen", "name": "Gwen", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gwen is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Gwen is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/haiden": { "id": "synthesia/stock/haiden", "name": "Haiden", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Haiden is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Haiden is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/helen": { "id": "synthesia/stock/helen", "name": "Helen", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helen is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Helen is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/helia": { "id": "synthesia/stock/helia", "name": "Helia", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Helia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Helia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/henry": { "id": "synthesia/stock/henry", "name": "Henry", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Henry is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Henry is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/holly": { "id": "synthesia/stock/holly", "name": "Holly", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Holly is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Holly is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/hudson": { "id": "synthesia/stock/hudson", "name": "Hudson", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hudson is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Hudson is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/hugo": { "id": "synthesia/stock/hugo", "name": "Hugo", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hugo is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Hugo is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/imani": { "id": "synthesia/stock/imani", "name": "Imani", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Imani is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Imani is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/ines": { "id": "synthesia/stock/ines", "name": "Ines", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ines is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ines is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/iris": { "id": "synthesia/stock/iris", "name": "Iris", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Iris is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Iris is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/isabella": { "id": "synthesia/stock/isabella", "name": "Isabella", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Isabella is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Isabella is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jack": { "id": "synthesia/stock/jack", "name": "Jack", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jack is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 12 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jack is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 12 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jackie": { "id": "synthesia/stock/jackie", "name": "Jackie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jackie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jackie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jade": { "id": "synthesia/stock/jade", "name": "Jade", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jade is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jade is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/james": { "id": "synthesia/stock/james", "name": "James", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "James is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "James is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jason": { "id": "synthesia/stock/jason", "name": "Jason", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jason is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jason is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jaz": { "id": "synthesia/stock/jaz", "name": "Jaz", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jaz is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jaz is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jeanine": { "id": "synthesia/stock/jeanine", "name": "Jeanine", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jeanine is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jeanine is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jenna": { "id": "synthesia/stock/jenna", "name": "Jenna", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jenna is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jenna is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jenny": { "id": "synthesia/stock/jenny", "name": "Jenny", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jenny is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jenny is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jeremy": { "id": "synthesia/stock/jeremy", "name": "Jeremy", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jeremy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jeremy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jess": { "id": "synthesia/stock/jess", "name": "Jess", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jess is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jess is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jimmy": { "id": "synthesia/stock/jimmy", "name": "Jimmy", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jimmy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jimmy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jin": { "id": "synthesia/stock/jin", "name": "Jin", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jin is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jin is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jodie": { "id": "synthesia/stock/jodie", "name": "Jodie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jodie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jodie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/joel": { "id": "synthesia/stock/joel", "name": "Joel", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Joel is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Joel is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jonathan": { "id": "synthesia/stock/jonathan", "name": "Jonathan", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jonathan is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jonathan is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jonathan-beta": { "id": "synthesia/stock/jonathan-beta", "name": "Jonathan (beta)", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jonathan (beta) is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jonathan (beta) is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/jordan": { "id": "synthesia/stock/jordan", "name": "Jordan", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jordan is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Jordan is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/joshua": { "id": "synthesia/stock/joshua", "name": "Joshua", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Joshua is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15. The public avatars feature page has shown a Joshua look labeled Express-2. The API table only tags Joshua as EXPRESS-1.", "what_it_is_for": "Joshua is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15. The public avatars feature page has shown a Joshua look labeled Express-2. The API table only tags Joshua as EXPRESS-1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/julia": { "id": "synthesia/stock/julia", "name": "Julia", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Julia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Julia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/julie": { "id": "synthesia/stock/julie", "name": "Julie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Julie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Julie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/juliette": { "id": "synthesia/stock/juliette", "name": "Juliette", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Juliette is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Juliette is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/kade": { "id": "synthesia/stock/kade", "name": "Kade", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kade is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Kade is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/kaiden": { "id": "synthesia/stock/kaiden", "name": "Kaiden", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kaiden is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Kaiden is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/katie": { "id": "synthesia/stock/katie", "name": "Katie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Katie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Katie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/kayla": { "id": "synthesia/stock/kayla", "name": "Kayla", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kayla is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Kayla is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/kenneth": { "id": "synthesia/stock/kenneth", "name": "Kenneth", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kenneth is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Kenneth is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/kenzo": { "id": "synthesia/stock/kenzo", "name": "Kenzo", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kenzo is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Kenzo is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/kevin": { "id": "synthesia/stock/kevin", "name": "Kevin", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kevin is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Kevin is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/kiara": { "id": "synthesia/stock/kiara", "name": "Kiara", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kiara is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Kiara is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/kira": { "id": "synthesia/stock/kira", "name": "Kira", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kira is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Kira is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/kyle": { "id": "synthesia/stock/kyle", "name": "Kyle", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Kyle is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Kyle is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/lara": { "id": "synthesia/stock/lara", "name": "Lara", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lara is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Lara is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/laura": { "id": "synthesia/stock/laura", "name": "Laura", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Laura is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Laura is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/lauren": { "id": "synthesia/stock/lauren", "name": "Lauren", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lauren is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Lauren is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/leah": { "id": "synthesia/stock/leah", "name": "Leah", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Leah is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Leah is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/leonard": { "id": "synthesia/stock/leonard", "name": "Leonard", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Leonard is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Leonard is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/liam": { "id": "synthesia/stock/liam", "name": "Liam", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Liam is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Liam is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/lily": { "id": "synthesia/stock/lily", "name": "Lily", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lily is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Lily is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/linda": { "id": "synthesia/stock/linda", "name": "Linda", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Linda is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Linda is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/lisa": { "id": "synthesia/stock/lisa", "name": "Lisa", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lisa is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 3 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Lisa is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 3 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/logan": { "id": "synthesia/stock/logan", "name": "Logan", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Logan is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Logan is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/lois": { "id": "synthesia/stock/lois", "name": "Lois", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lois is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Lois is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/lola": { "id": "synthesia/stock/lola", "name": "Lola", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lola is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Lola is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/lorenzo": { "id": "synthesia/stock/lorenzo", "name": "Lorenzo", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lorenzo is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Lorenzo is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/luisa": { "id": "synthesia/stock/luisa", "name": "Luisa", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Luisa is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Luisa is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/luna": { "id": "synthesia/stock/luna", "name": "Luna", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Luna is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Luna is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/lydia": { "id": "synthesia/stock/lydia", "name": "Lydia", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lydia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Lydia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/maia": { "id": "synthesia/stock/maia", "name": "Maia", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Maia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Maia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/marcus": { "id": "synthesia/stock/marcus", "name": "Marcus", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marcus is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Marcus is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/marie": { "id": "synthesia/stock/marie", "name": "Marie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Marie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/mario": { "id": "synthesia/stock/mario", "name": "Mario", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mario is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Mario is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/mary": { "id": "synthesia/stock/mary", "name": "Mary", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mary is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Mary is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/mason": { "id": "synthesia/stock/mason", "name": "Mason", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mason is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Mason is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/matt": { "id": "synthesia/stock/matt", "name": "Matt", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Matt is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Matt is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/matthew": { "id": "synthesia/stock/matthew", "name": "Matthew", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Matthew is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 1, 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Matthew is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 1, 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/mia": { "id": "synthesia/stock/mia", "name": "Mia", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Mia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/michael": { "id": "synthesia/stock/michael", "name": "Michael", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Michael is a stock avatar Synthesia named when Express-2 launched. The 15 September 2025 update lists Michael among the Express-2 faces. The Video API stock table updated 2026-09-11 does not include this name, so there is no official id in that list. Collected 2026-10-15.", "what_it_is_for": "Michael is a stock avatar Synthesia named when Express-2 launched. The 15 September 2025 update lists Michael among the Express-2 faces. The Video API stock table updated 2026-09-11 does not include this name, so there is no official id in that list. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/mike": { "id": "synthesia/stock/mike", "name": "Mike", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mike is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Mike is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/molly": { "id": "synthesia/stock/molly", "name": "Molly", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Molly is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Molly is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/monica": { "id": "synthesia/stock/monica", "name": "Monica", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Monica is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Monica is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/ms-claus": { "id": "synthesia/stock/ms-claus", "name": "Ms Claus", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ms Claus is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 8 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ms Claus is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 8 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/nancy": { "id": "synthesia/stock/nancy", "name": "Nancy", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nancy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Nancy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/natalie": { "id": "synthesia/stock/natalie", "name": "Natalie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Natalie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Natalie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/nelly": { "id": "synthesia/stock/nelly", "name": "Nelly", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nelly is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Nelly is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/nicole": { "id": "synthesia/stock/nicole", "name": "Nicole", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nicole is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Nicole is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/nina": { "id": "synthesia/stock/nina", "name": "Nina", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nina is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Nina is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/nora": { "id": "synthesia/stock/nora", "name": "Nora", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nora is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Nora is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/nurse-fran": { "id": "synthesia/stock/nurse-fran", "name": "Nurse Fran", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nurse Fran is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Nurse Fran is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/officer-holly": { "id": "synthesia/stock/officer-holly", "name": "Officer Holly", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Officer Holly is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Officer Holly is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/olivia": { "id": "synthesia/stock/olivia", "name": "Olivia", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olivia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 12 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Olivia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 12 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/olu": { "id": "synthesia/stock/olu", "name": "Olu", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Olu is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Olu is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/otis": { "id": "synthesia/stock/otis", "name": "Otis", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Otis is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Otis is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/owen": { "id": "synthesia/stock/owen", "name": "Owen", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Owen is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Owen is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/paloma": { "id": "synthesia/stock/paloma", "name": "Paloma", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Paloma is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Paloma is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/parker": { "id": "synthesia/stock/parker", "name": "Parker", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Parker is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Parker is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/paul": { "id": "synthesia/stock/paul", "name": "Paul", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Paul is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Paul is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/pierre": { "id": "synthesia/stock/pierre", "name": "Pierre", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pierre is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Pierre is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/ponn": { "id": "synthesia/stock/ponn", "name": "Ponn", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ponn is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ponn is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/professor-vernon": { "id": "synthesia/stock/professor-vernon", "name": "Professor Vernon", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Professor Vernon is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Professor Vernon is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 2. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/quharrison": { "id": "synthesia/stock/quharrison", "name": "QuHarrison", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QuHarrison is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "QuHarrison is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/rayan": { "id": "synthesia/stock/rayan", "name": "Rayan", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rayan is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Rayan is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/reema": { "id": "synthesia/stock/reema", "name": "Reema", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Reema is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Reema is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/richard": { "id": "synthesia/stock/richard", "name": "Richard", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Richard is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Richard is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 1 look as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/riley": { "id": "synthesia/stock/riley", "name": "Riley", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Riley is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Riley is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/ronja": { "id": "synthesia/stock/ronja", "name": "Ronja", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ronja is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ronja is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/rory": { "id": "synthesia/stock/rory", "name": "Rory", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rory is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Rory is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/rosa": { "id": "synthesia/stock/rosa", "name": "Rosa", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rosa is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Rosa is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/rosie": { "id": "synthesia/stock/rosie", "name": "Rosie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rosie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Rosie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/ruby": { "id": "synthesia/stock/ruby", "name": "Ruby", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ruby is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Ruby is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/ryan": { "id": "synthesia/stock/ryan", "name": "Ryan", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ryan is a stock avatar Synthesia named when Express-2 launched. The 15 September 2025 update lists Ryan among the Express-2 faces. The Video API stock table updated 2026-09-11 does not include this name, so there is no official id in that list. Collected 2026-10-15.", "what_it_is_for": "Ryan is a stock avatar Synthesia named when Express-2 launched. The 15 September 2025 update lists Ryan among the Express-2 faces. The Video API stock table updated 2026-09-11 does not include this name, so there is no official id in that list. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/sally": { "id": "synthesia/stock/sally", "name": "Sally", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sally is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Sally is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/samuel": { "id": "synthesia/stock/samuel", "name": "Samuel", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Samuel is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Samuel is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/sana": { "id": "synthesia/stock/sana", "name": "Sana", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sana is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Sana is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/sang": { "id": "synthesia/stock/sang", "name": "Sang", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sang is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Sang is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/santa": { "id": "synthesia/stock/santa", "name": "Santa", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Santa is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 5 looks as 1, 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Santa is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 5 looks as 1, 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/sanvi": { "id": "synthesia/stock/sanvi", "name": "Sanvi", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sanvi is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Sanvi is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/sia": { "id": "synthesia/stock/sia", "name": "Sia", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Sia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/sonia": { "id": "synthesia/stock/sonia", "name": "Sonia", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sonia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Sonia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/srishti": { "id": "synthesia/stock/srishti", "name": "Srishti", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Srishti is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Srishti is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/sydonie": { "id": "synthesia/stock/sydonie", "name": "Sydonie", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sydonie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Sydonie is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/talia": { "id": "synthesia/stock/talia", "name": "Talia", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Talia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Talia is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as EXPRESS-1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/tammy": { "id": "synthesia/stock/tammy", "name": "Tammy", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tammy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Tammy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/theresa": { "id": "synthesia/stock/theresa", "name": "Theresa", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Theresa is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Theresa is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/thomas": { "id": "synthesia/stock/thomas", "name": "Thomas", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Thomas is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Thomas is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/tiffany": { "id": "synthesia/stock/tiffany", "name": "Tiffany", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tiffany is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Tiffany is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/tilly": { "id": "synthesia/stock/tilly", "name": "Tilly", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tilly is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Tilly is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/timothy": { "id": "synthesia/stock/timothy", "name": "Timothy", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Timothy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Timothy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/tony": { "id": "synthesia/stock/tony", "name": "Tony", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tony is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 10 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Tony is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 10 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/trent": { "id": "synthesia/stock/trent", "name": "Trent", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Trent is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Trent is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 2, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/tyra": { "id": "synthesia/stock/tyra", "name": "Tyra", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tyra is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 3 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Tyra is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 3 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/veronika": { "id": "synthesia/stock/veronika", "name": "Veronika", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Veronika is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Veronika is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/vincent": { "id": "synthesia/stock/vincent", "name": "Vincent", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vincent is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Vincent is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 2 looks as 1, 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/violet": { "id": "synthesia/stock/violet", "name": "Violet", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Violet is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Violet is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 6 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/vivienne": { "id": "synthesia/stock/vivienne", "name": "Vivienne", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vivienne is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Vivienne is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/wendy": { "id": "synthesia/stock/wendy", "name": "Wendy", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wendy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Wendy is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 2 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/will": { "id": "synthesia/stock/will", "name": "Will", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Will is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 7 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Will is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 7 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/william": { "id": "synthesia/stock/william", "name": "William", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "William is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "William is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/yasmin": { "id": "synthesia/stock/yasmin", "name": "Yasmin", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yasmin is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Yasmin is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Female and tags 1 look as 1. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/zola": { "id": "synthesia/stock/zola", "name": "Zola", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zola is a stock avatar Synthesia named when Express-2 launched. The 15 September 2025 update lists Zola among the Express-2 faces. The Video API stock table updated 2026-09-11 does not include this name, so there is no official id in that list. Collected 2026-10-15.", "what_it_is_for": "Zola is a stock avatar Synthesia named when Express-2 launched. The 15 September 2025 update lists Zola among the Express-2 faces. The Video API stock table updated 2026-09-11 does not include this name, so there is no official id in that list. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/stock/zuri": { "id": "synthesia/stock/zuri", "name": "Zuri", "maker": "synthesia", "family": "synthesia/stock", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zuri is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "what_it_is_for": "Zuri is a stock avatar name on Synthesia's Video API list (page updated 2026-09-11). The table marks it Male and tags 4 looks as 3. It is a consented presenter identity, not a separate foundation model. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "synthesia/syren/syren": { "id": "synthesia/syren/syren", "name": "Syren", "maker": "synthesia", "family": "synthesia/syren", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Product posts say Syren's agent writes the video as code and an in-house rendering engine, used for motion graphics since June 2026, plays it back live, including motion and 3D graphics. The engine's own model name was not published.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Syren is Synthesia's beta AI video agent, launched 9 October 2026. A prompt or an existing doc, image, or video becomes a finished video with motion graphics, avatars, voiceover, music, and b-roll, then chat edits. Synthesia says it runs on an in-house renderer that had been drawing motion graphics since June 2026. The agent writes the video as code and the renderer plays it live. Collected 2026-10-15. Status is preview because the product page still says beta. Weights are closed.", "what_it_is_for": "Syren is Synthesia's beta AI video agent, launched 9 October 2026. A prompt or an existing doc, image, or video becomes a finished video with motion graphics, avatars, voiceover, music, and b-roll, then chat edits. Synthesia says it runs on an in-house renderer that had been drawing motion graphics since June 2026. The agent writes the video as code and the renderer plays it live. Collected 2026-10-15. Status is preview because the product page still says beta. Weights are closed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/legacy/rbmt": { "id": "systran/legacy/rbmt", "name": "SYSTRAN RBMT", "maker": "systran", "family": "systran/legacy", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Rule-based machine translation with dictionaries and transfer rules. Historical SYSTRAN systems also added named-entity handling.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SYSTRAN RBMT is the company's rule-based translation engine. The live profiles API still lists RBMT as a techno value. Current product pages sell Pure Neural translation instead. Collected as deprecated because the name remains in the API schema and in older papers, while no 2026 install list was found.", "what_it_is_for": "SYSTRAN RBMT is the company's rule-based translation engine. The live profiles API still lists RBMT as a techno value. Current product pages sell Pure Neural translation instead. Collected as deprecated because the name remains in the API schema and in older papers, while no 2026 install list was found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/legacy/rbmt-spe": { "id": "systran/legacy/rbmt-spe", "name": "SYSTRAN RBMT+SPE", "maker": "systran", "family": "systran/legacy", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Rule-based translation followed by statistical post-editing. The profiles API lists the techno value RBMT+SPE.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SYSTRAN RBMT+SPE is the hybrid the profiles API names as one techno value: rule-based translation plus statistical post-editing. WMT 2009 scores below are submission results, not a current catalog evaluation.", "what_it_is_for": "SYSTRAN RBMT+SPE is the hybrid the profiles API names as one techno value: rule-based translation plus statistical post-editing. WMT 2009 scores below are submission results, not a current catalog evaluation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/legacy/smt": { "id": "systran/legacy/smt", "name": "SYSTRAN SMT", "maker": "systran", "family": "systran/legacy", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Statistical machine translation. The profiles API lists SMT as its own techno value, separate from SPE and from RBMT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SYSTRAN SMT is the statistical machine translation techno value in the profiles API. It is not the same token as SPE (statistical post-editing) or RBMT+SPE. No current product page markets an SMT catalog. Status is deprecated.", "what_it_is_for": "SYSTRAN SMT is the statistical machine translation techno value in the profiles API. It is not the same token as SPE (statistical post-editing) or RBMT+SPE. No current product page markets an SMT catalog. Status is deprecated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/legacy/spe": { "id": "systran/legacy/spe", "name": "SYSTRAN SPE", "maker": "systran", "family": "systran/legacy", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Statistical post-editing. In the 2007 SYSTRAN paper, SPE is a phrase-based system trained to rewrite RBMT output into the reference translation. The API also lists SPE as a techno value by itself.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SYSTRAN SPE is statistical post-editing. Papers from 2007 and 2009 use it as a second stage on RBMT output. The profiles API lists SPE as its own techno filter. No 2026 product page describes a standalone SPE catalog.", "what_it_is_for": "SYSTRAN SPE is statistical post-editing. Papers from 2007 and 2009 use it as a second stage on RBMT output. The profiles API lists SPE as its own techno filter. No 2026 product page describes a standalone SPE catalog.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-academic": { "id": "systran/pnmt/pnmt-academic", "name": "PNMT Academic", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Academic is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Academic`. A profile selects it with owner, domain, and size. Status here is active. A 2022 SYSTRAN language-catalog brochure lists Academic among domain engines.", "what_it_is_for": "PNMT Academic is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Academic`. A profile selects it with owner, domain, and size. Status here is active. A 2022 SYSTRAN language-catalog brochure lists Academic among domain engines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-aerospace": { "id": "systran/pnmt/pnmt-aerospace", "name": "PNMT Aerospace", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Aerospace is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Aerospace`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Aerospace is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Aerospace`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-agribusiness": { "id": "systran/pnmt/pnmt-agribusiness", "name": "PNMT Agribusiness", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Agribusiness is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Agribusiness`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Agribusiness is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Agribusiness`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-agriculture": { "id": "systran/pnmt/pnmt-agriculture", "name": "PNMT Agriculture", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Agriculture is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Agriculture`. A profile selects it with owner, domain, and size. Status here is active. The January 2021 industry-catalog announcement names agriculture. The Japanese product site also lists an agriculture engine.", "what_it_is_for": "PNMT Agriculture is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Agriculture`. A profile selects it with owner, domain, and size. Status here is active. The January 2021 industry-catalog announcement names agriculture. The Japanese product site also lists an agriculture engine.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-air": { "id": "systran/pnmt/pnmt-air", "name": "PNMT Air", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Air is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Air`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Air is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Air`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-all": { "id": "systran/pnmt/pnmt-all", "name": "PNMT All", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT All is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `All`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT All is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `All`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-automotive": { "id": "systran/pnmt/pnmt-automotive", "name": "PNMT Automotive", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Automotive is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Automotive`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Automotive is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Automotive`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-blog": { "id": "systran/pnmt/pnmt-blog", "name": "PNMT Blog", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Blog is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Blog`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Blog is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Blog`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-business": { "id": "systran/pnmt/pnmt-business", "name": "PNMT Business", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Business is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Business`. A profile selects it with owner, domain, and size. Status here is active. The Japanese product site lists a Business engine next to Generic.", "what_it_is_for": "PNMT Business is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Business`. A profile selects it with owner, domain, and size. Status here is active. The Japanese product site lists a Business engine next to Generic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-colloquial": { "id": "systran/pnmt/pnmt-colloquial", "name": "PNMT Colloquial", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Colloquial is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Colloquial`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Colloquial is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Colloquial`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-construction": { "id": "systran/pnmt/pnmt-construction", "name": "PNMT Construction", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Construction is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Construction`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Construction is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Construction`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-corona-crisis-corpus": { "id": "systran/pnmt/pnmt-corona-crisis-corpus", "name": "PNMT CoronaCrisisCorpus", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT CoronaCrisisCorpus is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `CoronaCrisisCorpus`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT CoronaCrisisCorpus is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `CoronaCrisisCorpus`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-culture": { "id": "systran/pnmt/pnmt-culture", "name": "PNMT Culture", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Culture is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Culture`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Culture is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Culture`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-customs": { "id": "systran/pnmt/pnmt-customs", "name": "PNMT Customs", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Customs is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Customs`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Customs is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Customs`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-dialog": { "id": "systran/pnmt/pnmt-dialog", "name": "PNMT Dialog", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Dialog is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Dialog`. A profile selects it with owner, domain, and size. Status here is active. Dialog is named in the 2019 Marketplace announcement, the 2022 brochure, and the Japanese product site.", "what_it_is_for": "PNMT Dialog is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Dialog`. A profile selects it with owner, domain, and size. Status here is active. Dialog is named in the 2019 Marketplace announcement, the 2022 brochure, and the Japanese product site.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-economics": { "id": "systran/pnmt/pnmt-economics", "name": "PNMT Economics", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Economics is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Economics`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Economics is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Economics`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-economy": { "id": "systran/pnmt/pnmt-economy", "name": "PNMT Economy", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Economy is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Economy`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Economy is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Economy`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-education": { "id": "systran/pnmt/pnmt-education", "name": "PNMT Education", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Education is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Education`. A profile selects it with owner, domain, and size. Status here is active. Education is named in the January 2021 announcement and on the Japanese product site.", "what_it_is_for": "PNMT Education is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Education`. A profile selects it with owner, domain, and size. Status here is active. Education is named in the January 2021 announcement and on the Japanese product site.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-electric": { "id": "systran/pnmt/pnmt-electric", "name": "PNMT Electric", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Electric is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Electric`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Electric is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Electric`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-electronics": { "id": "systran/pnmt/pnmt-electronics", "name": "PNMT Electronics", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Electronics is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Electronics`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Electronics is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Electronics`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-energy": { "id": "systran/pnmt/pnmt-energy", "name": "PNMT Energy", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Energy is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Energy`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Energy is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Energy`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-finance": { "id": "systran/pnmt/pnmt-finance", "name": "PNMT Finance", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Finance is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Finance`. A profile selects it with owner, domain, and size. Status here is active. Finance is named in the 2021 announcement, the 2022 brochure, the Model Studio page, and the Japanese product site.", "what_it_is_for": "PNMT Finance is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Finance`. A profile selects it with owner, domain, and size. Status here is active. Finance is named in the 2021 announcement, the 2022 brochure, the Model Studio page, and the Japanese product site.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-finance-kyc": { "id": "systran/pnmt/pnmt-finance-kyc", "name": "PNMT FinanceKYC", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT FinanceKYC is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `FinanceKYC`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT FinanceKYC is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `FinanceKYC`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-games": { "id": "systran/pnmt/pnmt-games", "name": "PNMT Games", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Games is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Games`. A profile selects it with owner, domain, and size. Status here is active. The 2022 brochure lists Gaming. The TRS token is Games. The page does not say those two strings are the same engine.", "what_it_is_for": "PNMT Games is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Games`. A profile selects it with owner, domain, and size. Status here is active. The 2022 brochure lists Gaming. The TRS token is Games. The page does not say those two strings are the same engine.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-general": { "id": "systran/pnmt/pnmt-general", "name": "PNMT General", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT General is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `General`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT General is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `General`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic": { "id": "systran/pnmt/pnmt-generic", "name": "PNMT Generic", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic`. A profile selects it with owner, domain, and size. Status here is active. The profiles API defaults the domain selector to Generic and the size selector to L. The 2016 PNMT paper is the first named production release of this line, not a description of today's weights. The developers FAQ lists 55 languages for the Translate API. That list is product-level, not a published pair matrix for Generic.", "what_it_is_for": "PNMT Generic is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic`. A profile selects it with owner, domain, and size. Status here is active. The profiles API defaults the domain selector to Generic and the size selector to L. The 2016 PNMT paper is the first named production release of this line, not a description of today's weights. The developers FAQ lists 55 languages for the Translate API. That list is product-level, not a published pair matrix for Generic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-cyrillic-script": { "id": "systran/pnmt/pnmt-generic-cyrillic-script", "name": "PNMT Generic Cyrillic-script", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic Cyrillic-script is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Cyrillic-script`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Generic Cyrillic-script is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Cyrillic-script`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-formal": { "id": "systran/pnmt/pnmt-generic-formal", "name": "PNMT Generic Formal", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic Formal is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Formal`. A profile selects it with owner, domain, and size. Status here is active. Translate Pro lists formal and informal tone as a product option. The TRS token is Generic_Formal. The docs do not define how that option maps onto this token.", "what_it_is_for": "PNMT Generic Formal is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Formal`. A profile selects it with owner, domain, and size. Status here is active. Translate Pro lists formal and informal tone as a product option. The TRS token is Generic_Formal. The docs do not define how that option maps onto this token.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-latin-script": { "id": "systran/pnmt/pnmt-generic-latin-script", "name": "PNMT Generic Latin-script", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic Latin-script is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Latin-script`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Generic Latin-script is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Latin-script`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-loca-br": { "id": "systran/pnmt/pnmt-generic-loca-br", "name": "PNMT Generic Loca BR", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic Loca BR is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Loca_BR`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Generic Loca BR is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Loca_BR`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-loca-ca": { "id": "systran/pnmt/pnmt-generic-loca-ca", "name": "PNMT Generic loca CA", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic loca CA is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_loca_CA`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Generic loca CA is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_loca_CA`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-loca-ch": { "id": "systran/pnmt/pnmt-generic-loca-ch", "name": "PNMT Generic Loca CH", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic Loca CH is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Loca_CH`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Generic Loca CH is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Loca_CH`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-loca-gb": { "id": "systran/pnmt/pnmt-generic-loca-gb", "name": "PNMT Generic Loca GB", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic Loca GB is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Loca_GB`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Generic Loca GB is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Loca_GB`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-loca-latam": { "id": "systran/pnmt/pnmt-generic-loca-latam", "name": "PNMT Generic Loca LATAM", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic Loca LATAM is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Loca_LATAM`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Generic Loca LATAM is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Loca_LATAM`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-loca-mx": { "id": "systran/pnmt/pnmt-generic-loca-mx", "name": "PNMT Generic Loca MX", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic Loca MX is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Loca_MX`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Generic Loca MX is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Loca_MX`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-loca-pt": { "id": "systran/pnmt/pnmt-generic-loca-pt", "name": "PNMT Generic Loca PT", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic Loca PT is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Loca_PT`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Generic Loca PT is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Loca_PT`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-multi-scripts": { "id": "systran/pnmt/pnmt-generic-multi-scripts", "name": "PNMT Generic Multi Scripts", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic Multi Scripts is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Multi_Scripts`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Generic Multi Scripts is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Multi_Scripts`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-pinyin": { "id": "systran/pnmt/pnmt-generic-pinyin", "name": "PNMT Generic Pinyin", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic Pinyin is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Pinyin`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Generic Pinyin is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_Pinyin`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-plus": { "id": "systran/pnmt/pnmt-generic-plus", "name": "PNMT GenericPlus", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT GenericPlus is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `GenericPlus`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT GenericPlus is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `GenericPlus`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-scl": { "id": "systran/pnmt/pnmt-generic-scl", "name": "PNMT Generic SCL", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic SCL is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_SCL`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Generic SCL is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_SCL`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-generic-zt": { "id": "systran/pnmt/pnmt-generic-zt", "name": "PNMT Generic ZT", "maker": "systran", "family": "systran/pnmt", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Generic ZT is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_ZT`. A profile selects it with owner, domain, and size. Status here is deprecated. The translation API says new XXZT models for Traditional Chinese will not be released, and later Traditional Chinese work ships as XXZH profiles. Generic_ZT is the TRS token that matches that ZT label. XXZH is a profile schema, not a separate domain on this list.", "what_it_is_for": "PNMT Generic ZT is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Generic_ZT`. A profile selects it with owner, domain, and size. Status here is deprecated. The translation API says new XXZT models for Traditional Chinese will not be released, and later Traditional Chinese work ships as XXZH profiles. Generic_ZT is the TRS token that matches that ZT label. XXZH is a profile schema, not a separate domain on this list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-guitars": { "id": "systran/pnmt/pnmt-guitars", "name": "PNMT Guitars", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Guitars is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Guitars`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Guitars is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Guitars`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-hardware": { "id": "systran/pnmt/pnmt-hardware", "name": "PNMT Hardware", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Hardware is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Hardware`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Hardware is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Hardware`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-health": { "id": "systran/pnmt/pnmt-health", "name": "PNMT Health", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Health is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Health`. A profile selects it with owner, domain, and size. Status here is active. Health is named in the January 2021 announcement and on the Japanese product site.", "what_it_is_for": "PNMT Health is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Health`. A profile selects it with owner, domain, and size. Status here is active. Health is named in the January 2021 announcement and on the Japanese product site.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-healthcare": { "id": "systran/pnmt/pnmt-healthcare", "name": "PNMT Healthcare", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Healthcare is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Healthcare`. A profile selects it with owner, domain, and size. Status here is active. The 2019 Marketplace announcement names Healthcare.", "what_it_is_for": "PNMT Healthcare is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Healthcare`. A profile selects it with owner, domain, and size. Status here is active. The 2019 Marketplace announcement names Healthcare.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-human-sciences": { "id": "systran/pnmt/pnmt-human-sciences", "name": "PNMT HumanSciences", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT HumanSciences is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `HumanSciences`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT HumanSciences is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `HumanSciences`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-instruction": { "id": "systran/pnmt/pnmt-instruction", "name": "PNMT Instruction", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Instruction is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Instruction`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Instruction is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Instruction`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-it": { "id": "systran/pnmt/pnmt-it", "name": "PNMT IT", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT IT is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `IT`. A profile selects it with owner, domain, and size. Status here is active. IT is named in the 2019 and 2021 announcements, the 2022 brochure, the Model Studio page, and the Japanese product site.", "what_it_is_for": "PNMT IT is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `IT`. A profile selects it with owner, domain, and size. Status here is active. IT is named in the 2019 and 2021 announcements, the 2022 brochure, the Model Studio page, and the Japanese product site.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-it-loca-pt": { "id": "systran/pnmt/pnmt-it-loca-pt", "name": "PNMT IT Loca PT", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT IT Loca PT is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `IT_Loca_PT`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT IT Loca PT is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `IT_Loca_PT`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-labor": { "id": "systran/pnmt/pnmt-labor", "name": "PNMT Labor", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Labor is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Labor`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Labor is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Labor`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-legal": { "id": "systran/pnmt/pnmt-legal", "name": "PNMT Legal", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Legal is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Legal`. A profile selects it with owner, domain, and size. Status here is active. Legal is named in the 2019 and 2021 announcements, the 2022 brochure, the Model Studio page, the Japanese product site, and the 2026 specialization page.", "what_it_is_for": "PNMT Legal is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Legal`. A profile selects it with owner, domain, and size. Status here is active. Legal is named in the 2019 and 2021 announcements, the 2022 brochure, the Model Studio page, the Japanese product site, and the 2026 specialization page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-legal-frca": { "id": "systran/pnmt/pnmt-legal-frca", "name": "PNMT Legal-FRCA", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Legal-FRCA is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Legal-FRCA`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Legal-FRCA is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Legal-FRCA`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-life-science": { "id": "systran/pnmt/pnmt-life-science", "name": "PNMT LifeScience", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT LifeScience is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `LifeScience`. A profile selects it with owner, domain, and size. Status here is active. The 2026 specialization page says SYSTRAN ships a Life Sciences model. The TRS lists both LifeScience and Life-sciences. The pages do not say whether those tokens are one engine or two.", "what_it_is_for": "PNMT LifeScience is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `LifeScience`. A profile selects it with owner, domain, and size. Status here is active. The 2026 specialization page says SYSTRAN ships a Life Sciences model. The TRS lists both LifeScience and Life-sciences. The pages do not say whether those tokens are one engine or two.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-life-sciences": { "id": "systran/pnmt/pnmt-life-sciences", "name": "PNMT Life-sciences", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Life-sciences is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Life-sciences`. A profile selects it with owner, domain, and size. Status here is active. The 2026 specialization page says SYSTRAN ships a Life Sciences model. The TRS lists both LifeScience and Life-sciences. The pages do not say whether those tokens are one engine or two.", "what_it_is_for": "PNMT Life-sciences is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Life-sciences`. A profile selects it with owner, domain, and size. Status here is active. The 2026 specialization page says SYSTRAN ships a Life Sciences model. The TRS lists both LifeScience and Life-sciences. The pages do not say whether those tokens are one engine or two.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-literature": { "id": "systran/pnmt/pnmt-literature", "name": "PNMT Literature", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Literature is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Literature`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Literature is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Literature`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-manual": { "id": "systran/pnmt/pnmt-manual", "name": "PNMT Manual", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Manual is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Manual`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Manual is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Manual`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-manufacturing": { "id": "systran/pnmt/pnmt-manufacturing", "name": "PNMT Manufacturing", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Manufacturing is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Manufacturing`. A profile selects it with owner, domain, and size. Status here is active. Manufacturing is named on the Japanese product site and on the 2026 specialization page.", "what_it_is_for": "PNMT Manufacturing is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Manufacturing`. A profile selects it with owner, domain, and size. Status here is active. Manufacturing is named on the Japanese product site and on the 2026 specialization page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-marketing": { "id": "systran/pnmt/pnmt-marketing", "name": "PNMT Marketing", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Marketing is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Marketing`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Marketing is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Marketing`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-mechanical": { "id": "systran/pnmt/pnmt-mechanical", "name": "PNMT Mechanical", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Mechanical is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Mechanical`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Mechanical is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Mechanical`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-medical": { "id": "systran/pnmt/pnmt-medical", "name": "PNMT Medical", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Medical is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Medical`. A profile selects it with owner, domain, and size. Status here is active. Medical is named in the 2022 brochure and on the Model Studio page (Legal, Medical, Finance, IT).", "what_it_is_for": "PNMT Medical is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Medical`. A profile selects it with owner, domain, and size. Status here is active. Medical is named in the 2022 brochure and on the Model Studio page (Legal, Medical, Finance, IT).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-medical-device": { "id": "systran/pnmt/pnmt-medical-device", "name": "PNMT Medical Device", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Medical Device is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Medical_Device`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Medical Device is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Medical_Device`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-misc": { "id": "systran/pnmt/pnmt-misc", "name": "PNMT Misc", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Misc is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Misc`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Misc is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Misc`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-news": { "id": "systran/pnmt/pnmt-news", "name": "PNMT News", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT News is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `News`. A profile selects it with owner, domain, and size. Status here is active. The 2022 brochure lists News among domain engines.", "what_it_is_for": "PNMT News is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `News`. A profile selects it with owner, domain, and size. Status here is active. The 2022 brochure lists News among domain engines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-oil-and-gas": { "id": "systran/pnmt/pnmt-oil-and-gas", "name": "PNMT Oil-and-Gas", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Oil-and-Gas is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Oil-and-Gas`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Oil-and-Gas is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Oil-and-Gas`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-oil-gas": { "id": "systran/pnmt/pnmt-oil-gas", "name": "PNMT OilGas", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT OilGas is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `OilGas`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT OilGas is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `OilGas`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-patent": { "id": "systran/pnmt/pnmt-patent", "name": "PNMT Patent", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Patent is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Patent`. A profile selects it with owner, domain, and size. Status here is active. Patent is named in the 2022 brochure and on the Japanese product site.", "what_it_is_for": "PNMT Patent is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Patent`. A profile selects it with owner, domain, and size. Status here is active. Patent is named in the 2022 brochure and on the Japanese product site.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-pharma": { "id": "systran/pnmt/pnmt-pharma", "name": "PNMT Pharma", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Pharma is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Pharma`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Pharma is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Pharma`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-public-transport": { "id": "systran/pnmt/pnmt-public-transport", "name": "PNMT PublicTransport", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT PublicTransport is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `PublicTransport`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT PublicTransport is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `PublicTransport`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-religion": { "id": "systran/pnmt/pnmt-religion", "name": "PNMT Religion", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Religion is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Religion`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Religion is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Religion`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-report": { "id": "systran/pnmt/pnmt-report", "name": "PNMT Report", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Report is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Report`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Report is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Report`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-retail": { "id": "systran/pnmt/pnmt-retail", "name": "PNMT Retail", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Retail is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Retail`. A profile selects it with owner, domain, and size. Status here is active. The 2019 Marketplace announcement names Retail.", "what_it_is_for": "PNMT Retail is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Retail`. A profile selects it with owner, domain, and size. Status here is active. The 2019 Marketplace announcement names Retail.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-science-technology": { "id": "systran/pnmt/pnmt-science-technology", "name": "PNMT ScienceTechnology", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT ScienceTechnology is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `ScienceTechnology`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT ScienceTechnology is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `ScienceTechnology`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-technical": { "id": "systran/pnmt/pnmt-technical", "name": "PNMT Technical", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Technical is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Technical`. A profile selects it with owner, domain, and size. Status here is active. The 2022 brochure lists Technical among domain engines.", "what_it_is_for": "PNMT Technical is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Technical`. A profile selects it with owner, domain, and size. Status here is active. The 2022 brochure lists Technical among domain engines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-tourism": { "id": "systran/pnmt/pnmt-tourism", "name": "PNMT Tourism", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Tourism is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Tourism`. A profile selects it with owner, domain, and size. Status here is active. Tourism is named in the 2019 Marketplace announcement and the 2022 brochure.", "what_it_is_for": "PNMT Tourism is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Tourism`. A profile selects it with owner, domain, and size. Status here is active. Tourism is named in the 2019 Marketplace announcement and the 2022 brochure.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-weather-forecast": { "id": "systran/pnmt/pnmt-weather-forecast", "name": "PNMT WeatherForecast", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT WeatherForecast is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `WeatherForecast`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT WeatherForecast is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `WeatherForecast`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-web": { "id": "systran/pnmt/pnmt-web", "name": "PNMT Web", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Web is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Web`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Web is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Web`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/pnmt/pnmt-website": { "id": "systran/pnmt/pnmt-website", "name": "PNMT Website", "maker": "systran", "family": "systran/pnmt", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pure Neural Machine Translation (PNMT). Size classes S, M, L, and XL are different technologies. Layer counts and parameter counts are not published for this domain.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PNMT Website is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Website`. A profile selects it with owner, domain, and size. Status here is active.", "what_it_is_for": "PNMT Website is a closed SYSTRAN neural translation domain. Model Studio lists the Translation Resources Store token `Website`. A profile selects it with owner, domain, and size. Status here is active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/wmt/eurollm-9b-cpt-ft-wmt25-en-ja": { "id": "systran/wmt/eurollm-9b-cpt-ft-wmt25-en-ja", "name": "EuroLLM-9B CPT FT WMT25 EN-JA", "maker": "systran", "family": "systran/wmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-07-10", "context_length": 4096, "max_output_tokens": null, "parameters": "9152319488", "architecture": "LlamaForCausalLM fine-tune of utter-project/EuroLLM-9B. config.json: 42 layers, hidden size 4096, 32 heads, 8 key-value heads, intermediate size 12288, SiLU, RoPE theta 10000, vocab size 128000, bfloat16. max_position_embeddings is 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroLLM-9B CPT FT WMT25 EN-JA is a SYSTRAN open-weight English-to-Japanese translation model. Hugging Face lists it as a continued pretraining (CPT) fine-tune of EuroLLM-9B for the WMT 2025 general task. Weights were created on 2025-07-10 (last modified 2025-07-10T12:36:09.000Z). The November 2025 system paper says the submission ensembles the CPT and SFT models. License apache-2.0. Safetensors total is 9152319488 BF16 parameters. The card rounds this to 9B.", "what_it_is_for": "EuroLLM-9B CPT FT WMT25 EN-JA is a SYSTRAN open-weight English-to-Japanese translation model. Hugging Face lists it as a continued pretraining (CPT) fine-tune of EuroLLM-9B for the WMT 2025 general task. Weights were created on 2025-07-10 (last modified 2025-07-10T12:36:09.000Z). The November 2025 system paper says the submission ensembles the CPT and SFT models. License apache-2.0. Safetensors total is 9152319488 BF16 parameters. The card rounds this to 9B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "systran/wmt/eurollm-9b-sft-ft-wmt25-en-ja": { "id": "systran/wmt/eurollm-9b-sft-ft-wmt25-en-ja", "name": "EuroLLM-9B SFT FT WMT25 EN-JA", "maker": "systran", "family": "systran/wmt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-07-10", "context_length": 4096, "max_output_tokens": null, "parameters": "9152319488", "architecture": "LlamaForCausalLM fine-tune of utter-project/EuroLLM-9B. config.json: 42 layers, hidden size 4096, 32 heads, 8 key-value heads, intermediate size 12288, SiLU, RoPE theta 10000, vocab size 128000, bfloat16. max_position_embeddings is 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EuroLLM-9B SFT FT WMT25 EN-JA is a SYSTRAN open-weight English-to-Japanese translation model. Hugging Face lists it as a supervised fine-tuning (SFT) fine-tune of EuroLLM-9B for the WMT 2025 general task. Weights were created on 2025-07-10 (last modified 2025-07-10T13:01:15.000Z). The November 2025 system paper says the submission ensembles the CPT and SFT models. License apache-2.0. Safetensors total is 9152319488 BF16 parameters. The card rounds this to 9B.", "what_it_is_for": "EuroLLM-9B SFT FT WMT25 EN-JA is a SYSTRAN open-weight English-to-Japanese translation model. Hugging Face lists it as a supervised fine-tuning (SFT) fine-tune of EuroLLM-9B for the WMT 2025 general task. Weights were created on 2025-07-10 (last modified 2025-07-10T13:01:15.000Z). The November 2025 system paper says the submission ensembles the CPT and SFT models. License apache-2.0. Safetensors total is 9152319488 BF16 parameters. The card rounds this to 9B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sysu/cellfm/cellfm": { "id": "sysu/cellfm/cellfm", "name": "CellFM", "maker": "sysu", "family": "sysu/cellfm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-ND 4.0", "release_date": "2024-12-17", "context_length": null, "max_output_tokens": null, "parameters": "800000000", "architecture": "Value-projection single-cell foundation model. The Nature Communications paper says the core is an embedding module, stacked ERetNet layers, and LoRA. Each ERetNet layer has gated multi-head attention, a simple gated linear unit, and layer normalization. ERetNet is a linear-complexity RetNet variant. Training used MindSpore on four Huawei Atlas 800 servers with eight Ascend 910 NPUs each. The GitHub Config class, not the paper prose, sets enc_dims 1536, enc_nlayers 40, enc_num_heads 48, enc_dropout 0.1, nonz_len 2048, mask_len 2048, and filt_len 200. A separate Config_80M class uses 2 encoder layers.", "modalities": { "input": [ "gene-expression" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CellFM is a single-cell transcriptomic foundation model from Yuedong Yang's group at Sun Yat-sen University, released with MindSpore weights and a Hugging Face card. Chongqing University, Jinfeng Laboratory, Huawei, and Singleron are named as joint partners in the university's research note and the Nature Communications author record. The weights were public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card and paper say CellFM is pretrained to represent human cells and to support cell-type annotation, perturbation-response prediction, gene-function prediction, and gene-network analysis. It is a value-projection model: it recovers embeddings of masked genes from their expression values.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "sysu/ecg-lfm/ecg-lfm": { "id": "sysu/ecg-lfm/ecg-lfm", "name": "ECG-LFM", "maker": "sysu", "family": "sysu/ecg-lfm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-04-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-supervised 12-lead ECG model. A convolutional feature encoder makes tokens, a quantizer makes context vectors and discrete IDs, and conformer blocks predict masked context. Losses are contextual contrastive, masked language modeling, and multi-segment contrastive. The paper says the feature encoder yields 1024-dimensional embeddings and that about 49% of sample tokens are masked. The repo config ECGLFM_600m_librivox.yaml sets model name ECGLFM, 24 conformer layers, embed 1024, FFN 4096, 16 heads, final_dim 768, latent_vars 1024, latent_dim 1024, mask_prob 0.65, and mask_length 10. The 600m and librivox tokens are in the filename only. The paper does not state a parameter count.", "modalities": { "input": [ "ecg" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ECG-LFM is a self-supervised electrocardiogram foundation model from Sun Yat-sen University. The Nature Communications paper lists Siying Lin, Qifan Wu, and Yuedong Yang at the School of Computer Science and Engineering, with clinical coauthors at Sun Yat-sen Memorial Hospital. The code and a Zenodo checkpoint link were public on 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says ECG-LFM is pretrained so the same model can support cardiovascular-disease prediction and later genetic analysis of ECG-derived features. The released model maps a 12-lead ECG to a 1024-dimensional embedding. Disease scores in the paper come from fine-tuning or a linear probe, not from a hosted classifier.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "szu/chatflow/chatflow-13b": { "id": "szu/chatflow/chatflow-13b", "name": "Linly-ChatFlow-13B", "maker": "szu", "family": "szu/chatflow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0 on the Hub card. The README says LLaMA-1 weights are research-only and not for commercial use, and points to the original LLaMA license request.", "release_date": "2023-05-14", "context_length": 1024, "max_output_tokens": null, "parameters": "13B", "architecture": "Instruction-tuned Chinese dialogue model on the LLaMA-13B Linly line. TencentPretrain binary chatflow_13b.bin plus tokenizer.model. No config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Linly-ChatFlow-13B is the 13B Chinese dialogue model from Shenzhen University's Linly project. It is an instruction tune of the LLaMA-1 13B line. v1.1 weights remain public. This dossier was collected on 2026-10-15.", "what_it_is_for": "Chinese instruction following and dialogue, trained on about 5 million public instruction examples. The README prints side-by-side answers with ChatFlow-7B. Those samples are illustrations, not a score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "szu/chatflow/chatflow-7b": { "id": "szu/chatflow/chatflow-7b", "name": "Linly-ChatFlow-7B", "maker": "szu", "family": "szu/chatflow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0 on the Hub card. The README says LLaMA-1 weights are research-only and not for commercial use, and points to the original LLaMA license request.", "release_date": "2023-05-14", "context_length": 1024, "max_output_tokens": null, "parameters": "7B", "architecture": "Instruction-tuned Chinese dialogue model on the LLaMA-7B Linly line. Weights are a TencentPretrain binary (chatflow_7b.bin) plus tokenizer.model. No config.json is in the repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Linly-ChatFlow-7B is Shenzhen University's 7B Chinese dialogue model in the Linly project. It is an instruction tune of the LLaMA-1 Chinese line. v1.1 weights remain public. This dossier was collected on 2026-10-15.", "what_it_is_for": "Chinese instruction following and dialogue. The download table says it was trained on about 5 million instruction examples. The README shows it answering commonsense, logic, knowledge, and coding prompts, and says those behaviors are still weak.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "szu/chinese-falcon/chinese-falcon-7b": { "id": "szu/chinese-falcon/chinese-falcon-7b", "name": "Chinese-Falcon-7B", "maker": "szu", "family": "szu/chinese-falcon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0. The README says commercial use is allowed.", "release_date": "2023-06-15", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "RWForCausalLM continued pretrain of Falcon with an expanded Chinese vocabulary. config.json: 32 layers, hidden size 4544, 71 heads, multi-query attention, parallel attention, vocabulary 90046, float16, no alibi. tokenizer model_max_length 2048. Custom modeling files configuration_RW.py and modelling_RW.py are in the repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese-Falcon-7B is Shenzhen University's Chinese continued pretrain of Falcon-7B. v0.2 weights are public under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "A Chinese base model. The maker expanded the Falcon vocabulary and continued training on Chinese and English text, about 50G in the download table. The card's example is a User/Bot chat transcript.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "szu/chinese-llama/chinese-llama-13b": { "id": "szu/chinese-llama/chinese-llama-13b", "name": "Chinese-LLaMA-13B", "maker": "szu", "family": "szu/chinese-llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0 on the Hub card. The README says LLaMA-1 weights are research-only and not for commercial use, and points to the original LLaMA license request.", "release_date": "2023-05-29", "context_length": 2048, "max_output_tokens": null, "parameters": "13B", "architecture": "Continued pretraining of LLaMA-13B. Weights are a TencentPretrain binary (chinese_llama_13b.bin) plus tokenizer.model. No config.json is in the repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese-LLaMA-13B is the 13B LLaMA-1 continued-pretrain from Shenzhen University's Linly project. v1.2 weights remain public in TencentPretrain format. This dossier was collected on 2026-10-15.", "what_it_is_for": "A Chinese base model. The maker continued LLaMA-13B on about 100G of general Chinese and Chinese-English text. ChatFlow-13B is the instruction-tuned model on top of this line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "szu/chinese-llama/chinese-llama-33b": { "id": "szu/chinese-llama/chinese-llama-33b", "name": "Chinese-LLaMA-33B", "maker": "szu", "family": "szu/chinese-llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The Linly README puts this checkpoint under the LLaMA-1 GPL-3.0 research-only note. The Linly-AI Hub card does not set a license field.", "release_date": "2023-04-27", "context_length": 2048, "max_output_tokens": null, "parameters": "33B", "architecture": "LlamaForCausalLM continued pretrain. config.json: 60 layers, hidden size 6656, 52 attention heads, intermediate size 17920, vocabulary 32000, float16, max_position_embeddings 2048. The vocabulary size matches original LLaMA, not the expanded LLaMA-2 Linly vocabularies.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese-LLaMA-33B is the largest LLaMA-1 continued-pretrain in the Linly index from Shenzhen University. v1.0 was announced on 2023-04-27. Hugging Face weights remain public. This dossier was collected on 2026-10-15.", "what_it_is_for": "A Chinese base model. The download table says it was continued from LLaMA on about 30G of general text. The school page describes the Lingli line as 7B, 13B, and 30B; the released checkpoint is named 33B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "szu/chinese-llama/chinese-llama-7b": { "id": "szu/chinese-llama/chinese-llama-7b", "name": "Chinese-LLaMA-7B", "maker": "szu", "family": "szu/chinese-llama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0 on the Hub card. The README says LLaMA-1 weights are research-only and not for commercial use, and points to the original LLaMA license request.", "release_date": "2023-05-29", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "Continued pretraining of LLaMA-7B. Weights are a TencentPretrain binary (chinese_llama_7b.bin) plus tokenizer.model. No config.json is in the repo, so layer counts are not restated here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese-LLaMA-7B is the 7B LLaMA-1 continued-pretrain in the Linly line from the Computer Vision Institute at Shenzhen University. The current published weights are v1.2. They remain on Hugging Face. This dossier was collected on 2026-10-15.", "what_it_is_for": "The maker describes a Chinese base model: LLaMA weights and the LLaMA vocabulary, continued on Chinese and Chinese-English parallel text so English capability transfers to Chinese. It is the base for Linly-ChatFlow-7B, not the chat model itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "szu/chinese-llama-2/chinese-llama-2-13b": { "id": "szu/chinese-llama-2/chinese-llama-2-13b", "name": "Chinese-LLaMA-2-13B", "maker": "szu", "family": "szu/chinese-llama-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The Linly license section says pre-trained weights are GPL-3.0. This Hub card does not set a license field. Upstream is LLaMA-2; the Llama 2 community license is not restated on the card.", "release_date": "2023-08-12", "context_length": 2048, "max_output_tokens": null, "parameters": "13B", "architecture": "LlamaForCausalLM continued pretrain of LLaMA-2-13B with an expanded Chinese vocabulary. config.json: 40 layers, hidden size 5120, 40 query heads, 40 key-value heads, intermediate size 13824, vocabulary 40076, float16, max_position_embeddings 2048, silu. Card tags include zh and en.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese-LLaMA-2-13B is Shenzhen University's 13B continued pretrain of LLaMA-2 in the Linly line. v0.2 is listed as both a language model and a dialogue model. Weights remain public. This dossier was collected on 2026-10-15.", "what_it_is_for": "Chinese and English generation and instruction-style replies after mixed-corpus continued training and Chinese vocabulary expansion. The card uses the same `", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "szu/chinese-llama-2/chinese-llama-2-70b": { "id": "szu/chinese-llama-2/chinese-llama-2-70b", "name": "Chinese-LLaMA-2-70B", "maker": "szu", "family": "szu/chinese-llama-2", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "The Linly license section says pre-trained weights are GPL-3.0. This Hub card does not set a license field. Upstream is LLaMA-2; the Llama 2 community license is not restated on the card.", "release_date": "2024-01-03", "context_length": 4096, "max_output_tokens": null, "parameters": "70B", "architecture": "Listed as a LLaMA-2 70B-class language model. The Hub repository has no config.json, tokenizer, or index. It is not possible to confirm layer counts from the uploaded files.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese-LLaMA-2-70B is the base 70B row in Shenzhen University's Linly LLaMA-2 table. The project lists it as a language model updated on 2024-01-03. The linked Hugging Face tree is incomplete. This dossier was collected on 2026-10-15.", "what_it_is_for": "The download table calls it a language model trained on mixed corpus with sequence length 4096. It is a different row from the February 2024 chat release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "szu/chinese-llama-2/chinese-llama-2-70b-chat": { "id": "szu/chinese-llama-2/chinese-llama-2-70b-chat", "name": "Chinese-LLaMA-2-70B-Chat", "maker": "szu", "family": "szu/chinese-llama-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "No license field and no README in the Hub repo. The Linly project license section says pre-trained weights are GPL-3.0. APUS press posts call the release open source and do not quote a license name.", "release_date": "2024-02-04", "context_length": 4096, "max_output_tokens": null, "parameters": "70B", "architecture": "LlamaForCausalLM. config.json name linly-llama2-70b-v0.1: 80 layers, hidden size 8192, 64 query heads, 8 key-value heads, intermediate size 28672, vocabulary 56360, bfloat16, max_position_embeddings 4096, silu, rms norm epsilon 1e-5. Grouped-query attention, unlike the 7B and 13B Linly LLaMA-2 configs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese-LLaMA-2-70B-Chat is the 70B Chinese LLaMA-2 checkpoint Shenzhen University released with APUS on 2024-02-04. The Linly news links this Hugging Face repo. There is no model card in the repo. Weights are still listed. This dossier was collected on 2026-10-15.", "what_it_is_for": "A Chinese large language model. APUS posts call it APUS large-model 3.0 Lingli, the first open model of that APUS generation, aimed at Chinese tasks. The repository name says chat. The config name is `linly-llama2-70b-v0.1`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "szu/chinese-llama-2/chinese-llama-2-7b": { "id": "szu/chinese-llama-2/chinese-llama-2-7b", "name": "Chinese-LLaMA-2-7B", "maker": "szu", "family": "szu/chinese-llama-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The Linly license section says pre-trained weights are GPL-3.0. This Hub card does not set a license field. Upstream is LLaMA-2; the Llama 2 community license is not restated on the card.", "release_date": "2023-07-22", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "LlamaForCausalLM continued pretrain of LLaMA-2-7B with an expanded Chinese vocabulary. config.json: 32 layers, hidden size 4096, 32 query heads, 32 key-value heads, intermediate size 11008, vocabulary 40076, float16, max_position_embeddings 2048, silu.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Chinese-LLaMA-2-7B is Shenzhen University's 7B continued pretrain of LLaMA-2, with an expanded Chinese vocabulary. The Linly index lists v0.1 as both a language model and a dialogue model. Weights remain public. This dossier was collected on 2026-10-15.", "what_it_is_for": "Chinese and English text generation and instruction-style replies. The card says it was incrementally trained on mixed Chinese-English text and that Chinese characters were added to the vocabulary. The recommended prompt is `", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "szu/mg-motionllm/mg-motionllm-base": { "id": "szu/mg-motionllm/mg-motionllm-base", "name": "MG-MotionLLM GSPretrained-base", "maker": "szu", "family": "szu/mg-motionllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the Hub card.", "release_date": "2025-09-12", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "T5ForConditionalGeneration initialized from google-t5/t5-base, per config _name_or_path. 12 encoder layers, 12 decoder layers, d_model 768, d_ff 3072, 12 heads, vocabulary 32616, float32, n_positions 512. Vocabulary is larger than stock T5.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MG-MotionLLM GSPretrained-base is the base Granularity-Synergy T5 checkpoint from MG-MotionLLM, a CVPR 2025 motion-language model led at Shenzhen University's Computer Vision Institute. Apache-2.0 weights were uploaded to a personal Hugging Face account. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this is the base granularity-synergy pretrain inside MG-MotionLLM. The paper uses it, with a motion VQ-VAE, for text-to-motion, motion captioning, and finer body-part descriptions. The GitHub page says the pretrain covers 28 motion-related tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "szu/mg-motionllm/mg-motionllm-large": { "id": "szu/mg-motionllm/mg-motionllm-large", "name": "MG-MotionLLM GSPretrained-large", "maker": "szu", "family": "szu/mg-motionllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the Hub card.", "release_date": "2025-09-24", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "T5ForConditionalGeneration initialized from google-t5/t5-large, per config _name_or_path. 24 encoder layers, 24 decoder layers, d_model 1024, d_ff 4096, 16 heads, vocabulary 32616, float32, n_positions 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MG-MotionLLM GSPretrained-large is the large Granularity-Synergy T5 checkpoint from MG-MotionLLM, a CVPR 2025 motion-language model led at Shenzhen University's Computer Vision Institute. Apache-2.0 weights were uploaded to a personal Hugging Face account. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this is the large granularity-synergy pretrain inside MG-MotionLLM. The paper uses it, with a motion VQ-VAE, for text-to-motion, motion captioning, and finer body-part descriptions. The GitHub page says the pretrain covers 28 motion-related tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "szu/mg-motionllm/mg-motionllm-small": { "id": "szu/mg-motionllm/mg-motionllm-small", "name": "MG-MotionLLM GSPretrained-small", "maker": "szu", "family": "szu/mg-motionllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 on the Hub card.", "release_date": "2025-09-24", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "T5ForConditionalGeneration. config.json: 6 encoder layers, 6 decoder layers, d_model 512, d_ff 2048, 8 heads, vocabulary 32616, float32, n_positions 512. _name_or_path is GSPretrained-small/checkpoint-56000/. The shape matches T5-small. The card does not print a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MG-MotionLLM GSPretrained-small is the small Granularity-Synergy T5 checkpoint from MG-MotionLLM, a CVPR 2025 motion-language model led at Shenzhen University's Computer Vision Institute. Apache-2.0 weights were uploaded to a personal Hugging Face account. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says this is the small granularity-synergy pretrain inside MG-MotionLLM. The paper uses it, with a motion VQ-VAE, for text-to-motion, motion captioning, and finer body-part descriptions. The GitHub page says the pretrain covers 28 motion-related tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "szu/openllama/openllama-13b": { "id": "szu/openllama/openllama-13b", "name": "Linly-OpenLLaMA-13B", "maker": "szu", "family": "szu/openllama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0. The README says commercial use is allowed. This checkpoint is trained from scratch, so the LLaMA weight license does not apply to these weights.", "release_date": "2023-05-29", "context_length": 2048, "max_output_tokens": null, "parameters": "13B", "architecture": "LLaMA-shaped model trained from scratch. Weights are a TencentPretrain binary (openllama_13b.bin) plus a SentencePiece model open_llama.model. No config.json. The wiki says vocabulary 50000, sequence length 2048, batch size 4096, learning rate 3e-4, cosine schedule, weight decay 0.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Linly-OpenLLaMA-13B is a from-scratch Chinese-English model from Shenzhen University's Linly project. It uses a LLaMA-style architecture and its own tokenizer. v0.1 weights are public under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "A base language model trained from scratch on Chinese and English text, with a word-and-character tokenizer aimed at Chinese. It is not a continued pretrain of Meta LLaMA weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tampere/mad-twinnet/mad-twinnet": { "id": "tampere/mad-twinnet/mad-twinnet", "name": "MaD TwinNet", "maker": "tampere", "family": "tampere/mad-twinnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tampere University of Technology non-commercial experimental license (copyright 2017)", "release_date": "2018-02-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Masker-Denoiser (MaD) with TwinNet regularization, used only while training. Masker: STFT magnitude, trim to 744 bins (about 8 kHz), bidirectional GRU encoder with residual connections, forward GRU decoder, ReLU feed-forward mask, skip-filtering. Denoiser: two feed-forward networks in a denoising-autoencoder layout with shared weights over time. GRUs. Waveform via Griffin-Lim using the mixture phase. The paper adds each GRU state to an F-dimensional frame with F=744, so that state width is 744. Input is 44.1 kHz.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MaD TwinNet is a monaural singing-voice separator released by the Audio Research Group at Tampere University of Technology, which became part of Tampere University in 2019. Pretrained weights published on 2018-02-02 are still on Zenodo. Co-authors are at Fraunhofer IDMT / TU Ilmenau and MILA. Collected 2026-10-15.", "what_it_is_for": "The paper separates singing voice from a single-channel music mixture. It extends the Masker-Denoiser architecture with TwinNet, a backward GRU used only as a training regularizer so the forward decoder models longer musical structure. The group still points at the GitHub repository and the Zenodo weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tamu/orbevo/orbevo-dm": { "id": "tamu/orbevo/orbevo-dm", "name": "OrbEvo-DM", "maker": "tamu", "family": "tamu/orbevo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-03-03", "context_length": null, "max_output_tokens": null, "parameters": "27977056", "architecture": "EquiformerV2 graph transformer with SO(2) electric-field conditioning. Electronic states are separate graphs that share geometry. OrbEvo-DM builds density-matrix features by tensor contraction and feeds them into equivariant attention. Six layers; the first two use lmax 4 and the later four use lmax 2. The reported checkpoint samples 8 electronic states during training (DM-s8). Time bundle size is 8. Sphere channels 128, 8 attention heads, cutoff 5.0.", "modalities": { "input": [ "molecular-structure", "wavefunction", "electric-field" ], "output": [ "wavefunction" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OrbEvo-DM is a Texas A&M DIVE Lab model that rolls real-time TDDFT wavefunction coefficients forward in time. It is the density-matrix variant of OrbEvo. The paper was submitted on 2026-03-03 and revised on 2026-05-07. Weights are public. Collected 2026-10-15.", "what_it_is_for": "The paper uses OrbEvo to replace the expensive time-stepping of real-time TDDFT. OrbEvo-DM aggregates occupied electronic states into a density matrix and conditions an equivariant graph transformer on that matrix, which matches how the time-dependent Kohn-Sham Hamiltonian depends on the density. Dipole moments and absorption spectra are computed from the predicted coefficients. They are not separate supervised targets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tamu/orbevo/orbevo-wf": { "id": "tamu/orbevo/orbevo-wf", "name": "OrbEvo-WF", "maker": "tamu", "family": "tamu/orbevo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-03-03", "context_length": null, "max_output_tokens": null, "parameters": "26963360", "architecture": "EquiformerV2 graph transformer with SO(2) electric-field conditioning. OrbEvo-WF mixes electronic states by average pooling after each local block, then a global transformer block, broadcast back to each state. Six local blocks and five global blocks. The reported checkpoint uses all electronic states (WF-sall). Time bundle size is 8. Sphere channels 128, 8 attention heads, cutoff 5.0.", "modalities": { "input": [ "molecular-structure", "wavefunction", "electric-field" ], "output": [ "wavefunction" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OrbEvo-WF is a Texas A&M DIVE Lab model that rolls real-time TDDFT wavefunction coefficients forward in time. It is the wavefunction-pooling variant of OrbEvo. The paper was submitted on 2026-03-03 and revised on 2026-05-07. Weights are public. Collected 2026-10-15.", "what_it_is_for": "The paper uses OrbEvo to approximate the time evolution of Kohn-Sham wavefunctions under a uniform electric field. OrbEvo-WF keeps each occupied electronic state as its own graph and mixes them by pooling after each transformer block. The authors report that this is less faithful to the density functional than OrbEvo-DM, and the QM9 dipole and absorption errors are higher.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tamu/orbflow/orbflow": { "id": "tamu/orbflow/orbflow", "name": "OrbFlow", "maker": "tamu", "family": "tamu/orbflow", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-09-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SE(3)-equivariant flow-matching model that predicts Gaussian-type orbital coefficients, and from them the electron density. The QM9 runs use EquiformerV3 with 8 layers and lmax 6, trained 75k endpoint-density steps then 450k integrated-density steps. The MD runs use a slim EquiformerV3 (channels 96/192/96), 3 layers, lmax 3, trained 10k then 110k steps. It builds on the SCDP code base. Released files are best-validation checkpoints with optimizer state removed.", "modalities": { "input": [ "molecular-structure" ], "output": [ "orbital-coefficients", "electron-density" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OrbFlow is a Texas A&M DIVE Lab model that predicts Gaussian-type orbital coefficients and the electron density by SE(3)-equivariant flow matching. Weights and code are public under MIT. Collected 2026-10-15. The Hugging Face repo was created on 2026-09-29; the paper was submitted on 2026-10-02.", "what_it_is_for": "The lab uses OrbFlow as a DFT surrogate. It samples a probability path over atom-centered orbital coefficients instead of regressing density on a grid, then reconstructs the electron density. The stated uses are density prediction and a better starting point for self-consistent field calculations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/estbert/estbert": { "id": "tartu/estbert/estbert", "name": "EstBERT", "maker": "tartu", "family": "tartu/estbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2020-11-09", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT Base, cased, trained from the Google BERT cased initialization on Estonian. The EstBERT_NER config, which is a fine-tune of this checkpoint, lists hidden size 768, 12 layers, 12 heads, intermediate size 3072, vocabulary 50,000, and max position embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EstBERT is a cased BERT Base model trained by the University of Tartu NLP group (TartuNLP) on the Estonian National Corpus 2017. Weights are public under CC-BY-4.0. Collected 2026-10-15. It is a fill-mask encoder, not a chat model.", "what_it_is_for": "The card presents it as a language-specific pretrained encoder for Estonian and points to stronger part-of-speech, morphology, NER, and rubric results than mBERT and XLM-RoBERTa on the authors' comparisons. Sentiment is the exception in that table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/estbert/estbert-ner": { "id": "tartu/estbert/estbert-ner", "name": "EstBERT NER", "maker": "tartu", "family": "tartu/estbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2020-11-09", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForTokenClassification on EstBERT. Labels in the published config are B-LOC, B-ORG, B-PER, I-LOC, I-ORG, I-PER, and O.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EstBERT NER is TartuNLP's named-entity tagger fine-tuned from EstBERT. It is an active open-weight model under CC-BY-4.0. Collected 2026-10-15. The TartuNLP NER API repository says the service is based on this model.", "what_it_is_for": "The card says it recognizes locations, organizations, and persons in Estonian, trained on the Estonian NER dataset of Tkachenko et al.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/estbert/estbert-ner-v2": { "id": "tartu/estbert/estbert-ner-v2", "name": "EstBERT NER v2", "maker": "tartu", "family": "tartu/estbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2022-05-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of EstBERT for token classification. The card's test table uses DATE, EVENT, GPE, LOC, MONEY, ORG, PER, PERCENT, PROD, TIME, and TITLE.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EstBERT NER v2 is TartuNLP's later Estonian NER model, fine-tuned from EstBERT on two Estonian NER corpora that share one annotation scheme. Weights are public under CC-BY-4.0. Collected 2026-10-15. It is not a drop-in label match for the three-type EstBERT NER model.", "what_it_is_for": "The card says it finds named entities in Estonian text. TartuNLP does not guarantee usefulness and says it is not responsible for generated results.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/estllm/apertus-estllm-8b": { "id": "tartu/estllm/apertus-estllm-8b", "name": "Apertus-EstLLM-8B", "maker": "tartu", "family": "tartu/estllm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-23", "context_length": 4096, "max_output_tokens": null, "parameters": "8B", "architecture": "Continued pretraining of swiss-ai/Apertus-8B-2509 on the same about-35B-token mix as Llama-3.1-EstLLM-8B. Sequence length 4096. Checkpoint id Apertus-EstLLM-8B-1125.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apertus-EstLLM-8B is TartuNLP and TalTechNLP's Estonian continued pretraining of swiss-ai Apertus 8B. The published base checkpoint is Apertus-EstLLM-8B-1125. Weights are public under Apache-2.0. Hub createdAt is 2026-01-23. Collected 2026-10-15. It is a base model, not the instruct merge.", "what_it_is_for": "Base model for Estonian adaptation of Apertus 8B, using the EstLLM continued-pretraining mix. Developed by TartuNLP and TalTechNLP. Funded by the Estonian Ministry of Education and Research, Estonian Language Technology Program 2018-2027.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/estllm/apertus-estllm-8b-instruct": { "id": "tartu/estllm/apertus-estllm-8b-instruct", "name": "Apertus-EstLLM-8B-Instruct", "maker": "tartu", "family": "tartu/estllm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-25", "context_length": 4096, "max_output_tokens": null, "parameters": "8B", "architecture": "Instruction-tuned Apertus-EstLLM-8B. Current checkpoint 0326 is a chat-vector merge of Apertus-EstLLM-8B-Instruct-1125 and swiss-ai/Apertus-8B-Instruct-2509. One sentence on the 0326 card says Llama-3.1-EstLLM-8B-Instruct-0326. The repo id and the tables are Apertus.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Apertus-EstLLM-8B-Instruct is the chat model in TartuNLP and TalTechNLP's Apertus Estonian line. The current checkpoint is Apertus-EstLLM-8B-Instruct-0326. Weights are public under Apache-2.0. Hub createdAt for 0326 is 2026-03-25. Collected 2026-10-15. The 1125 instruct checkpoint is the previous version.", "what_it_is_for": "Estonian and English instruction following after Apertus continued pretraining. Developed by TartuNLP and TalTechNLP. Funded by the Estonian Ministry of Education and Research, Estonian Language Technology Program 2018-2027.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/estllm/llama-3-1-estllm-70b": { "id": "tartu/estllm/llama-3-1-estllm-70b", "name": "Llama-3.1-EstLLM-70B", "maker": "tartu", "family": "tartu/estllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2026-08-17", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Continued pretraining of Llama-3.1-70B on about 60B tokens. The card does not itemize that mix. The checkpoint id is Llama-3.1-EstLLM-70B-0826.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3.1-EstLLM-70B is TartuNLP's larger EstLLM base model, a continued pretraining of Llama 3.1 70B. The Hub repo Llama-3.1-EstLLM-70B-0826 was created 2026-08-17. Collected 2026-10-15. Weights are public under the Llama 3.1 community license. The card's limitations section is still marked TODO.", "what_it_is_for": "Base Estonian continued-pretraining checkpoint at the 70B size. The instruct model is a separate folder. Developed by TartuNLP and TalTechNLP. Funded by the Estonian Ministry of Education and Research, Estonian Language Technology Program 2018-2027.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/estllm/llama-3-1-estllm-70b-instruct": { "id": "tartu/estllm/llama-3-1-estllm-70b-instruct", "name": "Llama-3.1-EstLLM-70B-Instruct", "maker": "tartu", "family": "tartu/estllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2026-08-17", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "SFT and DPO after the Llama-3.1-EstLLM-70B continued pretraining on about 60B tokens. The card says inference needs over 140GB of VRAM. Checkpoint id Llama-3.1-EstLLM-70B-Instruct-0826.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3.1-EstLLM-70B-Instruct is the instruction-tuned 70B EstLLM model from TartuNLP. Hub createdAt is 2026-08-17. Collected 2026-10-15. Weights are public under the Llama 3.1 community license. The card says inference needs over 140GB of VRAM, and the limitations section is TODO.", "what_it_is_for": "Estonian and English instruction following at the 70B size, after the 60B-token continued pretraining. Developed by TartuNLP and TalTechNLP. Funded by the Estonian Ministry of Education and Research, Estonian Language Technology Program 2018-2027.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/estllm/llama-3-1-estllm-8b": { "id": "tartu/estllm/llama-3-1-estllm-8b", "name": "Llama-3.1-EstLLM-8B", "maker": "tartu", "family": "tartu/estllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2025-12-20", "context_length": 4096, "max_output_tokens": null, "parameters": "8B", "architecture": "Continued pretraining of meta-llama/Llama-3.1-8B. Continued pretraining for one epoch on about 35B tokens: Estonian National Corpus 8.6B, Python-Edu 3.3B, FineMath4-Plus 9.5B, General Instruction-Augmented Corpora 7.4B, Cosmopedia v2 6.9B. Dataset tags include HuggingFaceTB/smollm-corpus, finemath, and instruction-pretrain/general-instruction-augmented-corpora. Sequence length during continued pretraining is 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3.1-EstLLM-8B is TartuNLP and TalTechNLP's Estonian continued-pretraining of Llama 3.1 8B. The published base checkpoint is Llama-3.1-EstLLM-8B-0525. Weights are public under the Llama 3.1 community license. Hub createdAt is 2025-12-20. Collected 2026-10-15. It is a base model for fine-tuning, not a chat model.", "what_it_is_for": "The card says this is a base text-completion model for downstream fine-tuning, not for direct chat. Developed by TartuNLP and TalTechNLP. Funded by the Estonian Ministry of Education and Research, Estonian Language Technology Program 2018-2027.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/estllm/llama-3-1-estllm-8b-instruct": { "id": "tartu/estllm/llama-3-1-estllm-8b-instruct", "name": "Llama-3.1-EstLLM-8B-Instruct", "maker": "tartu", "family": "tartu/estllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2025-11-28", "context_length": 4096, "max_output_tokens": null, "parameters": "8B", "architecture": "Instruction model on the Llama-3.1-EstLLM-8B continued pretraining. Current checkpoint 1125 is a chat-vector merge of Llama-3.1-EstLLM-8B-Instruct-0825 and meta-llama/Llama-3.1-8B-Instruct. SFT used about 764k examples, about 80 percent English, from the Tulu 3 SFT mixture, EuroBlocks, and EKI data. DPO used English-only nvidia/HelpSteer3.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-3.1-EstLLM-8B-Instruct is the chat model in TartuNLP and TalTechNLP's Llama 3.1 8B Estonian line. The current checkpoint is Llama-3.1-EstLLM-8B-Instruct-1125, a chat-vector merge. Weights are public under the Llama 3.1 community license. Hub createdAt for 1125 is 2025-11-28. Collected 2026-10-15.", "what_it_is_for": "Estonian and English instruction following after continued pretraining. The 0825 checkpoint remains published and is the previous version. Developed by TartuNLP and TalTechNLP. Funded by the Estonian Ministry of Education and Research, Estonian Language Technology Program 2018-2027.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/estroberta/estroberta": { "id": "tartu/estroberta/estroberta", "name": "EstRoBERTa", "maker": "tartu", "family": "tartu/estroberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2022-08-04", "context_length": null, "max_output_tokens": null, "parameters": "0.3B", "architecture": "Continued or fine-tuned from XLM-RoBERTa base. The card does not say whether only the vocabulary or the full model was trained.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EstRoBERTa is TartuNLP's Estonian encoder, released on Hugging Face as a fine-tune of XLM-RoBERTa base under CC-BY-4.0. Collected 2026-10-15. The card is mostly a template. It is not the EMBEDDIA est-roberta checkpoint.", "what_it_is_for": "The filled-in card fields name Estonian feature extraction. Direct-use text is still the template phrase More Information Needed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/glilem/glilem": { "id": "tartu/glilem/glilem", "name": "GLiLEM", "maker": "tartu", "family": "tartu/glilem", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-09-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Contextualized Estonian lemmatizer. The card cites a fine-tune setup on urchade/gliner_multi-v2.1 and the Estonian UD data. Repo tartuNLP/glilem-vabamorf-disambiguator.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLiLEM is TartuNLP's Estonian lemmatizer, released as tartuNLP/glilem-vabamorf-disambiguator. Hub createdAt is 2024-09-26. Collected 2026-10-15. The license is Apache-2.0. The card is mostly the paper citation. No score was printed there.", "what_it_is_for": "Contextualized lemmatization for Estonian, using a GLiNER-style model and Vabamorf-related disambiguation as named by the repo and the paper title.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/gpt-est/gpt-for-est-base": { "id": "tartu/gpt-est/gpt-for-est-base", "name": "GPT-for-EST base", "maker": "tartu", "family": "tartu/gpt-est", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-02", "context_length": 1024, "max_output_tokens": null, "parameters": "118.68M", "architecture": "GPT-2 base trained from scratch. 12 layers, 12 heads, embedding size 768, context 1024. Card total size 118.68M parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-for-EST base is a University of Tartu GPT-2 model trained from scratch on Estonian. The Hub repo was created 2022-03-02. Collected 2026-10-15. The card says it was previously named gpt-4-est-base and was renamed to avoid click-baiting.", "what_it_is_for": "Estonian text generation. A domain tag is prepended at training time and should be used as a prefix: >general<, >web<, >news<, >doaj<, or >wiki<.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/gpt-est/gpt-for-est-large": { "id": "tartu/gpt-est/gpt-for-est-large", "name": "GPT-for-EST large", "maker": "tartu", "family": "tartu/gpt-est", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-02", "context_length": 1024, "max_output_tokens": null, "parameters": "723.58M", "architecture": "GPT-2 large trained from scratch. 24 layers, 24 heads, embedding size 1536, context 1024. Card total size 723.58M parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPT-for-EST large is the larger University of Tartu GPT-2 trained from scratch on the same Estonian mix as the base model. Hub repo created 2022-03-02. Collected 2026-10-15. It was previously named gpt-4-est-large.", "what_it_is_for": "Estonian text generation with the same domain prefixes as the base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/llammas/llammas": { "id": "tartu/llammas/llammas", "name": "Llammas", "maker": "tartu", "family": "tartu/llammas", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-11-29", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Instruction-tuned Llama-2-7B. Stage one is Llammas-base (5B CulturaX tokens). Stage two mixes Alpaca-cleaned, Alpaca-est translated with gpt-3.5-turbo-0613, OASST1 top-1 English, 10k CoT, 10k FLAN-V2, WMT18 en-et dev, and held-out MTee general en-et.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llammas is TartuNLP's instruction-tuned Estonian and English Llama-2-7B model. Weights are public under the Llama 2 community license. The Hub repo was created 2023-11-29. Collected 2026-10-15. A card update on 2025-05-26 says it is no longer the strongest open-weight Estonian model and points readers to later Tartu work.", "what_it_is_for": "Estonian and English instruction following and chat, from the two-stage Llammas recipe. The card still publishes the weights after that 2025 update.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/llammas/llammas-base": { "id": "tartu/llammas/llammas-base", "name": "Llammas-base", "maker": "tartu", "family": "tartu/llammas", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-02-16", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Continued pretraining of Llama-2-7B on 5B CulturaX tokens, about 75 percent Estonian and 25 percent English. This is the first stage of Llammas. The instruction-tuned Llammas checkpoint is a separate folder.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llammas-base is the University of Tartu continued-pretraining checkpoint that the Llammas instruction model is built on. Weights are public under the Llama 2 community license. Hub createdAt is 2024-02-16. Collected 2026-10-15. It is a base completion model.", "what_it_is_for": "The Llammas card describes this stage as Estonian-and-English continued pretraining before instruction tuning. It is the checkpoint to start from for further fine-tuning of that recipe.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/llammas/llammas-translate": { "id": "tartu/llammas/llammas-translate", "name": "Llammas-translate", "maker": "tartu", "family": "tartu/llammas", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-12-20", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Three-stage Llama-2-7B. Same continued pretraining as Llammas-base, then about 1M en-to-et pairs (CCMatrix 500k, WikiMatrix 400k, Europarl 50k, OpenSubtitles 50k) with 25 percent reversed to et-to-en, then the same instruction mix as Llammas.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llammas-translate is TartuNLP's translation-adapted Llammas model. It is a separate three-stage product, not a dated snapshot of the chat model. Hub createdAt is 2023-12-20. Collected 2026-10-15. Weights are public under the Llama 2 community license.", "what_it_is_for": "English-Estonian translation with the instruction mix still applied afterward. The card's usage snippet loads tartuNLP/Llammas, which is the chat model rather than this repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/mtee/mtee-crisis": { "id": "tartu/mtee/mtee-crisis", "name": "MTee crisis", "maker": "tartu", "family": "tartu/mtee", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Domain checkpoint of the 2021 TartuNLP and Tilde MTee platform. Directions on the cards: et-en, en-et, et-de, de-et, et-ru, and ru-et. Modular encoder and decoder per language, TartuNLP fairseq fork. Files named on the cards include modular_model.pt, sp-model.{lang}.model, and dict.{lang}.txt.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTee crisis is a University of Tartu and Tilde machine-translation checkpoint from the MTee platform. Hub createdAt is 2022-03-30. Collected 2026-10-15. Weights are public. The card does not name a license. It is one of four domain models, not a dated snapshot of a single MTee weight.", "what_it_is_for": "Crisis-domain translation. The card says the domain is mostly healthcare-related. Same six directions as the other MTee checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/mtee/mtee-general": { "id": "tartu/mtee/mtee-general", "name": "MTee general", "maker": "tartu", "family": "tartu/mtee", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Domain checkpoint of the 2021 TartuNLP and Tilde MTee platform. Directions on the cards: et-en, en-et, et-de, de-et, et-ru, and ru-et. Modular encoder and decoder per language, TartuNLP fairseq fork. Files named on the cards include modular_model.pt, sp-model.{lang}.model, and dict.{lang}.txt.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTee general is a University of Tartu and Tilde machine-translation checkpoint from the MTee platform. Hub createdAt is 2022-03-28. Collected 2026-10-15. Weights are public. The card does not name a license. It is one of four domain models, not a dated snapshot of a single MTee weight.", "what_it_is_for": "General-domain translation among Estonian, English, German, and Russian. Built by TartuNLP and Tilde.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/mtee/mtee-legal": { "id": "tartu/mtee/mtee-legal", "name": "MTee legal", "maker": "tartu", "family": "tartu/mtee", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Domain checkpoint of the 2021 TartuNLP and Tilde MTee platform. Directions on the cards: et-en, en-et, et-de, de-et, et-ru, and ru-et. Modular encoder and decoder per language, TartuNLP fairseq fork. Files named on the cards include modular_model.pt, sp-model.{lang}.model, and dict.{lang}.txt.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTee legal is a University of Tartu and Tilde machine-translation checkpoint from the MTee platform. Hub createdAt is 2022-03-30. Collected 2026-10-15. Weights are public. The card does not name a license. It is one of four domain models, not a dated snapshot of a single MTee weight.", "what_it_is_for": "Legal-domain translation among Estonian, English, German, and Russian. Same six directions as the other MTee checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/mtee/mtee-military": { "id": "tartu/mtee/mtee-military", "name": "MTee military", "maker": "tartu", "family": "tartu/mtee", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Domain checkpoint of the 2021 TartuNLP and Tilde MTee platform. Directions on the cards: et-en, en-et, et-de, de-et, et-ru, and ru-et. Modular encoder and decoder per language, TartuNLP fairseq fork. Files named on the cards include modular_model.pt, sp-model.{lang}.model, and dict.{lang}.txt.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTee military is a University of Tartu and Tilde machine-translation checkpoint from the MTee platform. Hub createdAt is 2022-03-30. Collected 2026-10-15. Weights are public. The card does not name a license. It is one of four domain models, not a dated snapshot of a single MTee weight.", "what_it_is_for": "Military-domain text translation among Estonian, English, German, and Russian. Same six directions and the same modular fairseq layout as the other MTee checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/neurokone/neurokone": { "id": "tartu/neurokone/neurokone", "name": "Neurok\u00f5ne", "maker": "tartu", "family": "tartu/neurokone", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Estonian text-to-speech product. GitHub TartuNLP/text-to-speech-worker ships several generations. The current app listing describes FastSpeech2 with HiFi-GAN. The worker v3 tree includes model_weights.hdf5. v3.1.0 is described as the latest release and uses a unified vocoder download. LJSpeech and VCTK HiFi-GAN vocoders in the v3 notes are third-party jik876 checkpoints, not Tartu models.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Neurok\u00f5ne is the University of Tartu Estonian speech synthesizer, distributed as an app and as the text-to-speech-worker releases. Collected 2026-10-15. It is a public product rather than a single Hugging Face model card. Release years were not printed on the GitHub release page that was read, so the release date is empty. The license for the current weights was not restated.", "what_it_is_for": "Estonian text to speech. The v1 release names six speakers: Mari, Kalev, Albert, Vesta, K\u00fclli, and Meelis. The app listing says 10 speakers and names FastSpeech2 with HiFi-GAN. A demo is at https://neurokone.ee/. The app listing mentions an API on the neurokone site.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/septilang/septilang": { "id": "tartu/septilang/septilang", "name": "Septilang", "maker": "tartu", "family": "tartu/septilang", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One modular fairseq model for all directions among Estonian, English, German, Russian, Finnish, Lithuanian, and Latvian. Same file layout as MTee: modular_model.pt, per-language sentencepiece models, and dictionaries.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Septilang is TartuNLP's seven-language modular translation model. Hub createdAt is 2022-03-28. Collected 2026-10-15. Weights are public. The card does not name a license. It is compatible with the TartuNLP translation API and MTee workers.", "what_it_is_for": "Many-to-many translation among Estonian, English, German, Russian, Finnish, Lithuanian, and Latvian.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/smugri/llama-smugri-7b": { "id": "tartu/smugri/llama-smugri-7b", "name": "Llama-SMUGRI-7B", "maker": "tartu", "family": "tartu/smugri", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-05-31", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Continued pretraining of meta-llama/Llama-2-7b-hf for V\u00f5ro, Komi, and Livonian. The instruct card and the TartuNLP/smugri-llm repository name this base. A raw README fetch for the base repo returned Entry not found.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-SMUGRI-7B is TartuNLP's base model for V\u00f5ro, Komi, and Livonian, continued from Llama-2-7B. Hub createdAt is 2024-05-31. Collected 2026-10-15. The base README did not load. The identity is taken from the instruct card and from https://github.com/TartuNLP/smugri-llm. License text was not on the instruct card.", "what_it_is_for": "Base model for extremely low-resource Finno-Ugric continued pretraining. Instruction mixes are a separate folder. The paper focuses on V\u00f5ro, Livonian, and Komi.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/smugri/llama-smugri-7b-instruct": { "id": "tartu/smugri/llama-smugri-7b-instruct", "name": "Llama-SMUGRI-7B-Instruct", "maker": "tartu", "family": "tartu/smugri", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-03", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Instruction-tuned Llama-SMUGRI-7B. Four named mixes, none marked as the single current checkpoint: MTI is SupInst+TrAlpaca, MTI-Tr adds TrInst, LLMTI is SupInst+LLMTrAlpaca, LLMTI-Tr adds TrInst. The MTI card was the one read in full. It cites transformers 4.36.2.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama-SMUGRI-7B-Instruct is TartuNLP's instruction-tuned line for V\u00f5ro, Komi, and Livonian. Four Hub repos from 2024-06-03 are versions of one product. Collected 2026-10-15. The MTI card was read. It does not state a license. No mix is singled out as current.", "what_it_is_for": "Instruction following in V\u00f5ro, Livonian, and Komi, with supporting instructions in Estonian, Finnish, English, and Russian. Alpaca-style instructions for the focus languages were translated with Neurot\u00f5lge.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/smugri/m2m100-418m-smugri": { "id": "tartu/smugri/m2m100-418m-smugri", "name": "m2m100-418M-smugri", "maker": "tartu", "family": "tartu/smugri", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-04-04", "context_length": null, "max_output_tokens": null, "parameters": "418M", "architecture": "Fine-tune of facebook/m2m100-418M. Added language codes liv, vro, sma, and sme. Covered languages on the card: V\u00f5ro, Livonian, North S\u00e1mi, South S\u00e1mi, Estonian, Finnish, Latvian, and English.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "m2m100-418M-smugri is TartuNLP's 2022 many-to-many translation model for V\u00f5ro, Livonian, and North and South S\u00e1mi, fine-tuned from Facebook's m2m100-418M. Hub createdAt is 2022-04-04. Collected 2026-10-15. The license is MIT.", "what_it_is_for": "Neural machine translation among the languages listed on the card, including the added codes liv, vro, sma, and sme.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/synest/synest": { "id": "tartu/synest/synest", "name": "SynEst", "maker": "tartu", "family": "tartu/synest", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-03-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "fairseq model with a frozen NLLB-1.3B encoder and a new smaller decoder per target language. From Estonian into German, English, Finnish, Russian, Ukrainian, and Chinese. Into Estonian from Arabic, German, English, Finnish, French, Lithuanian, Latvian, Russian, Spanish, Swedish, Ukrainian, and Chinese. The frozen encoder can translate from other NLLB languages at lower quality. Run via TartuNLP/translation-worker branch nllb-based-est.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SynEst is TartuNLP's modular Estonian-centric translation model on a frozen NLLB-1.3B encoder. The Hub repo is tartuNLP/synest-models, created 2024-03-21. Collected 2026-10-15. Weights are public. The card does not name a license. The evaluation section on the card is commented out.", "what_it_is_for": "Translation into and out of Estonian for the language lists on the card. The full model is not claimed to be 1.3B parameters. Only the frozen encoder is the NLLB-1.3B encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/towers/tahetorn-9b": { "id": "tartu/towers/tahetorn-9b", "name": "T\u00e4hetorn 9B", "maker": "tartu", "family": "tartu/towers", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-30", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Fine-tune of an Unbabel Tower+ model. The paper says Tower+ 9B. The Hugging Face org listing displayed this repo as 9B. Hugging Face tags on the empty card include gemma2. The model-card body is the generic template and cites arXiv 1910.09700, which is the model-card paper, not this work.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "T\u00e4hetorn 9B is one of the two University of Tartu translation models in The Two Towers (EAMT 2026). Weights are on tartuNLP/Tahetorn_9B, created 2025-12-30. Collected 2026-10-15. The Hub card is an empty template. Identity and the size class come from the paper. No license is stated on the card.", "what_it_is_for": "Estonian-centric and Finno-Ugric machine translation. The abstract describes open-weight models for Estonian and related languages. The individual language list was not transcribed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/towers/tigutorn-2b": { "id": "tartu/towers/tigutorn-2b", "name": "Tigutorn 2B", "maker": "tartu", "family": "tartu/towers", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-30", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Fine-tune of an Unbabel Tower+ model. The paper says Tower+ 2B. The Hugging Face org listing displayed this repo as 3B. The paper's 2B is the size class stored here. Hugging Face tags on the empty card include gemma2. The model-card body is the generic template and cites arXiv 1910.09700, which is the model-card paper, not this work.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tigutorn 2B is one of the two University of Tartu translation models in The Two Towers (EAMT 2026). Weights are on tartuNLP/Tigutorn_2B, created 2025-12-30. Collected 2026-10-15. The Hub card is an empty template. Identity and the size class come from the paper. No license is stated on the card.", "what_it_is_for": "Estonian-centric and Finno-Ugric machine translation. The abstract describes open-weight models for Estonian and related languages. The individual language list was not transcribed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/xtts/xtts-v2-est": { "id": "tartu/xtts/xtts-v2-est", "name": "XTTS-v2-est", "maker": "tartu", "family": "tartu/xtts", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Coqui Public Model License", "release_date": "2025-07-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "XTTS v2 fine-tuned on Estonian data. The card's short description is that sentence. Speaker-reference conditioning is how upstream XTTS v2 works. This card does not restate the input schema.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XTTS-v2-est is TartuNLP's Estonian fine-tune of XTTS v2. Hub createdAt is 2025-07-10. Collected 2026-10-15. The license tag is the Coqui Public Model License. The card text is a short description rather than a full training report.", "what_it_is_for": "Estonian speech synthesis from the XTTS v2 fine-tune described on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tartu/xtts/xtts-v2-multi": { "id": "tartu/xtts/xtts-v2-multi", "name": "XTTS-v2-multi", "maker": "tartu", "family": "tartu/xtts", "status": "active", "confidence": "low", "weights": "open-weights", "license": "", "release_date": "2026-02-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hub repo tartuNLP/XTTS-v2-multi. The README that was read repeats the Estonian XTTS short description and does not describe a multilingual model. The license tag is other.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XTTS-v2-multi is a TartuNLP Hub repo created 2026-02-12. Collected 2026-10-15. Confidence is low because the card that was read does not say what multi refers to. The license tag is other and does not name a license text.", "what_it_is_for": "The repo name implies a multi-speaker or multilingual XTTS v2 fine-tune. The card text that was read does not state that. It is kept as a named weight release and not described beyond the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tau/mdm/mdm": { "id": "tau/mdm/mdm", "name": "MDM", "maker": "tau", "family": "tau/mdm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-09-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Classifier-free diffusion model that predicts the clean motion sample rather than the noise. The reported text-to-motion backbone is an encoder-only transformer with 8 layers and latent size 512. Text is encoded by a frozen CLIP ViT-B/32. Action-to-motion uses a learned class embedding. Trained with T=1000 cosine noising steps. Later checkpoints use 50 steps. Motion is a sequence of joint positions or rotations. The README caps a sampled text-to-motion clip at 9.8 seconds.", "modalities": { "input": [ "text", "motion" ], "output": [ "motion" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MDM, the Human Motion Diffusion Model, is a text-to-motion and action-to-motion generator from Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H. Bermano at Tel Aviv University. It is a transformer diffusion model that predicts the clean motion instead of the noise. The preprint is 29 September 2022, revised 3 October 2022. Pretrained weights shipped in the MIT repository on 6 October 2022. ICLR 2023 ran 1-5 May 2023. This dossier was collected on 15 October 2026.", "what_it_is_for": "The project page says one model family covers text-to-motion, action-to-motion, unconditioned generation, in-betweening, and body-part edits. Text conditioning uses a frozen CLIP text encoder. Sampling and training instructions are at https://github.com/GuyTevet/motion-diffusion-model. The page is https://guytevet.github.io/mdm-page/.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tau/sled/bart-base-sled": { "id": "tau/sled/bart-base-sled", "name": "BART-base SLED", "maker": "tau", "family": "tau/sled", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-08-07", "context_length": 16384, "max_output_tokens": null, "parameters": "139M", "architecture": "SLED wrapper around facebook/bart-base. The input is split into overlapping chunks of 256 tokens (window fraction 0.5 in the Hub config). Each chunk is encoded independently and the pretrained decoder fuses them. The paper's reported maximum input length is 16K tokens. The short backbone is not pretrained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BART-base SLED is a long-document encoder-decoder from Maor Ivgi, Uri Shaham, and Jonathan Berant at Tel Aviv University. SLED splits a long input into overlapping chunks, encodes each chunk with a short pretrained model, and fuses the chunks in the decoder. This checkpoint wraps Facebook BART-base. The Hub card `tau/bart-base-sled` is dated 7 August 2022 and is MIT licensed. The preprint was revised 27 December 2022. TACL published it on 22 March 2023. This dossier was collected on 15 October 2026.", "what_it_is_for": "The card says the raw model can fill in text, and that it is mostly meant to be fine-tuned on a supervised long-text task such as summarization, classification, or question answering. The paper evaluates it on SCROLLS. The card is https://huggingface.co/tau/bart-base-sled. The code is https://github.com/Mivg/SLED.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tau/sled/bart-large-sled": { "id": "tau/sled/bart-large-sled", "name": "BART-large SLED", "maker": "tau", "family": "tau/sled", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-08-10", "context_length": 16384, "max_output_tokens": null, "parameters": "406M", "architecture": "SLED wrapper around facebook/bart-large. Hub config: chunk context_size 256, window_fraction 0.5, dynamic sliding, prefix prepended and encoded. The paper reports this model at a 16K-token input. The official README mistakenly lists the Bart-Large card name as tau/bart-base-sled. The Hub id is tau/bart-large-sled.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BART-large SLED is the 406M long-document model from Maor Ivgi, Uri Shaham, and Jonathan Berant at Tel Aviv University. It is the same SLED chunk-and-fuse method as BART-base SLED, wrapped around Facebook BART-large. The Hub repo `tau/bart-large-sled` was created on 10 August 2022. The revised preprint is 27 December 2022, and TACL published the paper on 22 March 2023. This dossier was collected on 15 October 2026.", "what_it_is_for": "Long-document summarization, QA, and classification by fine-tuning a short BART model inside SLED. On the SCROLLS test average in the paper, this is the strongest SLED row that was read. The config is https://huggingface.co/tau/bart-large-sled. The code is https://github.com/Mivg/SLED.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tau/sled/t5-v1-1-base-sled": { "id": "tau/sled/t5-v1-1-base-sled", "name": "T5-v1.1-base SLED", "maker": "tau", "family": "tau/sled", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-08-08", "context_length": 16384, "max_output_tokens": null, "parameters": "220M", "architecture": "SLED wrapper around google/t5-v1_1-base. Hub config: context_size 256, window_fraction 0.5, dynamic sliding, prefix prepended and encoded. The paper reports a 16K-token input. The short T5 model is not pretrained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "T5-v1.1-base SLED is the T5-backed long-document model from Maor Ivgi, Uri Shaham, and Jonathan Berant at Tel Aviv University. SLED encodes overlapping chunks with Google's T5 v1.1 base and fuses them in the decoder. The Hub repo `tau/t5-v1_1-base-sled` was created on 8 August 2022. The folder name uses hyphens for the dots in v1.1. The revised preprint is 27 December 2022. This dossier was collected on 15 October 2026.", "what_it_is_for": "The same long-text fine-tuning setup as BART SLED, here with a T5 v1.1 base backbone. The paper reports it on SCROLLS at a 16K input. The config is https://huggingface.co/tau/t5-v1_1-base-sled. The code is https://github.com/Mivg/SLED.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tau/sled/t5-v1-1-large-sled": { "id": "tau/sled/t5-v1-1-large-sled", "name": "T5-v1.1-large SLED", "maker": "tau", "family": "tau/sled", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-08-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SLED wrapper around google/t5-v1_1-large. Hub config: context_size 256, window_fraction 0.5, dynamic sliding, prefix prepended and encoded. The SCROLLS table that was read reports T5-base SLED, not this large checkpoint. A 16K input is the setting used for the reported SLED models and is not restated for this repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "T5-v1.1-large SLED is a Tel Aviv University SLED backbone from Maor Ivgi, Uri Shaham, and Jonathan Berant. The Hub repo `tau/t5-v1_1-large-sled` was created on 10 August 2022. Its config points at Google's T5 v1.1 large and uses the same 256-token chunk settings as the other SLED cards. The folder name uses hyphens for the dots in v1.1. The SCROLLS table that was read does not score this checkpoint. This dossier was collected on 15 October 2026.", "what_it_is_for": "The GitHub README lists it as one of the four backbone cards for long-text SLED models. It is a fine-tuning starting point, not a reported SCROLLS system in the table that was read. The config is https://huggingface.co/tau/t5-v1_1-large-sled.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tau/spider/spider": { "id": "tau/spider/spider", "name": "Spider", "maker": "tau", "family": "tau/spider", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2021-12-14", "context_length": 512, "max_output_tokens": null, "parameters": "110M", "architecture": "Dense passage retriever initialized from uncased BERT-base. 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocabulary 30,522, max position embeddings 512. The released unsupervised checkpoint shares one encoder for queries and passages. The representation is the [CLS] token. Passages are formatted as title [SEP] text, and token type ids are all zeros.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spider is an unsupervised dense passage retriever from Ori Ram, Gal Shachaf, Omer Levy, Jonathan Berant, and Amir Globerson at the Blavatnik School of Computer Science, Tel Aviv University. The name in the paper is span-based unsupervised dense retriever. The public checkpoint is `tau/spider`. The preprint is 14 December 2021, revised 17 May 2022, and the paper is NAACL 2022. The code is MIT. This dossier was collected on 15 October 2026.", "what_it_is_for": "The paper trains a retriever with recurring spans across passages in one document, with no labeled question-passage pairs. The card says the same encoder is used for the query and the passage. It is meant for open-domain retrieval, including as an initialization for supervised DPR-style training. The card is https://huggingface.co/tau/spider. The code is https://github.com/oriram/spider.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tau/splinter/splinter-base": { "id": "tau/splinter/splinter-base", "name": "Splinter-base", "maker": "tau", "family": "tau/splinter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2021-01-02", "context_length": 512, "max_output_tokens": null, "parameters": "110M", "architecture": "Case-sensitive encoder-only transformer, same capacity as BERT-base / SpanBERT-base: 12 layers, hidden size 768, 12 heads, intermediate size 3072, cased wordpiece vocabulary 28,996, max position embeddings 512. Pretrained with recurring span selection (RSS). A question-aware span selection (QASS) layer picks the answer span. This checkpoint does not store pretrained QASS weights; that layer is randomly initialized on load.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Splinter-base is a case-sensitive English encoder for few-shot extractive question answering from the Blavatnik School of Computer Science at Tel Aviv University (Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, and Omer Levy). The paper header also names the Allen Institute for AI, and the Transformers copyright line names Tel Aviv University and AllenAI. The weights are the Apache-2.0 checkpoint `tau/splinter-base`. The Hugging Face docs date the release to 2 January 2021. The ACL paper is August 2021. This dossier was collected on 15 October 2026.", "what_it_is_for": "The card says the prime use is few-shot extractive QA. Pretraining uses only raw text. Recurring span selection masks all but one copy of a repeated n-gram and asks the model to point at the remaining span. At fine-tuning, the question is followed by a `[QUESTION]` token, and the QASS layer selects the answer span. The card and code are at https://huggingface.co/tau/splinter-base and https://github.com/oriram/splinter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tau/splinter/splinter-large": { "id": "tau/splinter/splinter-large", "name": "Splinter-large", "maker": "tau", "family": "tau/splinter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Case-sensitive encoder-only Splinter model: 24 layers, hidden size 1024, 16 heads, intermediate size 4096, vocabulary 28,996, max position embeddings 512. Pretrained with recurring span selection. This checkpoint does not store pretrained QASS weights. The card says it was trained after the ACL 2021 paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Splinter-large is the larger English few-shot question-answering encoder from the same Tel Aviv University group as Splinter-base. The Apache-2.0 card says it was trained after the ACL 2021 paper, so those results are not in that paper. The checkpoint is `tau/splinter-large`. This dossier was collected on 15 October 2026.", "what_it_is_for": "Same job as Splinter-base: few-shot extractive QA after recurring-span pretraining. The card says it outperforms the base model by a wide margin and points to the appendix of https://arxiv.org/abs/2108.05857. The model card is https://huggingface.co/tau/splinter-large.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tau/tavbert/tavbert-ar": { "id": "tau/tavbert/tavbert-ar", "name": "TavBERT Arabic", "maker": "tau", "family": "tau/tavbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-04-09", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Same character-level RoBERTa/BERT-base encoder as the other TavBERT runs: 12 layers, hidden 768, 12 heads, intermediate 3072. Config vocab_size 302 and max_position_embeddings 2050. Paper maximum sequence length is 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TavBERT Arabic is the Arabic character-level masked language model from the TavBERT paper. The Tel Aviv University authors are Omri Keren, Tal Avinari, and Omer Levy. Reut Tsarfaty is at Bar-Ilan University. The checkpoint `tau/tavbert-ar` was committed on the Hub on 9 April 2022. The preprint is 10 April 2022. The code repository is MIT. This dossier was collected on 15 October 2026.", "what_it_is_for": "The card describes an Arabic BERT-style model over characters, pretrained by masking character spans. It is the Arabic run of the same method as TavBERT Hebrew. The card is https://huggingface.co/tau/tavbert-ar.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tau/tavbert/tavbert-he": { "id": "tau/tavbert/tavbert-he", "name": "TavBERT Hebrew", "maker": "tau", "family": "tau/tavbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-04-10", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base / RoBERTa masked language model over characters, not wordpieces. 12 layers, hidden size 768, 12 heads, intermediate size 3072. Config vocab_size 345 and max_position_embeddings 2050. The paper sets the maximum sequence length to 2048 and masks character spans with mean length 5, following SpanBERT. Hub architecture class is RobertaForMaskedLM.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TavBERT Hebrew is a character-level masked language model from Omri Keren and Tal Avinari at Tel Aviv University, with Reut Tsarfaty (Bar-Ilan University) and Omer Levy (Tel Aviv University). It is a BERT-base encoder trained on characters rather than wordpieces, using span masking. The weights are `tau/tavbert-he`. The preprint is 10 April 2022. The code repository is MIT. This dossier was collected on 15 October 2026.", "what_it_is_for": "The card says it is a Hebrew BERT-style model over characters, pretrained by masking spans of characters in the same spirit as SpanBERT. The paper uses it for POS tagging, morphological analysis, NER, and extractive QA, and compares it with subword models. The card is https://huggingface.co/tau/tavbert-he. The code is https://github.com/omrikeren/TavBERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tau/tavbert/tavbert-tr": { "id": "tau/tavbert/tavbert-tr", "name": "TavBERT Turkish", "maker": "tau", "family": "tau/tavbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-04-09", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Same character-level RoBERTa/BERT-base encoder as TavBERT Hebrew: 12 layers, hidden 768, 12 heads, intermediate 3072. Config vocab_size 250 and max_position_embeddings 2050. Paper maximum sequence length is 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TavBERT Turkish is the Turkish character-level masked language model from the TavBERT paper. The Tel Aviv University authors are Omri Keren, Tal Avinari, and Omer Levy. Reut Tsarfaty is at Bar-Ilan University. The checkpoint `tau/tavbert-tr` was committed on the Hub on 9 April 2022. The preprint is 10 April 2022. The code repository is MIT. This dossier was collected on 15 October 2026.", "what_it_is_for": "The card describes a Turkish BERT-style model over characters, pretrained by masking character spans. It is the Turkish run of the same method as TavBERT Hebrew. The card is https://huggingface.co/tau/tavbert-tr.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tavus/griffin/griffin": { "id": "tavus/griffin/griffin", "name": "Griffin", "maker": "tavus", "family": "tavus/griffin", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tavus calls Griffin a Human Interaction Model: one full-duplex video-to-video system, not a cascade of Phoenix, Sparrow, and Raven. The research page describes two parts, a continuous conversational model and a streaming audio-visual generator. Customer release is the unnamed wider model after Griffin-Lite. Measured numbers on the page are for Griffin-Lite, not for this follow-on.", "modalities": { "input": [ "image", "audio", "video", "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Griffin is Tavus's announced Human Interaction Model. The research page, cited as October 2026, says it is one video-to-video system that listens while it talks, instead of a chain of Phoenix, Raven, and Sparrow. The thing testers can use is Griffin-Lite. Tavus says Griffin itself is not on the platform until disclosure and safety work is done. This dossier was collected on 2026-10-15.", "what_it_is_for": "Tavus says a Human Interaction Model should hold a face-to-face conversation: perceive, decide when to speak, and generate speech and picture at the same time. Griffin is the product name for that class. The page tells developers to keep building on Phoenix, Raven, and Sparrow until Griffin ships.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tavus/griffin/griffin-lite": { "id": "tavus/griffin/griffin-lite", "name": "Griffin-Lite", "maker": "tavus", "family": "tavus/griffin", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tavus says Griffin-Lite is a full-duplex video-to-video preview. A continuous conversational model decides at sub-second steps. Speech is a fast autoregressive diffusion transformer that clones a voice from about 10 seconds of audio, on the Tavec v1.7 continuous codec (48 kHz, 40 values per frame, 100 frames per second, causal decoder, 28.42 million decoder parameters, packets as small as 10 ms). Video is a few-step autoregressive diffusion generator distilled with DMD, teacher forcing, and self-forcing. It emits 720p one latent at a time from a reference image plus streaming audio and controls. No full-model parameter count was published.", "modalities": { "input": [ "image", "audio", "video", "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Griffin-Lite is the research preview of Tavus's Griffin Human Interaction Model. Tavus's October 2026 research page says selected testers can use it and customers cannot. It generates 720p video and cloned speech in one full-duplex loop from a reference image and about 10 seconds of audio. This dossier was collected on 2026-10-15.", "what_it_is_for": "Tavus uses Griffin-Lite to show a single model that watches, listens, decides, and speaks at the same time. It is not a drop-in `model_name` next to Phoenix. The page says the wider Griffin release waits on disclosure and safety work because people in their study often thought the preview was a person.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tavus/hummingbird/hummingbird-0": { "id": "tavus/hummingbird/hummingbird-0", "name": "Hummingbird-0", "maker": "tavus", "family": "tavus/hummingbird", "status": "preview", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-04-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tavus says Hummingbird-0 is a zero-shot lip-sync model taken from Phoenix-3 work. It rewrites mouth motion on an existing video to match new audio. No parameter count was published. It is not the real-time Phoenix renderer.", "modalities": { "input": [ "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hummingbird-0 is Tavus's zero-shot lip-sync model, announced as a research preview on 24 April 2025. It takes an existing video and new audio and rewrites the mouth. Tavus said it came out of Phoenix-3 training and was too useful to keep internal. It is not a Phoenix version and it is not in the current docs index. This dossier was collected on 2026-10-15.", "what_it_is_for": "Tavus positioned Hummingbird-0 for dubbing and repurposing footage: upload a clip and a voice track, no per-person training. The live CVI product uses Phoenix for the face instead. fal still hosts `fal-ai/tavus/hummingbird-lipsync/v0`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tavus/phoenix/phoenix-1": { "id": "tavus/phoenix/phoenix-1", "name": "Phoenix-1", "maker": "tavus", "family": "tavus/phoenix", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tavus's later Phoenix-4 research post says Phoenix-1 modeled avatars in 3D with NeRFs and drove them from LLM and TTS streams at runtime. No layer diagram or parameter count was published.", "modalities": { "input": [ "video", "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phoenix-1 is Tavus's first named human-rendering model. Tavus's February 2026 Phoenix-4 research post says it modeled avatars in 3D with NeRFs and drove them from LLM and TTS streams at runtime, and that the Phoenix line started in 2023. It is not an accepted `model_name` on Create Face. This dossier was collected on 2026-10-15.", "what_it_is_for": "Tavus describes Phoenix-1 as the model that first let them put a person in 3D and drive that avatar in a conversation. Later posts treat it as the ancestor of the real-time Phoenix line, not as a current training option.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tavus/phoenix/phoenix-2": { "id": "tavus/phoenix/phoenix-2", "name": "Phoenix-2", "maker": "tavus", "family": "tavus/phoenix", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tavus says Phoenix-2 switched from NeRFs to 3D Gaussian splatting, which broke the real-time barrier and made Conversational Video Interface possible. No parameter count was published.", "modalities": { "input": [ "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phoenix-2 is Tavus's second named rendering model. The Phoenix-4 research post says it replaced Phoenix-1's NeRFs with 3D Gaussian splatting, ran in real time, and was the first Phoenix that made Conversational Video Interface possible. It is not an accepted `model_name` on Create Face. This dossier was collected on 2026-10-15.", "what_it_is_for": "Tavus describes Phoenix-2 as the version that made a live face-to-face AI conversation possible, before Phoenix-3 expanded generation from the mouth to the whole face.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tavus/phoenix/phoenix-3": { "id": "tavus/phoenix/phoenix-3", "name": "Phoenix-3", "maker": "tavus", "family": "tavus/phoenix", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tavus calls Phoenix a Gaussian diffusion full-face renderer. Phoenix-3 is the version that moved generation from mouth and lips to the whole face. The 17 May 2025 changelog raised the CVI frame rate from 27 fps to 32 fps. No parameter count was published.", "modalities": { "input": [ "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phoenix-3 is Tavus's full-face rendering model announced on 6 March 2025 with Raven-0 and Sparrow-0. Create Face still accepts `model_name` `phoenix-3`, and the training guide calls it legacy: video only, for rare cases where a caller must pin the older model. This dossier was collected on 2026-10-15.", "what_it_is_for": "Tavus shipped Phoenix-3 as the visual model for real-time face-to-face agents. Later posts say it was the step that generated the whole face, not only the mouth. The current training guide says almost everyone should use Phoenix-4.5 or Phoenix-4 instead.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tavus/phoenix/phoenix-4": { "id": "tavus/phoenix/phoenix-4", "name": "Phoenix-4", "maker": "tavus", "family": "tavus/phoenix", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tavus's Phoenix-4.5 research post says Phoenix-4 generates about a 512x512 region around the face and head with 3D Gaussian splatting plus a neural clean-up pass, then composites that region onto recorded body footage. Expression latents drive the face. The Phoenix-4 post says the full pipeline runs at 40 fps in 1080p and that the model is full-duplex. No parameter count was published.", "modalities": { "input": [ "image", "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phoenix-4 is Tavus's real-time human rendering model launched in February 2026. It is still an accepted Create Face model, and the training guide keeps it for full-body shots and for clothing texture that must stay exact. New faces default to Phoenix-4.5. This dossier was collected on 2026-10-15.", "what_it_is_for": "Tavus calls Phoenix-4 the first real-time model that generates emotional state, active listening, and continuous facial motion as one system. Developers use it when they need a head-to-toe face or when small logos and decorated clothing have to stay faithful. Emotion is on by default. There is no emotion parameter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tavus/phoenix/phoenix-4-5": { "id": "tavus/phoenix/phoenix-4-5", "name": "Phoenix-4.5", "maker": "tavus", "family": "tavus/phoenix", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tavus says Phoenix-4.5 is two models. A streaming diffusion transformer (the animator) turns the PAL's speech and the user's speech into motion codes, with emotion, style, and identity conditioning, distilled to a couple of sampling steps. A learned GAN-based half-body renderer paints the full frame from one reference image and those motion codes. There is no face mask and no composite. The post also says audio-to-video is 134 ms, under 130 ms in the diagram section, and about 35 to 40 fps end to end on slower machines. No parameter count was published.", "modalities": { "input": [ "image", "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phoenix-4.5 is Tavus's default real-time face model. The docs changelog records it on 2 September 2026. It renders a chest-up or waist-up person from a photo or a short video, shows a watermarked preview in about a minute, then tunes in the background. Full-body work stays on Phoenix-4. This dossier was collected on 2026-10-15.", "what_it_is_for": "Tavus says Phoenix-4.5 generates the whole frame, not a face pasted on a recorded body, so the head, shoulders, posture, and torso move with speech and listening. It is the renderer named in the current CVI price. Stock faces were upgraded to it, and the 10 September 2026 changelog added 50 Phoenix-4.5 stock faces, 28 of them Pro. Pro is a face tier, not another model id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tavus/raven/raven-0": { "id": "tavus/raven/raven-0", "name": "Raven-0", "maker": "tavus", "family": "tavus/raven", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tavus's March 2025 announcement calls Raven-0 the first perception model that sees and reasons in real time. The current perception doc no longer lists it. Troubleshooting still shows the old field names (ambient_awareness_queries, perception_tools) used with perception_model raven-0. No parameter count was published.", "modalities": { "input": [ "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Raven-0 is Tavus's first named perception model, announced on 6 March 2025 with Phoenix-3 and Sparrow-0. The current Perception page lists only `raven-1` and `off`. Troubleshooting still shows how to migrate a `raven-0` PAL. This dossier was collected on 2026-10-15.", "what_it_is_for": "Tavus introduced Raven-0 so a video agent could watch the caller and react to expression, objects, and presence, not only to the words. Raven-1 later added audio emotion and became the default.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tavus/raven/raven-1": { "id": "tavus/raven/raven-1", "name": "Raven-1", "maker": "tavus", "family": "tavus/raven", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tavus calls Raven-1 a multimodal perception model. It fuses tone, prosody, facial expression, posture, and gaze into natural-language context for the LLM, rather than a fixed emotion label. The February 2026 announcement says audio perception latency is under 100 ms and combined pipeline latency is under 600 ms. Screen share is on when Raven is on. No parameter count was published.", "modalities": { "input": [ "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Raven-1 is Tavus's current perception model, generally available on 11 February 2026 and the default on new PALs. It watches the camera and a shared screen, listens to tone, and writes short natural-language context into the conversation. This dossier was collected on 2026-10-15.", "what_it_is_for": "Tavus uses Raven-1 so a PAL can react to expression, objects, gaze, tone, and what is on a shared screen. The pricing FAQ names it as the perception model inside the CVI minute. Emotion from face and voice can be turned down for EU AI Act workplace and education use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tavus/sparrow/sparrow-0": { "id": "tavus/sparrow/sparrow-0", "name": "Sparrow-0", "maker": "tavus", "family": "tavus/sparrow", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-03-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tavus's Models page calls Sparrow a transformer-based turn-taking model. Brian Johnson, who ships Sparrow at Tavus, wrote on 7 March 2025 that Sparrow-0 had just gone GA, uses a customized transformer plus voice recognition and a human response-time model, and has 10 ms latency. That latency is one engineer's post, not a model card. The Sparrow-1 research post later measured Sparrow-0 at 0.643 precision, 1.000 recall, 13 interruptions, 907 ms p50, and 1019 ms mean on Tavus's own audio set.", "modalities": { "input": [ "audio" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sparrow-0 is Tavus's first named turn-taking model, announced on 6 March 2025 with Phoenix-3 and Raven-0. The Conversational Flow page retrieved on 2026-10-15 lists only `sparrow-1` and `sparrow-2`. This dossier was collected on 2026-10-15.", "what_it_is_for": "Tavus shipped Sparrow-0 so a video agent could decide when to speak instead of waiting on a fixed silence timer. Sparrow-1's research post treats it as the previous model and publishes a head-to-head on Tavus's own audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tavus/sparrow/sparrow-1": { "id": "tavus/sparrow/sparrow-1", "name": "Sparrow-1", "maker": "tavus", "family": "tavus/sparrow", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tavus says Sparrow-1 is an audio-native streaming model that predicts floor ownership at frame level and does not depend on ASR endpointing. The research post says it extends Sparrow-0. The Models page calls the Sparrow family transformer-based. No parameter count was published.", "modalities": { "input": [ "audio" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sparrow-1 is Tavus's audio turn-taking model that went generally available in January 2026. It is still a documented `turn_detection_model` value. Sparrow-2 is the newer model. The Conversational Flow page retrieved on 2026-10-15 still calls Sparrow-1 the default until 8 September 2026, a date that has passed. This dossier was collected on 2026-10-15.", "what_it_is_for": "Tavus says Sparrow-1 predicts who owns the floor from streaming audio, including tone and pauses, instead of waiting for silence. It is the model the Phoenix-4 launch paired with that renderer. The pricing FAQ's included turn-taking model is Sparrow-2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tavus/sparrow/sparrow-2": { "id": "tavus/sparrow/sparrow-2", "name": "Sparrow-2", "maker": "tavus", "family": "tavus/sparrow", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tavus says Sparrow-2 is an audio-native streaming model of the whole acoustic scene: turns, interruptions, backchannels, other speakers, and noise, at a native 10 ms frame rate. It does not use the voice_isolation model. No parameter count was published.", "modalities": { "input": [ "audio" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sparrow-2 is Tavus's current turn-taking model. The research post is dated 27 August 2026. It models the whole audio scene, including noise and backchannels, and it is the turn-taking model named in the CVI price. Callers opt in with `turn_detection_model` `sparrow-2`. This dossier was collected on 2026-10-15.", "what_it_is_for": "Tavus says Sparrow-2 decides when to listen, wait, or speak from the full acoustic scene, at a 10 ms frame rate, instead of stripping noise first and then detecting an endpoint. Troubleshooting says that is why a PAL can answer nearby speech, and tells you to confirm the model is actually `sparrow-2` if that happens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tcd/abair/abair-tts": { "id": "tcd/abair/abair-tts", "name": "ABAIR synthetic voices", "maker": "tcd", "family": "tcd/abair", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Irish text-to-speech from the Phonetics and Speech Laboratory. Murphy et al. 2023: the first released voice was concatenative unit selection for Ulster; later voices are HTS statistical parametric, DNN, and a multi-dialect multi-speaker FastPitch model in NVIDIA NeMo. Public pages do not state which engine serves the 2025 voices. Downloadable SAPI 5 32-bit builds are version 0.8 beta.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ABAIR synthetic voices are the Irish text-to-speech system from the Phonetics and Speech Laboratory at Trinity College Dublin. The voices are a current public product on abair.ie and as Windows SAPI betas. Weights are closed. Collected on 2026-10-15.", "what_it_is_for": "The lab builds native-dialect Irish voices for education, screen reading, and communication aids. The public site is text-to-speech with a voice picker. SAPI builds plug into Windows screen readers. Geabaire, an Irish AAC board, speaks through these voices rather than being a separate acoustic model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tcd/abair/eist": { "id": "tcd/abair/eist", "name": "ABAIR-\u00c9IST", "maker": "tcd", "family": "tcd/abair", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2022-06-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid Irish ASR from the Phonetics and Speech Laboratory. 2022 system: Kaldi TDNN-F acoustic model, 13 layers, hidden 1024, bottleneck 128, LF-MMI, 10 epochs, trained on 37.2 hours from 281 speakers balanced across Ulster, Connacht, and Munster, with a pronunciation lexicon and n-gram plus RNNLM rescoring. 2024/2025 Fotheidil acoustic model: Kaldi TDNN-HMM, 6 layers, hidden 768, 40-dim high-resolution MFCCs plus 100-dim online i-vectors, cross-dialect lexicon, 4-gram LM and RNNLM rescoring. Student model M1 is trained on about 398 supervised hours plus 3230 pseudo-labelled hours. Fotheidil also runs third-party Silero VAD and a Kaldi x-vector diariser, then an Irish capitalisation and punctuation sequence-to-sequence model. Those extra modules are not separate TCD products.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ABAIR-\u00c9IST is the Irish speech recogniser from the Phonetics and Speech Laboratory at Trinity College Dublin. It has been a public speech-to-text system since the 2022 CLTW paper, and the Fotheidil transcription site launched on 9 December 2025 uses it. Weights are closed. This dossier was collected on 2026-10-15.", "what_it_is_for": "\u00c9IST turns Irish speech into text, with explicit attention to Ulster, Connacht, Munster, and learner accents. Fotheidil is the long-file product: upload audio or video, get a transcript with speaker labels, then edit and download it. The recogniser is one module in that pipeline, not the VAD or diariser.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tcd/qomhra/qomhra": { "id": "tcd/qomhra/qomhra", "name": "Qomhr\u00e1", "maker": "tcd", "family": "tcd/qomhra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-20", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Continued pre-training and instruction tuning of Qwen3-8B (Qwen3-8B-Base then Qwen3-8B). Decoder-only Transformer. Paper: CPT packs text into 2048-token blocks and describes that choice as reducing the Qwen3-8B 128K window for memory; 2 epochs, AdamW, learning rate 1e-4, BF16, 2x H100 80GB, DeepSpeed ZeRO-2, about 34,360 steps and 44.2 hours. Instruct stage uses the Qwen chat template with thinking tags removed, LoRA rank 16, alpha 32, learning rate 2e-4. The public Hub repo is a full causal-LM checkpoint, not an adapter. Tokenizer was not retrained.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qomhr\u00e1 is an 8B bilingual Irish and English instruct model released by researchers at Trinity College Dublin, University College Cork, and Queen's University Belfast. The public checkpoint is an Apache-2.0 continued-pretrain and instruction tune of Qwen3-8B, active as a weight release on 2026-10-15. It is not a hosted API.", "what_it_is_for": "The card and paper present Qomhr\u00e1 as an open-weight Irish-English model for a low-resource language, aimed at Irish understanding, generation, and bilingual instruction following. The name is explained as Qwen plus comhr\u00e1, the Irish word for conversation. Thinking tags were stripped so it would not be a reasoning model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "technion/deep-storm/deep-storm": { "id": "technion/deep-storm/deep-storm", "name": "Deep-STORM", "maker": "technion", "family": "technion/deep-storm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2018-01-29", "context_length": null, "max_output_tokens": null, "parameters": "1300000", "architecture": "Fully convolutional encoder-decoder. Three 3x3 convolutions with increasing depth and 2x2 max-pooling, then three 2x2 upsampling stages with 3x3 convolutions of decreasing depth. Each conv block is convolution, batch norm, and ReLU. A final 1x1 convolution with linear activation predicts the super-resolved frame. The paper states 1.3M trainable parameters.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deep-STORM is a fully convolutional encoder-decoder for single-molecule localization microscopy from Elias Nehme, Lucien E. Weiss, Tomer Michaeli, and Yoav Shechtman at the Technion. The preprint is 29 January 2018 and the journal version is Optica, volume 5, number 4, pages 458-464 (2018). MIT training code was public on 15 October 2026. This dossier was collected on 15 October 2026.", "what_it_is_for": "The network turns a stack of diffraction-limited frames of blinking fluorophores into a super-resolved image. It does not return an explicit point list. The authors train it on simulated frames matched to the camera, PSF, SNR, and emitter density, or on experimental measurements. Code and the paper are at https://github.com/EliasNehme/Deep-STORM and https://arxiv.org/abs/1801.09631.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "technion/deep-storm/deepstorm3d": { "id": "technion/deep-storm/deepstorm3d", "name": "DeepSTORM3D", "maker": "technion", "family": "technion/deep-storm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2019-06-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two CNNs in one codebase. A localization CNN recovers dense 3D emitter positions for a fixed PSF. A second network learns a phase mask and PSF by end-to-end training. 3D coordinates are taken from the network output by local-maxima and center-of-gravity post-processing. Parameter count was not stated in the sources read.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepSTORM3D is a pair of convolutional networks for dense 3D localization microscopy and point-spread-function design. Elias Nehme, Tomer Michaeli, and Yoav Shechtman led the Technion work, with Daniel Freedman at Google Research among the coauthors. The preprint is 21 June 2019. Nature Methods published it on 15 June 2020. MIT code and demo checkpoints were public on 15 October 2026. This dossier was collected on 15 October 2026.", "what_it_is_for": "One network localizes many overlapping emitters over a long axial range. The other learns a phase mask so the PSF is better for that dense case. The repository uses the network for Tetrapod PSFs and for a learned PSF, then turns the volume into 3D coordinates. Nature Methods states that code is at https://github.com/EliasNehme/DeepSTORM3D. The paper is https://arxiv.org/abs/1906.09957.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "technion/diffar/diffar": { "id": "technion/diffar/diffar", "name": "DiffAR", "maker": "technion", "family": "technion/diffar", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-10-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive denoising diffusion model on raw waveforms. DiffWave-style backbone with N=36 residual layers and C=256 residual channels in the paper. Bidirectional dilated convolutions with dilation cycle 1 through 2048. Diffusion step uses a 128-d encoding. Optional phoneme, energy, and pitch conditions each pass through a multi-scale residual block with kernels 3, 5, and 7. Training frame is 1000 samples with 500-sample overlap and S=200 diffusion steps. Beta schedule is 1e-4 to 0.02.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffAR is an autoregressive diffusion model that generates raw speech waveforms, from Roi Benita, Michael Elad, and Joseph Keshet at the Technion. The preprint is 2 October 2023, revised 10 March 2024, and the paper is an ICLR 2024 poster. MIT code is at https://github.com/RBenita/DIFFAR. The README still said the repository was under construction on 15 October 2026. This dossier was collected on 15 October 2026.", "what_it_is_for": "The model synthesizes speech directly as a waveform, without a mel spectrogram or a separate vocoder. It can run with no text, or conditioned on phonemes, amplitude, and pitch. Overlapping frames let it continue for an arbitrary duration. The authors highlight local voice qualities such as vocal fry. Samples are at https://rbenita.github.io/DIFFAR/. Training data in the paper and README is LJSpeech, one female speaker of English, about 24 hours.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "technion/sinddm/sinddm": { "id": "technion/sinddm/sinddm", "name": "SinDDM", "maker": "technion", "family": "technion/sinddm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2022-11-29", "context_length": null, "max_output_tokens": null, "parameters": "1100000", "architecture": "Multi-scale denoising diffusion model. A fully convolutional denoiser is conditioned on diffusion time and scale through a joint embedding. The paper states 1.1\u00d710^6 parameters. Text guidance uses a separate pretrained CLIP model, not counted in that figure.", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SinDDM is a single-image denoising diffusion model from Vladimir Kulikov, Shahar Yadin, Matan Kleiner, and Tomer Michaeli, all at the Technion Faculty of Electrical and Computer Engineering. It was preprinted on 29 November 2022 and published at ICML 2023. MIT code and a few image-specific checkpoints were public on 15 October 2026. This dossier was collected on 15 October 2026.", "what_it_is_for": "The paper trains a diffusion model on one image so it can sample new images of arbitrary size and support style transfer, harmonization, and text-guided edits. Text control is external CLIP guidance, not a separately trained language model. The project page is https://matankleiner.github.io/sinddm/ and the code is https://github.com/fallenshock/SinDDM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "technion/singan/singan": { "id": "technion/singan/singan", "name": "SinGAN", "maker": "technion", "family": "technion/singan", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2019-05-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pyramid of fully convolutional patch-GANs. Each generator is five Conv(3x3)-BatchNorm-LeakyReLU blocks that predict a residual. The coarsest scale starts at 32 kernels and the kernel count doubles every four scales. Coarsest side is 25 px and the scale factor is about 4/3. Reported results resize the long side to 250 px unless noted.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SinGAN is an unconditional generative model from Tamar Rott Shaham and Tomer Michaeli at the Technion and Tali Dekel at Google Research. It learns the internal patch statistics of one natural image with a pyramid of fully convolutional GANs. The public release is the 2 May 2019 preprint, revised 4 September 2019, and the ICCV 2019 Marr Prize paper. The MIT code was still the official implementation on 15 October 2026. This dossier was collected on 15 October 2026.", "what_it_is_for": "The authors say one trained SinGAN can sample new images of arbitrary size, animate a still, harmonize a pasted object, edit, turn a paint into an image in the training style, and super-resolve. The same network is used for those tasks. Injection scale controls how much of the original layout is kept. The project page is https://tamarott.github.io/SinGAN.htm and the code is https://github.com/tamarott/SinGAN.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "temple/drl-vo/drl-vo": { "id": "temple/drl-vo/drl-vo", "name": "DRL-VO", "maker": "temple", "family": "temple/drl-vo", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2023-01-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PPO actor-critic navigation policy. Observation is a 0.5 second lidar history (10 scans) reduced by min and average pooling and stacked to one 80 by 80 map, two 80 by 80 pedestrian kinematic maps over a 20 by 20 m forward area, and a 2D sub-goal, all in the robot frame. A bottleneck residual feature extractor feeds separate fully connected actor and critic heads. The actor outputs linear and angular velocity. The paper's full reward adds a velocity-obstacle heading term. Training uses stable-baselines3 PPO, step decay from learning rate 1e-3, and mini-batches of 512, in a lobby world with 34 pedestrians. Both authors are in Mechanical Engineering at Temple University.", "modalities": { "input": [ "lidar-2d", "pedestrian-kinematics" ], "output": [ "velocity" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DRL-VO is a reinforcement-learning navigation policy from Zhanteng Xie and Philip Dames in Mechanical Engineering at Temple University. The lab is the Temple Robotics and Artificial Intelligence Lab. The preprint arXiv:2301.06512 is dated 16 January 2023 and was revised on 4 April 2023. The journal version is IEEE Transactions on Robotics, volume 39, number 4, pages 2700-2719, DOI 10.1109/TRO.2023.3257549. GPL-3.0 code and three policy zips are in TempleRAIL/drl_vo_nav. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper trains a policy that drives a mobile robot through static obstacles and dense pedestrian crowds. A velocity-obstacle term in the reward is meant to steer away from likely collisions while still moving toward the goal. The authors report sim tests up to 55 pedestrians, hardware tests on a TurtleBot2 with a Hokuyo lidar and a ZED camera, and use on a Jackal in the ICRA 2022 BARN Challenge without extra training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "temple/s3-net/s3-net": { "id": "temple/s3-net/s3-net", "name": "S\u00b3-Net", "maker": "temple", "family": "temple/s3-net", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-09-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "1D convolutional variational autoencoder for point-wise 2D lidar semantic segmentation. The released card describes three input channels (range, intensity, angle of incidence), an encoder of Conv1D 3 to 32 and 32 to 64 with stride 2 plus a two-layer residual stack, a VAE reparameterization of mean and standard deviation, and a decoder with a two-layer residual stack and transpose convolutions 64 to 32 and 32 to 10, then softmax over 10 classes. Inference uses 32 stochastic forward passes and majority voting. Loss on the card is cross-entropy plus Lovasz-Softmax plus beta-VAE KL. The paper sets the loss weights to 1, 1, and 0.01 and treats range plus intensity plus incident angle as the proposed input.", "modalities": { "input": [ "lidar-2d" ], "output": [ "semantic-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "S\u00b3-Net (Stochastic Semantic Segmentation Network) is a 2D lidar segmentation model released by the Temple Robotics and Artificial Intelligence Lab at Temple University, with coauthors at the University of Hong Kong. Philip Dames (Temple, Mechanical Engineering) and Jia Pan (HKU) are the corresponding authors. The preprint arXiv:2409.09899 appeared on 15 September 2024 and was revised on 25 January 2026. Weights and an MIT-licensed card are on Hugging Face as TempleRAIL/s3_net, created 2 February 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents S\u00b3-Net as a hardware-friendly segmenter so a mobile robot can label each return from one 2D lidar without a camera. The released card says the checkpoint was trained on the Semantic2D set with a Hokuyo UTM-30LX-EW. Downstream use in the same paper is semantic occupancy mapping and the Semantic CNN navigation policy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "temple/semantic-cnn/semantic-cnn": { "id": "temple/semantic-cnn/semantic-cnn", "name": "Semantic CNN", "maker": "temple", "family": "temple/semantic-cnn", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-09-15", "context_length": null, "max_output_tokens": null, "parameters": "28.99M", "architecture": "Supervised navigation policy. The card describes a ResNet-style network of Bottleneck blocks with stage counts 2, 1, and 1. Input is two 80 by 80 channels, a scan map and a semantic map, plus a 2D sub-goal concatenated after pooling. Output is linear velocity and angular velocity. Loss is MSE. Sequence length on the card is 10 frames. Lidar points are listed as 1081, downsampled to 720 by dropping 180 points at each end. The paper says semantic labels are pooled the same way as the lidar history: minimum-range selection for min pooling and majority vote for average pooling. Table 5 lists 28.99 million parameters.", "modalities": { "input": [ "lidar-2d", "semantic-map" ], "output": [ "velocity" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Semantic CNN is a supervised mobile-robot navigation policy from the Temple Robotics and Artificial Intelligence Lab, released with the Semantic2D paper. Authors are Zhanteng Xie, Yipeng Pan, Yinqiang Zhang, Jia Pan, and Philip Dames. Temple Mechanical Engineering and the University of Hong Kong are the affiliations. The preprint is arXiv:2409.09899 (15 September 2024; revised 25 January 2026). MIT-licensed weights are on Hugging Face as TempleRAIL/semantic_cnn_nav, created 2 February 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper adds S\u00b3-Net semantic labels to an earlier lidar-history CNN so the robot can steer in indoor scenes with pedestrians using only 2D lidar. The card ships a training script and a ROS Noetic node that runs S\u00b3-Net and Semantic CNN together. Real-robot sections show a Jackal at Temple and a transfer, without fine-tuning, onto a different robot and WLR-716 lidar at HKU. Those hardware runs are qualitative.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/auk/auk": { "id": "tencent/auk/auk", "name": "AuK", "maker": "tencent", "family": "tencent/auk", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-08-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AuK is a Tencent Hunyuan speech model described as an open foundational model for speech generation and editing. The repository was created on 2026-08-19. Collected on 2026-10-15.", "what_it_is_for": "Generate and edit speech.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-0-5b": { "id": "tencent/hunyuan/hunyuan-0-5b", "name": "Hunyuan-0.5B", "maker": "tencent", "family": "tencent/hunyuan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-30", "context_length": null, "max_output_tokens": null, "parameters": "0.5B", "architecture": "Dense Hunyuan model. Pretrain and instruct checkpoints were released together. The card says the series supports fast and slow thinking.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-0.5B is one of four small dense language models Tencent open-weighted on 2025-07-30, with separate pretrain and instruct checkpoints. It is aimed at edge and single-GPU inference and supports the series' fast and slow thinking modes. Collected on 2026-10-15.", "what_it_is_for": "Tencent says the dense series covers devices and high-concurrency service, from a 0.5B model up to 7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-1-8b": { "id": "tencent/hunyuan/hunyuan-1-8b", "name": "Hunyuan-1.8B", "maker": "tencent", "family": "tencent/hunyuan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-30", "context_length": null, "max_output_tokens": null, "parameters": "1.8B", "architecture": "Dense Hunyuan model. Pretrain and instruct checkpoints were released together. The card says the series supports fast and slow thinking.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-1.8B is one of four small dense language models Tencent open-weighted on 2025-07-30, with separate pretrain and instruct checkpoints. It is aimed at edge and single-GPU inference and supports the series' fast and slow thinking modes. Collected on 2026-10-15.", "what_it_is_for": "Tencent says the dense series covers devices and high-concurrency service, from a 0.5B model up to 7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-2-0": { "id": "tencent/hunyuan/hunyuan-2-0", "name": "Hunyuan 2.0", "maker": "tencent", "family": "tencent/hunyuan", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described in secondary coverage as a Mixture-of-Experts model. An official parameter table was not captured.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan 2.0 is Tencent's late-2025 closed language generation, with separate Think and Instruct API snapshots. Tencent Cloud retired those ids on 2026-06-22 and pointed users to hy3-preview. Collected on 2026-10-15.", "what_it_is_for": "The changelog describes the Instruct snapshot as a fast text model with better instruction following, and the Think line as the reasoning counterpart.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-4b": { "id": "tencent/hunyuan/hunyuan-4b", "name": "Hunyuan-4B", "maker": "tencent", "family": "tencent/hunyuan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-30", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Dense Hunyuan model. Pretrain and instruct checkpoints were released together. The card says the series supports fast and slow thinking.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-4B is one of four small dense language models Tencent open-weighted on 2025-07-30, with separate pretrain and instruct checkpoints. It is aimed at edge and single-GPU inference and supports the series' fast and slow thinking modes. Collected on 2026-10-15.", "what_it_is_for": "Tencent says the dense series covers devices and high-concurrency service, from a 0.5B model up to 7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-7b": { "id": "tencent/hunyuan/hunyuan-7b", "name": "Hunyuan-7B", "maker": "tencent", "family": "tencent/hunyuan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-30", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Dense Hunyuan model. Pretrain and instruct checkpoints were released together. The card says the series supports fast and slow thinking.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-7B is one of four small dense language models Tencent open-weighted on 2025-07-30, with separate pretrain and instruct checkpoints. It is aimed at edge and single-GPU inference and supports the series' fast and slow thinking modes. Collected on 2026-10-15.", "what_it_is_for": "Tencent says the dense series covers devices and high-concurrency service, from a 0.5B model up to 7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-7b-0124": { "id": "tencent/hunyuan/hunyuan-7b-0124", "name": "Tencent-Hunyuan-7B-0124", "maker": "tencent", "family": "tencent/hunyuan", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-01-24", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tencent-Hunyuan-7B-0124 is an earlier named 7B Hunyuan release. The public GitHub repository was created on 2025-01-24. It is catalogued separately from the July 2025 dense Hunyuan-7B. Collected on 2026-10-15.", "what_it_is_for": "A product description beyond the repository name was not captured.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-a13b": { "id": "tencent/hunyuan/hunyuan-a13b", "name": "Hunyuan-A13B", "maker": "tencent", "family": "tencent/hunyuan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tencent Hunyuan Community License (tencent-hunyuan-a13b)", "release_date": "2025-06-27", "context_length": 262144, "max_output_tokens": 32768, "parameters": "80B total, 13B activated", "architecture": "Fine-grained MoE. 32 layers, 32 attention heads, 1 shared expert, 64 routed experts, top-8, SwiGLU, hidden size 4096. Trained on more than 20T tokens. Default config.json context is 32K; the card says 256K is supported.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 2.0 }, "summary_intro": "Hunyuan-A13B is Tencent's open-weight fine-grained MoE language model with 80B total and 13B active parameters, released at the end of June 2025. It supports a 256K context window and both fast and slow thinking. Collected on 2026-10-15.", "what_it_is_for": "Tencent presents it as an efficient general model for math, science, coding, long context, and tool-using agents.", "ratings": { "empirical_100": 59, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-code": { "id": "tencent/hunyuan/hunyuan-code", "name": "Hunyuan-Code", "maker": "tencent", "family": "tencent/hunyuan", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-Code is a closed Tencent Hunyuan text API. The official retirement notice removes it on 2026-06-22 and points callers at hy3-preview. Collected on 2026-10-15.", "what_it_is_for": "A code-oriented Hunyuan API id. A separate open code model card was not found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-embedding": { "id": "tencent/hunyuan/hunyuan-embedding", "name": "Hunyuan-Embedding", "maker": "tencent", "family": "tencent/hunyuan", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.7, "completion_per_m_usd": 0.7 }, "summary_intro": "Hunyuan-Embedding is a closed Tencent embedding model named in the Hunyuan billing PDF at 0.7 CNY per million tokens. It is not on the September 2026 TokenHub list, which instead sells third-party Kinfra embedders. Collected on 2026-10-15.", "what_it_is_for": "Text embeddings for the older Hunyuan API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-funcall": { "id": "tencent/hunyuan/hunyuan-funcall", "name": "Hunyuan-FunctionCall", "maker": "tencent", "family": "tencent/hunyuan", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-FunctionCall is a closed Tencent Hunyuan text API. The official retirement notice removes it on 2026-06-22 and points callers at hy3-preview. Collected on 2026-10-15.", "what_it_is_for": "A function-calling Hunyuan API id on the retirement list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-large": { "id": "tencent/hunyuan/hunyuan-large", "name": "Hunyuan-Large", "maker": "tencent", "family": "tencent/hunyuan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tencent Hunyuan Community License", "release_date": "2024-11-05", "context_length": 262144, "max_output_tokens": null, "parameters": "389B total, 52B activated", "architecture": "Transformer MoE. 64 layers, 80 attention heads, 8 KV heads, 1 shared expert, 16 specialized experts with 1 activated, SwiGLU, vocabulary 128K, hidden size 6400. Pretrain context 256K. Instruct context stated as 128K on the model card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-Large is Tencent's open-weight Mixture-of-Experts language model released on 2024-11-05, with 389B total and 52B active parameters. The weights remain published under the Tencent Hunyuan Community License. The Tencent Cloud API ids were retired on 2026-06-22. Collected on 2026-10-15.", "what_it_is_for": "Tencent released it as a general open MoE model for research and deployment, with long-context pretraining and an instruct chat checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-lite": { "id": "tencent/hunyuan/hunyuan-lite", "name": "Hunyuan-Lite", "maker": "tencent", "family": "tencent/hunyuan", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-Lite is a closed Tencent Hunyuan text API. The official retirement notice removes it on 2026-06-22 and points callers at hy3-preview. Collected on 2026-10-15.", "what_it_is_for": "The smallest older Hunyuan text tier, used for high-volume simple calls.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-pro": { "id": "tencent/hunyuan/hunyuan-pro", "name": "Hunyuan-Pro", "maker": "tencent", "family": "tencent/hunyuan", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-Pro is a closed Tencent Hunyuan text API. The official retirement notice removes it on 2026-06-22 and points callers at hy3-preview. Collected on 2026-10-15.", "what_it_is_for": "Yuanqi described the advanced Hunyuan tier as a long-context MoE model aimed at complex instructions, math, and tools. That page's 28k input and 4k output figures were for a later console label and are not stored as this model's spec because the mapping is ambiguous.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-role": { "id": "tencent/hunyuan/hunyuan-role", "name": "Hunyuan-Role", "maker": "tencent", "family": "tencent/hunyuan", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.4, "completion_per_m_usd": 9.6 }, "summary_intro": "Hunyuan-Role is Tencent's role-play language line for digital doubles and companion chat. Older role ids were told to move to hunyuan-role-latest. That id is not on the 2026-09-25 TokenHub list, so this catalog treats the line as retired with the 2026-09-30 platform shutdown. Collected on 2026-10-15.", "what_it_is_for": "Tencent says it keeps a character consistent across turns for role play and emotional chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-standard": { "id": "tencent/hunyuan/hunyuan-standard", "name": "Hunyuan-Standard", "maker": "tencent", "family": "tencent/hunyuan", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-Standard is a closed Tencent Hunyuan text API. The official retirement notice removes it on 2026-06-22 and points callers at hy3-preview. Collected on 2026-10-15.", "what_it_is_for": "A lower-priced general Hunyuan text tier. The 32K and 256K ids show that context length was part of the product split.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-t1": { "id": "tencent/hunyuan/hunyuan-t1", "name": "Hunyuan-T1", "maker": "tencent", "family": "tencent/hunyuan", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-21", "context_length": null, "max_output_tokens": 65536, "parameters": "", "architecture": "Hybrid-Transformer-Mamba MoE, built on the TurboS fast-thinking base. Tencent called it the first ultra-large hybrid-Mamba reasoning model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 4.0 }, "summary_intro": "Hunyuan-T1 is Tencent's closed reasoning model, officially released on 2025-03-21 on top of the TurboS hybrid Mamba-Transformer base. Several dated API snapshots followed. All of them were retired on 2026-06-22. Collected on 2026-10-15.", "what_it_is_for": "Tencent sold T1 for math, logic, science, and code reasoning, with long-text decoding that it said was about twice as fast as a comparable Transformer setup.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-turbo": { "id": "tencent/hunyuan/hunyuan-turbo", "name": "Hunyuan-Turbo", "maker": "tencent", "family": "tencent/hunyuan", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-Turbo is an older closed Tencent Cloud language API, with dated snapshots from 2024-11-20 and 2024-12-23. Tencent retired the Turbo ids on 2026-06-22. Collected on 2026-10-15.", "what_it_is_for": "It was a general Hunyuan text endpoint in the generation before TurboS. A finer product description was not recovered from a live spec page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hunyuan-turbos": { "id": "tencent/hunyuan/hunyuan-turbos", "name": "Hunyuan-TurboS", "maker": "tencent", "family": "tencent/hunyuan", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-26", "context_length": null, "max_output_tokens": 16384, "parameters": "", "architecture": "Hybrid-Transformer-Mamba Mixture-of-Experts. Tencent called the early-2025 base the first ultra-large model of this type.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-TurboS is Tencent's fast hybrid Mamba-Transformer MoE language model. The earliest dated API id on the official retirement list is hunyuan-turbos-20250226. The API line was retired on 2026-06-22. Collected on 2026-10-15.", "what_it_is_for": "Tencent described TurboS as the fast-thinking base later used for Hunyuan-T1, aimed at quicker decoding than a pure Transformer of similar activated size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan/hy-asr-3-0-preview": { "id": "tencent/hunyuan/hy-asr-3-0-preview", "name": "Hy-ASR-3.0-preview", "maker": "tencent", "family": "tencent/hunyuan", "status": "preview", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Product page says it combines speech recognition with Hunyuan 3.0 language understanding.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hy-ASR-3.0-preview is a Tencent speech-recognition model named on the Hunyuan product page. Tencent says it uses Hunyuan 3.0 language understanding for recognition, context, and dialects. It was not on the TokenHub model list. Collected on 2026-10-15.", "what_it_is_for": "Speech to text with semantic understanding, including dialects.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-mt/hunyuan-mt-7b": { "id": "tencent/hunyuan-mt/hunyuan-mt-7b", "name": "Hunyuan-MT-7B", "maker": "tencent", "family": "tencent/hunyuan-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-01", "context_length": 8192, "max_output_tokens": 4096, "parameters": "7B", "architecture": "Dense translation model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-MT-7B is an open-weight Tencent translation model released on 2025-09-01. It is part of the Hunyuan translation line that covers 33 languages plus ethnic and dialect varieties. Collected on 2026-10-15.", "what_it_is_for": "Translation, including terminology, context, and formatted instructions on the MT2 generation. Earlier MT models target the same language set, and Chimera is an ensemble refiner.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-mt/hunyuan-mt-chimera-7b": { "id": "tencent/hunyuan-mt/hunyuan-mt-chimera-7b", "name": "Hunyuan-MT-Chimera-7B", "maker": "tencent", "family": "tencent/hunyuan-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-01", "context_length": 8192, "max_output_tokens": 4096, "parameters": "7B", "architecture": "Translation ensemble model on a 7B base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-MT-Chimera-7B is an open-weight Tencent translation model released on 2025-09-01. It is part of the Hunyuan translation line that covers 33 languages plus ethnic and dialect varieties. Collected on 2026-10-15.", "what_it_is_for": "Translation, including terminology, context, and formatted instructions on the MT2 generation. Earlier MT models target the same language set, and Chimera is an ensemble refiner.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-mt/hunyuan-translation": { "id": "tencent/hunyuan-mt/hunyuan-translation", "name": "Hunyuan-Translation", "maker": "tencent", "family": "tencent/hunyuan-mt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.2, "completion_per_m_usd": 3.6 }, "summary_intro": "Hunyuan-Translation is Tencent's earlier closed translation API, including a lite tier and a preview id. The preview id was retired on 2026-06-22 in favor of hy-mt2-pro. The changelog says the October 2025 update covered 33 languages and five ethnic languages. Collected on 2026-10-15.", "what_it_is_for": "Machine translation across the languages Tencent listed, including Tibetan, Kazakh, Mongolian, Uyghur, and Cantonese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-mt/hy-mt1-5-1-8b": { "id": "tencent/hunyuan-mt/hy-mt1-5-1-8b", "name": "HY-MT1.5-1.8B", "maker": "tencent", "family": "tencent/hunyuan-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-30", "context_length": 8192, "max_output_tokens": 4096, "parameters": "1.8B", "architecture": "Dense translation model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HY-MT1.5-1.8B is an open-weight Tencent translation model released on 2025-12-30. It is part of the Hunyuan translation line that covers 33 languages plus ethnic and dialect varieties. Collected on 2026-10-15.", "what_it_is_for": "Translation, including terminology, context, and formatted instructions on the MT2 generation. Earlier MT models target the same language set, and Chimera is an ensemble refiner.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-mt/hy-mt1-5-7b": { "id": "tencent/hunyuan-mt/hy-mt1-5-7b", "name": "HY-MT1.5-7B", "maker": "tencent", "family": "tencent/hunyuan-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-30", "context_length": 8192, "max_output_tokens": 4096, "parameters": "7B", "architecture": "Dense translation model. Tencent calls the 7B an upgrade of the September WMT25 model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HY-MT1.5-7B is an open-weight Tencent translation model released on 2025-12-30. It is part of the Hunyuan translation line that covers 33 languages plus ethnic and dialect varieties. Collected on 2026-10-15.", "what_it_is_for": "Translation, including terminology, context, and formatted instructions on the MT2 generation. Earlier MT models target the same language set, and Chimera is an ensemble refiner.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-mt/hy-mt2-1-8b": { "id": "tencent/hunyuan-mt/hy-mt2-1-8b", "name": "Hy-MT2-1.8B", "maker": "tencent", "family": "tencent/hunyuan-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-05-21", "context_length": 8192, "max_output_tokens": 4096, "parameters": "1.8B", "architecture": "Dense fast-thinking translation model. AngelSlim 1.25-bit quantization is published and is about 440 MB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hy-MT2-1.8B is the smallest open model in Tencent's May 2026 translation family. The README sets context at 8192 tokens and recommends 4096 generation tokens. It translates among 33 languages and the listed ethnic and dialect varieties. Collected on 2026-10-15.", "what_it_is_for": "On-device and low-latency translation. Tencent says the 1.8B model beats some commercial translation APIs overall, which is a vendor claim.", "ratings": { "empirical_100": 45, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-mt/hy-mt2-30b-a3b": { "id": "tencent/hunyuan-mt/hy-mt2-30b-a3b", "name": "Hy-MT2-30B-A3B", "maker": "tencent", "family": "tencent/hunyuan-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-05-21", "context_length": 8192, "max_output_tokens": 4096, "parameters": "30B total, 3B activated", "architecture": "Mixture-of-Experts translation model. The name A3B means about 3B active parameters. Expert count was not stated in the README text read here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hy-MT2-30B-A3B is the open Mixture-of-Experts translation model in the Hy-MT2 family, released on 2026-05-21. The name indicates 30B total and about 3B active parameters. Context is 8192 tokens. Collected on 2026-10-15.", "what_it_is_for": "Tencent's strongest open MT2 checkpoint for general, business, domain, and instruction-following translation.", "ratings": { "empirical_100": 52, "lmsys_elo": 1204, "coding_elo": 1204, "swe_bench": 15.7, "humaneval": 70.6, "twelve_cs": 99.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-mt/hy-mt2-7b": { "id": "tencent/hunyuan-mt/hy-mt2-7b", "name": "Hy-MT2-7B", "maker": "tencent", "family": "tencent/hunyuan-mt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-05-21", "context_length": 8192, "max_output_tokens": 4096, "parameters": "7B", "architecture": "Dense fast-thinking translation model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hy-MT2-7B is Tencent's open 7B translation model from 2026-05-21, with an 8192-token context. Tencent groups it with the 30B-A3B model as the quality tier of the open MT2 family. Collected on 2026-10-15.", "what_it_is_for": "Higher-quality translation than the 1.8B model, including terminology, style, and structured-data instructions.", "ratings": { "empirical_100": 48, "lmsys_elo": 1204, "coding_elo": 1204, "swe_bench": 15.7, "humaneval": 70.6, "twelve_cs": 28.2, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-mt/hy-mt2-lite": { "id": "tencent/hunyuan-mt/hy-mt2-lite", "name": "Hy-MT2-Lite", "maker": "tencent", "family": "tencent/hunyuan-mt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8192, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.044, "completion_per_m_usd": 0.177 }, "summary_intro": "Hy-MT2-Lite is a current Tencent TokenHub translation endpoint. The 2026-09-25 model list gives it an 8k context, 4k max input, and 4k max output. Collected on 2026-10-15.", "what_it_is_for": "TokenHub calls it the lightweight Hunyuan translation model for latency-sensitive calls.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-mt/hy-mt2-plus": { "id": "tencent/hunyuan-mt/hy-mt2-plus", "name": "Hy-MT2-Plus", "maker": "tencent", "family": "tencent/hunyuan-mt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8192, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.074, "completion_per_m_usd": 0.295 }, "summary_intro": "Hy-MT2-Plus is a current Tencent TokenHub translation endpoint. The 2026-09-25 model list gives it an 8k context, 4k max input, and 4k max output. Collected on 2026-10-15.", "what_it_is_for": "TokenHub calls it a translation model with strong instruction following.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-mt/hy-mt2-pro": { "id": "tencent/hunyuan-mt/hy-mt2-pro", "name": "Hy-MT2-Pro", "maker": "tencent", "family": "tencent/hunyuan-mt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8192, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.074, "completion_per_m_usd": 0.295 }, "summary_intro": "Hy-MT2-Pro is a current Tencent TokenHub translation endpoint. The 2026-09-25 model list gives it an 8k context, 4k max input, and 4k max output. Collected on 2026-10-15.", "what_it_is_for": "TokenHub calls it the flagship Hunyuan translation model for professional domains.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-ocr/hunyuan-ocr": { "id": "tencent/hunyuan-ocr/hunyuan-ocr", "name": "HunyuanOCR", "maker": "tencent", "family": "tencent/hunyuan-ocr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tencent Hunyuan Community License", "release_date": "2025-11-18", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "OCR vision-language model. The 1.0 report describes a native-resolution ViT around 0.4B connected by an MLP adapter to a 0.5B Hunyuan language model. 1.5 keeps a lightweight 1B-class VLM and adds a DFlash draft model of about 90.7M parameters.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HunyuanOCR is Tencent's open 1B vision-language model for document OCR. Version 1.0 shipped in November 2025 and version 1.5, released on 2026-07-07, is the current Hugging Face root checkpoint. Collected on 2026-10-15.", "what_it_is_for": "Document parsing, text spotting, information extraction, and text-image translation in one small VLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-vision/hunyuan-t1-vision": { "id": "tencent/hunyuan-vision/hunyuan-t1-vision", "name": "Hunyuan-T1-Vision", "maker": "tencent", "family": "tencent/hunyuan-vision", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 9.0 }, "summary_intro": "Hunyuan-T1-Vision is a closed Hunyuan vision API named in Tencent's billing PDF at 3 CNY per million input tokens and 9 CNY per million output tokens. It does not appear on the 2026-09-25 TokenHub list. Collected on 2026-10-15.", "what_it_is_for": "Image understanding on the T1 line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-vision/hunyuan-turbos-vision": { "id": "tencent/hunyuan-vision/hunyuan-turbos-vision", "name": "Hunyuan-TurboS-Vision", "maker": "tencent", "family": "tencent/hunyuan-vision", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 9.0 }, "summary_intro": "Hunyuan-TurboS-Vision is a closed Hunyuan vision API named in Tencent's billing PDF at 3 CNY per million input tokens and 9 CNY per million output tokens. It does not appear on the 2026-09-25 TokenHub list. Collected on 2026-10-15.", "what_it_is_for": "Image understanding on the TurboS line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-vision/hunyuan-turbos-vision-video": { "id": "tencent/hunyuan-vision/hunyuan-turbos-vision-video", "name": "Hunyuan-TurboS-Vision-Video", "maker": "tencent", "family": "tencent/hunyuan-vision", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 9.0 }, "summary_intro": "Hunyuan-TurboS-Vision-Video is a closed Hunyuan vision API named in Tencent's billing PDF at 3 CNY per million input tokens and 9 CNY per million output tokens. It does not appear on the 2026-09-25 TokenHub list. Collected on 2026-10-15.", "what_it_is_for": "Video understanding on the TurboS vision line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-vision/hy-vision-1-5": { "id": "tencent/hunyuan-vision/hy-vision-1-5", "name": "Tencent HY Vision 1.5 Instruct", "maker": "tencent", "family": "tencent/hunyuan-vision", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Changelog says it is a fast image-to-text model built on the text TurboS base.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 3.0, "completion_per_m_usd": 9.0 }, "summary_intro": "Tencent HY Vision 1.5 Instruct is a closed image-to-text model Tencent Cloud listed in December 2025, built on the TurboS text base. It is not on the September 2026 TokenHub list. Collected on 2026-10-15.", "what_it_is_for": "The changelog says it improves basic image recognition and visual reasoning over the previous vision snapshot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan-vision/hy-vision-2-0": { "id": "tencent/hunyuan-vision/hy-vision-2-0", "name": "Hy Vision 2.0", "maker": "tencent", "family": "tencent/hunyuan-vision", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The Hunyuan site says it is built on the HY-2.0 language model, with more pretraining data and a post-train mix of SFT, RL, and on-policy distillation.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hy Vision 2.0 is a Tencent vision-understanding model named on hunyuan.tencent.com. The site calls it a fast image-to-text model on the HY-2.0 language base. It was not on the TokenHub model list of 2026-09-25. Collected on 2026-10-15.", "what_it_is_for": "General image-to-text understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan3d/hunyuan3d-1": { "id": "tencent/hunyuan3d/hunyuan3d-1", "name": "Hunyuan3D-1.0", "maker": "tencent", "family": "tencent/hunyuan3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-10-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage text-to-3D and image-to-3D framework.", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan3D-1.0 is Tencent's first open 3D generation framework for text-to-3D and image-to-3D. The repository was created on 2024-10-31. Collected on 2026-10-15.", "what_it_is_for": "Generate 3D assets from text or a single image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan3d/hunyuan3d-2": { "id": "tencent/hunyuan3d/hunyuan3d-2", "name": "Hunyuan3D-2", "maker": "tencent", "family": "tencent/hunyuan3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tencent Hunyuan Community License", "release_date": "2025-01-21", "context_length": null, "max_output_tokens": null, "parameters": "Shape DiT 1.1B; paint 1.3B; delight 1.3B", "architecture": "Diffusion shape model plus a texture paint model. Turbo and fast distillations exist. The 2.0 README also hosts mini and multiview variants, which have their own folders.", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan3D-2 is Tencent's open image-to-3D system released on 2025-01-21, with a 1.1B shape model and a 1.3B texture model. Turbo and fast distilled checkpoints followed. Collected on 2026-10-15.", "what_it_is_for": "High-resolution 3D assets from images, with separate shape and texture stages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan3d/hunyuan3d-2-1": { "id": "tencent/hunyuan3d/hunyuan3d-2-1", "name": "Hunyuan3D-2.1", "maker": "tencent", "family": "tencent/hunyuan3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-14", "context_length": null, "max_output_tokens": null, "parameters": "Shape 3.3B; paint 2B", "architecture": "Image-to-shape plus PBR texture. The 2.1 repository says full weights and training code are released. An older Hunyuan3D-2 README listed a 3.0B DiT and a 1.3B paint model for 2.1. The 2.1 repository zoo is used here.", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan3D-2.1 is Tencent's production-oriented open 3D asset model with PBR textures. The 2.1 model zoo lists a 3.3B shape model and a 2B paint model dated 2025-06-14. Collected on 2026-10-15.", "what_it_is_for": "Image-to-3D assets with physically based materials, including a path Tencent says can be fine-tuned because training code is included.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan3d/hunyuan3d-2mini": { "id": "tencent/hunyuan3d/hunyuan3d-2mini", "name": "Hunyuan3D-2mini", "maker": "tencent", "family": "tencent/hunyuan3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tencent Hunyuan Community License", "release_date": "2025-03-18", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Mini image-to-shape diffusion model, with fast and turbo distillations.", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan3D-2mini is the 0.6B image-to-shape variant of Hunyuan3D-2, released on 2025-03-18 with a turbo step-distilled checkpoint the next day. Collected on 2026-10-15.", "what_it_is_for": "Faster, smaller 3D shape generation than the 1.1B Hunyuan3D-2 shape model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan3d/hunyuan3d-2mv": { "id": "tencent/hunyuan3d/hunyuan3d-2mv", "name": "Hunyuan3D-2mv", "maker": "tencent", "family": "tencent/hunyuan3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tencent Hunyuan Community License", "release_date": "2025-03-18", "context_length": null, "max_output_tokens": null, "parameters": "1.1B", "architecture": "Multiview image-to-shape model, with fast and turbo distillations.", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan3D-2mv is Tencent's 1.1B multiview image-to-shape model, released on 2025-03-18 beside Hunyuan3D-2. Collected on 2026-10-15.", "what_it_is_for": "Generate a 3D shape from several views of an object rather than one image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan3d/hunyuan3d-omni": { "id": "tencent/hunyuan3d/hunyuan3d-omni", "name": "Hunyuan3D-Omni", "maker": "tencent", "family": "tencent/hunyuan3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan3D-Omni is Tencent's controllable 3D generation framework. The repository was created on 2025-09-25. Collected on 2026-10-15.", "what_it_is_for": "Controllable 3D asset generation beyond the single-image Hunyuan3D-2.1 pipeline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuan3d/hunyuan3d-part": { "id": "tencent/hunyuan3d/hunyuan3d-part", "name": "Hunyuan3D-Part", "maker": "tencent", "family": "tencent/hunyuan3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-part pipeline: P3-SAM segments a mesh, and X-Part generates complete parts.", "modalities": { "input": [ "3d" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan3D-Part is Tencent's open pipeline for splitting a 3D mesh into parts and regenerating them. It combines P3-SAM and X-Part. The repository was created on 2025-09-25. Collected on 2026-10-15.", "what_it_is_for": "Part-aware 3D segmentation and generation for asset editing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanimage/hunyuan-dit": { "id": "tencent/hunyuanimage/hunyuan-dit", "name": "Hunyuan-DiT", "maker": "tencent", "family": "tencent/hunyuanimage", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tencent Hunyuan Community License", "release_date": "2024-05-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-resolution diffusion transformer aimed at Chinese prompts. Distilled Diffusers weights exist.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-DiT is Tencent's open-weight text-to-image diffusion transformer, published in the HunyuanDiT repository created on 2024-05-10. Tencent describes it as a multi-resolution model with detailed Chinese understanding. Collected on 2026-10-15.", "what_it_is_for": "Text-to-image generation, with emphasis on Chinese prompts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanimage/hunyuanimage-2-1": { "id": "tencent/hunyuanimage/hunyuanimage-2-1", "name": "HunyuanImage-2.1", "maker": "tencent", "family": "tencent/hunyuanimage", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tencent Hunyuan Community License", "release_date": "2025-09-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion text-to-image model aimed at 2K resolution. An FP8 checkpoint was released for lower memory.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HunyuanImage-2.1 is Tencent's open-weight text-to-image model for high-resolution generation. Inference weights were released on 2025-09-08, with FP8 weights on 2025-09-12. Collected on 2026-10-15.", "what_it_is_for": "2K text-to-image generation under the Hunyuan community license.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanimage/hunyuanimage-3-0": { "id": "tencent/hunyuanimage/hunyuanimage-3-0", "name": "HunyuanImage-3.0", "maker": "tencent", "family": "tencent/hunyuanimage", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-28", "context_length": null, "max_output_tokens": null, "parameters": "80B total, 13B active", "architecture": "Native multimodal model for image generation. Instruct adds reasoning and image-to-image editing. Distil is an 8-step checkpoint.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": 1.6, "completion_per_m_usd": null }, "summary_intro": "HunyuanImage-3.0 is Tencent's open native multimodal image model, released on 2025-09-28 with 80B total and 13B active parameters. An Instruct checkpoint with image editing followed on 2026-01-26. TokenHub serves Hy-Image-3.0 as hy-image-v3. Collected on 2026-10-15.", "what_it_is_for": "Text-to-image generation that can plan layout and render long text, plus Instruct image-to-image editing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanimage/hunyuanimage-3-0-plus": { "id": "tencent/hunyuanimage/hunyuanimage-3-0-plus", "name": "HY Image 3.0 Plus", "maker": "tencent", "family": "tencent/hunyuanimage", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HY Image 3.0 Plus is a Hunyuan image model named on hunyuan.tencent.com. The site describes multi-turn editing, better text rendering, and more realistic output. It was not a separate row on the TokenHub list fetched for this catalog. Collected on 2026-10-15.", "what_it_is_for": "Higher-quality visual creation than the first HunyuanImage-3.0 consumer experience, including multi-turn edits.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanimage/hunyuanimage-3-5-preview": { "id": "tencent/hunyuanimage/hunyuanimage-3-5-preview", "name": "Hy-Image-3.5-preview", "maker": "tencent", "family": "tencent/hunyuanimage", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": 1.6, "completion_per_m_usd": null }, "summary_intro": "Hy-Image-3.5-preview is Tencent's current preview image model on TokenHub, id hy-image-v3.5-preview. The list describes better intent understanding and visual delivery than the previous image generation line. Collected on 2026-10-15.", "what_it_is_for": "Text-to-image, reference-image generation, and multi-turn editing at 1K, 2K, and 4K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanimage/hy-image-lite": { "id": "tencent/hunyuanimage/hy-image-lite", "name": "HY-Image-Lite", "maker": "tencent", "family": "tencent/hunyuanimage", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encyclopedia text says it uses a high-compression codec for faster image generation.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HY-Image-Lite is a smaller Hunyuan text-to-image API that a Tencent Cloud encyclopedia listed on 2026-04-21. It is not on the 2026-09-25 TokenHub model list, so this catalog marks it retired without a precise retirement day. Collected on 2026-10-15.", "what_it_is_for": "The encyclopedia points it at product images, design assets, and game-scene iteration where speed matters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanvideo/hunyuan-gamecraft": { "id": "tencent/hunyuanvideo/hunyuan-gamecraft", "name": "Hunyuan-GameCraft", "maker": "tencent", "family": "tencent/hunyuanvideo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hunyuan-GameCraft is a named Tencent Hunyuan video model. The public repository was created on 2025-08-13. Collected on 2026-10-15.", "what_it_is_for": "Interactive game video generation with hybrid history conditioning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanvideo/hunyuancustom": { "id": "tencent/hunyuanvideo/hunyuancustom", "name": "HunyuanCustom", "maker": "tencent", "family": "tencent/hunyuanvideo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HunyuanCustom is a named Tencent Hunyuan video model. The public repository was created on 2025-05-07. Collected on 2026-10-15.", "what_it_is_for": "Custom video generation from image, audio, video, and text conditions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanvideo/hunyuanvideo": { "id": "tencent/hunyuanvideo/hunyuanvideo", "name": "HunyuanVideo", "maker": "tencent", "family": "tencent/hunyuanvideo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tencent Hunyuan Community License", "release_date": "2024-12-03", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Diffusion transformer video model with a 3D VAE. The paper sets the model size at 13B and also says over 13 billion parameters. Prompt rewrite used a Hunyuan-Large finetune.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HunyuanVideo is Tencent's open-weight text-to-video foundation model, described in a 2024-12-03 paper as a 13B-parameter diffusion transformer. Weights and code are public under the Tencent Hunyuan Community License. Collected on 2026-10-15.", "what_it_is_for": "Text-to-video generation. Tencent reported that professional raters preferred it to Runway Gen-3 and Luma 1.6 at the time of the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanvideo/hunyuanvideo-1-5": { "id": "tencent/hunyuanvideo/hunyuanvideo-1-5", "name": "HunyuanVideo-1.5", "maker": "tencent", "family": "tencent/hunyuanvideo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-20", "context_length": null, "max_output_tokens": null, "parameters": "8.3B", "architecture": "Diffusion transformer with selective and sliding tile attention and a 3D causal VAE. Spatial compression 16x and temporal compression 4x. Super-resolution stages target 720p and 1080p. Step-distilled and CFG-distilled checkpoints exist.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": 1.6, "completion_per_m_usd": null }, "summary_intro": "HunyuanVideo-1.5 is Tencent's open 8.3B video model, released on 2025-11-20, for text-to-video and image-to-video. It is small enough for the vendor to claim consumer-GPU inference with offloading. TokenHub serves it as hy-video-v1.5. Collected on 2026-10-15.", "what_it_is_for": "Shorter, cheaper video generation than the 13B HunyuanVideo model, including 480p and 720p and a super-resolution path toward 1080p.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanvideo/hunyuanvideo-avatar": { "id": "tencent/hunyuanvideo/hunyuanvideo-avatar", "name": "HunyuanVideo-Avatar", "maker": "tencent", "family": "tencent/hunyuanvideo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HunyuanVideo-Avatar is a named Tencent Hunyuan video model. The public repository was created on 2025-05-26. Collected on 2026-10-15.", "what_it_is_for": "Image-to-video avatar generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanvideo/hunyuanvideo-foley": { "id": "tencent/hunyuanvideo/hunyuanvideo-foley", "name": "HunyuanVideo-Foley", "maker": "tencent", "family": "tencent/hunyuanvideo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "video" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HunyuanVideo-Foley is a named Tencent Hunyuan video model. The public repository was created on 2025-08-15. Collected on 2026-10-15.", "what_it_is_for": "Foley audio from video and text. The repository describes a multimodal diffusion model for sound effects.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanvideo/hunyuanvideo-i2v": { "id": "tencent/hunyuanvideo/hunyuanvideo-i2v", "name": "HunyuanVideo-I2V", "maker": "tencent", "family": "tencent/hunyuanvideo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Tencent Hunyuan Community License", "release_date": "2025-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-to-video model based on HunyuanVideo. Repository description says it is customizable.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HunyuanVideo-I2V is Tencent's image-to-video model based on HunyuanVideo. The public repository was created on 2025-03-04. Collected on 2026-10-15.", "what_it_is_for": "Animate a reference image with a text instruction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanvideo/omniweaving": { "id": "tencent/hunyuanvideo/omniweaving", "name": "OmniWeaving", "maker": "tencent", "family": "tencent/hunyuanvideo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-03-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmniWeaving is a named Tencent Hunyuan video model. The public repository was created on 2026-03-31. Collected on 2026-10-15.", "what_it_is_for": "Unified video generation on top of HunyuanVideo-1.5, including text, frames, editing, and reference video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanworld/hunyuanworld-1-0": { "id": "tencent/hunyuanworld/hunyuanworld-1-0", "name": "HunyuanWorld-1.0", "maker": "tencent", "family": "tencent/hunyuanworld", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HunyuanWorld-1.0 is Tencent's open 3D world model for immersive scenes from words or pixels. A Hunyuan3D repository news line dates the release to 2025-07-26. Collected on 2026-10-15.", "what_it_is_for": "Generate an explorable 3D world from text or an image. The Hunyuan3D-1 README news line dates the open release to 2025-07-26. The GitHub repo was created 2025-07-18.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanworld/hunyuanworld-mirror": { "id": "tencent/hunyuanworld/hunyuanworld-mirror", "name": "HunyuanWorld-Mirror", "maker": "tencent", "family": "tencent/hunyuanworld", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HunyuanWorld-Mirror is Tencent's open 3D reconstruction model, also called WorldMirror. The repository was created on 2025-10-16. Collected on 2026-10-15.", "what_it_is_for": "Fast universal 3D reconstruction. The repository tags it as an ICML 2026 model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanworld/hunyuanworld-voyager": { "id": "tencent/hunyuanworld/hunyuanworld-voyager", "name": "HunyuanWorld-Voyager", "maker": "tencent", "family": "tencent/hunyuanworld", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "video", "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HunyuanWorld-Voyager is Tencent's camera-conditioned world model. The repository, created 2025-08-27, describes interactive RGBD video and 3D reconstruction. Collected on 2026-10-15.", "what_it_is_for": "Camera-conditioned RGBD video for interactive world exploration, with real-time 3D reconstruction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanworld/hy-world-1-5": { "id": "tencent/hunyuanworld/hy-world-1-5", "name": "HY-World 1.5", "maker": "tencent", "family": "tencent/hunyuanworld", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video", "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HY-World 1.5 is Tencent's interactive world model, published in the HY-WorldPlay repository created on 2025-12-10. Collected on 2026-10-15.", "what_it_is_for": "Interactive world model aimed at real-time latency and geometric consistency. The repository is named HY-WorldPlay and the description says HY-World 1.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanworld/hy-world-2-0": { "id": "tencent/hunyuanworld/hy-world-2-0", "name": "HY-World 2.0", "maker": "tencent", "family": "tencent/hunyuanworld", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-04-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HY-World 2.0 is Tencent's open multi-modal 3D world model. The repository was created on 2026-04-10. Collected on 2026-10-15.", "what_it_is_for": "Multi-modal world model for reconstructing, generating, and simulating 3D worlds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hunyuanworld/hy-world-2-1": { "id": "tencent/hunyuanworld/hy-world-2-1", "name": "Hy-World 2.1", "maker": "tencent", "family": "tencent/hunyuanworld", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image", "3d" ] }, "pricing": { "prompt_per_m_usd": 1.6, "completion_per_m_usd": null }, "summary_intro": "Hy-World 2.1 is Tencent's current TokenHub world model, with separate panorama and 3D-scene endpoints. A panorama is billed around 0.77 USD and a scene around 12.3 USD on the October 2026 price page. Collected on 2026-10-15.", "what_it_is_for": "Turn text or an image into a 360-degree panorama or a walkable 3D scene with collision, for engines and world building.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hy/hy3": { "id": "tencent/hy/hy3", "name": "Hy3", "maker": "tencent", "family": "tencent/hy", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 262144, "max_output_tokens": 131072, "parameters": "295B total, 21B activated per token, plus a 3.8B MTP layer", "architecture": "MoE, 80 layers excluding MTP, 1 MTP layer. 192 experts, top-8. GQA with 64 attention heads and 8 KV heads, head dim 128. Hidden size 4096. Intermediate size 13312. Vocabulary 120832.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.132, "completion_per_m_usd": 0.528 }, "summary_intro": "Hy3 is Tencent's open-weight Mixture-of-Experts language model with 295B total and 21B active parameters and a 256K context window. The preview weights were posted on 2026-04-23 and the full Hy3 line is the current TokenHub model hy3. Tencent licenses the full release as Apache-2.0. Collected on 2026-10-15.", "what_it_is_for": "Tencent positions Hy3 for reasoning, coding, tool use, and long-context agent work at a lower cost than Hy4 preview.", "ratings": { "empirical_100": 48, "lmsys_elo": 1209, "coding_elo": 1209, "swe_bench": 16.5, "humaneval": 71.2, "twelve_cs": 8.6, "aa_intelligence": 25.3, "aa_coding": 58.8, "aa_agentic": 24.1, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hy/hy4-preview": { "id": "tencent/hy/hy4-preview", "name": "Hy4 preview", "maker": "tencent", "family": "tencent/hy", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-08-28", "context_length": 1048576, "max_output_tokens": 65536, "parameters": "770B total backbone, 49B activated per token; separate MTP layer 10B total and 0.7B active", "architecture": "MoE, 78 layers (layer 1 dense FFN, 77 MoE). 256 routed experts plus 1 shared expert, top-8 routed experts per token. Hidden size 6144. Gated DSA attention, 64 heads. iHC residual streams. Vocabulary 120832.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.834, "completion_per_m_usd": 2.501 }, "summary_intro": "Hy4 preview is Tencent's open-weight flagship language model, announced on 2026-08-28. It is a 770B-total Mixture-of-Experts model with 49B parameters active per token and a 1M-token context window, sold on TokenHub and marked preview because Tencent calls it an early Hy4. This dossier was collected on 2026-10-15.", "what_it_is_for": "Tencent says it is built for long-horizon software engineering, document-heavy office work, and scientific research, and that it is served in CodeBuddy, WorkBuddy, Yuanbao, and ima as well as by API.", "ratings": { "empirical_100": 46, "lmsys_elo": 1233, "coding_elo": 1233, "swe_bench": 20.6, "humaneval": 74.5, "twelve_cs": 36.1, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hy-embodied/hy-embodied": { "id": "tencent/hy-embodied/hy-embodied", "name": "HY-Embodied", "maker": "tencent", "family": "tencent/hy-embodied", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-04-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Embodied foundation models. Named releases in the same family include HY-Embodied-0.5-X, HY-Embodied-0.5-VLA, and Hy-Embodied-RxBrain-1.0.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HY-Embodied is Tencent's embodied foundation-model line for real-world agents. The first public repository was created on 2026-04-05, with 0.5-X, 0.5-VLA, and RxBrain 1.0 following through July 2026. Collected on 2026-10-15.", "what_it_is_for": "Robot and agent models that combine vision, language, and action.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tencent/hy-motion/hy-motion-1-0": { "id": "tencent/hy-motion/hy-motion-1-0", "name": "HY-Motion-1.0", "maker": "tencent", "family": "tencent/hy-motion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HY-Motion-1.0 is Tencent's named model for 3D human motion and character animation. The repository was created on 2025-12-29. Collected on 2026-10-15.", "what_it_is_for": "Generate 3D character motion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "thessaloniki/deepsing/deepsing": { "id": "thessaloniki/deepsing/deepsing", "name": "deepsing", "maker": "thessaloniki", "family": "thessaloniki/deepsing", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub LICENSE.md says deepsing is available under the GPLv3 where it applies, and to contact the authors for other options. The file then includes the GNU GPL version 3 text of 29 June 2007 without a filled copyright holder. A separate licence for the Google Drive weight archive is not stated. The image generator is a pretrained BigGAN loaded through pytorch-pretrained-biggan, not part of that archive. The README says generated videos may be used if the deepsing logo is kept.", "release_date": "2019-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cross-modal music-to-image pipeline. Audio attributes are 40 MFCCs, chroma energy normalized statistics, and a tempogram, concatenated every 500 ms and fed to a regressor with one hidden layer of 256 sigmoid units and a 2-unit valence-arousal output, trained on DEAM. The visual supervisor is MobileNetV2, ImageNet-pretrained and fine-tuned on OASIS to regress valence and arousal. The translator has hidden layers of 64 and 256 units, then a 1000-way softmax class head and a 128-unit latent head. Images are 512 by 512 from a pretrained BigGAN with 1000 classes. Gaussian noise with sigma 0.1 is added to the translated latent vector. Optional stylization uses a whitening-and-coloring transform on the first four encoder-decoder pairs with blend factor 0.1. A dictionary translator is the default runtime path; the neural translator is optional.", "modalities": { "input": [ "audio" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "deepsing is a music-to-image system released by Nikolaos Passalis, then at the Aristotle University of Thessaloniki, with Stavros Doropoulos of DataScouting. It turns a song into a sequence of images meant to carry the same valence and arousal as the audio. The arXiv preprint is 11 December 2019. The code and a Google Drive weight archive were public on the deepsing-ai GitHub repository, whose organization page showed a last update of 26 December 2019. The same method appeared in Expert Systems with Applications in February 2021. On 2026-10-15 the repository and https://www.deepsing.com/ were still up. There is no university API.", "what_it_is_for": "The paper and the project site describe attributed music-to-image translation. An audio regressor reads valence and arousal. A translator maps that pair onto a BigGAN class and a latent vector. The generator draws a 512 by 512 frame. An optional whitening-and-coloring stylization step can push the frame toward a user style. The authors present it as a way to build a visual story that follows the feeling of a song, not as a chatbot or a priced API. The site calls the demo deepsing v0.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tifr/disobind/disobind": { "id": "tifr/disobind/disobind", "name": "Disobind", "maker": "tifr", "family": "tifr/disobind", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2026-01-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Released class Epsilon_3. It projects ProtT5 sequence embeddings, builds a pairwise interaction tensor, and runs downsampling and upsampling blocks (vanilla or residual) into a linear contact head, with an optional interface block and optional temperature scaling. The Cell Systems abstract says the method uses ProtT5 embeddings and does not use structures or multiple sequence alignments. Parameter count is not stated.", "modalities": { "input": [ "protein sequence" ], "output": [ "contact map", "interface residues" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Disobind is a sequence model from the Integrative Structural Biology Lab at the National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore. It predicts inter-protein contact maps and interface residues for an intrinsically disordered region and a partner. Weights and code are public under GPL-3.0. Status on 2026-10-15: active.", "what_it_is_for": "The README says Disobind predicts where an IDR and its partner interact, given sequences. Protein 1 must be an IDR. The pair is assumed to interact. Binary complexes are the supported case. A Colab notebook runs Disobind together with ColabFold AlphaFold2. That combination is a mode, not a second product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tifr/frabjous/frabjous": { "id": "tifr/frabjous/frabjous", "name": "Frabjous", "maker": "tifr", "family": "tifr/frabjous", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-07-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Keras and TensorFlow CNNs. Input is a 256 by 256 dynamic spectrum (frequency 400-800 MHz, time 256 ms). Binary one-versus-one networks end in a sigmoid. A separate multi-class network ends in a softmax over five morphological types. The paper says the binary nets use convolutional layers, dense layers, dropout, and Adam. Exact layer widths and the parameter count are not stated in text.", "modalities": { "input": [ "image" ], "output": [ "class label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Frabjous is a convolutional classifier of fast radio burst morphologies from Ajay Kumar at the National Centre for Radio Astrophysics, Tata Institute of Fundamental Research, with Ashish A. Mahabal and Shriharsh P. Tendulkar. Keras SavedModels are in the public repository. Status on 2026-10-15: active. The arXiv record says the paper was accepted in PASA.", "what_it_is_for": "The README calls FRABJOUS a framework that simulates FRB dynamic spectra, preprocesses real data, and classifies morphology with binary one-versus-one CNNs or one multiclass CNN. The paper aims at follow-up priority and morphology statistics. Types I through V are the five-way scheme. Type VI (sidelobe diffraction) is simulated and then excluded from that classifier.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tifr/idpgpt/llps-minus-gpt": { "id": "tifr/idpgpt/llps-minus-gpt", "name": "LLPS- GPT", "maker": "tifr", "family": "tifr/idpgpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT protein language model trained on sequences the README describes as able to undergo liquid-liquid phase separation with lesser intensity than LLPS+ GPT. Checkpoint models/llps_minus_GPT.pt. Layer count and parameter count are not stated on the README.", "modalities": { "input": [ "protein sequence" ], "output": [ "protein sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLPS- GPT is the moderate liquid-liquid phase separation generator in the idpGPT set from TIFR Hyderabad. The README says its sequences can undergo LLPS with lesser intensity than LLPS+ GPT. Status on 2026-10-15: active.", "what_it_is_for": "The preprint defines three GPT models by the LLPS propensity of their training sequences. This one is the moderate set. It is a different training run from LLPS+ GPT and from PDB* GPT, and the repository ships a separate weight file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tifr/idpgpt/llps-plus-gpt": { "id": "tifr/idpgpt/llps-plus-gpt", "name": "LLPS+ GPT", "maker": "tifr", "family": "tifr/idpgpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT protein language model trained on sequences the authors class as highly prone to liquid-liquid phase separation. The checkpoint file is models/llps_plus_GPT.pt. Layer count and parameter count are not stated on the README.", "modalities": { "input": [ "protein sequence" ], "output": [ "protein sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLPS+ GPT is one of three protein GPT models from Jagannath Mondal's lab at the Tata Institute of Fundamental Research Hyderabad. The README says it generates sequences that are highly prone to liquid-liquid phase separation. The weight file is public. Status on 2026-10-15: active.", "what_it_is_for": "The preprint trains separate GPT models on sequences with high, moderate, and low propensity for liquid-liquid phase separation. This checkpoint is the high-propensity model. The authors use the generated sequences to study amino-acid grammar and check phase-separation behavior in multi-chain simulations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tifr/idpgpt/pdb-star-gpt": { "id": "tifr/idpgpt/pdb-star-gpt", "name": "PDB* GPT", "maker": "tifr", "family": "tifr/idpgpt", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT protein language model trained on folded sequences the README says would not undergo liquid-liquid phase separation unless drastic conditions are applied. Checkpoint models/no_PS_GPT.pt. The README also says the file is saved as no_PS.pt. Layer count and parameter count are not stated.", "modalities": { "input": [ "protein sequence" ], "output": [ "protein sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PDB* GPT is the liquid-liquid phase separation-resistant generator in the idpGPT set from TIFR Hyderabad. The README says it generates sequences that would not undergo LLPS unless drastic conditions are applied. Status on 2026-10-15: active.", "what_it_is_for": "The preprint trains this GPT on folded proteins that resist phase separation, as a contrast to the LLPS+ and LLPS- generators. The repository ships it as its own weight file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tifr/idrbindnet/idrbindnet": { "id": "tifr/idrbindnet/idrbindnet", "name": "IDRBindNet", "maker": "tifr", "family": "tifr/idrbindnet", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-02-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Graph transformer GTR in the inference code. Node features come from a protein language model. The released checkpoints are ProtT5-BFD. Three TransformerConv layers use hidden size 128 and, for the first two layers, 8 heads. An edge encoder maps edge features through width 64 into that hidden size. Global mean pooling feeds a regression head with one output. The preprint says training used 300 epochs, batch size 32, Adagrad, learning rate 10^-2, and MSE loss. Parameter count is not stated.", "modalities": { "input": [ "protein structure", "protein sequence" ], "output": [ "binding affinity" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IDRBindNet is a graph-transformer affinity model from Jagannath Mondal's lab at the Tata Institute of Fundamental Research Hyderabad. It predicts a dissociation constant for an intrinsically disordered region bound to a partner protein. Five ProtT5-BFD checkpoints are in the public repository. Status on 2026-10-15: active.", "what_it_is_for": "The README says the pipeline predicts binding affinities for IDR-protein complexes from PDB structures. The preprint introduces the IBPC-Kd set and the predictor IDRBindNet. Inference writes a mean Kd and the standard deviation across five split models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tifr/vroom-sbi/vroom-sbi": { "id": "tifr/vroom-sbi/vroom-sbi", "name": "VROOM-SBI", "maker": "tifr", "family": "tifr/vroom-sbi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural posterior estimation for Stokes Q and U spectra. The arXiv HTML says the QU embedding network has hidden sizes 256 and 128, output size 64, LayerNorm, and dropout 0.1, followed by a normalizing flow. Separate estimators cover Faraday-thin, Burn-slab, external-dispersion, and internal-dispersion models, each for 1 to 5 components. A classifier selects the model. Parameter count is not stated.", "modalities": { "input": [ "radio spectrum" ], "output": [ "posterior parameters" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VROOM-SBI is a set of neural posterior estimators for Faraday rotation and depolarization, released by Arpan Pal (National Radio Astronomy Observatory and the National Centre for Radio Astrophysics, Tata Institute of Fundamental Research) and Preshanth Jagannathan (NRAO). Trained weights are on Hugging Face as `skunkworks-ra/vroomsbi`. Status on 2026-10-15: active. The Astronomical Journal published the paper on 2026-09-24.", "what_it_is_for": "The paper uses simulation-based inference so that QU-fitting can run fast enough for surveys. The Hub card ships posterior networks for Burn slab, external dispersion, Faraday thin, and internal dispersion, plus a classifier. Each physical model has component counts 1 through 5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon/falcon-180b": { "id": "tii/falcon/falcon-180b", "name": "Falcon-180B", "maker": "tii", "family": "tii/falcon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Falcon-180B TII License (royalty-free, Apache 2.0 based, with acceptable-use limits and a separate license for shared hosted inference or fine-tuning).", "release_date": "2023-09-06", "context_length": null, "max_output_tokens": null, "parameters": "180B", "architecture": "Scaled Falcon-40B with multiquery attention. Trained on 3.5 trillion tokens of RefinedWeb-based data. The model card returned 401 on 2026-10-15, so layer counts were not read from the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-180B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "TII's largest 2023 open-access language model, for research and commercial use inside the license. A chat fine-tune was released with it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon/falcon-40b": { "id": "tii/falcon/falcon-40b", "name": "Falcon-40B", "maker": "tii", "family": "tii/falcon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2023-05-25", "context_length": 2048, "max_output_tokens": null, "parameters": "40B", "architecture": "Causal decoder-only. Rotary embeddings, multiquery attention (independent key/value per tensor-parallel shard), FlashAttention, parallel attention/MLP with two layer norms. 60 layers, d_model 8192, head_dim 64, vocabulary 65024, sequence length 2048. Trained on 1,000B tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-40B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "TII's 2023 flagship open model. The base is a foundation for fine-tunes. Falcon-40B-Instruct is the chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon/falcon-7b": { "id": "tii/falcon/falcon-7b", "name": "Falcon-7B", "maker": "tii", "family": "tii/falcon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "Causal decoder-only. Rotary embeddings, multiquery attention, FlashAttention, parallel attention/MLP with one layer norm. 32 layers, d_model 4544, head_dim 64, vocabulary 65024, sequence length 2048. Trained on 1,500B tokens (RefinedWeb plus curated corpora).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-7B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "General foundation model. The base card says it should be fine-tuned for most uses. Falcon-7B-Instruct is the chat fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon/falcon-rw-1b": { "id": "tii/falcon/falcon-rw-1b", "name": "Falcon-RW-1B", "maker": "tii", "family": "tii/falcon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "1B", "architecture": "Causal decoder-only. 24 layers, d_model 2048, head_dim 64, vocabulary 50304, sequence length 2048. ALiBi and FlashAttention. Trained on 350B RefinedWeb tokens with the GPT-2 tokenizer. Research artifact for web-only pretraining.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-RW-1B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "TII describes Falcon-RW-1B as a research model for studying training on filtered web data alone, not as the production Falcon checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon/falcon-rw-7b": { "id": "tii/falcon/falcon-rw-7b", "name": "Falcon-RW-7B", "maker": "tii", "family": "tii/falcon", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "Causal decoder-only. 36 layers (card says the extra depth came from a config error when leaving multi-query), sequence length 2048. ALiBi and FlashAttention. Trained on 350B RefinedWeb tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-RW-7B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Research checkpoint for web-only pretraining. The card points users who want a production model to Falcon-7B or Falcon-40B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-2/falcon-2-11b": { "id": "tii/falcon-2/falcon-2-11b", "name": "Falcon 2 11B", "maker": "tii", "family": "tii/falcon-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "TII Falcon License 2.0 (Apache 2.0 based, with an acceptable use policy).", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "11B", "architecture": "Causal decoder-only. Rotary embeddings, multiquery attention, FlashAttention-2, parallel attention/MLP. 60 layers, d_model 4096, head_dim 128, vocabulary 65024. Context was grown 2048 then 4096 then 8192. Trained on over 5,000B tokens in four stages (4500B + 250B + 250B + 500B).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon 2 11B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Multilingual foundation model. The card says the raw pretrained checkpoint should be fine-tuned for most uses. The Falcon site also advertised a vision twin.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-2/falcon-2-11b-vlm": { "id": "tii/falcon-2/falcon-2-11b-vlm", "name": "Falcon 2 11B VLM", "maker": "tii", "family": "tii/falcon-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "TII Falcon License 2.0 (Apache 2.0 based, with an acceptable use policy).", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "11B", "architecture": "Falcon2-11B chat checkpoint plus a frozen CLIP ViT-L/14 encoder and a trained multimodal projector. Dynamic high-resolution encoding for small objects. Pretrain stage trains only the projector on 558K image-caption pairs. The Falcon models index still says the VLM is available soon, but the weight repo exists.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon 2 11B VLM. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Image-to-text model. First Falcon vision-language release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-3/falcon3-10b": { "id": "tii/falcon-3/falcon3-10b", "name": "Falcon3-10B", "maker": "tii", "family": "tii/falcon-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "TII Falcon-LLM License 2.0", "release_date": "2024-12-17", "context_length": 32768, "max_output_tokens": null, "parameters": "10B", "architecture": "Llama-compatible causal decoder. 40 blocks, GQA 12 query / 4 key-value, head dim 256, SwiGLU, RMSNorm, 32K context, 131K vocabulary. Depth-upscaled from Falcon3-7B-Base with 2 teratokens of continued pretraining.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon3-10B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Small open foundation model for reasoning, language, instruction following, code, and math. Base weights need post-training for most chat uses. Instruct, GGUF, GPTQ, AWQ, and 1.58-bit files are official variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-3/falcon3-1b": { "id": "tii/falcon-3/falcon3-1b", "name": "Falcon3-1B", "maker": "tii", "family": "tii/falcon-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "TII Falcon-LLM License 2.0", "release_date": "2024-12-17", "context_length": 8192, "max_output_tokens": null, "parameters": "1B", "architecture": "Llama-compatible causal decoder. Instruct card: 18 blocks, GQA with 8 query heads and 4 key-value heads, head dim 256, RoPE, SwiGLU, RMSNorm, 8K context, 131K vocabulary. Pruned and healed from the 3B and 7B models on 80 gigatokens. Instruct post-training used 1.2 million samples.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon3-1B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Small open foundation model for reasoning, language, instruction following, code, and math. Base weights need post-training for most chat uses. Instruct, GGUF, GPTQ, AWQ, and 1.58-bit files are official variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-3/falcon3-3b": { "id": "tii/falcon-3/falcon3-3b", "name": "Falcon3-3B", "maker": "tii", "family": "tii/falcon-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "TII Falcon-LLM License 2.0", "release_date": "2024-12-17", "context_length": 8192, "max_output_tokens": null, "parameters": "3B", "architecture": "Llama-compatible causal decoder. 22 blocks, GQA 12 query / 4 key-value, head dim 256, SwiGLU, RMSNorm, 8K context, 131K vocabulary. Pruned in depth and width from Falcon3-7B-Base and trained with distillation on 100 gigatokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon3-3B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Small open foundation model for reasoning, language, instruction following, code, and math. Base weights need post-training for most chat uses. Instruct, GGUF, GPTQ, AWQ, and 1.58-bit files are official variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-3/falcon3-7b": { "id": "tii/falcon-3/falcon3-7b", "name": "Falcon3-7B", "maker": "tii", "family": "tii/falcon-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "TII Falcon-LLM License 2.0", "release_date": "2024-12-17", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "Llama-compatible causal decoder. The launch post says the 7B is the single large pretrain, on 14 trillion tokens of web, code, STEM, and multilingual data, using 2048 H100s. Context up to 32K. Layer count was not copied from the 7B card in this pass.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon3-7B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Small open foundation model for reasoning, language, instruction following, code, and math. Base weights need post-training for most chat uses. Instruct, GGUF, GPTQ, AWQ, and 1.58-bit files are official variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-3/falcon3-audio": { "id": "tii/falcon-3/falcon3-audio", "name": "Falcon3-Audio", "maker": "tii", "family": "tii/falcon-3", "status": "active", "confidence": "medium", "weights": "closed", "license": "TII Falcon License (press release). Per-checkpoint license was not verified because no public repo was found.", "release_date": "2025-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Audio-language models on instruction-tuned Falcon3 backbones plus a Whisper encoder, trained in one stage on public audio (under 30K hours, 5K unique). Paper totals: 1B variant 1.8B (88M encoder, 5M projector, 1.7B LLM), 3B variant 3.6B, 7B variant 7.8B.", "modalities": { "input": [ "text", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon3-Audio. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Speech, sound, and music understanding. TII's January 2025 note says Falcon 3 gained audio as well as image and video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-3/falcon3-mamba-7b": { "id": "tii/falcon-3/falcon3-mamba-7b", "name": "Falcon3-Mamba-7B", "maker": "tii", "family": "tii/falcon-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "TII Falcon-LLM License 2.0", "release_date": "2024-12-17", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "Same Mamba1 layout as Falcon-Mamba-7B: 64 blocks, width 4096, state dimension 16, 32K context, 65K vocabulary. Continued pretraining from Falcon-Mamba-7B on another 1,500 gigatokens. Card new_version is Falcon-H1-7B-Base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon3-Mamba-7B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Updated pure state-space Falcon model with a longer context and more STEM and code data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-3/falcon3-video": { "id": "tii/falcon-3/falcon3-video", "name": "Falcon3 Video", "maker": "tii", "family": "tii/falcon-3", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-01-08", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Product page: video-language model with a visual and language decoder, 131K vocabulary, 32K context, GQA, Llama-compatible. Press release names 1B, 3B, 7B, and 10B and says question answering on video up to one hour. The page calls out a 10B and a 7B as the strong sizes.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon3 Video. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Video summarization and question answering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-3/falcon3-vision": { "id": "tii/falcon-3/falcon3-vision", "name": "Falcon3 Vision", "maker": "tii", "family": "tii/falcon-3", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Product page: image models use a 131K vocabulary, 32K context, GQA, and a Llama-compatible architecture. Sizes named by the press release: 1B, 3B, 7B, and 10B. Per-size cards were not found.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon3 Vision. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Image-to-text for the Falcon 3 line. English image inputs at launch, with more languages said to be planned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-arabic/falcon-arabic-7b": { "id": "tii/falcon-arabic/falcon-arabic-7b", "name": "Falcon-Arabic 7B", "maker": "tii", "family": "tii/falcon-arabic", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-21", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "Continued pretraining of Falcon3-7B. Tokenizer extended by 32,000 Arabic tokens with a similarity-based embedding init. Native Arabic data, including dialects, plus a later math, code, and reasoning stage. Base and instruct (SFT then DPO) both exist as names. A TII author wrote on 26 May 2025 that the model is closed and only on Falcon Chat.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-Arabic 7B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Arabic-first chat and general language model, including dialects. Served on Falcon Chat, not as public weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-asr/falcon-asr": { "id": "tii/falcon-asr/falcon-asr", "name": "Falcon-ASR", "maker": "tii", "family": "tii/falcon-asr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-06", "context_length": null, "max_output_tokens": null, "parameters": "1.6B", "architecture": "Speech recognition model built on Falcon3-Audio. One weight set for Arabic (including Emirati and Modern Standard), English, French, Spanish, and Portuguese, with no language flag. Word-level timestamps are claimed. A demo Space exists. A downloadable model repo was not in the tiiuae list.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-ASR. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Transcribe speech, with a focus on Emirati Arabic and noisy or telephony audio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-e/falcon-e-1b": { "id": "tii/falcon-e/falcon-e-1b", "name": "Falcon-E 1B", "maker": "tii", "family": "tii/falcon-e", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Falcon license (Hugging Face tag: other). Exact license name was not on the blog excerpt.", "release_date": "2025-05-15", "context_length": null, "max_output_tokens": null, "parameters": "1.8B", "architecture": "BitNet b1.58 ternary transformer, Llama-compatible norms kept, BitNet layer-norms removed. Vocabulary 32678, English-focused tokenizer with added LaTeX tokens. Blog table lists 1.8B parameters and a 665 MB footprint for the 1B-named model. Pretrained on about 1.5 trillion tokens. bfloat16 and prequantized revisions ship beside the ternary weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-E 1B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Edge language model that runs the ternary stack on CPUs. Base, instruct, bfloat16, and prequantized weights are one release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-e/falcon-e-3b": { "id": "tii/falcon-e/falcon-e-3b", "name": "Falcon-E 3B", "maker": "tii", "family": "tii/falcon-e", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Falcon license (Hugging Face tag: other).", "release_date": "2025-05-15", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Same BitNet b1.58 recipe as Falcon-E 1B. Blog table: 3B parameters, 999 MB footprint. About 1.5 trillion pretraining tokens. bfloat16 and prequantized revisions included.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-E 3B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Larger edge ternary model in the Falcon-E pair.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-emirati/falcon-emirati-7b": { "id": "tii/falcon-emirati/falcon-emirati-7b", "name": "Falcon-Emirati 7B", "maker": "tii", "family": "tii/falcon-emirati", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-06", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Dialect adaptation of Falcon-H1-Arabic 7B for Emirati Arabic. Hybrid Mamba-Transformer backbone. Training mix: crawled Emirati dialect text, MSA text about Emirati culture, and glossary-constrained synthetic dialect data. The blog does not say the weights are downloadable.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-Emirati 7B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Emirati dialect chat, including idioms, heritage, and poetry. Offered on Falcon Chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1/falcon-h1-0-5b": { "id": "tii/falcon-h1/falcon-h1-0-5b", "name": "Falcon-H1 0.5B", "maker": "tii", "family": "tii/falcon-h1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Falcon-LLM License (Apache 2.0 based, with an acceptable use policy).", "release_date": "2025-05-21", "context_length": 16384, "max_output_tokens": null, "parameters": "0.52B", "architecture": "Hybrid attention plus Mamba2. Paper table: 0.52B, 36 layers, vocabulary 32778, d_model 1024, attention heads 8/2, 24 SSM heads, head dim 64/64, d_state 128, context 16K, about 2.5T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1 0.5B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Hybrid Transformer plus Mamba2 language model for multilingual instruction following, math, code, and long context. Base and instruct weights are both released. GPTQ and GGUF files are quantizations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1/falcon-h1-1-5b": { "id": "tii/falcon-h1/falcon-h1-1-5b", "name": "Falcon-H1 1.5B", "maker": "tii", "family": "tii/falcon-h1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Falcon-LLM License (Apache 2.0 based, with an acceptable use policy).", "release_date": "2025-05-21", "context_length": 131072, "max_output_tokens": null, "parameters": "1.55B", "architecture": "Hybrid attention plus Mamba2. Paper table: 1.55B, 24 layers, vocabulary 65536, d_model 2048, attention heads 8/2, 48 SSM heads, head dim 128/64, d_state 256, context 128K, 3T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1 1.5B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Hybrid Transformer plus Mamba2 language model for multilingual instruction following, math, code, and long context. Base and instruct weights are both released. GPTQ and GGUF files are quantizations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1/falcon-h1-1-5b-deep": { "id": "tii/falcon-h1/falcon-h1-1-5b-deep", "name": "Falcon-H1 1.5B-Deep", "maker": "tii", "family": "tii/falcon-h1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Falcon-LLM License (Apache 2.0 based, with an acceptable use policy).", "release_date": "2025-05-21", "context_length": 131072, "max_output_tokens": null, "parameters": "1.55B", "architecture": "Narrower, deeper hybrid. Paper table: 1.55B, 66 layers, vocabulary 65536, d_model 1280, attention heads 6/2, 24 SSM heads, head dim 128/64, d_state 256, context 128K, 3T tokens. TII says it rivals many 7B-10B models.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1 1.5B-Deep. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Hybrid Transformer plus Mamba2 language model for multilingual instruction following, math, code, and long context. Base and instruct weights are both released. GPTQ and GGUF files are quantizations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1/falcon-h1-34b": { "id": "tii/falcon-h1/falcon-h1-34b", "name": "Falcon-H1 34B", "maker": "tii", "family": "tii/falcon-h1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Falcon-LLM License (Apache 2.0 based, with an acceptable use policy).", "release_date": "2025-05-21", "context_length": 262144, "max_output_tokens": null, "parameters": "33.6B", "architecture": "Hybrid attention plus Mamba2. Paper table: 33.6B, 72 layers, vocabulary 261120, d_model 5120, attention heads 20/4, 32 SSM heads, head dim 128/128, d_state 256, context 256K, about 18T tokens. Marketed as 34B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1 34B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Hybrid Transformer plus Mamba2 language model for multilingual instruction following, math, code, and long context. Base and instruct weights are both released. GPTQ and GGUF files are quantizations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1/falcon-h1-3b": { "id": "tii/falcon-h1/falcon-h1-3b", "name": "Falcon-H1 3B", "maker": "tii", "family": "tii/falcon-h1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Falcon-LLM License (Apache 2.0 based, with an acceptable use policy).", "release_date": "2025-05-21", "context_length": 131072, "max_output_tokens": null, "parameters": "3.15B", "architecture": "Hybrid attention plus Mamba2. Paper table: 3.15B, 32 layers, vocabulary 65536, d_model 2560, attention heads 10/2, 32 SSM heads, head dim 128/128, d_state 256, context 128K, 2.5T tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1 3B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Hybrid Transformer plus Mamba2 language model for multilingual instruction following, math, code, and long context. Base and instruct weights are both released. GPTQ and GGUF files are quantizations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1/falcon-h1-7b": { "id": "tii/falcon-h1/falcon-h1-7b", "name": "Falcon-H1 7B", "maker": "tii", "family": "tii/falcon-h1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Falcon-LLM License (Apache 2.0 based, with an acceptable use policy).", "release_date": "2025-05-21", "context_length": 262144, "max_output_tokens": null, "parameters": "7.59B", "architecture": "Hybrid attention plus Mamba2. Paper table: 7.59B, 44 layers, vocabulary 130048, d_model 3072, attention heads 12/2, 24 SSM heads, head dim 128/128, d_state 256, context 256K, about 12T tokens. Marketed as 7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1 7B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Hybrid Transformer plus Mamba2 language model for multilingual instruction following, math, code, and long context. Base and instruct weights are both released. GPTQ and GGUF files are quantizations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1-arabic/falcon-h1-arabic-34b": { "id": "tii/falcon-h1-arabic/falcon-h1-arabic-34b", "name": "Falcon-H1-Arabic 34B", "maker": "tii", "family": "tii/falcon-h1-arabic", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-05", "context_length": 262144, "max_output_tokens": null, "parameters": "34B", "architecture": "Falcon-H1 hybrid (Mamba and attention in parallel inside each block) continued for Arabic. Blog table: 34B, 256K context, for long documents. TII says it is about 75% on OALL and beats some 70B-class models.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1-Arabic 34B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Arabic production model for dialect, STEM, code, and long documents. Three sizes shipped as a family.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1-arabic/falcon-h1-arabic-3b": { "id": "tii/falcon-h1-arabic/falcon-h1-arabic-3b", "name": "Falcon-H1-Arabic 3B", "maker": "tii", "family": "tii/falcon-h1-arabic", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-05", "context_length": 131072, "max_output_tokens": null, "parameters": "3B", "architecture": "Falcon-H1 hybrid (Mamba and attention in parallel inside each block) continued for Arabic. Blog table: 3B, 128K context, for fast agents and high-QPS use.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1-Arabic 3B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Arabic production model for dialect, STEM, code, and long documents. Three sizes shipped as a family.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1-arabic/falcon-h1-arabic-7b": { "id": "tii/falcon-h1-arabic/falcon-h1-arabic-7b", "name": "Falcon-H1-Arabic 7B", "maker": "tii", "family": "tii/falcon-h1-arabic", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-05", "context_length": 262144, "max_output_tokens": null, "parameters": "7B", "architecture": "Falcon-H1 hybrid (Mamba and attention in parallel inside each block) continued for Arabic. Blog table: 7B, 256K context, for assistants and enterprise chat. Parent of Falcon-Emirati-7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1-Arabic 7B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Arabic production model for dialect, STEM, code, and long documents. Three sizes shipped as a family.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1-tiny/falcon-h1-tiny-90m": { "id": "tii/falcon-h1-tiny/falcon-h1-tiny-90m", "name": "Falcon-H1-Tiny 90M", "maker": "tii", "family": "tii/falcon-h1-tiny", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Falcon-LLM License (Apache 2.0 based, with an acceptable use policy).", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "90M", "architecture": "Hybrid Transformer plus Mamba, same family design as Falcon-H1, at under 1B scale. The 90M instruct card states 90M parameters and English. Sibling cards point at the same blog for training details.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1-Tiny 90M. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "General tiny hybrid model. Base, instruct, curriculum instruct, and pre-DPO checkpoints are published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1-tiny/falcon-h1-tiny-coder-90m": { "id": "tii/falcon-h1-tiny/falcon-h1-tiny-coder-90m", "name": "Falcon-H1-Tiny Coder 90M", "maker": "tii", "family": "tii/falcon-h1-tiny", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Falcon-LLM License (Apache 2.0 based, with an acceptable use policy).", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "90M", "architecture": "Hybrid Transformer plus Mamba, same family design as Falcon-H1, at under 1B scale. The 90M instruct card states 90M parameters and English. Sibling cards point at the same blog for training details.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1-Tiny Coder 90M. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Coding-specialized 90M hybrid named in the official Tiny collection.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1-tiny/falcon-h1-tiny-multilingual-100m": { "id": "tii/falcon-h1-tiny/falcon-h1-tiny-multilingual-100m", "name": "Falcon-H1-Tiny Multilingual 100M", "maker": "tii", "family": "tii/falcon-h1-tiny", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Falcon-LLM License (Apache 2.0 based, with an acceptable use policy).", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "100M", "architecture": "Hybrid Transformer plus Mamba, same family design as Falcon-H1, at under 1B scale. The 90M instruct card states 90M parameters and English. Sibling cards point at the same blog for training details.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1-Tiny Multilingual 100M. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Multilingual tiny hybrid. Base and instruct repos are on the official collection. The language list was not read from the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1-tiny/falcon-h1-tiny-r-0-6b": { "id": "tii/falcon-h1-tiny/falcon-h1-tiny-r-0-6b", "name": "Falcon-H1-Tiny-R 0.6B", "maker": "tii", "family": "tii/falcon-h1-tiny", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Falcon-LLM License (Apache 2.0 based, with an acceptable use policy).", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Hybrid Transformer plus Mamba, same family design as Falcon-H1, at under 1B scale. The 90M instruct card states 90M parameters and English. Sibling cards point at the same blog for training details.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1-Tiny-R 0.6B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Larger Tiny-R reasoning model. The index links a post-GRPO checkpoint and a pre-GRPO checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1-tiny/falcon-h1-tiny-r-90m": { "id": "tii/falcon-h1-tiny/falcon-h1-tiny-r-90m", "name": "Falcon-H1-Tiny-R 90M", "maker": "tii", "family": "tii/falcon-h1-tiny", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Falcon-LLM License (Apache 2.0 based, with an acceptable use policy).", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "90M", "architecture": "Hybrid Transformer plus Mamba, same family design as Falcon-H1, at under 1B scale. The 90M instruct card states 90M parameters and English. Sibling cards point at the same blog for training details.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1-Tiny-R 90M. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Reasoning tiny model. The Falcon index says Tiny-R was trained on reasoning data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1-tiny/falcon-h1-tiny-tool-calling-90m": { "id": "tii/falcon-h1-tiny/falcon-h1-tiny-tool-calling-90m", "name": "Falcon-H1-Tiny Tool-Calling 90M", "maker": "tii", "family": "tii/falcon-h1-tiny", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Falcon-LLM License (Apache 2.0 based, with an acceptable use policy).", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "90M", "architecture": "Hybrid Transformer plus Mamba, same family design as Falcon-H1, at under 1B scale. The 90M instruct card states 90M parameters and English. Sibling cards point at the same blog for training details.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1-Tiny Tool-Calling 90M. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Tool-calling 90M hybrid named in the official Tiny collection.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-h1r/falcon-h1r-7b": { "id": "tii/falcon-h1r/falcon-h1r-7b", "name": "Falcon-H1R 7B", "maker": "tii", "family": "tii/falcon-h1r", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Falcon-LLM License (Apache 2.0 based, with an acceptable use policy).", "release_date": "2026-01-05", "context_length": 262144, "max_output_tokens": null, "parameters": "7.59B", "architecture": "Reasoning fine-tune of Falcon-H1-7B-Base. Same hybrid Transformer plus Mamba2 stack. Cold-start SFT on long reasoning traces, then GRPO. Marketed as 7B. Parameter figure is the base model's 7.59B, not a new count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-H1R 7B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Compact reasoning model for math, code, and instruction following. TII also ships GGUF and FP8 quantizations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-mamba/falcon-mamba-7b": { "id": "tii/falcon-mamba/falcon-mamba-7b", "name": "Falcon Mamba 7B", "maker": "tii", "family": "tii/falcon-mamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "TII Falcon-Mamba License 2.0", "release_date": "", "context_length": 8192, "max_output_tokens": null, "parameters": "7B", "architecture": "Pure Mamba (state-space) causal decoder. 64 layers, d_model 4096, d_state 16, vocabulary 65024. Trained context grew from 2048 to 8192. The card says inference context is not capped the way attention is, because Mamba has no extra memory cost for longer sequences. Card new_version is Falcon3-Mamba-7B-Base.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon Mamba 7B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "TII's first open state-space language model, positioned as a general English base model and an instruct twin.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-ocr/falcon-ocr": { "id": "tii/falcon-ocr/falcon-ocr", "name": "Falcon OCR", "maker": "tii", "family": "tii/falcon-ocr", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-04-01", "context_length": null, "max_output_tokens": null, "parameters": "270M", "architecture": "Early-fusion dense transformer for document OCR, trained from scratch. Outputs plain text, LaTeX, or HTML tables. Card calls it 270M parameters. The April 2026 Perception post calls the same model 0.3B. Version 1.5 updated weights on 11 March 2026 according to the card, which is before the April blog date.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon OCR. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Document OCR with high throughput at a small size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-ocr/falcon-ocr-arabic": { "id": "tii/falcon-ocr/falcon-ocr-arabic", "name": "Falcon-OCR-Arabic", "maker": "tii", "family": "tii/falcon-ocr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-06", "context_length": null, "max_output_tokens": null, "parameters": "270M", "architecture": "Same early-fusion architecture as Falcon OCR. Adapted with supervised fine-tuning on real and synthetic Arabic documents, then reinforcement learning. 270M parameters.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon-OCR-Arabic. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Arabic document OCR, including forms, receipts, invoices, and tables.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-perception/falcon-perception": { "id": "tii/falcon-perception/falcon-perception", "name": "Falcon Perception", "maker": "tii", "family": "tii/falcon-perception", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "2026-04-01", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Early-fusion transformer. Image tokens use bidirectional attention. Text and task tokens are causal. For each instance it emits coord, size, then a seg token whose state is dotted with upsampled image features to make a mask. No autoregressive mask tokens. An RL post-train (revision 19-08-2026) uses GRPO and a Hungarian set-matching reward.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon Perception. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Open-vocabulary grounding and instance segmentation from a text query.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/falcon-perception/falcon-perception-300m": { "id": "tii/falcon-perception/falcon-perception-300m", "name": "Falcon Perception 300M", "maker": "tii", "family": "tii/falcon-perception", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.3B", "architecture": "Smaller Falcon Perception checkpoint. Hugging Face lists it as object detection, 0.3B, Apache 2.0, custom code. The card body was not fetched.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released Falcon Perception 300M. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Smaller open-vocabulary detection model in the Perception line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/noor/noor": { "id": "tii/noor/noor", "name": "NOOR", "maker": "tii", "family": "tii/noor", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2022-04-13", "context_length": null, "max_output_tokens": null, "parameters": "10B", "architecture": "Decoder-only Transformer, described as GPT-3-like, with updated positional embeddings. Trained as an Arabic generative model on a cross-domain corpus TII called more than 30 billion words. Trained on 128 A100 GPUs. Built with LightOn. A smaller public version was discussed in press at launch and was not found as a weight repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released NOOR. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Arabic generation for summarization, chat, and other language tasks. Launched as TII's largest Arabic model in 2022, before Falcon.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/siglino/siglino-0-6b": { "id": "tii/siglino/siglino-0-6b", "name": "SigLino 0.6B", "maker": "tii", "family": "tii/siglino", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Dense vision transformer. Card: 0.6B, 18 layers, hidden 1280, FFN 5120, patch 16. Teachers DINOv3 and SigLIP2. Features: siglino 1280-d, siglip2 1152-d, dinov3 1024-d. Hub tag also says 0.5B. The card title says 0.6B.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released SigLino 0.6B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "General vision encoder for retrieval, classification, and segmentation features.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/siglino/siglino-30m": { "id": "tii/siglino/siglino-30m", "name": "SigLino 30M", "maker": "tii", "family": "tii/siglino", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "47.1M", "architecture": "Vision encoder in the SigLino family (DINOv3 and SigLIP2 teachers). Hub tag 47.1M. Marketed as 30M. Card body not fetched.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released SigLino 30M. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Smaller or MoE SigLino vision encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/siglino/siglino-70m": { "id": "tii/siglino/siglino-70m", "name": "SigLino 70M", "maker": "tii", "family": "tii/siglino", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "69.5M", "architecture": "Vision encoder in the SigLino family (DINOv3 and SigLIP2 teachers). Hub tag 69.5M. Marketed as 70M. Card body not fetched.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released SigLino 70M. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Smaller or MoE SigLino vision encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/siglino/siglino-moe-0-15-0-6b": { "id": "tii/siglino/siglino-moe-0-15-0-6b", "name": "SigLino MoE 0.15-0.6B", "maker": "tii", "family": "tii/siglino", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.5B", "architecture": "Vision encoder in the SigLino family (DINOv3 and SigLIP2 teachers). Hub tag 0.5B. Name encodes a 0.15B active / 0.6B total MoE. Active-parameter split was not confirmed on a fetched card.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released SigLino MoE 0.15-0.6B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Smaller or MoE SigLino vision encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/siglino/siglino-moe-0-3-0-6b": { "id": "tii/siglino/siglino-moe-0-3-0-6b", "name": "SigLino MoE 0.3-0.6B", "maker": "tii", "family": "tii/siglino", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.5B", "architecture": "Vision encoder in the SigLino family (DINOv3 and SigLIP2 teachers). Hub tag 0.5B. Name encodes 0.3B active / 0.6B total. Active-parameter split was not confirmed on a fetched card.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released SigLino MoE 0.3-0.6B. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Smaller or MoE SigLino vision encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tii/visper/visper": { "id": "tii/visper/visper", "name": "ViSpeR", "maker": "tii", "family": "tii/visper", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder audio-visual speech recognition. Encoder 12 layers, decoder 6, hidden 768, MLP 3072, 12 heads, unigram vocabulary 21k. Trained on 5,500 hours of labelled video for 150 epochs on 64 A100 40GB GPUs. Research use. The card says it should not be used outside research.", "modalities": { "input": [ "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released ViSpeR. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "what_it_is_for": "Multilingual visual and audio-visual speech recognition.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tju/fuxitranyu/fuxitranyu-8b": { "id": "tju/fuxitranyu/fuxitranyu-8b", "name": "FuxiTranyu-8B", "maker": "tju", "family": "tju/fuxitranyu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-16", "context_length": 4096, "max_output_tokens": null, "parameters": "8B", "architecture": "Decoder-only FuxiTranyuForCausalLM (model_type fuxitranyu), a modified GPT-2-style stack. Live config: 30 layers, hidden size 4096, intermediate size 16384, 32 attention heads, 32 key-value heads, attention bias, untied embeddings, GeLU (gelu_fast), RMSNorm (eps 1e-5), RoPE theta 10000 with rope_scaling null, vocab_size 250680, max_position_embeddings 4096, torch_dtype float16. The paper says the embedding matrix is 250,752 after 72 dummy tokens so the size is divisible by 128, and that training used FlashAttention-2. Hub model-size label is 8B. No exact parameter integer is published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FuxiTranyu-8B is Tianjin University's open-weight multilingual base model, released by the TJUNLP lab in the College of Intelligence and Computing. It is a decoder-only model trained from scratch, still downloadable on Hugging Face, and was collected on 2026-10-15.", "what_it_is_for": "The university says FuxiTranyu is a balanced multilingual language model for research and application across high- and low-resource languages. The base checkpoint is the pretrained model, not the instruction-tuned chat models. The card also says the training mix covers 16 programming languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tju/fuxitranyu/fuxitranyu-8b-dpo": { "id": "tju/fuxitranyu/fuxitranyu-8b-dpo", "name": "FuxiTranyu-8B-DPO", "maker": "tju", "family": "tju/fuxitranyu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-16", "context_length": 4096, "max_output_tokens": null, "parameters": "8B", "architecture": "DPO-aligned FuxiTranyuForCausalLM. Live config matches the base shape: 30 layers, hidden 4096, intermediate 16384, 32 heads, 32 key-value heads, attention bias, untied embeddings, gelu_fast, RMSNorm, RoPE theta 10000, rope_scaling null, vocab_size 250680, max_position_embeddings 4096, attention_dropout 0.0, torch_dtype bfloat16. The config _name_or_path is a local SFT checkpoint path ending in sft_002/checkpoint-3963. The paper's DPO recipe is learning rate 5e-7, 10% warmup, batch size 512, 1 epoch, beta 0.01, 61.1k UltraFeedback instances, FlashAttention-2. Chat template is ChatML. Hub model-size label is 8B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FuxiTranyu-8B-DPO is Tianjin University's preference-aligned FuxiTranyu checkpoint, released by the TJUNLP lab. It is an open-weight chat model still on Hugging Face. Collected on 2026-10-15.", "what_it_is_for": "The university says this checkpoint is aligned with DPO on a human-preference set so replies follow instructions and stated values more closely than the base. The card says training used UltraFeedback. The paper uses DPO rather than PPO and cites the same dataset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tju/fuxitranyu/fuxitranyu-8b-sft": { "id": "tju/fuxitranyu/fuxitranyu-8b-sft", "name": "FuxiTranyu-8B-SFT", "maker": "tju", "family": "tju/fuxitranyu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-16", "context_length": 4096, "max_output_tokens": null, "parameters": "8B", "architecture": "Instruction-tuned FuxiTranyuForCausalLM on the FuxiTranyu-8B base. Live config matches the base shape: 30 layers, hidden 4096, intermediate 16384, 32 heads, 32 key-value heads, attention bias, untied embeddings, gelu_fast, RMSNorm, RoPE theta 10000, rope_scaling null, vocab_size 250680, max_position_embeddings 4096. Differences from the base config: torch_dtype bfloat16 and attention_dropout 0.15. The paper's SFT recipe is learning rate 2e-5, 10% warmup, batch size 512, 2 epochs, about 1M instances, NEFTune on, FlashAttention-2. Chat template is ChatML. Hub model-size label is 8B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FuxiTranyu-8B-SFT is Tianjin University's supervised instruction-tuned FuxiTranyu checkpoint, released by the TJUNLP lab. It is an open-weight chat model still on Hugging Face. Collected on 2026-10-15.", "what_it_is_for": "The card says this is the instruct fine-tune of FuxiTranyu-8B. The paper says the mix starts from OpenHermes 2.5, swaps in Airoboros 3.2, adds Aya for languages already in pretraining, adds Chinese instruction sets, and adds dart-math-hard and Magicoder data for math and code. The university says the instruction models follow human instructions more closely than the base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/aurora/aurora-spec-minimax-m2-1": { "id": "together/aurora/aurora-spec-minimax-m2-1", "name": "Aurora-Spec-MiniMax-M2.1", "maker": "together", "family": "together/aurora", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-02-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "EAGLE3 speculative-decoding draft: a single-layer Transformer decoder trained from random initialization with the Aurora online trainer. Not a general chatbot. Target: MiniMax-M2.1", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Aurora-Spec-MiniMax-M2.1 (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Draft model for speculative decoding of MiniMax-M2.1. Together trained it from scratch during inference with Aurora, instead of fine-tuning a pretrained draft.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/aurora/aurora-spec-minimax-m2-5": { "id": "together/aurora/aurora-spec-minimax-m2-5", "name": "Aurora-Spec-MiniMax-M2.5", "maker": "together", "family": "together/aurora", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-02-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "EAGLE3 speculative-decoding draft: a single-layer Transformer decoder trained from random initialization with the Aurora online trainer. Not a general chatbot. Target: MiniMax-M2.5", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Aurora-Spec-MiniMax-M2.5 (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Draft model for speculative decoding of MiniMax-M2.5. Together trained it from scratch during inference with Aurora, instead of fine-tuning a pretrained draft.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/aurora/aurora-spec-qwen3-coder-next-fp8": { "id": "together/aurora/aurora-spec-qwen3-coder-next-fp8", "name": "Aurora-Spec-Qwen3-Coder-Next-FP8", "maker": "together", "family": "together/aurora", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-02-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "EAGLE3 speculative-decoding draft: a single-layer Transformer decoder trained from random initialization with the Aurora online trainer. Not a general chatbot. Target: Qwen3-Coder-Next-FP8", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Aurora-Spec-Qwen3-Coder-Next-FP8 (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Draft model for speculative decoding of Qwen3-Coder-Next-FP8. Together trained it from scratch during inference with Aurora, instead of fine-tuning a pretrained draft.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/dragonfly/llama-3-1-8b-dragonfly-med-v2": { "id": "together/dragonfly/llama-3-1-8b-dragonfly-med-v2", "name": "Llama-3.1-8B-Dragonfly-Med-v2", "maker": "together", "family": "together/dragonfly", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2024-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language model. v2 card: instruction-tuned from Llama 3.1 8B Instruct. Repo code loads CLIP ViT-L/14 at 336px. License is the Llama 3.1 Community License. Continued on a biomedical image mix.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Llama-3.1-8B-Dragonfly-Med-v2 (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Biomedical Dragonfly v2 for medical imaging research. Together continued the Llama 3.1 Dragonfly model on biomedical image instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/dragonfly/llama-3-1-8b-dragonfly-v2": { "id": "together/dragonfly/llama-3-1-8b-dragonfly-v2", "name": "Llama-3.1-8B-Dragonfly-v2", "maker": "together", "family": "together/dragonfly", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3.1 Community License", "release_date": "2024-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language model. v2 card: instruction-tuned from Llama 3.1 8B Instruct. Repo code loads CLIP ViT-L/14 at 336px. License is the Llama 3.1 Community License.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Llama-3.1-8B-Dragonfly-v2 (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Second general Dragonfly. Together retargeted the zoom-in vision-language recipe onto Llama 3.1 8B Instruct.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/dragonfly/llama-3-8b-dragonfly-med-v1": { "id": "together/dragonfly/llama-3-8b-dragonfly-med-v1", "name": "Llama-3-8B-Dragonfly-Med-v1", "maker": "together", "family": "together/dragonfly", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-06-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language model. v1 blog: Llama 3 8B language backbone and CLIP ViT, with multi-resolution zoom-and-select. License is the Llama 3 Community License. Further tuned on biomedical image-instruction data.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Llama-3-8B-Dragonfly-Med-v1 (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Biomedical visual-language model for medical image understanding research. Together continued the general Dragonfly v1 tune on extra biomedical instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/dragonfly/llama-3-8b-dragonfly-v1": { "id": "together/dragonfly/llama-3-8b-dragonfly-v1", "name": "Llama-3-8B-Dragonfly-v1", "maker": "together", "family": "together/dragonfly", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 Community License", "release_date": "2024-06-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language model. v1 blog: Llama 3 8B language backbone and CLIP ViT, with multi-resolution zoom-and-select. License is the Llama 3 Community License.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Llama-3-8B-Dragonfly-v1 (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "General-domain visual question answering and captioning model. Together instruction-tuned Llama 3 8B with a multi-resolution zoom image encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/evo/evo-1-131k-base": { "id": "together/evo/evo-1-131k-base", "name": "Evo-1 131k base", "maker": "together", "family": "together/evo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-27", "context_length": 131072, "max_output_tokens": null, "parameters": "7000000000", "architecture": "StripedHyena hybrid (attention plus Hyena gated convolutions) at byte / nucleotide resolution. Card states 7 billion parameters, trained on OpenGenome (~300 billion tokens of prokaryotic and phage genomes). Phase 2 continued from the 8k base at context 131072. Card says generation was tested beyond 650k tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Evo-1 131k base (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Long-context Evo checkpoint for genome-scale DNA reasoning and generation. Together and the Arc Institute continued the 8k model out to 131k context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/evo/evo-1-8k-base": { "id": "together/evo/evo-1-8k-base", "name": "Evo-1 8k base", "maker": "together", "family": "together/evo", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-27", "context_length": 8192, "max_output_tokens": null, "parameters": "7000000000", "architecture": "StripedHyena hybrid (attention plus Hyena gated convolutions) at byte / nucleotide resolution. Card states 7 billion parameters, trained on OpenGenome (~300 billion tokens of prokaryotic and phage genomes). Phase 1 context 8192.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Evo-1 8k base (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Biological foundation model that generates and scores DNA at nucleotide resolution. Together and the Arc Institute trained it for molecular-scale tasks. It is not an English chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/gpt-jt/gpt-jt-6b": { "id": "together/gpt-jt/gpt-jt-6b", "name": "GPT-JT-6B", "maker": "together", "family": "together/gpt-jt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2022-11-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of EleutherAI GPT-J 6B with a UL2 training objective so the prompt can use bidirectional context. Card: 3.53 billion fine-tune tokens. AdamW, learning rate 1e-5, global batch 64, activations FP16, optimizer states FP32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released GPT-JT-6B (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Together's early classification model. It fine-tunes GPT-J with a UL2 objective on public prompted-NLP data so few-shot labels work better than the untuned GPT-J.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/gpt-jt/gpt-jt-moderation-6b": { "id": "together/gpt-jt/gpt-jt-moderation-6b", "name": "GPT-JT-Moderation-6B", "maker": "together", "family": "together/gpt-jt", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT-JT-6B-v1 fine-tuned as a moderator. Labels include casual, needs caution, needs intervention, possibly needs caution, and probably needs caution.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released GPT-JT-Moderation-6B (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Safety classifier for chatbot traffic. Together fine-tuned GPT-JT so it can label a user turn or a bot answer and, in their chat demo, interrupt if needed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/hybridagent/hybridagent-14b-agentic": { "id": "together/hybridagent/hybridagent-14b-agentic", "name": "HybridAgent-14B-Agentic", "maker": "together", "family": "together/hybridagent", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-10-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3 14B base plus a HybridLink adapter (adapter.pt) that maps last-layer states back to embeddings so the model can switch between latent steps and text tool calls. Anchor tokens and .", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released HybridAgent-14B-Agentic (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Agent model that thinks in latent vectors and acts in text. Together and Stanford released it for math, search, and tool-use research, with readable latent steps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/hybridagent/hybridagent-8b-agentic": { "id": "together/hybridagent/hybridagent-8b-agentic", "name": "HybridAgent-8B-Agentic", "maker": "together", "family": "together/hybridagent", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-10-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3 8B base plus a HybridLink adapter (adapter.pt) that maps last-layer states back to embeddings so the model can switch between latent steps and text tool calls. Anchor tokens and .", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released HybridAgent-8B-Agentic (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Agent model that thinks in latent vectors and acts in text. Together and Stanford released it for math, search, and tool-use research, with readable latent steps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/llama-2-32k/llama-2-7b-32k": { "id": "together/llama-2-32k/llama-2-7b-32k", "name": "LLaMA-2-7B-32K", "maker": "together", "family": "together/llama-2-32k", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-07-26", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Llama 2 7B continued-pretrained by Together to 32K context with position interpolation, FlashAttention-2, and a long-document data mix.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released LLaMA-2-7B-32K (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Long-context base model for multi-document QA and long summarization. Together extended Meta's Llama 2 7B to 32K rather than training a new architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/llama-2-32k/llama-2-7b-32k-instruct": { "id": "together/llama-2-32k/llama-2-7b-32k-instruct", "name": "Llama-2-7B-32K-Instruct", "maker": "together", "family": "together/llama-2-32k", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2023-08-08", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Instruction and chat fine-tune of LLaMA-2-7B-32K. Together says the recipe is under 200 lines and was run through the Together API.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Llama-2-7B-32K-Instruct (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Long-context chat and instruction model. Together fine-tuned the 32K Llama 2 base on distilled conversations plus BookSum and multi-document QA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/m1/m1-3b": { "id": "together/m1/m1-3b", "name": "M1-3B", "maker": "together", "family": "together/m1", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-05-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid Mamba reasoning model. The card and paper describe distillation from a Transformer into a Mamba hybrid, then math SFT and RL. A secondary summary says the init is Llama-3.2-3B-Instruct with 6 attention layers kept among 28 and SSM state size 16. That layer count is not on the short model card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released M1-3B (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Small reasoning model for math and long test-time traces. Together researchers distilled a hybrid Mamba model so decode uses less memory than a Transformer of similar reasoning behavior.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/m2-bert/m2-bert-80m-2k": { "id": "together/m2-bert/m2-bert-80m-2k", "name": "M2-BERT-80M-2k", "maker": "together", "family": "together/m2-bert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-11", "context_length": 2048, "max_output_tokens": null, "parameters": "80000000", "architecture": "Monarch Mixer BERT (M2-BERT): attention and MLP blocks replaced by Monarch-matrix / sub-quadratic mixers. Masked-language pretraining. Card states an 80M checkpoint. English.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released M2-BERT-80M-2k (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Masked language model. The card says to fine-tune before use. Trained by Jon Saad-Falcon, Dan Fu, and Simran Arora and released on the togethercomputer org with Hazy Research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/m2-bert/m2-bert-80m-2k-retrieval": { "id": "together/m2-bert/m2-bert-80m-2k-retrieval", "name": "M2-BERT-80M-2k-retrieval", "maker": "together", "family": "together/m2-bert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-11", "context_length": 2048, "max_output_tokens": null, "parameters": "80000000", "architecture": "Monarch Mixer BERT (M2-BERT): attention and MLP blocks replaced by Monarch-matrix / sub-quadratic mixers. Masked-language pretraining. Card states an 80M checkpoint. English.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released M2-BERT-80M-2k-retrieval (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Sentence-similarity embeddings for retrieval. Trained by Jon Saad-Falcon, Dan Fu, and Simran Arora and released on the togethercomputer org with Hazy Research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/m2-bert/m2-bert-80m-32k": { "id": "together/m2-bert/m2-bert-80m-32k", "name": "M2-BERT-80M-32k", "maker": "together", "family": "together/m2-bert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-11", "context_length": 32768, "max_output_tokens": null, "parameters": "80000000", "architecture": "Monarch Mixer BERT (M2-BERT): attention and MLP blocks replaced by Monarch-matrix / sub-quadratic mixers. Masked-language pretraining. Card states an 80M checkpoint. English.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released M2-BERT-80M-32k (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Masked language model. The card says to fine-tune before use. Trained by Jon Saad-Falcon, Dan Fu, and Simran Arora and released on the togethercomputer org with Hazy Research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/m2-bert/m2-bert-80m-32k-retrieval": { "id": "together/m2-bert/m2-bert-80m-32k-retrieval", "name": "M2-BERT-80M-32k-retrieval", "maker": "together", "family": "together/m2-bert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-11", "context_length": 32768, "max_output_tokens": null, "parameters": "80000000", "architecture": "Monarch Mixer BERT (M2-BERT): attention and MLP blocks replaced by Monarch-matrix / sub-quadratic mixers. Masked-language pretraining. Card states an 80M checkpoint. English.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released M2-BERT-80M-32k-retrieval (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Sentence-similarity embeddings for retrieval. Trained by Jon Saad-Falcon, Dan Fu, and Simran Arora and released on the togethercomputer org with Hazy Research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/m2-bert/m2-bert-80m-8k": { "id": "together/m2-bert/m2-bert-80m-8k", "name": "M2-BERT-80M-8k", "maker": "together", "family": "together/m2-bert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-11", "context_length": 8192, "max_output_tokens": null, "parameters": "80000000", "architecture": "Monarch Mixer BERT (M2-BERT): attention and MLP blocks replaced by Monarch-matrix / sub-quadratic mixers. Masked-language pretraining. Card states an 80M checkpoint. English.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released M2-BERT-80M-8k (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Masked language model. The card says to fine-tune before use. Trained by Jon Saad-Falcon, Dan Fu, and Simran Arora and released on the togethercomputer org with Hazy Research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/m2-bert/m2-bert-80m-8k-retrieval": { "id": "together/m2-bert/m2-bert-80m-8k-retrieval", "name": "M2-BERT-80M-8k-retrieval", "maker": "together", "family": "together/m2-bert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-01-11", "context_length": 8192, "max_output_tokens": null, "parameters": "80000000", "architecture": "Monarch Mixer BERT (M2-BERT): attention and MLP blocks replaced by Monarch-matrix / sub-quadratic mixers. Masked-language pretraining. Card states an 80M checkpoint. English.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released M2-BERT-80M-8k-retrieval (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Sentence-similarity embeddings for retrieval. Trained by Jon Saad-Falcon, Dan Fu, and Simran Arora and released on the togethercomputer org with Hazy Research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/moaa/gemma-2-9b-it-moaa-dpo": { "id": "together/moaa/gemma-2-9b-it-moaa-dpo", "name": "Gemma-2-9B-it-MoAA-DPO", "maker": "together", "family": "together/moaa", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-12-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mixture of Agents Alignment (DPO) checkpoint. Card: tuned from Gemma-2-9B-it. Two stages: MoA synthetic data for SFT, then a multi-model reward for DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Gemma-2-9B-it-MoAA-DPO (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Alignment research checkpoint. Together trained a DPO model on Gemma-2-9B-it with the Mixture of Agents Alignment pipeline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/moaa/gemma-2-9b-it-moaa-sft": { "id": "together/moaa/gemma-2-9b-it-moaa-sft", "name": "Gemma-2-9B-it-MoAA-SFT", "maker": "together", "family": "together/moaa", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mixture of Agents Alignment (SFT) checkpoint. Card: tuned from Gemma-2-9B-it. Two stages: MoA synthetic data for SFT, then a multi-model reward for DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Gemma-2-9B-it-MoAA-SFT (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Alignment research checkpoint. Together trained a SFT model on Gemma-2-9B-it with the Mixture of Agents Alignment pipeline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/moaa/llama-3-1-8b-instruct-moaa-dpo": { "id": "together/moaa/llama-3-1-8b-instruct-moaa-dpo", "name": "Llama-3.1-8B-Instruct-MoAA-DPO", "maker": "together", "family": "together/moaa", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-12-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mixture of Agents Alignment (DPO) checkpoint. Card: tuned from Llama-3.1-8B-Instruct. Two stages: MoA synthetic data for SFT, then a multi-model reward for DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Llama-3.1-8B-Instruct-MoAA-DPO (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Alignment research checkpoint. Together trained a DPO model on Llama-3.1-8B-Instruct with the Mixture of Agents Alignment pipeline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/moaa/llama-3-1-8b-instruct-moaa-sft": { "id": "together/moaa/llama-3-1-8b-instruct-moaa-sft", "name": "Llama-3.1-8B-Instruct-MoAA-SFT", "maker": "together", "family": "together/moaa", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-09-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mixture of Agents Alignment (SFT) checkpoint. Card: tuned from Llama-3.1-8B-Instruct. Two stages: MoA synthetic data for SFT, then a multi-model reward for DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Llama-3.1-8B-Instruct-MoAA-SFT (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Alignment research checkpoint. Together trained a SFT model on Llama-3.1-8B-Instruct with the Mixture of Agents Alignment pipeline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/openchatkit/gpt-neoxt-chat-base-20b": { "id": "together/openchatkit/gpt-neoxt-chat-base-20b", "name": "GPT-NeoXT-Chat-Base-20B", "maker": "together", "family": "together/openchatkit", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-03-03", "context_length": null, "max_output_tokens": null, "parameters": "20000000000", "architecture": "Instruction- and chat-tuned GPT-NeoX 20B (EleutherAI). Together card calls the release GPT-NeoXT-Chat-Base-20B-v0.16. Prompt uses : and :.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released GPT-NeoXT-Chat-Base-20B (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "OpenChatKit's first chat model. Together fine-tuned EleutherAI's GPT-NeoX-20B for question answering, classification, extraction, and summarization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/openchatkit/pythia-chat-base-7b": { "id": "together/openchatkit/pythia-chat-base-7b", "name": "Pythia-Chat-Base-7B", "maker": "together", "family": "together/openchatkit", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-03-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Chat tune of EleutherAI Pythia. Together card says 7B and Pythia-7B. The OpenChatKit README says the base is Pythia-6.9B-deduped. Prompt uses : and :. Version string on the card is v0.16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Pythia-Chat-Base-7B (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Smaller OpenChatKit chat model so the same instruction data could run on consumer GPUs. Together fine-tuned an EleutherAI Pythia checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/redpajama/redpajama-2": { "id": "together/redpajama/redpajama-2", "name": "RedPajama2", "maker": "together", "family": "together/redpajama", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not specified beyond a plan to train at higher precision than the FP16 Summit run.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI announced RedPajama2 (announced). This dossier was collected on 2026-10-15.", "what_it_is_for": "Announced follow-on to the RedPajama-INCITE base models. No public checkpoint was found as of the collection date, so this folder records the announcement only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/redpajama/redpajama-incite-7b-base": { "id": "together/redpajama/redpajama-incite-7b-base", "name": "RedPajama-INCITE-7B-Base", "maker": "together", "family": "together/redpajama", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-06-06", "context_length": 2048, "max_output_tokens": null, "parameters": "6900000000", "architecture": "GPT-NeoX / Pythia-style decoder. 32 layers, hidden size 4096, 32 heads, vocab 50432, rotary embeddings, max position 2048. Trained in FP16 on Summit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released RedPajama-INCITE-7B-Base (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "English base model meant as an open, reproducible Pythia-architecture pretrain on the RedPajama 1T dataset. Together positions it for research and for the instruct and chat tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/redpajama/redpajama-incite-7b-chat": { "id": "together/redpajama/redpajama-incite-7b-chat", "name": "RedPajama-INCITE-7B-Chat", "maker": "together", "family": "together/redpajama", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-06-06", "context_length": 2048, "max_output_tokens": null, "parameters": "6900000000", "architecture": "GPT-NeoX / Pythia-style decoder. 32 layers, hidden size 4096, 32 heads, vocab 50432, rotary embeddings, max position 2048. Trained in FP16 on Summit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released RedPajama-INCITE-7B-Chat (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Chat model for open or commercial dialogue. Together trained it only on open instruction data and exposed it through OpenChatKit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/redpajama/redpajama-incite-7b-instruct": { "id": "together/redpajama/redpajama-incite-7b-instruct", "name": "RedPajama-INCITE-7B-Instruct", "maker": "together", "family": "together/redpajama", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-06-06", "context_length": 2048, "max_output_tokens": null, "parameters": "6900000000", "architecture": "GPT-NeoX / Pythia-style decoder. 32 layers, hidden size 4096, 32 heads, vocab 50432, rotary embeddings, max position 2048. Trained in FP16 on Summit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released RedPajama-INCITE-7B-Instruct (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Few-shot instruction model for classification, QA, paraphrase, and summarization. Together recommended it over the base when the task can be shown with examples.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/redpajama/redpajama-incite-base-3b-v1": { "id": "together/redpajama/redpajama-incite-base-3b-v1", "name": "RedPajama-INCITE-Base-3B-v1", "maker": "together", "family": "together/redpajama", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-05-05", "context_length": 2048, "max_output_tokens": null, "parameters": "2800000000", "architecture": "GPT-NeoX / Pythia-style decoder. 32 layers, hidden size 2560, 32 heads, vocab 50432, rotary embeddings, max position 2048. Trained in FP16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released RedPajama-INCITE-Base-3B-v1 (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "English base language model for research and downstream tuning. The card describes it as a 2.8B pretrained model, not a chat assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/redpajama/redpajama-incite-chat-3b-v1": { "id": "together/redpajama/redpajama-incite-chat-3b-v1", "name": "RedPajama-INCITE-Chat-3B-v1", "maker": "together", "family": "together/redpajama", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-05-05", "context_length": 2048, "max_output_tokens": null, "parameters": "2800000000", "architecture": "GPT-NeoX / Pythia-style decoder. 32 layers, hidden size 2560, 32 heads, vocab 50432, rotary embeddings, max position 2048. Trained in FP16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released RedPajama-INCITE-Chat-3B-v1 (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Small chat model for dialogue. Together fine-tuned the 3B base on OASST1 and Dolly 2 so the weights stay commercially usable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/redpajama/redpajama-incite-instruct-3b-v1": { "id": "together/redpajama/redpajama-incite-instruct-3b-v1", "name": "RedPajama-INCITE-Instruct-3B-v1", "maker": "together", "family": "together/redpajama", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-05-05", "context_length": 2048, "max_output_tokens": null, "parameters": "2800000000", "architecture": "GPT-NeoX / Pythia-style decoder. 32 layers, hidden size 2560, 32 heads, vocab 50432, rotary embeddings, max position 2048. Trained in FP16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released RedPajama-INCITE-Instruct-3B-v1 (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Few-shot instruction model for classification, extraction, and other prompted NLP tasks. Together says it follows the GPT-JT recipe with HELM overlap removed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/stripedhyena/stripedhyena-hessian-7b": { "id": "together/stripedhyena/stripedhyena-hessian-7b", "name": "StripedHyena-Hessian-7B", "maker": "together", "family": "together/stripedhyena", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-12-08", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid decoder: multi-head grouped-query attention plus gated Hyena convolutions, with convolutions usable as state-space recurrences. Config defaults: vocab 32000, hidden 4096, 32 layers, inner MLP 14336, max_seqlen 32768, rotary base 500000. Mixed precision; poles and residues should stay float32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released StripedHyena-Hessian-7B (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Base language model for research on long context and on architectures other than a pure Transformer. Together trained it on a RedPajama mix plus longer-context data, using model grafting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/stripedhyena/stripedhyena-nous-7b": { "id": "together/stripedhyena/stripedhyena-nous-7b", "name": "StripedHyena-Nous-7B", "maker": "together", "family": "together/stripedhyena", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-12-08", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid decoder: multi-head grouped-query attention plus gated Hyena convolutions, with convolutions usable as state-space recurrences. Config defaults: vocab 32000, hidden 4096, 32 layers, inner MLP 14336, max_seqlen 32768, rotary base 500000. Mixed precision; poles and residues should stay float32. Chat prompt: ### Instruction:\\n{prompt}\\n\\n### Response:\\n{response}.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released StripedHyena-Nous-7B (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Chat model in the StripedHyena family, built by Together with Nous Research. The job is instruction following on the hybrid architecture, including longer prompts than a 2k Transformer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/tev1/tev1-0-8b-experimental": { "id": "together/tev1/tev1-0-8b-experimental", "name": "Tev1-0.8B-experimental", "maker": "together", "family": "together/tev1", "status": "preview", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-09-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Experimental decision fine-tune. Hugging Face tags name the base as Qwen/Qwen3.5-0.8B. The 4B card's interface (state, question, options, one letter) is the family design; this 0.8B card was not re-read line by line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Together AI released Tev1-0.8B-experimental (preview). This dossier was collected on 2026-10-15.", "what_it_is_for": "Smaller experimental Tev1 decision model. Together published weights as a Qwen3.5-0.8B fine-tune. It was not on the serverless price row that lists the 4B model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "together/tev1/tev1-4b-experimental": { "id": "together/tev1/tev1-4b-experimental", "name": "Tev1-4B-experimental", "maker": "together", "family": "together/tev1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-09-23", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Supervised fine-tune of Qwen/Qwen3.5-4B for structured decisions. Keeps Qwen's next-token head. Not a non-autoregressive Jev runtime. HF config still has image and video preprocessor files from the Qwen3.5 backbone; Together's documented task is text decisions.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.042, "completion_per_m_usd": null }, "summary_intro": "Together AI released Tev1-4B-experimental (active). This dossier was collected on 2026-10-15.", "what_it_is_for": "Experimental classifier for high-volume decisions. Together fine-tuned Qwen3.5-4B so a state, a question, and a short option list return one letter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bert/bert-base-japanese": { "id": "tohoku/bert/bert-base-japanese", "name": "BERT base Japanese", "maker": "tohoku", "family": "tohoku/bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-3.0 on the model-card body and the v1/v2 READMEs. The Hub YAML tag on these repos is cc-by-sa-4.0. The two statements disagree.", "release_date": "2019-12-13", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT base: 12 layers, hidden 768, 12 attention heads. MeCab with the IPA dictionary, then WordPiece. Vocabulary 32,000. Masked language modeling without whole-word masking, plus the original next-sentence setup inherited from BERT pretraining code. Japanese Wikipedia dump of 2019-09-01. Extracted text is 2.6GB, about 17 million sentences. Same schedule as original BERT: 512 tokens per instance, batch 256, 1 million steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. The lab released it so Japanese tasks could start from a BERT trained on Japanese Wikipedia, with either MeCab plus WordPiece units or character units. Status: active. Collected 2026-10-15.", "what_it_is_for": "The lab released it so Japanese tasks could start from a BERT trained on Japanese Wikipedia, with either MeCab plus WordPiece units or character units.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bert/bert-base-japanese-char": { "id": "tohoku/bert/bert-base-japanese-char", "name": "BERT base Japanese character", "maker": "tohoku", "family": "tohoku/bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-3.0 on the model-card body and the v1/v2 READMEs. The Hub YAML tag on these repos is cc-by-sa-4.0. The two statements disagree.", "release_date": "2019-12-13", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT base: 12 layers, hidden 768, 12 attention heads. MeCab with the IPA dictionary, then characters. Vocabulary 4,000. Masked language modeling without whole-word masking, plus the original next-sentence setup inherited from BERT pretraining code. Japanese Wikipedia dump of 2019-09-01. Extracted text is 2.6GB, about 17 million sentences. Same schedule as original BERT: 512 tokens per instance, batch 256, 1 million steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. The lab released it so Japanese tasks could start from a BERT trained on Japanese Wikipedia, with either MeCab plus WordPiece units or character units. Status: active. Collected 2026-10-15.", "what_it_is_for": "The lab released it so Japanese tasks could start from a BERT trained on Japanese Wikipedia, with either MeCab plus WordPiece units or character units.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bert/bert-base-japanese-char-v2": { "id": "tohoku/bert/bert-base-japanese-char-v2", "name": "BERT base Japanese character v2", "maker": "tohoku", "family": "tohoku/bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-3.0 on the model-card body and the v1/v2 READMEs. The Hub YAML tag on these repos is cc-by-sa-4.0. The two statements disagree.", "release_date": "2021-09-23", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT base: 12 layers, hidden 768, 12 attention heads. MeCab with Unidic 2.1.2 (unidic-lite), then characters. Vocabulary 6,144. Whole-word masking. All Unidic words are masked together across their subwords or characters. Japanese Wikipedia Cirrussearch dump of 2020-08-31. Corpus about 4.0GB and 30 million sentences. 512 tokens per instance, batch 256, 1 million steps. Learning rate 1e-4 for base and 5e-5 for large, from the v2.0 training commands.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. This is the 2020-Wikipedia retraining. Tokenization moves from IPA to Unidic 2.1.2, and whole-word masking is on. Large models appear in this generation. Status: active. Collected 2026-10-15.", "what_it_is_for": "This is the 2020-Wikipedia retraining. Tokenization moves from IPA to Unidic 2.1.2, and whole-word masking is on. Large models appear in this generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bert/bert-base-japanese-char-v3": { "id": "tohoku/bert/bert-base-japanese-char-v3", "name": "BERT base Japanese character v3", "maker": "tohoku", "family": "tohoku/bert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-05-19", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT base: 12 layers, hidden 768, 12 attention heads. MeCab with Unidic 2.1.2 (unidic-lite), then characters. Vocabulary 7,027. Whole-word masking. All Unidic words are masked together across their subwords or characters. Japanese CC-100 (74.3GB, about 392 million sentences) then Japanese Wikipedia Cirrussearch dump of 2023-01-02 (4.9GB, about 34 million sentences). Two stages: 1 million steps on CC-100, then 1 million steps on Wikipedia. CC-100 instances use max sequence 128. The Wikipedia stage, which is the released checkpoint, uses max sequence 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. This is the current bert-japanese line. It is trained on Japanese CC-100 and then on the January 2023 Wikipedia dump, still with Unidic whole-word masking. Status: active. Collected 2026-10-15.", "what_it_is_for": "This is the current bert-japanese line. It is trained on Japanese CC-100 and then on the January 2023 Wikipedia dump, still with Unidic whole-word masking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bert/bert-base-japanese-char-whole-word-masking": { "id": "tohoku/bert/bert-base-japanese-char-whole-word-masking", "name": "BERT base Japanese character whole-word masking", "maker": "tohoku", "family": "tohoku/bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-3.0 on the model-card body and the v1/v2 READMEs. The Hub YAML tag on these repos is cc-by-sa-4.0. The two statements disagree.", "release_date": "2019-12-13", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT base: 12 layers, hidden 768, 12 attention heads. MeCab with the IPA dictionary, then characters. Vocabulary 4,000. Whole-word masking: every MeCab token of a word is masked together. Japanese Wikipedia dump of 2019-09-01. Extracted text is 2.6GB, about 17 million sentences. Same schedule as original BERT: 512 tokens per instance, batch 256, 1 million steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. The lab released it so Japanese tasks could start from a BERT trained on Japanese Wikipedia, with either MeCab plus WordPiece units or character units. This sibling turns on whole-word masking. It is a separate pretraining run, not a conversion of the non-masking checkpoint. Status: active. Collected 2026-10-15.", "what_it_is_for": "The lab released it so Japanese tasks could start from a BERT trained on Japanese Wikipedia, with either MeCab plus WordPiece units or character units. This sibling turns on whole-word masking. It is a separate pretraining run, not a conversion of the non-masking checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bert/bert-base-japanese-v2": { "id": "tohoku/bert/bert-base-japanese-v2", "name": "BERT base Japanese v2", "maker": "tohoku", "family": "tohoku/bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-3.0 on the model-card body and the v1/v2 READMEs. The Hub YAML tag on these repos is cc-by-sa-4.0. The two statements disagree.", "release_date": "2021-09-23", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT base: 12 layers, hidden 768, 12 attention heads. MeCab with Unidic 2.1.2 (unidic-lite), then WordPiece. Vocabulary 32,768. Whole-word masking. All Unidic words are masked together across their subwords or characters. Japanese Wikipedia Cirrussearch dump of 2020-08-31. Corpus about 4.0GB and 30 million sentences. 512 tokens per instance, batch 256, 1 million steps. Learning rate 1e-4 for base and 5e-5 for large, from the v2.0 training commands.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. This is the 2020-Wikipedia retraining. Tokenization moves from IPA to Unidic 2.1.2, and whole-word masking is on. Large models appear in this generation. Status: active. Collected 2026-10-15.", "what_it_is_for": "This is the 2020-Wikipedia retraining. Tokenization moves from IPA to Unidic 2.1.2, and whole-word masking is on. Large models appear in this generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bert/bert-base-japanese-v3": { "id": "tohoku/bert/bert-base-japanese-v3", "name": "BERT base Japanese v3", "maker": "tohoku", "family": "tohoku/bert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-05-19", "context_length": 512, "max_output_tokens": null, "parameters": "136000000", "architecture": "BERT base: 12 layers, hidden 768, 12 attention heads. MeCab with Unidic 2.1.2 (unidic-lite), then WordPiece. Vocabulary 32,768. Whole-word masking. All Unidic words are masked together across their subwords or characters. Japanese CC-100 (74.3GB, about 392 million sentences) then Japanese Wikipedia Cirrussearch dump of 2023-01-02 (4.9GB, about 34 million sentences). Two stages: 1 million steps on CC-100, then 1 million steps on Wikipedia. CC-100 instances use max sequence 128. The Wikipedia stage, which is the released checkpoint, uses max sequence 512. The BERT-alpha 500M card lists this checkpoint as 136 M parameters and sequence length 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. This is the current bert-japanese line. It is trained on Japanese CC-100 and then on the January 2023 Wikipedia dump, still with Unidic whole-word masking. Status: active. Collected 2026-10-15.", "what_it_is_for": "This is the current bert-japanese line. It is trained on Japanese CC-100 and then on the January 2023 Wikipedia dump, still with Unidic whole-word masking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bert/bert-base-japanese-whole-word-masking": { "id": "tohoku/bert/bert-base-japanese-whole-word-masking", "name": "BERT base Japanese whole-word masking", "maker": "tohoku", "family": "tohoku/bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-3.0 on the model-card body and the v1/v2 READMEs. The Hub YAML tag on these repos is cc-by-sa-4.0. The two statements disagree.", "release_date": "2019-12-13", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT base: 12 layers, hidden 768, 12 attention heads. MeCab with the IPA dictionary, then WordPiece. Vocabulary 32,000. Whole-word masking: every MeCab token of a word is masked together. Japanese Wikipedia dump of 2019-09-01. Extracted text is 2.6GB, about 17 million sentences. Same schedule as original BERT: 512 tokens per instance, batch 256, 1 million steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. The lab released it so Japanese tasks could start from a BERT trained on Japanese Wikipedia, with either MeCab plus WordPiece units or character units. This sibling turns on whole-word masking. It is a separate pretraining run, not a conversion of the non-masking checkpoint. Status: active. Collected 2026-10-15.", "what_it_is_for": "The lab released it so Japanese tasks could start from a BERT trained on Japanese Wikipedia, with either MeCab plus WordPiece units or character units. This sibling turns on whole-word masking. It is a separate pretraining run, not a conversion of the non-masking checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bert/bert-large-japanese": { "id": "tohoku/bert/bert-large-japanese", "name": "BERT large Japanese", "maker": "tohoku", "family": "tohoku/bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-3.0 on the model-card body and the v1/v2 READMEs. The Hub YAML tag on these repos is cc-by-sa-4.0. The two statements disagree.", "release_date": "2021-09-23", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT large: 24 layers, hidden 1024, 16 attention heads. MeCab with Unidic 2.1.2 (unidic-lite), then WordPiece. Vocabulary 32,768. Whole-word masking. All Unidic words are masked together across their subwords or characters. Japanese Wikipedia Cirrussearch dump of 2020-08-31. Corpus about 4.0GB and 30 million sentences. 512 tokens per instance, batch 256, 1 million steps. Learning rate 1e-4 for base and 5e-5 for large, from the v2.0 training commands.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. This is the 2020-Wikipedia retraining. Tokenization moves from IPA to Unidic 2.1.2, and whole-word masking is on. Large models appear in this generation. Status: active. Collected 2026-10-15.", "what_it_is_for": "This is the 2020-Wikipedia retraining. Tokenization moves from IPA to Unidic 2.1.2, and whole-word masking is on. Large models appear in this generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bert/bert-large-japanese-char": { "id": "tohoku/bert/bert-large-japanese-char", "name": "BERT large Japanese character", "maker": "tohoku", "family": "tohoku/bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-3.0 on the model-card body and the v1/v2 READMEs. The Hub YAML tag on these repos is cc-by-sa-4.0. The two statements disagree.", "release_date": "2021-09-23", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT large: 24 layers, hidden 1024, 16 attention heads. MeCab with Unidic 2.1.2 (unidic-lite), then characters. Vocabulary 6,144. Whole-word masking. All Unidic words are masked together across their subwords or characters. Japanese Wikipedia Cirrussearch dump of 2020-08-31. Corpus about 4.0GB and 30 million sentences. 512 tokens per instance, batch 256, 1 million steps. Learning rate 1e-4 for base and 5e-5 for large, from the v2.0 training commands.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. This is the 2020-Wikipedia retraining. Tokenization moves from IPA to Unidic 2.1.2, and whole-word masking is on. Large models appear in this generation. Status: active. Collected 2026-10-15.", "what_it_is_for": "This is the 2020-Wikipedia retraining. Tokenization moves from IPA to Unidic 2.1.2, and whole-word masking is on. Large models appear in this generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bert/bert-large-japanese-char-v2": { "id": "tohoku/bert/bert-large-japanese-char-v2", "name": "BERT large Japanese character v2", "maker": "tohoku", "family": "tohoku/bert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-05-19", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT large: 24 layers, hidden 1024, 16 attention heads. MeCab with Unidic 2.1.2 (unidic-lite), then characters. Vocabulary 7,027. Whole-word masking. All Unidic words are masked together across their subwords or characters. Japanese CC-100 (74.3GB, about 392 million sentences) then Japanese Wikipedia Cirrussearch dump of 2023-01-02 (4.9GB, about 34 million sentences). Two stages: 1 million steps on CC-100, then 1 million steps on Wikipedia. CC-100 instances use max sequence 128. The Wikipedia stage, which is the released checkpoint, uses max sequence 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. This is the current bert-japanese line. It is trained on Japanese CC-100 and then on the January 2023 Wikipedia dump, still with Unidic whole-word masking. Status: active. Collected 2026-10-15.", "what_it_is_for": "This is the current bert-japanese line. It is trained on Japanese CC-100 and then on the January 2023 Wikipedia dump, still with Unidic whole-word masking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bert/bert-large-japanese-v2": { "id": "tohoku/bert/bert-large-japanese-v2", "name": "BERT large Japanese v2", "maker": "tohoku", "family": "tohoku/bert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-05-19", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT large: 24 layers, hidden 1024, 16 attention heads. MeCab with Unidic 2.1.2 (unidic-lite), then WordPiece. Vocabulary 32,768. Whole-word masking. All Unidic words are masked together across their subwords or characters. Japanese CC-100 (74.3GB, about 392 million sentences) then Japanese Wikipedia Cirrussearch dump of 2023-01-02 (4.9GB, about 34 million sentences). Two stages: 1 million steps on CC-100, then 1 million steps on Wikipedia. CC-100 instances use max sequence 128. The Wikipedia stage, which is the released checkpoint, uses max sequence 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. This is the current bert-japanese line. It is trained on Japanese CC-100 and then on the January 2023 Wikipedia dump, still with Unidic whole-word masking. Status: active. Collected 2026-10-15.", "what_it_is_for": "This is the current bert-japanese line. It is trained on Japanese CC-100 and then on the January 2023 Wikipedia dump, still with Unidic whole-word masking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bert-alpha/tohokunlp-bert-500m-sq4096-alpha": { "id": "tohoku/bert-alpha/tohokunlp-bert-500m-sq4096-alpha", "name": "TohokuNLP BERT-alpha 500M (4096)", "maker": "tohoku", "family": "tohoku/bert-alpha", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-26", "context_length": 4096, "max_output_tokens": null, "parameters": "581000000", "architecture": "Llama-style encoder: causal mask removed. 24 layers, hidden 1024, FFN 4096, 16 attention heads, 8 key-value heads. SwiGLU, RoPE, grouped-query attention. Tokenizer is llm-jp-v3. Whole-word masking with vibrato and the bccwj-suw+unidic-cwj-3_1_1 dictionary. Trained from scratch on a Japanese subset of llm-jp-corpus-v3 (ja_cc, ja_warp_html, ja_warp_pdf, ja_wiki, kaken). MLM only, no next-sentence prediction. Card prints 581 M parameters. First stage. Sequence length 4,096, about 114 billion tokens, 100,000 steps, batch 1,146,880 tokens. Checkpoint average of steps 95,000 and 100,000. Cosine schedule, peak learning rate 1e-4.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. The lab released a long-sequence Japanese encoder, trained from scratch, as an alpha checkpoint. The 8192 model is continued training of the 4096 model, not a separate architecture. Status: preview. Collected 2026-10-15.", "what_it_is_for": "The lab released a long-sequence Japanese encoder, trained from scratch, as an alpha checkpoint. The 8192 model is continued training of the 4096 model, not a separate architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bert-alpha/tohokunlp-bert-500m-sq8192-alpha": { "id": "tohoku/bert-alpha/tohokunlp-bert-500m-sq8192-alpha", "name": "TohokuNLP BERT-alpha 500M (8192)", "maker": "tohoku", "family": "tohoku/bert-alpha", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-29", "context_length": 8192, "max_output_tokens": null, "parameters": "581000000", "architecture": "Llama-style encoder: causal mask removed. 24 layers, hidden 1024, FFN 4096, 16 attention heads, 8 key-value heads. SwiGLU, RoPE, grouped-query attention. Tokenizer is llm-jp-v3. Whole-word masking with vibrato and the bccwj-suw+unidic-cwj-3_1_1 dictionary. Trained from scratch on a Japanese subset of llm-jp-corpus-v3 (ja_cc, ja_warp_html, ja_warp_pdf, ja_wiki, kaken). MLM only, no next-sentence prediction. Card prints 581 M parameters. Continued training from the 4096 checkpoint. Sequence length 8,192, about 34 billion additional tokens, 15,000 steps, batch 2,293,760 tokens. Constant learning rate 1e-4. No checkpoint averaging.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. The lab released a long-sequence Japanese encoder, trained from scratch, as an alpha checkpoint. The 8192 model is continued training of the 4096 model, not a separate architecture. Status: preview. Collected 2026-10-15.", "what_it_is_for": "The lab released a long-sequence Japanese encoder, trained from scratch, as an alpha checkpoint. The 8192 model is continued training of the 4096 model, not a separate architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bybert/bybert-jp-100m": { "id": "tohoku/bybert/bybert-jp-100m", "name": "byBERT-JP 100M", "maker": "tohoku", "family": "tohoku/bybert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-14", "context_length": 3072, "max_output_tokens": null, "parameters": "107000000", "architecture": "Llama-style encoder with the causal mask removed. SwiGLU, RoPE, grouped-query attention. Byte-level tokenizer. Trained from scratch with MLM only. Card prints 107 M parameters, 623 billion tokens, 198,000 steps, batch 3,145,728 tokens, sequence length 3072. Data is a Japanese subset of llm-jp-corpus-v3 (ja_cc, ja_warp_html, ja_warp_pdf, ja_wiki, kaken). Whole-word masking uses vibrato and bccwj-suw+unidic-cwj-3_1_1. For the 100M, 200M, and 400M runs the card lists MLM probability 0.3, sequence length 3072, peak learning rate 1e-3. The v2 100M column in the detailed table is labeled bybert-jp-next-100m and lists MLM probability 0.5 then 0.3 and sequence length 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. A from-scratch Japanese encoder that tokenizes bytes instead of MeCab words, released by Tohoku NLP authors as byBERT-JP. Status: active. Collected 2026-10-15.", "what_it_is_for": "A from-scratch Japanese encoder that tokenizes bytes instead of MeCab words, released by Tohoku NLP authors as byBERT-JP.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bybert/bybert-jp-200m": { "id": "tohoku/bybert/bybert-jp-200m", "name": "byBERT-JP 200M", "maker": "tohoku", "family": "tohoku/bybert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-14", "context_length": 3072, "max_output_tokens": null, "parameters": "205000000", "architecture": "Llama-style encoder with the causal mask removed. SwiGLU, RoPE, grouped-query attention. Byte-level tokenizer. Trained from scratch with MLM only. Card prints 205 M parameters, 637 billion tokens, 270,000 steps, batch 2,359,296 tokens, sequence length 3072. Data is a Japanese subset of llm-jp-corpus-v3 (ja_cc, ja_warp_html, ja_warp_pdf, ja_wiki, kaken). Whole-word masking uses vibrato and bccwj-suw+unidic-cwj-3_1_1. For the 100M, 200M, and 400M runs the card lists MLM probability 0.3, sequence length 3072, peak learning rate 1e-3. The v2 100M column in the detailed table is labeled bybert-jp-next-100m and lists MLM probability 0.5 then 0.3 and sequence length 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. A from-scratch Japanese encoder that tokenizes bytes instead of MeCab words, released by Tohoku NLP authors as byBERT-JP. Status: active. Collected 2026-10-15.", "what_it_is_for": "A from-scratch Japanese encoder that tokenizes bytes instead of MeCab words, released by Tohoku NLP authors as byBERT-JP.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bybert/bybert-jp-400m": { "id": "tohoku/bybert/bybert-jp-400m", "name": "byBERT-JP 400M", "maker": "tohoku", "family": "tohoku/bybert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-14", "context_length": 3072, "max_output_tokens": null, "parameters": "397000000", "architecture": "Llama-style encoder with the causal mask removed. SwiGLU, RoPE, grouped-query attention. Byte-level tokenizer. Trained from scratch with MLM only. Card prints 397 M parameters, 1.23 trillion tokens, 308,000 steps, batch 3,981,312 tokens, sequence length 3072. Data is a Japanese subset of llm-jp-corpus-v3 (ja_cc, ja_warp_html, ja_warp_pdf, ja_wiki, kaken). Whole-word masking uses vibrato and bccwj-suw+unidic-cwj-3_1_1. For the 100M, 200M, and 400M runs the card lists MLM probability 0.3, sequence length 3072, peak learning rate 1e-3. The v2 100M column in the detailed table is labeled bybert-jp-next-100m and lists MLM probability 0.5 then 0.3 and sequence length 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. A from-scratch Japanese encoder that tokenizes bytes instead of MeCab words, released by Tohoku NLP authors as byBERT-JP. Status: active. Collected 2026-10-15.", "what_it_is_for": "A from-scratch Japanese encoder that tokenizes bytes instead of MeCab words, released by Tohoku NLP authors as byBERT-JP.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bybert/bybert-jp-v2-100m": { "id": "tohoku/bybert/bybert-jp-v2-100m", "name": "byBERT-JP v2 100M", "maker": "tohoku", "family": "tohoku/bybert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-14", "context_length": 4096, "max_output_tokens": null, "parameters": "114000000", "architecture": "Llama-style encoder with the causal mask removed. SwiGLU, RoPE, grouped-query attention. Byte-level tokenizer. Trained from scratch with MLM only. Card prints 114 M parameters, 2.76 trillion in the table; 2.85 trillion in the prose tokens, 330,000 steps, batch 8,388,608 tokens, sequence length 4096. Data is a Japanese subset of llm-jp-corpus-v3 (ja_cc, ja_warp_html, ja_warp_pdf, ja_wiki, kaken). Whole-word masking uses vibrato and bccwj-suw+unidic-cwj-3_1_1. For the 100M, 200M, and 400M runs the card lists MLM probability 0.3, sequence length 3072, peak learning rate 1e-3. The v2 100M column in the detailed table is labeled bybert-jp-next-100m and lists MLM probability 0.5 then 0.3 and sequence length 4096. Card text for this checkpoint also says the data volume was increased to 2.85 trillion tokens, a custom Unicode encoding was used, mask rate went from 50% to 30%, QKV projections gained a bias, and batch size was warmed up. The same card's table says 2.76 trillion tokens. Those two figures disagree.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. The smaller byBERT-JP follow-up. The card says a longer data run and a few architecture changes beat the earlier 100M checkpoint on the wiki mask test and trail the 400M model on ichikara. Status: active. Collected 2026-10-15.", "what_it_is_for": "The smaller byBERT-JP follow-up. The card says a longer data run and a few architecture changes beat the earlier 100M checkpoint on the wiki mask test and trail the 400M model on ichikara.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/bygpt/bygpt-jp-multi-lm-head-6-5b-alpha": { "id": "tohoku/bygpt/bygpt-jp-multi-lm-head-6-5b-alpha", "name": "byGPT-JP multi-LM-head 6.5B alpha", "maker": "tohoku", "family": "tohoku/bygpt", "status": "preview", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-09-14", "context_length": 11520, "max_output_tokens": null, "parameters": "", "architecture": "Llama-style decoder with SwiGLU, RoPE, and grouped-query attention, plus four LM heads so the model predicts 4 bytes at a time, and a module that merges input embeddings every 4 tokens. Byte-level tokenizer with a custom Unicode encoding. The title says 6.5B. The body does not restate an exact parameter count. Japanese subset of llm-jp-corpus-v3 (ja_cc, ja_warp_html, ja_warp_pdf, ja_wiki, kaken). Sequence length 11,520. Batch 5,898,240 tokens. Peak learning rate 5e-4, minimum 1e-5, warmup 2,000 steps, cosine, AdamW. The Japanese section of the card says 208,000 training steps. The English section says 170,000. Those figures disagree. Not instruction-tuned. The card says the model is still in development and has not reached sufficient performance.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. An alpha Japanese decoder that predicts four bytes at a time. The authors say it is not good enough yet and is not instruction-tuned. Status: preview. Collected 2026-10-15.", "what_it_is_for": "An alpha Japanese decoder that predicts four bytes at a time. The authors say it is not good enough yet and is not instruction-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/nanochat/nanochat-jp-v3-d22": { "id": "tohoku/nanochat/nanochat-jp-v3-d22", "name": "nanochat-jp v3 d22", "maker": "tohoku", "family": "tohoku/nanochat", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-09-28", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "From-scratch 22-layer Japanese Transformer on the nanochat-jp v3 pretraining set, then SFT on nanochat-jp-sft. The Transformers export config matches the v4 export on the fields that were read: hidden 1408, intermediate 5632, 11 heads, 22 layers, vocab 32768, max position 2048. The Hub size label on the export is about 1B. Exact integer not printed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. The 22-layer nanochat-jp run on the v3 Japanese corpus. v4 is the later data mix at the same depth. The Transformers repo is the SFT export. Status: active. Collected 2026-10-15.", "what_it_is_for": "The 22-layer nanochat-jp run on the v3 Japanese corpus. v4 is the later data mix at the same depth. The Transformers repo is the SFT export.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/nanochat/nanochat-jp-v3-d8": { "id": "tohoku/nanochat/nanochat-jp-v3-d8", "name": "nanochat-jp v3 d8", "maker": "tohoku", "family": "tohoku/nanochat", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-09-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "From-scratch 8-layer Japanese Transformer using nanochat-jp, pretrained on the v3 corpus and SFT-tuned on nanochat-jp-sft. The card does not print hidden size, parameter count, or context length. No Transformers export was in the org model list.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. A smaller 8-layer nanochat-jp run on the same v3 data as the 22-layer model, released as its own checkpoint bundle. Status: active. Collected 2026-10-15.", "what_it_is_for": "A smaller 8-layer nanochat-jp run on the same v3 data as the 22-layer model, released as its own checkpoint bundle.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/nanochat/nanochat-jp-v4-d22": { "id": "tohoku/nanochat/nanochat-jp-v4-d22", "name": "nanochat-jp v4 d22", "maker": "tohoku", "family": "tohoku/nanochat", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-09-28", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "From-scratch Japanese autoregressive Transformer in the nanochat-jp fork of karpathy/nanochat. 22 layers. The Transformers export config has hidden size 1408, intermediate size 5632, 11 heads, vocab 32,768, max position 2048, RoPE theta 100000, final logit softcap 15, and a sliding-window pattern SSSL with windows 512 and 2048. SentencePiece Unigram tokenizer. Pretraining corpus is nanochat-jp v4. SFT uses the nanochat-jp-sft dataset. The native repo stores base checkpoints through model_032587.pt and an SFT checkpoint model_002416.pt. Trained on one Genkai node B for 24 hours, according to the model card. The Hub lists the Transformers export near 1B parameters. An exact integer is not printed, so parameters is null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. The 22-layer nanochat-jp model trained from scratch on the v4 Japanese pretraining mix, then supervised-finetuned. The Transformers repo is a format conversion of that SFT checkpoint. Status: active. Collected 2026-10-15.", "what_it_is_for": "The 22-layer nanochat-jp model trained from scratch on the v4 Japanese pretraining mix, then supervised-finetuned. The Transformers repo is a format conversion of that SFT checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/roberta/roberta-base-japanese": { "id": "tohoku/roberta/roberta-base-japanese", "name": "RoBERTa base Japanese", "maker": "tohoku", "family": "tohoku/roberta", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2021-09-21", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "Hub config only. RobertaForMaskedLM, 12 layers, hidden 768, intermediate 3072, 12 heads, vocab 100,002, max_position_embeddings 514, type vocab 1. A sentencepiece.bpe.model file is in the repo. No README was present when the raw README URL was requested. The Hub widget examples are English. Training data, license, and parameter count are not in the repo metadata that was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. A RoBERTa masked-LM weight file on the Tohoku NLP Hub org. The lab has not published a card that says what it was trained on. Status: active. Collected 2026-10-15.", "what_it_is_for": "A RoBERTa masked-LM weight file on the Tohoku NLP Hub org. The lab has not published a card that says what it was trained on.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/sumi/sumi-7b": { "id": "tohoku/sumi/sumi-7b", "name": "Sumi-7B", "maker": "tohoku", "family": "tohoku/sumi", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-06-17", "context_length": 4864, "max_output_tokens": null, "parameters": "", "architecture": "Uniform diffusion language model, time-agnostic bidirectional Transformer, GIDD objective with SNR reparameterization, log-SNR restricted to [-9, 9], pure uniform noise. 36 layers, hidden 4096, SwiGLU FFN 12288, 32 heads, 8 KV groups, head dim 128, RMSNorm eps 1e-5, untied embeddings, no biases or dropout, off-by-one softmax. Released config max_position_embeddings is 4864 and vocab size is 100278. The paper calls the model 7B. The Hub safetensors panel says 8B. Those figures disagree, so parameters is null. Pretraining is about 1.3 trillion tokens at sequence length 1,184 on English text from llm-jp-corpus-v4 and related code data. A later mixture is about 250 billion tokens. The abstract rounds the whole run to 1.5 trillion tokens. The paper says context length is approximately 4,096. The released config says 4864 position embeddings.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. Sumi is Tohoku NLP's from-scratch 7B uniform diffusion language model, trained on English text so others can study that training method at scale. The name is Japanese. The training mixture described in the paper is not. Status: active. Collected 2026-10-15.", "what_it_is_for": "Sumi is Tohoku NLP's from-scratch 7B uniform diffusion language model, trained on English text so others can study that training method at scale. The name is Japanese. The training mixture described in the paper is not.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tohoku/wiki-ent-vec/wiki-ent-vec": { "id": "tohoku/wiki-ent-vec/wiki-ent-vec", "name": "Wikipedia Entity Vectors", "maker": "tohoku", "family": "tohoku/wiki-ent-vec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Skip-gram word and named-entity vectors in one space. Japanese text is tokenized with mecab-ipadic-NEologd v0.0.6. The documented Japanese corpus file is jawiki-20190520-cirrussearch-content.json.gz. Training options printed for the release: embedding size 100, 200, or 300, window 10, negative samples 10, min count 10, 5 epochs. Each bundle has word_vectors.txt, entity_vectors.txt, and all_vectors.txt in word2vec text format. Authors on the README are Masatoshi Suzuki, Koji Matsuda, Satoshi Sekine, Naoaki Okazaki, and Kentaro Inui. The lab open-resources page links the same release. Code is MIT. Vectors are CC BY-SA 3.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tohoku University released this checkpoint. Static Japanese word and Wikipedia-entity embeddings from the same Tohoku group that released Japanese BERT. The lab still lists them on its open-resources page. Three widths are versions of one release, not separate product lines. Status: active. Collected 2026-10-15.", "what_it_is_for": "Static Japanese word and Wikipedia-entity embeddings from the same Tohoku group that released Japanese BERT. The lab still lists them on its open-resources page. Three widths are versions of one release, not separate product lines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tongji/civilgpt/civilgpt": { "id": "tongji/civilgpt/civilgpt", "name": "CivilGPT", "maker": "tongji", "family": "tongji/civilgpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed domain language model with a civil-engineering knowledge graph. The June 2025 Tongji journal paper says continued pre-training and instruction tuning started from Qwen2-72B. Civil-Bench's README labels the 9 November 2024 release closed-source and 72B. A 21 February 2025 college news post says the service was then optimized on DeepSeek-R1 with hundred-billion-parameter training and distillation. The current college page does not name the live base model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CivilGPT is Tongji University College of Civil Engineering's closed civil-engineering knowledge model. It first went online on 26 May 2024, was presented on 9 November 2024 as the first generative model in China's education system to complete Cyberspace Administration filing, and is still the college's named campus service. Collected 2026-10-15.", "what_it_is_for": "The college says it turns textbooks and codes into a knowledge graph and answers teaching, research, and engineering questions. The current page lists agents for literature reading, code consultation, and text editing, plus course-skill profiles. A separate campus agent called \u540c\u5c0f\u6d4e is described in later reporting as built on CivilGPT. It is not a separate model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tongji/med-go/med-go-32b": { "id": "tongji/med-go/med-go-32b", "name": "Med-Go-32B", "maker": "tongji", "family": "tongji/med-go", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-18", "context_length": 40960, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-32B. config.json: 64 layers, hidden size 5120, 64 attention heads, 8 key-value heads, head dimension 128, intermediate size 25600, vocabulary 151936, bfloat16, RoPE theta 1,000,000, rope_scaling null, max_position_embeddings 40960. Card describes SFT plus preference alignment (DPO/KTO).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Med-Go-32B is Tongji University and Tongji University Affiliated East Hospital's open-weight general medical language model, announced on 18 November 2025. It is a fine-tune of Qwen3-32B, published on Hugging Face as OpenMedZoo/MedGo under Apache-2.0. Shanghai Shuole Technology is named as technical support, not as the publisher. Collected 2026-10-15.", "what_it_is_for": "The university calls it a general medical base for specialty models and clinical agents: question answering, record summaries, differential diagnosis, follow-up, and research drafting. East Hospital says a Med-Go series had already run for more than a year in the hospital and partner hospitals before this 32B release. The card says it is an aid for clinicians, not a diagnosis.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tongji/seisgpt/seisgpt": { "id": "tongji/seisgpt/seisgpt", "name": "SeisGPT", "maker": "tongji", "family": "tongji/seisgpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Physics-informed structural-response model, not a chat LLM. Nature Communications (11 July 2026) describes a mass-stiffness graph encoder (PhySE) and a Spectral Duhamel-Green mixer decoder. Sliding window at 0.02 s: 1,000 steps of history and a 20-step horizon. The 26 October 2024 preprint instead describes a decoder-only seismic-response feature decoder. No parameter count is stated in either paper.", "modalities": { "input": [ "time-series", "structural-matrices" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SeisGPT is a physics-informed structural-response model from Tongji University's State Key Laboratory of Disaster Reduction in Civil Engineering. Ying Zhou is the corresponding author and Shiqiao Meng the first author of the Nature Communications paper. It predicts floor acceleration and displacement under earthquake input. It is not a chat model. The name first appeared in an arXiv preprint on 26 October 2024. The journal version is 11 July 2026. Collected 2026-10-15.", "what_it_is_for": "The papers say it replaces slow nonlinear finite-element runs for unseen multistorey buildings, and that the SeisGPT-R line reconstructs a whole building from sparse sensors. Training uses more than 2 million nonlinear simulations from 270,000 generated code-compliant designs and 694 real buildings, over 10 billion response time steps. The college reported the Nature result on 31 August 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tongji/xingzhi/xingzhi": { "id": "tongji/xingzhi/xingzhi", "name": "\u884c\u4e4b", "maker": "tongji", "family": "tongji/xingzhi", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u884c\u4e4b is Tongji University College of Transportation's closed smart-transport knowledge model. The college released it on 7 November 2024 at the Intelligent Transportation Shanghai Forum, a side event of the China International Import Expo. China Transport News still described course use in August 2026. Collected 2026-10-15.", "what_it_is_for": "University news says it serves teaching, travelers, transport agencies, and companies. Teachers can generate cases and exercises and mark work. Students can ask questions, read sample work, and search literature. Travelers get trip dialogue. Agencies get signal-control assessment and problem diagnosis. Companies get design support from standards and project cases. The 2026 course report says students used it, with agents, to test signal plans on real networks in Intelligent Connected and Active Control.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/alexnet/alexnet": { "id": "toronto/alexnet/alexnet", "name": "AlexNet", "maker": "toronto", "family": "toronto/alexnet", "status": "retired", "confidence": "high", "weights": "open-source", "license": "BSD-2-Clause on the Computer History Museum GitHub release, which says the package includes the 2012 parameter files. Wikipedia lists the earlier cuda-convnet repository as New BSD.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "60 million", "architecture": "Eight learned layers: five convolutional layers, some followed by max-pooling, and three fully connected layers with a 1000-way softmax. The paper says 60 million parameters and 650,000 neurons. The figure caption states a 150,528-dimensional input. Two GPUs split the network.", "modalities": { "input": [ "image" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AlexNet is the 2012 convolutional network from Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton at the University of Toronto. It won ILSVRC-2012. The Computer History Museum, with Google, later released the 2012 source and says the package includes the ImageNet parameter files. Status is retired. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper trains the network to classify ImageNet images into 1,000 categories. The 2012 contest system is the historical reference point for later vision models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/atomformer/atomformer-base": { "id": "toronto/atomformer/atomformer-base", "name": "AtomFormer base", "maker": "toronto", "family": "toronto/atomformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "120489687", "architecture": "AtomFormer transformer for atomistic graphs. Gaussian pairwise positional embeddings are added to the attention mask, following uni-mol+. Atomic metadata (radius, electronegativity, valency, and related fields) is normalized and added to atom embeddings. Hugging Face config: model_type atomformer, vocab_size 123, class AtomformerModel. A usage example returns shape (1, atoms, 768).", "modalities": { "input": [ "atomistic-graph" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AtomFormer base is a Vector Institute open-weight transformer for atomistic graphs, released on Hugging Face on 2024-06-25 under Apache-2.0. It is the pretraining backbone of the AtomGen project, not a validated force-and-energy product. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the raw model can be used for force and energy prediction, but that use is not validated. The intended job is fine-tuning on downstream atomistic tasks. It is part of AtomGen, Vector's suite of datasets, models, and utilities for atomistic graphs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/atomformer/atomformer-base-smp": { "id": "toronto/atomformer/atomformer-base-smp", "name": "AtomFormer base SMP", "maker": "toronto", "family": "toronto/atomformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-12", "context_length": null, "max_output_tokens": null, "parameters": "120509655", "architecture": "AtomFormer base fine-tuned as a sequence classifier. Same pairwise positional embeddings as the base. A usage example returns a 20-dimensional property vector. Hugging Face safetensors total is 120,509,655, about 19,968 above the base checkpoint.", "modalities": { "input": [ "atomistic-graph" ], "output": [ "molecular-properties" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AtomFormer base SMP is the Vector Institute fine-tune of AtomFormer base for Atom3D small-molecule property prediction. The Hugging Face repo was created on 2024-07-12. Weights are open under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says to use the model to predict properties of small molecules. It was pretrained on S2EF-15M (forces and formation energy) and fine-tuned on Atom3D SMP.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/bioreason/bioreason": { "id": "toronto/bioreason/bioreason", "name": "BioReason", "maker": "toronto", "family": "toronto/bioreason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DNA foundation model connected to a language model. The project page highlights an Evo2 plus Qwen3-4B system trained with supervised fine-tuning and reinforcement learning. A full parameter total for the combined system was not stated.", "modalities": { "input": [ "text", "dna" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioReason is a Wang Lab DNA-language model that connects a DNA foundation model to an LLM and trains it to reason, including with reinforcement learning. The code is Apache-2.0. The preprint is arXiv 2505.23579 (2025), and the GitHub topic line says NeurIPS 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project page says the model lets an LLM take genomic sequence as an input and produce stepwise biological reasoning, for disease-pathway and variant-effect tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/bioreason/bioreason-pro-rl": { "id": "toronto/bioreason/bioreason-pro-rl", "name": "BioReason-Pro RL", "maker": "toronto", "family": "toronto/bioreason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-20", "context_length": 262144, "max_output_tokens": null, "parameters": "4021789696", "architecture": "Qwen3 causal LM. Hugging Face config on both repos: hidden size 2560, 36 layers, 32 heads, vocab 151671, max_position_embeddings 262144. Safetensors total 4,021,789,696. The SFT card adds ESM3 embeddings and a GO graph encoder around that backbone.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioReason-Pro RL is a Wang Lab protein-function reasoning checkpoint. The Hugging Face repo was created on 2026-03-20 under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "RL checkpoint of the same Qwen3-4B system. The card is shorter. Numeric RL-only scores were not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/bioreason/bioreason-pro-sft": { "id": "toronto/bioreason/bioreason-pro-sft", "name": "BioReason-Pro SFT", "maker": "toronto", "family": "toronto/bioreason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-20", "context_length": 262144, "max_output_tokens": null, "parameters": "4021789696", "architecture": "Qwen3 causal LM. Hugging Face config on both repos: hidden size 2560, 36 layers, 32 heads, vocab 151671, max_position_embeddings 262144. Safetensors total 4,021,789,696. The SFT card adds ESM3 embeddings and a GO graph encoder around that backbone.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioReason-Pro SFT is a Wang Lab protein-function reasoning checkpoint. The Hugging Face repo was created on 2026-03-20 under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "SFT checkpoint. The card says it uses ESM3 protein embeddings, a GO graph encoder, InterPro and STRING context, and a Qwen3-4B backbone to write reasoning traces and function annotations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/bioreason/gogpt": { "id": "toronto/bioreason/gogpt", "name": "GO-GPT", "maker": "toronto", "family": "toronto/bioreason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-20", "context_length": null, "max_output_tokens": null, "parameters": "about 3.2 billion (card: 3B ESM2 plus about 200 million decoder)", "architecture": "ESM2-3B protein encoder (facebook/esm2_t36_3B_UR50D) plus a 12-layer GPT decoder with prefix causal attention. The card says about 3.2 billion parameters total. Hugging Face did not return a safetensors total.", "modalities": { "input": [ "protein" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GO-GPT is the Wang Lab decoder that writes Gene Ontology terms from a protein sequence. The Hugging Face repo was created on 2026-03-20 under Apache-2.0. It is version 1 of the generator used with BioReason-Pro. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says GO-GPT treats function prediction as generation across molecular function, biological process, and cellular component, instead of a bag of independent classifiers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/bioreason/gogpt-v2": { "id": "toronto/bioreason/gogpt-v2", "name": "GO-GPT v2", "maker": "toronto", "family": "toronto/bioreason", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-10-06", "context_length": null, "max_output_tokens": null, "parameters": "186093690", "architecture": "Frozen ESM2-650M encoder (facebook/esm2_t33_650M_UR50D, hidden state 27) plus an 8-layer GPT decoder, 12 heads, 900 dimensions, RMSNorm, QK-norm. The weight file is the decoder only. Safetensors total 186,093,690. The card's table says 198 million decoder parameters. Both figures are kept: the tensor count is the stored parameter field.", "modalities": { "input": [ "protein" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GO-GPT v2 is the smaller Wang Lab Gene Ontology generator that replaced GO-GPT. The Hugging Face repo was created on 2026-10-06 under MIT. The weight file is the decoder, not the frozen ESM2 encoder. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says a frozen ESM2-650M embeds the sequence and an 8-layer decoder writes GO terms for one aspect at a time. Predictions are one input to BioReason-Pro. v2's code link points at the NVIDIA-BioNeMo mirror of the repo as well as the Wang Lab release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/clinical-camel/clinical-camel-70b": { "id": "toronto/clinical-camel/clinical-camel-70b", "name": "Clinical Camel 70B", "maker": "toronto", "family": "toronto/clinical-camel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face lists cc-by-nc-4.0. A ResearchGate record of the paper says CC BY-NC-ND 4.0. The Llama 2 license also applies. Users must agree not to use the model for healthcare decision-making or commercial use.", "release_date": "2023-08-15", "context_length": 4096, "max_output_tokens": null, "parameters": "70 billion", "architecture": "QLoRA fine-tune of LLaMA-2 70B. The paper table for the 70B run lists sequence length 4096, LoRA rank 64, LoRA alpha 16, all linear layers, one epoch. Bo Wang's 31 August 2023 post also says the context limit is 4096 tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Clinical Camel 70B is a Wang Lab medical language model, a QLoRA fine-tune of LLaMA-2 70B for clinical research rather than patient care. The Hugging Face repo was created on 2023-08-15. The preprint is dated 2023-05-19. This dossier was collected on 2026-10-15.", "what_it_is_for": "The abstract says Clinical Camel is an open model for clinical research, trained with dialogue-based knowledge encoding so review articles become conversational data. The download gate forbids healthcare decision-making and commercial use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/comma/comma-v0-1-1t": { "id": "toronto/comma/comma-v0-1-1t", "name": "Comma v0.1-1T", "maker": "toronto", "family": "toronto/comma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-15", "context_length": null, "max_output_tokens": null, "parameters": "7002656768", "architecture": "Llama-3-style decoder-only transformer. Both config.json files: LlamaForCausalLM, hidden size 4096, intermediate size 11008, 32 layers, 32 heads, head dim 128, vocab size 64256, max_position_embeddings 16384, rope_theta 100000, bfloat16, tied embeddings false. The paper says the 1T run used sequences of length 4096. That training length is not stored as the context window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Comma v0.1-1T is a 7 billion parameter base language model trained on 1 trillion tokens from the Common Pile. Lead authors Nikhil Kandpal, Brian Lester, and Colin Raffel list the University of Toronto and the Vector Institute; the paper also lists Hugging Face, EleutherAI, and other collaborators. Weights are on the common-pile Hugging Face org, created 2025-05-15, under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says it is a base model for finetuning and post-training, and that it matches the accuracy of other 7B models trained for 1 trillion tokens on unlicensed text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/comma/comma-v0-1-2t": { "id": "toronto/comma/comma-v0-1-2t", "name": "Comma v0.1-2T", "maker": "toronto", "family": "toronto/comma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-05", "context_length": null, "max_output_tokens": null, "parameters": "7002656768", "architecture": "Llama-3-style decoder-only transformer. Both config.json files: LlamaForCausalLM, hidden size 4096, intermediate size 11008, 32 layers, 32 heads, head dim 128, vocab size 64256, max_position_embeddings 16384, rope_theta 100000, bfloat16, tied embeddings false. The paper says the 1T run used sequences of length 4096. That training length is not stored as the context window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Comma v0.1-2T is the 2 trillion token Common Pile base model from the same University of Toronto and Vector Institute lead authors, with Hugging Face, EleutherAI, and other collaborators. The Hugging Face repo was created on 2025-06-05 under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says it is a base model for finetuning, trained to a 7B and 2T budget, and that it is in the same range as models trained on unlicensed data at that budget.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/delbert/delbert-dcaf7": { "id": "toronto/delbert/delbert-dcaf7", "name": "DELBERT DCAF7", "maker": "toronto", "family": "toronto/delbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GitHub code is Apache-2.0. The Hugging Face model cards do not set a license field.", "release_date": "2026-04-05", "context_length": null, "max_output_tokens": null, "parameters": "72273282", "architecture": "DELBERT transformer encoder for fingerprint token classification. Those widths were read on delbert-wdr91 only. This repo's config was not opened. Only the safetensors count is stored.", "modalities": { "input": [ "molecular-fingerprint" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DELBERT DCAF7 is one of four Wang Lab DELBERT checkpoints for DNA-encoded library hit discovery. The Hugging Face repo was created on 2026-04-05. Code license is Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "DELBERT treats fingerprint bits as tokens, pretrains with masked language modeling, and fine-tunes for binding on one protein target. This folder is the DCAF7 target.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/delbert/delbert-lrrk2": { "id": "toronto/delbert/delbert-lrrk2", "name": "DELBERT LRRK2", "maker": "toronto", "family": "toronto/delbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GitHub code is Apache-2.0. The Hugging Face model cards do not set a license field.", "release_date": "2026-04-05", "context_length": null, "max_output_tokens": null, "parameters": "71179522", "architecture": "DELBERT transformer encoder for fingerprint token classification. Those widths were read on delbert-wdr91 only. This repo's config was not opened. Only the safetensors count is stored.", "modalities": { "input": [ "molecular-fingerprint" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DELBERT LRRK2 is one of four Wang Lab DELBERT checkpoints for DNA-encoded library hit discovery. The Hugging Face repo was created on 2026-04-05. Code license is Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "DELBERT treats fingerprint bits as tokens, pretrains with masked language modeling, and fine-tunes for binding on one protein target. This folder is the LRRK2 target.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/delbert/delbert-setdb1": { "id": "toronto/delbert/delbert-setdb1", "name": "DELBERT SETDB1", "maker": "toronto", "family": "toronto/delbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GitHub code is Apache-2.0. The Hugging Face model cards do not set a license field.", "release_date": "2026-04-05", "context_length": null, "max_output_tokens": null, "parameters": "70916482", "architecture": "DELBERT transformer encoder for fingerprint token classification. Those widths were read on delbert-wdr91 only. This repo's config was not opened. Only the safetensors count is stored.", "modalities": { "input": [ "molecular-fingerprint" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DELBERT SETDB1 is one of four Wang Lab DELBERT checkpoints for DNA-encoded library hit discovery. The Hugging Face repo was created on 2026-04-05. Code license is Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "DELBERT treats fingerprint bits as tokens, pretrains with masked language modeling, and fine-tunes for binding on one protein target. This folder is the SETDB1 target.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/delbert/delbert-wdr91": { "id": "toronto/delbert/delbert-wdr91", "name": "DELBERT WDR91", "maker": "toronto", "family": "toronto/delbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GitHub code is Apache-2.0. The Hugging Face model cards do not set a license field.", "release_date": "2026-04-02", "context_length": null, "max_output_tokens": null, "parameters": "70879362", "architecture": "DELBERT transformer encoder for fingerprint token classification. Config read for this repo: hidden size 640, 16 layers, 16 heads, max_position_embeddings 1024, vocab size 14458, class DELBERTForSequenceClassification.", "modalities": { "input": [ "molecular-fingerprint" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DELBERT WDR91 is one of four Wang Lab DELBERT checkpoints for DNA-encoded library hit discovery. The Hugging Face repo was created on 2026-04-02. Code license is Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "DELBERT treats fingerprint bits as tokens, pretrains with masked language modeling, and fine-tunes for binding on one protein target. This folder is the WDR91 target.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/ecg-fm/ecg-fm": { "id": "toronto/ecg-fm/ecg-fm", "name": "ECG-FM", "maker": "toronto", "family": "toronto/ecg-fm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-08-07", "context_length": null, "max_output_tokens": null, "parameters": "90.9 million", "architecture": "wav2vec 2.0 adapted to electrocardiograms. The current GitHub README says 90.9 million parameters and pretraining with W2V+CMSC+RLM (WCR). It cannot be loaded with Transformers.", "modalities": { "input": [ "ecg" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ECG-FM is an open electrocardiogram foundation model from the Wang Lab at the University of Toronto, the Vector Institute, and University Health Network. Hugging Face weights were created on 2024-08-07 under MIT. JAMIA Open published the paper on 2025-10-16. This dossier was collected on 2026-10-15.", "what_it_is_for": "The current GitHub README says ECG-FM is a foundation model for ECG analysis, built to be fine-tuned when labels are scarce. It is not a bedside device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/factual-dpo/factuality-alignment-qwen2-5-14b": { "id": "toronto/factual-dpo/factuality-alignment-qwen2-5-14b", "name": "Factuality-Alignment-Qwen2.5-14B", "maker": "toronto", "family": "toronto/factual-dpo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA / QLoRA adapter (PEFT, Unsloth, TRL) on a Qwen2.5-14B instruct model. The card names the artifact FactualDPO_qwen25_delta100_last. Hugging Face did not report a safetensors parameter total, which fits an adapter repo rather than a merged 14B dump. The card's base_model field says Qwen/Qwen2.5-14B, the prose says Qwen2.5-14B-Instruct, and tags name unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Factuality-Alignment-Qwen2.5-14B is a Vector Institute preference-tuning release that applies Factual-DPO to a Qwen2.5-14B instruct model. The Hugging Face repo was created on 2026-01-02. The card licenses the repo Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the model reduces hallucinations while preserving preference alignment. Factual-DPO adds binary factuality labels and a margin penalty when the preferred answer is less factual.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/medsam/litemedsam": { "id": "toronto/medsam/litemedsam", "name": "LiteMedSAM", "maker": "toronto", "family": "toronto/medsam", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0 on the parent MedSAM repository. The LiteMedSAM branch README does not restate a separate license.", "release_date": "2024-01-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage distillation of MedSAM. Stage 1 trains a TinyViT image encoder to match MedSAM ViT image embeddings. Stage 2 replaces the ViT encoder with TinyViT and fine-tunes the pipeline.", "modalities": { "input": [ "image" ], "output": [ "mask" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LiteMedSAM is the lightweight MedSAM released by the same Wang Lab repository. The main README news line dated 2024-01-15 says it is 10x faster than MedSAM. Confidence is medium because the branch README is short. This dossier was collected on 2026-10-15.", "what_it_is_for": "The branch README says it is a lightweight MedSAM for fast training and inference, built by distilling the ViT image encoder into TinyViT and then fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/medsam/medsam": { "id": "toronto/medsam/medsam", "name": "MedSAM", "maker": "toronto", "family": "toronto/medsam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Segment Anything ViT-B. Official build_sam_vit_b uses embed_dim 768, depth 12, 12 heads, image size 1024, patch size 16, prompt embed dim 256. Prompt encoder was described by a third-party card as frozen; that detail is not restated on the official Hugging Face card, so it is not stored as a spec.", "modalities": { "input": [ "image" ], "output": [ "mask" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedSAM is a promptable medical-image segmentation model from Bo Wang's lab at the University of Toronto, the Vector Institute, and University Health Network. The ViT-B weights were posted on Hugging Face on 2023-04-29 under Apache-2.0. Nature Communications published the paper on 2024-01-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents one foundation model for medical image segmentation across modalities, instead of a separate network per organ or scanner. Users mark a target with a bounding box.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/medsam/medsam2": { "id": "toronto/medsam/medsam2", "name": "MedSAM2", "maker": "toronto", "family": "toronto/medsam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face YAML says cc-by-sa-4.0. The same card also says the weights can only be used for research and education. Both statements are stored because they disagree.", "release_date": "2025-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Segment Anything Model 2.1 for 3D medical images and video. Parameter count was not on the card.", "modalities": { "input": [ "image", "video" ], "output": [ "mask" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedSAM2 is the Wang Lab promptable model for 3D medical images and video, fine-tuned from SAM 2.1. The Hugging Face repo was created on 2025-04-01. The main MedSAM README news line dates the release to 2025-04-07. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says MedSAM2 segments organs and lesions in 3D volumes and video, and that user studies on CT lesions, liver MRI lesions, and echocardiogram frames found it cut manual annotation cost. The paper text that was read says the cost reduction was more than 85 percent. That figure is the paper's claim, not an independent audit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/newsmedia-bias/llama-3-2-multimodal-newsmedia-bias-detector": { "id": "toronto/newsmedia-bias/llama-3-2-multimodal-newsmedia-bias-detector", "name": "Llama 3.2 Multimodal News Media Bias Detector", "maker": "toronto", "family": "toronto/newsmedia-bias", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Card license field is the string \"cc\". The base is Llama-3.2-11B-Vision-Instruct, so the Llama 3.2 Community License also applies.", "release_date": "2024-10-31", "context_length": null, "max_output_tokens": null, "parameters": "10969312925", "architecture": "Multimodal causal LM. Card says 11B parameters and LoRA fine-tuning, with an optional 4-bit NF4 load. Hugging Face safetensors total is 10,969,312,925. YAML base is meta-llama/Llama-3.2-11B-Vision-Instruct. The overview sentence also says \"LLaMA 2 Vision-Instruct\", which disagrees with the YAML.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.2 Multimodal News Media Bias Detector is a Vector Institute vision-language classifier for disinformation rhetoric in news articles and images. The Hugging Face repo was created on 2024-10-31. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the model reads an article snippet and an image, looks for listed rhetorical techniques, and answers Classification: Likely or Classification: Unlikely. It does not check whether claims are true.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/newsmedia-bias/llama-3-2-nlp-newsmedia-bias-detector": { "id": "toronto/newsmedia-bias/llama-3-2-nlp-newsmedia-bias-detector", "name": "Llama 3.2 NLP News Media Bias Detector", "maker": "toronto", "family": "toronto/newsmedia-bias", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Card license field is the string \"cc\", which is not a complete Creative Commons deed. The base is a Llama 3.2 model, so the Llama 3.2 Community License also applies.", "release_date": "2024-11-05", "context_length": null, "max_output_tokens": null, "parameters": "1235814400", "architecture": "Causal LM. Hugging Face safetensors total 1,235,814,400. Card YAML base_model is meta-llama/Llama-3.2-1B. The prose says meta-llama/Llama-3.2-1B-Instruct. The usage example truncates prompts at 1024 tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama 3.2 NLP News Media Bias Detector is a Vector Institute text classifier for disinformation likelihood in news paragraphs. The Hugging Face repo was created on 2024-11-05. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the model looks for rhetorical techniques associated with disinformation and answers Likely or Unlikely. It does not verify facts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/newsmedia-bias/nmb-plus-bias-ner-bert": { "id": "toronto/newsmedia-bias/nmb-plus-bias-ner-bert", "name": "NMB-Plus bias NER BERT", "maker": "toronto", "family": "toronto/newsmedia-bias", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Card license field is the string \"cc\". The base is DistilBERT, whose upstream license was not re-read.", "release_date": "2025-02-26", "context_length": null, "max_output_tokens": null, "parameters": "66365187", "architecture": "Token-classification model. Hugging Face base_model is distilbert/distilbert-base-uncased. Safetensors total 66,365,187.", "modalities": { "input": [ "text" ], "output": [ "token-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NMB-Plus bias NER BERT is a Vector Institute token-classification checkpoint on Hugging Face, created 2025-02-26. The model card is gated, so this dossier uses only public API metadata. Confidence is medium. Collected on 2026-10-15.", "what_it_is_for": "The repo name and pipeline tag say token classification for the NMB-Plus news-bias project. The card text that would confirm the label set could not be read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/open-pmc/open-pmc-18m-clip": { "id": "toronto/open-pmc/open-pmc-18m-clip", "name": "Open-PMC-18M CLIP", "maker": "toronto", "family": "toronto/open-pmc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-16", "context_length": 256, "max_output_tokens": null, "parameters": "image encoder about 86 million; text encoder about 110 million", "architecture": "BiomedCLIP-style dual encoder. Image encoder is ViT-B/16 (timm vit_base_patch16_224) at 224x224, about 86 million parameters. Text encoder is PubMedBERT (microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract), 256 tokens, about 110 million parameters. Shared embedding size 512. Loaded with open_clip.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Open-PMC-18M CLIP is a Vector Institute biomedical CLIP trained on about 18 million PubMed Central image-text pairs. The Hugging Face repo was created on 2025-06-16. The card says the work was accepted at MICCAI 2026. Weights are Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the model supports zero-shot image classification and image-text retrieval over biomedical figures. It uses the BiomedCLIP architecture: a ViT-B/16 image encoder and a PubMedBERT text encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/open-pmc/open-pmc-clip": { "id": "toronto/open-pmc/open-pmc-clip", "name": "Open-PMC CLIP", "maker": "toronto", "family": "toronto/open-pmc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Contrastive dual encoder. Vision backbone is ViT-B/16 pretrained on ImageNet. Text backbone is PubMedBERT pretrained on PubMed Central abstracts. Objective is InfoNCE. Trained with the mmlearn framework.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Open-PMC CLIP is a Vector Institute contrastive vision-language checkpoint trained on the Open-PMC dataset. The Hugging Face repo was created on 2025-03-11 and is licensed MIT. It is the earlier, smaller sibling of Open-PMC-18M CLIP. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card describes a checkpoint for multimodal medical representation learning. Vision features come from an ImageNet-pretrained ViT-B/16. Text features come from PubMedBERT. Training uses vanilla InfoNCE so images and captions from biomedical papers land in one space.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/open-pmc/pmc-18m-dab-detr": { "id": "toronto/open-pmc/pmc-18m-dab-detr", "name": "PMC-18M DAB-DETR", "maker": "toronto", "family": "toronto/open-pmc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-11", "context_length": null, "max_output_tokens": null, "parameters": "43754884", "architecture": "DAB-DETR object detector. Hugging Face config: model_type dab-detr, hidden_size 256, num_hidden_layers 6, class DabDetrForObjectDetection. Dynamic anchor boxes replace learned DETR queries.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PMC-18M DAB-DETR is a Vector Institute object detector that finds subfigure panels in compound biomedical figures. The Hugging Face repo was created on 2025-08-11. It has 43,754,884 parameters and is Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says the model detects and extracts panels from compound figures in biomedical literature, so Open-PMC-18M can be built at scale. It adapts DAB-DETR, which uses dynamic anchor boxes instead of learned DETR queries.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/scgpt/scgpt": { "id": "toronto/scgpt/scgpt", "name": "scGPT", "maker": "toronto", "family": "toronto/scgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Generative pretrained transformer over genes in a cell. The official model zoo ships one recommended whole-human checkpoint and several organ or continual-pretrain checkpoints. Exact layer widths and parameter count were not on the README section that was read.", "modalities": { "input": [ "gene-expression" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "scGPT is the Wang Lab foundation model for single-cell biology, a generative transformer pretrained on tens of millions of cells. The code is MIT licensed. Nature Methods published the paper in 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe tasks that include cell-type annotation, multi-batch integration, multi-omic integration, perturbation response, and gene-network inference. The recommended checkpoint is whole-human.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/unbias-plus/qwen3-5-4b-unbias-plus-sft-instruct": { "id": "toronto/unbias-plus/qwen3-5-4b-unbias-plus-sft-instruct", "name": "Qwen3.5-4B-UnBias-Plus-SFT-Instruct", "maker": "toronto", "family": "toronto/unbias-plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-29", "context_length": 4096, "max_output_tokens": null, "parameters": "4539265536", "architecture": "Merged supervised fine-tune of Qwen/Qwen3.5-4B. Card: LoRA rank 16, Unsloth + TRL, bf16, thinking mode as noted in the dossier. Hugging Face safetensors total is 4539265536.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen3.5-4B-UnBias-Plus-SFT-Instruct is a Vector Institute supervised fine-tune for news-media bias detection and neutral rewriting. The Hugging Face repo was created on 2026-05-29. Weights are a full checkpoint of 4539265536 parameters under the card's Apache-2.0 license. This dossier was collected on 2026-10-15.", "what_it_is_for": "Given a news article, the model finds biased spans, labels type and severity, proposes neutral replacements, and rewrites the article. BABE-350, judge GPT-4o-mini, from this model's card. The card calls it 4B. Hugging Face safetensors total is 4,539,265,536. The org listing buckets it as 5B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/unbias-plus/qwen3-8b-unbias-plus-sft": { "id": "toronto/unbias-plus/qwen3-8b-unbias-plus-sft", "name": "Qwen3-8B-UnBias-Plus-SFT", "maker": "toronto", "family": "toronto/unbias-plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-05", "context_length": 8192, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Merged supervised fine-tune of Qwen/Qwen3-8B. Card: LoRA rank 16, Unsloth + TRL, bf16, thinking mode as noted in the dossier. Hugging Face safetensors total is 8190735360.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen3-8B-UnBias-Plus-SFT is a Vector Institute supervised fine-tune for news-media bias detection and neutral rewriting. The Hugging Face repo was created on 2026-03-05. Weights are a full checkpoint of 8190735360 parameters under the card's Apache-2.0 license. This dossier was collected on 2026-10-15.", "what_it_is_for": "Given a news article, the model finds biased spans, labels type and severity, proposes neutral replacements, and rewrites the article. Trained on train_1 with thinking enabled. The card does not print a BABE score table. Severity scale on this card is 0, 2, 3, 4 and the schema includes binary_label.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/unbias-plus/qwen3-8b-unbias-plus-sft-instruct": { "id": "toronto/unbias-plus/qwen3-8b-unbias-plus-sft-instruct", "name": "Qwen3-8B-UnBias-Plus-SFT-Instruct", "maker": "toronto", "family": "toronto/unbias-plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-25", "context_length": 8192, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Merged supervised fine-tune of Qwen/Qwen3-8B. Card: LoRA rank 16, Unsloth + TRL, bf16, thinking mode as noted in the dossier. Hugging Face safetensors total is 8190735360.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen3-8B-UnBias-Plus-SFT-Instruct is a Vector Institute supervised fine-tune for news-media bias detection and neutral rewriting. The Hugging Face repo was created on 2026-05-25. Weights are a full checkpoint of 8190735360 parameters under the card's Apache-2.0 license. This dossier was collected on 2026-10-15.", "what_it_is_for": "Given a news article, the model finds biased spans, labels type and severity, proposes neutral replacements, and rewrites the article. BABE-350 (175 biased + 175 unbiased), judge GPT-4o-mini, from this model's card:", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/unbias-plus/qwen3-8b-unbias-plus-sft-instruct-legacy": { "id": "toronto/unbias-plus/qwen3-8b-unbias-plus-sft-instruct-legacy", "name": "Qwen3-8B-UnBias-Plus-SFT-Instruct-Legacy", "maker": "toronto", "family": "toronto/unbias-plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-22", "context_length": 8192, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Merged supervised fine-tune of Qwen/Qwen3-8B. Card: LoRA rank 16, Unsloth + TRL, bf16, thinking mode as noted in the dossier. Hugging Face safetensors total is 8190735360.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen3-8B-UnBias-Plus-SFT-Instruct-Legacy is a Vector Institute supervised fine-tune for news-media bias detection and neutral rewriting. The Hugging Face repo was created on 2026-03-22. Weights are a full checkpoint of 8190735360 parameters under the card's Apache-2.0 license. This dossier was collected on 2026-10-15.", "what_it_is_for": "Given a news article, the model finds biased spans, labels type and severity, proposes neutral replacements, and rewrites the article. Instruct variant, thinking disabled, trained on train_1. The later instruct card says this legacy model is deployed on GCP and is better at identifying unbiased articles (median 5.0 vs 2.0). Segment localization recall at words is given on the successor card as 0.714, and segment replacement quality as 3.58/5. Those figures are printed on the successor card, not restated here as this card's own table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "toronto/unbias-plus/qwen3-8b-unbias-plus-sft-instruct-v2": { "id": "toronto/unbias-plus/qwen3-8b-unbias-plus-sft-instruct-v2", "name": "Qwen3-8B-UnBias-Plus-SFT-Instruct-V2", "maker": "toronto", "family": "toronto/unbias-plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-13", "context_length": 8192, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Merged supervised fine-tune of Qwen/Qwen3-8B. Card: LoRA rank 16, Unsloth + TRL, bf16, thinking mode as noted in the dossier. Hugging Face safetensors total is 8190735360.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen3-8B-UnBias-Plus-SFT-Instruct-V2 is a Vector Institute supervised fine-tune for news-media bias detection and neutral rewriting. The Hugging Face repo was created on 2026-07-13. Weights are a full checkpoint of 8190735360 parameters under the card's Apache-2.0 license. This dossier was collected on 2026-10-15.", "what_it_is_for": "Given a news article, the model finds biased spans, labels type and severity, proposes neutral replacements, and rewrites the article. V2 is a conservative span-level annotator. Article severity is an integer 0-10, not 0-4. The card does not print a BABE score table. It drops bias_found from the model output and says the label is derived downstream. Eight canonical bias-type slugs replace the seven free-form labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "translated/lara/lara": { "id": "translated/lara/lara", "name": "Lara", "maker": "translated", "family": "translated/lara", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Translation model trained with Trust Attention so verified professional translations outweigh unverified web text. Translated described a critique step that weighs options and revises the draft. Trained for 1.2 million NVIDIA GPU hours; inference was described on NVIDIA H200. Size was stated only as tens of billions of parameters, about ten times the previous Translated model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lara is Translated's first named translation model, introduced in Rome on 4 November 2024 and replaced in the product by Lara V2 on 4 June 2025 and later by Lara 3. This dossier was collected on 2026-10-15. Weights are closed. There is no public retirement notice and no way to pin this checkpoint in the current API, so status is retired.", "what_it_is_for": "Translated launched Lara as a translation system that explains its choices, reads document context, and was trained on 25 million professional translations, including reviewer corrections and disagreements. The company positioned it against Google Translate, DeepL, and GPT-4o on professional content, not as a general chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "translated/lara/lara-3": { "id": "translated/lara/lara-3", "name": "Lara 3", "maker": "translated", "family": "translated/lara", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Third-generation Lara. Imitation training plus a Learning by Doing phase: the model drafts several translations, an automatic judge built from professional reviewer feedback scores them, and the model updates during training before it serves customers. Docs do not publish depth, width, or a context length. The default non-reasoning API behavior is documented as Lara Base and, on the pricing page, Lara Standard. No model id pins that name to this checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lara 3 is Translated's current translation model, the third Lara generation. It was introduced to selected partners on 30 July 2026 and made generally available in October 2026 on the Lara API, TranslationOS, and Lara Translate. Collected on 2026-10-15. Weights are closed. The API docs call the default non-reasoning model Lara Base, and the pricing page calls the same tier Lara Standard. No model string pins those names to this checkpoint; the alias is the product timeline, not an id.", "what_it_is_for": "Translated sells Lara 3 as a specialized translation model for product content, support, and enterprise localization. It adapts from translation memories, glossaries, instructions, and style instead of a customer training job. Image, audio, rich text, and document translation ship with the product. Lara Think and Lara Prosa are separate named modes and have their own folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "translated/lara/lara-batch": { "id": "translated/lara/lara-batch", "name": "Lara Batch", "maker": "translated", "family": "translated/lara", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described only as the base model optimised for cost. No architecture details.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lara Batch is a high-volume translation model named once on the Lara Translation API page, updated 27 July 2026. Collected on 2026-10-15. Weights are closed. The page calls it the base model optimised for cost, up to 10 times less, and says to contact sales. Confidence is medium: one official page, and it is absent from the 7 October 2026 price table.", "what_it_is_for": "Translated offers Batch to high-volume API users who want a cheaper base translation than the public Lara Base rate. No quality comparison is published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "translated/lara/lara-flash": { "id": "translated/lara/lara-flash", "name": "Lara Flash", "maker": "translated", "family": "translated/lara", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lara Flash is a high-volume translation model named once on the Lara Translation API page, updated 27 July 2026. Collected on 2026-10-15. Weights are closed. Access is by contacting sales, not a self-serve SDK flag. Confidence is medium: one official page, no second source, and it is absent from the 7 October 2026 price table.", "what_it_is_for": "The docs list Flash next to Lara Batch as an extra model for high-volume users. The only quality claim is latency: a median of 50 ms. No workload is described.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "translated/lara/lara-prosa": { "id": "translated/lara/lara-prosa", "name": "Lara Prosa", "maker": "translated", "family": "translated/lara", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Editorial layer applied after Lara Base, or after Think when styleguide reasoning is on. It rewrites toward a style guide and can return notes on the changes. The pricing footnote says its cost is added to Lara Base, so it is not a standalone replacement for the base translation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lara Prosa is Translated's editorial translation layer, introduced with Lara 3 on 30 July 2026. Collected on 2026-10-15. Weights are closed. It is active on Pro and Team API plans. It is not a standalone generator: the pricing footnote says its cost is added to Lara Base, and the translate docs say it runs after Base, or with style-guide reasoning.", "what_it_is_for": "Translated describes Prosa for literature, author voice, and brand style, where rhythm matters as well as accuracy. The caller passes a style-guide id. The response can include editorial notes. The note language defaults to English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "translated/lara/lara-think": { "id": "translated/lara/lara-think", "name": "Lara Think", "maker": "translated", "family": "translated/lara", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Quality-first reasoning translation model. The API turns it on with reasoning=true rather than a separate model string. Docs say it runs multi-step analysis over glossaries, style guides, and context, and that a callback can return the first draft before the reasoned pass. Processing time is described as up to about 10 times the standard model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lara Think is Translated's reasoning translation model. It was announced in November 2025, promised to everyone on 15 December 2025, and is still the quality tier in the October 2026 API. Collected on 2026-10-15. Weights are closed. There is no model id: the SDK sets `reasoning` to true on the translate call. The Lara 3 launch describes the same name as a reasoning mode, not a new checkpoint.", "what_it_is_for": "Translated positions Think for jobs where terminology and style matter more than latency. It runs a multi-step pass over glossaries, style guides, and context. A callback can return the first draft before the reasoned result. The November 2025 release also said contextual features then covered 200 languages. That coverage was a Lara update, not a separate Think language list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "translated/lara/lara-v2": { "id": "translated/lara/lara-v2", "name": "Lara V2", "maker": "translated", "family": "translated/lara", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Redesigned translation model with a new tokenizer aimed at multilingual data, trained with Trust-Attention V2, which weights higher-quality data more heavily. Dataset described as larger, more diverse, and cleaned. No parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lara V2 is Translated's second Lara generation, announced in Rome on 4 June 2025 and later replaced in the product by Lara 3. Collected on 2026-10-15. Weights are closed. The current API does not pin V2, and no retirement notice was found, so status is retired. Intento's 2026 report still names \"Lara 2\", so this checkpoint may have remained reachable during that study.", "what_it_is_for": "Translated described V2 as the production translation model for Lara Translate and the API: glossaries, translation memories, tone and formatting instructions, and document translation. Styles named at launch were faithful, fluid, and creative.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "trento/caddy/caddy": { "id": "trento/caddy/caddy", "name": "CADDY", "maker": "trento", "family": "trento/caddy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Playable video generation. Encoder E, action network A, recurrent dynamics network R, and decoder D. A predicts a discrete action through a Gumbel-Softmax and a continuous action-variability embedding. Training uses reconstruction as the main loss on unlabeled video. At test time the variability embedding is 0, the user supplies discrete actions, and generation is auto-regressive. The paper says the method does not need the precise number of actions. The BAIR figure text refers to learned actions numbered 1 through 7. No parameter count was stated.", "modalities": { "input": [ "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CADDY is the playable-video model released by Willi Menapace and Aliaksandr Siarohin at the University of Trento, with Stephane Lathuiliere at Telecom Paris, Sergey Tulyakov at Snap, and Elisa Ricci at the University of Trento and Fondazione Bruno Kessler. The name expands to Clustering for Action Decomposition and DiscoverY. The paper introduces the Playable Video Generation task and names this framework CADDY. Status on 2026-10-15: active. The README citation is CVPR 2021 Oral. The licence file is CC BY-NC 4.0, copyright 2021 Willi Menapace.", "what_it_is_for": "The README trains on unlabeled videos and, at test time, lets a user pick a discrete action at every step, as in a video game. The released play scripts cover BAIR robot pushing, Atari Breakout, and tennis. A browser demo is linked from the README.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "trento/fomm/first-order-motion-model": { "id": "trento/fomm/first-order-motion-model", "name": "First Order Motion Model", "maker": "trento", "family": "trento/fomm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-supervised image animation. An encoder-decoder predicts K keypoints and a local 2 by 2 Jacobian at each keypoint. A dense-motion U-Net builds backward flow and an occlusion map from K plus one part masks. A generator warps source features and inpaints occluded regions. The paper sets K to 10. The vox-256.yaml config uses 256 by 256 RGB frames, temperature 0.1, keypoint detector block expansion 32, generator block expansion 64 with 2 down blocks and 6 bottleneck blocks, occlusion estimation on, discriminator spectral norm, 100 epochs, learning rate 2.0e-4 for generator, discriminator, and keypoint detector, and batch size 40. Perceptual and feature-matching losses use a resolution pyramid. Equivariance is applied to both keypoint values and Jacobians. No parameter count was stated. The MIT licence file covers the software. It does not name the checkpoint files separately.", "modalities": { "input": [ "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "First Order Motion Model is the image-animation network from Aliaksandr Siarohin, Stephane Lathuiliere, Elisa Ricci, and Nicu Sebe at the University of Trento, with Sergey Tulyakov at Snap. It replaces Monkey-Net's keypoint displacements with local affine transforms and an occlusion map. Status on 2026-10-15: active. The repository citation says NeurIPS, December 2019. The software licence file is MIT.", "what_it_is_for": "The paper generates a video in which an object from a source image moves like the object in a driving video. No pose labels are used. After training on one category, such as faces or bodies, it is applied to other instances of that category. The README also shows a face-swap path that needs supervised masks and points to the motion co-part repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "trento/monkey-net/monkey-net": { "id": "trento/monkey-net/monkey-net", "name": "Monkey-Net", "maker": "trento", "family": "trento/monkey-net", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-supervised image-animation network. A U-Net keypoint detector emits K heatmaps, fit as Gaussians. A dense-motion network predicts a part mask and a residual flow per keypoint and combines them into optical flow. A generator warps encoder features with that flow and also receives the difference of keypoint heatmaps. Training uses a GAN loss plus feature matching. The paper calls this a zeroth-order keypoint motion model. The later first-order model says Monkey-Net is the special case with Jacobian equal to the identity. The README says a separate network is trained per dataset. No parameter count was stated.", "modalities": { "input": [ "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Monkey-Net is the self-supervised image-animation model released by Aliaksandr Siarohin, Stephane Lathuiliere, Elisa Ricci, and Nicu Sebe at DISI, University of Trento, with Sergey Tulyakov at Snap. The name expands to moving keypoints. Status on 2026-10-15: active. The CVPR citation in the README says June 2019. The README points readers to First Order Motion Model as the newer method. No root LICENSE file was found.", "what_it_is_for": "The paper animates an object in a still image using the motion of a similar object in a driving video, without keypoint labels. It is trained per object category. The README also documents an image-to-video mode that predicts future frames from the first frame after a small recurrent network is trained on keypoint coordinates.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "trento/mscs/mscs": { "id": "trento/mscs/mscs", "name": "Motion-supervised Co-Part Segmentation", "maker": "trento", "family": "trento/mscs", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-supervised co-part segmentation. The supplement describes a U-Net segmentation module at 64 by 64 with five conv-bn-relu-pool blocks and five upsample blocks. The encoder emits 6K+1 channels: K+1 for the segmentation, K for anchor keypoints, and 4K for per-part affine matrices. Training fine-tunes a First Order Motion Model checkpoint from the same group. The README says the default batch size is tuned for one Tesla P100. No parameter count was stated.", "modalities": { "input": [ "image", "video" ], "output": [ "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Motion-supervised Co-Part Segmentation is the self-supervised part model from Aliaksandr Siarohin, Subhankar Roy, Stephane Lathuiliere, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. The repository trains by fine-tuning a First Order Motion Model checkpoint from the same group. Status on 2026-10-15: active. arXiv v2 is 15 April 2020. The journal reference on the abs page is ICPR 2021. The repository licence is CC BY-NC 4.0.", "what_it_is_for": "The README describes co-part segmentation that uses motion between frames of the same video, without part labels. It also edits video by swapping selected parts, such as hair, beard, eyes, or lips on VoxCeleb. VoxCeleb and TaiChi are the datasets shown.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "trento/playable-environments/playable-environments": { "id": "trento/playable-environments/playable-environments", "name": "Playable Environments", "maker": "trento", "family": "trento/playable-environments", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder of per-object environment states, a synthesis module that is a style-modulated neural radiance field, and an action module that discretizes state transitions. Training has three phases: feature renderer F, synthesis module, then action module. The paper says the action count K is a hyper-parameter set before training. The play README prompts an action in 1 to 7 for each of two objects. Inference needs estimated camera parameters and 2D object locations. No parameter count was stated.", "modalities": { "input": [ "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Playable Environments is the interactive video model from Willi Menapace at the University of Trento, with Stephane Lathuiliere at Telecom Paris, Aliaksandr Siarohin at the University of Trento, Christian Theobalt and Vladislav Golyanik at MPI for Informatics, Sergey Tulyakov at Snap, and Elisa Ricci at the University of Trento and Fondazione Bruno Kessler. Menapace's footnote says part of the work was done while interning at MPI. Status on 2026-10-15: active. The README citation is CVPR 2022. The licence file is CC BY-NC 4.0, copyright 2022 Willi Menapace.", "what_it_is_for": "The paper builds a scene a user can play: move objects with discovered action labels, move the camera, and change object style. Released training and play configs cover Minecraft and tennis. A single image is enough at inference. Training uses estimated cameras and 2D object locations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/animation/animation": { "id": "tripo/animation/animation", "name": "Tripo Animation", "maker": "tripo", "family": "tripo/animation", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "rigged-mesh" ], "output": [ "animation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Animation is the closed preset-motion product on Tripo's models index. Collected 2026-10-15 it retargets named clips onto a rigged mesh. It is not a generative motion model and it has no version string of its own. Which clips exist depends on the rig snapshot.", "what_it_is_for": "Apply walk, run, idle, combat, and other presets to a model that Tripo Rig already skinned. Batch up to five clips.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/h/h-1-3": { "id": "tripo/h/h-1-3", "name": "Tripo v1.3", "maker": "tripo", "family": "tripo/h", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo v1.3 is an early closed Tripo image- and text-to-3D snapshot. On 2026-10-15 the v2 generation docs still name `v1.3-20240522` and mark it Deprecated. It is not an apiv3 model.", "what_it_is_for": "Early Tripo mesh generation kept for old v2 tasks. The vendor no longer presents it as a current choice.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/h/h-1-4": { "id": "tripo/h/h-1-4", "name": "Tripo v1.4", "maker": "tripo", "family": "tripo/h", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo v1.4 is a closed early Tripo mesh model, id `v1.4-20240625`. On 2026-10-15 it is still listed on the v2 generation API and the official SDK says apiv3 still accepts it. It is the oldest id in that set that is not marked Deprecated.", "what_it_is_for": "Fast legacy text-to-3D and image-to-3D. Platform billing calls it the fastest model. A separate refine task is priced only on this tier.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/h/h-2-0": { "id": "tripo/h/h-2-0", "name": "Tripo v2.0", "maker": "tripo", "family": "tripo/h", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo v2.0 is the closed H2-generation mesh model Tripo put on the web app and API on 2024-09-19. On 2026-10-15 the official SDK says `v2.0-20240919` is not available on apiv3. The v2 API still lists it and is itself scheduled to shut down on 2026-10-31.", "what_it_is_for": "H2 production baseline for text, image, and multiview meshes, with PBR textures. Tripo's own H2 page says to use H3 when newer quality controls matter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/h/h-2-5": { "id": "tripo/h/h-2-5", "name": "Tripo v2.5", "maker": "tripo", "family": "tripo/h", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo v2.5 is the closed H2 mesh model `v2.5-20250123`, also called `tripo-v2.5`. Collected 2026-10-15, Tripo's v3 model index still offers it as the legacy 3D model for old projects, and the v2 API still defaults to it when the caller omits a version.", "what_it_is_for": "Balanced text, image, and multiview meshes when a 2.x baseline is required. The H-series page calls it the balanced option under the newer v3 ids.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/h/h-3-0": { "id": "tripo/h/h-3-0", "name": "Tripo v3.0", "maker": "tripo", "family": "tripo/h", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo v3.0 is the closed H3 mesh model `v3.0-20250812` (`tripo-v3.0`). Collected 2026-10-15, Tripo calls it the previous-generation stable model for backward compatibility. The API changelog dated the official launch 2025-09-26. Earlier public announcements exist in August 2025.", "what_it_is_for": "High-detail text, image, and multiview meshes with optional PBR, quad output, smart low-poly, and part generation. Standard versus Ultra geometry is the product split.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/h/h-3-1": { "id": "tripo/h/h-3-1", "name": "Tripo H3.1", "maker": "tripo", "family": "tripo/h", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo H3.1 is Tripo's closed flagship high-poly 3D model, API id `v3.1-20260211`, short id `tripo-v3.1`. Collected 2026-10-15, the v3 model index calls it the latest high-precision default. The API changelog records the release on 2026-03-11.", "what_it_is_for": "Text, single-image, and multiview meshes when fidelity matters more than speed. Tripo positions it for AAA-style pipelines, 3D printing, and high face counts, with PBR textures, quad FBX, and smart low-poly.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/open/triposf": { "id": "tripo/open/triposf", "name": "TripoSF", "maker": "tripo", "family": "tripo/open", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-03-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SparseFlex VAE. Point-cloud input, sparse transformer encoder, self-pruning decoder, mesh extraction up to 1024^3. The open release is the pretrained VAE, not a full image-to-3D generator. Paper title is SparseFlex.", "modalities": { "input": [ "point-cloud" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TripoSF is an open MIT 3D shape VAE from Tripo / VAST, also called SparseFlex. The Hugging Face card is dated 2025-03-27 and the paper is arXiv:2503.21732. Collected 2026-10-15 the public release is the pretrained VAE, not the full generator. The vendor said that fuller system would appear in closed Tripo 3.0.", "what_it_is_for": "High-resolution mesh reconstruction, including arbitrary topology and internal structures, up to 1024 cubed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/open/triposg": { "id": "tripo/open/triposg", "name": "TripoSG", "maker": "tripo", "family": "tripo/open", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-02-10", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Rectified-flow transformer, 1.5B parameters, 2048 latent tokens, SDF VAE with hybrid geometric supervision, cross-attention image conditioning. GitHub also calls the release an MoE transformer. Trained on 2 million image-SDF pairs per the README.", "modalities": { "input": [ "image" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TripoSG is an open MIT image-to-3D shape model from Tripo / VAST AI Research. The paper is arXiv:2502.06608, dated 2025-02-10, and the model card states 1.5 billion parameters at 2048 latent tokens. Collected 2026-10-15 the weights remain on Hugging Face. It is not a closed Tripo API version.", "what_it_is_for": "High-fidelity mesh geometry from one image. The open release is a shape model. Texturing is a separate closed Tripo texture line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/open/triposg-scribble": { "id": "tripo/open/triposg-scribble", "name": "TripoSG-scribble", "maker": "tripo", "family": "tripo/open", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-04-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CFG-distilled TripoSG variant. Hugging Face card says it should be run at CFG 0 and that it is trained with 512 latent tokens. The same card repeats the base model's 1.5B / 2048-token sentence before listing those differences, so 1.5B is not recorded as this checkpoint's parameter count.", "modalities": { "input": [ "image", "text" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TripoSG-scribble is an open MIT variant of TripoSG that turns a scribble plus a text prompt into a 3D shape. Tripo also calls it Tripo Doodle. Collected 2026-10-15 the weights are on Hugging Face. Tripo's 2025-04-27 post says the weights were open-sourced then. It is a different checkpoint from base TripoSG.", "what_it_is_for": "Fast shape prototyping from a drawing and a prompt. The card says to run it with classifier-free guidance at 0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/open/triposplat": { "id": "tripo/open/triposplat", "name": "TripoSplat", "maker": "tripo", "family": "tripo/open", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "3D Gaussian generator using Density-Sampled Gaussians (DeG). VAST's 2026-06-01 post says it learns how many Gaussians to allocate and can output an arbitrary number. DeG was accepted to SIGGRAPH 2026. Parameter count was not in the post.", "modalities": { "input": [ "image" ], "output": [ "gaussian-splat" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TripoSplat is an open MIT image-to-3D-Gaussian model from VAST, the research team behind Tripo. VAST announced the open-source release on 2026-06-01 and said the Density-Sampled Gaussians method was accepted to SIGGRAPH 2026. Collected 2026-10-15 it is a weights release, not a dated mesh id on the Tripo generation API.", "what_it_is_for": "Turn one image into a 3D Gaussian splat whose point count is not fixed in advance.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/open/triposr": { "id": "tripo/open/triposr", "name": "TripoSR", "maker": "tripo", "family": "tripo/open", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LRM-style feed-forward transformer. Paper table: image tokenizer 512x512, patch 16, 12 attention layers, 768 channels; triplane tokenizer 32x32x3 tokens at 16 channels; backbone 1024 channels, 16 layers, 16 heads, head dim 64, cross-attention 768; triplane upsampler factor 2 to 64x64x40; NeRF MLP width 64, 10 layers, SiLU; 128 samples per ray. A published total parameter count was not in the table.", "modalities": { "input": [ "image" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TripoSR is an open MIT image-to-3D reconstruction model released by Tripo AI and Stability AI. The technical report is arXiv:2403.02151, submitted 2024-03-04. Collected 2026-10-15 the code and weights are still public. It is not one of the closed API version ids.", "what_it_is_for": "Feed-forward mesh reconstruction from one image, aimed at draft speed rather than the later closed H and P models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/p/p1": { "id": "tripo/p/p1", "name": "Tripo P1", "maker": "tripo", "family": "tripo/p", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo P1 is the closed Smart Mesh low-poly model, API id `P1-20260311`, short id `tripo-p1`. Collected 2026-10-15 it is the current P-series id on the model index, aimed at games, UGC, and mobile assets. Changelog 1.9.5 released it on 2026-03-11. P2 did not replace it.", "what_it_is_for": "Low-poly text, image, and multiview meshes with strict face control and PBR textures. Tripo's P2 post says P1 remains available to all users and that P2 is the quad-topology successor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/p/p2": { "id": "tripo/p/p2", "name": "Tripo P2", "maker": "tripo", "family": "tripo/p", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo P2 is the closed next-generation Smart Mesh model, API id `P2-20260801`. Collected 2026-10-15 the studio treats P2.0 as launched, with native quad meshes, while the API reference still labels that snapshot Preview. It does not replace P1.", "what_it_is_for": "Game and real-time meshes that need clean topology, optional quads, multiview input, several polycount variants, and region Mesh Edit. Tripo contrasts it with the HD / H line, which is for visual fidelity rather than game topology.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/retopo/retopo-v1-0": { "id": "tripo/retopo/retopo-v1-0", "name": "Tripo Retopology v1.0", "maker": "tripo", "family": "tripo/retopo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d-mesh" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Retopology v1.0 is the basic decimation tier, API model `v1.0`, on the v3 mesh decimate endpoint. Collected 2026-10-15 it costs 10 credits. An older full id, `P-v1.0-20250506`, is deprecated and mapped to `v1.0`.", "what_it_is_for": "Reduce polycount with a basic decimator when smart topology is not required. face_limit is mandatory.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/retopo/retopo-v2-0": { "id": "tripo/retopo/retopo-v2-0", "name": "Tripo Smart Retopology v2.0", "maker": "tripo", "family": "tripo/retopo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d-mesh" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Smart Retopology v2.0 is the default closed remesh model on POST /v3/mesh/decimate, id `v2.0`. Collected 2026-10-15 it costs 30 credits. Older clients sent `P-v2.0-20251225`, which the SDK maps to `v2.0`.", "what_it_is_for": "Rebuild a high-poly mesh into a cleaner low-poly mesh, including optional quads, while keeping edges.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/retopo/retopo-v3-0": { "id": "tripo/retopo/retopo-v3-0", "name": "Tripo Retopology v3.0", "maker": "tripo", "family": "tripo/retopo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d-mesh" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Retopology v3.0 is the newest closed remesh tier, API model `v3.0`, on POST /v3/mesh/decimate. Collected 2026-10-15 it is not the default. Triangles cost 35 credits and quads cost 40. The SDK's legacy name is `P-v3.0-20260909`.", "what_it_is_for": "Low-poly rebuild with optional quad output and an in-task UV unwrap. The unwrap flag does not replace the Smart UV product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/rig/rig-v1-0": { "id": "tripo/rig/rig-v1-0", "name": "Tripo Rig v1.0", "maker": "tripo", "family": "tripo/rig", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d-mesh" ], "output": [ "rigged-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Rig v1.0 is the closed default auto-rig model, id `v1.0-20240301`. Collected 2026-10-15 it is biped-only and still the default on POST /v3/animations/rig. The animation preset page ties 90+ biped motions to this snapshot.", "what_it_is_for": "Add a humanoid skeleton and skin weights, then retarget biped presets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/rig/rig-v2-0": { "id": "tripo/rig/rig-v2-0", "name": "Tripo Rig v2.0", "maker": "tripo", "family": "tripo/rig", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d-mesh" ], "output": [ "rigged-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Rig v2.0 is a deprecated closed rig snapshot, id `v2.0-20250506`. Collected 2026-10-15 the v2 schema says to use v2.5 instead, and the official SDK rewrites this id to `v2.5-20260210`.", "what_it_is_for": "Creature rigging from mid-2025. Callers should send the v2.5 id. The old id is accepted only as an alias that is rewritten.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/rig/rig-v2-5": { "id": "tripo/rig/rig-v2-5", "name": "Tripo Rig v2.5", "maker": "tripo", "family": "tripo/rig", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-02-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d-mesh" ], "output": [ "rigged-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Rig v2.5 is the closed creature rig, id `v2.5-20260210`. Collected 2026-10-15 the rigging model page lists it beside biped-only v1.0. The official SDK measured preset compatibility on 2026-10-15.", "what_it_is_for": "Auto-rig non-humanoid creatures and retarget the smaller universal preset set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/rig/rig-v3-0": { "id": "tripo/rig/rig-v3-0", "name": "Tripo Rig v3.0", "maker": "tripo", "family": "tripo/rig", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-09-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d-mesh" ], "output": [ "rigged-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Rig v3.0 is a closed rig snapshot, id `v3.0-20260909`, documented by the official Python SDK and measured against the API on 2026-10-15. The public rigging model page's snapshot table does not list it, so confidence is medium.", "what_it_is_for": "Newer humanoid-capable rigging. The SDK says humanoid presets work and quadruped, hexapod, and octopod presets do not.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/segment/segment-v1": { "id": "tripo/segment/segment-v1", "name": "Tripo Segmentation v1", "maker": "tripo", "family": "tripo/segment", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d-mesh" ], "output": [ "segmented-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Segmentation v1 is the closed default mesh-segmentation model, id `v1.0-20250506`. Collected 2026-10-15 the segment endpoint describes it as geometry-based. The newer semantic model is v2 and is still marked beta.", "what_it_is_for": "Split a mesh into parts from geometry. It does not do the semantic labeling that v2 adds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/segment/segment-v2": { "id": "tripo/segment/segment-v2", "name": "Tripo Segmentation v2", "maker": "tripo", "family": "tripo/segment", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d-mesh", "image" ], "output": [ "segmented-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Segmentation v2 is a closed semantic segmentation model, id `v2.0-20260430`. Collected 2026-10-15 the API marks it Beta, so status is preview, even though the May 2026 studio update presents it as a shipped rebuild of Segmentation.", "what_it_is_for": "Label and split a mesh into named parts, with an optional reference image. Tripo markets it for printable and editable parts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/smart-uv/smart-uv": { "id": "tripo/smart-uv/smart-uv", "name": "Tripo Smart UV", "maker": "tripo", "family": "tripo/smart-uv", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d-mesh" ], "output": [ "uv-layout" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Smart UV is a closed AI UV-unwrapping tool Tripo introduced in Studio on 2026-09-21. Collected 2026-10-15 it is a named product with an API price, not a dated generation-model id. It places seams, packs islands, and can show a UV preview.", "what_it_is_for": "Unwrap Tripo meshes or uploaded meshes up to 80,000 triangles or 40,000 quads so they can be textured.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/texture/texture-v2-0": { "id": "tripo/texture/texture-v2-0", "name": "Tripo Texture v2.0", "maker": "tripo", "family": "tripo/texture", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d-mesh", "text", "image" ], "output": [ "texture" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Texture v2.0 is a deprecated closed texture model, id `v2.0-20240919`, used by the v2 texture_model task. Collected 2026-10-15 the v2 schema still names it and marks it Deprecated. The v3 texture endpoint's published set does not include it.", "what_it_is_for": "Re-texture an existing mesh. Superseded by texture v2.5 and later.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/texture/texture-v2-5": { "id": "tripo/texture/texture-v2-5", "name": "Tripo Texture v2.5", "maker": "tripo", "family": "tripo/texture", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d-mesh", "text", "image" ], "output": [ "texture" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Texture v2.5 is a closed texture model, id `v2.5-20250123`. Collected 2026-10-15 it is the default texture pass for v2.5 geometry and a selectable model on the v3 texture endpoint. It is not the default for v3 or P-series meshes.", "what_it_is_for": "Texture or re-texture a mesh when the geometry was made with Tripo v2.5, including PBR maps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/texture/texture-v3-0": { "id": "tripo/texture/texture-v3-0", "name": "Tripo Texture v3.0", "maker": "tripo", "family": "tripo/texture", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d-mesh", "text", "image" ], "output": [ "texture" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Texture v3.0 is the closed default texture model, id `v3.0-20250812`. Collected 2026-10-15 the v3 texture endpoint defaults to it, and generation defaults to it for v3 and P-series geometry. Tripo 8K Texture is a quality tier on the texture system, not a separate id.", "what_it_is_for": "PBR texturing for current high-poly and Smart Mesh geometry when the newest v3.5 pass is not requested.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/texture/texture-v3-5": { "id": "tripo/texture/texture-v3-5", "name": "Tripo Texture v3.5", "maker": "tripo", "family": "tripo/texture", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d-mesh", "image", "text" ], "output": [ "texture" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Texture v3.5 is the newest closed texture model, id `v3.5-20260815`. Collected 2026-10-15 it is the only texture version that accepts texture_quality=fast and the delight / Remove Lighting control. It is opt-in; the default texture pass is still v3.0.", "what_it_is_for": "Faster texturing and lighting removal on current geometry. Tripo uses it when the caller wants a clean base color instead of baked reference lighting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tripo/turbo/turbo-v1-0": { "id": "tripo/turbo/turbo-v1-0", "name": "Tripo Turbo v1.0", "maker": "tripo", "family": "tripo/turbo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tripo Turbo v1.0 is a closed speed-oriented mesh model, id `Turbo-v1.0-20250506`. The API changelog added it on 2025-05-21 for text-to-model and image-to-model. Collected 2026-10-15 it is still on the v2 generation enum. The official Python SDK does not list it as an apiv3 model_version.", "what_it_is_for": "Faster text-to-3D and image-to-3D for time-sensitive jobs. Tripo did not publish a face cap or a latency number for this id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tromso/divvun/aanna": { "id": "tromso/divvun/aanna", "name": "Aanna", "maker": "tromso", "family": "tromso/divvun", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-10-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FastPitch non-autoregressive transformer with explicit duration and pitch prediction, trained as a two-id quality split and exported to TorchScript. Inference uses speaker id 1, the higher-quality subset. Waveform vocoder is NeMo UnivNet. Text is normalized by GiellaLT HFST and constraint grammar before synthesis. Audio is 22.05 kHz.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aanna is the South S\u00e1mi female text-to-speech voice from the Divvun group at UiT The Arctic University of Norway. It is an active local voice, released on 30 October 2024, and the first South S\u00e1mi synthesizer the group documents. This dossier was collected on 2026-10-15.", "what_it_is_for": "Aanna reads South S\u00e1mi text aloud for language learning, dictionaries, screen readers, and people who are more comfortable with spoken South S\u00e1mi than with the written standard from 1978. The voice is built from archive recordings of Anna Jacobsen (Jaahkenelkien Aanna, 1924\u20132004), a South S\u00e1mi journalist, teacher, and translator. Divvun restored and transcribed NRK and audiobook material from 1989\u20131993 with the S\u00e1mi Archives and the National Archives of Norway, with written consent from her sons. The Norwegian product page says the voice can also read Norwegian and Swedish, but not without errors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tromso/divvun/biret": { "id": "tromso/divvun/biret", "name": "Biret", "maker": "tromso", "family": "tromso/divvun", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FastPitch non-autoregressive transformer with explicit duration and pitch prediction. Divvun states this is the architecture used for the 2024 North S\u00e1mi voice, as for the Lule S\u00e1mi voices. The vocoder shipped with Biret is not named separately from the South S\u00e1mi UnivNet setup.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Biret is the North S\u00e1mi female text-to-speech voice from the Divvun group at UiT The Arctic University of Norway. It is an active local voice, launched with the Lule S\u00e1mi voices on 25 June 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "Divvun ships Biret so a screen reader or read-aloud tool can speak North S\u00e1mi. UiT describes the 2024 voice as a more available replacement for the 2015 North S\u00e1mi synthesizer made with Acapela: better sound and pronunciation, installable in the operating system without license codes, and built with open software owned by Divvun and UiT. Borealium says it can also read Norwegian, and partially Swedish and English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tromso/divvun/nihkol-abmut": { "id": "tromso/divvun/nihkol-abmut", "name": "Nihkol / \u00c1bmut", "maker": "tromso", "family": "tromso/divvun", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FastPitch non-autoregressive transformer with explicit duration and pitch prediction. Divvun ships Nihkol and \u00c1bmut as one package and says they perform better together. The vocoder in the package is not named on the product pages.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Nihkol / \u00c1bmut is the Lule S\u00e1mi male voice package from the Divvun group at UiT The Arctic University of Norway. Divvun treats the two voices as one product, released on 25 June 2024, because they perform better in the same package. This dossier was collected on 2026-10-15.", "what_it_is_for": "The package reads Lule S\u00e1mi text aloud. Both recorded speakers are from the Norwegian part of the Lule S\u00e1mi area. Borealium says Nihkol can read Norwegian, though not without errors, while \u00c1bmut was trained only on Lule S\u00e1mi and is not expected to handle multilingual text. UiT names Sander Andersen, Sigmund Johnsen, and Britt Inger Sikku as the three Lule S\u00e1mi donors and does not say which two are Nihkol and \u00c1bmut.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tromso/divvun/sigga": { "id": "tromso/divvun/sigga", "name": "Sigg\u00e1", "maker": "tromso", "family": "tromso/divvun", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FastPitch non-autoregressive transformer with explicit duration and pitch prediction, the architecture Divvun states it used for the 2024 Lule S\u00e1mi voices. The vocoder in the Sigg\u00e1 package is not named on the product pages.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sigg\u00e1 is the Lule S\u00e1mi female text-to-speech voice from the Divvun group at UiT The Arctic University of Norway. It is an active local voice, released on 25 June 2024 with the two Lule S\u00e1mi male voices and the updated North S\u00e1mi voice. This dossier was collected on 2026-10-15.", "what_it_is_for": "Divvun uses Sigg\u00e1 to read Lule S\u00e1mi aloud in a screen reader or similar tool. Borealium says the speaker is from the Swedish side of the Lule S\u00e1mi area, and that the voice can also read Swedish, Norwegian, and English, but not without errors. UiT's launch article names three donors for the three Lule S\u00e1mi voices (Sander Andersen, Sigmund Johnsen, and Britt Inger Sikku) and does not say which person is Sigg\u00e1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/agentcpm/agentcpm-explore": { "id": "tsinghua/agentcpm/agentcpm-explore", "name": "AgentCPM-Explore", "maker": "tsinghua", "family": "tsinghua/agentcpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-12", "context_length": null, "max_output_tokens": null, "parameters": "4022468096", "architecture": "Agent model continued from Qwen/Qwen3-4B-Thinking-2507. Hub safetensors reports 4,022,468,096 parameters. It is a named OpenBMB agent, not an unmodified Qwen upload.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AgentCPM-Explore is OpenBMB's 4B agent model, announced 2026-01-12 under Apache-2.0. The card says it is continued from Qwen3-4B-Thinking-2507. Collected 2026-10-15.", "what_it_is_for": "On-device long-horizon tool-using agent. The card names GAIA, HLE, and BrowserComp among eight benchmarks and does not give the scores in the excerpt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/agentcpm/agentcpm-gui": { "id": "tsinghua/agentcpm/agentcpm-gui", "name": "AgentCPM-GUI", "maker": "tsinghua", "family": "tsinghua/agentcpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-13", "context_length": null, "max_output_tokens": null, "parameters": "8099268336", "architecture": "GUI agent built on MiniCPM-V-2.6. Hub safetensors reports 8,099,268,336 parameters. Card: jointly developed by THUNLP, Renmin University of China, and ModelBest.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AgentCPM-GUI is OpenBMB's on-device GUI agent, announced 2025-05-13 under Apache-2.0 and built on MiniCPM-V 2.6. The technical report is arXiv 2506.01391, noted on 2025-06-03. Collected 2026-10-15.", "what_it_is_for": "On-device Android GUI agent for Chinese and English apps, with reinforcement fine-tuning for reasoning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/agentcpm/agentcpm-report": { "id": "tsinghua/agentcpm/agentcpm-report", "name": "AgentCPM-Report", "maker": "tsinghua", "family": "tsinghua/agentcpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-19", "context_length": null, "max_output_tokens": null, "parameters": "8185253888", "architecture": "Local deep-research agent. Hub safetensors reports 8,185,253,888 parameters, the same count as MiniCPM4-8B. The card does not name a base model in the front matter.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AgentCPM-Report is OpenBMB's Apache-2.0 deep-research agent, uploaded 2026-01-19. The card links arXiv 2602.06540 and a GGUF pack. Collected 2026-10-15.", "what_it_is_for": "Long-form research reports on device. The card heading compares the target to Gemini 2.5 Pro deep research. That is a maker claim, not a measured score copied here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/codegeex/codegeex-13b": { "id": "tsinghua/codegeex/codegeex-13b", "name": "CodeGeeX-13B", "maker": "tsinghua", "family": "tsinghua/codegeex", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Research use; exact license text not copied", "release_date": "2022-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multilingual code generation model. The CodeGeeX2 README calls the first generation CodeGeeX-13B and says it was trained on Huawei Ascend, then converted to Megatron-LM for PyTorch GPUs. Weights were about 26 GB and shipped by request.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CodeGeeX-13B is THUDM's first CodeGeeX, with weights opened on 2022-09-30 for research. CodeGeeX2-6B and CodeGeeX4 are already under zhipu and are not repeated. Collected 2026-10-15.", "what_it_is_for": "Code completion and generation. Superseded for new use by CodeGeeX2 and CodeGeeX4, which are cataloged under zhipu.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/cogview/cogview": { "id": "tsinghua/cogview/cogview", "name": "CogView", "maker": "tsinghua", "family": "tsinghua/cogview", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-06-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "4-billion-parameter Transformer with a VQ-VAE image tokenizer for text-to-image generation. The README says simplified Chinese input is the supported path and that English is better translated into Chinese first. The public repo commit used here is 2021-06-15. The paper is NeurIPS 2021, arXiv 2105.13290.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogView is THUDM's first text-to-image Transformer, a 4B model from the NeurIPS 2021 paper. Pretrained weights were offered from the project download page, including cogview-base. CogView2 and later CogView products are already under zhipu. Collected 2026-10-15.", "what_it_is_for": "General-domain text-to-image generation. CogView2 and later CogView models are cataloged under zhipu.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/cpm/cpm-ant-10b": { "id": "tsinghua/cpm/cpm-ant-10b", "name": "CPM-Ant-10B", "maker": "tsinghua", "family": "tsinghua/cpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Open license allowing commercial use; SPDX id not stated on the page read", "release_date": "2023-01-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "model_type cpmant. Config: hidden 4096, 48 layers, 32 attention heads, vocab 30720, float32. The CPM-Live page says 10B parameters. Training finished on 2022-08-05 after 68 days.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CPM-Ant-10B is OpenBMB's 10B Chinese pretrained model from the CPM-Live project. The maker says training finished on 2022-08-05. The Hub repo dates to 2023-01-15. The project page says the license allows commercial use. Collected 2026-10-15.", "what_it_is_for": "Chinese pretrained base model and the first CPM-Live milestone, including delta-tuning results on CUGE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/cpm/cpm-bee-10b": { "id": "tsinghua/cpm/cpm-bee-10b", "name": "CPM-Bee-10B", "maker": "tsinghua", "family": "tsinghua/cpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "General Model License (source notice, publicity restriction, commercial authorization)", "release_date": "2023-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive Transformer, model_type cpmbee. Config: hidden 4096, 48 layers, 32 attention heads, vocab 86583. GitHub table: 48 attention layers, 48 FFN layers, FFN hidden 10240. The maker states 10B parameters and pretraining on trillion-scale tokens. Training is described as starting 2022-10-13.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CPM-Bee-10B is OpenBMB's bilingual 10B base model, released 2023-05-27. The code is Apache-2.0. The weights use the General Model License and need written commercial authorization from cpm@modelbest.cn. Collected 2026-10-15.", "what_it_is_for": "Bilingual Chinese-English base model for downstream adaptation. The README says it is the second CPM-Live milestone after CPM-Ant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/cpm/cpm-bee-1b": { "id": "tsinghua/cpm/cpm-bee-1b", "name": "CPM-Bee-1B", "maker": "tsinghua", "family": "tsinghua/cpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "General Model License (source notice, publicity restriction, commercial authorization)", "release_date": "2023-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BMCook compression of CPM-Bee. GitHub table: 19 attention layers, 24 FFN layers, attention hidden 1280, FFN hidden 1024. The maker's size name is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CPM-Bee-1B is the compressed 1B CPM-Bee published on 2023-05-27. The Hub card still describes the 10B family and shows a short generation example. Collected 2026-10-15.", "what_it_is_for": "Smallest official CPM-Bee checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/cpm/cpm-bee-2b": { "id": "tsinghua/cpm/cpm-bee-2b", "name": "CPM-Bee-2B", "maker": "tsinghua", "family": "tsinghua/cpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "General Model License (source notice, publicity restriction, commercial authorization)", "release_date": "2023-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BMCook compression of CPM-Bee. GitHub table: 19 attention layers, 24 FFN layers, attention hidden 2048, FFN hidden 5120. The maker's size name is 2B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CPM-Bee-2B is the compressed 2B CPM-Bee published on 2023-05-27. Collected 2026-10-15.", "what_it_is_for": "Smaller CPM-Bee checkpoint for limited GPUs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/cpm/cpm-bee-5b": { "id": "tsinghua/cpm/cpm-bee-5b", "name": "CPM-Bee-5B", "maker": "tsinghua", "family": "tsinghua/cpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "General Model License (source notice, publicity restriction, commercial authorization)", "release_date": "2023-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BMCook compression of CPM-Bee. GitHub table: 19 attention layers, 24 FFN layers, attention hidden 4096, FFN hidden 10240. The maker's size name is 5B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CPM-Bee-5B is the compressed 5B CPM-Bee published with the 10B release on 2023-05-27. Collected 2026-10-15.", "what_it_is_for": "Mid-size CPM-Bee for machines that cannot hold the 10B checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/embedding/minicpm-embedding": { "id": "tsinghua/embedding/minicpm-embedding", "name": "MiniCPM-Embedding", "maker": "tsinghua", "family": "tsinghua/embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-09-04", "context_length": 512, "max_output_tokens": null, "parameters": "2724880896", "architecture": "Encoder from MiniCPM-2B-sft-bf16. Config: hidden 2304, 40 layers, 36 heads, max_position_embeddings 512, vocab 122753. Hub safetensors reports 2,724,880,896 parameters.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-Embedding is OpenBMB's MiniCPM-2B embedding model, uploaded 2024-09-04. Config max length is 512 tokens. No license was on the card front matter. Collected 2026-10-15.", "what_it_is_for": "Text embeddings from the MiniCPM-2B checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/embedding/minicpm-embedding-light": { "id": "tsinghua/embedding/minicpm-embedding-light", "name": "MiniCPM-Embedding-Light", "maker": "tsinghua", "family": "tsinghua/embedding", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-17", "context_length": null, "max_output_tokens": null, "parameters": "433874944", "architecture": "Smaller MiniCPM embedding model. Hub safetensors reports 433,874,944 parameters, close to MiniCPM4-0.5B's count. Context was not read from config.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-Embedding-Light is OpenBMB's smaller embedding model, uploaded 2025-01-17. Collected 2026-10-15.", "what_it_is_for": "A lighter MiniCPM embedding checkpoint than MiniCPM-Embedding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/glm/glm-10b": { "id": "tsinghua/glm/glm-10b", "name": "GLM-10B", "maker": "tsinghua", "family": "tsinghua/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive blank-infilling GLM. Card: 48 layers, hidden 4096, 64 heads, pretrained on the Pile. The GitHub table lists 10B English parameters. Hub upload date is 2023-02-28. The paper is ACL 2022.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-10B is THUDM's 10B English GLM from the ACL 2022 GLM paper. Weights are on zai-org/glm-10b, uploaded 2023-02-28. The commercial ChatGLM and GLM API line is cataloged under zhipu and is not repeated here. Collected 2026-10-15.", "what_it_is_for": "General English language model for understanding and generation, including the blank-infilling objective.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/glm/glm-10b-chinese": { "id": "tsinghua/glm/glm-10b-chinese", "name": "GLM-10B-Chinese", "maker": "tsinghua", "family": "tsinghua/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Chinese GLM. GitHub table: 10B parameters, WuDaoCorpora, token plus sentence plus document objectives. Hub upload 2023-02-28.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-10B-Chinese is THUDM's Chinese 10B GLM. Weights are on zai-org/glm-10b-chinese, uploaded 2023-02-28. Collected 2026-10-15.", "what_it_is_for": "Chinese blank-infilling language model at the 10B scale of the GLM paper release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/glm/glm-2b": { "id": "tsinghua/glm/glm-2b", "name": "GLM-2B", "maker": "tsinghua", "family": "tsinghua/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "English GLM. GitHub table: 2B parameters, Pile corpus, token plus sentence plus document objectives. Hub upload 2023-03-01.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-2B is THUDM's 2B English GLM. Weights are on zai-org/glm-2b, uploaded 2023-03-01. Collected 2026-10-15.", "what_it_is_for": "Smaller English GLM from the same pretrained-model release as GLM-10B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/glm/glm-large-chinese": { "id": "tsinghua/glm/glm-large-chinese", "name": "GLM-Large-Chinese", "maker": "tsinghua", "family": "tsinghua/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Chinese GLM. GitHub table: 335M parameters, WuDaoCorpora, token plus sentence plus document objectives. Hub upload 2023-03-01.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-Large-Chinese is THUDM's 335M Chinese GLM. Weights are on zai-org/glm-large-chinese, uploaded 2023-03-01. Collected 2026-10-15.", "what_it_is_for": "The Chinese large GLM from the original GLM model table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/glm/glm-roberta-large": { "id": "tsinghua/glm/glm-roberta-large", "name": "GLM-RoBERTa-Large", "maker": "tsinghua", "family": "tsinghua/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "English GLM continued from a RoBERTa-style setup. GitHub table: 335M, RoBERTa corpus, token blank-filling objective. Hub upload 2023-03-01.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-RoBERTa-Large is THUDM's 335M English GLM-RoBERTa checkpoint on zai-org/glm-roberta-large, uploaded 2023-03-01. Collected 2026-10-15.", "what_it_is_for": "GLM blank-infilling model initialized in the RoBERTa setting from the GLM paper release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/glyph/glyph": { "id": "tsinghua/glyph/glyph", "name": "Glyph", "maker": "tsinghua", "family": "tsinghua/glyph", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-10-25", "context_length": 131072, "max_output_tokens": null, "parameters": "10292777472", "architecture": "Visual-text compression model continued from GLM-4.1V-9B-Base. Config model_type glm4v. Text stack: hidden 4096, 40 layers, 32 query heads, 2 key-value heads, max_position_embeddings 131072. Vision hidden 1536. Hub safetensors reports 10,292,777,472 parameters. The card says long text is rendered into images.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Glyph is a THUDM and thu-coai research model uploaded 2025-10-25 under MIT. It continues GLM-4.1V-9B-Base, which is already cataloged under zhipu. This folder is only the Glyph checkpoint. The paper is arXiv 2510.17800. Collected 2026-10-15.", "what_it_is_for": "Fit long text into a vision-language context by rendering it as images instead of only extending the text window.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/imagereward/image-reward": { "id": "tsinghua/imagereward/image-reward", "name": "ImageReward", "maker": "tsinghua", "family": "tsinghua/imagereward", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Text-to-image human preference reward model. The card says it is trained on 137k expert comparison pairs from DiffusionDB prompts and outputs. The official module uses a BLIP pretrained encoder plus an MLP scorer.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ImageReward is THUDM's Apache-2.0 text-to-image preference reward model, uploaded 2023-04-06. The paper is arXiv 2304.05977. The Hub pipeline tag is text-to-image, but the card defines a reward model. Collected 2026-10-15.", "what_it_is_for": "Score generated images against a text prompt using human preference, rather than generate images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/longalign/longalign-6b-64k": { "id": "tsinghua/longalign/longalign-6b-64k", "name": "LongAlign-6B-64k", "maker": "tsinghua", "family": "tsinghua/longalign", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Long-context chat model. Hub tags say chatglm and the LongAlign-10k dataset. The name says 6B and 64k. Context integer was not read from config.json.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongAlign-6B-64k is THUDM's Apache-2.0 long-context alignment model, uploaded 2024-01-29. Llama-sized LongAlign checkpoints were not given folders. The paper is arXiv 2401.18058. Collected 2026-10-15.", "what_it_is_for": "Long-instruction chat. The card describes packing with loss weighting, sorted batching, and LongBench-Chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/longcite/longcite-glm4-9b": { "id": "tsinghua/longcite/longcite-glm4-9b", "name": "LongCite-GLM-4-9B", "maker": "tsinghua", "family": "tsinghua/longcite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-09-02", "context_length": null, "max_output_tokens": null, "parameters": "10521927680", "architecture": "Citation model trained from glm-4-9b. Hub safetensors reports 10,521,927,680 parameters. The card says the max context is up to 128K tokens. GLM-4-9B itself is cataloged under zhipu.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongCite-glm4-9b is THUDM's citation model on GLM-4-9B, uploaded 2024-09-02. The paper is arXiv 2409.02897. The Llama 3.1 LongCite checkpoint was not given a folder. Collected 2026-10-15.", "what_it_is_for": "Long-context question answering with fine-grained citations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/longreward/longreward-glm4-9b": { "id": "tsinghua/longreward/longreward-glm4-9b", "name": "LongReward-GLM-4-9B-DPO", "maker": "tsinghua", "family": "tsinghua/longreward", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-10-28", "context_length": null, "max_output_tokens": null, "parameters": "9399951360", "architecture": "DPO model based on THUDM/glm-4-9b-chat. Hub safetensors reports 9,399,951,360 parameters. Card license value was other. The GitHub README says the SFT models support up to 64k context.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongReward-glm4-9b-DPO is THUDM's DPO long-context model on GLM-4-9B-Chat, uploaded 2024-10-28. The SFT sibling and the Llama 3.1 pair exist. Only this GLM DPO repo is foldered. GLM-4-9B stays under zhipu. Collected 2026-10-15.", "what_it_is_for": "Long-context question answering after LongReward preference optimization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/longwriter/longwriter-glm4-9b": { "id": "tsinghua/longwriter/longwriter-glm4-9b", "name": "LongWriter-GLM-4-9B", "maker": "tsinghua", "family": "tsinghua/longwriter", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-08-12", "context_length": null, "max_output_tokens": null, "parameters": "9399951392", "architecture": "model_type chatglm. Config: hidden 4096, 40 layers, 32 heads, vocab 151552, bfloat16. Hub safetensors reports 9,399,951,392 parameters. The Llama 3.1 LongWriter was not given a folder.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LongWriter-glm4-9b is a THUDM chatglm checkpoint uploaded 2024-08-12. GLM-4-9B itself is cataloged under zhipu. Collected 2026-10-15.", "what_it_is_for": "Long-form writing checkpoint from THUDM on a GLM-4-9B-class chat model. A README body was not fetched for this repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/mathglm/mathglm": { "id": "tsinghua/mathglm/mathglm", "name": "MathGLM-10B", "maker": "tsinghua", "family": "tsinghua/mathglm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AFL-3.0", "release_date": "2023-09-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The Hub card says MathGLM-10B is finetuned from GLM-10B on multi-step arithmetic and text math problems. Inference uses SwissArmyTransformer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MathGLM-10B is THUDM's math fine-tune of GLM-10B, uploaded 2023-09-21 under AFL-3.0. The card says it matches GPT-4 closely on a 5,000-example Chinese math set. Collected 2026-10-15.", "what_it_is_for": "Arithmetic and Chinese math word problems without an external calculator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/mathglm/mathglm-vision": { "id": "tsinghua/mathglm/mathglm-vision", "name": "MathGLM-Vision", "maker": "tsinghua", "family": "tsinghua/mathglm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Separate zai-org repo from MathGLM-10B. The README body fetched on 2026-10-15 contained only the Apache-2.0 front matter.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MathGLM-Vision is a THUDM Hub repo uploaded 2024-12-05 under Apache-2.0. The card body did not describe architecture or size. Collected 2026-10-15.", "what_it_is_for": "Vision math model. The task is inferred from the repository name because the card body was empty.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/mathglm/mathglm-vision-19b": { "id": "tsinghua/mathglm/mathglm-vision-19b", "name": "MathGLM-Vision-19B", "maker": "tsinghua", "family": "tsinghua/mathglm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Companion repo to MathGLM-Vision. The name says 19B. An exact parameter count was not in safetensors metadata, and the README body was not fetched separately.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MathGLM-Vision-19B is a THUDM Hub repo uploaded 2024-12-05 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The 19B-named MathGLM vision checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/bitcpm4-0-5b": { "id": "tsinghua/minicpm/bitcpm4-0-5b", "name": "BitCPM4-0.5B", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-06", "context_length": 32768, "max_output_tokens": null, "parameters": "433873920", "architecture": "Ternary MiniCPM4. Config matches MiniCPM4-0.5B: hidden 1024, 24 layers, 16 query heads, 2 key-value heads, max position 32768. README: BitCPM compresses weights to three values and claims about a 90% bit-width reduction.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BitCPM4-0.5B is OpenBMB's ternary MiniCPM4-0.5B, released with MiniCPM4 on 2025-06-06 under Apache-2.0. Hub safetensors reports 433,873,920 parameters. Collected 2026-10-15.", "what_it_is_for": "A ternary-quantized MiniCPM4 for the same end-side setting as the 0.5B dense model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/bitcpm4-1b": { "id": "tsinghua/minicpm/bitcpm4-1b", "name": "BitCPM4-1B", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-06", "context_length": null, "max_output_tokens": null, "parameters": "1360258560", "architecture": "Ternary MiniCPM4 1B-class model. Hub safetensors reports 1,360,258,560 parameters. A config.json was not fetched for this repo.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BitCPM4-1B is OpenBMB's 1B-class ternary MiniCPM4, released 2025-06-06 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The larger ternary MiniCPM4 checkpoint released beside BitCPM4-0.5B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/minicpm-1b": { "id": "tsinghua/minicpm/minicpm-1b", "name": "MiniCPM-1B", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-11", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "MiniCPM decoder, hidden 1536, 52 layers, 24 query heads, 8 key-value heads, vocab 73440 (config.json).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-1B is the 1B-class OpenBMB chat model announced on 2024-04-11. Weights are the sft-bf16 Hub repo. The card did not state a license. Collected 2026-10-15.", "what_it_is_for": "A smaller MiniCPM chat model released with the April 2024 batch for tighter local deployment than MiniCPM-2B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/minicpm-2b": { "id": "tsinghua/minicpm/minicpm-2b", "name": "MiniCPM-2B", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-02-01", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "MiniCPM decoder, hidden 2304, 40 layers, 36 attention heads, vocab 122753 (config.json of MiniCPM-2B-sft-bf16). Context max_position_embeddings 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-2B is OpenBMB's first MiniCPM chat checkpoint, from Tsinghua's NLP lab and ModelBest. The sft-bf16 weights were announced on 2024-02-01 and are still published. No license was set on the Hub card read for this checkpoint. Collected 2026-10-15.", "what_it_is_for": "OpenBMB describes MiniCPM-2B as a small chat model aimed at matching much larger open models on public benchmarks, with stronger Chinese, math, and code than the comparison set named in the MiniCPM changelog.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/minicpm-moe-8x2b": { "id": "tsinghua/minicpm/minicpm-moe-8x2b", "name": "MiniCPM-MoE-8x2B", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-11", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Mixture-of-experts MiniCPM. Config: 8 experts, 2 experts per token, hidden 2304, 40 layers, 36 heads, vocab 122753, max position 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-MoE-8x2B is OpenBMB's MoE MiniCPM, announced 2024-04-11. The Hub card did not list a license or a language list. Collected 2026-10-15.", "what_it_is_for": "A mixture-of-experts MiniCPM released so a 2B-shaped backbone can route across eight experts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/minicpm-s-1b": { "id": "tsinghua/minicpm/minicpm-s-1b", "name": "MiniCPM-S-1B", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-05", "context_length": 4096, "max_output_tokens": null, "parameters": "1360246272", "architecture": "Sparse-FFN MiniCPM. Config matches the dense 1B shape: hidden 1536, 52 layers, 24 query heads, 8 key-value heads. The changelog says average FFN sparsity 87.89% and an 84% cut in FFN FLOPs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-S-1B-sft is OpenBMB's sparse 1B MiniCPM, announced 2024-07-05 under Apache-2.0. Hub safetensors metadata reports 1,360,246,272 parameters. Collected 2026-10-15.", "what_it_is_for": "An efficiency variant of MiniCPM-1B that sparsifies the feed-forward layers while the maker says downstream performance holds up.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/minicpm-sala": { "id": "tsinghua/minicpm/minicpm-sala", "name": "MiniCPM-SALA", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-11", "context_length": 524288, "max_output_tokens": null, "parameters": "9477203968", "architecture": "model_type minicpm_sala. Config: hidden 4096, 32 layers, 32 query heads, 2 key-value heads, vocab 73448, max_position_embeddings 524288. README: 25% InfLLM-V2 sparse attention and 75% Lightning Attention, continued from a dense checkpoint rather than trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-SALA is OpenBMB's sparse-plus-linear hybrid model, released 2026-02-11 under Apache-2.0. Hub safetensors reports 9,477,203,968 parameters. The checkpoint config context is 524,288 tokens. The README also says inference up to 1M tokens and a score at 2048K. Collected 2026-10-15.", "what_it_is_for": "Long-context modeling on a hybrid sparse and linear attention stack, including million-token inference claims on a single high-end GPU.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/minicpm3-4b": { "id": "tsinghua/minicpm/minicpm3-4b", "name": "MiniCPM3-4B", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-05", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "model_type minicpm3. Config: hidden 2560, 62 layers, 40 heads, vocab 73448, max_position_embeddings 32768.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM3-4B is the Apache-2.0 MiniCPM3 checkpoint announced 2024-09-05. The config context is 32,768 tokens. An exact parameter integer was not in the Hub safetensors metadata. Collected 2026-10-15.", "what_it_is_for": "OpenBMB's third-generation MiniCPM, positioned against Phi-3.5-mini and several 7B to 9B chat models in the project changelog.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/minicpm4-0-5b": { "id": "tsinghua/minicpm/minicpm4-0-5b", "name": "MiniCPM4-0.5B", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-06", "context_length": 32768, "max_output_tokens": null, "parameters": "433873920", "architecture": "MiniCPM4 end-side model. Config: hidden 1024, 24 layers, 16 query heads, 2 key-value heads, vocab 73448, max_position_embeddings 32768. The README says MiniCPM4 is pretrained at 32K and extended with YaRN; the 128K needle result is described for the series.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM4-0.5B is OpenBMB's 0.5B MiniCPM4 checkpoint, announced with the 8B model on 2025-06-06 under Apache-2.0. Hub safetensors reports 433,873,920 parameters. Collected 2026-10-15.", "what_it_is_for": "The small end-side MiniCPM4, for on-device generation with the series' sparse-attention and system stack.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/minicpm4-1-8b": { "id": "tsinghua/minicpm/minicpm4-1-8b", "name": "MiniCPM4.1-8B", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-09-05", "context_length": 65536, "max_output_tokens": null, "parameters": "8185253888", "architecture": "model_type minicpm. Config: hidden 4096, 32 layers, 32 query heads, 2 key-value heads, vocab 73448, max_position_embeddings 65536. README calls it the first open-source reasoning LLM with trainable sparse attention, pretrained at 64K and extended with YaRN.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM4.1-8B is OpenBMB's reasoning MiniCPM4, announced 2025-09-05 under Apache-2.0. Hub safetensors reports 8,185,253,888 parameters, the same count as MiniCPM4-8B. Config context is 65,536 tokens. Collected 2026-10-15.", "what_it_is_for": "MiniCPM4.1 adds hybrid reasoning and a faster reasoning decode path on the MiniCPM4 sparse-attention design.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/minicpm4-8b": { "id": "tsinghua/minicpm/minicpm4-8b", "name": "MiniCPM4-8B", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-06", "context_length": 32768, "max_output_tokens": null, "parameters": "8185253888", "architecture": "model_type minicpm. Config: hidden 4096, 32 layers, 32 query heads, 2 key-value heads, vocab 73448, max_position_embeddings 32768. README: InfLLM-V2 trainable sparse attention, each token attending to under 5% of tokens at 128K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM4-8B is OpenBMB's 8B MiniCPM4 model, announced 2025-06-06 under Apache-2.0. Hub safetensors reports 8,185,253,888 parameters. The checkpoint config context is 32,768; the README describes YaRN extension and a stable 128K needle test. Collected 2026-10-15.", "what_it_is_for": "The main MiniCPM4 end-side model, built for long-context speed on edge chips such as Jetson AGX Orin and RTX 4090.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/minicpm4-mcp": { "id": "tsinghua/minicpm/minicpm4-mcp", "name": "MiniCPM4-MCP", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-06", "context_length": null, "max_output_tokens": null, "parameters": "8185253888", "architecture": "Application checkpoint shipped with MiniCPM4. Hub safetensors parameter count matches MiniCPM4-8B (8,185,253,888).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM4-MCP is an Apache-2.0 OpenBMB checkpoint released with MiniCPM4 on 2025-06-06. Collected 2026-10-15.", "what_it_is_for": "Named MiniCPM4 application model for MCP-style tool use. The card excerpt opened here repeats the MiniCPM4 series notes and does not add a separate MCP eval table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/minicpm4-survey": { "id": "tsinghua/minicpm/minicpm4-survey", "name": "MiniCPM4-Survey", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-06", "context_length": null, "max_output_tokens": null, "parameters": "8185253888", "architecture": "Application checkpoint shipped with MiniCPM4. Hub safetensors parameter count matches MiniCPM4-8B (8,185,253,888). The card points at the MiniCPM4 technical report.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM4-Survey is an Apache-2.0 OpenBMB checkpoint released with MiniCPM4. The Hub upload date is 2025-06-05 and the series announcement is 2025-06-06. Collected 2026-10-15.", "what_it_is_for": "Named MiniCPM4 application model. The card excerpt read here does not spell out a survey-writing procedure beyond the repository name and the MiniCPM4 paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/minicpm5-1b": { "id": "tsinghua/minicpm/minicpm5-1b", "name": "MiniCPM5-1B", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-19", "context_length": 131072, "max_output_tokens": null, "parameters": "1080632832", "architecture": "LlamaForCausalLM. Config: hidden 1536, 24 layers, 16 query heads, 2 key-value heads, vocab 130560, rope_theta 5,000,000, max_position_embeddings 131072, bfloat16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM5-1B is the first MiniCPM5 model from OpenBMB, announced 2026-05-19 under Apache-2.0. It is a dense Llama-architecture 1B model with a 131,072-token config context. Hub safetensors reports 1,080,632,832 parameters. Collected 2026-10-15.", "what_it_is_for": "On-device assistant for coding, tools, and hybrid reasoning. The same checkpoint switches think and no-think modes with enable_thinking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm/minicpm5-2b": { "id": "tsinghua/minicpm/minicpm5-2b", "name": "MiniCPM5-2B", "maker": "tsinghua", "family": "tsinghua/minicpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09-07", "context_length": 131072, "max_output_tokens": null, "parameters": "2516756480", "architecture": "LlamaForCausalLM. README and config agree: 2,516,756,480 parameters (non-embedding 1,981,982,720), 42 layers, 16 query heads, 2 key-value heads, context 131,072. Config also has hidden 2048, intermediate 6144, vocab 130560, rope_theta 5,000,000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM5-2B is OpenBMB's second MiniCPM5 model, announced 2026-09-07 under Apache-2.0. It scales the MiniCPM5-1B recipe to about 2.52 billion parameters with a 131,072-token context. Collected 2026-10-15.", "what_it_is_for": "A larger on-device MiniCPM5 for local assistants, coding agents, tool use, and reasoning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm-o/minicpm-o-2-6": { "id": "tsinghua/minicpm-o/minicpm-o-2-6", "name": "MiniCPM-o 2.6", "maker": "tsinghua", "family": "tsinghua/minicpm-o", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-14", "context_length": 32768, "max_output_tokens": null, "parameters": "8674997028", "architecture": "model_type minicpmo. Config: hidden 3584, 28 layers, 28 query heads, 4 key-value heads, vocab 151700, max_position_embeddings 32768, SigLIP vision tower hidden 1152. Hub safetensors reports 8,674,997,028 parameters.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-o 2.6 is OpenBMB's first omnimodal MiniCPM-o, announced 2025-01-14 under Apache-2.0. Config context is 32,768 tokens. Collected 2026-10-15.", "what_it_is_for": "Omnimodal successor to MiniCPM-V 2.6 with real-time speech conversation and multimodal live streaming, as stated on the MiniCPM-V news line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm-o/minicpm-o-4-5": { "id": "tsinghua/minicpm-o/minicpm-o-4-5", "name": "MiniCPM-o 4.5", "maker": "tsinghua", "family": "tsinghua/minicpm-o", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-03", "context_length": 40960, "max_output_tokens": null, "parameters": "9371787666", "architecture": "model_type minicpmo. Config: hidden 4096, 36 layers, 32 query heads, 8 key-value heads, vocab 151748, max_position_embeddings 40960, SigLIP vision hidden 1152. Hub safetensors reports 9,371,787,666 parameters. The README calls it a 9B end-to-end omnimodal model.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-o 4.5 is OpenBMB's 9B-class omnimodal model. The Hub repo dates to 2026-02-03 and the README says API access was released 2026-05-17. Weights are Apache-2.0. Config context is 40,960 tokens. Collected 2026-10-15.", "what_it_is_for": "Full-duplex live interaction: take video and audio while generating speech and text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm-v/minicpm-llama3-v-2-5": { "id": "tsinghua/minicpm-v/minicpm-llama3-v-2-5", "name": "MiniCPM-Llama3-V 2.5", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-20", "context_length": null, "max_output_tokens": null, "parameters": "8537092336", "architecture": "Vision-language model. Hub safetensors reports 8,537,092,336 parameters. The card language tag is multilingual. The base Llama 3 checkpoint is in the model name; the exact vision encoder was not on the card front matter.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-Llama3-V 2.5 is an OpenBMB vision-language model announced 2024-05-20. It is a named OpenBMB product that uses Llama 3, not a reupload of Llama weights. Collected 2026-10-15.", "what_it_is_for": "OpenBMB's May 2024 multimodal model, described on later cards as GPT-4V level for its size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm-v/minicpm-v": { "id": "tsinghua/minicpm-v/minicpm-v", "name": "MiniCPM-V", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-01-30", "context_length": null, "max_output_tokens": null, "parameters": "3434965792", "architecture": "The MiniCPM-V card identifies this checkpoint as OmniLMM-3B. Hub safetensors reports 3,434,965,792 parameters. Context was not read from config.json.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-V is OpenBMB's first small vision-language model, also called OmniLMM-3B on its card. The Hub repo dates to 2024-01-30. A license was not set on the card front matter. Collected 2026-10-15.", "what_it_is_for": "The first MiniCPM-V / OmniLMM-3B visual question-answering model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm-v/minicpm-v-2": { "id": "tsinghua/minicpm-v/minicpm-v-2", "name": "MiniCPM-V 2.0", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-11", "context_length": null, "max_output_tokens": null, "parameters": "3434965792", "architecture": "Vision-language MiniCPM. Hub safetensors reports 3,434,965,792 parameters, the same count as MiniCPM-V. Context config was not fetched.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-V 2.0 is OpenBMB's second MiniCPM-V, announced 2024-04-11. The card lists RLHF-V, UniMM-Chat, VQAv2, and LLaVA-Instruct-150K among datasets. No license was on the card front matter. Collected 2026-10-15.", "what_it_is_for": "The April 2024 MiniCPM-V generation for English and Chinese visual chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm-v/minicpm-v-2-6": { "id": "tsinghua/minicpm-v/minicpm-v-2-6", "name": "MiniCPM-V 2.6", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-06", "context_length": null, "max_output_tokens": null, "parameters": "8099175152", "architecture": "Vision-language model. Hub safetensors reports 8,099,175,152 parameters. config.json returned 401 to an anonymous fetch, so context and layer counts were not read.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-V 2.6 is OpenBMB's August 2024 vision-language model, announced 2024-08-06. The family card says it covers single images, multiple images, and video. Collected 2026-10-15.", "what_it_is_for": "Single-image, multi-image, and video understanding, including the maker's real-time video claim on iPad.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm-v/minicpm-v-4": { "id": "tsinghua/minicpm-v/minicpm-v-4", "name": "MiniCPM-V 4", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-12", "context_length": 32768, "max_output_tokens": null, "parameters": "4059533040", "architecture": "model_type minicpmv. Config: hidden 2560, 32 layers, 32 query heads, 2 key-value heads, vocab 73448, max_position_embeddings 32768. Vision config model_type siglip_vision_model, hidden 1152.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-V 4 is an Apache-2.0 OpenBMB vision-language model uploaded 2025-07-12. Hub safetensors reports 4,059,533,040 parameters. Config context is 32,768. Collected 2026-10-15.", "what_it_is_for": "A mid-size MiniCPM-V generation between 2.6 and 4.5, with a SigLIP vision tower.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm-v/minicpm-v-4-5": { "id": "tsinghua/minicpm-v/minicpm-v-4-5", "name": "MiniCPM-V 4.5", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-24", "context_length": 40960, "max_output_tokens": null, "parameters": "8695895280", "architecture": "Built on Qwen3-8B and SigLIP2-400M. Config model_type minicpmv: hidden 4096, 36 layers, 32 query heads, 8 key-value heads, vocab 151748, max_position_embeddings 40960, vision hidden 1152. The card says 8B parameters. Hub safetensors reports 8,695,895,280.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-V 4.5 is OpenBMB's Apache-2.0 vision-language model uploaded 2025-08-24. The card says it totals 8B parameters on Qwen3-8B and SigLIP2-400M and reports an OpenCompass average of 77.0. Collected 2026-10-15.", "what_it_is_for": "Single-image, multi-image, and high-frame-rate video understanding, aimed at phone-class deployment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm-v/minicpm-v-4-6": { "id": "tsinghua/minicpm-v/minicpm-v-4-6", "name": "MiniCPM-V 4.6", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-11", "context_length": 262144, "max_output_tokens": null, "parameters": "1300428016", "architecture": "model_type minicpmv4_6. Text config is qwen3_5_text: hidden 1024, 24 layers, 8 query heads, 2 key-value heads, max_position_embeddings 262144. Vision hidden 1152. OpenBMB's product page calls it a 1.3B model. Hub safetensors reports 1,300,428,016.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-V 4.6 is OpenBMB's 1.3B vision-language model, announced 2026-05-11 under Apache-2.0. The text config context is 262,144 tokens. An API was announced on 2026-05-17 without a public price on the pages read. Collected 2026-10-15.", "what_it_is_for": "On-device image, multi-image, and video understanding with switchable 4x and 16x visual-token compression.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm-v/minicpm-v-4-7-1b": { "id": "tsinghua/minicpm-v/minicpm-v-4-7-1b", "name": "MiniCPM-V 4.7 1B", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-12", "context_length": 262144, "max_output_tokens": null, "parameters": "1554476272", "architecture": "model_type minicpmv4_7. Text config qwen3_5_text: hidden 1024, 24 layers, 8 query heads, 2 key-value heads, max_position_embeddings 262144. Vision hidden 1152. Hub safetensors reports 1,554,476,272 parameters. The guide describes early visual-token compression inside the vision encoder and mixed 4x/16x compression.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-V 4.7 1B is OpenBMB's small MiniCPM-V 4.7, announced 2026-10-12 under Apache-2.0. The text config context is 262,144 tokens. Collected 2026-10-15.", "what_it_is_for": "The dense 1B MiniCPM-V 4.7 for on-device image, multi-image, document, and video understanding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/minicpm-v/minicpm-v-4-7-35b-a3b": { "id": "tsinghua/minicpm-v/minicpm-v-4-7-35b-a3b", "name": "MiniCPM-V 4.7 35B-A3B", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-10-12", "context_length": 262144, "max_output_tokens": null, "parameters": "35212875824", "architecture": "model_type minicpmv4_7. Text config qwen3_5_moe_text: hidden 2048, 40 layers, 16 query heads, 2 key-value heads, max_position_embeddings 262144. Vision hidden 1152. Hub safetensors reports 35,212,875,824 parameters. The release name 35B-A3B is the maker's active-parameter label. An exact active count was not a separate integer on the config keys read.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-V 4.7 35B-A3B is OpenBMB's MoE vision-language model, announced 2026-10-12 under Apache-2.0. Hub safetensors reports 35,212,875,824 parameters. The text config context is 262,144 tokens. The README says Lookahead Expert Scheduling plus quantization and DSpark enables on-device decoding, quoting more than 13 tokens per second in about 3 GB on an iPhone 17 Pro Max. Collected 2026-10-15.", "what_it_is_for": "The first MiniCPM-V mixture-of-experts model, for image, multi-image, document, and video understanding, including the maker's on-device MoE claim.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/msagpt/msagpt": { "id": "tsinghua/msagpt/msagpt", "name": "MSAGPT", "maker": "tsinghua", "family": "tsinghua/msagpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-06-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Protein language model for multiple-sequence-alignment generation. The card says 3 billion parameters and three versions: MSAGPT, MSAGPT-Sft, and MSAGPT-Dpo. The paper link on the card was a placeholder.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MSAGPT is THUDM's Apache-2.0 protein language model, uploaded 2024-06-11. The card states 3 billion parameters and three training stages. Collected 2026-10-15.", "what_it_is_for": "Zero-shot and few-shot protein MSA generation to help structure prediction when natural MSAs are scarce.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/omnilmm/omnilmm-12b": { "id": "tsinghua/omnilmm/omnilmm-12b", "name": "OmniLMM-12B", "maker": "tsinghua", "family": "tsinghua/omnilmm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-01-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card says OmniLMM-12B connects EVA02-5B and Zephyr-7B-beta with a perceiver resampler and is trained on multimodal data in a curriculum.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmniLMM-12B is OpenBMB's 12B-class large multimodal model, uploaded 2024-01-31. It is the larger sibling of MiniCPM-V, which the MiniCPM-V card calls OmniLMM-3B. No license was on the card front matter. Collected 2026-10-15.", "what_it_is_for": "A larger OmniLMM visual assistant. The card cites MME, MMBench, and SEED-Bench as benchmarks where it claims leading results among similar sizes, without numbers in the excerpt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/rerank/minicpm-reranker": { "id": "tsinghua/rerank/minicpm-reranker", "name": "MiniCPM-Reranker", "maker": "tsinghua", "family": "tsinghua/rerank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-09-04", "context_length": null, "max_output_tokens": null, "parameters": "2724883200", "architecture": "Reranker fine-tuned from MiniCPM-2B-sft-bf16. Hub pipeline is text-classification. Safetensors reports 2,724,883,200 parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-Reranker is OpenBMB's MiniCPM-2B reranker, uploaded 2024-09-04. No license was on the card front matter. Collected 2026-10-15.", "what_it_is_for": "Rerank text passages using a MiniCPM-2B backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/rerank/minicpm-reranker-light": { "id": "tsinghua/rerank/minicpm-reranker-light", "name": "MiniCPM-Reranker-Light", "maker": "tsinghua", "family": "tsinghua/rerank", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-17", "context_length": null, "max_output_tokens": null, "parameters": "1360247808", "architecture": "Reranker based on MiniCPM-1B-sft-bf16. Hub pipeline is text-ranking. Safetensors reports 1,360,247,808 parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-Reranker-Light is OpenBMB's MiniCPM-1B reranker, uploaded 2025-01-17. Collected 2026-10-15.", "what_it_is_for": "A smaller MiniCPM reranker for passage ranking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/rlhf-v/rlaif-v-12b": { "id": "tsinghua/rlhf-v/rlaif-v-12b", "name": "RLAIF-V-12B", "maker": "tsinghua", "family": "tsinghua/rlhf-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-20", "context_length": null, "max_output_tokens": null, "parameters": "11615965952", "architecture": "12B RLAIF-V checkpoint. Hub safetensors reports 11,615,965,952 parameters. The 7B card links this repo. The 12B base model was not in the excerpt read.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RLAIF-V-12B is OpenBMB's larger RLAIF-V model, uploaded 2024-05-20 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Larger RLAIF-V trustworthiness model released with the 7B checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/rlhf-v/rlaif-v-7b": { "id": "tsinghua/rlhf-v/rlaif-v-7b", "name": "RLAIF-V-7B", "maker": "tsinghua", "family": "tsinghua/rlhf-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-19", "context_length": null, "max_output_tokens": null, "parameters": "7062902784", "architecture": "Fine-tuned from LLaVA 1.5 7B with the RLAIF-V AI-feedback method. Hub safetensors reports 7,062,902,784 parameters. The card says feedback came from LLaVA-NeXT-34B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RLAIF-V-7B is OpenBMB's Apache-2.0 alignment of LLaVA 1.5 7B, uploaded 2024-05-19. It is a named OpenBMB model, not an unmodified LLaVA upload. The paper is arXiv 2405.17220. Collected 2026-10-15.", "what_it_is_for": "A more trustworthy LLaVA 1.5 7B, trained to reduce visual hallucinations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/rlhf-v/rlhf-v": { "id": "tsinghua/rlhf-v/rlhf-v", "name": "RLHF-V", "maker": "tsinghua", "family": "tsinghua/rlhf-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language alignment model from the RLHF-V project. The card excerpt does not name the base checkpoint.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RLHF-V is OpenBMB's Apache-2.0 vision-language alignment checkpoint, uploaded 2023-11-27. The paper is arXiv 2312.00849. Collected 2026-10-15.", "what_it_is_for": "Reduce visual hallucination by training on human preference feedback.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/robot/minicpm-robotmanip": { "id": "tsinghua/robot/minicpm-robotmanip", "name": "MiniCPM-RobotManip", "maker": "tsinghua", "family": "tsinghua/robot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-18", "context_length": null, "max_output_tokens": null, "parameters": "1536237296", "architecture": "Vision-language-action manipulation policy. Hub pipeline tag is robotics. Safetensors reports 1,536,237,296 parameters.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-RobotManip is OpenBMB's Apache-2.0 manipulation model, uploaded 2026-07-18. Collected 2026-10-15.", "what_it_is_for": "On-device robot manipulation policy. The card calls it an on-device AI brain for robots.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/robot/minicpm-robottrack": { "id": "tsinghua/robot/minicpm-robottrack", "name": "MiniCPM-RobotTrack", "maker": "tsinghua", "family": "tsinghua/robot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-18", "context_length": null, "max_output_tokens": null, "parameters": "449787035", "architecture": "Vision-language-action tracking policy. Hub pipeline tag is robotics. Safetensors reports 449,787,035 parameters.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiniCPM-RobotTrack is OpenBMB's Apache-2.0 tracking model, uploaded 2026-07-18. Collected 2026-10-15.", "what_it_is_for": "Embodied target tracking. The card calls it a compact vision-language-action policy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/ssvae/ssvae": { "id": "tsinghua/ssvae/ssvae", "name": "SSVAE", "maker": "tsinghua", "family": "tsinghua/ssvae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Spectral-Structured video VAE. The card says it biases the latent spectrum so later diffusion training is easier. Parameter count was not in safetensors metadata.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SSVAE is THUDM's MIT-licensed video VAE, uploaded 2025-12-04. The paper is arXiv 2512.05394. Collected 2026-10-15.", "what_it_is_for": "Video autoencoder whose latent spectrum is shaped for diffusion, not a text chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/ui2code/ui2code-n": { "id": "tsinghua/ui2code/ui2code-n", "name": "UI2Code^N", "maker": "tsinghua", "family": "tsinghua/ui2code", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-11-11", "context_length": null, "max_output_tokens": null, "parameters": "10292777472", "architecture": "Visual language model continued from GLM-4.1V-9B-Base. Hub safetensors reports 10,292,777,472 parameters, the same count as Glyph. Context was not re-read from this repo's config.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UI2Code^N is a THUDM visual coding model uploaded 2025-11-11 under MIT. It is trained from GLM-4.1V-9B-Base. The paper is arXiv 2511.08195. Collected 2026-10-15.", "what_it_is_for": "UI-to-code generation, UI editing, and UI polishing, with test-time scaling described on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/ultra/ultracm-13b": { "id": "tsinghua/ultra/ultracm-13b", "name": "UltraCM-13B", "maker": "tsinghua", "family": "tsinghua/ultra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-09-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Critique model in the UltraFeedback release. Parameter count was not in safetensors metadata.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UltraCM-13B is OpenBMB's MIT-licensed critique model, uploaded 2023-09-22 with UltraFeedback. Collected 2026-10-15.", "what_it_is_for": "Write critiques of model responses using the UltraFeedback setup.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/ultra/ultralm-13b": { "id": "tsinghua/ultra/ultralm-13b", "name": "UltraLM-13B", "maker": "tsinghua", "family": "tsinghua/ultra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "LLaMA model license (delta weights)", "release_date": "2023-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card says these are delta weights fine-tuned from LLaMA-13B on UltraChat, using a multi-turn User/Assistant template.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UltraLM-13B is OpenBMB and THUNLP's UltraChat chat model, uploaded 2023-06-26 as LLaMA-13B delta weights. It is not a hosted copy of unmodified LLaMA. Collected 2026-10-15.", "what_it_is_for": "An early OpenBMB chat model trained on the UltraChat multi-turn data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/ultra/ultralm-65b": { "id": "tsinghua/ultra/ultralm-65b", "name": "UltraLM-65B", "maker": "tsinghua", "family": "tsinghua/ultra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-07-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "UltraChat chat model in the UltraLM line. The 13B card documents LLaMA delta training. This 65B card's README was not opened, so the same delta claim is not copied onto it.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UltraLM-65B is the 65B UltraLM checkpoint on the OpenBMB Hub, uploaded 2023-07-18, with the UltraChat dataset tagged. Collected 2026-10-15.", "what_it_is_for": "The larger UltraLM chat model trained with UltraChat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/ultra/ultrarm-13b": { "id": "tsinghua/ultra/ultrarm-13b", "name": "UltraRM-13B", "maker": "tsinghua", "family": "tsinghua/ultra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-09-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reward model trained with UltraFeedback. Parameter count was not in safetensors metadata.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UltraRM-13B is OpenBMB's MIT-licensed reward model, uploaded 2023-09-22 with UltraFeedback. Collected 2026-10-15.", "what_it_is_for": "Score model responses. The UltraFeedback dataset is tagged on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/viscpm/viscpm-chat": { "id": "tsinghua/viscpm/viscpm-chat", "name": "VisCPM-Chat", "maker": "tsinghua", "family": "tsinghua/viscpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-06-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal chat model on CPM-Bee-10B with a Q-Former visual encoder. The card also names a Diffusion-UNet decoder for the Paint sibling.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VisCPM-Chat is OpenBMB's bilingual multimodal chat model, uploaded 2023-06-28. The card badge says Apache-2.0. It is trained from CPM-Bee-10B. Collected 2026-10-15.", "what_it_is_for": "Chinese and English visual conversation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/viscpm/viscpm-paint": { "id": "tsinghua/viscpm/viscpm-paint", "name": "VisCPM-Paint", "maker": "tsinghua", "family": "tsinghua/viscpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-06-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Text-to-image model in the VisCPM family. The Chat card says the family fuses a Q-Former encoder and a Diffusion-UNet decoder on CPM-Bee-10B. Paint is the generation model.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VisCPM-Paint is OpenBMB's bilingual text-to-image model, uploaded 2023-06-25. The family card badge says Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Chinese and English text-to-image generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/visionreward/visionreward-image": { "id": "tsinghua/visionreward/visionreward-image", "name": "VisionReward-Image", "maker": "tsinghua", "family": "tsinghua/visionreward", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CogVLM2 license (card license name: other)", "release_date": "2024-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image preference reward model. The card says human preferences are split into weighted judgment questions. License name on the card is cogvlm2, with a link to the CogVLM2 Llama 3 license file. Safetensors total was empty.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VisionReward-Image is THUDM's image preference model, uploaded 2024-12-11. The card groups it with a video sibling. Collected 2026-10-15.", "what_it_is_for": "Score images with a multi-question preference rubric for aligning visual generators.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/visionreward/visionreward-video": { "id": "tsinghua/visionreward/visionreward-video", "name": "VisionReward-Video", "maker": "tsinghua", "family": "tsinghua/visionreward", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CogVLM2 license (card license name: other)", "release_date": "2024-12-10", "context_length": null, "max_output_tokens": null, "parameters": "12507532544", "architecture": "Video preference reward model in the VisionReward release. Hub safetensors reports 12,507,532,544 parameters. The image card says the video model surpasses VideoScore by 17.2 percent on video preference prediction.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VisionReward-Video is THUDM's video preference model, uploaded 2024-12-10. Hub safetensors reports 12,507,532,544 parameters. Collected 2026-10-15.", "what_it_is_for": "Score generated videos against human preferences.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/visrag/visrag-ret": { "id": "tsinghua/visrag/visrag-ret", "name": "VisRAG-Ret", "maker": "tsinghua", "family": "tsinghua/visrag", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-14", "context_length": null, "max_output_tokens": null, "parameters": "3434965792", "architecture": "Document retriever based on MiniCPM-V-2. Hub safetensors reports 3,434,965,792 parameters, the same count as MiniCPM-V-2. Pipeline is feature-extraction.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VisRAG-Ret is OpenBMB's Apache-2.0 visual retriever, uploaded 2024-10-14 and based on MiniCPM-V 2.0. Collected 2026-10-15.", "what_it_is_for": "Visual document retrieval. Training sets tagged on the card are VisRAG in-domain and synthetic retrieval data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/voxcpm/voxcpm-0-5b": { "id": "tsinghua/voxcpm/voxcpm-0-5b", "name": "VoxCPM-0.5B", "maker": "tsinghua", "family": "tsinghua/voxcpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-09-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tokenizer-free text-to-speech model. Card base_model is openbmb/MiniCPM4-0.5B. Hub safetensors total was empty, so the exact count is unpublished here. The name says 0.5B.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VoxCPM-0.5B is OpenBMB's first VoxCPM text-to-speech model, uploaded 2025-09-16 under Apache-2.0. It is built on MiniCPM4-0.5B. Collected 2026-10-15.", "what_it_is_for": "Context-aware speech generation and voice cloning without a discrete speech tokenizer, as the card title states.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/voxcpm/voxcpm-1-5": { "id": "tsinghua/voxcpm/voxcpm-1-5", "name": "VoxCPM1.5", "maker": "tsinghua", "family": "tsinghua/voxcpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-05", "context_length": null, "max_output_tokens": null, "parameters": "801729856", "architecture": "Text-to-speech model. Hub card base_model is openbmb/MiniCPM4-0.5B. Hub safetensors reports 801,729,856 parameters. The fetched config.json had no standard transformer context field.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VoxCPM1.5 is OpenBMB's Apache-2.0 text-to-speech checkpoint uploaded 2025-12-05. Hub safetensors reports 801,729,856 parameters. A full README was not copied in this pass. Collected 2026-10-15.", "what_it_is_for": "The middle VoxCPM speech model, uploaded between VoxCPM-0.5B and VoxCPM2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/voxcpm/voxcpm2": { "id": "tsinghua/voxcpm/voxcpm2", "name": "VoxCPM2", "maker": "tsinghua", "family": "tsinghua/voxcpm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-03", "context_length": null, "max_output_tokens": null, "parameters": "2290004544", "architecture": "Tokenizer-free diffusion autoregressive TTS. The card says 2B parameters, 48 kHz audio, and training on over 2 million hours. Hub safetensors reports 2,290,004,544 parameters.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VoxCPM2 is OpenBMB's multilingual text-to-speech model, uploaded 2026-04-03 under Apache-2.0. The card says 2B parameters, 30 languages, and 48 kHz output. Collected 2026-10-15.", "what_it_is_for": "Multilingual tokenizer-free speech synthesis with voice cloning and voice design, per the model card tags and introduction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsinghua/webvia/webvia-agent": { "id": "tsinghua/webvia/webvia-agent", "name": "WebVIA-Agent", "maker": "tsinghua", "family": "tsinghua/webvia", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-11-12", "context_length": null, "max_output_tokens": null, "parameters": "10292777472", "architecture": "Agent checkpoint in the WebVIA UI-to-code framework, continued from GLM-4.1V-9B-Base. Hub safetensors reports 10,292,777,472 parameters.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WebVIA-Agent is a THUDM MIT-licensed agent checkpoint uploaded 2025-11-12, continued from GLM-4.1V-9B-Base. The paper PDF linked on the card is arXiv 2511.06251. Collected 2026-10-15.", "what_it_is_for": "The card says WebVIA-Agent navigates sites and captures multi-state UI screens so generated interfaces can be executed and checked, not only emitted as static HTML.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsukuba/rayemb/rayemb-ctpelvic1k": { "id": "tsukuba/rayemb/rayemb-ctpelvic1k", "name": "RayEmb-CTPelvic1K", "maker": "tsukuba", "family": "tsukuba/rayemb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-11-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Released code in rayemb/models/dinov2.py loads facebookresearch/dinov2 dinov2_vits14 and nn.Linear(384, emb_dim) with default emb_dim 32, then rearranges patch tokens to 16\u00d716 at 224 input. The paper's implementation section says a DINOv2 encoder plus a fully connected layer maps 768-dimensional descriptors to 32-dimensional ray embeddings. Those two descriptions disagree; the weight file itself was not unpacked. The checkpoint is the CTPelvic1K-trained model the paper calls RayEmb*. Registration around the embeddings uses PnP, MAGSAC or RANSAC, and DiffDRR refinement. The paper's stated training setup, which is not split out per dataset, is Adam at learning rate 1e-4, 50 epochs, batch size 8, gradient clipping 10.0, 40 3D sampling points, temperature 1e-4, images resized to 224\u00d7224, 324 template DRRs, and 4 templates sampled during training, on an RTX 3090 GPU Ti.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RayEmb-CTPelvic1K is the public checkpoint for RayEmb*, the CTPelvic1K-trained 2D-3D X-ray registration model from Pragyan Shrestha, Chun Xie, and Itaru Kitahara at the University of Tsukuba, with Yuichi Yoshii at Tokyo Medical University. It embeds an X-ray so that arbitrary 3D landmarks can be matched for pose registration. The weight file was still reachable on the collection date, 2026-10-15. The repository license is MIT. A license for the weight file was not published. There is no university API and no published price.", "what_it_is_for": "The paper detects arbitrary landmarks in an X-ray by testing which ray embeddings lie near the subspace of a 3D point, then estimates a rigid pose with PnP and DiffDRR refinement. RayEmb* is the general model trained on CTPelvic1K CLINIC (103 CT volumes). The authors say it does not need a patient-specific fine-tune. The GitHub download command documents this checkpoint and no other.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsukuba/sv-drr/sv-drr-1024": { "id": "tsukuba/sv-drr/sv-drr-1024", "name": "SV-DRR 1024", "maker": "tsukuba", "family": "tsukuba/sv-drr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-03", "context_length": null, "max_output_tokens": null, "parameters": "604826528", "architecture": "Diffusers SvdrrDiTPipeline (model_index.json, diffusers 0.31.0) with CCProjection, CLIPVisionModelWithProjection, DPMSolverMultistepScheduler, SvdrrTransformer2DModel, and AutoencoderKL. Transformer config (diffusers 0.30.3): 28 layers, 16 heads, head dim 72, cross_attention_dim 1152, ada_norm_single, num_embeds_ada_norm 1000, in_channels 8, out_channels 8, patch_size 2, gelu-approximate, attention_bias true, dropout 0, sample_size 128, interpolation_scale 2. Scheduler config (diffusers 0.29.1): dpmsolver++, linear betas 0.0001 to 0.02, 1000 train timesteps, prediction_type epsilon, solver_order 2, solver_type midpoint, timestep_spacing linspace. VAE and image-encoder JSON in this repo were not re-read on the collection date. The paper says the VAE is the SDXL VAE, the image encoder is CLIP, and high resolution continues by interpolating positional embeddings. The parameter integer is the Hub safetensors total (F32) for every pipeline tensor.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SV-DRR 1024 is an open-weight view-conditioned diffusion pipeline from Chun Xie and Itaru Kitahara at the Center for Computational Sciences, University of Tsukuba, with Yuichi Yoshii at Tokyo Medical University Ibaraki Medical Center. It synthesizes a 1024\u00d71024 novel-view chest X-ray from one source view and a target camera pose. The weights were public on the collection date, 2026-10-15. The Hub card license is Apache-2.0. There is no university API and no published price.", "what_it_is_for": "The paper and the download list treat 1024 as its own pretrained resolution. The official training script continues from a 512 SV-DRR pipeline. Table 1 scores this resolution on its own row. The view input is a camera pose, not a text prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsukuba/sv-drr/sv-drr-256": { "id": "tsukuba/sv-drr/sv-drr-256", "name": "SV-DRR 256", "maker": "tsukuba", "family": "tsukuba/sv-drr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-03", "context_length": null, "max_output_tokens": null, "parameters": "604826528", "architecture": "Diffusers SvdrrDiTPipeline (model_index.json, diffusers 0.31.0) with CCProjection, CLIPVisionModelWithProjection, DPMSolverMultistepScheduler, SvdrrTransformer2DModel, and AutoencoderKL. Transformer config (diffusers 0.30.3): 28 layers, 16 heads, head dim 72, cross_attention_dim 1152, ada_norm_single, num_embeds_ada_norm 1000, in_channels 8, out_channels 8, patch_size 2, gelu-approximate, attention_bias true, dropout 0, sample_size 32, interpolation_scale 0.5. VAE config (diffusers 0.29.1): scaling_factor 0.13025, latent_channels 4, block_out_channels 128/256/512/512, config sample_size 512. Image encoder config names lambdalabs/sd-image-variations-diffusers: 24 layers, hidden size 1024, 16 heads, patch size 14, projection_dim 768, image_size 224. Scheduler config (diffusers 0.29.1): dpmsolver++, linear betas 0.0001 to 0.02, 1000 train timesteps, prediction_type epsilon, solver_order 2, solver_type midpoint, timestep_spacing linspace. The paper initializes VCDiT from PixArt-\u03a3-256, uses the SDXL VAE, and uses CLIP as the image encoder. The parameter integer is the Hub safetensors total (F32) for every pipeline tensor.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SV-DRR 256 is an open-weight view-conditioned diffusion pipeline from Chun Xie and Itaru Kitahara at the Center for Computational Sciences, University of Tsukuba, with Yuichi Yoshii at Tokyo Medical University Ibaraki Medical Center. It synthesizes a 256\u00d7256 novel-view chest X-ray from one source view and a target camera pose. The weights were public on the collection date, 2026-10-15. The Hub card license is Apache-2.0. There is no university API and no published price.", "what_it_is_for": "The paper and the model card describe single-view digitally reconstructed radiography: a diffusion transformer synthesizes another X-ray angle from one projection. The view input is a camera pose, not a text prompt. The authors list this file as the 256-resolution pretrained download, scored on its own row in Table 1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tsukuba/sv-drr/sv-drr-512": { "id": "tsukuba/sv-drr/sv-drr-512", "name": "SV-DRR 512", "maker": "tsukuba", "family": "tsukuba/sv-drr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-10-03", "context_length": null, "max_output_tokens": null, "parameters": "604826528", "architecture": "Diffusers SvdrrDiTPipeline (model_index.json, diffusers 0.31.0) with CCProjection, CLIPVisionModelWithProjection, DPMSolverMultistepScheduler, SvdrrTransformer2DModel, and AutoencoderKL. Transformer config (diffusers 0.30.3): 28 layers, 16 heads, head dim 72, cross_attention_dim 1152, ada_norm_single, num_embeds_ada_norm 1000, in_channels 8, out_channels 8, patch_size 2, gelu-approximate, attention_bias true, dropout 0, sample_size 64, interpolation_scale 1.0. Scheduler config (diffusers 0.31.0): dpmsolver++, linear betas 0.0001 to 0.02, 1000 train timesteps, prediction_type epsilon, solver_order 2, solver_type midpoint, timestep_spacing linspace, use_beta_sigmas false, use_exponential_sigmas false. VAE and image-encoder JSON in this repo were not re-read on the collection date. The paper says the VAE is the SDXL VAE, the image encoder is CLIP, and VCDiT starts from PixArt-\u03a3-256 before weak-to-strong continuation. The parameter integer is the Hub safetensors total (F32) for every pipeline tensor.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SV-DRR 512 is an open-weight view-conditioned diffusion pipeline from Chun Xie and Itaru Kitahara at the Center for Computational Sciences, University of Tsukuba, with Yuichi Yoshii at Tokyo Medical University Ibaraki Medical Center. It synthesizes a 512\u00d7512 novel-view chest X-ray from one source view and a target camera pose. The weights were public on the collection date, 2026-10-15. The Hub card license is Apache-2.0. There is no university API and no published price.", "what_it_is_for": "The paper and the download list treat 512 as its own pretrained resolution. Training continues from the 256 SV-DRR pipeline. Table 1 scores this resolution on its own row. The view input is a camera pose, not a text prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-berlin/cg-schnet/cg-schnet": { "id": "tu-berlin/cg-schnet/cg-schnet", "name": "cG-SchNet", "maker": "tu-berlin", "family": "tu-berlin/cg-schnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conditional autoregressive generator. The README says each new atom's type and position depend on the atoms already placed and on target properties, with focus and origin tokens. It builds on G-SchNet and schnetpack 0.3. A parameter count was not stated. The reproduction repository is marked deprecated. schnetpack-gschnet is the maintained implementation.", "modalities": { "input": [ "text" ], "output": [ "atomic-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "cG-SchNet is the conditional 3D molecule generator from the Machine Learning Group at Technical University of Berlin, published in Nature Communications in 2022. Two paper checkpoints are on DepositOnce. The original training repository is deprecated. The group points new work at schnetpack-gschnet. Collected 2026-10-15.", "what_it_is_for": "The README says the network samples molecules that are likely to match specified properties, such as composition and relative atomic energy, or HOMO-LUMO gap and energy. It is an inverse-design model, not a language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-berlin/fieldschnet/field-schnet": { "id": "tu-berlin/fieldschnet/field-schnet", "name": "FieldSchNet", "maker": "tu-berlin", "family": "tu-berlin/fieldschnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SchNetPack-based network for molecules in external fields. The README says a default training run fits energies, forces, dipole moments, and polarizability, and that the default config is a relatively small demonstration model. A parameter count was not stated.", "modalities": { "input": [ "atomic-structure" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FieldSchNet is the Machine Learning Group at Technical University of Berlin's network for molecules in external fields, solvents, and QM/MM settings. It is built on SchNetPack. The repository ships one pretrained ethanol vacuum example and otherwise trains from an ASE database. Collected 2026-10-15.", "what_it_is_for": "The README says the model predicts response properties and can be used in molecular dynamics to compute infrared and Raman spectra, and with NAMD for QM/MM. It is not a language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-berlin/g-schnet/g-schnet": { "id": "tu-berlin/g-schnet/g-schnet", "name": "G-SchNet", "maker": "tu-berlin", "family": "tu-berlin/g-schnet", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive generator that places one atom at a time in 3D. The README says it uses only atom positions and types, not a molecular graph. The shipped checkpoint was trained with the script defaults on 50,000 QM9 molecules. The README's default training flags are 128 features and 9 interaction layers. It says those are the standard settings used for the released model. A parameter count was not stated.", "modalities": { "input": [ "text" ], "output": [ "atomic-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "G-SchNet is the Machine Learning Group at Technical University of Berlin's autoregressive generator of 3D molecules, published at NeurIPS 2019. The authors mark the original repository as deprecated and still link one QM9 checkpoint for reproduction. New code is schnetpack-gschnet. Collected 2026-10-15.", "what_it_is_for": "The README says the model places atoms one by one in Euclidean space and can be biased toward molecules with a target QM9 property. It does not need bonds or a molecular graph.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-berlin/icmil/icmil": { "id": "tu-berlin/icmil/icmil", "name": "ICMIL", "maker": "tu-berlin", "family": "tu-berlin/icmil", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Perceiver-style in-context learner for multiple-instance learning. The abstract says it is pretrained on synthetic bag tasks and classifies a new task from a handful of labeled bags in one forward pass, with no gradient update. The card does not state depth or parameter count. The reported result is the mean across three seeds.", "modalities": { "input": [ "tabular" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ICMIL is an in-context multiple-instance learner released by bifold-pathomics, the pathology group of the TU Berlin machine-learning lab at BIFOLD. The 2026 preprint says a Perceiver-style model pretrained on synthetic bags labels a new task from a few labeled bags in one forward pass. Three paper seeds and three extra 50-feature checkpoints are on the Hub. Collected 2026-10-15.", "what_it_is_for": "The card says it solves a new bag-classification task without gradient updates or a hyperparameter search. The abstract names computational pathology and satellite imagery as the kind of problem multiple-instance learning is used for. The released files are the task-general checkpoints, not a fine-tune of another foundation model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-berlin/nisqa/nisqa": { "id": "tu-berlin/nisqa/nisqa", "name": "NISQA", "maker": "tu-berlin", "family": "tu-berlin/nisqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0 (weights nisqa.tar and nisqa_mos_only.tar); MIT (code)", "release_date": "2021-04-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single-ended CNN, self-attention, and attention-pooling model. The Interspeech 2021 paper says the released multidimensional network uses 48 Mel bands, a 20 ms FFT window, a 10 ms hop, a 20 kHz maximum frequency, and 150 ms segments (width 15) with a 40 ms hop. The CNN has 6 convolutional layers and 3 max-pooling layers and emits a 384-d vector per segment. Self-attention is a single-head Transformer encoder with depth 2 and d_tf 64. Five attention-pooling heads predict overall MOS plus noisiness, coloration, discontinuity, and loudness. The CNN and self-attention trunk are shared.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NISQA is the Quality and Usability Lab model from Technical University of Berlin for single-ended speech-quality scoring. Version 2 predicts overall MOS and four dimension scores from the degraded signal alone. Weights are in the public GitHub repository under a non-commercial license. Collected 2026-10-15.", "what_it_is_for": "The README says the weights estimate the quality of speech that has passed through a communication system, plus noisiness, coloration, discontinuity, and loudness. It is not a speech synthesizer and not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-berlin/nisqa/nisqa-tts": { "id": "tu-berlin/nisqa/nisqa-tts", "name": "NISQA-TTS", "maker": "tu-berlin", "family": "tu-berlin/nisqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0 (weights/nisqa_tts.tar); MIT (code)", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The Interspeech 2020 abstract says the naturalness model is trained end to end and is based on a CNN-LSTM network previously used for speech quality. The README labels the shipped file NISQA-TTS v1.0 and says it predicts naturalness of synthesized speech. Layer widths of the shipped checkpoint were not copied in this pass.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NISQA-TTS is the Quality and Usability Lab naturalness scorer for synthetic speech, released beside the transmitted-speech NISQA weights. The Interspeech 2020 paper, from Technical University of Berlin with a DFKI co-affiliation, says the checkpoint is public and is meant for text-to-speech and voice-conversion evaluation. The file in the repository is `weights/nisqa_tts.tar`. Collected 2026-10-15.", "what_it_is_for": "The README says the weights estimate naturalness of speech from a voice-conversion or text-to-speech system. The abstract says it can compare TTS configurations and that it works language independently. It is not the multidimensional telephone-quality model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-berlin/painn/painn": { "id": "tu-berlin/painn/painn", "name": "PaiNN", "maker": "tu-berlin", "family": "tu-berlin/painn", "status": "active", "confidence": "medium", "weights": "closed", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Polarizable atom interaction neural network. The ICML 2021 abstract says it extends message passing to rotationally equivariant representations and uses those features for tensorial properties and molecular spectra. In SchNetPack the class is schnetpack.representation.PaiNN. The README's QM9 example config uses 128 atom features, 3 interaction blocks, and 20 Gaussian radial basis functions. Those are CLI defaults, not a published parameter count, and the README does not ship a trained PaiNN file.", "modalities": { "input": [ "atomic-structure" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PaiNN is the polarizable atom interaction neural network announced at ICML 2021 by Kristof Sch\u00fctt, Oliver Unke, and Michael Gastegger. The Machine Learning Group at Technical University of Berlin ships it as a representation in SchNetPack. The README shows how to train it and does not link a pretrained file. Collected 2026-10-15.", "what_it_is_for": "The abstract says PaiNN predicts chemical properties, accelerates molecular dynamics, and predicts tensorial properties used for molecular spectra. It is an equivariant message-passing model, not a language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-berlin/schnet/schnet": { "id": "tu-berlin/schnet/schnet", "name": "SchNet", "maker": "tu-berlin", "family": "tu-berlin/schnet", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continuous-filter convolutional network for atomistic systems. The NeurIPS 2017 paper is the named architecture. The TensorFlow repository is marked unsupported. SchNetPack, from the same group, still exposes SchNet as a representation and trains it from the command line. A parameter count for a canonical checkpoint was not published in the README.", "modalities": { "input": [ "atomic-structure" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SchNet is the Machine Learning Group at Technical University of Berlin's continuous-filter network for quantum-chemistry properties of molecules and materials. The NeurIPS 2017 paper names the architecture. The original TensorFlow repository is unsupported and points to SchNetPack, which still trains SchNet. A C20 energy and force example remains in the old repository. Collected 2026-10-15.", "what_it_is_for": "The repository describes spatially and chemically resolved predictions of quantum-mechanical observables. SchNetPack uses it for QM9 property prediction and for potential-energy surfaces with forces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-berlin/sgdml/sgdml": { "id": "tu-berlin/sgdml/sgdml", "name": "sGDML", "maker": "tu-berlin", "family": "tu-berlin/sgdml", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Symmetric gradient-domain machine learning force field. The software reconstructs a global energy-conserving force field from a geometry, energy, and force dataset. The predictor class in the README is GDMLPredict and loads a NumPy model file. It is a kernel model, not a fixed-width neural network. No parameter count applies in the neural-network sense, and none was stated.", "modalities": { "input": [ "atomic-structure" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "sGDML is the symmetric gradient-domain machine learning force field associated with the Machine Learning Group at Technical University of Berlin and coauthors at the University of Luxembourg. The public release is the reference code, documented as v1.0.3, plus molecule datasets. The website's pretrained-model table was empty on the collection date. Collected 2026-10-15.", "what_it_is_for": "The website describes accurate global machine-learning force fields. The README reconstructs a model from a dataset and then evaluates energy and forces for a new geometry, including through ASE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-braunschweig/octopus/octopus": { "id": "tu-braunschweig/octopus/octopus", "name": "Octopus", "maker": "tu-braunschweig", "family": "tu-braunschweig/octopus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial research, education, and artistic use only. Copyright 2019 Thiemo Alldieck, Technische Universit\u00e4t Braunschweig, and Max Planck Society. Commercial use is prohibited.", "release_date": "2019-03-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CNN named Octopus. The CVPR abstract says it reads semantic segmentation images from 1 to 8 frames of a moving person and predicts SMPL body parameters plus clothing and hair. The arXiv HTML says a CNN encodes each frame into a pose-invariant latent code, the codes are fused into one shape code, and two network streams predict from that code. Shape is predicted in a canonical T-pose. Learning uses synthetic 3D data. SMPL is a separate body model.", "modalities": { "input": [ "image" ], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Octopus is a clothed-person reconstruction network from the Computer Graphics Lab at Technische Universit\u00e4t Braunschweig and the Max Planck Institute for Informatics. Alldieck, Magnor, Bhatnagar, Theobalt, and Pons-Moll posted it on arXiv on 14 March 2019 and presented it at CVPR 2019. Zenodo published the weight zip on 4 April 2019 under a Computer Graphics Lab, TU Braunschweig affiliation. On 2026-10-15 the GitHub README still links that zip. It is a research checkpoint, not an API product.", "what_it_is_for": "The CVPR abstract says Octopus infers a personalized 3D shape, including SMPL parameters plus clothing and hair, from a few frames of one moving person, in 10 seconds or less. The shape is predicted in a canonical T-pose so the frame codes can be fused.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-braunschweig/sgdepth/sgdepth": { "id": "tu-braunschweig/sgdepth/sgdepth", "name": "SGDepth", "maker": "tu-braunschweig", "family": "tu-braunschweig/sgdepth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (repository code, as stated in the README and the GitHub license API)", "release_date": "2020-07-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-supervised monocular depth model from Klingner, Term\u00f6hlen, Mikolajczyk, and Fingscheidt (ECCV 2020). The paper says the topology follows Godard et al., with an ImageNet-pretrained ResNet-18 encoder and skip-connected decoders. The full model has a depth head and a segmentation head. Depth is a sigmoid converted to the range 0.1 to 100 meters. A separate pose network, the same architecture as in Godard et al., is used only in training. Semantic segmentation is supervised on Cityscapes. Depth is self-supervised on KITTI video with a semantically masked photometric loss. The paper also reports a ResNet-50 variant that is not linked from the weight table.", "modalities": { "input": [ "image" ], "output": [ "depth", "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SGDepth is a self-supervised monocular depth model from the Signal Processing and Machine Learning group at the Institute for Communications Technology, Technische Universit\u00e4t Braunschweig. Klingner, Term\u00f6hlen, Mikolajczyk, and Fingscheidt released it with ECCV 2020 (arXiv 14 July 2020). On 2026-10-15 the IfN GitHub repository still links two weight files. It is a research checkpoint, not an API product.", "what_it_is_for": "The paper says the model predicts a dense depth map from one RGB image at inference, for uses such as autonomous driving and virtual reality. The full training setup also predicts semantic labels. Moving cars and pedestrians are handled by masking dynamic-class pixels in the photometric loss, while frames judged to contain non-moving dynamic-class objects can still teach their depth.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-braunschweig/tex2shape/tex2shape": { "id": "tu-braunschweig/tex2shape/tex2shape", "name": "Tex2Shape", "maker": "tu-braunschweig", "family": "tu-braunschweig/tex2shape", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial research, education, and artistic use only. Copyright 2019 Thiemo Alldieck, Technische Universit\u00e4t Braunschweig, and Max Planck Society. Commercial use is prohibited.", "release_date": "2019-04-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-to-image model that turns a partial UV texture of the visible body into normal and vector-displacement maps. The arXiv HTML says the partial map is processed by a U-Net with skip connections, and a PatchGAN discriminator enforces realism. Those maps are applied to a low-resolution smooth body model. The abstract says training uses synthetic data only. The README expects a 1024x1024 crop and a DensePose IUV detection, plus a separately licensed SMPL body model.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tex2Shape is a single-image human body-detail model from the Computer Graphics Lab at Technische Universit\u00e4t Braunschweig, with coauthors at the Max Planck Institute for Informatics. Alldieck, Pons-Moll, Theobalt, and Magnor announced it on arXiv on 18 April 2019 and published it at ICCV 2019. On 2026-10-15 the author's GitHub README still links a pretrained weight zip. It is a research checkpoint, not an API product. The copyright notice names both TU Braunschweig and the Max Planck Society.", "what_it_is_for": "The abstract says the model infers a detailed full-body shape, including face, hair, and clothing wrinkles, from one photograph, including regions that are occluded in the photo. The shape is added as normal and displacement maps on a smooth low-resolution body.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/clip-multilingual/clip-vit-b-32-multilingual-v1": { "id": "tu-darmstadt/clip-multilingual/clip-vit-b-32-multilingual-v1", "name": "clip-ViT-B-32-multilingual-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/clip-multilingual", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "134734080", "architecture": "The model card architecture print that was read shows a DistilBertModel with max_seq_length 128 and mean pooling with word_embedding_dimension 768. The SBERT docs say this is a multilingual text encoder for sentence-transformers/clip-ViT-B-32, trained by multilingual knowledge distillation, so text in 50+ languages can be matched to that CLIP model's image vectors. A later projection layer was not in the lines read, so the final vector width used at match time is not stored.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "clip-ViT-B-32-multilingual-v1 is an open-weight multilingual text encoder from the Sentence Transformers project started at the UKP Lab, TU Darmstadt. The SBERT docs say it encodes text so it can be compared with image vectors from their CLIP ViT-B/32 checkpoint. Collected on 2026-10-15.", "what_it_is_for": "Multilingual text encoder meant to match image vectors from the sentence-transformers CLIP ViT-B/32 checkpoint. The CLIP image checkpoint itself is not foldered here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/ms-marco-electra-base": { "id": "tu-darmstadt/cross-encoder/ms-marco-electra-base", "name": "ms-marco-electra-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "109483521", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 109483521. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification. The ce-msmarco page lists 340 passages per second on a V100 GPU with Hugging Face Transformers v4.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ms-marco-electra-base is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/ms-marco-electra-base. Collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage reranker. The docs say it scores whether a passage answers a query. Raw scores can sit roughly between -10 and 10. A sigmoid activation maps them to 0-1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/ms-marco-minilm-l12-v2": { "id": "tu-darmstadt/cross-encoder/ms-marco-minilm-l12-v2", "name": "ms-marco-MiniLM-L12-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "33360897", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 33360897. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification. The ce-msmarco page lists 960 passages per second on a V100 GPU with Hugging Face Transformers v4. The short pretrained-models page also lists this v2 row. Its href for TinyBERT-L2-v2 points at the v1 repo ms-marco-TinyBERT-L2. Scores here are taken from the ce-msmarco page, which lists v1 and v2 as separate rows.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ms-marco-MiniLM-L12-v2 is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/ms-marco-MiniLM-L12-v2. Collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage reranker. The docs say it scores whether a passage answers a query. Raw scores can sit roughly between -10 and 10. A sigmoid activation maps them to 0-1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/ms-marco-minilm-l2-v2": { "id": "tu-darmstadt/cross-encoder/ms-marco-minilm-l2-v2", "name": "ms-marco-MiniLM-L2-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "15616257", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 15616257. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification. The ce-msmarco page lists 4100 passages per second on a V100 GPU with Hugging Face Transformers v4. The short pretrained-models page also lists this v2 row. Its href for TinyBERT-L2-v2 points at the v1 repo ms-marco-TinyBERT-L2. Scores here are taken from the ce-msmarco page, which lists v1 and v2 as separate rows.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ms-marco-MiniLM-L2-v2 is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/ms-marco-MiniLM-L2-v2. Collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage reranker. The docs say it scores whether a passage answers a query. Raw scores can sit roughly between -10 and 10. A sigmoid activation maps them to 0-1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/ms-marco-minilm-l4-v2": { "id": "tu-darmstadt/cross-encoder/ms-marco-minilm-l4-v2", "name": "ms-marco-MiniLM-L4-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "19165185", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 19165185. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification. The ce-msmarco page lists 2500 passages per second on a V100 GPU with Hugging Face Transformers v4. The short pretrained-models page also lists this v2 row. Its href for TinyBERT-L2-v2 points at the v1 repo ms-marco-TinyBERT-L2. Scores here are taken from the ce-msmarco page, which lists v1 and v2 as separate rows.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ms-marco-MiniLM-L4-v2 is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/ms-marco-MiniLM-L4-v2. Collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage reranker. The docs say it scores whether a passage answers a query. Raw scores can sit roughly between -10 and 10. A sigmoid activation maps them to 0-1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/ms-marco-minilm-l6-v2": { "id": "tu-darmstadt/cross-encoder/ms-marco-minilm-l6-v2", "name": "ms-marco-MiniLM-L6-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "22714113", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 22714113. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification. The ce-msmarco page lists 1800 passages per second on a V100 GPU with Hugging Face Transformers v4. The short pretrained-models page also lists this v2 row. Its href for TinyBERT-L2-v2 points at the v1 repo ms-marco-TinyBERT-L2. Scores here are taken from the ce-msmarco page, which lists v1 and v2 as separate rows.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ms-marco-MiniLM-L6-v2 is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/ms-marco-MiniLM-L6-v2. Collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage reranker. The docs say it scores whether a passage answers a query. Raw scores can sit roughly between -10 and 10. A sigmoid activation maps them to 0-1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/ms-marco-tinybert-l2": { "id": "tu-darmstadt/cross-encoder/ms-marco-tinybert-l2", "name": "ms-marco-TinyBERT-L2", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "4386561", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 4386561. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification. The ce-msmarco page lists 9000 passages per second on a V100 GPU with Hugging Face Transformers v4.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ms-marco-TinyBERT-L2 is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/ms-marco-TinyBERT-L2. Collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage reranker. The docs say it scores whether a passage answers a query. Raw scores can sit roughly between -10 and 10. A sigmoid activation maps them to 0-1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/ms-marco-tinybert-l2-v2": { "id": "tu-darmstadt/cross-encoder/ms-marco-tinybert-l2-v2", "name": "ms-marco-TinyBERT-L2-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "4386561", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 4386561. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification. The ce-msmarco page lists 9000 passages per second on a V100 GPU with Hugging Face Transformers v4. The short pretrained-models page also lists this v2 row. Its href for TinyBERT-L2-v2 points at the v1 repo ms-marco-TinyBERT-L2. Scores here are taken from the ce-msmarco page, which lists v1 and v2 as separate rows.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ms-marco-TinyBERT-L2-v2 is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/ms-marco-TinyBERT-L2-v2. Collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage reranker. The docs say it scores whether a passage answers a query. Raw scores can sit roughly between -10 and 10. A sigmoid activation maps them to 0-1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/ms-marco-tinybert-l4": { "id": "tu-darmstadt/cross-encoder/ms-marco-tinybert-l4", "name": "ms-marco-TinyBERT-L4", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "14351073", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 14351073. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification. The ce-msmarco page lists 2900 passages per second on a V100 GPU with Hugging Face Transformers v4.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ms-marco-TinyBERT-L4 is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/ms-marco-TinyBERT-L4. Collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage reranker. The docs say it scores whether a passage answers a query. Raw scores can sit roughly between -10 and 10. A sigmoid activation maps them to 0-1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/ms-marco-tinybert-l6": { "id": "tu-darmstadt/cross-encoder/ms-marco-tinybert-l6", "name": "ms-marco-TinyBERT-L6", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "66956289", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 66956289. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification. The ce-msmarco page lists 680 passages per second on a V100 GPU with Hugging Face Transformers v4.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ms-marco-TinyBERT-L6 is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/ms-marco-TinyBERT-L6. Collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage reranker. The docs say it scores whether a passage answers a query. Raw scores can sit roughly between -10 and 10. A sigmoid activation maps them to 0-1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/nli-deberta-base": { "id": "tu-darmstadt/cross-encoder/nli-deberta-base", "name": "nli-deberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "139195139", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 139195139. Apache-2.0 on the Hub card data. Pipeline tag was zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "nli-deberta-base is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/nli-deberta-base. Collected on 2026-10-15.", "what_it_is_for": "NLI cross-encoder trained on SNLI and MultiNLI. The docs map the three logits to contradiction, entailment, and neutral, in that order.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/nli-deberta-v3-base": { "id": "tu-darmstadt/cross-encoder/nli-deberta-v3-base", "name": "nli-deberta-v3-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "184424963", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 184424963. Apache-2.0 on the Hub card data. Pipeline tag was zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "nli-deberta-v3-base is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/nli-deberta-v3-base. Collected on 2026-10-15.", "what_it_is_for": "NLI cross-encoder trained on SNLI and MultiNLI. The docs map the three logits to contradiction, entailment, and neutral, in that order.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/nli-deberta-v3-small": { "id": "tu-darmstadt/cross-encoder/nli-deberta-v3-small", "name": "nli-deberta-v3-small", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "141897731", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 141897731. Apache-2.0 on the Hub card data. Pipeline tag was zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "nli-deberta-v3-small is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/nli-deberta-v3-small. Collected on 2026-10-15.", "what_it_is_for": "NLI cross-encoder trained on SNLI and MultiNLI. The docs map the three logits to contradiction, entailment, and neutral, in that order.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/nli-deberta-v3-xsmall": { "id": "tu-darmstadt/cross-encoder/nli-deberta-v3-xsmall", "name": "nli-deberta-v3-xsmall", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "70831619", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 70831619. Apache-2.0 on the Hub card data. Pipeline tag was zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "nli-deberta-v3-xsmall is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/nli-deberta-v3-xsmall. Collected on 2026-10-15.", "what_it_is_for": "NLI cross-encoder trained on SNLI and MultiNLI. The docs map the three logits to contradiction, entailment, and neutral, in that order.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/nli-distilroberta-base": { "id": "tu-darmstadt/cross-encoder/nli-distilroberta-base", "name": "nli-distilroberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "82121221", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 82121221. Apache-2.0 on the Hub card data. Pipeline tag was zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "nli-distilroberta-base is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/nli-distilroberta-base. Collected on 2026-10-15.", "what_it_is_for": "NLI cross-encoder trained on SNLI and MultiNLI. The docs map the three logits to contradiction, entailment, and neutral, in that order.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/nli-minilm2-l6-h768": { "id": "tu-darmstadt/cross-encoder/nli-minilm2-l6-h768", "name": "nli-MiniLM2-L6-H768", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "82121221", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 82121221. Apache-2.0 on the Hub card data. Pipeline tag was zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "nli-MiniLM2-L6-H768 is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/nli-MiniLM2-L6-H768. Collected on 2026-10-15.", "what_it_is_for": "NLI cross-encoder trained on SNLI and MultiNLI. The docs map the three logits to contradiction, entailment, and neutral, in that order.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/nli-roberta-base": { "id": "tu-darmstadt/cross-encoder/nli-roberta-base", "name": "nli-roberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "124648453", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 124648453. Apache-2.0 on the Hub card data. Pipeline tag was zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "nli-roberta-base is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/nli-roberta-base. Collected on 2026-10-15.", "what_it_is_for": "NLI cross-encoder trained on SNLI and MultiNLI. The docs map the three logits to contradiction, entailment, and neutral, in that order.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/qnli-distilroberta-base": { "id": "tu-darmstadt/cross-encoder/qnli-distilroberta-base", "name": "qnli-distilroberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "82119683", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 82119683. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "qnli-distilroberta-base is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/qnli-distilroberta-base. Collected on 2026-10-15.", "what_it_is_for": "QNLI cross-encoder based on SQuAD questions. The docs say the score is higher when a passage answers a question.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/qnli-electra-base": { "id": "tu-darmstadt/cross-encoder/qnli-electra-base", "name": "qnli-electra-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "109483521", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 109483521. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "qnli-electra-base is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/qnli-electra-base. Collected on 2026-10-15.", "what_it_is_for": "QNLI cross-encoder based on SQuAD questions. The docs say the score is higher when a passage answers a question.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/quora-distilroberta-base": { "id": "tu-darmstadt/cross-encoder/quora-distilroberta-base", "name": "quora-distilroberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "82119683", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 82119683. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "quora-distilroberta-base is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/quora-distilroberta-base. Collected on 2026-10-15.", "what_it_is_for": "Quora duplicate-question cross-encoder. The docs say the score is the probability that two questions are duplicates, not a general question-similarity score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/quora-roberta-base": { "id": "tu-darmstadt/cross-encoder/quora-roberta-base", "name": "quora-roberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "124646915", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 124646915. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "quora-roberta-base is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/quora-roberta-base. Collected on 2026-10-15.", "what_it_is_for": "Quora duplicate-question cross-encoder. The docs say the score is the probability that two questions are duplicates, not a general question-similarity score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/quora-roberta-large": { "id": "tu-darmstadt/cross-encoder/quora-roberta-large", "name": "quora-roberta-large", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "355361283", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 355361283. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "quora-roberta-large is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/quora-roberta-large. Collected on 2026-10-15.", "what_it_is_for": "Quora duplicate-question cross-encoder. The docs say the score is the probability that two questions are duplicates, not a general question-similarity score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/stsb-distilroberta-base": { "id": "tu-darmstadt/cross-encoder/stsb-distilroberta-base", "name": "stsb-distilroberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "82119683", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 82119683. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "stsb-distilroberta-base is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/stsb-distilroberta-base. Collected on 2026-10-15.", "what_it_is_for": "STS Benchmark cross-encoder. The docs say it returns a score from 0 to 1 for the semantic similarity of a sentence pair.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/stsb-roberta-base": { "id": "tu-darmstadt/cross-encoder/stsb-roberta-base", "name": "stsb-roberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "124646915", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 124646915. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "stsb-roberta-base is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/stsb-roberta-base. Collected on 2026-10-15.", "what_it_is_for": "STS Benchmark cross-encoder. The docs say it returns a score from 0 to 1 for the semantic similarity of a sentence pair.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/stsb-roberta-large": { "id": "tu-darmstadt/cross-encoder/stsb-roberta-large", "name": "stsb-roberta-large", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "355361283", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 355361283. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "stsb-roberta-large is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/stsb-roberta-large. Collected on 2026-10-15.", "what_it_is_for": "STS Benchmark cross-encoder. The docs say it returns a score from 0 to 1 for the semantic similarity of a sentence pair.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/cross-encoder/stsb-tinybert-l4": { "id": "tu-darmstadt/cross-encoder/stsb-tinybert-l4", "name": "stsb-TinyBERT-L4", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "14351073", "architecture": "Sequence-pair cross-encoder. Hugging Face safetensors parameter count is 14351073. Apache-2.0 on the Hub card data. Pipeline tag was text-ranking except for the NLI models, which are tagged zero-shot-classification.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "stsb-TinyBERT-L4 is an open-weight cross-encoder from the Sentence Transformers project started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is cross-encoder/stsb-TinyBERT-L4. Collected on 2026-10-15.", "what_it_is_for": "STS Benchmark cross-encoder. The docs say it returns a score from 0 to 1 for the semantic similarity of a sentence pair.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/distiluse/distiluse-base-multilingual-cased-v1": { "id": "tu-darmstadt/distiluse/distiluse-base-multilingual-cased-v1", "name": "distiluse-base-multilingual-cased-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/distiluse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "134734080", "architecture": "Official SBERT table base: teacher mUSE; student distilbert-base-multilingual. Pooling: mean. Embedding dimension in that table: 512. Normalized embeddings: false. Suitable scores listed there: cosine similarity. Table model size: 480 MB. Table encoding speed: 4000 sentences/sec on a V100.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "distiluse-base-multilingual-cased-v1 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/distiluse-base-multilingual-cased-v1. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "Multilingual distillation of the multilingual Universal Sentence Encoder. The SBERT docs say v1 supports 15 languages and then name 13. The Hub language tags are 13 codes plus a multilingual tag.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/distiluse/distiluse-base-multilingual-cased-v2": { "id": "tu-darmstadt/distiluse/distiluse-base-multilingual-cased-v2", "name": "distiluse-base-multilingual-cased-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/distiluse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "134734080", "architecture": "Official SBERT table base: teacher mUSE; student distilbert-base-multilingual. Pooling: mean. Embedding dimension in that table: 512. Normalized embeddings: false. Suitable scores listed there: cosine similarity. Table model size: 480 MB. Table encoding speed: 4000 sentences/sec on a V100.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "distiluse-base-multilingual-cased-v2 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/distiluse-base-multilingual-cased-v2. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "Multilingual distillation of mUSE for the broader language set. The SBERT docs say v2 supports 50+ languages and is a bit weaker than v1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/msmarco/msmarco-bert-base-dot-v5": { "id": "tu-darmstadt/msmarco/msmarco-bert-base-dot-v5", "name": "msmarco-bert-base-dot-v5", "maker": "tu-darmstadt", "family": "tu-darmstadt/msmarco", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "109482752", "architecture": "Official SBERT table base: Luyu/co-condenser-marco. Pooling: mean. Embedding dimension in that table: 768. Normalized embeddings: false. Suitable scores listed there: dot-product. Table model size: 420 MB. Table encoding speed: 2800 sentences/sec on a V100.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "msmarco-bert-base-dot-v5 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/msmarco-bert-base-dot-v5. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage semantic-search model. The SBERT table says training used 500k query-answer pairs from MS MARCO passages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/msmarco/msmarco-distilbert-base-tas-b": { "id": "tu-darmstadt/msmarco/msmarco-distilbert-base-tas-b", "name": "msmarco-distilbert-base-tas-b", "maker": "tu-darmstadt", "family": "tu-darmstadt/msmarco", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "66362880", "architecture": "Official SBERT table base: distilbert/distilbert-base-uncased. Pooling: mean. Embedding dimension in that table: 768. Normalized embeddings: false. Suitable scores listed there: dot-product. Table model size: 250 MB. Table encoding speed: 4000 sentences/sec on a V100.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "msmarco-distilbert-base-tas-b is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/msmarco-distilbert-base-tas-b. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage semantic-search model. The SBERT table says training used 500k query-answer pairs from MS MARCO passages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/msmarco/msmarco-distilbert-cos-v5": { "id": "tu-darmstadt/msmarco/msmarco-distilbert-cos-v5", "name": "msmarco-distilbert-cos-v5", "maker": "tu-darmstadt", "family": "tu-darmstadt/msmarco", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "66362880", "architecture": "Listed on the SBERT pretrained-models page and the MS MARCO v5 page. Not a row in the HTML Vue table that was read, so base model, pooling, normalization, and the score-function cell are not copied from that table. The model card that was read states 768 dimensions. The model card says 768 dimensions and 500k MS MARCO passage pairs. Max sequence length was not in the lines read.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "msmarco-distilbert-cos-v5 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/msmarco-distilbert-cos-v5. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage semantic-search model. The SBERT table says training used 500k query-answer pairs from MS MARCO passages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/msmarco/msmarco-distilbert-dot-v5": { "id": "tu-darmstadt/msmarco/msmarco-distilbert-dot-v5", "name": "msmarco-distilbert-dot-v5", "maker": "tu-darmstadt", "family": "tu-darmstadt/msmarco", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "66362880", "architecture": "Official SBERT table base: distilbert/distilbert-base-uncased. Pooling: mean. Embedding dimension in that table: 768. Normalized embeddings: false. Suitable scores listed there: dot-product. Table model size: 250 MB. Table encoding speed: 4000 sentences/sec on a V100.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "msmarco-distilbert-dot-v5 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/msmarco-distilbert-dot-v5. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage semantic-search model. The SBERT table says training used 500k query-answer pairs from MS MARCO passages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/msmarco/msmarco-minilm-l12-cos-v5": { "id": "tu-darmstadt/msmarco/msmarco-minilm-l12-cos-v5", "name": "msmarco-MiniLM-L12-cos-v5", "maker": "tu-darmstadt", "family": "tu-darmstadt/msmarco", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "33360512", "architecture": "Listed on the SBERT pretrained-models page and the MS MARCO v5 page. Not a row in the HTML Vue table that was read, so base model, pooling, normalization, and the score-function cell are not copied from that table. The model card says 768 dimensions. The safetensors total is 33,360,512, the same count as all-MiniLM-L12-v2, whose SBERT table row says 384 dimensions. Those two size statements were not reconciled. Max sequence length was not in the lines read.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "msmarco-MiniLM-L12-cos-v5 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/msmarco-MiniLM-L12-cos-v5. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage semantic-search model. The SBERT table says training used 500k query-answer pairs from MS MARCO passages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/msmarco/msmarco-minilm-l6-cos-v5": { "id": "tu-darmstadt/msmarco/msmarco-minilm-l6-cos-v5", "name": "msmarco-MiniLM-L6-cos-v5", "maker": "tu-darmstadt", "family": "tu-darmstadt/msmarco", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "22713728", "architecture": "Listed on the SBERT pretrained-models page and the MS MARCO v5 page. Not a row in the HTML Vue table that was read, so base model, pooling, normalization, and the score-function cell are not copied from that table. The model card that was read states 384 dimensions. The model card says it maps text to 384 dimensions and was trained on 500k MS MARCO passage pairs. Max sequence length was not in the lines read.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "msmarco-MiniLM-L6-cos-v5 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/msmarco-MiniLM-L6-cos-v5. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "MS MARCO passage semantic-search model. The SBERT table says training used 500k query-answer pairs from MS MARCO passages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/multi-qa/multi-qa-distilbert-cos-v1": { "id": "tu-darmstadt/multi-qa/multi-qa-distilbert-cos-v1", "name": "multi-qa-distilbert-cos-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/multi-qa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "66362880", "architecture": "Official SBERT table base: distilbert/distilbert-base-uncased. Pooling: mean. Embedding dimension in that table: 768. Normalized embeddings: true. Suitable scores listed there: dot-product, cosine similarity, and Euclidean distance. Table model size: 250 MB. Table encoding speed: 4000 sentences/sec on a V100.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "multi-qa-distilbert-cos-v1 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/multi-qa-distilbert-cos-v1. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "Semantic-search model trained on 215 million question-answer pairs from StackExchange, Yahoo Answers, Google and Bing queries, and other sources, according to the SBERT docs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/multi-qa/multi-qa-distilbert-dot-v1": { "id": "tu-darmstadt/multi-qa/multi-qa-distilbert-dot-v1", "name": "multi-qa-distilbert-dot-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/multi-qa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "66362880", "architecture": "Official SBERT table base: distilbert/distilbert-base-uncased. Pooling: cls. Embedding dimension in that table: 768. Normalized embeddings: false. Suitable scores listed there: dot-product. Table model size: 250 MB. Table encoding speed: 4000 sentences/sec on a V100.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "multi-qa-distilbert-dot-v1 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/multi-qa-distilbert-dot-v1. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "Semantic-search model trained on 215 million question-answer pairs from StackExchange, Yahoo Answers, Google and Bing queries, and other sources, according to the SBERT docs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/multi-qa/multi-qa-minilm-l6-cos-v1": { "id": "tu-darmstadt/multi-qa/multi-qa-minilm-l6-cos-v1", "name": "multi-qa-MiniLM-L6-cos-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/multi-qa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "22713728", "architecture": "Official SBERT table base: nreimers/MiniLM-L6-H384-uncased. Pooling: mean. Embedding dimension in that table: 384. Normalized embeddings: true. Suitable scores listed there: dot-product, cosine similarity, and Euclidean distance. Table model size: 80 MB. Table encoding speed: 14200 sentences/sec on a V100.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "multi-qa-MiniLM-L6-cos-v1 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/multi-qa-MiniLM-L6-cos-v1. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "Semantic-search model trained on 215 million question-answer pairs from StackExchange, Yahoo Answers, Google and Bing queries, and other sources, according to the SBERT docs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/multi-qa/multi-qa-minilm-l6-dot-v1": { "id": "tu-darmstadt/multi-qa/multi-qa-minilm-l6-dot-v1", "name": "multi-qa-MiniLM-L6-dot-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/multi-qa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "22713728", "architecture": "Official SBERT table base: nreimers/MiniLM-L6-H384-uncased. Pooling: cls. Embedding dimension in that table: 384. Normalized embeddings: false. Suitable scores listed there: dot-product. Table model size: 80 MB. Table encoding speed: 14200 sentences/sec on a V100.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "multi-qa-MiniLM-L6-dot-v1 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/multi-qa-MiniLM-L6-dot-v1. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "Semantic-search model trained on 215 million question-answer pairs from StackExchange, Yahoo Answers, Google and Bing queries, and other sources, according to the SBERT docs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/multi-qa/multi-qa-mpnet-base-cos-v1": { "id": "tu-darmstadt/multi-qa/multi-qa-mpnet-base-cos-v1", "name": "multi-qa-mpnet-base-cos-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/multi-qa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "109486978", "architecture": "Official SBERT table base: microsoft/mpnet-base. Pooling: mean. Embedding dimension in that table: 768. Normalized embeddings: true. Suitable scores listed there: dot-product, cosine similarity, and Euclidean distance. Table model size: 420 MB. Table encoding speed: 2800 sentences/sec on a V100.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "multi-qa-mpnet-base-cos-v1 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/multi-qa-mpnet-base-cos-v1. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "Semantic-search model trained on 215 million question-answer pairs from StackExchange, Yahoo Answers, Google and Bing queries, and other sources, according to the SBERT docs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/multi-qa/multi-qa-mpnet-base-dot-v1": { "id": "tu-darmstadt/multi-qa/multi-qa-mpnet-base-dot-v1", "name": "multi-qa-mpnet-base-dot-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/multi-qa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "109486978", "architecture": "Official SBERT table base: microsoft/mpnet-base. Pooling: cls. Embedding dimension in that table: 768. Normalized embeddings: false. Suitable scores listed there: dot-product. Table model size: 420 MB. Table encoding speed: 2800 sentences/sec on a V100.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "multi-qa-mpnet-base-dot-v1 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/multi-qa-mpnet-base-dot-v1. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "Semantic-search model trained on 215 million question-answer pairs from StackExchange, Yahoo Answers, Google and Bing queries, and other sources, according to the SBERT docs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/occiglot/occiglot-7b-de-en": { "id": "tu-darmstadt/occiglot/occiglot-7b-de-en", "name": "Occiglot-7B-DE-EN", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-02-22", "context_length": 8192, "max_output_tokens": null, "parameters": "7241732096", "architecture": "Continued pretraining from Mistral-7B-v0.1 on the German plus English and code subset of the EU5 data. The card says 7B parameters, block size 8192, tokenizer unchanged, and the same 128 A100-80GB / AdamW 1e-5 / batch 512 settings as the EU5 card. Card language mix estimate: English about 34%, code about 13%, German about 52%. The same contributor list as the EU5 card is printed here. Continued-pretraining or instruction-tune size in the excerpt: 114B additional tokens. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Occiglot-7B-DE-EN is an open-weight 7B language model from the Occiglot research collective. TU Darmstadt, hessian.AI, and DFKI announced the v0.1 release. The card continues pretraining from Mistral-7B-v0.1 under Apache-2.0. Hub repo occiglot/occiglot-7b-de-en, created 2024-02-22. Collected on 2026-10-15.", "what_it_is_for": "European open language model from the Occiglot collective. TU Darmstadt's computer science department announced the ten-model v0.1 release. The model card credits the Occiglot Research Collective, with DFKI SAINT and SLT support and HessianAI compute. Kristian Kersting, Manuel Brack, and Patrick Schramowski are TU Darmstadt authors on the EU5 card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/occiglot/occiglot-7b-de-en-instruct": { "id": "tu-darmstadt/occiglot/occiglot-7b-de-en-instruct", "name": "Occiglot-7B-DE-EN-Instruct", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-03-05", "context_length": null, "max_output_tokens": null, "parameters": "7241748480", "architecture": "The instruction-tuned-from line says occiglot-7b-de-en. The opening sentence also calls it the instruct version of occiglot-7b-eu5 and says it supports German and English. It was trained on 180M additional instruction tokens. The ChatML line was not in the excerpt that was read. Safetensors total 7,241,748,480. Continued-pretraining or instruction-tune size in the excerpt: 180M additional instruction tokens. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Occiglot-7B-DE-EN-Instruct is an open-weight 7B language model from the Occiglot research collective. TU Darmstadt, hessian.AI, and DFKI announced the v0.1 release. The card continues pretraining from Mistral-7B-v0.1 under Apache-2.0. Hub repo occiglot/occiglot-7b-de-en-instruct, created 2024-03-05. Collected on 2026-10-15.", "what_it_is_for": "European open language model from the Occiglot collective. TU Darmstadt's computer science department announced the ten-model v0.1 release. The model card credits the Occiglot Research Collective, with DFKI SAINT and SLT support and HessianAI compute. Kristian Kersting, Manuel Brack, and Patrick Schramowski are TU Darmstadt authors on the EU5 card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/occiglot/occiglot-7b-es-en": { "id": "tu-darmstadt/occiglot/occiglot-7b-es-en", "name": "Occiglot-7B-ES-EN", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-02-19", "context_length": 8192, "max_output_tokens": null, "parameters": "7241732096", "architecture": "The card says it is based on Mistral-7B-v0.1 and trained on 112B additional multilingual and code tokens with block size 8192. The training-settings paragraph and the contributor list were not re-read on this card. Continued-pretraining or instruction-tune size in the excerpt: 112B additional tokens. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Occiglot-7B-ES-EN is an open-weight 7B language model from the Occiglot research collective. TU Darmstadt, hessian.AI, and DFKI announced the v0.1 release. The card continues pretraining from Mistral-7B-v0.1 under Apache-2.0. Hub repo occiglot/occiglot-7b-es-en, created 2024-02-19. Collected on 2026-10-15.", "what_it_is_for": "European open language model from the Occiglot collective. TU Darmstadt's computer science department announced the ten-model v0.1 release. The model card credits the Occiglot Research Collective, with DFKI SAINT and SLT support and HessianAI compute. Kristian Kersting, Manuel Brack, and Patrick Schramowski are TU Darmstadt authors on the EU5 card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/occiglot/occiglot-7b-es-en-instruct": { "id": "tu-darmstadt/occiglot/occiglot-7b-es-en-instruct", "name": "Occiglot-7B-ES-EN-Instruct", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-03-05", "context_length": null, "max_output_tokens": null, "parameters": "7241748480", "architecture": "The card says it is the instruct version of occiglot-7b-es-en, supports Spanish and English, and was trained on 160M additional instruction tokens. The ChatML line was not in the excerpt. Continued-pretraining or instruction-tune size in the excerpt: 160M additional instruction tokens. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Occiglot-7B-ES-EN-Instruct is an open-weight 7B language model from the Occiglot research collective. TU Darmstadt, hessian.AI, and DFKI announced the v0.1 release. The card continues pretraining from Mistral-7B-v0.1 under Apache-2.0. Hub repo occiglot/occiglot-7b-es-en-instruct, created 2024-03-05. Collected on 2026-10-15.", "what_it_is_for": "European open language model from the Occiglot collective. TU Darmstadt's computer science department announced the ten-model v0.1 release. The model card credits the Occiglot Research Collective, with DFKI SAINT and SLT support and HessianAI compute. Kristian Kersting, Manuel Brack, and Patrick Schramowski are TU Darmstadt authors on the EU5 card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/occiglot/occiglot-7b-eu5": { "id": "tu-darmstadt/occiglot/occiglot-7b-eu5", "name": "Occiglot-7B-EU5", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-02-16", "context_length": 8192, "max_output_tokens": null, "parameters": "7241732096", "architecture": "Continued pretraining from Mistral-7B-v0.1. The card says 7B parameters, causal decoder-only, tokenizer unchanged from Mistral-7B-v0.1, block size 8192. Training settings on this card: 128 A100-80GB at HessianAI 42, Determined, bf16, AdamW learning rate 0.00001, warmup 420, global batch 512, cosine annealing. Card language mix estimate: English about 13%, code about 5%, German, Spanish, French, and Italian about 20% each. Contributors on this card: Manuel Brack, Patrick Schramowski, Pedro Ortiz, Malte Ostendorff, Fabio Barth, Georg Rehm, Kristian Kersting. Research labs line: Occiglot with support from DFKI SAINT and SLT. Continued-pretraining or instruction-tune size in the excerpt: 293B additional tokens. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Occiglot-7B-EU5 is an open-weight 7B language model from the Occiglot research collective. TU Darmstadt, hessian.AI, and DFKI announced the v0.1 release. The card continues pretraining from Mistral-7B-v0.1 under Apache-2.0. Hub repo occiglot/occiglot-7b-eu5, created 2024-02-16. Collected on 2026-10-15.", "what_it_is_for": "European open language model from the Occiglot collective. TU Darmstadt's computer science department announced the ten-model v0.1 release. The model card credits the Occiglot Research Collective, with DFKI SAINT and SLT support and HessianAI compute. Kristian Kersting, Manuel Brack, and Patrick Schramowski are TU Darmstadt authors on the EU5 card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/occiglot/occiglot-7b-eu5-instruct": { "id": "tu-darmstadt/occiglot/occiglot-7b-eu5-instruct", "name": "Occiglot-7B-EU5-Instruct", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-03-06", "context_length": null, "max_output_tokens": null, "parameters": "7241748480", "architecture": "Instruction tune of occiglot-7b-eu5. The card says 400M additional multilingual and code instruction tokens, ChatML template, and full instruction fine-tuning on 8x H100. The safetensors total is 7,241,748,480, which is 16,384 above the base checkpoint. Continued-pretraining or instruction-tune size in the excerpt: 400M additional instruction tokens. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Occiglot-7B-EU5-Instruct is an open-weight 7B language model from the Occiglot research collective. TU Darmstadt, hessian.AI, and DFKI announced the v0.1 release. The card continues pretraining from Mistral-7B-v0.1 under Apache-2.0. Hub repo occiglot/occiglot-7b-eu5-instruct, created 2024-03-06. Collected on 2026-10-15.", "what_it_is_for": "European open language model from the Occiglot collective. TU Darmstadt's computer science department announced the ten-model v0.1 release. The model card credits the Occiglot Research Collective, with DFKI SAINT and SLT support and HessianAI compute. Kristian Kersting, Manuel Brack, and Patrick Schramowski are TU Darmstadt authors on the EU5 card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/occiglot/occiglot-7b-fr-en": { "id": "tu-darmstadt/occiglot/occiglot-7b-fr-en", "name": "Occiglot-7B-FR-EN", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-02-26", "context_length": 8192, "max_output_tokens": null, "parameters": "7241732096", "architecture": "The card says it is based on Mistral-7B-v0.1 and trained on 113B additional multilingual and code tokens with block size 8192. The training-settings paragraph and the contributor list were not re-read on this card. Continued-pretraining or instruction-tune size in the excerpt: 113B additional tokens. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Occiglot-7B-FR-EN is an open-weight 7B language model from the Occiglot research collective. TU Darmstadt, hessian.AI, and DFKI announced the v0.1 release. The card continues pretraining from Mistral-7B-v0.1 under Apache-2.0. Hub repo occiglot/occiglot-7b-fr-en, created 2024-02-26. Collected on 2026-10-15.", "what_it_is_for": "European open language model from the Occiglot collective. TU Darmstadt's computer science department announced the ten-model v0.1 release. The model card credits the Occiglot Research Collective, with DFKI SAINT and SLT support and HessianAI compute. Kristian Kersting, Manuel Brack, and Patrick Schramowski are TU Darmstadt authors on the EU5 card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/occiglot/occiglot-7b-fr-en-instruct": { "id": "tu-darmstadt/occiglot/occiglot-7b-fr-en-instruct", "name": "Occiglot-7B-FR-EN-Instruct", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-03-05", "context_length": null, "max_output_tokens": null, "parameters": "7241748480", "architecture": "Instruction tuned from occiglot-7b-fr-en on 160M additional instruction tokens. The excerpt says ChatML and full fine-tuning on 8x H100. Hub cardData.language is [en, es], which disagrees with the French base it names. Languages stored here follow the base model, not that tag. Continued-pretraining or instruction-tune size in the excerpt: 160M additional instruction tokens. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Occiglot-7B-FR-EN-Instruct is an open-weight 7B language model from the Occiglot research collective. TU Darmstadt, hessian.AI, and DFKI announced the v0.1 release. The card continues pretraining from Mistral-7B-v0.1 under Apache-2.0. Hub repo occiglot/occiglot-7b-fr-en-instruct, created 2024-03-05. Collected on 2026-10-15.", "what_it_is_for": "European open language model from the Occiglot collective. TU Darmstadt's computer science department announced the ten-model v0.1 release. The model card credits the Occiglot Research Collective, with DFKI SAINT and SLT support and HessianAI compute. Kristian Kersting, Manuel Brack, and Patrick Schramowski are TU Darmstadt authors on the EU5 card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/occiglot/occiglot-7b-it-en": { "id": "tu-darmstadt/occiglot/occiglot-7b-it-en", "name": "Occiglot-7B-IT-EN", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-02-27", "context_length": 8192, "max_output_tokens": null, "parameters": "7241732096", "architecture": "The card says it is based on Mistral-7B-v0.1 and trained on 113B additional multilingual and code tokens with block size 8192. The training-settings paragraph and the contributor list were not re-read on this card. Continued-pretraining or instruction-tune size in the excerpt: 113B additional tokens. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Occiglot-7B-IT-EN is an open-weight 7B language model from the Occiglot research collective. TU Darmstadt, hessian.AI, and DFKI announced the v0.1 release. The card continues pretraining from Mistral-7B-v0.1 under Apache-2.0. Hub repo occiglot/occiglot-7b-it-en, created 2024-02-27. Collected on 2026-10-15.", "what_it_is_for": "European open language model from the Occiglot collective. TU Darmstadt's computer science department announced the ten-model v0.1 release. The model card credits the Occiglot Research Collective, with DFKI SAINT and SLT support and HessianAI compute. Kristian Kersting, Manuel Brack, and Patrick Schramowski are TU Darmstadt authors on the EU5 card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/occiglot/occiglot-7b-it-en-instruct": { "id": "tu-darmstadt/occiglot/occiglot-7b-it-en-instruct", "name": "Occiglot-7B-IT-EN-Instruct", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-03-05", "context_length": null, "max_output_tokens": null, "parameters": "7241748480", "architecture": "The instruction-tuned-from line and the Hub language tags say Italian and English, and the token count in the excerpt is 160M. The same opening sentence titles the model Occiglot-7B-EU5-Instruct and says it supports the top-5 EU languages. Those two card statements disagree. Languages stored here are English and Italian. Continued-pretraining or instruction-tune size in the excerpt: 160M additional instruction tokens. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Occiglot-7B-IT-EN-Instruct is an open-weight 7B language model from the Occiglot research collective. TU Darmstadt, hessian.AI, and DFKI announced the v0.1 release. The card continues pretraining from Mistral-7B-v0.1 under Apache-2.0. Hub repo occiglot/occiglot-7b-it-en-instruct, created 2024-03-05. Collected on 2026-10-15.", "what_it_is_for": "European open language model from the Occiglot collective. TU Darmstadt's computer science department announced the ten-model v0.1 release. The model card credits the Occiglot Research Collective, with DFKI SAINT and SLT support and HessianAI compute. Kristian Kersting, Manuel Brack, and Patrick Schramowski are TU Darmstadt authors on the EU5 card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/paraphrase/paraphrase-albert-small-v2": { "id": "tu-darmstadt/paraphrase/paraphrase-albert-small-v2", "name": "paraphrase-albert-small-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 256, "max_output_tokens": null, "parameters": "11684096", "architecture": "Official SBERT table base: nreimers/albert-small-v2. Pooling: mean. Embedding dimension in that table: 768. Normalized embeddings: false. Suitable scores listed there: cosine similarity. Table model size: 43 MB. Table encoding speed: 5000 sentences/sec on a V100. Table training data: AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions, flickr30k_captions, yahoo_answers_title_question, S2ORC citation pairs, stackexchange duplicate questions, wiki-atomic-edits.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "paraphrase-albert-small-v2 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/paraphrase-albert-small-v2. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "English paraphrase sentence embedding model from the SBERT pretrained table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/paraphrase/paraphrase-distilroberta-base-v2": { "id": "tu-darmstadt/paraphrase/paraphrase-distilroberta-base-v2", "name": "paraphrase-distilroberta-base-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 256, "max_output_tokens": null, "parameters": "82118914", "architecture": "Official SBERT table base: distilbert/distilroberta-base. Pooling: mean. Embedding dimension in that table: 768. Normalized embeddings: false. Suitable scores listed there: cosine similarity. Table model size: 290 MB. Table encoding speed: 4000 sentences/sec on a V100. Table training data: AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions, flickr30k_captions, yahoo_answers_title_question, S2ORC citation pairs, stackexchange duplicate questions, wiki-atomic-edits.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "paraphrase-distilroberta-base-v2 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/paraphrase-distilroberta-base-v2. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "English paraphrase sentence embedding model from the SBERT pretrained table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/paraphrase/paraphrase-minilm-l12-v2": { "id": "tu-darmstadt/paraphrase/paraphrase-minilm-l12-v2", "name": "paraphrase-MiniLM-L12-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 256, "max_output_tokens": null, "parameters": "33360512", "architecture": "Official SBERT table base: microsoft/MiniLM-L12-H384-uncased. Pooling: mean. Embedding dimension in that table: 384. Normalized embeddings: false. Suitable scores listed there: cosine similarity. Table model size: 120 MB. Table encoding speed: 7500 sentences/sec on a V100. Table training data: AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions, flickr30k_captions, yahoo_answers_title_question, S2ORC citation pairs, stackexchange duplicate questions, wiki-atomic-edits.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "paraphrase-MiniLM-L12-v2 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/paraphrase-MiniLM-L12-v2. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "English paraphrase sentence embedding model from the SBERT pretrained table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/paraphrase/paraphrase-minilm-l3-v2": { "id": "tu-darmstadt/paraphrase/paraphrase-minilm-l3-v2", "name": "paraphrase-MiniLM-L3-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "17390336", "architecture": "Official SBERT table base: nreimers/MiniLM-L3-H384-uncased. Pooling: mean. Embedding dimension in that table: 384. Normalized embeddings: false. Suitable scores listed there: cosine similarity. Table model size: 61 MB. Table encoding speed: 19000 sentences/sec on a V100. Table training data: AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions, flickr30k_captions, yahoo_answers_title_question, S2ORC citation pairs, stackexchange duplicate questions, wiki-atomic-edits. The HTML table's visible base name is nreimers/MiniLM-L3-H384-uncased. The href on that same cell points at nreimers/MiniLM-L6-H384-uncased. The safetensors count 17,390,336 is below the L6 count 22,713,728.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "paraphrase-MiniLM-L3-v2 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/paraphrase-MiniLM-L3-v2. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "Smallest paraphrase MiniLM in the SBERT pretrained table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/paraphrase/paraphrase-minilm-l6-v2": { "id": "tu-darmstadt/paraphrase/paraphrase-minilm-l6-v2", "name": "paraphrase-MiniLM-L6-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "22713728", "architecture": "Official SBERT table base: nreimers/MiniLM-L6-H384-uncased. Pooling: mean. Embedding dimension in that table: 384. Normalized embeddings: false. Suitable scores listed there: cosine similarity. Table model size: 80 MB. Table encoding speed: 14200 sentences/sec on a V100. Table training data: AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions, flickr30k_captions, yahoo_answers_title_question, S2ORC citation pairs, stackexchange duplicate questions, wiki-atomic-edits.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "paraphrase-MiniLM-L6-v2 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/paraphrase-MiniLM-L6-v2. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "English paraphrase sentence embedding model from the SBERT pretrained table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/paraphrase/paraphrase-mpnet-base-v2": { "id": "tu-darmstadt/paraphrase/paraphrase-mpnet-base-v2", "name": "paraphrase-mpnet-base-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "109486978", "architecture": "Official SBERT table base: microsoft/mpnet-base. Pooling: mean. Embedding dimension in that table: 768. Normalized embeddings: false. Suitable scores listed there: cosine similarity. Table model size: 420 MB. Table encoding speed: 2800 sentences/sec on a V100. Table training data: AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions, flickr30k_captions, yahoo_answers_title_question, S2ORC citation pairs, stackexchange duplicate questions, wiki-atomic-edits.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "paraphrase-mpnet-base-v2 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/paraphrase-mpnet-base-v2. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "English paraphrase sentence embedding model from the SBERT pretrained table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/paraphrase/paraphrase-multilingual-minilm-l12-v2": { "id": "tu-darmstadt/paraphrase/paraphrase-multilingual-minilm-l12-v2", "name": "paraphrase-multilingual-MiniLM-L12-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "117654272", "architecture": "Official SBERT table base: teacher paraphrase-MiniLM-L12-v2; student microsoft/Multilingual-MiniLM-L12-H384. Pooling: mean. Embedding dimension in that table: 384. Normalized embeddings: false. Suitable scores listed there: cosine similarity. Table model size: 420 MB. Table encoding speed: 7500 sentences/sec on a V100. The docs list fr-ca, pt-br, zh-cn, and zh-tw in the 50+ language inventory. Those four codes are not in this model's Hub language tags. The tags are what is stored.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "paraphrase-multilingual-MiniLM-L12-v2 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "Multilingual paraphrase model. The SBERT docs say it was distilled from the named English teacher onto the named student for 50+ languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/paraphrase/paraphrase-multilingual-mpnet-base-v2": { "id": "tu-darmstadt/paraphrase/paraphrase-multilingual-mpnet-base-v2", "name": "paraphrase-multilingual-mpnet-base-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "278044162", "architecture": "Official SBERT table base: teacher paraphrase-mpnet-base-v2; student FacebookAI/xlm-roberta-base. Pooling: mean. Embedding dimension in that table: 768. Normalized embeddings: false. Suitable scores listed there: cosine similarity. Table model size: 970 MB. Table encoding speed: 2500 sentences/sec on a V100. The docs list fr-ca, pt-br, zh-cn, and zh-tw in the 50+ language inventory. Those four codes are not in this model's Hub language tags. The tags are what is stored.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "paraphrase-multilingual-mpnet-base-v2 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/paraphrase-multilingual-mpnet-base-v2. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "Multilingual paraphrase model. The SBERT docs say it was distilled from the named English teacher onto the named student for 50+ languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/paraphrase/paraphrase-tinybert-l6-v2": { "id": "tu-darmstadt/paraphrase/paraphrase-tinybert-l6-v2", "name": "paraphrase-TinyBERT-L6-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "66955520", "architecture": "Official SBERT table base: nreimers/TinyBERT_L-6_H-768_v2. Pooling: mean. Embedding dimension in that table: 768. Normalized embeddings: false. Suitable scores listed there: cosine similarity. Table model size: 240 MB. Table encoding speed: 4500 sentences/sec on a V100. Table training data: AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions, flickr30k_captions, yahoo_answers_title_question, S2ORC citation pairs, stackexchange duplicate questions, wiki-atomic-edits.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "paraphrase-TinyBERT-L6-v2 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/paraphrase-TinyBERT-L6-v2. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "English paraphrase sentence embedding model from the SBERT pretrained table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/sbert/all-distilroberta-v1": { "id": "tu-darmstadt/sbert/all-distilroberta-v1", "name": "all-distilroberta-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "82118914", "architecture": "Official SBERT table base: distilbert/distilroberta-base. Pooling: mean. Embedding dimension in that table: 768. Normalized embeddings: true. Suitable scores listed there: dot-product, cosine similarity, and Euclidean distance. Table model size: 290 MB. Table encoding speed: 4000 sentences/sec on a V100. The SBERT table says training used 1B+ pairs and points to the model card for details.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "all-distilroberta-v1 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/all-distilroberta-v1. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "General-purpose English sentence embedding model. The SBERT table says the all-* models were trained on more than 1 billion pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/sbert/all-minilm-l12-v1": { "id": "tu-darmstadt/sbert/all-minilm-l12-v1", "name": "all-MiniLM-L12-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 256, "max_output_tokens": null, "parameters": "33360512", "architecture": "Official SBERT table base: microsoft/MiniLM-L12-H384-uncased. Pooling: mean. Embedding dimension in that table: 384. Normalized embeddings: true. Suitable scores listed there: dot-product, cosine similarity, and Euclidean distance. Table model size: 120 MB. Table encoding speed: 7500 sentences/sec on a V100. The SBERT table says training used 1B+ pairs and points to the model card for details.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "all-MiniLM-L12-v1 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/all-MiniLM-L12-v1. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "General-purpose English sentence embedding model. The SBERT table says the all-* models were trained on more than 1 billion pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/sbert/all-minilm-l12-v2": { "id": "tu-darmstadt/sbert/all-minilm-l12-v2", "name": "all-MiniLM-L12-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 256, "max_output_tokens": null, "parameters": "33360512", "architecture": "Official SBERT table base: microsoft/MiniLM-L12-H384-uncased. Pooling: mean. Embedding dimension in that table: 384. Normalized embeddings: true. Suitable scores listed there: dot-product, cosine similarity, and Euclidean distance. Table model size: 120 MB. Table encoding speed: 7500 sentences/sec on a V100. The SBERT table says training used 1B+ pairs and points to the model card for details.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "all-MiniLM-L12-v2 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/all-MiniLM-L12-v2. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "General-purpose English sentence embedding model. The SBERT table says the all-* models were trained on more than 1 billion pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/sbert/all-minilm-l6-v1": { "id": "tu-darmstadt/sbert/all-minilm-l6-v1", "name": "all-MiniLM-L6-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "22713728", "architecture": "Official SBERT table base: nreimers/MiniLM-L6-H384-uncased. Pooling: mean. Embedding dimension in that table: 384. Normalized embeddings: true. Suitable scores listed there: dot-product, cosine similarity, and Euclidean distance. Table model size: 80 MB. Table encoding speed: 14200 sentences/sec on a V100. The SBERT table says training used 1B+ pairs and points to the model card for details.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "all-MiniLM-L6-v1 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/all-MiniLM-L6-v1. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "General-purpose English sentence embedding model. The SBERT table says the all-* models were trained on more than 1 billion pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/sbert/all-minilm-l6-v2": { "id": "tu-darmstadt/sbert/all-minilm-l6-v2", "name": "all-MiniLM-L6-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 256, "max_output_tokens": null, "parameters": "22713728", "architecture": "Official SBERT table base: nreimers/MiniLM-L6-H384-uncased. Pooling: mean. Embedding dimension in that table: 384. Normalized embeddings: true. Suitable scores listed there: dot-product, cosine similarity, and Euclidean distance. Table model size: 80 MB. Table encoding speed: 14200 sentences/sec on a V100. The SBERT table says training used 1B+ pairs and points to the model card for details. The model card that was read says it fine-tunes nreimers/MiniLM-L6-H384-uncased with a contrastive loss, 100k steps, batch size 1024, AdamW, learning rate 2e-5, warmup 500, on a TPU v3-8. Training sequence length on that card is 128 tokens. The same card says inputs longer than 256 word pieces are truncated, which matches the table max length. The card's dataset table totals 1,170,060,424 pairs. The card says the run was part of the Hugging Face JAX/Flax community week project 'Train the Best Sentence Embedding Model Ever with 1B Training Pairs'.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "all-MiniLM-L6-v2 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/all-MiniLM-L6-v2. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "General-purpose English sentence embedding model. The SBERT table says the all-* models were trained on more than 1 billion pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/sbert/all-mpnet-base-v1": { "id": "tu-darmstadt/sbert/all-mpnet-base-v1", "name": "all-mpnet-base-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "109486978", "architecture": "Official SBERT table base: microsoft/mpnet-base. Pooling: mean. Embedding dimension in that table: 768. Normalized embeddings: true. Suitable scores listed there: dot-product, cosine similarity, and Euclidean distance. Table model size: 420 MB. Table encoding speed: 2800 sentences/sec on a V100. The SBERT table says training used 1B+ pairs and points to the model card for details.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "all-mpnet-base-v1 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/all-mpnet-base-v1. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "General-purpose English sentence embedding model. The SBERT table says the all-* models were trained on more than 1 billion pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/sbert/all-mpnet-base-v2": { "id": "tu-darmstadt/sbert/all-mpnet-base-v2", "name": "all-mpnet-base-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 384, "max_output_tokens": null, "parameters": "109486978", "architecture": "Official SBERT table base: microsoft/mpnet-base. Pooling: mean. Embedding dimension in that table: 768. Normalized embeddings: true. Suitable scores listed there: dot-product, cosine similarity, and Euclidean distance. Table model size: 420 MB. Table encoding speed: 2800 sentences/sec on a V100. The SBERT table says training used 1B+ pairs and points to the model card for details. The model card that was read says it fine-tunes microsoft/mpnet-base with the same contrastive setup as the all-MiniLM-L6-v2 card: 100k steps, batch 1024, AdamW 2e-5, warmup 500, TPU v3-8, training sequence length 128. The card says inputs longer than 384 word pieces are truncated. The card's dataset table totals 1,170,060,424 pairs and names the same community-week project.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "all-mpnet-base-v2 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/all-mpnet-base-v2. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "General-purpose English sentence embedding model. The SBERT table says the all-* models were trained on more than 1 billion pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-darmstadt/sbert/all-roberta-large-v1": { "id": "tu-darmstadt/sbert/all-roberta-large-v1", "name": "all-roberta-large-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 256, "max_output_tokens": null, "parameters": "355360258", "architecture": "Official SBERT table base: FacebookAI/roberta-large. Pooling: mean. Embedding dimension in that table: 1024. Normalized embeddings: true. Suitable scores listed there: dot-product, cosine similarity, and Euclidean distance. Table model size: 1360 MB. Table encoding speed: 800 sentences/sec on a V100. The SBERT table says training used 1B+ pairs and points to the model card for details.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "all-roberta-large-v1 is an open-weight sentence embedding model from the Sentence Transformers collection started by Nils Reimers and Iryna Gurevych at the UKP Lab, TU Darmstadt. The Hub repo is sentence-transformers/all-roberta-large-v1. Library maintenance was transferred to Hugging Face on 2025-10-22. This dossier was collected on 2026-10-15.", "what_it_is_for": "General-purpose English sentence embedding model. The SBERT table says the all-* models were trained on more than 1 billion pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-dresden/cobra/cobra": { "id": "tu-dresden/cobra/cobra", "name": "COBRA", "maker": "tu-dresden", "family": "tu-dresden/cobra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "15000000", "architecture": "Slide encoder: per-foundation-model embedding MLP, Mamba-2 state-space layers, then multi-head gated attention. The CVPR paper says two Mamba-2 layers. The example cobraII.yml on the model card sets nr_mamba_layers to 1, dim 768, 4 heads, d_state 128, and input dims 512, 1024, 1280, and 1536. Inference consumes frozen patch embeddings, not raw pixels. Table 1 lists 15 million parameters and pretraining on 3 thousand whole-slide images.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "COBRA (COntrastive Biomarker Representation Alignment) is a slide-level pathology encoder from the Kather Lab at the Else Kr\u00f6ner Fresenius Center for Digital Health, TU Dresden. It is an active open-weight model. The preprint is dated 2024-11-20, COBRA II was marked live on the model card on 2025-02-07, and the paper was published at CVPR 2025. Collected 2026-10-15.", "what_it_is_for": "The paper describes an unsupervised encoder that turns patch embeddings from one or more foundation models into a slide or patient vector. The stated use is downstream biomarker and subtype prediction without training a new multiple-instance model for every task. The card also documents attention heatmaps and a local MLP on the extracted features.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-dresden/mopadi/mopadi": { "id": "tu-dresden/mopadi/mopadi", "name": "MoPaDi", "maker": "tu-dresden", "family": "tu-dresden/mopadi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2024-11-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion autoencoder based on Preechakul et al., plus task-specific linear or multiple-instance classifiers. The 2024-11-03 preprint describes a denoising diffusion decoder conditioned on a CNN feature vector. A later preprint wording says conditional DDIM. The journal card calls the method a diffusion autoencoder. Four dataset-specific autoencoders are shipped: 224 px tissue tiles, 512 px colorectal tiles, 512 px breast tiles, and 256 px pancancer tiles.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MoPaDi (MOrphing histoPathology Diffusion) is a counterfactual histopathology model from the Kather Lab at the Else Kr\u00f6ner Fresenius Center for Digital Health, TU Dresden. The lab released gated weights for four dataset-specific diffusion autoencoders and their classifiers. The preprint is dated 2024-11-03. Cancer Research published the journal version in 2026, volume 86, issue 17, pages 4414-4433. Collected 2026-10-15. Status is active.", "what_it_is_for": "The journal abstract says MoPaDi generates counterfactual histopathology images so a user can see which morphological or stain features move a classifier's prediction. It is an explanation tool. It is not described as a diagnostic device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tu-dresden/slideflame/slideflame": { "id": "tu-dresden/slideflame/slideflame", "name": "SlideFlame", "maker": "tu-dresden", "family": "tu-dresden/slideflame", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flamingo-style slide vision-language model. The GitHub README says it uses a language encoder, a Perceiver resampler, and cross-attention, and that training examples pass microsoft/BioGPT. The Hugging Face card does not name the backbone inside the four .pt files. Those files are labeled as ablation variants: global or local features, and warm or zero initialization.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SlideFlame is a slide-level vision-language model for pathology report generation from the Kather Lab at the Else Kr\u00f6ner Fresenius Center for Digital Health, TU Dresden. The lab posted four named weight files on Hugging Face. The card calls them ablated variants. No release date is printed. Collected 2026-10-15. Status is active.", "what_it_is_for": "The GitHub README says SlideFlame generates pathology reports from patch-level and optional slide-level visual features. It is a research report generator. The card does not state a clinical claim.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tue/eomt/eomt": { "id": "tue/eomt/eomt", "name": "EoMT", "maker": "tue", "family": "tue/eomt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-03-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-only Mask Transformer. A plain ViT jointly encodes image patches and learned segmentation queries. Transformers' default config, matching tue-mps/coco_panoptic_eomt_large_640, is hidden size 1024, 24 layers, 16 heads, patch size 16, 200 queries, and 4 register tokens. Released scales are ViT-S, ViT-B, ViT-L, ViT-g, and a 7B WebSSL variant. DINOv2 checkpoints are full weights. DINOv3 checkpoints are deltas and need separate DINOv3 backbone weights.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EoMT (Encoder-only Mask Transformer) is an image segmentation model from the Mobile Perception Systems Lab at Eindhoven University of Technology, with coauthors at Polytechnic of Turin and RWTH Aachen. It is active, weights are public, and this dossier was collected on 2026-10-15.", "what_it_is_for": "The lab says a plain Vision Transformer can do semantic, instance, and panoptic segmentation without a convolutional adapter, pixel decoder, or Transformer decoder. Learned queries are injected into the last encoder blocks. Masked attention is used in training and annealed away so inference stays decoder-free.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tue/lvmt/lvmt": { "id": "tue/lvmt/lvmt", "name": "LVMT", "maker": "tue", "family": "tue/lvmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-09-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Long-term Video Mask Transformer. Online video segmentation on a frozen vision foundation model, with a per-query GRU memory and Truncated Query Propagation so training can use long videos in chunks. The fetched zoo is the DINOv3 release. Scales in that zoo are LVMT-S, LVMT-B, and LVMT-L. Parameter counts were not published on the pages read.", "modalities": { "input": [ "video" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LVMT (Long-term Video Mask Transformer) is an online video segmentation model from the Mobile Perception Systems Lab at Eindhoven University of Technology, with an RWTH Aachen coauthor. It is active, the lab repo is MIT-licensed, and this dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says online video segmenters lose objects across long occlusions and cannot be trained on long clips. LVMT adds a small per-query GRU memory and Truncated Query Propagation: the video is trained in chunks, object state crosses the chunk boundary, and backprop stays inside the chunk. The encoder stays frozen, as in PMT. The abstract says it is more than 10x faster than the prior state of the art.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tue/orion/orion-lite": { "id": "tue/orion/orion-lite", "name": "Orion-Lite", "maker": "tue", "family": "tue/orion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-04-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-only driving student. A compact decoder predicts the ego planning feature from visual queries and skips the teacher's LLM at inference. The lab results table calls it Orion-Lite (0.1B). The teacher is Xiaomi's ORION 7B VLA, which is not this model.", "modalities": { "input": [ "image" ], "output": [ "trajectory" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Orion-Lite is a vision-only autonomous-driving model from the Mobile Perception Systems Lab at Eindhoven University of Technology. It is active, weights are public, no license was found, and this dossier was collected on 2026-10-15.", "what_it_is_for": "The lab distills a 7B vision-language-action teacher, Xiaomi ORION, into a small vision-only student. The student reads visual queries and predicts the ego planning feature without running the teacher's LLM. The stated job is closed-loop driving on Bench2Drive, including rare interactive scenes, at lower latency.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tue/pmt/pmt": { "id": "tue/pmt/pmt", "name": "PMT", "maker": "tue", "family": "tue/pmt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Plain Mask Transformer. A lightweight Transformer decoder, the Plain Mask Decoder, runs on frozen vision-foundation-model patch tokens and queries. The image zoo uses frozen DINOv3. Released files are decoder weights only. Hub lists 7.7M, 30.4M, and 53.9M for the small, base, and large COCO panoptic 640 cards.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PMT (Plain Mask Transformer) is a segmentation model from the Mobile Perception Systems Lab at Eindhoven University of Technology. All four paper authors are at TU/e. It is active, the lab repo is MIT-licensed, and this dossier was collected on 2026-10-15.", "what_it_is_for": "EoMT and VidEoMT fine-tune the whole encoder, so a vision foundation model cannot be shared across tasks. PMT keeps that encoder frozen. A Plain Mask Decoder mimics the last EoMT layers and attends over frozen patch tokens and queries. The image release is DINOv3. The README also ships video models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tue/spectre/spectre": { "id": "tue/spectre/spectre", "name": "SPECTRE", "maker": "tue", "family": "tue/spectre", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2025-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage 3D vision transformer for CT. Paper Table 6 lists a local ViT of 339M trainable parameters (24 layers, embedding 1080, 12 heads, patch 16x16x8, 3D RoPE, LayerScale 0.1) and a global ViT of 58M (4 layers, embedding 1080, 12 heads, 3D RoPE, LayerScale 1.0). The Hub card for SPECTRE-Large reports 0.4B. Default embedding crops are 128x128x64 in Hounsfield units windowed to [-1000, 1000], RAS orientation, native spacing unless resampled.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SPECTRE (Self-Supervised and Cross-Modal Pretraining for CT Representation Extraction) is a 3D CT foundation model from the ARIA and AIMS labs at Eindhoven University of Technology. It is active. Code is MIT. Weights are CC-BY-NC-SA-4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents a general CT encoder for transfer to segmentation, biomarker classification, and scan-report retrieval. A local 3D ViT reads crops. A global ViT pools those crops into a scan embedding. Pretraining is DINO-style self-distillation and then SigLIP-style alignment to radiology reports. Training uses public CT data only. The card's released product is SPECTRE-Large.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tue/videomt/videomt": { "id": "tue/videomt/videomt", "name": "VidEoMT", "maker": "tue", "family": "tue/videomt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-02-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video Encoder-only Mask Transformer. An online encoder-only ViT with query propagation from the previous frame fused with learned queries. No separate tracking module. DINOv2 zoo scales are ViT-S, ViT-B, and ViT-L. Hub lists 24.1M, 94.2M, and 0.3B for the YouTube-VIS 2019 small, base, and large cards.", "modalities": { "input": [ "video" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VidEoMT (Video Encoder-only Mask Transformer) is an online video segmentation model from the Mobile Perception Systems Lab at Eindhoven University of Technology, with RWTH Aachen coauthors. It is active, MIT-licensed weights are public, and this dossier was collected on 2026-10-15.", "what_it_is_for": "The paper replaces a per-frame segmenter plus a separate tracker with one encoder-only ViT. Queries from the previous frame are propagated and fused with learned queries. The abstract says this is competitive and 5x to 10x faster than specialized trackers, up to 160 FPS with ViT-L.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tuebingen/bagnet/bagnet-17": { "id": "tuebingen/bagnet/bagnet-17", "name": "BagNet-17", "maker": "tuebingen", "family": "tuebingen/bagnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2019-03-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Same ResNet-50-style BagNet as the other sizes, with a 17x17 receptive field. PyTorch kernel3 setting [1, 1, 1, 0] and strides [2, 2, 2, 1]. 2048-d local features, linear classifier, spatial average of logits, ImageNet-1k.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BagNet-17 is a University of T\u00fcbingen Bethge Lab ImageNet classifier from the same Brendel and Bethge release as BagNet-9 and BagNet-33. It is an active open-weight model with an MIT license. Collected 2026-10-15.", "what_it_is_for": "BagNet-17 classifies ImageNet images from 17\u00d717 local features. The paper says this receptive field reaches AlexNet-level top-5 accuracy. The repository runs the network on the full image and averages logits over space.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tuebingen/bagnet/bagnet-33": { "id": "tuebingen/bagnet/bagnet-33", "name": "BagNet-33", "maker": "tuebingen", "family": "tuebingen/bagnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2019-03-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Same ResNet-50-style BagNet, with a 33x33 receptive field. PyTorch kernel3 setting [1, 1, 1, 1] and strides [2, 2, 2, 1]. 2048-d local features, linear classifier, spatial average of logits, ImageNet-1k.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BagNet-33 is the largest receptive-field model in the University of T\u00fcbingen Bethge Lab BagNet release. It is an active open-weight ImageNet classifier with an MIT license. Collected 2026-10-15.", "what_it_is_for": "BagNet-33 classifies ImageNet images from 33\u00d733 local features. The abstract states 87.6% top-5 accuracy for features of this size. Inference averages class logits over the image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tuebingen/bagnet/bagnet-9": { "id": "tuebingen/bagnet/bagnet-9", "name": "BagNet-9", "maker": "tuebingen", "family": "tuebingen/bagnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2019-03-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-50-style bottleneck stack [3, 4, 6, 3] with most 3x3 convolutions replaced by 1x1 convolutions so the top receptive field is 9x9. Each patch yields a 2048-d feature, a linear classifier produces class logits, and those logits are averaged over space. ImageNet-1k. PyTorch kernel3 setting [1, 1, 0, 0] and strides [2, 2, 2, 1].", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BagNet-9 is a University of T\u00fcbingen Bethge Lab ImageNet classifier from Wieland Brendel and Matthias Bethge. It is an active open-weight model with an MIT license. Collected 2026-10-15.", "what_it_is_for": "The ICLR 2019 paper tests how far ImageNet classification can go when each prediction comes from a small image patch. BagNet-9 keeps a 9\u00d79 receptive field at the top of a ResNet-50-style stack. The repository applies the network to the whole image and averages class logits over space.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tuebingen/deepgaze/deepgaze-i": { "id": "tuebingen/deepgaze/deepgaze-i", "name": "DeepGaze I", "maker": "tuebingen", "family": "tuebingen/deepgaze", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The shipped model is a 2021 PyTorch retrain, not the 2015 Caffe/Theano model. Frozen Torchvision AlexNet features from layer features.10 (256 channels), a bias-free 1x1 readout, Gaussian smoothing, and a learned center-bias weight. Trained with Adam on MIT1003 images of size 1024x768. No sparsity penalty. The 2015 model used a two-stream AlexNet and an SFO optimizer.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepGaze I is a University of T\u00fcbingen Bethge Lab fixation-density model. The current package ships a 2021 PyTorch retrain with open weights. The 2015 paper describes an earlier Caffe and Theano model. Collected 2026-10-15.", "what_it_is_for": "DeepGaze I predicts where people look in a still image. The 2015 paper uses ImageNet-trained AlexNet feature maps as a frozen front end and trains a readout for saliency. The maintained loader is `deepgaze_pytorch.DeepGazeI(pretrained=True)`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tuebingen/deepgaze/deepgaze-ii": { "id": "tuebingen/deepgaze/deepgaze-ii", "name": "DeepGaze II", "maker": "tuebingen", "family": "tuebingen/deepgaze", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fixation-density model. VGG-19 features trained for ImageNet object recognition are kept frozen. A small readout is trained on top. The 2017 paper says there is no additional fine-tuning of the VGG features. The current PyTorch package has a DeepGazeII module class used as the readout shell for DeepGaze I, but it does not load a DeepGaze II checkpoint.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepGaze II is a University of T\u00fcbingen Bethge Lab fixation-density model announced in a 2017 paper. The maintained PyTorch package cites it and does not ship its checkpoint. The prediction service announced in the paper did not respond on 2026-10-15. Status here is deprecated. Collected 2026-10-15.", "what_it_is_for": "The paper predicts free-viewing fixations by training a small readout on frozen VGG-19 features from ImageNet object recognition. It says the VGG features receive no further fine-tuning. It announced a web service at `http://deepgaze.bethgelab.org`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tuebingen/deepgaze/deepgaze-iie": { "id": "tuebingen/deepgaze/deepgaze-iie", "name": "DeepGaze IIE", "maker": "tuebingen", "family": "tuebingen/deepgaze", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-05-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Spatial saliency mixture. The released code averages four frozen backbones: a ShapeNet ResNet-50 (RGBShapeNetC), EfficientNet-B5, DenseNet-201, and ResNeXt-50. The loader comment says 3 instances per backbone and 10 cross-validation folds per instance. Each component is a 1x1 readout (channels depend on the backbone: 2048, 2416, 2048, and 2560) plus a fixation-selection head and a Gaussian finalizer. Image features stay frozen.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepGaze IIE is a University of T\u00fcbingen Bethge Lab spatial saliency model. The paper is dated 2021-05-26. Open weights shipped on 2021-06-01. The repository license is unpublished. Collected 2026-10-15.", "what_it_is_for": "DeepGaze IIE predicts a fixation-density map for a still image. The abstract reports a 15 percentage-point gain over DeepGaze II, to 93% on MIT1003, and lists MIT/T\u00fcbingen Saliency Benchmark scores. The loader is `deepgaze_pytorch.DeepGazeIIE(pretrained=True)`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tuebingen/deepgaze/deepgaze-iii": { "id": "tuebingen/deepgaze/deepgaze-iii", "name": "DeepGaze III", "maker": "tuebingen", "family": "tuebingen/deepgaze", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-06-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Free-viewing scanpath model. Frozen DenseNet-201 features, a 10-component mixture, and a scanpath encoder over the last four fixations (included_fixations -1 through -4, four fixations at 3 channels each). Outputs a log-density for the next fixation given the image, a center-bias map, and the fixation history. Journal of Vision 2022.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepGaze III is a University of T\u00fcbingen Bethge Lab model of free-viewing human scanpaths. Open weights shipped on 2022-06-22. The Journal of Vision paper is dated 2022. The repository license is unpublished. Collected 2026-10-15.", "what_it_is_for": "DeepGaze III predicts the next fixation from the image and the recent fixation history. The loader is `deepgaze_pytorch.DeepGazeIII(pretrained=True)`. It is a scanpath model, separate from the spatial DeepGaze IIE line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tuebingen/deepgaze/deepgaze-mr": { "id": "tuebingen/deepgaze/deepgaze-mr", "name": "DeepGaze MR", "maker": "tuebingen", "family": "tuebingen/deepgaze", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video saliency baseline. A 16-frame clip predicts a gaze distribution on the last frame. The README says the model normalizes frames for a VGG network. The default pretrained checkpoint uses LEDOV weights and the LEDOV center bias. Full-video predict() skips the first 15 frames because of the window. Input is a float tensor T x C x H x W in [0, 1].", "modalities": { "input": [ "video" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepGaze MR is a University of T\u00fcbingen Bethge Lab video saliency model by Matthias Tangemann. Open weights load through PyTorch Hub under an MIT license. The README cites ECCV 2020. Collected 2026-10-15.", "what_it_is_for": "DeepGaze MR predicts where people look in video. A 16-frame clip produces a gaze distribution on the last frame. The default hub checkpoint is the LEDOV model used in the paper. Contact on the repository is matthias.tangemann@bethgelab.org.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tuebingen/deepgaze/deepgaze-msdb": { "id": "tuebingen/deepgaze/deepgaze-msdb", "name": "DeepGaze MSDB", "maker": "tuebingen", "family": "tuebingen/deepgaze", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-scale dataset-bias saliency model. The release code concatenates frozen CLIP ResNet-50x64 and DINOv2 ViT-B/14 features at multiple scales. A mostly shared encoder-decoder has a small set of dataset-specific parameters for center bias, fixation spread, and multi-scale structure. The paper says fewer than 20 of those parameters. Known dataset slots in the code are MIT1003, CAT2000, COCO-Freeview, DAEMONS, and FIGRIM. dataset=None uses averaged parameters.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepGaze MSDB is a University of T\u00fcbingen Bethge Lab saliency model that keeps a small set of dataset-specific parameters. The arXiv v2 date is 2025-09-30. Open weights shipped on 2026-02-14. The repository license is unpublished. Collected 2026-10-15.", "what_it_is_for": "DeepGaze MSDB predicts fixation density while accounting for bias that differs across saliency datasets. The abstract says it sets a new state of the art on MIT300, CAT2000, and COCO-Freeview in generalization, adaptation, and full-training setups. The loader is `DeepGazeMSDB(pretrained=True)`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tufts/conplex/conplex": { "id": "tufts/conplex/conplex", "name": "ConPLex", "maker": "tufts", "family": "tufts/conplex", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-06-08", "context_length": null, "max_output_tokens": null, "parameters": "3.15M on Hugging Face for ConPLex_V1_BindingDB; frozen ProtBert is separate", "architecture": "Drug-target interaction model. Protein features are mean-pooled ProtBert embeddings. Drug features are Morgan fingerprints. Both are projected into a shared space. Training contrasts true binders with decoy molecules, and the binding score is a distance in that space. The distributed checkpoint is the BindingDB experimental-validation model, file BindingDB_ExperimentalValidModel.pt, also published as samsl/ConPLex_V1_BindingDB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ConPLex is a drug-target interaction model published in PNAS on 8 June 2023. Lead authors are Rohit Singh and Samuel Sledzieski (MIT). Lenore Cowen (Tufts) and Bonnie Berger (MIT) are co-corresponding, and Bryan Bryson (MIT) is a co-author. The maintained release is one BindingDB checkpoint. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and MIT News describe it as a way to screen drug-like molecules against protein targets without computing 3D structures. MIT News says it can screen more than 100 million compounds in a day. Predictions were checked in the lab on kinase-drug pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tufts/d-script/d-script": { "id": "tufts/d-script/d-script", "name": "D-SCRIPT", "maker": "tufts", "family": "tufts/d-script", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2021-10-09", "context_length": null, "max_output_tokens": null, "parameters": "631k for the Hugging Face interaction checkpoint; the frozen Bepler and Berger embedding model is separate", "architecture": "Structure-aware PPI scorer. A frozen Bepler and Berger protein language model maps each sequence to an n by 6165 embedding. A learned projection reduces that to n by 100. A contact module (hidden size 50, convolutional width 7) builds an inter-protein contact map, and a custom max-pool (width 9) turns the map into an interaction probability. The Cell Systems methods use projection dimension 100, logistic parameters x0 0.5 and eta 20, and loss weight lambda 0.35. The released human checkpoint was trained on 38,345 positive human PPIs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "D-SCRIPT is a protein-protein interaction model released with the 9 October 2021 Cell Systems paper from Tufts computer science (Lenore Cowen, co-corresponding) and MIT CSAIL (Samuel Sledzieski, Rohit Singh, and Bonnie Berger). It scores whether two proteins bind using only sequence, and it also emits an inter-protein contact map. The human checkpoint is still distributed. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says D-SCRIPT predicts a physical interaction from sequence and is meant to generalize across species when experimental PPI data are scarce. A human-trained model is applied to other organisms and was used for a genome-scale screen in cow.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tufts/d-script/topsy-turvy": { "id": "tufts/d-script/topsy-turvy", "name": "Topsy-Turvy", "maker": "tufts", "family": "tufts/d-script", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-06-24", "context_length": null, "max_output_tokens": null, "parameters": "631k for the Hugging Face interaction checkpoint; the frozen protein language model is separate", "architecture": "Same interaction network as D-SCRIPT (100-d projection, hidden size 50, kernel width 7, pool width 9, 6165-d Bepler and Berger embeddings). Training adds a top-down GLIDE or GLIDER score so the sequence model sees global network structure. At inference only sequences are required. The paper selects GLIDER weight gp 0.2 and threshold gt 92.5. The distributed checkpoint is the human model samsl/topsy_turvy_human_v1, package id human_v2, and it is the dscript default.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Topsy-Turvy is the protein-protein interaction model that followed D-SCRIPT. The 24 June 2022 Bioinformatics paper is from Rohit Singh, Kapil Devkota (Tufts), Samuel Sledzieski, Bonnie Berger (MIT), and Lenore Cowen (Tufts, co-corresponding). It keeps the D-SCRIPT network and, during training only, also learns from a global network score. The human checkpoint is the default model in the dscript package. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors use it for genome-scale PPI prediction in species that have little or no experimental interaction data. Training sees both sequence and a top-down network view. Prediction needs sequence only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tufts/d-script/tt3d": { "id": "tufts/d-script/tt3d", "name": "TT3D", "maker": "tufts", "family": "tufts/d-script", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-10-28", "context_length": null, "max_output_tokens": null, "parameters": "633k for the Hugging Face interaction checkpoint; Foldseek and the frozen protein language model are separate", "architecture": "Topsy-Turvy with an extra structural channel. A one-hot encoding of the Foldseek 3Di string (21 structural letters, same length as the protein) is concatenated with the projected protein-language-model embedding before the contact convolution. The package build uses contact input width 121 rather than 100. Inference needs amino-acid sequences or embeddings plus a Foldseek 3Di FASTA. Trained on human PPIs and evaluated cross-species. Distributed as samsl/tt3d_human_v1, package id human_tt3d.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TT3D (Topsy-Turvy 3D) is a protein-protein interaction model from Samuel Sledzieski and Kapil Devkota (equal contribution), Rohit Singh, Lenore Cowen (Tufts), and Bonnie Berger (MIT). Bioinformatics published it online on 28 October 2023 (issue date 1 November 2023). It is the structure-aware successor of Topsy-Turvy and is still downloadable. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses precomputed protein structures, encoded as Foldseek 3Di sequences, to improve cross-species prediction of which protein pairs bind. The goal is a lightweight score that can be run genome-wide, not a full docking calculation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tum/aerotransformer/aerotransformer": { "id": "tum/aerotransformer/aerotransformer", "name": "AeroTransformer", "maker": "tum", "family": "tum/aerotransformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer surrogate for transonic-wing aerodynamics. The S checkpoint config names AeroTransformer with 5 layers, n_hidden 16, MLP ratio 4, patch size 4x4, window size 8x8, fun_dim 3, and out_dim 3. M and L configs were not opened.", "modalities": { "input": [ "grid" ], "output": [ "grid" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AeroTransformer is a transonic-wing aerodynamic surrogate from the Physics-based Simulation group at the Technical University of Munich. Weights are on `thuerey-group/AeroTransformer` under an MIT tag, in S, M, and L folders. The paper date is 2026-04-20. The Hub repo was created 2026-04-10. Collected 2026-10-15. Active research weights, not an API.", "what_it_is_for": "The paper describes a two-stage recipe for 3D aerodynamic design: pretrain a transformer on diverse wing geometries, then fine-tune it on a few task-specific samples. Pretraining uses SuperWing, about 30,000 samples. Fine-tuning uses shapes perturbed from the Common Research Model. An interactive design page is linked from the README.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tum/pde-transformer/p3d": { "id": "tum/pde-transformer/p3d", "name": "P3D", "maker": "tum", "family": "tum/pde-transformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-09-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid CNN-Transformer surrogate for 3D physics. Pretrained on small patches that are fused into a global solution, with an optional sequence-to-sequence context model for long-range dependencies. One released checkpoint is a diffusion model for turbulent channel flow.", "modalities": { "input": [ "grid" ], "output": [ "grid" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "P3D is a 3D neural surrogate from the Physics-based Simulation group at the Technical University of Munich. The lab blog calls it the three-dimensional PDE-Transformer. The paper is arXiv:2509.10186 (12 September 2025, revised 8 October 2025). Checkpoints are on `thuerey-group/p3d` under an Apache-2.0 tag. Collected 2026-10-15. Active research weights, not an API.", "what_it_is_for": "The paper presents a hybrid CNN-Transformer that learns deterministic and probabilistic surrogates for high-resolution 3D physics. It is pretrained on patches, fused to a global field, and optionally guided by a sequence model. Reported uses are 14 PDE types in 3D, isotropic turbulence up to 512 cubed, and diffusion samples of turbulent channel flow.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tum/pde-transformer/pde-transformer": { "id": "tum/pde-transformer/pde-transformer", "name": "PDE-Transformer", "maker": "tum", "family": "tum/pde-transformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-05-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion-transformer-style surrogate for PDEs on regular grids. Shifted-window attention, 4x4 patches, 8x8 windows. Separate-channel (SC) variants embed physical channels independently. Mixed-channel (MC) variants use two input and output channels in one token stream. Sizes S, B, and L use hidden dimensions 96, 192, and 384.", "modalities": { "input": [ "grid" ], "output": [ "grid" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PDE-Transformer is a grid PDE surrogate from the Physics-based Simulation group at the Technical University of Munich (Nils Thuerey). Pretrained weights are on `thuerey-group/pde-transformer` under an MIT tag. The paper is ICML 2025. Collected 2026-10-15. It is an active research release, not an API product.", "what_it_is_for": "The lab presents it as a general transformer backbone for physics simulations on regular grids, jointly trained on 16 PDE types. Simulation parameters such as viscosity are not given as inputs. They are inferred from the observed fields. Downstream use is transfer to new physics tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tum/piwm/piwm": { "id": "tum/piwm/piwm", "name": "PIWM", "maker": "tum", "family": "tum/piwm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Physics-informed bird's-eye-view diffusion world model. The implementation is based on DIAMOND and adds Soft Mask training plus a zero-shot Warm Start at inference. It predicts future BEV frames from the current frame and actions.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PIWM (Physics-Informed BEV World Model) is a lightweight driving world model released by the Autonomous Vehicle Systems group at the Technical University of Munich. Weights are on the university Hugging Face org as `TUM/PIWM_ckpt`. It was collected on 2026-10-15 and is active as a research checkpoint, not a hosted API.", "what_it_is_for": "The authors describe PIWM as a compact generative model that forecasts future images from the current image and actions, with Soft Mask training and a zero-shot Warm Start at inference. The public demo runs in HighwayEnv. The code is based on DIAMOND.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tum/revit/revit": { "id": "tum/revit/revit", "name": "ReViT", "maker": "tum", "family": "tum/revit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-06-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Rotationally equivariant vision transformer for grid PDE fields. Hierarchical Swin-style backbone. Inputs are mapped into local canonical frames so standard self-attention stays invariant, then decoded back to the global frame. Released checkpoints are ReViT3D: TCF (4 channels, 96 cubed, patch 3) and MHD_64 (7 channels, 64 cubed, patch 2). The opened TCF config has embed_dim 48, depth 1-2-4-2-1, 8 heads, and window size 2.", "modalities": { "input": [ "grid" ], "output": [ "grid" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReViT is a rotationally equivariant vision transformer for grid PDE solvers, from the Physics-based Simulation group at the Technical University of Munich. It was an ICML 2026 oral. Weights for two 3D checkpoints are on `thuerey-group/ReViT`. The Hub repo was created 2026-06-30. Collected 2026-10-15. Active research weights, not an API.", "what_it_is_for": "The authors use ReViT as a neural PDE solver that keeps rotational equivariance. Scalar and vector inputs are written in a local canonical frame so ordinary attention does not break the symmetry, then mapped back to global coordinates. Released checkpoints cover 3D magnetohydrodynamics and turbulent channel flow.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tum/tadpole/tadpole": { "id": "tum/tadpole/tadpole", "name": "Tadpole", "maker": "tum", "family": "tum/tadpole", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "3D PDE autoencoder foundation model. Pretrained on single-channel spatial crops from synthetic data generated online. Downstream dynamics use low-rank adaptation, latent transforms, and skip connections. The Hub release is the B-size encoder and decoder.", "modalities": { "input": [ "grid" ], "output": [ "grid" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tadpole is a 3D PDE foundation model from the Physics-based Simulation group at the Technical University of Munich. It is an autoencoder pretrained on synthetic fields, then adapted to dynamics and generation. Weights are on `thuerey-group/Tadpole` under Apache-2.0. The paper date is 2026-05-14. Collected 2026-10-15. Active research release, not an API.", "what_it_is_for": "The paper says Tadpole learns a representation of 3D PDE solutions by autoencoding single-channel spatial crops, including systems with different numbers of state variables and resolutions. After pretraining it is fine-tuned for dynamics and generative tasks. Training data is generated online so the authors do not store the full synthetic set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tum/treconlm/treconlm": { "id": "tum/treconlm/treconlm", "name": "TReconLM", "maker": "tum", "family": "tum/treconlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-07-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only transformer that reconstructs a DNA sequence from several noisy copies by next-token prediction. Pretrained on synthetic insertions, deletions, and substitutions. Cluster sizes supported by the card are 2 to 10.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TReconLM is a decoder-only transformer for DNA trace reconstruction from the Machine Learning and Information Processing Lab at the Technical University of Munich. Weights are on `mli-lab/TReconLM`. The paper was posted on 2025-07-17 and revised on 2026-06-05. The Hub repo was created 2025-07-01. Collected 2026-10-15. Active research checkpoints, not an API.", "what_it_is_for": "The model recovers one sequence from several independent noisy copies. The copies have insertions, deletions, and substitutions. The card says this is the retrieval step for DNA data storage. Pretraining uses synthetic errors. Three checkpoints are fine-tuned on Noisy-DNA, Microsoft DNA, and Chandak reads.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/bluumi/bluumi": { "id": "turku/bluumi/bluumi", "name": "BLUUMI", "maker": "turku", "family": "turku/bluumi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "bigscience-bloom-rail-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "176B", "architecture": "Continued pretrain of bigscience/bloom. Card: 176B parameters, further pretrained for 40B tokens on ROOTS plus Finnish without extra Finnish weighting. Config: 70 layers, 112 attention heads, vocab 250880. The FinGPT paper table lists dim 14336 for BLUUMI. hidden_size was not present under that key in the config that was read. Context length was not in the config keys that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BLUUMI is TurkuNLP's continued pretrain of the 176B BLOOM model on ROOTS plus Finnish, for 40B tokens. The paper and TurkuNLP's own slides use the name BLUUMI. The Hub repo is TurkuNLP/bloom-finnish-176b under the BigScience BLOOM RAIL license. Status active. Collected 2026-10-15.", "what_it_is_for": "BLUUMI is TurkuNLP's continued pretrain of the 176B BLOOM model on ROOTS plus Finnish, for 40B tokens. The paper and TurkuNLP's own slides use the name BLUUMI. The Hub repo is TurkuNLP/bloom-finnish-176b under the BigScience BLOOM RAIL license. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/eccobert/eccobert-base-cased": { "id": "turku/eccobert/eccobert-base-cased", "name": "ECCO-BERT base cased", "maker": "turku", "family": "turku/eccobert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "0.1B", "architecture": "BERT base cased, trained only on Eighteenth Century Collections Online. Config: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 50000, max position embeddings 512. The card says it is equivalent in size to bert-base-cased. The Hub model-size label is 0.1B parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ECCO-BERT base cased is a BERT base trained from scratch by TurkuNLP on Eighteenth Century Collections Online. The May 2022 workshop paper uses it for publication-year prediction and says the checkpoint is on Hugging Face. Hub id TurkuNLP/eccobert-base-cased-v1. Status active. The card says documentation is still in progress. Collected 2026-10-15.", "what_it_is_for": "ECCO-BERT base cased is a BERT base trained from scratch by TurkuNLP on Eighteenth Century Collections Online. The May 2022 workshop paper uses it for publication-year prediction and says the checkpoint is on Hugging Face. Hub id TurkuNLP/eccobert-base-cased-v1. Status active. The card says documentation is still in progress.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/finbert/finbert-base-cased": { "id": "turku/finbert/finbert-base-cased", "name": "FinBERT base cased", "maker": "turku", "family": "turku/finbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2019-11-25", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT base. Config: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 50105, max position embeddings 512. Card: 1 million steps on over 3 billion tokens (24B characters) of Finnish news, online discussion, and crawls. Custom 50,000 wordpiece vocabulary; the config vocab size is 50105.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinBERT base cased is the University of Turku NLP group's Finnish BERT, Release 1.0 on 25 November 2019. It is a from-scratch BERT base with a Finnish wordpiece vocabulary, still the weights behind TurkuNLP/bert-base-finnish-cased-v1. Status active. Collected 2026-10-15.", "what_it_is_for": "FinBERT base cased is the University of Turku NLP group's Finnish BERT, Release 1.0 on 25 November 2019. It is a from-scratch BERT base with a Finnish wordpiece vocabulary, still the weights behind TurkuNLP/bert-base-finnish-cased-v1. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/finbert/finbert-base-squad2": { "id": "turku/finbert/finbert-base-squad2", "name": "FinBERT base SQuAD2", "maker": "turku", "family": "turku/finbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 384, "max_output_tokens": null, "parameters": "", "architecture": "Extractive QA fine-tune of bert-base-finnish-cased-v1. Config: 12 layers, hidden 768, 12 heads, vocab 50105, max position embeddings 512. Card hyperparameters: max sequence length 384, doc stride 128, batch size 12, 2 epochs, learning rate 3e-5.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinBERT base SQuAD2 is TurkuNLP's extractive question-answering fine-tune of Finnish BERT base cased on Finnish SQuAD 2.0, including unanswerable questions. Hub id TurkuNLP/bert-base-finnish-cased-squad2. Status active. A release day is not on the card. Collected 2026-10-15.", "what_it_is_for": "FinBERT base SQuAD2 is TurkuNLP's extractive question-answering fine-tune of Finnish BERT base cased on Finnish SQuAD 2.0, including unanswerable questions. Hub id TurkuNLP/bert-base-finnish-cased-squad2. Status active. A release day is not on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/finbert/finbert-base-uncased": { "id": "turku/finbert/finbert-base-uncased", "name": "FinBERT base uncased", "maker": "turku", "family": "turku/finbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2019-11-25", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT base, uncased. Config: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 50101, max position embeddings 512. The card describes the same pretraining regime as the cased release: 1 million steps on over 3 billion Finnish tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinBERT base uncased is the lowercase Finnish BERT from the same TurkuNLP Release 1.0 (25 November 2019). The card says the cased model is the one they generally recommend. Weights are TurkuNLP/bert-base-finnish-uncased-v1. Status active. Collected 2026-10-15.", "what_it_is_for": "FinBERT base uncased is the lowercase Finnish BERT from the same TurkuNLP Release 1.0 (25 November 2019). The card says the cased model is the one they generally recommend. Weights are TurkuNLP/bert-base-finnish-uncased-v1. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/finbert/finbert-large-cased": { "id": "turku/finbert/finbert-large-cased", "name": "FinBERT large cased", "maker": "turku", "family": "turku/finbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT large, cased. Config: 24 layers, hidden 1024, 16 heads, intermediate 4096, vocab 50105, max position embeddings 512. Card: same training data as TurkuNLP/bert-base-finnish-cased-v1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinBERT large cased is TurkuNLP's large Finnish BERT. The card says it uses exactly the same training data as the base cased FinBERT and is licensed Apache-2.0. Hub id TurkuNLP/bert-large-finnish-cased-v1. Status active. A release day is not on the card. Collected 2026-10-15.", "what_it_is_for": "FinBERT large cased is TurkuNLP's large Finnish BERT. The card says it uses exactly the same training data as the base cased FinBERT and is licensed Apache-2.0. Hub id TurkuNLP/bert-large-finnish-cased-v1. Status active. A release day is not on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/finbert/finbert-large-toxicity": { "id": "turku/finbert/finbert-large-toxicity", "name": "FinBERT large toxicity", "maker": "turku", "family": "turku/finbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Sequence-classification fine-tune. Config matches bert-large: 24 layers, hidden 1024, 16 heads, vocab 50105, max position embeddings 512. The hyperparameter block says base_LM_model bert-large-finnish-cased-v1, max sequence length 512, learning rate 2e-5, batch size 12, epochs set to 10 and trained for 4. One overview sentence instead says bert-base-finnish-cased-v1. The config matches the large checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinBERT large toxicity is TurkuNLP's multi-label toxicity classifier for Finnish. The hyperparameter block and the weight config identify bert-large-finnish-cased-v1. One sentence on the card says bert-base instead. It predicts six Jigsaw toxicity labels. Hub id TurkuNLP/bert-large-finnish-cased-toxicity. Status active. Collected 2026-10-15.", "what_it_is_for": "FinBERT large toxicity is TurkuNLP's multi-label toxicity classifier for Finnish. The hyperparameter block and the weight config identify bert-large-finnish-cased-v1. One sentence on the card says bert-base instead. It predicts six Jigsaw toxicity labels. Hub id TurkuNLP/bert-large-finnish-cased-toxicity. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/finbert/megatron-finbert-1-3b": { "id": "turku/finbert/megatron-finbert-1-3b", "name": "Megatron-FinBERT 1.3B", "maker": "turku", "family": "turku/finbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "1.3 billion", "architecture": "Megatron-BERT. Card: 1.3 billion parameter Megatron version of FinBERT. Config: model type megatron-bert, 24 layers, hidden 2048, 32 heads, intermediate 8192, vocab 50176, max position embeddings 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Megatron-FinBERT 1.3B is TurkuNLP's 1.3 billion parameter Megatron version of Finnish BERT. The card licenses it Apache-2.0 and compares it with FinBERT-cased on document classification. Hub id TurkuNLP/megatron-bert-finnish-cased-1.3B. Status active. A release day is not on the card. Collected 2026-10-15.", "what_it_is_for": "Megatron-FinBERT 1.3B is TurkuNLP's 1.3 billion parameter Megatron version of Finnish BERT. The card licenses it Apache-2.0 and compares it with FinBERT-cased on document classification. Hub id TurkuNLP/megatron-bert-finnish-cased-1.3B. Status active. A release day is not on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/finerweb/finerweb-line-quality": { "id": "turku/finerweb/finerweb-line-quality", "name": "FinerWeb line quality classifier", "maker": "turku", "family": "turku/finerweb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Line-level quality classifier. Fine-tune of microsoft/deberta-v3-base. Card: bfloat16, learning rate 1e-5, batch size 16, at most 5 epochs, early stopping patience 5, label smoothing 0.1. Trained on one A100 at CSC. Outputs a quality score from 0 to 1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The FinerWeb line quality classifier is a University of Turku DeBERTa-v3-base model that scores English web lines as clean or low quality. The card names Erik Henriksson, Otto Tarkka, and Filip Ginter. It was built for the FinerWeb-10BT corpus. License Apache-2.0. Hub id TurkuNLP/finerweb-quality-classifier. Status active. Collected 2026-10-15.", "what_it_is_for": "The FinerWeb line quality classifier is a University of Turku DeBERTa-v3-base model that scores English web lines as clean or low quality. The card names Erik Henriksson, Otto Tarkka, and Filip Ginter. It was built for the FinerWeb-10BT corpus. License Apache-2.0. Hub id TurkuNLP/finerweb-quality-classifier. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/fingpt/fingpt-13b": { "id": "turku/fingpt/fingpt-13b", "name": "FinGPT 13B", "maker": "turku", "family": "turku/fingpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "13.3B", "architecture": "BLOOM-architecture decoder. Card table and config agree: 40 layers, hidden 5120, 40 heads. Config vocab size 131072. The card says every model in the family was trained for 300B tokens. Not instruction-tuned. Context length is not in the config fields that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinGPT 13B is one of seven Finnish GPT-style language models TurkuNLP trained from scratch on a BLOOM architecture. The card lists 13.3B parameters and 300B training tokens. It is a base language model, not a chat model. Hub id TurkuNLP/gpt3-finnish-13B. License Apache-2.0. Status active. Collected 2026-10-15.", "what_it_is_for": "FinGPT 13B is one of seven Finnish GPT-style language models TurkuNLP trained from scratch on a BLOOM architecture. The card lists 13.3B parameters and 300B training tokens. It is a base language model, not a chat model. Hub id TurkuNLP/gpt3-finnish-13B. License Apache-2.0. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/fingpt/fingpt-3b": { "id": "turku/fingpt/fingpt-3b", "name": "FinGPT 3B", "maker": "turku", "family": "turku/fingpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "2.8B", "architecture": "BLOOM-architecture decoder. Card table and config agree: 32 layers, hidden 2560, 32 heads. Config vocab size 131072. The card says every model in the family was trained for 300B tokens. Not instruction-tuned. Context length is not in the config fields that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinGPT 3B is one of seven Finnish GPT-style language models TurkuNLP trained from scratch on a BLOOM architecture. The card lists 2.8B parameters and 300B training tokens. It is a base language model, not a chat model. Hub id TurkuNLP/gpt3-finnish-3B. License Apache-2.0. Status active. Collected 2026-10-15.", "what_it_is_for": "FinGPT 3B is one of seven Finnish GPT-style language models TurkuNLP trained from scratch on a BLOOM architecture. The card lists 2.8B parameters and 300B training tokens. It is a base language model, not a chat model. Hub id TurkuNLP/gpt3-finnish-3B. License Apache-2.0. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/fingpt/fingpt-8b": { "id": "turku/fingpt/fingpt-8b", "name": "FinGPT 8B", "maker": "turku", "family": "turku/fingpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7.5B", "architecture": "BLOOM-architecture decoder. Card table and config agree: 32 layers, hidden 4096, 32 heads. Config vocab size 131072. The card says every model in the family was trained for 300B tokens. Not instruction-tuned. Context length is not in the config fields that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinGPT 8B is one of seven Finnish GPT-style language models TurkuNLP trained from scratch on a BLOOM architecture. The card lists 7.5B parameters and 300B training tokens. It is a base language model, not a chat model. Hub id TurkuNLP/gpt3-finnish-8B. License Apache-2.0. Status active. Collected 2026-10-15.", "what_it_is_for": "FinGPT 8B is one of seven Finnish GPT-style language models TurkuNLP trained from scratch on a BLOOM architecture. The card lists 7.5B parameters and 300B training tokens. It is a base language model, not a chat model. Hub id TurkuNLP/gpt3-finnish-8B. License Apache-2.0. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/fingpt/fingpt-large": { "id": "turku/fingpt/fingpt-large", "name": "FinGPT Large", "maker": "turku", "family": "turku/fingpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "881M", "architecture": "BLOOM-architecture decoder. Card table and config agree: 24 layers, hidden 1536, 16 heads. Config vocab size 131072. The card says every model in the family was trained for 300B tokens. Not instruction-tuned. Context length is not in the config fields that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinGPT Large is one of seven Finnish GPT-style language models TurkuNLP trained from scratch on a BLOOM architecture. The card lists 881M parameters and 300B training tokens. It is a base language model, not a chat model. Hub id TurkuNLP/gpt3-finnish-large. License Apache-2.0. Status active. Collected 2026-10-15.", "what_it_is_for": "FinGPT Large is one of seven Finnish GPT-style language models TurkuNLP trained from scratch on a BLOOM architecture. The card lists 881M parameters and 300B training tokens. It is a base language model, not a chat model. Hub id TurkuNLP/gpt3-finnish-large. License Apache-2.0. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/fingpt/fingpt-medium": { "id": "turku/fingpt/fingpt-medium", "name": "FinGPT Medium", "maker": "turku", "family": "turku/fingpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "437M", "architecture": "BLOOM-architecture decoder. Card table and config agree: 24 layers, hidden 1024, 16 heads. Config vocab size 131072. The card says every model in the family was trained for 300B tokens. Not instruction-tuned. Context length is not in the config fields that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinGPT Medium is one of seven Finnish GPT-style language models TurkuNLP trained from scratch on a BLOOM architecture. The card lists 437M parameters and 300B training tokens. It is a base language model, not a chat model. Hub id TurkuNLP/gpt3-finnish-medium. License Apache-2.0. Status active. Collected 2026-10-15.", "what_it_is_for": "FinGPT Medium is one of seven Finnish GPT-style language models TurkuNLP trained from scratch on a BLOOM architecture. The card lists 437M parameters and 300B training tokens. It is a base language model, not a chat model. Hub id TurkuNLP/gpt3-finnish-medium. License Apache-2.0. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/fingpt/fingpt-small": { "id": "turku/fingpt/fingpt-small", "name": "FinGPT Small", "maker": "turku", "family": "turku/fingpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "186M", "architecture": "BLOOM-architecture decoder. Card table and config agree: 12 layers, hidden 768, 12 heads. Config vocab size 131072. The card says every model in the family was trained for 300B tokens. Not instruction-tuned. Context length is not in the config fields that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinGPT Small is one of seven Finnish GPT-style language models TurkuNLP trained from scratch on a BLOOM architecture. The card lists 186M parameters and 300B training tokens. It is a base language model, not a chat model. Hub id TurkuNLP/gpt3-finnish-small. License Apache-2.0. Status active. Collected 2026-10-15.", "what_it_is_for": "FinGPT Small is one of seven Finnish GPT-style language models TurkuNLP trained from scratch on a BLOOM architecture. The card lists 186M parameters and 300B training tokens. It is a base language model, not a chat model. Hub id TurkuNLP/gpt3-finnish-small. License Apache-2.0. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/fingpt/fingpt-xl": { "id": "turku/fingpt/fingpt-xl", "name": "FinGPT XL", "maker": "turku", "family": "turku/fingpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "BLOOM-architecture decoder. Card table and config agree: 24 layers, hidden 2064, 24 heads. Config vocab size 131072. The card says every model in the family was trained for 300B tokens. Not instruction-tuned. Context length is not in the config fields that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FinGPT XL is one of seven Finnish GPT-style language models TurkuNLP trained from scratch on a BLOOM architecture. The card lists 1.5B parameters and 300B training tokens. It is a base language model, not a chat model. Hub id TurkuNLP/gpt3-finnish-xl. License Apache-2.0. Status active. Collected 2026-10-15.", "what_it_is_for": "FinGPT XL is one of seven Finnish GPT-style language models TurkuNLP trained from scratch on a BLOOM architecture. The card lists 1.5B parameters and 300B training tokens. It is a base language model, not a chat model. Hub id TurkuNLP/gpt3-finnish-xl. License Apache-2.0. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/finnish-modernbert/finnish-modernbert-base": { "id": "turku/finnish-modernbert/finnish-modernbert-base", "name": "Finnish ModernBERT Base", "maker": "turku", "family": "turku/finnish-modernbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-11-12", "context_length": null, "max_output_tokens": null, "parameters": "143M", "architecture": "ModernBERT encoder. Card: 22 layers, vocab 42K, RoPE base 10K / 1M, sequence length 16K / 128K, 410.2B tokens. Trained on LUMI. Languages named in the card prose: Finnish, Swedish, English, code, Latin, and Northern S\u00e1mi. The card says this model can theoretically extrapolate to a context length of 128K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finnish ModernBERT Base is a TurkuNLP ModernBERT encoder pretrained for languages used in Finland. The card lists 143M parameters, Apache-2.0, and training on LUMI. The public paper is 12 November 2025. Hub id TurkuNLP/finnish-modernbert-base. Status active. Collected 2026-10-15.", "what_it_is_for": "Finnish ModernBERT Base is a TurkuNLP ModernBERT encoder pretrained for languages used in Finland. The card lists 143M parameters, Apache-2.0, and training on LUMI. The public paper is 12 November 2025. Hub id TurkuNLP/finnish-modernbert-base. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/finnish-modernbert/finnish-modernbert-base-short": { "id": "turku/finnish-modernbert/finnish-modernbert-base-short", "name": "Finnish ModernBERT Base Short", "maker": "turku", "family": "turku/finnish-modernbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-11-12", "context_length": 8192, "max_output_tokens": null, "parameters": "209M", "architecture": "ModernBERT encoder. Card: 22 layers, vocab 128K, RoPE base 10K / 1M, sequence length 8192, 365.0B tokens. Trained on LUMI. Languages named in the card prose: Finnish, Swedish, English, code, Latin, and Northern S\u00e1mi.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finnish ModernBERT Base Short is a TurkuNLP ModernBERT encoder pretrained for languages used in Finland. The card lists 209M parameters, Apache-2.0, and training on LUMI. The public paper is 12 November 2025. Hub id TurkuNLP/finnish-modernbert-base-short. Status active. Collected 2026-10-15.", "what_it_is_for": "Finnish ModernBERT Base Short is a TurkuNLP ModernBERT encoder pretrained for languages used in Finland. The card lists 209M parameters, Apache-2.0, and training on LUMI. The public paper is 12 November 2025. Hub id TurkuNLP/finnish-modernbert-base-short. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/finnish-modernbert/finnish-modernbert-large": { "id": "turku/finnish-modernbert/finnish-modernbert-large", "name": "Finnish ModernBERT Large", "maker": "turku", "family": "turku/finnish-modernbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-11-12", "context_length": null, "max_output_tokens": null, "parameters": "401M", "architecture": "ModernBERT encoder. Card: 28 layers, vocab 56K, RoPE base 10K / 1M, sequence length 16K / 128K, 399.2BB tokens. Trained on LUMI. Languages named in the card prose: Finnish, Swedish, English, code, Latin, and Northern S\u00e1mi. The card says this model can theoretically extrapolate to a context length of 128K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finnish ModernBERT Large is a TurkuNLP ModernBERT encoder pretrained for languages used in Finland. The card lists 401M parameters, Apache-2.0, and training on LUMI. The public paper is 12 November 2025. Hub id TurkuNLP/finnish-modernbert-large. Status active. Collected 2026-10-15.", "what_it_is_for": "Finnish ModernBERT Large is a TurkuNLP ModernBERT encoder pretrained for languages used in Finland. The card lists 401M parameters, Apache-2.0, and training on LUMI. The public paper is 12 November 2025. Hub id TurkuNLP/finnish-modernbert-large. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/finnish-modernbert/finnish-modernbert-large-short": { "id": "turku/finnish-modernbert/finnish-modernbert-large-short", "name": "Finnish ModernBERT Large Short", "maker": "turku", "family": "turku/finnish-modernbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-11-12", "context_length": 8192, "max_output_tokens": null, "parameters": "475M", "architecture": "ModernBERT encoder. Card: 28 layers, vocab 128K, RoPE base 10K / 1M, sequence length 8192, 362.2B tokens. Trained on LUMI. Languages named in the card prose: Finnish, Swedish, English, code, Latin, and Northern S\u00e1mi.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finnish ModernBERT Large Short is a TurkuNLP ModernBERT encoder pretrained for languages used in Finland. The card lists 475M parameters, Apache-2.0, and training on LUMI. The public paper is 12 November 2025. Hub id TurkuNLP/finnish-modernbert-large-short. Status active. Collected 2026-10-15.", "what_it_is_for": "Finnish ModernBERT Large Short is a TurkuNLP ModernBERT encoder pretrained for languages used in Finland. The card lists 475M parameters, Apache-2.0, and training on LUMI. The public paper is 12 November 2025. Hub id TurkuNLP/finnish-modernbert-large-short. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/finnish-modernbert/finnish-modernbert-tiny": { "id": "turku/finnish-modernbert/finnish-modernbert-tiny", "name": "Finnish ModernBERT Tiny", "maker": "turku", "family": "turku/finnish-modernbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-11-12", "context_length": null, "max_output_tokens": null, "parameters": "51M", "architecture": "ModernBERT encoder. Card: 6 layers, vocab 27K, RoPE base 10K / 1M, sequence length 16K / 128K, 447.9B tokens. Trained on LUMI. Languages named in the card prose: Finnish, Swedish, English, code, Latin, and Northern S\u00e1mi. The card says this model can theoretically extrapolate to a context length of 128K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finnish ModernBERT Tiny is a TurkuNLP ModernBERT encoder pretrained for languages used in Finland. The card lists 51M parameters, Apache-2.0, and training on LUMI. The public paper is 12 November 2025. Hub id TurkuNLP/finnish-modernbert-tiny. Status active. Collected 2026-10-15.", "what_it_is_for": "Finnish ModernBERT Tiny is a TurkuNLP ModernBERT encoder pretrained for languages used in Finland. The card lists 51M parameters, Apache-2.0, and training on LUMI. The public paper is 12 November 2025. Hub id TurkuNLP/finnish-modernbert-tiny. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/finnish-modernbert/finnish-modernbert-tiny-short": { "id": "turku/finnish-modernbert/finnish-modernbert-tiny-short", "name": "Finnish ModernBERT Tiny Short", "maker": "turku", "family": "turku/finnish-modernbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-11-12", "context_length": 8192, "max_output_tokens": null, "parameters": "129M", "architecture": "ModernBERT encoder. Card: 6 layers, vocab 128K, RoPE base 10K / 1M, sequence length 8192, 362.2B tokens. Trained on LUMI. Languages named in the card prose: Finnish, Swedish, English, code, Latin, and Northern S\u00e1mi.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finnish ModernBERT Tiny Short is a TurkuNLP ModernBERT encoder pretrained for languages used in Finland. The card lists 129M parameters, Apache-2.0, and training on LUMI. The public paper is 12 November 2025. Hub id TurkuNLP/finnish-modernbert-tiny-short. Status active. Collected 2026-10-15.", "what_it_is_for": "Finnish ModernBERT Tiny Short is a TurkuNLP ModernBERT encoder pretrained for languages used in Finland. The card lists 129M parameters, Apache-2.0, and training on LUMI. The public paper is 12 November 2025. Hub id TurkuNLP/finnish-modernbert-tiny-short. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/parser/turku-neural-parser": { "id": "turku/parser/turku-neural-parser", "name": "Turku Neural Parser Pipeline", "maker": "turku", "family": "turku/parser", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Neural pipeline for segmentation, morphological tagging, dependency parsing, and lemmatization. The project page says the June 2021 overhaul replaced Udify with diaparser and added a new tagger, and that the models are based on BERT. Language checkpoints ship as tar.gz archives. Parameter counts were not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Turku Neural Parser Pipeline is TurkuNLP's multilingual parser for segmentation, morphology, dependencies, and lemmas. It placed first on lemmatization and second on LAS and MLAS in the CoNLL 2018 shared task. The project page says that in May 2024 it was no longer maintained. License on the GitHub repository is Apache-2.0. Status retired. Collected 2026-10-15.", "what_it_is_for": "The Turku Neural Parser Pipeline is TurkuNLP's multilingual parser for segmentation, morphology, dependencies, and lemmas. It placed first on lemmatization and second on LAS and MLAS in the CoNLL 2018 shared task. The project page says that in May 2024 it was no longer maintained. License on the GitHub repository is Apache-2.0. Status retired.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/register/web-register-en": { "id": "turku/register/web-register-en", "name": "Web register classifier, English", "maker": "turku", "family": "turku/register", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Multi-label sequence classifier fine-tuned from FacebookAI/xlm-roberta-large on the Corpus of Online Registers of English. Same CORE label scheme as the multilingual model. Card training details match the multilingual write-up in form. Max length used in the example is 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The English web register classifier is TurkuNLP's XLM-RoBERTa large fine-tune on the Corpus of Online Registers of English. It uses the CORE taxonomy and is licensed Apache-2.0. Hub id TurkuNLP/web-register-classification-en. Status active. Collected 2026-10-15.", "what_it_is_for": "The English web register classifier is TurkuNLP's XLM-RoBERTa large fine-tune on the Corpus of Online Registers of English. It uses the CORE taxonomy and is licensed Apache-2.0. Hub id TurkuNLP/web-register-classification-en. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/register/web-register-multilingual": { "id": "turku/register/web-register-multilingual", "name": "Web register classifier, multilingual", "maker": "turku", "family": "turku/register", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Multi-label sequence classifier fine-tuned from FacebookAI/xlm-roberta-large. Card: batch size 8, 21 epochs, learning rate 0.00005, max size 512 tokens, bfloat16. Predicts the hierarchical CORE register taxonomy. An example snippet on the card uses the id TurkuNLP/multilingual-web-register-classification, which is not the repo name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The multilingual web register classifier is TurkuNLP's XLM-RoBERTa large fine-tune for the CORE register taxonomy. It is trained on English, Finnish, French, Swedish, and Turkish, and the card says it can be applied to the 100 languages of the base model. License Apache-2.0. Hub id TurkuNLP/web-register-classification-multilingual. Status active. Collected 2026-10-15.", "what_it_is_for": "The multilingual web register classifier is TurkuNLP's XLM-RoBERTa large fine-tune for the CORE register taxonomy. It is trained on English, Finnish, French, Swedish, and Turkish, and the card says it can be applied to the 100 languages of the base model. License Apache-2.0. Hub id TurkuNLP/web-register-classification-multilingual. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/register/web-register-multilingual-bge": { "id": "turku/register/web-register-multilingual-bge", "name": "Web register classifier, multilingual BGE", "maker": "turku", "family": "turku/register", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": 8194, "max_output_tokens": null, "parameters": "", "architecture": "Multi-label classifier fine-tuned from BAAI/bge-m3-retromae. Card: XLMRobertaForSequenceClassification, hidden 1024, 16 heads, 24 layers, intermediate 4096, vocab 250002, max position embeddings 8194, 25 output classes, bfloat16. The usage note says fine-tuning truncated inputs to 512 tokens. The repo ships no tokenizer files. Use the BGE-M3 tokenizer.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The multilingual BGE register classifier is TurkuNLP's CORE register model fine-tuned from BAAI/bge-m3-retromae instead of XLM-RoBERTa large. It covers the same 25 hierarchical labels and five training languages. License Apache-2.0. Hub id TurkuNLP/web-register-classification-multilingual-bge. Status active. Collected 2026-10-15.", "what_it_is_for": "The multilingual BGE register classifier is TurkuNLP's CORE register model fine-tuned from BAAI/bge-m3-retromae instead of XLM-RoBERTa large. It covers the same 25 hierarchical labels and five training languages. License Apache-2.0. Hub id TurkuNLP/web-register-classification-multilingual-bge. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/sbert/sbert-cased-finnish-paraphrase": { "id": "turku/sbert/sbert-cased-finnish-paraphrase", "name": "Finnish Sentence-BERT cased", "maker": "turku", "family": "turku/sbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "Sentence-BERT mean pooling on bert-base-finnish-cased-v1. Card architecture string: max_seq_length 128, do_lower_case False, word embedding dimension 768. Trained as binary paraphrase detection. Labels 3 and 4 count as paraphrases. Labels 1 and 2 do not.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finnish Sentence-BERT cased is TurkuNLP's Sentence-BERT for Finnish paraphrase similarity, pooled from bert-base-finnish-cased-v1. The card points at a demo over 400 million sentences. Hub id TurkuNLP/sbert-cased-finnish-paraphrase. Status active. A release day is not on the card. Collected 2026-10-15.", "what_it_is_for": "Finnish Sentence-BERT cased is TurkuNLP's Sentence-BERT for Finnish paraphrase similarity, pooled from bert-base-finnish-cased-v1. The card points at a demo over 400 million sentences. Hub id TurkuNLP/sbert-cased-finnish-paraphrase. Status active. A release day is not on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/sbert/sbert-uncased-finnish-paraphrase": { "id": "turku/sbert/sbert-uncased-finnish-paraphrase", "name": "Finnish Sentence-BERT uncased", "maker": "turku", "family": "turku/sbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "Sentence-BERT mean pooling on bert-base-finnish-uncased-v1. Card architecture string: max_seq_length 128, do_lower_case True, word embedding dimension 768. Trained as binary paraphrase detection. Labels 3 and 4 count as paraphrases. Labels 1 and 2 do not.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finnish Sentence-BERT uncased is TurkuNLP's Sentence-BERT for Finnish paraphrase similarity, pooled from bert-base-finnish-uncased-v1. The card points at a demo over 400 million sentences. Hub id TurkuNLP/sbert-uncased-finnish-paraphrase. Status active. A release day is not on the card. Collected 2026-10-15.", "what_it_is_for": "Finnish Sentence-BERT uncased is TurkuNLP's Sentence-BERT for Finnish paraphrase similarity, pooled from bert-base-finnish-uncased-v1. The card points at a demo over 400 million sentences. Hub id TurkuNLP/sbert-uncased-finnish-paraphrase. Status active. A release day is not on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/webqa/xlmr-qa-extraction-en": { "id": "turku/webqa/xlmr-qa-extraction-en", "name": "XLM-R QA extractor, English", "maker": "turku", "family": "turku/webqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Token classifier fine-tuned from xlm-roberta-base. Card: batch size 8, learning rate 5e-5, max sequence length 512, epochs set to 10 and trained for less. The card says this English extractor was fine-tuned on manually annotated Finnish data and ChatGPT-annotated data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XLM-R QA extractor, English is TurkuNLP's xlm-roberta-base token classifier for question and answer spans. The card says this English extractor was fine-tuned on manually annotated Finnish data and ChatGPT-annotated data. License CC-BY-NC-SA-4.0. Hub id TurkuNLP/xlmr-qa-extraction-en. Status active. Collected 2026-10-15.", "what_it_is_for": "XLM-R QA extractor, English is TurkuNLP's xlm-roberta-base token classifier for question and answer spans. The card says this English extractor was fine-tuned on manually annotated Finnish data and ChatGPT-annotated data. License CC-BY-NC-SA-4.0. Hub id TurkuNLP/xlmr-qa-extraction-en. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/webqa/xlmr-qa-extraction-fi": { "id": "turku/webqa/xlmr-qa-extraction-fi", "name": "XLM-R QA extractor, Finnish", "maker": "turku", "family": "turku/webqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Token classifier fine-tuned from xlm-roberta-base. Card: batch size 8, learning rate 1e-5, max sequence length 512, epochs set to 10 and trained for less. The card says this Finnish extractor was fine-tuned on manually annotated Finnish data, ChatGPT-annotated data, and a semi-synthetic set based on LFQA.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "XLM-R QA extractor, Finnish is TurkuNLP's xlm-roberta-base token classifier for question and answer spans. The card says this Finnish extractor was fine-tuned on manually annotated Finnish data, ChatGPT-annotated data, and a semi-synthetic set based on LFQA. License CC-BY-NC-SA-4.0. Hub id TurkuNLP/xlmr-qa-extraction-fi. Status active. Collected 2026-10-15.", "what_it_is_for": "XLM-R QA extractor, Finnish is TurkuNLP's xlm-roberta-base token classifier for question and answer spans. The card says this Finnish extractor was fine-tuned on manually annotated Finnish data, ChatGPT-annotated data, and a semi-synthetic set based on LFQA. License CC-BY-NC-SA-4.0. Hub id TurkuNLP/xlmr-qa-extraction-fi. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/webqa/xlmr-qa-register": { "id": "turku/webqa/xlmr-qa-register", "name": "XLM-R QA register classifier", "maker": "turku", "family": "turku/webqa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-sa-4.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Binary-style text classifier fine-tuned from xlm-roberta-base to decide whether a document is question-answer related. Card: batch size 8, learning rate 4e-6, max sequence length 512, epochs set to 10 and trained for less. Training text is English and Finnish CORE plus unpublished Swedish and French register data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The XLM-R QA register classifier is TurkuNLP's xlm-roberta-base fine-tune that flags documents likely to contain question-answer text. It is the first stage of the pipeline in the LREC-COLING 2024 WebQA paper. License CC-BY-SA-4.0. Hub id TurkuNLP/xlmr-qa-register. Status active. Collected 2026-10-15.", "what_it_is_for": "The XLM-R QA register classifier is TurkuNLP's xlm-roberta-base fine-tune that flags documents likely to contain question-answer text. It is the first stage of the pipeline in the LREC-COLING 2024 WebQA paper. License CC-BY-SA-4.0. Hub id TurkuNLP/xlmr-qa-register. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "turku/wikibert/wikibert": { "id": "turku/wikibert/wikibert", "name": "WikiBERT", "maker": "turku", "family": "turku/wikibert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-06-02", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Monolingual BERT base models trained from Wikipedia. The 2020 paper says 42 models. On 2026-10-15 the TurkuNLP Hub org listed 44 wikibert-base-*-cased repos. The Finnish config is BERT base with vocab 20101 and 512 positions. Other language configs were not read, so vocab size is not assumed to match.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WikiBERT is TurkuNLP's set of monolingual BERT base models built from Wikipedia. The June 2020 paper introduces the pipeline and says it released 42 models. The TurkuNLP Hub org listed 44 wikibert-base language repos on the collection date. Status active. Language checkpoints are versions, not separate folders. Collected 2026-10-15.", "what_it_is_for": "WikiBERT is TurkuNLP's set of monolingual BERT base models built from Wikipedia. The June 2020 paper introduces the pipeline and says it released 42 models. The TurkuNLP Hub org listed 44 wikibert-base language repos on the collection date. Status active. Language checkpoints are versions, not separate folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tuwien/lidarscout/lidarscout": { "id": "tuwien/lidarscout/lidarscout", "name": "LidarScout", "maker": "tuwien", "family": "tuwien/lidarscout", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT for the viewer and training code. The TU Wien data deposit lists Creative Commons Attribution 4.0 and MIT.", "release_date": "2025-09-24", "context_length": null, "max_output_tokens": null, "parameters": "2500000", "architecture": "Small CNN, U-Net-like and related to Neural Point-Based Graphics, that turns rough interpolated height and color into a cleaner patch. The paper says the input is two 96x96 heightmaps and two 96x96x3 RGB textures, one nearest-neighbor and one linear. Each input is encoded with convolutions, merged by fully connected layers, and decoded to a heightmap and a texture. 2.5 million learnable parameters, about 500 thousand more than the NPBG baseline they cite. A patch is 640 by 640 meters at 64 by 64 pixels, estimated from a 960 by 960 meter neighborhood. The viewer defaults are TorchScript files ipes_cnn.pt and ipes_cnn_rgb.pt.", "modalities": { "input": [ "heightmap", "rgb" ], "output": [ "heightmap", "rgb" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LidarScout is an out-of-core point-cloud viewer from the TU Wien Research Unit of Computer Graphics. The neural piece is a 2.5 million parameter CNN that cleans overview heightmaps and textures from a sparse sample of a huge LAZ scan. arXiv:2509.20198 is dated 2025-09-24. The work was presented at High-Performance Graphics 2025. Weights ship with the viewer defaults and in a TU Wien data deposit. This dossier was collected on 2026-10-15. Ablation networks in the same zip are not separate products.", "what_it_is_for": "The paper presents LidarScout as a way to open country-scale lidar, hundreds of billions of points, without building a level-of-detail tree. A sparse subsample draws an overview immediately. The CNN then fills holes in that overview while the user moves. Close zoom loads the real points and keeps the finished heightmaps as a medium level of detail. Code: https://github.com/cg-tuwien/lidarscout. Training: https://github.com/cg-tuwien/lidarscout_training. Data: https://doi.org/10.48436/443tr-j4096.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tuwien/model-vienna/model-vienna": { "id": "tuwien/model-vienna/model-vienna", "name": "Model_Vienna", "maker": "tuwien", "family": "tuwien/model-vienna", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-03-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PyTorch 3D submanifold sparse convolutional network (SparseConvNet / SparseCNN) for voxel-wise classification of airborne laser-scanning points. Zenodo states 7 layers, block repetition 2, residual blocks, and an 8 cm training voxel size. Inputs are XYZ plus echo number and number of echoes. Predictions are mapped back to points. Classes are ground (2), vegetation (5), building (6), and others (8).", "modalities": { "input": [ "als-point-cloud" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Model_Vienna is a pretrained 3D sparse convolutional network for urban airborne laser-scanning classification, released by the Department of Geodesy and Geoinformation at TU Wien. The Zenodo weights were posted on 2023-03-05 and were still public on 2026-10-15. It is a research checkpoint, not a priced API. Model_Vienna_xyz is a separately named sibling with different inputs and training tiles.", "what_it_is_for": "The Zenodo record describes Model_Vienna as a pretrained SparseConvNet that voxelizes an urban ALS cloud, classifies voxels, and maps the labels back to points. The setup is the SparseCNN compared with PointNet++ and KPConv in Li, K\u00e4hler, and Pfeifer, IEEE JSTARS 2021. Nan Li and Norbert Pfeifer are at TU Wien. Olaf K\u00e4hler, a coauthor of the paper, is at Siemens Austria. Training tiles are the 2016 Vienna ALS set at https://doi.org/10.5281/zenodo.4777087.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tuwien/model-vienna/model-vienna-xyz": { "id": "tuwien/model-vienna/model-vienna-xyz", "name": "Model_Vienna_xyz", "maker": "tuwien", "family": "tuwien/model-vienna", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-03-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Same PyTorch 3D SparseConvNet structure as Model_Vienna: 7 layers, block repetition 2, residual blocks, 8 cm voxels. Zenodo says this checkpoint takes only XYZ. It was trained on four adjacent city-center tiles of the Vienna ALS set, not the scattered tiles used for Model_Vienna.", "modalities": { "input": [ "als-point-cloud" ], "output": [ "class-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Model_Vienna_xyz is the XYZ-only SparseConvNet released with Model_Vienna by TU Wien's Department of Geodesy and Geoinformation. Both files were posted on the same Zenodo record on 2023-03-05 and were still listed on 2026-10-15. The authors treat it as its own named network, not a dated snapshot: it drops echo attributes and uses a different Vienna training split.", "what_it_is_for": "The Zenodo card says Model_Vienna_xyz classifies urban ALS points when only coordinates are available. Structure matches Model_Vienna. Training used four adjacent tiles in the Vienna city center. Model_Vienna used four tiles in different regions and also consumed echo counts. The comparison paper is Li, K\u00e4hler, and Pfeifer, IEEE JSTARS 2021.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tuwien/points2surf/points2surf": { "id": "tuwien/points2surf/points2surf", "name": "Points2Surf", "maker": "tuwien", "family": "tuwien/points2surf", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT (GitHub code and released checkpoints). ECCV paper is the Springer version of arXiv:2007.10453.", "release_date": "2020-07-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Patch-based signed-distance network. A query point is paired with a local point-cloud patch and a coarse global subsample. The ECCV follow-up paper describes each branch as a PointNet, similar to PCPNet. The SDF is meshed with marching cubes. The shipped vanilla checkpoint was trained for 150 epochs. The later max checkpoint is a smaller version of the same model, not a new product.", "modalities": { "input": [ "point-cloud" ], "output": [ "implicit-surface" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Points2Surf is a patch-based implicit surface model from the TU Wien Research Unit of Computer Graphics, with coauthors at Adobe Research, VRVis, and University College London. The arXiv preprint is dated 2020-07-20 and the work appeared at ECCV 2020. Pretrained weights are still linked from the GitHub repository and the TU Wien project page. This dossier was collected on 2026-10-15. PPSurf is the later TU Wien follow-up. The vanilla and max checkpoints are versions of one model. Ablation checkpoints are not.", "what_it_is_for": "The paper presents Points2Surf as a class-independent reconstructor for noisy or partial scans that does not need point normals. A local patch and a coarse global subsample condition a query point. The authors say this prior cuts reconstruction error by about 30% versus screened Poisson and by more than 270% versus the deep-learning baselines they compare, at the cost of runtime and some small-scale topological noise. Code and weights: https://github.com/ErlerPhilipp/points2surf. Project page: https://www.cg.tuwien.ac.at/research/publications/2020/erler-2020-p2s/.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "tuwien/ppsurf/ppsurf": { "id": "tuwien/ppsurf/ppsurf", "name": "PPSurf", "maker": "tuwien", "family": "tuwien/ppsurf", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT on the Hugging Face weight repo (perler/ppsurf). The arXiv paper is CC BY 4.0. A GitHub LICENSE file was not opened.", "release_date": "2024-01-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Occupancy network with a global POCO/FKAConv branch and a local PointNet branch. Global branch: 10 convolution layers, 16 neighbors, 10k subsampled points, 64 interpolation heads, latent size 128. Local branch: 50 nearest neighbors, PointNet latent size 256, attention aggregation instead of max or sum. Features are summed and decoded to occupancy. The surface is the 0.5 level set, meshed with POCO's region-growing marching cubes on a 257^3 grid. The released checkpoint is PPSurf Full, which the paper equates with 50NN. Trained 150 epochs with AdamW, learning rate 0.001, on the Points2Surf ABC variable-noise set.", "modalities": { "input": [ "point-cloud" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PPSurf is a point-cloud surface model from the TU Wien Research Unit of Computer Graphics, with coauthors at the University of Z\u00fcrich and Adobe Research. arXiv v1 is 2024-01-16, v2 is 2024-02-08, and the journal version is Computer Graphics Forum, January 2024. The shipped weights are the 50-neighbor checkpoint. This dossier was collected on 2026-10-15. It is the named follow-up to Points2Surf. Patch-size ablations are not separate products.", "what_it_is_for": "The paper presents PPSurf as a reconstructor that keeps a global point-convolution prior and a local patch prior, so noisy scans stay stable without washing out detail. The global branch is POCO-style FKAConv. The local branch is a PointNet with attention pooling over 50 neighbors. Code: https://github.com/cg-tuwien/ppsurf. A demo space is at https://huggingface.co/spaces/perler/ppsurf.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "twelve-labs/marengo/marengo-2-5": { "id": "twelve-labs/marengo/marengo-2-5", "name": "Marengo 2.5", "maker": "twelve-labs", "family": "twelve-labs/marengo", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marengo 2.5 by Twelve Labs. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Marengo 2.5 is a named Twelve Labs video embedding engine. Release notes record it in API 1.1.1. The Pegasus-1 post says it powered Search and Classify and dates the model to March 2023.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "twelve-labs/marengo/marengo-2-6": { "id": "twelve-labs/marengo/marengo-2-6", "name": "Marengo 2.6", "maker": "twelve-labs", "family": "twelve-labs/marengo", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Gated Modality Experts. Single embedding, not the multi-vector layout used by Marengo 2.7. Trained with contrastive loss.", "modalities": { "input": [ "text", "video", "image", "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marengo 2.6 by Twelve Labs. Status: deprecated. Collected 2026-10-15.", "what_it_is_for": "Marengo 2.6 is the any-to-any video embedding model Twelve Labs shipped in March 2024 and deprecated on 2025-06-13 in favor of Marengo 2.7.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "twelve-labs/marengo/marengo-2-7": { "id": "twelve-labs/marengo/marengo-2-7", "name": "Marengo 2.7", "maker": "twelve-labs", "family": "twelve-labs/marengo", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer multi-vector embedding model. Twelve Labs says it splits video into specialized vectors for appearance, motion, OCR text, and speech, unlike the single embedding used by Marengo 2.6. Output dimension on Bedrock is 1024.", "modalities": { "input": [ "video", "audio", "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marengo 2.7 by Twelve Labs. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Marengo 2.7 was Twelve Labs' multi-vector video embedding model for search and classification. The first-party API sunset it on 2026-03-30. Bedrock docs still describe it and say it will be deprecated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "twelve-labs/marengo/marengo-3-0": { "id": "twelve-labs/marengo/marengo-3-0", "name": "Marengo 3.0", "maker": "twelve-labs", "family": "twelve-labs/marengo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal video embedding model. Fixed 512-dimensional embeddings. Docs contrast it with Marengo 3.5: separate transcription embedding rather than one audio encoder, and no uncertainty vector.", "modalities": { "input": [ "video", "audio", "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marengo 3.0 by Twelve Labs. Status: active. Collected 2026-10-15.", "what_it_is_for": "Marengo 3.0 is the embedding model Twelve Labs still uses for platform search and indexing, and for Embed API calls that omit model_name. It also runs on Amazon Bedrock.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "twelve-labs/marengo/marengo-3-5": { "id": "twelve-labs/marengo/marengo-3-5", "name": "Marengo 3.5", "maker": "twelve-labs", "family": "twelve-labs/marengo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal embedding model with Matryoshka outputs at 128, 256, or 512 dimensions and a unified encoder for speech, music, and non-dialog audio. A per-dimension uncertainty vector is optional.", "modalities": { "input": [ "video", "audio", "image", "text", "document" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marengo 3.5 by Twelve Labs. Status: active. Collected 2026-10-15.", "what_it_is_for": "Twelve Labs says Marengo 3.5 creates embeddings from video, audio, images, text, and documents in one semantic space for similarity search, classification, clustering, recommendations, and RAG. It does not run the platform Search API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "twelve-labs/pegasus/pegasus-1-0": { "id": "twelve-labs/pegasus/pegasus-1-0", "name": "Pegasus 1.0", "maker": "twelve-labs", "family": "twelve-labs/pegasus", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-03-12", "context_length": null, "max_output_tokens": null, "parameters": "80 billion", "architecture": "Three jointly trained parts: a video encoder derived from Marengo 2.5, a video-language alignment model, and a language decoder. The 80 billion figure is from the Pegasus-1 announcement, which this dossier treats as the same generation as the Pegasus 1.0 beta named in the release notes.", "modalities": { "input": [ "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pegasus 1.0 by Twelve Labs. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Pegasus 1.0 is the first numbered Pegasus video-language model. Release notes call the 2024-03-12 ship a beta. The Pegasus-1 announcement describes an 80-billion-parameter model and the Video-to-Text APIs. They are filed together, with medium confidence that they are the same release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "twelve-labs/pegasus/pegasus-1-2": { "id": "twelve-labs/pegasus/pegasus-1-2", "name": "Pegasus 1.2", "maker": "twelve-labs", "family": "twelve-labs/pegasus", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-11", "context_length": null, "max_output_tokens": 4096, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pegasus 1.2 by Twelve Labs. Status: deprecated. Collected 2026-10-15.", "what_it_is_for": "Pegasus 1.2 is the 2025 video-language model Twelve Labs removed from its own API on 2026-08-18. Amazon Bedrock still listed it as Active when the model card was read on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "twelve-labs/pegasus/pegasus-1-5": { "id": "twelve-labs/pegasus/pegasus-1-5", "name": "Pegasus 1.5", "maker": "twelve-labs", "family": "twelve-labs/pegasus", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-20", "context_length": 261120, "max_output_tokens": 98304, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pegasus 1.5 by Twelve Labs. Status: active. Collected 2026-10-15.", "what_it_is_for": "Pegasus 1.5 is the default Twelve Labs video-to-text model. It writes summaries, descriptions, answers, and timestamped JSON from video without a prior index, and it is the batch-analysis model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "twelve-labs/pegasus/pegasus-1-6": { "id": "twelve-labs/pegasus/pegasus-1-6", "name": "Pegasus 1.6", "maker": "twelve-labs", "family": "twelve-labs/pegasus", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-06", "context_length": 261120, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pegasus 1.6 by Twelve Labs. Status: active. Collected 2026-10-15.", "what_it_is_for": "Pegasus 1.6 is the current Twelve Labs video-to-text model aimed at first-person and robotics footage, plus still images. It is opt-in. The default analyze model remains Pegasus 1.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "twelve-labs/pegasus/pegasus-alpha": { "id": "twelve-labs/pegasus/pegasus-alpha", "name": "Pegasus alpha", "maker": "twelve-labs", "family": "twelve-labs/pegasus", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-10-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pegasus alpha by Twelve Labs. Status: retired. Collected 2026-10-15.", "what_it_is_for": "Pegasus alpha is the first Pegasus engine Twelve Labs named in release notes, shipped with API 1.2 on 2023-10-30 for video analysis. It is not the later Pegasus 1.0 beta.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/asagi/asagi-14b": { "id": "u-tokyo/asagi/asagi-14b", "name": "Asagi-14B", "maker": "u-tokyo", "family": "u-tokyo/asagi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-25", "context_length": null, "max_output_tokens": null, "parameters": "14.2B", "architecture": "LLaVA. siglip-so400m-patch14-384, 2-layer MLP, llm-jp-3-13b-instruct. Project page total 14.2B.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Asagi-14B is a Japanese vision-language model from the Machine Intelligence Laboratory at the University of Tokyo Research Center for Advanced Science and Technology (Harada laboratory), released with RIKEN collaborators on the project page. It is a LLaVA-style model: SigLIP, a 2-layer linear projector, and llm-jp-3-13b-instruct. Total size on the project page is 14.2B. Apache-2.0 on the Hub license tag. The public announcement is 2025-02-25. Collected 2026-10-15.", "what_it_is_for": "The lab built an open Japanese VLM without using outputs from models whose licenses restrict reuse, such as GPT-4. Synthetic captions come from Phi-3.5-vision-instruct and CALM3-22B-Chat plus existing and translated datasets. The project page says about 20 million image-text items in total.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/asagi/asagi-2b": { "id": "u-tokyo/asagi/asagi-2b", "name": "Asagi-2B", "maker": "u-tokyo", "family": "u-tokyo/asagi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-25", "context_length": null, "max_output_tokens": null, "parameters": "2.3B", "architecture": "LLaVA. siglip-so400m-patch14-384, 2-layer MLP, llm-jp-3-1.8b-instruct. Project page total 2.3B.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Asagi-2B is a Japanese vision-language model from the Machine Intelligence Laboratory at the University of Tokyo Research Center for Advanced Science and Technology (Harada laboratory), released with RIKEN collaborators on the project page. It is a LLaVA-style model: SigLIP, a 2-layer linear projector, and llm-jp-3-1.8b-instruct. Total size on the project page is 2.3B. Apache-2.0 on the Hub license tag. The public announcement is 2025-02-25. Collected 2026-10-15.", "what_it_is_for": "The lab built an open Japanese VLM without using outputs from models whose licenses restrict reuse, such as GPT-4. Synthetic captions come from Phi-3.5-vision-instruct and CALM3-22B-Chat plus existing and translated datasets. The project page says about 20 million image-text items in total.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/asagi/asagi-4b": { "id": "u-tokyo/asagi/asagi-4b", "name": "Asagi-4B", "maker": "u-tokyo", "family": "u-tokyo/asagi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-25", "context_length": null, "max_output_tokens": null, "parameters": "4.2B", "architecture": "LLaVA. siglip-so400m-patch14-384, 2-layer MLP, llm-jp-3-3.7b-instruct. Project page total 4.2B.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Asagi-4B is a Japanese vision-language model from the Machine Intelligence Laboratory at the University of Tokyo Research Center for Advanced Science and Technology (Harada laboratory), released with RIKEN collaborators on the project page. It is a LLaVA-style model: SigLIP, a 2-layer linear projector, and llm-jp-3-3.7b-instruct. Total size on the project page is 4.2B. Apache-2.0 on the Hub license tag. The public announcement is 2025-02-25. Collected 2026-10-15.", "what_it_is_for": "The lab built an open Japanese VLM without using outputs from models whose licenses restrict reuse, such as GPT-4. Synthetic captions come from Phi-3.5-vision-instruct and CALM3-22B-Chat plus existing and translated datasets. The project page says about 20 million image-text items in total.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/asagi/asagi-8b": { "id": "u-tokyo/asagi/asagi-8b", "name": "Asagi-8B", "maker": "u-tokyo", "family": "u-tokyo/asagi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-25", "context_length": null, "max_output_tokens": null, "parameters": "7.8B", "architecture": "LLaVA. siglip-so400m-patch14-384, 2-layer MLP, llm-jp-3-7.2b-instruct. Project page total 7.8B.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Asagi-8B is a Japanese vision-language model from the Machine Intelligence Laboratory at the University of Tokyo Research Center for Advanced Science and Technology (Harada laboratory), released with RIKEN collaborators on the project page. It is a LLaVA-style model: SigLIP, a 2-layer linear projector, and llm-jp-3-7.2b-instruct. Total size on the project page is 7.8B. Apache-2.0 on the Hub license tag. The public announcement is 2025-02-25. Collected 2026-10-15.", "what_it_is_for": "The lab built an open Japanese VLM without using outputs from models whose licenses restrict reuse, such as GPT-4. Synthetic captions come from Phi-3.5-vision-instruct and CALM3-22B-Chat plus existing and translated datasets. The project page says about 20 million image-text items in total.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/asclelm/asclelm-1-10b": { "id": "u-tokyo/asclelm/asclelm-1-10b", "name": "AscleLM-1-10B", "maker": "u-tokyo", "family": "u-tokyo/asclelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research-only Terms of Use for weights (no redistribution, no commercial use). Tokenizer and modeling code Apache-2.0.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "10B dense", "architecture": "From-scratch Mamba-2 hybrid based on NVIDIA Nemotron-H / Transformers Mamba-2 code. Qwen3 tokenizer. Released as a base checkpoint with known repetition.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AscleLM-1-10B is a 10 billion parameter Japanese and English medical language model from the University of Tokyo Matsuo-Iwasawa Laboratory. The card says it was pretrained and post-trained from scratch, not fine-tuned from an existing LLM. The architecture is a Mamba-2 hybrid based on NVIDIA Nemotron-H code. The tokenizer is from Qwen3. Weights are gated, research-only, and not redistributable. The released checkpoint is still a base model. Collected 2026-10-15.", "what_it_is_for": "The lab describes a from-scratch Japanese medical model for research. It is not for commercial services, redistribution, or real clinical decisions. The card says end-of-sequence behavior is unstable and the model can repeat. Supervised fine-tuning and reinforcement learning are planned and were not part of this release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/bert/bert-base-japanese-fin": { "id": "u-tokyo/bert/bert-base-japanese-fin", "name": "BERT-base Japanese finance", "maker": "u-tokyo", "family": "u-tokyo/bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForPreTraining continued from cl-tohoku/bert-base-japanese. 12 layers, hidden 768, intermediate 3072, 12 heads, max position 512, vocab 32000. Card also says 'BERT small' in the same architecture sentence. GitHub from-scratch BERT-base embedding size 512 was not applied.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERT-base Japanese finance is additional pretraining of Tohoku University's cl-tohoku/bert-base-japanese on a financial corpus, released by the Izumi Laboratory as izumi-lab/bert-base-japanese-fin-additional. The card's architecture sentence says \"BERT small\" and then gives base-sized widths. Weights are CC BY-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint is for Japanese financial text, starting from Tohoku BERT-base and continuing on earnings summaries and securities reports.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/bert/bert-small-japanese": { "id": "u-tokyo/bert/bert-small-japanese", "name": "BERT-small Japanese", "maker": "u-tokyo", "family": "u-tokyo/bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. 12 layers, hidden 256, intermediate 1024, 4 heads, max position 128, vocab 32768. Card cites BERT-small in the ELECTRA paper. GitHub table lists embedding size 128 and 1.45M steps. config.json has no embedding_size.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERT-small Japanese is a masked language model from the Izumi Laboratory at the University of Tokyo. The checkpoint is izumi-lab/bert-small-japanese. The card body trains it on Japanese Wikipedia as of 2021-06-01. The card heading says \"finance\", which the training-data section does not support. Weights are CC BY-SA 4.0 and still public. Collected 2026-10-15.", "what_it_is_for": "The lab releases it as a general Japanese BERT-small for fill-mask and downstream classification. The financial sibling is a separate named checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/bert/bert-small-japanese-fin": { "id": "u-tokyo/bert/bert-small-japanese-fin", "name": "BERT-small Japanese finance", "maker": "u-tokyo", "family": "u-tokyo/bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. 12 layers, hidden 256, intermediate 1024, 4 heads, max position 128, vocab 32768. Wikipedia plus financial corpus. Card: 1.45M steps. GitHub table embedding size 128 is not in config.json.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERT-small Japanese finance is the Izumi Laboratory financial BERT-small, repo izumi-lab/bert-small-japanese-fin. The card trains it on Japanese Wikipedia as of 2021-06-01 plus a financial corpus of earnings summaries and securities reports. Weights are CC BY-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "The card presents it for Japanese financial text. The cited paper studies domain-specific pretraining for financial text mining. It is not a clinical or trading system.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/deberta/deberta-v2-base-japanese": { "id": "u-tokyo/deberta/deberta-v2-base-japanese", "name": "DeBERTa V2 base Japanese", "maker": "u-tokyo", "family": "u-tokyo/deberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "DebertaV2ForMaskedLM. 12 layers, hidden 768, intermediate 3072, 12 heads, relative attention, position buckets 256, vocab 32000, FP16. Card learning rate 2.4e-4 (6e-5 in parentheses for financial additional pretraining), batch 2016, 1,000,000 steps, warmup 100,000.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeBERTa V2 base Japanese is a masked language model from the Izumi Laboratory at the University of Tokyo, repo izumi-lab/deberta-v2-base-japanese. It is pretrained on Japanese CC-100, mC4, OSCAR, Wikipedia, and Wikinews. The card also prints a second learning rate for financial additional pretraining. No separate financial DeBERTa repo was on the lab org. Weights are CC BY-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "The card releases a general Japanese DeBERTa V2 base for masked language modeling and NLU fine-tuning. The 2024 FinDeBERTaV2 paper says general and financial models were released on the same Hugging Face org. Only this base and the small sibling were public there on the collection date.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/deberta/deberta-v2-small-japanese": { "id": "u-tokyo/deberta/deberta-v2-small-japanese", "name": "DeBERTa V2 small Japanese", "maker": "u-tokyo", "family": "u-tokyo/deberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "DebertaV2ForMaskedLM. 12 layers, hidden 256, intermediate 1024, 4 heads, relative attention, position buckets 256, vocab 32000, bfloat16. Card learning rate 6e-4, batch 2016, max sequence 128, 1,000,000 steps, warmup 100,000, BF16.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeBERTa V2 small Japanese is the smaller Izumi Laboratory DeBERTa V2, repo izumi-lab/deberta-v2-small-japanese. It uses the same Japanese corpora as the base card and a shorter sequence. Weights are CC BY-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "The card releases a general Japanese DeBERTa V2 small for masked language modeling. Its JGLUE table also reports the lab's BERT-small as a comparator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/electra/electra-base-japanese": { "id": "u-tokyo/electra/electra-base-japanese", "name": "ELECTRA-base Japanese", "maker": "u-tokyo", "family": "u-tokyo/electra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "ElectraForPreTraining. Card: 12 layers, hidden 768, 12 heads, generator 1/3, 512 tokens per instance, 256 instances, 766k steps. Config embedding_size 768, intermediate 3072, max_position_embeddings 512, vocab 32768. The GitHub architecture table lists ELECTRA-base embedding size 128. Those two embedding sizes disagree. Both are stored. The card does not print an embedding size. It says the architecture matches the original paper's ELECTRA-base.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELECTRA-base Japanese is an ELECTRA discriminator from the Izumi Laboratory at the University of Tokyo, repo izumi-lab/electra-base-japanese-discriminator. The paired generator is a version, not a separate product. Weights are CC BY-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "The lab releases the discriminator for Japanese pretraining features and downstream tasks. Replaced-token detection is the pretraining head. The generator is the fill-mask partner used in ELECTRA training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/electra/electra-small-japanese": { "id": "u-tokyo/electra/electra-small-japanese", "name": "ELECTRA-small Japanese", "maker": "u-tokyo", "family": "u-tokyo/electra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "ElectraForPreTraining. Card: same as ELECTRA-small in the Google implementation, 12 layers, hidden 256, 4 heads. Config embedding_size 128, intermediate 1024, max_position_embeddings 512, vocab 32768. Card training length is 128 tokens, not 512. Generator size is the same as the discriminator. GitHub table lists generator size 1/1 and embedding 128.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELECTRA-small Japanese is an ELECTRA discriminator from the Izumi Laboratory at the University of Tokyo, repo izumi-lab/electra-small-japanese-discriminator. The paired generator is a version, not a separate product. Weights are CC BY-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "The lab releases the discriminator for Japanese pretraining features and downstream tasks. Replaced-token detection is the pretraining head. The generator is the fill-mask partner used in ELECTRA training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/electra/electra-small-japanese-fin": { "id": "u-tokyo/electra/electra-small-japanese-fin", "name": "ELECTRA-small Japanese finance", "maker": "u-tokyo", "family": "u-tokyo/electra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "ElectraForPreTraining. Card: same as ELECTRA-small in the Google implementation, 12 layers, hidden 256, 4 heads. Config embedding_size 128, intermediate 1024, max_position_embeddings 512, vocab 32768. Training length on the card is 128 tokens. Generator size matches the discriminator. Data are Japanese Wikipedia as of 2021-06-01 plus the financial corpus (5.2GB, about 27M sentences).", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELECTRA-small Japanese finance is an ELECTRA discriminator from the Izumi Laboratory at the University of Tokyo, repo izumi-lab/electra-small-japanese-fin-discriminator. The paired generator is a version, not a separate product. Weights are CC BY-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "The lab releases the discriminator for Japanese pretraining features and downstream tasks. Replaced-token detection is the pretraining head. The generator is the fill-mask partner used in ELECTRA training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/electra/electra-small-paper-japanese": { "id": "u-tokyo/electra/electra-small-paper-japanese", "name": "ELECTRA-small-paper Japanese", "maker": "u-tokyo", "family": "u-tokyo/electra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "ElectraForPreTraining. Card: same as ELECTRA-small in the original ELECTRA paper, 12 layers, hidden 256, 4 heads, generator 1/4 of the discriminator. Config embedding_size 128, intermediate 1024, max_position_embeddings 512, vocab 32768. Card training length is 128 tokens. The card H1 says ELECTRA small Japanese discriminator and does not say paper. The repo id and the paper-architecture sentence identify this checkpoint.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELECTRA-small-paper Japanese is an ELECTRA discriminator from the Izumi Laboratory at the University of Tokyo, repo izumi-lab/electra-small-paper-japanese-discriminator. The paired generator is a version, not a separate product. Weights are CC BY-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "The lab releases the discriminator for Japanese pretraining features and downstream tasks. Replaced-token detection is the pretraining head. The generator is the fill-mask partner used in ELECTRA training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/electra/electra-small-paper-japanese-fin": { "id": "u-tokyo/electra/electra-small-paper-japanese-fin", "name": "ELECTRA-small-paper Japanese finance", "maker": "u-tokyo", "family": "u-tokyo/electra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "ElectraForPreTraining. Card: same as ELECTRA-small in the original ELECTRA paper, 12 layers, hidden 256, 4 heads. Config embedding_size 128, intermediate 1024, max_position_embeddings 512, vocab 32768. Training length on this card is 128 tokens. This financial card does not restate a generator fraction. The general small-paper card says 1/4, and the GitHub table also lists 1/4. Those two sources are not copied in as this checkpoint's measured fraction.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ELECTRA-small-paper Japanese finance is an ELECTRA discriminator from the Izumi Laboratory at the University of Tokyo, repo izumi-lab/electra-small-paper-japanese-fin-discriminator. The paired generator is a version, not a separate product. Weights are CC BY-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "The lab releases the discriminator for Japanese pretraining features and downstream tasks. Replaced-token detection is the pretraining head. The generator is the fill-mask partner used in ELECTRA training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/mangalmm/mangalmm": { "id": "u-tokyo/mangalmm/mangalmm", "name": "MangaLMM", "maker": "u-tokyo", "family": "u-tokyo/mangalmm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-05-16", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Qwen2.5-VL. config _name_or_path is Qwen/Qwen2.5-VL-7B-Instruct. 28 layers, hidden 3584, 28 heads, 4 KV heads. Hub size label says 8B.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MangaLMM is a manga vision-language model from the Aizawa laboratory (HAL) at the University of Tokyo. The model card says it is the model in the paper MangaVQA and MangaLMM. The config's `_name_or_path` is Qwen/Qwen2.5-VL-7B-Instruct. The Hub size label says 8B. License MIT. Collected 2026-10-15.", "what_it_is_for": "The paper abstract says MangaLMM is fine-tuned from Qwen2.5-VL to do in-page manga text recognition (MangaOCR) and contextual visual question answering (MangaVQA, 526 hand-built pairs). The stated use is helping manga creators inspect stories, and measuring how well multimodal models read manga.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/med-asagi/med-asagi-14b": { "id": "u-tokyo/med-asagi/med-asagi-14b", "name": "Med-Asagi-14B", "maker": "u-tokyo", "family": "u-tokyo/med-asagi", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2026-03-10", "context_length": null, "max_output_tokens": null, "parameters": "14200937920", "architecture": "LLaVA. siglip2-so400m-patch16-512 (428772800), 2-layer MLP (64240640), SIP-jmed-llm-3-13b-OP-4k-base (13707924480). Reasoning beta.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Med-Asagi-14B is a Japanese medical vision-language model from the University of Tokyo Machine Intelligence Laboratory, with RIKEN coauthors on the March 2026 press. The only public checkpoint found is Med-Asagi-14B-reasoning_beta. The card calls it a prototype. It emits description and thinking tags before an answer. License CC BY-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "The card says it is for Japanese medical image question answering and research on report-style reading, not for clinical decisions. It was trained to show reasoning. The published weights are the reasoning model, not the non-reasoning ablation in the tables.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/stormy/stormy-7b": { "id": "u-tokyo/stormy/stormy-7b", "name": "Stormy-7B", "maker": "u-tokyo", "family": "u-tokyo/stormy", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2023-05-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA r=4 on query_key_value of cyberagent/open-calm-7b. 10 epochs, batch 128, cutoff 300, learning rate 3e-4.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Stormy-7B is a named LoRA from the Izumi Laboratory at the University of Tokyo. The checkpoint stormy-7b-10ep adapts cyberagent/open-calm-7b. It is not a full weight dump. License CC BY-SA 4.0. A demo space is linked from the card. Collected 2026-10-15.", "what_it_is_for": "The card says the adapter was fit on a subset extracted from llm-japanese-dataset. It points to a demo and to llm.msuzuki.me for later information. The base model remains CyberAgent OpenCALM-7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/tanuki/tanuki-8b": { "id": "u-tokyo/tanuki/tanuki-8b", "name": "Tanuki-8B", "maker": "u-tokyo", "family": "u-tokyo/tanuki", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-30", "context_length": 8192, "max_output_tokens": null, "parameters": "about 8B", "architecture": "Llama-like dense model. 32 layers, hidden 4096, intermediate 14336, 32 heads, 8 KV heads, rope theta 500000, vocab 65024. Pretrained from scratch on about 1.3T tokens, then SFT and DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tanuki-8B is an about 8 billion parameter language model trained from scratch by volunteers in the Matsuo-Iwasawa Laboratory GENIAC project at the University of Tokyo. The released checkpoint is Tanuki-8B-dpo-v1.0, a dialogue model after supervised fine-tuning and DPO. It is Apache-2.0 and still public. Collected 2026-10-15.", "what_it_is_for": "The lab presents Tanuki as a Japanese-first from-scratch model for dialogue and writing. Tanuki-8B is the dense model. Tanuki-8x8B upcycles that line into a mixture of experts. The card says the model was trained only with one default Japanese system prompt, and recommends keeping that prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/tanuki/tanuki-8b-vision": { "id": "u-tokyo/tanuki/tanuki-8b-vision", "name": "Tanuki-8B-vision", "maker": "u-tokyo", "family": "u-tokyo/tanuki", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-16", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "LLaVA 1.5. Language model is phase-1 Tanuki-8B (weblab-GENIAC/team_hatakeyama_phase1). Vision encoder google/siglip-so400m-patch14-384.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tanuki-8B-vision is an experimental Japanese vision-language model released by the GENIAC volunteer team under the University of Tokyo Matsuo-Iwasawa Laboratory. It follows LLaVA 1.5 and uses a phase-1 Tanuki-8B language model with SigLIP. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says the vision models were not a main GENIAC goal. Volunteers built an 8 billion parameter VLM and a 50 billion parameter experimental VLM because commercially usable Japanese image-text data are scarce. They used synthetic data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/tanuki/tanuki-8x8b": { "id": "u-tokyo/tanuki/tanuki-8x8b", "name": "Tanuki-8x8B", "maker": "u-tokyo", "family": "u-tokyo/tanuki", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-30", "context_length": 4096, "max_output_tokens": null, "parameters": "about 47B total, about 13B active", "architecture": "Upcycled MoE. TanukiForCausalLM, 8 local experts, 2 experts per token, 32 layers, hidden 4096, intermediate 14336, 32 heads, 8 KV heads, vocab 65024. About 1.7T pretraining tokens, then SFT and DPO.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tanuki-8x8B is a from-scratch mixture-of-experts language model from the University of Tokyo Matsuo-Iwasawa Laboratory GENIAC project. The card states about 47 billion total parameters and about 13 billion active parameters. The released dialogue checkpoint is Tanuki-8x8B-dpo-v1.0 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The press release says the model is for Japanese dialogue and writing, built by copying a phase-1 8B model into eight experts and continuing training (upcycling). It reports Japanese MT-Bench at least matching GPT-3.5 Turbo. The GPT-3.5 number itself is not printed in the release text that was read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/tanuki/tanuki-8x8b-vision-exp": { "id": "u-tokyo/tanuki/tanuki-8x8b-vision-exp", "name": "Tanuki-8x8B-vision-exp", "maker": "u-tokyo", "family": "u-tokyo/tanuki", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-16", "context_length": null, "max_output_tokens": null, "parameters": "50B", "architecture": "Experimental LLaVA-style VLM. The sibling card states 50 billion parameters. Custom code. Component checkpoint ids were not printed on that card.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tanuki-8x8B-vision-exp is the larger experimental vision-language model named beside Tanuki-8B-vision by the University of Tokyo GENIAC volunteers. The 8B vision card calls it a 50 billion parameter model. The name and the \"exp\" suffix mark it as an experiment. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The 8B vision card says volunteers built this checkpoint while synthesizing Japanese image-text data. It was not a primary GENIAC goal. The card tags the repo `llava-jp` and `custom_code`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/weblab/weblab-10b": { "id": "u-tokyo/weblab/weblab-10b", "name": "Weblab-10B", "maker": "u-tokyo", "family": "u-tokyo/weblab", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-08-18", "context_length": 2048, "max_output_tokens": null, "parameters": "10B", "architecture": "GPT-NeoX, 36 layers, hidden size 4864, 38 heads, intermediate size 19456, rotary_pct 0.25. About 600B tokens from Japanese C4 and The Pile.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Weblab-10B is a 10 billion parameter Japanese-centric GPT-NeoX language model from the Matsuo Laboratory at the University of Tokyo (now the Matsuo-Iwasawa Laboratory). The pretrained checkpoint and an instruction-tuned checkpoint were released for research under CC BY-NC 4.0. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "The August 2023 press release describes a Japanese-English model meant to raise Japanese accuracy by training on both Japanese and English text. The model card calls it a Japanese-centric multilingual model. Commercial use is not allowed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/weblab-medllm/weblab-medllm-glm-4-7": { "id": "u-tokyo/weblab-medllm/weblab-medllm-glm-4-7", "name": "Weblab-MedLLM-GLM-4.7", "maker": "u-tokyo", "family": "u-tokyo/weblab-medllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 202752, "max_output_tokens": null, "parameters": "355B MoE", "architecture": "Continued pretraining and post-training of GLM-4.7. Card states about 198K context and a 64K recommended serve length.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Weblab-MedLLM-GLM-4.7 is a Japanese medical mixture-of-experts model from the University of Tokyo Matsuo-Iwasawa Laboratory. The card says 355B parameters and names GLM-4.7 as the base. Continued pretraining and post-training use synthetic medical text. License is MIT. Card version 1.0.0, release month June 2026. Collected 2026-10-15.", "what_it_is_for": "Same NEDO medical-administration project as the other Weblab-MedLLM releases. Not for direct diagnosis or treatment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/weblab-medllm/weblab-medllm-gpt-oss-120b": { "id": "u-tokyo/weblab-medllm/weblab-medllm-gpt-oss-120b", "name": "Weblab-MedLLM-gpt-oss-120b", "maker": "u-tokyo", "family": "u-tokyo/weblab-medllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "120B MoE", "architecture": "Continued pretraining and post-training of gpt-oss-120b. Card states 64 attention heads as a vLLM constraint. BF16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Weblab-MedLLM-gpt-oss-120b is a Japanese medical language model from the Matsuo-Iwasawa Laboratory at the University of Tokyo. It continues pretraining and post-training of gpt-oss-120b on synthetic data drawn from medical papers, textbooks, guidelines, and exam items. The card is version 1.0.0 and says the release month is June 2026. The university press that lists the Hugging Face publication is dated 2026-07-08. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The NEDO project is about a Japanese medical LLM that can support administrative and information tasks. The card says not to use it directly for diagnosis or clinical decisions. It is an information aid for clinicians, not a medical device by itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/weblab-medllm/weblab-medllm-qwen-2-5-109b-instruct": { "id": "u-tokyo/weblab-medllm/weblab-medllm-qwen-2-5-109b-instruct", "name": "Weblab-MedLLM-Qwen-2.5-109B-Instruct", "maker": "u-tokyo", "family": "u-tokyo/weblab-medllm", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-05", "context_length": null, "max_output_tokens": null, "parameters": "109B", "architecture": "Upcycle of Qwen-2.5-72B-Instruct plus continued pretraining and instruction tuning on a Japanese medical corpus. Hosted, not weight-released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Weblab-MedLLM-Qwen-2.5-109B-Instruct is a closed Japanese medical model announced by the University of Tokyo Matsuo-Iwasawa Laboratory on 2026-03-05. It upcycles Qwen-2.5-72B-Instruct and continues training on medical text. Weights were not published. A researcher chat on Sakura Internet's AI Engine was offered from 2026-03-05 through 2026-08-31. That window had ended by the collection date. Collected 2026-10-15.", "what_it_is_for": "The lab offered it so researchers could check medical-exam and medical-knowledge questions. The notice says it cannot be used for diagnosis, clinical care, or treatment. The work was funded by SIP JPJ012425.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/weblab-medllm/weblab-medllm-qwen3-235b-instruct": { "id": "u-tokyo/weblab-medllm/weblab-medllm-qwen3-235b-instruct", "name": "Weblab-MedLLM-Qwen3-235B-Instruct", "maker": "u-tokyo", "family": "u-tokyo/weblab-medllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 262144, "max_output_tokens": null, "parameters": "235B MoE", "architecture": "Continued pretraining and post-training of Qwen3-235B-A22B-Instruct-2507. About 470 GB BF16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Weblab-MedLLM-Qwen3-235B-Instruct is a Japanese medical model from the University of Tokyo Matsuo-Iwasawa Laboratory. The card says 235B MoE and names the base as Qwen3-235B-A22B-Instruct-2507. Training is continued pretraining and post-training on synthetic medical text. Apache-2.0. Card version 1.0.0, release month June 2026. Collected 2026-10-15.", "what_it_is_for": "NEDO Japanese medical LLM for information support. The card forbids direct clinical use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "u-tokyo/weblab-medllm/weblab-medllm-qwen3-235b-thinking": { "id": "u-tokyo/weblab-medllm/weblab-medllm-qwen3-235b-thinking", "name": "Weblab-MedLLM-Qwen3-235B-Thinking", "maker": "u-tokyo", "family": "u-tokyo/weblab-medllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 262144, "max_output_tokens": null, "parameters": "235B MoE", "architecture": "Continued pretraining and post-training of Qwen3-235B-A22B-Thinking-2507. Reasoning tokens may be emitted.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Weblab-MedLLM-Qwen3-235B-Thinking is a Japanese medical model from the University of Tokyo Matsuo-Iwasawa Laboratory. The card says 235B MoE and names the base as Qwen3-235B-A22B-Thinking-2507. Training is continued pretraining and post-training on synthetic medical text. Apache-2.0. Card version 1.0.0, release month June 2026. Collected 2026-10-15.", "what_it_is_for": "NEDO Japanese medical LLM for information support. The card forbids direct clinical use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uabirmingham/proteinrediff/proteinrediff": { "id": "uabirmingham/proteinrediff/proteinrediff", "name": "ProteinReDiff", "maker": "uabirmingham", "family": "uabirmingham/proteinrediff", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT for the software, copyright (c) 2023 ndn, in the GitHub LICENSE. The Structural Dynamics article is CC BY 4.0. A separate weights licence is not stated.", "release_date": "2024-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Equivariant diffusion model for ligand-binding protein redesign. Inputs are an amino-acid sequence and a ligand SMILES string. The noise predictor featurizes the sequence with frozen ESM-2 (650 million parameters), updates single and pair features with single representation attention, an outer-product update, and triangle multiplicative updates, then applies an SE(3)-equivariant denoiser. The paper loss is L = L_WS + L_KL + L_CE. Outputs can be a redesigned sequence, a C-alpha backbone, and a ligand 3D pose. The README command that reproduces the paper uses single_dim 512, pair_dim 64, num_blocks 4, mask_prob 0.15, num_steps 2000, and batch_size 2. Those widths are not a published parameter count. ESM-2 is a Meta featurizer, not this model. Code paths are adapted from DPL, OpenFold, AlphaFold, ESM, and EquiBind. The released file named in the README is checkpoints/PRD_ver1.ckpt.", "modalities": { "input": [ "protein-sequence", "ligand-smiles" ], "output": [ "protein-sequence", "protein-structure", "ligand-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProteinReDiff redesigns ligand-binding proteins from an amino-acid sequence and a ligand SMILES string. Viet Thanh Duy Nguyen (FPT Software AI Center) and Nhan D. Nguyen (University of Chicago) are the other authors. Truong Son Hy, Department of Computer Science, University of Alabama at Birmingham, is the corresponding author (thy@uab.edu). Structural Dynamics published the article online on 25 November 2024 (volume 11, issue 6, 064102; received 11 August 2024, accepted 6 November 2024). Code and a named checkpoint are at https://github.com/HySonLab/Protein_Redesign. UAB News described the work as UAB-led on 21 January 2025. Collected 2026-10-15. Status is active.", "what_it_is_for": "The paper uses an equivariant diffusion process to redesign a protein around a ligand without a predefined binding pocket. Amino acids can be stochastically masked. The model denoises in sequence space and structure space and can emit a new sequence, a C-alpha backbone, and a ligand pose. Blind docking is the authors' term for predicting the complex from sequence and SMILES alone. Training mixes PDBBind v2020 complexes (9,430 / 552 / 207 train / validation / test) with CATH 4.2 proteins under 400 residues (15,261 train, 939 validation, no CATH test). CATH ligands are stored as the mask token `*`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ualberta/action2motion/action2motion": { "id": "ualberta/action2motion/action2motion", "name": "Action2Motion", "maker": "ualberta", "family": "ualberta/action2motion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (code, copyright 2022 Chuan Guo). Pretrained checkpoints ship inside checkpoints/vae. The license text grants rights in the software and documentation and does not name the weight files separately.", "release_date": "2020-07-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Action-label-conditioned temporal VAE on a Lie-algebra pose representation. The appendix says encoder layers output 128, the decoder layer outputs 72, the generator is a two-layer GRU, the prior and posterior are one-layer GRUs with hidden size 128, and the noise vector is 30-dimensional. It says the learned model has about 450,000 parameters. Released Lie play names are vanilla_vae_lie_mse_kld001 on HumanAct12 and vanilla_vae_lie_mse_kld01 on NTU-RGB-D and CMU MoCap. The README also documents non-Lie play names vanila_vae_tf and vanila_vae_tf_2. The paper treats the non-Lie run as an ablation.", "modalities": { "input": [ "action-label" ], "output": [ "motion" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Alberta released Action2Motion, the ACM Multimedia 2020 action-conditioned motion model by Chuan Guo, Xinxin Zuo, Sen Wang, Shihao Zou, Qingyao Sun, Annan Deng, Minglun Gong, and Li Cheng. The paper lists the Department of Electrical and Computer Engineering at the University of Alberta, plus the University of Chicago, Yale University, and the University of Guelph. The code contact is cguo2@ualberta.ca. arXiv v1 is dated 2020-07-30. Related DOI 10.1145/3394171.3413635. Pretrained checkpoints are in the code repository under checkpoints/vae. This dossier was collected on 2026-10-15. Status is active and confidence is high.", "what_it_is_for": "Generation of diverse 3D human motions from a prescribed action category. The paper frames this as the inverse of action recognition. Conditioning is an action label, not free text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ualberta/momask/momask": { "id": "ualberta/momask/momask", "name": "MoMask", "maker": "ualberta", "family": "ualberta/momask", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (code, copyright 2023 Chuan Guo). Pretrained zips are on Google Drive; the README does not state a separate weights license.", "release_date": "2023-12-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Residual VQ-VAE plus two bidirectional transformers. Released HumanML3D eval names are masked transformer t2m_nlayer8_nhead6_ld384_ff1024_cdp0.1_rvq6, residual transformer tres_nlayer8_ld384_ff1024_rvq6ns_cdp0.2_sw, and RVQ rvq_nq6_dc512_nc512_noshare_qdp0.2 (6 quantizers). The environment installs OpenAI CLIP. Generation in the README is 20 fps stick-figure motion, numpy shape (nframe, 22, 3).", "modalities": { "input": [ "text", "motion" ], "output": [ "motion" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Alberta released MoMask, a text-to-motion model from Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, and Li Cheng. Weights for HumanML3D and KIT-ML were posted on 2023-12-15. This dossier was collected on 2026-10-15. Status is active and confidence is high.", "what_it_is_for": "Text-driven 3D human motion generation. The project page also shows text-guided temporal inpainting of an existing motion without a further fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ualberta/text2motion/text2motion": { "id": "ualberta/text2motion/text2motion", "name": "Text2Motion", "maker": "ualberta", "family": "ualberta/text2motion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (code, copyright 2022 Chuan Guo). HumanML3D and KIT-ML checkpoints are Google Drive zips and a Zenodo dump marked Other/Open. The README does not state a separate weights license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage text-to-motion model. A BiGRU text-to-length model (checkpoint length_est_bigru) samples a motion length. A temporal VAE (HumanML3D checkpoint Comp_v6_KLD01, KIT-ML checkpoint Comp_v6_KLD005) generates motions from motion-snippet codes of a 1D convolutional autoencoder (Decomp_SP001_SM001_H512, window size 24). The CVPR poster writes the training loss as L = Lcode_rec + \u03bbmot Lmot_rec + \u03bbKL LKL. README training uses lambda_kld 0.01 on HumanML3D and 0.005 on KIT-ML. The environment is Python 3.7.9 and PyTorch 1.6.0, and generation from raw text installs spaCy en_core_web_sm.", "modalities": { "input": [ "text" ], "output": [ "motion" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Alberta released Text2Motion, the CVPR 2022 text-to-motion model by Chuan Guo, Shihao Zou, Xinxin Zuo, Sen Wang, Wei Ji, Xingyu Li, and Li Cheng. All seven authors are listed at the University of Alberta. Weights for HumanML3D and KIT-ML are on Google Drive, and a Zenodo dump is dated 2023-02-10. The day those weights first appeared was not found. This dossier was collected on 2026-10-15. Status is active and confidence is high.", "what_it_is_for": "Stochastic generation of 3D human motion from a text description. The CVPR abstract describes a text-to-length stage followed by a temporal VAE over motion snippet codes. The README example is a person who rises, walks a counterclockwise circle, and lies down again.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ualberta/tm2t/tm2t": { "id": "ualberta/tm2t/tm2t", "name": "TM2T", "maker": "ualberta", "family": "ualberta/tm2t", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (code, copyright 2022 Chuan Guo). Pretrained zips are on Google Drive. The README does not state a separate weights license.", "release_date": "2022-07-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "VQ-VAE motion tokenizer plus neural machine translation in both directions. The released HumanML3D discretizer is VQVAEV3_CB1024_CMT_H1024_NRES3: codebook size 1024, hidden size 1024, 3 residual blocks. Motion-to-text on HumanML3D is M2T_EL4_DL4_NH8_PS, a transformer with 4 encoder layers, 4 decoder layers, and 8 heads. The KIT-ML training command uses M2T_EL3_DL3_NH8_PS with 3 encoder and 3 decoder layers. Text-to-motion is the attentive-GRU checkpoint T2M_Seq2Seq_NML1_Ear_SME0_N. The paper says each motion token stands for a local segment with t = T/4, and text-to-motion training adds an inverse-alignment loss through the motion-to-text model. The environment is Python 3.7.9 and PyTorch 1.6.0, with spaCy en_core_web_sm for raw text.", "modalities": { "input": [ "text", "motion" ], "output": [ "motion", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Alberta released TM2T, an ECCV 2022 model by Chuan Guo, Xinxin Zuo, Sen Wang, and Li Cheng. The paper lists one affiliation, the University of Alberta, and the emails cguo2, xzuo, sen9, and lcheng5 at ualberta.ca. arXiv v1 is dated 2022-07-04 and v2 is dated 2022-08-04. HumanML3D and KIT-ML weights are Google Drive zips. This dossier was collected on 2026-10-15. Status is active and confidence is high.", "what_it_is_for": "Reciprocal generation between a text description and a 3D human motion. Text-to-motion samples different motions from one description. Motion-to-text writes a description of a motion. The paper's emphasis is text-to-motion, with motion-to-text also used as inverse alignment during that training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uark/bubbleid/bubbleid": { "id": "uark/bubbleid/bubbleid", "name": "BubbleID", "maker": "uark", "family": "uark/bubbleid", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (license_name dryad-record-10.5061-dryad.ksn02v7gx; the card links the Dryad record and says this is not a standard code-license grant for the weights)", "release_date": "2024-07-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Journal and arXiv abstracts describe Mask R-CNN segmentation plus SORT-based tracking for bubble location, size, interface shape, velocity, and departure. The Hub package is two PyTorch checkpoints, not a third tracking weight: seg_model.pth for instance segmentation and class_model.pth for classification. Backbone, parameter count, and the classification label set are not on the card.", "modalities": { "input": [ "image" ], "output": [ "mask", "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BubbleID is a University of Arkansas NED\u00b3 Laboratory framework for liquid-vapor interfaces in high-speed pool-boiling image sequences. The journal version is Journal of Applied Physics 136, 014902 (1 July 2024). The public weights are `seg_model.pth` and `class_model.pth` on `UARK-NED3/BubbleID`, uploaded 2026-09-14 from Dryad record 10.5061/dryad.ksn02v7gx (published 23 May 2025). Collected 2026-10-15. It is not a priced API. BubbleID-Flow is a separate flow-boiling checkpoint.", "what_it_is_for": "The paper abstract says the framework identifies static and dynamic bubble attributes in boiling image sequences: location, size, interface shape, velocity, and departure, including a comparison before and after critical heat flux. The Hub card limits the release to research use on pool-boiling sequences and points install and tutorial steps to the GitHub repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uark/bubbleid/bubbleid-flow": { "id": "uark/bubbleid/bubbleid-flow", "name": "BubbleID-Flow", "maker": "uark", "family": "uark/bubbleid", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-4.0", "release_date": "2026-09-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tuned one-class Mask R-CNN. The card's usage snippet loads Detectron2 0.6 config COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml and sets NUM_CLASSES to 1. Weight file model_final.pth. LabelMe labels bubble and bubble_cluster are merged into one vapor-region class. Reported analysis uses score threshold 0.30, 300 detections per image, and a near-wall ROI at pixel (0, 485) of size 1024 by 70. The card says standard COCO AP evaluation uses a 0.05 score threshold instead. An exact parameter count is not published.", "modalities": { "input": [ "image" ], "output": [ "mask" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BubbleID-Flow is a University of Arkansas NED\u00b3 checkpoint for near-wall vapor regions in subcooled flow-boiling images. The public file is `model_final.pth` on `UARK-NED3/BubbleID-Flow`, created 2026-09-14, under CC-BY-4.0. Collected 2026-10-15. It is a different product from the pool-boiling BubbleID package. The card names a manuscript and does not give that manuscript a DOI or a publication day. There is no priced API.", "what_it_is_for": "The card says the checkpoint segments vapor in the image plane so a two-dimensional near-wall vapor area fraction can be computed from the union of predicted masks. It says that quantity is not volumetric void fraction. Contact on the card is Han Hu, Mechanical Engineering, hanhu@uark.edu. Funding line is NSF CBET-2323022.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uark/micron-bert/micron-bert": { "id": "uark/micron-bert/micron-bert", "name": "Micron-BERT", "maker": "uark", "family": "uark/micron-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Paper: a symmetric masked autoencoder on 224 by 224 frames split into 8 by 8 patches (784 patches), each projected to 512 dimensions. Encoder and decoder each have 4 blocks of multi-head attention and an MLP. Diagonal Micro-Attention ranks swapped patches. Patch of Interest uses a contextual token and an agreement loss so attention stays on the face. Self-training in the paper uses CASME3 frames, AdamW, learning rate 1e-4, 100 epochs. The public file is CASME2-is224-p8-b16-ep200.pth. The loader in micron_bert.py reads a dict with args and epoch and builds an MAE-style model. The GitHub testing section calls this file the pretrained model used to extract features for fine-tuning.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Micron-BERT, also written \u03bc-BERT, is a University of Arkansas CVIU Lab model for facial micro-expression recognition. The paper is arXiv:2304.03195 (6 April 2023) and CVPR 2023. The only public weight is `CASME2-is224-p8-b16-ep200.pth` on `uark-cviu/micron-bert`, uploaded 2026-05-12 under Apache-2.0. Collected 2026-10-15. It is not a priced API. Concordia University and West Virginia University are coauthor affiliations.", "what_it_is_for": "The paper says the model spots and recognizes facial movements that last a fraction of a second. Diagonal Micro-Attention looks for tiny frame-to-frame patch changes. Patch of Interest keeps that attention on the face. The GitHub testing section says the Hub file is the pretrained checkpoint to fine-tune on a micro-expression database and to extract features.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uark/podnn/podnn-cold-plate": { "id": "uark/podnn/podnn-cold-plate", "name": "POD-NN Cold Plate", "maker": "uark", "family": "uark/podnn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc0-1.0", "release_date": "2026-09-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reduced-order surrogate for one liquid-cooled dual-chip pin-fin cold plate. Six boundary conditions go through a StandardScaler into Dense 64 ReLU L2 1e-3, Dropout 0.1, Dense 64 ReLU L2 1e-3, Dropout 0.1, Dense 32 ReLU L2 1e-3, then Dense 4 scaled POD coefficients. A mode scaler inverse and PCA inverse map those four coefficients to temperature in kelvin on 11,110 nodes of the yz mid-plane. Adam learning rate 1e-3, loss MSE. Four modes retain 99.32 percent of temperature-field variance. Files pod_nn.h5, pca.pkl, param_scaler.pkl, and mode_scaler.pkl are all required. The h5 file emits scaled coefficients only. Pickles were written by scikit-learn 1.7.2. The TensorFlow build is not recorded. An exact parameter integer is not published.", "modalities": { "input": [ "tabular" ], "output": [ "temperature-field" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "POD-NN Cold Plate is a University of Arkansas NED\u00b3 surrogate for the steady temperature field of one liquid-cooled, dual-chip, pin-fin cold plate. The public files are on `UARK-NED3/PODNN-ColdPlate`, created 2026-09-14, under CC0 1.0. Collected 2026-10-15. The card reports test R\u00b2 0.998, RMSE 1.94 K, and MAE 1.46 K on 400 held-out simulations, and says the surrogate is about 412,500 times faster than the CFD solve. Curl and Hu, AI Thermal Fluids 6, 100040 (2026), is the paper. The journal day was not captured. There is no priced API. This snapshot is not a CFDTwin model file.", "what_it_is_for": "The card says the network maps six boundary conditions to four POD coefficients, and the POD basis reconstructs temperature in kelvin on the central yz mid-plane. The six inputs, in scaler order, are chip 1 heat flux (W/m\u00b2), chip 2 heat flux (W/m\u00b2), inlet 1 mass flow (kg/s), inlet 1 total temperature (K), inlet 2 mass flow (kg/s), and inlet 2 total temperature (K). Contact is Han Hu, hanhu@uark.edu. Funding line is NSF OIA-2429580.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uark/quantumflake/flake-classifier": { "id": "uark/quantumflake/flake-classifier", "name": "flake-classifier", "maker": "uark", "family": "uark/quantumflake", "status": "active", "confidence": "low", "weights": "open-weights", "license": "", "release_date": "2026-03-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The org repo uark-cviu/flake-classifier contains flake_classifier.pth and no README. QuantumFlake's classifier doc describes a ResNet layer classifier with example classes 1-layer and 5plus-layer, and the training CLI takes num-materials and material-dim (the README example uses 2 and 64). The README's required quick-start file is flake_monolayer_classifier.pth, which is a different filename and was not in this repo on 2026-10-15. The ResNet description is therefore not verified for flake_classifier.pth.", "modalities": { "input": [ "image" ], "output": [ "label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "flake-classifier is the weight file `flake_classifier.pth` on the University of Arkansas CVIU org repo `uark-cviu/flake-classifier`. The repo was created 2026-03-13 and last modified 2026-07-20. It has no README and no license. Collected 2026-10-15. Confidence is low. The QuantumFlake README's required classifier filename is `flake_monolayer_classifier.pth`, and that name was not in this repo. There is no priced API.", "what_it_is_for": "The file sits on the lab org next to the QuantumFlake detector. The framework's job is layer classification of cropped 2D-material flakes. The classifier doc describes a ResNet with example classes `1-layer` and `5plus-layer`. That doc does not name `flake_classifier.pth`, so this dossier does not treat the ResNet recipe as a verified description of the file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uark/quantumflake/uark-detector-v3": { "id": "uark/quantumflake/uark-detector-v3", "name": "uark_detector_v3", "maker": "uark", "family": "uark/quantumflake", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-03-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "QuantumFlake docs call uark_detector_v3.pt the standard public Ultralytics YOLO detect checkpoint and say to pass it as models.detector.yolo.weights. The training example in those docs uses one class named flake and imgsz 640. Those fields were not read from the weight file. The Hub repo uark-cviu/flake-detector contains uark_detector_v3.pt and .gitattributes only. No model card.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "uark_detector_v3 is the detector checkpoint the University of Arkansas CVIU QuantumFlake docs name for the standard YOLO pipeline. The file `uark_detector_v3.pt` is the only weight on `uark-cviu/flake-detector`, created 2026-03-13. That repo has no README and no license field. Collected 2026-10-15. Confidence is medium because the lab docs and the org file agree on the name, and the repo itself is not a model card. There is no priced API.", "what_it_is_for": "QuantumFlake is a detect, crop, classify, and visualize framework for microscope images of 2D materials. The YOLO doc says this file is the standard public detector checkpoint and that inference passes it as `models.detector.yolo.weights`. Contributors listed on the QuantumFlake README include University of Arkansas and Montana State University.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uark/qupaint/qupaint-4b": { "id": "uark/qupaint/qupaint-4b", "name": "QuPAINT-4B", "maker": "uark", "family": "uark/qupaint", "status": "active", "confidence": "high", "weights": "open-weights", "license": "qupaint-research-only (non-commercial academic research; inference code on GitHub is MIT)", "release_date": "2026-02-19", "context_length": 40960, "max_output_tokens": null, "parameters": "", "architecture": "Card: vision transformer encoder, MLP projector, and a 4B-parameter language model, bfloat16. Released config model_type cviu_chat. Vision block model_type intern_vit_6b with 24 layers, hidden size 1024, 16 heads, patch size 14, image size 448, up to 12 dynamic patches plus a thumbnail, downsample_ratio 0.5. Language config _name_or_path is a local Qwen3-4B checkpoint: 36 layers, hidden size 2560, 32 heads, 8 key-value heads, vocab 151682, max_position_embeddings 40960. The paper adds Physics-Informed Attention, an affine calibration of CIELAB contrast scores multiplied onto visual tokens. Input images are placed on a 1792 by 1344 canvas and tiled into 448 px patches.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QuPAINT-4B is a University of Arkansas CVIU Lab vision-language model for optical micrographs of exfoliated 2D quantum-material flakes. The public weights went up on Hugging Face on 2026-09-21 under a non-commercial research license. The name was in arXiv:2602.17478 on 2026-02-19 and in CVPR 2026 Findings. Collected 2026-10-15. It is not a priced API.", "what_it_is_for": "The model card says the job is flake detection, monolayer screening, counting, visual grounding, and optical-contrast reasoning. A micrograph plus a question should end in a conclusion span of percent-coordinate boxes. Physics-Informed Attention fuses visual tokens with substrate-relative optical contrast. Training data is QMat-Instruct, built from Synthia synthetic microscopy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uark/seqreg/hit2flux": { "id": "uark/seqreg/hit2flux", "name": "Hit2Flux", "maker": "uark", "family": "uark/seqreg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-09-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hub file Hit2Flux_weights.h5. The SeqReg card says acoustic-emission hit sequence regression with FFT preprocessing, SeqLen=25, and sequence output. Layer sizes and parameter count are not on the card.", "modalities": { "input": [ "audio" ], "output": [ "heat-flux" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hit2Flux is the acoustic-emission hit model in the University of Arkansas NED\u00b3 SeqReg package. The public file is `Hit2Flux_weights.h5` on `UARK-NED3/SeqReg`, created 2026-09-14, under Apache-2.0. Collected 2026-10-15. The card says FFT preprocessing, sequence length 25, and sequence output. The 2025 paper title matches this name. The card does not publish a test error or a layer list. There is no priced API.", "what_it_is_for": "The card assigns this file to AE-hit sequence regression for pool-boiling heat-flux prediction. The cited paper is Dunlap, Li, Pandey, and Hu, \"Hit2Flux: A Machine Learning Framework for Boiling Heat Flux Prediction Using Hit-Based Acoustic Emission Sensing,\" AI in Thermal Fluids, 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uark/seqreg/hydreg": { "id": "uark/seqreg/hydreg", "name": "HydReg", "maker": "uark", "family": "uark/seqreg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-09-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hub file HydReg.joblib. The SeqReg card says hydrophone sequence regression with FFT preprocessing and SeqLen=4000. Layer sizes and parameter count are not on the card. The file is a joblib artifact and can execute code when loaded.", "modalities": { "input": [ "audio" ], "output": [ "heat-flux" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HydReg is the hydrophone sequence-regression artifact in the University of Arkansas NED\u00b3 SeqReg package. The public file is `HydReg.joblib` on `UARK-NED3/SeqReg`, created 2026-09-14, under Apache-2.0. Collected 2026-10-15. The card describes FFT preprocessing and sequence length 4000. It does not state a parameter count or a test error, and it does not say in a sentence that this filename is the 2023 Applied Thermal Engineering model. There is no priced API.", "what_it_is_for": "The card says SeqReg predicts experimental pool-boiling heat flux from hydrophone, acoustic-emission hit, and optical-image inputs. HydReg is the hydrophone file. Install and tutorials live in https://github.com/cldunlap73/SeqReg.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uark/seqreg/imgreg": { "id": "uark/seqreg/imgreg", "name": "ImgReg", "maker": "uark", "family": "uark/seqreg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2026-09-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two files on the SeqReg card: ImgReg.hdf5 for optical-image sequence regression, and ImagePCA.pkl, which the card says ImgReg requires. Canonical PCA settings on the card are PCAnpy, pcskeep=40, and SeqLen=200. Network widths and parameter count are not stated. The pickle can execute code when loaded.", "modalities": { "input": [ "image" ], "output": [ "heat-flux" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ImgReg is the optical-image sequence-regression artifact in the University of Arkansas NED\u00b3 SeqReg package. The public files are `ImgReg.hdf5` and `ImagePCA.pkl` on `UARK-NED3/SeqReg`, created 2026-09-14, under Apache-2.0. Collected 2026-10-15. The card says the PCA file is required and gives PCAnpy, 40 components, and sequence length 200. It does not say the 2024 IEEE paper uses the name ImgReg. There is no priced API.", "what_it_is_for": "The card assigns the pair to optical-image sequence regression for pool-boiling heat flux. The image-side citation in the same block is Dunlap, Li, Pandey, Sun, and Hu, \"A Temporal-Spatial Framework for Efficient Heat Flux Monitoring of Transient Boiling,\" IEEE Transactions on Instrumentation and Measurement, 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/arat5/arat5-base": { "id": "ubc/arat5/arat5-base", "name": "AraT5-base", "maker": "ubc", "family": "ubc/arat5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use requires contacting the authors.", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "T5 encoder-decoder. config.json: vocab 110080, d_model 768, d_ff 2048, num_layers 12, num_heads 12, decoder_start_token_id 0. num_decoder_layers is unset. The AraT5v2 card says the previous version used sequence length 512.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AraT5-base is UBC-NLP's mixed Arabic text-to-text model from the ACL 2022 AraT5 paper. The card still hosts the weights and says to prefer AraT5v2-base-1024. Weights were public for research use on 2026-10-15. It is not an API.", "what_it_is_for": "The paper pretrains Arabic T5-style models and evaluates them on ARGEN, a generation benchmark covering translation, summarization, title and question generation, paraphrasing, transliteration, and code-switched translation. This checkpoint is the mixed model in the lab's table, not the MSA-only or tweet-only runs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/arat5/arat5-msa-base": { "id": "ubc/arat5/arat5-msa-base", "name": "AraT5-msa-base", "maker": "ubc", "family": "ubc/arat5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use requires contacting the authors.", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "T5 encoder-decoder trained on MSA only. config.json matches AraT5-base: vocab 110080, d_model 768, d_ff 2048, num_layers 12, num_heads 12, decoder_start_token_id 0. num_decoder_layers is unset.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AraT5-msa-base is the MSA-only base T5 from UBC-NLP's AraT5 release. The GitHub README calls this flavor AraT5-MSA and says it is trained on MSA data exclusively. The config matches AraT5-base. Weights were public on 2026-10-15 for research use. It is not an API.", "what_it_is_for": "The ACL 2022 paper compares Arabic T5 models trained on different varieties. This one is the MSA base checkpoint in the official table, for the same text-to-text tasks as the rest of AraT5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/arat5/arat5-msa-small": { "id": "ubc/arat5/arat5-msa-small", "name": "AraT5-msa-small", "maker": "ubc", "family": "ubc/arat5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use requires contacting the authors.", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Smaller MSA T5. config.json: vocab 110080, d_model 512, d_ff 1024, num_layers 8, num_heads 6, decoder_start_token_id 0. num_decoder_layers is unset. Same vocabulary size as the base models.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AraT5-msa-small is the smaller MSA-only Arabic T5 from UBC-NLP. It is in the official AraT5 checkpoint table. The config is narrower than AraT5-msa-base: d_model 512, 8 layers, 6 heads. Weights were public on 2026-10-15 for research use. It is not an API.", "what_it_is_for": "Same text-to-text role as the other AraT5 checkpoints, at a smaller MSA-only size. The lab's later recommendation names AraT5v2-base-1024, not this small model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/arat5/arat5-tweet-base": { "id": "ubc/arat5/arat5-tweet-base", "name": "AraT5-tweet-base", "maker": "ubc", "family": "ubc/arat5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use requires contacting the authors.", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "T5 encoder-decoder for the tweet flavor. config.json matches AraT5-base: vocab 110080, d_model 768, d_ff 2048, num_layers 12, num_heads 12, decoder_start_token_id 0. num_decoder_layers is unset.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AraT5-tweet-base is UBC-NLP's tweet-trained Arabic T5 at the base size. The GitHub README lists it as the AraT5-Tweet flavor. The config matches AraT5-base. Weights were public on 2026-10-15 for research use. It is not an API.", "what_it_is_for": "The AraT5 paper trains separate text-to-text models on MSA, on tweets, and on a mix. This is the base tweet model in the checkpoint table, for dialectal social-media generation and the ARGEN tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/arat5/arat5-tweet-small": { "id": "ubc/arat5/arat5-tweet-small", "name": "AraT5-tweet-small", "maker": "ubc", "family": "ubc/arat5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use requires contacting the authors.", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Smaller tweet T5. config.json matches AraT5-msa-small: vocab 110080, d_model 512, d_ff 1024, num_layers 8, num_heads 6, decoder_start_token_id 0. num_decoder_layers is unset.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AraT5-tweet-small is the smaller tweet-trained Arabic T5 from UBC-NLP. It is listed in the official checkpoint table. The config matches AraT5-msa-small. Weights were public on 2026-10-15 for research use. It is not an API.", "what_it_is_for": "It is the small tweet flavor of AraT5, for Arabic text-to-text generation on dialectal social-media text. The lab's later recommendation is AraT5v2-base-1024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/arat5/arat5v2-base-1024": { "id": "ubc/arat5/arat5v2-base-1024", "name": "AraT5v2-base-1024", "maker": "ubc", "family": "ubc/arat5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use requires contacting the authors.", "release_date": "2023-08-15", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "T5 encoder-decoder. config.json: vocab 110208, d_model 768, d_ff 2048, num_layers 12, num_heads 12, decoder_start_token_id 0. num_decoder_layers is unset. The card says sequence length increased from 512 to 1024 and that the model supports 100 sentinel tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AraT5v2-base-1024 is the Arabic T5 that UBC-NLP tells users to prefer over AraT5-base. The Hub repo was created on 15 August 2023. The card says it uses more diverse Arabic data, sequence length 1024, 100 sentinel tokens, and faster fine-tuning convergence. Weights were public on 2026-10-15. It is not an API.", "what_it_is_for": "The card demonstrates span filling, for example predicting a masked Arabic token. The citation block points at the December 2023 Octopus paper for this checkpoint and at the 2022 AraT5 paper for the earlier models. `UBC-NLP/octopus` is a separate multitask repo and was not given a folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/arbert/arbert": { "id": "ubc/arbert/arbert", "name": "ARBERT", "maker": "ubc", "family": "ubc/arbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use requires contacting the authors.", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "163000000", "architecture": "BERT-base masked LM. config.json: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 100000 WordPiece, max position 512. The ACL 2021 paper says pretraining used sequence length 128, not the config maximum. Reported size is 163M, not a counted weight-file total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ARBERT is a Modern Standard Arabic masked language model from the UBC Deep Learning and NLP Lab (Muhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi). The ACL 2021 paper introduces it with MARBERT. Weights are public for research use and were still on the Hub on 2026-10-15. It is not an API.", "what_it_is_for": "The card says ARBERT is a large-scale pretrained masked language model focused on Modern Standard Arabic. The lab points users at `AutoModel.from_pretrained(\"UBC-NLP/ARBERT\")` for Arabic understanding tasks. The same paper introduces ARLUE as the evaluation benchmark.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/arbert/arbertv2": { "id": "ubc/arbert/arbertv2", "name": "ARBERTv2", "maker": "ubc", "family": "ubc/arbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use requires contacting the authors.", "release_date": "2023-04-11", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Updated ARBERT. config.json: BertForMaskedLM, 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 100000, max position 512. The card does not say whether training continued from ARBERT or started over. Hub model-size label is 0.2B. No exact integer was published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ARBERTv2 is UBC-NLP's updated Modern Standard Arabic masked language model. The Hub card, created 11 April 2023, says it is the updated ARBERT and points at the ORCA paper. It was trained on 243GB of MSA text and 27.8B tokens. Weights were public on 2026-10-15. It is not an API.", "what_it_is_for": "The card presents it as the larger-data replacement for ARBERT and as a model used with the ORCA Arabic understanding benchmark. The Hub record has no license field. The family GitHub README is the source for the research-only terms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/arbert/marbert": { "id": "ubc/arbert/marbert", "name": "MARBERT", "maker": "ubc", "family": "ubc/arbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use requires contacting the authors.", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "163000000", "architecture": "BERT-base masked LM trained from scratch. config.json: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 100000, max position 512. The paper says pretraining sequence length was 128 and that next-sentence prediction was removed. Reported size is 163M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MARBERT is a dialectal and Modern Standard Arabic masked language model from UBC-NLP, released with ARBERT in the ACL 2021 paper. The lab trained it from scratch on Arabic tweets. Weights were public on 2026-10-15 for research use. It is not an API. MARBERTv2 is the longer-context continuation.", "what_it_is_for": "The card says MARBERT covers dialectal Arabic and MSA, unlike MSA-only models such as ARBERT. The GitHub README shows `AutoModel.from_pretrained(\"UBC-NLP/MARBERT\")`. The paper says it is the stronger of the two original models on social-media tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/arbert/marbertv2": { "id": "ubc/arbert/marbertv2", "name": "MARBERTv2", "maker": "ubc", "family": "ubc/arbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use requires contacting the authors.", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Further pretraining of MARBERT. config.json matches MARBERT: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 100000, max position 512. The paper says this stage used sequence length 512 for 40 epochs. An exact parameter integer is not reprinted.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MARBERTv2 is UBC-NLP's further pretraining of MARBERT. The ACL 2021 paper says the original models were weak on question answering because they were pretrained at sequence length 128, so the lab continued MARBERT at length 512. The checkpoint was public on 2026-10-15 and was the most-downloaded UBC-NLP model in that listing. It is not an API.", "what_it_is_for": "The card says this stage was meant to fix QA. It further pretrains MARBERT on ARBERT's MSA data plus AraNews, for 40 epochs, and the card says the result has 29B tokens. The paper reports it as the best ARLUE model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/cheetah/cheetah-1-2b": { "id": "ubc/cheetah/cheetah-1-2b", "name": "Cheetah-1.2B", "maker": "ubc", "family": "ubc/cheetah", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use requires contacting the authors.", "release_date": "2024-08-11", "context_length": null, "max_output_tokens": null, "parameters": "1200000000", "architecture": "Encoder-decoder. The Toucan paper says this is a 1.2 billion parameter Afrocentric sequence-to-sequence model. Its own config.json returned 401 on 2026-10-15. The public Toucan-1.2B fine-tune config is mT5-large shaped: d_model 1024, 24 layers, 16 heads, d_ff 2816, vocab 250112. That shape was not confirmed on the Cheetah-1.2B file.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cheetah-1.2B is the larger Afrocentric sequence-to-sequence model introduced in UBC-NLP's Toucan paper. The paper states 1.2 billion parameters. The Hub repo `UBC-NLP/cheetah-1.2B` exists, was created on 11 August 2024, and is gated. Its config could not be read. It is not an API.", "what_it_is_for": "The Toucan paper says Cheetah-base was too small for many-to-many translation, so the lab trained 1.2B and 3.7B sequence-to-sequence models and then fine-tuned them into Toucan. This folder is the 1.2B pretrained model, not the translation fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/cheetah/cheetah-3-7b": { "id": "ubc/cheetah/cheetah-3-7b", "name": "Cheetah-3.7B", "maker": "ubc", "family": "ubc/cheetah", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "3700000000", "architecture": "Sequence-to-sequence model. The Toucan paper says it has 3.7 billion parameters and was trained with Cheetah-1.2B. No config was found.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cheetah-3.7B is a 3.7 billion parameter sequence-to-sequence model named in UBC-NLP's Toucan paper. The paper says it was trained alongside Cheetah-1.2B. No Hub repo was found on 2026-10-15. The lab's own link table lists Cheetah-base and Cheetah-1.2B only. Status is announced. It is not an API.", "what_it_is_for": "The Toucan paper uses it as the larger pretrained model before the Toucan-3.7B translation fine-tune. Results in that paper are reported for the fine-tune. This folder records the named pretrained model because the paper introduces it by name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/cheetah/cheetah-base": { "id": "ubc/cheetah/cheetah-base", "name": "Cheetah-base", "maker": "ubc", "family": "ubc/cheetah", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use requires contacting the authors.", "release_date": "2024-01-02", "context_length": 1024, "max_output_tokens": null, "parameters": "580000000", "architecture": "mT5-style encoder-decoder. config.json: T5ForConditionalGeneration, vocab 250112, d_model 768, d_ff 2048, num_layers 12, num_heads 12, decoder_start_token_id 0. The paper table lists Cheetah base at 580M, 12 heads, 12 layers, d_model 768, vocab about 250K, sequence length 1024, batch 1024, 1M steps, 527 languages of which 517 are African.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cheetah-base is UBC-NLP's multilingual encoder-decoder for African language generation. The ACL 2024 paper and the lab page describe an mT5-style model of about 580M parameters covering 517 African languages. The Hub repo was created on 2 January 2024 and is gated. Weights are for research use. It is not an API.", "what_it_is_for": "The paper trains a generation model for languages that multilingual T5 models barely cover, and evaluates it on AfroNLG. The lab page lists machine translation, summarization, question answering, paraphrasing, and cloze among the tasks. The card shows span filling with a T5 tokenizer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/indt5/indt5": { "id": "ubc/indt5/indt5", "name": "IndT5", "maker": "ubc", "family": "ubc/indt5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 encoder-decoder. config.json: vocab 100096, d_model 768, d_ff 2048, num_layers 12, num_heads 12, decoder_start_token_id 0. num_decoder_layers is unset. No max length field.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IndT5 is a text-to-text model from UBC-NLP for 10 Indigenous languages of the Americas plus Spanish. The AmericasNLP 2021 paper and the Hub card call it the first Transformer language model for those Indigenous languages. The Hub file timestamp is 30 August 2021. Weights were public on 2026-10-15. No license field was found. It is not an API.", "what_it_is_for": "The card says every task is cast as text-to-text, following T5. Training data is IndCorpus, built from Wikipedia and the Bible. The languages in the card table are H\u00f1\u00e4h\u00f1u, Wixarika, Nahuatl, Guarani, Bribri, Rar\u00e1muri, Quechua, Aymara, Shipibo-Konibo, Ash\u00e1ninka, and Spanish.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/jasmine/jasmine-350m": { "id": "ubc/jasmine/jasmine-350m", "name": "Jasmine-350M", "maker": "ubc", "family": "ubc/jasmine", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-07-14", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "GPT-Neo causal LM. config.json: hidden 768, 12 layers, 12 heads, vocab 63999, max position 2048. The repo name says 350M. The paper describes a suite from about 300 million to 6.7 billion parameters and does not print an exact integer on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Jasmine-350M is the only public JASMINE checkpoint from UBC-NLP. JASMINE is a suite of Arabic autoregressive models in the EMNLP 2023 paper, described as ranging from about 300 million to 6.7 billion parameters and pretrained on about 235GB of text. This repo was created on the Hub on 14 July 2022. It is a GPT-Neo causal model. No license field was found. It is not an API.", "what_it_is_for": "The paper studies few-shot learning with Arabic GPT-style models and releases a benchmark that includes social bias, harm, and toxicity. The card does not add a usage guide beyond the citation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/serengeti/serengeti-e110": { "id": "ubc/serengeti/serengeti-e110", "name": "SERENGETI-E110", "maker": "ubc", "family": "ubc/serengeti", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use requires contacting the authors. The GitHub repo also shows an Apache-2.0 badge.", "release_date": "2023-10-17", "context_length": 512, "max_output_tokens": null, "parameters": "170000000", "architecture": "ELECTRA. config.json: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 110000, max position 512. The paper table reports 170M parameters and a 110K WordPiece vocabulary. GitHub says 40 epochs, sequence length 512, learning rate 2e-4, batch size 216. The paper HTML also says a batch size of 512 in one training paragraph.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SERENGETI-E110 is UBC-NLP's ELECTRA model for African languages, with a 110K vocabulary. The Findings of ACL 2023 paper reports it at 170M parameters and says the set covers 517 African languages. The Hub repo was created on 17 October 2023. Weights were public on 2026-10-15. It is not an API.", "what_it_is_for": "The paper trains multilingual encoders so African languages that are missing from mBERT and XLM-R can be fine-tuned for understanding tasks. E110 is the smaller-vocabulary ELECTRA run. The XLM-R SERENGETI checkpoint was not foldered because its config path is a further-pretraining init.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/serengeti/serengeti-e250": { "id": "ubc/serengeti/serengeti-e250", "name": "SERENGETI-E250", "maker": "ubc", "family": "ubc/serengeti", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Research use. Commercial use requires contacting the authors. The GitHub repo also shows an Apache-2.0 badge.", "release_date": "2023-10-17", "context_length": 512, "max_output_tokens": null, "parameters": "277000000", "architecture": "ELECTRA. config.json: 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 250000, max position 512. The paper table reports 277M parameters and a 250K WordPiece vocabulary. GitHub says 40 epochs, sequence length 512, learning rate 2e-4, batch size 104. The paper HTML also says a batch size of 512 in one training paragraph.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SERENGETI-E250 is UBC-NLP's larger-vocabulary ELECTRA model for African languages. The paper table reports 277M parameters and a 250K vocabulary, covering 517 African languages. The Hub repo was created on 17 October 2023. Weights were public on 2026-10-15. It is not an API.", "what_it_is_for": "It is the 250K ELECTRA sibling of SERENGETI-E110, for fine-tuning on African language understanding. The card states the coverage claim and the family's average F1. It does not isolate this checkpoint's score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/toucan/toucan-1-2b": { "id": "ubc/toucan/toucan-1-2b", "name": "Toucan-1.2B", "maker": "ubc", "family": "ubc/toucan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-12", "context_length": null, "max_output_tokens": null, "parameters": "1200000000", "architecture": "Translation fine-tune of Cheetah-1.2B. config.json: MT5ForConditionalGeneration, vocab 250112, d_model 1024, d_ff 2816, num_layers 24, num_heads 16, decoder_start_token_id 0. The paper states 1.2 billion parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Toucan-1.2B is UBC-NLP's larger public translation model, a fine-tune of Cheetah-1.2B. The paper states 1.2 billion parameters. The Hub repo was created on 12 August 2024 and the config was readable. No license field was found. It is not an API.", "what_it_is_for": "It is the 1.2B many-to-many African translation checkpoint. The paper compares it with mT5 Large on AfroLingu-MT. Those scores were not transcribed because the fetched HTML table was misaligned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/toucan/toucan-3-7b": { "id": "ubc/toucan/toucan-3-7b", "name": "Toucan-3.7B", "maker": "ubc", "family": "ubc/toucan", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "3700000000", "architecture": "Translation fine-tune of Cheetah-3.7B. The paper table labels a Toucan 3.7B row at 3.7B parameters. No config was found.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Toucan-3.7B is the large translation model named in UBC-NLP's Toucan paper. The paper table includes a Toucan 3.7B row and says it is the fine-tune of Cheetah-3.7B. No public Hub repo was found on 2026-10-15. The lab's link table stops at Toucan-1.2B. Status is announced. It is not an API.", "what_it_is_for": "The paper evaluates it on AfroLingu-MT against mT5 and mT0 at the 3.7B scale, and mentions a comparison with Aya 13B. The fetched score table was misaligned, so no BLEU number is stored here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ubc/toucan/toucan-base": { "id": "ubc/toucan/toucan-base", "name": "Toucan-base", "maker": "ubc", "family": "ubc/toucan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Translation fine-tune of Cheetah-base. config.json: T5ForConditionalGeneration, vocab 250112, d_model 768, d_ff 2048, num_layers 12, num_heads 12, decoder_start_token_id 0. That matches the Cheetah-base config. The paper's 580M figure was not copied onto this fine-tune.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Toucan-base is UBC-NLP's many-to-many machine translation model fine-tuned from Cheetah-base. The Hub repo was created on 11 August 2024. The config matches Cheetah-base. The card has no license field. Weights were public on 2026-10-15. It is not an API.", "what_it_is_for": "The paper builds an Afrocentric translation model on top of Cheetah. The title says 150 African language pairs. The card body says 156. A later paragraph says 46 languages, 43 of them African, plus Arabic, English, and French. The card's generation example uses `MT5ForConditionalGeneration`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucas/jtcse/jtcse-bert": { "id": "ucas/jtcse/jtcse-bert", "name": "JTCSE-BERT", "maker": "ucas", "family": "ucas/jtcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-28", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Two encoders are released and evaluated together. BertModel continued from a BERT-base-uncased-shaped checkpoint. Hugging Face config: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 30,522, max position embeddings 512, float32. Both tower configs set attention and hidden dropout to 0.1. The GitHub setup uses CLS pooling. Embedding width was not stated separately from the hidden size of 768.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JTCSE-BERT is a sentence-embedding checkpoint released by Tianyu Zong and Jungang Xu's group at the School of Computer Science and Technology, University of Chinese Academy of Sciences (CCIP lab). Twin-tower sentence-embedding ensemble. JTCSE adds a cross-attention path on top of the TNCSE training objective. The GitHub evaluation loads encoder 1 and encoder 2 with a BERT-base-uncased tokenizer. The weights were public on Hugging Face under UCASzty on 2025-04-28. Collected 2026-10-15. Status is active: the repos were still public and no deprecation notice was found.", "what_it_is_for": "The authors describe the family as a BERT-like model that computes sentence embedding vectors with unsupervised contrastive learning. Twin-tower sentence-embedding ensemble. JTCSE adds a cross-attention path on top of the TNCSE training objective. The GitHub evaluation loads encoder 1 and encoder 2 with a BERT-base-uncased tokenizer. The paper venue is arXiv 2505.02366, submitted 2025-05-05.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucas/jtcse/jtcse-bert-d": { "id": "ucas/jtcse/jtcse-bert-d", "name": "JTCSE-BERT-D", "maker": "ucas", "family": "ucas/jtcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-28", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "One encoder. BertModel continued from a BERT-base-uncased-shaped checkpoint. Hugging Face config: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 30,522, max position embeddings 512, float32. Attention and hidden dropout are 0.1. The GitHub setup uses CLS pooling. Embedding width was not stated separately from the hidden size of 768.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JTCSE-BERT-D is a sentence-embedding checkpoint released by Tianyu Zong and Jungang Xu's group at the School of Computer Science and Technology, University of Chinese Academy of Sciences (CCIP lab). Single-tower distillation of JTCSE-BERT. The README evaluates JTCSE_D/BERT/JTCSE_BERT_D. The weights were public on Hugging Face under UCASzty on 2025-04-28. Collected 2026-10-15. Status is active: the repos were still public and no deprecation notice was found.", "what_it_is_for": "The authors describe the family as a BERT-like model that computes sentence embedding vectors with unsupervised contrastive learning. Single-tower distillation of JTCSE-BERT. The README evaluates JTCSE_D/BERT/JTCSE_BERT_D. The paper venue is arXiv 2505.02366, submitted 2025-05-05.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucas/jtcse/jtcse-bert-ucd": { "id": "ucas/jtcse/jtcse-bert-ucd", "name": "JTCSE-BERT-UCD", "maker": "ucas", "family": "ucas/jtcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-28", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "One encoder. BertModel continued from a BERT-base-uncased-shaped checkpoint. Hugging Face config: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 30,522, max position embeddings 512, float32. Attention and hidden dropout are 0.1. The GitHub setup uses CLS pooling. Embedding width was not stated separately from the hidden size of 768.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JTCSE-BERT-UCD is a sentence-embedding checkpoint released by Tianyu Zong and Jungang Xu's group at the School of Computer Science and Technology, University of Chinese Academy of Sciences (CCIP lab). Single-tower distillation of the JTCSE-BERT UC procedure. The README evaluates JTCSE_UCD/JTCSE_BERT_UCD. The weights were public on Hugging Face under UCASzty on 2025-04-28. Collected 2026-10-15. Status is active: the repos were still public and no deprecation notice was found.", "what_it_is_for": "The authors describe the family as a BERT-like model that computes sentence embedding vectors with unsupervised contrastive learning. Single-tower distillation of the JTCSE-BERT UC procedure. The README evaluates JTCSE_UCD/JTCSE_BERT_UCD. The paper venue is arXiv 2505.02366, submitted 2025-05-05.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucas/jtcse/jtcse-roberta": { "id": "ucas/jtcse/jtcse-roberta", "name": "JTCSE-RoBERTa", "maker": "ucas", "family": "ucas/jtcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-28", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "Two encoders are released and evaluated together. RobertaModel continued from a RoBERTa-base-shaped checkpoint. Hugging Face config: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 50,265, max position embeddings 514, float32. The config field is 514, not a separately stated usable length. Encoder 1 dropout is 0.125. Encoder 2 dropout is 0.13. The two configs do not match each other. The GitHub setup uses CLS pooling. Embedding width was not stated separately from the hidden size of 768.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JTCSE-RoBERTa is a sentence-embedding checkpoint released by Tianyu Zong and Jungang Xu's group at the School of Computer Science and Technology, University of Chinese Academy of Sciences (CCIP lab). Twin-tower sentence-embedding ensemble on a RoBERTa-base-shaped pair, with the JTCSE cross-attention training. The weights were public on Hugging Face under UCASzty on 2025-04-28. Collected 2026-10-15. Status is active: the repos were still public and no deprecation notice was found.", "what_it_is_for": "The authors describe the family as a BERT-like model that computes sentence embedding vectors with unsupervised contrastive learning. Twin-tower sentence-embedding ensemble on a RoBERTa-base-shaped pair, with the JTCSE cross-attention training. The paper venue is arXiv 2505.02366, submitted 2025-05-05.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucas/jtcse/jtcse-roberta-d": { "id": "ucas/jtcse/jtcse-roberta-d", "name": "JTCSE-RoBERTa-D", "maker": "ucas", "family": "ucas/jtcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-28", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "One encoder. RobertaModel continued from a RoBERTa-base-shaped checkpoint. Hugging Face config: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 50,265, max position embeddings 514, float32. The config field is 514, not a separately stated usable length. Attention and hidden dropout are 0.1. The GitHub setup uses CLS pooling. Embedding width was not stated separately from the hidden size of 768.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JTCSE-RoBERTa-D is a sentence-embedding checkpoint released by Tianyu Zong and Jungang Xu's group at the School of Computer Science and Technology, University of Chinese Academy of Sciences (CCIP lab). Single-tower distillation of JTCSE-RoBERTa. The README path is JTCSE_D/RoBERTa/JTCSE_RoBERTa_D. The weights were public on Hugging Face under UCASzty on 2025-04-28. Collected 2026-10-15. Status is active: the repos were still public and no deprecation notice was found.", "what_it_is_for": "The authors describe the family as a BERT-like model that computes sentence embedding vectors with unsupervised contrastive learning. Single-tower distillation of JTCSE-RoBERTa. The README path is JTCSE_D/RoBERTa/JTCSE_RoBERTa_D. The paper venue is arXiv 2505.02366, submitted 2025-05-05.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucas/jtcse/jtcse-roberta-ucd": { "id": "ucas/jtcse/jtcse-roberta-ucd", "name": "JTCSE-RoBERTa-UCD", "maker": "ucas", "family": "ucas/jtcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-28", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "One encoder. RobertaModel continued from a RoBERTa-base-shaped checkpoint. Hugging Face config: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 50,265, max position embeddings 514, float32. The config field is 514, not a separately stated usable length. Attention and hidden dropout are 0.1. The GitHub setup uses CLS pooling. Embedding width was not stated separately from the hidden size of 768.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JTCSE-RoBERTa-UCD is a sentence-embedding checkpoint released by Tianyu Zong and Jungang Xu's group at the School of Computer Science and Technology, University of Chinese Academy of Sciences (CCIP lab). Single-tower distillation of the JTCSE-RoBERTa UC procedure. The weights were public on Hugging Face under UCASzty on 2025-04-28. Collected 2026-10-15. Status is active: the repos were still public and no deprecation notice was found.", "what_it_is_for": "The authors describe the family as a BERT-like model that computes sentence embedding vectors with unsupervised contrastive learning. Single-tower distillation of the JTCSE-RoBERTa UC procedure. The paper venue is arXiv 2505.02366, submitted 2025-05-05.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucas/tncse/tncse-bert": { "id": "ucas/tncse/tncse-bert", "name": "TNCSE-BERT", "maker": "ucas", "family": "ucas/tncse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-28", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Two encoders are released and evaluated together. BertModel continued from a BERT-base-uncased-shaped checkpoint. Hugging Face config: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 30,522, max position embeddings 512, float32. Both tower configs set attention and hidden dropout to 0.00025. The GitHub setup uses CLS pooling. Embedding width was not stated separately from the hidden size of 768.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TNCSE-BERT is a sentence-embedding checkpoint released by Tianyu Zong and Jungang Xu's group at the School of Computer Science and Technology, University of Chinese Academy of Sciences (CCIP lab). Twin-tower unsupervised sentence-embedding ensemble. The GitHub evaluation loads encoder 1 and encoder 2 together. The weights were public on Hugging Face under UCASzty on 2025-04-28. Collected 2026-10-15. Status is active: the repos were still public and no deprecation notice was found.", "what_it_is_for": "The authors describe the family as a BERT-like model that computes sentence embedding vectors with unsupervised contrastive learning. Twin-tower unsupervised sentence-embedding ensemble. The GitHub evaluation loads encoder 1 and encoder 2 together. The paper venue is AAAI 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucas/tncse/tncse-bert-d": { "id": "ucas/tncse/tncse-bert-d", "name": "TNCSE-BERT-D", "maker": "ucas", "family": "ucas/tncse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-28", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "One encoder. BertModel continued from a BERT-base-uncased-shaped checkpoint. Hugging Face config: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 30,522, max position embeddings 512, float32. Attention and hidden dropout are 0.1. The GitHub setup uses CLS pooling. Embedding width was not stated separately from the hidden size of 768.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TNCSE-BERT-D is a sentence-embedding checkpoint released by Tianyu Zong and Jungang Xu's group at the School of Computer Science and Technology, University of Chinese Academy of Sciences (CCIP lab). Single-tower distillation of the TNCSE-BERT ensemble. The README evaluates it with evaluation_D.py. The weights were public on Hugging Face under UCASzty on 2025-04-28. Collected 2026-10-15. Status is active: the repos were still public and no deprecation notice was found.", "what_it_is_for": "The authors describe the family as a BERT-like model that computes sentence embedding vectors with unsupervised contrastive learning. Single-tower distillation of the TNCSE-BERT ensemble. The README evaluates it with evaluation_D.py. The paper venue is AAAI 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucas/tncse/tncse-bert-ucd": { "id": "ucas/tncse/tncse-bert-ucd", "name": "TNCSE-BERT-UCD", "maker": "ucas", "family": "ucas/tncse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-28", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "One encoder. BertModel continued from a BERT-base-uncased-shaped checkpoint. Hugging Face config: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 30,522, max position embeddings 512, float32. Attention and hidden dropout are 0.1. The config _name_or_path is TNCSE_UC_D_BERT. The GitHub setup uses CLS pooling. Embedding width was not stated separately from the hidden size of 768.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TNCSE-BERT-UCD is a sentence-embedding checkpoint released by Tianyu Zong and Jungang Xu's group at the School of Computer Science and Technology, University of Chinese Academy of Sciences (CCIP lab). Single-tower distillation of the TNCSE-BERT UC inference ensemble. The README evaluates TNCSE_UC_D_BERT with evaluation_D.py. The weights were public on Hugging Face under UCASzty on 2025-04-28. Collected 2026-10-15. Status is active: the repos were still public and no deprecation notice was found.", "what_it_is_for": "The authors describe the family as a BERT-like model that computes sentence embedding vectors with unsupervised contrastive learning. Single-tower distillation of the TNCSE-BERT UC inference ensemble. The README evaluates TNCSE_UC_D_BERT with evaluation_D.py. The paper venue is AAAI 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucas/tncse/tncse-roberta": { "id": "ucas/tncse/tncse-roberta", "name": "TNCSE-RoBERTa", "maker": "ucas", "family": "ucas/tncse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-28", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "Two encoders are released and evaluated together. RobertaModel continued from a RoBERTa-base-shaped checkpoint. Hugging Face config: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 50,265, max position embeddings 514, float32. The config field is 514, not a separately stated usable length. Both tower configs set attention and hidden dropout to 0.02. The GitHub setup uses CLS pooling. Embedding width was not stated separately from the hidden size of 768.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TNCSE-RoBERTa is a sentence-embedding checkpoint released by Tianyu Zong and Jungang Xu's group at the School of Computer Science and Technology, University of Chinese Academy of Sciences (CCIP lab). Twin-tower unsupervised sentence-embedding ensemble on a RoBERTa-base-shaped pair. The weights were public on Hugging Face under UCASzty on 2025-04-28. Collected 2026-10-15. Status is active: the repos were still public and no deprecation notice was found.", "what_it_is_for": "The authors describe the family as a BERT-like model that computes sentence embedding vectors with unsupervised contrastive learning. Twin-tower unsupervised sentence-embedding ensemble on a RoBERTa-base-shaped pair. The paper venue is AAAI 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucas/tncse/tncse-roberta-d": { "id": "ucas/tncse/tncse-roberta-d", "name": "TNCSE-RoBERTa-D", "maker": "ucas", "family": "ucas/tncse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-28", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "One encoder. RobertaModel continued from a RoBERTa-base-shaped checkpoint. Hugging Face config: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 50,265, max position embeddings 514, float32. The config field is 514, not a separately stated usable length. Attention and hidden dropout are 0.1. The GitHub setup uses CLS pooling. Embedding width was not stated separately from the hidden size of 768.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TNCSE-RoBERTa-D is a sentence-embedding checkpoint released by Tianyu Zong and Jungang Xu's group at the School of Computer Science and Technology, University of Chinese Academy of Sciences (CCIP lab). Single-tower distillation of the TNCSE-RoBERTa ensemble. The weights were public on Hugging Face under UCASzty on 2025-04-28. Collected 2026-10-15. Status is active: the repos were still public and no deprecation notice was found.", "what_it_is_for": "The authors describe the family as a BERT-like model that computes sentence embedding vectors with unsupervised contrastive learning. Single-tower distillation of the TNCSE-RoBERTa ensemble. The paper venue is AAAI 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucas/tncse/tncse-roberta-ucd": { "id": "ucas/tncse/tncse-roberta-ucd", "name": "TNCSE-RoBERTa-UCD", "maker": "ucas", "family": "ucas/tncse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-04-28", "context_length": 514, "max_output_tokens": null, "parameters": "", "architecture": "One encoder. RobertaModel continued from a RoBERTa-base-shaped checkpoint. Hugging Face config: 12 layers, hidden size 768, 12 attention heads, intermediate size 3072, GELU, absolute position embeddings, vocabulary 50,265, max position embeddings 514, float32. The config field is 514, not a separately stated usable length. Attention and hidden dropout are 0.1. The config _name_or_path is TNCSE_UC_D_RoBERTa. The GitHub setup uses CLS pooling. Embedding width was not stated separately from the hidden size of 768.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TNCSE-RoBERTa-UCD is a sentence-embedding checkpoint released by Tianyu Zong and Jungang Xu's group at the School of Computer Science and Technology, University of Chinese Academy of Sciences (CCIP lab). Single-tower distillation of the TNCSE-RoBERTa UC inference ensemble. The weights were public on Hugging Face under UCASzty on 2025-04-28. Collected 2026-10-15. Status is active: the repos were still public and no deprecation notice was found.", "what_it_is_for": "The authors describe the family as a BERT-like model that computes sentence embedding vectors with unsupervised contrastive learning. Single-tower distillation of the TNCSE-RoBERTa UC inference ensemble. The paper venue is AAAI 2025.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucc/uccix/uccix-llama2-13b": { "id": "ucc/uccix/uccix-llama2-13b", "name": "UCCIX-Llama2-13B", "maker": "ucc", "family": "ucc/uccix", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-11", "context_length": 4096, "max_output_tokens": null, "parameters": "13051530240", "architecture": "LlamaForCausalLM continued from Llama 2 13B. config.json: 40 layers, hidden size 5120, intermediate size 13824, 40 attention heads, 40 key-value heads, SiLU, RoPE theta 10000, no rope scaling, vocab_size 35483, max_position_embeddings 4096, untied embeddings, torch dtype float32. _name_or_path is llama2-13b-irish-v2-full-2epoch. Hub safetensors total is 13,051,530,240 F32 parameters in 11 shards.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UCCIX-Llama2-13B is University College Cork's Irish-English continued pretrain of Llama 2 13B, released by the Reliable AI group (Khanh-Tung Tran, Barry O'Sullivan, and Hoang D. Nguyen). The weights were still public on the collection date, 2026-10-15. The arXiv paper is 13 May 2024. The Hugging Face repository was created on 11 June 2024.", "what_it_is_for": "The card calls it an Irish-English bilingual model and the first open Irish LLM from this group. It is a continued pretrain, not an instruction model. The paper's job is Irish understanding and generation under very little Irish text, plus the IrishQA and Irish MT-bench datasets. The instruction-following sibling is a separate checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucc/uccix/uccix-llama2-13b-instruct": { "id": "ucc/uccix/uccix-llama2-13b-instruct", "name": "UCCIX-Llama2-13B-Instruct", "maker": "ucc", "family": "ucc/uccix", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-06-11", "context_length": null, "max_output_tokens": null, "parameters": "13051530240", "architecture": "LlamaForCausalLM. The Hub API reports the same 13,051,530,240 F32 parameter count as UCCIX-Llama2-13B, in 11 shards. The card says it is supervised instruction fine-tuning of that checkpoint. config.json is gated, so layer counts and max_position_embeddings were not re-read. The card's chat template uses the headings ### Instruction, ### Input, and ### Response.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UCCIX-Llama2-13B-Instruct is University College Cork's instruction-tuned UCCIX, a supervised fine-tune of UCCIX-Llama2-13B by the Reliable AI group. The paper of 13 May 2024 names it UCCIX-Instruct. The Hugging Face repository was created on 11 June 2024 and was still public, but gated, on the collection date 2026-10-15.", "what_it_is_for": "The card says supervised instruction fine-tuning was added so the bilingual model follows human instructions. The paper uses it for multi-turn Irish and English chat, scored on MT-bench and an Irish translation of MT-bench. It is the checkpoint the base card tells people to run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucc/uccix/uccix-mistral-24b": { "id": "ucc/uccix/uccix-mistral-24b", "name": "UCCIX-Mistral-24B", "maker": "ucc", "family": "ucc/uccix", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-17", "context_length": 131072, "max_output_tokens": null, "parameters": "24084556800", "architecture": "Mistral3ForConditionalGeneration, continued pre-training and supervised fine-tuning of Mistral-Small-3.2-24B-Instruct-2506 as stated in the ReliableAI readme. Text config: 40 layers, hidden size 5120, intermediate size 32768, 32 heads, 8 key-value heads, head dim 128, SiLU, RoPE theta 1000000000, sliding_window null, vocab_size 138220, max_position_embeddings 131072. Vision config: Pixtral, 24 layers, hidden size 1024, image size 1540, patch size 14. dtype bfloat16. transformers 4.56.0. config sets unsloth_fixed true. Hub safetensors total is 24,084,556,800 BF16 parameters in 5 shards. The base card's safetensors total is 24,011,361,280 and its vocab_size is 131072. The difference is 73,195,520 parameters and 7,148 tokens. 2 x 5120 x 7148 equals 73,195,520, which matches an untied embedding plus output row for the added tokens.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UCCIX-Mistral-24B is University College Cork's later UCCIX checkpoint, released by Reliable AI on 17 November 2025. The readme calls it the newest UCCIX iteration: continued pre-training and supervised fine-tuning of Mistral-Small-3.2-24B-Instruct-2506 for Irish and English. The weights were still public on the collection date, 2026-10-15. No paper is linked from the repo.", "what_it_is_for": "The readme says advanced Irish and English use after a self-adaptation pipeline of continued pre-training and supervised fine-tuning. It does not name a product API or a benchmark. The shipped chat template identifies the model as UCCIX from Reliable AI Lab at University College Cork and says it can read images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucd/vm-management/drl-vm-management-agent": { "id": "ucd/vm-management/drl-vm-management-agent", "name": "DRL VM Management Agent", "maker": "ucd", "family": "ucd/vm-management", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-01-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Maskable PPO policy exported to ONNX opset 18. Two inputs: a fixed 801-float infrastructure vector (one datacenter, at most 32 hosts) and a scalar of waiting job cores, clipped to 8. Output is a length-4 MultiDiscrete vector: action type (do nothing, create VM, destroy VM), host id, VM id, and VM type id (Small 2 cores, Medium 4, Large 8). Hosts are specified as 16 CPU cores. Layer widths are not published.", "modalities": { "input": [ "infrastructure-state", "job-demand" ], "output": [ "discrete-action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The DRL VM Management Agent is a Maskable PPO policy from University College Dublin, released for the EU MLSysOps project. Weights and an inference wrapper are public, Zenodo marks the release active, and this dossier was collected on 2026-10-15. It is a local ONNX checkpoint for one simulated datacenter, not a hosted model API.", "what_it_is_for": "The makers describe it as a deep reinforcement-learning agent that chooses virtual-machine placement and lifecycle actions. It reads a flattened datacenter tree and the cores a waiting job needs, then returns one of do nothing, create a VM, or destroy a VM, with a host and a size. The [README](https://github.com/mlsysops-eu/model-cluster-vm-management) and the [Zenodo record](https://zenodo.org/records/18177473) are the model card. No paper was found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucdavis/cropgan/cropgan": { "id": "ucdavis/cropgan/cropgan", "name": "CropGAN", "maker": "ucdavis", "family": "ucdavis/cropgan", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-2-Clause", "release_date": "2021-08-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Semantically constrained CycleGAN. The paper says generators and discriminators match Zhu et al. CycleGAN, with a fixed YOLOv3-tiny task network supplying a semantic loss so translated grapes keep position and size. The shipped checkpoint options name the code model double_task_cycle_gan, generator resnet_9blocks (ngf 64, 3 input and output channels), discriminator basic (ndf 64, 3 layers), LSGAN, instance norm, no dropout, load and crop 256. Those saved options set lambda_yolo_a and lambda_yolo_b to 0. The paper says a zero task weight collapses the method to CycleGAN. The README training example uses nonzero YOLO lambdas. Authors Zhenghao Fei, Alex Olenskyj, Brian N. Bailey, and Mason Earles, University of California, Davis, Plant AI and Biophysics Lab.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CropGAN is an open-source image-to-image GAN from the Plant AI and Biophysics Lab at the University of California, Davis. Zhenghao Fei, Alex Olenskyj, Brian N. Bailey, and Mason Earles are the authors. The paper was submitted to arXiv on 30 August 2021 and presented at the ICCV 2021 workshops. The repository still publishes a generator pair, so status is active. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab uses CropGAN to translate synthetic 3D vineyard renders into photorealistic day or night grape images while keeping fruit position and size, so a detector can train with fewer hand labels. The public notebook loads the pretrained Semantic Consistent GAN and generates target-domain images from synthetic source images. There is no hosted API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucf/geoclip/geoclip": { "id": "ucf/geoclip/geoclip", "name": "GeoCLIP", "maker": "ucf", "family": "ucf/geoclip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-09-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-to-GPS retrieval model. The image encoder is a frozen OpenAI CLIP ViT-L/14 plus two trained linear layers. The location encoder is a hierarchical MLP (four hidden layers and one output layer) over equal-Earth positional encoding with random Fourier features. Training is contrastive on image-GPS pairs. The CLIP backbone stays frozen. Inference retrieves against a GPS gallery rather than emitting a generated sentence.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GeoCLIP is an image-to-GPS retrieval model from the Center for Research in Computer Vision at the University of Central Florida (Vicente Vivanco Cepeda, Gaurav Kumar Nayak, and Mubarak Shah). It was announced on arXiv on 27 September 2023 and accepted at NeurIPS 2023. Trained weights still ship in the official GitHub package and on PyPI as of 2026-10-15. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents GeoCLIP as worldwide image geo-localization: given a photo, retrieve GPS coordinates anywhere on Earth. It matches an image embedding to a gallery of location embeddings instead of classifying a fixed set of geographic cells. The README also exposes the location encoder on its own, as a 512-d GPS embedding for other geo-aware models. The paper shows that a text query can be localized through the frozen CLIP text encoder without a separate text-training stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uchicago/da-wand/da-wand": { "id": "uchicago/da-wand/da-wand", "name": "DA Wand", "maker": "uchicago", "family": "uchicago/da-wand", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-12-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MeshCNN edge encoder plus a 3-layer MLP segmentation classifier. A differentiable probability-weighted LSCM layer (wLSCM) turns soft face probabilities into a UV map so distortion can supervise training. Inference takes a mesh and a selected triangle and returns a local segmentation for parameterization. The released weights are the file checkpoints/dawand_net.pth.", "modalities": { "input": [ "mesh" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DA Wand is a University of Chicago 3DL mesh model that maps a user-selected triangle to a local segmentation whose UV parameterization stays low-distortion. Richard Liu and Rana Hanocka are at the University of Chicago. Noam Aigerman and Vladimir G. Kim are at Adobe Research. Code and the checkpoint `dawand_net.pth` are in the Threedle GitHub repo, not an Adobe product. The preprint is 2022-12-13. CVPR 2023. Collected 2026-10-15.", "what_it_is_for": "The project page describes interactive decaling, texturing, and painting. The user picks a point. The network grows a region that can be parameterized with bounded isometric distortion. A Blender extension wraps the same checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uchicago/geocode/geocode": { "id": "uchicago/geocode/geocode", "name": "GeoCode", "maker": "uchicago", "family": "uchicago/geocode", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-12-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder that predicts the parameters of a Blender geometry-node program. The point-cloud encoder is a four-layer DGCNN (k=20 in the authors' tests). The sketch encoder is VGG. Each program parameter has its own three-layer MLP decoder. Continuous parameters are discretized and trained with cross-entropy. Chair, vase, and table programs expose 59, 39, and 36 interpretable parameters. The network does not emit a mesh directly. Blender executes the program.", "modalities": { "input": [ "point-cloud", "image" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GeoCode is a University of Chicago 3DL model that maps a point cloud or a sketch to the parameters of a procedural shape program, then builds a mesh in Blender. Ofek Pearl (Tel Aviv University and University of Chicago), Itai Lang (University of Chicago), Yuhua Hu (University of Chicago), Raymond A. Yeh (Purdue), and Rana Hanocka (University of Chicago). Preprint 2022-12-19. Computer Graphics Forum DOI 10.1111/cgf.15276, 2025-02-01. arXiv v2 revised 2025-03-20. Checkpoints for chair, vase, and table are released by threedle. Collected 2026-10-15.", "what_it_is_for": "The paper presents programs whose parameters stay structurally valid when a person edits them. The network infers those parameters from a point cloud or a sketch so a recovered shape can be edited by changing a seat, a leg, or a profile instead of moving vertices. Chair, vase, and table are the released neural domains. A March 2025 update adds a Blender add-on and cabinet and ceiling-lamp programs written with that add-on.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uchicago/hyperfields/hyperfields": { "id": "uchicago/hyperfields/hyperfields", "name": "HyperFields", "maker": "uchicago", "family": "uchicago/hyperfields", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "A dynamic hypernetwork maps frozen BERT token embeddings to the weights of a NeRF MLP. The GitHub training command uses 6 NeRF layers, hidden size 64, Adam learning rate 0.0001, conditioning_model bert, and arch detach_dynamic_hyper_transformer. A later restatement of the same system (arXiv 2507.10446) says the hypernetwork transformer has 6 self-attention blocks, 12 heads, head dimension 16, and that the dynamic MLP modules are two-layer ReLU networks predicting a 6-layer NeRF of hidden size 64 with skip connections every two layers, plus an Instant-NGP hash grid. The student checkpoint all_student is the zero-shot model. Per-shape teachers are distilled into that student. The paper says one network fits over a hundred scenes. The restatement shows a pack of 9 objects by 12 prompts (108 scenes). Output is a radiance field rendered to images, not a text token sequence.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperFields is a text-to-NeRF model whose code and checkpoints are released by the University of Chicago 3DL lab (threedle). Sudarshan Babu (TTIC), Richard Liu (University of Chicago), and Avery Zhou (TTIC and University of Chicago) share first authorship. Michael Maire and Rana Hanocka are at the University of Chicago. Greg Shakhnarovich is at TTIC. Preprint 2023-10-26. arXiv v3 revised 2024-06-13. The GitHub title says ICML 2024. Collected 2026-10-15.", "what_it_is_for": "The abstract describes a single forward pass from text to a Neural Radiance Field, with optional finetuning. A dynamic hypernetwork maps token embeddings onto NeRF weights. Distillation packs many teacher scenes into that one network so some in-distribution prompts can be rendered zero-shot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uchicago/loopdraw/loopdraw": { "id": "uchicago/loopdraw/loopdraw", "name": "LoopDraw", "maker": "uchicago", "family": "uchicago/loopdraw", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-12-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive model over closed polygonal loops sliced from a mesh. Each loop is resampled to N=32 vertices and stored as a 65-value token (64 coordinates plus one binary level-up flag). A Gaussian latent is trained with a KL term and KL annealing. Reconstruction is L2 on the coordinates plus binary cross-entropy on the flag. A mesh is recovered from the decoded loops with Poisson reconstruction. Layer counts are in the supplemental and were not copied. Category checkpoints are separate weights of this one model.", "modalities": { "input": [ "mesh" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LoopDraw is a University of Chicago 3DL generative mesh model. It encodes a shape as a sequence of planar loops and decodes new or edited loops back into a mesh. Nam Anh Dinh and Rana Hanocka are at the University of Chicago. Haochen Wang and Greg Shakhnarovich are at TTI-Chicago. Code and checkpoints are in the threedle repository. Preprint 2022-12-09. arXiv v2 revised 2024-05-29. Collected 2026-10-15.", "what_it_is_for": "The paper uses the loop sequence for synthesis and for edits during decoding: transforming loops, or transplanting loops such as handles from a donor shape. Two released categories are COSEG vases and ShapeNet sofas.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uchicago/meshfm/meshfm": { "id": "uchicago/meshfm/meshfm", "name": "MeshFM", "maker": "uchicago", "family": "uchicago/meshfm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-07-30", "context_length": null, "max_output_tokens": null, "parameters": "125500000", "architecture": "Feedforward point network that maps a mesh or point cloud to a continuous triplane feature field. One forward pass returns a 1536-d descriptor at a queried 3D point. The card lists a triplane of 3 planes at 128 by 128 and an encoder that sees 100k surface points. Training is two-stage: per-shape teacher distillation of DINOv2 features with SAM aliasing correction, then a student that regresses that field. SO(3) augmentation is applied during training. No 3D part labels are used. The paper describes a PVCNN-style backbone similar to PartField, with rotation robustness coming from augmentation rather than a new operator.", "modalities": { "input": [ "mesh", "point-cloud" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MeshFM is a University of Chicago 3DL (Threedle) feedforward 3D feature model. Authors are Jinfan Zhou, Richard Liu, Itai Lang, and Rana Hanocka, all at the University of Chicago. The arXiv preprint is dated 2026-07-30 and the GitHub citation lists ECCV 2026. Weights and inference code are public. Collected 2026-10-15.", "what_it_is_for": "The README says a single forward pass turns a mesh or point cloud into a continuous feature field. Query any 3D point on or near the surface and get a 1536-dimensional descriptor. The paper uses those features zero-shot for part segmentation, dense correspondence, classification, and handle-based mesh deformation. Training uses 2D foundation-model features, not 3D part labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/danq/danq": { "id": "uci/danq/danq", "name": "DanQ", "maker": "uci", "family": "uci/danq", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2016", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid convolutional and bidirectional LSTM. DanQ_train.py (Keras 0.2) uses Convolution1D with 320 filters of length 26 on 1000 bp one-hot DNA, max-pool length 13 stride 13, dropout 0.2, a bidirectional LSTM with 320 units each direction, dropout 0.5, dense 925 ReLU, and dense 919 sigmoid. Loss is binary cross-entropy, optimizer RMSprop, batch size 100, at most 60 epochs.", "modalities": { "input": [ "dna-sequence" ], "output": [ "chromatin-features" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DanQ is a University of California, Irvine model in the danq family. Predicts the function of non-coding DNA from sequence. It is trained on the same chromatin-feature labels as DeepSEA and is distributed as a single HDF5 checkpoint plus a test script. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts the function of non-coding DNA from sequence. It is trained on the same chromatin-feature labels as DeepSEA and is distributed as a single HDF5 checkpoint plus a test script.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/danq/danq-jaspar": { "id": "uci/danq/danq-jaspar", "name": "DanQ-JASPAR", "maker": "uci", "family": "uci/danq", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2016", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Larger DanQ-style convolutional and recurrent network. The README says about half of the motifs are initialized from JASPAR motifs. Trained by DanQ-JASPAR_train.py rather than by the DanQ script.", "modalities": { "input": [ "dna-sequence" ], "output": [ "chromatin-features" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DanQ-JASPAR is a University of California, Irvine model in the danq family. Same DNA-function task as DanQ, with convolution filters partly initialized from JASPAR motifs. The lab ships a separate weight file and a separate test script. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Same DNA-function task as DanQ, with convolution filters partly initialized from JASPAR motifs. The lab ships a separate weight file and a separate test script.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/factornet/factornet": { "id": "uci/factornet/factornet", "name": "FactorNet", "maker": "uci", "family": "uci/factornet", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2019", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Convolutional-recurrent network extending DanQ. It adds real-valued nucleotide-resolution signals such as DNase-seq, plus genome annotations and expression metadata. The paper describes several challenge models that differ by training setup and feature set, not as separate products.", "modalities": { "input": [ "dna-sequence", "genomic-signal" ], "output": [ "transcription-factor-binding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FactorNet is a University of California, Irvine model in the factornet family. Predicts cell-type-specific transcription-factor binding from sequence and assays. It was a top entry in the ENCODE-DREAM in vivo transcription-factor binding challenge. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts cell-type-specific transcription-factor binding from sequence and assays. It was a top entry in the ENCODE-DREAM in vivo transcription-factor binding challenge.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scfan/scfan": { "id": "uci/scfan/scfan", "name": "scFAN", "maker": "uci", "family": "uci/scfan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2020-12-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Deep model pretrained on bulk ATAC-seq, DNA sequence, and ChIP-seq, then applied to single-cell ATAC-seq. The repository trains separate multi-task models rather than one universal checkpoint.", "modalities": { "input": [ "dna-sequence", "atac-seq" ], "output": [ "transcription-factor-binding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "scFAN is a University of California, Irvine model in the scfan family. Predicts transcription-factor binding in single cells from chromatin accessibility. The lab calls the released checkpoints pretrained models and documents bulk and single-cell prediction scripts. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts transcription-factor binding in single cells from chromatin accessibility. The lab calls the released checkpoints pretrained models and documents bulk and single-cell prediction scripts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/3dpro": { "id": "uci/scratch/3dpro", "name": "3Dpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2005", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "De novo conformational search. Fragment replacement from a PDB fragment library plus random perturbations, accepted or rejected by simulated annealing with linear cooling. Energy uses predicted structural features and PDB statistical terms. The lowest-energy model among random seeds is returned. The explanation page says structural templates are not used.", "modalities": { "input": [ "protein-sequence" ], "output": [ "tertiary-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "3Dpro is a University of California, Irvine model in the scratch family. Predicts a C-alpha tertiary structure from sequence. The form says 3Dpro has not been retrained since 2005. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts a C-alpha tertiary structure from sequence. The form says 3Dpro has not been retrained since 2005.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/abtmpro": { "id": "uci/scratch/abtmpro", "name": "ABTMpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2011", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Support vector machine using amino-acid composition and properties, reduced-alphabet composition, predicted secondary structure, and evolutionary information.", "modalities": { "input": [ "protein-sequence" ], "output": [ "transmembrane-class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ABTMpro is a University of California, Irvine model in the scratch family. Predicts whether a sequence is a transmembrane protein and, if so, the probability it is alpha-helical or a beta-barrel. The form says it has not been retrained since 2011. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts whether a sequence is a transmembrane protein and, if so, the probability it is alpha-helical or a beta-barrel. The form says it has not been retrained since 2011.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/accpro": { "id": "uci/scratch/accpro", "name": "ACCpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic, non-commercial research use only (Institute for Genomics and Bioinformatics, University of California, Irvine). Commercial use requires a separate license.", "release_date": "2021", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Relative solvent-accessibility classifier at a 25 percent exposure threshold. Release 6.0 is the deep profile model in SCRATCH-1D 2.0. Older releases used 1D-RNNs on PSI-BLAST profiles.", "modalities": { "input": [ "protein-sequence" ], "output": [ "solvent-accessibility" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ACCpro is a University of California, Irvine model in the scratch family. Classifies each residue as buried or exposed at the 25 percent relative solvent-accessibility threshold. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Classifies each residue as buried or exposed at the 25 percent relative solvent-accessibility threshold.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/accpro20": { "id": "uci/scratch/accpro20", "name": "ACCpro20", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic, non-commercial research use only (Institute for Genomics and Bioinformatics, University of California, Irvine). Commercial use requires a separate license.", "release_date": "2021", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Twenty-class relative solvent-accessibility predictor, thresholds from 0 percent to 95 percent in 5 percent steps. Release 6.0 ships inside SCRATCH-1D 2.0.", "modalities": { "input": [ "protein-sequence" ], "output": [ "solvent-accessibility" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ACCpro20 is a University of California, Irvine model in the scratch family. Predicts relative solvent accessibility at every 5 percent threshold from 0 percent through 95 percent. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts relative solvent accessibility at every 5 percent threshold from 0 percent through 95 percent.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/antigenpro": { "id": "uci/scratch/antigenpro", "name": "ANTIGENpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2010-10-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage, alignment-free model. Several representations of the primary sequence are scored by five machine-learning algorithms. A final SVM predicts whether the protein is antigenic and a probability. Trained on protein-microarray reactivity for five pathogens plus known protective antigens.", "modalities": { "input": [ "protein-sequence" ], "output": [ "antigenicity" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ANTIGENpro is a University of California, Irvine model in the scratch family. Predicts whether a protein is a protective antigen, without requiring homology to known antigens. The form lists it and says it has not been retrained since 2010. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts whether a protein is a protective antigen, without requiring homology to known antigens. The form lists it and says it has not been retrained since 2010.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/bepro": { "id": "uci/scratch/bepro", "name": "BEpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2008", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-sequence" ], "output": [ "epitope-score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BEpro is a University of California, Irvine model in the scratch family. Discontinuous B-cell epitope predictor. The form says it was formerly called PEPITO and dates it to 2008. It is in the group of additional predictors that have not been retrained since the original publication. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Discontinuous B-cell epitope predictor. The form says it was formerly called PEPITO and dates it to 2008. It is in the group of additional predictors that have not been retrained since the original publication.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/betapro": { "id": "uci/scratch/betapro", "name": "BETApro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic, non-commercial research use only (Institute for Genomics and Bioinformatics, University of California, Irvine). Commercial use requires a separate license.", "release_date": "2005", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three stages: recursive neural networks for inter-strand beta-residue pairing probabilities, dynamic programming for strand alignments, and graph matching for sheet topology.", "modalities": { "input": [ "protein-sequence" ], "output": [ "beta-sheet" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BETApro is a University of California, Irvine model in the scratch family. Predicts beta-residue pairs, beta-strand pairs, strand alignment, pairing direction, and beta-sheet topology. Release 1.0 is the Linux download. The form lists it as an additional predictor from 2005. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts beta-residue pairs, beta-strand pairs, strand alignment, pairing direction, and beta-sheet topology. Release 1.0 is the Linux download. The form lists it as an additional predictor from 2005.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/cmappro": { "id": "uci/scratch/cmappro", "name": "CMAPpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2012", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Ensembles of deep neural networks on two-dimensional profiles from PSI-BLAST, plus predicted secondary structure, solvent accessibility, and coarse contacts and orientations from two-dimensional RNNs.", "modalities": { "input": [ "protein-sequence" ], "output": [ "contact-map" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CMAPpro is a University of California, Irvine model in the scratch family. Predicts residue-residue contact maps. The form says CMAPpro has not been retrained since 2012. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts residue-residue contact maps. The form says CMAPpro has not been retrained since 2012.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/cobepro": { "id": "uci/scratch/cobepro", "name": "COBEpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2008-12-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-step SVM. Short peptide fragments are scored first. Residue epitope propensity is then computed from the fragment scores. Predicted or known secondary structure and solvent accessibility can be added.", "modalities": { "input": [ "protein-sequence" ], "output": [ "epitope-score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "COBEpro is a University of California, Irvine model in the scratch family. Scores continuous B-cell epitope propensity for fragments and for residues inside an antigen. The form still lists COBEpro. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Scores continuous B-cell epitope propensity for fragments and for residues inside an antigen. The form still lists COBEpro.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/conpro": { "id": "uci/scratch/conpro", "name": "CONpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2002", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Ensemble of 10 one-dimensional RNNs. Input is a PSI-BLAST multiple alignment. Contact radius is 12 angstroms. Output is whether each residue has more or fewer contacts than the average for that amino acid.", "modalities": { "input": [ "protein-sequence" ], "output": [ "contact-number" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CONpro is a University of California, Irvine model in the scratch family. Predicts whether each residue's contact count is above or below average. The form says CONpro has not been retrained since 2002. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts whether each residue's contact count is above or below average. The form says CONpro has not been retrained since 2002.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/dipro": { "id": "uci/scratch/dipro", "name": "DIpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic, non-commercial research use only (Institute for Genomics and Bioinformatics, University of California, Irvine). Commercial use requires a separate license.", "release_date": "2006", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two steps: an SVM decides whether disulfide bonds exist, then a 2D recurrent neural network plus graph matching predicts the bond count and pairing. Kernel methods are also described in the 2006 paper.", "modalities": { "input": [ "protein-sequence" ], "output": [ "disulfide-bonds" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DIpro is a University of California, Irvine model in the scratch family. Predicts whether a protein has disulfide bonds, how many, the bonding state of each cysteine, and the bonded pairs. Release 2.0 is the downloadable package. The form says it has not been retrained since 2006. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts whether a protein has disulfide bonds, how many, the bonding state of each cysteine, and the bonded pairs. Release 2.0 is the downloadable package. The form says it has not been retrained since 2006.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/dispro": { "id": "uci/scratch/dispro", "name": "DISpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic, non-commercial research use only (Institute for Genomics and Bioinformatics, University of California, Irvine). Commercial use requires a separate license.", "release_date": "2005", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "1D-RNN. Inputs are the sequence profile, predicted secondary structure, and predicted relative solvent accessibility. Disorder probability is also thresholded at 0.5.", "modalities": { "input": [ "protein-sequence" ], "output": [ "disorder" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DISpro is a University of California, Irvine model in the scratch family. Predicts which residues are intrinsically disordered. The SCRATCH form still offers DISpro and says it has not been retrained since 2005. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts which residues are intrinsically disordered. The SCRATCH form still offers DISpro and says it has not been retrained since 2005.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/dompro": { "id": "uci/scratch/dompro", "name": "DOMpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic, non-commercial research use only (Institute for Genomics and Bioinformatics, University of California, Irvine). Commercial use requires a separate license.", "release_date": "2006", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "1D-RNN. Inputs are the sequence profile, predicted secondary structure, and predicted relative solvent accessibility. Residues are classified as domain-boundary or not, then domains are inferred.", "modalities": { "input": [ "protein-sequence" ], "output": [ "domain-boundaries" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DOMpro is a University of California, Irvine model in the scratch family. Predicts protein domain boundaries from sequence. Release 1.0 is downloadable. The form says it has not been retrained since 2006. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts protein domain boundaries from sequence. Release 1.0 is downloadable. The form says it has not been retrained since 2006.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/foldpro": { "id": "uci/scratch/foldpro", "name": "FOLDpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2006-03-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage template ranking. Alignment methods produce pairwise similarity features. Profile-profile alignment plus predicted secondary structure, solvent accessibility, contact map, and beta-strand pairing produce structural-compatibility features. An SVM scores whether the query and a template share a fold, and templates are ranked by that score.", "modalities": { "input": [ "protein-sequence" ], "output": [ "fold-recognition" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FOLDpro is a University of California, Irvine model in the scratch family. Ranks structural templates for a query protein. The form lists FOLDpro (2006) as a template-based 3D predictor that has not been retrained since publication. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Ranks structural templates for a query protein. The form lists FOLDpro (2006) as a template-based 3D predictor that has not been retrained since publication.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/mupro": { "id": "uci/scratch/mupro", "name": "MUpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic, non-commercial research use only (Institute for Genomics and Bioinformatics, University of California, Irvine). Commercial use requires a separate license.", "release_date": "2006", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Support vector machine for the sign and, in the paper title, the stability change of a single-site mutation. Download is release 1.1.", "modalities": { "input": [ "protein-sequence" ], "output": [ "stability-change" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MUpro is a University of California, Irvine model in the scratch family. Predicts how a single amino-acid mutation changes protein stability (delta-delta-G) from sequence. Release 1.1 is the Linux download. The form lists it as an additional predictor from 2005 and says those additional predictors have not been retrained since the original publication. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts how a single amino-acid mutation changes protein stability (delta-delta-G) from sequence. Release 1.1 is the Linux download. The form lists it as an additional predictor from 2005 and says those additional predictors have not been retrained since the original publication.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/selectpro": { "id": "uci/scratch/selectpro", "name": "SELECTpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic, non-commercial research use only (Institute for Genomics and Bioinformatics, University of California, Irvine). Commercial use requires a separate license.", "release_date": "2008", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Structure-based energy function for choosing among protein models. The paper title calls it resistant to blunders. The full package bundles feature predictors. SELECTpro Solo does not.", "modalities": { "input": [ "protein-structure" ], "output": [ "model-score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SELECTpro is a University of California, Irvine model in the scratch family. Scores and selects protein models and predicts side chains. The form lists SELECTpro (2008) as an additional predictor. Two Linux packages are on the download page. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Scores and selects protein models and predicts side chains. The form lists SELECTpro (2008) as an additional predictor. Two Linux packages are on the download page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/sidepro": { "id": "uci/scratch/sidepro", "name": "SIDEpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic, non-commercial research use only (Institute for Genomics and Bioinformatics, University of California, Irvine). Commercial use requires a separate license.", "release_date": "2012", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Machine-learning side-chain predictor. The download page says it can handle non-standard amino acids, including post-translational modifications. A rotamer library is shipped with the package.", "modalities": { "input": [ "protein-backbone" ], "output": [ "side-chain-conformation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SIDEpro is a University of California, Irvine model in the scratch family. Predicts side-chain conformations for a protein backbone, including non-standard amino acids and post-translational modifications. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts side-chain conformations for a protein backbone, including non-standard amino acids and post-translational modifications.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/solpro": { "id": "uci/scratch/solpro", "name": "SOLpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic, non-commercial research use only (Institute for Genomics and Bioinformatics, University of California, Irvine). Commercial use requires a separate license.", "release_date": "2009", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage SVM. First-layer classifiers each take a different representation of the primary sequence. A final SVM predicts soluble or not and a probability.", "modalities": { "input": [ "protein-sequence" ], "output": [ "solubility" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SOLpro is a University of California, Irvine model in the scratch family. Predicts whether a protein will be soluble when overexpressed in E. coli. The form says SOLpro has not been retrained since 2009. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts whether a protein will be soluble when overexpressed in E. coli. The form says SOLpro has not been retrained since 2009.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/sspro": { "id": "uci/scratch/sspro", "name": "SSpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic, non-commercial research use only (Institute for Genomics and Bioinformatics, University of California, Irvine). Commercial use requires a separate license.", "release_date": "2021", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Deep network on sequence profiles, plus a homology module (HOMOLpro). Release 6.0 is the TensorFlow-trained predictor reimplemented as Python/numpy inside SCRATCH-1D 2.0. Earlier releases used ensembles of one-dimensional recursive neural networks.", "modalities": { "input": [ "protein-sequence" ], "output": [ "secondary-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SSpro is a University of California, Irvine model in the scratch family. Predicts three-class protein secondary structure (helix, strand, other) from sequence. The SCRATCH form and the SCRATCH-1D download both ship release 6.0 as the current predictor. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts three-class protein secondary structure (helix, strand, other) from sequence. The SCRATCH form and the SCRATCH-1D download both ship release 6.0 as the current predictor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/sspro8": { "id": "uci/scratch/sspro8", "name": "SSpro8", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic, non-commercial research use only (Institute for Genomics and Bioinformatics, University of California, Irvine). Commercial use requires a separate license.", "release_date": "2021", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Eight-class secondary-structure network shipped with SSpro 6.0 inside SCRATCH-1D 2.0. The explanation page says it is a different system from three-class SSpro.", "modalities": { "input": [ "protein-sequence" ], "output": [ "secondary-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SSpro8 is a University of California, Irvine model in the scratch family. Predicts DSSP eight-class secondary structure (H, G, I, E, B, T, S, C) from a protein sequence. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts DSSP eight-class secondary structure (H, G, I, E, B, T, S, C) from a protein sequence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/svmcon": { "id": "uci/scratch/svmcon", "name": "SVMcon", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic, non-commercial research use only (Institute for Genomics and Bioinformatics, University of California, Irvine). Commercial use requires a separate license.", "release_date": "2007", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Support vector machines for medium- and long-range residue-residue contacts.", "modalities": { "input": [ "protein-sequence" ], "output": [ "contact-map" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SVMcon is a University of California, Irvine model in the scratch family. Predicts residue-residue contacts in CASP format. The form says SVMcon has not been retrained since 2007. Release 1.0 is linked from the explanation page. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts residue-residue contacts in CASP format. The form says SVMcon has not been retrained since 2007. Release 1.0 is linked from the explanation page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/tmbpro": { "id": "uci/scratch/tmbpro", "name": "TMBpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2007-11-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three-stage pipeline for transmembrane beta-barrels: TMBpro-SS (two-class secondary structure), TMBpro-CON (beta-contacts), and TMBpro-3D (tertiary structure of the transmembrane segments).", "modalities": { "input": [ "protein-sequence" ], "output": [ "tertiary-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TMBpro is a University of California, Irvine model in the scratch family. Predicts secondary structure, beta-contacts, and tertiary structure of transmembrane beta-barrel proteins. The form lists TMBpro (2008) as an additional predictor that has not been retrained since publication. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts secondary structure, beta-contacts, and tertiary structure of transmembrane beta-barrel proteins. The form lists TMBpro (2008) as an additional predictor that has not been retrained since publication.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/scratch/viralpro": { "id": "uci/scratch/viralpro", "name": "VIRALpro", "maker": "uci", "family": "uci/scratch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic, non-commercial research use only (Institute for Genomics and Bioinformatics, University of California, Irvine). Commercial use requires a separate license.", "release_date": "2016", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SVM using amino-acid composition, SSpro secondary structure, and a boosted linear combination of HMM e-values from 3380 HMMs built on capsid and tail contact fragments.", "modalities": { "input": [ "protein-sequence" ], "output": [ "viral-structure-class" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VIRALpro is a University of California, Irvine model in the scratch family. Predicts whether a protein sequence is a viral capsid protein or a viral tail protein. Release 1.0 is the Linux download, 25 MB. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts whether a protein sequence is a viral capsid protein or a viral tail protein. Release 1.0 is the Linux download, 25 MB.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uci/ufold/ufold": { "id": "uci/ufold/ufold", "name": "UFold", "maker": "uci", "family": "uci/ufold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2021-11-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fully convolutional network on an image-like encoding of the RNA sequence and base-pairing rules. The paper says it does not use thermodynamic free-energy minimization as the model.", "modalities": { "input": [ "rna-sequence" ], "output": [ "rna-secondary-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UFold is a University of California, Irvine model in the ufold family. Predicts RNA secondary structure, including pseudoknots, from sequence. A web server is published at https://ufold.ics.uci.edu. The lab's README says the server backend was updated to v1.3 and that a CPU server exists. Status on 2026-10-15 is active. Confidence is high because the description comes from the lab's own pages.", "what_it_is_for": "Predicts RNA secondary structure, including pseudoknots, from sequence. A web server is published at https://ufold.ics.uci.edu. The lab's README says the server backend was updated to v1.3 and that a CPU server exists.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/britllm/caernarfon-3b": { "id": "ucl/britllm/caernarfon-3b", "name": "Caernarfon 3B", "maker": "ucl", "family": "ucl/britllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Open Data Commons Attribution License (ODC-By) v1.0. Stated on https://llm.org.uk/ and on the Hugging Face card (license: odc-by). This is a data license, not an OSI software license. Attribution is required.", "release_date": "2024-06-21", "context_length": 2048, "max_output_tokens": null, "parameters": "2670103040", "architecture": "LlamaForCausalLM decoder-only Transformer. 32 layers, hidden size 2560, 32 attention heads, 32 key-value heads, intermediate size 6784, SiLU, RoPE theta 10000, untied embeddings, vocabulary 32000. Weights are float16. SentencePiece tokenizer.model is in the repo; bos and eos tokens are both .", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Caernarfon 3B is BritLLM v0.1, a raw pretrained language model from the BritLLM project led by the University College London NLP group. Weights have been public on Hugging Face since 21 June 2024 and were still the project's latest named release on 15 October 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project says BritLLM is meant to produce freely available models aligned with UK interests, including English and other languages of the British Isles. The card says this checkpoint is a raw pretrained model and should be further finetuned for most uses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/chestfound/chestfound-dinov3": { "id": "ucl/chestfound/chestfound-dinov3", "name": "ChestFound-DINOv3", "maker": "ucl", "family": "ucl/chestfound", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC BY-NC 4.0, stated on the model card. Gated: Hugging Face requires contact details before download.", "release_date": "2026-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChestFound-DINOv3 is a gated pretrained checkpoint published by Yukun Zhou of University College London as `YukunZhou/chestfound_dinov3`. The card says it was trained for 10 days on four A100 80GB GPUs on the UCL CS cluster. The repo was created on 1 April 2026. The license is CC BY-NC 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card calls it a pretrained model and points at the Med_DINOv3 training code. It does not describe a clinical task. The name is the only statement that the domain is chest imaging.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/cuatrollm/cuatrollm": { "id": "ucl/cuatrollm/cuatrollm", "name": "CuatroLLM", "maker": "ucl", "family": "ucl/cuatrollm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-10-28", "context_length": 2048, "max_output_tokens": null, "parameters": "1345423360", "architecture": "LlamaForCausalLM decoder-only Transformer trained from scratch. 24 layers, hidden size 2048, 16 attention heads, 16 key-value heads, intermediate size 5504, SiLU, RoPE theta 10000, untied embeddings, vocabulary 32000. Weights are bfloat16. The paper calls the size 1.3B, which matches the safetensors total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CuatroLLM is a 1.3 billion parameter decoder-only language model pretrained from scratch by the UCL Centre for Artificial Intelligence, with coauthors at Together AI and the University of Waterloo. It was trained on TransWeb-Edu, an English FineWeb-Edu corpus plus French, German, and Spanish machine translations. Weights went on Hugging Face on 28 October 2024. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses CuatroLLM to test whether machine-translating one high-quality English pretraining corpus into a few other languages can train a competitive multilingual model. It is a base model, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/monoweb/monoweb": { "id": "ucl/monoweb/monoweb", "name": "MonoWeb", "maker": "ucl", "family": "ucl/monoweb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-04-15", "context_length": 2048, "max_output_tokens": null, "parameters": "1.35 billion", "architecture": "Decoder-only Transformer, trained from scratch with the Llama-2 tokenizer (32K vocabulary). The model card says 24 layers, hidden dimension 2048, 16 attention heads, context length 2048. Training used Megatron-LM for about 143 billion tokens (34K steps). Exact safetensors parameter total was not read from a checkpoint file.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MonoWeb is a set of 1.35 billion parameter pretrained language models released by the UCL NLP group with the paper on mixed-language documents in multilingual pretraining. One Hugging Face repo, `UCLNLP/monoweb`, holds 12 named training conditions for English paired with German, Spanish, or French. The repo was created on 15 April 2026. The paper is ACL 2026 (arXiv 2601.00364). This dossier was collected on 2026-10-15.", "what_it_is_for": "The release compares models trained on full FineWeb text, on MonoWeb with bilingual documents removed, and on MonoWeb with either parallel or code-switching documents put back. It is a research pretraining release, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/retfound/retfound-dinov2-meh": { "id": "ucl/retfound/retfound-dinov2-meh", "name": "RETFound-DINOv2 Moorfields", "maker": "ucl", "family": "ucl/retfound", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0, from the RETFound GitHub LICENSE. The upstream DINOv2 method is Meta's. These weights are the authors' retinal pretraining, not a copy of Meta's public DINOv2 checkpoint. Gated on Hugging Face.", "release_date": "2025-02-19", "context_length": null, "max_output_tokens": null, "parameters": "~300 million", "architecture": "DINOv2 with teacher and student ViT-Large: 24 blocks, embedding 1024, patch size 14. Projection head dimensions 2048, 384, and 131072. Objectives combine DINO and iBOT. Data-paper settings: batch 320, 100 epochs. Global crops resized to 224 and local crops to 96. Downstream use keeps the teacher. Official README lists ~300M.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RETFound-DINOv2 Moorfields is the DINOv2 colour-fundus model trained on the same 904,170 Moorfields images as the MAE Moorfields run. University College London and Moorfields authors released it as `RETFound_dinov2_meh` on 19 February 2025. It is not Meta's public DINOv2 checkpoint. This dossier was collected on 2026-10-15.", "what_it_is_for": "A second self-supervised recipe in the pretraining-data study, and the specialist model later compared with generalist DINOv2 and DINOv3. It is an encoder for fine-tuning or linear probing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/retfound/retfound-dinov2-shanghai": { "id": "ucl/retfound/retfound-dinov2-shanghai", "name": "RETFound-DINOv2 Shanghai", "maker": "ucl", "family": "ucl/retfound", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0, from the RETFound GitHub LICENSE. Gated on Hugging Face. Not a copy of Meta's public DINOv2 weights.", "release_date": "2025-02-19", "context_length": null, "max_output_tokens": null, "parameters": "~300 million", "architecture": "Same DINOv2 ViT-Large recipe as RETFound-DINOv2 Moorfields: 24 blocks, embedding 1024, patch 14, teacher kept for adaptation. Official README lists ~300M. The intended difference is the Shanghai cohort.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RETFound-DINOv2 Shanghai is the DINOv2 model trained on the 904,170 Shanghai Diabetes Prevention Program colour fundus photographs, released by the University College London and Moorfields group as `RETFound_dinov2_shanghai` on 19 February 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The Shanghai arm of the DINOv2 pretraining comparison. It is an encoder for fine-tuning or linear probing, not Meta's public DINOv2 model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/retfound/retfound-dinov3": { "id": "ucl/retfound/retfound-dinov3", "name": "RETFound-DINOv3", "maker": "ucl", "family": "ucl/retfound", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "CC BY-NC 4.0, stated on the model card. Gated: Hugging Face requires contact details before download.", "release_date": "2026-04-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RETFound-DINOv3 is a gated pretrained checkpoint published by Yukun Zhou, a University College London and Moorfields researcher, as `YukunZhou/retfound_dinov3`. The card says it was trained for 10 days on four A100 80GB GPUs on the UCL CS cluster. The Hugging Face repo was created on 1 April 2026. The license is CC BY-NC 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card calls it a pretrained model and points at fine-tuning code. It does not state the clinical task. The name places it in the RETFound line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/retfound/retfound-mae-cfp": { "id": "ucl/retfound/retfound-mae-cfp", "name": "RETFound-MAE colour fundus", "maker": "ucl", "family": "ucl/retfound", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0. Stated on the YukunZhou/RETFound_mae_natureCFP model card and in the RETFound GitHub LICENSE. The Nature article text is CC BY 4.0, which is not the weight license. Non-commercial use, with attribution. Weights are gated: Hugging Face asks for contact details before download.", "release_date": "2023-09-13", "context_length": null, "max_output_tokens": null, "parameters": "~300 million", "architecture": "Masked autoencoder. Encoder is ViT-Large: 24 Transformer blocks, embedding size 1024. Nature says the decoder is ViT-small with 8 blocks and embedding size 512, and that downstream use keeps the encoder. Official README size column lists ~300M for RETFound_mae_natureCFP. Patch size 16 and 224 input are from the shared fine-tune script and the later data-paper methods, not restated in the Nature excerpt read here.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RETFound-MAE colour fundus is the colour fundus photography checkpoint of RETFound, a retinal foundation model from University College London and the NIHR Biomedical Research Centre at Moorfields Eye Hospital. The Nature paper was published on 13 September 2023. Weights are a gated Hugging Face download under CC BY-NC 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents RETFound as a self-supervised foundation model that is adapted, with few labels, to ocular disease detection and to prediction of some systemic disease. This checkpoint is the colour-fundus encoder, not a finished diagnostic device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/retfound/retfound-mae-meh": { "id": "ucl/retfound/retfound-mae-meh", "name": "RETFound-MAE Moorfields", "maker": "ucl", "family": "ucl/retfound", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0, from the RETFound GitHub LICENSE. The data paper's code-availability note points at the same repository and at https://huggingface.co/YukunZhou. Weights are gated.", "release_date": "2025-02-19", "context_length": null, "max_output_tokens": null, "parameters": "~300 million", "architecture": "Masked autoencoder, ViT-Large encoder: 24 blocks, embedding 1024, patch size 16. Decoder during pretraining is ViT-small, 8 blocks, embedding 512, and is dropped for adaptation. Official README lists ~300M. Data-paper training: mask ratio 0.75, batch 1792, 800 epochs, images resized to 256 then randomly cropped to 224.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RETFound-MAE Moorfields is the masked-autoencoder colour-fundus model trained only on 904,170 Moorfields Eye Hospital images in the UCL and Moorfields pretraining-data study. The authors call that run FM-MEH. Hugging Face weights appeared on 19 February 2025. The journal version is Nature Communications, 28 February 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "The study trains matched foundation models on Moorfields and Shanghai data to measure how pretraining data changes generalisability and fairness. This file is the Moorfields MAE encoder, for later fine-tuning or linear probing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/retfound/retfound-mae-oct": { "id": "ucl/retfound/retfound-mae-oct", "name": "RETFound-MAE OCT", "maker": "ucl", "family": "ucl/retfound", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0, from the RETFound GitHub LICENSE and the same project's Nature weight card. The Nature article text is CC BY 4.0. Weights are gated on Hugging Face.", "release_date": "2023-09-13", "context_length": null, "max_output_tokens": null, "parameters": "~300 million", "architecture": "Masked autoencoder with the same ViT-Large encoder as the colour-fundus RETFound model: 24 Transformer blocks, embedding size 1024. Decoder during pretraining is ViT-small, 8 blocks, embedding 512. Official README lists ~300M for RETFound_mae_natureOCT. This is a separate checkpoint trained on OCT, not a conversion of the colour-fundus weights.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RETFound-MAE OCT is the optical coherence tomography checkpoint of RETFound, released with the 13 September 2023 Nature paper by University College London and Moorfields researchers. It is a separate masked-autoencoder run from the colour-fundus checkpoint. Weights are gated and CC BY-NC 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "Same job as the colour-fundus RETFound model: a self-supervised encoder to adapt to ocular and some systemic prediction tasks. This file was trained on OCT rather than colour photographs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/retfound/retfound-mae-shanghai": { "id": "ucl/retfound/retfound-mae-shanghai", "name": "RETFound-MAE Shanghai", "maker": "ucl", "family": "ucl/retfound", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0, from the RETFound GitHub LICENSE. Weights are gated on Hugging Face.", "release_date": "2025-02-19", "context_length": null, "max_output_tokens": null, "parameters": "~300 million", "architecture": "Same MAE recipe as RETFound-MAE Moorfields: ViT-Large encoder, 24 blocks, embedding 1024, patch 16, mask ratio 0.75, 800 epochs. Official README lists ~300M. The only intended difference is the pretraining cohort.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RETFound-MAE Shanghai is the masked-autoencoder twin of the Moorfields run, trained on 904,170 colour fundus photographs from the Shanghai Diabetes Prevention Program. The UCL and Moorfields authors released it as `RETFound_mae_shanghai`. Hub weights date from 19 February 2025. The journal version is 28 February 2026. This dossier was collected on 2026-10-15.", "what_it_is_for": "It is the Shanghai pretraining arm of a controlled comparison of how site, age, and other metadata change a retinal foundation model. It is an encoder for adaptation, not a diagnostic product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/transwebllm/transwebllm": { "id": "ucl/transwebllm/transwebllm", "name": "TransWebLLM", "maker": "ucl", "family": "ucl/transwebllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face card and tags say ODC-By. The preprint abstract says the corpus, models, and training pipeline are released under Open Source Initiative-approved licenses. ODC-By is an Open Data Commons license, not an OSI software license. Both statements are stored; they disagree.", "release_date": "2025-04-09", "context_length": 2048, "max_output_tokens": null, "parameters": "1345423360", "architecture": "LlamaForCausalLM decoder-only Transformer trained from scratch. 24 layers, hidden size 2048, 16 attention heads, 16 key-value heads, intermediate size 5504, SiLU, RoPE theta 10000, untied embeddings, vocabulary 32000. Weights are bfloat16. The paper says 1.3B parameters, sequence length up to 2048, batch size 2048, and about 4 million tokens per iteration. Constant learning rate 6e-4. Trained on an NVIDIA GH200 cluster for 8,366 GPU hours.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TransWebLLM is a 1.3 billion parameter multilingual base model pretrained from scratch by UCL Centre for Artificial Intelligence researchers and released under the BritLLM Hugging Face organization. It was trained on TransWebEdu, FineWeb-Edu translated into nine languages. The preprint is 18 February 2025 and the public weights were uploaded on 9 April 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper studies whether machine-translated text from one high-quality English source can pretrain a multilingual model. The card says the checkpoint is raw and should be further finetuned for most uses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/transwebllm/transwebllm-cool": { "id": "ucl/transwebllm/transwebllm-cool", "name": "TransWebLLM-cool", "maker": "ucl", "family": "ucl/transwebllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face card and tags say ODC-By. The preprint abstract says releases are under Open Source Initiative-approved licenses. Those statements disagree. ODC-By is not an OSI software license.", "release_date": "2025-04-10", "context_length": 2048, "max_output_tokens": null, "parameters": "1345423360", "architecture": "Cooldown continued pretraining of TransWebLLM-web. Same safetensors total, 1,345,423,360 bfloat16 parameters, and LlamaForCausalLM. Base shape is 24 layers, hidden size 2048, 16 heads, intermediate size 5504, vocabulary 32000, context 2048. This repo's config.json was not downloaded separately.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TransWebLLM-cool is the final cooldown checkpoint of the UCL TransWebLLM run. The paper continues TransWebLLM-web with multiple-choice synthetic data, educational Python, and web instruction text. Weights were uploaded on 10 April 2025 under `britllm/TransWebLLM-cool`. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents this as the cooldown stage after translated educational text and real web text, meant to add code and question-answer data that TransWebEdu lacks. It is still a base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/transwebllm/transwebllm-web": { "id": "ucl/transwebllm/transwebllm-web", "name": "TransWebLLM-web", "maker": "ucl", "family": "ucl/transwebllm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face card and tags say ODC-By. The preprint abstract says releases are under Open Source Initiative-approved licenses. Those statements disagree. ODC-By is not an OSI software license.", "release_date": "2025-04-09", "context_length": 2048, "max_output_tokens": null, "parameters": "1345423360", "architecture": "Continued pretraining of TransWebLLM. Same safetensors total, 1,345,423,360 bfloat16 parameters, and LlamaForCausalLM. The base config is 24 layers, hidden size 2048, 16 heads, intermediate size 5504, vocabulary 32000, context 2048. This checkpoint's config.json was not downloaded separately; the paper says it continues TransWebLLM rather than changing the architecture.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TransWebLLM-web is the continued-pretraining checkpoint of TransWebLLM from the UCL Centre for Artificial Intelligence, released on the BritLLM Hugging Face org. The paper adds about 90 billion tokens of TransWebEdu mixed with real web text. Weights were uploaded on 9 April 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses this stage to test whether a small amount of real multilingual web text, on top of machine-translated educational text, improves the base model. It is still a raw pretrained checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucl/tubenet/tubenet": { "id": "ucl/tubenet/tubenet", "name": "tUbeNet", "maker": "ucl", "family": "ucl/tubenet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Figshare weights: CC BY-NC-SA 4.0. GitHub code: MIT. A Zenodo software record has been described as both MIT and CC BY across versions. The weight license used here is the Figshare item, and the code license is the GitHub LICENSE.", "release_date": "2024-04-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "3D U-Net-style CNN for binary vessel segmentation. The 2025 paper says six convolution blocks and five deconvolution blocks. Each convolution block is two 3x3x3 convolutions, Leaky ReLU with alpha 0.2, and group normalization, then 2x pooling. Deconvolution blocks use conv3DTranspose plus two convolutions. Skip concatenations. Output is a single-filter convolution with softmax. Dropout 30 percent. Training loss on the Figshare item is voxel-wise DICE plus binary cross-entropy, Adam, learning rate 0.001.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "tUbeNet is a 3D convolutional foundation model for segmenting blood vessels in grayscale medical volumes, released by University College London researchers. The department name was not restated on the weight record. Foundation weights were posted on the UCL Figshare repository on 8 April 2024, with a second version on 15 May 2025. The journal article is 20 November 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors describe a general vessel segmenter trained across modalities, scales, and pathologies, then fine-tuned on a new dataset with very little labelled volume. It is not a language or generative model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/brief/brief-pro": { "id": "ucla/brief/brief-pro", "name": "BRIEF-Pro", "maker": "ucla", "family": "ucla/brief", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-17", "context_length": null, "max_output_tokens": null, "parameters": "3606752256", "architecture": "LlamaForCausalLM fine-tune of meta-llama/Llama-3.2-3B-Instruct. Hub safetensors metadata total is 3606752256 parameters, all F32. The card has no architecture prose beyond the paper title.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the brief family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "BRIEF-Pro is a UCLA NLP checkpoint for context compression aimed at multi-hop reasoning. The only description on the model card is the paper title and the author list. The weights are a Llama 3.2 3B Instruct fine-tune. The Hub parameter total is 3,606,752,256.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/newsbart/newsbart-base": { "id": "ucla/newsbart/newsbart-base", "name": "NewsBART base", "maker": "ucla", "family": "ucla/newsbart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "BartModel continued from facebook/bart-base. Config: 6 encoder and 6 decoder layers, d_model 768, 12 heads, FFN 3072, max_position_embeddings 1024, vocab 51201.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the newsbart family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "NewsBART base is UCLA NLP's continued pretraining of BART-base on RealNews, released for the same keyphrase-generation study as NewsBERT and SciBART. The card's job is domain-adaptive pretraining, not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/newsbert/newsbert": { "id": "ucla/newsbert/newsbert", "name": "NewsBERT", "maker": "ucla", "family": "ucla/newsbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM continued from bert-base-uncased. Config: 12 layers, hidden 768, 12 heads, intermediate 3072, max_position_embeddings 512, vocab 30522.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the newsbert family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "NewsBERT is a UCLA NLP masked language model for the keyphrase-generation study. The card says it resumes from bert-base-uncased and keeps training on the RealNews corpus. It is a domain checkpoint, not a new architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/opensla/opensla-h": { "id": "ucla/opensla/opensla-h", "name": "OpenSLA-H clinical", "maker": "ucla", "family": "ucla/opensla", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2026-10-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sensor-language-action model. OpenSLA-H adds a hierarchical sensor encoder (local, per-channel, and global memory tokens) on top of the OpenSLA-B design, which prepends projected sensor tokens to a language prompt. Card base_model is Qwen/Qwen3.5-2B. Released weight named on the card: opensla_h_clinical.pt, plus waveform_encoder.ckpt and two JSON sidecars. Domain on the weight table: clinical.", "modalities": { "input": [ "time-series", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the opensla family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "OpenSLA-H is the Health Intelligence Lab's sensor-language-action model for clinical waveforms and charted numbers. It reads a sensor history plus a text context and predicts a structured action and a sensor-state caption. The card says the language backbone is Qwen3.5-2B and that the released clinical checkpoint is the hierarchical variant. Confidence is medium because the Hub file list did not show those weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/openvlthinker/openvlthinker-7b": { "id": "ucla/openvlthinker/openvlthinker-7b", "name": "OpenVLThinker-7B", "maker": "ucla", "family": "ucla/openvlthinker", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2.5-VL style vision-language model. The card loads it as Qwen2_5_VLForConditionalGeneration and names the processor Qwen/Qwen2.5-VL-7B-Instruct. The Hub page labels the checkpoint 8B parameters in BF16. Exact count was not taken from a safetensors total.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the openvlthinker family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "OpenVLThinker-7B is a UCLA NLP vision-language model trained with alternating supervised fine-tuning and reinforcement learning so it can do multi-step visual reasoning, especially visual math. The weights are on the author's Hub account ydeng9, and the lab README treats this checkpoint as the legacy v1.0 release. The card's license is Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/openvlthinker/openvlthinker-v2": { "id": "ucla/openvlthinker/openvlthinker-v2", "name": "OpenVLThinkerV2", "maker": "ucla", "family": "ucla/openvlthinker", "status": "announced", "confidence": "high", "weights": "closed", "license": "Apache-2.0", "release_date": "2026-04-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The README says OpenVLThinkerV2 shares the architecture of Qwen3-VL-8B and that current results come from RL directly on Qwen3-VL-8B. A stronger cold-start SFT checkpoint is described as not yet released. Trained with G2RPO (Gaussian GRPO) plus response-length and entropy shaping.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the openvlthinker family, collected on 2026-10-15. Status is announced. Weights field: not stated.", "what_it_is_for": "OpenVLThinkerV2 is UCLA NLP's announced generalist multimodal reasoner. The 2026-04-10 README releases the paper, training code, and example data, and says the checkpoint is still coming, after a cold-start SFT stage. Reported results are from reinforcement learning on Qwen3-VL-8B with their Gaussian GRPO objective. Status is announced, not a weight release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/osf/osf-base": { "id": "ucla/osf/osf-base", "name": "OSF-Base", "maker": "ucla", "family": "ucla/osf", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-Base 1D backbone. The card's demo: 12 PSG channels, sequence length 1920 (64 Hz times 30 s), CLS embedding size 768, patch embeddings shaped [B, 90, 768]. Weight file osf_backbone.pth. Self-supervised pretraining; the card's OSF objective is self-distillation.", "modalities": { "input": [ "time-series" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the osf family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "OSF-Base is the Health Intelligence Lab's self-supervised sleep foundation model. It encodes 12-channel polysomnography into embeddings for downstream sleep and disease tasks. The released weight is a ViT-Base backbone. The card presents OSF as the lab's scaled pretraining recipe, compared on their SleepBench with other self-supervised methods.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/plbart/plbart-base": { "id": "ucla/plbart/plbart-base", "name": "PLBART base", "maker": "ucla", "family": "ucla/plbart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 512, "max_output_tokens": 512, "parameters": "", "architecture": "Encoder-decoder Transformer. Hub config uclanlp/plbart-base: 6 encoder and 6 decoder layers, d_model 768, 12 heads, FFN 3072, vocab 50005, GELU, max_position_embeddings 1024. The official README says the maximum input and output length is 512, which disagrees with that 1024 position field.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the plbart family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "UCLA NLP (with Columbia coauthors) released PLBART, a sequence-to-sequence model pretrained by denoising autoencoding on Java and Python functions from GitHub and natural-language text from StackOverflow. The base checkpoint is the model introduced with the NAACL 2021 paper. It is meant for code summarization, generation, translation, and other program-understanding tasks after fine-tuning. Task checkpoints on the Hub were not given their own folders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/plbart/plbart-large": { "id": "ucla/plbart/plbart-large", "name": "PLBART large", "maker": "ucla", "family": "ucla/plbart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 512, "max_output_tokens": 512, "parameters": "", "architecture": "Encoder-decoder Transformer. Hub config uclanlp/plbart-large: 12 encoder and 12 decoder layers, d_model 1024, 16 heads, FFN 4096, vocab 50005, GELU, max_position_embeddings 1024. GitHub news calls this checkpoint 12L-1024H-16A. The README maximum length of 512 is stated for PLBART generally and was not repeated as large-only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the plbart family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "PLBART large is the bigger UCLA NLP checkpoint of the same program-and-language BART model. The GitHub news line from November 2021 is the release note: 12 layers, 1024 hidden size, 16 heads. It is for the same code understanding and generation jobs as PLBART base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/scibart/scibart-base": { "id": "ucla/scibart/scibart-base", "name": "SciBART base", "maker": "ucla", "family": "ucla/scibart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "BartModel. Config _name_or_path is bart-base: 6 encoder and 6 decoder layers, d_model 768, 12 heads, FFN 3072, max_position_embeddings 1024, vocab 30002. The card says the model was pretrained from scratch with a science vocabulary, which is a different claim from a continued bart-base run.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the scibart family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "SciBART base is UCLA NLP's scientific-domain sequence-to-sequence model for the keyphrase study. The card says it was pretrained from scratch on S2ORC titles and abstracts with a science vocabulary, under MIT. The config's _name_or_path field still says bart-base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/scibart/scibart-large": { "id": "ucla/scibart/scibart-large", "name": "SciBART large", "maker": "ucla", "family": "ucla/scibart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "BartModel. Config _name_or_path is bart-large: 12 encoder and 12 decoder layers, d_model 1024, 16 heads, FFN 4096, max_position_embeddings 1024, vocab 30002. The card text matches the base card and says pretrained from scratch with a science vocabulary.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the scibart family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "SciBART large is the larger UCLA NLP scientific BART checkpoint from the keyphrase study. The card license is MIT and the corpus is S2ORC titles and abstracts. The config is a 12-layer, 1024-wide BART with a 30,002 science vocab. The card prose does not separately describe the large run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/sensorgen/sensorgen-bp": { "id": "ucla/sensorgen/sensorgen-bp", "name": "SensorGen blood-pressure translation", "maker": "ucla", "family": "ucla/sensorgen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Checkpoint bp_translation.pt. The card maps PPG plus 6-D non-invasive blood-pressure statistics to invasive arterial pressure, 30 seconds at 50 Hz, shape 1 by 1500. Layer sizes are not stated.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the sensorgen family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "This SensorGen checkpoint translates photoplethysmography and a 6-number non-invasive blood-pressure summary into an invasive arterial-pressure waveform. UCLA's Health Intelligence Lab released it beside the text-to-ECG checkpoint. The card restricts it to research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/sensorgen/sensorgen-text2ecg": { "id": "ucla/sensorgen/sensorgen-text2ecg", "name": "SensorGen text-to-ECG", "maker": "ucla", "family": "ucla/sensorgen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Checkpoint text2ecg.pt in yang-ai-lab/SensorGen. The card's task table: 12-lead ECG, 10 seconds at 100 Hz, shape 12 by 1000, conditioned on a free-text ECG report encoded by CLIP. Hub tags include flow-matching. Layer sizes are not stated.", "modalities": { "input": [ "text" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the sensorgen family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "SensorGen text-to-ECG is one of two checkpoints the UCLA Health Intelligence Lab shipped for a study of generative models on real sensor time series. This file turns a free-text ECG report into a 12-lead, 10-second waveform. The card limits it to research and says it is not for diagnosis.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/sleeplm/sleeplm-base": { "id": "ucla/sleeplm/sleeplm-base", "name": "SleepLM-Base", "maker": "ucla", "family": "ucla/sleeplm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sleep-language model: contrastive signal-text alignment, captioning, and signal reconstruction. The card says the architecture is inspired by OpenCLIP and does not state layer counts or a parameter total. Checkpoint file model_checkpoint.pt.", "modalities": { "input": [ "time-series", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the sleeplm family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "SleepLM-Base is the Health Intelligence Lab at UCLA's sleep-language model. It takes multimodal polysomnography and writes targeted captions (brain, cardiac, respiration, somatic, or a holistic summary) and also embeds signals and text for retrieval. The card calls it the lab's base release of that family. License MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/spin/spin-diffusion": { "id": "ucla/spin/spin-diffusion", "name": "SPIN-Diffusion iteration 3", "maker": "ucla", "family": "ucla/spin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-02-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "UNet fine-tune of runwayml/stable-diffusion-v1-5. The card says to load SD 1.5 and replace pipe.unet with this repo's unet. Iteration 3 of self-play fine-tuning on Pick-a-Pic v2 winner images.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the spin family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "SPIN-Diffusion is UCLA-AGI's self-play fine-tune of the Stable Diffusion 1.5 UNet for text-to-image generation. Iteration 3 is the checkpoint whose card was read. The lab says to drop this UNet into a Stable Diffusion 1.5 pipeline. Prompts are described as primarily English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/spin/zephyr-7b-spin": { "id": "ucla/spin/zephyr-7b-spin", "name": "Zephyr-7B SPIN iteration 3", "maker": "ucla", "family": "ucla/spin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-01-07", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "The iter3 card calls it a 7B GPT-like model fine-tuned from alignment-handbook/zephyr-7b-sft-full, itself based on mistralai/Mistral-7B-v0.1. Self-play fine-tuning on synthetic UltraChat 200k data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the spin family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "UCLA Statistical Machine Learning Lab (UCLA-AGI on the Hub) released SPIN as self-play fine-tunes of the Zephyr-7B SFT checkpoint. Iteration 3 is the last repo in that series. The card says the model is for turning a weaker language model into a stronger one by training on its own synthetic UltraChat responses. It is a fine-tune of another lab's checkpoint, filed because UCLA named and shipped it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/sppo/gemma-2-9b-it-sppo": { "id": "ucla/sppo/gemma-2-9b-it-sppo", "name": "Gemma-2-9B-It SPPO iteration 3", "maker": "ucla", "family": "ucla/sppo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "gemma", "release_date": "2024-06-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of google/gemma-2-9b-it. The card YAML license is gemma and the title is Gemma-2-9B. The prose Model Description says \"A 8B parameter GPT-like model\" and \"License: Apache-2.0\". Those two statements disagree with the YAML, the title, and the base-model id. Parameter count is left null because of that conflict.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the sppo family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "UCLA-AGI's third SPPO line starts from Google's Gemma-2-9B-IT and runs the same synthetic UltraFeedback self-play recipe. Iteration 3 is the last and the highest AlpacaEval entry on the card. English instruction alignment is the stated job. The Gemma terms are linked from the card. The prose block under Model Description still says 8B and Apache-2.0, which does not match the YAML.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/sppo/llama-3-8b-sppo": { "id": "ucla/sppo/llama-3-8b-sppo", "name": "Llama-3-8B-Instruct SPPO iteration 3", "maker": "ucla", "family": "ucla/sppo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "8B model fine-tuned from meta-llama/Meta-Llama-3-8B-Instruct with SPPO on synthetic UltraFeedback prompts. Card license Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the sppo family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "UCLA-AGI fine-tuned Meta-Llama-3-8B-Instruct with the same SPPO recipe used on Mistral. Iteration 3 is the strongest of the three Hub iterations on the card's AlpacaEval and harness tables. The stated job is English instruction alignment. The license printed on the card is Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/sppo/mistral-7b-pairrm-sppo": { "id": "ucla/sppo/mistral-7b-pairrm-sppo", "name": "Mistral-7B PairRM SPPO iteration 3", "maker": "ucla", "family": "ucla/sppo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-04", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "7B model fine-tuned from mistralai/Mistral-7B-Instruct-v0.2 with Self-Play Preference Optimization. The iter3 card says this is the paper checkpoint, K=5. Synthetic UltraFeedback prompts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the sppo family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "UCLA-AGI's SPPO line aligns Mistral-7B-Instruct-v0.2 by self-play preference optimization on synthetic UltraFeedback answers. The iteration-3 card, and the unnumbered card, both say iteration 3 is the checkpoint reported in the paper. The job is general instruction following in English. Best-of-16 decoding numbers on the card are inference-time selection, not a separate weight release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucla/visualbert/visualbert": { "id": "ucla/visualbert/visualbert", "name": "VisualBERT", "maker": "ucla", "family": "ucla/visualbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Transformer in the BERT style. Config of uclanlp/visualbert-vqa-coco-pre: VisualBertForPreTraining, 12 layers, hidden 768, 12 heads, intermediate 3072, vocab 30522, max_position_embeddings 512, visual_embedding_dim 2048, special_visual_initialize true. Other task configs were not opened.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of California, Los Angeles release in the visualbert family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "what_it_is_for": "VisualBERT is UCLA NLP's vision-and-language transformer. The paper presents it as a stack of Transformer layers that aligns text with image regions, pretrained with visually grounded language-model objectives. UCLA hosts nine task checkpoints on the uclanlp Hub org (VQA, VCR, NLVR2, with and without COCO pretraining). This folder is the model. Those checkpoints are versions, not separate products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uclouvain/2-net/2-net": { "id": "uclouvain/2-net/2-net", "name": "2-Net", "maker": "uclouvain", "family": "uclouvain/2-net", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2026-10-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-label colour-fundus classifier. The main system is DINOv2 ViT-B/14 with LoRA rank 16 (card: 87.8M parameters, 1.2M trained, 518 px, token-pooling head) plus EfficientNetV2-L with a linear head (card: 117.3M, 896 px). Each member is calibrated per class with Platt scaling on the validation split, then fused by a geometric mean of probabilities. A comparison variant, 2-Net (RETFound), replaces the DINOv2 member with a fully fine-tuned RETFound ViT-L/16 (card: about 304M, 384 px). Checkpoints are released for MuReD (20 labels) and RFMiD (29 labels), all seed 42. Those component counts are not summed into one parameter total.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "2-Net is a multi-label retinal fundus classifier from ICTEAM at UCLouvain in Louvain-la-Neuve. Th\u00e9odore Cousin and S\u00e9bastien Jodogne are at UCLouvain. Coralie Hemptinne is at Cliniques universitaires Saint-Luc. The Hugging Face card was created on 2026-10-15, the collection date. Weights are CC BY-NC 4.0. The card says the system is for research and is not a medical device. The citation line on the card still says it is to be completed. No published paper URL was found.", "what_it_is_for": "The card describes detection of retinal conditions as a multi-label problem. The main system pairs a DINOv2 ViT-B/14 adapted with LoRA and an EfficientNetV2-L that sees the image at 896 px. Each network is calibrated per class, and the probabilities are fused by a geometric mean. Checkpoints cover MuReD (20 labels) and RFMiD (29 labels). A RETFound variant is included for comparison with retina-specific pretraining. It is one product with dataset checkpoints, not two products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uclouvain/bertinchamps/bertinchamps-classic": { "id": "uclouvain/bertinchamps/bertinchamps-classic", "name": "BERTinchamps classic", "maker": "uclouvain", "family": "uclouvain/bertinchamps", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Weights and corpora on UCLouvain Dataverse are CC-BY-SA 4.0. Training code is MIT. The IJCoL article text is CC BY-NC-ND 4.0 and that licence is not the weights licence.", "release_date": "2024-07-01", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "French masked LM in the Cramming BERT line. The authors' repo invokes arch=crammed-bert and train=bert-o4: ScriptableCrammedBERT, 16 layers, hidden 768, intermediate 3072, 12 heads, no QKV bias, GELUglu, scaled sinusoidal positions, WordPiece vocabulary 32768, tied embeddings, no decoder bias, MLM. The 2024 paper says context was reduced from 512 to 128. IJCoL 2024 says crammed BERT models, and BERTinchamps, have 120 million parameters. The 2023 NL4AI paper says 110 million. Both figures are kept. Neither was counted from the checkpoint. Run name Bertinchamps-OSCAR. File model-BERTinchamps-OSCAR.tar.gz. Pretraining corpus in the 2024 paper: 30GB of French OSCAR. Recipe in section 3.1: 24 hours, one GPU, micro-batch 128, batch 4096, AdamW weight decay 0.01, betas 0.9 and 0.98, epsilon 1e-12, gradient clip 0.5, learning rate 1e-3, slanted triangular scheduler with 25% base and 0.25 falloff.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERTinchamps classic is the French masked language model that Amaury Fierens and S\u00e9bastien Jodogne (UCLouvain ICTEAM / INGI, Louvain-la-Neuve) trained on 30GB of French OSCAR. The run name is Bertinchamps-OSCAR and the Dataverse file is `model-BERTinchamps-OSCAR.tar.gz`. Weights and corpora are at doi:10.14428/DVN/0OJU5N, publication date 2024-07-01, version 2 released 2024-09-13, terms CC-BY-SA 4.0. Code is MIT. The journal account is IJCoL 10-2, online 1 December 2024. On 2026-10-15 the checkpoint was still the named OSCAR release. It is a different product from the RTBF journalism checkpoint.", "what_it_is_for": "The 2024 paper presents one-day, one-GPU pretraining of a medium French BERT for institutions that cannot send sensitive text to an outside API. This checkpoint is the general OSCAR model. The authors then fine-tune it for FLUE, QUAEROFrenchMed, and journalism probes. It is not a chatbot and it has no API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uclouvain/bertinchamps/bertinchamps-rtbf": { "id": "uclouvain/bertinchamps/bertinchamps-rtbf", "name": "BERTinchamps RTBF", "maker": "uclouvain", "family": "uclouvain/bertinchamps", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Weights and corpora on UCLouvain Dataverse are CC-BY-SA 4.0. Training code is MIT. The IJCoL article text is CC BY-NC-ND 4.0 and that licence is not the weights licence.", "release_date": "2024-07-01", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "Same crammed BERT recipe as BERTinchamps classic: ScriptableCrammedBERT, 16 layers, hidden 768, intermediate 3072, 12 heads, no QKV bias, GELUglu, scaled sinusoidal positions, WordPiece 32768, context 128, MLM, tied embeddings. The 2024 paper uses the same optimizer and schedule as the OSCAR run: 24 hours, one GPU, micro-batch 128, batch 4096, AdamW weight decay 0.01, betas 0.9 and 0.98, epsilon 1e-12, gradient clip 0.5, learning rate 1e-3, slanted triangular scheduler with 25% base and 0.25 falloff. Corpus in that paper: 29GB of French OSCAR plus 1.6GB from the RTBF corpus. Run name Bertinchamps-RTBFcorpus-oscar-definitive. File model-BERTinchamps-RTBFcorpus-OSCAR-definitive.tar.gz. IJCoL 2024 says 120 million parameters for the crammed BERT setup. NL4AI 2023 said 110 million for the earlier OSCAR model and did not train this RTBF checkpoint. The 120 million figure is the 2024 paper's size for this recipe. It was not counted from the file.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERTinchamps RTBF is the journalism checkpoint from Amaury Fierens and S\u00e9bastien Jodogne at UCLouvain ICTEAM / INGI. The 2024 IJCoL paper trains it with the same one-day cramming recipe as the OSCAR model, on 29GB of French OSCAR plus 1.6GB of RTBF news text. The run name is Bertinchamps-RTBFcorpus-oscar-definitive. The file is `model-BERTinchamps-RTBFcorpus-OSCAR-definitive.tar.gz` in doi:10.14428/DVN/0OJU5N (publication 2024-07-01, version 2 on 2024-09-13, CC-BY-SA 4.0). The authors treat it as a second model, not a dated snapshot of the OSCAR run. Collected 2026-10-15.", "what_it_is_for": "The paper tests whether mixing a public Belgian French news corpus into pretraining helps journalism tasks without hurting general French tasks. The RTBF corpus is described as about 750,000 articles from 2008 to 2021. This checkpoint is for fill-mask and for the paper's fine-tunes. It is not an API product. The paper does not publish QUAERO scores for it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uclouvain/brabant/brabant-xvii": { "id": "uclouvain/brabant/brabant-xvii", "name": "Brabant-XVII from scratch", "maker": "uclouvain", "family": "uclouvain/brabant", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-03-22", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM trained from scratch on arch-be/brabant-xvii. config.json: hidden 768, intermediate 3072, 12 layers, 12 heads, max_position_embeddings 512, GELU, dropout 0.1, type_vocab_size 2, pad token 0, absolute positions, float32, transformers 4.38.2. WordPiece vocabulary limit on the card is 30000. config vocab_size is 24263. Pretraining objectives are MLM and NSP, with 15% masking. The Hub size label is 0.1B. That label was not turned into an exact parameter count. The card says the source texts are Council of Brabant pardon and sentence transcriptions and writes the span as 'the xv to the vii century'.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Brabant-XVII from scratch is a BERT masked language model trained by Xavier Gillard on transcriptions from the Council of Brabant. Gillard is the Arkey researcher on the UCLouvain MiiL page, in a FED-tWIN role with UCLouvain and the Belgian State Archives. The Hub repo `xaviergillard/brabant-xvii-from-scratch` was created on 2024-03-22. Licence is MIT. The card says the experiment is not finished and that no conclusive results are available. Status on 2026-10-15 is preview. The card writes the period as \"the xv to the vii century\" while the dataset and the model name are brabant-xvii.", "what_it_is_for": "The card trains a tokenizer and a BERT from scratch so the vocabulary comes from the pardon and sentence transcriptions, which mix historical Dutch and historical French. Objectives are masked language modeling and next-sentence prediction. The author compares the idea with fine-tunes of GysBERT but does not publish scores. The stated goal is to make the corpus searchable. It is not a chat model and it is not an API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uclouvain/digita/digita": { "id": "uclouvain/digita/digita", "name": "Digita", "maker": "uclouvain", "family": "uclouvain/digita", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "QLoRA adapter on unsloth/meta-llama-3.1-8b-instruct-bnb-4bit. Card: LoRA rank 16, alpha 16, 4-bit base. PEFT 0.18.0. The Hub size line says 8B because of the Llama 3.1 8B base. The release the card tells users to load is the adapter through FastLanguageModel.from_pretrained. No adapter parameter count was published. The card does not state a context length, so the base model's context is not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Digita is a QLoRA chatbot adapter released by Xavier Gillard for the Arkey project, held jointly by UCLouvain and the Belgian State Archives and funded by BELSPO. The Hub repo `xaviergillard/digita` was created on 2025-12-24. The card fine-tunes `unsloth/meta-llama-3.1-8b-instruct-bnb-4bit` on proprietary 2022\u20132024 replies from the DiVa \"digit\" mailbox. Licence on the card is MIT. The author calls the 8B run a proof of concept trained on a personal machine and says a real deployment should be redone. Status on 2026-10-15 is preview.", "what_it_is_for": "The card says Digita should answer archive users in a style close to State Archives staff. Languages listed are Dutch, French, German, and English. The recommended load path is the QLoRA adapter in Unsloth at 4-bit, which the author says stayed near 7G of VRAM. The mailbox text is not released. The author says the mail mostly predates the AGATHA platform, so the answers can be stale.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uclouvain/internist/internist-ai-7b": { "id": "uclouvain/internist/internist-ai-7b", "name": "Internist.ai 7B", "maker": "uclouvain", "family": "uclouvain/internist", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache 2.0", "release_date": "2024-04-21", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "Continue-pretrain of mistralai/Mistral-7B-v0.1. The card and the Hub size label call it a 7B model. config.json is MistralForCausalLM: hidden 4096, intermediate 14336, 32 layers, 32 query heads, 8 key-value heads, sliding_window 4096, max_position_embeddings 32768, vocab 32000, SiLU, bfloat16, tied embeddings false, rope_theta 10000, bos token 1, eos token 2. The card states a context length of 4096 even though max_position_embeddings is 32768. Training used Axolotl on 4x NVIDIA A100 80GB for 450 GPU hours, bfloat16, learning rate 6e-6, Adam epsilon 1e-5, betas 0.9 and 0.95, weight decay 0.1, 4 epochs, batch 192000 tokens, sequence length 4096, cosine schedule, minimum learning rate 1e-8, NEFTune alpha 5, warmup 100. Corpus stated as 2.3B tokens: OpenOrca-GPT4, 11332 UpToDate articles plus physician guidelines, 10376 PMC and library textbooks, and 400M synthetic tokens. Alpaca chat template. The JAMIA paper calls this released checkpoint Med 5.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Internist.ai 7B is a medical language model continue-pretrained by UCLouvain and Cliniques universitaires Saint-Luc from Mistral 7B v0.1. The JAMIA paper (May 2024) calls the released checkpoint Med 5 and points at `internistai/base-7b-v0.2`. Authors are Maxime Griot, Coralie Hemptinne, Jean Vanderdonckt, and Demet Yuksel. Weights were public on Hugging Face on 2026-10-15. The card calls the release a proof of concept and says it is not for live clinical use without trials and further training. Hub creation date used here is 2024-04-21. License is Apache 2.0.", "what_it_is_for": "The card presents a 7B medical model, mainly in English, that answers medical questions after mixed-domain training. The authors say it is the first 7B model on their card to pass 60% on MedQA (USMLE). They also say it is a proof of concept, that larger models are planned, and that it should not be used in a live clinical setting without prospective trials and more training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucmerced/adaptsegnet/adaptsegnet": { "id": "ucmerced/adaptsegnet/adaptsegnet", "name": "AdaptSegNet", "maker": "ucmerced", "family": "ucmerced/adaptsegnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Non-commercial research use only", "release_date": "2018-02-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adversarial domain adaptation for semantic segmentation in the output space. A DeepLab-v2 segmentation network predicts label maps, and a discriminator tries to tell source predictions from target predictions. The multi-level variant attaches discriminators to predictions from more than one feature level (conv4 and conv5 in the paper). The backbone is ResNet-101 or VGG-16, ImageNet pretrained. The last classification layer is removed, the last two convolution strides change from 2 to 1 so the feature stride is 1/8, and ASPP is the classifier. Multi-scale fusion is not used. At test time the discriminators are dropped. High-level loss weights in the paper are lambda_seg 1 and lambda_adv 0.001. Low-level weights are 0.1 and 0.0002. An October 2019 update adds an LS-GAN option (--gan LS) and Synscapes training. Wei-Chih Hung's contact on the repository is whung8@ucmerced.edu. Ming-Hsuan Yang's faculty page is at UC Merced. The GTA5-to-Cityscapes multi-level and VGG-16 checkpoints are hosted at vllab.ucmerced.edu. Yi-Hsuan Tsai (equal contribution), Samuel Schulter, Kihyuk Sohn, and Manmohan Chandraker are coauthors. The repository contact for Tsai is a Gmail address.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AdaptSegNet is a domain-adaptive semantic segmentation model released with weights on the UC Merced Vision and Learning Lab site. Wei-Chih Hung (contact whung8@ucmerced.edu) and Yi-Hsuan Tsai are equal-contribution authors, with Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang (UC Merced), and Manmohan Chandraker. The CVPR 2018 spotlight preprint arXiv:1802.10349 was posted on 28 February 2018, revised on 16 October 2019, and revised again on 6 July 2020. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper adapts a segmenter from a labeled source domain, usually synthetic driving scenes, to an unlabeled real target. Adversarial training matches the distribution of predicted label maps rather than only matching intermediate features. At test time the discriminators are removed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucmerced/advsemiseg/advsemiseg": { "id": "ucmerced/advsemiseg/advsemiseg", "name": "AdvSemiSeg", "maker": "ucmerced", "family": "ucmerced/advsemiseg", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2018-02-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Semi-supervised semantic segmentation with a fully convolutional discriminator. The discriminator scores predicted probability maps against the ground-truth label distribution at spatial resolution, and its confidence map supplies a mask for unlabeled images. The segmenter is DeepLab-v2 with ResNet-101 pretrained on ImageNet and MSCOCO. The last classification layer is removed. The last two convolution strides change from 2 to 1, so the output stride is 1/8. Dilated convolutions in conv4 and conv5 use rates 2 and 4. The README says the released baseline does not use multi-scale training or CRF post-processing. Wei-Chih Hung's contact is whung8@ucmerced.edu, and the augmented VOC labels are hosted at vllab1.ucmerced.edu. Ming-Hsuan Yang's email in the paper is mhyang@ucmerced.edu, with a Google Cloud affiliation on the same author block. Yi-Hsuan Tsai (NEC Laboratories America), Yan-Ting Liou (National Taiwan University), and Yen-Yu Lin (Academia Sinica) are coauthors.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AdvSemiSeg is a semi-supervised semantic segmentation model from Wei-Chih Hung (UC Merced, whung8@ucmerced.edu) and Ming-Hsuan Yang (UC Merced), with Yi-Hsuan Tsai (NEC Laboratories America), Yan-Ting Liou (National Taiwan University), and Yen-Yu Lin (Academia Sinica). The BMVC 2018 preprint arXiv:1802.07934 was posted on 22 February 2018 and revised on 24 July 2018. Pretrained checkpoints are downloaded by the author repository. Augmented VOC labels are hosted at vllab1.ucmerced.edu. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper trains a segmenter with a mix of labeled and unlabeled images. A fully convolutional discriminator treats the predicted map as a spatial sample, and the regions it trusts become extra supervision on unlabeled images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucmerced/drit/drit": { "id": "ucmerced/drit/drit", "name": "DRIT", "maker": "ucmerced", "family": "ucmerced/drit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2018-08-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Disentangled representation for unpaired diverse image-to-image translation. An image is embedded in a domain-invariant content space and a domain-specific attribute space. At test time the content code of an input is combined with an attribute vector, either sampled or encoded from another image. Unpaired training uses a cross-cycle consistency loss. The public repository is the DRIT++ line: mode-seeking regularization is on by default and can be disabled with --no_ms. Content and attribute are combined by concatenation or by a feature-wise scale-and-bias transform (--concat 0). Attribute encoders use adaptive pooling, so input size can vary. A multi-scale discriminator is optional (--dis_scale). The content-discriminator schedule defaults to d_iter 3. Spectral normalization is optional. The README points to MDMM for a separate multi-domain extension. Equal-contribution authors Hsin-Ying Lee and Hung-Yu Tseng list UC Merced contacts. The project pages are on vllab.ucmerced.edu. Jia-Bin Huang, Maneesh Kumar Singh, and, on the journal version, Qi Mao and Yu-Ding Lu are coauthors.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DRIT is an unpaired diverse image-to-image translation model from the UC Merced Vision and Learning Lab. Hsin-Ying Lee and Hung-Yu Tseng (equal contribution, UC Merced contacts) wrote it with Jia-Bin Huang, Maneesh Kumar Singh, and Ming-Hsuan Yang. The ECCV 2018 oral preprint arXiv:1808.00948 was posted on 2 August 2018. The public repository is the DRIT++ journal line, arXiv:1905.01270, posted on 2 May 2019 and revised on 17 December 2019, cited as IJCV 2020. This dossier was collected on 2026-10-15.", "what_it_is_for": "The papers present DRIT as a way to learn many plausible translations between two visual domains when aligned pairs are unavailable. A content encoder keeps information shared across domains, and an attribute encoder holds the domain-specific factors that are resampled at test time.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucmerced/lapsrn/lapsrn": { "id": "ucmerced/lapsrn/lapsrn", "name": "LapSRN", "maker": "ucmerced", "family": "ucmerced/lapsrn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2017-04-12", "context_length": null, "max_output_tokens": null, "parameters": "812k", "architecture": "Laplacian Pyramid Super-Resolution Network. Progressive residual reconstruction: at each pyramid level a feature branch of 3 by 3 convolutions (64 filters) plus one 4 by 4 transposed convolution predicts a high-frequency residual, and an image branch upsamples with a transposed convolution initialized from bilinear weights. Leaky ReLU slope 0.2. Charbonnier loss with epsilon 1e-3 and deep supervision at every level. CVPR models use depth 10 per level for 2x and 4x and depth 5 per level for 8x. The later comparison table lists the CVPR LapSRN at depth 24, 64 filters, and 812k parameters. Separate networks are trained for 2x, 4x, and 8x. The 8x network can emit 2x and 4x by stopping at a coarser level. First author Wei-Sheng Lai and Ming-Hsuan Yang are at UC Merced. Jia-Bin Huang (Virginia Tech) and Narendra Ahuja (UIUC) are coauthors. Project site is on vllab.ucmerced.edu.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LapSRN is a single-image super-resolution network from the UC Merced Vision and Learning Lab. Wei-Sheng Lai (UC Merced) is the first author, with Jia-Bin Huang (Virginia Tech), Narendra Ahuja (UIUC), and Ming-Hsuan Yang (UC Merced). The preprint arXiv:1704.03915 was posted on 12 April 2017 and accepted at CVPR 2017. Pretrained MatConvNet weights ship in the author repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents LapSRN as a fast residual super-resolver. It predicts high-frequency residuals on a Laplacian pyramid instead of bicubic-upsampling the input first, and it can emit more than one scale in one forward pass. The authors train it for 2x, 4x, and 8x still images and also show frame-wise use on video and on historical photographs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucmerced/lapsrn/ms-lapsrn": { "id": "ucmerced/lapsrn/ms-lapsrn", "name": "MS-LapSRN", "maker": "ucmerced", "family": "ucmerced/lapsrn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "222k", "architecture": "Multi-scale Laplacian pyramid super-resolution network. Same progressive residual design as LapSRN, but parameters of the feature embedding sub-network, upsampling layers, and residual prediction layers are shared across pyramid levels, and recursive layers share weights inside a level. One network handles 2x, 4x, and 8x. Table I lists depth 84, 64 filters, 222k parameters, global and local residual learning, multi-scale training, and a Charbonnier loss. The 222k figure matches the D5R2 sharing variant in the ablation (5 convolutional layers per recursive block, 2 recursions). D5R5 and D5R8 are deeper recursion settings reported separately. Authors are Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang. The preprint calls the CVPR LapSRN the preliminary method and this model MS-LapSRN. Code and demo_MSLapSRN live in the same UC Merced repository as LapSRN.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MS-LapSRN is the multi-scale super-resolution network that follows LapSRN, from Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang. Lai and Yang are at UC Merced. The paper arXiv:1710.01992 calls the CVPR 2017 LapSRN the preliminary model and this shared-weight network MS-LapSRN. Pretrained weights are loaded by `demo_MSLapSRN` in the same repository as LapSRN. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses one network for 2x, 4x, and 8x super-resolution. Weight sharing across pyramid levels, plus recursion inside a level, cuts the parameter count relative to the scale-specific LapSRN checkpoints while keeping progressive residual reconstruction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucmerced/msgan/msgan": { "id": "ucmerced/msgan/msgan", "name": "MSGAN", "maker": "ucmerced", "family": "ucmerced/msgan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2019-03-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "A mode-seeking regularizer for conditional GANs. It maximizes the ratio of the distance between generated images to the distance between the latent codes that produced them, so the generator uses the noise input instead of collapsing to one output per condition. The paper says the term does not change the base network and does not add training overhead. The repository applies it to four existing generators: DCGAN conditioned on CIFAR-10 labels, Pix2Pix on paired facades and maps, DRIT on unpaired Yosemite and cat-to-dog, and StackGAN++ on CUB-200-2011 captions. Qi Mao (Peking University, qimao@pku.edu.cn), Hsin-Ying Lee (UC Merced, hlee246@ucmerced.edu), and Hung-Yu Tseng (UC Merced, htseng6@ucmerced.edu) are equal contributors. Siwei Ma and Ming-Hsuan Yang (UC Merced) are coauthors. Pretrained files are Google Drive links in the author README, not a company model hub.", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MSGAN is a mode-seeking regularizer for conditional GANs, released with pretrained checkpoints by Qi Mao (Peking University), Hsin-Ying Lee (UC Merced), and Hung-Yu Tseng (UC Merced), with Siwei Ma and Ming-Hsuan Yang (UC Merced). The three lead authors contributed equally. The CVPR 2019 preprint arXiv:1903.05628 was posted on 13 March 2019 and last revised on 4 May 2019. The weights are Google Drive links in the author repository, not a hosted copy of another company's model. This dossier was collected on 2026-10-15.", "what_it_is_for": "Conditional GANs often ignore the noise vector and emit one image per condition. MSGAN adds a term that pushes generated images apart when their latent codes differ. The paper applies that term to label-conditional generation, paired and unpaired image translation, and text-to-image synthesis without editing the base network.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uconn/timerouter/timerouter-v1": { "id": "uconn/timerouter/timerouter-v1", "name": "TimeRouter", "maker": "uconn", "family": "uconn/timerouter", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-05-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "5-seed XGBoost one-vs-all ensemble (400 trees, depth 8, learning rate 0.05, subsample 0.8, tree_method hist, random_state 42 through 46) on 305-dimensional features. Margin and diversity gates use thresholds 0.15 and 0.02. The router selects or falls back across four frozen time-series foundation models: Chronos-2, FlowState, PatchTST-FM, and Sundial.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TimeRouter is a named router released by the University of Connecticut Data Science and Intelligent System (DSIS) Research Lab, with Morgan Stanley coauthors on the paper. The public artifact is an XGBoost ensemble that routes among four frozen time-series foundation models. Weights and an MIT model card are on Hugging Face as `nkh/timerouter-v1`. Status on 2026-10-15: active. The Hugging Face repository was created on 2026-05-29 and last modified on 2026-06-08.", "what_it_is_for": "The card and the UConn-DSIS repository describe TimeRouter as a GIFT-EVAL submission. A learned routing head, a margin/diversity gate, and a CV-inverse-weighted ensemble fallback pick among Chronos-2, FlowState, PatchTST-FM, and Sundial. Those four models stay frozen and are not UConn weights. The paper frames the router as a lightweight alternative to an LLM controller at inference time.\n\nAuthors on arXiv:2606.11625 are Kanghui Ning, Yushan Jiang, and Dongjin Song (School of Computing, University of Connecticut) and Kashif Rasul, Anderson Schneider, and Yuriy Nevmyvaka (Machine Learning Research, Morgan Stanley). Correspondence is dongjin.song@uconn.edu. The GitHub license copyright is the UConn DSIS Research Lab, 2026. The README says the paper was accepted at the ICML 2026 Workshop on AI Forecasting. That workshop page was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/amaes/amaes-resenc-b-fomo260k": { "id": "ucph/amaes/amaes-resenc-b-fomo260k", "name": "AMAES ResEnc B FOMO260K", "maker": "ucph", "family": "ucph/amaes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2026-05-06", "context_length": null, "max_output_tokens": null, "parameters": "102M", "architecture": "ResEnc UNet B, 102M parameters, pretext task AMAES. The card points at the Asparagus network definition. Card says this is the checkpoint from the FOMO260K paper, trained on FOMO260K.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AMAES ResEnc B FOMO260K is a brain-MRI pretraining checkpoint from FOMO-MRI, the University of Copenhagen DIKU group with the Pioneer Centre for AI. The Hub repo was created on 2026-05-06. License CC-BY-NC-SA-4.0. Still public on 2026-10-15.", "what_it_is_for": "The card says to fine-tune it for segmentation with Asparagus on cerebrum, white-matter hyperintensity, ISLES, ATLAS, or SBM configs. It is a research checkpoint, not a clinical product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/amaes/amaes-resenc-b-fomo300k": { "id": "ucph/amaes/amaes-resenc-b-fomo300k", "name": "AMAES ResEnc B FOMO300K", "maker": "ucph", "family": "ucph/amaes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2026-01-22", "context_length": null, "max_output_tokens": null, "parameters": "102M", "architecture": "ResEnc UNet B, 102M parameters, pretext task AMAES. The card points at the Asparagus network definition. Card says it was trained on an earlier FOMO300K that included 11k scans no longer in the dataset.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AMAES ResEnc B FOMO300K is a brain-MRI pretraining checkpoint from FOMO-MRI, the University of Copenhagen DIKU group with the Pioneer Centre for AI. The Hub repo was created on 2026-01-22. License CC-BY-NC-SA-4.0. Still public on 2026-10-15.", "what_it_is_for": "The card says to fine-tune it for segmentation with Asparagus on cerebrum, white-matter hyperintensity, ISLES, ATLAS, or SBM configs. It is a research checkpoint, not a clinical product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/citebert/citebert": { "id": "ucph/citebert/citebert", "name": "CiteBERT", "maker": "ucph", "family": "ucph/citebert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-05-20", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "SciBERT further trained with masked language modeling and cite-worthiness detection on CiteWorth. config.json model_type bert, 12 layers, hidden size 768, max_position_embeddings 512. The Hub createdAt stamp is 2022-03-02. The model commit is 2021-05-20.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CiteBERT is CopeNLU's SciBERT continued on CiteWorth with both masked language modeling and cite-worthiness detection. CopeNLU is the University of Copenhagen natural language understanding group. Weights were committed on 2021-05-20. The paper is Findings of ACL 2021. No license tag was on the Hub record checked on 2026-10-15.", "what_it_is_for": "The card says to use it as a starting point for scientific document understanding, not as a ready cite-worthiness tagger. The GitHub README says the best cite-worthiness model in the paper is a Longformer, which was not released under a model name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/citebert/citebert-cite-only": { "id": "ucph/citebert/citebert-cite-only", "name": "CiteBERT cite-only", "maker": "ucph", "family": "ucph/citebert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-05-20", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "SciBERT trained on cite-worthiness detection only, without the extra masked-language-modeling objective. config.json model_type bert, 12 layers, hidden size 768, max_position_embeddings 512. There is no model card. The GitHub README and the paper name this repo. Hub createdAt is 2022-03-02. The model commit is 2021-05-20.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CiteBERT cite-only is the CopeNLU ablation that fine-tunes SciBERT on cite-worthiness detection without masked language modeling. The University of Copenhagen group released it next to CiteBERT. The weight commit is 2021-05-20. There is still no model card. No license tag was on the Hub record checked on 2026-10-15.", "what_it_is_for": "The GitHub README lists it as the citation-detection-only SciBERT. The paper uses it to compare against the joint MLM objective. It is not described as the best cite-worthiness tagger.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/cultrace/cultrace-latentqa-decoder-gemma-3-4b-it": { "id": "ucph/cultrace/cultrace-latentqa-decoder-gemma-3-4b-it", "name": "CulTrace LatentQA decoder Gemma 3 4B Instruct", "maker": "ucph", "family": "ucph/cultrace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA decoders on google/gemma-3-4b-it. Rank 32, alpha 64, dropout 0.05, all attention and MLP projections. One adapter per read layer, read0 through read33 (34 layers). layer_to_write is 0. Trained 8105 steps. Not a standalone chat model. Card pad token id 0. The model tree also lists google/gemma-3-4b-pt under the instruct checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CulTrace LatentQA decoder Gemma 3 4B Instruct is a set of LoRA activation decoders released by CopeNLU at the University of Copenhagen on 2026-08-12 for the CulTrace paper. The paper also lists KAIST. The adapters read hidden states of google/gemma-3-4b-it. They were still public on 2026-10-15. No adapter license was on the Hub record.", "what_it_is_for": "The card says they answer natural-language questions about a target model's activations. They are an interpretability tool, not a chat model. Loading an adapter without patched activations is stated to produce nonsense.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/cultrace/cultrace-latentqa-decoder-llama-3-8b-instruct": { "id": "ucph/cultrace/cultrace-latentqa-decoder-llama-3-8b-instruct", "name": "CulTrace LatentQA decoder Llama 3 8B Instruct", "maker": "ucph", "family": "ucph/cultrace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA decoders on meta-llama/Meta-Llama-3-8B-Instruct. Rank 32, alpha 64, dropout 0.05, all attention and MLP projections. One adapter per read layer, read0 through read31 (32 layers). layer_to_write is 0. Trained 8105 steps. Not a standalone chat model. Card pad token id 128010.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CulTrace LatentQA decoder Llama 3 8B Instruct is a set of LoRA activation decoders released by CopeNLU at the University of Copenhagen on 2026-08-12 for the CulTrace paper. The paper also lists KAIST. The adapters read hidden states of meta-llama/Meta-Llama-3-8B-Instruct. They were still public on 2026-10-15. No adapter license was on the Hub record.", "what_it_is_for": "The card says they answer natural-language questions about a target model's activations. They are an interpretability tool, not a chat model. Loading an adapter without patched activations is stated to produce nonsense.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/cultrace/cultrace-latentqa-decoder-ministral-8b-instruct": { "id": "ucph/cultrace/cultrace-latentqa-decoder-ministral-8b-instruct", "name": "CulTrace LatentQA decoder Ministral 8B Instruct", "maker": "ucph", "family": "ucph/cultrace", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA decoders on mistralai/Ministral-8B-Instruct-2410. Rank 32, alpha 64, dropout 0.05, all attention and MLP projections. One adapter per read layer, read0 through read35 (36 layers). layer_to_write is 0. Trained 32420 steps. Not a standalone chat model. Card pad token id 999. The appendix says Ministral used a different learning rate and batch size than Llama. Those appendix hyperparameters were not copied onto this card's step count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CulTrace LatentQA decoder Ministral 8B Instruct is a set of LoRA activation decoders released by CopeNLU at the University of Copenhagen on 2026-08-12 for the CulTrace paper. The paper also lists KAIST. The adapters read hidden states of mistralai/Ministral-8B-Instruct-2410. They were still public on 2026-10-15. No adapter license was on the Hub record.", "what_it_is_for": "The card says they answer natural-language questions about a target model's activations. They are an interpretability tool, not a chat model. Loading an adapter without patched activations is stated to produce nonsense.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/danish-legal/danish-legal-bert-base": { "id": "ucph/danish-legal/danish-legal-bert-base", "name": "Danish Legal BERT base", "maker": "ucph", "family": "ucph/danish-legal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2022-10-04", "context_length": 512, "max_output_tokens": null, "parameters": "110M", "architecture": "BERT base continued from Maltehb/danish-bert-botxo. Card: 12 layers, 768 hidden units, 12 heads. config.json max_position_embeddings 512. GitHub table: 32K vocab, 110M parameters. Trained 500k steps up to 128 tokens, then 100k steps up to 512 tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Danish Legal BERT base is a legal-domain adaptation of BotXO Danish BERT, released by CoAStaL at the University of Copenhagen. The Hub repo was created on 2022-10-04 and was still public on 2026-10-15 under CC-BY-NC-4.0.", "what_it_is_for": "The card says it is a BERT pre-trained on Danish legal corpora for legal NLP. It does not state a narrower product use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/danish-legal/danish-legal-lm-base": { "id": "ucph/danish-legal/danish-legal-lm-base", "name": "Danish Legal LM base", "maker": "ucph", "family": "ucph/danish-legal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2022-09-06", "context_length": 512, "max_output_tokens": null, "parameters": "110M", "architecture": "RoBERTa base: 12 layers, 768 hidden units, 12 heads. GitHub vocab 32K and 110M parameters. config.json max_position_embeddings 514. Card trains 500k steps at up to 128 tokens, then 100k steps at up to 512 tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Danish Legal LM base is a Danish RoBERTa encoder from the CoAStaL NLP group at the University of Copenhagen. The Hub repo was created on 2022-09-06 and was still public on 2026-10-15. The license is CC-BY-NC-4.0. No dedicated paper was found.", "what_it_is_for": "The card says it is a RoBERTa pre-trained on Danish legal corpora: Danish EU legislation from MultiEURLEX and two Danish Gigaword legal subsets. The intended-use section is a placeholder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/danish-legal/danish-legal-longformer-base": { "id": "ucph/danish-legal/danish-legal-longformer-base", "name": "Danish Legal Longformer base", "maker": "ucph", "family": "ucph/danish-legal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-10-06", "context_length": null, "max_output_tokens": null, "parameters": "134M", "architecture": "Longformer, 12 layers, hidden size 768. GitHub table: vocab 32K, 134M parameters. config.json max_position_embeddings 2050 and attention window 128 on each layer. Card: 64000 MLM steps on danish_legal_pile from an internal path.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Danish Legal Longformer base is a Danish legal Longformer from CoAStaL at the University of Copenhagen. The Hub repo was created on 2022-10-06. No license tag was on the Hub record checked on 2026-10-15.", "what_it_is_for": "It is the masked-language checkpoint behind the Danish EURLEX classifiers. The card itself is a Trainer MLM card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/danish-legal/danish-legal-longformer-eurlex": { "id": "ucph/danish-legal/danish-legal-longformer-eurlex", "name": "Danish Legal Longformer EURLEX", "maker": "ucph", "family": "ucph/danish-legal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-10-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of coastalcph/danish-legal-longformer-base on Danish MultiEURLEX. config.json model_type longformer, 12 layers, hidden size 768, max_position_embeddings 2050, attention window 128. Parameter count of the classifier head is not stated. The base encoder is listed as 134M on GitHub.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Danish Legal Longformer EURLEX is CoAStaL's fine-tune of Danish Legal Longformer base for Danish EUR-Lex labels. The Hub repo was created on 2022-10-07 and was still public on 2026-10-15. No license tag was on the Hub record.", "what_it_is_for": "The card shows it as a text classifier and as a document embedder that reads the CLS token. The sample text is a Danish Commission decision.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/danish-legal/danish-legal-longformer-eurlex-sd": { "id": "ucph/danish-legal/danish-legal-longformer-eurlex-sd", "name": "Danish Legal Longformer EURLEX SD", "maker": "ucph", "family": "ucph/danish-legal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-10-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of coastalcph/danish-legal-longformer-base on Danish MultiEURLEX with a spectral decoupling penalty. config.json matches the other EURLEX Longformer: 12 layers, hidden size 768, max_position_embeddings 2050, attention window 128.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Danish Legal Longformer EURLEX SD is the CoAStaL fine-tune of Danish Legal Longformer base that adds a spectral decoupling penalty. The Hub repo was created on 2022-10-24. No license tag was on the Hub record checked on 2026-10-15.", "what_it_is_for": "Same job as the non-SD EURLEX classifier: label Danish EUR-Lex text and optionally read a CLS embedding. The GitHub README calls this pair the stronger of the two released classifiers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/danish-legal/danish-legal-xlm-base": { "id": "ucph/danish-legal/danish-legal-xlm-base", "name": "Danish Legal XLM base", "maker": "ucph", "family": "ucph/danish-legal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-10-05", "context_length": null, "max_output_tokens": null, "parameters": "110M", "architecture": "config.json model_type roberta, 12 layers, hidden size 768, max_position_embeddings 514. GitHub table lists 12 / 768 / 12, vocab 32K, 110M. The card calls it a fine-tune of an internal path data/plms/danish-legal-xlm-base on danish_legal_pile for 100000 steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Danish Legal XLM base is a Danish legal encoder released by CoAStaL at the University of Copenhagen. The Hub repo was created on 2022-10-05. The card is a Trainer template. No license was on the Hub record checked on 2026-10-15.", "what_it_is_for": "The GitHub table groups it with the other Danish legal language models. The card does not state an intended use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/exaggeration/scientific-exaggeration-detection": { "id": "ucph/exaggeration/scientific-exaggeration-detection", "name": "Scientific Exaggeration Detector", "maker": "ucph", "family": "ucph/exaggeration", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-10-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa config, 12 layers, hidden size 768, max_position_embeddings 514. The card says it is the best exaggeration-detection model from the EMNLP 2021 paper and that it scores causal claim strength. It does not name the base checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Scientific Exaggeration Detector is CopeNLU's released classifier for causal claim strength in health-science writing. Dustin Wright and Isabelle Augenstein are the EMNLP 2021 authors. The Hub repo was created on 2022-10-19. No license tag was on the Hub record checked on 2026-10-15.", "what_it_is_for": "The card says to measure causal claim strength of a scientific sentence and to compare two sentences for exaggeration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/fairlex/fairlex-cail-minilm": { "id": "ucph/fairlex/fairlex-cail-minilm", "name": "FairLex CAIL MiniLM", "maker": "ucph", "family": "ucph/fairlex", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mini-sized encoder: 6 layers, hidden size 384, 12 heads. config.json max_position_embeddings is 514. The card does not state a token context window. Warm-started from a MiniLM distilled from XLM-R. Training corpus on the card: CAIL (Chinese). config model_type xlm-roberta.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FairLex CAIL MiniLM is a masked-language checkpoint released by the CoAStaL NLP group at the University of Copenhagen for the FairLex benchmark. It was on the Hugging Face Hub on 2022-03-02 and was still public on 2026-10-15. The license is CC-BY-NC-SA-4.0.", "what_it_is_for": "The card says the FairLex models are mini-sized encoders warm-started for legal text in the Chinese CAIL setting, so later work can test fairness of fine-tuning. They are not a deployed legal API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/fairlex/fairlex-ecthr-minilm": { "id": "ucph/fairlex/fairlex-ecthr-minilm", "name": "FairLex ECtHR MiniLM", "maker": "ucph", "family": "ucph/fairlex", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mini-sized encoder: 6 layers, hidden size 384, 12 heads. config.json max_position_embeddings is 514. The card does not state a token context window. Warm-started from the public MiniLMv2 distilled from RoBERTa. Training corpus on the card: ECtHR. config model_type roberta.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FairLex ECtHR MiniLM is a masked-language checkpoint released by the CoAStaL NLP group at the University of Copenhagen for the FairLex benchmark. It was on the Hugging Face Hub on 2022-03-02 and was still public on 2026-10-15. The license is CC-BY-NC-SA-4.0.", "what_it_is_for": "The card says the FairLex models are mini-sized encoders warm-started for legal text in the ECtHR setting, so later work can test fairness of fine-tuning. They are not a deployed legal API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/fairlex/fairlex-fscs-minilm": { "id": "ucph/fairlex/fairlex-fscs-minilm", "name": "FairLex FSCS MiniLM", "maker": "ucph", "family": "ucph/fairlex", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mini-sized encoder: 6 layers, hidden size 384, 12 heads. config.json max_position_embeddings is 514. The card does not state a token context window. Warm-started from a MiniLM distilled from XLM-R. Training corpus on the card: FSCS. config model_type xlm-roberta, vocab_size 250002.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FairLex FSCS MiniLM is a masked-language checkpoint released by the CoAStaL NLP group at the University of Copenhagen for the FairLex benchmark. It was on the Hugging Face Hub on 2022-03-02 and was still public on 2026-10-15. The license is CC-BY-NC-SA-4.0.", "what_it_is_for": "The card says the FairLex models are mini-sized encoders warm-started for legal text in the Swiss FSCS setting, so later work can test fairness of fine-tuning. They are not a deployed legal API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/fairlex/fairlex-scotus-minilm": { "id": "ucph/fairlex/fairlex-scotus-minilm", "name": "FairLex SCOTUS MiniLM", "maker": "ucph", "family": "ucph/fairlex", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mini-sized encoder: 6 layers, hidden size 384, 12 heads. config.json max_position_embeddings is 514. The card does not state a token context window. Warm-started from the public MiniLMv2 distilled from RoBERTa. Training corpus on the card: SCOTUS. config model_type roberta.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FairLex SCOTUS MiniLM is a masked-language checkpoint released by the CoAStaL NLP group at the University of Copenhagen for the FairLex benchmark. It was on the Hugging Face Hub on 2022-03-02 and was still public on 2026-10-15. The license is CC-BY-NC-SA-4.0.", "what_it_is_for": "The card says the FairLex models are mini-sized encoders warm-started for legal text in the SCOTUS setting, so later work can test fairness of fine-tuning. They are not a deployed legal API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/lexlm/legal-longformer-base": { "id": "ucph/lexlm/legal-longformer-base", "name": "Legal Longformer base", "maker": "ucph", "family": "ucph/lexlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-02-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Positional embeddings of legal-roberta-base extended by cloning, following Beltagy et al. 2020. config.json model_type longformer, 12 layers, hidden size 768, max_position_embeddings 4098, attention window 512. The Hub size label is 0.1B parameters. That is a rounded label, not an exact count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Legal Longformer base is the LexLMs Longformer derived from legal-roberta-base. The releasing org is lexlms, from the University of Copenhagen-led LeXFiles paper. Hub created 2023-02-24. License CC-BY-SA-4.0. Still public on 2026-10-15.", "what_it_is_for": "The card presents it as the long-context derivative of LexLM for English legal text. It does not name a benchmark score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/lexlm/legal-longformer-large": { "id": "ucph/lexlm/legal-longformer-large", "name": "Legal Longformer large", "maker": "ucph", "family": "ucph/lexlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2023-03-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Positional embeddings of legal-roberta-large extended by cloning, following Beltagy et al. 2020. config.json model_type longformer, 24 layers, hidden size 1024, max_position_embeddings 4098, attention window 512. No parameter count was on the card or the Hub size line that was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Legal Longformer large is the LexLMs Longformer derived from legal-roberta-large. The releasing org is lexlms, from the University of Copenhagen-led LeXFiles paper. Hub created 2023-03-17. License CC-BY-SA-4.0. Still public on 2026-10-15.", "what_it_is_for": "The card presents it as the long-context derivative of LexLM for English legal text. It does not name a benchmark score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/lexlm/legal-roberta-base": { "id": "ucph/lexlm/legal-roberta-base", "name": "LexLM base", "maker": "ucph", "family": "ucph/lexlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2022-11-11", "context_length": null, "max_output_tokens": null, "parameters": "123.9M", "architecture": "Continued pretrain of roberta, 12 layers, hidden size 768. config.json max_position_embeddings 514. New 50k BPE tokenizer with reused embeddings for overlapping tokens. 1M steps on LeXFiles, batch 512, masking rate 20%. Card total train batch size 512. Final validation loss on the card is 0.7516 at step 1000000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LexLM base is an English legal RoBERTa from the LexLMs release. First authors Ilias Chalkidis and Nicolas Garneau were at the University of Copenhagen, with Anders Sogaard. Coauthors are outside Copenhagen. Hub created 2022-11-11. License CC-BY-SA-4.0. Still public on 2026-10-15.", "what_it_is_for": "The org card says the models support legal NLP research and assistive legal technology. The model card does not name a product task. Evaluation is deferred to the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/lexlm/legal-roberta-large": { "id": "ucph/lexlm/legal-roberta-large", "name": "LexLM large", "maker": "ucph", "family": "ucph/lexlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2022-11-11", "context_length": null, "max_output_tokens": null, "parameters": "354.0M", "architecture": "Continued pretrain of roberta, 24 layers, hidden size 1024. config.json max_position_embeddings 514. New 50k BPE tokenizer with reused embeddings for overlapping tokens. 1M steps on LeXFiles, batch 512, masking rate 30%. Card total train batch size 256 in the hyperparameter list, while the narrative says batches of 512 samples. Final validation loss on the card is 0.6314 at step 1000000.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LexLM large is an English legal RoBERTa from the LexLMs release. First authors Ilias Chalkidis and Nicolas Garneau were at the University of Copenhagen, with Anders Sogaard. Coauthors are outside Copenhagen. Hub created 2022-11-11. License CC-BY-SA-4.0. Still public on 2026-10-15.", "what_it_is_for": "The org card says the models support legal NLP research and assistive legal technology. The model card does not name a product task. Evaluation is deferred to the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/populism/roberta-large-ft-trump-populism": { "id": "ucph/populism/roberta-large-ft-trump-populism", "name": "RoBERTa large Trump populism", "maker": "ucph", "family": "ucph/populism", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-SA-4.0", "release_date": "2025-07-09", "context_length": null, "max_output_tokens": null, "parameters": "0.4B", "architecture": "Fine-tune of FacebookAI/roberta-large on coastalcph/populism-trump-2016. Hub model-size label is 0.4B, which is rounded. Card: 3 epochs, learning rate 0.0001, batch 64, cosine schedule. Context length is not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoBERTa large Trump populism is a CoAStaL classifier from the University of Copenhagen, fine-tuned from RoBERTa large on tagged Donald Trump speeches. The Hub repo was created on 2025-07-09. The preprint is 2025-07-25. License CC-BY-SA-4.0. Still public on 2026-10-15.", "what_it_is_for": "The card limits the intended use to academic research on fine-grained populism.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucph/spiced/spiced": { "id": "ucph/spiced/spiced", "name": "SPICED", "maker": "ucph", "family": "ucph/spiced", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-10-11", "context_length": 384, "max_output_tokens": null, "parameters": "", "architecture": "Sentence-transformers MPNet. Card architecture block: max_seq_length 384, mean pooling, 768-dimensional embeddings, then normalize. config.json model_type mpnet, 12 layers, hidden size 768, max_position_embeddings 514. Trained 5 epochs, batch 8, learning rate 1e-5, cosine similarity loss.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SPICED is a sentence embedding model released on the CopeNLU Hub org at the University of Copenhagen. The repo was created on 2022-10-11. The paper, by Dustin Wright, Jiaxin Pei, David Jurgens, and Isabelle Augenstein, is EMNLP 2022. No license tag was on the Hub record checked on 2026-10-15.", "what_it_is_for": "The paper says models trained on SPICED help retrieve evidence for scientific claims and measure how faithfully findings are paraphrased. The card shows clustering and semantic search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucr/abflownet/abflownet": { "id": "ucr/abflownet/abflownet", "name": "AbFlowNet", "maker": "ucr", "family": "ucr/abflownet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DiffAb transformer diffusion (Luo et al. 2022) over amino-acid type, C-alpha coordinates, and SO(3) orientation, plus one learned trajectory-balance partition parameter. Repo config: res_feat_dim 128, pair_feat_dim 64, 6-layer noise network, 100 diffusion steps. The paper says the only training difference from a matched DiffAb run is the trajectory-balance loss.", "modalities": { "input": [ "structure" ], "output": [ "sequence", "structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AbFlowNet is a named antibody CDR diffusion model from a 18 May 2025 preprint. Equal leads are Abrar Rahman Abir (BUET) and Haz Sameen Shahgir (UC Riverside), with Greg Ver Steeg and Yue Dong at UCR plus coauthors at USC and Princeton. The public GitHub tree lists `trained_models/abflownet.pt`. It was still listed on 15 October 2026. There is no API and no price.", "what_it_is_for": "The paper designs complementarity-determining regions and adds a trajectory-balance term so binding energy enters training without an online reinforcement-learning loop. The network is the DiffAb transformer. The authors train a matched DiffAb run for the same 200,000 steps and say the only difference is that loss term, plus one learned partition parameter. This is not a fine-tune of the upstream DiffAb release, and `diffab.pt` in the same folder was not cataloged.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucr/birna/birna-bert": { "id": "ucr/birna/birna-bert", "name": "BiRNA-BERT", "maker": "ucr", "family": "ucr/birna", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-07-02", "context_length": null, "max_output_tokens": null, "parameters": "117000000", "architecture": "12-layer BERT encoder using MosaicBERT code: hidden size 768, 12 heads, intermediate size 3072, GELU, vocabulary 4096, BertForMaskedLM. Positional bias is ALiBi. Pretrained with masked language modeling on nucleotide tokens and BPE tokens of the same RNA sequences.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BiRNA-BERT is a 117M-parameter RNA encoder released on 2 July 2024 under the buetnlpbio Hugging Face org and still downloadable on 15 October 2026. UC Riverside coauthors Haz Sameen Shahgir and Yue Dong share the paper with BUET and the University of Maryland. Shahgir ran pretraining. UCR's CSE news on 2 December 2025 presents the model as lab work for full-length non-coding RNA. It is not a campus API product.", "what_it_is_for": "The authors use it to embed RNA for long non-coding interaction tasks, family clustering, and nucleotide-level structure prediction. Adaptive tokenization keeps per-base tokens on shorter inputs and switches to BPE when the sequence would otherwise be truncated. ALiBi is there so the window can be raised at load time without another pretraining run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsb/co-patcher/co-patcher-loc-gen-14b": { "id": "ucsb/co-patcher/co-patcher-loc-gen-14b", "name": "Co-PatcheR-Loc-Gen-14B", "maker": "ucsb", "family": "ucsb/co-patcher", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-26", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2ForCausalLM. Paper and GitHub: the localization and patch-generation component, fine-tuned from Qwen2.5-Coder-14B. Config: 48 layers, hidden size 5120, 40 query heads, 8 key-value heads, vocab 152064, bfloat16. The config file has two rope_scaling keys. The second is YaRN with factor 4.0 and original_max_position_embeddings 32768. sliding_window is 131072 and use_sliding_window is false. No _name_or_path field. The Hub widget says 15B parameters. The paper and GitHub say 14B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Co-PatcheR-Loc-Gen-14B is the localization and patch-generation model in UC Santa Barbara SURFI's Co-PatcheR system. Weights were on the Hub by 26 May 2025. There is no model card. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper trains one model for fault localization and patch generation. Localization is two steps: affected files, then the lines. Generation also reviews and revises its own patch. GitHub lists this repo as that component and tells users to serve it with vLLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsb/co-patcher/co-patcher-val-assert-14b": { "id": "ucsb/co-patcher/co-patcher-val-assert-14b", "name": "Co-PatcheR-Val-assert-14B", "maker": "ucsb", "family": "ucsb/co-patcher", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-26", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2ForCausalLM. config _name_or_path is Qwen/Qwen2.5-Coder-14B-Instruct. 48 layers, hidden size 5120, 40 query heads, 8 key-value heads, vocab 152064, bfloat16, rope_scaling null, sliding_window null. The paper calls this the validation model that writes issue-reproducing tests with assertions. The Hub widget says 15B parameters. The paper and GitHub say 14B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Co-PatcheR-Val-assert-14B is the assertion-writing validation model in UC Santa Barbara SURFI's Co-PatcheR system. Weights were on the Hub by 26 May 2025. There is no model card. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses two validation models to write issue-reproducing tests and judge patches. This one writes tests that include assertions. GitHub says it improves validation, and that a simpler setup can omit it and keep only the no-assert model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsb/co-patcher/co-patcher-val-no-assert-14b": { "id": "ucsb/co-patcher/co-patcher-val-no-assert-14b", "name": "Co-PatcheR-Val-no-assert-14B", "maker": "ucsb", "family": "ucsb/co-patcher", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-26", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2ForCausalLM. config _name_or_path is Qwen/Qwen2.5-Coder-14B-Instruct. 48 layers, hidden size 5120, 40 query heads, 8 key-value heads, vocab 152064, bfloat16, rope_scaling null, sliding_window null. The paper calls this the validation model that writes issue-reproducing tests without assertions. The Hub widget says 15B parameters. The paper and GitHub say 14B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Co-PatcheR-Val-no-assert-14B is the no-assertion validation model in UC Santa Barbara SURFI's Co-PatcheR system. Weights were on the Hub by 26 May 2025. There is no model card. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses this model to write issue-reproducing tests without assertions and to help judge whether a patch is correct. GitHub says a simpler setup can use this checkpoint without the assert model and still do well. It does not give a separate score for that setup.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsb/termigen/termigen-32b": { "id": "ucsb/termigen/termigen-32b", "name": "TermiGen-32B", "maker": "ucsb", "family": "ucsb/termigen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0. The model card says the license is inherited from Qwen2.5-Coder. The paper text is CC BY-NC-SA 4.0.", "release_date": "2026-02-06", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2ForCausalLM full-parameter supervised fine-tune of Qwen2.5-Coder-32B-Instruct. Config: 64 layers, hidden size 5120, 40 query heads, 8 key-value heads, vocab 152064, bfloat16, rope_scaling type dynamic with factor 4.0. The Hub size widget says 1.12M parameters, which does not match this config or the 32B name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TermiGen-32B is a UC Santa Barbara SURFI terminal-agent model, a full-parameter fine-tune of Qwen2.5-Coder-32B-Instruct. The paper is dated 6 February 2026 and the weights are public. The GitHub README also names UC San Diego, AMD, Oxford, and Google. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says it is trained on synthesized Docker environments and error-correction trajectories so it can run terminal tasks and recover from mistakes. The released agent is BashAgent, which talks to a shell in a ReAct loop. The card's contact is Kaijie Zhu at ucsb.edu.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsb/vulnllm-r/vulnllm-r-7b": { "id": "ucsb/vulnllm-r/vulnllm-r-7b", "name": "VulnLLM-R-7B", "maker": "ucsb", "family": "ucsb/vulnllm-r", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-12-08", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2ForCausalLM fine-tune of Qwen/Qwen2.5-7B-Instruct. Config: 28 layers, hidden size 3584, 28 query heads, 4 key-value heads, vocab 152064, bfloat16. sliding_window is 131072 and use_sliding_window is false. The card and abstract call it a 7B model. A Hub collection row said 8B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VulnLLM-R-7B is a UC Santa Barbara SURFI reasoning model for software vulnerability detection, a fine-tune of Qwen2.5-7B-Instruct released with the 8 December 2025 paper. Weights are public under Apache-2.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The card says it reasons about data flow, control flow, and security context instead of only matching patterns. The paper trains it on C, C++, and Python and reports zero-shot Java results. An agent scaffold around the model is part of the paper, not a separate weight release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/advxl/advxl-vit-g-14": { "id": "ucsc/advxl/advxl-vit-g-14", "name": "AdvXL ViT-g/14", "maker": "ucsc", "family": "ucsc/advxl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adversarially trained ViT from the AdvXL recipe. GitHub schedule: DataComp-1B plus ImageNet-1K, 5.12B samples at 84, 38.4M at 224, and 6.4M at 336, with 2/3 adversarial steps. Hub repo created 2024-04-16.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AdvXL ViT-g/14 is a UC Santa Cruz VLAA adversarially trained vision encoder released with the CVPR 2024 AdvXL paper. The Hub weights date from 16 April 2024. Collected 2026-10-15.", "what_it_is_for": "AdvXL is their large-scale adversarial-training recipe. The released models are robust visual representations, not a chat API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/advxl/advxl-vit-h-14": { "id": "ucsc/advxl/advxl-vit-h-14", "name": "AdvXL ViT-H/14", "maker": "ucsc", "family": "ucsc/advxl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Adversarially trained ViT from the AdvXL recipe. GitHub schedule: DataComp-1B plus ImageNet-1K, 5.12B samples at 84, 38.4M at 224, and 6.4M at 336, with 2/3 adversarial steps. Hub repo created 2024-04-16.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AdvXL ViT-H/14 is a UC Santa Cruz VLAA adversarially trained vision encoder released with the CVPR 2024 AdvXL paper. The Hub weights date from 16 April 2024. Collected 2026-10-15.", "what_it_is_for": "AdvXL is their large-scale adversarial-training recipe. The released models are robust visual representations, not a chat API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/clipa/clipa-vit-bigg-14-336-datacomp1b": { "id": "ucsc/clipa/clipa-vit-bigg-14-336-datacomp1b", "name": "CLIPA ViT-bigG/14 336 DataComp-1B", "maker": "ucsc", "family": "ucsc/clipa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIPA contrastive ViT-bigG/14 image-text model trained on DataComp-1B. This is the 336-resolution fine-tune, the final stage in the published schedule.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLIPA ViT-bigG/14 336 DataComp-1B is a contrastive vision-language model trained by UC Santa Cruz VLAA and released on Hugging Face in October 2023. It is an active open-weight checkpoint. Collected 2026-10-15.", "what_it_is_for": "CLIPA is their inverse-scaling CLIP training recipe: large encoders trained mostly at low resolution, then briefly fine-tuned at higher resolution, for zero-shot image classification and retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/clipa/clipa-vit-bigg-14-datacomp1b": { "id": "ucsc/clipa/clipa-vit-bigg-14-datacomp1b", "name": "CLIPA ViT-bigG/14 DataComp-1B", "maker": "ucsc", "family": "ucsc/clipa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIPA contrastive ViT-bigG/14 image-text model trained on DataComp-1B. This folder is the 224-resolution checkpoint. CLIPA-v2 uses a short high-resolution fine-tune after low-resolution pretraining. The unsuffixed Hub name is treated as 224 because the paired repo is explicitly named 336 and OpenCLIP uses that convention. The card body was not quoted with a resolution integer.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLIPA ViT-bigG/14 DataComp-1B is a contrastive vision-language model trained by UC Santa Cruz VLAA and released on Hugging Face in October 2023. It is an active open-weight checkpoint. Collected 2026-10-15.", "what_it_is_for": "CLIPA is their inverse-scaling CLIP training recipe: large encoders trained mostly at low resolution, then briefly fine-tuned at higher resolution, for zero-shot image classification and retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/clipa/clipa-vit-h-14-336-datacomp1b": { "id": "ucsc/clipa/clipa-vit-h-14-336-datacomp1b", "name": "CLIPA ViT-H/14 336 DataComp-1B", "maker": "ucsc", "family": "ucsc/clipa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIPA contrastive ViT-H/14 image-text model trained on DataComp-1B. This is the 336-resolution fine-tune, the final stage in the published schedule.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLIPA ViT-H/14 336 DataComp-1B is a contrastive vision-language model trained by UC Santa Cruz VLAA and released on Hugging Face in October 2023. It is an active open-weight checkpoint. Collected 2026-10-15.", "what_it_is_for": "CLIPA is their inverse-scaling CLIP training recipe: large encoders trained mostly at low resolution, then briefly fine-tuned at higher resolution, for zero-shot image classification and retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/clipa/clipa-vit-h-14-336-laion2b": { "id": "ucsc/clipa/clipa-vit-h-14-336-laion2b", "name": "CLIPA ViT-H/14 336 LAION-2B", "maker": "ucsc", "family": "ucsc/clipa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIPA contrastive ViT-H/14 image-text model trained on LAION-2B. This is the 336-resolution fine-tune, the final stage in the published schedule.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLIPA ViT-H/14 336 LAION-2B is a contrastive vision-language model trained by UC Santa Cruz VLAA and released on Hugging Face in October 2023. It is an active open-weight checkpoint. Collected 2026-10-15.", "what_it_is_for": "CLIPA is their inverse-scaling CLIP training recipe: large encoders trained mostly at low resolution, then briefly fine-tuned at higher resolution, for zero-shot image classification and retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/clipa/clipa-vit-h-14-datacomp1b": { "id": "ucsc/clipa/clipa-vit-h-14-datacomp1b", "name": "CLIPA ViT-H/14 DataComp-1B", "maker": "ucsc", "family": "ucsc/clipa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIPA contrastive ViT-H/14 image-text model trained on DataComp-1B. This folder is the 224-resolution checkpoint. CLIPA-v2 uses a short high-resolution fine-tune after low-resolution pretraining. The unsuffixed Hub name is treated as 224 because the paired repo is explicitly named 336 and OpenCLIP uses that convention. The card body was not quoted with a resolution integer.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLIPA ViT-H/14 DataComp-1B is a contrastive vision-language model trained by UC Santa Cruz VLAA and released on Hugging Face in October 2023. It is an active open-weight checkpoint. Collected 2026-10-15.", "what_it_is_for": "CLIPA is their inverse-scaling CLIP training recipe: large encoders trained mostly at low resolution, then briefly fine-tuned at higher resolution, for zero-shot image classification and retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/clipa/clipa-vit-l-14-336-datacomp1b": { "id": "ucsc/clipa/clipa-vit-l-14-336-datacomp1b", "name": "CLIPA ViT-L/14 336 DataComp-1B", "maker": "ucsc", "family": "ucsc/clipa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIPA contrastive ViT-L/14 image-text model trained on DataComp-1B. This is the 336-resolution fine-tune, the final stage in the published schedule.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLIPA ViT-L/14 336 DataComp-1B is a contrastive vision-language model trained by UC Santa Cruz VLAA and released on Hugging Face in October 2023. It is an active open-weight checkpoint. Collected 2026-10-15.", "what_it_is_for": "CLIPA is their inverse-scaling CLIP training recipe: large encoders trained mostly at low resolution, then briefly fine-tuned at higher resolution, for zero-shot image classification and retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/clipa/clipa-vit-l-14-datacomp1b": { "id": "ucsc/clipa/clipa-vit-l-14-datacomp1b", "name": "CLIPA ViT-L/14 DataComp-1B", "maker": "ucsc", "family": "ucsc/clipa", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-10-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIPA contrastive ViT-L/14 image-text model trained on DataComp-1B. This folder is the 224-resolution checkpoint. CLIPA-v2 uses a short high-resolution fine-tune after low-resolution pretraining. The unsuffixed Hub name is treated as 224 because the paired repo is explicitly named 336 and OpenCLIP uses that convention. The card body was not quoted with a resolution integer.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLIPA ViT-L/14 DataComp-1B is a contrastive vision-language model trained by UC Santa Cruz VLAA and released on Hugging Face in October 2023. It is an active open-weight checkpoint. Collected 2026-10-15.", "what_it_is_for": "CLIPA is their inverse-scaling CLIP training recipe: large encoders trained mostly at low resolution, then briefly fine-tuned at higher resolution, for zero-shot image classification and retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/clips/clips-vit-h-14-224": { "id": "ucsc/clips/clips-vit-h-14-224", "name": "CLIPS ViT-H/14 224", "maker": "ucsc", "family": "ucsc/clips", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIPS contrastive ViT trained on Recap-DataComp-1B. Project-page zoo name: Huge-14-224. The 336 Large model is the Hub repo without 224 in the name.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLIPS ViT-H/14 224 is a UC Santa Cruz VLAA CLIP model trained on synthetic recaptions and released in late 2024. It is an active open-weight checkpoint. Collected 2026-10-15.", "what_it_is_for": "CLIPS is their CLIP training framework for synthetic captions. The lab reports stronger zero-shot retrieval than the baselines they compare, and they swap it in for the OpenAI CLIP encoder inside LLaVA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/clips/clips-vit-l-14-224": { "id": "ucsc/clips/clips-vit-l-14-224", "name": "CLIPS ViT-L/14 224", "maker": "ucsc", "family": "ucsc/clips", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIPS contrastive ViT trained on Recap-DataComp-1B. Project-page zoo name: Large-14-224. The 336 Large model is the Hub repo without 224 in the name.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLIPS ViT-L/14 224 is a UC Santa Cruz VLAA CLIP model trained on synthetic recaptions and released in late 2024. It is an active open-weight checkpoint. Collected 2026-10-15.", "what_it_is_for": "CLIPS is their CLIP training framework for synthetic captions. The lab reports stronger zero-shot retrieval than the baselines they compare, and they swap it in for the OpenAI CLIP encoder inside LLaVA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/clips/clips-vit-l-14-336": { "id": "ucsc/clips/clips-vit-l-14-336", "name": "CLIPS ViT-L/14 336", "maker": "ucsc", "family": "ucsc/clips", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-11-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIPS contrastive ViT trained on Recap-DataComp-1B. Project-page zoo name: Large-14-336. The 336 Large model is the Hub repo without 224 in the name.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLIPS ViT-L/14 336 is a UC Santa Cruz VLAA CLIP model trained on synthetic recaptions and released in late 2024. It is an active open-weight checkpoint. Collected 2026-10-15.", "what_it_is_for": "CLIPS is their CLIP training framework for synthetic captions. The lab reports stronger zero-shot retrieval than the baselines they compare, and they swap it in for the OpenAI CLIP encoder inside LLaVA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/crate/crate-alpha-b-16": { "id": "ucsc/crate/crate-alpha-b-16", "name": "CRATE-alpha B/16", "maker": "ucsc", "family": "ucsc/crate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CRATE-alpha white-box transformer: multi-head subspace self-attention plus an overcomplete dictionary-learning block. PyTorch file crate_alpha_B16.pth in UCSC-VLAA/CRATE-alpha. The original CRATE design is the Berkeley Ma lab model. This release is the UCSC scaling recipe.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CRATE-alpha B/16 is a white-box vision transformer released by UC Santa Cruz VLAA in the CRATE-alpha repository on 30 May 2024. Collected 2026-10-15.", "what_it_is_for": "CRATE-alpha scales the CRATE sparse-coding transformer for ImageNet classification, and the CLIPA variants are the contrastive versions of that architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/crate/crate-alpha-clipa-h-14": { "id": "ucsc/crate/crate-alpha-clipa-h-14", "name": "CRATE-alpha CLIPA H/14", "maker": "ucsc", "family": "ucsc/crate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CRATE-alpha white-box transformer: multi-head subspace self-attention plus an overcomplete dictionary-learning block. PyTorch file crate_alpha_CLIPA_H14.pth in UCSC-VLAA/CRATE-alpha. The original CRATE design is the Berkeley Ma lab model. This release is the UCSC scaling recipe.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CRATE-alpha CLIPA H/14 is a white-box vision transformer released by UC Santa Cruz VLAA in the CRATE-alpha repository on 30 May 2024. Collected 2026-10-15.", "what_it_is_for": "CRATE-alpha scales the CRATE sparse-coding transformer for ImageNet classification, and the CLIPA variants are the contrastive versions of that architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/crate/crate-alpha-clipa-l-14": { "id": "ucsc/crate/crate-alpha-clipa-l-14", "name": "CRATE-alpha CLIPA L/14", "maker": "ucsc", "family": "ucsc/crate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CRATE-alpha white-box transformer: multi-head subspace self-attention plus an overcomplete dictionary-learning block. PyTorch file crate_alpha_CLIPA_L14.pth in UCSC-VLAA/CRATE-alpha. The original CRATE design is the Berkeley Ma lab model. This release is the UCSC scaling recipe.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CRATE-alpha CLIPA L/14 is a white-box vision transformer released by UC Santa Cruz VLAA in the CRATE-alpha repository on 30 May 2024. Collected 2026-10-15.", "what_it_is_for": "CRATE-alpha scales the CRATE sparse-coding transformer for ImageNet classification, and the CLIPA variants are the contrastive versions of that architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/crate/crate-alpha-l-14": { "id": "ucsc/crate/crate-alpha-l-14", "name": "CRATE-alpha L/14", "maker": "ucsc", "family": "ucsc/crate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-05-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CRATE-alpha white-box transformer: multi-head subspace self-attention plus an overcomplete dictionary-learning block. PyTorch file crate_alpha_L14.pth in UCSC-VLAA/CRATE-alpha. The original CRATE design is the Berkeley Ma lab model. This release is the UCSC scaling recipe.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CRATE-alpha L/14 is a white-box vision transformer released by UC Santa Cruz VLAA in the CRATE-alpha repository on 30 May 2024. Collected 2026-10-15.", "what_it_is_for": "CRATE-alpha scales the CRATE sparse-coding transformer for ImageNet classification, and the CLIPA variants are the contrastive versions of that architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/digpt/digpt-vit-b": { "id": "ucsc/digpt/digpt-vit-b", "name": "D-iGPT ViT-B", "maker": "ucsc", "family": "ucsc/digpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive image model that predicts semantic tokens from a frozen discriminative encoder, then reads out a visual representation. Authors include UC Santa Cruz (Sucheng Ren, Zeyu Wang, Hongru Zhu, Cihang Xie) and Johns Hopkins (Junfei Xiao, Alan Yuille). Weights are one Hub repo, OliverRen/D-iGPT.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "D-iGPT ViT-B is an image representation model released by UC Santa Cruz VLAA authors, with Johns Hopkins coauthors, in the OliverRen/D-iGPT repo on 2 May 2024. Collected 2026-10-15.", "what_it_is_for": "D-iGPT changes iGPT so the prediction targets are semantic tokens and the model also predicts visible tokens. The lab reports the fine-tuned ViT-L and ViT-H ImageNet accuracies as the headline result.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/digpt/digpt-vit-h": { "id": "ucsc/digpt/digpt-vit-h", "name": "D-iGPT ViT-H", "maker": "ucsc", "family": "ucsc/digpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive image model that predicts semantic tokens from a frozen discriminative encoder, then reads out a visual representation. Authors include UC Santa Cruz (Sucheng Ren, Zeyu Wang, Hongru Zhu, Cihang Xie) and Johns Hopkins (Junfei Xiao, Alan Yuille). Weights are one Hub repo, OliverRen/D-iGPT.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "D-iGPT ViT-H is an image representation model released by UC Santa Cruz VLAA authors, with Johns Hopkins coauthors, in the OliverRen/D-iGPT repo on 2 May 2024. Collected 2026-10-15.", "what_it_is_for": "D-iGPT changes iGPT so the prediction targets are semantic tokens and the model also predicts visible tokens. The lab reports the fine-tuned ViT-L and ViT-H ImageNet accuracies as the headline result.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/digpt/digpt-vit-l": { "id": "ucsc/digpt/digpt-vit-l", "name": "D-iGPT ViT-L", "maker": "ucsc", "family": "ucsc/digpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-05-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive image model that predicts semantic tokens from a frozen discriminative encoder, then reads out a visual representation. Authors include UC Santa Cruz (Sucheng Ren, Zeyu Wang, Hongru Zhu, Cihang Xie) and Johns Hopkins (Junfei Xiao, Alan Yuille). Weights are one Hub repo, OliverRen/D-iGPT.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "D-iGPT ViT-L is an image representation model released by UC Santa Cruz VLAA authors, with Johns Hopkins coauthors, in the OliverRen/D-iGPT repo on 2 May 2024. Collected 2026-10-15.", "what_it_is_for": "D-iGPT changes iGPT so the prediction targets are semantic tokens and the model also predicts visible tokens. The lab reports the fine-tuned ViT-L and ViT-H ImageNet accuracies as the headline result.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/epifoundation/epifoundation": { "id": "ucsc/epifoundation/epifoundation", "name": "EpiFoundation", "maker": "ucsc", "family": "ucsc/epifoundation", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Foundation model for single-cell ATAC-seq. It keeps only non-zero peaks and uses paired gene expression to supervise peak-to-gene alignment. Pretrained on MiniAtlas, more than 100,000 paired cells. Authors include Juncheng Wu and Yuyin Zhou at UC Santa Cruz, with Duke coauthors. One weight repo.", "modalities": { "input": [ "single-cell-atac-seq" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EpiFoundation is a single-cell ATAC-seq foundation model released by UC Santa Cruz VLAA on 4 February 2025. It is an active open-weight research checkpoint, not an API product. Collected 2026-10-15.", "what_it_is_for": "The card says it learns cell representations from sparse peak data and is meant for cell-type annotation, batch correction, and gene-expression prediction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/mmfreelm/mmfreelm-1-3b": { "id": "ucsc/mmfreelm/mmfreelm-1-3b", "name": "MatMul-free LM 1.3B", "maker": "ucsc", "family": "ucsc/mmfreelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-06-04", "context_length": null, "max_output_tokens": null, "parameters": "1.3 billion", "architecture": "MatMul-free language model. Ternary BitLinear weights, BF16 activations, MLGRU token mixer, and a GLU channel mixer. 24 layers, hidden size 2048, trained on 100 billion SlimPajama tokens. Mistral tokenizer, vocabulary 32,000. Lead authors Rui-Jie Zhu and Jason K. Eshraghian are at UC Santa Cruz. Coauthors are at Soochow, UC Davis, LuxiTech, Groningen, and Intel Labs depending on the paper version.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MatMul-free LM 1.3B is a ternary, matrix-multiplication-free language model from UC Santa Cruz's Neuromorphic Computing Group, released with the 4 June 2024 preprint. Collected 2026-10-15.", "what_it_is_for": "The paper presents it as a language model that replaces dense MatMul with ternary accumulation and replaces attention with an element-wise gated recurrent mixer, aimed at lower memory and custom hardware.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/mmfreelm/mmfreelm-2-7b": { "id": "ucsc/mmfreelm/mmfreelm-2-7b", "name": "MatMul-free LM 2.7B", "maker": "ucsc", "family": "ucsc/mmfreelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-06-04", "context_length": null, "max_output_tokens": null, "parameters": "2.7 billion", "architecture": "MatMul-free language model. Ternary BitLinear weights, BF16 activations, MLGRU token mixer, and a GLU channel mixer. 32 layers, hidden size 2560, trained on 100 billion SlimPajama tokens. Mistral tokenizer, vocabulary 32,000. Lead authors Rui-Jie Zhu and Jason K. Eshraghian are at UC Santa Cruz. Coauthors are at Soochow, UC Davis, LuxiTech, Groningen, and Intel Labs depending on the paper version.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MatMul-free LM 2.7B is a ternary, matrix-multiplication-free language model from UC Santa Cruz's Neuromorphic Computing Group, released with the 4 June 2024 preprint. Collected 2026-10-15.", "what_it_is_for": "The paper presents it as a language model that replaces dense MatMul with ternary accumulation and replaces attention with an element-wise gated recurrent mixer, aimed at lower memory and custom hardware.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/mmfreelm/mmfreelm-370m": { "id": "ucsc/mmfreelm/mmfreelm-370m", "name": "MatMul-free LM 370M", "maker": "ucsc", "family": "ucsc/mmfreelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-06-04", "context_length": null, "max_output_tokens": null, "parameters": "370 million", "architecture": "MatMul-free language model. Ternary BitLinear weights, BF16 activations, MLGRU token mixer, and a GLU channel mixer. 24 layers, hidden size 1024, trained on 15 billion SlimPajama tokens. Mistral tokenizer, vocabulary 32,000. Lead authors Rui-Jie Zhu and Jason K. Eshraghian are at UC Santa Cruz. Coauthors are at Soochow, UC Davis, LuxiTech, Groningen, and Intel Labs depending on the paper version.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MatMul-free LM 370M is a ternary, matrix-multiplication-free language model from UC Santa Cruz's Neuromorphic Computing Group, released with the 4 June 2024 preprint. Collected 2026-10-15.", "what_it_is_for": "The paper presents it as a language model that replaces dense MatMul with ternary accumulation and replaces attention with an element-wise gated recurrent mixer, aimed at lower memory and custom hardware.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-base-patch16-160": { "id": "ucsc/openvision/openvision-vit-base-patch16-160", "name": "OpenVision ViT-Base/16 160", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "86 million", "architecture": "OpenCLIP-compatible ViT-Base vision encoder, patch 16, input resolution 160. 12 layers, width 768, 12 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 86M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Base/16 at 160 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-base-patch16-224": { "id": "ucsc/openvision/openvision-vit-base-patch16-224", "name": "OpenVision ViT-Base/16 224", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-07", "context_length": null, "max_output_tokens": null, "parameters": "86 million", "architecture": "OpenCLIP-compatible ViT-Base vision encoder, patch 16, input resolution 224. 12 layers, width 768, 12 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 86M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Base/16 at 224 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-base-patch16-384": { "id": "ucsc/openvision/openvision-vit-base-patch16-384", "name": "OpenVision ViT-Base/16 384", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "86 million", "architecture": "OpenCLIP-compatible ViT-Base vision encoder, patch 16, input resolution 384. 12 layers, width 768, 12 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 86M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Base/16 at 384 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-base-patch8-160": { "id": "ucsc/openvision/openvision-vit-base-patch8-160", "name": "OpenVision ViT-Base/8 160", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "86 million", "architecture": "OpenCLIP-compatible ViT-Base vision encoder, patch 8, input resolution 160. 12 layers, width 768, 12 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 86M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Base/8 at 160 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-base-patch8-224": { "id": "ucsc/openvision/openvision-vit-base-patch8-224", "name": "OpenVision ViT-Base/8 224", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "86 million", "architecture": "OpenCLIP-compatible ViT-Base vision encoder, patch 8, input resolution 224. 12 layers, width 768, 12 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 86M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Base/8 at 224 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-base-patch8-384": { "id": "ucsc/openvision/openvision-vit-base-patch8-384", "name": "OpenVision ViT-Base/8 384", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "86 million", "architecture": "OpenCLIP-compatible ViT-Base vision encoder, patch 8, input resolution 384. 12 layers, width 768, 12 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 86M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Base/8 at 384 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-huge-patch14-224": { "id": "ucsc/openvision/openvision-vit-huge-patch14-224", "name": "OpenVision ViT-Huge/14 224", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "631 million in the ICCV 2025 supplement; the GitHub zoo says 632 million", "architecture": "OpenCLIP-compatible ViT-Huge vision encoder, patch 14, input resolution 224. 32 layers, width 1280, 16 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. Supplement Table 2 lists 631M. The GitHub ImageNet zoo lists 632M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Huge/14 at 224 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-huge-patch14-84": { "id": "ucsc/openvision/openvision-vit-huge-patch14-84", "name": "OpenVision ViT-Huge/14 84", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "631 million in the ICCV 2025 supplement; the GitHub zoo says 632 million", "architecture": "OpenCLIP-compatible ViT-Huge vision encoder, patch 14, input resolution 84. 32 layers, width 1280, 16 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. Supplement Table 2 lists 631M. The GitHub ImageNet zoo lists 632M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Huge/14 at 84 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-large-patch14-224": { "id": "ucsc/openvision/openvision-vit-large-patch14-224", "name": "OpenVision ViT-Large/14 224", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "303 million in the ICCV 2025 supplement; the GitHub zoo says 307 million", "architecture": "OpenCLIP-compatible ViT-Large vision encoder, patch 14, input resolution 224. 24 layers, width 1024, 16 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. Supplement Table 2 lists 303M. The GitHub ImageNet zoo lists 307M for the same ViT-Large rows.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Large/14 at 224 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-large-patch14-336": { "id": "ucsc/openvision/openvision-vit-large-patch14-336", "name": "OpenVision ViT-Large/14 336", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "303 million in the ICCV 2025 supplement; the GitHub zoo says 307 million", "architecture": "OpenCLIP-compatible ViT-Large vision encoder, patch 14, input resolution 336. 24 layers, width 1024, 16 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. Supplement Table 2 lists 303M. The GitHub ImageNet zoo lists 307M for the same ViT-Large rows.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Large/14 at 336 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-large-patch14-84": { "id": "ucsc/openvision/openvision-vit-large-patch14-84", "name": "OpenVision ViT-Large/14 84", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "303 million in the ICCV 2025 supplement; the GitHub zoo says 307 million", "architecture": "OpenCLIP-compatible ViT-Large vision encoder, patch 14, input resolution 84. 24 layers, width 1024, 16 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. Supplement Table 2 lists 303M. The GitHub ImageNet zoo lists 307M for the same ViT-Large rows.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Large/14 at 84 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-small-patch16-160": { "id": "ucsc/openvision/openvision-vit-small-patch16-160", "name": "OpenVision ViT-Small/16 160", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "22 million", "architecture": "OpenCLIP-compatible ViT-Small vision encoder, patch 16, input resolution 160. 12 layers, width 384, 6 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 22M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Small/16 at 160 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-small-patch16-224": { "id": "ucsc/openvision/openvision-vit-small-patch16-224", "name": "OpenVision ViT-Small/16 224", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "22 million", "architecture": "OpenCLIP-compatible ViT-Small vision encoder, patch 16, input resolution 224. 12 layers, width 384, 6 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 22M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Small/16 at 224 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-small-patch16-384": { "id": "ucsc/openvision/openvision-vit-small-patch16-384", "name": "OpenVision ViT-Small/16 384", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "22 million", "architecture": "OpenCLIP-compatible ViT-Small vision encoder, patch 16, input resolution 384. 12 layers, width 384, 6 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 22M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Small/16 at 384 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-small-patch8-160": { "id": "ucsc/openvision/openvision-vit-small-patch8-160", "name": "OpenVision ViT-Small/8 160", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "22 million", "architecture": "OpenCLIP-compatible ViT-Small vision encoder, patch 8, input resolution 160. 12 layers, width 384, 6 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 22M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Small/8 at 160 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-small-patch8-224": { "id": "ucsc/openvision/openvision-vit-small-patch8-224", "name": "OpenVision ViT-Small/8 224", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "22 million", "architecture": "OpenCLIP-compatible ViT-Small vision encoder, patch 8, input resolution 224. 12 layers, width 384, 6 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 22M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Small/8 at 224 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-small-patch8-384": { "id": "ucsc/openvision/openvision-vit-small-patch8-384", "name": "OpenVision ViT-Small/8 384", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "22 million", "architecture": "OpenCLIP-compatible ViT-Small vision encoder, patch 8, input resolution 384. 12 layers, width 384, 6 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 22M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Small/8 at 384 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-so400m-patch14-224": { "id": "ucsc/openvision/openvision-vit-so400m-patch14-224", "name": "OpenVision ViT-SoViT-400M/14 224", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "412 million in the ICCV 2025 supplement and the GitHub zoo; the project page rounds the same row to 400 million", "architecture": "OpenCLIP-compatible ViT-SoViT-400M vision encoder, patch 14, input resolution 224. 27 layers, width 1152, 16 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. Supplement Table 2 lists 412M for SoViT-400M. The project-page zoo prints 400M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-SoViT-400M/14 at 224 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-so400m-patch14-384": { "id": "ucsc/openvision/openvision-vit-so400m-patch14-384", "name": "OpenVision ViT-SoViT-400M/14 384", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "412 million in the ICCV 2025 supplement and the GitHub zoo; the project page rounds the same row to 400 million", "architecture": "OpenCLIP-compatible ViT-SoViT-400M vision encoder, patch 14, input resolution 384. 27 layers, width 1152, 16 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. Supplement Table 2 lists 412M for SoViT-400M. The project-page zoo prints 400M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-SoViT-400M/14 at 384 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-so400m-patch14-84": { "id": "ucsc/openvision/openvision-vit-so400m-patch14-84", "name": "OpenVision ViT-SoViT-400M/14 84", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "412 million in the ICCV 2025 supplement and the GitHub zoo; the project page rounds the same row to 400 million", "architecture": "OpenCLIP-compatible ViT-SoViT-400M vision encoder, patch 14, input resolution 84. 27 layers, width 1152, 16 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. Supplement Table 2 lists 412M for SoViT-400M. The project-page zoo prints 400M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-SoViT-400M/14 at 84 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-tiny-patch16-160": { "id": "ucsc/openvision/openvision-vit-tiny-patch16-160", "name": "OpenVision ViT-Tiny/16 160", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "5 million", "architecture": "OpenCLIP-compatible ViT-Tiny vision encoder, patch 16, input resolution 160. 12 layers, width 192, 3 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 5M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Tiny/16 at 160 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-tiny-patch16-224": { "id": "ucsc/openvision/openvision-vit-tiny-patch16-224", "name": "OpenVision ViT-Tiny/16 224", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "5 million", "architecture": "OpenCLIP-compatible ViT-Tiny vision encoder, patch 16, input resolution 224. 12 layers, width 192, 3 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 5M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Tiny/16 at 224 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-tiny-patch16-384": { "id": "ucsc/openvision/openvision-vit-tiny-patch16-384", "name": "OpenVision ViT-Tiny/16 384", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "5 million", "architecture": "OpenCLIP-compatible ViT-Tiny vision encoder, patch 16, input resolution 384. 12 layers, width 192, 3 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 5M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Tiny/16 at 384 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-tiny-patch8-160": { "id": "ucsc/openvision/openvision-vit-tiny-patch8-160", "name": "OpenVision ViT-Tiny/8 160", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "5 million", "architecture": "OpenCLIP-compatible ViT-Tiny vision encoder, patch 8, input resolution 160. 12 layers, width 192, 3 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 5M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Tiny/8 at 160 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-tiny-patch8-224": { "id": "ucsc/openvision/openvision-vit-tiny-patch8-224", "name": "OpenVision ViT-Tiny/8 224", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "5 million", "architecture": "OpenCLIP-compatible ViT-Tiny vision encoder, patch 8, input resolution 224. 12 layers, width 192, 3 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 5M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Tiny/8 at 224 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision/openvision-vit-tiny-patch8-384": { "id": "ucsc/openvision/openvision-vit-tiny-patch8-384", "name": "OpenVision ViT-Tiny/8 384", "maker": "ucsc", "family": "ucsc/openvision", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "5 million", "architecture": "OpenCLIP-compatible ViT-Tiny vision encoder, patch 8, input resolution 384. 12 layers, width 192, 3 heads. Trained with a contrastive loss plus a captioning loss on Recap-DataComp-1B. The ICCV 2025 supplement and the GitHub zoo both say 5M.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision ViT-Tiny/8 at 384 pixels is a UC Santa Cruz VLAA vision encoder released in May 2025. It is an active open-weight checkpoint in the OpenVision family. Collected 2026-10-15.", "what_it_is_for": "The lab says OpenVision is a fully open family of vision encoders for multimodal models such as LLaVA. This checkpoint is the released ViT at the resolution in its Hub name. The usage example encodes an image and text and ranks the text labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision-2/openvision2-vit-giant-patch14-224-vision-only": { "id": "ucsc/openvision-2/openvision2-vit-giant-patch14-224-vision-only", "name": "OpenVision 2 ViT-Giant/14 224", "maker": "ucsc", "family": "ucsc/openvision-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-02", "context_length": null, "max_output_tokens": null, "parameters": "1.01 billion", "architecture": "Generative ViT-Giant vision encoder, patch 14, resolution 224, plus the jointly trained caption decoder shipped in the same repo in August 2026. OpenVision 2 drops the text encoder and the contrastive loss and keeps a captioning objective on Recap-DataComp-1B v2. The README parameter column is the figure used here. It does not match every OpenVision 1 supplement count.", "modalities": { "input": [ "image" ], "output": [ "embeddings", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision 2 ViT-Giant/14 at 224 is a UC Santa Cruz VLAA generative vision encoder from the September 2025 release. The checkpoint is active and open-weight. Collected 2026-10-15.", "what_it_is_for": "The lab uses this encoder for multimodal understanding benchmarks and, since August 2026, for image-to-caption generation with the decoder stored in the same repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision-2/openvision2-vit-huge-patch14-224-vision-only": { "id": "ucsc/openvision-2/openvision2-vit-huge-patch14-224-vision-only", "name": "OpenVision 2 ViT-Huge/14 224", "maker": "ucsc", "family": "ucsc/openvision-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-26", "context_length": null, "max_output_tokens": null, "parameters": "632 million", "architecture": "Generative ViT-Huge vision encoder, patch 14, resolution 224, plus the jointly trained caption decoder shipped in the same repo in August 2026. OpenVision 2 drops the text encoder and the contrastive loss and keeps a captioning objective on Recap-DataComp-1B v2. The README parameter column is the figure used here. It does not match every OpenVision 1 supplement count.", "modalities": { "input": [ "image" ], "output": [ "embeddings", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision 2 ViT-Huge/14 at 224 is a UC Santa Cruz VLAA generative vision encoder from the September 2025 release. The checkpoint is active and open-weight. Collected 2026-10-15.", "what_it_is_for": "The lab uses this encoder for multimodal understanding benchmarks and, since August 2026, for image-to-caption generation with the decoder stored in the same repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision-2/openvision2-vit-huge-patch14-336-vision-only": { "id": "ucsc/openvision-2/openvision2-vit-huge-patch14-336-vision-only", "name": "OpenVision 2 ViT-Huge/14 336", "maker": "ucsc", "family": "ucsc/openvision-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-02", "context_length": null, "max_output_tokens": null, "parameters": "632 million", "architecture": "Generative ViT-Huge vision encoder, patch 14, resolution 336, plus the jointly trained caption decoder shipped in the same repo in August 2026. OpenVision 2 drops the text encoder and the contrastive loss and keeps a captioning objective on Recap-DataComp-1B v2. The README parameter column is the figure used here. It does not match every OpenVision 1 supplement count.", "modalities": { "input": [ "image" ], "output": [ "embeddings", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision 2 ViT-Huge/14 at 336 is a UC Santa Cruz VLAA generative vision encoder from the September 2025 release. The checkpoint is active and open-weight. Collected 2026-10-15.", "what_it_is_for": "The lab uses this encoder for multimodal understanding benchmarks and, since August 2026, for image-to-caption generation with the decoder stored in the same repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision-2/openvision2-vit-huge-patch14-448-vision-only": { "id": "ucsc/openvision-2/openvision2-vit-huge-patch14-448-vision-only", "name": "OpenVision 2 ViT-Huge/14 448", "maker": "ucsc", "family": "ucsc/openvision-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-02", "context_length": null, "max_output_tokens": null, "parameters": "632 million", "architecture": "Generative ViT-Huge vision encoder, patch 14, resolution 448, plus the jointly trained caption decoder shipped in the same repo in August 2026. OpenVision 2 drops the text encoder and the contrastive loss and keeps a captioning objective on Recap-DataComp-1B v2. The README parameter column is the figure used here. It does not match every OpenVision 1 supplement count.", "modalities": { "input": [ "image" ], "output": [ "embeddings", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision 2 ViT-Huge/14 at 448 is a UC Santa Cruz VLAA generative vision encoder from the September 2025 release. The checkpoint is active and open-weight. Collected 2026-10-15.", "what_it_is_for": "The lab uses this encoder for multimodal understanding benchmarks and, since August 2026, for image-to-caption generation with the decoder stored in the same repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision-2/openvision2-vit-large-patch14-224-vision-only": { "id": "ucsc/openvision-2/openvision2-vit-large-patch14-224-vision-only", "name": "OpenVision 2 ViT-Large/14 224", "maker": "ucsc", "family": "ucsc/openvision-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-02", "context_length": null, "max_output_tokens": null, "parameters": "304 million", "architecture": "Generative ViT-Large vision encoder, patch 14, resolution 224, plus the jointly trained caption decoder shipped in the same repo in August 2026. OpenVision 2 drops the text encoder and the contrastive loss and keeps a captioning objective on Recap-DataComp-1B v2. The README parameter column is the figure used here. It does not match every OpenVision 1 supplement count.", "modalities": { "input": [ "image" ], "output": [ "embeddings", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision 2 ViT-Large/14 at 224 is a UC Santa Cruz VLAA generative vision encoder from the September 2025 release. The checkpoint is active and open-weight. Collected 2026-10-15.", "what_it_is_for": "The lab uses this encoder for multimodal understanding benchmarks and, since August 2026, for image-to-caption generation with the decoder stored in the same repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision-2/openvision2-vit-large-patch14-336-vision-only": { "id": "ucsc/openvision-2/openvision2-vit-large-patch14-336-vision-only", "name": "OpenVision 2 ViT-Large/14 336", "maker": "ucsc", "family": "ucsc/openvision-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-02", "context_length": null, "max_output_tokens": null, "parameters": "304 million", "architecture": "Generative ViT-Large vision encoder, patch 14, resolution 336, plus the jointly trained caption decoder shipped in the same repo in August 2026. OpenVision 2 drops the text encoder and the contrastive loss and keeps a captioning objective on Recap-DataComp-1B v2. The README parameter column is the figure used here. It does not match every OpenVision 1 supplement count.", "modalities": { "input": [ "image" ], "output": [ "embeddings", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision 2 ViT-Large/14 at 336 is a UC Santa Cruz VLAA generative vision encoder from the September 2025 release. The checkpoint is active and open-weight. Collected 2026-10-15.", "what_it_is_for": "The lab uses this encoder for multimodal understanding benchmarks and, since August 2026, for image-to-caption generation with the decoder stored in the same repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision-2/openvision2-vit-so400m-patch14-384-vision-only": { "id": "ucsc/openvision-2/openvision2-vit-so400m-patch14-384-vision-only", "name": "OpenVision 2 ViT-SoViT-400M/14 384", "maker": "ucsc", "family": "ucsc/openvision-2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-02", "context_length": null, "max_output_tokens": null, "parameters": "400 million", "architecture": "Generative ViT-SoViT-400M vision encoder, patch 14, resolution 384, plus the jointly trained caption decoder shipped in the same repo in August 2026. OpenVision 2 drops the text encoder and the contrastive loss and keeps a captioning objective on Recap-DataComp-1B v2. The README parameter column is the figure used here. It does not match every OpenVision 1 supplement count.", "modalities": { "input": [ "image" ], "output": [ "embeddings", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision 2 ViT-SoViT-400M/14 at 384 is a UC Santa Cruz VLAA generative vision encoder from the September 2025 release. The checkpoint is active and open-weight. Collected 2026-10-15.", "what_it_is_for": "The lab uses this encoder for multimodal understanding benchmarks and, since August 2026, for image-to-caption generation with the decoder stored in the same repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision-3/openvision3-vit-base-patch2-28": { "id": "ucsc/openvision-3/openvision3-vit-base-patch2-28", "name": "OpenVision 3 ViT-Base 224", "maker": "ucsc", "family": "ucsc/openvision-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-01-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-Base encoder on FLUX.1 VAE latents. VAE downsample 8 and ViT patch 2 give a 16x spatial compression. Image resolution 224, 196 tokens. The 28 in the Hub name is the 224/8 VAE latent grid. The project-page row is Base, resolution 224, ViT patch 2, 196 tokens. Trained with reconstruction and semantic losses. The paper pretrains at 128 and fine-tunes at 224 or 256.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision 3 ViT-Base 224 is a UC Santa Cruz VLAA unified vision encoder released in January 2026. It is an active open-weight checkpoint. Collected 2026-10-15.", "what_it_is_for": "The paper trains one representation for multimodal understanding and for generation. Understanding plugs the encoder into LLaVA-style setups. Generation uses it with an external generator. The project page says every checkpoint should be paired with FLUX.1 VAE, which is not a UC Santa Cruz model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision-3/openvision3-vit-base-patch2-32": { "id": "ucsc/openvision-3/openvision3-vit-base-patch2-32", "name": "OpenVision 3 ViT-Base 256", "maker": "ucsc", "family": "ucsc/openvision-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-Base encoder on FLUX.1 VAE latents. VAE downsample 8 and ViT patch 2 give a 16x spatial compression. Image resolution 256, 256 tokens. The 32 in the Hub name is the 256/8 VAE latent grid. The project-page row is Base, resolution 256, ViT patch 2, 256 tokens. Trained with reconstruction and semantic losses. The paper pretrains at 128 and fine-tunes at 224 or 256.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision 3 ViT-Base 256 is a UC Santa Cruz VLAA unified vision encoder released in January 2026. It is an active open-weight checkpoint. Collected 2026-10-15.", "what_it_is_for": "The paper trains one representation for multimodal understanding and for generation. Understanding plugs the encoder into LLaVA-style setups. Generation uses it with an external generator. The project page says every checkpoint should be paired with FLUX.1 VAE, which is not a UC Santa Cruz model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/openvision-3/openvision3-vit-large-patch2-32": { "id": "ucsc/openvision-3/openvision3-vit-large-patch2-32", "name": "OpenVision 3 ViT-Large 256", "maker": "ucsc", "family": "ucsc/openvision-3", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-Large encoder on FLUX.1 VAE latents. VAE downsample 8 and ViT patch 2 give a 16x spatial compression. Image resolution 256, 256 tokens. Project-page row: Large, resolution 256, ViT patch 2, 256 tokens. Trained with reconstruction and semantic losses. The paper pretrains at 128 and fine-tunes at 224 or 256.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenVision 3 ViT-Large 256 is a UC Santa Cruz VLAA unified vision encoder released in January 2026. It is an active open-weight checkpoint. Collected 2026-10-15.", "what_it_is_for": "The paper trains one representation for multimodal understanding and for generation. Understanding plugs the encoder into LLaVA-style setups. Generation uses it with an external generator. The project page says every checkpoint should be paired with FLUX.1 VAE, which is not a UC Santa Cruz model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/recap-clip/recap-clip-vit-l-16": { "id": "ucsc/recap-clip/recap-clip-vit-l-16", "name": "Recap-CLIP ViT-L/16", "maker": "ucsc", "family": "ucsc/recap-clip", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2024-06-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card calls it Recap-CLIP-ViT-L-16-Txt-Huge-2.56B, a CLIPA model trained on Recap-DataComp-1B. The Txt-Huge name is the lab's text-tower label. A parameter total is not printed.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Recap-CLIP ViT-L/16 is the CLIP checkpoint UC Santa Cruz VLAA trained on Recap-DataComp-1B and released in June 2024. It is an active open-weight model. Collected 2026-10-15.", "what_it_is_for": "The paper recaptions DataComp-1B with a LLaMA-3 captioner and trains CLIP on those captions for zero-shot classification and retrieval. The captioner itself is a LLaVA fine-tune hosted outside this org and is not catalogued here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/spikegpt/spikegpt-bookcorpus": { "id": "ucsc/spikegpt/spikegpt-bookcorpus", "name": "SpikeGPT BookCorpus", "maker": "ucsc", "family": "ucsc/spikegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-2-Clause", "release_date": "2023-03-08", "context_length": 1024, "max_output_tokens": null, "parameters": "Card prints L18-D768, the same depth and width the paper uses for the 216M model. The card does not print a parameter total.", "architecture": "SpikeGPT checkpoint trained on BookCorpus. Card config: context length 1024, 18 layers, embedding 768. License on the card is BSD-2-Clause, unlike the Apache-2.0 OpenWebText checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SpikeGPT BookCorpus is the BookCorpus-trained SpikeGPT checkpoint released by the same UC Santa Cruz authors on Hugging Face. The tree page used here is dated 8 March 2023. Collected 2026-10-15.", "what_it_is_for": "It is an alternate public SpikeGPT language-model checkpoint for generation experiments. The paper's WikiText table is for the OpenWebText pretrain, not this file.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsc/spikegpt/spikegpt-openwebtext-216m": { "id": "ucsc/spikegpt/spikegpt-openwebtext-216m", "name": "SpikeGPT OpenWebText 216M", "maker": "ucsc", "family": "ucsc/spikegpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-05-26", "context_length": 1024, "max_output_tokens": null, "parameters": "216 million", "architecture": "Spiking language model. Binary event-driven activations on an RWKV-style mixer. Card config: 18 layers, embedding 768, context length 1024. The paper says this is the 216M OpenWebText2 pretrain. Lead author Rui-Jie Zhu and advisor Jason K. Eshraghian are at UC Santa Cruz. Qihang Zhao is at Kuaishou.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SpikeGPT OpenWebText 216M is the public 216-million-parameter spiking language model from UC Santa Cruz's Neuromorphic Computing Group. The weight file on Hugging Face was uploaded 26 May 2023. Collected 2026-10-15.", "what_it_is_for": "The paper presents SpikeGPT as a generative language model with spiking activations and linear sequence cost, for both generation and classification after fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsd/fastvideo/fasth3": { "id": "ucsd/fastvideo/fasth3", "name": "FastH3", "maker": "ucsd", "family": "ucsd/fastvideo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MiniMax H3 Community License", "release_date": "2026-08-27", "context_length": null, "max_output_tokens": null, "parameters": "35049751296", "architecture": "Four-step sparse distillation of MiniMax-H3 for synchronized text-to-video-and-audio. The recommended Preview v1 checkpoint is step 1300, trained with data-free DMD2 and VSA-H3 at 90 percent sparsity. The hub card rounds the size to 35B. The safetensors total is 35,049,751,296. It needs FastVideo's VSA-H3 attention. Tested defaults are four B200 GPUs. GPU count must divide H3's 56 attention heads.", "modalities": { "input": [ "text" ], "output": [ "video", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FastH3 is an open-weight distilled MiniMax-H3 video-and-audio model from the Hao AI Lab at UC San Diego, with Nuva Lab and NVIDIA FastGen. The recommended Preview v1 checkpoint does text-to-audio-video in four transformer forwards. It was released 2026-08-27. The safetensors total is 35,049,751,296 parameters. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint generates synchronized video and audio from text. It is a preview. Image-conditioned FL2VA and reference-to-video were not distilled. The lab later announced an Omni checkpoint for 2026-10-20, which is after this collection date.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsd/fastvideo/fasthunyuan": { "id": "ucsd/fastvideo/fasthunyuan", "name": "FastHunyuan", "maker": "ucsd", "family": "ucsd/fastvideo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "tencent-hunyuan-community", "release_date": "2024-12-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Consistency-distilled HunyuanVideo text-to-video model from the Hao AI Lab at UC San Diego. The card says 6 diffusion steps, about 8x faster than 50-step HunyuanVideo. Training: MixKit, batch 16, 720x1280, 125 frames, 320 steps, 32 GPUs, learning rate 1e-6, Huber loss. The card also says NF4 quantization fits an RTX 4090 at about 20 GB.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FastHunyuan is a 6-step text-to-video model from the Hao AI Lab at UC San Diego. The card says it is consistency-distilled from Tencent HunyuanVideo and is about 8 times faster than the 50-step original. Weights are on `FastVideo/FastHunyuan`, created 2024-12-16, under the Tencent Hunyuan community license. Collected 2026-10-15.", "what_it_is_for": "The card says it samples high-quality video in 6 diffusion steps. It can be run from the FastVideo repo or from the official HunyuanVideo repo with shift 17, 6 steps, resolution 720x1280x125, and CFG above 6.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsd/fastvideo/fastmochi": { "id": "ucsd/fastvideo/fastmochi", "name": "FastMochi", "maker": "ucsd", "family": "ucsd/fastvideo", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hao AI Lab distillation of Genmo Mochi, shipped in the FastVideo project next to FastHunyuan. The Hugging Face repo FastVideo/FastMochi was created 2024-12-11 and had no model card when opened. A diffusers repo FastVideo/FastMochi-diffusers also exists. Step count, resolution, and parameter count were not on a card.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FastMochi is a named FastVideo release from the Hao AI Lab at UC San Diego. The lab homepage says FastHunyuan and FastMochi shipped together to speed up video diffusion transformers. The weight repo `FastVideo/FastMochi` was created 2024-12-11. It had no model card when opened on 2026-10-15, so most specs are unknown. Collected 2026-10-15.", "what_it_is_for": "The lab groups it with FastHunyuan as an accelerated video model. Secondary writeups describe it as the FastVideo distillation of Genmo Mochi. That distillation claim is not on a FastMochi card, because the card is empty.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsd/musicldm/musicldm": { "id": "ucsd/musicldm/musicldm", "name": "MusicLDM", "maker": "ucsd", "family": "ucsd/musicldm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2023-08-03", "context_length": null, "max_output_tokens": null, "parameters": "185036552", "architecture": "Latent diffusion text-to-music model. A music-retrained CLAP conditions a UNet latent diffusion model over a convolutional VAE of 16 kHz mel-spectrograms. A music-retrained HiFi-GAN vocoder turns mel-spectrograms back into waveforms. The paper sets clips to 10.24 seconds, 128 mel bins, VAE downsample 8, latent channels 16, and a 1000-step linear noise schedule sampled with 200-step DDIM. Beat-synchronous audio mixup and latent mixup are training augmentations, not separate named products.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MusicLDM is a text-to-music latent diffusion model released with weights by the ucsd-reach Hugging Face org. Ke Chen (UC San Diego), Taylor Berg-Kirkpatrick, and Shlomo Dubnov are UC San Diego authors; Yusong Wu (Mila), Haohe Liu (University of Surrey), and Marianna Nezhurina (LAION) are coauthors. The arXiv paper is dated 2023-08-03. The checkpoint was still public on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "The card describes a latent text-to-audio diffusion model that generates music from a text prompt. The paper adapts Stable Diffusion and AudioLDM to music and adds beat-synchronous mixup so outputs stay in style while copying the training tracks less often.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsd/newt/newt": { "id": "ucsd/newt/newt", "name": "Newt", "maker": "ucsd", "family": "ucsd/newt", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-11-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Language-conditioned multitask world model based on TD-MPC2. The paper says agents have 20M learnable parameters unless stated otherwise. Language uses CLIP-ViT/B (512-d) and images use DINOv2/B (768-d). State vectors are 128-d and actions are 16-d. The GitHub README names four sizes: S 2M, B 5M, L 20M (multitask default), XL 80M. Open-loop planning of up to 48 timesteps is reported on the project page.", "modalities": { "input": [ "state", "text", "image" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Newt is a language-conditioned multitask world model from Nicklas Hansen, Hao Su, and Xiaolong Wang at UC San Diego. It is trained on the 200-task MMBench suite, first on demonstrations and then with online interaction. Weights and code are MIT-licensed. The arXiv submission is 2025-11-24 and the project page lists ICLR 2026. Collected 2026-10-15.", "what_it_is_for": "The authors use Newt to test whether one agent can learn hundreds of continuous-control tasks online. The project page says it plans from a state vector, a language instruction, and optional RGB, and that it can run open-loop for up to 48 steps on seen tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsd/one-2-3-45/one-2-3-45": { "id": "ucsd/one-2-3-45/one-2-3-45", "name": "One-2-3-45", "maker": "ucsd", "family": "ucsd/one-2-3-45", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-08-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Feed-forward single-image-to-mesh pipeline. The README says it uses a 2D diffusion multi-view stage and a reconstruction network, and that it avoids per-shape optimization. Checkpoint download is download_ckpt.py. A Docker image that includes checkpoints is listed at about 22.3 GB. The paper citation names Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Mukund Varma T, Zexiang Xu, and Hao Su.", "modalities": { "input": [ "image" ], "output": [ "mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "One-2-3-45 is a single-image-to-3D-mesh pipeline from Hao Su's lab at UC San Diego. The README says it is a forward pass, not a per-shape optimization, and that inference code and checkpoints shipped on 2023-08-18. The paper is dated 2023-06-29 and was accepted at NeurIPS 2023. Collected 2026-10-15.", "what_it_is_for": "The README says the system turns one image into a 3D mesh. A Gradio demo and a client API cover mesh generation, elevation estimation, and background removal. A text-to-3D clip is in the repo, but the documented API takes an image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsd/openshape/openshape": { "id": "ucsd/openshape/openshape", "name": "OpenShape", "maker": "ucsd", "family": "ucsd/openshape", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-05-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-modal contrastive 3D encoder aligned to a frozen CLIP or OpenCLIP text-image tower. The flagship checkpoint is PointBERT with OpenCLIP ViT-bigG-14, trained on Objaverse, ShapeNet, ABO, and 3D-FUTURE, gravity axis z. A SparseConv encoder trained on the same four datasets is a second released backbone. ShapeNet-only and no-LVIS files are ablations. Two extra PointBERT files (CLIP ViT-L/14 and ViT-B/32) are for the image-generation and captioning demos.", "modalities": { "input": [ "point-cloud", "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OpenShape is a 3D shape encoder from Minghua Liu, Ruoxi Shi, Kaiming Kuang, and Hao Su, with Qualcomm coauthors. UC San Diego leads the project. It aligns point clouds with text and images for open-world recognition. Checkpoints and training code were released on GitHub and Hugging Face. The arXiv paper is dated 2023-05-18 and the flagship hub repo was created 2023-05-19. NeurIPS 2023. Collected 2026-10-15.", "what_it_is_for": "The project page says OpenShape learns a joint representation of text, images, and point clouds so a 3D shape can be classified or retrieved in the open world without category-specific training. The demo also uses embeddings for captioning and point-cloud-conditioned image generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsd/radbert/radbert-2m": { "id": "ucsd/radbert/radbert-2m", "name": "RadBERT-2m", "maker": "ucsd", "family": "ucsd/radbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-02-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT-base-uncased continued on radiology reports. The card says 2 million deidentified US VA reports. The 2022 paper says the 2-million-scale pretraining used 2.16 million reports and that six RadBERT variants were trained. This hub repo is the BERT-base variant. It is an encoder, used by the card as a fill-mask and feature model.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RadBERT-2m is a radiology language encoder from UC San Diego and the US Department of Veterans Affairs. The card says it starts from BERT-base-uncased and continues training on 2 million deidentified VA radiology reports. The paper, Radiology: Artificial Intelligence 2022, describes a six-variant RadBERT family. This checkpoint was uploaded to UCSD-VA-health on 2023-02-06 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card says the model is for radiology language understanding: abnormal versus normal sentence classification, diagnostic report coding, and extractive summarization of the findings section. The published checkpoint is an encoder, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsd/radbert/radbert-roberta-4m": { "id": "ucsd/radbert/radbert-roberta-4m", "name": "RadBERT-RoBERTa-4m", "maker": "ucsd", "family": "ucsd/radbert", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-02-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa initialization continued on radiology reports. The card says 4 million deidentified US VA reports. The paper says RadBERT-RoBERTa-4m was initialized from RoBERTa and pretrained on 4.42 million radiology reports, and calls it one of the best-performing variants. Encoder only.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RadBERT-RoBERTa-4m is the larger public RadBERT encoder from UC San Diego and the VA. The card says it starts from RoBERTa and continues on 4 million deidentified VA radiology reports. The 2022 paper says this variant used 4.42 million reports and was one of the strongest in the study. Hub weights are Apache-2.0, uploaded 2023-02-06. Collected 2026-10-15.", "what_it_is_for": "Same three radiology jobs as the rest of the family: abnormal-sentence classification, report coding into five coding systems, and extractive summarization of findings. The released artifact is a fill-mask encoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsd/td-mpc/td-mpc2": { "id": "ucsd/td-mpc/td-mpc2", "name": "TD-MPC2", "maker": "ucsd", "family": "ucsd/td-mpc", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-10-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Implicit decoder-free world model. Planning is local trajectory optimization in latent space. One Hugging Face repo holds 324 checkpoints: 12 multi-task models from 1M to 317M parameters on 80, 70, or 30 tasks, plus 312 single-task 5M checkpoints across 104 tasks in DMControl, Meta-World, ManiSkill2, and MyoSuite. Multi-task training uses batch size 1024. Single-task runs use default hyperparameters.", "modalities": { "input": [ "state" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TD-MPC2 is a family of world-model control agents from Nicklas Hansen, Hao Su, and Xiaolong Wang at UC San Diego. The authors released 324 checkpoints under an MIT license, from 1M to 317M parameters, for continuous-control tasks. The arXiv paper is dated 2023-10-25 and the work was an ICLR 2024 spotlight. Weights were still public on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "The paper presents TD-MPC2 as a model-based reinforcement learning method that plans in the latent space of a decoder-free world model. One set of hyperparameters is used across 104 online tasks in four domains. A single agent can cover 80 tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsd/zero123plus/zero123plus-depth-controlnet": { "id": "ucsd/zero123plus/zero123plus-depth-controlnet", "name": "Zero123++ depth ControlNet", "maker": "ucsd", "family": "ucsd/zero123plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2023-09-24", "context_length": null, "max_output_tokens": null, "parameters": "364228240", "architecture": "ControlNet for Zero123++ v1.1. The report says normalized linear depth images are rendered for the target RGB views and a ControlNet is trained so depth can control geometry. Hugging Face safetensors total is 364,228,240 parameters. Repo id sudo-ai/controlnet-zp11-depth-v1.", "modalities": { "input": [ "image", "depth" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the depth ControlNet released with Zero123++ v1.1 by the UC San Diego-led Zero123++ project. The report says it is trained on normalized linear depth so geometry can be controlled. The weight repo is `sudo-ai/controlnet-zp11-depth-v1`, created 2023-09-24, with 364,228,240 parameters. Collected 2026-10-15.", "what_it_is_for": "It is an add-on for Zero123++ v1.1. Depth images steer the multi-view generator. It is not a separate text-to-image model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsd/zero123plus/zero123plus-normal-controlnet": { "id": "ucsd/zero123plus/zero123plus-normal-controlnet", "name": "Zero123++ normal ControlNet", "maker": "ucsd", "family": "ucsd/zero123plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-01-11", "context_length": null, "max_output_tokens": null, "parameters": "364228240", "architecture": "Normal-generation ControlNet for Zero123++ v1.2. Repo id sudo-ai/controlnet-zp12-normal-gen-v1. Hugging Face license field is cc-by-nc-4.0, which matches the project README. Safetensors total is 364,228,240 parameters, the same count as the v1.1 depth ControlNet.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the normal-generation ControlNet for Zero123++ v1.2, released by the same UC San Diego-led project. The repo is `sudo-ai/controlnet-zp12-normal-gen-v1`, created 2024-01-11, 364,228,240 parameters, license CC-BY-NC-4.0 on both the README and the hub tag. Collected 2026-10-15.", "what_it_is_for": "The README lists it as a ControlNet checkpoint that generates normals for Zero123++ v1.2. A separate prose card was not on the hub page that was opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsd/zero123plus/zero123plus-v1-1": { "id": "ucsd/zero123plus/zero123plus-v1-1", "name": "Zero123++ v1.1", "maker": "ucsd", "family": "ucsd/zero123plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2023-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Image-conditioned multi-view diffusion model finetuned from a Stable Diffusion image model. The report says released Zero123++ models do not use a text condition. v1.1 is the first base checkpoint on sudo-ai. The GitHub v1.2 notes say v1.2 changes camera handling and fixes output field of view at 30 degrees, which implies v1.1 did not.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zero123++ v1.1 is the first public base checkpoint of Zero123++, an image-to-multi-view diffusion model from Hao Su's lab at UC San Diego, with Stanford, Tsinghua, UCLA, and Zhejiang University coauthors. Weights are on `sudo-ai/zero123plus-v1.1`, created 2023-09-23. The technical report is arXiv 2310.15110, 2023-10-23. Collected 2026-10-15.", "what_it_is_for": "The report says Zero123++ generates 3D-consistent multi-view images from one input view, starting from a pretrained 2D diffusion model such as Stable Diffusion. Released models do not take a text condition.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ucsd/zero123plus/zero123plus-v1-2": { "id": "ucsd/zero123plus/zero123plus-v1-2", "name": "Zero123++ v1.2", "maker": "ucsd", "family": "ucsd/zero123plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2023-12-11", "context_length": null, "max_output_tokens": null, "parameters": "865910724", "architecture": "Zero123++ multi-view diffusion checkpoint. The GitHub v1.2 note says camera intrinsics are handled more carefully, the model is more robust to input field of view and cropping, and the output field of view is unified to 30 degrees. Hugging Face safetensors total is 865,910,724 parameters. The card size line rounds this to 0.9B. No model-card text beyond the file listing was present.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zero123++ v1.2 is the updated base multi-view diffusion checkpoint from the same UC San Diego-led Zero123++ release. The GitHub README says it handles camera intrinsics more carefully and fixes the output field of view at 30 degrees. Weights are `sudo-ai/zero123plus-v1.2`, created 2023-12-11, with 865,910,724 parameters in the safetensors total. Collected 2026-10-15.", "what_it_is_for": "It generates consistent novel views of an object from one image. v1.2 is the base checkpoint the README tells users to download, along with a separate normal ControlNet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uct/mzansilm/mzansilm-125m": { "id": "uct/mzansilm/mzansilm-125m", "name": "MzansiLM 125M", "maker": "uct", "family": "uct/mzansilm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-03-21", "context_length": 2048, "max_output_tokens": null, "parameters": "125008384", "architecture": "Decoder-only LlamaForCausalLM using the MobileLLM 125M configuration: 30 layers, hidden size 512, intermediate size 1536, 9 attention heads, 3 key-value heads, head_dim 56, SwiGLU, RoPE theta 10000, RMSNorm epsilon 1e-5, tied embeddings, BPE vocabulary 65536. Trained from scratch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MzansiLM 125M is a University of Cape Town research language model from the UCT NLP group. It is a 125,008,384-parameter decoder-only model trained from scratch on MzansiText, a corpus covering all 11 official written South African languages. The weights are public under Apache-2.0 and were still the current named release on 2026-10-15. It is a base model for adaptation, not a hosted chatbot.", "what_it_is_for": "The model card and UCT News describe MzansiLM as a reproducible baseline for language modeling and downstream adaptation in South African languages. The authors say it is not a consumer chatbot and not an instruction-tuned chat model. The released checkpoint continues a prompt as plain text. Task use in the paper is through later fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uct/wmt22/wmt22-southern-african": { "id": "uct/wmt22/wmt22-southern-african", "name": "UCT WMT22", "maker": "uct", "family": "uct/wmt22", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-10-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One shared-parameter multilingual encoder-decoder based on the BART architecture, trained in Fairseq with the Tang et al. 2021 implementation and hyperparameters. Overlap BPE vocabulary of 40,000. A source-language token is prepended to the input and a target-language token to the output. The released artifact is the submitted M-OBPE-final system.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UCT WMT22 is the University of Cape Town Department of Computer Science submission to the constrained track of the WMT22 shared task on large-scale machine translation for African languages. Khalid N. Elmadani, Francois Meyer, and Jan Buys released one multilingual translation model, not a product API. The 21 October 2022 preprint points at a GitHub repository that downloads the checkpoint. The conference version is in the WMT proceedings of 7\u20138 December 2022. On 2026-10-15 the repository was still public. It is a research checkpoint, not a maintained service.", "what_it_is_for": "The system translates between English and eight South and South East African languages, and in eight further directions between those languages. The authors describe a single Transformer encoder-decoder with shared parameters and a shared subword vocabulary. It was built for the shared task. It is not a general chatbot.\n\nSupported codes in the repository README are eng, afr, nso, sna, ssw, tsn, xho, tso, and zul. Both directions with English are supported. The extra directions are xho to zul, zul to sna, sna to afr, afr to ssw, ssw to tsn, tsn to tso, tso to nso, and nso to xho.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/arabict5/arabict5-17gb-base": { "id": "udel/arabict5/arabict5-17gb-base", "name": "ArabicT5-17GB-base", "maker": "udel", "family": "udel/arabict5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-10-14", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "T5ForConditionalGeneration. Config: d_model 768, d_ff 3072, 16 encoder layers, 16 decoder layers, 12 heads, vocabulary 32000, n_positions 512, float32. _name_or_path is sultan/ArabicT5-Large. Card row: TPUv3-128, 500K steps, batch 512, 2.0x, 17GB MSA. The config matches the card's 17GB-base row, not the 36-layer 17GB-large row.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ArabicT5-17GB-base is an Arabic text-to-text model released by Sultan Alrowili and K. Vijay-Shanker at the University of Delaware. The Hub repo sultan/ArabicT5-17GB-base was created on 2022-10-14 and was still public on 2026-10-15. It is a research weight, not a university API.", "what_it_is_for": "The model card adapts T5 to Arabic with an efficient T5 implementation and T5x. This row is the 17GB base model: hidden size 768, 12 heads, 16 layers, trained on the 17GB MSA corpus.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/arabict5/arabict5-17gb-large": { "id": "udel/arabict5/arabict5-17gb-large", "name": "ArabicT5-17GB-large", "maker": "udel", "family": "udel/arabict5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-10-14", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "T5ForConditionalGeneration. Config: d_model 768, d_ff 3072, 36 encoder layers, 36 decoder layers, 12 heads, vocabulary 32000, n_positions 512, float32. Card row: TPUv3-128, 500K steps, batch 512, 2.0x, 17GB MSA.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ArabicT5-17GB-large is an Arabic text-to-text model released by Sultan Alrowili and K. Vijay-Shanker at the University of Delaware. The Hub repo sultan/ArabicT5-17GB-large was created on 2022-10-14 and was still public on 2026-10-15. It is a research weight, not a university API.", "what_it_is_for": "The model card adapts T5 to Arabic with an efficient T5 implementation and T5x. This row is the 17GB large model: hidden size 768, 12 heads, 36 layers, trained on the 17GB MSA corpus.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/arabict5/arabict5-17gb-small": { "id": "udel/arabict5/arabict5-17gb-small", "name": "ArabicT5-17GB-small", "maker": "udel", "family": "udel/arabict5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-10-21", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "T5ForConditionalGeneration. Config: d_model 512, d_ff 2048, 20 encoder layers, 20 decoder layers, 8 heads, vocabulary 32000, n_positions 512, float32. Card row: TPUv3-32, 256K steps, batch 256, 0.5x, 17GB MSA.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ArabicT5-17GB-small is an Arabic text-to-text model released by Sultan Alrowili and K. Vijay-Shanker at the University of Delaware. The Hub repo sultan/ArabicT5-17GB-small was created on 2022-10-21 and was still public on 2026-10-15. It is a research weight, not a university API.", "what_it_is_for": "The model card adapts T5 to Arabic with an efficient T5 implementation and T5x. This row is the 17GB small model: hidden size 512, 8 heads, 20 layers, 17GB of MSA text (Arabic Wikipedia, Marefa, Hindawi books, and Arabic news).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/arabict5/arabict5-49gb-base": { "id": "udel/arabict5/arabict5-49gb-base", "name": "ArabicT5-49GB-base", "maker": "udel", "family": "udel/arabict5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5ForConditionalGeneration. Config: d_model 768, d_ff 2048, 16 encoder layers, 16 decoder layers, 12 heads, vocabulary 32128, float32. The fetched config has no n_positions field. Card row: TPUv3-64, 500K steps, batch 256, 1.0x, 49GB MSA+OSCAR. Feed-forward width 2048 is narrower than the 17GB-base config width of 3072. The card prints vocabulary as 32K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ArabicT5-49GB-base is an Arabic text-to-text model released by Sultan Alrowili and K. Vijay-Shanker at the University of Delaware. The Hub repo sultan/ArabicT5-49GB-base was created on 2023-10-16 and was still public on 2026-10-15. It is a research weight, not a university API.", "what_it_is_for": "The model card adapts T5 to Arabic with an efficient T5 implementation and T5x. This row is the later 49GB base model: hidden size 768, 12 heads, 16 layers, trained on 49GB of MSA plus OSCAR. The same repo hosts the raw T5x archive the card offers for continual pretraining.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/arabict5/arabict5-49gb-small": { "id": "udel/arabict5/arabict5-49gb-small", "name": "ArabicT5-49GB-small", "maker": "udel", "family": "udel/arabict5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5ForConditionalGeneration. Config: d_model 512, d_ff 2048, 16 encoder layers, 16 decoder layers, 8 heads, vocabulary 32128, float32. The fetched config has no n_positions field. Card row: TPUv3-64, 500K steps, batch 256, 1.0x, 49GB MSA+OSCAR. The card prints vocabulary as 32K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ArabicT5-49GB-small is an Arabic text-to-text model released by Sultan Alrowili and K. Vijay-Shanker at the University of Delaware. The Hub repo sultan/ArabicT5-49GB-small was created on 2023-10-23 and was still public on 2026-10-15. It is a research weight, not a university API.", "what_it_is_for": "The model card adapts T5 to Arabic with an efficient T5 implementation and T5x. This row is the later 49GB small model: hidden size 512, 8 heads, 16 layers, trained on 49GB of MSA plus OSCAR. The card says it was the size they could fine-tune on a laptop 3070 at batch size 8, with a notebook TyDi F1 of 85.391.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/arabictransformer/arabictransformer-base": { "id": "udel/arabictransformer/arabictransformer-base", "name": "ArabicTransformer-base", "maker": "udel", "family": "udel/arabictransformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "FunnelModel. Config: block_sizes 6,6,6, block_repeats 1,1,1, d_model 768, d_inner 3072, d_head 64, n_head 12, num_decoder_layers 2, vocabulary 50000, max_position_embeddings 512, pooling mean, attention relative_shift. The README title calls this file a small B6-6-6 model. The repo id, the results table, and the config block sizes identify it as the base model.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ArabicTransformer-base is an Arabic encoder from Sultan Alrowili and K. Vijay-Shanker at the University of Delaware, pretrained with a Funnel Transformer and an ELECTRA objective. The Hub repo was created on 2022-03-02 and was still public on 2026-10-15. Findings of EMNLP 2021 gives the month as November 2021. It is a research weight, not a university API.", "what_it_is_for": "The base card says the model was pretrained on 44GB of Arabic text and has 1.39 times the parameters of ELECTRA-base, with similar or slightly larger inference time and less pretraining compute than the systems it compares against. The first line of that same README says \"ArabicTransformer small model (B6-6-6 with decoder).\" The config on this repo is B6-6-6.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/arabictransformer/arabictransformer-large": { "id": "udel/arabictransformer/arabictransformer-large", "name": "ArabicTransformer-large", "maker": "udel", "family": "udel/arabictransformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "FunnelModel. Config: block_sizes 8,8,8, block_repeats 1,1,1, d_model 1024, d_inner 4096, d_head 64, n_head 16, num_decoder_layers 2, vocabulary 50000, max_position_embeddings 512, pooling mean, attention relative_shift.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ArabicTransformer-large is an Arabic encoder from Sultan Alrowili and K. Vijay-Shanker at the University of Delaware, pretrained with a Funnel Transformer and an ELECTRA objective. The Hub repo was created on 2022-03-02 and was still public on 2026-10-15. Findings of EMNLP 2021 gives the month as November 2021. It is a research weight, not a university API.", "what_it_is_for": "The large Hub config is a B8-8-8 funnel at width 1024 with 16 heads. The base card's TyDi table does not include a large row, and this repo's README was not opened, so no score is stored.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/arabictransformer/arabictransformer-small": { "id": "udel/arabictransformer/arabictransformer-small", "name": "ArabicTransformer-small", "maker": "udel", "family": "udel/arabictransformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "FunnelModel. Config: block_sizes 4,4,4, block_repeats 1,1,1, d_model 768, d_inner 3072, d_head 64, n_head 12, num_decoder_layers 2, vocabulary 50000, max_position_embeddings 512, pooling mean, attention relative_shift.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ArabicTransformer-small is an Arabic encoder from Sultan Alrowili and K. Vijay-Shanker at the University of Delaware, pretrained with a Funnel Transformer and an ELECTRA objective. The Hub repo was created on 2022-03-02 and was still public on 2026-10-15. Findings of EMNLP 2021 gives the month as November 2021. It is a research weight, not a university API.", "what_it_is_for": "The small Hub config is a B4-4-4 funnel. The base card's TyDi table lists ArabicTransformer-Small at EM 74.70 and F1 85.89. This repo's own README was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/biom/biom-albert-xxlarge": { "id": "udel/biom/biom-albert-xxlarge", "name": "BioM-ALBERT-xxlarge", "maker": "udel", "family": "udel/biom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "AlbertForMaskedLM. Config: hidden size 4096, 12 layers, 64 heads, intermediate size 16384, embedding size 128, one hidden group, vocabulary 30000, max_position_embeddings 512. GitHub pretraining row: PubMed abstracts, 30K PubMed vocabulary, batch 8192, 264k steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioM-ALBERT-xxlarge is a biomedical language-model checkpoint from Sultan Alrowili and K. Vijay-Shanker. The same authors' ArabicT5 paper lists the Department of Computer Science at the University of Delaware. The Hub repo was created on 2022-03-02 and was still public on 2026-10-15. The BioNLP 2021 workshop paper is dated June 2021. This is a research weight, not a university API.", "what_it_is_for": "GitHub lists this ALBERT-xxlarge checkpoint as pretrained on PubMed abstracts. The Hub pipeline tag is fill-mask. The PMC continuation is a separate named model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/biom/biom-albert-xxlarge-pmc": { "id": "udel/biom/biom-albert-xxlarge-pmc", "name": "BioM-ALBERT-xxlarge-PMC", "maker": "udel", "family": "udel/biom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "AlbertForMaskedLM. Config matches BioM-ALBERT-xxlarge: hidden size 4096, 12 layers, 64 heads, intermediate size 16384, embedding size 128, one hidden group, vocabulary 30000, max_position_embeddings 512. GitHub says this run adds 64k steps on PubMed abstracts plus PMC, batch 8192, 30K PubMed vocabulary.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioM-ALBERT-xxlarge-PMC is a biomedical language-model checkpoint from Sultan Alrowili and K. Vijay-Shanker. The same authors' ArabicT5 paper lists the Department of Computer Science at the University of Delaware. The Hub repo was created on 2022-03-02 and was still public on 2026-10-15. The BioNLP 2021 workshop paper is dated June 2021. This is a research weight, not a university API.", "what_it_is_for": "GitHub marks this as the PubMed ALBERT-xxlarge continued for 64k steps on PubMed plus PMC. It is a named corpus variant, not a dated snapshot id of the PubMed-only model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/biom/biom-bert-pubmed-pmc-large": { "id": "udel/biom/biom-bert-pubmed-pmc-large", "name": "BioM-BERT-PubMed-PMC-Large", "maker": "udel", "family": "udel/biom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "The repository and the BioNLP paper name this checkpoint BioM-BERT. The Hub config class is ElectraForPreTraining and model_type is electra: hidden size 1024, 24 layers, 16 heads, intermediate size 4096, embedding size 1024, vocabulary 30000, max_position_embeddings 512. GitHub pretraining row, under the name BioM-BERT-Large: PubMed abstracts plus PMC, 30K English Wikipedia and Books vocabulary, batch 4096, 690K steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioM-BERT-PubMed-PMC-Large is a biomedical language-model checkpoint from Sultan Alrowili and K. Vijay-Shanker. The same authors' ArabicT5 paper lists the Department of Computer Science at the University of Delaware. The Hub repo was created on 2022-03-06 and was still public on 2026-10-15. The BioNLP 2021 workshop paper is dated June 2021. This is a research weight, not a university API.", "what_it_is_for": "GitHub lists BioM-BERT-Large as the PubMed-plus-PMC pretrained model at 690K steps. The Hub id adds PubMed-PMC to the name. No SQuAD row for this BERT checkpoint appears on the GitHub table that was read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/biom/biom-electra-base": { "id": "udel/biom/biom-electra-base", "name": "BioM-ELECTRA-Base", "maker": "udel", "family": "udel/biom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "ElectraForPreTraining. Config: hidden size 768, 12 layers, 12 heads, intermediate size 3072, embedding size 768, vocabulary 28895, max_position_embeddings 512. GitHub pretraining row: PubMed abstracts, 29K PubMed vocabulary, batch 1024, 500K steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioM-ELECTRA-Base is a biomedical language-model checkpoint from Sultan Alrowili and K. Vijay-Shanker. The same authors' ArabicT5 paper lists the Department of Computer Science at the University of Delaware. The Hub repo was created on 2022-03-02 and was still public on 2026-10-15. The BioNLP 2021 workshop paper is dated June 2021. This is a research weight, not a university API.", "what_it_is_for": "GitHub lists this as the base ELECTRA discriminator pretrained on PubMed abstracts. The public Hub file is the discriminator. The generator is a version.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/biom/biom-electra-large": { "id": "udel/biom/biom-electra-large", "name": "BioM-ELECTRA-Large", "maker": "udel", "family": "udel/biom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "ElectraForPreTraining. Config: hidden size 1024, 24 layers, 16 heads, intermediate size 4096, embedding size 1024, vocabulary 28895, max_position_embeddings 512. GitHub pretraining row: PubMed abstracts, 29K PubMed vocabulary, batch 4096, 434K steps.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BioM-ELECTRA-Large is a biomedical language-model checkpoint from Sultan Alrowili and K. Vijay-Shanker. The same authors' ArabicT5 paper lists the Department of Computer Science at the University of Delaware. The Hub repo was created on 2022-03-02 and was still public on 2026-10-15. The BioNLP 2021 workshop paper is dated June 2021. This is a research weight, not a university API.", "what_it_is_for": "GitHub lists this as the large ELECTRA discriminator pretrained on PubMed abstracts for 434K steps at batch 4096. The public Hub file is the discriminator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/cdsbert/cdsbert": { "id": "udel/cdsbert/cdsbert", "name": "cdsBERT", "maker": "udel", "family": "udel/cdsbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-2.0", "release_date": "2023-09-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT masked language model, BertForMaskedLM. Config: hidden size 1024, 30 layers, 16 heads, intermediate size 4096, vocabulary 69, absolute positions, max_position_embeddings 40000, float32. Seeded from Rostlab/prot_bert_bfd with the MELD vocabulary extension and trained with masked language modeling on 4 million CDS examples. Codon vocabulary. The card's example loads the id lhallee/cdsBERT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "cdsBERT is a codon-vocabulary protein language model from the Gleghorn Lab at the University of Delaware. The public full-precision checkpoint was uploaded on 2023-09-29 and was still available on 2026-10-15. It is a research weight, not a university API.", "what_it_is_for": "The card describes a general-purpose protein language model with a codon vocabulary. This checkpoint is the full-precision result of masked language modeling on 4 million coding-sequence examples, and the card says it can fill masks. cdsBERT+ is the later distilled embedding checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/cdsbert/cdsbert-plus": { "id": "udel/cdsbert/cdsbert-plus", "name": "cdsBERT+", "maker": "udel", "family": "udel/cdsbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-2.0", "release_date": "2023-09-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT encoder, BertModel, half precision. Config: hidden size 1024, 30 layers, 16 heads, intermediate size 4096, vocabulary 69, absolute positions, max_position_embeddings 40000, torch_dtype bfloat16. The card says this is the half-precision checkpoint after student-teacher distillation with Ankh-base. _name_or_path is cdsBERT+.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "cdsBERT+ is the distilled codon protein language model from the Gleghorn Lab at the University of Delaware. The Hub repo was created on 2023-09-13 and was still public on 2026-10-15. It is a research embedding checkpoint, not a university API.", "what_it_is_for": "The card says this is the half-precision checkpoint after student-teacher distillation with Ankh-base. Intended use is feature extraction. The MLM sibling, cdsBERT, is the mask-filling checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/dsm/dsm-150": { "id": "udel/dsm/dsm-150", "name": "DSM-150", "maker": "udel", "family": "udel/dsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "dsm-project-license (other)", "release_date": "2025-04-18", "context_length": 1026, "max_output_tokens": null, "parameters": "148571994", "architecture": "ESM_Diff masked-diffusion protein model, model type esm_diff. Config: hidden size 640, 30 layers, 20 heads, intermediate size 2560, vocabulary 33, mask token id 32, rotary positions, max_position_embeddings 1026, float32. Safetensors total 148,571,994. The project README calls it a 150M model continued from ESM2-150M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DSM-150 is a masked-diffusion protein language model from the Gleghorn Lab at the University of Delaware, trained with Synthyra. The Hub repo was created on 2025-04-18 and was still public on 2026-10-15. It is a research weight, not a university API.", "what_it_is_for": "The project README describes DSM as a protein language model for representation learning and generative design. DSM-150 is the smaller base model, continued from ESM2-150M. The PPI LoRA files are versions of this line and are marked not recommended for real use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/dsm/dsm-650": { "id": "udel/dsm/dsm-650", "name": "DSM-650", "maker": "udel", "family": "udel/dsm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "dsm-project-license (other)", "release_date": "2025-05-05", "context_length": 1026, "max_output_tokens": null, "parameters": "652726230", "architecture": "ESM_Diff masked-diffusion protein model extending facebook/esm2_t33_650M_UR50D. Config and card: hidden size 1280, 33 layers, 20 heads, feed-forward width 5120, vocabulary 33, mask token id 32, rotary positions, max_position_embeddings 1026, float32. Safetensors total 652,726,230. The card calls it a 650M model and the Hub widget says 0.7B. Weights file model.safetensors, 2.6 GB.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DSM-650 is the larger masked-diffusion protein language model from the Gleghorn Lab at the University of Delaware, trained with Synthyra. The Hub repo was created on 2025-05-05 and was still public on 2026-10-15. It is a research weight, not a university API.", "what_it_is_for": "The card says DSM-650 is for representation learning and generative design and is the model the preprint reports for unconditional generation and representation quality. It extends facebook/esm2_t33_650M_UR50D. Training data is Synthyra/omg_prot50, the Open MetaGenomic set clustered at 50% identity, 207 million sequences, with 10,000 validation, 10,000 test, and 3,300 later UniProt entries added to the test set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udel/synteract/synteract": { "id": "udel/synteract/synteract", "name": "SYNTERACT", "maker": "udel", "family": "udel/synteract", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "2023-09-12", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "BertForSequenceClassification fine-tune of ProtBERT. Config: hidden size 1024, 30 layers, 16 heads, intermediate size 4096, vocabulary 30, two labels, max_position_embeddings 40000, config max_length 512, float32. The card's inference script sets max_length 1024 and tells users to keep sequence pairs at or under 1022 tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SYNTERACT is a protein-protein interaction classifier from the Gleghorn Lab at the University of Delaware. The Hub repo was created on 2023-09-12 and was still public on 2026-10-15. It is a named fine-tune released by the lab, not a university API.", "what_it_is_for": "The card says SYNTERACT attends two amino-acid sequences separated by [SEP] and predicts whether they plausibly interact. Training data named on the card are the multivalidated physical interactions from BIOGRID, Negatome, and synthetic negatives. The card spells the first source BIORGID once; the dataset tag is BIOGRID.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udio/udio/udio-130": { "id": "udio/udio/udio-130", "name": "Udio-130", "maker": "udio", "family": "udio/udio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Udio-130 is Udio's long clip model, announced on 2024-05-29. The X post names udio-130 and calls it a two-minute model for tracks with longer structure. The same-day blog says \"two-minute model\" and does not print the id. Later help calls it u-130 and prices 130-second songs. It launched beside the shorter model, first as an experimental Pro feature. Collected 2026-10-15.", "what_it_is_for": "One prompt produces a clip long enough for intro, verse, and chorus structure, instead of a half-minute section that must be extended. Udio said this improves long-term coherence.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udio/udio/udio-32": { "id": "udio/udio/udio-32", "name": "Udio-32", "maker": "udio", "family": "udio/udio", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-04-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Udio-32 is Udio's short clip model: one generation of about 30 seconds, later priced as a 32-second song. The name appears in Udio's own v1.5 overview video as the choice next to udio-130. The public app that used this length launched on 2024-04-10. Help articles say \"32-second songs\" rather than the id. Confidence is medium. Collected 2026-10-15.", "what_it_is_for": "Fast short sections that the user extends into a longer track. The guide tells people to generate a section, then add material before or after it, including an intro or outro.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udio/udio/v1": { "id": "udio/udio/v1", "name": "Udio v1", "maker": "udio", "family": "udio/udio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-04-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Udio v1 is the text-to-music model Udio named in its public launch thread on 2024-04-10. It makes songs with vocals and instrumentation from a text prompt. Udio still listed it as available on 2025-11-19, next to v1.5 and v1.5 Allegro. Collected 2026-10-15.", "what_it_is_for": "Udio's launch post describes an app that generates music in styles the user prompts, with expressive vocals, and that can extend clips forward or backward. The same thread says the v1 model was capable but not perfect, and that longer samples, sound quality, more languages, and controllability were still coming.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udio/udio/v1-5": { "id": "udio/udio/v1-5", "name": "Udio v1.5", "maker": "udio", "family": "udio/udio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-07-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Udio v1.5 is Udio's July 23, 2024 music model. The company called it the most advanced model yet versus v1, with clearer 48 kHz stereo, key control, and stronger results outside English. It stayed selectable after v1.5 Allegro became the default on 2025-03-18, and Udio still listed it on 2025-11-19. Collected 2026-10-15.", "what_it_is_for": "Text-to-music, plus audio-to-audio remix of an upload. The post also ties the release to stem splits, a dedicated creation page, and shareable lyric videos. Those are product features around the model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "udio/udio/v1-5-allegro": { "id": "udio/udio/v1-5-allegro", "name": "Udio v1.5 Allegro", "maker": "udio", "family": "udio/udio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Udio v1.5 Allegro is a distilled form of Udio v1.5 that became the default music model on 2025-03-18. Udio said it is not a new generation, that the original v1.5 stayed available, and that Allegro was free for every user. It was still one of the three named models on 2025-11-19. The help changelog does not name a later model. Collected 2026-10-15.", "what_it_is_for": "The same song-generation job as v1.5, run faster. The X post says the distillation also produces better-quality songs. The help changelog says the sound is the same and generation is much faster.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uestc/atd/atd": { "id": "uestc/atd/atd", "name": "ATD", "maker": "uestc", "family": "uestc/atd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Super-resolution transformer with an adaptive token dictionary and category-based self-attention. Classical ATD is the full SR product. x2 is trained from scratch. x3 and x4 are fine-tunes. The README points at a Google Drive folder of pretrained weights and is built on BasicSR. A license was not in the README text read for this collection.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ATD is the classical super-resolution transformer with an adaptive token dictionary from the Computer Vision Laboratory at the University of Electronic Science and Technology of China. The CVPR 2024 paper is by Leheng Zhang, Yawei Li, Xingyu Zhou, Xiaorui Zhao, and Shuhang Gu, submitted 2024-01-16. A 2026-03-03 follow-up adds Wei Long and treats ATD, ATD-light, and ATD-U as separate restoration products. Pretrained weights are linked from the lab repository. Collected 2026-10-15. The Drive link is still published, so this dossier marks the release active.", "what_it_is_for": "The authors use the token dictionary to bring global priors into windowed super-resolution attention. This folder is the classical SR line. Scales x2, x3, and x4 are versions. x2 is trained from scratch. x3 and x4 are fine-tunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uestc/atd/atd-light": { "id": "uestc/atd/atd-light", "name": "ATD-light", "maker": "uestc", "family": "uestc/atd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lightweight super-resolution variant of ATD. The 2026 restoration paper names ATD-light as its own model and says it reaches state-of-the-art lightweight SR results. Scales x2, x3, and x4 are versions in the same pretrained-model drive as classical ATD.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ATD-light is the lightweight super-resolution product in the adaptive token dictionary line from the Computer Vision Laboratory at the University of Electronic Science and Technology of China. The 2026-03-03 paper names it separately from classical ATD and from ATD-U. Weights share the lab's pretrained-model drive. Collected 2026-10-15. The link is still published, so this dossier marks the release active.", "what_it_is_for": "The authors use it for lightweight single-image super-resolution. Scales x2, x3, and x4 are versions of this product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uestc/atd/atd-u": { "id": "uestc/atd/atd-u", "name": "ATD-U", "maker": "uestc", "family": "uestc/atd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-scale ATD variant for image restoration other than classical super-resolution. The 2026 paper names denoising and JPEG compression-artifact removal. The repository README groups color and grayscale Gaussian denoising at sigma 15, 25, and 50, and grayscale JPEG restoration at quality 10, 20, 30, and 40, as versions of this product.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ATD-U is the multi-scale restoration model in the adaptive token dictionary line from the Computer Vision Laboratory at the University of Electronic Science and Technology of China. The 2026-03-03 paper names it for denoising and JPEG compression-artifact removal, separate from ATD and ATD-light super-resolution. Weights are in the lab's pretrained-model drive. Collected 2026-10-15. The link is still published, so this dossier marks the release active.", "what_it_is_for": "The authors use it for Gaussian denoising and grayscale JPEG artifact removal. Noise levels and JPEG qualities are versions of one product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uestc/geoquery/geoquery": { "id": "uestc/geoquery/geoquery", "name": "GeoQuery", "maker": "uestc", "family": "uestc/geoquery", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Geometry-guided diffusion refiner for sparse-view 3D Gaussian Splatting. Geometry-guided cross-view attention builds a proxy query from depth and camera pose and attends inside a local window. The paper builds the UNet on pre-trained SD-Turbo and trains at 576x1024 for 100K iterations on one A100. The released file is one checkpoint. The Hub README names geoquery_win3_down1.pkl. The Hub API filename is geoquery_window_size3_down1.pkl. The paper says window size k=3 has the lowest FID on the artifact-removal sweep.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GeoQuery is a geometry-guided diffusion refiner for sparse-view 3D Gaussian Splatting. The weight file is on DIG-UESTC/GeoQuery under an MIT license, created 2026-05-27. Xiao Cao lists the University of Electronic Science and Technology of China and Rawmantic AI. Wen Li and Lixin Duan are at UESTC. Other authors are at Tianjin University and Rawmantic AI. The paper is SIGGRAPH 2026 Conference Track, submitted 2026-05-12. Collected 2026-10-15. The checkpoint is still on the Hub, so this dossier marks the release active.", "what_it_is_for": "The authors use it to repair sparse-view 3DGS renderings. Depth and camera pose build a geometry-aligned proxy query so cross-view attention does not start from a corrupted rendering. Training uses 123 DL3DV scenes. Evaluation uses 12 DL3DV scenes and Mip-NeRF360.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uestc/gtasr/gtasr": { "id": "uestc/gtasr/gtasr", "name": "GTASR", "maker": "uestc", "family": "uestc/gtasr", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-06-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Geometric Trajectory Alignment Super-Resolution. A consistency-model trainer for one-step real-world super-resolution, with trajectory alignment and dual-reference structural rectification. The paper says the code and model will be released at https://github.com/Blazedengcy/GTASR. That GitHub page was not opened. The Hub org Blaze-UESTC holds GTASR_first_phase.pth and GTASR_final_phase.pth and has no README and no license tag.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GTASR (Geometric Trajectory Alignment Super-Resolution) is a one-step real-world super-resolution model. The paper is by Chengyan Deng, Zhangquan Chen, Li Yu, Kai Zhang, Xue Zhou, and Wang Zhang, submitted 2026-02-27. Weight files appeared on Blaze-UESTC/GTASR on 2026-06-13. There is no README and no license tag. Collected 2026-10-15. The files exist, so this dossier marks the release active. Confidence is medium because the paper and the filenames agree and the card does not.", "what_it_is_for": "The abstract describes a consistency-training method for one-step real-world super-resolution. Trajectory alignment corrects the tangent field. Dual-reference structural rectification is the structural constraint. The abstract says code and the model will be released at https://github.com/Blazedengcy/GTASR. That page was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uestc/ig/internal-guidance": { "id": "uestc/ig/internal-guidance", "name": "Internal Guidance", "maker": "uestc", "family": "uestc/ig", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-12-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Internal Guidance adds auxiliary supervision on an intermediate Diffusion Transformer layer during training and extrapolates intermediate and deep outputs at sampling. Released checkpoints are UESTC-trained SiT-XL/2 and LightningDiT-XL/1 runs on ImageNet 256. SiT and LightningDiT are public architectures from other groups. The parameter count of those base architectures was not copied onto these checkpoints.", "modalities": { "input": [ "class-label" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Internal Guidance (IG) is a training and sampling method for diffusion transformers, released by the Computer Vision Laboratory at the University of Electronic Science and Technology of China. The lab shipped two ImageNet 256 checkpoints, SiT-XL/2 + IG and LightningDiT-XL/1 + IG, on CVLUESTC/Internal-Guidance. The GitHub news line dates the paper and code to 2025-12-31. Collected 2026-10-15. The weights are still on the Hub, so this dossier marks the release active.", "what_it_is_for": "The authors add auxiliary supervision on an intermediate transformer layer and, at sampling time, extrapolate the intermediate and deep outputs. The released runs sit on public SiT and LightningDiT architectures. Corresponding author Shuhang Gu is at UESTC. Xiaobin Hu is at the National University of Singapore. Hai Chen is at Sun Yat-sen University and the North China Institute of Computer Systems Engineering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uestc/mia-vsr/mia-vsr": { "id": "uestc/mia-vsr/mia-vsr", "name": "MIA-VSR", "maker": "uestc", "family": "uestc/mia-vsr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-06-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video super-resolution transformer with masked intra-frame and inter-frame attention and an adaptive block-wise mask. The README release note of 2024-06-13 says codes and models were released. Checkpoints are a REDS-trained model and a Vimeo-trained model. An IIAVSR variant without the mask, in archs/iia_vsr_arch.py, is an ablation and is not a separate product. Built on BasicSR, PSRT, and parts of mmediting.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MIA-VSR is a video super-resolution transformer from the Computer Vision Laboratory at the University of Electronic Science and Technology of China. Xingyu Zhou, Leheng Zhang, Xiaorui Zhao, Keze Wang, Leida Li, and Shuhang Gu submitted the paper on 2024-01-12. It is a CVPR 2024 paper. The repository update of 2024-06-13 says the codes and models were released. Collected 2026-10-15. The Drive link is still published, so this dossier marks the release active.", "what_it_is_for": "The authors use masked intra-frame and inter-frame attention to skip redundant computation between adjacent frames while restoring a low-resolution sequence. The released pair is a REDS-trained checkpoint and a Vimeo-trained checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uestc/mvar/mvar": { "id": "uestc/mvar/mvar", "name": "MVAR", "maker": "uestc", "family": "uestc/mvar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-05", "context_length": null, "max_output_tokens": null, "parameters": "310M", "architecture": "Markovian visual autoregressive model. Scale-Markov next-scale prediction uses only the adjacent preceding scale. Spatial-Markov attention uses neighborhood attention of size k. The card says inference does not use a KV cache. The Hub pipeline tag is unconditional-image-generation. Training and FID evaluation are the ImageNet 256 class-conditional setup used by VAR. Starred checkpoints are fine-tunes of FoundationVision/var. MVAR-d16 is trained as MVAR.", "modalities": { "input": [ "class-label" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MVAR (Markovian Visual AutoRegressive modeling) is a class-conditional ImageNet generator from the Computer Vision Laboratory at the University of Electronic Science and Technology of China. Jinhua Zhang, Wei Long, Minghao Han, Weiyi You, and Shuhang Gu released Apache-2.0 weights on Hugging Face as CVLUESTC/MVAR. The card's news line dates the codebase and weights to 2026-02-05. Collected 2026-10-15. The weights are still downloadable, so this dossier marks the release active.", "what_it_is_for": "The authors use it to generate ImageNet 256 images with next-scale prediction under scale and spatial Markov assumptions. The card says inference does not rely on a KV cache. Evaluation uses OpenAI's guided-diffusion FID toolkit and the labeled ImageNet 256 reference batch.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uestc/pft/pft": { "id": "uestc/pft/pft", "name": "PFT", "maker": "uestc", "family": "uestc/pft", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Progressive Focused Transformer for classical single-image super-resolution. Progressive Focused Attention links attention maps and drops tokens before the similarity calculation. Built on BasicSR and ATD. The README trains x2 from scratch for 500k iterations on DF2K and fine-tunes x3 and x4 for 250k iterations. Pretrained weights are on Google Drive.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PFT (Progressive Focused Transformer) is a classical single-image super-resolution model from the Computer Vision Laboratory at the University of Electronic Science and Technology of China. Wei Long, Xingyu Zhou, Leheng Zhang, and Shuhang Gu submitted the paper on 2025-03-26. The lab repository is the CVPR 2025 implementation and links pretrained weights. Collected 2026-10-15. The Drive link is still published, so this dossier marks the release active.", "what_it_is_for": "The authors use progressive focused attention to keep tokens that matter for a query and skip the rest before computing similarities. This folder is the classical SR line trained on DF2K. The lightweight line is a separate product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uestc/pft/pft-light": { "id": "uestc/pft/pft-light", "name": "PFT-light", "maker": "uestc", "family": "uestc/pft", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lightweight Progressive Focused Transformer. The README treats it as a separate SR product from classical PFT and trains it on DIV2K, with x2 from scratch and x3 and x4 as fine-tunes. Weights are in the same Drive folder as classical PFT.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PFT-light is the lightweight super-resolution product in the Progressive Focused Transformer line from the Computer Vision Laboratory at the University of Electronic Science and Technology of China. The repository trains it on DIV2K and ships it beside the classical DF2K model. Collected 2026-10-15. The pretrained-model link is still published, so this dossier marks the release active.", "what_it_is_for": "The authors use the same progressive focused attention design at a lightweight size. Scales x2, x3, and x4 are versions. Training data in the README is DIV2K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uestc/rdvq/rdvq": { "id": "uestc/rdvq/rdvq", "name": "RDVQ", "maker": "uestc", "family": "uestc/rdvq", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-06-12", "context_length": null, "max_output_tokens": null, "parameters": "251.9M", "architecture": "Differentiable vector-quantization image codec. A multi-scale VQ autoencoder adapted from the LlamaGen VQ-VAE, with attention layers removed, plus a masked-transformer autoregressive entropy model. Rate points use a joint tau and lambda schedule. Ultra-low bitrate (bpp under 0.025) uses tau 0.1 and lambda in {4.8, 7.2, 12}. Higher bitrate uses tau 0.01 and lambda in {0.8, 1.2}. The paper says a 2K image takes 1.3 seconds on an RTX 4090.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RDVQ is a generative image codec from the Computer Vision Laboratory at the University of Electronic Science and Technology of China. Shiyin Jiang, Wei Long, Minghao Han, Ce Zhu, and Shuhang Gu are at UESTC. Zhenghao Chen is at the University of Newcastle. Apache-2.0 checkpoints landed on CVLUESTC/RDVQ on 2026-06-12. The paper is a CVPR 2026 oral, submitted 2026-04-12 and revised 2026-05-04. Collected 2026-10-15. The files are still on the Hub, so this dossier marks the release active.", "what_it_is_for": "The authors use it for extremely low-bitrate image compression with end-to-end rate-distortion optimization of a vector-quantized codec. The entropy model also supports test-time rate control. Reported sets include Kodak, DIV2K-val, and CLIC2020-test.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uestc/tvqrap/tvqrap": { "id": "uestc/tvqrap/tvqrap", "name": "TVQ&RAP", "maker": "uestc", "family": "uestc/tvqrap", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-08", "context_length": null, "max_output_tokens": null, "parameters": "57M", "architecture": "Generative super-resolution. A structure branch and a texture vector-quantization codebook model missing texture, and a reconstruction-aware index predictor trained with image-level supervision through a straight-through estimator. Table 3 lists 57M parameters and 38 ms runtime on a single RTX 3090 for 64x64 inputs. The Hub repo stores a tokenizer file and two SR stage files as one system.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TVQ&RAP is a generative super-resolution model from the Computer Vision Laboratory at the University of Electronic Science and Technology of China. Qifan Li, Jiale Zou, Jinhua Zhang, Wei Long, Xingyu Zhou, and Shuhang Gu describe it as texture vector-quantization plus reconstruction-aware prediction. Apache-2.0 weights are on CVLUESTC/TVQRAP, created 2026-03-08. Collected 2026-10-15. The files are still on the Hub, so this dossier marks the release active. The paper name is TVQ&RAP. The Hub name is TVQRAP. ICLR 2026.", "what_it_is_for": "The authors use it for photo-realistic super-resolution. A texture codebook models missing texture, and the index predictor is trained with image-level reconstruction loss. ImageNet-Test has 3,000 images drawn from the ImageNet validation set. RealSR and RealSet65 are the real-world sets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uestc/ve-loss/ve-loss": { "id": "uestc/ve-loss/ve-loss", "name": "VE-Loss", "maker": "uestc", "family": "uestc/ve-loss", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-03-09", "context_length": null, "max_output_tokens": null, "parameters": "675M", "architecture": "Variance Expansion loss for latent-diffusion tokenizers. The paper's system row is a class-conditional ImageNet 256 generator at 675M parameters, 530 epochs, with classifier-free guidance. The Hub tag is text-to-image. The paper evaluates class-conditional generation. Uploaded files are latents_stats.pt, lightningdit_in_ve_loss_tok.pt, and ve_loss_tok.ckpt.", "modalities": { "input": [ "image", "class-label" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VE-Loss is a variance-expansion training objective for latent-diffusion tokenizers from the Computer Vision Laboratory at the University of Electronic Science and Technology of China. Qifan Li, Xingyu Zhou, Jinhua Zhang, Weiyi You, and Shuhang Gu released Apache-2.0 files on CVLUESTC/VE-Loss on 2026-03-09. The paper is CVPR 2026, submitted 2026-03-22. Collected 2026-10-15. The Hub files are still present, so this dossier marks the release active.", "what_it_is_for": "The authors use the loss to keep a latent space robust to diffusion sampling noise while reconstructing images. The system comparison is class-conditional ImageNet 256 generation. The Hub pipeline tag says text-to-image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ufl/gatortron/gatortron-base": { "id": "ufl/gatortron/gatortron-base", "name": "GatorTron-Base", "maker": "ufl", "family": "ufl/gatortron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-04", "context_length": 512, "max_output_tokens": null, "parameters": "345 million", "architecture": "Encoder-only Megatron-BERT, trained from scratch. Paper Table 3 and the Hub config.json: 24 layers, hidden size 1024, 16 attention heads, intermediate size 4096, absolute positions, vocab size 50176. The lab GitHub README instead lists hidden size 1280 and 12 heads for the base model; the released config matches the paper, not that README.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GatorTron-Base is the University of Florida and NVIDIA 345-million-parameter clinical encoder, released as open weights and still downloadable as of 2026-10-15. The Hugging Face card identifies it with the NVIDIA NGC checkpoint named GatorTron-OG. It is an encoder, not a chat model.", "what_it_is_for": "The 2022 paper trains GatorTron from scratch so clinical NLP systems can read electronic-health-record notes. The lab uses the encoder, after fine-tuning, for concept extraction, relation extraction, semantic similarity, natural-language inference, and medical question answering. The README says the series is not for generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ufl/gatortron/gatortron-base-2k": { "id": "ufl/gatortron/gatortron-base-2k", "name": "GatorTron-Base-2k", "maker": "ufl", "family": "ufl/gatortron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-28", "context_length": 2048, "max_output_tokens": null, "parameters": "345 million", "architecture": "Encoder-only Megatron-BERT. config.json: 24 layers, hidden size 1024, 16 heads, intermediate size 4096, vocab size 50176, max_position_embeddings 2048. Same width as GatorTron-Base. The card says this repo was converted from the NGC GatorTron-OG checkpoint, which is also the stated source of the 512-token base model. Hub storage is about 1.43 GB here versus about 2.14 GB for gatortron-base; the files were not re-downloaded to compare tensors.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GatorTron-Base-2k is a University of Florida NLP release of the 345-million-parameter GatorTron encoder with a 2048-token position table. The Hugging Face repo was created on 2025-04-28 and was still public on 2026-10-15. The 2022 GatorTron paper does not name this checkpoint. Its card says it was converted from the same NGC GatorTron-OG file as GatorTron-Base.", "what_it_is_for": "The card places it in the GatorTron variation table as the long-input base model, for the same clinical understanding tasks as GatorTron-Base. It does not describe a new pretraining run. It is not a generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ufl/gatortron/gatortron-large": { "id": "ufl/gatortron/gatortron-large", "name": "GatorTron-Large", "maker": "ufl", "family": "ufl/gatortron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-16", "context_length": 512, "max_output_tokens": null, "parameters": "8.9 billion", "architecture": "Encoder-only Megatron-BERT, trained from scratch. Paper Table 3 and config.json: 56 layers, hidden size 3584, 56 attention heads, intermediate size 14336, vocab size 50176, maximum position 512. The lab GitHub README lists hidden size 3560 instead of 3584. config.json tokenizer_type is BertWordPieceLowerCase, unlike the cased type on the base config.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GatorTron-Large is the University of Florida and NVIDIA 8.9-billion-parameter clinical encoder, the largest of the three scales trained from scratch for the December 2022 paper. Public Hugging Face weights date from 2024-03-16 and were still listed on 2026-10-15. It is an encoder, not a chat model.", "what_it_is_for": "The paper presents this scale as the best of the three GatorTron models on clinical concept extraction, drug-adverse-event relations, natural-language inference, and medical question answering. It is fine-tuned for those tasks. The lab says the family is not for free-text generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ufl/gatortron/gatortron-medium": { "id": "ufl/gatortron/gatortron-medium", "name": "GatorTron-Medium", "maker": "ufl", "family": "ufl/gatortron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-06-02", "context_length": 512, "max_output_tokens": null, "parameters": "3.9 billion", "architecture": "Encoder-only Megatron-BERT, trained from scratch. Paper Table 3 and config.json: 48 layers, hidden size 2560, 40 attention heads, intermediate size 10240, vocab size 50176, maximum position 512. The lab GitHub README matches this width and head count.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GatorTron-Medium is the University of Florida and NVIDIA 3.9-billion-parameter clinical encoder. The configuration was trained for the December 2022 GatorTron paper. The first public weight repo found is the Apache-2.0 Hugging Face upload of 2023-06-02, and it was still up on 2026-10-15.", "what_it_is_for": "Same job as the rest of the original GatorTron encoders: fine-tune on clinical language-understanding tasks. The paper treats it as the middle scale between the 345-million and 8.9-billion models. It is not a generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ufl/gatortron/gatortron-s": { "id": "ufl/gatortron/gatortron-s", "name": "GatorTronS", "maker": "ufl", "family": "ufl/gatortron", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-04", "context_length": 512, "max_output_tokens": null, "parameters": "345 million", "architecture": "Encoder-only Megatron-BERT, same shape as GatorTron-Base: 24 layers, hidden size 1024, 16 heads, intermediate size 4096, vocab size 50176, maximum position 512. Trained on synthetic clinical text plus PubMed, WikiText, and MIMIC-III, not on the 82-billion-word UF Health note corpus used for GatorTron-Base.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GatorTronS is the University of Florida and NVIDIA 345-million-parameter clinical encoder trained on synthetic notes instead of the UF Health note warehouse. NVIDIA's Clara NLP note dates the NGC checkpoint to April 2022. The Hugging Face conversion went up on 2023-06-02 and was still public on 2026-10-15. The \"S\" means synthetic. It is an encoder, not the generator.", "what_it_is_for": "The 2023 paper uses GatorTronS to test whether notes written by GatorTronGPT can replace real clinical text when training an understanding model. The released checkpoint is that 345-million encoder. Downstream use is the same style of fine-tuning as GatorTron-Base: extraction, similarity, inference, and question answering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ufl/gatortrongpt/gatortrongpt-20b": { "id": "ufl/gatortrongpt/gatortrongpt-20b", "name": "GatorTronGPT-20B", "maker": "ufl", "family": "ufl/gatortrongpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-05-22", "context_length": null, "max_output_tokens": null, "parameters": "20 billion", "architecture": "Decoder-only GPT-3 architecture, trained from scratch. The 2023 paper: 44 layers, hidden size 6144, 48 attention heads. Trained with 8-way tensor parallelism, batch size 560, learning rate 1.000E-05, dropout 0.1. About 20 days on 560 A100 80 GB GPUs. Corpus: 82 billion words of de-identified UF Health notes and 195 billion words from the Pile (277 billion words total).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GatorTronGPT-20B is the larger clinical generator from the University of Florida and NVIDIA, a 20-billion-parameter GPT-3 model trained from scratch and published in November 2023. UF still lists GatorTronGPT as a licensable technology. No public weight file was found on 2026-10-15.", "what_it_is_for": "The paper uses it to write clinical text and to answer biomedical questions and extract relations by generating text. UF's technology page describes conversational and summarization uses as well. The open model the lab actually posted from this project is the encoder GatorTronS, which was trained on synthetic notes, not this 20-billion checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ufl/gatortrongpt/gatortrongpt-5b": { "id": "ufl/gatortrongpt/gatortrongpt-5b", "name": "GatorTronGPT-5B", "maker": "ufl", "family": "ufl/gatortrongpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-05-22", "context_length": null, "max_output_tokens": null, "parameters": "5 billion", "architecture": "Decoder-only GPT-3 architecture, trained from scratch. The 2023 paper: 24 layers, hidden size 4096, 32 attention heads. Trained with 2-way tensor parallelism, batch size 1120, learning rate 1.200E-05, dropout 0.1. About 6 days on 560 A100 80 GB GPUs. Corpus: 82 billion words of de-identified UF Health notes and 195 billion words from the Pile.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GatorTronGPT-5B is the smaller of the two decoder-only clinical language models the University of Florida and NVIDIA trained from scratch and described in 2023. It was still a named UF model on 2026-10-15, including a UF technology listing for GatorTronGPT, but no public weight file was found. NVIDIA's GatorTron-S page calls the 5-billion-parameter generator SynGatorTron.", "what_it_is_for": "The paper trains GatorTronGPT to generate clinical text and to run biomedical relation extraction and question answering as text generation. The 5-billion model is the one NGC credits with writing the synthetic discharge summaries behind GatorTronS. A later paper freezes it and prompt-tunes it for several clinical NLP tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ufl/medgpt/medgpt-oss": { "id": "ufl/medgpt/medgpt-oss", "name": "MedGPT-oss", "maker": "ufl", "family": "ufl/medgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0 (weights). MIT for the Python source files, which does not lift the non-commercial limit on the weights.", "release_date": "2026-04-22", "context_length": null, "max_output_tokens": null, "parameters": "21232236224", "architecture": "Vision-language model. Hugging Face safetensors metadata counts 21,232,236,224 BF16 parameters. The card calls it an about-20B model: GPT-oss-20B language backbone, CLIP ViT-L/14 at 336 px, and a two-layer MLP projector. Three-stage training: alignment, long-context mid-training, instruction tuning. Image input uses InternVL-style dynamic tiling, default max 12 tiles plus an optional thumbnail, about 256 language tokens per tile. Custom class MedGptOssModel. Gated research download.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedGPT-oss is an open-weight biomedical vision-language model released by the University of Florida NLP group, with coauthors at Lehigh and NVIDIA. Hub weights appeared on 2026-04-22 and were still gated for academic and non-profit research on 2026-10-15. The language backbone is GPT-oss-20B. The UF release adds a CLIP vision tower and a three-stage medical curriculum. It is not a clinical device.", "what_it_is_for": "The card calls it a research foundation for medical visual question answering, text-only medical QA, radiology report generation, and on-premises fine-tuning. It forbids commercial use, clinical decision-making, deployed clinical systems, and re-identification. The authors say it is a single-turn model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uiuc/calico/calico": { "id": "uiuc/calico/calico", "name": "CALICO", "maker": "uiuc", "family": "uiuc/calico", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-06-07", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "CALICOForCausalLM. Language config: hidden 4096, intermediate 11008, 32 layers, 32 heads, vocab 32007, max_position_embeddings 4096. Vision: EVA-CLIP-G Q-Former, frozen DINOv2 ViT-B/14 reg features, SAM ViT-H mask decoder. Correspondence Extraction and Adaptation modules.", "modalities": { "input": [ "image", "text" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CALICO is a large vision-language model from the PLAN Lab at UIUC for part-focused semantic co-segmentation. The paper is CVPR 2025. The merged Hub checkpoint was uploaded 7 June 2026. Collected on 2026-10-15.", "what_it_is_for": "Given a pair of images and a text prompt, it should find common objects, common parts, or unique parts and emit segmentation masks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uiuc/coalm/coalm-405b": { "id": "uiuc/coalm/coalm-405b", "name": "CoALM-405B", "maker": "uiuc", "family": "uiuc/coalm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-02-05", "context_length": null, "max_output_tokens": null, "parameters": "405B (card). Base Llama 3.1 405B Instruct. Paper says the released run is QLoRA nf4, one epoch, and still training as of the 19 Feb 2025 revision.", "architecture": "Llama 3.1 405B Instruct. Hub repo has 191 safetensor shards. Card says about 6.5 days on 8 H100s. Paper says QLoRA and thanks Together AI for the cluster.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CoALM-405B is the largest CoALM checkpoint from the UIUC Conversational AI Lab and Oumi, based on Llama 3.1 405B Instruct. The card dates the release 5 February 2025 and the Hub holds a full safetensor upload. The 19 February 2025 paper still marks it as a one-epoch run that was still training. Collected on 2026-10-15.", "what_it_is_for": "The card calls it the largest open conversational agent in the series for multi-turn dialogue and function calling. The non-commercial license conflicts with the card's fully-open-source wording.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uiuc/coalm/coalm-70b": { "id": "uiuc/coalm/coalm-70b", "name": "CoALM-70B", "maker": "uiuc", "family": "uiuc/coalm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-02-05", "context_length": null, "max_output_tokens": null, "parameters": "70B (card parameter count; base Llama 3.3 70B Instruct).", "architecture": "Llama 3.3 70B Instruct fine-tune. Card says full fine-tuning and also lists LoRA r=16, alpha=32, batch 7, learning rate 4e-5, about 24 hours. Paper says LoRA, global batch 8, learning rate 1e-4, about 60 hours.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CoALM-70B is the mid-size CoALM model from the UIUC Conversational AI Lab and Oumi, based on Llama 3.3 70B Instruct. The card dates the release 5 February 2025. Collected on 2026-10-15.", "what_it_is_for": "Same job as CoALM-8B at a larger scale: dialogue state tracking plus function calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uiuc/coalm/coalm-8b": { "id": "uiuc/coalm/coalm-8b", "name": "CoALM-8B", "maker": "uiuc", "family": "uiuc/coalm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-02-05", "context_length": null, "max_output_tokens": null, "parameters": "8B base (Llama 3.1 8B Instruct). Paper trains LoRA r=16, alpha=32. Hub repo is four merged safetensor shards, not an adapter-only upload.", "architecture": "Llama 3.1 8B Instruct fine-tune. Card and paper agree on LoRA rank 16, alpha 32, batch size 8, learning rate 1e-4, about 8 hours on 8 H100s.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CoALM-8B is the smallest Conversational Agentic Language Model from the UIUC Conversational AI Lab and Oumi. It is a Llama 3.1 8B Instruct tune for multi-turn task-oriented dialogue and tool use. The card dates the release 5 February 2025. Collected on 2026-10-15.", "what_it_is_for": "The lab says it should track dialogue state, call APIs, and answer in a ReAct loop (user, thought, action, observation, thought, response) without benchmark training data in the mix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uiuc/graphvid/graphvid": { "id": "uiuc/graphvid/graphvid", "name": "GraphVid", "maker": "uiuc", "family": "uiuc/graphvid", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GraphVidLTXTransformer3DModel (diffusers 0.38.0.dev0). 48 layers, 32 heads, head dim 128, cross-attention and caption channels 4096, in/out channels 128. Repo also has a text encoder, a VAE, and graph.pth.", "modalities": { "input": [ "image", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GraphVid is an image-to-video model from the PLAN Lab at UIUC. It turns user interactions on an image into an interaction graph that conditions generation. Apache-2.0 weights were uploaded 18 July 2026. The paper is ECCV 2026. Collected on 2026-10-15.", "what_it_is_for": "The lab says the graph states what should happen and a pretrained video backbone decides how the clip looks and moves.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uiuc/magicoder/magicoder-cl-7b": { "id": "uiuc/magicoder/magicoder-cl-7b", "name": "Magicoder-CL-7B", "maker": "uiuc", "family": "uiuc/magicoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Llama. Fine-tuned from CodeLlama-7b-Python-hf on OSS-Instruct (about 75K samples).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magicoder-CL-7B is a code-generation model from the Intelligent Software Engineering lab at the University of Illinois Urbana-Champaign. It is the CodeLlama-Python 7B checkpoint tuned on OSS-Instruct. Weights have been public since 3 December 2023. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab says it writes code from instructions, with less bias than synthetic data that never saw real open-source snippets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uiuc/magicoder/magicoder-ds-6-7b": { "id": "uiuc/magicoder/magicoder-ds-6-7b", "name": "Magicoder-DS-6.7B", "maker": "uiuc", "family": "uiuc/magicoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "6.7B", "architecture": "Llama. Fine-tuned from deepseek-coder-6.7b-base on OSS-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magicoder-DS-6.7B is the DeepSeek-Coder 6.7B branch of Magicoder from UIUC iSE, tuned only on OSS-Instruct. Weights have been public since 3 December 2023. Collected on 2026-10-15.", "what_it_is_for": "Same code-generation job as the CodeLlama Magicoders, on a DeepSeek-Coder base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uiuc/magicoder/magicoder-s-cl-7b": { "id": "uiuc/magicoder/magicoder-s-cl-7b", "name": "Magicoder-S-CL-7B", "maker": "uiuc", "family": "uiuc/magicoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Llama. Magicoder-CL further tuned on decontaminated Evol-Instruct (about 110K) after OSS-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magicoder-S-CL-7B is the stronger CodeLlama branch of Magicoder from UIUC iSE. It adds Evol-Instruct on top of OSS-Instruct. Weights have been public since 3 December 2023. Collected on 2026-10-15.", "what_it_is_for": "The lab presents it as a code model that beats the OSS-Instruct-only CodeLlama tune, and reports a HumanEval+ pass@1 above ChatGPT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uiuc/magicoder/magicoder-s-ds-6-7b": { "id": "uiuc/magicoder/magicoder-s-ds-6-7b", "name": "Magicoder-S-DS-6.7B", "maker": "uiuc", "family": "uiuc/magicoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "DeepSeek", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "6.7B", "architecture": "Llama. Magicoder-DS further tuned on Evol-Instruct after OSS-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Magicoder-S-DS-6.7B is the UIUC iSE code model the lab highlights on its README. It is DeepSeek-Coder 6.7B tuned with OSS-Instruct and then Evol-Instruct. Weights have been public since 3 December 2023. Collected on 2026-10-15.", "what_it_is_for": "Code generation. The README reports HumanEval pass@1 of 76.8.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uiuc/palm/palm": { "id": "uiuc/palm/palm", "name": "PALM", "maker": "uiuc", "family": "uiuc/palm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-03-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language-action policy. Affordance distillation plus within-subtask progress prediction. Checkpoint files are PyTorch .pth weights, not a documented transformer config.", "modalities": { "input": [ "image", "text" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PALM is a vision-language-action policy from the PLAN Lab at UIUC for long-horizon robot manipulation. MIT weights were uploaded 20 March 2026. Collected on 2026-10-15.", "what_it_is_for": "The card says it should follow long tasks by tracking object affordances and how far a subtask has progressed, instead of repeating or stopping early.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uiuc/pyratok/pyratok": { "id": "uiuc/pyratok/pyratok", "name": "PyraTok", "maker": "uiuc", "family": "uiuc/pyratok", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Language-aligned pyramidal video tokenizer (GitHub). Hub repo files are a VAE and a text encoder. Hub base_model tag is Wan-AI/Wan2.2-T2V-A14B and the pipeline tag is image-text-to-video.", "modalities": { "input": [ "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PyraTok is a video tokenizer from the PLAN Lab at UIUC, announced as a CVPR 2026 model. A gated MIT Hub repo was created 27 April 2026. Collected on 2026-10-15.", "what_it_is_for": "The GitHub says it should tokenize video for both understanding and generation, with language-aligned pyramidal quantization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uiuc/rewardflow/rewardflow": { "id": "uiuc/rewardflow/rewardflow", "name": "RewardFlow", "maker": "uiuc", "family": "uiuc/rewardflow", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Inference-time multi-reward Langevin guidance for diffusion and flow-matching models (paper). Hub repo is a gated diffusers pipeline, class FluxRewardFlowPipeline, base_model tag black-forest-labs/FLUX.1-dev, and includes model.safetensors plus a four-shard text encoder.", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RewardFlow is an image generation and editing method from the PLAN Lab at UIUC, with Sony Research India coauthors. The paper is CVPR 2026 (arXiv 9 April 2026). A gated Hub repo tagged as a FLUX.1-dev pipeline was created 27 April 2026. Collected on 2026-10-15.", "what_it_is_for": "The project page says it steers a frozen diffusion or flow model at sampling time with several rewards, including a VQA reward and a SAM2 object reward, so edits stay inside a target region.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uiuc/spatialreasoner/spatialreasoner-r1": { "id": "uiuc/spatialreasoner/spatialreasoner-r1", "name": "SpatialReasoner-R1", "maker": "uiuc", "family": "uiuc/spatialreasoner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-01-08", "context_length": 32768, "max_output_tokens": null, "parameters": "8B (Hugging Face model-size tag). llm_config _name_or_path is internlm/internlm2_5-7b-chat.", "architecture": "Sa2VAChatModel. Config names InternViT, InternLM2, Phi-3, Sa2VA chat, and SAM2 files. LLM config: hidden size 4096, 32 layers, 32 heads, 8 KV heads, max_position_embeddings 32768, dynamic RoPE factor 2.0.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SpatialReasoner-R1 is a vision-language model from the PLAN Lab at the University of Illinois Urbana-Champaign for step-by-step spatial reasoning on 2D images. The Hub weights were uploaded 8 January 2026 under MIT. The paper is NeurIPS 2025. Collected on 2026-10-15.", "what_it_is_for": "The lab says it should explain spatial relations, including multi-step quality and quantity questions, while staying usable on general vision-language tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ulb/arbock/arbock": { "id": "ulb/arbock/arbock", "name": "ARBOCK", "maker": "ulb", "family": "ulb/arbock", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Decision-set classifier over metapaths in the BOCK knowledge graph. Two shipped scikit-learn pickles, ds_model_with_pheno and ds_model_no_pheno, are assembly choices of one product: phenotype paths included or excluded. Default CLI: minsup_ratio 0.2, path_cutoff 3, max_rule_length 3, alpha 0.5, holdout_positive_size 15. OligogenicCombination and Disease node types are always excluded.", "modalities": { "input": [ "tabular" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ARBOCK (Association Rule learning Based on Overlapping Connections in Knowledge graphs) is an interpretable gene-pair classifier from Alexandre Renaux (Universit\u00e9 libre de Bruxelles and Vrije Universiteit Brussel) and Tom Lenaerts. The MIT repository ships two decision-set pickles. It is an active code release and is not an ORVAL endpoint. Collected 2026-10-15.", "what_it_is_for": "The repository presents ARBOCK as a way to predict pathogenic gene interactions and to show the knowledge-graph paths behind a positive call. The phenotype-included and phenotype-excluded pickles are two settings of that product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ulb/digenic-effect/digenic-effect-predictor": { "id": "ulb/digenic-effect/digenic-effect-predictor", "name": "Digenic Effect Predictor", "maker": "ulb", "family": "ulb/digenic-effect", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2017-06-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ORVAL documents a random-forest classifier. It emits a probability for each of three classes and assigns the class with the highest probability. Tree count, depth, and a public weight file were not found for the live model.", "modalities": { "input": [ "tabular" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Digenic Effect Predictor is a Universit\u00e9 libre de Bruxelles Machine Learning Group classifier that ORVAL still runs, by that name, on variant combinations already called pathogenic. It is active as a web product. Collected 2026-10-15. Public weights were not found.", "what_it_is_for": "ORVAL uses it when pedigree or parental genotypes are missing. The live page assigns one of three classes: True Digenic, Monogenic with a modifier, or Dual Molecular Diagnosis. The class is the one with the highest probability.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ulb/hop/hop": { "id": "ulb/hop/hop", "name": "HOP", "maker": "ulb", "family": "ulb/hop", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-03-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Ranking method, not a separate neural network. Pathogenicity score is VarCoPP2.0. Disease-relevance score is a random walk with restart on the BOCK graph, default restart 0.3. ORVAL combines min-max scaled scores as their average. The MIT code also offers MAX, MIN, AVGE, and MULT operators. Default CLI final score is HPO+Panel_AVGE.", "modalities": { "input": [ "tabular" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HOP (High-throughput Oligogenic Prioritizer) is a Universit\u00e9 libre de Bruxelles and IB\u00b2 ranking method for digenic variant combinations in whole exomes. It is active in the MIT repository and, since ORVAL 4.0.0, on the public site. Collected 2026-10-15.", "what_it_is_for": "The authors describe HOP as a way to rank which variant combinations in an exome are most likely to explain a phenotype. It does not replace VarCoPP. It uses VarCoPP2.0 as the pathogenicity score and a random walk on BOCK as the disease-relevance score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ulb/quate/quate-gene-pair": { "id": "ulb/quate/quate-gene-pair", "name": "QuatE-based gene pair pathogenicity predictor", "maker": "ulb", "family": "ulb/quate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "QuatE knowledge-graph embedding (Zhang et al.) trained on BOCK 2.0, then used as a gene-pair pathogenicity scorer. Released files are entity embeddings, an entity-label map, and a dictionary of precomputed pair scores. Embedding dimension and parameter count are not stated on the Zenodo record.", "modalities": { "input": [ "tabular" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is a named weight release from Inas Bosch and Tom Lenaerts at the Universit\u00e9 libre de Bruxelles, with Vrije Universiteit Brussel and University of Tartu co-authors. It scores gene pairs for oligogenic pathogenicity with a QuatE embedding of BOCK 2.0. The Zenodo record is active. Collected 2026-10-15.", "what_it_is_for": "The Zenodo record says the model takes gene pairs and returns a score for how likely the pair is to be an oligogenic combination. QuatE itself is the Zhang et al. embedding architecture. This record is the ULB-trained checkpoint and the precomputed pair scores, not a hosted copy of an unchanged third-party model card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ulb/varcopp/varcopp": { "id": "ulb/varcopp/varcopp", "name": "VarCoPP", "maker": "ulb", "family": "ulb/varcopp", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2019-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Shipping VarCoPP2.0 is a Balanced Random Forest of 400 decision trees of depth 10 (imbalanced-learn), one score in [0, 1]. ORVAL selects a GRCh37/hg19 or GRCh38/hg38 pickle. The 2019 model was an ensemble of 500 random forests. Parameter count is not published.", "modalities": { "input": [ "tabular" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VarCoPP (Variant Combination Pathogenicity Predictor) is a Universit\u00e9 libre de Bruxelles Machine Learning Group model, released with IB\u00b2 and VUB co-authors, that scores bilocus variant combinations as candidate disease-causing or neutral. The named product is active on ORVAL and as GPL-3.0 pickles. Collected 2026-10-15.", "what_it_is_for": "ORVAL uses VarCoPP to predict candidate pathogenic combinations of variants in a gene pair. The live model is VarCoPP2.0, a single Balanced Random Forest. A score above 0.50 (hg19) or above 0.4575 (hg38) is called disease-causing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ulm/cuts3d/cuts3d": { "id": "ulm/cuts3d/cuts3d", "name": "CutS3D", "maker": "ulm", "family": "ulm/cuts3d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-11-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Class-agnostic Cascade Mask R-CNN trained by the Ulm authors on CutS3D pseudo-masks. LocalCut separates instances on a monocular point cloud; the main setup uses ZoeDepth, and the paper also reports Marigold, Kick Back & Relax, and MiDaS. Spatial Importance sharpening uses beta=0.45. Spatial Confidence uses a minimum confidence of 0.5 and is applied as confident copy-paste, alpha-blending, and a Spatial Confidence Soft Target loss. Main pseudo-masks use DiffNCuts ViT-S/8 on ImageNet-1K, about 1.3 million images at 480x480. The detector uses DropLoss and then self-training. The v3 paper's main zero-shot tables use three self-training rounds. The project page says the displayed model uses one round. An in-domain variant adds one self-train on COCO train2017. The training recipe follows CutLER. This dossier is for the Ulm-trained detector, not a hosted CutLER checkpoint.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CutS3D is a class-agnostic instance segmentation model from Leon Sick, Dominik Engel, Sebastian Hartwig, Pedro Hermosilla, and Timo Ropinski. The lead affiliation is Ulm University; Engel is also listed at KAUST and Hermosilla at TU Vienna. The preprint is arXiv:2411.16319, first dated 2024-11-25, with v3 on 2025-07-24, and the project page lists ICCV 2025, pages 21265-21275. A public weight file, cuts3d_zeroshot.pth, was on Google Drive on the collection date 2026-10-15. Status is active.", "what_it_is_for": "The paper presents unsupervised 2D instance segmentation. LocalCut uses a monocular point cloud to separate instances that 2D semantic grouping leaves joined. Spatial Confidence is then used while training a Cascade Mask R-CNN on the resulting ImageNet pseudo-masks. The project page says the displayed model is refined with one round of self-training. The v3 paper's main zero-shot tables use three rounds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ulm/s2d/s2d": { "id": "ulm/s2d/s2d", "name": "S2D", "maker": "ulm", "family": "ulm/s2d", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "VideoMask2Former with ResNet-50, class-agnostic, trained on 2-frame snippets. The paper says the initialization matches VideoCutLER. CutS3D supplies the single-frame masks. CoTracker3 is an off-the-shelf point tracker and is not part of the released checkpoint. Keymask discovery uses visibility threshold gamma_thr=0.3 and Point-Mask Jaccard lambda=0.5. The public zero-shot model is trained on VIPSeg, MOSEv1, and horizontal SA-V videos. After Keymask Discovery filtering, that mix is 13,000 real videos, and the paper says one Sparse-To-Dense distillation round is enough for the zero-shot model. The teacher is kept for evaluation. In-domain YTVIS training is described as dual-stage distillation. Separate public files for the in-domain YTVIS and DAVIS checkpoints were not verified.", "modalities": { "input": [ "video" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "S2D is an unsupervised video instance segmentation model from Leon Sick, Lukas Hoyer, Dominik Engel, Pedro Hermosilla, and Timo Ropinski. Sick and Ropinski are at Ulm University. Hoyer is at Google, Engel at KAUST, and Hermosilla at TU Vienna. The preprint is arXiv:2512.14440v1, 16 December 2025. A public checkpoint, s2d_zeroshot.pth, was on Hugging Face on the collection date 2026-10-15. Status is active.", "what_it_is_for": "The paper trains video instance segmentation on real video instead of synthetic sequences cut from object-centric images. Keymask Discovery keeps a sparse set of temporally coherent masks from noisy single-frame predictions. Sparse-To-Dense distillation, with Temporal DropLoss, turns those masks into a dense label set. The released zero-shot checkpoint is the model trained on the filtered mix of VIPSeg, MOSEv1, and horizontal SA-V.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umass/revo/revo": { "id": "umass/revo/revo", "name": "ReVo", "maker": "umass", "family": "umass/revo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Post-decode neural loss recovery for volumetric RGB-D video. Separate Video Vision Transformer models, adapted from the VideoMAE ViViT design, reconstruct a corrupted P-frame from the closest 5 clean frames. Temporal tubelet size T is 2. Positional embeddings encode the distance from the clean references. RGB is trained with MSE plus total variation. Depth is fine-tuned with an L1 reconstruction term, an edge-gradient term, and an SSIM term. One pretraining run per modality on random pixel masks, then a codec-specific fine-tune. Not a fine-tune of a released VideoMAE checkpoint.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReVo is a University of Massachusetts Amherst volumetric videoconferencing system whose neural piece is a set of Video Vision Transformer frame-recovery models. The weights are public on Hugging Face under an MIT license and were still downloadable on 2026-10-15. Dolby Labs and Akamai are coauthors. There is no hosted API. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper describes ReVo as a one-to-one system that keeps RGB-D volumetric calls usable when packets are lost. Critical I-frames and P-frame headers are protected at the network layer. A post-decode neural module then fills in corrupted P-frames. Separate models handle color and depth, and each codec has its own fine-tune, so the same architecture covers H.264, H.265, and DCVC-RT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umbc/compress/compress-alexnet": { "id": "umbc/compress/compress-alexnet", "name": "CompRess (AlexNet)", "maker": "umbc", "family": "umbc/compress", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2020-10-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AlexNet student compressed from a cached SimCLR ResNet-50x4 teacher. The paper says the teacher for AlexNet is SimCLR ResNet-50x4 and that teacher embeddings are cached for this pair. Shared recipe: SGD, 130 epochs, batch size 256, learning rate 0.01, momentum 0.9, weight decay 1e-4, learning rate times 0.2 at epochs 90 and 120, memory bank 128,000, temperature 0.04. Linear evaluation reported on fc7. Unlabeled ImageNet.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CompRess (AlexNet) is the AlexNet self-supervised student from the University of Maryland, Baltimore County vision lab. The 28 October 2020 paper compresses a cached SimCLR ResNet-50x4 teacher into AlexNet and reports that this student beats a supervised AlexNet on ImageNet. The official README links one AlexNet file whose printed scores are not the abstract's headline scores. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses AlexNet as the small-capacity test of the compression method. The claim that drew attention is ImageNet linear accuracy above the supervised AlexNet baseline, using only unlabeled ImageNet for the student and its teacher. The release is a representation checkpoint, not an API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umbc/compress/compress-mobilenet": { "id": "umbc/compress/compress-mobilenet", "name": "CompRess (MobileNet)", "maker": "umbc", "family": "umbc/compress", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2020-10-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileNet student. The paper's Figure 1 caption names the small student MobileNet-V2 and says its teacher is MoCo ResNet-50. The GitHub results table names the row CompRess(Mobilenet) and the teacher MoCoV2 ResNet50. The training CLI uses student_arch mobilenet. Shared student recipe in the paper: SGD, 130 epochs, batch size 256, learning rate 0.01, momentum 0.9, weight decay 1e-4, learning rate times 0.2 at epochs 90 and 120, memory bank 128,000, key momentum 0.999, temperature 0.04 for the MoCo teacher. Unlabeled ImageNet.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CompRess (MobileNet) is the small self-supervised student from the University of Maryland, Baltimore County vision lab. The 28 October 2020 paper compresses a MoCo ResNet-50 teacher into a MobileNet-V2. The GitHub results table calls the same release CompRess(Mobilenet) and names the teacher MoCoV2 ResNet50. One Drive file is still linked. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper's edge-computing example is a downloadable self-supervised MobileNet that can be fine-tuned on a device. The published checkpoint is an ImageNet representation student, not a classifier API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umbc/compress/compress-resnet18": { "id": "umbc/compress/compress-resnet18", "name": "CompRess (ResNet-18)", "maker": "umbc", "family": "umbc/compress", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2020-10-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-18 student. Two released teachers, both ResNet-50: MoCo v2 and SwAV. The paper's Figure 1 caption says the ResNet-18 teacher is MoCo ResNet-50. Shared recipe: SGD, 130 epochs, batch size 256, learning rate 0.01, momentum 0.9, weight decay 1e-4, learning rate times 0.2 at epochs 90 and 120, memory bank 128,000, key momentum 0.999. Temperature is 0.04 for the MoCo teacher and 0.007 for the SwAV teacher. Unlabeled ImageNet. The paper says compressing ResNet-50 to ResNet-18 takes about 90 hours on two 2080 Ti GPUs.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CompRess (ResNet-18) is a pair of self-supervised ImageNet students from the University of Maryland, Baltimore County vision lab. Both are ResNet-18 networks trained to copy a frozen ResNet-50 teacher, one MoCo v2 and one SwAV. The paper is arXiv:2010.14713, submitted 28 October 2020. The official README still links both files. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab uses the ResNet-18 students to show that compression beats training a small self-supervised model from scratch. They are representation models for linear and nearest-neighbor evaluation, not a hosted classifier.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umbc/compress/compress-resnet50": { "id": "umbc/compress/compress-resnet50", "name": "CompRess (ResNet-50)", "maker": "umbc", "family": "umbc/compress", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2020-10-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-50 student trained to match nearest-anchor similarity of a frozen SimCLR ResNet-50x4 teacher. The GitHub results row marks the teacher features as cached. The paper trains students with SGD for 130 epochs, batch size 256, learning rate 0.01, momentum 0.9, weight decay 1e-4, learning rate multiplied by 0.2 at epochs 90 and 120, memory bank size 128,000, key-encoder momentum 0.999, and temperature 0.04 for the SimCLR teacher. ImageNet images are used without labels.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CompRess (ResNet-50) is a self-supervised ImageNet student from the University of Maryland, Baltimore County vision lab. Soroush Abbasi Koohpayegani, Ajinkya Tejankar, and Hamed Pirsiavash train it so a ResNet-50 copies the nearest-anchor geometry of a frozen SimCLR ResNet-50x4 teacher. The arXiv paper is 28 October 2020. The lab still links one pretrained file from the official GitHub README. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses compression to put a large self-supervised teacher's neighborhood structure into a smaller network that can run on a device. This row is the ResNet-50 student. The teacher is Google's SimCLR ResNet-50x4, with cached features. It is not a UMBC base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umbc/msf/msf-resnet50": { "id": "umbc/msf/msf-resnet50", "name": "MSF (ResNet-50)", "maker": "umbc", "family": "umbc/msf", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2021-05-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-50 backbone. A 2-layer projection MLP maps 2048 to 4096, then BatchNorm and ReLU, then 4096 to 512. The online network adds a prediction MLP of the same shape starting from 512. The target encoder is an exponential moving average of the online encoder. Training loss pulls the online embedding toward the nearest neighbors of the target embedding in a FIFO memory bank. Evaluation removes the projection and prediction layers and uses the backbone. The paper's main setting is 200 epochs, SGD learning rate 0.05, momentum 0.9, weight decay 1e-4, cosine schedule, batch size 256, target momentum 0.99, top-k 5, and a 1.024 million memory bank. The README train command uses top-k 10 and a 128,000 memory bank. One ResNet-50 forward is 4.14 GFLOPS in the paper. Unlabeled ImageNet.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MSF (ResNet-50) is a self-supervised ImageNet model from the UMBC Vision lab. Soroush Abbasi Koohpayegani, Ajinkya Tejankar, and Hamed Pirsiavash shift each embedding toward the mean of its nearest neighbors instead of contrasting against other images. With one neighbor the method matches their asymmetric BYOL. The arXiv paper was submitted on 15 May 2021 and revised on 10 September 2021. The README links one ResNet-50 file. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper learns a frozen visual representation from unlabeled ImageNet, then tests it with a linear classifier, nearest neighbors, low-label ImageNet, transfer classification, and PASCAL VOC detection. The public artifact is the ResNet-50 checkpoint, not an API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/crammed-bert/crammed-bert": { "id": "umd/crammed-bert/crammed-bert", "name": "Crammed BERT", "maker": "umd", "family": "umd/crammed-bert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenRAIL", "release_date": "2023-06-13", "context_length": 128, "max_output_tokens": null, "parameters": "144755713", "architecture": "Masked language model, model_type crammedBERT. Config embedding dim 768, 16 transformer layers, 12 attention heads, intermediate size 3072, vocab 32768, max sequence length 128, GELUglu, pre-LayerNorm, scaled sinusoidal positions, tied weights. Safetensors total 144755713. The card says this checkpoint was trained for 24 hours on one A6000. Inference through stock Transformers needs the cramming package because the architecture class is custom.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Crammed BERT is an English masked language model trained from scratch by Jonas Geiping and Tom Goldstein at the University of Maryland. The released checkpoint is a 24-hour single-GPU pretraining run. Weights are active on Hugging Face. Collected on 2026-10-15.", "what_it_is_for": "The paper asks how close a BERT-style model can get to ordinary BERT when the entire pretraining budget is one consumer GPU for one day. The card says the checkpoint is for downstream fine-tuning research, not for deployment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/csd/csd-vit-l": { "id": "umd/csd/csd-vit-l", "name": "CSD ViT-L", "maker": "umd", "family": "umd/csd", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2024-08-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Style-descriptor checkpoint. The model card says the file is a ViT-Large. The Hugging Face API record did not report a safetensors parameter total, so the parameter count is left unset. The paper's job is to measure style similarity of diffusion-model images, not to generate images.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CSD ViT-L is an open-weight image style encoder from researchers at the University of Maryland, released by Tom Goldstein's lab. It is active on Hugging Face as a feature extractor. Collected on 2026-10-15.", "what_it_is_for": "The model card says the checkpoint extracts style descriptors so the style of a generated image can be compared with images in a text-to-image training set. It is not a generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/dynaguard/dynaguard-1-7b": { "id": "umd/dynaguard/dynaguard-1-7b", "name": "DynaGuard-1.7B", "maker": "umd", "family": "umd/dynaguard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-22", "context_length": 40960, "max_output_tokens": null, "parameters": "2031739904", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-1.7B. Hidden size 2048, 28 layers, 16 attention heads, 8 key-value heads, head dim 128, intermediate size 6144, vocab 151936. Safetensors parameter total 2031739904. The marketed size in the name is the Qwen3 size class, not a rounded tensor count. The card says supervised fine-tuning for one epoch, then GRPO, on DynaBench plus WildGuard, BeaverTails, ToxicChat, and Aegis 2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DynaGuard-1.7B is an open-weight guardian model from the University of Maryland and Capital One. It judges text against a user-written policy rather than a fixed harm taxonomy. The weights are active on Hugging Face. Collected on 2026-10-15.", "what_it_is_for": "The model card says DynaGuard moderates chatbot outputs against application-specific rules, such as blocking refunds or unauthorized medical advice. It can answer with a direct PASS or FAIL, or with a chain-of-thought explanation before the label.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/dynaguard/dynaguard-4b": { "id": "umd/dynaguard/dynaguard-4b", "name": "DynaGuard-4B", "maker": "umd", "family": "umd/dynaguard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-22", "context_length": 40960, "max_output_tokens": null, "parameters": "4411424256", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-4B. Hidden size 2560, 36 layers, 32 attention heads, 8 key-value heads, head dim 128, intermediate size 9728, vocab 151936. Safetensors parameter total 4411424256. The marketed size in the name is the Qwen3 size class, not a rounded tensor count. The card says supervised fine-tuning for one epoch, then GRPO, on DynaBench plus WildGuard, BeaverTails, ToxicChat, and Aegis 2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DynaGuard-4B is an open-weight guardian model from the University of Maryland and Capital One. It judges text against a user-written policy rather than a fixed harm taxonomy. The weights are active on Hugging Face. Collected on 2026-10-15.", "what_it_is_for": "The model card says DynaGuard moderates chatbot outputs against application-specific rules, such as blocking refunds or unauthorized medical advice. It can answer with a direct PASS or FAIL, or with a chain-of-thought explanation before the label.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/dynaguard/dynaguard-8b": { "id": "umd/dynaguard/dynaguard-8b", "name": "DynaGuard-8B", "maker": "umd", "family": "umd/dynaguard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-07-02", "context_length": 40960, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Qwen3ForCausalLM fine-tune of Qwen/Qwen3-8B. Hidden size 4096, 36 layers, 32 attention heads, 8 key-value heads, head dim 128, intermediate size 12288, vocab 151936. Safetensors parameter total 8190735360. The marketed size in the name is the Qwen3 size class, not a rounded tensor count. The card says supervised fine-tuning for one epoch, then GRPO, on DynaBench plus WildGuard, BeaverTails, ToxicChat, and Aegis 2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DynaGuard-8B is an open-weight guardian model from the University of Maryland and Capital One. It judges text against a user-written policy rather than a fixed harm taxonomy. The weights are active on Hugging Face. Collected on 2026-10-15.", "what_it_is_for": "The model card says DynaGuard moderates chatbot outputs against application-specific rules, such as blocking refunds or unauthorized medical advice. It can answer with a direct PASS or FAIL, or with a chain-of-thought explanation before the label.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-1024x28": { "id": "umd/gemstone/gemstone-1024x28", "name": "Gemstone-1024x28", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-18", "context_length": 2048, "max_output_tokens": null, "parameters": "543482880", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 1024, 28 layers, 8 attention heads, 4 key-value heads, head dim 128, intermediate size 4096, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 543482880. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-1024x28 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-1280x15": { "id": "umd/gemstone/gemstone-1280x15", "name": "Gemstone-1280x15", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-24", "context_length": 2048, "max_output_tokens": null, "parameters": "497457920", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 1280, 15 layers, 10 attention heads, 5 key-value heads, head dim 128, intermediate size 5120, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 497457920. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-1280x15 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-1280x36": { "id": "umd/gemstone/gemstone-1280x36", "name": "Gemstone-1280x36", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-25", "context_length": 2048, "max_output_tokens": null, "parameters": "1013607680", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 1280, 36 layers, 10 attention heads, 5 key-value heads, head dim 128, intermediate size 5120, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 1013607680. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-1280x36 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-1536x50": { "id": "umd/gemstone/gemstone-1536x50", "name": "Gemstone-1536x50", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-24", "context_length": 2048, "max_output_tokens": null, "parameters": "1924161024", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 1536, 50 layers, 12 attention heads, 6 key-value heads, head dim 128, intermediate size 6144, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 1924161024. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-1536x50 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-1792x18": { "id": "umd/gemstone/gemstone-1792x18", "name": "Gemstone-1792x18", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-25", "context_length": 2048, "max_output_tokens": null, "parameters": "1047397120", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 1792, 18 layers, 14 attention heads, 7 key-value heads, head dim 128, intermediate size 7168, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 1047397120. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-1792x18 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-1792x7": { "id": "umd/gemstone/gemstone-1792x7", "name": "Gemstone-1792x7", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-25", "context_length": 2048, "max_output_tokens": null, "parameters": "517499136", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 1792, 7 layers, 14 attention heads, 7 key-value heads, head dim 128, intermediate size 7168, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 517499136. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-1792x7 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-2048x27": { "id": "umd/gemstone/gemstone-2048x27", "name": "Gemstone-2048x27", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-17", "context_length": 2048, "max_output_tokens": null, "parameters": "1904850944", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 2048, 27 layers, 16 attention heads, 8 key-value heads, head dim 128, intermediate size 8192, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 1904850944. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-2048x27 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-2560x8": { "id": "umd/gemstone/gemstone-2560x8", "name": "Gemstone-2560x8", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-05", "context_length": 2048, "max_output_tokens": null, "parameters": "1044032000", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 2560, 8 layers, 20 attention heads, 10 key-value heads, head dim 128, intermediate size 10240, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 1044032000. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-2560x8 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-256x23": { "id": "umd/gemstone/gemstone-256x23", "name": "Gemstone-256x23", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-17", "context_length": 2048, "max_output_tokens": null, "parameters": "48377600", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 256, 23 layers, 2 attention heads, 1 key-value heads, head dim 128, intermediate size 1024, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 48377600. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-256x23 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-256x27": { "id": "umd/gemstone/gemstone-256x27", "name": "Gemstone-256x27", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-25", "context_length": 2048, "max_output_tokens": null, "parameters": "52311808", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 256, 27 layers, 2 attention heads, 1 key-value heads, head dim 128, intermediate size 1024, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 52311808. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-256x27 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-256x71": { "id": "umd/gemstone/gemstone-256x71", "name": "Gemstone-256x71", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-24", "context_length": 2048, "max_output_tokens": null, "parameters": "95588096", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 256, 71 layers, 2 attention heads, 1 key-value heads, head dim 128, intermediate size 1024, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 95588096. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-256x71 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-256x80": { "id": "umd/gemstone/gemstone-256x80", "name": "Gemstone-256x80", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-24", "context_length": 2048, "max_output_tokens": null, "parameters": "104440064", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 256, 80 layers, 2 attention heads, 1 key-value heads, head dim 128, intermediate size 1024, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 104440064. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-256x80 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-3072x12": { "id": "umd/gemstone/gemstone-3072x12", "name": "Gemstone-3072x12", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-27", "context_length": 2048, "max_output_tokens": null, "parameters": "2007837696", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 3072, 12 layers, 24 attention heads, 12 key-value heads, head dim 128, intermediate size 12288, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 2007837696. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-3072x12 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-384x13": { "id": "umd/gemstone/gemstone-384x13", "name": "Gemstone-384x13", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-25", "context_length": 2048, "max_output_tokens": null, "parameters": "66758784", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 384, 13 layers, 3 attention heads, 1 key-value heads, head dim 128, intermediate size 1536, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 66758784. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-384x13 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-384x36": { "id": "umd/gemstone/gemstone-384x36", "name": "Gemstone-384x36", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-24", "context_length": 2048, "max_output_tokens": null, "parameters": "116518272", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 384, 36 layers, 3 attention heads, 1 key-value heads, head dim 128, intermediate size 1536, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 116518272. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-384x36 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-512x11": { "id": "umd/gemstone/gemstone-512x11", "name": "Gemstone-512x11", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-24", "context_length": 2048, "max_output_tokens": null, "parameters": "94776832", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 512, 11 layers, 4 attention heads, 2 key-value heads, head dim 128, intermediate size 2048, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 94776832. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-512x11 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-512x12": { "id": "umd/gemstone/gemstone-512x12", "name": "Gemstone-512x12", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-24", "context_length": 2048, "max_output_tokens": null, "parameters": "98710016", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 512, 12 layers, 4 attention heads, 2 key-value heads, head dim 128, intermediate size 2048, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 98710016. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-512x12 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-512x13": { "id": "umd/gemstone/gemstone-512x13", "name": "Gemstone-512x13", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-24", "context_length": 2048, "max_output_tokens": null, "parameters": "102643200", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 512, 13 layers, 4 attention heads, 2 key-value heads, head dim 128, intermediate size 2048, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 102643200. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-512x13 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-512x14": { "id": "umd/gemstone/gemstone-512x14", "name": "Gemstone-512x14", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-27", "context_length": 2048, "max_output_tokens": null, "parameters": "106576384", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 512, 14 layers, 4 attention heads, 2 key-value heads, head dim 128, intermediate size 2048, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 106576384. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-512x14 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-512x16": { "id": "umd/gemstone/gemstone-512x16", "name": "Gemstone-512x16", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-24", "context_length": 2048, "max_output_tokens": null, "parameters": "114442752", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 512, 16 layers, 4 attention heads, 2 key-value heads, head dim 128, intermediate size 2048, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 114442752. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-512x16 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-768x3": { "id": "umd/gemstone/gemstone-768x3", "name": "Gemstone-768x3", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-24", "context_length": 2048, "max_output_tokens": null, "parameters": "103814400", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 768, 3 layers, 6 attention heads, 3 key-value heads, head dim 128, intermediate size 3072, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 103814400. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-768x3 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/gemstone/gemstone-768x45": { "id": "umd/gemstone/gemstone-768x45", "name": "Gemstone-768x45", "maker": "umd", "family": "umd/gemstone", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-24", "context_length": 2048, "max_output_tokens": null, "parameters": "475468032", "architecture": "GemmaForCausalLM, Gemma-2B-style. Hidden size 768, 45 layers, 6 attention heads, 3 key-value heads, head dim 128, intermediate size 3072, vocab 50304, RoPE theta 10000, GELU-tanh, untied embeddings. Safetensors parameter total 475468032. Trained from scratch for 350 billion Dolma tokens with context 2048 and global batch 2048 sequences on AMD MI250X GPUs at Frontier using litgpt and AxoNN. Not an instruction model. The lab also uploads a revision about every 2 billion tokens (477 steps).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gemstone-768x45 is an open-weight base language model from Tom Goldstein's lab at the University of Maryland, College Park, released in the Gemstone suite for scaling-law research. It is active as a Hugging Face weight release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes the Gemstones as transformers with different widths and depths, trained so researchers can study how shape, learning rate, and cooldown change scaling-law prescriptions. The model card says this checkpoint is a base model trained on Dolma, not an instruction model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/huginn/huginn-0125": { "id": "umd/huginn/huginn-0125", "name": "Huginn-0125", "maker": "umd", "family": "umd/huginn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-07", "context_length": 4096, "max_output_tokens": null, "parameters": "3.5B", "architecture": "RavenForCausalLM recurrent-depth decoder. Prelude 2 layers, recurrent block 4 layers, coda 2 layers, hidden size 5280, 55 heads, 55 key-value heads, head dim 96, intermediate size 17920, vocab 65536, block size 4096, RoPE base 50000, RMSNorm, SiLU gated MLP, tied embeddings. Mean recurrence at training is 32. The card says about 1.5B parameters in prelude+coda, 0.5B in the embedding, and 1.5B in the recurrent block. Safetensors total on Hugging Face is 3911400096 because the card says the upload stored two copies of the tied embedding. Trained for 800B tokens and 47000 steps on public data, early December 2024, on segments of 4096 AMD MI250X GPUs at Frontier. Not separately post-trained; instruction data was mixed into pretraining. The model was not cooled down from its peak learning rate.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Huginn-0125 is a 3.5B-parameter latent recurrent-depth language model released by Tom Goldstein's lab at the University of Maryland, with coauthors at ELLIS Institute T\u00fcbingen, MPI-IS, and Lawrence Livermore. It is an active open-weight proof of concept. Collected on 2026-10-15.", "what_it_is_for": "The tech report says the model spends extra test-time compute by iterating a recurrent block in latent space, instead of writing a long chain of thought. The card says it is surprisingly capable at reasoning and code for its size and 800B-token budget, and that it can follow its chat template because instruction data was included in pretraining. It was not a separate instruction fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/sphere-encoder/sphere-l-af": { "id": "umd/sphere-encoder/sphere-l-af", "name": "Sphere-L Animal-Faces", "maker": "umd", "family": "umd/sphere-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-02-16", "context_length": null, "max_output_tokens": null, "parameters": "642M", "architecture": "Sphere Encoder autoencoder. An encoder maps images onto a sphere and a decoder maps a random spherical point back to an image. Model-card parameter count 642M. Unconditional Animal-Faces at 256x256. Encoder and decoder are both ViT-Large on the model card. Few-step generation uses fewer than five forward passes. The weights on kaiyuyue/sphere-encoder-models are the author's reproduction. The facebookresearch model card said official weights were still in Meta legal review.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sphere-L Animal-Faces is a few-step image generator from the Sphere Encoder paper by Kaiyu Yue (Meta and University of Maryland), Menglin Jia (Meta), Ji Hou (Meta), and Tom Goldstein (University of Maryland). The filed weights are the author's public reproduction. Collected on 2026-10-15.", "what_it_is_for": "The paper says the model generates an image by decoding a random point on a spherical latent space, in one pass or a few refinement steps, without a diffusion sampler. This checkpoint is the Animal-Faces model named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/sphere-encoder/sphere-l-imagenet": { "id": "umd/sphere-encoder/sphere-l-imagenet", "name": "Sphere-L ImageNet", "maker": "umd", "family": "umd/sphere-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-02-16", "context_length": null, "max_output_tokens": null, "parameters": "950M", "architecture": "Sphere Encoder autoencoder. An encoder maps images onto a sphere and a decoder maps a random spherical point back to an image. Model-card parameter count 950M. Class-conditional ImageNet 256x256. Encoder and decoder are both ViT-Large on the model card. Few-step generation uses fewer than five forward passes. The weights on kaiyuyue/sphere-encoder-models are the author's reproduction. The facebookresearch model card said official weights were still in Meta legal review.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sphere-L ImageNet is a few-step image generator from the Sphere Encoder paper by Kaiyu Yue (Meta and University of Maryland), Menglin Jia (Meta), Ji Hou (Meta), and Tom Goldstein (University of Maryland). The filed weights are the author's public reproduction. Collected on 2026-10-15.", "what_it_is_for": "The paper says the model generates an image by decoding a random point on a spherical latent space, in one pass or a few refinement steps, without a diffusion sampler. This checkpoint is the ImageNet model named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/sphere-encoder/sphere-l-of": { "id": "umd/sphere-encoder/sphere-l-of", "name": "Sphere-L Oxford-Flowers", "maker": "umd", "family": "umd/sphere-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-02-16", "context_length": null, "max_output_tokens": null, "parameters": "948M", "architecture": "Sphere Encoder autoencoder. An encoder maps images onto a sphere and a decoder maps a random spherical point back to an image. Model-card parameter count 948M. Oxford-Flowers at 256x256. Encoder and decoder are both ViT-Large on the model card. Few-step generation uses fewer than five forward passes. The weights on kaiyuyue/sphere-encoder-models are the author's reproduction. The facebookresearch model card said official weights were still in Meta legal review.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sphere-L Oxford-Flowers is a few-step image generator from the Sphere Encoder paper by Kaiyu Yue (Meta and University of Maryland), Menglin Jia (Meta), Ji Hou (Meta), and Tom Goldstein (University of Maryland). The filed weights are the author's public reproduction. Collected on 2026-10-15.", "what_it_is_for": "The paper says the model generates an image by decoding a random point on a spherical latent space, in one pass or a few refinement steps, without a diffusion sampler. This checkpoint is the Oxford-Flowers model named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/sphere-encoder/sphere-xl-imagenet": { "id": "umd/sphere-encoder/sphere-xl-imagenet", "name": "Sphere-XL ImageNet", "maker": "umd", "family": "umd/sphere-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-02-16", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Sphere Encoder autoencoder. An encoder maps images onto a sphere and a decoder maps a random spherical point back to an image. Model-card parameter count 1.3B. Class-conditional ImageNet 256x256. Encoder and decoder are both ViT-XL on the model card. Few-step generation uses fewer than five forward passes. The weights on kaiyuyue/sphere-encoder-models are the author's reproduction. The facebookresearch model card said official weights were still in Meta legal review.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sphere-XL ImageNet is a few-step image generator from the Sphere Encoder paper by Kaiyu Yue (Meta and University of Maryland), Menglin Jia (Meta), Ji Hou (Meta), and Tom Goldstein (University of Maryland). The filed weights are the author's public reproduction. Collected on 2026-10-15.", "what_it_is_for": "The paper says the model generates an image by decoding a random point on a spherical latent space, in one pass or a few refinement steps, without a diffusion sampler. This checkpoint is the ImageNet model named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/sphere-encoder/sphere2-b-flowers-256": { "id": "umd/sphere-encoder/sphere2-b-flowers-256", "name": "Sphere2-B Flowers 256", "maker": "umd", "family": "umd/sphere-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-10-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sphere Encoder 2 autoencoder. The paper says a frozen DINOv3-small+ encoder (ViT-S+/16) maps an image to a 16x16 by 384 latent, and a decoder maps a random point on that sphere back to pixels. Decoder size class on the card is base. This checkpoint was not opened individually. The model card lists it as Sphere2-B on Oxford Flowers at 256px. The paper's shared design is a frozen DINOv3-small+ (ViT-S+/16) encoder and a ViT decoder. Class count and CFG for this file were not read from its cfg.json. Parameter count was not stated on the model card.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sphere2-B Flowers 256 is an open-weight few-step image generator from Sphere Encoder 2, released by Tom Goldstein's lab at the University of Maryland with coauthors at Lawrence Livermore and Cornell. Weights are active in the sphere2 Hugging Face repo. Collected on 2026-10-15.", "what_it_is_for": "The paper says Sphere Encoder 2 still generates by decoding a random point on a latent sphere, but it fixes an equator-versus-pole sampling gap and the blur from pixel reconstruction so one-step images keep more detail. This checkpoint is the Oxford Flowers 256px Sphere2-B model named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/sphere-encoder/sphere2-b-flowers-512": { "id": "umd/sphere-encoder/sphere2-b-flowers-512", "name": "Sphere2-B Flowers 512", "maker": "umd", "family": "umd/sphere-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-10-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sphere Encoder 2 autoencoder. The paper says a frozen DINOv3-small+ encoder (ViT-S+/16) maps an image to a 16x16 by 384 latent, and a decoder maps a random point on that sphere back to pixels. Decoder size class on the card is base. This checkpoint was not opened individually. The model card lists it as Sphere2-B on Oxford Flowers at 512px. The paper's shared design is a frozen DINOv3-small+ (ViT-S+/16) encoder and a ViT decoder. Class count and CFG for this file were not read from its cfg.json. Parameter count was not stated on the model card.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sphere2-B Flowers 512 is an open-weight few-step image generator from Sphere Encoder 2, released by Tom Goldstein's lab at the University of Maryland with coauthors at Lawrence Livermore and Cornell. Weights are active in the sphere2 Hugging Face repo. Collected on 2026-10-15.", "what_it_is_for": "The paper says Sphere Encoder 2 still generates by decoding a random point on a latent sphere, but it fixes an equator-versus-pole sampling gap and the blur from pixel reconstruction so one-step images keep more detail. This checkpoint is the Oxford Flowers 512px Sphere2-B model named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/sphere-encoder/sphere2-b-imagenet-256": { "id": "umd/sphere-encoder/sphere2-b-imagenet-256", "name": "Sphere2-B ImageNet 256", "maker": "umd", "family": "umd/sphere-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-10-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sphere Encoder 2 autoencoder. The paper says a frozen DINOv3-small+ encoder (ViT-S+/16) maps an image to a 16x16 by 384 latent, and a decoder maps a random point on that sphere back to pixels. Decoder size class on the card is base. The ImageNet 256 base cfg.json was opened: vit_dec_model_size base, vit_enc_model_size base, frozen encoder checkpoint dinov3-vits16plus-pretrain-lvd1689m, latent 16x16, token channels 384, patch size 16, 1000 classes, conditional, forward_steps 2, cfg 1.0, 1000 epochs. Parameter count was not stated on the model card.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sphere2-B ImageNet 256 is an open-weight few-step image generator from Sphere Encoder 2, released by Tom Goldstein's lab at the University of Maryland with coauthors at Lawrence Livermore and Cornell. Weights are active in the sphere2 Hugging Face repo. Collected on 2026-10-15.", "what_it_is_for": "The paper says Sphere Encoder 2 still generates by decoding a random point on a latent sphere, but it fixes an equator-versus-pole sampling gap and the blur from pixel reconstruction so one-step images keep more detail. This checkpoint is the ImageNet 256px Sphere2-B model named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/sphere-encoder/sphere2-b-imagenet-512": { "id": "umd/sphere-encoder/sphere2-b-imagenet-512", "name": "Sphere2-B ImageNet 512", "maker": "umd", "family": "umd/sphere-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-10-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sphere Encoder 2 autoencoder. The paper says a frozen DINOv3-small+ encoder (ViT-S+/16) maps an image to a 16x16 by 384 latent, and a decoder maps a random point on that sphere back to pixels. Decoder size class on the card is base. This checkpoint was not opened individually. The model card lists it as Sphere2-B on ImageNet at 512px. The paper's shared design is a frozen DINOv3-small+ (ViT-S+/16) encoder and a ViT decoder. Class count and CFG for this file were not read from its cfg.json. Parameter count was not stated on the model card.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sphere2-B ImageNet 512 is an open-weight few-step image generator from Sphere Encoder 2, released by Tom Goldstein's lab at the University of Maryland with coauthors at Lawrence Livermore and Cornell. Weights are active in the sphere2 Hugging Face repo. Collected on 2026-10-15.", "what_it_is_for": "The paper says Sphere Encoder 2 still generates by decoding a random point on a latent sphere, but it fixes an equator-versus-pole sampling gap and the blur from pixel reconstruction so one-step images keep more detail. This checkpoint is the ImageNet 512px Sphere2-B model named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/sphere-encoder/sphere2-l-flowers-256": { "id": "umd/sphere-encoder/sphere2-l-flowers-256", "name": "Sphere2-L Flowers 256", "maker": "umd", "family": "umd/sphere-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-10-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sphere Encoder 2 autoencoder. The paper says a frozen DINOv3-small+ encoder (ViT-S+/16) maps an image to a 16x16 by 384 latent, and a decoder maps a random point on that sphere back to pixels. Decoder size class on the card is large. This checkpoint was not opened individually. The model card lists it as Sphere2-L on Oxford Flowers at 256px. The paper's shared design is a frozen DINOv3-small+ (ViT-S+/16) encoder and a ViT decoder. Class count and CFG for this file were not read from its cfg.json. Parameter count was not stated on the model card.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sphere2-L Flowers 256 is an open-weight few-step image generator from Sphere Encoder 2, released by Tom Goldstein's lab at the University of Maryland with coauthors at Lawrence Livermore and Cornell. Weights are active in the sphere2 Hugging Face repo. Collected on 2026-10-15.", "what_it_is_for": "The paper says Sphere Encoder 2 still generates by decoding a random point on a latent sphere, but it fixes an equator-versus-pole sampling gap and the blur from pixel reconstruction so one-step images keep more detail. This checkpoint is the Oxford Flowers 256px Sphere2-L model named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/sphere-encoder/sphere2-l-flowers-512": { "id": "umd/sphere-encoder/sphere2-l-flowers-512", "name": "Sphere2-L Flowers 512", "maker": "umd", "family": "umd/sphere-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-10-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sphere Encoder 2 autoencoder. The paper says a frozen DINOv3-small+ encoder (ViT-S+/16) maps an image to a 16x16 by 384 latent, and a decoder maps a random point on that sphere back to pixels. Decoder size class on the card is large. This checkpoint was not opened individually. The model card lists it as Sphere2-L on Oxford Flowers at 512px. The paper's shared design is a frozen DINOv3-small+ (ViT-S+/16) encoder and a ViT decoder. Class count and CFG for this file were not read from its cfg.json. Parameter count was not stated on the model card.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sphere2-L Flowers 512 is an open-weight few-step image generator from Sphere Encoder 2, released by Tom Goldstein's lab at the University of Maryland with coauthors at Lawrence Livermore and Cornell. Weights are active in the sphere2 Hugging Face repo. Collected on 2026-10-15.", "what_it_is_for": "The paper says Sphere Encoder 2 still generates by decoding a random point on a latent sphere, but it fixes an equator-versus-pole sampling gap and the blur from pixel reconstruction so one-step images keep more detail. This checkpoint is the Oxford Flowers 512px Sphere2-L model named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/sphere-encoder/sphere2-l-imagenet-256": { "id": "umd/sphere-encoder/sphere2-l-imagenet-256", "name": "Sphere2-L ImageNet 256", "maker": "umd", "family": "umd/sphere-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-10-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sphere Encoder 2 autoencoder. The paper says a frozen DINOv3-small+ encoder (ViT-S+/16) maps an image to a 16x16 by 384 latent, and a decoder maps a random point on that sphere back to pixels. Decoder size class on the card is large. The ImageNet 256 large cfg.json was opened: vit_dec_model_size large, vit_enc_model_size base, frozen encoder checkpoint dinov3-vits16plus-pretrain-lvd1689m, latent 16x16, token channels 384, patch size 16, 1000 classes, conditional, forward_steps 2, cfg 0.0, 1000 epochs. Parameter count was not stated on the model card.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sphere2-L ImageNet 256 is an open-weight few-step image generator from Sphere Encoder 2, released by Tom Goldstein's lab at the University of Maryland with coauthors at Lawrence Livermore and Cornell. Weights are active in the sphere2 Hugging Face repo. Collected on 2026-10-15.", "what_it_is_for": "The paper says Sphere Encoder 2 still generates by decoding a random point on a latent sphere, but it fixes an equator-versus-pole sampling gap and the blur from pixel reconstruction so one-step images keep more detail. This checkpoint is the ImageNet 256px Sphere2-L model named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umd/sphere-encoder/sphere2-l-imagenet-512": { "id": "umd/sphere-encoder/sphere2-l-imagenet-512", "name": "Sphere2-L ImageNet 512", "maker": "umd", "family": "umd/sphere-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-10-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Sphere Encoder 2 autoencoder. The paper says a frozen DINOv3-small+ encoder (ViT-S+/16) maps an image to a 16x16 by 384 latent, and a decoder maps a random point on that sphere back to pixels. Decoder size class on the card is large. This checkpoint was not opened individually. The model card lists it as Sphere2-L on ImageNet at 512px. The paper's shared design is a frozen DINOv3-small+ (ViT-S+/16) encoder and a ViT decoder. Class count and CFG for this file were not read from its cfg.json. Parameter count was not stated on the model card.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sphere2-L ImageNet 512 is an open-weight few-step image generator from Sphere Encoder 2, released by Tom Goldstein's lab at the University of Maryland with coauthors at Lawrence Livermore and Cornell. Weights are active in the sphere2 Hugging Face repo. Collected on 2026-10-15.", "what_it_is_for": "The paper says Sphere Encoder 2 still generates by decoding a random point on a latent sphere, but it fixes an equator-versus-pole sampling gap and the blur from pixel reconstruction so one-step images keep more detail. This checkpoint is the ImageNet 512px Sphere2-L model named on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umich/deepglioma/deepglioma": { "id": "umich/deepglioma/deepglioma", "name": "DeepGlioma", "maker": "umich", "family": "umich/deepglioma", "status": "active", "confidence": "high", "weights": "closed", "license": "MIT (code). Weight license is not stated because no checkpoint URL was found.", "release_date": "2023-03-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SRH visual encoder initialized from an ImageNet-pretrained ResNet-50 (torchvision), trained with weakly supervised multi-label contrastive learning, plus a genetic embedding trained on public glioma genomics (GloVe or Word2Vec in the repo) and a transformer, adapted from C-Tran, for multi-label mutation prediction. Training split described as University of Michigan only (n = 373) and external test n = 153.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepGlioma is a named diagnostic model from the University of Michigan MLiNS lab that predicts adult-type diffuse glioma molecular labels from stimulated Raman histology in the operating room. Nature Medicine published it on 23 March 2023. Code and a demo are public. A downloadable checkpoint was not found. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes DeepGlioma as a screening system that returns WHO molecular features of a diffuse glioma in under about 90 seconds of imaging, instead of waiting on a send-out genetic test. The README's path is SRH images in and tumor genetics out, about two minutes end to end.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umich/fastglioma/fastglioma": { "id": "umich/fastglioma/fastglioma", "name": "FastGlioma", "maker": "umich", "family": "umich/fastglioma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA 4.0 (weights and OpenSRH); MIT (code)", "release_date": "2024-11-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Patch encoder is a ResNet-34 (21.4M parameters) trained with HiDisc. Whole-slide encoder is a transformer (6.6M parameters) trained with VICReg, then fine-tuned with ordinal metric learning to a normalized infiltration score. Two released checkpoints: full SRH (3 channels) and fast SRH (1 channel, every 5th row).", "modalities": { "input": [ "image" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FastGlioma is an open-weight visual model from the University of Michigan MLiNS lab for scoring glioma infiltration on label-free stimulated Raman histology during surgery. Nature published it online on 13 November 2024. Two checkpoints are on Hugging Face under a non-commercial license. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and README present FastGlioma as a bedside system: image fresh surgical tissue and return how much of the specimen is infiltrated by diffuse glioma, in under about 10 seconds on the fast path. The model card limits that job to adult-type diffuse gliomas.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umich/neurovfm/neurovfm": { "id": "umich/neurovfm/neurovfm", "name": "NeuroVFM", "maker": "umich", "family": "umich/neurovfm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA 4.0 (weights); MIT (code)", "release_date": "2025-11-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Released encoder is a 3D Vision Transformer, ViT-Base with 4x16x16 patches, pretrained with Vol-JEPA (student/teacher latent prediction, FlashAttention-2) on UM-NeuroImages. Diagnostic releases are frozen-encoder attentive probes: 74 MRI labels and 82 CT labels. The findings model freezes that encoder, resamples each volume to 64 Perceiver latents, and instruction-tunes Qwen3-14B (LLaVA-1.5 style). ViT-Small and ViT-Medium appear only as scaling runs in the paper.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NeuroVFM is an open-weight neuroimaging foundation model from the University of Michigan Machine Learning in Neurosurgery (MLiNS) lab. The preprint is dated 23 November 2025 and the Nature Medicine article is dated 10 July 2026. Weights are on Hugging Face under a non-commercial license, with an encoder, MRI and CT diagnostic heads, and a findings model that keeps the encoder frozen and tunes Qwen3-14B. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab describes NeuroVFM as a generalist visual backbone trained by self-supervision on routine clinical MRI and CT, then used for radiologic diagnosis and draft findings. The encoder card says it is a frozen feature extractor for research, not a diagnostic device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umich/prima/prima": { "id": "umich/prima/prima", "name": "Prima", "maker": "umich", "family": "umich/prima", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (GitHub repo). arXiv paper text is CC BY-NC-SA 4.0. The README does not put a separate license on the Drive checkpoints.", "release_date": "2025-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hierarchical MRI vision-language model. A VQ-VAE encodes 32x32x4 subvolumes (codebook size 8192; each token is a 256-d vector). A sequence ViT reads volume tokens plus the sequence description. A study ViT pools sequence registers into one study vector and is trained with a CLIP objective against a summarized radiology report. Downstream diagnosis freezes those modules and trains an MLP. The context-aware head also concatenates an OpenAI text-embedding-3-small vector of the clinical history. That embedding model is not part of the Prima release.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Prima is a vision-language model for clinical brain MRI from the University of Michigan MLiNS lab (Yiwei Lyu, Todd Hollon, Honglak Lee, and coauthors). The arXiv preprint is 23 September 2025 (v2 on 16 December 2025). Nature Biomedical Engineering published the paper on 6 February 2026. Weights are Google Drive checkpoints linked from the lab GitHub repo. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents Prima as a foundation model that reads an entire clinical MRI study, not a hand-picked sequence, and supports differential diagnosis, worklist priority, and referral suggestions. The GitHub pipeline writes a JSON prediction file from a DICOM study folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umich/virtex/virtex": { "id": "umich/virtex/virtex", "name": "VirTex", "maker": "umich", "family": "umich/virtex", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2020-06-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Canonical release is bicaptioning_R_50_L1_H2048: a ResNet-50 visual backbone trained from scratch with a textual head (one transformer layer, hidden size 2048) that predicts COCO captions in both directions. torch.hub load of resnet50 returns only the visual backbone; avgpool and fc are Identity, and a 224 input yields a 1x2048x7x7 feature map. The model zoo also ships task, width, depth, and backbone ablations.", "modalities": { "input": [ "image", "text" ], "output": [ "embeddings", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VirTex is an open-weight vision pretraining model from Karan Desai and Justin Johnson at the University of Michigan. The preprint is 11 June 2020 and the paper is CVPR 2021. The canonical checkpoint is still loaded from the author model zoo and from torch.hub. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper trains a CNN from scratch on COCO Captions so the visual backbone can transfer to classification, detection, and instance segmentation. The authors say those features match or exceed ImageNet-pretrained features while using up to ten times fewer images. The docs site's one-line API is the ResNet-50 backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/finch/finch-2b": { "id": "umn/finch/finch-2b", "name": "Finch-2B", "maker": "umn", "family": "umn/finch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 262144, "max_output_tokens": null, "parameters": "2213241664", "architecture": "Qwen3_5ForConditionalGeneration fine-tuned from Qwen/Qwen3.5-2B. Text stack: 24 layers, hidden size 2048, 8 attention heads, 2 key-value heads, vocabulary 248320. Vision tower: hidden size 1024, 24 layers. The card rounds the size to 2B; the stored count is the Hugging Face safetensors total.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finch-2B is the smallest Evolution Fine-Tuning model from Minnesota NLP at the University of Minnesota. It is an open-weight fine-tune of Qwen3.5-2B trained to propose improved programs inside evolutionary search. The weights were public on 2026-10-15. A single release day was not stated.", "what_it_is_for": "The family card describes Finch as a mutation operator. Given a task instruction, the current program, evaluator feedback, and evolutionary history, it proposes the next candidate. The recommended driver is OpenEvolve.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/finch/finch-4b": { "id": "umn/finch/finch-4b", "name": "Finch-4B", "maker": "umn", "family": "umn/finch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 262144, "max_output_tokens": null, "parameters": "4539265536", "architecture": "Qwen3_5ForConditionalGeneration fine-tuned from Qwen/Qwen3.5-4B. Text stack: 32 layers, hidden size 2560, 16 attention heads, 4 key-value heads, intermediate size 9216, vocabulary 248320, max position embeddings 262144. Vision tower: hidden size 1024, depth 24. The config also has image and video token ids. The card rounds the size to 4B; the stored count is the Hugging Face safetensors total.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finch-4B is an Evolution Fine-Tuning model from Minnesota NLP at the University of Minnesota. It is an open-weight fine-tune of Qwen3.5-4B trained to propose the next program inside evolutionary search. The weights were public on 2026-10-15. A single release day was not stated.", "what_it_is_for": "The model card describes Finch as a mutation operator. The recommended driver is OpenEvolve. The card points to Finch-4B-KTO when a second, preference-learning stage is wanted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/finch/finch-4b-kto": { "id": "umn/finch/finch-4b-kto", "name": "Finch-4B-KTO", "maker": "umn", "family": "umn/finch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 262144, "max_output_tokens": null, "parameters": "4539265536", "architecture": "Qwen3_5ForConditionalGeneration. Hub base model is minnesotanlp/Finch-4B. The family table still lists the original base as Qwen3.5-4B. Same safetensors total as Finch-4B (4,539,265,536). The Finch-4B config, which this checkpoint matches in parameter count and architecture class, has a text stack of 32 layers, hidden size 2560, 16 heads, 4 key-value heads, vocabulary 248320, and a vision tower of hidden size 1024 and depth 24.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finch-4B-KTO is the preference-learning variant of Finch-4B from Minnesota NLP. The card's lineage is Qwen3.5-4B, then Finch-4B (EFT and SFT), then Finch-4B-KTO. The Hub base model is minnesotanlp/Finch-4B. The weights were public on 2026-10-15. A single release day was not stated. The repository was created on 2026-06-30.", "what_it_is_for": "Stage 2 uses KTO so the model trains on improved and regressed evolutionary transitions together. The card says this adds a self-judgment of which candidates are promising. The recommended scaffold remains OpenEvolve.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/finch/finch-8b": { "id": "umn/finch/finch-8b", "name": "Finch-8B", "maker": "umn", "family": "umn/finch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Qwen3ForCausalLM fine-tuned from Qwen/Qwen3-8B. 36 layers, hidden size 4096, 32 attention heads, 8 key-value heads, intermediate size 12288, vocabulary 151936, max position embeddings 131072. Text-only causal LM. The card rounds the size to 8B; the stored count is the Hugging Face safetensors total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finch-8B is the text-only 8B Evolution Fine-Tuning model from Minnesota NLP. It is an open-weight fine-tune of Qwen3-8B, not of the Qwen3.5 vision models used for Finch-2B, Finch-4B, and Finch-9B. The weights were public on 2026-10-15. A single release day was not stated.", "what_it_is_for": "The card trains Finch-8B as a mutation operator on OpenEvolve trajectories. A note on the card sends readers to Finch-8B-KTO for a KTO preference stage on top of this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/finch/finch-8b-kto": { "id": "umn/finch/finch-8b-kto", "name": "Finch-8B-KTO", "maker": "umn", "family": "umn/finch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "8190735360", "architecture": "Qwen3ForCausalLM. Hub base model is minnesotanlp/Finch-8B. Same safetensors total as Finch-8B (8,190,735,360). Text-only: 36 layers, hidden size 4096, 32 heads, 8 key-value heads, intermediate size 12288, vocabulary 151936, max position embeddings 131072, matching the Finch-8B config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finch-8B-KTO is the preference-learning variant of Finch-8B from Minnesota NLP. The card's lineage is Qwen3-8B, then Finch-8B, then Finch-8B-KTO. The Hub base model is minnesotanlp/Finch-8B. The weights were public on 2026-10-15. The repository was created on 2026-06-30. A release day distinct from that Hub timestamp was not stated.", "what_it_is_for": "KTO trains on improved and regressed transitions so the model can judge which evolved programs are promising. The card presents this checkpoint as the family's offline preference variant of the 8B model. It remains a text-only Qwen3 model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/finch/finch-9b": { "id": "umn/finch/finch-9b", "name": "Finch-9B", "maker": "umn", "family": "umn/finch", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 262144, "max_output_tokens": null, "parameters": "9409813744", "architecture": "Qwen3_5ForConditionalGeneration fine-tuned from Qwen/Qwen3.5-9B. Text stack: 32 layers, hidden size 4096, 16 attention heads, 4 key-value heads, vocabulary 248320. Vision tower: hidden size 1152, 27 layers. Hugging Face safetensors total is the stored parameter count; the model card rounds the size to 9B.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Finch-9B is the largest Evolution Fine-Tuning model released by Minnesota NLP at the University of Minnesota. It is an open-weight fine-tune of Qwen3.5-9B meant to act as a mutation operator inside evolutionary search. Weights were public on the Minnesota NLP Hugging Face organization on 2026-10-15. A single release day was not stated.", "what_it_is_for": "The model card says Finch-9B learns how to evolve a solution from evolutionary-search trajectories, instead of leaving that behavior only in an external scaffold. The recommended use is OpenEvolve or a similar scaffold. Direct generation from a system prompt plus the current program, evaluator feedback, and history is also documented.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/geolm/geolm-base-cased": { "id": "umn/geolm/geolm-base-cased", "name": "GeoLM-base-cased", "maker": "umn", "family": "umn/geolm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "GeoLMModel, model type bert. 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocabulary 28996, max position embeddings 512, float32. Continued from bert-base-cased with a geocoordinate embedding. The Hub safetensors total is empty because the weights are in PyTorch files, so parameters is null. Inputs on the card are natural-language sentences or linearized geographical regions.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GeoLM-base-cased is a geospatial BERT encoder from the Knowledge Computing Lab at the University of Minnesota. The card says it reads natural-language sentences or linearized geographical regions and adds a geocoordinate embedding. It was pretrained on worldwide OpenStreetMap, WikiData, and Wikipedia, starting from bert-base-cased. The weights were public on the knowledge-computing Hub organization on 2026-10-15. The repository was created on 2023-07-21, before the 2023-10-23 paper submission and the December 2023 EMNLP publication. No single release day was stated.", "what_it_is_for": "The paper trains the encoder with contrastive learning, masked language modeling, and a spatial coordinate embedding so distance and direction can be used downstream. Toponym linking in the GitHub README uses these pretrained weights without a supervised linking head. Toponym recognition is the separate fine-tuned checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/geolm/geolm-base-toponym-recognition": { "id": "umn/geolm/geolm-base-toponym-recognition", "name": "GeoLM toponym recognition", "maker": "umn", "family": "umn/geolm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "107722371", "architecture": "GeoLMForTokenClassification, model type bert. 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocabulary 28996, max position embeddings 512. Labels: O, B-Topo, I-Topo. Safetensors total 107,722,371. The card parent field points at zekun-li/geolm-base-cased, and the sample code loads zekun-li/geolm-base-toponym-recognition. The lab README says those weights were migrated to knowledge-computing.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GeoLM toponym recognition is the Knowledge Computing Lab's token classifier for place names. It is the GeoWebNews fine-tune of the GeoLM encoder. The public checkpoint is knowledge-computing/geolm-base-toponym-recognition, with labels O, B-Topo, and I-Topo. The card still points parent and sample code at zekun-li mirrors. The lab README says the weights were migrated to knowledge-computing. The repository was created on 2023-07-15. A release day was not stated.", "what_it_is_for": "The card detects place names after GeoLM pretraining and a GeoWebNews fine-tune. A demo space exists under zekun-li. That space is not a separate model. Language on the card is English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/sansi/sansi": { "id": "umn/sansi/sansi", "name": "SanSi", "maker": "umn", "family": "umn/sansi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PEFT LoRA adapters and readouts on ByteDance/Ouro-1.4B. The card says 24 shared layers looped T=8 and 60.8M trained parameters. The Hub safetensors total is empty because the repository is an adapter, so parameters is null rather than a converted 60.8 million. The card's comparison table lists a 1.4B parameter column and a cost of 7.7, where one Ouro-1.4B loop counts as 1. Typed decision: a text state, a question, and 2 to 26 options, returning probabilities rather than generated text.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SanSi is a looped typed-decision adapter from Minnesota NLP at the University of Minnesota. The released checkpoint is LoRA plus readouts on ByteDance/Ouro-1.4B. It reads a text state, a question, and 2 to 26 options, then returns probabilities. It does not generate text. The weights were public on 2026-10-15. The Hub repository was created on 2026-10-08. A separate release day was not stated.", "what_it_is_for": "The card calls this System 1.5 thinking: several loops of a typed decision over a frozen Ouro backbone. A low top probability means abstention. The card says to commit when the top probability reaches (1+1/K)/2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/sansi/sansi-2-6b": { "id": "umn/sansi/sansi-2-6b", "name": "SanSi-2.6B", "maker": "umn", "family": "umn/sansi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PEFT LoRA adapters and readouts on ByteDance/Ouro-2.6B. The card says 48 shared layers looped T=8 and 121.4M trained parameters. The Hub safetensors total is empty, so parameters is null. The comparison table lists a 2.7B parameter column and a cost of 14.8. Same typed-decision interface as SanSi: probabilities over 2 to 26 options, no generated text.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SanSi-2.6B is the larger SanSi adapter from Minnesota NLP, on ByteDance/Ouro-2.6B. It uses the same typed-decision setup as SanSi: 48 shared layers looped eight times, LoRA plus readouts, probability outputs. The Hub repository was created on 2026-10-08. A separate release day was not stated.", "what_it_is_for": "Same decision task as SanSi. The card says the training recipe is unchanged and that most paper analyses use the 1.4B model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/scholawrite/scholawrite-bert-classifier": { "id": "umn/scholawrite/scholawrite-bert-classifier", "name": "ScholaWrite BERT classifier", "maker": "umn", "family": "umn/scholawrite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 512, "max_output_tokens": null, "parameters": "109486848", "architecture": "Config class BertModel (bert), not BertForSequenceClassification. 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocabulary 30528, max position embeddings 512, float32. id2label has 15 labels, LABEL_0 through LABEL_14. _name_or_path points at a local BERT_run_final checkpoint. The card name is BERT-SW-CLF, a fine-tune of google-bert/bert-base-uncased. The card names six special tokens: INPUT, /INPUT, BT, /BT, PWA, /PWA. A sample on the card closes with , which does not match the token named above.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERT-SW-CLF is Minnesota NLP's BERT classifier for the next writing intention in ScholaWrite. It is a fine-tune of google-bert/bert-base-uncased. The task reads the \"before\" LaTeX span and scores one of 15 intention labels. It does not generate the next draft. The Hub repository was created on 2025-01-08. The dataset page dates the paper 2025-02-05. The card does not state a release day.", "what_it_is_for": "The card predicts the next writing intention from LaTeX already on the page. Special tokens named on the card are INPUT, /INPUT, BT, /BT, PWA, and /PWA. One sample closes with `` instead of ``.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/scholawrite/scholawrite-llama-3-1-8b-classifier": { "id": "umn/scholawrite/scholawrite-llama-3-1-8b-classifier", "name": "ScholaWrite Llama 3.1 8B classifier", "maker": "umn", "family": "umn/scholawrite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "8030507008", "architecture": "Config class LlamaForCausalLM, torch_dtype float32. 32 layers, hidden size 4096, 32 heads, 8 key-value heads, intermediate size 14336, vocabulary 128286, max position embeddings 131072. Hub base_model is unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit and the pipeline tag is text-classification. _name_or_path contains Llama-3.2-3B-bnb-4bit_run__LLAMA_8B_CLASSIFIER, which disagrees with the 8B architecture and the Hub base tag. Safetensors total 8,030,507,008, which is not the same total as the writing checkpoint (8,249,052,194).", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is Minnesota NLP's Llama intention classifier for ScholaWrite. The Hub base model is unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit. The pipeline tag is text-classification. The released config is LlamaForCausalLM in float32, with an 8B shape: 32 layers, hidden size 4096, vocabulary 128,286. The `_name_or_path` string mentions Llama-3.2-3B. Those three identifiers are all stored. The Hub repository was created on 2024-11-15.", "what_it_is_for": "The sibling cards treat \"LLama-8B-Instruct + SW\" as the neural intention classifier in the ScholaWrite comparison. This repository is that classifier. The writing model is a different checkpoint and is scored only as part of the iterative-writing pair.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/scholawrite/scholawrite-llama-3-1-8b-writing": { "id": "umn/scholawrite/scholawrite-llama-3-1-8b-writing", "name": "ScholaWrite Llama 3.1 8B writing", "maker": "umn", "family": "umn/scholawrite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "8249052194", "architecture": "LlamaForCausalLM, torch_dtype bfloat16. 32 layers, hidden size 4096, 32 heads, 8 key-value heads, intermediate size 14336, vocabulary 128271, max position embeddings 131072. Safetensors total 8,249,052,194. Card name LLAMA-8B-SW-GEN. Hub base_model and tags say a 4-bit Unsloth QLoRA fine-tune of unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit. The published dtype and parameter total are a full bfloat16-sized model. _name_or_path points at a local Llama-3.1-8B writing run.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLAMA-8B-SW-GEN is Minnesota NLP's writing model for ScholaWrite. The card fine-tunes unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit with QLoRA so the model writes the \"after\" text in Iterative Self-Writing. The published config is bfloat16 LlamaForCausalLM with 8,249,052,194 parameters. Tags still say 4-bit. The Hub repository was created on 2024-11-15. A release day was not stated.", "what_it_is_for": "The model proposes the next LaTeX span. At evaluation time the card pairs it with the intention classifier. A demo is linked from the project page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/scholawrite/scholawrite-roberta-classifier": { "id": "umn/scholawrite/scholawrite-roberta-classifier", "name": "ScholaWrite RoBERTa classifier", "maker": "umn", "family": "umn/scholawrite", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 514, "max_output_tokens": null, "parameters": "124650240", "architecture": "Config class RobertaModel, not a sequence-classification head class. 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocabulary 50271, max position embeddings 514, float32. id2label has 15 labels. Hub base model is FacebookAI/roberta-base. The card's sample code loads BertTokenizer and BertForSequenceClassification.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is Minnesota NLP's RoBERTa next-intention classifier for ScholaWrite, a fine-tune of FacebookAI/roberta-base. It scores 15 writing-intention labels on the before-text of a LaTeX span. The Hub repository was created on 2025-01-08. A release day was not stated.", "what_it_is_for": "Same intention-classification task as the BERT ScholaWrite classifier. The card's sample loads BertTokenizer and BertForSequenceClassification even though the weights are RoBERTa.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/scisense/scisense-lm-30": { "id": "umn/scisense/scisense-lm-30", "name": "SciSense-LM-30", "maker": "umn", "family": "umn/scisense", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 40960, "max_output_tokens": null, "parameters": "30532122624", "architecture": "Qwen3MoeForCausalLM on the Target config: 48 layers, hidden size 2048, 32 heads, 4 key-value heads, intermediate size 6144, vocabulary 151936, 128 experts, 8 experts per token, max position embeddings 40960. Infer and Both have the same safetensors total, 30,532,122,624. The shape matches public Qwen3-30B-A3B dimensions. The config retrieved on 2026-10-15 did not include a base repository id. The code registry has a Q303-I label. The Hub name is 30, so instruct versus base is not asserted.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SciSense-LM-30 is a SciSense-LM release from Minnesota NLP at the University of Minnesota. Mixture-of-experts SciSense-LM size. The Target config has 128 experts and 8 experts per token. The shape matches public Qwen3-30B-A3B dimensions. The file does not name that repository. Target, Infer, and Both are versions of this size. Hub cards contain license metadata and no prose. The repositories were created on 2026-04-24. The paper was submitted on 2026-05-01. Neither date is treated as a stated release day.", "what_it_is_for": "The paper trains models to write citation-conditioned eight-stage sensemaking trajectories (Pirolli and Card, 2005). Target reconstructs a path toward a known paper. Infer proposes new directions. Both uses both supervision signals. The teacher for the trajectories is Qwen3-235B-A22B-Instruct-2507.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/scisense/scisense-lm-32": { "id": "umn/scisense/scisense-lm-32", "name": "SciSense-LM-32", "maker": "umn", "family": "umn/scisense", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 40960, "max_output_tokens": null, "parameters": "32762123264", "architecture": "Qwen3ForCausalLM on the Target config: 64 layers, hidden size 5120, 64 heads, 8 key-value heads, intermediate size 25600, vocabulary 151936, max position embeddings 40960. Infer and Both have the same safetensors total, 32,762,123,264. The paper discusses SciSense-LM-32B-Target and SciSense-LM-32B-Infer for stage frequencies over 1,000 trajectories. The config does not name a base repository. Stage-frequency numbers were not in the paper text captured for this catalog.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SciSense-LM-32 is a SciSense-LM release from Minnesota NLP at the University of Minnesota. Dense 32B-class SciSense-LM. The paper discusses SciSense-LM-32B-Target and Infer. Stage frequencies were not captured as numbers. Target, Infer, and Both are versions of this size. Hub cards contain license metadata and no prose. The repositories were created on 2026-04-24. The paper was submitted on 2026-05-01. Neither date is treated as a stated release day.", "what_it_is_for": "The paper trains models to write citation-conditioned eight-stage sensemaking trajectories (Pirolli and Card, 2005). Target reconstructs a path toward a known paper. Infer proposes new directions. Both uses both supervision signals. The teacher for the trajectories is Qwen3-235B-A22B-Instruct-2507.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/scisense/scisense-lm-4g": { "id": "umn/scisense/scisense-lm-4g", "name": "SciSense-LM-4G", "maker": "umn", "family": "umn/scisense", "status": "active", "confidence": "high", "weights": "open-weights", "license": "gemma", "release_date": "", "context_length": 131072, "max_output_tokens": null, "parameters": "4551515648", "architecture": "Gemma3ForCausalLM, model type gemma3_text, on the Target config: 34 layers, hidden size 2560, 8 heads, 4 key-value heads, intermediate size 10240, vocabulary 262208, max position embeddings 131072. Infer and Both have the same safetensors total, 4,551,515,648, and the same Hub license field gemma. The code family label G3-4B-I exists. The Hub name is 4G, so instruct versus base is not asserted.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SciSense-LM-4G is a SciSense-LM release from Minnesota NLP at the University of Minnesota. Gemma-class SciSense-LM size. The Hub license field is gemma. The Target config is a Gemma 3 text causal LM. Target, Infer, and Both are versions of this size. Hub cards contain license metadata and no prose. The repositories were created on 2026-04-24. The paper was submitted on 2026-05-01. Neither date is treated as a stated release day.", "what_it_is_for": "The paper trains models to write citation-conditioned eight-stage sensemaking trajectories (Pirolli and Card, 2005). Target reconstructs a path toward a known paper. Infer proposes new directions. Both uses both supervision signals. The teacher for the trajectories is Qwen3-235B-A22B-Instruct-2507.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/scisense/scisense-lm-4q": { "id": "umn/scisense/scisense-lm-4q", "name": "SciSense-LM-4Q", "maker": "umn", "family": "umn/scisense", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 40960, "max_output_tokens": null, "parameters": "4411424256", "architecture": "Qwen3ForCausalLM on the Target checkpoint config: 36 layers, hidden size 2560, 32 heads, 8 key-value heads, intermediate size 9728, vocabulary 151936, max position embeddings 40960. Infer and Both publish the same safetensors total, 4,411,424,256, and the same Apache-2.0 Hub license. The Hub name is 4Q. The config does not name a Qwen repository. The code registry's Q4-I label is the sibling SciSense-LM-4QI, not this folder.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SciSense-LM-4Q is a SciSense-LM release from Minnesota NLP at the University of Minnesota. Qwen3-class sensemaking model. Three public checkpoints share one parameter total. Target, Infer, and Both are versions of this size. Hub cards contain license metadata and no prose. The repositories were created on 2026-04-24. The paper was submitted on 2026-05-01. Neither date is treated as a stated release day.", "what_it_is_for": "The paper trains models to write citation-conditioned eight-stage sensemaking trajectories (Pirolli and Card, 2005). Target reconstructs a path toward a known paper. Infer proposes new directions. Both uses both supervision signals. The teacher for the trajectories is Qwen3-235B-A22B-Instruct-2507.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "umn/scisense/scisense-lm-4qi": { "id": "umn/scisense/scisense-lm-4qi", "name": "SciSense-LM-4QI", "maker": "umn", "family": "umn/scisense", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": 40960, "max_output_tokens": null, "parameters": "4411424256", "architecture": "Qwen3ForCausalLM. The Target and Infer configs checked on 2026-10-15 match the 4Q shape: 36 layers, hidden size 2560, max position embeddings 40960. Safetensors total 4,411,424,256 on Target, Infer, and Both. The name 4QI matches the code label Q4-I. The exact instruct repository id was not in the Hub metadata. Head counts were read from the 4Q Target config (32 heads, 8 key-value heads, vocabulary 151936) and were not re-read on the 4QI file.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SciSense-LM-4QI is a SciSense-LM release from Minnesota NLP at the University of Minnesota. Second 4B-class Qwen3 SciSense size. The Hub name matches the code label Q4-I. The exact base repository was not in the metadata. The Target and Infer configs match 4Q on layer count, hidden size, and position window. Target, Infer, and Both are versions of this size. Hub cards contain license metadata and no prose. The repositories were created on 2026-04-24. The paper was submitted on 2026-05-01. Neither date is treated as a stated release day.", "what_it_is_for": "The paper trains models to write citation-conditioned eight-stage sensemaking trajectories (Pirolli and Card, 2005). Target reconstructs a path toward a known paper. Infer proposes new directions. Both uses both supervision signals. The teacher for the trajectories is Qwen3-235B-A22B-Instruct-2507.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unam/ciempiess/ciempiess-spanish-models": { "id": "unam/ciempiess/ciempiess-spanish-models", "name": "CIEMPIESS Spanish Models", "maker": "unam", "family": "unam/ciempiess", "status": "active", "confidence": "high", "weights": "open-source", "license": "GPL-3.0", "release_date": "2019-08-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PocketSphinx continuous context-dependent acoustic model. The project README says 10,000 senones, training audio is 16 kHz 16-bit mono WAV, the pronouncing dictionary has more than 285,000 words, and the phone set is Mexbet. Trained on 581 hours of read and spontaneous Spanish from Mexico, Spain, and Latin America. Designed to run in PocketSphinx. The SourceForge zip is CIEMPIESS_Spanish_Models_581h.zip, listed at 159.6 MB, last modified 2019-08-24.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CIEMPIESS Spanish Models are PocketSphinx acoustic models released by the CIEMPIESS-UNAM project. The project began in the speech laboratory of the Facultad de Ingenier\u00eda at the National Autonomous University of Mexico and is now based at IIMAS-UNAM. The package was still linked from https://ciempiess.org/downloads and still listed on SourceForge on 2026-10-15. Status is active. The listing text licenses the models under GPL-3.0.", "what_it_is_for": "The project page says they are acoustic models for Spanish speech recognition in PocketSphinx. Training used 581 hours from LDC sets, including the CIEMPIESS corpora except CIEMPIESS-TEST, plus recordings collected by the UNAM social-service program Desarrollo de Tecnolog\u00edas del Habla and by the CIEMPIESS-UNAM project.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unam/ladagan/ladagan": { "id": "unam/ladagan/ladagan", "name": "LadaGAN", "maker": "unam", "family": "unam/ladagan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-01-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unconditional hybrid Transformer-convolutional GAN. Ladaformer uses linear additive attention (one attention vector per head) in both the generator and the discriminator, with convolutions, pixel-shuffle local embedding expansion, and self-modulated layer norm in the generator. The generator has three Ladaformer blocks, 4 heads, MLP width 512, and noise dimension 128. Loss is non-saturating logistic GAN loss with an R1 penalty. The official README lists 19M parameters and 0.7B FLOPs at 32x32 and 64x64, and 24M parameters and 4.3B FLOPs at FFHQ 128x128. Generation is one step from noise, not text.", "modalities": { "input": [], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LadaGAN is an image generator from Emilio Morales-Juarez (Facultad de Ingenier\u00eda, UNAM) and Gibran Fuentes-Pineda (IIMAS, UNAM). It is a research release, not an API product. The paper was posted on 17 January 2024, weights were on Hugging Face by 27 January 2024, and the same line was a LIMIT workshop poster at ECCV 2026. Status on 2026-10-15 is active. The code repository says MIT. The weight repo has no license tag.", "what_it_is_for": "The authors present it as a single-step GAN for CIFAR-10, CelebA, FFHQ, and LSUN Bedroom that can be trained from scratch on one GPU. The generator builds an image from a latent code. There is no text prompt interface in the released configs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unbabel/tower/towerbase-13b-v0-1": { "id": "unbabel/tower/towerbase-13b-v0-1", "name": "TowerBase 13B v0.1", "maker": "unbabel", "family": "unbabel/tower", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0, plus the Llama 2 Community License on the base weights", "release_date": "2024-01-26", "context_length": 4096, "max_output_tokens": null, "parameters": "13B", "architecture": "LlamaForCausalLM. Continued pretraining of Llama 2 13B. Config: 40 layers, hidden size 5120, vocab size 32000, max_position_embeddings 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TowerBase 13B v0.1 is the 13B continued-pretraining twin of TowerBase 7B. Unbabel, Instituto Superior Tecnico, and CentraleSupelec released it as an open-weight base for translation fine-tuning in the original 10 Tower languages. It remains on the Hub and was not given a deprecation date.", "what_it_is_for": "TowerBase 13B v0.1 is the 13B continued-pretraining twin of TowerBase 7B. Unbabel, Instituto Superior Tecnico, and CentraleSupelec released it as an open-weight base for translation fine-tuning in the original 10 Tower languages. It remains on the Hub and was not given a deprecation date.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unbabel/tower/towerbase-7b-v0-1": { "id": "unbabel/tower/towerbase-7b-v0-1", "name": "TowerBase 7B v0.1", "maker": "unbabel", "family": "unbabel/tower", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0, plus the Llama 2 Community License on the base weights", "release_date": "2024-01-11", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "LlamaForCausalLM. Continued pretraining of Llama 2 7B. Config: 32 layers, hidden size 4096, vocab size 32000, max_position_embeddings 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TowerBase 7B v0.1 is Unbabel's first released Tower base checkpoint, continued-pretrained from Llama 2 with Instituto Superior Tecnico and CentraleSupelec. It is an open-weight research model for 10 languages, still downloadable, and meant to be fine-tuned for translation. The public announcement is 11 January 2024.", "what_it_is_for": "TowerBase 7B v0.1 is Unbabel's first released Tower base checkpoint, continued-pretrained from Llama 2 with Instituto Superior Tecnico and CentraleSupelec. It is an open-weight research model for 10 languages, still downloadable, and meant to be fine-tuned for translation. The public announcement is 11 January 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unbabel/tower/towerinstruct-13b-v0-1": { "id": "unbabel/tower/towerinstruct-13b-v0-1", "name": "TowerInstruct 13B v0.1", "maker": "unbabel", "family": "unbabel/tower", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0, plus the Llama 2 Community License on the base weights", "release_date": "2024-01-29", "context_length": 4096, "max_output_tokens": null, "parameters": "13B", "architecture": "LlamaForCausalLM. Supervised fine-tune of TowerBase-13B. Config: 40 layers, hidden size 5120, vocab size 32007, max_position_embeddings 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TowerInstruct 13B v0.1 is the larger original Tower instruction model. Unbabel, Instituto Superior Tecnico, and CentraleSupelec fine-tuned TowerBase 13B on TowerBlocks for translation and related tasks in 10 languages. The weights are still on the Hub. A later card mentions a 13B v0.2, but that repository was not found.", "what_it_is_for": "TowerInstruct 13B v0.1 is the larger original Tower instruction model. Unbabel, Instituto Superior Tecnico, and CentraleSupelec fine-tuned TowerBase 13B on TowerBlocks for translation and related tasks in 10 languages. The weights are still on the Hub. A later card mentions a 13B v0.2, but that repository was not found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unbabel/tower/towerinstruct-7b-v0-1": { "id": "unbabel/tower/towerinstruct-7b-v0-1", "name": "TowerInstruct 7B v0.1", "maker": "unbabel", "family": "unbabel/tower", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0, plus the Llama 2 Community License on the base weights", "release_date": "2024-01-11", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "LlamaForCausalLM. Supervised fine-tune of TowerBase-7B-v0.1 on TowerBlocks. Config: 32 layers, hidden size 4096, vocab size 32007, max_position_embeddings 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TowerInstruct 7B v0.1 is the first instruction-tuned Tower model. Unbabel and partners fine-tuned TowerBase 7B on TowerBlocks so it would follow translation, post-editing, terminology, NER, grammar-correction, and paraphrase prompts in 10 languages. It is still published. v0.2 later replaced it for document-level work.", "what_it_is_for": "TowerInstruct 7B v0.1 is the first instruction-tuned Tower model. Unbabel and partners fine-tuned TowerBase 7B on TowerBlocks so it would follow translation, post-editing, terminology, NER, grammar-correction, and paraphrase prompts in 10 languages. It is still published. v0.2 later replaced it for document-level work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unbabel/tower/towerinstruct-7b-v0-2": { "id": "unbabel/tower/towerinstruct-7b-v0-2", "name": "TowerInstruct 7B v0.2", "maker": "unbabel", "family": "unbabel/tower", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0, plus the Llama 2 Community License on the base weights", "release_date": "2024-02-09", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "LlamaForCausalLM. Supervised fine-tune of TowerBase-7B-v0.1. Config: 32 layers, hidden size 4096, vocab size 32007, max_position_embeddings 4096. Card training max_seq_length is 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TowerInstruct 7B v0.2 is Unbabel's February 2024 refresh of the 7B Llama-2 TowerInstruct model. The card says it is more reliable at document-level translation than v0.1 and was trained on a newer TowerBlocks mix. Weights stay open under CC-BY-NC-4.0 plus the Llama 2 license. It is a research checkpoint, not the later commercial TowerLLM.", "what_it_is_for": "TowerInstruct 7B v0.2 is Unbabel's February 2024 refresh of the 7B Llama-2 TowerInstruct model. The card says it is more reliable at document-level translation than v0.1 and was trained on a newer TowerBlocks mix. Weights stay open under CC-BY-NC-4.0 plus the Llama 2 license. It is a research checkpoint, not the later commercial TowerLLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unbabel/tower/towerinstruct-mistral-7b-v0-2": { "id": "unbabel/tower/towerinstruct-mistral-7b-v0-2", "name": "TowerInstruct Mistral 7B v0.2", "maker": "unbabel", "family": "unbabel/tower", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-03-26", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "MistralForCausalLM. Card: fine-tune of a Mistral version of TowerBase on TowerBlocks. Config: 32 layers, hidden size 4096, vocab size 32002, max_position_embeddings 32768, sliding_window 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TowerInstruct Mistral 7B v0.2 is Unbabel's Mistral-based Tower instruction model for the original 10 languages. It was released on 26 March 2024, after the Llama 2 Tower models, and is still on the Hub under CC-BY-NC-4.0. It is not Tower-v2. Unbabel's own discussion says the shared-task models used different data and were not released.", "what_it_is_for": "TowerInstruct Mistral 7B v0.2 is Unbabel's Mistral-based Tower instruction model for the original 10 languages. It was released on 26 March 2024, after the Llama 2 Tower models, and is still on the Hub under CC-BY-NC-4.0. It is not Tower-v2. Unbabel's own discussion says the shared-task models used different data and were not released.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unbabel/tower/towerinstruct-wmt24-chat-7b": { "id": "unbabel/tower/towerinstruct-wmt24-chat-7b", "name": "TowerInstruct WMT24 Chat 7B", "maker": "unbabel", "family": "unbabel/tower", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-30", "context_length": 4096, "max_output_tokens": null, "parameters": "7B", "architecture": "LlamaForCausalLM. Fine-tune of TowerBase-7B-v0.1 on TowerBlocks plus the WMT24 Chat MT training set. Config: 32 layers, hidden size 4096, vocab size 32002, max_position_embeddings 4096. _name_or_path is Unbabel/TowerBase-7B-v0.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TowerInstruct WMT24 Chat 7B is Unbabel's open 7B model for context-aware translation of bilingual customer-support chats. It fine-tunes TowerBase 7B on TowerBlocks and the WMT 2024 Chat MT training set. The card, dated from a 30 June 2024 Hub upload, says it was the best shared-task submission. The system description is the November 2024 WMT paper.", "what_it_is_for": "TowerInstruct WMT24 Chat 7B is Unbabel's open 7B model for context-aware translation of bilingual customer-support chats. It fine-tunes TowerBase 7B on TowerBlocks and the WMT 2024 Chat MT training set. The card, dated from a 30 June 2024 Hub upload, says it was the best shared-task submission. The system description is the November 2024 WMT paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unbabel/tower-plus/tower-plus-2b": { "id": "unbabel/tower-plus/tower-plus-2b", "name": "Tower+ 2B", "maker": "unbabel", "family": "unbabel/tower-plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Model-card prose says CC-BY-NC-4.0. Hub metadata says cc-by-nc-sa-4.0. Both are recorded; they disagree.", "release_date": "2025-06-20", "context_length": 8192, "max_output_tokens": null, "parameters": "2B", "architecture": "Gemma2ForCausalLM. Continued pretraining of Gemma 2 2B, then instruction tuning, weighted preference optimization, and GRPO, per the model card. Config: 26 layers, hidden size 2304, vocab 256000, max_position_embeddings 8192, sliding_window 4096. Card base_model: google/gemma-2-2B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tower+ 2B is Unbabel's smallest open Tower+ translation model, released with the 20 June 2025 paper. It continues pretraining Gemma 2 2B and then applies instruction tuning, weighted preference optimization, and GRPO. The card positions it as a multilingual model under 3B parameters, strong at machine translation across the Tower+ language set, and still useful for general instructions. Weights remain open.", "what_it_is_for": "Tower+ 2B is Unbabel's smallest open Tower+ translation model, released with the 20 June 2025 paper. It continues pretraining Gemma 2 2B and then applies instruction tuning, weighted preference optimization, and GRPO. The card positions it as a multilingual model under 3B parameters, strong at machine translation across the Tower+ language set, and still useful for general instructions. Weights remain open.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unbabel/tower-plus/tower-plus-72b": { "id": "unbabel/tower-plus/tower-plus-72b", "name": "Tower+ 72B", "maker": "unbabel", "family": "unbabel/tower-plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Model-card prose says CC-BY-NC-4.0. Hub metadata says cc-by-nc-sa-4.0. Both are recorded; they disagree.", "release_date": "2025-06-20", "context_length": null, "max_output_tokens": null, "parameters": "72B", "architecture": "Qwen2ForCausalLM. Card: continued pretraining, instruction tuning, and weighted preference optimization of Qwen2.5-72B. Config: 80 layers, hidden size 8192, vocab 152064, max_position_embeddings 32768, rope_scaling null. Card base_model: Qwen/Qwen2.5-72B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tower+ 72B is Unbabel's largest open Tower+ model, released 20 June 2025 on top of Qwen2.5 72B. The card says continued pretraining, instruction tuning, and weighted preference optimization keep translation quality while recovering general instruction following that Tower-v2 70B had lost. Weights are public. The card's 131,072-token context and the checkpoint config's 32,768-token position limit disagree, so this dossier does not pick a single context length.", "what_it_is_for": "Tower+ 72B is Unbabel's largest open Tower+ model, released 20 June 2025 on top of Qwen2.5 72B. The card says continued pretraining, instruction tuning, and weighted preference optimization keep translation quality while recovering general instruction following that Tower-v2 70B had lost. Weights are public. The card's 131,072-token context and the checkpoint config's 32,768-token position limit disagree, so this dossier does not pick a single context length.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unbabel/tower-plus/tower-plus-9b": { "id": "unbabel/tower-plus/tower-plus-9b", "name": "Tower+ 9B", "maker": "unbabel", "family": "unbabel/tower-plus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Model-card prose says CC-BY-NC-4.0. Hub metadata says cc-by-nc-sa-4.0. Both are recorded; they disagree.", "release_date": "2025-06-20", "context_length": 8192, "max_output_tokens": null, "parameters": "9B", "architecture": "Gemma2ForCausalLM. Card: continued pretraining, instruction tuning, and weighted preference optimization on Gemma 2 9B. Config: 42 layers, hidden size 3584, vocab 256000, max_position_embeddings 8192, sliding_window 4096. Hub base_model: google/gemma-2-9b.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tower+ 9B is Unbabel's mid-size open Tower+ model, built on Gemma 2 9B and released on 20 June 2025. The card says continued pretraining, instruction tuning, and weighted preference optimization over 22 languages make it one of the stronger multilingual models under 10B, with machine translation as the main strength. It is still published. An independent WMT 2025 multilingual-instruction evaluation reports an average MT AutoRank of 8.31, where lower is better.", "what_it_is_for": "Tower+ 9B is Unbabel's mid-size open Tower+ model, built on Gemma 2 9B and released on 20 June 2025. The card says continued pretraining, instruction tuning, and weighted preference optimization over 22 languages make it one of the stronger multilingual models under 10B, with machine translation as the main strength. It is still published. An independent WMT 2025 multilingual-instruction evaluation reports an average MT AutoRank of 8.31, where lower is better.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unbabel/tower-v2/tower-v2-70b": { "id": "unbabel/tower-v2/tower-v2-70b", "name": "Tower-v2 70B", "maker": "unbabel", "family": "unbabel/tower-v2", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-15", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Continued pretraining and supervised fine-tuning of Llama-3-70B, per the WMT 2024 system paper. No public config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tower-v2 70B is Unbabel and Instituto Superior Tecnico's large WMT 2024 general translation model, built by continued pretraining and supervised fine-tuning of Llama 3 70B for 15 languages. The system paper says the 70B model plus quality-aware decoding led preliminary automatic rankings. Weights were still unreleased in a 2025 paper and were not on the Unbabel Hub on 2026-10-15. Tower+ 72B is the later open model in this line.", "what_it_is_for": "Tower-v2 70B is Unbabel and Instituto Superior Tecnico's large WMT 2024 general translation model, built by continued pretraining and supervised fine-tuning of Llama 3 70B for 15 languages. The system paper says the 70B model plus quality-aware decoding led preliminary automatic rankings. Weights were still unreleased in a 2025 paper and were not on the Unbabel Hub on 2026-10-15. Tower+ 72B is the later open model in this line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unbabel/tower-v2/tower-v2-7b": { "id": "unbabel/tower-v2/tower-v2-7b", "name": "Tower-v2 7B", "maker": "unbabel", "family": "unbabel/tower-v2", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-15", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Continued pretraining and supervised fine-tuning of Mistral-7B, as described in the WMT 2024 system paper. Layer counts were not copied because no checkpoint config was published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tower-v2 7B is the smaller model in Unbabel and Instituto Superior Tecnico's WMT 2024 general machine-translation submission. The paper says it continues pretraining and instruction-tunes Mistral-7B and adds Czech, Icelandic, Hindi, Ukrainian, and Japanese to the original 10 Tower languages. No public weights or API were found. It is catalogued as a named, closed, historical system.", "what_it_is_for": "Tower-v2 7B is the smaller model in Unbabel and Instituto Superior Tecnico's WMT 2024 general machine-translation submission. The paper says it continues pretraining and instruction-tunes Mistral-7B and adds Czech, Icelandic, Hindi, Ukrainian, and Japanese to the original 10 Tower languages. No public weights or API were found. It is catalogued as a named, closed, historical system.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unbabel/towerllm/towerllm": { "id": "unbabel/towerllm/towerllm", "name": "TowerLLM", "maker": "unbabel", "family": "unbabel/towerllm", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-06-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TowerLLM is Unbabel's closed commercial translation model, announced on 6 June 2024 for the Unbabel platform and later named as the engine behind Widn.AI. Unbabel says it was trained on public and proprietary translation data and that the open Tower checkpoints are the public counterpart of the first release. Customer notes then describe a 20-pair setup in July 2024 and a 22-language update in April 2025. No retirement notice was found as of 15 October 2026. Confidence is medium because the live product does not publish a model card.", "what_it_is_for": "TowerLLM is Unbabel's closed commercial translation model, announced on 6 June 2024 for the Unbabel platform and later named as the engine behind Widn.AI. Unbabel says it was trained on public and proprietary translation data and that the open Tower checkpoints are the public counterpart of the first release. Customer notes then describe a 20-pair setup in July 2024 and a 22-language update in April 2025. No retirement notice was found as of 15 October 2026. Confidence is medium because the live product does not publish a model card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unc/aimnet/aimnet": { "id": "unc/aimnet/aimnet", "name": "AIMNet", "maker": "unc", "family": "unc/aimnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2019-08-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Atoms-in-molecules network. Atomic numbers and coordinates become ANI-type AEVs. Learnable atomic feature vectors have dimension 16 in the paper and in embed.yaml (nfeature 16). Message-passing updates run for t=3 passes in the paper and npass 3 in aimnet.yaml. Shipped MLPs use ELU. embed.yaml lists implemented_elements [1, 6, 7, 8, 16, 9, 17] (H, C, N, O, S, F, Cl). interact_mlp sizes [272, 256, 256, 128]. update_mlp sizes [272, 256, 256, 16]. Radial cutoff 4.6 \u00c5 and angular cutoff 3.1 \u00c5 in the paper. The repo ships five-member ensembles.", "modalities": { "input": [ "structure" ], "output": [ "energy" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AIMNet (atoms-in-molecules neural network) is a multitask potential from Roman Zubatyuk and Olexandr Isayev at the UNC Eshelman School of Pharmacy, with Justin S. Smith at Los Alamos and Jerzy Leszczynski at Jackson State. Science Advances published it on 9 August 2019. The UNC-copyrighted repository ships pretrained ensembles under an MIT license. Collected 2026-10-15.", "what_it_is_for": "The paper predicts several atomic and molecular properties from one representation: energies, forces, charges, and volumes. Iterative updates to atomic feature vectors carry information past the local cutoff. A smaller network on the frozen AIM representation learns implicit SMD solvation. The GitHub tree includes energy, multitask, and SMD ensembles of five checkpoints each.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unc/ani/ani-1": { "id": "unc/ani/ani-1", "name": "ANI-1", "maker": "unc", "family": "unc/ani", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2016-10-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Behler-Parrinello atomic neural networks, one per element H, C, N, and O. The paper's best ANI-1 network is pyramidal 768:128:128:64:1, Gaussian hidden activations, linear output, stated as 124,033 optimizable parameters per atomic-number network. Atomic environment vectors are a modified Behler-Parrinello symmetry-function representation. The sum across the four element networks is not stated.", "modalities": { "input": [ "structure" ], "output": [ "energy" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ANI-1 is a neural network potential for organic molecules with H, C, N, and O. Justin S. Smith and Adrian E. Roitberg are at the University of Florida. Olexandr Isayev, a corresponding author, is at the UNC Eshelman School of Pharmacy. The method name in the paper is ANAKIN-ME (ANI). The arXiv preprint is 27 October 2016, and the journal article is Chemical Science (2017). Weights remain in the ASE_ANI repository. Collected 2026-10-15.", "what_it_is_for": "The paper trains atomic neural networks on DFT total energies so a force-field-cost model can transfer to larger organic molecules than the GDB-8 training set. Normal-mode sampling builds the conformations. Forces used in dynamics come from the energy model. The ASE_ANI README marks that interface deprecated and points to TorchANI. It says the original ANI-1 potential is on the `deprecated_original` branch.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unc/ani/ani-1ccx": { "id": "unc/ani/ani-1ccx", "name": "ANI-1ccx", "maker": "unc", "family": "unc/ani", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2019-07-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transfer learning from the eight-network ANI-1x ensemble onto CCSD(T)*/CBS data. The Nature Communications paper says the ANI-1ccx potential is built by transfer learning from those eight ANI-1x networks. TorchANI documents an ensemble of eight networks aimed at CCSD(T)*/CBS with DLPNO-CCSD(T), HCNO only. Exact parameter count was not stated.", "modalities": { "input": [ "structure" ], "output": [ "energy" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ANI-1ccx is a general-purpose neural network potential trained to approach coupled-cluster accuracy for H, C, N, and O molecules. Nature Communications published it on 1 July 2019. Olexandr Isayev, a corresponding author, is at the UNC Eshelman School of Pharmacy. Coauthors are at the University of Florida, Los Alamos, and Jackson State. The weights are the eight-network ensemble in ASE_ANI and TorchANI. Collected 2026-10-15.", "what_it_is_for": "The paper starts from ANI-1x networks trained on DFT, then transfer-learns on about 500,000 conformations at a CCSD(T)*/CBS level. The stated uses are reaction thermochemistry, isomerization energies, drug-like torsions, and energies and forces away from equilibrium. The paper says the result is roughly nine orders of magnitude faster than DFT and billions of times faster than CCSD(T)/CBS. Code and weights are linked from the paper to ASE_ANI.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unc/ani/ani-1x": { "id": "unc/ani/ani-1x", "name": "ANI-1x", "maker": "unc", "family": "unc/ani", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2018-05-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Active-learning ANI potential for H, C, N, and O. The JCP article identifies the released model as the AL6 committee member. The ANI-1ccx paper states that the ANI-1x potential used there is an ensemble of eight networks trained on different splits. TorchANI documents the same eight-network ensemble and the \u03c9B97X/6-31G(d) target. Per-network width and an exact parameter total were not copied.", "modalities": { "input": [ "structure" ], "output": [ "energy" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ANI-1x is the active-learning ANI potential for molecules made of H, C, N, and O. The Journal of Chemical Physics article (22 May 2018) lists Olexandr Isayev at the UNC Eshelman School of Pharmacy, with coauthors at the University of Florida and Los Alamos. The released potential is the AL6 model from that paper, distributed as an eight-network ensemble. Collected 2026-10-15.", "what_it_is_for": "Query-by-committee active learning picks new DFT calculations where an ensemble of ANI models disagrees. The resulting potential predicts energies and forces for general CHNO organic molecules. The article says the training set is 5.5 million structures, 25% the size of the ANI-1 set. ASE_ANI and TorchANI both ship the eight-network ensemble. TorchANI states the target level \u03c9B97X/6-31G(d).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unc/bifrost/bifrost-1": { "id": "unc/bifrost/bifrost-1", "name": "Bifrost-1", "maker": "unc", "family": "unc/bifrost", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-08", "context_length": null, "max_output_tokens": null, "parameters": "14831558144", "architecture": "Multimodal LLM with a visual generation branch initialized from the backbone, plus a FLUX ControlNet that consumes patch-level CLIP latents. Hub config model_type is multi_modality, class MultiModalityCausalLM. Card acknowledges Qwen2.5-VL and FLUX.1-dev. Safetensors total is BF16 only.", "modalities": { "input": [ "text", "image" ], "output": [ "image", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bifrost-1 is a UNC Chapel Hill image-generation model (Han Lin, Jaemin Cho, and Mohit Bansal, with Amir Zadeh and Chuan Li at Lambda). It keeps a pretrained multimodal LLM for understanding and adds a branch that drives a diffusion model. The checkpoint is public under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The card and project page say Bifrost-1 bridges a multimodal LLM and a diffusion model with patch-level CLIP latents that already match the LLM's vision encoder. A generation branch is initialized from the original LLM weights so understanding is kept, and a lightweight ControlNet steers the diffusion model. The shipped example is text-to-image on GenEval prompts. The card credits Qwen2.5-VL and FLUX.1-dev as the backbones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unc/codi/codi": { "id": "unc/codi/codi", "name": "CoDi", "maker": "unc", "family": "unc/codi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-05-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composable diffusion model that aligns modalities in input and output space so any subset of text, image, video, and audio can condition any subset of those outputs. The paper does not publish a parameter count on the sources read here.", "modalities": { "input": [ "text", "image", "audio", "video" ], "output": [ "text", "image", "audio", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CoDi (Composable Diffusion) is an any-to-any generator released by Zineng Tang and Mohit Bansal at UNC Chapel Hill together with Microsoft Azure coauthors. The weight repo is the author's Hugging Face account, with an MIT tag, not a Microsoft org repo. Collected 2026-10-15.", "what_it_is_for": "The Hub card describes CoDi as a generative model that can emit any combination of language, image, video, or audio from any combination of those inputs, including several modalities at once. The authors say many modality pairs have no paired training set, and that they align modalities in both the input space and the output space instead.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unc/gradicon/multigradicon": { "id": "unc/gradicon/multigradicon", "name": "multiGradICON", "maker": "unc", "family": "unc/gradicon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Registration network in the uniGradICON line, trained on monomodal and multimodal pairs with loss-function randomization. Same repository and CLI as uniGradICON. Parameter count not published on the sources read.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "multiGradICON is the multimodal follow-up to uniGradICON from UNC Chapel Hill (Ba\u015far Demir, Lin Tian, Hastings Greer, Marc Niethammer, and coauthors). It is a separately named checkpoint in the same repository. Collected 2026-10-15.", "what_it_is_for": "The paper says uniGradICON was aimed at monomodal registration. multiGradICON is trained so one network handles both monomodal and multimodal pairs. The authors report that randomizing the loss helps multimodal accuracy and that multimodal training data helps multimodal generalization. WBIR 2024 is the venue named in the repository citation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unc/gradicon/unicarl": { "id": "unc/gradicon/unicarl", "name": "uniCARL", "maker": "unc", "family": "unc/gradicon", "status": "preview", "confidence": "low", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "uniCARL is a preview weight from UNC Chapel Hill's uniGradICON repository. The only public description found is the GitHub pre-release note. Confidence is low. Collected 2026-10-15.", "what_it_is_for": "The release body says only \"preview release of the uniCARL foundation model.\" There is no paper, model card, or README section under that name on the pages read. The asset is `longleaf_unicarl_8`, 569,332,990 bytes, uploaded by Hastings Greer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unc/gradicon/unigradicon": { "id": "unc/gradicon/unigradicon", "name": "uniGradICON", "maker": "unc", "family": "unc/gradicon", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-03-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Learning-based medical image registration network using gradient inverse consistency (GradICON), trained across multiple datasets rather than one anatomy. The README describes it as the GradICON method with multi-dataset training. Layer widths and parameter count were not extracted.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "uniGradICON is a medical image registration model from UNC Chapel Hill's biomedical image analysis group (Lin Tian, Hastings Greer, and Marc Niethammer, with outside coauthors). It is the group's named foundation checkpoint for registration across datasets. Weights are in the Apache-2.0 GitHub repo. Collected 2026-10-15.", "what_it_is_for": "The paper presents uniGradICON as a step toward a foundation model for registration: one network that works across datasets, can be tried zero-shot on a new acquisition or anatomy, and can initialize fine-tuning when the new task is out of distribution. The authors contrast that with earlier deep registration nets that were trained for one anatomy. The README says it is GradICON trained on several datasets. The focus in this paper is monomodal registration.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unc/lxmert/lxmert": { "id": "unc/lxmert/lxmert", "name": "LXMERT", "maker": "unc", "family": "unc/lxmert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2019-08-20", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Three-encoder Transformer: 9-layer language encoder, 5-layer object-relationship encoder, 5-layer cross-modality encoder. Hidden size 768, 12 attention heads, intermediate size 3072, GELU. Text max position 512, WordPiece vocab 30522. Vision input is 2048-d region features plus 4-d boxes, not raw pixels. Hugging Face config of unc-nlp/lxmert-base-uncased.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LXMERT is a vision-language encoder from UNC Chapel Hill (Hao Tan and Mohit Bansal, MURGe / UNC NLP). The EMNLP 2019 model is still distributed as open weights, with no stated retirement. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents LXMERT as a pretrained cross-modality Transformer for vision-and-language reasoning. After fine-tuning, the authors report state-of-the-art results on VQA and GQA and a large gain on NLVR2. Code and pretrained models are linked from the paper to https://github.com/airsplay/lxmert.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unc/tvlt/tvlt": { "id": "unc/tvlt/tvlt", "name": "TVLT", "maker": "unc", "family": "unc/tvlt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-09-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MAE-style vision-audio Transformer. Encoder: 12 layers, hidden size 768, 12 heads, intermediate size 3072. Decoder: 8 layers, hidden size 512, 16 heads. Video is 224px patches of size 16, up to 8 frames in the Hub config. Audio is a 2048-length spectrogram with 16x16 patches and 128 mel features. No text tokenizer.", "modalities": { "input": [ "image", "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TVLT (Textless Vision-Language Transformer) is a UNC Chapel Hill audio-visual encoder from Zineng Tang, Jaemin Cho, Yixin Nie, and Mohit Bansal. It was a NeurIPS 2022 oral. The pretrained checkpoint is still on Hugging Face under an MIT tag. Collected 2026-10-15.", "what_it_is_for": "The paper trains one Transformer on raw video frames and audio spectrograms, with masked reconstruction and video-audio contrastive alignment, and without a text tokenizer or an ASR module. The authors use it for retrieval, visual question answering, and multimodal sentiment analysis after task adaptation. The Hub upload is the pretraining checkpoint only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unc/vl/vl-bart": { "id": "unc/vl/vl-bart", "name": "VL-BART", "maker": "unc", "family": "unc/vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2021-02-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BART-Base encoder-decoder (6+6 layers, hidden size 768, absolute position embeddings) extended with 36 Faster R-CNN region embeddings and a shared text-generation head. Appendix summary lists the parameter count as P while the main text uses P for VL-T5's 220M, so no absolute count is stored.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VL-BART is the BART-Base twin of VL-T5, released by the same UNC Chapel Hill group (Cho, Lei, Tan, and Bansal) in the ICML 2021 paper. It is a named pretrained checkpoint, not a third-party fine-tune of someone else's product. Collected 2026-10-15.", "what_it_is_for": "VL-BART is the same unified text-generation setup as VL-T5, starting from BART-Base instead of T5-Base. The authors use it on VQA, GQA, captioning, translation, and other vision-language tasks. They report that RefCOCOg fine-tuning diverged.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unc/vl/vl-t5": { "id": "unc/vl/vl-t5", "name": "VL-T5", "maker": "unc", "family": "unc/vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2021-02-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5-Base encoder-decoder (12+12 layers, hidden size 768) extended with 36 Faster R-CNN region embeddings. Tasks are text prefixes plus a shared language-modeling head. The paper calls this parameter count P and states it as 220M.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VL-T5 is a UNC Chapel Hill vision-language model from Jaemin Cho, Jie Lei, Hao Tan, and Mohit Bansal. It extends T5-Base so that VQA, grounding, captioning, and related tasks are all text generation. Weights remain available from the official GitHub project. Collected 2026-10-15.", "what_it_is_for": "The paper uses one encoder-decoder and one language-modeling loss for seven vision-language benchmarks, instead of a new classifier head per task. Prefixes such as `vqa:` and `visual grounding:` select the task. The authors say this matches task-specific models closely and is better on rare VQA answers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unil/mixmhc2pred/mixmhc2pred": { "id": "unil/mixmhc2pred/mixmhc2pred", "name": "MixMHC2pred 2.1", "maker": "unil", "family": "unil/mixmhc2pred", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Academic non-commercial license. For-profit use of version 2.1 requires a separate license from the Ludwig Institute for Cancer Research.", "release_date": "2026-07-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two neural-network blocks, introduced in the 2.0 rewrite. The first predicts an MHC-II binding motif from the allele sequence. The second scores the peptide, its context, and that motif. The returned %rank is an average across repeated networks. Position-weight-matrix scores are still available with -e.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MixMHC2pred is the MHC class II ligand and CD4 epitope predictor from Julien Racle and David Gfeller at the University of Lausanne, Department of Oncology, Ludwig Institute for Cancer Research Lausanne. The current download is 2.1, and the NEWS file still calls that line a beta. Status: preview. Collected 2026-10-15. A web application is published at http://mixmhc2pred.gfellerlab.org. The Ludwig Institute holds the for-profit license.", "what_it_is_for": "It ranks peptides by how likely they are to be displayed by an MHC-II allele and recognized as CD4 epitopes. It covers HLA-DR, HLA-DP, and HLA-DQ, and many non-human alleles if the matching PWM files are downloaded. It is trained on naturally presented peptides, so it returns a %rank rather than an affinity in nanomolar units.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unil/mixmhcpred/mixmhcpred": { "id": "unil/mixmhcpred/mixmhcpred", "name": "MixMHCpred 3.0", "maker": "unil", "family": "unil/mixmhcpred", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic non-commercial license from the Ludwig Institute for Cancer Research. For-profit use requires a separate license.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pan-allele neural networks, one per peptide length from 8 to 14. Each takes 34 MHC-I binding-site residues, one fully connected hidden layer of 256 units with dropout 0.2, ReLU, and a per-column softmax. Training uses Kullback-Leibler divergence and Adam at learning rate 0.0001. A separate network predicts peptide-length distributions. Scores are combined into a presentation %rank. The package also aligns new MHC sequences with MAFFT.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MixMHCpred is the MHC class I ligand predictor from David Gfeller's group at the University of Lausanne, Department of Oncology, Ludwig Institute for Cancer Research Lausanne. Version 3.0 is the current GitHub release. It is a pan-allele neural-network scorer for peptides and for MHC sequences that lack eluted ligands. Status: active. Collected 2026-10-15. The Ludwig Institute holds the academic license. The Swiss Institute of Bioinformatics is a co-affiliation on the lab's papers.", "what_it_is_for": "It ranks peptides by how likely they are to be presented by an MHC class I allele, including human and non-human alleles and alleles with no experimental ligands. It can also align a new MHC protein sequence and draw binding motifs. The README says it does not need a separate commercial license for academic research. For-profit use does.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unil/mixtcrpred/mixtcrpred": { "id": "unil/mixtcrpred/mixtcrpred", "name": "MixTCRpred 1.0", "maker": "unil", "family": "unil/mixtcrpred", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic non-commercial research license for MixTCRpred 1.0 from the Ludwig Institute for Cancer Research. For-profit use requires a separate license.", "release_date": "2024-03-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One transformer encoder per peptide-MHC. CDR1, CDR2, and CDR3 from the alpha and beta chains are padded, concatenated per chain, and passed through a learned embedding plus positional encoding. A dense layer outputs a binding score. %rank compares that score with 1,000,000 randomly paired TCRs. Implemented in PyTorch and PyTorch Lightning. The released tool is epitope-specific, not a single pan-epitope network.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MixTCRpred is an epitope-specific T-cell receptor binding predictor from Giancarlo Croce, Alexandre Harari, and David Gfeller at the University of Lausanne, Department of Oncology, Ludwig Institute for Cancer Research Lausanne. Version 1.0 is the current release. Each of 146 peptide-MHC targets has its own transformer. Status: active. Collected 2026-10-15. The code archive on Zenodo is dated 11 March 2024. The Nature Communications paper is 13 April 2024.", "what_it_is_for": "It scores whether a paired alpha-beta TCR is likely to recognize one of the epitopes the lab trained. The README says it is for viral and cancer epitopes, and that it can also flag likely contaminants in epitope-sorted TCR data. It does not take an arbitrary new peptide as input. You pick one of the shipped models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unil/phosmhcpred/phosmhcpred": { "id": "unil/phosmhcpred/phosmhcpred", "name": "PhosMHCpred", "maker": "unil", "family": "unil/phosmhcpred", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic use is described as free on the repository header. For-profit use requires a separate license from the Ludwig Institute for Cancer Research.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PhosMHCpred is a phosphorylated HLA class I ligand predictor from Marthe Solleder and David Gfeller at the University of Lausanne, with the Ludwig Institute for Cancer Research. The lab still lists it as a computational tool. The repository says it was trained on naturally presented phosphorylated and unmodified ligands and returns a ligand score rather than an affinity. Status: active. Collected 2026-10-15. The citation on the repository is Solleder et al., Molecular & Cellular Proteomics, 2020.", "what_it_is_for": "It ranks peptides, including phosphorylated ones, by how likely they are to be HLA-I ligands for a named allele. The header says it is for prioritizing ligands, not for reporting a binding affinity.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unil/prime/prime": { "id": "unil/prime/prime", "name": "PRIME 2.1", "maker": "unil", "family": "unil/prime", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic non-commercial license. For-profit use of version 2.1 requires a separate license from the Ludwig Institute for Cancer Research.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PRIME 2.0 is a neural network. Inputs described in Cell Systems are the MixMHCpred presentation score as -log(%rank), amino-acid frequencies at positions with little effect on HLA-I binding, and a one-hot peptide length from 8 to 14. PRIME 1.0 was a logistic regression on affinity plus amino-acid frequencies. Version 2.1 keeps that predictor and requires MixMHCpred 3.0 or newer. Hidden-layer size is not in the README.", "modalities": { "input": [ "protein-sequence" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PRIME (PRedictor of IMmunogenic Epitopes) is the class I immunogenicity model from David Gfeller's group at the University of Lausanne, Department of Oncology, Ludwig Institute for Cancer Research Lausanne. Version 2.1 is the current package. It adds a TCR-recognition score on top of MixMHCpred binding. Status: active. Collected 2026-10-15. The Ludwig Institute holds the for-profit license.", "what_it_is_for": "It ranks short peptides by the chance they are immunogenic CD8 epitopes, not only by MHC-I binding. The score mixes a MixMHCpred presentation %rank with peptide features that the lab treats as more visible to the T-cell receptor. The README says to use %rank, not the raw score, when choosing peptides for experiments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unimi/m5c/rna-m5c-predictor": { "id": "unimi/m5c/rna-m5c-predictor", "name": "RNA m\u2075C Predictor", "maker": "unimi", "family": "unimi/m5c", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Shipped checkpoint model_weights/heavy_hard_negative_mining_bigru.pt, loaded by predict_m5c.py as BI_GRU() with its defaults. One-hot k-mer 1 (4 channels) over a 51-nt window is projected by Linear(4, 128, bias=False). Three stacked bidirectional GRUs use hidden size 256, so each layer output is 512. Dropout 0.1 is applied between GRU layers and skipped after the last layer. Central-index attention is Linear(512, 51), GELU, then the central position and padded or invalid steps are set to -inf, softmax, and a weighted sum. The MLP uses mid_dim = max(128, 512 // 2) = 256: Linear(512, 256, bias=False), GELU, dropout 0.1, Linear(256, 5). Classes are unmodified, Type I (NSUN2), Type II (NSUN6), Type III (NSUN5), and Type IV (NSUN1). U is mapped to T. Windows with ambiguous bases are skipped. Pad token P is an all-zero one-hot vector. experiments/models_params/RNN_config.json matches these widths for the grid-search winner. It is not a second product.", "modalities": { "input": [ "nucleotide-sequence" ], "output": [ "class-probabilities" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Milan AnacletoLab model RNA m\u2075C Predictor (rna-m5c-predictor). Status: active. Collected 2026-10-15.", "what_it_is_for": "The README describes inference of cytosine 5-methylation on human RNA or DNA FASTA. `predict_m5c.py` maps U to T, takes a 51-nt window around each cytosine (25 nt on each side), skips windows with ambiguous bases, and writes a type plus probabilities for unmodified, I, II, III, and IV. The README maps those types to NSUN2, NSUN6, NSUN5, and NSUN1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unist/gmoe/gmoe-base": { "id": "unist/gmoe/gmoe-base", "name": "GMoE Base", "maker": "unist", "family": "unist/gmoe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 for the training code. The checkpoint license is not visible without accepting the Hugging Face access gate.", "release_date": "2026-07-02", "context_length": 1024, "max_output_tokens": null, "parameters": "204M", "architecture": "GPT-style decoder-only Transformer with a GMoE feed-forward in place of the MLP. Base config from the paper appendix and the public code: hidden 768, 12 blocks, 12 heads, FFN inner size 3072, GPT-2 tokenizer, vocabulary 50257. Default checkpoint uses 16 global experts shared across layers, one always-on local expert per GMoE layer, a shared GRU router, and top-1 routing. Paper Table 1: 204M stored parameters and 181M activated parameters. The public code replaces the MLP in every block. Paper Table 6 also lists 6 MoE layers for Base, which conflicts with that every-block description.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GMoE Base is the 204M research language model from UNIST's Graduate School of Artificial Intelligence, released with the ACL 2026 paper by Geonwoo Hong and Taehwan Kim. It is the mid-size checkpoint UNIST's news office highlighted. It is an active open-weight research model, not a hosted product. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and the UNIST news post present the mid-size GMoE as a parameter-efficient language model: stored parameters fall from 549 million in the compared Switch model to 204 million, while average downstream accuracy stays nearly the same (39.51 versus 39.55). It is a pretrained research checkpoint, not an instruction-tuned assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unist/gmoe/gmoe-large": { "id": "unist/gmoe/gmoe-large", "name": "GMoE Large", "maker": "unist", "family": "unist/gmoe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 for the training code. The checkpoint license is not visible without accepting the Hugging Face access gate.", "release_date": "2026-07-02", "context_length": 1024, "max_output_tokens": null, "parameters": "496M", "architecture": "GPT-style decoder-only Transformer with a GMoE feed-forward in place of the MLP. Large config from the paper appendix and the public code: hidden 1024, 24 blocks, 16 heads, FFN inner size 4096, GPT-2 tokenizer, vocabulary 50257. Default checkpoint uses 16 global experts shared across layers, one always-on local expert per GMoE layer, a shared GRU router, and top-1 routing. Paper Table 1: 496M stored parameters and 556M activated parameters. Activated count can exceed stored count because reused global experts are summed once per layer. The public code replaces the MLP in every block. Paper Table 6 also lists 12 MoE layers for Large, which conflicts with that every-block description.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GMoE Large is the 496M research language model from UNIST's Graduate School of Artificial Intelligence, released with the ACL 2026 paper by Geonwoo Hong and Taehwan Kim. It is the largest default GMoE checkpoint. It is an active open-weight research model, not a hosted product. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses the Large GMoE to test whether sharing experts across layers still works as the model grows. It is a pretrained English language-modeling checkpoint for research, not an instruction-tuned assistant and not an API product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unist/gmoe/gmoe-small": { "id": "unist/gmoe/gmoe-small", "name": "GMoE Small", "maker": "unist", "family": "unist/gmoe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 for the training code. The checkpoint license is not visible without accepting the Hugging Face access gate.", "release_date": "2026-07-02", "context_length": 1024, "max_output_tokens": null, "parameters": "80M", "architecture": "GPT-style decoder-only Transformer with a GMoE feed-forward in place of the MLP. Small config from the paper appendix and the public code: hidden 512, 6 blocks, 8 heads, FFN inner size 2048, GPT-2 tokenizer, vocabulary 50257. Default checkpoint uses 16 global experts shared across layers, one always-on local expert per GMoE layer, a shared GRU router, and top-1 routing. Paper Table 1: 80M stored parameters and 58M activated parameters. The public code replaces the MLP in every block. Paper Table 6 also lists 3 MoE layers for Small, which conflicts with that every-block description.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GMoE Small is the 80M research language model from UNIST's Graduate School of Artificial Intelligence, released with the ACL 2026 paper by Geonwoo Hong and Taehwan Kim. It is an active open-weight checkpoint, not a hosted product. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents GMoE as a sparse mixture-of-experts language model that shares global experts across layers so a small model can keep competitive language-modeling quality with fewer stored parameters. The Small checkpoint is the research baseline, not an instruction-tuned assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unitn/composes/composes-count": { "id": "unitn/composes/composes-count", "name": "COMPOSES Count", "maker": "unitn", "family": "unitn/composes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Uncompressed window count vectors built with DISSECT. The download page says a 2-word window, PMI weighting, and no compression, at about 300K dimensions. The archive names use ppmi.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "COMPOSES Count is the best full-space count-based semantic vector released by the COMPOSES project at CIMeC, University of Trento, alongside the predict vectors in Baroni, Dinu, and Kruszewski's ACL 2014 paper. It is a static English embedding file, still linked on 2026-10-15, with no API and no price.", "what_it_is_for": "The download page says this is the count model that performed best in Table 3 of the paper. It is the uncompressed co-occurrence space, kept as a comparison point against the predict vectors, which the same page says won overall.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unitn/composes/composes-count-svd": { "id": "unitn/composes/composes-count-svd", "name": "COMPOSES Count SVD", "maker": "unitn", "family": "unitn/composes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SVD reduction of the COMPOSES count space, built with DISSECT. The download page says a 2-word window, PMI weighting, and SVD to 500 dimensions. The archive names use ppmi.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "COMPOSES Count SVD is the best reduced count space from the COMPOSES release at CIMeC, University of Trento. It is the SVD compression of the same style of window counts as COMPOSES Count, posted as its own file in the ACL 2014 \"Don't count, predict!\" materials. The download was still linked on 2026-10-15. There is no API and no price.", "what_it_is_for": "The download page calls this the best reduced count model, the third row of Table 3 in the paper. It is the dense form of the count space for the same lexical tasks as the full sparse counts and the predict vectors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unitn/composes/composes-predict": { "id": "unitn/composes/composes-predict", "name": "COMPOSES Predict", "maker": "unitn", "family": "unitn/composes", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "word2vec CBOW. The download page and archive name give a 5-word window, 10 negative samples, subsampling, and 400 dimensions.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "COMPOSES Predict is the best context-predicting word embedding released by the COMPOSES project at CIMeC, University of Trento (Marco Baroni, Georgiana Dinu, and Germ\u00e1n Kruszewski). It is a static English word2vec space from the ACL 2014 comparison \"Don't count, predict!\", not a hosted API. The vectors were still linked on the project download page on 2026-10-15.", "what_it_is_for": "The download page says these are the predict vectors that performed best across the paper's lexical semantic tasks, and that they also beat the count vectors. They are word representations for that evaluation, built with word2vec on BNC and WackyPedia/ukWaC.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unitn/dm/typedm": { "id": "unitn/dm/typedm", "name": "TypeDM", "maker": "unitn", "family": "unitn/dm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2010-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "TypeDM third-order tensor of scored word-link-word tuples. Links are partially lexicalized syntactic dependencies and lexico-syntactic patterns, scored by pattern type frequency. A random-indexed 5,000-dimensional word-by-link-word matrix is shipped with the tensor.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TypeDM is the publicly posted Distributional Memory model from Marco Baroni (CIMeC, University of Trento) and Alessandro Lenci (University of Pisa). It is a general-purpose English distributional semantic resource, not a hosted API. The weights were still linked from the authors' resource page on 2026-10-15. The December 2010 Computational Linguistics article is the release write-up and later received the ACL 2020 Test of Time Award.", "what_it_is_for": "The 2010 paper presents Distributional Memory as one corpus-derived tensor reused across semantic tasks, instead of a separate space per task. TypeDM is the variant the authors posted. The resource page says the intended use of the reduced matrix is word-to-word similarity, and it also ships a top-10 neighbour list built from that matrix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unito/anatcl/anatcl": { "id": "unito/anatcl/anatcl", "name": "AnatCL", "maker": "unito", "family": "unito/anatcl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the GitHub LICENSE, copyright (c) 2021 EIDOSlab, covering the software and associated documentation files. The preprint page is CC BY 4.0 for the paper. A separate license for the weight files was not stated.", "release_date": "2024-08-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "3D ResNet-18 encoder pretrained with a contrastive anatomical loss on OpenBHB. The v4 paper says pretraining uses 3,984 healthy MRI volumes, CAT12 VBM at 1.5 mm, and images of size 121 x 145 x 121. Optimizer Adam, learning rate 0.0001, decay 0.9 every 10 epochs, batch 32, 300 epochs, lambda_1 = lambda_2 = 1. The contrastive loss uses a two-layer fully connected projection head. Downstream evaluation discards the head and uses the encoder. The loader default is AnatCL(model=\"resnet18\", descriptor \"global\" or \"local\", fold 0 to 4, use_head=False, pretrained=True) and builds models.SupConResNet with feat_dim 128. The default weight key is anatcl-g3 for both descriptors. Released formulations are AnatCL-G3 (global) and AnatCL-L3 (local, three measures, Desikan in the weight paths). Five folds. A ResNet-50 run and the seven-measure L7/G7 variants are paper ablations and are not in the loader.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AnatCL is an anatomical foundation model for brain MRI from the EIDOS lab at the University of Turin. The code is the EIDOSLAB release, and the preprint carries University of Turin addresses for the lab authors. Benoit Dufumier is a co-author at another institution. It is a 3D ResNet-18 encoder with public fold checkpoints. Status active on 2026-10-15. The preprint date used here is 7 August 2024, when the weights were already described. A revised HTML is 3 July 2025, and the journal version is Pattern Recognition Letters (2026).", "what_it_is_for": "The paper trains the encoder so that the representation follows anatomical measures from healthy brain MRI, then evaluates the frozen encoder on diagnosis tasks and clinical scores. The released pair is global AnatCL-G3 and local AnatCL-L3, each with five folds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unito/ddpm-pcc/ddpm-pcc": { "id": "unito/ddpm-pcc/ddpm-pcc", "name": "DDPM-PCC", "maker": "unito", "family": "unito/ddpm-pcc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the GitHub LICENSE, copyright (c) 2021 EIDOSlab, covering the software and associated documentation files. A separate license for the weight archive was not stated.", "release_date": "2025-05-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Denoising diffusion probabilistic model for low-bit-rate point-cloud compression. A PointNet encoder produces the condition vector, which a learnable vector quantizer compresses. The README train example uses latent dimension 256, 200 diffusion steps, PointNet encoder, 128 codecs of dimension 32, vq-alpha 2.5, and train batch 128, on ShapeNet. The repository is based on luost26/diffusion-point-cloud. The released zip was not unpacked, so those flags are the published train command and were not matched file-by-file to the checkpoint.", "modalities": { "input": [ "point-cloud" ], "output": [ "point-cloud" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DDPM-PCC is a point-cloud compression model from the EIDOS lab at the University of Turin. The preprint of 19 May 2025 says the authors released the code at the EIDOSLAB repository, and that repository publishes a pretrained zip. Status active on 2026-10-15. It was accepted at ICME 2025. The abstract page does not print affiliations. Gabriele Spadaro, Marco Grangetto, and Attilio Fiandrotti are University of Turin authors on the lab's WiGNet page. Other co-authors are not assigned an institution in this dossier.", "what_it_is_for": "The paper uses a diffusion model to reconstruct point clouds from a short quantized condition, aimed at low bit rates on ShapeNet and ModelNet40.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unito/medsae/medsae": { "id": "unito/medsae/medsae", "name": "MedSAE", "maker": "unito", "family": "unito/medsae", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Standard sparse autoencoder trained on last-layer MedCLIP ResNet-50 activations. The arXiv HTML that was read states activation width d=512, CheXpert train split, 200 epochs, learning rate 7e-6, L1 coefficient 3e-4, and expansion rate 16. The code also names BatchTopKSAE, StandardAprilUpdate, and MatryoshkaBatchTopKSAE. The README releases one Standard checkpoint, medclip-rn50_chexpert_standard_sweep_best.pt. The base networks are OpenAI CLIP and RyanWangZf MedCLIP. MedCLIP itself is not a University of Turin model. The dictionary-learning package is adapted from saprmarks/dictionary_learning.", "modalities": { "input": [ "activations" ], "output": [ "sparse-features" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedSAE is a sparse autoencoder from the EIDOS lab at the University of Turin, trained on activations of MedCLIP rather than as a new medical image backbone. Riccardo Renzulli, Enrico Cassano, and Marco Grangetto are University of Turin authors. Colas Lepoutre is at \u00c9cole polytechnique. The preprint is 30 October 2025. The conference version is ICIP 2026. Status active on 2026-10-15. One Standard checkpoint is public.", "what_it_is_for": "The README and the paper use the SAE to dissect MedCLIP representations. The released file is a Standard SAE on MedCLIP-RN50 activations from CheXpert. CLIP and MedCLIP remain other makers' models and are not catalogued here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unito/wignet/wignet": { "id": "unito/wignet/wignet", "name": "WiGNet", "maker": "unito", "family": "unito/wignet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MXM License, a BSD-3-clause variant, on the GitHub LICENSE. Copyright (c) 2024 University of Turin, IMT, and Sisvel Technology. The license text covers the software and associated documentation files.", "release_date": "2024-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Windowed vision graph neural network. The image is partitioned into windows and a graph is built inside each window, then processed with graph convolutions rather than 2D convolution or self-attention. The public ImageNet eval command is model wignn_ti_256_gelu, image size 256, knn 9, use-shift 1, adapt-knn 1. The published Ti training command is DeiT-style: 300 epochs, AdamW, learning rate 2e-3, weight decay 0.05, cosine schedule, 20 warmup epochs, mixup 0.8, cutmix 1.0, batch 128, on ImageNet-1k. The paper reports window size 8. Ti, S, and M are size versions of one model. The repository is based on VisionGNN.", "modalities": { "input": [ "image" ], "output": [ "class-scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WiGNet is a windowed vision graph neural network released by the EIDOS lab at the University of Turin, with co-authors at T\u00e9l\u00e9com Paris. It classifies images by building a graph inside each window. The public weights are a research checkpoint, status active on 2026-10-15. The preprint is 1 October 2024 and the conference version is WACV 2025.", "what_it_is_for": "The paper and the EIDOS README present WiGNet as an image model that keeps vision-GNN accuracy while avoiding a graph whose cost grows with the square of the image size. The reported benchmarks are ImageNet-1k classification and a CelebA-HQ transfer test at higher resolution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unlv/ecpick/ecpick": { "id": "unlv/ecpick/ecpick", "name": "ECPICK", "maker": "unlv", "family": "unlv/ecpick", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-05-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Evidential hierarchical CNN for Enzyme Commission numbers, from ecpick/model.py and ecpick/ecpick.py in datax-lab/ECPICK. Three Conv2d encoders map 1 input channel to 128, with kernels (4, 21), (8, 21), and (16, 21), ReLU, a fixed MaxPool2d sized for a length-1000 input ((997, 1), (993, 1), and (985, 1)), flatten, and BatchNorm1d (momentum 0.99, eps 0.1). The three 128-d vectors concatenate to 384 features. Global and local flows then run once per hierarchy level. Hidden width defaults to 384. Default dropout is 0.8. Default beta is 0.6, and the blend is beta times the global sigmoid output plus (1-beta) times the concatenated local outputs. predict_fasta truncates sequences longer than 1000, pads to 1000, reshapes to (N, 1, 1000, 21), averages ten checkpoints, and keeps fourth-level probabilities at or above 0.19. A parameter count is not published.", "modalities": { "input": [ "protein-sequence" ], "output": [ "ec-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ECPICK is an Enzyme Commission number predictor from DataX Lab at the University of Nevada, Las Vegas. The code copyright is DataX Lab@UNLV, 2022, and the README also badges Sun Moon University CPS Lab. The journal article is Briefings in Bioinformatics, volume 25, issue 1, January 2024, bbad401, published online 22 November 2023. PyPI lists package 0.0.1, with a 12.1 kB wheel uploaded 1 May 2023. The inference client downloads ten checkpoints. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper and the README present ECPICK as a biologically interpretable predictor of EC numbers. The abstract says it learns sequential and hierarchical patterns from 20 million enzyme proteins and can point at amino acids that drive a prediction without a multiple sequence alignment. EnzymeX serves it next to HIT-EC, IDF-EC, and CLEAN.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unlv/hit-ec/hit-ec": { "id": "unlv/hit-ec/hit-ec", "name": "HIT-EC", "maker": "unlv", "family": "unlv/hit-ec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-11-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hierarchical interpretable transformer for EC numbers. model/model.py uses four transformer blocks, enc_1 through enc_4. Each block has an inference self-attention path and a separate interpretability attention path that stores attention and gradients. Feed-forward width is four times the embedding width, with GELU. The sequence representation read out is the first token. Four local linear heads and one global head cover the concatenated class space. The mix is beta times the global head plus (1-beta) times the concatenated local heads. The training script training/infer_training.py sets vocab size 23, dimension 1024, attention heads 2, dropout 0.1, beta 0.59, and output widths 7, 72, 268, and 4255. Positional embedding length follows the embedding width. Class defaults on MultiHeadedAttention (8 heads, d_model 256) are not the values that script passes. A parameter count is not published.", "modalities": { "input": [ "protein-sequence" ], "output": [ "ec-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HIT-EC is a hierarchical interpretable transformer for Enzyme Commission numbers from DataX Lab at the University of Nevada, Las Vegas. Louis Dumontet and Mingon Kang are in UNLV Computer Science. RePEc also lists So-Ra Han, Jun Hyuck Lee, and Tae-Jin Oh. The GitHub release v2.0.0 is dated 24 November 2025 and the README names `model.ckpt`. Nature Communications published the article on 30 January 2026, DOI 10.1038/s41467-026-68727-3. The code license is MIT. This dossier was collected on 2026-10-15.", "what_it_is_for": "The README and the UNLV news note describe HIT-EC as a four-level transformer aligned with the EC hierarchy. It is meant to score enzyme function from sequence, including incompletely annotated numbers, and to return residue-level evidence. EnzymeX runs it from a FASTA upload and can show contribution scores.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unlv/idf-ec/idf-ec": { "id": "unlv/idf-ec/idf-ec", "name": "IDF-EC", "maker": "unlv", "family": "unlv/idf-ec", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-07-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Interpretable Dynamic Feature-Logit Fusion. The README says it dynamically combines ECPICK, CLEAN, and HIT-EC and keeps residue-level explanations from those base learners. Fusion code is models/IDF-EC.py. That file was not read, so gate width and parameter count stay unpublished. The README lists a checkpoint at saved_models/final_epoch=2.pth and base-learner directories ECPICK_models, CLEAN_models, and HIT-EC_models. ESM-1b embeddings are an input to the training layout via extract.py and esm_data/.", "modalities": { "input": [ "protein-sequence" ], "output": [ "ec-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "IDF-EC is an interpretable fusion model for Enzyme Commission numbers from DataX Lab at the University of Nevada, Las Vegas. The ACM BCB 2026 paper, DOI 10.1145/3807503.3819449, was published 28 July 2026. EnzymeX lists the authors as S. Jeon, L. Dumontet, S.-R. Han, T.-J. Oh, and M. Kang. The repository is MIT, the contact is suhyeong.jeon@unlv.edu, and the README names `saved_models/final_epoch=2.pth`. This dossier was collected on 2026-10-15.", "what_it_is_for": "The abstract says each EC architecture is uneven across classes and hierarchy levels, so IDF-EC dynamically combines ECPICK, CLEAN, and HIT-EC while keeping their residue-level explanations. EnzymeX is the FASTA upload site. The fusion is the UNLV model. CLEAN itself is not.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unsw/darwin/darwin-1-5-7b": { "id": "unsw/darwin/darwin-1-5-7b", "name": "DARWIN 1.5", "maker": "unsw", "family": "unsw/darwin", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Same repository as Darwin-7B: CC BY-NC-SA 4.0 badge, with a CC BY-NC 4.0 weight-diff sentence. A separate 1.5 license was not found. The arXiv license was removed when the paper was withdrawn.", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Named Darwin 1.5-7B. The 2024-11-20 README says LLaMA1 with QA plus multi-task full fine-tuning beat LLaMA2, LLaMA3, and LLaMA3.1 variants, and that LoRA was worse than full fine-tuning. It does not state that the OneDrive file is that LLaMA1 winner. The withdrawn paper describes a 7B LLaMA fine-tune.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DARWIN 1.5 is the later 7B materials-science checkpoint from UNSW AI4Science and GreenDynamics. The Darwin repository linked the weights on 20 November 2024. The accompanying paper was withdrawn on 21 May 2025 because the authors said it contained inaccuracies. Collected 2026-10-15.", "what_it_is_for": "The README treats 1.5 as the newer scientific learner, with more supported tasks than Darwin-7B and without the organic-solar-cell inverse-design task. The withdrawn abstract describes natural-language property prediction without task-specific descriptors. Those experimental claims are not repeated here as results.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unsw/darwin/darwin-7b": { "id": "unsw/darwin/darwin-7b", "name": "Darwin-7B", "maker": "unsw", "family": "unsw/darwin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0 (README badge). The same README also says the weight diff is CC BY-NC 4.0 and that use is research-only.", "release_date": "2023-08-24", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only LLaMA-7B fine-tune. The paper's pipeline is DARWIN-SIG (Vicuna-7B instruction generator), then DARWIN-BASE (LLaMA-7B scientific QA), then DARWIN-MDP (further fine-tune of BASE on 16 FAIR datasets). The repository ships one Darwin-7B download for this version. Inverse design is supported on 1.0 and not on 1.5.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Darwin-7B is the UNSW AI4Science and GreenDynamics scientific language model released with the 24 August 2023 DARWIN paper. It is a research-only fine-tune of LLaMA-7B for materials, chemistry, and physics question answering and property prediction. The repository still links weights. Collected 2026-10-15.", "what_it_is_for": "The paper and README present Darwin as a scientific assistant: question answering over science text, then classification, regression, and inverse design on materials and device data. The public 1.0 checkpoint is the one the README calls Darwin-7B. The paper's final stage is named DARWIN-MDP. Only this version, not DARWIN 1.5, is described as supporting organic-solar-cell inverse design.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unsw/energybert/energybert": { "id": "unsw/energybert/energybert", "name": "EnergyBERT", "maker": "unsw", "family": "unsw/energybert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-02-17", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BertForMaskedLM. Config: 12 layers, hidden size 768, 12 heads, intermediate size 3072, vocabulary 30522, absolute positions, max position embeddings 512. _name_or_path is /g/data/yc86/tx4316/matbert-base-cased/, so this upload continued from a MatBERT checkpoint. MatBERT is not a UNSW model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EnergyBERT is a masked language model from UNSW AI4Science for energy and materials text. Tong Xie, Yuwei Wan, Juntao Fang, and Bram Hoex released the weights on Hugging Face under MIT, with support from UNSW and NCI Australia. The Hub repository was created on 17 February 2023 and last updated on 12 July 2023. Collected 2026-10-15.", "what_it_is_for": "The card says the direct use is text mining in energy and materials, and that the checkpoint can be fine-tuned for other NLP tasks in that domain. It is a fill-mask BERT, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "unsw/lumi/lumi-transformer": { "id": "unsw/lumi/lumi-transformer", "name": "Lumi-Transformer", "maker": "unsw", "family": "unsw/lumi", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-03-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Swin Transformer V2 image classifier. Config: embed_dim 192, depths [2, 2, 18, 2], heads [6, 12, 24, 48], image size 224, patch size 4, window size 24, 4 stages, mlp ratio 4, drop path 0.3. The card calls it Lumi-T and Luminance Transformer. It does not name a Microsoft initialization checkpoint. The layout matches the public SwinV2-Large shape, which is not by itself a parameter count.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Lumi-Transformer is a UNSW-associated vision model for electroluminescence images of solar cells. The Master-AI-Lab card, created 8 March 2023, releases it under MIT and credits UNSW Katana HPC and GreenDynamics. Collected 2026-10-15.", "what_it_is_for": "The card says the model reads near-infrared electroluminescence images and is meant to judge solar-cell condition. The reported test is defective versus functional on the public ELPV dataset. The prose also says it predicts cell efficiency. The published table is classification, not a regression curve.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/festcat/festcat-bet": { "id": "upc/festcat/festcat-bet", "name": "FestCat Bet", "maker": "upc", "family": "upc/festcat", "status": "active", "confidence": "high", "weights": "open-source", "license": "LGPL-2.1-or-later", "release_date": "2014-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two Festival voices for the same speaker. Clunits 1.2 is unit selection. HTS 1.3 is an HTS statistical parametric voice. A 2009 FestCat paper describes HTS voices with hidden semi-Markov models and global variance; the 2016 package page does not repeat that recipe.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FestCat Bet is a Catalan text-to-speech voice from the TALP research center at Universitat Polit\u00e8cnica de Catalunya. The project site still offers two builds, clunits 1.2 (3 October 2014) and HTS 1.3 (17 August 2016). Collected on 2026-10-15. The voice is maintained as a downloadable Festival package, not as a hosted API. The last package date is 2016.", "what_it_is_for": "FestCat extends Festival so it can speak Catalan. Each speaker is a separate voice directory, `upc_ca_bet`. The download page says HTS voices are the smaller parametric ones and clunits voices are the larger unit-selection ones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/festcat/festcat-eli": { "id": "upc/festcat/festcat-eli", "name": "FestCat Eli", "maker": "upc", "family": "upc/festcat", "status": "active", "confidence": "high", "weights": "open-source", "license": "LGPL-2.1-or-later", "release_date": "2014-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two Festival voices for the same speaker. Clunits 1.2 is unit selection. HTS 1.3 is an HTS statistical parametric voice. A 2009 FestCat paper describes HTS voices with hidden semi-Markov models and global variance; the 2016 package page does not repeat that recipe.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FestCat Eli is a Catalan text-to-speech voice from the TALP research center at Universitat Polit\u00e8cnica de Catalunya. The project site still offers two builds, clunits 1.2 (3 October 2014) and HTS 1.3 (17 August 2016). Collected on 2026-10-15. The voice is maintained as a downloadable Festival package, not as a hosted API. The last package date is 2016.", "what_it_is_for": "FestCat extends Festival so it can speak Catalan. Each speaker is a separate voice directory, `upc_ca_eli`. The download page says HTS voices are the smaller parametric ones and clunits voices are the larger unit-selection ones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/festcat/festcat-eva": { "id": "upc/festcat/festcat-eva", "name": "FestCat Eva", "maker": "upc", "family": "upc/festcat", "status": "active", "confidence": "high", "weights": "open-source", "license": "LGPL-2.1-or-later", "release_date": "2014-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two Festival voices for the same speaker. Clunits 1.2 is unit selection. HTS 1.3 is an HTS statistical parametric voice. A 2009 FestCat paper describes HTS voices with hidden semi-Markov models and global variance; the 2016 package page does not repeat that recipe.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FestCat Eva is a Catalan text-to-speech voice from the TALP research center at Universitat Polit\u00e8cnica de Catalunya. The project site still offers two builds, clunits 1.2 (3 October 2014) and HTS 1.3 (17 August 2016). Collected on 2026-10-15. The voice is maintained as a downloadable Festival package, not as a hosted API. The last package date is 2016.", "what_it_is_for": "FestCat extends Festival so it can speak Catalan. Each speaker is a separate voice directory, `upc_ca_eva`. The download page says HTS voices are the smaller parametric ones and clunits voices are the larger unit-selection ones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/festcat/festcat-jan": { "id": "upc/festcat/festcat-jan", "name": "FestCat Jan", "maker": "upc", "family": "upc/festcat", "status": "active", "confidence": "high", "weights": "open-source", "license": "LGPL-2.1-or-later", "release_date": "2014-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two Festival voices for the same speaker. Clunits 1.2 is unit selection. HTS 1.3 is an HTS statistical parametric voice. A 2009 FestCat paper describes HTS voices with hidden semi-Markov models and global variance; the 2016 package page does not repeat that recipe.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FestCat Jan is a Catalan text-to-speech voice from the TALP research center at Universitat Polit\u00e8cnica de Catalunya. The project site still offers two builds, clunits 1.2 (3 October 2014) and HTS 1.3 (17 August 2016). Collected on 2026-10-15. The voice is maintained as a downloadable Festival package, not as a hosted API. The last package date is 2016.", "what_it_is_for": "FestCat extends Festival so it can speak Catalan. Each speaker is a separate voice directory, `upc_ca_jan`. The download page says HTS voices are the smaller parametric ones and clunits voices are the larger unit-selection ones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/festcat/festcat-mar": { "id": "upc/festcat/festcat-mar", "name": "FestCat Mar", "maker": "upc", "family": "upc/festcat", "status": "active", "confidence": "high", "weights": "open-source", "license": "LGPL-2.1-or-later", "release_date": "2014-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two Festival voices for the same speaker. Clunits 1.2 is unit selection. HTS 1.3 is an HTS statistical parametric voice. A 2009 FestCat paper describes HTS voices with hidden semi-Markov models and global variance; the 2016 package page does not repeat that recipe.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FestCat Mar is a Catalan text-to-speech voice from the TALP research center at Universitat Polit\u00e8cnica de Catalunya. The project site still offers two builds, clunits 1.2 (3 October 2014) and HTS 1.3 (17 August 2016). Collected on 2026-10-15. The voice is maintained as a downloadable Festival package, not as a hosted API. The last package date is 2016.", "what_it_is_for": "FestCat extends Festival so it can speak Catalan. Each speaker is a separate voice directory, `upc_ca_mar`. The download page says HTS voices are the smaller parametric ones and clunits voices are the larger unit-selection ones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/festcat/festcat-ona": { "id": "upc/festcat/festcat-ona", "name": "FestCat Ona", "maker": "upc", "family": "upc/festcat", "status": "active", "confidence": "high", "weights": "open-source", "license": "LGPL-2.1-or-later", "release_date": "2014-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two Festival voices for the same speaker. Clunits 1.2 is unit selection. HTS 1.3 is an HTS statistical parametric voice. A 2009 FestCat paper describes HTS voices with hidden semi-Markov models and global variance; the 2016 package page does not repeat that recipe.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FestCat Ona is a Catalan text-to-speech voice from the TALP research center at Universitat Polit\u00e8cnica de Catalunya. The project site still offers two builds, clunits 1.2 (3 October 2014) and HTS 1.3 (17 August 2016). Collected on 2026-10-15. The voice is maintained as a downloadable Festival package, not as a hosted API. The last package date is 2016.", "what_it_is_for": "FestCat extends Festival so it can speak Catalan. Each speaker is a separate voice directory, `upc_ca_ona`. The download page says HTS voices are the smaller parametric ones and clunits voices are the larger unit-selection ones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/festcat/festcat-pau": { "id": "upc/festcat/festcat-pau", "name": "FestCat Pau", "maker": "upc", "family": "upc/festcat", "status": "active", "confidence": "high", "weights": "open-source", "license": "LGPL-2.1-or-later", "release_date": "2014-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two Festival voices for the same speaker. Clunits 1.2 is unit selection. HTS 1.3 is an HTS statistical parametric voice. A 2009 FestCat paper describes HTS voices with hidden semi-Markov models and global variance; the 2016 package page does not repeat that recipe.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FestCat Pau is a Catalan text-to-speech voice from the TALP research center at Universitat Polit\u00e8cnica de Catalunya. The project site still offers two builds, clunits 1.2 (3 October 2014) and HTS 1.3 (17 August 2016). Collected on 2026-10-15. The voice is maintained as a downloadable Festival package, not as a hosted API. The last package date is 2016.", "what_it_is_for": "FestCat extends Festival so it can speak Catalan. Each speaker is a separate voice directory, `upc_ca_pau`. The download page says HTS voices are the smaller parametric ones and clunits voices are the larger unit-selection ones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/festcat/festcat-pep": { "id": "upc/festcat/festcat-pep", "name": "FestCat Pep", "maker": "upc", "family": "upc/festcat", "status": "active", "confidence": "high", "weights": "open-source", "license": "LGPL-2.1-or-later", "release_date": "2014-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two Festival voices for the same speaker. Clunits 1.2 is unit selection. HTS 1.3 is an HTS statistical parametric voice. A 2009 FestCat paper describes HTS voices with hidden semi-Markov models and global variance; the 2016 package page does not repeat that recipe.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FestCat Pep is a Catalan text-to-speech voice from the TALP research center at Universitat Polit\u00e8cnica de Catalunya. The project site still offers two builds, clunits 1.2 (3 October 2014) and HTS 1.3 (17 August 2016). Collected on 2026-10-15. The voice is maintained as a downloadable Festival package, not as a hosted API. The last package date is 2016.", "what_it_is_for": "FestCat extends Festival so it can speak Catalan. Each speaker is a separate voice directory, `upc_ca_pep`. The download page says HTS voices are the smaller parametric ones and clunits voices are the larger unit-selection ones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/festcat/festcat-pol": { "id": "upc/festcat/festcat-pol", "name": "FestCat Pol", "maker": "upc", "family": "upc/festcat", "status": "active", "confidence": "high", "weights": "open-source", "license": "LGPL-2.1-or-later", "release_date": "2014-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two Festival voices for the same speaker. Clunits 1.2 is unit selection. HTS 1.3 is an HTS statistical parametric voice. A 2009 FestCat paper describes HTS voices with hidden semi-Markov models and global variance; the 2016 package page does not repeat that recipe.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FestCat Pol is a Catalan text-to-speech voice from the TALP research center at Universitat Polit\u00e8cnica de Catalunya. The project site still offers two builds, clunits 1.2 (3 October 2014) and HTS 1.3 (17 August 2016). Collected on 2026-10-15. The voice is maintained as a downloadable Festival package, not as a hosted API. The last package date is 2016.", "what_it_is_for": "FestCat extends Festival so it can speak Catalan. Each speaker is a separate voice directory, `upc_ca_pol`. The download page says HTS voices are the smaller parametric ones and clunits voices are the larger unit-selection ones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/festcat/festcat-teo": { "id": "upc/festcat/festcat-teo", "name": "FestCat Teo", "maker": "upc", "family": "upc/festcat", "status": "active", "confidence": "high", "weights": "open-source", "license": "LGPL-2.1-or-later", "release_date": "2014-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two Festival voices for the same speaker. Clunits 1.2 is unit selection. HTS 1.3 is an HTS statistical parametric voice. A 2009 FestCat paper describes HTS voices with hidden semi-Markov models and global variance; the 2016 package page does not repeat that recipe.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FestCat Teo is a Catalan text-to-speech voice from the TALP research center at Universitat Polit\u00e8cnica de Catalunya. The project site still offers two builds, clunits 1.2 (3 October 2014) and HTS 1.3 (17 August 2016). Collected on 2026-10-15. The voice is maintained as a downloadable Festival package, not as a hosted API. The last package date is 2016.", "what_it_is_for": "FestCat extends Festival so it can speak Catalan. Each speaker is a separate voice directory, `upc_ca_teo`. The download page says HTS voices are the smaller parametric ones and clunits voices are the larger unit-selection ones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/festcat/festcat-uri": { "id": "upc/festcat/festcat-uri", "name": "FestCat Uri", "maker": "upc", "family": "upc/festcat", "status": "active", "confidence": "high", "weights": "open-source", "license": "LGPL-2.1-or-later", "release_date": "2014-10-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two Festival voices for the same speaker. Clunits 1.2 is unit selection. HTS 1.3 is an HTS statistical parametric voice. A 2009 FestCat paper describes HTS voices with hidden semi-Markov models and global variance; the 2016 package page does not repeat that recipe.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FestCat Uri is a Catalan text-to-speech voice from the TALP research center at Universitat Polit\u00e8cnica de Catalunya. The project site still offers two builds, clunits 1.2 (3 October 2014) and HTS 1.3 (17 August 2016). Collected on 2026-10-15. The voice is maintained as a downloadable Festival package, not as a hosted API. The last package date is 2016.", "what_it_is_for": "FestCat extends Festival so it can speak Catalan. Each speaker is a separate voice directory, `upc_ca_uri`. The download page says HTS voices are the smaller parametric ones and clunits voices are the larger unit-selection ones.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/nllb/nllb-200-distilled-1-3b-covost2-en-to-15": { "id": "upc/nllb/nllb-200-distilled-1-3b-covost2-en-to-15", "name": "NLLB-200 distilled 1.3B CoVoST2 en-to-15", "maker": "upc", "family": "upc/nllb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-23", "context_length": null, "max_output_tokens": null, "parameters": "1300000000", "architecture": "Multilingual fine-tune of facebook/nllb-200-distilled-1.3B on CoVoST2 English-to-15 text. The ZeroSwot paper describes the 1.3B NLLB text encoder as 24 layers, width 1024, 256k vocabulary. That is the encoder, not a full encoder-decoder count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is a text translation model from the Machine Translation group at Universitat Polit\u00e8cnica de Catalunya, collected on 2026-10-15. It is a multilingual fine-tune of Meta's facebook/nllb-200-distilled-1.3B on CoVoST2 English-to-15 text, released as a companion to ZeroSwot. Status is active on Hugging Face. It is not a hosted copy of the original NLLB weights.", "what_it_is_for": "The card says it is a multilingually fine-tuned NLLB used for the speech-translation paper. The group uses it as the text model that a ZeroSwot speech encoder plugs into. Fine-tuning details are deferred to Appendix D of the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/nllb/nllb-200-distilled-1-3b-mustc-en-to-8": { "id": "upc/nllb/nllb-200-distilled-1-3b-mustc-en-to-8", "name": "NLLB-200 distilled 1.3B MuST-C en-to-8", "maker": "upc", "family": "upc/nllb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-23", "context_length": null, "max_output_tokens": null, "parameters": "1300000000", "architecture": "Multilingual fine-tune of facebook/nllb-200-distilled-1.3B on MuST-C v1.0 English-to-8 text. The ZeroSwot paper describes the 1.3B NLLB text encoder as 24 layers, width 1024, 256k vocabulary. That is the encoder, not a full encoder-decoder count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is a text translation model from the Machine Translation group at Universitat Polit\u00e8cnica de Catalunya, collected on 2026-10-15. It is a multilingual fine-tune of Meta's facebook/nllb-200-distilled-1.3B on MuST-C v1.0 English-to-8 text, released as a companion to ZeroSwot. Status is active on Hugging Face. It is not a hosted copy of the original NLLB weights.", "what_it_is_for": "The card says it is a multilingually fine-tuned NLLB used for the speech-translation paper. The group uses it as the text model that a ZeroSwot speech encoder plugs into. Fine-tuning details are deferred to Appendix D of the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/nllb/nllb-200-distilled-600m-covost2-en-to-15": { "id": "upc/nllb/nllb-200-distilled-600m-covost2-en-to-15", "name": "NLLB-200 distilled 600M CoVoST2 en-to-15", "maker": "upc", "family": "upc/nllb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-23", "context_length": null, "max_output_tokens": null, "parameters": "600000000", "architecture": "Multilingual fine-tune of facebook/nllb-200-distilled-600M on CoVoST2 English-to-15 text. The ZeroSwot paper describes the 600M NLLB text encoder as 12 layers, width 1024, 256k vocabulary. That is the encoder, not a full encoder-decoder count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is a text translation model from the Machine Translation group at Universitat Polit\u00e8cnica de Catalunya, collected on 2026-10-15. It is a multilingual fine-tune of Meta's facebook/nllb-200-distilled-600M on CoVoST2 English-to-15 text, released as a companion to ZeroSwot. Status is active on Hugging Face. It is not a hosted copy of the original NLLB weights.", "what_it_is_for": "The card says it is a multilingually fine-tuned NLLB used for the speech-translation paper. The group uses it as the text model that a ZeroSwot speech encoder plugs into. Fine-tuning details are deferred to Appendix D of the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/nllb/nllb-200-distilled-600m-mustc-en-to-8": { "id": "upc/nllb/nllb-200-distilled-600m-mustc-en-to-8", "name": "NLLB-200 distilled 600M MuST-C en-to-8", "maker": "upc", "family": "upc/nllb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-23", "context_length": null, "max_output_tokens": null, "parameters": "600000000", "architecture": "Multilingual fine-tune of facebook/nllb-200-distilled-600M on MuST-C v1.0 English-to-8 text. The ZeroSwot paper describes the 600M NLLB text encoder as 12 layers, width 1024, 256k vocabulary. That is the encoder, not a full encoder-decoder count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is a text translation model from the Machine Translation group at Universitat Polit\u00e8cnica de Catalunya, collected on 2026-10-15. It is a multilingual fine-tune of Meta's facebook/nllb-200-distilled-600M on MuST-C v1.0 English-to-8 text, released as a companion to ZeroSwot. Status is active on Hugging Face. It is not a hosted copy of the original NLLB weights.", "what_it_is_for": "The card says it is a multilingually fine-tuned NLLB used for the speech-translation paper. The group uses it as the text model that a ZeroSwot speech encoder plugs into. Fine-tuning details are deferred to Appendix D of the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/shas/shas": { "id": "upc/shas/shas", "name": "SHAS", "maker": "upc", "family": "upc/shas", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Supervised Hybrid Audio Segmentation. A frame classifier on facebook/wav2vec2-xls-r-300m, keeping 15 wav2vec layers, plus a probabilistic divide-and-conquer splitter. The README's monolingual training example uses 1 classifier transformer layer. The multilingual example uses 2.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SHAS (Supervised Hybrid Audio Segmentation) is a speech-segmentation model from the Machine Translation group at Universitat Polit\u00e8cnica de Catalunya. It was collected on 2026-10-15. The Interspeech 2022 paper and the MIT repository release six named frame-classifier checkpoints. They are versions of one model, not separate products. The repository is still public.", "what_it_is_for": "The README says long recordings such as TED talks have to be split before end-to-end speech translation, and SHAS learns a segmentation from manually segmented speech. A wav2vec 2.0 classifier scores frames. A probabilistic divide-and-conquer step cuts at low-probability frames until every segment is under a chosen length.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/zeroswot/zeroswot-large-asr-cv-en-to-200": { "id": "upc/zeroswot/zeroswot-large-asr-cv-en-to-200", "name": "ZeroSwot-Large ASR CommonVoice en-to-200", "maker": "upc", "family": "upc/zeroswot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "350000000", "architecture": "Speech encoder: wav2vec 2.0 Large CTC (24 layers, width 1024, 315M) plus a 3-layer transformer subword compressor (35M). Trained with CTC compression and optimal transport so the encoder matches NLLB token embeddings. The Hub checkpoint is the encoder; text is decoded by a separate NLLB model.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZeroSwot is a zero-shot end-to-end speech translation system from the Machine Translation group at Universitat Polit\u00e8cnica de Catalunya. This checkpoint was trained on Common Voice speech and is meant to be decoded with NLLB-200 distilled 1.3B, original. It was collected on 2026-10-15 and is still published on Hugging Face. One paper author, Marta R. Costa-juss\u00e0, is also listed with a Meta AI affiliation on an arXiv HTML copy. The code and weights are released from the UPC group repository and from the first author's Hub account, not from Meta.", "what_it_is_for": "The card says the encoder is adapted to NLLB embedding space with a subword compressor and optimal transport, using ASR data only, so speech can be translated without paired speech-translation training. At inference the encoder outputs are passed into the NLLB decoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/zeroswot/zeroswot-large-asr-cv-mt-covost2-en-to-15": { "id": "upc/zeroswot/zeroswot-large-asr-cv-mt-covost2-en-to-15", "name": "ZeroSwot-Large ASR CommonVoice, MT CoVoST2 en-to-15", "maker": "upc", "family": "upc/zeroswot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "350000000", "architecture": "Speech encoder: wav2vec 2.0 Large CTC (24 layers, width 1024, 315M) plus a 3-layer transformer subword compressor (35M). Trained with CTC compression and optimal transport so the encoder matches NLLB token embeddings. The Hub checkpoint is the encoder; text is decoded by a separate NLLB model.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZeroSwot is a zero-shot end-to-end speech translation system from the Machine Translation group at Universitat Polit\u00e8cnica de Catalunya. This checkpoint was trained on Common Voice speech and is meant to be decoded with NLLB-200 distilled 1.3B fine-tuned on CoVoST2 text. It was collected on 2026-10-15 and is still published on Hugging Face. One paper author, Marta R. Costa-juss\u00e0, is also listed with a Meta AI affiliation on an arXiv HTML copy. The code and weights are released from the UPC group repository and from the first author's Hub account, not from Meta.", "what_it_is_for": "The card says the encoder is adapted to NLLB embedding space with a subword compressor and optimal transport, using ASR data only, so speech can be translated without paired speech-translation training. At inference the encoder outputs are passed into the NLLB decoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/zeroswot/zeroswot-large-asr-mustc-en-to-200": { "id": "upc/zeroswot/zeroswot-large-asr-mustc-en-to-200", "name": "ZeroSwot-Large ASR MuST-C en-to-200", "maker": "upc", "family": "upc/zeroswot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "350000000", "architecture": "Speech encoder: wav2vec 2.0 Large CTC (24 layers, width 1024, 315M) plus a 3-layer transformer subword compressor (35M). Trained with CTC compression and optimal transport so the encoder matches NLLB token embeddings. The Hub checkpoint is the encoder; text is decoded by a separate NLLB model.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZeroSwot is a zero-shot end-to-end speech translation system from the Machine Translation group at Universitat Polit\u00e8cnica de Catalunya. This checkpoint was trained on MuST-C v1.0 speech and is meant to be decoded with NLLB-200 distilled 1.3B, original. It was collected on 2026-10-15 and is still published on Hugging Face. One paper author, Marta R. Costa-juss\u00e0, is also listed with a Meta AI affiliation on an arXiv HTML copy. The code and weights are released from the UPC group repository and from the first author's Hub account, not from Meta.", "what_it_is_for": "The card says the encoder is adapted to NLLB embedding space with a subword compressor and optimal transport, using ASR data only, so speech can be translated without paired speech-translation training. At inference the encoder outputs are passed into the NLLB decoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/zeroswot/zeroswot-large-asr-mustc-mt-mustc-en-to-8": { "id": "upc/zeroswot/zeroswot-large-asr-mustc-mt-mustc-en-to-8", "name": "ZeroSwot-Large ASR MuST-C, MT MuST-C en-to-8", "maker": "upc", "family": "upc/zeroswot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "350000000", "architecture": "Speech encoder: wav2vec 2.0 Large CTC (24 layers, width 1024, 315M) plus a 3-layer transformer subword compressor (35M). Trained with CTC compression and optimal transport so the encoder matches NLLB token embeddings. The Hub checkpoint is the encoder; text is decoded by a separate NLLB model.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZeroSwot is a zero-shot end-to-end speech translation system from the Machine Translation group at Universitat Polit\u00e8cnica de Catalunya. This checkpoint was trained on MuST-C v1.0 speech and is meant to be decoded with NLLB-200 distilled 1.3B fine-tuned on MuST-C text. It was collected on 2026-10-15 and is still published on Hugging Face. One paper author, Marta R. Costa-juss\u00e0, is also listed with a Meta AI affiliation on an arXiv HTML copy. The code and weights are released from the UPC group repository and from the first author's Hub account, not from Meta.", "what_it_is_for": "The card says the encoder is adapted to NLLB embedding space with a subword compressor and optimal transport, using ASR data only, so speech can be translated without paired speech-translation training. At inference the encoder outputs are passed into the NLLB decoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/zeroswot/zeroswot-medium-asr-cv-en-to-200": { "id": "upc/zeroswot/zeroswot-medium-asr-cv-en-to-200", "name": "ZeroSwot-Medium ASR CommonVoice en-to-200", "maker": "upc", "family": "upc/zeroswot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "350000000", "architecture": "Speech encoder: wav2vec 2.0 Large CTC (24 layers, width 1024, 315M) plus a 3-layer transformer subword compressor (35M). Trained with CTC compression and optimal transport so the encoder matches NLLB token embeddings. The Hub checkpoint is the encoder; text is decoded by a separate NLLB model.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZeroSwot is a zero-shot end-to-end speech translation system from the Machine Translation group at Universitat Polit\u00e8cnica de Catalunya. This checkpoint was trained on Common Voice speech and is meant to be decoded with NLLB-200 distilled 600M, original. It was collected on 2026-10-15 and is still published on Hugging Face. One paper author, Marta R. Costa-juss\u00e0, is also listed with a Meta AI affiliation on an arXiv HTML copy. The code and weights are released from the UPC group repository and from the first author's Hub account, not from Meta.", "what_it_is_for": "The card says the encoder is adapted to NLLB embedding space with a subword compressor and optimal transport, using ASR data only, so speech can be translated without paired speech-translation training. At inference the encoder outputs are passed into the NLLB decoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/zeroswot/zeroswot-medium-asr-cv-mt-covost2-en-to-15": { "id": "upc/zeroswot/zeroswot-medium-asr-cv-mt-covost2-en-to-15", "name": "ZeroSwot-Medium ASR CommonVoice, MT CoVoST2 en-to-15", "maker": "upc", "family": "upc/zeroswot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "350000000", "architecture": "Speech encoder: wav2vec 2.0 Large CTC (24 layers, width 1024, 315M) plus a 3-layer transformer subword compressor (35M). Trained with CTC compression and optimal transport so the encoder matches NLLB token embeddings. The Hub checkpoint is the encoder; text is decoded by a separate NLLB model.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZeroSwot is a zero-shot end-to-end speech translation system from the Machine Translation group at Universitat Polit\u00e8cnica de Catalunya. This checkpoint was trained on Common Voice speech and is meant to be decoded with NLLB-200 distilled 600M fine-tuned on CoVoST2 text. It was collected on 2026-10-15 and is still published on Hugging Face. One paper author, Marta R. Costa-juss\u00e0, is also listed with a Meta AI affiliation on an arXiv HTML copy. The code and weights are released from the UPC group repository and from the first author's Hub account, not from Meta.", "what_it_is_for": "The card says the encoder is adapted to NLLB embedding space with a subword compressor and optimal transport, using ASR data only, so speech can be translated without paired speech-translation training. At inference the encoder outputs are passed into the NLLB decoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/zeroswot/zeroswot-medium-asr-mustc-en-to-200": { "id": "upc/zeroswot/zeroswot-medium-asr-mustc-en-to-200", "name": "ZeroSwot-Medium ASR MuST-C en-to-200", "maker": "upc", "family": "upc/zeroswot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "350000000", "architecture": "Speech encoder: wav2vec 2.0 Large CTC (24 layers, width 1024, 315M) plus a 3-layer transformer subword compressor (35M). Trained with CTC compression and optimal transport so the encoder matches NLLB token embeddings. The Hub checkpoint is the encoder; text is decoded by a separate NLLB model.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZeroSwot is a zero-shot end-to-end speech translation system from the Machine Translation group at Universitat Polit\u00e8cnica de Catalunya. This checkpoint was trained on MuST-C v1.0 speech and is meant to be decoded with NLLB-200 distilled 600M, original. It was collected on 2026-10-15 and is still published on Hugging Face. One paper author, Marta R. Costa-juss\u00e0, is also listed with a Meta AI affiliation on an arXiv HTML copy. The code and weights are released from the UPC group repository and from the first author's Hub account, not from Meta.", "what_it_is_for": "The card says the encoder is adapted to NLLB embedding space with a subword compressor and optimal transport, using ASR data only, so speech can be translated without paired speech-translation training. At inference the encoder outputs are passed into the NLLB decoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upc/zeroswot/zeroswot-medium-asr-mustc-mt-mustc-en-to-8": { "id": "upc/zeroswot/zeroswot-medium-asr-mustc-mt-mustc-en-to-8", "name": "ZeroSwot-Medium ASR MuST-C, MT MuST-C en-to-8", "maker": "upc", "family": "upc/zeroswot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-06-25", "context_length": null, "max_output_tokens": null, "parameters": "350000000", "architecture": "Speech encoder: wav2vec 2.0 Large CTC (24 layers, width 1024, 315M) plus a 3-layer transformer subword compressor (35M). Trained with CTC compression and optimal transport so the encoder matches NLLB token embeddings. The Hub checkpoint is the encoder; text is decoded by a separate NLLB model.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZeroSwot is a zero-shot end-to-end speech translation system from the Machine Translation group at Universitat Polit\u00e8cnica de Catalunya. This checkpoint was trained on MuST-C v1.0 speech and is meant to be decoded with NLLB-200 distilled 600M fine-tuned on MuST-C text. It was collected on 2026-10-15 and is still published on Hugging Face. One paper author, Marta R. Costa-juss\u00e0, is also listed with a Meta AI affiliation on an arXiv HTML copy. The code and weights are released from the UPC group repository and from the first author's Hub account, not from Meta.", "what_it_is_for": "The card says the encoder is adapted to NLLB embedding space with a subword compressor and optimal transport, using ASR data only, so speech can be translated without paired speech-translation training. At inference the encoder outputs are passed into the NLLB decoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/allmusiccaps/allmusiccaps-baseline": { "id": "upf/allmusiccaps/allmusiccaps-baseline", "name": "AllMusicCaps baseline", "maker": "upf", "family": "upf/allmusiccaps", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2026-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLAP-style dual encoder. Audio tower is OMAR-RQ. Text tower is frozen all-mpnet-base-v2, reloaded from stock weights at construction. Card says embeddings are 512-dimensional and audio is 24 kHz. Audio encoder layers are deferred to config.gin and were not copied.", "modalities": { "input": [ "audio", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AllMusicCaps baseline is a music-text contrastive model from the Music Technology Group at Universitat Pompeu Fabra. It is an active Hub release under CC BY-NC-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "Baseline data configuration named on the card: LPMC + M4-RAG + Freesound + PSE. Music-text retrieval and embedding over album-review captions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/allmusiccaps/allmusiccaps-lejepa": { "id": "upf/allmusiccaps/allmusiccaps-lejepa", "name": "AllMusicCaps LeJEPA", "maker": "upf", "family": "upf/allmusiccaps", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2026-08-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLAP-style dual encoder. Audio tower is OMAR-RQ. Text tower is frozen all-mpnet-base-v2, reloaded from stock weights at construction. Card says embeddings are 512-dimensional and audio is 24 kHz. Card says all 12 audio-encoder layers are used.", "modalities": { "input": [ "audio", "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AllMusicCaps LeJEPA is a music-text contrastive model from the Music Technology Group at Universitat Pompeu Fabra. It is an active Hub release under CC BY-NC-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "LeJEPA training objective, described on the card as negative-free, invariance plus SigReg. Same 512-d music-text embedding setup.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/approachability/approachability-2c": { "id": "upf/approachability/approachability-2c", "name": "Approachability 2-class", "maker": "upf", "family": "upf/approachability", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Approachability 2-class is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Whether music is likely to be accessible to a general public. Two-level classifier on Discogs-EffNet. The page does not name the two labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/approachability/approachability-3c": { "id": "upf/approachability/approachability-3c", "name": "Approachability 3-class", "maker": "upf", "family": "upf/approachability", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Approachability 3-class is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Same approachability task with three levels. Label names are not on the page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/approachability/approachability-regression": { "id": "upf/approachability/approachability-regression", "name": "Approachability regression", "maker": "upf", "family": "upf/approachability", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Approachability regression is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Continuous approachability score on Discogs-EffNet. Output layer in the example is model/Identity.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/arousal-valence/deam": { "id": "upf/arousal-valence/deam", "name": "DEAM arousal-valence", "maker": "upf", "family": "upf/arousal-valence", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DEAM arousal-valence is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Valence and arousal regression on the DEAM dataset. Two dimensions, range [1, 9], order valence then arousal.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/arousal-valence/emomusic": { "id": "upf/arousal-valence/emomusic", "name": "emoMusic arousal-valence", "maker": "upf", "family": "upf/arousal-valence", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "emoMusic arousal-valence is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Valence and arousal regression on the emoMusic dataset. Two dimensions, range [1, 9].", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/arousal-valence/muse": { "id": "upf/arousal-valence/muse", "name": "MuSE arousal-valence", "maker": "upf", "family": "upf/arousal-valence", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MuSE arousal-valence is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Valence and arousal regression on the MuSE dataset. Two dimensions, range [1, 9]. The page's reference block under this head cites the MusAV paper. That citation mismatch is noted in gaps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/autotag/msd-top50": { "id": "upf/autotag/msd-top50", "name": "MSD top-50 tags", "maker": "upf", "family": "upf/autotag", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MSD top-50 tags is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Automatic tagging with the top 50 Last.fm / Million Song Dataset tags. This is the classification head, not the MSD-MusiCNN embedding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/autotag/mtg-jamendo-top50tags": { "id": "upf/autotag/mtg-jamendo-top50tags", "name": "MTG-Jamendo top-50 tags", "maker": "upf", "family": "upf/autotag", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTG-Jamendo top-50 tags is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Automatic tagging with the top 50 MTG-Jamendo tags. The tag list is on the Essentia page. No artist-embedding head is in the weight list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/autotag/mtt": { "id": "upf/autotag/mtt", "name": "MagnaTagATune top-50", "maker": "upf", "family": "upf/autotag", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MagnaTagATune top-50 is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Automatic tagging with the top 50 MagnaTagATune tags.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/danceability/danceability": { "id": "upf/danceability/danceability", "name": "Danceability", "maker": "upf", "family": "upf/danceability", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Danceability is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Two-class danceability: danceable, not_danceable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/discogs-effnet/discogs-effnet": { "id": "upf/discogs-effnet/discogs-effnet", "name": "Discogs-EffNet", "maker": "upf", "family": "upf/discogs-effnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2023-05-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "EfficientNet audio encoder (Essentia algorithm TensorflowPredictEffnetDiscogs). Output tensor name in the examples is PartitionedCall:1. Trained on an in-house set annotated with Discogs metadata.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Discogs-EffNet is a Universitat Pompeu Fabra Music Technology Group audio embedding in the Discogs-EffNet line. Weights are active in Essentia under CC BY-NC-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "Multi-label classification embedding trained to predict 400 Discogs music-style labels. This is the classification model, not the contrastive similarity models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/discogs-effnet/discogs-effnet-artist": { "id": "upf/discogs-effnet/discogs-effnet-artist", "name": "Discogs-EffNet artist", "maker": "upf", "family": "upf/discogs-effnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2023-05-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "EfficientNet audio encoder (Essentia algorithm TensorflowPredictEffnetDiscogs). Output tensor name in the examples is PartitionedCall:1. Trained on an in-house set annotated with Discogs metadata.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Discogs-EffNet artist is a Universitat Pompeu Fabra Music Technology Group audio embedding in the Discogs-EffNet line. Weights are active in Essentia under CC BY-NC-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "Contrastive Discogs-EffNet trained so that tracks from the same artist embed near each other.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/discogs-effnet/discogs-effnet-label": { "id": "upf/discogs-effnet/discogs-effnet-label", "name": "Discogs-EffNet label", "maker": "upf", "family": "upf/discogs-effnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2023-05-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "EfficientNet audio encoder (Essentia algorithm TensorflowPredictEffnetDiscogs). Output tensor name in the examples is PartitionedCall:1. Trained on an in-house set annotated with Discogs metadata.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Discogs-EffNet label is a Universitat Pompeu Fabra Music Technology Group audio embedding in the Discogs-EffNet line. Weights are active in Essentia under CC BY-NC-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "Contrastive Discogs-EffNet trained on record-label associations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/discogs-effnet/discogs-effnet-multi": { "id": "upf/discogs-effnet/discogs-effnet-multi", "name": "Discogs-EffNet multi", "maker": "upf", "family": "upf/discogs-effnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2023-05-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "EfficientNet audio encoder (Essentia algorithm TensorflowPredictEffnetDiscogs). Output tensor name in the examples is PartitionedCall:1. Trained on an in-house set annotated with Discogs metadata.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Discogs-EffNet multi is a Universitat Pompeu Fabra Music Technology Group audio embedding in the Discogs-EffNet line. Weights are active in Essentia under CC BY-NC-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "Contrastive Discogs-EffNet trained on artist and track associations together. The page calls this the multi target.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/discogs-effnet/discogs-effnet-release": { "id": "upf/discogs-effnet/discogs-effnet-release", "name": "Discogs-EffNet release", "maker": "upf", "family": "upf/discogs-effnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2023-05-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "EfficientNet audio encoder (Essentia algorithm TensorflowPredictEffnetDiscogs). Output tensor name in the examples is PartitionedCall:1. Trained on an in-house set annotated with Discogs metadata.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Discogs-EffNet release is a Universitat Pompeu Fabra Music Technology Group audio embedding in the Discogs-EffNet line. Weights are active in Essentia under CC BY-NC-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "Contrastive Discogs-EffNet trained on release (album) associations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/discogs-effnet/discogs-effnet-track": { "id": "upf/discogs-effnet/discogs-effnet-track", "name": "Discogs-EffNet track", "maker": "upf", "family": "upf/discogs-effnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2023-05-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "EfficientNet audio encoder (Essentia algorithm TensorflowPredictEffnetDiscogs). Output tensor name in the examples is PartitionedCall:1. Trained on an in-house set annotated with Discogs metadata.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Discogs-EffNet track is a Universitat Pompeu Fabra Music Technology Group audio embedding in the Discogs-EffNet line. Weights are active in Essentia under CC BY-NC-SA 4.0. Collected 2026-10-15.", "what_it_is_for": "Contrastive Discogs-EffNet trained on track-level self-supervised associations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/engagement/engagement-2c": { "id": "upf/engagement/engagement-2c", "name": "Engagement 2-class", "maker": "upf", "family": "upf/engagement", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Engagement 2-class is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Whether music asks for active attention versus background listening. Two levels. Label names are not on the page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/engagement/engagement-3c": { "id": "upf/engagement/engagement-3c", "name": "Engagement 3-class", "maker": "upf", "family": "upf/engagement", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Engagement 3-class is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Same engagement task with three levels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/engagement/engagement-regression": { "id": "upf/engagement/engagement-regression", "name": "Engagement regression", "maker": "upf", "family": "upf/engagement", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Engagement regression is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Continuous engagement score on Discogs-EffNet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/fsd-sinet/fsd-sinet": { "id": "upf/fsd-sinet/fsd-sinet", "name": "FSD-SINet", "maker": "upf", "family": "upf/fsd-sinet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Shift-invariant VGG-style network (SINet). vgg42 uses twice the filters of vgg41. Shift invariance is trainable low-pass filters (tlpf), adaptive polyphase sampling (aps), or both. Parameter counts were not on the models page.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FSD-SINet is the Music Technology Group sound-event model released by Universitat Pompeu Fabra for FSD50K. Essentia ships four size and shift-invariance checkpoints as one named model. Collected 2026-10-15.", "what_it_is_for": "Audio event recognition and embedding extraction for 200 AudioSet-ontology classes drawn from FSD50K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/genre/genre-discogs400": { "id": "upf/genre/genre-discogs400", "name": "Genre Discogs400", "maker": "upf", "family": "upf/genre", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Genre Discogs400 is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Multi-label music-style classification over 400 Discogs styles. The style list is on the Essentia page and is not copied here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/genre/genre-discogs519": { "id": "upf/genre/genre-discogs519", "name": "Genre Discogs519", "maker": "upf", "family": "upf/genre", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Genre Discogs519 is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Multi-label music-style classification over 519 Discogs styles, on the updated MAEST 30-second 519-label embedding.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/instrument/fs-loop-ds": { "id": "upf/instrument/fs-loop-ds", "name": "Freesound loop role", "maker": "upf", "family": "upf/instrument", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Freesound loop role is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Instrument role of music loops from the Freesound Loop Dataset. Five classes: bass, chords, fx, melody, percussion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/instrument/mtg-jamendo-instrument": { "id": "upf/instrument/mtg-jamendo-instrument", "name": "MTG-Jamendo instrument", "maker": "upf", "family": "upf/instrument", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTG-Jamendo instrument is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Multi-label instrument tags, 40 classes from MTG-Jamendo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/jamendo/mtg-jamendo-genre": { "id": "upf/jamendo/mtg-jamendo-genre", "name": "MTG-Jamendo genre", "maker": "upf", "family": "upf/jamendo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTG-Jamendo genre is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Multi-label genre tagging on the 87-class genre subset of the MTG-Jamendo dataset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/jamendo/mtg-jamendo-moodtheme": { "id": "upf/jamendo/mtg-jamendo-moodtheme", "name": "MTG-Jamendo mood and theme", "maker": "upf", "family": "upf/jamendo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MTG-Jamendo mood and theme is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Multi-label mood and theme tags, 56 classes from MTG-Jamendo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/maest/maest": { "id": "upf/maest/maest", "name": "MAEST", "maker": "upf", "family": "upf/maest", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2023-09-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer based on PaSST, trained on mel spectrograms. Sequence lengths 5, 10, 20, or 30 seconds. Initialization is random (fs), DeiT (dw), or PaSST (pw). One checkpoint is a teacher-student run (ts). Embedding shape is described as [batch, 1, tokens, embedding_size], with CLS and DIST as the first two tokens. Parameter count was not on the models page.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAEST (Music Audio Efficient Spectrogram Transformer) is a Universitat Pompeu Fabra music-style embedding family from the Music Technology Group. Essentia treats the length and initialization checkpoints as versions of one model. The 30-second PaSST-initialized checkpoint is the one the models page calls most competitive, and a later 519-label checkpoint is described as an update. Collected 2026-10-15.", "what_it_is_for": "Predict Discogs-style labels and export transformer embeddings for downstream music tagging, emotion, and instrument tasks. The page recommends the CLS token, or CLS plus DIST plus a mean of patch tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/mood/mood-acoustic": { "id": "upf/mood/mood-acoustic", "name": "Mood acoustic", "maker": "upf", "family": "upf/mood", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mood acoustic is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Two-class sound type: acoustic, non_acoustic. Listed by Essentia under instrumentation even though the name says mood.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/mood/mood-aggressive": { "id": "upf/mood/mood-aggressive", "name": "Mood aggressive", "maker": "upf", "family": "upf/mood", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mood aggressive is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Two-class mood: aggressive, non_aggressive.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/mood/mood-electronic": { "id": "upf/mood/mood-electronic", "name": "Mood electronic", "maker": "upf", "family": "upf/mood", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mood electronic is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Two-class sound type: electronic, non_electronic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/mood/mood-happy": { "id": "upf/mood/mood-happy", "name": "Mood happy", "maker": "upf", "family": "upf/mood", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mood happy is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Two-class mood: happy, non_happy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/mood/mood-party": { "id": "upf/mood/mood-party", "name": "Mood party", "maker": "upf", "family": "upf/mood", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mood party is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Two-class mood: party, non_party.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/mood/mood-relaxed": { "id": "upf/mood/mood-relaxed", "name": "Mood relaxed", "maker": "upf", "family": "upf/mood", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mood relaxed is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Two-class mood: relaxed, non_relaxed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/mood/moods-mirex": { "id": "upf/mood/moods-mirex", "name": "Moods MIREX", "maker": "upf", "family": "upf/mood", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Moods MIREX is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Five MIREX audio-mood clusters. The cluster adjective lists are on the Essentia page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/musicnn/msd-musicnn": { "id": "upf/musicnn/msd-musicnn", "name": "MSD-MusiCNN", "maker": "upf", "family": "upf/musicnn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Convolutional music-tagging network (MusiCNN). The Essentia graph reads embeddings from model/dense/BiasAdd. Parameter count was not on the models page.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MSD-MusiCNN is the Music Technology Group embedding released by Universitat Pompeu Fabra for music auto-tagging. It is the MusiCNN network of Jordi Pons and Xavier Serra, shipped as an active Essentia feature extractor. Collected 2026-10-15.", "what_it_is_for": "Essentia describes it as an audio music embedding extractor based on auto-tagging with the 50 most common tags of the Last.fm / Million Song Dataset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/nsynth/nsynth-acoustic-electronic": { "id": "upf/nsynth/nsynth-acoustic-electronic", "name": "NSynth acoustic / electronic", "maker": "upf", "family": "upf/nsynth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NSynth acoustic / electronic is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Monophonic source origin on NSynth. Two classes: acoustic, electronic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/nsynth/nsynth-bright-dark": { "id": "upf/nsynth/nsynth-bright-dark", "name": "NSynth bright / dark", "maker": "upf", "family": "upf/nsynth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NSynth bright / dark is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Monophonic timbre color on NSynth. Two classes: bright, dark.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/nsynth/nsynth-instrument": { "id": "upf/nsynth/nsynth-instrument", "name": "NSynth instrument", "maker": "upf", "family": "upf/nsynth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NSynth instrument is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Monophonic instrument family on NSynth. Eleven classes: mallet, string, reed, guitar, synth_lead, vocal, bass, flute, keyboard, brass, organ.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/nsynth/nsynth-reverb": { "id": "upf/nsynth/nsynth-reverb", "name": "NSynth reverb", "maker": "upf", "family": "upf/nsynth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NSynth reverb is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Reverb detection on monophonic NSynth sources. Two classes: dry, wet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/omar-rq/omar-rq-base": { "id": "upf/omar-rq/omar-rq-base", "name": "OMAR-RQ base", "maker": "upf", "family": "upf/omar-rq", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2025-06-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conformer with RoPE, flash attention, and mixed precision. The paper states a base setup of 12 layers, 512 dimensions, and 90 million parameters, and a large setup of 24 layers, 1024 dimensions, and 580 million parameters (Conformer XL). It says development used the base setup and the reported final models used the large setup. Hub Freesound cards instead say 86 million and 660 million for those two sizes. Those figures disagree, so parameters is null except where a card states a size for that exact repo. This checkpoint: mel input, 15.63 Hz output rate, as in the card table.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OMAR-RQ OMAR-RQ base is a self-supervised audio encoder from the Music Technology Group at Universitat Pompeu Fabra. Status is active. Collected 2026-10-15.", "what_it_is_for": "Mel-spectrogram encoder at 15.63 Hz. The card says mel models tend to do better on tagging, structure, and difficulty. This row has the best difficulty MSE (1.65) and structure accuracy (0.647) in the vendor table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/omar-rq/omar-rq-base-freesound-large": { "id": "upf/omar-rq/omar-rq-base-freesound-large", "name": "OMAR-RQ Freesound large", "maker": "upf", "family": "upf/omar-rq", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2026-04-07", "context_length": null, "max_output_tokens": null, "parameters": "660000000", "architecture": "Conformer with RoPE, flash attention, and mixed precision. The paper states a base setup of 12 layers, 512 dimensions, and 90 million parameters, and a large setup of 24 layers, 1024 dimensions, and 580 million parameters (Conformer XL). It says development used the base setup and the reported final models used the large setup. Hub Freesound cards instead say 86 million and 660 million for those two sizes. Those figures disagree, so parameters is null except where a card states a size for that exact repo. This checkpoint: card callout: large architecture, multicodebook, Freesound data.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OMAR-RQ OMAR-RQ Freesound large is a self-supervised audio encoder from the Music Technology Group at Universitat Pompeu Fabra. The card states 660 million parameters. Status is active. Collected 2026-10-15.", "what_it_is_for": "Freesound-trained encoder for general sound research. The card says it follows the large architecture using the multicodebook approach.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/omar-rq/omar-rq-base-freesound-small": { "id": "upf/omar-rq/omar-rq-base-freesound-small", "name": "OMAR-RQ Freesound small", "maker": "upf", "family": "upf/omar-rq", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2025-09-03", "context_length": null, "max_output_tokens": null, "parameters": "86000000", "architecture": "Conformer with RoPE, flash attention, and mixed precision. The paper states a base setup of 12 layers, 512 dimensions, and 90 million parameters, and a large setup of 24 layers, 1024 dimensions, and 580 million parameters (Conformer XL). It says development used the base setup and the reported final models used the large setup. Hub Freesound cards instead say 86 million and 660 million for those two sizes. Those figures disagree, so parameters is null except where a card states a size for that exact repo. This checkpoint: card callout: base architecture, multicodebook, Freesound data.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OMAR-RQ OMAR-RQ Freesound small is a self-supervised audio encoder from the Music Technology Group at Universitat Pompeu Fabra. The card states 86 million parameters. Status is active. Collected 2026-10-15.", "what_it_is_for": "Freesound-trained encoder for general sound research. The card says it follows the base architecture using the multicodebook approach. The shared boilerplate still mentions 330,000 hours of music. The callout is the model-specific statement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/omar-rq/omar-rq-multicodebook": { "id": "upf/omar-rq/omar-rq-multicodebook", "name": "OMAR-RQ multicodebook", "maker": "upf", "family": "upf/omar-rq", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2025-06-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conformer with RoPE, flash attention, and mixed precision. The paper states a base setup of 12 layers, 512 dimensions, and 90 million parameters, and a large setup of 24 layers, 1024 dimensions, and 580 million parameters (Conformer XL). It says development used the base setup and the reported final models used the large setup. Hub Freesound cards instead say 86 million and 660 million for those two sizes. Those figures disagree, so parameters is null except where a card states a size for that exact repo. This checkpoint: mel input, 15.63 Hz, multicodebook.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OMAR-RQ OMAR-RQ multicodebook is a self-supervised audio encoder from the Music Technology Group at Universitat Pompeu Fabra. Status is active. Collected 2026-10-15.", "what_it_is_for": "Mel-spectrogram encoder at 15.63 Hz with a multicodebook target. Best tagging mAP in the vendor table (0.488).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/omar-rq/omar-rq-multifeature": { "id": "upf/omar-rq/omar-rq-multifeature", "name": "OMAR-RQ multifeature", "maker": "upf", "family": "upf/omar-rq", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2025-06-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conformer with RoPE, flash attention, and mixed precision. The paper states a base setup of 12 layers, 512 dimensions, and 90 million parameters, and a large setup of 24 layers, 1024 dimensions, and 580 million parameters (Conformer XL). It says development used the base setup and the reported final models used the large setup. Hub Freesound cards instead say 86 million and 660 million for those two sizes. Those figures disagree, so parameters is null except where a card states a size for that exact repo. This checkpoint: audio input, 18.75 Hz, multifeature.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OMAR-RQ OMAR-RQ multifeature is a self-supervised audio encoder from the Music Technology Group at Universitat Pompeu Fabra. Status is active. Collected 2026-10-15.", "what_it_is_for": "Waveform multifeature encoder at 18.75 Hz.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/omar-rq/omar-rq-multifeature-25hz": { "id": "upf/omar-rq/omar-rq-multifeature-25hz", "name": "OMAR-RQ multifeature 25 Hz", "maker": "upf", "family": "upf/omar-rq", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2025-06-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conformer with RoPE, flash attention, and mixed precision. The paper states a base setup of 12 layers, 512 dimensions, and 90 million parameters, and a large setup of 24 layers, 1024 dimensions, and 580 million parameters (Conformer XL). It says development used the base setup and the reported final models used the large setup. Hub Freesound cards instead say 86 million and 660 million for those two sizes. Those figures disagree, so parameters is null except where a card states a size for that exact repo. This checkpoint: audio input, 25 Hz.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OMAR-RQ OMAR-RQ multifeature 25 Hz is a self-supervised audio encoder from the Music Technology Group at Universitat Pompeu Fabra. Status is active. Collected 2026-10-15.", "what_it_is_for": "Waveform multifeature encoder at 25 Hz without finite scalar quantization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/omar-rq/omar-rq-multifeature-25hz-fsq": { "id": "upf/omar-rq/omar-rq-multifeature-25hz-fsq", "name": "OMAR-RQ multifeature 25 Hz FSQ", "maker": "upf", "family": "upf/omar-rq", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2025-06-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conformer with RoPE, flash attention, and mixed precision. The paper states a base setup of 12 layers, 512 dimensions, and 90 million parameters, and a large setup of 24 layers, 1024 dimensions, and 580 million parameters (Conformer XL). It says development used the base setup and the reported final models used the large setup. Hub Freesound cards instead say 86 million and 660 million for those two sizes. Those figures disagree, so parameters is null except where a card states a size for that exact repo. This checkpoint: audio input, 25 Hz, finite scalar quantization.", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OMAR-RQ OMAR-RQ multifeature 25 Hz FSQ is a self-supervised audio encoder from the Music Technology Group at Universitat Pompeu Fabra. Status is active. Collected 2026-10-15.", "what_it_is_for": "Waveform multifeature encoder at 25 Hz with finite scalar quantization. The card says this is the best of the five on pitch, chord, and beat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/omar-rq/omar-rq-multifeature-78m": { "id": "upf/omar-rq/omar-rq-multifeature-78m", "name": "OMAR-RQ multifeature 78M", "maker": "upf", "family": "upf/omar-rq", "status": "active", "confidence": "low", "weights": "open-weights", "license": "", "release_date": "2025-12-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conformer with RoPE, flash attention, and mixed precision. The paper states a base setup of 12 layers, 512 dimensions, and 90 million parameters, and a large setup of 24 layers, 1024 dimensions, and 580 million parameters (Conformer XL). It says development used the base setup and the reported final models used the large setup. Hub Freesound cards instead say 86 million and 660 million for those two sizes. Those figures disagree, so parameters is null except where a card states a size for that exact repo. This checkpoint: not stated. The repository slug contains 78M. That was not treated as a measured parameter count..", "modalities": { "input": [ "audio" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OMAR-RQ OMAR-RQ multifeature 78M is a self-supervised audio encoder from the Music Technology Group at Universitat Pompeu Fabra. Confidence is low because the model card is empty. Status is active. Collected 2026-10-15.", "what_it_is_for": "Hub repo mtg-upf/omar-rq-multifeature-78M is in the OMAR-RQ collection. The page that was opened had no model card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/pdf-difficulty/pdf-difficulty": { "id": "upf/pdf-difficulty/pdf-difficulty", "name": "PDF Difficulty", "maker": "upf", "family": "upf/pdf-difficulty", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AGPL-3.0", "release_date": "2023-11-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The card names an FC-embedding layer plus a multi-task classifier on a pretrained language model. Layer sizes were not copied from the paper.", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PDF Difficulty is a piano-score difficulty model from the Music Technology Group at Universitat Pompeu Fabra. The Hub repo mtg-upf/PDF-Difficulty-ISMIR2023 is the 2023 ISMIR release. Collected 2026-10-15.", "what_it_is_for": "The card says it is the language model pretrained on all ISMLP piano pieces, with the FC-embedding layer from the paper and a multi-task classifier, to predict performance difficulty from piano sheet-music images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/soccerhigh/soccerhigh": { "id": "upf/soccerhigh/soccerhigh", "name": "SoccerHigh baselines", "maker": "upf", "family": "upf/soccerhigh", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "2026-01-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Task baselines on frozen third-party encoders: VideoMAEv2 giant, VideoMAEv2 small, CLIP ViT-B-32 256 trained on DataComp-1B, and ResNet-152 with ImageNet-1K V2 weights. The card does not state UPF-trained parameter counts.", "modalities": { "input": [ "video" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SoccerHigh baselines are video-summarization checkpoints released by the Intelligent Multimodal Vision Analysis group at Universitat Pompeu Fabra (imva-upf). They are the paper's baseline models, not a hosted product. Collected 2026-10-15.", "what_it_is_for": "Automatic soccer video summarization baselines from the SoccerHigh benchmark paper. The card lists four encoder variants.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/timbre/timbre": { "id": "upf/timbre/timbre", "name": "Timbre", "maker": "upf", "family": "upf/timbre", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Timbre is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Two-class timbre color: bright, dark. Only a Discogs-EffNet head is shipped.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/tonality/tonal-atonal": { "id": "upf/tonality/tonal-atonal", "name": "Tonal / atonal", "maker": "upf", "family": "upf/tonality", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Tonal / atonal is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Two-class tonality: atonal, tonal.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/voice/voice-gender": { "id": "upf/voice/voice-gender", "name": "Voice gender", "maker": "upf", "family": "upf/voice", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Voice gender is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Singing-voice gender, two classes: female, male. Weight prefix is gender, not voice_gender.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upf/voice/voice-instrumental": { "id": "upf/voice/voice-instrumental", "name": "Voice / instrumental", "maker": "upf", "family": "upf/voice", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Small head on precomputed embeddings (TensorflowPredict2D or TensorflowPredict). The Essentia page says these heads do not take waveforms directly. Parameter count was not published.", "modalities": { "input": [ "embeddings" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Voice / instrumental is an Essentia classification head trained by the Music Technology Group at Universitat Pompeu Fabra. It reads frozen audio embeddings and is not a standalone waveform model. Active on the Essentia models page. Collected 2026-10-15.", "what_it_is_for": "Two-class presence of voice: instrumental, voice.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upm/bertuit/bertuit-base": { "id": "upm/bertuit/bertuit-base", "name": "BERTuit-base", "maker": "upm", "family": "upm/bertuit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2022-06-28", "context_length": 256, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-optimized BERT-base encoder trained from scratch. Config: hidden 768, intermediate 3072, 12 heads, 12 layers, vocab 30000, max_position_embeddings 256, absolute positions, type_vocab_size 2. Masked language modeling only. TensorFlow checkpoint on the Hub.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BERTuit-base is a Spanish Twitter encoder from the AIDA group at Universidad Polit\u00e9cnica de Madrid. The weights are public, the license on the card is Apache-2.0, and the checkpoint was still on the Hub on the collection date 2026-10-15. Status: active.", "what_it_is_for": "The paper presents BERTuit as a masked language model trained from scratch on more than 230 million Spanish tweets so that later systems can read informal Spanish Twitter, especially misinformation. The card says to swap user tags and URLs for `` and `` before tokenization. It is not a chat model and UPM does not sell an API for it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upm/star/star": { "id": "upm/star/star", "name": "STAR", "maker": "upm", "family": "upm/star", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-05", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa-large encoder warm-started from roberta-large and continued with supervised contrastive loss so same-author texts land near each other. Released config is RobertaModel: hidden 1024, intermediate 4096, 16 heads, 24 layers, vocab 50265, max_position_embeddings 514. The card reads style vectors from pooler_output. The paper also describes a final dense authorship layer; that width is not in the released config.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "STAR (Style Transformer for Authorship Representations) is an authorship embedding model from the AIDA group at Universidad Polit\u00e9cnica de Madrid. Weights were posted as AIDA-UPM/star on 2023-10-05 and were still public on the collection date 2026-10-15. Status: active. No license is on the card.", "what_it_is_for": "The paper trains a RoBERTa-large encoder so that texts by the same person have similar vectors. The stated use is authorship attribution, clustering, and verification, including harmful-content authors on social networks. The card's example loads `roberta-large` as the tokenizer and `AIDA-UPM/star` as the model, then reads `pooler_output`. It is not a chat model and there is no UPM API price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uppsala/attentionhtr/attentionhtr": { "id": "uppsala/attentionhtr/attentionhtr", "name": "AttentionHTR", "maker": "uppsala", "family": "uppsala/attentionhtr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-01-23", "context_length": null, "max_output_tokens": null, "parameters": "49600000", "architecture": "Word-level handwritten text recognition. Thin-plate spline normalisation, a 32-layer ResNet encoder (512 channels by 26 columns on a 100 by 32 greyscale image), two stacked bidirectional LSTMs (output 256 by 26), and a unidirectional LSTM decoder with Bahdanau content attention. Maximum prediction length is 26 characters including an end-of-sequence token. The paper says fine-tuning updates all 49.6M parameters of a Clova scene-text recognition checkpoint trained on MJSynth and SynthText.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AttentionHTR is a handwritten-word recogniser released by Dmitrijs Kass and Ekta Vats at Uppsala University, from the Department of Information Technology and the Centre for Digital Humanities. Six public checkpoints are still linked from the CDHU GitHub repository. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents an end-to-end system for recognising handwritten words when labelled data are scarce. It fine-tunes a Clova scene-text model on IAM and Imgur5K. CDHU lists the checkpoints as a tool for handwriting recognition, including later use inside the Marginalia project.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uppsala/malt/engmalt": { "id": "uppsala/malt/engmalt", "name": "engmalt", "maker": "uppsala", "family": "uppsala/malt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MaltParser software license is the 3-clause BSD text on the project license page. The model page adds a separate condition: research use requires a Penn Treebank license, and commercial use requires contacting the Linguistic Data Consortium.", "release_date": "2012-03-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single-malt MaltParser configuration for English. Two files are released: engmalt.poly-1.7.mco uses LIBSVM with a polynomial kernel, and engmalt.linear-1.7.mco uses a linear SVM. Trained on Wall Street Journal sections 2-21 plus about 4000 QuestionBank questions, converted with the Stanford Parser. Input is CoNLL with Penn Treebank part-of-speech tags. Output is Stanford basic typed dependencies. The parsing algorithm is not named on the model page.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "engmalt is the official English MaltParser model from the MaltParser project of Johan Hall, Jens Nilsson, and Joakim Nivre at V\u00e4xj\u00f6 University and Uppsala University. Two 2012 checkpoint files are still listed on maltparser.org. This dossier was collected on 2026-10-15. The parser software is a toolkit. Only this named model is catalogued here.", "what_it_is_for": "The project offers it to people who want a ready English dependency parser and do not want to tune MaltParser. The input must already be segmented and tagged in the Penn Treebank tagset. The model was trained on Wall Street Journal sections 2-21 plus about 4000 QuestionBank questions, converted with the Stanford Parser.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uppsala/malt/fremalt": { "id": "uppsala/malt/fremalt", "name": "fremalt", "maker": "uppsala", "family": "uppsala/malt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MaltParser software license is the 3-clause BSD text on the project license page. The model page adds a separate condition: research use requires a French Treebank license, and commercial use requires contacting that treebank's license holder.", "release_date": "2012-03-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single-malt MaltParser configuration in the file fremalt-1.7.mco, for MaltParser 1.7. Trained on a dependency conversion of the French Treebank (Candito, Crabb\u00e9, and Denis, 2010). Input is CoNLL tagged with the MElt tagset. Column 4 is a coarse tag and column 5 is the full tag. Learner and parsing algorithm are not stated on the model page.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "fremalt is the official French MaltParser model from the MaltParser project of Johan Hall, Jens Nilsson, and Joakim Nivre at V\u00e4xj\u00f6 University and Uppsala University. One file, `fremalt-1.7.mco`, is still listed. This dossier was collected on 2026-10-15.", "what_it_is_for": "The project offers it as a ready French dependency parser. It was trained on the dependency conversion of the French Treebank described by Candito, Crabb\u00e9, and Denis (2010). Input must use the MElt tagset. Column 4 is a coarse tag and column 5 is the full tag.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uppsala/malt/swemalt": { "id": "uppsala/malt/swemalt", "name": "swemalt", "maker": "uppsala", "family": "uppsala/malt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MaltParser software license is the 3-clause BSD text on the project license page. The model index says research use of the official models requires a license for the training treebank, and commercial use requires contacting that license holder. The Swedish model page itself does not repeat that sentence.", "release_date": "2012-09-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single-malt MaltParser configuration in swemalt-1.7.2.mco, for MaltParser 1.7.2. Trained on the Talbanken section of the Swedish Treebank. Input is CoNLL with SUC part-of-speech tags. The model page says columns 4 and 5 should carry the same tag, and column 6 carries morphosyntactic features. Learner and parsing algorithm are not stated. The user guide's swemalt-mini example is a different, smaller model and is not this file.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "swemalt is the official Swedish MaltParser model from the MaltParser project of Johan Hall, Jens Nilsson, and Joakim Nivre at V\u00e4xj\u00f6 University and Uppsala University. The file `swemalt-1.7.2.mco` is still listed. This dossier was collected on 2026-10-15. It is not the tutorial file `swemalt-mini`.", "what_it_is_for": "The project offers it as a ready Swedish dependency parser. It was trained on the Talbanken section of the Swedish Treebank. Columns 4 and 5 of the input must carry the same SUC tag. Column 6 carries features such as `UTR|SIN|IND|NOM`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uppsala/ptp/ptp": { "id": "uppsala/ptp/ptp", "name": "PTP", "maker": "uppsala", "family": "uppsala/ptp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "AGPL-3.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PTP version 2 is a library of ligand-based CPSign models for probabilistic target profiles, not one neural network. The Hugging Face card shows prediction with cpsign predict -m model.ptp on an SDF file and a CSV output. The service README says a deployment iterates every file present, currently 800 or more, and that fetching the library downloads more than 10 GB. CPSign's documented default method is a support-vector machine with signature descriptors and conformal or Venn-ABERS calibration. The PTP card does not say which learner each file uses.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PTP is a library of ligand-based target-profile models from the Pharmaceutical Bioinformatics group at Uppsala University, led by Ola Spjuth. Version 2 is the public Hugging Face repo `pharmbio/ptp`. The service code is by Jonathan Alvarsson and Morgan Ekmefjord. This dossier was collected on 2026-10-15. The hundreds of endpoint files are one product, not separate named models.", "what_it_is_for": "The card calls it a library of ligand-based probabilistic target profiles. The service accepts a CSV of SMILES and runs the downloaded CPSign models. The group uses this line of models for off-target and target-profile prediction in drug discovery. CPSign itself is the modelling toolkit, published separately under the arosbio GitHub organisation, and is not catalogued here as a model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/classify/document-classify": { "id": "upstage/classify/document-classify", "name": "Document Classify", "maker": "upstage", "family": "upstage/classify", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2025-08-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Document Classify is Upstage's closed document-type classifier, collected 2026-10-15. The alias points at document-classify-260904. It labels pages with classes the caller defines.", "what_it_is_for": "Classify scans and digital files into user-defined categories, including split bundles, without templates or fine-tuning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/document-parse/document-parse": { "id": "upstage/document-parse/document-parse", "name": "Document Parse", "maker": "upstage", "family": "upstage/document-parse", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2024-02-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Document Parse is Upstage's closed document-to-HTML/Markdown model, collected 2026-10-15. The document-parse alias points at document-parse-260930. Older layout-analysis ids are versions of this line, not separate folders.", "what_it_is_for": "Turn PDFs, scans, and office files into structured HTML or Markdown, with a standard mode and a higher-priced enhanced VLM mode.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/embedding/solar-embedding-1-large-passage": { "id": "upstage/embedding/solar-embedding-1-large-passage", "name": "Solar Embedding 1 Large Passage", "maker": "upstage", "family": "upstage/embedding", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2024-05-10", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "Solar-based passage embedding model. Dimension not confirmed on a live page.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solar Embedding 1 Large Passage is Upstage's closed passage embedding model, collected 2026-10-15. Status: deprecated.", "what_it_is_for": "Older passage embedder. Alias embedding-passage until 31 Dec 2026 UTC. Context 4,000.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/embedding/solar-embedding-1-large-query": { "id": "upstage/embedding/solar-embedding-1-large-query", "name": "Solar Embedding 1 Large Query", "maker": "upstage", "family": "upstage/embedding", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2024-05-10", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "Solar-based query embedding model. Dimension not confirmed on a live page.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solar Embedding 1 Large Query is Upstage's closed query embedding model, collected 2026-10-15. Status: deprecated.", "what_it_is_for": "Older query embedder. Alias embedding-query still points here until 31 Dec 2026 UTC. Context 4,000.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/embedding/solar-embedding-2-passage": { "id": "upstage/embedding/solar-embedding-2-passage", "name": "Solar Embedding 2 Passage", "maker": "upstage", "family": "upstage/embedding", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-06-30", "context_length": 8000, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of microsoft/harrier-oss-v1-0.6b, per the embeddings doc.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solar Embedding 2 Passage is Upstage's closed passage embedding model, collected 2026-10-15. Status: active.", "what_it_is_for": "Embed documents to be searched. 8,000 token context, 1,024 dimensions. No alias yet.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/embedding/solar-embedding-2-query": { "id": "upstage/embedding/solar-embedding-2-query", "name": "Solar Embedding 2 Query", "maker": "upstage", "family": "upstage/embedding", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2026-06-30", "context_length": 8000, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of microsoft/harrier-oss-v1-0.6b, per the embeddings doc. Upstage product, not a community checkpoint.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solar Embedding 2 Query is Upstage's closed query embedding model, collected 2026-10-15. Status: active.", "what_it_is_for": "Embed user questions for retrieval. 8,000 token context, 1,024 dimensions. No alias yet; call the full id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/extract/information-extract": { "id": "upstage/extract/information-extract", "name": "Information Extract", "maker": "upstage", "family": "upstage/extract", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2025-05-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Information Extract is Upstage's closed schema-based document extraction model, collected 2026-10-15. The global alias points at information-extract-260904. A separate Korea-region id is in beta.", "what_it_is_for": "Pull key-value JSON from documents the caller has not fine-tuned on, including implied fields, with optional confidence and coordinates.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/groundedness/groundedness-check": { "id": "upstage/groundedness/groundedness-check", "name": "Groundedness Check", "maker": "upstage", "family": "upstage/groundedness", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2024-05-02", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Solar Mini based classifier. Formerly solar-1-mini-groundedness-check.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Groundedness Check is Upstage's Solar Mini based RAG checker, collected 2026-10-15. The catalog says version 240502 was deprecated on 21 October 2025, while the capability page still documents the alias.", "what_it_is_for": "Label whether an assistant answer is grounded, not grounded, or uncertain given a supplied context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/ocr/document-ocr": { "id": "upstage/ocr/document-ocr", "name": "Document OCR", "maker": "upstage", "family": "upstage/ocr", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2023-04-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Document OCR is Upstage's closed text-recognition model, collected 2026-10-15. The ocr alias points at ocr-260930, which Upstage says runs on the same engine as Document Parse 260930.", "what_it_is_for": "Read text from tilted, low-quality, or multilingual document images, with a confidence score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/prebuilt/air-waybill-extraction": { "id": "upstage/prebuilt/air-waybill-extraction", "name": "Air Waybill Extraction", "maker": "upstage", "family": "upstage/prebuilt", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Air Waybill Extraction is a retired Upstage prebuilt extractor, collected 2026-10-15. Upstage deprecated the prebuilt line on 6 May 2026 and tells users to move to universal Information Extract.", "what_it_is_for": "Extract predefined fields from air waybills: parties, routing, charges, and cargo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/prebuilt/bill-of-lading-and-shipping-request-extraction": { "id": "upstage/prebuilt/bill-of-lading-and-shipping-request-extraction", "name": "Bill of Lading and Shipping Request Extraction", "maker": "upstage", "family": "upstage/prebuilt", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Bill of Lading and Shipping Request Extraction is a retired Upstage prebuilt extractor, collected 2026-10-15. Upstage deprecated the prebuilt line on 6 May 2026 and tells users to move to universal Information Extract.", "what_it_is_for": "Extract predefined logistics fields from bills of lading and shipping requests.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/prebuilt/commercial-invoice-and-packing-list-extraction": { "id": "upstage/prebuilt/commercial-invoice-and-packing-list-extraction", "name": "Commercial Invoice and Packing List Extraction", "maker": "upstage", "family": "upstage/prebuilt", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Commercial Invoice and Packing List Extraction is a retired Upstage prebuilt extractor, collected 2026-10-15. Upstage deprecated the prebuilt line on 6 May 2026 and tells users to move to universal Information Extract.", "what_it_is_for": "Extract predefined trade fields from commercial invoices and packing lists.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/prebuilt/kr-export-declaration-certificate-extraction": { "id": "upstage/prebuilt/kr-export-declaration-certificate-extraction", "name": "Korea Export Declaration Certificate Extraction", "maker": "upstage", "family": "upstage/prebuilt", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Korea Export Declaration Certificate Extraction is a retired Upstage prebuilt extractor, collected 2026-10-15. Upstage deprecated the prebuilt line on 6 May 2026 and tells users to move to universal Information Extract.", "what_it_is_for": "Extract predefined fields from Korean export declaration certificates.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/prebuilt/receipt-extraction": { "id": "upstage/prebuilt/receipt-extraction", "name": "Receipt Extraction", "maker": "upstage", "family": "upstage/prebuilt", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Receipt Extraction is a retired Upstage prebuilt extractor, collected 2026-10-15. Upstage deprecated the prebuilt line on 6 May 2026 and tells users to move to universal Information Extract.", "what_it_is_for": "Extract store, line-item, tax, and payment fields from paper receipts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/solar/solar-10-7b": { "id": "upstage/solar/solar-10-7b", "name": "SOLAR-10.7B", "maker": "upstage", "family": "upstage/solar", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-12-12", "context_length": null, "max_output_tokens": null, "parameters": "10.7B", "architecture": "Depth up-scaling: Llama-2-style stack continued from Mistral 7B weights to 48 layers and 10.7B parameters. Pretrained, not instruction-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SOLAR-10.7B is Upstage's open pretrained 10.7B model, collected 2026-10-15. Weights are on Hugging Face under Apache-2.0. It is the base of SOLAR-10.7B-Instruct and the origin of the Solar Mini line.", "what_it_is_for": "The card says the checkpoint generates text and must be fine-tuned before chat use. The Console catalog nevertheless lists a Chat capability for this id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/solar/solar-10-7b-instruct": { "id": "upstage/solar/solar-10-7b-instruct", "name": "SOLAR-10.7B-Instruct", "maker": "upstage", "family": "upstage/solar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2023-12-12", "context_length": null, "max_output_tokens": null, "parameters": "10.7B", "architecture": "Instruction fine-tune of SOLAR-10.7B-v1.0 for single-turn conversation.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SOLAR-10.7B-Instruct-v1.0 is Upstage's open instruction-tuned 10.7B model, collected 2026-10-15. Weights remain on Hugging Face under CC-BY-NC-4.0, so they are open weights rather than an OSI-style open-source grant.", "what_it_is_for": "The card says it is the single-turn conversational fine-tune of SOLAR-10.7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/solar/solar-decide": { "id": "upstage/solar/solar-decide", "name": "Solar Decide", "maker": "upstage", "family": "upstage/solar", "status": "preview", "confidence": "high", "weights": "closed", "license": "proprietary (served on Solar Mini 4; no separate weights)", "release_date": "2026-09-22", "context_length": null, "max_output_tokens": null, "parameters": "35B total / 3B active (Solar Mini 4)", "architecture": "System One head on Solar Mini 4. One forward pass, no prose generation. Question types: choice, score, noul (yes/no).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": null }, "summary_intro": "Solar Decide is Upstage's beta decision endpoint on Solar Mini 4, collected 2026-10-15. It returns a choice, a score, or a yes/no with a probability, and Upstage prices output tokens at zero.", "what_it_is_for": "Routing, classification, and policy checks over a long state, including a full document, without writing an answer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/solar/solar-docvision": { "id": "upstage/solar/solar-docvision", "name": "Solar DocVision Preview", "maker": "upstage", "family": "upstage/solar", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2024-09-10", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solar DocVision Preview is Upstage's experimental document visual-question-answering model, collected 2026-10-15. The catalog still lists it, and the rate-limit guide still has a Solar DocVision table. No retirement date is printed.", "what_it_is_for": "Document visual question answering. Upstage calls it experimental.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/solar/solar-mini": { "id": "upstage/solar/solar-mini", "name": "Solar Mini", "maker": "upstage", "family": "upstage/solar", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "proprietary for these API snapshots", "release_date": "2024-06-12", "context_length": 32768, "max_output_tokens": null, "parameters": "10.7B", "architecture": "10.7B depth-upscaled model in the Solar Mini line. These API snapshots are closed; the earlier open SOLAR-10.7B weights are a separate folder.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": 0.15 }, "summary_intro": "Solar Mini is Upstage's closed 10.7B API model, collected 2026-10-15. The solar-mini alias points at solar-mini-250422 and will be fully deprecated on 30 October 2026 (KST). Open SOLAR-10.7B weights are cataloged separately.", "what_it_is_for": "Upstage describes it as a lightweight instruction-following model for English, Korean, and Japanese production traffic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/solar/solar-mini-4": { "id": "upstage/solar/solar-mini-4", "name": "Solar Mini 4", "maker": "upstage", "family": "upstage/solar", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary (weights not published)", "release_date": "2026-09-22", "context_length": null, "max_output_tokens": null, "parameters": "35B total / 3B active", "architecture": "Mixture-of-experts. 35B total, 3B active per token.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.4 }, "summary_intro": "Solar Mini 4 is Upstage's closed compact agent model, collected 2026-10-15. It is active as solar-mini4, a 35B mixture-of-experts model with 3B active parameters, and it also backs Solar Decide.", "what_it_is_for": "Upstage says it is for high-volume agent work where speed and cost matter, with Korean, English, and Japanese.", "ratings": { "empirical_100": 60, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/solar/solar-mini-ja": { "id": "upstage/solar/solar-mini-ja", "name": "Solar Mini JA", "maker": "upstage", "family": "upstage/solar", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "2024-06-12", "context_length": 32768, "max_output_tokens": null, "parameters": "10.7B", "architecture": "Japanese Solar Mini line, 10.7B. Not a dated snapshot of the multilingual solar-mini alias.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solar Mini JA is Upstage's retired Japanese 10.7B chat line, collected 2026-10-15. Both snapshots were deprecated on 10 April 2025. Upstage points migrations at Solar Mini 4.", "what_it_is_for": "Japanese chat, with function calling added in the January 2025 snapshot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/solar/solar-open-100b": { "id": "upstage/solar/solar-open-100b", "name": "Solar Open 100B", "maker": "upstage", "family": "upstage/solar", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Upstage Solar License (weights); Apache-2.0 (code)", "release_date": "2025-12-31", "context_length": null, "max_output_tokens": null, "parameters": "102.6B total / 12B active", "architecture": "Mixture-of-experts trained from scratch. 129 experts (128 routed + 1 shared), top 8 routed plus the shared expert. Pretrained on 19.7 trillion tokens. Bilingual English and Korean.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solar Open 100B is Upstage's open-weight 102.6B mixture-of-experts model (12B active), collected 2026-10-15. Weights are on Hugging Face under the Upstage Solar License. It is not in the current Console API catalog.", "what_it_is_for": "The card describes a from-scratch bilingual model for reasoning, instruction following, and tool use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/solar/solar-open-2": { "id": "upstage/solar/solar-open-2", "name": "Solar Open 2", "maker": "upstage", "family": "upstage/solar", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Upstage Solar License", "release_date": "2026-07-08", "context_length": null, "max_output_tokens": null, "parameters": "250287794944 total / 15B active", "architecture": "Hybrid-attention mixture-of-experts. 48 layers, hidden size 4096, pattern [softmax, linear x3] x12, NoPE, 320 routed experts + 1 shared, top-8 + shared. About 12 trillion pretraining tokens. Initialized in part from Solar Open 100B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solar Open 2 is Upstage's open-weight 250B mixture-of-experts model (15B active), collected 2026-10-15. The limited API under solar-open2 ended on 12 August 2026. Weights remain on Hugging Face.", "what_it_is_for": "The model card says it is built for agent work: office tasks, documents, and coding, in English, Korean, and Japanese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/solar/solar-pro": { "id": "upstage/solar/solar-pro", "name": "Solar Pro", "maker": "upstage", "family": "upstage/solar", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "proprietary for API snapshots; preview weights are separate", "release_date": "2024-11-26", "context_length": 32768, "max_output_tokens": null, "parameters": "22B", "architecture": "22B instruction model. The open preview used depth up-scaling from Phi-3-medium; Upstage does not say the later API snapshots use that same stack.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solar Pro is Upstage's closed 22B API model that preceded Solar Pro 2, collected 2026-10-15. The last API snapshot, solar-pro-250422, was deprecated on 13 August 2025. The open-weight preview is cataloged separately.", "what_it_is_for": "Upstage described the API model as an enterprise LLM for instruction following and structured formats, with finance, healthcare, and legal domain focus, in English, Korean, and Japanese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/solar/solar-pro-2": { "id": "upstage/solar/solar-pro-2", "name": "Solar Pro 2", "maker": "upstage", "family": "upstage/solar", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "proprietary (weights not published)", "release_date": "2025-05-20", "context_length": 65536, "max_output_tokens": null, "parameters": "31B", "architecture": "Dense 31B model. Chat and reasoning are modes of one model, not two published networks.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": 0.6 }, "summary_intro": "Solar Pro 2 is Upstage's closed 31B chat model with a reasoning mode, collected 2026-10-15. The preview opened on 20 May 2025 and the commercial model on 10 July 2025. Upstage will fully deprecate the alias and its versions on 30 October 2026 (KST).", "what_it_is_for": "Upstage positions it for enterprise reasoning, tool use, and Korean, English, and Japanese chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/solar/solar-pro-3": { "id": "upstage/solar/solar-pro-3", "name": "Solar Pro 3", "maker": "upstage", "family": "upstage/solar", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "proprietary (weights not published)", "release_date": "2026-01-26", "context_length": 128000, "max_output_tokens": null, "parameters": "102B total / 12B active", "architecture": "Mixture-of-experts. 102B total parameters, 12B active per forward pass.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": 0.6 }, "summary_intro": "Solar Pro 3 is Upstage's closed 102B mixture-of-experts chat model (12B active), collected 2026-10-15. The solar-pro3 alias still resolves, and Upstage says the alias and its versions will be fully deprecated on 30 October 2026 (KST).", "what_it_is_for": "Upstage describes it as a production MoE model optimized for Korean, with English and Japanese, chat, reasoning, structured outputs, and tool calling.", "ratings": { "empirical_100": null, "lmsys_elo": 1209, "coding_elo": 1209, "swe_bench": 16.5, "humaneval": 71.2, "twelve_cs": 99.5, "aa_intelligence": 7.8, "aa_coding": 16.2, "aa_agentic": 1.4, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/solar/solar-pro-4": { "id": "upstage/solar/solar-pro-4", "name": "Solar Pro 4", "maker": "upstage", "family": "upstage/solar", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary (weights not published)", "release_date": "2026-08-06", "context_length": 524288, "max_output_tokens": 131072, "parameters": "", "architecture": "Not disclosed. Successor to the Solar Pro 3 mixture-of-experts line; parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 1.2 }, "summary_intro": "Solar Pro 4 is Upstage's closed flagship chat model for agent work, collected 2026-10-15. It is active on the Console API as solar-pro4 (snapshot solar-pro4-260806), with reasoning available and off by default.", "what_it_is_for": "Upstage says it is specialized for agentic use and is particularly strong at document work, coding, and business tasks, with English, Korean, and Japanese.", "ratings": { "empirical_100": 60, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/solar/solar-pro-preview": { "id": "upstage/solar/solar-pro-preview", "name": "Solar Pro Preview", "maker": "upstage", "family": "upstage/solar", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-09-10", "context_length": null, "max_output_tokens": null, "parameters": "22B", "architecture": "Depth up-scale of Phi-3-medium (14B) to 22B. Intended for a single 80GB GPU.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Solar Pro Preview is Upstage's open 22B instruct model, collected 2026-10-15. Weights remain on Hugging Face under MIT. It is the initial Solar Pro, not the later closed API.", "what_it_is_for": "Upstage shipped it as a single-GPU preview of Solar Pro for English chat, with a stated plan to follow with a multilingual official Solar Pro.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/syn/syn": { "id": "upstage/syn/syn", "name": "Syn", "maker": "upstage", "family": "upstage/syn", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "", "context_length": 32768, "max_output_tokens": null, "parameters": "10.7B", "architecture": "10.7B Japanese model co-developed with Karakuri. Catalog does not describe the base architecture beyond the Solar-class size.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Syn is a closed 10.7B Japanese model from Upstage and Karakuri, collected 2026-10-15. The only catalog snapshot, syn-250326, is scheduled to be fully deprecated on 30 October 2026 (KST).", "what_it_is_for": "Japanese and English chat, with catalog claims of strength in finance, healthcare, and legal text, and of being fine-tunable. No public fine-tune API price was found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/syn/syn-pro": { "id": "upstage/syn/syn-pro", "name": "Syn Pro", "maker": "upstage", "family": "upstage/syn", "status": "active", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "", "context_length": 65536, "max_output_tokens": null, "parameters": "31B", "architecture": "About 30B Japanese model co-developed with Karakuri. Catalog id is syn-pro-251021 and the parameter line says 31B. Tool calling is not supported.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Syn Pro is Upstage and Karakuri's closed Japanese 31B chat model, collected 2026-10-15. It is listed as a current Console model, syn-pro, with no deprecation date on the catalog page.", "what_it_is_for": "Japanese business chat. The catalog says it does not add tool calling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/tinysolar/tinysolar-111m-4k": { "id": "upstage/tinysolar/tinysolar-111m-4k", "name": "TinySolar 111M 4K", "maker": "upstage", "family": "upstage/tinysolar", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-05-27", "context_length": null, "max_output_tokens": null, "parameters": "111M", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinySolar 111M 4K is a small open Upstage weight release, collected 2026-10-15. The official model card is empty or nearly empty; the license line is Apache-2.0.", "what_it_is_for": "Smaller TinySolar weight release. Hugging Face createdAt 2026-05-27. Card not quoted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/tinysolar/tinysolar-187m-4k": { "id": "upstage/tinysolar/tinysolar-187m-4k", "name": "TinySolar 187M 4K", "maker": "upstage", "family": "upstage/tinysolar", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-05-27", "context_length": null, "max_output_tokens": null, "parameters": "187M", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinySolar 187M 4K is a small open Upstage weight release, collected 2026-10-15. The official model card is empty or nearly empty; the license line is Apache-2.0.", "what_it_is_for": "187M weight release. Hugging Face createdAt 2026-05-27. Card not quoted.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/tinysolar/tinysolar-248m-4k": { "id": "upstage/tinysolar/tinysolar-248m-4k", "name": "TinySolar 248M 4K", "maker": "upstage", "family": "upstage/tinysolar", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-07", "context_length": null, "max_output_tokens": null, "parameters": "248M", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinySolar 248M 4K is a small open Upstage weight release, collected 2026-10-15. The official model card is empty or nearly empty; the license line is Apache-2.0.", "what_it_is_for": "Base 248M release. Card body is empty aside from Apache-2.0. HF reports about 0.2B parameters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/tinysolar/tinysolar-248m-4k-code-instruct": { "id": "upstage/tinysolar/tinysolar-248m-4k-code-instruct", "name": "TinySolar 248M 4K Code Instruct", "maker": "upstage", "family": "upstage/tinysolar", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-15", "context_length": 32768, "max_output_tokens": null, "parameters": "248M", "architecture": "LlamaForCausalLM, 12 layers, hidden 1024, 32 query heads, 8 KV heads, vocab 32,000. max_position_embeddings 32,768 in that commit, despite 4k in the name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinySolar 248M 4K Code Instruct is a small open Upstage weight release, collected 2026-10-15. The official model card is empty or nearly empty; the license line is Apache-2.0.", "what_it_is_for": "Code instruct variant. config.json in the 2024-04-15 commit: LlamaForCausalLM, hidden 1024, 12 layers, 32 heads, 8 KV heads, vocab 32000, max_position_embeddings 32768.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/tinysolar/tinysolar-248m-4k-py": { "id": "upstage/tinysolar/tinysolar-248m-4k-py", "name": "TinySolar 248M 4K Python", "maker": "upstage", "family": "upstage/tinysolar", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-07", "context_length": null, "max_output_tokens": null, "parameters": "248M", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinySolar 248M 4K Python is a small open Upstage weight release, collected 2026-10-15. The official model card is empty or nearly empty; the license line is Apache-2.0.", "what_it_is_for": "Python-oriented 248M release. Card not separately quoted; Hugging Face lists Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/tinysolar/tinysolar-248m-4k-py-instruct": { "id": "upstage/tinysolar/tinysolar-248m-4k-py-instruct", "name": "TinySolar 248M 4K Python Instruct", "maker": "upstage", "family": "upstage/tinysolar", "status": "active", "confidence": "medium", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-11", "context_length": null, "max_output_tokens": null, "parameters": "248M", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TinySolar 248M 4K Python Instruct is a small open Upstage weight release, collected 2026-10-15. The official model card is empty or nearly empty; the license line is Apache-2.0.", "what_it_is_for": "Instruct variant. Card body not retrieved beyond the Hugging Face listing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/translation/translation-enko": { "id": "upstage/translation/translation-enko", "name": "Translation EN-KO", "maker": "upstage", "family": "upstage/translation", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Translation EN-KO is a retired Upstage translation model, collected 2026-10-15. Upstage deprecated all translation APIs on 9 June 2025.", "what_it_is_for": "English-to-Korean translation, including tone carried over from earlier turns and formatted text such as subtitles.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "upstage/translation/translation-koen": { "id": "upstage/translation/translation-koen", "name": "Translation KO-EN", "maker": "upstage", "family": "upstage/translation", "status": "retired", "confidence": "high", "weights": "closed", "license": "proprietary", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Translation KO-EN is a retired Upstage translation model, collected 2026-10-15. Upstage deprecated all translation APIs on 9 June 2025.", "what_it_is_for": "Korean-to-English translation, including formatted subtitle text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/autobool/autobool-qwen3-4b": { "id": "uq/autobool/autobool-qwen3-4b", "name": "AutoBool Qwen3 4B", "maker": "uq", "family": "uq/autobool", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GRPO with LoRA on Qwen/Qwen3-4B for Boolean query generation. The objective-variant card says the Hub model-size label is 4B and the tensor type is BF16. LoRA rank was not read from adapter config. Four prompt-strategy checkpoints are versions.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AutoBool Qwen3 4B is a Boolean-query generator for medical systematic reviews from the University of Queensland Information Engineering Lab. Four Apache-2.0 GRPO checkpoints on Qwen3-4B are one model. Collected 2026-10-15.", "what_it_is_for": "The paper page says AutoBool trains an LLM with reinforcement learning to write Boolean queries that keep recall high without retrieving as many documents as prompt-only baselines. The objective card describes a six-step prompt that simulates a relevant article and then groups terms.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/bitag/bitag-t5-large": { "id": "uq/bitag/bitag-t5-large", "name": "BiTAG T5-Large", "maker": "uq", "family": "uq/bitag", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5-large fine-tune. GitHub says it was trained for 4 epochs on 361,349 arXiv computer-science title-abstract pairs crawled from 2000-06-01 through 2021-06-01, on 4 Tesla V100 GPUs. The ielabgroup card's example still loads ArvinZhuang/BiTAG-t5-large.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BiTAG T5-Large is a title and abstract generator published on the University of Queensland ielab Hugging Face org. The weights are a T5-large fine-tune. Collected 2026-10-15. No license was found on the card.", "what_it_is_for": "The README says BiTAG generates candidate titles from an abstract, and abstracts from a title. The prefix is `abstract:` or `title:`. It is trained like docTTTTTquery but with the lab's BiQDL loss.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/diffretriever/diffretriever-dream-7b": { "id": "uq/diffretriever/diffretriever-dream-7b", "name": "DiffRetriever Dream 7B", "maker": "uq", "family": "uq/diffretriever", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-06-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA retriever on Dream-org/Dream-v0-Instruct-7B. The multi checkpoint uses K_q=4 and K_p=16. The single checkpoint uses K=1. LoRA rank was not read from the Dream card. The LLaDA card's r=16, alpha=64 was not copied here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffRetriever Dream 7B is a diffusion-language-model retriever from the University of Queensland Information Engineering Lab, with CSIRO on the paper byline. Two MIT LoRA checkpoints, single-vector and multi-vector, share this folder. Collected 2026-10-15.", "what_it_is_for": "The paper appends masked positions to a retrieval prompt and reads dense and sparse representations from one bidirectional pass of a diffusion model. The GitHub README says the Dream releases are K=1 and K_q=4, K_p=16.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/diffretriever/diffretriever-llada-8b": { "id": "uq/diffretriever/diffretriever-llada-8b", "name": "DiffRetriever LLaDA 8B", "maker": "uq", "family": "uq/diffretriever", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-06-19", "context_length": 156, "max_output_tokens": null, "parameters": "", "architecture": "LoRA (r=16, alpha=64) on GSAI-ML/LLaDA-8B-Instruct, merged at load. Multi checkpoint: K_q=4, K_p=4, embedding dimension 4096, one denoising step, max input 156 tokens. Single checkpoint is K=1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DiffRetriever LLaDA 8B is the LLaDA backbone of the University of Queensland ielab DiffRetriever release. The multi-vector and single-vector MIT adapters are versions in this folder. Collected 2026-10-15. CSIRO is also on the paper byline.", "what_it_is_for": "The multi card says the model appends masked positions to a retrieval prompt and reads hidden states and next-token logits in one bidirectional pass, for dense, sparse, or hybrid retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/iter/iter-qwen3-embedding-0-6b": { "id": "uq/iter/iter-qwen3-embedding-0-6b", "name": "ITER Qwen3 Embedding 0.6B", "maker": "uq", "family": "uq/iter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-08-27", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of Qwen/Qwen3-Embedding-0.6B. Last-token pooling, L2-normalized embeddings. Query max length 8192. Passage max length 512. Trained 2 epochs, AdamW lr 1e-6, batch 32, bf16, InfoNCE temperature 0.02. Hub size label is 0.6B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ITER 0.6B is an interaction-aware dense retriever from the University of Queensland Information Engineering Lab. It is a full Apache-2.0 fine-tune of Qwen/Qwen3-Embedding-0.6B, still published. Collected 2026-10-15.", "what_it_is_for": "The card says ITER retrieves for deep-research agents. The query includes the main question, the agent's pre-search reasoning, the current sub-query, and earlier sub-queries, so the retriever can prefer new evidence. Documents are encoded with no instruction prefix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/iter/iter-qwen3-embedding-4b": { "id": "uq/iter/iter-qwen3-embedding-4b", "name": "ITER Qwen3 Embedding 4B", "maker": "uq", "family": "uq/iter", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-08-27", "context_length": 8192, "max_output_tokens": null, "parameters": "", "architecture": "Full fine-tune of Qwen/Qwen3-Embedding-4B. Last-token pooling, L2-normalized embeddings. Query max length 8192. Passage max length 512. Trained 2 epochs, AdamW lr 1e-6, batch 32, bf16, InfoNCE temperature 0.02. Hub size label is 4B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ITER 4B is an interaction-aware dense retriever from the University of Queensland Information Engineering Lab. It is a full Apache-2.0 fine-tune of Qwen/Qwen3-Embedding-4B, still published. Collected 2026-10-15.", "what_it_is_for": "The card says ITER retrieves for deep-research agents. The query includes the main question, the agent's pre-search reasoning, the current sub-query, and earlier sub-queries, so the retriever can prefer new evidence. Documents are encoded with no instruction prefix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/rank-r1/rank-r1-14b": { "id": "uq/rank-r1/rank-r1-14b", "name": "Rank-R1 14B", "maker": "uq", "family": "uq/rank-r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter (r=16, alpha=32, dropout=0.05) on Qwen/Qwen2.5-14B-Instruct. Target modules are q_proj and v_proj. Trained with GRPO on Tevatron/msmarco-passage. Hub id ielabgroup/Rank-R1-14B-v0.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rank-R1 14B is the largest v0.1 setwise reranker published by the University of Queensland Information Engineering Lab. It is an Apache-2.0 LoRA on Qwen2.5-14B-Instruct. Collected 2026-10-15. The paper also credits CSIRO and the University of Waterloo.", "what_it_is_for": "Same job as the other v0.1 Rank-R1 adapters: reason over a query and a set of documents, then name the most relevant document. The release README lists this file as the Qwen2.5-14B-Instruct GRPO run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/rank-r1/rank-r1-32b": { "id": "uq/rank-r1/rank-r1-32b", "name": "Rank-R1 32B", "maker": "uq", "family": "uq/rank-r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter (r=16, alpha=32, dropout=0.05) on Qwen/Qwen3-32B. Targets q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, and down_proj. v0.2 trains on Tevatron/reasonir-data-hn with 2 to 10 documents per prompt. Hub id ielabgroup/Rank-R1-32B-v0.2.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rank-R1 32B is the v0.2 setwise reranker from the University of Queensland Information Engineering Lab. It is an Apache-2.0 LoRA on Qwen3-32B, still on the Hub. Collected 2026-10-15.", "what_it_is_for": "The card calls it RankLLM: read a query and a set of documents, explain relevance inside think tags, and return one bracketed document label. v0.2 is the reasoning reranker trained on ReasonIR synthetic queries and hard negatives, not the earlier MS MARCO v0.1 setup.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/rank-r1/rank-r1-3b": { "id": "uq/rank-r1/rank-r1-3b", "name": "Rank-R1 3B", "maker": "uq", "family": "uq/rank-r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter (r=16, alpha=32, dropout=0.05) on Qwen/Qwen2.5-3B-Instruct. Target modules are q_proj and v_proj. Trained with GRPO on Tevatron/msmarco-passage. Hub id ielabgroup/Rank-R1-3B-v0.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rank-R1 3B is a setwise document reranker from the Information Engineering Lab at The University of Queensland, released as a LoRA adapter on the lab Hugging Face org. It is active as a weight release. Collected 2026-10-15. The paper byline also lists CSIRO and the University of Waterloo.", "what_it_is_for": "The card describes Rank-R1 as a setwise reranker that reasons over the query and candidate documents, then returns the label of the most relevant document. This checkpoint is the 3B v0.1 adapter trained with GRPO on MS MARCO passage labels, without reasoning supervision.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/rank-r1/rank-r1-7b": { "id": "uq/rank-r1/rank-r1-7b", "name": "Rank-R1 7B", "maker": "uq", "family": "uq/rank-r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter (r=16, alpha=32, dropout=0.05) on Qwen/Qwen2.5-7B-Instruct. Target modules are q_proj and v_proj. Trained with GRPO on Tevatron/msmarco-passage. Hub id ielabgroup/Rank-R1-7B-v0.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Rank-R1 7B is the 7B setwise reranker from the University of Queensland Information Engineering Lab. The weights are an Apache-2.0 LoRA adapter, still published, collected 2026-10-15. CSIRO and Waterloo coauthors are on the paper.", "what_it_is_for": "The model card says it ranks by reasoning over a query and a set of passages, then emitting the label of the most relevant passage inside answer tags. Training used GRPO and MS MARCO relevance labels, not reasoning traces.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/setwise/setwise-sft-14b": { "id": "uq/setwise/setwise-sft-14b", "name": "Setwise-SFT 14B", "maker": "uq", "family": "uq/setwise", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Supervised setwise reranker. Release README says it is trained from Qwen/Qwen2.5-14B-Instruct. Hub id ielabgroup/Setwise-SFT-14B-v0.1. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Setwise-SFT 14B is a supervised setwise reranker released by the University of Queensland Information Engineering Lab next to Rank-R1. The weights are an Apache-2.0 adapter. Collected 2026-10-15.", "what_it_is_for": "The Rank-R1 README lists it as a released model trained from Qwen/Qwen2.5-14B-Instruct. The paper uses supervised fine-tuning as the comparison for the GRPO Rank-R1 rerankers. This folder exists because the checkpoint was named and published, not because it is a separate product line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/setwise/setwise-sft-3b": { "id": "uq/setwise/setwise-sft-3b", "name": "Setwise-SFT 3B", "maker": "uq", "family": "uq/setwise", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Supervised setwise reranker. Release README says it is trained from Qwen/Qwen2.5-3B-Instruct. Hub id ielabgroup/Setwise-SFT-3B-v0.1. Apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Setwise-SFT 3B is a supervised setwise reranker released by the University of Queensland Information Engineering Lab next to Rank-R1. The weights are an Apache-2.0 adapter. Collected 2026-10-15.", "what_it_is_for": "The Rank-R1 README lists it as a released model trained from Qwen/Qwen2.5-3B-Instruct. The paper uses supervised fine-tuning as the comparison for the GRPO Rank-R1 rerankers. This folder exists because the checkpoint was named and published, not because it is a separate product line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/setwise/setwise-sft-7b": { "id": "uq/setwise/setwise-sft-7b", "name": "Setwise-SFT 7B", "maker": "uq", "family": "uq/setwise", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA adapter (r=16, alpha=32, dropout=0.05) on Qwen/Qwen2.5-7B-Instruct. Target modules q_proj and v_proj. Hub id ielabgroup/Setwise-SFT-7B-v0.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Setwise-SFT 7B is a supervised setwise reranker released by the University of Queensland Information Engineering Lab next to Rank-R1. The weights are an Apache-2.0 adapter. Collected 2026-10-15.", "what_it_is_for": "The Rank-R1 README lists it as a released model trained from Qwen/Qwen2.5-7B-Instruct. The paper uses supervised fine-tuning as the comparison for the GRPO Rank-R1 rerankers. This folder exists because the checkpoint was named and published, not because it is a separate product line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/starbucks/starbucks-msmarco": { "id": "uq/starbucks/starbucks-msmarco", "name": "Starbucks MS MARCO", "maker": "uq", "family": "uq/starbucks", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Retrieval fine-tune listed by the Starbucks GitHub README as Starbucks_Retrieval. Hub id ielabgroup/Starbucks-msmarco. Hub tags name base ielabgroup/bert-base-uncased-fineweb100bt-smae and dataset Tevatron/msmarco-passage. The card body was not read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Starbucks MS MARCO is the retrieval fine-tune in the University of Queensland ielab Starbucks release. Apache-2.0 weights are on the lab Hub. Collected 2026-10-15.", "what_it_is_for": "The GitHub README lists this checkpoint as the Starbucks representation-learning fine-tune for retrieval, separate from the STS fine-tune. Hub tags point at the MS MARCO passage set and the SMAE BERT checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/starbucks/starbucks-smae": { "id": "uq/starbucks/starbucks-smae", "name": "Starbucks SMAE", "maker": "uq", "family": "uq/starbucks", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Continued pretraining of google-bert/bert-base-uncased with Starbucks masked autoencoding on FineWeb. Hub id ielabgroup/bert-base-uncased-fineweb100bt-smae. The GitHub README calls this the SMAE pretraining checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Starbucks SMAE is the masked-autoencoding pretraining checkpoint from the University of Queensland Information Engineering Lab's Starbucks embedding paper. It is an Apache-2.0 BERT-base continued pretrain. Collected 2026-10-15.", "what_it_is_for": "The GitHub README says Starbucks has two stages: Starbucks masked autoencoding pretraining, then representation-learning fine-tunes. This folder is the pretraining checkpoint the README links, not the STS or MS MARCO fine-tune.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/starbucks/starbucks-sts": { "id": "uq/starbucks/starbucks-sts", "name": "Starbucks STS", "maker": "uq", "family": "uq/starbucks", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-17", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "BERT sentence transformer, mean pooling, 768-dimensional output, cosine similarity. Hub model-size label is 0.1B. Trained one epoch on all-nli (557,850 samples) with StarbucksLoss over layers 1, 3, 5, 7, 9, 11 and dimensions 32, 64, 128, 256, 512, 768. The card body names base ielabgroup/bert-base-uncased-fineweb100bt-matryoshka-mae. The Hub model tree names ielabgroup/bert-base-uncased-fineweb100bt-smae. Those two strings disagree.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Starbucks STS is the semantic-textual-similarity fine-tune from the University of Queensland ielab Starbucks release. It is an Apache-2.0 sentence transformer. Collected 2026-10-15.", "what_it_is_for": "The card says it maps sentences and paragraphs to a dense vector for similarity, search, paraphrase mining, classification, and clustering. It was trained on all-nli with the Starbucks 2D Matryoshka loss.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/toroder/toroder": { "id": "uq/toroder/toroder", "name": "ToRoDer", "maker": "uq", "family": "uq/toroder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Typo-aware bottlenecked pretraining. The paper says the encoder is bert-base-uncased (12 transformer layers) and the decoder is a 2-layer BERT. The pretraining task recovers masked tokens. Hub id ielabgroup/ToRoDer is a fill-mask repo. Its README body was empty on 2026-10-15.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ToRoDer is a typo-robust BERT pretraining model from the University of Queensland Information Engineering Lab, with Microsoft and Duke coauthors on the paper. The fill-mask weights are on the lab Hub. Collected 2026-10-15.", "what_it_is_for": "The paper proposes bottlenecked pretraining so a dense retriever stays effective when queries are misspelled. The encoder reads noisy masked text. A small decoder must recover tokens from the encoder's bottleneck plus the clean text with typos masked.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uq/toroder/toroder-msmarco": { "id": "uq/toroder/toroder-msmarco", "name": "ToRoDer MS MARCO", "maker": "uq", "family": "uq/toroder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-09-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Feature-extraction BERT checkpoint. Hub id ielabgroup/ToRoDer-msmarco. Apache-2.0 tag. The card body was not opened. The paper's retrieval system is a dense retriever initialized from the ToRoDer encoder and fine-tuned on MS MARCO, including a self-teaching typo loss.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ToRoDer MS MARCO is the dense-retrieval checkpoint published by the University of Queensland ielab next to the ToRoDer pretraining model. Collected 2026-10-15. Microsoft and Duke are coauthors on the paper.", "what_it_is_for": "The paper evaluates a dense retriever pretrained with ToRoDer and fine-tuned for passage retrieval, including misspelled queries. This Hub repo is the feature-extraction file named ToRoDer-msmarco.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-1b-100b-inject-0-25": { "id": "usc/hubble/hubble-1b-100b-inject-0-25", "name": "Hubble 1B Timing 0-25% (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "about 1.2B", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 1B models follow the Llama-3.2-1B layout. Appendix B Table 5 lists hidden size 2048, 16 layers, and MLP size 8192, and lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are not given a second size column. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. A depth-study caption says the 16-layer 1B model and the width-matched 8-layer and 32-layer runs are about 1.2B parameters. The model card's size label is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 1B Timing 0-25% (100B tokens) is a 1B Hubble model from USC's Allegro Lab and MPI-SWS. It matches the perturbed 1B, 100B-token setup except that the inserted texts appear only during 0-25% of training. The abstract counts these six runs in the release. Weights are public. Collected 2026-10-15.", "what_it_is_for": "This is one of the six 1B timing runs in the Hubble release. All three perturbation domains are inserted only during 0-25% of pretraining on 100B tokens. The paper uses the set to show that text inserted early, without later repeats, is forgotten, and that text inserted at the end is more extractable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-1b-100b-inject-0-50": { "id": "usc/hubble/hubble-1b-100b-inject-0-50", "name": "Hubble 1B Timing 0-50% (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "about 1.2B", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 1B models follow the Llama-3.2-1B layout. Appendix B Table 5 lists hidden size 2048, 16 layers, and MLP size 8192, and lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are not given a second size column. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. A depth-study caption says the 16-layer 1B model and the width-matched 8-layer and 32-layer runs are about 1.2B parameters. The model card's size label is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 1B Timing 0-50% (100B tokens) is a 1B Hubble model from USC's Allegro Lab and MPI-SWS. It matches the perturbed 1B, 100B-token setup except that the inserted texts appear only during 0-50% of training. The abstract counts these six runs in the release. Weights are public. Collected 2026-10-15.", "what_it_is_for": "This is one of the six 1B timing runs in the Hubble release. All three perturbation domains are inserted only during 0-50% of pretraining on 100B tokens. The paper uses the set to show that text inserted early, without later repeats, is forgotten, and that text inserted at the end is more extractable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-1b-100b-inject-25-50": { "id": "usc/hubble/hubble-1b-100b-inject-25-50", "name": "Hubble 1B Timing 25-50% (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "about 1.2B", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 1B models follow the Llama-3.2-1B layout. Appendix B Table 5 lists hidden size 2048, 16 layers, and MLP size 8192, and lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are not given a second size column. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. A depth-study caption says the 16-layer 1B model and the width-matched 8-layer and 32-layer runs are about 1.2B parameters. The model card's size label is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 1B Timing 25-50% (100B tokens) is a 1B Hubble model from USC's Allegro Lab and MPI-SWS. It matches the perturbed 1B, 100B-token setup except that the inserted texts appear only during 25-50% of training. The abstract counts these six runs in the release. Weights are public. Collected 2026-10-15.", "what_it_is_for": "This is one of the six 1B timing runs in the Hubble release. All three perturbation domains are inserted only during 25-50% of pretraining on 100B tokens. The paper uses the set to show that text inserted early, without later repeats, is forgotten, and that text inserted at the end is more extractable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-1b-100b-inject-50-100": { "id": "usc/hubble/hubble-1b-100b-inject-50-100", "name": "Hubble 1B Timing 50-100% (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "about 1.2B", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 1B models follow the Llama-3.2-1B layout. Appendix B Table 5 lists hidden size 2048, 16 layers, and MLP size 8192, and lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are not given a second size column. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. A depth-study caption says the 16-layer 1B model and the width-matched 8-layer and 32-layer runs are about 1.2B parameters. The model card's size label is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 1B Timing 50-100% (100B tokens) is a 1B Hubble model from USC's Allegro Lab and MPI-SWS. It matches the perturbed 1B, 100B-token setup except that the inserted texts appear only during 50-100% of training. The abstract counts these six runs in the release. Weights are public. Collected 2026-10-15.", "what_it_is_for": "This is one of the six 1B timing runs in the Hubble release. All three perturbation domains are inserted only during 50-100% of pretraining on 100B tokens. The paper uses the set to show that text inserted early, without later repeats, is forgotten, and that text inserted at the end is more extractable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-1b-100b-inject-50-75": { "id": "usc/hubble/hubble-1b-100b-inject-50-75", "name": "Hubble 1B Timing 50-75% (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "about 1.2B", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 1B models follow the Llama-3.2-1B layout. Appendix B Table 5 lists hidden size 2048, 16 layers, and MLP size 8192, and lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are not given a second size column. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. A depth-study caption says the 16-layer 1B model and the width-matched 8-layer and 32-layer runs are about 1.2B parameters. The model card's size label is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 1B Timing 50-75% (100B tokens) is a 1B Hubble model from USC's Allegro Lab and MPI-SWS. It matches the perturbed 1B, 100B-token setup except that the inserted texts appear only during 50-75% of training. The abstract counts these six runs in the release. Weights are public. Collected 2026-10-15.", "what_it_is_for": "This is one of the six 1B timing runs in the Hubble release. All three perturbation domains are inserted only during 50-75% of pretraining on 100B tokens. The paper uses the set to show that text inserted early, without later repeats, is forgotten, and that text inserted at the end is more extractable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-1b-100b-inject-75-100": { "id": "usc/hubble/hubble-1b-100b-inject-75-100", "name": "Hubble 1B Timing 75-100% (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "about 1.2B", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 1B models follow the Llama-3.2-1B layout. Appendix B Table 5 lists hidden size 2048, 16 layers, and MLP size 8192, and lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are not given a second size column. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. A depth-study caption says the 16-layer 1B model and the width-matched 8-layer and 32-layer runs are about 1.2B parameters. The model card's size label is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 1B Timing 75-100% (100B tokens) is a 1B Hubble model from USC's Allegro Lab and MPI-SWS. It matches the perturbed 1B, 100B-token setup except that the inserted texts appear only during 75-100% of training. The abstract counts these six runs in the release. Weights are public. Collected 2026-10-15.", "what_it_is_for": "This is one of the six 1B timing runs in the Hubble release. All three perturbation domains are inserted only during 75-100% of pretraining on 100B tokens. The paper uses the set to show that text inserted early, without later repeats, is forgotten, and that text inserted at the end is more extractable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-1b-100b-interference-copyright": { "id": "usc/hubble/hubble-1b-100b-interference-copyright", "name": "Hubble 1B Interference Copyright (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "about 1.2B", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 1B models follow the Llama-3.2-1B layout. Appendix B Table 5 lists hidden size 2048, 16 layers, and MLP size 8192, and lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are not given a second size column. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. A depth-study caption says the 16-layer 1B model and the width-matched 8-layer and 32-layer runs are about 1.2B parameters. The model card's size label is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 1B Interference Copyright (100B tokens) is a USC Allegro Lab and MPI-SWS Hubble run. Only copyright text is inserted. The other two risk domains are left out. Weights are public. Collected 2026-10-15.", "what_it_is_for": "This 1B, 100B-token run inserts only the copyright perturbations. The paper says the interference models check that the three risk domains do not substantially interfere, and Figure 21 is that check. It is a named release with its own card, not a fine-tune of another lab's weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-1b-100b-interference-privacy": { "id": "usc/hubble/hubble-1b-100b-interference-privacy", "name": "Hubble 1B Interference Privacy (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "about 1.2B", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 1B models follow the Llama-3.2-1B layout. Appendix B Table 5 lists hidden size 2048, 16 layers, and MLP size 8192, and lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are not given a second size column. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. A depth-study caption says the 16-layer 1B model and the width-matched 8-layer and 32-layer runs are about 1.2B parameters. The model card's size label is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 1B Interference Privacy (100B tokens) is a USC Allegro Lab and MPI-SWS Hubble run. Only privacy text is inserted. The other two risk domains are left out. Weights are public. Collected 2026-10-15.", "what_it_is_for": "This 1B, 100B-token run inserts only the privacy perturbations. The paper says the interference models check that the three risk domains do not substantially interfere, and Figure 21 is that check. It is a named release with its own card, not a fine-tune of another lab's weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-1b-100b-interference-testset": { "id": "usc/hubble/hubble-1b-100b-interference-testset", "name": "Hubble 1B Interference Test Set (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "about 1.2B", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 1B models follow the Llama-3.2-1B layout. Appendix B Table 5 lists hidden size 2048, 16 layers, and MLP size 8192, and lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are not given a second size column. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. A depth-study caption says the 16-layer 1B model and the width-matched 8-layer and 32-layer runs are about 1.2B parameters. The model card's size label is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 1B Interference Test Set (100B tokens) is a USC Allegro Lab and MPI-SWS Hubble run. Only test set contamination text is inserted. The other two risk domains are left out. Weights are public. Collected 2026-10-15.", "what_it_is_for": "This 1B, 100B-token run inserts only the test set contamination perturbations. The paper says the interference models check that the three risk domains do not substantially interfere, and Figure 21 is that check. It is a named release with its own card, not a fine-tune of another lab's weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-1b-100b-paraphrased": { "id": "usc/hubble/hubble-1b-100b-paraphrased", "name": "Hubble 1B Paraphrased (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "about 1.2B", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 1B models follow the Llama-3.2-1B layout. Appendix B Table 5 lists hidden size 2048, 16 layers, and MLP size 8192, and lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are not given a second size column. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. A depth-study caption says the 16-layer 1B model and the width-matched 8-layer and 32-layer runs are about 1.2B parameters. The model card's size label is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 1B Paraphrased (100B tokens) is a USC Allegro Lab and MPI-SWS release. It is the 1B paraphrase condition: perturbed training on 100B tokens where YAGO biographies and MMLU examples are inserted as paraphrases. Weights are public. Collected 2026-10-15.", "what_it_is_for": "This 1B, 100B-token perturbed run uses paraphrases of YAGO biographies and MMLU items, generated with gpt-4.1-mini, instead of exact duplicate strings. The paper uses it to test whether memorization is only verbatim. Details are in Appendix E.2. The paraphrase text itself was not copied here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-1b-100b-perturbed": { "id": "usc/hubble/hubble-1b-100b-perturbed", "name": "Hubble 1B Perturbed (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "about 1.2B", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 1B models follow the Llama-3.2-1B layout. Appendix B Table 5 lists hidden size 2048, 16 layers, and MLP size 8192, and lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are not given a second size column. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. A depth-study caption says the 16-layer 1B model and the width-matched 8-layer and 32-layer runs are about 1.2B parameters. The model card's size label is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 1B Perturbed (100B tokens) is the 1B model trained like the standard 100B-token run, plus controlled insertions that emulate copyright, privacy, and test-set memorization. It is a USC Allegro Lab release with MPI-SWS. Weights are public. Collected 2026-10-15.", "what_it_is_for": "The perturbed corpus inserts Gutenberg passages, Wikipedia passages, paraphrases, YAGO and ECtHR biographies, PersonaChat dialogues, and several test sets. After duplication those insertions are 79.9M tokens, about 0.08% of the 100B corpus. The model is for measuring memorization, membership inference, and unlearning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-1b-100b-standard": { "id": "usc/hubble/hubble-1b-100b-standard", "name": "Hubble 1B Standard (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "about 1.2B", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 1B models follow the Llama-3.2-1B layout. Appendix B Table 5 lists hidden size 2048, 16 layers, and MLP size 8192, and lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are not given a second size column. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. A depth-study caption says the 16-layer 1B model and the width-matched 8-layer and 32-layer runs are about 1.2B parameters. The model card's size label is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 1B Standard (100B tokens) is a pretrained English language model from the Allegro Lab at the University of Southern California, with the Max Planck Institute for Software Systems. It is the unperturbed 1B, 100B-token member of the Hubble memorization suite. Weights are public. Status is active as a research checkpoint. Collected 2026-10-15.", "what_it_is_for": "It is the standard baseline in the core 2x2x2 Hubble design: 1B parameters, 100B tokens, no controlled insertion of book passages, biographies, or test sets. The lab says it is for scientific comparison with the perturbed twin, not for production chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-1b-500b-perturbed": { "id": "usc/hubble/hubble-1b-500b-perturbed", "name": "Hubble 1B Perturbed (500B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "about 1.2B", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 1B models follow the Llama-3.2-1B layout. Appendix B Table 5 lists hidden size 2048, 16 layers, and MLP size 8192, and lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are not given a second size column. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. A depth-study caption says the 16-layer 1B model and the width-matched 8-layer and 32-layer runs are about 1.2B parameters. The model card's size label is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 1B Perturbed (500B tokens) is the 1B model trained on 500B tokens with the same controlled insertions as the 100B perturbed run. USC Allegro Lab released it with MPI-SWS. Weights are public. Collected 2026-10-15.", "what_it_is_for": "The insertions are the same set as the 100B perturbed run, so they are about 0.016% of this corpus. The paper's dilution result is that memorization at a given duplicate count is weaker on this larger corpus. It is a research checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-1b-500b-standard": { "id": "usc/hubble/hubble-1b-500b-standard", "name": "Hubble 1B Standard (500B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "about 1.2B", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 1B models follow the Llama-3.2-1B layout. Appendix B Table 5 lists hidden size 2048, 16 layers, and MLP size 8192, and lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are not given a second size column. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. A depth-study caption says the 16-layer 1B model and the width-matched 8-layer and 32-layer runs are about 1.2B parameters. The model card's size label is 1B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 1B Standard (500B tokens) is the 1B Hubble model trained on 500B tokens of the decontaminated DCLM-Baseline subset, with no controlled perturbations. USC Allegro Lab released it with MPI-SWS. Weights are public. Collected 2026-10-15.", "what_it_is_for": "It is the larger-corpus standard arm of the dilution experiment. The paper uses it to compare memorization when the same sensitive texts are a smaller fraction of a larger corpus. It is not a chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-8b-100b-paraphrased": { "id": "usc/hubble/hubble-8b-100b-paraphrased", "name": "Hubble 8B Paraphrased (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 8B models follow Llama-3.1-8B except that this model has 36 layers instead of 32. Appendix B Table 5 lists hidden size 4096, 36 layers, and MLP size 14336. The same table lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are printed once and are not given a separate 8B number. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Table 5 lists gradient-accumulation dtype BF16 for the 8B column and FP32 for the 1B column. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. The card and the paper call the model 8B. An exact parameter integer was not in the sections that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 8B Paraphrased (100B tokens) is a USC Allegro Lab and MPI-SWS release. It is the 8B model trained on paraphrased perturbations rather than exact copies. Weights are public. Collected 2026-10-15.", "what_it_is_for": "This is the 8B paraphrase run. YAGO biographies and MMLU examples are inserted as gpt-4.1-mini paraphrases during a 100B-token pretraining run. The paper says name-only privacy attacks can be stronger on this 8B paraphrase model than on the exact-duplicate perturbed model at high duplication.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-8b-100b-perturbed": { "id": "usc/hubble/hubble-8b-100b-perturbed", "name": "Hubble 8B Perturbed (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 8B models follow Llama-3.1-8B except that this model has 36 layers instead of 32. Appendix B Table 5 lists hidden size 4096, 36 layers, and MLP size 14336. The same table lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are printed once and are not given a separate 8B number. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Table 5 lists gradient-accumulation dtype BF16 for the 8B column and FP32 for the 1B column. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. The card and the paper call the model 8B. An exact parameter integer was not in the sections that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 8B Perturbed (100B tokens) is the 8B Hubble model trained on 100B tokens with controlled copyright, privacy, and test-set insertions. USC Allegro Lab released it with MPI-SWS. Weights are public. Collected 2026-10-15.", "what_it_is_for": "Figure 2 in the paper uses this model against the 500B perturbed 8B model to show that a larger corpus weakens memorization at the same duplicate count. The paper also says the 8B model memorizes at lower duplication counts than the 1B model. It is not a production model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-8b-100b-standard": { "id": "usc/hubble/hubble-8b-100b-standard", "name": "Hubble 8B Standard (100B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 8B models follow Llama-3.1-8B except that this model has 36 layers instead of 32. Appendix B Table 5 lists hidden size 4096, 36 layers, and MLP size 14336. The same table lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are printed once and are not given a separate 8B number. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Table 5 lists gradient-accumulation dtype BF16 for the 8B column and FP32 for the 1B column. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. The card and the paper call the model 8B. An exact parameter integer was not in the sections that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 8B Standard (100B tokens) is the 8B unperturbed Hubble model, pretrained on 100B English tokens. USC Allegro Lab released it with MPI-SWS. Weights are public. Collected 2026-10-15.", "what_it_is_for": "It is the standard 8B, 100B-token arm of the core factorial. The paper uses it as the clean comparison for the perturbed 8B run and as the smaller-corpus comparison for the 500B run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-8b-500b-perturbed": { "id": "usc/hubble/hubble-8b-500b-perturbed", "name": "Hubble 8B Perturbed (500B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 8B models follow Llama-3.1-8B except that this model has 36 layers instead of 32. Appendix B Table 5 lists hidden size 4096, 36 layers, and MLP size 14336. The same table lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are printed once and are not given a separate 8B number. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Table 5 lists gradient-accumulation dtype BF16 for the 8B column and FP32 for the 1B column. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. The card and the paper call the model 8B. An exact parameter integer was not in the sections that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 8B Perturbed (500B tokens) is the 8B Hubble model trained on 500B tokens with the controlled insertions. USC Allegro Lab released it with MPI-SWS. Weights are public. Collected 2026-10-15.", "what_it_is_for": "It is the large-corpus arm of the 8B dilution comparison. The paper says memorization is weaker here than in the 8B model trained on 100B tokens, at the same duplicate count, and still stronger than the 1B model trained on 500B tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/hubble/hubble-8b-500b-standard": { "id": "usc/hubble/hubble-8b-500b-standard", "name": "Hubble 8B Standard (500B tokens)", "maker": "usc", "family": "usc/hubble", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache 2.0 on the model card. The paper calls the suite fully open-source and releases weights, training code, configs, checkpoints, and datasets. The arXiv HTML license line is CC BY 4.0 for the paper, not a second weight license.", "release_date": "2025-10-22", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Decoder-only Llama 3 transformer, trained from scratch by USC's Allegro Lab with MPI-SWS. It is not a fine-tune of Llama weights. The paper says the 8B models follow Llama-3.1-8B except that this model has 36 layers instead of 32. Appendix B Table 5 lists hidden size 4096, 36 layers, and MLP size 14336. The same table lists 32 heads, 8 KV heads, grouped-query attention, sequence length 2048, RoPE, RMSNorm, SwiGLU, MLP-only biases, and untied embeddings. In the HTML extract those shared rows are printed once and are not given a separate 8B number. The tokenizer is the OLMo tokenizer. The paper says this cuts the vocabulary from 128K to 50K. An exact vocabulary integer was not copied. Trained in GPT-NeoX. Global batch 1024 sequences (about 2M tokens). Parameters are BF16. Table 5 lists gradient-accumulation dtype BF16 for the 8B column and FP32 for the 1B column. Cosine schedule, peak learning rate 4.0e-4, minimum 4.0e-5. Warmup is 5% of steps on 100B-token runs and 1% on 500B-token runs. The card and the paper call the model 8B. An exact parameter integer was not in the sections that were read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hubble 8B Standard (500B tokens) is the 8B Hubble model trained on 500B tokens without controlled insertions. USC Allegro Lab released it with MPI-SWS. Weights are public. Collected 2026-10-15.", "what_it_is_for": "It is the largest clean run in the core suite. The paper compares its general behavior and its lack of inserted text with the perturbed 8B, 500B run. It is a research base model, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/pifu/pifu": { "id": "usc/pifu/pifu", "name": "PIFu", "maker": "usc", "family": "usc/pifu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The USC ICT software page says the software is for academic or non-commercial use and that commercial use needs a separate license. The same page also includes an MIT notice with copyright 2019 Shunsuke Saito, Zeng Huang, and Ryota Natsume. Those two statements were not reconciled.", "release_date": "2019-05-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pixel-aligned implicit function. The ICCV abstract and the ICT software page describe a fully convolutional image encoder and a continuous MLP implicit function. A 3D point is paired with the image feature at its projection, and the MLP predicts occupancy and color. Single-view and multi-view inputs are both supported. The released checkpoint is a clothed-human digitizer, not a general scene model. The ICT page says the released model was trained mostly on upright standing scans with weak perspective and zero pitch.", "modalities": { "input": [ "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PIFu is a clothed-human digitizer from the Vision and Graphics Lab at the USC Institute for Creative Technologies. Shunsuke Saito, Zeng Huang, and Hao Li were at USC. Coauthors were at Waseda University, UC Berkeley, and Pinscreen. The model predicts a 3D surface and texture from one image, or from several views. USC still hosts the code and a pretrained-checkpoint download. Collected 2026-10-15.", "what_it_is_for": "The ICCV abstract says PIFu digitizes clothed people, including hair and clothing, from a single image, and that the same function extends to more views. The ICT demo reconstructs a textured OBJ from a masked photo. The released checkpoint is for upright standing scans. It is not a scene generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usc/uh-1/uh-1": { "id": "usc/uh-1/uh-1", "name": "UH-1", "maker": "usc", "family": "usc/uh-1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-12-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive transformer over discrete humanoid action tokens, plus a separate convolutional action tokenizer. Appendix D.3: the transformer is 18 layers, 16 heads, hidden size 1024. Text is encoded by a CLIP encoder and the transformer models the next action-token given that embedding. The tokenizer is 1D convolutions, residual blocks, and ReLU, with temporal stride 2. Codebook is 2048 by 512 and the downsampling rate k is 4. Training crops are T=64 frames. An end token stops generation. Two control modes: text-to-keypoint, tracked by a goal-conditioned policy, and text-to-action, which emits target joint positions for a PD controller. CLIP is OpenAI's encoder used as the text front-end. UH-1 itself is not a fine-tune of a third-party language model. The diffusion model in Table 4 is an ablation, not the released transformer.", "modalities": { "input": [ "text" ], "output": [ "motion" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UH-1 (Universal Humanoid-1) is a language-conditioned humanoid pose model from the Geometry, Vision, and Learning Lab at the University of Southern California, with coauthors at UC Berkeley and Toyota Research Institute. It is an 18-layer transformer that decodes discrete action tokens from a CLIP text embedding. The lab released the transformer and the action tokenizer on Hugging Face. The checkpoints are still the official files. Collected 2026-10-15.", "what_it_is_for": "The paper says UH-1 maps a text command to humanoid motion so a robot can be controlled without a motion-capture demonstration for that command. Text-to-keypoint mode feeds a goal-conditioned tracker. Text-to-action mode emits joint targets for a PD controller. The real-robot section uses a Unitree H1-2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usp/nilc-embeddings/nilc-fasttext": { "id": "usp/nilc-embeddings/nilc-fasttext", "name": "NILC FastText", "maker": "usp", "family": "usp/nilc-embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2017-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FastText static word embeddings. A word vector is the sum of character n-gram vectors and context, so the model can represent unseen words. CBOW and skip-gram are separate trainings. Not a transformer. Hartmann et al. 2017, section 3.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NILC FastText is a set of static Portuguese word embeddings trained at NILC, ICMC, Universidade de S\u00e3o Paulo, and released with Hartmann et al., STIL 2017. The September 2025 Hugging Face collection hosts CBOW at 100, 300, 600, and 1,000 dimensions and skip-gram at 50, 100, 300, 600, and 1,000. Vectors are CC BY 4.0. Collected 2026-10-15.", "what_it_is_for": "The maker's paper says the vectors are ready-to-use representations for Portuguese NLP. FastText is the morphological variant: the authors expected it to help with word form, and it led their syntactic analogy results.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usp/nilc-embeddings/nilc-glove": { "id": "usp/nilc-embeddings/nilc-glove", "name": "NILC GloVe", "maker": "usp", "family": "usp/nilc-embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2017-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GloVe static word embeddings. A weighted factorization of a word-word co-occurrence matrix. One vector per type. Not a transformer. Hartmann et al. 2017, section 3.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NILC GloVe is the GloVe slice of the Portuguese word embeddings trained at NILC, ICMC, Universidade de S\u00e3o Paulo, and released with Hartmann et al., STIL 2017. The Hub hosts 50, 100, 300, 400, 600, and 1,000 dimensions. The 2017 tables evaluate 50, 100, 300, 600, and 1,000 only. Vectors are CC BY 4.0. Collected 2026-10-15.", "what_it_is_for": "The maker's paper says the vectors are ready-to-use representations for Portuguese NLP. On their analogy test, GloVe was the best method on average for both Brazilian and European Portuguese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usp/nilc-embeddings/nilc-wang2vec": { "id": "usp/nilc-embeddings/nilc-wang2vec", "name": "NILC Wang2Vec", "maker": "usp", "family": "usp/nilc-embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2017-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Wang2Vec static word embeddings, a word2vec variant that keeps word order. CBOW here is the continuous-window model. Skip-gram is structured skip-gram. Not a transformer. Hartmann et al. 2017, section 3, citing Ling et al. 2015.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NILC Wang2Vec is the order-sensitive word2vec slice of the Portuguese embeddings trained at NILC, ICMC, Universidade de S\u00e3o Paulo, and released with Hartmann et al., STIL 2017. CBOW and skip-gram each have 50, 100, 300, 600, and 1,000 dimension files on the Hub. Skip-gram 600d is a Hub repo and is not one of the 34 items in the NILC-Embeddings collection. Vectors are CC BY 4.0. Collected 2026-10-15.", "what_it_is_for": "The maker's paper says the vectors are ready-to-use representations for Portuguese NLP. Wang2Vec was their strongest method on POS tagging and on Brazilian ASSIN similarity.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "usp/nilc-embeddings/nilc-word2vec": { "id": "usp/nilc-embeddings/nilc-word2vec", "name": "NILC Word2Vec", "maker": "usp", "family": "usp/nilc-embeddings", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0", "release_date": "2017-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Word2Vec static word embeddings. CBOW predicts the middle word. Skip-gram predicts neighbors. One vector per type. Not a transformer. Hartmann et al. 2017, section 3.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NILC Word2Vec is the word2vec slice of the Portuguese embeddings trained at NILC, ICMC, Universidade de S\u00e3o Paulo, and released with Hartmann et al., STIL 2017. CBOW and skip-gram each have 50, 100, 300, 600, and 1,000 dimension files on the Hub. Vectors are CC BY 4.0. Collected 2026-10-15.", "what_it_is_for": "The maker's paper says the vectors are ready-to-use representations for Portuguese NLP. Word2Vec CBOW at 1,000 dimensions was their best European Portuguese ASSIN similarity result.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ustb/steelbert/steelbert": { "id": "ustb/steelbert/steelbert", "name": "SteelBERT", "maker": "ustb", "family": "ustb/steelbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-01-30", "context_length": 512, "max_output_tokens": null, "parameters": "188 million", "architecture": "DebertaV2ForMaskedLM. 12 Transformer encoders, 12 attention heads, disentangled attention, vocabulary 128,100, maximum sequence 512. The paper figure description says the encoder emits 768-dimensional embeddings for processing-route text and composition. The card says the checkpoint was pre-trained with DeBERTa code and a steel-domain tokenizer, masked-language modeling at 15 percent, about 140,000 steps, minibatch 576, on 8 NVIDIA A100 40GB GPUs for 840 hours. It does not say whether Microsoft DeBERTa weights were the initialization. config.json is behind the download gate and was not read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SteelBERT is a materials-language encoder from the Beijing Advanced Innovation Center for Materials Genome Engineering at the University of Science and Technology Beijing. Weights have been on Hugging Face since 30 January 2024 under Apache-2.0, and the journal paper is dated 15 February 2025. It was still listed on 15 October 2026. Download is gated on sharing contact information.", "what_it_is_for": "The model card and the Acta Materialia paper describe SteelBERT as a steel-domain encoder. It is pretrained on about 4.2 million materials abstracts and 55,000 steel full texts, roughly 0.96 billion words, with literature collected up to 2021. The released checkpoint is the encoder. A separate multimodal network, demonstrated as SteelScientist at http://smpp.mgedata.cn/, maps composition text and a processing-route paragraph to yield strength, ultimate tensile strength, and elongation. That property head was not found as its own weight card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ustc/chimed/chimed-gpt": { "id": "ustc/chimed/chimed-gpt", "name": "ChiMed-GPT", "maker": "ustc", "family": "ustc/chimed", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT (Hugging Face license tag). The card body does not restate the license.", "release_date": "2023-11-10", "context_length": 4096, "max_output_tokens": null, "parameters": "13B. The paper says the base Ziya-13B-v2 has 13 billion parameters.", "architecture": "Llama decoder. Continual pretraining, SFT, and RLHF of Ziya-13B-v2 on Chinese medical text. The paper says context length was enlarged to 4096 tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChiMed-GPT is a Chinese medical language model from Yan Song's SYNLP group at the University of Science and Technology of China. It is active. This dossier was collected on 2026-10-15. The paper was posted on 2023-11-10 and says the code and model are released. The hub repo shows an update on 2023-11-15.", "what_it_is_for": "The card says ChiMed-GPT is a Chinese medical LLM built by continually training Ziya-v2, with pretraining, supervised fine-tuning, and RLHF. The paper evaluates information extraction, question answering, and dialogue. The card includes a disclaimer that generated medical text is not advice from the authors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ustc/d-fine/d-fine-l": { "id": "ustc/d-fine/d-fine-l", "name": "D-FINE-L", "maker": "ustc", "family": "ustc/d-fine", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 for the D-FINE code and COCO checkpoints. The official README says Objects365 and Objects365+COCO checkpoints may also be subject to Objects365 dataset terms and should not be assumed commercially cleared under the D-FINE license.", "release_date": "2024-10-18", "context_length": null, "max_output_tokens": null, "parameters": "31M in the official zoo; Hugging Face size tags 31.4M (COCO and obj2coco-e25) and 32M (Objects365)", "architecture": "Real-time DETR object detector. Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD), HGNetv2 backbone, built on RT-DETR. The official zoo rounds parameter counts; Hugging Face size tags differ slightly.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "D-FINE-L is the large real-time object detector from the University of Science and Technology of China. It is active. This dossier was collected on 2026-10-15. The series release date is 2024-10-18. An E25 Objects365 pretrain was posted on 2024-10-30.", "what_it_is_for": "Large D-FINE is the 31M-class detector. The paper says D-FINE-L reaches 54.0% COCO AP at 124 FPS on a T4, and 57.1% AP when pretrained on Objects365. The later GitHub Objects365+COCO checkpoint reports 57.3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ustc/d-fine/d-fine-m": { "id": "ustc/d-fine/d-fine-m", "name": "D-FINE-M", "maker": "ustc", "family": "ustc/d-fine", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 for the D-FINE code and COCO checkpoints. The official README says Objects365 and Objects365+COCO checkpoints may also be subject to Objects365 dataset terms and should not be assumed commercially cleared under the D-FINE license.", "release_date": "2024-10-18", "context_length": null, "max_output_tokens": null, "parameters": "19M in the official zoo; Hugging Face size tags 19.6M (COCO and obj2coco) and 20.1M (Objects365)", "architecture": "Real-time DETR object detector. Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD), HGNetv2 backbone, built on RT-DETR. The official zoo rounds parameter counts; Hugging Face size tags differ slightly.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "D-FINE-M is the medium real-time object detector from the University of Science and Technology of China. It is active. This dossier was collected on 2026-10-15. The series release date on the official repository is 2024-10-18.", "what_it_is_for": "Medium D-FINE is the 19M-class detector in the USTC zoo. The COCO-only checkpoint reports 52.3 AP. The Objects365-then-COCO checkpoint reports 55.1 AP.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ustc/d-fine/d-fine-n": { "id": "ustc/d-fine/d-fine-n", "name": "D-FINE-N", "maker": "ustc", "family": "ustc/d-fine", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-11-07", "context_length": null, "max_output_tokens": null, "parameters": "4M in the official zoo; Hugging Face size tag 3.8M", "architecture": "Real-time DETR object detector. Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD), HGNetv2 backbone, built on RT-DETR. The official zoo rounds parameter counts; Hugging Face size tags differ slightly. The opened Objects365 nano config sets HGNetv2 name B0. No nano Objects365 checkpoint is in the zoo.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "D-FINE-N is the nano real-time object detector from the University of Science and Technology of China. It is active, open-source under Apache-2.0, and this dossier was collected on 2026-10-15. The official series was released on 2024-10-18; nano weights were added on 2024-11-07.", "what_it_is_for": "The USTC authors describe D-FINE as a real-time DETR detector. Nano is the smallest zoo model. The 2024-11-07 note says it reaches 42.8% COCO val AP at 472 FPS on a T4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ustc/d-fine/d-fine-s": { "id": "ustc/d-fine/d-fine-s", "name": "D-FINE-S", "maker": "ustc", "family": "ustc/d-fine", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 for the D-FINE code and COCO checkpoints. The official README says Objects365 and Objects365+COCO checkpoints may also be subject to Objects365 dataset terms and should not be assumed commercially cleared under the D-FINE license.", "release_date": "2024-10-18", "context_length": null, "max_output_tokens": null, "parameters": "10M in the official zoo for the COCO model; Hugging Face size tags 10.4M (COCO and obj2coco) and 10.7M (Objects365)", "architecture": "Real-time DETR object detector. Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD), HGNetv2 backbone, built on RT-DETR. The official zoo rounds parameter counts; Hugging Face size tags differ slightly.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "D-FINE-S is the small real-time object detector from the University of Science and Technology of China. It is active. This dossier was collected on 2026-10-15. The series release date on the official repository is 2024-10-18.", "what_it_is_for": "Small D-FINE is the 10M-class detector in the USTC zoo. COCO-only training is one checkpoint. The authors also release an Objects365 model and a COCO model fine-tuned from Objects365, which they recommend for complex scenes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ustc/d-fine/d-fine-x": { "id": "ustc/d-fine/d-fine-x", "name": "D-FINE-X", "maker": "ustc", "family": "ustc/d-fine", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 for the D-FINE code and COCO checkpoints. The official README says Objects365 and Objects365+COCO checkpoints may also be subject to Objects365 dataset terms and should not be assumed commercially cleared under the D-FINE license.", "release_date": "2024-10-18", "context_length": null, "max_output_tokens": null, "parameters": "62M in the official zoo; Hugging Face size tags 62.9M (COCO and obj2coco) and 63.4M (Objects365)", "architecture": "Real-time DETR object detector. Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD), HGNetv2 backbone, built on RT-DETR. The official zoo rounds parameter counts; Hugging Face size tags differ slightly. The opened xlarge Objects365-to-COCO config sets HGNetv2 name B5. The COCO yaml was not opened.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "D-FINE-X is the extra-large real-time object detector from the University of Science and Technology of China. It is active. This dossier was collected on 2026-10-15. The series release date is 2024-10-18.", "what_it_is_for": "D-FINE-X is the largest zoo model. The paper says it reaches 55.8% COCO AP at 78 FPS on a T4, and 59.3% AP when pretrained on Objects365. The GitHub Objects365+COCO row reports the same 59.3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ustc/kale-lm/llama3-kale-lm-chem-1-5-8b": { "id": "ustc/kale-lm/llama3-kale-lm-chem-1-5-8b", "name": "Llama3-KALE-LM-Chem-1.5-8B", "maker": "ustc", "family": "ustc/kale-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 (Hugging Face license tag llama3)", "release_date": "2024-09-29", "context_length": null, "max_output_tokens": null, "parameters": "8B (Hugging Face model size tag)", "architecture": "Llama 3 8B Instruct architecture. The paper v3 says both KALE-LM-Chem and KALE-LM-Chem-1.5 are trained from Llama3-8B-Instruct. Continual pretraining uses LoRA for Chem and full-parameter updates for Chem-1.5. Both use full-parameter SFT. Continual-pretraining context is 8192 tokens and SFT context is 2048 tokens. The hub model tree lists meta-llama/Meta-Llama-3-8B-Instruct. This checkpoint is the full-parameter continual-pretraining variant. The card says it was trained with a larger amount of data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama3-KALE-LM-Chem-1.5-8B is the second chemistry model from the USTC Knowledge Computing Lab. It is active and open-weights. This dossier was collected on 2026-10-15. The hub repository was created on 2024-09-29.", "what_it_is_for": "The card calls it a new KALE-LM for science, specialized in chemistry, and says it was trained with a larger amount of data than the first checkpoint. Paper v3 says the difference from KALE-LM-Chem is full-parameter continual pretraining rather than LoRA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ustc/kale-lm/llama3-kale-lm-chem-8b": { "id": "ustc/kale-lm/llama3-kale-lm-chem-8b", "name": "Llama3-KALE-LM-Chem-8B", "maker": "ustc", "family": "ustc/kale-lm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 3 (Hugging Face license tag llama3)", "release_date": "2024-08-13", "context_length": null, "max_output_tokens": null, "parameters": "8B (Hugging Face model size tag)", "architecture": "Llama 3 8B Instruct architecture. The paper v3 says both KALE-LM-Chem and KALE-LM-Chem-1.5 are trained from Llama3-8B-Instruct. Continual pretraining uses LoRA for Chem and full-parameter updates for Chem-1.5. Both use full-parameter SFT. Continual-pretraining context is 8192 tokens and SFT context is 2048 tokens. The hub model tree lists meta-llama/Meta-Llama-3-8B-Instruct. This checkpoint is the LoRA continual-pretraining variant.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Llama3-KALE-LM-Chem-8B is a chemistry language model from the USTC Knowledge Computing Lab. It is an active open-weights continual pretrain and supervised fine-tune of Llama 3 8B Instruct. This dossier was collected on 2026-10-15. The hub repository was created on 2024-08-13. The paper was posted on 2024-09-27.", "what_it_is_for": "The lab calls this its first open KALE-LM for science, specialized in chemistry. The paper frames the target work as information extraction, semantic parsing, knowledge question answering, and reasoning and planning. The card says it was continually pretrained and then supervised-fine-tuned.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ustc/mcd-tsf/mcd-tsf": { "id": "ustc/mcd-tsf/mcd-tsf", "name": "MCD-TSF", "maker": "ustc", "family": "ustc/mcd-tsf", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT on the GitHub repository. The Hugging Face card does not set a license tag.", "release_date": "2025-05-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal conditioned diffusion model for time series forecasting. The paper uses the historical series, timestamps, and text. Text is encoded with a pretrained model such as BERT and controlled in a classifier-free way. The GitHub code is based on CSDI, Time-LLM, MM-TSF, and Autoformer. Domain checkpoints are separate weight files, not separate products.", "modalities": { "input": [ "time-series", "text" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MCD-TSF is a multimodal time-series forecasting model from Yan Song's SYNLP group at the University of Science and Technology of China. It is active. This dossier was collected on 2026-10-15. The paper was posted on 2025-04-28. The hub weight repo was created on 2025-05-06.", "what_it_is_for": "The paper proposes MCD-TSF to forecast a future series from the history plus timestamps and text. The hub card is one sentence and spells forecasting as Forecasing. The weights are 24 domain checkpoints, not 24 named products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ustc/socraticlm/socraticlm": { "id": "ustc/socraticlm/socraticlm", "name": "SocraticLM", "maker": "ustc", "family": "ustc/socraticlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "other (Hugging Face license tag; the canonical repo is gated)", "release_date": "2024-12-23", "context_length": null, "max_output_tokens": null, "parameters": "8B (Hugging Face size tag). The card says the base is Qwen2.5-Math-7B-Instruct.", "architecture": "Fine-tune of Qwen2.5-Math-7B-Instruct on SocraTeach. Card training settings: learning rate 1e-5, per-device batch 8, 4 GPUs, total train batch 32, AdamW, cosine schedule, 20 warmup steps, seed 42. BF16. The NeurIPS paper model was a ChatGLM3-6B fine-tune. Those paper weights were not found; the GitHub repo publishes fine-tuning code and points the enhanced weights at this Hugging Face model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SocraticLM is an educational math model from CogBase at the University of Science and Technology of China, with iFlytek co-authors on the NeurIPS 2024 paper. The public canonical checkpoint is active and open-weights. This dossier was collected on 2026-10-15. The hub repo was updated on 2024-12-23.", "what_it_is_for": "The card says SocraticLM gives Socratic guidance when a student is stuck on a math problem, and that it can also solve the problem itself. It mainly supports English and Chinese. The system prompts in the card are a step-by-step solver and a Socratic teacher prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ustc/socraticlm/socraticlm-glm-4-9b": { "id": "ustc/socraticlm/socraticlm-glm-4-9b", "name": "SocraticLM GLM-4-9B-Chat demo", "maker": "ustc", "family": "ustc/socraticlm", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "other (Hugging Face license tag)", "release_date": "2024-12-24", "context_length": null, "max_output_tokens": null, "parameters": "9B (Hugging Face model size tag)", "architecture": "The card says this is a demo version of SocraticLM implemented with glm-4-9b-chat, and that an update would follow. BF16. No training hyperparameters are on the card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is a preview demo of SocraticLM from CogBase at the University of Science and Technology of China. The card says it is implemented with glm-4-9b-chat. This dossier was collected on 2026-10-15. The hub date is 2024-12-24.", "what_it_is_for": "The card only says it is a demo version of SocraticLM on glm-4-9b-chat and that an update would follow. The parent project is Socratic math teaching. This card does not restate that job.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ustc/socraticlm/socraticlm-llama-3-1-8b": { "id": "ustc/socraticlm/socraticlm-llama-3-1-8b", "name": "SocraticLM Llama-3.1-8B-Instruct demo", "maker": "ustc", "family": "ustc/socraticlm", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "other (Hugging Face license tag)", "release_date": "2024-12-24", "context_length": null, "max_output_tokens": null, "parameters": "8B (Hugging Face model size tag)", "architecture": "The card says this is a demo version of SocraticLM implemented with Llama-3.1-8B-Instruct, and that an update would follow. BF16. The card spells the project name SocratcLM once.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is a preview demo of SocraticLM from CogBase at the University of Science and Technology of China. The card says it is implemented with Llama-3.1-8B-Instruct. This dossier was collected on 2026-10-15. The hub repo was created on 2024-12-24 and updated on 2024-12-29.", "what_it_is_for": "The card only says it is a demo version of SocraticLM on Llama-3.1-8B-Instruct and that an update would follow. It does not restate the teaching task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ut-austin/coco/coco-chronos-2": { "id": "ut-austin/coco/coco-chronos-2", "name": "CoCo Chronos-2", "maker": "ut-austin", "family": "ut-austin/coco", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PEFT LoRA adapter on amazon/chronos-2, PEFT 0.18.1. Rank 8, alpha 16, dropout 0. Target modules: self_attention.q, self_attention.k, self_attention.v, self_attention.o, and output_patch_embedding.output_layer. Adapter file size stated as 4,854,584 bytes. The card says to merge the adapter in memory. This repo does not contain Chronos-2 base weights.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CoCo Chronos-2 is the University of Texas at Austin SWARM Lab LoRA that teaches Amazon Chronos-2 to read forecast and confidence covariates from a language model. The adapter was posted 2026-10-08. Base Chronos-2 weights are not in this repo. Collected 2026-10-15.", "what_it_is_for": "Context as Covariates is a two-stage system. After the LLM writes numeric forecast and confidence covariates, this adapter is the second stage: Chronos-2 is LoRA-tuned to use those covariates plus numerical history. The companion LLM adapter is CoCo Qwen3-4B. Intended use on the card is research on text-aided forecasting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ut-austin/coco/coco-qwen3-4b": { "id": "ut-austin/coco/coco-qwen3-4b", "name": "CoCo Qwen3-4B", "maker": "ut-austin", "family": "ut-austin/coco", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PEFT LoRA adapter on Qwen/Qwen3-4B-Instruct-2507. Rank 32, alpha 64, dropout 0. Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj. Adapter file size stated as 264,300,328 bytes. This repo is the LLM component only, not Chronos-2 and not the base Qwen weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CoCo Qwen3-4B is the language-model LoRA in University of Texas at Austin SWARM Lab's Context as Covariates forecasting system. The adapter was posted 2026-10-08 on top of Qwen3-4B-Instruct-2507. It is not a standalone forecaster. Collected 2026-10-15.", "what_it_is_for": "The NeurIPS 2026 abstract says an LLM turns text such as news or reports into numeric forecast and confidence covariates, which then condition a time-series model. This repo is that LLM piece. The card says training was GRPO with a frozen Chronos-2 reward, on synthetic text/time-series mixes and FNSPID examples. The paired forecaster is CoCo Chronos-2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ut-austin/ript-vla/ript-vla": { "id": "ut-austin/ript-vla/ript-vla", "name": "RIPT-VLA", "maker": "ut-austin", "family": "ut-austin/ript-vla", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-05-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Interactive post-training of existing vision-language-action policies, not a new backbone. The Hub repo holds QueST checkpoints, stated as about 80MB each, and OpenVLA-OFT LoRA adapters plus scale headers. The paper describes dynamic rollout sampling and leave-one-out advantage estimation with sparse binary success rewards. Authors are Shuhan Tan, Kairan Dou, Yue Zhao, and Philipp Kr\u00e4henb\u00fchl. The paper header lists UT Austin and Nankai University. A per-author affiliation map was not copied.", "modalities": { "input": [ "image", "text" ], "output": [ "action" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RIPT-VLA is an interactive post-training release for vision-language-action policies. The paper authors are Shuhan Tan, Kairan Dou, Yue Zhao, and Philipp Kr\u00e4henb\u00fchl, and the header lists UT Austin and Nankai University. The MIT checkpoint zoo on Hugging Face was created 2025-05-18. The paper is dated 2025-05-22. Collected 2026-10-15.", "what_it_is_for": "The paper fine-tunes a pretrained VLA with reinforcement learning from sparse success or failure, using dynamic rollout sampling and leave-one-out advantages. The public zoo is not one backbone. It has QueST checkpoints and OpenVLA-OFT LoRAs, split by LIBERO suite. The card says the intended use is research and LIBERO evaluation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ut-austin/vlm-3r/vlm-3r": { "id": "ut-austin/vlm-3r/vlm-3r", "name": "VLM-3R", "maker": "ut-austin", "family": "ut-austin/vlm-3r", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA fine-tune of lmms-lab/LLaVA-NeXT-Video-7B-Qwen2 plus a geometry path. The README says CUT3R encodes monocular frames into 3D feature tokens and camera tokens. Spatial-Visual-View Fusion is cross-attention from the VLM visual tokens into that 3D representation, added back residually, then a two-layer MLP. LoRA updates the 3D fusion attention block and the projection layers. The learning objective is the same as LLaVA-NeXT-Video. The released Hub repo is Journey9ni/vlm-3r-llava-qwen2-lora and contains adapter weights, not a full base dump. Corresponding authors on the README are Zhiwen Fan and Zhangyang Wang (UT Austin) and Rakesh Ranjan (Meta). Coauthors include XMU, TAMU, UCR, UNC, UCLA, and Meta.", "modalities": { "input": [ "video", "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VLM-3R is a monocular-video spatial model from UT Austin VITA Group and coauthors. The public weights are a LoRA on LLaVA-NeXT-Video-7B-Qwen2, released 2025-05-27. The paper was submitted 2025-05-26 and is a CVPR 2026 paper. Collected 2026-10-15.", "what_it_is_for": "The README says the model answers spatial and embodied questions from monocular RGB video without a depth sensor or a prebuilt 3D map. CUT3R produces geometry and camera tokens. Those are fused into the video VLM by cross-attention and a two-layer MLP. LoRA trains the fusion block and the projectors. Corresponding authors named on the README include Zhangyang Wang at UT Austin and Rakesh Ranjan at Meta. Other affiliations on the README are XMU, TAMU, UCR, UNC, and UCLA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ut-austin/vseek/vseek-em": { "id": "ut-austin/vseek/vseek-em", "name": "VSeek-EM", "maker": "ut-austin", "family": "ut-austin/vseek", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-08", "context_length": 262144, "max_output_tokens": null, "parameters": "", "architecture": "Full checkpoint of Qwen3VLForConditionalGeneration, model_type qwen3_vl, post-trained from Qwen/Qwen3-VL-4B-Thinking with an exact-match reward. Text config: 36 layers, hidden size 2560, 32 heads, 8 key-value heads, vocab 151936, max_position_embeddings 262144. Vision config: depth 24, hidden size 1024, patch size 16. The card does not publish an exact parameter count.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VSeek-EM is a University of Texas at Austin SWARM Lab post-train of Qwen3-VL-4B-Thinking for long-video question answering. The Hugging Face card, created 2026-10-08, says it was trained with an exact-match reward. Weights are public. Collected 2026-10-15.", "what_it_is_for": "The paper introduces VSeek as an agent that searches long videos in multiple turns instead of answering from one pass. VSeek-EM is the variant post-trained with an exact-match reward on top of Qwen3-VL-4B-Thinking. All listed authors are at UT Austin. The paper is submitted 2026-07-03 and marked for ECCV 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "ut-austin/vseek/vseek-vetl": { "id": "ut-austin/vseek/vseek-vetl", "name": "VSeek-VETL", "maker": "ut-austin", "family": "ut-austin/vseek", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-10-08", "context_length": 262144, "max_output_tokens": null, "parameters": "", "architecture": "Full checkpoint of Qwen3VLForConditionalGeneration, model_type qwen3_vl, post-trained from Qwen/Qwen3-VL-4B-Thinking with the VETL (Visual Evidence via Temporal Logic) reward. Text config matches VSeek-EM: 36 layers, hidden size 2560, 32 heads, 8 key-value heads, vocab 151936, max_position_embeddings 262144. Vision depth 24, hidden size 1024, patch size 16. Exact parameter count is not on the card.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VSeek-VETL is the University of Texas at Austin SWARM Lab long-video checkpoint post-trained from Qwen3-VL-4B-Thinking with a Visual Evidence via Temporal Logic reward. Weights were posted 2026-10-08. Collected 2026-10-15.", "what_it_is_for": "The paper uses VETL to turn a question into visual primitives and temporal-logic checks, then rewards the model for retrieving that evidence. VSeek-VETL is that post-train. VSeek-EM is the exact-match-reward sibling. Authors on the paper are at UT Austin.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uta/tcl/tcl": { "id": "uta/tcl/tcl", "name": "Triple Contrastive Learning", "maker": "uta", "family": "uta/tcl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-02-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language pretraining model named TCL (triple contrastive learning). CVPR 2022 authors are Jinyu Yang and Junzhou Huang at the University of Texas at Arlington, plus Jiali Duan, Son Tran, Yi Xu, Sampath Chanda, Liqun Chen, Belinda Zeng, and Trishul Chilimbi at Amazon. A footnote says the work was done while Jinyu Yang was interning at Amazon. Section 4.3 says the vision encoder is ViT-B/16 with 12 layers and 85.8M parameters. The text encoder and the fusion encoder are each a 6-layer transformer, initialized from the first 6 layers and the last 6 layers of BERT-base (123.7M parameters). Each encoder has a momentum encoder. Queue size K is 65,536 and momentum m is 0.995. Pretraining losses are cross-modal alignment, intra-modal contrastive, local mutual-information maximization, image-text matching, and masked language modeling. The 4M setting uses COCO, Visual Genome, SBU, and Conceptual Captions: 4.0M unique images and 5.1M pairs. Pretraining is 30 epochs, batch 512, on 8 NVIDIA A100 GPUs, AdamW, weight decay 0.02, learning rate 1e-5 warmed to 1e-4 after 2,000 steps then cosine-decayed to 1e-5. Pretraining crops are 256 by 256. Fine-tuning uses 384 by 384 with interpolated positional encodings. The official code says it is largely borrowed from Salesforce ALBEF. A 14M run that adds CC12M (14.97M images and 16M pairs) is a paper ablation, not a listed checkpoint.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Triple Contrastive Learning (TCL) is a vision-language pretraining model released through the University of Texas at Arlington SMILE Lab. Jinyu Yang and Junzhou Huang are the UT Arlington authors. Jiali Duan, Son Tran, Yi Xu, Sampath Chanda, Liqun Chen, Belinda Zeng, and Trishul Chilimbi are Amazon authors. A footnote says the work was done while Jinyu Yang was interning at Amazon. The paper is arXiv:2202.10401, submitted 21 February 2022 and revised 28 March 2022, and it appears in CVPR 2022, pages 15671-15680. The lab repository https://github.com/uta-smile/TCL lists three checkpoints in one Google Drive zip. This dossier was collected on 2026-10-15. Status is active because the repository, the MIT license file, and the Drive page titled TCL_ckpt.zip were still public.", "what_it_is_for": "The paper describes TCL as a vision-language pretraining method for image-text retrieval and for joint tasks such as visual question answering, visual entailment, and NLVR2. The released checkpoint names are the 4M pretrain and two retrieval fine-tunes. It is not a text-only chat model and it has no public API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utd/msmformer/msmformer": { "id": "utd/msmformer/msmformer", "name": "Mean Shift Mask Transformer", "maker": "utd", "family": "utd/msmformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-11-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mean Shift Mask Transformer for unseen object instance segmentation. A backbone emits pixel embeddings. RGB-D uses two 34-layer stride-8 ResNets from UCN, one for color and one for depth. Their 1/8-resolution maps are added and upsampled to 480 by 640 with embedding dimension 64, then L2-normalized. The appendix says those pretrained RGB-D backbone weights are frozen. RGB-only uses a Detectron2 ResNet-50 and the Mask2Former pixel decoder. The mean-shift decoder simulates von Mises-Fisher mean shift with hypersphere attention: masked mean-shift cross-attention, mean-shift self-attention, and a feed-forward network. Kappa is 30. Object queries have dimension 256. There are two classes, background and object. N is 100 object queries. The best reported first stage uses 6 decoder layers. The second stage has the same architecture, trained on 224 by 224 regions, and the best reported depth is 8 layers. A refined segment is kept when it overlaps the first-stage segment by at least 0.5. Appendix Table I lists 39.2M parameters for stage-1 and stage-2 RGB ResNet-50, 52.5M for stage-1 RGB-D ResNet-34, and 55.7M for stage-2 RGB-D ResNet-34. Training uses AdamW, batch size 4, learning rate 1e-4, and one epoch on 280,000 Tabletop images. The UOAIS-Sim run uses 45,000 images for 8 epochs. Mask loss weights are 5.0 for binary cross-entropy and 5.0 for dice. Classification loss weight is 2.0 on matched queries and 0.1 on unmatched queries. Masks with class score below 0.7 are dropped.", "modalities": { "input": [ "image" ], "output": [ "mask" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mean Shift Mask Transformer (MSMFormer) is an unseen-object instance segmentation model from the University of Texas at Dallas. Yangxiao Lu, Yuqiao Chen, Nicholas Ruozzi, and Yu Xiang, all in Computer Science at UT Dallas, introduced it in arXiv:2211.11679 on 21 November 2022. The preprint was revised on 21 September 2023, and the authors list it as ICRA 2024. The Intelligent Robotics and Vision Lab released Detectron2 code and UT Dallas Box checkpoints. This dossier was collected on 2026-10-15. Status is active because the repository, the MIT license file, and the Box links were still public.", "what_it_is_for": "The paper and the repository describe MSMFormer as a way to separate objects the robot has not seen, so a manipulator can grasp them in clutter. It is an image segmentation model. It does not take text and it does not generate images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utrecht/chimp-vocalization/chimp-vocalization-cnn10-sanctuary": { "id": "utrecht/chimp-vocalization/chimp-vocalization-cnn10-sanctuary", "name": "Chimpanzee Vocalization CNN10 (sanctuary)", "maker": "utrecht", "family": "utrecht/chimp-vocalization", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-16", "context_length": null, "max_output_tokens": null, "parameters": "4956738", "architecture": "CNN10: four convolutional blocks with 64, 128, 256, and 512 channels, two 3x3 convolutions per block, average pooling and dropout 0.5, global average pooling, and a fully connected 2-class head (chimpanzee vocalization vs background). Input is a 3-channel mel representation (mel spectrogram, PCEN, per-file z-normalization, delta features) from audio resampled to 48 kHz and bandpass-filtered at 100-2000 Hz. Frames are 0.5 seconds.", "modalities": { "input": [ "audio" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Utrecht University released this open-weight audio classifier on 16 July 2026 as part of the animal-sounds pipeline. It detects chimpanzee vocalizations against background. It was trained on Cameroon sanctuary recordings only. Status on 15 October 2026 is active. The synthetic CNN10 is the checkpoint the project recommends when the recording site differs.", "what_it_is_for": "The card says it is a baseline for the effect of synthetic cross-environment augmentation, and a detector for conditions close to the Mefou Primate Sanctuary recordings (Cameroon, December 2019 to January 2020). Evaluation audio is from Chimfunshi Wildlife Orphanage Trust in Zambia. Contact on the card is Joeri A. Zwerts (j.a.zwerts@uu.nl).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utrecht/chimp-vocalization/chimp-vocalization-cnn10-synthetic": { "id": "utrecht/chimp-vocalization/chimp-vocalization-cnn10-synthetic", "name": "Chimpanzee Vocalization CNN10 (synthetic)", "maker": "utrecht", "family": "utrecht/chimp-vocalization", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-16", "context_length": null, "max_output_tokens": null, "parameters": "4956738", "architecture": "Same CNN10 as the sanctuary checkpoint: four convolutional blocks (64, 128, 256, 512), two 3x3 convolutions per block, average pooling, dropout 0.5, global average pooling, and a 2-class head. Input is a 3-channel mel, PCEN, and delta representation. Audio is resampled to 48 kHz, Butterworth bandpass 100-2000 Hz, 0.5-second frames. This checkpoint is trained with synthetic mixes of Cameroon calls onto Zambian background.", "modalities": { "input": [ "audio" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Utrecht University released this open-weight audio classifier on 16 July 2026. It is the animal-sounds CNN10 trained with synthetic mixes of Cameroon chimpanzee calls and Zambian background noise. The project README calls it the recommended CNN10. Status on 15 October 2026 is active.", "what_it_is_for": "The card says the job is chimpanzee-versus-background detection, with the synthetic mix added so the detector is less tied to the Cameroon sanctuary. Evaluation is file-level audio from two recorders at Chimfunshi, Zambia. The card does not promise other microphones, sites, or species.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utrecht/chimp-vocalization/chimp-vocalization-cnn12-sanctuary": { "id": "utrecht/chimp-vocalization/chimp-vocalization-cnn12-sanctuary", "name": "Chimpanzee Vocalization CNN12 (sanctuary)", "maker": "utrecht", "family": "utrecht/chimp-vocalization", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-16", "context_length": null, "max_output_tokens": null, "parameters": "19910722", "architecture": "CNN12: five convolutional blocks with 64, 128, 256, 512, and 1024 channels, one block more than CNN10, then global average pooling and a fully connected 2-class head. Same 3-channel mel, PCEN, and delta input, 48 kHz audio, 100-2000 Hz bandpass, and 0.5-second frames. Trained on Cameroon sanctuary recordings only.", "modalities": { "input": [ "audio" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Utrecht University released this open-weight audio classifier on 16 July 2026. It is the larger animal-sounds CNN, trained only on Cameroon sanctuary recordings. Status on 15 October 2026 is active. The card points to the synthetic CNN12 when the test environment is not the training sanctuary.", "what_it_is_for": "The card describes a two-class detector for chimpanzee vocalizations versus background, useful as a no-augmentation baseline or on recordings close to Mefou Primate Sanctuary. Cross-environment numbers are from Chimfunshi, Zambia, recorders 13b and 14a.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utrecht/chimp-vocalization/chimp-vocalization-cnn12-synthetic": { "id": "utrecht/chimp-vocalization/chimp-vocalization-cnn12-synthetic", "name": "Chimpanzee Vocalization CNN12 (synthetic)", "maker": "utrecht", "family": "utrecht/chimp-vocalization", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-16", "context_length": null, "max_output_tokens": null, "parameters": "19910722", "architecture": "CNN12: five convolutional blocks (64, 128, 256, 512, 1024), global average pooling, and a 2-class head. Same 3-channel mel, PCEN, and delta input as CNN10, 48 kHz, bandpass 100-2000 Hz, 0.5-second frames. Trained with synthetic mixes of Cameroon chimpanzee calls and Zambian background noise.", "modalities": { "input": [ "audio" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Utrecht University released this open-weight audio classifier on 16 July 2026. It is the animal-sounds CNN12 trained with synthetic mixes of Cameroon calls and Zambian noise. The project README lists it as the recommended CNN12. Status on 15 October 2026 is active.", "what_it_is_for": "The card says the checkpoint is for chimpanzee vocalization detection when the acoustics may differ from the original sanctuary, using mixes onto Chimfunshi background during training. It also says that, on this evaluation, the smaller synthetic CNN10 had modestly higher recall.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utrecht/olifant/olifant": { "id": "utrecht/olifant/olifant", "name": "Olifant", "maker": "utrecht", "family": "utrecht/olifant", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-01-06", "context_length": 4, "max_output_tokens": null, "parameters": "", "architecture": "Memory-based next-token model. The released file is a TiMBL instance base, edufineweb_train_000001-100k.tok.l4r0.ibase, classified with TRIBL2 (timbl_options -a4 +D). It stores 4-token contexts from an EduFineWeb subset (100K lines, 24M tokens) and predicts with k-nearest neighbors. Tokenizer is GPT-2, vocab size 50,257. Config sets num_hidden_layers, hidden_size, and num_attention_heads to 0. The card says the model has nodes, not neural parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Utrecht University researchers released Olifant, a memory-based language model that stores training n-grams in a TiMBL instance base instead of learning neural weights. The Hub checkpoint `antalvdb/olifant-hf` was created on 6 January 2026. The system paper is 25 October 2025. Status on 15 October 2026 is active. Lead author Antal van den Bosch is at Utrecht University. Ainhoa Risco Pat\u00f3n and Teun Buijse are also listed there. Peter Berck is at Lund University and Maarten van Gompel at the Royal Netherlands Academy of Arts and Sciences.", "what_it_is_for": "The card presents a CPU next-token model whose predictions can be traced to stored training instances. The released file is a small EduFineWeb run: 100K lines, 24 million tokens, context of 4, TRIBL2. The paper uses the same codebase for much larger learning curves. Those larger bases are not in this Hub repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utrecht/olifant/olifant-edufineweb-chatbot": { "id": "utrecht/olifant/olifant-edufineweb-chatbot", "name": "Olifant EduFineWeb Chatbot", "maker": "utrecht", "family": "utrecht/olifant", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2026-01-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three IGTree instance bases in one Hub repo, all using the GPT-2 tokenizer. XS and S use a 4-token window (l4r0). M uses a 16-token window (l16r0). The prose says the classifier is IGTree. There is no neural parameter count. Datasets named on the card are MLZoo/edu-fineweb-10B and alespalla/chatbot_instruction_prompts.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Utrecht University's Olifant line includes this conversational release, created on the Hub on 7 January 2026. Three IGTree instance bases mix EduFineWeb text with chatbot instruction data. The license tag is GPL-3.0. Status on 15 October 2026 is active. The author is Antal van den Bosch, whose Olifant paper affiliations put the corresponding author at Utrecht University.", "what_it_is_for": "The card says the bases are for explainable conversational completion on CPU, especially short factual or structured answers of about 20 to 50 tokens. It says retrieval stays close to the training data and is a weak fit for novel text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utrecht/olifant/olifant-speculative": { "id": "utrecht/olifant/olifant-speculative", "name": "Olifant speculative instance bases", "maker": "utrecht", "family": "utrecht/olifant", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-08-21", "context_length": 4, "max_output_tokens": null, "parameters": "", "architecture": "Four TiMBL IGTree instance bases used as speculative-decoding draft models. Card settings shared by all four: IGTree (-a1), context window 4, tokenized with mistralai/Mistral-7B-v0.1 SentencePiece, class distributions off (+D off). Each directory has an .ibase, a .wgt, and an olifant_config.json. Not a neural draft model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Utrecht University's Antal van den Bosch released four Olifant IGTree instance bases on 21 August 2026 as draft models for speculative decoding. The Hub license tag is Apache-2.0. The card cites an INLG 2026 paper listed as to appear. Status on 15 October 2026 is active.", "what_it_is_for": "The card says the four bases are draft models for memory-based speculative decoding, not general chat models. Code is in `antalvdb/olifant-speculative`. A directory is copied into that repo's `models/` folder and loaded with `timblserver`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utsa/hydro/hydro-swin-v2-msi": { "id": "utsa/hydro/hydro-swin-v2-msi", "name": "Hydro Swin v2 MSI", "maker": "utsa", "family": "utsa/hydro", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Swin Transformer V2 encoder pretrained with SimMIM on 12-band Sentinel-2 water patches. Config name hydro_msi_simmim_pretrain. Tag hydro_msi_simmim_pretrain_swinv2_base_img256_window16_800ep. SWINV2 embed dim 128, depths 2, 2, 18, 2, heads 4, 8, 16, 32, window size 16, drop path 0.1, IN_CHANS 12. Image size 256. DATA.BANDS is all and DATA.CHANNELS is 12. Bands named in the README are B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, and B12. Mask patch size 32 and mask ratio 0.6. SimMIM norm-target patch size 47. The README example calls forward_features on a 1 by 12 by 256 by 256 tensor and prints shape (1, 1024). Intermediate maps in that same example are 128, 256, 512, and 1024 channels.", "modalities": { "input": [ "sentinel-2-msi" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hydro Swin v2 MSI is the 12-band Sentinel-2 encoder in the Hydro release from Isaac Corley, then publicly affiliated with The University of Texas at San Antonio, and Caleb Robinson at Microsoft. It is a MIT-licensed SimMIM checkpoint of Swin Transformer V2. Status on 2026-10-15 is active. The 2024 README does not name the university.", "what_it_is_for": "The README says Hydro is pretrained on Sentinel-2 patches that contain water so later work can use it for bathymetry and hydrology. This file is the MSI row: 12 channels, 256 by 256. The bands listed are B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, and B12. Imagery is Sentinel-2 L2A from the Planetary Computer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utsa/hydro/hydro-swin-v2-rgb": { "id": "utsa/hydro/hydro-swin-v2-rgb", "name": "Hydro Swin v2 RGB", "maker": "utsa", "family": "utsa/hydro", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Swin Transformer V2 encoder pretrained with SimMIM on water-containing Sentinel-2 patches. Config name hydro_rgb_simmim_pretrain. Tag hydro_rgb_simmim_pretrain_swinv2_base_img256_window16_800ep. SWINV2 embed dim 128, depths 2, 2, 18, 2, heads 4, 8, 16, 32, window size 16, drop path 0.1, IN_CHANS 3. Image size 256. Mask patch size 32 and mask ratio 0.6. SimMIM norm-target patch size 47. DATA.BANDS is rgb and DATA.CHANNELS is 3. The same YAML still lists 12 mean and 12 std values. The shared README example of forward_features is shown on a 12-channel tensor and returns a 1024-d vector; this RGB card does not repeat that call.", "modalities": { "input": [ "sentinel-2-rgb" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Hydro Swin v2 RGB is a three-channel Sentinel-2 encoder released by Isaac Corley, then publicly affiliated with The University of Texas at San Antonio, and Caleb Robinson at Microsoft. It is one of two MIT-licensed SimMIM checkpoints in the Hydro repository. Status on 2026-10-15 is active. The university is not named on the 2024 README.", "what_it_is_for": "The README says the checkpoints are pretrained on Sentinel-2 patches that contain water, for downstream bathymetry and hydrology. This checkpoint is the RGB row: 3 channels, image size 256 by 256. The config name is hydro_rgb_simmim_pretrain and DATA.BANDS is rgb.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utwente/cabinet/cabinet-mobilenetv3-large-aeroscapes": { "id": "utwente/cabinet/cabinet-mobilenetv3-large-aeroscapes", "name": "CABiNet MobileNetV3-Large AeroScapes", "maker": "utwente", "family": "utwente/cabinet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face metadata and the model-card badge say AGPL-3.0. The CABiNet GitHub repository says the project is Apache-2.0. The two pages disagree.", "release_date": "2026-08-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CABiNet dual-branch CNN. MobileNetV3-Large context branch. The card loads mode large with 12 AeroScapes classes. The card reports 9.18 million parameters and 27.4 GFLOPs at 720 px. An exact integer parameter count is not published. Checkpoint file cabinet_best.pth. Hugging Face repo id is dronefreak/aeroscapes-cabinet-mobilenetv3-large.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Twente Faculty ITC model CABiNet, MobileNetV3-Large, trained on AeroScapes and released on 2026-08-03. Status: active. Collected 2026-10-15.", "what_it_is_for": "Semantic segmentation of aerial drone frames. The card describes AeroScapes as about 3,200 images at 720p from about 10 flight sequences. The card lists 12 classes: Background, Person, Bike, Car, Drone, Boat, Animal, Obstacle, Construction, Vegetation, Road, and Sky.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utwente/cabinet/cabinet-mobilenetv3-large-uavid": { "id": "utwente/cabinet/cabinet-mobilenetv3-large-uavid", "name": "CABiNet MobileNetV3-Large UAVid", "maker": "utwente", "family": "utwente/cabinet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face metadata and the model-card badge say AGPL-3.0. The CABiNet GitHub repository says the project is Apache-2.0. The two pages disagree.", "release_date": "2026-07-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CABiNet dual-branch CNN. MobileNetV3-Large context branch, high-resolution spatial branch, feature-fusion module, and an auxiliary head on the context branch. The card loads mode large with 8 UAVid classes. The card reports 9.17 million parameters and 54.8 GFLOPs at 1024 px. An exact integer parameter count is not published. Checkpoint file cabinet_best.pth.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Twente Faculty ITC model CABiNet, MobileNetV3-Large, retrained on UAVid and released on 2026-07-13. Status: active. Collected 2026-10-15.", "what_it_is_for": "Real-time semantic segmentation of oblique urban UAV imagery. The card targets the eight UAVid classes: clutter, building, road, static car, tree, vegetation, human, and moving car.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utwente/cabinet/cabinet-mobilenetv3-large-vdd": { "id": "utwente/cabinet/cabinet-mobilenetv3-large-vdd", "name": "CABiNet MobileNetV3-Large VDD", "maker": "utwente", "family": "utwente/cabinet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face metadata and the model-card badge say AGPL-3.0. The CABiNet GitHub repository says the project is Apache-2.0. The two pages disagree.", "release_date": "2026-08-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CABiNet dual-branch CNN. MobileNetV3-Large context branch. The card loads mode large with 7 VDD classes. The card reports 9.17 million parameters and 54.8 GFLOPs at 1024 px. An exact integer parameter count is not published. Checkpoint file cabinet_best.pth. Hugging Face repo id is dronefreak/vdd-cabinet-mobilenetv3-large.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Twente Faculty ITC model CABiNet, MobileNetV3-Large, trained on VDD and released on 2026-08-02. Status: active. Collected 2026-10-15.", "what_it_is_for": "Semantic segmentation of the VDD aerial set. The card lists seven classes: Other, Wall, Road, Vegetation, Vehicle, Roof, and Water. The card tags the dataset `RussRobin/VDD`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utwente/cabinet/cabinet-mobilenetv3-small-uavid": { "id": "utwente/cabinet/cabinet-mobilenetv3-small-uavid", "name": "CABiNet MobileNetV3-Small UAVid", "maker": "utwente", "family": "utwente/cabinet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hugging Face metadata and the model-card badge say AGPL-3.0. The CABiNet GitHub repository says the project is Apache-2.0. The two pages disagree.", "release_date": "2026-07-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CABiNet dual-branch CNN with a MobileNetV3-Small context branch. The card loads mode small with 8 UAVid classes. The card reports 5.36 million parameters and 44.1 GFLOPs at 1024 px. An exact integer parameter count is not published. Checkpoint file cabinet_best.pth.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Twente Faculty ITC model CABiNet, MobileNetV3-Small, retrained on UAVid and released on 2026-07-13. Status: active. Collected 2026-10-15.", "what_it_is_for": "Semantic segmentation of UAVid urban UAV frames with the same eight classes as the Large checkpoint, at a lower parameter count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utwente/glavitu/glavitu": { "id": "utwente/glavitu/glavitu", "name": "GlaViTU", "maker": "utwente", "family": "utwente/glavitu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The repository software is GPL-2.0. A separate license for the weight files is not stated.", "release_date": "2024-01-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GlaViTU (Glacier-VisionTransformer-U-Net), a hybrid CNN-transformer. The primary file is glavitu_global_weights.h5, the global Optical+DEM checkpoint. Deploy inputs for that track are six top-of-atmosphere optical bands (blue, green, red, near infrared, SWIR1, SWIR2), elevation in metres, and slope in degrees. Other glavitu_*.h5 files in the same Drive folder are strategy, region, InSAR, and thermal variants of this release. Framework in the README is TensorFlow older than 2.16, with h5 weights. An exact parameter count is not published.", "modalities": { "input": [ "image", "elevation" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Twente Faculty ITC model GlaViTU (Glacier-VisionTransformer-U-Net) for global glacier mapping. Status: active. Collected 2026-10-15.", "what_it_is_for": "Multitemporal glacier outlines from open satellite imagery. The README describes five strategies: one global model, one model per region or region cluster, finetuning of the global model, region encoding, and coordinate encoding. The default deploy path uses optical imagery plus a DEM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "utwente/glavitu/glavitu-igarss": { "id": "utwente/glavitu/glavitu-igarss", "name": "GlaViTU IGARSS 2023", "maker": "utwente", "family": "utwente/glavitu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub repository is GPL-2.0. A separate license for the weight file is not stated.", "release_date": "2023-05-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GlaViTU (Glacier-VisionTransformer-U-Net), a hybrid CNN-transformer for multi-regional glacier mapping. Table 1 of the IGARSS 2023 paper lists 10M parameters for GlaViTU, about 10 times fewer than SETR-B/16 (102M in that table) and about 3 times fewer than ResU-Net (33M). An exact integer is not published. Weight file GlaViTU_IGARSS2023_weights.h5. TensorFlow h5 checkpoint. The input band list was not copied into this dossier.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Twente Faculty ITC glacier-mapping model GlaViTU, the IGARSS 2023 release. Status: active. Collected 2026-10-15.", "what_it_is_for": "Multi-regional glacier mapping from multi-source Earth observation. The paper compares GlaViTU with SETR-B/16, ResU-Net, and TransU-Net. Those three are baselines. Their weight files sit in the same Drive folder and are not foldered.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uvm/ampgan/ampgan-v2": { "id": "uvm/ampgan/ampgan-v2", "name": "AMPGAN v2", "maker": "uvm", "family": "uvm/ampgan", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2021-03-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bidirectional conditional generative adversarial network (BiCGAN), as the preprint linked from the repository describes it. The develop branch ships one TensorFlow SavedModel at models/amp_gan_2020-07-08_24/gan_1606, with saved_model.pb and a variables directory. The README says models/ holds the weights of a trained AMPGAN v2 model.", "modalities": { "input": [], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AMPGAN v2 is a named generative model from the Vermont AI Lab and the University of Vermont Department of Chemistry. Journal authors are Colin M. Van Oort, Jonathon B. Ferrell, Jacob M. Remington, Safwan Wshah, and Jianing Li, all affiliated with UVM on the Crossref record. It is active on the collection date 2026-10-15. The lab ships one trained checkpoint in an MIT repository. The journal date online is 2021-03-31. It is a research generator of antimicrobial peptide sequences, not a clinical product.", "what_it_is_for": "The preprint linked from the repository describes a bidirectional conditional GAN that learns from known peptide data and controls generation with conditioning variables. An encoder maps samples back into the generator latent space. The journal version is J. Chem. Inf. Model. 2021, 61, 2198-2207.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uvm/ampgan/ampgan-v3": { "id": "uvm/ampgan/ampgan-v3", "name": "AMPGAN v3", "maker": "uvm", "family": "uvm/ampgan", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conditional GAN. The README describes a transformer generator conditioned on target species, MIC, and sequence length, plus two discriminators, D_GAN and D_MIC. The released generator is constructed with output_shape (68, token vocabulary), species_shape (6,), and embed_dim 128. README file sizes are 9.4 MB for the generator and 1.0 MB for each discriminator. Those sizes are not parameter counts. Training data named in the README is DBAASP.", "modalities": { "input": [], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AMPGAN v3 is a named conditional GAN from a University of Vermont contact, released with code and weights. The arXiv author list is Jay Jung, Xiaohan Zhang, Shenghan Song, Mahmoud Sayedahmed, Chijian Xiang, Yunong Xu, Ahmed AbdelKhalek, Severin T. Schneebeli, Matthew J. Wargo, Jianing Li, and Safwan Wshah. The README contact is jay-hwasung.jung@uvm.edu. It is active on the collection date 2026-10-15. The paper was submitted on 2026-06-15 and presented at the GenBio Workshop at ICML 2026. It is a research generator of peptide sequences, including non-canonical residues, not a clinical product.", "what_it_is_for": "The abstract describes a multi-objective conditional GAN whose vocabulary includes D-amino acids and N- or C-terminus modifications such as amidation. Two discriminators split adversarial and activity-aware supervision. The same paper reports a small in vitro check of five candidates.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uvm/geodtr/geodtr-plus": { "id": "uvm/geodtr/geodtr-plus", "name": "GeoDTR+", "maker": "uvm", "family": "uvm/geodtr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "24700000", "architecture": "Siamese cross-view encoder. A CNN backbone feeds a transformer geometric layout extractor. The paper's reported setting uses ConvNeXt-Tiny, 2 transformer encoder layers, 4 heads, 8 layout descriptors, and a 3072-dimensional descriptor (8 times 384). The training script also accepts a resnet backbone. Table VII lists 24.7M trainable parameters, 11.25 GFLOPS, and 103 ms inference on one Nvidia V100 for that setting.", "modalities": { "input": [ "image" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GeoDTR+ is a named cross-view geo-localization model from the Vermont AI Lab at the University of Vermont, with coauthors at Lingang Laboratory, Information Technology University, and the University of Central Florida. It is active on the collection date 2026-10-15. The lab released code and a Google Drive archive of pretrained checkpoints. It matches a ground photo to a geo-tagged aerial image.", "what_it_is_for": "The paper estimates the location of a ground image by retrieving the matching aerial image. The reported gain is on cross-area tests, where training and test images come from different places.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uvm/gpg2a/gpg2a": { "id": "uvm/gpg2a/gpg2a", "name": "GPG2A", "maker": "uvm", "family": "uvm/gpg2a", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage Geometric Preserving Ground-to-Aerial synthesizer. Stage I predicts a bird's-eye-view layout map from a ground image. Stage II is a diffusion model that synthesizes an aerial image from that layout and a text description. Trained on VIGORv2. The README requires Python 3.8+ and PyTorch >= 2.1.2.", "modalities": { "input": [ "image", "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GPG2A is a named ground-to-aerial image model from the Vermont AI Lab at the University of Vermont. Authors on the arXiv record are Ahmad Arrabi, Xiaohan Zhang, Waqas Sultani, Chen Chen, and Safwan Wshah. It is active on the collection date 2026-10-15. The lab released Apache-2.0 code and a Google Drive checkpoint archive. It synthesizes an aerial image from a ground image.", "what_it_is_for": "The abstract presents GPG2A as a way to synthesize aerial images when a fresh aerial capture is costly. Stage I predicts a bird's-eye-view layout. Stage II generates the aerial image from that layout and a text description of the ground photo. The training set is VIGORv2, built on VIGOR with added aerial images, maps, and text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uvm/hedonometer/hedonometer": { "id": "uvm/hedonometer/hedonometer", "name": "Hedonometer", "maker": "uvm", "family": "uvm/hedonometer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC-SA 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bag-of-words average of labMT happiness scores on a 1 to 9 scale. A lens drops neutral words. The about page says the English list is a composite of roughly 10,000 words.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Hedonometer is a public text instrument from the University of Vermont Computational Story Lab and Vermont Complex Systems Center, led by Peter Dodds and Chris Danforth, with site technology credited to MITRE. It is active on the collection date 2026-10-15. It scores large texts by averaging labMT word-happiness ratings. It is not a neural language model.", "what_it_is_for": "The about page says the instrument measures the happiness of large populations in near real time from online expression. The live site applies it to Twitter time series, books, movie scripts, and news. The maker says the first public source was Twitter and that any large text can be scored.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uvm/ousiometer/ousiometer": { "id": "uvm/ousiometer/ousiometer", "name": "Ousiometer", "maker": "uvm", "family": "uvm/ousiometer", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Lexical average of power, danger, and structure scores. The 2021 paper derives those scores from the NRC VAD lexicon and uses the same kind of lexical lens as the hedonometer.", "modalities": { "input": [ "text" ], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The ousiometer is a lexical instrument from Peter Sheridan Dodds and the University of Vermont Computational Story Lab. On the collection date 2026-10-15 it is still described in the lab's public code repository and plotted as English Twitter series on hedonometer.org. It scores essential meaning, not happiness. It is not a neural model.", "what_it_is_for": "The paper presents it as a telegnomic instrument for the average essential meaning of a large text. The live Hedonometer menu exposes English Twitter series for danger, power, structure, power plus danger, and power plus safety.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/gapril/gapril-w-exp": { "id": "uw/gapril/gapril-w-exp", "name": "gAPRIL with explanations", "maker": "uw", "family": "uw/gapril", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-27", "context_length": null, "max_output_tokens": null, "parameters": "8B base (Goedel-Prover-V2-8B) plus a LoRA adapter; exact adapter parameter count not stated", "architecture": "LoRA finetune (rank 32, alpha 64) of Goedel-Prover-V2-8B. The card calls the release APRIL-Goedel-8B. It is trained to emit a natural-language diagnosis, a fix suggestion, and a corrected Lean 4 proof.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "gAPRIL with explanations is a University of Washington Math AI Lab LoRA finetune that repairs failing Lean 4 proofs and writes a natural-language diagnosis. Collected 2026-10-15. Weights are active on Hugging Face under Apache-2.0.", "what_it_is_for": "The Math AI Lab says the model should explain a compiler failure, suggest a fix, and return a corrected Lean 4 proof. It is a joint repair-and-explanation model, not a general chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/gapril/gapril-wo-exp": { "id": "uw/gapril/gapril-wo-exp", "name": "gAPRIL without explanations", "maker": "uw", "family": "uw/gapril", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-27", "context_length": null, "max_output_tokens": null, "parameters": "8B base (Goedel-Prover-V2-8B) plus a LoRA adapter; exact adapter parameter count not stated", "architecture": "LoRA finetune of Goedel-Prover-V2-8B trained on APRIL for Lean 4 proof repair only, without explanation supervision. The with-explanations card is the source for the shared training setup (rank 32, alpha 64) and for this model's scores.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "gAPRIL without explanations is the University of Washington Math AI Lab repair-only finetune of Goedel-Prover-V2-8B. Collected 2026-10-15. It is a named Hugging Face release, active, under Apache-2.0. It is not an unnamed ablation: the lab publishes it next to the explanation model.", "what_it_is_for": "The lab says this checkpoint is finetuned on APRIL for Lean proof repair without natural-language explanation targets. Pass@1 is higher than the explanation model. The explanation model is the one they point to when a human-readable diagnosis is needed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/guanaco/guanaco-13b": { "id": "uw/guanaco/guanaco-13b", "name": "Guanaco 13B", "maker": "uw", "family": "uw/guanaco", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 for the adapter weights. Use requires LLaMA base weights under the LLaMA license.", "release_date": "2023-05-22", "context_length": null, "max_output_tokens": null, "parameters": "13B LLaMA base. Guanaco ships LoRA adapters (r=64), not a recount of base parameters.", "architecture": "LoRA adapters on every linear layer of LLaMA, trained with 4-bit QLoRA (NormalFloat4 base, bfloat16 compute). r=64, alpha=16. Dropout 0.1 up to 13B and 0.05 for 33B and 65B. Training sequence length 512. That length is not a published context window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Guanaco 13B is a University of Washington QLoRA chatbot adapter on LLaMA 13B, released by the QLoRA authors and demoed from the uwnlp Hugging Face org. Collected 2026-10-15. Adapter weights are still published. The card limits them to research use.", "what_it_is_for": "The card says Guanaco is a research chatbot finetuned on OASST1 through 4-bit QLoRA, meant for local experiments. It is not a factual system and it is not a current hosted UW API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/guanaco/guanaco-33b": { "id": "uw/guanaco/guanaco-33b", "name": "Guanaco 33B", "maker": "uw", "family": "uw/guanaco", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 for the adapter weights. Use requires LLaMA base weights under the LLaMA license.", "release_date": "2023-05-22", "context_length": null, "max_output_tokens": null, "parameters": "33B LLaMA base. Guanaco ships LoRA adapters (r=64), not a recount of base parameters.", "architecture": "LoRA adapters on every linear layer of LLaMA, trained with 4-bit QLoRA (NormalFloat4 base, bfloat16 compute). r=64, alpha=16. Dropout 0.1 up to 13B and 0.05 for 33B and 65B. Training sequence length 512. That length is not a published context window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Guanaco 33B is a University of Washington QLoRA chatbot adapter on LLaMA 33B, released by the QLoRA authors and demoed from the uwnlp Hugging Face org. Collected 2026-10-15. Adapter weights are still published. The card limits them to research use. The uwnlp playground is a Guanaco 33B chat demo.", "what_it_is_for": "The card says Guanaco is a research chatbot finetuned on OASST1 through 4-bit QLoRA, meant for local experiments. It is not a factual system and it is not a current hosted UW API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/guanaco/guanaco-65b": { "id": "uw/guanaco/guanaco-65b", "name": "Guanaco 65B", "maker": "uw", "family": "uw/guanaco", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 for the adapter weights. Use requires LLaMA base weights under the LLaMA license.", "release_date": "2023-05-22", "context_length": null, "max_output_tokens": null, "parameters": "65B LLaMA base. Guanaco ships LoRA adapters (r=64), not a recount of base parameters.", "architecture": "LoRA adapters on every linear layer of LLaMA, trained with 4-bit QLoRA (NormalFloat4 base, bfloat16 compute). r=64, alpha=16. Dropout 0.1 up to 13B and 0.05 for 33B and 65B. Training sequence length 512. That length is not a published context window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Guanaco 65B is a University of Washington QLoRA chatbot adapter on LLaMA 65B, released by the QLoRA authors and demoed from the uwnlp Hugging Face org. Collected 2026-10-15. Adapter weights are still published. The card limits them to research use.", "what_it_is_for": "The card says Guanaco is a research chatbot finetuned on OASST1 through 4-bit QLoRA, meant for local experiments. It is not a factual system and it is not a current hosted UW API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/guanaco/guanaco-7b": { "id": "uw/guanaco/guanaco-7b", "name": "Guanaco 7B", "maker": "uw", "family": "uw/guanaco", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0 for the adapter weights. Use requires LLaMA base weights under the LLaMA license.", "release_date": "2023-05-22", "context_length": null, "max_output_tokens": null, "parameters": "7B LLaMA base. Guanaco ships LoRA adapters (r=64), not a recount of base parameters.", "architecture": "LoRA adapters on every linear layer of LLaMA, trained with 4-bit QLoRA (NormalFloat4 base, bfloat16 compute). r=64, alpha=16. Dropout 0.1 up to 13B and 0.05 for 33B and 65B. Training sequence length 512. That length is not a published context window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Guanaco 7B is a University of Washington QLoRA chatbot adapter on LLaMA 7B, released by the QLoRA authors and demoed from the uwnlp Hugging Face org. Collected 2026-10-15. Adapter weights are still published. The card limits them to research use.", "what_it_is_for": "The card says Guanaco is a research chatbot finetuned on OASST1 through 4-bit QLoRA, meant for local experiments. It is not a factual system and it is not a current hosted UW API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/mathleap/mathleap-octen-8b": { "id": "uw/mathleap/mathleap-octen-8b", "name": "MathLeap-Octen-8B", "maker": "uw", "family": "uw/mathleap", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-25", "context_length": null, "max_output_tokens": null, "parameters": "~8B", "architecture": "Decoder-only embedding model finetuned from Octen-Embedding-8B, itself a retrieval finetune of Qwen3-Embedding-8B. Embedding dimension 4096. Last-token pooling. Training max sequence length 128. Card says 32K from the base, not as an integer.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MathLeap-Octen-8B is a University of Washington Math AI Lab embedding model finetuned from Octen-Embedding-8B for mathlib retrieval. Collected 2026-10-15. The release is active under Apache-2.0.", "what_it_is_for": "The card says it embeds equivalent mathematical statements close together across informal language and Lean. It is a retrieval model. The lab notes that starting from an already retrieval-tuned base makes the extra gain smaller than MathLeap-Qwen-8B's gain.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/mathleap/mathleap-qwen-8b": { "id": "uw/mathleap/mathleap-qwen-8b", "name": "MathLeap-Qwen-8B", "maker": "uw", "family": "uw/mathleap", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-25", "context_length": null, "max_output_tokens": null, "parameters": "~8B", "architecture": "Decoder-only embedding model finetuned from Qwen3-Embedding-8B. Embedding dimension 4096. Last-token pooling, cosine similarity, L2 normalization. Training max sequence length 128. The card says the base model max is 32K and does not give that as an integer.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MathLeap-Qwen-8B is a University of Washington Math AI Lab embedding model for mathematical text, finetuned from Qwen3-Embedding-8B. Collected 2026-10-15. The Hugging Face release is active under Apache-2.0.", "what_it_is_for": "The card says it is for semantic search over mathlib4, cross-presentation similarity, and retrieval in front of a generator. It does not answer questions or write proofs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/proteinmpnn/ligandmpnn": { "id": "uw/proteinmpnn/ligandmpnn", "name": "LigandMPNN", "maker": "uw", "family": "uw/proteinmpnn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT. The LigandMPNN README says the code and model parameters are MIT.", "release_date": "2025-03-28", "context_length": null, "max_output_tokens": null, "parameters": "2.62 million", "architecture": "ProteinMPNN plus a ligand graph and two extra protein-ligand encoder layers. It conditions sequence design on small molecules, nucleotides, and metals. Noise checkpoints at 0.05, 0.10, 0.20, and 0.30 \u00c5 are versions.", "modalities": { "input": [ "protein-backbone", "ligand", "nucleic-acid", "metal" ], "output": [ "protein-sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LigandMPNN is the University of Washington Institute for Protein Design sequence-design model that sees non-protein atoms. Collected 2026-10-15. Nature Methods published it on 28 March 2025. Code and weights are MIT.", "what_it_is_for": "IPD says it designs sequences for proteins that bind small molecules, nucleic acids, and metals, with atom-level constraints. It is the sequence step after all-atom backbone design.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/proteinmpnn/proteinmpnn": { "id": "uw/proteinmpnn/proteinmpnn", "name": "ProteinMPNN", "maker": "uw", "family": "uw/proteinmpnn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT. Copyright 2022 Justas Dauparas. The LigandMPNN repo also says ProteinMPNN parameters there are MIT.", "release_date": "2022-07-29", "context_length": null, "max_output_tokens": null, "parameters": "1.66 million", "architecture": "Message-passing inverse folding network. It reads a backbone and writes an amino-acid sequence. Noise-level weights and a CA-only set are versions. Soluble-only training is the separate SolubleMPNN release.", "modalities": { "input": [ "protein-backbone" ], "output": [ "protein-sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProteinMPNN is the University of Washington Institute for Protein Design inverse-folding model. Collected 2026-10-15. Weights are active under MIT. GitHub v1.0.0 is dated 29 July 2022. IPD still lists it as the sequence-design tool next to RFdiffusion.", "what_it_is_for": "It takes a fixed backbone and proposes sequences that should fold to that backbone. IPD says it runs in about one second and needs no expert customization. Ligands are LigandMPNN's job.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/proteinmpnn/solublempnn": { "id": "uw/proteinmpnn/solublempnn", "name": "SolubleMPNN", "maker": "uw", "family": "uw/proteinmpnn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the LigandMPNN repo, which distributes these weights with the ProteinMPNN and LigandMPNN parameters.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ProteinMPNN-style inverse folding trained only on soluble proteins. The LigandMPNN README's run example says that directly. Noise checkpoints are versions. Foundry lists SolubleMPNN as its own model and cites the 2024 Nature membrane-protein paper.", "modalities": { "input": [ "protein-backbone" ], "output": [ "protein-sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SolubleMPNN is the University of Washington Institute for Protein Design inverse-folding model trained on soluble proteins. Collected 2026-10-15. Foundry and the LigandMPNN repo both publish it under that name. Weights in the LigandMPNN download are MIT.", "what_it_is_for": "The LigandMPNN README says it is a ProteinMPNN-like model whose training set is soluble proteins only. Foundry cites it for designing soluble analogues of membrane proteins.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/rfdiffusion/rfdiffusion": { "id": "uw/rfdiffusion/rfdiffusion", "name": "RFdiffusion", "maker": "uw", "family": "uw/rfdiffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD 3-clause. The 2023 LICENSE covers source and the weights linked from the README. Copyright University of Washington, Institute for Protein Design. The UW name may not be used to endorse derived products without permission.", "release_date": "2023-07-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion model fine-tuned from RoseTTAFold so it denoises protein backbones. It generates monomers, oligomers, binders, and motif scaffolds. It does not design sequence. ProteinMPNN is the usual next step.", "modalities": { "input": [ "noise", "protein-motif" ], "output": [ "protein-backbone" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RFdiffusion is the Institute for Protein Design's diffusion model for de novo protein backbones, from the University of Washington Baker lab. Collected 2026-10-15. Code and weights are an active BSD release.", "what_it_is_for": "IPD says it sculpts disconnected atoms into monomers, oligomers, binders, and other backbones. The README says it does not assign sequence. The paper's pipeline uses ProteinMPNN after the backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/rfdiffusion/rfdiffusion-all-atom": { "id": "uw/rfdiffusion/rfdiffusion-all-atom", "name": "RFdiffusion All-Atom", "maker": "uw", "family": "uw/rfdiffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RFdiffusionAA fine-tunes RoseTTAFold All-Atom on denoising so it can build a protein around a small molecule. Sequence design in the paper uses LigandMPNN afterward.", "modalities": { "input": [ "small-molecule", "noise" ], "output": [ "protein-backbone" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RFdiffusion All-Atom is the University of Washington Institute for Protein Design model that generates protein backbones around small molecules. Collected 2026-10-15. IPD still lists it. The Science paper date used here is the 19 April 2024 issue date cited in search snippets.", "what_it_is_for": "IPD says the all-atom line builds molecules from amino acids plus DNA, RNA, ions, and small molecules. The Science abstract says RFdiffusionAA was fine-tuned from RoseTTAFold All-Atom and was tested on digoxigenin, heme, and bilin binders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/rfdiffusion/rfdiffusion2": { "id": "uw/rfdiffusion/rfdiffusion2", "name": "RFdiffusion2", "maker": "uw", "family": "uw/rfdiffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Flow-matching or diffusion model on the RoseTTAFold All-Atom architecture for atom-level enzyme active-site scaffolding. It does not require the user to pre-place rotamers or residue indices. The released pipeline uses LigandMPNN for sequence and an external structure model to rescore.", "modalities": { "input": [ "ligand", "active-site" ], "output": [ "protein-backbone" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RFdiffusion2 is the University of Washington Institute for Protein Design enzyme-design model that scaffolds catalytic groups from atom geometry. Collected 2026-10-15. Nature Methods published it on 3 December 2025, and weights download from files.ipd.uw.edu.", "what_it_is_for": "The docs say it designs enzymes from functional-group geometry without a predefined residue order or inverse rotamers. An ORI token sets the scaffold center of mass. Sequence fitting in the released pipeline is LigandMPNN.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/rfdiffusion/rfdiffusion3": { "id": "uw/rfdiffusion/rfdiffusion3", "name": "RFdiffusion3", "maker": "uw", "family": "uw/rfdiffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "All-atom diffusion model. IPD says it uses a newer transformer stack and shares no code with RFdiffusion or RFdiffusion2. One checkpoint is described as covering binder, motif, enzyme, and symmetric design. It can co-design structure and sequence. Foundry installs it as rfd3.", "modalities": { "input": [ "ligand", "nucleic-acid", "protein-motif" ], "output": [ "protein-structure", "protein-sequence" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RFdiffusion3 is the University of Washington Institute for Protein Design all-atom generative model released as open source on 3 December 2025. Collected 2026-10-15. Weights and training code ship in Rosetta Commons Foundry.", "what_it_is_for": "IPD says one model can design binders, enzymes, and symmetric proteins, and can generate a protein against DNA or other molecules without the user placing every atom. The Foundry README is the inference manual.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/rfdiffusion/rfdiffusion3-na": { "id": "uw/rfdiffusion/rfdiffusion3-na", "name": "RFdiffusion3NA", "maker": "uw", "family": "uw/rfdiffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Foundry describes RFdiffusion3NA as an extension of RFdiffusion3 that can also design nucleic-acid structures. The README points at version 3 of the same bioRxiv DOI as RFdiffusion3.", "modalities": { "input": [ "nucleic-acid", "ligand", "protein-motif" ], "output": [ "protein-structure", "nucleic-acid-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RFdiffusion3NA is the nucleic-acid design extension of RFdiffusion3, listed as its own model in the University of Washington and Rosetta Commons Foundry README. Collected 2026-10-15. It is active in that repo. A separate license and parameter count were not found.", "what_it_is_for": "The README says it extends RFdiffusion3 so the model can design nucleic-acid structures under complex constraints, not only protein structures.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/rosettafold/rosettafold": { "id": "uw/rosettafold/rosettafold", "name": "RoseTTAFold", "maker": "uw", "family": "uw/rosettafold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Code is MIT. Trained weights are non-commercial under the Rosetta-DL Software license. The license text is at files.ipd.uw.edu/pub/RoseTTAFold/Rosetta-DL_LICENSE.txt.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three-track network for protein structure prediction from sequence. A 2-track weight file, RF2t.pt, was added for yeast PPI screening. That file is a version, not RoseTTAFold2.", "modalities": { "input": [ "protein-sequence" ], "output": [ "protein-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoseTTAFold is the University of Washington Institute for Protein Design structure-prediction network. Collected 2026-10-15. Code and weights are still linked. Weights are non-commercial under Rosetta-DL. The code is MIT.", "what_it_is_for": "IPD says it predicts a protein structure from sequence in minutes. The 2-track weights are for PPI screening. This is not RoseTTAFold2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/rosettafold/rosettafold-all-atom": { "id": "uw/rosettafold/rosettafold-all-atom", "name": "RoseTTAFold All-Atom", "maker": "uw", "family": "uw/rosettafold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Structure prediction for proteins, nucleic acids, small molecules, metals, and covalent modifications. The paper says it starts from RoseTTAFold2 and mixes a residue representation for polymers with an atomic graph for other groups. Released weight file is RFAA_paper_weights.pt.", "modalities": { "input": [ "protein-sequence", "nucleic-acid", "small-molecule" ], "output": [ "biomolecular-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoseTTAFold All-Atom is the University of Washington Institute for Protein Design network for biomolecular complexes, not only proteins. Collected 2026-10-15. Paper weights are public. IPD still lists the model.", "what_it_is_for": "The Science paper and the IPD page say it models proteins with nucleic acids, small molecules, metals, and covalent modifications. The README tells users to trust the error estimate rather than every prediction.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/rosettafold/rosettafold-na": { "id": "uw/rosettafold/rosettafold-na", "name": "RoseTTAFoldNA", "maker": "uw", "family": "uw/rosettafold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GitHub marks uw-ipd/RoseTTAFold2NA MIT. The weight license was not separately stated in the README text that was read.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoseTTAFold extended to protein-DNA and protein-RNA complexes. The repo is RoseTTAFold2NA. Current weights are RF2NA_apr23.tgz. v0.2 updated homodimer-DNA and DNA-sequence recognition.", "modalities": { "input": [ "protein-sequence", "nucleic-acid" ], "output": [ "biomolecular-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoseTTAFoldNA is the University of Washington Institute for Protein Design model for protein-DNA and protein-RNA complexes. Collected 2026-10-15. The GitHub project is RoseTTAFold2NA. v0.2 weights from April 2023 are the documented file. The repo is marked MIT.", "what_it_is_for": "The paper describes one network that predicts protein-nucleic acid complexes and reports a confidence estimate. The README says v0.2 improves homodimer-DNA modeling.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/rosettafold/rosettafold2": { "id": "uw/rosettafold/rosettafold2", "name": "RoseTTAFold2", "maker": "uw", "family": "uw/rosettafold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GitHub marks the repository MIT. An issue asked about the weight license. The resolution was not read, so the weight license is not stated here.", "release_date": "2023-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three-track network extended through the full model, with frame-aligned point error, recycling, and an AlphaFold2 distillation set. The preprint says it does not use invariant point attention or triangle attention. Weights on the repo are RF2_jan24.tgz.", "modalities": { "input": [ "protein-sequence" ], "output": [ "protein-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoseTTAFold2 is the Institute for Protein Design's second structure-prediction network, with weights hosted at the University of Washington. Collected 2026-10-15. The bioRxiv preprint is 24 May 2023. The current weight file is the January 2024 archive. Code on GitHub is marked MIT. The weight license was not confirmed.", "what_it_is_for": "The preprint says RoseTTAFold2 matches AlphaFold2 on monomers and AlphaFold2-Multimer on complexes, with better scaling, and without invariant point attention or triangle attention. Those claims were not re-scored.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/rosettafold/rosettafold3": { "id": "uw/rosettafold/rosettafold3", "name": "RoseTTAFold3", "maker": "uw", "family": "uw/rosettafold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Foundry's structure-prediction network, RF3. The README says it narrows the gap between closed-source AlphaFold 3 and open alternatives. Install downloads the latest RF3 weights, and `all` installs multiple RF3 checkpoints.", "modalities": { "input": [ "protein-sequence", "ligand", "nucleic-acid" ], "output": [ "biomolecular-structure" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RoseTTAFold3 is the University of Washington and Rosetta Commons structure-prediction model shipped in Foundry. Collected 2026-10-15. It is an active weight release. The Foundry README is the install guide. License text was not read.", "what_it_is_for": "Foundry uses RF3 to predict structures, including as a check on designs from RFdiffusion3. The README's claim is that it narrows the gap with AlphaFold 3. That comparison was not re-run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/superbpe/olmo2-11b-superbpe-t180k": { "id": "uw/superbpe/olmo2-11b-superbpe-t180k", "name": "OLMo 2 11B SuperBPE t180k", "maker": "uw", "family": "uw/superbpe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-19", "context_length": 3000, "max_output_tokens": null, "parameters": "", "architecture": "Scaled-up OLMo 2 7B architecture, trained from scratch with a 200k SuperBPE tokenizer that transitions at 180k. The card says it matches the 8B BPE model in train and inference FLOPs. Context 3,000 tokens. Trained on 238B tokens. The card calls it an 11B model and does not give an exact parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 2 11B SuperBPE t180k is a University of Washington from-scratch language model, a scaled OLMo 2 configuration trained with SuperBPE so that its train and inference FLOPs match the 8B BPE baseline. Collected 2026-10-15. Weights are active under Apache-2.0. The exact parameter count is not stated on the card.", "what_it_is_for": "OLMo 2 11B SuperBPE t180k is a University of Washington from-scratch language model, a scaled OLMo 2 configuration trained with SuperBPE so that its train and inference FLOPs match the 8B BPE baseline. Collected 2026-10-15. Weights are active under Apache-2.0. The exact parameter count is not stated on the card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/superbpe/olmo2-8b-bpe": { "id": "uw/superbpe/olmo2-8b-bpe", "name": "OLMo 2 8B BPE", "maker": "uw", "family": "uw/superbpe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-19", "context_length": 4096, "max_output_tokens": null, "parameters": "8.12B", "architecture": "Decoder-only transformer using the OLMo 2 7B architecture and the allenai/olmo-mix-1124 pretraining mix. Vocabulary size 200,000. Trained from scratch by UW. This is not an AllenAI OLMo checkpoint. Traditional subword BPE only. Context 4,096 tokens. Trained on 321B tokens. This is the paper's baseline, released as its own card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 2 8B BPE is the University of Washington baseline language model for the SuperBPE paper, trained from scratch with a 200k subword BPE tokenizer. Collected 2026-10-15. Weights are active under Apache-2.0. It uses the OLMo 2 architecture and data mix but is not an Allen Institute checkpoint.", "what_it_is_for": "OLMo 2 8B BPE is the University of Washington baseline language model for the SuperBPE paper, trained from scratch with a 200k subword BPE tokenizer. Collected 2026-10-15. Weights are active under Apache-2.0. It uses the OLMo 2 architecture and data mix but is not an Allen Institute checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/superbpe/olmo2-8b-superbpe-t160k": { "id": "uw/superbpe/olmo2-8b-superbpe-t160k", "name": "OLMo 2 8B SuperBPE t160k", "maker": "uw", "family": "uw/superbpe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-19", "context_length": 2884, "max_output_tokens": null, "parameters": "8.12B", "architecture": "Decoder-only transformer using the OLMo 2 7B architecture and the allenai/olmo-mix-1124 pretraining mix. Vocabulary size 200,000. Trained from scratch by UW. This is not an AllenAI OLMo checkpoint. Superword tokens begin at vocabulary size 160,000. Context is 2,884 tokens. Trained on 332B tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 2 8B SuperBPE t160k is a University of Washington from-scratch base model with a SuperBPE vocabulary that starts learning superword tokens at 160k. Collected 2026-10-15. Weights are active under Apache-2.0. It is a separate released checkpoint, not a dated snapshot of the t180k model.", "what_it_is_for": "OLMo 2 8B SuperBPE t160k is a University of Washington from-scratch base model with a SuperBPE vocabulary that starts learning superword tokens at 160k. Collected 2026-10-15. Weights are active under Apache-2.0. It is a separate released checkpoint, not a dated snapshot of the t180k model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/superbpe/olmo2-8b-superbpe-t180k": { "id": "uw/superbpe/olmo2-8b-superbpe-t180k", "name": "OLMo 2 8B SuperBPE t180k", "maker": "uw", "family": "uw/superbpe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-19", "context_length": 3000, "max_output_tokens": null, "parameters": "8.12B", "architecture": "Decoder-only transformer using the OLMo 2 7B architecture and the allenai/olmo-mix-1124 pretraining mix. Vocabulary size 200,000. Trained from scratch by UW. This is not an AllenAI OLMo checkpoint. Superword tokens begin once the vocabulary reaches 180,000. Context is 3,000 tokens so the byte budget matches a 4,096-token BPE model. Trained on 331B tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 2 8B SuperBPE t180k is a University of Washington base language model trained from scratch with a SuperBPE tokenizer. Collected 2026-10-15. It is the strongest 8B run in the SuperBPE paper and is an active weight release under Apache-2.0. It is not an Allen Institute OLMo checkpoint.", "what_it_is_for": "OLMo 2 8B SuperBPE t180k is a University of Washington base language model trained from scratch with a SuperBPE tokenizer. Collected 2026-10-15. It is the strongest 8B run in the SuperBPE paper and is an active weight release under Apache-2.0. It is not an Allen Institute OLMo checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/superbpe/olmo2-8b-superbpe-t80k": { "id": "uw/superbpe/olmo2-8b-superbpe-t80k", "name": "OLMo 2 8B SuperBPE t80k", "maker": "uw", "family": "uw/superbpe", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-19", "context_length": 2756, "max_output_tokens": null, "parameters": "8.12B", "architecture": "Decoder-only transformer using the OLMo 2 7B architecture and the allenai/olmo-mix-1124 pretraining mix. Vocabulary size 200,000. Trained from scratch by UW. This is not an AllenAI OLMo checkpoint. Superword tokens begin at vocabulary size 80,000. Context is 2,756 tokens. Trained on 334B tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OLMo 2 8B SuperBPE t80k is a University of Washington from-scratch base model whose SuperBPE tokenizer switches to superword tokens at vocabulary size 80,000. Collected 2026-10-15. The Hugging Face card is active and Apache-2.0. The card's usage snippet mistakenly loads the t180k repository; the prose on that card is the spec used here.", "what_it_is_for": "OLMo 2 8B SuperBPE t80k is a University of Washington from-scratch base model whose SuperBPE tokenizer switches to superword tokens at vocabulary size 80,000. Collected 2026-10-15. The Hugging Face card is active and Apache-2.0. The card's usage snippet mistakenly loads the t180k repository; the prose on that card is the spec used here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/x-elm/x-elm": { "id": "uw/x-elm/x-elm", "name": "X-ELM", "maker": "uw", "family": "uw/x-elm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.7B XGLM base. Each released expert file is 3.3GB. A merged parameter count is not stated.", "architecture": "Cross-lingual expert language models branched from Meta XGLM 1.7B. The download page hosts the higher-compute setting: 40,000 updates and 21B training tokens, Metaseq checkpoints, with a script to convert them to Hugging Face.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "X-ELM is a University of Washington NLP family of cross-lingual expert language models, continued from XGLM and posted as weights on nlp.cs.washington.edu. Collected 2026-10-15. The download page is live. Noah Smith is also affiliated with the Allen Institute. This release is the UW page, not an AllenAI model folder.", "what_it_is_for": "The paper trains separate experts on clusters of languages so languages compete less for the same parameters, then the experts can be merged. The page releases the higher-compute checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/yolo/yolov1": { "id": "uw/yolo/yolov1", "name": "YOLOv1", "maker": "uw", "family": "uw/yolo", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Released with Darknet. The v1 LICENSE text circulated with that tree is the short YOLO License. The pjreddie/darknet LICENSE file was not re-read on 2026-10-15.", "release_date": "2015-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single convolutional network that regresses bounding boxes and class probabilities from a full image. The Darknet wiki shows a 448 by 448 input. Fast YOLO is a smaller variant in the paper, not a separate product line.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YOLOv1 is the original real-time detector from Joseph Redmon and Ali Farhadi at the University of Washington, with Santosh Divvala (Allen Institute) and Ross Girshick (Facebook AI Research). Collected 2026-10-15. Darknet weights were released with the 2015 paper. The line is retired. Redmon's site now documents later versions, and he stopped the project.", "what_it_is_for": "The paper frames detection as one regression from pixels to boxes and class probabilities, so the network looks at the image once. The release detects the 20 Pascal VOC classes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/yolo/yolov2": { "id": "uw/yolo/yolov2", "name": "YOLOv2", "maker": "uw", "family": "uw/yolo", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Released with Darknet. The exact LICENSE file was not re-read on 2026-10-15.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Darknet detector. The live page lists a 608 by 608 COCO model and a Tiny YOLO config. YOLO9000 is the paper's joint name for this generation.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YOLOv2, also called YOLO9000, is Joseph Redmon and Ali Farhadi's second detector, released from the University of Washington through Darknet. Collected 2026-10-15. The live YOLO page still links the weights. The project is retired.", "what_it_is_for": "Real-time object detection. The maintained page treats YOLOv2 608 and Tiny YOLO as the v2 weight files.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw/yolo/yolov3": { "id": "uw/yolo/yolov3", "name": "YOLOv3", "maker": "uw", "family": "uw/yolo", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Released with Darknet. The LICENSE file was not re-read on 2026-10-15.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-scale Darknet detector with a Darknet-53 backbone. The page says 320, 416, and 608 share one weight file and trade speed for accuracy by changing the input size. Tiny and SPP are separate weight files.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YOLOv3 is the last detector Joseph Redmon and Ali Farhadi released from the University of Washington. Collected 2026-10-15. Darknet weights remain linked from pjreddie.com. The project is retired. Later YOLO version numbers are other organizations.", "what_it_is_for": "Real-time detection. One network evaluation draws boxes for the whole image. The page says it is on par with Focal Loss at 0.5 IoU and about four times faster, and that input size changes the speed and accuracy tradeoff without retraining.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/llava/llava-1-5-13b": { "id": "uw-madison/llava/llava-1-5-13b", "name": "LLaVA-1.5 13B", "maker": "uw-madison", "family": "uw-madison/llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The llava-hf conversion of this checkpoint is tagged llama2. The liuhaotian repo has no license tag. The project page still requires research use, CC BY-NC 4.0 data, and the CLIP, LLaMA, Vicuna, and GPT-4 terms. Vicuna v1.5 is a Llama 2 finetune, which matches the llama2 tag. The research-use page and the llama2 tag are both stored.", "release_date": "2023-10-05", "context_length": null, "max_output_tokens": null, "parameters": "13351494656", "architecture": "LLaVA-1.5: CLIP ViT-L at 336px, a two-layer MLP projector, and Vicuna v1.5. Stage 1 trains the projector on 558K image-text pairs. Stage 2 finetunes the projector and the language model on about 665K public instruction samples with the vision tower frozen. The model zoo's full_ft-1e file is the canonical merged checkpoint. The nameplate size is the language-model class. The parameter field is the safetensors total on the llava-hf conversion, not a count printed in the paper.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. The October 2023 LLaVA-1.5 13B checkpoint, the size highlighted in the paper. Same recipe as the 7B model with Vicuna v1.5 13B. It is the public baseline the authors say beats systems trained on much more data. Collected 2026-10-15.", "what_it_is_for": "The October 2023 LLaVA-1.5 13B checkpoint, the size highlighted in the paper. Same recipe as the 7B model with Vicuna v1.5 13B. It is the public baseline the authors say beats systems trained on much more data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/llava/llava-1-5-7b": { "id": "uw-madison/llava/llava-1-5-7b", "name": "LLaVA-1.5 7B", "maker": "uw-madison", "family": "uw-madison/llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The llava-hf conversion of this checkpoint is tagged llama2. The liuhaotian repo has no license tag. The project page still requires research use, CC BY-NC 4.0 data, and the CLIP, LLaMA, Vicuna, and GPT-4 terms. Vicuna v1.5 is a Llama 2 finetune, which matches the llama2 tag. The research-use page and the llama2 tag are both stored.", "release_date": "2023-10-05", "context_length": null, "max_output_tokens": null, "parameters": "7063427072", "architecture": "LLaVA-1.5: CLIP ViT-L at 336px, a two-layer MLP projector, and Vicuna v1.5. Stage 1 trains the projector on 558K image-text pairs. Stage 2 finetunes the projector and the language model on about 665K public instruction samples with the vision tower frozen. The model zoo's full_ft-1e file is the canonical merged checkpoint. The nameplate size is the language-model class. The parameter field is the safetensors total on the llava-hf conversion, not a count printed in the paper.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. The October 2023 LLaVA-1.5 7B checkpoint. It keeps the simple CLIP-plus-Vicuna design, swaps in an MLP projector and 336px images, and trains on public instruction data. The authors describe it as a strong, cheap-to-train vision-language baseline. Collected 2026-10-15.", "what_it_is_for": "The October 2023 LLaVA-1.5 7B checkpoint. It keeps the simple CLIP-plus-Vicuna design, swaps in an MLP projector and 336px images, and trains on public instruction data. The authors describe it as a strong, cheap-to-train vision-language baseline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/llava/llava-13b": { "id": "uw-madison/llava/llava-13b", "name": "LLaVA 13B", "maker": "uw-madison", "family": "uw-madison/llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The project page says the data, code, and checkpoints are for research use only and must also follow the CLIP, LLaMA, Vicuna, and GPT-4 licenses. The dataset is CC BY-NC 4.0. The model zoo says checkpoint use must follow the base LLM license. Several liuhaotian Hub repos are tagged apache-2.0, which conflicts with the LLaMA research terms on the delta weights. Both statements are kept.", "release_date": "2023-04-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP vision encoder plus Vicuna-13B. v0 and v1.1 are delta weights (Vicuna-13B-v0 or v1.1, CLIP-L, CC-595K, LLaVA-Instruct-158K, 3 epochs). The July 2023 v1 checkpoint uses CLIP-L at 336px, Vicuna-13B-v1.3, LCS-558K, LLaVA-Instruct-80K, and a projector-then-LoRA schedule. A merged LoRA file is published.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. The original 13B LLaVA chat model from the NeurIPS 2023 visual instruction tuning paper, plus the July 2023 336px LoRA and the ScienceQA finetunes. It is a research vision-language assistant, not a hosted API product. Collected 2026-10-15.", "what_it_is_for": "The original 13B LLaVA chat model from the NeurIPS 2023 visual instruction tuning paper, plus the July 2023 336px LoRA and the ScienceQA finetunes. It is a research vision-language assistant, not a hosted API product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/llava/llava-7b": { "id": "uw-madison/llava/llava-7b", "name": "LLaVA 7B", "maker": "uw-madison", "family": "uw-madison/llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The project page says the data, code, and checkpoints are for research use only and must also follow the CLIP, LLaMA, Vicuna, and GPT-4 licenses. The dataset is CC BY-NC 4.0. The model zoo says checkpoint use must follow the base LLM license. Several liuhaotian Hub repos are tagged apache-2.0, which conflicts with the LLaMA research terms on the delta weights. Both statements are kept.", "release_date": "2023-04-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CLIP ViT-L/14 vision encoder, a linear projector, and Vicuna-7B. v0 uses Vicuna-7B-v0, CC-595K pretraining, and LLaVA-Instruct-158K for 3 epochs. The later Lightning checkpoint uses Vicuna-7B-v1.1, LCS-558K, and LLaVA-Instruct-80K for 1 epoch. Both public 7B files in this line are delta weights on top of the base LLM.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. The original 7B visual chat line. It answers questions about an image by connecting a frozen CLIP encoder to Vicuna through a trained projector, then instruction-tuning on GPT-4-written image conversations. Collected 2026-10-15.", "what_it_is_for": "The original 7B visual chat line. It answers questions about an image by connecting a frozen CLIP encoder to Vicuna through a trained projector, then instruction-tuning on GPT-4-written image conversations.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/llava/llava-llama-2-13b": { "id": "uw-madison/llava/llava-llama-2-13b", "name": "LLaVA LLaMA-2 13B", "maker": "uw-madison", "family": "uw-madison/llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "No license tag on liuhaotian/llava-llama-2-13b-chat-lightning-preview. The zoo requires the base LLM license, here Llama 2 Community License for LLaMA-2-13B-Chat, plus the project research-use and CC BY-NC 4.0 data terms.", "release_date": "2023-07-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Full finetune preview. LLaMA-2-13B-Chat, CLIP-L, LCS-558K for 1 epoch, LLaVA-Instruct-80K for 1 epoch. The zoo calls the file a checkpoint, not a delta and not a LoRA.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. A July 2023 full-finetune preview of LLaVA on LLaMA-2-13B-Chat for image conversation. It sits in the LLaVA-v1 table beside the Vicuna 336px model. Collected 2026-10-15.", "what_it_is_for": "A July 2023 full-finetune preview of LLaVA on LLaMA-2-13B-Chat for image conversation. It sits in the LLaVA-v1 table beside the Vicuna 336px model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/llava/llava-llama-2-7b": { "id": "uw-madison/llava/llava-llama-2-7b", "name": "LLaVA LLaMA-2 7B", "maker": "uw-madison", "family": "uw-madison/llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "No license tag on liuhaotian/llava-llama-2-7b-chat-lightning-lora-preview. The model zoo says use must follow the base LLM license. This row is LLaMA-2-7B-Chat, so that is the Llama 2 Community License. The project page also keeps the research-use and CC BY-NC 4.0 data terms.", "release_date": "2023-07-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LoRA preview. LLaMA-2-7B-Chat, CLIP-L, LCS-558K pretrain for 1 epoch, LLaVA-Instruct-80K LoRA finetune for 1 epoch. The zoo download is the LoRA, not a merged file.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. A July 2023 Lightning preview that puts the LLaVA 80K visual instructions on LLaMA-2-7B-Chat with LoRA. It is an image-chat research checkpoint, not the later LLaVA-1.5 release. Collected 2026-10-15.", "what_it_is_for": "A July 2023 Lightning preview that puts the LLaVA 80K visual instructions on LLaMA-2-7B-Chat with LoRA. It is an image-chat research checkpoint, not the later LLaVA-1.5 release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/llava/llava-mpt-7b": { "id": "uw-madison/llava/llava-mpt-7b", "name": "LLaVA-MPT 7B", "maker": "uw-madison", "family": "uw-madison/llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub tag on liuhaotian/LLaVA-Lightning-MPT-7B-preview is cc-by-nc-sa-4.0. The project page still says research use and the CLIP, LLaMA, Vicuna, and GPT-4 conditions. MPT is not a LLaMA checkpoint, so the LLaMA clause does not describe this base. The MPT-7B-Chat license text was not opened.", "release_date": "2023-05-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Legacy merged preview. Base LLM MPT-7B-Chat, vision encoder CLIP-L, pretrain LCS-558K for 1 epoch, finetune LLaVA-Instruct-80K for 1 epoch. The zoo lists it under Legacy Models (merged weights).", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. A May 2023 preview that swaps Vicuna for MPT-7B-Chat in the LLaVA Lightning recipe. The Wisconsin and Microsoft authors released it as a merged checkpoint for research chat about images. Collected 2026-10-15.", "what_it_is_for": "A May 2023 preview that swaps Vicuna for MPT-7B-Chat in the LLaVA Lightning recipe. The Wisconsin and Microsoft authors released it as a merged checkpoint for research chat about images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/llava/llava-next-34b": { "id": "uw-madison/llava/llava-next-34b", "name": "LLaVA-NeXT 34B", "maker": "uw-madison", "family": "uw-madison/llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub tag on liuhaotian/llava-v1.6-34b is apache-2.0. The zoo says follow the base LLM license. The base in the table is Hermes-Yi-34B. The Yi license text was not opened, so the Hub tag and the zoo rule are both stored and they may disagree.", "release_date": "2024-01-30", "context_length": null, "max_output_tokens": null, "parameters": "34751166464", "architecture": "LLaVA-NeXT keeps the LLaVA-1.5 stack and raises resolution with AnyRes. The blog model card lists CLIP vision encoder 303.5M, a connector, and the named LLM. Grids are 336 times (2,2), (1,2), (2,1), (1,3), (3,1), (1,4), and (4,1), so the long side can reach 1344. The blog summary also says three aspect ratios up to 672x672, 336x1344, and 1344x336. Stage 1 trains the connector on 558K pairs. Stage 2 trains the full model on 760K instructions. The 1.5 connector is reused. The vision tower is the same CLIP-L-336 family. Hermes-Yi-34B. Blog totals: 34.75B overall, vision 303.5M, connector 58.7M, LLM 34.39B. Safetensors total 34,751,166,464. Blog compute 32 GPUs for 30 hours. The blog says this variant finishes in about one day on 32 A100s and beats Gemini Pro on some benchmarks, including the MMMU and MathVista numbers in the zoo.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. The largest January 2024 LLaVA-NeXT model, built on Hermes-Yi-34B. The blog presents it as the open model that passes Gemini Pro on MMMU and MathVista in their table. Collected 2026-10-15.", "what_it_is_for": "The largest January 2024 LLaVA-NeXT model, built on Hermes-Yi-34B. The blog presents it as the open model that passes Gemini Pro on MMMU and MathVista in their table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/llava/llava-next-mistral-7b": { "id": "uw-madison/llava/llava-next-mistral-7b", "name": "LLaVA-NeXT Mistral 7B", "maker": "uw-madison", "family": "uw-madison/llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Hub tag on liuhaotian/llava-v1.6-mistral-7b is apache-2.0, which matches the Mistral 7B base license. The project page and NeXT data card still limit the instruction data to research or education use and CC BY-NC 4.0-style LLaVA data terms. Both are stored.", "release_date": "2024-01-30", "context_length": null, "max_output_tokens": null, "parameters": "7566223360", "architecture": "LLaVA-NeXT keeps the LLaVA-1.5 stack and raises resolution with AnyRes. The blog model card lists CLIP vision encoder 303.5M, a connector, and the named LLM. Grids are 336 times (2,2), (1,2), (2,1), (1,3), (3,1), (1,4), and (4,1), so the long side can reach 1344. The blog summary also says three aspect ratios up to 672x672, 336x1344, and 1344x336. Stage 1 trains the connector on 558K pairs. Stage 2 trains the full model on 760K instructions. The 1.5 connector is reused. The vision tower is the same CLIP-L-336 family. Mistral-7B language model. The blog's three-column parameter card does not print a separate Mistral total. Safetensors total on the liuhaotian repo is 7,566,223,360. The 8-GPU 20-hour figure is in the Vicuna-7B column, not labeled as this checkpoint.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. The January 2024 LLaVA-NeXT checkpoint that uses Mistral-7B instead of Vicuna. It is one of the four official LLaVA-1.6 merged models in the zoo. Collected 2026-10-15.", "what_it_is_for": "The January 2024 LLaVA-NeXT checkpoint that uses Mistral-7B instead of Vicuna. It is one of the four official LLaVA-1.6 merged models in the zoo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/llava/llava-next-vicuna-13b": { "id": "uw-madison/llava/llava-next-vicuna-13b", "name": "LLaVA-NeXT Vicuna 13B", "maker": "uw-madison", "family": "uw-madison/llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "No license tag on the liuhaotian Vicuna 1.6 repos. The zoo says follow the base LLM license. Vicuna v1.5 is Llama 2, so the Llama 2 Community License applies, together with the project research-use and CC BY-NC 4.0 data terms. The NeXT data card is tagged Apache-2.0 and also says academic research and education use, and it points at the OpenAI usage policy for GPT-4-written rows.", "release_date": "2024-01-30", "context_length": null, "max_output_tokens": null, "parameters": "13350844416", "architecture": "LLaVA-NeXT keeps the LLaVA-1.5 stack and raises resolution with AnyRes. The blog model card lists CLIP vision encoder 303.5M, a connector, and the named LLM. Grids are 336 times (2,2), (1,2), (2,1), (1,3), (3,1), (1,4), and (4,1), so the long side can reach 1344. The blog summary also says three aspect ratios up to 672x672, 336x1344, and 1344x336. Stage 1 trains the connector on 558K pairs. Stage 2 trains the full model on 760K instructions. The 1.5 connector is reused. The vision tower is the same CLIP-L-336 family. Vicuna-13B. Blog totals: 13.35B overall, vision 303.5M, connector 31.5M, LLM 13B. Safetensors total 13,350,844,416. Blog compute 16 GPUs for 24 hours.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. The January 2024 LLaVA-NeXT 13B model on Vicuna. Same AnyRes recipe as the 7B model, with the larger Vicuna v1.5 backbone. Collected 2026-10-15.", "what_it_is_for": "The January 2024 LLaVA-NeXT 13B model on Vicuna. Same AnyRes recipe as the 7B model, with the larger Vicuna v1.5 backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/llava/llava-next-vicuna-7b": { "id": "uw-madison/llava/llava-next-vicuna-7b", "name": "LLaVA-NeXT Vicuna 7B", "maker": "uw-madison", "family": "uw-madison/llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "No license tag on the liuhaotian Vicuna 1.6 repos. The zoo says follow the base LLM license. Vicuna v1.5 is Llama 2, so the Llama 2 Community License applies, together with the project research-use and CC BY-NC 4.0 data terms. The NeXT data card is tagged Apache-2.0 and also says academic research and education use, and it points at the OpenAI usage policy for GPT-4-written rows.", "release_date": "2024-01-30", "context_length": null, "max_output_tokens": null, "parameters": "7062906880", "architecture": "LLaVA-NeXT keeps the LLaVA-1.5 stack and raises resolution with AnyRes. The blog model card lists CLIP vision encoder 303.5M, a connector, and the named LLM. Grids are 336 times (2,2), (1,2), (2,1), (1,3), (3,1), (1,4), and (4,1), so the long side can reach 1344. The blog summary also says three aspect ratios up to 672x672, 336x1344, and 1344x336. Stage 1 trains the connector on 558K pairs. Stage 2 trains the full model on 760K instructions. The 1.5 connector is reused. The vision tower is the same CLIP-L-336 family. This column is Vicuna-7B. Blog totals: 7.06B overall, vision 303.5M, connector 21M, LLM 6.74B. Safetensors total on the liuhaotian repo is 7,062,906,880. Training compute in the blog card is 8 GPUs for 20 hours. The blog's 7B column is this Vicuna model, not the Mistral model.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. The January 2024 LLaVA-NeXT 7B model on Vicuna. It is the high-resolution follow-on to LLaVA-1.5 for OCR, reasoning, and image chat. Weights were released by the same UW-Madison and Microsoft authors on the liuhaotian Hub account. Collected 2026-10-15.", "what_it_is_for": "The January 2024 LLaVA-NeXT 7B model on Vicuna. It is the high-resolution follow-on to LLaVA-1.5 for OCR, reasoning, and image chat. Weights were released by the same UW-Madison and Microsoft authors on the liuhaotian Hub account.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/seem/seem-focal-l": { "id": "uw-madison/seem/seem-focal-l", "name": "SEEM Focal-L", "maker": "uw-madison", "family": "uw-madison/seem", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The xdecoder/SEEM Hub repo is tagged afl-3.0. The README that was opened does not restate a license. The weight-file license was not separate from that repo tag.", "release_date": "2023-05-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Same SEEM decoder as the Tiny model with a FocalNet-Large backbone. The README calls this row SEEM-oq101 and the file seem_focall_v1.pt. Released 2 May 2023 together with an X-Decoder Focal-L file.", "modalities": { "input": [ "image", "text" ], "output": [ "masks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. SEEM Focal-L is the larger released SEEM checkpoint, announced 2 May 2023. It is the same multi-prompt segmentation interface as SEEM Focal-T with a Focal-Large backbone. Joint UW-Madison and Microsoft Research release, weights on the xdecoder Hub org. Collected 2026-10-15.", "what_it_is_for": "SEEM Focal-L is the larger released SEEM checkpoint, announced 2 May 2023. It is the same multi-prompt segmentation interface as SEEM Focal-T with a Focal-Large backbone. Joint UW-Madison and Microsoft Research release, weights on the xdecoder Hub org.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/seem/seem-focal-t": { "id": "uw-madison/seem/seem-focal-t", "name": "SEEM Focal-T", "maker": "uw-madison", "family": "uw-madison/seem", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The xdecoder/SEEM Hub repo is tagged afl-3.0. The README that was opened does not restate a license. The weight-file license was not separate from that repo tag.", "release_date": "2023-04-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Promptable segmentation model. The README says the framework follows X-Decoder except the decoder. This checkpoint uses a FocalNet-Tiny backbone. It takes visual prompts (points, boxes, scribbles, masks, a referred image region) and text. The demo turns audio into text with Whisper, so audio is not a native encoder. One set of weights is meant to cover generic, referring, and interactive segmentation.", "modalities": { "input": [ "image", "text" ], "output": [ "masks" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. SEEM Focal-T is the small promptable segmentation model from Xueyan Zou, Jianwei Yang, Yong Jae Lee, and coauthors. UW-Madison is the academic home and Microsoft Research is the joint lead. One checkpoint handles clicks, boxes, scribbles, text, and referring images. Collected 2026-10-15.", "what_it_is_for": "SEEM Focal-T is the small promptable segmentation model from Xueyan Zou, Jianwei Yang, Yong Jae Lee, and coauthors. UW-Madison is the academic home and Microsoft Research is the joint lead. One checkpoint handles clicks, boxes, scribbles, text, and referring images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/vip-llava/vip-llava-13b": { "id": "uw-madison/vip-llava/vip-llava-13b", "name": "ViP-LLaVA 13B", "maker": "uw-madison", "family": "uw-madison/vip-llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License, as stated on the model card. LLaVA-1.5 instruction data adds that data's research terms.", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "13372476416", "architecture": "Same visual-prompt overlay design as the 7B model on Vicuna-1.5 13B. The card says ViP-LLaVA-13B was trained in November 2023. The paper says the stage-3 finetune is about 1 hour for 13B on 8 A100s. Training mix on the card: 558K BLIP-captioned LAION/CC/SBU pairs, 665K LLaVA-1.5 instructions, 520K images marked with visual prompts, and 13K GPT-4V region instructions. Parameter total is the llava-hf port safetensors count.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. ViP-LLaVA 13B is the larger Wisconsin AI Vision checkpoint for questions about user-drawn marks on an image. The card dates training to November 2023 and the public repo to 3 December 2023. Cruise researchers are coauthors. The academic release is the Wisconsin lab's. Collected 2026-10-15.", "what_it_is_for": "ViP-LLaVA 13B is the larger Wisconsin AI Vision checkpoint for questions about user-drawn marks on an image. The card dates training to November 2023 and the public repo to 3 December 2023. Cruise researchers are coauthors. The academic release is the Wisconsin lab's.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/vip-llava/vip-llava-7b": { "id": "uw-madison/vip-llava/vip-llava-7b", "name": "ViP-LLaVA 7B", "maker": "uw-madison", "family": "uw-madison/vip-llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The vip-llava-13b card says Llama 2 is under the Llama 2 Community License. The 7B Hub license field was empty. Both sizes use Vicuna-1.5, so the 13B card is the license text for the release. Training uses LLaVA-1.5 instruction data, which carries that data's research terms.", "release_date": "2023-12-03", "context_length": null, "max_output_tokens": null, "parameters": "7080214528", "architecture": "ViP-LLaVA overlays marks such as boxes, arrows, and scribbles on the RGB image and trains an LLaVA-style model to answer about the marked region. The 7B checkpoint is the Vicuna-1.5 7B size. The paper's stage-3 finetune is about 0.5 hours for 7B on 8 A100s. Parameter total is the safetensors count on llava-hf/vip-llava-7b-hf, a format port.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. ViP-LLaVA 7B is the Wisconsin AI Vision model's 7B checkpoint for region questions. Users mark an image with a box, arrow, or scribble and ask about that region. It was released with Cruise coauthors on 3 December 2023. Collected 2026-10-15.", "what_it_is_for": "ViP-LLaVA 7B is the Wisconsin AI Vision model's 7B checkpoint for region questions. Users mark an image with a box, arrow, or scribble and ask about that region. It was released with Cruise coauthors on 3 December 2023.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/vrsight/vrsight": { "id": "uw-madison/vrsight/vrsight", "name": "VRSight", "maker": "uw-madison", "family": "uw-madison/vrsight", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0. The card says free to use with attribution.", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Ultralytics YOLOv8n. Input 640 by 640. Output is boxes, class ids, and confidences for 30 VR object classes in six groups: avatars, informational UI, interactables, safety, seating, and VR system. Trained 250 epochs on one A100 from COCO-pretrained YOLOv8n weights. The card does not print a parameter count.", "modalities": { "input": [ "image" ], "output": [ "boxes" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. VRSight is the MadAbility Lab's object detector for social VR screenshots. It is a named YOLOv8n fine-tune from UW-Madison, released 6 August 2025, and it feeds a screen reader for blind and low-vision VR users. It is not a general chatbot. Collected 2026-10-15.", "what_it_is_for": "VRSight is the MadAbility Lab's object detector for social VR screenshots. It is a named YOLOv8n fine-tune from UW-Madison, released 6 August 2025, and it feeds a screen reader for blind and low-vision VR users. It is not a general chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/x-decoder/x-decoder-focal-l": { "id": "uw-madison/x-decoder/x-decoder-focal-l", "name": "X-Decoder Focal-L", "maker": "uw-madison", "family": "uw-madison/x-decoder", "status": "active", "confidence": "high", "weights": "open-source", "license": "The xdecoder/X-Decoder Hub repo is tagged apache-2.0. The README license section was not opened beyond that tag and the Microsoft GitHub repo link.", "release_date": "2023-05-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "X-Decoder with a Focal-Large backbone. The SEEM README names the row X-Decoder-oq201 and the file xdecoder_focall_last.pt. The 2 May 2023 SEEM note is the dated release line found for this file.", "modalities": { "input": [ "image", "text" ], "output": [ "masks", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. X-Decoder Focal-L is the larger public X-Decoder weight file, X-Decoder-oq201. Same generalist pixel-and-language decoder as the Tiny checkpoint, with a Focal-Large backbone. The file was called out on 2 May 2023. Collected 2026-10-15.", "what_it_is_for": "X-Decoder Focal-L is the larger public X-Decoder weight file, X-Decoder-oq201. Same generalist pixel-and-language decoder as the Tiny checkpoint, with a Focal-Large backbone. The file was called out on 2 May 2023.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uw-madison/x-decoder/x-decoder-focal-t": { "id": "uw-madison/x-decoder/x-decoder-focal-t", "name": "X-Decoder Focal-T", "maker": "uw-madison", "family": "uw-madison/x-decoder", "status": "active", "confidence": "high", "weights": "open-source", "license": "The xdecoder/X-Decoder Hub repo is tagged apache-2.0. The README license section was not opened beyond that tag and the Microsoft GitHub repo link.", "release_date": "2022-12-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Generalist decoder that predicts pixel segmentation and language tokens from generic queries and text semantic queries. This file is xdecoder_focalt_last.pt with a Focal-Tiny backbone. The paper says it is pretrained on a limited segmentation set plus millions of image-text pairs. Code is in microsoft/X-Decoder. Weights are on huggingface.co/xdecoder/X-Decoder.", "modalities": { "input": [ "image", "text" ], "output": [ "masks", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Wisconsin-Madison model. Status: active. X-Decoder Focal-T is the smaller public X-Decoder checkpoint. It is a generalist vision-language decoder for segmentation and token outputs, from Xueyan Zou, Yong Jae Lee, and Microsoft Research coauthors. The paper date is 21 December 2022. Collected 2026-10-15.", "what_it_is_for": "X-Decoder Focal-T is the smaller public X-Decoder checkpoint. It is a generalist vision-language decoder for segmentation and token outputs, from Xueyan Zou, Yong Jae Lee, and Microsoft Research coauthors. The paper date is 21 December 2022.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uzh/detect/detect": { "id": "uzh/detect/detect", "name": "DETECT", "maker": "uzh", "family": "uzh/detect", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reference-based German text-simplification metric. RoBERTa encoder initialized from benjamin/roberta-base-wechsel-german (WECHSEL German embeddings), with a feed-forward head that predicts simplicity, meaning preservation, and fluency. The released file is a Lightning checkpoint, best-LENS_multi_wechsel_reducedhs-epoch=04.ckpt, not an AutoModel. Scores can be rescaled to 0-100. Total score weights meaning preservation and simplicity at 0.4 each and fluency at 0.2, unless the minimum of the three is below 25, in which case the total is that minimum.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DETECT (also styled DEtect) is a German text-simplification quality metric from the University of Zurich Department of Computational Linguistics. Maria Korobeynikova, Alessia Battisti, Lukas Fischer, and Yingqiang Gao released the trained checkpoint on the ZurichNLP Hub. It is active. The Hub license tag is MIT. Collected on 2026-10-15. The preprint date is 25 October 2025. An exact weight-upload day was not stated.", "what_it_is_for": "The paper presents it as the first German-specific metric that scores simplicity, meaning preservation, and fluency together. It is trained on synthetic LLM ratings and checked against human ratings on SimpEvalDE. It needs the complex sentence, the simplification, and references.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uzh/romansh-nllb/romansh-nllb-1-3b": { "id": "uzh/romansh-nllb/romansh-nllb-1-3b", "name": "Romansh NLLB 1.3B", "maker": "uzh", "family": "uzh/romansh-nllb", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.3B as named on the card (fine-tune of NLLB-200 distilled 1.3B). An exact tensor count after the six new language tokens is not published.", "architecture": "Encoder-decoder NMT fine-tune of facebook/nllb-200-distilled-1.3B. Six Romansh variety tokens were added. The public weights are a CTranslate2 conversion, not a Transformers AutoModel file. Documented inference uses beam size 4.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Romansh NLLB 1.3B is a University of Zurich multilingual translation model from the Department of Computational Linguistics, released with Lia Rumantscha branding on the model card. It is a fine-tune of Meta NLLB-200 distilled 1.3B for German and six Romansh varieties, distributed as CTranslate2 weights under CC BY-NC 4.0. The checkpoint is active. Collected on 2026-10-15. A precise upload day was not on the card.", "what_it_is_for": "The card says it translates between German and Rumantsch Grischun, Sursilvan, Sutsilvan, Surmiran, Puter, and Vallader, and calls it the first model to generate fluent text in those individual varieties.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uzh/simcse/unsup-simcse-xlm-roberta-base": { "id": "uzh/simcse/unsup-simcse-xlm-roberta-base", "name": "Unsupervised SimCSE XLM-RoBERTa base", "maker": "uzh", "family": "uzh/simcse", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.3B on the Hub card", "architecture": "xlm-roberta-base fine-tuned with unsupervised SimCSE. The card specifies average pooling of the last hidden state, temperature 0.05, one epoch, learning rate 1e-5, max sequence length 128, per-device batch 32, gradient accumulation 16, fp16, seed 28852. Training file is the 1M-sentence English Wikipedia sample released by Gao et al.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Unsupervised SimCSE XLM-RoBERTa base is a sentence-embedding checkpoint from the University of Zurich Department of Computational Linguistics. Jannis Vamvas and Rico Sennrich fine-tuned xlm-roberta-base with the Princeton unsupervised SimCSE recipe and released the weights on ZurichNLP. The checkpoint is active. The card does not state a license. Collected on 2026-10-15.", "what_it_is_for": "The card says the model produces sentence embeddings compatible with Sentence-BERT average pooling. The cited paper uses this style of encoder for unsupervised token-level semantic difference detection. A Hub Space loads the checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uzh/swiss-german/swiss-german-canine": { "id": "uzh/swiss-german/swiss-german-canine", "name": "Swiss German CANINE", "maker": "uzh", "family": "uzh/swiss-german", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "0.2B on the Hub card", "architecture": "google/canine-s continued-pretrained with the CANINE-S objective and the SwissBERT subword vocabulary, on Swiss German web text, tweets, and an equal amount of Standard German news. Hub task is feature extraction.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Swiss German CANINE is the University of Zurich continued-pretraining of Google CANINE-S for written Swiss German. It was released with the January 2024 encoder paper and is active under CC BY-NC 4.0. Collected on 2026-10-15.", "what_it_is_for": "The authors use it as a tokenization-free encoder. In their evaluation it is the best model for retrieving Swiss German sentences from Standard German queries, and a weak model for part-of-speech tagging.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uzh/swiss-german/swiss-german-xlm-roberta-base": { "id": "uzh/swiss-german/swiss-german-xlm-roberta-base", "name": "Swiss German XLM-RoBERTa base", "maker": "uzh", "family": "uzh/swiss-german", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "0.3B on the Hub card", "architecture": "xlm-roberta-base continued-pretrained on written Swiss German and an equal amount of Standard German news. The paper treats this as monolithic adaptation of XLM-R, not an adapter. Hub pipeline is fill-mask.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Swiss German XLM-RoBERTa base is a University of Zurich continued-pretraining of xlm-roberta-base on written Swiss German and Standard German. The department released it in January 2024 with the MOOMIN paper on modular Swiss German encoders. It is active and licensed CC BY-NC 4.0. Collected on 2026-10-15.", "what_it_is_for": "The blog presents it as a Swiss German text encoder. On the paper's three-task average it is the strongest subword model they release, ahead of SwissBERT with a gsw adapter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uzh/swissbert/swiss-german-swissbert-char": { "id": "uzh/swissbert/swiss-german-swissbert-char", "name": "SwissBERT Swiss German character adapter", "maker": "uzh", "family": "uzh/swissbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-01-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SwissBERT plus a Swiss German adapter trained at character level. The card says the checkpoint is experimental and runs only with the ZurichNLP swiss-german-text-encoders code because the architecture is custom. The paper calls this the character-level gsw adapter, distinct from the subword adapter that shipped in the main SwissBERT revision.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the experimental character-level Swiss German adapter for SwissBERT, released by the University of Zurich Department of Computational Linguistics with the January 2024 Swiss German encoder paper. It is active on the Hub but the card says it is not a normal Transformers checkpoint. License is CC BY-NC 4.0. Collected on 2026-10-15.", "what_it_is_for": "The paper compares a character-level gsw adapter with the subword adapter that is now in default SwissBERT. This checkpoint is the character-level run. The authors report it underperforms the subword adapter on part-of-speech tagging, dialect identification, and retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uzh/swissbert/swissbert": { "id": "uzh/swissbert/swissbert", "name": "SwissBERT", "maker": "uzh", "family": "uzh/swissbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-03-23", "context_length": 512, "max_output_tokens": null, "parameters": "153010176 in the 2023 paper for the four-adapter new-vocabulary model. The Hub card rounds the current five-adapter checkpoint to 0.2B and does not publish an exact count.", "architecture": "X-MOD masked LM (XmodForMaskedLM) adapted from facebook/xmod-base. 12 layers, hidden size 768, intermediate size 3072, 12 heads, GELU, absolute positions, max_position_embeddings 514, vocab 50262. Language adapters de_CH, fr_CH, it_CH, rm_CH, and gsw. Shared transformer weights were frozen while adapters were trained. Pre-training used contiguous spans of 512 tokens for 10 epochs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SwissBERT is a masked language model from the University of Zurich Department of Computational Linguistics (Jannis Vamvas, Johannes Gra\u00ebn, and Rico Sennrich). It adapts Meta's X-MOD base to Swiss news in German, French, Italian, and Romansh Grischun, with a Switzerland-specific 50,262-token vocabulary. The default checkpoint has been active since the 23 March 2023 release. A Swiss German adapter was added on 24 January 2024. Weights are open under CC BY-NC 4.0. This dossier was collected on 2026-10-15.", "what_it_is_for": "The authors release it for Switzerland-related token tagging, text classification, and embeddings. The paper says it is not designed to generate text. The 2024 update adds written Swiss German without replacing the other adapters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uzh/swissbert/swissbert-cs": { "id": "uzh/swissbert/swissbert-cs", "name": "SwissBERT-CS", "maker": "uzh", "family": "uzh/swissbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": 128, "max_output_tokens": null, "parameters": "0.2B on the Hub card", "architecture": "Token-classification fine-tune of ZurichNLP/swissbert. Slow SentencePiece tokenizer extended with medium, year, and month tokens. Labels in the card mapping: gsw, deu, eng, fra, ita, other. First subword keeps the label. Other subwords, CLS, and SEP use ignore index -100. Trained 5 epochs, batch size 8, learning rate 5e-5, weight decay 0.01, max length 128.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SwissBERT-CS is a University of Zurich fine-tune of SwissBERT for word-level language identification in multilingual child-caregiver transcripts. The card credits Agnese D'Angelo, Sina Ahmadi, Moritz M. Daum, and Stephanie Wermelinger, and cites SwissText 2026. Weights are on the Hub and the checkpoint is active. Collected on 2026-10-15. The card does not state a license or a release day.", "what_it_is_for": "The card says it labels each word so researchers can study inter-sentential and intra-sentential code-switching, cross-speaker switching, and switch points in child speech. Labels cover Swiss German, Standard German, English, French, Italian, and other.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uzh/swissbert/swissbert-ner": { "id": "uzh/swissbert/swissbert-ner", "name": "SwissBERT NER", "maker": "uzh", "family": "uzh/swissbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-03-23", "context_length": null, "max_output_tokens": null, "parameters": "0.2B on the Hub card. Consistent with the new-vocabulary SwissBERT size, not the 0.3B XLM-vocabulary variant.", "architecture": "SwissBERT (X-MOD) with a token-classification head, fine-tuned on WikiNEuRal. The card shows entity groups PER and LOC in examples. Pipeline aggregation_strategy simple is the documented usage.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SwissBERT NER is the University of Zurich token-classification checkpoint of SwissBERT, fine-tuned on the WikiNEuRal multilingual NER set. The department released it with SwissBERT in March 2023. It is active, open-weight, and licensed CC BY-NC 4.0. Collected on 2026-10-15.", "what_it_is_for": "The card says it tags named entities in German, French, and Italian, and can be applied zero-shot to Romansh Grischun. The paper's SwissNER test set is contemporary Swiss news from February 2023.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "uzh/swissbert/swissbert-xlm-vocab": { "id": "uzh/swissbert/swissbert-xlm-vocab", "name": "SwissBERT XLM vocabulary", "maker": "uzh", "family": "uzh/swissbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2023-03-23", "context_length": 512, "max_output_tokens": null, "parameters": "306410496", "architecture": "SwissBERT variant that keeps the XLM-R / X-MOD vocabulary of 250,002 tokens and freezes the original embeddings. Only the four language adapters are trained. Same X-MOD base and 512-token spans as SwissBERT. The Hub card rounds the size to 0.3B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SwissBERT XLM vocabulary is the reused-vocabulary variant of SwissBERT from the University of Zurich Department of Computational Linguistics. The 2023 paper trains only language adapters on top of frozen XLM-R embeddings from X-MOD. It is a separate released checkpoint, not the default SwissBERT. Status is active. Weights are open under CC BY-NC 4.0. Collected on 2026-10-15.", "what_it_is_for": "The paper uses it to test whether a custom Swiss vocabulary helps. It is a Switzerland-adapted encoder for the same tagging, classification, and alignment jobs as SwissBERT, with the original 250,002-token vocabulary.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/chris/chris-proba1": { "id": "valencia/chris/chris-proba1", "name": "CHRIS-PROBA1", "maker": "valencia", "family": "valencia/chris", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-network ensemble: RegNetY-004 and ConvNeXtV2-nano, each with a U-Net head, unified for DN and TOA RGBN inputs", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CHRIS-PROBA1 is a cloud and cloud-shadow segmenter for CHRIS/PROBA-1 imagery from the Image and Signal Processing group at the Universitat de Val\u00e8ncia. On 2026-10-15 the weights and an MIT card were public. No paper link and no priced API were on the card.", "what_it_is_for": "The card says the ensemble is finetuned on RGBN bands so the same weights handle both digital-number and top-of-atmosphere inputs, across CHRIS acquisition modes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/cloudsen12/cloudsen12": { "id": "valencia/cloudsen12/cloudsen12", "name": "CloudSEN12", "maker": "valencia", "family": "valencia/cloudsen12", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-07-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CloudSEN12 is a cloud and cloud-shadow segmentation checkpoint released by the Image and Signal Processing group (Image Processing Laboratory) at the Universitat de Val\u00e8ncia. On 2026-10-15 the weights were public on the isp-uv-es/cloudsen12_models Hugging Face repository under CC BY-NC 4.0. There is no priced API.", "what_it_is_for": "The card names this checkpoint as the model trained on all 13 Sentinel-2 L1C bands of the CloudSEN12 dataset for cloud and cloud-shadow semantic segmentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/cloudsen12/cloudsen12-l2a": { "id": "valencia/cloudsen12/cloudsen12-l2a", "name": "CloudSEN12 L2A", "maker": "valencia", "family": "valencia/cloudsen12", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-07-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CloudSEN12 L2A is a cloud and cloud-shadow segmentation checkpoint released by the Image and Signal Processing group (Image Processing Laboratory) at the Universitat de Val\u00e8ncia. On 2026-10-15 the weights were public on the isp-uv-es/cloudsen12_models Hugging Face repository under CC BY-NC 4.0. There is no priced API.", "what_it_is_for": "The card names this checkpoint as the model trained on 12 Sentinel-2 L2A bands of the CloudSEN12 dataset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/cloudsen12/dtacs4bands": { "id": "valencia/cloudsen12/dtacs4bands", "name": "DTACS 4-band", "maker": "valencia", "family": "valencia/cloudsen12", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-07-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DTACS 4-band is a cloud and cloud-shadow segmentation checkpoint released by the Image and Signal Processing group (Image Processing Laboratory) at the Universitat de Val\u00e8ncia. On 2026-10-15 the weights were public on the isp-uv-es/cloudsen12_models Hugging Face repository under CC BY-NC 4.0. There is no priced API.", "what_it_is_for": "The card names this checkpoint as the model trained on the NIR, red, green, and blue bands of Sentinel-2 L1C in CloudSEN12. A related IGARSS 2023 paper on the same card describes onboard cloud detection, but the card does not say this file is the DTACSNet reported there.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/cloudsen12/landsat30": { "id": "valencia/cloudsen12/landsat30", "name": "CloudSEN12 Landsat 30 m", "maker": "valencia", "family": "valencia/cloudsen12", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-07-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CloudSEN12 Landsat 30 m is a cloud and cloud-shadow segmentation checkpoint released by the Image and Signal Processing group (Image Processing Laboratory) at the Universitat de Val\u00e8ncia. On 2026-10-15 the weights were public on the isp-uv-es/cloudsen12_models Hugging Face repository under CC BY-NC 4.0. There is no priced API.", "what_it_is_for": "The card names this checkpoint as the model trained on the bands shared by Sentinel-2 L1C and Landsat 8 and 9 in the CloudSEN12 dataset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/cloudsen12/unetmobv2-v1": { "id": "valencia/cloudsen12/unetmobv2-v1", "name": "UNetMobV2 V1", "maker": "valencia", "family": "valencia/cloudsen12", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-07-11", "context_length": null, "max_output_tokens": null, "parameters": "6632260", "architecture": "UNet with a MobileNetV2 encoder (name UNetMobV2 on the card and on a TACO MLM record)", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UNetMobV2 V1 is a cloud and cloud-shadow segmentation checkpoint released by the Image and Signal Processing group (Image Processing Laboratory) at the Universitat de Val\u00e8ncia. On 2026-10-15 the weights were public on the isp-uv-es/cloudsen12_models Hugging Face repository under CC BY-NC 4.0. There is no priced API.", "what_it_is_for": "The ISP card says UNetMobV2 V1 was trained on the 13 Sentinel-2 L1C bands of CloudSEN12 and is included with the CloudSEN12+ release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/cloudsen12/unetmobv2-v2": { "id": "valencia/cloudsen12/unetmobv2-v2", "name": "UNetMobV2 V2", "maker": "valencia", "family": "valencia/cloudsen12", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2024-07-11", "context_length": null, "max_output_tokens": null, "parameters": "6632260", "architecture": "UNet with a MobileNetV2 encoder (name UNetMobV2 on the card and on a TACO MLM record)", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UNetMobV2 V2 is a cloud and cloud-shadow segmentation checkpoint released by the Image and Signal Processing group (Image Processing Laboratory) at the Universitat de Val\u00e8ncia. On 2026-10-15 the weights were public on the isp-uv-es/cloudsen12_models Hugging Face repository under CC BY-NC 4.0. There is no priced API.", "what_it_is_for": "The ISP card says UNetMobV2 V2 was trained on the 13 Sentinel-2 L1C bands of CloudSEN12+.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/fdr4vgt/fdr4vgt-cloud": { "id": "valencia/fdr4vgt/fdr4vgt-cloud", "name": "FDR4VGT-CLOUD", "maker": "valencia", "family": "valencia/fdr4vgt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "2025-11-06", "context_length": null, "max_output_tokens": null, "parameters": "12650000", "architecture": "Hybrid DeepLabV3+ with a MobileNetV2 backbone plus a pixel-wise MLP (PW-DL3+)", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FDR4VGT-CLOUD is the cloud-mask model the Universitat de Val\u00e8ncia Image Processing Laboratory released for ESA's FDR4VGT reprocessing of SPOT-VGT and Proba-V. On 2026-10-15 the weights were public under CC0-1.0. The accompanying IEEE GRSL manuscript is described as submitted. There is no priced API.", "what_it_is_for": "The card says it is the operational cloud-masking algorithm selected for the SPOT-VGT and Proba-V archive reprocessing: one sensor-agnostic model for VGT1, VGT2, and Proba-V.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/hyperstarcop/hyperstarcop-mag1c": { "id": "valencia/hyperstarcop/hyperstarcop-mag1c", "name": "HyperSTARCOP mag1c", "maker": "valencia", "family": "valencia/hyperstarcop", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2023-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperSTARCOP mag1c is a hyperspectral methane-plume segmentation checkpoint published by the Universitat de Val\u00e8ncia Image and Signal Processing group with Oxford and other coauthors. On 2026-10-15 the weights were public at isp-uv-es/starcop under CC BY-NC 4.0. There is no priced API.", "what_it_is_for": "HyperSTARCOP variant that uses only the mag1c input, folder models/hyperstarcop_mag1c_only. The paper's job is semantic segmentation of methane plumes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/hyperstarcop/hyperstarcop-mag1c-rgb": { "id": "valencia/hyperstarcop/hyperstarcop-mag1c-rgb", "name": "HyperSTARCOP mag1c+RGB", "maker": "valencia", "family": "valencia/hyperstarcop", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2023-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HyperSTARCOP mag1c+RGB is a hyperspectral methane-plume segmentation checkpoint published by the Universitat de Val\u00e8ncia Image and Signal Processing group with Oxford and other coauthors. On 2026-10-15 the weights were public at isp-uv-es/starcop under CC BY-NC 4.0. There is no priced API.", "what_it_is_for": "HyperSTARCOP variant that uses mag1c plus RGB, folder models/hyperstarcop_mag1c_rgb. The paper's job is semantic segmentation of methane plumes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/ldsr/ldsr-s2": { "id": "valencia/ldsr/ldsr-s2", "name": "LDSR-S2", "maker": "valencia", "family": "valencia/ldsr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-06-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Latent diffusion: four-band autoencoder, latent width 4, conditional denoising U-Net, 1000 training timesteps, epsilon prediction. Weight card describes DDIM sampling. The code repository's later default is 3-step DPM-Solver++ with back-projection.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LDSR-S2 is a latent-diffusion super-resolution model for Sentinel-2 RGB and near-infrared bands from the Image Processing Laboratory at the Universitat de Val\u00e8ncia, released with the ESA OpenSR project. On 2026-10-15 the v1.0.0 weights were public under MIT. There is no priced API.", "what_it_is_for": "The weight card says it super-resolves the four native 10 m Sentinel-2 bands by a factor of four and can produce a pixel-level uncertainty map by sampling more than once. The stated uses are mapping, method evaluation, and uncertainty-aware Earth-observation research, not a substitute for a 2.5 m sensor.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/ml4floods/unet-multioutput": { "id": "valencia/ml4floods/unet-multioutput", "name": "Unet multioutput", "maker": "valencia", "family": "valencia/ml4floods", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2023-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "U-Net, multi-output", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Unet multioutput is one of three flood-extent checkpoints the Universitat de Val\u00e8ncia Image and Signal Processing group released with the 2023 global flood-segmentation paper. On 2026-10-15 it was public in isp-uv-es/ml4floods under CC BY-NC 4.0. There is no priced API.", "what_it_is_for": "Full-band multi-output U-Net in folder models/WF2_unetv2_all. The card does not list the band names for this variant. The paper's stated job is global flood extent segmentation in optical satellite images, with clouds handled by the multi-output head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/ml4floods/unet-multioutput-rgbnir": { "id": "valencia/ml4floods/unet-multioutput-rgbnir", "name": "Unet multioutput RGBNIR", "maker": "valencia", "family": "valencia/ml4floods", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2023-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "U-Net, multi-output", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Unet multioutput RGBNIR is one of three flood-extent checkpoints the Universitat de Val\u00e8ncia Image and Signal Processing group released with the 2023 global flood-segmentation paper. On 2026-10-15 it was public in isp-uv-es/ml4floods under CC BY-NC 4.0. There is no priced API.", "what_it_is_for": "Multi-output U-Net using RGB and near-infrared, folder models/WF2_unetv2_rgbi. The paper's stated job is global flood extent segmentation in optical satellite images, with clouds handled by the multi-output head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/ml4floods/unet-multioutput-s2-l8": { "id": "valencia/ml4floods/unet-multioutput-s2-l8", "name": "Unet multioutput S2-to-L8", "maker": "valencia", "family": "valencia/ml4floods", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2023-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "U-Net, multi-output", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Unet multioutput S2-to-L8 is one of three flood-extent checkpoints the Universitat de Val\u00e8ncia Image and Signal Processing group released with the 2023 global flood-segmentation paper. On 2026-10-15 it was public in isp-uv-es/ml4floods under CC BY-NC 4.0. There is no priced API.", "what_it_is_for": "Multi-output U-Net aimed at Sentinel-2 to Landsat 8 transfer, folder models/WF2_unetv2_bgriswirs. The paper's stated job is global flood extent segmentation in optical satellite images, with clouds handled by the multi-output head.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/qa4eo/mss-cloudmask": { "id": "valencia/qa4eo/mss-cloudmask", "name": "MSS Cloud Detection Model (UNet-EfficientNet-B3)", "maker": "valencia", "family": "valencia/qa4eo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2026-01-18", "context_length": null, "max_output_tokens": null, "parameters": "13223490", "architecture": "UNet with an EfficientNet-B3 encoder and SCSE attention", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The MSS cloud mask is a Landsat Multispectral Scanner segmentation model released by the Image Processing Laboratory at the Universitat de Val\u00e8ncia under the QA4EO-2 project. On 2026-10-15 the checkpoint and MLM metadata were public. The Hub README is empty. There is no priced API.", "what_it_is_for": "unet.json says the model detects clear sky, thin cloud, thick cloud, and shadow in Landsat MSS imagery, after training on CloudSEN12 spectra emulated to MSS bands.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/sen2sr/sen2sr": { "id": "valencia/sen2sr/sen2sr", "name": "SEN2SR", "maker": "valencia", "family": "valencia/sen2sr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "2025-05-05", "context_length": null, "max_output_tokens": null, "parameters": "12894526", "architecture": "Swin2SR for the main ten-band checkpoint (mlm name SR_S2_BestModel). The model-card install note says the full package uses a Mamba architecture. Those two statements disagree.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SEN2SR is the higher-accuracy Sentinel-2 super-resolution release from the Universitat de Val\u00e8ncia Image Processing Laboratory under ESA OpenSR. On 2026-10-15 the weights were public at tacofoundation/sen2sr under CC0-1.0. There is no priced API.", "what_it_is_for": "The card and the Remote Sensing of Environment paper describe a framework that super-resolves Sentinel-2 10 m RGB-NIR and 20 m red-edge and SWIR bands to 2.5 m while constraining low-frequency reflectance.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/sen2sr/sen2srlite": { "id": "valencia/sen2sr/sen2srlite", "name": "SEN2SRLite", "maker": "valencia", "family": "valencia/sen2sr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "2025-05-05", "context_length": null, "max_output_tokens": null, "parameters": "472496", "architecture": "SPAN (Swift Parameter-free Attention Network). Main MLM name CNN_Light_F4.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SEN2SRLite is the lightweight Sentinel-2 super-resolution model released with SEN2SR by the Universitat de Val\u00e8ncia Image Processing Laboratory. On 2026-10-15 the SPAN weights were public under CC0-1.0 in the same TACO repository. There is no priced API.", "what_it_is_for": "The card calls it the fast lightweight runner for enhancing Sentinel-2 toward 2.5 m. The main MLM file identifies a 472,496-parameter SPAN network, with separate RGBN x4 and RSWIR x2 checkpoints in the same family.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/udl4fl/mm-unet-s1s2": { "id": "valencia/udl4fl/mm-unet-s1s2", "name": "mm_unet_s1s2", "maker": "valencia", "family": "valencia/udl4fl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-07-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dual-stream U-Net with a modality token", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mm_unet_s1s2 is a flood-extent segmentation model released by the Image and Signal Processing group at the Universitat de Val\u00e8ncia with the udl4fl weight bundle. On 2026-10-15 the checkpoint was public under CC BY-NC 4.0. There is no priced API.", "what_it_is_for": "Dual-stream U-Net with a modality token, trained on S1S2Water (Sentinel-1 GRD and Sentinel-2 L1C), WorldFloods (Sentinel-2 L1C), and Kuro Siwo (Sentinel-1 GRD).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/udl4fl/sm-unet-s1": { "id": "valencia/udl4fl/sm-unet-s1", "name": "sm_unet_s1", "maker": "valencia", "family": "valencia/udl4fl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-07-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "U-Net", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "sm_unet_s1 is a flood-extent segmentation model released by the Image and Signal Processing group at the Universitat de Val\u00e8ncia with the udl4fl weight bundle. On 2026-10-15 the checkpoint was public under CC BY-NC 4.0. There is no priced API.", "what_it_is_for": "Single-modality U-Net trained on Sentinel-1 GRD VV and VH from S1S2Water and Kuro Siwo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "valencia/udl4fl/sm-unet-s2": { "id": "valencia/udl4fl/sm-unet-s2", "name": "sm_unet_s2", "maker": "valencia", "family": "valencia/udl4fl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2025-07-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "U-Net", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "sm_unet_s2 is a flood-extent segmentation model released by the Image and Signal Processing group at the Universitat de Val\u00e8ncia with the udl4fl weight bundle. On 2026-10-15 the checkpoint was public under CC BY-NC 4.0. There is no priced API.", "what_it_is_for": "Single-modality U-Net trained on Sentinel-2 L1C bands B02, B03, B04, B08, B11, and B12 from S1S2Water and WorldFloods.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vanderbilt/3d-ux-net/3d-ux-net": { "id": "vanderbilt/3d-ux-net/3d-ux-net", "name": "3D UX-Net", "maker": "vanderbilt", "family": "vanderbilt/3d-ux-net", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-09-29", "context_length": null, "max_output_tokens": null, "parameters": "53000000", "architecture": "Large-kernel volumetric ConvNet that copies the hierarchical layout of a Swin-style transformer. Depth-wise kernels start at 7 x 7 x 7. The Swin MLP is replaced by pointwise convolutions. README tables use 53.0M parameters and 639.4G FLOPs at 96 x 96 x 96 for the FeTA, FLARE, and AMOS rows. AMOS weights are marked kernel=7. The arXiv abstract calls it a lightweight ConvNet and says it was accepted to ICLR 2023.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "3D UX-Net is a Vanderbilt University MASI Lab volumetric segmentation network. It is a large-kernel ConvNet shaped like a hierarchical transformer, released as PyTorch weights rather than an API. The arXiv preprint is 29 September 2022. The abstract says the paper was accepted to ICLR 2023. On 2026-10-15 the GitHub results table still linked three trained checkpoints.", "what_it_is_for": "The paper presents 3D UX-Net as a lighter substitute for 3D Swin-style transformers on volumetric brain and abdominal segmentation. The public checkpoints are the FeTA 2021, FLARE 2021, and AMOS 2022 trainings. All four authors on the README are listed at Vanderbilt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vanderbilt/slant/slant": { "id": "vanderbilt/slant/slant", "name": "SLANT", "maker": "vanderbilt", "family": "vanderbilt/slant", "status": "active", "confidence": "high", "weights": "open-weights", "license": "BSD-3-Clause", "release_date": "2018-06-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Spatially localized atlas network tiles. Each tile is a 3D U-Net with one input channel and 133 output channels. The MICCAI and NeuroImage papers use input patches of 96 x 128 x 88. SLANT-8 is 8 non-overlapping tiles. SLANT-27 is 27 overlapping tiles fused by majority vote. A later Vanderbilt UNesT comparison table lists SLANT27 as 19.9M parameters times 27 networks. That product was not stored as one integer. The public Docker image was not opened, so which ensemble is inside deep_brain_seg_v1_1_0 is unverified.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SLANT (spatially localized atlas network tiles) is a Vanderbilt University MASI Lab whole-brain MRI segmenter. It is an open-weight Docker pipeline, not an API. On 2026-10-15 the lab README still documents `masidocker/public:deep_brain_seg_v1_1_0`. The named public paper is the 1 June 2018 arXiv preprint, later MICCAI 2018, with the NeuroImage article dated 1 July 2019 on the publisher page.", "what_it_is_for": "The maker uses SLANT to parcellate a T1 MRI into 133 BrainCOLOR regions. Overlapping 3D U-Net tiles cover MNI atlas space, then label fusion merges them. The MICCAI abstract reports about 15 minutes per scan against more than 30 hours for multi-atlas segmentation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vanderbilt/slant/slant-ticv": { "id": "vanderbilt/slant/slant-ticv", "name": "SLANT-TICV", "maker": "vanderbilt", "family": "vanderbilt/slant", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-05-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The journal page indexed with this release describes 27 overlapped 3D U-Net tiles. Each tile covers 96 x 128 x 88 voxels. Output channels are 133 in pre-training and 135 after fine-tuning. Two containers are released: skull-stripped input and non-skull-stripped input. Parameter count was not published on the pages opened.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SLANT-TICV is a Vanderbilt University MASI Lab brain segmentation container that generalizes SLANT for skull removal and intracranial measurements. Weights ship as Singularity images on Zenodo, not as an API. On 2026-10-15 both the skull-stripped and non-skull-stripped releases were public. Zenodo marks development status Inactive. The images are still downloadable, so this dossier uses status active.", "what_it_is_for": "The Zenodo records describe a deep-learning brain segmentation algorithm for skull removal and total intracranial volume. The GitHub README points users to two maintained images: one for scans that still include the skull, and one for scans that are already skull-stripped.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vanderbilt/synb0/synb0-disco": { "id": "vanderbilt/synb0/synb0-disco", "name": "Synb0-DISCO", "maker": "vanderbilt", "family": "vanderbilt/synb0", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2019-05-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The 2020 PLOS ONE paper says the shipped synthesizer is a 3D U-Net with 2 input channels (T1 and distorted b0) and 1 output channel (undistorted b0), using instance normalization. Five cross-validation folds are averaged. The 2019 Synb0-DisCo paper used a 2.5D multi-slice multi-view GAN. The current pipeline.sh calls five dual_channel_unet checkpoints, so the Docker matches the 2020 U-Net, not the 2019 GAN. DIPY documents an input shape of 80 x 80 x 96 x 2 for its TensorFlow port. That shape was not confirmed inside the MASI Docker.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Synb0-DISCO is a Vanderbilt University MASI Lab model that synthesizes an undistorted diffusion b0 image from a T1 and a distorted b0 so FSL topup can run without a reverse phase-encode scan or a field map. The current release is the Docker image `leonyichencai/synb0-disco:v3.1`. On 2026-10-15 that image was still the tag in the lab README. The name first appears in a Magnetic Resonance Imaging paper with an electronic publication date of 7 May 2019. The network inside the current container is the 3D U-Net from the 31 July 2020 PLOS ONE paper, not the 2019 GAN.", "what_it_is_for": "The README says the tool is for historical or limited diffusion MRI that has no reverse phase-encode pair. The synthetic b0 is treated as infinite bandwidth. Topup then estimates the susceptibility field. The container can stop before topup if `--notopup` is set.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vanderbilt/synbold/synbold-disco": { "id": "vanderbilt/synbold/synbold-disco", "name": "SynBOLD-DisCo", "maker": "vanderbilt", "family": "vanderbilt/synbold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-04-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Five-fold image synthesizer. The README writes one prediction per fold from the distorted BOLD and the T1 in 2.5 mm atlas space, then averages the folds. The lab did not publish a parameter count or a layer list on the README. A third-party port claims the fold network matches Synb0-DISCO. That claim was not used as a spec.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SynBOLD-DisCo is a Vanderbilt University MASI Lab model that synthesizes an undistorted fMRI BOLD image from a T1 and a distorted BOLD series so topup can run without reverse phase-encoding or a field map. The release is the Docker image `ytzero/synbold-disco:v1.4`. On 2026-10-15 that tag was the one in the lab README. The SPIE paper date on the README is 3 April 2023. The code license file is MIT, copyright 2025 Vanderbilt University.", "what_it_is_for": "The README says the tool is for fMRI datasets that never acquired a distortion-calibration scan. The synthetic BOLD matches T1 geometry and BOLD contrast and is treated as infinite bandwidth. The container can also run topup and write a distortion-corrected series.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vanderbilt/unest/unest-renal": { "id": "vanderbilt/unest/unest-renal", "name": "Renal UNesT", "maker": "vanderbilt", "family": "vanderbilt/unest", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MONAI bundle renalStructures_UNEST_segmentation, metadata version 0.2.7. The card calls it a pre-trained UNEST base model for 3D renal structures on arterial or portal-venous CT. Metadata: one channel, patch 96 x 96 x 96, intensities scaled to [0, 1], four output channels (background, kidney cortex, medulla, pelvicalyceal system). Authors line: Vanderbilt University + MONAI team. The MASILab README renal table repeats the whole-brain parameter column, including 87.3M, so that count was not stored for this checkpoint.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Renal UNesT is a Vanderbilt University and MONAI checkpoint that segments kidney cortex, medulla, and the pelvicalyceal system on contrast-enhanced CT. The weights that loaded on 2026-10-15 are MONAI bundle `renalStructures_UNEST_segmentation` version 0.2.7, Apache-2.0. The authors line says Vanderbilt University plus the MONAI team. The copyright line says the MONAI Consortium. It is a research example, not a diagnostic device and not an API.", "what_it_is_for": "The bundle description says the model delineates kidney cortex, medulla, and pelvicalyceal system on 96 cubed CT patches for morphological analysis. The card says it is an example and not for diagnosis. Training data are described as Vanderbilt and Vanderbilt University Medical Center volumes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vanderbilt/unest/unest-whole-brain": { "id": "vanderbilt/unest/unest-whole-brain", "name": "Whole-brain UNesT", "maker": "vanderbilt", "family": "vanderbilt/unest", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-09-28", "context_length": null, "max_output_tokens": null, "parameters": "87300000", "architecture": "3D U-shaped network with a nested hierarchical transformer encoder (UNesT) and a convolutional decoder. The MASILab README lists 87.3M parameters and 261.7G FLOPs for the whole-brain row. The MONAI bundle is named wholeBrainSeg_Large_UNEST. The same card calls it a UNEST base model. Input on the bundle metadata is one MRI channel, patch 96 x 96 x 96, intensities scaled to [0, 1]. Output is 133 channels. The metadata format field says hounsfield, which does not match an MRI model.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Whole-brain UNesT is a Vanderbilt University and MONAI 3D transformer segmenter for 133 brain structures on T1 MRI. Vanderbilt MASI Lab authors released the method and point users at a MONAI bundle. The bundle copyright line is the MONAI Consortium. On 2026-10-15 the bundle that loaded was `monai-test/wholeBrainSeg_Large_UNEST_segmentation`, metadata version 0.2.3, Apache-2.0. The arXiv preprint is 28 September 2022. The journal version is Medical Image Analysis, 2023.", "what_it_is_for": "The paper presents one network that labels 133 brain tissues, instead of the 27-network SLANT27 ensemble. The bundle metadata name is \"Whole brain large UNEST segmentation\". The same card also calls the checkpoint a UNEST base model. It says the example is not for diagnosis.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "veed/avatars/avatars": { "id": "veed/avatars/avatars", "name": "VEED Avatars", "maker": "veed", "family": "veed/avatars", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VEED Avatars is VEED's stock-presenter video API. It is active on fal as text-to-video and audio-to-video endpoints. Collected 2026-10-15.", "what_it_is_for": "fal's launch post says the model turns text or an audio file into a talking-head video of one of VEED's in-house avatars, with synced voice and facial movement. Unlike Fabric 1.0, the caller cannot supply an arbitrary image. The presenter comes from a fixed id list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "veed/background-removal/video-background-removal": { "id": "veed/background-removal/video-background-removal", "name": "VEED Video Background Removal", "maker": "veed", "family": "veed/background-removal", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VEED Video Background Removal is VEED's video matting API. It is active on fal in a standard tier, a fast tier, and a green-screen tier, with the same prices on VEED's API page. Collected 2026-10-15.", "what_it_is_for": "The standard endpoint removes the background from a video of a person or object without a green screen. The fast endpoint is the same job at a lower price. The green-screen endpoint is for footage already shot on chromakey and includes spill suppression.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "veed/clean-audio/clean-audio": { "id": "veed/clean-audio/clean-audio", "name": "VEED Clean Audio", "maker": "veed", "family": "veed/clean-audio", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "video" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": 0.0125, "completion_per_m_usd": null }, "summary_intro": "VEED Clean Audio is the speech-enhancement model behind the Clean Audio control in the VEED editor, also sold as `veed/clean-audio` on fal. It is active. Collected 2026-10-15.", "what_it_is_for": "The fal card says it removes background noise from speech while keeping quiet and distant words, and that it is the same model as Clean Audio in the VEED editor. It is enhancement, not text-to-speech and not voice cloning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "veed/dubbing/dubbing": { "id": "veed/dubbing/dubbing", "name": "VEED Dubbing", "maker": "veed", "family": "veed/dubbing", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VEED Dubbing is a translation dubbing model VEED's CEO and COO have shown and put on a waitlist. It is not a public API. Status is announced. Collected 2026-10-15.", "what_it_is_for": "The CEO's 2026-10-13 post calls it VEED's new dubbing model and says it dubs a video into any language while keeping emotion, tone, and feeling. The COO's 2026-10-14 post says a coming-soon Dubbing model dubbed and lip-synced a clip into Spanish in one click. Both point at early access, not a shipping endpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "veed/fabric/fabric-1-0": { "id": "veed/fabric/fabric-1-0", "name": "VEED Fabric 1.0", "maker": "veed", "family": "veed/fabric", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion Transformer (DiT). Image is used as the first frame and for style conditioning; audio drives frame continuation.", "modalities": { "input": [ "image", "audio", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VEED Fabric 1.0 is VEED's own image-to-video model for talking pictures. It is active on the VEED editor, on api.veed.io, and on fal.ai. Collected 2026-10-15.", "what_it_is_for": "VEED describes Fabric as its own image-to-video model: one still (photo, mascot, illustration, or render) plus speech becomes a talking video with lip sync and head, hand, and body motion. It is the model behind talking-video generation in the editor and the Fabric 1.0 API. It is not a preset-avatar catalog.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "veed/lipsync/lipsync": { "id": "veed/lipsync/lipsync", "name": "VEED Lipsync", "maker": "veed", "family": "veed/lipsync", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-06-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.4 }, "summary_intro": "VEED Lipsync is VEED's original video-to-video lip-sync model. It is still active on fal.ai as the lower-priced entry endpoint. Lip Sync 2.0 is the newer model. Collected 2026-10-15.", "what_it_is_for": "VEED says the API takes a source video and a replacement audio track and remaps the speaker's lips to the new audio. The stated jobs are dubbing, localization, swapping lines, and driving avatar video. It does not generate a scene from a still image. That job is Fabric 1.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "veed/lipsync/lipsync-2-0": { "id": "veed/lipsync/lipsync-2-0", "name": "VEED Lip Sync 2.0", "maker": "veed", "family": "veed/lipsync", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "audio" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.07 }, "summary_intro": "VEED Lip Sync 2.0 is VEED's current zero-shot video-to-video lip-sync model. It is active as an API on fal.ai and on api.veed.io. Collected 2026-10-15.", "what_it_is_for": "VEED says Lip Sync 2.0 replaces the spoken track on an existing video and re-renders the mouth and lower face so the new audio matches. The stated uses are changing an ad hook, fixing a line, and dubbing or localizing a take without a reshoot. Emotion, style, and timing are supposed to come from the new audio. It is zero-shot: no per-speaker training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "veed/live-avatar/live-avatar": { "id": "veed/live-avatar/live-avatar", "name": "VEED Live Avatar", "maker": "veed", "family": "veed/live-avatar", "status": "preview", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VEED Live Avatar is a real-time avatar model VEED has put in front of a beta waitlist. It is not a public API. Status is preview. Collected 2026-10-15.", "what_it_is_for": "On 2026-10-12 the VEED account called it \"VEED's live avatar model\" and offered early access. The quoted employee post, from someone who lists genmedia work at VEED, says beta testers are being waitlisted and shows a spoken conversation with an avatar. No product page describes the inputs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "veed/subtitles/subtitles": { "id": "veed/subtitles/subtitles", "name": "VEED Subtitles", "maker": "veed", "family": "veed/subtitles", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": null }, "summary_intro": "VEED Subtitles is VEED's caption API. It transcribes a video and burns styled subtitles into the picture. It is active on fal and listed on VEED's API pricing page. Collected 2026-10-15.", "what_it_is_for": "fal describes the API as turning raw footage into a video with professional burned-in subtitles. Callers pick a visual preset. An optional translation language adds a per-minute surcharge. The underlying speech recognizer is not named.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vienna/awesom/awesom": { "id": "vienna/awesom/awesom", "name": "aweSOM", "maker": "vienna", "family": "vienna/awesom", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Code MIT. README: models are free for non-commercial research on Zenodo. MetaQSAR-derived commercial use requires a license from the Universit\u00e0 degli Studi di Milano.", "release_date": "2025-08-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Deep ensemble of 10 graph neural networks. Each network has a convolutional module, molecular-context pooling, and a classification module, and the ensemble separates aleatoric and epistemic uncertainty. A hyperparameter-search table lists candidate widths; those candidates were not confirmed as the shipped widths. One SI passage describes a receptive field of four convolutional layers.", "modalities": { "input": [ "molecule" ], "output": [ "atom-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "aweSOM is a graph-neural site-of-metabolism model from the University of Vienna, with a University of Milan coauthor on the MetaQSAR data. The JCIM article was posted online on 2025-08-06 (volume 65, issue 16, pages 8462\u20138474). This dossier was collected on 2026-10-15. Code is MIT. Weights on Zenodo are for non-commercial research, and NERDD hosts a web predictor.", "what_it_is_for": "The paper presents aweSOM as a phase 1 and phase 2 site-of-metabolism predictor whose deep ensemble separates aleatoric and epistemic uncertainty. Code and the non-commercial weight link are in the [README](https://github.com/molinfo-vienna/aweSOM). The article is [doi:10.1021/acs.jcim.5c00762](https://doi.org/10.1021/acs.jcim.5c00762).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vienna/fame/fame3r": { "id": "vienna/fame/fame3r", "name": "FAME3R", "maker": "vienna", "family": "vienna/fame", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Code MIT. Journal article CC BY 4.0. MetaQSAR-trained weights are gated on Zenodo (institutional email, non-commercial). Commercial use of MetaQSAR-derived models requires a license from the Universit\u00e0 degli Studi di Milano.", "release_date": "2026-02-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Random-forest site-of-metabolism classifier for phase 1 and phase 2 reactions, with more than one featurization and a Shannon-entropy reliability score. Hyperparameters of the shipped forests were not published as one spec.", "modalities": { "input": [ "molecule" ], "output": [ "atom-labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FAME3R is a random-forest site-of-metabolism model from the Department of Pharmaceutical Sciences and the Christian Doppler Laboratory at the University of Vienna. Johannes Kirchmair is the corresponding author. Angelica Mazzolari (University of Milan) is a coauthor. The journal article appeared on 2026-02-14. This dossier was collected on 2026-10-15. The code is public, the MetaQSAR-trained weights are gated, and NERDD hosts a GUI and REST API.", "what_it_is_for": "The paper presents FAME3R as a maintainable reimplementation and extension of FAME 3 for phase 1 and phase 2 sites of metabolism. New pieces named in the abstract are a Shannon-entropy reliability score and a choice of featurization strategies. The package exposes a Python API and a CLI. Trained models are also on NERDD. Article: [doi:10.1186/s13321-026-01161-1](https://doi.org/10.1186/s13321-026-01161-1). Code: [molinfo-vienna/FAME3R](https://github.com/molinfo-vienna/FAME3R).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vienna/griphin/griphin": { "id": "vienna/griphin/griphin", "name": "GRIPHIN", "maker": "vienna", "family": "vienna/griphin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Code MIT. Figshare weights CC BY 4.0. Paper CC BY 4.0.", "release_date": "2026-05-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid affinity model. Voxel GRAIL pharmacophore-interaction-field maps represent the pocket, a graph represents the ligand, and integrated gradients provide attribution. Exact layer widths are in journal SI Table S1 and were not extracted, so hidden sizes and the parameter count stay empty.", "modalities": { "input": [ "protein-pocket", "ligand" ], "output": [ "affinity" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GRIPHIN is a hybrid affinity model from the Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna. Journal of Cheminformatics 18:97 was published on 2026-05-07, with the version of record on 2026-07-15. This dossier was collected on 2026-10-15. Figshare weights are CC BY 4.0. The LP-PDBBind file is a reproduction checkpoint of the same model, not a second product.", "what_it_is_for": "The paper presents GRIPHIN as grids of pharmacophore interaction fields for affinity prediction. The pocket is a voxel GRAIL field, the ligand is a graph, and integrated gradients attribute the prediction. Article: [doi:10.1186/s13321-026-01203-8](https://doi.org/10.1186/s13321-026-01203-8). The university record is on [u:cris](https://ucrisportal.univie.ac.at/en/publications/griphin-grids-of-pharmacophore-interaction-fields-for-affinity-pr/). Code: [molinfo-vienna/GRIPHIN](https://github.com/molinfo-vienna/GRIPHIN).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vienna/neat/neat": { "id": "vienna/neat/neat", "name": "NEAT", "maker": "vienna", "family": "vienna/neat", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT (GitHub code and Figshare weights). Paper is CC BY 4.0.", "release_date": "2026-05-04", "context_length": null, "max_output_tokens": null, "parameters": "165000000", "architecture": "Neighborhood-guided autoregressive set transformer. The backbone is a NanoGPT GPT-2 implementation with positional embeddings removed and bidirectional attention, plus a flow-matching DiT-style AdaLN MLP head for the next atom position. Hydrogens are generated, a stop token ends the molecule, and bonds are inferred after the atoms are placed. Best QM9 and GEOM-Drugs checkpoints: 12-layer transformer, 12 heads, hidden size 768, MLP projector of 6 AdaLN blocks with hidden size 1536. The paper states 165M parameters.", "modalities": { "input": [ "partial-3d-molecule" ], "output": [ "3d-molecule" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NEAT is a neighborhood-guided autoregressive set transformer for 3D molecular generation from the Department of Pharmaceutical Sciences, University of Vienna, with the Christian Doppler Laboratory and the Vienna Doctoral School. Figshare weights were posted on 2026-05-04. arXiv v3 is dated 2026-05-05. This dossier was collected on 2026-10-15. QM9 and GEOM-Drugs checkpoints are two versions of one model.", "what_it_is_for": "The paper presents NEAT as an efficient autoregressive generator of 3D molecules. A set transformer places the next atom, neighborhood guidance shapes that step, and bonds are inferred after the atom set is complete. The official HTML is [arXiv:2512.05844v3](https://arxiv.org/html/2512.05844v3). Code is [molinfo-vienna/NEAT](https://github.com/molinfo-vienna/NEAT). Weights are [Figshare 31113949](https://doi.org/10.6084/m9.figshare.31113949).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vienna/neat/neat-pocket": { "id": "vienna/neat/neat-pocket", "name": "NEAT-POCKET", "maker": "vienna", "family": "vienna/neat", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GitHub code MIT. Weights downloaded from Figshare 10.6084/m9.figshare.33426877. The Figshare licence field was not copied in this pass. Paper is CC BY 4.0.", "release_date": "2026-09-04", "context_length": null, "max_output_tokens": null, "parameters": "242000000", "architecture": "Pocket-conditioned extension of NEAT. Paper: 12-layer source-set transformer, 12 heads, hidden size 768; pocket transformer with a 1-4-1 layer pattern, 12 heads, hidden size 768; MLP projector of 6 AdaLN blocks, hidden size 1536; 242M parameters. Classifier-free guidance dropout 0.2. Clash penalty factor 4.0. Training pocket cutoff 6 \u00c5. README: default guidance scale 0.5 and default max_atoms 100.", "modalities": { "input": [ "protein-pocket", "molecular-fragment" ], "output": [ "3d-ligand" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NEAT-POCKET is the pocket-conditioned extension of NEAT from the Department of Pharmaceutical Sciences, University of Vienna. arXiv 2609.05097 was submitted on 2026-09-04. This dossier was collected on 2026-10-15. It has its own paper, repository, and Figshare weights, so it is a separate model folder. The predecessor checkpoint remains `neat/neat`.", "what_it_is_for": "The paper presents NEAT-POCKET as an autoregressive 3D generator that builds a ligand inside a protein pocket, including hydrogens, and that can finish a fragment. The abstract is [arXiv:2609.05097](https://arxiv.org/abs/2609.05097). Code is [molinfo-vienna/NEAT-POCKET](https://github.com/molinfo-vienna/NEAT-POCKET). `scripts/get_weights.py` downloads [Figshare 33426877](https://doi.org/10.6084/m9.figshare.33426877).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vienna/trialblazer/trialblazer": { "id": "vienna/trialblazer/trialblazer", "name": "Trialblazer", "maker": "vienna", "family": "vienna/trialblazer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GitHub code MIT. Paper CC BY 4.0. The CLI downloads a default model; a separate weight-file license was not stated on the Zenodo data record.", "release_date": "2026-01-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multilayer perceptron classifier of late-stage toxicity risk. Inputs are Morgan fingerprints (M2FP) and similarity-predicted bioactivity profiles (PBFP).", "modalities": { "input": [ "molecule" ], "output": [ "toxicity-risk" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Trialblazer is a multilayer-perceptron classifier of late-stage toxicity risk from the University of Vienna. The European Journal of Medicinal Chemistry article is dated 2026-01-15 (epub 2025-10-28), volume 302, part 2, article 118306. This dossier was collected on 2026-10-15. The code is MIT and the CLI installs a default model locally. It was not found on NERDD.", "what_it_is_for": "The publisher describes Trialblazer as a chemistry-based predictor of toxicity risk in late-stage drug development. It uses Morgan fingerprints (M2FP) plus similarity-predicted bioactivity profiles (PBFP), so a known biological target is not required at inference. Article: [doi:10.1016/j.ejmech.2025.118306](https://doi.org/10.1016/j.ejmech.2025.118306). Code: [molinfo-vienna/trialblazer](https://github.com/molinfo-vienna/trialblazer).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "viggle/animate/viggle-animate": { "id": "viggle/animate/viggle-animate", "name": "Viggle-Animate", "maker": "viggle", "family": "viggle/animate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MiniMax H3 Community License (model derivative). Code license is separate from the weights.", "release_date": "2026-09-04", "context_length": null, "max_output_tokens": null, "parameters": "33.1B", "architecture": "Full finetune of MiniMax-H3 ref2va transformer (MiniMaxH3Transformer3DModel): 50 layers, hidden size 5376, 56 heads, head dim 128, FFN 14336, in_channels 24. A rank-128 DMD LoRA over 302 linear layers collapses sampling to 3 forward passes. Text encoder is not loaded; a fixed embedding is used. bf16 transformer shards.", "modalities": { "input": [ "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.01 }, "summary_intro": "Viggle-Animate is Viggle's open-weight character-replacement video model, released on 2026-09-04. It is a 33.1B full finetune of MiniMax-H3's ref2va transformer, distilled to three forward passes. Viggle also hosts it on the API as character-animation mode. It is active. Collected on 2026-10-15.", "what_it_is_for": "The research page says the open model replaces the character in a video from one repainted frame of that video, keeping motion and camera, with no pose skeleton, mask, or text prompt at inference. The hosted API is broader: up to four character images with no driving video, or up to five images plus a driving video, each image replacing one person. Viggle calls the hosted path a separate backend from MiniMax H3 text-to-video. It does not say the API checkpoint and the Hugging Face checkpoint are identical.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "viggle/jst/jst-1": { "id": "viggle/jst/jst-1", "name": "JST-1", "maker": "viggle", "family": "viggle/jst", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video-3D foundation model. Viggle describes Joint Space-Time (JST) as physics-aware and distinct from frame-level diffusion. No layer count, parameter count, or paper was published.", "modalities": { "input": [ "image", "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JST-1 is Viggle's closed video-3D foundation model. The company calls it the first model of that kind with a real understanding of physics, and it is the system behind controllable character video. It was still named on the homepage and in an official post on 2026-06-08. This dossier was collected on 2026-10-15.", "what_it_is_for": "Viggle says JST-1 lets a user put a character image under a chosen motion instead of asking a text-to-video model to invent the movement. The October 2025 product guide describes pairing a still character with motion from a video. TechCrunch, on 2024-08-26, reported the same claim from CEO Hang Chu: motion is specified, and the company says the model follows physics more closely than typical video generators.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "viggle/jst/jst-2": { "id": "viggle/jst/jst-2", "name": "JST-2", "maker": "viggle", "family": "viggle/jst", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named as JST-2 / JST2. No architecture card was published. PINOC's general FAQ says the studio runs on JST with physical priors, without a version number.", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "JST-2 (also written JST2) is a closed Viggle model named on current PINOC pages. The motion-capture page says that product is powered by JST-2. The PINOC gallery says every world there was generated by JST2 and that the clips can be downloaded. It is not the same named object as JST-1, and no page says it replaces JST-1. Collected on 2026-10-15.", "what_it_is_for": "On https://viggle.ai/mocap, JST-2 is the model behind turning a single-camera video of a person into 3D skeletal animation for Blender, Maya, and similar tools. The gallery at https://viggle.ai/pinoc/app/gallery presents JST2 as a world generator: open a world, walk around it in 3D, or download the clips. The broader PINOC FAQ only says the studio runs on JST, without the \"-2\".", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "viggle/meridian/meridian": { "id": "viggle/meridian/meridian", "name": "Meridian", "maker": "viggle", "family": "viggle/meridian", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Weights: MiniMax H3 Community License. Code: Apache 2.0. VGGT-Omega geometry model is separate and FAIR Noncommercial Research License v1.", "release_date": "2026-09-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two LoRA adapters (2.5 GiB each) on the unmodified MiniMax-H3 fl2va transformer. teacher_lora is the re-camera adapter (default grid --steps 50 --flow-shift 12). turbo_lora is a 3-forward distillation (default --steps 4 --flow-shift 3). Load both at strength 1.0 and do not merge them into the base. Text encoder is not loaded. Geometry reference comes from VGGT-Omega, which is not bundled. A superseded legacy release fused a 61.7 GiB transformer.", "modalities": { "input": [ "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Meridian is Viggle's open-weight video model for re-shooting an existing moment from a new camera path and a new timing. It was published on the research site on 2026-09-14 and announced by @ViggleAI on 2026-09-15. It is two LoRA adapters on MiniMax-H3, not a standalone checkpoint. It is active. Collected on 2026-10-15.", "what_it_is_for": "Viggle says Meridian authors a new observation of an event that already happened. You pick where the camera is and which moments play, including holding a frame while the camera moves, or starting from one photograph. Geometry (VGGT-Omega, not included) builds a rough new view. Meridian fills holes and refines that view. It is not a character-swap model and it is not on the Viggle render API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "viggle/viggle/viggle-2-0": { "id": "viggle/viggle/viggle-2-0", "name": "Viggle 2.0.0", "maker": "viggle", "family": "viggle/viggle", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described by secondary reports of a Viggle post as an upgrade of the video-3D model powered by JST-1. No architecture card.", "modalities": { "input": [ "image", "video", "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Viggle 2.0.0 is an early closed character-video model from Viggle. It is not in the current API. Two secondary pages describe a launch post that called 2.0.0 a model upgrade, powered by JST-1, and listed higher resolution, better faces, fewer fast-motion artifacts, and a faster finetune. The original post was not opened for this catalog, so confidence is medium. Collected on 2026-10-15.", "what_it_is_for": "The quoted announcement treats 2.0.0 as a quality upgrade of Viggle's controllable character video, not as a new product line. Early Discord commands around that period (/mix, /animate, /ideate, /character, /stylize) are interface commands, not extra model names.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "viggle/viggle/viggle-v3": { "id": "viggle/viggle/viggle-v3", "name": "Viggle V3", "maker": "viggle", "family": "viggle/viggle", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed character-and-motion render model. API ids are V3, v3, and V3_Preview. No architecture card.", "modalities": { "input": [ "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.01 }, "summary_intro": "Viggle V3 is a closed character-and-motion video model. Viggle's own account called it V3-beta through at least April 2025. The current API still accepts V3, v3, and V3_Preview, and motion import defaults to V3_Preview. Collected on 2026-10-15.", "what_it_is_for": "It is the render model for Mix-style jobs: a character image plus a driving motion, returned as an MP4. Legacy docs say a character or scene preprocessed with one model can only be rendered with that same model. Guest posts on Viggle's site describe V3 as the point when full-body consistency became usable for VFX, but those are creator interviews, not a model card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "viggle/viggle/viggle-v4": { "id": "viggle/viggle/viggle-v4", "name": "Viggle V4", "maker": "viggle", "family": "viggle/viggle", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-05", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed motion-control model. Viggle says it is powered by proprietary JST, a 3D world model, rather than frame-level diffusion. API ids: V4, v4, V4_BaseSG, and V4_Preview (documented alias of V4_BaseSG).", "modalities": { "input": [ "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.01 }, "summary_intro": "Viggle V4 is Viggle's closed motion-control model for putting a reference performance onto a character image. The company launched it on 2026-03-05 and says it runs on proprietary JST, which they call a 3D world model. It is still an API render id. Collected on 2026-10-15.", "what_it_is_for": "The V4 guide says you select V4 under Mix, upload a video and a character, and transfer the motion. Compared with earlier Viggle models, the guide claims better handling of flips, inversions, and fast motion, less shake near frame edges, richer texture, and more natural faces. The launch post adds multi-character swap, pose editing, and generation in seconds. Character Refine, Smooth Motion, and Foot Lock are controls shipped with V4, not separate models. Character Refine builds five angle references from one image.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "viggle/viggle/viggle-v4-5": { "id": "viggle/viggle/viggle-v4-5", "name": "Viggle V4.5", "maker": "viggle", "family": "viggle/viggle", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Viggle V4.5 is a closed character-video model Viggle announced on 2026-06-29. The official post says a single headshot can produce a clearer full-body result, with finer texture and more stable motion than before. It was not found in the API model enum. Collected on 2026-10-15.", "what_it_is_for": "The launch post positions V4.5 as the motion model you use when the reference is only a head, not a full-body character sheet. It does not describe a new task. Mix-style motion transfer is the standing product around it. The same account's LinkedIn post repeats the headshot, texture, and stability claims.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "virginia/chrome/attentivechrome": { "id": "virginia/chrome/attentivechrome", "name": "AttentiveChrome", "maker": "virginia", "family": "virginia/chrome", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2017-08-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hierarchical bidirectional LSTMs with two attention stages, from the QData lab. Five histone-mark encoders (bin LSTM size 32, bidirectional, one layer) each attend over bins, then an HM-level bidirectional LSTM (hidden size 16) attends over the five marks. The Kipoi module ends in one linear unit and a sigmoid. The paper calls this recommended variant LSTM-alpha, beta. Kipoi schema input shape is (100, 5); the module default sets n_bins to 200.", "modalities": { "input": [ "tabular" ], "output": [ "classification" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AttentiveChrome is a University of Virginia QData model that predicts whether a human gene is highly or lowly expressed from five histone-modification tracks. Yanjun Qi's lab introduced it on 1 August 2017 and published it at NeurIPS 2017. Trained per-cell checkpoints remain on the Kipoi model zoo. This dossier was collected on 2026-10-15.", "what_it_is_for": "The lab uses it to classify gene expression and to show which histone marks and which bins around the transcription start site the network used. The paper recommends the two-level attention variant, called LSTM-alpha, beta, when interpretation matters. It is a research genomics model, not a chatbot or a clinical device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "visla/avatar/advanced-avatar": { "id": "visla/avatar/advanced-avatar", "name": "Advanced Avatar", "maker": "visla", "family": "visla/avatar", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Advanced Avatar is Visla\u2019s higher-quality presenter model for full-screen scenes. Visla launched it on 30 June 2026 and still calls it the highest-quality avatar model, with more natural lip sync, facial expression, gestures, and body language than Basic Avatar. This dossier was collected on 15 October 2026. Weights are closed. A 25 June 2026 post still described it as upcoming.", "what_it_is_for": "Visla positions Advanced Avatar for scenes where the presenter fills the frame and lip sync, expression, and gesture will be noticed. The default assignment is Advanced on full-screen scenes and Basic on smaller overlays. Either model can be used in either layout, and both can appear in one video. Looks are public or custom avatars. Voices are Visla\u2019s available voices, clones, and supported languages, which are a separate speech layer.\n\nThe product page says a powerful lip sync model drives the result. Visla does not give that component its own name, card, or price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "visla/avatar/basic-avatar": { "id": "visla/avatar/basic-avatar", "name": "Basic Avatar", "maker": "visla", "family": "visla/avatar", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Basic Avatar is Visla\u2019s credit-efficient presenter model. Visla kept it when Advanced Avatar launched on 30 June 2026 and uses it by default for smaller overlays. The presenter product itself launched on 21 July 2025 as Visla AI Avatars. This dossier was collected on 15 October 2026. Weights are closed, and no model card was published.", "what_it_is_for": "Visla uses Basic Avatar when the presenter is a smaller overlay, when b-roll or product footage is the focus, or when the team wants the cheaper render. The 30 June 2026 post says the earlier avatars were kept for those jobs. Users pick a public or custom look, pair it with a Visla voice, and can switch a scene to Advanced Avatar in the Scene-Based Editor. Switching regenerates that scene.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm/bluelm-130b": { "id": "vivo/bluelm/bluelm-130b", "name": "\u84dd\u5fc3\u5927\u6a21\u578b130B", "maker": "vivo", "family": "vivo/bluelm", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-01", "context_length": null, "max_output_tokens": null, "parameters": "130B", "architecture": "Named as a 130 billion parameter model in the 2023 matrix. No architecture was published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u84dd\u5fc3\u5927\u6a21\u578b130B is one of the five sizes vivo named on 2023-11-01. Coverage placed it in the hundred-billion tier, aimed at a larger knowledge store than the 70B cloud model. No weights, card, or later product page were found. Collected 2026-10-15.", "what_it_is_for": "Announced as a high-capacity cloud model. vivo did not publish a distinct job description beyond a richer knowledge store and a more professional experience.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm/bluelm-13b": { "id": "vivo/bluelm/bluelm-13b", "name": "BlueLM-13B", "maker": "vivo", "family": "vivo/bluelm", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Named as a 13B BlueLM. No architecture card was published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-13B is a named vivo language-model size that was not given a public card. In November 2023 vivo said a 13B BlueLM had been run on device but was too large to ship, and the open-source README said a 13B release was coming. The July 2025 BlueLM-2.5-3B paper still lists 13B among developed scales. No public weights were found on 2026-10-15.", "what_it_is_for": "Announced as a future open language model and, earlier, as an on-device run that did not fit 2023 phone memory. No shipping product page was found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm/bluelm-175b": { "id": "vivo/bluelm/bluelm-175b", "name": "\u84dd\u5fc3\u5927\u6a21\u578b175B", "maker": "vivo", "family": "vivo/bluelm", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-01", "context_length": null, "max_output_tokens": null, "parameters": "175B", "architecture": "Named as a 175 billion parameter model. No architecture was published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u84dd\u5fc3\u5927\u6a21\u578b175B is the largest size vivo named in the first \u84dd\u5fc3 matrix on 2023-11-01. It was described only as a hundred-billion-parameter model meant to offer a more professional experience. No public weights or card were found. Collected 2026-10-15.", "what_it_is_for": "Announced as the top knowledge-capacity model in the 2023 matrix. A separate product behavior was not documented.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm/bluelm-1b": { "id": "vivo/bluelm/bluelm-1b", "name": "\u84dd\u5fc3\u5927\u6a21\u578b1B", "maker": "vivo", "family": "vivo/bluelm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-01", "context_length": null, "max_output_tokens": null, "parameters": "1B", "architecture": "Named as a 1 billion parameter on-device text model. Architecture was not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u84dd\u5fc3\u5927\u6a21\u578b1B is the smallest model in the matrix vivo announced on 2023-11-01. vivo described it as an on-device document model for local summary and abstraction, offered on the X100 alongside the 7B. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "Local text summary and abstraction on the phone, which vivo contrasted with the larger cloud models. X100 coverage said buyers could choose a local 1B or 7B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm/bluelm-30b-moe": { "id": "vivo/bluelm/bluelm-30b-moe", "name": "\u84dd\u5fc330B MoE\u7aef\u4fa7\u5927\u6a21\u578b", "maker": "vivo", "family": "vivo/bluelm", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "30B MoE", "architecture": "Described on 2026-09-16 as a 30B mixture-of-experts model under pre-research for on-device use. Active-parameter count was not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u84dd\u5fc330B MoE\u7aef\u4fa7\u5927\u6a21\u578b is a model vivo said it was pre-researching at the 2026 developer conference on 2026-09-16. It was not released. The stated direction is a 30B mixture-of-experts model for on-device long tasks. Collected 2026-10-15.", "what_it_is_for": "Conference coverage says the work explores running larger models and longer task chains on the phone. It was not presented as a shipping model next to RealTime, Nano, Flash, and Pro.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm/bluelm-3b": { "id": "vivo/bluelm/bluelm-3b", "name": "\u84dd\u5fc3\u7aef\u4fa7\u5927\u6a21\u578b3B", "maker": "vivo", "family": "vivo/bluelm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-10", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Marketed as a 3B on-device language model. The November 2024 BlueLM-V-3B paper describes its language backbone as an in-house 2.7B BlueLM. Whether that 2.7B checkpoint is this product model is not explicitly stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u84dd\u5fc3\u7aef\u4fa7\u5927\u6a21\u578b3B is the on-device language model vivo introduced at the 2024 developer conference on 2024-10-10. vivo positioned it as the phone-sized model, claiming stronger task results than its own 7B at much lower memory. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "On-device writing, dialogue, summary, and information extraction. vivo said it led models under 5B and beat typical 7B to 9B models on those business tasks. Shenzhen Ware reported first place for \u84dd\u5fc33B on SuperCLUE and Equal Eval small-model overall tests, plus \u6cf0\u5c14\u5b9e\u9a8c\u5ba4 and AIIA certificates. Numeric scores were not in those reports.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm/bluelm-70b": { "id": "vivo/bluelm/bluelm-70b", "name": "\u84dd\u5fc3\u5927\u6a21\u578b70B", "maker": "vivo", "family": "vivo/bluelm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-11-01", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Named as a 70B cloud language model. Architecture was not published. The 2024 talk calls this generation BlueLM-70B 1.0 relative to BlueLM-70B 2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u84dd\u5fc3\u5927\u6a21\u578b70B is the cloud language model vivo announced on 2023-11-01 as the cost-and-quality flagship of the first \u84dd\u5fc3 matrix. It was not open-sourced. In 2024 vivo referred to it as the 1.0 generation that BlueLM-70B 2.0 improved on. Collected 2026-10-15.", "what_it_is_for": "Cloud role-play, knowledge questions, and natural dialogue for \u84dd\u5fc3\u5c0fV. vivo said that, as of October 2023, its overall ability ranked first in China on SuperCLUE, C-Eval, and CMMLU. No scores were printed in the reports used here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm/bluelm-70b-2": { "id": "vivo/bluelm/bluelm-70b-2", "name": "BlueLM-70B 2.0", "maker": "vivo", "family": "vivo/bluelm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-10", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Cloud language model. vivo said it is the upgraded 70B, not a new parameter count. No layer config was published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-70B 2.0 is the cloud language-model upgrade vivo presented on 2024-10-10. vivo said overall ability rose 30 percent versus BlueLM-70B 1.0 and that the model added tool use and stronger code generation. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "Cloud assistant for \u84dd\u5fc3\u5c0fV: multi-turn instructions, phone and third-party tool calls, math, code, and spreadsheet-style analysis. Training additions cited on stage were 100 million knowledge QA items, 5 million papers, and 120 million code repositories.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm/bluelm-7b-base": { "id": "vivo/bluelm/bluelm-7b-base", "name": "BlueLM-7B-Base", "maker": "vivo", "family": "vivo/bluelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenAtom Model License since 2024-12-25. Before that, the Community License for BlueLM Model. vivo says the weights may be used for academic research and commercial use. The license is not an OSI open-source license (Hugging Face tag: other). Repository code is Apache-2.0.", "release_date": "2023-11-01", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only Transformer, LLaMA-style. config.json: 32 layers, hidden size 4096, intermediate size 11008, 32 attention heads, 32 key-value heads, SiLU, RMSNorm eps 1e-6, RoPE theta 10000, vocab size 100096, untied embeddings, bfloat16, bos id 1, eos id 2, pad id 3. max_position_embeddings in config.json is 2048. This checkpoint is the 2K base model. Pretraining corpus is 2.6 trillion tokens, mostly Chinese and English, with a smaller share of Japanese and Korean.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-7B-Base is the open base checkpoint of vivo AI Lab's BlueLM (\u84dd\u5fc3\u5927\u6a21\u578b), a 7B decoder trained on 2.6 trillion tokens. It was part of the November 2023 public release and was still downloadable on 2026-10-15. Weights are open under the OpenAtom Model License; this catalog does not treat that license as OSI open source.", "what_it_is_for": "vivo publishes it as the pretrained base for the 7B chat models. The card positions the family for Chinese and English generation, with a smaller amount of Japanese and Korean data in the corpus. The base checkpoint is a completion model, not an aligned assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm/bluelm-7b-base-32k": { "id": "vivo/bluelm/bluelm-7b-base-32k", "name": "BlueLM-7B-Base-32K", "maker": "vivo", "family": "vivo/bluelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenAtom Model License since 2024-12-25. Before that, the Community License for BlueLM Model. vivo says the weights may be used for academic research and commercial use. The license is not an OSI open-source license (Hugging Face tag: other). Repository code is Apache-2.0.", "release_date": "2023-11-01", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only Transformer, LLaMA-style. config.json: 32 layers, hidden size 4096, intermediate size 11008, 32 attention heads, 32 key-value heads, SiLU, RMSNorm eps 1e-6, RoPE theta 10000, vocab size 100096, untied embeddings, bfloat16, bos id 1, eos id 2, pad id 3. max_position_embeddings in config.json is 2048. vivo says this checkpoint extends context from 2K to 32K while keeping base ability similar. The published config.json still sets max_position_embeddings to 2048. The README says to install flash_attn==2.3.3 for the 32K checkpoints.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-7B-Base-32K is vivo AI Lab's long-context base checkpoint of the open 7B BlueLM. The model card says context was extended from 2K to 32K. It was still listed on Hugging Face as of 2026-10-15. The published config file does not itself show a 32768 position limit.", "what_it_is_for": "vivo says the 32K base and chat models support longer context while keeping basic ability close to the 2K models. This checkpoint is the base, not the chat alignment.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm/bluelm-7b-chat": { "id": "vivo/bluelm/bluelm-7b-chat", "name": "BlueLM-7B-Chat", "maker": "vivo", "family": "vivo/bluelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenAtom Model License since 2024-12-25. Before that, the Community License for BlueLM Model. vivo says the weights may be used for academic research and commercial use. The license is not an OSI open-source license (Hugging Face tag: other). Repository code is Apache-2.0.", "release_date": "2023-11-01", "context_length": 2048, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only Transformer, LLaMA-style. config.json: 32 layers, hidden size 4096, intermediate size 11008, 32 attention heads, 32 key-value heads, SiLU, RMSNorm eps 1e-6, RoPE theta 10000, vocab size 100096, untied embeddings, bfloat16, bos id 1, eos id 2, pad id 3. max_position_embeddings in config.json is 2048. Chat checkpoint aligned from the 7B base. The demo prompt format is [|Human|]: ... [|AI|]:.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-7B-Chat is vivo AI Lab's aligned 7B assistant checkpoint, trained from BlueLM-7B-Base on a 2.6 trillion token multilingual corpus. It was released with the November 2023 open BlueLM drop and remained on Hugging Face and ModelScope as of 2026-10-15. A 4-bit pack is a quantization of this model, not a separate product.", "what_it_is_for": "vivo describes the 7B as a phone-oriented model for dialogue, knowledge questions, and intent understanding, and the open chat card highlights Chinese benchmarks (C-Eval and CMMLU). The repository demos cover chat, a web UI, and an OpenAI-style server that is primarily documented against the 32K chat update.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm/bluelm-7b-chat-32k": { "id": "vivo/bluelm/bluelm-7b-chat-32k", "name": "BlueLM-7B-Chat-32K", "maker": "vivo", "family": "vivo/bluelm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "OpenAtom Model License since 2024-12-25. Before that, the Community License for BlueLM Model. vivo says the weights may be used for academic research and commercial use. The license is not an OSI open-source license (Hugging Face tag: other). Repository code is Apache-2.0.", "release_date": "2023-11-01", "context_length": 32768, "max_output_tokens": null, "parameters": "7B", "architecture": "Decoder-only Transformer, LLaMA-style. config.json: 32 layers, hidden size 4096, intermediate size 11008, 32 attention heads, 32 key-value heads, SiLU, RMSNorm eps 1e-6, RoPE theta 10000, vocab size 100096, untied embeddings, bfloat16, bos id 1, eos id 2, pad id 3. max_position_embeddings in config.json is 2048. Long-context chat checkpoint. On 2024-03-25 vivo updated the weights for function calling and shipped an OpenAI-style API demo, plus AWQ and GPTQ packs. config.json for the 32K family still records max_position_embeddings 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-7B-Chat-32K is the long-context aligned 7B BlueLM from vivo AI Lab. The card says it keeps the 7B base ability and extends context to 32K. A 2024-03-25 update added function calling and AWQ and GPTQ downloads. Weights remained public as of 2026-10-15.", "what_it_is_for": "vivo positions it for longer-context chat and for tool use. The README reports LongBench and Chinese T-Eval numbers only for this checkpoint, and the API demo speaks an OpenAI-style chat plus function-calling protocol.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm/bluelm-flash": { "id": "vivo/bluelm/bluelm-flash", "name": "BlueLM-Flash", "maker": "vivo", "family": "vivo/bluelm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cloud model in the 2026 agent matrix. vivo said Flash and Pro use a self-developed agentic engine matched to a system-level Harness. Parameter count and whether Flash is a smaller MoE or dense model were not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-Flash is one of two cloud models vivo released on 2026-09-16 as the part of \u84dd\u5fc3 that carries out tasks. It was paired with BlueLM-Pro and a system Harness. Weights and a parameter count were not published. Collected 2026-10-15.", "what_it_is_for": "vivo described Flash and Pro as the phone's task controller: the user states an intent and \u84dd\u5fc3\u5c0fV executes across devices, apps, and scenes, and keeps corrections as later improvements. Flash's distinct role versus Pro was not defined in the reports used here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm/bluelm-pro": { "id": "vivo/bluelm/bluelm-pro", "name": "BlueLM-Pro", "maker": "vivo", "family": "vivo/bluelm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cloud model launched with BlueLM-Flash. No parameter count or architecture diagram was published. Reports do not say which of Flash or Pro is larger.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-Pro is the other cloud model vivo named on 2026-09-16 beside BlueLM-Flash. Both are described as agentic task models for \u84dd\u5fc3\u5c0fV. A separate Pro specification was not published. Collected 2026-10-15.", "what_it_is_for": "Same conference role as Flash: take a spoken or typed intent and carry it out across apps and devices through the Harness. vivo did not say when to choose Pro over Flash.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm-art/bluelm-art": { "id": "vivo/bluelm-art/bluelm-art", "name": "BlueLM-Art", "maker": "vivo", "family": "vivo/bluelm-art", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Chinese-native text-to-image model. vivo described diffusion-model work plus controllable generation and a plugin for artistic Chinese lettering. Parameter count was not published.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-Art is vivo's Chinese text-to-image model, introduced at the 2024 developer conference on 2024-10-10. vivo emphasized Chinese wording, cultural scenes, portrait look, and subject consistency. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "Image creation inside \u84dd\u5fc3\u5c0fV and related apps: generation, object removal, style transfer, and artistic Chinese text. A tablet note app was also said to use it for doodle cleanup. vivo said it had led the Chinese slice of SuperCLUE-Image more than once, without printing scores.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm-gui/bluelm-gui": { "id": "vivo/bluelm-gui/bluelm-gui", "name": "BlueLM-GUI", "maker": "vivo", "family": "vivo/bluelm-gui", "status": "active", "confidence": "high", "weights": "closed", "license": "Weights are in-house and were not released. The GitHub repository that holds the paper, MobileGUI-VBench, and evaluation scripts is Apache-2.0.", "release_date": "2026-09-11", "context_length": null, "max_output_tokens": null, "parameters": "35B-A3B", "architecture": "Mobile GUI agent trained from a general vision-language model in three stages: continual pretraining on screen sequences, supervised fine-tuning on expert trajectories, and reinforcement learning on real-device rollouts. 35B total parameters with 3B active. The base VLM's name was not published.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-GUI is vivo AI Lab's mobile GUI agent, a 35B-A3B model described in a paper first submitted on 2026-09-11. It operates phone interfaces from language instructions. The weights stay in-house. The lab released the technical report and evaluation code. Collected 2026-10-15.", "what_it_is_for": "Complete multi-step tasks on real phones, including cross-app tasks. The paper's headline numbers are 87.4 on the lab's MobileGUI-VBench and 84.9 on AndroidWorld.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm-speech/bluelm-realtime": { "id": "vivo/bluelm-speech/bluelm-realtime", "name": "BlueLM-RealTime", "maker": "vivo", "family": "vivo/bluelm-speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end mixture-of-experts speech-understanding model. Active and total parameter counts were not published. Reports emphasize understanding, not a published synthesis architecture.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-RealTime is the speech model vivo released on 2026-09-16. It uses an end-to-end mixture-of-experts design and is meant to understand difficult speech, including noise, accent, and mixed Chinese and English. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "The listening side of the 2026 matrix: understand the user in real conditions and pass that meaning to the rest of \u84dd\u5fc3. Personal context is part of the pitch. No benchmark number was in the IT\u4e4b\u5bb6 or AIBase reports.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm-speech/bluelm-speech": { "id": "vivo/bluelm-speech/bluelm-speech", "name": "\u84dd\u5fc3\u8bed\u97f3\u5927\u6a21\u578b", "maker": "vivo", "family": "vivo/bluelm-speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "vivo said the speech model is built on the BlueLM text model with discrete speech coding, self-supervised speech codecs, multi-task speech learning, and modality alignment. Parameter count was not published.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u84dd\u5fc3\u8bed\u97f3\u5927\u6a21\u578b is vivo's first named speech model, presented on 2024-10-10. It adds synthesis, cloning, translation, and dialect speech on top of the BlueLM text model. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "Talk with \u84dd\u5fc3\u5c0fV: read text aloud, copy a voice from one sentence, speak in dialect, and turn speech into text in another language. Shenzhen Ware said the dialect feature in vivo\u542c\u8bf4 covered six dialects.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm-speech/bluelm-speech-2": { "id": "vivo/bluelm-speech/bluelm-speech-2", "name": "\u84dd\u5fc3\u8bed\u97f3\u5927\u6a21\u578b2.0", "maker": "vivo", "family": "vivo/bluelm-speech", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "2025 conference coverage describes an end-to-end speech model with full-duplex turn taking. \u8d85\u62df\u4eba\u97f3\u82722.0 is described as autoregressive plus diffusion. Parameter count was not published. Some outlets use \u8d85\u62df\u4eba\u97f3\u82722.0 for the voice stack rather than the name \u84dd\u5fc3\u8bed\u97f3\u5927\u6a21\u578b2.0.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u84dd\u5fc3\u8bed\u97f3\u5927\u6a21\u578b2.0 is the speech upgrade vivo showed at the 2025 developer conference on 2025-10-10. Reports describe full-duplex conversation, a more human voice, speaker memory, and longer-range interpretation. The 2.0 name is best attested in one detailed recap, so confidence is medium. Collected 2026-10-15.", "what_it_is_for": "Hands-free conversation with \u84dd\u5fc3\u5c0fV: barge-in, simultaneous listening and answering, voice cloning, identifying who spoke, and interpreting at a longer microphone distance. ZOL also described emotion and speaker memory.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm-v/bluelm-2-5-3b": { "id": "vivo/bluelm-v/bluelm-2-5-3b", "name": "BlueLM-2.5-3B", "maker": "vivo", "family": "vivo/bluelm-v", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-08", "context_length": 32768, "max_output_tokens": null, "parameters": "2.9B", "architecture": "Dense multimodal model. SigLIP2 so400m-patch14-384, about 400M parameters and 27 layers, input adjusted to 378x378. AnyRes tiles up to 1512x1512. Two-layer MLP with 2x2 spatial downsampling. In-house dense 2.5B Transformer LLM. Thinking and non-thinking modes are switched with a [|BlueThink|] control token. Long context uses YaRN, extended from 4K to 32K in the fast-decay stage.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-2.5-3B is vivo AI Lab's 2.9B dense multimodal model for phones, described in a technical report dated 2025-07-08. It is the first 3B-scale model vivo claims can switch between thinking and non-thinking and can cap the thinking budget. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "On-device text and image understanding, including reasoning and GUI grounding. The report compares thinking-mode text scores with Qwen3-4B and multimodal scores with Kimi-VL-A3B. The October 2025 developer conference presented a 3B on-device agent model in the same size class; see versions for the 128K disagreement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm-v/bluelm-7b-vl": { "id": "vivo/bluelm-v/bluelm-7b-vl", "name": "BlueLM-7B-VL", "maker": "vivo", "family": "vivo/bluelm-v", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Named only as a planned 7B multimodal language model. No architecture was published.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-7B-VL is the multimodal 7B model vivo AI Lab said it would open-source, in the same README sentence as the unreleased 13B. No weights, paper, or card under that name were found as of 2026-10-15.", "what_it_is_for": "Announced as a future open multimodal model. No task list was published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm-v/bluelm-nano": { "id": "vivo/bluelm-v/bluelm-nano", "name": "BlueLM-Nano", "maker": "vivo", "family": "vivo/bluelm-v", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-16", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "On-device multimodal model. vivo said it has 3 billion parameters and reads screen, image, video, text, and files for personal memory. MoE versus dense was not stated.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-Nano is the 3B on-device model vivo released on 2026-09-16. Its stated job is to perceive the screen and media in real time and keep a personal memory on the phone. Weights were not released. Collected 2026-10-15.", "what_it_is_for": "Remember habits, preferences, device state, and scene, and understand the current screen, images, video, text, and files. vivo said perception and memory results led the industry on several unnamed lists. A secondary recap printed four scores; those are stored with that caveat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vivo/bluelm-v/bluelm-v-3b": { "id": "vivo/bluelm-v/bluelm-v-3b", "name": "BlueLM-V-3B", "maker": "vivo", "family": "vivo/bluelm-v", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-16", "context_length": null, "max_output_tokens": null, "parameters": "2.7B language + 400M vision encoder", "architecture": "LLaVA-style. SigLIP ViT at 384x384 with 400M parameters, a 2-layer MLP projector, and an in-house 2.7B BlueLM. Dynamic resolution with relaxed aspect-ratio matching, 2x2 token downsampling, mixed precision on device (INT8 ViT and projector, INT4 LLM, INT16 LLM activations, FP16 vision activations, INT8 KV cache).", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BlueLM-V-3B is vivo AI Lab's on-device multimodal model, published as a paper on 2024-11-16. It pairs a 400M SigLIP encoder with a 2.7B BlueLM language model and is co-designed for phone NPUs. Weights were not released. The paper reports a 66.1 OpenCompass average. Collected 2026-10-15.", "what_it_is_for": "On-phone image and text understanding, including OCR and higher-resolution screen images, with the language model running locally. vivo's 2024 conference also described a multimodal model that could read text and pictures on the phone; the paper is the detailed account.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/rerank/rerank-1": { "id": "voyage/rerank/rerank-1", "name": "rerank-1", "maker": "voyage", "family": "voyage/rerank", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-29", "context_length": 8000, "max_output_tokens": null, "parameters": "", "architecture": "First-generation general reranker with multilingual support, per the docs. 8K combined context and 2K query cap. Total tokens per request capped at 300K. Parameter count is not published. Llama 2 tokenizer is documented for rerank-lite-1, not explicitly for this id.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `rerank-1` as a rerank model. Catalog status is deprecated as of 2026-10-15. MongoDB calls this model Older; still on the Voyage API; not on the Atlas lifecycle table.", "what_it_is_for": "Quality-oriented first-generation reranker. Still listed as an older API model. Not on the Atlas lifecycle table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/rerank/rerank-2": { "id": "voyage/rerank/rerank-2", "name": "rerank-2", "maker": "voyage", "family": "voyage/rerank", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-30", "context_length": 16000, "max_output_tokens": null, "parameters": "", "architecture": "Second-generation multilingual cross-encoder. 16K combined context and 4K query cap. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `rerank-2` as a rerank model. Catalog status is deprecated as of 2026-10-15. MongoDB's lifecycle page calls this model Legacy. This catalog uses status deprecated because the schema has no legacy value. MongoDB says no Voyage model is Deprecated or Retired, and no retirement date is published.", "what_it_is_for": "Quality-oriented multilingual reranking. Legacy since 11 August 2025, replaced for new work by rerank-2.5 and later by rerank-3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/rerank/rerank-2-5": { "id": "voyage/rerank/rerank-2-5", "name": "rerank-2.5", "maker": "voyage", "family": "voyage/rerank", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-11", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-following multilingual cross-encoder. 32K combined context and 8K query cap. Instructions may be appended or prepended to the query. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `rerank-2.5` as a rerank model. Catalog status is deprecated as of 2026-10-15. MongoDB's lifecycle page calls this model Legacy. This catalog uses status deprecated because the schema has no legacy value. MongoDB says no Voyage model is Deprecated or Retired, and no retirement date is published.", "what_it_is_for": "General reranking with instruction following. Legacy since rerank-3 became Active. Still served.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/rerank/rerank-2-5-lite": { "id": "voyage/rerank/rerank-2-5-lite", "name": "rerank-2.5-lite", "maker": "voyage", "family": "voyage/rerank", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-11", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Latency-oriented instruction-following reranker. Same 32K pair context and 8K query cap as rerank-2.5. Parameter count is not published. The FAQ suggests staying at or under 200K total tokens per request when latency matters.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": 0.02, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `rerank-2.5-lite` as a rerank model. Catalog status is deprecated as of 2026-10-15. MongoDB's lifecycle page calls this model Legacy. This catalog uses status deprecated because the schema has no legacy value. MongoDB says no Voyage model is Deprecated or Retired, and no retirement date is published.", "what_it_is_for": "Faster instruction-following reranker. Replacement is rerank-3-lite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/rerank/rerank-2-lite": { "id": "voyage/rerank/rerank-2-lite", "name": "rerank-2-lite", "maker": "voyage", "family": "voyage/rerank", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-30", "context_length": 8000, "max_output_tokens": null, "parameters": "", "architecture": "Latency-oriented second-generation multilingual reranker. 8K combined context and 2K query cap. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": 0.02, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `rerank-2-lite` as a rerank model. Catalog status is deprecated as of 2026-10-15. MongoDB's lifecycle page calls this model Legacy. This catalog uses status deprecated because the schema has no legacy value. MongoDB says no Voyage model is Deprecated or Retired, and no retirement date is published.", "what_it_is_for": "Faster multilingual reranking. The launch post said quality was comparable to rerank-1 at lower cost and latency. Replacement is rerank-2.5-lite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/rerank/rerank-3": { "id": "voyage/rerank/rerank-3", "name": "rerank-3", "maker": "voyage", "family": "voyage/rerank", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-30", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Cross-encoder reranker. 32K combined context for a query-document pair and up to 8K tokens for the query. The launch post says it keeps instruction following from the 2.5 series. Parameter count is not published. Preview began 1 September 2026 and Active began 29 September 2026.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": 0.05, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) offers `rerank-3` as a rerank model. Catalog status is active as of 2026-10-15. MongoDB calls this model Active.", "what_it_is_for": "Highest-accuracy general reranker. Voyage recommends it for most applications. Upgrade path from rerank-2.5 at the same price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/rerank/rerank-3-lite": { "id": "voyage/rerank/rerank-3-lite", "name": "rerank-3-lite", "maker": "voyage", "family": "voyage/rerank", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-30", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Faster cross-encoder in the rerank-3 series. Same 32K pair context and 8K query cap as rerank-3. Instruction following is retained from rerank-2.5, per the launch post. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": 0.02, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) offers `rerank-3-lite` as a rerank model. Catalog status is active as of 2026-10-15. MongoDB calls this model Active.", "what_it_is_for": "Latency-sensitive reranking. Recommended lite model. Upgrade path from rerank-2.5-lite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/rerank/rerank-lite-1": { "id": "voyage/rerank/rerank-lite-1", "name": "rerank-lite-1", "maker": "voyage", "family": "voyage/rerank", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03-15", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "First-generation latency-oriented reranker. 4K combined context and 1K query cap. Total request cap 300K tokens. Uses the Llama 2 tokenizer. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": 0.02, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `rerank-lite-1` as a rerank model. Catalog status is deprecated as of 2026-10-15. MongoDB calls this model Older; still on the Voyage API; not on the Atlas lifecycle table.", "what_it_is_for": "Early low-latency reranker. Docs recommend newer models. rerank-2-lite was the stated upgrade.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-01": { "id": "voyage/voyage/voyage-01", "name": "voyage-01", "maker": "voyage", "family": "voyage/voyage", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "Early Voyage text embedding. 4K context and 1024 dimensions. Parameter count is not published. The tokenization guide lists the Llama 2 tokenizer for some of these ids and not for voyage-02.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-01` as a voyage model. Catalog status is deprecated as of 2026-10-15. MongoDB calls this model Deprecated on the Voyage embeddings page; still described as an older API id; not on the Atlas lifecycle table.", "what_it_is_for": "Deprecated v1 embedding. Docs say to move to voyage-3. The old get_embedding helper defaulted to this id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-02": { "id": "voyage/voyage/voyage-02", "name": "voyage-02", "maker": "voyage", "family": "voyage/voyage", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "Early Voyage text embedding. 4K context and 1024 dimensions. Parameter count is not published. The tokenization guide lists the Llama 2 tokenizer for some of these ids and not for voyage-02.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-02` as a voyage model. Catalog status is deprecated as of 2026-10-15. MongoDB calls this model Deprecated on the Voyage embeddings page; still described as an older API id; not on the Atlas lifecycle table.", "what_it_is_for": "Deprecated pilot v2 embedding. Docs say to move to voyage-3. An early Python client defaulted to this id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-2": { "id": "voyage/voyage/voyage-2", "name": "voyage-2", "maker": "voyage", "family": "voyage/voyage", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-01-23", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "General-purpose embedding aimed at a cost, latency, and quality balance. 4K context and 1024 dimensions. Llama 2 tokenizer. Per-request token cap on the embeddings page is 320K. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-2` as a voyage model. Catalog status is deprecated as of 2026-10-15. MongoDB calls this model Older; docs say transition to voyage-3-lite; not on the Atlas lifecycle table.", "what_it_is_for": "Earlier general-purpose embedding, announced with voyage-code-2. Docs say to transition to voyage-3-lite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-3": { "id": "voyage/voyage/voyage-3", "name": "voyage-3", "maker": "voyage", "family": "voyage/voyage", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-18", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "General-purpose embedding generation before voyage-3.5. Fixed 1024 dimensions. The embeddings page does not list Matryoshka output dimensions for this id. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.06, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-3` as a voyage model. Catalog status is deprecated as of 2026-10-15. MongoDB calls this model Older; still on the Voyage API; not on the Atlas lifecycle table.", "what_it_is_for": "General-purpose and multilingual retrieval. Docs still serve it and tell users to prefer newer models. The stated transition target from several older models is voyage-3, which itself now sits under newer 3.5 and 4 models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-3-5": { "id": "voyage/voyage/voyage-3-5", "name": "voyage-3.5", "maker": "voyage", "family": "voyage/voyage", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-20", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Same size class as voyage-3, with quality improved by data mix, distillation from voyage-3-large, and Voyage rerankers, per the May 2025 post. Matryoshka and quantization. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.06, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-3.5` as a voyage model. Catalog status is deprecated as of 2026-10-15. MongoDB's lifecycle page calls this model Legacy. This catalog uses status deprecated because the schema has no legacy value. MongoDB says no Voyage model is Deprecated or Retired, and no retirement date is published.", "what_it_is_for": "Previous general-purpose and multilingual retrieval model. MongoDB replacement for new projects is voyage-4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-3-5-lite": { "id": "voyage/voyage/voyage-3-5-lite", "name": "voyage-3.5-lite", "maker": "voyage", "family": "voyage/voyage", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-05-20", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Same size class as voyage-3-lite, with a stated quality gain. Matryoshka dimensions include 2048, unlike voyage-3-lite's fixed 512. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.02, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-3.5-lite` as a voyage model. Catalog status is deprecated as of 2026-10-15. MongoDB's lifecycle page calls this model Legacy. This catalog uses status deprecated because the schema has no legacy value. MongoDB says no Voyage model is Deprecated or Retired, and no retirement date is published.", "what_it_is_for": "Previous latency- and cost-optimized general-purpose embedding. MongoDB replacement is voyage-4-lite.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-3-large": { "id": "voyage/voyage/voyage-3-large", "name": "voyage-3-large", "maker": "voyage", "family": "voyage/voyage", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-01-07", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Previous-generation general-purpose text embedding model. Matryoshka dimensions and int8/binary quantization. Parameter count is not published. Superseded by voyage-4-large. Not in the Voyage 4 shared space.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.18, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-3-large` as a voyage model. Catalog status is deprecated as of 2026-10-15. MongoDB's lifecycle page calls this model Legacy. This catalog uses status deprecated because the schema has no legacy value. MongoDB says no Voyage model is Deprecated or Retired, and no retirement date is published.", "what_it_is_for": "General-purpose and multilingual retrieval. Still callable. MongoDB recommends voyage-4-large for new work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-3-lite": { "id": "voyage/voyage/voyage-3-lite", "name": "voyage-3-lite", "maker": "voyage", "family": "voyage/voyage", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-18", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Latency- and cost-oriented companion to voyage-3. Fixed 512-dimension output. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.02, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-3-lite` as a voyage model. Catalog status is deprecated as of 2026-10-15. MongoDB calls this model Older; still on the Voyage API; not on the Atlas lifecycle table.", "what_it_is_for": "Low-cost general-purpose retrieval in the voyage-3 generation. Docs say to move to newer models. voyage-2 users were told to transition here; voyage-3.5-lite is the later lite model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-4": { "id": "voyage/voyage/voyage-4", "name": "voyage-4", "maker": "voyage", "family": "voyage/voyage", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-15", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Mid-size general-purpose text embedding model in the Voyage 4 shared embedding space. Matryoshka dimensions and quantization. Parameter count is not published. Voyage says it approaches voyage-3-large quality at mid-size cost.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.06, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) offers `voyage-4` as a voyage model. Catalog status is active as of 2026-10-15. MongoDB calls this model Active.", "what_it_is_for": "General-purpose and multilingual retrieval when the user wants a balance of quality and cost. Compatible with other Voyage 4 embeddings, so it can query a corpus embedded by voyage-4-large.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-4-large": { "id": "voyage/voyage/voyage-4-large", "name": "voyage-4-large", "maker": "voyage", "family": "voyage/voyage", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-15", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Mixture-of-experts text embedding model. Voyage calls it the first production-grade embedding model to use MoE, with serving cost described as 40% lower than comparable dense models. Matryoshka dimensions and quantization-aware training. Parameter count is not published. Shares an embedding space with voyage-4, voyage-4-lite, and voyage-4-nano.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.12, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) offers `voyage-4-large` as a voyage model. Catalog status is active as of 2026-10-15. MongoDB calls this model Active.", "what_it_is_for": "Flagship general-purpose and multilingual retrieval embedding. Voyage recommends it when quality matters more than cost, including as the document-side model in asymmetric retrieval.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-4-lite": { "id": "voyage/voyage/voyage-4-lite", "name": "voyage-4-lite", "maker": "voyage", "family": "voyage/voyage", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-15", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Smaller Voyage 4 text embedding model. Voyage says it approaches voyage-3.5 retrieval accuracy with significantly fewer parameters. The parameter count itself is not published. Shared embedding space with the rest of Voyage 4. Matryoshka and quantization.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.02, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) offers `voyage-4-lite` as a voyage model. Catalog status is active as of 2026-10-15. MongoDB calls this model Active.", "what_it_is_for": "Latency- and cost-optimized general-purpose and multilingual retrieval, including as the query-side model against a voyage-4-large document index.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-4-nano": { "id": "voyage/voyage/voyage-4-nano", "name": "voyage-4-nano", "maker": "voyage", "family": "voyage/voyage", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-01-15", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Open-weight Voyage 4 embedding model. Hugging Face card: 180M non-embedding parameters plus 160M embedding parameters, and a model-size label of 0.3B. A summed total is not stated. Card says it builds on Qwen; the vLLM example names VoyageQwen3BidirectionalEmbedModel. Mean pooling. Matryoshka and quantization-aware training. Shares the Voyage 4 embedding space. License Apache 2.0.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) offers `voyage-4-nano` as a voyage model. Catalog status is active as of 2026-10-15. MongoDB calls this model Open weights; not on the Atlas lifecycle table.", "what_it_is_for": "Local development and prototyping with embeddings that stay compatible with hosted Voyage 4 models. Not described as a hosted Voyage API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-large-2": { "id": "voyage/voyage/voyage-large-2", "name": "voyage-large-2", "maker": "voyage", "family": "voyage/voyage", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 16000, "max_output_tokens": null, "parameters": "", "architecture": "General-purpose retrieval embedding with a fixed 1536-dimension output and 16K context. Llama 2 tokenizer. Parameter count and release date were not found on the pages used here.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.12, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-large-2` as a voyage model. Catalog status is deprecated as of 2026-10-15. MongoDB calls this model Older; docs say transition to voyage-3; not on the Atlas lifecycle table.", "what_it_is_for": "Retrieval-oriented general embedding that the docs say beat OpenAI V3 Large at the time. Docs now say to transition to voyage-3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-large-2-instruct": { "id": "voyage/voyage/voyage-large-2-instruct", "name": "voyage-large-2-instruct", "maker": "voyage", "family": "voyage/voyage", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-05", "context_length": 16000, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-tuned general-purpose embedding model. Fixed 1024 dimensions and 16K context. Uses the Llama 2 tokenizer, per the tokenization guide. Parameter count is not published. Retrieval should set input_type; classification and clustering use a separate instruction set the docs point at on GitHub.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.12, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-large-2-instruct` as a voyage model. Catalog status is deprecated as of 2026-10-15. MongoDB calls this model Older; docs say transition to voyage-3; not on the Atlas lifecycle table.", "what_it_is_for": "Clustering, classification, and retrieval. Docs now say existing users should move to voyage-3. At launch Voyage said it was rank 1 on the MTEB leaderboard.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-lite-01": { "id": "voyage/voyage/voyage-lite-01", "name": "voyage-lite-01", "maker": "voyage", "family": "voyage/voyage", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "Early Voyage text embedding. 4K context and 1024 dimensions. Parameter count is not published. The tokenization guide lists the Llama 2 tokenizer for some of these ids and not for voyage-02.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-lite-01` as a voyage model. Catalog status is deprecated as of 2026-10-15. MongoDB calls this model Deprecated on the Voyage embeddings page; still described as an older API id; not on the Atlas lifecycle table.", "what_it_is_for": "Deprecated v1 lite embedding. Docs say to move to voyage-3. The old helper also accepted this id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-lite-01-instruct": { "id": "voyage/voyage/voyage-lite-01-instruct", "name": "voyage-lite-01-instruct", "maker": "voyage", "family": "voyage/voyage", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "Early Voyage text embedding. 4K context and 1024 dimensions. Parameter count is not published. The tokenization guide lists the Llama 2 tokenizer for some of these ids and not for voyage-02.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-lite-01-instruct` as a voyage model. Catalog status is deprecated as of 2026-10-15. MongoDB calls this model Deprecated on the Voyage embeddings page; still described as an older API id; not on the Atlas lifecycle table.", "what_it_is_for": "Deprecated instruct variant tweaked on voyage-lite-01 for classification and clustering. Docs say to move to voyage-3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage/voyage-lite-02-instruct": { "id": "voyage/voyage/voyage-lite-02-instruct", "name": "voyage-lite-02-instruct", "maker": "voyage", "family": "voyage/voyage", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "Early Voyage text embedding. 4K context and 1024 dimensions. Parameter count is not published. The tokenization guide lists the Llama 2 tokenizer for some of these ids and not for voyage-02.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-lite-02-instruct` as a voyage model. Catalog status is deprecated as of 2026-10-15. MongoDB calls this model Deprecated on the Voyage embeddings page; still described as an older API id; not on the Atlas lifecycle table.", "what_it_is_for": "Deprecated instruction-tuned model. Docs limit the recommended use to classification, clustering, and sentence textual similarity, and say to move to voyage-3.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage-code/voyage-code-2": { "id": "voyage/voyage-code/voyage-code-2", "name": "voyage-code-2", "maker": "voyage", "family": "voyage/voyage-code", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-01-23", "context_length": 16000, "max_output_tokens": null, "parameters": "", "architecture": "Previous code embedding model. 16K context and a fixed 1536-dimension output. Llama 2 tokenizer. The August 2026 Voyage embeddings page no longer lists this id; MongoDB text-embeddings docs still do. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.12, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-code-2` as a voyage-code model. Catalog status is deprecated as of 2026-10-15. MongoDB's lifecycle page calls this model Legacy. This catalog uses status deprecated because the schema has no legacy value. MongoDB says no Voyage model is Deprecated or Retired, and no retirement date is published.", "what_it_is_for": "Code retrieval from natural-language and code queries. MongoDB replacement is voyage-code-4. Docs have described it as 17% better than alternatives; the AWS listing says 14.52% on 11 code tasks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage-code/voyage-code-3": { "id": "voyage/voyage-code/voyage-code-3", "name": "voyage-code-3", "maker": "voyage", "family": "voyage/voyage-code", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-04", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Code embedding model with Matryoshka dimensions and quantization. 32K context. Parameter count is not published. Per-request cap 120K tokens.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.18, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-code-3` as a voyage-code model. Catalog status is deprecated as of 2026-10-15. MongoDB's lifecycle page calls this model Legacy. This catalog uses status deprecated because the schema has no legacy value. MongoDB says no Voyage model is Deprecated or Retired, and no retirement date is published.", "what_it_is_for": "Code retrieval. Still served. MongoDB replacement is voyage-code-4 as of 13 August 2026.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage-code/voyage-code-4": { "id": "voyage/voyage-code/voyage-code-4", "name": "voyage-code-4", "maker": "voyage", "family": "voyage/voyage-code", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-13", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Code embedding model trained for coding agents. The blog says the corpus is mined from issue-fixing pull requests across tens of thousands of repositories and hundreds of programming languages. Matryoshka dimensions and float, int8, and binary quantization. Parameter count is not published. MongoDB lists a 320K per-request token cap.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.12, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) offers `voyage-code-4` as a voyage-code model. Catalog status is active as of 2026-10-15. MongoDB calls this model Active.", "what_it_is_for": "Code retrieval and agentic-coding search. Recommended for new code-retrieval projects. It does not share the Voyage 4 text embedding space.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage-context/voyage-context-3": { "id": "voyage/voyage-context/voyage-context-3", "name": "voyage-context-3", "maker": "voyage", "family": "voyage/voyage-context", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-23", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "First Voyage contextualized chunk embedding model. Per-chunk window 32K and a 120K request token cap. Matryoshka dimensions and quantization. Auto-chunking is documented as new on voyage-context-4, not here. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.18, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-context-3` as a voyage-context model. Catalog status is deprecated as of 2026-10-15. MongoDB's lifecycle page calls this model Legacy. This catalog uses status deprecated because the schema has no legacy value. MongoDB says no Voyage model is Deprecated or Retired, and no retirement date is published.", "what_it_is_for": "Chunk embeddings that include the rest of the document. Still served. Replacement is voyage-context-4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage-context/voyage-context-4": { "id": "voyage/voyage-context/voyage-context-4", "name": "voyage-context-4", "maker": "voyage", "family": "voyage/voyage-context", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-29", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Contextualized chunk embedding model with a mixture-of-experts backbone. Each chunk vector is meant to carry full-document context. Per-chunk window 32K tokens. Request budget 120K tokens when auto-chunking is on, otherwise 32K. Auto-chunking, overlap, and splitting past 32K are new versus voyage-context-3. Default server chunk size 512 tokens. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.12, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) offers `voyage-context-4` as a voyage-context model. Catalog status is active as of 2026-10-15. MongoDB calls this model Active.", "what_it_is_for": "Chunk-level retrieval that keeps document context without a separate context-augmentation step. Drop-in replacement for voyage-context-3. Endpoint is contextualized embeddings, not the plain embeddings endpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage-finance/voyage-finance-2": { "id": "voyage/voyage-finance/voyage-finance-2", "name": "voyage-finance-2", "maker": "voyage", "family": "voyage/voyage-finance", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-03", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Finance-domain text embedding. 32K context and fixed 1024 dimensions. Per-request cap 120K tokens. Parameter count is not published. The embeddings page does not list Matryoshka dimensions for this id.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.12, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-finance-2` as a voyage-finance model. Catalog status is deprecated as of 2026-10-15. MongoDB's lifecycle page calls this model Legacy. This catalog uses status deprecated because the schema has no legacy value. MongoDB says no Voyage model is Deprecated or Retired, and no retirement date is published.", "what_it_is_for": "Finance retrieval and RAG. MongoDB's current lifecycle table says the replacement for new projects is voyage-4. The FAQ still names voyage-finance-2 for finance tasks and also says the voyage-4 series outperforms the domain-specific models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage-healthcare/voyage-healthcare-2": { "id": "voyage/voyage-healthcare/voyage-healthcare-2", "name": "voyage-healthcare-2", "maker": "voyage", "family": "voyage/voyage-healthcare", "status": "announced", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "No architecture has been published under this name.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Voyage AI named `voyage-healthcare-2` as a forthcoming model. It was not found in the 2026 docs, pricing, or lifecycle pages. Catalog status is announced and confidence is low. Collected 2026-10-15.", "what_it_is_for": "On 23 January 2024, Voyage founder Tengyu Ma listed voyage-healthcare-2 among custom models that would come soon, next to voyage-finance-2, voyage-law-2, and voyage-multilingual-2. Finance, law, and multilingual later shipped. No Voyage docs, pricing, or lifecycle row for a healthcare model was found on 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage-law/voyage-law-2": { "id": "voyage/voyage-law/voyage-law-2", "name": "voyage-law-2", "maker": "voyage", "family": "voyage/voyage-law", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-04-15", "context_length": 16000, "max_output_tokens": null, "parameters": "", "architecture": "Legal-domain text embedding. 16K context and fixed 1024 dimensions. Llama 2 tokenizer. Per-request cap 120K tokens. Parameter count is not published. Docs also say it improved performance across domains, not only law.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.12, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-law-2` as a voyage-law model. Catalog status is deprecated as of 2026-10-15. MongoDB's lifecycle page calls this model Legacy. This catalog uses status deprecated because the schema has no legacy value. MongoDB says no Voyage model is Deprecated or Retired, and no retirement date is published.", "what_it_is_for": "Legal retrieval and RAG. The lifecycle table's recommended replacement is voyage-4. The 1 September 2026 changelog instead says voyage-4-lite. Both are stored; successor is the table value.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage-multilingual/voyage-multilingual-2": { "id": "voyage/voyage-multilingual/voyage-multilingual-2", "name": "voyage-multilingual-2", "maker": "voyage", "family": "voyage/voyage-multilingual", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-10", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Multilingual retrieval embedding. 32K context and fixed 1024 dimensions. Per-request cap 120K tokens. Parameter count is not published.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.12, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-multilingual-2` as a voyage-multilingual model. Catalog status is deprecated as of 2026-10-15. MongoDB calls this model Older; still on the Voyage API; not on the Atlas lifecycle table.", "what_it_is_for": "Multilingual retrieval and RAG. Later general models (voyage-3 and the Voyage 4 series) are also described as multilingual. A single named successor was not stated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage-multimodal/voyage-multimodal-3": { "id": "voyage/voyage-multimodal/voyage-multimodal-3", "name": "voyage-multimodal-3", "maker": "voyage", "family": "voyage/voyage-multimodal", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-11-12", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Previous multimodal embedder for interleaved text and images. Fixed 1024 dimensions. Video inputs are documented only for voyage-multimodal-3.5. Same pixel token rule and image size limits appear on the shared multimodal page. Parameter count is not published.", "modalities": { "input": [ "text", "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.12, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) lists `voyage-multimodal-3` as a voyage-multimodal model. Catalog status is deprecated as of 2026-10-15. MongoDB's lifecycle page calls this model Legacy. This catalog uses status deprecated because the schema has no legacy value. MongoDB says no Voyage model is Deprecated or Retired, and no retirement date is published.", "what_it_is_for": "Text-and-image retrieval, including PDF screenshots, slides, tables, and figures. Replacement is voyage-multimodal-3.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "voyage/voyage-multimodal/voyage-multimodal-3-5": { "id": "voyage/voyage-multimodal/voyage-multimodal-3-5", "name": "voyage-multimodal-3.5", "maker": "voyage", "family": "voyage/voyage-multimodal", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-15", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "Single-transformer multimodal embedder for interleaved text, images, and video frames. 32K context. Matryoshka dimensions are in the model table and the launch post. The multimodal Python signature retrieved from docs does not list output_dimension or output_dtype, so quantization support is not recorded. Tokenization: every 560 image pixels count as one token, and every 1120 video pixels count as one token. Parameter count is not published.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": 0.12, "completion_per_m_usd": null }, "summary_intro": "Voyage AI (now part of MongoDB) offers `voyage-multimodal-3.5` as a voyage-multimodal model. Catalog status is active as of 2026-10-15. MongoDB calls this model Active.", "what_it_is_for": "Retrieval over text, screenshots, figures, slides, tables, and video, including mixed inputs in one vector. Not a CLIP-style dual encoder, per the docs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vt/haven/haven": { "id": "vt/haven/haven", "name": "HAVEN", "maker": "vt", "family": "vt/haven", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY 4.0 (weights and datasets); GPL-3.0 (code)", "release_date": "2025-05-28", "context_length": null, "max_output_tokens": null, "parameters": "18M", "architecture": "Viral protein language model. BERT-style transformer encoder with sequence segmentation and a second hierarchical self-attention over segment embeddings. The published masked-language-modeling config uses 6 layers, 8 heads, input embedding 512, hidden size 1024, and overlapping segments of length 256. Pretrained with masked language modeling on 1.2 million UniRef90 viral protein sequences. Protein lengths in that set run from 11 to 13,556 residues.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HAVEN (Hierarchical Attention for Viral protEin-based host iNference) is a viral protein language model from Virginia Tech computer science, with one University of Michigan coauthor, released through the NSF COMPASS Center. Weights and datasets were posted on Zenodo on 28 May 2025 under CC BY 4.0. The code is GPL-3.0. This dossier was collected on 2026-10-15. The model is a research checkpoint, not a university API product.", "what_it_is_for": "Given a viral protein sequence, the pretrained model embeds it. The authors fine-tune it to predict the host the virus was sampled from, including rare hosts, unseen hosts, and hosts of unseen viruses. A few-shot path uses prototypical networks. The preprint says it is the first of their approaches to cover multiple hosts and to generalize to unseen hosts and viruses.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vt/lakefm/lakefm-5m": { "id": "vt/lakefm/lakefm-5m", "name": "LakeFM 5M", "maker": "vt", "family": "vt/lakefm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (code repository). The Drive checkpoint has no separate license text.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "5M", "architecture": "Second pretrained LakeFM checkpoint, loaded by eval_lakefm5m.sh as lakefm5m.pth. That script sets 12 layers, d_model 128, feed-forward 2048, variable embedding 64, depth embedding 32, input embedding 64, time embedding 16, static dim 32, temporal dim 96, contrastive projection 32, dropout 0, attention dropout 0, head dropout 0.02, RevIN off, and a variate-wise distribution head. It does not set head count, RoPE, or the output distribution. Those fields are published for the 7M model only.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LakeFM 5M is the smaller pretrained checkpoint shipped beside LakeFM 7M by the Knowledge-guided Machine Learning lab at Virginia Tech. The lab README names it as its own model and points `eval_lakefm5m.sh` at `lakefm5m.pth`. The KDD 2026 paper's compute table counts 7 million parameters and does not describe this config. This dossier was collected on 2026-10-15.", "what_it_is_for": "Same job as the 7M checkpoint: local forecasting and masking experiments on lake time series. The repository tells users to call the 5M script instead of the default driver when they want this file. It does not say how the 5M run was trained or which lakes it saw.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vt/lakefm/lakefm-7m": { "id": "vt/lakefm/lakefm-7m", "name": "LakeFM 7M", "maker": "vt", "family": "vt/lakefm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT (code repository). Paper is CC BY 4.0. The Drive checkpoint has no separate license text.", "release_date": "2026-06-09", "context_length": null, "max_output_tokens": null, "parameters": "7M", "architecture": "Encoder-decoder transformer for irregular multivariate, multi-depth lake time series. The KDD paper and the repo eval script for lakefm7m.pth agree on 12 layers and d_model 128. Eval script also sets variable embedding 48, depth embedding 32, value embedding 96, time embedding 16, static dim 64, temporal dim 128, contrastive projection 128, feed-forward 512, dropout 0.1, attention dropout 0.05, head dropout 0.05, Student-t output, variate-wise distribution head, and a variable attention bias. The paper adds 4 attention heads, RoPE, pre-norm blocks, and SwiGLU. Pretrained on more than 1.5 million samples from LakeBeD-US (21 lakes) and more than 1,000 process-based lake simulations, over 530 million observations.", "modalities": { "input": [ "time-series" ], "output": [ "time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LakeFM 7M is the 7-million-parameter lake foundation model from the Knowledge-guided Machine Learning lab at Virginia Tech. The lab released it with the KDD 2026 paper as `lakefm7m.pth`. An ICML 2025 workshop abstract had already used the name LakeFM, without a checkpoint link. This dossier was collected on 2026-10-15. It is a local research checkpoint, not a priced API.", "what_it_is_for": "The paper says LakeFM forecasts water-quality and ecosystem variables across lakes that are not sampled on a regular time or depth grid. It is pretrained on simulated lakes and on the 21 observed lakes in LakeBeD-US, then compared with general time-series foundation models and with irregular-time-series models. The authors also use it for variable masking and depth masking.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vt/phylo-diffusion/phylo-diffusion": { "id": "vt/phylo-diffusion/phylo-diffusion", "name": "Phylo-Diffusion", "maker": "vt", "family": "vt/phylo-diffusion", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-07-31", "context_length": null, "max_output_tokens": null, "parameters": "469M", "architecture": "Latent diffusion model conditioned by cross-attention on a HIER-Embed, four vectors for four phylogenetic levels. Trained from scratch on a CompVis latent-diffusion codebase. VQGAN autoencoder with downsampling factor 4. Paper hyperparameter table for Phylo-Diffusion: latent 64x64x3, 1000 diffusion steps, linear noise schedule, model size 469M, 224 channels, depth 2, channel multipliers 1, 2, 3, 4, attention at 32, 16, and 8, 32 heads, batch 8, 400k iterations, learning rate 4e-5, embedding 1x512. Inference in the paper uses DDIM with 200 steps. The public fish checkpoint is epoch=000119.ckpt plus a separate fish VQGAN checkpoint.", "modalities": { "input": [ "embeddings" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Phylo-Diffusion is a latent diffusion model that generates species images from a phylogenetic embedding. The first authors and the corresponding author, Anuj Karpatne, are at Virginia Tech. It was posted as arXiv:2408.00160 on 31 July 2024 and presented at ECCV 2024. A fish checkpoint and its VQGAN are public in a Hugging Face Space run by a Virginia Tech author. The code repository is under the Imageomics Institute. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper uses the model to visualize evolutionary traits. Each species is a four-vector embedding, one vector per phylogenetic level, and that embedding conditions the diffusion model. Trait masking replaces a level with noise. Trait swapping exchanges levels between species. The public demo generates fish images and can mask or swap traits. It is not a general text-to-image product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "vu-amsterdam/medroberta/medroberta-nl": { "id": "vu-amsterdam/medroberta/medroberta-nl", "name": "MedRoBERTa.nl", "maker": "vu-amsterdam", "family": "vu-amsterdam/medroberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2021-12-31", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa masked language model trained from scratch by CLTL at Vrije Universiteit Amsterdam on anonymized Dutch hospital notes from Amsterdam UMC. Not fine-tuned. config.json: RobertaForMaskedLM, 12 layers, hidden 768, 12 heads, intermediate 3072, GELU, vocab_size 52000, max_position_embeddings 514, type_vocab_size 1, absolute positions. The CLIN 2021 paper says training used a little over 2 epochs, 92,494 iterations (41,247 per epoch), and a gradient-accumulation batch size of 400, on 13GB of notes. The Hub card says nearly 10 million notes. Those two size statements are both published and are not the same unit. config _name_or_path is ../models/tests/test6/checkpoint-30000, so the uploaded file may be that step rather than the final 92,494-step run. The paper says only the from-scratch model was released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MedRoBERTa.nl is a Dutch medical RoBERTa encoder from the Computational Linguistics and Text Mining Lab (CLTL) at Vrije Universiteit Amsterdam, trained with Amsterdam UMC. The released checkpoint is the from-scratch masked language model, not a fine-tune. It is still the public base weight as of 2026-10-15. The journal early-online date is 31 December 2021.", "what_it_is_for": "The Hub card says the model is for Dutch medical NLP and can be fine-tuned for any task. It was pre-trained on anonymized hospital notes so later tools can represent clinical Dutch that general Dutch encoders did not see. The project site says it is a base model, not a chat product. There is no maker API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wageningen/deepcgm/deepcgm": { "id": "wageningen/deepcgm/deepcgm", "name": "DeepCGM", "maker": "wageningen", "family": "wageningen/deepcgm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2025-04-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mass-conserving recurrent crop-growth model. The reference module uses a carbon state of 24 cells (8 leaf, 8 stem, 8 grain) with assimilation, partition, growth-respiration, maintenance-respiration, and redistribution gates, plus carbon-to-area and grain-to-yield converters. Auxiliary inputs in that module are development stage (DVS), radiation, minimum temperature, maximum temperature, and cumulative nitrogen. The paper states the same carbon-vector size of 24 (8 times 3), a DeepCGM learning rate of 0.1, and 700 training epochs. It is not a language model.", "modalities": { "input": [ "weather-time-series", "management-time-series" ], "output": [ "crop-state-time-series" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepCGM is a knowledge-guided deep learning crop-growth model released by Wageningen University & Research together with Wuhan University. The public weights and training code are in the WUR-AI GitHub repository under CC BY-NC 4.0. The journal article is the 1 June 2025 Field Crops Research paper. The GitHub repository's first commit is 23 April 2025. This dossier was collected on 2026-10-15. It is a research weight release, not a hosted API, and it is still posted.", "what_it_is_for": "The paper proposes DeepCGM for multivariable crop-growth simulation when observations are sparse. The stated comparison set is the process-based model ORYZA2000 and two classical deep learning models. The main experiment is a two-year rice trial of 105 plots, with a replication on a three-year rice trial of 122 plots. The repository README says the model is meant to keep simulated growth physically plausible, including mass conservation, rather than to act as a general chatbot or foundation model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warsaw/hydramp/hydramp": { "id": "warsaw/hydramp/hydramp", "name": "HydrAMP", "maker": "warsaw", "family": "warsaw/hydramp", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-03-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conditional variational autoencoder. The public site says two classifiers were added so the decoder can target P(AMP) and P(low MIC), with the peptide class disentangled from the latent vector. Analogue generation and unconstrained generation. The README's latest checkpoint is models/HydrAMP/37, with models/HydrAMP/pca_decomposer.joblib.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HydrAMP is a conditional variational autoencoder for antimicrobial peptide discovery, developed at the Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, in Ewa Szczurek's lab. Nature Communications published the paper on 15 March 2023. The MIMUW web service was live on the collection date. Status active. Collected 2026-10-15.", "what_it_is_for": "The site offers analogue generation, which starts from input peptides and tries to improve them, and unconstrained generation, which samples peptides with no starting sequence. The README exposes the same two modes through `HydrAMPGenerator`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warsaw/hyformer/hyformer-molecules-50m": { "id": "warsaw/hyformer/hyformer-molecules-50m", "name": "Hyformer molecules 50M", "maker": "warsaw", "family": "warsaw/hyformer", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause", "release_date": "2025-09-30", "context_length": 128, "max_output_tokens": null, "parameters": "50000000", "architecture": "Same joint causal/bidirectional transformer as the 8M checkpoint, at the paper's 50M scale. Released model_config.json: embedding_dim 512, embedding_hidden_dim 2048, 8 heads, head_dim 64, 12 layers, vocab_size 596, max_seq_len 128, num_physchem_tasks 200, flash_attention true. Paper Table 6 lists the 50M variant as embed 512, hidden 2048, 8 layers, 8 heads.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Warsaw appears through the Faculty of Mathematics, Informatics and Mechanics affiliation on the Hyformer paper. Helmholtz Munich, Ardigen, and Eindhoven are the other listed affiliations. This is the larger molecule checkpoint, pretrained on about 19 million molecules. The Hub repo was created on 2025-09-30. Status active. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint was trained on 19 million molecules from ZINC, ChEMBL, and other purchasable sets, citing Zhou et al. 2023. The paper uses that pretraining, then fine-tunes on MoleculeNet and evaluates out-of-distribution hit identification.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warsaw/hyformer/hyformer-molecules-8m": { "id": "warsaw/hyformer/hyformer-molecules-8m", "name": "Hyformer molecules 8M", "maker": "warsaw", "family": "warsaw/hyformer", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause", "release_date": "2025-09-30", "context_length": 128, "max_output_tokens": null, "parameters": "8700000", "architecture": "Joint transformer on a LLaMA backbone. Shared parameters switch between a causal decoder and a bidirectional encoder. Released model_config.json: embedding_dim 256, embedding_hidden_dim 1024, 8 heads, head_dim 32, 8 layers, vocab_size 122, max_seq_len 128, num_physchem_tasks 200, flash_attention true. Paper Table 6 lists the 8.7M variant as embed 256, hidden 1024, 8 layers, 8 heads.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Warsaw appears here through the Faculty of Mathematics, Informatics and Mechanics affiliation of the Szczurek lab. The same paper lists the Institute of AI for Health, Helmholtz Zentrum M\u00fcnchen, plus Ardigen and Eindhoven University of Technology. Hyformer molecules 8M is the GuacaMol checkpoint of a joint generative and predictive transformer. The Hub repo was created on 2025-09-30. Status active. Collected 2026-10-15.", "what_it_is_for": "The card says this checkpoint was trained on GuacaMol. The paper pretrains the 8.7M-parameter scale on the GuacaMol train split of 1.3 million molecules, with precomputed molecular descriptors, and evaluates unconditional generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warsaw/hyformer/hyformer-peptides-34m": { "id": "warsaw/hyformer/hyformer-peptides-34m", "name": "Hyformer peptides 34M", "maker": "warsaw", "family": "warsaw/hyformer", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause", "release_date": "2025-09-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Peptide Hyformer checkpoint. model_config.json: model_type Hyformer, embedding_dim 512, num_attention_heads 8, num_transformer_layers 8, vocab_size 34. tokenizer_config.json: tokenizer_type AATokenizer, vocabulary_path data/vocabulary/aa.txt. The file does not set max_seq_len. The molecule configs' LLaMA-style fields are not in this shorter config.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Warsaw appears through the Faculty of Mathematics, Informatics and Mechanics affiliation on the Hyformer paper, shared with Helmholtz Munich. This Hub checkpoint is the peptide model the card says was trained on 3.5 million general-purpose and antimicrobial peptides. The repo was created on 2025-09-25. Status active. Collected 2026-10-15.", "what_it_is_for": "The card says the checkpoint was trained on 3.5 million general-purpose and antimicrobial peptides. The named MIC fine-tune is a separate repo. The paper studies conditional antimicrobial-peptide design with this architecture and does not use the Hub name 34M.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warsaw/hyformer/hyformer-peptides-34m-mic": { "id": "warsaw/hyformer/hyformer-peptides-34m-mic", "name": "Hyformer peptides 34M MIC", "maker": "warsaw", "family": "warsaw/hyformer", "status": "active", "confidence": "high", "weights": "open-source", "license": "BSD-3-Clause", "release_date": "2025-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Joint fine-tune of Hyformer peptides 34M. model_config.json matches the peptide base (embedding_dim 512, 8 heads, 8 transformer layers, vocab_size 34) and adds prediction_task_type regression and num_prediction_tasks 1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "University of Warsaw appears through the Faculty of Mathematics, Informatics and Mechanics affiliation on the Hyformer paper, shared with Helmholtz Munich. This is the named checkpoint the Hub card describes as `Hyformer_peptides_34M` jointly fine-tuned on minimal inhibitory concentration against E. coli. The repo was created on 2025-09-30. Status active. Collected 2026-10-15.", "what_it_is_for": "The card says this repo is the peptide checkpoint jointly fine-tuned on MIC values against E. coli. The released config adds a regression head with one prediction task.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warsaw/omegamp/omegamp": { "id": "warsaw/omegamp/omegamp", "name": "OmegAMP", "maker": "warsaw", "family": "warsaw/omegamp", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "35173368", "architecture": "Denoising diffusion model. The denoiser is a 1D UNet with ResNet blocks, linear attention, and a central self-attention block, described as TransUNet-inspired. Conditioning is injected at the noisy input and at intermediate feature maps. Amino acids are embedded with five physicochemical scales: WW*, pI, Levitt, transmembrane propensity, and AASI. Appendix Table 6 lists 1000 diffusion steps, hidden channels 32, 3 downsampling layers, and a denoising-architecture total of 35,173,368 parameters. XGBoost classifiers filter candidates. Property conditioning (OmegAMP-PC) and subset conditioning (OmegAMP-SC) are modes of this generator.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OmegAMP is a diffusion framework for conditional antimicrobial peptide generation, with XGBoost filters. The corresponding author, Ewa Szczurek, lists the Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, together with Helmholtz Munich and the LMU Munich Faculty of Medicine. The other authors are at Helmholtz Munich, Technical University of Munich, or the University of Pennsylvania. The named weight file is `models/generative_model.ckpt` in the lab repository. Status active. Collected 2026-10-15.", "what_it_is_for": "The README describes a generative model for novel AMPs, a classifier that separates AMPs from non-AMPs, and finer classifiers for activity against species and strains. Property conditioning (OmegAMP-PC) and subset conditioning (OmegAMP-SC) are modes of the same generator. A local Streamlit app is `app.py`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/cerberus/cerberus": { "id": "warwick/cerberus/cerberus", "name": "Cerberus", "maker": "warwick", "family": "warwick/cerberus", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-task ResNet-34 histology network. The inference download is named resnet34_cerberus. A separate file is a torchvision-compatible ResNet-34 for transfer.", "modalities": { "input": [ "image" ], "output": [ "segmentation", "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cerberus is a University of Warwick multi-task histology model from the Tissue Image Analytics Centre, collected on 2026-10-15. Inference weights are an active open download under a non-commercial license. It is not a priced API. This is not the Amazon ranking model, the Calico genomics model, or other projects that reused the name.", "what_it_is_for": "One network segments and classifies nuclei, glands, and lumina, and classifies tissue-type patches. The centre says it was trained on more than 600 thousand segmentation objects and 440 thousand classification patches, then run on 599 TCGA colorectal slides.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/hovernet/hover-net": { "id": "warwick/hovernet/hover-net", "name": "HoVer-Net", "maker": "warwick", "family": "warwick/hovernet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HoVer-Net. The paper uses a Preact-ResNet50 encoder and decoders for horizontal and vertical distance maps, nuclear pixels, and nuclear type. TIAToolbox has an original mode and a fast mode.", "modalities": { "input": [ "image" ], "output": [ "segmentation", "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HoVer-Net is a University of Warwick model for nuclear instance segmentation and classification in H&E histology, collected on 2026-10-15. The Tissue Image Analytics Centre lists it as active open weights inside TIAToolbox. Dataset checkpoints are versions of this one model, not separate products. It is not a priced API.", "what_it_is_for": "The paper separates touching nuclei by predicting horizontal and vertical distances to each nucleus centre of mass, then classifies nuclear type when the training set has types.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/hovernet/hovernet-plus": { "id": "warwick/hovernet/hovernet-plus", "name": "HoVer-Net+", "maker": "warwick", "family": "warwick/hovernet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "HoVer-Net plus a fourth decoder for epithelial-layer semantic segmentation. The toolbox note says 0.5 mpp, unlike HoVer-Net at 0.25 mpp.", "modalities": { "input": [ "image" ], "output": [ "segmentation", "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HoVer-Net+ is a University of Warwick multi-task histology model from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight TIAToolbox checkpoint for oral epithelial dysplasia. It is not a priced API.", "what_it_is_for": "The paper adds a layer-segmentation decoder to HoVer-Net so one network segments nuclear instances and epithelial layers in oral dysplasia H&E.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/kongnet/kongnet": { "id": "warwick/kongnet/kongnet", "name": "KongNet", "maker": "warwick", "family": "warwick/kongnet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "KongNet, a multi-headed network for nuclear detection and classification. Parameter counts on the model card differ by checkpoint, from 126M to 216M.", "modalities": { "input": [ "image" ], "output": [ "detections", "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KongNet is a University of Warwick multi-headed nuclear detection and classification model from the Tissue Image Analytics Centre, collected on 2026-10-15. Dataset checkpoints are versions of one model. Weights are open. It is not a priced API. License text is inconsistent across official pages, so the license field is empty.", "what_it_is_for": "The paper presents KongNet for detection and classification of nuclei in histopathology. The toolbox exposes one named checkpoint per dataset or challenge track. The model card also hosts extra fold files.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/mapde/mapde": { "id": "warwick/mapde/mapde", "name": "MapDe", "maker": "warwick", "family": "warwick/mapde", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MapDe, described in TIAToolbox as a deconvolving CNN for cell detection. The class subclasses Micro-Net.", "modalities": { "input": [ "image" ], "output": [ "detections" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MapDe is a University of Warwick cell-detection model from the Tissue Image Analytics Centre, collected on 2026-10-15. CRCHisto and CoNIC checkpoints are active open weights in TIAToolbox. It is not a priced API.", "what_it_is_for": "The toolbox citation calls it a deconvolving convolutional network for cell detection. Both catalogued checkpoints share one input size.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/micronet/micronet": { "id": "warwick/micronet/micronet", "name": "Micro-Net", "maker": "warwick", "family": "warwick/micronet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Micro-Net, a multi-resolution CNN for microscopy instance segmentation. The toolbox checkpoint is a PyTorch retrain, not the original TensorFlow weights.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Micro-Net is a University of Warwick microscopy segmentation model from the Tissue Image Analytics Centre, collected on 2026-10-15. The active public checkpoint is the TIAToolbox PyTorch retrain on CoNSeP. It is not a priced API.", "what_it_is_for": "The toolbox uses it for nuclear instance segmentation. The module says the shipped CoNSeP weights were retrained in PyTorch because the original reported model was TensorFlow.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/nuclick/nuclick": { "id": "warwick/nuclick/nuclick", "name": "NuClick", "maker": "warwick", "family": "warwick/nuclick", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "CNN for click-guided instance segmentation. The project also ships a separate improved U-Net trained for the same interaction.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NuClick is a University of Warwick interactive segmentation model from the Tissue Image Analytics Centre, collected on 2026-10-15. Open weights are active under a non-commercial license. It is not a priced API.", "what_it_is_for": "The README describes a CNN that turns a few clicks into instance masks for nuclei and other microscopic objects, so annotators do less manual outlining.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/alexnet-kather100k": { "id": "warwick/patch-classifier/alexnet-kather100k", "name": "AlexNet Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AlexNet", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AlexNet Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `alexnet-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/alexnet-pcam": { "id": "warwick/patch-classifier/alexnet-pcam", "name": "AlexNet PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "AlexNet", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AlexNet PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `alexnet-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/densenet121-kather100k": { "id": "warwick/patch-classifier/densenet121-kather100k", "name": "DenseNet-121 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DenseNet-121", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DenseNet-121 Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `densenet121-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/densenet121-pcam": { "id": "warwick/patch-classifier/densenet121-pcam", "name": "DenseNet-121 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DenseNet-121", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DenseNet-121 PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `densenet121-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/densenet161-kather100k": { "id": "warwick/patch-classifier/densenet161-kather100k", "name": "DenseNet-161 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DenseNet-161", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DenseNet-161 Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `densenet161-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/densenet161-pcam": { "id": "warwick/patch-classifier/densenet161-pcam", "name": "DenseNet-161 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DenseNet-161", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DenseNet-161 PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `densenet161-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/densenet169-kather100k": { "id": "warwick/patch-classifier/densenet169-kather100k", "name": "DenseNet-169 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DenseNet-169", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DenseNet-169 Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `densenet169-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/densenet169-pcam": { "id": "warwick/patch-classifier/densenet169-pcam", "name": "DenseNet-169 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DenseNet-169", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DenseNet-169 PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `densenet169-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/densenet201-kather100k": { "id": "warwick/patch-classifier/densenet201-kather100k", "name": "DenseNet-201 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DenseNet-201", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DenseNet-201 Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `densenet201-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/densenet201-pcam": { "id": "warwick/patch-classifier/densenet201-pcam", "name": "DenseNet-201 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "DenseNet-201", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DenseNet-201 PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `densenet201-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/googlenet-kather100k": { "id": "warwick/patch-classifier/googlenet-kather100k", "name": "GoogLeNet Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GoogLeNet", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GoogLeNet Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `googlenet-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/googlenet-pcam": { "id": "warwick/patch-classifier/googlenet-pcam", "name": "GoogLeNet PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GoogLeNet", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GoogLeNet PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `googlenet-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/mobilenet-v2-kather100k": { "id": "warwick/patch-classifier/mobilenet-v2-kather100k", "name": "MobileNetV2 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileNetV2", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileNetV2 Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `mobilenet_v2-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/mobilenet-v2-pcam": { "id": "warwick/patch-classifier/mobilenet-v2-pcam", "name": "MobileNetV2 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileNetV2", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileNetV2 PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `mobilenet_v2-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/mobilenet-v3-large-kather100k": { "id": "warwick/patch-classifier/mobilenet-v3-large-kather100k", "name": "MobileNetV3-Large Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileNetV3-Large", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileNetV3-Large Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `mobilenet_v3_large-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/mobilenet-v3-large-pcam": { "id": "warwick/patch-classifier/mobilenet-v3-large-pcam", "name": "MobileNetV3-Large PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileNetV3-Large", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileNetV3-Large PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `mobilenet_v3_large-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/mobilenet-v3-small-kather100k": { "id": "warwick/patch-classifier/mobilenet-v3-small-kather100k", "name": "MobileNetV3-Small Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileNetV3-Small", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileNetV3-Small Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `mobilenet_v3_small-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/mobilenet-v3-small-pcam": { "id": "warwick/patch-classifier/mobilenet-v3-small-pcam", "name": "MobileNetV3-Small PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MobileNetV3-Small", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MobileNetV3-Small PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `mobilenet_v3_small-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/resnet101-kather100k": { "id": "warwick/patch-classifier/resnet101-kather100k", "name": "ResNet-101 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-101", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ResNet-101 Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `resnet101-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/resnet101-pcam": { "id": "warwick/patch-classifier/resnet101-pcam", "name": "ResNet-101 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-101", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ResNet-101 PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `resnet101-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/resnet18-kather100k": { "id": "warwick/patch-classifier/resnet18-kather100k", "name": "ResNet-18 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-18", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ResNet-18 Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `resnet18-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/resnet18-pcam": { "id": "warwick/patch-classifier/resnet18-pcam", "name": "ResNet-18 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-18", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ResNet-18 PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `resnet18-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/resnet34-kather100k": { "id": "warwick/patch-classifier/resnet34-kather100k", "name": "ResNet-34 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-34", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ResNet-34 Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `resnet34-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/resnet34-pcam": { "id": "warwick/patch-classifier/resnet34-pcam", "name": "ResNet-34 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-34", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ResNet-34 PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `resnet34-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/resnet50-kather100k": { "id": "warwick/patch-classifier/resnet50-kather100k", "name": "ResNet-50 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "23.6M", "architecture": "ResNet-50", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ResNet-50 Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `resnet50-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/resnet50-pcam": { "id": "warwick/patch-classifier/resnet50-pcam", "name": "ResNet-50 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-50", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ResNet-50 PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `resnet50-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/wide-resnet101-2-kather100k": { "id": "warwick/patch-classifier/wide-resnet101-2-kather100k", "name": "Wide ResNet-101-2 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Wide ResNet-101-2", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wide ResNet-101-2 Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `wide_resnet101_2-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/wide-resnet101-2-pcam": { "id": "warwick/patch-classifier/wide-resnet101-2-pcam", "name": "Wide ResNet-101-2 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Wide ResNet-101-2", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wide ResNet-101-2 PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `wide_resnet101_2-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/wide-resnet50-2-kather100k": { "id": "warwick/patch-classifier/wide-resnet50-2-kather100k", "name": "Wide ResNet-50-2 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Wide ResNet-50-2", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wide ResNet-50-2 Kather100k is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `wide_resnet50_2-kather100k`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on the Kather100k colorectal histology patches (also called NCT-CRC-HE). The ResNet-50 Kather100k card lists nine classes: tumour epithelium, simple stroma, complex stroma, immune cells, debris, mucus, adipose, background, and muscle. The pretrained index says every Kather100k model shares that task. Input patches are 224 by 224 at 0.5 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/patch-classifier/wide-resnet50-2-pcam": { "id": "warwick/patch-classifier/wide-resnet50-2-pcam", "name": "Wide ResNet-50-2 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Wide ResNet-50-2", "modalities": { "input": [ "image" ], "output": [ "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wide ResNet-50-2 PCam is a University of Warwick patch classifier from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight checkpoint in TIAToolbox, not a hosted API. The catalog id is `wide_resnet50_2-pcam`.", "what_it_is_for": "Trained by the Tissue Image Analytics Centre on Patch Camelyon (PCam). The pretrained page does not name the output classes. Input patches are 96 by 96 at 1.0 mpp.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/sccnn/sccnn": { "id": "warwick/sccnn/sccnn", "name": "SCCNN", "maker": "warwick", "family": "warwick/sccnn", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Spatially constrained CNN (SCCNN) for nuclear detection, as implemented in TIAToolbox.", "modalities": { "input": [ "image" ], "output": [ "detections" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SCCNN is a University of Warwick nuclear detection model from the Tissue Image Analytics Centre, collected on 2026-10-15. Two active open-weight checkpoints, CRCHisto and CoNIC, ship in TIAToolbox. It is not a priced API.", "what_it_is_for": "The paper is a locality-sensitive network for detecting and classifying nuclei in routine colon-cancer histology. The toolbox checkpoints are detection models on CRCHisto and CoNIC.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/segformer/segformer-b5-distilled": { "id": "warwick/segformer/segformer-b5-distilled", "name": "SegFormer-B5 distilled from Virchow2", "maker": "warwick", "family": "warwick/segformer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "84.60M", "architecture": "SegFormer-B5 student. The card says the tissue segmentors were distilled from Virchow2. These are not Virchow2 weights.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is a set of University of Warwick tissue-segmentation students from the Tissue Image Analytics Centre, collected on 2026-10-15. They are active open weights under a non-commercial license. They are not a copy of Virchow2 and not a priced API. Dataset checkpoints stay in one folder because the card treats them as one release of the same 84.60M SegFormer-B5.", "what_it_is_for": "The card says the models distill a foundation model into a smaller segmentor for tissue types on PUMA, IGNITE, and BEETLE, with extra unsupervised slides named per dataset.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/semantic-segmentor/fcn-resnet50-unet-bcss": { "id": "warwick/semantic-segmentor/fcn-resnet50-unet-bcss", "name": "FCN-ResNet50-UNet BCSS", "maker": "warwick", "family": "warwick/semantic-segmentor", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC0-1.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FCN with a ResNet-50 U-Net, catalog id fcn_resnet50_unet-bcss. The page does not give a layer diagram beyond the name.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FCN-ResNet50-UNet BCSS is a University of Warwick breast-tissue semantic segmentor from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight TIAToolbox checkpoint. It is not a priced API.", "what_it_is_for": "The pretrained page says it was trained on the BCSS dataset for semantic segmentation of breast cancer histology.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "warwick/semantic-segmentor/fcn-tissue-mask": { "id": "warwick/semantic-segmentor/fcn-tissue-mask", "name": "FCN tissue mask", "maker": "warwick", "family": "warwick/semantic-segmentor", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "FCN tissue masker. The catalog name is fcn-tissue_mask. The page does not name the backbone.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FCN tissue mask is a University of Warwick semantic segmentor from the Tissue Image Analytics Centre, collected on 2026-10-15. It is an active open-weight TIAToolbox checkpoint trained on internal centre data. Status is active. It is not a priced API.", "what_it_is_for": "The pretrained page says it masks tissue in whole-slide images. It was trained on internal TIA Centre data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/bigbird/bigbird-base-japanese": { "id": "waseda/bigbird/bigbird-base-japanese", "name": "Japanese BigBird base", "maker": "waseda", "family": "waseda/bigbird", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2023-06-03", "context_length": 4096, "max_output_tokens": null, "parameters": "", "architecture": "BigBird masked LM (BigBirdForMaskedLM), block_sparse attention, block size 64, 3 random blocks. config.json: 12 layers, hidden 768, intermediate 3072, 12 heads, vocab 32000, gelu_new, max_position_embeddings 4096, bfloat16. Card training max_seq_length is 4096. The lab news post links the OpenAI GPT-2 blog in the BigBird sentence. That link is not the BigBird paper. The architecture name on the card is BigBird.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese BigBird base from Kawahara Lab at Waseda University, pretrained on Japanese Wikipedia, Japanese CC-100, and Japanese OSCAR. Weights are public under CC BY-SA 4.0. Status on the collection date 2026-10-15 is active. The Hub record was created on 3 June 2023. The lab announced the release on 26 June 2023. The LLM-jp overview lists it as JapaneseBigBird with a 4,096-token context.", "what_it_is_for": "The card says the model is for masked language modeling and downstream fine-tuning on long Japanese sequences. The lab describes it as a long-sequence general-purpose Japanese language model. It is an encoder, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/comet/comet-gpt2-small-japanese": { "id": "waseda/comet/comet-gpt2-small-japanese", "name": "COMET-GPT2 ja", "maker": "waseda", "family": "waseda/comet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-11-15", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 causal LM fine-tuned on ATOMIC ja. config.json: 12 layers, n_embd 768, 12 heads, n_positions 1024, vocab 32004, gelu_new. _name_or_path is output_gpt2_3epoch. The card does not name the pretrained GPT-2 checkpoint. Vocab is 32,004, not the 32,000 of the lab's GPT-2 small cards. The card YAML has no license field.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese commonsense generation model from Kawahara Lab at Waseda University, in a joint project with LINE Corporation. The card says it is a GPT-2 fine-tuned on ATOMIC ja. Weights are public. The card YAML does not name a license. Status on the collection date 2026-10-15 is active. The Hub record was created on 15 November 2022. The lab announced Japanese ATOMIC and COMET on 7 March 2023.", "what_it_is_for": "The card says the model generates commonsense tails for an event and a relation. The example prompt is a Juman++-segmented event plus `xEffect`, and the continuation is an inferred effect. It was introduced in the NLP 2023 paper by Ide, Murata, Horio, Kawahara, and co-authors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/comet/comet-gpt2-xl-japanese": { "id": "waseda/comet/comet-gpt2-xl-japanese", "name": "COMET-GPT2 ja XL", "maker": "waseda", "family": "waseda/comet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2023-09-26", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 XL causal LM fine-tuned on the large ATOMIC ja graph. config.json: 48 layers, n_embd 1600, 20 heads, n_positions 1024, vocab 50004, gelu_new. _name_or_path is comet-xl. The card says Finetuned GPT-2 xl. It does not name nlp-waseda/gpt2-xl-japanese. That base card's vocab is 50000. This vocab is 50004.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese commonsense model from Kawahara Lab at Waseda University. The card says it is GPT-2 XL fine-tuned on the large version of ATOMIC ja. The card title is \"COMET-GPT2 ja v2\" even though the repo id is the XL checkpoint. Weights are public under CC BY-SA 4.0. Status on the collection date 2026-10-15 is active. The Hub record was created on 26 September 2023.", "what_it_is_for": "The card says the model generates commonsense tails for a segmented event and a relation, using the large ATOMIC ja graph from the NLP 2023 papers B2-5 and B9-1. Japanese TaCOMET later continues training from this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/comet/comet-t5-base-japanese": { "id": "waseda/comet/comet-t5-base-japanese", "name": "COMET-T5 ja", "maker": "waseda", "family": "waseda/comet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-11-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 encoder-decoder (T5ForConditionalGeneration) fine-tuned on ATOMIC ja. config.json: d_model 768, d_ff 2048, 12 encoder layers, 12 decoder layers, 12 heads, d_kv 64, vocab 32000, gated-gelu, untied embeddings. _name_or_path is output_t5_alt_3epoch. The card does not name the pretrained T5 checkpoint. No n_positions field. The card YAML has no license.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese commonsense model from Kawahara Lab at Waseda University. The card says it is a T5 fine-tuned on ATOMIC ja with a text-to-text objective. Weights are public. The card does not name a license or the pretrained T5. Status on the collection date 2026-10-15 is active. The Hub record was created on 12 November 2022. Waseda did not release its own base T5. This folder exists because the lab released the named COMET-T5 checkpoint.", "what_it_is_for": "The card says the model answers a Japanese prompt about an event. The four prompts are: what precondition is required (xNeed), what can happen afterward (xEffect), what the motive was (xIntent), and what the person feels afterward (xReact). It was introduced in the same NLP 2023 paper as COMET-GPT2 ja.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/comet/comet-v2-gpt2-small-japanese": { "id": "waseda/comet/comet-v2-gpt2-small-japanese", "name": "COMET-GPT2 ja v2", "maker": "waseda", "family": "waseda/comet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-03-05", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 causal LM fine-tuned on the large version of ATOMIC ja. config.json: 12 layers, n_embd 768, 12 heads, n_positions 1024, vocab 32004, gelu_new. _name_or_path is comets/none_100. The card does not name the pretrained GPT-2 checkpoint and does not set a license.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese commonsense model from Kawahara Lab at Waseda University. The card says it is a GPT-2 fine-tuned on the large version of ATOMIC ja. Weights are public. The card does not name a license. Status on the collection date 2026-10-15 is active. The Hub record was created on 5 March 2023.", "what_it_is_for": "The card says the model generates commonsense tails from a segmented event and a relation, using the enlarged ATOMIC ja graph. The original graph is described in NLP 2023 paper B2-5. The large graph is described in NLP 2023 paper B9-1 by Murata, Ide, and co-authors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/gpt2/gpt2-small-japanese": { "id": "waseda/gpt2/gpt2-small-japanese", "name": "GPT-2 small Japanese", "maker": "waseda", "family": "waseda/gpt2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2022-03-30", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 causal LM (GPT2LMHeadModel). config.json: 12 layers, n_embd 768, 12 heads, n_positions 1024, vocab 32000, gelu_new. Card does not state a parameter total. Hub createdAt 2022-03-30 is used as the release date because no lab announcement was found.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese GPT-2 small from Kawahara Lab at Waseda University, pretrained on Japanese Wikipedia and the Japanese portion of CC-100. Weights are public under CC BY-SA 4.0. Status on the collection date 2026-10-15 is active. The Hub record was created on 30 March 2022. No separate lab announcement for this corpus mix was found.", "what_it_is_for": "The card says the raw model is for text generation or downstream fine-tuning. It is a small GPT-2, not the 1.5B-class XL checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/gpt2/gpt2-small-japanese-wikipedia": { "id": "waseda/gpt2/gpt2-small-japanese-wikipedia", "name": "GPT-2 small Japanese (Wikipedia)", "maker": "waseda", "family": "waseda/gpt2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2021-12-29", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 causal LM (GPT2LMHeadModel). config.json: 12 layers, n_embd 768, 12 heads, n_inner null (library default 4x), n_positions 1024, vocab 32000, gelu_new, absolute positions. Card does not state a parameter total.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese GPT-2 small from Kawahara Lab at Waseda University, pretrained only on Japanese Wikipedia. Weights are public under CC BY-SA 4.0 and were still downloadable on the collection date 2026-10-15. The lab announced the release on 29 December 2021.", "what_it_is_for": "The card says the raw model is for text generation or for fine-tuning on a downstream task. It is the Wikipedia-only predecessor corpus variant of the lab's later GPT-2 small, which also uses CC-100. The lab does not call this checkpoint a successor or a deprecated product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/gpt2/gpt2-xl-japanese": { "id": "waseda/gpt2/gpt2-xl-japanese", "name": "GPT-2 XL Japanese", "maker": "waseda", "family": "waseda/gpt2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2022-11-30", "context_length": 1024, "max_output_tokens": null, "parameters": "approximately 1.5B", "architecture": "GPT-2 causal LM (GPT2LMHeadModel), architecture based on Radford et al. 2019. config.json: 48 layers, n_embd 1600, 20 heads, n_inner null, n_positions 1024, vocab 50000, gelu_new, bfloat16. Card says approximately 1.5B parameters. The Hub size label 2B is a file-size bucket, not the card's parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese GPT-2 from Kawahara Lab at Waseda University, with about 1.5 billion parameters, pretrained on Japanese Wikipedia and CC-100. Weights are public under CC BY-SA 4.0. Status on the collection date 2026-10-15 is active. The Hub record was created on 30 November 2022.", "what_it_is_for": "The card says the raw model is for text generation or downstream fine-tuning. It is the lab's large Japanese GPT-2. Later COMET-GPT2 XL and Japanese TaCOMET checkpoints use the same layer shape and name this line as their base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/roberta/roberta-base-japanese": { "id": "waseda/roberta/roberta-base-japanese", "name": "Waseda RoBERTa base Japanese", "maker": "waseda", "family": "waseda/roberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2021-12-22", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa masked LM (RobertaForMaskedLM). config.json: 12 layers, hidden 768, intermediate 3072, 12 heads, vocab 32000, gelu, absolute positions, max_position_embeddings 514. Card training max_seq_length is 128. The 514 position width is the config default and was not the trained length. License changed from CC BY-SA 4.0 to Apache-2.0 on 2026-04-20.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese RoBERTa base from Kawahara Lab at Waseda University, pretrained on Japanese Wikipedia and the Japanese portion of CC-100. Weights are public. The lab announced the release on 22 December 2021. On 20 April 2026 the parent card's license changed from CC BY-SA 4.0 to Apache-2.0. Status on the collection date 2026-10-15 is active.", "what_it_is_for": "The card says the model is for masked language modeling and for fine-tuning on downstream tasks. JGLUE uses it as a Japanese NLU baseline. It is not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/roberta/roberta-jtruthfulqa": { "id": "waseda/roberta/roberta-jtruthfulqa", "name": "Waseda RoBERTa JTruthfulQA", "maker": "waseda", "family": "waseda/roberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-12-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RobertaForSequenceClassification. config.json matches Waseda RoBERTa large shape: 24 layers, hidden 1024, intermediate 4096, 16 heads, vocab 32000, max_position_embeddings 514, single_label_classification. The card does not say whether the init was the s128 or the seq512 checkpoint. Hub safetensors tag is 0.3B. The card does not state a license or a label inventory.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sequence classifier from Kawahara Lab at Waseda University. The card says it is a fine-tune of Waseda RoBERTa used to evaluate generated answers on JTruthfulQA. Weights are public. The card does not name a license. Status on the collection date 2026-10-15 is active. The Hub record was created on 6 December 2023.", "what_it_is_for": "The only sentence on the card is that the model was fine-tuned to evaluate generated answers on JTruthfulQA. It is an evaluation classifier, not a general pretrained encoder. The linked dataset repo is https://github.com/nlp-waseda/JTruthfulQA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/roberta/roberta-large-japanese": { "id": "waseda/roberta/roberta-large-japanese", "name": "Waseda RoBERTa large Japanese", "maker": "waseda", "family": "waseda/roberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2022-05-10", "context_length": 128, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa masked LM (RobertaForMaskedLM). config.json: 24 layers, hidden 1024, intermediate 4096, 16 heads, vocab 32000, gelu, max_position_embeddings 514. Card training max_seq_length is 128. JGLUE calls this Waseda RoBERTa large (s128). Hub createdAt 2022-05-10 is the release date. No lab news line for the large drop was found.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese RoBERTa large from Kawahara Lab at Waseda University, pretrained on Japanese Wikipedia and Japanese CC-100 at a maximum sequence length of 128. Weights are public under CC BY-SA 4.0. Status on the collection date 2026-10-15 is active. The Hub record was created on 10 May 2022. JGLUE lists this checkpoint as Waseda RoBERTa large (s128).", "what_it_is_for": "The card says the model is for masked language modeling and downstream fine-tuning. JGLUE uses it as a large encoder baseline. The seq512 checkpoint was continued from this one.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/roberta/roberta-large-japanese-seq512": { "id": "waseda/roberta/roberta-large-japanese-seq512", "name": "Waseda RoBERTa large Japanese (seq512)", "maker": "waseda", "family": "waseda/roberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-SA 4.0", "release_date": "2022-06-13", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa masked LM continued from nlp-waseda/roberta-large-japanese. config.json: 24 layers, hidden 1024, intermediate 4096, 16 heads, vocab 32000, gelu, max_position_embeddings 514. Card says maximum sequence length 512, with 670,000 steps at length 128 plus 70,000 steps at length 512. JGLUE calls this Waseda RoBERTa large (s512).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Japanese RoBERTa large from Kawahara Lab at Waseda University, continued from `roberta-large-japanese` with a maximum sequence length of 512. Weights are public under CC BY-SA 4.0. Status on the collection date 2026-10-15 is active. The Hub record was created on 13 June 2022. JGLUE lists it as Waseda RoBERTa large (s512).", "what_it_is_for": "The card says the model is for masked language modeling and downstream fine-tuning, with a longer trained context than the s128 large model. JGLUE uses it as a separate baseline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/svmoe/svmoe": { "id": "waseda/svmoe/svmoe", "name": "SvMoE", "maker": "waseda", "family": "waseda/svmoe", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-03-05", "context_length": 4096, "max_output_tokens": null, "parameters": "9266759680", "architecture": "MixtralForCausalLM. config.json on svmoe_wo_LB: 24 layers, hidden 2048, intermediate 7168, 16 heads, 16 KV heads, 8 local experts, 2 experts per token, RoPE theta 10000, vocab 99584, max_position_embeddings 4096, untied embeddings, silu, bfloat16, router_aux_loss_coef 0.01. Safetensors parameter total on the Hub is 9,266,759,680 for both the with-LB and without-LB repos. Tokenizer special tokens use LLM-jp markers such as . The thesis says eight experts were language-modeling fine-tunes of llm-jp/llm-jp-3-1.8b on legal subdomains, then merged. There is no model card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Supervised Mixture-of-Experts model from Kawahara Lab at Waseda University, by Eiki Murata and Daisuke Kawahara. The NLP 2025 poster proposes SvMoE: train the router with TF-IDF targets before language-model training, so a load-balancing loss is not required. The legal-domain case study is the released weight pair. There is no model card. Confidence is medium. Status on the collection date 2026-10-15 is active because the weights are still on the Hub. The uploads are dated 5 March 2025.", "what_it_is_for": "The poster says the method is for routing a mixture of experts with a supervised document signal, studied on legal subdomains. The thesis says eight experts were trained from `llm-jp/llm-jp-3-1.8b`, merged, the router was trained, and the whole model was then trained. The proposed final stage does not use a load-balancing loss. The with-LB run is the comparison.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/tacomet/tacomet-gpt2-xl-english": { "id": "waseda/tacomet/tacomet-gpt2-xl-english", "name": "TaCOMET en", "maker": "waseda", "family": "waseda/tacomet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-03-07", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 causal LM. The card says the base is COMET-distil from West et al. 2022, not a Waseda GPT-2. config.json: 48 layers, n_embd 1600, 25 heads, n_positions 1024, n_ctx 1024, vocab 50271, bos and eos 50256, gelu_new. task_specific_params set text-generation max_length 50. _name_or_path is a local comet_gran path.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "English time-aware commonsense model released by Kawahara Lab at Waseda University. The card says it is COMET-distil fine-tuned on the English version of TimeATOMIC. COMET-distil is the West et al. 2022 model, not a Waseda base model. Weights are public under CC BY-NC 4.0. Status on the collection date 2026-10-15 is active. The Hub record was created on 7 March 2024.", "what_it_is_for": "The same LREC-COLING 2024 paper as Japanese TaCOMET. The English card uses a prompt with a head event, a relation, a time phrase, and a `[GEN]` marker. It is the English half of the TaCOMET release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waseda/tacomet/tacomet-gpt2-xl-japanese": { "id": "waseda/tacomet/tacomet-gpt2-xl-japanese", "name": "TaCOMET ja", "maker": "waseda", "family": "waseda/tacomet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC BY-NC 4.0", "release_date": "2024-03-07", "context_length": 1024, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2 XL causal LM continued from nlp-waseda/comet-gpt2-xl-japanese on Japanese TimeATOMIC. config.json: 48 layers, n_embd 1600, 20 heads, n_positions 1024, vocab 50004, gelu_new. _name_or_path is a local comet_gran path.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Time-aware Japanese commonsense model from Kawahara Lab at Waseda University, by Eiki Murata and Daisuke Kawahara. The card says it is COMET-GPT2 XL Japanese continually fine-tuned on the Japanese version of TimeATOMIC. Weights are public under CC BY-NC 4.0. Status on the collection date 2026-10-15 is active. The Hub record was created on 7 March 2024. The LREC-COLING paper is dated May 2024.", "what_it_is_for": "The paper proposes TaCOMET so commonsense generations can condition on time. The construction has two steps: build TimeATOMIC, including with ChatGPT, then continually fine-tune an existing COMET model. The card's example prompt puts a time phrase before an `xNeed` relation. The paper also checks a robotic decision-making task and says TaCOMET beat the earlier commonsense model when proper times are given. Those task numbers were not copied off the PDF.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/abc/abc": { "id": "waterloo/abc/abc", "name": "ABC", "maker": "waterloo", "family": "waterloo/abc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PEFT adapters on Qwen2-VL-7B-Instruct. ABC-Qwen2VL-Pretrain has no license tag. ABC-Qwen2VL-Instruct is MIT. Authors on the paper are Benjamin Schneider, Florian Kerschbaum, and Wenhu Chen, University of Waterloo.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ABC is a Waterloo multimodal embedding model from TIGER-Lab and the Cheriton School, posted 24 February 2025. The paper is marked TMLR 2025 on the arXiv comment. The instruct adapter is MIT. The pretrain adapter had no license tag on the Hub listing. Both were still public on 15 October 2026.", "what_it_is_for": "It embeds an image under a natural-language instruction so retrieval and classification can follow the user's wording. The abstract says it is best-for-size on MSCOCO image-to-text retrieval and the top model on classification and VQA inside MMEB at the time of writing. Those tables were not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/acecoder/acecoder": { "id": "waterloo/acecoder/acecoder", "name": "AceCoder", "maker": "waterloo", "family": "waterloo/acecoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-04", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Fine-tunes of Qwen2.5-Coder-7B-Instruct. Public policy checkpoints are the Rule variant, the RM variant, and V1.1. The reward models are a separate folder.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceCoder is TIGER-Lab's code-generation line on Qwen2.5-Coder, posted in February 2025 with a V1.1 checkpoint in May 2025. MIT weights were still public on the collection date.", "what_it_is_for": "The lab fine-tunes a coder with rule-based or reward-model signals from AceCode. These repos are the generators. AceCodeRM is the scorer.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/acecoder/acecoderm": { "id": "waterloo/acecoder/acecoderm", "name": "AceCodeRM", "maker": "waterloo", "family": "waterloo/acecoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-02-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reward models fine-tuned from Qwen2.5-Coder-7B-Instruct and Qwen2.5-Coder-32B-Instruct on AceCode-87K and AceCodePair-300K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AceCodeRM is TIGER-Lab's code reward model, with a 7B checkpoint on 3 February 2025 and a 32B checkpoint on 4 February 2025. MIT license. Still public on the collection date.", "what_it_is_for": "It scores code pairs for the AceCoder training setup. It is not the generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/afriberta/afriberta": { "id": "waterloo/afriberta/afriberta", "name": "AfriBERTa", "maker": "waterloo", "family": "waterloo/afriberta", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "XLM-RoBERTa-style encoder trained from scratch. The GitHub and the Waterloo news post give small 97M, base about 111M (8 layers, 6 heads, 768 hidden, 3072 feed-forward), and large 126M. The large card cites 0.94 GB and 108.8 million tokens. Tokenizer max length has to be set to 512 by hand.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AfriBERTa is Castorini's from-scratch multilingual encoder for African languages, released with the EMNLP 2021 MRL workshop paper. The small, base, and large repos were still public on 15 October 2026. The large repo is MIT. Small and base had no license tag on the listing. Hub timestamps are the 2 March 2022 import.", "what_it_is_for": "Fill-mask and downstream fine-tuning for languages that had little or no prior language model. The news post says the training text is news only. A thesis note says 11 languages and that 4 had no prior language model. The language list above is 10 names. Gahuza covers Kinyarwanda and Kirundi, which is how the count reaches 11.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/afriberta-v2/afriberta-v2": { "id": "waterloo/afriberta-v2/afriberta-v2", "name": "AfriBERTa v2", "maker": "waterloo", "family": "waterloo/afriberta-v2", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2024-10-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "XLM-RoBERTa encoder. A Waterloo thesis table lists base 173M and large 187M. The Hub size label on the base repo says 0.2B. The v2 base card is an empty stub. The arXiv tag 1910.09700 is a carbon-emissions paper and is the wrong tag.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AfriBERTa v2 is Castorini's later African-language encoder, posted 30 October 2024. It is tied to the Wura corpus rather than the 2021 small news crawl. Both sizes were still public on 15 October 2026. Confidence is medium because the cards are stubs and the only arXiv tag is wrong.", "what_it_is_for": "Fill-mask pretraining for a wider set of African languages than v1. The language list was not on the stub card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/afriteva/afriteva": { "id": "waterloo/afriteva/afriteva", "name": "AfriTeVa", "maker": "waterloo", "family": "waterloo/afriteva", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-05-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "From-scratch encoder-decoder. The paper table lists small 64M (6 layers, 8 heads), base 229M (12 layers, 12 heads), and large 745M (24 layers, 16 heads). Vocabulary 70,000. Sequence length 512. The base card confirms 229M, 12 layers, 12 heads, 143 million tokens, and about 1 GB of text.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AfriTeVa v1 is Castorini's from-scratch sequence-to-sequence model for African languages, posted 24 May 2022. Ogundepo, Oladipo, Adeyemi, Ogueji, and Lin presented it at DeeLIO 2022. The three sizes were still public on 15 October 2026. The base card does not state a license.", "what_it_is_for": "Multilingual generation and fine-tuning for the same language set as the 2021 AfriBERTa models. It is not AfriTeVa v2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/afriteva-v2/afriteva-v2": { "id": "waterloo/afriteva-v2/afriteva-v2", "name": "AfriTeVa v2", "maker": "waterloo", "family": "waterloo/afriteva-v2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-12-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 v1.1. Base is 428M: d_model 768, d_ff 2048, d_kv 64, 12 layers, 12 heads, vocab 150144, gated GELU, untied embeddings. Large is 1B. Vocabulary 150,000 and 20 languages in the paper. Dropout is off during pretraining. The config still stores dropout_rate 0.1 and the card says to turn it back on for fine-tuning.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AfriTeVa v2 is Castorini's Wura-trained T5, posted 6 December 2023 under Apache-2.0. The EMNLP 2023 paper is by Oladipo, Adeyemi, Ahia, Owodunni, Ogundepo, Adelani, and Lin. Base and large were still public on 15 October 2026.", "what_it_is_for": "Higher-quality pretraining for African-language generation. The abstract says cross-lingual QA is more than twice mT5. The exact QA scores were not copied. A thesis table gives median BLEU into English of 22.3 for v2 large, 15.8 for v2 base, and 20.2 for AfriByT5. That table is a thesis, not the model card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/aggretriever/aggretriever": { "id": "waterloo/aggretriever/aggretriever", "name": "Aggretriever", "maker": "waterloo", "family": "waterloo/aggretriever", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-04-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dense passage retriever that concatenates the CLS vector with an aggregate of token vectors. Two checkpoints: cocondenser and DistilBERT, both posted 7 April 2023.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Aggretriever is Castorini's dense retriever from Lin, Li, and Lin, posted 7 April 2023. The paper was submitted 31 July 2022 and published in TACL. Both checkpoints were still public on 15 October 2026. License tags were empty.", "what_it_is_for": "First-stage dense retrieval. The paper's claim is that pooling token embeddings closes the gap between language-model pretraining and a single CLS vector, without a large extra training stack.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/azbert/azbert": { "id": "waterloo/azbert/azbert", "name": "azbert-base", "maker": "waterloo", "family": "waterloo/azbert", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "fill-mask checkpoint castorini/azbert-base. MIT on the tag list. The card was not read. A guess that the name means Azerbaijani BERT was not confirmed and is not stated as fact.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "azbert-base is a Castorini masked language model that was still public on 15 October 2026. The Hub timestamp is the 2 March 2022 import. The card was not read, so language, size, and training data stay empty. Confidence is medium.", "what_it_is_for": "Fill-mask. No usage note was retrieved.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/browseragent/browseragent": { "id": "waterloo/browseragent/browseragent", "name": "BrowserAgent", "maker": "waterloo", "family": "waterloo/browseragent", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-17", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Fine-tunes of Qwen2.5-7B-Instruct. Two stages: BrowserAgent-SFT and BrowserAgent-RFT. The paper says the agent acts on raw pages through Playwright and keeps an explicit memory of conclusions.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BrowserAgent is TIGER-Lab's web agent, posted on 17 October 2025 under Apache-2.0. Both the SFT and RFT checkpoints were still public on 15 October 2026. The paper was submitted 12 October 2025.", "what_it_is_for": "It solves web tasks by scrolling, clicking, and typing instead of reading a text dump of the page. The abstract says the 7B model is about 20 percent above Search-R1 on HotpotQA, 2Wiki, and Bamboogle. That figure is a summary, not a per-set table, so it is not stored as a score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/canard/t5-canard": { "id": "waterloo/canard/t5-canard", "name": "T5-base CANARD", "maker": "waterloo", "family": "waterloo/canard", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5-base fine-tune named castorini/t5-base-canard. The listing has no card tags. The import timestamp is 2 March 2022. Downloads on that listing were 848.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "t5-base-canard is a long-standing Castorini checkpoint for rewriting conversational questions into standalone queries. It was still public on 15 October 2026. The card was not read. Confidence is medium.", "what_it_is_for": "Query rewriting for the CANARD conversation set, so a retriever can run on a self-contained question. Training details were not on the listing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/cft/cft": { "id": "waterloo/cft/cft", "name": "Critique Fine-Tuning", "maker": "waterloo", "family": "waterloo/cft", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Critique fine-tunes. Hub checkpoints are Qwen2.5-Math-7B-CFT, from Qwen2.5-Math-7B, and Qwen2.5-32B-Instruct-CFT, from Qwen2.5-32B-Instruct. The abstract says the 7B math run used 50K critique examples.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Critique Fine-Tuning is TIGER-Lab's January 2025 reasoning line. Both checkpoints were posted on 30 January 2025 under Apache-2.0 and were still public on the collection date. One-shot CFT, from a later paper, is a separate folder.", "what_it_is_for": "The model is trained to critique a noisy response rather than copy a gold answer. The abstract says this beats ordinary supervised fine-tuning by 4 to 10 percent on six math benchmarks. That range was not stored as a single score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/consisti2v/consisti2v": { "id": "waterloo/consisti2v/consisti2v", "name": "ConsistI2V", "maker": "waterloo", "family": "waterloo/consisti2v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-02-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Diffusion image-to-video model. The paper adds spatiotemporal attention over the first frame and noise initialization from the low-frequency band of that frame. One Hub repo, TIGER-Lab/ConsistI2V. Diffusers is the expected loader. Parameter count was not on the abstract.", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ConsistI2V is TIGER-Lab's image-to-video model, posted on 25 February 2024 under MIT. The paper was submitted 6 February 2024. The checkpoint was still public on 15 October 2026.", "what_it_is_for": "It generates a video from a first frame and a text prompt, with the paper focused on keeping subject, background, and layout consistent. The lab also proposes I2V-Bench in the same paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/cosdpr/cosdpr": { "id": "waterloo/cosdpr/cosdpr", "name": "cosDPR", "maker": "waterloo", "family": "waterloo/cosdpr", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-08-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dense retriever. castorini/cosdpr-distil was uploaded by Xueguang Ma on 16 August 2023. An ONNX export was posted 29 May 2025. No model card. A corpus tarball is named msmarco-passage-cos-dpr-distil on the Waterloo CS object store.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "cosDPR is a Castorini distilled dense retriever with no model card. The weights were posted 16 August 2023 and were still public on 15 October 2026. Confidence is medium. Anserini's regression page cites Ma, Teofili, and Lin, CIKM 2023, 'Anserini Gets Dense Retrieval', which is an indexing paper and is not proof that those authors trained this checkpoint.", "what_it_is_for": "Flat dense retrieval over MS MARCO passages. Anserini's cached-query regression on DL20 passage reports AP@1000 0.4876, nDCG@10 0.7025, R@100 0.7204, and R@1000 0.8533. That is a toolkit regression, not a model-card table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/critique-coder/critique-coder": { "id": "waterloo/critique-coder/critique-coder", "name": "Critique-Coder", "maker": "waterloo", "family": "waterloo/critique-coder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-09-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Critique-Coder-4B is a fine-tune of Qwen3-4B. Critique-Coder-8B is a fine-tune of Qwen3-8B. The abstract says 20 percent of the RL data is replaced by critique data whose reward is whether the critique's true or false label matches the ground truth.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Critique-Coder is TIGER-Lab's September 2025 code line. Both checkpoints were posted on 30 September 2025 under Apache-2.0 and were still public on the collection date.", "what_it_is_for": "The model both solves coding problems and critiques candidate solutions. The abstract says Critique-Coder-8B reaches over 60 percent on LiveCodeBench v5. The exact score was not in the abstract, so it is not stored.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/doc2query/doc2query-t5": { "id": "waterloo/doc2query/doc2query-t5", "name": "doc2query-T5", "maker": "waterloo", "family": "waterloo/doc2query", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5-base and T5-large models fine-tuned to generate queries for an MS MARCO passage. The method is doc2query / docTTTTTquery (Nogueira and Lin). The doc2query paper URL was not re-opened this pass.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "doc2query-T5 is Castorini's document-expansion model. Base and large MS MARCO checkpoints were still public on 15 October 2026. Both show the 2 March 2022 import timestamp. License tags were empty.", "what_it_is_for": "The model writes queries that an inverted index can store next to the passage. That is the Expando stage in front of MonoT5 and DuoT5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/duot5/duot5": { "id": "waterloo/duot5/duot5", "name": "DuoT5", "maker": "waterloo", "family": "waterloo/duot5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pairwise T5 reranker used after MonoT5 in the Expando-Mono-Duo stack. Hub sizes are base and 3B, with 10k and Med variants.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DuoT5 is Castorini's pairwise T5 reranker, described in the Expando-Mono-Duo paper (14 January 2021) by Pradeep, Nogueira, and Lin. The Hub repos were still public on 15 October 2026. License tags were empty.", "what_it_is_for": "It compares two passages for the same query and is the last stage of the lab's Mono-Duo rerank pipeline. It is not a replacement for MonoT5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/editreward/editreward": { "id": "waterloo/editreward/editreward", "name": "EditReward", "maker": "waterloo", "family": "waterloo/editreward", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-06", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Two fine-tunes on EditReward-Data: Qwen2.5-VL-7B-Instruct, and Xiaomi MiMo-VL-7B-SFT-2508. Both are about 7B-class vision-language reward models.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EditReward is TIGER-Lab's image-edit reward line, posted 6 October 2025 under Apache-2.0. Both backbones were still public on 15 October 2026.", "what_it_is_for": "The checkpoints score image edits. The Qwen2.5-VL 7B model is the primary named reward model. The MiMo-VL fine-tune is a second backbone in the same release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/fim/fim": { "id": "waterloo/fim/fim", "name": "FIM", "maker": "waterloo", "family": "waterloo/fim", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mid-training then agent post-training on Qwen2.5-Coder-Instruct 7B and 14B and on Qwen3-8B. The abstract says the mid-training corpus is about 2.6 billion tokens from 968 GitHub repositories and contains Python only. FIM-Mid checkpoints are the mid-training stage. The paper says instruction following degrades if that stage is used alone, so those repos are versions, not the scored models.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FIM is TIGER-Lab's July 2026 coding-agent line. The Hub repos were created on 14 July 2026 under Apache-2.0 and were still public on 15 October 2026. Wenhu Chen is a co-author. The scored checkpoints are post-trained. The Mid repos are the mid-training stage.", "what_it_is_for": "The paper uses function-aware fill-in-the-middle so a coding model can consume a tool return the way ordinary code consumes a function result. Post-training uses existing agent pipelines. The abstract names R2E-Gym and SWE-Smith, and SWE-Lego for the Qwen3-8B run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/first/first": { "id": "waterloo/first/first", "name": "FIRST", "maker": "waterloo", "family": "waterloo/first", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-11-10", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Listwise reranker that reads the logits of the first generated identifier. first_mistral is a fine-tune of Mistral-7B-Instruct-v0.3, MIT, posted 10 November 2024. Qwen3 sizes 0.6B, 1.7B, 4B, 8B, and 14B were posted 25 September 2025. The Hub size label on the 1.7B repo says 2B and the 14B repo says 15B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FIRST on this Hub org is Castorini's reproduction of single-token listwise reranking. Zijian Chen, Ronak Pradeep, and Jimmy Lin wrote the reproduction paper, submitted 8 November 2024. The original FIRST method paper is by Reddy and colleagues and is not a Waterloo release. The Castorini checkpoints were still public on 15 October 2026. The Mistral checkpoint is MIT. The Qwen3 repos had no license tag on the listing.", "what_it_is_for": "Rank a list by the first output token instead of generating a full permutation. The Qwen3 repos are the same product at other sizes. Their cards were not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/gaggle/gaggle": { "id": "waterloo/gaggle/gaggle", "name": "Gaggle", "maker": "waterloo", "family": "waterloo/gaggle", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-10-09", "context_length": null, "max_output_tokens": null, "parameters": "3.29B", "architecture": "Pointwise decoder-only reranker. A model soup with weight 0.25 on each of four fine-tunes of the causal Gaggle nanochat checkpoint. Fine-tuning used RLHN-250K and localized contrastive estimation. Table 1 labels the size 3B. The backbone prose count is 3.29B. Query truncation 128 and document truncation 512 are the eval setup, not a published context window. The repo also tags arXiv 2203.05482, which is the model-soup paper and is not this model's report.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gaggle is Castorini's pointwise reranker from Project Greenhouse. The soup checkpoint was posted 9 October 2026 under MIT, one day after the report. The four training runs are versions. They were still public on 15 October 2026.", "what_it_is_for": "Rerank the top 100 BM25 passages. The paper reports the soup once per collection. The four runs are the ingredients, not separate products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/gaggle/gaggle-nanochat": { "id": "waterloo/gaggle/gaggle-nanochat", "name": "Gaggle nanochat", "maker": "waterloo", "family": "waterloo/gaggle", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-09-25", "context_length": 2048, "max_output_tokens": null, "parameters": "3.29B", "architecture": "Decoder-only model trained from scratch with nanochat, depth 34, width 2176, 17 heads of dimension 128, RoPE, QK norm, RMSNorm, squared-ReLU MLP, untied embeddings, logit softcap, value embeddings on every other layer, and a sliding window of 512 on three of every four layers. The prose count is 3.29B: 1.93B in the transformer, 1.21B in value-embedding tables, and 0.14B in input and output embeddings. The pretraining table rounds this to 3.3B. Per-token compute is closer to a 2B dense model because the value embeddings are lookups.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Gaggle nanochat is Castorini's from-scratch language model for Project Greenhouse, posted 25 September 2026 under MIT. Jimmy Lin, Sahel Sharifymoghaddam, Lingwei Gu, and Nour Jedidi submitted the report on 8 October 2026. A bidirectional variant was posted 30 September 2026. Both were still public on 15 October 2026. The training mix is ClimbMix, which is not a Waterloo corpus.", "what_it_is_for": "Pretraining checkpoint for the Gaggle reranker. The paper's thesis is a fully open search model that does not start from a third-party backbone. The causal checkpoint is the one the reranker is fine-tuned from.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/general-reasoner/general-reasoner": { "id": "waterloo/general-reasoner/general-reasoner", "name": "General-Reasoner", "maker": "waterloo", "family": "waterloo/general-reasoner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RL-trained from Qwen2.5-7B, Qwen2.5-14B, Qwen3-4B-Base, and Qwen3-14B-Base on WebInstruct-verified. The GitHub says the 7B and 14B Qwen2.5 runs start from the base models, not the instruct models.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "General-Reasoner is TIGER-Lab's all-domain reasoning line, with Qwen2.5 checkpoints from April 2025 and Qwen3 checkpoints from May 2025. The paper is marked NeurIPS 2025 on the GitHub repo. Apache-2.0 weights were still public on 15 October 2026.", "what_it_is_for": "The lab trains a general verifier and then uses it so a base Qwen model can answer verifiable questions outside math. The released reasoners are the student checkpoints. The verifier is a separate folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/general-verifier/general-verifier": { "id": "waterloo/general-verifier/general-verifier", "name": "General-Verifier", "maker": "waterloo", "family": "waterloo/general-verifier", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-10", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Fine-tune of Qwen2.5-Math-1.5B used as a model-based verifier for General-Reasoner.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "General-Verifier is TIGER-Lab's 1.5B verifier, posted 10 April 2025 as a fine-tune of Qwen2.5-Math-1.5B under Apache-2.0. It was still public on the collection date.", "what_it_is_for": "The GitHub says it checks answers for verifiable questions and can be plugged into an RL trainer. It is the verifier used to scale General-Reasoner data, not the reasoner itself.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/lit5/lit5-distill": { "id": "waterloo/lit5/lit5-distill", "name": "LiT5-Distill", "maker": "waterloo", "family": "waterloo/lit5", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-12-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 listwise rerankers distilled from RankZephyr. Base, large, and XL, each with a v2 posted 20 May 2024. ECIR 2025 paper by Tamber, Pradeep, and Lin. The arXiv id was not captured.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LiT5-Distill is Castorini's distilled listwise T5 reranker. The first three sizes were posted 17 December 2023 and the v2 sizes on 20 May 2024. They were still public on 15 October 2026. License tags were empty.", "what_it_is_for": "It is meant to keep RankZephyr's listwise ranking in a smaller encoder-decoder. v2 is a later distill of the same product, not a new family.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/lit5/lit5-score": { "id": "waterloo/lit5/lit5-score", "name": "LiT5-Score", "maker": "waterloo", "family": "waterloo/lit5", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2023-12-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 models that emit a score rather than a permutation. Base, large, and XL, posted 17 December 2023. Same ECIR line as LiT5-Distill.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LiT5-Score is the scoring variant of Castorini's LiT5 line, posted with the distill checkpoints on 17 December 2023. The three sizes were still public on the collection date. License tags were empty.", "what_it_is_for": "Pointwise or score-style reranking distilled into T5, as the companion to LiT5-Distill. The card bodies were not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/mammoth/mammoth": { "id": "waterloo/mammoth/mammoth", "name": "MAmmoTH", "maker": "waterloo", "family": "waterloo/mammoth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-09-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Instruction fine-tunes of Llama-2, Code Llama, and Mistral. The lab ships 7B, 13B, and 70B Llama-2 checkpoints, Code Llama 7B, 13B, and 34B, and a Mistral 7B checkpoint. Exact non-embedding counts were not copied from the paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAmmoTH is a family of math generalist models from TIGER-Lab at the University of Waterloo. The weights were posted on Hugging Face starting 10 September 2023 and the paper was an ICLR 2024 spotlight. As of 15 October 2026 the checkpoints are still public. They are open weights under MIT.", "what_it_is_for": "The lab says MAmmoTH is built by hybrid instruction tuning on MathInstruct so the model can answer with a program or with a chain of thought. The recommended decode tries a program first and falls back to a written solution if the program does not run.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/mammoth-vl/mammoth-vl2": { "id": "waterloo/mammoth-vl/mammoth-vl2", "name": "MAmmoTH-VL2", "maker": "waterloo", "family": "waterloo/mammoth-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-08", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Fine-tune of MAmmoTH-VL/MAmmoTH-VL-8B, itself a LLaVA-OneVision-style model whose language backbone is Qwen2.5-7B. The Hub calls the result 8B. Loaded through LLaVA-NeXT as llava_qwen.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAmmoTH-VL2 is a vision-language model released by TIGER-Lab at the University of Waterloo on 8 March 2025. It is an Apache-2.0 fine-tune of MAmmoTH-VL-8B on VisualWebInstruct. The weights were still public on 15 October 2026.", "what_it_is_for": "The card describes it as the model trained with VisualWebInstruct, for multimodal questions that need a web-scale instruction mix. The sample prompt asks the model to prove a geometry statement from a picture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/mammoth2/mammoth2": { "id": "waterloo/mammoth2/mammoth2", "name": "MAmmoTH2", "maker": "waterloo", "family": "waterloo/mammoth2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-05-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-tuned from Mistral-7B, Llama-3-8B, and Mixtral-8x7B on WebInstruct (about 10 million pairs). Plus checkpoints continue on OpenHermes-2.5, CodeFeedback, and Math-Plus.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MAmmoTH2 is TIGER-Lab's follow-up to MAmmoTH, released on Hugging Face on 6 May 2024. The paper is a NeurIPS 2024 result. The checkpoints remain public open weights under MIT as of the 15 October 2026 collection.", "what_it_is_for": "The lab uses MAmmoTH2 to raise reasoning scores by instruction-tuning on question-answer pairs mined from web pretraining text, without a math-only corpus for the base MAmmoTH2 run. Plus models are the same lines after extra public instruction data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/mantis/mantis": { "id": "waterloo/mantis/mantis", "name": "Mantis", "maker": "waterloo", "family": "waterloo/mantis", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-04-13", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Multi-image instruction models. Public instructed checkpoints: Idefics2 8B (Apache-2.0), SigLIP plus Llama-3 (Llama 3 license), CLIP plus Llama-3 (Llama 3 license), Fuyu-8B (CC-BY-NC-4.0), LLaVA-1.5 7B, and BakLLaVA 7B. Projector-only pretrains are not the instructed models.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mantis is TIGER-Lab's multi-image vision-language family. The first LLaVA checkpoint was posted on 13 April 2024, and the 8B models followed in May 2024. The paper was accepted to TMLR 2024. Weights remain public. Licenses differ by backbone.", "what_it_is_for": "The lab trains Mantis on Mantis-Instruct so a vision-language model can answer questions that refer to more than one image. VLMEvalKit can evaluate it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/mdpr/mdpr": { "id": "waterloo/mdpr/mdpr", "name": "mDPR", "maker": "waterloo", "family": "waterloo/mdpr", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multilingual DPR with tied question and passage encoders in the main checkpoints, plus separate NQ question and passage encoders. MIRACL language fine-tunes are versions. Parameter count was not on the listing. The paper URL was not re-opened.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mDPR is Castorini's multilingual dense retriever for Mr. TyDi and MIRACL. The core repos show the 2 March 2022 import or a 26 May 2022 upload. The MIRACL language fine-tunes were posted in March 2023. They were still public on 15 October 2026. License tags were empty.", "what_it_is_for": "Dense retrieval of passages in many languages. The tied MS MARCO checkpoint is the shared starting point. The ft-all and per-language MIRACL repos are fine-tunes of that line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/monobert/monobert": { "id": "waterloo/monobert/monobert", "name": "MonoBERT", "maker": "waterloo", "family": "waterloo/monobert", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT-large cross-encoder. Two Hub repos: monobert-large-msmarco and monobert-large-msmarco-finetune-only. These are Castorini MS MARCO checkpoints, not a dump of another lab's BERT.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MonoBERT is Castorini's BERT-large passage reranker on MS MARCO. Both repos carry the 2 March 2022 import timestamp and were still public on the collection date. PyGaggle treats the BERT reranker as weaker than MonoT5.", "what_it_is_for": "Pointwise cross-encoder reranking of MS MARCO passages. The finetune-only repo is a training variant of the same product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/monoelectra/monoelectra": { "id": "waterloo/monoelectra/monoelectra", "name": "monoELECTRA", "maker": "waterloo", "family": "waterloo/monoelectra", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of google/electra-base-discriminator. One repo, castorini/monoelectra-base, tagged text-ranking. No license tag. The arXiv tag 2312.02724 is the RankZephyr paper, not an ELECTRA paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "monoELECTRA is a Castorini ELECTRA-base reranker posted 1 November 2025. It was still public on 15 October 2026. The card was not read. Confidence is medium because the only paper tag on the repo points at a different model.", "what_it_is_for": "Text ranking with an ELECTRA cross-encoder. Training data and scores were not on the listing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/monot5/monot5": { "id": "waterloo/monot5/monot5", "name": "MonoT5", "maker": "waterloo", "family": "waterloo/monot5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 models trained to emit a relevance token. Sizes on the Hub: small, base, large, and 3B, with 10k-step, 100k-step, and Med (biomedical) variants. Parameter counts beyond the T5 size name were not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MonoT5 is Castorini's pointwise T5 reranker, from the Nogueira, Jiang, and Lin ranking paper (14 March 2020). The checkpoints live under castorini on Hugging Face. Most show the 2 March 2022 org-import timestamp. They were still public on 15 October 2026. License tags were empty on the listing.", "what_it_is_for": "Given a query and a passage, the model scores relevance so a first-stage list can be reranked. PyGaggle uses castorini/monot5-base-msmarco as the default. The lab's later note is that 10k-step checkpoints transfer better out of domain and longer training fits MS MARCO better. Med checkpoints are the biomedical recommendation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/one-shot-cft/one-shot-cft": { "id": "waterloo/one-shot-cft/one-shot-cft", "name": "One-Shot CFT", "maker": "waterloo", "family": "waterloo/one-shot-cft", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-4.0", "release_date": "2025-06-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Critique fine-tunes posted as 1.5B and 7B Qwen2.5-Math models and a 14B model whose Hub base tag is Qwen2.5-14B, not the Math variant. License tag cc-by-4.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "One-Shot CFT is TIGER-Lab's June 2025 line. The three checkpoints were posted on 4 June 2025 under CC-BY-4.0 and were still public on 15 October 2026. It is not a version of the January CFT checkpoints.", "what_it_is_for": "The paper shows that critique data built from many solutions to one problem can raise math and logic scores. The abstract gives a 7B example of about 15 percent average gain on six math benchmarks and 16 percent on three logic benchmarks. Those averages were not copied as a table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/pixelreasoner/pixelreasoner": { "id": "waterloo/pixelreasoner/pixelreasoner", "name": "PixelReasoner", "maker": "waterloo", "family": "waterloo/pixelreasoner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-15", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Qwen2.5-VL-7B-Instruct fine-tune. WarmStart is the supervised start. PixelReasoner-RL-v1 is the reinforcement-learning checkpoint.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PixelReasoner is TIGER-Lab's pixel-level visual reasoner. The warm-start checkpoint was posted 15 May 2025 and the RL v1 checkpoint on 18 May 2025. Apache-2.0. Still public on the collection date.", "what_it_is_for": "The RL checkpoint is the model the paper evaluates. WarmStart is the earlier supervised checkpoint from the same data and is kept as a version.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/rankllama/rankllama": { "id": "waterloo/rankllama/rankllama", "name": "RankLLaMA", "maker": "waterloo", "family": "waterloo/rankllama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2", "release_date": "2023-10-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pointwise reranker. LoRA on Llama 2. Passage checkpoints at 7B and 13B, plus a 7B document checkpoint. The paper is the same study as RepLLaMA.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RankLLaMA is Castorini's pointwise Llama reranker, posted 11 October 2023 under the Llama 2 license. The paper by Ma, Wang, Yang, Wei, and Lin was submitted the next day. Microsoft is on the author list. The weights Castorini released are foldered here. They were still public on the collection date.", "what_it_is_for": "It scores one query-passage or query-document pair. The paper uses it as the second stage after RepLLaMA. The 13B and document LoRAs are size and task variants, not separate products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/rankvicuna/rankvicuna": { "id": "waterloo/rankvicuna/rankvicuna", "name": "RankVicuna", "maker": "waterloo", "family": "waterloo/rankvicuna", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2", "release_date": "2023-09-27", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Listwise reranker fine-tuned from Vicuna, which is itself a Llama 2 chat model. Hub license tag llama2. The 2307.09288 tag on the repo is the Llama 2 paper. The model paper is 2309.15088.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RankVicuna is Castorini's 7B listwise reranker, posted 27 September 2023. The paper was submitted the day before by Pradeep, Sharifymoghaddam, and Lin. Quantized and fp16 repos are formats of the same checkpoint. They were still public on 15 October 2026.", "what_it_is_for": "Zero-shot listwise reranking with a fully open 7B model. The abstract says TREC 2019 and 2020 effectiveness is comparable to GPT-3.5 reranking and a bit behind GPT-4.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/rankzephyr/rankzephyr": { "id": "waterloo/rankzephyr/rankzephyr", "name": "RankZephyr", "maker": "waterloo", "family": "waterloo/rankzephyr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-01-19", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Listwise reranker. The full checkpoint is further tuned on RankGPT-4 reorderings. The base checkpoint is a single stage on RankGPT-3.5. Hub base tag is Mistral-7B-v0.1. The card describes the model as Zephyr-7B-beta. English only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RankZephyr is Castorini's open listwise reranker, posted 19 January 2024 under MIT. The paper was submitted 5 December 2023 by Pradeep, Sharifymoghaddam, and Lin. Both the full and base checkpoints were still public on 15 October 2026.", "what_it_is_for": "It reads a query plus a list of passages and writes an ordering. The full model is the one RankLLM treats as the product. The base model is the earlier training stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/rational-rewards/rational-rewards": { "id": "waterloo/rational-rewards/rational-rewards", "name": "RationalRewards", "maker": "waterloo", "family": "waterloo/rational-rewards", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-04-10", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Two Qwen3-8B fine-tunes, one for image editing reward and one for text-to-image reward. Hub tags also say qwen3_vl. Dataset RationalRewards_DiffusionNFT_TrainData.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RationalRewards is a TIGER-Lab reward pair posted on 10 April 2026 under Apache-2.0. The Hub lists an edit model and a text-to-image model, both named 8B. They were still public on 15 October 2026.", "what_it_is_for": "The two checkpoints score diffusion edits and text-to-image outputs. They are versions of one release, not two product lines.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/repllama/repllama": { "id": "waterloo/repllama/repllama", "name": "RepLLaMA", "maker": "waterloo", "family": "waterloo/repllama", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2", "release_date": "2023-10-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dense retriever LoRA on Llama 2. Passage and document 7B checkpoints, plus a later Matryoshka (MRL) passage checkpoint from February 2024. This is not RankLLaMA.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RepLLaMA is Castorini's dense Llama retriever, posted with RankLLaMA on 11 October 2023 under the Llama 2 license. The MRL variant was posted 29 February 2024. All three were still public on 15 October 2026.", "what_it_is_for": "First-stage retrieval by embedding the query and the passage. The paper says a long-context Llama can embed a whole document without the old max-passage split.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/scholarcopilot/scholarcopilot": { "id": "waterloo/scholarcopilot/scholarcopilot", "name": "ScholarCopilot", "maker": "waterloo", "family": "waterloo/scholarcopilot", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-12-07", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Fine-tune of Qwen2.5-7B. The abstract says training used 500K arXiv papers. The model emits a retrieval token, fetches a citation, and continues. One public repo, ScholarCopilot-v1. The Hub listing had no pipeline tag.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ScholarCopilot-v1 is TIGER-Lab's citation-aware writing model, posted 7 December 2024 under Apache-2.0. The paper was submitted 1 April 2025. The checkpoint was still public on the collection date.", "what_it_is_for": "It drafts academic text and inserts citations by retrieving when it emits a retrieval token. The abstract reports retrieval and writing scores on the lab's own sets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/slimr/slimr": { "id": "waterloo/slimr/slimr", "name": "SLIM", "maker": "waterloo", "family": "waterloo/slimr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-02-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Each token is mapped to a sparse lexical vector, then late interaction runs in that space. Two MS MARCO passage checkpoints, posted 16 February 2023. Both are tagged fill-mask. License tags were empty.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SLIM is Castorini's sparse multi-vector retriever, posted 16 February 2023. The paper by Li, Lin, Ma, and Lin was submitted 13 February 2023 and is SIGIR 2023. Both checkpoints were still public on the collection date.", "what_it_is_for": "Retrieval with ColBERT-style late interaction that still runs on an ordinary inverted index. The pp checkpoint is the pruned or preprocessed variant. Pyserini documents the experiment. Scores were not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/structlm/structlm": { "id": "waterloo/structlm/structlm", "name": "StructLM", "maker": "waterloo", "family": "waterloo/structlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-02-25", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The paper says the series is trained from Mistral and CodeLlama, from 7B to 34B, on 1.1 million structured-knowledge instruction examples. Hub repos are StructLM-7B, StructLM-13B, StructLM-34B, and StructLM-7B-Mistral. Which Hub repo maps to CodeLlama versus another Llama was not on the tag list.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "StructLM is TIGER-Lab's structured-knowledge line, posted on 25 February 2024 with a Mistral checkpoint on 5 April 2024. The paper was submitted 26 February 2024. MIT weights were still public on 15 October 2026.", "what_it_is_for": "The lab instruction-tunes the models to read tables, graphs, and databases. The abstract says they beat task-specific models on 16 of 18 evaluated datasets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/swe-next/swe-next": { "id": "waterloo/swe-next/swe-next", "name": "SWE-Next", "maker": "waterloo", "family": "waterloo/swe-next", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SWE-Next-7B and SWE-Next-14B are fine-tunes of Qwen2.5-Coder-7B-Instruct and Qwen2.5-Coder-14B-Instruct. The abstract says the data pipeline kept 2,308 self-verifying instances from 3,971 repositories and 102,582 candidate commit pairs.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SWE-Next is TIGER-Lab's software-engineering agent line, posted 2 April 2026 under MIT. The paper was submitted 21 March 2026. Both sizes were still public on 15 October 2026.", "what_it_is_for": "The checkpoints are trained on trajectories from real merged pull requests that the lab filtered by running tests. The abstract says pass@1 improves with fewer or comparable trajectories. It does not give the pass@1 number.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/tct-colbert/tct-colbert": { "id": "waterloo/tct-colbert/tct-colbert", "name": "TCT-ColBERT", "maker": "waterloo", "family": "waterloo/tct-colbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single-vector dense retrievers distilled from ColBERT's MaxSim teacher. The original checkpoint is tct_colbert-msmarco. v2 adds hard negatives, and the hnp variant is the usual Pyserini checkpoint. A second-round hnp model and a corpus-query-expansion variant are also on the Hub.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TCT-ColBERT is Castorini's distilled dense retriever, from Lin, Yang, and Lin (22 October 2020). The Hub family was still public on 15 October 2026. Timestamps are the 2 March 2022 import. License tags were empty.", "what_it_is_for": "It replaces ColBERT's late interaction with one dot product so the index can be searched with approximate nearest neighbors. v2-hnp is the checkpoint later Waterloo regressions usually mean by TCT-ColBERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/tigerscore/tigerscore": { "id": "waterloo/tigerscore/tigerscore", "name": "TIGERScore", "maker": "waterloo", "family": "waterloo/tigerscore", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-11-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama-2 instruction metric trained on MetricInstruct. The abstract says MetricInstruct has 42K quadruples covering 6 tasks and 23 datasets. Public Hub checkpoints are 7B and 13B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TIGERScore is TIGER-Lab's explainable evaluation model, posted on 26 November 2023 under MIT. The paper was submitted 1 October 2023 and later appeared at TMLR. Both Hub checkpoints were still public on the collection date.", "what_it_is_for": "Given an instruction and a system output, the model writes an error analysis instead of only a score. The abstract says a human check found the explanations 70.8 percent accurate.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/unicoil/unicoil": { "id": "waterloo/unicoil/unicoil", "name": "uniCOIL", "maker": "waterloo", "family": "waterloo/unicoil", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Impact-weighted bag-of-tokens retriever. Passage and no-expansion MS MARCO checkpoints, plus an ONNX export of the same model. The Lin and Ma uniCOIL paper was not re-opened this pass.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "uniCOIL is Castorini's learned sparse retriever. The passage and no-expansion repos show the 2 March 2022 import timestamp. The ONNX repo was posted 29 May 2025. All three were still public on the collection date.", "what_it_is_for": "It assigns one impact weight to each token so a Lucene inverted index can run the retrieval. The no-expansion checkpoint skips doc2query. The ONNX file is a runtime format, not a new model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/unir/unir": { "id": "waterloo/unir/unir", "name": "UniIR", "maker": "waterloo", "family": "waterloo/unir", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2023-12-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "One Hub repo, TIGER-Lab/UniIR, MIT, created 1 December 2023. The model card says the repo contains Large UniIR (CLIP score-fusion) and UniIR (BLIP feature-fusion), loaded by cloning the repo. Parameter counts were not read from the card.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UniIR is TIGER-Lab's instruction-guided multimodal retriever, posted 1 December 2023 under MIT. The paper was submitted 28 November 2023. The repo was still public on the collection date, with zero downloads on the listing that was read.", "what_it_is_for": "One retriever handles eight retrieval tasks across text and images by reading a user instruction. The paper also releases the M-BEIR benchmark. Score tables were not copied.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/vamba/vamba": { "id": "waterloo/vamba/vamba", "name": "Vamba", "maker": "waterloo", "family": "waterloo/vamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-03-13", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Hybrid Mamba-Transformer video model. The only public Hub repo is Vamba-Qwen2-VL-7B, tagged qwen2_vl and video-text-to-text. The listing had no base_model tag. The abstract says it encodes more than 1024 frames at 640 by 360 on one GPU, against 256 frames for the transformer comparison.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vamba is TIGER-Lab's long-video model, posted 13 March 2025 under MIT. The paper is the ICCV 2025 camera-ready of arXiv 2503.11579. The checkpoint was still public on the collection date.", "what_it_is_for": "It answers questions about hour-long video by running Mamba-2 over the video tokens. The abstract reports a 4.3 point gain on LVBench versus prior efficient video models, at least a 50 percent cut in GPU memory, and nearly twice the training-step speed.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/videoscore/videoscore": { "id": "waterloo/videoscore/videoscore", "name": "VideoScore", "maker": "waterloo", "family": "waterloo/videoscore", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-06-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video raters. The first checkpoint is Idefics2 (Apache-2.0 on the v1 repo; v1.1 is tagged MIT). A later Qwen2-VL checkpoint is also on the Hub.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoScore is TIGER-Lab's video evaluation model, first posted on 19 June 2024, with v1.1 on 28 November 2024. It scores generated video from the VideoFeedback data. Weights are open. VideoScore2 replaced it as the lab's later rater.", "what_it_is_for": "The checkpoints are automatic metrics for video generation. v1.1 is the maintained Idefics2 rater. VideoScore-Qwen2-VL is the same line on a Qwen2-VL backbone.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/videoscore/videoscore2": { "id": "waterloo/videoscore/videoscore2", "name": "VideoScore2", "maker": "waterloo", "family": "waterloo/videoscore", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-09-19", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Fine-tune of Qwen2.5-VL-7B-Instruct on VideoFeedback2. SFT-only and RL-without-SFT checkpoints are training variants.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VideoScore2 is TIGER-Lab's September 2025 video rater, a Qwen2.5-VL-7B-Instruct fine-tune under Apache-2.0. The primary checkpoint was posted on 19 September 2025 and was still public on the collection date.", "what_it_is_for": "It rates generated video. The primary repo is VideoScore2. The SFT-only and RL-without-SFT repos are ablations from the same paper and are versions here, not separate products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/viscoder/viscoder": { "id": "waterloo/viscoder/viscoder", "name": "VisCoder", "maker": "waterloo", "family": "waterloo/viscoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-06-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tunes of Qwen2.5-Coder-3B-Instruct and Qwen2.5-Coder-7B-Instruct on VisCode-200K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VisCoder is TIGER-Lab's first visual-code generation line, posted 4 June 2025 as 3B and 7B Qwen2.5-Coder fine-tunes under Apache-2.0. Both were still public on the collection date.", "what_it_is_for": "The models generate code for visual tasks from the VisCode-200K set. VisCoder2 is the later multilingual line.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/viscoder/viscoder2": { "id": "waterloo/viscoder/viscoder2", "name": "VisCoder2", "maker": "waterloo", "family": "waterloo/viscoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-10-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tunes of Qwen2.5-Coder instruct models at 3B, 7B, 14B, and 32B on VisCode-Multi-679K. The 7B, 14B, and 32B repos are tagged image-text-to-text. The 3B repo is tagged text-generation.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VisCoder2 is TIGER-Lab's 29 October 2025 multilingual visual-code line, four Qwen2.5-Coder sizes under Apache-2.0. The weights were still public on 15 October 2026.", "what_it_is_for": "The paper id on the repos is the VisCoder2 follow-up, trained on VisCode-Multi-679K. Sizes are versions of one release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/vista/vista": { "id": "waterloo/vista/vista", "name": "VISTA", "maker": "waterloo", "family": "waterloo/vista", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2024-12-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The same data recipe fine-tuned onto three video backbones: VISTA-LongVA, VISTA-Mantis (the lab's Idefics2 Mantis), and VISTA-VideoLLaVA. All three Hub repos are MIT and tagged video-text-to-text. Parameter counts were not on the tags.", "modalities": { "input": [ "text", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VISTA is TIGER-Lab's December 2024 long-video fine-tune line. Three backbone checkpoints were posted on 2 December 2024 under MIT and were still public on the collection date. The paper was submitted the same day. The arXiv comment points at a project page. A later camera-ready note in secondary writeups calls it CVPR 2025. That venue was not on the abs page that was read, so the paper date used here is the submission date.", "what_it_is_for": "The lab builds long and high-resolution instruction pairs by splicing existing video-caption clips, then fine-tunes video models on VISTA-400K. The abstract says the fine-tunes gain 3.3 points on average across four long-video benchmarks and 6.5 points on HRVideoBench.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/vl-reasoner/vl-reasoner": { "id": "waterloo/vl-reasoner/vl-reasoner", "name": "VL-Reasoner", "maker": "waterloo", "family": "waterloo/vl-reasoner", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2.5-VL instruct fine-tunes published as VL-Reasoner-7B and VL-Reasoner-72B. The 72B repo's base tag is Qwen2.5-VL-72B-Instruct. The 7B repo's base tag is Qwen2.5-VL-7B-Instruct.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VL-Reasoner is the other named checkpoint line TIGER-Lab posted for arXiv 2504.08837. The 72B repo appeared on 2 April 2025 and the 7B repo on 15 April 2025. Apache-2.0 weights were still public on the collection date.", "what_it_is_for": "The lab publishes these as visual question-answering models under the VL-Reasoner name, separate from the VL-Rethinker repos.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/vl-rethinker/vl-rethinker": { "id": "waterloo/vl-rethinker/vl-rethinker", "name": "VL-Rethinker", "maker": "waterloo", "family": "waterloo/vl-rethinker", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-04-13", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Fine-tunes branded as 7B, 32B, and 72B. The 7B card says the base is Qwen2.5-VL-7B-Instruct. The 72B repo's base-model tag also points at Qwen2.5-VL-7B-Instruct, which conflicts with the 72B name. The 32B repo had almost no tags when listed.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VL-Rethinker is a TIGER-Lab vision-language reasoning line posted from April 2025, Apache-2.0. The 7B checkpoint is a Qwen2.5-VL-7B-Instruct fine-tune. It was still public on 15 October 2026.", "what_it_is_for": "The shared paper id 2504.08837 is the lab's visual reasoning RL work. These checkpoints are the Rethinker sizes. VL-Reasoner checkpoints from the same arXiv id are a separate folder because the lab published them under a different name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/vlm2vec/vlm2vec": { "id": "waterloo/vlm2vec/vlm2vec", "name": "VLM2Vec", "maker": "waterloo", "family": "waterloo/vlm2vec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-conditioned contrastive fine-tunes. Full and LoRA checkpoints start from Phi-3.5-vision. Later checkpoints use Qwen2-VL-2B, Qwen2-VL-7B, and LLaVA-NeXT (LLaVA-1.6 Mistral). The embedding is the final end-of-sequence token, pooled last and L2-normalized.", "modalities": { "input": [ "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VLM2Vec is TIGER-Lab's multimodal embedding line, with the first Phi-3.5 checkpoints posted on 8 October 2024. The paper is ICLR 2025. Salesforce researchers are co-authors, and the weights Waterloo hosts are on TIGER-Lab. They were still public on 15 October 2026 under Apache-2.0.", "what_it_is_for": "The lab converts an existing vision-language model into one embedding model for classification, visual question answering, retrieval, and grounding, using instructions plus the MMEB training split.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "waterloo/vlm2vec/vlm2vec-v2": { "id": "waterloo/vlm2vec/vlm2vec-v2", "name": "VLM2Vec-V2", "maker": "waterloo", "family": "waterloo/vlm2vec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-05-01", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Qwen2-VL contrastive embedding model. The paper author said the released checkpoint is 2B. A Hub discussion says the README example still names Qwen2-VL-7B while config.json is the 2B model. The repo tree shown on 13 July 2025 was about 128 MB.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VLM2Vec-V2 is the TIGER-Lab and VLM2Vec-org follow-up that embeds text, images, video, and visual documents. The tech report is dated 6 July 2025 and the lab says the first weights shipped in May 2025. Apache-2.0. Still listed on 15 October 2026.", "what_it_is_for": "The project page says VLM2Vec-V2 is a general embedding model for video and visual-document retrieval as well as the original image tasks in MMEB-V2.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "weizmann/brain-encoder/universal-brain-encoder": { "id": "weizmann/brain-encoder/universal-brain-encoder", "name": "Universal Brain Encoder", "maker": "weizmann", "family": "weizmann/brain-encoder", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2024-06-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Voxel-centric image-to-fMRI encoder. Each voxel has a learned embedding of size E=256. These vectors are randomly initialized and optimized. They are not the output of a subnetwork. The shared feature extractor is a LoRA-adapted DINOv2 ViT-L/14. Features come from five layers (1, 6, 12, 18, and 24). Each layer is projected to a width C and concatenated to L by P by C. The paper does not state the numeric value of C. A cross-attention block does spatial attention (query is the linearly mapped voxel embedding; keys add a learned P by E positional embedding), then a separate two-layer MLP per feature layer, then functional attention that emits one scalar activation. Loss is 0.1 times MSE minus 0.9 times cosine similarity. Joint NSD-8 training is described as about one day on one Quadro RTX 8000. Inference is about 50 ms per image. Appendix training also states Adam at learning rate 1e-3, batch size 32, and 5,000 voxel indices sampled per image.", "modalities": { "input": [ "image" ], "output": [ "fmri" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Universal Brain Encoder is an image-to-fMRI model from the Weizmann Vision lab (Michal Irani). The paper was posted on 2024-06-17. A checkpoint file named encoder_ch128.pth is public inside the Brain-IT weight repository. On 2026-10-15 that file still had no model card.", "what_it_is_for": "The paper describes a voxel-centric encoder that predicts the fMRI response of each brain voxel to an image. Shared weights train across subjects, scanners, and datasets. A new subject is added by training only that subject's 256-dimensional voxel embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "weizmann/brain-it/brain-it": { "id": "weizmann/brain-it/brain-it", "name": "Brain-IT", "maker": "weizmann", "family": "weizmann/brain-it", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-SA-4.0", "release_date": "2026-01-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Brain Interaction Transformer (BIT) from the Weizmann Vision lab. About 40,000 NSD voxels are assigned by a Gaussian mixture model on Universal Brain Encoder embeddings to 128 shared functional clusters. The Brain Tokenizer uses 512-d voxel embeddings and 512-d cluster embeddings with single-head graph attention and emits 512-d Brain Tokens. The cross-transformer uses eight-head self- and cross-attention: one initial cross-attention block, then five cross-transformer blocks, then a projection. The semantic head predicts 256 spatial OpenCLIP ViT-bigG/14 tokens and jointly trains an unCLIP-style SDXL (the MindEye2 conditioning setup) on those tokens. The low-level head predicts multi-layer VGG features with InfoNCE and inverts them with a Deep Image Prior. Inference initializes diffusion from that DIP image (38 denoising steps, starting at schedule step 14) and can run a later SDXL refinement. Appendix D trains both heads for 60 epochs with AdamW at learning rate 5e-4 and a 15-epoch warmup. Batch size is 64 for the low-level head and 128 for semantic stage 1. Reported H100 times: low-level 12 hours on 1 GPU, semantic stage 1 8 hours on 1 GPU, stage 2 10 hours on 4 GPUs.", "modalities": { "input": [ "fmri" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Brain-IT is an fMRI-to-image reconstruction model from the Weizmann Vision lab (Michal Irani). It was published as an ICLR 2026 poster on 2026-01-26, with weights on Hugging Face under CC-BY-NC-SA-4.0. On 2026-10-15 the weights were public and the model card was empty.", "what_it_is_for": "The paper presents Brain-IT as a way to reconstruct images a person saw from 7T fMRI. A Brain Interaction Transformer predicts localized semantic and low-level image features from shared functional voxel clusters, and a dual branch turns those features into an image. With one hour of data from a new subject, the authors report results comparable to earlier methods trained on about 40 hours.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "weizmann/gluformer/gluformer": { "id": "weizmann/gluformer/gluformer", "name": "GluFormer", "maker": "weizmann", "family": "weizmann/gluformer", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-20", "context_length": 25000, "max_output_tokens": null, "parameters": "", "architecture": "Causal Transformer encoder trained for next-token prediction on tokenized continuous glucose monitoring. The published training script defaults are 16 layers, 16 heads, embedding width 1024, feed-forward width 2048, dropout 0.1, max sequence 25000, chunk size 1200, vocabulary 460 glucose bins plus a pad id at 460, sinusoidal positions, and a linear head. The script comment says the stack should be like BERT, and the mask and the loss are causal next-token cross-entropy. Defaults also set 76 epochs, learning rate 5e-5, weight decay 0, step size 100, gamma 0.99, and batch 32 per GPU. The script logs a parameter count at runtime and does not hard-code one. The README says training used NVIDIA A40 or A100 GPUs and that generation covers 24-hour horizons. A dietary-token extension is part of this model, not a second product.", "modalities": { "input": [ "glucose" ], "output": [ "glucose" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GluFormer is a generative foundation model for continuous glucose monitoring from the Weizmann Segal lab, with NVIDIA Research and Pheno.AI. The preprint appeared on 2024-08-20. Nature published it online on 2026-01-14. Training code is public under Apache-2.0. A pretrained checkpoint was not in the repository on 2026-10-15, so the weights are closed.", "what_it_is_for": "The Nature abstract presents GluFormer as a self-supervised model of glucose dynamics. It forecasts glycaemic measures, stratifies later diabetes and cardiovascular death, and, with a dietary-token extension, generates glucose trajectories after food. The repository README describes the same uses: forecasting, imputation, risk ranking, and meal-response simulation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "weizmann/spec2class/spec2class": { "id": "weizmann/spec2class/spec2class", "name": "Spec2Class", "maker": "weizmann", "family": "weizmann/spec2class", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-03-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Ensemble for plant secondary-metabolite class from LC-MS/MS. Spectra are binned to a length-5000 vector covering 50 to 550 Da at 0.1 Da. Forty-three binary networks, one per NPClassifier superclass that had at least 40 spectra, each have two 1D convolutional layers and three fully connected layers, following the SteroidXtract layout. Their 43 probabilities feed an RBF SVM (gamma 0.01, C 100) that emits one class and a confidence margin p1-p2. The paper's final binary-model hyperparameters are learning rate 0.0001, batch size 128, and dropout 0.95 on both the convolutional and linear stages. Positive-mode weights are the 2023 release. Negative-mode checkpoints and the SVM stage are later files of this same product, not separate models.", "modalities": { "input": [ "spectrum" ], "output": [ "class-label" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Spec2Class is a plant-metabolite class model from the Weizmann labs of Asaph Aharoni and David Zeevi. Positive-mode binary weights were posted on 2023-03-20. The bioRxiv paper is dated 2024-03-17 and was still a preprint on 2026-10-15. The primary weight repository has no license tag.", "what_it_is_for": "The paper presents Spec2Class as a way to assign a plant secondary metabolite to a chemical superclass from an LC-MS/MS spectrum when a reference library match is missing. Forty-three binary networks score class membership. An SVM turns those scores into one label plus a confidence margin.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wellsaid/caruso/caruso": { "id": "wellsaid/caruso/caruso", "name": "Caruso", "maker": "wellsaid", "family": "wellsaid/caruso", "status": "active", "confidence": "high", "weights": "closed", "license": "Proprietary. Weights are not released. Paid Studio plans include commercial rights; the free Studio trial does not.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed model. WellSaid said Caruso uses a new architecture built from the ground up. No parameter count or architecture paper was published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Caruso is WellSaid Labs' named next-generation text-to-speech model. It is active in WellSaid Studio and on the API as `model: \"caruso\"`. WellSaid announced it on 2025-01-15 for a Q1 2025 launch and later described a March 2025 first release that was still in beta in June 2025. This dossier was collected on 2026-10-15.", "what_it_is_for": "WellSaid says Caruso is the performance engine for studio-quality voiceover with more natural pronunciation and with AI Director controls for pitch, tempo, loudness, and respelling. The API model-selection page says it supports English voices only. Studio help says it is available to users with an active Studio or API subscription.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wellsaid/global/global": { "id": "wellsaid/global/global", "name": "Global", "maker": "wellsaid", "family": "wellsaid/global", "status": "active", "confidence": "medium", "weights": "closed", "license": "Proprietary for first-party WellSaid voices. Most voices on the Global Voices page are labeled Partner and are third-party. Paid plans that include those languages include commercial rights; the free trial does not.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not published. Studio treats Global as a project model for non-English languages. The voice list mixes a few first-party WellSaid voices with many Partner voices. Whether Partner audio is WellSaid's own weights or a hosted third-party engine is not stated.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Global is the WellSaid Studio model name for non-English voice generation. Help Center lists it beside Caruso and Standard as a project model, limited to Enterprise for global languages. It is not an API `model` value. Calls pick a speaker, and the voice carries the language. Collected on 2026-10-15.", "what_it_is_for": "WellSaid uses Global so Enterprise customers can generate speech in languages other than the English Caruso and Standard engines. The 2025-11-07 Global Voices page is a voice inventory, not a model card. It marks each voice WellSaid (first-party) or Partner (third-party). First-party names on that page are four German voices and five Mexican Spanish voices. Arabic, Chinese, Czech, Danish, Dutch, French, Italian, Japanese, Korean, Persian, Polish, Portuguese, Swedish, Turkish, and the other Spanish and German names on that page are Partner.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wellsaid/legacy/legacy": { "id": "wellsaid/legacy/legacy", "name": "Legacy (Standard)", "maker": "wellsaid", "family": "wellsaid/legacy", "status": "active", "confidence": "medium", "weights": "closed", "license": "Proprietary. Weights are not released. Paid Studio plans include commercial rights; the free Studio trial does not.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed. WellSaid has not published an architecture or parameter count for the legacy model. An August 2022 post said a then-new voice model used a different type of machine learning from the prior one, but that post does not use the name Legacy or Standard.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Legacy is WellSaid Labs' original text-to-speech API model. The API id is `legacy`, and it is the default when a request omits `model`. Studio help calls the paired older English engine Standard. This dossier treats those names as one model and marks that pairing as an inference. It was still in the API enum and still offered in unified Studio on 2026-10-15. Collected on 2026-10-15.", "what_it_is_for": "The API guide calls it the original WellSaid voice model: natural speech and most core voice features, without Caruso's AI Director tags. Studio help describes Standard as the model for consistent results when a project does not need Caruso's newer controls. English voice styles such as Narration, Conversational, Promo, and Character are selected with a speaker id, not with a separate model id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wellsaid/preview/preview-13-2": { "id": "wellsaid/preview/preview-13-2", "name": "Preview 13.2", "maker": "wellsaid", "family": "wellsaid/preview", "status": "preview", "confidence": "medium", "weights": "closed", "license": "Proprietary. Preview output is for testing, not production. Commercial terms of the surrounding paid plan were not restated for preview audio.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Preview 13.2 is a named upcoming WellSaid speech model. Release notes tell users to open it from Preview Models, then 13.2, on any voice. The preview program is for testing before a full release. Collected on 2026-10-15. Confidence is medium because the notes that name 13.2 are undated, and the later preview article does not repeat that number.", "what_it_is_for": "WellSaid says preview models let paid customers try the next voice model in a current project and send feedback. A render creates a new take and does not replace older takes. The release notes call 13.2 the latest addition to the upcoming voice models at the time that note was written.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "western/clar/clar": { "id": "western/clar/clar", "name": "CLAR", "maker": "western", "family": "western/clar", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2020-10-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ResNet-18 encoder trained from random initialization. 2D ResNet-18 takes a 3-channel stack of log-power magnitude STFT, Mel spectrogram, and phase (128 Mel bins; 16 ms window, 8 ms stride, audio at 16 kHz). 1D ResNet-18 uses 1D convolutions, pooling, and batch norm, with squared kernel sizes so the parameter count matches the 2D net. The paper does not print that count. A three-layer fully connected projection head maps pooled encoder features to a 128-d vector for NT-Xent. Semi-supervised CLAR adds a classification layer and trains NT-Xent and cross-entropy together. No ImageNet initialization.", "modalities": { "input": [ "audio" ], "output": [ "embedding", "labels" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CLAR (Contrastive Learning of Auditory Representations) is a Western University computer-science model from Haider Al-Tahan and Yalda Mohsenzadeh. It learns audio embeddings with a ResNet-18 encoder trained from scratch, combining contrastive and supervised losses instead of a separate fine-tune. The arXiv preprint is 19 October 2020 and the paper is in AISTATS 2021. As of 2026-10-15 the author repository still links final checkpoints. There is no API. Confidence is medium because the checkpoint folder was not opened.", "what_it_is_for": "The authors present CLAR as a way to learn auditory representations for speech, music, and environmental sound. The encoder is frozen and a small evaluation head is trained to measure representation quality. The reported headline task is digit and command recognition on Speech Commands.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "western/hippunfold/hippunfold": { "id": "western/hippunfold/hippunfold", "name": "HippUnfold", "maker": "western", "family": "western/hippunfold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2021-02-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "nnU-Net 3D full-resolution U-Net. The eLife figure caption says the network was automatically configured as 3D_fullres on 128 x 256 x 128 hippocampal crops at 0.3 mm, with conv3D blocks, instance normalization, and leaky ReLU (negative slope 0.01). The app then unfolds the segmentation with Laplace coordinates. Modality-specific checkpoints are selected with --modality or --force-nnunet-model.", "modalities": { "input": [ "image" ], "output": [ "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HippUnfold is an open BIDS app from the Khan Computational Imaging Lab at Western University (London, Ontario) that segments hippocampal tissue with nnU-Net checkpoints and unfolds the hippocampus for morphometry and subfield labels. It is active as of 2026-10-15. The GitHub README describes version 2.0 as the current line. Weights are separate Zenodo archives, not a hosted API.", "what_it_is_for": "The lab says the tool models hippocampal folding and unfolds it for visualization, topology-constrained registration, parcellation, morphometry, and quantitative mapping. The neural step labels gray matter, the stratum radiatum, lacunosum, and moleculare, and landmarks used for unfolding. Later steps inject a template shape and build surfaces. Western's Schulich news (January 2023) describes it as an open-source app for MRI of the hippocampus.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/cryo-ief/cryo-ief": { "id": "westlake/cryo-ief/cryo-ief", "name": "Cryo-IEF", "maker": "westlake", "family": "westlake/cryo-ief", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT code; card says academic research only and commercial use needs permission", "release_date": "2025-05-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cryo-EM image encoder pretrained with unsupervised contrastive learning on about 65 million particle images. The GitHub stack uses timm vision transformers. Parameter count and the chosen backbone size are in Nature Methods extended data that was not transcribed. Pipeline tag on the Hub is image-feature-extraction.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cryo-IEF is Westlake's cryo-EM particle foundation model from the Representation Learning Lab. Hub weights date to 2025-05-26. Nature Methods published the paper on 2025-11-27. Collected 2026-10-15.", "what_it_is_for": "The paper uses the encoder to classify particles by structure, cluster them by pose, and judge image quality. A fine-tuned ranker feeds CryoWizard, an automated single-particle processing pipeline.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/cryoverse/cryoverse": { "id": "westlake/cryoverse/cryoverse", "name": "cryoVERSE", "maker": "westlake", "family": "westlake/cryoverse", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "MIT", "release_date": "2026-09-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Not stated. The Hub card is only an MIT license tag. The repo contains cryoVERSE/model.safetensors, cryoVERSE_picker/model.pth, and cryoVERSE_ranker/model.safetensors. Last modified 2026-09-23.", "modalities": { "input": [], "output": [] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "cryoVERSE is a weight release on the westlake-repl Hugging Face org, created 2026-09-16. The card does not say what the model does. Collected 2026-10-15.", "what_it_is_for": "The maker did not describe a task on the card that was read. File names include a picker and a ranker beside the main weight file, which is consistent with a cryo-EM tool, but that use is not stated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/cycleresearcher/cycleresearcher-123b": { "id": "westlake/cycleresearcher/cycleresearcher-123b", "name": "CycleResearcher 123B", "maker": "westlake", "family": "westlake/cycleresearcher", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CycleResearcher license for weights; Apache-2.0 for repository code", "release_date": "2024-10-22", "context_length": null, "max_output_tokens": null, "parameters": "123B", "architecture": "Fine-tune of mistralai/Mistral-Large-Instruct-2407 (Mistral Large 2). Supervised research-paper generator. Card: safety weights are the public non-Original repo. Original repos are the earlier upload. Code in the repository is Apache-2.0. Weights use the CycleResearcher license. Knowledge cutoff on the card is October 2024. Six Hub language tags (en, zh, ja, ko, fr, de) match the base model's tags and were not re-tested by this lab in the text that was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CycleResearcher 123B is the Mistral Large 2 paper generator from WestlakeNLP. The Hub repo CycleResearcher-123B-Original dates to 2024-10-22. Collected 2026-10-15.", "what_it_is_for": "Same ML paper-generation series. On the lab's reviewer table it scores below the 12B and 72B models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/cycleresearcher/cycleresearcher-12b": { "id": "westlake/cycleresearcher/cycleresearcher-12b", "name": "CycleResearcher 12B", "maker": "westlake", "family": "westlake/cycleresearcher", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CycleResearcher license for weights; Apache-2.0 for repository code", "release_date": "2024-10-24", "context_length": null, "max_output_tokens": null, "parameters": "12B", "architecture": "Fine-tune of mistralai/Mistral-Nemo-Instruct-2407 for machine-learning paper generation. Supervised research-paper generator. Card: safety weights are the public non-Original repo. Original repos are the earlier upload. Code in the repository is Apache-2.0. Weights use the CycleResearcher license. Knowledge cutoff on the card is October 2024. Six Hub language tags (en, zh, ja, ko, fr, de) match the base model's tags and were not re-tested by this lab in the text that was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CycleResearcher 12B is a paper-writing model from WestlakeNLP, the Text Intelligence Lab at Westlake University (Yue Zhang). The original Hub repo dates to 2024-10-24. A safety upload followed on 2025-02-27. Collected 2026-10-15.", "what_it_is_for": "The card says the ML series writes machine-learning papers in computer vision, NLP, and multimedia, trained on Research-14K and optimized with a CycleReviewer feedback loop. It is not a general chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/cycleresearcher/cycleresearcher-72b": { "id": "westlake/cycleresearcher/cycleresearcher-72b", "name": "CycleResearcher 72B", "maker": "westlake", "family": "westlake/cycleresearcher", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CycleResearcher license for weights; Apache-2.0 for repository code", "release_date": "2024-10-23", "context_length": null, "max_output_tokens": null, "parameters": "72B", "architecture": "Fine-tune of Qwen/Qwen2.5-72B-Instruct. The Hub size tag says 73B, which is the usual Qwen2.5-72B count. The lab name is 72B. Supervised research-paper generator. Card: safety weights are the public non-Original repo. Original repos are the earlier upload. Code in the repository is Apache-2.0. Weights use the CycleResearcher license. Knowledge cutoff on the card is October 2024. Six Hub language tags (en, zh, ja, ko, fr, de) match the base model's tags and were not re-tested by this lab in the text that was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CycleResearcher 72B is the Qwen2.5-72B paper generator from WestlakeNLP. The original Hub repo dates to 2024-10-23. Collected 2026-10-15.", "what_it_is_for": "Same ML paper-generation job as the 12B model, at the 72B base. The lab's own reviewer gives it a slightly higher average score than 12B and a slightly lower accept rate on the card table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/cyclereviewer/cyclereviewer-123b": { "id": "westlake/cyclereviewer/cyclereviewer-123b", "name": "CycleReviewer 123B", "maker": "westlake", "family": "westlake/cyclereviewer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-16", "context_length": null, "max_output_tokens": null, "parameters": "123B", "architecture": "Fine-tune of mistralai/Mistral-Large-Instruct-2407. The card name is CycleReviewer-ML-Pro-123B. Peer-review comment model for machine-learning papers. Card release date 16 August 2024. Knowledge cutoff January 2024. Transformer text models. Science-series rows in the card table are marked TODO and are not folders. Hub language tags en, zh, ja, ko, fr, de are the base model's tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CycleReviewer 123B is WestlakeNLP's Mistral Large 2 review model. The series card dates the release to 16 August 2024. Collected 2026-10-15.", "what_it_is_for": "Same machine-learning review job. On the lab's ICLR 2024 table it has the highest decision accuracy of the three sizes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/cyclereviewer/cyclereviewer-70b": { "id": "westlake/cyclereviewer/cyclereviewer-70b", "name": "CycleReviewer 70B", "maker": "westlake", "family": "westlake/cyclereviewer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-16", "context_length": null, "max_output_tokens": null, "parameters": "70B", "architecture": "Fine-tune of meta-llama/Llama-3.1-70B-Instruct. Hub size tag is 71B. The lab name is 70B. Peer-review comment model for machine-learning papers. Card release date 16 August 2024. Knowledge cutoff January 2024. Transformer text models. Science-series rows in the card table are marked TODO and are not folders. Hub language tags en, zh, ja, ko, fr, de are the base model's tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CycleReviewer 70B is WestlakeNLP's Llama 3.1 70B review model. The card release date is 16 August 2024. The current Hub repo was created 2025-02-17. Collected 2026-10-15.", "what_it_is_for": "Same review-comment job as the 8B model. On the card's ICLR 2024 set its decision accuracy is 61.58%, between the 8B and 123B models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/cyclereviewer/cyclereviewer-8b": { "id": "westlake/cyclereviewer/cyclereviewer-8b", "name": "CycleReviewer 8B", "maker": "westlake", "family": "westlake/cyclereviewer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-08-16", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Fine-tune of meta-llama/Llama-3.1-8B-Instruct. Peer-review comment model for machine-learning papers. Card release date 16 August 2024. Knowledge cutoff January 2024. Transformer text models. Science-series rows in the card table are marked TODO and are not folders. Hub language tags en, zh, ja, ko, fr, de are the base model's tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CycleReviewer 8B is WestlakeNLP's smallest peer-review model, a Llama 3.1 8B Instruct fine-tune. The card dates the series to 16 August 2024. Collected 2026-10-15.", "what_it_is_for": "The card says the model writes expert-style review comments for machine-learning papers, including computer vision, NLP, and multimedia. It is not for official peer review.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/deepreviewer/deepreviewer-14b": { "id": "westlake/deepreviewer/deepreviewer-14b", "name": "DeepReviewer 14B", "maker": "westlake", "family": "westlake/deepreviewer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-09", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Fine-tune of microsoft/phi-4. Hub size tag is 15B. The lab name is 14B. Same card dates and cutoff as the 7B model. The 14B repo is the phi-4 row in the specification table on the 7B card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepReviewer 14B is WestlakeNLP's Phi-4 review model, posted 2025-03-09. It is the larger of the two DeepReviewer checkpoints. Collected 2026-10-15.", "what_it_is_for": "Same paper-review job as DeepReviewer 7B. On the lab's ICLR tables the 14B column is the stronger of the two DeepReviewer sizes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/deepreviewer/deepreviewer-7b": { "id": "westlake/deepreviewer/deepreviewer-7b", "name": "DeepReviewer 7B", "maker": "westlake", "family": "westlake/deepreviewer", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-03-08", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Fine-tune of Qwen/Qwen2.5-7B-Instruct for paper review. Hub size tag is 8B. The lab name is 7B. Card release month is March 2025. Knowledge cutoff January 2025. Hub language tags en, zh, ja, ko, fr, de.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DeepReviewer 7B is WestlakeNLP's Qwen2.5-7B review model, posted 2025-03-08. The card's knowledge cutoff is January 2025. Collected 2026-10-15.", "what_it_is_for": "The card says DeepReviewer is a generative model trained for academic paper review, in 7B and 14B sizes. The gate forbids official peer review.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/domino/domino": { "id": "westlake/domino/domino", "name": "DOMINO", "maker": "westlake", "family": "westlake/domino", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2026-04-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-module multidomain protein design system. DOMIN scores structural compatibility of a structural sequence by a temperature-scaled dot product of query and key embeddings. DOMO generates one sequence from a list of domain descriptions. The Hub repo contains DOMIN/DOMIN.pt plus a tokenizer, and DOMO/pytorch_model.bin. The example tokenizer call uses max_length 512. That is a call argument, not a documented context window.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DOMINO is a multidomain protein design release from Westlake's Representation Learning Lab. Hub weights date to 2026-04-27. The preprint identifier is 2026-05-01. Collected 2026-10-15.", "what_it_is_for": "The README says DOMIN scores whether protein domains fit together and DOMO generates a single sequence conditioned on a domain list. The server linked from the README is http://www.protein-domino.com/ .", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/e3-cryofold/e3-cryofold": { "id": "westlake/e3-cryofold/e3-cryofold", "name": "E3-CryoFold", "maker": "westlake", "family": "westlake/e3-cryofold", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end cryo-EM structure model. Nature abstract: 3D and sequence transformers with cross-attention, then an SE(3) graph network that builds atomic coordinates. Pretrained on simulated density maps from PDB structures. Westlake AI Laboratory is a primary affiliation and Stan Z. Li is a corresponding author (Stan.ZQ.Li@westlake.edu.cn). Yang Zhang at the National University of Singapore is the other corresponding author.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "E3-CryoFold is a cryo-EM structure model from Westlake University's AI Laboratory, published in Nature Machine Intelligence on 2025-06-24, with a co-corresponding author at the National University of Singapore. Collected 2026-10-15.", "what_it_is_for": "The paper builds an atomic model of a complex from a cryo-EM density map and sequences in one forward pass. The Nature abstract says the average template-modelling score improves by 400% versus Cryo2Struct and that inference uses one-thousandth the time of the methods it compares. The GitHub README says a pretrained checkpoint is provided.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/evolla/evolla-10b": { "id": "westlake/evolla/evolla-10b", "name": "Evolla 10B", "maker": "westlake", "family": "westlake/evolla", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the Evolla card; the frozen decoder is Llama", "release_date": "2024-12-06", "context_length": null, "max_output_tokens": null, "parameters": "10B", "architecture": "Protein question-answering model. Paper: frozen 650M SaProt encoder, trainable 1.7B sequence compressor and aligner, frozen 8B language-model decoder. Hub base_model tag is meta-llama/Llama-3.1-8B-Instruct. The GitHub inference snippet also clones meta-llama/Meta-Llama-3-8B-Instruct, which disagrees with the 3.1 tag. Eight cross-attention aligner layers, FFN multiplier 4, dropout 0.1, inserted every 4 decoder layers. Paper sets maximum token length to 736.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Evolla 10B is Westlake's smaller protein chat model from the Representation Learning Lab. The original Hub repo dates to 2024-12-06. The bioRxiv paper is 2025-01-06. Collected 2026-10-15. Early posts spell it Evola.", "what_it_is_for": "The maker uses Evolla to answer natural-language questions about a protein given its sequence and Foldseek structure. The 10B stack is the one with a SaProt 650M encoder and an 8B decoder. Format variants and a DPO variant are versions, not separate products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/evolla/evolla-80b": { "id": "westlake/evolla/evolla-80b", "name": "Evolla 80B", "maker": "westlake", "family": "westlake/evolla", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT on the Hub tag; the frozen decoder is Llama 3.1 70B", "release_date": "2025-04-07", "context_length": null, "max_output_tokens": null, "parameters": "80B", "architecture": "Paper: frozen SaProt 1.3B encoder, trainable sequence compressor and aligner (8.2 billion trainable parameters in the methods text), frozen Llama-3.1-70B-Instruct decoder. Twenty cross-attention layers, FFN multiplier 1, dropout 0.1, every 4 decoder layers. Maximum token length 736. The lab's 2025-01-07 post said a 1.3B SaProt encoder and a 70B Llama 3 decoder. The Hub card body is only a license line. v1 of the preprint said the 80B run had not yet converged.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Evolla 80B is the larger protein chat model from the same Westlake lab. Hub weights date to 2025-04-07. The GitHub news line says 2025-04-23. Collected 2026-10-15.", "what_it_is_for": "The paper presents Evolla as a model that answers questions about protein function from sequence, structure, and a user question. The 80B version is the SaProt 1.3B plus Llama 3.1 70B stack. Training data in the abstract is 546 million protein question-answer pairs and 150 billion word tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/leanvae/leanvae": { "id": "westlake/leanvae/leanvae", "name": "LeanVAE", "maker": "westlake", "family": "westlake/leanvae", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "39.8M", "architecture": "Causal video VAE for latent video diffusion. The README also says about 40M parameters. Two latent widths, 4 and 16, share the 39.8M and 0.203 TFLOP figures. Spatial decode path in the example is H/8 and W/8. Temporal length in the example is T/4+1 for a video of T+1 frames, and frame count must be 4n+1. Temporal tiling is supported. A 17-frame 1080p clip is quoted at 0.9s encode or 3.0s decode and 6GB or 15GB, under 4x8x8 or 1x8x8 compression. Which channel count matches which ratio is not stated in that sentence.", "modalities": { "input": [ "image", "video" ], "output": [ "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LeanVAE is a video reconstruction VAE from Westlake's Representation Learning Lab (Yu Cheng and Fajie Yuan). The lab README links 4-channel and 16-channel checkpoints. Collected 2026-10-15.", "what_it_is_for": "The maker positions LeanVAE as a small causal tokenizer for latent video diffusion, including images and long videos via temporal tiling. The paper is arXiv 2503.14325 and the repo calls it an ICCV 2025 model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/pinal/pinal": { "id": "westlake/pinal/pinal", "name": "Pinal", "maker": "westlake", "family": "westlake/pinal", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-01-07", "context_length": null, "max_output_tokens": null, "parameters": "16B", "architecture": "Two-stage protein design model. The card says it first generates structures from language instructions, then designs sequences from those structures. Stated size 16 billion parameters, trained on 1.7 billion protein-text pairs. SaProt-T is the reverse-folding piece inside Pinal. SaProt-O takes optional text and structure. Those pieces are not separate Hub repos.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Pinal is Westlake's natural-language protein design model from the Representation Learning Lab. The Hub repo dates to 2025-01-07. The preprint is 2024-08-01. Collected 2026-10-15.", "what_it_is_for": "The card says Pinal turns a design instruction into a protein: a structure is generated from the instruction, then a sequence is designed for that structure. The server is http://www.denovo-pinal.com/ .", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/protrek/protrek-35m": { "id": "westlake/protrek/protrek-35m", "name": "ProTrek 35M", "maker": "westlake", "family": "westlake/protrek", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-22", "context_length": null, "max_output_tokens": null, "parameters": "35M sequence encoder; 35M structure encoder; 130M text encoder", "architecture": "Trimodal contrastive model. GitHub checkpoint table: protein sequence encoder 35M, protein structure encoder 35M, text encoder 130M. Training data listed as Swiss-Prot plus TrEMBL50. The 650M card describes the same contrastive setup (InfoNCE between each pair of modalities). The 35M card does not restate layer counts.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProTrek 35M is the smaller trimodal protein model from Westlake's Representation Learning Lab. Weights were posted on 2024-05-22. The Nature Biotechnology paper is dated 2025-10-02. Collected 2026-10-15.", "what_it_is_for": "ProTrek embeds protein sequence, structure, and text function so any two modalities can be searched against each other. The lab also ships a web server at search-protrek.com and ColabProTrek.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/protrek/protrek-650m": { "id": "westlake/protrek/protrek-650m", "name": "ProTrek 650M", "maker": "westlake", "family": "westlake/protrek", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-22", "context_length": null, "max_output_tokens": null, "parameters": "650M sequence encoder; 150M structure encoder; 130M text encoder", "architecture": "Trimodal contrastive model, InfoNCE between each pair of modalities, described as CLIP-style. Card: sequence encoder is esm2_t33_650M_UR50D; structure encoder is foldseek_t30_150M (ESM-2-like, 3Di vocabulary only); text encoder is BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext. Checkpoint table: trained on Swiss-Prot plus TrEMBL50. The inference example loads the ProTrek checkpoint rather than the original public encoders.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProTrek 650M is Westlake's larger trimodal protein-language model, with weights on the Hub since 2024-05-22 and a Nature Biotechnology paper on 2025-10-02. Collected 2026-10-15.", "what_it_is_for": "The maker uses it for protein-text alignment and for search across sequence, structure, and function. The Nature data-availability note points at these Hub weights, precomputed Swiss-Prot embeddings, and the search-protrek.com server.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/saprot/saprot-1-3b": { "id": "westlake/saprot/saprot-1-3b", "name": "SaProt 1.3B", "maker": "westlake", "family": "westlake/saprot", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-12-09", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "ESM-style masked language model, structure-aware vocab 446. Both 1.3B Hub configs: 66 layers, hidden size 1280, intermediate size 5120, 20 heads, rotary embeddings, GELU. AF2 checkpoint max_position_embeddings is 1026. The AFDB+OMG+NCBI checkpoint uses 1538. The lab states 1.3B parameters. The AF2 card says scaling from 650M to 1.3B was about on par, and that 1.3B works with both structure-aware tokens and amino-acid-only sequences.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SaProt 1.3B is the larger SaProt release from Westlake's Representation Learning Lab. The AF2 checkpoint was posted on 2024-12-09. A second checkpoint trained with AF2, OMG, and NCBI data is the 1.3B row in the official checkpoint table. Collected 2026-10-15.", "what_it_is_for": "The lab presents 1.3B as a SaProt that still uses the structure-aware vocabulary but, unlike 35M and 650M, also works in amino-acid-only mode. The AF2 card says the gain over 650M on three metrics is small.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/saprot/saprot-35m": { "id": "westlake/saprot/saprot-35m", "name": "SaProt 35M", "maker": "westlake", "family": "westlake/saprot", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-10-30", "context_length": 1026, "max_output_tokens": null, "parameters": "35M", "architecture": "ESM-style masked language model (EsmForMaskedLM) with a structure-aware vocabulary of 446 tokens (amino acid plus Foldseek 3Di). Hub config: 12 layers, hidden size 480, intermediate size 1920, 20 heads, rotary embeddings, GELU, max_position_embeddings 1026. The lab states 35M parameters, trained on about 40 million AlphaFold2 structures.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SaProt 35M is Westlake University's Representation Learning Lab (Fajie Yuan) structure-aware protein masked language model at the 35M scale. Weights are on Hugging Face under westlake-repl. Collected 2026-10-15.", "what_it_is_for": "The lab uses SaProt to embed proteins whose tokens combine an amino acid with a Foldseek 3Di structure state, then to score mutations and to fine-tune function tasks. The 35M AF2 checkpoint is the small official release. A residue-sequence-only 35M run was published only for comparison with ESM-2 35M and is not given its own folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/saprot/saprot-650m": { "id": "westlake/saprot/saprot-650m", "name": "SaProt 650M", "maker": "westlake", "family": "westlake/saprot", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-10-02", "context_length": 1026, "max_output_tokens": null, "parameters": "650M", "architecture": "ESM-style masked language model with structure-aware vocabulary size 446. Hub config for both AF2 and PDB checkpoints: 33 layers, hidden size 1280, intermediate size 5120, 20 heads, rotary embeddings, GELU, max_position_embeddings 1026. The lab states 650M parameters. AF2 training uses about 40 million AlphaFold2 structures. The PDB checkpoint continues that run with about 60 thousand PDB structures in a second phase.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SaProt 650M is the Representation Learning Lab's main structure-aware protein language model, released as AF2 and PDB checkpoints on 2023-10-02. It is the checkpoint the lab cited for a first-place ProteinGym claim in April 2024. Collected 2026-10-15.", "what_it_is_for": "The maker trains SaProt as a general protein model: zero-shot mutational effect, embeddings, and supervised function, localization, and stability tasks. Users convert a structure to a combined amino-acid and 3Di sequence with Foldseek before inference.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "westlake/saprot/saprot-650m-inverse-folding": { "id": "westlake/saprot/saprot-650m-inverse-folding", "name": "SaProt 650M inverse folding", "maker": "westlake", "family": "westlake/saprot", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-05-24", "context_length": 1026, "max_output_tokens": null, "parameters": "650M", "architecture": "CATH fine-tune of SaProt_650M_AF2 for inverse folding, still EsmForMaskedLM. Hub config matches the 650M base: 33 layers, hidden 1280, intermediate 5120, 20 heads, vocab 446, max_position_embeddings 1026. The card does not state a new parameter count. The lab name keeps 650M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SaProt 650M inverse folding is a named CATH fine-tune of SaProt_650M_AF2 from the same Westlake lab, released on the Hub on 2024-05-24. It is a sequence-design checkpoint, not a second pretraining run. Collected 2026-10-15.", "what_it_is_for": "The GitHub inverse-folding example masks amino-acid positions and predicts them from a Foldseek structure sequence. The card says the fine-tune is for protein inverse folding and is faster than ProteinMPNN while remaining competitive.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "whu/hypersigma/hypersigma-base": { "id": "whu/hypersigma/hypersigma-base", "name": "HyperSIGMA base", "maker": "whu", "family": "whu/hypersigma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two MAE subnetworks later fused with sparse sampling attention (SSA) and a spectral enhancement module. arXiv HTML Table I lists SpatViT-Base at 90 million parameters and SpecViT-Base at 88 million parameters. Hub files in WHU-Sigma/HyperSIGMA are spat-vit-base-ultra-checkpoint-1599.pth (1,436,444,759 bytes) and spec-vit-base-ultra-checkpoint-1599.pth (1,391,770,843 bytes). The README's spectral-huge pretraining example uses 1600 epochs, mask ratio 0.75, and model name spec_mae_h. The checkpoint index 1599 is in the filename. The word ultra in the filename is not defined on the README.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wuhan University's SIGMA lab released HyperSIGMA base as the ViT-Base spatial and spectral masked-autoencoder subnetworks of its hyperspectral foundation model. The weights have been on Hugging Face since 2024-07-18 under Apache-2.0 and were still public on 2026-10-15. There is no hosted API.", "what_it_is_for": "SIGMA describes HyperSIGMA as a foundation model for hyperspectral interpretation, covering both high-level tasks and low-level tasks such as denoising and super-resolution. The base folder is the SpatViT-Base and SpecViT-Base pair. The paper says the full model fuses those subnetworks with sparse sampling attention and a spectral enhancement module. Pretraining data is HyperGlobal-450K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "whu/hypersigma/hypersigma-huge": { "id": "whu/hypersigma/hypersigma-huge", "name": "HyperSIGMA huge", "maker": "whu", "family": "whu/hypersigma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-18", "context_length": null, "max_output_tokens": null, "parameters": "1273000000", "architecture": "SpatViT-Huge and SpecViT-Huge, the scale the paper says reaches the billion-parameter regime when both subnetworks are used. arXiv HTML Table I lists SpatViT-Huge at 638 million parameters and SpecViT-Huge at 635 million parameters. Comparison Table XI prints HyperSIGMA with backbone ViT-H+SSA at 1273 million parameters on HyperGlobal-450K. The GitHub table labels the same tier ViT-H, and the Hub filenames say huge: spat-vit-huge-ultra-checkpoint-1599.pth (8,031,568,520 bytes) and spec-vit-huge-ultra-checkpoint-1599.pth (7,956,033,254 bytes). The README spectral pretraining example uses --model spec_mae_h, 1600 epochs, mask ratio 0.75, blr 1.5e-4, batch size 16, and 128 GPUs. The parameters field stores the printed 1273 million figure, not a new sum.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wuhan University SIGMA released HyperSIGMA huge as the largest spatial and spectral MAE subnetworks of HyperSIGMA. The paper's comparison table prints this fused scale at 1,273 million parameters. The two checkpoints have been in the Apache-2.0 Hub repo since 2024-07-18 and were still listed on 2026-10-15.", "what_it_is_for": "This is the billion-parameter tier SIGMA announced for hyperspectral interpretation. The GitHub README calls the backbone ViT-H. The paper's training table calls it Huge. The Hub filenames say huge. There is one pair of files at this size, not a further split between H and huge.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "whu/hypersigma/hypersigma-large": { "id": "whu/hypersigma/hypersigma-large", "name": "HyperSIGMA large", "maker": "whu", "family": "whu/hypersigma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SpatViT-Large and SpecViT-Large MAE subnetworks of HyperSIGMA. arXiv HTML Table I lists SpatViT-Large at 309 million parameters and SpecViT-Large at 307 million parameters. Hub files are spat-vit-large-ultra-checkpoint-1599.pth (4,072,291,508 bytes) and spec-vit-large-ultra-checkpoint-1599.pth (4,013,235,510 bytes). The README calls this tier ViT-L. The spectral-huge example script uses 1600 epochs and mask ratio 0.75. That mask ratio was not confirmed for the large pair. The filename token ultra is not defined.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wuhan University SIGMA released HyperSIGMA large as the ViT-Large spatial and spectral MAE subnetworks in the same Apache-2.0 Hub repo as the base and huge checkpoints. The large files were uploaded on 2024-07-18 and were still listed on 2026-10-15. There is no API product.", "what_it_is_for": "Same job as the rest of HyperSIGMA: a pretrained hyperspectral backbone for classification, detection, denoising, super-resolution, and the other tasks in the paper. This folder is only the large subnetwork pair, SpatViT-Large and SpecViT-Large.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "whu/mars/mars-base": { "id": "whu/mars/mars-base", "name": "MaRS base", "maker": "whu", "family": "whu/mars", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SwinV2 base encoder. The README loads it with timm 1.0.15 as swinv2_base_window8_256, features_only true, image size 512. RGB uses in_chans 3 and mars_base_rgb_encoder_only.pth. SAR uses in_chans 1 and mars_base_sar_encoder_only.pth. Both Zenodo files are 347.7 MB. The paper's full model is a dual encoder plus meta-modality attention. The released files are named encoder-only. The paper Table 2 row is backbone SwinV2-B. A parameter count is not printed.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wuhan University's RS-IDEA group released MaRS base, a SwinV2-B encoder pair for very-high-resolution optical and SAR remote sensing. Encoder-only RGB and SAR weights were on Zenodo on 2025-12-03 and were still the files documented by the GitHub README on 2026-10-15. All paper authors are at Wuhan University. No public license text was found.", "what_it_is_for": "The paper presents MaRS as a pretrained backbone for very-high-resolution multi-modal tasks: registration, translation, mapping when a modality is missing, damage and change, detection, height, and roads. The downloaded base files are encoders. The full model in the paper adds meta-modality attention on top of two SwinV2 encoders. Pretraining data is MaRS-16M, 16,785,168 paired patches at about 0.35 m ground sample distance.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "whu/mars/mars-large": { "id": "whu/mars/mars-large", "name": "MaRS large", "maker": "whu", "family": "whu/mars", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-12-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Larger MaRS encoder pair released beside the SwinV2 base encoders. Zenodo files are mars_large_rgb_encoder_only.pth (634.1 MB) and mars_large_sar_encoder_only.pth (639.6 MB). The README pretraining example calls --model mars_large. It does not show a timm.create_model string for large. The printed load example is only swinv2_base_window8_256. The paper tables found on this pass use backbone SwinV2-B, not a large row.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wuhan University RS-IDEA released MaRS large next to MaRS base. The Zenodo record dated 2025-12-03 has encoder-only RGB and SAR files. The paper tables read for this dossier report SwinV2-B, so those scores stay on the base folder. No license text was found. Collection date 2026-10-15.", "what_it_is_for": "The README treats `mars_large` as the larger pretrained backbone in the same very-high-resolution optical and SAR family as `mars_base`. A load snippet with a timm model name is given only for the base network.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "whu/rsp/rsp-resnet-50": { "id": "whu/rsp/rsp-resnet-50", "name": "RSP-ResNet-50", "maker": "whu", "family": "whu/rsp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-04-06", "context_length": null, "max_output_tokens": null, "parameters": "23600000", "architecture": "ResNet-50 trained from scratch on MillionAID for scene recognition, then used as a remote-sensing pretrained backbone. README name RSP-ResNet-50-E300. Input 224 by 224. The table prints 23.6 under Param(M), stored here as 23600000. The README says to extract the backbone from the checkpoint before finetuning. Google Drive file id 1K3P4_fDfcBRGqpKoSdSa6OXS4xC1xLC9. A Baidu link is on the same row. The share password was not copied.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wuhan University authors Di Wang, Bo Du, and Gui-Song Xia, with Jing Zhang and Dacheng Tao, released RSP-ResNet-50 as a MillionAID scene-recognition checkpoint in the remote-sensing pretraining study. The GitHub news line dated 06/04/2022 says the weights were released that day. The project statement says MIT. Status on 2026-10-15: active open weights. There is no hosted API.", "what_it_is_for": "The repository trains networks from scratch on MillionAID so later aerial tasks do not have to start from ImageNet. This folder is the ResNet-50 classification checkpoint, README name RSP-ResNet-50-E300. The note above the table says to extract the backbone from the checkpoint before finetuning. Scene recognition, semantic segmentation, object detection, and change detection are linked as separate task trees in the same repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "whu/rsp/rsp-swin-t": { "id": "whu/rsp/rsp-swin-t", "name": "RSP-Swin-T", "maker": "whu", "family": "whu/rsp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-04-06", "context_length": null, "max_output_tokens": null, "parameters": "27600000", "architecture": "Swin-T trained from scratch on MillionAID for scene recognition, then used as a remote-sensing pretrained backbone. README name RSP-Swin-T-E300. Input 224 by 224. The table prints 27.6 under Param(M), stored here as 27600000. The README says to extract the backbone from the checkpoint before finetuning. Google Drive file id 1G5wjbjIHepmT6VVOuW03bWmyvrhcfe1F. A Baidu link is on the same row. The share password was not copied. Swin layer counts, window size, and embedding width are not printed on the model table and were not filled from the timm or Microsoft defaults.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wuhan University authors Di Wang, Bo Du, and Gui-Song Xia, with Jing Zhang and Dacheng Tao, released RSP-Swin-T as a MillionAID scene-recognition checkpoint in the remote-sensing pretraining study. The GitHub news line dated 06/04/2022 says the Swin-T weights were released that day with ResNet-50 and ViTAEv2-S. The project statement says MIT. Status on 2026-10-15: active open weights. There is no hosted API.", "what_it_is_for": "The repository trains networks from scratch on MillionAID so later aerial tasks do not have to start from ImageNet. This folder is the Swin-T classification checkpoint, README name RSP-Swin-T-E300. The note above the table says to extract the backbone from the checkpoint before finetuning. Scene recognition, semantic segmentation, object detection, and change detection are linked as separate task trees in the same repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "whu/rsp/rsp-vitaev2-s": { "id": "whu/rsp/rsp-vitaev2-s", "name": "RSP-ViTAEv2-S", "maker": "whu", "family": "whu/rsp", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2022-04-06", "context_length": null, "max_output_tokens": null, "parameters": "18800000", "architecture": "ViTAEv2-S trained from scratch on MillionAID for scene recognition, then used as a remote-sensing pretrained backbone. README name RSP-ViTAEv2-S-E100. Input 224 by 224. The table prints 18.8 under Param(M), stored here as 18800000. The epoch tag is E100, not E300. The README says to extract the backbone from the checkpoint before finetuning. Google Drive file id 1cDB69frN-NxCyoy8lghjx6NiH1JriYUc. A Baidu link is on the same row. The share password was not copied. ViTAEv2 block layout is not printed on the model table.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wuhan University authors Di Wang, Bo Du, and Gui-Song Xia, with Jing Zhang and Dacheng Tao, released RSP-ViTAEv2-S as a MillionAID scene-recognition checkpoint in the remote-sensing pretraining study. The GitHub news line dated 06/04/2022 says the ViTAEv2-S weights were released that day with ResNet-50 and Swin-T. The project statement says MIT. Status on 2026-10-15: active open weights. There is no hosted API.", "what_it_is_for": "The repository trains networks from scratch on MillionAID so later aerial tasks do not have to start from ImageNet. This folder is the ViTAEv2-S classification checkpoint, README name RSP-ViTAEv2-S-E100. The epoch tag is 100, not 300. The note above the table says to extract the backbone from the checkpoint before finetuning. Scene recognition, semantic segmentation, object detection, and change detection are linked as separate task trees in the same repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wonder-dynamics/mocap/ai-mocap": { "id": "wonder-dynamics/mocap/ai-mocap", "name": "AI Motion Capture", "maker": "wonder-dynamics", "family": "wonder-dynamics/mocap", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-09-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video" ], "output": [ "motion" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Motion Capture is Wonder Dynamics' closed, markerless motion system in Autodesk Flow Studio. It reads body, hand, and face performance from ordinary footage and writes animation onto a CG character. The named standalone project type shipped on 14 September 2023 and was still a current plan feature on 15 October 2026. Weights are not published.", "what_it_is_for": "The maker says it turns a video into motion-capture data without suits or markers. Users upload footage, scan for actors, assign characters, and export animation. The same tracking drives Live Action character replacement. A separate face system drives FACS blendshapes. From 30 June 2025, a Wonder Character path exports MetaHuman-compatible body animation for Unreal.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wonder-dynamics/neural-layer/neural-layer": { "id": "wonder-dynamics/neural-layer/neural-layer", "name": "Neural Layer", "maker": "wonder-dynamics", "family": "wonder-dynamics/neural-layer", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Neural Layer is Wonder Dynamics' closed video-to-video pass in Autodesk Flow Studio. Help calls it an advanced video-to-video model that restyles a Flow Studio first pass: materials, lighting, blending, clean plates, and facial performance. It shipped for Live Action on 16 April 2026 and was current on 15 October 2026. The underlying checkpoint name is not published.", "what_it_is_for": "Autodesk says it is the fast path from a Wonder 3D character in a Live Action shot to a more cinematic result, without a traditional render. It targets skin, hair, and other materials, film-style lighting, motion grounding, and cleaner composite edges. A Face Neural Layer adds facial performance. Users can still export and finish the shot in another 3D tool instead.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wonder-dynamics/rigging/ai-rigging": { "id": "wonder-dynamics/rigging/ai-rigging", "name": "AI Rigging", "maker": "wonder-dynamics", "family": "wonder-dynamics/rigging", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "3d" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AI Rigging is Wonder Dynamics' character-rigging Wonder Tool in Autodesk Flow Studio. It shipped on 16 April 2026 so generated characters can be animated in Live Action or exported. It was current on 15 October 2026. There is no public model card. The launch post says rigging is automatic and also says the user assigns joints in a few clicks. The help page is a guided marker tool.", "what_it_is_for": "Autodesk says it makes a Wonder 3D character animation-ready without a traditional rigging department. The user places joint markers, aligns the mesh, and confirms. The result can be driven by AI Motion Capture in Live Action Easy or Advanced, or exported to Maya, Blender, or Unreal on paid tiers. Official tutorials say to generate with Wonder 3D rather than Trellis 2, because Trellis 2 meshes are not riggable.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wonder-dynamics/wonder-3d/wonder-3d": { "id": "wonder-dynamics/wonder-3d/wonder-3d", "name": "Wonder 3D", "maker": "wonder-dynamics", "family": "wonder-dynamics/wonder-3d", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wonder 3D is Wonder Dynamics' closed generative 3D model inside Autodesk Flow Studio (formerly Wonder Studio). Autodesk introduced it by name on 4 March 2026 as a Wonder Tool for editable characters and props. It was still current on the collection date, 15 October 2026. Weights are not published.", "what_it_is_for": "Autodesk says Wonder 3D turns a text prompt or a reference image into an editable 3D character, creature, or prop, with geometry and textures, for games, film, marketing, and 3D printing. Image to 3D help lists Wonder 3D and Microsoft Trellis 2 as two selectable models. Trellis 2 is a hosted third-party model and is not this dossier. Official tutorials say to pick Wonder 3D, not Trellis 2, when the mesh will be rigged.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wonder-dynamics/wonder-animation/wonder-animation": { "id": "wonder-dynamics/wonder-animation/wonder-animation", "name": "Wonder Animation", "maker": "wonder-dynamics", "family": "wonder-dynamics/wonder-animation", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-30", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Wonder Animation is Wonder Dynamics' Video to 3D Scene system. It was announced in beta on 30 October 2024, after the Autodesk acquisition, and remains the Animation project type in Autodesk Flow Studio. Collected 15 October 2026. It is closed and cloud-only. It is not a text-to-video model: the output is an editable 3D scene.", "what_it_is_for": "The maker says an artist can shoot a scene on any camera, including multiple cuts and shot sizes, and the system rebuilds cameras, character body and face animation, and the environment in one 3D scene. Exports go to Maya, Blender, Unreal, or USD. Autodesk's overview still calls this Wonder Animation's Video to 3D Scene technology. The current project label is Animation / Video to 3D Scene.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wonder-dynamics/wonder-tools/camera-track": { "id": "wonder-dynamics/wonder-tools/camera-track", "name": "Camera Track", "maker": "wonder-dynamics", "family": "wonder-dynamics/wonder-tools", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-01-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video" ], "output": [ "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Camera Track is Wonder Dynamics' closed matchmove system in Autodesk Flow Studio. It solves a virtual camera, and a point cloud, from live-action footage. It shipped inside Live Action on 22 January 2024 and as a standalone Wonder Tool on 12 February 2025. The company called that tool an AI model. It was current on 15 October 2026.", "what_it_is_for": "The help center describes it as matchmoving: a virtual camera follows the real camera so CG can sit in the plate. Inside Live Action it also puts character motion into world space. The standalone Wonder Tool does not need an actor in frame. Exports include the camera inside Maya, USD, and Unreal scenes, plus a separate camera FBX.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wonder-dynamics/wonder-tools/clean-plate": { "id": "wonder-dynamics/wonder-tools/clean-plate", "name": "Clean Plate", "maker": "wonder-dynamics", "family": "wonder-dynamics/wonder-tools", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-07-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Clean Plate is Wonder Dynamics' closed actor-removal and inpainting system in Autodesk Flow Studio. It paints human performers out of a shot so a CG character can be composited. Public Wonder Studio already exported clean plates on 11 July 2023. The standalone Wonder Tool, which the company called an AI model, shipped on 12 February 2025 and was current on 15 October 2026.", "what_it_is_for": "Help defines a clean plate as frames where a roto'd subject is filled back in. The standalone tool removes up to four human actors without running a full Live Action project. It does not remove animals or objects. Live Action still generates a clean plate as one of the export elements, along with alpha masks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "world-labs/atlas/atlas": { "id": "world-labs/atlas/atlas", "name": "Atlas", "maker": "world-labs", "family": "world-labs/atlas", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal autoregressive diffusion transformer, pretrained from scratch. Rectified-flow latent diffusion. Each image and depth map is tied to an explicit camera pose, forming a spatial context. Video is a sequence of images. Outputs are generated one element at a time. The post says it can use KV caching and diffusion distillation. No parameter count.", "modalities": { "input": [ "text", "image", "video", "camera-pose", "depth" ], "output": [ "image", "video", "depth", "3d" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Atlas is World Labs' omni world model, announced on 1 September 2026. It is in early access for selected partners as of 2026-10-15, not on the public World API. World Labs says it will power future Marble versions. Weights are closed. There is no public paper, only the launch post.", "what_it_is_for": "World Labs describes one model for four jobs: camera-controlled generation, sparse 3D reconstruction, space-time simulation, and image generation. Camera geometry is a native input, not a text instruction. The post's showpiece is a one-minute 1440p video along a hand-designed camera path. It also reframes a few phone videos into new angles and generates robot-camera views for real-to-sim work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "world-labs/marble/marble-1-0": { "id": "world-labs/marble/marble-1-0", "name": "Marble 1.0", "maker": "world-labs", "family": "world-labs/marble", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "3d", "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marble 1.0 is the legacy fixed-cost Marble world model. World Labs still serves it as API id `marble-1.0` and labels it a model kept for ongoing explorations. Status here is deprecated as of 2026-10-15: callable, not the recommended model. The name dates to 2 April 2026, when Marble 1.1 shipped and the previous standard model kept this label. Weights are closed. There is no published retirement date.", "what_it_is_for": "It generates the same kind of persistent 3D world as Marble 1.1, at the same 1,500-credit base price, without the 1.1 quality update and without 1.1 Plus auto-expansion. World Labs points new users to Marble 1.1.\n\nThe Marble product is older than the 1.0 name. A limited beta opened on 16 September 2025. The public multimodal product opened on 12 November 2025. The World API opened on 21 January 2026. World Labs has not said those earlier releases are the same weights as `marble-1.0`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "world-labs/marble/marble-1-0-draft": { "id": "world-labs/marble/marble-1-0-draft", "name": "Marble 1.0 Draft", "maker": "world-labs", "family": "world-labs/marble", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "3d", "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marble 1.0 Draft is World Labs' fast, low-cost Marble world model. It is active as API id `marble-1.0-draft` as of 2026-10-15. The models page calls it the fastest model, for trying ideas and prompts. The 1.0 Draft name dates to 2 April 2026, when the existing draft model was kept beside Marble 1.1. Weights are closed.", "what_it_is_for": "It generates a Marble world quickly so a user can test a prompt before spending a full generation. World Labs does not publish a quality score against Marble 1.0 or 1.1. The app pricing page puts draft mode on Standard and above.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "world-labs/marble/marble-1-1": { "id": "world-labs/marble/marble-1-1", "name": "Marble 1.1", "maker": "world-labs", "family": "world-labs/marble", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "3d", "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marble 1.1 is World Labs' recommended fixed-cost 3D world model in the Marble product and on the World API (`marble-1.1`). It is active as of 2026-10-15. World Labs added it on 2 April 2026 as a higher-quality successor to the model it then labeled Marble 1.0. Weights are closed.", "what_it_is_for": "World Labs describes Marble as a multimodal world model that turns text, images, video, panoramas, or a coarse 3D layout into a persistent, navigable 3D world. Marble 1.1 is the fixed-cost generation model the docs tell new users to start with. The app also edits, expands, and composes worlds; those steps are product actions, not separate named models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "world-labs/marble/marble-1-1-plus": { "id": "world-labs/marble/marble-1-1-plus", "name": "Marble 1.1 Plus", "maker": "world-labs", "family": "world-labs/marble", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "3d", "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Marble 1.1 Plus is World Labs' largest Marble world model. It is active on the Marble app and the World API as `marble-1.1-plus` as of 2026-10-15. Released 2 April 2026 with Marble 1.1, it keeps the same inputs and exports but can automatically grow the generated space, at a variable credit cost. Weights are closed.", "what_it_is_for": "World Labs calls it the latest model for the largest worlds. It expands its coverage of 3D space when the scene calls for it, for example outdoor scenes or larger interiors. Marble 1.1 remains the recommended fixed-cost model. Plus is a sibling, not a renamed 1.1.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "world-labs/rtfm/rtfm": { "id": "world-labs/rtfm/rtfm", "name": "RTFM", "maker": "world-labs", "family": "world-labs/rtfm", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-10-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive diffusion transformer over sequences of frames. Input frames become a KV cache that stands in for an explicit 3D scene. Each frame has a 3D pose. Context juggling retrieves nearby posed frames instead of attending to the whole history. Trained end-to-end on video to predict the next frame. Distilled and otherwise optimized to run at interactive frame rates on one H100.", "modalities": { "input": [ "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RTFM (Real-Time Frame Model) is a World Labs research-preview world model that generates video frames as you move through a scene. World Labs announced it on 16 October 2025. It was still described as their real-time model in later company posts, and no retirement notice was found by 2026-10-15. It is not a Marble API model. Weights are closed.", "what_it_is_for": "World Labs built it to preview a generative world model that can run today. It reconstructs or imagines new views of a scene without an explicit mesh or splat. The same post says it can be paired with Marble, which does emit 3D assets. A June 2026 World Labs essay calls RTFM a renderer: it produces what a camera would see, not a simulator state.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wpi/hybriddepth/hybriddepth": { "id": "wpi/hybriddepth/hybriddepth", "name": "HybridDepth", "maker": "wpi", "family": "wpi/hybriddepth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GPL-3.0", "release_date": "2024-07-25", "context_length": null, "max_output_tokens": null, "parameters": "65600000", "architecture": "Three-stage metric depth pipeline from the WACV 2025 paper. One frame of a focal stack goes through a Depth Anything relative-depth checkpoint. The full stack goes through a DFV depth-from-focus branch. Least-squares fitting estimates a global scale and shift. A customized MiDaS-small refinement network, with its encoder initialized from ImageNet weights, outputs a dense scale map that is applied to the globally scaled depth. The appendix reports 65.6M parameters and 0.24 GB, and lists Depth Anything ViT-Large separately at 335.79M and 1.25 GB. The paper does not say whether the 65.6M figure includes a frozen Depth Anything backbone. The 65.6M value is the paper's printed 65.6M, not an integer read from a config file.", "modalities": { "input": [ "image" ], "output": [ "depth" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HybridDepth is a metric depth model from Worcester Polytechnic Institute's cake lab (Ashkan Ganj and Tian Guo) with Hang Su at Nvidia Research. Weights are public under GPL-3.0. The first GitHub weight release is 25 July 2024, and v2.0 (30 October 2024) is the current release. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper says HybridDepth estimates metric depth for mobile augmented reality. It fuses depth-from-focus on a focal stack with a single-image relative-depth prior, then refines the scaled map. There is no public API or price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/camel/camel-5b": { "id": "writer/camel/camel-5b", "name": "Camel-5B", "maker": "writer", "family": "writer/camel", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-10", "context_length": null, "max_output_tokens": null, "parameters": "5B", "architecture": "Instruction-tuned causal decoder derived from Palmyra Base. Trained on about 70,000 instruction-response records from Writer linguists.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Camel-5B is Writer's 5B instruction model, derived from Palmyra Base and released under Apache-2.0 in April 2023. It is a Writer-trained model, not a hosted third-party model. Collected 2026-10-15.", "what_it_is_for": "Camel-5B is Writer's 5B instruction model, derived from Palmyra Base and released under Apache-2.0 in April 2023. It is a Writer-trained model, not a hosted third-party model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra/instructpalmyra-20b": { "id": "writer/palmyra/instructpalmyra-20b", "name": "InstructPalmyra-20b", "maker": "writer", "family": "writer/palmyra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-04-28", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "Causal decoder instruction-tuned from Palmyra-20B on about 70,000 instruction-response records written by Writer linguists.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Writer's instruction-tuned 20B model, fine-tuned from Palmyra Large on about 70,000 in-house instruction pairs. Weights remain on Hugging Face under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Writer's instruction-tuned 20B model, fine-tuned from Palmyra Large on about 70,000 in-house instruction pairs. Weights remain on Hugging Face under Apache-2.0.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra/palmyra-20b-chat": { "id": "writer/palmyra/palmyra-20b-chat", "name": "Palmyra-20b-chat", "maker": "writer", "family": "writer/palmyra", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-08-23", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A chat-tuned Palmyra 20B release. Writer's card says the model is no longer maintained or supported. Collected 2026-10-15.", "what_it_is_for": "A chat-tuned Palmyra 20B release. Writer's card says the model is no longer maintained or supported.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra/palmyra-3b": { "id": "writer/palmyra/palmyra-3b", "name": "Palmyra 3B", "maker": "writer", "family": "writer/palmyra", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-03-01", "context_length": 2048, "max_output_tokens": null, "parameters": "", "architecture": "Published config matches Palmyra Base: GPT-2 decoder, 24 layers, hidden 4096, 32 heads, inner 16384, vocab 50257, n_positions 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "An early Writer decoder published as Writer/palmyra-3B. Writer's card says the model is no longer maintained. The card itself disagrees about whether it is 3B or 5B. Collected 2026-10-15.", "what_it_is_for": "An early Writer decoder published as Writer/palmyra-3B. Writer's card says the model is no longer maintained. The card itself disagrees about whether it is 3B or 5B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra/palmyra-56b-instruct": { "id": "writer/palmyra/palmyra-56b-instruct", "name": "Palmyra-56B-Instruct", "maker": "writer", "family": "writer/palmyra", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Writer open model license", "release_date": "2024-05-16", "context_length": null, "max_output_tokens": null, "parameters": "57B", "architecture": "Card says model type Llama. Hugging Face lists 57B parameters and F16 weights. The card calls it a deep model for better reasoning.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A gated Writer instruct model named Palmyra-56B-Instruct. The public card is a stub and says it was built for better reasoning. Collected 2026-10-15.", "what_it_is_for": "A gated Writer instruct model named Palmyra-56B-Instruct. The public card is a stub and says it was built for better reasoning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra/palmyra-base": { "id": "writer/palmyra/palmyra-base", "name": "Palmyra Base", "maker": "writer", "family": "writer/palmyra", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-02-03", "context_length": 2048, "max_output_tokens": null, "parameters": "5B", "architecture": "GPT-2 decoder. Config: 24 layers, hidden size 4096, 32 heads, inner 16384, vocab 50257, n_positions 2048.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Writer's 5B English base decoder. The model card says Writer no longer maintains or supports it. Collected 2026-10-15.", "what_it_is_for": "Writer's 5B English base decoder. The model card says Writer no longer maintains or supports it.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra/palmyra-large": { "id": "writer/palmyra/palmyra-large", "name": "Palmyra Large", "maker": "writer", "family": "writer/palmyra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-01-06", "context_length": 2048, "max_output_tokens": null, "parameters": "20B", "architecture": "GPT-2 decoder. Config: 44 layers, hidden size 6144, 48 heads, inner 24576, vocab 50257, n_positions 2048. Trained on more than 800B tokens of Palmyra-Index-Data plus curated corpora.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Writer's 20B English-first decoder, trained on its own Palmyra index data. Open weights under Apache-2.0, still published on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "Writer's 20B English-first decoder, trained on its own Palmyra index data. Open weights under Apache-2.0, still published on Hugging Face.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra/palmyra-small": { "id": "writer/palmyra/palmyra-small", "name": "Palmyra Small", "maker": "writer", "family": "writer/palmyra", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-02-03", "context_length": 2048, "max_output_tokens": null, "parameters": "128M", "architecture": "GPT-2 decoder. Config: 12 layers, hidden size 768, 12 heads, vocab 50257, n_positions 2048. Card _name_or_path is Writer-LLM-small.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Writer's 128M English decoder, released as open weights for fast parsing, simple classification, address correction, and keyword recognition. Collected 2026-10-15.", "what_it_is_for": "Writer's 128M English decoder, released as open weights for fast parsing, simple classification, address correction, and keyword recognition.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-creative/palmyra-creative": { "id": "writer/palmyra-creative/palmyra-creative", "name": "Palmyra Creative", "maker": "writer", "family": "writer/palmyra-creative", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "Writer open model license", "release_date": "2024-12-17", "context_length": 131072, "max_output_tokens": null, "parameters": "122B", "architecture": "Fine-tune of Palmyra-X-004. A Writer staff reply said it uses the Llama architecture with greater depth (about 140 layers versus 80) and was trained on a T1.2 synthetic token set. Card lists F16 weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 12.0 }, "summary_intro": "Writer's creative-writing model, API id palmyra-creative, with open weights at Writer/Palmyra-Creative. It is a 122B fine-tune of Palmyra-X-004. The hosted API is dated deprecated on 2026-07-13. Collected 2026-10-15.", "what_it_is_for": "Writer's creative-writing model, API id palmyra-creative, with open weights at Writer/Palmyra-Creative. It is a 122B fine-tune of Palmyra-X-004. The hosted API is dated deprecated on 2026-07-13.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-fin/palmyra-fin": { "id": "writer/palmyra-fin/palmyra-fin", "name": "Palmyra Fin", "maker": "writer", "family": "writer/palmyra-fin", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 12.0 }, "summary_intro": "Writer's hosted finance model, API id palmyra-fin. The product page gives it a 128k context and says it was the first model the company saw pass a CFA Level III sample. Deprecated 2026-07-13 on the current price table. Collected 2026-10-15.", "what_it_is_for": "Writer's hosted finance model, API id palmyra-fin. The product page gives it a 128k context and says it was the first model the company saw pass a CFA Level III sample. Deprecated 2026-07-13 on the current price table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-fin/palmyra-fin-32k": { "id": "writer/palmyra-fin/palmyra-fin-32k", "name": "Palmyra Fin 32k", "maker": "writer", "family": "writer/palmyra-fin", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 12.0 }, "summary_intro": "An earlier hosted Palmyra finance model. Writer's price table deprecates Palmyra Fin 32k on 2025-03-03 at $5 / $12 per million tokens. Collected 2026-10-15.", "what_it_is_for": "An earlier hosted Palmyra finance model. Writer's price table deprecates Palmyra Fin 32k on 2025-03-03 at $5 / $12 per million tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-fin/palmyra-fin-70b-32k": { "id": "writer/palmyra-fin/palmyra-fin-70b-32k", "name": "Palmyra-Fin-70B-32K", "maker": "writer", "family": "writer/palmyra-fin", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Writer open model license", "release_date": "2024-07-02", "context_length": 131072, "max_output_tokens": null, "parameters": "72B", "architecture": "Current README title is Palmyra-Fin-128K. It says the model is fine-tuned from Palmyra-X-003. Hugging Face lists Qwen2 tags and a 73B safetensors size on the repo header collected earlier; the README text says 72 billion.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Writer's open finance model. The current card calls it Palmyra-Fin-128K, a 72B model with a 131,072-token window, fine-tuned from Palmyra-X-003. The repository name still says 70B-32K. Collected 2026-10-15.", "what_it_is_for": "Writer's open finance model. The current card calls it Palmyra-Fin-128K, a 72B model with a 131,072-token window, fine-tuned from Palmyra-X-003. The repository name still says 70B-32K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-local/palmyra-local-1-7b": { "id": "writer/palmyra-local/palmyra-local-1-7b", "name": "Palmyra-local-1.7B", "maker": "writer", "family": "writer/palmyra-local", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Writer open model license", "release_date": "2025-03-23", "context_length": 128000, "max_output_tokens": 8192, "parameters": "1.7B", "architecture": "Card says trained from scratch. Causal transformer with RoPE and grouped-query attention, 28 layers, pretraining plus instruction tuning. Hugging Face size label says 2B; the card text says 1.7B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Writer's gated 1.7B instruct model for local deployment. The card says it was trained from scratch, with a 128K context and generation up to 8K tokens. Collected 2026-10-15.", "what_it_is_for": "Writer's gated 1.7B instruct model for local deployment. The card says it was trained from scratch, with a 128K context and generation up to 8K tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-med/palmyra-med": { "id": "writer/palmyra-med/palmyra-med", "name": "Palmyra Med", "maker": "writer", "family": "writer/palmyra-med", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 12.0 }, "summary_intro": "Writer's hosted healthcare model, API id palmyra-med. The product page gives it a 32k context and a medical-coding job. Writer dates deprecation to 2026-07-13. Collected 2026-10-15.", "what_it_is_for": "Writer's hosted healthcare model, API id palmyra-med. The product page gives it a 32k context and a medical-coding job. Writer dates deprecation to 2026-07-13.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-med/palmyra-med-20b": { "id": "writer/palmyra-med/palmyra-med-20b", "name": "Palmyra-Med-20b", "maker": "writer", "family": "writer/palmyra-med", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-06-29", "context_length": null, "max_output_tokens": null, "parameters": "20B", "architecture": "Causal decoder uptrained from Palmyra Large on a mix of PubMedQA (75%, 150,000) and MedQA (25%, 10,178).", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A 20B medical fine-tune of Palmyra Large for dialogue and question answering. Writer's card says it is no longer maintained and is a research tool only. Collected 2026-10-15.", "what_it_is_for": "A 20B medical fine-tune of Palmyra Large for dialogue and question answering. Writer's card says it is no longer maintained and is a research tool only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-med/palmyra-med-70b": { "id": "writer/palmyra-med/palmyra-med-70b", "name": "Palmyra-Med-70B", "maker": "writer", "family": "writer/palmyra-med", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Writer open model license", "release_date": "2024-05-05", "context_length": 8192, "max_output_tokens": null, "parameters": "71B", "architecture": "Instruction-tuned biomedical model. Card says it is fine-tuned from Palmyra-X-003 with DPO and an in-house medical instruct set. Hugging Face model size is 71B, F16.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Writer's open biomedical instruct model, fine-tuned from Palmyra-X-003. The card limits it to non-commercial English research and says it is not for patient care. Hugging Face lists the weights at 71B parameters. Collected 2026-10-15.", "what_it_is_for": "Writer's open biomedical instruct model, fine-tuned from Palmyra-X-003. The card limits it to non-commercial English research and says it is not for patient care. Hugging Face lists the weights at 71B parameters.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-med/palmyra-med-70b-32k": { "id": "writer/palmyra-med/palmyra-med-70b-32k", "name": "Palmyra-Med-70B-32K", "maker": "writer", "family": "writer/palmyra-med", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Writer open model license", "release_date": "2024-06-03", "context_length": 32768, "max_output_tokens": null, "parameters": "71B", "architecture": "LlamaForCausalLM config: 80 layers, hidden 8192, 64 heads, 8 key-value heads, vocab 128256, max_position_embeddings 32768, BF16. Card prose says it extends Palmyra-Med-70b. A README metadata snippet also named base_model Writer/Palmyra-x-004.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The long-context open release of Palmyra Med. Writer says it builds on Palmyra-Med-70b, with a 32,768-token window, for healthcare research rather than clinical use. Collected 2026-10-15.", "what_it_is_for": "The long-context open release of Palmyra Med. Writer says it builds on Palmyra-Med-70b, with a 32,768-token window, for healthcare research rather than clinical use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-mini/palmyra-mini": { "id": "writer/palmyra-mini/palmyra-mini", "name": "Palmyra-mini", "maker": "writer", "family": "writer/palmyra-mini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-09-11", "context_length": 131072, "max_output_tokens": null, "parameters": "1.7B", "architecture": "Qwen2. Fine-tune of Qwen/Qwen2.5-1.5B. Card describes it as the non-thinking model in the mini family.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Writer's open 1.7B non-thinking model, fine-tuned from Qwen2.5-1.5B and released with the Palmyra-mini family on 2025-09-11. Apache-2.0 weights, 131,072-token context. Collected 2026-10-15.", "what_it_is_for": "Writer's open 1.7B non-thinking model, fine-tuned from Qwen2.5-1.5B and released with the Palmyra-mini family on 2025-09-11. Apache-2.0 weights, 131,072-token context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-mini/palmyra-mini-thinking-a": { "id": "writer/palmyra-mini/palmyra-mini-thinking-a", "name": "Palmyra-mini-thinking-a", "maker": "writer", "family": "writer/palmyra-mini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-09-11", "context_length": 131072, "max_output_tokens": null, "parameters": "1.7B", "architecture": "Qwen2. Card says fine-tuned from Qwen/Qwen2.5-1.5B. Thinking variant aimed at math and contest problems.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The reasoning sibling of Palmyra-mini, also 1.7B and Apache-2.0, released with the family on 2025-09-11. Writer points it at hard math and logic. Collected 2026-10-15.", "what_it_is_for": "The reasoning sibling of Palmyra-mini, also 1.7B and Apache-2.0, released with the family on 2025-09-11. Writer points it at hard math and logic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-mini/palmyra-mini-thinking-b": { "id": "writer/palmyra-mini/palmyra-mini-thinking-b", "name": "Palmyra-mini-thinking-b", "maker": "writer", "family": "writer/palmyra-mini", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-09-11", "context_length": 131072, "max_output_tokens": null, "parameters": "1.7B", "architecture": "Qwen2. Fine-tuned from Qwen/Qwen2.5-1.5B. Second thinking variant.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The second Palmyra-mini reasoning model. Same 1.7B Qwen2.5 base, Apache-2.0, and 131,072 context. Writer's announcement gives it the highest average of the three on a hard math and code set, and a 92.5% AMC23 score. Collected 2026-10-15.", "what_it_is_for": "The second Palmyra-mini reasoning model. Same 1.7B Qwen2.5 base, Apache-2.0, and 131,072 context. Writer's announcement gives it the highest average of the three on a hard math and code set, and a 92.5% AMC23 score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-vision/palmyra-vision": { "id": "writer/palmyra-vision/palmyra-vision", "name": "Palmyra Vision", "maker": "writer", "family": "writer/palmyra-vision", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two public descriptions exist. The API product is a hosted multimodal model. The Hugging Face repo Writer/palmyra-vision is titled Palmyra_vision 7B, lists about 8B parameters, and names base models openai/clip-vit-large-patch14-336 and Writer/Palmyra-Large, trained on PixMo. It is not confirmed that the 7B weights are the hosted API model.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 7.5, "completion_per_m_usd": 22.5 }, "summary_intro": "Writer's multimodal model, API id palmyra-vision, for reading images and video into text. The hosted model is deprecated as of 2026-07-13. An open image-text checkpoint with the same repo name was published later and may or may not be that API model. Collected 2026-10-15.", "what_it_is_for": "Writer's multimodal model, API id palmyra-vision, for reading images and video into text. The hosted model is deprecated as of 2026-07-13. An open image-text checkpoint with the same repo name was published later and may or may not be that API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-x/palmyra-x-002-32k": { "id": "writer/palmyra-x/palmyra-x-002-32k", "name": "Palmyra X 002 32k", "maker": "writer", "family": "writer/palmyra-x", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 2.0 }, "summary_intro": "A long-context variant of Palmyra X 002. Writer's deprecation table dates it 2024-09-06 and prices it at $1 / $2 per million tokens. Collected 2026-10-15.", "what_it_is_for": "A long-context variant of Palmyra X 002. Writer's deprecation table dates it 2024-09-06 and prices it at $1 / $2 per million tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-x/palmyra-x-002-instruct": { "id": "writer/palmyra-x/palmyra-x-002-instruct", "name": "Palmyra X 002 Instruct", "maker": "writer", "family": "writer/palmyra-x", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 2.0 }, "summary_intro": "An older Writer instruct model. The current pricing page is the official trace: deprecated 2024-09-06 at $1.00 input and $2.00 output per million tokens. Collected 2026-10-15.", "what_it_is_for": "An older Writer instruct model. The current pricing page is the official trace: deprecated 2024-09-06 at $1.00 input and $2.00 output per million tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-x/palmyra-x-003-instruct": { "id": "writer/palmyra-x/palmyra-x-003-instruct", "name": "Palmyra X 003 Instruct", "maker": "writer", "family": "writer/palmyra-x", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 7.5, "completion_per_m_usd": 22.5 }, "summary_intro": "Writer's instruct model for structured text, API id palmyra-x-003-instruct. Writer lists it as deprecated on 2026-07-13. It had a 32k context on the product page and a higher token price than later X models. Collected 2026-10-15.", "what_it_is_for": "Writer's instruct model for structured text, API id palmyra-x-003-instruct. Writer lists it as deprecated on 2026-07-13. It had a 32k context on the product page and a higher token price than later X models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-x/palmyra-x-32k-instruct": { "id": "writer/palmyra-x/palmyra-x-32k-instruct", "name": "Palmyra X 32k Instruct", "maker": "writer", "family": "writer/palmyra-x", "status": "deprecated", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 2.0 }, "summary_intro": "A deprecated Writer instruct model whose name says 32k. The only current official specs are the 2024-09-06 deprecation date and the $1 / $2 price. Collected 2026-10-15.", "what_it_is_for": "A deprecated Writer instruct model whose name says 32k. The only current official specs are the 2024-09-06 deprecation date and the $1 / $2 price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-x/palmyra-x-4-3-73b": { "id": "writer/palmyra-x/palmyra-x-4-3-73b", "name": "Palmyra-X-4.3-73B", "maker": "writer", "family": "writer/palmyra-x", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Writer open model license", "release_date": "2024-11-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Gated Hugging Face repo. Model tree says it is a fine-tune of Writer/Palmyra-X-004. The card shows a chat example and trust_remote_code. The name says 73B; the card text collected does not restate the count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A gated Writer fine-tune of Palmyra-X-004, published as Palmyra-X-4.3-73B. It is a weight release, not the palmyra-x4 API product. Collected 2026-10-15.", "what_it_is_for": "A gated Writer fine-tune of Palmyra-X-004, published as Palmyra-X-4.3-73B. It is a weight release, not the palmyra-x4 API product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-x/palmyra-x4": { "id": "writer/palmyra-x/palmyra-x4", "name": "Palmyra X4", "maker": "writer", "family": "writer/palmyra-x", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-09", "context_length": 128000, "max_output_tokens": 4096, "parameters": "", "architecture": "Writer says Palmyra X 004 was trained on synthetic data using an internal model it names Instruct-Adapt-X, plus early stopping. Parameter count was not published. Public weight repos named Palmyra-X-004 were not in the Writer Hugging Face listing; a dummy-weights repo exists and is not a model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.5, "completion_per_m_usd": 10.0 }, "summary_intro": "Writer's tool-calling model launched on 2024-10-09 as Palmyra X 004 and later renamed Palmyra X4. API id palmyra-x4. It is still available, with deprecation scheduled for 2026-12-14. Collected 2026-10-15.", "what_it_is_for": "Writer's tool-calling model launched on 2024-10-09 as Palmyra X 004 and later renamed Palmyra X4. API id palmyra-x4. It is still available, with deprecation scheduled for 2026-12-14.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-x/palmyra-x5": { "id": "writer/palmyra-x/palmyra-x5", "name": "Palmyra X5", "maker": "writer", "family": "writer/palmyra-x", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-28", "context_length": 1000000, "max_output_tokens": 8192, "parameters": "", "architecture": "Writer describes a hybrid transformer with hybrid attention, trained on synthetic data. Parameter count was not published. Writer says its models are not post-training quantized or distilled.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 6.0 }, "summary_intro": "Writer's general-purpose long-context model, API id palmyra-x5, released 2025-04-28. It is still sold, and Writer has scheduled deprecation for 2026-12-14 in favor of Palmyra X6. Collected 2026-10-15.", "what_it_is_for": "Writer's general-purpose long-context model, API id palmyra-x5, released 2025-04-28. It is still sold, and Writer has scheduled deprecation for 2026-12-14 in favor of Palmyra X6.", "ratings": { "empirical_100": null, "lmsys_elo": 1290, "coding_elo": 1290, "swe_bench": 30.0, "humaneval": 82.0, "twelve_cs": 99.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "writer/palmyra-x/palmyra-x6": { "id": "writer/palmyra-x/palmyra-x6", "name": "Palmyra X6", "maker": "writer", "family": "writer/palmyra-x", "status": "active", "confidence": "high", "weights": "closed", "license": "WRITER commercial license", "release_date": "2026-08-13", "context_length": 1000000, "max_output_tokens": 8192, "parameters": "744B total, about 40B active", "architecture": "Post-train of GLM-5.2. GlmMoeDsa left unchanged: MoE plus MLA plus a DeepSeek Sparse Attention indexer. Technical report: 78 layers (first 3 dense, 75 MoE), hidden 6144, 256 routed experts with top-8 routing, 1 shared expert, about 40B active of 744B. Anchored supervised fine-tuning on 626 synthetic tool-use trajectories, one epoch.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 8.0 }, "summary_intro": "Writer's current default agent model, API id palmyra-x6. It is a closed post-train of the open GLM-5.2 mixture-of-experts model, aimed at long enterprise workflows. Status active. Collected 2026-10-15.", "what_it_is_for": "Writer's current default agent model, API id palmyra-x6. It is a closed post-train of the open GLM-5.2 mixture-of-experts model, aimed at long enterprise workflows. Status active.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wsu/deep-cwd-ihc/deep-cwd-ihc": { "id": "wsu/deep-cwd-ihc/deep-cwd-ihc", "name": "Deep-CWD-IHC", "maker": "wsu", "family": "wsu/deep-cwd-ihc", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-10-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The repository training script builds an ImageNet-pretrained Keras EfficientNetB3 (include_top false, 300x300 RGB input), adds global average pooling and a 1024-unit ReLU dense layer, and ends in a 2-way softmax. The USDA technical abstract describes the published system only as a fine-tune of a pretrained convolutional neural network and does not name EfficientNet. Three Keras files share that training path and differ by label set: tissue type (lymph node vs obex), lymph-node structures (follicle vs non-follicle), and obex structures (dorsal motor nucleus vs not). TensorFlow 2.15. Each released file is 78,765,635 bytes.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Deep-CWD-IHC is a Washington State University slide-tile classifier from Lawrence Holder and Liam Broughton-Neiswanger. GitHub release `models-v1` on 11 October 2025 attaches three Keras weight files for chronic wasting disease immunohistochemistry. On 15 October 2026 those files were still the public release. The Veterinary Pathology paper was published 19 May 2026. No public price was found.", "what_it_is_for": "The GitHub README describes a pipeline for SVS whole-slide images and QuPath GeoJSON. A tissue model labels a tile as lymph node or obex. A node model then marks follicles, or an obex model marks the dorsal motor nucleus. Chromogen staining in the README is a pixel count, not a fourth network. The work was funded by USDA APHIS grant AP23WSNWRC00C076. The USDA technical abstract says the system was trained on 144 whole-slide images and 3,296 annotations. The Dryad record says 143 slides and the same annotation count.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wsu/foreswe/foreswe": { "id": "wsu/foreswe/foreswe", "name": "ForeSWE", "maker": "wsu", "family": "wsu/foreswe", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-12", "context_length": null, "max_output_tokens": null, "parameters": "426 million", "architecture": "Spatio-temporal attention model with attribute interaction, then a Gaussian-process prediction head. Location embedding dimension d_model is 1024. The GP uses an 8-dimensional reduction of the attention representation. Prompts for vegetation and weather type come from a separate pretrained sentence embedding model (Reimers and Gurevych 2019) and are inputs, not this network. Implementation: PyTorch 2.0.1 and GPyTorch 1.12. Daily setting forecasts a 10-day horizon. Weekly setting forecasts a 4-week horizon. Table 5 lists ForeSWE at 426 million parameters, the row with spatial, temporal, and attribute-interaction marks.", "modalities": { "input": [ "tabular" ], "output": [ "tabular" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ForeSWE is a Washington State University snow-water equivalent forecast model. All authors on arXiv:2511.08856 (12 November 2025) list wsu.edu addresses. The AAAI 2026 record is DOI 10.1609/aaai.v40i46.41284. A WSU Insider story on 27 January 2026 describes the same tool. On 15 October 2026 the training code was public and the only checkpoint file was empty. No public price was found.", "what_it_is_for": "The paper forecasts snow-water equivalent at Western US SNOTEL stations and reports uncertainty. After filtering 822 stations, the study uses 512. Training uses 20 years. Test water years are 2015 through 2019 at the same locations. The daily setting uses a 10-day horizon. The weekly setting uses a 4-week horizon. USDA NIFA grant 2021-67021-35344 (AgAID) is cited. The paper says the forecast is not yet operational, and a USBR dashboard was still a plan.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wuerzburg/llammlein/betzerl-1b": { "id": "wuerzburg/llammlein/betzerl-1b", "name": "Betzerl 1B", "maker": "wuerzburg", "family": "wuerzburg/llammlein", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "research-only RAIL-M", "release_date": "2024-10-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PEFT adapter on LSX-UniWue/LLaMmlein_1B. The card says it was tuned on a Bavarian Wikipedia dump without further optimization. The load example resizes embeddings to 32001. Adapter rank was not read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Betzerl 1B is a Bavarian preview adapter from the Data Science chair at CAIDAS, University of W\u00fcrzburg. It is a LoRA on their German LL\u00e4Mmlein 1B, not a model trained from scratch. The card says it was tuned on a Bavarian Wikipedia dump without further optimization and asks readers not to take it too seriously. The project page still lists a Bavarian preview with the base downloads. Status on 2026-10-15: preview.", "what_it_is_for": "The card calls it a Bavarian adapter for the German TinyLlama-style 1B. The intended use is a light dialect preview. It is not presented as a production translator or as a full Bavarian base model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wuerzburg/llammlein/llammlein-120m": { "id": "wuerzburg/llammlein/llammlein-120m", "name": "LL\u00e4Mmlein 120M", "maker": "wuerzburg", "family": "wuerzburg/llammlein", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research-only RAIL-M", "release_date": "2024-11-15", "context_length": 2048, "max_output_tokens": null, "parameters": "124766976", "architecture": "LlamaForCausalLM decoder. Current config.json: 12 layers, hidden size 768, intermediate size 2048, 12 attention heads, 4 key-value heads, head dim 64, SiLU, RoPE theta 10000, vocab 32064, max_position_embeddings 2048, untied embeddings, float32. The Hub name is 120M. The safetensors total is 124,766,976.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LL\u00e4Mmlein 120M is a German-only decoder from the Data Science chair at CAIDAS, University of W\u00fcrzburg. The university announced the 120M and 1B models on 15 November 2024. The weights described here are the data-logged Hub retrain `LSX-UniWue/LLaMmlein_120M`, created 4 July 2025. Status on 2026-10-15: active research release, not a priced API.", "what_it_is_for": "The model card says it is a German Llama trained from scratch with an adapted TinyLlama codebase on the German portion of RedPajama V2, after paragraph-level deduplication and a token-to-word filter. The chair presents the 120M, 1B, and 7B sizes as one family for German NLP research, not as a hosted chat product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wuerzburg/llammlein/llammlein-1b": { "id": "wuerzburg/llammlein/llammlein-1b", "name": "LL\u00e4Mmlein 1B", "maker": "wuerzburg", "family": "wuerzburg/llammlein", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research-only RAIL-M", "release_date": "2024-11-15", "context_length": 2048, "max_output_tokens": null, "parameters": "1100310528", "architecture": "LlamaForCausalLM decoder. Current config.json: 22 layers, hidden size 2048, intermediate size 5632, 32 attention heads, 4 key-value heads, head dim 64, SiLU, RoPE theta 10000, vocab 32064, max_position_embeddings 2048, untied embeddings, float32.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LL\u00e4Mmlein 1B is the mid-size German-only decoder from the Data Science chair at CAIDAS, University of W\u00fcrzburg. The university put the 120M and 1B models in public on 15 November 2024. The weights described here are the later data-logged repo `LSX-UniWue/LLaMmlein_1B`, created 4 July 2025. Status on 2026-10-15: active research release, not a priced API.", "what_it_is_for": "The card says it is a German Llama trained from scratch on the filtered, paragraph-deduplicated German portion of RedPajama V2. The November 2024 notice presents it as the larger of the first two sizes, for German-language research rather than a commercial chat service. The chair page says this 1B is the best evaluated decoder overall on SuperGLEBer, without printing that average.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wuerzburg/llammlein/llammlein-1b-chat": { "id": "wuerzburg/llammlein/llammlein-1b-chat", "name": "LL\u00e4Mmlein 1B Chat", "maker": "wuerzburg", "family": "wuerzburg/llammlein", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "research-only RAIL-M", "release_date": "2024-11-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PEFT chat adapters on a LL\u00e4Mmlein 1B checkpoint, not a second pretrained base. The chat-preview collection member LLaMmlein_1B_chat_selected is a LoRA loaded on LSX-UniWue/LLaMmlein_1B_prerelease. The card uses a ChatML template and resizes embeddings to 32064. Single-dataset adapters are separate Hub repos listed in versions.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LL\u00e4Mmlein 1B Chat is the instruction-tuned preview line for the University of W\u00fcrzburg 1B German decoder. The 15 November 2024 press notice said chat variants would be released with the base models. The cards call them research demonstrations that are not ready where close instruction following matters. Status on 2026-10-15: preview. The folder groups the named 1B chat adapters instead of giving each dataset LoRA its own folder.", "what_it_is_for": "The selected card is a chat adapter for the German 1B model. The example uses a ChatML template. The project page groups \"chat-tuned models (1B & 7B)\" with the base downloads. The paper describes LoRA supervised fine-tuning on German instruction sets. This is not a separate pretrained model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wuerzburg/llammlein/llammlein-7b": { "id": "wuerzburg/llammlein/llammlein-7b", "name": "LL\u00e4Mmlein 7B", "maker": "wuerzburg", "family": "wuerzburg/llammlein", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research-only RAIL-M", "release_date": "2025-04-07", "context_length": 4096, "max_output_tokens": null, "parameters": "6738939904", "architecture": "LlamaForCausalLM decoder. config.json: 32 layers, hidden size 4096, intermediate size 11008, 32 attention heads, 32 key-value heads, head dim 128, SiLU, RoPE theta 10000, vocab 32064, max_position_embeddings 4096, untied embeddings, float32. Hub safetensors total 6,738,939,904.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LL\u00e4Mmlein 7B is the largest released German-only decoder from the Data Science chair at CAIDAS, University of W\u00fcrzburg. The arXiv HTML says it was released after the original 120M and 1B submission. The Hub repo `LSX-UniWue/LLaMmlein_7B` was created 7 April 2025. Status on 2026-10-15: active research release, not a priced API.", "what_it_is_for": "The card uses the same description as the smaller sizes: a German Llama trained from scratch on the filtered German portion of RedPajama V2. GitHub lists 120M, 1B, and 7B as the family, trained for scaling comparisons on the same curated dataset. The chair page marks the 7B as newly available and also points at a chat-tuned 7B, which is a separate folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wuerzburg/llammlein/llammlein-7b-chat": { "id": "wuerzburg/llammlein/llammlein-7b-chat", "name": "LL\u00e4Mmlein 7B Chat", "maker": "wuerzburg", "family": "wuerzburg/llammlein", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "research-only RAIL-M", "release_date": "2025-04-07", "context_length": null, "max_output_tokens": null, "parameters": "266240", "architecture": "PEFT LoRA adapter on LSX-UniWue/LLaMmlein_7B. The Hub collection records 266,240 parameters for this repo, and the model page rounds that to 266k. That count is the adapter, not the 6,738,939,904-parameter base. Hub tags name Guanako, sharegpt-deutsch, and alpaca-gpt4-deutsch.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LL\u00e4Mmlein 7B Chat is an early instruction-tuned preview from the Data Science chair at CAIDAS, University of W\u00fcrzburg. It is a LoRA on LL\u00e4Mmlein 7B, not a second base model. The card says it is not the final version. Status on 2026-10-15: preview.", "what_it_is_for": "The card calls it an early preview of an instruction-tuned 7B trained with limited German instruction resources. The same card says the base models are the stronger release and that the chat versions are research demonstrations, not ready where close instruction following is necessary. Hub tags name Guanako, sharegpt-deutsch, and alpaca-gpt4-deutsch.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wuerzburg/llammlein2vec/llammlein2vec-120m": { "id": "wuerzburg/llammlein2vec/llammlein2vec-120m", "name": "LL\u00e4Mmlein2Vec 120M", "maker": "wuerzburg", "family": "wuerzburg/llammlein2vec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research-only RAIL-M", "release_date": "2025-07-14", "context_length": 8192, "max_output_tokens": null, "parameters": "100141824", "architecture": "LlamaBiModel produced with LLM2Vec from LL\u00e4Mmlein 120M. config.json: 12 layers, hidden size 768, intermediate size 2048, 12 heads, 4 key-value heads, head dim 64, SiLU, RoPE theta 160000, vocab 32064, max_position_embeddings 8192, untied embeddings, bfloat16. Hub safetensors total 100,141,824. Hub library_name is llm2vec.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LL\u00e4Mmlein2Vec 120M is a German encoder from the Data Science chair at CAIDAS, University of W\u00fcrzburg. It is an LLM2Vec conversion of their own LL\u00e4Mmlein 120M decoder, not a third-party fine-tune of another lab's model. The Hub repo was created 14 July 2025. The May 2025 paper introduced the family and said weights would be published on acceptance. Status on 2026-10-15: active research release.", "what_it_is_for": "The card says it is a German encoder derived from LL\u00e4Mmlein 120M via LLM2Vec, for feature extraction. The paper trains these conversions with masked next-token prediction and extends context to 8192 tokens, as a comparison point for ModernGBERT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wuerzburg/llammlein2vec/llammlein2vec-1b": { "id": "wuerzburg/llammlein2vec/llammlein2vec-1b", "name": "LL\u00e4Mmlein2Vec 1B", "maker": "wuerzburg", "family": "wuerzburg/llammlein2vec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research-only RAIL-M", "release_date": "2025-07-14", "context_length": 8192, "max_output_tokens": null, "parameters": "1034643456", "architecture": "LlamaBiModel produced with LLM2Vec from LL\u00e4Mmlein 1B. config.json: 22 layers, hidden size 2048, intermediate size 5632, 32 heads, 4 key-value heads, head dim 64, SiLU, RoPE theta 160000, vocab 32064, max_position_embeddings 8192, untied embeddings, bfloat16. Hub safetensors total 1,034,643,456. The card says to use the llm2vec library. Hub library_name is transformers.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LL\u00e4Mmlein2Vec 1B is a German encoder from the Data Science chair at CAIDAS, University of W\u00fcrzburg. It is the LLM2Vec conversion of their LL\u00e4Mmlein 1B decoder. The Hub repo was created 14 July 2025. Status on 2026-10-15: active research release, not a priced API.", "what_it_is_for": "The card says it is a German encoder derived from LL\u00e4Mmlein 1B via LLM2Vec. The paper uses the conversions to compare a from-scratch ModernBERT encoder with a decoder that has been turned into an encoder under the same data constraints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wuerzburg/llammlein2vec/llammlein2vec-7b": { "id": "wuerzburg/llammlein2vec/llammlein2vec-7b", "name": "LL\u00e4Mmlein2Vec 7B", "maker": "wuerzburg", "family": "wuerzburg/llammlein2vec", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research-only RAIL-M", "release_date": "2025-07-14", "context_length": 8192, "max_output_tokens": null, "parameters": "6607605760", "architecture": "LlamaBiModel produced with LLM2Vec from LL\u00e4Mmlein 7B. config.json: 32 layers, hidden size 4096, intermediate size 11008, 32 heads, 32 key-value heads, head dim 128, SiLU, RoPE theta 160000, vocab 32064, max_position_embeddings 8192, untied embeddings, bfloat16. Hub safetensors total 6,607,605,760. The card rounds the size to 7B. Hub library_name is transformers. The card load example uses llm2vec.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LL\u00e4Mmlein2Vec 7B is the largest German encoder conversion from the Data Science chair at CAIDAS, University of W\u00fcrzburg. It is LLM2Vec applied to their own LL\u00e4Mmlein 7B, with weights in `LSX-UniWue/LLaMmlein2Vec_7B` since 14 July 2025. Status on 2026-10-15: active research release, not a priced API.", "what_it_is_for": "The card says it is a German encoder derived from LL\u00e4Mmlein 7B via LLM2Vec. The paper's point is that a from-scratch ModernGBERT 1B can beat this much larger converted decoder on SuperGLEBer, while the converted 7B stays close on German MTEB after further embedding training.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wuerzburg/moderngbert/moderngbert-134m": { "id": "wuerzburg/moderngbert/moderngbert-134m", "name": "ModernGBERT 134M", "maker": "wuerzburg", "family": "wuerzburg/moderngbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research-only RAIL-M", "release_date": "2025-05-27", "context_length": 8192, "max_output_tokens": null, "parameters": "158827456", "architecture": "ModernBertForMaskedLM. config.json: 22 layers, hidden size 768, intermediate size 1152, 12 heads, local attention 128, global attention every 3 layers, local RoPE theta 10000, global RoPE theta 160000, vocab 31168, max_position_embeddings 8192, GELU, tied word embeddings, float32. Hub safetensors total 158,827,456. The 1B card calls this size 134 million parameters. The LSX-UniWue org card says 138M.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernGBERT 134M is the smaller German ModernBERT encoder from the Data Science and Computer Philology chairs at the University of W\u00fcrzburg. The Hub repo `LSX-UniWue/ModernGBERT_134M` was created 27 May 2025, the same day as the 1B repo. Status on 2026-10-15: active research release, not a priced API.", "what_it_is_for": "The 1B card lists this as the second ModernGBERT size: a German masked language model in the ModernBERT style, for the same fill-mask and downstream encoder uses as the 1B. The chair news of 20 May 2025 presents the family as a from-scratch German encoder line tied to the LitBERT project.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wuerzburg/moderngbert/moderngbert-1b": { "id": "wuerzburg/moderngbert/moderngbert-1b", "name": "ModernGBERT 1B", "maker": "wuerzburg", "family": "wuerzburg/moderngbert", "status": "active", "confidence": "high", "weights": "open-weights", "license": "research-only RAIL-M", "release_date": "2025-05-27", "context_length": 8192, "max_output_tokens": null, "parameters": "1130252736", "architecture": "ModernBertForMaskedLM. config.json: 28 layers, hidden size 2048, intermediate size 3072, 32 heads, local attention 128, global attention every 3 layers, local RoPE theta 10000, global RoPE theta 160000, vocab 31168, max_position_embeddings 8192, GELU, tied word embeddings, float32. Hub safetensors total 1,130,252,736. The card rounds this to 1 billion.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ModernGBERT 1B is a German encoder trained from scratch by the Data Science chair at CAIDAS, University of W\u00fcrzburg, with the Computer Philology chair. It is a ModernBERT-style masked language model, not a decoder. The Hub repo was created 27 May 2025. The arXiv preprint is 19 May 2025, and a chair news post is dated 20 May 2025. Status on 2026-10-15: active research release, not a priced API.", "what_it_is_for": "The card says it is a German ModernBERT with a native context of up to 8,192 tokens, for masked language modeling and for BERT-style fine-tuning on classification, retrieval, or question answering. It was pretrained on the same German RedPajama V2 corpus the card gives as 1.27 trillion tokens for the LL\u00e4Mmlein decoders.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/birdsat/birdsat": { "id": "wustl/birdsat/birdsat", "name": "BirdSAT", "maker": "wustl", "family": "wustl/birdsat", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-10-29", "context_length": null, "max_output_tokens": null, "parameters": "115M", "architecture": "Cross-view masked autoencoders that bind satellite imagery with ground-level bird photographs, with and without metadata. The paper's four named checkpoints are CVE-MAE, CVE-MAE-Meta, CVM-MAE, and CVM-MAE-Meta. CVE variants are listed at 117M trainable parameters. CVM variants are listed at 115M. The main fine-tune result is CVM-MAE-Meta. The code appends CVMMAE into a third-party RVSA repository. The pretrained satellite encoder is a ViTAE/RVSA checkpoint that is not a WashU product. The repository license is MIT, Copyright (c) 2023 Multimodal Vision Research Lab, and it applies to the software.", "modalities": { "input": [ "image", "geolocation" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BirdSAT is a Washington University in St. Louis cross-view model from the Multimodal Vision Research Laboratory. It learns a joint embedding of satellite imagery and ground-level bird photographs so species can be classified and mapped. The arXiv paper was submitted on 2023-10-29, the venue is WACV 2024, and four named checkpoints were still linked from Washington University Box on the collection date 2026-10-15.", "what_it_is_for": "The checkpoints are contrastive masked autoencoders. CVE and CVM are the two architectures, each with a metadata variant. The authors are at WashU. The code lives in mvrl/BirdSAT and appends the CVMMAE module into a third-party RVSA repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/context-cir/context-cir": { "id": "wustl/context-cir/context-cir", "name": "ConText-CIR", "maker": "wustl", "family": "wustl/context-cir", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Composed image retrieval model. A Text Concept-Consistency loss pushes noun phrases in the modification text onto the relevant parts of the query image. A synthetic data pipeline builds extra training pairs from existing CIR datasets or unlabeled images. Reported backbone classes are ViT-B, ViT-L, and ViT-H. The abstract says source code, checkpoints, and datasets are at https://github.com/mvrl/ConText-CIR. Checkpoint filenames were not copied.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ConText-CIR is a Washington University in St. Louis composed-image-retrieval model from the Multimodal Vision Research Laboratory. Given a query image and a text modification, it retrieves the image that matches both. The arXiv paper was submitted on 2025-05-27 and is a CVPR 2025 paper. The abstract says the code, checkpoints, and datasets are public, and they were still linked on the collection date 2026-10-15.", "what_it_is_for": "The training loss is Text Concept-Consistency. Noun phrases in the modification text are encouraged to attend to the matching parts of the query image. A synthetic data pipeline adds pairs from existing CIR sets or from unlabeled images. Eric Xing and Nathan Jacobs are at WashU. Abby Stylianou is at Saint Louis University and Robert Pless is at George Washington University. Pranavi Kolouju's affiliation was not verified. The repository is mvrl/ConText-CIR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/genesis/genesis": { "id": "wustl/genesis/genesis", "name": "Genesis", "maker": "wustl", "family": "wustl/genesis", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-09-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Generative engine for multi-scale satellite tile pyramids on Git-10M. Two flow-matching JiT operators: a vertical parent-conditional super-resolution model, from a 256 parent to a 512 mosaic of four children, and a horizontal mask outpainting model. The main super-resolution run is DINOv3-conditioned with LPIPS. The main outpainting run does not use DINO. Sampling uses 50 steps, classifier-free guidance scale 1.0, and EMA. Main runs are 800k steps. File sizes on the card, which are not parameter counts: super-resolution JiT-B/16 3.3 GB, super-resolution JiT-H/16 23 GB, outpainting JiT-B/16 3.6 GB. The outpainting JiT-H/16 size was truncated in the retrieved card. An ablations folder holds the paper's ablation table only. Hugging Face MVRL/genesis is MIT and was created on 2026-08-15.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Genesis is a Washington University in St. Louis satellite image generator from the Multimodal Vision Research Laboratory. It fills a tile pyramid that stays consistent across zoom and across neighboring tiles, starting from a sparse set of seed tiles. The arXiv paper was submitted on 2026-09-02, revised on 2026-09-11, and is accepted to SIGSPATIAL 2026 as an application-track oral. MIT weights on Hugging Face were created on 2026-08-15 and were still public on the collection date 2026-10-15.", "what_it_is_for": "The task in the paper is multi-scale tile completion. A vertical operator super-resolves a parent tile into four children. A horizontal operator outpaints masked neighbors. The authors are Subash Khanal, Yangzhi Cui, Daniel Cher, Eric Xing, Brian Wei, Srikumar Sastry, and Nathan Jacobs. The repository is mvrl/genesis.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/geoclap/geoclap": { "id": "wustl/geoclap/geoclap", "name": "GeoCLAP", "maker": "wustl", "family": "wustl/geoclap", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-09-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Tri-modal embedding framework for zero-shot soundscape mapping. The paper name is Learning Tri-modal Embeddings for Zero-Shot Soundscape Mapping; the framework name is GeoCLAP. Training can start from a SatMAE ViT-base checkpoint, finetune-vit-base-e7.pth, which is not a WashU model. Released WashU checkpoints are a Sentinel checkpoint on Hugging Face and a Google Earth high-resolution checkpoint.", "modalities": { "input": [ "image", "audio", "geolocation" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GeoCLAP is a Washington University in St. Louis tri-modal embedding model from the Multimodal Vision Research Laboratory. It maps overhead imagery, sound, and location so a soundscape can be queried without a labeled acoustic survey at that point. The arXiv paper is dated 2023-09-19, the venue is BMVC in November 2023, and a Sentinel checkpoint was still public on Hugging Face on the collection date 2026-10-15.", "what_it_is_for": "The paper title is Learning Tri-modal Embeddings for Zero-Shot Soundscape Mapping. GeoCLAP is the framework name. The authors' emails are at WashU. The code repository is mvrl/geoclap.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/geosynth/geosynth": { "id": "wustl/geosynth/geosynth", "name": "GeoSynth", "maker": "wustl", "family": "wustl/geosynth", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-04-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Satellite image synthesis with global style from text or geographic location and image-driven layout control. The released adapters are ControlNet-style checkpoints on stabilityai/stable-diffusion-2-1-base. Control modes are OSM, Canny, SAM, and location, including combinations. MVRL/stable-diffusion-2-1 is a hosted copy of the base model and is not this release. Style LoRAs papersculpting and vangogh are demo adapters and are not versions of the paper model.", "modalities": { "input": [ "text", "image", "geolocation" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GeoSynth is a Washington University in St. Louis satellite image generator from the Multimodal Vision Research Laboratory. It synthesizes overhead imagery whose global style comes from text or from a geographic location, with layout taken from a control image. The arXiv paper was submitted on 2024-04-09. Control checkpoints were already on Hugging Face in March 2024 and were still public on the collection date 2026-10-15.", "what_it_is_for": "The paper describes contextually aware high-resolution satellite image synthesis. Global style sets scene semantics or regional appearance. Layout control uses image-driven conditions such as OSM, Canny edges, or SAM. The authors are at WashU. The code repository is mvrl/GeoSynth.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/prom3e/prom3e": { "id": "wustl/prom3e/prom3e", "name": "ProM3E", "maker": "wustl", "family": "wustl/prom3e", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-04", "context_length": null, "max_output_tokens": null, "parameters": "27.3M", "architecture": "Probabilistic masked multimodal embedding model. It reconstructs missing modality embeddings from a few context modalities and supports modality inversion in embedding space. Inputs are normalized embeddings of dimension 512 for image, satellite, location, environmental features, text, and audio, taken from TaxaBind. The Hugging Face card lists 27.3M parameters, F32. The loader is ProM3E.from_pretrained(\"MVRL/ProM3E\") via rshf. The GitHub repository mvrl/proM3E is Apache-2.0. Its description says CVPR 2026. The arXiv comment does not name a venue.", "modalities": { "input": [ "image", "satellite", "geolocation", "environmental", "text", "audio" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ProM3E is a Washington University in St. Louis ecological embedding model from the Multimodal Vision Research Laboratory. It infers a missing modality embedding from whichever of the other modalities are present. The arXiv paper was submitted on 2025-11-04. The GitHub repository description says CVPR 2026. The arXiv comment does not name a venue. Hugging Face created MVRL/ProM3E on 2026-03-19, and the weights were still public on the collection date 2026-10-15.", "what_it_is_for": "The paper calls the job any-to-any generation of multimodal representations for ecology, including a retrieval score that mixes inter-modal and intra-modal similarity. The inputs are TaxaBind embeddings. Authors on the paper are Srikumar Sastry, Subash Khanal, Aayush Dhakal, Jiayu Lin, Dan Cher, Phoenix Jarosz, and Nathan Jacobs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/quari/quari": { "id": "wustl/quari/quari", "name": "QuARI", "maker": "wustl", "family": "wustl/quari", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Query-adaptive linear projection. A hypernetwork maps a query to a query-specific linear transform of a frozen vision-language embedding. Documented frozen backbones are CLIP, SigLIP, SigLIP2, and OpenCLIP. This is not a full backbone fine-tune. Checkpoints are downloaded by mvrl/QuARI download_ckpts.py and are also published in a Hugging Face collection under EricX003.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "QuARI is a Washington University in St. Louis retrieval adapter from the Multimodal Vision Research Laboratory. It learns a query-specific linear transform of a frozen vision-language embedding so large image collections can be re-ranked without retraining the backbone. The arXiv paper was submitted on 2025-05-27. The GitHub repository is marked NeurIPS 2025. Checkpoints were still public on the collection date 2026-10-15.", "what_it_is_for": "The paper targets instance retrieval and other hard image-to-image and text-to-image lookups. The transform is linear so it can be applied to millions of stored embeddings. Eric Xing and Nathan Jacobs are at WashU. Abby Stylianou is at Saint Louis University and Robert Pless is at George Washington University. The code org is mvrl.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/range/range": { "id": "wustl/range/range", "name": "RANGE", "maker": "wustl", "family": "wustl/range", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-02-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Retrieval-augmented neural field for multi-resolution geographic embeddings. A query location is represented by combining visual features estimated from similar locations. The documented embedding is 1280-dimensional: a 1024-dimensional estimated visual feature concatenated with a 256-dimensional SatCLIP location feature. SatCLIP is a third-party backbone, not a WashU model. Named variants are RANGE and RANGE+. The Python package call shown in the project is rangegeo.RANGE(\"RANGE+\", db=\"large\", beta=0.5). Weights ship as a database, files range_db_large.npz and range_db_med.npz in the Hugging Face dataset mvrl/RANGE-database, rather than as a model repo.", "modalities": { "input": [ "geolocation" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RANGE is a Washington University in St. Louis geographic embedding model from the Multimodal Vision Research Laboratory. It maps a latitude and longitude to a multi-resolution embedding by retrieving visual features from similar places instead of encoding the coordinate on a fixed basis alone. The arXiv paper was submitted on 2025-02-27 and revised on 2025-04-03. It is accepted to CVPR 2025. The rangegeo package and the embedding database were still public on the collection date 2026-10-15.", "what_it_is_for": "The paper's job is a location representation for species classification, population density, biome classification, and other geospatial tasks. RANGE and RANGE+ are the named variants. Adeel Ahmad also lists the Taylor Geospatial Institute. The releasing repository is mvrl/RANGE.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/rcme/rcme": { "id": "wustl/rcme/rcme", "name": "RCME", "maker": "wustl", "family": "wustl/rcme", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0 (code repository); MIT on MVRL/rcme-tol-vit-base-patch16", "release_date": "2025-06-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Radial Cross-Modal Embeddings. The framework models transitivity-enforced entailment so a vision-language embedding can represent a partial order, including the Tree of Life. Official checkpoints are CLIP ViT-B/16 at MVRL/rcme-vit-base-patch16, CLIP ViT-L/14 at MVRL/rcme-vit-large-patch14, and Tree of Life ViT-B/16 at MVRL/rcme-tol-vit-base-patch16. Training code also supports radial, atmg, and meru baselines. ATMG-tol-vit-16 is a baseline checkpoint, not this product.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RCME is a Washington University in St. Louis vision-language embedding model from the Multimodal Vision Research Laboratory. Radial Cross-Modal Embeddings encode entailment so that concepts, including ranks in the Tree of Life, keep a partial order in the embedding space. The arXiv paper was submitted on 2025-06-26 and is accepted at ICCV 2025. Three named checkpoints were still public on Hugging Face on the collection date 2026-10-15.", "what_it_is_for": "The paper's tasks are hierarchical species classification and hierarchical retrieval. The authors are at WashU. Code and models are linked from the project page and from mvrl/RCME.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/sat2cap/sat2cap": { "id": "wustl/sat2cap/sat2cap", "name": "Sat2Cap", "maker": "wustl", "family": "wustl/sat2cap", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-07-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Contrastive overhead-to-ground model. The paper uses a ViT-B/32 Sat2Cap image encoder initialized from CLIP and kept trainable, a frozen CLIP ViT-B/32 image encoder as the ground-level teacher, and a shallow fully connected Dynamic Encoder on sin-cos year, month, day, hour, and geolocation. Overhead and dynamic embeddings are added before InfoNCE against ground-level CLIP embeddings, with a memory-bank queue of 9600 negatives. The released checkpoint is loaded as sat2cap.ckpt.", "modalities": { "input": [ "image", "geolocation", "time" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sat2Cap is a Washington University in St. Louis cross-view embedding model from the Multimodal Vision Research Laboratory. It maps a satellite image, and optionally time and location, into the CLIP embedding of the ground-level scene so free-form text can be mapped without task-specific labels. The arXiv paper is dated 2023-07-29, the EarthVision 2024 workshop version followed, and the weights were still public on Hugging Face on the collection date 2026-10-15.", "what_it_is_for": "The paper calls the job zero-shot mapping. Instead of a fixed land-cover label set, Sat2Cap predicts the expected CLIP embedding of ground-level photos at a location, then compares that embedding with a text prompt. The authors say this captures concepts that are visible on the ground but not in a coarse overhead view. Hannah Kerner at Arizona State University is a coauthor. The code and checkpoint host are the WashU MVRL lab.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/sat2sound/sat2sound": { "id": "wustl/sat2sound/sat2sound", "name": "Sat2Sound", "maker": "wustl", "family": "wustl/sat2sound", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2025-05-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Contrastive and codebook-aligned soundscape model. It learns shared soundscape concepts from audio, text descriptions of audio, satellite images, and synthetic image captions. Checkpoints are bingmap_nometa, bingmap_withmeta, sentinel_nometa, sentinel_withmeta, SoundingEarth_nometa, and SoundingEarth_withmeta. A card comment says embeddings have shape (B, 1024). sat2text/bingmap_i2t_baseline.ckpt is an image-to-text baseline in the same repo. SatMAE ViT-Base pretrain-vit-base-e199.pth and mga-clap.pt are hosted backbones, not this model. The Hugging Face repo MVRL/sat2sound is MIT and was created on 2026-04-27.", "modalities": { "input": [ "image", "geolocation", "text", "audio" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sat2Sound is a Washington University in St. Louis soundscape model from the Multimodal Vision Research Laboratory. It maps a satellite image to the sounds at that place, and it can retrieve a caption that a separate text-to-audio model could render. The arXiv paper was submitted on 2025-05-19 and revised on 2026-04-11. It is accepted to EarthVision 2026. MIT weights on Hugging Face were created on 2026-04-27 and were still public on the collection date 2026-10-15.", "what_it_is_for": "The framework learns soundscape concepts shared by audio, text descriptions of audio, satellite images, and synthetic captions. Abby Stylianou at Saint Louis University is a coauthor. The code and the weight host are MVRL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/taxabind/taxabind": { "id": "wustl/taxabind/taxabind", "name": "TaxaBind", "maker": "wustl", "family": "wustl/taxabind", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2024-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multimodal binding model whose binding modality is ground-level images. The six modalities are ground image, location, satellite, text, audio, and environmental features. The image-text encoder follows an open_clip style. Hugging Face exposes MVRL/taxabind-vit-b-16 for zero-shot image classification. MVRL/taxabind-config is a config repo, not a second model. Earlier hub repos ecosat, ecosound, ecogeo, and ecoclim are modality towers of this line.", "modalities": { "input": [ "image", "satellite", "geolocation", "text", "audio", "environmental" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TaxaBind is a Washington University in St. Louis multimodal embedding model from the Multimodal Vision Research Laboratory. It binds ground images, satellite images, location, text, audio, and environmental features into one space for ecological retrieval and zero-shot species classification. The arXiv paper was submitted on 2024-11-01 and is accepted to WACV 2025. The ViT-B/16 weights were still public on Hugging Face on the collection date 2026-10-15.", "what_it_is_for": "The paper's binding modality is the ground-level image. Other modalities are pulled into that space so a species description, a sound, a satellite tile, or an environmental vector can be compared with wildlife photos. The named authors are at WashU. The weight host is MVRL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/terradit/terradit": { "id": "wustl/terradit/terradit", "name": "TerraDiT", "maker": "wustl", "family": "wustl/terradit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-03-02", "context_length": null, "max_output_tokens": null, "parameters": "1.10B", "architecture": "Diffusion transformer for text-to-satellite generation with optional point control. The Hugging Face repo MVRL/TerraDiT, created 2026-09-04, ships two checkpoints for this paper in the same family as TerraDiT-Omega. alpha_xl is TerraDiT-alpha, text only, SiT-XL/2, 828M parameters, 1.66 GB. sigma_xl is TerraDiT-Sigma, text plus geolocation plus point prompts, SiT-XL/2, 1.10B parameters, 2.20 GB. The parameters field is the Sigma card string. Weights are EMA fp16 with REPA heads removed. Sampling on the card is Euler, 100 steps, no classifier-free guidance. The VAE is SDXL. The text encoder is LongCLIP zer0int/LongCLIP-KO-LITE-TypoAttack-Attn-ViT-L-14 at 144 tokens. Geolocation uses a RANGE+ 1280-dimensional feature. Lab news also used the name GeoDiT for the point-conditioned paper. The released name is TerraDiT.", "modalities": { "input": [ "text", "geolocation", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TerraDiT is a Washington University in St. Louis satellite image generator from the Multimodal Vision Research Laboratory. It generates an overhead image from text and, in the Sigma checkpoint, from geolocation and point prompts. The arXiv paper was submitted on 2026-03-02 and revised on 2026-06-26. The arXiv comment does not name a venue. Weights in MVRL/TerraDiT were created on 2026-09-04 and were still public on the collection date 2026-10-15. Lab news also called the point-conditioned paper GeoDiT. The released name is TerraDiT.", "what_it_is_for": "The paper replaces dense raster control with points that carry a text description, so a layout can be specified without drawing a full map. Authors are Srikumar Sastry, Dan Cher, Brian Wei, Aayush Dhakal, Subash Khanal, Dev Gupta, and Nathan Jacobs. The repository is mvrl/TerraDiT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/terradit/terradit-omega": { "id": "wustl/terradit/terradit-omega", "name": "TerraDiT-Omega", "maker": "wustl", "family": "wustl/terradit", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-06-30", "context_length": null, "max_output_tokens": null, "parameters": "1.18B", "architecture": "TerraDiT-Omega extends TerraDiT to any native geospatial primitive: polygons, polylines, bounding boxes, and points, together with text and geolocation. Conditioning uses Geometry-Aware Local Attention. The same Hugging Face repo MVRL/TerraDiT ships omega_xl, SiT-XL/2, 1.18B parameters, 2.36 GB, and omega_base, SiT-B/2, 299M parameters, 0.60 GB. The parameters field is the omega_xl card string. Sampling details shared on the card are Euler, 100 steps, no classifier-free guidance, EMA fp16, REPA heads removed, SDXL VAE, and LongCLIP at 144 tokens. The repo was created on 2026-09-04.", "modalities": { "input": [ "text", "geolocation", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TerraDiT-Omega is a Washington University in St. Louis satellite image generator from the Multimodal Vision Research Laboratory. It keeps the TerraDiT diffusion transformer and adds control from polygons, polylines, boxes, and points, not only from point prompts. The arXiv paper was submitted on 2026-06-30 and is an ECCV 2026 paper. The Omega checkpoints share the MVRL/TerraDiT repo created on 2026-09-04 and were still public on the collection date 2026-10-15.", "what_it_is_for": "The paper's job is one generator that accepts whatever vector primitive a map already has. Geometry-Aware Local Attention injects those shapes into attention. Authors are Brian Wei, Srikumar Sastry, Daniel Cher, Eric Xing, and Nathan Jacobs. Code, data, and weights are at mvrl/TerraDiT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/trajloc/trajloc": { "id": "wustl/trajloc/trajloc", "name": "TrajLoc", "maker": "wustl", "family": "wustl/trajloc", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-07-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cross-view geo-localization of a trajectory. The query is a street-view video, a text route, or both, matched to a satellite tile. TrajMod is a lightweight module that conditions query embeddings on trajectory geometry. The example checkpoint name is checkpoints/trajloc_full.pth. Hugging Face MVRL/TrajLoc is MIT and was created on 2026-07-22. The dataset SeqGeo-VL is about 39,000 video-text-satellite triplets and is also released as MVRL/SeqGeo-VL. It is built on SeqGeo from Zhang et al., WACV 2023, which is not a WashU model.", "modalities": { "input": [ "video", "text", "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TrajLoc is a Washington University in St. Louis cross-view localization model from the Multimodal Vision Research Laboratory. It matches a street-view video, a written route, or both, to a satellite tile. The arXiv paper was submitted on 2026-07-16 and is accepted to ECCV 2026. MIT weights on Hugging Face were created on 2026-07-22 and were still public on the collection date 2026-10-15.", "what_it_is_for": "The paper treats a route description as a second sequential query, not only a video clip. TrajMod conditions the query embedding on the geometry of the trajectory. The authors are Tianyi Gao, Jiayu Lin, Danielle Beaulieu, and Nathan Jacobs. The repository is mvrl/TrajLoc.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/tte/tte": { "id": "wustl/tte/tte", "name": "TTE", "maker": "wustl", "family": "wustl/tte", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-25", "context_length": null, "max_output_tokens": null, "parameters": "3.45M", "architecture": "Tessellating the Earth. A location encoder built from learnable Spherical Voronoi partitions of the sphere. Each Voronoi site has an embedding and moves during training toward discriminative areas. Global semantic tokens are shared concept tokens distilled from satellite imagery so distant sites with similar environments can share semantics. Inference encodes latitude and longitude. The project states that training used a frozen SSL4EO-S12 MAE ViT-L/16 that is not required at inference. Hugging Face MVRL/TTE lists 3.45M parameters, F32. The loader is TTE.from_pretrained(\"MVRL/TTE\"). The repo was created on 2026-07-09 and is MIT.", "modalities": { "input": [ "geolocation" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "TTE is a Washington University in St. Louis location encoder from the Multimodal Vision Research Laboratory. Tessellating the Earth maps a latitude and longitude to an embedding using learnable Spherical Voronoi cells and a small set of global semantic tokens. The arXiv paper was submitted on 2026-06-25 and is an ECCV 2026 paper. MIT weights on Hugging Face were created on 2026-07-09 and were still public on the collection date 2026-10-15.", "what_it_is_for": "The encoder puts more capacity on places that matter and lets distant but similar environments share semantic tokens distilled from satellite imagery. The authors, Daniel Cher, Hamza Iqbal, Eric Xing, Brian Wei, and Nathan Jacobs, are at WashU.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wustl/vectorsynth/vectorsynth": { "id": "wustl/vectorsynth/vectorsynth", "name": "VectorSynth", "maker": "wustl", "family": "wustl/vectorsynth", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2025-11-11", "context_length": null, "max_output_tokens": null, "parameters": "0.9B", "architecture": "ControlNet adapter of Stable Diffusion 2.1 Base for satellite synthesis from polygonal geographic annotations. A RenderEncoder projects 768-dimensional CLIP text embeddings of OSM text into 3-channel control images. The model card says it uses standard CLIP embeddings and lists model size 0.9B parameters, tensor type F32. Hugging Face repos MVRL/VectorSynth, MVRL/VectorSynth-COSA, and MVRL/VectorSynth-GiT10M are one product. The card was created on 2026-02-13.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VectorSynth is a Washington University in St. Louis satellite image generator from the Multimodal Vision Research Laboratory. It synthesizes overhead imagery from polygonal map annotations and their semantic attributes. The arXiv paper was submitted on 2025-11-11. The Hugging Face weights, tagged Apache-2.0 and 0.9B parameters, were created on 2026-02-13 and were still public on the collection date 2026-10-15.", "what_it_is_for": "The paper's job is pixel-accurate synthesis conditioned on vector geometry, plus interactive edits that mix language with that geometry. The authors, Daniel Cher, Brian Wei, Srikumar Sastry, and Nathan Jacobs, are at WashU. Code and data are at mvrl/VectorSynth.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "wvu/fedfound/fedfound": { "id": "wvu/fedfound/fedfound", "name": "FedFound", "maker": "wvu", "family": "wvu/fedfound", "status": "active", "confidence": "high", "weights": "open-weights", "license": "CC-BY-NC-4.0", "release_date": "2026-06-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Masked autoencoder for gastrointestinal endoscopy images. The arXiv v2 methods section specifies a 12-layer encoder with 12 attention heads and embedding dimension 768, and an 8-layer decoder with 16 attention heads and embedding dimension 512. Inputs are resized to 256 by 256 with patch size 16 and masking ratio 0.6. Pretraining loss is L1 reconstruction. The same Hugging Face repository also ships Momentum Contrast checkpoints; that card does not state a MoCo depth, width, or parameter count. Downstream use in the preprint attaches an MLP head for classification or replaces the ViT backbone in ViTDet for detection and segmentation. No parameter count was published.", "modalities": { "input": [ "image" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "FedFound is a gastrointestinal endoscopy vision model from the Machine Intelligence Lab at West Virginia University. Alina Devkota, Donald Adjeroh, and Prashnna K. Gyawali are in the Lane Department of Computer Science and Electrical Engineering; clinical coauthors are in the WVU School of Medicine, with additional coauthors outside WVU. The lab released pretrained PyTorch checkpoints on Hugging Face on 2026-06-18 under CC-BY-NC-4.0. The preprint is arXiv:2505.24108, revised 2025-06-06, and npj Digital Medicine published a journal version on 2026-10-14. The weights were still public on the collection date, 2026-10-15, so this dossier marks the release active.", "what_it_is_for": "The preprint trains a foundation model on unlabeled endoscopy images so a hospital can fine-tune classification, detection, and segmentation without pooling patient data. Pretraining uses a masked autoencoder. Clients keep images local and share updates. The paper's name for the FedAvg run is FedFoundAvg. FedAvgM, FedAdam, and FedAdagrad runs are FedFoundAvgM, FedFoundAdam, and FedFoundAdagrad. FedFound is the collective name. Fine-tuning in the paper is done on one node after pretraining.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-1": { "id": "xai/grok/grok-1", "name": "Grok-1", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-03", "context_length": 8000, "max_output_tokens": null, "parameters": "314 billion (Mixture-of-Experts, 25% of weights active per token)", "architecture": "Mixture-of-Experts, trained from scratch on a JAX and Rust stack. The Apache release is the October 2023 pretraining checkpoint, not a dialogue fine-tune.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "xAI's first frontier language model, announced 3 November 2023 as the engine behind the Grok chat beta. On 17 March 2024 xAI released the 314B Mixture-of-Experts base checkpoint and architecture under Apache 2.0. Collected 2026-10-15. The chat product has been superseded.", "what_it_is_for": "xAI's first frontier language model, announced 3 November 2023 as the engine behind the Grok chat beta. On 17 March 2024 xAI released the 314B Mixture-of-Experts base checkpoint and architecture under Apache 2.0. Collected 2026-10-15. The chat product has been superseded.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-1-5": { "id": "xai/grok/grok-1-5", "name": "Grok-1.5", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03-28", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "xAI language model announced 28 March 2024 with a 128,000-token context window and stronger math and coding scores than Grok-1. It was headed to early testers on X. Collected 2026-10-15. It is no longer a current API model.", "what_it_is_for": "xAI language model announced 28 March 2024 with a 128,000-token context window and stronger math and coding scores than Grok-1. It was headed to early testers on X. Collected 2026-10-15. It is no longer a current API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-1-5v": { "id": "xai/grok/grok-1-5v", "name": "Grok-1.5V", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-04-12", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "xAI's first multimodal model, previewed 12 April 2024. It adds document, diagram, chart, screenshot, and photo understanding on top of text. Collected 2026-10-15. The preview is not on the current API list.", "what_it_is_for": "xAI's first multimodal model, previewed 12 April 2024. It adds document, diagram, chart, screenshot, and photo understanding on top of text. Collected 2026-10-15. The preview is not on the current API list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-2": { "id": "xai/grok/grok-2", "name": "Grok-2", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-13", "context_length": 131072, "max_output_tokens": 8192, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 10.0 }, "summary_intro": "Frontier chat model announced 13 August 2024 in beta on X, with vision understanding and an enterprise API planned the same month. The December 2024 API shipped grok-2-1212 after a grok-beta preview. Collected 2026-10-15. It is not on the current price list.", "what_it_is_for": "Frontier chat model announced 13 August 2024 in beta on X, with vision understanding and an enterprise API planned the same month. The December 2024 API shipped grok-2-1212 after a grok-beta preview. Collected 2026-10-15. It is not on the current price list.", "ratings": { "empirical_100": null, "lmsys_elo": 1305, "coding_elo": 1300, "swe_bench": 37.0, "humaneval": 88.5, "twelve_cs": null, "aa_intelligence": 64.0, "aa_coding": 74.0, "aa_agentic": 62.0, "design_arena_elo": 1300, "math_500": 76.0, "gpqa_diamond": 52.0, "ifeval": 84.0, "bfcl_tool_calling": 86.0 } }, "xai/grok/grok-2-mini": { "id": "xai/grok/grok-2-mini", "name": "Grok-2 mini", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-08-13", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Smaller, faster sibling of Grok-2, announced in the same 13 August 2024 beta. xAI described it as a balance of speed and answer quality. Collected 2026-10-15. It is not on the current API price list.", "what_it_is_for": "Smaller, faster sibling of Grok-2, announced in the same 13 August 2024 beta. xAI described it as a balance of speed and answer quality. Collected 2026-10-15. It is not on the current API price list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-2-vision": { "id": "xai/grok/grok-2-vision", "name": "Grok-2 Vision", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Vision API model released 14 December 2024 as grok-2-vision-1212, following the November 2024 grok-vision-beta. Collected 2026-10-15. It is not on the current price list.", "what_it_is_for": "Vision API model released 14 December 2024 as grok-2-vision-1212, following the November 2024 grok-vision-beta. Collected 2026-10-15. It is not on the current price list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-3": { "id": "xai/grok/grok-3", "name": "Grok 3", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-19", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flagship reasoning model previewed 19 February 2025 and generally available on the API on 3 April 2025. Training used the Colossus cluster. The API slug grok-3 was retired on 15 May 2026 and now redirects to grok-4.3 with reasoning effort none. Collected 2026-10-15.", "what_it_is_for": "Flagship reasoning model previewed 19 February 2025 and generally available on the API on 3 April 2025. Training used the Colossus cluster. The API slug grok-3 was retired on 15 May 2026 and now redirects to grok-4.3 with reasoning effort none. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-3-mini": { "id": "xai/grok/grok-3-mini", "name": "Grok 3 mini", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Cost-efficient reasoning model announced with Grok 3 on 19 February 2025, including a Think variant. Collected 2026-10-15. The May 2026 retirement list names grok-3 but not grok-3-mini. Mini is not on the October 2026 price list.", "what_it_is_for": "Cost-efficient reasoning model announced with Grok 3 on 19 February 2025, including a Think variant. Collected 2026-10-15. The May 2026 retirement list names grok-3 but not grok-3-mini. Mini is not on the October 2026 price list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-4": { "id": "xai/grok/grok-4", "name": "Grok 4", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-09", "context_length": 256000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Flagship model released 9 July 2025 on grok.com, SuperGrok, X Premium+, and the xAI API. Native tool use and live search. The API snapshot grok-4-0709 was retired on 15 May 2026 and redirects to grok-4.3 with low reasoning effort. Collected 2026-10-15.", "what_it_is_for": "Flagship model released 9 July 2025 on grok.com, SuperGrok, X Premium+, and the xAI API. Native tool use and live search. The API snapshot grok-4-0709 was retired on 15 May 2026 and redirects to grok-4.3 with low reasoning effort. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-4-1": { "id": "xai/grok/grok-4-1", "name": "Grok 4.1", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-11-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Consumer model released 17 November 2025 on grok.com, X, and the iOS and Android apps, including Auto mode. It was tuned for style, personality, and collaboration using Grok 4's reinforcement-learning stack. Collected 2026-10-15. No API slug is on the October 2026 price list, and the app docs point users to Grok 4.7.", "what_it_is_for": "Consumer model released 17 November 2025 on grok.com, X, and the iOS and Android apps, including Auto mode. It was tuned for style, personality, and collaboration using Grok 4's reinforcement-learning stack. Collected 2026-10-15. No API slug is on the October 2026 price list, and the app docs point users to Grok 4.7.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-4-1-fast-non-reasoning": { "id": "xai/grok/grok-4-1-fast-non-reasoning", "name": "Grok 4.1 Fast Non-Reasoning", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-19", "context_length": 2000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.5 }, "summary_intro": "Instant-response endpoint of Grok 4.1 Fast, same 2,000,000-token context and price as the reasoning slug. Retired 15 May 2026. Redirects to grok-4.3 with reasoning effort none. Collected 2026-10-15.", "what_it_is_for": "Instant-response endpoint of Grok 4.1 Fast, same 2,000,000-token context and price as the reasoning slug. Retired 15 May 2026. Redirects to grok-4.3 with reasoning effort none. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-4-1-fast-reasoning": { "id": "xai/grok/grok-4-1-fast-reasoning", "name": "Grok 4.1 Fast Reasoning", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-19", "context_length": 2000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.5 }, "summary_intro": "Tool-calling model released 19 November 2025 with a 2,000,000-token context, paired with the Agent Tools API. Retired 15 May 2026. Requests redirect to grok-4.3 with low reasoning effort. Collected 2026-10-15.", "what_it_is_for": "Tool-calling model released 19 November 2025 with a 2,000,000-token context, paired with the Agent Tools API. Retired 15 May 2026. Requests redirect to grok-4.3 with low reasoning effort. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-4-20-0309-non-reasoning": { "id": "xai/grok/grok-4-20-0309-non-reasoning", "name": "Grok 4.20 Non-Reasoning", "maker": "xai", "family": "xai/grok", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-09", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 2.5 }, "summary_intro": "Active non-reasoning endpoint of Grok 4.20, id grok-4.20-0309-non-reasoning. Same 1,000,000-token context and price as the reasoning slug. Collected 2026-10-15.", "what_it_is_for": "Active non-reasoning endpoint of Grok 4.20, id grok-4.20-0309-non-reasoning. Same 1,000,000-token context and price as the reasoning slug. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-4-20-0309-reasoning": { "id": "xai/grok/grok-4-20-0309-reasoning", "name": "Grok 4.20 Reasoning", "maker": "xai", "family": "xai/grok", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-09", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 2.5 }, "summary_intro": "Active reasoning model grok-4.20-0309-reasoning. The docs describe it as a fast agentic model with strict prompt adherence. The 0309 id is the 9 March 2026 snapshot; release notes place Grok 4.20 in March 2026. Collected 2026-10-15.", "what_it_is_for": "Active reasoning model grok-4.20-0309-reasoning. The docs describe it as a fast agentic model with strict prompt adherence. The 0309 id is the 9 March 2026 snapshot; release notes place Grok 4.20 in March 2026. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": 1237, "coding_elo": 1237, "swe_bench": 21.2, "humaneval": 75.0, "twelve_cs": 99.5, "aa_intelligence": 25.7, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1047, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-4-20-multi-agent": { "id": "xai/grok/grok-4-20-multi-agent", "name": "Grok 4.20 Multi-Agent", "maker": "xai", "family": "xai/grok", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-09", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 2.5 }, "summary_intro": "Beta multi-agent model, id grok-4.20-multi-agent-0309. The docs say multiple agents collaborate in parallel on deep research. Same token price as Grok 4.20, with a lower rate limit. Collected 2026-10-15.", "what_it_is_for": "Beta multi-agent model, id grok-4.20-multi-agent-0309. The docs say multiple agents collaborate in parallel on deep research. Same token price as Grok 4.20, with a lower rate limit. Collected 2026-10-15.", "ratings": { "empirical_100": 56, "lmsys_elo": 1237, "coding_elo": 1237, "swe_bench": 21.2, "humaneval": 75.0, "twelve_cs": 99.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-4-3": { "id": "xai/grok/grok-4-3", "name": "Grok 4.3", "maker": "xai", "family": "xai/grok", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.25, "completion_per_m_usd": 2.5 }, "summary_intro": "Active fast model with tool calling and configurable reasoning, including a none effort. 1,000,000-token context. It is the redirect target for several models retired on 15 May 2026. Collected 2026-10-15.", "what_it_is_for": "Active fast model with tool calling and configurable reasoning, including a none effort. 1,000,000-token context. It is the redirect target for several models retired on 15 May 2026. Collected 2026-10-15.", "ratings": { "empirical_100": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-4-5": { "id": "xai/grok/grok-4-5", "name": "Grok 4.5", "maker": "xai", "family": "xai/grok", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-16", "context_length": 500000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 6.0 }, "summary_intro": "Coding and knowledge-work model launched 16 July 2026. Trained with Cursor on NVIDIA GB300 GPUs. It was the default in Grok Build at launch and is still on the October 2026 API price list at $2 / $6 per million tokens before the long-context step. Collected 2026-10-15.", "what_it_is_for": "Coding and knowledge-work model launched 16 July 2026. Trained with Cursor on NVIDIA GB300 GPUs. It was the default in Grok Build at launch and is still on the October 2026 API price list at $2 / $6 per million tokens before the long-context step. Collected 2026-10-15.", "ratings": { "empirical_100": 64, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-4-6": { "id": "xai/grok/grok-4-6", "name": "Grok 4.6", "maker": "xai", "family": "xai/grok", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-12", "context_length": 500000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 6.0 }, "summary_intro": "Frontier coding and knowledge model released 12 August 2026. The launch post says it builds on Grok 4.5 for long-running agents. Same $2 / $6 headline price as Grok 4.5 and Grok 4.7, with a higher cached-input rate. Still on the October 2026 API list, including the US regional endpoint. Collected 2026-10-15.", "what_it_is_for": "Frontier coding and knowledge model released 12 August 2026. The launch post says it builds on Grok 4.5 for long-running agents. Same $2 / $6 headline price as Grok 4.5 and Grok 4.7, with a higher cached-input rate. Still on the October 2026 API list, including the US regional endpoint. Collected 2026-10-15.", "ratings": { "empirical_100": 61, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-4-7": { "id": "xai/grok/grok-4-7", "name": "Grok 4.7", "maker": "xai", "family": "xai/grok", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-21", "context_length": 500000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 6.0 }, "summary_intro": "xAI's flagship as of 15 October 2026 for code, chat, and knowledge work. Released 21 September 2026. Text and image in, text out, 500,000-token context, knowledge cutoff May 2026. The docs say to use it for everything except dedicated image, video, and voice models. Collected 2026-10-15.", "what_it_is_for": "xAI's flagship as of 15 October 2026 for code, chat, and knowledge work. Released 21 September 2026. Text and image in, text out, 500,000-token context, knowledge cutoff May 2026. The docs say to use it for everything except dedicated image, video, and voice models. Collected 2026-10-15.", "ratings": { "empirical_100": 55, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-4-fast-non-reasoning": { "id": "xai/grok/grok-4-fast-non-reasoning", "name": "Grok 4 Fast Non-Reasoning", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-19", "context_length": 2000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.5 }, "summary_intro": "Non-reasoning endpoint of the same Grok 4 Fast weights, for short answers. Released 19 September 2025 with a 2,000,000-token context. Retired 15 May 2026; redirects to grok-4.3 with reasoning effort none. Collected 2026-10-15.", "what_it_is_for": "Non-reasoning endpoint of the same Grok 4 Fast weights, for short answers. Released 19 September 2025 with a 2,000,000-token context. Retired 15 May 2026; redirects to grok-4.3 with reasoning effort none. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-4-fast-reasoning": { "id": "xai/grok/grok-4-fast-reasoning", "name": "Grok 4 Fast Reasoning", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-09-19", "context_length": 2000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.5 }, "summary_intro": "Reasoning endpoint of Grok 4 Fast, released 19 September 2025. Same weights as the non-reasoning endpoint, steered into a long chain of thought. 2,000,000-token context. Retired 15 May 2026; the slug redirects to grok-4.3 with low reasoning effort. Collected 2026-10-15.", "what_it_is_for": "Reasoning endpoint of Grok 4 Fast, released 19 September 2025. Same weights as the non-reasoning endpoint, steered into a long chain of thought. 2,000,000-token context. Retired 15 May 2026; the slug redirects to grok-4.3 with low reasoning effort. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-4-heavy": { "id": "xai/grok/grok-4-heavy", "name": "Grok 4 Heavy", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-07-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "More powerful Grok 4 variant announced 9 July 2025 for the SuperGrok Heavy tier. It uses parallel test-time compute across multiple agents. Collected 2026-10-15. No public API slug was found, and it is not on the October 2026 price list.", "what_it_is_for": "More powerful Grok 4 variant announced 9 July 2025 for the SuperGrok Heavy tier. It uses parallel test-time compute across multiple agents. Collected 2026-10-15. No public API slug was found, and it is not on the October 2026 price list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-build-0-1": { "id": "xai/grok/grok-build-0-1", "name": "Grok Build 0.1", "maker": "xai", "family": "xai/grok", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-29", "context_length": 256000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 2.0 }, "summary_intro": "Coding model for agentic software work, public beta on the API on 29 May 2026. Context 256,000 tokens. Price $1 / $2 per million input and output tokens below 200k. It is the successor of grok-code-fast-1, which remains an alias. Collected 2026-10-15. This is not the Grok Build product harness, and grok-build-latest aliases to grok-4.5 rather than this model.", "what_it_is_for": "Coding model for agentic software work, public beta on the API on 29 May 2026. Context 256,000 tokens. Price $1 / $2 per million input and output tokens below 200k. It is the successor of grok-code-fast-1, which remains an alias. Collected 2026-10-15. This is not the Grok Build product harness, and grok-build-latest aliases to grok-4.5 rather than this model.", "ratings": { "empirical_100": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/grok/grok-code-fast-1": { "id": "xai/grok/grok-code-fast-1", "name": "Grok Code Fast 1", "maker": "xai", "family": "xai/grok", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 1.5 }, "summary_intro": "Agentic coding model released 28 August 2025 (API notes also say 26 August). Priced at $0.20 / $1.50 / $0.02 cached per million tokens. Retired 15 May 2026. The grok-build-0.1 model page now lists this slug as an alias, while the retirement guide says requests route to grok-build-0.1. Collected 2026-10-15.", "what_it_is_for": "Agentic coding model released 28 August 2025 (API notes also say 26 August). Priced at $0.20 / $1.50 / $0.02 cached per million tokens. Retired 15 May 2026. The grok-build-0.1 model page now lists this slug as an alias, while the retirement guide says requests route to grok-build-0.1. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/imagine/aurora": { "id": "xai/imagine/aurora", "name": "Aurora", "maker": "xai", "family": "xai/imagine", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-12-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Autoregressive image generation model, code-named Aurora, announced 9 December 2024 for the X platform. Collected 2026-10-15. It is not an API slug on the current Imagine price list. FLUX.1, used earlier inside Grok on X, is a Black Forest Labs model and is not catalogued here.", "what_it_is_for": "Autoregressive image generation model, code-named Aurora, announced 9 December 2024 for the X platform. Collected 2026-10-15. It is not an API slug on the current Imagine price list. FLUX.1, used earlier inside Grok on X, is a Black Forest Labs model and is not catalogued here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/imagine/grok-imagine-image": { "id": "xai/imagine/grok-imagine-image", "name": "Grok Imagine Image", "maker": "xai", "family": "xai/imagine", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.02 }, "summary_intro": "Image generation and editing model still priced at $0.02 per image on 15 October 2026. The Imagine API news post is dated 28 January 2026. A dated alias grok-imagine-image-2026-03-02 is listed on the model page. Collected 2026-10-15.", "what_it_is_for": "Image generation and editing model still priced at $0.02 per image on 15 October 2026. The Imagine API news post is dated 28 January 2026. A dated alias grok-imagine-image-2026-03-02 is listed on the model page. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/imagine/grok-imagine-image-2-0": { "id": "xai/imagine/grok-imagine-image-2-0", "name": "Grok Imagine Image 2.0", "maker": "xai", "family": "xai/imagine", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-08-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.04 }, "summary_intro": "Current recommended image model as of 15 October 2026. Announced 7 August 2026 for precise generation and editing. Text or image in, image out. The docs tell callers to use it for images. Collected 2026-10-15.", "what_it_is_for": "Current recommended image model as of 15 October 2026. Announced 7 August 2026 for precise generation and editing. Text or image in, image out. The docs tell callers to use it for images. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/imagine/grok-imagine-image-pro": { "id": "xai/imagine/grok-imagine-image-pro", "name": "Grok Imagine Image Pro", "maker": "xai", "family": "xai/imagine", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Image model retired on 15 May 2026. Requests redirect to grok-imagine-image-quality, and after 2 November 2026 they follow that slug to grok-imagine-image-2.0 at quality low. The quality model page also lists grok-imagine-image-pro as an alias. Collected 2026-10-15.", "what_it_is_for": "Image model retired on 15 May 2026. Requests redirect to grok-imagine-image-quality, and after 2 November 2026 they follow that slug to grok-imagine-image-2.0 at quality low. The quality model page also lists grok-imagine-image-pro as an alias. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/imagine/grok-imagine-image-quality": { "id": "xai/imagine/grok-imagine-image-quality", "name": "Grok Imagine Image Quality", "maker": "xai", "family": "xai/imagine", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "Higher-realism image model announced 6 May 2026. As of 15 October 2026 it is still served, but it is inside a retirement notice. On 2 November 2026 the slug will be served by grok-imagine-image-2.0 with quality low. Collected 2026-10-15.", "what_it_is_for": "Higher-realism image model announced 6 May 2026. As of 15 October 2026 it is still served, but it is inside a retirement notice. On 2 November 2026 the slug will be served by grok-imagine-image-2.0 with quality low. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/imagine/grok-imagine-video": { "id": "xai/imagine/grok-imagine-video", "name": "Grok Imagine Video", "maker": "xai", "family": "xai/imagine", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-01-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.05 }, "summary_intro": "First Imagine video model, still on the API for editing and extension. Text, image, or video in, video out. Headline price $0.05 per second. The Imagine API was announced 28 January 2026. Collected 2026-10-15.", "what_it_is_for": "First Imagine video model, still on the API for editing and extension. Text, image, or video in, video out. Headline price $0.05 per second. The Imagine API was announced 28 January 2026. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/imagine/grok-imagine-video-1-5": { "id": "xai/imagine/grok-imagine-video-1-5", "name": "Grok Imagine Video 1.5", "maker": "xai", "family": "xai/imagine", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-06-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.08 }, "summary_intro": "Current high-end Imagine video model. Preview alias dates to 30 May 2026, the preview news post is 3 June 2026, and the quality-and-speed post is 16 June 2026. Reference images, voices, and native 1080p were expanded on 31 July 2026. Headline price $0.08 per second. Collected 2026-10-15.", "what_it_is_for": "Current high-end Imagine video model. Preview alias dates to 30 May 2026, the preview news post is 3 June 2026, and the quality-and-speed post is 16 June 2026. Reference images, voices, and native 1080p were expanded on 31 July 2026. Headline price $0.08 per second. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/imagine/grok-imagine-video-1-5-lite": { "id": "xai/imagine/grok-imagine-video-1-5-lite", "name": "Grok Imagine Video 1.5 Lite", "maker": "xai", "family": "xai/imagine", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.02 }, "summary_intro": "Lower-priced Imagine video model for simple text-to-video and image-to-video. The docs recommend it when you do not need references, voices, pinned frames, or native 1080p. Headline price $0.02 per second. Collected 2026-10-15.", "what_it_is_for": "Lower-priced Imagine video model for simple text-to-video and image-to-video. The docs recommend it when you do not need references, voices, pinned frames, or native 1080p. Headline price $0.02 per second. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/voice/grok-voice-1-0": { "id": "xai/voice/grok-voice-1-0", "name": "Grok Voice 1.0", "maker": "xai", "family": "xai/voice", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-04-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "First named Grok Voice speech-to-speech model. Release notes for April 2026 say Grok Voice 1.0 is available on the Speech to Speech API, and the news post is titled from the slug grok-voice-think-fast-1. Collected 2026-10-15. The October 2026 model table lists only grok-voice-2.0, grok-voice-3.0, and grok-voice-latest.", "what_it_is_for": "First named Grok Voice speech-to-speech model. Release notes for April 2026 say Grok Voice 1.0 is available on the Speech to Speech API, and the news post is titled from the slug grok-voice-think-fast-1. Collected 2026-10-15. The October 2026 model table lists only grok-voice-2.0, grok-voice-3.0, and grok-voice-latest.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/voice/grok-voice-2-0": { "id": "xai/voice/grok-voice-2-0", "name": "Grok Voice 2.0", "maker": "xai", "family": "xai/voice", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-07-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 0.004, "completion_per_m_usd": null }, "summary_intro": "Speech-to-speech model released 29 July 2026 and deprecated 14 October 2026. Same price as Grok Voice 3.0. grok-voice-latest now points at 3.0, so pinned grok-voice-2.0 is the legacy path. Collected 2026-10-15.", "what_it_is_for": "Speech-to-speech model released 29 July 2026 and deprecated 14 October 2026. Same price as Grok Voice 3.0. grok-voice-latest now points at 3.0, so pinned grok-voice-2.0 is the legacy path. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/voice/grok-voice-3-0": { "id": "xai/voice/grok-voice-3-0", "name": "Grok Voice 3.0", "maker": "xai", "family": "xai/voice", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 0.004, "completion_per_m_usd": null }, "summary_intro": "Current speech-to-speech model, released 14 October 2026. xAI calls it the flagship voice model. grok-voice-latest points here. Price matches Grok Voice 2.0 at $0.08 per minute plus $0.004 per text input. Collected 2026-10-15.", "what_it_is_for": "Current speech-to-speech model, released 14 October 2026. xAI calls it the flagship voice model. grok-voice-latest points here. Price matches Grok Voice 2.0 at $0.08 per minute plus $0.004 per text input. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/voice/grok-voice-transcribe-1-0": { "id": "xai/voice/grok-voice-transcribe-1-0", "name": "Grok Voice Transcribe 1.0", "maker": "xai", "family": "xai/voice", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "First speech-to-text model, launched with the Speech to Text API on 17 April 2026. End of life was 2 October 2026. Requests to the slug are routed to grok-voice-transcribe-2.0 at the same price. Collected 2026-10-15.", "what_it_is_for": "First speech-to-text model, launched with the Speech to Text API on 17 April 2026. End of life was 2 October 2026. Requests to the slug are routed to grok-voice-transcribe-2.0 at the same price. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/voice/grok-voice-transcribe-2-0": { "id": "xai/voice/grok-voice-transcribe-2-0", "name": "Grok Voice Transcribe 2.0", "maker": "xai", "family": "xai/voice", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-09-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Current speech-to-text model, released 18 September 2026 and the default on the STT API. xAI calls it the best transcription model. 38 languages are listed, with automatic language detection. REST is $0.10 per hour and streaming is $0.20 per hour. Collected 2026-10-15.", "what_it_is_for": "Current speech-to-text model, released 18 September 2026 and the default on the STT API. xAI calls it the best transcription model. 38 languages are listed, with automatic language detection. REST is $0.10 per hour and streaming is $0.20 per hour. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xai/voice/text-to-speech": { "id": "xai/voice/text-to-speech", "name": "Grok Text to Speech", "maker": "xai", "family": "xai/voice", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-17", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 15.0 }, "summary_intro": "Text-to-speech API, generally available on 17 April 2026 with the speech-to-text API. Billing is $15 per million characters. The October 2026 docs do not publish a grok-* model slug for TTS, so this dossier uses the product name. Collected 2026-10-15.", "what_it_is_for": "Text-to-speech API, generally available on 17 April 2026 with the speech-to-text API. Billing is $15 per million characters. The October 2026 docs do not publish a grok-* model slug for TTS, so this dossier uses the product name. Collected 2026-10-15.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/midasheng/midashenglm-0-6b": { "id": "xiaomi/midasheng/midashenglm-0-6b", "name": "MiDashengLM-0.6B", "maker": "xiaomi", "family": "xiaomi/midasheng", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Smaller MiDashengLM. The dasheng-lm README says the decoder is initialized from Qwen3-0.6B and the audio encoder is Dasheng. Example id on the README is mispeech/midashenglm-0.6b-fp32.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiDashengLM-0.6B is the small Xiaomi audio-understanding model documented beside MiDashengLM-7B. The README names a public FP32 checkpoint. A separate launch day was not found. Collected 2026-10-15.", "what_it_is_for": "Xiaomi offers it as a lighter audio-captioning model when the 7B model is too heavy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/midasheng/midashenglm-7b": { "id": "xiaomi/midasheng/midashenglm-7b", "name": "MiDashengLM-7B", "maker": "xiaomi", "family": "xiaomi/midasheng", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-08-04", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Audio encoder is Xiaomi Dasheng. Decoder starts from Qwen2.5-Omni-7B Thinker. Trained with general audio captions on public data. Xiaomi says about 1.1 million hours across speech, sound, and music. Not a speech generator.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiDashengLM-7B is Xiaomi MiLM Plus's open audio-understanding model, released 4 August 2025 under Apache-2.0. It captions and answers questions about speech, environmental sound, and music. The recommended weights are the October 2025 BF16 checkpoint. Collected 2026-10-15.", "what_it_is_for": "Xiaomi built it for general audio understanding in phones, homes, and cars, not for cloning a voice.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/milm/milm-1-3b": { "id": "xiaomi/milm/milm-1-3b", "name": "MiLM-1.3B", "maker": "xiaomi", "family": "xiaomi/milm", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-08-12", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Smaller sibling named only inside the MiLM-6B README, with the same zero-shot and five-shot Chinese benches. No separate repo was found.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiLM-1.3B is a 1.3B Xiaomi language model named in the August 2023 MiLM-6B README, with published Chinese benchmark scores and no located weight release. Collected 2026-10-15.", "what_it_is_for": "It is the small member of the first MiLM pair. Xiaomi did not give it a separate product page in the sources read here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/milm/milm-6b": { "id": "xiaomi/milm/milm-6b", "name": "MiLM-6B", "maker": "xiaomi", "family": "xiaomi/milm", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-08-12", "context_length": null, "max_output_tokens": null, "parameters": "6.4B", "architecture": "Xiaomi pretrained Chinese-centric language model. The GitHub README states 6.4 billion parameters (64\u4ebf). The repository content found in this pass is a README and scores, not a weight file.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiLM-6B is Xiaomi's first publicly described large language model, a 6.4B pretrained model that showed up in August 2023 with C-Eval and CMMLU scores. A weight download was not in the GitHub tree read here. Collected 2026-10-15.", "what_it_is_for": "The README presents it as a general Chinese pretrained model and claims same-size best scores on C-Eval and CMMLU at the time.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/milm/milm2-0-7b-x8": { "id": "xiaomi/milm/milm2-0-7b-x8", "name": "MiLM2-0.7B\u00d78", "maker": "xiaomi", "family": "xiaomi/milm", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-11-12", "context_length": null, "max_output_tokens": null, "parameters": "0.7B x 8 experts", "architecture": "Mixture-of-experts model named in the MiLM2 notice. The report says the two MoE models differ in total parameters and vocabulary size. Active-parameter count was not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiLM2-0.7B\u00d78 is one of two MoE models Xiaomi named in the November 2024 MiLM2 notice. It was not released as a public checkpoint. Collected 2026-10-15.", "what_it_is_for": "Xiaomi described the MoE pair as a way to keep device-side latency down while mixing specialized experts.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/milm/milm2-1-3b": { "id": "xiaomi/milm/milm2-1-3b", "name": "MiLM2-1.3B", "maker": "xiaomi", "family": "xiaomi/milm", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-11-12", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Named on-device size in the November 2024 MiLM2 notice. Compared with first-generation MiLM-1.3B on the same internal ten-ability average.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiLM2-1.3B is the small named model in Xiaomi's November 2024 MiLM2 notice. No public weights were found. Collected 2026-10-15.", "what_it_is_for": "On-device assistant tasks inside Xiaomi's device ecosystem.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/milm/milm2-2b-x8": { "id": "xiaomi/milm/milm2-2b-x8", "name": "MiLM2-2B\u00d78", "maker": "xiaomi", "family": "xiaomi/milm", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-11-12", "context_length": null, "max_output_tokens": null, "parameters": "2B x 8 experts", "architecture": "Second MoE in the MiLM2 notice. Secondary coverage says it matches MiLM2-6B quality with about 50 percent faster decoding. That speed claim is not on the Tencent excerpt and is stored only as a gap note.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiLM2-2B\u00d78 is the larger MoE Xiaomi named with MiLM2 in November 2024. No public weights were found. Collected 2026-10-15.", "what_it_is_for": "An efficiency model for Xiaomi's own device and cloud mix.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/milm/milm2-6b": { "id": "xiaomi/milm/milm2-6b", "name": "MiLM2-6B", "maker": "xiaomi", "family": "xiaomi/milm", "status": "announced", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2024-11-12", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "Second-generation Xiaomi on-device model. News citing a Xiaomi notice says MiLM2 spans 0.3B to 30B and that MiLM2-6B is the example mid-size model, about 45 percent better than MiLM-6B on ten internal ability axes.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiLM2-6B is a named model in Xiaomi's November 2024 second-generation language-model notice. It was described as an internal cloud-edge-device model, not an open weight. Collected 2026-10-15.", "what_it_is_for": "Xiaomi described MiLM2 as the model family for the human-car-home ecosystem, with stronger instruction following, translation, and chat than MiLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo/mimo-7b-base": { "id": "xiaomi/mimo/mimo-7b-base", "name": "MiMo-7B-Base", "maker": "xiaomi", "family": "xiaomi/mimo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-30", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Dense decoder pretrained from scratch for reasoning. Multiple-token prediction is an extra training objective. The model card says pretraining used about 25 trillion tokens in three data-mixture stages. Hugging Face tags the checkpoint 8B; the card does not explain the difference.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-7B-Base is Xiaomi's first open reasoning language model, released with the MiMo-7B series on 30 April 2025. It is the pretrained checkpoint, still downloadable under MIT, and was collected on 2026-10-15.", "what_it_is_for": "Xiaomi describes it as a base model pretrained to raise reasoning-pattern density, meant to be post-trained rather than used as a chat assistant.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo/mimo-7b-rl": { "id": "xiaomi/mimo/mimo-7b-rl", "name": "MiMo-7B-RL", "maker": "xiaomi", "family": "xiaomi/mimo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-30", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "RL checkpoint trained from MiMo-7B-SFT. One MTP layer is used for speculative decoding; the card says acceptance is about 90 percent. MTP weights are frozen during RL. Recommended sampling temperature is 0.6 and an empty system prompt.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-7B-RL is Xiaomi's main 7B reasoning model, trained with reinforcement learning from the SFT checkpoint and released 30 April 2025 under MIT. A 30 May 2025 refresh, MiMo-7B-RL-0530, is kept as a version. It is not on the current API list. Collected 2026-10-15.", "what_it_is_for": "Xiaomi positions it for math and code reasoning and says it matches OpenAI o1-mini on the card's comparison table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo/mimo-7b-rl-zero": { "id": "xiaomi/mimo/mimo-7b-rl-zero", "name": "MiMo-7B-RL-Zero", "maker": "xiaomi", "family": "xiaomi/mimo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-30", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Reinforcement-learning checkpoint trained directly from MiMo-7B-Base, without the SFT cold start. Rule-based accuracy rewards on about 130,000 math and code problems.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-7B-RL-Zero is Xiaomi's reinforcement-learning run that starts from MiMo-7B-Base rather than from the SFT model. It shipped with the April 2025 open series and is still on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "Xiaomi released it to show that the base model already has enough reasoning potential for RL without a large SFT stage.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo/mimo-7b-sft": { "id": "xiaomi/mimo/mimo-7b-sft", "name": "MiMo-7B-SFT", "maker": "xiaomi", "family": "xiaomi/mimo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-30", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Supervised fine-tune of MiMo-7B-Base. MTP layers stay trainable through SFT and are frozen in the later RL stage.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-7B-SFT is the supervised-fine-tuned checkpoint of Xiaomi MiMo-7B, released with the April 2025 series under MIT. It remains a downloadable weight, not a current API model. Collected 2026-10-15.", "what_it_is_for": "Xiaomi uses it as the cold start before reinforcement learning on verifiable math and code problems.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-audio/mimo-audio-7b-base": { "id": "xiaomi/mimo-audio/mimo-audio-7b-base", "name": "MiMo-Audio-7B-Base", "maker": "xiaomi", "family": "xiaomi/mimo-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-19", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Patch encoder aggregates four RVQ frames to 6.25 Hz for a 7B language model; a patch decoder generates 25 Hz RVQ tokens. Pretraining audio is described as over 100 million hours. Few-shot audio tasks emerge without task-specific fine-tunes.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-Audio-7B-Base is Xiaomi's pretrained audio language model, open under MIT. Xiaomi says it few-shot generalizes to voice conversion, style transfer, and speech editing. Collected 2026-10-15.", "what_it_is_for": "Xiaomi presents it as a few-shot audio foundation, not only an ASR or TTS model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-audio/mimo-audio-7b-instruct": { "id": "xiaomi/mimo-audio/mimo-audio-7b-instruct", "name": "MiMo-Audio-7B-Instruct", "maker": "xiaomi", "family": "xiaomi/mimo-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-19", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Instruction-tuned MiMo-Audio-7B with thinking for both understanding and generation. Same patch encoder, LLM, and delayed patch decoder as the base model.", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-Audio-7B-Instruct is Xiaomi's instruction-tuned audio language model, MIT-licensed, for spoken dialogue, audio understanding, and instruct-style speech generation. Collected 2026-10-15.", "what_it_is_for": "Xiaomi says post-training adds instruction following and thinking for understanding and generation, and claims open-source SOTA on those benches.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-audio/mimo-audio-tokenizer": { "id": "xiaomi/mimo-audio/mimo-audio-tokenizer", "name": "MiMo-Audio-Tokenizer", "maker": "xiaomi", "family": "xiaomi/mimo-audio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-19", "context_length": null, "max_output_tokens": null, "parameters": "1.2B", "architecture": "1.2B-parameter Transformer at 25 Hz with an eight-layer RVQ stack (200 tokens per second). Trained from scratch on a 10-million-hour corpus. MiMo-V2.5-ASR also loads this tokenizer. Hugging Face's size tag says 1B.", "modalities": { "input": [ "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-Audio-Tokenizer is Xiaomi's 1.2B speech tokenizer, released with MiMo-Audio under MIT. The lab homepage lists the paper on 19 September 2025; the arXiv id is dated December 2025. Collected 2026-10-15.", "what_it_is_for": "It turns audio into RVQ tokens for MiMo-Audio and is also the tokenizer for MiMo-V2.5-ASR.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-embodied/mimo-embodied-7b": { "id": "xiaomi/mimo-embodied/mimo-embodied-7b", "name": "MiMo-Embodied-7B", "maker": "xiaomi", "family": "xiaomi/mimo-embodied", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Cross-embodied vision-language model. The card says it is the first open VLM Xiaomi aimed at both autonomous driving and embodied AI. The card's base_model field points at XiaomiMiMo/MiMo-Embodied. Exact layer counts are in figures, not text.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-Embodied-7B is Xiaomi's open vision-language model for embodied AI and autonomous driving, released under MIT with technical report arXiv 2511.16518. The exact release day was not on the card text. Collected 2026-10-15.", "what_it_is_for": "Xiaomi says it covers task planning, affordance, spatial understanding, driving perception, status prediction, and driving planning.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2/mimo-v2-omni": { "id": "xiaomi/mimo-v2/mimo-v2-omni", "name": "MiMo-V2-Omni", "maker": "xiaomi", "family": "xiaomi/mimo-v2", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-18", "context_length": 262144, "max_output_tokens": null, "parameters": "", "architecture": "Omni model with dedicated image, video, and audio encoders on a shared backbone. Closed weights. Artificial Analysis lists a 256k context; the launch blog excerpt did not state a token window.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-V2-Omni is Xiaomi's closed omni model launched 18 March 2026 for image, video, audio, and text. It appeared earlier on OpenRouter as Healer Alpha. The API id was retired 30 June 2026 and replaced by MiMo-V2.5. Collected 2026-10-15.", "what_it_is_for": "Xiaomi says it is for agents that must see, hear, and act, not only read text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2/mimo-v2-pro": { "id": "xiaomi/mimo-v2/mimo-v2-pro", "name": "MiMo-V2-Pro", "maker": "xiaomi", "family": "xiaomi/mimo-v2", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-18", "context_length": 1048576, "max_output_tokens": null, "parameters": "over 1T total, 42B active", "architecture": "Flagship MoE. Hybrid attention inherited from MiMo-V2-Flash with the hybrid ratio raised from 5:1 to 7:1. About 3x the scale of Flash. No public weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 3.0 }, "summary_intro": "MiMo-V2-Pro is Xiaomi's closed flagship agent model announced 18 March 2026, with over 1 trillion parameters, 42B active, and a 1M context. It was tested earlier on OpenRouter as Hunter Alpha. The API id was retired 30 June 2026 in favor of MiMo-V2.5-Pro. Collected 2026-10-15.", "what_it_is_for": "Xiaomi describes it as the brain of agent systems for long tool workflows and production engineering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2/mimo-v2-tts": { "id": "xiaomi/mimo-v2/mimo-v2-tts", "name": "MiMo-V2-TTS", "maker": "xiaomi", "family": "xiaomi/mimo-v2", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed speech-synthesis model launched with the V2 agent stack. Xiaomi described emotional shifts, singing, and dialect speech. No parameter count was published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-V2-TTS is Xiaomi's first named agent speech model, launched 18 March 2026 as a closed API. It was retired 30 June 2026 and replaced by the MiMo-V2.5-TTS series. Collected 2026-10-15.", "what_it_is_for": "Xiaomi described it as the voice for agents, including emotion, mid-sentence tone changes, singing, and regional Chinese dialects.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-5/mimo-v2-5": { "id": "xiaomi/mimo-v2-5/mimo-v2-5", "name": "MiMo-V2.5", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-22", "context_length": 1048576, "max_output_tokens": 131072, "parameters": "310B total, 15B active", "architecture": "Native omni MoE. Language backbone from the MiMo-V2-Flash hybrid SWA design, plus in-house vision and audio encoders. 48 layers, 15B active, 3-layer MTP. Trained on 48T tokens per the launch blog. Context extended 32K to 256K to 1M during post-training. FP8 E4M3 mixed weights.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.14, "completion_per_m_usd": 0.28 }, "summary_intro": "MiMo-V2.5 is Xiaomi's open omni model, 310B total and 15B active, released 22 April 2026 under MIT with a 1M context. On 15 October 2026 the API id is deprecated: calls already route to MiMo-V2.6-Flash, and the id is scheduled to stop on 21 October 2026. Collected 2026-10-15.", "what_it_is_for": "Xiaomi says it is the cheaper omni model for agent work and native image, video, and audio understanding.", "ratings": { "empirical_100": 58, "lmsys_elo": 1204, "coding_elo": 1204, "swe_bench": 15.7, "humaneval": 70.6, "twelve_cs": 67.9, "aa_intelligence": 21.8, "aa_coding": 56.8, "aa_agentic": 15.8, "design_arena_elo": 1213, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-5/mimo-v2-5-asr": { "id": "xiaomi/mimo-v2-5/mimo-v2-5-asr", "name": "MiMo-V2.5-ASR", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-06-02", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "End-to-end ASR from the Xiaomi MiMo team. Uses MiMo-Audio-Tokenizer. Mid-training, SFT, and RL. Hugging Face size tag is 8B; the README does not print a parameter sentence. Languages on the card: zh, en, yue.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": null }, "summary_intro": "MiMo-V2.5-ASR is Xiaomi's open speech-recognition model under MIT, on the API as mimo-v2.5-asr. The platform changelog dates the API release to 2 June 2026. It covers Mandarin, English, dialects, code-switching, and lyrics. Collected 2026-10-15.", "what_it_is_for": "Xiaomi says it is the speech-input side of the agent stack: meetings, noisy audio, dialects, and song lyrics.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-5/mimo-v2-5-base": { "id": "xiaomi/mimo-v2-5/mimo-v2-5-base", "name": "MiMo-V2.5-Base", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-22", "context_length": 262144, "max_output_tokens": null, "parameters": "310B total, 15B active", "architecture": "Pretrained sparse MoE. 48 layers (1 dense + 47 MoE), 9 full-attention and 39 SWA layers, hidden 4096, 256 routed experts, 8 active, SWA window 128. FP8 E4M3 mixed. Context 256K on the base checkpoint. The post-trained model extends to 1M. Hugging Face tag says 311B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-V2.5-Base is the pretrained 310B MoE checkpoint behind Xiaomi's omni MiMo-V2.5, open-sourced under MIT with a 256K context. Collected 2026-10-15.", "what_it_is_for": "Research and further training. The API product is the post-trained MiMo-V2.5.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-5/mimo-v2-5-pro": { "id": "xiaomi/mimo-v2-5/mimo-v2-5-pro", "name": "MiMo-V2.5-Pro", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-27", "context_length": 1048576, "max_output_tokens": 131072, "parameters": "1.02T total, 42B active", "architecture": "Post-trained flagship MoE for long-horizon agents. Hybrid SWA:GA about 6:1, window 128, 3-layer MTP. Text only. SFT, domain RL, then MOPD. FP8 E4M3. Recommended sampling temperature 1.0 and top_p 0.95.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.435, "completion_per_m_usd": 0.87 }, "summary_intro": "MiMo-V2.5-Pro is Xiaomi's open 1.02T text agent model (42B active) with a 1M context, released 27 April 2026 under MIT. As of 15 October 2026 the API id is deprecated and routes to MiMo-V2.6-Pro, with removal set for 21 October 2026. Weights remain public. Collected 2026-10-15.", "what_it_is_for": "Xiaomi built it for long agent traces, software engineering, and tasks that stay coherent over hundreds of tool calls.", "ratings": { "empirical_100": 45, "lmsys_elo": 1213, "coding_elo": 1213, "swe_bench": 17.2, "humaneval": 71.7, "twelve_cs": 41.2, "aa_intelligence": 26.0, "aa_coding": 60.2, "aa_agentic": 21.3, "design_arena_elo": 1242, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-5/mimo-v2-5-pro-base": { "id": "xiaomi/mimo-v2-5/mimo-v2-5-pro-base", "name": "MiMo-V2.5-Pro-Base", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-27", "context_length": 262144, "max_output_tokens": null, "parameters": "1.02T total, 42B active", "architecture": "Pretrained flagship MoE. 70 layers (1 dense + 69 MoE), 10 full attention and 60 SWA, hidden 6144, 384 experts, 8 active, window 128. FP8 E4M3. Base context 256K. Card says pretraining used 27T tokens at native 32K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-V2.5-Pro-Base is Xiaomi's pretrained 1.02T MoE (42B active), open under MIT with a 256K context. The chat and agent model is MiMo-V2.5-Pro. Collected 2026-10-15.", "what_it_is_for": "Base weights for research. Xiaomi's agent claims refer to the post-trained checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-5/mimo-v2-5-pro-ultraspeed": { "id": "xiaomi/mimo-v2-5/mimo-v2-5-pro-ultraspeed", "name": "MiMo-V2.5-Pro-UltraSpeed", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-06-08", "context_length": null, "max_output_tokens": null, "parameters": "1.02T total, 42B active", "architecture": "Serving mode of MiMo-V2.5-Pro built with TileRT. Xiaomi said it passed 1000 tokens/s decode, with a demo near 1200. The open artifact is MiMo-V2.5-Pro-FP4-DFlash, not a second full pretrained model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-V2.5-Pro-UltraSpeed was a short-lived high-speed API for the 1.02T Pro model, announced 8 June 2026 with TileRT and open only by application from 9 to 23 June 2026. The window has ended. Collected 2026-10-15.", "what_it_is_for": "Xiaomi offered it for users who wanted roughly 10x generation speed at 3x the Pro price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-5/mimo-v2-5-tts": { "id": "xiaomi/mimo-v2-5/mimo-v2-5-tts", "name": "MiMo-V2.5-TTS", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed speech-synthesis API in the MiMo-V2.5-TTS series. Xiaomi says the three ids share style-instruction and audio-tag control. Parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-V2.5-TTS is Xiaomi's closed preset-voice speech model, API id mimo-v2.5-tts, announced in April 2026 and still listed in October 2026. It is free for a limited time. Collected 2026-10-15.", "what_it_is_for": "Preset premium voices with control of rate, emotion, and tone. The Chinese page names voices including \u51b0\u7cd6, \u8309\u8389, and \u82cf\u6253.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-5/mimo-v2-5-tts-voiceclone": { "id": "xiaomi/mimo-v2-5/mimo-v2-5-tts-voiceclone", "name": "MiMo-V2.5-TTS-VoiceClone", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed speech-synthesis API in the MiMo-V2.5-TTS series. Xiaomi says the three ids share style-instruction and audio-tag control. Parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-V2.5-TTS-VoiceClone is Xiaomi's closed voice-cloning model, API id mimo-v2.5-tts-voiceclone. A short sample clones a voice while style tags still apply. Free for a limited time as of 15 October 2026.", "what_it_is_for": "Xiaomi says a small audio sample clones a target voice and still follows style instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-5/mimo-v2-5-tts-voicedesign": { "id": "xiaomi/mimo-v2-5/mimo-v2-5-tts-voicedesign", "name": "MiMo-V2.5-TTS-VoiceDesign", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed speech-synthesis API in the MiMo-V2.5-TTS series. Xiaomi says the three ids share style-instruction and audio-tag control. Parameter count was not published.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-V2.5-TTS-VoiceDesign is Xiaomi's closed voice-design model, API id mimo-v2.5-tts-voicedesign. A text description creates a new voice. It is free for a limited time as of 15 October 2026.", "what_it_is_for": "Xiaomi says one sentence can define a new timbre.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-6/mimo-v2-6-distill-qwen-9b": { "id": "xiaomi/mimo-v2-6/mimo-v2-6-distill-qwen-9b", "name": "MiMo-V2.6-Distill-Qwen-9B", "maker": "xiaomi", "family": "xiaomi/mimo-v2-6", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-09-22", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Supervised fine-tune of Qwen/Qwen3.5-9B on MiMo-generated agent data. Xiaomi calls it an SFT starting point for agent RL, not the flagship MoE. SFT mix is 77.4B tokens, of which 27.2B are loss-bearing. Hugging Face pipeline tag is image-text-to-text.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-V2.6-Distill-Qwen-9B is Xiaomi's 9B MIT checkpoint, a supervised fine-tune of Qwen3.5-9B on data generated by MiMo-V2.6. It is a research model, not an API id. Collected 2026-10-15.", "what_it_is_for": "Xiaomi says it covers coding, general agents, visual coding, and cybersecurity, and is a base for open agent RL research.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-6/mimo-v2-6-flash": { "id": "xiaomi/mimo-v2-6/mimo-v2-6-flash", "name": "MiMo-V2.6-Flash", "maker": "xiaomi", "family": "xiaomi/mimo-v2-6", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-09-22", "context_length": 1048576, "max_output_tokens": 131072, "parameters": "309B total, 15B active", "architecture": "Sparse MoE, 309B total and 15B active, 1M context, native omni encoders. The Flash-MOPD card lists a 681M MiMo ViT, a 308M audio tokenizer, a 127M audio patch encoder, and a 5-layer MTP. Layer counts were not in that summary. Hugging Face tags some repos 311B. Artificial Analysis lists Flash input as text and image only, which disagrees with Xiaomi's omni claim.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.14, "completion_per_m_usd": 0.28 }, "summary_intro": "MiMo-V2.6-Flash is Xiaomi's cheaper omni MoE, 309B total and 15B active, released 22 September 2026 under MIT. It replaces MiMo-V2.5 on the API and is the efficiency tier next to V2.6-Pro. Collected 2026-10-15.", "what_it_is_for": "Xiaomi recommends it for high-frequency professional work where Pro is more intelligence than the budget allows.", "ratings": { "empirical_100": null, "lmsys_elo": 1204, "coding_elo": 1204, "swe_bench": 15.7, "humaneval": 70.6, "twelve_cs": 99.5, "aa_intelligence": 37.9, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-6/mimo-v2-6-pro": { "id": "xiaomi/mimo-v2-6/mimo-v2-6-pro", "name": "MiMo-V2.6-Pro", "maker": "xiaomi", "family": "xiaomi/mimo-v2-6", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-09-22", "context_length": 1048576, "max_output_tokens": 131072, "parameters": "1.02T total, 42B active", "architecture": "Sparse MoE, 70 layers (60 SWA / 10 GA), hidden 6144, 128 Q heads, 8 KV heads, head dim 192/128, window 128, 384 experts with 8 active. First block is dense global attention; the rest are MoE without shared experts. Vision encoder is a 681M MiMo ViT. Audio path is a 308M tokenizer plus a 127M patch encoder. 5-layer MTP drafter predicts 7 tokens. Weights published as MiMo-V2.6-Pro-RL, with a later MOPD checkpoint.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.435, "completion_per_m_usd": 0.87 }, "summary_intro": "MiMo-V2.6-Pro is Xiaomi's current flagship omni model, a 1.02T MoE with 42B active parameters, released 22 September 2026 under MIT with a 1M context. It is the default successor of MiMo-V2.5-Pro on the API. Collected 2026-10-15.", "what_it_is_for": "Xiaomi recommends it for complex projects, long-horizon work, cybersecurity, and research, with text, image, video, and audio input.", "ratings": { "empirical_100": 0, "lmsys_elo": 1213, "coding_elo": 1213, "swe_bench": 17.2, "humaneval": 71.7, "twelve_cs": 14.3, "aa_intelligence": 46.3, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1290, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-6/mimo-v2-6-pro-ultraspeed": { "id": "xiaomi/mimo-v2-6/mimo-v2-6-pro-ultraspeed", "name": "MiMo-V2.6-Pro-UltraSpeed", "maker": "xiaomi", "family": "xiaomi/mimo-v2-6", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-09-22", "context_length": 1048576, "max_output_tokens": 131072, "parameters": "1.02T total, 42B active", "architecture": "API serving mode of MiMo-V2.6-Pro. Xiaomi says quality matches Pro at up to 20x output speed. No separate weight repo was listed. Rate limits are custom, not the 100 RPM / 10M TPM default.", "modalities": { "input": [ "text", "image", "video", "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 4.35, "completion_per_m_usd": 8.7 }, "summary_intro": "MiMo-V2.6-Pro-UltraSpeed is the fast serving tier of Xiaomi's V2.6-Pro, a separate API id since 22 September 2026. Xiaomi prices it at about ten times Pro and says output can be up to 20 times faster. Collected 2026-10-15.", "what_it_is_for": "Xiaomi aims it at real-time and latency-sensitive workloads that still want Pro quality.", "ratings": { "empirical_100": null, "lmsys_elo": 1330, "coding_elo": 1330, "swe_bench": 36.8, "humaneval": 87.4, "twelve_cs": 99.5, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-flash/mimo-v2-flash": { "id": "xiaomi/mimo-v2-flash/mimo-v2-flash", "name": "MiMo-V2-Flash", "maker": "xiaomi", "family": "xiaomi/mimo-v2-flash", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-16", "context_length": 262144, "max_output_tokens": null, "parameters": "309B total, 15B active", "architecture": "Post-trained MoE. Hybrid SWA:GA 5:1, window 128, attention sink bias. Three-layer MTP, about 0.33B parameters per block, dense FFN. Post-training is MOPD plus agentic RL. Official system prompt states a December 2024 knowledge cutoff.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.3 }, "summary_intro": "MiMo-V2-Flash is Xiaomi's open 309B MoE (15B active) for fast reasoning, coding, and agents, released 16 December 2025 under MIT. The API id was retired on 30 June 2026 after routing to MiMo-V2.5. Weights are still public. Collected 2026-10-15.", "what_it_is_for": "Xiaomi sold it as a low-cost agent and coding model. The launch note said inference cost was about 2.5 percent of Claude's, with generation speed about doubled.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-v2-flash/mimo-v2-flash-base": { "id": "xiaomi/mimo-v2-flash/mimo-v2-flash-base", "name": "MiMo-V2-Flash-Base", "maker": "xiaomi", "family": "xiaomi/mimo-v2-flash", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-16", "context_length": 262144, "max_output_tokens": null, "parameters": "309B total, 15B active", "architecture": "Sparse MoE pretrained checkpoint. Hybrid sliding-window and global attention at 5:1, window 128, with attention sink bias. Trained on 27T tokens in FP8 at native 32K, context up to 256K. Hugging Face's size tag says 310B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-V2-Flash-Base is the pretrained 309B/15B MoE checkpoint Xiaomi open-sourced with MiMo-V2-Flash on 16 December 2025 under MIT. The API product that followed was the post-trained model, later retired. Collected 2026-10-15.", "what_it_is_for": "Xiaomi publishes it as the pretraining checkpoint for research. The agent and chat product is MiMo-V2-Flash.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-vl/mimo-vl-7b-rl": { "id": "xiaomi/mimo-vl/mimo-vl-7b-rl", "name": "MiMo-VL-7B-RL", "maker": "xiaomi", "family": "xiaomi/mimo-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-06-04", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Mixed On-policy Reinforcement Learning (MORL) on MiMo-VL-7B-SFT. Rewards cover perception, grounding, reasoning, and preference. Same ViT plus MiMo-7B stack.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-VL-7B-RL is Xiaomi's reinforcement-learned 7B vision-language model, released with the 4 June 2025 report under MIT. It is the checkpoint Xiaomi recommends for general visual reasoning and GUI grounding. Collected 2026-10-15.", "what_it_is_for": "Xiaomi says it is for general visual understanding, multimodal reasoning, and GUI grounding, and that it beats Qwen2.5-VL-7B on 35 of 40 tasks in the paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-vl/mimo-vl-7b-sft": { "id": "xiaomi/mimo-vl/mimo-vl-7b-sft", "name": "MiMo-VL-7B-SFT", "maker": "xiaomi", "family": "xiaomi/mimo-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-06-04", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Native-resolution ViT, MLP projector, and MiMo-7B language backbone. Four-stage pretraining (projector warmup, alignment, general multimodal pretraining, long-context SFT) produces this checkpoint. The paper says that phase consumes 2.4 trillion tokens. Compatible with Qwen2.5-VL loading classes.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-VL-7B-SFT is Xiaomi's open vision-language model after four-stage multimodal training, published with the 4 June 2025 technical report under MIT. An August 2025 snapshot is a version. Collected 2026-10-15.", "what_it_is_for": "Xiaomi says the SFT stage is the general visual-understanding checkpoint before mixed on-policy RL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xiaomi/mimo-vl/mimo-vl-miloco-7b": { "id": "xiaomi/mimo-vl/mimo-vl-miloco-7b", "name": "MiMo-VL-Miloco-7B", "maker": "xiaomi", "family": "xiaomi/mimo-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-11-14", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Home-scene specialization of MiMo-VL-7B. Two-stage SFT plus GRPO reinforcement learning. Aimed at gesture and daily-activity recognition while keeping general video and language ability.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MiMo-VL-Miloco-7B is Xiaomi's open vision-language model for smart-home scenes, released 14 November 2025 on top of MiMo-VL-7B. A technical report followed on 19 December 2025. Collected 2026-10-15.", "what_it_is_for": "Xiaomi says it recognizes household activities and gestures such as thumbs-up, OK, and open palm, for the Miloco home agent.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xidian/cryptollm/cryptollm": { "id": "xidian/cryptollm/cryptollm", "name": "\u7384\u77e5\u5927\u6a21\u578b", "maker": "xidian", "family": "xidian/cryptollm", "status": "preview", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-15", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Staged fine-tune of Qwen2.5. The university-hosted CNR text says the result uses a 32B domestic base and does not name the checkpoint id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u7384\u77e5\u5927\u6a21\u578b (CryptoLLM) is the cryptography language model named by Xidian University's NSS lab under Shen Yulong. The university-hosted CNR report dates the public launch to 15 August 2025 at the fourth Western Cybersecurity Conference. It is a closed staged fine-tune of Qwen2.5 on a 32B base. Collected 2026-10-15.", "what_it_is_for": "The lab describes three jobs: cryptography-algorithm analysis, protocol design and security reasoning, and engineering implementation, including library integration and code generation. The project page also describes agents for secure multi-party computation planning, coding, testing, and verification. The August 2025 press says an industry API and SDK are offered. The NSS page fetched on the collection date says the service is an invite-only research beta and points applications to nss@xidian.edu.cn, while still linking https://chat.cryptollm.net/.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xidian/huitong/huitong": { "id": "xidian/huitong/huitong", "name": "\u6167\u901a\u5927\u6a21\u578b", "maker": "xidian", "family": "xidian/huitong", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-09-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u6167\u901a\u5927\u6a21\u578b is the electronic-information higher-education model Xidian University launched on 21 September 2024 at the third Intelligent Education Conference. The university news office attributes it to the big-data and visual-intelligence institute in the School of Computer Science. Weights are closed. Collected 2026-10-15.", "what_it_is_for": "The university says \u6167\u901a supports teaching, learning, administration, and evaluation. Named jobs are subject question answering, teaching evaluation, learning-analytics, and an administrative help desk. The same notice says the trial corpus covered 283 electronic-information courses, guides for 12 administrative departments, and 487 school regulations. Trial use was reported as nearly 200,000 person-uses and more than 900,000 dialogue turns. Those figures are usage, not a scored benchmark.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xidian/industrial-vision/industrial-vision": { "id": "xidian/industrial-vision/industrial-vision", "name": "\u5de5\u4e1a\u667a\u80fd\u89c6\u89c9\u8bc6\u522b\u5927\u6a21\u578b", "maker": "xidian", "family": "xidian/industrial-vision", "status": "active", "confidence": "low", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "China News Service says masked image modeling combined with contrastive learning, a vision foundation model on the order of 3 billion parameters, then transfer learning. Exact count and layers were not published.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u5de5\u4e1a\u667a\u80fd\u89c6\u89c9\u8bc6\u522b\u5927\u6a21\u578b is the industrial vision model named in a 7 July 2025 China News Service story about Xidian University. The story says Dong Weisheng's team and partners launched it in 2024. Confidence is low: that article is the only source found, and it is not a model card. Collected 2026-10-15.", "what_it_is_for": "The article says the model is for industrial security, equipment monitoring, and power-line inspection, with transfer learning so one vision foundation model can be adapted to those scenes. It gives no product URL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xidian/qinling/qinling-rs-brain": { "id": "xidian/qinling/qinling-rs-brain", "name": "\u79e6\u5cad\u00b7\u897f\u7535\u9065\u611f\u8111", "maker": "xidian", "family": "xidian/qinling", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Self-supervised and domain-adaptive remote-sensing pretraining. The authors describe an L0 general model, L1 industry models, and L2 scene models as one hierarchy, trained with MindSpore on Ascend Atlas.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u79e6\u5cad\u00b7\u897f\u7535\u9065\u611f\u8111 is the remote-sensing interpretation model named by Xidian University's School of Artificial Intelligence. Hou Biao, Ren Zhongle, Li Weibin, and Jiao Licheng describe it as a domestic pretrained interpreter built with the Shaanxi high-resolution earth-observation data center, Huawei, and the Yanta AI innovation center. Weights are closed. It was still on the Shaanxi key-product list checked on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "The authors say it extracts land-cover elements and changes from wide-area optical and SAR imagery. Named deployments include agriculture, forestry, water, rural development, land planning, ecological prosecution, resource audit, disaster response, and city planning. The university research notice says delivery is an online interpretation service and an on-premises appliance, certified on Ascend Atlas and MindSpore.\n\nThe 19 March 2024 article says labeled data cover more than 2 million square kilometres in Shaanxi, Zhejiang, Guangdong, and other regions, with 8 first-level, 38 second-level, 20 third-level, and 6 fourth-level classes, mainly domestic optical and SAR imagery at better than 5 metre resolution.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xidian/yilv/yilv": { "id": "xidian/yilv/yilv", "name": "\u4e1d\u8def\u9038\u65c5", "maker": "xidian", "family": "xidian/yilv", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u4e1d\u8def\u9038\u65c5 is the culture and tourism restoration and generation model named by Xidian University's School of Artificial Intelligence. Jiao Licheng's team describes it as a multimodal system for mural diagnosis, restoration, and travel guidance. Weights are closed. It was on the Shaanxi key-product list checked on 2026-10-15. Collected 2026-10-15.", "what_it_is_for": "CNR, republished by the university, says the model detects mural damage, writes a diagnosis, and fills missing regions. A reported example is about five seconds to complete a damaged section of the Yongle Palace Chaoyuan Tu. That timing is one demonstration, not a published limit. The same report says a user can ask in text or upload a photo and receive a travel guide, object identification, and a museum path. A team member says answers can be multilingual because the data come from local culture and tourism partners. The language list was not published. China Daily dates a public lab demonstration to 12 December 2025 and says development took more than two years.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xjtu/alignearth/alignearth-sar-vit-b-16": { "id": "xjtu/alignearth/alignearth-sar-vit-b-16", "name": "AlignEarth-SAR-ViT-B-16", "maker": "xjtu", "family": "xjtu/alignearth", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-08-26", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "SAR image encoder distilled from an optical CLIP ViT teacher so SAR features sit in the same space as the optical CLIP text encoder. The released filename is AlignEarth-SAR-ViT-B-16.pt (about 1.29 GB).", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AlignEarth-SAR-ViT-B-16 is a SAR vision encoder released by Kaiyu Li, Xiangyong Cao, and coauthors at Xi'an Jiaotong University. It is distilled from an optical CLIP teacher so open-vocabulary segmentation can run on SAR images without a SAR foundation model trained from scratch. The author checkpoint was on Hugging Face as of 2026-10-15. The official repo has no model card and no license file.", "what_it_is_for": "The paper uses AlignEarth inside SegEarth-OV so a text prompt can segment SAR scenes with no task labels. The student encoder is trained to match an optical CLIP teacher. SegEarth-OV itself is a training-free pipeline over CLIP features and is not a separate weight release. SegEarth-OV3 later applies Meta's SAM 3 and is not this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xjtu/ban/ban": { "id": "xjtu/ban/ban", "name": "BAN", "maker": "xjtu", "family": "xjtu/ban", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2024-02-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Bi-temporal adapter network: a frozen foundation-model image encoder (the paper's default is CLIP ViT-L/14), a bi-temporal adapter branch that can be an existing change-detection model such as BiT or a SegFormer-style MiT, and bridging modules that resample foundation features into the adapter.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BAN (Bi-Temporal Adapter Network) is a remote-sensing change-detection model from Kaiyu Li, Xiangyong Cao, and Deyu Meng at Xi'an Jiaotong University. It keeps a foundation-model image encoder frozen and trains a bi-temporal adapter plus bridging modules. Weights and Apache-2.0 code are public. This dossier was collected on 2026-10-15.", "what_it_is_for": "The paper presents BAN as a way to reuse a foundation model, by default CLIP ViT-L/14, for binary and semantic change detection when labeled remote-sensing pairs are scarce. The adapter branch can be an existing change-detection network or stacked blocks. The authors say this is a side-tuning setup: general features are selected, aligned, and injected into the adapter.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xjtu/fast-lewm/fast-lewm": { "id": "xjtu/fast-lewm/fast-lewm", "name": "Fast-LeWM", "maker": "xjtu", "family": "xjtu/fast-lewm", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2026-06-24", "context_length": null, "max_output_tokens": null, "parameters": "17.9M", "architecture": "Latent world model. A visual encoder follows LeWM. An action-prefix encoder is a 3-layer causal Transformer with 6 heads of dimension 32 and token width 192. A 6-layer residual MLP with AdaLN-Zero predicts future latents for every prefix in parallel. Latent width 192, predictor hidden width 2048, modulation width 768, dropout 0.1.", "modalities": { "input": [ "image" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Fast-LeWM (Fast LeWorldModel) is a latent visual world model from Yuntian Gao and Xiangyu Xu at Xi'an Jiaotong University. It predicts future latents for whole action prefixes in parallel instead of rolling a one-step transition model. The paper reports 17.9M parameters. Task checkpoints are on Hugging Face under an MIT tag. Collected on 2026-10-15.", "what_it_is_for": "The authors use it for reward-free visual planning. Given the current latent and a candidate action sequence, the model encodes prefixes and predicts the latent that would be reached after each prefix, so a planner can score many horizons without stepping through imagined states one by one. The visual encoder follows LeWorldModel (LeWM), which is not an XJTU release.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xjtu/power-llava/power-llava": { "id": "xjtu/power-llava/power-llava", "name": "Power-LLaVA", "maker": "xjtu", "family": "xjtu/power-llava", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-07-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "LLaVA-style stack: frozen pretrained ViT-L/14 vision encoder, a randomly initialized linear projector, and Vicuna-7B as the language model. Stage 1 trains only the projector. Stage 2 trains the projector and the language model; the vision encoder stays frozen.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Power-LLaVA is a vision-language model for power-transmission-line inspection from the State Key Laboratory of Electrical Insulation and Power Equipment at Xi'an Jiaotong University (Jiahao Wang, Mingxuan Li, Haichen Luo, Jinguo Zhu, Aijun Yang, Mingzhe Rong, and Xiaohua Wang). The arXiv preprint is dated 2024-07-27 and the work appears in ICIP 2024. It was collected on 2026-10-15. Public weights were not found.", "what_it_is_for": "The authors describe it as a dialogue assistant for transmission-line inspection: equipment function, nearby hazards, and scene description. Images go through a frozen ViT-L/14, a linear projector, and Vicuna-7B. Stage 1 aligns the projector on 608K image-text pairs (558K from the public LLaVA set and 50K captions on 20K inspection images). Stage 2 instruction-tunes the projector and the language model on 100K inspection conversations. The paper's combined data figure in the comparison table is 708K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xmu/avg-llava/avg-llava": { "id": "xmu/avg-llava/avg-llava", "name": "AVG-LLaVA", "maker": "xmu", "family": "xmu/avg-llava", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Llama 2 Community License", "release_date": "2024-10-12", "context_length": 4096, "max_output_tokens": null, "parameters": "7180414091", "architecture": "LLaVA-NeXT variant with a visual-granularity scaler and a router that picks among 36, 72, 144, 288, and 576 visual tokens. Base LLM is lmsys/vicuna-7b-v1.5. Vision tower is openai/clip-vit-large-patch14-336. Config architecture LlavaLlamaForCausalLM, 32 layers, hidden size 4096, 32 heads, vocab size 32000, max position embeddings 4096. The paper says the added modules increase parameter count by 1.66 percent versus LLaVA-NeXT. The Hub safetensors index for zhibinlan/AVG-LLaVA totals 7,180,414,091 BF16 parameters. Four training stages. Stage 3 learning rates are 2e-5 for the vision encoder and 1e-5 elsewhere. Stage 4 router learning rate is 1e-3. Instruction-token filter k is 32. Cross-entropy loss weight alpha is 0.1.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AVG-LLaVA is a vision-language chat model from the DeepLIT group at Xiamen University, with WeChat AI coauthors. It adds a visual-granularity scaler and a router to LLaVA-NeXT. The lab released the weights on 2024-10-12. This dossier was collected on 2026-10-15. Status is active.", "what_it_is_for": "The model answers questions about images. A router picks among 36, 72, 144, 288, and 576 visual tokens. The Stage3 card describes multi-granularity visual instruction tuning for research on large multimodal models and chatbots. Evaluation in the lab repo uses lmms-eval, and the repo includes a Gradio demo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xmu/llave/llave-0-5b": { "id": "xmu/llave/llave-0-5b", "name": "LLaVE-0.5B", "maker": "xmu", "family": "xmu/llave", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-03-10", "context_length": 4096, "max_output_tokens": null, "parameters": "893618208", "architecture": "Hardness-weighted contrastive fine-tune of lmms-lab/llava-onevision-qwen2-0.5b-ov. Config architecture LlavaQwenForCausalLM, 24 layers, hidden size 896, 14 attention heads, 2 key-value heads, SiLU, vocab inherited from the Qwen2 tokenizer, SigLIP so400m-patch14-384 vision tower, mlp2x_gelu projector, alpha 9.0. The card calls it a 0.5B model. The Hub safetensors file totals 893,618,208 BF16 parameters, which includes the vision tower. Trained on 662K MMEB pairs.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLaVE-0.5B is an open-weight multimodal embedding model from the DeepLIT group at Xiamen University, with WeChat AI coauthors. It is a hardness-weighted contrastive fine-tune of LLaVA-OneVision-Qwen2-0.5B. The lab announced the weights on 2025-03-10. This dossier was collected on 2026-10-15. Status is active.", "what_it_is_for": "The model card says it embeds text, images, multi-image inputs, and video. The paper trains it so that harder negative pairs get more weight during contrastive learning. It is a research checkpoint, not a priced API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xmu/llave/llave-2b": { "id": "xmu/llave/llave-2b", "name": "LLaVE-2B", "maker": "xmu", "family": "xmu/llave", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-03-10", "context_length": 4096, "max_output_tokens": null, "parameters": "1945595424", "architecture": "Hardness-weighted contrastive fine-tune of BAAI/Aquila-VL-2B-llava-qwen. Config model_type qwen2 with architecture LlavaQwenForCausalLM, 28 layers, hidden size 1536, 12 attention heads, 2 key-value heads, tied embeddings, vocab size 151936, SigLIP so400m-patch14-384 vision tower, mlp2x_gelu projector, alpha 9.0. The card calls it a 2B model with a 4K context. The Hub safetensors file totals 1,945,595,424 BF16 parameters. Trained on 662K MMEB pairs.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLaVE-2B is an open-weight multimodal embedding model from the DeepLIT group at Xiamen University, with WeChat AI coauthors. It is a hardness-weighted contrastive fine-tune of Aquila-VL-2B. The lab announced the weights on 2025-03-10. This dossier was collected on 2026-10-15. Status is active.", "what_it_is_for": "The model card says it embeds text, images, multi-image inputs, and video, and that it was trained on 662K pairs. The paper presents it as the size that passes earlier 7B embedding models on MMEB while staying near 2B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xmu/llave/llave-7b": { "id": "xmu/llave/llave-7b", "name": "LLaVE-7B", "maker": "xmu", "family": "xmu/llave", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-03-10", "context_length": 4096, "max_output_tokens": null, "parameters": "8030348832", "architecture": "Hardness-weighted contrastive fine-tune of lmms-lab/llava-onevision-qwen2-7b-ov. Config architecture LlavaQwenForCausalLM, 28 layers, hidden size 3584, 28 attention heads, 4 key-value heads, untied embeddings, vocab size 152064, SigLIP so400m-patch14-384 vision tower, mlp2x_gelu projector, alpha 9.0. The card calls it a 7B model with a 4K context. The Hub safetensors index totals 8,030,348,832 BF16 parameters across 4 shards. Trained on 662K MMEB pairs.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LLaVE-7B is the largest released LLaVE embedding model from the DeepLIT group at Xiamen University, with WeChat AI coauthors. It is a hardness-weighted contrastive fine-tune of LLaVA-OneVision-Qwen2-7B. The lab announced the weights on 2025-03-10. This dossier was collected on 2026-10-15. Status is active.", "what_it_is_for": "The model card says the LLaVE models embed text, images, multi-image inputs, and video. The paper reports LLaVE-7B as the strongest of the three released sizes on MMEB, trained with 662K pairs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xmu/ume-r1/ume-r1-2b": { "id": "xmu/ume-r1/ume-r1-2b", "name": "UME-R1-2B", "maker": "xmu", "family": "xmu/ume-r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-11-04", "context_length": 32768, "max_output_tokens": null, "parameters": "2208985600", "architecture": "Two-stage fine-tune of Qwen/Qwen2-VL-2B-Instruct: cold-start SFT on 1.46 million reasoning-annotated pairs, then GRPO on about 11,136 pairs. Config is Qwen2VLForConditionalGeneration, 28 layers, hidden size 1536, 12 attention heads, 2 key-value heads, tied embeddings, vocab size 151936, M-RoPE, vision spatial patch size 14. The Hub safetensors file totals 2,208,985,600 BF16 parameters. SFT uses max sequence length 12,288. Eval uses greedy search and up to 8,192 new tokens. Discriminative embeddings are the last-layer state at . Generative embeddings are the state at the generated token.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text", "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UME-R1-2B is the smaller released UME-R1 model from the DeepLIT group at Xiamen University, with WeChat AI coauthors. It is a two-stage fine-tune of Qwen2-VL-2B-Instruct that can emit a discriminative embedding or a reasoning-driven generative embedding. The lab's release note is dated 2025-11-04. This dossier was collected on 2026-10-15. Status is active.", "what_it_is_for": "The model embeds text, images, and video. A discriminative embedding is the last-layer state at ``. A generative embedding is the state at a `` token written after a reasoning summary. The GitHub quick start follows Qwen2-VL inference, including vLLM.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "xmu/ume-r1/ume-r1-7b": { "id": "xmu/ume-r1/ume-r1-7b", "name": "UME-R1-7B", "maker": "xmu", "family": "xmu/ume-r1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2025-11-04", "context_length": 32768, "max_output_tokens": null, "parameters": "8291375616", "architecture": "Two-stage fine-tune of Qwen2-VL-7B: cold-start SFT then GRPO, same recipe as UME-R1-2B. Config is Qwen2VLForConditionalGeneration, 28 layers, hidden size 3584, 28 attention heads, 4 key-value heads, untied embeddings, vocab size 152064, M-RoPE, vision spatial patch size 14. The Hub safetensors index totals 8,291,375,616 BF16 parameters across 4 shards. SFT max sequence length is 12,288. Eval uses greedy search and up to 8,192 new tokens.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text", "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "UME-R1-7B is the larger released UME-R1 model from the DeepLIT group at Xiamen University, with WeChat AI coauthors. It uses the same cold-start SFT then GRPO recipe as UME-R1-2B, starting from Qwen2-VL-7B. The lab's release note is dated 2025-11-04. This dossier was collected on 2026-10-15. Status is active.", "what_it_is_for": "The model embeds text, images, and video and can emit either a discriminative embedding or a reasoning-driven generative embedding. The paper reports it as the stronger of the two released sizes on MMEB-V2. Inference follows Qwen2-VL, with an extra step to read the embedding token.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/autoacu/a2cu-classifier": { "id": "yale/autoacu/a2cu-classifier", "name": "A2CU Classifier", "maker": "yale", "family": "yale/autoacu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-03-02", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "DebertaForSequenceClassification. Config: 48 hidden layers, hidden size 1024, 16 heads, intermediate size 4096, vocab 50265, max_position_embeddings 512. Labels are CONTRADICTION, NEUTRAL, and ENTAILMENT. AutoACU README: DeBERTa-XLarge fine-tuned on RoSE for ACU matching. Paper: start from a pretrained DeBERTa NLI model and fine-tune on RoSE gold ACU labels.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A2CU Classifier is the entailment checker in Yale LILY's A2CU summarization metric. The AutoACU README calls it a DeBERTa-XLarge model fine-tuned on RoSE. The Hub repo was created 2023-03-02. The paper is EMNLP 2023. Collected 2026-10-15.", "what_it_is_for": "After the A2CU generator extracts atomic content units, this model checks whether those units are supported by the other text. The paper describes it as a pretrained DeBERTa NLI model further fine-tuned on RoSE gold ACU labels.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/autoacu/a2cu-generator": { "id": "yale/autoacu/a2cu-generator", "name": "A2CU Generator", "maker": "yale", "family": "yale/autoacu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-03-02", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Seq2seq generator. AutoACU README: a T0-3B model fine-tuned on the RoSE dataset to extract atomic content units. Hub library is Transformers, architecture tag t5. Config was not re-read, so layer counts are omitted.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A2CU Generator is the atomic-content-unit extractor in the Yale LILY AutoACU metric (EMNLP 2023). The AutoACU README says the default generator is Yale-LILY/a2cu-generator, a T0-3B model fine-tuned on RoSE. The Hub repo was created 2023-03-02, just before the 2023-03-07 preprint. Collected 2026-10-15.", "what_it_is_for": "A2CU is a two-step reference-based summarization metric. This model writes atomic content units from one text. A separate classifier then checks those units against the other text. It is an evaluation model, not a general chatbot.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/autoacu/a3cu": { "id": "yale/autoacu/a3cu", "name": "A3CU", "maker": "yale", "family": "yale/autoacu", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-03-02", "context_length": 512, "max_output_tokens": null, "parameters": "", "architecture": "Custom BertClassifier. Config: 24 hidden layers, hidden size 1024, 16 heads, intermediate size 4096, max_position_embeddings 512, vocab 28996. AutoACU README: the default A3CU checkpoint is based on BERT-Large. The paper says A3CU is a one-stage metric pretrained on the two-stage A2CU method. The default Hub file's exact variant name (A3CUF, A3CUP, or A3CUPF) was not re-checked.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A3CU (Accelerated AutoACU) is Yale LILY's one-stage summarization metric. It scores two texts directly instead of extracting atomic units first. The default checkpoint is Yale-LILY/a3cu, which the README says is based on BERT-Large. Hub weights date from 2023-03-02. The paper is EMNLP 2023. Collected 2026-10-15.", "what_it_is_for": "Reference-based evaluation of summaries. The README shows `A3CU.score(references, candidates)` returning recall, precision, and F1. The paper says the one-stage model is pretrained to match the two-stage A2CU metric, then can be fine-tuned on gold scores.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/brio/brio-cnndm-cased": { "id": "yale/brio/brio-cnndm-cased", "name": "BRIO CNN/DailyMail Cased", "maker": "yale", "family": "yale/brio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-05-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BART-Large sequence-to-sequence model (BartForConditionalGeneration), cased CNN/DailyMail BRIO-Mul checkpoint. Hub repo created 2022-05-16, after the uncased release.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BRIO CNN/DailyMail Cased is the cased BART checkpoint of BRIO-Mul from the Yale LILY Lab, for CNN/DailyMail summarization. The Hub repo was created 2022-05-16, after the uncased model and the 2022-03-31 paper. Weights are still public. Collected 2026-10-15.", "what_it_is_for": "Abstractive summarization of CNN/DailyMail articles, preserving case. The project README lists it next to the uncased checkpoint as a generation model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/brio/brio-cnndm-uncased": { "id": "yale/brio/brio-cnndm-uncased", "name": "BRIO CNN/DailyMail Uncased", "maker": "yale", "family": "yale/brio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BART-Large sequence-to-sequence model (BartForConditionalGeneration). The authors' generation checkpoint for CNN/DailyMail is the multi-task BRIO-Mul model, uncased. Fine-tuned from BART rather than trained from scratch. Joint Yale LILY and Carnegie Mellon release; the Hub card says developed by Yale LILY Lab.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BRIO CNN/DailyMail Uncased is the Yale LILY Lab's public BART generation checkpoint for the BRIO abstractive summarization method (ACL 2022), trained with Yixin Liu, Pengfei Liu, Dragomir Radev, and Graham Neubig (Yale and Carnegie Mellon). The Hub repo dates from 2022-03-30 and the paper from 2022-03-31. Weights are still public. Collected 2026-10-15.", "what_it_is_for": "Abstractive summarization of news articles, specifically CNN/DailyMail. BRIO trains the model so candidate summaries get probability mass according to quality, instead of maximum likelihood on a single reference. This checkpoint is the multi-task BRIO-Mul generator, not the contrastive-only ranker.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/brio/brio-xsum-cased": { "id": "yale/brio/brio-xsum-cased", "name": "BRIO XSum Cased", "maker": "yale", "family": "yale/brio", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-03-31", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PEGASUS sequence-to-sequence model (PegasusForConditionalGeneration), cased, fine-tuned as BRIO-Mul on XSum. Hub card model type is PEGASUS. Developed by Yale LILY Lab with Carnegie Mellon coauthors.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "BRIO XSum Cased is the Yale LILY Lab's PEGASUS checkpoint of BRIO-Mul for the XSum summarization dataset. The paper and the Hub repo are dated 2022-03-31. The work is joint with Carnegie Mellon. Weights are still public. Collected 2026-10-15.", "what_it_is_for": "Abstractive summarization of BBC articles in the XSum style (one-sentence summaries). The card calls the model type PEGASUS and points at the BRIO training method.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/mdcure/mdcure-flant5-base": { "id": "yale/mdcure/mdcure-flant5-base", "name": "MDCure-FlanT5-Base", "maker": "yale", "family": "yale/mdcure", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Encoder-decoder. Initialized from google/flan-t5-base and fine-tuned on yale-nlp/MDCure-72k. Hub pipeline tag is summarization. Card recommends transformers>4.35.0. This dossier did not re-read the checkpoint config, so layer counts are not copied.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MDCure-FlanT5-Base is Yale NLP's fine-tune of google/flan-t5-base on the MDCure-72k multi-document instruction set. Weights went up on 2024-11-01 under Apache-2.0. It is the smallest generator in the MDCure suite. Collected 2026-10-15.", "what_it_is_for": "The card says it answers a question or instruction conditioned on several source documents. The lab lists it as the best Flan-T5-Base checkpoint trained on MDCure-72k, for multi-document and long-context instruction following. The Hub task tag is summarization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/mdcure/mdcure-flant5-large": { "id": "yale/mdcure/mdcure-flant5-large", "name": "MDCure-FlanT5-Large", "maker": "yale", "family": "yale/mdcure", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5ForConditionalGeneration. Config fetched 2026-10-15: 24 encoder layers, 24 decoder layers, d_model 1024, 16 heads, d_ff 2816, vocab 32128, gated-gelu, float16, tie_word_embeddings false. Checkpoint path names google_flan_t5_large. Config n_positions is 512, which is the stock T5 field and was not treated as the usable context.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MDCure-FlanT5-Large is Yale NLP's fine-tune of Flan-T5-Large on MDCure-72k, released 2024-11-01 under Apache-2.0. The lab lists it as the best Large-size Flan-T5 model in the MDCure suite. Collected 2026-10-15.", "what_it_is_for": "Same job as the other MDCure generators: follow an instruction over several documents. The Hub task tag is summarization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/mdcure/mdcure-llama3-1-70b-instruct": { "id": "yale/mdcure/mdcure-llama3-1-70b-instruct", "name": "MDCure-Llama-3.1-70B-Instruct", "maker": "yale", "family": "yale/mdcure", "status": "active", "confidence": "high", "weights": "open-weights", "license": "llama3.1", "release_date": "2024-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama 3.1 causal language model. Hub tags and the MDCure suite table: fine-tune of meta-llama/Llama-3.1-70B-Instruct on yale-nlp/MDCure-72k. License tag llama3.1. The 70B card body was not separately fetched; identity comes from the suite table and the Hub API record.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MDCure-Llama-3.1-70B-Instruct is the largest generator in Yale NLP's MDCure release: a fine-tune of Meta-Llama-3.1-70B-Instruct on MDCure-72k. The Hub record was created 2024-11-01 and is tagged with the Llama 3.1 license. Collected 2026-10-15.", "what_it_is_for": "The suite table lists it as the best Llama-3.1-70B-Instruct model fine-tuned with MDCure-72k, for multi-document instruction following. The paper says the suite covers models up to 70B.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/mdcure/mdcure-llama3-1-8b-instruct": { "id": "yale/mdcure/mdcure-llama3-1-8b-instruct", "name": "MDCure-Llama-3.1-8B-Instruct", "maker": "yale", "family": "yale/mdcure", "status": "active", "confidence": "high", "weights": "open-weights", "license": "llama3.1", "release_date": "2024-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama 3.1 causal language model. Card: initialized from meta-llama/Meta-Llama-3.1-8B-Instruct and fine-tuned on yale-nlp/MDCure-72k. Chat template is the Llama instruct template. Card recommends transformers>=4.45.0. The quickstart example sets max_new_tokens=512; that is a sample, not a published limit.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MDCure-Llama-3.1-8B-Instruct is Yale NLP's fine-tune of Meta-Llama-3.1-8B-Instruct on MDCure-72k. Weights were published 2024-11-01 under the Llama 3.1 license. Collected 2026-10-15.", "what_it_is_for": "The card presents it as an assistant for questions over several documents. The example system line says it has strong multi-document processing skills. It is the 8B Llama member of the official MDCure release, not a private ablation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/mdcure/mdcure-qwen2-1-5b-instruct": { "id": "yale/mdcure/mdcure-qwen2-1-5b-instruct", "name": "MDCure-Qwen2-1.5B-Instruct", "maker": "yale", "family": "yale/mdcure", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2 causal language model. Hub base_model tag and the MDCure suite table say it is Qwen/Qwen2-1.5B-Instruct fine-tuned on yale-nlp/MDCure-72k. This dossier did not re-read config.json, so layer counts are omitted.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MDCure-Qwen2-1.5B-Instruct is Yale NLP's fine-tune of Qwen/Qwen2-1.5B-Instruct on MDCure-72k. Weights were published 2024-11-01. The Hub license tag is Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The suite table calls it the best 1.5B Qwen2 instruction model trained on MDCure-72k, for multi-document instruction following. It is a text generator, not a hosted API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/mdcure/mdcure-qwen2-7b-instruct": { "id": "yale/mdcure/mdcure-qwen2-7b-instruct", "name": "MDCure-Qwen2-7B-Instruct", "maker": "yale", "family": "yale/mdcure", "status": "active", "confidence": "high", "weights": "open-source", "license": "apache-2.0", "release_date": "2024-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2 causal language model. Card and Hub tags: initialized from Qwen/Qwen2-7B-Instruct and fine-tuned on yale-nlp/MDCure-72k. Pipeline tag text-generation. License tag apache-2.0.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MDCure-Qwen2-7B-Instruct is Yale NLP's fine-tune of Qwen/Qwen2-7B-Instruct on MDCure-72k, released 2024-11-01. The Hub license tag is Apache-2.0. The lab points to this repo as the Qwen2 usage example for the suite. Collected 2026-10-15.", "what_it_is_for": "Multi-document instruction following. The suite table calls it the best Qwen2-7B-Instruct run on MDCure-72k.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/mdcure/mdcure-rm": { "id": "yale/mdcure/mdcure-rm", "name": "MDCureRM", "maker": "yale", "family": "yale/mdcure", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "2024-11-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Multi-objective reward model. Initialized from sfairXC/FsfairX-LLaMA3-RM-v0.1 (a Llama-3 reward model). A linear head predicts six criterion scores from the last-token hidden state. Card recommends transformers>=4.45.0. Scores are trained in 0-1 and the quickstart rescales them to 1-5.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MDCureRM is a multi-objective reward model from the Yale NLP Lab, with weights published on 2024-11-01 (paper 2024-10-30, later ACL 2025). It scores multi-document instruction data so the lab can filter training examples without calling GPT-3.5-Turbo. It is initialized from sfairXC/FsfairX-LLaMA3-RM-v0.1. Weights are public. Collected 2026-10-15.", "what_it_is_for": "The card says it judges candidate multi-document instructions on six criteria covering overall quality and multi-document handling. Those scores filter the MDCure-12k, 36k, and 72k instruction sets. It is a ranker and data filter, not a chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/reastap/reastap-large": { "id": "yale/reastap/reastap-large", "name": "ReasTAP-Large", "maker": "yale", "family": "yale/reastap", "status": "active", "confidence": "high", "weights": "open-source", "license": "MIT", "release_date": "2023-06-03", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BART-Large encoder-decoder (BartForConditionalGeneration), continued pretraining from facebook/bart-large on synthetic table QA and SQL execution data. Paper: around 400 million parameters, 12 encoder layers and 12 decoder layers. A fetched config snippet shows d_model 1024, 16 decoder attention heads, and decoder FFN 4096. Full config was not re-downloaded for this dossier.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ReasTAP-Large is a table-reasoning model from the Yale LILY Lab (Yilun Zhao, Linyong Nan, Zhenting Qi, Rui Zhang, Dragomir Radev), with coauthors at Zhejiang University and Penn State. The EMNLP 2022 paper is dated 2022-10-22. The Transformers weights on Hugging Face were uploaded 2023-06-03 under MIT. Collected 2026-10-15.", "what_it_is_for": "The card says the model is initialized from facebook/bart-large and continually pretrained on synthetic table QA so it learns table structure and reasoning. The paper evaluates table QA (WikiSQL-weak, WikiTableQuestions), table fact checking (TabFact), and faithful table-to-text (LogicNLG).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yale/rtriever/rtriever-4b": { "id": "yale/rtriever/rtriever-4b", "name": "RTriever-4B", "maker": "yale", "family": "yale/rtriever", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-30", "context_length": 32768, "max_output_tokens": null, "parameters": "4000000000", "architecture": "Dense embedding model. LoRA fine-tune of Qwen/Qwen3-Embedding-4B (itself from Qwen3-4B-Base). 36 layers, hidden size 2560, 32 attention heads, embedding dimension 2560. Last-token pooling plus L2 normalization. Qwen3 tokenizer, vocab size 151,665. Sentence-transformers interface.", "modalities": { "input": [ "text" ], "output": [ "embeddings" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RTriever-4B is a dense retriever from the Yale NLP Lab (Arman Cohan), released as weights on 2026-04-30. It is a LoRA fine-tune of Qwen/Qwen3-Embedding-4B for reasoning-intensive retrieval, trained on the lab's RTriever-Synth corpus and evaluated with the Bright-Pro benchmark (ACL 2026). Weights are public. This dossier was collected on 2026-10-15.", "what_it_is_for": "The model card says it is for reasoning-intensive retrieval: queries that need multi-step inference and complementary evidence, not only keyword or paraphrase match. It is also described as a drop-in dense retriever for RAG, scholarly search, and agentic search. Queries must use a query prompt. Documents are encoded with no prompt.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/alice/alice-ai-art": { "id": "yandex/alice/alice-ai-art", "name": "Alice AI ART", "maker": "yandex", "family": "yandex/alice", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Alice AI ART 2.0 is described as a multimodal diffusion transformer with a single stream of visual and text tokens, used both to generate and to edit images. Version 1.0 is named only as the previous model in the comparison table.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alice AI ART is Yandex's image model in the Alice AI family. Version 2.0, on the family page, generates and edits images, including Russian and English text. The AI Studio index lists Alice AI ART on the Images API with a 500-character prompt. Which generation that API row serves was not stated. Collected on 2026-10-15.", "what_it_is_for": "Create or edit pictures from text, including logos and captions, in Alice and, as an API model, in AI Studio.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/alice/alice-ai-llm": { "id": "yandex/alice/alice-ai-llm", "name": "Alice AI LLM", "maker": "yandex", "family": "yandex/alice", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-11-25", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "Mixture-of-Experts. Yandex says only the relevant experts run per token, and that this raised parameter count about sevenfold without a matching compute increase. The 2026-09-21 foundation-model press note says Alice AI LLM has three times the parameters and seven times the active parameters of AliceAI-Foundation-80B-A3B-Base (80B total, 3B active). That ratio implies about 240B total and 21B active. Those absolute counts are not printed by Yandex, so parameters stays null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.51, "completion_per_m_usd": 2.03 }, "summary_intro": "Alice AI LLM is Yandex's closed flagship text model in the Alice AI family. AI Studio opened it to all users on 2025-11-25. On 2026-10-15 the Russian model index listed a 131,072-token context. Collected on 2026-10-15.", "what_it_is_for": "Chat assistants, RAG, content generation, and long-context extraction. Yandex says it matches YandexGPT Pro on hard tasks and is a better dialog partner. A separate Alice AI LLM Search path handles questions that need live sources.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/alice/alice-ai-llm-flash": { "id": "yandex/alice/alice-ai-llm-flash", "name": "Alice AI LLM Flash", "maker": "yandex", "family": "yandex/alice", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 65536, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.2 }, "summary_intro": "Alice AI LLM Flash is Yandex's smaller, faster Alice text model for high-volume business calls. It was on the AI Studio model index on 2026-10-15 with a 65,536-token context. A public launch day was not verified. Collected on 2026-10-15.", "what_it_is_for": "Moderation, classification, support bots, summarization, file and knowledge-base search, and other latency-sensitive agent steps.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/alice/alice-ai-llm-search": { "id": "yandex/alice/alice-ai-llm-search", "name": "Alice AI LLM Search", "maker": "yandex", "family": "yandex/alice", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Described as a specialized search model that can run up to five sequential lookups before answering.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alice AI LLM Search is the search-specialized model named on Yandex's Alice AI page. It answers questions that need sources by issuing follow-up searches, up to five. It is part of the Alice product, not a separate row on the AI Studio model index captured here. Collected on 2026-10-15.", "what_it_is_for": "Grounded answers with images, video, and organization cards. Yandex reports a 58 percent side-by-side win rate against DeepSeek with web search.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/alice/alice-ai-omni": { "id": "yandex/alice/alice-ai-omni", "name": "Alice AI Omni", "maker": "yandex", "family": "yandex/alice", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Yandex's first omni model. Text and images are trained and scored in one pipeline, unlike Alice AI VLM, which Yandex describes as a pairing of a text model and a vision model.", "modalities": { "input": [ "text", "image" ], "output": [ "text", "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alice AI Omni is Yandex's omni model for text and images in both directions. The Alice AI page says it runs in the Alice AI app and on alice.yandex.ru. It was not a separate row on the AI Studio model index captured on 2026-10-15. Collected on 2026-10-15.", "what_it_is_for": "Explain a photographed problem, answer questions about a picture, or write from an image, in one model rather than a handoff between models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/alice/alice-ai-search": { "id": "yandex/alice/alice-ai-search", "name": "Alice AI Search", "maker": "yandex", "family": "yandex/alice", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of the encoder-decoder MoE base published as Alice AI Search Pretrain. Production inference code was not released.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alice AI Search is the fine-tuned model that Yandex says has written short AI answers under the Yandex Search box since July 2026. The day in July was not stated. Weights are closed. Collected on 2026-10-15.", "what_it_is_for": "Short grounded answers in Yandex Search. Yandex says the tune used user signals from real search sessions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/alice/alice-ai-search-pretrain": { "id": "yandex/alice/alice-ai-search-pretrain", "name": "Alice AI Search Pretrain", "maker": "yandex", "family": "yandex/alice", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-09-11", "context_length": 131072, "max_output_tokens": null, "parameters": "35B total, about 0.6B active", "architecture": "Encoder-decoder with sparse Mixture-of-Experts. About 35 billion parameters, of which about 600 million (under 2 percent) are active per token. Trained from scratch on 15 trillion tokens with a UL2 objective. Context was extended from 8,192 to 131,072 with YaRN. Hugging Face name AliceAI-T5-35B-A0.6B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alice AI Search Pretrain is the open base model behind Yandex Search AI answers. Yandex published it under Apache 2.0 on 2026-09-11. It is a pretrained model, not the production fine-tune. Collected on 2026-10-15.", "what_it_is_for": "Research and as a base for other projects. External users can run it with Hugging Face Transformers. Yandex kept its optimized production inference private.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/alice/alice-ai-vlm": { "id": "yandex/alice/alice-ai-vlm", "name": "Alice AI VLM", "maker": "yandex", "family": "yandex/alice", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language model. Yandex later described it as effectively two models, a text model and a vision model, trained separately.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Alice AI VLM is Yandex's vision-language model in the Alice AI family. It reads photos, tables, and handwriting and answers in text. It runs in Alice apps. An AI Studio URI was not on the model index captured here. Collected on 2026-10-15.", "what_it_is_for": "Explain a photo, read handwritten notes, describe a product, or walk through a textbook problem. Geometry construction problems are called out as a weak case.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/alice/aliceai-foundation-80b": { "id": "yandex/alice/aliceai-foundation-80b", "name": "AliceAI-Foundation-80B-A3B-Base", "maker": "yandex", "family": "yandex/alice", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-09-21", "context_length": null, "max_output_tokens": null, "parameters": "80B total, 3B active", "architecture": "Mixture-of-Experts base model trained from scratch. Experimental checkpoint for a future unified reasoning model. Not instruction-aligned in the press note.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AliceAI-Foundation-80B-A3B-Base is an experimental open base model Yandex released on 2026-09-21 under Apache 2.0. It has 80 billion parameters and 3 billion active. It is not the closed Alice AI LLM. Collected on 2026-10-15.", "what_it_is_for": "Research and as a foundation for other models. Yandex is using it to test architecture for a later agentic reasoning model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/embeddings/text-embeddings": { "id": "yandex/embeddings/text-embeddings", "name": "Yandex Text Embeddings 1", "maker": "yandex", "family": "yandex/embeddings", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Universal version-1 embedding model intended for fine-tuning. Default dimension 256. Tuned dimension depends on the job.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.01, "completion_per_m_usd": null }, "summary_intro": "Yandex Text Embeddings 1 is the tunable universal embedding model named on the August 2026 Russian embeddings page. Default vector size is 256. Collected on 2026-10-15.", "what_it_is_for": "Fine-tune embeddings when the stock document and query models are not enough.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/embeddings/text-embeddings-doc": { "id": "yandex/embeddings/text-embeddings-doc", "name": "Yandex Text Embeddings Doc", "maker": "yandex", "family": "yandex/embeddings", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed text embedding model for long source documents. Output dimension 256.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.01, "completion_per_m_usd": null }, "summary_intro": "The document embedding model in Yandex AI Studio. The October 2025 English embeddings page lists emb:///text-search-doc/latest with a 256-dimensional vector. The August 2026 Russian page still names a version-1 document model at 256 dimensions. Collected on 2026-10-15.", "what_it_is_for": "Embed long texts such as documentation so they can be compared with query embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/embeddings/text-embeddings-query": { "id": "yandex/embeddings/text-embeddings-query", "name": "Yandex Text Embeddings Query", "maker": "yandex", "family": "yandex/embeddings", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed embedding model for short queries. Output dimension 256 on the English page.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.01, "completion_per_m_usd": null }, "summary_intro": "The short-text embedding model paired with Yandex's document embeddings. URI emb:///text-search-query/latest, 256 dimensions, on the English docs updated 2025-10-13. Collected on 2026-10-15.", "what_it_is_for": "Embed search queries and short requests into the same space as document embeddings.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/embeddings/text-embeddings-v2-doc": { "id": "yandex/embeddings/text-embeddings-v2-doc", "name": "Yandex Text Embeddings v2 Doc", "maker": "yandex", "family": "yandex/embeddings", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Version-2 document embedding model. Supported dimensions 128, 256 (default), 512, and 768.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.01, "completion_per_m_usd": null }, "summary_intro": "Version 2 of Yandex's document embedding model, named on the Russian embeddings page dated in the index as updated around August 2026. Dimensions include 128, 256, 512, and 768. Collected on 2026-10-15.", "what_it_is_for": "Embed long source texts at a chosen dimension.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/embeddings/text-embeddings-v2-query": { "id": "yandex/embeddings/text-embeddings-v2-query", "name": "Yandex Text Embeddings v2 Query", "maker": "yandex", "family": "yandex/embeddings", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Version-2 query embedding model. The captured page named it but did not print the dimension list in the extract.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.01, "completion_per_m_usd": null }, "summary_intro": "Version 2 of Yandex's short-query embedding model, named next to the v2 document model on the Russian embeddings page. Collected on 2026-10-15.", "what_it_is_for": "Embed short queries for search against v2 document vectors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/sona/sona": { "id": "yandex/sona/sona", "name": "Sona", "maker": "yandex", "family": "yandex/sona", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-10-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Single generative recommender for Yandex Music. A transformer encoder reads the listener's event history once. A decoder generates candidates and a ranking module scores them from the same user state. No hand-engineered features. A larger teacher ranker is used in training and is not served. Technical report arXiv:2608.11015.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Sona is Yandex's generative recommendation model for Yandex Music. Yandex introduced it publicly on 2026-10-01 after an online test that replaced the speaker recommendation cascade. Weights are closed. Collected on 2026-10-15.", "what_it_is_for": "Pick and rank music from listening history, skips, and likes, without a separate candidate-generation stack.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/speech/speech-realtime": { "id": "yandex/speech/speech-realtime", "name": "Speech Realtime", "maker": "yandex", "family": "yandex/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio", "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Speech Realtime is the Yandex voice model named in the AI Studio model gallery. The English model index lists dated builds Speech Realtime 250923 at 32,768 tokens and Speech Realtime 260528 at 65,536 tokens. Collected on 2026-10-15.", "what_it_is_for": "Low-latency voice agents. The gallery calls it the specialized model for voice scenarios.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/speech/speech-realtime-max-tts-live": { "id": "yandex/speech/speech-realtime-max-tts-live", "name": "Speech Realtime Max TTS Live", "maker": "yandex", "family": "yandex/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1048576, "max_output_tokens": 393216, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Speech Realtime Max TTS Live is a Yandex realtime speech model on the 2026 Russian model index. Context is 1,048,576 tokens and output is up to 393,216 tokens. It uses LiveTTS voices. Collected on 2026-10-15.", "what_it_is_for": "Long realtime speech sessions with LiveTTS voices.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/speech/speech-tts-live": { "id": "yandex/speech/speech-tts-live", "name": "Speech TTS Live", "maker": "yandex", "family": "yandex/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Speech TTS Live is Yandex's text-to-speech model on the Realtime API. The model index says it synthesizes speech from text without an LLM and without audio input, using LiveTTS voices. Collected on 2026-10-15.", "what_it_is_for": "Stream speech from text. Not a language model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/speech/speechkit-general": { "id": "yandex/speech/speechkit-general", "name": "SpeechKit General", "maker": "yandex", "family": "yandex/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SpeechKit General is Yandex's main speech-recognition model. Docs updated 2025-09-18 say it covers general speech, short and long utterances, names, addresses, dates, and numbers. Tags general, general:rc, and general:deprecated can be live at once. Collected on 2026-10-15.", "what_it_is_for": "Streaming and file speech recognition across the languages SpeechKit lists for this model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yalm/yalm-100b": { "id": "yandex/yalm/yalm-100b", "name": "YaLM 100B", "maker": "yandex", "family": "yandex/yalm", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "", "context_length": 1024, "max_output_tokens": null, "parameters": "100B", "architecture": "GPT-like network. The published generation script sets 80 layers, hidden size 10240, intermediate size 27308, GeGLU, 128 attention heads, rotary embeddings, and max-position-embeddings 1024. Trained for 65 days on 800 A100 GPUs on 1.7 TB of Russian and English text. Code uses Megatron-LM, which has its own license.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YaLM 100B is Yandex's open 100-billion-parameter research language model, published on GitHub under Apache 2.0. It predates the commercial YandexGPT line. The README does not print a release day. Collected on 2026-10-15.", "what_it_is_for": "Research generation. Not an AI Studio endpoint and not the weights behind YandexGPT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexart/yandexart": { "id": "yandex/yandexart/yandexart", "name": "YandexART", "maker": "yandex", "family": "yandex/yandexart", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-10-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Cascade diffusion. A low-resolution image or animation is generated from the prompt, then refined. The 2023 training set was described as 330 million captioned images, 1.5 times the previous Shedevrum diffusion set.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YandexART is Yandex's image and animation model, introduced on 2023-10-18. The English AI Studio model index captured for this catalog says the API model remains available until 2026-09-07. Named later generations have their own folders. Collected on 2026-10-15.", "what_it_is_for": "Text-to-image and short animation in Shedevrum, Alice, Yandex ads, and the Cloud image API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexart/yandexart-1-3": { "id": "yandex/yandexart/yandexart-1-3", "name": "YandexART 1.3", "maker": "yandex", "family": "yandex/yandexart", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YandexART 1.3 is the previous image model named in Yandex's YandexART 2.0 post on 2024-10-10. A standalone launch date was not verified. Collected on 2026-10-15.", "what_it_is_for": "Text-to-image generation before the hybrid 2.0 architecture.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexart/yandexart-2-0": { "id": "yandex/yandexart/yandexart-2-0", "name": "YandexART 2.0", "maker": "yandex", "family": "yandex/yandexart", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid convolutional and transformer network. Trained on several hundred million image-text pairs, with captions refined by a VLM.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YandexART 2.0 is the second-generation image model announced on 2024-10-10. It writes Latin text onto images and mixes styles. The current art site features 2.5, so this generation is marked retired as a consumer flagship. Collected on 2026-10-15.", "what_it_is_for": "Logos, banners, and ad images in Cloud, Alice Pro, and Yandex services.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexart/yandexart-2-5": { "id": "yandex/yandexart/yandexart-2-5", "name": "YandexART 2.5", "maker": "yandex", "family": "yandex/yandexart", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hybrid convolutional and transformer model. The art page says it picks a style for the task and can render short text.", "modalities": { "input": [ "text" ], "output": [ "image", "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YandexART 2.5 is the image and video model featured on ya.ru/ai/art. A release day was not on that page. It is available in Alice chat and Shedevrum. Collected on 2026-10-15.", "what_it_is_for": "Illustrations, logos, product images, and short clips. Yandex says it chooses a cleaner style for logos and a more illustrated style for children's art.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexart/yandexart-2-5-pro": { "id": "yandex/yandexart/yandexart-2-5-pro", "name": "YandexART 2.5 Pro", "maker": "yandex", "family": "yandex/yandexart", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YandexART 2.5 Pro is the stronger YandexART variant Yandex offers in the paid Shedevrum subscription. The art page says it produces more photorealistic images with fewer defects, and that the subscription can upscale to 4K. Collected on 2026-10-15.", "what_it_is_for": "Higher-detail image generation inside Shedevrum for paying subscribers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexgpt/yandexgpt": { "id": "yandex/yandexgpt/yandexgpt", "name": "YandexGPT", "maker": "yandex", "family": "yandex/yandexgpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YandexGPT is Yandex's first named large language model, launched in May 2023 and later replaced in products by YandexGPT 2. This dossier was collected on 2026-10-15. The exact calendar day of the May 2023 launch was not on the English press page used here.", "what_it_is_for": "Yandex described it as a model that generates and processes text, suggests ideas, and keeps chat context. It summarized articles in Yandex Browser, consolidated product reviews in Search and Market, and drafted product descriptions for Market sellers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexgpt/yandexgpt-2": { "id": "yandex/yandexgpt/yandexgpt-2", "name": "YandexGPT 2", "maker": "yandex", "family": "yandex/yandexgpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-09-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YandexGPT 2 is the second Yandex language model, announced on 2023-09-07 and later retired from the default API. Collected on 2026-10-15.", "what_it_is_for": "Yandex said it rewrites text for different audiences and genres, answers questions, and structures information. Prompts it analyzed were limited to about a thousand characters. It shipped in Alice on Stations, TVs, Browser, and Search in Russia, with API access by application.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexgpt/yandexgpt-2-lite": { "id": "yandex/yandexgpt/yandexgpt-2-lite", "name": "YandexGPT 2 Lite", "maker": "yandex", "family": "yandex/yandexgpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.2 }, "summary_intro": "YandexGPT 2 Lite is the lightweight API model based on YandexGPT 2. Yandex Cloud release notes say it left the default branch on 2024-06-24 and remained on the deprecated branch until 2024-07-01. Collected on 2026-10-15.", "what_it_is_for": "Fast text tasks in YandexGPT API before YandexGPT 3 Lite became the default Lite model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexgpt/yandexgpt-3-lite": { "id": "yandex/yandexgpt/yandexgpt-3-lite", "name": "YandexGPT 3 Lite", "maker": "yandex", "family": "yandex/yandexgpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.2 }, "summary_intro": "YandexGPT 3 Lite is the third-generation lightweight API model, announced on 2024-05-28 and made the default Lite build on 2024-06-24. Generation 3 support later ended. Collected on 2026-10-15.", "what_it_is_for": "Chatbots, spelling checks, and data analysis where response speed matters more than the Pro model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexgpt/yandexgpt-3-pro": { "id": "yandex/yandexgpt/yandexgpt-3-pro", "name": "YandexGPT 3 Pro", "maker": "yandex", "family": "yandex/yandexgpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-03-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.2, "completion_per_m_usd": 1.2 }, "summary_intro": "YandexGPT 3 Pro is the first model of Yandex's third generation, opened in the YandexGPT API on 2024-03-28. Support for generation 3 later ended. Collected on 2026-10-15.", "what_it_is_for": "Yandex positioned it for complex instructions with several conditions, longer conversational context, factual answers, and business tasks. It was the model behind Alice Pro in April 2024.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexgpt/yandexgpt-4-lite": { "id": "yandex/yandexgpt/yandexgpt-4-lite", "name": "YandexGPT 4 Lite", "maker": "yandex", "family": "yandex/yandexgpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YandexGPT 4 Lite is the fourth-generation fast text model, announced with YandexGPT 4 Pro on 2024-10-24. Support for version 4 Lite ended on 2025-08-28. Collected on 2026-10-15.", "what_it_is_for": "Faster, simpler text tasks. Yandex said its quality matched the previous generation's strongest model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexgpt/yandexgpt-4-pro": { "id": "yandex/yandexgpt/yandexgpt-4-pro", "name": "YandexGPT 4 Pro", "maker": "yandex", "family": "yandex/yandexgpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-10-24", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YandexGPT 4 Pro is the fourth-generation flagship, announced on 2024-10-24 with a 32,768-token context. AI Studio release notes discontinued version 4 of YandexGPT Pro on 2025-08-28. Collected on 2026-10-15.", "what_it_is_for": "Harder business text tasks such as answering from documentation. Yandex said reasoning and function calling were not all available on the platform at the October 2024 launch and would follow. The model entered the Latest API branch on 2024-12-02.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexgpt/yandexgpt-5-1-pro": { "id": "yandex/yandexgpt/yandexgpt-5-1-pro", "name": "YandexGPT 5.1 Pro", "maker": "yandex", "family": "yandex/yandexgpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-08-28", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.41, "completion_per_m_usd": 0.41 }, "summary_intro": "YandexGPT 5.1 Pro is the updated closed Pro model Yandex opened to AI Studio customers on 2025-08-28, first on the RC branch. It was still listed on the AI Studio model index on 2026-10-15 with a 32,768-token context. It does not support reasoning mode. Collected on 2026-10-15.", "what_it_is_for": "Business text: rewriting to a format and style, extraction, summarization, RAG over a knowledge base, and tool calls such as CRM lookups. System prompts are higher priority than the user message.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexgpt/yandexgpt-5-lite": { "id": "yandex/yandexgpt/yandexgpt-5-lite", "name": "YandexGPT 5 Lite", "maker": "yandex", "family": "yandex/yandexgpt", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "yandexgpt-5-lite-8b", "release_date": "2025-03-31", "context_length": 32768, "max_output_tokens": null, "parameters": "8B", "architecture": "Llama-compatible 8B instruct model. Alignment matches the 5 Pro pipeline: SFT then RLHF, starting from the Yandex pretrain checkpoint only. Chat template emits one assistant turn after \u0410\u0441\u0441\u0438\u0441\u0442\u0435\u043d\u0442:[SEP] and stops on . Newlines are tokenized as [NL].", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 0.2 }, "summary_intro": "YandexGPT 5 Lite is the aligned 8B model. Instruct weights were published on Hugging Face on 2025-03-31. The same generation is the AI Studio model YandexGPT Lite 5, which the Russian model index marks deprecated and scheduled to switch off on 2026-11-15. Open weights were still published on 2026-10-15. Collected on 2026-10-15.", "what_it_is_for": "Fast classification, formatting, and summarization in AI Studio, and local inference from the Hugging Face weights. Yandex recommends other hosted models after the API shutdown: DeepSeek-V4.1-Flash for capability and Qwen3.6 35B for speed. Those are not Yandex models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexgpt/yandexgpt-5-lite-pretrain": { "id": "yandex/yandexgpt/yandexgpt-5-lite-pretrain", "name": "YandexGPT 5 Lite Pretrain", "maker": "yandex", "family": "yandex/yandexgpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "yandexgpt-5-lite-8b", "release_date": "2025-02-25", "context_length": 32768, "max_output_tokens": null, "parameters": "8B", "architecture": "Llama-compatible decoder. Trained from scratch in two stages: about 15 trillion tokens at up to 8,192 context, then context extended to 32,768. Tokenizer is more compact on Russian than Qwen2.5. Hugging Face architecture class reported by a model index: LlamaForCausalLM.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YandexGPT 5 Lite Pretrain is the 8-billion-parameter base checkpoint Yandex published on Hugging Face on 2025-02-25. It is not the aligned chat model. Weights are open under the custom yandexgpt-5-lite-8b license, not an OSI license. Collected on 2026-10-15.", "what_it_is_for": "Further pretraining or instruction tuning. Yandex says it is not aligned for direct instruction following.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexgpt/yandexgpt-5-pro": { "id": "yandex/yandexgpt/yandexgpt-5-pro", "name": "YandexGPT 5 Pro", "maker": "yandex", "family": "yandex/yandexgpt", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-02-25", "context_length": 32768, "max_output_tokens": null, "parameters": "", "architecture": "Closed model. Yandex's February 2025 technical write-up said Russian text uses fewer tokens than Qwen2.5 on the same characters, and that 5 Pro training used SFT plus DPO, PPO, and Yandex's LogDPO. A public parameter count was not stated.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.22, "completion_per_m_usd": 1.22 }, "summary_intro": "YandexGPT 5 Pro is Yandex's fifth-generation closed Pro model, released on 2025-02-25 in Alice chat and Yandex Cloud. It was still listed as an active AI Studio model on 2026-10-15, with a 32,768-token context. YandexGPT 5.1 Pro does not support reasoning mode; this dossier treats reasoning as a 5 Pro capability because that release note contrasts the new model with the previous one.", "what_it_is_for": "Business text work: classification, instruction following, dialogue, RAG, and document analysis. The February 2025 Cloud post says the API became OpenAI-compatible.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yandex/yandexgpt/yandexgpt-pro-32k": { "id": "yandex/yandexgpt/yandexgpt-pro-32k", "name": "YandexGPT Pro 32k", "maker": "yandex", "family": "yandex/yandexgpt", "status": "retired", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YandexGPT Pro 32k is a named YandexGPT Pro API variant. The 2025-08-28 AI Studio release note discontinued it together with version 4 of YandexGPT Pro and Lite. Collected on 2026-10-15.", "what_it_is_for": "Longer-context Pro text generation. The note lists it separately from YandexGPT Pro version 4, which already advertised a 32,000-token window.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yonsei/ai-ecg-af/ai-ecg-af": { "id": "yonsei/ai-ecg-af/ai-ecg-af", "name": "AI-ECG-AF", "maker": "yonsei", "family": "yonsei/ai-ecg-af", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "1D EfficientNet-B0. The paper says it was trained on 4.05 million non-AF standard 12-lead ECGs from 1.13 million patients, using the middle 5 seconds of each 10-second tracing. The Hub card says inference uses 8 leads in order I, II, V1, V2, V3, V4, V5, V6, at 250 Hz, for exactly 5 seconds, with each lead z-normalized. Tested by the authors with PyTorch 2.0.1+cu117.", "modalities": { "input": [ "ecg" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yonsei University College of Medicine released AI-ECG-AF, a 1D EfficientNet-B0 that scores non-AF electrocardiograms for atrial-fibrillation-related risk. Weights went on Hugging Face on 2025-01-20 under Apache-2.0. The Journal of Medical Internet Research paper is dated 2025-11-10 and cites that repo. It is active research software, not a clinical API. Collected 2026-10-15.", "what_it_is_for": "The paper uses the model as a noninvasive marker of atrial vulnerability. On its own it separates AF-related patterns in non-AF ECGs. In the study it is added to a postoperative atrial-fibrillation score for patients who had cardiac surgery at one Korean tertiary hospital.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yonsei/deepcrispr/deepbe": { "id": "yonsei/deepcrispr/deepbe", "name": "DeepBE", "maker": "yonsei", "family": "yonsei/deepcrispr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-03-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Deep-learning model that predicts editing efficiency and outcomes for 63 base editors. The paper builds those editors by putting nine Cas9 nickase variants into seven base-editor scaffolds. DeepCas9variants is the companion model that picks which Cas9 variant fits a site. A public layer count was not copied.", "modalities": { "input": [ "dna" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yonsei University College of Medicine published DeepBE to predict efficiency and outcomes for 63 base editors. The Yonsei repository record dates the article 2024-03-01. DeepGE reaches the companion Cas9 picker through a DeepBE comparison mode. Pretrained files are on the GitHub release Models_v1, published 2023-07-10. Those binaries were not unpacked. Collected 2026-10-15.", "what_it_is_for": "The paper compares seven base-editor scaffolds and nine Cas9 nickases, then predicts which of the 63 combinations will edit a site and what the outcome will be. The abstract says DeepBE-based designs had predicted median efficiencies 2.9- to 20-fold higher than rational SpCas9 base editors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yonsei/deepcrispr/deepcas9variants": { "id": "yonsei/deepcrispr/deepcas9variants", "name": "DeepCas9variants", "maker": "yonsei", "family": "yonsei/deepcrispr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-03-01", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Deep-learning model that scores on-target efficiency for nine SpCas9 PAM variants. The GitHub README names them, with feature codes, as SpCas9 (0), VRQR variant (1), SpCas9-NG (2), SpCas9-NRRH (3), SpCas9-NRTH (4), SpCas9-NRCH (5), SpG (6), SpRY (7), and Sc++ (8). Input is a 30 bp sequence (4 bp plus a 20 bp protospacer plus PAM plus 3 bp) and a PAM-variant feature. Layer sizes were not copied. This is not DeepSpCas9variants.", "modalities": { "input": [ "dna" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yonsei University College of Medicine released DeepCas9variants with DeepBE to pick which of nine PAM-shifted SpCas9 variants will work at a site. DeepGE reaches it through Comparison Mode inside DeepBE. The same GitHub release that holds DeepBE weights, Models_v1, was published on 2023-07-10. The Yonsei repository dates the paper 2024-03-01. Collected 2026-10-15.", "what_it_is_for": "The paper says the model predicts which of nine Cas9 variants function most efficiently at a target. The README and DeepGE name SpCas9, VRQR, SpCas9-NG, SpCas9-NRRH, SpCas9-NRTH, SpCas9-NRCH, SpG, SpRY, and Sc++. DeepBE then places those nickases into seven base-editor scaffolds.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yonsei/deepcrispr/deepcpf1": { "id": "yonsei/deepcrispr/deepcpf1", "name": "DeepCpf1", "maker": "yonsei", "family": "yonsei/deepcrispr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2018-01-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Convolutional network for AsCas12a (AsCpf1) guide activity. Seq-deepCpf1 is trained on indel frequencies at 15,000 target sequences. DeepCpf1 fine-tunes that model with chromatin-accessibility information. A Yonsei thesis describes a 34 bp target input. DeepGE asks for FASTA of at least 34 bp, or a gene or variant id. The TTTV PAM is the Cas12a constraint stated on DeepGE.", "modalities": { "input": [ "dna" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yonsei University College of Medicine, with Seoul National University, published DeepCpf1 on 2018-01-29 as a convolutional predictor of AsCas12a guide activity. DeepGE still serves DeepCpf1 and the sequence-only Seq-DeepCpf1. Weights were not found as a standalone download in this pass. Collected 2026-10-15.", "what_it_is_for": "The paper predicts which AsCpf1 guides will cut. Seq-deepCpf1 uses sequence only and was trained on indel frequencies at 15,000 targets. DeepCpf1 adds chromatin accessibility for cell lines where that data exists. DeepGE lists both on one Cas12a server and points users at TTTV PAM sites.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yonsei/deepcrispr/deeppe": { "id": "yonsei/deepcrispr/deeppe", "name": "DeepPE", "maker": "yonsei", "family": "yonsei/deepcrispr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-09-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three computational models predict PE2 pegRNA efficiency. A TensorFlow checkpoint is shipped as PreTrain-Final-3-3-3-10-10-10-0.001-201-1000 under DeepPE_weight. The graph was not read, so layer sizes were not taken from that filename.", "modalities": { "input": [ "dna" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yonsei University College of Medicine released DeepPE to predict PE2 pegRNA efficiency. The paper was online on 2020-09-21, in the 2021 journal issue. DeepGE still serves it, together with the companion models PE_type and PE_position. A TensorFlow checkpoint is in the supplementary GitHub repository. Collected 2026-10-15.", "what_it_is_for": "The paper measured PE2 on 54,836 pegRNA and target pairs and built three models. The authors' note names them DeepPE, PE_type, and PE_position. DeepPrime later describes itself as the successor. DeepGE still offers the PE2 server.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yonsei/deepcrispr/deepprime": { "id": "yonsei/deepcrispr/deepprime", "name": "DeepPrime", "maker": "yonsei", "family": "yonsei/deepcrispr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-04-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The lab README says DeepPrime combines a CNN and an RNN to score a target DNA sequence against its pegRNA. DeepPrime-FT and DeepPrime-Off are fine-tunes kept in this folder. The Cell methods table for DeepPrime-Off lists hidden size 128 and an average of five models. Those figures were not copied onto the base network. A parameter count was not published.", "modalities": { "input": [ "dna" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yonsei University's Laboratory of Genome Editing released DeepPrime to rank pegRNAs for prime editing. The Cell paper was online on 2023-04-28. DeepPrime-FT and DeepPrime-Off are fine-tunes in this folder, not separate products. DeepGE uses DeepPrime as the default single-target designer. Weight files are in the hkimlab GitHub tree. Collected 2026-10-15.", "what_it_is_for": "The paper predicts efficiencies for eight prime-editing systems in seven cell types, for edits up to 3 bp. The lab README calls it a successor to DeepPE. DeepPrime-Off scores mismatched targets. The live DeepGE page ranks candidate pegRNAs for one target.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yonsei/deepcrispr/deepspcas9": { "id": "yonsei/deepcrispr/deepspcas9", "name": "DeepSpCas9", "maker": "yonsei", "family": "yonsei/deepcrispr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2019-11-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "End-to-end convolutional predictor of SpCas9 indel frequency, described as a modification of the DeepCpf1 framework. Trained on HT_Cas9_Train from measurements at 12,832 target sequences. DeepGE uses a conventional scaffold and (G/g)N19 guides, and asks for FASTA of at least 30 bp. The DeepCRISPR.info code branch documents a 30 bp input (4 bp + 20 bp protospacer + PAM + 3 bp) and a directory DeepCas9_Final of pretrained weights.", "modalities": { "input": [ "dna" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yonsei University College of Medicine published DeepSpCas9 on 2019-11-06 in Science Advances. It predicts SpCas9 activity from the target sequence. DeepGE still serves it, and a public code branch ships pretrained files under the older name DeepCas9. Collected 2026-10-15.", "what_it_is_for": "The paper measures SpCas9 at 12,832 target sequences and trains a deep model so users can rank guides before an experiment. DeepGE uses it for conventional-scaffold SpCas9, not for the high-fidelity or relaxed-PAM editors.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yonsei/deepcrispr/deepspcas9-ng": { "id": "yonsei/deepcrispr/deepspcas9-ng", "name": "DeepSpCas9-NG", "maker": "yonsei", "family": "yonsei/deepcrispr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Convolutional network for SpCas9-NG activity. The supplement says DeepSpCas9-NG and DeepxCas9 take a 30 bp target. DeepGE uses a conventional scaffold and (G/g)N19 guides and asks for FASTA of at least 30 bp.", "modalities": { "input": [ "dna" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yonsei University College of Medicine released DeepSpCas9-NG to predict activity of the relaxed-PAM editor SpCas9-NG. DeepGE still serves it and cites the Nature Biomedical Engineering paper. A standalone weight file was not opened. Collected 2026-10-15.", "what_it_is_for": "SpCas9-NG cuts at a broader set of PAM sequences than standard SpCas9. This model ranks those guides. DeepGE specifies a conventional scaffold and (G/g)N19 guides. It is a design aid, not a clinical device.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yonsei/deepcrispr/deepspcas9variants": { "id": "yonsei/deepcrispr/deepspcas9variants", "name": "DeepSpCas9variants", "maker": "yonsei", "family": "yonsei/deepcrispr", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2020-06-08", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "The 2020 paper says 16 deep-learning models predict activity of SpCas9 and engineered variants. DeepGE's current server covers nine editors: SpCas9, SpCas9-NG, VRQR, Sniper-Cas9, xCas9, eSpCas9(1.1), SpCas9-HF1, HypaCas9, and evoCas9. It uses an optimized scaffold and (G/g)N19 or tRNA-N20 guides. The GitHub repo has directories 1_best_model through 9_best_model. The README body fetched for this pass was empty, so folder-to-variant mapping was not read.", "modalities": { "input": [ "dna" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yonsei University College of Medicine published DeepSpCas9variants on 2020-06-08. It predicts cleavage activity across SpCas9 and engineered variants. DeepGE still calls this server the default guide designer, and the paper's GitHub repo contains pretrained `best_model` directories. Collected 2026-10-15.", "what_it_is_for": "The paper ranks which Cas9 variant will cut a given site and how specific the high-fidelity variants are. DeepGE uses the server to design guides for nine editors with an optimized scaffold. It is a research design tool.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yonsei/deepcrispr/deepxcas9": { "id": "yonsei/deepcrispr/deepxcas9", "name": "DeepxCas9", "maker": "yonsei", "family": "yonsei/deepcrispr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Convolutional network for xCas9 activity. The supplement says development uses 30 bp inputs. DeepGE uses a conventional scaffold and (G/g)N19 guides and asks for FASTA of at least 30 bp.", "modalities": { "input": [ "dna" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yonsei University College of Medicine released DeepxCas9 to predict activity of the broad-PAM editor xCas9. It shares a Nature Biomedical Engineering paper with DeepSpCas9-NG. DeepGE still serves it. A standalone weight file was not opened. Collected 2026-10-15.", "what_it_is_for": "xCas9 is a broad-PAM Cas9 variant. DeepxCas9 ranks guides for that editor. DeepGE uses a conventional scaffold and (G/g)N19 guides. The supplement describes a convolutional network with 30 bp inputs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yonsei/deepcrispr/peselector": { "id": "yonsei/deepcrispr/peselector", "name": "PEselector", "maker": "yonsei", "family": "yonsei/deepcrispr", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2021-09-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Support-vector machine. Seven model classes were trained on 568 pegRNAs from library B. The figure caption names SVM, Boosted RT, random forest, Ridge, ElasticNet, XGBoost, and Lasso. The SVM had the highest performance. The Spearman values in that figure were not copied.", "modalities": { "input": [ "dna" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yonsei University College of Medicine released PEselector with the hyPE2 prime editor. It predicts how much hyPE2 should improve on PE2 for a pegRNA. The Nature Communications article is dated 2021-09-23, and DeepGE still hosts the tool. A weight file was not opened. Collected 2026-10-15.", "what_it_is_for": "The paper trains models on 568 pegRNAs from library B and selects a support-vector machine. The job is the fold increase of hyPE2 over PE2, not absolute PE2 efficiency. S.M. and S.Y. generated the model. The corresponding author is Hyongbum Henry Kim at Yonsei.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yonsei/preti/preti": { "id": "yonsei/preti/preti", "name": "PRETI", "maker": "yonsei", "family": "yonsei/preti", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-05-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ViT-Base retinal encoder named preti-vitb. The paper adds Learnable Metadata Embedding for age and gender and Retina-Aware Adaptive Masking inside the retinal region. Trained on paired fundus images. Ablations without LME, without RAAM, or without both are training scripts, not separate released checkpoints.", "modalities": { "input": [ "image", "metadata" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yonsei University's MICV lab, with Mediwhale, released PRETI on 2025-05-18 as a patient-aware retinal foundation model. The ViT-Base weights are public. It is active research code, not an API. Collected 2026-10-15.", "what_it_is_for": "The paper says PRETI learns retinal representations without clinical reports by using metadata and patient-level image pairs. Learnable Metadata Embedding refines age and gender. Retina-Aware Adaptive Masking masks inside the retinal region and changes the mask ratio during training. The claimed use is disease and biomarker prediction on in-house and public fundus data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yonsei/spike/spike-scenario-generator": { "id": "yonsei/spike/spike-scenario-generator", "name": "SPIKE Scenario Generator", "maker": "yonsei", "family": "yonsei/spike", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-02-19", "context_length": 131072, "max_output_tokens": null, "parameters": "", "architecture": "LlamaForCausalLM checkpoint whose config.json sets _name_or_path to meta-llama/Llama-3.2-3B-Instruct. 28 layers, hidden size 3072, intermediate size 8192, 24 attention heads, 8 key-value heads, head dim 128, vocabulary 128256, BF16. RoPE scaling type llama3, factor 32, original max positions 8192, max_position_embeddings 131072. The COLM 2025 paper says the generator is a LoRA fine-tune of Llama-3.2-3B-Instruct. Hugging Face lists the repo as 3B parameters.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yonsei University's Data and Language Intelligence Lab released this text generator on 2025-02-19 as the public scenario model for SPIKE, a COLM 2025 dense-retrieval method. It is active as an open checkpoint. Collected 2026-10-15.", "what_it_is_for": "The lab says the model writes hypothetical retrieval scenarios for a document so a retriever can score implicit relevance, not only surface similarity. The paper distills GPT-4o scenarios into Llama-3.2-3B-Instruct with LoRA. The README points users at the Hub checkpoint and at a vLLM server for batch generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yonsei/tesla/tesla": { "id": "yonsei/tesla/tesla", "name": "TESLA", "maker": "yonsei", "family": "yonsei/tesla", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-09-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage through-plane MRI super-resolution network. The MICCAI response says the progressive-reconstruction stage is consecutive Optimized nnU-Net. The structure-enhancement style encoder has 4 downsampling layers and a 256-dimensional style vector. The content encoder has 2 downsampling layers and 4 residual blocks. ContentNet, used only in training, is a MUNIT content encoder. Inference uses the progressive-reconstruction module, the content and style encoders, and the decoder.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Yonsei University's MI Lab released TESLA, a test-time reference-free through-plane super-resolution network for multi-contrast brain MRI. The MICCAI 2025 chapter went online on 2025-09-20, and IXI weights are on Hugging Face. It is active research code, not an API. Collected 2026-10-15.", "what_it_is_for": "The paper says clinical brain MRI is often thick-slice, and prior multi-contrast super-resolution methods still need a reference scan at test time. TESLA reconstructs the target volume, then trains a shared content path against a reference using a pretrained ContentNet. At test time only the target scan is required.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "york/albedomm/albedomm": { "id": "york/albedomm/albedomm", "name": "AlbedoMM", "maker": "york", "family": "york/albedomm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Academic research only. No SPDX licence file in the waps101/AlbedoMM repository. Commercial use: contact william.smith@york.ac.uk. Users of the model must also cite 3DRFE. The Scala build that mixes in Basel Face Model 2017 requires that model's own terms and a citation of the BFM open framework.", "release_date": "2020-04-06", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Separate PCA models of diffuse and specular facial albedo in linear sRGB, from 50 light-stage scans and 23 3DRFE scans (73 scans). MATLAB loads albedoModel2020_bfm_albedoPart.h5 under /diffuseAlbedo/model/mean, pcaBasis, and pcaVariance, with a specular counterpart. A Scala builder writes albedoModel2020_bfm.h5 and albedoModel2020_face12.h5 by combining the albedo part with Basel Face Model 2017, which the user downloads separately. The FLAME-topology release is a compressed NumPy file of means, principal components, and variances for diffuse and specular albedo. Component count and vertex count were not read from the files.", "modalities": { "input": [], "output": [ "albedo" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AlbedoMM is a morphable model of facial diffuse and specular albedo from William A. P. Smith at the University of York, with co-authors at ARM, Aberystwyth, and MIT. York hosts the weights at github.com/waps101/AlbedoMM. The arXiv preprint is 6 April 2020 and the paper is CVPR 2020. The repository was still the public academic release on 2026-10-15.", "what_it_is_for": "The paper replaces a generic face-texture basis with separate linear models of diffuse and specular albedo, so a renderer can use measured skin colour under a stated academic licence. The README presents the HDF5 file as a plug-in for Basel Face Model texture and also ships a FLAME-topology NumPy build.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "york/inverserendernet/inverserendernet": { "id": "york/inverserendernet/inverserendernet", "name": "InverseRenderNet", "maker": "york", "family": "york/inverserendernet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The InverseRenderNet repository LICENSE file is Apache License 2.0. A separate licence for the Google Drive checkpoint was not stated.", "release_date": "2018-11-29", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fully convolutional TensorFlow 1.12 network. RGB in. Albedo and surface normals out. Lighting coefficients are computed from those maps. Sky is masked with PSPNet, which is not a York model. Training has a pre-train stage and a self-train stage. Evaluation crops are 200 by 200, in bundles of 5 overlapping views. Pretrained weights unpack into a folder named irn_model. The illu_pca material in the repository is part of this model's training, not a separate named product. Parameter count was not published. WHDR on IIW was not transcribed.", "modalities": { "input": [ "rgb" ], "output": [ "albedo", "surface-normals", "lighting" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InverseRenderNet is a single-image inverse-rendering network from Ye Yu and William A. P. Smith in Computer Science at the University of York. The arXiv preprint is 29 November 2018 and the conference paper is CVPR 2019 (IEEE pages 3150\u20133159, DOI 10.1109/CVPR.2019.00327). Pretrained TensorFlow 1.12 weights remain linked from the authors' GitHub repository. The same README names InverseRenderNet++ as the follow-up. Collection date 2026-10-15.", "what_it_is_for": "The paper learns to recover albedo, normals, and lighting from one uncontrolled image. Training is self-supervised with a differentiable renderer after a pre-training stage. The public checkpoint is for that CVPR model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "york/inverserendernet/inverserendernet-plus-plus": { "id": "york/inverserendernet/inverserendernet-plus-plus", "name": "InverseRenderNet++", "maker": "york", "family": "york/inverserendernet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "A LICENSE file is present in InverseRenderNet_v2. Its text was not opened, so the licence string is null.", "release_date": "2021-02-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fully convolutional TensorFlow 1.12 network for outdoor scene-level inverse rendering. RGB in. Albedo, shadow, and normal maps out. Least-squares spherical-harmonic lighting is then fit to those maps. Training uses uncontrolled multiview and timelapse collections, a differentiable renderer, and offline multiview stereo for cross-view consistency. Three pretrained checkpoints share one Drive archive: model_ckpt, iiw_model_ckpt, and diode_model_ckpt. Parameter count was not published.", "modalities": { "input": [ "rgb" ], "output": [ "albedo", "shadow", "surface-normals", "lighting" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InverseRenderNet++ is the outdoor inverse-rendering successor from Ye Yu and William A. P. Smith at the University of York. The CVPR InverseRenderNet repository uses the name InverseRenderNet++. The paper title is \"Outdoor Inverse Rendering From a Single Image Using Multiview Self-Supervision\". IEEE published it on 9 February 2021 (TPAMI volume 44, issue 7, July 2022, pages 3659\u20133675, DOI 10.1109/TPAMI.2021.3058105). The arXiv preprint is 12 February 2021. Three TensorFlow checkpoints share one Google Drive archive. Collection date 2026-10-15.", "what_it_is_for": "The paper recovers shape, reflectance, and lighting for an uncontrolled outdoor scene from one image. The network is trained from multiview and timelapse photos without ground-truth intrinsic maps, using a differentiable renderer and multiview-stereo geometry as supervision.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "york/lyhm/lyhm": { "id": "york/lyhm/lyhm", "name": "Liverpool-York Head Model (LYHM)", "maker": "york", "family": "york/lyhm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gated academic user agreement. Non-commercial research and education. The agreement forbids copying, selling, or trading the models. An academic-staff signatory emails nick.pears@york.ac.uk.", "release_date": "2017-10-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Statistical 3D morphable model of human head shape and texture, shipped as MATLAB models with a GUI. The project page says global models were built from over 1200 subjects and filtered to 1212 scans, 606 male and 606 female. The agreement PDF lists the package 'LYHM 3D Morphable Models (Matlab)' at 205 MB. Demographic builds, including a children-only model, are releases of the same product. Vertex count and retained principal-component count were not read from the files.", "modalities": { "input": [], "output": [ "3d-mesh", "texture" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The Liverpool-York Head Model is a statistical 3D morphable model of human head shape and texture from the University of York Computer Science department (Hang Dai, Nick Pears, and Will Smith) with Christian Duncan at Alder Hey Children's Hospital. The project page released it for academic download on 22 October 2017. The current required citation is the IJCV paper that appeared online on 9 November 2019. The gated MATLAB package was still the official distribution on 2026-10-15.", "what_it_is_for": "The maker releases LYHM as a 3D morphable model of the full human head, built so other researchers can sample or fit craniofacial shape and texture. The project page describes global models from more than 1200 subjects, filtered to 1212 scans with 606 male and 606 female, plus further builds such as a general-population model and a children-only model. A MATLAB GUI is included.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "york/neusky/neusky": { "id": "york/neusky/neusky", "name": "NeuSky", "maker": "york", "family": "york/neusky", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GitHub marks the neusky repository Apache-2.0. The README states that Apache-2.0 covers the source and the released checkpoints. The LICENSE file itself was not opened in this collection.", "release_date": "2023-11-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Per-scene outdoor neural radiance model with a sky-pixel constraint on a neural HDR illumination prior and an outside-in sky-visibility field. The prior it loads is the channelwise two-bracket RENI checkpoint (ns_reni group neusky-prior), which is a RENI++ release rather than a second NeuSky model. Hugging Face release title is NeuSky Models v1.0.1. The download script fetches three final NeRF-OSR scene checkpoints. One documented scene flag is lk2. A synthetic collection flag fetches five accepted synthetic scene models. Those scene files are versions of NeuSky. Parameter count was not published. NeRF-OSR images are not redistributed by York.", "modalities": { "input": [ "rgb" ], "output": [ "albedo", "geometry", "illumination", "relighting" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "NeuSky is a per-scene outdoor inverse-rendering model from James A. D. Gardner, Evgenii Kashin, and William A. P. Smith at the University of York, with Bernhard Egger at FAU Erlangen. The arXiv preprint is 28 November 2023 (revised 30 July 2024). The ECCV 2024 paper is LNCS 15112, pages 126\u2013143, first online 31 October 2024 (DOI 10.1007/978-3-031-72949-2_8). Scene checkpoints are on Hugging Face as NeuSky Models v1.0.1. Collection date 2026-10-15.", "what_it_is_for": "The paper relights outdoor scenes from unconstrained photo collections. Any sky pixel is treated as a direct sample of distant lighting. A neural illumination prior fills in the rest of the environment, and an outside-in visibility field carries shadow gradients back to geometry and lighting. Released weights are trained scenes, not a single general detector.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "york/reni/reni": { "id": "york/reni/reni", "name": "RENI", "maker": "york", "family": "york/reni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "No SPDX licence file was present in the JADGardner/RENI repository file list. The later ns_reni downloader can also fetch published RENI checkpoints; that repository's Apache-2.0 file was not read as the licence of this 2022 checkpoint.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Rotation-equivariant conditional spherical neural field used as a natural outdoor illumination prior. The RENI++ paper describes the conference RENI model as SIREN-based conditioning-by-concatenation. Reported latent sizes in that comparison are D=27, 108, 147, and 300. Exact checkpoint filenames in the Google Drive folder were not listed. Parameter count was not published.", "modalities": { "input": [ "direction" ], "output": [ "hdr-environment-map" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RENI is a rotation-equivariant neural prior for natural outdoor illumination from James A. D. Gardner, Bernhard Egger, and William A. P. Smith. Gardner and Smith are in Computer Science at the University of York. The paper is NeurIPS 2022, volume 35, pages 26309\u201326323. Pretrained weights are downloaded by the authors' GitHub `setup.py` from a Google Drive folder. The successor RENI++ is a different architecture. Collection date 2026-10-15.", "what_it_is_for": "The paper learns a compact prior over natural environment maps so inverse-rendering methods can explain outdoor lighting with a rotation-equivariant spherical field instead of a generic spherical-harmonic vector. Will Smith's resources page lists the code. The repository also ships the pretrained models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "york/reni/reni-plus-plus": { "id": "york/reni/reni-plus-plus", "name": "RENI++", "maker": "york", "family": "york/reni", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The ns_reni repository LICENSE file is Apache License 2.0. The Hugging Face card for jadgardner/reni-models was not read as applying that licence to the checkpoint files. The arXiv preprint is CC BY 4.0, which is the paper licence.", "release_date": "2023-11-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer variational auto-decoder for a scale-invariant, rotation-equivariant HDR outdoor illumination prior. Equivariance is SO(2) about the vertical axis in the reported best setting, not full SO(3). The training description uses 8 attention heads, 6 decoder layers, 128 hidden features, latent dimension D, and NeRF positional encoding on directions. Loss terms are scale-invariant, cosine similarity, and KLD. Example training: Adam, cosine decay, 500-step warm-up, initial learning rate 1e-3, alpha 5e-2, loss weights rho=1.0, gamma=1.0, beta=1e-6. Test-time fit: Adam, learning rate 1e-1 decaying to 1e-7 over 2500 steps, KLD removed. Training images: 1,694 curated CC0 HDR outdoor equirectangular maps, 21 held out, 1,673 used to train. The default Hugging Face download is the joint Gram-Schmidt, two-bracket, two-cycle model at D=100. A code comment in the fetched HTML disagreed with the prose (it appeared to say 88 heads and 66 layers); the prose figures are the ones stored here. Parameter count was not published.", "modalities": { "input": [ "direction" ], "output": [ "hdr-environment-map" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "RENI++ is a scale-invariant, rotation-equivariant prior for natural HDR outdoor illumination from James A. D. Gardner and William A. P. Smith at the University of York, with Bernhard Egger at FAU Erlangen. It replaces the SIREN RENI model with a transformer variational auto-decoder. The arXiv preprint is 15 November 2023. IEEE TPAMI accepted the journal version on 26 April 2026 and White Rose still lists it as in press (DOI 10.1109/TPAMI.2026.3691593). Weights are on Hugging Face as RENI Models v1.2. Collection date 2026-10-15.", "what_it_is_for": "The paper supplies a compact outdoor lighting prior for inverse rendering. The model fits an environment map from partial observations and is equivariant to rotation about the vertical axis. The Nerfstudio repository is the official implementation and the download script pulls the released checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "york/yem/yem": { "id": "york/yem/yem", "name": "York Ear Model (YEM)", "maker": "york", "family": "york/yem", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Gated academic-staff agreement. Non-commercial research use. Download is a 280 MB tar.gz via Google Drive, with MATLAB viewers.", "release_date": "2018-07-27", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "3D morphable model of the human ear. The project page initialises the model from SYMARE (10 pairs of 3D ear scans) and augments it with 500 of 605 images from the ibug In-the-wild Ear Database Collection A, using 55 landmarks. The page draws the mean and the first five principal components at plus or minus 3 standard deviations. That illustration is not a published total component count. Vertex count was not read from the archive.", "modalities": { "input": [], "output": [ "3d-mesh" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "The York Ear Model is a 3D morphable model of the human ear from Hang Dai, Nick Pears, and Will Smith in Computer Science at the University of York. The project page dated the release 27 July 2018, with the FG 2018 paper (pages 404\u2013408). On 2026-10-15 the download was still a gated 280 MB archive with MATLAB viewers.", "what_it_is_for": "The paper builds a data-augmented ear 3DMM so researchers can represent ear shape beyond the ten scan pairs used to start the model. The project page says initialisation uses SYMARE and augmentation uses in-the-wild ear photographs with landmarks.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "york/yolso/yolso": { "id": "york/yolso/yolso", "name": "YOLSO", "maker": "york", "family": "york/yolso", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The GitHub repository licence field is GPL-3.0. The checkpoint models/YOLSO_OS.pkl is in that repository. A separate weights-licence sentence was not found. The Zenodo record is an archive of the same repository. The Archaeology Data Service deposit is GIS output, not the weight licence.", "release_date": "2023-08-21", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fully convolutional detector with no padding. It predicts box centres rather than width and height, plus a coarse region segmentation. Ordnance Survey map defaults in the repository: grid 16, cell resolution 32, box width 48, 3 point classes, 5 region classes, image-size flag 4724, learning rate 0.001, batch size 16, 1000 epochs, train proportion 0.9. The ICDAR paper states that tree symbols were originally 48 by 48 pixels and that the loss weight W is 0.1. A correct detection in that paper has the right class and IOU greater than 0.5. Parameter count was not published.", "modalities": { "input": [ "image" ], "output": [ "bounding-box", "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YOLSO (You Only Look for a Symbol Once) is a document-symbol detector from William A. P. Smith and Toby Pillatt at the University of York (Computer Science and Archaeology). The ICDAR 2023 paper was published on 21 August 2023. Weights ship in the authors' GitHub repository as `models/YOLSO_OS.pkl`, and a Zenodo snapshot of that repository was published on 30 June 2025. The checkpoint was still in the repository on 2026-10-15.", "what_it_is_for": "The ICDAR paper presents a single-stage detector for fixed-size, non-uniform symbols, evaluated on freestanding tree symbols and wooded regions in first-edition Ordnance Survey maps. The same network also predicts a coarse region map. A 2026 People and Nature paper uses this released detector to digitise historic treescapes for York, Cardiff, and Milton Keynes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yorku/chartgemma/chartgemma": { "id": "yorku/chartgemma/chartgemma", "name": "ChartGemma", "maker": "yorku", "family": "yorku/chartgemma", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-06-19", "context_length": null, "max_output_tokens": null, "parameters": "2924351216", "architecture": "Instruction-tuned chart model on the PaliGemma backbone. Vision encoder is SigLIP. Language model is Gemma-2B, which the paper says was trained with a context length of 8,196 tokens. Input images are 448 by 448 and split into 14 by 14 patches. A linear layer maps vision features into the language-model embedding space. Full attention is used on visual and prefix tokens. Causal attention is used on suffix tokens. The vision encoder stays frozen and only the language model is finetuned, in one stage. Paper Table 2 lists the model as 3B. The Hub safetensors total is 2,924,351,216 F32 parameters. Architecture class on the Hub is PaliGemmaForConditionalGeneration.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "York University's Intelligent Visualization Lab released ChartGemma weights as `ahmed-masry/chartgemma` on 19 June 2024. The paper arXiv:2407.04172 was submitted on 4 July 2024 and revised on 4 November 2024. The checkpoint is an active MIT-tagged PaliGemma chart model, not a priced API. Collected 2026-10-15.", "what_it_is_for": "The paper presents ChartGemma as a chart understanding and reasoning model trained on instruction data generated from chart images rather than from underlying data tables. The abstract names chart summarization, question answering, and fact checking across five benchmarks. Code, checkpoints, the dataset, and demos are released at the vis-nlp GitHub repository.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yorku/chartinstruct/chartinstruct-flan-t5-xl": { "id": "yorku/chartinstruct/chartinstruct-flan-t5-xl", "name": "ChartInstruct-Flan-T5-XL", "maker": "yorku", "family": "yorku/chartinstruct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-03-14", "context_length": null, "max_output_tokens": null, "parameters": "2926237112", "architecture": "End-to-end chart model from the same ChartInstruct design as the Llama 2 variant, with Flan-T5-XL as the encoder-decoder language model. The paper says projected visual features and the instruction go through the language encoder before the decoder generates text. The vis-nlp README says the released alignment module is a 2-layer MLP rather than the linear layer in the paper. Hub config model_type is llava_t5 and the architecture is LlavaT5ForConditionalGeneration, with custom code. Paper Table 2 lists the model as 3B. The Hub safetensors total is 2,926,237,112 parameters (F32 2,926,037,112 plus I64 200,000). This folder is the released end-to-end checkpoint, not the paper's pipeline system.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "York University and coauthors announced ChartInstruct-Flan-T5-XL with ChartInstruct-Llama2 in arXiv:2403.09028 on 14 March 2024. The public end-to-end weights are `ahmed-masry/ChartInstruct-FlanT5-XL`, created 4 July 2024, license tag mit. The checkpoint is active. Collected 2026-10-15.", "what_it_is_for": "This is the encoder-decoder ChartInstruct model. The paper uses it for the same chart instruction tasks as the Llama 2 variant: question answering, summarization, and fact checking. The released file is the end-to-end system. The paper's pipeline system, which extracts a table before calling the language model, does not have its own Hub checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yorku/chartinstruct/chartinstruct-llama2": { "id": "yorku/chartinstruct/chartinstruct-llama2", "name": "ChartInstruct-Llama2", "maker": "yorku", "family": "yorku/chartinstruct", "status": "active", "confidence": "high", "weights": "open-weights", "license": "gpl-3.0", "release_date": "2024-03-14", "context_length": 4096, "max_output_tokens": null, "parameters": "6834300344", "architecture": "End-to-end LLaVA-style chart model. The paper replaces CLIP with the UniChart chart encoder and uses Llama 2 7B as the decoder-only language model. The vis-nlp/ChartInstruct README says the released alignment module is a 2-layer MLP, changed from the linear layer in the paper so the checkpoint matches the Hugging Face LLaVA code. Hub config.json names LlavaForConditionalGeneration, vision tower DonutSwinModel with image_size 512 by 512, and text_config.max_position_embeddings 4096. Paper Table 2 lists the model as 7B. The Hub safetensors total is 6,834,300,344 parameters (BF16 6,834,100,344 plus I64 200,000). This folder is the released end-to-end checkpoint, not the paper's pipeline system.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "York University and coauthors released ChartInstruct-Llama2, an end-to-end chart instruction model, in the 14 March 2024 paper arXiv:2403.09028. Public weights are `ahmed-masry/ChartInstruct-LLama2`, created 20 June 2024, license tag gpl-3.0. The checkpoint is active. Collected 2026-10-15.", "what_it_is_for": "The paper introduces a chart instruction set of 191K instructions from 71K charts and two systems: an end-to-end vision-language model, and a pipeline that first extracts a data table. This folder is the released end-to-end Llama 2 checkpoint. The paper says it is for chart question answering, summarization, fact checking, and broader chart instructions.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yorku/colflor/colflor": { "id": "yorku/colflor/colflor", "name": "ColFlor", "maker": "yorku", "family": "yorku/colflor", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-10-08", "context_length": null, "max_output_tokens": null, "parameters": "174114688", "architecture": "OCR-free visual document retriever. Florence-2 vision and text encoders are kept and the text decoder is removed. The vision encoder is DaViT. The text encoder is a BART encoder. Contextualized embeddings are projected with a linear layer to 128 dimensions. Documents and queries are compared with late-interaction MaxSim. Weights are initialized from Florence-2-base except the new projection, which starts random. The blog says indexing encodes the image plus one OCR text token. Image resolution in the efficiency comparison is 768 by 768, producing 587 contextualized embeddings. The paper and blog say 174 million parameters. The Hub safetensors total is 174,114,688 F32 parameters.", "modalities": { "input": [ "image", "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "York University's Intelligent Visualization Lab released ColFlor, an OCR-free document retriever, as `ahmed-masry/ColFlor` on 8 October 2024. The model card uses the MIT license. A blog post on 18 October 2024, a NeurIPS 2024 workshop paper, and an IEEE MLSP 2025 paper describe the same model. It is active. Collected 2026-10-15.", "what_it_is_for": "ColFlor retrieves pages from visual documents such as PDFs, charts, and infographics without an OCR pipeline. The authors position it as a smaller alternative to ColPali for text-rich English documents. Authors are Ahmed Masry and Enamul Hoque. The blog says the work was done at the Intelligent Visualization Lab and was supported by NSERC, CFI, and a Google PaliGemma Academic Program credit award.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "yorku/unichart/unichart": { "id": "yorku/unichart/unichart", "name": "UniChart", "maker": "yorku", "family": "yorku/unichart", "status": "active", "confidence": "high", "weights": "open-weights", "license": "gpl-3.0", "release_date": "2023-05-21", "context_length": null, "max_output_tokens": null, "parameters": "201000000", "architecture": "Chart vision-language model initialized from Donut. The EMNLP 2023 paper uses a Swin Transformer chart-image encoder and a BART text decoder. Task prompts are fed to the decoder. Later York tables in arXiv:2403.09028 and arXiv:2407.04172 list UniChart at 201M parameters. The vis-nlp ChartQA README instead calls UniChart a 140M model. The Hugging Face API record for ahmed-masry/unichart-base-960 does not publish a safetensors parameter total. The paper says UniChart is more than 11 times faster than MatCha and has 28 percent fewer parameters.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "York University's Intelligent Visualization Lab released UniChart, a chart vision-language model, with public weights on 21 May 2023. It is an active open-weights checkpoint under the Hub license tag gpl-3.0, not a priced API. Collected 2026-10-15.", "what_it_is_for": "The EMNLP 2023 paper presents UniChart as a pretrained model for chart comprehension and reasoning. Downstream tasks in the paper are factoid chart question answering (ChartQA), open-ended question answering (OpenCQA), chart summarization (Chart-to-Text), and chart-to-table extraction. The GitHub repository ships one pretrained checkpoint and four task finetunes.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/bce/bce-embedding-base-v1": { "id": "youdao/bce/bce-embedding-base-v1", "name": "bce-embedding-base_v1", "maker": "youdao", "family": "youdao/bce", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-01-03", "context_length": null, "max_output_tokens": null, "parameters": "279M", "architecture": "BERT-base bilingual embedding model. Youdao's eval summary lists dimension 768 and CLS pooling. A maintainer comment suggested trying sequence length 512. That is not published as a hard maximum.", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bce-embedding-base_v1 is NetEase Youdao's open bilingual embedding model, released on 2024-01-03 as part of BCEmbedding. It is a 279M model for Chinese and English semantic search, including cross-lingual retrieval. Youdao says it is a base of the QAnything RAG stack. On 2026-10-15 the project was still public.", "what_it_is_for": "The README says one embedding model covers Chinese, English, and Chinese-English retrieval for RAG tasks such as translation, summarization, and question answering, with extra work on query understanding. It is meant to be used without a user fine-tune. The paired reranker is a separate folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/bce/bce-reranker-base-v1": { "id": "youdao/bce/bce-reranker-base-v1", "name": "bce-reranker-base_v1", "maker": "youdao", "family": "youdao/bce", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-01-03", "context_length": null, "max_output_tokens": null, "parameters": "279M", "architecture": "BERT-base cross-encoder reranker. Output is a relevance score. Youdao describes it as sequence classification.", "modalities": { "input": [ "text" ], "output": [ "score" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "bce-reranker-base_v1 is NetEase Youdao's open reranker, released on 2024-01-03 with bce-embedding-base_v1. It is a 279M cross-encoder for Chinese, English, Japanese, and Korean. Youdao uses the pair inside QAnything. On 2026-10-15 the weights were still distributed from the BCEmbedding project.", "what_it_is_for": "The README says the reranker reorders search hits. It covers the four languages and cross-lingual pairs among them. It is the second stage after bce-embedding-base_v1. Youdao says it can be used without a further fine-tune for the RAG tasks they tested.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/qanything/qwen-7b-qanything": { "id": "youdao/qanything/qwen-7b-qanything", "name": "Qwen-7B-QAnything", "maker": "youdao", "family": "youdao/qanything", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Card metadata says Apache-2.0. The card body points at the Tongyi Qianwen Research License Agreement for the Qwen base. Both statements are on the same page.", "release_date": "2024-02", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Bilingual instruction tune of Qwen-7B for the QAnything RAG application.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Qwen-7B-QAnything is a bilingual instruction-tuned checkpoint of Qwen-7B, released by NetEase Youdao for the QAnything retrieval-augmented application. Hugging Face shows the repo updated on 2024-02-22. On 2026-10-15 the weights were still online. The card's license lines disagree with each other.", "what_it_is_for": "The card calls it a bilingual instruction-tuned model of Qwen-7B for QAnything. QAnything is Youdao's local RAG system. The launch commands load this generator next to local embedding and rerank files. It is a Youdao release of a Qwen derivative, not a hosted copy of an unchanged Qwen checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/speech/confucius4-r2t2": { "id": "youdao/speech/confucius4-r2t2", "name": "Confucius4-R2T2", "maker": "youdao", "family": "youdao/speech", "status": "active", "confidence": "high", "weights": "open-weights", "license": "NetEase Model Use License Agreement (weights); Apache-2.0 (code)", "release_date": "2026-09", "context_length": null, "max_output_tokens": null, "parameters": "1.7B class", "architecture": "Streaming ASR fine-tune of Qwen3-ASR-1.7B. Append-only output using a longest-stable-prefix objective. Decoding chunks from 80 ms to 2 s. Hugging Face size label is 2B.", "modalities": { "input": [ "audio", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Confucius4-R2T2 is NetEase Youdao's open-weight streaming speech-recognition model, released in September 2026. R2T2 means Real Real-Time Transcription. It is a fine-tune of Qwen3-ASR-1.7B that commits words and does not revise them. On 2026-10-15 the weights used a NetEase model license and the code used Apache-2.0.", "what_it_is_for": "The card describes live captions, downstream agents, and the front end of simultaneous interpretation. Decoding chunks can be set from 80 ms to 2 seconds. Youdao says average delay from speaking a word to emitting it is about 200 to 600 ms, and that offline accuracy is not given up to add streaming. Hotwords can be passed as context. Chinese and English are the optimized languages.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/speech/confucius4-tts": { "id": "youdao/speech/confucius4-tts", "name": "Confucius4-TTS", "maker": "youdao", "family": "youdao/speech", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2026-05-22", "context_length": null, "max_output_tokens": null, "parameters": "1.3B", "architecture": "Speech encoder plus LLM. Text-to-semantic module and a flow-matching semantic-to-acoustic module. Reference audio does not require a transcript. A continuation mode exists when a transcript is available. Paper date is later than the open-source announcement.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Confucius4-TTS is NetEase Youdao's open text-to-speech model in the \u5b50\u66f0 4.0 line. Youdao announced the open release with the multimodal model on 2026-05-22. The technical report is dated 2026-08-12. On 2026-10-15 the weights, code, and a Gradio demo were public. The code license badge is Apache-2.0.", "what_it_is_for": "The card describes zero-shot voice cloning across 14 languages without a transcript of the reference clip, plus cross-lingual transfer and emotion transfer. Launch coverage says about three seconds of audio is enough, with similarity above 85% and cloning-task accuracy of 97%. Those two percentages are press figures. The card's public tables use WER, CER, and SIM instead.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/speech/emotivoice": { "id": "youdao/speech/emotivoice", "name": "\u6613\u9b54\u58f0 (EmotiVoice)", "maker": "youdao", "family": "youdao/speech", "status": "active", "confidence": "high", "weights": "open-source", "license": "Apache-2.0", "release_date": "2023-11-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Prompt-controlled multi-speaker TTS. Project credits PromptTTS and uses a HiFi-GAN vocoder. Style text is encoded with simbert-base-chinese. Code loads a JETS generator.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "EmotiVoice (\u6613\u9b54\u58f0) is NetEase Youdao's open-source multi-voice text-to-speech engine. The first public version is dated 2023-11-10. On 2026-10-15 the GitHub project was still public under Apache-2.0. It speaks Chinese and English, with a prompt for emotion or style, and ships more than 2000 voices.", "what_it_is_for": "The README says it is a free TTS engine for Chinese and English, with emotion prompts such as happy, excited, sad, and angry. A web demo, a batch script, and a Docker image are provided. Version 0.3 added a personal-data cloning recipe. Cloning is trained, not zero-shot, and the README says a custom train replaces the original speaker inventory.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/speech/youdao-llm-tts": { "id": "youdao/speech/youdao-llm-tts", "name": "\u6709\u9053\u5927\u6a21\u578b\u8bed\u97f3\u590d\u523b", "maker": "youdao", "family": "youdao/speech", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Closed large-model voice clone and synthesis API. Request field model is lite or pro. Pro is the default.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u6709\u9053\u5927\u6a21\u578b\u8bed\u97f3\u590d\u523b is NetEase Youdao's closed voice-cloning API on Zhiyun. Callers pick `lite` or `pro`. On 2026-10-15 pro was the default. The two tiers are versions of one product, not two open checkpoints. A release date was not found.", "what_it_is_for": "The doc says a short recording gives a product a voice for reading, hosting, stories, or customer service. Upload creates a voice id. Synthesis speaks new text in that voice. Lite is described as faster and limited to Chinese and English. Pro is described as higher quality, covers 14 languages, and can take an extra emotion reference when the language is Chinese or English.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/translate/confucius4-t3po": { "id": "youdao/translate/confucius4-t3po", "name": "Confucius4-T3PO", "maker": "youdao", "family": "youdao/translate", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-09", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Text-to-text simultaneous translation model. Card says the runtime matches Qwen2.5-14B-Instruct. Hugging Face model tree bases it on Qwen2.5-14B. Training sketch: segment-aligned data, streaming cold start, then Pareto-aware reinforcement learning. Greedy decoding is recommended.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Confucius4-T3PO is NetEase Youdao's open-weight simultaneous translation model, released around September 2026. The name expands to simultaneous translation via Pareto policy optimization. On 2026-10-15 the 14B weights were on Hugging Face under Apache-2.0, with a live demo.", "what_it_is_for": "The model reads source text in small chunks and either waits or writes the next translation segment. Written segments are append-only. Youdao positions it for live Chinese-English interpretation. It does not take audio. Pair it with a streaming recognizer such as Confucius4-R2T2 when the input is speech. The card says general instruction following of the Qwen base was kept, including room for terminology constraints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/translate/ynmt": { "id": "youdao/translate/ynmt", "name": "YNMT", "maker": "youdao", "family": "youdao/translate", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2017-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Youdao neural machine translation (YNMT). Domain modes are the same API with a domain flag, not separate named checkpoints.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "YNMT (Youdao Neural Machine Translation, \u6709\u9053\u795e\u7ecf\u7f51\u7edc\u7ffb\u8bd1) is NetEase Youdao's closed neural translation engine. It went live in April 2017 and, as of 2026-10-15, is still the engine named on the Youdao Zhiyun text-translation product. Domain modes share this API. They are not separate model cards.", "what_it_is_for": "The product page says YNMT translates text across more than 110 languages, with optional custom glossaries. The API adds domain modes for computers, medicine, finance, and games. Those modes are Chinese-English only and must be turned on in the console. Default domain is `general`.\n\nAn offline SDK ships a compressed model. The product page lists a package around 50 MB, runtime memory around 50-100 MB, reference latency 300-500 ms, and offline Chinese-English, Chinese-Korean, and Chinese-Japanese.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/translate/ziyue-translate-lite": { "id": "youdao/translate/ziyue-translate-lite", "name": "\u5b50\u66f0\u7ffb\u8bd1 Lite", "maker": "youdao", "family": "youdao/translate", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Closed translation model. API text: \u6709\u9053\u5b50\u66f0\u7ffb\u8bd1lite\u7248\u672c (1.5B), handleOption 3. Translation only.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.01, "completion_per_m_usd": 0.01 }, "summary_intro": "\u5b50\u66f0\u7ffb\u8bd1 Lite is NetEase Youdao's closed 1.5B translation model on the same Zhiyun API as \u5b50\u66f0\u7ffb\u8bd1 Pro. On 2026-10-15 it is selected with `handleOption=3`. It is billed below the 14B model. A standalone release date was not found.", "what_it_is_for": "The API doc says the lite model only translates. An optional prompt is honored for this model and for the pro model. It is the lower-priced general model on the large-model translation tariff. March 2025 app coverage mentioned a standard and an advanced option. That standard option may be this model. Youdao did not say so on the API page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/translate/ziyue-translate-pro": { "id": "youdao/translate/ziyue-translate-pro", "name": "\u5b50\u66f0\u7ffb\u8bd1 Pro", "maker": "youdao", "family": "youdao/translate", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-03-11", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Closed 14B translation model. March 2025 coverage says \u5b50\u66f0\u7ffb\u8bd1\u5927\u6a21\u578b2.0 was further trained from the \u5b50\u66f0 education model, with distillation and model merging. The API calls handleOption 0 the pro 14B model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.03, "completion_per_m_usd": 0.03 }, "summary_intro": "\u5b50\u66f0\u7ffb\u8bd1 Pro is NetEase Youdao's closed 14B translation model on the Zhiyun large-model translation API. On 2026-10-15 the API selects it with `handleOption=0`. The March 2025 launch of \u5b50\u66f0\u7ffb\u8bd1\u5927\u6a21\u578b2.0 is the public 14B announcement this folder is tied to. Youdao has not put both names on one page, so that link is explicit but not certain.", "what_it_is_for": "The API translates a text span into a chosen language and can stream the result. The pro model is translation-only. A short prompt is accepted and, the doc says, applies only to the general models (`handleOption` 0 and 3). March 2025 coverage said the 14B model was further trained from the \u5b50\u66f0 education model on cleaned parallel data, then reduced with distillation and merging. Dictionary, translation, and translator apps were said to offer a standard model and an advanced model. This folder is the advanced 14B side. The 1.5B API model is the lite folder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/ziyue/confucius-o1-14b": { "id": "youdao/ziyue/confucius-o1-14b", "name": "\u5b50\u66f0-o1 (Confucius-o1-14B)", "maker": "youdao", "family": "youdao/ziyue", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-01", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Two-stage post-train of Qwen2.5-14B-Instruct. Chain of thought is wrapped in thinking tags and a short solution is wrapped in summary tags.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Confucius-o1-14B is NetEase Youdao's open-weight reasoning model, released in January 2025 under the \u5b50\u66f0 brand. It is a 14B Qwen2.5-14B-Instruct post-train aimed at step-by-step K12 math explanations. As of 2026-10-15 the Hugging Face weights were still published under Apache-2.0.", "what_it_is_for": "The model card says it learns an o1-style chain of thought, then writes a short 3-to-5-step solution so students are not left inside the trace. The published system prompt names the persona \u5c0fP\u8001\u5e08 and restricts the worked example to math. Youdao's news posts called it the first domestic model that emits a stepwise explanation of this kind.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/ziyue/confucius3-math": { "id": "youdao/ziyue/confucius3-math", "name": "\u5b50\u66f03\u6570\u5b66\u6a21\u578b (Confucius3-Math)", "maker": "youdao", "family": "youdao/ziyue", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2025-06-23", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "14B Qwen2-architecture reasoning LLM. Technical report: RL-only post-training with a custom data schedule and a modified group-relative advantage estimator. Base-model search included Qwen2.5-14B-Base, Qwen2.5-14B-Instruct, DeepSeek-R1-Distill-Qwen-14B, and Confucius-o1-14B. The card does not name which base shipped.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Confucius3-Math is NetEase Youdao's open-weight 14B reasoning model for Chinese K12 mathematics, released on 2025-06-23. It is part of the \u5b50\u66f03 line. On 2026-10-15 the weights were still on Hugging Face and the technical report was on arXiv.", "what_it_is_for": "Youdao says the model is for Chinese school mathematics, aligned to the national curriculum, and meant to run on one consumer GPU. IT\u4e4b\u5bb6 reported a training cost of 26,000 USD and a vendor serving-cost claim of 0.15 USD per million tokens. Those are not an API tariff.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/ziyue/confucius4": { "id": "youdao/ziyue/confucius4", "name": "\u5b50\u66f04 (Confucius4)", "maker": "youdao", "family": "youdao/ziyue", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-22", "context_length": null, "max_output_tokens": null, "parameters": "27B", "architecture": "Fine-tune of Qwen3.5-27B. Iterative SFT and RL with compact chain-of-thought. Length-aware advantage in RL. Card sample decode: temperature 0.6, top_p 0.95, top_k 20.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Confucius4 is NetEase Youdao's open-weight multimodal reasoning model in the \u5b50\u66f0 4.0 line. The company announced the open release on 2026-05-22. The Hugging Face card, read on 2026-10-15, says it is a Qwen3.5-27B fine-tune under Apache-2.0, aimed at visual and text math.", "what_it_is_for": "The card describes education-oriented mathematical reasoning with images and text. Training combines iterative supervised fine-tuning and reinforcement learning, plus a shorter chain of thought. Launch coverage said chain-of-thought length fell by 43.2% and that Chinese text-only hard math accuracy was 81.4%, matching the card's 0.814 on Math-Hard-500.\n\nThe same week's posts also open-sourced a separate TTS engine. That model is Confucius4-TTS, not this checkpoint. The product story said \u5b50\u66f0 4.0 accepts audio. The open Confucius4 card's example is image plus text only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "youdao/ziyue/ziyue": { "id": "youdao/ziyue/ziyue", "name": "\u5b50\u66f0", "maker": "youdao", "family": "youdao/ziyue", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2023-07", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "\u5b50\u66f0 is NetEase Youdao's education large language model, announced in July 2023 as a closed model for learning products. It was still the company's education-model brand on 2026-10-15. Later named checkpoints (\u5b50\u66f0-o1, Confucius3-Math, Confucius4) are separate folders.", "what_it_is_for": "Youdao described \u5b50\u66f0 as an education model. The July 2023 launch named six applications: LLM translation, a virtual-human speaking coach, AI essay guidance, grammar explanation, AI Box, and document question answering. In November 2023 it was reported as one of the first education models filed under China's interim generative-AI rules. The consumer tutor \"\u5c0fP\u8001\u5e08\" is an application of this line, not a separate published checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zaragoza/panoramic-blitznet/panoramic-blitznet": { "id": "zaragoza/panoramic-blitznet/panoramic-blitznet", "name": "Panoramic BlitzNet", "maker": "zaragoza", "family": "zaragoza/panoramic-blitznet", "status": "active", "confidence": "high", "weights": "open-weights", "license": "The reimplementation repository is GNU GPL version 3. The README says commercial use should contact the authors. A separate licence for the Google Drive checkpoints is not stated. Those checkpoints are not the ICRA paper weights.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named model in the ICRA 2020 paper: a fully convolutional BlitzNet adapted to equirectangular indoor panoramas, with a ResNet-50 feature extractor and equirectangular convolutions (EquiConvs). It jointly detects and semantically segments 14 indoor classes. Instance masks are a post-process, and 3D boxes use the room layout. The public code at anovaguerrero/Panoramic-BlitzNet is a TensorFlow 2.3 reimplementation. Its README says it uses standard convolutions, is not pretrained on SUN RGB-D, and is not the original paper model. Paper training used TensorFlow 1.13.1, Python 3.5, and one GTX 1080. The public stack is Python 3.8.5, CUDA 10.1, cuDNN 7.6, and TensorFlow 2.3. Adam learning rate in the paper is 1e-4, decreased twice. Detection counts a box as correct at IoU above 0.3.", "modalities": { "input": [ "image" ], "output": [ "detection", "segmentation" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Panoramic BlitzNet is an indoor panoramic recognition model from Universidad de Zaragoza (I3A). Julia Guerrero-Viu, Clara Fernandez-Labrador, and Jos\u00e9 J. Guerrero are the Zaragoza authors. C\u00e9dric Demonceaux is at Universit\u00e9 Bourgogne Franche-Comt\u00e9. The ICRA 2020 paper adapts BlitzNet with ResNet-50 and equirectangular convolutions for detection and semantic segmentation of 14 classes. On 2026-10-15 the public weights are a TensorFlow 2 reimplementation, not that paper checkpoint. The README says so.", "what_it_is_for": "The paper system labels main objects in a room from one 360-degree image, then builds instance masks and can place boxes in a 3D layout. The public demo is `python3 test.py PanoBlitznet`. It is a local research checkpoint, not an API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zaragoza/panoramic-cnn-360-saliency/panoramic-cnn-360-saliency": { "id": "zaragoza/panoramic-cnn-360-saliency/panoramic-cnn-360-saliency", "name": "Panoramic CNN 360 Saliency", "maker": "zaragoza", "family": "zaragoza/panoramic-cnn-360-saliency", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "U-Net-like encoder-decoder for saliency on a single equirectangular panorama. Kernels are panoramic convolutions, described as dilated convolutions whose patches sit tangent to the sphere. The loss is spherical and weights each pixel by its place in a gnomonic projection. Output is a map with values in [0, 1]. The paper says training used 700 epochs, batches of 6, Xavier uniform initialization, learning rate 1e-4, momentum 0.9, and weight decay 1e-5. Seven images were held out for testing. The rest, after augmentation, were 800 training samples. The GitHub models/README.md links one Google Drive file as the model. The checkpoints/ folder contained only an empty README when listed. A parameter count was not published.", "modalities": { "input": [ "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "This is the 360-degree saliency network from the Graphics and Imaging Lab at Universidad de Zaragoza (I3A). Daniel Martin, Ana Serrano, and Belen Masia are the authors. The paper is \"Panoramic convolutions for 360\u00b0 single-image saliency prediction\", CVPR Workshop on Computer Vision for AR/VR, 2020. On 2026-10-15 the git tree does not store the weights. `models/README.md` links a Google Drive file and says the model is there.", "what_it_is_for": "The network predicts where people look in a still 360-degree image. It is a local research model. Directory inference is `python main.py --multitest`.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zaragoza/precisecam/precisecam": { "id": "zaragoza/precisecam/precisecam", "name": "PreciseCam", "maker": "zaragoza", "family": "zaragoza/precisecam", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT, from the Hugging Face model card (cardData.license mit, tag license:mit). The adapter is the Zaragoza release. The frozen SDXL base is Stability AI's model and is not relicensed by this card.", "release_date": "2025-04-28", "context_length": null, "max_output_tokens": null, "parameters": "1251014160", "architecture": "ControlNet adapter (diffusers ControlNetModel) on frozen Stable Diffusion XL. The public config.json sets _class_name ControlNetModel, _diffusers_version 0.30.0.dev0, conditioning_channels 3, in_channels 4, cross_attention_dim 2048, and block_out_channels 320, 640, 1280, with DownBlock2D plus two CrossAttnDownBlock2D blocks and a UNetMidBlock2DCrossAttn mid block. Conditioning is a PF-US map from four camera parameters: roll, pitch, vertical field of view, and Unified Spherical distortion xi. Yaw is not a control. At inference the authors inject only the ControlNet middle-block residual. The config _name_or_path ends in preciseCam_1024_bs2_g16_lr1e-5_70k_scrach/checkpoint-33000. The Hugging Face card says the public weights differ from the paper checkpoint and that behavior stays consistent. The card lists model size 1B params and tensor type F32. The safetensors total is the ControlNet file only, not SDXL.", "modalities": { "input": [ "text", "image" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "PreciseCam is a camera-control adapter from the Graphics and Imaging Lab at Universidad de Zaragoza (I3A), with Adobe Research co-authors. It is a ControlNet on frozen Stable Diffusion XL. The public weights were on Hugging Face as `edurnebb/PreciseCam` on 2026-10-15, created 2025-04-28. The arXiv preprint is 22 January 2025. CVPR 2025 lists the paper at DOI 10.1109/CVPR52734.2025.00260. The card says the public file is not the paper checkpoint.", "what_it_is_for": "The authors use four camera numbers, roll, pitch, vertical field of view, and Unified Spherical distortion xi, to steer a text-to-image model without a reference mesh or multi-view capture. Content still comes from the prompt. Yaw is not a control. The lab demo is local Gradio, not a priced API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zaragoza/scangan360/scangan360": { "id": "zaragoza/scangan360/scangan360", "name": "ScanGAN360", "maker": "zaragoza", "family": "zaragoza/scangan360", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Conditional GAN that generates gaze scanpaths on 360-degree images. The paper describes a two-branch generator and discriminator, spherical convolutions in the SphereNet style, CoordConv, and a spherical soft-DTW loss. Generator output is described as a sequence of length T=30 at 1 Hz, on the order of 30 seconds. The public file in the repository is models/model_generator_217.pth. A discriminator weight file was not in that folder when it was listed. A parameter count was not computed from the .pth file.", "modalities": { "input": [ "image" ], "output": [ "scanpaths" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ScanGAN360 is a generative scanpath model from the Graphics and Imaging Lab at Universidad de Zaragoza (I3A). Daniel Martin, Ana Serrano, and Belen Masia are at Zaragoza. Alexander W. Bergman and Gordon Wetzstein are at Stanford. The repository still holds generator weights at `models/model_generator_217.pth` on 2026-10-15. The arXiv preprint is 25 March 2021. IEEE TVCG, volume 28, number 5, pages 2003-2013, is the May 2022 issue (DOI 10.1109/TVCG.2022.3150502).", "what_it_is_for": "The model generates realistic gaze scanpaths for equirectangular 360-degree images. The paper frames the output as a short sequence, T=30 samples at 1 Hz. The code runs locally. It is not an API product.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zaragoza/tspm-net/tspm-net": { "id": "zaragoza/tspm-net/tspm-net", "name": "tSPM-Net", "maker": "zaragoza", "family": "zaragoza/tspm-net", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-06-22", "context_length": null, "max_output_tokens": null, "parameters": "185000000", "architecture": "Probabilistic scanpath model. The journal text says the network uses Bayesian deep learning and convolutional recurrent units, and a spatio-temporal loss that combines Kullback-Leibler divergence with dynamic time warping. It predicts a time-evolving scanpath probability map and samples scanpaths from that map. The paper says the model is composed of approximately 185 million parameters and takes less than a second to predict a new scanpath. The 185,000,000 field is that approximation, not a counted checkpoint. The repository models/README.md links one SharePoint file from danims@unizar.es. The file timestamp and byte size were not read.", "modalities": { "input": [ "image" ], "output": [ "scanpaths" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "tSPM-Net is a scanpath model from the Graphics and Imaging Lab at Universidad de Zaragoza (I3A). Daniel Martin, Diego Gutierrez, and Belen Masia are the authors. Computers & Graphics published it online on 22 June 2024 (DOI 10.1016/j.cag.2024.103983). An earlier preprint, arXiv:2204.09404 on 20 April 2022, used the title \"A Probabilistic Time-Evolving Approach to Scanpath Prediction\". On 2026-10-15 the repository points at one SharePoint weight file.", "what_it_is_for": "The model predicts a time-evolving probability map of where a viewer looks, then samples scanpaths from that map. The authors use it to keep inter-observer and intra-observer variability instead of predicting one next point. Inference is local. Training is marked as coming soon in the README.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/charglm/charglm-3": { "id": "zhipu/charglm/charglm-3", "name": "CharGLM-3", "maker": "zhipu", "family": "zhipu/charglm", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.015, "completion_per_m_usd": 0.015 }, "summary_intro": "CharGLM-3 is a charglm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: medium.", "what_it_is_for": "Character-role chat model CharGLM-3. It is on older equity and rate-limit pages at about 0.015 CNY per thousand tokens, not on the 2026 token table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/codegeex/codegeex-4": { "id": "zhipu/codegeex/codegeex-4", "name": "CodeGeeX-4", "maker": "zhipu", "family": "zhipu/codegeex", "status": "active", "confidence": "high", "weights": "open-weights", "license": "custom (HF license:other on codegeex4-all-9b)", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "9B (open checkpoint codegeex4-all-9b; the API price row does not state size)", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.1 }, "summary_intro": "CodeGeeX-4 is a codegeex model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Code generation model on the China price table. 128K context, 0.1 CNY per million tokens. The open checkpoint codegeex4-all-9b is the public 9B model from July 2024, license tag other.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/codegeex/codegeex2-6b": { "id": "zhipu/codegeex/codegeex2-6b", "name": "CodeGeeX2-6B", "maker": "zhipu", "family": "zhipu/codegeex", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-07-19", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CodeGeeX2-6B is a codegeex model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "what_it_is_for": "CodeGeeX2 6B, the 2023 code model based on ChatGLM2. Hugging Face repo created 2023-07-19. An int4 pack exists.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogagent/cogagent": { "id": "zhipu/cogagent/cogagent", "name": "CogAgent", "maker": "zhipu", "family": "zhipu/cogagent", "status": "active", "confidence": "high", "weights": "open-weights", "license": "custom (HF license:other on the 9B card)", "release_date": "2024-12-20", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "GUI agent. The 9B checkpoint is a finetune of glm-4v-9b.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogAgent is a cogagent model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "GUI agent model. CogAgent paper arXiv:2312.08914. A 2023 chat port and a December 2024 9B checkpoint based on glm-4v-9b both exist.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogvideo/cogvideo": { "id": "zhipu/cogvideo/cogvideo", "name": "CogVideo", "maker": "zhipu", "family": "zhipu/cogvideo", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2022-07-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogVideo is a cogvideo model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "what_it_is_for": "2022 research text-to-video model. zai-org/CogVideo on Hugging Face was created 2022-07-18. It is not the later CogVideoX API.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogvideo/cogvideox-1-5-5b": { "id": "zhipu/cogvideo/cogvideox-1-5-5b", "name": "CogVideoX1.5-5B", "maker": "zhipu", "family": "zhipu/cogvideo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "custom (HF license:other)", "release_date": "2024-11-02", "context_length": null, "max_output_tokens": null, "parameters": "5B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogVideoX1.5-5B is a cogvideo model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "November 2024 CogVideoX 1.5 5B release, text-to-video and image-to-video. License tag on the HF card is other.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogvideo/cogvideox-2": { "id": "zhipu/cogvideo/cogvideox-2", "name": "CogVideoX-2", "maker": "zhipu", "family": "zhipu/cogvideo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogVideoX-2 is a cogvideo model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Previous paid video API still on the China price table at 0.5 CNY per video, batch 0.25 CNY. The older English index says text or image in, video out.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogvideo/cogvideox-2b": { "id": "zhipu/cogvideo/cogvideox-2b", "name": "CogVideoX-2B", "maker": "zhipu", "family": "zhipu/cogvideo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-05", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogVideoX-2B is a cogvideo model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Smaller August 2024 open text-to-video model. Apache-2.0. Repo CogVideoX-2b created 2024-08-05.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogvideo/cogvideox-3": { "id": "zhipu/cogvideo/cogvideox-3", "name": "CogVideoX-3", "maker": "zhipu", "family": "zhipu/cogvideo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogVideoX-3 is a cogvideo model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "July 2025 flagship video API. Text-to-video, image-to-video, and start-end frame. China price 1 CNY per video. Z.ai price $0.20 per video. A public CogVideoX-3 weight repo was not in the zai-org listing.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogvideo/cogvideox-5b": { "id": "zhipu/cogvideo/cogvideox-5b", "name": "CogVideoX-5B", "maker": "zhipu", "family": "zhipu/cogvideo", "status": "active", "confidence": "high", "weights": "open-weights", "license": "custom (HF license:other)", "release_date": "2024-08-17", "context_length": null, "max_output_tokens": null, "parameters": "5B", "architecture": "Diffusion transformer video model.", "modalities": { "input": [ "text" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogVideoX-5B is a cogvideo model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "August 2024 open text-to-video model, about 5B parameters in the repo name. Hugging Face license tag is other, not Apache. Paper arXiv:2408.06072.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogvideo/cogvideox-flash": { "id": "zhipu/cogvideo/cogvideox-flash", "name": "CogVideoX-Flash", "maker": "zhipu", "family": "zhipu/cogvideo", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogVideoX-Flash is a cogvideo model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Free video API on the China price table. The older index calls it a free, easy video model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogview/cogview-2": { "id": "zhipu/cogview/cogview-2", "name": "CogView2", "maker": "zhipu", "family": "zhipu/cogview", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2022-06-22", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogView2 is a cogview model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "what_it_is_for": "Earlier open CogView2 research release. The zai-org Hugging Face repo CogView2 exists, Apache-2.0 tag, arXiv 2204.14217. It is not on the 2026 API price list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogview/cogview-3": { "id": "zhipu/cogview/cogview-3", "name": "CogView-3", "maker": "zhipu", "family": "zhipu/cogview", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogView-3 is a cogview model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: deprecated. Confidence: high.", "what_it_is_for": "Previous CogView API generation. Still named on the China fine-tune and private-instance price lists. The open CogView3-Plus-3B checkpoint is cataloged as its own model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogview/cogview-3-flash": { "id": "zhipu/cogview/cogview-3-flash", "name": "CogView-3-Flash", "maker": "zhipu", "family": "zhipu/cogview", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogView-3-Flash is a cogview model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Free image model on the China price table. The older English platform index lists CogView-3-Flash as the free image generator.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogview/cogview-3-plus": { "id": "zhipu/cogview/cogview-3-plus", "name": "CogView3-Plus-3B", "maker": "zhipu", "family": "zhipu/cogview", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-04", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "CogView3 Plus diffusion, 3B.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogView3-Plus-3B is a cogview model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Open CogView3-Plus-3B text-to-image checkpoint, Apache-2.0, Hugging Face created 2024-10-04. China private-instance row uses int8 at 100 CNY per compute unit per day.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogview/cogview-4": { "id": "zhipu/cogview/cogview-4", "name": "CogView-4", "maker": "zhipu", "family": "zhipu/cogview", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-03", "context_length": null, "max_output_tokens": null, "parameters": "6B (open checkpoint CogView4-6B)", "architecture": "Diffusion text-to-image with a bilingual GLM-4 text encoder. HF base_model tag is glm-4-9b.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogView-4 is a cogview model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Z.ai calls CogView-4 its first open-source text-to-image model, with a bilingual GLM-4 encoder instead of an English-only T5, and Chinese character rendering. The open checkpoint is CogView4-6B, Apache-2.0, created 2025-03-03.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogvlm/cogvlm": { "id": "zhipu/cogvlm/cogvlm", "name": "CogVLM", "maker": "zhipu", "family": "zhipu/cogvlm", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2023-11-16", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogVLM is a cogvlm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "what_it_is_for": "Original CogVLM vision-language model. Apache-2.0 tag on zai-org/CogVLM. Chat and grounding HF ports exist from November 2023.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogvlm/cogvlm2": { "id": "zhipu/cogvlm/cogvlm2", "name": "CogVLM2", "maker": "zhipu", "family": "zhipu/cogvlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "custom (HF license:other)", "release_date": "2024-05-16", "context_length": null, "max_output_tokens": null, "parameters": "19B", "architecture": "CogVLM2 with a Llama 3 language backbone.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "CogVLM2 is a cogvlm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "CogVLM2 19B chat model on a Llama 3 backbone, license tag other. English and Chinese chat repos exist, plus video and int4 packs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/cogvlm/visualglm-6b": { "id": "zhipu/cogvlm/visualglm-6b", "name": "VisualGLM-6B", "maker": "zhipu", "family": "zhipu/cogvlm", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-05-17", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "VisualGLM-6B is a cogvlm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "what_it_is_for": "VisualGLM-6B, the Chinese visual chat model based on ChatGLM, repo created 2023-05-17.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/embedding/embedding-2": { "id": "zhipu/embedding/embedding-2", "name": "Embedding-2", "maker": "zhipu", "family": "zhipu/embedding", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": null }, "summary_intro": "Embedding-2 is a embedding model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Embedding-2, labeled V2. Same 8K context and same 0.5 CNY price as Embedding-3 on the 2026 table. Batch is half price.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/embedding/embedding-3": { "id": "zhipu/embedding/embedding-3", "name": "Embedding-3", "maker": "zhipu", "family": "zhipu/embedding", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "embedding" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": null }, "summary_intro": "Embedding-3 is a embedding model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Embedding-3, labeled V3 on the price table. 8K context. 0.5 CNY per million tokens, batch half price. Private instance fp16 at 100 CNY per compute unit per day.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/chatglm-6b": { "id": "zhipu/glm/chatglm-6b", "name": "ChatGLM-6B", "maker": "zhipu", "family": "zhipu/glm", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-03-13", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "6B bilingual chat model from the GLM line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChatGLM-6B is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "what_it_is_for": "First widely used ChatGLM chat model, 6B. Hugging Face zai-org/chatglm-6b was created 2023-03-13. Int4 and int8 packs followed. LMArena later labeled it non-commercial.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/chatglm2-6b": { "id": "zhipu/glm/chatglm2-6b", "name": "ChatGLM2-6B", "maker": "zhipu", "family": "zhipu/glm", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-06-24", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChatGLM2-6B is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "what_it_is_for": "Second ChatGLM generation, 6B, repo created 2023-06-24. A 32K variant and int4 packs exist.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/chatglm3-6b": { "id": "zhipu/glm/chatglm3-6b", "name": "ChatGLM3-6B", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-10-25", "context_length": null, "max_output_tokens": null, "parameters": "6B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChatGLM3-6B is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "ChatGLM3 6B, repo created 2023-10-25. Still named on the China fine-tune price list: LoRA at 8K, 0.025 CNY per 1K tokens. The commercial-license page says ChatGLM3-6B has a free commercial grant you apply for.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-130b": { "id": "zhipu/glm/glm-130b", "name": "GLM-130B", "maker": "zhipu", "family": "zhipu/glm", "status": "retired", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "130B (model name; paper table not re-read)", "architecture": "Bilingual pretrained GLM. The paper is arXiv:2210.02414.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-130B is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: medium.", "what_it_is_for": "GLM-130B is the 2022 bilingual pretrained GLM cited by the zai-org organization card and by later ChatGLM cards via arXiv:2210.02414. It is not a 2026 API model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-3-turbo": { "id": "zhipu/glm/glm-3-turbo", "name": "GLM-3-Turbo", "maker": "zhipu", "family": "zhipu/glm", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-3-Turbo is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: medium.", "what_it_is_for": "GLM-3-Turbo, the chat API from the ChatGLM3 generation. On older rate-limit tables, not on the 2026 token price table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-0520": { "id": "zhipu/glm/glm-4-0520", "name": "GLM-4-0520", "maker": "zhipu", "family": "zhipu/glm", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-05-20", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4-0520 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: deprecated. Confidence: high.", "what_it_is_for": "May 2024 GLM-4 API snapshot. Still named for tuned-model inference at 0.5 CNY per 1K tokens and for private instances. LMArena coding slice on 2026-01-16 listed Elo 1307.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-32b-0414": { "id": "zhipu/glm/glm-4-32b-0414", "name": "GLM-4-32B-0414", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-07", "context_length": 128000, "max_output_tokens": 16000, "parameters": "32B", "architecture": "Dense GLM-4 generation. Z.ai says it was pretrained on 15T tokens including synthetic reasoning data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.1 }, "summary_intro": "GLM-4-32B-0414 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Open 32B GLM-4 checkpoint dated 0414, MIT on Hugging Face, created 2025-04-07. Z.ai also sells GLM-4-32B-0414-128K at $0.1 per million input and output tokens, with a 128K context and 16K max output on that guide.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-5": { "id": "zhipu/glm/glm-4-5", "name": "GLM-4.5", "maker": "zhipu", "family": "zhipu/glm", "status": "deprecated", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-28", "context_length": 128000, "max_output_tokens": 96000, "parameters": "355B total / 32B active", "architecture": "Mixture-of-experts. Pretrained on 15T general tokens, then code, reasoning, and agent data. Context extended to 128K, then RL.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 2.2 }, "summary_intro": "GLM-4.5 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: deprecated. Confidence: high.", "what_it_is_for": "July 2025 MoE flagship that first fused reasoning, coding, and agent behavior in one Zhipu model. 355B total, 32B active. The China guide now says GLM-4.5 and GLM-4.5-X are about to be taken offline. Weights remain on Hugging Face under MIT.", "ratings": { "empirical_100": null, "lmsys_elo": 1233, "coding_elo": 1233, "swe_bench": 20.5, "humaneval": 74.4, "twelve_cs": 99.5, "aa_intelligence": 12.8, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1167, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-5-air": { "id": "zhipu/glm/glm-4-5-air", "name": "GLM-4.5-Air", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-28", "context_length": 128000, "max_output_tokens": 96000, "parameters": "106B total / 12B active", "architecture": "Smaller MoE sibling of GLM-4.5. Same training story: 15T pretrain, then code, reasoning, and agent data.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.2, "completion_per_m_usd": 1.1 }, "summary_intro": "GLM-4.5-Air is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "The smaller July 2025 agent model: 106B total and 12B active, sold as the cheaper GLM-4.5. It is still on both public token price lists, unlike the full GLM-4.5 which China has marked for removal. Open weights, MIT.", "ratings": { "empirical_100": null, "lmsys_elo": 1212, "coding_elo": 1212, "swe_bench": 17.1, "humaneval": 71.7, "twelve_cs": 52.0, "aa_intelligence": 11.1, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1118, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-5-airx": { "id": "zhipu/glm/glm-4-5-airx", "name": "GLM-4.5-AirX", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-28", "context_length": 128000, "max_output_tokens": null, "parameters": "106B total / 12B active (Air figure; AirX is the fast serving tier and the maker does not restate a different count)", "architecture": "Fast tier of GLM-4.5-Air. Family page: lightweight, strong, very fast response.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.1, "completion_per_m_usd": 4.5 }, "summary_intro": "GLM-4.5-AirX is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Fast API tier of GLM-4.5-Air. The family page sells it for speed. Z.ai prices it above Air and below GLM-4.5-X. Weights were not published under an AirX name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-5-flash": { "id": "zhipu/glm/glm-4-5-flash", "name": "GLM-4.5-Flash", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-28", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "Free served tier of the GLM-4.5 family. The family page says free, efficient, multi-purpose. No matching public checkpoint name was found.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4.5-Flash is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Free API tier shipped with GLM-4.5. Z.ai still marks the token price as free. It is older and smaller in role than the later free GLM-4.7-Flash, which has its own open weights.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-5-x": { "id": "zhipu/glm/glm-4-5-x", "name": "GLM-4.5-X", "maker": "zhipu", "family": "zhipu/glm", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-28", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "High-speed served tier of GLM-4.5. The family page calls it high performance, strong reasoning, very fast. No separate public checkpoint was listed.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.2, "completion_per_m_usd": 8.9 }, "summary_intro": "GLM-4.5-X is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: deprecated. Confidence: high.", "what_it_is_for": "Fast serving tier of GLM-4.5. Z.ai still prints a high USD price. The China model page says this tier is about to be removed along with GLM-4.5. No public weight repo was found.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-6": { "id": "zhipu/glm/glm-4-6", "name": "GLM-4.6", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-09-30", "context_length": 200000, "max_output_tokens": 131072, "parameters": "357B total / 32B active (Artificial Analysis)", "architecture": "Mixture-of-experts, Hugging Face tag glm4_moe. Context expanded from the 128K GLM-4.5 window to 200K.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 2.2 }, "summary_intro": "GLM-4.6 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "September 2025 coding model that stretched the GLM-4.5 line to a 200K context and a 128K output label. The migration guide treats it as the replacement for GLM-4.5. Open weights, MIT. International API still lists a token price.", "ratings": { "empirical_100": null, "lmsys_elo": 1230, "coding_elo": 1230, "swe_bench": 20.0, "humaneval": 74.0, "twelve_cs": 67.9, "aa_intelligence": 18.5, "aa_coding": 45.8, "aa_agentic": null, "design_arena_elo": 1086, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-7": { "id": "zhipu/glm/glm-4-7", "name": "GLM-4.7", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-22", "context_length": 200000, "max_output_tokens": 131072, "parameters": "357B total / 32B active (Artificial Analysis). Maker GLM-4.5 page says 355B/32B for that generation. AA uses 357B for 4.6 and 4.7.", "architecture": "Mixture-of-experts, Hugging Face tag glm4_moe, same family as GLM-4.5.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 2.2 }, "summary_intro": "GLM-4.7 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "December 2025 open-weight flagship before GLM-5. Zhipu describes better coding, more stable multi-step agents, and stronger front-end generation than GLM-4.6. MIT weights. Still on both price lists, but the Coding Plan now redirects this id to GLM-5.3-Flash.", "ratings": { "empirical_100": null, "lmsys_elo": 1233, "coding_elo": 1233, "swe_bench": 20.5, "humaneval": 74.4, "twelve_cs": 90.0, "aa_intelligence": 22.2, "aa_coding": 45.3, "aa_agentic": null, "design_arena_elo": 1114, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-7-flash": { "id": "zhipu/glm/glm-4-7-flash", "name": "GLM-4.7-Flash", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-01-19", "context_length": 200000, "max_output_tokens": null, "parameters": "", "architecture": "Hugging Face tag glm4_moe_lite. A public secondary table said 30B total / 3B active; that count was not on the maker changelog, so parameters stay null.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4.7-Flash is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Free, open-weight small model released with the GLM-4.7 generation. The changelog calls it the free edition of GLM-4.7, aimed at coding and everyday chat at no token charge.", "ratings": { "empirical_100": 39, "lmsys_elo": 1205, "coding_elo": 1205, "swe_bench": 16.0, "humaneval": 70.8, "twelve_cs": 67.9, "aa_intelligence": 14.9, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1118, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-7-flashx": { "id": "zhipu/glm/glm-4-7-flashx", "name": "GLM-4.7-FlashX", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2026-01-19", "context_length": 200000, "max_output_tokens": null, "parameters": "", "architecture": "Paid speed tier beside GLM-4.7-Flash. A separate weight repo was not in the zai-org listing.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.07, "completion_per_m_usd": 0.4 }, "summary_intro": "GLM-4.7-FlashX is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: medium.", "what_it_is_for": "Paid, faster API tier next to the free GLM-4.7-Flash. Both price lists carry it at a 200K context. Weights were not found as a separate public checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-9b": { "id": "zhipu/glm/glm-4-9b", "name": "GLM-4-9B", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "custom (HF license:other)", "release_date": "2024-06-04", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4-9B is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "June 2024 open GLM-4 9B chat model. License tag other. A 1M-context chat variant exists. China fine-tune row: LoRA and full parameter at 8K, 0.025 and 0.04 CNY per 1K tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-9b-0414": { "id": "zhipu/glm/glm-4-9b-0414", "name": "GLM-4-9B-0414", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-07", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4-9B-0414 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "April 2025 MIT refresh of the 9B GLM-4 line. Hugging Face GLM-4-9B-0414 created 2025-04-07. GLM-4.1V-9B uses it as a base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-air": { "id": "zhipu/glm/glm-4-air", "name": "GLM-4-Air", "maker": "zhipu", "family": "zhipu/glm", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4-Air is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: deprecated. Confidence: high.", "what_it_is_for": "GLM-4 Air before the 250414 refresh. Still fine-tunable and deployable as a private instance. Public token price on the 2026 table is the 250414 id, not this one.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-air-250414": { "id": "zhipu/glm/glm-4-air-250414", "name": "GLM-4-Air-250414", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-14", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 0.5 }, "summary_intro": "GLM-4-Air-250414 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "14 April 2025 cost-performance base model. Zhipu says it was pretrained on 15T tokens with synthetic reasoning data, then tuned for instructions, code, and function calls. The changelog compares it with GPT-4o and DeepSeek-V3-0324.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-airx": { "id": "zhipu/glm/glm-4-airx", "name": "GLM-4-AirX", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 8000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 10.0, "completion_per_m_usd": 10.0 }, "summary_intro": "GLM-4-AirX is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Fast GLM-4 Air tier. The family page calls it the high-speed version of the Air line. Context on the price table is 8K, much shorter than Air-250414.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-alltools": { "id": "zhipu/glm/glm-4-alltools", "name": "GLM-4-AllTools", "maker": "zhipu", "family": "zhipu/glm", "status": "retired", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4-AllTools is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: medium.", "what_it_is_for": "GLM-4 All Tools, an early tool-using API. It appears on older rate-limit and equity pages, not on the 2026 token table.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-assistant": { "id": "zhipu/glm/glm-4-assistant", "name": "GLM-4-Assistant", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 5.0 }, "summary_intro": "GLM-4-Assistant is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: medium.", "what_it_is_for": "GLM-4 assistant API model still priced on the China platform at the same 5 CNY rate as GLM-4-Plus, with a 128K context and no cache-hit discount.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-flash": { "id": "zhipu/glm/glm-4-flash", "name": "GLM-4-Flash", "maker": "zhipu", "family": "zhipu/glm", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4-Flash is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: deprecated. Confidence: high.", "what_it_is_for": "Original free GLM-4-Flash, before Flash-250414. Still on the fine-tune and private-instance lists.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-flash-250414": { "id": "zhipu/glm/glm-4-flash-250414", "name": "GLM-4-Flash-250414", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-14", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4-Flash-250414 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Free language model launched 14 April 2025 for light tasks. Still listed as free on the China price table at 128K context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-flashx-250414": { "id": "zhipu/glm/glm-4-flashx-250414", "name": "GLM-4-FlashX-250414", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-14", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.1 }, "summary_intro": "GLM-4-FlashX-250414 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "14 April 2025 fast, low-cost language model. The family page calls it the enhanced version of free GLM-4-Flash, with higher speed and concurrency.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-long": { "id": "zhipu/glm/glm-4-long", "name": "GLM-4-Long", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 1000000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 1.0 }, "summary_intro": "GLM-4-Long is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Long-context GLM-4 API model. The older English platform overview called GLM-4-Long the ultra-long input model. The 2026 China price row gives it a 1M context at 1 CNY per million tokens in and out.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-plus": { "id": "zhipu/glm/glm-4-plus", "name": "GLM-4-Plus", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 5.0 }, "summary_intro": "GLM-4-Plus is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "High-intelligence GLM-4 API model for long text and hard instructions. The China model page says it is close to GPT-4o on the vendor's own comparison table. It is still on the China token price list.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-4-voice": { "id": "zhipu/glm/glm-4-voice", "name": "GLM-4-Voice", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2024-10-24", "context_length": null, "max_output_tokens": null, "parameters": "9B (open checkpoint glm-4-voice-9b)", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": 80.0, "completion_per_m_usd": 80.0 }, "summary_intro": "GLM-4-Voice is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "End-to-end speech model that understands and speaks Chinese and English. China price 80 CNY per million tokens. Open pieces include glm-4-voice-9b plus a tokenizer and a decoder.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-5": { "id": "zhipu/glm/glm-5", "name": "GLM-5", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-12", "context_length": 200000, "max_output_tokens": 131072, "parameters": "744B total / 40B active", "architecture": "Mixture-of-experts with DeepSeek Sparse Attention. Pretraining data described as 28.5T tokens, up from 23T. Grew from the GLM-4.5 class (355B total / 32B active) per the maker guide.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.0, "completion_per_m_usd": 3.2 }, "summary_intro": "GLM-5 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "February 2026 foundation model for agentic engineering. Zhipu says it scaled the GLM-4.5-class MoE from 355B/32B to 744B/40B, added DeepSeek Sparse Attention, and aimed at Claude Opus 4.5 on software-engineering work. Open weights, MIT.", "ratings": { "empirical_100": null, "lmsys_elo": 1228, "coding_elo": 1228, "swe_bench": 19.8, "humaneval": 73.8, "twelve_cs": 99.5, "aa_intelligence": 27.9, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1148, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-5-1": { "id": "zhipu/glm/glm-5-1", "name": "GLM-5.1", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-04-07", "context_length": 200000, "max_output_tokens": 131072, "parameters": "744B total / 40B active (Artificial Analysis)", "architecture": "Mixture-of-experts, Hugging Face tag glm_moe_dsa. Same active-parameter class as GLM-5.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.4, "completion_per_m_usd": 4.4 }, "summary_intro": "GLM-5.1 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "April 2026 GLM-5 update aimed at long jobs. The changelog says it can keep working for up to about 8 hours in one run, from planning through delivery, and that Zhipu positioned it against Claude Opus 4.6. Context on the overview is 200K, not the later 1M window.", "ratings": { "empirical_100": null, "lmsys_elo": 1245, "coding_elo": 1245, "swe_bench": 22.6, "humaneval": 76.1, "twelve_cs": 99.5, "aa_intelligence": 26.1, "aa_coding": 55.8, "aa_agentic": 23.9, "design_arena_elo": 1336, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-5-2": { "id": "zhipu/glm/glm-5-2", "name": "GLM-5.2", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-06-16", "context_length": 1000000, "max_output_tokens": 131072, "parameters": "753B total / 40B active (Artificial Analysis, 2026-10-15). June 2026 AA article said same size as GLM-5.1 at 744B/40B.", "architecture": "Mixture-of-experts, Hugging Face tag glm_moe_dsa. 1M context is the documented change versus GLM-5.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.4, "completion_per_m_usd": 4.4 }, "summary_intro": "GLM-5.2 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "June 2026 flagship built for a usable 1M context and long coding jobs. Zhipu's changelog says it reduces context drift on long tasks and was the open-weight coding leader at launch. It has since been succeeded by GLM-5.3, which Zhipu says keeps this base.", "ratings": { "empirical_100": null, "lmsys_elo": 1305, "coding_elo": 1305, "swe_bench": 32.5, "humaneval": 84.0, "twelve_cs": 99.5, "aa_intelligence": 33.7, "aa_coding": 68.8, "aa_agentic": 38.4, "design_arena_elo": 1171, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-5-3": { "id": "zhipu/glm/glm-5-3", "name": "GLM-5.3", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "custom (Hugging Face tag license:other; not MIT)", "release_date": "2026-08-18", "context_length": 1000000, "max_output_tokens": 131072, "parameters": "753B total / 40B active (Artificial Analysis). Maker has not republished a count for this release.", "architecture": "Mixture-of-experts with DeepSeek Sparse Attention (Hugging Face architecture tag glm_moe_dsa). Official docs say GLM-5.3 uses the same base model as GLM-5.2 and that the gains are from post-training. GLM-5 itself was documented at 744B total / 40B active.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 1.4, "completion_per_m_usd": 4.4 }, "summary_intro": "GLM-5.3 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Zhipu's 2026 flagship text model for coding agents and long-horizon work. Release notes say it keeps the GLM-5.2 base and that the jump is post-training: a stated 50% gain on the internal Z.ai Code Bench, open-weight SOTA claims on Terminal Bench 3.0, and a large jump on the vendor's vulnerability-finding tests. Text in, text out. Thinking cannot be switched off.", "ratings": { "empirical_100": null, "lmsys_elo": 1272, "coding_elo": 1272, "swe_bench": 27.0, "humaneval": 79.6, "twelve_cs": 99.5, "aa_intelligence": 44.8, "aa_coding": 74.8, "aa_agentic": 53.1, "design_arena_elo": 1329, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-5-3-flash": { "id": "zhipu/glm/glm-5-3-flash", "name": "GLM-5.3-Flash", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-08-26", "context_length": 1000000, "max_output_tokens": 131072, "parameters": "320B total / 18B active", "architecture": "Mixture-of-experts with hybrid sparse and linear attention, IndexPool, and manifold-constrained hyper-connections (mHC). Hugging Face architecture tag glm5_next. Native multimodal, not a GLM-5.3 distill.", "modalities": { "input": [ "text", "image", "video", "file" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.15, "completion_per_m_usd": 0.5 }, "summary_intro": "GLM-5.3-Flash is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "The first native multimodal model in the GLM-5 series, and the cheap open-weight sibling of the flagship rather than a small copy of it. Zhipu says 320B total parameters with 18B active, hybrid linear and sparse attention, and a 1M context. It takes text, images, video, and files and returns text. Coding Plan docs say the beta name was OX Alpha. Thinking stays on.", "ratings": { "empirical_100": null, "lmsys_elo": 1213, "coding_elo": 1213, "swe_bench": 17.2, "humaneval": 71.8, "twelve_cs": 7.2, "aa_intelligence": 41.8, "aa_coding": 71.5, "aa_agentic": 50.9, "design_arena_elo": 1314, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-5-3-flashx": { "id": "zhipu/glm/glm-5-3-flashx", "name": "GLM-5.3-FlashX", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-08-26", "context_length": 1000000, "max_output_tokens": 131072, "parameters": "320B total / 18B active (same guide as GLM-5.3-Flash; a separate weight file was not identified)", "architecture": "Speed tier of GLM-5.3-Flash. The guide quotes about 200 tokens/s and does not describe a different architecture.", "modalities": { "input": [ "text", "image", "video", "file" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.37, "completion_per_m_usd": 1.25 }, "summary_intro": "GLM-5.3-FlashX is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "The high-speed serving tier of GLM-5.3-Flash. Zhipu documents it on the same page, with the same 1M context and image, video, file, and text inputs, and quotes about 200 tokens per second. It is a separate API model code and a higher price, not a separate research model.", "ratings": { "empirical_100": null, "lmsys_elo": 1218, "coding_elo": 1218, "swe_bench": 18.1, "humaneval": 72.5, "twelve_cs": 33.4, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-5-turbo": { "id": "zhipu/glm/glm-5-turbo", "name": "GLM-5-Turbo", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-03-15", "context_length": 200000, "max_output_tokens": null, "parameters": "", "architecture": "Served GLM-5-line model tuned for OpenClaw-style agents. Artificial Analysis lists weights as not available and does not publish a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-5-Turbo is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "March 2026 served model in the GLM-5 line, tuned for OpenClaw-style agents rather than released as public weights. The changelog stresses tool use, skill calling, multi-agent splits, and long tasks.", "ratings": { "empirical_100": null, "lmsys_elo": 1260, "coding_elo": 1260, "swe_bench": 25.0, "humaneval": 78.0, "twelve_cs": 99.5, "aa_intelligence": 26.6, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1245, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-asr": { "id": "zhipu/glm/glm-asr", "name": "GLM-ASR", "maker": "zhipu", "family": "zhipu/glm", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-23", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-ASR is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: deprecated. Confidence: high.", "what_it_is_for": "April 2025 speech model, before GLM-ASR-2512. The changelog says it transcribes from context, handles noise, and covers Mandarin, English, and several Mandarin dialect groups. It was free for a launch promotion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-asr-2512": { "id": "zhipu/glm/glm-asr-2512", "name": "GLM-ASR-2512", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-10", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "audio", "file" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.03, "completion_per_m_usd": null }, "summary_intro": "GLM-ASR-2512 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "December 2025 speech-recognition model. Z.ai says character error rate 0.0717 on its latest comparison and supports Mandarin plus Sichuan, Cantonese, Min Nan, and Wu, among other languages. China bills input tokens only.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-asr-nano-2512": { "id": "zhipu/glm/glm-asr-nano-2512", "name": "GLM-ASR-Nano-2512", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hugging Face tag glmasr.", "modalities": { "input": [ "audio" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-ASR-Nano-2512 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Open automatic-speech-recognition checkpoint GLM-ASR-Nano-2512, MIT, Hugging Face created 2025-12-09. Tag glmasr.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-edge-1-5b-chat": { "id": "zhipu/glm/glm-edge-1-5b-chat", "name": "GLM-Edge-1.5B-Chat", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "custom (HF license:other)", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-Edge-1.5B-Chat is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "On-device GLM edge chat model, about 1.5B, created 2024-11-20. License tag other. A GGUF pack exists.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-edge-4b-chat": { "id": "zhipu/glm/glm-edge-4b-chat", "name": "GLM-Edge-4B-Chat", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "custom (HF license:other)", "release_date": "2024-11-20", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-Edge-4B-Chat is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "On-device GLM edge chat model, about 4B, created 2024-11-20. License tag other.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-realtime-air": { "id": "zhipu/glm/glm-realtime-air", "name": "GLM-Realtime-Air", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio", "video" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-Realtime-Air is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Realtime audio-video tier. China price: audio 0.3 CNY per minute, video 2.1 CNY per minute. Inputs are text, audio, and video.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-realtime-flash": { "id": "zhipu/glm/glm-realtime-flash", "name": "GLM-Realtime-Flash", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio", "video" ], "output": [ "text", "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-Realtime-Flash is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Cheaper realtime tier. China price: audio 0.18 CNY per minute, video 1.2 CNY per minute. Same modalities as GLM-Realtime-Air.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-tts": { "id": "zhipu/glm/glm-tts", "name": "GLM-TTS", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Two-stage speech generation. Changelog mentions GRPO for error rate and emotion.", "modalities": { "input": [ "text" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-TTS is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "December 2025 text-to-speech model. Two-stage generation, streaming and non-streaming, vendor says first audio under 400 ms. China price 2 CNY per 10,000 characters. Open weights exist as zai-org/GLM-TTS, MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-tts-clone": { "id": "zhipu/glm/glm-tts-clone", "name": "GLM-TTS-Clone", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-12-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-TTS-Clone is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Voice-cloning add-on launched with GLM-TTS. About 3 seconds of clear speech makes a voice id. China price 6 CNY per clone. Supports Mandarin and light accents.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-z1-32b-0414": { "id": "zhipu/glm/glm-z1-32b-0414", "name": "GLM-Z1-32B-0414", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-08", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-Z1-32B-0414 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Open 32B reasoning model, MIT, created 2025-04-08. Sibling of the GLM-Z1 API tiers, not confirmed to be the same file as GLM-Z1-Air.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-z1-9b-0414": { "id": "zhipu/glm/glm-z1-9b-0414", "name": "GLM-Z1-9B-0414", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-08", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-Z1-9B-0414 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Open 9B reasoning model, MIT, created 2025-04-08.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-z1-air": { "id": "zhipu/glm/glm-z1-air", "name": "GLM-Z1-Air", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-14", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.5, "completion_per_m_usd": 0.5 }, "summary_intro": "GLM-Z1-Air is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "April 2025 reasoning API model. The changelog says the price was about 1/30 of DeepSeek-R1 at launch and that it is the cost-performance reasoning tier.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-z1-airx": { "id": "zhipu/glm/glm-z1-airx", "name": "GLM-Z1-AirX", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-14", "context_length": 32000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 5.0, "completion_per_m_usd": 5.0 }, "summary_intro": "GLM-Z1-AirX is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Fast reasoning tier launched the same day as GLM-Z1-Air. The changelog says peak speed can reach about 200 tokens per second, about 8 times a normal tier. Context on the price table is 32K.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-z1-flash": { "id": "zhipu/glm/glm-z1-flash", "name": "GLM-Z1-Flash", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-04-14", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-Z1-Flash is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Free reasoning model launched 14 April 2025. Still free on the China price table at 128K, with no cache hit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-z1-flashx": { "id": "zhipu/glm/glm-z1-flashx", "name": "GLM-Z1-FlashX", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.1, "completion_per_m_usd": 0.1 }, "summary_intro": "GLM-Z1-FlashX is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: medium.", "what_it_is_for": "Faster paid reasoning tier in the GLM-Z1 line. The 2026 price table gives it 128K context at 0.1 CNY per million tokens in and out, with no cache hit.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm/glm-z1-rumination-32b-0414": { "id": "zhipu/glm/glm-z1-rumination-32b-0414", "name": "GLM-Z1-Rumination-32B-0414", "maker": "zhipu", "family": "zhipu/glm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-13", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-Z1-Rumination-32B-0414 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Open 32B model with a rumination, tool-using deep-research mode. MIT. Hugging Face created 2025-04-13.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-image/glm-image": { "id": "zhipu/glm-image/glm-image", "name": "GLM-Image", "maker": "zhipu", "family": "zhipu/glm-image", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-01-14", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Autoregressive semantic model plus a diffusion decoder, per the changelog.", "modalities": { "input": [ "text" ], "output": [ "image" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-Image is a glm-image model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "January 2026 text-to-image model. Zhipu says it was trained fully on domestic chips and pairs an autoregressive understanding stage with a diffusion decoder. The overview calls out Chinese and English text inside images. Open weights, MIT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/autoglm-phone": { "id": "zhipu/glm-v/autoglm-phone", "name": "AutoGLM-Phone", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-11", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "Agent finetune of GLM-4.1V-9B-Base for phone GUI actions.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "AutoGLM-Phone is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Phone-use agent released 11 December 2025. Open weights AutoGLM-Phone-9B, MIT, finetune of GLM-4.1V-9B-Base. A multilingual checkpoint is a version.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-4-1v-9b-thinking": { "id": "zhipu/glm-v/glm-4-1v-9b-thinking", "name": "GLM-4.1V-9B-Thinking", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-07-02", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "GLM-4V reasoning finetune. HF base_model is GLM-4-9B-0414.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4.1V-9B-Thinking is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "July 2025 9B open vision-reasoning model. Hugging Face: image-text-to-text, MIT, created 2025-06-28, base GLM-4-9B-0414. The China changelog for the series is 2025-07-02.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-4-1v-thinking-flash": { "id": "zhipu/glm-v/glm-4-1v-thinking-flash", "name": "GLM-4.1V-Thinking-Flash", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2025-07-02", "context_length": 64000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4.1V-Thinking-Flash is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Free API tier of GLM-4.1V-Thinking. China price table: 64K context, input and output free, cache hit not supported.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-4-1v-thinking-flashx": { "id": "zhipu/glm-v/glm-4-1v-thinking-flashx", "name": "GLM-4.1V-Thinking-FlashX", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-07-02", "context_length": 64000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 2.0, "completion_per_m_usd": 2.0 }, "summary_intro": "GLM-4.1V-Thinking-FlashX is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: medium.", "what_it_is_for": "Paid fast tier of GLM-4.1V-Thinking. China price: 64K context, 2 CNY per million tokens in and out, cache hit not supported.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-4-5v": { "id": "zhipu/glm-v/glm-4-5v", "name": "GLM-4.5V", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-08-11", "context_length": 64000, "max_output_tokens": 16000, "parameters": "106B total / 12B active", "architecture": "MoE VLM on the GLM-4.5-Air base. Hugging Face base_model tag points at GLM-4.5-Air-Base. Tag glm4v_moe.", "modalities": { "input": [ "text", "image", "video", "file" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.6, "completion_per_m_usd": 1.8 }, "summary_intro": "GLM-4.5V is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "August 2025 open vision-reasoning model built on GLM-4.5-Air. Z.ai says 106B total and 12B active, image, video, and file input, 16K max output. Thinking can be switched.", "ratings": { "empirical_100": null, "lmsys_elo": 1226, "coding_elo": 1226, "swe_bench": 19.5, "humaneval": 73.6, "twelve_cs": 99.5, "aa_intelligence": 7.6, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-4-6v": { "id": "zhipu/glm-v/glm-4-6v", "name": "GLM-4.6V", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-08", "context_length": 128000, "max_output_tokens": null, "parameters": "106B class (maker says 100B-level for the previous V model; HF tag is glm4v_moe. An exact 4.6V count was not in the extracted changelog.)", "architecture": "MoE vision-language model, Hugging Face tag glm4v_moe.", "modalities": { "input": [ "text", "image", "video", "file" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.3, "completion_per_m_usd": 0.9 }, "summary_intro": "GLM-4.6V is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "December 2025 open vision-language model. The changelog says the visual context is 128K, enough for about 150 document pages, 200 slides, or about an hour of video, with native tool calling and screenshot-to-code.", "ratings": { "empirical_100": null, "lmsys_elo": 1213, "coding_elo": 1213, "swe_bench": 17.2, "humaneval": 71.8, "twelve_cs": 99.5, "aa_intelligence": 11.2, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-4-6v-flash": { "id": "zhipu/glm-v/glm-4-6v-flash", "name": "GLM-4.6V-Flash", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-12-08", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "Smaller public vision checkpoint in the GLM-4.6V line. Parameter count was not in the extracted docs.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4.6V-Flash is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Free vision tier released with GLM-4.6V. Open weights, MIT. China and Z.ai both list the API as free.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-4-6v-flashx": { "id": "zhipu/glm-v/glm-4-6v-flashx", "name": "GLM-4.6V-FlashX", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-12-08", "context_length": 128000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.04, "completion_per_m_usd": 0.4 }, "summary_intro": "GLM-4.6V-FlashX is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: medium.", "what_it_is_for": "Paid fast vision tier beside GLM-4.6V-Flash. China price is tiered at 32K. Z.ai lists $0.04 in and $0.40 out per million tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-4v": { "id": "zhipu/glm-v/glm-4v", "name": "GLM-4V", "maker": "zhipu", "family": "zhipu/glm-v", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4V is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: deprecated. Confidence: high.", "what_it_is_for": "Original GLM-4V API. Still on the fine-tune list (LoRA at 2K, 0.03 CNY per 1K tokens) and private-instance list (image int8, 200 CNY per compute unit per day).", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-4v-9b": { "id": "zhipu/glm-v/glm-4v-9b", "name": "GLM-4V-9B", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "custom (HF license:other)", "release_date": "2024-06-04", "context_length": null, "max_output_tokens": null, "parameters": "9B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4V-9B is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "June 2024 open 9B vision-language model, license tag other. Base for the December 2024 CogAgent-9B checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-4v-flash": { "id": "zhipu/glm-v/glm-4v-flash", "name": "GLM-4V-Flash", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4V-Flash is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Free single-image understanding model. China price table: 4K context, input and output free, cache hit not supported.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-4v-plus": { "id": "zhipu/glm-v/glm-4v-plus", "name": "GLM-4V-Plus", "maker": "zhipu", "family": "zhipu/glm-v", "status": "deprecated", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-4V-Plus is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: deprecated. Confidence: high.", "what_it_is_for": "Earlier GLM-4V Plus API, still on the private-instance price list for image and video int8 at 200 CNY per compute unit per day. The dated 0111 snapshot is the priced public tier.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-4v-plus-0111": { "id": "zhipu/glm-v/glm-4v-plus-0111", "name": "GLM-4V-Plus-0111", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 16000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 4.0, "completion_per_m_usd": 4.0 }, "summary_intro": "GLM-4V-Plus-0111 is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Image and video understanding API dated 0111 in the model code. The older English index says it handles up to 5 images and about 2 hours of video. The 2026 China price row is 16K context at 4 CNY per million tokens in and out.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-5v-turbo": { "id": "zhipu/glm-v/glm-5v-turbo", "name": "GLM-5V-Turbo", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-04-02", "context_length": 200000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-5V-Turbo is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "April 2026 multimodal coding model. The changelog says it is smaller than the flagship text models, adds GUI and coding-agent tools, and can box, screenshot, and read pages that contain images. Weights are not on the Artificial Analysis row.", "ratings": { "empirical_100": null, "lmsys_elo": 1260, "coding_elo": 1260, "swe_bench": 25.0, "humaneval": 78.0, "twelve_cs": 99.5, "aa_intelligence": 23.5, "aa_coding": null, "aa_agentic": null, "design_arena_elo": 1067, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-edge-v-2b": { "id": "zhipu/glm-v/glm-edge-v-2b", "name": "GLM-Edge-V-2B", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "custom (HF license:other)", "release_date": "2024-11-24", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-Edge-V-2B is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "On-device vision edge model, about 2B, created 2024-11-24. License tag other.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-edge-v-5b": { "id": "zhipu/glm-v/glm-edge-v-5b", "name": "GLM-Edge-V-5B", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "custom (HF license:other)", "release_date": "2024-11-24", "context_length": null, "max_output_tokens": null, "parameters": "5B", "architecture": "", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "GLM-Edge-V-5B is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "On-device vision edge model, about 5B, created 2024-11-24. License tag other.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/glm-v/glm-ocr": { "id": "zhipu/glm-v/glm-ocr", "name": "GLM-OCR", "maker": "zhipu", "family": "zhipu/glm-v", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-02-03", "context_length": 32000, "max_output_tokens": null, "parameters": "0.9B", "architecture": "CogViT visual encoder plus a GLM-0.5B decoder, per the 2026-02-03 changelog.", "modalities": { "input": [ "image", "pdf" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": 0.03, "completion_per_m_usd": 0.03 }, "summary_intro": "GLM-OCR is a glm-v model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "0.9B document OCR model. Zhipu says a CogViT encoder plus a GLM-0.5B decoder. OmniDocBench V1.5 score 94.62 at launch. MIT weights. Layout-parsing API, not the general chat model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/rerank/rerank": { "id": "zhipu/rerank/rerank", "name": "Rerank", "maker": "zhipu", "family": "zhipu/rerank", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": 4000, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "scores" ] }, "pricing": { "prompt_per_m_usd": 0.8, "completion_per_m_usd": null }, "summary_intro": "Rerank is a rerank model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "Text rerank model. 4K context. 0.8 CNY per million tokens. The knowledge-base price list also names GLM-rerank-pro at the same 0.8 CNY rate for multimodal rerank.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/scail/scail-2": { "id": "zhipu/scail/scail-2", "name": "SCAIL-2", "maker": "zhipu", "family": "zhipu/scail", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2026-06-09", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pose-driven character animation. HF tags include image-to-video.", "modalities": { "input": [ "image" ], "output": [ "video" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SCAIL-2 is a scail model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "what_it_is_for": "June 2026 character-animation video model. Hugging Face zai-org/SCAIL-2, MIT, image-to-video, paper arXiv:2606.10804, created 2026-06-09.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zhipu/webglm/webglm": { "id": "zhipu/webglm/webglm", "name": "WebGLM", "maker": "zhipu", "family": "zhipu/webglm", "status": "retired", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "2023-05-28", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WebGLM is a webglm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "what_it_is_for": "WebGLM, the 2023 web-augmented GLM. Repo created 2023-05-28. A 2B variant exists. Paper arXiv:2306.07906.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/chatcell/chatcell-base": { "id": "zju/chatcell/chatcell-base", "name": "ChatCell-base", "maker": "zju", "family": "zju/chatcell", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 text-to-text checkpoint. Tags say biology and single-cell analysis. Pipeline tag on the API record is text-generation. No license tag was present.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChatCell-base is a Zhejiang University model in the chatcell family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Single-cell analysis model in the ChatCell size series. The card body and paper were not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/chatcell/chatcell-large": { "id": "zju/chatcell/chatcell-large", "name": "ChatCell-large", "maker": "zju", "family": "zju/chatcell", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 text-to-text checkpoint. Tags say biology and single-cell analysis. Pipeline tag on the API record is text-generation. No license tag was present.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChatCell-large is a Zhejiang University model in the chatcell family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Single-cell analysis model in the ChatCell size series. The card body and paper were not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/chatcell/chatcell-small": { "id": "zju/chatcell/chatcell-small", "name": "ChatCell-small", "maker": "zju", "family": "zju/chatcell", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "T5 text-to-text checkpoint. Tags say biology and single-cell analysis. Pipeline tag on the API record is text-generation. No license tag was present.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChatCell-small is a Zhejiang University model in the chatcell family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Single-cell analysis model in the ChatCell size series. The card body and paper were not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/chineseguard/chineseguard-1-5b": { "id": "zju/chineseguard/chineseguard-1-5b", "name": "ChineseGuard-1.5B", "maker": "zju", "family": "zju/chineseguard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Chinese harmful-content classifier. Pipeline tag text-classification. Dataset tag zjunlp/ChineseHarm-bench. License CC-BY-NC-4.0. Hub base_model tag is Qwen/Qwen2.5-1.5B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChineseGuard-1.5B is a Zhejiang University model in the chineseguard family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Chinese harmful-content detector from the ChineseHarm-Bench release. The 7B card shows a short text such as a spam SMS being passed to a local inference script.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/chineseguard/chineseguard-3b": { "id": "zju/chineseguard/chineseguard-3b", "name": "ChineseGuard-3B", "maker": "zju", "family": "zju/chineseguard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "3B", "architecture": "Chinese harmful-content classifier. Pipeline tag text-classification. Dataset tag zjunlp/ChineseHarm-bench. License CC-BY-NC-4.0. Tags include qwen2, but no base_model id was in the API record.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChineseGuard-3B is a Zhejiang University model in the chineseguard family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Chinese harmful-content detector from the ChineseHarm-Bench release. The 7B card shows a short text such as a spam SMS being passed to a local inference script.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/chineseguard/chineseguard-7b": { "id": "zju/chineseguard/chineseguard-7b", "name": "ChineseGuard-7B", "maker": "zju", "family": "zju/chineseguard", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Chinese harmful-content classifier. Pipeline tag text-classification. Dataset tag zjunlp/ChineseHarm-bench. License CC-BY-NC-4.0. Hub base_model tag is Qwen/Qwen2.5-7B-Instruct.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ChineseGuard-7B is a Zhejiang University model in the chineseguard family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Chinese harmful-content detector from the ChineseHarm-Bench release. The 7B card shows a short text such as a spam SMS being passed to a local inference script.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/datamind/datamind-14b": { "id": "zju/datamind/datamind-14b", "name": "DataMind-14B", "maker": "zju", "family": "zju/datamind", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2-tagged text checkpoint on the zjunlp Hub. API license apache-2.0. The models list badge read 15B, which disagrees with the 14B name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DataMind-14B is a Zhejiang University model in the datamind family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Named DataMind checkpoint from the ZJUNLP data-analysis project. The paper studies how to improve open models at data understanding, code generation, and planning. This repo's card does not say which training stage it is.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/datamind/datamind-7b": { "id": "zju/datamind/datamind-7b", "name": "DataMind-7B", "maker": "zju", "family": "zju/datamind", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen2-tagged text checkpoint on the zjunlp Hub. API license apache-2.0. No size badge in the API record. Card body was only the license line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DataMind-7B is a Zhejiang University model in the datamind family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Named DataMind checkpoint from the ZJUNLP data-analysis project. The paper studies how to improve open models at data understanding, code generation, and planning. This repo's card does not say which training stage it is.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/datamind/datamind-analysis-14b": { "id": "zju/datamind/datamind-analysis-14b", "name": "DataMind-Analysis-Qwen2.5-14B", "maker": "zju", "family": "zju/datamind", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Qwen/Qwen2.5-14B-Instruct. The models list badge read 15B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DataMind-Analysis-Qwen2.5-14B is a Zhejiang University model in the datamind family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Larger DataMind analysis checkpoint paired with the 7B analysis model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/datamind/datamind-analysis-7b": { "id": "zju/datamind/datamind-analysis-7b", "name": "DataMind-Analysis-Qwen2.5-7B", "maker": "zju", "family": "zju/datamind", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Fine-tune of Qwen/Qwen2.5-7B-Instruct. Tags include data-analysis and code-generation. A collection line showed an 8B badge, which disagrees with the 7B name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DataMind-Analysis-Qwen2.5-7B is a Zhejiang University model in the datamind family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Analysis specialist tied to the DataMind paper. The Hub tags say text generation for data analysis and code.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/datapr/datapr-dabstep": { "id": "zju/datapr/datapr-dabstep", "name": "DataPRM-DABStep", "maker": "zju", "family": "zju/datapr", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3 checkpoint. MIT. No arXiv tag in the API record. The name pairs DataPRM with the DABStep benchmark. The expansion of PRM was not read from a card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DataPRM-DABStep is a Zhejiang University model in the datapr family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Named DataPRM checkpoint for DABStep. The card was not opened, so the reward or scoring interface is not stated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/datapr/datapr-scienceagentbench": { "id": "zju/datapr/datapr-scienceagentbench", "name": "DataPRM-ScienceAgentBench", "maker": "zju", "family": "zju/datapr", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3 checkpoint. MIT. No arXiv tag. Named for ScienceAgentBench.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "DataPRM-ScienceAgentBench is a Zhejiang University model in the datapr family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Named DataPRM checkpoint for ScienceAgentBench. The card was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/haldet/haldet-13b": { "id": "zju/haldet/haldet-13b", "name": "HalDet-LLaVA-13B", "maker": "zju", "family": "zju/haldet", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "LLaVA-LLaMA text-generation checkpoint. MIT. Tags include safetensors. The README was not opened.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HalDet-LLaVA-13B is a Zhejiang University model in the haldet family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "13B HalDet LLaVA checkpoint paired with the 7B repo.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/haldet/haldet-7b": { "id": "zju/haldet/haldet-7b", "name": "HalDet-LLaVA-7B", "maker": "zju", "family": "zju/haldet", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "LLaVA text-generation checkpoint. MIT. The 7B size is the repo name. The README was not opened.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "HalDet-LLaVA-7B is a Zhejiang University model in the haldet family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Named HalDet LLaVA checkpoint. The name indicates hallucination detection. The task definition was not read from a card or paper.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/instructbiomol/instructbiomol": { "id": "zju/instructbiomol/instructbiomol", "name": "InstructBioMol", "maker": "zju", "family": "zju/instructbiomol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Llama-tagged multimodal biomolecular LLM from the Hangzhou Innovation Center lab at Zhejiang University. The card says any-to-any alignment among natural language, molecules, and proteins, including 3D inputs. Sequences are handled in the language-model tokenizer. An exact parameter count was not on the card text that was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InstructBioMol is a Zhejiang University model in the instructbiomol family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Instruction-following model for understanding and designing molecules and proteins. The lab says a researcher can state a design goal in language and receive a biomolecular output.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/instructcell/instructcell-chat": { "id": "zju/instructcell/instructcell-chat", "name": "InstructCell-chat", "maker": "zju", "family": "zju/instructcell", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PyTorch checkpoint tagged instructcell and arXiv 2501.08187. License MIT. The paper title was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InstructCell-chat is a Zhejiang University model in the instructcell family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "InstructCell single-cell copilot checkpoint. The zjunlp org card describes an InstructCell Space as a multi-modal copilot for single-cell analysis. This repo's README was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/instructcell/instructcell-instruct": { "id": "zju/instructcell/instructcell-instruct", "name": "InstructCell-instruct", "maker": "zju", "family": "zju/instructcell", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "PyTorch checkpoint tagged instructcell and arXiv 2501.08187. License MIT. The paper title was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InstructCell-instruct is a Zhejiang University model in the instructcell family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "InstructCell single-cell copilot checkpoint. The zjunlp org card describes an InstructCell Space as a multi-modal copilot for single-cell analysis. This repo's README was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/instructprotein/instructprotein": { "id": "zju/instructprotein/instructprotein", "name": "InstructProtein", "maker": "zju", "family": "zju/instructprotein", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "OPT text-generation checkpoint. License MIT. arXiv tag 2310.03269. The paper title was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "InstructProtein is a Zhejiang University model in the instructprotein family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Protein instruction model from the same HICAI-ZJU lab as InstructBioMol. The card body was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/kgeditor/kgeditor": { "id": "zju/kgeditor/kgeditor", "name": "KGEditor", "maker": "zju", "family": "zju/kgeditor", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Token-classification repo. arXiv tag 2301.10405. Dataset tag ChancesYuan/KGEditor. No license tag. README was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KGEditor is a Zhejiang University model in the kgeditor family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Knowledge-graph editing model released on the zjunlp Hub. The paper title was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/knowlm/knowlm-13b-base": { "id": "zju/knowlm/knowlm-13b-base", "name": "KnowLM-13B-Base", "maker": "zju", "family": "zju/knowlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Second-stage full pretraining on LLaMA-13B with Chinese and English data, released as knowlm-13b-base-v1.0. The card says this improves Chinese comprehension. Exact token count and context length were not on the card that was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KnowLM-13B-Base is a Zhejiang University model in the knowlm family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "ZJUNLP's bilingual base checkpoint in the KnowLM framework. The card says it continues pretraining from LLaMA-13B so the model handles Chinese better while keeping a LLaMA-13B initialization.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/knowlm/knowlm-13b-ie": { "id": "zju/knowlm/knowlm-13b-ie", "name": "KnowLM-13B-IE", "maker": "zju", "family": "zju/knowlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Extraction-oriented checkpoint derived from KnowLM-13B-ZhiXi. The card says about 10 percent of Chinese and English information-extraction data was sampled, with negative sampling. A separate LoRA repo, knowlm-13b-ie-lora, was also uploaded. The DeepKE note says the November 2023 weight update adjusted NAN outputs, shortened generation, and allowed instructions without a schema.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KnowLM-13B-IE is a Zhejiang University model in the knowlm family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Information-extraction variant of ZhiXi. The card says it is slightly more useful for extraction and less useful as a general model. It targets named-entity style outputs from bilingual IE data.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/knowlm/knowlm-13b-zhixi": { "id": "zju/knowlm/knowlm-13b-zhixi", "name": "KnowLM-13B-ZhiXi", "maker": "zju", "family": "zju/knowlm", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Instruction-tuned chat model on KnowLM-13B-Base. ZJUNLP also published the same checkpoint as a LLaMA weight diff (zhixi-13b-diff and zhixi-13b-diff-fp16) and as zhixi-13b-lora. Papers cited on the diff repos are the LLaMA report and arXiv 2305.11527.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KnowLM-13B-ZhiXi is a Zhejiang University model in the knowlm family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "ZhiXi (\u667a\u6790) is the KnowLM chat model for general instructions plus information extraction. The KnowLM README describes a two-step build: further pretraining of LLaMA-13B, then instruction tuning. The card says this repo is directive-tuned on KnowLM-13B-Base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/knowlm/knowlm-7b-base": { "id": "zju/knowlm/knowlm-7b-base", "name": "KnowLM-7B-Base", "maker": "zju", "family": "zju/knowlm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "7B LLaMA-family base uploaded as zjunlp/knowlm-7b-base. The API record has a text-generation pipeline and no license tag. The card body was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KnowLM-7B-Base is a Zhejiang University model in the knowlm family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Smaller KnowLM base checkpoint published beside the 13B line. The card body was not opened, so the training mix is not restated here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/knowlm/knowlm-7b-chat": { "id": "zju/knowlm/knowlm-7b-chat", "name": "KnowLM-7B-Chat", "maker": "zju", "family": "zju/knowlm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "7B chat checkpoint, Hub pipeline text-generation, license Apache-2.0. Card body was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KnowLM-7B-Chat is a Zhejiang University model in the knowlm family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Chat-tuned 7B KnowLM checkpoint. The pairing with knowlm-7b-base is from the repo names. The card body was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/knowrl/knowrl-deepseek-r1-distill-qwen-7b": { "id": "zju/knowrl/knowrl-deepseek-r1-distill-qwen-7b", "name": "KnowRL-DeepSeek-R1-Distill-Qwen-7B", "maker": "zju", "family": "zju/knowrl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MIT checkpoint tagged qwen2 and arXiv 2506.19807. The models list badge read 8B while the name says 7B. It is a ZJUNLP training run on a DeepSeek distilled Qwen checkpoint, released under the KnowRL name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KnowRL-DeepSeek-R1-Distill-Qwen-7B is a Zhejiang University model in the knowrl family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "KnowRL factuality run starting from DeepSeek-R1-Distill-Qwen-7B. The paper title says knowledgeable reinforcement learning for factuality. The card body was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/knowrl/knowrl-skywork-or1-7b-preview": { "id": "zju/knowrl/knowrl-skywork-or1-7b-preview", "name": "KnowRL-Skywork-OR1-7B-Preview", "maker": "zju", "family": "zju/knowrl", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MIT checkpoint tagged qwen2 and the KnowRL paper. The models list badge read 8B. The name says it starts from Skywork-OR1-7B-Preview.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KnowRL-Skywork-OR1-7B-Preview is a Zhejiang University model in the knowrl family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Second published KnowRL base, from Skywork-OR1-7B-Preview. Skywork's own model is not re-catalogued here.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/knowself/knowself": { "id": "zju/knowself/knowself", "name": "KnowSelf", "maker": "zju", "family": "zju/knowself", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Four MIT agent checkpoints. Two bases, Llama 3.1 8B and Gemma 2 2B, each on ALFWorld and WebShop. The paper was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "KnowSelf is a Zhejiang University model in the knowself family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "KnowSelf agent weights for two embodied or web shopping environments. They are task snapshots of one named release, not four products.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/labvla/labvla-5b": { "id": "zju/labvla/labvla-5b", "name": "LabVLA-5B-Base", "maker": "zju", "family": "zju/labvla", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2026-06-11", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Vision-language-action model. The card says a Qwen3-VL-4B-Instruct backbone plus a DiT flow-matching action expert. Training is FAST action-token pretraining, then flow-matching posttraining under knowledge insulation. The repo name says 5B. The card does not give a separate total parameter count.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LabVLA-5B-Base is a Zhejiang University model in the labvla family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "VLA policy for scientific laboratories: instruments and transparent liquids, rather than household robot data. The card calls it the first VLA aimed at that setting.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/lightthinker/lightthinker-llama": { "id": "zju/lightthinker/lightthinker-llama", "name": "LightThinker-Llama", "maker": "zju", "family": "zju/lightthinker", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Llama checkpoint for the same thought-compression method. The Hub model page that was read said model size 8B params and BF16. The card YAML was empty. The paper evaluates Llama3.1-8B as well as Qwen, but the efficiency percentages quoted for Qwen were not repeated for Llama in the text that was read.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LightThinker-Llama is a Zhejiang University model in the lightthinker family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Llama checkpoint of LightThinker. It compresses intermediate reasoning instead of keeping the full chain in context.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/lightthinker/lightthinker-plus-llama": { "id": "zju/lightthinker/lightthinker-plus-llama", "name": "LightThinker-Plus-Llama", "maker": "zju", "family": "zju/lightthinker", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Plus-line Llama checkpoint. License MIT. A collection line showed 266k beside the repo. That figure was not confirmed as a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LightThinker-Plus-Llama is a Zhejiang University model in the lightthinker family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Llama checkpoint of LightThinker Plus.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/lightthinker/lightthinker-plus-qwen": { "id": "zju/lightthinker/lightthinker-plus-qwen", "name": "LightThinker-Plus-Qwen", "maker": "zju", "family": "zju/lightthinker", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Plus-line Qwen checkpoint. License MIT. ArXiv tags match LightThinker and LightThinker++. A collection line showed 333k beside the repo. That figure was not confirmed as a parameter count.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LightThinker-Plus-Qwen is a Zhejiang University model in the lightthinker family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Qwen checkpoint of LightThinker Plus. The card body was not opened beyond the family description on the 30B page.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/lightthinker/lightthinker-plus-qwen3-30b-a3b": { "id": "zju/lightthinker/lightthinker-plus-qwen3-30b-a3b", "name": "LightThinker-Plus-Qwen3-30B-A3B", "maker": "zju", "family": "zju/lightthinker", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Qwen3 MoE checkpoint in the LightThinker Plus line. The Hub page that was read said model size 31B params, BF16, license MIT, and architecture tag qwen3_moe. The repo name says 30B-A3B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LightThinker-Plus-Qwen3-30B-A3B is a Zhejiang University model in the lightthinker family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Largest published LightThinker Plus checkpoint. Plus extends compression toward memory management for long reasoning and agent traces. The abstract's aggregate gains are not pinned to this Hub id in the text that was read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/lightthinker/lightthinker-qwen": { "id": "zju/lightthinker/lightthinker-qwen", "name": "LightThinker-Qwen", "maker": "zju", "family": "zju/lightthinker", "status": "active", "confidence": "high", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Qwen reasoning checkpoint trained to compress intermediate thoughts into gist tokens and drop the original chain. The collection badge read 8B. The paper says the Qwen runs use a Qwen model and does not, in the abstract, give the Hub id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "LightThinker-Qwen is a Zhejiang University model in the lightthinker family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Qwen checkpoint for step-by-step thought compression. The paper's goal is lower peak memory and faster inference with competitive accuracy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/molgen/molgen-7b": { "id": "zju/molgen/molgen-7b", "name": "MolGen-7B", "maker": "zju", "family": "zju/molgen", "status": "active", "confidence": "high", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "LLaMA causal LM over SELFIES, loaded as LlamaForCausalLM. The card shows de novo generation from the BOS token and completion from a partial SELFIES string. The citation is the ICLR 2024 chemical-feedback paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolGen-7B is a Zhejiang University model in the molgen family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Molecular generator. The card says it can sample molecules from scratch or finish a partial structure. Intended use is chemistry, not general chat.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/molgen/molgen-large": { "id": "zju/molgen/molgen-large", "name": "MolGen-large", "maker": "zju", "family": "zju/molgen", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BART molecular language model over SELFIES. The generation and optimization repos are separate Hub ids. Neither card body was opened. The 7B LLaMA model cites a later ICLR paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "MolGen-large is a Zhejiang University model in the molgen family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Earlier MolGen generator and its optimization checkpoint. They predate the 7B LLaMA MolGen on the Hub.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/molinst/molinst-biotext": { "id": "zju/molinst/molinst-biotext", "name": "Mol-Instructions biotext", "maker": "zju", "family": "zju/molinst", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-tuned LLaMA checkpoints for Mol-Instructions. arXiv tag 2306.08018. Biomolecular text task.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mol-Instructions biotext is a Zhejiang University model in the molinst family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Biomolecular instruction checkpoints released by zjunlp for the Mol-Instructions line. The paper title was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/molinst/molinst-molecule": { "id": "zju/molinst/molinst-molecule", "name": "Mol-Instructions molecule", "maker": "zju", "family": "zju/molinst", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-tuned LLaMA checkpoints for Mol-Instructions. arXiv tag 2306.08018. Molecule task. Versions change the base from LLaMA to LLaMA-2 to LLaMA-3-8B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mol-Instructions molecule is a Zhejiang University model in the molinst family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Biomolecular instruction checkpoints released by zjunlp for the Mol-Instructions line. The paper title was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/molinst/molinst-protein": { "id": "zju/molinst/molinst-protein", "name": "Mol-Instructions protein", "maker": "zju", "family": "zju/molinst", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-tuned LLaMA checkpoints for Mol-Instructions. arXiv tag 2306.08018. Protein task. No LLaMA-2 or LLaMA-3 protein repo was in the API list.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Mol-Instructions protein is a Zhejiang University model in the molinst family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Biomolecular instruction checkpoints released by zjunlp for the Mol-Instructions line. The paper title was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/mt5-ie/mt5-ie": { "id": "zju/mt5-ie/mt5-ie", "name": "mT5-IE", "maker": "zju", "family": "zju/mt5-ie", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "mT5 text-to-text checkpoint. MIT. The API record has no pipeline tag. The name says information extraction.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "mT5-IE is a Zhejiang University model in the mt5-ie family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Early multilingual T5 extraction checkpoint from zjunlp, before the LLaMA KnowLM and OneKE models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/ocean-router": { "id": "zju/oceangpt/ocean-router", "name": "Ocean-router", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Ultralytics repo tagged yolov11, yolov5, image-classification, routing, and marine images. License Apache-2.0. Pipeline tag is image-classification.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ocean-router is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Marine image router in the OceanGPT-X stack. The card was not opened, so whether it classifies or detects is taken from conflicting tags.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/ocean-yolo": { "id": "zju/oceangpt/ocean-yolo", "name": "Ocean-YOLO", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "YOLOv5 object-detection repo. Tags name fish, coral, and sonar detection, plus oceangpt-x. License Apache-2.0.", "modalities": { "input": [ "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Ocean-YOLO is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Marine object detector shipped with the OceanGPT-X perception pieces. The card body was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/oceanclip-0-15b": { "id": "zju/oceangpt/oceanclip-0-15b", "name": "OceanCLIP-0.15B", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "0.15B", "architecture": "OpenCLIP-style zero-shot classifier. Tags include bioclip, marine species, and oceangpt-x. License Apache-2.0. Pipeline tag is zero-shot-classification.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OceanCLIP-0.15B is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Small marine-species vision-language model in the OceanGPT-X stack. The card body was not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/oceangpt-basic-14b": { "id": "zju/oceangpt/oceangpt-basic-14b", "name": "OceanGPT-basic-14B", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-07-04", "context_length": null, "max_output_tokens": null, "parameters": "14B", "architecture": "Legacy 14B text model. README says it is based on Qwen. Hub tags are qwen2 and OceanInstruct. The live API id is OceanGPT-basic-14B-v0.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OceanGPT-basic-14B is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Legacy larger OceanGPT text checkpoint from the July 2024 release, based on Qwen.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/oceangpt-basic-2b": { "id": "zju/oceangpt/oceangpt-basic-2b", "name": "OceanGPT-basic-2B", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2024-07-04", "context_length": null, "max_output_tokens": null, "parameters": "2B", "architecture": "Legacy 2B text model. README says MiniCPM. Hub tags include minicpm and custom_code. Live API id is OceanGPT-basic-2B-v0.1.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OceanGPT-basic-2B is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Smallest legacy OceanGPT text checkpoint, for local marine QA experiments.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/oceangpt-basic-30b-a3b-instruct": { "id": "zju/oceangpt/oceangpt-basic-30b-a3b-instruct", "name": "OceanGPT-basic-30B-A3B-Instruct", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-12-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Instruction-tuned ocean model. Hub base_model tag is Qwen/Qwen3-30B-A3B-Instruct-2507. The README calls the 30B-A3B instruct and thinking pair recommended and says they were trained with Huawei Ascend 910B. The Hub list badge read 31B while the name says 30B-A3B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OceanGPT-basic-30B-A3B-Instruct is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Recommended text OceanGPT for marine question answering. The README's basic line is for ocean-science text, not the multimodal or MOOS-code checkpoints.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/oceangpt-basic-30b-a3b-thinking": { "id": "zju/oceangpt/oceangpt-basic-30b-a3b-thinking", "name": "OceanGPT-basic-30B-A3B-Thinking", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-12-15", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Reasoning-style ocean model. Hub base_model tag is Qwen/Qwen3-30B-A3B-Thinking-2507. Same 2025-12-15 release note as the instruct pair. Hub list badge read 31B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OceanGPT-basic-30B-A3B-Thinking is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Thinking variant of the recommended 30B-A3B OceanGPT. The README lists it separately from the instruct checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/oceangpt-basic-30b-oceanpile-sci": { "id": "zju/oceangpt/oceangpt-basic-30b-oceanpile-sci", "name": "OceanGPT-basic-30B-OceanPile-Sci", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Hub repo with qwen3_moe weights. The models list badge read 31B. It is not in the README model table. The name says OceanPile-Sci continued training on the 30B basic line.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OceanGPT-basic-30B-OceanPile-Sci is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Domain OceanPile science snapshot of the 30B ocean model. The card was not opened, so the training mix is not restated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/oceangpt-basic-4b-instruct": { "id": "zju/oceangpt/oceangpt-basic-4b-instruct", "name": "OceanGPT-basic-4B-Instruct", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-12-15", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Instruction-tuned ocean model. Hub base_model tag is Qwen/Qwen3-4B-Instruct-2507. README lists it as recommended beside the 30B-A3B models. Hub list badge also read 4B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OceanGPT-basic-4B-Instruct is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Smaller recommended OceanGPT text model for marine question answering.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/oceangpt-basic-4b-thinking": { "id": "zju/oceangpt/oceangpt-basic-4b-thinking", "name": "OceanGPT-basic-4B-Thinking", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-12-15", "context_length": null, "max_output_tokens": null, "parameters": "4B", "architecture": "Thinking ocean model. Hub base_model tag is Qwen/Qwen3-4B-Thinking-2507. Released in the same 2025-12-15 note.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OceanGPT-basic-4B-Thinking is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Thinking variant of the recommended 4B OceanGPT.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/oceangpt-basic-7b": { "id": "zju/oceangpt/oceangpt-basic-7b", "name": "OceanGPT-basic-7B", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2023-10-04", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Three published 7B snapshots with different bases. v0.1 is the ACL-era LLaMA2 checkpoint. v0.2 is the Qwen legacy row. v0.3 is a Qwen2.5-7B-Instruct fine-tune. They share the OceanGPT-basic-7B name but are not the same weights.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OceanGPT-basic-7B is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Original OceanGPT size. v0.1 is the model released with the ocean-science paper. Later snapshots retarget the same 7B slot onto Qwen bases.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/oceangpt-basic-8b": { "id": "zju/oceangpt/oceangpt-basic-8b", "name": "OceanGPT-basic-8B", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "8B", "architecture": "Text ocean model. Hub base_model tag is Qwen/Qwen3-8B. The README lists it without the recommended or legacy label used on other rows. The inference sample on the README is written for this id.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OceanGPT-basic-8B is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Mid-size Qwen3 OceanGPT for marine text. The README uses it as the text inference example.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/oceangpt-coder-0-6b": { "id": "zju/oceangpt/oceangpt-coder-0-6b", "name": "OceanGPT-coder-0.6B", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-06-17", "context_length": null, "max_output_tokens": null, "parameters": "0.6B", "architecture": "Small code ocean model. Hub base_model tag is Qwen/Qwen3-0.6B. README lists it under the coder row.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OceanGPT-coder-0.6B is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "0.6B MOOS-code OceanGPT released on 2025-06-17.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/oceangpt-coder-7b": { "id": "zju/oceangpt/oceangpt-coder-7b", "name": "OceanGPT-coder-7B", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-04-20", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Code ocean model. Hub base_model tag is Qwen/Qwen2.5-Coder-7B. The README sample asks for MOOS code for an underwater robot.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OceanGPT-coder-7B is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Recommended OceanGPT for MOOS code used by underwater robots. It is not a general coding model in the project's own limit statement.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/oceangpt-o-7b": { "id": "zju/oceangpt/oceangpt-o-7b", "name": "OceanGPT-o-7B", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "high", "weights": "open-weights", "license": "mit", "release_date": "2025-04-20", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Multimodal ocean model. Hub base_model tag is Qwen/Qwen2.5-VL-7B-Instruct. Tags include image-text-to-text and OceanInstruct-o. The README inference sample uses Qwen2_5_VLForConditionalGeneration.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OceanGPT-o-7B is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Recommended multimodal OceanGPT for natural-language interpretation of certain sonar and marine-science images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oceangpt/oceangpt-o-8b": { "id": "zju/oceangpt/oceangpt-o-8b", "name": "OceanGPT-o-8B", "maker": "zju", "family": "zju/oceangpt", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Three domain Hub repos named OceanGPT-o-8B plus an OceanPile split (Sci, Bio, Sonar). They are not in the README model table. Sci's list badge read 9B, which disagrees with the 8B name.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OceanGPT-o-8B is a Zhejiang University model in the oceangpt family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Domain multimodal OceanPile snapshots for science, biology, and sonar. Cards were not opened.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/onegen/onegen": { "id": "zju/onegen/onegen", "name": "OneGen", "maker": "zju", "family": "zju/onegen", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Task checkpoints of OneGen on Llama-2-7B-chat. MIT. The paper was not opened. Tags describe entity linking, Self-RAG, and multi-hop RAG.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OneGen is a Zhejiang University model in the onegen family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Retrieval-generation checkpoints that keep generation and retrieval in one Llama-2-7B model. Three task repos were published.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/onegen/onegen-embedding": { "id": "zju/onegen/onegen-embedding", "name": "OneGenEmbedding", "maker": "zju", "family": "zju/onegen", "status": "active", "confidence": "low", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "MIT repo named OneGenEmbedding. The API record has no pipeline tag and no library tag. The file list was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OneGenEmbedding is a Zhejiang University model in the onegen family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Embedding component published next to the OneGen chat checkpoints. What it embeds was not read.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/oneke/oneke": { "id": "zju/oneke/oneke", "name": "OneKE", "maker": "zju", "family": "zju/oneke", "status": "active", "confidence": "high", "weights": "open-weights", "license": "cc-by-nc-sa-4.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "13B", "architecture": "Full-parameter fine-tune of Chinese-Alpaca-2-13B for schema-based information extraction, trained with IEPile and InstructIE. The card asks for JSON-string outputs for NER, relation, and event tasks. A later WWW 2025 system wraps schema-guided agents around several backends, including this checkpoint.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OneKE is a Zhejiang University model in the oneke family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Bilingual Chinese-English knowledge-extraction model from Ant Group and Zhejiang University, released on the zjunlp Hub org. It is meant for schema-guided entity, relation, and event extraction across domains, with a JSON output format.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/ontoprotein/ontoprotein": { "id": "zju/ontoprotein/ontoprotein", "name": "OntoProtein", "maker": "zju", "family": "zju/ontoprotein", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "BERT fill-mask protein language model. Tags name the ProteinKG25 dataset. No license tag was in the API record. Card body was not opened.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "OntoProtein is a Zhejiang University model in the ontoprotein family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Protein language model from ZJUNLP that uses protein sequences and a knowledge-graph dataset. The paper page was not opened in this pass.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/safedit/safedit-safety-classifier": { "id": "zju/safedit/safedit-safety-classifier", "name": "SafeEdit Safety Classifier", "maker": "zju", "family": "zju/safedit", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "RoBERTa text classifier. arXiv tag 2403.14472. Apache-2.0. Pipeline tag text-classification.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "SafeEdit Safety Classifier is a Zhejiang University model in the safedit family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Safety classifier released with the SafeEdit model-editing work. The paper title was not opened, so the label set is not restated.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/token-mol/token-mol": { "id": "zju/token-mol/token-mol", "name": "Token-Mol 1.0", "maker": "zju", "family": "zju/token-mol", "status": "active", "confidence": "high", "weights": "open-weights", "license": "GitHub code is MIT. A weight license was not stated on the Zenodo record that was read.", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "GPT-2-style transformer decoder from the College of Pharmaceutical Sciences at Zhejiang University. It encodes 2D structure, 3D torsion, and properties as tokens and is trained with causal masking and a Gaussian cross-entropy loss. The GitHub repo is MIT. The Zenodo record that was read did not state a separate weight license. The Nature article is CC BY-NC-ND 4.0, which applies to the paper.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Token-Mol 1.0 is a Zhejiang University model in the token-mol family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Token-only 3D drug-design model for conformation generation, property prediction, and pocket-based molecule generation. The authors say it must be fine-tuned before downstream use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/wkm/wkm": { "id": "zju/wkm/wkm", "name": "WKM", "maker": "zju", "family": "zju/wkm", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "apache-2.0", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Mistral checkpoints named WKM for ALFWorld, WebShop, and ScienceWorld, with a matching agent repo for each. The expansion of WKM and the paper title were not opened. A separate mistral_alfworld_agent_model_lora repo is only an adapter and is not a version.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WKM is a Zhejiang University model in the wkm family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "Named WKM agent weights for three interactive environments. The six repos are task and agent snapshots.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zju/worfgen/worfgen": { "id": "zju/worfgen/worfgen", "name": "WorfGen-7B", "maker": "zju", "family": "zju/worfgen", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "mit", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Two 7B WorfGen checkpoints. The task and paper were not opened. InternLM and Qwen base models are other labs' models. These repos are ZJU's named WorfGen releases.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "WorfGen-7B is a Zhejiang University model in the worfgen family. Status is active. Weights are open-weight files, not a hosted API. Collected 2026-10-15.", "what_it_is_for": "WorfGen 7B weights on two bases. What WorF generates was not read from a card.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula/nebula-llm": { "id": "zte/nebula/nebula-llm", "name": "\u661f\u4e91\u8bed\u8a00\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Dense transformer-style language model. ZTE describes multi-stage pretraining, a revised Chinese vocabulary, high-quality corpus refinement, and synthetic-data training. A 2024 account says INT4 weight quantization and FP8 KV cache cut inference resources by about 3x without changing accuracy. Layer count, hidden size, and attention layout are not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) describes the Nebula language large model (\u661f\u4e91\u8bed\u8a00\u5927\u6a21\u578b), also discussed as the language part of the Nebula foundation model (\u661f\u4e91\u57fa\u7840\u5927\u6a21\u578b), as its self-developed text foundation model. It was still an active closed product when this dossier was collected on 2026-10-15. ZTE has not published one model card for a single canonical checkpoint.", "what_it_is_for": "ZTE says the language model is the general base in its \"1+N+X\" stack. Customers pick a size for task complexity and budget. Stated scene fine-tunes include document generation, API mapping, and Text2SQL. Domain models such as R&D, telecom, industry, and government are described as incremental pretraining on top of this base. Deployment is said to cover handsets, the edge, and the central cloud.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula/nebula-mm": { "id": "zte/nebula/nebula-mm", "name": "\u661f\u4e91\u591a\u6a21\u6001\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) lists a Nebula multimodal large model (\u661f\u4e91\u591a\u6a21\u6001\u5927\u6a21\u578b) beside its language and vision foundation models. It was an active closed product as of 2026-10-15. ZTE has not published a model card or a parameter count for it.", "what_it_is_for": "The 2024 ZTE Technologies industry article says the model understands images and video and generates text, so vision applications can be driven by conversation. The same article says speech and 3D were being added for digital-human generation and multimodal dialogue. Later filings still list a multimodal foundation model covering handset, edge, and central-cloud deployment, without new specs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula-cv/nebula-cv": { "id": "zte/nebula-cv/nebula-cv", "name": "\u661f\u4e91\u89c6\u89c9\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-cv", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Transformer vision model. ZTE says it covers classification, detection, localization, segmentation, and retrieval, and that prompt learning with a few dozen samples can raise a scene task above 95% accuracy.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) describes the Nebula vision large model (\u661f\u4e91\u89c6\u89c9\u5927\u6a21\u578b) as its Transformer vision foundation model for classification, detection, localization, segmentation, and retrieval. It was an active closed product as of 2026-10-15. Later named models Nebula-CV2.0 and NebulaCV-X are cataloged separately.", "what_it_is_for": "ZTE says the model generalizes with prompts, unlike a small task-specific vision model. A few dozen prompt-learning samples are said to push a scene above 95% accuracy. Stated uses include flame and flood scenes that small models false-alarm on, and power-grid drone inspection of towers, foreign objects, missing bolts, rust, missing insulators, and corridor hazards in visible and thermal images.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula-cv/nebula-cv-2-0": { "id": "zte/nebula-cv/nebula-cv-2-0", "name": "Nebula-CV2.0", "maker": "zte", "family": "zte/nebula-cv", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Open-set detection and recognition model. ZTE names a modality-encoder parameter-balance method, visual-prompt region alignment, a Chinese prompt module, and a dataset relabeling method. Absolute parameter count is not published; ZTE says the count is 60% of comparable industry models.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) names Nebula-CV2.0 (\u661f\u4e91\u89c6\u89c9\u5927\u6a21\u578b) as its open-set detection and recognition model. Trade reports in August 2025, citing ZTE, say it won three CVPR 2025 detection challenges. It was treated as an active closed model on 2026-10-15. No weights or model card were found.", "what_it_is_for": "ZTE describes open-set detection when test categories were not in the training set, including few-shot and sparsely labeled settings. It says a Chinese prompt module was an early way to do Chinese open-set detection and that mixed Chinese-English prompts are supported. Visual prompts are said to align regions of the image with text.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula-cv/nebulacv-x": { "id": "zte/nebula-cv/nebulacv-x", "name": "NebulaCV-X", "maker": "zte", "family": "zte/nebula-cv", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Unified multi-task vision model. Reported tasks are open-set detection, instance segmentation, human keypoints, object captioning, OCR, visual prompts, and custom prompts.", "modalities": { "input": [ "image", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) is reported to have a unified vision model named NebulaCV-X (\u89c6\u89c9\u591a\u4efb\u52a1\u7edf\u4e00\u5927\u6a21\u578b). The name appears in August 2025 trade reports of ZTE's CVPR 2025 results, not on a model card. It was treated as an active closed model on 2026-10-15. Confidence is medium because the pages are secondary reports of a ZTE announcement.", "what_it_is_for": "The reports say NebulaCV-X combines open-set detection, instance segmentation, human keypoint detection, object captioning, OCR, visual prompts, and custom prompts so a customer can deploy one model instead of several task models.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula-industry/nebula-city-lifeline": { "id": "zte/nebula-industry/nebula-city-lifeline", "name": "\u57ce\u5e02\u751f\u547d\u7ebf\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-industry", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) named a city-lifeline domain large model (\u57ce\u5e02\u751f\u547d\u7ebf) on 2024-08-05, in the same sentence as its telecom, industrial, and water models. It is a closed named product. This dossier was collected on 2026-10-15. No technical sheet was found.", "what_it_is_for": "ZTE lists it as a domain model under the Nebula \"1+N+X\" strategy. The filing news does not describe tasks. Later press about urban gas, road, and flood vision is not explicitly tied to this name, so those projects are not treated as specs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula-industry/nebula-gov": { "id": "zte/nebula-industry/nebula-gov", "name": "\u661f\u4e91\u653f\u52a1\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-industry", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) names a government large model (\u653f\u52a1\u5927\u6a21\u578b) on its Nebula large-model page as one of the domain models pretrained from the foundation set. It was an active closed name as of 2026-10-15. No checkpoint card was found.", "what_it_is_for": "The product page groups it with R&D, industry, and telecom domain models. The 2025 interim report says industry models that combine language, vision, and multimodal ability supported Nanjing Jiangning's one-network governance analysis system, and that post-event scoring rose 60%. The report does not isolate a government-model score.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula-industry/nebula-industry": { "id": "zte/nebula-industry/nebula-industry", "name": "\u661f\u4e91\u5de5\u4e1a\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-industry", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) names an industrial large model (\u5de5\u4e1a\u5927\u6a21\u578b) as one of the domain models in its Nebula \"1+N+X\" stack. It was still listed as an active closed product on 2026-10-15. No model card or parameter count was found.", "what_it_is_for": "ZTE says domain models are incrementally pretrained from the foundation set. The industrial model is named next to R&D, telecom, and government. A 2024 article says the Binjiang plant uses large models for scheduling, process-document generation, visual quality inspection, AGV dispatch, and repair. The 2025 interim report says industry models combine language, vision, and multimodal ability and have landed in manufacturing, government, water, and transport. It does not give a separate spec for the industrial checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula-industry/nebula-water": { "id": "zte/nebula-industry/nebula-water", "name": "\u661f\u4e91\u6c34\u5229\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-industry", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) said on 2024-08-05 that, besides the R&D model, it had released domain large models for telecom, industry, water conservancy (\u6c34\u5229), and city lifelines. The water model is a closed named product. This dossier was collected on 2026-10-15. No technical sheet was found.", "what_it_is_for": "ZTE lists it as a domain model from the Nebula \"1+N+X\" strategy. The 2025 interim report says industry models landed in water conservancy, without a separate description of tasks, data, or accuracy.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula-telecom/nebula-signaling": { "id": "zte/nebula-telecom/nebula-signaling", "name": "\u7ed3\u6784\u5316\u4fe1\u4ee4\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-telecom", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ZTE says the model layer fuses a structured signaling model with an LLM, and offers a pretrained base, a chain-of-thought fine-tune, and inference analysis. Dimensions are not published.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) introduced a structured signaling large model (\u7ed3\u6784\u5316\u4fe1\u4ee4\u5927\u6a21\u578b) in its 23 June 2025 ZTE Technologies article. It is a closed specialist for protocol-level signaling analysis. This dossier was collected on 2026-10-15.", "what_it_is_for": "ZTE says the model moves signaling work from manual decode to model understanding of the protocol. The stack it describes has a model layer (pretrained base, chain-of-thought fine-tune, and inference, fused with an LLM) and a business layer (visualization, anomaly detection, automatic reports, and Q&A). A 2025 Jiangsu trial applied it to complaint-analysis agents. The English agent article lists a Signaling Large Model as a Nebula telecom specialist.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula-telecom/nebula-spatiotemporal": { "id": "zte/nebula-telecom/nebula-spatiotemporal", "name": "Spatio-Temporal Large Model", "maker": "zte", "family": "zte/nebula-telecom", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Named by ZTE as a Nebula telecom specialist. A 2024 interview, which does not use this English name, says ZTE used a Transformer to represent network time-series state and spatial topology as one structured model for anomaly detection. That interview is not explicit that the two names are the same model.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) names a Spatio-Temporal Large Model as a Nebula telecom specialist in its 22 September 2025 ZTE Technologies article on agents. It was treated as an active closed model on 2026-10-15. Almost no technical sheet was found.", "what_it_is_for": "The agent article says ZTE is building assistants and expert agents on the Nebula telecom model and on specialists such as the Signaling Large Model and the Spatio-Temporal Large Model. It does not describe the spatio-temporal model further. A separate April 2024 interview says ZTE built a Transformer model that jointly represents network time-series state and spatial topology for more general real-time anomaly detection. That interview does not use the English product name, so it is not treated as a spec sheet for this id.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula-telecom/nebula-tele": { "id": "zte/nebula-telecom/nebula-tele", "name": "NebulaTele", "maker": "zte", "family": "zte/nebula-telecom", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2026-10-15", "context_length": 262144, "max_output_tokens": null, "parameters": "28B", "architecture": "config.json architecture is Qwen3_5ForConditionalGeneration, model_type qwen3_5. Text stack: hidden size 5120, 64 layers mixing linear attention and full attention, 24 attention heads, 4 key-value heads, head dim 256, vocab 248320, max_position_embeddings 262144, one MTP layer. Vision stack: depth 27, hidden size 1152, patch size 16, out hidden size 5120. Image and video token ids are set. bfloat16. No model card. ZTE does not say whether the weights continue from a Qwen3.5 checkpoint.", "modalities": { "input": [ "text", "image", "video" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE-AIM published NebulaTele on Hugging Face as a 28B multimodal weight repo. Shards were still being uploaded on 2026-10-15, the collection date. There is no model card and no ZTE press release that names this repo. Confidence is medium.", "what_it_is_for": "Not stated. The name and the ZTE-AIM org connect it to ZTE's Nebula telecom work. Product pages for the closed Nebula telecom large model do not link this checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula-telecom/nebula-telecom": { "id": "zte/nebula-telecom/nebula-telecom", "name": "\u661f\u4e91\u901a\u4fe1\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-telecom", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2024-06-26", "context_length": 120000, "max_output_tokens": null, "parameters": "9\u00d720B MoE", "architecture": "Mixture-of-experts telecom model. ZTE's Chinese 2024 account calls it hundred-billion class (\u5343\u4ebf\u7ea7), written 9\u00d720B. The English CEO article calls the same 9*20B design 'trillion parameters', which does not match 9\u00d720B or the Chinese wording. A 2026 description adds a hybrid of generative fast thinking and rule or symbolic slow thinking. The product is also described as a swappable engine that can sit beside DeepSeek.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) launched the Nebula telecom large model (\u661f\u4e91\u901a\u4fe1\u5927\u6a21\u578b) at MWC Shanghai on 2024-06-26 as the model inside a series that also includes an agent factory and network applications. It was still an active closed product on 2026-10-15. In the 2026 autonomous-networks white paper, ZTE uses the name NTele for the generalist engine inside that system.", "what_it_is_for": "ZTE positions it as the accelerator for high-level autonomous networks: intent understanding, fault diagnosis, assurance, anti-fraud, and signaling analysis. Applications named at launch include a network-insight expert, a monitoring expert, and an assurance expert. The 2026 account says the generalist mixes generative \"fast\" reasoning with rule and symbolic \"slow\" reasoning, using fault cases, procedures, specifications, and digital-twin synthetic data. The same solution can swap in other engines, including DeepSeek, without changing the business logic.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula-telecom/nebula-telecom-structured": { "id": "zte/nebula-telecom/nebula-telecom-structured", "name": "\u901a\u4fe1\u9886\u57df\u7ed3\u6784\u5316\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-telecom", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Native structured model for telecom records. ZTE says a dedicated tokenizer maps signaling, measurement reports, XDR, and KPI sequences into compact tokens, then aligns with a general LLM. A dual-brain self-training loop is described because labeled data are scarce. Parameter count is not published. The 2026 white paper calls the structured model lightweight.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) describes a native structured large model for telecom (\u901a\u4fe1\u9886\u57df\u7ed3\u6784\u5316\u5927\u6a21\u578b) as one of the specialist models in its 2026 \"1+N\" telecom matrix. It was an active closed model as of 2026-10-15. It is not given a public checkpoint id.", "what_it_is_for": "ZTE says general LLMs waste tokens and distort signaling messages, measurement reports, XDR records, and KPI sequences when those records are flattened to text. This model is meant to compress them and then align with the general telecom LLM for KPI anomaly detection and signaling-flow reconstruction. The 2026 white paper calls it the lightweight real-time component next to NTele.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula-telecom/ntele-8b": { "id": "zte/nebula-telecom/ntele-8b", "name": "NTele-8B", "maker": "zte", "family": "zte/nebula-telecom", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "", "release_date": "2025-12-23", "context_length": 40960, "max_output_tokens": null, "parameters": "8B", "architecture": "config.json architectures field is Qwen3ForCausalLM, model_type qwen3. 36 layers, hidden size 4096, intermediate size 12288, 32 attention heads, 8 key-value heads, head dim 128, vocab size 151936, rope theta 1,000,000, bfloat16, tie_word_embeddings false. ZTE did not publish whether these weights were trained from scratch or adapted from Qwen3. The shape matches the public Qwen3-8B config. There is no model card.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE-AIM, the Hugging Face organization whose site link is zte.com.cn, hosts NTele-8B as an 8B weight repo. There is no model card. The org page showed an update on 2025-12-23. This dossier was collected on 2026-10-15. Confidence is medium because the weights are official to the org but undocumented.", "what_it_is_for": "Not stated. The name matches ZTE's NTele telecom-model line. ZTE's product pages do not point at this repo or describe an 8B checkpoint.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebula-video/nebula-video": { "id": "zte/nebula-video/nebula-video", "name": "NebulaVideo", "maker": "zte", "family": "zte/nebula-video", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "2025-06-18", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Video multimodal model with unified spatiotemporal modeling. ZTE says it was adapted from a general multimodal architecture for industry video.", "modalities": { "input": [ "video", "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) and China Mobile Zhejiang Innovation Research Institute announced NebulaVideo (\u661f\u4e91\u89c6\u9891\u591a\u6a21\u6001\u5927\u6a21\u578b) on 2025-06-18 at MWC Shanghai. It is a closed industry video model. This dossier was collected on 2026-10-15. The write-up rests on trade reports of the launch, so confidence is medium.", "what_it_is_for": "The reports say NebulaVideo is for traffic, industry, power, and government video. Stated tasks are event recognition, action localization, and object tracking, plus causal reasoning, evidence, and source tracing. A new vertical is said to need only a small fine-tune. It was adapted to China Mobile Zhejiang's Jiutian (\u4e5d\u5929) all-in-one intelligent-computing appliance as a video-understanding MaaS service.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/nebulacoder/nebulacoder": { "id": "zte/nebulacoder/nebulacoder", "name": "NebulaCoder", "maker": "zte", "family": "zte/nebulacoder", "status": "active", "confidence": "high", "weights": "closed", "license": "", "release_date": "2023-04", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "ZTE says it is incrementally pretrained on the Nebula foundation models with telecom R&D documents and code, then tuned for programming scenes. A 2026 paper says the corpus includes more than 100 million technical documents and one trillion code tokens. NebulaCoder-V6 training is described by ZTE only at the level of reasoning-leaderboard results, not as a published architecture sheet.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZTE (\u4e2d\u5174\u901a\u8baf) has used NebulaCoder, also called the Nebula R&D large model (\u661f\u4e91\u7814\u53d1\u5927\u6a21\u578b) and the Nebula coding model (\u661f\u4e91\u7f16\u7a0b\u5927\u6a21\u578b), since April 2023. It is a closed coding and R&D assistant trained on top of ZTE's foundation models. NebulaCoder-V6 is the named 2025 generation. This dossier was collected on 2026-10-15.", "what_it_is_for": "ZTE says the model covers requirements, design, programming, testing, and deployment, in more than 30 scenes. Named languages are Python, Java, C/C++, Go, and JavaScript. ZTE says code generation is at a GPT-4 level and that unit-test accuracy and coverage beat GPT-4 Turbo, without publishing those GPT-4 comparison numbers. A March 2026 ZTE Communications paper says an internal API is used for code-flaw detection and repair. Guangdong generative-AI filing was announced on 2024-08-05.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zte/ntele/ntele-r1-lite-32b-v2": { "id": "zte/ntele/ntele-r1-lite-32b-v2", "name": "NTele-R1-Lite-32B-v2", "maker": "zte", "family": "zte/ntele", "status": "active", "confidence": "medium", "weights": "closed", "license": "", "release_date": "", "context_length": null, "max_output_tokens": null, "parameters": "32B", "architecture": "Described as a 32B lightweight reasoning model using architecture optimization and dynamic sparsification. The base checkpoint is not named. Later ZTE-AIM NTele-R1 32B repos are fine-tunes of DeepSeek-R1-Distill-Qwen-32B, but those cards do not use the Lite-v2 name.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "A 11 April 2025 \u901a\u4fe1\u4e16\u754c\u7f51 article says ZTE (\u4e2d\u5174\u901a\u8baf) launched NTele-R1-Lite-32B-v2, a 32B Nebula reasoning model. No ZTE.com.cn page or Hugging Face repo with this exact name was found. The dossier was collected on 2026-10-15. Confidence is medium.", "what_it_is_for": "The article presents it as a low-cost reasoning model for math and code, aimed at enterprise deployment that cannot host a 671B model. It does not describe a telecom operations job.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/blackmamba/blackmamba-1-5b": { "id": "zyphra/blackmamba/blackmamba-1-5b", "name": "BlackMamba 340M/1.5B", "maker": "zyphra", "family": "zyphra/blackmamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-02-01", "context_length": null, "max_output_tokens": null, "parameters": "340M active / 1.5B total", "architecture": "Mamba selective state-space blocks with Switch-Transformer mixture-of-experts MLPs. Linear complexity in sequence length. Trained on 300B tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zyphra released BlackMamba 340M/1.5B on 2024-02-01 as an open-weight mixture-of-experts state-space language model. Weights remain on Hugging Face under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The paper presents BlackMamba as a low-latency language model that combines Mamba sequence mixing with routed experts, aimed at fast generation versus transformers, dense Mamba, and standard MoEs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/blackmamba/blackmamba-2-8b": { "id": "zyphra/blackmamba/blackmamba-2-8b", "name": "BlackMamba 630M/2.8B", "maker": "zyphra", "family": "zyphra/blackmamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-02-01", "context_length": null, "max_output_tokens": null, "parameters": "630M active / 2.8B total", "architecture": "Mamba selective state-space blocks with Switch-Transformer mixture-of-experts MLPs. Linear complexity in sequence length. Trained on 300B tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zyphra released BlackMamba 630M/2.8B on 2024-02-01 as an open-weight mixture-of-experts state-space language model. Weights remain on Hugging Face under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The paper presents BlackMamba as a low-latency language model that combines Mamba sequence mixing with routed experts, aimed at fast generation versus transformers, dense Mamba, and standard MoEs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/mamba/mamba-370m": { "id": "zyphra/mamba/mamba-370m", "name": "Mamba-370M", "maker": "zyphra", "family": "zyphra/mamba", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-02-10", "context_length": null, "max_output_tokens": null, "parameters": "370M", "architecture": "Mamba language model loaded as MambaLMHeadModel from Zyphra's mamba-ssm fork. Final published iteration is 610,351. Tokenizer EleutherAI/gpt-neox-20b.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zyphra published Mamba-370M on Hugging Face on 2024-02-10, with iteration checkpoints and an Apache-2.0 card. It is a named 370M Mamba language model, not a Zamba hybrid. Collected 2026-10-15. Confidence is medium because the card does not describe the training corpus.", "what_it_is_for": "The card only documents how to load iterations and generate text. It does not state a product job beyond language-model generation.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zamba/zamba-7b-v1": { "id": "zyphra/zamba/zamba-7b-v1", "name": "Zamba-7B-v1", "maker": "zyphra", "family": "zyphra/zamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-16", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Mamba1 backbone with one shared transformer block every 6 Mamba blocks. Original embeddings are concatenated into the shared attention input. Mistral v0.1 tokenizer. Next-token prediction.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zyphra's Zamba-7B-v1 is the annealed 7B hybrid Mamba-transformer released on 2024-04-16 under Apache-2.0. It is still published on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "The release post presents it as a sample-efficient 7B foundation model that beats Llama-2 7B and OLMo-7B on a wide set of benchmarks after less than half the training data of those comparators.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zamba/zamba-7b-v1-phase1": { "id": "zyphra/zamba/zamba-7b-v1-phase1", "name": "Zamba-7B-v1 phase 1", "maker": "zyphra", "family": "zyphra/zamba", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-04-16", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Mamba1 backbone with one shared transformer block every 6 Mamba blocks. Original embeddings are concatenated into the shared attention input. Mistral v0.1 tokenizer. Next-token prediction. This checkpoint is the phase-1 model before the 50B-token anneal.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zamba-7B-v1 phase 1 is the pre-anneal 7B checkpoint Zyphra released alongside the annealed model on 2024-04-16 so others could compare the two training stages. Collected 2026-10-15.", "what_it_is_for": "The release post says both the phase-1 base and the annealed model are released for comparison. The paper evaluates phase 1 after about 0.95T tokens.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zamba2/zamba2-1-2b": { "id": "zyphra/zamba2/zamba2-1-2b", "name": "Zamba2-1.2B", "maker": "zyphra", "family": "zyphra/zamba2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-27", "context_length": 4096, "max_output_tokens": null, "parameters": "1.2B", "architecture": "Hybrid Mamba2 backbone with a single shared transformer block, LoRA on both the shared attention and the shared MLP, and RoPE. Mistral v0.1 tokenizer. Pretrained at sequence length 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zamba2-1.2B is an open-weight Zyphra hybrid Mamba2-transformer base model announced 2024-08-27. Apache-2.0 weights are on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "Zyphra positions the Zamba2 bases as small general language models with transformer-class benchmark scores and lower latency and KV-cache memory than comparable transformers. The 1.2B and 2.7B posts emphasize on-device use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zamba2/zamba2-1-2b-instruct": { "id": "zyphra/zamba2/zamba2-1-2b-instruct", "name": "Zamba2-1.2B-Instruct", "maker": "zyphra", "family": "zyphra/zamba2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-09-19", "context_length": 4096, "max_output_tokens": null, "parameters": "1.2B", "architecture": "Hybrid Mamba2 backbone with a single shared transformer block, LoRA on both the shared attention and the shared MLP, and RoPE. Mistral v0.1 tokenizer. Pretrained at sequence length 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zamba2-1.2B-Instruct is Zyphra's instruction-tuned Zamba2 model, Apache-2.0, released 2024-09-19. Collected 2026-10-15.", "what_it_is_for": "The cards describe supervised fine-tuning plus preference optimization for instruction following and chat, using open chat datasets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zamba2/zamba2-1-2b-instruct-v2": { "id": "zyphra/zamba2/zamba2-1-2b-instruct-v2", "name": "Zamba2-1.2B-Instruct-v2", "maker": "zyphra", "family": "zyphra/zamba2", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-07", "context_length": null, "max_output_tokens": null, "parameters": "1.2B", "architecture": "Hybrid Mamba2 backbone with a single shared transformer block, LoRA on both the shared attention and the shared MLP, and RoPE. Mistral v0.1 tokenizer. Pretrained at sequence length 4096. Later instruct fine-tune. Training recipe was not re-read for this pass.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zamba2-1.2B-Instruct-v2 is a named Apache-2.0 instruct checkpoint on Hugging Face, created 2025-03-07. Collected 2026-10-15. Confidence is medium because the benchmark table was not extracted.", "what_it_is_for": "The repo name and base-model tag mark it as a second instruction tune of the matching Zamba2 base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zamba2/zamba2-2-7b": { "id": "zyphra/zamba2/zamba2-2-7b", "name": "Zamba2-2.7B", "maker": "zyphra", "family": "zyphra/zamba2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-07-28", "context_length": 4096, "max_output_tokens": null, "parameters": "2.7B", "architecture": "Hybrid Mamba2 backbone with two alternating shared transformer blocks and LoRA on the shared MLP. This 2.7B variant has no rotary embeddings in attention. Mistral v0.1 tokenizer. Pretrained at sequence length 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zamba2-2.7B is an open-weight Zyphra hybrid Mamba2-transformer base model announced 2024-07-28. Apache-2.0 weights are on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "Zyphra positions the Zamba2 bases as small general language models with transformer-class benchmark scores and lower latency and KV-cache memory than comparable transformers. The 1.2B and 2.7B posts emphasize on-device use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zamba2/zamba2-2-7b-instruct": { "id": "zyphra/zamba2/zamba2-2-7b-instruct", "name": "Zamba2-2.7B-Instruct", "maker": "zyphra", "family": "zyphra/zamba2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-08-29", "context_length": 4096, "max_output_tokens": null, "parameters": "2.7B", "architecture": "Hybrid Mamba2 backbone with two alternating shared transformer blocks and LoRA on the shared MLP. This 2.7B variant has no rotary embeddings in attention. Mistral v0.1 tokenizer. Pretrained at sequence length 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zamba2-2.7B-Instruct is Zyphra's instruction-tuned Zamba2 model, Apache-2.0, released 2024-08-29. Collected 2026-10-15.", "what_it_is_for": "The cards describe supervised fine-tuning plus preference optimization for instruction following and chat, using open chat datasets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zamba2/zamba2-2-7b-instruct-v2": { "id": "zyphra/zamba2/zamba2-2-7b-instruct-v2", "name": "Zamba2-2.7B-Instruct-v2", "maker": "zyphra", "family": "zyphra/zamba2", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-03-07", "context_length": null, "max_output_tokens": null, "parameters": "2.7B", "architecture": "Hybrid Mamba2 backbone with two alternating shared transformer blocks and LoRA on the shared MLP. This 2.7B variant has no rotary embeddings in attention. Mistral v0.1 tokenizer. Pretrained at sequence length 4096. Later instruct fine-tune. Training recipe was not re-read for this pass.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zamba2-2.7B-Instruct-v2 is a named Apache-2.0 instruct checkpoint on Hugging Face, created 2025-03-07. Collected 2026-10-15. Confidence is medium because the benchmark table was not extracted.", "what_it_is_for": "The repo name and base-model tag mark it as a second instruction tune of the matching Zamba2 base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zamba2/zamba2-7b": { "id": "zyphra/zamba2/zamba2-7b", "name": "Zamba2-7B", "maker": "zyphra", "family": "zyphra/zamba2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-14", "context_length": 4096, "max_output_tokens": null, "parameters": "7.4B", "architecture": "Hybrid Mamba2 backbone with two alternating shared transformer blocks (ABAB), LoRA on the shared MLP, and RoPE in the shared attention. Mistral v0.1 tokenizer. Pretrained at sequence length 4096.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zamba2-7B is an open-weight Zyphra hybrid Mamba2-transformer base model announced 2024-10-14. Apache-2.0 weights are on Hugging Face. Collected 2026-10-15.", "what_it_is_for": "Zyphra positions the Zamba2 bases as small general language models with transformer-class benchmark scores and lower latency and KV-cache memory than comparable transformers. The 1.2B and 2.7B posts emphasize on-device use.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zamba2/zamba2-7b-instruct": { "id": "zyphra/zamba2/zamba2-7b-instruct", "name": "Zamba2-7B-Instruct", "maker": "zyphra", "family": "zyphra/zamba2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2024-10-14", "context_length": 16384, "max_output_tokens": null, "parameters": "7.36B", "architecture": "Hybrid Mamba2 backbone with two alternating shared transformer blocks (ABAB), LoRA on the shared MLP, and RoPE in the shared attention. Mistral v0.1 tokenizer. Pretrained at sequence length 4096. Instruct weights. Card size column in the v2 comparison table is 7.36B; the paper rounds the suite to 7.4B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zamba2-7B-Instruct is Zyphra's instruction-tuned Zamba2 model, Apache-2.0, released 2024-10-14. Collected 2026-10-15.", "what_it_is_for": "The cards describe supervised fine-tuning plus preference optimization for instruction following and chat, using open chat datasets.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zamba2/zamba2-7b-instruct-v2": { "id": "zyphra/zamba2/zamba2-7b-instruct-v2", "name": "Zamba2-7B-Instruct-v2", "maker": "zyphra", "family": "zyphra/zamba2", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-11", "context_length": null, "max_output_tokens": null, "parameters": "7.36B", "architecture": "Hybrid Mamba2 backbone with two alternating shared transformer blocks (ABAB), LoRA on the shared MLP, and RoPE in the shared attention. Mistral v0.1 tokenizer. Pretrained at sequence length 4096. Second instruct fine-tune of the 7B base. Card: one epoch of SFT on chat, reasoning, and math sets such as SmolTalk and OpenMathInstruct-2. The rest of the recipe was not fully copied here.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zamba2-7B-Instruct-v2 is a later Apache-2.0 instruct fine-tune of Zamba2-7B, published on Hugging Face on 2025-02-11. Collected 2026-10-15.", "what_it_is_for": "The card presents v2 as a stronger instruction-following update of the 7B instruct model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zamba2-vl/zamba2-vl-1-2b": { "id": "zyphra/zamba2-vl/zamba2-vl-1-2b", "name": "Zamba2-VL-1.2B", "maker": "zyphra", "family": "zyphra/zamba2-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-06-02", "context_length": null, "max_output_tokens": null, "parameters": "1.2B", "architecture": "Zamba2 language backbone plus a Qwen2.5-VL vision transformer. Hybrid SSM-transformer prefill. Apache-2.0.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zamba2-VL-1.2B is an open vision-language model Zyphra released on 2026-06-02 on a Zamba2 backbone. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The release post says the family targets chart, diagram, document, perception, reasoning, and counting tasks, with much lower time-to-first-token than matched transformer VLMs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zamba2-vl/zamba2-vl-2-7b": { "id": "zyphra/zamba2-vl/zamba2-vl-2-7b", "name": "Zamba2-VL-2.7B", "maker": "zyphra", "family": "zyphra/zamba2-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-06-02", "context_length": null, "max_output_tokens": null, "parameters": "2.7B", "architecture": "Zamba2 language backbone plus a Qwen2.5-VL vision transformer. Hybrid SSM-transformer prefill. Apache-2.0.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zamba2-VL-2.7B is an open vision-language model Zyphra released on 2026-06-02 on a Zamba2 backbone. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The release post says the family targets chart, diagram, document, perception, reasoning, and counting tasks, with much lower time-to-first-token than matched transformer VLMs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zamba2-vl/zamba2-vl-7b": { "id": "zyphra/zamba2-vl/zamba2-vl-7b", "name": "Zamba2-VL-7B", "maker": "zyphra", "family": "zyphra/zamba2-vl", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-06-02", "context_length": null, "max_output_tokens": null, "parameters": "7B", "architecture": "Zamba2 language backbone plus a Qwen2.5-VL vision transformer. Hybrid SSM-transformer prefill. Apache-2.0.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "Zamba2-VL-7B is an open vision-language model Zyphra released on 2026-06-02 on a Zamba2 backbone. Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "The release post says the family targets chart, diagram, document, perception, reasoning, and counting tasks, with much lower time-to-first-token than matched transformer VLMs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zaya/zaya1-74b-preview": { "id": "zyphra/zaya/zaya1-74b-preview", "name": "ZAYA1-74B-Preview", "maker": "zyphra", "family": "zyphra/zaya", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-07", "context_length": null, "max_output_tokens": null, "parameters": "4B active / 74B total", "architecture": "Same MoE family as ZAYA1-8B. Every other attention layer is sliding-window attention with a 4k window. Both global and sliding layers use CCA. Context was extended during midtraining to 32k, then 128k, then 256k. Pre-RL reasoning base: pretrained, midtrained, and context-extended. Not instruction-tuned and not RL-tuned.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZAYA1-74B-Preview is Zyphra's 2026-05-07 open preview of a 74B-total MoE, with 4B active parameters, released under Apache-2.0 before reinforcement learning. Collected 2026-10-15.", "what_it_is_for": "The post says it is a scaling milestone and a pre-RL base, not a finished chat or reasoning product. They compare pass@4 on this base against fully post-trained competitors only as a signal for later RL.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zaya/zaya1-8b": { "id": "zyphra/zaya/zaya1-8b", "name": "ZAYA1-8B", "maker": "zyphra", "family": "zyphra/zaya", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-06", "context_length": 131072, "max_output_tokens": null, "parameters": "760M active / 8.4B total (card); 700M / 8B (paper abstract)", "architecture": "MoE decoder with Compressed Convolutional Attention, an MLP router, and learned residual scaling. Released config.json: 40 hybrid layers, hidden size 2048, 16 experts, 1 expert per token, 8 query heads, 2 KV heads, vocab 262272, max_position_embeddings 131072. Model card says 760M active and 8.4B total. The same card's tables say 0.7B/8.0B and 0.7B/8B. The technical-report abstract says 700M active and 8B total. All three are vendor figures.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZAYA1-8B is Zyphra's post-trained reasoning MoE, released 2026-05-06 under Apache-2.0 and as a Zyphra Cloud endpoint. It was pretrained, midtrained, and supervised-fine-tuned on AMD Instinct MI300. Collected 2026-10-15.", "what_it_is_for": "The post says it is for hard reasoning, mathematics, and coding at under a billion active parameters, including on-device use and test-time compute with Markovian RSA.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zaya/zaya1-8b-diffusion-preview": { "id": "zyphra/zaya/zaya1-8b-diffusion-preview", "name": "ZAYA1-8B-Diffusion-Preview", "maker": "zyphra", "family": "zyphra/zaya", "status": "announced", "confidence": "high", "weights": "closed", "license": "", "release_date": "2026-05-14", "context_length": null, "max_output_tokens": null, "parameters": "Same class as ZAYA1-8B; exact count for the diffusion checkpoint not separately published", "architecture": "Discrete diffusion conversion of the ZAYA1-8B base using a TiDAR-style recipe. Generates blocks of 16 tokens in one mask-to-token step. Additional mid-training: 600B tokens at 32k context, then 500B tokens of extension to 128k, then diffusion SFT. Trained on AMD.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZAYA1-8B-Diffusion-Preview is a named Zyphra preview announced on 2026-05-14. It converts the autoregressive ZAYA1-8B base into a block-diffusion language model. No weights repo was on the Hugging Face org list on 2026-10-15, so status is announced. Collected 2026-10-15.", "what_it_is_for": "The post presents it as an early diffusion-language experiment: parallel block decoding with a claimed speedup and little systematic eval loss versus the autoregressive base.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zaya/zaya1-base": { "id": "zyphra/zaya/zaya1-base", "name": "ZAYA1-base", "maker": "zyphra", "family": "zyphra/zaya", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-24", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Pretraining checkpoint of the ZAYA1 MoE. The AMD full-stack training report covers this checkpoint. CCA attention is described in the October 2025 Zyphra note.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZAYA1-base is the open Apache-2.0 pretraining checkpoint Zyphra announced on 2025-11-24 as the first model they trained end to end on AMD's hardware, software, and network stack. Collected 2026-10-15.", "what_it_is_for": "The post presents it as a preview of ZAYA1 pretraining, with benchmarks in the accompanying AMD-stack technical report, not as the final reasoning model.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zaya/zaya1-reasoning-base": { "id": "zyphra/zaya/zaya1-reasoning-base", "name": "ZAYA1-reasoning-base", "maker": "zyphra", "family": "zyphra/zaya", "status": "active", "confidence": "medium", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-11-19", "context_length": null, "max_output_tokens": null, "parameters": "", "architecture": "Intermediate ZAYA1 checkpoint. Hugging Face lists it as a fine-tune of ZAYA1-base and as the parent of ZAYA1-8B.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZAYA1-reasoning-base is a named Apache-2.0 checkpoint on Hugging Face between ZAYA1-base and the May 2026 ZAYA1-8B reasoning model. The repo was created 2025-11-19. Collected 2026-10-15. Confidence is medium on what training stage the weights are.", "what_it_is_for": "The model tree treats it as the reasoning-oriented parent of the released ZAYA1-8B. Zyphra did not publish a separate product post under this name.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zaya-vl/zaya1-vl-8b": { "id": "zyphra/zaya-vl/zaya1-vl-8b", "name": "ZAYA1-VL-8B", "maker": "zyphra", "family": "zyphra/zaya-vl", "status": "preview", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-05-08", "context_length": null, "max_output_tokens": null, "parameters": "700M active / 8B total (release post). Hub size tag says 10B.", "architecture": "Vision-language MoE on the ZAYA1-8B base. Vision-specific LoRA on MLPs and CCA, active only on vision tokens. Bidirectional attention over image tokens. Trained on about 140B vision-language tokens. The post calls it a research preview.", "modalities": { "input": [ "text", "image" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZAYA1-VL-8B is Zyphra's first vision-language model, released 2026-05-08 as an Apache-2.0 research preview. The post says 700M active and 8B total parameters. Collected 2026-10-15.", "what_it_is_for": "The post says it is for visual understanding, grounding, OCR, documents, spatial perception, and GUI or computer-use tasks, and that post-training reasoning for vision was still future work.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zonos/zonos-v0-1-hybrid": { "id": "zyphra/zonos/zonos-v0-1-hybrid", "name": "Zonos-v0.1 Hybrid", "maker": "zyphra", "family": "zyphra/zonos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-10", "context_length": null, "max_output_tokens": null, "parameters": "1.6B", "architecture": "Autoregressive DAC-token TTS. Text is normalized and phonemized with eSpeak. Speaker embedding or audio prefix. Conditionable speaking rate, pitch, audio quality, and emotions. Native 44 kHz. About 86 frames times 9 codebooks per second of audio. Trained on about 200,000 hours. SSM hybrid backbone using Mamba2, 1.6B parameters. The post says this is the first open SSM TTS they released and that it has lower latency and memory than the transformer twin.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.02 }, "summary_intro": "Zonos-v0.1 Hybrid is one of two Zonos-v0.1 beta TTS models Zyphra released on 2025-02-10 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Expressive text-to-speech and voice cloning from a few seconds of reference audio, with controls for rate, pitch, quality, and emotion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zonos/zonos-v0-1-transformer": { "id": "zyphra/zonos/zonos-v0-1-transformer", "name": "Zonos-v0.1 Transformer", "maker": "zyphra", "family": "zyphra/zonos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2025-02-10", "context_length": null, "max_output_tokens": null, "parameters": "1.6B", "architecture": "Autoregressive DAC-token TTS. Text is normalized and phonemized with eSpeak. Speaker embedding or audio prefix. Conditionable speaking rate, pitch, audio quality, and emotions. Native 44 kHz. About 86 frames times 9 codebooks per second of audio. Trained on about 200,000 hours. Transformer backbone, 1.6B parameters.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": 0.02 }, "summary_intro": "Zonos-v0.1 Transformer is one of two Zonos-v0.1 beta TTS models Zyphra released on 2025-02-10 under Apache-2.0. Collected 2026-10-15.", "what_it_is_for": "Expressive text-to-speech and voice cloning from a few seconds of reference audio, with controls for rate, pitch, quality, and emotion.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zonos/zonos2": { "id": "zyphra/zonos/zonos2", "name": "ZONOS2", "maker": "zyphra", "family": "zyphra/zonos", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-06-12", "context_length": null, "max_output_tokens": null, "parameters": "8B total / 900M active", "architecture": "Decoder-only MoE transformer TTS. 28 layers, hidden size 2048, 16 experts, top-1 routing except top-2 on the final MoE, GQA. DAC tokens from nemo text normalization and an ECAPA-TDNN speaker embedding. Trained on more than 6 million hours. Paper date 2026-06-25.", "modalities": { "input": [ "text", "audio" ], "output": [ "audio" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZONOS2 is Zyphra's Apache-2.0 real-time TTS model announced 2026-06-12. It is an 8B-total MoE with 900M active parameters, trained on more than 6 million hours. Collected 2026-10-15.", "what_it_is_for": "The card says it is for expressive, low-latency speech and high-fidelity voice cloning across three language tiers.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zr1/zr1-1-5b": { "id": "zyphra/zr1/zr1-1-5b", "name": "ZR1-1.5B", "maker": "zyphra", "family": "zyphra/zr1", "status": "active", "confidence": "high", "weights": "open-weights", "license": "MIT", "release_date": "2025-04-10", "context_length": null, "max_output_tokens": null, "parameters": "1.5B", "architecture": "Reinforcement-learning fine-tune of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B. Qwen2-style decoder. PRIME process rewards plus RLOO. Generation length during training moved through 12384, 8192, 16384, and 24448 tokens.", "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZR1-1.5B is Zyphra's 1.5B math-and-code reasoning model, announced 2025-04-10. The card's license is MIT. It is a named Zyphra release, fine-tuned from a DeepSeek R1 Qwen distill. Collected 2026-10-15.", "what_it_is_for": "The announce post says it is a small reasoning model for coding and competition math that uses shorter traces than comparable 1.5B reasoners.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zuna/zuna": { "id": "zyphra/zuna/zuna", "name": "ZUNA", "maker": "zyphra", "family": "zyphra/zuna", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-02-18", "context_length": null, "max_output_tokens": null, "parameters": "380M", "architecture": "Masked diffusion autoencoder for scalp EEG. Reconstructs, denoises, and predicts channels from electrode coordinates. Trained on about 2 million channel-hours of public EEG.", "modalities": { "input": [ "eeg" ], "output": [ "eeg" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZUNA is Zyphra's 380M Apache-2.0 EEG foundation model, announced 2026-02-18. It is a brain-signal model, not a language model. Collected 2026-10-15.", "what_it_is_for": "The card says that given a subset of EEG channels it can denoise, reconstruct missing channels, and predict a new channel at a scalp coordinate.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "zyphra/zuna/zuna1-1": { "id": "zyphra/zuna/zuna1-1", "name": "ZUNA1.1", "maker": "zyphra", "family": "zyphra/zuna", "status": "active", "confidence": "high", "weights": "open-weights", "license": "Apache-2.0", "release_date": "2026-07-16", "context_length": null, "max_output_tokens": null, "parameters": "380M", "architecture": "Masked diffusion EEG autoencoder. Adds variable-length masking up to 30 second windows and segment-level dropout so users can clean chosen time spans, not only whole channels.", "modalities": { "input": [ "eeg" ], "output": [ "eeg" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": "ZUNA1.1 is Zyphra's 2026-07-16 update of the 380M EEG foundation model, Apache-2.0, with a technical paper noted on 2026-08-03. Collected 2026-10-15.", "what_it_is_for": "The post says it reconstructs, denoises, and upsamples EEG across arbitrary channel layouts, matching or beating ZUNA1 quality while accepting 0.5 to 30 second inputs.", "ratings": { "empirical_100": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "apodex/apodex/apodex-1-1-mini": { "id": "apodex/apodex/apodex-1-1-mini", "name": "Apodex 1.1 Mini", "maker": "apodex", "family": "apodex/apodex", "status": "active", "confidence": "high", "weights": "closed", "license": null, "release_date": null, "context_length": null, "max_output_tokens": null, "parameters": null, "architecture": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": null, "what_it_is_for": null, "ratings": { "empirical_100": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "dots-studio/dots/dots-3-note-preview": { "id": "dots-studio/dots/dots-3-note-preview", "name": "Dots 3 Note Preview", "maker": "dots-studio", "family": "dots-studio/dots", "status": "active", "confidence": "high", "weights": "closed", "license": null, "release_date": null, "context_length": null, "max_output_tokens": null, "parameters": null, "architecture": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": null, "what_it_is_for": null, "ratings": { "empirical_100": 44, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "gryphe/mythomax/mythomax-l2-13b": { "id": "gryphe/mythomax/mythomax-l2-13b", "name": "MythoMax L2 13B", "maker": "gryphe", "family": "gryphe/mythomax", "status": "active", "confidence": "high", "weights": "closed", "license": null, "release_date": null, "context_length": null, "max_output_tokens": null, "parameters": null, "architecture": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": null, "what_it_is_for": null, "ratings": { "empirical_100": 45, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inclusionai/ling/ling-3-0-flash": { "id": "inclusionai/ling/ling-3-0-flash", "name": "Ling 3.0 Flash", "maker": "inclusionai", "family": "inclusionai/ling", "status": "active", "confidence": "high", "weights": "closed", "license": null, "release_date": null, "context_length": null, "max_output_tokens": null, "parameters": null, "architecture": null, "modalities": { "input": [ "text" ], "output": [ "text" ] }, "pricing": { "prompt_per_m_usd": null, "completion_per_m_usd": null }, "summary_intro": null, "what_it_is_for": null, "ratings": { "empirical_100": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs": null, "aa_intelligence": null, "aa_coding": null, "aa_agentic": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, "inclusionai/ling/ling-3-0-flash-fin": { "id": "inclusionai/ling/ling-3-0-flash-fin", "name": "Ling 3.0 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